Search Results (1,055)

Background/Objectives: Puerto Rican Hispanic/Latino (PR H/L) men experience a heightened incidence and mortality rate of aggressive forms of prostate cancer. The underlying causes of this increased disease burden likely include a complex interplay of socio-economic and biological factors. This pilot study leveraged the first cancer tissue biobank at a Hispanic-Serving Institution (Puerto Rico BioBank) and aimed to provide an initial description of the genomic features of prostate cancer in 35 PR H/L men. Methods: Whole-exome and RNA sequencing were performed on prostate adenocarcinoma tumor samples to investigate the genomic features associated with prostate cancer. Results: Our analysis suggests that mutation profiles and gene expression pattern differences are observed in this population and may be associated with disease aggressiveness and progression. Notably, mutations in TP53 and TMPRSS2-ERG gene fusions, which are common in broader populations, were less prevalent in the PR H/L cohort. Conclusions: While this study contributes to the understanding of ethnicity-specific genetic factors in prostate cancer, underscoring the need for inclusive genomic studies, continued expansion to larger cohorts of patients under-represented in large genomic studies will be needed to more robustly characterize the full range of genomic features of prostate cancer. A broader understanding of the genomic features of prostate cancer in PR H/L men may lead to future opportunities for delivering more personalized prognoses and treatment options, helping to ensure that treatment advances and better outcomes are available to all patients. Full article

The escalating production of sewage sludge presents a significant environmental challenge, while the construction industry simultaneously seeks sustainable raw materials to improve its circularity. This review analyses the technical and regulatory landscape for valorizing SS within the Latvian construction sector, set against the divergent strategies of its Baltic neighbours. While global research confirms the technical viability of using SS in fired-clay bricks and as a supplementary cementitious material (SCM), national management approaches differ starkly. Lithuania has adopted widespread incineration, and Estonia has focused on advanced composting. In contrast, Latvia’s national strategy is failing, with 51% of its 2024 sludge production diverted to “temporary storage”. This review identifies this crisis as a unique opportunity, arguing that incorporating dewatered digestate into fired-clay bricks is the most logical and economically viable pathway for Latvia, as it leverages existing industrial infrastructure. The primary obstacle to this circular solution is not technical but legal, specifically the lack of a national “End-of-Waste” (EoW) criterion for sludge-derived construction materials. Therefore, this article proposes a strategic roadmap for Latvia, centred on developing this essential legal framework, creating a national sludge characterization map, and initiating a pilot project to bridge the research-to-industry gap. Although Latvia is the primary focus of this review, the regulatory, infrastructural and material constraints analysed here are common in many small and mid-sized countries, making the insights applicable beyond the Latvian context. Full article

The transition from a linear to a circular, resource-efficient economy is crucial in order to address the growing scarcity of resources, environmental degradation and the rapid increase in electronic waste and end-of-life products. Artificial Intelligence (AI) has emerged as a key enabling technology, capable of enhancing decision making, automation and optimization across Circular Economy (CE) pathways, including reuse, remanufacturing and recycling. This perspective paper presents a comprehensive and critical overview of AI’s role in supporting the transition to a circular, resource-efficient economy, introducing the Digital CE Architecture (DCEA-4) as a novel framework for integrating AI across the circular value chain. Recent advances in machine learning, deep learning and data-driven optimization are analyzed in the context of electronic waste and used battery management. This highlights how AI-based solutions can improve material recovery rates, reduce environmental impact and enhance system-level efficiency. Additionally, we examine major challenges concerning data availability, model generalization, industrial deployment, and explainability, together with relevant industrial case studies. Although AI offers substantial potential for optimizing circular resource systems, its environmental benefits must be balanced against the computational energy demands of large-scale AI models. This perspective discusses the potential rebound effects associated with AI deployment and emphasizes the importance of energy-efficient algorithms and sustainable digital infrastructures. By bringing together current developments and highlighting future opportunities, this paper aims to help researchers, practitioners and policymakers leverage AI to speed up the transition to sustainable, circular and resource-efficient systems. Full article

Construction accident reports contain rich causal evidence; however, their unstructured narratives make systematic analysis difficult. Recent advances in large language models (LLMs) have created new opportunities to leverage such information at scale. This study develops an integrated LLM–BERT–network framework for analyzing construction accident causation. Based on 347 official construction accident investigation reports, a DeepSeek-based pipeline with human-in-the-loop quality control was used to extract causal keywords describing direct and indirect causes, yielding 2572 keywords. A BERT-based semantic normalization procedure then consolidated synonymous expressions, reducing 811 deduplicated keywords to 104 normalized terms (an 87.2% reduction in vocabulary size). A manual sample-based evaluation further supported the reliability of the LLM-based extraction and BERT-based normalization procedures. The normalized keywords were further organized into a hierarchical taxonomy and used to construct a directed keyword-association network linking indirect and direct causes for structured relational analysis. To strengthen methodological rigor, additional validation and analytical experiments were conducted, including manual sample-based evaluation of keyword extraction, sensitivity analysis of normalization settings, and examination of representative failure cases. The results support the reliability and robustness of the proposed framework. The analysis indicates that behavior-related factors and management deficiencies occupy structurally important positions in the directed network. Overall, the findings suggest that construction accidents arise from the interaction of human, managerial, environmental, material, and technical factors rather than isolated single causes. Effective prevention therefore requires system-oriented interventions that strengthen worker competence, supervision, training, accountability, and hazard identification. Full article

