Programming Research Articles

Beyond The Basics: A Detailed Survey of Advanced Python Applications and Innovations

Abstract: Python is the general purpose, high-level, scriptable, interpreted, object oriented and easy to learn language was developed by Guido van Rossum. Python is a popular programming language because of its simplicity, ease of use, open-source licensing, and accessibility. It provides various libraries and frameworks for learning and real world programming. Python’s versatility and active community make it an ideal language for Machine Learning, Data Science, Artificial Intelligence, Networking, Game Development, Web Development and various other domains. In this paper, we will provide an introduction to the main Python programming software tools used for Web development, Data science, Machine learning techniques, Educational Development, etc. In this paper will first introduce Python as a language, and give introduction about Web Development, Data science, Machine learning, Educational Development & Financial Analysis and then describe packages that are popular in that sector

Enhancing computational thinking in children through logical puzzles: A machine learning approach for early coding education

This recent growth in early education in coding evidences the rising need for the computing-thinking-skills development in children. Though most of the programming curricula are based upon syntax and other basic concepts of coding, computational thinking encompasses broader cognitive skills, such as problem-solving, abstraction, and logical reasoning. This research therefore intends to fill a gap in research and will investigate the ways pre-coding games employ logical puzzles as an enhancement in developing computational thinking skills in children aged 7-10 years and how these affect the acquisition of programming knowledge. In all, there will be two groups assigned: one will be exposed to logical-oriented puzzle training, Group A, and the other will be a control group, Group B, which receives only regular standard lessons in programming introduction. Pre- and post-measures of computational thinking and basic programming skills will be obtained from the participants. The machine learning algorithms, such as Random Forest for prediction in terms of improvement in subject performance in computational thinking and K-Means clustering to identify patterns in their development of skills, will be implemented in their pure or integrated forms to analyze data. Logistic Regression will be executed in order to model the odds of improved performance in programming given an intervention of exposure to puzzles. The research study will, therefore, apply these algorithms in comparing the performance of the two groups and reveal the aspects of computational thinking to which the greatest influence in a positive way through puzzle activities is exerted. It is expected that children exposed to logical puzzles may show significant improvements in both computational thinking and programming skills. These findings should be useful to educators in their effort to show how logical puzzles can be integrated into the curricula of early coding education to build foundational skills and thereby contribute to a refinement of best practices in STEM education.

Fully automated CT analysis with a deep-learning based algorithm for pre-procedural planning in transcatheter aortic valve implantation

Abstract Background Pre-procedural cardiac computed tomography analysis has emerged to an essential tool in transcatheter aortic valve implantation (TAVI). It facilitates the valve choice and allows for optimal vascular access planning. For this purpose, software programs have been specifically developed to assist a fast and precise analysis of the generated data set in high quality. However, these software programs only assist the evaluator and are not able to perform a complete analysis on their own. Purpose We report the performance of a fully-automated, deep-learning based algorithm for pre-procedural CT analysis as compared to the current clinical standard. Methods Ninety-eight patients with symptomatic severe aortic stenosis undergoing TAVI were retrospectively enrolled. The pre-procedural data set was analyzed in two ways: - conventionally with a commercially available TAVI CT-analysis software by experiences users . - by a fully-automated CT-analysis platform with a deep-learning based algorithm. The current beta-version of the platform allows the determination of the annulus diameter, -area, -perimeter and the distance measurements of the coronary arteries to the aortic annulus. Hence, analyzable parameters were limited to the aforementioned, to ensure comparability. Statistical analysis: Mean, standard deviation, mean of difference and the interclass-correlation coefficient (ICC) were calculated and Bland-Altman plots created. Results The mean annulus diameter was 24.5±2.5mm (conventional) vs. 24.1±2.4 mm (deep learning). The mean of difference was 0.42±0.82mm (Bland-Altmann diagram. Figure 1). The ICC was 0.964 (0.925; 0.980), showing an excellent correlation between the two values. The mean annulus perimeter was 77.7±7.4mm (conventional) vs. 75.6±9.3mm (deep learning), showing a very good correlation of the means [mean of difference: 2.11±5.75mm; ICC: 0.852 (0.759; 0.906)]. The distance from the annulus to the left coronary artery was 14.0±3.2mm (conventional) vs. 12.6±2.8mm (deep learning). We depicted a good correlation between the two values [mean of difference: 1.45±2.52mm; ICC 0.736 (IQR 0.483; 0.850)]. The distance from the annulus to the right coronary artery was 17.1±2.7mm (conventional) vs. 16.5±3.2mm (deep learning), respectively. A very good correlation between the two values could be shown [mean of difference 0.57±2.2mm; ICC 0.822 (0.730; 0.882)], as well. Additionally, both methods visualize the measurements and allow manual adjustments (Figure 2). Conclusion In this retrospective analysis, deep-learning based analysis of peri-procedural derived CT data sets showed an very good correlation with conventional assessment. If results can be sustained in a bigger patient cohort and extended to more complex measurements, this artificial intelligence based fully-automated CT-analysis could emerge to a valuable alternative to conventional CT assessment in the pre-procedural planning for TAVI.Figure 1 - Bland-Altman plotFigure 2- Measurements by both methods

