Search Results (584)

The Enhanced Cipher Block Chaining scheme (eCBC) is an authentication encryption scheme (AE) improved from the CBC encryption scheme. It is shown that eCBC scheme fails to achieve ciphertext integrity (INT-CTXT): the IV is unauthenticated and the tag is a linear XOR of ciphertext hashes, enabling trivial forgeries such as IV substitution, block cancellation, and permutation. Furthermore, the medical image application diagonal block encryption based on eCBC scheme is also insecure. Its deterministic design leaks structural information, breaking confidentiality (IND-CPA). At the same time, it also inherits the forgery weaknesses of eCBC scheme, breaking authenticity. The results highlight that neither eCBC scheme nor its application meet AE security goals. And it is recommended to use standardized AE schemes such as SIV, GCM, or Ascon instead of ad hoc designs. Full article

In mineral processing and metallurgy, total iron grade serves as a critical indicator guiding the entire production chain from crushing to smelting, directly influencing the quality and yield of steel products. To address the limitations of conventional matrix effect correction methods in X-ray fluorescence (XRF) analysis—such as low accuracy, high time consumption, and labor-intensive procedures—this study proposes a novel hybrid model (DSCN-LS) integrating least squares (LS) with dynamically regularized stochastic configuration networks (DSCNs) for total iron ore grade quantification. Through feature analysis, we decompose the grade modeling problem into a linear structural component and nonlinear residual terms. The linear component is resolved by means of LS, while the nonlinear terms are processed by the DSCN with a dynamic regularization strategy. This strategy implements node-specific weighted regularization: weak constraints preserve salient features in high-weight-norm nodes, while strong regularization suppresses redundant information in low-weight-norm nodes, collectively enhancing model generalizability and robustness. Notably, the model was trained and validated using datasets collected directly from industrial sites, ensuring that the results reflect real-world production scenarios. Industrial validation demonstrates that the proposed method achieves an average absolute error of 0.3092, a root mean square error of 0.5561, and a coefficient of determination (R²) of 99.91% in total iron grade estimation. All metrics surpass existing benchmarks, confirming significant improvements in accuracy and operational practicality for XRF detection under complex industrial conditions Full article

In recent years, data-driven deep learning has yielded fruitful results in synthetic aperture radar (SAR) ship detection; weakly supervised learning methods based on horizontal bounding boxes (HBBs) train oriented bounding box (OBB) detectors using HBB labels, effectively addressing scarce OBB annotation data and advancing SAR ship OBB detection. However, current methods for oriented SAR ship detection still suffer from issues such as insufficient quantity and quality of pseudo-labels, low inference efficiency, large model parameters, and limited global information capture, making it difficult to balance detection performance and efficiency. To tackle these, we propose the weakly supervised oriented SAR ship detection algorithm based on optimized pseudo-label generation and guidance. The method introduces pseudo-labels into a single-stage detector via a two-stage training process: the first stage coarsely learns target angles and scales using horizontal bounding box weak supervision and angle self-supervision, while the second stage refines angle and scale learning guided by pseudo-labels, improving performance and reducing missed detections. To generate high-quality pseudo-labels in large quantities, we propose three optimization strategies: Adaptive Kernel Growth Pseudo-Label Generation Strategy (AKG-PLGS), Pseudo-Label Selection Strategy based on PCA angle estimation and horizontal bounding box constraints (PCA-HBB-PLSS), and Long-Edge Scanning Refinement Strategy (LES-RS). Additionally, we designed a backbone and neck network incorporating window attention and adaptive feature fusion, effectively enhancing global information capture and multiscale feature integration while reducing model parameters. Experiments on SSDD and HRSID show that our algorithm achieves an mAP50 of 85.389% and 82.508%, respectively, with significantly reduced model parameters and computational consumption. Full article

