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To enhance biogenic coalbed methane production, it is imperative to comprehend the processes by which methane is generated when microbes decompose coal in perched water zones situated in steeply inclined fire zones. Low-rank coal from Fukang mining was used to simulate biogenic gas production and investigate methane generation in perched water zones in steeply inclined coal fire zones. This study integrated characterization techniques, multifractal theory, and field data. Microbial activity increased oxygen-containing functional groups, reduced aromatic rings, and disrupted aliphatic clusters in samples. Aromatic nucleus interlayer spacing expanded; microcrystalline stacking, extensibility, and aromatic layers decreased. Coal structure degraded as microbes targeted aromatic then aliphatic carbon. Increased pore and fracture density enhanced pore connectivity (H) and reduced pore heterogeneity (△α), while pore types remained unchanged. Methane production via microbial degradation follows acetate and hydrogenotrophic pathways. Methane rates were affected by perched water zone distance, coal seam salinity, and pH. • Identified methane production mechanisms in steep coal seams under water recharge. • Microbial degradation improved pore connectivity and reduced heterogeneity in coal. • Biogenic methane generation via acetate and hydrogenotrophic pathways was confirmed. • Findings provide guidance for biogenic gas recovery in steeply dipping coal seams. [ABSTRACT FROM AUTHOR]

Nearly 80 % of severe neonatal hyperbilirubinemia cases are identified too late for medical treatment in lower-middle-income countries. Parents' understanding of neonatal jaundice is crucial for early detection and effective treatment. This study evaluated the effectiveness of prenatal sensitization in improving maternal knowledge and attitude toward neonatal hyperbilirubinemia management in Bangladesh. Cluster-randomized trial. Between September 2019 and March 2021, a prospective home-based intervention trial on neonatal hyperbilirubinemia was conducted in 20 interventions and comparison clusters through community health workers (CHW). Participants were pregnant women in their second or third trimester of pregnancy. Proportion-based difference-in-difference (DID) was calculated to determine changes in the mother's knowledge and attitude towards newborn jaundice. A total of 539 pregnant women were enrolled, 273 in the intervention, and 266 in the comparison arm. Following the intervention, participant's overall knowledge level about neonatal hyperbilirubinemia increased significantly (DID: 24.9, 95%CI: 19.2, 30.7) from baseline (33.2 %) to endline (70.5 %). Knowledge of newborn jaundice management showed the most improvement (DID: 60), followed by danger signs detection (DID: 40.0). The intervention also enhanced participants' positive attitudes regarding neonatal hyperbilirubinemia (DID: 13.1, 95%CI: 8.1, 18.0). Mothers with improved knowledge of neonatal hyperbilirubinemia were nearly five times more likely to have a favorable attitude toward it than those with counterparts. CHW-led home-based prenatal sensitization interventions substantially improved mothers' awareness and attitudes toward neonatal hyperbilirubinemia detection and management. Integrating such sensitization into prenatal care of the existing health system could be a practical approach to improve neonatal health outcomes in Bangladesh. [ABSTRACT FROM AUTHOR]

Dentists often focus on masticatory performance (MP) rather than masticatory ability (MA) and treatment expectations when evaluating older adults. This study aimed to determine if MA aligns with MP and to explore influencing factors. This cross-sectional study utilized a multi-stage stratified cluster sampling, including one thousand community-dwelling adults aged 60 and older. Participants underwent oral exams and questionnaire interviews, assessing tooth count and Eichner index (EI) levels. MP and MA were quantified using a standard gummy jelly test and a 14-food group questionnaire, respectively. Multivariable linear regressions analyzed correlations between MP, MA, and related factors. Approximately 25 % of participants exhibited poor MP. Factors contributing to poor MP included increased age rise, low education, number of chronic diseases, tooth loss, without the assistance of removable dentures, and being in EI group C (all P < 0.05). Tooth count showed a significant negative correlation with MP (P < 0.001). Participants' MP declined significantly from good to poor, with MA scores decreasing from 11.96 to 7.52 (P < 0.001). The tooth number was a common factor influencing MA across food groups (all P < 0.05). The decline in MA among older adults was linked to age rise, tooth loss, lack of assistance of removable dentures, and being in EI group C. Self-rated MA in older adults effectively reflects their MP. Maintaining a high tooth count to meet occlusal support and the use of dentures to assist with tooth loss in older adults help to improve their MA. [ABSTRACT FROM AUTHOR]

The outbreak of the coronavirus disease 2019 (COVID-19) pandemic threatened adolescents' mental health and livelihoods, which can worsen their non-suicidal self-injury (NSSI) behaviors. With the significant increase of total online time use, adolescents become more prone to problematic internet use (PIU). This study examined whether depression mediated the relationship between PIU and NSSI among adolescence during the COVID-19 outbreak. Constructed with a cross-sectional design during the COVID-19 outbreak in Taiwan, 1060 participants were drawn from junior high schools through stratified and cluster sampling, and completed a set of comprehensive surveys. The mediation model demonstrated a good fit to the data, GFI =.96, CFI =.97, NFI =.97, NNFI =.95, IFI =.97, and SRMR =.02. The overall fit of the mediational model was adequate. The path from PIU to depression, β =.41, p <.001, and the path from depression to NSSI, β =.40, p <.001, were both significant. Moreover, the effect of PIU to NSSI decreased from.23 (p <.001) to.05 (p =.099) when depression was incorporated into the analysis. Moreover, results in bootstrapping analysis displayed that the indirect effect (PIU on NSSI via depression) was statistically significant (p <.001) and the direct effect (PIU on NSSI) was statistically non-significant (p =.134). The full mediation model was confirmed. The findings of the structure equation modeling and bootstrap analysis showed that PIU significantly and positively predicted NSSI, and that depression fully mediated this relationship. [ABSTRACT FROM AUTHOR]

The Transformer architecture has shown to be a powerful tool for a wide range of tasks. It is based on the self-attention mechanism, which is an inherently computationally expensive operation with quadratic computational complexity: memory usage and compute time increase quadratically with the length of the input sequences, thus limiting the application of Transformers. In this work, we propose a novel Clustering self-Attention mechanism using Surrogate Tokens (CAST), to optimize the attention computation and achieve efficient transformers. CAST utilizes learnable surrogate tokens to construct a cluster affinity matrix, used to cluster the input sequence and generate novel cluster summaries. The self-attention from within each cluster is then combined with the cluster summaries of other clusters, enabling information flow across the entire input sequence. CAST improves efficiency by reducing the complexity from O (N 2) to O (α N) where N is the sequence length, and α is constant according to the number of clusters and samples per cluster. We show that CAST performs better than or comparable to the baseline Transformers on long-range sequence modeling tasks, while also achieving higher results on time and memory efficiency than other efficient transformers. • Computation of self-attention Transformers is limited by the input sequence length. • We propose CAST, an efficient self-attention mechanism with clustered attention. • We propose the use of surrogate tokens to optimize self-attention in transformers. • We observe that cluster summaries enhance training efficiency and results. • CAST reduces complexity of self-attention computation from O (N 2) to O (α N). [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Variation in forage composition decreases the accuracy of diets delivered to dairy cows. However, variability of forages can be managed using a renewal reward model (RRM) and genetic algorithm (GA) to optimize sampling and monitoring practices for farm conditions. Specifically, use of quality control charts to monitor forage composition can identify changes in composition for which adjustment in the formulated diet will result in a better match of the nutrients delivered to cows. The objectives of this study were (1) to assess the use of a clustering algorithm to estimate the mean time (d) the process is stable or in control (T Stable) and the magnitude of the change in forage composition between stable periods (Δ Forage) for corn silage and alfalfa-grass silage, which are input parameters for the RRM; (2) to compare optimized farm-specific sampling practices (number of samples, sampling interval, and control limits [Δ Limit ]) using previously proposed defaults and our estimates for the T Stable and Δ Forage input parameters; and (3) to conduct a simulation study to compare the number of recommended diet changes under the proposed sampling and monitoring protocols. We estimated the T Stable and Δ Forage parameters for corn silage NDF and starch and alfalfa-grass silage NDF and CP using a k-means clustering approach applied to forage samples collected from 8 farms, 3×/wk during a 16-wk period. We compared 4 sampling and monitoring protocols that resulted from the 2 methods for estimating T Stable and Δ Forage (default values and our proposed method) and either optimizing only the control limit or optimizing the control limits, the number of samples, and the number of days between sampling. We simulated the outcomes of implementing the optimized monitoring protocols using a quality control chart for corn silage and alfalfa-grass silage of each farm. Estimates of T ^ S t a b l e and Δ ^ F o r a g e from the k-means clustering analysis were, respectively, shorter and larger than previously proposed default values. In the simulated quality control monitoring, larger Δ ^ F o r a g e estimates increased the optimized Δ Limit , resulting in fewer detected shifts in composition of forages, a lower frequency of false alarms, and a lower quality control cost ($/d). Recommended diet reformulation intervals from the simulated quality control analysis were specific for the type of forage and farm management practices. The median of the diet reformulation intervals for all farms using our optimal protocols was 14 d (quartile [Q] 1 = 8, Q 3 = 26) for corn silage and 16 d (Q 1 = 8, Q 3 = 26) for alfalfa-grass silage. [ABSTRACT FROM AUTHOR]

