Actual frequency response (
\r\n\tThis book chapter’s main theme will be focused on transmission dynamics, pathogenesis, mechanisms of host interaction and response, epigenetics and markers, molecular diagnosis, RNA interacting proteins, RNA binding proteins, advanced development of tools for diagnosis, possible development of concepts for vaccines and anti drugs for RNA viruses, immunological mechanisms, treatment, prevention and control.
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With the increasing threat of global warming and the waning of fossil fuel reserves, renewable energy is playing more and more important roles in both environmental and economic aspect of lives. Solar photovoltaic (PV), as one of the most important renewable energy sources, has been widely used in more than a hundred of countries [1]. Solar panels are commonly deployed in harsh environments with high temperature, high humidity and strong winds [2], and are expected to be functional for over 30 years. Thus the glass panel, where the PV material will be located, has to be specially designed to sustain the harsh environments over its life of service [3]. As a necessary validation step, the strength and quality of the manufactured glass sheet must be tested and qualified, especially before the mass production stage. A rigorous test would require the strength of the solar glass sheet to be verified under a controlled environment with simulated temperature, humidity and induced vibration or forces on the PV glass sheet. Although commercial solutions exist for the various types of material-strength testing, environment simulated glass sheet strength testing is not available. As the booming of the PV industry and the increasing researching into stronger and better PV glass sheet, there has been a greater demand into the research and development of specially designed PV glass sheet test machines. This chapter presented the design of an environment controlled force loading machine specifically for solar panel glass sheet strength testing.
The glass sheet testing machine is basically a real-time electro-mechanical control system which is composed of actuators for simulated loading generation, sensors for monitoring and control feedback, a digital controller for the control of the overall system performance and mechanical supporting structures to hold everything together. The key part is the real-time digital controller which serves as the brain of such a system. Although computer-based control system with great computational power is able to host complex control algorithm, testing facilities with space, power and budget constraint cannot afford the size, power and cost of the computer-based design. Moreover, many extended and long hour testing scenarios require great robustness on the control system, where computer with standard operating system cannot meet the requirement. Embedded control systems with much lower power consumption, compact space, less cost and increased robustness is preferred over computer-based systems in many custom designed solutions [4–6]. Although embedded control systems have many advantages, they usually have limited or reduced computational powers that may not be able to host complex control algorithms. This poses challenges into the design of efficient real-time control algorithms. Specific embedded system-oriented control techniques must be carried out to guarantee control performances. For this reason, a simple and effective control law is preferred on the embedded control system and the Cerebellar Model Articulation Controller (CMAC) is a good choice.
The CMAC proposed by Albus [7] is a lookup-table adaptive neural network for estimating complicated nonlinear functions. The basic idea of CMAC is to quantify states from input, find memory addresses of states according to their locations in memory, add the content in the memory address, generate the CMAC output, compare the output with desired output and update the content in memory based on learning algorithms. Compared with other neural networks, advantages of the CMAC [8] are: (1) it is based on local learning and stores information in local memory, hence weights are changed slightly in each step and its learning speed is fast and suitable for real-time control; (2) it owns definite generalization property, so that the close inputs generate the close outputs, and the different inputs produce the different outputs; (3) it can receive continuous input and produce continuous output; (4) it can accelerate the response speed by using addressing mode and (5) it is a nonlinear approximation and robust to the sequences of training data. Owing to above predominant merits, the nonlinear approximating capacity of the CMAC is superior to that of other traditional neural networks and is more suitable for real-time control in the real world.
In summary, various CMAC control algorithms have been formed so far, such as CMAC feedforward control [9–11], CMAC feedback control [12–14], CMAC optimal control [15, 16], CMAC fuzzy control [17–22], CMAC H-infinity control [23] and CMAC adaptive control [24–26]. The previous works can be divided into two categories: one is to improve the control structures of the CMAC, such as the CMAC feedforward control; the other is to improve the learning algorithms with other intelligent techniques, such as the fuzzy CMAC (FCMAC). Although a lot of complicated advanced CMAC control algorithms can well perform the control tasks in the simulations or with the computer-based control systems, the performances raised by some of them are very limited compared with the basic CMAC structure, even several advanced CMAC control algorithms, such as the FCMAC with the wavelet, cannot be implemented in real time with an embedded system due to the complicated improvement algorithms.
In this chapter, we adopt CMAC feedforward and PD feedback control, because [10, 11]: (1) the CMAC carries out the feedforward part to approximate the inverse model of the plant; (2) the PD controller actualizes the feedback part to train the CMAC and guarantee the stability of the closed-loop system; (3) compared with other neural networks or other complicated CMACs, limited computation cost of the simple and effective CMAC plus PD control removes CPU burden, so that the microcontroller can have enough computational power available.