Aiming to solve the challenges of the weak spatial and temporal correlation of medium- and long-term photovoltaic (PV) power data, as well as data redundancy and low forecasting efficiency brought about by long-time forecasting, this paper proposes a medium- and long-term PV power forecasting method based on the Transformer, SP-Transformer (spatiotemporal probsparse transformer), which aims to effectively capture the spatiotemporal correlation between meteorological and geographical elements and PV power. The method embeds the geographic location information of PV sites into the model through spatiotemporal positional encoding and designs a spatiotemporal probsparse self-attention mechanism, which reduces model complexity while allowing the model to better capture the spatiotemporal correlation between input data. To further enhance the model’s ability to capture and generalize potential patterns in complex PV power data, this paper proposes a feature pyramid self-attention distillation module to ensure the accuracy and robustness of the model in long-term forecasting tasks. The SP-Transformer model performs well in the PV power forecasting task, with a medium-term (48 h) forecasting accuracy of 93.8% and a long-term (336 h) forecasting accuracy of 90.4%, both of which are better than all the comparative algorithms involved in the experiment. Full article

Resilience through urban planning has gained prominence since the adoption of the Sendai Framework for Disaster Risk Reduction (2015–2030), particularly in regions exposed to multiple natural hazards. This study examines how six Western Mediterranean countries—Spain, France, Italy, Tunisia, Algeria, and Morocco—address disaster risk prevention through urban and spatial planning. Although these countries share a similar geodynamic and climatic context, their approaches to integrating hazard prevention into planning frameworks vary significantly due to institutional, technical, and legal factors. Special attention is given to the case of Morocco, where delays in hazard integration are evident, particularly in the Maghreb region. Limited access to historical data, weak inter-agency coordination, and insufficient scientific capacity hinder effective planning. In response, Morocco has developed the Urbanization Suitability Map (USM) program, a non-binding planning tool inspired by the French Natural Risk Prevention Plan (PPRN). The USM tool overlays hazard information to guide land use decisions and mitigate risks such as floods, landslides, and seismic activity. Using a qualitative comparative analysis of regulatory texts, national planning strategies, and mapping instruments, this study identifies contrasting levels of disaster risk reduction integration across the six countries. The Moroccan USM initiative stands out as a pragmatic response to governance gaps and offers a transferable model for other countries with similar constraints. The findings underscore the need for clearer legislation, improved data systems, and multi-level coordination to enhance urban resilience. Recommendations are provided to strengthen hazard-informed planning practices and support more adaptive and sustainable land management in risk-prone areas. Full article

In recent years, remote data management environments have been increasingly deployed across diverse infrastructures, accompanied by a rapid surge in the demand for sharing and collaborative processing of sensitive data. Consequently, ensuring data security and privacy protection remains a fundamental challenge. A representative example of such an environment is the cloud, where efficient mechanisms for secure data sharing and access control are essential. In domains such as finance, healthcare, and public administration, where large volumes of sensitive information are processed by multiple participants, traditional access-control techniques often fail to satisfy the stringent security requirements. To address these limitations, Key-Aggregate Proxy Re-Encryption (KA-PRE) has emerged as a promising cryptographic primitive that simultaneously provides efficient key management and flexible authorization. However, existing KA-PRE constructions still suffer from several inherent security weaknesses, including aggregate-key leakage, ciphertext insertion and regeneration attacks, metadata exposure, and the lack of participant anonymity within the data-management framework. To overcome these limitations, this study systematically analyzes potential attack models in the KA-PRE setting and introduces a novel KA-PRE scheme designed to mitigate the identified vulnerabilities. Furthermore, through theoretical comparison with existing approaches and an evaluation of computational efficiency, the proposed scheme is shown to enhance security while maintaining practical performance and scalability. Full article

