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Automated interior construction progress monitoring (ICPM) has gained increasing academic attention. This emerging research field faces numerous technical challenges that have been noted in previous studies but lack a holistic examination to analyze these challenges and their potential impacts. This study addresses this gap by conducting a systematic review of ICPM technical challenges, collecting related literature from Scopus, Web of Science, and ScienceDirect databases, and utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) framework for filtering and selecting literature. The filtration results in 44 strongly related technical papers. Alongside summarizing these challenges, the study explores their impacts on the entire ICPM automation process and proposes innovative solutions. Specifically, this study highlights the key phases of ICPM automation development, including data acquisition, 3D reconstruction, as-planned modeling, as-built modeling, progress comparison, and progress quantification, and subsequently identifies challenges for each phase, totaling 11 major challenges composed of 41 subchallenges. The data acquisition phase is found to have the most and most severe challenges, and challenges in other phases also impact the automation system performance. This review encompasses the identification of potential issues and proposes corresponding solutions, enabling future researchers to anticipate challenges and develop more advanced and user-friendly monitoring systems. [ABSTRACT FROM AUTHOR]

Spatio-temporal models can be used to analyze data collected at various spatial locations throughout multiple time points. However, even with a finite number of spatial locations, there may be insufficient resources to collect data from every spatial location at every time point. We develop a spatio-temporal finite-population block kriging (ST-FPBK) method to predict a quantity of interest, such as a mean or total, across a finite number of spatial locations. This ST-FPBK predictor incorporates an appropriate variance reduction for sampling from a finite population. Through an application to moose surveys in the east-central region of Alaska, we show that the predictor has a substantially smaller standard error compared to a predictor from the purely spatial model that is currently used to analyze moose surveys in the region. We also show how the model can be used to forecast a prediction for abundance in a time point for which spatial locations have not yet been surveyed. A separate simulation study shows that the spatio-temporal predictor is unbiased and that prediction intervals from the ST-FPBK predictor attain appropriate coverage. For ecological monitoring surveys completed with some regularity through time, use of ST-FPBK could improve precision. We also give an R package that ecologists and resource managers could use to incorporate data from past surveys in predicting a quantity from a current survey. Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

This paper purports to enrich the burgeoning field of research on the content of people's beliefs about inequality by studying the structure of these beliefs. We develop a theoretical and methodological framework that combines Correlational Class Analysis and Exploratory Graph Analysis, and we test it empirically with original survey data collected in the United States and the Netherlands (n = 2,501 and 1,618). Using CCA, we identify groups of individuals who share construals of inequality, while EGA allows us to model these structures as inequality belief systems, which are networks of perceptions, explanations and attitudes about inequality. Results reveal the presence of two distinct belief systems in each country. These systems exhibit structural differences and are related to different sociodemographic factors in the U.S. and the Netherlands. Moreover, we show that inequality belief systems are more socially patterned in the former country. Finally, we demonstrate that belief systems, in both countries, are associated with different levels of support for redistribution. We discuss the significance of our findings for the politics of inequality and stress that overlooking attitudinal structures impedes a full understanding of people's views on inequality and their support for redistribution. [ABSTRACT FROM AUTHOR]

Increasing advancements in building digitization, smart sensing, and metering technologies have allowed large amounts of timeseries data to be collected for monitoring, analyzing, and controlling building systems. However, due to sensor or communication failures, the data collected are often incomplete and poor in quality. Data imputation approaches to replace the missing values, specifically based on either statistical or predictive models have been widely adopted for multivariate datasets in other domains. It is hence of interest to find an effective way to impute timeseries data collected from a building system. In this paper, we evaluate multiple data imputation approaches using data collected from a medium sized building situated in Stockholm, Sweden and a small commercial building from the ASHRAE RP-1312 research project. Sensors with widely varying characteristics from the case study buildings were selected to evaluate the imputation methods. The imputation accuracy and the impact of each chosen imputation method on information entropy, short-term building forecasting model performance, and fault detection strategy were evaluated. Results demonstrate that incorporating time-lagged cross correlations within a k -nearest neighbor ( k NN) model provide the most accurate imputations without affecting the quality of subsequent data analysis. [ABSTRACT FROM AUTHOR]

This paper draws from our own experiences of sexism within Business Schools to bring attention to the effects of the operation of a highly masculinized, white, cis‐gendered, and patriarchal culture, whether enacted by men or women, and to how we come to be silenced within it. Our work reflects on intersectional issues of race, health (mental and physical), and care‐work, using faction built from six paired interviews to tell a truth we feel unable to tell individually. This piece highlights the real fear of repercussions that still persist for female academics, and uses the acts of collecting data and writing differently to offer the authors a safe space in which to resist both overt and structural sexism in Business Schools. It highlights the need to take seriously those subtleties of sexism that we are often expected to put up with, those difficult‐to‐name aspects of our working lives that leave us feeling it would be "silly" to complain and act as a form of micro‐coercive control over our lives. We operationalize our collective voice as a form of activism in the academy that is situated within our individual silences. [ABSTRACT FROM AUTHOR]

Despite total shoulder arthroplasty (TSA) and reverse TSA (rTSA) being fundamentally different procedures, and indicated in different pathologies (rTSA for rotator cuff deficiency [RCD] and proximal humeral fractures [PHFx] and anatomic TSA [aTSA] for glenohumeral osteoarthritis [GHOA]), they have the same Current Procedural Terminology (CPT) code (23472). This paper's aim is to investigate differences in operative time and work-related value units (wRVUs) per hour among these pathologies, and ultimately determine if there is a need to assign separate CPTs for aTSA and rTSA. A retrospective cohort of data from the American College of Surgeons–National Surgical Quality Improvement Program was collected, all patients who underwent aTSA or rTSA (CPT: 23472) between the years of 2006 and 2019 for diagnoses of GHOA, RCD, and PHFx were included. Data collected included patient age, body mass index, operative time, and wRVUs per hour. Compared to GHOA (reference group), the average operative time for the RCD cohort was 12.242 minutes shorter (P <.001), while the wRVUs were higher by 1.627 (P <.001). The average operative time for rTSAs in the PHFx cohort were 17.615 minutes longer (P <.001), while the wRVUs were lower by 2.205 (P <.001). The average operative time for rTSAs for both RCDs and PHFx were longer than that for aTSAs for GHOA. Additionally, wRVUs were lower for rTSAs for RCD and PHFx compared to aTSAs for GHOA. This elucidates inconsistency in reimbursement structure for the procedures, which should be revisited. [ABSTRACT FROM AUTHOR]

