Performance Evaluation Procedure Research Articles

The Impact of Performance Appraisal and Training and Development on The Financial Performance of Small and Medium-Sized Enterprises (SMEs)

This study aims to measure the influence of human resource management (HRM) practices on the financial performance of small and medium enterprises (SMEs). It notably focuses on analyzing the effects of training and development programs and performance appraisal methodologies. In order to carry out this study, a quantitative methodology was employed, which entailed the use of self-administered questionnaires. A linear regression analysis was conducted on data obtained from 130 small and medium-sized enterprises (SMEs) to examine, assess, and improve the relationship between performance appraisal, training and development, and the financial performance of SMEs. The results indicate a clear and direct association between training and development, performance appraisal systems, and the financial success of small and medium-sized enterprises (SMEs). The findings highlight the critical importance of HRM practices as strategic investments rather than just administrative tasks. These findings provide useful insights for scholars and practitioners, offering a framework for implementing training and development and performance evaluation procedures to achieve financial success in small and medium-sized enterprises (SMEs), not just in Kosovo but also in similar global contexts.

Multiplier Design Based on Booth and Sequential Algorithm

Research on cellular networks and low-power electronic devices has ramped up in recent years, thanks to the proliferation of portable and mobile systems. There are many portable applications in the present day that require low power (smaller and more efficient batteries) and more milli ampere hours than before. As a result, designing low-power devices has grown in importance as a performance criterion. Multipliers arrangement (it is an organized set of adders) and delay make up the basic construction of a infinite impulse response filter. This paper deals with analytic procedure for performance evaluation to reduce time and RAM usage throughout the computation process. The Booth and Sequential multipliers have been used to simulate, and implement in this article. Using the Sequential and Booth Algorithms, respectively. Comparative study is carried out utilising Xilinx 14.7 with the virtex 5 family, devices for bit lengths 4, 8, 16, and 32.

Comparator Data Characteristics and Testing Procedures for the Clinical Performance Evaluation of Continuous Glucose Monitoring Systems.

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.

A Novel Knowledge-augmented Model Customization Approach for Arabic Offensive Language Detection

Multiple attempts to develop systems for detecting online Arabic offensive language have been explored in previous studies. However, most of these attempts do not consider the variation of Arabic dialects, cultures, and offensive phrases. In contrast, this study aims to extract knowledge from multiple offensive language datasets to build a cross-dialect and culture knowledge-based repository. This knowledge-based repository is utilized to develop novel system architecture based on customizing the AraBERT model in a unique method to preserve dialectal knowledge and offensive cultural knowledge within the contextual word embedding of BERT architecture. Performance evaluation procedures consist of statistical evaluation metrics and a behavioral checklist. Results report more effective predictions by the customized model than the uncustomized one, particularly for offensive text. The customization process allows the model to gain more knowledge of informal text in general, and a better understanding of dialectal Arabic.

On the validity of retrospective predictive performance evaluation procedures in just-in-time software defect prediction

Just-In-Time Software Defect Prediction (JIT-SDP) is concerned with predicting whether software changes are defect-inducing or clean. It operates in scenarios where labels of software changes arrive over time with delay, which in part corresponds to the time we wait to label software changes as clean (waiting time). However, clean labels decided based on waiting time may be different from the true labels of software changes, i.e., there may be label noise. This typically overlooked issue has recently been shown to affect the validity of continuous performance evaluation procedures used to monitor the predictive performance of JIT-SDP models during the software development process. It is still unknown whether this issue could potentially also affect evaluation procedures that rely on retrospective collection of software changes such as those adopted in JIT-SDP research studies, affecting the validity of the conclusions of a large body of existing work. We conduct the first investigation of the extent with which the choice of waiting time and its corresponding label noise would affect the validity of retrospective performance evaluation procedures. Based on 13 GitHub projects, we found that the choice of waiting time did not have a significant impact on the validity and that even small waiting times resulted in high validity. Therefore, (1) the estimated predictive performances in JIT-SDP studies are likely reliable in view of different waiting times, and (2) future studies can make use of not only larger (5k+ software changes), but also smaller (1k software changes) projects for evaluating performance of JIT-SDP models.

