Search Results (6,005)

The paper investigates the design and operation of microgrid arrangements, with a focus on renewable power systems, system architectures, and storage solutions. The research evaluates stochastic and multi-objective optimization methods to show how demand response systems improve operational flexibility. The study evaluates 183 journal articles to select those that address microgrid design in conjunction with optimization models and demand response approaches. The articles are classified into three essential categories, which include microgrid design optimization methods and demand response integration. The review establishes that microgrid performance depends on three fundamental design parameters, which include energy generation systems, storage capabilities, and load demand control mechanisms. The review demonstrates that advanced optimization approaches, such as stochastic and multi-objective optimization methods, offer effective solutions for managing renewable energy variability. The paper demonstrates that demand response strategies are crucial for reducing costs and enhancing system flexibility. However, current published research falls short of establishing an integrated system that combines real-time demand response with stochastic optimization. This integration, while not yet fully realized, is suggested as a critical advancement for ensuring both system performance optimization and long-term sustainability. Therefore, this paper calls for further research to develop resilient hybrid renewable microgrids that integrate flexibility with sustainability through advanced optimization models and demand response strategies. Full article

As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laboratory conditions, a critical gap exists between algorithm performance evaluation under idealized conditions and their actual effectiveness in realistic deployment scenarios. This study presents a comprehensive comparative analysis of two distinct preprocessing methodologies for 6G network slicing classification: Pure Raw Data Analysis (PRDA) and Literature-Validated Realistic Transformations (LVRTs). We evaluate the impact of these strategies on algorithm performance, resilience characteristics, and practical deployment feasibility to bridge the laboratory–reality gap in 6G network optimization. Our experimental methodology involved testing eleven machine learning algorithms—including traditional ML, ensemble methods, and deep learning approaches—on a dataset comprising 10,000 network slicing samples (expanded to 21,033 through realistic transformations) across five network slice types. The LVRT methodology incorporates realistic operational impairments including market-driven class imbalance (9:1 ratio), multi-layer interference patterns, and systematic missing data reflecting authentic 6G deployment challenges. The experimental results revealed significant differences in algorithm behavior between the two preprocessing approaches. Under PRDA conditions, deep learning models achieved perfect accuracy (100% for CNN and FNN), while traditional algorithms ranged from 60.9% to 89.0%. However, LVRT results exposed dramatic performance variations, with accuracies spanning from 58.0% to 81.2%. Most significantly, we discovered that algorithms achieving excellent laboratory performance experience substantial degradation under realistic conditions, with CNNs showing an 18.8% accuracy loss (dropping from 100% to 81.2%), FNNs experiencing an 18.9% loss (declining from 100% to 81.1%), and Naive Bayes models suffering a 34.8% loss (falling from 89% to 58%). Conversely, SVM (RBF) and Logistic Regression demonstrated counter-intuitive resilience, improving by 14.1 and 10.3 percentage points, respectively, under operational stress, demonstrating superior adaptability to realistic network conditions. This study establishes a resilience-based classification framework enabling informed algorithm selection for diverse 6G deployment scenarios. Additionally, we introduce a comprehensive explainable artificial intelligence (XAI) framework using SHAP analysis to provide interpretable insights into algorithm decision-making processes. The XAI analysis reveals that Packet Loss Budget emerges as the dominant feature across all algorithms, while Slice Jitter and Slice Latency constitute secondary importance features. Cross-scenario interpretability consistency analysis demonstrates that CNN, LSTM, and Naive Bayes achieve perfect or near-perfect consistency scores (0.998–1.000), while SVM and Logistic Regression maintain high consistency (0.988–0.997), making them suitable for regulatory compliance scenarios. In contrast, XGBoost shows low consistency (0.106) despite high accuracy, requiring intensive monitoring for deployment. This research contributes essential insights for bridging the critical gap between algorithm development and deployment success in next-generation wireless networks, providing evidence-based guidelines for algorithm selection based on accuracy, resilience, and interpretability requirements. Our findings establish quantitative resilience boundaries: algorithms achieving >99% laboratory accuracy exhibit 58–81% performance under realistic conditions, with CNN and FNN maintaining the highest absolute accuracy (81.2% and 81.1%, respectively) despite experiencing significant degradation from laboratory conditions. Full article

