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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]

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]

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]

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]

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 use of Unmanned Aerial Systems (UAS) in remote sensing applications, specifically using the Trimble UX5 HP platform for aerial data collection over Karakol city in Kyrgyzstan. Photogrammetric technique used to identify and match common features in the overlapping aerial images to create a sparse point cloud, which were further processed to create a Digital Surface Model (DSM). A slope-based filtering algorithm was applied to the DSM data for generating a Digital Terrain Model (DTM). The normalized Digital Surface Model (nDSM) was derived from the DSM by subtracting the DTM. Object-based image analysis applied to UAS datasets for the extraction of building footprints in an urban area. The results indicate that extracted building footprints have been generated accurately with an overall completeness of 92.4% and correctness of 95,2%. [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]

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]

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]

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]

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]

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]

Data are generated at an unprecedented rate and scale these days across many disciplines. The field of streaming data analysis has emerged as a result of new data collection and storage technologies in various areas, such as air pollution monitoring, detection of traffic congestion, disease surveillance, and recommendation systems. In this paper, we consider the problem of model estimation for data streams in reproducing kernel Hilbert spaces. We propose an adaptive supervised learning method with a data sparsity constraint that uses limited storage spaces and can handle nonstationary models. We demonstrate the competitive performance of the proposed method using simulations and analysis of the bike sharing dataset. [ABSTRACT FROM AUTHOR]

The modernization of the production of official statistics faces challenges related to technological developments, budget cuts, and growing privacy concerns. At the same time, there is a need for shareable and scalable platforms to support comparable data, leading to several online data collection strategies being rolled out. Time Use Surveys (TUS) are particularly affected by these challenges and needs as they (while producing rich data) are complex, time-intensive studies (because they include multiple tasks and are administered at the household level). This article introduces the Modular Online Time Use Survey (MOTUS) data collection platform and explains how it accommodates the challenges of and changes in the production of a TUS that is carried out in line with the Harmonized European Time Use Survey guidelines. It argues that MOTUS supports a shift in the methodological paradigm of conducting TUS by being timelier and more cost efficient, by lowering respondent burden, and by improving the reliability of the data collected. Importantly, the modular structure allows MOTUS to be easily deployed for various TUS configurations. Moreover, this versatile structure allows comparable, complex diary surveys (such as the household budget survey) to be performed on the same platform and with the same applications. [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]

Background: Existing research documented the connection between individuals' childhood trauma exposure and adverse health and behavioural health outcomes. However, less attention has been paid to understanding trauma in an intergenerational context. Objective: This study aims to fill this research gap by (1) exploring the association between parents' trauma histories and child behavioural outcomes; (2) unpacking the effects of different types of parental trauma on child behavioural outcomes; (3) examining whether these associations vary by parental roles. Participants and Setting: This study recruited 202 parents from an online data collection platform – Amazon Mechanical Turk. Methods: We conducted regression analyses to examine the associations between parental adverse childhood experiences (ACEs) and their child's behavioural problems and functioning. Results: Findings showed that only parental childhood maltreatment ACEs, but not family dysfunction ACEs, were significantly associated with child behavioural problems and functioning. The impact of parental child maltreatment ACEs on child behavioural problems was equally significant between fathers and mothers. In contrast, mothers' childhood maltreatment ACEs exerted a more potent effect on child functioning. Conclusion: Study findings highlighted the need for child behavioural health services to incorporate the screening and treatment of parental ACEs in treatment programmes to break the cycle of intergenerational trauma. [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]

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]