Since there is not much effort dealing with the systematic design problem of an embedded-control glass strength testing machine, the contribution of this chapter is to present a three-step systematic design approach to address the embedded control issue: Firstly, the mathematical description of the system is studied using both theoretical and experimental method. A mathematical model is derived from the physical models of each component used, and an experiment is retrieved by employing Levy’s method to identify parameters of the mathematical model. Secondly, an adaptive CMAC feedforward plus PD feedback controller is designed and simulated based on the identified system model as a preparation for the embedded system implementation. Finally, the proposed algorithm is applied to the embedded system with the same parameters as those of simulations, and experiments are conducted to verify both the identified model and designed controller. To design a machine systematically for practical use, these three steps are closely linked and indispensable. The three-step systematic design approach could benefit engineers in measurement and control as a guide.
Rest of this chapter is organized as follows. Section 2 analyses the system requirements and formulates a mathematical system model. Section 3 performs the system identification. Section 4 proposes the simulation-based controller design. Embedded system-based experiments are conducted in Section 5 and Section 6 concludes this work.
The schematic diagram of the force loader unit is shown in Figures 1 and 2. The loader unit consists of one loader and one steel bar, which is connected to the load cell and linear motor actuator. The linear motor actuator, used to apply force, consists of a motor with linear motion and an encoder. The loader is attached to the steel bar and then through a load cell to the linear motor. In short, the force is applied on the material plate by a loader unit which is connected to a linear motor actuator through a steel bar, and the applied force is recorded by a force sensor.
Schematic drawing of the loader unit.
Photo of the loader unit.
The load applied on the material plate varies as a ramp function. The user can select the slope of the ramp function by setting the maximum force in a finite time period on the touch screen, and can perform the test under different forces ranging from 0 to 300 lbs. The control objective is to ensure the applied force track the reference force command for measuring the material plate’s yield stress qualified or not.
DC motors are widely used as actuators for high-precision servo control owing to its good working characteristics and simple mathematical model. Mechanical resonance phenomena are ubiquitous because the transmission shaft is not completely rigid and will be distorted under force. For the servo-motor-drive system, considering the mechanical resonance phenomena, the double-mass structure model is commonly used to describe such dynamical systems.
The electrical equilibrium equation can be written as [27]
where
Motor and load double mass diagram.
The motor output torque
where
The torque equilibrium equation of the motor is
where
where
The torque equilibrium equation of the load is
where
The force generated on the solar panel glass has an almost linear relationship with the deformation of the glass, which can be represented as
The control objective is to track the input command through feedback control based on the signal measured by the force sensor. In addition to the motor-loader and the micro controller unit (MCU), other components included in the control loop are a digital to analog converter (DAC), an amplifier and a load cell. Figure 4 illustrates the open-loop plant structure.
Open-loop block diagram.
The DAC converter is basically a zero-order hold. Assume the controller’s output is
Considering the sampling process, by replacing
which is a low-pass filter.
In a high-precision servo system, PWM-based amplifiers are commonly used as the motor drivers [28]. The PWM power amplifiers can be represented as
where
The load cell signal conditioner linearly converts the force into a voltage signal, and its model can be simplified as
Without considering the disturbance torque
By considering
where
If the motor armature inductance is regarded as
And the transfer function between the input voltage and output rotational angle is
where
The mathematical model of the open-loop plant is the cascade of the units described previously:
Because the system bandwidth is much lower than the system sampling rate and the modulation frequency of the PWM amplifier, the time constant of the DAC converter and the amplifier is very small. In the lower frequency range, the effect of the DAC converter and the amplifier can be omitted. Therefore, the open-loop plant can be simplified as
where
Obvious, it is a second-order model. Only four parameters need to be identified.
Levy’s method [29] and least square estimation are widely used in system identification. Assume that the identified transfer function is given as
where
The frequency response is
For each frequency point
Define an objective function:
Minimize the objective function, and prompt ∂
Table 1 shows actual frequency response obtained by experiments. We employ different sinusoidal input
1 | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 | 0.4 | 0.3 | 0.2 | 0.1 | 1 | |
---|---|---|---|---|---|---|---|---|---|---|---|
16 | 22 | 25 | 27 | 30 | 31 | 40 | 46 | 64 | 122 | 16 | |
1.71 | 1.82 | 1.78 | 1.77 | 1.89 | 1.92 | 1.90 | 2.00 | 1.94 | 2.23 | 1.71 |
Actual frequency response (
The output signals can be decomposed as
First, we select the sampling interval
Second, by defining Y
Third,
where
After
As shown in Figure 5, in the CMAC plus PD control scheme, the PD controller is used to train the weights of the CMAC at the early stage of control. The output
CMAC feedforward plus PD feedback control block diagram.
