Appl. Syst. Innov., Volume 5, Issue 1 (February 2022) – 28 articles

Stock tape reading involves surveilling stock prices once in a while and recording stock prices. The method of observing stock prices may be television or stock exchange. The time step for recoding stock prices is every stock user’s experience and their theory, perhaps 3 min or 2 h and so on. As an example, the Taiwan stock market starts at 9:00 a.m. to 13:30 p.m. It will have a 4 h operating time. Splitting the 4 h operating time for tape reading is the skill of stock users. The stock price sequence generated by tape reading can be predicted by stock users, but finally, it is the stock user’s experience. Therefore, the meaning of tape reading is to record the stock price, but its concept should have no prediction purpose. This study used thread technology and proposed a tape-reading method with web scraping. This method can periodically scrape stock prices and generate a stock price sequence to Excel file. This application can satisfy the demand of these stock users, who are called day trading users. Because these day trading users want to gain stock price sequences minute by minute, rather than the stock exchange format day by day, and also ones which are better than the those provided by the stock website service, because its stock sequence format is limited and not normalized, these day trading users think that minute-by-minute stock price sequences are very clear to forecast. This study implemented the prior scheme and designed the GUI to query a company’s stock price and its stock news, even per second, etc., and how long it took to update the stock price, and the GUI also included a time-up feature to stop scraping stock prices if users just wanted to scrape stock prices periodically. Full article

The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing the various themes and research trends of what Industry 5.0 is using text mining tools and techniques. Toward this, the abstracts of 196 published papers based on the keyword “Industry 5.0” search in IEEE, science direct and MDPI data bases were extracted. Data cleaning and preprocessing were performed for further analysis to apply text mining techniques of key terms extraction and frequency analysis. Further topic mining i.e., unsupervised machine learning method was used for exploring the data. It is observed that the terms artificial intelligence (AI), big data, supply chain, digital transformation, machine learning, internet of things (IoT), are among the most often used and among several enablers that have been identified by researchers to drive Industry 5.0. Five major themes of Industry 5.0 addressing, supply chain evaluation and optimization, enterprise innovation and digitization, smart and sustainable manufacturing, transformation driven by IoT, AI, and Big Data, and Human-machine connectivity were classified among the published literature, highlighting the research themes that can be further explored. It is observed that the theme of Industry 5.0 as a gateway towards human machine connectivity and co-existence is gaining more interest among the research community in the recent years. Full article

New technologies’ advances offer innovative automations to people’s daily lives. More and more devices are continuously connected to the internet allowing people to control them remotely. The smart home is such a technological development. However, it is uncertain whether and to what extent the average consumer will accept smart home technology. The purpose of this study is to investigate the factors that affect the intention of Greek consumers to use smart home technology. The results of this study show that Greek consumers are beginning to have a positive attitude towards smart home technology. Important factors that contribute to their intention to use smart home technology include their perceived usefulness, compatibility, and ease of use of smart home technology. On the contrary, they do not think that they are influenced by their social environment regarding their intention to use smart home technology. Finally, they think that the major benefits of using smart home technology include the health monitoring, home security, and cost savings. Full article

After studying the professional practices related to the management of medical records in Kuwaiti hospitals, it became utterly evident that there is inadequate understanding about maintaining the health information of patients. This dire situation is further compounded by the absence of official regulations established by the Ministry of Health for hospitals to effectively assess and manage medical records. Through this study, the researchers aimed to assess the medical records system in multiple healthcare settings in Kuwait, comprising government, private, and oil sectors. The study was carried out from May 2019 to July 2020 and used a self-developed, pilot-tested questionnaire measuring various aspects of the medical records management system. A total of 98 participants responded to the survey. The study results revealed that 43% of hospitals were using paper-based medical records, as compared to 53% that used both paper-based and electronic media. Moreover, 40% of hospitals in Kuwait did not adhere to the Ministry of Health policies regarding medical records disposition; instead, they developed their own hospital-based disposition policy. Moreover, the study findings showed that there were clear discrepancies in record retention policies among the participating hospitals, and the duration of record retention varied from 2 years, 5 years, 10 years, and more than 20 years across hospitals in Kuwait. In conclusion, national policies and guidelines need to be established to monitor the medical record systems in Kuwaiti hospitals to further enable better patient care and improve healthcare facilities. Furthermore, it has become indispensable to develop and maintain electronic health records as they constitute an integral part of modern healthcare. Full article

