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BACKGROUND: Ménière's disease is a disabling condition causing vertigo and hearing loss yet remains incompletely understood. Registry studies have the potential to answer important questions about phenotypes and natural history of clinical conditions. The aim of this study was to explore the feasibility of a patient-centered national Ménière's disease registry. METHODS: This was an observational study carried out at 4 state-funded hospitals and 4 independent clinics, within 3 distinct urban and rural regions within the UK. Adults with Ménière's disease were eligible to participate. A range of patient reported data, questionnaire data and clinical data (audiometric, radiological, and specialist balance testing data) was inputted into a bespoke database. RESULTS: The study recruited 411 participants. The majority of participants chose online recruitment (73%) and 27% chose via paper-based methods for participation. A small majority (57%) of participants were female. 96% of participants were of white ethnicity. Data completeness from online or postal data collection was similar. Around 20% of participants had audiological evidence of bilateral Ménière's disease. CONCLUSION: This feasibility study has successfully piloted methods for recruitment of hundreds of participants diagnosed with Ménière's disease. Participants actively contributed their data to a robust and extensive data collection platform. The positive outcomes from this initial feasibility study are anticipated to serve as a foundation for the future expansion of the registry. This expansion holds the potential to address a broad spectrum of request, encompassing all aspects of the nature of Ménière's disease. [ABSTRACT FROM AUTHOR]

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

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

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

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]

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

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

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

The article introduces ScDB, a comprehensive database dedicated to Saccharum, which facilitates functional genomics and molecular biology studies in sugarcane. Sugarcane is an important sugar crop used for the production of sugar and biofuels. The complexity of the sugarcane genome has historically posed challenges for researchers, but recent advancements in genome research have led to the assembly of the genomes of various sugarcane species. ScDB provides access to genomic assembly sequences, annotations, and transcriptome data for six Saccharum species and Erianthus rufipilus. The database includes various tools and modules for search, analysis, and visualization, and it will continue to be updated with more sugarcane genome data and other omics data in the future. [Extracted from the article]

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]

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]

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

The 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]

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 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]

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]

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]

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]

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 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]

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]

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]

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]

This article discusses the key factors that influence quality in contemporary surgical nursing. The factors include education and training, technology and innovation, patient-centered care, and patient safety. The article emphasizes the importance of continuous improvement and adaptation in healthcare to ensure high-quality care and patient safety. It also highlights the need for ongoing education in the use of new technologies and the importance of communication skills and cultural awareness in providing individualized care. The article concludes by emphasizing the role of surgical nurses in promoting quality and safety in modern medical practice. [Extracted from the article]

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]

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]

Background: In Ethiopia, child malnutrition is a significant public health problem. To address the problem, Nutrition-Sensitive Agriculture (NSA) program was introduced. However, there is a paucity of evidence about the prevalence of child undernutrition in NSA-implemented districts. Therefore, this study aimed to assess the prevalence of undernutrition among children aged 6–59 months in NSA-implemented districts. Method: A community-based cross-sectional study was conducted by enrolling 422 children aged 6–59 months paired with their mothers. A systematic sampling technique was used to select respondents. Data were collected by Open Data Kit (ODK) data collection platform, and Stata version 16 was used for analysis. The multivariable logistic analysis model was fitted to assess the association between variables, and 95% CI was estimated to measure the strength of the association. The level of statistical significance was declared at a p-value of less than 0.05 in the multivariable model. Result: Overall, 406 respondents participated in the study, and a response rate of 96.2% was obtained. The prevalence of stunting, wasting, and underweight was 24.1% (95% CI: 19.9–28.4), 8.87% (95% CI: 6.3–12.1) and 19.95% (95% CI: 16.2–24.2), respectively. Household food insecurity was significantly associated with being underweight (AOR: 3.31, 95% CI (1.7–6.3). Child dietary diversity (AOR: 0.06, 95% CI: 0.01–0.48) and being a beneficiary of the NSA (AOR: 0.12, 95% CI: 0.02–0.96) program were associated with wasting. Lack of ANC visits and diarrhea in the past two weeks was associated with stunting and wasting, respectively. Conclusion: The prevalence of malnutrition was a moderate public health problem. Wasting was more prevalent than the recent national and Amhara region averages. However, the prevalence of stunting and underweight was lower than the national average and other studies conducted in Ethiopia. Healthcare providers should work to increase dietary diversity, ANC visits, and reduce diarrheal disease. [ABSTRACT FROM AUTHOR]

