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We consider the public‐good aspect of platform's data collection on users. Data have commercial benefits to the platform, personal benefits to the user, and public benefits to other users. We ask who should decide which data the platform commercializes. We find that the answer depends on the type of heterogeneity in the disutility of data commercialization. When heterogeneity is across users (data items) and the public benefit of data is high (low), it is welfare enhancing to let the platform (users) control the data. Furthermore, dynamic data accumulation strengthens our results. [ABSTRACT FROM AUTHOR]

Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection. [ABSTRACT FROM AUTHOR]

Access to water has improved globally; however, nearly 800 million people continue to lack access, particularly in resource constrained contexts. Current efforts are not on track to meet the Sustainable Development Goals aim of safe and affordable drinking water for all. Innovative and contextualized solutions are required. This article describes a university–government collaboration in Ethiopia that addressed key data limitations, without which equity-based decision-making was not possible. These efficient and cost-effective ways for obtaining and managing data support improved decision-making, and also identified avenues for improving water system governance through policy shifts and citizen participation. [ABSTRACT FROM AUTHOR]

Taking the example of conspiracy-related communication online as one form of contentious politics, this study examines the data collection challenges for multidimensional comparative research across platforms, time, and cultural embeddings. It compares the architectures and features relevant to data collection, access regimes, and use cultures for a set of digital platforms and communication venues. Differentiating between actor- and content-based strategies, this study discusses the potentials and limitations of these approaches, considering differences in platforms, temporal dynamics, and cultural embeddings as well as several layers of equivalence. The discussion highlights crucial insights into designing data collection strategies in multidimensional comparative studies. [ABSTRACT FROM AUTHOR]

Understanding the clinical spectrum of SARS‐CoV‐2 infection, including the asymptomatic fraction, is important as asymptomatic individuals are still able to infect other individuals and contribute to ongoing transmission. The WHO Unity Household transmission investigation (HHTI) protocol provides a platform for the prospective and systematic collection of high‐quality clinical, epidemiological, serological and virological data from SARS‐CoV‐2 confirmed cases and their household contacts. These data can be used to understand key severity and transmissibility parameters—including the asymptomatic proportion—in relation to local epidemic context and help inform public health response. We aimed to estimate the asymptomatic proportion of SARS‐CoV‐2 Omicron variant infections in Unity‐aligned HHTIs. We conducted a systematic review and meta‐analysis in alignment with the PRISMA 2020 guidelines and registered our systematic review on PROSPERO (CRD42022378648). We searched EMBASE, Web of Science, MEDLINE and bioRxiv and medRxiv from 1 November 2021 to 22 August 2023. We identified 8368 records, of which 98 underwent full text review. We identified only three studies for data extraction, with substantial variation in study design and corresponding estimates of the asymptomatic proportion. As a result, we did not generate a pooled estimate or I2 metric. The limited number of quality studies that we identified highlights the need for improved preparedness and response capabilities to facilitate robust HHTI implementation, analysis and reporting, to better inform national, regional and global risk assessments and policymaking. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: The Royal Canadian Mounted Police (RCMP) Study includes longitudinal multimodal assessments of RCMP cadets from pre-training (i.e., starting the Cadet Training Program [CTP]) to post-deployment and for five years thereafter. The data allow for investigating the multidimensionality of volitional participation in digital health data collection frameworks within serial data collection platforms and the impact of participation inequalities by classifying cadets using the 90-9-1 rule. By classifying cadets as Lurkers, Contributors, and Superusers formally described by the 90-9-1 rule, where 90% of actors do not participate, 9% seldom contribute, and 1% contribute substantially allows for the assessing of relationships between participation inequalities in self-monitoring behaviors as well as whether mental health disorder symptoms at pre-training (i.e., starting the CTP) were associated with subsequent participation. Methods: Participants were asked to complete a Full Assessment prior to their training at CTP, as well as short daily surveys throughout their training. Participation frequency was described using a process where participants were rank ordered by the number of daily surveys completed and classified into one of three categories. Full assessment surveys completed prior to their training at CTP included screening tools for generalized anxiety disorder (GAD), major depressive disorder (MDD), posttraumatic stress disorder (PTSD), alcohol use disorder (AUD), and panic disorder (PD). The Kruskal-Wallis H test was used to assess differences in participation rates between mental health disorder symptom screening groups for each measure at pre-training, and Spearman's Rho was used to test for associations amongst self-reported Full Assessment screening tool responses and the number of daily surveys completed during CTP. Results: There were 18557 daily survey records collected from 772 participants. The rank-ordering of cadets by the number of daily surveys completed produced three categories in line with the 90-9-1 rule: Superusers who were the top 1% of cadets (n=8) and produced 6.4% of all recordings; Contributors who were the next 9% of cadets (n=68) and produced 49.2% of the recordings; and Lurkers who were the next 90% of cadets (n=695) and produced 44.4% of daily survey recordings. Lurkers had the largest proportion of positive screens for selfreported mental health disorders at pre-training. Conclusion: The creation of highly individualized, population-based mental health injury programs has been limited by an incomplete understanding of the causal relationships between protective factors and mental health. Disproportionate rates of disengagement from persons who screen positive for mental health disorders further compounds the difficulty in understanding the relationships between training programs and mental health. The current results suggest persons with mental health challenges may be less likely to engage in some forms of proactive mental health training. The current results also provide useful information about participation, adherence, and engagement that can be used to inform evidence-based paradigm shifts in health-related data collection in occupational populations. [ABSTRACT FROM AUTHOR]

