Search Results (55,534)

In recent decades, residential mobility has emerged as a fundamental interpretative key lens for understanding contemporary urban transformations, particularly in polycentric and fragmented urban contexts. Movements between different residential settings reflect economic, social and cultural changes, impacting the organisation of urban spaces, the demand for services and mobility systems. In territories characterised by dispersed settlement patterns and strong functional polarisation, these dynamics tend to promote the intensive use of private means, with consequent negative impacts on environmental sustainability, social equity and economic efficiency. In response to these critical issues, there is growing interest in sustainable mobility models based on proximity and on the integration between daily travel, access to services and the quality of public space. Within this perspective, greenways are configured as hybrid infrastructures, capable of reorganising mobility while contributing to the regeneration of urban spaces. In the Caserta area, in the Campania region, the disused route of the former Alifana railway represents a topic of great interest, both for research and planning. Its potential strategic conversion into a greenway opens a broader perspective than that so far considered at the regional level, which has mainly focused on the infrastructure dimension. The paper analyses the strengths and weaknesses of an approach limited to infrastructural mobility, proposing a comparative evaluation of project scenarios—including the non-intervention hypothesis—both through the application of the MACBETH approach and preliminary parametric estimation of construction costs, in order to emphasise the importance of integrating social and environmental benefits, as well as quality of life, into decision-making processes. Full article

Pharmaceutical residues are increasingly detected in aquatic environments and are recognized as contaminants of emerging concern. This systematic literature review compiled and evaluated published concentrations of pharmaceutical residues in bottled water, tap water, and surface water in Portugal, applying risk quotient (RQ) and screening-level risk quotient (RQ_screen) approaches to evaluate potential human health risks and prioritize contaminants. Assessment based on the compiled literature data across age groups showed bottled and tap water posed low risk, while surface water presented the highest concern, with compounds spanning the full risk spectrum. Key contributors to potential human health risk included hormones (17-alpha-ethinylestradiol, 17-beta-estradiol, estrone), ramipril, betamethasone, citalopram, and amoxicillin. RQ_screen highlighted compounds relevant for ongoing monitoring even in treated waters, such as carbamazepine, diclofenac, salicylic acid, warfarin, fluoxetine, and erythromycin, due to their persistence and toxicological significance. Both RQ and RQ_screen indicated higher risk values for infants and children, reflecting lower body weight and higher water intake per unit mass, underscoring the need for age-specific evaluations. The RQ_screen method proved useful for contaminant prioritization, identifying substances relevant for monitoring despite low concentrations. Overall, this systematic review highlights pharmaceutical residues as an emerging public and environmental health concern in Portugal and emphasizes the importance of targeted monitoring and risk-based management within a One Health framework. Full article

This study evaluates publicness and spatial quality along the Çoruh Riverfront in the city center of Bayburt through the Project for Public Spaces (PPS) framework. The research is based on a literature review, interpretation of satellite imagery, on-site observation, production of visual documentation, and surveys conducted with a total of 210 participants, including 30 users in each of seven public spaces. The findings show that the presence of parks along the riverfront alone does not produce continuous and inclusive public life. In the composite PPS index, July 15 Martyrs Park showed the strongest profile (63.4%), whereas the surroundings of Kıyasi Şentürk Mosque showed the most vulnerable profile (44.1%). The mean values indicate that Access and Linkages are relatively strong (62.5%), whereas Comfort and Image constitute the weakest dimension (46.2%). This result suggests that although the Çoruh Riverfront is physically accessible, it has difficulty producing continuity in terms of staying, shade, orientation, maintenance, safety, and inclusive social programming. The study argues that, in small-scale Anatolian cities, riverfronts should be planned not as fragmented recreational areas but as accessible, socially inclusive, and ecologically sensitive blue-green public spines. Therefore, this study does not use PPS merely as a scoring tool. It uses it as a placemaking framework that makes it possible to read public continuity, place-led management, and social-ecological sustainability together on small-city riverfronts. The findings are interpreted as exploratory and diagnostic evidence for planning and design, rather than as inferential statistical proof of site-level differences or direct ecological performance. Full article