A growing interest has been expressed in the issue of building adaptability over the past decade, perceiving it as an intrinsic criterion for sustainability. In light of the circular economy (CE) and its application in the construction sector, more attention has been paid to buildings’ design for adaptability (DfA) towards the realization of circular buildings. DfA is considered a key enabler for other circular design strategies such as design for disassembly (DfD), multi-functionality, spatial transformability, and design reversibility. However, implementation and assessment frameworks, and design-support tools for the circular building, are still in development as the characterization of circular buildings continues with endeavors to draw a defined shape by identifying the prerequisites for circularity where the design takes an important place. For the sake of objectifying the role of DfA in circularity frameworks in buildings, this paper carries out an analytical review and discussion on two types of assessment and design-support frameworks; the first addresses adaptability criteria and considerations in assessment frameworks that handle the concept individually while the second classifies existing circularity assessment endeavors into four main categories under which multiple tools are reviewed. A reflection on the scope and objectives for both types is later performed, illustrating the state of adaptability evaluation and criteria as well as its role in circularity frameworks. Results show that the concept of building adaptability lacks quantitative methods that quantify a building’s capacity to adapt as well as empirical data that prioritize the most valuable criteria facilitating adaptations. Moreover, many circularity assessment frameworks fail to consider adaptability criteria at all hierarchal levels of a building composition. To address this shortcoming, a series of conceptual considerations and requirements is proposed towards a potential establishment of an inclusive framework of a circularity design-support tool in buildings. The study is concluded by identifying gaps and recommendations for further developments in the field. Full article

Big data have been used widely in many areas, including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted to improve the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized, and off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies. Full article

Many industries and systems have faced consequences as a result of the COVID-19 outbreak. In this time, we are happy to had the publication of a Special Issue of Applied System Innovation titled “Systems and Industries in Response to the COVID-19 Crisis”. This editorial article contains the special issue’s closing remarks. However, while the scope of this given special issue grabbed the interest of practitioners and scholars, there is still much to be learned from COVID-19’s experience. Thus, suggestions for future special issues are included with this letter. Full article

The purpose of this research is to develop a model for measuring the efficiency of the TTO incubation process performance to accelerate the commercialization of research results in universities. The method of analyzing the efficiency used in this research is the Data Envelopment Analysis (DEA) method based on Banker, Charnes, and Cooper (BCC), which is output-oriented. The software used in analyzing the efficiency of TTO performance is MaxDEA 8. The output of this research is a mathematical model tool for measuring the efficiency of TTO performance by DEA, which considered 17 parameters and proposed recommendations for TTO performance strategies. The limitation of this research is the object of research in one university that has succeeded in the commercialization of research. This research implies that the performance efficiency measurement model is an alternative predictive way to increase the acceleration of commercialization. The practical implications of this research are that it can evaluate performance or inefficient strategies in the incubation process of higher education research results to the Technology Transfer Office (TTO). This research also provides recommendations on strengthening the TTO function that can be used as a reference for improving performance at universities. This research measures the level of performance evaluation of TTO in the incubation process, which refers to the Death Valley framework. This incubation process is the main process accelerating the commercialization of research results in universities. Full article

The global vaccination program provides hope for ending the present worldwide pandemic. Due to the current pandemic, COVID-19 vaccines must be delivered and administered to communities once supplies are available. However, there are significant challenges in the sourcing, allocation, distribution, and uptake of vaccinations. A successful vaccination program would necessitate adequate risk management across the vaccination supply chain. This article has collected the predictable risks of the COVID-19 vaccines considered within the vaccine delivery system framework. The risks are presented based on relevant literature. Hence, this work’s framework is expected to contribute to better risk management of vaccination programs and similar future projects. Full article