The advantages of employing i-a-SiOX:H as a buffer layer in hydrogenated amorphous silicon oxide solar cells

Abstract This study focuses on a p-i-n single junction solar cell made of hydrogenated amorphous silicon oxide (a-SiOx:H), aiming to enhance solar cell efficiency by mitigating the impact of discontinuities and mismatches occurring at the i/p defect-rich interface between the window layer and the absorber layer. To address this concern, the impact of adding a thin i-a-SiOx:H buffer layer between the p-a-SiOx:H window layer and the i-a-SiOx:H active layer was investigated through numerical modeling using the AMPS-1D (Analysis of Micro-electronic and Photonic Structures) computer program. Implementing these changes led to a remarkable increase in conversion efficiency, rising from 5.714% to an impressive 8.929%. The increase in short-circuit current (JSC), however, is due to improved quantum efficiency at short wavelengths between 350 and 550 nm. Furthermore, enhancing the built-in potential (Vbi) at the i/p interface, combined with the buffer layer’s appropriate band gap energy, increases VOC (open-circuit voltage) from 850 to 993 mV. The substantial improvement in the fill factor (FF) from 63.1 to 83.1% can be largely attributed to the smoothed band offset, primarily facilitated by the presence of the buffer layer at the p/i interface, which led to more efficient extraction of photogenerated holes. To ensure effective usage of the buffer layer, the thickness of a-SiOx:H (buffer layer) varied between 3 nm and 9 nm, while the p-type doping concentration of the same layer was adjusted between 0 and 1020 cm−3. In summary, adding a 3 nm thick a-SiOx:H buffer layer with an intermediate band gap and with a p-type doping concentration (NA) below 1018 cm−3 at the i/p interface improves the electrical and optical properties of the p-i-n solar cells (EFF = 8.951%; VOC = 0.994 V; FF = 83.1%; JSC = 10.842 mA.cm−2).

The impact of a block-based visual programming curriculum: Untangling coding skills and computational thinking

Backgroundand Context: Computer programming is increasingly being taught to young children worldwide. Quality programming curricula are needed to enable students to explore computer science concepts and develop a positive attitude towards programming. However, few studies have been conducted on the available curricula for young learners. ObjectiveThis study aims to provide an evidence-based, public, and free computer science curriculum for early childhood education by investigating the efficacy of the CAL-ScratchJr curriculum, among second graders. MethodA cluster-randomized controlled trial involving 11 schools and 21 second-grade classrooms was conducted to examine the impact of the 24-lesson curriculum on students’ coding proficiency and computational thinking. FindingsThe results indicated that the CAL-ScratchJr curriculum intervention had a medium positive effect on second graders’ coding skills, but no significant impact on computational thinking. ImplicationsResults caution against incorporating disconnected abstract computational thinking practices in education. The features of the curriculum were discussed.