CMAC employs the supervisory learning algorithm. At the end of each control step
The control law can be described as
where
And the learning function of the CMAC is given as
where
Once the system starts running, the controller initializes
Choose the identified model (26) as the simulation plant to tune the controller and verify its performance. The force control is aimed at tracking a ramp command, of which the expression is given as
The CMAC plus PD controller is tuned and trained by simulations. Figure 6 demonstrates that the PD controller plays a main role of control at the beginning. Then the control effect of the CMAC will gradually increase via learning from the output of the PD controller. Owing to the help of the CMAC, control performances are better than those of the only PD controller. Overshoots are reduced dramatically and control actions are speeded up while choosing the ramp signal as an input. Figure 7(a) shows an overview of control performances from 0 to 20 s. Figure 7(b) is an enlarged view of control performances from 19 to 20s. It can be seen that the system output well tracks the input command. Figure 8 further exhibits the superiority of the CMAC: the tracking errors are tiny, mostly between −0.2 and 0.2 lbs. Since there exists a modeling error between identified system and actual controlled plant, the controller’s performances will be further validated in the real material-strength testing experiments.
Output of the controller.
Simulation results.
Tracking error.
For verifying the controller’s adaption to modeling error in the real-time control experiments, we implement above control law in C language and download compiled files into the embedded system. The central processing unit is an ARM7-based processor LPC2294. The LPC2294 is a 32-bit reduced instruction set computer (RISC) processor with low power consumption and high performance. Although there is no Float Point Unit (FPU) in this processor, the 70 million instructions per second (MIPS) processing speed makes it ideal for the real-time control system. The control step size is set as 50 ms.
The actual force tracking control diagram is shown in Figure 9. The force is applied to the glass sheet specimen by a loader which is connected to a linear motor actuator through a steel bar. A three point bending test is utilized under this configuration. The load force followed a ramp function with time as the independent variable. The slope of the ramp function can be programmed through the LCD touch screen. The force range can be from 0 to 300 lbs. Displacement of the motor, which also reflected the deformation of the glass sheet specimen, is recorded during the test. The signal flow of the control system is as follows. The DAC generates the control outputs as a voltage signal, which is amplified by a power amplifier and exerted on the linear motor. The linear motor then transforms the voltage signal into rotation, and generates linear displacements. The force sensor and conditioner measure the displacements and generates charges, which are transformed back to a voltage signal and fed back through ADC to the microcontroller.
Actual force tracking control diagram.
The input command and control parameters are the same as those of simulations. Figure 10(a) shows that the output force basically tracks the input command well and the controller implemented on the embedded system can perform the control task in real time within the control step size. From Figure 10(b), it can be seen that tracking errors of the experiment are mostly between −2.5 and 2.5 lbs, which are larger than those of the simulation. The reason is that uncertainties and disturbances always exist in the real world, which mainly reflects in modeling error. However, the maximum absolute tracking error is 3.79, and the variance of tracking errors is 1.69, which are still acceptable in the actual real-time control environment. Moreover, it verifies that the controller tuned by simulations is also available for actual experiments, and indirectly proves the effectiveness of identified system model.
Experimental results.
As shown in Figure 11, an inverse model feedforward control is compared with the CMAC feedforward control by embedded-system-based experiments. Its basic idea is to directly employ the inverse model 1/G(s) of the plant’s identified transfer function G(s) in Eq. (26) to be the feedforward part. Obviously, this control scheme is also suitable for real-time implementations; even its computation cost is less than that of the CMAC scheme.
Block diagram of the inverse model feedforward control.
Figure 12(a) shows overall performances of the tracking control with the inverse model scheme. It can be seen that the output force basically tracks the input command and the controller implemented on the embedded system can perform the control task in real time within the control step size; however, the tracking control is not performed very well. As shown in Figure 12(b), tracking errors are mostly between −5 and −2 lbs, which are larger than those of the CMAC scheme. Table 2 also shows that the maximum absolute tracking error and variance of tracking errors of the inverse model scheme are both larger than those of the CMAC scheme, so that performances of the CMAC scheme are superior to those of the inverse model scheme. Although the CMAC scheme and inverse model scheme both utilize inverse model idea, the difference is that the latter directly employs the unchanged inverse model of the identified transfer function and the modeling error always exists, but the inverse model approximated by the CMAC is dynamical and adaptive.
Experimental results (inverse model feedforward control).
Method | Maximum absolute error | Variance |
---|---|---|
CMAC | 3.79 | 1.69 |
Inverse model | 6.13 | 3.85 |
Comparisons between the CMAC and the inverse model.
Compared with the traditional PID control method, the learning behavior and adaptive feature of the proposed CMAC control algorithm are embodied in the freedom of tuning control parameters and the robustness to the disturbances in the real world.