Growth-Regulating Factors (GRFs) are plant-specific transcription factors that, together with GRF-Interacting Factors (GIFs) and under post-transcriptional control by miR396, coordinate cell proliferation and expansion to define organ size. This GRF–GIF–miR396 regulatory module holds major agronomic importance, shaping leaf architecture, source–sink relationships, nitrogen-use efficiency (NUE), and stress resilience in crops. Upregulation of specific GRF genes has been shown to enhance leaf width, yield potential, and other important agronomic traits. Synthetic GRF–GIF chimeras have revolutionized regeneration and genome editing in multiple crop species, revealing both successes and species-specific limitations. Expanding GRF/GIF gene families and functional analyses across various crops highlight conserved developmental functions with variable outcomes, including improved drought and salinity tolerance through sustained canopy growth. This review, focused on crop systems, integrates current advances in GRF-regulated leaf development, their contributions to abiotic and biotic stress adaptation, and the emerging utility of GRF–GIF chimeras. Finally, it outlines key challenges and future opportunities for leveraging GRFs in designing climate-resilient, high-efficiency crop ideotypes. Full article

Engine oil degradation critically influences the performance, efficiency, and longevity of internal combustion engines. Conventional mileage or time-based replacement schedules often result in premature oil changes or delayed servicing, both of which compromise engine health and increase costs. This review examines recent advances in real-time oil condition monitoring and evaluates the feasibility of a low-cost microcontroller-based system that integrates physical sensors with machine learning models for continuous on-board oil health assessment. Drawing on established techniques from industrial lubrication monitoring, we propose an experimental framework that leverages electrical engineering principles, including sensor interface, analog front-end design, signal acquisition, and embedded AI deployment to enable accurate, affordable, and scalable oil health diagnostics. The review highlights opportunities for innovation in embedded systems and electrical engineering design, positioning AI-driven monitoring as a practical solution for predictive automotive maintenance. Full article

The Industrial Metaverse (IM) integrates digital twins, IoT, AI, and immersive technologies to create interconnected, data-driven production environments. While its potential for enhancing efficiency and collaboration is widely acknowledged, its operationalization, particularly in alignment with sustainability goals, remains underexplored. This paper investigates how manufacturing firms transition from isolated pilots to strategic adoption of IM technologies, using a Digital Maturity Model as an analytical lens. Drawing on two industrial case studies, a university-based smart production lab, and expert roundtable discussions, we identify key barriers such as interoperability, governance, and skills gaps, alongside opportunities for circular material flows and resource optimization. Based on these insights, we propose three pathways for implementation: (1) digital maturity and Infrastructure Readiness, (2) organizational transformation for metaverse-enabled workflows, and (3) strategic value realization through sustainable business models. The study contributes a roadmap for managers and policymakers seeking to leverage the IM as a catalyst for resilience, circularity, and long-term competitiveness in smart manufacturing ecosystems. Full article

Background/Objectives: Early detection of cardiometabolic irregularities is crucial for averting cardiovascular illness; however, demographic cohorts that consistently engage with healthcare systems like habitual blood donors are inadequately leveraged for metabolic monitoring. Methods: This study performed lipid profiling and cardiovascular risk assessment among blood donors identified with visually lipemic plasma during routine screening, in order to explore metabolic variability within this selected donor subgroup. Of 13,818 screened donors, 160 with lipemic plasma were included, and multivariable and machine-learning analyses were restricted to 90 donors with complete clinical data. Results: We observed substantial variability in triglyceride levels, with males displaying higher and more dispersed values. Correlation analysis indicated that triglycerides were associated with BMI and composite cardiovascular risk metrics, while age was the strongest contributor to the calculated 10-year cardiovascular risk score. Using a Random Forest classifier, elevated triglyceride levels were predicted with an AUC of 0.86; however, given the limited sample size, this analysis should be interpreted as exploratory and proof-of-concept in nature. Conclusions: In this selected subgroup of donors with lipemic plasma, clinically relevant hypertriglyceridemia was frequently observed. These findings suggest that routine donor data may provide opportunities for targeted metabolic monitoring, although the results cannot be generalized to the broader blood donor population. Further studies in larger and more representative cohorts are warranted. Full article

