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We tested whether sexual orientation disparities in depressive symptoms are partially explained by a path through attachment anxiety or avoidance leading to ineffective coping strategies. A convenience sample of 1290 individuals from two reliable data collection platforms (Credamo and Wenjuanxing) in China were included. Structural equation modeling was performed, with sexual orientation treated as the independent variable, ineffective coping strategies, attachment avoidance and anxiety as mediators, and depressive symptoms as the dependent variable. We found that lesbian, gay and bisexual (LGB) individuals reported higher levels of attachment anxiety and avoidance than heterosexual individuals, regardless of sex, with Cohen’s ds ranging from 0.25 to 0.76. For both sexes, LGB individuals reported more depressive symptoms than heterosexual individuals, with total effects (standardized regression coefficients) ranging from 0.22 to 0.26. More depressive symptoms reported by LGB individuals compared to heterosexual individuals were partially explained by a path involving attachment avoidance or anxiety leading to ineffective coping strategies, with indirect effects ranging from 0.04 to 0.09 (small effect sizes). Overall, our results suggest that psychological factors, such as the cognitive or emotional representations of self and others, as well as ineffective coping strategies, may partially explain sexual orientation disparities in depressive symptoms. [ABSTRACT FROM AUTHOR]

Developing air pollution monitoring systems utilizing Unmanned Aerial Vehicles (UAVs) and a real-time platform is a significant advancement in environmental monitoring. The paper described a system incorporating multiple sensors, including particulate matter, temperature and humidity sensors, and a barometric pressure sensor to assess air quality parameters using UAVs at different altitudes. The system monitors air quality parameters from 0 to 120 meters at various altitudes. This vertical profiling capability provides a more detailed understanding of air quality variations in the vertical dimension, which is crucial for identifying pollution sources and patterns. The system leverages the Internet of Things (IoT) platform for realtime data collection, transmission, and analysis. The study suggests that the developed system could serve as an alternative for particulate matter monitoring at an urban scale. Combining UAVs, air quality sensors, and IoT technologies offers a comprehensive and dynamic approach to air quality monitoring in urban environments. [ABSTRACT FROM AUTHOR]

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]

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

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

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

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]

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]

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]

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]

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]

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

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

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]

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

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

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 energy of Wireless Sensor Network (WSN) in edge network is limited. In order to save energy, data bundling is proposed to reduce the energy consumed in setting up connection links by joining several packets into a single unit. However, although current data bundling schemes can reduce energy consumption, they will increase the delay of data routing, so it makes the multi-hop end to end delay a lot in WSN. Also, there is no appropriate solution to tackle the delayed growing problem caused by data bundling. And the energy consumption of the Acknowledgment (ACK) transmission is not comprehensively considered either. Regarding to those issues of previous researches, in this paper, a Data and ACK Bundling Scheme (DAB) is proposed to aggregate several data or ACK packets into a single unit to reduce the energy consumption as well as the end to end delay of the WSN. In the proposed DAB scheme, not only multiple packets but also ACK bundling into a single unit can reduce energy consumption. More importantly, the parameters of bundling technology are optimized from a global perspective in DAB scheme, so that WSN can not only improve the network lifetime, but also reduce the end to end delay which is impossible in previous studies. The theoretical analysis and experimental results illustrate that, while ensuring the reliability of data collection, our DAB scheme improves the energy efficiency by 29.03 % and reduces the delay by 22.29 % when compared with our traditional data collection scheme. [ABSTRACT FROM AUTHOR]

Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots are the two popular platforms for data collection in fields. They all contribute to site-specific weed management (SSWM) to maintain crop yield. Currently, the data from these two platforms is processed separately, though sharing the same semantic objects (weed and crop). In our paper, we have proposed a novel method with a new deep learning-based model and the enhanced data augmentation pipeline to train field images alone and subsequently predict both field images and UAV images for weed segmentation and mapping. The network learning process is visualized by feature maps at shallow and deep layers. The results show that the mean intersection of union (IOU) values of the segmentation for the crop (maize), weeds, and soil background in the developed model for the field dataset are 0.744, 0.577, 0.979, respectively, and the performance of aerial images from an UAV with the same model, the IOU values of the segmentation for the crop (maize), weeds and soil background are 0.596, 0.407, and 0.875, respectively. To estimate the effect on the use of plant protection agents, we quantify the relationship between herbicide spraying saving rate and grid size (spraying resolution) based on the predicted weed map. The spraying saving rate is up to 90 % when the spraying resolution is at 1.78 × 1.78 cm2. The study shows that the developed deep convolutional neural network could be used to classify weeds from both field and aerial images and delivers satisfactory results. To achieve this performance, it is crucial to perform preprocessing techniques that reduce dataset differences between two distinct domains. [ABSTRACT FROM AUTHOR]