To enhance ecosystem services (ESs) benefits and promote ecological–economic–sociologic sustainability in highly urbanized regions such as the Beijing–Tianjin–Hebei (BTH) region, it is essential to assess the dynamic changes in ESs within these regions from a functional zoning perspective and to explore the interactions between ESs. This research delved into how ESs change over space and time, using land-use projections for 2035 based on Natural Development (ND), Ecological Protection (EP), Economic Construction (EC) scenarios. This study also took a close look at the interplay of these ESs across BTH and its five distinct functional zones: the Bashang Plateau Ecological Protection Zone (BS), the Northwestern Ecological Conservation Zone (ST), the Central Core Functional Zone (HX), the Southern Functional Expansion Zone (TZ), and the Eastern Coastal Development Zone (BH). We utilize the Multiple Ecosystem Service Landscape Index (MESLI) to assess the capacity to supply multiple ESs. Key results include the following: (1) Projected land-use changes for 2035 scenarios consistently show cropland and grassland declining, while forest and urbanland expand, though the magnitude of change varies by scenario. (2) Habitat quality, carbon storage, and soil conservation displayed a “high northwest–low southeast” gradient, opposite to water yield. The average MESLI value declined in all scenarios relative to 2020, with the highest value under the EP scenario. (3) Synergies prevailed between habitat quality, carbon storage, and soil conservation, while trade-offs occurred with water yield. These relationships varied spatially—for instance, habitat quality and soil conservation were weakly synergistic in the BS but showed weak trade-offs in the HX. These insights can inform management strategies in other rapidly urbanizing regions. Full article

Environmental awareness is widely recognized as a key factor of environmentally friendly behavior, especially as human activities persist in exacerbating global environmental issues. While previous research has largely focused on environmental regulations to promote green technology, such approaches often fall short in developing countries due to weak enforcement mechanisms and the prominence of informal economic activities. This study takes a different approach by exploring how environmental awareness can foster the adoption of green technology in informal manufacturing enterprises, thereby enhancing both environmental and social outcomes. Enterprise-level survey data, collected from a major city in a developing country, serves as the basis for this analysis. The survey captures information related to knowledge attitudes and the behavioral practices of owners or managers with respect to the environment, as well as pollution and its management. Utilizing the collected data, and guided by established theoretical frameworks, the study develops an environmental awareness (EA) index. This index is then applied in probit and logit models to estimate its effect on the likelihood of adopting pollution-reducing technologies. The marginal effect analysis reveals that informal SMEs with a higher environmental awareness are 28.5% more likely to adopt green technologies. This probability increases to 30.1% when demographic- and business-related variables are incorporated into the model. Based on empirical findings, this study recommends targeted investments in awareness building initiatives, alongside long-term educational and training programs for enterprise owners and managers to instill environmental values and practices across operations. Given the financial constraints faced by informal enterprises, this study also recommends both public and private sector support to make this transition feasible and sustainable. Full article

Grinding surface texture is random and feature information is weak, so it is difficult to extract effective features by deep learning network. In addition, the existing deep learning methods mostly adopt a large parameter model in grinding surface roughness recognition task, and the cost of deployment in embedded end is high. In order to solve these problems, a new lightweight network model GE-MobileNet (Ghost-ECA-MobileNetV3) is proposed. Based on MobileNetV3, a feature extractor is introduced into the shallow network part of the model to enhance the ability of the network to extract and suppress the surface texture feature and noise. At the same time, SE (Squeeze-and-Excitation) attention mechanism is replaced with ECA (Efficient Channel) attention mechanism with stronger performance. Finally, the deep network layer is removed to reduce the model size. The experimental results show that the accuracy of GE-MobileNet-based grinding surface roughness measurement model on test set is 94.97%, which is better than other networks. This study proves the effectiveness of the roughness measurement method based on GE-MobileNet. Full article