The PID control method has been widely used because of its simpleness, but the tuning problem of PID parameters (proportional, integral and differential) is difficult. At present, PID parameter-tuning optimization depends on the experiences of technical staffs and needs a lot of manpower and time, which means that the optimal PID parameters are difficult to be obtained by people’s tuning, and the inappropriate parameters cannot guarantee the control performances to meet the control requirements. In addition, the PID control law is a kind of the linear control law which owns few robustness to the disturbances. It means that even though the PID parameters are tuned optimally and perfectly in the simulations, the tuned parameters may perform poor in the real world due to disturbances and uncertainties.
Thus, combining the adaptive CMAC and the traditional PID to construct an intelligent neural network PID controller, can automatically identify the controlled plant and adaptively adapt the control parameters of the CMAC, which can solve the difficult problem of tuning parameters of the traditional PID controller. As shown in Sections 4 and 5, the PID parameters of the proposed CMAC algorithm in the experiments are same as those in the simulations, which verifies control performances of the proposed CMAC algorithm are independent of tuning PID parameters. Experimental results in the real world in Section 5 also demonstrate the robustness of the proposed algorithm owing to the CMAC neural network, while the traditional PID controller does not have this capacity.
In this chapter, a three-step systematic design approach is proposed to design an adaptive control system for practical use. We firstly study the system model identification problem of the embedded control material-strength testing system, including mathematical modeling of all the open-loop physical components and parameters identification of the mathematical model. Both theoretical analyses and experimental comparisons validate the identified transfer function of the system model is applicable for controller design and simulation. Next, benefited from limited computation cost and compensation ability to the modeling error, a simple and effective CMAC plus PD controller is simulated based on the identified system model, and then applied to the embedded control system for real-time force tracking. Both numerical simulations and actual experiments illustrate the proposed algorithm satisfactorily performs the tracking control task under real-time constraints of the embedded system.
On the other hand, different strength features of different types of the material plates will affect the control performances. Since the yield strength generated by the solar panel glass has an almost linear relationship with the deformation of the glass sheet,
This work is supported by National Key R&D Program of China under Grant Nos. 2016YFD0200700 and 2017YFD0701000, and Chinese Universities Scientific Fund under Grant Nos. 2017QC139 and 2017GX001.
Poor soil fertility is the major biophysical factor affecting crop production in the world [1]. It is a major threat to food security considering the ever-increasing growth rate in human population which is projected to reach about 10 billion by 2050 [2]. In times of old, forests and marginal lands were converted to farmlands to meet the food demands of the growing population. This practice caused the extinction and endangerment of many plant and animal species; hence it is frowned upon by many stakeholders. As such, today, it would not be prudent to encroach land reserves and other marginal lands for agricultural purposes. It is therefore imperative that we improve soil fertility and health of the available land, to increase food production and to ensure the world’s food security under the current and projected climate change.
Previously, the use of mineral fertilizer was thought of as the most appropriate remedy to soil fertility problems due to its rapid nutrient release [3]. However, mineral fertilizer lacks the ability to improve the soil’s physical properties causing fertility improvement by fertilizers alone to be unsustainable. Over-reliance on mineral fertilizer without due diligence to the organics may lead to increased soil erosion, surface and groundwater contamination, increased greenhouse gas emission and reduced biodiversity [4]. In addition, mineral fertilizers are expensive, and many farmers may not have the purchasing power to acquire it [5]. As a result, the attention of various stakeholders has been drawn to use of organic resources [6]. The application of organic fertilizers presents a more sustainable method of food production. There is unending literature reporting the efficiency and effectiveness of organic nutrient sources in maintaining soil quality (physical, biological and chemical properties), improving crop yields and sustaining productivity [6, 7, 8]. The benefits of applying organic fertilizers to the soil are elaborated in this chapter.
Organic nutrient sources are specifically derived from plant and animal origins [9]. They include plant residues, animal wastes and biofertilizers. In this era where climate change and the COVID-19 pandemic has impacted agricultural production and the financial capabilities of all workforces including farmers, farmers could use organic fertilizers available to them for soil fertility purposes because they are cheaper and more environmentally friendly when they are locally available [10].
Organic fertilizers include poultry manure, cattle manure, green manure (often legumes), field crop residues, composts, bone meal, household waste, blood meal, slurry, cocoa pod husks, palm kernel cake, among others. Biofertilizers are products containing single micro-organisms or combinations of them which when applied help fix atmospheric N, solubilize nutrients, mobilize nutrients, or secrete growth promoting substances to aid crop growth. These products do not supply nutrients themselves but enhance the activities of soil microbes to make more nutrients available to crops. They are categorized into N-fixing biofertilizers, phosphorus solubilizing biofertilizers, composting accelerators and plant growth promoting rhizobacteria [9]. Most of the plant and animal residues are often by-products and nuisance to the environment. Using them as nutrient sources would help reduce waste and greenhouse gas emission.