In September 2017, two strong earthquakes hit the central region of Mexico, producing substantial damage to the historical buildings. A retroactive analysis for assessing the pre-event seismic vulnerability of these constructions allowed for testing the suitability of an existing parameter-based approach based on material and geometrical features. More than 160 adobe buildings in four municipalities of the State of Morelos were surveyed and included in a vulnerability-oriented GIS database. Data were collected on-site and managed by resorting to open-source GIS software combined with a Python-based database management tool and a cloud-based platform for onsite data collection using mobile devices. The parameter-based approach was used for assessing the analytical seismic vulnerability of the buildings and implementing a secondary, more conservative assessment that considers uncertainties associated with the data acquisition process. The capabilities of the database were further used to train a Machine Learning algorithm aimed at overcoming some representativeness limitations of the parameter-based analytical method. This third approach was found to be suitable for assessing the vulnerability of the building typologies addressed in this investigation. Although the implementation discussed in this paper is limited to a specific vernacular typology, it can be used to conduct customized local calibrations. [ABSTRACT FROM AUTHOR]

In the book review titled "Digital working lives: worker autonomy and the gig-economy" by Tim Christiaens, the author explores the impact of the gig economy on worker autonomy. Christiaens argues that the gig economy has negative consequences for worker autonomy and criticizes existing political theories for failing to address this issue adequately. The book provides a diagnostic analysis of the problems with gig work, drawing on post-workerist thought, and proposes an alternative concept of "convivial autonomy" as a solution. While the book offers a valuable critique and vision for addressing the problems of gig work, there are some tensions and unresolved issues in the proposed concept of convivial autonomy. [Extracted from the article]

Data collection is one of the initial steps in almost all research practices. This study describes the challenges and issues faced by a researcher, in a developing country like Pakistan, during the execution of time-use research on nurses and paramedics, working in public sector hospitals of District Bahawalpur. We collected the data from the year 2018 to 2021, in three phases from 364 participants. Obstacles faced included: non-cooperative behavior of health department officials, non-response, reluctance, disinformation and misinterpretation on behalf of participants during working hours or after working hours and limited resources regarding funds and time. This paper also highlights the social and religious restrictions & obligations of being a female, Muslim researcher in fieldwork. It also includes the problems faced during COVID-19 confinement. This article may help novice researchers, especially from developing countries who are planning to execute field research on nurses and paramedics. [ABSTRACT FROM AUTHOR]

The current review explores multiple definitions of school shootings used by myriad data collection platforms and by various scholars. Importantly, the impacts of definitional discrepancies on inclusion criteria, data divergence, research, policy, and public perception are discussed at length. The review concludes with a call to Criminologists and school gun violence scholars to better collaborate on what should be considered a "school shooting" and lists five benefits that may result from modifying school gun violence definitions and data collection methodologies. [ABSTRACT FROM AUTHOR]

Data collection using Mobile Sink (MS) is one of the best approaches to address the hot spot issue resulting from multihop data collection and extend the lifetime of Wireless Sensor Networks wherein the MS tours a few specific locations called sojourn locations that serve as data collecting points (DCPs). The best choice of these locations is an NP-hard problem, and the optimum or nearly optimum results can be achieved by applying meta-heuristic optimization methods. It is challenging to create an effective algorithm that allows MS for data collection irrespective of the network topology changes caused by node failures since these changes affect node coverage, data transmission, and network lifespan. Hence, an effort must be made to ensure a trade-off between the MS trajectory and the number of hops. Different MS-based techniques have been proposed; however, most of them fell short of addressing the above goals. With this inspiration, we propose iCapS-MS, which is an integrated approach that utilizes an improved Capuchin Search Algorithm (iCapSA) to determine the best set of DCPs and enhanced Ant Colony Optimization (e-ACO)-based MS trajectory design. Using iCapSA, the best DCPs are selected such that almost every node is served in one-hop communication with the shortest feasible hop distance and minimum coverage intersection between DCPs. The best trajectory for MS is established using e-ACO method. The results demonstrate that iCapS-MS outperforms existing methods based on several performance metrics. [ABSTRACT FROM AUTHOR]

Introduction: Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning methodologies, researchers have created various algorithms to automatically score and extract clinically relevant features from polysomnography, but less research has been devoted to how exactly the algorithms should be incorporated into the workflow of sleep technologists. This paper presents a sophisticated data collection platform developed under the Sleep Revolution project, to harness polysomnographic data from multiple European centers. Methods: A tripartite platform is presented: a user-friendly web platform for uploading three-night polysomnographic recordings, a dedicated splitter that segments these into individual one-night recordings, and an advanced processor that enhances the one-night polysomnography with contemporary automatic scoring algorithms. The platform is evaluated using real-life data and human scorers, whereby scoring time, accuracy, and trust are quantified. Additionally, the scorers were interviewed about their trust in the platform, along with the impact of its integration into their workflow. Results: We found that incorporating AI into the workflow of sleep technologists both decreased the time to score by up to 65 min and increased the agreement between technologists by as much as 0.17 κ. Discussion: We conclude that while the inclusion of AI into the workflow of sleep technologists can have a positive impact in terms of speed and agreement, there is a need for trust in the algorithms. [ABSTRACT FROM AUTHOR]

This is a first-of-its-kind dataset containing detailed purchase histories from 5027 U.S. Amazon.com consumers, spanning 2018 through 2022, with more than 1.8 million purchases. Consumer spending data are customarily collected through government surveys to produce public datasets and statistics, which serve public agencies and researchers. Companies now collect similar data through consumers' use of digital platforms at rates superseding data collection by public agencies. We published this dataset in an effort towards democratizing access to rich data sources routinely used by companies. The data were crowdsourced through an online survey and shared with participants' informed consent. Data columns include order date, product code, title, price, quantity, and shipping address state. Each purchase history is linked to survey data with information about participants' demographics, lifestyle, and health. We validate the dataset by showing expenditure correlates with public Amazon sales data (Pearson r = 0.978, p < 0.001) and conduct analyses of specific product categories, demonstrating expected seasonal trends and strong relationships to other public datasets. [ABSTRACT FROM AUTHOR]

