ADF stationarity tests.
\r\n\tCell viability is defined as the number of healthy cells in a sample and proliferation of cells is a vital indicator for understanding the mechanisms inaction of certain genes, proteins, and pathways involved in cell survival or death after exposure to toxic agents. The methods used to determine viability are also common for the detection of cell proliferation. A cell viability assay is performed based on the ratio of live and dead cells. This assay is based on an analysis of cell viability in cell culture for evaluating in vitro drug effects in cell-mediated cytotoxicity assays for monitoring cell proliferation. Various methods are involved in performing a cell viability assay, including the dilution method, surface viable count, roll tube technique, nalidixic acid method, fluorogenic dye assay, and the Trypan Blue Cell Viability Assay. The cell viability assays can determine the effect of drug candidates on cells and be used to optimize the cell culture conditions. The parameters that define cell viability can be as diverse as the redox potential of the cell population, the integrity of cell membranes, or the activity of cellular enzymes.
\r\n\tCytotoxicity is the degree to which a substance can cause damage to a cell. Cytotoxicity assays measure the ability of cytotoxic compounds to cause cell damage or cell death. Cytotoxicity assays are widely used in fundamental research and drug discovery to screen libraries for toxic compounds. The cell cytotoxicity and proliferation assays are mainly used for drug screening to detect whether the test molecules have effects on cell proliferation or display direct cytotoxic effects. In a cell-based assay, it is important to know how many viable cells are remaining at the end of the experiment. There are a variety of assay methods based on various cell functions such as enzyme activity, cell membrane permeability, cell adherence, ATP production, co-enzyme production, and nucleotide uptake activity. These methods could be classified in to different categories: (I) dye exclusion methods such as trypan blue dye exclusion assay, (II) methods based on metabolic activity, (III) ATP assay, (IV) sulforhodamine B assay, (V) protease viability marker assay, (VI) clonogenic cell survival assay, (VII) DNA synthesis cell proliferation assays and (V) Raman micro-spectroscopy.
\r\n\tMedical devices have been widely used in various clinical disciplines and these devices have direct contact with the tissues and cells of the body, they should have good physical and chemical properties as well as good biocompatibility. Biocompatibility testing assesses the compatibility of medical devices with a biological system. It studies the interaction between the device and the various types of living tissues and cells exposed to the device when it comes into contact with patients.
\r\n\t
\r\n\tThe book will cover original studies, reviews, all aspects of Cell Viability and Cytotoxicity assays, methods, Biocompatibility of studies of biomedical devices, and related topics.
Crude petroleum is one of the fundamental sources of energy in the world and plays an important role in economic growth and development of many economies. Because of the need for this product, the oil market is subjected to the market forces of demand and supply, which do lead to the fluctuation in the pricing. Hamilton [1], Blanchard and Gali [2], viewed, changes in the price of oil as an imperative source of economic fluctuations, in which the resultant effect led to global shock, capable of affecting many economic activities instantaneously. This shock is perceived generally to have a similar impact due to events like fall in growth rate, high unemployment rate, and high inflation rate, while the magnitude and the causes of the effect of these shocks may differ. For import-based economy, hike in the oil price will lead to shock in the economy, vice versa for the export-based economy [1, 3].
There are many established empirical analyses on the macroeconomic consequence of oil price shocks to net exporting countries, this is based on the dependency between oil price and the business cycle which can be explained through the impact of the oil price shocks on aggregate demand. Practitioners opined that an increase in oil price reduces aggregate supply since high energy prices mean that firms will purchase less energy. As a consequence, the productivity of any given volume of capital and labor will decline and leads to potential output loss. This invariably will lead to a decline in factors of production and real wages ([4, 5], p. 23; [6, 7]).
To expatiate further the influence of the oil price shocks on aggregate demand, Riaz et al. [5] submitted that oil is one of the basic inputs in manufacturing industries, any positive oil price shock increases the cost of manufacturing. As the cost of manufacturing rises the profit margins on investments fall will influence investors to postpone their irrevocable investments. Reductions in investment causes cuts in production level, consequently exports of the country are negatively affected and economy has to face adverse balance of trade. So also the effect permeates into households, oil price fluctuation induces the consumers to reschedule their expenditures on durable goods. This suggested that oil price shocks have serious concerns for all types of economies as aggregate demand is reduced from both consumption and investment sides. Increase in both oil prices and uncertainty in oil prices is detrimental for the economy (p. 24).
The negative effects of oil price shocks are more on the net-exporters of oil of the developing economies, the effect could be attributed to over-dependence on oil revenue, importation of basic necessity and susceptibility of their tradable lagging sectors to Dutch disease syndrome, the consequences of externalities, and economic pass-through (inflation) [8, 9, 10, 11, 12].
In the submissions of Abeng [8], opined that theoretically, an increase in oil price should reflect more revenue dividend for oil-exporting countries as it is expected to enhance foreign exchange earnings and build reserve in the short-run. Conversely, for net-importers of refined petroleum products for instance Nigeria with domestic regulation of oil prices (subsidies), oil price increase may not transform to the anticipated economic advantage, due to fiscal difficulties, restraining government’s ability to finance import in addition to meeting other international obligations (p.3). Nigerian has a deficit of ₦7114.49 and ₦8324.76 billion Naira for 2017 and 2018 periods for importation of non-oil products and spent about ₦2618.97 and ₦3833.82 billion on importation of refined petroleum product for the period of 2017 and 2018 [13]. These figures stress the vulnerability of the economy to the impulses of international oil price. The consequences may be unfavorable to economic growth arising from increased domestic production cost and decline in aggregate demand (p. 23).
In Ibrahim [14] remarks in studying the responses of non-oil productive sectors that is agriculture, manufacturing and service to shocks in change in oil price in Nigeria. In his submissions, the results obtained reveal that oil price impacted positively on aggregate output but negatively on agricultural, manufacturing and service sector suggesting that at the aggregate level, oil price is incline to increase aggregate output whereas an increase in oil price impacted negatively on the outputs of productive sectors as oil serves as an input factor in the production process of these sectors. This specifies that fluctuation in oil price creates uncertainty in the production capacity of the productive sectors and it also destabilizes the effectiveness of the government fiscal management of crude oil revenue.
Also Ayadi [15] posited that the forecast errors in industrial production are credited to volatility in real exchange rates and that changes in oil prices are only slightly important in influencing industrial production in Nigeria. Moreover, oil price changes affect real exchange rates, which, in turn, affect industrial production. He remarked that it should be noted that the indirect effect of oil prices on industrial production is not statistically significant. Therefore, the implication of the results presented in his paper is that an increase in oil prices does not cause an increase in industrial production in Nigeria.
According to [16, 17], the economy of Nigeria was affected by the decline in the revenue due to a fall in the price of crude oil alongside production. They cited that in about twenty months, the oil price has nosedived rapidly from as high as about one hundred and thirty dollars per barrel to as low as twenty-eight dollars and quantity also dropped from 2.15 Mbpd to 1.81 Mbpd in the earlier months of 2016, this resulted to a recession.
The crude petroleum industry is among the largest contributors to the economic growth, before the recession experienced by the country, in 2016 the growth rate shrank by −13.65%, a more substantial decline than that in 2015 of −5.45%. This reduced the oil sectors share of real GDP to 8.42% in 2016, compared to 9.61 per cent in 2015, (NBS, Q4 [18]). Aside from the contribution to the growth rate, the industry affects monetary variable and high unemployment rate [2]. According to Nweze and Edeme [19], as quoted by Adedokun [16], CBN [20] opined that on average, 75% of government revenues and on average 93% of foreign earnings from trade in goods and services, in the last ten years come from oil export, which informs part of the major sources used in financing the country’s imports.
Fluctuate in the price of natural resources is a term more related to the oil shocks because the majority of the problems encountered concerning recession is aggravated by a change in oil price. Hamilton [1], in his abstract, he opined that historical oil price shocks were principally caused by physical disruptions of supply, the price hike of 2007–2008 was caused by supply not meeting the excessive world demand. The consequences of recession are very similar with significant effects on consumption. According to Hamilton (1983) as cited by Sabiu [21], opined that ten out of eleven economic recessions were preceded by a sharp increase in oil prices in the United States.
Although, In a more recent development in the investigation of the causes of oil price shocks, many practitioners do not see supply as the sole cause of oil price shocks. The neo-monetarist, the likes of Bernanke et al. [22] sees oil and energy costs as insignificant relative to total production costs to account for the entire decline in output that, at least some events, has followed increases in the price of oil, they foresee that the monetary policy taken during spikes in the price of oil as the major contributing factors to the economic shocks.
Kilian [23] opined that historically, the decompositions of fluctuations in the real price of oil shows that oil price shocks have been driven mainly by a combination of global aggregate demand shocks and precautionary demand shocks, rather than oil supply shocks.
In furtherance to clear the air on the causes of oil price fluctuations, which was generally believed to have outgrown the traditional demand and supply factors, Humbatova and Hajiyev [24] made references, to the Er-Riad summit of 2007 where conclusions where reached on the oil market trend that, it is not related to OPEC decisions. They concluded that the current trend is due to financialisation factors, lack of production capacities in oil production, reduction in the world oil reserves, natural disasters, political events and processes.
The financialisation of oil market made oil a speculative commodity in the financial market contrary to the real commodity. This has been one among the major sources of oil price volatility [25, 26].
The exposure of the oil market to commodity market brought about the issue of speculation, that is investors’ expectations about future oil supply and demand. This breeds in the issue of inventory, either below or above the ground since oil can be stored. Others factors are the price of dollars, for net oil importers appreciation of dollar mean lower consumption of oil whereas the net exporters mean more revenue from the sales of oil, the reverse is the case when dollar price depreciate [26, 27].
The most recent factor in the front burner affecting fluctuation of oil price is the improvement of shale-oil technology (the shale revolution in the United States). The technological innovations that decreased the liquid fuel consumption and influenced the global energy markets to the point that many countries that are solely dependent on the oil resource plunged into economic crisis in 2016 due to falling in oil demand [26, 28]. Davig et al. [29] added that the fall in demand led to shifts in precautionary demand in the mid-2014 to mid-2015, this played a fundamental role in driving oil prices lower due to market glut and exacerbate the oil crisis to net exporters in 2016.
