Search Results (506)

The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO₂ emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article

This study examines how green talent management (GTM) practices foster sustainable supply chains in micro, small, and medium-sized enterprises (MSMEs) in Quito, Ecuador. It analyzes how sustainable leadership, green organizational culture, and sustainability-oriented training influence employees’ pro-environmental motivation, organizational commitment, and sustainability attitudes, which in turn mediate the adoption of green logistics practices, supply chain efficiency, and organizational resilience. A quantitative design was employed, using survey data from 280 MSMEs analyzed through structural equation modeling. The findings demonstrate that GTM enhances employees’ motivation, commitment, and sustainability attitudes, which act as the primary behavioral mechanisms translating managerial practices into sustainability outcomes. Theoretically, the study integrates Green HRM and supply chain research with multiple organizational behavior theories, including Social Exchange Theory, the AMO model, the Theory of Planned Behavior, and the Resource-Based View. Empirically, it contributes novel evidence from Ecuadorian MSMEs, a context often underexplored in sustainability research. Practically, the study highlights leadership, culture, and training as strategic levers for building greener, more efficient, and more resilient supply chains. The results offer actionable recommendations for MSME managers and policymakers in Ecuador, highlighting the importance of investing in people as the foundation of sustainable competitiveness. Full article

The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem. Full article

The Okapi Wildlife Reserve (OWR) in northeastern Democratic Republic of the Congo represents both a biodiversity hotspot and the ancestral homeland of the Indigenous Mbuti and Efe peoples, whose livelihoods and knowledge systems are closely tied to forest resources. This study investigates how Indigenous knowledge and practices contribute to sustainable resource management under conditions of rapid socio-cultural transformation. A mixed-methods approach was applied, combining socio-demographic surveys (n = 80), focus group discussions, floristic inventories, and statistical analyses (ANOVA, logistic regressions, chi-square, MCA). Results show that hunting, fishing, gathering, and honey harvesting remain central livelihood activities, governed by customary taboos and restrictions that act as de facto ecological regulations. Agriculture, recently introduced through intercultural exchange with neighboring Bantu populations, complements rather than replaces traditional practices and demonstrates emerging agroecological hybridization. Nevertheless, evidence of biodiversity decline (including local disappearance of species such as Dioscorea spp.), erosion of intergenerational knowledge transmission, and increased reliance on monetary income indicate vulnerabilities. Multiple Correspondence Analysis revealed a highly structured socio-ecological gradient (98.5% variance explained; Cronbach’s α = 0.977), indicating that perceptions of environmental change are strongly coupled with demographic identity and livelihood strategies. Floristic inventories confirmed significant differences in species abundance across camps (ANOVA, p < 0.001), highlighting site-specific pressures and the protective effect of persistent customary norms. The findings underscore the resilience and adaptability of Indigenous Peoples but also their exposure to ecological and cultural disruptions. We conclude that formal recognition of Indigenous institutions and integration of their knowledge systems into co-management frameworks are essential to strengthen ecological resilience, secure Indigenous rights, and align conservation policies with global biodiversity and climate agendas. 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

This study examined social and physical environmental exposures, health, and healthcare utilization among children aged 0–12 in urban areas. A population-based cross-sectional design was used, incorporating general practitioners’ data (2018–2019, n = 14,547), and societal and environmental data. Latent class analysis identified three distinct classes based on child and family demographics: ‘Dutch-origin two-parent household’ (n = 7267), ‘households with diverse countries of origin’ (n = 4313), and ‘single-parent household’ (n = 2967). Binary and multinomial logistic regression examined associations with environmental factors and child health outcomes. Children from the Dutch-origin class most often had favorable family demographics, neighborhood conditions, and health outcomes. Children from the diverse countries of origin class most often faced adverse neighborhood conditions, had higher rates of physical or somatic health conditions, and higher healthcare costs. Children from the single-parent class more often had less favorable family demographics, a higher likelihood of mental health problems, more frequent general practitioner visits, and were often in contact with youth care. This study highlights how child and family demographics and social and neighborhood conditions impact child health and healthcare utilization. Future approaches should focus on strategies to build and strengthen family and community resilience and adopt family-centered, context-sensitive interventions. Full article

