Search Results (11,900)

Urban models support sustainable, resilient, and equitable planning, but their validity hinges on underlying spatial data. This study examines the epistemological and technical consequences of relying on two dominant yet divergent platforms—OpenStreetMap (OSM) and Google Maps—for extracting proximity-based amenities within the 15-min city framework. Across four European contexts—Versilia, Gothenburg, Nice, and Vienna—we compare (i) data completeness and spatial coverage; (ii) semantic categories; and (iii) the effects of data heterogeneity on accessibility modelling. Findings show that OSM, while semantically consistent and openly accessible, systematically underrepresents peripheral amenities, introducing bias towards urban cores in accessibility metrics. Conversely, Google Maps provides broader coverage but is constrained by dependencies on extraction methods, opaque data structures, and ambiguous classification schemes, which hinder reproducibility, reduce interpretability, and limit its analytical robustness. These divergences yield distinct accessibility landscapes and competing readings of functionality and spatial equity. We argue that data source choice and protocol design are epistemological decisions and advocate transparent, hybrid strategies with cross-platform semantic harmonisation to strengthen robustness, equity, and policy relevance. Full article

Background: Appendiceal adenocarcinoma is a rare malignancy, and data guiding its systemic treatment in metastatic settings are limited. This study aimed to determine the clinical outcomes, treatment efficacy, biomarkers, and prognostic factors in patients with metastatic or unresectable appendiceal adenocarcinoma receiving palliative chemotherapy. Methods: We retrospectively reviewed patients with metastatic appendiceal adenocarcinoma who received first-line palliative systemic chemotherapy at the Peter MacCallum Cancer Centre between January 2015 and December 2024. Results: Of the 40 patients included, fluoropyrimidine-based doublet regimens were most commonly used (82.5%) in first-line setting, achieving an objective response rate of 39.4%. Median overall survival (OS) was 21.6 months, and median first-line progression-free survival (PFS) was 8.9 months. 22 patients (55.0%) received second-line treatment. Median OS and PFS were 21.6 and 8.9 months, respectively, among patients treated with oxaliplatin-based doublet regimens, and 66.4 and 10.8 months, respectively, among those treated with irinotecan-based doublet regimens. Molecular biomarker testing was performed in 35 patients (87.5%). KRAS and NRAS mutations were identified in 68.6% and 2.9% of tested patients, respectively. Factors associated with poorer OS included male sex, elevated carcinoembryonic antigen levels, and overweight status. Bevacizumab use was not clearly associated with survival. Conclusions: Palliative systemic chemotherapy, particularly fluoropyrimidine-based doublet regimens, appears to be a reasonable and effective treatment option for patients with advanced appendiceal adenocarcinoma. Although this study was underpowered for formal comparison, the numerically longer OS and PFS of irinotecan-based regimens are hypothesis-generating and support further prospective evaluation. Molecular profiling emphasizes the need for personalized targeted therapeutic strategies. The identified prognostic factors may help guide risk stratification and patient counseling for treatment planning. Full article

The Atlantic blue crab (Callinectes sapidus) is an opportunistic invasive species in the Mediterranean that is negatively affecting biodiversity, fisheries, and tourism. In Italy, it is appreciated for its good meat quality, but the processing yield is low (21.87 ± 2.38%), generating a significant amount of by-products (72.45 ± 4.08%), which are underutilized. Valorizing this biomass is in line with circular economy principles and can improve both environmental and economic sustainability. This study aimed to valorize Atlantic blue crab by-products (BCBP), producing protein hydrolysates and assessing their in vitro bioactivities, in order to plan applications in animal food and related sectors. BCBP hydrolysates were obtained by enzymatic hydrolysis using Alcalase and Protamex enzymes. The treatment with Alcalase resulted in a higher degree of hydrolysis (DH = 23% in 205 min) compared to Protamex (DH = 14% in 175 min). Antioxidant activity of the hydrolisates was evaluated through DPPH, ABTS, reducing power and FRAP assays, as well as in vitro test in fibroblasts (HS-68). At 10 mg/mL, hydrolysates from both enzymes exhibited the maximum radical scavenging activity in DPPH and ABTS assays. In HS-68 cells, 0.5 mg/mL hydrolysates protected against H₂O₂-induced oxidative stress, showing a cell viability comparable to cells treated with 0.5 mM N-acetyl cysteine (NAC), as an antioxidant. Statistical analyses were performed using one-way ANOVA followed by Student–Newman–Keuls (SNK) or Games–Howell post hoc tests, with significance set at p < 0.05. Overall, both enzymes efficiently hydrolyzed BCBP proteins, generating hydrolysates with significant antioxidant activity and cytoprotective effects. These results demonstrate the potential to produce high-quality bioactive compounds from BCBPs, suitable for food, nutraceutical, and health applications. Scaling up this valorization process represents a viable strategy to improve sustainability and add economic value to the management of this invasive species, turning a problem in a resource. Full article