Fluctuation in the price of oil as a result of the aforesaid causes create the effect of uncertainty in the outputs of industries, not only to the manufacturing sector but also to the energy management sectors in process industries, that is oil and gas industries. According to Elder and Serletis [30] they posited that the theories of investment under uncertainty and real options predict that uncertainty about oil prices will tend to depress current investment. This uncertainty can be due to rise or fall in the oil prices.
Higher oil prices do come with a glade tidings for some industries. Apparently, they benefit oil and gas industries, but have both positive and negative multiplier effects to other components of an economy [31]. According to Hayes upstream firms face more hitches when oil prices fall since market forces is the determining factor at which oil is sold, and their costs of production are largely fixed. The higher the cost of production the higher the losses incurred by the producer. Downstream companies suffer a lesser consequences since they profit by purchasing crude oil and selling the refined products at a premium. Their earnings and profit margins always remain fairly stable even with fluctuating in oil prices. The submissions of Hayes is line with the suggestions of Jobert et al. [32] they posited that rise in the prices of oil are much desirable to the oil industries because they will make higher turnover, simultaneously, the rise in the oil prices correlate with waning outcomes for large capital expenditure projects for oil recovery. Large and capital-intensive drilling operations are hit harder in contrast to the smaller rigs, which can decide to shut down pending on when prices rise again.
Energy and the development of the shale oil is among the current drivers of US economy, new jobs opportunities has sprang up due to economy of scale (internal and external) for the Americans. Persistence, fall in oil price, could lead to folding up of operations for many onshore fracking wells that lack the working capital to continue drilling. Although the hydraulic fracturing is more expensive than typical drilling, so shale gas companies will be among the first hit if the cost of production prevail over profits [33].
According to Adesina [34], he made references to the local key oil and gas corporation having a rough time due to the fall in oil price in the recent time with prices lower than local production in Nigeria. The local oil firms are fighting hard to survive as Crude and remains at the $20, which means Nigeria’s crude is being sold at a loss, coupled with the fact that oil demand has plummeted to the lowest level in more than a generation.
While on the other side Deloitte [35] views was on the impact of the oil price collapse on company accounts, fall in oil price tends to increase risk of loss of assets. They opined that lower oil price forecasts mean lower future profits from an asset. These leads to reduction in the present value of the asset, and the asset values on balance sheets cannot be fully recovered, this results in write-off, and tendencies of knock-on effect connected to deferring taxes and holding company investment balances.
In Nigeria one of the major contributing factors for 2016 recession was fall in the price of oil coupled with decreased in quantity of production, the recession was accompanied by high inflation rate on basic commodities (cost-push) [16]. Monetary policy on inflation is always been informed by the general price level. Before the recession, the inflation rate was at a single digit of 8.0% and 9.55% per cent for 2014 and 2015 [36]. During the recession, the inflation rate was about 18.55% per cent that is in 2016 and as expected, the monetary authority introduced a tight monetary policy by raising the cost of borrowing, the interest rate was steady at 14% from July 2017 to the first quarter of 2018 against 2016 which was 200 points higher. This is against the backdrop of relative improvement in the global economy.
Saban et al. [37] Investigated the responses of monetary policy variables of select emerging markets to oil market shocks. Using conventional and Fourier Toda Yamamoto methods. In their findings, the oil prices are sensitive to structural shifts and, the causality approach with gradual/smooth shifts indicates oil price shocks influencing the currencies of Indonesia and South Africa, interest rates in Brazil and India, and inflation in South Africa and Turkey.
Also in the summaries of Santos and Chris [38], used Johansen (1992) co-integration approach and the Toda and Yamamoto [39] causality testing procedure. Applying Wald coefficient test, the nominal interest rates, and expected inflation co-move together, in the long run, there is a uni-directional causality from expected inflation to nominal interest rates as suggested by the Fisher hypothesis in the closed economy context. While in the open economy context, the result showed that the expected inflation and international variables do not contain information that predicts the nominal interest rate.
In the empirical findings of Mohammed and Jauhari [40], they employed asymmetric causality test based on Toda and Yamamoto [39] causality approach to further the causal relationship between exchange rate and inflation differentials in Brunei, Malaysia, and Singapore. The results show the existence of Granger causality running from positive cumulative exchange rate shocks to shocks in inflation differentials for Brunei and Malaysia. Also, the asymmetric causality for Singapore runs from both positive and negative cumulative domestic inflation shocks to positive and negative exchange rate shocks respectively.
Chibvalo et al. [41] in their submissions, they employed the Toda-Yamamoto approach to Granger causality to test for a causal relationship between inflation and trade openness in Zambia. They established a bi-directional causality between inflation and trade openness. Further, there exists a positive relationship between inflation and trade openness in Zambia.
This analysis aims at investigating the effect and the interrelations existing between the impact of oil price fluctuation on the monetary instrument (Exchange rate, Inflation, Interest rate). The data were sourced from the Central Bank of Nigeria (CBN), National Bureau of Statistics (NBS) and Nigeria National Petroleum Corporation (NNPC). The data cover a period of 1995–2018 and the data is monthly. All our variables are in local currency. Therefore we used oil price, the interbank exchange rate as a proxy for exchange rate data, while the prime lending rate was used as a proxy for data on the interest rate and we used consumer price index for all commodity as a proxy for inflation.
A Toda and Yamamoto model (1995) (TY-VAR) was adopted in estimating the Modified WALD Granger Non-causality test (MWALD), Forecast Error Variance Decomposition (FEVD) and Impulse Response Function (IRF).
According to Salisu [42], Sims [43] and Toda and Yamamoto (TY-VAR) [39], Vector auto-regressions (VARs) are one of the widely used classes of models in applied econometrics, used as tools both for prediction and for model building and evaluation. It success lied on its flexibility and ease of application when dealing with the analysis of multivariate time series.
Practitioners have recently shown that the conventional asymptotic theory does not apply to hypothesis testing in levels VAR’s if the variables are integrated or co-integrated [39, 43]. And one of the deficiencies of the VAR application is the inability to ascertain the a priori expectation of the variables whether the variables are integrated, co-integrated, or (trend) stationary. This necessitates pretesting(s) for a unit root(s) and co-integration in the economic time series, asarequisite for estimating the VAR model, and also when the intentions are prioritized towards the estimation of cointegration and vector error correction model [44].
Conversely, the powers of the unit and also simulation experiments of Johansen tests for co-integrating are very sensitive to the values of the nuisance parameters in finite samples and hence not very reliable for sample sizes that are typical for economic time series [39, 45, 46].
To alleviate these problems, Toda and Yamamoto [39] as quoted by Shakya [47], Giles [48] proposes the augmented VAR modeling, that is the modified Wald test statistic (MWALD), which is more superiority to the ordinary Granger - causality tests, the method is flexible and easy to apply, since one can test linear or nonlinear restrictions on the coefficients by estimating a levels VAR and applying the Wald criterion, paying little attention or circumventing the integration and cointegration properties of the time series data [42, 44]. However, the model is not a substitute for the conventional pre-testing in time series analysis, but as a complementary to the conventional VAR [49].
In estimating the MWALD test for Granger causality, it is prerequisite to determine the maximum possible order of the integration of the basic variables (dmax). Although, the variables could be a mixture of I (0), I (1), and I (2), in such condition, dmax = 2. The determination of the optimal lag length (k) is very important, to avoid overstating or understating the true value of lag, to evade biased estimates of accepting the null hypothesis when it should be rejected, vice versa. By identifying dmax and k, a level VAR model of order (k + dmax) is estimated and zero restrictions test is conducted on lagged coefficients of the regressors up to lag k. This process certifies that the Wald test statistics have an asymptotical chi-square (χ2) distribution whose critical values can be used to draw a valid inference and conclusion [39, 44].
The model used in this research work borrowed a leave from the Toda and Yamamoto model (1995) as iterated in the work of Saban et al. [37], their model was adopted in this paper, to finding the inter-relationship between oil price and monetary variables. While they consider Granger Non-causality and structural shift, in our model we considered Granger Non-causality test, and substitute structural shift with Impulse Response Function (IRFs) and Forecast Error Variance Decomposition (FEVD). The TY-VAR is given by:
Where
The analysis aims at establishing the interrelationship that exist among the variables; i.e. oil price (lnoilpr), and monetary policy variable i.e. exchange rate (lnexchr), interest rates (lnintr), and inflation (lncpi). The specification considers each variable expressed as independent in the model as a function of its lag and the lag of other variables in the model. Here the exogenous error terms
Where
Although, the Todo-Yamamoto model, the MWALD test was introduced for ease of estimation by circumventing the presence of unit roots pre-testing problem, nevertheless, there is the need to determine the maximum order of integration of the variables, which is necessary for estimation of The MWALD test for Granger causality by Toda and Yamamoto [39]. Therefore, we ran the test for the Augmented Dickey-Fuller (ADF) test, Phillips – Perron (PP) test and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) unit root test, to ascertain the stationarity of the variables [45, 50, 51, 52, 53, 54].
From Tables 1 and 2, the unit-roots tests confirmed all our process to be considered integrated at the first difference and 1% level of significance using Augmented Dickey-Fuller (ADF) test and Phillips – Perron (PP).
Variable | ADF | |||||||
---|---|---|---|---|---|---|---|---|
Level | First Difference | |||||||
Constant | Prob. | Constant & Trend | Prob. | Constant | Prob. | Constant & Trend | Prob. | |
lnoilpr | −1.2206 | 0.6663 | −2.3779 | 0.3904 | −14.3220*** | 0.0000 | −14.3037*** | 0.0000 |
lnexchr | 0.3070 | 0.9784 | −1.5899 | 0.7949 | −11.6443*** | 0.0000 | −11.6786*** | 0.0000 |
lncpi | −1.4401 | 0.5626 | −5.3282*** | 0.0000 | −13.3181*** | 0.0000 | −13.3666*** | 0.0000 |
lnintr | −1.8216 | 0.3696 | −2.3214 | 0.4250 | −16.2688*** | 0.0000 | −16.2400*** | 0.0000 |
ADF stationarity tests.
Note: ***, ** and * denote significance at 1%, 5% and 10% respectively. ADF test the null hypothesis of ‘not stationary’ against the alternative of ‘stationary’. Source: E-views Version 9 software was used in the estimation.