Background: Only a few studies explore the role of nurses in genetic counselling and genetic health care, and none of them is related to orphan diseases. In addition, few studies address the issue of finding variables that might affect the economy of a service or perform a cost-effectiveness analysis of a having genetic nurse at a unit. Methods: A multidisciplinary panel of experts working in the hospital was set up to identify sensitive indicators and remove confounding variables. This panel evaluated efficiency and effectiveness indicators and drafted a questionnaire to estimate patient perception of the quality of the service. Data were captured from different sources, including the hospital patient database and a web-accessible platform for data collection. More than 600 clinical evaluations of 400 patients were considered, and economic parameters were studied by applying Porter's Time-Driven Activity-Based Costing methodology to evaluate costs and outcomes. Additionally, an anonymous, semi-structured, paper-and-pencil interview questionnaire was given to patients at their periodic follow-ups. Results: The results showed an increase in the quality of patient management, more accurate data capturing, and higher quality ambulatory care. In fact, approximately 70% of the respondents reported positive changes. In addition, a parallel economic analysis explored indicators influencing economic impact, and outcomes showed positive results with the quality of outcomes improving more compared to the increase in costs. Conclusions: The variety of evaluated issues highlighted that having a nurse in a genetic service and at day clinic activities resulted in better access, better scheduling, more satisfaction, and proved to be a cost-effective solution for patients affected by rare diseases. [ABSTRACT FROM AUTHOR]

Background: CommunityRx is an evidence-based social care intervention delivered to family and friend caregivers ("caregivers") at the point of healthcare to address health-related social risks (HRSRs). Two CommunityRx randomized controlled trials (RCTs) are being fielded concurrently on Chicago's South Side, a predominantly African American/Black community. CommunityRx-Hunger is a double-blind RCT enrolling caregivers of hospitalized children. CommunityRx-Dementia is a single-blind RCT enrolling caregivers of community-residing people with dementia. RCTs with caregivers face recruitment barriers, including caregiver burden and lack of systematic strategies to identify caregivers in clinical settings. COVID-19 pandemic-related visitor restrictions exacerbated these barriers and prompted the need for iteration of the protocols from in-person to remote operations. This study describes these protocols and methods used for successful iteration to overcome barriers. Methods and findings: CommunityRx uses individual-level data to generate personalized, local community resource referrals for basic, health and caregiving needs. In early 2020, two in-person RCT protocols were pre-tested. In March 2020, when pandemic conditions prohibited face-to-face clinical enrollment, both protocols were iterated to efficient, caregiver-centered remote operations. Iterations were enabled in part by the Automated Randomized Controlled Trial Information-Communication System (ARCTICS), a trial management system innovation engineered to integrate the data collection database (REDCap) with community resource referral (NowPow) and SMS texting (Mosio) platforms. Enabled by engaged Community Advisory Boards and ARCTICS, both RCTs quickly adapted to remote operations. To accommodate these adaptations, launch was delayed until November (CommunityRx-Hunger) and December (CommunityRx-Dementia) 2020. Despite the delay, 65% of all planned participants (CommunityRx-Hunger n = 417/640; CommunityRx-Dementia n = 222/344) were enrolled by December 2021, halfway through our projected enrollment timeline. Both trials enrolled 13% more participants in the first 12 months than originally projected for in-person enrollment. Discussion: Our asset-based, community-engaged approach combined with widely accessible institutional and commercial information technologies facilitated rapid migration of in-person trials to remote operations. Remote or hybrid RCT designs for social care interventions may be a viable, scalable alternative to in-person recruitment and intervention delivery protocols, particularly for caregivers and other groups that are under-represented in traditional health services research. Trial registration: ClinicalTrials.gov: CommunityRx-Hunger (NCT04171999, 11/21/2019); CommunityRx for Caregivers (NCT04146545, 10/31/2019). [ABSTRACT FROM AUTHOR]

This study designs a spectrum data collection device and system based on the Internet of Things technology, aiming to solve the tedious process of chlorophyll collection and provide a more convenient and accurate method for predicting chlorophyll content. The device has the advantages of integrated design, portability, ease of operation, low power consumption, low cost, and low maintenance requirements, making it suitable for outdoor spectrum data collection and analysis in fields such as agriculture, environment, and geology. The core processor of the device uses the ESP8266-12F microcontroller to collect spectrum data by communicating with the spectrum sensor. The spectrum sensor used is the AS7341 model, but its limited number of spectral acquisition channels and low resolution may limit the exploration and analysis of spectral data. To verify the performance of the device and system, this experiment collected spectral data of Hami melon leaf samples and combined it with a chlorophyll meter for related measurements and analysis. In the experiment, twelve regression algorithms were tested, including linear regression, decision tree, and support vector regression. The results showed that in the original spectral data, the ETR method had the best prediction effect at a wavelength of 515 nm. In the training set, RMSEc was 0.3429, and Rc2 was 0.9905. In the prediction set, RMSEp was 1.5670, and Rp2 was 0.8035. In addition, eight preprocessing methods were used to denoise the original data, but the improvement in prediction accuracy was not significant. To further improve the accuracy of data analysis, principal component analysis and isolation forest algorithm were used to detect and remove outliers in the spectral data. After removing the outliers, the RFR model performed best in predicting all wavelength combinations of denoised spectral data using PBOR. In the training set, RMSEc was 0.8721, and Rc2 was 0.9429. In the prediction set, RMSEp was 1.1810, and Rp2 was 0.8683. [ABSTRACT FROM AUTHOR]

