\r\n\tManagement of these disorders requires good clinical evaluation, diagnostic tests, appropriate therapy and huge healthcare cost. Sometimes multiple specialties (gastroenterologists, \r\n\tgastrointestinal motility specialists, otolaryngologists, surgeons, speech therapists, medical oncologists and radiation oncologists) are involved in the management of dysphonia and dysphagia. In the recent years, there have been many updates in the management of these disorders. This book will discuss systematically the different etiologies and management of dysphonia, maxillofacial, oropharyngeal and esophageal dysphagia. This book will be a good \r\n\tguide to the practicing physicians for the management of voice and swallowing disorders.
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1. Introduction
The monitoring of chemical species in pharmaceutical and biomedical samples is a field in which analytical chemistry plays an important role, contributing new procedures of analysis and instrumentation. Many methods have been developed for pharmaceutical and biomedical analysis including chromatographic, electrophoretic, and spectrophotometric methods. However, there are inherent difficulties associated with the types of samples involved. The most practical difficulty encountered is the preservation and integrity of the species during sampling, storage, and sample pretreatment. In the medical area, the main matrices are blood, serum, and urine, while in the pharmaceutical industry, there are many types of samples and variations in their compositions. This hinders the application of analytical techniques for the fast and accurate monitoring of pharmaceutical and biomedical species in real samples.
Spectrophotometry is the technique most commonly employed in chemical analysis, and it provides advantages in terms of the availability of instruments, simplicity of procedures, speed, precision, accuracy, and applicability to a wide range of bio-medically important substances. Due to recent advances, increasing attention is being given to the coupling of a spectrometer to a continuous flow system to determine low concentrations of several chemical species in different kinds of pharmaceutical and biological samples. The flow-injection analysis technique has found wide application, which can be mainly attributed to its versatility, ease of automation, high sampling frequency, and the requirement for minimum sample treatment prior to injection into the system.
This chapter draws attention to some of the important and unique aspects of the applications of flow-injection spectrophotometry, addressed within the context of pharmaceutical and biomedical analysis. A short historical background of the flow-injection analysis technique and a brief discussion of the basic principles and potential are presented.
A notable feature of this chapter is the large number of papers on chemiluminescence discussed herein. In addition, considerable attention is given to sample preparation techniques and the characteristics of analytical methods such as precision, accuracy, and sampling frequency. Representative applications of flow-injection spectrophotometry to pharmaceutical and biomedical analysis are also described.
2. FIA origin and development
The process of flow-injection analysis (FIA) was initially proposed in the 1970s by Prof. Dr. Jaromir Ruzicka of the Technical University of Denmark and was subsequently consolidated as a state-of-the-art technology for the automation and mechanization of chemical systems. At that time, the cited researcher spent a year in Brazil advising on the installation of the Laboratory of Analytical Chemistry at the Center of Nuclear Energy in Agriculture of the University of São Paulo (CENA/USP), where pioneering work was carried out that led to the FIA process becoming very well established. Since its introduction, more than 20,000 papers have been published reporting the development of advanced instrumentation methods in the context of chemical analysis, which are available for environmental, food, and clinical services involving pharmaceutical and biomedical samples [1, 2].
The FIA process involves the insertion of the sample into a carrier fluid that transports it to a suitable detection system. During this process, the sample can be brought into contact with reagents that are also inserted by propulsion, resulting in a controlled dispersion of the sample. The processes that characterize FIA systems have gained great prominence in contemporary analytical chemistry since several limitations have been overcome in the development of improved analytical procedures.
The origin and development of flow analysis systems was strongly influenced by the work of Skeggs, who proposed an approach called continuous flow analysis (CFA) [3]. For approximately 20 years, it was accepted that segmentation by dividing the flow into small regular compartments separated by air bubbles was the best strategy to avoid contamination and the widening of the discrete zone of the sample along the course, which is known today as dispersion [3–5]. It was only in the mid-1970s that it was widely accepted that segmentation could be omitted following an innovative proposal for a method employing the continuous flow of the samples and reagents with adequate dimensions and flow rates. The system was subsequently simplified, increasing the frequency of samples analyzed per unit of time, referred to as the analytical frequency. Due to the advantage of good reproducibility offered by FIA systems, it has also become possible to quantify the analyte even before the reaction between the sample and the reagent reaches equilibrium since the interval between the injection and detection is the same for the standard solutions and the sample.
In general, the FIA process consists of fluid propulsion, usually performed by a peristaltic pump that operates at constant flow, sample injection, reaction promoted in a homogenizing mixing coil with suitable geometry and a compatible detection technique, such as molecular spectrometry and atomic, chromatographic, and electroanalytical techniques.
Initially, Ruzicka used a hypodermic syringe to promote the injection of the sample, which gave rise to the name of the process [6]. Since then, other devices have been proposed for the insertion of the sample into the loader fluid, such as the proportional commutator injector and the rotary valve. The FIA systems have thus evolved and independent injections by multicomutation can be performed, enabling binary sampling [7]. In recent years, FIA systems have evolved in ways that have led to the development of sequential injection analysis (SIA) systems. In this case, the injection of the sample and the contact with the reagent flow occur through the selection of the port of a central selector valve in which the mixture is provided with bi-directional movement, alternating the propulsion direction occurring in a single line, in the absence of confluences [2, 8]. Due to these characteristics, SIA systems can be considered as differentiated from conventional FIA systems.
The classification of FIA systems has become necessary considering the variety of analytical procedures available. This can be based on the way in which the sample is introduced (continuous or intermittent) and on the flow characteristic (segmented, monosegmented, nonsegmented). Figure 1 shows a classification scheme for flow analysis methods.
Figure 1.
Schematic diagram of classification of flow analysis procedures.
FIA systems have become commonly used and a number of variations in the configurations have been proposed in order to minimize the consumption of sample and reagents and to enhance the sensitivity of detection and the selectivity and precision of the analytical measurements.
The simplest configuration is the single-line flow diagram, where the loading fluid is the reagent itself, and the mixing occurs exclusively by dispersion. When the ratio between the volumes of the injected sample aliquot and its pathway is inappropriate, the addition of reagents by confluence may provide a more effective reaction where inert solutions, such as carriers, are employed. In order to overcome the excessive waste of reagents, which are continuously consumed, the system of flow injection through coalescing zones was proposed, thus minimizing the amount of waste generated. Since then, FIA systems have proven to be highly versatile and robust, making it possible to obtain strategically various arrangements and configurations that have been satisfactorily employed for extractive, separation, and preconcentration purposes. In addition, there are a number of approaches through which clinical formulation products can be efficiently monitored for the certification of their quality.
3. Spectrophotometric flow-injection procedures for pharmaceutical samples
Spectrophotometric methods are the most commonly used techniques in chemical analysis due to the availability of instruments, simplicity of procedures, precision, and wide applicability. Based on the laws governing absorption and emission phenomena, it is possible to determine the concentrations of compounds in solutions, notably those of biological, chemical, or pharmaceutical interest [9].
Drug analysis, involving the pharmaceutical preparations or the raw materials used for their production, and the determination of drugs together with metabolites in biological samples (serum, plasma, saliva, urine, and some secretions) constitutes a large part of the activities carried out by pharmaceutical and clinical laboratories.
Spectrophotometry is the most commonly used technique for the determination of drugs, and it is based on chromogenic reactions or light absorption by the analyte. Chromogenic reactions for drugs include metal-ion complexes, redox reactions, and the formation of charge-transfer complexes.
Some of the flow-injection spectrophotometry procedures for the quantification of pharmaceutical samples [10–14] are detailed in Table 1. An important observation is the choice of carrier, aiming to avoid matrix effects and even clogging of the flow channels due to precipitation [14, 15].
Analyte
Methodology
Detection limit
Ref.
Ketoprofen
Distilled water as a carrier for gels and citrate buffer, pH 6.5, for ampoules at 261 nm.
0.436 and 0.303 g ml−1 for gels and ampoules, respectively.
Flow-injection spectrophotometry procedures for the quantification of pharmaceutical samples.
Flow techniques, characterized by great flexibility, versatility, and ease of automation, allow the development and implementation of many analytical systems, which are compatible with a wide range of sample manipulation techniques, under highly reproducible conditions. Some flow-injection procedures are based on oxidation-reduction reactions. For the determination of N-acetyl-L-cysteine [16], this procedure involves the oxidation of the analyte of interest by Fe(III). The Fe(II) produced can be determined using 1,10-phenanthroline, and the chromophore formed is analyzed at 510 nm. On-line oxidation by Ce(IV) in acid medium—a procedure based on oxy-reduction—has been used to determine pyrazine. The colored free radical produced by the reaction was monitored at 510 nm [17].
Other procedures are based on the formation of a colored complex between the analyte of interest and metal-ions. For example, the determination of cimetidine with Cu(II) in acetate buffer (pH 5.9) can be carried out at a wavelength of 330 nm [18] and epinephrine with Fe(II) in amino acetic carbonate buffer (pH 8.3) at a wavelength of 530 nm [19].
A procedure for the determination of paracetamol (4-acetaminophen) has been described by Fatibello-Filho and Vieira [20]. The method is based on paracetamol oxidation by sodium hypochlorite, and the determination of the oxidant using o-toluidine dichloride as the chromogenic reagent at 430 nm. The analytical curve for paracetamol was linear in the concentration range of 8.50 × 10−6 to 2.51 × 10−4 mol L−1 with a detection limit of 5.0 × 10−6 mol L−1. The relative standard deviation was less than 1.2% for a paracetamol solution of 1.20 × 10−4 mol L−1 (n = 10).
