Search Results (1,275)

Freshwater fish habitat simulation is a vital technology for assessing the state and dynamics of aquatic ecosystems under changing environments. Based on a comprehensive dataset spanning 1991–2024, this study constructs a domain knowledge map by integrating co-citation analysis, keyword burst detection, and social network metrics. The bibliometric results quantitatively identify leading contributors and trace the field’s exponential growth. Complementing this, a critical technical review reveals a significant paradigm shift in modeling methodologies: moving from traditional univariate suitability curves to advanced multivariate and artificial intelligence (AI)-based frameworks. Despite these advancements, our analysis highlights critical gaps in addressing habitat connectivity and broad environmental stressors. To overcome these limitations, we propose a novel framework that integrates landscape pattern indices with circuit theory to quantify habitat patch arrangement and ecological flows. Furthermore, we advocate for future research to explicitly incorporate climate change scenarios (e.g., thermal regime shifts) and geomorphological processes. This study offers both a macroscopic overview of the discipline’s evolution and a roadmap for developing robust, ecosystem-based management tools. Full article

The proliferation of biometric authentication systems in critical security applications has highlighted the urgent need for robust defense mechanisms against sophisticated adversarial attacks. This paper presents ShieldNet, an adversarially resilient Convolutional Neural Network (CNN) framework specifically designed for secure iris biometric authentication. Unlike existing approaches that apply adversarial training or gradient regularization independently, ShieldNet introduces a synergistic dual-layer defense framework featuring three key components: (1) an attack-aware adaptive weighting mechanism that dynamically balances defense priorities across multiple attack types, (2) a smoothness-regularized gradient penalty formulation that maintains differentiable gradients while encouraging locally smooth loss landscapes, and (3) a consistency loss component that enforces prediction stability between clean and adversarial inputs. Through extensive experimental validation across three diverse iris datasets, MMU1, CASIA-Iris-Africa, and UBIRIS.v2, and rigorous evaluation against strong adaptive attacks including AutoAttack, PGD-100 with random restarts, and transfer-based black-box attacks, ShieldNet demonstrated robust performance, achieving 87.3% adversarial accuracy under AutoAttack on MMU1, 85.1% on CASIA-Iris-Africa, and 82.4% on UBIRIS.v2, while maintaining competitive clean data accuracies of 94.7%, 93.9%, and 92.8%, respectively. The proposed framework outperforms existing state-of-the-art defense methods including TRADES, MART, and AWP, achieving an equal error rate (EER) as low as 2.8% and demonstrating consistent robustness across both gradient-based and gradient-free attack scenarios. Comprehensive ablation studies validate the complementary contributions of each defense component, while latent space analysis confirms that ShieldNet learns genuinely robust feature representations rather than relying on gradient obfuscation. These results establish ShieldNet as a practical and reliable solution for deployment in high-security biometric authentication environments. Full article

Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min–max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training–testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics—accuracy, precision, recall, F1-score, specificity, and AUC—alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care. Full article

The rise of “Internet + Recycling” platforms is transforming the domestic waste management landscape, creating dual-channel reverse supply chains where new platforms interact with traditional recyclers. However, these platforms face critical strategic decisions regarding their service portfolios (convenient but costly door-to-door vs. economical fixed-point drop-off) and their relationship with incumbents (cooperation vs. competition). This study aims to determine the optimal pricing, service level, and relationship strategies for an “Internet + Recycling” center to maximize profitability under the influence of consumer channel preferences and government subsidies. We developed four Stackelberg game-theoretic models representing different scenarios of service modes (fixed-point only vs. fixed-point with door-to-door) and relationship structures (cooperation vs. competition). We derived equilibrium solutions for recycling prices, service levels, and profits. Our results reveal that while cooperation generally leads to higher systemic profits, the addition of a door-to-door service significantly alters the strategic landscape. We find that a higher consumer preference for the platform channel allows the center to lower prices while increasing profits, and that government subsidies are the most effective at enhancing service levels in cooperative models. Crucially, intense competition incentivizes recycling centers to reduce rather than increase their service levels to cut costs. This research provides a decision-making framework for recycling enterprises to select optimal service and competitive strategies. It also offers insights for policymakers on how to design subsidies to effectively promote high-convenience recycling services and foster a more efficient circular economy. Full article

