Search Results (2,716)

Under carbon peaking and neutrality goals, building integrated with photovoltaics and energy storage system clusters (BIPECs) enable efficient on-site renewable energy use and can act as dispatch units for the public grid. However, BIPECs face significant uncertainties and are still under development. This study proposes a decentralized cooperative power dispatch model coupling a multi-agent proximal policy optimization (MAPPO) algorithm and offline digital twin (ODT) technology to optimize the photovoltaic (PV) power consumption of clusters despite limited data availability. An integrated BIPEC energy system model is established, and by leveraging the multi-agent system model of the BIPEC, the decentralized dispatch problem is converted into a fully cooperative multi-agent reinforcement learning (MARL) problem. A simulation-assisted ODT framework constructs a digital environment for MAPPO to augment data, conduct MAPPO training, and optimize the reward function, thereby obtaining power dispatch strategies. The results show that the proposed optimization model can obtain dispatch strategies that reflect a high degree of collaboration, reducing the cumulative power supply from the public grid by 0.55–2.56% per month compared to the non-cooperative self-generating and self-using strategy. This study presents the application of MARL in BIPECs by introducing a decentralized collaborative power dispatch methodology for building clusters, enhancing building energy efficiency and facilitating flexible collaborative power dispatch. Full article

Background/Objectives: The expansion of direct-to-consumer (DTC) genetic testing has generated civilian genomic databases containing tens of millions of profiles, some of which may be available, under specific conditions, for criminal investigations. Meanwhile, artificial intelligence (AI) is reshaping forensic genetics through applications such as kinship inference, DNA mixture deconvolution, probabilistic phenotyping, and the prioritization of investigative leads. This review examines the scientific, legal, and ethical implications of the convergence between DTC genetic databases, forensic investigative genetic genealogy (FIGG), and AI-assisted forensic analysis. Methods: This article presents a multidisciplinary narrative review at the intersection of forensic genomics, FIGG, artificial intelligence, genomic data governance, and bioethics, with particular attention to French, European, and international regulatory frameworks. Results: Six major dimensions structure the field: (i) the current state of forensic genomic technologies, including STRs, SNPs, and next-generation sequencing; (ii) the contribution of AI to forensic genetics and FIGG; (iii) the governance of large-scale genomic data; (iv) regulatory fragmentation across jurisdictions; (v) the principal bioethical tensions raised by the forensic use of DTC genetic databases; and (vi) future governance needs and operational recommendations. Across these dimensions, three findings emerge. First, genealogical matches and AI-supported outputs should be understood primarily as investigative leads rather than autonomous judicial evidence. Second, the relational nature of genomic data exposes non-consenting relatives to potential forensic scrutiny, thereby challenging traditional models of individual consent and privacy. Third, the absence of harmonized standards for validation, transparency, and oversight remains a major obstacle to legal certainty, judicial admissibility, and public legitimacy. Conclusions: The forensic use of DTC genetic databases should not be understood as a purely technical extension of conventional DNA profiling. It reflects a broader transformation in the relationship between genomic knowledge, criminal investigation, and fundamental rights. Its long-term legitimacy and operational viability will depend on the combined strength of scientific reliability, legal proportionality, ethical safeguards, and meaningful democratic oversight. Full article

Decommissioned airfields are increasingly recognized as strategic sites for ecological regeneration, climate adaptation, and the creation of new public spaces. However, research on their transformation has predominantly focused on the environmental performance of Nature-based Solutions (NBS), often overlooking the role of inherited spatial morphology in structuring regeneration processes and outcomes. This paper proposes an AI-assisted, morphology-based proto-ontological framework for analyzing and designing post-airfield architecture. The framework was developed through the inductive and comparative analysis of a corpus of 32 urban post-airfield regeneration projects, from which recurrent inherited morphologies, transformation actions, spatial devices, and NBS were identified and structured into a relational sequence. The framework was then applied to two contrasting case studies: Maurice Rose Airfield Park (Frankfurt) and Xuhui Runway Park (Shanghai); these were selected for their different transformation logics. The results show that similar airfield morphologies can generate markedly different climatic, ecological, social, and memory-related outcomes depending on how they are transformed and linked to NBS. The study demonstrates that inherited airfield morphologies are not passive remnants but operative spatial structures, and that NBS should be understood as spatially embedded and form-generating design components. The proposed proto-ontology offers a transferable analytical model and a basis for future computational and generative design applications. Full article

