Search Results (1,134)

Urban heritage revitalization relies on public cultural cognition, yet the psychological impact of spatial morphology remains under-quantified. This study integrates space syntax with survey data to evaluate the relationship between five spatial metrics and six cultural dimensions, including authenticity, integrity, vitality, involvement, diversity, and identity, across three heritage cases. Correlation and clustering analyses reveal that spatial configuration dictates distinct cultural cognitions rather than universally positive effects. This study identified three spatial–cultural typologies: an authentic traditional revitalized area, where high visual entropy preserves authenticity but limits involvement; a high-quality hub, which utilizes high integration and connectivity to holistically enhance all cultural cognitions; and a functional and vital art area, where spatial control for modern vitality is maximized at the expense of historical identity. These findings demonstrate that morphological variables serve as active, quantifiable predictors of cultural impact, moving the discourse of space and culture from a metaphor to an empirical framework. This study provides actionable design strategies for balancing functional efficiency with historical preservation in sustainable urban (re)development. Full article

This study presents GAGER, a governance-aware ontology-based framework for scholarly knowledge graph analytics evaluated as a single-university case study. The framework integrates bibliometric normalization, semantic organization, thematic clustering, provenance control, governance validation, and budgeted semantic coverage optimization. It transforms Scopus-based institutional bibliographic records into an operational knowledge infrastructure in which documents, authors, institutions, countries, publication venues, document types, knowledge areas, thematic clusters, and Sustainable Development Goal tags are jointly represented through a traceable semantic graph. GAGER uses the ontology to define candidate semantic anchors and applies a maximum-coverage optimization model to select the most informative governance-valid anchors under explicit budget constraints. Experiments using a real single-university corpus show that the proposed governance-validated ontology-plus-optimization strategy achieves higher semantic coverage and aggregate decision quality than ontology-only selection, random selection, and frequency-based selection baselines evaluated under the same budget constraints. The governance layer also has a measurable operational effect, reducing the initial candidate-anchor space from 27,542 anchors to 8320 validated anchors before optimization. The contribution is therefore a methodologically reproducible single-institution analytical framework that connects semantic governance, provenance, graph-based bibliometric representation, and budgeted optimization to support institutional research analytics, thematic prioritization, and evidence-based decision-making within the analyzed university context. Full article

3D Gaussian Splatting (3DGS) SLAM provides an explicit scene representation for dense tracking and mapping, which is useful for underwater robotic perception. However, underwater monocular 3DGS SLAM lacks reliable metric depth cues: monocular depth estimation can provide dense structural priors, but its scale and reliability often degrade under underwater appearance changes. Forward-looking sonar (FLS) provides range–azimuth acoustic measurements whose range coordinate is related to physical distance, but raw sonar observations are sparse, noisy, and ambiguous. Our key insight is that FLS returns can serve as sparse metric depth anchors when they are associated with visually detected object regions. Based on this insight, we propose SonarReg-GS SLAM, an underwater visual–acoustic 3DGS SLAM framework with sparse sonar-guided depth regularization. Given synchronized RGB and sonar inputs, SonarReg-GS SLAM uses object masks to constrain the search space for acoustic range association. Filtered sonar responses are selected as sparse metric anchors through object-aware sampling, bearing-to-beam gating, and valid-pair checking. These anchors regularize the scale of monocular depth and generate metric depth priors for Gaussian initialization and tracking. An object-aware RGB mask loss further increases supervision on detected object regions while preserving full-scene mapping. Experiments on two public RGB–sonar underwater datasets show that SonarReg-GS SLAM improves tracking accuracy and mapping quality compared with representative classical SLAM and Gaussian Splatting SLAM baselines. Compared with Splat-SLAM, our method reduces the average ATE RMSE from 0.1296 m to 0.1015 m on UXO and from 0.5687 m to 0.4640 m on OPTI, corresponding to relative reductions of 21.7% and 18.4%, respectively. For rendering-based mapping, it increases the average PSNR from 28.05 dB to 29.61 dB on UXO and from 20.91 dB to 28.63 dB on OPTI while reducing the average LPIPS from 0.345 to 0.173 and from 0.450 to 0.303, respectively. Full article

