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Due to their lack of periodontal ligaments (PLs) and the differences between dental implants and natural teeth, it is necessary to improve and generate a new occlusal scheme to prolong the life of implants and prostheses. The age and the sex of patients must be considered because of their effects on the stomatognathic system's physiology. Operators must manage all the changes to obtain good sensations during mastication and a better occlusal scheme for implanting fixed partial prostheses. Dentists should try to protect this type of prosthesis using adjacent teeth and the PL. This is why new digital systems were created. The combination of T-Scan® (digital software for occlusal analysis) and electromyography (EMG) could allow doctors to find areas where it is necessary to act and to find suitable solutions for the problems generated by using conventional methods of occlusal analysis (such as articulating paper). In this study, a new method for establishing occlusion on fixed partial implant prostheses has been created, combining digital systems with conventional articulating paper. This method consists of asking the patient to bite down with different forces and situations in an attempt to achieve Implant-Protected Occlusion (IPO). The use of digital systems has been shown to be more effective than using only conventional systems. This new method allows a safer mode of occlusion which protects implants and prostheses, saving all the differences between them and natural teeth, and increasing the satisfaction of patients. This method also helps to overcome the changes in the stomatognathic system as age increases, adjusting the occlusion to changes in PLs with age. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the traditional rapidly exploring random tree (RRT) algorithm only considers the global path of unmanned vehicles in a static environment, which has the limitation of not being able to avoid unknown dynamic obstacles in real time, and that the traditional dynamic window approach (DWA) algorithm is prone to fall into a local optimum during local path planning, this paper proposes a path planning method for unmanned vehicles that integrates improved RRT and DWA algorithms. The RRT algorithm is improved by introducing strategies such as target-biased random sampling, adaptive step size, and adaptive radius node screening, which enhance the efficiency and safety of path planning. The global path key points generated by the improved RRT algorithm are used as the subtarget points of the DWA algorithm, and the DWA algorithm is optimized through the design of an adaptive evaluation function weighting method based on real-time obstacle distances to achieve more reasonable local path planning. Through simulation experiments, the fusion algorithm shows promising results in a variety of typical static and dynamic mixed driving scenarios, can effectively plan a path that meets the driving requirements of an unmanned vehicle, avoids unknown dynamic obstacles, and shows higher path optimization efficiency and driving stability in complex environments, which provides strong support for an unmanned vehicle's path planning in complex environments. [ABSTRACT FROM AUTHOR]

To achieve an accurate measurement of tread size after fixed-length cutting, this paper proposes a point-cloud-based tread size measurement method. Firstly, a mathematical model of corner points and a reprojection error is established, and the optimal solution of the number of corner points is determined by the non-dominated sorting genetic algorithm II (NSGA-II), which reduces the reprojection error of the RGB-D camera. Secondly, to address the problem of the low accuracy of the traditional pixel metric ratio measurement method, the random sampling consensus point cloud segmentation algorithm (RANSAC) and the oriented bounding box (OBB) collision detection algorithm are introduced to complete the accurate detection of the tread size. By comparing the absolute error and relative error data of several groups of experiments, the accuracy of the detection method in this paper reaches 1 mm, and the measurement deviation is between 0.14% and 2.67%, which is in line with the highest accuracy standard of the national standard. In summary, the RGB-D visual inspection method constructed in this paper has the characteristics of low cost and high inspection accuracy, which is a potential solution to enhance the pickup guidance of tread size measurement. [ABSTRACT FROM AUTHOR]

Background: The gold standard treatment for periprosthetic joint infections is the two-stage revision that includes the spacer placement before definitive reimplantation. The management of PJI affects patients' joint function and, subsequently, their mental health. Even though significant advances have been achieved, little to no attention has been paid to the psychological implications. So, based on standardized patient-reported outcome measures (PROMs), this study aimed to clarify the effect of spacer treatment of infected hip and knee arthroplasties on patients' mental health. Methods: We performed research on the literature on PJIs in the English language using the MEDLINE database with the search strings "spacer" OR "spacers" AND "hip" OR "knee" AND "SF-12" OR "SF-36" OR "EQ-5" OR "mental" OR "depression" OR "anxiety." The reference lists of selected articles were also hand-searched for any additional articles. Results: A total of 973 published papers were extracted, and 9 papers were finally included. A total of 384 patients who underwent spacer placement for PJI were identified. Of these 384 patients, 54% were female. The mean age ranged from 62 to 78.2 years. Of the11 papers identified for this review, 4 analyzed only hip spacers, including 119 patients; 4 only knee spacers, evaluating 153 patients; while a single study included 112 patients for both joints. Conclusions: Patients with the spacer are living in a state of mental upset, albeit better than the preoperative state. Clinical improvement with the review is not assured. The alteration of mental state turns out not to be transient for all the patients. [ABSTRACT FROM AUTHOR]

Ensuring the integrity of scientific literature is essential for advancing knowledge and research. However, the credibility and trustworthiness of scholarly publications are compromised by manipulated citations. Traditional methods, such as manual inspection and basic statistical analyses, have limitations in detecting intricate patterns and subtle manipulations of citations. In recent years, network-based approaches have emerged as promising techniques for identifying and understanding citation manipulation. This study introduces a novel method to identify potential citation manipulation in academic papers using perturbations of a deep embedding model. The key idea is to reconstruct meaningful connections represented by citations within a network by exploring, to some extent, longer alternative paths. These indirect pathways enable the recovery of reliable citations while estimating their trustworthiness. The investigation takes a comprehensive approach to link prediction, leveraging the consistent behavior of prominent connections when exposed to network perturbations. Through numerical experiments, the method demonstrates a high capability to identify reliable citations as the core of the analyzed data and to raise suspicions about unreliable references that may have been manipulated. This research presents a refined method for tackling the urgent problem of citation manipulation in academic papers. It harnesses statistical sampling and graph-embedding techniques to evaluate the credibility of scholarly publications with a substantial assessment of the whole citation graph. [ABSTRACT FROM AUTHOR]

One of the most widely used robust regression methods for solving simple linear regression problems is the Theil-Sen (TS) estimator. This estimator has some notable advantages; however, it does not belong to the most robust estimation methods (called high-breakdown estimators) and is prone to outliers whose distribution is highly asymmetric with respect to the correct data points. This paper presents a modification of the TS estimator, the Robustified Theil-Sen (RTS) estimator. The new method uses a heuristic-based selection procedure to reduce the number of initial estimates of the regression function parameters computed with at least one outlier, thereby improving the regression results. The use of this heuristic procedure only slightly increases the computational time required for using the RTS estimator compared to the TS estimator. Preliminary results of two numerical experiments presented in the paper show that the RTS estimator outperforms other comparable estimators, i.e., the TS estimator and the repeated median estimator, in terms of robustness. The results presented also suggest that the breakpoint value (which is a measure of the robustness of estimators) of the RTS estimator is higher than the breakpoint value of the TS estimator and equal to the breakpoint value of the high-breakpoint estimators. [ABSTRACT FROM AUTHOR]

