Software Applications Research Articles

Why "Artificial Intelligence" Should Not Be Regulated

Lawmakers all over the world have started to draft new regulations for Artificial Intelligence (AI). While the European Union is currently leading the way with its AI Act, many other legislators will follow and already positioned themselves with white papers and other publications. This commentary argues that “Artificial Intelligence”, including Generative AI, should not be used as a regulatory category. Not because there is no potential for harm from AI systems and not because AI systems should not be regulated, but because “Artificial Intelligence” is a vaguely defined label that is neither suitable nor necessary for comprehensive regulation of technological risks. Instead of regulating a particular set of approaches and algorithms, lawmakers should focus and double down on regulating high-risk applications of software, independent of whether it is labelled as AI or not.

A - 4 The Potential Applications of Software Supporting the Early Detection of Cognitive Disorders and Dementia in Diagnostics and Prevention

Abstract Objective In Hungary, the process of diagnosing dementia is slow and time-consuming. Currently, there are no well-developed systems and strategies at the local level for screening, and there is no artificial intelligence algorithm that could determine early signs of dementia based on the patient’s digital behavioral patterns. Methods Two PILOTs were conducted using the PreDem platform. Over the PILOTs’ duration (2021.09.01.-2024.04.09), 61,902 test data were analyzed. The 259 participants completed SDMT-type tests, Stroop tests, and memory/word games. Results We established a unified system for task evaluation, facilitating cross-test result comparisons. Participants (n = 43,902) were grouped into three categories: 1st - presumed dementia patients (13,932), 2nd - diagnosed MS patients (16,175), and 3rd - presumably normal population (13,795). Comparing the first two groups to the normal population revealed significant differences, vividly illustrated by density functions. A key finding is the substantial improvement in individuals dealing with memory disorders and forgetfulness through regular cognitive game performance. Conclusion Study results reveal the PreDem platform’s potential as a valuable tool for early dementia detection and prevention. Combining cognitive tests and artificial intelligence, the platform can successfully identify early signs of dementia and cognitive impairment (90% accuracy), potentially leading to early diagnosis, treatment, and improved quality of life for patients and families.

A systematic literature review on software applications used to support curriculum development and delivery in primary and secondary education

The evolution of educational software applications has revolutionised teaching and learning methodologies in primary and secondary education over the past decade. This paper conducts a review of primary studies based on N = 21 papers published between 2013 and 2023, focusing on the diverse landscape of software applications designed for student learning, curriculum development, delivery, and assessment. Findings from this study showcases a range of software solutions ranging from assessment tools to tutoring applications. Distinctive features supporting various aspects of teaching and learning, including lesson planning, delivery, management, assessment, and self-directed learning, were also identified. Regarding the features of software solutions used in primary and secondary schools, some differences were identified in terms of complexity, interactivity, assessment methodologies, and the collaborative functionalities of these tools. While highlighting the potential benefits, findings from this study also showed that challenges such as deployment costs, user self-efficacy, and technology anxiety are influential factors affecting the adoption of these technologies in primary and secondary educational settings. The evidence presented in this study serves as a resource for educational leaders and practitioners, facilitating a deeper understanding of available educational tools and essential considerations in the design and adoption of future tools.

Bibliometric Analysis Using Vosviewer on Research Trends in Marketing with the Theme of Green Buying Behavior in Google Scholar

This paper aim to explores the emerging focus on environmentally conscious consumer practices. Utilizing a bibliometric approach, the study examines 352 scholarly articles sourced from Google Scholar, utilizing the Publish or Perish application and VOSviewer software for data visualization and mapping. Key findings highlight a steady increase in research interest, particularly after 2021, indicating a growing academic and practical emphasis on green buying behaviors. The study presents a chronological publication trend and identifies the most prolific publishers and researchers in the field. Additionally, the research network's mapping illustrates the central themes and connections among various factors influencing green buying behavior, such as consumer awareness, marketing strategies, and the environmental impact of consumer choices. This comprehensive bibliometric analysis not only charts the evolution of research in this area but also suggests that increased attention to green marketing strategies could significantly influence consumer behavior. This is visually supported by network and density visualizations that depict the relationships and research intensity in the field, pointing to potential future directions for both academic study and practical marketing applications