Acceptability of Digital Mental Health Interventions for Depression and Anxiety: Systematic Review.

Depression and anxiety disorders are common, and treatment often includes psychological interventions. Digital health interventions, delivered through technologies such as web-based programs and mobile apps, are increasingly used in mental health treatment. Acceptability, the extent to which an intervention is viewed positively, has been identified as contributing to patient adherence and engagement with digital health interventions. Acceptability, therefore, impacts the benefit derived from using digital health interventions in treatment. Understanding the acceptability of digital mental health interventions among patients with depression or anxiety disorders is essential to maximize the effectiveness of their treatment. This review investigated the acceptability of technology-based interventions among patients with depression or anxiety disorders. A systematic review was performed based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and PROSPERO (International Prospective Register of Systematic Reviews) guidelines. We searched PubMed, Web of Science, and Ovid in May 2022. Studies were included if they evaluated digital interventions for the treatment of depression or anxiety disorders and investigated their acceptability among adult patients. Studies were excluded if they targeted only specific populations (eg, those with specific physical health conditions), investigated acceptability in healthy individuals or patients under the age of 18 years, involved no direct interaction between patients and technologies, used technology only as a platform for traditional care (eg, videoconferencing), had patients using technologies only in clinical or laboratory settings, or involved virtual reality technologies. Acceptability outcome data were narratively synthesized by the direction of acceptability using vote counting. Included studies were evaluated using levels of evidence from the Oxford Centre for Evidence-Based Medicine. The risk of bias was assessed using a tool designed for this review and GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). A total of 143 articles met the inclusion criteria, comprising 67 (47%) articles on interventions for depression, 65 (45%) articles on interventions for anxiety disorders, and 11 (8%) articles on interventions for both. Overall, 90 (63%) were randomized controlled trials, 50 (35%) were other quantitative studies, and 3 (2%) were qualitative studies. Interventions used web-based programs, mobile apps, and computer programs. Cognitive behavioral therapy was the basis of 71% (102/143) of the interventions. Digital mental health interventions were generally acceptable among patients with depression or anxiety disorders, with 88% (126/143) indicating positive acceptability, 8% (11/143) mixed results, and 4% (6/143) insufficient information to categorize the direction of acceptability. The available research evidence was of moderate quality. Digital mental health interventions seem to be acceptable to patients with depression or anxiety disorders. Consistent use of validated measures for acceptability would enhance the quality of evidence. Careful design of acceptability as an evaluation outcome can further improve the quality of evidence and reduce the risk of bias. Open Science Framework Y7MJ4; https://doi.org/10.17605/OSF.IO/SPR8M.

The Mutscore metapredictor: a new application of artificial intelligence in cardiogenetics

