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This paper proposes diary studies as a methodological choice for addressing crucial questions in qualitative process research. Diary studies unearth within-individual relationships that capture individual experiences and sense-making of these experiences in a processual manner. Therefore, using diary studies not only addresses the need for more dynamic methodological designs in qualitative research, but is advantageous for providing a process view of how the individual engages with the constructs studied. While process-studies, studied longitudinally, are often fraught with challenges of attrition or disengagement, this paper discusses how a methodological choice which is perceived as being beneficial by the participant can sustain engagement and consequently, research rigour. On one hand, a comparison of interview and diary data from the same participants show evidence of memory decay in interviews and reinforce the rigour of diary studies in the study of processual phenomenon. On the other hand, this study also showcases how researcher know-how, unintentionally imparted through reflective and processual questions asked in the study positively impacts the participants, and when reflected on, enhanced learning. This paper therefore demonstrates the ability of a research method to benefit both the research and the researched, and suggests a step-by-step design to implement a rigorous and impactful diary study using user-friendly forms and reflective questioning. [ABSTRACT FROM AUTHOR]

Background : The current UK standard for major trauma patients is to record notes in a paper trauma booklet. Through an innovative collaboration between a major trauma centre and a digital transformation industry partner, a TraumaApp was developed. Electronic notes have been shown to have fewer errors, granular data collection and enable time stamped contemporaneous record keeping. Implementation of digital clinical records presents a challenge within the context of trauma multidisciplinary trauma resuscitation. Data can be easily accessible and shared for quality improvement, audit and research purposes. This study compared paper and electronic notes for completeness and for acceptability data following the implementation of the TraumaApp. Methods: Trauma team members who performed scribe function attended training for the newly launched TraumaApp. Two staff members acted as scribe, using either the paper trauma booklet or TraumaApp, and attended major trauma calls. A framework for comparison of paper and electronic notes was created and used for a retrospective review of major trauma patients' notes. Statistical analysis was performed using a two-tailed t-test. Staff using the TraumaApp completed a System Usability Score questionnaire. Results: There was a total of 37 data points for collection per case. The mean numbers collected were paper notes 24.1 of 37 (65.1%) and electronic notes, 25.7 of 37 (69.5%). There was no statistical significance between the completeness of paper and electronic notes. The mean System Usability Score was 68.4. Discussion: Recording accurate patient information during a major trauma call can be challenging and the role of the scribe to accurately record events is critical for immediate and future care. There was no statistically significant difference in completeness of paper and electronic notes, however the mean System Usability Score was 68.4, which is greater than the internationally validated standard of acceptable usability. Conclusion: It is feasible to introduce digital data collection tools enabling accurate record keeping during trauma resuscitation and improve information sharing between clinicians. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method of generating the elemental image array (EIA) for the integral imaging (InIm) based light field display (LFD). Firstly, we reveal the intrinsic voxel spatial distributions of the InIm‐based LFD and analyze how the voxel space affects the display performances such as spatial resolution, depth of field, and smoothness of parallax. Then, we present an algorithm to generate an EIA from a pair of RGB and depth (RGBD) images and an optimally selected voxel space. In the experiments, we tested the display performances of the voxels on different depth planes and obtained results consistent with the theoretical analyses. We also experimented with a computer 3D model and a real‐world scene on two InIm‐based LFD prototypes working in different modes, one in real mode and the other in virtual mode, and obtained favorable 3D display effects. The proposed method simplifies the light field data acquisition and image synthesis process, and supports handling both the virtual and the real‐world 3D scenes. We expect the proposed system to help the practical application of the InIm‐based LFD technology. [ABSTRACT FROM AUTHOR]

Background Research theses can have a significant impact on the development of applied sciences, especially when their results are published. Objective This study aims to determine publication rate of papers extracted from the medical residency and master’s degree theses of Guilan University of Medical Sciences (GUMS) during 2008-2018. Methods This is a descriptive cross-sectional study. The data were collected from the medical residency and master’s degree theses of GUMS from 2008 to 2018. Then, they were searched in international and national databases according to the title, keywords, and names of supervisors, advisors, and students. Results Of 774 theses, 449 (58 %) papers had been extracted and published in journals indexed by ISI/ PubMed (41.2%), Scopus (11.8%), BIOSIS, Chemical Abstract, and Embase (0.44%) and others (46.56%). Most of them (98.66%) were original research. Moreover, in 383(85.3%) of the theses, there was no statistician or data advisor. The median time from defending theses to the publication of a paper from them was two years. Conclusion The publication of papers extracted from the theses of GUMS has increased, and more than 50% of them have been published in journals indexed by ISI, PubMed, and Scopus. However, the factors associated with the publication of papers need to be surveyed. [ABSTRACT FROM AUTHOR]

Applying the concept of diaspora, this paper examines an organization in Kampala, Uganda that utilizes sport to foster cultural belonging and increase livelihood opportunities for refugees. A participatory action research approach was implemented with multiple forms of data collection including semistructured interviews, photovoice, and photocollaging. Findings highlight how sport and livelihoods are used by refugees to resist exclusion and other inequalities in Kampala and to express diaspora space and consciousness. Discussion highlights the usefulness of the concept of diaspora for understanding the intersections of sport, refugees, livelihoods, and, more importantly, to stimulate a homing desire for refugees forcibly displaced. [ABSTRACT FROM AUTHOR]