Neuroinflammation is a central hallmark of numerous neurological disorders, including Alzheimer’s disease, Parkinson’s disease, traumatic brain injury, and spinal cord damage. Its persistent and dysregulated nature not only accelerates neuronal loss but also impedes endogenous repair, posing a major challenge for effective therapeutic intervention. Recent advances in nanobiotechnology have opened transformative opportunities to modulate neuroinflammation with unprecedented precision while simultaneously supporting neural regeneration. This review highlights emerging nanomaterial-based strategies including lipid-based, polymeric, inorganic nanoparticles designed to traverse the blood–brain barrier (BBB), deliver anti-inflammatory agents, modulate immune cell behavior, and attenuate glial activation. Extending beyond nanoparticle-based delivery systems, recent advances also emphasize the integration of nanomaterials into biomimetic architectures to provide structural and functional cues for neural repair. We further summarize how these functional nanostructured scaffolds, such as extracellular matrix (ECM) mimetic, nanofibrous and conductive hydrogels, are being leveraged in neural tissue engineering to direct stem cell fate, promote axonal outgrowth, and rebuild damaged neuroarchitectures. Moreover, pharmacokinetics, biodistribution, safety, clinical trials, regulatory considerations and limitations of nanotherapeutics in neurodegenerative diseases are discussed. By outlining the current progress, mechanistic insights, and translational challenges, this review underscores the potential of nanobiotechnology-enabled therapeutics to revolutionize the treatment of neuroinflammatory conditions and advance next-generation neural repair technologies. Full article

Many universities acknowledge a responsibility to address climate change and are actively working to meet this goal in academic programs and undergraduate curricula. This paper provides insights from interviews with university leaders from 20 American and Canadian institutions pursuing climate action via education. Interviewees described a range of initiatives, including new General Education requirements (GEs), cross-disciplinary courses, domain-specific classes, and certificate programs, as well as the establishment of dedicated climate schools. Pathways for curricular change include academic senate climate committees, top-down support from university leadership, bottom-up advocacy and activism from faculty and students, and opportunities to leverage evolving systems. To increase climate-teaching capacity, interviewees reported instituting team teaching, supporting faculty learning opportunities, hiring faculty with climate expertise, and partnering with organizations outside academia. Qualitative data collected during these interviews were thematically coded, revealing significant takeaways including the need to appropriately reward faculty for climate-teaching efforts and to recognize the complementary virtues of high-level courses like GEs with broad reach versus deeper dives for climate-related majors with targeted reach. This paper synthesizes advice from educators who succeeded in increasing climate education at their institutions and concludes with suggestions on how to integrate climate more fully into academia’s educational mission. Full article

Retaining the green workforce, employees driving sustainability and environmental innovation, is essential for organizational resilience and long-term environmental goals. While prior Green HRM research has primarily relied on survey-based methodologies and theoretical frameworks to examine retention factors, these approaches lack predictive capability and fail to provide actionable, employee-specific insights. This study advances beyond descriptive and correlational analyses by employing explainable artificial intelligence (XAI) to develop a transparent, data-driven framework for identifying attrition drivers and quantitatively evaluating retention strategies. Unlike existing studies that rely on self-reported perceptions, our approach leverages objective HR data and machine learning to predict individual-level attrition risk with calibrated probabilities. Leveraging the IBM HR Analytics dataset as a proxy for sustainability-focused roles, we construct an interpretable logistic regression model with strong predictive performance and isotonic regression calibration. Global and local interpretability techniques, including SHAP, LIME, and permutation importance, show that non-monetary factors, such as excessive overtime, frequent business travel, and limited promotion opportunities, have a greater impact on turnover risk than salary levels. These findings align with Green Human Management (Green HRM) principles, which emphasize work–life balance and employee well-being. Crucially, our policy simulation framework, absent from prior Green HRM studies, demonstrates that eliminating overtime could reduce predicted attrition probability by 17.35% for affected employees, potentially retaining 31 staff members, substantially outperforming modest salary adjustments. This work expands the value of predictive AI into HR analytics by consolidating HR analytics with Green HRM through a novel methodology that bridges the gap between prediction and actionable intervention. It represents the first systematic integration of XAI-based predictive modeling with counterfactual policy simulation in environmentally conscious sustainable organizations. Full article

