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Background: This study aims to identify trends, research gaps, leading journals, institutions, countries, and authors by examining the top 100 cited Yttrium-90 Radioembolization (Y90) research articles. The insights gained will assist funding and collaboration efforts in Y90 research. The team analyzed 981 articles from 36 peer-reviewed journals, gathering data on authors, publication year, journal, citations, affiliations, keywords, and references. Author affiliations were scrutinized to pinpoint institutions and countries. p values < 0.05 were deemed statistically significant. The most cited paper (703 citations) was authored by Salem et al. (2010), and the journal with the most publications (16) was Journal of Vascular and Interventional Radiology. There was no significant correlation between journal impact factor and citation metrics. A decreasing trend in the number of top 100 articles was observed since the peak period (2008–2013). Most papers were published in high-impact factor (IF > 3, n = 31/35) and SCImago Q1 journals (86/100), with Northwestern University, Clínica Universidad de Navarra, and Ludwig Maximilian University of Munich being the leading institutions. The most prolific authors were Salem, R., Lewandowski, R.J., and Mulcahy, M.F., with Salem, R. as the most influential. A statistically significant positive correlation between collaborative links and published articles was also discovered. Short conclusion: The top 100 cited articles were primarily published in high-impact journals, and Northwestern University and the USA showed greater productivity and collaboration. These findings have crucial implications for researchers, policymakers, and institutions, aiding in the improvement of Y90 application and understanding in clinical practice. [ABSTRACT FROM AUTHOR]

Digital games are one of the most engaging forms of media. Players can have a first-hand experience and the possibility of feeling immersed within a digital setting where agency is exerted. This paper seeks to understand the role of digital games as new means of communication and their potential to convey messages that may defy gamers to question the moral grand narratives that shape contemporary societies. In a moment when many countries have opted to close or limit the circulation in their borders, it is important to think about the role played by the border as a historic liminal entity for the representation of societies and communities, distinguishing those who belong from those who do not. The representation of the border in digital games have tended to follow one of the main tropes explored in other media: the border as a physical space that set boundaries between different nations and different regions of the world. In order to assert the potential of digital games to offer a more comprehensive representation of the border as a liminal space, the case of Papers, Please! (2013) will be analyzed. Based on a qualitative methodology, having content analysis as primary data collection method, the paper intends to contribute to the discussion on how history may narrativized and, eventually, reinterpreted in digital games. Papers, Please! analysis will be centered on the relationship that is set between player and border, that is presented as a liminal space where the game's action takes places entirely. Moreover, the paper intends to discuss the role that has been played by digital games in the representation of serious topics and how they have evolved as communication tools beyond their role as entertainment products. [ABSTRACT FROM AUTHOR]

Background : The current UK standard for major trauma patients is to record notes in a paper trauma booklet. Through an innovative collaboration between a major trauma centre and a digital transformation industry partner, a TraumaApp was developed. Electronic notes have been shown to have fewer errors, granular data collection and enable time stamped contemporaneous record keeping. Implementation of digital clinical records presents a challenge within the context of trauma multidisciplinary trauma resuscitation. Data can be easily accessible and shared for quality improvement, audit and research purposes. This study compared paper and electronic notes for completeness and for acceptability data following the implementation of the TraumaApp. Methods: Trauma team members who performed scribe function attended training for the newly launched TraumaApp. Two staff members acted as scribe, using either the paper trauma booklet or TraumaApp, and attended major trauma calls. A framework for comparison of paper and electronic notes was created and used for a retrospective review of major trauma patients' notes. Statistical analysis was performed using a two-tailed t-test. Staff using the TraumaApp completed a System Usability Score questionnaire. Results: There was a total of 37 data points for collection per case. The mean numbers collected were paper notes 24.1 of 37 (65.1%) and electronic notes, 25.7 of 37 (69.5%). There was no statistical significance between the completeness of paper and electronic notes. The mean System Usability Score was 68.4. Discussion: Recording accurate patient information during a major trauma call can be challenging and the role of the scribe to accurately record events is critical for immediate and future care. There was no statistically significant difference in completeness of paper and electronic notes, however the mean System Usability Score was 68.4, which is greater than the internationally validated standard of acceptable usability. Conclusion: It is feasible to introduce digital data collection tools enabling accurate record keeping during trauma resuscitation and improve information sharing between clinicians. [ABSTRACT FROM AUTHOR]

Background Research theses can have a significant impact on the development of applied sciences, especially when their results are published. Objective This study aims to determine publication rate of papers extracted from the medical residency and master’s degree theses of Guilan University of Medical Sciences (GUMS) during 2008-2018. Methods This is a descriptive cross-sectional study. The data were collected from the medical residency and master’s degree theses of GUMS from 2008 to 2018. Then, they were searched in international and national databases according to the title, keywords, and names of supervisors, advisors, and students. Results Of 774 theses, 449 (58 %) papers had been extracted and published in journals indexed by ISI/ PubMed (41.2%), Scopus (11.8%), BIOSIS, Chemical Abstract, and Embase (0.44%) and others (46.56%). Most of them (98.66%) were original research. Moreover, in 383(85.3%) of the theses, there was no statistician or data advisor. The median time from defending theses to the publication of a paper from them was two years. Conclusion The publication of papers extracted from the theses of GUMS has increased, and more than 50% of them have been published in journals indexed by ISI, PubMed, and Scopus. However, the factors associated with the publication of papers need to be surveyed. [ABSTRACT FROM AUTHOR]

The vibration of the rolling mill has been a persistent issue affecting its safe and stable operation. To address the vibration problem in the F2 stand of a continuous rolling mill, this paper establishes a digital twin model of the rolling mill. Based on the digital twin model, a rolling mill virtual commissioning framework has been proposed to enhance the accuracy of real-time data and improve the utilization of production data, laying the foundation for reducing vibration during the operation of the rolling mill. In this process, we first construct a digital twin framework for the rolling mill, consisting of four levels: physical rolling mill, virtual rolling mill, twin data, and services. Next, an online data acquisition and virtual commissioning framework is established to achieve a state mapping from the physical rolling mill to the virtual rolling mill. Finally, using the digital twin model, the effects of real-time process parameter optimization are analyzed. The impact of process parameter optimization on the dynamic response of the rolling mill system is examined. The optimized rolling process parameters effectively reduce the vertical vibration of the rolling mill system. [ABSTRACT FROM AUTHOR]

