Search Results (14,217)

Against the backdrop of high-quality urbanization in cities, the rapid expansion of metro networks has led to severe spatial mismatches in land use around station areas, which seriously restricts the full exertion of the comprehensive benefits of the transit-oriented development (TOD) model. Taking 139 operational metro stations in Xi’an in 2024 as the research sample, this study constructs a multi-objective land use optimization model with the richness of public services, transportation accessibility and population distribution balance as the three core maximization objectives. A hierarchically adaptive improved NSGA-III algorithm is proposed, with the following four key technical optimizations implemented: multi-dimensional adaptive reference point adjustment, design of real-integer hybrid coding genetic operators, construction of an enhanced multi-criteria environmental selection mechanism, and dynamic regulation of algorithm iteration. Experimental results show that the performance of the improved algorithm is significantly superior to that of the traditional NSGA-III algorithm: the values of the three core objectives are increased by 59.58%, 12.94% and 7.35% respectively compared with the original data; the algorithm achieves stable convergence after 25 iterations, with the convergence efficiency improved by 30%. The obtained Pareto optimal front features good uniformity (U = 0.92) and coverage (C = 0.95), and all the 80 non-dominated solutions meet all constraint conditions, with the solution set highly coupled with the urban functional zoning and spatial planning of Xi’an. This study proposes a zoned, prioritized and phased hierarchical land use optimization strategy for the areas around metro stations in Xi’an. The research findings provide a replicable research framework and methodological reference for the TOD practice and land use optimization of metro station areas in other rapidly urbanizing central cities in China and developing countries worldwide with the characteristic of rapid rail transit expansion. Full article

The accurate and rapid prediction of hazard gas dispersion fields in urban environments is essential for guiding emergency sensor deployment and enabling real-time risk assessment. However, the computational cost associated with Computational Fluid Dynamics (CFD) simulations hinders their use as real-time forward models, while simplified Gaussian plume models lack the fidelity to resolve building obstruction effects. This study proposes a morphology-guided conditional Generative Adversarial Network (cGAN) framework designed to achieve real-time gas dispersion field modeling in urban environments with complex building configurations. The urban area is discretized into 50 × 50 m grid cells, each characterized by six morphological parameters describing building geometry. K-means clustering categorizes these cells into distinct morphological types. High-fidelity dispersion datasets are then generated for each type using Lattice Boltzmann Method (LBM) simulations. Each sample encodes building geometry, release location, wind speed, and time as multi-channel input images, with the corresponding gas dispersion concentration field is recorded as the output. Two cGAN architectures, Image-to-Image Translation (Pix2Pix) and its high-resolution variant (Pix2PixHD), are employed to learn the mapping from input features to dispersion fields. Model performance is evaluated using four complementary metrics: Fraction within a Factor of Two (FAC2) for prediction accuracy, Normalized Root Mean Square Error (NRMSE) for precision, Fractional Bias (FB) for systematic error, and Structural Similarity Index (SSIM) for spatial pattern fidelity. A case study is conducted across a 1176 km² urban district in China. The results demonstrate that under varying wind speeds (0.5–1.5 m/s) and temporal scales (5–60 s), and across five morphological categories, the Pix2PixHD-based model achieves 92.5% prediction accuracy and reproduces 97.6% of the spatial patterns. The proposed framework accelerates computation by approximately 18,000 times compared to traditional CFD, reducing inference time to under 0.1 s per scenario. This sub-second capability enables real-time concentration field estimation for emergency management, and provides a physically informed, computationally feasible forward model that can potentially support sensor-based gas source localization and detection network planning in complex urban environments. Full article