Machine learning techniques for network-based intrusion detection systems (NIDS) have advanced considerably over the past decade. Still, improvements are inhibited by handcrafted feature pipelines, isolated public benchmark data, and evaluation procedures that do not reflect real-life deployment. AutoML, a branch of ML automating model selection, automated architecture search, and the creation of model pipelines, may help overcome these shortcomings. While numerous NIDS applications employing automated ML techniques have been proposed, and recent surveys have mapped the AutoML framework landscape for network intrusion detection, no existing review critically audits the evaluation practice of this literature: the quality of its benchmark datasets, the reproducibility of its reported results, and the realism of its deployment assumptions. This paper critically reviews 26 research works published between January 2023 and June 2026, collected via a two-phase structured search: a documented keyword search across five databases (Scopus, IEEE Xplore, Web of Science, ACM Digital Library, and Google Scholar), followed by full-text eligibility screening, citation chaining, and expert evaluation. Findings drawn from this collection capture trends observed among the selected studies, rather than reflecting the broader state of the field. Analysis of the corpus reveals that 88% of dataset-verified studies evaluate exclusively or partly on the legacy benchmark family (KDD-derived, CICIDS, UNSW-NB15, CIDDS), 21% evaluate on a single dataset only, and among attribute-verified studies only 32% release source code, 40% report statistical significance testing, and 36% include variance analysis, findings that collectively motivate the four contributions of this study. First, a recommended evaluation framework is proposed, addressing baseline parity, transparent search-space and budget reporting, nested cross-validation for selection-bias control, and stability reporting across multiple random seeds. Second, a dataset quality scoring framework is introduced, assessing five dimensions: overlap rate, duplication rate, label correctness, attack-type representativeness, and coverage of benign, IoT, and IIoT traffic. Third, a cross-domain justification is provided for neural architecture search (NAS) and meta-learning in NIDS, grounded in advances in federated NAS, out-of-distribution robustness, edge-constrained search cost reduction, and few-shot adaptation. Fourth, a structured research roadmap is outlined, targeting real-world validation, standardized benchmarks, curated datasets, resource-aware AutoML, and privacy-preserving federated NAS. In contrast to prior surveys of AutoML for network intrusion detection, which map frameworks and computational paradigms, this review contributes a formalized evaluation checklist, an explicit and partially empirically validated dataset quality scoring scheme, and evidence-based methodological guidance grounded in a transparent, fully enumerated study corpus. Full article

In parallel CNC machining systems, machine deterioration can simultaneously increase energy-related operating costs, affect delivery performance, and change the timing of preventive maintenance. This study develops a reliability- and energy-aware decision-support framework for production-maintenance scheduling in a two-machine parallel CNC cell. The model integrates priority sequencing, reliability-based machine assignment during decoding, degradation-dependent energy cost, tardiness penalties, and threshold-triggered preventive maintenance in a unified cost-minimization formulation. A normalized reliability index is updated by a short-horizon exponential degradation function, and the energy term is amplified when machines operate in degraded states. Preventive maintenance is triggered when post-job reliability falls below a specified threshold, restoring the machine’s condition for subsequent production or the next planning horizon. The computational study combines application-inspired machining instances, decoder-space full enumeration for small cases, repeated GA/PSO comparisons, a new algorithm-budget sensitivity experiment, and adapted OR-Library weighted-tardiness benchmarks. Across 270 paired budget-sensitivity runs, GA obtained a lower total cost in 265 cases, whereas PSO retained a shorter average runtime in the matched-budget experiments. Across 90 adapted public-benchmark comparisons, GA obtained a lower total cost in 86 cases. These results show that the framework generates feasible schedules and reveals energy–maintenance–tardiness trade-offs. The algorithmic findings are interpreted as a quality–time trade-off under the tested scalarized cost model, not as a claim of universal algorithmic superiority. Full article