Accounting information research is developing rapidly, the security of accounting information has been an important issue. Traditional cryptography has many disadvantages, such as low reliability and low security. This paper studies the method of accounting data encryption based on KMCA, classifies the accounting data according to specific characteristics, and encrypts each classification to ensure the confidentiality of accounting data. The K-Means clustering algorithm is used to classify the accounting data, and the data set is divided into several categories by calculating the distance between the cluster center and the samples. Experimental results show that the data encryption based on KMA significantly improves the security (Max:9.74) and reliability of data, compared with the decision tree method, KMA has significant advantages in data encryption. [ABSTRACT FROM AUTHOR]

Assessing diet quality has been challenging, and the associations of diet quality and daily free sugar intake with depressive and/or anxiety symptoms have shown inconsistency. A total of 1749 students aged 12–16 years were recruited using stratified random cluster sampling across three cities in China. The overall Global Dietary Recommendations (GDR) score, a novel indicator of diet quality, was constructed using the low-burden Diet Quality Questionnaire. Free sugar intake, including from beverages and foods, was measured using the Food Frequency Questionnaire. Daily free sugar intake was divided into low, medium, and high categories based on the latest version of the dietary guidelines for Chinese residents. Binomial and multinominal logistic regression analyses were used to examine the associations of the overall GDR score and daily free sugar intake categories or different sources of free sugar intake with depressive and/or anxiety symptoms. The overall GDR score was negatively associated with depressive symptoms (adjusted odds ratio[aOR] = 0.92, 95%CI: 0.87–0.98) and anxiety symptoms (0.95, 0.90–0.99), particularly with comorbid depression and anxiety (0.90, 0.84–0.97). Conversely, daily free sugar intake was positively associated with symptoms of depression and/or anxiety, the multiple aOR for symptoms depression or anxiety were significantly increased with higher daily free sugar intake (all P trend < 0.05). Both free sugar from beverages and from foods were positively associated with depressive and/or anxiety symptoms. These associations were consistent between males and females. Cross-sectional design and self-reported symptoms. Diet quality was negatively associated with symptoms of depression or anxiety, especially with comorbid symptoms among adolescents. Daily free sugar intake, regardless of its sources, was positively associated with symptoms of depression and/or anxiety. • This study examined the associations of diet quality and daily free sugar intake with depressive and/or anxiety symptoms. • This study also examined the association of different sources of free sugar intake with depressive and/or anxiety symptoms. • The overall GDR score, a novel indicator of diet quality, was constructed by the low-burden Diet Quality Questionnaire. • Free sugar including from eight beverages and six foods were evaluated. [ABSTRACT FROM AUTHOR]

Semi-supervised clustering methods enhance the performance of completely unsupervised clustering tasks by incorporating pairwise relationship information from a subset of samples. However, previous methods based on pairwise constraints have struggled to effectively leverage available prior knowledge and fully exploit the similarity relationships between samples. To address these issues, this paper proposes an advanced approach called Deep Semi-Supervised Clustering based on Pairwise Constraints and Sample Similarity (DSCPS). Specifically, DSCPS consists of two training stages: the coarse clustering stage and the fine clustering stage. In the coarse clustering stage, DSCPS assigns probabilities to each sample indicating its belongingness to each cluster by computing the distance to the cluster centers. In the fine clustering stage, DSCPS introduces a predictor network in the clustering space to predict the class labels of each sample. Simultaneously, the thresholds for similarity and dissimilarity between samples are determined based on pairwise constraints. Finally, the encoder and prediction network are further optimized using sample similarity relationships and pairwise constraints as loss conditions, resulting in the final clustering results. Extensive experiments demonstrate that DSCPS outperforms state-of-the-art methods, achieving the highest performance. • Proposing a two-stage deep learning network for clustering with pairwise constraints. • Designing an iterative clustering training process with weighted assignments. • Larger weight losses are assigned to samples at the cluster boundaries. • Introducing a similarity loss to train the prediction network. [ABSTRACT FROM AUTHOR]

Fuzzy K-Means (FKM) based on fuzzy theory is a classic method to effectively handle overlapping regions between clusters. However, redundant features and noises brought by increasing data dimensions affect the effectiveness of FKM. To cope with this issue, we propose a Discriminative Projection Fuzzy K-Means with adaptive neighbors (DPFKM) model, which embeds a discriminative subspace into FKM to facilitate learning of global structure and the most discriminative information. Firstly, a novel projection space with uncorrelated constraints are adopted to promote statistical independence among the data in the subspace as well as to enhance the ability of FKM to discern and utilize discriminative information. Secondly, the Frobenius norm is introduced as the regularization term to eliminate discrete solutions, while preserving the fuzziness and enhancing the sparsity of FKM. Finally, we propose a novel optimization method to finetune the model, with a particular focus on adaptive adjustment of the regularization parameter based on the proximity relationship between the samples and clusters. Comprehensive experiments are conducted on multiple data sets, and the results can prove the superiority of the proposed model. • The discriminative projection fuzzy K-means (DPFKM) is proposed to handle subspace data clustering. • DPFKM utilizes uncorrelated constraints to maintain discriminative information and global structure in subspace. • DPFKM adaptively determines the regularization parameter using neighbor relationship of samples and clusters. • Experiments on nine data sets show that our method realize a competitive performance in clustering results. [ABSTRACT FROM AUTHOR]

Cardiac arrest is one of the fatal medical emergencies which need to be treated immediately. Poor survival rates in the community settings are common because of limited and ineffective bystander basic life support (BLS). This study aimed to identify factors that are associated with the willingness to perform BLS in communities in Yogyakarta, Indonesia A descriptive study was conducted with a cross-sectional design. Participants (n = 251) were enrolled from the general population consisting of teachers, security personnel, and police officers recruited through cluster random sampling. Data were gathered using both digital or printed questionnaires. Ordinal logistic regression with adjusted odds ratio (AOR) was used to analyze the association between BLS predictors and willingness to perform BLS. Most participants were willing to perform BLS for all genders (55.55%). The inability to perform BLS and fear of causing harm were the main barriers to performing BLS accounting for 61.35% and 43.82%, respectively. Compared to other independent predictors, ages 40–59 were found to be the highest predictors of willingness to perform BLS (AOR:1.44) followed by experience of seeing real or simulation of the emergency case (AOR:1.38) More than half of the respondents were eager to perform BLS although some barriers were also found. This study provides some understanding of the predictor factors associated with BLS performance and shows respondents with some training or experience were more likely to perform BLS. The results inform policymakers to develop a strategic plan for increasing willingness to apply BLS in the community. WC:250 [ABSTRACT FROM AUTHOR]