Objective: This retrospective observational four-year review (2019–2022) evaluated neonatal admissions to Malta’s NICU and their outcomes. Study Design: Data from neonates up to 28 days meeting NICU admission criteria with available EMRs were analyzed, focusing on demographic data such as gestation and birth weight, need for resuscitation at birth, admission reasons, and outcomes related to nutrition, respiratory support, congenital anomalies, prematurity-related complications, phototherapy, and infection. Results: Total admissions numbered 1303 (7.3% of total births), out of which 1234 had available electronic medical records and were included in the final analysis. The main reasons for admission were respiratory distress syndrome (27.7%), transient tachypnoea (16.3%), and sepsis (13.5%). Among preterm infants, conditions related to prematurity were observed at expected frequencies and are reported descriptively. Feeding practice resulted in delayed attainment of full enteral nutrition compared to international standards, with an exclusive breastfeeding rate below the EU average. Sepsis and CLABSI rates were low, indicative of robust infection prevention and control measures. Conclusions: This study provides a descriptive overview of NICU admissions and outcomes stratified by gestational age at a single tertiary center in Malta, and highlights areas for improvement. The findings highlight expected patterns of prematurity-related morbidity and differences in clinical management, particularly in nutritional and respiratory support. Future prospective studies incorporating standardized data collection and detailed maternal and demographic variables are needed to better inform neonatal care and service planning. Full article

While generative artificial intelligence (GenAI) is increasingly used by university students for writing support, less is known about its role in discipline-specific professional tasks. This study examines how pre-service primary teachers integrate and conceptualise GenAI when designing Teaching Units for English for Young Learners (EYL), with a focus on whether AI is positioned as a substitute for pedagogical reasoning or as a support within teacher decision-making. The qualitative study involved 75 fifth-year pre-service teachers at the Free University of Bozen-Bolzano (Italy), working in 23 groups. Data included 23 Teaching Units and 10 AI Use Reports, analysed through document analysis and thematic coding. GenAI was used mainly for material production (visual and text generation, idea generation, and text revision) and resource adaptation, with limited evidence of use for macro- or micro-planning decisions (objectives, sequencing, assessment). Prompts were often underspecified, but reports described iterative refinement and critical adaptation to improve age appropriateness and reduce lexical overload. Overall, within a transparent course framework, pre-service teachers retained pedagogical ownership while using GenAI as a supplementary resource, underscoring the need to develop pedagogically grounded AI literacy (prompt design, evaluation, and disclosure). Full article

Labor shortages and the need for increased productivity have accelerated the development of robotic harvesting systems for greenhouse crops; however, reliable operation under fruit occlusion and clustered arrangements remains a major challenge, particularly due to the limited integration between perception and motion planning modules. The paper presents the design and experimental validation of an autonomous robotic system for greenhouse tomato harvesting. The proposed platform integrates a rail-guided mobile base, a six-degrees-of-freedom robotic manipulator, and an adaptive end effector with a hybrid vision framework that combines convolutional neural networks and watershed-based segmentation to enable robust fruit detection and localization under occluded conditions. The proposed approach enables improved separation of overlapping fruits and provides accurate spatial localization through stereo vision combined with IMU-assisted camera-to-robot coordinate transformation. An occlusion-aware trajectory planning strategy was developed to generate collision-free manipulation paths in the presence of leaves and stems, enhancing harvesting safety and reliability. The system was trained and evaluated using a dataset of real greenhouse images supplemented with synthetic data augmentation. Experimental trials conducted under practical greenhouse conditions demonstrated a fruit detection precision of 96.9%, recall of 93.5%, and mean Intersection-over-Union of 79.2%. The robotic platform achieved an overall harvesting success rate of 78.5%, reaching 85% for unobstructed fruits, with an average cycle time of 15 s per fruit in direct harvesting scenarios. The rail-guided mobility significantly improved positioning stability and repeatability during manipulation compared with fully mobile platforms. The results confirm that integrating hybrid perception with occlusion-aware motion planning can substantially improve the functionality of robotic harvesting systems in protected cultivation environments. The proposed solution contributes to the advancement of automation technologies for greenhouse vegetable production and supports the transition toward more sustainable and labor-efficient agricultural practices. Full article

