Software Apps Research Articles

Social Media Platform Based Evaluation of Teaching Style on Online Education System using Heuristic Search with Stacked Sparse Autoencoder Model

As online education has become increasingly prominent, the primary objective of this study is to evaluate students' opinions of online classes taught by teachers with no prior experience in online teaching, focusing on their teaching style, teaching efficiency, and pedagogy in the online classroom. Online teaching is a kind of teaching system that depends on network management technology. It concludes the teaching method by the process of live courses or recorded courses employing software containing special online teaching environments and any APP software employed for teaching. Social media, with its massive pool of user-generated content and instant feedback, offers a great opportunity to calculate teaching styles in online class management. Therefore, this study offers a Social Media Based Evaluation of Teaching Style in Online Education Systems using Heuristic Search (SMBETS-OESHS) Algorithm. The main objective of the SMBETS-OESHS technique for evaluate teaching styles in online education systems using insights derived from social media platforms. At primary stage, the SMBETS-OESHS model takes place linear scaling normalization (LSN) is implemented for scaling the input data. Next, the bayesian optimization algorithm (BOA) based feature selection process can be employed to allow for the detection of the most relevant features from the data. In addition, the SMBETS-OESHS model exploits stacked sparse autoencoder (SSAE) technique for classification process. In order to achieve optimal performance, the SSAE model parameters are fine-tuned using the improved beetle optimization algorithm (IBOA), ensuring robust evaluation accuracy. The experimental validation outcome of the SMBETS-OESHS algorithm undergoes and the performances are examined over various measures. The simulation outcome stated that the enhanced solution of the SMBETS-OESHS system over the recent techniques.

Reproducibility and reliability of dental arch measurements: comparing of manual, digital, and app-based methods

BackgroundKnowing the available dental space in a patient is crucial for orthodontists to develop a good treatment plan.ObjectivesTo compare and evaluate the reliability, reproducibility, and accuracy of three measurement methods on models: conventional, mobile app, and digital software. Materials and methods: Maxillary and mandibular dental plaster models of 20 subjects with permanent dentition were analyzed. Three measurements were taken: manually using a digital caliper, using Nemocast 3D® software, and using the mobile app “Ruler.” These were performed by the same observer at the same initial time (T1) and repeated after 15 days (T2).ResultsThe three methods measured each of the quadrants and each of the dental pieces similarly, with no significant differences found. Significant differences were found between the different measurement times in the manual and app ruler measurement groups. Intra-observer consistency was also observed through Pearson correlations. The measurement from the mobile app “Ruler” presented lower values compared to the other two models, especially with the measurements taken for each quadrant.ConclusionThe three methods used for measuring dentoskeletal discrepancy are similar in terms of results. However, Nemocast 3D software showed greater reproducibility and reliability compared to the other two methods presenting consistency in the results measured at both time points, which may be useful for our diagnoses.Trial registrationThe study was conducted according to the ethical standards set by the Declaration of Helsinki for Biomedical Research and the protocol was approved by the Bioethics Committee of the University of Salamanca with number 23/1074.

Metaverse and education: identifying key themes and future research trajectories

PurposeThe present study aims to undertake an extensive review of scholarly literature by exploring the intersection of the metaverse and education.Design/methodology/approachThe researchers used the relevant documents from the Scopus database to conduct bibliometric analysis. The data were retrieved from 2010 to February 2024. Citation, co-citation and author’s keyword analysis were conducted for bibliometric analysis. The study was performed using VOSviewer and the Biblioshiny app software packages.FindingsThe extant literature related to the metaverse and education is presented in the paper. The paper identified four key themes in the literature, i.e. Metaverse and education, Contemporary application of metaverse: a multisectoral perspective, Metaverse: spatial dimensions and concerns and Metaverse: shaping the future of digital interaction. Other information related to the most influential authors, journals and countries concerning metaverse and education is also presented.Originality/valueThe paper studies the gradual evolution of the present research domain over time. The study outlines key areas that have emerged from the literature review, suggesting directions for future research.