mHealth applications (apps) are tools that can enhance research by efficiently collecting and storing large amounts of data. However, data collection alone does not lead to change. Innovation and practice change occur through utilisation of evidence. The volume of data collected raises questions regarding utilisation of data by nurses and midwives, and how data from mHealth apps can be used to improve person-centred practice. There is limited empirical evidence and a lack of direction from global health authorities to guide nurses and midwives in this area. To describe strategies for nurses and midwives that could enhance the effective use of data generated by mHealth apps to inform person-centred practice. The purpose of this paper is to stimulate reflection and generate actions for data utilisation when using mHealth apps in nursing research and practice. This discussion paper has been informed by current evidence, the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, and research experience as part of doctoral study. Before engaging in data collection using mHealth apps, nurses and midwives would benefit from considering the nature of the evidence collected, available technological infrastructure, and staff skill levels. When collecting data and interpreting results, use of a team approach supported by engaged leadership and external facilitation is invaluable. This provides support to operate apps, and more importantly use the data collected to inform person-centred practice. This paper addresses the limited available evidence to guide nurses and midwives when using mHealth apps to collect and use data to inform practice change. It highlights the need for appropriate technology, external facilitative support, engaged leadership, and a team approach to collect meaningful evidence using mHealth apps. Clinicians, leaders, and researchers can apply the strategies provided to enhance the use of mHealth apps and ensure translation of evidence into practice. [ABSTRACT FROM AUTHOR]

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This paper presents TimelinePTC, a web-based tool developed to improve the collection and analysis of Pathways to Care (PTC) data in first episode psychosis (FEP) research. Accurately measuring the duration of untreated psychosis (DUP) is essential for effective FEP treatment, requiring detailed understanding of the patient's journey to care. However, traditional PTC data collection methods, mainly manual and paper-based, are time-consuming and often fail to capture the full complexity of care pathways. TimelinePTC addresses these limitations by providing a digital platform for collaborative, real-time data entry and visualization, thereby enhancing data accuracy and collection efficiency. Initially created for the Specialized Treatment Early in Psychosis (STEP) program in New Haven, Connecticut, its design allows for straightforward adaptation to other healthcare contexts, facilitated by its open-source codebase. The tool significantly simplifies the data collection process, making it more efficient and user-friendly. It automates the conversion of collected data into a format ready for analysis, reducing manual transcription errors and saving time. By enabling more detailed and consistent data collection, TimelinePTC has the potential to improve healthcare access research, supporting the development of targeted interventions to reduce DUP and improve patient outcomes. [ABSTRACT FROM AUTHOR]

The global rise of data volume from Zettabyte to Yottabyte raises concerns about effectively managing its use and storage. One concern is dark data, which needs to be tapped and used for critical decision-making and insights generation. This phenomenon is a reality in the data economy that requires a solution through productization. The term data economy refers to the expanding economic activity centered around data collection, analysis, and sale. With the digitization of society and the exponential growth of the internet and social media, individuals, organizations, and society are continuously generating and collecting massive amounts of data. This data can be invaluable for companies and institutions to gain insights into customer behavior, market trends, and the performance of their businesses. Despite the scholarly literature that provides some insights, there remains a need for further exploration and clarification of the intersection between the data economy and productization. This study addresses these unresolved issues by thoroughly analyzing academic and industrial literature. The study contributes to the existing body of knowledge by extending the TTF (Task-Technology Fit) theory to conceptualize the relationship between data economy and productization. The study also proposes a comprehensive framework for countries, organizations, and institutions and offers theoretical and managerial implications. It is important to note that this study has its limitations, and future research should address these limitations and further advance the understanding in this research domain. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper is to enhance understanding of the use of sourcing teams (STs) by organisations in their procurement and supply chain management. The paper achieves this by exploring, within the context of the supply chain directorate of a global aerospace manufacturing company (GAMC), both the relationship between sourcing teamwork effectiveness (TE) and sourcing task-work effectiveness (TA) and the relationship between individual team member knowledge, skills and abilities (KSAs) and TE. Design/methodology/approach: The authors develop a theoretical model positing positive links between both KSAs and TE and TE and TA. The model is empirically validated using partial least squares structural equation modelling in a survey of 108 ST members from a GAMC. Findings: The authors identify that, within GAMC, four of five KSAs drive TE and further discover the direct effects of TE on improved TA. Additionally, the authors observe within GAMC the indirect effects of KSAs on TA cascading through TE. Research limitations/implications: Limitations include the use of a single firm and self-report measures for data collection. In spite of this, the paper has numerous research implications. Previous research on STs has combined factors related to TE and TA. In this paper, TE and TA were disaggregated, and the relationships between them were explored. The relationships were found to be positive within GAMC, a finding that strengthens the evidence base supporting the use of STs by organisations in their procurement and supply chain management. In addition, the paper also strengthened the evidence base regarding the importance of KSAs to TE, which complements existing research highlighting the importance of team-level factors and individual technical attributes. Practical implications: The findings from GAMC suggest that executives/managers should take an individual as well as a team-level perspective when developing STs and should consider KSAs as well as technical knowledge when judging individuals' suitability for inclusion within an ST. There are established KSA tests in the literature that could be used by managers for this task. The findings also inform executives/managers that TE matters for TA and needs attention and investment, especially where sourcing tasks concern high-value areas and/or critical incidents within supply chains. Originality/value: To the best of the authors' knowledge, this is the first paper to explore the relationship between TE and TA. Establishing that this relationship is a positive one provides critically important evidence regarding the efficacy of STs, which are widely used within procurement and supply chain management. It is also a rare study looking at TE from the perspective of individual team member KSAs, with further positive relationships revealed. Both findings enhance what is a very limited literature on a widely used practice within procurement and supply chain management. [ABSTRACT FROM AUTHOR]