This paper proposes a novel camera–LiDAR calibration method that utilizes an iterative random sampling and intersection line-based quality evaluation using a foldable plane pair. Firstly, this paper suggests using a calibration object consisting of two small planes with ChArUco patterns, which is easy to make and convenient to carry. Secondly, the proposed method adopts an iterative random sampling to make the calibration procedure robust against sensor data noise and incorrect object recognition. Lastly, this paper proposes a novel quality evaluation method based on the dissimilarity between two intersection lines of the plane pairs from the two sensors. Thus, the proposed method repeats random sampling of sensor data, extrinsic parameter estimation, and quality evaluation of the estimation result in order to determine the most appropriate calibration result. Furthermore, this method can also be used for the LiDAR–LiDAR calibration with a slight modification. In experiments, the proposed method was quantitively evaluated using simulation data and qualitatively assessed using real-world data. The experimental results show that the proposed method can successfully perform both camera–LiDAR and LiDAR–LiDAR calibrations while outperforming the previous approaches. [ABSTRACT FROM AUTHOR]

Research suggests parental ability to recognise when their child has overweight is limited. It is hypothesised that recognition of child overweight/obesity is fundamental to its prevention, acting as a potential barrier to parental action to improve their child's health-related behaviours and/or help seeking. The purpose of this study was to investigate the efficacy of an intervention (MapMe) to improve parental ability to correctly categorise their child as having overweight one-month post-intervention, and reduce child body mass index (BMI) z-score 12 months post-intervention. MapMe consists of body image scales of known child BMI and information on the consequences of childhood overweight, associated health-related behaviours and sources of support. We conducted a three-arm (paper-based MapMe, web-based MapMe and control) randomised control trial in fifteen English local authority areas with parents/guardians of 4–5- and 10–11-year-old children. Parental categorisation of child weight status was assessed using the question 'How would you describe your child's weight at the moment?' Response options were: underweight, healthy weight, overweight, and very overweight. Child weight status and BMI z-scores were calculated using objectively measured height and weight data and UK90 clinical thresholds. There was no difference in the percentage of parents correctly categorising their child as having overweight/very overweight (n = 264: 41% control, 48% web-based, and 43% paper-based, p = 0.646). BMI z-scores were significantly reduced for the intervention group at 12 months post-intervention compared to controls (n = 338, mean difference in BMI z-score change −0.11 (95% CI −0.202 to −0.020, p = 0.017). MapMe was associated with a decrease in BMI z-score 12 months post-intervention, although there was no direct evidence of improved parental ability to correctly categorise child overweight status. Further work is needed to replicate these findings in a larger sample of children, investigate mechanisms of action, and determine the use of MapMe as a public health initiative. [ABSTRACT FROM AUTHOR]

Night-time vehicle detection plays a vital role due to the high incidence of abnormal events in our daily security field. However, existing studies mainly focus on vehicle detection in autonomous driving and traffic intersection scenes, but ignore urban scenes. There are vast differences between these scenes, such as viewpoint, position, illumination, etc. In this paper, the authors present a night-time vehicle detection dataset collected from urban scenes, named Vehicle Detection in Night-Time Urban Scene (VD-NUS). The VD-NUS dataset consists of more than 100 K challenging images, comprising a total of about 500 K labelled vehicles. This paper introduces a vehicle detection framework via an active auxiliary mechanism (AAM) to reduce the annotation workload. The proposed AAM framework can actively select the informative sample for annotation by estimating its uncertainty and locational instability. Furthermore, this paper proposes a computer-assisted detection module embedded in the AAM framework to help human annotators to rapidly and accurately label the selected data. AAM outperformed the baseline method (random sampling) by up to 0.91 AP and 3.0 MR−2 on the VD-NUS dataset. [ABSTRACT FROM AUTHOR]

Photoacoustic microscopy (PAM) is a medical-imaging technique with the merits of high contrast and resolution. Nevertheless, conventional PAM scans specimens in a diameter-by-diameter fashion, resulting in a time-consuming process. Furthermore, deep-learning-based PAM image enhancement necessitates acquiring ground-truth data for training purposes. In this paper, we built an optical-resolution photoacoustic microscopy system and introduced an innovative unsupervised-learning algorithm. First, we enhanced the rotational-scanning method, transitioning from a diameter-by-diameter approach to a sector-by-sector one, significantly reducing imaging time (from 280 s to 109 s). Second, by establishing a metric for unsupervised learning, we eliminated the need for collecting reliable and high-quality ground truth, which is a challenging task in photoacoustic microscopy. A total of 324 pairs of datasets (mouse ears) were collected for unsupervised learning, with 274 for training and 50 for testing. Additionally, carbon-fiber data were sampled for lateral resolution and contrast evaluation, as well as the effective rate evaluation of the algorithm. The enhanced images demonstrated superior performance compared with that of maximum projection, both subjectively and objectively. A 76% improvement in the lateral resolution was observed. The effective rate of the algorithm was measured to be 100%, which was tested on 50 random samples. The technique presented in this paper holds substantial potential for image postprocessing and opens new avenues for unsupervised learning in photoacoustic microscopy. [ABSTRACT FROM AUTHOR]

To estimate the finite population variance of the study variable, this paper proposes an improved class of efficient estimators using different transformations. When both the minimum and maximum values of the auxiliary variable are known and the ranks of the auxiliary variable are associated with the study variable, these estimators are particularly useful. Therefore, the precision of the estimators can be effectively improved through the utilization of these rankings. We examine the properties of the proposed class of estimators, including bias and mean squared error (MSE), using a first-order approximation through a stratified random sampling method. To determine the performances and validate the findings mathematically, a simulation study is carried out. Based on the results, the proposed class of estimators performs better in terms of the mean squared error (M S E) and percent relative efficiency (P R E) as compared to other estimators in all scenarios. Furthermore, in order to prove that the performances of the improved class of estimators are better than those of the existing estimators, three data sets are examined in the application section. [ABSTRACT FROM AUTHOR]