The Internet of Vehicles (IoV) is reshaping intelligent transportation through pervasive connectivity, real-time data exchange, cooperative perception, and vehicle–edge–cloud services, while also expanding cybersecurity and privacy risks across heterogeneous cyber–physical environments. This paper presents a PRISMA 2020-informed systematic review of IoV security and privacy protection research. A cross-layer and lifecycle-oriented analytical framework is developed by integrating a four-layer IoV architecture—sensing layer, network access layer, coordinative computing layer, and application layer—with a five-stage data lifecycle covering data collection, transmission, storage, usage, and disposal. Based on this framework, the paper examines representative threat surfaces, vehicle-to-everything (V2X) communication security, public key infrastructure (PKI) based authentication, trust management, privacy-preserving data sharing, intrusion detection, active defense, and AI-assisted security analytics. Privacy-preserving mechanisms, including differential privacy, federated learning, blockchain, homomorphic encryption, and secure multi-party computation, are further compared in terms of deployment layer, lifecycle stage, real-time suitability, and representative performance evidence. In addition, the review discusses the engineering relevance of UNECE WP.29 R155/R156, ISO/SAE 21434, and related national standards, with emphasis on compliance evidence, over-the-air (OTA) governance, supply-chain coordination, and lifecycle cybersecurity management. The review shows that no single protection mechanism can simultaneously satisfy the requirements of real-time performance, scalability, privacy preservation, trustworthiness, and regulatory compliance in dynamic IoV environments. Future research should emphasize lightweight and adaptive protection, cross-layer trust coordination, privacy–utility co-optimization, trustworthy AI-assisted security operations, and evidence-based lifecycle governance. This review provides a structured reference for researchers and a practical basis for secure and privacy-aware IoV system design. Full article

Objective: Minimally invasive surgery in Hirschsprung disease (HSCR) management was introduced in the mid-1990s. Despite decades of clinical application of various laparoscopic approaches, there remains a paucity of high-powered prospective studies and comprehensive systematic reviews in the literature. This study aimed to systematically review and summarize published techniques and outcomes of laparoscopic- and robotic-assisted surgery in HSCR. Methods: A systematic literature review was conducted using PubMed and the Cochrane Library. Studies reporting technical and outcome data of laparoscopic- or robotic-assisted surgery for HSCR were included. Data extraction and analysis were performed in accordance with the PRISMA 2020 guidelines. Parameters of interest included surgical technique, age at primary pull-through (PT), operative time, and functional outcomes. Outcomes of laparoscopic- versus robotic-assisted Yancey–Soave PT were compared. Results: A total of 700 publications were screened, of which seven studies met the inclusion criteria. Data from 556 patients were analyzed. A total of 338 underwent laparoscopic-assisted, and 218 underwent robotic-assisted pull-through. Large variability of the reported transanal resection technique (modified Yancey–Soave PT) was reported. Four studies reported functional outcomes in patients aged over four years. Three studies directly compared laparoscopic- and robotic-assisted PT; two reported no difference in the incidence of postoperative Hirschsprung-associated enterocolitis (HAEC). Functional outcomes were assessed using the Krickenbeck classification in three studies and the bowel function score in one study, with no significant differences reported in patients aged >4 years. Conclusions: Laparoscopic- and robotic-assisted Yancey–Soave PT appears to be safe for HSCR. Large variability in the applied surgical technique—despite being commonly classified as modified Yancey–Soave PT—as well as heterogeneity in the bowel function assessment, limit direct comparability between studies. To date, no single minimally invasive approach has demonstrated clear superiority over others. Prospective, randomized controlled studies are required to enable robust comparative evaluation of techniques, overall costs, and outcomes. Full article

During the COVID-19 pandemic, family support for older adults living with disabilities was disrupted due to “Shelter-in-Place (SIP)” orders. This study examined the impact of SIP policies on caregiving changes for people with disabilities who were not married. We used the National Health and Aging Trends Survey (NHATS) round 10 data and previously published data regarding SIP policies. The study sample included NHATS community-dwelling respondents who needed assistance with activities of daily living (ADLs) or instrumental activities of daily living (IADLs) and who were not married (n = 512). More than half of people (55.1%) reported no change in receiving help, approximately one-third (36.7%) reported receiving less help, and 8.2% reported receiving more help during COVID-19 than before. Compared with people who lived in areas that had fewer than 30%, people living in areas with 30–59% and with 60% or more of SIP days had 12 percentage points lower probability of reporting they received more help during COVID-19 (p = 0.02 for 30–59% and p = 0.03 for ≥60%). It is crucial to address caregiving needs during public health emergencies and to examine how policy disruptions impact support for individuals reliant on family assistance. Full article