Many methods for the determination of pharmaceuticals also involve the flow-injection procedure based on homogeneous reactions. For example, the indirect determination of olanzapine from the reaction with hexacyanoferrate (III) in which the measurement of the unreacted oxidant is collected at 425 nm [21]. Diclofenac and mefenamic acid can also be oxidized in a flow system by hexacyanoferrate (III) and spectrophotometrically determined [22]. Other analytes that can be determined in homogeneous reactions are shown in Table 2.
FIA procedures using based on color-forming reactions.
4. Spectrophotometric flow-injection procedures for biomedical samples
The FIA technique can be coupled to various detection systems, such as a spectrophotometer, which allows a wide range of analytical devices to be combined [32].
A flow-injection analysis procedure using spectrophotometry was used to determine urea in blood plasma, employing the legume Cajanus cajan as a source of urease enzyme, in a mini-column coupled to the FIA collector. A confidence level of 90% and a relative standard deviation of 1.4% (n = 12) were obtained [33]. In another study, the development of a flow analysis procedure for the determination of total protein in a bovine blood plasma was carried out using the Biuret method. Samples of bovine plasma with 12.5 and 100.0 g L−1 of total protein were analyzed, and the analytical range was 2.5–20.0 g L−1. The relative standard deviation of the procedure was 2.8%, and the analytical frequency was 76 determinations per hour. The results were compared with the traditional method of analysis (Biuret), and no statistically significant differences were observed at the 95% confidence level [34].
Sensors based on optical techniques are widely applied in different types of analysis, including biomedical sensing, and when connected to flow-injection analysis, a much faster analysis procedure is obtained [35]. In this context, a multicomutation flow system was used, which incorporates a sol-gel optical sensor (sensor: base catalyzed 4-(2-pyridylazo) resorcinol (PAR)) for the spectrophotometric determination of Cu(II) in urine samples using a photodiode detector with a maximum absorbance at 500 nm. The results were in agreement with those obtained by inductively coupled plasma mass spectrometry (ICP-MS), with a confidence level of 95% [35].
A FIA system was used to determine copper and zinc in water, pharmaceuticals, soils, and human hair samples. The product of the reaction with 2-carboxyl-2-hydroxy-5-sulfoformazylbenzene (Zincon) was introduced into a stream of carrier solution in the flow system. A sequential reaction of Cu(II) and Zn(II) was performed using Zincon, with the formation of two complexes and monitoring at 627 nm [36].
A flow procedure with spectrophotometric detection to determine bromopride in different matrices has been studied [37]. To increase the sensitivity of the reaction, a micellar medium (sodium dodecyl sulfate—SDS) was employed. Factorial planning was carried out to optimize the experimental parameters. The limit of detection was 1.07 × 10−7 mol L−1. The method developed was satisfactorily applied in the determination of bromopride in pharmaceuticals and human urine, and recoveries were in the ranges 99.6–101.2 and 98.6–102.1%, respectively.
The application of a very sensitive and selective on-line flow-injection method for the deter-mination of thorium(IV) after preconcentration in a minicolumn with N-benzoylphenylhydroxylamine-impregnated XAD-4 resin to biological samples has been described [38]. Sample rates of 40 and 11 h−1 were obtained at the 60 and 300 seconds preconcentration times, respectively; the preconcentration factors were 32 and 162, with detection limits of 0.76 and 0.150 μg L−1, respectively [38].
Sarcosine has been investigated as a new marker for prostate cancer. A method for detecting sarcosine in biological samples (urine or blood plasma) has been proposed [39]. Ion exchange liquid chromatography with photometric detection at 570 nm was used as a separation method, which proved insufficient for the detection of sarcosine (70 μM). An off-line approach to the ninhydrin derivatization of the fractions collected was optimized, after which a known amount of ninhydrin was added followed by incubation of the mixture under the optimized temperature and time conditions. FIA system with electrochemical detection was used. In this case, 5 μL of sample was injected through a manual valve with a cell phase flow rate of 1 mL min−1 and spectrophotometric detection in the wavelength range of 450–800 nm. A detection limit of 1.7 μM was obtained for sarcosine [39].
5. On-line sample processing methods for in flow analysis
Flow analysis systems are widely used in analytical chemistry, contributing to increased reproducibility and accuracy of the methods. They also enable a reduction in the reagent consumption and the development of cleaner methods, meeting the requirements of “green” chemistry.
However, there are still limitations inherent to the procedures involved in preparing the samples, and these need to be suitable for each matrix.
Several procedures for analyzing pharmaceutical and biological samples have been developed where the sample preparation method performed on a laboratory bench is replaced by a flow procedure coupled directly to the instrument (spectrometer, chromatograph, electrophoresis unit, etc.). This increases the reliability of the method since it minimizes the potential for contamination inherent to the analysis, increases the reproducibility of the results, and increases the analytical frequency. These characteristics are due to the automation and processing of samples in closed systems under highly reproducible mixing and timing conditions. One of the factors that contributes to the success of this sample processing procedure carried out in closed systems is the combination of techniques and methodologies, known as hyphenation, which promotes faster analysis that is more efficient with less interference. The pretreatment of pharmaceutical and biological samples in flow is an important step in closed systems. Due to the complexity of these samples, the determination of chemical species presents significant challenges [40]. Thus, different types of procedures can be developed for the preparation of on-line samples for each matrix, according to its characteristics, such as solid phase extraction, solid phase microextraction, liquid-liquid microextraction, and chemical derivatization.
Some articles using on-line processes for the determination of compounds in pharmaceutical and biological samples are shown in Table 3. Two on-line procedures have been reported for the determination of ranitidine: chemiluminescence and UV-Vis detection [41, 42]. Several methodologies for the on-line preparation of saliva samples with detection by UV-Vis [43], ICP-OES [44], and AFS [45] are described in the literature.
Sample
Detection technique
Strategies for analysis
Ref.
Ranitidine and salbutamol
FIA-chemiluminescence
For Ru(bipy)32+ chemiluminescence, a sulfuric acid carrier stream was employed into which Ru(bipy)33+ and sulfuric acid was injected (20 μL), while a second stream delivered the analyte standard and sample solutions.
On-line treatment procedures for pharmaceutical and biological samples using spectrometric techniques.
The complexity of the matrices of the pharmaceutical and biomedical samples requires an efficient decomposition process, without losing the necessary characteristics for a precise quantification, maintaining the integrity of the analyte. The use of microwave radiation energy was found to be an efficient alternative to conventional sample preparation methods since the processing time is reduced, minimizing problems associated with the loss of the more volatile components. In spite of these advantages, the process requires the manual transfer of volumes, addition of reagents, and excessive dilutions, which are all potential sources of errors, for instance, contamination. The mechanization of the microwave sample preparation processes in a continuous stream decomposition system has contributed to improving the sample processing and, therefore, the analytical performance of the method.
The use of flow systems coupled to a microwave oven for the preparation of samples was first proposed by Burguera et al. [47], where urine samples were decomposed for further determination of lead. A volume of up to 100 μL of the sample was decomposed using a home microwave oven with a maximum power of 700 W and a 100 μL mixture of 0.4 M HNO3 and 0.3 M HCl. The application of this system allowed an analytical frequency of 80 samples per hour. Since the first work of exploring the coupling of a microwave oven and a flow system, several systems have been developed and applied to a wide variety of samples, for instance, water, effluents, plants, food and minerals, along with biological fluids and tissues. The analysis of biological fluids is of great importance since it allows the diagnosis of various diseases, nutritional, and metabolic research, therapeutic monitoring involving the biological action of some metals, such as calcium, magnesium, iron, cobalt, zinc, and manganese, and the detection of some drugs (including cocaine and marijuana) [48, 49].
Coelho and collaborators developed an on-line decomposition system for urine samples using a microwave oven prior to the determination of calcium and magnesium by flame atomic absorption spectrometry (FAAS). The decomposition efficiency allowed a rapid treatment of the urine sample with an analytical frequency of 45 samples per hour. The system consisted of three decomposition coils inserted into the cavity of the microwave oven and a valve that allowed the interruption of the passage of the flow and confined the sample to the inside of the oven [50].
The preparation of flow samples in biomedical and pharmaceutical matrices employing spectroscopic techniques remains a challenge, and few studies have been reported in the literature when compared with the chromatographic methods. When spectrometric techniques are subjected to hyphenation, they are promising for the preparation of one or more samples, since the on-line detection systems cited in the literature favor a decrease in the use of batch procedures, thus minimizing the potential for contamination and automating the sample processing procedure.
6. Combination of FIA and other analytical systems
The advance of laboratory research has enabled the identification and quantification of analytes, individually or simultaneously.
One of the techniques that has contributed to the simultaneous determination of analytes is FIA system combined with other analytical systems, such as high performance liquid chromatography (HPLC), enzymatic reactions, gas chromatography, biosensors, electrophoresis, electrochemical, and immunoassays. According to Saurina [51], in most cases, these combinations enable analysts to detect and quantify up to three compounds simultaneously. The methods required to increase this number may not be compatible with the physical resources used in systems involving flow injection.
The combination of the FIA system with other analytical techniques enables reductions in the analysis time and the reagent/sample consumption and improved accuracy, sensitivity, selectivity, and sampling frequency. In addition, the analyst’s contact with the sample is minimized, decreasing the potential for contamination. Thus, by combining the pretreatment (digestion, preconcentration, sample clean-up, and solvent-solvent extraction, etc.) with on-line sample introduction, the FIA system becomes a very efficient and advantageous technique [52–57].