The COVID-19 pandemic exposed the urgent need for innovative tools to strengthen pandemic preparedness and health defense, especially in low- and middle-income countries (LMICs). While vaccination has been the cornerstone of the defense strategy against many infectious agents, there is a critical gap in vaccine equity, ensuring it is accessible to all, especially among the most vulnerable populations. The conventional vaccine delivery platforms, through parenteral administration, face notable limitations, including reliance on trained personnel, sterile conditions, and cold chain logistics. The parenteral vaccines often fail to induce robust mucosal immunity, which is critical for preventing infections at mucosal surfaces, the primary entry point for many pathogens. Bacillus subtilis, a Gram-positive, spore-forming bacterium, has emerged as a promising platform for mucosal vaccine delivery owing to its Generally Recognized as Safe (GRAS) status. Its robust spores are highly resilient to harsh environmental conditions, which may eliminate the need for cold chain storage and further facilitate distribution in LMICs. This review explores the potential of B. subtilis as a next-generation vaccine delivery platform, focusing on its unique characteristics, mechanisms of action, and applications in addressing global health challenges. This review also examines existing research demonstrating the safety, immunogenicity, and efficacy of B. subtilis spore-based vaccines while identifying limitations and future directions for optimization as a scalable and adaptable solution for resilient health defense, particularly in LMICs. Full article

Natural disasters often strike unexpectedly, leaving thousands of victims and affected individuals each year. Effective disaster preparedness is critical to reducing these consequences and accelerating recovery. This paper presents Eghatha, a blockchain-based decentralized system designed to optimize humanitarian aid delivery during crises. By enabling secure and transparent transfers of donations and relief from donors to beneficiaries, the system enhances trust and operational efficiency. All transactions are immutably recorded and verified on a blockchain network, reducing fraud and misuse while adapting to local contexts. The platform is volunteer-driven, coordinated by civil society organizations with humanitarian expertise, and supported by government agencies involved in disaster response. Eghatha’s design accounts for disaster-related constraints—including limited mobility, varying levels of technological literacy, and resource accessibility—by offering a user-friendly interface, support for local currencies, and integration with locally available technologies. These elements ensure inclusivity for diverse populations. Aligned with Morocco’s “Digital Morocco 2030” strategy, the system contributes to both immediate crisis response and long-term digital transformation. Its scalable architecture and contextual sensitivity position the platform for broader adoption in similarly affected regions worldwide, offering a practical model for ethical, decentralized, and resilient humanitarian logistics. Full article

This study reviews the role of Child-Friendly Green Spaces (CFGS) in supporting children’s psychological, physical, and educational recovery following natural disasters. The main research question guiding this review is the following: how do CFGS contribute to holistic child well-being and resilience in disaster-affected contexts, and what barriers and strategies influence their effective integration into recovery frameworks? Employing a rigorous literature review methodology, we synthesized interdisciplinary evidence from environmental psychology, urban planning, public health, and education, encompassing studies published between 2000 and 2024. Findings demonstrate that CFGS significantly reduce trauma-related symptoms such as anxiety, depression, and post-traumatic stress, promotes physical health through active play, and foster educational engagement by improving concentration, attendance, and informal learning opportunities. Furthermore, CFGS contribute directly to multiple Sustainable Development Goals, particularly SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), and SDG 11 (Sustainable Cities and Communities). Despite these advantages, CFGS are often overlooked in formal disaster recovery planning due to prioritization of immediate relief, financial and logistical challenges, and socio-cultural factors. To address these challenges, this study proposes a participatory, culturally sensitive framework for CFGS implementation, which integrates inclusive design, multi-sector collaboration, and ongoing monitoring and evaluation. Grounded in theoretical perspectives such as the Biophilia Hypothesis, Bronfenbrenner’s Ecological Systems Theory, and restorative environments, CFGS are reframed as critical infrastructures for children’s holistic recovery and resilience. The findings underscore the urgent need to embed CFGS within disaster recovery and urban planning policies to promote child-centered, sustainable community development. Full article