Rough set methodology has gained broad acceptance as a potent mathematical apparatus for feature selection within data mining and machine learning. Yet, classical rough sets hinge on equivalence relations to partition the universe, thereby demanding strict reflexivity, symmetry, and transitivity conditions that are arduous to satisfy in realistic settings. Although fuzzy rough sets have been explored to mitigate this rigidity, the entropy-based uncertainty measures employed in fuzzy approximation spaces remain acutely sensitive to data quality and noise corruption, potentially inducing severe bias in feature evaluation. Moreover, the literature currently lacks noise-tolerant uncertainty measures capable of accommodating a controlled fraction of classification errors while safeguarding the discriminative strength of feature subsets. Inspired by these gaps, this study develops a feature selection framework grounded in variable precision fuzzy entropy within the fuzzy rough set context. To this end, fuzzy decision is adopted to portray the membership degree of samples relative to decision classes, thereby enabling more precise detection and elimination of redundant attributes during approximation. An uncertainty quantifier termed fuzzy relational entropy is then introduced to appraise the distinguishing power of fuzzy similarity relations generated by attribute subsets. Leveraging fuzzy decision, a portfolio of uncertainty measure variants, specifically the variable precision joint discriminant index, the variable precision conditional discriminant index, and the variable precision mutual discriminant index, is developed to counteract noisy data effects. These variable precision discriminant indexes sanction a regulated error proportion and afford a measure of noise resistance. Finally, knowledge reduction for fuzzy decision systems is attacked from the angle of discriminative capability preservation, and a heuristic feature selection algorithm is crafted around the variable precision conditional discriminant index. Evaluation on twelve public UCI datasets reveals that the proposed algorithm effectively prunes redundant features and delivers competitive results against three representative alternatives: classical rough set, neighbourhood-based discriminant index, and fuzzy rough set feature selection. Additionally, it sustains stable classification performance across an extensive sweep of the variable precision parameter. Full article

Green exposure in public spaces plays a critical role in promoting mental health and well-being, especially for populations experiencing elevated psychological pressure in academically demanding environments such as university campuses. However, existing research remains largely limited to coarse-scale assessments, often overlooking the nuanced and nonlinear interactions between visual environmental characteristics and restorative quality across diverse spatial typologies. Addressing this gap, this study proposes a fine-scale, data-driven framework that integrates computer vision (CV) with perception-based evaluations using Shandong Jianzhu University as a case study. A semantic segmentation model (Mask2Former) was employed to extract 14 visual environmental elements from 193 campus photographs, while 426 students provided 4260 image-based evaluations of Perceived Restorative Quality (PRQ). Ridge regression optimized through cross-validation was used to identify key environmental correlates of PRQ, K-means clustering was applied to classify Campus Restorative Space (CRS) typologies, and Generalized Propensity Score Matching (GPSM) was employed to examine nonlinear dose–response relationships between sensory dimensions and restorative quality. The results indicate that natural elements, particularly vegetation and water-related features, are generally associated with higher PRQ scores; however, restorative responses do not consistently follow linear patterns. Several sensory dimensions exhibited threshold effects and diminishing marginal returns, suggesting that restorative benefits depend on balanced environmental composition rather than the simple accumulation of green elements. Furthermore, three CRS typologies—Greenery-Dominant Spaces, Open Landscape Spaces, and Courtyard Spaces—demonstrated distinct restorative pathways, indicating that the effects of environmental characteristics vary across spatial contexts. These findings support a context-dependent understanding of restorative environments and highlight the importance of spatial typology, environmental composition, and nonlinear responses in shaping restorative experiences. The study proposes a transferable analytical framework for assessing campus restorative environments and offers practical implications for evidence-based campus planning and design. Full article