Background/Objectives: Japan’s mental healthcare system is characterized by the world’s highest number of psychiatric beds, widespread “social hospitalization,” and a structurally entrenched managerial support model that frequently undermines patient autonomy. Joint Crisis Plans (JCPs)—collaboratively developed crisis management documents—have been increasingly adopted as a care coordination tool; however, their role in transforming professional practice towards recovery-oriented support remains underexplored. This study aimed to elucidate the experiences of professionals utilizing JCPs across diverse facility types and to develop a theoretical understanding of the process by which they redefine their role from ‘manager’ to ‘recovery companion’. Methods: A qualitative design using the Modified Grounded Theory Approach (M-GTA), grounded in symbolic interactionism, was employed. Semi-structured interviews were conducted with 13 professionals (7 nurses, 6 mental health and welfare workers) across nine facilities (psychiatric hospitals, 24-h residential facilities, outpatient facilities) in the Kanto region of Japan. Theoretical sampling continued until saturation. Data were analyzed using the constant comparative method, with validity ensured through team checking. Results: Nine categories and 23 subcategories were extracted. A three-stage support transformation process emerged: (1) Stage of Motivation and Initial Support, in which professionals confronted the limitations of managerial practice; (2) Stage of Collaborative Role Redefinition and Practice, involving joint crisis management, strength-based support, and network building; and (3) Stage of Integration of Support Perspectives and Recovery-Oriented Practice, in which professionals witnessed individual recovery and integrated new support values into their practice. Negative cases revealed that JCP effectiveness is contingent on the co-construction of shared meaning rather than procedural compliance. Conclusions: JCP was suggested to function as a potential tool to facilitate navigating and reframing structural managerial barriers in Japanese mental healthcare. The creation of a shared language through JCP was associated with supporting conditions for individual self-determination, alleviating professional conflicts, and contributing to shifts in organizational culture. Full article

Deep learning is increasingly integrated into oral rehabilitation workflows, particularly in implant planning, prosthodontic design automation, and peri-implant diagnosis. However, reported performance is heterogeneous and difficult to compare across tasks, modalities, and validation designs. The goal of this study was to critically analyze deep learning architecture families applied to oral rehabilitation and to provide task-driven selection guidance supported by an evidence table reporting dataset characteristics, validation strategy, and performance metrics. A focused narrative review was conducted using transparent, database-specific search criteria (final n = 10 included studies), emphasizing implant planning (cone–beam computed tomography [CBCT]-based segmentation), prosthodontic design (intraoral scan [IOS]/mesh inputs), and peri-implant diagnosis (periapical/panoramic radiographs). Evidence certainty for each clinical task was assessed using GRADE-informed ratings (High/Moderate/Low/Very Low). Extracted variables included clinical task, imaging modality, dataset size, architecture, validation strategy (internal vs. internal + external), split level, ground truth protocol, and performance metrics. A structured computational and hardware feasibility analysis was conducted for each architecture family to support real-world deployment planning. Encoder–decoder networks (U-Net/nnU-Net) dominate CBCT segmentation for implant planning, while detection architectures (Faster R-CNN, YOLO) support implant localization and peri-implant assessment on radiographs. Generative models (3D GANs, transformer-based point-to-mesh networks) enable crown design from three-dimensional scans. Hybrid CNN–Transformer architectures show promise for multimodal CBCT–IOS fusion, though direct evidence from the included studies remains limited to a single study. External validation remains uncommon yet essential given the risk of domain shift. In conclusion, architecture selection should be anchored to task geometry (2D vs. 3D), artifact burden, and required clinical output type. Reporting standards should prioritize dataset transparency, validation rigor, multi-center external testing, and uncertainty-aware outputs. Full article