This paper proposes diary studies as a methodological choice for addressing crucial questions in qualitative process research. Diary studies unearth within-individual relationships that capture individual experiences and sense-making of these experiences in a processual manner. Therefore, using diary studies not only addresses the need for more dynamic methodological designs in qualitative research, but is advantageous for providing a process view of how the individual engages with the constructs studied. While process-studies, studied longitudinally, are often fraught with challenges of attrition or disengagement, this paper discusses how a methodological choice which is perceived as being beneficial by the participant can sustain engagement and consequently, research rigour. On one hand, a comparison of interview and diary data from the same participants show evidence of memory decay in interviews and reinforce the rigour of diary studies in the study of processual phenomenon. On the other hand, this study also showcases how researcher know-how, unintentionally imparted through reflective and processual questions asked in the study positively impacts the participants, and when reflected on, enhanced learning. This paper therefore demonstrates the ability of a research method to benefit both the research and the researched, and suggests a step-by-step design to implement a rigorous and impactful diary study using user-friendly forms and reflective questioning. [ABSTRACT FROM AUTHOR]

Introduction: The utilization of internet-based data collection in mental health research has gained popularity for its convenience and affordability. However, concerns often arise regarding the validity and reliability of data collected via the internet. The Autism Spectrum Quotient (AQ) is a self-report questionnaire to measure the traits associated with autism spectrum disorder (Baron-Cohen et al. J Autism Dev Disord, 2001; 31 5-17) and the online usage of AQ is common and conducted with large numbers of participants across many studies. However, the effect of using internet-based data collection for AQ rather than conventional paper-based procedures is unknown. Objectives: To address this issue, we conducted a study comparing the effectiveness of internet-based and paper-based data collection procedures for both the AQ and Depression Anxiety Stress Scale-21 (DASS-21, Lovibond & Lovibond, Behav Res Ther 1995; 33 335–343), which is also a prevalent mental health measurement in the literature and often used for online data collection (Zlomke, Comput Hum Behav 2009; 25 841-843). In addition, to compare internet-based and paper-based methods more fully, we included another variable (type of supervision) where a researcher was either present or absent during the completion of the questionnaires. Methods: A power analysis was conducted, and a minimum of 90 participants were needed to reach a medium effect size of.30 with an adequate power of.80 at a=.05. Accordingly, 96 participants were used and randomly assigned across 4 data collection groups: internet-based (supervision, no supervision) and paper-based (supervision, no supervision). In addition to a Demographic Form, AQ, and DASS-21 were used to obtain the data. Three independent variables were used in the current study: type of presentation (internet-based and paper-based) and type of supervision as between factors, and type of assessment as a within factor. Results: Using a 2 x 2 x 2 mixed design ANOVA, no significant main effects were found for any independent variables (all p >.33) or interaction (all ps >.17). Conclusions: The results of using AQ and DASS-21 were not altered by using internet-based or paper-based data collection procedures, suggesting that both methodologies are equally valid for this purpose. Moreover, these effects were also unaffected by the presence or absence of a researcher during data collection, suggesting that supervision by an authoritative figure does not alter the responses made. Disclosure of Interest: None Declared [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1186/s12874-023-01833-8.Correction: BMC Med Res Methodol 23, 10 (2023)https://doi.org/10.1186/s12874-023-01833-8Following publication of the original article [[1]], the authors reported an error in the Fig. 4: the colours in the pie charts in Fig. 4 do not all correspond with the legend. See the Fig. 4 corrected.Graph: Fig. 4 Breakdown of total costs across the surveysThe original article [[1]] has been updated.By Siân Harrison; Fiona Alderdice and Maria A. QuigleyReported by Author; Author; Author [Extracted from the article]

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This paper presents TimelinePTC, a web-based tool developed to improve the collection and analysis of Pathways to Care (PTC) data in first episode psychosis (FEP) research. Accurately measuring the duration of untreated psychosis (DUP) is essential for effective FEP treatment, requiring detailed understanding of the patient's journey to care. However, traditional PTC data collection methods, mainly manual and paper-based, are time-consuming and often fail to capture the full complexity of care pathways. TimelinePTC addresses these limitations by providing a digital platform for collaborative, real-time data entry and visualization, thereby enhancing data accuracy and collection efficiency. Initially created for the Specialized Treatment Early in Psychosis (STEP) program in New Haven, Connecticut, its design allows for straightforward adaptation to other healthcare contexts, facilitated by its open-source codebase. The tool significantly simplifies the data collection process, making it more efficient and user-friendly. It automates the conversion of collected data into a format ready for analysis, reducing manual transcription errors and saving time. By enabling more detailed and consistent data collection, TimelinePTC has the potential to improve healthcare access research, supporting the development of targeted interventions to reduce DUP and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance. [ABSTRACT FROM AUTHOR]

Collision accidents involving construction vehicles and workers frequently occur at construction sites. Computer vision (CV) technology presents an efficient solution for collision-risk pre-warning. However, CV-based methods are still relatively rare and need an enhancement of their performance. Therefore, a novel three-stage collision-risk pre-warning model for construction vehicles and workers is proposed in this paper. This model consists of an object-sensing module (OSM), a trajectory prediction module (TPM), and a collision-risk assessment module (CRAM). In the OSM, the YOLOv5 algorithm is applied to identify and locate construction vehicles and workers; meanwhile, the DeepSORT algorithm is applied to the real-time tracking of the construction vehicles and workers. As a result, the historical trajectories of vehicles and workers are sensed. The original coordinates of the data are transformed to common real-world coordinate systems for convenient subsequent data acquisition, comparison, and analysis. Subsequently, the data are provided to a second stage (TPM). In the TPM, the optimized transformer algorithm is used for a real-time trajectory prediction of the construction vehicles and workers. In this paper, we enhance the reliability of the general object detection and trajectory prediction methods in the construction environments. With the assistance afforded by the optimization of the model's hyperparameters, the prediction horizon is extended, and this gives the workers more time to take preventive measures. Finally, the prediction module indicates the possible trajectories of the vehicles and workers in the future and provides these trajectories to the CRAM. In the CRAM, the worker's collision-risk level is assessed by a multi-factor-based collision-risk assessment rule, which is innovatively proposed in the present work. The multi-factor-based assessment rule is quantitatively involved in three critical risk factors, i.e., velocity, hazardous zones, and proximity. Experiments are performed within two different construction site scenarios to evaluate the effectiveness of the collision-risk pre-warning model. The research results show that the proposed collision pre-warning model can accurately predict the collision-risk level of workers at construction sites, with good tracking and predicting effect and an efficient collision-risk pre-warning strategy. Compared to the classical models, such as social-GAN and social-LSTM, the transformer-based trajectory prediction model demonstrates a superior accuracy, with an average displacement error of 0.53 m on the construction sites. Additionally, the optimized transformer model is capable of predicting six additional time steps, which equates to approximately 1.8 s. The collision pre-warning model proposed in this paper can help improve the safety of construction vehicles and workers. [ABSTRACT FROM AUTHOR]