To quantify the associations between general self-efficacy, subjective meaning in life, and posttraumatic stress and depressive symptoms and symptom clusters in US veterans, both cross-sectionally and longitudinally. Data from a Veteran Affairs (VA) funded intervention study (n = 191) were examined. Self-report measures of depressive symptoms, general self-efficacy, and meaning in life were collected, along with clinician-rated symptoms of PTSD. Meaning in life was consistently inversely associated with posttraumatic stress and depressive symptoms and symptom clusters cross-sectionally, whereas general self-efficacy was only inversely associated with some aspects of depressive symptoms. Longitudinal analyses further revealed that meaning in life was inversely associated with the cluster D symptoms of PTSD and the cognitive-affective symptoms of depression. Higher meaning in life is associated with less severe symptoms of posttraumatic stress and depressive symptoms, particularly those related to mood. Additional research is needed to determine whether interventions designed to increase meaning in life attenuate these symptoms. • Posttraumatic stress and depressive symptoms are common in U.S. miliary veterans. • Meaning in life and general self-efficacy have been shown to be inversely associated with these symptoms. • No studies have quantified these associations longitudinally, including how meaning in life and general self-efficacy may relate to symptom clusters. • Meaning in life, but not general self-efficacy, was longitudinally associated with less severe symptoms of posttraumatic stress and depressive symptoms. [ABSTRACT FROM AUTHOR]

Traumatic Dental Injuries (TDI) have emerged as a very significant public health and social problem, especially among children and adolescents. The aim of this study was to investigate the prevalence and associated risk factors of traumatic dental injuries to permanent anterior teeth in school going children of Kolkata aged 7-14 years. A cross-sectional study was conducted among 3762 school going children attending various private and public schools of Kolkata aged 7–14 years. A multistage random clustering sampling technique was adopted to select the children.Type of trauma using Ellis and Davey classification of fractures along with Andresen's Epidemiological Classification of Traumatic Injuries to Anterior Teeth, including WHO codes, was used. All values were considered statistically significant at P < 0.05. Prevalence of TDI to anterior teeth was found to be 9.89%. The mean age of children who presented with TDI was 11.06 ± 1.99.years. The most common place of occurrence of TDI was home. Falls were the most common causes of trauma. Children belonging to higher socioeconomic status were observed to have an increased prevalence of TDIs.The highest potential risk factor for the occurrence of trauma was a past history of trauma. Present study found a prevalence of 9.89%, and a very low percentage of children had received treatment. [ABSTRACT FROM AUTHOR]

Contact-sports are known to have higher risk of orofacial injuries due to the bodily contact with other players or equipment. Kabaddi is a traditional contact-sport of India that has gained an international recognition. There is a paucity of data regarding prevalence of traumatic dental and orofacial injuries among the Kabaddi players. Hence this study aimed to assess the prevalence, distribution and pattern of orofacial injuries among the Kabaddi players of Delhi and NCR region. This observational cross-sectional study was designed as per the best practices of epidemiology and reported as per the STROBE guidelines. The sample size was calculated as 440 and comprised of 10–18 years old players of either gender. Cluster random sampling was performed and the information was collected by one-calibrated evaluator by using a self-designed proforma. The injuries were classified as per standard criteria and the description of each episode was recorded. Statistical analysis was performed using the Statistical software-STATA-version-14(StataCorpLLC,Texas,USA). Total of 393 players were included in the study (Males-295, Females-98) with majority of them between 15 and 18 years of age. The prevalence of traumatic episode was 49.11% and the prevalence orofacial-trauma was 44.02%.The frequency of extraoral-injuries was higher when compared to the intraoral injuries, with orbit(26.67%) being the commonest-site. Most of the players had not received adequate emergency treatment in the first incident which had improved in the second. The prevalence of traumatic orofacial injuries in 10–18 years old Kabaddi players of Delhi-NCR region was found to be 44.02% while the prevalence of traumatic episodes was 49.11%. The prevalence of dental injuries was 5.1%. Majority of the players perceived that Kabaddi can lead to orofacial-injuries. [ABSTRACT FROM AUTHOR]

Multi-view clustering (MVC) enhances the clustering performance of data by combining correlation information from different views. However, most existing MVC approaches process each sample independently and ignore the correlation amongst samples, resulting in reduced clustering performance. Although graph convolution network (GCN) can naturally capture correlation amongst samples by integrating the neighbors and structural information into representation learning, it is used in the semi-supervised learning scenario. In this paper, we propose a neighbor-aware deep MVC framework based on GCN (NMvC-GCN) for clustering multi-view samples and training GCN in a fully unsupervised manner. In addition, we design a consensus regularization to learn the common representations and introduce a clustering embedding layer to jointly optimize the clustering task and representation learning, so that the correlation amongst samples and that between the clustering task and representation learning can be fully explored. Extensive experiments on 10 datasets illustrate that NMvC-GCN significantly outperforms the state-of-the-art MVC methods. Our code will be released at https://github.com/dugzzuli/NMvC-GCN. • Graph Convolution Networks are adopted to fuse topological structure and sample features. • An unsupervised way is proposed to jointly train GCN of each view. • Consensus regularization pushes data from each view to generate consensus representation. • Experiments with real datasets are carried out to evaluate our methods. [ABSTRACT FROM AUTHOR]

To examine the effectiveness of a 12-month MHBC intervention in the prevention of onset depression in primary health care (PHC). Twenty-two PHC centres took part in the cluster-randomized controlled trial. Patients were randomized to receive either usual care or an MHBC intervention. The endpoints were onset of major depression and reduction of depressive symptoms in participants without baseline depression at a 12-month follow-up. 2531 patients agreed and were eligible to participate. At baseline, around 43% were smokers, 82% were non-adherent to the Mediterranean diet and 55% did not perform enough physical activity. The intervention group exhibited a greater positive change in two or more behaviours (OR 1.75 [95%CI: 1.17 to 2.62]; p = 0.006); any behaviour (OR 1.58 [95%CI: 1.13 to 2.20]; p = 0.007); and adherence to the Mediterranean diet (OR 1.94 [95%CI: 1.29 to 2.94]; p = 0.002), while this increase was not statistically significant for smoking and physical activity. The intervention was not effective in preventing major depression (OR 1.17; [95% CI 0.53 to 2.59)]; p = 0.690) or reducing depressive symptoms (Mean difference: 0.30; [95% CI -0.77 to 1.36]; p = 0.726) during follow-up. As compared to usual care, the MHBC intervention provided a non-significant reduction in the incidence of major depression. Trial registration: ClinicalTrials.gov , NCT03136211. • Health behaviour may be involved in the onset of depressive disorders. • Multiple health behaviour change (MHBC) interventions can be implemented in primary health care (PHC) to prevent depression. • The MHBC intervention tested was not effective in preventing major depression and in reducing depressive symptoms in PHC. • More studies are needed to draw robust conclusions about the implementation of MHCB interventions to prevent depression. [ABSTRACT FROM AUTHOR]

The generalized multiplicative fuzzy C-means (GMFCM) clustering is a novel and potentially competitive fuzzy clustering method. However, the convergence speed of this method is relatively slow. Therefore, a fast generalized multiplicative fuzzy C-means clustering algorithm is proposed in this paper. First, an affinity filtering method based on triangular inequalities is utilized to identify a complete set of non-affinity cluster centers for each sample with low computational complexity. Then, a new multiplicative membership scaling scheme is proposed to modify the multiplicative fuzzy membership between each sample and its non-affinity cluster centers to a non-zero minimum, while increasing the multiplicative fuzzy membership between each sample and its affinity cluster centers. Finally, a new fast algorithm combining these two techniques is proposed to accelerate the convergence process of GMFCM. Extensive experiments on numerical data and images confirm that the proposed algorithm is faster and significantly reduces the number of GMFCM iterations. Specifically, the algorithm reduces the number of iterations by more than 60% compared with the original GMFCM algorithm. At the same time, the running time of the algorithmic process is also greatly reduced. [Display omitted] • An affinity filtering method for searching non-affinity cluster centers of samples is developed. • A scaling scheme for modifying multiplicative fuzzy membership is originally proposed. • A fast algorithm of multiplicative fuzzy partition clustering is designed in this paper. • Experimental results indicate that the proposed algorithm has higher running efficiency. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common malignant tumors among women, and early diagnosis can significantly mitigate its impact. Despite substantial advancements in breast cancer diagnosis using deep learning methods, many challenges persist. In clinical practice, transferring trained deep learning models to new, unlabeled patient samples is essential but challenging due to substantial variability among patient domains. Furthermore, existing domain adaptation models often neglect class-aware sub-domain gaps. Additionally, variations in image styles across domains further impede the accurate diagnostic in the target domain. To address these issues, we propose the category-weight instance fusion learning model for unsupervised domain adaptation in breast cancer diagnosis. This model employs a category-weighted contrast knowledge distillation module to align domains at the category level by selectively clustering similar samples and segregating dissimilar ones. Simultaneously, the meticulously designed instance-aware feature mixing module merges image styles across domains through a domain feature mixing algorithm, significantly enhancing breast cancer domain adaptation capability. Results on BreakHis and ICIAR-2018 datasets demonstrate that our model outperforms other state-of-the-art domain adaptation algorithms in diagnostic accuracy, proving the transferability and robustness of our model across diverse clinical patients. • Address unsupervised domain adaptation task on breast cancer histopathology images. • Enhance the class-aware sub-domain adaptation capability across different domains. • Mix features to fuse the style information across different patient domains. [ABSTRACT FROM AUTHOR]