Extend UTAUT2 Model to Analyze User Behavior of China Construction Bank Mobile App

The outbreak of COVID-19 has been accelerating the digital transformation of financial institutions all over the world. Mobile banking APPs become an important channel to provide financial services in the Internet and Big Data era. Factors that restrict its development have attracted the close attention of scholars and practitioners around the world. Different from a general APP software, mobile banking APPs naturally have some financial attributes which set high requirements for personal privacy protection and other security issues. For this reason, this paper extends the original UTAUT2 model by incorporating a new constructor: perceived risks. The study carefully selects China Construction Bank’s mobile banking app (short as “the CCB APP”), as she is the biggest bank in China and has good novelty and representativeness in financial technology. This paper constructs an extended UTAUT2-based model using questionnaires and structural equation modeling the user behavior of CCB APP and tests the influencing factors of the proposed model through empirical analysis. Similar to other studies using the UTAUT2 model, the findings of this paper indicate that performance expectations, effort expectations, social influence, facilitators, and habits, all have a positive impact on CCB APP users’ intentions. Their intention to use was a mediating variable of user behavior, while facilitators directly influenced CCB users’ behavior. However, the study finds that perceived risk negatively affects the CCB users’ intention while hedonic motivation and price value do not significantly affect users’ decision-making process. Based on these findings, the misdirection should to be avoided in regard to financial APP development by financial institutions is discussed and the future research direction concerning mobile banking app adoption is illustrated.

Analysing educational dialogue around shared artefacts in technology-mediated contexts: a new coding framework

ABSTRACT Multimodal interactions that incorporate technology use can support rich new forms of educational dialogue that highlight differences between participants’ perspectives and make reasoning processes more explicit. Learning with technology is mediated by digitally represented knowledge artefacts that participants construct together and manipulate through dialogue. However, there is a need for research into the methods by which researchers and practitioners can understand the learning taking place in such complex environments where verbal and nonverbal interactions with technology are inter-connected and entangled with activities and material learning resources. This paper addresses that need. It offers a critical discussion of the underlying theory and presents a new 13-category coding scheme, ‘Tech-SEDA’. This is the first public version of a systematic, research-informed and practical tool for analysing verbal and nonverbal dialogic communication at the micro- or meso- level across diverse educational settings. Application is illustrated with examples of dialogic interaction across diverse technology contexts by learners located in classrooms or remotely – including online discussions, 3-dimensional modelling in augmented reality, software apps on tablets, Knowledge Forum, Scratch programming, interactive whiteboards, and more. The paper considers the methodological issues arising in capturing dynamic multimodal dialogue and in identifying the learning taking place.

Extraction and empirical evaluation of GUI-level invariants as GUI Oracles in mobile app testing

ContextMobile apps (software) are used in almost all aspects of daily life by billions of people. Given the widespread use of mobile apps in various domains, the demand for systematic testing of their Graphical User Interfaces (GUI) is crucial. Despite the significant advances in automated mobile app testing over the last decade, certain challenges remain, most notably the app-specific GUI test-oracle problem, which can significantly hinder the effective detection of defects in mobile apps. In this study, we introduce the use of GUI-level invariants, referred to as GUI invariants, as app-specific GUI oracles in GUI test cases to address this challenge. MethodsWe propose a semi-automatic solution to extract GUI invariants and use them as app-specific GUI oracles in test cases. We use the mutation testing technique to evaluate the (fault detection) effectiveness of the GUI oracles used. In addition, we evaluate their quality aspects, namely correctness, understandability, and compatibility, from the perspective of human experts using a questionnaire survey. ResultsThe empirical results show that the GUI oracles used are effective and helpful, as they improved the fault-detection effectiveness of the empirical test suites ranging from 18% to 32%. These results also highlight the efficacy of GUI oracles used in identifying various defects, including crashing and non-crashing functional issues, and surpassing the performance of existing tools in fault-detection rates. Additionally, the questionnaire survey outcomes indicate that the GUI oracles used are correct, understandable, and compatible. ConclusionsBased on the empirical results, we can conclude that using GUI invariants as GUI oracles can be useful and effective in mobile app testing.

Wearable sensor and smartphone assisted vestibular physical therapy for multiple sclerosis: usability and outcomes.