Under the dual backdrop of the Healthy China strategy and the concept of sustainable development, optimizing the spatial layout of primary healthcare facilities is important for fairly distributing healthcare resources and strengthening the resilience of the public health system in a sustainable way. This study introduces an innovative 3D spatial resilience evaluation framework, covering transmission (service accessibility), diversity (facility type matching), and stability (supply demand balance). Unlike traditional accessibility studies, the concept of “resilience” here highlights a system’s ability to adapt to sudden public health events through spatial reorganization, contrasting sharply with vulnerable systems that lack resilience. Method-wise, the study uses an improved Gaussian two-step floating catchment area method (Ga2SFCA) to measure spatial accessibility, applies a geographically weighted regression model (GWR) to analyze spatial heterogeneity factors, combines network analysis tools to assess service coverage efficiency, and uses spatial overlay analysis to identify areas with supply demand imbalances. Harbin is located in northeastern China and is the capital of Heilongjiang Province. Since Harbin is a typical central city in the northeast region, with a large population and clear regional differences, it was chosen as the case study. The case study in Harbin’s main urban area shows clear spatial differences in medical accessibility. Daoli, Nangang, and Xiangfang form a highly accessible cluster, while Songbei and Daowai show clear service gaps. The GWR model reveals that population density and facility density are key factors driving differences in service accessibility. LISA cluster analysis identifies two typical hot spots with supply demand imbalances: northern Xiangfang and southern Songbei. Finally, based on these findings, recommendations are made to increase appropriate-level medical facilities, offering useful insights for fine-tuning the spatial layout of basic healthcare facilities in similar large cities. Full article

Background/Objectives: This study is part of an exploratory mixed-methods project investigating how companies and their employees in Germany dealt with adapted working conditions during the COVID-19 pandemic. Here, we identify predictive factors for employees’ attitudes towards the suitability of work-related technical, organisational, and personal SARS-CoV-2 infection control measures. Methods: In July 2021, when there was little evidence to suggest that the risk of work-related exposure to SARS-CoV-2 differed between occupations and workplaces, a standardised online and an optional paper-and-pencil survey were distributed across seven companies in southern Germany. Multivariate linear regression was used for analysis. Results: A total of 821 employees participated (average response rate: 24.5%). Most of the respondents (93%) worked in large companies, in the production industry (82%), with most of them having office jobs (82%). Around 29% reported doing most of their office work remotely during the pandemic. The perceived suitability of workplace infection control measures was rated quite high, with an overall mean score of 4.11 (SD 0.60) out of a possible 5. Workplace characteristics related to the COVID-19 pandemic as well as individual perception of SARS-CoV2 and COVID-19 in general were the most prominent predictors of attitudes towards the suitability of work-related SARS-CoV-2 infection control. For example, a higher COVID-19-specific reactance was negatively associated with attitudes towards technical (ß = −0.16), organisational (ß = −0.14), and personal (ß = −0.17) infection control measures (all p-values < 0.001). Furthermore, a higher rating of the employer’s commitment to occupational safety and health related to SARS-CoV-2, a higher individual disease perception, and a higher individual COVID-19-specific resilience had a positive association with attitudes towards the suitability of infection control measures. Finally, professional activity as well as company affiliation had statistically significant associations with employees’ attitudes towards the suitability of infection control measures. Conclusions: The results provide insight into factors relevant to pandemic prevention and control. In particular, our findings highlight the potential to implement organisational measures alongside compulsory technical occupational health measures. This could inform the development of pandemic preparedness strategies that prioritise adherence to established occupational infection control measures. Full article