Abstract Introduction The rapid development of high-throughput next generation sequencing has shifted the limits of genetic knowledge from variant sequencing to variant interpretation. The current standards recommend that sequence variants are interpreted according to a comprehensive analysis including, among others, computational data. The two major types of in-silico predictive tools encompass those predicting the theoretical variant effect on splicing and algorithms predicting the potential damage of missense variants. However, overall for missense variants, prediction accuracy is often limited and results are sometimes inconsistent between software programs. Purpose We aimed at testing the predictive performance of a new predictive algorithm (Mutscore) for missense variants based on a machine learning approach, in our cohort of families referred for hereditary cardiac diseases. Methods We retrospectively reviewed DNA sequencing results from 230 families (from 01.07.2014 till 01.07.2023) addressed to our center for channelopathy or hereditary cardiomyopathy. Missense variants were identified and evaluated according to the current standards of the American College of Medical Genetics and Genomics. Variants were analysed with the commonly used in-silico tools CADD, Polyphen, Alpha-missense and Revel. We further applied the Mutscore, a new metapredictor integrating 16 existing predictive tools to data on variant topographical location. Results Among our 230 families, we detected 252 missense variants (class I-II 2.4%, class III 62.3%, class IV and V 15.9% and 19.4% respectively). We observed a significant positive correlation (r = 0.59, p=0.000) between the Mutscore and the interpretation of variants according to standards. The Mutscore had a better predictive performance than Polyphen (AUC 0.83 vs 0.67, p=0.007), Alpha-missense (AUC 0.87 vs 0.79, p=0.047) and CADD (AUC 0.87 vs 0.70, p=0.0001). Compared to Revel, the Mutscore had a comparable predictive performance (0.89 vs 0.87, p=NS), but a better sensitivity (75% vs 66%) at the maximum tolerated false positive rate of 10%. Figure 1. Focusing on variants of uncertain significance (VUS), we applied Mutscore cut-off values which were identified to reclassify variants in our previous study (1). Importantly, the Mutscore reclassified 45% of VUS into likely benign/pathogenic variants. Figure 2. Conclusions The Mutscore, through its metaprediction approach integrating data on variant topographical location, improves the accuracy of pathogenicity prediction as compared to three out of four algorithms commonly used in clinical practice, and seems to represent a valuable tool contributing to VUS disambiguation.

Investigation of the Knowledge Level of Hospital Managers About Strategic Management Tools and Their Use

Objectives: Effective and efficient implementation of all elements of strategic management is important for health organizations to achieve their future goals. This study aims to examine the knowledge levels of hospital managers about strategic management tools, their usage of these tools, and the priorities and needs in tool selection. Materials and Methods: The population of the study is the managers working in private hospitals in Antalya Province. 90 managers constitute the sample of the study. The data were collected by face-to-face questionnaire method. The questionnaire includes sociodemographic characteristics form and statements to measure the level of knowledge and use of strategic management tools. Mann Whitney U and Kruskal-Wallis tests were applied to examine the differences between groups, and Spearman Correlation Analysis was applied to examine the relationships between variables. SPSS.25 Package Program was used to analyze the data obtained. Statistical significance was accepted at 0.05 level. Results: 59% of the participants were female, 63% had a bachelor's degree, and the majority (41%) were middle managers. Those who did not receive managerial training and strategic management training were the majority (54% and 71%, respectively). When the knowledge level of the managers about strategic management tools was analyzed, mission and vision (98.9%) received the highest score, and value chain analysis (73.3%) received the lowest score. It was observed that the priorities of the administrators in the selection of strategic management tools were different between the groups according to gender, level of education, and type of school graduated from. In addition, statistically significant relationships were found between the priorities in the selection of strategic management tools and age, professional experience and years of working in the organization. Conclusion: Managers have limited knowledge and use of strategic management tools and there are differences in the selection of strategic management tools according to their sociodemographic status.

The Effects of Post-Earthquake Trauma Levels of Nursing Students on their Academic Motivation and Career Decisions.

This study was carried out to determine the effects of post-earthquake trauma levels of nursing students on their academic motivation and career decisions. The sample of this descriptive study consisted of 228 students studying at Gaziantep Islamic Science and Technology University, Department of Nursing. The data were collected using an online questionnaire created by the researchers. This online questionnaire form consists of seven questions about socio-demographic characteristics, and it also includes the Determination of Post-Earthquake Trauma Levels Scale, Academic Motivation Scale, and Career Decision Scale. SPSS 23.0 package program was used for the analysis of the data, and P < 0.05 was accepted for the level of significance. It was determined that the post-earthquake trauma level of the students was above the moderate level (63.49 ± 17.29) and that the extrinsic motivation-identified regulation, extrinsic motivation-external motivation, and intrinsic-knowledge levels were more affected by the earthquake (P < 0.05). In addition, it was determined that the mean Career Decision Scale score of the students (74.20 ± 17.35) was below the average. It was determined that post-earthquake trauma level had positive effects on intrinsic motivation-stimulation, extrinsic motivation, and amotivation, but had no effect on career decision.