Performance Evaluation of Subcontractors Using Weighted Sum Method through KPI Measurement

Key Performance Indicators (KPIs) help measure objectives critical to business sustainability. This research takes place in a toy factory’s injection moulding department that has yet to develop indicators for subcontractor performance. Poor subcontractor performance significantly impacts the injection moulding production. In the last three months, the delivered quantity exceeded the target by 27%, delivery time consistency is only 78%, there were 1.31% rejects, and inventory accuracy and material efficiency were 81% and 93% respectively. The objective of the study is to determine the KPI and develop an evaluation procedure for the injection moulding supplier and select the best supplier. Multi-criteria decision-making, i.e., weighted sum method, was used. Five employees were involved in determining the criteria weights. The evaluation was based on each supplier’s performance. A written KPI for the company's subcontractor and performance evaluation procedures were developed. There were six subcontractors of which subcontractor H received the highest score (78.42) and subcontractor G received the lowest score (69.42). The subcontractor performance needs to be improved in the future. In the interest of the company’s sustainability, it is recommended that this subcontractor performance evaluation be institutionalised to support subcontractor performance. Successful subcontractors support the business performance.

Good Practices for the Laboratory Performance Testing of Aqueous Oral Inhaled Products (OIPs): an Assessment from the International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS).

Multiple sources must be consulted to determine the most appropriate procedures for the laboratory-based performance evaluation of aqueous oral inhaled products (OIPs) for the primary measures, dose uniformity/delivery, and aerodynamic particle (droplet) size distribution (APSD). These sources have been developed at different times, mainly in Europe and North America, during the past 25years by diverse organizations, including pharmacopeial chapter/monograph development committees, regulatory agencies, and national and international standards bodies. As a result, there is a lack of consistency across all the recommendations, with the potential to cause confusion to those developing performance test methods. We have reviewed key methodological aspects of source guidance documents identified by a survey of the pertinent literature and evaluated the underlying evidence supporting their recommendations for the evaluation of these performance measures. We have also subsequently developed a consistent series of solutions to guide those faced with the various associated challenges when developing OIP performance testing methods for oral aqueous inhaled products.

A Human-Adaptive Model for User Performance and Fatigue Evaluation during Gaze-Tracking Tasks

Eye gaze interfaces are an emerging technology that allows users to control graphical user interfaces (GUIs) simply by looking at them. However, using gaze-controlled GUIs can be a demanding task, resulting in high cognitive and physical load and fatigue. To address these challenges, we propose the concept and model of an adaptive human-assistive human–computer interface (HA-HCI) based on biofeedback. This model enables effective and sustainable use of computer GUIs controlled by physiological signals such as gaze data. The proposed model allows for analytical human performance monitoring and evaluation during human–computer interaction processes based on the damped harmonic oscillator (DHO) model. To test the validity of this model, the authors acquired gaze-tracking data from 12 healthy volunteers playing a gaze-controlled computer game and analyzed it using odd–even statistical analysis. The experimental findings show that the proposed model effectively describes and explains gaze-tracking performance dynamics, including subject variability in performance of GUI control tasks, long-term fatigue, and training effects, as well as short-term recovery of user performance during gaze-tracking-based control tasks. We also analyze the existing HCI and human performance models and develop an extension to the existing physiological models that allows for the development of adaptive user-performance-aware interfaces. The proposed HA-HCI model describes the interaction between a human and a physiological computing system (PCS) from the user performance perspective, incorporating a performance evaluation procedure that interacts with the standard UI components of the PCS and describes how the system should react to loss of productivity (performance). We further demonstrate the applicability of the HA-HCI model by designing an eye-controlled game. We also develop an analytical user performance model based on damped harmonic oscillation that is suitable for describing variability in performance of a PC game based on gaze tracking. The model’s validity is tested using odd–even analysis, which demonstrates strong positive correlation. Individual characteristics of users established by the damped oscillation model can be used for categorization of players under their playing skills and abilities. The experimental findings suggest that players can be categorized as learners, whose damping factor is negative, and fatiguers, whose damping factor is positive. We find a strong positive correlation between amplitude and damping factor, indicating that good starters usually have higher fatigue rates, but slow starters have less fatigue and may even improve their performance during play. The proposed HA-HCI model and analytical user performance models provide a framework for developing an adaptive human-oriented HCI that enables monitoring, analysis, and increased performance of users working with physiological-computing-based user interfaces. The proposed models have potential applications in improving the usability of future human-assistive gaze-controlled interface systems.