Accurate risk assessment is crucial for predicting potential financial losses. This paper introduces an innovative approach by employing expected risk models that utilize risk samples to capture comprehensive risk characteristics. The innovation lies in the integration of classical credibility theory with expected risk models, enhancing their stability and precision. In this study, two distinct expected risk models were developed, referred to as Model Type I and Model Type II. The Type I model involves independent and identically distributed random samples, while the Type II model incorporates time-varying stochastic processes, including heteroscedastic models like GARCH(p,q). However, these models often exhibit high variability and instability, which can undermine their effectiveness. To mitigate these issues, we applied classical credibility theory, resulting in credible expected risk models. These enhanced models aim to improve the accuracy of Value-at-Risk (VaR) forecasts, a key risk measure defined as the maximum potential loss over a specified period at a given confidence level. The credible expected risk models, referred to as CreVaR, provide more stable and precise VaR forecasts by incorporating credibility adjustments. The effectiveness of these models is evaluated through two complementary approaches: coverage probability, which assesses the accuracy of risk predictions; and scoring functions, which offer a more nuanced evaluation of prediction accuracy by comparing predicted risks with actual observed outcomes. Scoring functions are essential in further assessing the reliability of CreVaR forecasts by quantifying how closely the forecasts align with the actual data, thereby providing a more comprehensive measure of predictive performance. Our findings demonstrate that the CreVaR risk measure delivers more reliable and stable risk forecasts compared to conventional methods. This research contributes to quantitative risk management by offering a robust approach to financial risk prediction, thereby supporting better decision making for companies and financial institutions. [ABSTRACT FROM AUTHOR]

This paper reports on a service evaluation of PeerTalk, a nationwide charity that organises and facilitates peer support groups for individuals with depression. Therefore, the aim was to gather and synthesise benefits perceived by support group attendees. Thematic analysis was undertaken following the collection of data from two group interviews comprising PeerTalk support group attendees. Once those data were analysed, five key themes emerged: (1) talking/listening, (2) socialising, (3) contrast with other services, (4) personal benefits, and (5) structure and accessibility. Two further minor themes were also identified: (6) wider benefits and (7) areas for development that could lead to overall improvements to the service. PeerTalk's support groups provide multiple opportunities for attendees to meet others who have similar experiences within an environment that does not require formal engagement or commitment. Those that attend find benefit from supporting others and socialising within the group. These benefits are complementary to mainstream services that they may concurrently be involved with, rather than replacing or hindering them. Peer support groups can therefore provide a resource for healthcare professionals to which they can direct individuals who may feel benefit from engaging with other individuals with similar experiences. Sheffield Hallam University granted ethics approval for the study (ER:59716880) prior to its commencement (16 February 2024). [ABSTRACT FROM AUTHOR]

Objectives: This paper explores parent coaching experiences supporting parents of young children newly diagnosed with type 1 diabetes in a clinical trial. Methods: In a trial for 157 parents, those in the intervention arm (n = 116) were paired with a parent coach (n = 37; Mage = 37.9 years, SD = 3.9; 94.6% mothers, 81.1% White non-Hispanic). Parent coaches provided diabetes-specific social support. Parent coaches completed monthly surveys and satisfaction/feasibility surveys, with a subset (n = 7) undergoing qualitative interviews at the end of this study. Results: There were 2262 contacts between participants and their parent coaches, averaging 14.4 (SD = 9.3) per participant. Parent coaches reported that the most commonly used methods were text messages (67.9%) and emails (18.7%), with 33.6% having in-person visits. Coaches reported high satisfaction and belief in their usefulness to participants during the first 9 months after T1D diagnosis. Themes discussed by parent coaches about their experience in mentoring included relationship building, expertise sharing, personal growth, gratification, and intervention optimization suggestions. Conclusions: Parent coaching post T1D diagnosis involves regular, multi-method contacts. It is highly acceptable and valuable for parent coaches to mentor other parents of young children newly diagnosed with T1D. [ABSTRACT FROM AUTHOR]

Robust geometric fitting is one of the crucial and fundamental problems in computer vision and pattern recognition. While random sampling and consensus maximization have been popular strategies for robust fitting, finding a balance between optimization quality and computational efficiency remains a persistent obstacle. In this paper, we adopt an optimization perspective and introduce a novel maximum consensus robust fitting algorithm that incorporates the maximum entropy framework into the consensus maximization problem. Specifically, we incorporate the probability distribution of inliers calculated using maximum entropy with consensus constraints. Furthermore, we introduce an improved relaxed and accelerated alternating direction method of multipliers (R-A-ADMMs) strategy tailored to our framework, facilitating an efficient solution to the optimization problem. Our proposed algorithm demonstrates superior performance compared to state-of-the-art methods on both synthetic and contaminated real datasets, particularly when dealing with contaminated datasets containing a high proportion of outliers. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to decision-making based on a three-phase application of a new fuzzy logic model that embraces the principles of symmetry by balancing competing objectives in data collection and analysis. Our study, which employs a three-stage stratified random sample strategy with a randomized response technique, addresses the critical challenges of cost management and volatility reduction. Using the alpha-cut method, our model creates an effective allocation strategy that finds a balance between cost constraints and variance reduction objectives. We use numerical examples from real-world scenarios to demonstrate our approach's durability and practicality. Our revolutionary technique maintains data quality and cost-effectiveness while offering a game-changing answer to sensitive information acquisition concerns. By combining randomized response techniques and fuzzy logic, this study establishes a new standard for decision-making models that prioritizes both data-gathering precision and privacy preservation, encapsulating the essential principle of symmetry in balancing competing aims. [ABSTRACT FROM AUTHOR]

Accurate terrain mapping information is very important for foot landing planning and motion control in foot robots. Therefore, a terrain mapping method suitable for an indoor structured environment is proposed in this paper. Firstly, by constructing a terrain mapping framework and adding the estimation of the robot's pose, the algorithm converts the distance sensor measurement results into terrain height information and maps them into the voxel grid, and effectively reducing the influence of pose uncertainty in a robot system. Secondly, the height information mapped into the voxel grid is downsampled to reduce information redundancy. Finally, a preemptive random sample consistency (preemptive RANSAC) algorithm is used to divide the plane from the height information of the environment and merge the voxel grid in the extracted plane to realize the adaptive resolution 2D voxel terrain mapping (ARVTM) in the structured environment. Experiments show that the proposed mapping algorithm reduces the error of terrain mapping by 62.7% and increases the speed of terrain mapping by 25.1%. The algorithm can effectively identify and extract plane features in a structured environment, reducing the complexity of terrain mapping information, and improving the speed of terrain mapping. [ABSTRACT FROM AUTHOR]

To address the long-term statistical problem of ski-track area in the construction and operation of ski resorts, we propose a new ski-track point cloud boundary extraction method that improves the accuracy of boundary extraction and minimizes the offset of the area error. In this method, all point clouds are first projected onto the fitting plane using the random sample consensus (RANSAC) method. An improved point cloud boundary extraction algorithm is used to triangulate and extract the high-precision ski-track boundary. A discrete Green formula is then used to calculate and count the ski track's exact area. It is demonstrated through five sets of test experiments that the error offset of the method proposed in this paper is smaller than that of other classical methods, which confirms its benefits and feasibility. [ABSTRACT FROM AUTHOR]