In this context, the possibility of detecting multianalytes using various techniques involving the combination of the FIA system with other traditional analysis systems should be highlighted. Different separation and sample pretreatment procedures can be performed using detection techniques such as fluorescence, spectrophotometry, and electrochemistry, enabling the detection of innumerable analytes, including those present in samples involved in biological applications [58–63].
Several approaches to detection have been used, and electrochemical detectors are prominent in the scientific literature, notably in studies involving conventional amperometric detection coupled to an FIA system. The main characteristics of this combination are increased sensitivity, minimized contamination of the surface of the working electrode, the presence of negligible capacitive current and in situ measurement, etc. A limitation associated with this system is the instability of the electrochemical signal during the determination of some compounds, compromising the repeatability of the response and the reproducibility of the results [64].
Another example of combining FIA and an electrochemical system is found in the studies of Chaves et al. [65] in which three compounds were determined simultaneously: caffeine, ibuprofen, and paracetamol. The authors report results obtained by combining FIA with multiple pulse amperometry (MPA) using a wall-jet flow cell with a boron-doped diamond electrode. In this analysis, cyclic voltammetry (50 mVs−1) was used.
According to Llorent-Martinez et al. [66] and Oliveira et al. [67], most of the methods involving detection by UV-Vis using flow procedures offer many advantageous of this combination, being simple, fast and direct methods offering good selectivity and sensitivity in the separation and/or preconcentration steps.
Vidal et al. [68] address the simultaneous determination of a mixture of three analytes that are often combined in pharmaceutical formulations: two analgesics (paracetamol and propyphenazone) and a stimulant drug (caffeine). The quantification was performed by separating the three compounds using an FIA system combined with a precolumn containing C18 silica gel to avoid spectral overlap of the compounds under analysis. The detection was conducted with a spectrophotometric detector through UV absorbance measurements. The results were satisfactory, since the compounds were quantified at low concentration ranges, that is, 25–350 μg mL−1 for paracetamol, 5–75 μg mL−1 for caffeine, and 15–150 μg mL−1 for propylphenazone. Also, the proposed method provided low detection limits ranging from 0.65 to 7.5 μg mL−1.
Pistonesia et al. [69] carried out the simultaneous analysis of levodopa and benserazide in tablets of pharmaceutical formulations. The samples were not subjected to pretreatment, and the reaction mixture containing the sample and potassium periodate was directed to a flow cell (8 μL inner volume) inserted in a spectrophotometer with a UV-Vis diode array detector. The concentrations used for the construction of the calibration curves analyzed were 4.1 × 10−4 to 2.03 × 10−3 M for levodopa and 8.5 × 10−5 to 4.25 × 10−4 M for benserazide. During the analysis, the FIA system variables (flow velocity, reactor length, and injected volumes) were optimized and the validation of the method (considering the robustness, repeatability, reproducibility, and accuracy) was studied. The kinetic-spectrophotometric data provided by the diode array detection were treated applying partial least squares (PLS) multidimensional regression. Samples were injected into the HPLC system using preoptimized conditions. The results obtained using the HPLC analysis (197 and 49 for levodopa and benserazide, respectively) and the FIA/PLS system (200 and 48 for levodopa and benserazide, respectively) showed no discrepancy. Thus, a simple, fast, and direct method was obtained through the implementation of a flow-injection system coupled to UV-visible diode spectrophotometry.
Regarding the analysis of biological samples, Reis and Luca [70] described a procedure for the determination of total protein in bovine blood plasma using a FIA system. The use of the FIA system enabled the in-line dilution of bovine plasma samples containing between 12.5 and 100.0 g L−1 total protein. The conditions for the flow analysis were optimized and the results, when compared to those obtained with the traditional method (Biuret), did not indicate significant statistical differences (t-paired test) at the 95% confidence level. The proposed method provided fast results, low reagent consumption, and minimization of the sample handling, as well as an analytical frequency of 76 determinations per hour.
In addition to the combination of the FIA system with innumerous detectors, the coupling of flow analysis with pretreatment and separation systems has been employed in some studies, especially FIA combined with capillary electrophoresis (CE). The first studies involving this coupling were described in 1997 by Kuban et al. [71] and Fang et al. [72]. They described this as an advantageous combination, capable of overcoming certain limitations presented by CE, such as low sensitivity, precision, and analytical frequency. An important feature is that the sample pretreatment step in the FIA-CE system is performed online, minimizing the potential for sample contamination.
Numerous studies involving FIA-CE have been reported in the literature notably: Kuban and Karlberg [73] carried out the determination of small anions through dialysis; Chen and Fang [74] performed the preconcentration of samples; Arce et al. [75] determined cations and anions; Chen and Fang [74] monitored multi-components in drugs; Kuban et al. [76] determined trimethoprim and sulfamethoxazole in drug samples; and Kuban and Karlberg [77] determined pseudoephedrine in human plasma.
An example of an FIA-CE system is also described in a paper by Liu et al. [78], which proposes a method developed through the combination of flow injection and CE for the separation and determination of paracetamol (Par), pseudoephedrine hydrochloride (Pse), dextromethorphan, potassium bromide (Dex), and chlorphenamine hydrogen maleate (Chl) using uncoated fused silica capillaries. Detection was performed on a UV detector at 214 nm. During the analysis, a flow-injection analyzer was used to transport the background electrolytes and the samples. The system consisted of a double piston, a 16-way automatic switching valve with three sample loops, and a peristaltic pump. The limits of detection (LOD) values were 0.22, 0.29, 0.42, and 0.70 μg ml−1 for the compounds Dex, Chl, Pse, Par, respectively. The low LOD values, the separation of the baseline of the peak of each analyte and the low cost of this FIA-CE system are characteristics that indicate that the proposed system is suitable for the identification and quantification of the compounds investigated.
Kuban et al. [76] described the determination of small inorganic cations (K+, Na+, Mg2+, and Ca2+) in blood, milk, or plasma samples by electrokinetic injection using an FIA-CE system. Since the undesirable adsorption of proteins onto the capillary wall during electrophoresis was inhibited, pretreatment of the samples was not necessary, and they could be injected directly into the system. In the initial stage, two injection modes were tested for all electrolyte and standard solutions: electrokinetic (EK) and prehydrodynamic (HD). The results indicated that EK injection was the better option because it showed high sensitivity and low matrix effects, with good repeatability of the cation migration times, mainly in the case of human plasma samples. In addition, a better performance was observed for the FIA-CE system when compared to the commercial CE system.
Other researchers have reported the determination of multianalytes using the FIA system combined with other systems of separation, identification, or quantification (detectors). These include the following: an immunoassay system using detection by chemiluminescence [79]; an electrochemiluminescence immunosensor for the detection of tumor markers [80]; biosensors with the use of enzymes [81]; the quantification of carbohydrates with amperometric biosensors [82]; and the analysis of pharmaceutical formulations combining FIA with HPLC or CE [83]. Thus, it is clear that FIA coupling with other analytical techniques allows the detection/quantification of multianalytes in pharmaceutical and biological samples, etc., either for the development of methods in laboratory research or in routine analysis.
Tzanavaras and Themelis published a review on the application of flow injection to pharmaceutical analysis that covers the topics of spectrophotometric determination of active pharmaceutical ingredients [84]. According to Tzanavaras and Themelis [84], the discovery of new drugs, especially when many samples have to be analyzed in the minimum of time, demand the improvement or development of new analytical methods.
7. Conclusions and future prospects
Many methods aimed at the monitoring of chemical species in pharmaceutical and biomedical samples have been developed and investigated in recent decades. This is a field in which analytical chemistry plays an important role, contributing new analysis procedures and instrumentation. However, methods for the determination and monitoring of pharmaceuticals are still scarce.
Although some progress has been made in the development of methodologies for the monitoring of chemical species in pharmaceutical and biomedical samples, some important points still need to be addressed, such as the sample pretreatment.
In this context, a further challenge has emerged for scientists, which is the development of new clean environmentally acceptable technologies with commercial feasibility. Thus, laboratory researchers need to improve the techniques for the identification and quantification of analytes, individually or simultaneously, with a focus on this challenge.