Dam collapse is a catastrophic event involving an artificial reservoir usually filled with water for hydropower or irrigation purposes. Several cases of dam collapses have overwhelmed entire valleys, reconfiguring their geomorphology, redesigning their landscape, and causing several thousand casualties. These episodes led to more careful regulations and the activation of more effective monitoring and mitigation strategies. A fundamental tool in defining appropriate procedures for alert and risk scenarios is the Dam Emergency Plan (PED), an operational document that establishes the actions and procedures required to manage potential hazards (e.g., geo-hydrological and seismic risk). The aim of this study is to describe a reference methodology for identifying geo-hydrological criticalities based on historical and geomorphological data, applied to civil protection activities. A further objective is to provide a structured inventory of Italian reservoirs, assigning each a potential risk index based on an analytical approach considering several factors (age and construction methodology of the dam, morphological and environmental settings, anthropized environment, and exposed population). The approach identifies that the most significant change in risk over time is not only the dam itself but also the transformation of the territory. This methodology does not incorporate probabilistic forecasting of flood or climate change; instead, it objectively characterizes the exposed territory, offering insights into existing vulnerabilities on which to base effective mitigation strategies. Full article

Generative Artificial Intelligence (GAI) has become a topic of increasing societal and academic relevance, with its rapid diffusion reshaping public debate, policymaking, and scholarly inquiry across diverse disciplines. Building on this context, the present study explores the factors influencing GAI adoption among Spanish digital natives (Millennials and Zoomers), using data from a large national survey of 1533 participants (average age = 33.51 years). The theoretical foundation of this research is the Theory of Planned Behavior (TPB). Accordingly, the study examines how perceived usefulness (USEFUL), innovativeness (INNOV), privacy concerns (PRI), knowledge (KNOWL), perceived social performance (SPER), and perceived need for regulation (NREG), along with gender (FEM) and generational identity (GENZ), influence the frequency of GAI use. A mixed-methods design combines ordered logistic regression to assess average effects and fuzzy set qualitative comparative analysis (fsQCA) to uncover multiple causal paths. The results show that USEFUL, INNOV, KNOWL, and GENZ positively influence GAI use, whereas NREG discourages it. PRI and SPER show no statistically significant differences. The fsQCA reveals 17 configurations leading to GAI use and eight to non-use, confirming an asymmetric pattern in which all variables, including PRI, SPER, and FEM, are relevant in specific combinations. These insights highlight the multifaceted nature of GAI adoption and suggest tailored educational, communication, and policy strategies to promote responsible and inclusive use. Full article

Traffic sign detection is a crucial task for autonomous driving perception systems, as it directly impacts vehicle path planning and safety decisions. Existing algorithms face challenges such as feature information attenuation and model lightweighting requirements in the detection of small traffic signs at long distances. To address these issues, this paper proposes a dual-pooling dynamic grouping (DPDG) module. This module dynamically adjusts the number of groups to adapt to different input features, combines global average pooling and max pooling to enhance channel attention representation, and uses a lightweight 3 × 3 convolution-based spatial branch to generate spatial weights. Based on a hierarchical optimization strategy, the DPDG module is integrated into the YOLOv10n network. Experimental results on the traffic sign dataset demonstrate a significant improvement in the performance of the YOLO-DPDG network: Compared to the baseline YOLOv10n model, mAP@0.5 and mAP@0.5:0.95 improved by 8.77% and 10.56%, respectively, while precision and recall were enhanced by 6.16% and 6.62%, respectively. Additionally, inference speed (FPS) increased by 11.1%, with only a 4.89% increase in model parameters. Compared to the YOLOv10-Small model, this method achieves a similar detection accuracy while reducing the number of model parameters by 64.83%. This study provides a more efficient and lightweight solution for edge-based traffic sign detection. Full article