Wildfires are increasingly recognized as a climatological hazard, able to threaten industrial and critical infrastructure safety and operations and lead to Natech disasters. Future projections of exacerbated fire regimes increase the likelihood of Natech disasters, therefore increasing expected direct damage costs, clean-up costs, and long-term economic losses due to business interruption and environmental remediation. While large industrial complexes, such as oil, gas, and chemical facilities have sufficient resources for the implementation of effective prevention and mitigation plans, small-to-medium-sized industrial hubs are particularly vulnerable due to their scattered distribution and limited resources for investing in comprehensive fire prevention systems. This study targets the vulnerability of these communities by proposing the deployment of Wireless Sensor Networks (WSNs) as cost-effective Early Wildfire Detection Systems (EWDSs) to safeguard wildland and industrial domains. The proposed approach leverages wildland–industrial interface (WII) geospatial data, simulated wildfire dynamics data, and mathematical optimization to maximize detection efficiency at minimal cost. The WII delimits the boundary where the presence of wildland fires impacts industrial activity, thus representing a proxy for potential Natech disasters. The methodology is tested in Cocentaina, Spain, a municipality characterized by a highly flammable Mediterranean landscape and medium-scale industrial parks. Results reveal the complex trade-offs between detection characteristics and the degree of protection in the combined wildland and WII areas, enabling stakeholders to make informed decisions. This methodology is easily replicable for any municipality and industrial installation, or for generic wildland–human interface (WHI) scenarios, provided there is access to wildfire dynamics data and geospatial boundaries delimiting the areas to protect. Full article

Rapid urbanization has exacerbated urban-stormwater challenges, highlighting the critical need for coordinated surface-water and groundwater management through rainfall recharge. However, current sponge city construction methods often overlook the crucial role of underground aquifers in regulating the water cycle and mostly rely on simplified engineering approaches. To address these limitations, this study proposes a spatial optimization framework for urban-scale sponge systems that integrates a hydrodynamic model (FVCOM), geographic information systems (GIS), and Monte Carlo simulations. This framework establishes a comprehensive evaluation system that synergistically integrates surface water inundation depth, geological lithology, and groundwater depth to quantitatively assess sponge city suitability. The FVCOM was employed to simulate surface water inundation processes under extreme rainfall scenarios, while GIS facilitated spatial analysis and data integration. The Monte Carlo simulation was utilized to optimize the spatial layout by objectively determining factor weights and evaluate result uncertainty. Using Shenzhen City in China as a case study, this research combined the “matrix-corridor-patch” theory from landscape ecology to optimize the spatial structure of the sponge system. Furthermore, differentiated planning and management strategies were proposed based on regional characteristics and uncertainty analysis. The research findings provide a replicable and verifiable methodology for developing sponge city systems in high-density urban areas. The core value of this methodology lies in its creation of a scientific decision-making tool for direct application in urban planning. This tool can significantly enhance a city’s climate resilience and facilitate the coordinated, optimal management of water resources amid environmental changes. Full article

Informal learning increasingly occurs in digital environments, where citizens access, evaluate and apply knowledge outside of formal education. In the context of disaster preparedness, such informal learning is crucial for promoting individual and collective self-protection. This study examines how disaster preparedness knowledge is represented in German-language online resources, and how these materials can be categorised from an adult education perspective. An exploratory mixed-methods design combining expert-guided sampling, a qualitatively developed coding scheme, large-scale web scraping and AI-assisted classification was employed. A total of 7305 webpages were analysed in terms of actor type, topic, media format, and didactic design. The findings suggest that government and commercial organisations dominate the online preparedness landscape, with limited contributions from civil society and individuals. Thematically, most resources focus on general preventive measures and checklists, whereas scenario-specific and procedural content is underrepresented. Didactically rich and interactive formats are rare, with most materials relying on static, text-based communication. From an adult education perspective, these results suggest a gap between raising awareness and active learning. While online resources offer easy access to preparedness knowledge, they rarely facilitate deeper understanding, participation or collaborative learning. Methodologically, the study illustrates how AI-assisted analysis can combine qualitative interpretive depth with computational scalability in educational research. Full article

Digital technology is profoundly transforming the experiential landscape of tourism. However, its application does not necessarily produce cultural experiences, creating a critical bottleneck that constrains the sustainable development of the cultural tourism industry and broader societal culture. To address this gap, this study introduces psychological ownership theory as an overarching explanatory framework. It constructs and validates an integrated model that examines how digital technology characteristics (interactivity and innovativeness) influence cultural experience through three parallel mediating pathways: cognitive evaluation (perceived usefulness and ease of use), scenario construction, and flow experience. Based on 540 visitor questionnaires, structural equation modeling validated the theoretical model. Findings reveal that the interactivity and innovation of digital technology jointly stimulate visitors’ psychological ownership through three parallel pathways. Specifically, technological innovativeness exhibited the strongest effect on perceived ease of use (β = 0.387, p < 0.001), while the indirect effect via the flow experience path was also significant (effect size = 0.036). This process stimulates visitors’ psychological ownership, ultimately leading to cultural experiences. The study systematically reveals the pathways through which digital technology empowers cultural experiences across three dimensions: as a rational tool, an emotional narrative medium, and an intrinsic psychological catalyst. It highlights that strategically allocating technological resources to cultivate visitors’ psychological ownership is crucial for driving high-quality industrial development. Furthermore, the research offers significant implications for cultural sustainability, suggesting that such internally motivated identification provides a more effective foundation for the living transmission of culture and socio-cultural sustainability than external regulations or imposed norms. Full article