Accurate and efficient estimation of Sobol’ sensitivity indices is a cornerstone of variance-based global sensitivity analysis, providing critical insights into how uncertainties in input parameters affect model outputs. This is particularly important for large-scale environmental, engineering, and financial models, where understanding parameter influence is essential for improving model reliability, guiding calibration, and supporting informed decision-making. However, computing Sobol’ indices requires evaluating high-dimensional integrals, presenting significant numerical and computational challenges. In this study, we present a comparative analysis of two of the best available Quasi-Monte Carlo (QMC) techniques: polynomial lattice rules (PLRs) and modified Sobol’ sequences. The performance of both approaches is systematically assessed in terms of performance and accuracy. Extensive numerical experiments demonstrate that the proposed PLR-based framework achieves superior precision for several sensitivity measures, while modified Sobol’ sequences remain competitive for lower-dimensional indices. Our results show that IPLR-α3 outperforms traditional QMC methods in estimating both dominant and weak sensitivity indices, offering a robust framework for high-dimensional models. These findings provide practical guidelines for selecting optimal QMC strategies, contributing to more reliable sensitivity analysis and enhancing the predictive power of complex computational models. Full article

The southwestern region of China is highly susceptible to landslides due to steep terrain, fractured geology, and intense rainfall. This study focuses on the Xiluodu Reservoir area in Yunnan Province and applies Geographic Information System (GIS) techniques together with ten key spatial factors—such as slope, lithology, elevation, and distance to rivers—to perform a quantitative landslide risk assessment. In addition to the individual Certainty Factor (CF) and Logistic Regression (LR) models, we developed an integrated CF–LR coupled model to overcome their respective limitations: the CF model’s sensitivity to specific factor attributes but neglect of factor interactions, and the LR model’s robust weight estimation but weak representation of attribute heterogeneity. By combining these strengths, the CF–LR model achieved superior predictive performance (AUC = 0.804), successfully capturing 92.5% of historical landslide events within moderate-to-high risk zones. The results show that lithology, slope angle, and proximity to rivers and roads are dominant controls on susceptibility, with landslides concentrated on soft rock slopes of 30–40° and within 600–900 m of rivers. Compared with previous coupled approaches in similar mountainous reservoir settings, our CF–LR model provides a more balanced and interpretable framework, enhancing both classification accuracy and practical applicability. These findings demonstrate that GIS-based CF–LR integration is a novel and reliable tool for landslide susceptibility mapping, offering important technical support for disaster prevention and risk management in large reservoir regions. Full article

Traditional multi-criteria decision-making (MCDM) methods face problems in sustainable product design evaluation, including weak semantic expression, single weight modeling, and insufficient ranking robustness. To address these issues, this paper proposes an evaluation framework based on Trapezoidal Intuitionistic Fuzzy (TrIF), named TrIF-DEC, which integrates Decision-Making Trial and Evaluation Laboratory (DEMATEL), Entropy, and Combined Compromise Solution (CoCoSo). Firstly, design criteria across four dimensions—social, economic, technological, and environmental—are identified based on sustainability considerations. Then, TrIF is used to capture the fuzziness and hesitation in expert judgments. The DEMATEL and Entropy methods are combined to extract causal relationships between criteria and quantify data variation, enabling the collaborative weighting of subjective and objective factors. Finally, multi-strategy integrated ranking is performed through TrIF-CoCoSo to enhance decision stability. An empirical case study on nursing bed design demonstrates the effectiveness of the proposed framework. Results demonstrate that TrIF-DEC can systematically integrate uncertainty information with multidimensional sustainability goals, providing reliable support for complex product design evaluation. Full article