Background: Managing repaired tetralogy of Fallot (TOF) patients is still challenging despite the fact that published studies identified prognostic clinical or imaging data with rather good negative predictive accuracy but weak positive predictive accuracy. Heterogeneity of the initial anatomy, the surgical approach, and the complexity of the mechanism leading to dilation and ventricular dysfunction explain the challenge of predicting the adverse event in this population. Therefore, risk stratification and management of this population remain poorly standardized. Design: The CMR/CT WG of the Italian Pediatric Cardiology Society set up a multicenter observational clinical database of repaired TOF evaluations. This registry will enroll patients retrospectively and prospectively assessed by CMR for clinical indication in many congenital heart diseases (CHD) Italian centers. Data collection in a dedicated platform will include surgical history, clinical data, imaging data, and adverse cardiac events at 6 years of follow-up. Summary: The multicenter repaired TOF clinical database will collect data on patients evaluated by CMR in many CHD centers in Italy. The registry has been set up to allow future research studies in this population to improve clinical/surgical management and risk stratification of this population. [ABSTRACT FROM AUTHOR]

This study aims to determine the attitude of class XI students of SMA Negeri 1 Merauke towards physical education learning activities. This research is a descriptive research using a survey method with data collection techniques using a questionnaire. The subjects in this study were class XI students of SMA Negeri 1 Merauke, totaling 54 respondents. The Instrument Reliability Test uses the Alpha Cronbach formula and the reliability gain coefficient is 0.922. temporary test validity of the 34 test items all declared valid. The data analysis technique uses descriptive analysis which is outlined in the form of proportions which are divided into 4 categories, namely very positive, positive, moderately positive, and less positive. The results showed that the attitude of class XI students of SMA Negeri 1 Merauke towards Physical Education, Sports and Health was stated to be positive. In detail, 0 people (0%) stated that they were less positive, 3 people (5.56%) stated that they were quite positive, 32 people (59.26%) stated that they were positive, and 19 people (35.19%) stated that they were very positive. [ABSTRACT FROM AUTHOR]

Official price statistics in the Philippines are mainly sourced from the conduct of regular surveys and censuses which entail high costs. As businesses move into digital platforms, alternatives to these traditional data sources have become more available; one of which is web scraping, a process of collecting information from the web. As digital and online platforms become increasingly utilized for commerce, web scraping offers a way to increase the frequency of data collection while reducing its cost compared to price surveys. This paper provides a survey of experiences of various government statistical agencies in their conduct of web scraping for the Consumer Price Index (CPI). Moreover, it details the Philippines' experience using web scraped data to estimate the food and alcoholic beverages CPI of the National Capital Region in the Philippines, and that is compared to the official CPI estimate of the Philippine Statistics Authority. Finally, this paper discusses the challenges encountered and the recommendations for enhancing the approach. [ABSTRACT FROM AUTHOR]

The safe operation of the fluid machine is greatly affected by fault states. With the development of data collection technology in process industrial systems, data-based methods are widely applied in fault diagnosis. The observed data of the fluid machine belongs to typical multivariable time series, so the Euclidean features related to the observed timestamps and the non-Euclidean features related to the observed variables need to be extracted simultaneously by the fault diagnosis method. However, the existing diagnostic studies do not involve the explainability analysis of the diagnostic process, which makes it hard to evaluate the contribution of these features to the accurate diagnosis. An explainable diagnosis model based on temporal and graph convolutional neural network is proposed. The class activation map algorithm is improved to perform explainability analysis of the diagnosis process related to the observed variables and timestamps. Using the simulation data of the fluid machine, features related to observed variables and timestamps of six operating states are fully extracted. Through data experiments, this method can be utilized to achieve high-precision fault diagnosis, and can intuitively display the contribution of each observed variable and its each timestamp to network decision-making. This helps to trace system faults and has significant benefits for process safety assurance. [ABSTRACT FROM AUTHOR]