Acknowledgments
The authors are grateful for financial support from the Brazilian governmental agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), the MG state government agency Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), and the GO state government agency Fundação de Amparo á Pesquisa do Estado de Goiás (FAPEG).
multivariate curve resolution-alternating least squares
MPA
multiple pulse amperometry
PAR
4-(2-pyridylazo) resorcinol
PLS
partial least squares
SDS
sodium dodecyl sulfate
SIA
sequential injection analysis
USP
University of São Paulo
UV
Ultraviolet
Vis
Visible
\n',keywords:"pharmaceutical, biomedical samples, flow-injection, spectrophotometry",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/56497.pdf",chapterXML:"https://mts.intechopen.com/source/xml/56497.xml",downloadPdfUrl:"/chapter/pdf-download/56497",previewPdfUrl:"/chapter/pdf-preview/56497",totalDownloads:760,totalViews:631,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"November 3rd 2016",dateReviewed:"June 20th 2017",datePrePublished:null,datePublished:"December 6th 2017",readingETA:"0",abstract:"The discovery of new drugs, especially when many samples have to be analyzed in the minimum of time, demand the improvement or development of new analytical methods. Various techniques may be employed for this purpose. In this context, this chapter gathers the collection of paper and represents the review of past work on spectrophotometric technique coupled to a continuous flow system to determine low concentrations of several chemical species in different kinds of pharmaceutical and biological samples. A short historical background of the flow-injection analysis technique and a brief discussion of the basic principles and potential are presented. Part of this chapter is devoted to describing the sample preparation techniques, principles, and figures of merit of analytical methods. Representative applications of flow-injection spectrophotometry to pharmaceutical and biomedical analysis are also described.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/56497",risUrl:"/chapter/ris/56497",book:{slug:"spectroscopic-analyses-developments-and-applications"},signatures:"Bruno E.S. Costa, Henrique P. Rezende, Liliam Q. Tavares, Luciana M.\nCoelho, Nívia M.M. Coelho, Priscila A.R. Sousa and Thais S. Néri",authors:[{id:"163731",title:"Prof.",name:"Nivia",middleName:null,surname:"Coelho",fullName:"Nivia Coelho",slug:"nivia-coelho",email:"nmmcoelho@ufu.br",position:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},{id:"177651",title:"Dr.",name:"Luciana",middleName:null,surname:"Coelho",fullName:"Luciana Coelho",slug:"luciana-coelho",email:"lucianacatalao@hotmail.com",position:null,institution:null},{id:"205230",title:"MSc.",name:"Bruno Elias S.",middleName:null,surname:"Costa",fullName:"Bruno Elias S. Costa",slug:"bruno-elias-s.-costa",email:"brunoeliassantos@yahoo.com.br",position:null,institution:null},{id:"205231",title:"MSc.",name:"Thais S.",middleName:null,surname:"Neri",fullName:"Thais S. Neri",slug:"thais-s.-neri",email:"thayshamdan@gmail.com",position:null,institution:null},{id:"205232",title:"MSc.",name:"Henrique P.",middleName:null,surname:"Rezende",fullName:"Henrique P. Rezende",slug:"henrique-p.-rezende",email:"henriqueprezende@gmail.com",position:null,institution:null},{id:"205233",title:"MSc.",name:"Priscila A.R.",middleName:null,surname:"Souza",fullName:"Priscila A.R. Souza",slug:"priscila-a.r.-souza",email:"priscilaarsouza@gmail.com",position:null,institution:null},{id:"205234",title:"MSc.",name:"Liliam Q.",middleName:null,surname:"Tavares",fullName:"Liliam Q. Tavares",slug:"liliam-q.-tavares",email:"liliamqtavares@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. FIA origin and development",level:"1"},{id:"sec_3",title:"3. Spectrophotometric flow-injection procedures for pharmaceutical samples",level:"1"},{id:"sec_4",title:"4. Spectrophotometric flow-injection procedures for biomedical samples",level:"1"},{id:"sec_5",title:"5. On-line sample processing methods for in flow analysis",level:"1"},{id:"sec_6",title:"6. Combination of FIA and other analytical systems",level:"1"},{id:"sec_7",title:"7. Conclusions and future prospects",level:"1"},{id:"sec_8",title:"Acknowledgments",level:"1"},{id:"sec_10",title:"Abbreviations",level:"1"}],chapterReferences:[{id:"B1",body:'Siddiqui MR, Alothman ZA, Rahman N. Analytical techniques in pharmaceutical analysis: A review. Arabian Journal of Chemistry. 2017;10:S1409-S1421'},{id:"B2",body:'Trojanowicz M, Kotacinska K. Recent advances in flow injection analysis. Analyst. 2016;141:2085-2139'},{id:"B3",body:'Skeggs LT. New dimensions in medical diagnoses. Analytical Chemistry. 1966;38:31A-44A'},{id:"B4",body:'Ruzicka J. From continuous flow analysis to programmable Flow Injection techniques. A history and tutorial of emerging methodologies. Talanta. 2016;158:299-305'},{id:"B5",body:'Oliveira HM, Fonseca AJM, Cabrita ARJ. Flow analysis as an analytical tool for soil monitoring: From wet chemistry assays to nanomaterials based sample preparation. 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Preconcentration and decomposition of perfluorinated carboxylic acids on an activated charcoal cartridge with sodium biphenyl reagent and its determination at μg L−1 level on the basis of flow injection-fluorimetric detection of fluoride ion. Talanta. 1998;45:477-484'},{id:"B74",body:'Chen HW, Fang ZL. Combination of flow injection with capillary electrophoresis: Part 4. Automated multicomponent monitoring of drug dissolution. Analytica Chimica Acta. 1998;376:209-220'},{id:"B75",body:'Arce L, Rios A, Valcarcel M. Flow injection–capillary electrophoresis coupling to automate on-line sample treatment for the determination of inorganic ions in waters. Journal of Chromatography A. 1997;791:279-287'},{id:"B76",body:'Kuban P, Oldhoff O, Karlberg B. Direct determination of small cations in proteinaceous samples using a flow injection–capillary electrophoresis system. Journal of Chromatography A. 1999;857:321-326'},{id:"B77",body:'Kuban P, Karlberg B. On-line monitoring of kraft pulping liquors with a valveless flow injection–capillary electrophoresis system. Analytica Chimica Acta. 2000;404:19-28'},{id:"B78",body:'Liu X, Liu L, Chen H, Chen X. Separation and determination of four active components in medicinal preparations by flow injection-capillary electrophoresis. Journal of Pharmaceutical and Biomedical Analysis. 2007;43:1700-1705'},{id:"B79",body:'Fu Z, Yan F, Liu H, Yang Z, Ju H. Channel-resolved multianalyte immunosensing system for flow-through chemiluminescent detection of α-fetoprotein and carcinoembryonic antigen. Biosensors Bioelectronics. 2008;23:1063-1069'},{id:"B80",body:'Zhang Y, Liu W, Ge S, Yan M, Wang S, Yu J, Li N, Song X. Multiplexed sandwich immunoassays using flow injection electrochemiluminescence with designed substrate spatial-resolved technique for detection of tumor markers. Biosensors Bioelectronics. 2013;41:684-690'},{id:"B81",body:'Suwansa-Ard S, Kanatharana P, Asawatreratanakul P, Limsakul C, Wongkittisuka B, Thavarungkul P. Semi-disposable reactor biosensors for detecting carbamate pesticides in water. Biosensors Bioelectronics. 2005;21:445-454'},{id:"B82",body:'Vargas E, Gamella M, Campuzano S, Guzmán-Vázquez de Prada A, Ruiz MA, Reviejo AJ, Pingarrón JM. Development of an integrated electrochemical biosensor for sucrose and its implementation in a continuous flow system for the simultaneous monitoring of sucrose, fructose and glucose. Talanta. 2013;105:93-100'},{id:"B83",body:'Dantan N, Frenzel W, Kuppers S. Flow injection analysis coupled with HPLC and CE for monitoring chemical production processes. Chromatography. 2001;54:187-190'},{id:"B84",body:'Tzanavaras PD, Themelis DG. Review of recent applications of flow injection spectrophotometry to pharmaceutical analysis. Analytica Chimica Acta. 2007;588:1-9'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Bruno E.S. Costa",address:null,affiliation:'
Institute of Chemistry, Federal University of Uberlândia, Uberlândia, Brazil
'},{corresp:null,contributorFullName:"Henrique P. Rezende",address:null,affiliation:'
Institute of Chemistry, Federal University of Uberlândia, Uberlândia, Brazil
Department of Chemistry, Federal University of Goiás, Catalão, Brazil
'},{corresp:null,contributorFullName:"Thais S. Néri",address:null,affiliation:'
Department of Chemistry, Federal University of Goiás, Catalão, Brazil
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1. Introduction
Risk is the chance of something happening that has an impact upon objectives. It is measured in terms of consequences and likelihood. Probability, likelihood, and chance are used synonymously, as also are consequences and impact. Everyone struggles to understand and deal with many risk situations—from a domestic to corporate level, personal to national level, activity to project level. Management of projects can be improved by raising awareness about risks, and then implementing formal processes to deal with them. Construction projects are fundamentally susceptible to risks. Projects can be successful if managers plan for risks—planning, identifying, analyzing, and providing response for undesirable events that can occur. Choudhry and Iqbal [1] reveal that risk management is a new area in the construction sector and is attaining importance in the construction industry. The application of systematic risk management system is necessary for managing project risks [1]. Risk management can be applied successfully by identifying the risk sources connected with activities of the project. These risks are quantified in terms of likelihood and impact. Akintoye and Macleod [2] revealed that risk affects the performance, quality, budget and productivity of projects in construction. The strategy is to decrease the probability and impact of a risk [3]. Dikemen et al. [4] defined risk management as a systematic procedure of controlling risk. Choudhry and Iqbal [1] defined risk management as a stepwise process comprising on identification, analysis and risk response. Other researchers defined risk analysis as the procedure of evaluating identified risk and opportunities for their magnitude to proceed for a matching response in the light of limited funds.
Projects related to construction are complicated because they contain a range of human and non-human factors. These projects are started in complicated and vibrant environment resulting in high uncertainty, which are multifaceted by challenging time restraints [5]. Identifying and analyzing prospective risks can increase effective completion of the project. Risk management offers an opportunity for project stakeholders to review the project through a collective dialog, to recognize better and evaluate the prospective problems and then formulate a suitable response [6].
Various methods and models are developed by investigators to analyze risks. A decision support framework called as Advanced Programmatic Risk Analysis and Management Model (APRAM) is useful for risk management [7, 8]. Nasir et al. [9] devised a schedule risk model called as Evaluating Risk in Construction—Schedule Model (ERIC-S) that estimates the pessimistic and optimistic duration of activities. Ökmen and Öztas [10] proposed the Construction Schedule Risk Analysis Model (CSRAM) that evaluates schedule networks under uncertainty when duration of activities and risk factors are correlated. All these models evaluate either the time schedule risks, cost risks or both.
