Programming Research Articles

Household food security contextualised: A comparison of Ambros and Maramanzhi villages, South Africa

Smallholder agriculture plays a crucial role in achieving food security, particularly at the household level. However, fallow fields are progressively increasing in former homelands of South Africa. While substantial efforts have been devoted towards addressing food insecurity, access to arable land has not translated to sustainable crop production for smallholder farmers in former homelands of the country. This paper analyses household food security in the context of deagrarianization in two villages, Ambros (Eastern Cape) and Maramanzhi (Limpopo). Using a mixed-method approach, a total of 106 semi-structured questionnaires were administered through face-to-face interviews with household heads. Descriptive statistics were analysed using the IBM SPSS Statistics 27.0 computer program. Meanwhile, qualitative data was coded and content analysis was conducted on NVivo 12 software. The key findings revealed that the primary household income in the study area was earned from social grants. Furthermore, home gardens, with an average size of 4100m2 in Ambros and 4400m2 in Maramanzhi village, played a crucial role in supporting household crop production. However, food insecurity threatened the sustainability of rural livelihoods because the Household Food Insecurity Access Scale (HFIAS) indicator highlighted that a majority of 54% of households in Ambros and 69% in Maramanzhi were mildly food insecure. Food insecurity challenges varied between the two villages but these were mainly perpetuated by food shortages caused by insufficient monthly income and waning household crop production. Although acquiring food was rated as a very important reason for cultivating in both villages, deagrarianization eroded opportunities for increased crop production. Among other solutions, this study recommends that the government improve the delivery of basic services such as water to promote household crop production and the revitalization of fallow fields. These transformations can potentially enhance food security, income and employment opportunities for rural households, contributing to the sustainability of rural livelihoods.

Improving the water quality in the Zandvlei Estuary, Cape Town, by retrofitting sustainable drainage systems in the Diep River catchment

The Zandvlei Estuary is the only functioning estuary along the False Bay coastline of Cape Town and is therefore of extreme local ecological importance. The most significant problems are eutrophication and siltation caused by the increased total inorganic nitrogen (TIN) and soluble ready phosphorus (SRP) levels due to urban development and the associated increased impervious surface area in the catchment that drains into it. In South Africa, stormwater drainage systems conventionally channel everything they collect into receiving water bodies without significant treatment. Sustainable drainage systems (SuDS) provide an alternative approach to managing stormwater runoff. They are designed to manage both stormwater quality and quantity while potentially improving biodiversity and amenity. This project modelled the potential improvement in the quality of the water entering Zandvlei Estuary resulting from the implementation of selected SuDS control measures in Zandvlei’s Diep River catchment using the software program, PCSWMM. SRP, TIN, total phosphorus (TP) and total suspended solids (TSS) were selected as pollutant indicators. Treatment trains that included a large, constructed wetland at the bottom of the catchment will likely provide the greatest improvements to the water quality entering Zandvlei – potentially reducing SRP, TIN, TP and TSS by approximately 59%, 53%, 53%, and 66%, respectively – as well as potentially reducing the runoff by 48%.

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.

Development of a Sexological Ontology.

This study aimed to show what role biomedical engineering can play in sexual health. A new concept of sexological ontology, an essential tool for building evidence-based models of sexual health, is proposed. This ontology should be based on properly validated mathematical models of sexual reactions identified using reliable measurements of physiological signals. This paper presents a review of the recommended measurement methods. Moreover, a general human sexual reaction model based on dynamic systems built at different levels of time × space × detail is presented, and the actual used modeling approaches are reviewed, referring to the introduced model. Lastly, examples of devices and computer programs designed for sexual therapy are described, indicating the need for legal regulation of their manufacturing, similar to that for other medical devices.

Triple Pillar for Sustainability of Accounting Evaluation of Carbon Emissions and Climate Change

This study is among the first to address the relationship between accounting and adaptation to climate change. Aramco is a well-known company in the Middle East that produces oil, and it was used as a case study. This study examines the theoretical underpinnings of accounting assessment of carbon emissions arising from climate change and their influence on sustainability. The questionnaire was distributed to approximately 150 Saudi Arabian managers. The researcher used an analytical descriptive technique and the Statistical Packages Program for Social Sciences (SPSS) to assess the sample, fill out, tabulate, analyze the data, and test the hypotheses. The findings demonstrate that accounting for carbon emissions affects sustainable development in its three pillars-economic, social, and environmental. Further, the sub-hypothesis’s results indicate that accounting for carbon emissions statistically impacts sustainable development’s economic and environmental aspects. Accounting for carbon emissions has no discernible impact on sustainable development through the social component. The researcher advocated that the social aspect of sustainable development should be given attention. Large corporations are made aware of their significance in achieving environmental sustainability and that failing to adopt labor and human rights standards will result in companies being held accountable.