Variable | PP | |||||||
---|---|---|---|---|---|---|---|---|
Level | First Difference | |||||||
Constant | Prob. | Constant & Trend | Prob. | Constant | Prob. | Constant & Trend | Prob. | |
lnoilpr | −1.2921 | −2.3897 | −14.3491*** | −14.3312*** | ||||
lnexchr | 1.0660 | −1.5040 | −9.8974*** | 0.0000 | −9.8872*** | 0.0000 | ||
lncpi | −1.7664 | 0.3968 | −5.5627*** | −13.2950*** | 0.0000 | −13.3455*** | 0.0000 | |
lnintr | −1.9316 | −2.4972 | −16.2641*** | 0.0000 | −16.2351*** | 0.0000 |
PP stationarity tests.
Note: Just like the ADF, the PP unit root test has the null hypothesis of ‘not stationary’ against the alternative, which is ‘stationary’. *, ** and *** indicate the level of significance at 10%, 5% and 1% respectively. Source: E-views Version 9 software was used in the estimation.
While Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) in Table 3 is in contrast to ADF and PP which indicated that the variables are at levels. This corroborates with the work of Yakubu and Abdul Jalil in their test of stationarity. A quick check on the line graphs in Figure 1 indicated that all the variables are at first difference I(1). Therefore, we stick to ADF and PP, and agree that dmax = 1.
Variable | KPSS | |||||||
---|---|---|---|---|---|---|---|---|
Level | First Difference | |||||||
Constant | Prob. | Constant & Trend | Prob. | Constant | Prob. | Constant & Trend | Prob. | |
lnoilpr | 1.8432*** | 0.2905*** | 0.0615 | 0.0359 | ||||
lnexchr | 1.7493*** | 0.2035** | 0.1959 | 0.0771 | ||||
lncpi | 0.2299*** | 0.1406* | 0.2440 | 0.1035 | ||||
Intr | 0.9826*** | 0.1353* | 0.0457 | 0.0454 |
KPSS stationarity tests.
Note: In contrast to ADF and PP, KPSS unit root test has the null hypothesis of ‘stationarity’ against the alternative, ‘not stationary’. ***, ** and * represent 1%, 5% and 10% level of significance respectively. Source: E-views Version 9 software was used in the estimation.
Graphical representation of original series at I(1) for oil price (doilpr), exchange rate (dexcri), CPI (dcpi) and interest rate (dintr).
The Modified Wald (MWALD) Test for Granger Causality requires the determination of optimal lag which is presented in Table 4. By default, we use LR: sequentially modified LR test statistic, FPE: Final prediction error, AIC; Akaike information criterion, SBC: Schwarz information criterion and Hannan-Quinn information criterion to determine the optimal lag for the estimation of VAR system. The SC and HQ minimize its value at lag 2 while LR and FPE minimizes at lag 3. According to Liew [55], Asghar and Abid [56] Estimating the lag length of the autoregressive process for a time series is imperative in econometrics. The selection is done to minimize the chance of underestimation while at the same time maximizing the chance of recovering the true lag length. Another important aspect of the lag selection criteria is to overcome the structural break. Though, studies indicated that HQC is found to surpass the rest by correctly identifying the true lag length. In contrast, AIC and FPE are better choices for a smaller sample. In Table 4 out of the two criteria, we propose three lags (lag 3) as the optimal lag.
Endogenous variables: LNOILPR LNEXCHR LNCPI LNINTR | ||||||
---|---|---|---|---|---|---|
Lag | LogL | LR | FPE | AIC | SC | HQ |
0 | 1024.270 | NA | 8.68e-09 | −7.210389 | −7.158863 | −7.189729 |
1 | 3293.435 | 4458.148 | 1.05e-15 | −23.13382 | −22.87619 | −23.03052 |
2 | 3342.568 | 95.13951 | 8.35e-16 | −23.36797 | −22.90424* | −23.18203* |
3 | 3364.257 | 41.38540* | 8.02e-16* | −23.40817* | −22.73834 | −23.13959 |
4 | 3375.620 | 21.36093 | 8.29e-16 | −23.37540 | −22.49947 | −23.02418 |
5 | 3381.763 | 11.37514 | 8.89e-16 | −23.30575 | −22.22371 | −22.87189 |
VAR lag order selection criteria.
indicates lag order selected by the criterion. LR: sequential modified LR test statistic (each test at 5% level), FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion and HQ: Hannan-Quinn information criterion. Source: E-views Version 9 software was used in the estimation.
The orthogonal impulse response are based on recursive causal ordering, if the ordering is reversed different sets of structural shocks will be identified, and this gives a different impulse response function (IRF) and forecast error variance decomposition (FEVD), except if the error terms contemporaneous correlations are low [57]. According to Lutkepohl [58] given a sample size of T, the determinant of the reordering of the variables is given by
The ordering of variables suggested by Sims (1981, 1980) as iterated in the work of Yakubu and Abdul Jalil [44], Duasa [46], is to start with the most exogenous variables in the system and ended by the most endogenous variable. Table 5 shows the residual correlation matrix result, the result shows that there is no instantaneous correlation between the variables because the variables are not significantly different from zero (at a 5% level of significance) [59]. This is based on the sample size in this analysis, we need at least a correlation of 31% that is above 5% level of significance to satisfy the call for reordering of the variables. Since there is no strong correlation among the variable we assumed the arrangement of our variables are in order.
LNOILPR | LNEXCHR | LNCPI | LNINTR | |
---|---|---|---|---|
LNOILPR | 1.000000 | |||
LNEXCHR | 0.156275 | 1.000000 | ||
LNCPI | 0.025236 | 0.038583 | 1.000000 | |
LNINTR | 0.052056 | 0.144681 | −0.057944 | 1.000000 |
Correlation matrix for TY-VAR.
Before the estimation of the Causality Test, Forecast Error Variance Decomposition (FEVD) and Impulse Response Functions (IRFs). The VAR residual serial correlation test is needed to verify the adequacy of the lag selection criterion used in the estimation of a chosen multivariate model, it is applied to test a set of restrictions on a model that is unrestricted, and it is based on the restricted maximum likelihood test (ML) [42, 60, 61]. From the TY-VAR estimated output for the residual serial correlation test in Table 6, the null hypothesis for the test is that there is no serial correlation. The result submits that there is no evidence of serial correlation. Which indicate the acceptance of the null hypothesis that the restriction (lags) place on the model is adequate.
Lags | LM-Stat | Prob |
---|---|---|
1 | 13.85744 | 0.6093 |
2 | 8.875657 | 0.9184 |
3 | 15.67327 | 0.4760 |
4 | 12.71378 | 0.6936 |
TY-VAR residual serial correlation LM tests.
Probs from chi-square with 16 df.
In the test for normality, to examine whether the residuals are normally distributed. We employed the null hypothesis H0: residuals are normally distributed. From Table 7 we rejected the null hypothesis of normality of residuals of each equation as well as all the equations combined at 5% level of significance since p-value of all the variables are zero. Hence, we concluded that residuals are not normally distributed [62].
Component | Jarque-Bera | df | Prob. |
---|---|---|---|
1 | 15.36714 | 2 | 0.0005 |
2 | 4572.449 | 2 | 0.0000 |
3 | 389.0131 | 2 | 0.0000 |
4 | 382.0722 | 2 | 0.0000 |
Joint | 5358.902 | 8 | 0.0000 |
Jarque-Bera normality test result.
df and Prob stands for the degree of freedom and probability
Although, the credibility of Iarque-Bera test of normality with application to VAR has been questioned specifically for an I(1). Jarque-Bera normality of the series does not guarantee normality of distributions, it only signifies normality of the first four moments of a distributions [58]. According to Lutz and Ufuk [63] in their remarks, they posited that Jarque-Bera test based on asymptotic critical values can be very unreliable. In their submissions, they gave the asymptotic critical values of 1–100% in their Monte Carlo analysis of VAR. They presented that the size distortions of the asymptotic test persevere even for sample sizes as large as 5000 observations.
From Table 8 we have the lnoilpr as the dependent variable, at 5% level of significance, we accept the null hypothesis that there is no causality between, the lnexchr, lncpi and lnintr on the dependent variable. Also, the combination of all the independent variables do not granger caused changes in the dependent variable. This indicates the exogeneity of oil price which is been determined by many factors that are exogenous to both net importers and exporters of oil, Nigerian inclusive. According to Humbatova and Hajiyev [24] posited that the determinants of oil price range from financial factors, lack of production capacities in oil production, the decline in the world oil reserves, natural disasters, political events and processes, and no one country has the monopoly of determining oil price.
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNEXCHR | 0.297326 | 3 | 0.9605 |
LNCPI | 2.517571 | 3 | 0.4721 |
LNINTR | 2.072927 | 3 | 0.5574 |
All | 5.503884 | 9 | 0.7884 |
Granger causality test WALD test for Eq. (2) for the dependent variable: LNOILPR.
From Table 9 we have the lnexchr as the dependent variable, at 10% level of significance, we reject the null hypothesis that there is no causality between loilpr and lnexchr. The exchange rate plays a significant role in determining the oil price both to net exporters and net importers. Specifically, oil is priced in U.S. dollars. According to Farley [64] submissions, each decrease and increase in the dollar or the price of the commodity (oil) generates an instantaneous realignment between the US dollar and other currencies. These correlated is more significant in countries with significant oil reserves that depend largely on crude exports and they experience more economic damage than those with more diverse resources. In the presentations of Bützer [65], he established that oil Net exporters tend to respond against depreciation pressures by running down foreign exchange reserves, particularly after oil demand shocks, but also global demand shocks (which also decrease oil prices). This is sometimes supplemented by a nominal depreciation of exchange rates. These invariably indicate that oil demand shocks are a relevant factor for their exchange rates. While we accept the null hypothesis that there is no causality between, the lncpi and lnintr on the dependent variable. Also, the combination of all the independent variables do not Granger cause changes in the dependent variable.
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNOILPR | 6.426225* | 3 | 0.0926 |
LNCPI | 2.889761 | 3 | 0.4089 |
LNINTR | 1.567570 | 3 | 0.6668 |
All | 11.29767 | 9 | 0.2559 |
Granger causality test WALD test for Eq. (3) for the dependent variable: LNEXCHR.