Whole slide imaging (WSI) of pathology glass slides using high‐resolution scanners has enabled the large‐scale application of artificial intelligence (AI) in pathology, to support the detection and diagnosis of disease, potentially increasing efficiency and accuracy in tissue diagnosis. Despite the promise of AI, it has limitations. 'Brittleness' or sensitivity to variation in inputs necessitates that large amounts of data are used for training. AI is often trained on data from different scanners but not usually by replicating the same slide across scanners. The utilisation of multiple WSI instruments to produce digital replicas of the same slides will make more comprehensive datasets and may improve the robustness and generalisability of AI algorithms as well as reduce the overall data requirements of AI training. To this end, the National Pathology Imaging Cooperative (NPIC) has built the AI FORGE (Facilitating Opportunities for Robust Generalisable data Emulation), a unique multi‐scanner facility embedded in a clinical site in the NHS to (1) compare scanner performance, (2) replicate digital pathology image datasets across WSI systems, and (3) support the evaluation of clinical AI algorithms. The NPIC AI FORGE currently comprises 15 scanners from nine manufacturers. It can generate approximately 4,000 WSI images per day (approximately 7 TB of image data). This paper describes the process followed to plan and build such a facility. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

High-resolution temperature forecasting plays a crucial role in various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. The existing low-resolution forecast data may not accurately capture the fine-grained temperature patterns required for localized predictions. These forecasts may contain biases that need to be corrected for accurate results. Therefore, there is a need for an effective framework that can downscale low-resolution forecast data and correct biases to generate high-resolution temperature forecasts. The paper proposes a machine learning-based spatial downscaling and bias-correction framework for high-resolution temperature forecasting. The framework utilizes low-resolution forecast data from the European Centre for Medium-Range Weather Forecasts and real-time 1km analysis product data from the National Meteorological Administration, to generate high-resolution 1km forecast temperature data. The framework consists of four modules: data acquisition module, data preprocessing module, downscaling and correction model module, and post-processing and visualization module. Through experiments, it demonstrated that the framework has superior performance and potential in meteorological data downscaling and correction and can be used to achieve real-time high-resolution temperature forecasting, which has important significance for various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. [ABSTRACT FROM AUTHOR]

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Increasing advancements in building digitization, smart sensing, and metering technologies have allowed large amounts of timeseries data to be collected for monitoring, analyzing, and controlling building systems. However, due to sensor or communication failures, the data collected are often incomplete and poor in quality. Data imputation approaches to replace the missing values, specifically based on either statistical or predictive models have been widely adopted for multivariate datasets in other domains. It is hence of interest to find an effective way to impute timeseries data collected from a building system. In this paper, we evaluate multiple data imputation approaches using data collected from a medium sized building situated in Stockholm, Sweden and a small commercial building from the ASHRAE RP-1312 research project. Sensors with widely varying characteristics from the case study buildings were selected to evaluate the imputation methods. The imputation accuracy and the impact of each chosen imputation method on information entropy, short-term building forecasting model performance, and fault detection strategy were evaluated. Results demonstrate that incorporating time-lagged cross correlations within a k -nearest neighbor ( k NN) model provide the most accurate imputations without affecting the quality of subsequent data analysis. [ABSTRACT FROM AUTHOR]

As an essential component of urban infrastructure construction, polyethylene (PE) pipelines face the challenging task of underground detection due to the complex and dynamic nature of the subsurface environment, diverse installation paths, and the inherent insulating properties of PE materials. In order to address the non-excavation detection of buried PE pipelines, this paper proposes an acoustic method based on the long short-term memory (LSTM) neural network. The study begins by analyzing the propagation and reflection mechanisms of elastic waves in the pipe-soil coupling system, and a impact excitation source is designed to generate the excitation signal. After establishing the experimental environment and collecting experimental data, a comprehensive analysis is conducted, and the LSTM neural network is employed for data classification to determine the presence of buried PE pipelines. Through neural network training, accurate identification of the PE pipeline's existence and prediction of its burial depth are achieved, providing an efficient and reliable solution for buried PE pipeline detection. The practical results demonstrate the significant application prospects of the combined acoustic method and LSTM neural network in buried PE pipeline detection. This research contributes a novel solution to the field of non-destructive PE pipeline detection, with both theoretical and practical implications. [ABSTRACT FROM AUTHOR]

There has been an explosion of data collected about sports. Because such data is extremely rich and complex, machine learning is increasingly being used to extract actionable insights from it. Typically, machine learning is used to build models and indicators that capture the skills, capabilities, and tendencies of athletes and teams. Such indicators and models are in turn used to inform decision-making at professional clubs. Designing these indicators requires paying careful attention to a number of subtle issues from a methodological and evaluation perspective. In this paper, we highlight these challenges in sports and discuss a variety of approaches for handling them. Methodologically, we highlight that dependencies affect how to perform data partitioning for evaluation as well as the need to consider contextual factors. From an evaluation perspective, we draw a distinction between evaluating the developed indicators themselves versus the underlying models that power them. We argue that both aspects must be considered, but that they require different approaches. We hope that this article helps bridge the gap between traditional sports expertise and modern data analytics by providing a structured framework with practical examples. [ABSTRACT FROM AUTHOR]

