Search Results (996)

This study investigates the regional drivers shaping the development of short food supply chains (SFSCs). SFSCs are increasingly recognized as sustainable alternatives to industrial food systems; however, their development dynamics at the regional level remain poorly understood. Drawing on structured interviews and surveys with regional agricultural institutions, combined with official statistical data, we applied Spearman’s rank correlation to test five hypotheses related to structural, institutional, historical, and demand-related factors across 16 provinces in Poland. The results revealed weak or statistically non-significant associations between most of the analyzed factors and the development of SFSCs, with the notable exception of a strong correlation with urban population size. These findings challenge conventional assumptions about the role of agrarian or policy conditions in supporting localized food systems and suggest that regional food policy should focus more on enhancing urban–rural linkages and consumer engagement to foster sustainable food supply models. Full article

Generating realistic and diverse driving scenarios is essential for effective scenario-based testing and validation in autonomous driving and the development of driver assistance systems. Traditionally, parametric models are used as standard approaches for scenario generation, but they require detailed domain expertise, suffer from scalability issues, and often introduce biases due to idealizations. Recent research has demonstrated that AI models can generate more realistic driving scenarios with reduced manual effort. However, these models typically focused on single scenario types, such as cut-in maneuvers, which limits their applicability to diverse real-world driving situations. This paper, therefore, proposes a unified generative framework that can simultaneously generate multiple types of driving scenarios, including cut-in, cut-out, and cut-through maneuvers from both directions, thus covering six distinct driving behaviors. The model not only learns to generate realistic trajectories but also reflects the same statistical properties as observed in real-world data, which is essential for risk assessment. Comprehensive evaluations, including quantitative metrics and visualizations from detailed latent and physical space analyses, demonstrate that the unified model achieves comparable performance to individually trained models. The shown approach reduces modeling complexity and offers a scalable solution for generating diverse, safety-relevant driving scenarios, supporting robust testing and validation. Full article

Stocking density is a key driver of performance in aquaponics, affecting both fish welfare and crop yield. This study evaluated the impact of three initial stocking densities (3.1, 4.1, and 6.2 kg/m³) on survival, growth, feeding behavior, carcass and filet quality of largemouth bass (Micropterus salmoides), as well as on the yield of lettuce (Lactuca sativa), sweet basil (Ocimum basilicum), and Swiss chard (Beta vulgaris var. cicla) cultivated in vertical coupled aquaponic units. A total of 184 fish (109 ± 28 g) were reared for 176 days in nine independent recirculating systems. Fish reared at the lowest density achieved the highest final live weight and specific growth rate, with a better feed conversion ratio, whereas performance declined at higher densities despite similar survival rates. Feeding behavior was generally consistent across groups, although feed intake rate was reduced at the highest density. Carcass and filet quality traits were unaffected by stocking density. Vegetable yield was enhanced by higher fish biomass, with significant increases in lettuce production and a positive trend for basil. These findings indicate that intermediate stocking densities may represent the most sustainable compromise, ensuring fish welfare and acceptable growth while supporting efficient plant production in largemouth bass–based aquaponics. Full article

Against the backdrop of global warming and intensified anthropogenic activities, groundwater reserves are rapidly depleting and facing unprecedented threats to their long-term sustainability. Consequently, investigating groundwater reserves is of critical importance for ensuring water security and promoting sustainable development. This study takes the Heilongjiang (Amur) River Basin as the research area. Groundwater storage was estimated using data from the Gravity Recovery and Climate Experiment (GRACE) satellite and the Global Land Data Assimilation System (GLDAS) covering the period from 2002 to 2024. A combination of Random Forest (RF), SHapley Additive exPlanation (SHAP) models, and Pearson partial correlation coefficients was employed to analyze the spatiotemporal evolution characteristics, driving mechanisms, and spatial linear correlations of the primary influencing factors. The results indicate that the basin’s groundwater storage anomaly (GWSA) exhibits an overall declining trend. GWSA is influenced by multiple factors, including climatic and anthropogenic drivers, with temperature (TEM) and precipitation (PRE) identified as the primary controlling variables. Spatiotemporal analysis reveals significant spatial heterogeneity in the relationship between GWSA evolution and its primary drivers. This study adopts a “retrieval–attribution–spatial analysis” framework to provide a scientific basis for enhancing regional groundwater security and supporting sustainable development goals. Full article