Background: Patients who have had a negative experience with the health care delivery bypass primary healthcare facilities and instead seek care in hospitals. There is a dearth of evidence on the role of users' perceptions of the quality of care on outpatient visits to primary care facilities. This study aimed to examine the relationship between perceived quality of care and the number of outpatient visits to nearby health centers. Methods: A community-based cross-sectional study was conducted in two rural districts of northeast Ethiopia among 1081 randomly selected rural households that had visited the outpatient units of a nearby health center at least once in the previous 12 months. Data were collected using an interviewer-administered questionnaire via an electronic data collection platform. A multivariable analysis was performed using zero-truncated negative binomial regression model to determine the association between variables. The degree of association was assessed using the incidence rate ratio, and statistical significance was determined at a 95% confidence interval. Results: A typical household makes roughly four outpatient visits to a nearby health center, with an annual per capita visit of 0.99. The mean perceived quality of care was 6.28 on a scale of 0–10 (SD = 1.05). The multivariable analysis revealed that perceived quality of care is strongly associated with the number of outpatient visits (IRR = 1.257; 95% CI: 1.094 to 1.374). In particular, a significant association was found for the dimensions of provider communication (IRR = 1.052; 95% CI: 1.012, 1.095), information provision (IRR = 1.088; 95% CI: 1.058, 1.120), and access to care (IRR = 1.058, 95% CI: 1.026, 1.091). Conclusions: Service users' perceptions of the quality of care promote outpatient visits to primary healthcare facilities. Effective provider communication, information provision, and access to care quality dimensions are especially important in this regard. Concerted efforts are required to improve the quality of care that relies on service users' perceptions, with a special emphasis on improving health care providers' communication skills and removing facility-level access barriers. [ABSTRACT FROM AUTHOR]

This new editorial discusses the promise and challenges of successful integration of natural language processing methods into electronic health records for timely, robust, and fair oncology pharmacovigilance. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas, area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower-atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UASs). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado Boulder RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 d was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. The quality-controlled TRACER-UAS observations from the CopterSonde and RAAVEN can be found at 10.5439/1969004 (Lappin, 2023) and 10.5439/1985470 (de Boer, 2023), respectively. [ABSTRACT FROM AUTHOR]

The continuous global urbanization with rapid and dynamic transitioning in traffic situations among highly populated cities results in difficulty for data collection and communication. Data collection for millions of vehicles hinders by various problems, i.e., higher cost of energy, time, space, and storage resources. Moreover, higher data traffic results in higher delays, larger throughput, excessive bottlenecks, and frequent repetition of data. To better facilitate the aforementioned challenges and to provide a solution, we have proposed a lightweight Machine Learning based data collection protocol named ML-TDG to effectively deal with higher data volumes in a real-time traffic environment capable of bringing the least burden on the network while utilizing less space, time, and energy. ML-TDG is functional based on Apache Spark, an effective data processing engine that indexes the data based on two logs, i.e., old commuters or frequent/daily commuters and second new/occasional commuters. The proposed protocol's main idea is to utilize real-time traffic, distinguish the indexes in parallel based on two assigned logs criteria to train the network, and collect data with the least sources. For energy and time optimization, dynamic segmentation switching is introduced which is an intelligent road segments division and switching for reducing bottlenecks and replication. ML-TDG is tested and verified on Dublin, Ireland's busiest motorway M50. ML-TDG performs the data collection, data sorting, and network training to decide the next execution altogether for better optimization every time. The experimental results verify that our proposed protocol is attaining higher performance with lower resource requirements along with rich and time-efficient sustainable data collection clusters in comparison with baseline protocols. [ABSTRACT FROM AUTHOR]

Amidst an energy crisis stemming from increased energy costs and the looming threat of war, there has been a burgeoning interest in energy conservation and management worldwide. Industrial complexes constitute a significant portion of total energy consumption. Hence, reducing energy consumption in these complexes is imperative for energy preservation. Typically, factories within similar industries aggregate in industrial complexes and share similar energy utilities. However, they often fail to capitalize on this shared infrastructure efficiently. To address this issue, a network system employing a virtual utility plant has been proposed. This system enables proactive measures to counteract energy surplus or deficit through AI-based predictions, thereby maximizing energy efficiency. Nevertheless, deploying conventional server systems within factories poses considerable challenges. Therefore, leveraging edge devices, characterized by low power consumption, high efficiency, and minimal space requirements, proves highly advantageous. Consequently, this study focuses on constructing and employing data collection and AI systems to utilize edge devices as standalone systems in each factory. To optimize the AI system for low-performance edge devices, we employed the integration-learning AI modeling technique. Evaluation results demonstrate that the proposed system exhibits high stability and reliability. [ABSTRACT FROM AUTHOR]

This paper explores the development of a Quadcopter-Rover System specifically designed for environmental survey applications. The system combines the capabilities of a quadcopter and a rover to provide a comprehensive and versatile solution for data collection and analysis. The paper presents a detailed overview of the system's modelling, design, and manufacturing of the two main components: the quadcopter and the differential wheel robot (the rover). The quadcopter’s main task is to carry the rover to/from the ground destination and collect aerial data while the rover`s main task is ground exploration and data collection. The paper discusses the development of a robust and efficient control algorithm that enables autonomous and coordinated operation between the quadcopter and the rover. Experimental results demonstrate the system's effectiveness in conducting environmental surveys, showcasing its ability to accurately navigate challenging terrains, and collect valuable data for environmental analysis. The Quadcopter-Rover System offers significant potential in applications such as ecological monitoring, disaster management, and precision agriculture, where comprehensive and efficient data collection is crucial for informed decision-making. [ABSTRACT FROM AUTHOR]