This paper focuses on the social information needs of people with albinism (PWA) in Khomas region, Namibia, and the sources of information used PWA to meet their information needs. The study applies Moore's model of social information needs and Wilson's model of information behaviour to investigate and understand the information needs and sources used by PWA to find information. The study was carried out using a qualitative case design within interpretivism paradigm and semi-structured face-to-face interviews were used to collect data from participants. Sixteen participants took part in the study, eight were males, and eight were female participants. Among these participants, two were children with albinism. The following information was identified as being crucial to PWA namely: skin- and eye-related information, information on what is albinism and its causes, disability grants, educational information, and emotional and psychological support. Additionally, the study established that PWA use formal, informal, and human sources of information to find information. [ABSTRACT FROM AUTHOR]

This paper examines strategic enforcement approaches relying on co-enforcement and transgovernmentalist. It examines three cases in the construction industry in Austria, Asturias (Spain), and Poland, as well as three cases in maritime shipping in Finland, Spain, and Poland, focussing on Labour Inspection's (LI)s motivations for engaging in co-enforcement and transnational cooperation. Data collection involved desk research, semi-structured interviews (39 construction and 14 in maritime), observation of inspections, and participant observation in EU seminars we organised together with regulatory actors (specific details are provided in the appendix). Findings show that LI's selection of co-enforcement and transnational alliances are driven by the specific and highly contingent challenges each organisation faces. LI organisations are seeking to exploit synergies, but both the material reality and perception of these depends on diverse pre-existing infrastructures and organisation-specific assets. [ABSTRACT FROM AUTHOR]

This exploratory study analyzes the mechanisms and barriers through which the Port State Control (PSC) regimes can promote or undermine a safety learning culture. PSC Officers (PSCOs), ideally positioned to collect ship data, have the potential to significantly enhance safety learning at national and global levels. The study, which involved semi-structured interviews with eleven seafarers and twenty PSCOs from China, underscores the inherent potential of PSC regimes to enhance safety and promote ship safety. This might be achieved through the establishment of mutually beneficial relationships with crew members, companies, and other maritime stakeholders, including regulators at national and international levels. However, the quality and diversity of ship feedback depend on seafarers’ willingness or reluctance to contribute. The paper concludes with actionable recommendations, including promoting trustful interactions during inspections, enhancing non-technical skills for PSCOs and in-depth safety assessments. [ABSTRACT FROM AUTHOR]

The increase in the popularity of field research and its digitization meant that the vast majority is carried out in teams. This means that collecting empirical data in the field requires coordination for the correctness of its effectiveness. The article presents the evaluation meeting as a tool for managing field research carried out on a team. The text presents the current challenges of field research and discusses the range of areas requiring coordination. The paper presented how the field research coordinator should organize and conduct the evaluation meeting. The range of problems that can be solved through the evaluation meeting was also presented, indicating its benefits and limitations. [ABSTRACT FROM AUTHOR]

Introduction: Vertical transmission of HIV remains a major challenge in Guinea, especially, in low-resource rural areas. This paper presents the results of a pilot project designed to enhance the prevention of vertical transmission through a comprehensive improvement plan implemented across 66 large-cohort sites. Methods: Data from 66 large-cohort of mother to child transmission prevention (PMTCT) sites from 2019 to 2022 were analysed to compare PMTCT metrics before (2019–2020) and after (2021–2022) the improvement initiative. Key indicators were reviewed, and trends were statistically analysed using Mann‒Whitney tests, with a p value less than 0.05 indicating statistical significance. Results: The implementation of this strategy significantly increased the antiretroviral therapy rate among HIV-positive pregnant women from 66 to 94%, and full antiretroviral prophylaxis coverage was achieved in infants. However, early infant diagnosis via polymerase chain reaction testing falls short of the national target, highlighting deficiencies in laboratory and specimen transport capacities. The study also revealed regional disparities in the use of PMTCT services. Conclusion: The improvement plan effectively enhanced antiretroviral therapy and prophylaxis use, demonstrating the benefits of structured interventions and capacity development. Despite improvements, challenges such as insufficient polymerase chain reaction (PCR) testing and uneven access to services remain. Future initiatives should aim to equip PMTCT sites with essential resources and promote community-driven health-seeking behaviours in underserved areas. [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]

For ensuring the proper operation of CPS, it is required to obtain a solution to the security problem. Applications for cyber-physical systems (CPS) have a big impact on several industrial sectors. Growing in both quantity and variety are cyber attacks try to manage both the industrial process itself as well as data collection from CPS. Attacks on CPS must be identified and stopped in order to avoid financial loss, production disruptions, and possible threats to national security. This paper models a cyber-attack detection and mitigation framework using a hybrid classifier (CADFHC) including the steps like (1) Pre-processing, (2) Feature Extraction (3) Feature Selection (4) Detection & (5) Mitigation. The initial data is preprocessed via z-score normalization. Higher-order statistical features, mutual information, correlation, and raw features, will be retrieved in the feature extraction step. From the extracted feature set, an improved PCA will be used for selecting the appropriate features. In the detection phase, the hybrid model combining improved Bi-GRU & RNN is used for detecting the presence of an attack. After the attack detection process, the mitigation process will be performed by eliminating the attackers from the network. For this, modified distribution entropy function-based mitigation is performed. Finally, the effectiveness of the accepted model is compared with the conventional models with different metrics. The CADFHC approach has attained the most excellent outcomes (0.99) under the best case scenario. [ABSTRACT FROM AUTHOR]

