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Urban inequality, the housing crisis and deteriorating water access in US cities

The housing unaffordability and cost-of-living crisis is affecting millions of people in US cities, yet the implications for urban dwellers’ well-being and social reproduction remain less clear. This Article presents a longitudinal analysis of household access to running water—a vital component of social infrastructure—in the 50 largest US cities since 1970. The results indicate that water access has worsened in an increasing number and typology of US cities since the 2008 global financial crash, disproportionately affecting households of color in 12 of the 15 largest cities. We provide evidence to suggest that a ‘reproductive squeeze’—systemic, compounding pressures on households’ capacity to reproduce themselves on a daily and societal basis—is forcing urban households into more precarious living arrangements, including housing without running water. We analyze the case study of Portland (Oregon) to illustrate the racialized nature of the reproductive squeeze under a housing crisis. Our insights reveal that plumbing poverty—a lack of household running water—is expanding in scope and severity to a broader array of US cities, raising doubts about equitable progress towards Sustainable Development Goals for clean water and sanitation for all (SDG 6) and sustainable cities (SDG 11) in an increasingly urbanized United States.

How effective is CBCT-guided endodontic access over ‘brain-guided’ accesses, and is this a likely addition to the general dental practitioner’s armamentarium?

Non-randomised prospective single-arm controlled clinical trial. The main inclusion criteria for both guided and freehand access groups was pulp canal obliteration (PCO). All teeth underwent cone-beam CT (CBCT) scan prior to access. Null hypothesis was that there is no difference in technical failure between guided and unguided access. The primary outcome was canal location success as a discrete measure (found, not found, perforated). The secondary outcome was conservativeness of drill pathway using discrete measures (optimal precision, acceptable precision, technical failure (included canal not found and perforation)). Patients underwent one subsequent annual follow-up.

Leveraging large language models to assist philosophical counseling: prospective techniques, value, and challenges

Large language models (LLMs) have emerged as transformative tools with the potential to revolutionize philosophical counseling. By harnessing their advanced natural language processing and reasoning capabilities, LLMs offer innovative solutions to overcome limitations inherent in traditional counseling approaches—such as counselor scarcity, difficulties in identifying mental health issues, subjective outcome assessment, and cultural adaptation challenges. In this study, we explore cutting‐edge technical strategies—including prompt engineering, fine‐tuning, and retrieval‐augmented generation—to integrate LLMs into the counseling process. Our analysis demonstrates that LLM-assisted systems can provide counselor recommendations, streamline session evaluations, broaden service accessibility, and improve cultural adaptation. We also critically examine challenges related to user trust, data privacy, and the inherent inability of current AI systems to genuinely understand or empathize. Overall, this work presents both theoretical insights and practical guidelines for the responsible development and deployment of AI-assisted philosophical counseling practices.

Professional demand analysis for teaching Chinese to speakers of other languages: a text mining approach on internet recruitment platforms

The rapid development of international education in China highlights the growing importance of employment analysis in Teaching Chinese to Speakers of Other Languages (TCSOL). This study explores the enterprise demands for TCSOL professionals using text mining techniques to analyze recruitment data collected from four major platforms: Boss Zhipin, Zhaopin.com, 51job.com, and Liepin.com. Combining descriptive statistics, LDA topic modeling, BERT-BiLSTM-CRF-based named entity recognition, and co-occurrence network analysis were used. Results show that there is a high demand for TCSOL professionals, especially for small-scale enterprises located in first-tier cities such as Beijing, Shanghai, Guangzhou, and Shenzhen. Employers tend to favor candidates with at least a bachelor’s degree and 1–3 years of work experience. The topic model highlighted three central themes in job descriptions, emphasizing a shift toward a more diverse skill set. Named entity recognition identified essential attributes such as “communication ability”, “teaching experience”, “bachelor’s degree or above” and “responsibility” as core recruitment requirements. The co-occurrence network analysis revealed the importance of “teaching” and “priority” as core skill nodes. Time series analysis showed seasonal fluctuations in recruitment demand, peaking during spring recruitment and graduation periods. A hierarchical model of talent demand and development in TCSOL is proposed, integrating the perspectives of employers, job seekers, educators, and policymakers. This study provides valuable insights for aspiring TCSOL professionals, offering guidance to better align talent training with market needs and improve employment prospects.

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