An adaptive EWMA control chart based on Hampel function to monitor the process location parameter

AbstractThe classical memory Exponential Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) control charts (CCs) are important to detect small‐to‐moderate sizes of shift in the process location and/or dispersion parameters. To make the memory control chart (CC) more efficient for broad range of shift, the score functions in particular Huber and Bi‐square functions are incorporated in the classical memory CCs. These score functions help to formulate the parameters of memory CCs and are called Adaptive EWMA (AEWMA) and Adaptive CUSUM (ACUSUM). The AEWMA and ACUSUM are capable to handle various sizes (i.e., small, moderate, and large) of shift. The aim of this study is to utilize Hampel function as the alternative of the mentioned score functions, and propose a new AEWMA CC, symbolized as AEWMAHample. To develop the proposed AEWMAHampleCC, the standard EWMA statistic and Hampel function are incorporated in the classical EWMA CC structure. The proposed AEWMAHampleCC is also efficient to handle certain sizes of shift in the process location parameter. Performance evaluation procedures, like average run length is considered for a particular shift. Similarly, the performance comparison index, relative average run length, and extra quadratic loss function are estimated over a range of shift. All these performance measures are calculated from run length (RL) characteristic of the proposed control chart. An algorithm in MATLAB is developed based on Monte Carlo simulation procedure to generate the RLs. Some existing control charts including EWMA, CUSUM, MEC, MCE, , , IACCUSUM, , and are taken for comparison reason. For specific sizes of shift, the proposed AEWMAHampleCC reveals the superiority. Besides, to address the practical significance for practitioners and quality engineers, the real‐life and simulated data examples are considered.

Practical Consideration in using Pre-trained Convolutional Neural Network (CNN) for Finger Vein Biometric

Using a pre-trained Convolutional Neural Network (CNN) model for a practical biometric authentication system requires specific procedures for training and performance evaluation. There are two criteria for a practical biometric system studied in this paper. First, the system’s ability to handle identity theft or impersonation attacks. Second, the ability of the system to generate high authentication performance with minimal enrollment period. We propose the use of the Multiple Clip Contrast Limited Adaptive Histogram Equalization (MC-CLAHE) technique to process finger images before being trained by CNN. A pre-trained CNN model called AlexNet is used to extract features as well as classify the MC-CLAHE images. The authentication performance of the pre-trained AlexNet model has increased by a maximum of 30% when using this technique. To ensure that the pre-trained AlexNet model is evaluated based on its ability to prevent impersonation attacks, a procedure to generate the Receiver Operating Characteristics (ROC) curve is proposed. An offline procedure for training the pre-trained AlexNet model is also proposed in this paper. The purpose is to minimize the user enrollment period without compromising the authentication performance. In this paper, this procedure successfully reduces the enrollment time by up to 95% compared to using on-line training.

A Procedure to Continuously Evaluate Predictive Performance of Just-In-Time Software Defect Prediction Models During Software Development

Just-In-Time Software Defect Prediction (JIT-SDP) uses machine learning to predict whether software changes are defect-inducing or clean. When adopting JIT-SDP, changes in the underlying defect generating process may significantly affect the predictive performance of JIT-SDP models over time. Therefore, being able to continuously track the predictive performance of JIT-SDP models during the software development process is of utmost importance for software companies to decide whether or not to trust the predictions provided by such models over time. However, there has been little discussion on how to continuously evaluate predictive performance in practice, and such evaluation is not straightforward. In particular, labeled software changes that can be used for evaluation arrive over time with a delay, which in part corresponds to the time we have to wait to label software changes as ‘clean’ (waiting time). A clean label assigned based on a given waiting time may not correspond to the true label of the software changes. This can potentially hinder the validity of any continuous predictive performance evaluation procedure for JIT-SDP models. This paper provides the first discussion of how to continuously evaluate predictive performance of JIT-SDP models over time during the software development process, and the first investigation of whether and to what extent waiting time affects the validity of such continuous performance evaluation procedure in JIT-SDP. Based on 13 GitHub projects, we found that waiting time had a significant impact on the validity. Though typically small, the differences in estimated predicted performance were sometimes large, and thus inappropriate choices of waiting time can lead to misleading estimations of predictive performance over time. Such impact did not normally change the ranking between JIT-SDP models, and thus conclusions in terms of which JIT-SDP model performs better are likely reliable independent of the choice of waiting time, especially when considered across projects.