The data that everyday consumers produce is becoming more and more important to the economy. Yet, as this data imbues tech corporations with tremendous wealth and power, we, the data producers, have no say as to how our data is collected or how it is used. The reign of data analytics to pursue profit above all else has led to a conflagration of data harms perpetuated against already marginalized groups. What is needed in this moment is a tool that equalizes the bargaining power between platforms and users, to give consumers meaningful control over the data they produce. In the early 20th century, labor organizers called for industrial democracy: the ability for workers to have substantial say over the conditions of their labor. For today’s datafied information economy, this Note instead calls for the need for informational democracy: the ability of consumers, as data producers, to exert meaningful control over the data that their lives engender. This Note advocates for data unions as one such tool to achieve informational democracy. It conceptualizes data unions as democratically elected organizations that aggregate data to create collective bargaining units to negotiate with platforms as to allowed uses for data. First, the Note gives an overview of how today’s economy creates both value and harm out of data processing. Then, it argues that due to the specific nature of this value and harm creation, data unions are uniquely situated regulatory tools that can enact meaningful consumer control. [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 collection and use of data on climate change and its impacts are crucial for effective climate adaptation and climate risk management. The revolution in internet access, technology and costs has led to a shift from using traditional paper‐based data collection to the use of Mobile Data Collection using Personal Digital Assistants (PDA) such as smartphones and tablets. In this paper, we report our experiences using both approaches for a household and business survey during a climate adaptation study in two Nigerian cities—Makurdi and Calabar. The focus of this paper is to evaluate and compare the effectiveness of using traditional paper‐based data collection and PDAs as data collection tools for climate change study in African societies. In Calabar, data were collected using paper questionnaires, while in Makurdi the questionnaires were developed on Open Data Kit (ODK) and administered using PDAs. Results show that data collection using PDA was faster, cheaper, more accurate and resulted in fewer omissions than paper‐based data collection. There was a time saving of four (4) minutes per questionnaire and a 24% cost saving when using PDA. PDA provides additional benefits where platforms can collect images, videos and coordinates. This significantly improved the credibility of the data collection process and provided further data that allowed for the mapping of environmental phenomena by linking survey research with geo‐referenced data in a geographic information systems platform to provide spatial representations of social and environmental system convergence. PDA offers a tool for collecting data that will make necessary socio‐environmental data available in a faster, reliable and cheaper manner; future research can build on this study by discovering other possible but less highlighted benefits of PDA. Although, with great benefits, there are lessons to be learnt and issues to consider when deploying PDA in large‐scale household surveys. [ABSTRACT FROM AUTHOR]

In precision agriculture, information technology is used to improve farm management practices. Thereby, productivity can be increased and challenges with overfertilization and water consumption can be addressed. This requires low-power and wireless underground sensor nodes for monitoring the physical, chemical and biological soil parameters at the position of the plant roots. Three ESP32-based nodes with these capabilities have been designed to measure soil moisture and temperature. A system has been developed to collect the measurement data from the sensor nodes with a drone and forward the data to a ground station, using the LoRa transmission standard. In the investigations of the deployed system, an increase in the communication range between the sensor node and the ground station, from 300 m to 1000 m by using a drone, was demonstrated. Further, the decrease in the signal strength with the increasing sensor node depth and flight height of the drone was characterized. The maximum readout distance of 550 m between the sensor node and drone was determined. From this, it was estimated that the system enables the readout of the sensor nodes distributed over an area of 470 hectares. Additionally, analysis showed that the antenna orientation at the sensor node and the drone influenced the signal strength distribution around the node due to the antenna radiation pattern. The reproducibility of the LoRa signal strength measurements was demonstrated to support the validity of the results presented. It is concluded that the system design is suitable for collecting the data of distributed sensor nodes in agriculture. [ABSTRACT FROM AUTHOR]