The article discusses the establishment of the international colic surgery audit and registry (INCISE), which aims to collect data on colic surgery in horses from clinics around the world. The data collection platform, launched in January 2020, allows clinics to enter and download data for their own clinics and generates audit reports. A total of 226 equine clinics from 40 countries were contacted, and 60 clinics from 24 countries submitted data for the initial process audit. The data collected will be used to develop benchmarking audit data for colic surgery. [Extracted from the article]

As the volume of open data from cloud platforms, including consumer, credit, and social data, experiences exponential growth, the problem of data collection for credit and lending has been effectively alleviated. However, this surge in massive data exhibits new characteristics of high dimensionality and imbalance, which makes the value information density of credit features become very sparse, resulting in the inability of existing data processing methods to extract latent information from the data, and the difficulty of prediction models to assign more accurate weights to crucial features. This affects the model's performance in assessing individual credit defaults. To address these issues, this paper optimizes the data processing process, then introduces the self-attention and cross-network credit default prediction fusion framework (SACN), which incorporates a cross-network and self-attention mechanism. This fusion framework optimizes the credit data feature engineering process, further reducing conflicts among features from multiple sources. Through experimental comparisons using publicly available credit datasets, SACN accurately captures explicit and implicit high-order data feature interactions within credit lending, enhancing the precise and efficient extraction of critical credit information. Its performance in credit default prediction surpasses that of other mainstream predictive models and maintains accuracy and stability across various types of datasets. The source code is publicly available at https://gitee.com/andyham_andy.ham/sacn-forecasting-framework.git. [Display omitted] • Firstly, the introduction of the cross-network effectively addresses the challenge faced by credit default prediction models in extracting latent information in high-dimensional data. • Secondly, combining the self-attention mechanism and cross-network into the SACN fusion framework can accurately assign weights to feature information and extract more information from high-order features and their interactions. • Finally, the feature preprocessing filtering mechanism is enhanced to reduce data conflicts. This improvement enhances the versatility of the SACN fusion framework for diverse credit default scenarios. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have been broadly employed as a data collection platform to assist in efficiently collecting data from IoT devices. However, the deployment optimization of UAVs has been challenged due to the need to minimize the energy consumption of UAVs and IoT devices. Several algorithms have been recently proposed for tackling this challenge, but they still have room for improvement due to their slow convergence speed and memory-wasting problems. Therefore, in this study, a new energy-aware approach has been proposed for accurately optimizing the entire deployment of UAVs, which could minimize the total energy consumption. This approach is based on presenting a new encoding mechanism, namely an optimized population size mechanism, for representing both location and number of stop points in an effective manner. In this mechanism, similar to some studies in the literature, the whole population is responsible for the entire deployment, and each individual is responsible for a stop point in this deployment. However, this mechanism presents a novel way to optimize the number of stop points based on adding an auxiliary variable to each stop point to determine whether it will be removed, inserted, or replaced in the newly generated deployment. This variable will be optimized by the optimization techniques during the optimization process to search for the optimal choice for each stop point that could achieve a better deployment. Two well-known optimization techniques, known as differential evolution (DE) and gradient-based optimizer (GBO), are adapted using this mechanism to present new variants, namely DEoPS and GBoPS, for accurately tackling the deployment optimization problem. Two energy consumption formulations are used in our work to investigate the performance of DEoPS and GBoPS. Several experiments have been conducted to compare the performance of both DEoPS and GBoPS with several algorithms on eleven instances. The experimental findings show the effectiveness of GBoPS for the first formulation and the effectiveness of DEoPS for the second formulation. [ABSTRACT FROM AUTHOR]

The article focuses on the launch of the Anti-microbial Sales and Use (ASU) Platform by the European Medicines Agency (EMA), mandated under EU regulation to collect annual data from Member States on antimicrobial sales and usage in animals starting January 2024. It mentions this platform replaces the ESVAC project, aiming to enhance data collection and analysis to combat antimicrobial resistance.