Vertigo, dizziness, gaze instability and disequilibrium are highly prevalent in people with MS (PwMS) and head movement induced dizziness is commonly reported. Vestibular physical therapy (VPT) is a specialised, non-invasive and effective therapy for these problems but usually involves travel for the person to a specialist center with both personal and carbon costs. The use of wearable sensors to track head movement and smartphone applications to deliver and track programs has potential to improve VPT in MS. This study investigated the usability and effects of a commercially available digital VPT system (wearable head sensor, smartphone app and clinician software) to deliver VPT to PwMS. A pre/post treatment design was employed and the primary outcome was the System Usability Scale (SUS). Other patient reported outcomes were the Service User Acceptability Questionnaire (SUTAQ), the Patient Enablement Instrument (PEI) and the Dizziness Handicap Inventory (DHI). Physical outcomes measurements included Mini-BESTest (MB), Modified Dynamic Gait Index (mDGI), Gait Speed (GS), Dynamic Visual Acuity (DVA) and head kinematics and symptoms during exercise. Sixteen PwMS (14 female), mean age 44(±14) years were recruited to the study and twelve completed VPT. Mean adherence to exercise, measured digitally was 60% (±18.4). SUS scores were high at 81 (±14) and SUTAQ scores also demonstrated high levels of satisfaction and acceptability of the system. Statistically significant improvements in MB (mean change 2.25; p = 0.004), mDGI (median change 1.00; p = 0.008), DVA (median change -1.00; p = 0.004) were found. Head frequencies significantly improved with concurrent decreased intensity of dizziness during head movements (mean change across 4 gaze stabilization exercises was 23 beats per minute; p < 0.05). Non-significant improvements were seen in DHI (p = 0.07) and GS (p = 0.15). 64.5% of follow up visits were conducted remotely (video or phone), facilitated by the system. This study had two main outcomes and benefits for PwMS. Firstly, we showed that the system used was both acceptable and could be used by PwMS. Secondly, we demonstrated an improvement in a range of dizziness, balance and gait metrics with remotely delivered care. This system has the potential to positively impact on MS physiotherapy service provision with the potential to deliver effective remote care.

Mining software insights: uncovering the frequently occurring issues in low-rating software applications.

In today's digital world, app stores have become an essential part of software distribution, providing customers with a wide range of applications and opportunities for software developers to showcase their work. This study elaborates on the importance of end-user feedback for software evolution. However, in the literature, more emphasis has been given to high-rating & popular software apps while ignoring comparatively low-rating apps. Therefore, the proposed approach focuses on end-user reviews collected from 64 low-rated apps representing 14 categories in the Amazon App Store. We critically analyze feedback from low-rating apps and developed a grounded theory to identify various concepts important for software evolution and improving its quality including user interface (UI) and user experience (UX), functionality and features, compatibility and device-specific, performance and stability, customer support and responsiveness and security and privacy issues. Then, using a grounded theory and content analysis approach, a novel research dataset is curated to evaluate the performance of baseline machine learning (ML), and state-of-the-art deep learning (DL) algorithms in automatically classifying end-user feedback into frequently occurring issues. Various natural language processing and feature engineering techniques are utilized for improving and optimizing the performance of ML and DL classifiers. Also, an experimental study comparing various ML and DL algorithms, including multinomial naive Bayes (MNB), logistic regression (LR), random forest (RF), multi-layer perception (MLP), k-nearest neighbors (KNN), AdaBoost, Voting, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short term memory (BiLSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (BiGRU), and recurrent neural network (RNN) classifiers, achieved satisfactory results in classifying end-user feedback to commonly occurring issues. Whereas, MLP, RF, BiGRU, GRU, CNN, LSTM, and Classifiers achieved average accuracies of 94%, 94%, 92%, 91%, 90%, 89%, and 89%, respectively. We employed the SHAP approach to identify the critical features associated with each issue type to enhance the explainability of the classifiers. This research sheds light on areas needing improvement in low-rated apps and opens up new avenues for developers to improve software quality based on user feedback.

Integration of Myrica rubra-based N-doped carbon dots with Fe3S4 as excellent peroxidase mimics for colorimetric assay and smartphone-based intelligent sensing of p-aminophenol in waters.