This study provides a systematic examination of global research trends and developments in the field of family and marriage over a twenty-five-year period (2000–2025). Employing a hybrid review design, the research integrates bibliometric analysis with PRISMA guidelines to ensure methodological rigor and transparency. Data were retrieved from the Web of Science, where an initial pool of 97,171 records was refined to 2974 eligible publications through a structured screening and inclusion process. The reduction to 2974 publications was the result of structure bibliometrics using CiteSpace, which employs algorithmic thresholds to identify the most structurally significant publications within a large corpus. Utilizing CiteSpace (version 6.4.R1), this analysis maps the intellectual structure and evolution of the field. By synthesizing co-citation, co-authorship, institutional, and keyword co-occurrence data, this study identifies critical collaboration networks, influential contributors, and dominant thematic domains. The findings reveal prominent research clusters, including premarital cohabitation, partner effects, family structure transitions, marital discord, systemic family functioning, and marriage education. Key contributors identified include influential scholars such as Catherine Walker O’Neal, Birditt, Kira S, Higginbotham Brian J, Beach Steven R. H., and Matthew D. Johnson. Leading institutions are the University System of Ohio, the University of California System, the Pennsylvania Commonwealth System of Higher Education (PCSHE), Pennsylvania State University, and Pennsylvania State University–University Park. At the country level, the United States, Canada, England, Australia, the Netherlands, and Belgium emerge as the most significant contributors. The findings offer a comprehensive synthesis of authorship trends, institutional influence, and shifting research trajectories within the field of family and marriage studies. Full article

With the rapid development of social media, the speed and breadth of information dissemination have increased substantially, leading to more complex patterns in the emergence and evolution of online public opinion. Compared to unsigned networks, signed networks more accurately capture supportive and adversarial relationships among users. Although the traditional Polarity-Related Independent Cascade model (IC-P) can describe opinion propagation in signed networks, its capability remains limited when applied to complex social environments. To address this issue, this paper improves the IC-P model by incorporating a Prisoner’s Dilemma game to establish a user propagation-choice mechanism. Furthermore, activation probability and activation thresholds are redesigned from the perspectives of authority effect, homophily, and temporal decay, resulting in an Independent Cascade model incorporating Communication Choice and Polarity (ICC-P). Using three real-world negative public opinion datasets collected from the Sina Weibo platform spanning from March to April 2024, Monte Carlo simulations were conducted and compared with the main baseline models. Experimental results indicate that, relative to the best existing baselines, ICC-P reduces the mean absolute error of the prediction of the propagation scale by approximately 43% and reduces the mean absolute error of the prediction of the sentiment distribution of the nodes by approximately 57%, demonstrating significant improvements in both propagation fitting accuracy and sentiment prediction performance. Full article

Domestic cats (Felis catus) are ubiquitous companion animals that provide substantial psychological and social benefits to children and adults alike, but they also serve as reservoirs and vectors for a wide range of zoonotic pathogens. Close physical contact between cats and children, frequent use of shared environments such as homes, playgrounds, and sandboxes, and still-developing hygiene behaviours increase opportunities for exposure to protozoa, helminths, bacteria, fungi, and ectoparasite-borne agents. This review synthesizes current evidence on key feline-associated zoonoses of pediatric concern—including Toxoplasma gondii, Toxocara cati, Ancylostoma spp., Dipylidium caninum, Bartonella henselae, Salmonella enterica, Campylobacter jejuni, Pasteurella multocida, Microsporum canis, flea-borne Rickettsia species, and rabies—with emphasis on transmission routes, clinical manifestations, and risk modifiers in children, pregnant women, and immunocompromised individuals. Within a One Health framework, we also summarize global publication trends on feline zoonoses, discuss how urban cat ecology and management (including free-ranging cats in child-frequented environments) may shape pediatric risk, and outline practical prevention strategies centred on hygiene, veterinary care, and targeted education for caregivers and children. Full article