Background: Confident chemical annotation in nontarget small-molecule mass spectrometry critically depends on the availability of high-quality tandem mass spectral (MS²) reference libraries. While community efforts have driven significant expansion of open-access repositories, technical challenges in assembling standardized, metadata-rich records continue to limit broader participation, underscoring the need for improved computational tools to assist contributors. Methods: To promote the creation and sharing of standardized reference MS² spectral records, we have developed Librarian, a free, open-access web application designed for rapid and scalable assembly of high-resolution MS² libraries. Librarian integrates automated retrieval and harmonization of chemical identifiers and metadata from PubChem, compound mixture design for high-resolution mass spectrometry (HRMS) acquisition, and assembly of curated MS² spectra into repository-ready records compatible with public spectral databases. Results: Through a simple in-browser interface, Librarian offers a flexible end-to-end workflow compatible with popular open-source pre-processing tools to lower technical barriers and facilitate broader community participation in library development. As a demonstration, we used Librarian to create and deposit a spectral library comprising over 1500 new MS² records into MassBank, which was further applied in retrospective analysis of environmental datasets. Conclusions: Librarian streamlines the creation of standardized, metadata-rich and repository-ready MS² reference records. Addressing a key bottleneck in community spectral library development and sharing, Librarian supports the continued growth of open-access resources for metabolomics, exposomics, and environmental mass spectrometry. The Librarian web application is publicly accessible via the SciLifeLab Serve platform. Full article

Authentication is essential to hold users accountable across online services. Conventional authentication systems rely on centralized architectures or third-party identity providers, which, however, introduce single points of failure, privacy concerns, and limited user autonomy. Conversely, fully decentralized authentication frameworks often struggle to provide reliable identity attestation mechanisms. This makes them vulnerable to Sybil attacks and self-asserted claims, while limiting their interoperability with trust-based systems. This paper presents dAuth, a hybrid blockchain-based authentication architecture based on Ethereum smart contracts to provide cryptographic tokens that enable authentication to services. These tokens, anchored to the smart contract, are derived by users from institutionally certified base credentials issued by an accredited verifying authority and enable authentication to services without further involvement of the authority. Each token is cryptographically bound to a specific service, constrained in scope and duration, and verifiable off-chain through data and cryptographic commitments provided by the user. No plaintext personal information is published on-chain: identity attributes are committed as cryptographic digests, which anchor certified identity data on-chain while keeping the underlying personal information private and auditable. This design removes the verifying authority from the authentication process, as all authentication steps are assisted by the user-controlled smart contract. The verifying authority’s role is limited to initial identity certification and exceptional update procedures. The result is a privacy-preserving and verifiable hybrid authentication framework that leverages the cryptographic security properties of the underlying blockchain infrastructure and inherits its scalability characteristics. The proposed design has been implemented and experimentally evaluated on the Ethereum platform, addressing public blockchain-specific challenges such as scalability constraints and transaction costs to ensure practical deployment. Full article

Higher education policy on generative artificial intelligence has developed rapidly, yet much of this development remains stronger on governance, permission, disclosure, and assurance than on pedagogy. Universities increasingly move beyond blanket prohibition by distinguishing between restricted and permitted contexts, requiring acknowledgement of tool use, and introducing verification mechanisms to protect authorship and understanding. However, publicly visible institutional approaches appear less developed in providing structured, student-facing workflows that guide responsible AI engagement during assessment completion. This article, informed by a bounded qualitative document analysis, uses the term pedagogical middle layer to describe the process guidance needed between institutional permission settings and academic-integrity or misconduct procedures. Drawing on recent literature and a purposive scan of selected publicly available university policy and guidance documents, the paper argues that current public-facing models are often effective at defining boundaries but less explicit in guiding disciplined, transparent, and defensible forms of human–AI collaboration. In response, the paper presents the Structured AI-Guided Education Assessment Policy (SAGE-AP) as a theoretically grounded policy proposal for AI-assisted assessment, rather than as an empirically validated policy intervention. SAGE-AP frames assessment as a staged process in which students begin from their own understanding, engage with AI critically, document evaluative decisions, refine outputs responsibly, and defend the reasoning represented in the final submission. The paper contributes to institutional policy development by clarifying how permission settings may be complemented by pedagogical process guidance in the generative AI era. Full article