Organic fertilizers supply all essential crop nutrients (N, P, K, S, Ca, Mg, B, Cl, Cu, Fe, Mn, Mo, Ni and Zn) in balanced forms, including micronutrients. This is often not the case for any one inorganic fertilizer. Since all these nutrients make up the biomass of organic residues, they are released during the decomposition process into the soil. The downside to applying organic fertilizers alone is that they contain very minimal amounts of these nutrients and as such must be applied in bulky quantities to meet crop nutrient demands [11]. Also, the fact that only a fraction of the nutrients in organic fertilizers can be released per season must be factored in when applying organic fertilizers. On the average, as a rule of thumb, only about 50% of nutrients in organic fertilizers are mineralized in the first season of application [12]. Usually, the focal nutrient used to calculate the amount of organic fertilizer to apply is its nitrogen (N) concentration. For example, 30% decomposed cattle manure (DCM) contains about 2% N [13]. Assuming a farmer grows maize, which requires about 90 kg/ha N, that means:
Therefore, to supply 90 kg N = (90 × 100)/2 = 4500 kg DCM.
Since the applied DCM will only supply half the amount of N required in a season, the amount must be doubled to make
To supply same amount of N through mineral fertilizer, a farmer would only need about 200 kg Urea, however, in organic applications, other nutrients are concurrently being applied. Since a large amount of DCM would supply the required N and other nutrients, it must be available to the farmer. Hence advocates of organic fertilizers must emphasize on ways to raise such large amounts of materials for application locally if sole organic production is desired.
Aside the balanced nutrient supply, organic fertilizers add organic matter to the soil if a long-term application is practiced. Organic matter improves the nutrient holding capacity of the soil because it contains organic acids that increase the H+ ions and surface charge of the soil, causing the soil’s cation exchange capacity to increase [15]. Thus, the soil’s ability to hold more cations (nutrients) at exchange sites is increased and hence the nutrient holding capacity of the soil is also improved. Organic matter also improves the buffer capacity of the soil and increases the soil’s ability to resist a change in pH, which in turn affects nutrient loss or gain to the soil [16]. Organic fertilizers increase microbial activity in the soil, causing increased nutrient mineralization rates for the benefit of crops. They stimulate the activities of aerobic and anaerobic bacteria [17] and arbuscular mycorrhizae fungi that form networks of root extension for extensive nutrient availability to crops. Upon the lysis and decomposition of soil microbes, nutrients retained in their biomass are made available in the soil and to crops.
Soil structure, texture, bulk density, and organic matter content are the controls on soil water holding capacity; therefore, any management practice that improves these soil properties, in turn, improves water holding capacity (WHC) of the soil. Soil moisture content is largely dependent on the specific surface area of the soil and the thickness of films of water surrounding the pores [18]. The addition of organic matter through organic fertilizer application improves soil aggregation and increases the surface area of the soil, presenting the soil with more room for soil particles to be surrounded by films of water. As a result, the soil can hold more water against the pull of gravity which drains water from the soil.
While soil organic matter binds soil particles, it also stimulates the activity of soil microfauna whose movement create micro and macropores in the soil, creating extra room for water infiltration [19]. Thus, soil water holding capacity can be improved by the addition of organic fertilizers. In the wake of climate change, where unexpected droughts may be imminent, improving the water holding capacities of the soil with the application of organic fertilizer is the way to go. Also, the physical presence of organic materials on the soil serves as mulch that reduces evaporation and retains moisture in the soil. It also reduces the speed of runoff water and allows rain or irrigation water to infiltrate the soil at favorable speed, thereby reducing erosion, soil and nutrient loss [19].
The soil binding properties of organic matter and improvement in soil aggregation helps to improve soil structure [20]. The addition of organic matter also improves soil texture and aeration. Soils with improved structure and texture allow easy air, water, and root movement to support healthy crop growth.
Many research works have observed extra crop yields with organic fertilizer application compared to when its nutrient equivalents are applied through mineral fertilizer [21, 22, 23]. Various mechanisms have been proposed to explain this added crop yields from organic fertilizer application. Some of which include
Under the improved nutrient synchrony mechanism proposed by Vanlauwe et al. [23], when organic fertilizers are applied, they supply microbes with energy from the carbon they contain, to drive decomposition processes. This leads to temporal immobilization of soil N [24, 25] to build their body tissues. The immobilized N is made available at a later stage of plant growth when the microbes have decomposed the organic material to make nutrients available and/or some microbes have lysed and released their nutrients to the plant when it needs nutrients most. In effect, the peak of nutrient supply coincides with highest crop nutrient demand point when crops have matured, so that the nutrients are efficiently utilized, and little is lost to the environment. Kapkiyai et al. [26] reported that a combination of organic and mineral nutrient sources has been shown to result into synergy and improved synchronization of nutrient release and nutrient demand and uptake by plants leading to higher yields.