This study investigated the concentration-dependent effects of zinc oxide nanoparticles (ZnO NPs, 30–70 nm) on the freshwater microalga Lobosphaera sp. under different salinity conditions (0–4 g L⁻¹ NaCl). ZnO NPs demonstrated dual effects: low concentration (0.75 mg L⁻¹) enhanced growth and alleviated salt stress, while higher concentrations (7.5–75 mg L⁻¹) caused significant growth inhibition (up to 52%) and induced oxidative stress. Salinity did not significantly affect NPs aggregation patterns, and neither salinity nor aggregation degree influenced toxicity outcomes. NPs concentration plays a dominant role of toxicological effects. Dose-dependent increases in catalase activity and ROS-positive cells confirmed NPs-induced oxidative stress. Crucially, zinc bioaccumulation correlated with NPs concentration but dissociated from dissolved Zn²⁺ release, demonstrating particle-driven toxicity. Our findings challenge the ion-release paradigm and highlight the potential of low-dose ZnO nanoparticles as effective stress-protectors in algal biotechnology, offering new strategies for enhancing microalgal resilience under environmental stress. Full article

Background/Objectives: Mahachai Betta (Betta mahachaiensis) is a bubble-nesting fighting fish endemic to brackish habitats in Bangkok, Samut Sakhon, and Samut Prakan, where rapid urbanization and industrial growth threaten persistence. We evaluated genetic structure and diversity across 10 populations (81 individuals) to inform conservation planning. Methods: This study combined microsatellite genotyping (13 loci) with ecological niche modeling to assess genetic variability, population connectivity, and landscape–environmental drivers of differentiation. Results: Habitat loss and fragmentation were associated with reduced gene flow and decreased genetic diversity. Mean allelic richness was 2.65 and expected heterozygosity ranged from 0.20 to 0.46, with F_ST values up to 0.400. Forward simulations predicted severe erosion of diversity within the next 12.5–37.5 years. Populations showed clear genetic subdivision, most pronounced in Samut Prakan and Samut Sakhon, with two Samut Sakhon populations (SKN3 and SKN7) reflecting strong environmental heterogeneity. Conclusions: Improving habitat connectivity and intensifying local community engagement are priority actions to enhance the resilience and long-term persistence of Mahachai Betta. This study provides the first integrated genetic and landscape-based assessment of the species, highlighting its rapid genetic erosion under urbanization and offering a foundation for targeted, evidence-based conservation strategies. Full article

The resilience and sustainable development of modern power distribution systems faces escalating challenges due to increasing renewable integration and extreme events. Traditional single-system approaches often overlook the spatiotemporal coordination of cross-domain restoration resources. In this paper, we propose a multi-stage resilience enhancement method that employs transportation and hydrogen energy systems. This approach coordinates the pre-event preventive allocation and multi-stage collaborative scheduling of diverse restoration resources, including remote-controlled switches (RCSs), mobile hydrogen emergency resources (MHERs), and hydrogen production and refueling stations (HPRSs). The proposed framework supports cross-stage dynamic optimization scheduling, enabling the development of adaptive resource dispatch strategies tailored to the characteristics of different stages, including prevention, fault isolation, and service restoration. The model is applicable to complex scenarios involving dynamically changing network topologies and is formulated as a mixed-integer linear programming (MILP) problem. Case studies based on the IEEE 33-bus system show that the proposed method can restore a distribution system’s resilience to approximately 87% of its normal level following extreme events. Full article

Chaotic systems appear in a wide range of natural and engineering contexts, making the design of reliable and flexible control strategies a crucial challenge. This work proposes a robust control scheme based on the Fractional-Order Backstepping Control (FOBC) method for the stabilization of non-commensurate fractional-order chaotic systems subject to bounded uncertainties and external disturbances. The method is developed through a rigorous stability analysis grounded in the Mittag–Leffler function, enabling the step-by-step stabilization of each subsystem. By incorporating fractional-order derivatives into carefully selected Lyapunov candidate functions, the proposed controller ensures global system stability. The performance of the FOBC approach is validated on fractional-order versions of the Duffing–Holmes system and the Rayleigh oscillator, with the results compared against those of a fractional-order PID (FOPID) controller. Numerical evaluations demonstrate the superior performance of the proposed strategy: the error dynamics converge rapidly to zero, the system exhibits strong robustness by restoring state variables to equilibrium quickly after disturbances, and the method achieves low energy dissipation with a high error convergence speed. These quantitative indices confirm the efficiency of FOBC over existing methods. The integration of fractional-order dynamics within the backstepping framework offers a powerful, robust, and resilient approach to stabilizing complex chaotic systems in the presence of uncertainties and external perturbations. Full article