Drought is one of the main climate-induced risks threatening agricultural sustainability in semi-arid regions. Northern Kazakhstan, a key grain-producing region in Central Asia, exhibits increasing vulnerability to droughts due to climatic variability and reliance on rainfed agriculture. This study evaluates the informativeness of drought indices based on the response of agricultural vegetation to dry conditions using remote sensing-based vegetation indices across Northern Kazakhstan from 1990 to 2024. Ground-based meteorological indices—the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Hydrothermal Coefficient (HTC), and the Modified China-Z Index (MCZI)—and vegetation indices—the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), and the Vegetation Health Index (VHI)—were analyzed using data from 11 representative meteorological stations. For the first time in Kazakhstan, the MCZI was calculated, demonstrating high sensitivity to local climate variability and strong agreement with the VHI. The SPI, MCZI, and HTC showed strong seasonal correlations with vegetation indices, whereas the SPEI had a weak correlation, limiting its applicability. The highest correlations (r ≥ 0.82) between meteorological and vegetation indices were recorded in summer, while spring and autumn were influenced by phenological and temperature factors. Persistent drying trends in the southern and southwestern areas contrasted with moderate wetting in the north. The combined use of the SPI, MCZI, HTC, and VHI proved effective for monitoring droughts. The results provide a reproducible foundation for local drought assessment and early warning systems, supporting climate-resilient agricultural planning and sustainable land and water resource management. The results also offer actionable insights to enhance adaptation strategies and support long-term agricultural and environmental sustainability in Central Asia and similar continental agroecosystems. Full article

Pedestrian crashes remain a leading cause of road traffic fatalities in developing countries (DCs); yet reliable crash data are scarce, constraining the ability to model pedestrian safety risks and evaluate countermeasure effectiveness. This study developed a methodological process for estimating the influence of contextual factors on pedestrian crashes using artificial data. The process integrated literature-derived trend analysis, artificial data generation, external face validity checks, correlation analysis, stepwise negative binomial regression, sensitivity testing, and mapping of results against the International Road Assessment Programme (iRAP) framework. Of the 26 contextual factors considered, 20 were retained in the negative binomial (NB) models, while six were excluded due to weak or inconsistent trend data. Results showed that behavioural and institutional factors, including ad hoc countermeasure implementation, gender composition of pedestrian flows, and vehicle age or technology, exerted stronger influence on crash outcomes than several geometric variables typically emphasised in global models. External validity testing confirmed broad alignment of the artificial dataset with published values, while sensitivity analysis demonstrated the robustness of factor influence values (Fi) across bootstrap resampling and scenario perturbations. The Fi values derived are illustrative rather than decision-ready, reflecting the artificial-data basis of this study. Nonetheless, the findings highlight methodological proof of concept that artificial-data modelling can provide credible and context-sensitive insights in data-scarce environments. Mapping results to the iRAP framework revealed complementarity, with opportunities to extend global models by incorporating behavioural and institutional variables more systematically. The approach provides a replicable pathway for improving pedestrian safety assessment in DCs and informs the development of an enhanced iRAP effectiveness model in subsequent research. Future applications should prioritise empirical calibration with real-world crash datasets and support policymakers in integrating behavioural and institutional factors into countermeasure prioritisation and safety planning. Full article

Commercial complexes, as major sources of urban energy consumption and carbon emissions, face urgent demands for efficiency improvement under the “dual-carbon” strategy. This paper develops a Building Information Modeling (BIM)-enabled life-cycle energy management framework to address fragmented monitoring, weak coordination, and data silos inherent in traditional approaches. Methodologically, a structured literature review was conducted to identify inefficiencies and draw lessons from global practices. An enhanced Delphi method was then applied to refine 12 key evaluation indicators spanning six dimensions—policy, economic, social, technological, environmental, and compliance—which were subsequently integrated into a BIM platform. This integration enables real-time energy monitoring, multi-system diagnostics, and cross-phase collaboration across the design, construction, and operation stages. An empirical case study of the Zhongjian Plaza project in Shanghai demonstrates that the proposed framework not only enhances energy efficiency and reduces life-cycle costs, but also improves user comfort while aligning with both domestic green building standards and international sustainability targets. Overall, the study provides a replicable methodology and practical reference for the smart and low-carbon operation of large-scale commercial complexes, thereby offering strategic insights for advancing sustainable urban development. Full article