The general fertility improvement mechanism [23] is based on the theory that organic matter, aside its addition of nutrients to the soil, improves other physical properties of the soil that helps to perpetuate the nutrient addition effect in real time. Some of these benefits include the improvement of soil structure, water and nutrient holding capacities as discussed above. It also adds micronutrients which is usually not the focus of inorganic fertilizer application.
Priming effect is another mechanism proposed by Kuzyakov et al. [27], in which organic fertilizers affect additional crop yields. Priming refers to strong short - term changes in the turnover of soil nutrients caused by the addition of easily decomposable organic materials. Changes may be positive or negative depending on whether nutrients are rapidly mineralized or immobilized. Under this mechanism, a sum of nutrients available in the soil after harvest and nutrients in crops from the field are higher than a sum of the initial soil nutrients and nutrients in the organic materials. Thus, the additional unaccountable nutrient is the result of organic fertilizer precursing a more rapid mineralization rate and dissolution of previously unavailable/fixed nutrients into solution. This is made effective by the improvement in microbial population, diversity and activity affected by the organic material addition.
These mechanisms, though proposed by different authors, all point to the fact that organic fertilizers are beneficial to the soil and consequently, crops.
Despite the benefits of organic fertilizers to the soil, organic resources application is limited by the large amounts required to meet nutrient demand [28]. Hence locally available organic resources must be used to overcome this limitation. In areas where animal production is common, feedlot manure is the most available organic fertilizer resource. Crop residue retention and cash crop- cover crops rotation is an option to increase on-farm residue production. One other option that has proven to be effective is an integrated nutrient management approach where organic and inorganic fertilizers are applied in right quantities [29]. This approach helps to harness the mechanisms underlying the effects of organic fertilizer application on crops, resulting in synergy in terms of crop yields.
In times when climate change is imminent and its effect on agriculture tends to endanger food security, it is paramount that farmers and other stakeholders use strategies and resources that adapt farming systems to the changing climate. Climate change is mainly driven by natural and anthropogenic activities that pump greenhouse gases (examples CO2, CH4, N2O) into the atmosphere [30, 31]. It may lead to extreme droughts or extreme floods, which may have devastating impacts on food production and agriculture. In this light, organic fertilizers are a great resort due to their replenishing effects on soil physical and chemical properties. Aside the benefits of organic fertilizers discussed above which may adapt the soil to drought conditions, soils should be well drained and loose in flood prone areas in wait of climate change. In compact and poorly drained soils, the addition of organic fertilizers would improve soil particle aggregation and structure to give the soil more room to infiltrate water without settling on the top for too long to cause floods. The addition of organic matter reduces the inventory of greenhouse gasses contributed to climate change by agriculture. This is achieved by the sequestration of carbon into the soil from organic fertilizers applied. The carbon would have been lost to the atmosphere as CO2 or CH4 if it had not been incorporated into the soil [32]. As a result, the application of organic fertilizers to the soil helps to reduce greenhouse gas emission leading to global warming and a consequent climate change and helps adapt the soil to the current and future changes in the climate.
Since organic materials are diverse in type and nutrient composition, it is difficult to give a general recommendation of an organic material. The lignin, polyphenol and nitrogen contents of organic material are important controls on its nutrient mineralization, once applied. It is important to evaluate the carbon to nitrogen (C:N) ratio of an organic material to determine if application of the material will lead to N mineralization or immobilization. A C:N ratio of 25 would enhance decomposition and mineralization by soil microbes while a C:N ratio above that would enhance N immobilization [14]. Hence the lower the C:N ratio, the more rapidly nutrients will be made available to the soil. Organic materials high in lignin (>15%) and polyphenol (>5%) contents usually have high C:N ratios and are resistant to microbial decomposition; hence will decompose slowly. If the N content of the material is 2.5% or more, it would likely decompose and mineralize faster [33].
The effectiveness of an organic material as a fertilizer is also dependent on how it is applied. Surface application of organic fertilizer enhances the loss of N through ammonia volatilization or loss of N and P through runoff and erosion. Judicious methods by which organic materials may be applied to reduce wastage and nutrient losses include band spreading, trailing hose method, burying method, rapid soil incorporation, and the addition of nitrogen inhibitors [34].
Band spreading is the application of the organic material(s) in narrow bands usually a few centimeters away from the crops. This reduces the surface area of the material to the atmosphere so that ammonia volatilization is reduced. To reduce the rate of denitrification as well, band spreading should be done during cool weather with no excessive soil moisture and at right rates. The crop canopies also serve as a physical barrier that further reduces the rate of ammonia volatilization from band spreading applications.
Slurries or liquefied organic fertilizers could be applied in these narrow bands through trailing hoses which hang down from a boom and run along or just above the surface of the soil.