FlawCheck: Detecting Smart Contract Vulnerabilities Based on Symbolic Execution

ABSTRACTSmart contracts are turing‐complete computer programs running on blockchains. Like traditional programs, smart contracts are also vulnerable. However, unlike traditional programs, it is very difficult to modify smart contracts once they are deployed on the blockchain. Therefore, reducing potential vulnerabilities in contracts before deployment to the blockchain is very important. The existing smart contract detection tools mostly fail to fully consider the complex control flow relationships within smart contracts, leading to false positives and false negatives. To address these problems, we propose FlawCheck, a vulnerability detection tool based on symbolic execution. First, it compiles smart contract source code into bytecode. Then, it disassembles bytecode into an opcode sequence, building the dependencies of contract control flow. Next, it performs preliminary analysis on the information generated during the simulated execution on the Ethereum virtual machine to identify suspicious vulnerability paths. Finally, it detects these suspicious paths by using symbolic execution. We verified that FlawCheck can detect five types of smart contract vulnerabilities. Experimental results on a dataset of 13016 real contracts shows that FlawCheck has higher accuracy than other tools.

Effect of build orientation on the fabrication trueness of additively manufactured implant-supported complete arch interim fixed prostheses.

To evaluate how build orientation affects the fabrication trueness of additively manufactured implant-supported complete arch prostheses by comparing them to subtractively manufactured high-impact polymer-based prostheses. An edentulous maxillary model with four implants at canine and first molar regions bilaterally was digitized (ATOS Core 80 5MP) to design a reference implant-supported complete arch prosthesis standard tessellation language file (RF-STL). The STL file was used to manufacture prostheses additively in five different orientations according to the build platform (AM-0, 0-degree; AM-15, 15-degree; AM-30, 30-degree; AM-45, 45-degree; AM-90, 90-degree) or subtractively (SM-HIP, control) (n = 10). The prostheses were digitized with an intraoral scanner (Trios 3) to generate their STLs (TF-STL). After superimposing TF-STLs over the RF-STL with a metrology-grade analysis software program (Geomagic Control X), surface deviations at four regions (overall, occlusal, overall without occlusal, and abutments), linear deviations at each abutment site, and interimplant distance deviations (canine-to-molar, canine-to-canine, and molar-to-molar) were calculated. One-way analysis of variance and Tukey HSD tests were used for the statistical analyses (α = 0.05). AM-90 mostly had the highest surface deviations, while AM-0 had the lowest overall, and lowest overall without occlusal region deviations (p ≤ 0.022). SM-HIP had the lowest occlusal region deviations (p < 0.001). AM-90 had the highest linear deviations (p < 0.001). AM-15 had higher canine-to-molar deviations than SM-HIP (p = 0.042). SM-HIP had the highest canine-to-canine deviations, while AM-90 had higher deviations than AM-0 and AM-30 (p ≤ 0.026). AM-45 and AM-90 had the highest and AM-0 had the lowest molar-to-molar deviations (p ≤ 0.013). AM-0 mostly had higher trueness and AM-90 mostly had lower trueness within tested outcomes. Additively manufactured prostheses mostly had lower canine-to-molar distances and higher molar-to-molar distances, whereas SM-HIP prostheses consistently had higher distances than the design file.