Accounting, Calculative Infrastructures and Commensuration Work

Accounting scholars are paying increasing attention to the notion of commensuration, or the translation of different objects into common metrics that make the entities that are subject to performance evaluation comparable, and the effects this has on organizational behavior. This body of research draws attention to organizations’ commensuration work, defined as the efforts that evaluated organizations expend in pursuing and dealing with resistance to commensuration, but has mainly explored how such work emerges in response to a single set of comparable metrics such as individual rankings or ratings. By contrast, we ask how commensuration work unfolds in relation to a broader assemblage of calculative infrastructures. Such infrastructures can entail multiple, inter-related performance evaluation procedures that influence organizations’ commensuration work and give rise to complex patterns of reactivity, denoting the propensity of organizations to alter their behavior as they internalize external pressures for performance evaluation. Our empirical findings underline the importance of adopting a multi-layered view of commensuration work, that entails both direct and indirect forms of reactivity, as it unfolds in relation to a broader assemblage of calculative infrastructures. We discuss the implications of adopting such a view for future research on commensuration and related research on performance evaluation.

Synthesis and Characterization of a Composite Anion Exchange Membrane for Water Electrolyzers (AEMWE).

Anion exchange membranes (AEM) have gained attention recently as a promising candidate for low-cost water electrolysis systems to produce hydrogen, linked with renewable energy resources as a sustainable alternative to fossil fuels. The development of potential materials for producing and analyzing AEM is an imperative step towards commercialization and plays a competitive role in the hydrogen production industry. In this article, we developed a composite anion exchange membrane prepared by activating a commercial support structure (Celgard® 3401) with a commercially available functional group (Fumion® FAA-3) through a phase-inversion process. Fourier-transform infrared spectroscopy (FTIR) and Scanning Electron Microscopy (SEM) analysis demonstrated the phase-inversion procedure as an effective methodology. Furthermore, the cell performance test result (with Celgard/Fumion) was very promising and even better in comparison with a commercial membrane commonly applied in alkaline electrolysis (Fumasep). We also developed a testing procedure for membrane performance evaluation during electrolysis which is very critical considering the effect of CO2 absorption on membrane conductivity.

A case study on an innovative seismic performance evaluation procedure for irregular RC buildings

Assessing the structural capacity of an irregular structure requires an appropriate and robust representation of the analytical model. Conventional procedures are flawed because they lack the reversal effects of higher modes. Therefore, more robust procedures have been tested to accurately capture the capacity of irregular building-type structures. Some of these may be classified as adaptive pushover techniques in which the lateral load vector is revised instantaneously. These adaptive techniques generally refer to SRSS as a modal combination rule to superpose the maximum values of the modal quantities. However, this results in an overestimated capacity value. Furthermore, the reversal effects of the modes are not included in the evaluation process. Hence, in this research, a force-based adaptive multimode pushover method was tested on a plan-irregular building using a code developed and implemented into ZEUS-NL. First, an eigenvalue analysis was used to obtain the modal quantities of the considered building, and subsequently, the modal story shear forces were obtained using the modal story forces. Furthermore, the lateral load patterns were calculated at each iterative step. Finally, the structure was pushed using this calculated lateral load pattern up to a previously calculated target displacement value using a three-step adaptive pushover analysis. The first step is to run an adaptive single-mode pushover analysis considering the first mode. After comparing the fundamental period with a constant threshold limit, it is then decided whether to run an adaptive multimode pushover analysis considering the first two modes or to run two adaptive multimode pushover analyses considering the contribution of both the second and third modes. Neither SRSS nor CQC was used to obtain the maximum response quantities; instead, the use of the envelope response is suggested. After evaluating the capacity diagram of the considered building, the story drifts were determined. The outcomes of the adaptive pushover analyses were later checked with the nonlinear dynamic results. It can be concluded that the result of the tested procedure is in good correlation with the dynamic results. Furthermore, the top displacement–time traces were plotted to check which ground motion record reveals the maximum values. Using the ground motion record, interstory drift ratio–time graphs were developed for each story level and compared with the code limit. Base shear–time history traces were then obtained and compared with the tested FAP. Lastly, story shear–interstory drift relations were plotted to investigate the absorbed energy level as an indicator of damage. As a result, for irregular RC structures, the tested procedure was found to be more accurate. However, in its current form, the procedure reflects the seismic behavior of irregular midrise buildings.