Didactic video game prototype with recognization of geometric figures in augmented reality

The goal is to offer an educational and entertaining experience through pattern recognition and with the help of Artificial intelligence (AI) and Augmented reality (AR), classified in 2D and 3D that encourages the learning of geometric figures while playing. Technology has made students active participants who can manipulate and explore figures in a virtual environment. It encourages a deeper understanding, as they can see how the shapes interact with each other and with their environment (Toledo, 2017). However, there is still a world to investigate with the behavior demonstrated by 3rd grade primary school students. The pedagogical methodology of Local Theoretical Models (LTM) and the Prototyping methodology that is used to design and develop software systems or applications by building simplified and experimental versions. Currently you can get easy access to Software that helps create video games. Beginning to consolidate theoretical and cognitive knowledge that benefits stimulation in their learning and in attention, perception, interest, memory, thinking and reasoning with the help of AI and AR. The research helps 3rd grade primary school students understand the impact of technology to improve study and learning. The large number of applications implemented to strengthen the learning of basic level students has increased, converging between gaming technology and education, offering a new way to learn and practice mathematical skills in a fun and interactive way.

A decade in review: bibliometric analysis of sustainable performance trends in higher education institutes

The sustainable performance of higher education institutes (HEIs) is key to efficiently manage educational institutes in the long run, to get financial aid from the government, and to keep the interest of stakeholders in the institutions. Researchers have focused on examining the impact of a sustainable curriculum, sustainable teaching methodologies, sustainable campuses, and tools for assessing sustainability in HEIs in past literature. The present bibliometric analysis, combined with network analysis, aims to explore the literature to identify research gaps in the field of sustainable performance or sustainability performance of HEIs. We have employed a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow diagram to analyze the results obtained from Scopus, Web of Science (WoS), and EBSCOhost databases. A bibliometric analysis using Biblioshiny web application and VOSviewer software was performed on the 263 articles included in the study after screening. The results revealed that the annual growth rate of publications is increasing, indicating that the sustainability performance of HEIs is gaining momentum after the introduction of the Sustainable Development Goals (SDGs) in 2017. The production of articles in the field is highest from advanced European countries and the United States, but only a few articles are published from Asian countries. The authors with the highest number of publications and the most cited research article in the field are Leal Filho and Wals, respectively. Authors keywords in HEIs (or institutions) not only have a strong link with sustainability, sustainable education, green campus, and sustainability assessment but also have a small network with sustainable performance, green knowledge sharing, green knowledge management, green human resource management practices, green teams, change management, and organizational commitment. These findings can be used by future researchers to identify unexplored research areas in different contexts.

The load-transfer method as a tool for determining the load-displacement curve of piles

The paper presents two applications (software packages) in which the load-transfer method is used for axially loaded Kelly drilled bored piles and displacement ductile iron piles. In the first, the ultimate friction is related to the effective stress via the so-called β method. The β method is refined into three stages to cover the variety of soils typical of Central Europe. For the driven piles, a different approach is presented in which the ultimate shaft friction is related to the reference hammering time. The recorded hammering time profile is fed directly into the software based on the load-transfer method. Analyses of five loading tests are presented proving that the load transfer method in combination with the β method or the recorded hammering time profile is able to compute the load-displacement curve of both replacement and displacement piles with a reasonable accuracy in various geological conditions.

A microfluidic patch for wireless wearable electrochemical detection of sweat metabolites

Wearable sweat sensors provide an innovative detection method for analyzing human physiological and biochemical parameters and play an important role in detecting the potential risks of chronic diseases. Existing commercial wearable sensors (Apple iWatch, HUAWEI smart band) have problems such as single detection index (heart rate or blood pressure), and mainly focus on the physical information. This work studied an integrated microfluidic patch to simultaneously detect several biomarkers (pH, sodium ions, and uric acid) in sweat. We achieved the detection of pH level, sodium, and uric acid in linear ranges of 4–8, 0–160 mM, and 5–160 μM, respectively, covering well the clinically relevant ranges in the healthy human sweat. Laser engraving was chosen to prepare a microfluidic layer, and the sweat collection function was realized by combining hydrophilic and hydrophobic microfluidic synergy. It could efficiently collect the required sweat sample volume (80 μL) within 30 s while avoiding the contamination caused by directly contact with the skin. In addition, a wireless signal acquisition and transmission system was designed with the signal reading, processing, and wireless transmission functions, which can display the testing results in real time on Android phone-specific application software. We combined the wireless system with a microfluidic patch for on-body testing to verify the good wearability of the sweat sensor. This research realized the wireless data processing and transmission while improving sweat collection efficiency. In addition, it could detect various sweat physiological components in real-time, and provides a multiparameter, real-time, and portable sensing platform.