• A novel algorithm framework is proposed for object detection in ocean engineering. • Created a dataset of bird droppings on the surface of PV panels. • Constructing YCrS texture space to enhance the texture features of bird droppings. • A case study demonstrates the effectiveness of the algorithm framework. Guano are an important factor affecting the cleanliness of photovoltaic modules on floating solar power plants at sea. It can lead to a decrease in photoelectric conversion efficiency, power loss, and even the occurrence of "hot spots", thereby causing damage to the components. Therefore, the segmentation and detection of guano are crucial for visual automation in the cleaning and inspection processes. However, the composition, density, and thickness of guano naturally vary, leading to inconsistent levels of transparency and color. The uneven intensity of guano images greatly reduces the accuracy of segmentation and detection. Addressing this issue, this study proposes a segmentation algorithm based on combining different color channels to segment guano on the surface of photovoltaic modules. The mean shift method is used for adaptive segmentation to facilitate the detection of guano. Furthermore, the segmentation results obtained by this method are introduced into the input space to improve the traditional Mask RCNN. In addition, this study successfully produced a dataset of guano on the surface of photovoltaic modules using on-site data collection and with the help of a platform built in-house in the laboratory. The experimental results on the self-constructed dataset demonstrate that the enhanced Mask R-CNN model has shown an approximate increase of 5.9% and 6.0% in mAP values for object recognition and segmentation compared to the traditional Mask R-CNN model. This indicates the effectiveness of the methodology proposed in this study. [ABSTRACT FROM AUTHOR]

Since the existing control strategy for electro-hydraulic composite braking (EHB) system is essentially "vehicle-centered", it is liable to cause incompatibility with current driving behavior, torque fluctuation and inconsistent braking feeling occur, which affects braking safety. In view of the above issue, a personalized MPC control strategy in accordance with the design methodology of characteristic E is presented. To do this, the data collection platform for characterizing driving behavior is constructed under typical vehicle-following conditions. Then, a generalized radial basis function (GRBF) neural network is adopted to accurately identify braking intensity of different driving behaviors. Next, an optimization model for the maximum energy recovery of EHB system is established in terms of required braking torque and motor speed, the distribution coefficient of braking torque is optimized by applying the adaptive particle swarm optimization (APSO) algorithm. Finally, the proposed personalized MPC control strategy is verified under different driving behaviors, the results display that: (1) the personalized MPC controller possess superiority of acquiring stable braking feeling, the torque tracking error is decreased by 96.8 %; (2) energy recovery for EHB system with optimized torque distribution is increased by 34.47 % under FTP-75 cycle conditions, and the response amplitude of braking feeling is increased by 5.6 %. • Braking torque distribution coefficients of EHB system is optimized for different driving behaviors. • The parameter setting issue of driving behaviors characteristics and MPC controller is addressed. • A personalized MPC controller in accordance with driving behavior characteristic E is presented. [ABSTRACT FROM AUTHOR]

One of the most significant sources of information from customers is customer complaints. Successful and effective complaint management can end complaint crises and ensure client loyalty, which is a sign of great service performance. In this paper, we proposed a novel customer temperament-centered and e-CCH system-based data collection and data mining method titled "3D" model for customer complaint data analysis. Three phases are (1) Development and launch of e-Customer Complaint Handling system, (2) Data collection and transfer of learning by e-Customer Complaint Handling system, and (3) Data mining by e-Customer Complaint Handling system. An advanced e lectronic C ustomer C omplaint H andling System called the e-CCH system was then developed and launched. This system adapts the seasonal associations model based on Hippocrates's customer temperament theory to the whole stages of customer complaint reporting and handling. With this system, we conducted a dataset collection work from restaurant chains of two brands over four years. As a result, we collect thousands of real-world temperament-centred customer complaint cases by four years to form the one-of-a-kind CCH dataset. This one-of-a-kind CCH dataset was open-sourced with detailed customer complaint attributes and heuristic decision-making for valuable industrial handling manner. After further analysis of this dataset, we found that customers with different temperament types tend to have different types of complaints. In addition, adapting the temperament theory to the e-CCH system can classify customer types better and provide personalized solutions. To our best knowledge, this rich and the one-of-a-kind CCH dataset reported in this paper is the first comprehensive study of customer complaint handling in an industrial service management context. Meanwhile, data mining with cross analysis and correspondence analysis and an ChatGPT experiment for transfer of learning based on this yearly and one-of-a-kind industrial customer complaint dataset was analyzed and discussed. In addition, how this dataset may contribute to more realistic complaint-handling theoretic studies for better service failure recovery and interactive marketing is discussed in-depth. [ABSTRACT FROM AUTHOR]