Organic amendments could also be buried at about 5-30 cm depth depending on the crop establishment. Deeper depth burying can be practiced before crops are grown while shallower depth is suited for already established crop fields. This method greatly reduced N loss through ammonia volatilization and the loss of material through erosion.
Manure could be rapidly incorporated into the soil during soil tillage (before planting) or with hand implements to reduce N and P losses in volatilization and runoff.
Under conditions with high denitrification potential, nitrification inhibitors could be added to organic fertilizers to delay the rate at which ammonium is converted to nitrates, which is a suitable substrate that precursors the denitrification process. It is important to apply organic fertilizers at cool times of the day and at the right rates to reduce nutrient losses.
The sole application of organic fertilizers has proved to be a slow means of nutrient supply to the soil. Hence the combined use of organic and inorganic nutrient sources has been proposed [29]. Such applications harness the benefits of synergistic interaction between the organic and inorganic nutrient sources. The main objective of this research was to increase maize yield with the application of organic manure or a combination of it with mineral fertilizer. To arrive at this objective, the yield of maize following varying rates of combined manure and mineral fertilizer applications were estimated at harvest, synergistic benefits of combined applications were quantified and the effect on soil nutrient stocks were analyzed.
A field experiment was conducted at the plantation section of the Kwame Nkrumah University of Science and Technology under rain-fed conditions. Nine treatments (three levels of mineral fertilizer at 0, 50 and 100% of the 90-60-70 kg/ha NPK recommended rate (RR) by three levels of manure at 0, 50, 100% of 5 t/ha RR) were applied on the field in a factorial fashion arranged in Randomized Complete Block Design (RCBD) with three replications. The land was slashed and burned and later plowed and harrowed to a fine tilt. Plot layouts were done with lines and pegs with each plot measuring 3 m by 2 m. There were 2 m alleys between replications and 1 m alleys between plots. Initial soil and manure sampling and analyses were done to characterize them. Randomized manure treatments units were allocated to their designated plots. The Akposoe maize variety developed by the Crops Research Institute of Ghana was planted 2 weeks after manure allocation. Weeding was done manually when necessary. Mineral fertilizer application was done 2 weeks after planting (WAP). The fertilizers were applied as urea, triple superphosphate and murate of potash. The urea was split applied in the first fertilizer application (2 WAP). The other half of the urea was applied 6 WAP. The manure was spread in the plots and raked in to about 5 cm depth. The fertilizer was applied by the band placement method, about 5 cm away from the maize plants. A final soil analysis was done after harvest to determine soil nitrogen (N), phosphorus (P) and potassium (K) levels. Data was subjected to analysis of variance (ANOVA) with the GENSTAT statistical package and significant means were separated with least significant difference at 5%.
Note: Rains were quite erratic at the start of the experiment until an unexpected shortage during the reproductive stage of maize growth. Though unfortunate, this was a good situation to determine if manure applied to the soil would help maintain more soil moisture and consequently impact maize yield.
The lack of rains crippled any effect of the manure alone or its combinations with mineral fertilizer to create differences in the yield of maize. Limited soil moisture has been reported to constraint maize yield [35], because all the processes involved in nutrient movement to roots, uptake by roots and translocation through the transpiration stream use water [36].
After harvest, soil and statistical analysis showed that plots receiving 50 and 100% rates of manure had a significant 20% more total soil N than the control and mineral fertilizer rates. It is possible that due to the rapid nutrient release mechanism of mineral fertilizer, most of its nutrients was released during the early stages of the maize growth, subject to rapid loss from the soil system. The C:N ratio of the manure was 23.08, which is an indication that N was being mineralized [35] into the soil system over a long period, even after the shortage of rains. A combined use of the full rate of manure and full rate of mineral fertilizer also had 20% more total soil N than each individual nutrient source. It is evident that combining organic and inorganic inputs creates a balance between increasing N availability for plant uptake over sole organic application and decreasing N availability for potential system losses compared to fertilizer alone [37].
The rather erratic rains at the beginning of the experiment might have caused soil P and K to leach beyond root zone, hence the lack of differences between the effects of sole manure and mineral fertilizer applications or their combinations at the end of the experiment.
Overall, it was concluded that organic manure had the potential to hold nutrients in the soil longer than inorganic fertilizers. In the advent of climate change, it could be a very useful tool especially in areas was droughts are expected.