Three-way clustering uses core region and fringe region to describe a cluster, which divide the dataset into three parts. The division helps identify the central core and outer sparse regions of a cluster. One of the main challenges in three-way clustering is the meaningful construction of the two sets. Aimed at handling high-dimensional data and improving the stability of clustering, this paper proposes a novel three-way clustering method. The proposed method uses dimensionality reduction techniques to reduce data dimensions and eliminate noise. Based on the reduced dataset, random sampling and feature extraction are performed multiple times to introduce randomness and diversity, enhancing the algorithm's robustness. Ensemble strategies are applied on these subsets, and the k-means algorithm is utilized to obtain multiple clustering results. Based on these results, we obtain co-association frequency between different samples and fused clustering result using the single-linkage method of hierarchical clustering. In order to describe the core region and fringe region of each cluster, the similar class of each sample is defined by co-association frequency. The lower and upper approximations of each cluster are obtained based on similar class. The samples in the lower approximation of each cluster belong to the core region of the cluster. The differences between lower and upper approximations of each cluster are defined as fringe region. Therefore, a three-way explanation of each cluster is naturally formed. By employing various UC Irvine Machine Learning Repository (UCI) datasets and comparing different clustering metrics such as Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Accuracy (ACC), the experimental results show that the proposed strategy is effective in improving the structure of clustering results. [ABSTRACT FROM AUTHOR]

In response to the issues of low model recognition accuracy and weak generalization in mechanical equipment fault diagnosis due to scarce data, this paper proposes an innovative solution, a cross-device secondary transfer-learning method based on EGRUN (efficient gated recurrent unit network). This method utilizes continuous wavelet transform (CWT) to transform source domain data into images. The EGRUN model is initially trained, and shallow layer weights are frozen. Subsequently, random overlapping sampling is applied to the target domain data to enhance data and perform secondary transfer learning. The experimental results demonstrate that this method not only significantly improves the model's ability to learn fault features but also enhances its classification accuracy and generalization performance. Compared to current state-of-the-art algorithms, the model proposed in this study shows faster convergence speed, higher diagnostic accuracy, and superior robustness and generalization, providing an effective approach to address the challenges arising from scarce data and varying operating conditions in practical engineering scenarios. [ABSTRACT FROM AUTHOR]

The Philippines is a source of labor for many countries. Roughly 10 million overseas Filipinos are working and living outside of the Philippines. This paper examines the association between sociodemographic characteristics (sex, age, educational level, and income) and self-rated physical and mental health, access to healthcare, and health habits among immigrant Filipinos living in Santa Cruz de Tenerife, Spain. Through convenience sampling, Filipino migrants (n = 103) aged 18 years and above participated in the online survey between October 2022 and March 2023. The data were analyzed using descriptive statistical analysis and chi-square. Almost all respondents self-rated their health as excellent and very good. Female respondents are more affected by mental health. Most are enrolled in the Universal Health System of Spain (public insurance). There is more utilization of private health insurance among respondents aged 60 years and above and high-wage earners. Cigarette smoking and alcohol drinking are associated with males. More than half of the respondents perform weekly exercise occasionally or never. These findings suggest a potential need for targeted interventions with an emphasis on the practice of preventive health and the promotion of healthy lifestyles, especially among financially disadvantaged migrants with lesser health access. [ABSTRACT FROM AUTHOR]

Feature point matching is a fundamental task in computer vision such as vision simultaneous localization and mapping (VSLAM) and structure from motion (SFM). Due to the similarity or interference of features, mismatches are often unavoidable. Therefore, how to eliminate mismatches is important for robust matching. Smoothness constraint is widely used to remove mismatch, but it cannot effectively deal with the issue in the rapidly changing scene. In this paper, a novel LCS-SSM (Local Cell Statistics and Structural Similarity Measurement) mismatch removal method is proposed. LCS-SSM integrates the motion consistency and structural similarity of a local image block as the statistical likelihood of matched key points. Then, the Random Sampling Consensus (RANSAC) algorithm is employed to preserve the isolated matches that do not satisfy the statistical likelihood. Experimental and comparative results on the public dataset show that the proposed LCS-SSM can effectively and reliably differentiate true and false matches compared with state-of-the-art methods, and can be used for robust matching in scenes with fast motion, blurs, and clustered noise. [ABSTRACT FROM AUTHOR]

Introduction: This study aims to verify if older adults with mixed anxiety-depressive disorder are more prone to euthanasia and identify factors that interfere with their satisfaction with health and capacity for well-informed decisions. Material and Methods: The study applied a paper questionnaire composed of a sociodemographic section and a battery of scales (to assess depression, anxiety, cognitive performance, suicide risk, therapeutic adhesion, functionality, loneliness, attitude towards euthanasia, decision pattern, personality, empathy, and health status) in the Psychogeriatric Unity of Senhora da Oliveira Hospital in Portugal. The sample was collected by convenience to include patients and controls of the same age. Six months later, a reassessment was performed. Patients and controls were compared using descriptive statistics and a multiple-regression model. Results: A total of 114 patients and 25 controls were included. Eighty-one point six percent of patients had four or fewer years of schooling. Contrary to controls, they presented mild depressive and anxiety symptoms, loneliness feelings, worse cognitive performance, a more fragile personality, higher personal distress, and a poorer health state. No statistically significant differences were found between controls and patients regarding their attitudes towards euthanasia. Patients more favourable to euthanasia had higher empathic concern, conscientiousness, and fantasy, and lower personal distress. Discussion and Conclusion: When addressing euthanasia in these patients, it is crucial to ensure they are fully self-determinate and that all the necessary treatment and support are available. It may not be the case when the educational level is low and a mild disease persists, significantly affecting their well-being and cognitive performance. [ABSTRACT FROM AUTHOR]

Nursing students can access massive amounts of online health data to drive cutting-edge evidence-based practice in clinical placement, to bridge the theory–practice gap. This activity requires investigation to identify the strategies nursing students apply to evaluate online health information. Online Think-Aloud sessions enabled 14 participants to express their cognitive processes in navigating various educational resources, including online journals and databases, and determining the reliability of sources, indicating their strategies for information-seeking, which helped to create this scoring system. Easy access and user convenience were clearly the instrumental factors in this behavior, which has troubling implications for the lack of use of higher-quality resources (e.g., from peer-reviewed academic journals). The identified challenges encountered during resource access included limited skills in the critical evaluation of information credibility and reliability, signaling a requirement for improved information literacy skills. Participants acknowledged the importance of evidence-based, high-quality information, but faced numerous barriers, such as restricted access to professional and specialty databases, and a lack of academic skills training. This paper develops and critiques a Performative Tool for assessing the process of seeking health information using an online Think-Aloud method, and explores factors and strategies contributing to evidence-based health information access and utilization in clinical practice, aiming to provide insight into individuals' information-seeking behaviors in online health contexts. [ABSTRACT FROM AUTHOR]