The performance assessment of routing protocols in vehicular ad hoc networks (VANETs) plays a critical role in testing the efficiency of the routing algorithms before deployment in real conditions. This research introduces the statistical design of experiments (DOE) methodology as an innovative alternative to the one factor at a time (OFAT) approach for the assessment and the modeling of VANET routing protocol performance. In this paper, three design of experiments methods are applied, namely the two-level full factorial method, the Plackett–Burman method and the Taguchi method, and their outcomes are comprehensively compared. The present work considers a case study involving four factors namely: node density, number of connections, black hole and worm hole attacks. Their effects on four measured outputs called responses are simultaneously evaluated: throughput, packet loss ratio, average end-to-end delay and routing overhead of the AODV routing protocol. Further, regression models using the least squares method are generated. First, we compare the main effects of factors resulted from the three DOE methods. Second, we perform analysis of variance (ANOVA) to explore the statistical significance and compare the percentage contributions of each factor. Third, the goodness of fit of regression models is assessed using the adjusted R-squared measure and the fitting plots of measured versus predicted responses. VANET simulations are implemented using the network simulator (NS-3) and the simulator of urban mobility (SUMO). The findings reveal that the design of experiments methodology offers powerful mathematical, graphical and statistical techniques for analyzing and modeling the performance of VANET routing protocols with high accuracy and low costs. The three methods give equivalent results in terms of the main effect and ANOVA analysis. Nonetheless, the Taguchi models show higher predictive accuracy. Full article

Digitization in the mining industry and machine learning applications have improved the production by showing insights in different components. Energy consumption is one of the key components to improve the industry’s performance in a smart way that requires a very low investment. This study represents a new hardware, software, and data processing infrastructure for open-pit mines to overcome the energy 4.0 transition and digital transformation. The main goal of this infrastructure is adding an artificial intelligence layer to energy use in an experimental open-pit mine and giving insights on energy consumption and electrical grid quality. The achievement of these goals will ease the decision-making stage for maintenance and energy managers according to ISO 50001 standards. In order to minimize the energy consumption, which impact directly the profit and the efficiency of the industry, a design of a monitoring and peak load forecasting system was proposed and tested on the experimental open-pit mine of Benguerir. The main challenges of the application were the monitoring of typical loads machines per stage, feeding the supervisors by real time energy data on the same process SCADA view, parallel integrating hardware solutions to the same process control system, proposing a fast forest quantile regression algorithm to predict the energy demand response based on the data of different historical scenarios, finding correlations between the KPIs of energy consumption, mine production process and giving global insights on the electrical grid quality. Full article

This paper describes a study of the elements of service innovation for artistic painting creation enterprises from the service-dominant logic point of view. This research mainly aimed to (1) explore how to integrate the concepts of service innovation for companies (service provider) providing painting as their service in their business model, and (2) to explore that how consumers (service receiver) can evaluate their experience value as well as achieve value co-creation through the service process under the service innovation model. Here, a multiple case-detailed CEO-interview-based methodology was used with four corporate companies that provide artistic painting creation services. The findings showed that, as painting is the core content of the service, the process not only meets the emotional needs of consumers through its operations, but also develops innovations in the business model to solve social issues. This research applies the viewpoint of art in the service science field and combines creative and innovative thinking with business operations. The outcome has practical implications for enhancing the social value of business structures and enabling value co-creation under the development of creative industries. In conclusion, the popularity and accessibility of using painting as a service reinforces painting creation to develop internal expression channels that can be used as service innovation for the development of businesses in the creative industries. Full article

The main objective of this study was to explore the current practical use of an AI robo-advisor algorithmic technique. This study utilizes Roger’s innovation diffusion theory as a basis to explore the application of robo-advisors for forecasting in the stock market by using an abductive reasoning approach. We used literature reviews and semi-structured interviews to interview representatives of fund companies to see if they had adopted AI big data forecasting models to invest in stock selection. This study summarizes the big data stock market forecasts of the literature. According to the summary, the accuracy of the prediction models of these scholars ranged from 52% to 97%, with the prediction results of the models varying significantly. Interviews with 21 representatives of these fund companies revealed that the stock market forecast model of big data robo-advisors have not become a reference basis for fund investment candidates, mainly because of the unstable model prediction rate, and the lack of apparent relative advantages and observability, as well as being too complex. Thus, from the view of innovation diffusion, there is a lack of diffusion for the robo-advisor. Knowledge occurs when an individual is exposed to the existence of innovation, and gains some understanding of how it functions. Thereby, when investors become more familiar with neural network-like stock prediction models, this novel AI stock market forecasting model is expected to become another indicator of technical analysis in the future. Full article