Currently, unsupervised person re-identification (Re-ID) typically directly computes the similarity matrix between samples and then applies clustering algorithms to generate pseudo labels for model training, achieving significant progress to some extent. However, distance matrices calculated from a single perspective cannot effectively handle complex and variable sample noise environments, leading to pseudo labels that are not comprehensive or accurate enough, with considerable noise that can misguide model optimization. To address this issue, this paper proposes a Multi-view Similarity Aggregation and Multi-level Gap Optimization (MSAMGO) to improve the accuracy of pseudo labels before and after clustering. Specifically, Multi-view Similarity Aggregation (MSA) involves integrating similarity matrices calculated from multiple perspectives to generate reliable pseudo labels for clustering, greatly reducing noise interference from the source. Additionally, the Multi-level Gap Optimization (MGO) is introduced to leverage the positive and negative centroids of similarity between cluster-level samples and the correlation between instance-level samples. Together, they ensure the overall quality of pseudo-labels, reduce noise interference, and collectively promote model optimization. Experiments were conducted on several datasets, demonstrating that our method surpasses the majority of existing unsupervised person re-identification to a large extent and exhibits excellent generalization. [Display omitted] • We propose a multi-view pseudo-label generation scheme. • We have designed a strategy for gap optimization between clusters and samples. • Our approach outperforms most existing methods across various datasets. [ABSTRACT FROM AUTHOR]

Consumer reviews posted on e-commerce platforms offer an assessment of their opinion and sentiments about various aspects of a product or service. The aspects may belong to a specific category at subsystem and component level. The irrealis often associated with an opinion term, if not handled carefully, may distort the sentiment scores. This paper introduces a clustering and negative-sampling backed feed-forward Unsupervised Attention Deep Neural Framework for Opinion-Aspect-Category-Irrealis-Sentiment (UAN-OACIS) Quintuple extraction. This framework splits the five subtasks into two stages. In the first stage , aspects and opinions are extracted using word embedding-based multi-layer Attention Deep Neural Network (DNN) models; while the second stage projects corresponding categories , irrealis , and sentiment scores by creating a hash table and a couple of algorithms. We also propose a POS-Tag-based algorithm to make an irrealis dictionary to suggest sentiment modification percentage scores; and another algorithm to modify existing sentiment-lexicon based on domain-specific words. We compare UAN-OACIS with four unsupervised and two semi-supervised approaches at subtask level, and two supervised approaches at quadruples level excluding irrealis, and have got comparable results. Since no method is available for overall comparison at quintuple level, we additionally study the impact of irrealis on sentiment scores and observe substantial improvement compared to state-of-the-art lexicons methods. • Our work proposes an Unsupervised Attention Neural Framework for Quintuple extraction. • Our attention model uses clustering and negative sampling for aspects and opinions. • We employ hashing strategy to extract aspect-category using centroids of clusters. • We use improved lexicon-based sentiment-analysis to quantify aspects' opinions. • Our algorithm creates a pos-tag based irrealis lexicon to improve sentiment accuracy. [ABSTRACT FROM AUTHOR]

An imbalanced learning problem is a challenge that has grabbed the interest of academics and industry. Class imbalance is a widespread problem in machine learning due to the unavailability of data in a specific category. Several under-sampling and oversampling methods handle class imbalance; however, finding the correct pattern heavily depends on the sub-sampling. This paper proposes a two-stage approach with a data sampling method where Mutually Disjoint Data Sets(MDS) created from majority samples and binded with minority samples. A set of heterogeneous machine learning algorithms were applied on these binded sub-samples to create ensemble models. MDS ensembles give importance to the minority samples with a double voting method. The proposed method has an improvement of 3.78, 13.78 percent of average recall on the train and test data compared to best-base model results [ABSTRACT FROM AUTHOR]

In pre-post factorial designs involving clustered units, parametric methods such as generalized linear mixed effects models are used to handle within subject correlations. However, the distributional and parametric model assumptions in these methods are not always satisfied, especially so with modern data sets, and are often difficult to verify in practice. Often times, the assumptions may not even be realistic when data are measured in a non-metric scale as commonly happens, for example, in Quality-of-Life outcomes. In this article, nonparametric effect-size measures for clustered data in factorial designs with pre-post measurements will be introduced. In our setup, the pre and post occasions may also be viewed as two treatment conditions. Arbitrary dependence among observations within a cluster and across treatment groups is allowed. The effect-size estimators along with their asymptotic properties for computing confidence intervals and performing hypotheses tests are investigated. The proposed methods handle within-cluster as well as between-cluster treatment assignments seamlessly. The methods are shown to be effective in finite samples using simulation studies and they have high powers, especially in situations with multiple forms of clustering. Applications are illustrated with data from a three-arm Randomized Trial of Indoor Wood Smoke reduction. • Nonparametric effect size measures applicable and more appropriate for nonmetric data are introduced. • Methods for confidence interval and hypothesis tests are developed in the pre- and post-measurements factorial design settings. • The developed methods are suitable for data from clustered sampling or clustered randomized trials. • Simulation results showcasing the favorable finite sample performance are presented. • Illustrations of the methods are shown with a real data set. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To test the hypothesis that a feedback-based intervention would reduce human papillomavirus (HPV) vaccine missed opportunities. METHODS: In a longitudinal cluster randomized controlled trial of 48 pediatric primary care practices, we allocated half the practices to receive a sequential, multicomponent intervention phased over consecutive periods. In a prior trial (period 1), communication skills training reduced missed opportunities for the initial HPV vaccine dose at well visits but not at acute/chronic visits. The current trial (period 2) evaluated the added value of performance feedback to clinicians after communication training. Performance feedback consisted of an introductory training module, weekly electronic "Quick Tips," and 3 individualized performance feedback reports to clinicians. We fit logistic regression models for the primary outcome of HPV vaccination missed opportunities using generalized estimating equations with independence working correlation, accounting for clustering at thepracticelevel. RESULTS: Performance feedback resulted in a 3.4 (95% confidence interval [CI]: -6.8, 0.0) percentage point greater reduction in missed HPV vaccine opportunities for the intervention versus control group during acute/chronic visits for subsequent HPV vaccinations (dose 2 or 3). However, during well visits for HPV vaccination dose #1, intervention practices increased missed opportunities (worsened) by 4.2 (95% CI: 1.0, 7.4) percentage points more than control practices, reducing the prior period 1 improvements and blunting the overall effect of performance feedback. We did not observe differences for the other visit/dose categories. CONCLUSIONS: Performance feedback improved HPV vaccination for one subset of visits (acute/chronic, subsequent HPV vaccinations due), but not for well visits. [ABSTRACT FROM AUTHOR]

A novel method for prediction of the extreme wind speed events based on a Hierarchical Classification/Regression (HCR) approach is proposed. The idea is to improve the prediction skills of different Machine Learning approaches on extreme wind speed events, while preserving the prediction performance for steady events. The proposed HCR architecture rests on three distinctive levels: first, a data preprocessing level, where training data are divided into clusters and accordingly associated labels. At this point, balancing techniques are applied to increase the significance of clusters with poorly represented wind gusts data. At a second level of the architecture, the classification of each sample into the corresponding cluster is carried out. Finally, once we have determined the cluster a sample belongs to, the third level carries out the prediction of the wind speed value, by using the regression model associated with that particular cluster. The performance of the proposed HCR approach has been tested in a real database of hourly wind speed values in Spain, considering Reanalysis data as predictive variables. The results obtained have shown excellent prediction skill in the forecasting of extreme events, achieving a 96% extremes detection, while maintaining a reasonable performance in the non-extreme samples. The performance of the methods has also been assessed using forecast data (GFS) as predictors. [ABSTRACT FROM AUTHOR]