The Knowledge Discovery from Data (KDD) process is widely used across various domains to get valuable insights from data. Many platforms, like KNIME or RapidMiner, offer effective tools for KDD analysts, allowing them to perform data analytics tasks in a visual fashion, without writing code. In recent years, the increasing availability of mobility data has led to a surge in KDD-based initiatives from both industry and academia in the Intelligent Transportation Systems (ITS) domain. Still, KDD platforms lack comprehensive support for some typical mobility data manipulation tasks. As a result, mobility data analysis still requires a significant coding phase, with reduced productivity and hindered replicability of results. To address this gap, this paper presents a novel solution aimed at supporting ITS data analysts in defining KDD processes more efficiently. More in detail, we extended the KNIME platform by introducing a collection of new components explicitly tailored to facilitate some peculiar KDD tasks from mobility data. These components encompass critical functionalities such as map coverage analysis, trajectory partitioning and map-matching. To showcase the effectiveness of the proposed solution, we used it to replicate a study published in the ITS data analytics domain. Thanks to our proposal, such replication can be accomplished in a few minutes and with just a few clicks, without any manual coding, resulting in a pipeline that is easier to understand, distribute and re-execute, also for domain experts with no programming experience. Our solution is open-source and freely downloadable from the Knime Hub. In this way, we aim to foster data-driven research and practice in the ITS field, by providing researchers and practitioners with more effective analytics tools to handle mobility data. • Visual analytics platforms lack support for handling mobility data in ITS scenarios. • We present a visual-based toolkit to support mobility data analytics for ITS. • The solution allows practitioners to perform complex analyses without writing code. • The solution is open-source and integrated within the KNIME Analytics Platform. • The effectiveness of the solution is assessed in a real-world ITS analytics scenario. [ABSTRACT FROM AUTHOR]

Completing continuous circular capsulorhexis (CCC) requires the operator to perform fine operations, which is difficult to do accurately when continuous fine actions are out of balance in the classification of CCC procedures. Multimodal deep learning can improve the classifier's performance, but the recognition accuracy of inferior classes is difficult to improve. To solve these problems, a bidirect-gate recurrent unit (Bi-GRU)-attention-based multimodal, multi-timescale data fusion network (BiMNet) is proposed, which contains a data extraction module called a skip-concatenate gate recurrent unit (SC-GRU), a bimodal data fusion attention computation, and a decoder module. The combination of these modules can fully extract the features of different temporal scales in multimodal action data and fuse them effectively. The model is validated using the ophthalmologist CCC multimodal maneuver dataset, which was collected by the data collection platform constructed in this research, achieving an accuracy of 0.9124 ± 0.0125 in continuous action sequence segmentation and improving the F1-score of minority class recognition to over 80%, making it more effective than baseline algorithms. • A Bi-GRU-attention-based multimodal data fusion network (BiMNet) is proposed. • The skip-concatenate GRU module and a bimodal data fusion module are proposed. • BiMNet can improve the classification effect of dealing with imbalanced data. • Established a multimodal maneuver dataset of continuous circular capsulorhexis. [ABSTRACT FROM AUTHOR]

• Truck drivers' working conditions: from working schedules to time away from family. • The load represents a main safety concern imposing careful storage on the vehicle. • Truck driver's attitudes and behaviours related to in-vehicle Technologies. • Truck Platooning and the new skills imposing new training needs. The platooning technology allows for two or more trucks running in convoy at a pre-defined distance between each other, being virtually connected using connectivity technology and automated driving support systems. It is recognized that truck platooning systems bring economical and environmental advantages. Thus, it is time for a transition from the existing truck freight activity towards truck platooning systems. This requires an important improvement in terms of in-vehicle technology, together with infrastructure improvement and truck drivers' acquisition of new technology-related skills. A holistic approach is previewed to identify both the requirements for the development of truck platooning services and the requests for their safe deployment in the real world. Then, qualitative data were collected from truck drivers working for two different Portuguese freight companies using Focus Groups (FG). Thus, three FG sessions were organized and carried out with a total of 22 truck drivers. Considering that age and experience on the job are important factors to take into consideration for technological changes on the job, their potential impact on truck drivers' activity was addressed on the focus group discussions. Anyway, the potential users' attitudes regarding any innovation on the job were addressed as a prevention of further negative attitudes or misuse. Having safety in mind as a permanent attitude toward on job innovation is actually the most important factor toward success. [ABSTRACT FROM AUTHOR]