Machine learning-based predictive models for equivalent damping ratio of RC shear walls

Energy-based seismic design is being rapidly developed and suggests that the seismic demands are met by the energy dissipation capacity of the structural members. Equivalent damping ratio is a measure of energy dissipation in structural members that accounts for the post-elastic behavior of the member and provides insight regarding the dynamic response reduction during a seismic event. The present study implements a machine learning algorithm to estimate the equivalent damping ratio in reinforced concrete shear walls at displacements corresponding to a 1.0% lateral drift ratio. Five different machine learning models, namely, Robust Linear Regression, K-Nearest Neighbor Regression, Kernel Ridge Regression, Support Vector Regression, and Gaussian process regression were evaluated in order to choose the model with the highest accuracy. Among all models, Gaussian process regression, a machine learning method with successful implementation experiences in civil/structural engineering related problems, is selected to identify the equivalent damping ratio. The developed GPR-based algorithm uses a database of 161 rectangular shear walls subjected to quasi-static reversed cyclic loading with geometry and mechanical properties commonly found in building stocks of many earthquake-prone countries. The proposed algorithm estimates the equivalent damping ratio for each specimen by predicting the cyclic dissipated energy and lateral force values as two dependent variables. The model validation results show a mean coefficient of determination (R2) of about 0.89; a relative root mean square error of about 0.14 and a mean absolute percentage error of 10.44%, which is considered a substantially accurate prediction for such a complex problem. An open-source model and the entire database are provided which can be used by researchers and also design engineers. The proposed predictive model enables comparing the damping capacity of shear walls and the outcomes of this study are believed to contribute to the energy-based design or performance evaluation procedures in terms of predicting the energy capacity of shear walls.

A comparative study on vectorization methods for non-functional requirements classification

Identifying non-functional requirements (NFRs) and their categories at the early phase is crucial for analysts to design software systems and recognize constraints. Automatic non-functional requirements classification methods have been studied for reducing the costs of that labor-intensive task. Our previous study focused on the differences among vectorization methods that converted requirements written in natural language into numerical vectors for classification. It had some limitations regarding the number of datasets used, the types of vectorization methods supporting pre-trained data, and the performance evaluation procedure.

Green hydrogen production: Analysis for different single or combined large-scale photovoltaic and wind renewable systems

Hydrogen is a promising energy carrier to provide sustainable energy use throughout the world. Researchers and policy-makers have focused on investigations in three areas of hydrogen-related technologies in the energy market: (1) alternative fuel production based on hydrogen and carbon dioxide; (2) hydrogen injection to the natural gas pipeline networks; (3) usage of hydrogen in transportation applications. One of the most important challenges facing hydrogen technology development is the production of green hydrogen, which can be achieved through water electrolysis coupled with renewable power generation. Although many studies have been conducted, there is still a need for further development, which requires open-source big data and models and standardization of the processes to compare different renewable-based hydrogen production systems. To fill this research gap, the performance of a grid-connected hybrid wind turbine and solar photovoltaic-based water electrolysis systems for large-scale green hydrogen production were investigated. The objective is to propose an accurate methodology to compare wind and solar systems, or hybrid ones, for green hydrogen production worldwide. A large dataset describing the hybrid wind turbine-photovoltaic hydrogen production in various locations was created by performing dynamic simulations using TRNSYS and analyzed using MATLAB and Excel. Several dimensionless indicators were employed and assessed for standardizing the performance evaluation procedure of renewable-based hydrogen production systems, including the electrolyser load portion satisfied by the wind turbine and photovoltaic, the generated wind turbine and photovoltaic energy portion supplied to the electrolyser load and the quantity of energy exported to and imported from the grid. The annual and monthly values of these indicators together with the total amount of green hydrogen production were estimated for 28 global locations. The procedure applied can be easily used to test small-scale applications like residential users or large-scale applications like industrial users, as well as for any hydrogen demand and climatic conditions.