Rapid urbanisation and climate change intensify heat, drought, wind exposure, and salinity risks in arid coastal cities, while simultaneously increasing dependence on trees for microclimate regulation and public-space quality. However, integrated evidence linking taxonomic concentration, spatial structure, tree dimensions, and current vitality remains scarce for Central Asian cities on the Caspian coast. This study characterised 13,951 trees across 10 green spaces in Aktau, Kazakhstan, belonging to 10 species, 10 genera, and 7 families, using a comprehensive inventory and an integrated taxonomic, spatial, and vitality framework. Stand density ranged from 27.1 to 510.3 trees ha⁻¹. Ailanthus altissima and Platycladus orientalis accounted for 42.74% and 23.13% of all trees, respectively, and together formed 65.87% of the inventory. Two species, two genera, and one family exceeded the indicative 10/20/30 diversification thresholds. The Shannon index ranged from 1.19 to 2.00, Pielou evenness from 0.52 to 0.87, and the share of the dominant species from 24.2% to 63.2%. Of all trees, 12,205 (87.48%) were classified as in good condition, but three sites accounted for 83.0% of all satisfactory or unsatisfactory trees. Maclura pomifera and Gleditsia triacanthos had the highest proportions of trees in good condition (96.3% and 94.2%), whereas Catalpa speciosa had the lowest (66.0%). The inventory, therefore, reveals a favourable current condition, but limited taxonomic redundancy and localised management hotspots. The findings provide a baseline for climate-resilient urban landscape planning based on gradual diversification, site-specific irrigation and soil management, control of invasive recruitment, and repeated monitoring of condition, growth, and mortality. Full article

Radiomics has produced tens of thousands of publications yet almost no handcrafted radiomic signatures in routine clinical use, and the reasons are increasingly understood to be problems of reproducibility and clinical translation rather than of algorithms. This critical narrative review argues that the field systematically generates paper-grade evidence—findings sufficient to publish—far faster than decision-grade evidence—findings sufficient to change clinical practice. Drawing on meta-scientific research, we describe seven fragility mechanisms (publication bias, analytical flexibility, underpowering, HARKing [hypothesizing after the results are known], citation distortion, cognitive bias, and misaligned incentives) and show why radiomics is structurally exposed to all of them simultaneously: high-dimensional feature spaces, acquisition-dependent measurement instability, segmentation variability, retrospective single-centre data, small samples, and leakage-prone validation. We then summarise empirical evidence on the radiomics literature, which remains pervaded by suboptimal methodological quality, near-absent negative results, limited external validation, sparse calibration and clinical-utility assessment, low data and code sharing, and a measurable retraction signal. We interpret these patterns as the output of a self-reinforcing system rather than isolated errors, and argue that better algorithms alone cannot resolve them. Finally, we argue that closing this gap requires not better models but evidentiary discipline: the consistent, enforceable application of standards the field already has, and the calibration of published claims to the strength of the underlying evidence. Full article

Background: Cancer-related psychological distress is a major concern in comprehensive oncology care because it substantially impairs patients’ quality of life and may adversely affect treatment adherence, outcomes, and prognosis. Over the past decade, research in this field has expanded rapidly; however, the overall knowledge structure, global research patterns, major contributors, and emerging hotspots remain insufficiently characterized. A bibliometric analysis is therefore needed to systematically map the development of cancer-related psychological distress research and identify evolving directions for future investigation. Methods: Publications related to cancer-related psychological distress published between 1 January 2015 and 31 December 2024 were retrieved from the Web of Science Core Collection and Scopus databases. The database searches were conducted on 10 March 2025. Bibliometric analyses were performed using VOSviewer (version 1.6.20), CiteSpace (version 6.3.R1), and the R package bibliometrix (version 5.3). Publication trends, country and institutional contributions, journal distribution, author collaboration networks, co-cited references, keyword co-occurrence, burst keywords, and thematic evolution were analyzed. Results: A total of 7063 publications were included in the bibliometric analysis, including 6162 articles and 901 reviews. Annual publication output increased from 465 publications in 2015 to 924 publications in 2024. The main contributing countries were the United States, Australia, China, Germany, and the United Kingdom. The United States ranked first in publication volume and total citations. Psycho-Oncology and Supportive Care in Cancer were the leading journals by publication volume and total citations. Major research themes included quality of life, psychological distress, depression, anxiety, breast cancer, distress screening, survivorship, and palliative care. Seven high-frequency keywords—“cancer,” “quality of life,” “psychological distress,” “depression,” “anxiety,” “breast cancer,” and “distress”—each appeared more than 500 times, representing the core research topics. Emerging keywords such as “informal caregivers,” “young adults,” “guidelines,” and “adult survivors” reflected increasing attention to caregiver support, age-specific psychosocial needs, survivorship care, and standardized clinical management. Conclusions: This bibliometric analysis provides a comprehensive overview of global research on cancer-related psychological distress from 2015 to 2024. The findings reveal publication trends, major contributors, collaboration patterns, core research themes, knowledge structures, and emerging topics in this field. Current research has gradually shifted from general descriptions of psychological distress toward survivorship care, caregiver support, standardized screening, and guideline-based management. Future studies should strengthen interdisciplinary collaboration, improve standardized assessment and screening approaches, and further explore emerging areas such as caregiver support, young adult cancer populations, survivorship care, and digital health and artificial intelligence-assisted approaches, which require further validation before routine clinical implementation. Full article