Risk management is vital in construction to minimize losses and improving profitability [2]. Williams et al. [11] proposed a method of risk management. Complicated projects, such as metro rail contains risks from the feasibility stage, construction to commissioning. Risks in heavy construction have a direct impact on the cost, schedule and performance. Reilly and Brown [12] reveal that infrastructure underground metro is inherently complex project having uncertain and variable ground situations. For these kinds of projects, it is vital to identify risk as early as possible in the project [12]. A risk management plan, if developed would ensure smooth attainment of project goals within given time, cost and quality. Moreover, it can safeguard better safety throughout the construction process and operative stage of the project.
Uncertainties in on-site and off-site project activities often result in the risk of delays and schedule overruns in construction projects. A risk analysis approach that assesses the integrating impact of uncertainties [13] show that growth in project size and work quantities intensifies pair and group interconnection of tasks within and between groups of on-site and off-site activities, resulting in lengthened completion times and deviations from project plans. Vu et al. [14] revealed that prolonged schedule delays have an extremely detrimental impact on a project’s efficiency, cost and investment reputation.
Experienced experts are involved in analysis of risks qualitatively. Analyst needs to systematic and experienced to identify effectively internal or external risks. Mulholland and Christian [5] reveal that the decision maker makes the best use of experience and information in identifying risks. Akintoye and Macleod [2] reveal that an individual’s attitudes, beliefs, and judgment can influence risk perception. Many professionals find that risk identification and risk analysis require involvement of experts and advanced techniques [1].
Choudhry and Iqbal [1] reveal that formal risk management is a rare in the construction industry of Pakistan. The authors explained that contractors are not practicing risk management formally. Major barriers to risk management are non-adaptation of formal risk management system [1]. Projects in the construction industry suffer in terms of low productivity, cost overruns and poor quality due not conducting risks management [1, 2]. The country is confronting the trauma of bridge failures and loss of lives every year due to floods. There is a need to develop risk analysis guidelines to avoid bridge failures. This chapter is to identify risks and critically rank them that affect the performance of project time and cost. Monte Carlo (MC) simulation on a case study project determines that risk analysis is helpful in managing schedule and costs risks. Identifying and analyzing schedule and costs risks on bridge project, this work makes a unique contribution and provides an insight into the risk management concepts.
2. Method
This work focuses on risk analysis by including a case study bridge project. The research investigates the impact of risks on costs, schedule and suggest guideline for bridge projects. To collect, data, a questionnaire is designed based on the previous studies [1, 15]. The questionnaire includes questions related to respondent identification, years of experience and 37 risk factors. Among the 37 risk factors, 7 are adopted from Choudhry and Iqbal [1], 8 are from Masood and Choudhry [15], 11 from the pilot survey, and 11 are developed by the researcher. These 37 risk factors are divided into 7 categories: design risks, contractual risks, construction risks, management risks, financial risks, health and safety risks, and external risks.
A pilot study is performed with a panel comprising five professionals having over 20 years of experience in construction. The questionnaire is modified based upon the pilot study. Based on importance of impact on the bridge project performance, respondents ranked each risk factor on a 5-point Likert scale (5 = extra ordinary, 4 = major, 3 = moderate, 2 = minor, 1 = insignificant). The respondents comprised on managers and engineers involved with numerous bridge projects. The targeted population for this work included private and public sector clients, consultants, and contractors. These include around 7000 enterprises that are involved in bridge construction projects and are registered with the Pakistan Engineering Council.
According to Dillman et al. [16], a sample size of 61 is fine with ±10% sampling error and a 95% confidence level. The respondents are approached through e-mails and personal visits to construction sites. Overall, 100 surveys forms are distributed on 25 construction sites. The response rate for this survey is 77%, but only 69 are analyzed. Eight surveys forms are not filled properly and thus discarded. Black et al. [17] stated that a 30% response rate is satisfactory in construction. The composition of the respondents is 35% public clients, 10% private clients, 12% contractors and 43% consultants. Public clients own most of the bridge projects. A majority of respondents are civil engineers holding a bachelor’s degree with over 16 years of experience. In addition, 25 interviews are conducted; one at each project. These interviews delivered valuable information about risk management and risk analysis guidelines.
The collected data are analyzed by using a software called as Statistical Package for the Social Sciences (SPSS). Statistical techniques such as preliminary analysis, internal consistency analysis, relative importance index, Pearson’s product–moment correlation are used in the analysis.
In addition, a case study of a bridge project is documented to establish costs and time risk analysis. The researcher obtained assistance from the five-member expert panel (comprising on scheduling manager, project manager, resident engineer, construction manager, an academia) and Monte Carlo simulation to analyze risks on of the case study project. The panel members are having more than 20 years of experience in industry and academics. This panel identifies the risks relevant to the case study project and assigned probability to the risk factors. This panel assigned the probabilistic (optimistic, most likely, pessimistic) durations and costs in Pakistan Rupees (PKR). These probabilistic durations and costs permitted us to practice triangular distribution in Primavera Pertmaster. A 3 days’ workshop is held with the attendance of all panel members. Their involvements for the risk analysis are documented.
3. Results and analysis
3.1 Tests for factor analysis
Kaiser-Mayer-Olkin (KMO) test and Bartlett’s test of sphericity are carried out to check the suitability for factor analysis. The suitability of a sample in relations to the distribution of the data is checked by KMO test. Pallant [18] stated that KMO value should be more than 0.5. The researchers [18, 19] revealed that factor analysis is meaningless with an identity matrix. The tests showed that KMO value is 0.689 that is more than 0.5 and Bartlett’s test of sphericity is large (chi-square value = 1626.4890 with small significance (p value < 0.001).
3.2 Analysis for internal consistency
Cronbach’s alpha (α) is used to check the internal consistency in the items involved in each factor [20] and the minimum recommended value is α = 0.7 [19]. Cronbach’s α [19] also measures the reliability of all factors. Factor analysis shows that all 7 themes of the 37 factors had Cronbach’s α ranging from 0.921 to 0.912, which means that all the variables are reliable [19]. For all 37 variables, the overall α is 0.917.
3.3 Relative importance index
Chan and Kumaraswamy [21] reveal that the mean and standard deviation of individual factor are not appropriate to decide the total ranking as they do not indicate any association among the factors. As a substitute, we calculate the weighted average for every factor and formerly divide them with the highest scale of the dimension. The researchers [19, 21, 22] indicate that this results in a relative importance index. Respondents provide their responses on a Likert scale about the standing of the 37 risks affecting the cost and schedule aims of the project. Shash [22] provided the formula for relative importance index as:
Relative importance indexRII=∑aX×100/5E1
where ‘n’ is the frequency of the responses; and ‘N’ is the total number of responses that gives X = n/N. Where ‘a’ is the constant that express weight specified to each response, ranging from 1 (insignificant) to 5 (extra ordinary).
The relative importance index categorized the seven risk factors in descending order as: financial risks (RII = 69.95), external risks (RII = 66.67), design risks (RII = 66.28), management risks (RII = 65.17), construction risks (RII = 62.72), contractual risks (RII = 59.42), and health and safety risks (RII = 53.82). According to the results, financial risks are vital in affecting the cost and schedule aims of projects (see Table 1). The 2nd and 3rd most important risks are external risks and design risks.
Risk category
Relative importance index (RII)
Financial risk
69.95
External risk
66.67
Design risk
66.28
Management risk
65.17
Construction risk
62.72
Contractual risk
59.42
Health and safety risk
53.82
Table 1.
RII of risk categories.
Among the 37 factors, the highest 10 risk factors in order of importance are: unavailability of funds (RII = 85.80), financial failure of contractor (RII = 76.52), poor site management and supervision (RII = 74.20), inadequate site investigation (RII = 73.91), inadequate project planning (RII = 73.91), construction delays (RII = 73.62), unavailability of land and/or right of way for site access (RII = 72.17), defective work and or quality issue (RII = 71.88), financial delays (RII = 71.01), insufficient technology (RII = 69.86). These risk factors are important for clients, consultants and contractors. There is a need for an effective risk management system on construction projects. Health and safety risks are ranked at the bottom in the 37 factors. This indicates that clients and consultants do not demand from contractors to implement a proper health and safety management system. Management risks are rated with high importance. There is lack of construction management experts and only few institutions offer program in construction management. Small contractors generally do not hire qualified engineers unless it is mandatory by the client. There is a need for construction management and risk management education as well as research in the industry.
3.4 Pearson’s product-moment correlation
The Pearson product-moment correlation (‘r’ Rho) is a measure of the degree of linear relationship among the variables. The correlation coefficient (‘r’ Rho) is any value between plus and minus and the sign (±) explains the direction of the relationship, either positive or negative. A positive coefficient means that the value of the variable increases with the increase in value of the other variable; or if one goes down, the other also reduces. A negative coefficient indicates that as one variable increases, the other decreases, and vice-versa. The absolute value of the coefficient indicates the strength of the correlation. A coefficient of r = 0.50 shows a robust degree of linear relationship than that of r = 0.30. A coefficient of zero (r = 0.0) shows the lack of a linear relationship and coefficients of r = +1.0 and r = −1.0 show a perfect linear relationship [19].