Urban expansion presents significant challenges and opportunities for ecological conservation in developing countries, particularly in regions such as the Table Bay Nature Reserve in Cape Town, South Africa, where urban development interfaces with sensitive ecosystems. This article examines the complex dynamics between urban growth and ecological implications in this unique landscape, employing multi-temporal remote sensing techniques to analyze changes over time. By investigating the historical trajectory of urbanization in Table Bay, alongside its impacts on biodiversity and ecosystem services, we aim to underscore the urgent need for sustainable urban planning and conservation strategies. To analyze land use/land cover (LULC) dynamics over a 24-year period, this study leveraged a time series of satellite imagery processed within the Google Earth Engine (GEE) platform. Data can be accessed using their respective collection IDs within the GEE platform. The use of remote sensing tools aligns with Sustainable Development Goal (SDG) 15, which focuses on the protection, restoration, and sustainable use of terrestrial ecosystems. Urban encroachment analysis indicates that approximately 0.324 km² of built-up area expanded directly within the reserve boundary, highlighting a measurable degree of infringement into protected zones. The dominance of built-up and bare land classes highlights the early encroachment of urban infrastructure and anthropogenic disturbance, setting the stage for subsequent land cover transformations observed in later years (2012 and 2024). These findings demonstrate a persistent trend of urban encroachment and ecological alteration within the Table Bay Nature Reserve. With the increase in global population levels, urban expansion into protected conservation areas has become a critical environmental concern, threatening biodiversity globally. This challenge is particularly acute in developing countries as seen in regions like the Table Bay Nature Reserve in Cape Town, South Africa, where urban development is interfaced with sensitive ecosystems. Full article

Utilising data from the selected companies listed on the Johannesburg Stock Exchange and using the Panel Autoregressive Distributed Lag (ARDL) specifically employing the Pooled Mean Group approach, this study examines the cointegrating and causal relationships among firm liquidity, performance and firm leverage. The results reveal a negative and significant long-run and short-run relationship between profitability and leverage. Conversely, higher leverage is found to diminish firm performance, consistent with trade-off theory implications regarding financial distress costs. On liquidity, results revealed a bidirectional long-run relationship among liquidity, leverage, and firm value as measured by Tobin’s Q. Also, liquidity plays a pivotal moderating role, where firms with stronger liquidity and profitability exhibit reduced reliance on external debt, highlighting the interplay between financial health and capital structure decisions. Additionally, a positive bidirectional relationship between Tobin’s Q and leverage suggests that growth opportunities and market valuation influence firms’ debt utilisation. The error correction terms confirm stable long-run equilibrium and moderate adjustment speeds. These results contribute to the understanding of optimal capital structure by integrating liquidity and performance factors and provide practical insights for corporate financial management and policy formulation. Full article

The integration of artificial intelligence (AI) into early childhood education presents both opportunities and challenges to longstanding Froebelian pedagogies, particularly regarding child agency and nature-based play. This mixed-methods study explores this tension within the Chinese context. It examines how Chinese Froebelian practitioners perceive the alignment between AI tools and core principles and investigates the strategies they employ to navigate the integration of technology with humanistic educational values. The survey results, from 50 practitioners, revealed that AI can support autonomous and holistic learning, yet significant concerns persisted regarding the displacement of sensory and nature-based experiences. Follow-up interviews uncovered a practitioner-led “dual-track integration” approach, which strategically blends physical manipulation and nature engagement with AI-enabled personalization. Through an iterative dialogue between theory and data, this study develops and refines the “dual-track integration” framework as an empirically grounded, sensitizing model. This framework offers principled strategies for hybrid learning that uphold the developmental primacy of play. Situated within the discourse on Sustainable Development Goal 4 (quality education) and Goal 10 (reduced inequalities), the analysis highlights AI’s dual potential to advance or hinder equity. By examining China’s hybrid position, which combines advanced digital infrastructure with persistent equity gaps, this research highlights the critical role of educator agency and pedagogical design in leveraging AI to advance equitable, high-quality early childhood education. Full article

Scientific collaboration is increasingly needed to address complex research challenges, yet identifying promising partners in the absence of prior co-authorship remains difficult. We present a decision-support pipeline for discovering researchers who have not previously worked together and whose collaboration is unlikely to emerge without deliberate intervention or institutional incentives. The approach leverages document-level semantic representations to estimate proximity between publications, aggregates these similarities at the author level, and surfaces collaboration opportunities that are not evident from the co-authorship graph. To support interpretation by decision makers, a separate LLM module proposes potential joint research directions, which are subsequently annotated with multi-label fields of study. We evaluate the pipeline through an institutional case study, analyzing 7531 publications from 2009 to 2024 using retrospective, temporally shifted windows. While only a small fraction of suggested pairs materialized spontaneously in subsequent periods, the collaborations that do emerge exhibit strong semantic alignment with the computed recommendations (high cosine similarity) and substantial thematic overlap. These results indicate that semantic proximity can act as an early indicator of latent complementarity between researchers without prior ties, supporting intentional institutional mediation and complementing topology-driven approaches that predict links under passive evolution. Full article