Wind power is one of the most important sources of renewable energy. In this research paper, we developed an approach to select the optimum site among four different locations in Iraq (Talafar, Nasiriyah, Baghdad and RAWA) according to wind power density. Based on the optimization process, it was found that the RAWA city is the optimal site. We adopted Mabchour’s Method (MMab) to estimate the Weibull distribution parameters (c, k) for RAWA city at two heights (10 m and 50 m) for the period (2017–2019). It was found that the Mabchour technique (MMab) produced accurate results with minimum consumed time and effort. This was because the values of k and c were close to each other. Additionally, the coefficient values of the results of the Weibull measurements were very close to the average wind speeds that we measured. The values of the correlation coefficients between the Weibull scale parameters and the form were calculated and were equal to R² = 0.9971. The minimum value of the coefficient of variation (COV) for turbulence intensity was found to be 26% in July 2018, when the wind speeds reached their maximum. The highest error of wind power density between measured data (P_M) and Weibull distribution (P_W) was found to be 4.48%, at a height of 50 m. Full article

Twitter sentiment detectors (TSDs) provide a better solution to evaluate the quality of service and product than other traditional technologies. The classification accuracy and detection performance of TSDs, which are extremely reliant on the performance of the classification techniques, are used, and the quality of input features is provided. However, the time required is a big problem for the existing machine learning methods, which leads to a challenge for all enterprises that aim to transform their businesses to be processed by automated workflows. Deep learning techniques have been utilized in several real-world applications in different fields such as sentiment analysis. Deep learning approaches use different algorithms to obtain information from raw data such as texts or tweets and represent them in certain types of models. These models are used to infer information about new datasets that have not been modeled yet. We present a new effective method of sentiment analysis using deep learning architectures by combining the “universal language model fine-tuning” (ULMFiT) with support vector machine (SVM) to increase the detection efficiency and accuracy. The method introduces a new deep learning approach for Twitter sentiment analysis to detect the attitudes of people toward certain products based on their comments. The extensive results on three datasets illustrate that our model achieves the state-of-the-art results over all datasets. For example, the accuracy performance is 99.78% when it is applied on the Twitter US Airlines dataset. Full article

COVID-19 pandemic has infected millions and led to a catastrophic loss of lives globally. It has also significantly disrupted the movement of people, businesses, and industries. Additionally, electric vehicle (EV) users have faced challenges in charging their vehicles in public charging locations where there is a risk of COVID-19 exposure. However, a case study of EV charging behavior and its impacts during the SARS-CoV-2 is not addressed in the existing literature. This paper investigates the impacts of COVID-19 on EV charging behavior by analyzing the charging activity during the pandemic using a dataset from a public charging facility in the USA. Data visualization of charging behavior alongside significant timelines of the pandemic was utilized for analysis. Moreover, a cluster analysis using k-means, hierarchical clustering, and Gaussian mixture models was performed to identify common groups of charging behavior based on the vehicle arrival and departure times. Although the number of vehicles using the charging station was reduced significantly due to lockdown restrictions, the charging activity started to pick up again since May 2021 due to an increase in vaccination and easing of public restrictions. However, the charging activity currently still remains around half of the activity pre-pandemic. A noticeable decline in charging session length and an increase in energy consumption can be observed as well. Clustering algorithms identified three groups of charging behavior during the pandemic and their analysis and performance comparison using internal validation measures were also presented. Full article

The simultaneous localization and mapping (SLAM) market is growing rapidly with advances in Machine Learning, Drones, and Augmented Reality (AR) technologies. However, due to the absence of an open source-based SLAM library for developing AR content, most SLAM researchers are required to conduct their own research and development to customize SLAM. In this paper, we propose an open source-based Mobile Markerless AR System by building our own pipeline based on Visual SLAM. To implement the Mobile AR System of this paper, we use ORB-SLAM3 and Unity Engine and experiment with running our system in a real environment and confirming it in the Unity Engine’s Mobile Viewer. Through this experimentation, we can verify that the Unity Engine and the SLAM System are tightly integrated and communicate smoothly. In addition, we expect to accelerate the growth of SLAM technology through this research. Full article