In recent years, with the significant decline in fine particulate matter (PM_2.5) concentrations, ozone (O₃) has emerged as a major composite air pollutant during the warm season in China, attracting increasing attention due to its associated health burden and economic costs. This study focuses on Tianjin, using ozone monitoring data from 2017 to 2023 combined with health statistics to assess the health impacts and economic losses attributable to ozone pollution. First, ozone exposure indicators and compliance criteria were constructed based on national air quality standards, and the interannual variation and spatial differences of O₃ levels were analyzed at both citywide and district scales. Second, multiple machine learning classification models, including logistic regression, decision tree, k-nearest neighbors, and gradient boosting, were developed using ozone and meteorological variables to predict the occurrence risks of five diseases: cardiovascular diseases, respiratory diseases, hand-foot-and-mouth disease (HFMD), influenza, and dengue fever. Finally, excess cases were estimated using health impact functions, and the associated economic losses were quantified by combining the value of a statistical life (VSL) with cost-of-illness and willingness-to-pay (WTP) approaches. The results showed that the annual evaluation value of ozone in Tianjin, defined as the 90th percentile of the daily maximum 8 h average O₃ concentration, exhibited a pattern of initially increasing, then decreasing, and subsequently rebounding. It peaked at 201 µg/m³ in 2018, declined to a minimum of 164 µg/m³ in 2021, and rebounded to 188 µg/m³ in 2023. Machine-learning results indicated that the logistic regression model showed relatively stable overall performance across predictions of different diseases, while the gradient boosting tree model also achieved high accuracy in predicting certain infectious diseases. Overall, ozone pollution exhibits significant heterogeneous effects across different disease types, and the associated health-related economic losses show stage-wise fluctuations in response to pollution levels. Based on these findings, it is recommended to implement refined control measures during periods of high ozone exceedance and in key regions, while strengthening protection for vulnerable populations such as the elderly, children, and patients with respiratory diseases, in order to achieve synergistic improvements in air quality management and public health outcomes. Full article

In 1894, a Christian revelation occurred in western Saxony through the weaver August Hermann Hain, who founded the Christliche Gemeinschaft Hirt und Herde (Christian Community Shepherd and Flock). In the 1910s, church and state authorities became aware of the new religious community and attempted to prevent its meetings. As a result of the pacifist and labor movement-inspired social attitudes of Hain and his followers, state authorities banned the public activities of Hirt und Herde in parts of the German Empire in 1916/17. Despite the ban, the community continued to grow and by 1925 was already the third-largest religious community in Saxony. In addition to the former kingdom of Saxony, people in Thuringia, northern Bavaria, and Czechoslovakia also professed their belief in the new doctrine at that time. With the death of August Hermann Hain in 1927 and the ban on the community by the National Socialists in 1933, the members of Hirt und Herde increasingly withdrew from public life into internal emigration. Despite being recognized as an official religious community in the German Democratic Republic (GDR), Hirt und Herde remained below the radar of public perception. In recent years, however, the community has begun—albeit slowly—to open up to the new social realities of a modern, pluralistic society. Using the pluralism paradigm of Peter L. Berger, this article traces the genesis of the generally unknown religious community over the last 125 years. In addition to the historical development of the community, it provides an explanatory approach to the changes in its teachings and public appearance. Full article