Time constraints, financial limitations, and inadequate tools restrict the flood data collection in undeveloped countries, especially in the Asian and African regions. Engaging citizens in data collection and contribution has the potential to overcome these challenges. This research demonstrates the applicability of citizen science for gathering flood risk-related data on residential flooding, land use information, and flood damage to paddy fields for the Bui River Basin in Vietnam. Locals living in or around flood-affected areas participated in data collection campaigns as citizen scientists using self-investigation or investigation with a data collection app, a web form, and paper forms. We developed a community-based rainfall monitoring network in the study area using low-cost rain gauges to draw locals' attention to the citizen science program. Fifty-nine participants contributed 594 completed questionnaires and measurements for four investigated subjects in the first year of implementation. Five citizen scientists were active participants and contributed more than 50 completed questionnaires or measurements, while nearly 50% of citizen scientists participated only one time. We compared the flood risk-related data obtained from citizen scientists with other independent data sources and found that the agreement between the two datasets on flooding points, land use classification, and the flood damage rate to paddy fields was acceptable (overall agreement above 73%). Rainfall monitoring activities encouraged the participants to proactively update data on flood events and land use situations during the data collection campaign. The study's outcomes demonstrate that citizen science can help to fill the gap in flood data in data-scarce areas. [ABSTRACT FROM AUTHOR]

The increasing use of technologies, particularly computing and communication paradigms, has significantly influenced our daily lives. Interconnecting devices and networks provides convenient platforms for information exchange and facilitates pervasive user data collection. This new environment presents serious privacy challenges. User activities can be continuously monitored in both digital and physical realms. Gathered data can be aggregated and analysed, revealing aspects of user behaviour that may not be apparent from a single data point. The very items that facilitate connectivity simultaneously increase the risk of privacy breaches. The data gathered to provide services can also be used for monitoring and surveillance. This paper discerns three novel categories of privacy concerns relating to pervasive user data collection: privacy and user activity in cyberspace, privacy in personal cyber–physical systems, and privacy in proactive user-driven data collection. We emphasise the primary challenges, ranging from identity tracking in browsing histories to intricate issues in opportunistic networks, situating each within practical, real-world scenarios. Furthermore, we assess the effectiveness of current countermeasures, investigating their strengths and limitations. This paper explores the challenges in preserving privacy in user interactions with dynamic interconnected systems and suggests countermeasures to mitigate identified privacy risks. [ABSTRACT FROM AUTHOR]

Introduction and Objective: The ConcePTION project aims to improve the way medication use during pregnancy is studied. This includes exploring the possibility of developing a distributed data processing and analysis infrastructure using a common data model that could form a foundational platform for future surveillance and research. A prerequisite would be that data from various data access providers (DAPs) can be harmonised according to an agreed set of standard rules concerning the structure and content of the data. To do so, a reference framework of core data elements (CDEs) recommended for primary data studies on drug safety during pregnancy was previously developed. The aim of this study was to assess the ability of several public and private DAPs using different primary data sources focusing on multiple sclerosis, as a pilot, to map their respective data variables and definitions with the CDE recommendations framework. Methods: Four pregnancy registries (Gilenya, Novartis; Aubagio, Sanofi; the Organization of Teratology Information Specialists [OTIS]; Aubagio, Sanofi; the Dutch Pregnancy Drug Register, Lareb), two enhanced pharmacovigilance programmes (Gilenya PRIM, Novartis; MAPLE-MS, Merck Healthcare KGaA) and four Teratology Information Services (UK TIS, Jerusalem TIS, Zerifin TIS, Swiss TIS) participated in the study. The ConcePTION primary data source CDE includes 51 items covering administrative functions, the description of pregnancy, maternal medical history, maternal illnesses arising in pregnancy, delivery details, and pregnancy and infant outcomes. For each variable in the CDE, the DAPs identified whether their variables were: identical to the one mentioned in the CDE; derived; similar but with a divergent definition; or not available. Results: The majority of the DAP data variables were either directly taken (85%, n = 305/357, range 73–94% between DAPs) or derived by combining different variables (12%, n = 42/357, range 0–24% between DAPs) to conform to the CDE variables and definitions. For very few of the DAP variables, alignment with the CDE items was not possible, either because of divergent definitions (1%, n = 3/357, range 0–2% between DAPs) or because the variables were not available (2%, n = 7/357, range 0–4% between DAPs). Conclusions: Data access providers participating in this study presented a very high proportion of variables matching the CDE items, indicating that alignment of definitions and harmonisation of data analysis by different stakeholders to accelerate and strengthen pregnancy pharmacovigilance safety data analyses could be feasible. [ABSTRACT FROM AUTHOR]

There has been an increasing number of papers which explore the representation of mental health on social media using various social media platforms and methodologies. It is timely to review methodologies employed in this growing body of research in order to understand their strengths and weaknesses. This systematic literature review provides a comprehensive overview and evaluation of the methods used to investigate the representation of mental ill-health on social media, shedding light on the current state of this field. Seven databases were searched with keywords related to social media, mental health, and aspects of representation (e.g., trivialisation or stigma). Of the 36 studies which met inclusion criteria, the most frequently selected social media platforms for data collection were Twitter (n = 22, 61.1%), Sina Weibo (n = 5, 13.9%) and YouTube (n = 4, 11.1%). The vast majority of studies analysed social media data using manual content analysis (n = 24, 66.7%), with limited studies employing more contemporary data analysis techniques, such as machine learning (n = 5, 13.9%). Few studies analysed visual data (n = 7, 19.4%). To enable a more complete understanding of mental ill-health representation on social media, further research is needed focussing on popular and influential image and video-based platforms, moving beyond text-based data like Twitter. Future research in this field should also employ a combination of both manual and computer-assisted approaches for analysis. [ABSTRACT FROM AUTHOR]