This study aims to examine the improvement of algebra problem-solving ability and metacognitive awareness among junior high school students through the use of visualization based on a deep learning approach. The research employed a quantitative method with a quasi-experimental design, specifically a pretest–posttest control group design. The population consisted of all students from public schools in Tangerang City, Indonesia. The sample comprised seventh-grade students studying algebra. A purposive sampling technique was used to determine the experimental and control groups, with a total sample size of 51 students. The instruments included an algebra problem-solving ability test consisting of nine essay questions and a metacognitive awareness questionnaire with 52 items. Data were collected using these two instruments, with a pretest administered before the intervention and a posttest administered afterward. Data analysis was conducted using a prerequisite test, continued with independent sample t-tests, nonparametric tests, ANCOVA, and multiple linear regression. The results based on statistics indicated a significant improvement in students’ algebra problem-solving ability with a large effect. Nevertheless, the absolute increase in problem-solving scores in the experimental group is very small (N-gain mean = 0.02). Additionally, metacognitive awareness was not found to be a significant predictor of problem-solving ability; instead, initial ability (pretest) emerged as the strongest predictor. Only understanding the problem has a moderate effect; planning strategies has a small effect, and otherwise there is no effect. In conclusion, the use of visualization-based worked examples with a deep learning approach has a statistically significant effect, but its impact on improving students’ abilities should be interpreted with caution. So the practical effects of the intervention are limited; however, metacognitive awareness is not the main predictor in algebra problem-solving ability. Full article

Due to their aesthetic qualities and versatile applications, medicinal and aromatic plants are an important component of landscape systems. The diversity of color, shape, and texture observed in the vegetative and reproductive organs of these plants contributes to visual composition, while their medicinal and aromatic properties enhance their ecological and socio-cultural significance. However, many taxa are underrepresented in landscape planning applications. This study examined the diversity of medicinal and aromatic plant taxa identified at the Iğdır University Şehit Bülent Yurtseven Campus in Iğdır Province, Turkey, using a descriptive approach. Plant taxa were evaluated based on their families, life forms, leaf characteristics, flowering periods, and medicinal and aromatic properties. Multivariate analyses were conducted to examine phenological similarities among the taxa. A total of 98 plant taxa were identified; 66 taxa possess only medicinal properties, one taxon possesses only aromatic properties, and 31 taxa possess both. These findings reveal that the campus is home to a wide variety of medicinal and aromatic plant taxa, with characteristics relevant to planting layout and species selection. Consequently, this study provides a descriptive foundation for further research on how such taxa can be incorporated into campus planting designs and green space planning. Full article

Extreme natural disasters may simultaneously disrupt power distribution infrastructures and their supporting communication systems, significantly degrading post-disaster recovery performance. To enhance coordinated restoration under such coupled failure conditions, this study proposes a unified optimization framework for pre-disaster deployment and post-disaster repair and service restoration in interdependent distribution–communication networks. First, an interdependency model is developed to characterize the physical and operational couplings between the distribution and communication networks. The impacts of communication outages on remotely controlled switches and repair crew dispatching are quantitatively analyzed, revealing how communication failures influence the restoration process. Based on this interdependency representation, a coordinated optimization model is established to jointly determine repair crew routing, mobile power allocation, and critical load restoration sequencing. The objective is to minimize cumulative outage losses over the recovery horizon, thereby achieving coordinated allocation and routing of multiple types of emergency repair resources. Furthermore, by jointly considering pre-disaster deployment planning and post-disaster restoration strategies, a two-stage emergency recovery framework is designed to integrate pre-event preparedness with post-event response for distribution networks. Case studies on a modified IEEE 33-bus cyber–physical distribution system demonstrate that the proposed coordinated restoration strategy restores approximately 50% of critical loads within the first 3 h, which is of direct significance for maintaining essential services such as hospitals and emergency shelters during the acute phase of a disaster. The proposed approach reduces the total load loss by 49.5% and shortens the restoration time by 120 min. In terms of pre-disaster deployment, the proposed strategy reduces average load shedding by 33.4% and 46.5% relative to the heuristic and random deployment strategies, respectively, demonstrating the effectiveness of proposed method for grid resilience enhancement. Full article