Anti-UAV multi-object tracking in remote sensing videos is challenging because UAV targets are small, weakly textured, and often affected by cluttered backgrounds, abrupt motion, occlusion, and intermittent visibility. To address these challenges, we formulate anti-UAV multi-object tracking as a context-aware identity prediction task, in which target identities and locations are inferred from historical trajectory priors instead of current-frame observations alone. Under this formulation, we propose a dual-track parallel tracking framework. The adaptive identity disambiguation (AID) module combines motion cues with appearance features according to their estimated reliability, improving short-term association when visual evidence is weak. In parallel, the motion-evolution temporal memory (METM) module models trajectory dynamics using motion anomaly detection and time-decayed memory, enabling spatiotemporal recovery after occlusion, temporary disappearance, or abrupt motion. The outputs of the two branches are integrated by a unified identity decision layer to produce stable tracking results. Experiments are conducted on the public 4th Anti-UAV Benchmark Track-3 and our newly constructed Anti-UAV Multi-Object Tracking dataset, AU-MOT. On the 4th Anti-UAV Benchmark Track-3, our method achieves 63.6% HOTA and 64.1% IDF1, outperforming the strongest competing method by 3.5% and 3.9%, respectively, while reducing identity switches and track fragments by 20.8% and 23.8%. On AU-MOT, it achieves 67.2% HOTA and 67.8% IDF1, with 20.2% fewer identity switches and 22.3% fewer track fragments. These results demonstrate its effectiveness under long-range observation, weak target appearance, cluttered backgrounds, abrupt motion, and intermittent target visibility. Full article

The increasing complexity and sophistication of cyberattacks have made machine learning (ML) and artificial intelligence (AI) central to modern cyber defense. However, existing surveys typically examine attacks, ML methods, or datasets separately, limiting understanding of how methodological choices align with adversarial behaviours and benchmark availability. This paper presents a systematic literature review (SLR) of AI- and ML-based cyber defense studies published between 2019 and 2025, framed as an ATT&CK-aligned tri-axis synthesis of cyberattacks, machine learning methods, and datasets. Across 99 primary studies, the review maps 312 attack labels to MITRE ATT&CK tactics and techniques, categorises the ML methods applied, and organizes 96 datasets into a refined taxonomy spanning NIDD, IoT-NIDD, malware, Spam and Phishing, ICS, Insider Threat, custom-collected, and other datasets. Rather than treating attacks, ML methods, and datasets as separate descriptive dimensions, the review analyses them jointly through a tri-axis cross-reference framework, enabling the identification of benchmark dependence, methodological concentration, and underexplored attack–method–dataset intersections that are not visible in single-axis or model-centred surveys. The synthesis shows that the literature is strongly concentrated on externally visible attacks associated with Impact, Initial Access, and Execution, that ensemble and deep learning models dominate high-frequency detection settings, and that dataset usage remains heavily skewed toward a small set of public benchmarks, particularly CSE-CIC-IDS2017, UNSW-NB15, and NSL-KDD. This review further identifies persistent blind spots, including limited coverage of post-compromise ATT&CK behaviours, sparse use of ICS and insider-threat datasets, and weak support for multi-stage or multi-dataset evaluation. These findings provide a more focused and actionable evidence base for future ML-based cyber defense research. Full article

Self-regulation is a foundational developmental skill in early childhood that supports academic readiness, social competence, and long-term health outcomes. Physical activity has been proposed as a modifiable behavior that may contribute to self-regulation development; however, evidence in preschool-aged children remains inconsistent and has primarily focused on cognitive outcomes. This systematic review and meta-analysis aimed to examine the association between physical activity and self-regulation across domains in early childhood using cross-sectional evidence. The review was conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD420261361512). Searches were conducted in PubMed, ERIC, CINAHL, and Web of Science. Fifteen studies met the inclusion criteria and were included in the qualitative synthesis and meta-analysis. A random-effects meta-analysis revealed a small but statistically significant positive association between physical activity and self-regulation (r = 0.099, 95% CI = 0.064–0.133, p < 0.001), although substantial heterogeneity was observed (I² = 91.1%). Subgroup analyses showed significant associations for both cognitive and non-cognitive self-regulation outcomes. Sensitivity analyses confirmed the robustness of the findings, and no substantial publication bias was detected. Physical activity may represent a promising modifiable factor that supports self-regulation development in early childhood, although longitudinal and experimental research is needed to clarify causal relationships. Full article