Collision accidents involving construction vehicles and workers frequently occur at construction sites. Computer vision (CV) technology presents an efficient solution for collision-risk pre-warning. However, CV-based methods are still relatively rare and need an enhancement of their performance. Therefore, a novel three-stage collision-risk pre-warning model for construction vehicles and workers is proposed in this paper. This model consists of an object-sensing module (OSM), a trajectory prediction module (TPM), and a collision-risk assessment module (CRAM). In the OSM, the YOLOv5 algorithm is applied to identify and locate construction vehicles and workers; meanwhile, the DeepSORT algorithm is applied to the real-time tracking of the construction vehicles and workers. As a result, the historical trajectories of vehicles and workers are sensed. The original coordinates of the data are transformed to common real-world coordinate systems for convenient subsequent data acquisition, comparison, and analysis. Subsequently, the data are provided to a second stage (TPM). In the TPM, the optimized transformer algorithm is used for a real-time trajectory prediction of the construction vehicles and workers. In this paper, we enhance the reliability of the general object detection and trajectory prediction methods in the construction environments. With the assistance afforded by the optimization of the model's hyperparameters, the prediction horizon is extended, and this gives the workers more time to take preventive measures. Finally, the prediction module indicates the possible trajectories of the vehicles and workers in the future and provides these trajectories to the CRAM. In the CRAM, the worker's collision-risk level is assessed by a multi-factor-based collision-risk assessment rule, which is innovatively proposed in the present work. The multi-factor-based assessment rule is quantitatively involved in three critical risk factors, i.e., velocity, hazardous zones, and proximity. Experiments are performed within two different construction site scenarios to evaluate the effectiveness of the collision-risk pre-warning model. The research results show that the proposed collision pre-warning model can accurately predict the collision-risk level of workers at construction sites, with good tracking and predicting effect and an efficient collision-risk pre-warning strategy. Compared to the classical models, such as social-GAN and social-LSTM, the transformer-based trajectory prediction model demonstrates a superior accuracy, with an average displacement error of 0.53 m on the construction sites. Additionally, the optimized transformer model is capable of predicting six additional time steps, which equates to approximately 1.8 s. The collision pre-warning model proposed in this paper can help improve the safety of construction vehicles and workers. [ABSTRACT FROM AUTHOR]

Aiming at the received signal strength index (RSSI) in wireless positioning system, an improved weighted KNN fingerprint positioning algorithm is proposed in this paper. The algorithm pre-processes fingerprint data in offline stage that including eliminating outliers and Kalman filtering first, in order to improve the accuracy of data acquisition. Secondly, the fingerprint data is partitioned by using RSSI to attenuate obstacles such as walls. Then, points with significant RSSI differences in each region are selected as regional feature points, and the distance between RSSI of test points and feature points in each region is calculated respectively to determine the region in which the test points are located. Geometric method is used to analyse and define the correlation degree, and KNN is re-weighted to achieve accurate positioning in the region. Finally, experiments were carried out in the indoor environment to complete the establishment of the fingerprint database. Compared with the existing NN, KNN and WKNN, the experimental analysis results show that the accumulated error and average error are better than the traditional algorithm with the increase of measurement points, which has reference value for the complex environment positioning technology. [ABSTRACT FROM AUTHOR]

Recently, deep learning (DL) based dynamic security assessment (DSA) methods have been very successful. However, although a DSA model can be trained well for a specific topology, it often does not perform well for other topologies. Since the topology in real‐world power systems is frequently changing, the performance reduction of DL‐based DSA methods is very serious, which is a challenging and urgent problem. This paper proposes a novel DSA method based on a convolutional neural network (CNN) to solve this problem. In the proposed method, a strong yet simple domain adaptation approach named adaptive batch normalization (AdaBN) is used, which significantly enhances the extensibility and generalizability of the DSA model when the topology changes and eliminates the need to train a large number of models. This approach achieves a deep adaptation effect by modulating the statistics from the source domain to the target domain in all batch normalization layers across the model. Unlike other domain adaptation methods, this method is parameter‐free, requires no additional components, and has advanced performance despite its simplicity. In addition, this paper introduces TGAN‐based data augmentation to deal with the difficulty of costly data collection and labelling. This data augmentation makes the proposed model applicable to small databases. The test results of the proposed method on IEEE 39‐bus and IEEE 118‐bus systems show that this method can evaluate system dynamic security during topology changes and in the face of data noise with high accuracy. [ABSTRACT FROM AUTHOR]

Over the past few years, the emergence of electrochemical wearable sensors has attracted considerable attention because of their promising application in point‐of‐care testing due to some features such as high sensitivity, simplicity, miniaturization, and low fabrication cost. Recent developments in new fabrication approaches and innovative substrates have resulted in sensors able to real‐time and on‐body measurements. Wearable electrochemical sensors have also been combined with paper‐based substrates and directly used on human skin for different applications for non‐invasive analyses. Furthermore, wearable electrochemical sensors enable monitoring analytes in different biofluids without complex procedures, such as pre‐treatment or sample manipulation. The coupling of IoT to various wearable sensors has also attracted attention due to real‐time data collection and handling in remote and resource‐limited conditions. This mini‐review presents the significant advances in developing wearable electrochemical devices, such as sampling, data collection, connection protocols, and power sources, and discusses some critical challenges for higher performance in this field. We also present an overview of the application of paper as an intelligent substrate for electrochemical wearable sensors and discuss their advantages and drawbacks. Lastly, conclude by highlighting the future advances in wearable sensors and diagnostics by coupling real‐time and on‐body measurements to multiplexed detection of different biomarkers simultaneously, reducing the cost and time of classical analysis to provide fast and complete overall physiological conditions to the wearer. [ABSTRACT FROM AUTHOR]

Ocean research currently relies heavily on bulky bioelectronic tags and sensory telemetry networks for data collection. Therefore, in this study, a compact and flexible waterproof bioelectronic tag with a kirigami (paper cutting)‐based design for underwater conductivity, temperature, depth, and strain sensing is designed and evaluated. Unlike invasive bioelectronic tags, the kirigami bioelectronic tag can be attached to robotic fish or aquatic animals without harming the host. The kirigami‐based design facilitates excellent stretchability and reduces stress within the sensor, thus inhibiting crosstalk induced by movement during multimodal sensing. A working bioelectronic tag is fabricated and tested. Notably, the performance of the kirigami sensors shows very little degradation even after >1000 stretching cycles. Specifically, the multimodal bioelectronic tag demonstrates a wide working pressure range of 0–5.0 MPa, and the kirigami design effectively suppresses signal coupling. The kirigami‐based bioelectronic tag is applied to a robotic dolphin and underwater environmental parameters and detailed movement parameters, such as swing angle, swing frequency, and typical gestures are detected simultaneously with high sensitivity and accuracy. Therefore, the device can be applied to a broad range of applications in various fields such as behavioral biology, ocean informatics, and underwater robotics. [ABSTRACT FROM AUTHOR]