Efficient and cost-effective biomass logistics remain a significant challenge due to the dynamic and nonlinear nature of supply chains, as well as the scarcity of comprehensive data on this topic. As biomass plays an increasingly important role in sustainable energy systems, managing its complex supply chains efficiently is crucial. Traditional logistics methods often struggle with the dynamic, nonlinear, and data-scarce nature of biomass supply, especially when integrating local and international sources. To address these challenges, this study aims to develop an innovative modular artificial neural network (ANN)-based Biomass Delivery Management (BDM) model to optimize biomass procurement and supply for a fluidized bed combined heat and power (CHP) plant. The comprehensive model integrates technical, economic, and geographic parameters to enable supplier selection, optimize transport routes, and inform fuel blending strategies, representing a novel approach in biomass logistics. A case study based on operational data confirmed the model’s ability to identify cost-effective and quality-compliant biomass sources. Evaluated using empirical operational data from a Polish CHP plant, the ANN-based model demonstrated high predictive accuracy (MAE = 0.16, MSE = 0.02, R² = 0.99) within the studied scope. The model effectively handled incomplete datasets typical of biomass markets, aiding in supplier selection decisions and representing a proof-of-concept for optimizing Central European biomass logistics. The model was capable of generalizing supplier recommendations based on input variables, including biomass type, unit price, and annual demand. The proposed framework supports both strategic and real-time logistics decisions, providing a robust tool for enhancing supply chain transparency, cost efficiency, and resilience in the renewable energy sector. Future research will focus on extending the dataset and developing hybrid models to strengthen supply chain stability and adaptability under varying market and regulatory conditions. Full article

This study investigates how AI-driven innovations are reshaping manufacturing value chains through the transition from Industry 4.0 to Industry 6.0, particularly in resource-intensive sectors such as ceramics. Addressing a gap in the literature, the research situates the evolution of manufacturing within the broader context of digital transformation, sustainability, and regulatory demands. A mixed-methods approach was employed, combining semi-structured interviews with key industry stakeholders and an extensive review of secondary data, to develop an Industry 6.0 model tailored to the ceramics industry. The findings demonstrate that artificial intelligence, digital twins, and cognitive automation significantly enhance predictive maintenance, real-time supply chain optimization, and regulatory compliance, notably with the Corporate Sustainability Reporting Directive (CSRD). These technological advancements also facilitate circular economy practices and cognitive logistics, thereby fostering greater transparency and sustainability in B2B manufacturing networks. The study concludes that integrating AI-driven automation and cognitive logistics into digital ecosystems and supply chain management serves as a strategic enabler of operational resilience, regulatory alignment, and long-term competitiveness. While the industry-specific focus may limit generalizability, the study underscores the need for further research in diverse manufacturing sectors and longitudinal analyses to fully assess the long-term impact of AI-enabled Industry 6.0 frameworks. Full article

This study introduces the SESG (Security, Environmental, Social, Governance) framework as a necessary evolution of traditional ESG, aimed at enhancing societal and corporate resilience in the face of hybrid threats, war, and climate crises. By integrating a security dimension, SESG responds to the growing inadequacy of classical ESG models in high-risk environments, particularly for countries like Ukraine. The research combines theoretical analysis with empirical data, including a nationwide survey of Ukrainian professionals across business, government, and civil society sectors. The findings reveal overwhelming support—over 90%—for incorporating security into ESG, especially in sectors such as IT, energy, and logistics. The article proposes a matrix of qualitative and quantitative indicators to assess SESG performance and highlights business-led contributions to national defense. The results demonstrate that security is not just a governmental concern but a key factor in corporate responsibility, investor trust, and sustainable development. The study concludes that SESG offers both a scientific reframing of resilience and a practical tool for policy and strategy, particularly under conditions of geopolitical and environmental instability. It urges cross-sector collaboration, standardization, and awareness building to embed SESG as a core principle in global sustainability agendas. Full article