For the estimation of global karst carbon sink, a few conventional methods usually require the parameters that are difficult to measure, resulting in the big cost. Moreover, under the constraints of incomplete and timeliness issues in the collection of data over a large region, it has remained a challenge for these methods to study global karst carbon sink. Therefore, this paper proposes estimating the global karst carbon sink, and analyzing the suitability of the response surface methodology and the fluctuating variation of karst carbon sink in global karst regions from 1951 to 2050. This paper shows that the proposed method can reduce the time of numerical calculation and is suitable for application in global weathering models; The global karst carbon sink in the future changes not only displays an upward trend but also exposures its fluctuating trend largely. This fluctuation is probably due to global warming. [ABSTRACT FROM AUTHOR]

Recently, deep learning (DL) based dynamic security assessment (DSA) methods have been very successful. However, although a DSA model can be trained well for a specific topology, it often does not perform well for other topologies. Since the topology in real‐world power systems is frequently changing, the performance reduction of DL‐based DSA methods is very serious, which is a challenging and urgent problem. This paper proposes a novel DSA method based on a convolutional neural network (CNN) to solve this problem. In the proposed method, a strong yet simple domain adaptation approach named adaptive batch normalization (AdaBN) is used, which significantly enhances the extensibility and generalizability of the DSA model when the topology changes and eliminates the need to train a large number of models. This approach achieves a deep adaptation effect by modulating the statistics from the source domain to the target domain in all batch normalization layers across the model. Unlike other domain adaptation methods, this method is parameter‐free, requires no additional components, and has advanced performance despite its simplicity. In addition, this paper introduces TGAN‐based data augmentation to deal with the difficulty of costly data collection and labelling. This data augmentation makes the proposed model applicable to small databases. The test results of the proposed method on IEEE 39‐bus and IEEE 118‐bus systems show that this method can evaluate system dynamic security during topology changes and in the face of data noise with high accuracy. [ABSTRACT FROM AUTHOR]

Background: Liver disease is an increasing cause of morbidity and mortality in the United Kingdom and can be challenging to live with in the advanced stages. There has been little research exploring the healthcare experiences of UK individuals with decompensated disease when the liver cannot carry out its functions properly. A PhD research project was developed with people who have liver disease to explore care experiences in decompensated advanced liver disease. Public involvement (PI) is an essential aspect of meaningful health research, and this paper reports on the progression of our PI approach in this ongoing study. Objective: To embed PI throughout the research project to ensure that the study is meaningful to individuals with liver disease and the people who support them. Methods: The research adopts a Constructivist Grounded Theory methodology to develop a theory of care experience. Various PI approaches were considered in developing the PI strategy for this qualitative study. Initially, Embedded consultation was the preferred model, which has evolved to include aspects of collaboration and coproduction. A PI group was set up to oversee the project through the national public engagement website VOICE, and reflections on PI from three members of the group are included in this paper to illuminate the PI process. Results: Six individuals with liver disease and three carers from across the United Kingdom are part of an ongoing PI group. Their role includes commenting on the findings of the systematic literature review for this project and contributing to decisions about recruitment, data collection and data analysis. Additionally, they had a direct impact on changing the focus of the research. The PI group will continue involvement until the completion of the project. Conclusion: Successfully embedding PI into doctoral research, as demonstrated in this project, requires commitment, planning and dedication to reciprocal working for the benefit of PI contributors as well as the research. This approach could be adopted by other postgraduate researchers. Patient or Public Contribution: This project is overseen by the PI group, whose contribution is described throughout, including reflections from three PI group members. [ABSTRACT FROM AUTHOR]

This article introduces the concept of load aggregation, which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems. The online identification method is a computer-involved approach for data collection, processing, and system identification, commonly used for adaptive control and prediction. This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration, aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods. The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics, economic efficiency, and comfort. The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes, the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57, indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term. Overall, the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective. [ABSTRACT FROM AUTHOR]

To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper. [ABSTRACT FROM AUTHOR]

Crashes involving large trucks have a significant association with increasing fatalities or serious injuries. Identifying the factors affecting large trucks crashes can be helpful in reducing the number of crashes and corresponded consequences. The purpose of this review was to appraise the published papers on factors affecting large truckinvolved crashes. Data were compiled through searching Google Scholar, Scopus, Science Direct, and Web of Science using keywords included "large truck and accident severity", "large truck and accident", "large truck and crash severity", "large truck and crash", "large truck and fatal accident", "large truck and fatal crash", "large truck and injury", and "large truck and collide". The accomplishment process had two steps: excluding unrelated studies and reviewing related ones to select papers meeting the inclusion criteria. According to data collected, 31 papers achieved the inclusion criteria. The results indicated that speed, driver age, location type, collision type, seat belt wearing, light condition, vehicle technical condition, time, road surface condition, and accident type were common factors in the included papers. From the perspective of our study, speed, location type, and light condition seem to be the most frequent factors in crashes involving large trucks. The present study helps to access the studies on factors affecting the large truck-involved crashes. Thereby, the experts can perform measures to decrease the probability and severity of traffic accidents. [ABSTRACT FROM AUTHOR]