E-commerce is quickly increasing purchasing behavior across the globe, but little is known about how psychological paradigms underscore online buying intentions for locally essential items as nomadic local foods. The primary goal of this research is to examine the effects of some important psychological constructs and motivational values on predicting consumers’ intention to purchase nomadic and local foods via online e-commerce platforms, such as Ashayershop. This study followed the Theory of Planned Behavior (TPB) and looked at direct and mediated effects of attitudes, perceived behavioral control, and subjective norms on intention to purchase. Structural Equation Modeling (SEM) was conducted, based on data collected from a representative sample of consumers who were familiar with online shopping for local foods. The results highlight that attitude towards online shopping for local foods was the strongest direct predictor of intention to purchase (β = 0.383, T = 9.487, p < 0.001). Perceived behavioral control (β = 0.220, T = 5.316, p < 0.001), hedonic value (β = 0.213, T = 4.907, p < 0.001), utilitarian value (β = 0.187, T = 3.719, p < 0.001), and subjective norms (β = 0.149, T = 3.493, p < 0.001), received a significant positive effect on intention. In addition, hedonic and utilitarian values bountifully mediated the relation between psychological antecedents (attitudes, perceived behavioral control, and subjective norms) and purchase intention. For instance, attitude indirect effect via hedonic value was β = 0.080 (T = 3.783, p < 0.01), and indirect effect via utilitarian value was β = 0.040 (T = 3.058, p < 0.01), indicating the importance of these values as mediators. This research makes a contribution to the literature by showing that motivational values serve as not only an outcome but also as cognitive–affective mediators in the behavioral process thus expanding the TPB in the context of digital food markets. In general, these results provide valuable insights to e-commerce platforms and policymakers who desire to promote consumer engagement with products stemming from culture and tradition on line by developing new integrated strategies that address the cognitive, emotional, and social components. Full article

This paper presents an optimization-based scheduling strategy for battery energy storage systems (BESS) in alternating current microgrids, considering both grid-connected and islanded operation. The study addresses two independent objectives: minimizing energy losses in the distribution network and reducing carbon dioxide emissions from dispatchable power sources. The problem is formulated using a full AC power flow model that simultaneously manages active and reactive power flows in BESS located in the microgrid, while enforcing detailed operational constraints for network components, generation units, and storage systems. To solve it, a parallel implementation of the Particle Swarm Optimization (PPSO) algorithm is applied. The PPSO is integrated into the objective functions and evaluated through a 24-h scheduling horizon, incorporating a strict penalty scheme to guarantee compliance with technical and operational limits. The proposed method generates coordinated charging and discharging plans for multiple BESS units, ensuring voltage stability, current limits, and optimal reactive power support in both operating modes. Tests are conducted on a 33-node benchmark microgrid that represents the power demand and generation from Medellín, Colombia. This is compared with two methodologies reported in the literature: Parallel Crow Search and Parallel JAYA optimizer. The results demonstrate that the strategy produces robust schedules across objectives, identifies the most critical network elements for monitoring, and maintains safe operation without compromising performance. This framework offers a practical and adaptable tool for microgrid energy management, capable of aligning technical reliability with environmental goals in diverse operational scenarios. Full article

Ecological restoration of the Loess Plateau plays a pivotal role in mitigating land degradation and promoting regional sustainability. In this study, landscape pattern metrics were integrated into the MaxEnt model to evaluate the influence of landscape configuration on restoration planning. Nine representative species from three vegetation strata—herbs, shrubs, and trees—were selected based on ecological suitability. A comprehensive set of variables, including environmental, anthropogenic, and landscape metrics, was constructed for modeling. Results demonstrate that incorporating landscape metrics significantly enhanced the spatial explanatory power, providing a robust supplement to traditional ecological restoration assessments. Distinct responses to landscape structure were observed among vegetation types: herb species were more sensitive to patch aggregation and connectivity, shrubs preferred regular edges and larger patch size, while tree species favored extensive, low-fragmentation core habitats. Vertical structure optimization revealed that while large areas were suitable for single vegetation layers, composite vegetation configurations were more appropriate in certain central and southern subregions. These findings underscore the importance of landscape structure in guiding restoration strategies and serve as a basis for designing ecologically coherent and spatially targeted vegetation restoration plans on the Loess Plateau. Full article