Table 2 shows the Pearson’s correlations for the risk factor categories. The maximum coefficient (0.756) is between the construction and management risks, which is significant at the p value = 0.01. This indicates that numerous construction and management risks are correlated to each other and they are to be jointly addressed with good risk management practices. There is another essential coefficient of 0.605 at significance p value = 0.01 between construction and external risks. External risks impact on project costs and schedule more than the construction risks (see Table 1). They are in fact the second most important risk factor category. A positive correlation of health and safety risks with construction (0.459) at a significance p value = 0.01 confirms the importance of health and safety on bridge projects. Higher rate of risks in construction indicate an increase in physical vulnerabilities. The health and safety risks are correlated positively with contractual risks (0.428) at a significance p value = 0.01, indicating improvement in health and safety in construction may reduce contractual and health and safety risks.
Pearson’s product-moment correlation of risk factor categories.
Correlation is significant at 0.05 level.
Correlation is significant at 0.01 level.
3.5 Bridge project: a case study
The case study project is a bridge construction in Islamabad that links the Islamabad highway with a residential community. The project is located in the capital city of Pakistan. It has the following features: (a) bridge total length 166 m (544.8 ft), (b) constructed over a river with an annual peak discharge of 11,170 cusecs, (c) 56 piles of diameter 762 mm (2.5 ft) and abutment piles 15.24 m (50 feet) deep, (d) 4 spans, (e) pier piles 9.14 m (30 feet) deep, (f) 12 pile caps, (g) 4 abutment walls, (h) 2 abutments, (i) 12 piers, (j) 6 transoms or cross-beams, (k) 24 precast girders of 44.09 m (144.66 ft), (l) 14.32 m (47 feet) width of deck slab on one side, (m) 3.66 m (12 feet) length of approach slab on each side and, (n) asphalt 166.12 m (545 ft) long and the bridge is designed for 3 + 3 lanes of traffic.
A baseline work schedule is prepared for the project. The project has a base cost-estimate. Each activity in the schedule had its cost allocated. The allocation includes cost estimate for materials, equipment, labor, and overhead costs for each activity. The risks that are identified in the project are presented to the experts. The expert panel identifies specific risks to the case study project. These risks are loaded into the schedule to determine the impact on project schedule and cost. Primavera Pertmaster is used for risk analysis. The inputs to Pertmaster for the risk register are: (a) risk description, (b) risk ID number, (c) threat or opportunity, (d) effect of this risk on activity, (e) probability of occurrence, (f) type of risk e.g. schedule or cost, (g) distribution e.g. triangular, (h) correlation with other risk factors. The risk register (see Figure 1) is developed for the whole project.
Figure 1.
Risk register of the project.
Pertmaster uses Monte Carlo simulation for risk analysis. Monte Carlo simulation uses random independent variables to obtain solutions of problems. Lian and Yen [23] reveal that Latin hypercube sampling and simple random number sampling are among the sampling techniques that are used with Monte Carlo simulations. This simple and elegant method delivered a means to solve equations with triangular probability distributions [24, 25]. Critical path is found and further calculations are documented with activities that are on the critical path. The time schedule loaded with costs and risks is analyzed. Real versus simulation outcomes are compared. A total of 1000 iterations are conducted for risk analysis.
The cumulative distribution for project cost, finish date, and duration are calculated with Monte Carlo simulations. The project duration (maximum = 792 days, minimum = 628 days, mean = 701 days) is displayed in Figure 2. The cumulative distribution for project duration and cost is determined. The results showed that the probability of finishing the project within the allotted time (628 days) is 4% and within the budget (PKR 129 million) is less than 1%. Terms P100 and P80 indicate the probabilities of 100 and 80% respectively. For instance, P80 shows that the project could be completed in 730 days with an amount of PKR 161 Million. There shall be 100% sure that the project would be completes in 792 days or even less with a cost amounting to PKR 166.5 Million or less.
Figure 2.
Monte Carlo simulation results.
The observations are performed for 5 months for the case study project. We have compared Pertmaster results with the actual completed activities. The researchers spent full time on-site to ensure extreme communication with the project implementation team. Documents are cautiously reviewed and are assimilated in analysis. On-site real data are equated with the simulation outcomes. The evaluation associated with schedule start dates is noted. For piling activity, actual start dates matched with the base line as well as with P80 and P100. For pier-shaft, actual start dates are between P80 and P100.
Finish dates are also compared for the case study project. Piles activity finishes between the forecasted dates of P80 and P100. Pile-cap activity finishes between the expected dates of P80 and P100. The ‘pier-shaft’ activity also accomplished 22 days before the P80 finish date. Transom activity finishes 19 days after the P100 completion. This indicates that the simulation results are precise as the activities are actually completing either within the predicted dates or within ±20 days.
For the case study, costs are compared that are important to the contract partners. The project cost is at all times important to the management team. Probabilistic cost calculation with the model is very precise as all the genuine costs fell within the P80 and P100. The project cost incurred up to the completion of transoms is PKR 72.8 Million, while that forecasted by simulation with 80% probability is PKR 69.8 Million. The evaluation is PKR 76.0 Million with 100% probability, indicating that the expected cost using Monte Carlo simulation is precise (P80 costs = 69.8 Million, Actual = 72.8 Million, P100 costs = 76.0 Million). The baseline cost of the project is only PKR 37.2 Million up to ‘Transoms’ construction that shows a cost overrun of 96%, portraying the absenteeism of monitoring and control of cost practices. This shows a clear requirement of risk management on bridge construction projects.
For the case study project, risk analysis shows that project management can obtain a fair idea of schedule and cost changes and variations. For the case study project, risks (see Figure 1) that affected the schedule and cost objectives are: (a) delay in approval from the regulatory authority i.e. delay in sanctioning relocation of the railway track, (b) unexpected weather i.e. excess rainfall during monsoon, (c) design variations i.e. design changes due to insufficient site investigation, (d) insufficient work space i.e. land not available for pre-casting of girders, (e) lack of technology i.e. breakdown in asphalt paving equipment, (f) unavailability of funds i.e. delay in payment to subcontractor, (g) unavailability of material i.e. quality issues and material failure to meet specifications.
3.6 Guidelines for risk analysis
The study advocates the succeeding guidelines for an effective risk analysis of any bridge project:
Context development: Developing the context for risk analysis is exceptionally vital as indicated in the 25 interviews documented. The expert panel emphasized the requirement for precise definition of the scope of the construction project; develop the project method statement, and conduct stakeholder analysis systematically. These points set the boundary for risk analysis as stressed by researchers [26]. Factors and variables contributing to project risks are required to be recognized.
Identification of risks: Tools and techniques such as checklists, historical data, brainstorming, and idea stimulating techniques may be employed [25, 26]. Nonetheless, risks are required to be identified and defined as is carried out in the design of questionnaire. Help from expert panel should be sought in identifying risks. Choudhry and Iqbal [1] have documented the risks identification techniques and they may be adopted. Especially, risk related to time and cost is to be evaluated as it plays a major role in affecting the project performance.
Quantifying risks: The risk quantification is the most important process that requires skills, extensive experience and good judgment. In this process, one has to assess the probability of each risk [24, 25]. Next is to evaluate the impact of time or cost, or both. Generally, expert panels play a major role in calculating the probability of risks and their impact. The correlation of risks either positive or negative is addressed in this study. Lastly, the risk quantification decides, whether they have an effect on cost or duration, or both.
Prepare cost-loaded schedule for the project: Mubarak [27] revealed that the project baseline schedule needed to be prepared at the initial stage the project to measure the project’s progress against it. Probabilistic or deterministic durations of time and costs of activities are to be estimated. The critical path needs to be determined based on the probabilistic durations. Project cost is determined based on the probabilistic costs of the activities’ information. The comparison of actual duration and actual cost of activities is carried out with computed results and with the baseline.
Schedule loading with risks: When cost-loaded schedule is complete, the next step is to allocate risks as they are quantified with each of the project activities. These risks are generally documented in a risk register. The risk register contains all particulars of each risk for the project. From the risk register, relevant risks are assigned with the cost-loaded schedule.
Running of Monte Carlo simulations: The schedule loaded with risk is run by Monte Carlo simulations to calculate the impact. One needs to perform the Monte Carlo simulations by using software, for example, @Risk. Pertmaster is employed in this this research.
Understand the output: The results that are produced by Monte Carlo simulations are easy to comprehend. Outputs reflects the probability of meeting the time and costs. The P80 and P100 values represent 80 and 100% probability. They specify the values of time and cost with 80 and 100% confidence level. The results shows how much an activity is behind from its initial time and how much cost can overrun (see Figure 2).
This work reveals a systematic process to identify and quantify major risks related to construction and predominantly to bridge construction affecting cost and schedule of the project. All projects have their own special conditions; nonetheless, experts can acquire valuable evidence from the results as all projects have risks that need to be managed. Risks related to schedule create cost risk. The case study demonstrates with the help of Monte Carlo simulation that how schedule and cost risk can be analyzed and managed. The case study shows that understanding the probabilistic cost is vital to forecast long-term budgets. The risk management guidelines are documented from surveys, interviews and analysis. One can determine the probabilistic cost of project by adopting these guidelines.
4. Conclusions
This work has identified and ranked the critical risks threatening the performance of bridge construction projects and evaluated the consequence of risks on project time and cost. This work is planned to developed consciousness of project stakeholders in relation to risk analysis in the construction industry. The major risks concerning a bridge construction project are identified. After carrying out risk analysis, the major results of this work are examination of critical risks affecting project costs and schedule. Relative importance index of important risk factors is calculated. This exercise categorized risk that include ‘financial risks’, ‘design risks’, ‘external risks’, ‘management risks’, ‘contractual risks’, ‘health & safety risks’ and ‘construction risks’. Financial risks are categorized at the top. The five highest ranked risk factors are ‘financial failure of contractor’, ‘unavailability of funds’, ‘poor site management & supervision’, ‘inadequate project planning’ and ‘inadequate site investigation’ among the 37 factors. Many risks are correlated and they need to be managed by applying management practices.