The proliferation of GPS-enabled devices and advances in positioning technologies have greatly facilitated the collection of user location data, making them valuable across various domains. One of the most common and practical uses of these location datasets is to recommend the most probable route between two locations to users. Traditional algorithms for route recommendation rely on true trajectory data collected from users, which raises significant privacy concerns due to the personal information often contained in location data. Therefore, in this paper, we propose a novel framework for computing optimal routes using location data collected through differential privacy (DP)-based privacy-preserving methods. The proposed framework introduces a method for accurately extracting transitional probabilities from perturbed trajectory datasets, addressing the challenge of low data utility caused by DP-based methods. Specifically, to effectively compute transitional probabilities, we present a density-adjusted sampling method that enables the collection of representative data across all areas. In addition, we introduce an effective scheme to approximately estimate transitional probabilities based on sampled datasets. Experimental results on real-world data demonstrate the practical applicability and effectiveness of our framework in computing optimal routes while preserving user privacy. [ABSTRACT FROM AUTHOR]

Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous survey data that need to be inverted. Conventional optimal inversion and fast imaging methods still take a long time to obtain conductivity and depth information, which affect the efficiency of real-time data interpretation. In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. The training sample dataset was generated via two ways of constructing geoelectric models through forward modelling. To check the effectiveness of our deep learning inversion strategy, we tested it on synthetic data and two types of survey data. The experimental results show that this inversion method is effective and that it can be applied to airborne time-domain electromagnetic data collected using different observation systems. The proposed inversion method can obtain better inversion results for both simple and complex stratigraphic structures and requires significantly less computation time compared to conventional optimal inversion methods. [ABSTRACT FROM AUTHOR]

Robotic grasping is a common operation scenario in industry and agriculture, in which the force sensing function is a significant factor to achieve reliable grasping. Existing force sensing methods of flexible grippers require intelligent materials or force sensors embedded in the flexible gripper, which causes such problems of higher manufacturing requirements and contact surface properties changing. In this paper, a novel rope-driven force sensing flexible gripper is designed based on the fin-shaped gripper structure, which can realize the grasping sensing functions of contact nodes and contact forces without the need for force sensors. Firstly, the rope-driven force sensing flexible gripper is designed, including the driving unit, the transmission part, the gripper unit, and the force sensing unit. The force sensing unit and the gripper unit are connected by rope, and the prototype of the rope-driven force sensing flexible gripper is completed. Secondly, a force sensing algorithm and control system based on finite element method and grasping geometric relationship are designed to realize the rope-driven force sensing flexible gripper grasping control and sensor data acquisition and processing. Finally, the experimental system of the rope-driven force sensing flexible gripper is built, and the grasping experimental tests of objects with different diameters and different contact nodes are carried out to verify the force sensing function of the rope-driven force sensing flexible gripper. The force sensing flexible gripper designed in this paper can provide a new idea for the design and force sensing method of intelligent robotic grasping system in robotic teaching, scientific research, and industrial applications. [ABSTRACT FROM AUTHOR]

Detrital zircon U–Pb chronology database of Precambrian deposits provides a context for the interpretation of the origin and evolution of ancient crust. Here, we tried to summarize the published literature containing detrital zircon U–Pb ages and Hf isotope data of Precambrian deposits in South China and then established a database, which contains details of information, such as reference, sample ID, locality, rock type, research institution, GPS coordinates, U–Pb ages and εHf(t) values. The data statistics of this paper rely on the OneSediment Working Group of The Deep‐time Digital Earth program (DDE). By November 2022, 610 samples with 38,278 U–Pb ages and 8,798 Lu‐Hf isotope data were collected from 136 papers, and these data can be downloaded from DDE Data Publish & Repository website, https://repository.deep‐time.org/. The purpose of the establishment of the dataset is to provide guidance and convenience for the research direction of future generations in South China and to improve the previous studies through the integrated data to avoid the waste of resources caused by a large number of repeated studies. [ABSTRACT FROM AUTHOR]

In this paper, we describe the challenges of collecting data in the Māori population for automatic detection of schizophrenia using natural language processing (NLP). Existing psychometric tools for detecting are wide ranging and do not meet the health needs of indigenous persons considered at risk of developing psychosis and/or schizophrenia. Automated methods using NLP have been developed to detect psychosis and schizophrenia but lack cultural nuance in their designs. Research incorporating the cultural aspects relevant to indigenous communities is lacking in the design of existing automatic prediction tools and one of the main reasons is the scarcity of data from indigenous populations. This paper explores the current design of the New Zealand health care system and its potential impacts on access and inequities in the Māori population and details the methodology used to collect speech samples of Māori at risk of developing psychosis and schizophrenia. The paper also describes the major obstacles faced during speech data collection, key findings, and probable solutions. [ABSTRACT FROM AUTHOR]

As the Internet of Vehicles advances, the demand for timely data acquisition by vehicle users continues to escalate, albeit confronted with the challenge of excessive data retrieval latency. The emergence of edge computing provides technical support for the development of vehicular networks by caching data in advance to reduce data acquisition latency. Therefore, how to effectively cache and query data becomes a key issue in addressing the timeliness of data acquisition in vehicular networks. In this paper, we investigate an efficient query optimization model to minimize data acquisition latency. Firstly, based on the distribution of data query frequencies across different servers, we propose an edge collaborative caching strategy using a tabu search algorithm. This strategy prioritizes high-traffic data by finding two optimal storage nodes for each high-traffic data in descending order of data popularity, ensuring a backup within the collaborative domain for each data segment. This not only reduces data transmission latency between nodes during task execution but also prevents single-point failures. Secondly, we deploy cuckoo filters on edge nodes to enable rapid localization of cached data nodes when users query data, thus reducing data processing latency. Finally, simulation results demonstrate that the proposed query optimization model outperforms other schemes in terms of average data query latency. [ABSTRACT FROM AUTHOR]

As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames. [ABSTRACT FROM AUTHOR]

This paper describes a measuring unit for synchronously collecting the air dose rate and measurement position for efficient dosimetry surveying and data logging. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and dosimetry part, which are integrated into a single measuring unit through an embedded computer that installs a ROS (robot operating system) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and measurement position. In this paper, we describe the functional requirements for the measuring unit, the prototype system configuration, and the experimental results obtained in the mockup environment and nuclear facility to discuss its performance. [ABSTRACT FROM AUTHOR]