Computational statistics is a critical skill for professionals in fields such as data science, statistics, and related disciplines. One essential aspect of computational statistics is the ability to simulate random variables from specified probability distributions. Commonly employed techniques for sampling random variables include the inverse transform method, acceptance–rejection method, and Box–Muller transformation, all of which rely on sampling from the uniform (0 , 1) distribution. A significant concept in statistics is the finite mixture model, characterized by a convex combination of multiple probability density functions. In this paper, we introduce a modified version of the composition method, a standard approach for sampling finite mixture models. Our modification offers the advantage of relying on sampling from the uniform (0 , 1) distribution, aligning with prevalent methods in computational statistics. This alignment simplifies teaching computational statistics courses, as well as having other benefits. We offer several examples to illustrate the approach. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative Particle Swarm Optimization (PSO) Algorithm incorporating Sobol and Halton random number samplings. It evaluates the enhanced PSO's performance against conventional PSO employing Monte Carlo random number samplings. The comparison involves assessing the algorithms across nine benchmark problems and the renowned Travelling Salesman Problem (TSP). The results reveal consistent enhancements achieved by the enhanced PSO utilizing Sobol/Halton samplings across the benchmark problems. Particularly noteworthy are the Sobol-based PSO improvements in iterations and the computational times for the benchmark problems. These findings underscore the efficacy of employing Sobol and Halton random number generation methods to enhance algorithm efficiency. [ABSTRACT FROM AUTHOR]

Stochastic simulation optimization has been proposed by several researchers to optimize construction operations. Traditionally, explicit averaging is used to estimate the objective functions of candidate solutions. This is carried out by calculating the average estimates of the objective functions obtained from a number of simulation replications. However, the computation effort increases as the number of replications and the size of the search space increase. The main objective of this paper is to study the benefits of using implicit averaging and common random numbers to improve the quality of the optimum solutions while reducing the computation time. The initial results of this study showed a 91% reduction in the computation time and 2.6% improvement in the quality of the optimum solutions. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed learning framework designed to protect user privacy, widely applied across various domains. However, existing federated learning algorithms face challenges, including slow convergence, significant loss fluctuations during aggregation, and imbalanced client sampling. To address these issues, this paper introduces a high-performance federated learning aggregation algorithm. This algorithm combines a cyclic adaptive learning rate adjustment strategy with client-weighted random sampling, addressing the aforementioned problems. Weighted random sampling assigns client weights based on their sampling frequency, balancing client sampling rates and contributions to enhance model aggregation. Additionally, it adapts the learning rate based on client loss variations and communication rounds, accelerating model convergence and reducing communication costs. To evaluate this high-performance algorithm, experiments are conducted using well-known datasets MNIST and CIFAR-10. The results demonstrate significant improvements in convergence speed and loss stability. Compared to traditional federated learning algorithms, our approach achieves faster and more stable convergence while effectively reducing training costs. [ABSTRACT FROM AUTHOR]

The accurate and efficient segmentation of large-scale urban point clouds is crucial for many higher-level tasks, such as boundary line extraction, point cloud registration, and deformation measurement. In this paper, we propose a novel supervoxel segmentation approach to address the problem of under-segmentation in local regions of point clouds at various resolutions. Our approach introduces distance constraints from boundary points to supervoxel planes in the merging stage to enhance boundary segmentation accuracy between non-coplanar supervoxels. Additionally, supervoxels with roughness above a threshold are re-segmented using random sample consensus (RANSAC) to address multi-planar coupling within local areas of the point clouds. We tested the proposed method on two publicly available large-scale point cloud datasets. The results show that the new method outperforms two classical methods in terms of boundary recall, under-segmentation error, and average entropy in urban scenes. [ABSTRACT FROM AUTHOR]

The goal of the present study is to find a method for improving the predictive capabilities of feedforward neural networks in cases where values distant from the input–output sample interval are predicted. This paper proposes an iterative prediction algorithm based on two assumptions. One is that predictions near the statistical sample have much lower error than those distant from the sample. The second is that a neural network can generate additional training samples and use them to train itself in order to get closer to a distant prediction point. This paper presents the results of multiple experiments with different univariate and multivariate functions and compares the predictions made by neural networks before and after their training with the proposed iterative algorithm. The results show that, having passed through the stages of the algorithm, artificial neural networks significantly improve their interpolation performance in long-term forecasting. The present study demonstrates that neural networks are capable of creating additional samples for their own training, thus increasing their approximating efficiency. [ABSTRACT FROM AUTHOR]

Data assimilation for unconfined seepage analysis has faced significant challenges due to hybrid causes, such as sparse measurements, heterogeneity of porous media, and computationally expensive forward models. To address these bottlenecks, this paper introduces a physics-informed neural network (PINN) model to resolve the data assimilation problem for seepage analysis of unsaturated earth–rockfill dams. This strategy offers a solution that decreases the reliance on numerical models and enables an accurate and efficient prediction of seepage parameters for complex models in the case of sparse observational data. For the first attempt in this study, the observed values are obtained by random sampling of numerical solutions, which are then contributed to the synchronous constraints in the loss function by informing both the seepage control equations and boundary conditions. To minimize the effects of sharp gradient shifts in seepage parameters within the research domain, a residual adaptive refinement (RAR) constraint is introduced to strategically allocate training points around positions with significant residuals in partial differential equations (PDEs), which could facilitate enhancing the prediction accuracy. The model's effectiveness and precision are evaluated by analyzing the proposed strategy against the numerical solutions. The results indicate that even with limited sparse data, the PINN model has great potential to predict seepage data and identify complex structures and anomalies inside the dam. By incorporating coupling constraints, the validity of our PINN model could lead to theoretically viable applications of hydrogeophysical inversion or multi-parameter seepage inversion. The results show that the proposed framework can predict the seepage parameters for the entire research domain with only a small amount of observation data. Furthermore, with a small amount of observation data, PINNs are able to obtain more accurate results than purely data-driven DNNs. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 serology may be helpful to retrospectively understand infection dynamics in specific settings including kindergartens. We assessed SARS-CoV-2 seroprevalence in individuals connected to kindergartens in Berlin, Germany in September 2021. Children, staff, and household members from 12 randomly selected kindergartens were interviewed on COVID-19 history and sociodemographic parameters. Blood samples were collected on filter paper. SARS-CoV-2 anti-S and anti-N antibodies were assessed using Roche Elecsys. We assessed seroprevalence and the proportion of so far unrecognized SARS-CoV-2 infections. We included 277 participants, comprising 48 (17.3%) kindergarten children, 37 (13.4%) staff, and 192 (69.3%) household members. SARS-CoV-2 antibodies were present in 65.0%, and 52.7% of all participants were vaccinated. Evidence of previous infection was observed in 16.7% of kindergarten children, 16.2% of staff, and 10.4% of household members. Undiagnosed infections were observed in 12.5%, 5.4%, and 3.6%, respectively. Preceding infections were associated with facemask neglect. In conclusion, two-thirds of our cohort were SARS-CoV-2 seroreactive in September 2021, largely as a result of vaccination in adults. Kindergarten children showed the highest proportion of non-vaccine-induced seropositivity and an increased proportion of previously unrecognized SARS-CoV-2 infection. Silent infections in pre-school children need to be considered when interpreting SARS-CoV-2 infections in the kindergarten context. [ABSTRACT FROM AUTHOR]