To enhance early electrical fire warning in power IoT systems, we propose a residual current modeling method combining grey correlation and neural networks. By analyzing 27985 sets of data from an intelligent fire monitoring system, effective data collection and processing with advanced sensor technology in an IoT context were demonstrated. The model, derived from correlation analysis and grey prediction algorithms, uses a trained neural network for predicting residual current. This method not only augments the efficiency and accuracy of data processing in IoT but also underscores the significance of sensor technology in electrical monitoring and fire prevention. The comparative analysis of predicted and actual residual currents, showing an error range of 0.18 to 3.21%, validates the accuracy of the model and the utility of sensor-driven methods in IoT applications. [ABSTRACT FROM AUTHOR]

Tourism management plays an important role in the context of Smart Cities. In this work, we have used thermal cameras for the development of an Object Detection solution in pedestrian areas. The solution can classify people, bikes, strollers, and count people in Real-Time by using telephoto and wide-angle thermal cameras, in hot squares where there is a relevant number of people passing by. This work has improved FASTER-R-CNN and YOLOv5 architectures with new data sets and fine-tuning approaches to enhance mean average precision and flexibility whether compared to state of the art solutions. Both top-down and bottom-up training adaptation approaches have been assessed in order to demonstrate that the proposed bottom-up approach can provide better results. Results have overcome the state-of-the-art in terms of mean Average Precision in counting (i) for relevant number of people in the scene (removing the limitation of previous state-of-the-art solutions that were set to provide good precision up to 10 people) and (ii) in terms of flexibility with respect to different kinds of camera and resolutions. The resulting model can produce results also when executed on thermal camera and in Real-Time on industrial PC of mid-level. The proposed solution has been developed and validated in the framework of the Herit-Data EC project and it has exploited the Snap4City platform for the final collection of data results, monitoring and their publication on real time dashboards. [ABSTRACT FROM AUTHOR]

Background: Pharmacy intravenous admixture service (PIVAS) center has emerged as an important department of hospitals as it can improve occupational protection and ensure the safety and effectiveness of intravenous infusions. However, there is little research on the standardized capability and risk evaluation of PIVAS by using modern information technology. In this research, we established Regional Pharmacy Intravenous Admixture Services Data Reporting and Analysis Platform (RPDRAP) to improve quality control ability for PIVAS management. RPDRAP including evaluation matrix for quality control monitoring. The construction of platform is based on guidelines for the Construction and Management of PIVAS and management specifications of PIVAS in China. Methods: RPDRAP was established in 2018. This platform comprises a data collection system and a data analysis system. The data collection system consists of 67 data items. Data collection relied on online platforms through data acquisition module. The collected data were analyzed using a model with 20 indicators within the data analysis system. Fifteen hospitals, public comprehensive healthcare facilities with more than 500 beds, participated in the platform's application evaluation. Results: The study revealed significant differences in PIVAS total score, supervisors, and workload between 2020 and 2022. The platform's application results demonstrated improvements in personnel management, work efficiency, and infection control within these PIVAS. Although statistical significance was observed in only 8 out of the 25 items, most of the scores showed an increase, with a small portion remaining unchanged and no decline in scores. Conclusions: This platform can be recommended for PIVAS homogeneous and regional efficient management. The use of this platform not only improves the quality control ability of PIVAS but also enables the management department to quickly grasp the current situation and characteristics of each PIVAS through standardized data collection and analysis. [ABSTRACT FROM AUTHOR]

Background: In health care industry, incident reporting systems are considered effective instruments to learn from adverse events and errors and improve the quality of health care of all the stake holders. The present study has analyzed the challenges and barriers facing the medical staff to report incidents and events in magnetic resonance imaging (MRI) practices in government hospitals of Saudi Arabia. Materials and Methods: A well-structured MRI safety questionnaire was the primary data collection method, utilizing a quantitative descriptive, cross-sectional survey to highlight the challenges and barriers to incidents reporting in MRI practices. Data were subjected to statistical analysis, and the results have been presented with comprehensive discussions. Results: The data showed that the fear of retribution, fear of being questioned or interrogated, getting the supervisorons.ection method, reporting, and fear of damage to workers' reputation are the most significant parries that can prevent MRI personnel from reporting, leading to safety issues. Conclusions: Our findings provide an overview of the hurdles to MRI event/incident reporting in the perceptions of governmental hospitals as well as a number of suggestions for how institutions might minimize these barriers to encourage MRI staff to report. [ABSTRACT FROM AUTHOR]

The traditional motion monitoring system can only provide limited information and cannot meet people's requirements for details and accuracy. Therefore, the purpose of this paper is to develop an optical detection system based on fuzzy decision algorithm, which aims to achieve accurate and comprehensive acquisition of motion performance data and provide reliable monitoring performance in practical applications. A fuzzy decision algorithm is used to process optical signals and extract motion performance data. By establishing a suitable fuzzy rule set, the optical signal is transformed into real motion data, and the accuracy and reliability of the algorithm are verified by experiments and tests. The experimental results show that the optical detection system based on fuzzy decision algorithm can accurately collect motion performance data and has good monitoring performance. The system can maintain stable work under different lighting conditions, accurately capture and analyze the details in motion, and the system can meet people's needs for accuracy and comprehensiveness, and show good performance in practical applications. [ABSTRACT FROM AUTHOR]

