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The degree of pilling and fuzzing in textile fabrics is a crucial indicator of textile product quality. Current evaluation methods predominantly rely on subjective judgments, leading to issues such as rating errors and inefficiency. To achieve objective assessment of pilling and fuzzing grades, this study proposes a Hybrid Feature-Based Machine Vision Method for Objective Evaluation of Textile Pilling and Fuzzing. The method incorporates a Hybrid Feature-based Depthwise Separable Attention Network for Objective Evaluation of Textile Pilling and Fuzzing (HDAN-PF), which effectively extracts and fuses frequency and Space domain features. A Channel Attention mechanism enhances the model’s ability to capture subtle features, while Depthwise Separable Convolutions reduce computational complexity, improving evaluation speed while maintaining high accuracy.The model size is approximately 327.37 MB with a total parameter count of 135,115,512. Experimental results demonstrate that the proposed method achieves a classification accuracy of 96.26% on diverse fabric images, showcasing robust generalization and practical utility.By leveraging this machine vision approach, the proposed method offers a transformative solution for achieving objective, consistent, and efficient assessments of pilling and fuzzing grades, advancing textile quality evaluation practices.

Role-play in the Large Language Model (LLM) is a crucial technique that enables models to adopt specific perspectives, enhancing their ability to generate contextually relevant and accurate responses. By simulating different roles, theis approach improves reasoning capabilities across various NLP benchmarks, making the model's output more aligned with diverse scenarios. However, in this work, we demonstrate that role-play also carries potential risks. We systematically evaluate the impact of role-play by asking the language model to adopt different roles and testing it on multiple benchmarks that contain stereotypical and harmful questions. Despite the significant fluctuations in the benchmark results in different experiments, we find that applying role-play often increases the overall likelihood of generating stereotypical and harmful outputs.

With the growing deployment of large language models (LLMs) across various applications, assessing the influence of gender biases embedded in LLMs becomes crucial. The topic of gender bias within the realm of natural language processing (NLP) has gained considerable focus, particularly in the context of English. Nonetheless, the investigation of gender bias in languages other than English is still relatively under-explored and insufficiently analyzed. In this work, We examine gender bias in LLMs-generated outputs for different languages. We use three measurements: 1) gender bias in selecting descriptive words given the gender-related context. 2) gender bias in selecting gender-related pronouns (she/he) given the descriptive words. 3) gender bias in the topics of LLM-generated dialogues. We investigate the outputs of the GPT series of LLMs in various languages using our three measurement methods. Our findings revealed significant gender biases across all the languages we examined.

Role-play in large language models (LLMs) enhances their ability to generate contextually relevant and high-quality responses by simulating diverse cognitive perspectives. However, our study identifies significant risks associated with this technique. First, we demonstrate that autotuning, a method used to auto-select models' roles based on the question, can lead to the generation of harmful outputs, even when the model is tasked with adopting neutral roles. Second, we investigate how different roles affect the likelihood of generating biased or harmful content. Through testing on benchmarks containing stereotypical and harmful questions, we find that role-play consistently amplifies the risk of biased outputs. Our results underscore the need for careful consideration of both role simulation and tuning processes when deploying LLMs in sensitive or high-stakes contexts.

Current common interactions with language models is through full inference. This approach may not necessarily align with the model's internal knowledge. Studies show discrepancies between prompts and internal representations. Most focus on sentence understanding. We study the discrepancy of word semantics understanding in internal and external mismatch across Encoder-only, Decoder-only, and Encoder-Decoder pre-trained language models.

Background This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in 18 F-FDG PET/CT images. Methods A total of 137 HCC patients from two institutions were retrospectively included. First, intratumoral habitats were achieved by a two-step unsupervised clustering process based on k-means clustering. Second, a total of 4032 radiomic features were extracted based on each habitat, including 2016 PET-based and 2016 CT-based radiomic features. Then, after feature selection, the stacking ensemble learning approach which combined six machine learning classifiers as the first-level learners with Cox proportional hazards regression as the second-level learner, was employed to build multiple radiomic models. Finally, the optimal model was selected based on the calculation of the C-index, and a combined model integrating with a clinical model was also constructed to identify the potentially complementary effect. Results Three spatially distinct habitats were identified in the two cohorts. Among a total of 30 stacking ensemble learning models established based on different combinations of 5 types of segmented volumes of interest (VOIs) with 6 types of classifiers, the MLP-Cox-habitat-2 model was selected as the optimal radiomic model with a C-index of 0.702 in the external validation cohort. Furthermore, the combined model integrating the optimal radiomic model with the clinical model achieved an improved C-index of 0.747. Consistently, the combined model outperformed the other models for OS prediction, with a time-dependent AUC of 0.835, 0.828, and 0.800 in the 1-year, 2-year, and 3-year OS, respectively. Conclusion 18 F-FDG PET/CT-based habitat radiomics outperformed traditional radiomics in OS prediction for HCC, with a further improved predictive power by integrating with the clinical model. The optimal combined habitat model was potentially promising in guiding individualized treatment for HCC. Trial registration This study was a retrospective study, so it was free from registration.