This study conducts a systematic review of salesperson sampling within the pages of the Journal of Personal Selling and Sales Management from 2013 to 2022. A total of 284 papers (all inclusive) were identified in the first step of the review. Next articles were qualified based on article types, methodology (research approach), and respondent type before additional analysis was conducted. This resulted in 109 published manuscripts with 147 unique salesperson samples for further review. Specifically, sample scope and participant profiles were examined based on the 109 articles. The sample scope included response metrics, study deployment, and contextual aspects. Participant profile includes the examination of demographics, experience, work details, and performance. Ensuing the systematic review, managerial importance and implications, best practices, and research insights and future research directions are provided. This includes guidance on the sampling frame overview, data collection, and sample contexts of participants for a better understanding of what information should be included in research studies. In turn, this study puts forth evidence that one type of salesperson sample does not fit all sales research, and the generalizability of the sample should be carefully considered. [ABSTRACT FROM AUTHOR]

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance. [ABSTRACT FROM AUTHOR]

This paper describes a measuring unit for synchronously collecting the air dose rate and measurement position for efficient dosimetry surveying and data logging. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and dosimetry part, which are integrated into a single measuring unit through an embedded computer that installs a ROS (robot operating system) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and measurement position. In this paper, we describe the functional requirements for the measuring unit, the prototype system configuration, and the experimental results obtained in the mockup environment and nuclear facility to discuss its performance. [ABSTRACT FROM AUTHOR]

The increasing frequency and sophistication of cyber-attacks pose significant threats to organizational entities and critical national infrastructure, leading to substantial financial and operational consequences. Detecting such attacks early and accurately remains a complex endeavour, compounded by challenges in intrusion detection system (IDS) design, the exploitation of zero-day attacks, and issues of reliability and resiliency in physical systems. This research addresses these challenges through a two-fold approach: firstly, implementing input data fusion from diverse and heterogeneous sources, and secondly, fusing classifiers from multiple deep learning (DL)-based algorithms. The success of machine learning (ML) and DL models for IDS relies on meticulous data collection and classifier selection. The paper underscores the limitations of relying on single datasets and ML/DL algorithms, emphasizing potential biases and training restrictions. Rigorous experiments were conducted to identify optimal DL architectures, ensuring the creation of models that exhibit robust generalization on new traffic instances, leading to trusted and unbiased results. The study demonstrates the efficacy of the proposed models through comprehensive evaluations and metrics. Results indicate that the fusion of data and classifiers significantly improves model generalization. The paper also outlines key challenges and future trends in data fusion, emphasizing its role in enhancing IDS performance for securing critical infrastructure. [ABSTRACT FROM AUTHOR]

Zadeh introduced fuzzy sets to study imprecision in real life after which many variants of fuzzy sets have been developed in literature. In this paper, new variants of fuzzy sets named Hybrid fuzzy sets and Hybrid intuitionistic fuzzy sets have been defined based on some real life scenarios where conventional fuzzy sets are not applicable for dealing Multi-Criteria Decision Making (MCDM) problems. Further, total ordering algorithms on those proposed sets based on scoring functions are defined and applied in MCDM problems where decision matrix is incomplete with respect to some criteria. The major drawback of MCDM problems is non-availability / missing of data values for some alternatives with respect to some criteria while collecting data from the experts. To overcome this, researchers infuse the missing data for one expert from the corresponding data collected from the other expert or deleting that criteria to form a single complete MCDM table. But there are situations in which decisions should be taken with single MCDM matrix with missing data points. In this paper, we have proposed a new method in which we find the unknown data from known values / other entries of the table using relative performances of the alternatives with respect to other criteria through which we attain a ranking of the alternatives of MCDM using total ordering defined on the hybrid intuitionistic fuzzy sets. Further, we proposed another method in which we obtain the score of missing values based on the scores defined on the hybrid intuitionistic fuzzy sets through which final decision / ranking is made. Finally, the applicabilities of new types of fuzzy sets and total ordering methods are established in the field of Fuzzy decision making by comparing with existing imputation methods in the literature. [ABSTRACT FROM AUTHOR]

Purpose: The rapid growth of the mobile money industry has been matched by a rise in mobile money fraud. The technology required to apprehend perpetrators of such fraud is nonexistent in most developing countries. Hence, the need for individuals to be willing to pay for insurance against such frauds is crucial. This paper aims to examine individuals' willingness to pay for insurance against mobile money fraud in Ghana. Design/methodology/approach: The paper uses nationally representative data collected from 4,266 adults (persons 18 years and above) in Ghana. Individuals' willingness to pay premiums for protection against mobile money fraud was elicited by a single-bound dichotomous choice and open-ended contingent valuation designs. Findings: On average, 24.34% of Ghanaians are willing to pay premiums for insurance against mobile money frauds, with more men (26.37%) being willing than women (22.56%). Similarly, the average monthly premium that men are willing to pay for protection against mobile money fraud is GH¢32.16 (US$8.16), while that of women is GH¢22.5 (US$5.62). Furthermore, the results show that years of schooling, income, previous fraud experience, and using the accounts for saving are all positively associated with willingness to pay. However, using other networks apart from MTN has a negative association with willingness to pay. Originality/value: To the best of our knowledge, this is the first study that examines willingness to pay for insurance against mobile money fraud. Thus, this is the first that estimate quantitatively how much mobile account holders will pay as premiums for insurance against mobile money fraud. [ABSTRACT FROM AUTHOR]