Effects of Block-Based Visual Programming on K-12 Students’ Learning Outcomes

Computer programming is regarded as an important skill for the future. However, many K-12 students face challenges and difficulties in learning traditional text-based programming. Block-based visual programming (BVP) can reduce the difficulty of learning programming and is seen as a potential programming education tool. Nevertheless, the effects of BVP on K-12 student learning outcomes remain unclear, and there is no evidence of meta-analysis. This is the first meta-analysis that aims to examine the effects of BVP on K-12 students’ learning based on 42 effect sizes from 29 independent experimental studies (2000–2023). The result suggests that BVP has an upper-medium effect on learning ( SMD = 0.769, 95% CI = [0.554, 0.984], p < .001), particularly in the cognitive outcome ( SMD = 0.698, p < .001). Moreover, the BVP tool, study design, and region have moderating effects. The BVP’s effects are larger when: (1) with 61∼100 students, (2) for junior high school, (3) with the learning duration of >1 month, (4) targeting math, (5) among novice students, (6) using Scratch, ScratchJr, Alice, and App Inventor, (7) employing project-based learning and project-based + game-based learning, (8) base on quasi-experimental design, (9) in Africa and Europe, (10) with latter publication years. Finally, discussions, implications, limitations, and future directions are put forward.

ARTIFICIAL INTELLIGENCE IN THE COURT JUSTICE SYSTEM

Modern society is characterised by the pervasive presence of information and communication technologies. The growing demands of today’s economy and society for enhanced and efficient products and services have led to the continual advance­ment of the technological sector. Among these advancements, artificial intelligence stands out as a particularly noteworthy phenomenon. Artificial intelligence entails the capacity of computer programmes to emulate human intelligence and perform a wide array of tasks. Its implementation has ushered in various advantages, allowing indi­viduals to accomplish tasks like online banking, virtual meetings, and digital conver­sations without the requirement of physical presence. Despite these benefits, the adop­tion of new technologies also introduces potential risks to fundamental rights and freedoms, including privacy, personal data protection, and individual liberty. The ap­plication of artificial intelligence (AI) in the justice system has been a topic of grow­ing interest and debate. AI technologies are being explored and implemented in vari­ous aspects of the justice system to improve efficiency, accuracy, and access to jus­tice.

Cleaning Efficacy of Toothpastes With Varying Abrasiveness-An InVitro Investigation on Bovine Dentin.

To determine how differences in abrasiveness (RDA) influence cleaning capabilities of toothpastes. For this invitro trial, 60 bovine dentin samples were prepared and divided into six groups (G1-G6; n = 10). Groups G1-G5 were arranged in order from low to high toothpaste abrasiveness (G1: RDA: 12, G2: RDA: 29, G3: RDA: 43, G4: RDA: 71, and G5: RDA: 143). Samples in group G6 were used as a negative control and later brushed with pure artificial saliva. In preparation for testing, the dentin samples were stained with a black tea mixture. The respective toothpastes were mixed with artificial saliva in a 1:2 ratio to produce a toothpaste slurry. The samples were brushed for a total of 25 min (with measurement intervals at 2-, 5-, 10-, and 25-min) using a manual toothbrush in an automated brushing machine. The cleaning efficacy of the toothpastes was determined using photographs connected to a planimeter computer program and statistically evaluated. Analysis of the median cleaning efficacy values showed that the toothpastes with a higher abrasiveness achieved a greater cleaning effect at each time point. All toothpastes showed a statistically significant difference (p < 0.05) in their cleaning efficacy (taking into account the median, interquartile range (IQR), minimum and maximum values) after 25 min of brushing. With increasing RDA value, the cleaning efficiency of the toothpastes tested rises. However, a doubling of the RDA value does not necessarily elicit a twofold increase in cleaning effect.

Femtosecond Laser-Assisted Graft Preparation and Implantation of Corneal Allogeneic Intrastromal Ring Segments for Corneal Ectasia: 1-Year Results.

To evaluate the outcomes of patients treated with corneal allogeneic intrastromal ring segments cut with femtosecond laser (Femto-CAIRS) without concomitant corneal crosslinking. Patients with keratoconus treated with Femto-CAIRS at the American University of Beirut Medical Center were included (May 2022-January 2023). A proprietary software program was developed on the femtosecond laser to cut allogeneic segments. Visual, refractive, tomographic, aberrometric, and epithelial data by anterior segment optical coherence tomography were measured at baseline and 1, 3, 6, and 12 months postoperatively. 20 eyes of 15 patients were included and followed up for 12 months. The manifest refraction spherical equivalent and cylinder improved from -6.79 ± 4.9 diopter (D) and -4.25 ± 1.8 D to -1.88 ± 2.9 D (P < 0.001) and -2.64 ± 1.4 D (P = 0.01) 12 months postoperatively, respectively. 75% of eyes gained 3 or more corrected distance visual acuity lines, most of which (65%) gained 4 lines or more 12 months postoperatively. The maximum keratometry and vertical coma decreased by 5.2 D (P < 0.001) and 1 μm (P = 0.001), respectively, 3 months postoperatively and remained stable until 12 months. The largest anterior stromal elevation over the central 5-mm diameter decreased from 36.0 ± 18.2 μm preoperatively to 19.9 ± 9.25 μm at 1 week postoperatively (P < 0.001) and remained relatively stable. Epithelial thickness over the cone increased relative to baseline starting 1 month postoperatively and becoming stable after 6 months while the mean epithelial thickness central to the ring peaked at 1 month after which it decreased to reach a plateau at 6 months. The Femto-CAIRS procedure improves visual and tomographic parameters and allows repeatable and safe results with the possibility of customization for individualized management.