Schedule and costs risks are investigated in a case study project of a bridge construction. Real data of the bridge construction project is compared with simulation results after the risk analysis. Simulation findings are precisely correct and comparable to those really performed in relations to project duration and cost. In addition, guidelines for risk analysis are developed that can assist management in ascertaining possible risks on construction project of bridges. The research stresses that management is required to perform risks analysis after identifying prospective risks at the initial stage of bridge projects. Predicting risks can enable policymakers to detect areas of anxiety for project managers to take preemptive actions.
Although, each project in construction has its particular circumstances, project managers can acquire positive information from this study for their projects as the risks recognized for the construction are alike to risks in all sorts of projects across the world. The evidence delivered through this investigation can empower engineering professionals to safeguard that their projects advance efficiently without making unnecessary errors. This would be supportive to improve the execution of their projects. Even though there is some body of awareness in relation to management of risks, the formation of guidelines for risk management persisted to be vague. This research provided guidelines for risks management accompanying with heavy engineering construction in the construction sector. The research is important for project managers, academicians and professionals who are linked with heavy engineering construction and the construction sector in common.
\n',keywords:"bridge project, cost risk, schedule risk, risk analysis, risk management, Monte Carlo simulation, risk guidelines",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65609.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65609.xml",downloadPdfUrl:"/chapter/pdf-download/65609",previewPdfUrl:"/chapter/pdf-preview/65609",totalDownloads:624,totalViews:780,totalCrossrefCites:0,dateSubmitted:"April 19th 2018",dateReviewed:"December 12th 2018",datePrePublished:"February 11th 2019",datePublished:"April 17th 2019",readingETA:"0",abstract:"The construction sector is subject to more risk than many other sectors. Managing risk is the hottest topic of discussion for engineers within the construction sector. It is difficult to imagine managing of projects without risk management in construction. Risk management is concerned with risk management planning, identification, analysis, responses, monitoring and controlling project risk. Risk analysis is an evaluative process that establishes the magnitude of risks on projects. This work is planned to identify and analyze risks in the construction of a bridge project. The data are collected through a survey approach by administering a questionnaire. Professionals involved in the construction of bridges identify the project risks. A case study is utilized to determine the impact of cost and schedule risks. The analysis is carried out using the Monte Carlo simulation. The findings of the Monte Carlo simulation are compared with the actual times and costs of the case-study project. The results show the actual times and costs fell within the expected distribution of the simulation. The results indicate that risk analysis is helpful in managing costs and schedule risks. Additionally, this work documents guidelines for risk analysis.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65609",risUrl:"/chapter/ris/65609",signatures:"Rafiq M. Choudhry",book:{id:"7573",title:"Perspectives on Risk, Assessment and Management Paradigms",subtitle:null,fullTitle:"Perspectives on Risk, Assessment and Management Paradigms",slug:"perspectives-on-risk-assessment-and-management-paradigms",publishedDate:"April 17th 2019",bookSignature:"Ali G. Hessami",coverURL:"https://cdn.intechopen.com/books/images_new/7573.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"108303",title:"Prof.",name:"Ali",middleName:null,surname:"Hessami",slug:"ali-hessami",fullName:"Ali Hessami"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"255346",title:"Prof.",name:"Rafiq M.",middleName:null,surname:"Choudhry",fullName:"Rafiq M. Choudhry",slug:"rafiq-m.-choudhry",email:"rchoudhry@imamu.edu.sa",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Method",level:"1"},{id:"sec_3",title:"3. Results and analysis",level:"1"},{id:"sec_3_2",title:"3.1 Tests for factor analysis",level:"2"},{id:"sec_4_2",title:"3.2 Analysis for internal consistency",level:"2"},{id:"sec_5_2",title:"3.3 Relative importance index",level:"2"},{id:"sec_6_2",title:"3.4 Pearson’s product-moment correlation",level:"2"},{id:"sec_7_2",title:"3.5 Bridge project: a case study",level:"2"},{id:"sec_8_2",title:"3.6 Guidelines for risk analysis",level:"2"},{id:"sec_10",title:"4. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Choudhry RM, Iqbal K. Identification of risk management system in construction industry in Pakistan. Journal of Management in Engineering. 2013;29:42-49'},{id:"B2",body:'Akintoye AS, Macleod MJ. Risk analysis and management in construction. International Journal of Project Management. 1997;15:31-38'},{id:"B3",body:'Zhang Y. Selecting risk response strategies considering project risk interdependence. International Journal of Project Management. 2016;34:819-830'},{id:"B4",body:'Dikmen I, Birgonul MT, Arikan AE. A critical review of risk management support tools. In: Khosrowshahi F, editor. 20th Annual ARCOM Conference. 2004. pp. 1145-1154'},{id:"B5",body:'Mulholland B, Christian J. Risk assessment in construction schedules. Journal of Construction Engineering and Management. 1999;125:8-15'},{id:"B6",body:'Iqbal S, Choudhry RM, Holschemacher K, et al. Risk management in construction projects. Technological and Economic Development of Economy. 2015;21:65-78'},{id:"B7",body:'Dillon RL, Pate-Cornell ME. APRAM: An advanced programmatic risk analysis method. International Journal of Technology, Policy and Management. 2001;1:47-55'},{id:"B8",body:'Imbeah W, Guikema S. Managing construction projects using the advanced programmatic risk analysis and management model. Journal of Construction Engineering and Management. 2009;135:772-781'},{id:"B9",body:'Nasir D, McCabe B, Hartono L. Evaluating risk in construction-schedule model (ERIC-S): Construction schedule risk model. Journal of Construction Engineering and Management. 2003;129:518-527'},{id:"B10",body:'Ökmen Ö, Öztaş A. Construction project network evaluation with correlated schedule risk analysis model. Journal of Construction Engineering and Management. 2008;134:49-63'},{id:"B11",body:'Williams RC, Walker JA, Dorofee AJ. Putting risk management into practice. IEEE Software. 1997;14:75-82'},{id:"B12",body:'Reilly J, Brown J. Tunnelling and underground space technology. In: International Tunneling Conference. Singapore: Pergamon; 2004. pp. 703-712'},{id:"B13",body:'Arashpour M, Wakefield R, Lee EWM, et al. Analysis of interacting uncertainties in on-site and off-site activities: Implications for hybrid construction. International Journal of Project Management. 2016;34:1393-1402'},{id:"B14",body:'Vu HA, Cu VH, Min LX, et al. Risk analysis of schedule delays in international highway projects in Vietnam using a structural equation model. Engineering Construction and Architectural Management. 2017;24:1018-1039'},{id:"B15",body:'Masood R, Choudhry RM. Identification of risks factors for construction contracting firms—Encompassing mitigation stance. In: Second International Conference on Construction in Developing Countries; Cairo, Egypt. 2010'},{id:"B16",body:'Dillman DA, Smyth JD, Christian LM. Internet, Phone, Mail and Mixed-Mode Surveys: The Tailored Design Method. New York: Wiley; 2014'},{id:"B17",body:'Black C, Akintoye A, Fitzgerald E. An analysis of success factors and benefits of partnering in construction. International Journal of Project Management. 2000;18:423-434'},{id:"B18",body:'Pallant J. SPSS Survival Manual: A Step by Step Guide to Data Analysis Using SPSS for Windows. NSW: Allen Unwin; 2007'},{id:"B19",body:'Ghosh S, Jintanapakanont J. Identifying and assessing the critical risk factors in an underground rail project in Thailand: A factor analysis approach. International Journal of Project Management. 2004;22:633-643'},{id:"B20",body:'Carmines EG, Zeller RA. Reliability and Validity Assessment. Newbury Park, London: Sage Publication; 1979'},{id:"B21",body:'Chan DW, Kumaraswamy MM. A comparative study of causes of time overruns in Hong Kong construction projects. International Journal of Project Management. 1997;15:55-63'},{id:"B22",body:'Shash AA. Factors considered in tendering decisions by top UK contractors. Construction Management and Economics. 1993;11:111-118'},{id:"B23",body:'Lian Y, Yen BC. Comparison of risk calculation methods for a culvert. Journal of Hydraulic Engineering. 2003;129:140-152'},{id:"B24",body:'Love PED, Wang X, Sing C, et al. Determining the probability of project cost overruns. Journal of Construction Engineering and Management. 2013;139:321-330'},{id:"B25",body:'Choudhry RM, Aslam MA, Hinze JW, et al. Cost and schedule risk analysis of bridge construction in Pakistan: Establishing risk guidelines. Journal of Construction Engineering and Management. 2014;140:04014020'},{id:"B26",body:'Loosemore M, Raftery J, Reilly C, et al. Risk Management in Projects. London: Taylor & Francis; 2006'},{id:"B27",body:'Mubarak SA. Construction Project Scheduling and Control. 3rd ed. New Jersey: Wiley; 2015'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Rafiq M. Choudhry",address:"rchoudhry@imamu.edu.sa, choudhry03@gmail.com",affiliation:'