Background: Co-occurring physical and mental health conditions are common, but effective and sustainable interventions are needed for primary care settings. Purpose: Our paper analyzes the effectiveness of a Solution-Focused Brief Therapy (SFBT) intervention for treating depression and co-occurring health conditions in primary care. We hypothesized that individuals receiving the SFBT intervention would have statistically significant reductions in depressive and anxiety symptoms, systolic blood pressure (SBP), hemoglobin A1C (HbA1c), and body mass index (BMI) when compared to those in the control group. Additionally, we hypothesized that the SFBT group would have increased well-being scores compared to the control group. Methods: A randomized clinical trial was conducted at a rural federally qualified health center. Eligible participants scored ≥ 10 on the Patient Health Questionnaire (PHQ-9) and met criteria for co-occurring health conditions (hypertension, obesity, diabetes) evidenced by chart review. SFBT participants (n = 40) received three SFBT interventions over three weeks in addition to treatment as usual (TAU). The control group (n = 40) received TAU over three weeks. Measures included depression (PHQ-9) and anxiety (GAD-7), well-being (Human Flourishing Index), and SFBT scores, along with physical health outcomes (blood pressure, body mass index, and hemoglobin A1c). Results: Of 80 consented participants, 69 completed all measures and were included in the final analysis. 80% identified as female and the mean age was 38.1 years (SD = 14.5). Most participants were white (72%) followed by Hispanic (15%) and Black (13%). When compared to TAU, SFBT intervention participants had significantly greater reductions in depression (baseline: M = 18.17, SD = 3.97, outcome: M = 9.71, SD = 3.71) and anxiety (baseline: M = 14.69, SD = 4.9, outcome: M = 8.43, SD = 3.79). SFBT intervention participants also had significantly increased well-being scores (baseline: M = 58.37, SD = 16.36, outcome: M = 73.43, SD = 14.70) when compared to TAU. Changes in BMI and blood pressure were not statistically significant. Conclusion: The SFBT intervention demonstrated efficacy in reducing depressive and anxiety symptoms and increasing well-being but did not affect cardio-metabolic parameters over a short period of intervention. Trial Registration: The study was pre-registered at ClinicalTrials.gov Identifier: NCT05838222 on 4/20/2023. *M = Mean, SD = Standard deviation. [ABSTRACT FROM AUTHOR]

High risk of braking easily causes more safety accidents. In this paper, the driving experiments on G350 Gengda‐Yingxiu section containing continuous downhill tunnel group. Three‐axle trucks under standard load and overload conditions were considered. The research proposes a method for ensuring safe truck driving on continuous downhill sections of mountain roads based on the rise in brake drum temperature. The study collects data on brake drum temperature and braking duration from an experimental vehicle under the coupling action of human‐vehicle‐road‐environment. Through comparative analysis, theoretical derivation, and model construction, conclusions are drawn. The results indicate that the rise in brake drum temperature is influenced by the factors such as overload, alignment, road slope, and sections with bright and dark lines. The initial brake drum temperature, operating speed, and total vehicle mass are identified as the main controlling factors for the change in brake drum temperature. The study also demonstrates that water drenching can significantly reduce the rate of brake drum temperature rise, thereby ensuring driving safety. Furthermore, a model is constructed based on the relationship between brake drum temperature rise and various factors, which allows for the calculation of the corresponding safe slope length and average slope gradient. This model can be used for evaluating or designing overall load requirements. The research on brake drum temperature rise characteristics and braking behaviour under drenching conditions provides effective support for route design, traffic management, and the establishment of safety service facilities. [ABSTRACT FROM AUTHOR]

To realize transparent monitoring and resilience improvement of low- voltage distribution network, both the data acquisition scope and frequency have been greatly expanded. Cloud-edge collaboration leverages the edge server's real-time response capabilities and the cloud server's robust data processing power to enhance the performance of high-frequency data acquisition processing. Nonetheless, it continues to confront challenges such as the entanglement of optimization variables, the presence of uncertain information, and a lack of awareness regarding acquisition frequencies. In this paper, we propose a machine learning-based cloud-edge collaborative data processing optimization algorithm to minimize the weighted sum of data processing delay and device energy consumption for distribution network resilience improvement. The joint optimization problem is decoupled into device-edge data offloading subproblem and edge-cloud data splitting subproblem, which are solved by the proposed upper confidence bound (UCB) based frequency-aware device-edge data offloading optimization algorithm and the exponential-weight algorithm for exploration and exploitation (EXP3) based edge-cloud data splitting optimization algorithm, respectively. Simulation results show that the proposed algorithm is superior to existing algorithms in performances of energy consumption and total processing delay. [ABSTRACT FROM AUTHOR]

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally. [ABSTRACT FROM AUTHOR]

The recruitment and use of children by armed groups continue to threaten the effective protection of children. Preventive approaches to child protection, call for robust early warning mechanisms that do not only focus on the recruitment and use of children, but capture the root causes and vulnerabilities of both boys and girls for recruitment. A comprehensive early warning mechanism for prevention requires a gender-sensitive approach. This paper seeks to examine the gender dynamics of the recruitment and use of children by armed groups, and how that can inform a gender sensitive data collection and analysis to improve early warning. [ABSTRACT FROM AUTHOR]

Rock quality designation (RQD) is a parameter that describes rock mass quality in terms of percentage recovery of core pieces greater than 10 cm. The RQD represents a basic element of several classification systems. This paper studies scale effects for RQD measurements using synthetic rock masses generated using discrete fracture network (DFN) models. RQD measurements are performed for rock masses with varying fracture intensities and by changing the orientation of the simulated boreholes to account for orientation bias. The objective is to demonstrate the existence of a representative elementary length (REL, 1D analogue of a 3D representative elementary volume, or REV) above which RQD measurements would represent an average indicator of rock mass quality. For the synthetic rock masses, RQD measurements were calculated using the relationship proposed by Priest and Hudson and compared to the simulated RQD measurements along the boreholes. DFN models generated for a room-and-pillar mine using mapped field data were then used as an initial validation, and the conclusion of the study was further validated using the RQD calculation results directly obtained from the depth data collected at an iron cap deposit. The relationship between rock mass scale and assumed threshold length used to calculate RQD is also studied. [ABSTRACT FROM AUTHOR]