Grand challenges such as achieving sustainability goals and managing aging infrastructure are creating an unprecedented demand for deconstruction. However, deconstructing aging infrastructure is inherently risky, repetitive, and costly, necessitating effective project planning. Virtual reality (VR) technology offers the potential for planners to improve deconstruction project risk, time, safety, and cost. We propose that planners leverage VR early in the planning phase to compare alternative feasible tools and processes. Planners can create low-fidelity models of various deconstruction process alternatives and collect metrics on their suitability with VR-capable three-dimensional (3D) game engines. We present and formalize a methodology for conducting comparisons of candidate deconstruction processes by modeling candidates in VR, conducting trials, and collecting analytics data from the VR engine. We present a case study that demonstrates our approach to a cutting and waste packing process for nuclear power plant (NPP) decommissioning. As a novel contribution in this paper, we show that VR simulations can efficiently produce detailed insights useful for critically analyzing and comparing deconstruction process alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we study the Greek wiretappings scandal, which was revealed in 2022 and attracted significant attention from the press and citizens. Specifically, we propose a methodology for collecting data and analyzing patterns of online public discussions on Twitter. We apply our methodology to the Greek wiretappings use case and present findings related to the evolution of the discussion over time, its polarization, and the role of the Media. The methodology can be of wider use and replicated to other topics. Finally, we publicly provide an open dataset and online resources with the results. [ABSTRACT FROM AUTHOR]

Aims: To describe a reflexive account of a mental health nurse as researcher undertaking a mixed methods study as part of a doctoral degree, with women in a secure forensic hospital. Design: A discursive paper that draws upon the primary author's experience of dual roles as a mental health nurse and a novice researcher, and relevant literature pertaining to positionality and reflexivity in the context of conducting mixed methods research. Methods: A mixed methods study was conducted within a secure forensic hospital in Australia, to identify factors associated with the frequency and duration of seclusion for women and, to explore their experiences of seclusion. Notes and a reflective diary were used to demonstrate the reflexive approach and strategies used throughout the study design and data collection stages of the research. Results: Women in the secure forensic hospital setting often have complex histories and experiences and are considered a vulnerable group, which can add additional challenges when undertaking research with this population. As a doctoral student with clinical experience in mental health undertaking research in the forensic hospital setting with women, positionality and the connection with reflexivity requires exploration to understand self and the influence on research. This understanding along with identified strategies to enhance reflexivity, supports the management of the dual nurse as researcher roles to enhance all stages of the research process. Conclusion: Doctoral nursing candidates undertaking clinical research in their area of clinical practice and at the location of previous employment, may experience challenges in relation to identity and the dichotomy of roles. Further challenges can be posed when research involves complex populations and/or controversial practices. Support to balance role conflict and maintain reflexivity is critical to understanding the role of self in research and to enhance credibility. Implications for the Profession and/or Clinical Practice: Women are a complex, yet minority population in forensic mental health settings, however the numbers of women being admitted to services is increasing. Future research may consider the use of the strategies explored here to enhance the reflexive process, and support future researchers in this field to conduct research that aims to support an often‐misheard population to better support health outcomes. Patient or Public Involvement: There was no patient or public contribution to this paper, however the study in which this paper is based on, is grounded in the experience of patients (women). [ABSTRACT FROM AUTHOR]

Cooperative multi-agent systems make it possible to employ miniature robots in order to perform different experiments for data collection in wide open areas to physical interactions with test subjects in confined environments such as a hive. This paper proposes a new multi-agent path-planning approach to determine a set of trajectories where the agents do not collide with each other or any obstacle. The proposed algorithm leverages a risk-aware probabilistic roadmap algorithm to generate a map, employs node classification to delineate exploration regions, and incorporates a customized genetic framework to address the combinatorial optimization, with the ultimate goal of computing safe trajectories for the team. Furthermore, the proposed planning algorithm makes the agents explore all subdomains in the workspace together as a formation to allow the team to perform different tasks or collect multiple datasets for reliable localization or hazard detection. The objective function for minimization includes two major parts, the traveling distance of all the agents in the entire mission and the probability of collisions between the agents or agents with obstacles. A sampling method is used to determine the objective function considering the agents' dynamic behavior influenced by environmental disturbances and uncertainties. The algorithm's performance is evaluated for different group sizes by using a simulation environment, and two different benchmark scenarios are introduced to compare the exploration behavior. The proposed optimization method establishes stable and convergent properties regardless of the group size. [ABSTRACT FROM AUTHOR]

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights. [ABSTRACT FROM AUTHOR]

The adoption of artificial intelligence into industrial settings promises notable enhancements in productivity, quality, efficiency, competitiveness, and innovations. However, transitioning AI models from concept to full-scale industrial applications involves various complexities and challenges. These challenges are not only technical but also extend into the ethical and regulatory realms, calling for a comprehensive approach to AI integration. This paper examines the diverse hurdles faced during developing and deploying AI applications in the industrial domain. It addresses challenges in collecting the right data, construction of AI models, and ensuring that these models work accurately and responsibly when deployed in real industrial environment. Furthermore, the paper presents strategic recommendations, underscoring the necessity of ethical considerations and regulatory compliance to effectively overcome these obstacles. We provide guidelines aimed at maximizing AI's benefits in industrial environments while minimizing potential risks. [ABSTRACT FROM AUTHOR]

Background: Hip fractures are a common and debilitating condition posing not only a huge health care but also socioeconomic burden. Surgical management for a neck of femur fracture is typically with arthroplasty in the form of total hip arthroplasty or hip hemiarthroplasty. Serial radiographs are typically performed routinely as part of follow-up to look for complications, although their clinical utility in asymptomatic patients is yet to be validated. Our paper therefore aims to review the utility and necessity of radiographic follow-up following arthroplasty for NOF fractures. Materials and Methods: Patients who underwent operative management for acute fragility neck of femur fractures in the year from 1st January 2018 to 31st December 2018 at the author's institution were identified. All patients who underwent surgery, and had at least one pre and one post-operative plain film radiograph of the affected hip were included in this study. Exclusion criteria included patients who had undergone surgery for chronic fractures, avascular necrosis of the femoral head, mortality within 1 year, peri-prosthetic fractures, pathological fractures from metastases, had concomitant injuries, or had inaccessible or incomplete records. Clinical records were assessed for the number of visits, an abnormal presenting history or clinical examination, as well as changes in management of the patient. The number and type of radiographs were also assessed, and each radiograph analyzed for abnormal findings. Results: A total of 157 patients were included in our study with a mean age of 79.5 at the time of surgery, and a mean follow up of 17.3 months. Data was collected from 626 clinical visits and a total of 973 radiographs. The 3 abnormal radiographic series identified with a corresponding normal consult did not result in a change of management for the patient. A negative change in management was only observed in 1 patient with an abnormal consult and a corresponding normal radiograph. Conclusion: Post-operative complications following arthroplasty for NOF fractures are likely to result in a symptomatic presentation of the patient. Routine radiographic follow-up provides limited utility in asymptomatic patients and should only be performed if clinically indicated. [ABSTRACT FROM AUTHOR]