Cooperative multi-agent systems make it possible to employ miniature robots in order to perform different experiments for data collection in wide open areas to physical interactions with test subjects in confined environments such as a hive. This paper proposes a new multi-agent path-planning approach to determine a set of trajectories where the agents do not collide with each other or any obstacle. The proposed algorithm leverages a risk-aware probabilistic roadmap algorithm to generate a map, employs node classification to delineate exploration regions, and incorporates a customized genetic framework to address the combinatorial optimization, with the ultimate goal of computing safe trajectories for the team. Furthermore, the proposed planning algorithm makes the agents explore all subdomains in the workspace together as a formation to allow the team to perform different tasks or collect multiple datasets for reliable localization or hazard detection. The objective function for minimization includes two major parts, the traveling distance of all the agents in the entire mission and the probability of collisions between the agents or agents with obstacles. A sampling method is used to determine the objective function considering the agents' dynamic behavior influenced by environmental disturbances and uncertainties. The algorithm's performance is evaluated for different group sizes by using a simulation environment, and two different benchmark scenarios are introduced to compare the exploration behavior. The proposed optimization method establishes stable and convergent properties regardless of the group size. [ABSTRACT FROM AUTHOR]

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights. [ABSTRACT FROM AUTHOR]

The adoption of artificial intelligence into industrial settings promises notable enhancements in productivity, quality, efficiency, competitiveness, and innovations. However, transitioning AI models from concept to full-scale industrial applications involves various complexities and challenges. These challenges are not only technical but also extend into the ethical and regulatory realms, calling for a comprehensive approach to AI integration. This paper examines the diverse hurdles faced during developing and deploying AI applications in the industrial domain. It addresses challenges in collecting the right data, construction of AI models, and ensuring that these models work accurately and responsibly when deployed in real industrial environment. Furthermore, the paper presents strategic recommendations, underscoring the necessity of ethical considerations and regulatory compliance to effectively overcome these obstacles. We provide guidelines aimed at maximizing AI's benefits in industrial environments while minimizing potential risks. [ABSTRACT FROM AUTHOR]

Background: Hip fractures are a common and debilitating condition posing not only a huge health care but also socioeconomic burden. Surgical management for a neck of femur fracture is typically with arthroplasty in the form of total hip arthroplasty or hip hemiarthroplasty. Serial radiographs are typically performed routinely as part of follow-up to look for complications, although their clinical utility in asymptomatic patients is yet to be validated. Our paper therefore aims to review the utility and necessity of radiographic follow-up following arthroplasty for NOF fractures. Materials and Methods: Patients who underwent operative management for acute fragility neck of femur fractures in the year from 1st January 2018 to 31st December 2018 at the author's institution were identified. All patients who underwent surgery, and had at least one pre and one post-operative plain film radiograph of the affected hip were included in this study. Exclusion criteria included patients who had undergone surgery for chronic fractures, avascular necrosis of the femoral head, mortality within 1 year, peri-prosthetic fractures, pathological fractures from metastases, had concomitant injuries, or had inaccessible or incomplete records. Clinical records were assessed for the number of visits, an abnormal presenting history or clinical examination, as well as changes in management of the patient. The number and type of radiographs were also assessed, and each radiograph analyzed for abnormal findings. Results: A total of 157 patients were included in our study with a mean age of 79.5 at the time of surgery, and a mean follow up of 17.3 months. Data was collected from 626 clinical visits and a total of 973 radiographs. The 3 abnormal radiographic series identified with a corresponding normal consult did not result in a change of management for the patient. A negative change in management was only observed in 1 patient with an abnormal consult and a corresponding normal radiograph. Conclusion: Post-operative complications following arthroplasty for NOF fractures are likely to result in a symptomatic presentation of the patient. Routine radiographic follow-up provides limited utility in asymptomatic patients and should only be performed if clinically indicated. [ABSTRACT FROM AUTHOR]

Background: Collection of detailed dietary data is labor intensive and expensive, harmonization of existing data sets has been proposed as an effective tool for research questions in which individual studies are underpowered. Methods: In this paper, we describe the methodology used to retrospectively harmonize nutritional data from multiple sources, based on the individual participant data of all available studies, which collected nutritional data in Israel between 1963 and 2014. This collaboration was established in order to study the association of red and processed meat with colorectal cancer. Two types of nutritional questionnaires, the Food Frequency Questionnaires (FFQ) and the 24-h dietary recall (24HR recall), and different food composition tables, were used by the participating studies. The main exposure of interest included type of meat (total meat, red meat, and poultry) and level of processing. Results: A total of 29,560 Israeli men and women were enrolled. In studies using FFQ,the weighted mean intakes of total, red, processed meat, and poultry were 95, 27, 37 and 58 gr/day and 92, 25, 10, and 66 gr/day in studies using 24HR recall, respectively.. Despite several methodological challenges, we successfully harmonized nutritional data from the different studies. Conclusions: This paper emphasizes the significance and feasibility of harmonization of previously collected nutritional data, offering an opportunity to examine associations between a range of dietary exposures and the outcome of interest, while minimizing costs and time in epidemiological studies. [ABSTRACT FROM AUTHOR]