Background Migrant and left-behind families are vulnerable in health services utilization, but little is known about their disparities in immunization of non-National Immunization Program (NIP) vaccines. This study aims to evaluate the immunization coverage, knowledge, satisfaction, and associated factors of non-NIP vaccines among local and migrant families in the urban areas and non-left-behind and left-behind families in the rural areas of China. Methods A cross-sectional survey was conducted in urban areas of Zhejiang and rural areas of Henan in China. A total of 1648 caregivers of children aged 1–6 years were interviewed face-to-face by a pre-designed online questionnaire, and their families were grouped into four types: local urban, migrant, non-left-behind, and left-behind. Non-NIP vaccines included Hemophilus influenza b (Hib) vaccine, varicella vaccine, rotavirus vaccine, enterovirus 71 vaccine (EV71) and 13-valent pneumonia vaccine (PCV13). Log-binomial regression models were used to calculate prevalence ratios ( PR s) and 95% confidence intervals ( CI s) for the difference on immunization coverage of children, and knowledge and satisfaction of caregivers among families. The network models were conducted to explore the interplay of immunization coverage, knowledge, and satisfaction. Logistic regression models with odds ratios ( OR s) and 95% CI s were used to estimate the associated factors of non-NIP vaccination. Results The immunization coverage of all non-NIP vaccines and knowledge of all items of local urban families was the highest, followed by migrant, non-left-behind and left-behind families. Compared with local urban children, the PR s (95% CI s) for getting all vaccinated were 0.65 (0.52–0.81), 0.29 (0.22–0.37) and 0.14 (0.09–0.21) among migrant children, non-left-behind children and left-behind children, respectively. The coverage-knowledge-satisfaction network model showed the core node was the satisfaction of vaccination schedule. Non-NIP vaccination was associated with characteristics of both children and caregivers, including age of children (> 2 years- OR : 1.69, 95% CI : 1.07–2.68 for local urban children; 2.67, 1.39–5.13 for migrant children; 3.09, 1.23–7.76 for non-left-behind children); and below caregivers’ characteristics: family role (parents: 0.37, 0.14–0.99 for non-left-behind children), age (≤ 35 years: 7.27, 1.39–37.94 for non-left-behind children), sex (female: 0.49, 0.30–0.81 for local urban children; 0.31, 0.15–0.62 for non-left-behind children), physical health (more than average: 1.58, 1.07–2.35 for local urban children) and non-NIP vaccines knowledge (good: 0.45, 0.30–0.68 for local urban children; 7.54, 2.64–21.50 for left-behind children). Conclusions There were immunization disparities in non-NIP vaccines among migrant and left-behind families compared with their local counterparts. Non-NIP vaccination promotion strategies, including education on caregivers, and optimization of the immunization information system, should be delivered particularly among left-behind and migrant families. Graphical Abstract

Background Diabetes mellitus (DM) has become an increasingly significant global health challenge, with rising complications and mortality rates. Patients with DM are at a higher risk for advanced Cardiovascular-kidney-metabolic (CKM) syndrome, underscoring the importance of early detection and precise prevention strategies. The neutrophil percentage to albumin ratio (NPAR), a composite biomarker, may be indicative of inflammatory dysregulation and nutritional status in patients with advanced CKM and DM. This study aims to explore the association between NPAR and advanced CKM in patients with diabetes. Methods Data were derived from six National Health and Nutrition Examination Survey (NHANES) cycles (1999–2020), including 9375 adults. Multivariable logistic regression analyses were conducted to assess the association of NPAR with advanced CKM in diabetes, and restricted cubic spline (RCS) regression was further applied to explore potential nonlinear relationships. Subgroup analyses were performed to explore differences across various population factors. Feature selection was carried out using the Boruta algorithm, and predictive performance was evaluated through receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Results This study found a positive association between NPAR and risk of advanced CKM in diabetes. The RCS analysis revealed that this positive correlation was linear. Subgroup analysis showed no significant interactions across groups. Feature selection identified 22 relevant variables, and the machine learning model demonstrated excellent predictive accuracy (Area under curve: 0.873 and 0.872 for training and validation sets). The calibration curves and DCA affirmed the model's clinical relevance. Conclusions This study suggests that an elevated NPAR may serve as a potential marker for advanced CKM in patients with diabetes. It holds potential as an adjunct tool for detection and management of advanced CKM in patients with DM, offering valuable insights for clinical practice.

Crossbar resistive memory architectures enable high-capacity storage and neuromorphic computing, accurate retrieval of the stored information is a prerequisite during read operation. However, conventional electrical readout normally suffer from complicated process, inaccurate and destructive reading due to crosstalk effect from sneak path current. Here we report a memristive-photoconductive transduction (MPT) methodology for precise and nondestructive readout in a memristive crossbar array. The individual devices present dynamic filament form/fuse for resistance modulation under electric stimulation, which leads to photogenerated carrier transport for tunable photoconductive response under subsequently light pulse stimuli. This coherent signal transduction can be used to directly detect the memorized on/off states stored in each cell, and a prototype 4 * 4 crossbar memories has been constructed and validated for the fidelity of crosstalk-free readout in recall process.A memristive-photoconductive transduction (MPT) behavior was firstly observed in a memristor based on porous P3HT, which is the direct signal for cell resistance states detection, enabling accurate and nondestructive readout.