To realize the reutilization of waste Myrica rubra in the analytical field, we synthesized Myrica rubra-based N-doped carbon dots (MN-CDs) and further anchored them onto the surface of Fe3S4 to fabricate Fe3S4@MN-CD nanocomposites. The as-fabricated nanocomposites possessed higher peroxidase-mimetic activity than its two precursors, resulting from the synergistic effect between them, and could catalyze colorless 3,3',5,5'-tetramethylbenzidine (TMB) into deep blue oxTMB with a strong 652-nm absorption. Under optimized conditions (initial solution pH, 3.5; incubation temperature, 35 ℃; Fe3S4@MN-CD concentration, 50µg mL-1, and 652-nm absorption), Fe3S4@MN-CDs were employed for colorimetric assay of p-aminophenol (p-AP) with wide linear range (LR, 2.9-100 µM), low detection limit (LOD, 0.87 µM), and satisfactory recoveries (86.3-105%) in environmental waters. Encouragingly, this colorimetric assay provided the relative accuracy of 97.0-99.4% as compared withconventional HPLC-UV detection. A portable smartphone-based colorimetric application was developed by combining the Fe3S4@MN-CD-based visually chromogenic reaction with a "Thing Identify" APP software. Besides, we engineered an image-capturing device feasible for field use, in which the internal-compact sealing prevented external light source from entering photography chamber, thereby reducing light interference, and also the bottom light source enhanced the intensity of blue imaging. This colorimetric platform exhibited satisfactory LR (1-500 µM), low LOD (0.3 µM), and fortification recoveries (86.6-99.6%). In the chromogenic reaction catalyzed by Fe3S4@MN-CDs, ·O2- played a key role in concomitant with the participation of •OH and h+. Both the colorimetric assay and smartphone-based intelligent sensing show great promising in on-site monitoring of p-AP under field conditions.

A Scoping Review of the Research on the Teaching Models of Online International Chinese Language Teaching

The realm of online international Chinese language teaching is undergoing significant transformations propelled by the internet and the pandemic. The digital teaching is the way forward for online international Chinese language teaching. There are significant differences between online international Chinese language teaching and traditional Chinese language teaching. To improve online teaching activities and guide future research, this growing field aims to analyze Chinese international online education research. Specifically, the goal is to develop a sustainable teaching model. By utilizing Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P), the authors analyzed 27 articles proposing a viable teaching model. The review identifies six models of online Chinese language teaching: flipped classroom, APP software, open online platform, online interaction, blended learning, and ChatGPT. The blended learning model is considered the most effective but still faces limitations such as technical issues, demanding teachers, lack of motivation, limited face-to-face interaction, and limited assessment options. Scholars suggest solutions including technical support, teacher training, motivation, improving face-to-face interaction, and using multiple assessment methods. Overall, the blended teaching model has the potential to advance online international Chinese language teaching in the future.

Research on Portable Intelligent Terminal and APP Application Analysis and Intelligent Monitoring Method of College Students' Health Status

As a carrier of college students' health status monitoring, portable intelligent terminal APP, the study of its APP application analysis not only provides a new way for college students' extracurricular physical exercise, guides college students to actively participate in extracurricular physical activities using intelligent terminal APP software, but also promotes college students' physical health monitoring and enhancement in various aspects. Aiming at the current portable terminal APP college students' health monitoring application analysis method research exists low precision, real-time poor and other problems, through the analysis of the basic functional framework and functional characteristics of the portable intelligent terminal APP, the establishment of the portable intelligent terminal APP analysis index system applied to college students' health monitoring, combined with the heuristic optimisation algorithm and the improvement of deep learning algorithms, the construction of the marine predator based heuristic optimisation algorithm and the attention mechanism to improve the gating control loop. Combining the heuristic optimisation algorithm and the improved deep learning algorithm, we construct the portable intelligent terminal APP application analysis method for college students' health monitoring based on the marine predator heuristic optimisation algorithm and the attention mechanism improved gated recurrent unit neural network. Through simulation analysis, the results show that the proposed method meets the real-time requirements while improving the prediction accuracy of the portable smart terminal APP application analysis method, and significantly improves the efficiency of portable smart terminal APP analysis.

Assistive Technology for Higher Education Students with Disabilities: A Qualitative Research

The objective of this qualitative investigation is to identify the assistive technology recognized by students with disabilities and to determine the assistive technology (software apps and devices) they require both at university and at home. A total of forty-two students, comprising 20 males and 22 females, were recruited from four different countries (Germany, Greece, Italy, and Spain) for participation in this study. The sample encompassed 10 students with visual impairments, 11 with hearing impairments, 11 with mobility impairments, and 10 with specific learning disabilities. Semi-structured interviews were conducted with the students either online or in person. Content analysis was employed to scrutinize the data obtained from these interviews. The outcomes of this analysis shed light on the assistive technology acknowledged, utilized, or desired by students with disabilities in both academic and domestic settings. The findings from this study carry practical implications for fostering inclusive and accessible education within higher education institutions, benefiting accessibility units/offices staff as well as teaching personnel.

Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students

With the development of Internet of Things (IoT) technology, the use of running APP to analyse college students' physical fitness indicators has gradually become a commonly used sports analysis method. Aiming at the problems of insufficient precision of the running APP usage analysis method, easy to fall into the local optimum, and insufficiently comprehensive evaluation effect, this paper proposes a running APP usage analysis method based on deep learning network for some college students' physical fitness indicators. Firstly, feature vectors are taken from the running APP user behaviour data to analyse the values of college students' physical fitness indicators and construct a mapping model of running APP usage analysis for the effects of college students' physical fitness indicators; then, the BiGRU neural network is improved by using the driver-training heuristic optimisation algorithm to construct a running APP usage analysis model for some of the college students' physical fitness indicators; finally, a mapping model is constructed for the effect of running APP usage analysis for some of the college students' physical fitness indicators by using college student-oriented running APP Finally, the effectiveness and robustness of the DTBO algorithm are compared with the user behaviour dataset of the running APP for college students. Finally, the effectiveness and robustness of the DTBO algorithm are compared using the user behaviour data set of the running app platform for college students.

Abstract 340: Validation of TSO500 NGS panel for comprehensive genomic profiling from FFPE tumor samples

Abstract Comprehensive genomic profiling (CGP) of tumor sample using large targeted NGS panels (e.g. MSK-IMPACT) has been shown to allow the detection of biomarkers from hundreds of cancer relevant genes involved in targeted and immunotherapy. Illumina TruSight Oncology 500 (TSO500), a commercial CGP assay, allows concurrent analysis of DNA and RNA to simultaneously detect multiple types of variants across 523 genes. The assay is intended to detect single nucleotide variants (SNV), insertions and deletions (indel), and multinucleotide variants (MNV), copy number gain (CNV), tumor mutational burden (TMB) and microsatellite instability (MSI) in DNA, and fusions and splicing variants (exon skipping) in RNA. Here we aimed to validate TSO500 assay for its analytical performance on FFPE tumor samples. The validation samples include commercial controls, cell lines, clinical FFPE from NSCLC, CRC, Uterus and GIST (tumor content ≥20%). Library preparation was performed from minimum of 40 ng DNA and 40ng RNA. The data analysis is performed using TruSight Oncology 500 Local App software (LRM v2.2). A custom script (v1.0) was used to add SNP annotation for small variants and combine data output into cumulative format. For SNV and indel detection QC, 61 of 64 DNA libraries (95%) passed the QC for library size (median insert size ≥70bp) and coverage (median exon coverage ≥150). The percentage of DNA libraries passing QC for CNV and MSI are 95% and 88%, respectively. All 64 RNA libraries passed QC. The analytical performance of TSO500 was summarized in the Table below. In summary, the results showed that all the acceptance criteria were met and the TSO500 assay can be utilized for genomic profiling of FFPE tumor samples. The study also identified a few limitations of the Illumina LRM software e.g. potential false positive SNV within a indel background, potential false negative of complex indel, and the challenge of long indel detection. Summary of analytical performance of TSO500 Sensitivity and specificity Assay LoD Accuracy by concordance Inter-run reproducibility SNV/Small indel Sensitivity for SNV and indel detection are 99.24% and 99.36%. The LoD for SNV is 3-5%, for DEL is 5-10% and for INS is 3-5%. PPA for SNV detections is 96.64% (115/119; 95% CI: 91.68%, 98.69%) and for small indel is 92.86% (13/14; 95% CI: 68.53%, 98.73%) between TSO500 and comparator assays. The positive call rate across all SNV and small indel variants is 98.7% (42842/43419). Specificity for SNV and small indel detection are 99.9998% and 100.00%, respectively. MSI Sensitivity and Specificity for MSI detection are both 100%. NA MSI status for all samples were detected with 100% of concordance by three assays; MSI concordance for MSI-H is 100% (95% CI: 60.97%, 100.0%) and for MSS is 100% (87.13%, 100.0%). PPA is 100% (95% CI: 51.01%, 100%) and NPA is 100% (95% CI: 34.24%, 100%). TMB NA NA TSO500 reported TMB is well correlated and comparable to exome determined TMB. The CV values for samples are acceptable. CNV NA NA All positive CNVs detected by the comparator assay were detected by the TSO500 assay with similar fold changes; The overall reproducible call rate is 91.5% (43/47). PPA between TSO500 and OCAV3 assays is 100% (95% CI: 51.01%, 100%). Fusion Sensitivity and specificity for fusion detection is 91.94% and 100%, respectively. NA All TSO500 assay detected fusions were confirmed by other assays;PPA is 100% (95% CI: 60.97%, 100%) and NPA is 100% (95% CI: 75.75%, 100%) The reproducibility of fusion call is 92.59% (25/27; 95% CI: 76.63%, 97.94%). Splicing Sensitivity and Specificity for splicing variant detection are 91.67% and 100.00%, respectively. NA The splicing variant detections were confirmed by OCAV3, OFA and Archer assays; The overall reproducibility of positive call is 100% with 95% CI (43.85%, 100%) and negative call reproducibility is 100% with 95% CI (87.13%, 100%). PPA is 100% (95%% CI: 20.65%, 100%) and NPA is 100% (95% CI: 83.18%, 100%) Citation Format: Jin Li, Zhenyu Yan, Weihua Liu, Lin Shi, Cynthia Spittle, Chad Galderisi. Validation of TSO500 NGS panel for comprehensive genomic profiling from FFPE tumor samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 340.