Online crowdsourcing platforms have rapidly become a popular source of data collection. Despite the various advantages these platforms offer, there are substantial concerns regarding not only data validity issues, but also the ethical, societal, and global ramifications arising from the prevalent use of online crowdsourcing platforms. This paper seeks to expand the dialogue by examining both the "internal" aspects of crowdsourcing research practices, such as data quality issues, reporting transparency, and fair compensation, and the "external" aspects, in terms of how the widespread use of crowdsourcing data collection shapes the nature of scientific communities and our society in general. Online participants in research studies are informal workers who provide labor in exchange for remuneration. The paper thus highlights the need for researchers to consider the markedly different political, economic, and socio-cultural characteristics of the Global North and the Global South when undertaking crowdsourcing research involving an international sample; such consideration is crucial for both increasing research validity and mitigating societal inequities. We encourage researchers to scrutinize the value systems underlying this popular data collection research method and its associated ethical, societal, and global ramifications, as well as provide a set of recommendations regarding the use of crowdsourcing platforms. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence of things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies and reducing costs. Utilizing a three-tiered cloud architecture, the AIoT system enables real-time data acquisition from sensors embedded in aircraft systems, followed by machine learning algorithms to analyze and interpret the data for proactive decision-making. This research examines the evolution from traditional to AIoT-enhanced monitoring, presenting a comprehensive architecture integrated with satellite communication and 6G technology. The mathematical models quantifying the benefits of increased diagnostic depth through AIoT, covering aspects such as predictive accuracy, cost savings, and safety improvements are introduced in this paper. The findings emphasize the strategic importance of investing in AIoT technologies to balance cost, safety, and efficiency in aviation maintenance and operations, marking a paradigm shift from traditional health monitoring to proactive health management in aviation. [ABSTRACT FROM AUTHOR]

Objective: To scope how the Australian Bureau of Statistics Socio-Economic Indexes for Areas (SEIFA) has been applied to measure socio-economic status (SES) in peer-reviewed cardiovascular disease (CVD) research. Methods: The Joanna Briggs Institute's scoping review methodology was used. Results: The search retrieved 2788 unique citations, and 49 studies were included. Studies were heterogeneous in their approach to analysis using SEIFA. Not all studies provided information as to what version was used and how SEIFA was applied in analysis. Spatial unit of analysis varied between studies, with participant postcode most frequently applied. Study quality varied. Conclusions: The use of SEIFA in Australian CVD peer-reviewed research is widespread, with variations in the application of SEIFA to measure SES as an exposure. There is a need to improve the reporting of how SEIFA is applied in the methods sections of research papers for greater transparency and to ensure accurate interpretation of CVD research. What is known about the topic? A socio-economic status (SES) gradient is well established for cardiovascular disease (CVD). Research has generally applied two approaches to classifying SES: at an individual level using income, education or occupation data, and at an area level using a range of existing socio-economic information, including the Australian Bureau of Statistics (ABS) Socio-Economic Indexes for Areas (SEIFA). What does this paper add? This review examined how SEIFA has been applied to measure SES in Australian peer-reviewed CVD research and to identify any variations in research practice. What are the implications for practitioners? It is recommended that researchers provide a clear explanation in the methods section of research papers as to which SEIFA version and index was applied, how it was applied, at what spatial unit, and whether the spatial unit was an ABS or non-ABS unit. [ABSTRACT FROM AUTHOR]

Anomaly detection systems based on artificial intelligence (AI) have demonstrated high performance and efficiency in a wide range of applications such as power plants and smart factories. However, due to the inherent reliance of AI systems on the quality of training data, they still demonstrate poor performance in certain environments. Especially in hazardous facilities with constrained data collection, deploying these systems remains a challenge. In this paper, we propose Generative Anomaly Detection using Prototypical Networks (GAD-PN) designed to detect anomalies using only a limited number of normal samples. GAD-PN is a structure that integrates CycleGAN with Prototypical Networks (PNs), learning from metadata similar to the target environment. This approach enables the collection of data that are difficult to gather in real-world environments by using simulation or demonstration models, thus providing opportunities to learn a variety of environmental parameters under ideal and normal conditions. During the inference phase, PNs can classify normal and leak samples using only a small number of normal data from the target environment by prototypes that represent normal and abnormal features. We also complement the challenge of collecting anomaly data by generating anomaly data from normal data using CycleGAN trained on anomaly features. It can also be adapted to various environments that have similar anomalous scenarios, regardless of differences in environmental parameters. To validate the proposed structure, data were collected specifically targeting pipe leakage scenarios, which are significant problems in environments such as power plants. In addition, acoustic ultrasound signals were collected from the pipe nozzles in three different environments. As a result, the proposed model achieved a leak detection accuracy of over 90% in all environments, even with only a small number of normal data. This performance shows an average improvement of approximately 30% compared with traditional unsupervised learning models trained with a limited dataset. [ABSTRACT FROM AUTHOR]

Data acquisition systems that receive analog signals, convert them to digital, and perform signal processing are used in a variety of systems that use acoustics, radar, sonar, indoor localization, and navigation. The previous systems, such as NI USRP-RIO, are expensive to build, and the number of signals a single device can receive is limited to between two and four. In order to receive more channels of signals, multi-channel data acquisition systems using ADCs operating at tens of MSPS have been proposed. However, these systems require additional processing time because data acquisition and signal processing are performed on different devices. In this paper, we propose a multi-channel data acquisition system using a 16-channel ADC that can support up to 100 MSPS. In particular, to reduce unnecessary signal transmission time, we propose a one-chip structure where all processes are performed on a single chip. Also, we propose a data acquisition system that applies pipelining techniques to enable real-time processing. To verify the proposed system, we used TI ADS52J90 and a Kintex UltraScale KCU105 evaluation board, and confirmed that it is possible to receive and process all channels simultaneously. Furthermore, it is possible to configure a real-time system by adjusting the speed of the signal-processing operation and the speed of the communication interface. Therefore, the proposed system is expected to reduce the cost of system construction by performing signal reception and processing with a single chip, and to reduce the time required for overall signal processing. [ABSTRACT FROM AUTHOR]

This paper revolves around the concept of start-ups in IT Cities of India. Start-ups are mostly created based on creative and innovative ideas and grow to achieve success. Start-up companies are newly established companies which generally strive for existence due to some challenges. The basic purpose of this paper is to put some light on the factors influencing the success of startups in IT Cities of India. factors include Government support, Incubation support and financial support which is again sub divided into various sub factors having an influence on the success of startups in IT Cities of India. An attempt is made to review the literature and based on which primary data were collected from various Start-up Unit, start- up owner, CEO through questionnaire and the total of 309 respondents have been analyzed. Based on data collected all the three factors have been analyzed and proposed some suggestions and strategies. [ABSTRACT FROM AUTHOR]