Background/Objectives: Since the establishment of the Korean Neonatal Network (KNN) in 2013, numerous studies have been conducted using its nationwide registry of very low birth weight infants (VLBWIs). Beyond serving as a clinical database, the KNN has evolved into a national healthcare research infrastructure supporting multicenter collaboration, evidence generation, and quality improvement in neonatal care. This study aimed to evaluate the scientific productivity, collaborative structure, and knowledge-generation capacity of the KNN through bibliometric network analysis and to identify future research directions in Korean neonatal research. Methods: Publications from 1 January 2015 to 5 June 2024 were retrieved from the Web of Science Core Collection. Literature searches were performed using Medical Subject Headings (MeSH), Emtree terms, and related keywords associated with very low birth weight infants, extremely low birth weight infants, and the Korean Neonatal Network. A total of 7060 global publications and 86 KNN-related publications were identified for analysis. Co-authorship and keyword co-occurrence analyses were conducted using VOSviewer to evaluate international collaborations, research themes, and temporal trends in keyword emergence. Scimago Graphica was used to visualize keyword evolution, and burst detection analysis was performed using CiteSpace to identify emerging research topics. Results: Among global publications on VLBWIs, South Korea ranked 13th worldwide with 226 publications and demonstrated substantial international collaboration, particularly with researchers in the United States. Analysis of the 86 KNN-related publications revealed the rapid development of observational and topic-focused research areas within a relatively short period. Keyword clustering and temporal analyses demonstrated a progressive expansion of research interests as the database matured. Burst analysis identified emerging themes related to mortality and morbidity risk factors, neurodevelopmental outcomes, and predictive modeling using machine learning techniques. Conclusions: Bibliometric analysis demonstrated that research based on the KNN has largely followed global trends in neonatal research despite its relatively recent establishment. The KNN has successfully functioned as a nationwide research infrastructure facilitating collaborative knowledge generation and scientific productivity. Nevertheless, several underrepresented research areas and opportunities for broader international collaboration were identified. These findings may help guide future research strategies and maximize the impact of national neonatal registry-based healthcare systems. Full article

With rapid population aging in China, the age-friendly upgrading of rural public spaces has become central to both rural revitalization and active aging. Existing evaluation frameworks are constrained by urban-oriented indicators, linear utility assumptions, and static assessments that overlook rural contextual needs. In response to these limitations, this study develops and validates a rural age-friendly public space evaluation system by coupling the Kano model with the Capability Maturity Model (CMM). Evaluation indicators were derived from in-depth interviews with rural older adults through three-level grounded-theory coding. The Kano model was then used to identify demand attributes and Better–Worse coefficients, while indicator weights were determined using an AHP–entropy method adjusted by Kano coefficients. Finally, a Kano–CMM correlation matrix was constructed to generate a five-level maturity evaluation model and applied to Xinan Village, Xiancun Town, Zengcheng District, Guangzhou. Results identify seven core dimensions: safety and protection; accessibility and mobility continuity; comfort and health friendliness; health and wellness support; social interaction and psychological belonging; environmental sanitation and quality; and maintenance, renewal, and durability. Rural older adults’ needs are dominated by must-be attributes, followed by attractive attributes, whereas one-dimensional attributes are comparatively less prominent. Safety- and accessibility-related indicators show the highest negative risk sensitivity, indicating that rural age-friendly design should prioritize risk reduction over experiential enhancement. The proposed framework moves beyond checklist-based assessment and provides an evidence-based, dynamic, and auditable tool for diagnosing and improving rural age-friendly public spaces. Full article