Digital activities of statutory bodies are an emerging area in environmental governance and green information systems (Green IS) research. Copyright societies in India, under Section 33 of the Copyright Act, 1957, are crucial gatekeepers of the cultural economy and manage royalties on behalf of millions of creators through vital web portals. In this study, we examine the interface between their statutory role and digital environmental accountability, filling a research void at the interface of information management, sustainability, and policymaking. The researcher undertook website carbon auditing to determine the emissions of all seven registered copyright societies and found that 66.7% have high-emitting websites, with an average emission rate of 2.49 g CO₂ per page view, compared to the benchmark of 0.615 g CO₂ per page view for compliant websites. Significantly, there is a policy void: while societies are subject to detailed rules on financial and tariff matters, there is no statutory requirement on the sustainability of their digital operations. Our analysis shows that green hosting is insufficient and that there is a risk of symbolic compliance, thereby extending Green IS theory to statutory digital ecosystems. The researcher recommends theoretically informed interventions that include amending the Copyright Rules to require digital carbon statements, using existing corporate social responsibility (CSR) requirements, green procurement, and developing a Green IS governance model that is applicable to digital infrastructure in the public sector. Full article

Background/Objectives: Proteolysis Targeting Chimeras (PROTACs) are heterobifunctional small molecules that induce ubiquitin–proteasome–mediated degradation of target proteins and have matured from proof-of-concept chemistry to a clinically validated therapeutic modality, with the first Phase 3 readout reported in 2025. A systematic bibliometric analysis covering this pivotal-trial era, however, has been lacking. This study aimed to map the historical trajectory, current research front, and emerging frontiers of PROTAC research. Methods: We analyzed 2630 PROTAC-related publications indexed in the Web of Science Core Collection (WoSCC) from 2001 to 2025 using a combined toolkit of CiteSpace, HistCite, the Alluvial Generator, and R (ggplot2), covering co-occurrence networks, burst detection, keyword clustering, citation historiography, alluvial flow analysis, and reference co-citation timeline visualization. Results: China and the USA led global output, and the Chinese Academy of Sciences, China Pharmaceutical University, and Harvard University were the most productive institutions; the Journal of Medicinal Chemistry was the leading publishing venue, and Alessio Ciulli, Jian Jin, and Craig M. Crews anchored the author network. Keyword burst analysis showed that early research centred on E3 ubiquitin ligase recruitment and small-molecule PROTAC design, whereas the current hotspots, resolved through keyword clustering and co-citation timelines, included structural basis and ternary complex design, EGFR-directed degradation, oral bioavailability optimization, applications in multiple myeloma and Alzheimer’s disease, tumour-targeted delivery, and computational/AI-driven design. Conclusions: This study extends the bibliometric record of PROTACs across 2001–2025 and identifies oral bioavailability, E3 ligase repertoire expansion, and CNS-penetrant degrader design as the emerging frontiers likely to shape the next phase of the field. Full article

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

Examining how artificial intelligence industry agglomeration (AIIA) affects carbon and pollution reduction is crucial for China’s agricultural sustainability. Existing research mainly examines the effect of artificial intelligence (AI) on the reduction of single pollutants while overlooking how industry agglomeration influences the marginal cost of coordinated abatement, a key issue for the agricultural resource–environment–economy system. Using panel data for 30 Chinese provinces from 2016 to 2024, this study constructs a marginal cost-based indicator of agricultural pollution–carbon reduction synergy (APCRS) and examines the effect of AIIA. The full-sample results reveal that AIIA has a U-shaped relationship with APCRS. Technological progress partially mediates this relationship. Agricultural socialized services and rural industrial integration buffer the initial negative association, whereas agricultural labor productivity strengthens the curvature of the estimated nonlinear pattern. The effect of AIIA also varies with external conditions and is more pronounced in regions with higher levels of marketization and industrialization while remaining significantly U-shaped across grain strategic zones. This dynamic process is more likely to emerge when public innovation investment and rural household income exceed critical thresholds. These findings provide new evidence for understanding how AI-driven agglomeration can support green agricultural transformation. Full article