The rapid identification of offensive language in social media is of great significance for preventing viral spread and reducing the spread of malicious information, such as cyberbullying and content related to self-harm. In existing research, the public datasets of offensive language are small; the label quality is uneven; and the performance of the pre-trained models is not satisfactory. To overcome these problems, we proposed a multi-semantic fusion model based on data augmentation (MSF). Data augmentation was carried out by back translation so that it reduced the impact of too-small datasets on performance. At the same time, we used a novel fusion mechanism that combines word-level semantic features and n-grams character features. The experimental results on the two datasets showed that the model proposed in this study can effectively extract the semantic information of offensive language and achieve state-of-the-art performance on both datasets. Full article

Unexpected worldwide disruptions brought various challenges to supply chain management thus manipulating the research direction towards resilience. Since the supplier is one of the important supply chain elements, the challenges can be overcome through resilient supplier selection. Supplier selection is a multi-criteria decision-making problem where several criteria are involved. In this study, GRA-BWM-TOPSIS was proposed to evaluate resilient suppliers. Seven resilience criteria which were Quality, Lead Time, Cost, Flexibility, Visibility, Responsiveness and Financial Stability have been proposed and five experts were selected to provide judgments for the selection process. By using the proposed method, the criteria importance levels were obtained using GRA and the criteria weights were computed using BWM, together with a consistency test. TOPSIS was applied to evaluate the suppliers’ performances. Through a case study in a food manufacturing company, 10 suppliers were evaluated and ranked. A validation process was carried out and the managerial implications were provided to ensure the effectiveness of the proposed model. GRA-BWM-TOPSIS is suitable for resilient supplier selection when there are uncertainties and incomplete data. Full article

Business process modeling and verification have become an essential way to control and assure organizational evolution. We overview the opportunities for the application of blockchain in Business Process Management and Modeling in Finance and we focus on in-depth analysis of claim process in insurance as a use case. We investigate the utilization of blockchain technology for model checking of Workflow, Business Processes to ensure consistency, integrity, and security in a dynamically changing business environment. We create a UML profile for the blockchain, then we combine it with a UML activity diagram followed by a verification using Petri nets to guarantee a distributed computing system and scalable with mutable data. Our paper creates a unified picture of the approaches towards business processes modeling used in the financial industry organized around the set of premises intending to develop a future research agenda for blockchain business process modeling, specifically for the financial industry domain. Full article

Fabricating objects with desired mechanical properties by utilizing 3D printing methods can be expensive and time-consuming, especially when based only on a trial-and-error test modus operandi. Digital twins (DT) can be proposed as a solution to understand, analyze and improve the fabricated item, service system or production line. However, the development of relevant DTs is still hampered by a number of factors, such as a lack of full understanding of the concept of DTs, their context and method of development. In addition, the connection between existing conventional systems and their data is under development. This work aims to summarize and review the current trends and limitations in DTs for additive manufacturing, in order to provide more insights for further research on DT systems. Full article

This article took the case of the adoption of a Machine Learning (ML) solution in a steel manufacturing process through a platform provided by a Canadian startup, Canvass Analytics. The content of the paper includes a study around the state of the art of AI/ML adoption in steel manufacturing industries to optimize processes. The work aimed to highlight the opportunities that bring new business models based on AI/ML to improve processes in traditional industries. Methodologically, bibliographic research in the Scopus database was performed to establish the conceptual framework and the state of the art in the steel industry, then the case was presented and analyzed, to finally evaluate the impact of the new business model on the operation of the steel mill. The results of the case highlighted the way the innovative business model, based on a No-Code/Low-Code solution, achieved results in less time than conventional approaches of analytics solutions, and the way it is possible to democratize artificial intelligence and machine learning in traditional industrial environments. This work was focused on opportunities that arise around new business models linked to AI. In addition, the study looked into the framework of the adoption of AI/ML in a traditional industrial environment toward a smart manufacturing approach. The contribution of this article was the proposal of an innovative methodology to put AI/ML in the hands of process operators. It aimed to show how it was possible to achieve better results in a less complex and time-consuming adoption process. The work also highlighted the need for an important quantity of data from the process to approach this kind of solution. Full article