The eDNA-Container App: A Simple-to-Use Cross-Platform Package for the Reproducible Analysis of eDNA Sequencing Data

The analysis of environmental DNA (eDNA) is a powerful and non-invasive method for monitoring the presence of species in ecosystems. However, ecologists and laboratory staff can find it challenging to use eDNA analysis software effectively due to the unfamiliar command-line interfaces used by many of these packages. Therefore, we developed the eDNA-container app, a free and open-source software package that provides a simple user-friendly interface for eDNA analysis. The application is based on the popular QIIME2 library and is distributed as a Docker image. The use of Docker makes it compatible with a wide range of operating systems and facilitates the reproducible analysis of data across different laboratories. The application includes a point-and-click user interface for selecting sequencing files, configuring parameters, and accessing the results. Key pipeline outputs, such as sequence quality plots, denoising, and ASV generation statistics, are automatically included in a PDF report. This open-source and freely available analysis package should be a valuable tool for scientists using eDNA in biodiversity and biosecurity applications.

Bibliometric Analysis of Research Papers on Academic Stress (1989-2023).

This extensive study provides a comprehensive overview of the contemporary research landscape about academic stress, emphasizing on identifying the most relevant contributors and understanding prevalent trends. The analysis included 5,375 results from the PubMed database and revealed a consistent upward trajectory with fluctuations in research paper publications over the years. Network analysis and visualization were performed using the Biblioshiny app and VOSviewer software. The analysis identified that the University of Oslo has published the highest number of research papers related to academic stress. In contrast, the Netherlands, the USA, and Australia demonstrated the highest frequency of collaboration. Analysis of keywords and their co-occurrence provides an overview of the research focus and the areas associated with psychological stress due to academics. Thematic evaluation and topic trend analysis provided insights into the evolving nature of research in academic stress. The thematic map depicts two categories of themes - motor themes, including psychological stress, its epidemiology, the influence of the COVID-19 pandemic, and the mental health of university students, particularly those in medical programs; and emerging themes, including oxidative stress and risk factors, indicated evolving areas of interest. A notable observation was the scarcity of research on primary school students, signaling a gap in the existing academic stress literature. Citation analysis identified the most cited authors, countries, universities, and sources. This multifaceted examination provides a nuanced understanding of academic stress research's current state and dynamics, offering valuable insights into trends, collaborations, and thematic shifts that will guide future research in this critical field.

Transformer Monitoring with Electromagnetic Energy Transmission and Wireless Sensing.

To ensure stable and normal transformer operation, light gas protection of the transformer Buchholz relay is essential. However, false alarms related to light gas protection are common, and troubleshooting them often requires on-site gas sampling by personnel. During this time, the transformer's operating state may rapidly deteriorate, posing a safety threat to field staff. To tackle these challenges, this work presents the near-field, thin-sliced transformer monitoring system that uses Electromagnetic Energy Transmission and Wireless Sensing Device (ETWSD). The system leverages external wireless energy input to power gas monitoring sensors. Simultaneously, it employs Near-Field Communication to obtain real-time concentrations of light gases, along with the electrified state and temperature. In field testing conducted on transformer relays' gas collection chambers, the ETWSD effortlessly monitors parameters within warning ranges, encompassing methane gas concentrations around 1000 ppm, leakage voltage ranging from 0-100 V, and relay working temperatures up to 90 °C. Additionally, to facilitate real-time diagnosis for electrical workers, we have developed an Android-based APP software that displays current light gas concentrations, leakage voltage collection values, and temperature, while also enabling threshold judgment, alarms, and data storage. The developed ETWSD is expected to aid on-site personnel in promptly and accurately evaluating transformer light gas protection error alarm faults. It provides a method for simultaneous, contactless, and rapid monitoring of multiple indicators.