Vitamin C intake, psychological distress and health outcomes in patients with heart failure

Abstract Background Patients with heart failure (HF) have lower dietary vitamin C intake than healthy adults, which may increase oxidative stress and worsen important patient-reported health outcomes (functional status and quality of life [QOL]). Patients with HF are more likely to have higher levels of psychological distress (anxiety and depressive symptoms). However, it is not known whether psychological distress is a link in the association of dietary vitamin C with health outcomes. Objective To examine the inter-relationships among dietary vitamin C intake, psychological distress (anxiety and depressive symptoms), and health outcomes (functional status and QOL) in patients with HF. Method We performed a secondary analysis of baseline data from 206 adults with HF (female: 33%, age: 61±12, NYHA class III/IV: 44%) with complete data on dietary vitamin C intake, anxiety, depressive symptoms, functional status, and QOL. Dietary vitamin C intake was assessed using a 4-day food diary and analyzed using the Nutrition Data System for Research software program. Low dietary vitamin C intake was defined as averaged daily intake below the estimated average requirement (EAR) from the Institute of Medicine of 75 mg/day for men and 60 mg/day for women. Anxiety was assessed using the Brief Symptom Inventory Anxiety subscale. Depressive symptoms were assessed using the Beck Depression Inventory II. Functional status was measured using the Duke Activity Status Index. Quality of life was measured using the Minnesota Living with Heart Failure Questionnaire. Linear regression was conducted using the PROCESS macro in SPSS. Results Eighty-four patients (41%) had low dietary vitamin C intake based on the EAR standard. In bivariate analyses, patients with low dietary vitamin C intake had higher levels of anxiety, depressive symptoms, poorer functional status, and worse QOL (all p &amp;lt;.005). In the mediation regression model, low dietary vitamin C intake was directly associated with higher levels of anxiety, but not with depressive symptoms. Depressive symptoms were directly associated with functional status and QOL. Dietary vitamin C intake was not directly associated with functional status or QOL. Low dietary vitamin C intake was indirectly associated with poor functional status through higher levels of anxiety and depressive symptoms (Figure 1). Similarly, low dietary vitamin C intake was indirectly associated with worse QOL (Figure 2). Conclusion Anxiety and depressive symptoms link the relationship between dietary vitamin C intake and functional status and between dietary vitamin C intake and QOL. Our findings highlight the importance of including interventions to enhance diet quality and psychological well-being as part of HF treatment to improve health outcomes.Vitamin C and functional statusVitamin C and quality of life

Clinical and immunological characteristics of COVID-19 in patients with disease dynamics

The aim is to study the clinical and immunological indicators of COVID-19 in the dynamics of the disease with the determination of predictors of the development of changes in the lungs. Materials and methods. The object of the study was 35 patients aged 22 to 75 years in the acute period and dynamics of COVID-19 disease, divided into two groups according to the identified clinical and immunological disorders. Markers of cellular (CD-3, CD-4, CD-8, CD-19) and humoral immunity, cytokines (IL-6,8, TGF-β, TNF-α), CEC were determined in all examined patients. Statistical processing of the obtained results was carried out using the computer program Statistica 10.0. Results. It was found that all the subjects retained clinical and immunological changes during the entire follow-up period, which indicates an ongoing disease. At the same time, significant differences in the severity of changes in individuals were revealed, taking into account age and the presence of chronic somatic pathology, expressed primarily in violation of the parameters of the T-system of immunity. In patients with the development of postcovoid changes in the lungs, characteristic immunological features were revealed, taking into account age. Conclusions. A violation of the detected indicators of the immune system may indicate the persistence of the virus, which means a prolongation of a specific inflammatory response with the risk of extensive tissue damage. Within three months after recovery, the tendency towards the formation of humoral immunity persists in both groups, which occurs by the end of 1 month after the disease in both groups and continues throughout the entire follow-up period. In the risk group for the development of pneumofibrosis in the outcome of a new coronavirus infection, the combination of IL-8 and TGF-β is the most optimal, despite their significant decrease in dynamics compared with the acute period.

Teaching Statistics for the Social Sciences Using Active Learning: A Case Study

US college students are typically required to take at least one mathematics or statistics course, either as part their major area of study, or as a general education requirement. College mathematics requirements are an obstacle for many college students. Active learning, a pedagogical approach that places emphasis on students’ collaborative work, has been shown to increase student learning and course success in STEM fields of study and in mathematics courses. Active learning has also been shown to be adaptable to courses involving computer software and programming, such as introductory statistics. This case study is based on the author’s experience implementing an active learning model in an introductory statistics course for students majoring in sociology and related social sciences. Results indicate that the active learning approach was adaptable to the structure and particular learning goals of the course. Students perceived greater learning relative to other courses despite doing less work outside of class, and attributed this to the active learning structure of the course. These findings align with more systematic studies on the impacts of active learning in science and mathematics courses.