Managers of small commercial building (SCB) portfolios need to understand occupant interactions with heating, ventilation, and air-conditioning (HVAC) systems to reduce energy use and greenhouse gas (GHG) emissions. In Canada, SCBs are currently underserved by energy conservation and thermal analysis tools because of their dispersion and lower payback potential. However, the emergence of smart thermostats (STs) and their central data collection platform provide a cost-effective solution to gather data from portfolios of SCBs and improve our understanding of occupant-HVAC interactions. This article analyzes the relationship between HVAC schedules (temperature set-points), indoor thermal conditions (dry-bulb temperature and relative humidity), and occupant behavior (thermostat overrides) in a portfolio of 30 SCBs in Ontario, Canada. The results reveal that temperature set-points were not properly selected in the portfolio of SCBs, leading to a large range of indoor thermal conditions and increased thermostat overrides. Specifically, the study demonstrates that building- and zone-specific HVAC schedules are necessary to minimize discomfort and reduce energy consumption in the portfolio of SCBs. The findings of this study can provide valuable insights for portfolio managers to improve HVAC schedules in a manner that reduces energy consumption and GHG emissions while accommodating occupants' thermal comfort and productivity. [ABSTRACT FROM AUTHOR]

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 (UAS). 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 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 days 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. [ABSTRACT FROM AUTHOR]

PURPOSE: To describe clinical research professionals (CRPs)' experiences with electronic patient-reported outcome (ePRO) data collection systems in oncology clinical trials and identify correlates of CRPs' attitude toward technology. METHODS: An online survey was conducted among 210 CRPs from 125 National Cancer Institute–funded research sites. Measures included CRPs' demographic characteristics, working years, employment locations, and previous experiences with various types of ePROs. Their attitude toward technology was measured by the Technology Attitude Scale-Adapted. The Wilcoxon signed-rank test was used to compare two subdomains of attitude (perceived usefulness [PU] and perceived ease of use [PEU]). Multiple linear regression was used to explore correlates of (1) overall attitude, (2) PU, and (3) PEU. The significance level was 5%. RESULTS: Participants' median age was 41 years (range, 21-67). Most were female (90%) and White (82%). More than half of the participants had previous experiences with web-based ePROs using patients' own devices (72%) or site-/sponsor-provided on-site devices (eg, kiosks or tablets; 64%). CRPs who were 60 years or older (β = −0.32, P <.05) or worked for 10-20 years (β = −0.11, P <.05) had relatively negative attitudes, controlling for other factors. Previous experiences with more ePRO types were associated with more positive attitudes (β = 0.08, P =.02). Similar correlates were found with PU but not with PEU. CONCLUSION: This study revealed that CRPs had various experiences with ePRO systems and attitudes toward technology. Age, working years, and previous experiences with ePROs were correlates of overall attitude toward technology and PU. These findings suggest necessary targeted training to facilitate ePRO use in oncology clinical trials by improving CRPs' awareness and attitude toward technology. [ABSTRACT FROM AUTHOR]

Experiments in which data are collected by multiple independent resources, including multicentre data, different laboratories within the same centre or with different operators, are challenging in design, data collection and interpretation. Indeed, inconsistent results across the resources are possible. In this paper, we propose a statistical solution for the problem of multi-resource consensus inferences when statistical results from different resources show variation in magnitude, directionality, and significance. Our proposed method allows combining the corrected p-values, effect sizes and the total number of centres into a global consensus score. We apply this method to obtain a consensus score for data collected by the International Mouse Phenotyping Consortium (IMPC) across 11 centres. We show the application of this method to detect sexual dimorphism in haematological data and discuss the suitability of the methodology. [ABSTRACT FROM AUTHOR]