Areas characterised by high ecological and cultural value are increasingly exposed to overtourism and intensifying land-use pressures, often exacerbated by mobility policies aimed at enhancing regional accessibility and promoting tourism. These dynamics create spatial tensions, particularly in environmentally sensitive areas such as those within the Natura 2000 network and Sites of Community Importance (SCIs), where intensified visitor flows, and infrastructure expansion can disrupt the balance between conservation and development. This study offers a geospatial analysis of the current state (2024) of such dynamics in the Lazio Region (Italy), evaluating the effects of mobility strategies on ecological vulnerability and tourism pressure. By applying isochrone-based accessibility modelling, GIS buffer analysis, and spatial overlays, the research maps the intersection of accessibility, heritage value, and environmental sensitivity. The methodology enables the identification of critical zones where accessibility improvements coincide with heightened ecological risk and tourism-related stress. The original contribution of this work lies in its integrated spatial framework, which combines accessibility metrics with indicators of ecological and heritage significance to visualise and assess emerging risk areas. The Lazio Region, distinguished by its heterogeneous landscapes and ambitious mobility planning initiatives, constitutes a significant case study for examining how policy-driven improvements in transport infrastructure may inadvertently exacerbate spatial disparities and intensify ecological vulnerabilities in peripheral and sensitive territorial contexts. The findings support the formulation of adaptive, place-based policy recommendations aimed at mitigating the unintended consequences of accessibility-led tourism strategies. These include prioritising soft mobility, enhancing regulatory protection in high-risk zones, and fostering coordinated governance across sectors. Ultimately, the study advances a replicable methodology to inform sustainable territorial governance and balance tourism development with environmental preservation. Full article

Life cycle cost (LCC) analysis has become a key tool for evaluating the long-term economic and environmental performance of built assets, yet its application in marinas and marine infrastructure remains underdeveloped. This review provides the first structured attempt to apply LCC to marina infrastructure, addressing the lack of sector-specific models for pontoons, mooring systems, and marina operations. It also synthesizes research on LCC methodologies, challenges, and emerging trends relevant to coastal facilities, with a particular focus on pontoons, mooring systems, and marina management practices. Studies reveal persistent barriers to effective implementation, including fragmented data systems, inconsistent regulations, and limited sector-specific tools. Existing models, largely adapted from other construction contexts, often overlook the unique technical, environmental, and operational demands of marine assets. The review critically examines international standards, procurement frameworks, and methodological approaches, highlighting opportunities to integrate sustainability considerations and address gaps in cost forecasting. It also identifies the need for standardized data collection practices and risk-based maintenance strategies tailored to harsh marine environments. By mapping current knowledge and methodological limitations, this work provides a foundation for developing more accurate, sector-specific LCC models and guidance. This literature review contributes to the advancement of sustainable coastal infrastructure planning by consolidating scattered research, emphasizing knowledge gaps, and outlining priorities for future studies, supporting policymakers, practitioners, and researchers seeking to optimize investment decisions in marinas and related facilities. Full article

The purpose of this study is to propose and critically assess a sustainable urban regeneration model for the redevelopment of the former K-2 military airbase in Daegu, Korea. Large-scale idle military sites pose significant challenges in terms of ecological remediation, social integration, and economic transformation, but also offer opportunities for redefining urban identity and global competitiveness. To address this, we develop the concept of the “Global Contents City,” a planning framework that integrates cultural exchange, creative industries, education, and tourism within a sustainable urban ecosystem. The research employs a qualitative methodology that combines theoretical review, comparative analysis of international precedents (e.g., Munich-Riem, Tempelhof, Stapleton, and Toronto), and design-oriented masterplanning. The findings highlight design strategies that spatially interconnect cultural, educational, industrial, and ecological functions while reinforcing low-carbon infrastructure and green open space. By situating the Daegu K-2 case in an international context, the study demonstrates how lessons from post-military redevelopments can be adapted to Korea, contributing to both scholarly debates and practical frameworks for sustainable city-making. Full article