Also from Table 10 we have the lncpi as the dependent variable, at 10% level of significance, we reject the null hypothesis and accept the alternative hypothesis that there is causality from lnexchr and linintr to lncpi. Exchange rate plays a vital role in determining prices in Nigeria, as an economy that has some element of a Dutch disease syndrome, and relied heavily on importation of basic necessity, when we factor out oil exportation from the total export, the non-oil balance of trade approximately stood at negative 7114 billion for 2017 as stated in our introduction. Therefore, appreciation in the exchange rate can cause inflation (lncpi) (Katz, 1973). The interest rate is one of the instruments used by the monetary authority to regulate the economy either during inflation or deflationary periods, the interest rate affects the demand and allocation of the available loanable funds the level, and pattern of consumption and investment ([66] p. 15). Before 2016 recession in Nigeria, the inflation rate was at a single digit of 9.55% in 2015, during the recession, the inflation rate was at double-digit 18.55% in 2016 and the central bank introduced a tight monetary policy, by raising the interest rate steady at 14 per cent from July 2017 to the first quarter of 2018 against 2016 which is 200 points higher [36].
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNOILPR | 1.151935 | 3 | 0.7646 |
LNEXCR | 6.824049* | 3 | 0.0777 |
LNINTR | 7.771454* | 3 | 0.0510 |
All | 14.75625** | 9 | 0.0979 |
Granger causality test WALD test for Eq. (4) for the dependent variable: LNCPI.
Also, the combination of all the independent variables (lnoilpr, lnexchr and lnintr) does Granger cause changes in the dependent variable lncpi at 5%, but lnexchr and lnintr are more pronounced in the causality. While we accept the null hypothesis that lnoilpr do not granger cause lncpi.
In Table 11 we have lnintr as the dependent variable, we reject the null hypothesis and accept the alternative hypothesis that at 5% levels of significance that there is a causality which is from lnoilpr and lnexchr to the endogenous variable lnintr, while there is no any causality with the log of lncpi on the dependent variable. Also, the combination of all the independent variables Granger cause changes in the dependent variable at a 5% level of significance. The relationship of lnoilpr and lnintr may not be exclusive but via the exchange rate, in the boom period the net exporter of oil has more dollars to expend, vice versa during deflationary periods, both periods has a direct link to economic growth. To avoid these inflationary or deflationary tendencies, the central bank may engage in the sterilization process through open market operation, by manipulating the short-term interest rate, that is by increasing interest rates to discourage borrowing during inflationary periods or decrease the interest rate to encourage borrowing during deflationary periods. The relation is said to be inverse and this shows how oil price and exchange rate influences the monetary policy of net oil exporters.
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNOILPR | 14.66233** | 3 | 0.0021 |
LNEXCR | 10.44319** | 3 | 0.0152 |
LNCPI | 3.488718 | 3 | 0.3222 |
All | 31.49615** | 9 | 0.0002 |
Granger causality test WALD test for Eq. (5) for dependent variable: LNINTR.
From the estimated TY-VAR, we compute forecast error variance decompositions (FEVD and impulse response functions (IRF), which serve as means for evaluating the dynamics of the interrelationship, interactions, and strength of causal relations among the variables in the system. The impulse response functions trace the effects of a shock to one endogenous variable on to the other variables in the VAR, variance decomposition separates the variation in an endogenous variable into the component shocks to the VAR [10, 46].
In simulating FEVD and IFRs, the VAR innovations can be contemporaneously correlated. That is a shock in one variable can work through the contemporaneous correlation with innovations in other variables. The responses of a variable to innovations in another variable of interest cannot be adequately represented in isolation, due to the facts that shock to individual variables cannot be separately identified due to contemporaneous correlation [46].
In our analyses, we applied Cholesky approach which uses the inverse of the Cholesky factor of the residual covariance matrix to orthogonalise impulses (innovations) as recommended by Sims (1980) as quoted by Duasa [46] and (Breitung, Bruggemann, and [58]) to solve this identification problem. The strategy requires a pre-specified causal ordering of the variables, which we estimated in Table 5 for the correlation matrix. The results of FEVD are displayed in Tables 12–15, while the IRFs represented in Figures 2–17 in appendix 1, respectively.
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
---|---|---|---|---|---|
1 | 0.039283 | 100.0000 | 0.000000 | 0.000000 | 0.000000 |
2 | 0.059667 | 99.56602 | 0.007555 | 0.357862 | 0.068566 |
3 | 0.074387 | 99.31622 | 0.077847 | 0.518729 | 0.087200 |
4 | 0.087239 | 99.17720 | 0.135794 | 0.615055 | 0.071949 |
5 | 0.099720 | 99.16728 | 0.123200 | 0.650960 | 0.058563 |
6 | 0.112282 | 99.16645 | 0.102858 | 0.650544 | 0.080151 |
12 | 0.191020 | 98.36406 | 0.104402 | 0.630791 | 0.900743 |
18 | 0.276129 | 96.71609 | 0.060657 | 0.908562 | 2.314688 |
24 | 0.366613 | 94.33976 | 0.064427 | 1.477383 | 4.118426 |
30 | 0.457642 | 91.03687 | 0.223173 | 2.331971 | 6.407984 |
36 | 0.541764 | 86.40256 | 0.693518 | 3.520289 | 9.383636 |
42 | 0.611323 | 79.78047 | 1.802594 | 5.120937 | 13.29600 |
43 | 0.621214 | 78.43050 | 2.090483 | 5.429983 | 14.04904 |
44 | 0.630655 | 77.00398 | 2.418306 | 5.749531 | 14.82819 |
45 | 0.639696 | 75.50135 | 2.790808 | 6.077919 | 15.62992 |
46 | 0.648412 | 73.92544 | 3.212962 | 6.412711 | 16.44889 |
47 | 0.656906 | 72.28230 | 3.689787 | 6.750497 | 17.27741 |
48 | 0.665310 | 70.58226 | 4.226078 | 7.086683 | 18.10498 |
Variance decomposition of LNOILPR.
Note: SE refers to the total variance error in forecasting LNOILPR. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables.
Variance Decomposition of LNEXCHR: | |||||
---|---|---|---|---|---|
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
1 | 0.008667 | 2.442191 | 97.55781 | 0.000000 | 0.000000 |
2 | 0.016018 | 1.303029 | 98.47056 | 0.226099 | 0.000307 |
3 | 0.020768 | 0.793908 | 98.43015 | 0.646775 | 0.129165 |
4 | 0.024011 | 0.693289 | 97.87271 | 1.034284 | 0.399717 |
5 | 0.026961 | 0.553215 | 97.54321 | 1.309243 | 0.594331 |
6 | 0.030343 | 0.647208 | 97.17916 | 1.485892 | 0.687736 |
12 | 0.059365 | 4.366737 | 92.68622 | 2.025025 | 0.922015 |
18 | 0.109801 | 12.31598 | 84.79549 | 2.160683 | 0.727839 |
24 | 0.199812 | 21.01359 | 76.31208 | 2.242682 | 0.431654 |
30 | 0.358345 | 28.27847 | 69.15410 | 2.361025 | 0.206413 |
36 | 0.633138 | 33.57260 | 63.83346 | 2.514318 | 0.079625 |
42 | 1.103690 | 37.11351 | 60.17595 | 2.683471 | 0.027067 |
43 | 1.209424 | 37.56150 | 59.70328 | 2.711909 | 0.023305 |
44 | 1.324903 | 37.97407 | 59.26509 | 2.740223 | 0.020615 |
45 | 1.451006 | 38.35307 | 58.85970 | 2.768351 | 0.018878 |
46 | 1.588692 | 38.70034 | 58.48544 | 2.796232 | 0.017984 |
47 | 1.739007 | 39.01764 | 58.14072 | 2.823810 | 0.017829 |
48 | 1.903092 | 39.30669 | 57.82396 | 2.851035 | 0.018316 |
Variance decomposition of LNEXCHR.
Note: SE refers to the total variance error in forecasting LNEXCHR. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables.
Variance Decomposition of LNCPI: | |||||
---|---|---|---|---|---|
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
1 | 0.006843 | 0.063687 | 0.122994 | 99.81332 | 0.000000 |
2 | 0.010614 | 0.111687 | 1.169617 | 97.78015 | 0.938541 |
3 | 0.013902 | 0.104867 | 1.709240 | 96.72400 | 1.461890 |
4 | 0.016436 | 0.118843 | 2.348794 | 96.05369 | 1.478675 |
5 | 0.018494 | 0.094052 | 3.766938 | 94.89832 | 1.240691 |
6 | 0.020348 | 0.110542 | 6.716716 | 92.14406 | 1.028684 |
12 | 0.034150 | 0.390382 | 43.39555 | 54.73213 | 1.481945 |
18 | 0.058790 | 0.800621 | 71.63000 | 26.51978 | 1.049596 |
24 | 0.102887 | 2.477121 | 83.86813 | 13.27275 | 0.382003 |
30 | 0.182422 | 6.425699 | 86.02039 | 7.403793 | 0.150115 |
36 | 0.326127 | 12.33701 | 82.73589 | 4.811637 | 0.115460 |
42 | 0.583692 | 18.92647 | 77.30909 | 3.668364 | 0.096074 |
43 | 0.642926 | 19.99991 | 76.35470 | 3.554354 | 0.091028 |
44 | 0.708053 | 21.05415 | 75.40480 | 3.455500 | 0.085552 |
45 | 0.779631 | 22.08580 | 74.46447 | 3.369987 | 0.079739 |
46 | 0.858270 | 23.09198 | 73.53809 | 3.296234 | 0.073694 |
47 | 0.944635 | 24.07026 | 72.62936 | 3.232858 | 0.067521 |
48 | 1.039451 | 25.01867 | 71.74135 | 3.178651 | 0.061323 |
Variance decomposition of LNCPI.
Note: SE refers to the total variance error in forecasting LNCPI. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables.