Thermodynamic and propulsive characterization of nitro-substituted quadricyclane

This investigation delves into the potential of Octa-nitro Quadricyclane (ONQC), a nitro-substituted quadricyclane, as a next-generation energetic material. Employing a robust B3LYP-gCP-D3/6-31G(d) computational approach, the study evaluates key material properties of ONQC, including strain energy (700.2 kJ/mol), enthalpy of formation (668.6 kJ/mol), and density (2.08 g/cm³). These enhanced properties result in predicted increases of 124 % in detonation pressure and 49.5 % in detonation velocity compared to TNT, significantly surpassing the performance of conventional high-energy density materials. Additionally, ONQC's suitability as an energetic additive in bipropellant formulations was evaluated with NASA's Chemical Equilibrium with Applications (CEA) software. The primary focus was on bipropellant formulations employing kerosene-based fuels (RP-1, JP-10, JP-5, etc.) and liquid oxygen (LOX) as the oxidizer. Incorporating ONQC into propellant formulations provides substantial improvements in specific impulse and reduces oxidizer requirements. This makes ONQC a promising candidate for advancing propulsion technology.

ARTIFICIAL INTELLIGENCE TOOLS IN ARCHITECTURE

Lately, architects have begun to favour a more result-oriented way of working based on artificial intelligence (AI) and its use for automation and software applications operation that support work with design data. The research aims to identify and systematise data on computer tools using artificial intelligence algorithms and the prospects for its development in architectural activities. The use of AI algorithms in architectural design is characteristic of all its stages, from the conceptual phase and basic design levels to detailing, development of design documentation, and future implementation, which is why various computer tools are available. The scientific paper describes three groups of AI software products. The first group includes software for architectural activities with AI-based functionality. The second group involves additional plug-ins with AI algorithms, i.e., independently compiled software modules installed and connected to the main programme, expanding its capabilities. The third group comprises online AI platforms, many of which are already available on the Internet and continue to grow in number. These services are usually not associated with professional software but can be used to perform specific architectural design tasks. The reviewed software products suggest that artificial intelligence technologies can transform traditional approaches to architecture and construction, offering architects powerful tools to increase efficiency and creativity. However, it is necessary to note significant doubts in the professional architectural community about whether to implement artificial intelligence fully. It is due to uncertainty about the impact of AI on traditional roles in the architectural industry, displacement from jobs, and the reliability and ethical implications of decision-making based on these particular algorithms. We should also note that the first group of software is the most promising for the future development of the architectural profession, as it provides full-fledged programmes with a comprehensive approach to architectural activities. Keywords: artificial intelligence, software, plugin, architecture.

Применение теории множеств для разработки ключевых параметров эффективности трудового сектора пенитенциарной системы

The article considers the development of information management systems, decision support and modelling for production processes in penitentiary institutions. The work outlines the problem of insufficient study of production and labour sector data both from a scientific viewpoint and from the standpoint of developing new software and practical application. It is found that the first steps to solve the above-mentioned problems will be proper data structuring and identifying key performance parameters. Presenting data as sets and studying the interaction between them can be a good solution to the problems posed. Thus, the work formulates the task of determining the key performance parameters of the labour sector of the penitentiary system, and considers the principles of set theory as tools for solving this problem. In addition, the authors use previously created developments such as an indexing system and a software package for collecting statistical data to settle this issue. As a result of the study, rules are determined, as well as relationships that can be applied to detect the key performance parameters and develop an information system, as well as to create databases with improved structuring that are more practically oriented.

