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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]

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]

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]

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]

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]

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]

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]

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]

The main purpose of this paper is to add empirical data to the nascent field of metaverse learning and teaching by examining factors affecting student participation and their perceived experiences of different metaverse platforms. For data collection, 57 Korean undergraduates participated in a self-administered questionnaire and a short reflective essay regarding their experiences on three metaverse platforms (ifland, Gather Town, & Frame VR). For data analysis, exploratory factor analysis was first executed to derive the underlying factors that can explain student participation in metaverse platforms. The social and interactive learning as well as individualized and behavioral learning were identified as two main contributing factors. While the three platforms had no statistical difference in terms of social presence, students' sentimentally perceived differences among them. The sentiment analysis shows that 60.00% of ifland users were positive, followed by 53.66% of Frame VR users and 51.22% of Gather Town users. Furthermore, the additional keyword analysis shows why students expressed the perceived experiences of each platform in a different way. Given that the success of metaverse instruction can be dependent upon whether students regard it as beneficial, such measurements of student perception on the effectiveness of learning on metaverse platforms can offer meaningful recommendations for tech-savvy educators. [ABSTRACT FROM AUTHOR]

The remote monitoring of patients using the internet of things (IoT) is essential for ensuring continuous observation, improving healthcare, and decreasing the associated costs (i.e., reducing hospital admissions and emergency visits). There has been much emphasis on developing methods and approaches for remote patient monitoring using IoT. Most existing frameworks cover parts or sub-parts of the overall system but fail to provide a detailed and well-integrated model that covers different layers. The leverage of remote monitoring tools and their coupling with health services requires an architecture that handles data flow and enables significant interventions. This paper proposes a cloud-based patient monitoring model that enables IoT-generated data collection, storage, processing, and visualization. The system has three main parts: sensing (IoT-enabled data collection), network (processing functions and storage), and application (interface for health workers and caretakers). In order to handle the large IoT data, the sensing module employs filtering and variable sampling. This pre-processing helps reduce the data received from IoT devices and enables the observation of four times more patients compared to not using edge processing. We also discuss the flow of data and processing, thus enabling the deployment of data visualization services and intelligent applications. [ABSTRACT FROM AUTHOR]

Social robots serve as autonomous systems for performing social behaviors and assuming social roles. However, there is a lack of research focusing on the specific measurement of facial trustworthiness toward anthropomorphic robots, particularly during initial interactions. To address this research gap, a hybrid deep convolution approach was employed in this study, involving a crowdsourcing platform for data collection and deep convolution and factor analysis for data processing. The goal was to develop a scale, called Facial Anthropomorphic Trustworthiness towards Social Robots (FATSR-17), to measure the trustworthiness of a robot's facial appearance. The final measurement scale comprised four dimensions, "ethics concern", "capability", "positive affect", and "anthropomorphism", consisting of 17 items. An iterative examination and a refinement process were conducted to ensure the scale's reliability and validity. The study contributes to the field of robot design by providing designers with a structured toolkit to create robots that appear trustworthy to users. [ABSTRACT FROM AUTHOR]

Public libraries reflect the contemporary way of life. In the era of the digital data age, actions carried out through libraries can be traced and analyzed to monitor the demand for library services. This study presents the development and utilization of Korea's National Library Big Data Platform (NLBDP), which was constructed to collect operational data from public libraries in the country and to build national open data platform for the participating libraries and for public use. This study analyzed 17 application cases that utilized NLBDP in order to understand the way the platform has been used. The findings showed that the types of data analyzed so far have been somewhat limited. It suggests needs for further enhancing the data quality and for diversifying data sources, such as individual local library operational data, surveys, demographics, news, and social media. The results also revealed that NLBDP has been utilized for seven distinct purposes: library operations and strategic planning; social trend analysis; collection management; redesigning library space; recruiting new patrons; library marketing; and non-library applications. Building an infrastructure for networked data analysis not only enables evidence-based decision-making in public libraries, but also extracts timely topics that can be developed as useful services for their community. The findings of this study highlighted the necessity to formulate national library data policies and gather data capable of measuring the social impact of libraries. [ABSTRACT FROM AUTHOR]

Many high-throughput sequencing datasets can be represented as objects with coordinates along a reference genome. Currently, biological investigations often involve a large number of such datasets, for example representing different cell types or epigenetic factors. Drawing overall conclusions from a large collection of results for individual datasets may be challenging and time-consuming. Meaningful interpretation often requires the results to be aggregated according to metadata that represents biological characteristics of interest. In this light, we here propose the hierarchical Genomic Suite HyperBrowser (hGSuite), an open-source extension to the GSuite HyperBrowser platform, which aims to provide a means for extracting key results from an aggregated collection of high-throughput DNA sequencing data. The hGSuite utilizes a metadata-informed data cube to calculate various statistics across the multiple dimensions of the datasets. With this work, we show that the hGSuite and its associated data cube methodology offers a quick and accessible way for exploratory analysis of large genomic datasets. The web-based toolkit named hGsuite Hyperbrowser is available at https://hyperbrowser.uio.no/hgsuite under a GPLv3 license. [ABSTRACT FROM AUTHOR]