Statistical modelling of growth curve for longitudinal data on a feeding trial in goat breeds

The growth curve parameters were estimated extending a linear regression to higher degree polynomial with attempts to use also the biologically appealing Gombertz function. The data contained monthly weight records from weaning to the age of 14 months of kids in two Omani goat breeds Sahrawi and Jabbali used in a feeding trial assessing the effect of concentrate supplement (14% crude protein). The growth curve parameters were estimated within the fixed effects breed and feed concentrate level (2 or 3% of live body weight) with an extension to mixed models using animals as random effects. The parameters and model contrasting were performed with ML or REML as appropriate with relative comparison relying on AIC and significance testing of estimated parameters. The mixed model analyses were performed with publicly available R software package programs. The fixed effect cubic regression with linear random effect model fitted into the data. The higher level of concentrates improved the immediate post-weaning growth in the same way in the breeds while the subsequent growth curve differed with more pronounced weight increments in the Jabbali breed.

ARTIFICIAL INTELLIGENCE AS AN INNOVATIVE TOOL OF CORRUPTION PREVENTION IN JUDICIARY: THE ADVANTAGES

There has been made an attempt in the article to assess the possibility and expediency of application of information and communication technologies and their highest manifestation – artificial intelligence – as an anti-corruption tool in the judiciary. The essence and modern forms of the artificial intelligence phenomenon and its connection with information and communication technologies have been analyzed. There has been researched national and foreign legal regulation on the application of artificial intelligence both in the field of public administration in general and in the field of the judiciary and administration of justice in particular. The cases of the most «significant» corruption events in the judiciary have been presented and an attempt to find out the reasons of the failure of traditional tools to prevent corruption has been made. There has been called into question the ability of the state to overcome such a negative socio-power phenomenon as corruption without the implementation of radical steps which have long been overdue and became even more actualized during the armed conflict. Some positive examples of the information and communication technologies use in the judiciary have been demonstrated. Separate advantages of using artificial intelligence as a new tool for preventing corruption in the administration of justice have been briefly systematized. There has been emphasized that artificial intelligence due to its impartiality is fully capable to ensure the realization of the right to a fair trial, preserving time, financial and human resources, and reducing the level of corruption. At the same time, there has been underlined that without studying the risks and threats of using computer programs at this level of their development, relying only on them as a panacea for the destruction of corruption practices is imprudent and premature. Finally, an impulse for further research of artificial intelligence and its features as a tool of corruption prevention in the judicial system has been given.

Bridging Between High School and University: Assessment of Students’ Readiness to First-Year Engineering Programs

This descriptive case study uses self-assessment tools to explore 26 prospective students’ preparedness regarding their knowledge and competencies for entering the first year of engineering programs at a large, research-intensive Canadian university. We aim to provide insight for developing the Brigde2Engg (B2E) Program to empower and support students in their transition to university. Applying Conley’s college readiness model as a theoretical framework, findings from this study reveal that most students appear confident about their knowledge and skill preparedness for the first-year engineering programs, including their critical thinking abilities, problem-solving skills, and understanding of engineering professions. However, they are less confident in physics and the use of engineering tools. Therefore, we suggest that, when they choose to participate in the bridging program, these students should focus mainly on the subject they deem inadequate. Results also show that an introduction to engineering tools is essential to familiarize students with the programming and spreadsheet software they will use throughout their university programs.