Background: The COVID-19 pandemic disrupted essential health services globally, including contraceptive provision. This study examined barriers to contraceptive access in Nigeria during the national lockdown and lessons for future health crisis preparedness. Methods: A cross-sectional online survey of 1273 respondents was conducted during the COVID-19 lockdown. Descriptive statistics and multivariate logistic regression were used to identify predictors of unmet contraceptive need. Online convenience sampling may limit representativeness. Results: Fear of contracting COVID-19 at health facilities (76.6%), closure of drug and chemist shops (53.7%), movement restrictions (48.4%), and inability to reach healthcare providers (43.5%) were the most reported barriers. Adults aged 26–33 years (AOR = 2.00, 95% CI: 1.05–3.73), those married or cohabiting (AOR = 3.87, 95% CI: 2.58–5.68), and Yoruba respondents (AOR = 1.70, 95% CI: 1.04–2.58) were significantly more likely to report unmet need. Tertiary education (AOR = 0.28, 95% CI: 0.13–0.55) and rural residence (AOR = 0.57, 95% CI: 0.37–0.86) were protective factors. Conclusion: COVID-19-related restrictions exposed systemic weaknesses in Nigeria’s contraceptive delivery. Addressing fragile supply chains, strengthening community-based alternatives, and embedding reproductive health into emergency preparedness plans will be critical to building resilient systems for future crises. Full article

This study analyzes the determinants of impulsive buying behavior in low-cost retail stores in Metropolitan Lima, with particular emphasis on psychological, economic, social, and personal factors. The research draws on survey data collected from 380 consumers aged 18 to 39 belonging to socioeconomic levels B and C who had made recent purchases in discount stores. Data were gathered through a structured and validated instrument and examined using ordinal logistic regression and multinomial discrete choice models. The dependent variable, impulsive buying, was measured through three dimensions—remembered, suggested, and pure—while explanatory variables were classified into low, medium, and high categories. The empirical results demonstrate that psychological and economic dimensions exert a strong and positive influence on impulsive consumption, whereas social factors show no significant effect. Personal factors, though less consistent, also reveal a positive role. Diagnostic tests, including robustness checks, confirm the stability of the estimations. Beyond its marketing relevance, the findings contribute to the sustainability debate by highlighting how understanding impulsive behavior can guide the design of retail strategies that foster responsible consumption, reduce the risks of over-spending in vulnerable households, and support inclusive and resilient consumption practices. Thus, the study links the analysis of changing consumption patterns with broader sustainability goals in emerging urban contexts. Full article

Based on provincial panel data from 2014 to 2023, this study employs the entropy weight method to construct an indicator system for measuring the logistical resilience of regions along China’s Belt and Road Initiative (BRI). The Dagum Gini coefficient is used to analyze regional disparities in resilience levels. Furthermore, when geographical factors are integrated, spatial autocorrelation analysis via Moran’s I index is conducted on the measurement results to explain the spatial heterogeneity among variables. The results reveal several key findings: (1) During the implementation of the BRI, the logistical resilience of regions along the route has improved to varying degrees, indicating enhanced ability of the logistics industry to withstand external risks and recover from disruptions. (2) The level of regional logistical resilience exhibits a spatial pattern similar to that of logistics industry development, characterized by a gradual decline from the southeastern coastal areas toward the northwestern inland regions. (3) Logistical resilience within the study areas has increasingly significant spatial spillover effects; that is, regions with developed logistics industries positively impact surrounding areas, driving improvements in their resilience levels. The results of this study suggest a growing trend of spatial convergence in logistical resilience across these regions. Based on these results, corresponding policy recommendations are proposed to provide insights for enhancing regional logistical resilience. Full article