This study evaluates four developed external Human–Machine Interface (eHMI) concepts for automated shuttles, focusing on improving communication with other road users, mainly pedestrians and cyclists. Without a human driver to signal intentions, eHMI systems can play a crucial role in conveying the shuttle’s movements and future path, fostering safety and trust. The four eHMI systems’ purple light projections, emotional eyes, auditory alerts, and informative text were tested in a virtual reality (VR) environment. Participant evaluations were collected using an approach inspired by Kansei engineering and Likert scales. Results show that auditory alerts and informative text-eHMI are most appreciated, with participants finding them relatively clear and easy to understand. In contrast, purple light projections were hard to see in daylight, and emotional eyes were often misinterpreted. Principal Component Analysis (PCA) identified three key factors for eHMI success: predictability, endangerment, and practicality. The findings underscore the need for intuitive, simple, and predictable designs, particularly in the absence of a driver. This study highlights how eHMI systems can support the integration of automated shuttles into public transport. It offers insights into design features that improve road safety and user experience, recommending further research on long-term effectiveness in real-world traffic conditions. Full article

Electroencephalography (EEG) provides insights into the neural mechanisms underlying attention, response inhibition, and distraction in cognitive tasks. This dataset was collected to examine neural activity in young drivers and non-drivers performing Go/No-Go tasks under visual and auditory distraction conditions. A total of 40 university students (20 drivers, 20 non-drivers; balanced by sex) completed eight experimental blocks combining visual or auditory stimuli with realistic distractions, such as text message notifications and phone call simulations. EEG was recorded using a 16-channel BrainAccess MIDI system at 250 Hz. Experiments 1, 3, 5, and 7 served as transitional blocks without participant responses and were excluded from behavioral and event-related potential analyses; however, their EEG recordings and event markers are included for baseline or exploratory analyses. The dataset comprises raw EEG files, event markers for Go/No-Go stimuli and distractions, and metadata on participant demographics and mobile phone usage. This resource enables studies of attentional control, inhibitory processes, and distraction-related neural dynamics, supporting research in cognitive neuroscience, brain–computer interfaces, and transportation safety. Full article

The urgent global challenge of environmental sustainability has intensified interest in integrating Lean Management practices with environmental objectives, particularly within the automotive industry, a sector known for both innovation and high environmental impact. This study investigates the systemic relationships between 16 lean practices and three environmental performance metrics: energy consumption, CO₂ emissions, and waste generation. Using the Fuzzy Decision-Making Trial And Evaluation Laboratory (DEMATEL) methodology, data were collected from seven lean experts in the Italian automotive industry to model the cause–effect dynamics among the selected practices. The analysis revealed that certain practices, such as Total Productive Maintenance (TPM), just-in-time (JIT), and one-piece-flow, consistently act as influential drivers across all environmental objectives. Conversely, practices like Statistical Process Control (SPC) and Total Quality Management (TQM) were identified as highly dependent, delivering full benefits only when preceded by foundational practices. The results suggest a strategic three-step implementation roadmap tailored to each environmental goal, providing decision-makers with actionable guidance for sustainable transformation. This study contributes to the literature by offering a structured perspective on lean and environmental sustainability in the context of the automotive sector in Italy. The research is supported by a data-driven method to prioritize practices based on their systemic influence and contextual effectiveness. Full article

The construction industry is critically reliant on efficient supply chains to ensure better project management, success, and profitability. This research examined the critical and ongoing challenges within the construction supply chain, with a particular emphasis on the persistent shortage of truck drivers and its far-reaching implications for construction logistics. Utilizing a structured multi-phase approach, the research integrated a comprehensive literature review, bibliometric analysis, and an empirical case study involving commercial motor vehicle (CMV) drivers. The literature review established the current state of knowledge on transportation labor shortages. At the same time, the bibliometric analysis revealed four key thematic clusters that collectively define the primary areas influencing construction supply chain performance. The case study reinforced these findings by highlighting key factors contributing to driver shortages, including demographic shifts, challenging working conditions, regulatory barriers, and geopolitical disruptions. The paper concludes with practical implications for industry practitioners, policymakers, and supply chain managers, emphasizing the necessity of integrative operational and strategic responses to build resilient and sustainable construction supply chains and logistics systems, with a specific focus on truck driver shortages. By examining both academic literature and field-based perspectives, this research provides a comprehensive understanding of how labor shortages, particularly in transportation, can destabilize construction supply chains and identifies the most effective measures to support their foundations. Full article