Earthquake-triggered rockfalls pose significant threats to human lives, critical infrastructure, and the natural environment, highlighting an urgent need for sustainable and effective mitigation strategies. Flexible barriers are effective against rockfall, but there is a lack of universal procedures for selecting appropriate sites. As a result, flexible barriers are often misused, and their protective effect significantly decreases. To address this, a method for quantitatively characterizing the “flexible barrier siting adaptability” is proposed. The concept of “flexible barrier siting adaptability” is used to assess the suitability of a selected site for flexible barrier installation. The assessment method consists of three parts: the evaluation index system, the evaluation index value standards, and the calculation method. The evaluation index system is based on the interaction matrix considering not only the factors influencing the flexible barrier siting adaptability but also the interactions between them. The interaction matrix is determined by the expert semi-quantitative method, which can quantitatively assess the flexible barrier siting adaptability. Furthermore, the proposed method is applied to a typical rockfall area in Jiuzhaigou county, Sichuan province, China. This method provides a resource-efficient and practical tool for preliminary site assessment, contributing to the development of sustainable infrastructure and enhancing community resilience in rockfall-prone regions. Full article

Efficient water management is critical for sustainable crop production in arid and semi-arid regions. This study investigated the effects of two irrigation regimes—25% and 50% Management Allowable Depletion (MAD) and two planting patterns (single-row and double-row) on evapotranspiration (ET) partitioning, water use efficiency (WUE), and water footprint (WF) in drip-irrigated faba bean (Vicia faba L.). Field data were combined with a leaf area index (LAI)-based model to estimate the relative contributions of transpiration (T) and evaporation (E) to total ET. The highest grain yield (6171 kg ha⁻¹) and the lowest blue (570 m³ ton⁻¹) and green (68 m³ ton⁻¹) water footprints were recorded under the 25% MAD with double-row planting. This treatment also achieved the highest proportion of transpiration in ET (70%), indicating a shift toward productive water use. In contrast, the lowest-performing treatment (50% MAD, single-row) had the highest total water footprint (792 m³ ton⁻¹) and the lowest transpiration share (44%). Although high-density planting slightly reduced WUE based on transpiration, it improved overall water efficiency when total input (ETc) was considered (1.57 kg m⁻³ for total input WUE, 4.17 kg/m⁻³ for T-based WUE). These findings highlight the importance of integrating irrigation scheduling and planting pattern to improve both physiological and agronomic water productivity. The approach offers a practical strategy for sustainable faba bean production in water-scarce environments and supports climate-resilient irrigation planning aligned with Iraq’s National Water Strategy. Full article

The societal effects of cybersecurity are widely discussed, but it remains less clear how software security regulations specifically contribute to building a resilient society, particularly in relation to Sustainable Development Goals 5 (Gender Equality), 10 (Reduced Inequalities), and 16 (Peace, Justice and Strong Institutions). This study investigates this connection by examining key EU and U.S. strategies through comparative legal analysis, software development (SDLC) case studies, and a normative–sociological lens. Our findings reveal that major regulations—such as the EU’s Cyber Resilience Act and the U.S. SBOM rules—are not merely reactive, but proactively embed resilience as a fundamental mode of operation. This approach structurally reallocates digital risks from users to manufacturers, reframing software security from a matter of compliance to one of social fairness and institutional trust. We conclude that integrating ‘resilience by design’ into technology rules is more than a technical fix; it is a mechanism that makes digital access fairer and better protects vulnerable populations, enabling technology and society to advance cohesively. Full article