Recent studies have shown the efficacy of mobile elements in optimizing the energy consumption of sensor nodes. Current data collection approaches for waste management applications focus on exploiting IoT-enabled technologies. However, these techniques are no longer sustainable in the context of smart city (SC) waste management applications due to the emergence of large-scale wireless sensor networks (LS-WSNs) in smart cities with sensor-based big data architectures. This paper proposes an energy-efficient swarm intelligence (SI) Internet of Vehicles (IoV)-based technique for opportunistic data collection and traffic engineering for SC waste management strategies. This is a novel IoV-based architecture exploiting the potential of vehicular networks for SC waste management strategies. The proposed technique involves deploying multiple data collector vehicles (DCVs) traversing the entire network for data gathering via a single-hop transmission. However, employing multiple DCVs comes with additional challenges including costs and network complexity. Thus, this paper proposes analytical-based methods to investigate critical tradeoffs in optimizing energy consumption for big data collection and transmission in an LS-WSN such as (1) finding the optimal number of data collector vehicles (DCVs) required in the network and (2) determining the optimal number of data collection points (DCPs) for the DCVs. These critical issues affect efficient SC waste management and have been overlooked by previous studies exploring waste management strategies. Simulation-based experiments using SI-based routing protocols validate the efficacy of the proposed method in terms of the evaluation metrics. [ABSTRACT FROM AUTHOR]

Organisms use color to serve a variety of biological functions, including camouflage, mate attraction and thermoregulation. The potential adaptive role of color is often investigated by examining patterns of variation across geographic, habitat and life‐history gradients. This approach, however, presents a data collection trade‐off whereby researchers must either maximize intraspecific detail or taxonomic and geographic coverage. This limits our ability to fully understand color variation across entire taxonomic groups at global scales. We provide a solution by extracting color data from more than 44 000 individual specimens of ants, representing over 14 000 species and morphospecies, using a computer vision algorithm on ant head images. Our analyses on this dataset reveal that ants are dominated by variation in the dark‐pale color spectrum, that much of this variation is held within species, and that, overall, a suite of popular ecogeographic hypotheses are unable to explain intra‐ and interspecific variation in ant color. This is in contrast to previous work at the assemblage level in ants and other invertebrates demonstrating clear and strong links between variables such as temperature and the average color of entire assemblages. Our work applies a novel computational approach to the study of large‐scale trait diversity. By doing so, we reveal previously unknown levels of intraspecific variation. Similar approaches may unlock a vast amount of data residing in museum and specimen databases and establish a digital platform for a data collection revolution in functional biogeography. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a very useful nondestructive evaluation (NDE) device for locating and mapping underground assets prior to digging and trenching efforts in construction. This paper presents a novel robotic system to automate the GPR data-collection process, localize underground utilities, and interpret and reconstruct the underground objects for better visualization, allowing regular nonprofessional users to understand the survey results. This system is composed of three modules: (1) an omnidirectional robotic data-collection platform that carries a RGB-D camera with inertial measurement unit (IMU) and a GPR antenna to perform automatic GPR data collection and tag each GPR measurement with visual positioning information at every sampling step, (2) a learning-based migration module to interpret the raw GPR B-scan image into a two-dimensional (2D) cross-section model of objects, and (3) a three-dimensional (3D) reconstruction module, i.e., 30.0% GPRNet, to generate underground utility model represented as fine 3D point cloud. Comparative studies were performed on synthetic data and field GPR raw data with various incompleteness and noise. Experimental results demonstrated that our proposed method achieves a higher GPR imaging accuracy in mean intersection over union (IoU) than the conventional back-projection (BP) migration approach, and 6.9%−7.2% less loss in Chamfer distance (CD) than point cloud model reconstruction baseline methods. The GPR-based robotic inspection provides an effective tool for civil engineers to detect and survey underground utilities before construction. [ABSTRACT FROM AUTHOR]

Land tenure is an individual relationship to the land. Vietnam applies a regime of universal ownership of land. The land belongs to all the people of Vietnam, but the State represents the owner. Land ownership in Vietnam includes three fundamental rights: the right to possess, use, and dispose of land. When the State allocates land to land users, it only assigns them the right to own and use land. This article evaluated one out of eight rights to dispose of land by the State, land acquisition. Data was collected from a survey with 100 land-expropriated households, five interviews with authorities at all levels, and five interviews with households living next to the study area. The results show that land acquisition does not ensure fairness for families who lose land. Compensation rates are six to eight times lower for agricultural land. The right to access land information is also not appreciated when people do not know their household compensation details. Another inequity was also seen between households who lost land and those living around the land acquisition project. [ABSTRACT FROM AUTHOR]