The nonuniform sampling and reconstruction of bandlimited random signals in the SAFT domain is discussed in the paper, where the nonuniform samples are obtained by randomly disturbing the uniform sampling. First, we prove that the concerned nonuniform problem is equivalent to the process of uniform sampling after a prefilter in the statistic sense. Then, an approximate reconstruction method based on sinc interpolation is proposed for the randomized nonuniform sampling of SAFT-bandlimited random signals. Finally, we offer the mean square error estimate for the corresponding approximate recovery approach. The results generalize the conclusions of nonuniform sampling of bandlimited random signals in the FrFT and LCT domains to the SAFT domain. [ABSTRACT FROM AUTHOR]

Given the randomness inherent in fluid dynamics problems and limitations in human cognition, Computational Fluid Dynamics (CFD) modeling and simulation are afflicted with non-negligible uncertainties, casting doubts on the credibility of CFD. Scientifically and rigorously quantifying the uncertainty of CFD is paramount for assessing its credibility and informing engineering decisions. In order to quantify the uncertainty of multidimensional flow field responses stemming from uncertain model parameters, this paper proposes a method based on Gappy Proper Orthogonal Decomposition (POD) for supplementing high-fidelity flow field data within a framework that leverages POD and surrogate models. This approach enables the generation of corresponding high-fidelity flow fields from low-fidelity ones, significantly reducing the cost of high-fidelity flow field computation in uncertainty propagation modeling. Through an analysis of the impact of uncertainty in the coefficients of the Spalart–Allmaras (SA) turbulence model on the distribution of wall friction coefficients for the NACA0012 airfoil and pressure coefficients for the M6 wing, the proposed multi-fidelity modeling approach is demonstrated to offer significant advancements in both accuracy and efficiency compared to single-fidelity methods, providing a robust and efficient prediction model for large-scale random sampling. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative and eco-friendly computational methodology to assess the wind potential of a location with the aid of high-resolution simulations with a mesoscale numerical weather prediction model (WRF), coupled with the statistical "10% sampling condition". The proposed methodology is tested for a location with complex terrain on the Greek island of Crete, where moderate to strong winds prevail for most of the year. The results are promising, indicating that this method has great potential for studying and assessing areas of interest. Adverse effects and challenges associated with wind energy production may be mitigated with methods such as the proposed one. Mitigating such effects should constitute the main focus and priority in research concerning wind energy production. [ABSTRACT FROM AUTHOR]

Maternal mental health (MMH) conditions and infant malnutrition are both major global public health concerns. Despite a well-established link between the two, many nutrition programmes do not routinely consider MMH. New World Health Organization (WHO) malnutrition guidelines do, however, emphasise MMH. To inform guideline rollout, we aimed to assess the feasibility and acceptability of MMH assessments in nutrition programmes in low-resource settings. Ten semi-structured interviews were conducted with international key informants who work on nutrition programmes or MMH research. Interview transcripts were coded using subthemes derived from the key points discussed. The benefits and risks were highlighted. These included ethical dilemmas of asking about MMH if local treatment services are suboptimal. Commonly reported challenges included governance, staff training and finance. Community and programme staff perceptions of MMH were primarily negative across the different settings. Many points were raised for improvements and innovations in practice, but fundamental developments were related to governance, care pathways, advocacy, training, funding and using existing community networks. Future implementation research is needed to understand whether assessment is safe/beneficial (as it is in other settings) to promote MMH screening. Current service providers in low-resource settings can undertake several steps, as recommended in this paper, to improve the care offered to mothers and infants. [ABSTRACT FROM AUTHOR]

Aiming to address problems such as low sampling success rate and long computation time in the motion planning of a dual-arm cooperative system with multiple constraints, this paper proposes an Informed-Bi-Quick RRT* algorithm based on offline sampling. First, in the process of pre-sampling, the new algorithm relaxes the approximation of constrained manifolds by introducing the idea of incremental construction, and it incorporates the stochastic gradient descent method to replace global random sampling with local random sampling, which enriches the data set and shortens the offline sampling time of the data set. Second, the new algorithm improves the original Quick-RRT* algorithm by combining the two-tree idea and the multi-target bias expansion strategy, and it improves the adaptability of the algorithm to different obstacle environments. In addition, the loosely constrained motion and tightly constrained motion in the two-arm cooperative system are analyzed, and the adaptive planning of the two-arm trajectory in different motions is described in detail. In this paper, the two-arm cooperative model constructed with UR5 and UR10 robot arms is studied, and the ability of the proposed algorithm to deal with multiple constraints is verified by simulating assembly and handling tasks. The experimental results show that compared with other methods, the proposed algorithm has obvious advantages in path quality and planning efficiency. [ABSTRACT FROM AUTHOR]

This paper examines changes in the completeness of documentation in clinical practice before and during the implementation of the Safer Births Bundle of Care (SBBC) project. This observational study enrolled parturient women with a gestation age of at least 28 weeks at the onset of labour. Data collectors extracted information from facility registers and then a central data manager summarised and reported weekly statistics. Variables of clinical significance for CQI were selected, and the proportion of non-documentation was analysed over time. A Pearson chi-square test was used to test for significant differences in non-documentation between the periods. Between 1 March 2021 and 31 July 2022, a total of 138,442 deliveries were recorded. Overall, 75% of all patient cases had at least one missing variable among the selected variables across both periods. A lack of variable documentation occurred more frequently at the district hospital level (81% of patient cases) and health centres (74%) than at regional referral hospitals (56%) (p < 0.001). Non-documentation decreased significantly from 79% to 70% after the introduction of the SBBC (p < 0.001). A tendency towards negative correlations was noted for most variables. We noted an increased attention to data quality and use which may have a positive impact on the completeness of documentation. However, halfway through the project's implementation, the completeness of documentation was still low. Our findings support the recommendation to establish short-spaced feedback loops of locally collected data using one data platform. [ABSTRACT FROM AUTHOR]