The impulsive live streaming consumption intentions of the Southeast Asian market are examined in this study. Online live streaming transactions, a new form of social media, are becoming popular due to their real-time communication and innovative business concept. Though little attention has been dedicated to it, a theoretical knowledge of Live Streaming Transaction (LST) is crucial given its broad application and unique features. The analysis of 8613 respondents from Southeast Asia in this study, based on flow, temperament, and personality theories, offers fresh perspectives on the mediating role of flow and the moderating effect of temperament in a cross-national setting. Cluster sampling was used to construct a mixed-method conditional indirect effects model. Since introverted temperament types look for voices that echo within them, our findings revealed that LSTs are more intriguing to this market segment. Theoretical and practical elements, as well as implications for future directions, are presented. [ABSTRACT FROM AUTHOR]

The Pichia pastoris fermentation process is with highly nonlinear and time-varying and strong coupling characteristics, traditional control methods such as classic PID cannot satisfy the actual needs of the fermentation process control. In order to effectively solve the control problem of the Pichia pastoris fermentation process, a multi-model predictive control method is presented based on the relative error weighting algorithm. First, the prior sample data are divided into multiple training sample sets (sample cluster) by fuzzy C-means clustering algorithm (FCM). Then, the corresponding sub prediction model is obtained by using least square support vector machine (LSSVM) and Improved particle swarm optimization (IPSO) algorithm for each sample cluster. Finally, the control strategy of predictive model is constructed based on multi model relative error weighting algorithm. The simulation in the Pichia pastoris fermentation process shows that the algorithm proposed in this paper could improve the transient response and perform good output tracking in a wide range. It improves the adaptive ability of the model and makes it more accurate to describe the actual state of the nonlinear system. [ABSTRACT FROM AUTHOR]

Human motion prediction is to forecast future pose sequence based on the observed pose sequence. Most recent works rely on large datasets for training. However, the model trained by large datasets cannot be directly applied to the few-shot motion prediction task, which requires the model to generalize well on novel motion dynamics with limited training samples. To deal with this issue, we propose a Motion Prediction Network (MoPredNet) for few shot human motion prediction, which elegantly models long term dependency in motion dynamics and can adapt to predict new forms of motion dynamics. Specifically, the MoPredNet dynamically captures the most informative poses in the data stream as masked poses and adaptively learns spatio-temporal structure from the past poses and the masked poses, and thus improves its encoding capability of motion dynamics. We also propose to cluster training samples into pseudo actions to accumulate prior knowledge, and use the accumulated prior knowledge and few training samples to adapt the MoPredNet to unseen motion dynamics. Experimental results demonstrate that our method achieves better performance over state-of-the-art methods in human motion prediction. [ABSTRACT FROM AUTHOR]

Background: A strong link between childhood maltreatment (CM) and borderline personality features (BPF) has been consistently demonstrated. However, little is known about the role of psychological adjustment (PA) as a mediator of this relationship. The purpose of the study was to explore the mediating role of PA in the CM-BPF linkage in a large sample of early adolescents.Methods: A total of 5724 students (mean age = 13.5, SD = 1.0) were recruited from three middle schools by using the multistage random cluster sampling method in Anhui Province, China. Participants were required to complete self-report questionnaires regarding CM experience, self-perceived PA and BPF. Mediation analyses were computed via structural equation modelling.Results: CM victimization was positively associated with BPF, and individuals with lower levels of PA were more likely to present with BPF. Mediation analysis showed a significant indirect effect of CM on BPF via PA (effect = 0.047, 95 % CI: 0.035, 0.058).Limitation: The cross-sectional design of this study did not allow testing causality. The retrospective investigation of CM history could cause recall bias.Conclusions: PA partially mediates the relationship between CM and BPF. These findings have important implications for considering PA as a protective factor in this relationship. [ABSTRACT FROM AUTHOR]

Object re-identification is a retrieval task to identify the same identity, usually utilizing the ranking list to sort the features. However, most existing ranking lists only calculate the distance from the probe sample to its k -nearest neighbor samples in the gallery set but ignore the distribution of the gallery set. Meanwhile, we find that the gallery set tends to converge into different groups. When the probe lies near the boundary, its k -nearest neighbors often contain samples of different identities from the probe sample. This paper presents a Distribution Corrected Ranking List (DCRL) to correct the ranking by considering the distance between the probe sample and its nearest distribution (i.e., the cluster center). Specifically, given a probe sample, first, we cluster the gallery samples with the agglomerative hierarchical clustering method to obtain the cluster centers. Second, we compute the distance between the probe and the cluster centers. Third, the final ranking list is considered as the weighted combination of the original ranking list (i.e., the distance between the probe sample and its k -nearest neighbor in the gallery) and the distribution ranking list (i.e., the distance between the probe sample and its nearest cluster center). The proposed distribution corrected ranking list is suitable for large datasets because it does not require human intervention or labeled data. Experimental results on several benchmark re-identification datasets, such as Market-1501, DukeMTMC-reID, MSMT17, VeRi-776, and VeRi-Wild, show that the proposed method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In the field of unsupervised learning, many methods such as clustering rely on exploring the correlations among features. However, considering these correlations is not always advantageous for learning models. The biased selection of data may lead to redundant and unstable correlations among features, adversely affecting the performance of learning models. Multi-view data presents more complex feature correlations with potential redundancy and varying distributions across views, necessitating detailed analysis. This paper proposes a causal regularized debiased multi-view k-means clustering (DMKC) method to counteract redundant feature correlations stemming from sample selection bias. This method introduces a covariate weighted balance method from causal inference to mitigate redundant bias in multi-view clustering by adjusting sample weights. The approach combines sample and view weights within a k-means loss framework, effectively eliminating feature redundancy and enhancing clustering performance amidst sample selection bias. The optimization process of the relevant parameters is detailed in this paper, and comprehensive experiments demonstrate the effectiveness of the method. • Analyzes the under-discussed issue of redundant feature correlations caused by selection bias in clustering, noting the bias's inconsistency across views and its negative impact on multi-view learning model performance. • Introduces a new algorithm that utilizes causal regularization from covariate balancing methods in causal inference, applying dual weighting of samples and views to reduce the impact of biased sample selection. • Presents an optimized approach for the proposed algorithm, validating its effectiveness through experiments and showing enhanced clustering accuracy and reliability. [ABSTRACT FROM AUTHOR]

In order to increase the accuracy of traditional methods for camera-based pulse rate estimation, we propose a novel systemic approach that extracts multiple remote photoplethysmography (rPPG) signals from a set of scattered facial patches and effectively separates good estimates from noisy ones via a novel unsupervised Power Spectral Density (PSD) clustering method. In contrast to commonly adopted rPPG pipelines, which are often challenged by rigid head movements, facial expressions, and rapidly changing lighting conditions, our patch-oriented solution leverages the key feature of patch recurrence in video sequences. Instead of focusing on a small group of specific Regions of Interest (ROIs), our method adaptively selects a set of patches tracked across successive frames. The spatio-temporal self-similarity among these patches provides powerful internal statistics that significantly enhance standard techniques for rPPG assessment. Our main contribution is a novel unsupervised discriminatory strategy called CircleClustering , which naturally separates PSDs into those with low intra-class variability from those with high intra- and inter-class inhomogeneity. Extensive experimental results demonstrate the overall superiority of our patch-based clustering method compared to both traditional signal processing-based rPPG techniques and recent supervised deep learning-based models for rPPG recovery. [ABSTRACT FROM AUTHOR]

