Metropolitan University

This article explores the capabilities, opportunities, and motivations (COM) for the employment of people with disabilities (PWD) of small and medium-sized enterprises (SMEs) in Norway and the Netherlands. This COM-B (behavior) model provides useful insights for developing demand-side policies aimed at promoting PWD’s labour-market participation. Four groups of SMEs are distinguished: inclusive SMEs, non-inclusive SMEs, SMEs with the intention to employ PWD (inclusion-willing), and SMEs with past experience in employing PWD (inclusion-experienced). Based on survey data, we found that whereas non-inclusive SMEs score significantly lower on capabilities and opportunities than inclusive SMEs, inclusion-willing and inclusion-experienced SMEs hardly differ from inclusive SMEs. No significant differences were found regarding motivations of the four groups. In addition, the groups’ COM factors hardly differ when they are compared across both countries. Nevertheless, SMEs in both countries do differ with regard to their specific motivations to employ PWD. Implications for policies and employer services are discussed.

Parenting children with special educational needs presents multiple and unique challenges, often resulting in elevated emotional distress among caregivers. This study examined the efficacy of a Coping and Parenting Competence-Early Intervention to improve mental well-being in parents of preschool children with special educational needs. In this parallel-group randomised controlled trial, 83 eligible parents were randomly allocated to either a 7-week intervention group (n = 44) or a treatment-as-usual control group (n = 39). Results showed significant improvements in the intervention group for anxiety, stress, and perceived parenting competence after receiving the intervention, with the reduction of anxiety and stress, as well as the enhancement of perceived parenting competence maintained at one-month short-term follow-up. No significant improvements were detected in the control group. Although no direct group-by-time effect was observed for depression, changes in perceived parenting competence were negatively associated with changes in anxiety, stress, and depression. These findings indicate that the intervention effectively enhanced perceived parenting competence and revealed both direct and indirect effects on mental health symptoms, demonstrating its overall effectiveness in improving parents’ mental well-being. Our findings also suggest that perceived parenting competence could be a resilience factor for parental stress in parents of children with special needs. This study was registered retrospectively in the Chinese Clinical Trial Registry on 6 June 2025 (Registration number: ChiCTR2500103881: https://www.chictr.org.cn/showprojEN.html?proj=274488 ).

ABSTRACT Rings … Through Rings II is a performance research system that engages the historical erasure of Hong Kong’s military cartographies through robotic drawing, laser-engraved discs, and AI-mediated sound. Central to the project is the concept of frictional historiography: a posthuman methodology that approaches memory through misalignment, material resistance, and machinic failure. Instead of following representational models of digital heritage, the work uses noise, drift, and breakdown as compositional tools. In this recursive ecology, comprising robotic plotters, laser-engraved discs, and AI synthesis, memory is not retrieved but enacted through instability. By integrating Somax2 and RAVE with material feedback loops, the system treats servo lag and stylus drag as generative constraints. Failure and sonic hallucination function as epistemic strategies, transforming the archive into a volatile, performative terrain. The project contributes to posthuman performance, machine listening, and experimental media archaeology by advancing misremembrance as a method for engaging with histories shaped by loss, erasure, and uncertainty.

Under global warming, the intensification of extreme precipitation poses a critical threat to dense coastal metropolises like Hong Kong, where complex terrain and high population density amplify flood risks. While daily rainfall trends are well-documented, existing studies largely focus on coarse regional simulations or daily-scale metrics, leaving a gap in understanding the granular evolution of sub-daily extremes and their specific drivers within complex intra-urban environments. This study investigates the spatiotemporal characteristics of hourly extreme rainfall in Hong Kong from 1991 to 2024, utilizing continuous hourly records from approximately 80 Geotechnical Engineering Office (GEO) stations. We spatially categorize the territory into four distinct subregions—Hong Kong Island, Kowloon, New Territories, and Lantau—to examine regional heterogeneity. The analysis employs indices including hourly precipitation percentiles (95th, 97.5th, 99th, and 99.9th) and Maximum Rolling Rainfall (MRR) across 1, 3, 6, and 12-hour durations to capture both short-term intensity and cumulative event magnitude. Furthermore, a Structural Equation Modelling (SEM) framework is developed to disentangle the contributions of key drivers, specifically quantifying the impact of urbanization (e.g., built-up area, patch density) alongside large-scale climate variability (e.g., ENSO, monsoons) and socioeconomic factors. We hypothesize that short-duration rainfall intensity (1-hour MRR) exhibits a more significant upward trend than longer durations, particularly in highly urbanized sectors like Kowloon. The SEM analysis is expected to reveal that urbanization acts as a primary localized driver exacerbating extreme rainfall frequency and intensity, distinct from background climatic warming. These findings will provide essential insights for refining urban drainage standards and disaster mitigation strategies in high-density mountainous cities.

Large Language Models (LLMs) are increasingly being integrated into recommender systems, offering contextual reasoning, cross-domain adaptability, and natural language interaction. However, their adoption also introduces vulnerabilities such as prompt injection, semantic poisoning, and shilling attacks, which can distort recommendations and erode user trust. Addressing these risks is essential for the safe deployment of LLM-based recommenders. We propose RoLLMRec, a defense oriented architectural framework and evaluation methodology for LLM-based recommender systems that integrates prompt filtering, retrieval augmented grounding, trust aware scoring, and an auditing feedback loop. RoLLMRec improves robustness under the evaluated prompt level and semantic adversarial settings, while multimodal support is included at the architectural level only and is not empirically evaluated in the current experimental setup.RoLLMRec unifies five core components: (1) prompt shielding and input filtering to detect and block adversarial instructions; (2) retrieval-augmented generation to enrich factual grounding and reduce hallucination; (3) multimodal LLM encoding for text, metadata, and image inputs; (4) trust-aware scoring and Top-K ranking; and (5) adaptive feedback loops for continual learning. Evaluations on benchmark datasets such as Yelp, MovieLens, and Amazon Books show that RoLLMRec surpasses BERT4Rec, RecVAE, and LightGCN, improving NDCG@10 and HR@10 by up to 6% and 5%, respectively. Under a 10% prompt-injection attack, it maintains a Robust Hit Rate (RHR@10) above 0.63 and a Perturbation Sensitivity Index (PSI) below 0.135, achieving 15%–25% higher resilience. It also sustains a Semantic Stability Score (SSS) above 0.60 in zero-shot cross-domain transfer, confirming stable semantic intent.