Beyond RGB 2.0: Further Improvements to a Free, Opensource, Spectral Image Processing Software Application for Workflow, Analysis, and Repeatability

Beyond RGB 2.0: Further Improvements to a Free, Opensource, Spectral Image Processing Software Application for Workflow, Analysis, and Repeatability

DxGoals: A Software Tool for Determining and Analyzing Clinically Meaningful Classification Accuracy Goals for Diagnostic Tests.

To evaluate diagnostic tests for low prevalence conditions, classification accuracy metrics such as sensitivity, specificity, and positive likelihood ratio (PLR) and negative likelihood ratio (NLR) are advantageous because they are prevalence-independent and thus estimable in studies enriched for the condition. However, classification accuracy goals are often chosen without a clear understanding of whether they are clinically meaningful. Pennello (2021) proposed a risk stratification framework for determining classification accuracy goals. A software application is needed to determine the goals and provide data analysis. We introduce DxGoals, a freely available, R-Shiny software application for determining, visualizing, and analyzing classification accuracy goals for diagnostic tests. Given prevalence p for the target condition and specification that a test's positive and negative predictive values PPVand NPV=1-cNPV should satisfy PPV>PPV* and cNPV<cNPV*, DxGoals uses Bayes Theorem to determine equivalent goals for PLR and NLR and implied goals for sensitivity and specificity. When study data are provided, DxGoals analyzes whether the determined goals are met with statistical significance. When comparing 2 tests, DxGoals translates a superiority or noninferiority goals for the differences PPV-p and p-cNPV to equivalent goals for PLR and NLR and analyzes the goals when data are provided. We illustrate DxGoals on tests for penicillin allergy, ovarian cancer, and cervical cancer. The inputs cNPV*,p, and PPV* were informed by clinical management guidelines. DxGoals facilitates determination, visualization, and analysis of clinically meaningful standalone and comparative classification accuracy goals. It is a potentially useful tool for diagnostic test evaluation.

Big data and supply chain crisis financial risk management: A bibliometric review

The presence of a crisis has consistently been an inherent aspect of the Supply Chain, mostly as a result of the substantial number of stakeholders involved and the intricate dynamics of their relationships. The objective of this study is to assess the potential of Big Data as a tool for planning risk management in Supply Chain crises. Specifically, it focuses on using computational analysis and modeling to quantitatively analyze financial risks. The “Web of Science—Elsevier” database was employed to fulfill the aims of this work by identifying relevant papers for the investigation. The data were inputted into VOS viewer, a software application used to construct and visualize bibliometric networks for subsequent research. Data processing indicates a significant rise in the quantity of publications and citations related to the topic over the past five years. Moreover, the study encompasses a wide variety of crisis types, with the COVID-19 pandemic being the most significant. Nevertheless, the cooperation among institutions is evidently limited. This has limited the theoretical progress of the field and may have contributed to the ambiguity in understanding the research issue.

Delta GUI change detection using inferred models

Recent software development methodologies emphasize iterative and incremental evolution to align with stakeholders’ needs. This perpetual and rapid software evolution demands ongoing research into verification practices and technologies that ensure swift responsiveness and effective management of software delta increments. Strategies such as code review have been widely adopted for development and verification, ensuring readability and consistency in the delta increments of software projects. However, the integration of techniques to detect and visually report delta changes within the Graphical User Interface (GUI) software applications remains an underutilized process. In this paper, we set out to achieve two objectives. First, we aim to conduct a comprehensive review of existing studies concerning GUI change detection in desktop, web, and mobile applications to recognize common practices. Second, we introduce a novel change detection tool capable of highlighting delta GUI changes for this diverse range of applications. To accomplish our first objective, we performed a systematic mapping of the literature using the Scopus database. To address the second objective, we designed and developed a GUI change detection tool. This tool simultaneously transits and compares state models inferred by a scriptless testing tool, enabling the detection and highlighting of GUI changes to detect the widgets or functionalities that have been added, removed, or modified. Our study reveals the existence of a multitude of techniques for change detection in specific GUI systems with different objectives. However, there is no widely adopted technique suitable for the diverse range of existing desktop, web, and mobile applications. Our tool and findings demonstrate the effectiveness of using inferred state models to highlight between 8 and 20 GUI changes in software delta increments containing a large number of changes over months and between 4 and 6 GUI changes in delta increments of small iterations performed over multiple weeks. Moreover, some of these changes were recognized by the software developers as GUI failures that required a fix. Finally, we expose the motivation for using this technique to help developers and testers analyze GUI changes to validate delta increments and detect potential GUI failures, thereby fostering knowledge dissemination and paving the way to standard practices.