• Maternal childhood polyvictimization can increase the risk of child neglect. • The nuanced nature of polyvictimization needs to be understood regarding its effects. • Multiplicative interaction effects are instrumental in measuring polyvictimization. Limited research has been conducted on child neglect among ethnic minority groups in China. The Salar, a Turkic-Muslim ethnic minority predominantly residing in western China, face unique challenges in this regard. Influenced by their ethnicity, Islam, and Chinese Confucianism, Salar women are particularly vulnerable to various forms of childhood victimization. These victimizations further compound the difficulties they face in providing adequate care for their children. The 3-D polyvictimization theory introduced three key concepts to measure polyvictimization: invasiveness, exploitativeness, and severity. These concepts aim to provide a nuanced understanding of an individual's victimization experiences. In light of this theory, this study aims to test a hypothesis regarding the relationship between childhood polyvictimization experienced by Salar mothers and the neglect of their children. A probability proportional to size (PPS) sampling approach was used to collect a random representative multi-stage cluster sample of 201 married Salar women in 2022. Random effects Poisson regression models were used to test the five hypotheses. The participants reported a 65.6% rate of child neglect, and all the mothers included in the study had a history of childhood polyvictimization. The Poisson regression in the main effect model demonstrated that invasive and exploitative of the childhood polyvictimization are associated with child neglect. Moreover, a two-way interaction between invasiveness and exploitativeness, as well as exploitativeness and severity of the childhood polyvictimization, showed a strong positive association with child neglect. Lastly, a three-way interaction involving invasiveness, exploitativeness, and severity of the childhood polyvictimization was also strongly positively associated with child neglect. This research indicates that a history of maternal childhood polyvictimization is associated with an increased risk of child neglect, especially when the experiences of polyvictimization involved high levels of invasiveness and exploitativeness. It emphasizes the importance of considering the nuanced nature of polyvictimization in understanding its relationship with child neglect. [ABSTRACT FROM AUTHOR]

This study examined the effect of physical maltreatment on online gaming disorder and investigated the mediating role of insecure attachment in the aforementioned relationship. Further, multi-group analysis was conducted aiming to explore whether the pathways in the conceptual model were significantly different across the groups of adolescents from single and non-single child families. Based on a multi-stage random cluster sampling method, a total of 2001 adolescents aged 12–18 years (51.3 % boys and 48.7 % girls) was obtained from Hebei Province, China. We used Structural Equation Modeling (SEM) and multi-group analysis in SEM for data analyses.The results showed that physical abuse was directly associated with online gaming disorder (B=0.139, β = 0.107, p < 0.01, 95 % CI [0.063, 0.218]). Only anxious attachment mediated the relationship between physical abuse and online gaming disorder (B=0.069, β = 0.053, p < 0.001, 95 % CI [0.045, 0.098]), and between physical neglect and online gaming disorder (B=0.025, β = 0.029, p < 0.001, 95 % CI [0.015, 0.038]). The results of multi-group analysis suggested the significant difference in the pathway coefficients between the two groups. Significant difference was observed in the specific structural pathway between physical neglect and anxious attachment (CRD= − 2.851, p < 0.05), with the effect being stronger for adolescents from single child families (B=0.248, β = 0.279, p < 0.001, 95 % CI [0.176, 0.392]) compared with those from non-single child families (B=0.115, β = 0.137, p < 0.001, 95 % CI [0.073, 0.199]). The findings suggest the indirect influencing mechanism of insecure attachment in the relationship between physical maltreatment and online gaming disorder. The results of the multi-group analysis require future practitioners to evaluate the family conditions of siblings when intervening in online gaming disorder of adolescents. [ABSTRACT FROM AUTHOR]

Semi-supervised semantic segmentation has attracted increasing attention in computer vision, aiming to leverage unlabeled data through latent supervision. To achieve this goal, prototype-based classification has been introduced and achieved lots of success. However, the current approaches isolate prototype generation from the main training framework, presenting a non-end-to-end workflow. Furthermore, most methods directly perform the K-Means clustering on features to generate prototypes, resulting in their proximity to category semantic centers, while overlooking the clear delineation of class boundaries. To address the above problems, we propose a novel end-to-end boundary-refined prototype generation (BRPG) method. Specifically, we perform online clustering on sampled features to incorporate the prototype generation into the whole training framework. In addition, to enhance the classification boundaries, we sample and cluster high- and low-confidence features separately based on confidence estimation, facilitating the generation of prototypes closer to the class boundaries. Moreover, an adaptive prototype optimization strategy is proposed to increase the number of prototypes for categories with scattered feature distributions, which further refines the class boundaries. Extensive experiments demonstrate the remarkable robustness and scalability of our method across diverse datasets, segmentation networks, and semi-supervised frameworks, outperforming the state-of-the-art approaches on three benchmark datasets: PASCAL VOC 2012, Cityscapes and MS COCO. The code is available at https://github.com/djh-dzxw/BRPG. [ABSTRACT FROM AUTHOR]

Until recently, research examining the application of Rational Emotive Behavior Therapy (REBT) in sports settings was virtually absent in South Africa. Despite the growing evidence of REBT's potential as a psychological intervention in Western nations, its use within the multicultural and sports-fervent context of South Africa remains unexplored. Moreover, limited research has addressed the impact of REBT on rugby players, with only a few case studies being reported. The current experiment employs a cluster randomized trial (CRT) to compare the effects of a 7-week preferential REBT program with a 7-week Mindfulness-Acceptance-Commitment (MAC) program on irrational beliefs, competitive anxiety and subjective performance, among adolescent South African rugby players. We also include a wait-list control group who received neither REBT nor MAC. Results indicate that athletes receiving REBT reported greater improvements in irrational beliefs, anxiety, and subjective performance, while that athletes receiving MAC also reported some improvements in anxiety. This study highlights the potential of REBT as a valuable psychological intervention in the context of South African adolescent rugby players. • Sport REBT research has never been done in South African sports. • Cluster Randomised Trial (CRT) compares a 7-week REBT with a 7-week MAC on rugby players. • REBT group showed a significant reduction in irrational beliefs. [ABSTRACT FROM AUTHOR]

The goal of this study was to evaluate changes in knowledge, attitudes, and other key suicide prevention issues for substance use disorder (SUD) counselors trained to administer the Preventing Addiction Related Suicide (PARS) module as part of a large-scale clinical trial. PARS is a 3-hour, PowerPoint based intervention designed with and for community SUD agencies for their Intensive Outpatient (IOP) group therapy programs. A previous randomized study of 906 patients from 15 community SUD sites showed positive changes in patients' suicide prevention knowledge, attitudes, and help seeking. Counselor participants completed measures of knowledge and attitudes about suicide and their confidence treating suicidal patients at each step of a large, stepped wedge cluster randomized trial of PARS, including after the final step. Data analysis compared scores in steps prior to counselors' training in PARS with scores in the steps following counselors' PARS training. A total of 126 counselors participated in the study (89 % of those approached; 89–92 % retention across follow-up). Evaluation of both PARS efficacy and the PARS training by SUD counselors was highly rated. Counselor scores after receiving PARS training (vs. scores prior to PARS training) showed greater suicide knowledge, less maladaptive attitudes about suicide, and greater confidence in working with suicidal patients. Based on this rigorous test of PARS training for SUD counselors working in community SUD intensive outpatient programs, PARS training, as well as doing the PARS intervention, was rated as highly effective and acceptable. PARS offers an integrated, brief, engaging, and effective training method to improve suicide care for both SUD counselors and SUD clients. • Counselors are not confident in their ability to help clients experiencing suicide. • Preventing Addiction Related Suicide (PARS) training is experiential. • Training increased counselor's suicide knowledge, attitudes, and confidence. • Counselors found the PARS training effective and fit well into their workload. [ABSTRACT FROM AUTHOR]

This study was conducted to explore the combination of different conditional variables that led to high career aspiration of intern nursing students. A severe shortage of nurses exists nationwide, and nurses' low career aspiration contribute to high turnover rates and hinder the high-quality development of the nursing field. Nursing students undergoing clinical practice will become the backbone of future nursing staff. Enhancing their career aspiration is crucial for developing nursing career education and stabilizing the nursing team. This was a cross-sectional study. Using the cluster sampling method, 521 intern nursing students from Henan and Anhui in China were selected as participants from December 2023 to January 2024. This study employed the fuzzy-set qualitative comparative analysis (fsQCA) to explore the configuration for paths of leading to high career aspiration among intern nursing students. General information questionnaires, career aspiration scale, clinical practice maladjustment scale, self-reflection and insight scale were used as measures in this study. The results showed that students' origin, professional attitude, GPA, self-reflection and insight, relationship with preceptors and clinical practice maladjustment have a combined effect on career aspiration. This study found that eight configuration combined into four models could explain 75.9 % of high-level career aspiration (solution consistency = 0.786; solution coverage = 0.759). The level of career aspiration among intern nursing students is the result of the combination of different conditional variables. Using a configurational perspective to intervene in nursing students with various characteristics is expected to enhance the career aspiration of intern nursing students. • The career aspiration of intern nursing students is pivotal for retaining and cultivating nursing talents. • Utlizied the fsQCA method to explore the configuration paths of career aspirations of intern nursing students; • Under the framework of the SCCT, the combined effect of students' origin, GPA, etc. generated eight configuration paths. • Nursing educators and managers can intervene in career aspirations of intern nursing students with different characteristics. [ABSTRACT FROM AUTHOR]