Variance Decomposition of LNINTR: | |||||
---|---|---|---|---|---|
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
1 | 0.011298 | 0.270981 | 1.911153 | 0.411721 | 97.40614 |
2 | 0.015682 | 1.162856 | 3.384292 | 0.236829 | 95.21602 |
3 | 0.019164 | 0.778732 | 7.551086 | 0.251113 | 91.41907 |
4 | 0.021868 | 1.545252 | 10.35243 | 0.690563 | 87.41175 |
5 | 0.024147 | 4.317860 | 10.80310 | 1.548061 | 83.33098 |
6 | 0.026278 | 8.517769 | 10.19189 | 2.639437 | 78.65090 |
12 | 0.042469 | 43.36535 | 7.083532 | 7.537991 | 42.01312 |
18 | 0.068739 | 68.22092 | 7.817425 | 6.432329 | 17.52932 |
24 | 0.105922 | 75.25005 | 13.09986 | 4.196117 | 7.453977 |
30 | 0.154692 | 69.22610 | 23.97069 | 2.633864 | 4.169344 |
36 | 0.219876 | 52.16768 | 42.34294 | 1.710601 | 3.778773 |
42 | 0.320347 | 28.71560 | 65.93762 | 1.220443 | 4.126342 |
43 | 0.343392 | 25.06914 | 69.62387 | 1.173901 | 4.133092 |
44 | 0.369026 | 21.71282 | 73.04019 | 1.136972 | 4.110024 |
45 | 0.397622 | 18.71892 | 76.11686 | 1.109481 | 4.054735 |
46 | 0.429590 | 16.14604 | 78.79614 | 1.091170 | 3.966652 |
47 | 0.465375 | 14.03524 | 81.03610 | 1.081687 | 3.846977 |
48 | 0.505456 | 12.40799 | 82.81296 | 1.080569 | 3.698485 |
Variance decomposition of LNINTR.
Cholesky Ordering: LNOILPR LNEXCHR LNCPI LNINTR. Note: SE refers to the total variance error in forecasting LNINTR. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables.
Impulse response function of lnoilpr to lnoilpr.
Impulse response function of lnoilpr to lnexchr.
Impulse response function of lnoilpr to lncpi.
Impulse response function of lnoilpr to lnintr.
Impulse response function of lnexchr to lnoilpr.
Impulse response function of lnexchr to lnexchr.
Impulse response function of lnexchr to lncpi.
Impulse response function of lnexchr to lnintr.
Impulse response function of lncpi to lnoilpr.
Impulse response function of lncpi to lnexchr.
Impulse response function of lncpi to lncpi.
Impulse response function of lncpi to lnintr.
Impulse response function of lnintr to lnoilpr.
Impulse response function of lnintr to lnexchr.
Impulse response function of lnintr to lncpi.
Impulse response function of lnintr to lnintr.
We explored the Cholesky factorization in the E-Views software and forecast the interrelationship of the variables up 48 months equal to 4 years. Table 10 is the Table for FEVD for lnoilpr as a dependent variable for 48 periods (4 years) forecast. In forecasting a variable, shocks in the residual of the forecasted variable contribute more to its variance than the shocks in other variables in the first period. The shocks in oil price-output contributed more to its variance, from 100% in the first period down to 70.58% in the 48 period (4th year) of the forecast period. This is followed by lnintr that contributed 4.11% in the 24th period to about 18.11% in the 48 period (4th year). This followed by lncpi that contributed 1.48% at the 24th period to 7.09 at the 48 periods and last is the lnexchr contributions from 0.06% in the 24th period to 4.22% in the 48 periods. This shows monetary policy influences the fluctuation inherent with the oil price and in the future, it shows that lnintr will respond highly to oil price shocks. While the contemporaneous relationship between the oil prices as the endogenous variables (lncpi and lnexchr) in our model are very insignificant. This is an indication that it will take a longer time into the future, for variables other than lnintr to influence the impact of oil prices.
Table 13, is the Variance Decomposition for dependent variable lnexchr, the contributions to itself were 97.56% in the 1st period, to about 57.82% in the 48 period (4th year) into the future. This followed by the contributions of lnoilpr with 28.28% at the 24th period and 39.31% at the 48th period. While lncpi and lnintr contributed 2.58% and 0.02% all at the 48th period. The error variance in forecasting lnexchr from lnoilpr is high, which indicates that shocks in the residuals of lnoilpr will have much effect in determining the lnexchr in the future.
Table 14 is forecast error variance decomposition of LNCPI as the predictant, the predictant contributes 99.81%, 54.73%, 3.18% in the 1st, 12th and 48th periods to itself, which indicates that the contributions of lncpi to itself declined in 4 years. While lnexchr contributes more to the error variance in forecasting lncpi, contributing about 43.40% up to 82.74% for the periods 12th and 36th then declined to 71.74%in the 48th period (4th year). While lnoilpr contributions started from 24th period with 2.47% and keep increasing up to 25.02% in the 48th period. Whereas lnintr contributions are insignificant. This has brought a clearer picture that lnexchr and lnoilpr are the major determinant of inflation in the economy.
Table 15 illustrated the forecast error variance decomposition of lnintr, contributing to its future error variation of 97.41%, 42.01% and 54.34% for the 1st, 12th and declined to 3.70% at the 48th period (4th year), this is followed by lnexchr which contributes 1.91%, 10.19% for the 1st and 6th periods, it declined for some periods and pick up again and continue rising to 82.81% in the 48th period (4th year).
This is trailed behind by lnoilpr, contributing 4.32% and 43.37% in the 6th and 12th, 75.25% at 24th period and started declining up to 12.41% at the 48th period (4th year). This indicates also a strong relationship into the future. The forecast error variance decomposition of the variables estimates also coincides with the result we obtained in the estimates we derived in Table 11, which also indicates that our estimates are good to go with for future implementation of policies.
In Figure 2, from appendix 1, the Oil price (lnoilp) responded contemporaneously by the change in its own shocks, which is positive and not dissipating. The implication is that hick in the price of oil may mean high revenue, but the consequences is, as an import based economic of non-oil goods and refined petroleum product, with domestic regulation of prices (subsidies), the policy will confine government’s ability to finance the import bills as well as meet other international obligations [8]. While the response of oil price (lnoilpr) to change in Exchange rate (lnexchr) is insignificant in Figure 3. Inflation (lncpi), and Interest rate (lnintr) in Figures 4, and 5 showed some level of positive response.
In Figure 6, there is a slightly positive response of Exchange (lnexchr) to change Oil price (lnoilpr) in the sixth lag period. This show how influential oil is in determining exchange rate, since high price of oil means more revenue (foreign income), also Exchange (lnexchr) responded instantaneously, a positive response, to change in its self (Figure 7.). In Figure 8, there is slight positive response of lnexchr to change in lncpi and Figure 9 showed a small inverse response of lnexchr to change in lnintr.
In Figures 10 and 13, Inflation (lncpi) did not show a meaningful response to orthogonal change in the price of oil (lnoilpr) and Interest rate (lnintr). While Figure 11, showed a positive response in Inflation (lncpi) to change in the Exchange rate (lnexchr), that is from the second lag period up to the tenth lag period in increasing order, this indicate that inflation will continue since the response is not dissipating unless there is a policy to induce deflation. Whereas in Figure 12 there is an instantaneous response of Inflation (lncpi) to change in Inflation (lncpi) in a high positive level, with a slight drop towards the tenth period which indicates tendencies of achieving normality in the future.
Figure 14, showed that there is an inverse response of Interest rate (lnintr) to one standard deviation change in the price of oil (lnoilpr) from the second lag period in an increasing order up to the tenth period, this is expected because the assumption is that interest rate has an inverse relationship with the oil price. Also Figure 15 indicated an instantaneous positive response of interest rate (lnintr) to change in the Exchange rate (lnexchr), in the third and fourth period, before it dying off which indicates that there is propensities of achieving normality in the long run. In Figure 16 Interest rate (lnintr) responds contemporaneously to change in Inflation (lncpi), with a positive increase from the fourth period and finally, in Figure 17 Inflation (lncpi) responded significantly to change Inflation (lncpi). The impulse response functions further complement the Forecast Error Variance Decomposition by given a portrait of the direction of the inter-relationships of variables.
In this research work, we explored the Toda-Yamamoto Modified Wald Test (MWALD) to examine the impact of oil price fluctuation on the monetary instrument in Nigeria, by looking at their causal relationships. The study covered the period 1995 to 2018 and the data are monthly data, to establish the contemporaneous relationships between these macroeconomic indicators. Among other analyses are the Granger Causality, FEVD and IRFs.
The review showed the direction of causality and FEVD into the future for 48 months equivalent to four years (short-run), between oil price, Exchange rate, Inflation, and Interest rate.
From the analyses of Toda-Yamamoto Granger Causality WALD Test, the review presented that there is unidirectional causality from lnoilpr to lnexchr in Table 9. This is consistence with the result we obtained in the estimation forecast error variance decomposition of lnexchr (Table 13) as the predictant, where the predictant contributes 97.56% in the 1st period, to about 57.82% in the 48 period (4th year) into the future. This was followed by the contributions of lnoilpr with 28.28% at the 24th period and 39.31% at the 48th period. While lncpi and lnintr contributed 2.58% and 0.02% all at the 48th period. This was also complemented by for IRFs in Figure 7 in the appendix.
Also from granger causality of lncpi as a dependent variable in Table 10 there is unidirectional causality from lnexchr and lnintr to lncpi, also the combination of all the three independent variables (lnoilpr, lnexchr and lnintr) granger cause lncpi but lnexchr and lnintr have more contributions. This is also in tandem with the result of FEVD for dependent variable lncpi in Table 14 where the dependent variable contributions to itself were 99.81%, 54.73%, 3.18% in the 1st, 12th and 48th periods, which indicates that the contributions of lncpi to itself declined in 4 years. While lnexchr contributes more to the error variance in forecasting lncpi, contributing about 43.40% up to 82.74% for the periods 12th and 36th periods (3rd years) then declined to 71.74%in the 48th period (4th year). While lnoilpr contributions started from 24th period with 2.47% and keep increasing up to 25.02% in the 48th period (4th year). This is also affirmed in Figure 11 in the appendix.
Similarly in the estimation of Granger Causality WALD Test for lnintr, it responded positively to change in lnoilpr and lnexchr. This is also in agreement with the estimation of forecast error variance decomposition of lnintr as an endogenous variable, contributing to its future error variation of 97.41%, 42.01% and 54.34% for the 1st, 12th periods and declined to 3.70% at the 48th period (4th year), this is followed by lnexchr which contributes 1.91%, 10.19% for the 1st and 6th perods, it declined for some periods and pick up again and continue rising to 82.81% in the 48th period (4th year). This is trailed behind by lnoilpr, contributing 4.32% and 43.37% in the 6th and 12th, 75.25% at 24th period and started declining up to 12.41% at the 48th period (4th year). This indicated that the major determinant factors of interest rate policy in Nigeria are change in price of oil and exchange rate in the long run. This also conforms to the outcome of the IRF in Figure 14, which specified further that the relation between lnintr and lnoilpr is an inverse relationship, while lnexchr, lncpi and lnintr in Figures 15–17 are positive.