Power quality measurements are essential to monitor, analyse and control the operation of smart grids within power systems. This work aims to develop and calibrate a PQ network analyser. As the penetration of non-linear loads connected to power systems is increasing every day, it is essential to measure power quality. In this sense, a power quality (PQ) analyser is based on the high-speed sampling of electrical signals in single-phase and three-phase electrical installations, which are available in real time for analysis using wireless Wi-Fi (Wireless-Fidelity) networks. The PQAE (Power Quality Analyser Embedded) power quality analyser has met the calibration standards for Class A devices from IEC 61000-4-30, IEC 61000-4-7 and IEC 62586-2. In this paper, a complete guide to the tests included in this standard has been provided. The Fast Fourier Transform (FFT) obtains the harmonic components from the measured signals and the window functions used reduce spectral leakage. The window size depends on the fundamental frequency of, intensity of and changes in the signal. Harmonic measurements from the 2nd to 50th harmonics for each phase of the voltage and each phase and neutral of the current have been performed, using the Fast Fourier transform algorithm with various window functions and their comparisons. PQAE is developed on an open-source platform that allows you to adapt its programming to the measurement needs of the users. [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]

In this paper, a new artificial detrusor system is proposed to achieve continuous control over the urination process and realize the normal voiding process of human (NVPH). The system is powered by a wireless power transfer (WPT) module, controlled by a control module and actuated by a shape memory alloy (SMA) spring. The design method of the SMA springs is proposed. Two control strategies are proposed to achieve NVPH. The first strategy is an open-loop control strategy based on the result of finite element analysis (FEM) simulation results and calculation of the derived system governing equations. The second strategy is a closed-loop control strategy based on the collection of feedback data and algorithmic processing. Both strategies are verified by experiments. The results show that the proposed system is feasible and both strategies can achieve NVPH. It can provide reference and guidance for the design of artificial detrusor system and formulation of control strategies. [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]

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant. [ABSTRACT FROM AUTHOR]

Background: Florida International University Herbert Wertheim College of Medicine (FIU‐HWCOM) participated in the AAMC Core Entrustable Professional Activities (EPA) implementation pilot. Entrustment decision processes based on data from workplace‐based assessments (WBAs) were piloted. Outcomes illustrated challenges including variability across EPAs with regards to learner level alignment and feasibility of data collection in the form of WBAs. In addition, students reported discomfort requesting WBA completion by preceptors and dissatisfaction with associated feedback. Approach: To guide future directions, we conducted a survey of third‐year students to better understand their experience with and perceptions of WBAs used to evaluate EPAs at FIU‐HWCOM. Evaluation: Survey response was 96% (n = 107/112). Most (84%) reported that WBAs were not valuable to their development and that preceptors often did not complete WBAs in a timely fashion. Many (47%) reported not receiving verbal feedback. Most students (78%) used language in written responses demonstrating confusion between the EPAs and the WBAs used to assess them. Implications: The use of WBAs to assess EPAs did not have its intended impact at FIU‐HWCOM. For future classes, WBA forms will consist of paper cards with questions directly assessing performance of skills aligned with EPAs 1, 5 and 6 only. To continue to promote feedback, students will be required to collect WBAs on all clerkships, but the number of required WBAs will be less than prior and no entrustment decisions will be made. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to describe and compare the proportion of patients classified as an emergency department (ED) mental health presentation under different definitions, including the Australian Institute of Health and Welfare (AIHW) definition. Methods: This retrospective cohort study enrolled all patients that presented to the EDs of a multi-centre Victorian health service between 1 January 2020 and 30 June 2023. Varying definitions of a mental health presentation were applied to each ED attendance, applying the current AIHW definition (using selected diagnosis codes), broader diagnosis-based coding, the presenting complaint recorded at triage and whether the patient was seen by or referred to the emergency psychiatric service (EPS). The proportion of all ED presentations meeting each definition and any overlap between definitions were calculated. The agreement between each definition and the AIHW definition was evaluated using Kappa's coefficient. Results: There were 813,078 presentations to ED of which 34,248 (4.2%) met the AIHW definition for a mental health presentation. Throughout the study, 45,376 (5.6%) patients were seen and/or referred to EPS, and 36,160 (4.4%) patients were allocated a mental health presenting complaint by triage staff. There was moderate interrater agreement between these definitions, with a kappa statistic (95% confidence interval) between the AIHW definition and a mental health presenting complaint recorded at triage of 0.58 (0.58–0.59) and between the AIHW definition and review by EPS of 0.58 (0.57–0.58). Conclusions: The AIHW definition is a conservative measure of ED mental health presentations and may underestimate emergency psychiatry workload in Australian EDs. What is known about this topic? It is unknown whether the Australian Institute of Health and Welfare (AIHW) definition of an emergency department (ED) mental health presentation definition accurately reflects ED mental health workload. What does this paper add? The AIHW definition appears to underestimate the clinical workload related to ED mental health presentations. There is moderate agreement between the AIHW definition and mental health presentations classified according to complaint recorded at triage, or whether the patient is reviewed by psychiatry services. What are the implications for practitioners? Accurate measurement of emergency psychiatry workload should not rely on the AIHW definition alone. [ABSTRACT FROM AUTHOR]

An approach for the crystallographic mapping of two‐phase alloys on the nanoscale using a combination of scanned precession electron diffraction and open‐source python libraries is introduced in this paper. This method is demonstrated using the example of a two‐phase α/β titanium alloy. The data were recorded using a direct electron detector to collect the patterns, and recently developed algorithms to perform automated indexing and analyse the crystallography from the results. Very high‐quality mapping is achieved at a 3 nm step size. The results show the expected Burgers orientation relationships between the α laths and β matrix, as well as the expected misorientations between α laths. A minor issue was found that one area was affected by 180° ambiguities in indexing occur due to this area being aligned too close to a zone axis of the α with twofold projection symmetry (not present in 3D) in the zero‐order Laue Zone, and this should be avoided in data acquisition in the future. Nevertheless, this study demonstrates a good workflow for the analysis of nanocrystalline two‐ or multi‐phase materials, which will be of widespread use in analysing two‐phase titanium and other systems and how they evolve as a function of thermomechanical treatments. [ABSTRACT FROM AUTHOR]