Saudi Arabia currently lacks a nationally representative, multi-day National Household Travel Survey comparable to the US, UK, or New Zealand programmes; existing official data products focus on aggregate road-transport indicators or general household statistics rather than detailed day-to-day travel diaries. This study develops a benchmark-driven framework for NHTS–KSA by comparing Saudi demographic, geographic, infrastructure, climate, and mobility indicators with those of the United States, United Kingdom, and New Zealand, and by systematically assessing 15 survey-design indicators across their national household travel surveys. Context benchmarking identifies the United States as the closest for highway-oriented interurban structure and motorisation level, New Zealand for geography and demographic structure (in particular, near-identical physiological density on limited arable land), and the United Kingdom as the most aspirationally aligned benchmark for the multimodal mobility patterns Saudi Arabia aims to develop under Vision 2030. Design benchmarking shows that the three surveys are closely matched in aggregate similarity but lead on distinct elements: New Zealand on diary length and integrated passive tracking, the US on digital tools and emerging-behaviour modules, and the UK on interviewer-led recruitment and multimodal analysis, a pattern that proves robust to plausible variation in individual scores. The resulting NHTS–KSA blueprint specifies a statistically justified, stratified multistage annual household sample, a two-day diary with rolling 12-month fieldwork, interviewer-assisted recruitment, a digital-first diary with optional GPS tracking, and modules on long-distance travel, telework, e-commerce, gendered mobility, accessibility, safety, and environmental attitudes. While preserving international comparability, the framework provides the data foundation required to steer public-transport investment, demand-management measures, and land-use policies in line with Saudi Arabia’s Vision 2030 objectives for sustainable, inclusive, and smart mobility. Full article

Artificial intelligence (AI) is reshaping the field of diagnostic radiology; however, its applications in interventional radiology and pediatric interventional radiology (PIR) remain limited despite clear clinical needs and the rich multimodal data environment characteristic of pediatric procedural care. In this narrative review, I summarize the current state of AI technologies relevant to PIR and outline future perspectives for their clinical integration. Peer-reviewed literature and position statements identified through MEDLINE/PubMed, Embase, Scopus, and major society publications up to the first quarter of 2026 are synthesized, focusing on AI applications across the PIR care pathway, including dose-sparing image acquisition and reconstruction, automated image interpretation and computer-aided diagnosis, data-driven procedural planning and navigation, and post-procedural risk prediction and monitoring. After briefly introducing core machine learning and deep learning concepts, pediatric-specific challenges are discussed, including radiation sensitivity, growth-related anatomical variability, regulatory constraints, and the scarcity of large, annotated datasets, as well as existing and emerging applications along the PIR care pathway: AI-assisted dose reduction and image reconstruction, automated image interpretation, segmentation, and computer-aided diagnosis; data-driven procedural planning, including three-dimensional modelling, augmented reality, AI-enabled/AI-adjacent robotics, and AI-directed procedural navigation; and post-procedural risk prediction and outcome monitoring. Finally, emerging paradigms, including explainable AI, federated learning, and multimodal integration, are highlighted, and research priorities, collaborative frameworks, and governance principles required to ensure safe, equitable, and effective AI deployment in PIR are outlined. In doing so, this review delineates the current evidence gaps and priority directions for clinically meaningful AI adoption in PIR. Although AI has the potential to improve patient care, it has not yet been specifically designed, validated, or deployed in children. Existing work demonstrates feasibility across the PIR workflow, but most tools remain weakly linked to pediatric clinical endpoints. Full article

Population aging has increased the need for public open spaces that older adults can use safely, comfortably, and confidently. In many urban parks and community squares, however, resting facilities are still designed as standardized street furniture, with cold materials, insufficient hand support, limited wheelchair-inclusive space, and weak support for everyday social interaction. This study examines age-friendly public facilities as micro-scale spatial elements that shape sitting, standing, staying, communication, and willingness to remain in small urban open spaces. Drawing on field observation, behavioral analysis, semi-structured interviews, and a multi-criteria design-evaluation process, the study identifies older adults’ key facility-use needs and translates them into design indicators and alternative facility schemes. The results show that physical support and inclusive spatial use are the most important design priorities. Standing-up assistance, sitting-posture support, perceived structural stability, and age-appropriate dimensional adaptation were more influential than purely decorative or auxiliary functions. Among the three alternative schemes, the modular pergola system performed best because it combined stable hand support, independent seating, an age-friendly interactive table, shaded resting space, wheelchair-inclusive layout, and wood-based sensory comfort. The sensitivity analysis further confirmed that this scheme maintained a stable advantage under most weight-adjustment conditions. The findings suggest that age-friendly public facility design should move beyond the improvement of individual furniture products and instead integrate bodily support, spatial accessibility, social interaction, material comfort, and environmental pattern quality. This study provides a design-decision framework for improving the inclusiveness, accessibility, and health-supportive capacity of urban public open spaces for older adults. Full article