A long-range wide-area network (LoRaWAN) targets both mobile and static Internet of Things (IoT) applications; it is suited to IoT applications, which require a large coverage area while consuming less power at a low data rate; it provides a solution for transferring data between IoT devices with a minimum cost in terms of power, at the expense of higher latency. LoRaWAN was designed for static low-power long-range networks. However, several IoT solution applications involve the use of mobility. Therefore, this study investigates the usage of LoRaWAN in the field of mobile Internet of Things applications such as bike rentals, fleet monitoring, and wildlife and animal tracking applications. Using the OMNeT++ simulator, two different well-known mobility models are used to investigate the influence of mobility on the performance of mobile LoRaWAN. The results show that intense LoRaWAN networks can operate under a high velocity and varying traffic load. It can be observed that the random waypoint model combination yields a better performance, but at the cost of higher collisions and energy consumption. As a consequence, the results suggest the reconsideration of mobile IoT solutions over LoRaWAN. Full article

A detection system for water adulteration in honey is proposed. It consists of a modified SMA-connector sensor and a vector network analyzer. A modified SMA-connector sensor is applied to measure complex relative permittivity, electrical conductivity, and phase constant of honey samples with the open-ended method. The system is tested in the frequency range of 0.5–4.0 GHz at the sample temperature of 25 °C. The relationships between the complex relative permittivity, electrical conductivity, the phase constant, and the honey samples with different concentrations (0–30%w/w) are determined. The experimental results show that the real part of the complex relative permittivity is significantly proportional in honey samples with adulteration of water in the range of 0–30%w/w. The frequency of 0.6 GHz is a suitable frequency for detection with a real part of complex relative permittivity as an indicator. The frequency of 3.74 GHz is an appropriate frequency for detection with electrical conductivity as in indicator while the frequency of 4.0 GHz is suitable for detection with phase constant as an indicator. In addition, the data are analyzed with regression analysis. This technique is also performed on natural latex samples to determine the dry rubber content. The frequency of 0.5 GHz is a suitable frequency with a real part of complex relative permittivity as an indicator while the frequency of 4.0 GHz is a suitable frequency with an imaginary part of complex relative permittivity, electrical conductivity, and phase constant as the indicators. The results demonstrate that it is possible to apply this technique to determine the dry rubber content in the natural latex samples as well. Full article

The COVID-19 pandemic has significantly impacted almost every sector. This impact has been especially felt in the healthcare sector, as the pandemic has affected its stability, which has highlighted the need for improvements in service. As such, we propose a collaborative decision-making framework that is capable of accounting for the goals of multiple stakeholders, which consequently enables an optimal, consensus decision to be identified. The proposed framework utilizes the best–worst method (BWM) and the Multi-Actor Multi-Criteria Analysis (MAMCA) methodology to capture and rank each stakeholder’s preferences, followed by the application of a Multi-Objective Linear Programming (MOLP) model to identify the consensus solution. To demonstrate the applicability of the framework, two hypothetical scenarios involving improving patient care in an intensive care unit (ICU) are considered. Scenario 1 reflects all selected criteria under each stakeholder, whereas in Scenario 2, every stakeholder identifies their preferred set of criteria based on their experience and work background. The results for both scenarios indicate that hiring part-time physicians and medical staff can be the effective solution for improving service quality in the ICU. The developed integrated framework will help the decision makers to identify optimal courses of action in real-time and to select sustainable and effective strategies for improving service quality in the healthcare sector. Full article

This paper proposes an electronic prototype of the Grooved Pegboard Test (GPT), which is normally used to test the presence of hand dexterity. The prototype imitates the geometrical dimensions of an on-the-market GPT device, but it is electronic, not manual like the one available now for users. The suggested electronic GPT device makes automated time calculation between placing the first and the last peg in their designated locations, instead of manually observing a stopwatch normally used during the GPT. The electronic GPT prototype consists of a fabricated wooden box, electronics (switches and microcontroller), and liquid crystal display (LCD). A set of 40 normal volunteers, 20 females and 20 males, tested the designed prototype. A set of six volunteers with chronic medical conditions also participated in evaluating the proposed model. The results on normal volunteers showed that the proposed electronic GPT device yielded time calculations that match the population mean value of similar calculations by the GPT device. The one-sample t-test showed no significant difference in calculations between the new electronic GPT and the manual GPT device. The p-value was much higher than 0.05, indicating the possible use of the suggested electronic GPT device. Full article