This research paper investigates the challenges encountered by Bachelor of Science in English (BS English) students in analyzing literature. It focuses on difficulties such as recognizing symbolism, understanding historical context, identifying literary devices, grasping narrative voice, interpreting stage directions, discerning intended meanings, and analyzing plot structure. A mixed-methods approach was employed, involving a comprehensive literature review and qualitative data collection through questionnaire conducted among BS English students. The literature review provided a theoretical framework for understanding the complexities of literary analysis, while the qualitative data collection offered insights into the practical experiences and perceptions of students. The findings reveal several noteworthy trends. Students often struggle to recognize symbolism, frequently misinterpreting or overlooking its significance in literary texts. Understanding the historical context of works proves challenging, with many students failing to connect the socio-political climate of the author's time with the themes of the text. Based on these findings, recommendations are proposed to investigate how cultural background influence the challenges encountered by BS English students in literature analysis, considering factors such as language proficiency and exposal to diverse literary traditions. [ABSTRACT FROM AUTHOR]

Objective: In this paper we report how the United Kingdom's National Institute for Health and Care Excellence (NICE) search filters for treating and managing COVID-19 were validated for use in MEDLINE (Ovid) and Embase (Ovid). The objective was to achieve at least 98.9% for recall and 64% for precision. Methods: We did two tests of recall to finalize the draft search filters. We updated the data from an earlier peer-reviewed publication for the first recall test. For the second test, we collated a set of systematic reviews from Epistemonikos COVID-19 L.OVE and extracted their primary studies. We calculated precision by screening all the results retrieved by the draft search filters from a targeted sample covering 2020-23. We developed a gold-standard set to validate the search filter by using all articles available from the "Treatment and Management" subject filter in the Cochrane COVID-19 Study Register. Results: In the first recall test, both filters had 99.5% recall. In the second test, recall was 99.7% and 99.8% in MEDLINE and Embase respectively. Precision was 91.1% in a deduplicated sample of records. In validation, we found the MEDLINE filter had recall of 99.86% of the 14,625 records in the gold-standard set. The Embase filter had 99.88% recall of 19,371 records. Conclusion: We have validated search filters to identify records on treating and managing COVID-19. The filters may require subsequent updates, if new SARS-CoV-2 variants of concern or interest are discussed in future literature. [ABSTRACT FROM AUTHOR]

What are the waste management methods' historical places? In order to provide answers from earlier study, the current research work aims to investigate these important questions. The paper employed a desktop approach to provide answers to the research objectives. The work used a descriptive methodology to collect data from peer-reviewed sources, including books, journal articles and reports from environmental organizations. It was found that, waste is to a large extent subjective in meaning as a substance can only be regarded as a waste when the owner labels it as such. This is particularly relevant as different people may view substances differently as a waste, while another may view the same substance as a resource. Nevertheless, it was argued that there is a need to clearly define what constitute wastes as this form the basis for regulation. [ABSTRACT FROM AUTHOR]

Global science is set to experience different times in the 2020s. China surpasses the USA in terms of the number of scientific papers in 2020 in most scientific databases. This scenario is expected to have implications not only in East Asia but also beyond the region. Against this backdrop, this paper investigates the scientific influence of the US-China competition on six major Muslim-majority science systems. Multiple data sources are used to collect data for the bibliometric analyses, which include the trend analysis of collaboration patterns, discipline-based collaborations, authorship patterns for responsibility in collaborations, and citation premium of collaborations with the USA versus China. All the analyses consistently demonstrate that the USA is losing its scientific influence on the selected Muslim-majority science systems to China. Analysis results indicate an astonishing increase in collaborations with China-based scientists in the last decade. Half of the selected Muslim-majority science systems collaborate more with China-based scientists, whereas the other half collaborate more with the USA in 2021, indicating a fifty-fifty split. The collaborations with China-based authors garner higher citation premiums for the selected Muslim-majority science systems than the collaborations with the USA. [ABSTRACT FROM AUTHOR]

The article presents a new technique for analyzing phenomena occurring during the measurement of the strength properties of paper in the conditions of compression of the tested samples with forces acting in the paper plane. The technique is based on collecting data on the current distance of the clamps holding the tested sample and the force exerted on the sample using a universal testing machine and on the simultaneous recording of image sequence of the sample during the measurement. Next, the resulting images are subjected to processing and analysis, the purpose of which is to extract information about the shape of the sample edge in all phases of the measurement. Its advantage is the ability to determine the deflection arrow of the sample and describe its shape using the selected function given by the analytical parametric formula. It will be helpful in further research on the development of an analytical model describing the phenomena occurring during paper compression, and a method to determine the mechanism of paper destruction and the corresponding maximum force that destroys a paper sample. [ABSTRACT FROM AUTHOR]