Background: Collection of detailed dietary data is labor intensive and expensive, harmonization of existing data sets has been proposed as an effective tool for research questions in which individual studies are underpowered. Methods: In this paper, we describe the methodology used to retrospectively harmonize nutritional data from multiple sources, based on the individual participant data of all available studies, which collected nutritional data in Israel between 1963 and 2014. This collaboration was established in order to study the association of red and processed meat with colorectal cancer. Two types of nutritional questionnaires, the Food Frequency Questionnaires (FFQ) and the 24-h dietary recall (24HR recall), and different food composition tables, were used by the participating studies. The main exposure of interest included type of meat (total meat, red meat, and poultry) and level of processing. Results: A total of 29,560 Israeli men and women were enrolled. In studies using FFQ,the weighted mean intakes of total, red, processed meat, and poultry were 95, 27, 37 and 58 gr/day and 92, 25, 10, and 66 gr/day in studies using 24HR recall, respectively.. Despite several methodological challenges, we successfully harmonized nutritional data from the different studies. Conclusions: This paper emphasizes the significance and feasibility of harmonization of previously collected nutritional data, offering an opportunity to examine associations between a range of dietary exposures and the outcome of interest, while minimizing costs and time in epidemiological studies. [ABSTRACT FROM AUTHOR]

Data acquisition systems that receive analog signals, convert them to digital, and perform signal processing are used in a variety of systems that use acoustics, radar, sonar, indoor localization, and navigation. The previous systems, such as NI USRP-RIO, are expensive to build, and the number of signals a single device can receive is limited to between two and four. In order to receive more channels of signals, multi-channel data acquisition systems using ADCs operating at tens of MSPS have been proposed. However, these systems require additional processing time because data acquisition and signal processing are performed on different devices. In this paper, we propose a multi-channel data acquisition system using a 16-channel ADC that can support up to 100 MSPS. In particular, to reduce unnecessary signal transmission time, we propose a one-chip structure where all processes are performed on a single chip. Also, we propose a data acquisition system that applies pipelining techniques to enable real-time processing. To verify the proposed system, we used TI ADS52J90 and a Kintex UltraScale KCU105 evaluation board, and confirmed that it is possible to receive and process all channels simultaneously. Furthermore, it is possible to configure a real-time system by adjusting the speed of the signal-processing operation and the speed of the communication interface. Therefore, the proposed system is expected to reduce the cost of system construction by performing signal reception and processing with a single chip, and to reduce the time required for overall signal processing. [ABSTRACT FROM AUTHOR]

This research paper presents the development and implementation of an integrated condition monitoring and fault detection system for AC induction motors using a combination of sensors, GSM communication, and a cloud-based Internet of Things (IoT) platform. The proposed system aims to enhance industrial motors' operational reliability and efficiency by providing real-time data monitoring and early fault detection. The key components of the system include temperature, vibration, current, voltage, and speed sensors, which are strategically placed to gather critical motor performance data. These sensors feed data to an Arduino-based control unit responsible for sensor data acquisition and processing. To ensure timely response to anomalies, the system is equipped with an alarm system and GSM alerts, which notify designated personnel in case of abnormal motor behavior. Moreover, the paper incorporates remote monitoring capabilities, enabling users to access motor health data and real-time status from a distance. Historical data is also stored for analysis and comparison through the integration of a cloud-based Blynk-IoT platform. Additionally, the system facilitates RPM control and utilizes relay modules for seamless motor control and protection. The proposed system was tested and validated using Proteus for circuit diagram simulation and Arduino for sensor coding. The results demonstrate its effectiveness in detecting abnormal motor behavior and its potential to prevent catastrophic failures by enabling predictive maintenance. The proposed system successfully detects and displays abnormalities in important parameters like vibration, temperature, speed, three-phase currents, and voltages with 99% accuracy. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence of things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies and reducing costs. Utilizing a three-tiered cloud architecture, the AIoT system enables real-time data acquisition from sensors embedded in aircraft systems, followed by machine learning algorithms to analyze and interpret the data for proactive decision-making. This research examines the evolution from traditional to AIoT-enhanced monitoring, presenting a comprehensive architecture integrated with satellite communication and 6G technology. The mathematical models quantifying the benefits of increased diagnostic depth through AIoT, covering aspects such as predictive accuracy, cost savings, and safety improvements are introduced in this paper. The findings emphasize the strategic importance of investing in AIoT technologies to balance cost, safety, and efficiency in aviation maintenance and operations, marking a paradigm shift from traditional health monitoring to proactive health management in aviation. [ABSTRACT FROM AUTHOR]

Anomaly detection systems based on artificial intelligence (AI) have demonstrated high performance and efficiency in a wide range of applications such as power plants and smart factories. However, due to the inherent reliance of AI systems on the quality of training data, they still demonstrate poor performance in certain environments. Especially in hazardous facilities with constrained data collection, deploying these systems remains a challenge. In this paper, we propose Generative Anomaly Detection using Prototypical Networks (GAD-PN) designed to detect anomalies using only a limited number of normal samples. GAD-PN is a structure that integrates CycleGAN with Prototypical Networks (PNs), learning from metadata similar to the target environment. This approach enables the collection of data that are difficult to gather in real-world environments by using simulation or demonstration models, thus providing opportunities to learn a variety of environmental parameters under ideal and normal conditions. During the inference phase, PNs can classify normal and leak samples using only a small number of normal data from the target environment by prototypes that represent normal and abnormal features. We also complement the challenge of collecting anomaly data by generating anomaly data from normal data using CycleGAN trained on anomaly features. It can also be adapted to various environments that have similar anomalous scenarios, regardless of differences in environmental parameters. To validate the proposed structure, data were collected specifically targeting pipe leakage scenarios, which are significant problems in environments such as power plants. In addition, acoustic ultrasound signals were collected from the pipe nozzles in three different environments. As a result, the proposed model achieved a leak detection accuracy of over 90% in all environments, even with only a small number of normal data. This performance shows an average improvement of approximately 30% compared with traditional unsupervised learning models trained with a limited dataset. [ABSTRACT FROM AUTHOR]