The use of modern information technology to develop new evaluation tools is critical to the reform of comprehensive student quality evaluation system and educational modernization. Applying blockchain technology to students' quality evaluation can improve the efficiency and value of the work of comprehensive quality evaluation. This research proposes a redactable blockchain-based comprehensive quality evaluation platform named CEP for postgraduate students, based on the document of implementing comprehensive quality evaluation issued by Zhongnan University of Economics and Law. The platform uses smart contracts to automatically award credits in real time and divides four organizations into different private data collections to maintain the privacy and safety of data of postgraduates during the entire evaluation process. Moreover, to tackle the problem of inconsistent data generated by multi-level submission and review, an on-chain data modification scheme based on "temporary block" has been established. The assessment platform developed in this research protects the validity and confidentiality of evaluation data during the procedural evaluation. The research also aims at providing a better understanding of the dynamics of postgraduates' learning development, improving the scientific, professional and objective standards of quality education. [ABSTRACT FROM AUTHOR]

A continuous and multi-decadal surface water extent (SWE) record is vital for water resources management, flood risk assessment, and comprehensive climate change impact studies. The advancements in remote sensing technologies offer a valuable tool for monitoring surface water with high temporal and spatial resolution. However, challenges persist due to image gaps resulting from sensor issues and adverse weather conditions during data collection. To address this issue, one way to fill the gaps is by leveraging in situ measurements such as streamflow discharges (SFDs). We investigate the relationship between SFDs and Landsat-derived SWE in the New England region watersheds (eight-digit hydrological unit code (HUC)) on a monthly scale. While previous studies indicate the relationship exists, it remains elusive for larger domains. Recent research suggests using monthly average SFD data from a single stream gage to fill the gaps in SWE. However, as SWE represents a monthly maximum value, relying on a single gage with average values may not capture the complex dynamics of surface water. Our study introduces a novel approach by replacing the monthly average SFD with the maximum day streamflow discharge anomaly (SFDA) within a month. This adjustment aims to better reflect extreme scenarios, and we explore the relationship using ridge regression, incorporating data from all stream gages in the study domain. The SWE and SFDA are both transformed to stabilize the variance. We found that there is no discernible correlation between the magnitude of the correlation and the size of the basins. The correlations vary based on HUC and display a wide range, indicating the variances of the importance of stream gages to each HUC. The maximum correlation is found when the stream gage is located outside of the target HUC, further verifying the complex relationship between SWE and SFDA. Covering over 30 years of data across 45 HUCs, the imputing technique using ridge regression shows satisfactory performance for most of the HUCs analyzed. The results show that 41 out of 45 HUCs achieve a root-mean-square error (RMSE) of less than 10, and 44 out of 45 HUCs exhibit a normalized root-mean-square error (NRMSE) of less than 0.1. Of 45 HUCs, 42 have an R-squared (R2) score higher than 0.7. The Nash–Sutcliffe efficiency index (Ef) shows consistent results with R2, with the relative bias ranging from –0.02 to 0.03. The established relationship serves as an effective imputing technique, filling gaps in the time series of SWE. Moreover, our approach facilitates the identification and visualization of the most significant gages for each HUC, contributing to a more refined understanding of surface water dynamics. [ABSTRACT FROM AUTHOR]

Time synchronization is vital for accurate data collection and processing in sensor networks. Sensors in these networks often operate under fluctuating conditions. However, an accurate timekeeping mechanism is critical even in varying network conditions. Consequently, a synchronization method is required in sensor networks to ensure reliable timekeeping for correlating data accurately across the network. In this research, we present a novel dynamic NTP (Network Time Protocol) algorithm that significantly enhances the precision and reliability of the generalized NTP protocol. It incorporates a dynamic mechanism to determine the Round-Trip Time (RTT), which allows accurate timekeeping even in varying network conditions. The proposed approach has been implemented on an FPGA and a comprehensive performance analysis has been made, comparing three distinct NTP methods: dynamic NTP (DNTP), static NTP (SNTP), and GPS-based NTP (GNTP). As a result, key performance metrics such as variance, standard deviation, mean, and median accuracy have been evaluated. Our findings demonstrate that DNTP is markedly superior in dynamic network scenarios, a common characteristic in sensor networks. This adaptability is important for sensors installed in time-critical networks, such as real-time industrial IoTs, where precise and reliable time synchronization is necessary. [ABSTRACT FROM AUTHOR]

The urban planning and management needs a lot of spatial data. The data for the needs must be collected by land survey in most of the cases. When we talk about collecting data, there is a "must" to define: "What kind of data to collect?". Certain rules are related to the acquired data for professional workflow implementation. The link between data capturing and data implementation can be found in the smart people contribution.The target of the research is how to improve the spatial data registration, documentation and visualization with low-cost hardware and software in easy accessible platform for data capture and collection by involved citizens for volunteered information sharing. The possibility of applying comprehensible hardware and software for collecting geospatial data and integration into a contribution platform is explored.The workflow shows that with the help of the citizen initiative, there are many data attributes that can be collected by publishing through free sources, such as: address number, type of street coverage, electrified pole coverage, utility equipment, et cetera. [ABSTRACT FROM AUTHOR]

The household is the basic unit of a residential community or building. High-resolution, long-term open data are necessary to help study residential electricity consumption, smart home technologies, and electricity flexibility technologies at household level. This paper introduces an IoT-based data collection platform (IDCP) consisting of gateways, sensors, and cloud servers. This platform can collect data on the occupant presence, indoor environment, window-opening states, and appliance electricity consumption simultaneously. This study deployed the IDCP in a single-family apartment in Beijing, China, and compiled a dataset, namely, CN-OBEE, including data on the per-minute occupant behavior, thermal environment, and appliance electricity use of the apartment for an entire year (from May 31, 2021, to May 31, 2022), and hourly meteorological data collected by the nearest national weather station during the same period. This dataset is the first detailed and publicly available occupant behavior and electricity use dataset for Chinese homes. As a regional feature, the dataset compiled by this study includes window-opening behavior and the use of split air conditioners (ACs). [ABSTRACT FROM AUTHOR]