As semiconductor process nodes shrink to 3 nm and beyond, traditional optical proximity correction (OPC) and resolution enhancement technologies (RETs) can no longer meet the high patterning precision needs of advanced chip manufacturing due to the sub-wavelength lithography limits. Inverse lithography technology (ILT), a key part of computational lithography, has become a critical solution for these issues. From an EDA industry perspective, this review provides an original and systematic summary of ILT’s development and applications, which helps integrate the scattered research into a clear framework for both academic and industrial use. Compared with traditional OPC, the latest ILT has three main advantages: (1) better patterning accuracy, as a result of the precise optical models that fix complex optical issues (like diffraction and interference) in advanced lithography systems; (2) a wider process window, as it optimizes mask designs by working backwards from the target wafer patterns, making lithography more stable against process changes; and (3) stronger adaptability to new lithography scenarios, such as High-NA EUV and extended DUV nodes. This review first explains ILT’s working principles (the basic concepts, mathematical formulae, and main methods like level-set and pixelated approaches) and its development history, highlighting key events that boosted its progress. It then analyzes ILT’s current application status in the industry (such as hotspot fixing, full-chip trials, and EUV-era use) and its main bottlenecks: a high computational complexity leading to long runtime, difficulties in mask manufacturing, challenges in model calibration, and a conservative market that slows large-scale adoption. Finally, it discusses promising future directions, including hybrid ILT-OPC-SMO strategies, improving model accuracy, AI/ML-driven design, GPU acceleration, multi-beam mask writer improvements, and open-source data to solve data shortage problems. By combining the latest research and industry practices, this review fills the gap of comprehensive ILT summaries that cover the principles, progress, applications, and prospects. It helps readers fully understand ILT’s technical landscape and offers practical insights for solving the key challenges, thus promoting ILT’s industrial use in advanced chip manufacturing. Full article

This article analyzes the applications of computational fluid dynamics (CFD) in addressing the issue of flow patterns in a realistic urban landscape, specifically in the Metropolitan Area of Monterrey. CFD enables the simulation of physical phenomena such as turbulence, which is useful for studying the transport behavior of pollutants in urban environments. The computational model was obtained from satellite imaging and covered a surface of about 1.134 km × 1.227 km. It was composed of 173 urban blocks, representing around 3570 houses, including hospitals, schools, recreation centers and other gathering places. The population of the urban landscape was estimated at around 11,400 inhabitants. Three velocity scenarios, low, average, and high (air gusts), were simulated, using data from a local weather station. The Reynolds numbers (Re) ranged from 1.9 × 10⁶ to 21.2 × 10⁶, falling within the fully developed turbulence regime, which was modeled using the renormalization group (RNG) k–ε turbulence model. Results showed that the mean velocity patterns were preserved independent of the Reynolds number (Re) and were characterized by regions of high velocity in the main avenues, as well as other regions of low velocity between urban blocks. This methodology may also be applicable for understanding the flow patterns of similar urban regions composed of irregularly arranged low-rise blocks. Full article

Recognizing the evolution of landscape patterns in the Chishui River source region is essential for protecting ecosystems and sustainable growth in the Yangtze River Basin and other similar areas. However, knowledge of landscape pattern evolution within the primary channel zone remains insufficient. To address this gap, the current study used 2000–2020 land-use, geography, and socio-economic data, integrating landscape pattern indices, land-use transfer matrices, dynamic degree, the GeoDetector model, and the PLUS model. Results revealed that forest and cropland remained the prevailing land-use types throughout 2000–2020, comprising over 85% of the landscape. Grassland had the highest dynamic degree (1.58%), and landscape evolution during the study period was characterized by increased fragmentation, enhanced diversity, and stable dominance of major forms of land use. Anthropogenic influence on different landscape types followed the order: construction land > cropland > grassland > forest > water bodies. Land-use change in this region is a complex process governed by the interrelationships among various factors. Scenario-based predictions demonstrate pronounced variability in various land types. These findings provided a more comprehensive understanding of landscape patterns in karst river source regions, provided evidence-based support for regional planning, and offered guidance for ecological management of similar global river sources. Full article