In order to understand the application of iot Big data information acquisition for heating control systems, a research on iot Big data information acquisition for thermal storage heating control systems is proposed. This paper first analyzes the shortcomings of the traditional heating system, the heating data is scattered and difficult to collect. The IoT technology is applied to the heating platform through data acquisition, data visualization, data mining, data management and other aspects. Secondly, the application of the IoT in heating system data acquisition is studied, including the types of heating data collected, data acquisition process, data acquisition standards, etc. Finally, this paper takes the energy-saving transformation of 26 boilers in a heating enterprise as an example. The research results show that the boiler efficiency obtained in real time by using the online monitoring system of this project is 95.36%, and the reverse balance boiler efficiency in the paper report of the special inspection office is 94.88%, with a deviation of 1.48%. The application of the IoT technology can obtain the boiler indicators in real time and effectively, and can realize the comprehensive analysis of the monitoring data, and realize the evaluation of the energy efficiency operation state of the gas boiler and the fault diagnosis and early warning. The IoT technology is an effective means of fine energy-saving management in heating enterprises. [ABSTRACT FROM AUTHOR]

Combining online and offline data collection during six months of ethnographic fieldwork in Southern Mozambique in early 2021, this paper explores how media and its everyday use in the Global South are impacting on, and are in turn being transformed by China. It specifically looks into the creative and personalised ways in which Chinese-made garments are sold and promoted in Mozambique via the messenger service WhatsApp. Examining how Mozambican and West African traders use WhatsApp groups and status updates to mediate the fashionability of these Chinese goods, this paper shows that the consequences of Chinese interactions with the Global South on the global media landscape transcend the mere spread of Chinese hardware and software. Going beyond dominant, state-centred narratives of Chinese influence abroad, this paper demonstrates the key role of individuals and their agency in shaping the digital impact of Global China. By doing so, it also highlights the importance of analysing media practices in their specific local contexts. [ABSTRACT FROM AUTHOR]

Background: As a result of the ten years (2012-2022) work under the critical revision of the genera of Reticulariaceae, a set of papers was published. Collection data of hundreds of specimens, used as a material for these studies, were provided as supplements of corresponding papers, but remained unpublished in biodiversity databases. New information: Here, we represent an occurrence dataset "Barcoded Reticulariaceae of the World", published in GBIF. It includes data on 523 myxomycete collections (including 36 types) gathered from five continents and spanning 24 countries. The dataset encompasses 43 distinct species and one subspecies of myxomycetes, including rare, endemic, and recently-described taxa. Species included to the database mainly belong to the genera Alwisia, Lycogala, Reticularia, Siphoptychium, Thecotubifera and Tubifera (Reticulariaceae), but as well Lindbladia and Licaethalium (Cribrariaceae). Nearly all of the research material, with the exception of several old collections, underwent molecular barcoding, primarily involving the 18S rDNA gene, but also the elongation factor 1α gene and mitochondrial cytochrome oxidase subunit I gene. For those sequences that are stored in the NCBI GenBank, accession numbers are provided in the dataset. Newly-described species make up a significant part of the studied herbarium collections; many of them can be characterised as common for their region. A particularly high level of taxonomic novelty is observed in Australia, which may be explained by the endemism of the local myxomycete biota. [ABSTRACT FROM AUTHOR]

Objective: In this paper we report how the United Kingdom's National Institute for Health and Care Excellence (NICE) search filters for treating and managing COVID-19 were validated for use in MEDLINE (Ovid) and Embase (Ovid). The objective was to achieve at least 98.9% for recall and 64% for precision. Methods: We did two tests of recall to finalize the draft search filters. We updated the data from an earlier peer-reviewed publication for the first recall test. For the second test, we collated a set of systematic reviews from Epistemonikos COVID-19 L.OVE and extracted their primary studies. We calculated precision by screening all the results retrieved by the draft search filters from a targeted sample covering 2020-23. We developed a gold-standard set to validate the search filter by using all articles available from the "Treatment and Management" subject filter in the Cochrane COVID-19 Study Register. Results: In the first recall test, both filters had 99.5% recall. In the second test, recall was 99.7% and 99.8% in MEDLINE and Embase respectively. Precision was 91.1% in a deduplicated sample of records. In validation, we found the MEDLINE filter had recall of 99.86% of the 14,625 records in the gold-standard set. The Embase filter had 99.88% recall of 19,371 records. Conclusion: We have validated search filters to identify records on treating and managing COVID-19. The filters may require subsequent updates, if new SARS-CoV-2 variants of concern or interest are discussed in future literature. [ABSTRACT FROM AUTHOR]

The Expanded National Nutrition Survey (ENNS), formerly the National Nutrition Survey, is a regularly conducted survey that records the Philippines' overall nutritional status. This is especially important not only for government intervention and policy-making but also for private sector entities that plan for their nutrition and development programs. First deployed during the conduct of the 2013 ENNS, the electronic data collection system (EDCS) is a tool that implemented computer-assisted personal interviewing (CAPI) functionalities. This paper records the improvements made to this system and its division into separate subsystems, various CAPI subsystems with specialized functionalities, and how they interact with each other. Descriptive analysis was used for the users' feedback and recommendations -- specifically, for usability and navigation, system performance and portability, technicality and security, and visual design. The system is able to support interviews during the ENNS and receive positive feedback -- garnering visual design the highest, whereas technicality and security the lowest acceptability rating -- thereby proving this division useful. Overall, the system is capable of supporting the ENNS and will continue its improvement through the continual user feedback received. [ABSTRACT FROM AUTHOR]