Background. Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease. As an infrequent disease, a Facebook group was created for patients to share experiences and problems. In fact, during the COVID-19 pandemic, patient analysis could only be done through remote connection systems. Therefore, to analyze patients' quality of life (QoL), we exploited social networks and online data collection platforms. Objectives. A survey was carried out to evaluate the QoL of patients with PBC during the COVID-19 pandemic. Materials and Methods. A Facebook group was used for patient enrolment. Age, sex, diagnosis, years since diagnosis, associated diseases, histological stage of the disease, value of elastography, and current therapy were collected. PBC 40 online questionnaire was submitted to patients to assess their QoL. Results. 78 patients participated in the study: 75 females, and 3 males, the mean (±SD) age was 46.4±11.5. The main diagnoses were PBC in 66 patients and overlapping syndrome PBC + autoimmune hepatitis in 10. Histology was available in 45 patients, of whom 34 were stages 1-2 and 11 stages 3-4. The main therapy was ursodeoxycholic acid in 56 pts. The questionnaire is divided into 6 domains, covering fatigue, emotional, social, and cognitive functions, general symptoms, and itching. The mean and standard deviation of the scores were computed. Interpretation of the results obtained by applying a quantitative scale showed no impairment for social, mild impairment for general symptoms, itching, cognitive and emotional function, and moderate impairment for fatigue. No correlation was found between scores and disease duration. Conclusions. This study demonstrates that online questionnaires are a viable substitute for paper questionnaires and that data collected from online surveys on Facebook can have scientific relevance; PBC had the greatest impact on QoL on fatigue and the least on social aspects. [ABSTRACT FROM AUTHOR]

The 110 m QiTai radio Telescope (QTT) will be equipped with multiple Ultra-WideBand (UWB) receivers in the primary and Gregory focus to achieve continuous frequency coverage from 270 MHz to 115 GHz, which poses great challenges to signal acquisition, transmission, and real-time processing. Aiming at 10 GHz and above full-bandwidth acquisition and multi-scientific processing for the QTT UWB signals, an experimental system with high-speed signal acquisition, 100 Gb network multi-path distribution, and fast recording is designed by using advanced direct RF-sampling technology and heterogeneous architecture. The system employs a ZCU111 board to digitize dual-polarization signals with a sampling rate of 4.096 GigaSamples-Per-Second and 12-bit quantization. The collected wideband signals are channelized into 2048 chunks, which are then assembled into 16 sets of digital narrow basebands with 128 MHz bandwidth and transmitted to the processing servers through two 100 Gb ports. A HASPIPE pipeline, UWB_HASHPIPE is designed to receive and store multiple subbands in parallel. Data distribution links can be flexibly configured based on IP addresses and port numbers. The system is verified by pulsar observation experiments on the Nanshan 26 m telescope. 512 MHz bandwidth is selected from the collected L -band receiver signals and recorded in VDIF file format with 8 parallel instances. The test results show that the data integrity is excellent, and the signal-to-noise ratio of the band-merged pulsar profile is stronger than single subband data. This paper provides a high-performance and flexible solution for the design of versatile UWB backends. Meanwhile, the developed platform can be integrated into QTT backends for baseband data collection and Very Long Baseline Interferometry observation. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, a Mobile Collector (MC) is used to gather data by periodically traversing the network to avoid hotspot or energy-hole issues. Although the MC's data collection process and network performance can be enhanced by determining suitable set of Stop Points (SPs), it is challenging to find the best set of SPs and schedule an effective MC trajectory. Much attention has been received to MC's path planning through SPs in a static environment where the path is determined during the initial phase, but they do not emphasize the nodes' coverage rate and cannot be adapted to network topology changes. In this context, we propose an Efficient Trajectory Planning method for Coverage Enhanced Data collection in WSN (ETP-CED). We introduce an enhanced method based on integrated Particle Swarm Optimization and Ant Colony Optimization for selecting the best set of SPs and planning efficient MC trajectory. ETP-CED is adaptive to node failures, allowing the nodes to reposition themselves to patch up coverage holes in the network. MC readjusts its planned path when there are less nodes in the network due to node failures, thereby shortening the trajectory length and speeding up data delivery. The results show that ETP-CED outperforms existing methods in the aspects of nodes' coverage and data collection efficiency. [ABSTRACT FROM AUTHOR]