The object of this is work is to establish a direct link between oil price and some selected monetary instruments in Nigeria, and our a priori expectations were achieved, we were able to established that oil price has a direct influence on the exchange rate, interest rate and inflation rate. It is known facts that Nigeria is an oil-producing economy and at the same time also an import-based economy of non-oil products. The major sources of financing the import come from oil revenue. As an oil-producing economy, there are tendencies of having Dutch disease syndrome and economic pass-through [9]. Both in theory and empirical analyses one can conclude that oil price is a strong determining factor of the rate of exchange, it has a direct link to inflationary or deflationary tendencies and also influences the monetary policies in Nigeria in terms of cost of borrowing.
Therefore, in implementation of monetary policy by the policymakers, attention should be drawn to price level of import from the external market, that is by concurrently monitoring the domestic market and the economy of the country’s trading partners. On a general note, there should be diversification of the economy from oil to the non-oil economy to avoid the Dutch disease syndrome.
Authors are listed below with their open access chapters linked via author name:
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\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nJocelyn Chanussot (chapter to be published soon...)
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\\n\\nAbdul Latif Ahmad 2016-18
\\n\\nKhalil Amine 2017, 2018
\\n\\nEwan Birney 2015-18
\\n\\nFrede Blaabjerg 2015-18
\\n\\nGang Chen 2016-18
\\n\\nJunhong Chen 2017, 2018
\\n\\nZhigang Chen 2016, 2018
\\n\\nMyung-Haing Cho 2016, 2018
\\n\\nMark Connors 2015-18
\\n\\nCyrus Cooper 2017, 2018
\\n\\nLiming Dai 2015-18
\\n\\nWeihua Deng 2017, 2018
\\n\\nVincenzo Fogliano 2017, 2018
\\n\\nRon de Graaf 2014-18
\\n\\nHarald Haas 2017, 2018
\\n\\nFrancisco Herrera 2017, 2018
\\n\\nJaakko Kangasjärvi 2015-18
\\n\\nHamid Reza Karimi 2016-18
\\n\\nJunji Kido 2014-18
\\n\\nJose Luiszamorano 2015-18
\\n\\nYiqi Luo 2016-18
\\n\\nJoachim Maier 2014-18
\\n\\nAndrea Natale 2017, 2018
\\n\\nAlberto Mantovani 2014-18
\\n\\nMarjan Mernik 2017, 2018
\\n\\nSandra Orchard 2014, 2016-18
\\n\\nMohamed Oukka 2016-18
\\n\\nBiswajeet Pradhan 2016-18
\\n\\nDirk Raes 2017, 2018
\\n\\nUlrike Ravens-Sieberer 2016-18
\\n\\nYexiang Tong 2017, 2018
\\n\\nJim Van Os 2015-18
\\n\\nLong Wang 2017, 2018
\\n\\nFei Wei 2016-18
\\n\\nIoannis Xenarios 2017, 2018
\\n\\nQi Xie 2016-18
\\n\\nXin-She Yang 2017, 2018
\\n\\nYulong Yin 2015, 2017, 2018
\\n"}]'},components:[{type:"htmlEditorComponent",content:'New for 2018 (alphabetically by surname).
\n\n\n\n\n\n\n\n\n\nJocelyn Chanussot (chapter to be published soon...)
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nYuekun Lai
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPrevious years (alphabetically by surname)
\n\nAbdul Latif Ahmad 2016-18
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Determination of wettability is based on the measurement of the material surface contact angle. Contact angle is the main parameter that characterizes the drop shape on the solid surface and is also one of the directly measurable properties of the phase interface. In this chapter, the wettability and its related properties of pristine and modified polymer foils will be described. The wettability depends on surface roughness and chemical composition. Changes of these parameters can adjust the values of contact angle and, therefore, wettability. In the case of pristine polymer materials, their wettability is unsuitable for a wide range of applications (such as tissue engineering, printing, and coating). Polymer surfaces can easily be modified by, e.g., plasma discharge, whereas the bulk properties remain unchanged. This modification leads to oxidation of the treated layer and creation of new chemical groups that mainly contain oxygen. Immediately after plasma treatment, the values of the contact angles of the modified polymer significantly decrease. In the case of a specific polymer, the strongly hydrophilic surface is created and leads to total spreading of the water drop. Wettability is strongly dependent on time from modification.",book:{id:"5319",slug:"wetting-and-wettability",title:"Wetting and Wettability",fullTitle:"Wetting and Wettability"},signatures:"Nikola Slepickova Kasalkova, Petr Slepicka, Zdenka Kolska and\nVaclav Svorcik",authors:[{id:"144929",title:"Prof.",name:"Vaclav",middleName:null,surname:"Svorcik",slug:"vaclav-svorcik",fullName:"Vaclav Svorcik"},{id:"146297",title:"Dr.",name:"Petr",middleName:null,surname:"Slepicka",slug:"petr-slepicka",fullName:"Petr Slepicka"},{id:"147600",title:"Ph.D.",name:"Nikola",middleName:null,surname:"Slepičková Kasálková",slug:"nikola-slepickova-kasalkova",fullName:"Nikola Slepičková Kasálková"},{id:"153983",title:"Dr.",name:"Zdeňka",middleName:null,surname:"Kolská",slug:"zdenka-kolska",fullName:"Zdeňka Kolská"}]},{id:"48822",doi:"10.5772/60808",title:"Wettability of Nanostructured Surfaces",slug:"wettability-of-nanostructured-surfaces",totalDownloads:3128,totalCrossrefCites:11,totalDimensionsCites:32,abstract:"There are many studies in literature concerning contact angle measurements on different materials/substrates. It is documented that textiles can be coated with multifunctional materials in form of thin films or nanoparticles to acquire characteristics that can improve the protection and comfort of the wearer. The capacity of oxide nanostructures to inhibit fungal development and neutralize bacteria is a direct consequence of their wetting behavior [1–6]. Moreover, the radical modification of wetting behavior of nanostructures from hydrophilic to hydrophobic when changing the pulsed laser deposition (PLD) ambient will be thoroughly discussed.",book:{id:"5319",slug:"wetting-and-wettability",title:"Wetting and Wettability",fullTitle:"Wetting and Wettability"},signatures:"L. Duta, A.C. Popescu, I. Zgura, N. Preda and I.N. Mihailescu",authors:[{id:"17636",title:"Prof.",name:"Ion N.",middleName:null,surname:"Mihailescu",slug:"ion-n.-mihailescu",fullName:"Ion N. Mihailescu"},{id:"23532",title:"Dr.",name:"Andrei",middleName:null,surname:"Popescu",slug:"andrei-popescu",fullName:"Andrei Popescu"},{id:"174343",title:"Dr.",name:"Liviu",middleName:null,surname:"Duta",slug:"liviu-duta",fullName:"Liviu Duta"},{id:"174344",title:"Dr.",name:"Irina",middleName:null,surname:"Zgura",slug:"irina-zgura",fullName:"Irina Zgura"},{id:"174345",title:"Dr.",name:"Ligia",middleName:null,surname:"Frunza",slug:"ligia-frunza",fullName:"Ligia Frunza"}]},{id:"49090",doi:"10.5772/61205",title:"The Wetting of Leaf Surfaces and Its Ecological Significances",slug:"the-wetting-of-leaf-surfaces-and-its-ecological-significances",totalDownloads:3521,totalCrossrefCites:13,totalDimensionsCites:25,abstract:"Leaf wettability, indicating the affinity for water on leaf surfaces, is a common phenomenon for plants in a wide variety of habitats. The contact angle (θ) of water on leaves measured at the gas, solid and liquid interface is an index of surface wettability. Leaves are termed as “super-hydrophilic” if θ < 40°, “highly wettable” if θ < 90°, and “wettable” if θ < 110°. If θ > 110°, the leaves are classified as being non-wettable, while θ > 130° for highly non-wettable and θ > 150° for super-hydrophobic. Both internal and external factors can influence leaf wettability. The chemical composition and structure of leaf surfaces are internal causes, but the external environment can also influence wettability by affecting the structure and composition of the surface. The main internal factors that affecting leaf wettability include the content and microstructure of the epidermal wax, the number, size and pattern of trichomes, stomatal density, the shape of epidermal cells, and leaf water status. The leaf contact angles increased with the increasing of leaf wax content. However, studies have shown that the contact angles were more dependent on the complexity of wax structure than on the absolute amount. For trichomes, there are three types of interaction between trichomes and water droplets, including (1) low trichomes density: no apparent influence of trichomes on the location of surface moisture, droplet formation and retention ; (2) medium trichomes density: trichomes appear to circle surface moisture into patches; (3) high trichomes density: trichomes appear to hold water droplets above the trichomes. In some cases, a higher stomatal density was accompanied with a higher contact angles. While, it was also observed that there was no significant correlation between contact angle and stomatal density for some species. For the effects of epidermal cells on leaf wettability, it was generally considered that the combination of a dense layer of surface wax and the convex epidermal cells was what created a hydrophobic leaf surface. However, the influence of leaf water content on contact angle of water droplets on different leaf surfaces was complex, e.g., contact angles increased with decreasing of leaf water content, contact angle remained to be constant with different leaf water content.",book:{id:"5319",slug:"wetting-and-wettability",title:"Wetting and Wettability",fullTitle:"Wetting and Wettability"},signatures:"Huixia Wang, Hui Shi and Yanhui Wang",authors:[{id:"173921",title:"Dr.",name:"Huixia",middleName:null,surname:"Wang",slug:"huixia-wang",fullName:"Huixia Wang"}]},{id:"40738",doi:"10.5772/49979",title:"Viscoelastic Properties of Biological Materials",slug:"viscoelastic-properties-of-biological-materials",totalDownloads:5572,totalCrossrefCites:12,totalDimensionsCites:24,abstract:null,book:{id:"2854",slug:"viscoelasticity-from-theory-to-biological-applications",title:"Viscoelasticity",fullTitle:"Viscoelasticity - From Theory to Biological Applications"},signatures:"Naoki Sasaki",authors:[{id:"140935",title:"Prof.",name:"Naoki",middleName:null,surname:"Sasaki",slug:"naoki-sasaki",fullName:"Naoki Sasaki"}]},{id:"40741",doi:"10.5772/50137",title:"Die Swell of Complex Polymeric Systems",slug:"die-swell-of-complex-polymeric-systems",totalDownloads:6070,totalCrossrefCites:3,totalDimensionsCites:17,abstract:null,book:{id:"2854",slug:"viscoelasticity-from-theory-to-biological-applications",title:"Viscoelasticity",fullTitle:"Viscoelasticity - From Theory to Biological Applications"},signatures:"Kejian Wang",authors:[{id:"141238",title:"Prof.",name:"Kejian",middleName:null,surname:"Wang",slug:"kejian-wang",fullName:"Kejian