Soybean production is greatly affected by different types of weeds such as horseweed, kochia, ragweed, redroot pigweed, and waterhemp in the midwestern region of the United States. Identification of the soybean plants and the weeds is crucial to controlling the weeds in precision agriculture. The objective of this study was to classify soybean plants and five weed species where a hyperspectral imaging camera with a spectral range of 400 to 1000 nm was used to acquire the images. To acquire the HSI images, a customized robotic hyperspectral data collection scanning platform was developed and used in the greenhouse. A total of 983 hyperspectral data cubes were captured from the greenhouse environment (n = 252, soybean; n = 731, weeds). Spectral information was extracted from the collected images and then a classification model was developed by applying partial least squares discriminant analysis (PLS-DA). To construct the calibration and validation data set, the images were each divided into 70% and 30% ratios for model training and testing, respectively. Seven types of data preprocessing techniques, including mean normalization, maximum normalization, range normalization, multiplicative scatter correction (MSC), standard normal variate (SNV), Savitzky-Golay first derivatives, and Savitzky-Golay second derivatives, were explored individually and accepted as the best preprocessing method based on the highest performance in calibration and validation results. The results showed maximum validation model performance was found at 86.2% by applying the Savitzky-Golay second derivatives preprocessing method for multiclass and 89.4% for binary class. The most important wavelength information was evaluated from the beta coefficient developed using the same preprocessing method. Finally, chemical images were generated using a best-performer model to identify the soybean plants from weeds. The generated images showed a significant difference in chemical composition between soybean and weed plants at 443 nm, 633 nm, 743 nm, and 968 nm. The correlation between these peaks and the chemical components of the plants involves a-carotenoid, chlorophyll, and moisture, respectively. This study shows promising results for the application of HSI in weed control systems for soybeans and relevant weed identification in precision agriculture applications. [ABSTRACT FROM AUTHOR]

Quality is a substantial pillar of construction success, as its failure poses a significant threat to the construction budget and schedule. Effective root cause (RC) analysis allows for the early identification of issues leading to quality failure and proactive defect-prevention measures. This study puts forward a flexible RC analysis method that extracts useful information from construction nonconformance reports (NCRs) to identify the future trend RCs of construction defects by employing a novel graph representation learning (GRL) approach called node2vec. Node2vec was used to connect high-cost impact RC information based on shared construction defects to determine the RCs of the construction defects. Compared with the conventional RC analysis in the literature (i.e., association rule mining), the proposed node2vec offers three advantages: (1) responsiveness to large itemset, allowing its application across multiple projects with different data collection systems. (2) It receives richer semantic information (defect-related features, RC connectivity, and different cost impacts), enabling a more comprehensive understanding of underlying defects. (3) Prediction ability of future connectivity RCs, resulting in more efficient defect-prevention actions. In contrast to unsupervised RC analysis approaches, the incorporated word2vec prediction model allows the measurement of the prediction performance of related RCs (73% accuracy and 2.31% loss), providing a noticeably more accountable RC analysis and holistic defect prevention. This in turn facilitates the integration of the proposed approach with decisions regarding quality improvement in construction projects, thereby accelerating targeted decisions and interventions within related defect-prevention policies. The proposed RC analysis approach works as a construction quality management (CQM) tool to improve the quality of construction activities and reduce delays, cost overruns, and client satisfaction by incorporating three elements: (1) a comprehensive RC network that factors in the direct and indirect relationships between RCs of defects. (2) A node2vec algorithm that can dynamically find the relationship between different RCs, which facilitates the development of more efficient defect-prevention strategies to prevent similar occurrences. (3) A cosine similarity that allows practitioners to prioritize RCs for specific construction activities, enabling more efficient utilization of resources in the quality delivery of construction activities. This improves the efficiency of the related strategies and data-oriented decisions. Overall, the developed RC analysis method can aid construction managers in improving the quality of construction activities. Although the proposed node2vec approach for improving CQM is not a universal quality solution, the continuous RC analysis of the collected NCRs facilitates error prevention in the long term. Constant identification, documentation, and prioritization of the RCs of construction defects allow construction managers to devise more effective CQM plans that can gradually address and eliminate underlying issues, thereby directing their actions toward achieving the zero-defect goal for each activity. [ABSTRACT FROM AUTHOR]