Most Heterogeneous Information Network (HIN) embedding methods use meta-paths to guide random walks to sample from HIN and perform representation learning in order to overcome the bias of traditional random walks that are more biased towards high-order nodes. Their performance depends on the suitability of the generated meta-paths for the current HIN. The definition of meta-paths requires domain expertise, which makes the results overly dependent on the meta-paths. Moreover, it is difficult to represent the structure of complex HIN with a single meta-path. In a meta-path guided random walk, some of the heterogeneous structures (e.g., node type(s)) are not among the node types specified by the meta-path, making this heterogeneous information ignored. In this paper, HeteEdgeWalk, a solution method that does not involve meta-paths, is proposed. We design a dynamically adjusted bidirectional edge-sampling walk strategy. Specifically, edge sampling and the storage of recently selected edge types are used to better sample the network structure in a more balanced and comprehensive way. Finally, node classification and clustering experiments are performed on four real HINs with in-depth analysis. The results show a maximum performance improvement of 2% in node classification and at least 0.6% in clustering compared to baselines. This demonstrates the superiority of the method to effectively capture semantic information from HINs. [ABSTRACT FROM AUTHOR]

Our paper analyzes lessons for medical education and health education stemming from the experience gained in the course of the COVID-19 pandemic. Moreover, it tackles the issue of the social health and psychological wellbeing of medical students involved in online education during the COVID-19 pandemic. The paper systematizes up-to-date data on how medical schools and universities have adapted to the conditions of the COVID-19 pandemic and implemented novel effective solutions for the learning process, such as transitioning from traditional in-person classes to online learning, incorporating virtual simulations and telemedicine experiences for clinical training, and collaborating with health authorities to provide support in testing and contact tracing efforts. The paper contains an analysis of various aspects of medical education, such as the changes in practical classes, the impact of the pandemic on the formation of communication skills, methods for assessing students' knowledge and skills, and many others. It also considers case studies related to the implementation of educational programs, methodologies, and novel digital technologies in a pandemic. Additionally, the paper features an empirical study that is based on the results of our own survey that was carried out with the help of a snowball convenient sampling that involved 710 medical students between 19 and 25 years of age (56% females and 44% males) from 4 Russian regions (Moscow, Krasnodar, Kazan, and Saint Petersburg). We applied the correlation between stress scores, anxiety scores, factors of stress, and strategies for coping with stress and various economic and demographic variables (age, environment, and gender) that were analyzed using the chi-square test. Our results demonstrate that over 85% of the students in our sample yielded an above-average vulnerability to stress due to the COVID-19 restrictions. At the same time, around 61% of the students experienced severe anxiety during online education in the COVID-19 pandemic. The important factors leading to stress and anxiety were the fear of getting infected and social distancing, and the best strategy to deal with stress and increase wellbeing was self-control. Through a comprehensive review of the literature and empirical estimations, our paper identifies key areas of improvement, including curriculum adaptation, technology integration, faculty development, student support, and interprofessional collaboration. The proposed recommendations aim at strengthening medical education systems and preparing healthcare professionals to effectively navigate future pandemics. [ABSTRACT FROM AUTHOR]

There is increasing research interest in the experiences of new fathers taking paternity leave, but less insight into men's experiences of returning to work after the birth of their first baby. For many men in the UK context, this could take place immediately after the birth or after one or two weeks of paternity leave. This paper utilizes data from a UK-based study whilst also drawing on international literature and policy contexts. A constructivist grounded theory method was adopted to generate theory from the data gathered. Twelve new fathers shared their experiences in this study by participating in audio-recorded, semi-structured interviews. This paper focuses on fathers' experiences of negotiating the workplace as part of an overall theoretical framework related to broader transitions to fatherhood and sheds light on the distress, guilt and psychological challenges that the participants experienced when they initially returned to work. Whether fathers did or did not explicitly describe distress at this time, they all described a change in their worker identity, which for some participants led to uncertainty in the workplace. Men returning to work at this time in the postnatal period are vulnerable to experiencing distress. Flexibility and support in the workplace could be protective of their mental health. Finally, policy and practice developments are offered to support men's transitions to fatherhood in the workplace context. [ABSTRACT FROM AUTHOR]

Simple Summary: In 2016, the World Health Organization (WHO) recommended the incorporation of molecular parameters, in addition to histology, for an optimal definition of the central nervous system (CNS) tumors. Gliomas, being among the most common types of CNS tumors, have distinct clinical outcomes and treatment strategies based on different tumor grades, isocitrate dehydrogenase (IDH) mutation, and 1p/19q codeletion statuses. This paper uses radiomics models to noninvasively predict the glioma subtype with clinical Magnetic Resonance Imaging (MRI) images. Different settings in the radiomics pipeline were investigated to achieve optimal performance, together with a better understanding of the exact role of each setting in the model performance. The characteristics of the radiomic features that best distinguish the glioma subtypes were also analyzed. This paper not only provides a radiomics pipeline which works well for predicting the glioma subtype, but it also contributes to the radiomics model development and interpretability. Gliomas are among the most common types of central nervous system (CNS) tumors. A prompt diagnosis of the glioma subtype is crucial to estimate the prognosis and personalize the treatment strategy. The objective of this study was to develop a radiomics pipeline based on the clinical Magnetic Resonance Imaging (MRI) scans to noninvasively predict the glioma subtype, as defined based on the tumor grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q codeletion status. A total of 212 patients from the public retrospective The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG) and The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) datasets were used for the experiments and analyses. Different settings in the radiomics pipeline were investigated to improve the classification, including the Z-score normalization, the feature extraction strategy, the image filter applied to the MRI images, the introduction of clinical information, ComBat harmonization, the classifier chain strategy, etc. Based on numerous experiments, we finally reached an optimal pipeline for classifying the glioma tumors. We then tested this final radiomics pipeline on the hold-out test data with 51 randomly sampled random seeds for reliable and robust conclusions. The results showed that, after tuning the radiomics pipeline, the mean AUC improved from 0.8935 (±0.0351) to 0.9319 (±0.0386), from 0.8676 (±0.0421) to 0.9283 (±0.0333), and from 0.6473 (±0.1074) to 0.8196 (±0.0702) in the test data for predicting the tumor grade, IDH mutation, and 1p/19q codeletion status, respectively. The mean accuracy for predicting the five glioma subtypes also improved from 0.5772 (±0.0816) to 0.6716 (±0.0655). Finally, we analyzed the characteristics of the radiomic features that best distinguished the glioma grade, the IDH mutation, and the 1p/19q codeletion status, respectively. Apart from the promising prediction of the glioma subtype, this study also provides a better understanding of the radiomics model development and interpretability. The results in this paper are replicable with our python codes publicly available in github. [ABSTRACT FROM AUTHOR]

In this paper, we study a new type of distribution that generalizes distributions from the gamma and beta classes that are widely used in applications. The estimators for the parameters of the digamma distribution obtained by the method of logarithmic cumulants are considered. Based on the previously proved asymptotic normality of the estimators for the characteristic index and the shape and scale parameters of the digamma distribution constructed from a fixed-size sample, we obtain a statement about the convergence of these estimators to the scale mixtures of the normal law in the case of a random sample size. Using this result, asymptotic confidence intervals for the estimated parameters are constructed. A number of examples of the limit laws for sample sizes with special forms of negative binomial distributions are given. The results of this paper can be widely used in the study of probabilistic models based on continuous distributions with an unbounded non-negative support. [ABSTRACT FROM AUTHOR]