Wang"}]}],mostDownloadedChaptersLast30Days:[{id:"48768",title:"TiO2 -Based Surfaces with Special Wettability – From Nature to Biomimetic Application",slug:"tio2-based-surfaces-with-special-wettability-from-nature-to-biomimetic-application",totalDownloads:5008,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"Super-wetting/antiwetting surfaces with extremely high contrast of surface energy and liquid adhesion have attracted a lot of interest in both fundamental research and industry. Various types of special wetting surfaces can be constructed by adjusting the topographical structure and chemical composition. In this chapter, recent advance of the super-wetting/antiwetting surfaces with special solid/liquid adhesion has been reviewed, with a focus on the biomimetic fabrication and applications of TiO2-based surfaces. Special super-wettability examples include lotus-leaf-inspired surfaces with low adhesion, rose-petal-inspired surfaces with high adhesion, spider silk bio-inspired surfaces with directional adhesion, fish-scale-inspired underwater superoleophobic surface, and artificial surfaces with controllable or stimuli-responsive liquid adhesion. In addition, we will review some potential applications related to artificial antiwetting surface with controllable adhesion, e.g., self-cleaning, antifogging/anti-icing, micro-droplet manipulation, fog/water collection, water/oil separation, anti-bioadhesion, micro-template for patterning, and friction reduction. Finally, the difficulty and prospects of this renascent and rapidly developing field are also briefly proposed and discussed.",book:{id:"5319",slug:"wetting-and-wettability",title:"Wetting and Wettability",fullTitle:"Wetting and Wettability"},signatures:"Jian-Ying Huang and Yue-Kun Lai",authors:[{id:"175512",title:"Prof.",name:"Yuekun",middleName:null,surname:"Lai",slug:"yuekun-lai",fullName:"Yuekun Lai"}]},{id:"62882",title:"Inside the Phenomenological Aspects of Wet Granulation: Role of Process Parameters",slug:"inside-the-phenomenological-aspects-of-wet-granulation-role-of-process-parameters",totalDownloads:1424,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Granulation is a size-enlargement process by which small particles are bonded, by means of various techniques, in coherent and stable masses (granules), in which the original particles are still identifiable. In wet granulation processes, the powder particles are aggregated through the use of a liquid phase called binder. The main purposes of size-enlargement process of a powder or mixture of powders are to improve technological properties and/or to realize suitable forms of commercial products. A modern and rational approach in the production of granular structures with tailored features (in terms of size and size distribution, flowability, mechanical and release properties, etc.) requires a deep understanding of phenomena involved during granules formation. By this knowledge, suitable predictive tools can be developed with the aim to choose right process conditions to be used in developing new formulations by avoiding or reducing costs for new tests. In this chapter, after introductive notes on granulation process, the phenomenological aspects involved in the formation of the granules with respect to the main process parameters are presented by experimental demonstration. Possible mathematical approaches in the granulation process description are also presented and the one involving the population mass balances equations is detailed.",book:{id:"7352",slug:"granularity-in-materials-science",title:"Granularity in Materials Science",fullTitle:"Granularity in Materials Science"},signatures:"Veronica De Simone, Diego Caccavo, Annalisa Dalmoro, Gaetano\nLamberti, Matteo d’Amore and Anna Angela Barba",authors:[{id:"140173",title:"Prof.",name:"Anna Angela",middleName:null,surname:"Barba",slug:"anna-angela-barba",fullName:"Anna Angela Barba"},{id:"143947",title:"Prof.",name:"Matteo",middleName:null,surname:"D'Amore",slug:"matteo-d'amore",fullName:"Matteo D'Amore"},{id:"176104",title:"Prof.",name:"Gaetano",middleName:null,surname:"Lamberti",slug:"gaetano-lamberti",fullName:"Gaetano Lamberti"},{id:"176239",title:"MSc.",name:"Diego",middleName:null,surname:"Caccavo",slug:"diego-caccavo",fullName:"Diego Caccavo"},{id:"181500",title:"Dr.",name:"Annalisa",middleName:null,surname:"Dalmoro",slug:"annalisa-dalmoro",fullName:"Annalisa Dalmoro"},{id:"260822",title:"MSc.",name:"Veronica",middleName:null,surname:"De Simone",slug:"veronica-de-simone",fullName:"Veronica De Simone"}]},{id:"49177",title:"Influence of Wettability and Reactivity on Refractory Degradation – Interactions of Molten Iron and Slags with Steelmaking Refractories at 1550°C",slug:"influence-of-wettability-and-reactivity-on-refractory-degradation-interactions-of-molten-iron-and-sl",totalDownloads:2067,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"Refractories, materials that can withstand high temperatures, play an important role in the iron and steel sector which alone accounts for ~70% of total refractories produced. In this chapter, detailed wettability and interfacial phenomena investigations on alumina-carbon and zirconia-carbon refractories at steelmaking temperatures. The wettability between refractory substrates and molten iron/slags was investigated at 1550°C using the sessile drop approach in a horizontal tube furnace equipped with a CCD camera. Detailed experimental results were obtained on alumina-carbon/molten iron system at high temperatures. Alumina is known to be non-wetting to molten iron while carbon can be easily wetted. Observed contact angles were found to depend strongly on the substrate composition and contact time. While the refractory substrates containing 50 and 60% carbon were found to be non-wetting to molten iron, the substrates containing higher amounts of C (≥ 70%) were found to become increasingly wetting. Molten iron droplets were seen to spread on these substrates.",book:{id:"5319",slug:"wetting-and-wettability",title:"Wetting and Wettability",fullTitle:"Wetting and Wettability"},signatures:"R. Khanna, M. Ikram-ul-Haq and V. Sahajwalla",authors:[{id:"19010",title:"Associate Prof.",name:"Rita",middleName:null,surname:"Khanna",slug:"rita-khanna",fullName:"Rita Khanna"}]},{id:"48822",title:"Wettability of Nanostructured Surfaces",slug:"wettability-of-nanostructured-surfaces",totalDownloads:3122,totalCrossrefCites:11,totalDimensionsCites:31,abstract:"There are many studies in literature concerning contact angle measurements on different materials/substrates. It is documented that textiles can be coated with multifunctional materials in form of thin films or nanoparticles to acquire characteristics that can improve the protection and comfort of the wearer. The capacity of oxide nanostructures to inhibit fungal development and neutralize bacteria is a direct consequence of their wetting behavior [1–6]. Moreover, the radical modification of wetting behavior of nanostructures from hydrophilic to hydrophobic when changing the pulsed laser deposition (PLD) ambient will be thoroughly discussed.",book:{id:"5319",slug:"wetting-and-wettability",title:"Wetting and Wettability",fullTitle:"Wetting and Wettability"},signatures:"L. Duta, A.C. Popescu, I. Zgura, N. Preda and I.N. Mihailescu",authors:[{id:"17636",title:"Prof.",name:"Ion N.",middleName:null,surname:"Mihailescu",slug:"ion-n.-mihailescu",fullName:"Ion N. Mihailescu"},{id:"23532",title:"Dr.",name:"Andrei",middleName:null,surname:"Popescu",slug:"andrei-popescu",fullName:"Andrei Popescu"},{id:"174343",title:"Dr.",name:"Liviu",middleName:null,surname:"Duta",slug:"liviu-duta",fullName:"Liviu Duta"},{id:"174344",title:"Dr.",name:"Irina",middleName:null,surname:"Zgura",slug:"irina-zgura",fullName:"Irina Zgura"},{id:"174345",title:"Dr.",name:"Ligia",middleName:null,surname:"Frunza",slug:"ligia-frunza",fullName:"Ligia Frunza"}]},{id:"62615",title:"Nanolevel Surface Processing of Fine Particles by Waterjet Cavitation and Multifunction Cavitation to Improve the Photocatalytic Properties of Titanium Oxide",slug:"nanolevel-surface-processing-of-fine-particles-by-waterjet-cavitation-and-multifunction-cavitation-t",totalDownloads:1161,totalCrossrefCites:3,totalDimensionsCites:6,abstract:"Titanium oxide particles were treated by water jet cavitation (WJC) generated and multifunction cavitation (MFC) using an ejector nozzle. Generation, growth, and collapse of cavitation are repeated with the particles of titanium oxide and platinum. Because the cavitation has an extremely high collapse pressure, the surface of the titanium oxide particles is processed by the microjets of cavitation in a reactor comprising the ejector nozzle. In the multifunction cavitation, ultrasonic irradiation of a waterjet during floating cavitation was used to generate microjets with hot spots. Hot working can be performed at the nanoscale on a material surface using this MFC process, resulting in morphological changes and variations in the surface electrochemical characteristics. The fundamental characteristics of multifunction cavitation were investigated theoretically and experimentally. Furthermore, the additional nozzle was put on the ejector nozzle in order to increase the temperature and pressure of bubble and the mechanism was clarified. The quantities of hydrogen and oxygen generated from titanium dioxide particles treated by multifunction cavitation in response to UV and visible light irradiation were remarkably increased compared to the amounts produced by particles treated by WJC processing. In this chapter, the methods and their results of processing particles by cavitation are introduced.",book:{id:"7385",slug:"cavitation-selected-issues",title:"Cavitation",fullTitle:"Cavitation - Selected Issues"},signatures:"Toshihiko Yoshimura, Kumiko Tanaka and Masataka Ijiri",authors:[{id:"246052",title:"Dr.",name:"Masataka",middleName:null,surname:"Ijiri",slug:"masataka-ijiri",fullName:"Masataka Ijiri"},{id:"246359",title:"Prof.",name:"Toshihiko",middleName:null,surname:"Yoshimura",slug:"toshihiko-yoshimura",fullName:"Toshihiko Yoshimura"},{id:"246433",title:"Dr.",name:"Kumiko",middleName:null,surname:"Tanaka",slug:"kumiko-tanaka",fullName:"Kumiko Tanaka"}]}],onlineFirstChaptersFilter:{topicId:"961",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:287,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 18th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:0,paginationItems:[]},overviewPageOFChapters:{paginationCount:0,paginationItems:[]},overviewPagePublishedBooks:{paginationCount:0,paginationItems:[]},openForSubmissionBooks:{paginationCount:5,paginationItems:[{id:"11669",title:"Fatty