iNaturalist is a widely-utilized platform for data collection and sharing among non-professional volunteers and is widely employed in citizen science. This platform's data are also used in scientific studies for a wide range of purposes, including tracking changes in species distribution, monitoring the spread of alien-invasive species, and assessing the impacts of urbanization and land-use change on biodiversity. Lichens, due to their year-round presence on trees, soil and rocks, and their diverse shapes and colours, have captured the attention of iNaturalist users, and lichen records are widely represented on the platform. However, due to the complexity of lichen identification, the use of data collected by untrained, or poorly trained volunteers in scientific investigation poses concerns among lichenologists. To address these concerns, this study assessed the reliability of lichen identification by iNaturalist users by comparing records on the platform with identifications carried out by experts (experienced lichenologists) in three cities where citizen science projects were developed. Results of this study caution against the use of unchecked data obtained from the platform in lichenology, demonstrating substantial inconsistency between results gathered by iNaturalist users and experts. [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]

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]

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]

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]

In today's dynamic business environment, achieving and sustaining operational efficiency is paramount for organizations. To remain competitive and meet evolving customer demands, companies strive to optimize their operations and enhance efficiency. Over time, various indicators have been developed to assess efficiency across multiple operational dimensions, aligning with technological advancements and industrial practices. One such indicator is Overall Equipment Effectiveness (OEE), which evaluates equipment efficiency based on availability, performance, and quality. However, with the advent of the digital industry, the management of data for efficiency calculations has become increasingly complex. In this context, this paper proposes a digitized, real-time OEE calculation system that leverages advanced technologies to continuously monitor OEE, enable real-time decision-making, and drive performance improvements. Harnessing the potential of digitalization, the system facilitates seamless data collection, analysis, and reporting while automating manual processes. By focusing on OEE as a comprehensive efficiency indicator, the proposed system aims to enhance organizational performance and establish a foundation for calculating other efficiency measures. [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]

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]

The detection of local extinctions is often hindered by the lack of long-term monitoring schemes, and thus relies on time series of presence data. Recently, citizen science has repeatedly shown its value in documenting species occurrences. We investigated the effectiveness of unstructured citizen science records in reducing the perception of local extinctions in butterfly populations across Italian National Parks. We addressed three research questions: (i) the ability of citizen science data to supplement existing knowledge to complete time series of occurrences, (ii) the impact on data collection of three species features (species size, distribution and length of flight period) determining their appearance, and (iii) the interplay between participant effort and species appearance in the amount of diversity recorded on the iNaturalist platform. Our analysis of 98,922 records of Italian butterflies (39,929 from literature and 58,993 from iNaturalist of which 7427 from National Parks) showed that the addition of iNaturalist data filled many recent gaps in time series, thus reducing the perception of potential local extinctions. Records from more engaged users encompassed a higher fraction of local biodiversity and were more likely to reduce the perception of local extinctions. User effort strongly interacted with species features in determining the frequency of records for individual species. In particular, more engaged users were less affected by species size. We provided updated butterfly checklists for Italian National Parks and a R package to calculate potential extinction upon time series. These results offer guidance for protected areas, conservationists, policymakers, and citizen scientists to optimize monitoring of local populations. [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]

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]

Digital twin (DT) technology has become an effective means of industrial digital transformation due to its ability to realize virtual-real mapping, data fusion, etc. In recent years, the application of DT technology to the assembly process of complex mechanical products in multiple fields can effectively overcome the drawbacks of the current assembly method, which is a new strategy for achieving high-precision and high-efficiency intelligent assembly. However, there are few reviews of DT's intelligent assembly technology and application for complex mechanical products. Based on the composition of the DT assembly system, the paper systematically summarizes the critical technologies of DT assembly, including DT model representation of the assembly objects, DT model construction of the assembly process, and DT data collection/management. The application of the DT assembly model based on virtual and real fusion in online monitoring, quality prediction, process planning, real-time control, etc. is comprehensively reviewed. Finally, the challenges faced by DT intelligent assembly of complex mechanical products are described, and future research directions are predicted. [ABSTRACT FROM AUTHOR]