In recent years, China has faced the dual challenge of achieving high-quality economic development (HQED) alongside carbon emission reduction (CER), with new-quality productivity (NQP) emerging as a key driver integrating both agendas. Research on the coordinated development of these three dimensions remains limited but is critical for effective policy-making. Based on panel data from 30 Chinese provinces (2014–2023), this study constructs the NQP-CER-HQED evaluation indicator system; calculates the composite index using the entropy weight method and composite index calculation model; computes the coupling coordination degree (CCD) of the three components via the CCD model; analyzes the temporal evolution and future trends of CCD using kernel density and GM(1,1) models; examines the spatial evolution of CCD through Moran’s I index; employs traditional Markov chains and spatial Markov chains to investigate the spatial-temporal evolution patterns of CCD; and applies the geographic detector method to analyze the influencing factors of CCD among NQP, CER and HQED. The findings reveal that (1) the CCD of China’s NQP-CER-HQED has undergone six levels, showing an overall upward trend; (2) temporally, CCD levels improve annually, with all provinces expected to achieve coordinated development by 2026; (3) spatially, the CCD exhibits a “high-east, low-west” tiered distribution; (4) spatially/temporally, the transition of the CCD levels is primarily gradual rather than leapfrogging; and (5) the level of opening up and new-quality labor resources are identified as dominant influencing factors, with the interaction between new-quality labor resources and government support showing the strongest explanatory power. This study provides an analytical framework for understanding the NQP-CER-HQED synergy and offers a scientific basis for sustainable policy formulation. Full article

Effectively managing truck driver fatigue is essential for ensuring driver safety, as fatigue negatively impacts health and performance. Since fatigue is a complex, multidimensional issue with various cause–effect relationships, it requires a comprehensive management approach. This systematic review investigates truck driver fatigue management strategies currently available in the road freight industry. The review analyses and synthesises fatigue management recommendations from 32 resources, leading to the identification of overarching themes and the development of suitable frameworks. The findings highlight that fatigue management focuses on operational systems and processes, safety culture and practices, and driver health and well-being. Effective fatigue management should encompass each of these themes, whereby the specific proactive, real-time, and reactive practices are interlinked to support feedback loops, emphasising the application of a change management model. Further research that develops and empirically tests an actionable toolkit integrating the proposed frameworks in a developing country will enhance our understanding of the unique operating environment. This review acts as a foundational tool for management and researchers, highlighting available strategies for managing truck driver fatigue while emphasising that fatigue risk cannot be addressed by a singular approach, but rather that a combination of interconnected systems is required. Full article

Mountainous and sparsely populated regions in Europe—here called “cold lands”—are particularly exposed to global megatrends such as climate change, demographic shifts, and economic restructuring. Addressing these interconnected challenges requires approaches that link foresight with local governance systems. This study presents a pilot methodological framework that integrates Ostrom’s Social-Ecological Systems (SES) model with the European Commission’s Megatrend Assessment method to support participatory foresight. The framework was tested in two illustrative cases, located in the Italian Alps and Norway, to demonstrate its feasibility and potential value. Through a structured discussion among researchers, key megatrends were prioritised, and qualitative scenarios were developed to explore how community preparedness can influence socio-ecological outcomes. The results highlight climate change, resource scarcity, and demographic imbalances as dominant drivers, while contrasting scenarios illustrate how proactive governance can mitigate vulnerability and foster adaptive capacity. The approach contributes a replicable and scalable foresight tool to bridge global trends and local sustainability strategies, supporting anticipatory and community-based governance in vulnerable territories. Full article

The identification of brain clearance failure as a precursor to a large variety of neurodegenerative diseases has shifted fluid dynamics from a secondary to a tertiary target of brain health. The identification of the glymphatic system, detailing cerebrospinal fluid entry along perivascular spaces and exit via perivenous and meningeal lymphatic pathways, provided a challenge to previous diffusion models and established aquaporin-4–dependent astroglial polarity as a governing principle of solute transport. Multiple lines of evidence now support a coupled glymphatic–venous axis, wherein vasomotion, venous outflow, and lymphatic drainage are functionally interrelated. Failure of any axis will cascade and affect the entire axis, linking venous congestion, aquaporin-4 disassembly, and meningeal lymphatic failure to protein aggregation, neuroinflammation, edema, and intracranial hypertension. Specific lines of evidence from diffusion tensor imaging along vascular spaces, clearance MRI, and multi-omic biomarkers can provide a measure of transport. Therapeutic strategies are rapidly advancing from experimental strategies to translational approval, including behavioral optimization, closed-loop sleep stimulation, vascular and lymphatic therapies, focused ultrasound, pharmacological polarity recoupling, and regenerative bioengineering. Novel computational approaches, such as digital twin dynamic modeling and adaptive trial designs, suggest that clearance measures may serve as endpoints to be approved by the FDA. This review is intended to bridge relevant mechanistic and translational reviews, focusing on impaired clearance as an exploitable systems defect rather than an incapacitating secondary effect. Improving our understanding of the glymphatic-venous axis Injury may lead to future target strategies that advance cognitive resilience, alleviate disease burden, and improve quality of life. By clarifying the glymphatic–venous axis, we provide a mechanistic link between impaired interstitial clearance and the pathological accumulation of amyloid-β, tau, and α-synuclein in neurodegenerative diseases. The repair of aquaporin-4 polarity, venous compliance, and lymphatic drainage might therefore open new avenues for the diagnosis and treatment of Alzheimer’s and Parkinson’s disease, supplying both biomarkers of disease progression and new targets for early intervention. These translational implications not only locate clearance failure as an epiphenomenon of neurodegeneration but, more importantly, as a modifiable driver of the course of neurodegeneration. Full article