Mapping the Evolution of Clinical Trials on Drug-Resistant Tuberculosis: A Bibliometric Overview (1965-2023).

This study provides a comprehensive overview of the current landscape in drug-resistant tuberculosis research. An extensive electronic search was conducted on PubMed and Scopus databases to identify clinical trials related to drug-resistant tuberculosis. Network analysis and visualization were performed on the data using the Biblioshiny App and VOSviewer software. This bibliometric study focuses on revealing publication trends, leading contributors, key institutions, thematic focuses, and citation patterns. The analysis of research paper publications reveals a consistent upward trajectory over the years, characterized by periodic declines and subsequent surges. Noteworthy peaks in 2013 and 2010 are observed in Scopus and PubMed, respectively, followed by marked declines, particularly notable between 2021 and 2023. PubMed and Scopus data indicate that the United States and South Africa are the leading contributors. According to the PubMed and Scopus databases, the University of Cape Town and Stellenbosch University are the institutions that contribute the most. Keyword and thematic analyses underscore the primary research focuses on drug-resistant tuberculosis (DR-TB), including drug combination therapy, microbiological analysis of sputum, therapeutic uses of antitubercular agents, drug resistance (DR), multidrug resistance (MDR), andMycobacterium tuberculosis.The trend-topic analysis reveals dynamic shifts in research focus over time, transitioning from single-drug therapy to addressing drug resistance and highlighting the emerging need for effective drug therapy in cases of multidrug-resistant tuberculosis. Notably, most research papers on drug-resistant tuberculosis are single-country publications. Citation analysis in the Scopus database indicates that the average citation per year and mean total citation per year peaked during 2005-2006. This suggests a period of heightened impact and recognition within the research community during that timeframe. The study's findings may inform strategic planning for combating drug-resistant tuberculosis, ultimately contributing to future enhanced prevention, diagnosis, and treatment strategies.

Comparative Analysis of Smartphones and Survey-Grade GNSS Receivers for Parcel Boundary Determination

This paper advances the existing body of knowledge on the suitability of the accuracy derivable from the use of smartphones for cadastral mapping. Zenvus App software was installed on two smartphones of a different make. A set of dual-frequency GPS Promark 3 receivers and two different smartphones of different makes were used for data acquisition. Observations were carried out at the boundaries of ten parcels of land, comprising 46 boundary points. The coordinates of these points were obtained using Differential Global Positioning System (DGPS) observation in static mode and two Android smartphones (the Samsung A70 and the Tecno Spark 3 Pro). Mean score, root mean square error, and one-way analysis of variance were used to show significant differences in the equipment used. Overall, both the accuracy (mean) and precision (RMSE) were lower than those obtained by Differential GPS. A one-way analysis of variance (ANOVA) was calculated on the values of both X and Y. For X, the analysis was not significant; F (1, 45) = 0.88, p = 0.419 and for Y, the analysis was also not significant; F (1, 45) = 0.97, p = 0.383. The total RMSE shows that the coordinates of points as obtained by the Samsung smartphone (3.368) were more precise than those obtained by Tecno (4.041). However, the two smartphones (Tecno and Samsung) were less accurate than differential GPS. This implies that there is a 95% chance that the errors in the estimates are less than 6.993m (for Tecno) and 5.848m (for Samsung), respectively. The variation in the observations obtainable with smartphones affects both linear and polygon estimates. The study concluded that the magnitude of these errors is significant in cadastral survey practices and hence not suitable for use. It is recommended that further studies be carried out on the use of the Zenvus app on centimeter grade smartphones; probably this could yield a better result suitable for cadastral mapping.

Systematic reviews in mobile app software engineering: A tertiary study

Systematic reviews in mobile app software engineering: A tertiary study