Construction Engineering and Management, Department of Civil Engineering, College of Engineering, Al Imam Mohammad Ibn Saud Islamic University, Riyadh, Kingdom of Saudi Arabia
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Hessami",coverURL:"https://cdn.intechopen.com/books/images_new/7573.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"108303",title:"Prof.",name:"Ali",middleName:null,surname:"Hessami",slug:"ali-hessami",fullName:"Ali Hessami"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"34845",title:"Prof.",name:"Renato",middleName:null,surname:"Mariani Costantini",email:"rmc@unich.it",fullName:"Renato Mariani Costantini",slug:"renato-mariani-costantini",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"2",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:null},booksEdited:[],chaptersAuthored:[{title:"Building Sustainable Capacity for Disease Diagnosis in Sub-Saharan Africa: Case Studies of Cooperation in Diagnostic Pathology",slug:"building-sustainable-capacity-for-disease-diagnosis-in-sub-saharan-africa-case-studies-of-cooperatio",abstract:null,signatures:"Renato Mariani-Costantini, Khalid Dafaallah Awadelkarim, Massimo Barberis, Claudio Clemente, Pasquale De Blasio, Mario Di Gioacchino, Agostino Faravelli, Marco Forni, Piergiovanni Grigolato, Lorenzo Leoncini, Karin Schuerfeld, Ahmed Abdalla Mohamedani, Giuseppe Lattanzio, Sandra Rosini and Vincenzo Stracca Pansa",authors:[{id:"34845",title:"Prof.",name:"Renato",surname:"Mariani Costantini",fullName:"Renato Mariani Costantini",slug:"renato-mariani-costantini",email:"rmc@unich.it"},{id:"43794",title:"Dr.",name:"Massimo",surname:"Barberis",fullName:"Massimo Barberis",slug:"massimo-barberis",email:"massimo.barberis@ieo.it"},{id:"43798",title:"Dr.",name:"Claudio",surname:"Clemente",fullName:"Claudio Clemente",slug:"claudio-clemente",email:"cclemente.ap@iol.it"},{id:"43800",title:"Dr.",name:"Pasquale",surname:"De Blasio",fullName:"Pasquale De Blasio",slug:"pasquale-de-blasio",email:"p.deblasio@biorep.it"},{id:"43802",title:"Prof.",name:"Agostino",surname:"Faravelli",fullName:"Agostino Faravelli",slug:"agostino-faravelli",email:"faravelli50@gmail.com"},{id:"43807",title:"Dr.",name:"Marco",surname:"Forni",fullName:"Marco Forni",slug:"marco-forni",email:"marcolforni@gmail.com"},{id:"43811",title:"Prof.",name:"Piergiovanni",surname:"Grigolato",fullName:"Piergiovanni Grigolato",slug:"piergiovanni-grigolato",email:"grigolat@med.unibs.it"},{id:"43812",title:"Prof.",name:"Lorenzo",surname:"Leoncini",fullName:"Lorenzo Leoncini",slug:"lorenzo-leoncini",email:"leoncinil@unisi.it"},{id:"43816",title:"Dr.",name:"Vincenzo",surname:"Stracca Pansa",fullName:"Vincenzo Stracca Pansa",slug:"vincenzo-stracca-pansa",email:"v.stracca@alice.it"},{id:"43819",title:"Prof.",name:"Ahmed",surname:"Mohamedani",fullName:"Ahmed Mohamedani",slug:"ahmed-mohamedani",email:"a_mohamedani@hotmail.com"},{id:"43821",title:"Dr.",name:"Khalid",surname:"Awadelkarim",fullName:"Khalid Awadelkarim",slug:"khalid-awadelkarim",email:"khalid_awadelkarim@yahoo.com"},{id:"43822",title:"Prof.",name:"Mario",surname:"Di Gioacchino",fullName:"Mario Di Gioacchino",slug:"mario-di-gioacchino",email:"m.digioacchino@unich.it"},{id:"43833",title:"Prof.",name:"Sandra",surname:"Rosini",fullName:"Sandra Rosini",slug:"sandra-rosini",email:"rosini@unich.it"},{id:"106849",title:"Dr.",name:"Karin",surname:"Schuerfeld",fullName:"Karin Schuerfeld",slug:"karin-schuerfeld",email:"awadelkarim@gmail.com"},{id:"106855",title:"Dr.",name:"Giuseppe",surname:"Lattanzio",fullName:"Giuseppe Lattanzio",slug:"giuseppe-lattanzio",email:"khalid2000suda@hotmail.com"}],book:{title:"New Knowledge in a New Era of Globalization",slug:"new-knowledge-in-a-new-era-of-globalization",productType:{id:"1",title:"Edited Volume"}}},{title:"Epidemiology, Pathology, Management and Open Challenges of Breast Cancer in Central Sudan: A Prototypical Limited Resource African Setting",slug:"epidemiology-pathology-management-and-open-challenges-of-breast-cancer-in-central-sudan-a-prototypic",abstract:"Little is known about breast cancer in Sudan. According to the recent data published by the Khartoum Cancer Registry, breast cancer was the most common cancer among Sudanese women. Generally, breast cancer in native African women is characterized by young age at onset, occurrence in multiparous premenopausal patients, advanced stage at diagnosis, large tumor size, high‐grade and triple‐negative phenotype, with correspondingly poor prognosis. In Sudan, it was reported that about 70% of the women diagnosed with breast cancer were younger than 50 years old. We present here data from local and international publications as well as primary information from the National Cancer Institute in Wad Medani (one of the only two cancer hospitals of the country, both located in Central Sudan in Khartoum and Wad Medani). We provide an up‐to‐date situation analysis of breast cancer in Central Sudan as an example for an African reality and the various open challenges of breast cancer in a limited resource setting. A better understanding of breast cancer in black African women is of global relevance, as there is an alarming increase in breast cancer among young black women worldwide, and these patients have the lowest survival rates.",signatures:"Renato Mariani-Costantini, Moawia Mohammed Ali Elhassan,\nGitana Maria Aceto, Ahmed Abdalla Mohamedani and Khalid\nDafaallah Awadelkarim",authors:[{id:"34845",title:"Prof.",name:"Renato",surname:"Mariani Costantini",fullName:"Renato Mariani Costantini",slug:"renato-mariani-costantini",email:"rmc@unich.it"},{id:"43821",title:"Dr.",name:"Khalid",surname:"Awadelkarim",fullName:"Khalid Awadelkarim",slug:"khalid-awadelkarim",email:"khalid_awadelkarim@yahoo.com"},{id:"190476",title:"Dr.",name:"Moawia",surname:"Elhassan",fullName:"Moawia Elhassan",slug:"moawia-elhassan",email:"moawia2@gmail.com"},{id:"190485",title:"Dr.",name:"Gitana",surname:"Aceto",fullName:"Gitana Aceto",slug:"gitana-aceto",email:"gaceto@unich.it"},{id:"194585",title:"Prof.",name:"Ahmed Abdalla",surname:"Mohamedani",fullName:"Ahmed Abdalla Mohamedani",slug:"ahmed-abdalla-mohamedani",email:"amohamedani@gmail.com"}],book:{title:"Breast Cancer",slug:"breast-cancer-from-biology-to-medicine",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"29989",title:"Prof.",name:"Patricia",surname:"Fox",slug:"patricia-fox",fullName:"Patricia Fox",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Patricia Fox is Associate Chair of the Computer, Information and Leadership Department and Clinical Assistant Professor of the Organizational Leadership and Supervision of the Purdue School of Engineering and Technology at Indiana University and Purdue University Indianapolis (IUPUI). Pat has been involved in engineering education for over 26 years. She served as an assistant/associate dean for nearly 20 of those years. Now she is teaching full time and serving as an Associate Chair of her department, Pat has been teaching sustainability and globalization in a study abroad course in Germany for over 10 years and just recently started a new study abroad course in France. Pat has served over 12 years as a volunteer in various ways within the American Society for Engineering Education (ASEE). She has served on the ASEE Board of Directors three times, currently serving as the Vice President for External Relations and First Vice President.",institutionString:null,institution:null},{id:"31772",title:"Dr.",name:"Bjoern",surname:"Jaeger",slug:"bjoern-jaeger",fullName:"Bjoern Jaeger",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"32123",title:"Prof.",name:"Cheng-Ta",surname:"Wu",slug:"cheng-ta-wu",fullName:"Cheng-Ta Wu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"34759",title:"Prof.",name:"Ronald",surname:"Welch",slug:"ronald-welch",fullName:"Ronald Welch",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"45640",title:"Dr.",name:"Stephen",surname:"Hundley",slug:"stephen-hundley",fullName:"Stephen Hundley",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"45761",title:"Mr.",name:"Chia-wei",surname:"Tang",slug:"chia-wei-tang",fullName:"Chia-wei Tang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"45764",title:"Mr",name:"Gregory",surname:"Ching",slug:"gregory-ching",fullName:"Gregory Ching",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"85048",title:"Prof.",name:"Dian-Fu",surname:"Chang",slug:"dian-fu-chang",fullName:"Dian-Fu Chang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Chi Nan University",institutionURL:null,country:{name:"Taiwan"}}},{id:"103532",title:"Dr.",name:"Berit Irene",surname:"Helgheim",slug:"berit-irene-helgheim",fullName:"Berit Irene Helgheim",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Molde University College",institutionURL:null,country:{name:"Norway"}}},{id:"104941",title:"Prof.",name:"Lin",surname:"Xiao",slug:"lin-xiao",fullName:"Lin Xiao",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"careers-at-intechopen",title:"Careers at IntechOpen",intro:"
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If this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Openness - We communicate honestly and transparently. We are open to constructive criticism and committed to learning from it.
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Disruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
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What makes IntechOpen a great place to work?
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
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If this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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