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation of battery parameters such as voltage, current, and temperature. The proposed system allows the precise control of charging parameters via a user-friendly interface. Utilizing a programmable DC power supply, it delivers specific charging waveforms, while data acquisition instruments record temperature changes. Key performance metrics, including charging time, efficiency, and temperature rise, are analyzed. Moreover, this paper conducts in-depth research on the constant temperature–constant voltage (CT-CV) charging technique and applies the Taguchi method to identify key parameter configurations that achieve the objectives of the shortest charging time, highest charging efficiency, and lowest average temperature rise. A comprehensive evaluation compares the optimized CT-CV method with conventional constant current–constant voltage (CC-CV) charging. The results demonstrate a 10.7% reduction in charging time compared to the 1C CC-CV method, indicating the efficacy of CT-CV in shortening charging duration while managing temperature rise. [ABSTRACT FROM AUTHOR]

With the increasing number of households owning pets, the importance of sensor data for recognizing pet behavior has grown significantly. However, challenges arise due to the costs and reliability issues associated with data collection. This paper proposes a method for classifying pet behavior using cleaned meta pseudo labels to overcome these issues. The data for this study were collected using wearable devices equipped with accelerometers, gyroscopes, and magnetometers, and pet behaviors were classified into five categories. Utilizing this data, we analyzed the impact of the quantity of labeled data on accuracy and further enhanced the learning process by integrating an additional Distance Loss. This method effectively improves the learning process by removing noise from unlabeled data. Experimental results demonstrated that while the conventional supervised learning method achieved an accuracy of 82.9%, the existing meta pseudo labels method showed an accuracy of 86.2%, and the cleaned meta pseudo labels method proposed in this study surpassed these with an accuracy of 88.3%. These results hold significant implications for the development of pet monitoring systems, and the approach of this paper provides an effective solution for recognizing and classifying pet behavior in environments with insufficient labels. [ABSTRACT FROM AUTHOR]

Real-time traffic signal acquisition and network transmission are essential components of intelligent transportation systems, facilitating real-time traffic monitoring, management, and analysis in urban environments. In this paper, we introduce a comprehensive system that incorporates live traffic signal acquisition, real-time data processing, and secure network transmission through a combination of hardware and software modules, called LIDATS. LIDATS stands for Live Intersection Data Acquisition for Traffic Simulators. The design and implementation of our system are detailed, encompassing signal acquisition hardware as well as a software platform that is used specifically for real-time data processing. The performance evaluation of our system was conducted by simulation in the lab, demonstrating its capability to reliably capture and transmit data in real time, and to effectively extract the relevant information from noisy and complex traffic data. Supporting a variety of intelligent transportation applications, such as real-time traffic flow management, intelligent traffic signal control, and predictive traffic analysis, our system enables remote data analysis and decisionmaking, providing valuable insights and enhancing the traffic efficiency while reducing the congestion in urban environments. [ABSTRACT FROM AUTHOR]

The revolution of the Internet of Things (IoT) and the Web of Things (WoT) has brought new opportunities and challenges for the information retrieval (IR) field. The exponential number of interconnected physical objects and real-time data acquisition requires new approaches and architectures for IR systems. Research and prototypes can be crucial in designing and developing new systems and refining architectures for IR in the WoT. This paper proposes a unified and holistic approach for IR in the WoT, called IR.WoT. The proposed system contemplates the critical indexing, scoring, and presentation stages applied to some smart cities' use cases and scenarios. Overall, this paper describes the research, architecture, and vision for advancing the field of IR in the WoT and addresses some of the remaining challenges and opportunities in this exciting area. The article also describes the design considerations, cloud implementation, and experimentation based on a simulated collection of synthetic XML documents with technical efficiency measures. The experimentation results show promising outcomes, whereas further studies are required to improve IR.WoT effectiveness, considering the WoT dynamic characteristics and, more importantly, the heterogeneity and divergence of WoT modeling proposals in the IR domain. [ABSTRACT FROM AUTHOR]

Introduction: Violence against healthcare workers in the workplace is a complex issue affecting various healthcare professionals. Workplace violence (WPV) poses significant occupational hazards to healthcare employees. Objectives: To identify the causes, types, and perpetrators of WPV and explore the effects of violence on healthcare workers' well-being. Methods: This literature review analyzes 15 selected research papers published between 2015 and 2020, focusing on WPV against healthcare workers, and gathers insights from healthcare professionals with prior experience of WPV. Through an analysis of the selected research papers, this study provides a comprehensive overview with WPV in the healthcare setting. The respondents in the included studies were healthcare professionals who had encountered WPV in their work. Results: While nurses remain particularly vulnerable to violence due to their constant patient interaction, this study reveals that verbal violence is the most prevalent type, significantly impacting the well-being of healthcare workers as a whole. The identified causes of WPV include long waiting times, understaffing, miscommunication, inadequate security measures, high patient expectations, and overcrowding. Respondents emphasized the importance of education, security enhancements, and administrative support to address or prevent WPV. Conclusion: The findings strongly highlight the crucial role of administrative support in reducing the incidence of WPV. It is crucial to implement broader measures to ensure the safety and well-being of all healthcare workers. [ABSTRACT FROM AUTHOR]

This paper focuses on mission planning and cooperative navigation algorithms for multi-drone systems aimed at LiDAR-based mapping. It aims at demonstrating how multi-UAV cooperation can be used to fulfill LiDAR data georeferencing accuracy requirements, as well as to improve data collection capabilities, e.g., increasing coverage per unit time and point cloud density. These goals are achieved by exploiting the CDGNSS/Vision paradigm and properly defining the formation geometry and the UAV trajectories. The paper provides analytical tools to estimate point density considering different types of scanning LIDAR and to define attitude/pointing requirements. These tools are then used to support centralized cooperation-aware mission planning aimed at complete coverage for different target geometries. The validity of the proposed framework is demonstrated through numerical simulations considering a formation of three vehicles tasked with a powerline inspection mission. The results show that cooperative navigation allows for the reduction of angular and positioning estimation uncertainties, which results in a georeferencing error reduction of an order of magnitude and equal to 16.7 cm in the considered case. [ABSTRACT FROM AUTHOR]