Background: Novel survey methods are needed to tackle declining response rates. The 2020 National Maternity Survey included a randomised controlled trial (RCT) and social media survey to compare different combinations of sampling and data collection methods with respect to: response rate, respondent representativeness, prevalence estimates of maternity indicators and cost. Methods: A two-armed parallel RCT and concurrent social media survey were conducted. Women in the RCT were sampled from ONS birth registrations and randomised to either a paper or push-to-web survey. Women in the social media survey self-selected through online adverts. The primary outcome was response rate in the paper and push-to-web surveys. In all surveys, respondent representativeness was assessed by comparing distributions of sociodemographic characteristics in respondents with those of the target population. External validity of prevalence estimates of maternity indicators was assessed by comparing weighted survey estimates with estimates from national routine data. Cost was also compared across surveys. Results: The response rate was higher in the paper survey (n = 2,446) compared to the push-to-web survey (n = 2,165)(30.6% versus 27.1%, difference = 3.5%, 95%CI = 2.1–4.9, p < 0.0001). Compared to the target population, respondents in all surveys were less likely to be aged < 25 years, of Black or Minority ethnicity, born outside the UK, living in disadvantaged areas, living without a partner and primiparous. Women in the social media survey (n = 1,316) were less representative of the target population compared to women in the paper and push-to-web surveys. For some maternity indicators, weighted survey estimates were close to estimates from routine data, for other indicators there were discrepancies; no survey demonstrated consistently higher external validity than the other two surveys. Compared to the paper survey, the cost saving per respondent was £5.45 for the push-to-web survey and £22.42 for the social media survey. Conclusions: Push-to-web surveys may cost less than paper surveys but do not necessarily result in higher response rates. Social media surveys cost significantly less than paper and push-to-web surveys, but sample size may be limited by eligibility criteria and recruitment window and respondents may be less representative of the target population. However, reduced representativeness does not necessarily introduce more bias in weighted survey estimates. [ABSTRACT FROM AUTHOR]

In this study, we aimed to understand and reduce the difference between self-report in a survey and the actual behavior. Thus, we investigated whether such a difference was caused by participants who engaged in insufficient effort responding (IER), which has been receiving increasing research attention. We collected and analyzed data of actual and self-reported smartphone game usage from behavior logs and survey responses including the items associated with the IER scale, respectively. The results confirmed a strong tendency of overreporting and low correlations between the behavior log and survey responses for IER participants. Although the distributions of survey responses differed between IER participants and others, those of the behavior log did not. In conclusion, when IER participants are excluded, the difference between the behavior log and survey responses reduces, but the distribution of the actual behavior remains the same without selection bias. [ABSTRACT FROM AUTHOR]

The data paper is an emerging academic genre that focuses on the description of research data objects. However, there is a lack of empirical knowledge about this rising genre in quantitative science studies, particularly from the perspective of its linguistic features. To fill this gap, this research aims to offer a first quantitative examination of which rhetorical moves—rhetorical units performing a coherent narrative function—are used in data paper abstracts, as well as how these moves are used. To this end, we developed a new classification scheme for rhetorical moves in data paper abstracts by expanding a well‐received system that focuses on English‐language research article abstracts. We used this expanded scheme to classify and analyze rhetorical moves used in two flagship data journals, Scientific Data and Data in Brief. We found that data papers exhibit a combination of introduction, method, results, and discussion‐ and data‐oriented moves and that the usage differences between the journals can be largely explained by journal policies concerning abstract and paper structure. This research offers a novel examination of how the data paper, a data‐oriented knowledge representation, is composed, which greatly contributes to a deeper understanding of research data and its publication in the scholarly communication system. [ABSTRACT FROM AUTHOR]

With the advancement of mobile technology, mobile-assisted language learning (MALL) has significant potential regarding its practical applications and benefits in foreign language learning. Nevertheless, little research was conducted to examine factors regarding the facilitation of oral communication through MALL based on established theories or models. The purpose of this review is to investigate the main constituents conducive to the intended outcomes based on an adapted model from Beatty (2010) and further provide guidelines for enthusiastic stakeholders in the field. Twenty-eight empirical studies were collected and categorized according to four key variables of the design model and analyzed qualitatively, with the key findings identified as follows. Due to the technical affordance of mobile technology, it is found that a student-centered self-regulated learning context is created, where students construct knowledge through self-instruction, self-evaluation, and self-correction. Furthermore, a speaking strategy-driven collaborative-based learning design enhances students' oral proficiency through strong social connections, interactions, and communication. Finally, given the pedagogical design and practices, high-level cognitive thinking is thereby promoted, with promising affective learning outcomes. In light of the findings, guidelines for educational practitioners, learners, and system designers are provided for pedagogical and practical application in the future. [ABSTRACT FROM AUTHOR]

This article introduces the concept of load aggregation, which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems. The online identification method is a computer-involved approach for data collection, processing, and system identification, commonly used for adaptive control and prediction. This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration, aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods. The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics, economic efficiency, and comfort. The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes, the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57, indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term. Overall, the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective. [ABSTRACT FROM AUTHOR]

Background: Liver disease is an increasing cause of morbidity and mortality in the United Kingdom and can be challenging to live with in the advanced stages. There has been little research exploring the healthcare experiences of UK individuals with decompensated disease when the liver cannot carry out its functions properly. A PhD research project was developed with people who have liver disease to explore care experiences in decompensated advanced liver disease. Public involvement (PI) is an essential aspect of meaningful health research, and this paper reports on the progression of our PI approach in this ongoing study. Objective: To embed PI throughout the research project to ensure that the study is meaningful to individuals with liver disease and the people who support them. Methods: The research adopts a Constructivist Grounded Theory methodology to develop a theory of care experience. Various PI approaches were considered in developing the PI strategy for this qualitative study. Initially, Embedded consultation was the preferred model, which has evolved to include aspects of collaboration and coproduction. A PI group was set up to oversee the project through the national public engagement website VOICE, and reflections on PI from three members of the group are included in this paper to illuminate the PI process. Results: Six individuals with liver disease and three carers from across the United Kingdom are part of an ongoing PI group. Their role includes commenting on the findings of the systematic literature review for this project and contributing to decisions about recruitment, data collection and data analysis. Additionally, they had a direct impact on changing the focus of the research. The PI group will continue involvement until the completion of the project. Conclusion: Successfully embedding PI into doctoral research, as demonstrated in this project, requires commitment, planning and dedication to reciprocal working for the benefit of PI contributors as well as the research. This approach could be adopted by other postgraduate researchers. Patient or Public Contribution: This project is overseen by the PI group, whose contribution is described throughout, including reflections from three PI group members. [ABSTRACT FROM AUTHOR]

To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper. [ABSTRACT FROM AUTHOR]