Investigating the spatiotemporal distribution patterns of regional ecosystem health and the methods for optimizing their zoning is essential for enhancing ecosystem management and sustainable development. This study takes Fujian Province, a pivotal forest region in southern China, as the research object to establish a county-level ecosystem health evaluation framework grounded in the vitality–organization–resilience–ecosystem (VORS) model. It further conducts a comprehensive spatial–temporal analysis of ecosystem health dynamics from 2000 to 2020 and explores ecological management zoning and optimization strategies. The results indicate that (1) from 2000 to 2020, the overall ecosystem health level in Fujian Province maintained a relatively high status and exhibited a steady upward trajectory, with the index rising from 0.4694 in 2000 to 0.4762 in 2010, and further increasing to 0.4865 in 2020. (2) The health of ecosystems in Fujian exhibits significant geographical autocorrelation and aggregation, characterized mostly by high–high and low–low clusters. Typically, it has a geographical distribution characterized by elevated values in the northwest and diminished values in the southeast. (3) In light of the present condition and temporal dynamics of ecosystem health, the study classifies Fujian’s counties into three ecological management categories—conservation, stabilization, and reshaping zones—and puts forward tailored optimization measures for each region. The methodology employed in this study provides a general framework for ecosystem health assessment, which can offer useful references and insights for forest ecosystem health evaluation and refined, zone-specific ecological management in forest regions. Full article

A key aspect of climate change’s impact on organisms lies in understanding their ability to adapt to shifting and stressful environmental conditions. Insects, such as parasitoid wasps, are particularly vulnerable due to limited heat tolerance. Adaptive strategies during mass rearing may enhance the efficacy and resilience of commercially reared biocontrol agents. This study assessed the effects of constant and fluctuating temperature regimens across four generations of mass-reared aphid parasitoids, examining their fitness traits and parasitism success under three thermal environments: colder [10 °C], standard [20 °C], and heat stress [28 °C]. Parasitoids reared under fluctuating temperatures [day/night: 25 °C/17 °C] showed increased parasitism, but reduced progeny survival compared to those reared at a constant temperature [20 °C]. Fluctuating regimens encouraged greater parasitism under heat stress, whereas constant regimens yielded intermediate parasitism across thermal environments, reflecting a pattern consistent with the evolution of specialist–generalist trade-offs. These findings underscore the value of developing adaptive temperature-rearing strategies for mass-rearing systems of parasitoids that more accurately simulate field conditions, improving their performance under climate stress. Future research involving diverse temperature regimens should deepen our understanding of trait trade-offs, such as survival and fecundity, and aid in identifying optimal thermal profiles to maximize efficacy in mass-rearing parasitoid wasps and their performance at the field level. Full article

Casablanca, Morocco’s largest Atlantic port city, faces increasing exposure to floods, drought, and other risks that align with legacies of urban transformations carried out during the colonial period. This study examines how early-20th-century interventions—including the canalization and burial of the Oued Bouskoura, extensive coastal reclamation, and the implementation of rigid zoning—were associated with a reconfiguration of the city’s hydrology and coincide with persistent socio-spatial inequalities. Using historical cartography, archival sources, and GIS-based overlays of colonial-era plans with contemporary hazard maps, the analysis reveals an indicative spatial correlation between today’s high-risk zones and areas transformed under the Protectorate, with the medina emerging as one of the most vulnerable districts. While previous studies have examined either colonial planning in architectural or contemporary climate risks through technical and governance lenses, this article illuminates historically conditioned relationships and long-term associations for urban resilience. In doing so, it empirically maps spatial associations and conceptually argues for reframing heritage not only as cultural memory but as a climate resource, illustrating how suppressed vernacular systems may inform adaptation strategies. This interdisciplinary approach provides a novel contribution to postcolonial city research, climate adaptation and heritage studies by proposing a historically conscious framework for resilience planning. Full article

Rapid urbanization and aging population present global challenges for smart cities, especially for equitable pandemic response and age friendly urban transitions. This paper through a two-round Delphi study assessed Amman’s efficiency in pandemic response focusing on digital inclusion for older adults and critical barriers to age-friendly urbanism. The results indicate moderate satisfaction with Amman’s overall pandemic response alongside significant limitations, particularly in digital equity for older adults. Key systemic barriers included compromised air quality, inadequate public transportation, notably poor public Wi-Fi, and deficient digital infrastructure. Furthermore, political and financial obstacles, such as high living costs and low governance transparency, significantly hindered progress. Experts prioritized solutions emphasizing improved physical accessibility, expanded green spaces, and enhanced digital literacy. This study underscores the urgent need for integrated, multi-dimensional strategies, including participatory governance and targeted digital inclusion programs, to foster sustainable and equitable smart city development that enhances resilience and inclusiveness for aging populations in post pandemic urban planning contexts. Full article