Protected areas play a fundamental role in the implementation of international environmental strategies in order to ensure effective management systems that support the conservation of biodiversity and the provision of ecosystem services. However, the actual capacity of national parks to generate a specific “park effect” remains an open question. This study aims to assess whether the transformations observed in Italian national parks between 1960 and 2018 can be attributed to a specific park effect or are instead the result of other territorial dynamics. We analyzed long-term changes in land use and land cover (LUMCs) and variations in ecosystem services (ES), both inside and outside park boundaries, taking into account the SNAI classification. The results show a significant expansion of forest areas (+52%) and sparse vegetation (+56%), alongside a marked decline in arable land (−60%) and permanent crops (−26%). At the same time, the overall value of ES remains stable at around EUR 4 billion per year, with regulating services—accounting for 80% of the total—increasing by 20% between 1960 and 2018 and provisioning services declining by 41%. Italy’s national parks represent strategic socioecological laboratories capable of generating benefits both locally and globally. To fully realize this potential, more integrated management is needed, enabling their transformation from mere conservation areas to drivers of territorial resilience and social cohesion. Full article

Supply chains are a primary contributor to global greenhouse gas (GHG) emissions, rendering their decarbonization an essential dimension of sustainable development. Artificial intelligence (AI) provides a transformative pathway by facilitating proactive emission avoidance through operational efficiency, transparency, and resilience, in contrast to post-emission mitigation approaches such as carbon capture. This study explores the potential of AI to support indirect carbon dioxide removal (CDR) via supply chain decarbonization, adopting a comparative case study methodology. Empirical evidence is drawn from Tunisian agri-food, textile, and port logistics sectors, based on multi-source datasets spanning 6–12 months and covering fleet sizes ranging from 40 to 250,000 units. Methodological robustness was ensured through the use of pre-intervention baselines, statistical imputation for missing data (<5%), and validation against 20% out-of-sample test sets. Results indicate that AI-enabled interventions achieved annual avoided emissions between 500 and 1500 tCO₂ and reduced fuel consumption by 12–15%, with sensitivity analyses incorporating ±8–12% error margins. Among the approaches tested, hybrid models integrating operational and strategic layers demonstrated the most pronounced impact, aligning immediate efficiency gains with long-term systemic decarbonization. Furthermore, AI facilitates renewable energy integration, digital twin applications, and compliance with international sustainability frameworks, notably the Paris Agreement and the United Nations Sustainable Development Goals. Nevertheless, challenges related to data quality, computational demands, limited expertise, and organizational resistance constrain scalability. The findings underscore AI’s dual role as a technological enabler and systemic driver of supply chain decarbonization, advancing its positioning within global environmental sustainability transitions. Full article

The forest-based bioeconomy is increasingly recognized as a key pillar of European bioeconomy strategies, with potential to drive sustainable innovation, rural development, and climate action. However, regional disparities persist, particularly in Southern Europe. This study assesses the development of a forest-based entrepreneurial ecosystem located in the Spanish region of Castilla-La Mancha, using an adapted multidimensional framework that considers institutional, supply, and demand-side drivers. Fifteen interviews were conducted with key players in the forestry sector. Results indicate an incipient and fragmented ecosystem: while initiatives such as UFIL Cuenca foster entrepreneurship and innovation, the region lacks three main and different aspects: (i) a coherent strategic vision, (ii) cluster development, and (iii) presents coordination failures. Coordination structures as sectoral roundtables are viewed as critical for the forestry value chain but currently underutilized. The study emphasizes the importance of aligning forest-based resources with supportive entrepreneurial environments—where networks, infrastructure, and institutional mechanisms interact—to enable systemic innovation and sustainable regional development. The findings highlight the need for integrated regional strategies, strengthened governance mechanisms, stable financial resources for regional structures and expanded entrepreneurship support to advance the forest-based entrepreneurial ecosystems in Spain. Full article