Large language models (LLMs) are increasingly adopted in life-science research for scientific writing, coding, literature synthesis, workflow troubleshooting, and preliminary data interpretation. In microbial genomics and bioinformatics, their appeal is clear because researchers routinely integrate genome annotations, antimicrobial resistance profiles, virulence determinants, taxonomic assignments, microbiome outputs, workflow scripts, and primary literature. Yet this domain also highlights major risks, including hallucinated biological claims, inaccurate citations, irreproducible code, unsupported genotype-to-phenotype inference, and inappropriate clinical or public health framing. This narrative review examines responsible LLM use in microbial genomics as a representative life-science setting where interpretation depends on database provenance, validated workflows, expert assessment, and reproducible evidence chains. It considers applications in genome annotation, antimicrobial resistance interpretation, virulence analysis, microbiome and metagenomics workflows, coding support, and scientific writing. The review further presents MicrobeGuardGPT as a conceptual reliability framework for assessing LLM-assisted microbial genomics outputs before scientific, clinical, or public health use. By connecting task domains, evidence verification, expert validation, and reliability classification, the framework supports risk-aware LLM integration in bioinformatics. Responsible implementation will require domain-specific benchmarks, curated database linkage, transparent reporting, reproducible workflows, human oversight, and governance standards tailored to biological interpretation across research, diagnostic, surveillance, outbreak-response, educational, and translational contexts. Full article

Extreme climatic conditions often intensify abiotic stress factors (such as drought, salinity, heat stress, ultraviolet radiation, and soil degradation), and are increasingly limiting crop productivity and threatening global food security. Secondary metabolites (SMs), traditionally viewed as defense compounds, are now recognized as key regulators of plant adaptation to environmental stress. This review synthesizes recent advances in understanding the role of SMs as biochemical targets for improving crop resilience to climate extremes. By integrating evidence from multi-omics studies, artificial-intelligence-driven analyses, and functional genomics, we examine how stress-specific metabolic signatures and regulatory networks can be exploited for crop improvement. We further discuss the application of genome editing, synthetic biology, and metabolomics-assisted breeding to modulate the SM pathways to enhance stress tolerance. Selected case studies highlight the contribution of flavonoids, alkaloids, and terpenoids to stress adaptation in major and underutilized crops grown under salinity, drought, and low-temperature conditions. Despite significant progress, challenges remain, including metabolic trade-offs between stress tolerance and yield, regulatory constraints, and public acceptance of genetically engineered crops. By linking molecular mechanisms with applied strategies, this review provides a conceptual framework for leveraging secondary metabolism in climate-resilient agriculture and identifies key gaps to guide future research and innovation. Full article

Mapping wildfire patterns in Wildland–Urban Interface (WUI) areas is a fundamental tool for fire management and prevention, particularly in regions where urban expansion occurs in close proximity to natural vegetation. This mapping approach makes it possible to identify critical zones and to support more effective interventions adapted to the specific conditions of each territory. This work analyzed wildfires in the state of Tocantins, Brazil, using detailed geospatial data and advanced analysis techniques and statistics to characterize the dynamics of burned areas. Data used for the project were retrieved from MapBiomas and the Geoprocessing Laboratory of the Public Ministry of Tocantins (LABGEO), applying logistic regression models to explore the relationship between the distance of WUIs and the frequency of wildfires. The methodology covered the spatial distribution of fires and the different dynamics observed by type and size of burned area, allowing for a more detailed analysis. The results indicated significant variations in the proportion of burned areas inside and outside the WUIs, suggesting that proximity to these interfaces plays a critical role in the occurrence pattern of fires. Notably, Palmas, the state capital, stood out as one of the municipalities with the highest concentration of impacts in WUI areas, highlighting the relevance of these zones in environmental risk management. The study emphasizes the importance of adopting regional approaches that consider local specificities in the management and prevention of wildfires. The integration of geospatial data with robust statistical methodologies can guide more effective management strategies, assisting in the planning of public policies adapted to the socio-environmental dynamics of Tocantins. Full article