Emulator-type load-based tests for dynamic performance characterization of air conditioners

Current air-conditioning performance evaluation procedures are based on experimental tests conducted at a fixed compressor speed while deactivating the native control system to simplify data acquisition and ensure reproducibility of the results. Conversely, the actual operation of air conditioners responds to thermal loads with variable or cyclic modulations of the compressor speed and expansion valve opening according to the native control system.This study aimed to establish a new reproducible and representative methodology for evaluating the dynamic performance of air conditioners. The developed emulator-type dynamic performance testing facility features hybrid characteristics consisting of an emulator, which is used to simulate the dynamic response of indoor and outdoor environments, and the hardware of a testing facility for performance measurement. This study demonstrates the integrity of the constructed testing facility and the ability of the emulator-type testing methodology to determine the dynamic performance of an air conditioning unit using its native control system, in response to variations in the room-side environment.

Experiment-based performance evaluation framework for isolated structures in underground subway station–soil–aboveground structure interaction system under seismic excitations

Owing to the complementary functions of aboveground and underground structures, the complex structure group has been practically applied in many real-world situations. For the aboveground structures, isolation technology is widely adopted as an efficient approach to improve their seismic performances. However, being limited to the isolation effectiveness and design of the single isolated structures, previous studies have ignored the inevitable influence of the underground structure–soil–aboveground structure interaction (USSI) on the aboveground structure. This study proposes a performance evaluation framework for the isolated structures facilitated by a sliding bearing that considers the USSI. A series of shaking table tests and numerical simulations are performed to establish a reference database of the proposed framework. The performance evaluation procedure is detailed for the USSI system, based on which the seismic performances are investigated for experimental and numerical models. Following the proposed framework, the influences of various soil conditions, sliding bearing parameters, and seismic excitations are systematically quantified, while emphasizing the necessity of the USSI in this study. The results show that the isolated structure in this case not only significantly improves the multiple seismic responses in terms of acceleration, displacement, and layer drift but also performs adequately in weakening the negative effect from the underground station. According to the parametric analysis results, the seismic performances of the isolated structure in the USSI system are extremely sensitive to the soil type, friction coefficient, spring stiffness, and input frequency, which can be generally incorporated into the proposed performance evaluation framework.

Development and Performance Evaluation of a Mini-potentiostat for Corrosion Experimentations

The exorbitant cost of laboratory equipment is chiefly responsible for the dearth of corrosion characterization equipment in material science laboratories of Universities from Sub-Saharan Africa and that of other developing countries across the globe. It has therefore become germane to exploit available resources for development of affordable potentiostat within the university community. In this work, detailed procedure and working principles for design, fabrication and performance evaluation of a portable and inexpensive microcontroller-based potentiostat are presented. PROTEUS® software was used for the design of the parent circuit board. Graphical User Interface (GUI) developed using Visual Studio and Origin PRO Software was used for acquisition of experimental results. Performance evaluation of the potentiostat was implemented by performing corrosion experiments in 5 wt.% NaCl solution using mild steel as a working electrode (WE). Ag/AgCl reference electrode (RE) was used as well as Platinum wire as the counter electrode (CE). The analysis of the working electrode potential against the log of current-density plot at a scan rate of 10 mV/s of the fabricated potentiostat revealed a Tafel plot that is in conformity with results from commercially available potentiostats.