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
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\\n\\nOA Publishing Fees
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The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\n\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
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\n\nWe aim at improving the quality and availability of scholarly communication by promoting and practicing:
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The main global health organizations have incorporated patient safety in their review of work practices. The data provided by the medical laboratories have a direct impact on patient safety and a fault in any of processes such as strategic, operational and support, could affect it. To provide appreciate and reliable data to the physicians, it is important to emphasize the need to design risk management plan in the laboratory. Failure Mode and Effect Analysis (FMEA) is an efficient technique for error detection and reduction. Technical Committee of the International Organization for Standardization (ISO) licensed a technical specification for medical laboratories suggesting FMEA as a method for prospective risk analysis of high-risk processes. FMEA model helps to identify quality failures, their effects and risks with their reduction/elimination, which depends on severity, probability and detection. 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Leadership in health services is important for following innovations and adapting to current situations. Nurses working together with other health personnel in hospitals providing health services constitute an important group in leadership. Nursing, which is a key force for patient safety and safe care, is a human-centered profession, and therefore leadership is a key skill for nurses at all levels. The leadership styles of nurse managers are believed to be an important determinant of job satisfaction and persistence of nurses. The need for nurses with leadership skills and the need for nurses to develop their leadership skills are increasing day by day. There are several leadership styles defined in nursing literature. These leadership styles are examined under the titles of relational leadership style, transformational leadership, resonant leadership, emotional intelligence leadership, and participatory leadership. The task-focused leadership style is explored under the headings of transactional and autocratic leadership, laissez-faire leadership, and instrumental leadership.",book:{id:"9047",slug:"nursing-new-perspectives",title:"Nursing",fullTitle:"Nursing - New Perspectives"},signatures:"Serpil Çelik Durmuş and Kamile Kırca",authors:null},{id:"58916",title:"Factors Affecting the Attitudes of Women toward Family Planning",slug:"factors-affecting-the-attitudes-of-women-toward-family-planning",totalDownloads:8485,totalCrossrefCites:9,totalDimensionsCites:18,abstract:"Everyone has the right to decide on the number and timing of children without discrimination, violence and oppression, to have the necessary information and facilities for it, to access sexual and reproductive health services at the highest standard. Deficient or incorrect family planning methods, wrong attitudes and behaviors toward the methods and consequent unplanned pregnancies, increased maternal and infant mortality rates are the main health problems in most countries. Individuals’ learning modern family planning methods and having positive attitude for these methods may increase the usage of these methods and contributes the formation of healthy communities. 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Over periods of time, some of these norms become standards that all members of the community are expected to adhere to. Deviance from these standards is seen as absurd, wrong, or frankly abnormal. However, many of these cultural mores have no scientific basis and, some of them actually promote behaviors with negative health consequences. This chapter examines the cultural practices of some communities in Africa and their health consequences and, explores ways to address the challenges.",book:{id:"9138",slug:"public-health-in-developing-countries-challenges-and-opportunities",title:"Public Health in Developing Countries",fullTitle:"Public Health in Developing Countries - Challenges and Opportunities"},signatures:"Radiance Ogundipe",authors:[{id:"302308",title:"Dr.",name:"Radiance",middleName:null,surname:"Ogundipe",slug:"radiance-ogundipe",fullName:"Radiance Ogundipe"}]},{id:"55808",title:"The Role of Legumes in Human Nutrition",slug:"the-role-of-legumes-in-human-nutrition",totalDownloads:5378,totalCrossrefCites:59,totalDimensionsCites:100,abstract:"Legumes are valued worldwide as a sustainable and inexpensive meat alternative and are considered the second most important food source after cereals. Legumes are nutritionally valuable, providing proteins (20–45%) with essential amino acids, complex carbohydrates (±60%) and dietary fibre (5–37%). Legumes also have no cholesterol and are generally low in fat, with ±5% energy from fat, with the exception of peanuts (±45%), chickpeas (±15%) and soybeans (±47%) and provide essential minerals and vitamins. In addition to their nutritional superiority, legumes have also been ascribed economical, cultural, physiological and medicinal roles owing to their possession of beneficial bioactive compounds. Research has shown that most of the bioactive compounds in legumes possess antioxidant properties, which play a role in the prevention of some cancers, heart diseases, osteoporosis and other degenerative diseases. Because of their composition, legumes are attractive to health conscious consumers, celiac and diabetic patients as well as consumers concerned with weight management. 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She has been a faculty member at the University of California, Riverside in the School of Education since 2016. Her research focuses on translational studies to explore the reward system in ASD, as well as how anxiety contributes to social challenges in ASD. She also investigates how behavioral interventions affect neural activity, behavior, and school performance in children with ASD. She is also involved in the diagnosis of children with ASD and is a licensed clinical psychologist in California. 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He graduated from the Physics Department of the University of Crete and continued his post-graduate studies at the University Paris 7-Denis Diderot (D.E.A. in Didactic of Physics), University Paris 5-René Descartes-Sorbonne (D.E.A. in Science Education) and received his Ph.D. degree at the University Paris 5-René Descartes-Sorbonne (PhD in Science Education). His research interests include science education in early childhood, science teaching and learning, e-learning, the use of ICT in science education, games simulations, and mobile learning. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. 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