Optimizing Disease Surveillance Through Pooled Testing with Application to Infectious Diseases

AbstractPooled (or group) testing has been widely used for the surveillance of infectious diseases of low prevalence. The potential benefits of pooled testing include savings in testing time and costs, reducing false positive tests, and estimating models or making predictions from limited observed data information (e.g., only initial pooled responses). However, realizing these benefits often critically depends on the pool size used. Statistical methods introduced in the literature for optimal pool size determination have been developed mainly to accommodate simpler pooling protocols or perfect diagnostic assays. In this article, we study these issues with the goal of presenting a general optimization technique. We evaluate the efficiency of the estimators of disease prevalence (i.e., the proportion of diseased individuals in a population) while accounting for testing costs. Then, we determine the optimal pool size by minimizing the measures of optimality, such as screening efficiency and estimation efficiency. Our findings are illustrated using data from an ongoing screening application at the Louisiana Department of Health. We show that when a pooling application is properly designed, substantial advantages can be realized. We provide an package and a software application to facilitate the implementation of our optimization techniques. Supplementary materials accompanying this paper appear online.

Accuracy of Tooth Segmentation in the Digital Kesling Setup of Two Different Software Programs: A Retrospective Study.

Introduction Precise virtual setup creation and orthodontic appliance fabrication depend on accurate teeth segmentation from intraoral scans. This accuracy is also fundamental for successful orthodontic treatment, as it ensures correct diagnosis and optimal treatment planning. A number of software packages that facilitate the building of virtual setups have been made available in recent years. The performance of these software packages on automatic tooth segmentation has not been widely studied. Hence, the aim of this study was to evaluate the accuracy of automated teeth segmentation in the digital Keslingsetup of Ortho Studio (Maestro 3D Dental Studio, Bordeaux, France) and OrthoAnalyzer (3Shape, Copenhagen, Denmark) software systems. Materials and methods All the scans were taken from the same intraoral scanner (Runyes 3D intraoral scanner; Runyes Medical, Ningbo, China). The scans were stored and imported as stereolithography (STL) files into the Maestro Ortho Studio and 3Shape OrthoAnalyzer software systems. Subsequently, the digital photos underwent alignment in both software applications, an essential stage in each respective workflow prior to any further processing. The digitized images were automatically segmented in Maestro and 3Shape software by a single researcher. For each software interface, the accuracy of teeth segmentation was assessed. An independent t-test, with a significance level set at p < 0.05, was used to evaluate the statistical significance between the two software segmentations. Results The total number of teeth segmented by both software programs utilizing the 12 intraoral scans was 336 for both groups. Successful identification of the tooth segments was 98.21% (n = 330) for 3Shape software and 98.8% (n = 332) for Maestro software. There was no significant difference in the accuracy of determining the tooth segmentations between anterior and posterior teeth, respectively, between both groups, with a p-value of 0.523. Conclusion There were no statistically significant differences between the two software programs, and both demonstrated high success rates for auto-tooth segmentation. Although both programs had excellent success rates, Maestro 3D performed more accurately than 3Shape OrthoAnalyzer.

UNCERTAINTY EVALUATION USING LAW OF PROPAGATION AND MONTE CARLO SIMULATION METHODS WITH THE AUTORFPOWER MEASUREMENT SOFTWARE

RF power measurement is essential in RF and microwave metrology. For reliable and accurate power measurement, automatic measurement is preferred. A software application in C#, named AutoRFPower, was developed for automatic RF power measurement and uncertainty calculations at this study. According to the GUM document, this application is enhanced for uncertainty calculations by utilizing the Law of Propagation method and the Monte Carlo Simulation method. Trial measurements were performed at different RF power levels and frequencies between 50 MHz and 18 GHz using the AutoRFPower software. Law of Propagation and Monte Carlo Simulation uncertainty calculations were carried out by AutoRFPower based on the trial measurements and by the Oracle Crystal Ball simulation application. All measurements and their uncertainty calculations were compared with each other, and this study validated the uncertainty calculation of AutoRFPower. In addition, it was observed that in the Monte Carlo Simulation, uncertainty calculation results were non-symmetrical normal distribution, contrary to the assumption of symmetrical normal distribution according to the Low of Propagation method. Moreover, it has been observed that the statistical distribution of uncertainty changes depending on the dominant component of the parameters in the model function used for the uncertainty calculation with the Monte Carlo Simulation method.