A huge effort has been directed towards research and development of new measurement systems for maximizing comfort and safety in public buildings by monitoring indoor air quality (IAQ). In fact, according to World Health Organization, exposure to chemical, biological, and physical agents in poorly ventilated spaces can lead to psycho-physical discomfort as well as respiratory and neurological diseases. Recent advances in the Internet of Things (IoT) have paved the ground for the design and implementation of distributed measurement systems with higher sensor density and computational capacity. While these systems provide accurate assessments of individual rooms, they do not account for personal exposure to varying air quality levels over time. In public buildings such as schools, universities, and workplaces, occupants frequently move between rooms according to predefined schedules, resulting in heterogeneous exposure patterns. To address this issue, this paper proposes an innovative IAQ measurement technique for public buildings, shifting the focus from room-based assessment to occupant-centered assessment. Unlike wearable or portable personal monitors, the proposed technique infers occupant location from the institutional timetable and combines it with the fixed sensor infrastructure already installed in the rooms, requiring no additional devices to be worn. Individual conditions are quantified through a new personalized metric that integrates instantaneous air quality, cumulative individual exposure over time, and thermal comfort into a single index that is evaluated against occupant-specific thresholds. The technique is validated using real-world data, demonstrating higher potential to ensure safety and comfort compared to the state of the art. Full article

Enclosed transport environments are becoming an integral part of compact urban structures; however, their air quality is monitored less systematically than that of the outdoor urban environment. This study evaluates particulate matter concentrations (PM₁, PM_2.5, PM₁₀) in two urban microenvironments in Banská Bystrica (Slovakia): an underground parking garage (representing private urban mobility) and an underground bus station (representing public transport). Continuous measurements using an enviDUST monitoring device were analysed in relation to occupancy rates and transport intensity. The results showed a dominance of the PM₁₀ fraction in both environments, suggesting the importance of non-exhaust sources and dust resuspension in enclosed urban transport spaces. In the parking facility, the immediate relationship between occupancy and PM concentrations was weak; however, a time lag effect was observed, indicating particle accumulation. The bus station exhibited higher average concentrations (PM₁: 8.67; PM_2.5: 14.12; PM₁₀: 34.12 µg/m³), while peak levels during nighttime highlighted the critical role of air stagnation and ventilation regimes. The study emphasizes the need to perceive such transport nodes as semi-public urban spaces requiring the integration of intelligent air quality management within sustainable urban development. Full article

The King Abdulaziz Public Transport Project in Riyadh is one of the massive undertakings that could transform mobility and quality of life in the Saudi capital. However, a full grasp of its many-sided consequences is still hard to obtain. The project is expected to deliver several positive outcomes, including decreased traffic congestion and better air quality, as well as increased mobility; however, it remains vital that the impact of this development on different aspects of urban life is studied using modern spatial analysis methods. This research seeks to address this gap by delving into the project’s influence on land use patterns, transportation behaviors, economic development, urban growth, environmental conditions, population dynamics, and road network efficiency. Using these techniques, some of the achievements and struggles of the project are identified in terms of service coverage, travel times, and how well it fits within Riyadh’s sustainability objectives to reduce car dependency and increase ridesharing. In conclusion, the study aims to contribute knowledge that supports urban planning, policy formulation, and future infrastructure projects so that the King Abdulaziz Public Transport Project is better aligned with the needs of this growing city for a workable, sustainable Riyadh. The King Abdulaziz Public Transport Project is a significant step towards improving Riyadh’s transportation system and achieving environmental, economic, and social sustainability goals. It will contribute to alleviating traffic congestion, improving air quality, boosting economic growth, and enhancing the quality of life for residents. Sustainable landuse planning around the stations, with the allocation of green spaces and public facilities, is essential. Full article