Beyond humanism: telling response-able stories about significant otherness in human–chatbot relations

AI-enabled chatbots intended to build social relations with humans are becoming increasingly common in the marketplace, with millions of registered users using these chatbots as virtual companions or therapists. These chatbots make use of what is often called the “Eliza effect”—the tendency of human users to attribute human-like knowledge and understanding to a computer program. A common interpretation of this phenomenon is to consider this form of relating in terms of delusion, error, or deception, where the user misunderstands or forgets they are talking to a computer. As an alternative, we draw on the work of feminist Science and Technology Studies scholars as providing a robust and capacious tradition of thinking and engaging with human–nonhuman relationships in non-reductive ways. We closely analyze two different stories about encounters with chatbots, taking up the feminist STS challenge to attend to the agency of significant otherness in the encounter. The first is Joseph Weizenbaum's story about rejecting the ELIZA chatbot technology he designed to mimic a therapist as a monstrosity, based on his experiences watching others engage with it. The second is a story about Julie, who experiences a mental health crisis, and her chatbot Navi, as told through her descriptions of her experiences with Navi in the recent podcast Radiotopia presents: Bot Love. We argue that a reactionary humanist narrative, as presented by Weizenbaum, is incapable of attending to the possibilities of pleasure, play, or even healing that might occur in human–chatbot relatings. Other forms of engaging with, understanding, and making sense of this new technology and its potentialities are needed both in research and mental health practice, particularly as more and more patients will begin to use these technologies alongside engaging in traditional human-led psychotherapy.

Chatbots in medicine: certification process and applied use case.

Chatbots are computer programs designed to engage in natural language conversations in an easy and understandable way. Their use has been accelerated recently with the advent of large language models. However, their application in medicine and healthcare has been limited due to concerns over data privacy, the risk of providing medical diagnoses, and ensuring regulatory and legal compliance. Medicine and healthcare could benefit from chatbots if their scope is carefully defined and if they are used appropriately and monitored long-term. The confIAnce chatbot, developed at the Geneva University Hospitals and the University of Geneva, is an informational tool aimed at providing simplified information to the general public about primary care and chronic diseases. In this paper, we describe the certification and regulatory aspects applicable to chatbots in healthcare, particularly in primary care medicine. We use the confIAnce chatbot as a case study to explore the definition and classification of a medical device and its application to chatbots, considering the applicable Swiss regulations and the European Union AI Act. Chatbots can be classified anywhere from non-medical devices (informational tools that do not handle patient data or provide recommendations for treatment or diagnosis) to Class III medical devices (high-risk tools capable of predicting potentially fatal events and enabling a pre-emptive medical intervention). Key considerations in the definition and certification process include defining the chatbot's scope, ensuring compliance with regulations, maintaining security and safety, and continuously evaluating performance, risks, and utility. A lexicon of relevant terms related to artificial intelligence in healthcare, medical devices, and regulatory frameworks is also presented in this paper. Chatbots hold potential for both patients and healthcare professionals, provided that their scope of practice is clearly defined, and that they comply with regulatory requirements. This review aims to provide transparency by outlining the steps required for certification and regulatory compliance, making it valuable for healthcare professionals, scientists, developers, and patients.

Using ChatGPT as a tool during occupational therapy intervention: A case report in mild cognitive impairment

ABSTRACT This case report examined the impact of computer programmed assistive technology developed, using ChatGPT as a tool when designing an occupational therapy intervention on a client’s independence in activities of daily living. A 66-year-old female client with mild cognitive impairment consulted an occupational therapist due to difficulties with activities of daily living. The occupational therapist developed two activity assistance computer programs using ChatGPT as a resource. The client did not interact directly with ChatGPT; instead, the occupational therapist used the technology to design and implement the intervention. A computer programmed assistive technology-based occupational therapy intervention was completed for eight weeks. The occupational therapist trained the client to use these programs in the clinical setting and at home. As a result of the intervention, the client’s performance and independence in daily activities improved. The results of this study emphasize that ChatGPT may help occupational therapists as a tool to design simple computer programmed assistive technology interventions without requiring additional professional input.