Following successful campaigns on the compact high field spherical tokamak ST40, resistive gold foil bolometers have now been installed to measure the radiated power profile. Positioned on the midplane, two bolometer cameras offer perspectives of the horizontal and vertical planes, while the third camera, situated above the midplane, provides a vertical view of the top divertor plates, including the X-point and the strike points. These cameras use a commercial off-the-shelf data acquisition platform with modules specifically designed for bolometers in a Wheatstone bridge configuration, providing both phase sensitive detection and in situ calibration. This paper presents an overview of the design and functionality of all three cameras and the commissioning of the horizontal plane camera. The commissioning of the horizontal camera shows data from a range of plasma pulses, presenting a positive correlation between the relative profiles measured from the bolometer camera and an absolute extreme ultraviolet diode camera viewing along similar lines of sight. [ABSTRACT FROM AUTHOR]

In response to the deficiencies in visible-near infrared spectral identification of coal gangue under different working conditions, this paper employs multiple algorithms to construct local and global recognition models and conducts analysis. Simulating five different working conditions in the laboratory, including different detection angles, illumination angles, detection distances, coal powder content, and moisture content. Each working condition is further divided into four levels. Collecting spectral data of coal and gangue samples. Constructing global and local datasets, which are respectively derived from all levels and single levels under certain working conditions. Training local and global models to identify samples based on machine learning algorithms such as Lightgbm and 1DCNN. The results of the experiment indicate that there are variations in the sample spectra and recognition accuracy based on different working conditions. Furthermore, the local model exhibits lower recognition accuracy and inadequate robustness for other level samples under the same working condition. The global model is more robust and practical, as it achieves higher or equal recognition accuracy for local data compared to the local model. Among the global models, Lightgbm has the best overall performance, with an average F1-score of 99.34% and 97.9% for local and global datasets, respectively. It also has fast training time of 0.75s and recognition time of 1 ms. The Lightgbm global model can accurately identify coal and gangue in various working conditions, making it a reliable solution. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate disparities in urine drug testing (UDT) during perinatal care at a single academic medical center. This retrospective cohort study included patients who had a live birth and received prenatal care at our institution between 10/1/2015 and 9/30/2020. The primary outcomes were maternal UDT during pregnancy (UDTPN) and UDT only at delivery (UDTDEL). Secondary outcomes included the number of UDTs (UDTNUM) and the association between a positive UDT test result and race/ethnicity. Mixed model logistic regression and negative binomial regression with clustering based on prenatal care locations were used to control for confounders. Of 6,240 live births, 2,265 (36.3%) and 167 (2.7%) received UDTPN and UDTDEL, respectively. Black (OR 2.09, 95% CI 1.54–2.84) and individuals of Other races (OR 1.64, 95% CI 1.03–2.64) had greater odds of UDTPN compared to non-Hispanic White individuals. Black (beta = 1.12, p < 0.001) and Hispanic individuals (beta = 0.78, p < 0.001) also had a positive relationship with UDTNUM. Compared to individuals with non-Medicaid insurance, those insured by Medicaid had greater odds of UDTPN (OR 1.66, 95% CI 1.11–2.49) and had a positive relationship with UDTNUM (beta = 0.89, p < 0.001). No significant associations were found for UDTDEL and race/ethnicity. Despite receiving more UDT, Black individuals were not more likely to have a positive test result compared to non-Hispanic White individuals (OR 0.95, 95% CI 0.72–1.25). Our findings demonstrate persistent disparities in substance use testing during the perinatal period. Significance: What is already known on this subject?: Prior literature has shown evidence of racial disparities in urine drug testing during pregnancy-related hospital admissions and at delivery. What this study adds?: This paper found that disparities in urine drug testing also exist during the prenatal care period at an urban academic institution. We also found a positive relationship between the number of tests received and being of Black or Hispanic race/ethnicity. Future research should investigate health care system-related interventions to mitigate disparities in urine drug testing. [ABSTRACT FROM AUTHOR]

Aiming at the received signal strength index (RSSI) in wireless positioning system, an improved weighted KNN fingerprint positioning algorithm is proposed in this paper. The algorithm pre-processes fingerprint data in offline stage that including eliminating outliers and Kalman filtering first, in order to improve the accuracy of data acquisition. Secondly, the fingerprint data is partitioned by using RSSI to attenuate obstacles such as walls. Then, points with significant RSSI differences in each region are selected as regional feature points, and the distance between RSSI of test points and feature points in each region is calculated respectively to determine the region in which the test points are located. Geometric method is used to analyse and define the correlation degree, and KNN is re-weighted to achieve accurate positioning in the region. Finally, experiments were carried out in the indoor environment to complete the establishment of the fingerprint database. Compared with the existing NN, KNN and WKNN, the experimental analysis results show that the accumulated error and average error are better than the traditional algorithm with the increase of measurement points, which has reference value for the complex environment positioning technology. [ABSTRACT FROM AUTHOR]

