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This study aims to assess the social network and perceived social support among community corrections offenders in China and compare the relationship between different sub-dimensions of social support and offenders’ social adaptation.

A descriptive design combined with self-administered questionnaires was used. A total of 54 valid responses were analysed, with participants displaying the following demographic characteristics: male offenders outnumbered female offenders, probationers constituted the highest proportion among all community corrections offenders and educational levels were generally low. Descriptive statistics and correlation analysis were conducted. The Lubben Social Network Scale, Interpersonal Support Evaluation List Scale and Person at Re-education-centre Social Adaptation Scale were used to measure the variables.

Participants exhibited generally good levels of social support, with moderately extensive social networks and perceived support. All sub-dimensions showed significant positive correlations with social adaptation, with the highest correlation for appraisal support (r = 0.743, p < 0.01), followed by instrumental support (r = 0.714, p < 0.01), emotional support (r = 0.687, p < 0.01), informational support (r = 0.685, P < 0.01) and social networks (r = 0.426, p < 0.01).

This study expands the understanding of community corrections offenders in China by focusing on the underexplored Sichuan Province, using empirical methods for data collection. It addresses a gap in previous research by examining both structural and functional support for offenders rather than focusing solely on one aspect. The study uses two scales, rarely used in Chinese research, to measure structural and functional support, thereby enhancing research on social support for offenders. Practically, it explores the correlation between social support and re-entry, offering valuable insights for improving offenders’ reintegration into society.

These results underscore the critical role of social networks and perceived social support in reintegrating Chinese community corrections offenders. Distinct from prior research, this study used a more comprehensive scale, less commonly used by Chinese scholars, to enable detailed measurement and analysis, highlighting the significance of both structural and functional support in informing practice in related fields.

This study aims to explore the use of modified graphene (MG) in copper wire drawing lubricants to enhance their friction-reducing and anti-wear capabilities.

Graphene was modified using oleic and stearic acids to improve its dispersibility in lubricants. Various concentrations of MG were then introduced into a copper wire drawing lubricant to investigate their tribological performance. Wear mechanisms were evaluated with scanning electron microscopy, optical microscopy, Raman spectroscopy and energy dispersive spectroscopy (EDS).

The best concentration of MG is 1.5 Wt.%, at which the copper wire drawing oil exhibits a friction coefficient and wear rate of 0.085 and 2.11 × 10⁻⁶ mm³/Nm, respectively, representing decreases of 22.7% and 47.6% compared to the base oil. It was further found that the addition of 1.5 Wt.% MG to a copper wire drawing fluid with a water content of 70% resulted in a 30.3% reduction in friction coefficient compared to the base oil. Raman spectroscopy and EDS analysis confirmed that the MG tribo-film formed on the worn copper disc effectively minimized friction and wear.

This study analyzes the tribological performance of different concentrations of MG in copper wire drawing oils, establishing a basis for the application of MG in copper wire drawing fluids.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0399/

To enhance the fit, comfort and overall satisfaction of lower body attire for online shoppers, this study introduces a reclassification method of the lower body profiles of young females in complex environments, which is used in the framework of remote clothing mass customization.

Frontal and lateral photographs were collected from 170 females prior, marked as size M. Employing a salient object detection algorithm suitable for complex backgrounds, precise segmentation of body profiles was achieved while refining the performance through transfer learning techniques. Subsequently, a skeletal detection algorithm was employed to delineate distinct human regions, from which 21 pivotal dimensional metrics were derived. These metrics underwent clustering procedures, thus establishing a systematic framework for categorizing the lower body shapes of young females. Building upon this foundation, a methodology for the body type combination across different body parts was proposed. This approach incorporated a frequency-based filtering mechanism to regulate the enumeration of body type combinations. The automated identification of body types was executed through a support vector machine (SVM) model, achieving an average accuracy exceeding 95% for each defined type.

Young females prior to being marked as the same lower garment size can be further subdivided based on their lower body types. Participants' torso types were classified into barrel-shaped, hip-convex and fat-accumulation types. Leg profile shapes were categorized into slender-elongated and short-stocky types. The frontal straightness of participants’ legs was classified as X-shaped, I-shaped and O-shaped types, while the leg side straightness was categorized based on the knee hyperextended degree. The number of combinations can be controlled based on the frequency of occurrence of combinations of different body types.

This methodological advancement serves as a robust cornerstone for optimizing clothing sizing and enabling remote clothing mass customization in E-commerce, providing assistance for body type database and clothing size database management as well as strategies for establishing a comprehensive remote customization supply chain and on-demand production model.

To systematically characterize and objectively evaluate basic railway safety management capability, creating a closed-loop management approach which allows continuous improvement and optimization.

A basic railway safety management capability evaluation index system based on a comprehensive analysis of national safety management standards, railway safety rules and regulations and existing safety data from railway transport enterprises is presented. The system comprises a guideline layer including safety committee formation, work safety responsibility, safety management organization and safety rules and regulations as its components, along with an index layer consisting of 12 quantifiable indexes. Game theory combination weighting is utilized to integrate subjective and objective weight values derived using AHP and CRITIC methods and further combined using the TOPSIS method in order to construct a comprehensive basic railway safety management capability evaluation model.

The case study presented demonstrates that this evaluation index system and comprehensive evaluation model are capable of effectively characterizing and evaluating basic railway safety management capability and providing directional guidance for its sustained improvement.

Construction of an evaluation index system that is quantifiable, generalizable and accessible, accurately reflects the main aspects of railway transportation enterprises’ basic safety management capability and provides interoperability across various railway transportation enterprises. The application of the game theoretic combination weighting method to derive composite weights which combine experts’ subjective evaluations with the objectivity of data.