Compare the effectiveness of 2 nonpharmacologic approaches to dementia care in nursing homes on 12- and 18-month resident outcomes, which was after the emergence of COVID-19. A cluster randomized pragmatic trial, which included an embedded convergent mixed-methods design. Nursing homes were randomized to a team-based (TB) or problem-based (PB) intervention arm. Residents (n = 2658 at baseline) and staff (n = 327) from 52 nursing homes in the United States. The exploratory quantitative analysis used a difference-in-differences model to compare the change in TB arm resident outcomes between baseline, 12 months, and 18 months relative to the PB arm using the Minimum Data Set 3.0. Outcomes included antipsychotic medication use, behavioral symptoms, wandering, and rejection of care. Staff interview data were analyzed in a 3-phase team-based rapid qualitative analysis approach, after which data were integrated. Rejection of care decreased by 5.9 percentage points in the TB arm relative to the PB arm at 12 months (95% CI –11.7 to −0.2). Emergent results indicated that COVID-19 visitation restrictions prevented PB staff from working with families to manage rejection of care as was done before the pandemic, whereas TB staff described using collaboration strategies not hindered by the pandemic. There was no statistically significant difference between arms in antipsychotic medication use, behavioral symptoms, and wandering at either follow-up period. When integrated, qualitative data provided contradictory information on antipsychotic medication use and confirmatory information on behavioral symptoms and wandering. The exploratory nature and mixed results of the quantitative data analysis limited the ability to determine a clear benefit of one approach over the other. Findings suggest that dementia care delivery faced complex challenges during COVID-19 and required engagement from a broad range of nursing home staff underscoring the importance of equipping all staff with foundational dementia care knowledge. [ABSTRACT FROM AUTHOR]

The health of incarcerated populations is intertwined with the health of security staff, but the social mechanisms, and especially the specific interventions, that might mitigate these health harms are underexplored. We examine one possible mechanism of interrelated health harms: whether and how jail security staff are willing and able to care for mentally ill detainees. We hypothesize that the attitudes of security staff towards care affect the well-being of everyone in a jail setting—staff, as well as detainees. Analyzing 539 anonymous respondent surveys administered to a stratified cluster sample of security staff working in a large U.S. county jail system, we (1) describe the prevalence of a perceived duty to care and availability of caring resources among security staff and (2) analyze whether variations in a duty to care and caring resources predict outcomes associated with staff and detainee well-being. Across five maximum likelihood models estimated, both perceived duty to care and availability of caring resources are significantly associated with collaborative relationships with medical staff, increased perceptions of personal safety, decreased frequency of hostile encounters, and better self-reported health outcomes. Our models explain 20 percent of the variation in self-reported health outcomes (R2 =.20), a meaningful effect of care on security personnel's well-being. Our findings suggest security staff have an often-overlooked duty to care akin to that experienced by healthcare staff. Among healthcare staff, dual loyalty trainings have successfully amplified caring duties relative to security duties; similar trainings for security staff might better leverage their caring duties to improve both staff and detainee well-being. • Security staff, like medical staff, experience "dual loyalty" conflicts in jails. • Jail security staff with access to more caring resources are healthier and safer. • Health and safety of jail security staff is closely related to that of detainees. • More access to caring resources can improve overall staff and detainee well-being. [ABSTRACT FROM AUTHOR]

Accurate individual cattle identification is crucial for modern precision cattle farming. However, practical applications encounter challenges due to factors such as shooting distance and angles, cattle movements, weather conditions, and cattle face posture. Existing models struggle with low recognition accuracy for low-recognizable images and poor robustness to facial pose variations, presenting urgent problems that need to be addressed. In this study, a novel cattle face recognition method was proposed, aiming to solve the aforementioned issues by analyzing and optimizing the embedding distribution of cattle faces. Firstly, MobileFaceNet was employed as the feature extraction network to sufficiently extract discriminative representations. Secondly, due to the tendency of low-recognizable samples to cluster in specific areas of the embedding space, an Embedding Enhancement Module (EEM) was proposed. This module drives embedding features away from ineffective embedding spaces, thereby enhancing the model's ability to extract identity features from less recognizable cattle faces. Finally, an Embedding Optimization Module (EOM) was proposed, which utilized a Sub-Center method to alleviate the model's learning difficulty during early training stages and achieved "pose-irrelevant" clusters by merging Sub-Centers, enhancing robustness to facial pose variations. Experimental results confirmed the effectiveness of the proposed method, with it outperforming the baseline by up to 2.12 % in accuracy, achieving a recognition accuracy of 98.69 %. Furthermore, the application of the proposed method in cattle farms further demonstrated the significant potential value of this work. • Face recognition in uncontrolled scenes was proposed to identify individual cows. • Embedding enhancement module enhanced recognition accuracy for low-recognizable faces. • Embedding optimization module improved robustness to face posture changes. • Experiments deployed in real scenes showed good performance of the proposed method. [ABSTRACT FROM AUTHOR]

Existing popular domain adaptation approaches typically assume that the source and target domains share the same label set. However, in industrial scenarios, the equipment may encounter unknown fault modes because of the harsh operating environment, which limits the application of existing diagnostic methods. To address the above problem, an intrinsic information-guided open set domain adaptation network is proposed for cross-domain fault diagnosis with unknown faults. First, a similarity-based discrimination framework is constructed to enhance the robustness of the model for unknown samples, which learns the similarity between samples and fault prototypes to enhance the classification performance. Then, a multi-information integrated weighting module is designed to quantify the transferability of samples through enhanced domain similarity information learning and prediction uncertainty information learning methods. Additionally, a self-supervised neighborhood clustering learning method is constructed, which enables the model to learn structural information about the target domain and encourages the target samples to cluster closely for better separability. Finally, the weighted open set adversarial training framework effectively facilitates diagnostic knowledge transfer and unknown fault recognition. Comprehensive experimental results on two datasets demonstrate the effectiveness of the proposed method in addressing the open set cross-domain diagnosis problem, which achieves promising performance over the comparison methods. • A novel IODAN is proposed for open set cross domain fault diagnosis. • IODAN learns intrinsic structural information about the source and target domains. • The proposed weighting strategy distinguishes known and unknown class weights. • Self-supervised neighborhood clustering promotes target sample clustering. • Extensive experimental results verify the effectiveness and superiority of IODAN. [ABSTRACT FROM AUTHOR]

In near-infrared spectroscopy analysis, the predictive performance of models may degrade due to variations in the measurement environment and sample properties. Maintenance or updates to the model are essential to uphold prediction accuracy. The state-of-the-art approach for model updates involves labeling new samples and incorporating them into the calibration set. However, this strategy leads to an escalating size of the calibration dataset, and the low ratio of new samples renders the model update process inefficient. To enhance update efficiency, a size-stable updating strategy is presented which involves the simultaneous addition of new samples and removal of old samples. This is achieved by utilizing the new set as a basis for identifying and deleting significantly different old labeled samples. To improve labeling efficiency, a batch mode update is employed. Initially, calibration samples are clustered to yield multiple clusters with comparable sizes to the new update set. Subsequently, differences between multiple old sample clusters and the new set are calculated. The old sample cluster that most different are removed, accompanied by the addition of the new sample set to maintain calibration set size stability. In calculating set similarity, a multi-indices fusion method is employed to ensure accurate judgments. The efficacy of this method is validated through simulated and real data. [ABSTRACT FROM AUTHOR]