Aim: To determine the prevalence and clustering of NANDA‐International nursing diagnoses in patients assisted by pre‐hospital emergency teams. Design: Retrospective descriptive study of electronic record review. Methods: Episodes recorded during 2019, including at least a nursing diagnosis, were recovered from the electronic health records of a Spanish public emergency agency (N = 28,847). Descriptive statistics were used to characterize the sample and determine prevalence. A two‐step cluster analysis was used to group nursing diagnoses. A comparison between clusters in sociodemographic and medical problems was performed. Data were accessed in November 2020. Results: Risk for falls (00155) (27.3%), Anxiety (00146) (23.2%), Acute pain (00132), Fear (00148) and Ineffective breathing pattern (00032) represented 96.1% of all recorded diagnoses. A six‐cluster solution (n = 26.788) was found. Five clusters had a single high‐prevalence diagnosis predominance: Risk for falls (00155) in cluster 1, Anxiety (00146) in cluster 2, Fear (00148) in cluster 3, Acute pain (00132) in cluster 4 and Ineffective breathing pattern (00032) in cluster 6. Cluster 5 had several high prevalence diagnoses which co‐occurred: Risk for unstable blood glucose level (00179), Ineffective coping (00069), Ineffective health management (00078), Impaired comfort (00214) and Impaired verbal communication (00051). Conclusion: Five nursing diagnoses accounted for almost the entire prevalence. The identified clusters showed that pre‐hospital patients present six patterns of nursing diagnoses. Five clusters were predominated by a predominant nursing diagnosis related to patient safety, coping, comfort, and activity/rest, respectively. The sixth cluster grouped several nursing diagnoses applicable to exacerbations of chronic diseases. Implications for the profession and/or patient care: Knowing the prevalence and clustering of nursing diagnoses allows a better understanding of the human responses of patients attended by pre‐hospital emergency teams and increases the evidence of individualized/standardized care plans in the pre‐hospital clinical setting. Impact: What problem did the study address? There are different models of pre‐hospital emergency care services.The use of standardized nursing languages in the pre‐hospital setting is not homogeneous.Studies on NANDA‐I nursing diagnoses in the pre‐hospital context are scarce, and those available are conducted on small samples. What were the main findings? This paper reports the study with the largest sample among the few published on NANDA‐I nursing diagnoses in the pre‐hospital care setting.Five nursing diagnoses represented 96.1% of all recorded. These diagnoses were related to patients' safety/protection and coping/stress tolerance.Patients attended by pre‐hospital care teams are grouped into six clusters based on the nursing diagnoses, and this classification is independent of the medical conditions the patient suffers. Where and on whom will the research have an impact? Knowing the prevalence of nursing diagnoses allows a better understanding of the human responses of patients treated in the pre‐hospital setting, increasing the evidence of individualized and standardized care plans for pre‐hospital care. Reporting method: STROBE checklist has been used as a reporting method. No Patient or Public Contribution: Only patients' records were reviewed without further involvement. [ABSTRACT FROM AUTHOR]

With the development of industry, more and more crucial mechanical machinery generate wildness demand of effective fault diagnosis to ensure the safe operation. Over the past few decades, researchers have explored and developed a variety of approaches. In recent years, fault diagnosis based on deep learning has developed rapidly, which achieved satisfied results in the filed of mechanical equipment fault diagnosis. However, there is few review to systematically summarize and sort out these special image deep learning methods. In order to fill this gap, this paper concentrates on reviewing comprehensively the development of special image deep learning for mechanical equipment fault diagnosis in past 5 years. In general, a typical image fault diagnosis based on fault image deep learning generally consists of data acquisition, signal processing, model construction, feature learning and decision-making. Firstly, the method of signal preprocessing is introduced, and several common methods of converting signals into images are briefly compared and analyzed. Then, the principles and variants of deep learning models are expounded. Further more, the difficulties and challenges encountered at this stage are summarized. Last but not least, the future development and potential trends of the work are concluded, and it hoped that this work will facilitate and inspire further exploration for researchers in this area. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent manufacturing, Digital Twin technology, as an advanced tool for the intelligentization of weaving workshops, has endowed weaving services with real-time simulation and dynamic optimization capabilities while also placing higher demands on the digital capabilities of workshops. The diverse and multi-manufacturer equipment in weaving workshops exacerbates the complexity of multi-source heterogeneous data. Moreover, traditional data collection methods, which are mostly based on fixed frequencies, increase the network load during real-time high-frequency data reception, making stable, long-term operation difficult. Conversely, low-frequency collection might miss important state changes, thus affecting the quality of weaving big data. To address these issues, this paper proposes a service-oriented Digital Twin modeling method for weaving workshops. This method combines OPC Unified Architecture (OPC UA) with a state change-based data collection approach, utilizing a sliding time window (STW) to identify anomalous data and employing median interpolation to correct these anomalies. The goal is to enhance the representation capability of the Digital Twin in the weaving workshop by improving the data quality. For a specific service of predicting the warp-out time of 288 air-jet looms in a workshop, the average error of the predicted warp-out time using the dynamic data set proposed in this study was reduced from 0.85 h to 0.78 h compared to the static data set based on fixed frequency, an improvement of 8.2%, thereby validating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of CNC (Computer Numerical Control) working time through the strategic positioning and grouping of cutting tools. Our analysis primarily focused on evaluating the previous arrangement of tools in the magazine and exploring the potential for repositioning them to enhance CNC operational efficiency. The work methodology is based on the collection of direct data from CNC as well as through CAD/CAM simulations. The collected data are analyzed with the Python programming language, which is one of the most used and efficient languages now. By grouping the tools according to the same function, tools were repositioned next to each other, which we obtained. After collecting the data and processing them in the Python programming language, we made a visualization of the data. Working time is reduced from 27.44 s to 26.46 s and results are analyzed in the Python Programming language. The working time is reduced by 0.98 s, which is approximately a 3.57% reduction in the working time. This indicates a significant improvement in efficiency due to the optimization of the cutting tool positions in the magazine. [ABSTRACT FROM AUTHOR]

Orbital flybys have been extensively studied for spacecraft missions, resulting in effective mathematical and physical models. However, these models' applicability to natural encounters involving asteroids has not been explored. This paper examines the applicability of two such theories, patched conics (PC) and the Keplerian map (KM), to asteroid encounters. A review of the two methods will be provided, highlighting their assumptions and range of applicability. Simulations of asteroid–asteroid encounters will then be performed to evaluate their effectiveness in these scenarios. The simulation parameters are set by collecting data on actual asteroid–asteroid encounters, hereby presented, generally characterised by high close approach distances and small masses of the perturbing bodies, if compared to those used to build the flyby theories. Results show that the PC theory's effectiveness diminishes with increasing approach distances, aligning with its assumptions. Moreover, the prediction of the model is better in the geometric configurations where the flyby has major effects on the orbital energy change. The KM theory has shown good effectiveness for encounters occurring outside the sphere of influence of the perturbing body, even for very high distances. This research investigates flyby models' strengths and weaknesses in asteroid encounters, offering practical insights and future directions. [ABSTRACT FROM AUTHOR]