Rain spots on green plum are superficial micro-defects. Defect detection based on a two-dimensional image is easily influenced by factors such as placement position and light and is prone to misjudgment and omission, which are the main problems affecting the accuracy of defect screening of green plum. In this paper, using computer vision technology, an improved structure from motion (SFM) and patch-based multi-view stereo (PMVS) algorithm based on similar graph clustering and graph matching is proposed to perform three-dimensional sparse and dense reconstruction of green plums. The results show that, compared with the traditional algorithm, the running time of this algorithm is lower, at only 26.55 s, and the mean values of camera optical center error and pose error are 0.019 and 0.631, respectively. This method obtains a higher reconstruction accuracy to meet the subsequent plum micro-defect detection requirements. Aiming at the dense point cloud model of green plums, through point cloud preprocessing, the improved adaptive segmentation algorithm based on the Lab color space realizes the effective segmentation of the point cloud of green plum micro-defects. The experimental results show that the average running time of the improved adaptive segmentation algorithm is 2.56 s, showing a faster segmentation speed and better effect than the traditional K-means and K-means++ algorithms. After clustering the micro-defect point cloud, the micro-defect information of green plums was extracted on the basis of random sample consensus (RANSAC) plane fitting, which provides a theoretical model for further improving the accuracy of sorting the appearance quality of green plums. [ABSTRACT FROM AUTHOR]

In this paper, we verified the applicability of immersive VR technology to street-level residential landscape evaluation. We used GSV images taken from pedestrian paths in residential areas of Seoul and selected evaluation images through random sampling. Then, we conducted web-based and VR-based residential streetscape evaluation experiments with those landscape images. The VR-based streetscape evaluation results differed significantly from the web-based streetscape evaluation results. Our multi-level ordered logistic analysis confirmed that the VR-based streetscape evaluation method had better explanatory power than the web-based streetscape evaluation method. In the immersive VR-based streetscape evaluation index, the naturalness, beauty, and safety indicators had particularly high explanatory power. This study concluded that the VR-based streetscape evaluation method over the web-based method is more suitable for evaluating street scenes experienced in daily life. In addition, the innovative methodological approaches, including big data, virtual reality, and visual experiences, will also provide new insights for the planning and management of sustainable landscapes. [ABSTRACT FROM AUTHOR]

The fast multi-pulse spectrum is a spectrum acquisition method that obtains an average pulse amplitude in a dynamic window, which improves the energy resolution by sharpening peaks in the acquired spectra, but produces the counting loss. Owing to the counting loss problem, a counting rate multiplication method based on uniform sampling, also called the pulse circulation method, is presented in this paper. Based on the theory of mathematical statistics and uniform sampling, this method adopted a dynamic sample pool to update the pulse amplitude sample in real time. Random numbers from the uniform distribution were sampled from the sample pool, and the sampled results were stored in the random pulse circulator so that the pulse amplitude information used for spectrum generation was uniformly expanded. In the experiment section, the obtained spectrum was analyzed to verify the multiplication effect of the pulse circulation method on the counting rate and the compensation effect of the fast multi-pulse spectrum algorithm on the counting rate loss. The results indicated that the characteristic peaks of each element in the X-ray spectrogram obtained by the pulse circulation method could realize counting rate multiplication uniformly, and the multiplication ratio of every element was approximately equal. This is of great significance for obtaining an accurate X-ray fluorescence spectrum. [ABSTRACT FROM AUTHOR]

In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different beacons by relabeling to the locations with less samples, resolving data imbalance. Standard deviation and Kullback–Leibler divergence between minority and majority classes are used to measure signal pattern to find matching beacons to relabel. By matching beacons between classes, two variations of relabeling are implemented, specifically full and partial matching. The performance is evaluated using the real-world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. A Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the beacon data with our proposed relabeling method for data augmentation, we achieve a higher minority class F1-score compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected beacon data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score. [ABSTRACT FROM AUTHOR]

Due to the complex interaction of urban and mountainous floods, assessing flood susceptibility in mountainous urban areas presents a challenging task in environmental research and risk analysis. Data-driven machine learning methods can evaluate flood susceptibility in mountainous urban areas lacking essential hydrological data, utilizing remote sensing data and limited historical inundation records. In this study, two ensemble learning algorithms, Random Forest (RF) and XGBoost, were adopted to assess the flood susceptibility of Kunming, a typical mountainous urban area prone to severe flood disasters. A flood inventory was created using flood observations from 2018 to 2022. The spatial database included 10 explanatory factors, encompassing climatic, geomorphic, and anthropogenic factors. Artificial Neural Network (ANN) and Support Vector Machine (SVM) were selected for model comparison. To minimize the influence of expert opinions on model training, this study employed a strategy of uniformly random sampling in historically non-flooded areas for negative sample selection. The results demonstrated that (1) ensemble learning algorithms offer higher accuracy than other machine learning methods, with RF achieving the highest accuracy, evidenced by an area under the curve (AUC) of 0.87, followed by XGBoost at 0.84, surpassing both ANN (0.83) and SVM (0.82); (2) the interpretability of ensemble learning highlighted the differences in the potential distribution of the training data's positive and negative samples. Feature importance in ensemble learning can be utilized to minimize human bias in the collection of flooded-site samples, more targeted flood susceptibility maps of the study area's road network were obtained; and (3) ensemble learning algorithms exhibited greater stability and robustness in datasets with varied negative samples, as evidenced by their performance in F1-Score, Kappa, and AUC metrics. This paper further substantiates the superiority of ensemble learning in flood susceptibility assessment tasks from the perspectives of accuracy, interpretability, and robustness, enhances the understanding of the impact of negative samples on such assessments, and optimizes the specific process for urban flood susceptibility assessment using data-driven methods. [ABSTRACT FROM AUTHOR]

In the construction of underground engineering, the determination of surrounding rock stresses has important theoretical significance for the design of tunnel structure reliability. In this paper, the nonlinear criterion is introduced to modify and improve the friction resistance in the calculation formula of Bierbaumer's loose surrounding rock stresses, and the improved application is carried out in view of the situation of composite formation. Due to the variability and discreteness of geotechnical parameters, combined with engineering examples, Monte Carlo random sampling is carried out for various geotechnical parameters in engineering, and the characteristics of loose surrounding rock stresses are analyzed from the perspective of statistical reliability by using the improved shallow tunnel formula. By using the standard regression coefficient, the weight influence of the parameters in various shallow surrounding rock stress formulas is analyzed, and the confidence degree of the calculated results of various formulas is analyzed from a statistical point of view, which verifies the rationality of the improved formula in the statistical sense. [ABSTRACT FROM AUTHOR]