Acids - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11669.jpg",hash:"9117bd12dc904ced43404e3383b6591a",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"May 3rd 2022",isOpenForSubmission:!0,editors:[{id:"415310",title:"Assistant Prof.",name:"Erik",surname:"Froyen",slug:"erik-froyen",fullName:"Erik Froyen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{id:"11672",title:"Chemokines Updates",coverURL:"https://cdn.intechopen.com/books/images_new/11672.jpg",hash:"c00855833476a514d37abf7c846e16e9",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"May 6th 2022",isOpenForSubmission:!0,editors:[{id:"14794",title:"Prof.",name:"Murat",surname:"Şentürk",slug:"murat-senturk",fullName:"Murat Şentürk"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{id:"11674",title:"Updates on Endoplasmic Reticulum",coverURL:"https://cdn.intechopen.com/books/images_new/11674.jpg",hash:"5d7d49bd80f53dad3761f78de4a862c6",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"May 6th 2022",isOpenForSubmission:!0,editors:[{id:"238047",title:"Dr.",name:"Gaia",surname:"Favero",slug:"gaia-favero",fullName:"Gaia Favero"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{id:"11670",title:"Chitin-Chitosan - Isolation, Properties, and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11670.jpg",hash:"69f009be08998711eecfb200adc7deca",secondStepPassed:!1,currentStepOfPublishingProcess:2,submissionDeadline:"May 26th 2022",isOpenForSubmission:!0,editors:[{id:"176093",title:"Dr.",name:"Brajesh",surname:"Kumar",slug:"brajesh-kumar",fullName:"Brajesh Kumar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{id:"11673",title:"Stem Cell Research",coverURL:"https://cdn.intechopen.com/books/images_new/11673.jpg",hash:"13092df328080c762dd9157be18ca38c",secondStepPassed:!1,currentStepOfPublishingProcess:2,submissionDeadline:"June 8th 2022",isOpenForSubmission:!0,editors:[{id:"203598",title:"Ph.D.",name:"Diana",surname:"Kitala",slug:"diana-kitala",fullName:"Diana Kitala"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},onlineFirstChapters:{paginationCount:17,paginationItems:[{id:"81751",title:"NanoBioSensors: From Electrochemical Sensors Improvement to Theranostic Applications",doi:"10.5772/intechopen.102552",signatures:"Anielle C.A. Silva, Eliete A. Alvin, Lais S. de Jesus, Caio C.L. de França, Marílya P.G. da Silva, Samaysa L. Lins, Diógenes Meneses, Marcela R. Lemes, Rhanoica O. Guerra, Marcos V. da Silva, Carlo J.F. de Oliveira, Virmondes Rodrigues Junior, Renata M. Etchebehere, Fabiane C. de Abreu, Bruno G. Lucca, Sanívia A.L. Pereira, Rodrigo C. Rosa and Noelio O. 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Paul",slug:"organoids-and-commercialization",totalDownloads:33,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Organoids",coverURL:"https://cdn.intechopen.com/books/images_new/11430.jpg",subseries:null}},{id:"81412",title:"Mathematical Morphology and the Heart Signals",doi:"10.5772/intechopen.104113",signatures:"Taouli Sidi Ahmed",slug:"mathematical-morphology-and-the-heart-signals",totalDownloads:18,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Biosignal Processing",coverURL:"https://cdn.intechopen.com/books/images_new/11153.jpg",subseries:{id:"7",title:"Bioinformatics and Medical Informatics"}}},{id:"81360",title:"Deep Learning Algorithms for Efficient Analysis of ECG Signals to Detect Heart Disorders",doi:"10.5772/intechopen.103075",signatures:"Sumagna Dey, Rohan Pal and Saptarshi Biswas",slug:"deep-learning-algorithms-for-efficient-analysis-of-ecg-signals-to-detect-heart-disorders",totalDownloads:31,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Biosignal Processing",coverURL:"https://cdn.intechopen.com/books/images_new/11153.jpg",subseries:{id:"7",title:"Bioinformatics and Medical Informatics"}}},{id:"81294",title:"Applications of Neural Organoids in Neurodevelopment and Regenerative Medicine",doi:"10.5772/intechopen.104044",signatures:"Jing Gong, Jiahui Kang, Minghui Li, Xiao Liu, Jun Yang and Haiwei Xu",slug:"applications-of-neural-organoids-in-neurodevelopment-and-regenerative-medicine",totalDownloads:25,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Organoids",coverURL:"https://cdn.intechopen.com/books/images_new/11430.jpg",subseries:null}},{id:"81318",title:"Retinal Organoids over the Decade",doi:"10.5772/intechopen.104258",signatures:"Jing Yuan and Zi-Bing Jin",slug:"retinal-organoids-over-the-decade",totalDownloads:39,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Organoids",coverURL:"https://cdn.intechopen.com/books/images_new/11430.jpg",subseries:null}},{id:"81068",title:"Characteristic Profiles of Heart Rate Variability in Depression and Anxiety",doi:"10.5772/intechopen.104205",signatures:"Toshikazu Shinba",slug:"characteristic-profiles-of-heart-rate-variability-in-depression-and-anxiety",totalDownloads:20,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Biosignal Processing",coverURL:"https://cdn.intechopen.com/books/images_new/11153.jpg",subseries:{id:"7",title:"Bioinformatics and Medical Informatics"}}},{id:"80691",title:"Applications of Quantum Mechanics, Laws of Classical Physics, and Differential Calculus to Evaluate Source Localization According to the Electroencephalogram",doi:"10.5772/intechopen.102831",signatures:"Kristin S. Williams",slug:"applications-of-quantum-mechanics-laws-of-classical-physics-and-differential-calculus-to-evaluate-so",totalDownloads:7,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Biosignal Processing",coverURL:"https://cdn.intechopen.com/books/images_new/11153.jpg",subseries:{id:"7",title:"Bioinformatics and Medical Informatics"}}}]},subseriesFiltersForOFChapters:[{caption:"Bioinformatics and Medical Informatics",value:7,count:13,group:"subseries"}],publishedBooks:{paginationCount:0,paginationItems:[]},subseriesFiltersForPublishedBooks:[],publicationYearFilters:[],authors:{paginationCount:249,paginationItems:[{id:"274452",title:"Dr.",name:"Yousif",middleName:"Mohamed",surname:"Abdallah",slug:"yousif-abdallah",fullName:"Yousif Abdallah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274452/images/8324_n.jpg",biography:"I certainly enjoyed my experience in Radiotherapy and Nuclear Medicine, particularly it has been in different institutions and hospitals with different Medical Cultures and allocated resources. Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University, Kuwait. His research interests include optimization, computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, and intelligent systems. Prof. Sarfraz has been a keynote/invited speaker at various platforms around the globe. He has advised/supervised more than 110 students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He has authored and/or edited around seventy books. Prof. Sarfraz is a member of various professional societies. He is a chair and member of international advisory committees and organizing committees of numerous international conferences. He is also an editor and editor in chief for various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:"Beijing University of Technology",institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Lakhno Igor Victorovich was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPhD – 1999, Kharkiv National Medical Univesity.\nDSc – 2019, PL Shupik National Academy of Postgraduate Education \nLakhno Igor has been graduated from an international training courses on reproductive medicine and family planning held in Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor of the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s a professor of the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education . He’s an author of about 200 printed works and there are 17 of them in Scopus or Web of Science databases. Lakhno Igor is a rewiever of Journal of Obstetrics and Gynaecology (Taylor and Francis), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for DSc degree \\'Pre-eclampsia: prediction, prevention and treatment”. Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: obstetrics, women’s health, fetal medicine, cardiovascular medicine.",institutionString:"V.N. Karazin Kharkiv National University",institution:{name:"Kharkiv Medical Academy of Postgraduate Education",country:{name:"Ukraine"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"243698",title:"M.D.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:"Shanxi Eye Hospital",institution:{name:"Shanxi Eye Hospital",country:{name:"China"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRZkkQAG/Profile_Picture_2022-05-09T12:55:18.jpg",biography:null,institutionString:null,institution:null},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. RELACION DE PONENCIAS DE LA SOCIEDAD ESPAÑOLA DE OFTALMOLOGIA. 10/2014.",institutionString:null,institution:null},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:null},{id:"318905",title:"Prof.",name:"Elvis",middleName:"Kwason",surname:"Tiburu",slug:"elvis-tiburu",fullName:"Elvis Tiburu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Ghana",country:{name:"Ghana"}}},{id:"336193",title:"Dr.",name:"Abdullah",middleName:null,surname:"Alamoudi",slug:"abdullah-alamoudi",fullName:"Abdullah Alamoudi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"318657",title:"MSc.",name:"Isabell",middleName:null,surname:"Steuding",slug:"isabell-steuding",fullName:"Isabell Steuding",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"318656",title:"BSc.",name:"Peter",middleName:null,surname:"Kußmann",slug:"peter-kussmann",fullName:"Peter Kußmann",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"338222",title:"Mrs.",name:"María José",middleName:null,surname:"Lucía Mudas",slug:"maria-jose-lucia-mudas",fullName:"María José Lucía Mudas",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Carlos III University of Madrid",country:{name:"Spain"}}},{id:"147824",title:"Mr.",name:"Pablo",middleName:null,surname:"Revuelta Sanz",slug:"pablo-revuelta-sanz",fullName:"Pablo Revuelta Sanz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Carlos III University of Madrid",country:{name:"Spain"}}}]}},subseries:{item:{id:"86",type:"subseries",title:"Business and Management",keywords:"Demographic shifts, Innovation, Technology, Next-gen leaders, Worldwide environmental issues and clean technology, Uncertainty and political risks, Radical adjacency, Emergence of new business ecosystem type, Emergence of different leader and leader values types, Universal connector, Elastic enterprise, Business platform, Supply chain complexity",scope:"