This paper presents a comprehensive life-cycle analysis of distribution transformers, based on realized measurements of the increased power losses as a result of their long-term service under real-world conditions. The study is based on aggregated measured data from extensive fleets of oil-immersed distribution transformers characterized by diverse designs, manufacturing vintages, and service lives. The evolution of no-load losses and short-circuit losses is analyzed as a function of operational duration, structural characteristics, and the specific technologies employed for windings and magnetic core construction. Statistical models describing the variation in these losses are presented, highlighting the limitations of the static assumptions commonly utilized in power distribution network planning. On this basis, an approximation of the time evolution of the transformer’s total power and energy losses is proposed as appropriate for implementation in a life-cycle analysis model. Furthermore, the impacts of thermal loading and abnormal operating conditions—such as unbalanced loads, frequent short circuits, and repeated overheating of the transformer oil—are analyzed as drivers of accelerated transformer aging. These effects are integrated into a unified life-cycle framework, enabling the quantitative assessment of loss variations and their associated operational expenditures (OPEX). A numerical example is provided to evaluate the cost-effectiveness of “repair vs. replacement” scenarios, utilizing a discounted cash flow analysis that incorporates a carbon component. The findings establish a methodological foundation for a broader assessment of technical condition and energy performance, identifying the optimal intervention point for repair or replacement to support decision-making for Distribution System Operators (DSOs) amidst increasing requirements for efficiency and decarbonization. Full article

Blue-green infrastructure in high-density communities has been found to be vital to the well-being of urban residents, particularly in 15 min walkable communities. However, residents’ environmental preferences for blue-green infrastructure in high-density urban areas have received little attention. This study uses a walking interview method with 90 participants to explore residents’ motivations, activities and preferences in both community and riverside green spaces. The study area centers on the Licun River and surrounding communities within a 15 min walking distance of the river in Qingdao, China, a high-density city promoting 15 min walkable communities. The findings showed that relaxation was the main reason for visiting both types of spaces. Riverside green spaces supported a wider variety of activities but notable differences in preferences for particular spaces, particularly across gender and age groups. Within community green spaces, artificial elements had a stronger impact on preferences, whereas in riverside green spaces, natural elements were more influential. Blue-green infrastructure planning in high-density cities should then consider diverse user needs by accounting for demographic differences and adapting design elements to various spatial contexts. Since a 15 min walk is not feasible for all residents, enhancing the safety, walkability and inclusivity of blue-green infrastructure is essential for everyday use. Full article

The dynamism of adventure tourism necessitates the precise identification of areas with suitable natural, infrastructural, and service capacities for hosting activities. The aim of this study is to assess the multi-scenario spatial suitability for the sustainable development of adventure tourism camps using a Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approach. The datasets used included topographic, climatic, environmental, accessibility, natural and cultural attraction, and service infrastructure indicators. The relevant criteria were first standardized, and their weights were determined using the Analytic Hierarchy Process (AHP). Subsequently, the layers were integrated through a Weighted Linear Combination (WLC) model. Four scenarios were designed for sensitivity analysis: the first scenario with balanced weight distribution (S_bal), the second prioritizing accessibility (S_acc), the third focusing on natural attractions (S_att), and the fourth emphasizing services (S_serv). The results indicated that approximately 21% and 9% of Chaharmahal and Bakhtiari province have high and very high potential for adventure activities, respectively, which were selected as initial options for the multi-scenario analysis. In the balanced (S_bal) scenario, 31% and 13% of the area of these options fell into high and very high suitability classes, respectively. The Service-Based Scenario (S_serv) increased the share of high and very high suitability areas to 34% and 19%, while Accessibility-Based Scenario (S_acc) reduced these classes to 27% and 10%. In the Attraction-Based Scenario (S_att), the areas in the high and very high suitability classes were 30% and 12%, respectively. The findings demonstrate that altering the priority of components can significantly change the spatial pattern of suitability, and sustainable planning of adventure tourism activities should be conducted based on management objectives and regional capacities. The proposed framework is generalizable to other regions and can serve as a basis for decision-making in balanced development, optimal infrastructure allocation, and sustainable management of adventure tourism. Full article