With the large-scale deployment of 5G technology, the rationality of communication base station siting is crucial for network performance, construction costs, and operational efficiency. Traditional site selection methods rely heavily on manual experience, exhibiting strong subjectivity and difficulty in balancing multi-objective optimization. Existing heuristic algorithms suffer from slow convergence speeds and susceptibility to local optima. To address these challenges, this paper constructs a multi-objective base station site selection model that simultaneously minimizes costs, maximizes coverage contributions, and minimizes interference. It achieves quantitative balance among objectives through normalization and weight fusion, while introducing constraints to ensure engineering feasibility. Concurrently, the genetic algorithm underwent targeted optimization by introducing an adaptive migration strategy based on population diversity and a cosine-type parameter adjustment strategy. This approach was integrated with the particle swarm optimization algorithm to balance exploration and exploitation while mitigating premature convergence. Experimental validation demonstrates that the improved algorithm achieves faster convergence and greater stability compared to traditional genetic algorithms and particle swarm optimization, while satisfying engineering constraints such as base station quantity, coverage, and interference. This research provides an efficient and feasible solution for intelligent base station site planning. Full article

Background: Multimodal Deep learning has emerged as a crucial method for automated patient-specific quality assurance (PSQA) in radiotherapy research. Integrating image-based dose matrices with tabular plan complexity metrics enables more accurate prediction of quality indicators, including the Gamma Passing Rate (GPR) and dose difference (DD). However, modality imbalance remains a significant challenge, as tabular encoders often dominate training, suppressing image encoders and reducing model robustness. This issue becomes more pronounced under task heterogeneity, with GPR prediction relying more on tabular data, whereas dose difference prediction (DDP) depends heavily on image features. Methods: We propose BMMQA (Balanced Multi-modal Quality Assurance), a novel framework that achieves modality balance by adjusting modality-specific loss factors to control convergence dynamics. The framework introduces four key innovations: (1) task-specific fusion strategies (softmax-weighted attention for GPR regression and spatial cascading for DD prediction); (2) a balancing mechanism supported by Shapley values to quantify modality contributions; (3) a fast network forward mechanism for efficient computation of different modality combinations; and (4) a modality-contribution-based task weighting scheme for multi-task multimodal learning. A large-scale multimodal dataset comprising 1370 IMRT plans was curated in collaboration with Peking Union Medical College Hospital (PUMCH). Results: Experimental results demonstrate that, under the standard 2%/3 mm GPR criterion, BMMQA outperforms existing fusion baselines. Under the stricter 2%/2 mm criterion, it achieves a 15.7% reduction in mean absolute error (MAE). The framework also enhances robustness in critical failure cases (GPR < 90%) and achieves a peak SSIM of 0.964 in dose distribution prediction. Conclusions: Explicit modality balancing improves predictive accuracy and strengthens clinical trustworthiness by mitigating overreliance on a single modality. This work highlights the importance of addressing modality imbalance for building trustworthy and robust AI systems in PSQA and establishes a pioneering framework for multi-task multimodal learning. Full article

The wind–solar–storage–thermal combined power generation system is one of the key measures for China’s energy structure transition, and rational capacity planning of each generation entity within the system is of critical importance. First, this paper addresses the uncertainty of wind and photovoltaic (PV) power outputs through scenario-based analysis. Considering the diversity of generation entities and their complex interest demands, a bi-level capacity optimization framework based on game theory is proposed. In the upper-level framework, a game-theoretic method is designed to analyze the multi-agent decision-making process, and the objective function of capacity allocation for multiple entities is established. In the lower-level framework, multi-objective optimization is performed on utility functions and node voltage deviations. The Nash equilibrium of the non-cooperative game and the Shapley value of the cooperative game are solved to study the differences in the capacity allocation, economic benefits, and power supply stability of the combined power generation system under different game modes. The case study results indicate that under the cooperative game mode, when the four generation entities form a coalition, the overall system achieves the highest supply stability, the lowest carbon emissions at 30,195.29 tons, and the highest renewable energy consumption rate at 53.93%. Moreover, both overall and individual economic and environmental performance are superior to those under the non-cooperative game mode. By investigating the capacity configuration and joint operation strategies of the combined generation system, this study effectively enhances the enthusiasm of each generation entity to participate in the energy market; reduces carbon emissions; and promotes the development of a more efficient, environmentally friendly, and economical power generation model. Full article