In path-constrained multi-hop sensor networks (M-WSNs), maximizing data collection with minimal energy consumption is critical, especially for delay-sensitive applications. Because a mobile element (ME) moving at a constant speed along a constrained-path must receive data from sensor nodes (SNs) within a given time bound. This issue can be addressed by efficiently scheduling the SNs' data transmission. The shortest path data transmission (SPT) or its variations are a simple and energy-efficient technique for scheduling data transmission from SNs. Although this method considerably minimizes total energy spent, it does not enhance data collection efficiency due to uneven data forwarding load. To solve this issue, this paper proposes a novel efficient heuristic method. The proposed method first computes a set of discrete sub-paths on a given path based on the start and end distance points of nearby SNs, the speed of ME, and the given time delay. Then, for each sub-path and SN-communication model, a network flow graph is used to schedule and optimize SN data transmission to ME. Finally, a sub-path is chosen among those which return maximum data with minimum energy consumption. The network flow graphs are created by considering two distinct SN-communication models: (1) energy-unrestricted SN-model and (2) energy-restricted SN-model. Finally, the simulation reports demonstrate the proposed method's efficacy over baseline schemes in terms of data collected, energy usage efficiency, and success-delivery ratio. [ABSTRACT FROM AUTHOR]

In marking 400 years since the first enslaved Africans arrived to Jamestown, United States in 1619, the Ghana government through the Ghana Tourism Authority initiated the Year of Return 2019 (#YOR2019). The goal was to unite Africans in the diaspora with those on the continent, especially in Ghana, through a year-long calendar of commercial and commemorative slavery heritage tourism activities ranging from visits to slavery sites, healing ceremonies, theatre and musical performances, festivals, investment forums and relocation conferences. When a destination tourism product is rooted in a less-than-desirable past, how is 'balance' achieved between commercialization and commemoration? In exploring this conceptual question, we developed a methodological innovation utilizing the social media platform Twitter for data collection. Using a social media crawler coded in Python programming language, we scrapped tweets from the accounts of the Ghana Tourism Authority prior, during, and after the YOR2019 based on hashtag searches. After data cleaning, 1010 tweets were inductively analysed using NVIVO qualitative data analysis software. The findings revealed three emergent themes along a commodification-commemoration continuum: (1) the eventification and festivalisation of slavery heritage tourism, (2) celebrity co-production of YOR2019 experiences through social media and (3) pivoting from a predominantly slavery heritage destination to a destination that focuses on other touristic and business travel. Ultimately, YOR2019 marked a significant push by Ghana to move into a 'Beyond the Return' phase that pivots away from slavery heritage towards a more well-rounded tourism product for roots, leisure, and business travellers. The research established that commodification in slavery heritage tourism does not inherently destroy cultural meanings but provide new commemorative meanings for a new generation of Black travellers searching for more than just their roots. [ABSTRACT FROM AUTHOR]

Objective: The overall goal of this work is to produce a set of recommendations (SoNHR–Social Networks in Health Research) that will improve the reporting and dissemination of social network concepts, methods, data, and analytic results within health sciences research. Methods: This study used a modified-Delphi approach for recommendation development consistent with best practices suggested by the EQUATOR health sciences reporting guidelines network. An initial set of 28 reporting recommendations was developed by the author team. A group of 67 (of 147 surveyed) experienced network and health scientists participated in an online feedback survey. They rated the clarity and importance of the individual recommendations, and provided qualitative feedback on the coverage, usability, and dissemination opportunities of the full set of recommendations. After examining the feedback, a final set of 18 recommendations was produced. Results: The final SoNHR reporting guidelines are comprised of 18 recommendations organized within five domains: conceptualization (how study research questions are linked to network conceptions or theories), operationalization (how network science portions of the study are defined and operationalized), data collection & management (how network data are collected and managed), analyses & results (how network results are analyzed, visualized, and reported), and ethics & equity (how network-specific human subjects, equity, and social justice concerns are reported). We also present a set of exemplar published network studies which can be helpful for seeing how to apply the SoNHR recommendations in research papers. Finally, we discuss how different audiences can use these reporting guidelines. Conclusions: These are the first set of formal reporting recommendations of network methods in the health sciences. Consistent with EQUATOR goals, these network reporting recommendations may in time improve the quality, consistency, and replicability of network science across a wide variety of important health research areas. [ABSTRACT FROM AUTHOR]