This study aims to create a stigma reduction framework for stuttering in the local context of Québec, Canada using the Participative Concept Mapping Approach (PCMA), focusing on both self and societal stigma. Employing a mixed-methods approach, this study engaged 17 experts—people who stutter, clinicians and health innovation specialists—in PCMA workshops. Via diverse steps, including generation, sorting and rating of ideas in response to the focus prompt, "To effectively address stuttering (self-)stigma, an intervention should..." these sessions led to a framework depicted in visual maps, then refined into actionable principles through qualitative analysis. Mixed-methods data analysis used the open-source R-CMap software to generate visual maps illustrating the relationships among ideas as well as importance and feasibility ratings. The collaborative workshops identified 95 ideas in response to the focus prompt, reunited in 7 clusters, evolving into 16 principles to mitigate stuttering stigma and self-stigma. At the therapy level, these principles emphasize personalized therapy, thorough assessments, stigma-free therapeutic environment, empowerment, and the importance of group inclusivity and educating the relational circles. Societally, they advocate for initiatives such as improved educational outreach, empathy enhancement, and better representation. This dual approach targets individual experiences and societal views on stuttering, stressing the need for an all-encompassing intervention framework. The findings demonstrate PCMA's usefulness in crafting local, culturally sensitive, tailored interventions for stigma reduction. The study emphasizes the necessity of holistic approaches that address individual experiences and societal perceptions, offering a model to conduct similar exercises in diverse local settings. • The participatory method PCMA is used to map a stigma reduction framework. • 17 experts unite in Québec to tackle stuttering stigma via PCMA mixed methods. • PCMA has the potential to help develop tailored stigma community interventions. • Cultural insight's role in stigma reduction is explored. • PCMA's wider use in stigma research is discussed. [ABSTRACT FROM AUTHOR]

Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally overlook the significance of weakly-supervised information and fail to preserve the feature properties of multiple views, thus resulting in unsatisfactory clustering performance. To address these issues, in this paper, we propose a novel D eep M ulti-view S emi-supervised C lustering (DMSC) method, which jointly optimizes three kinds of losses during networks finetuning, including multi-view clustering loss, semi-supervised pairwise constraint loss and multiple autoencoders reconstruction loss. Specifically, a KL divergence based multi-view clustering loss is imposed on the common representation of multi-view data to perform heterogeneous feature optimization, multi-view weighting and clustering prediction simultaneously. Then, we innovatively propose to integrate pairwise constraints into the process of multi-view clustering by enforcing the learned multi-view representation of must-link samples (cannot-link samples) to be similar (dissimilar), such that the formed clustering architecture can be more credible. Moreover, unlike existing rivals that only preserve the encoders for each heterogeneous branch during networks finetuning, we further propose to tune the intact autoencoders frame that contains both encoders and decoders. In this way, the issue of serious corruption of view-specific and view-shared feature space could be alleviated, making the whole training procedure more stable. Through comprehensive experiments on eight popular image datasets, we demonstrate that our proposed approach performs better than the state-of-the-art multi-view and single-view competitors. [ABSTRACT FROM AUTHOR]

Cross-domain sentiment classification (CDSC) is used to predict the sentiment polarity of a text in an unlabeled target domain by analyzing the reviews in the labeled source domain. Domain adaptive approaches have become the preferred solution in recent years to the unsupervised domain migration problem. Among them, adversarial learning aligns the sample distribution of the two domains through domain confusion to transfer sentiment across domains. However, traditional adversarial learning often roughly measures domain discrepancy. Although scholars have attempted to adjust the decision boundary of different categories to eliminate the domain shift, such as maximum classifier discrepancy model, there are still two problems with this approach. First, it ignores the intra-domain structure, which causes the samples distributed on the decision boundary to be easily misclassified. Second, it only realizes coarse-grained sentiment migration and lacks a refined evaluation of the transferable information in the inter-domain, which causes a negative transfer. To solve these problems, we propose domain adaptation with a shrinkable discrepancy strategy (DA-SDS) for the task of CDSC. Specifically, we propose to shrink the category subspace in the intra-domain while building the decision boundary of classifiers, which reduces the misclassification by clustering samples to the category center. We also propose to measure the weighted domain discrepancy in the inter-domain, which mitigates the negative transfer through the refined assessment of domain discrepancy. Extensive evaluations showed that DA-SDS outperformed state-of-the-art methods on the Amazon Review dataset. [ABSTRACT FROM AUTHOR]

Person re-identification, getting impressive performance under the single-domain setting, often suffers huge performance drop when deploying to the unseen target domain owing to domain gap. Current research mainly focuses on unsupervised domain adaptation to alleviate the domain gap, and the methods by clustering the target-domain samples have achieved significant results. However, some inaccurate pseudo-labels, i.e., noisy pseudo-labels, may be generated on clustering, which will seriously affect the performance of the model. In order to solve the above problem, we propose a novel unsupervised domain adaptive person re-identification method by exploiting reliable pseudo-labels (RPL) from two aspects, i.e., adaptive dynamic clustering (ADC) and cross-camera similarity evaluation (CCSE). Specifically, firstly, for the methods based on the density-based clustering algorithm, we propose the adaptive dynamic clustering which calculates the clustering radius adaptively and dynamically to obtain more reasonable clustering results in the iterative optimization of the model. Next, for noisy pseudo-labels caused by small inter-class variations under the same camera, we propose the cross-camera similarity evaluation to filter out these noises to further improve the discrimination of the model. Extensive experiments on three publicly available large-scale datasets show that the proposed method can achieve state-of-the-art performance on unsupervised domain adaptation person re-identification. [ABSTRACT FROM AUTHOR]

Clustering on graphs (networks) is becoming intractable due to increasing sizes. Nyström spectral graph clustering (NSC) is a popular method to circumvent the problem. However, NSC currently faces two issues: (1) how to efficiently obtain representative samples for large networks; (2) the NSC is irrational for the mapping of nodes. To address the issues, in this paper, we propose an improved Nyström spectral graph clustering based on k -core decomposition sampling for large networks. In the proposed method, we first employ k -core decomposition as a sampling method, then use the samples to conduct the NSC to acquire the clusters of the sample nodes and the nodes connected with the samples, and finally utilize the proposed label propagation algorithm to group the remaining nodes into previously found clusters. To evaluate the performance, we use spectral clustering and NSC as baselines and compare our algorithm with the baselines on 9 small networks. Moreover, the proposed method is applied to 5 large networks. The proposed method can achieve a Modularity of 0.355 and cost 1234.95 s for a large network with approximately 120 million edges, which demonstrates that the proposed method has higher accuracy than NSC and higher efficiency than spectral clustering. [ABSTRACT FROM AUTHOR]

Purpose: One of the effective models in health education is the health belief model that considers a person's behavior as a under the influence of knowledge and attitude. In the present study, we investigated the efficacy of a training program based on the health belief model in burn prevention knowledge in mothers of children aged between 1 and 3 years old.Design and Methods: This single blind randomized Control Trial study was conducted on 64 mothers with a child aged between 12 and 36 months who were referred to the Health Centers of Shiraz, Iran, in 2018. Accordingly, using cluster sampling, 4 health centers were selected and then 2 centers were assigned to the intervention group and 2 other centers to the control group by randomization. The intervention group received six sessions of training. However, in order to observe the ethical principles, the educational content was provided to the control group at the end of the present study. Both groups completed health belief model questionnaire before, after the intervention, and by passing 2 months from the intervention. The collected data were compared between the two groups using SPSS software, version 25.Results: We found no differences in the demographics of the two study groups (P > 0.05). Intragroup comparison in the intervention and control groups indicated a significant difference among the three intervals (both P = 0.001). As well, a difference was found between the intervention and the control groups immediately after the intervention and 2 months after the intervention (both P < 0.001). Additionally, in both groups, the relationship between mothers' knowledge score and each dimension of the health belief model was examined, which was a significant. (P = 0.001).Conclusion: Health belief model can be considered as an effective method for educating mothers in terms of the prevention of children's burn and the increased the mothers' knowledge.Practice Implications: The health belief model was shown to have a good effect on educating mothers regarding child burn's prevention. So, due to this reason, it is recommended to use this model for burn's prevention training programs. [ABSTRACT FROM AUTHOR]