In the context of advanced BRAF-mutant melanoma, the treatment landscape has undergone a paradigm shift due to the impact of immune checkpoint inhibitors and BRAF/MEK inhibitors. This article presents three clinically illustrative melanoma cases that served as focal points for in-depth discussions during 12 expert meetings held across Spain. These include a treatment-naïve metastatic melanoma patient, a patient experiencing a recurrence while on anti-PD-1 adjuvant therapy, and a third patient whose melanoma relapsed ≥6 months after the end of adjuvant therapy. The discussions revolved around optimal treatment sequencing, emphasizing the challenges and alternatives discussed in each scenario. The common view aligned towards a nuanced approach that involves navigating the complexities of treatment choices. The conclusions underscore the need for personalized therapeutic strategies and highlight the ongoing challenge of refining real-life evidence-based algorithms for the management of metastatic BRAF-mutant melanoma. Full article

The rapid economic and urban development in the Yellow River Delta Efficient Ecological Economic Zone (YRDEEZ) has intensified land use changes and aggravated ecological patch fragmentation. Constructing ecological networks (ENs) can reconnect fragmented patches and enhance ecosystem services. This study simulated land use patterns for 2040 under three scenarios: Natural Development (NDS), Ecological Protection (EPS), and Urban Development (UDS). Results indicated a consistent decline in agricultural land and an expansion of urban land across all scenarios, with the most pronounced urban growth under UDS (6.79%) and the largest ecological land area under EPS (5178.96 km²). Since 2000, the number of EN sources and corridors had decreased, with sources mainly concentrated along coastal areas. The source and corridor under UDS exhibited the highest area ratio (20.08%), while NDS showed the lowest (18.72%), with UDS demonstrating the strongest resilience. Through community detection, the UDS EN was divided into five ecological clusters, encompassing 127 intra-cluster corridors (2285.95 km) and 34 inter-cluster corridors (1171.32 km), among which the cluster near the Yellow River estuary was determined to be the most critical (Level 1). These findings will provide valuable insights for managing landscape fragmentation and biological habitat protection in YRDEEZ. Meanwhile, the multi-scenario simulations of ENs could play an important role in constructing ecological security patterns and protecting ecosystems. Full article

Urban expansion and its driving factors are frequently analyzed within administrative regions to inform regional urban planning, yet such analyses often fall short at the natural basin scale (referring to the spatial extent defined by hydrological drainage boundaries) due to the scarcity of statistical data. Geographic and socio-economic spatial data can offer more detailed information across various research scales compared to traditional data (such as administrative statistical data, survey-based data, etc.), providing a potential solution to this limitation. Thus, this study took the Indus Basin as an example to reveal its urban expansion patterns and driving mechanism based on natural–economic–social time-series (2000–2020) spatial data, landscape expansion index, and geographical detector model (GDM). Future urban expansion distribution under different scenarios was also projected using Cellular Automata and Markov model (CA-Markov). The results indicated the following: (1) The Indus River Basin experienced rapid urban expansion during 2000–2020 dominated by edge-expansion, with urban expansion intensity showing a continuous increase. (2) Between 2000 and 2010 as well as 2010 and 2020, the dominant factor influencing urban expansion shifted from altitude to population (Pop), while the strongest interacting factors shifted from fine particulate matter (PM_2.5) and altitude to Gross Domestic Product (GDP) and Pop. (3) Future urban expansion probably occupies substantial mountainous area under the normal scenario, while the expansion region shifts towards the central plains to protect more ecological zones under a sustainable development scenario. Findings in this study would deepen the understanding of urban expansion characteristics of the Indus Basin and benefit its future urban planning. Full article

Land use/cover change (LUCC) is a dominant driver of ecosystem service dynamics in arid inland basins. Focusing on the Yarkant River Basin (YRB), Xinjiang, we coupled the PLUS land-use simulation with the InVEST Habitat Quality Model to project 2040 land-use patterns under four policy scenarios—Natural Development (ND), Arable Protection (AP), Ecological Protection (EP), and Economic Development (ED)—and to quantify their impact on habitat quality. Model validation against the 2020 map indicated strong agreement (Kappa = 0.792; FOM = 0.342), supporting scenario inference. From 1990 to 2023, arable land expanded by 58.17% and construction land by 121.64%, while forest land declined by 37.45%; these shifts corresponded to a basin-wide decline and increasing spatial heterogeneity of habitat quality. Scenario comparisons showed the EP pathway performed best, with 32.11% of the basin classified as very high-quality habitat and only 8.36% as very low-quality. In contrast, under ED, the combined share of very low + low quality reached 11.17%, alongside greater fragmentation. Spatially, high-quality habitat concentrates in forest and grassland zones of the middle–upper basin, whereas low-quality areas cluster along the oasis–desert transition and urban peripheries. Expansion of arable and construction land emerges as the primary driver of degradation. These results underscore the need to prioritize ecological-protection strategies especially improving habitat quality in oasis regions and strengthening landscape connectivity to support spatial planning and ecological security in dryland inland river basins. Full article