Novel Crystalline Salts of 4-Piperidyl- and 4-Pyridylmethylamines Prepared by Catalytic Hydrogenation of 4-Pyridinecarbonitrile: Crystallographic Unit Cells Based on Powder XRD Patterns by Using the DASH Program Package

Structures of some hydrogenated products and intermediates, prepared by a heterogeneous Pd/C or Ru/C catalyst starting from 4-pyridinecarbonitrile (4PN), in water and in the presence of an acidic additive (HCl or H2SO4), were confirmed in various salt forms of 4-piperidylmethylamine (4PIPA) and 4-pyridylmethylamine (4PA). Crystallographic unit cell structure of the completely hydrogenated product salts (4PIPA·H2SO4 and 4PIPA·2HCl) showed a common double-protonated [4PIPA+2H]2+ divalent cation structure, also proved by FT-IR, and that of the 4PA·H2SO4 intermediate salt was also indexed and modeled by means of powder X-ray diffraction, applying the DASH 4.0 software package and crystal coordinates coming from former single-crystal X-ray structure determination. Formations of the anhydrous and hydrated forms of 4PA·0.5H2SO4·xH2O (x = 0 or x = 0.5, hemisulfates) were also studied by powder XRD and FT-IR spectroscopy for comparing these crystal structures.

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

Recent One Decade Trends in Mathematical Representation Research: Systematic Mapping Study

Research can change over time and can be influenced by technological developments, educational trends, and current social issues. Developments on trends in mathematical representation research over the past 10 years are discussed in this systematic mapping study (SMS). The question of review research with the systematic mapping study method is how the trend of mathematical representation research over the last 1 decade and what new topics have the opportunity to be researched by future researchers related to the themes of mathematical representation. The method used in this review is the PRISMA method which includes 5 stages carried out: 1) defining eligibility criteria, 2) determining information sources, 3) data selection, 4) data collection, and 5) data collection. In this systematic mapping study, it starts from searching for articles on the topic of mathematical representation during the last 1 decade, namely the range of 2013-2023, articles obtained from the scopus database in July 2023 with the keywords "representations" AND : Mathematics" initial data obtained 1907 articles after the exclusion process obtained 120 articles which were then analyzed using R-Studio and Vos-Viewer tools. The results of SMS obtained the relationship of representation topics in the last 10 years of research with the topics of visual, computer software, computer aid instruction, data mining, open source software, network architecture, natural language processing systems, intelligent systems and information analysis. In addition, research over the past 1 decade on the topic of representation has led more to data mining, security of data and STEM. Artificial Intelligence or artificial intelligence is the latest topic that over the past 3 years has grown in various articles that deal with the theme of mathematical representation and accompanied by the theme of multi-representation. The evolution of research topics related to mathematical representations that used to be only related to visual representations and image representations now leads to computer programs, programming algorithms and artificial intelligence, this shows that over the past 1 decade there has been an evolution of research in the implementation of representations in technology.

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.

A Scientometric Review and Analysis of Studies on the Barriers and Challenges of Sustainable Construction

Despite numerous concerns about climate change and the deterioration of nature, the construction industry is still one of the largest consumers of minerals and natural resources. In recent decades, sustainable construction using renewable and recyclable materials, reducing energy, and the adoption of more green technologies with the aim of reducing harmful impacts on the environment have received profound worldwide attention. The more key stakeholders involved strive to achieve sustainability, the more barriers they may face, which requires investigating them to have an effective plan to recognize, prevent, and control them. This paper reviews, classifies, and analyzes the major barriers of sustainable construction between January 2000 and April 2023. In this scientometric study, 153 articles were selected from the Web of Science database. Then, bibliometrics, the creation of maps from network data, as well as the illustration and exploration of those maps were conducted with the HistCite 12.03.1 and VOSviewer 1.6.20 software programs. The analytical results showed that the most profound barriers of sustainable construction are classified into 12 groups: price, economic parameters, awareness, technical, policy and regulations, design, management and government, environmental, social, materials, planning, and market.

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.