Background: Current endometriosis classification systems have important limitations in accurately describing total disease burden and predicting clinical outcomes. Existing staging frameworks often fail to integrate adenomyosis and do not adequately distinguish between genital and extragenital disease involvement. The aim of this article was to introduce the AGCES (American & Global College of Endometriosis Specialists) classification system, a novel framework designed to provide a more comprehensive and clinically meaningful approach to staging endometriosis. Methods: The AGCES classification system was developed through an expert consensus process involving scientific members of the American & Global College of Endometriosis Specialists (AGCES), informed by extensive surgical experience on thousands of endometriosis surgeries, synthesis of published evidence on disease pathophysiology and anatomical distribution, and systematic analysis of the limitations of existing classification systems (rASRM, ENZIAN, AAGL, EFI). Results: The framework integrates adenomyosis as a component of endometriosis staging and separates genital and extragenital disease into independent staging categories. Disease burden is reported using three parallel components representing adenomyosis (A), genital endometriosis (G), and extragenital endometriosis (E). A standardized operative reporting template and digital implementation through web-based applications were also developed to support clinical use. Conclusions: The AGCES classification system introduces a novel approach to endometriosis staging by integrating adenomyosis and separating genital and extragenital disease components. This framework provides a more complete assessment of disease burden and has the potential to improve clinical documentation, surgical planning, and research standardization in endometriosis care. Full article

Background: Vestibular schwannoma (VS) stereotactic radiosurgery (SRS) requires high target conformality and rapid dose falloff to spare adjacent organs at risk (OARs), particularly the brainstem. HyperArc (HA) is an automated noncoplanar volumetric-modulated arc therapy (VMAT) approach designed to standardize and streamline cranial SRS planning and delivery. We compared CyberKnife (CK) with HA for single-fraction VS SRS and evaluated the impact of multileaf collimator (MLC) leaf width. Methods: Fifteen VS cases previously treated with single-fraction CK SRS (12.5 Gy) were retrospectively replanned using HA. HA plans used four preconfigured noncoplanar partial arcs and were created with either a standard 5.0 mm MLC (HA-SMLC) or a 2.5 mm high-definition MLC (HA-HDMLC). HA plans were normalized to match the prescription dose target coverage of the corresponding CK plan for each of the patients. Endpoints included planning target volume (PTV) dosimetric statistics (Dmean, Dmin, Dmax, D98%), Paddick conformity index (PCI), Paddick gradient index (GI), ICRU Report 83 homogeneity index (HI), brain V12Gy, and brainstem Dmax. Because plans were generated for the same patients, paired comparisons were performed using two-sided Wilcoxon signed-rank tests (p < 0.05). Results: Both HA techniques achieved a higher near-minimum target dose than CK, with significantly higher PTV D98% (CK 12.35 ± 0.52 Gy; HA-SMLC 12.54 ± 0.28 Gy; HA-HDMLC 12.57 ± 0.35 Gy; p < 0.05). HA reduced target hotspots, with lower PTV Dmax than CK (CK 15.25 ± 0.32 Gy; HA-SMLC 14.70 ± 0.39 Gy; HA-HDMLC 14.73 ± 0.32 Gy; p < 0.05), and improved homogeneity and dose falloff as reflected by HI and GI (p < 0.05). CK achieved the highest conformity by PCI (p < 0.05), while HA-HDMLC improved PCI compared with HA-SMLC (p < 0.05). Brain V12Gy and brainstem Dmax were low and did not differ significantly among techniques. Conclusions: HA provides dosimetric performance comparable to CK for single-fraction VS SRS, with improved near-minimum PTV dose, reduced hotspots, and steeper dose gradients. Although CK showed the highest PCI overall, conformity improved with HA when a high-definition MLC was used. Overall, these findings support HA, particularly HA-HDMLC, as an efficient and clinically practical option for VS SRS treatment planning. Full article