Despite the recent development of wearable cardiopulmonary monitoring devices and their necessity in clinical settings, the evidence regarding their application in real-world intensive care units (ICUs) is limited. These devices have notable problems, such as inefficient manufacturing and cumbersome hardware for medical staff and patients. In this study, we propose a simplified cardiopulmonary monitoring system and present a protocol for a single-center prospective study to evaluate the efficacy of the proposed system compared with those from the conventional monitoring system. The system was designed to continuously measure electrocardiogram, respiration rate, and oxygen saturation in a stand-alone device with an intuitive data visualization platform and automatic data collection. The accuracy of the data measured from the proposed device will be pre-validated by comparing them with those from the reference device. Medical staff from the St. Vincent's Hospital ICU will complete a five-point Likert-type scale questionnaire regarding their experience with conventional ICU monitoring systems. The result will be compared with the second questionnaire conducted after deploying the system. Since this is a study proposal paper, we do not have any data on this study yet. However, compared with the conventional patient monitoring system, the proposed device should be a promising method to relieve medical staff fatigue and that of the patients who must wear and attach the monitoring device for a long time. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) has had a severe impact on the education system, involving nearly 1.6 billion students in more than 190 countries and on all continents. The closure of schools, higher education institutions and other learning spaces affected more than 94% of the global student population. In response, online teaching and learning has been used by teachers and students on an unprecedented scale since the government announced the lockdown. As a result, teachers have encountered various difficulties in adapting to these changes and challenges. Following which, Teachers who are highly committed will have an awareness and willingness to improve the teaching profession and establish an effective learning environment to enable students to reach their goals. Previous studies have identified that religiosity and motivation significantly impact teachers' level of commitment. Therefore, the purpose of this study is to investigate the influence of religiosity and motivation on teachers' commitment during the COVID-19 pandemic. This study was conducted on 66 teachers currently working in various Islamic religious schools in Perak, Malaysia using a purposive sampling technique. A quantitative method was used and data was collected through a survey questionnaire with appended consent. Due to the COVID-19 pandemic, the Google form was utilised as a data collection platform from respondents. The findings of the study revealed that religiosity (β = 0.310, p = 0,004 < 0.05) and motivation (β = 0.435, p = 0.000 < 0.05) significantly influenced the work commitment of teachers during the COVID-19 pandemic. Hence, the school management should highly emphasise the role of these two factors among teachers, as they could enhance the commitment of teachers, such as preparing the syllabus and educating students. [ABSTRACT FROM AUTHOR]

The increasing prevalence of chronic diseases is a driver for emerging big data technologies for healthcare including digital platforms for data collection, systems for active patient engagement and education, therapy specific predictive models, optimized patient pathway models. Powerful bioelectronic medicine tools for data collection, analysis and visualization allow for joint processing of large volumes of heterogeneous data, which in turn can produce new insights about patient outcomes and alternative interpretations of clinical patterns that can lead to implementation of optimized clinical decisions and clinical patient pathway by healthcare professionals. With this perspective, we identify innovative solutions for disease management and evaluate their impact on patients, payers and society, by analyzing their impact in terms of clinical outcomes (effectiveness, safety, and quality of life) and economic outcomes (cost-effectiveness, savings, and productivity). As a result, we propose a new approach based on the main pillars of innovation in the disease management area, i.e. progressive patient care models, patient-centric approaches, bioelectronics for precise medicine, and lean management that, combined with an increase in appropriate private-public-citizen-partnership, leads towards Patient-Centric Healthcare. [ABSTRACT FROM AUTHOR]

Extensive streamflow data sources exist beyond the largest streamflow data provider in the United States, the U.S. Geological Survey. We developed and distributed a survey to about 300 individuals and organizations that collect streamflow data across the Pacific Northwest (Idaho, Oregon, Washington). We received 100 responses with 56% of those sufficiently complete to include in the analysis. From these responses, there are about 2000 streamflow monitoring locations in the region beyond the USGS monitoring network. The duration of record for gages is related to the size of the streamflow gaging network, with small and large networks generally operating monitoring locations for less than 5 years and more than 10 years, respectively. Quality assurance and quality control are variable across organizations, with 41% of respondents having at least two review steps and 13% that audit their data for long‐term consistency. Results of this survey begin to establish the differing capabilities of large and small stream gaging networks and highlight how supporting the overall quality streamflow data collection and management within the water resources community will improve our ability to harmonize these datasets in the future. [ABSTRACT FROM AUTHOR]

In this study, we explore innovative strategies for enhancing energy efficiency in Wireless Sensor Networks (WSNs), with a focus on the IQRF network. Our approach integrates dynamic sleep scheduling and data collection methods to optimize battery usage and extend the network's operational lifespan. We introduce a battery life estimation model, taking into account various factors such as data collection frequency and network size. This model is instrumental in predicting battery longevity under different operational scenarios. Additionally, we develop a practical tool in the form of an API and an online calculator, aimed at assisting network designers in planning and maintaining energy-efficient WSNs. Our results, derived from a case study involving a CO2 sensor network, demonstrate the effectiveness of our methodologies in real-world applications. The study concludes that implementing dynamic data collection and sleep scheduling significantly enhances battery life, offering a valuable contribution to the sustainability and reliability of WSNs. [ABSTRACT FROM AUTHOR]

The foundation of textile machinery digitization and intelligence is condition monitoring and identification. Online condition monitoring of looms is of great significance to ensure their long-term stable operation and improve their digital management level. However, the existing loom condition monitoring methods have problems such as insufficient depth of information mining, low condition recognition rate and poor system versatility. As a result, the loom on-board condition monitoring technology based on fuzzy rough set and improved DSmT theory is studied. To begin, we examine the loom operation mechanism, loom state characterization, and loom state feature data composition. Then, using the fuzzy rough set method, we analyze and make decisions on the loom state feature data, apply the theory of uncertainty and importance improvement DSmT fusion to solve the uncertainty problem of the rough set method's decision rules, and build the loom state feature decision network on the embedded terminal using the decision rules. Meanwhile, to collect, communicate, display, and alarm loom characteristic data, this paper employs the STM32F407ZET6 microcontroller and designs a loom system status data collection platform with the AD7730 as the core, as well as tests loom status monitoring data collection and loom status data analysis and decision method based on this platform. The experimental findings show the usefulness of attribute data gathering as well as data analysis and decision-making processes. The technology enhances the precision of loom condition identification and decision making, as well as the safety and quality of manufacturing. It is critical for carrying out applications like as problem detection, remote monitoring, efficiency optimization, and intelligent weaving machine management. [ABSTRACT FROM AUTHOR]