Objectives This study aims to describe the demographic and clinical profile and ascertain the determinants of outcome among hospitalized coronavirus disease 2019 (COVID-19) adult patients enrolled in the National Clinical Registry for COVID-19 (NCRC). Methods NCRC is an on-going data collection platform operational in 42 hospitals across India. Data of hospitalized COVID-19 patients enrolled in NCRC between 1st September 2020 to 26th October 2021 were examined. Results Analysis of 29 509 hospitalized, adult COVID-19 patients [mean (SD) age: 51.1 (16.2) year; male: 18 752 (63.6%)] showed that 15 678 (53.1%) had at least one comorbidity. Among 25 715 (87.1%) symptomatic patients, fever was the commonest symptom (72.3%) followed by shortness of breath (48.9%) and dry cough (45.5%). In-hospital mortality was 14.5% (n  = 3957). Adjusted odds of dying were significantly higher in age group ≥60 years, males, with diabetes, chronic kidney diseases, chronic liver disease, malignancy and tuberculosis, presenting with dyspnoea and neurological symptoms. WHO ordinal scale 4 or above at admission carried the highest odds of dying [5.6 (95% CI: 4.6–7.0)]. Patients receiving one [OR: 0.5 (95% CI: 0.4–0.7)] or two doses of anti-SARS CoV-2 vaccine [OR: 0.4 (95% CI: 0.3–0.7)] were protected from in-hospital mortality. Conclusions WHO ordinal scale at admission is the most important independent predictor for in-hospital death in COVID-19 patients. Anti-SARS-CoV2 vaccination provides significant protection against mortality. [ABSTRACT FROM AUTHOR]

With the growing need to obtain information about power consumption in buildings, it is necessary to investigate how to collect, store, and visualize such information using low-cost solutions. Currently, the available building management solutions are expensive and challenging to support small and medium-sized buildings. Unfortunately, not all buildings are intelligent, making it difficult to obtain such data from energy measurement devices and appliances or access such information. The internet of things (IoT) opens new opportunities to support real-time monitoring and control to achieve future smart buildings. This work proposes an IoT platform for remote monitoring and control of smart buildings, which consists of four-layer architecture: power layer, data acquisition layer, communication network layer, and application layer. The proposed platform allows data collection for energy consumption, data storage, and visualization. Various sensor nodes and measurement devices are considered to collect information on energy use from different building spaces. The proposed solution has been designed, implemented, and tested on a university campus considering three scenarios: an office, a classroom, and a laboratory. This work provides a guideline for future implementation of intelligent buildings using low-cost open-source solutions to enable building automation, minimize power consumption costs, and guarantee end-user comfort. [ABSTRACT FROM AUTHOR]

Introduction Establishing and maintaining a cancer registry in a tertiary high volume centre is fraught with difficulty, inaccuracy and missed data entry. Further, the raw unstructured data must be converted into a structured digital data, so that scientists can identify trends in cancer diagnoses and treatment responses. Objective We test the feasibility of establishing a cancer registry of Head and Neck malignancy patients through a research oriented artificial intelligence (AI) enabled data collection platform, using its smartphone application version. Materials and Methods This prospective observational study was conducted in the Department of Otolaryngology & Head and Neck Surgery, Post Graduate Institute of Medical Education And Research, Chandigarh in collaboration with Departments of Radiotherapy and Community Medicine. After taking due clearance from the Institute ethical committee, HNC patients, who were biopsy proven, were enrolled from October 2019 up to March 2021. The obtained data was entered, followed up and analysed through Jiyyo Research application which is a commercially available dedicated research oriented AI enabled data collection platform. Results The Jiyyo Research site was browsed and after proper registration, the patient data was entered into a proforma/questionnaire. The entered patient details were browsed for review, follow up and addition of new information. The whole process of data capture for each patient, took approximately 5-8 min, while any updates or review for the same patient required less than a minute. Search and data retrieval was very quick, and can be done in 1-2 min. Through this platform, a total of 1214 HNC patients were collected, followed and analysed during the study period. Conclusion It was feasible to establish a Head and Neck Cancer Registry using an AI based smartphone app. This AI based tumor registry could benefit in further studies with longer follow up of 5 and 10 years and in future AI studies. [ABSTRACT FROM AUTHOR]