The application of IoT (Internet of Things) technology to the health monitoring of expansion joints is of great importance in enhancing the efficiency of bridge expansion joint maintenance. In this study, a low-power, high-efficiency, end-to-cloud coordinated monitoring system analyzes acoustic signals to identify faults in bridge expansion joints. To address the issue of scarce authentic data related to bridge expansion joint failures, an expansion joint damage simulation data collection platform is established for well-annotated datasets. Based on this, a progressive two-level classifier mechanism is proposed, combining template matching based on AMPD (Automatic Peak Detection) and deep learning algorithms based on VMD (Variational Mode Decomposition), denoising, and utilizing edge and cloud computing power efficiently. The simulation-based datasets were used to test the two-level algorithm, with the first-level edge-end template matching algorithm achieving fault detection rates of 93.3% and the second-level cloud-based deep learning algorithm achieving classification accuracy of 98.4%. The proposed system in this paper has demonstrated efficient performance in monitoring the health of expansion joints, according to the aforementioned results. [ABSTRACT FROM AUTHOR]

As psychological researchers increasingly collect data from Workers on Amazon’s Mechanical Turk (MTurk), we examine the history of this data collection platform, reasons for both confidence and caution in the use of the data, and factors that differentiate MTurk data from data collected using more traditional methods. This discussion includes issues of recruitment, responder bias, the psychometric quality of the data, and ethical issues relating to incentives and compensation. [ABSTRACT FROM AUTHOR]

In this research, the Turkish validity and reliability study of the "Child Health Internet Research Parent Scale (CSSAI)" was conducted and it was aimed to gain the scale to the Turkish society. The data of this research, which was carried out in methodological design, were collected by reaching the parents via the online data collection platform in June-August 2021. To the study, 355 parents who met the study criteria were included. The language validity analysis of the scale was performed with the translation-retranslation method, and the content validity was performed with expert opinions. The Content Validity Index (CGI) was found to be 0.960. It was determined that the scale formed as the result of the translations showed parallelism with the original scale. The scale was applied to the sample group after the pilot application was done. As the result of the internal consistency analysis of the scale, it was found that the item-total score correlations in the scale were sufficient and the Cronbach's alpha coefficient was 0.916. Our research result showed that the "Child Health Internet Research Parent Scale (CSSAI)" is a valid and reliable tool for evaluating parents' internet use in health research. [ABSTRACT FROM AUTHOR]

Variation in the electrical conductivity (EC) of water can reveal environmental disturbance and natural dynamics, including factors such as anthropogenic salinization. Broader application of open source (OS) EC sensors could provide an inexpensive method to measure water quality. While studies show that other water quality parameters can be robustly measured with sensors, a similar effort is needed to evaluate the performance of OS EC sensors. To address this need, we evaluated the accuracy (mean error, %) and precision (sample standard deviation) of OS EC sensors in the laboratory via comparison to EC calibration standards using three different OS and OS/commercial-hybrid (OS/C) EC sensors and data logger configurations and two commercial (C) EC sensors and data logger configurations. We also evaluated the effect of cable length (7.5 m and 30 m) and sensor calibration on OS sensor accuracy and precision. We found a significant difference between OS sensor mean accuracy (3.08%) and all other sensors combined (9.23%). Our study also found that EC sensor precision decreased across all sensor configurations with increasing calibration standard EC. There was also a significant difference between OS sensor mean precision (2.85 μS/cm) and the mean precision of all other sensors combined (9.12 μS/cm). Cable length did not affect OS sensor precision. Furthermore, our results suggest that future research should include evaluating how performance is impacted by combining OS sensors with commercial data loggers as this study found significantly decreased performance in OS/commercial-hybrid sensor configurations. To increase confidence in the reliability of OS sensor data, more studies such as ours are needed to further quantify OS sensor performance in terms of accuracy and precision across different settings and OS sensor and data collection platform configurations. [ABSTRACT FROM AUTHOR]

Researchers are combining self-reports from mobile surveys with passive data collection using sensors and apps on smartphones increasingly more often. While smartphones are commonly used in some groups of individuals, smartphone penetration is significantly lower in other groups. In addition, different operating systems (OSs) limit how mobile data can be collected passively. These limitations cause concern about coverage error in studies targeting the general population. Based on data from the Panel Study Labour Market and Social Security (PASS), an annual probability-based mixed-mode survey on the labor market and poverty in Germany, we find that smartphone ownership and ownership of smartphones with specific OSs are correlated with a number of sociodemographic and substantive variables. The use of weighting techniques based on sociodemographic information available for both owners and nonowners reduces these differences but does not eliminate them. [ABSTRACT FROM AUTHOR]