Developing crossplatform software applications to enhance compatibility across devices and systems

In an increasingly interconnected world, the need for software applications that function seamlessly across diverse devices and operating systems is paramount. Developing crossplatform software applications addresses this need by providing a unified user experience and operational efficiency regardless of the hardware or system being used. This approach eliminates the need for multiple versions of the same application, streamlining development and reducing costs while improving accessibility and consistency. Crossplatform development involves creating software that is compatible with various operating systems such as Windows, macOS, iOS, and Android, as well as different device types including desktops, tablets, and smartphones. Key methodologies in this domain include the use of frameworks and tools such as React Native, Flutter, and Xamarin, which allow developers to write code once and deploy it across multiple platforms. These frameworks offer a range of features to enhance user interfaces, manage system resources efficiently, and ensure robust performance across devices. The benefits of crossplatform applications are manifold. They provide a consistent user experience, as the same application behaves similarly across different devices, enhancing usability and customer satisfaction. Additionally, they simplify maintenance and updates, as changes need only be implemented once rather than across multiple codebases. This approach also accelerates timetomarket by leveraging shared codebases, thereby enabling faster development cycles and quicker deployment. However, developing crossplatform applications also presents challenges. Ensuring consistent performance and functionality across diverse systems can be complex, requiring careful design and testing. Developers must also navigate varying hardware capabilities and user interface guidelines for different platforms. Despite these challenges, advances in development frameworks and tools continue to improve the efficiency and effectiveness of crossplatform solutions. In conclusion, crossplatform software development represents a strategic approach to enhancing compatibility and accessibility across devices and systems. By leveraging modern frameworks and tools, organizations can deliver cohesive, highquality applications that meet the needs of a diverse user base while optimizing development resources and costs. Keywords: : Developing, CrossPlatform, Software Applications, Compatibility, Devices.

Post Natal Mothers Satisfied and Happy with the Outcomes of their Deliveries after Individualized Precision Maternal Monitoring using 3D-Printing Technology in a Maternity Unit in the Developing World - A Case Study of Jaramogi Oginga Odinga Teaching and Referral Hospital in Western Kenya

Background: In the low and middle income countries’ healthcare system for medicine and surgery training and service provision, the use of computing technologies would be of great help in creating faster and transformational solutions by creating software applications and hardware fabrications for faster diagnosis, which are absent currently, hence leading to efficient treatment interventions in the developing world. Objective: The objective of this study was to assess the potential of using computing innovation of 3-D printing technology in producing medical solutions using 3-D printing of fetoscopes to solve the challenge of the frequency of monitoring fetal heart rates in the maternity department. Methodology: A randomized case-control study was used to analyze the impact of having one fetoscope per mother on shortening the frequency of monitoring of fetal heart rates and promptly intervening in case of non-reassuring fetal status. This was done by 3-D printing fetoscopes increasing the numbers at the Maternity department of Jaramogi Oginga Odinga Teaching and Referral Hospital. Findings: The frequency of monitoring heart rates increased to hourly instead of 3 hourly in 24 hours, leading to timely interventions, as compared to previous days when there was only one fetoscope serving the whole unit. Mothers with a 3-D printed Fetoscope reported a lower proportion of emergency caesarean section done compared to mothers who were sharing a 3-D printed Fetoscope (13% vs. 38%, p = 0.021). No fetal death was reported among mothers with a 3-D printed Fetoscope each, while maternal satisfaction was higher among mothers with a 3-D printed Fetoscope as compared to those who were sharing (94% vs. 63%, p = 0.002). Conclusion: We concluded that computing technology is useful in designing and fabricating medical equipment and parts, which help save lives in the maternity, emergency room and the operating room in low and middle income countries.