Helichrysum italicum (Roth) G. Don is a typical Mediterranean plant, with limited distribution on the islands of Sardinia, Corsica, and the Iberian Peninsula, as well as the islands of the Adriatic Sea and the Balkan Peninsula. In these regions, H. italicum is mainly collected from spontaneous nature, while in recent years, there has been a pronounced cultivation trend due to increased demand and market requirements for constant quality of raw materials. Bearing in mind that biological activity is linked with chemical composition, this review aimed to collect data from different scientific databases (Scopus, PubMed, Web of Science, and Google Scholar) on the antimicrobial activity of essential oil and its chemical composition. A total of 20 papers investigating the antibacterial, antibiofilm, and antifungal activities of H. italicum essential oil were found. Furthermore, in these samples, several compounds occurred as dominant: neryl acetate, α-pinene, and γ-curcumene. These compounds are known for their antimicrobial properties, which likely contribute to the essential oil's efficacy against various microbial strains. [ABSTRACT FROM AUTHOR]

Introduction: Digitalization in medicine is steadily increasing. Complex treatments, scarce personnel resources and a high level of documentation are a constant burden on healthcare systems. The balancing between correct manual documentation in the digital records and limited staff resources is rarely successful. The aim of this study is to evaluate the adherence between documentation and lack of documentation in the treatment of critical care patients. Material and Methods: For the evaluation, data from the hospital information system (HIS) of several intensive care units (ICU) were examined in conjunction with data collected from a checklist. All boluses of sedatives, analgesics and catecholamines were documented paper based across all shifts and all weekdays and compared with corresponding digital data from the HIS (2019–2022) of previous years. Results: 939 complete digital patient records revealed a massive under-documentation of the medication administration compared to that applied according to the checklist. Only 12% of all administered catecholamines, 11% of α2-agonists, 33% of propofol, 92% of midazolam and 46% of opioids were found in the digital recordings. In comparison, the effect was more pronounced on weekdays compared to weekends. In addition, the highest documentation gap was found in the comparison of early shifts. Comparing neurosurgical vs. internal vs. anesthesiologic ICUs there was a highly significant difference between anesthesiologic ICUs compared with other disciplines (p < 0.0001). Discussion: Our data shows that there is a remarkable documentation gap and incongruence in the area of applied boli. Automated documentation by connecting syringe pumps that enter data directly into the HIS can not only reduce the workload, but also lead to comprehensive and legally required documentation of all administered medication. [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]

Innovative methods for remote data collection, developed during the COVID-19 pandemic, also carry value for extending the reach of future research. Using the case study of an evaluation of children's learning from Sesame Street, this methodological paper discusses challenges of assessing preschool children's hands-on problem solving remotely via video chat, as well as techniques that were used to overcome these challenges, to yield rich, reliable data. These techniques pertained to every phase of the research: materials and preparatiory work prior to data collection, procedural and technical considerations during sessions (including enlisting assistance from the children's parents), managing children's behavior, preventing parents from influencing their children's responses, and considerations during data analysis. Although remote methods should not replace in-person research in all cases, they hold the potential for increasing the geographic and demographic diversity of samples, thus producing more representative and generalizable data. [ABSTRACT FROM AUTHOR]

Expansion joints render bridge structures highly vulnerable to damage during strong ground motions. Failures of expansion joints triggered by earthquakes not only jeopardize the post-earthquake serviceability of the bridge but also have a significant impact on the bridge's overall seismic performance. Despite extensive investigations and efforts to integrate these measures into design specifications aimed at mitigating the consequences of relative movements between adjacent bridge spans, major earthquakes have still revealed instances of damage related to expansion joints. On 6 February 2023, strong earthquake sequences occurred in Kahramanmaraş, Turkey, with magnitudes of M7.7 and M7.6. The fault lines and epicenters of these shallow earthquakes were near the city and town centers and caused severe structural damage to buildings and infrastructures. There are approximately 1000 railway and highway bridges in the earthquake-affected region. Although both highway and railway bridges have generally performed well, some bridges experienced structural damage during the Kahramanmaraş earthquakes. A large number of damage on the bridges is due to pounding and opening relative movements in expansion joints. This paper presents a comprehensive seismic evaluation of expansion joint failure mechanisms on bridges without viscous dampers during the 2023 Kahramanmaraş earthquake sequences and an in-depth investigation into the seismic performance of bridge expansion joints equipped with viscous dampers and shock transmission unit devices are implemented utilizing the strong ground motion data collected throughout the earthquake sequences. It can be stated that the near-fault induced significant directivity and fling effects, resulting in notable velocity pulses and permanent tectonic deformations, and that these effects contributed to the failures of expansion joints, viscous damper devices, pot bearings, and shear keys. [ABSTRACT FROM AUTHOR]

The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain and Paillier homomorphic cryptosystem, providing unchangeable and traceable data while overcoming privacy concerns. Secondly, based on the uploaded data, we propose a method to determine the approximate correct value region without knowing the exact value. Finally, we offer a data filtering mechanism based on the Paillier cryptosystem using this value region. The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection. [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 paper presents a multi-agent license plate recognition system, specifically designed to address the diverse and challenging nature of license plates. Utilizing a multi-agent architecture with agents operating in individual Docker containers and orchestrated by Kubernetes, the system demonstrates remarkable adaptability and scalability. It leverages advanced neural networks, trained on a comprehensive dataset, to accurately identify various license plate types under dynamic conditions. The system's efficacy is showcased through its threelayered approach, encompassing data collection, processing, and result compilation, significantly outperforming traditional license plate recognition (LPR) systems. This innovation not only marks a technological leap in license plate recognition but also offers strategic solutions for enhancing traffic management and smart city infrastructure globally. [ABSTRACT FROM AUTHOR]