Today, the use of information and communication technology is very important in making the internet of things (IoT) elements distributable around the earth. With the development of IoT topics, today unmanned aerial vehicles (UAV) are utilized as a platform for gathering data from various IoT devices located worldwide. Determining the number and optimal locations of drones can minimize energy consumption in this data-collection system in the IoT. Using a promising multi-objective optimization algorithm (MOA) can achieve this goal. In this research, a bio-inspired MOA, termed the multi-objective spotted hyena optimizer (MOSHO), is employed on the data-collection platform for a group of IoT devices in a geographical area. The results of this method have been compared with other evolutionary MOAs. The analysis of the results shows that the MOSHO has a noteworthy consequence on the process of optimal energy consumption in this system, in addition to a high convergence associated with better diversity and robustness. The results of this research can be used to identify the optimization parameters in this system. [ABSTRACT FROM AUTHOR]

Methods to collect data in construction engineering and management (CEM) research are evolving, informed by recent technological advancements. One such method is research charrettes that allow effective interactions and knowledge sharing between expert industry practitioners and academic researchers, all colocated in a single venue, enabling rich data collection and live communication. A pivot point in technological evolution occurred with the COVID-19 pandemic, forcing a global shift to remote work. Hence, planned in-person research charrettes had to shift to remote sessions, relying on virtual conferencing platforms and online data collection mechanisms. Technology-enabled charrettes have allowed the authors to collect significantly richer data sets and ensure a more diverse representation of participants, while saving tremendous amounts of time. With the continuing emergence of technological applications, the world might not go back to functioning fully in person. The authors believe remote research charrettes (RRCs) will still be used in a post-COVID-19 world because of their superior performance. This paper builds on a previous publication that described traditional research charrettes as a method to enhance CEM research a decade ago; it offers a significantly updated and improved RRC method based on the knowledge gained from transitioning a dozen in-person charrettes into RRCs. It also presents performance comparisons between RRCs and traditional charrettes by quantifying metrics indicating how RRCs are more time-efficient and cost-saving, harness more participants from more diverse locations, and enable the collection of richer data sets and four times more industry comments and expert feedback. This paper also provides guidance on the integration of technology with traditional research charrettes, hence contributing to the CEM body of knowledge. [ABSTRACT FROM AUTHOR]

The past decade has witnessed a rapid evolution in rare disease (RD) research, fueled by the availability of genome-wide (exome and genome) sequencing. In 2011, as this transformative technology was introduced to the research community, the Care4Rare Canada Consortium was launched: initially as FORGE, followed by Care4Rare, and Care4Rare SOLVE. Over what amounted to three eras of diagnosis and discovery, the Care4Rare Consortium used exome sequencing and, more recently, genome and other 'omic technologies to identify the molecular cause of unsolved RDs. We achieved a diagnostic yield of 34% (623/1,806 of participating families), including the discovery of deleterious variants in 121 genes not previously associated with disease, and we continue to study candidate variants in novel genes for 145 families. The Consortium has made significant contributions to RD research, including development of platforms for data collection and sharing and instigating a Canadian network to catalyze functional characterization research of novel genes. The Consortium was instrumental to implementing genome-wide sequencing as a publicly funded test for RD diagnosis in Canada. Despite the successes of the past decade, the challenge of solving all RDs remains enormous, and the work is far from over. We must leverage clinical and 'omic data for secondary use, develop tools and policies to support safe data sharing, continue to explore the utility of new and emerging technologies, and optimize research protocols to delineate complex disease mechanisms. Successful approaches in each of these realms is required to offer diagnostic clarity to all families with RDs. After a decade of collaborative network science in Canada and three eras of RD gene discovery, the Care4Rare Canada Consortium recognized it was time to reflect on the lessons learned from our successes to best meet the challenges of RD diagnosis and discovery in the decade to come. [ABSTRACT FROM AUTHOR]