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Background: The clinical translation of cell-based therapies for knee articular cartilage repair is fundamentally restricted by the severe biological unpredictability of autologous cell sources, inherent manufacturing bottlenecks, and the rapid phenotypic dedifferentiation of cells expanded in conventional 2D monolayers. To overcome these translational hurdles, this study engineered a scaffold-free, 3D formulation of highly characterized allogeneic FE002-Cart chondroprogenitor spheroids. Methods: We systematically investigated the specific microenvironmental cues and Good Manufacturing Practice (GMP) formulation parameters required to direct functional chondrogenesis. The structural and biochemical performance of this allogeneic formulation was benchmarked against multiple primary adult autologous chondrocyte types. Finally, we evaluated the phenotypic resilience of the microtissues in simulated osteoarthritic (OA) environments and investigated both short-term liquid storage and advanced terminal preservation strategies to establish off-the-shelf logistical viability. Results: Precise microenvironmental regulation proved to be a critical biological prerequisite. The synergistic combination of physiological hypoxia (2% O₂) and stringent glucocorticoid limitation (10 nM dexamethasone) induced robust glycosaminoglycan (GAG) deposition and a > 200-fold upregulation of ACAN and COL2, while suppressing the terminal hypertrophic drift observed in adult chondrocytes. Benchmarking revealed that the allogeneic FE002-Cart formulation substantially mitigates the profound morphological and biochemical unpredictability inherent to adult autologous cell sources. Furthermore, the scaffold-free spheroid geometry yielded a 10-fold increase in GAG production per cell compared to traditional matrix-seeded (MACI) platforms. Transitioning to a GMP-compatible manufacturing process revealed extreme cellular sensitivities; excipients within standard pharmaceutical-grade dexamethasone severely aborted chondrogenic differentiation, emphasizing the necessity of rigorous raw-material qualification. Functionally, the 3D architecture acted as a protective physical shield, sustaining high cellular viability when subjected to severe inflammatory stress and 100% OA patient synovial fluid. Logistically, the viable spheroids maintained matrix integrity and inter-spheroid fusion potential for up to 7 days at ambient temperature in transport medium. Finally, advanced spheroid preservation via lyophilization and high-dose gamma irradiation eliminated biological viability but successfully transitioned the microtissues into highly organized, terminally irradiated matrices capable of heterologous in vitro structural merging. Conclusions: These findings define the critical biological thresholds for manufacturing, demonstrate the enhanced in vitro biosynthetic efficiency of 3D allogeneic microtissues compared to specific autologous and matrix-dependent baselines, and establish a highly practical, off-the-shelf logistical framework for the regenerative treatment of large knee chondral defects. Full article

Urban transport emissions are a major contributor to climate change and urban air pollution. Although previous studies have demonstrated that traffic demand, fleet electrification, and driving behavior individually influence vehicular emissions, their combined effects under different congestion conditions remain insufficiently understood. This study investigates the interactions among these factors using the microscopic traffic simulator SUMO (Simulation of Urban MObility). A synthetic urban corridor consisting of five signalized intersections was developed to represent arterial roads in medium-sized cities. A full factorial experimental design was implemented by considering three traffic demand levels, three electric vehicle adoption percentage levels, and three driving behavior profiles, resulting in 27 experimental scenarios with 10 stochastic replications per scenario. Traffic performance and pollutant emissions were evaluated to quantify both the individual and interaction effects of the experimental factors. The results indicate that traffic demand is the primary determinant of CO₂ and NO_x emissions, while fleet electrification substantially reduces emissions, particularly under congested conditions. Driving behavior also plays a role by influencing acceleration and deceleration patterns. Furthermore, statistically significant interaction effects among the experimental factors ($p<0.05$) reveal the benefits of fleet electrification considering the traffic demand and the driving behavior. These findings contribute to the understanding of sustainable urban mobility by providing a comprehensive assessment of how traffic demand, fleet electrification, and driving behavior jointly influence urban traffic performance and vehicle emissions, offering valuable insights for the design of integrated transportation and environmental policies. Full article

Amid the well-researched negative effects of road congestion and increased private car use, there is a need for more sustainable modes of transport. The literature points towards trains as being a vital part of the solution to the current problems, but their success depends on a number of variables, especially when it comes to the main railway stations in the largest cities. The first goal of this study was to identify the relationship between passenger numbers at major railway stations in Poland and selected parameters characterizing public transport services; the second was to assess the usefulness of the NOAH (Nest of Apes Heuristic) method for data analysis. In Poland, the number of major transfer hubs is limited, and there is a lack of an existing method allowing comparison of variables in such small datasets in a way that infers statistical significance. This is a research gap that the authors aimed to address using the NOAH algorithm combined with an analysis of regression. The initial dataset had been successfully expanded in a way that dependencies could be observed, with both goals being met. Passenger numbers relied most on the number of trains departing at each station daily, while walking distance during transfers impacted that number most negatively. The results point towards other variables influencing the passenger numbers, which were not considered in this study but could form the basis of further research. The utilized method could also be applied to a different group of cities, and in other countries. Additionally, the study added to the development of the NOAH algorithm itself, improving the method. Full article

As a major beef cattle producing country, China’s beef industry is expanding and undergoing structural transformation. A systematic assessment of the spatiotemporal evolution and driving factors of its water footprint is of great significance for the green and sustainable development of the beef cattle industry, and also provides a reference for understanding the current status of beef cattle water footprint, formulating environmental policies, and supporting the sustainable development of other livestock and poultry species. Based on the life cycle assessment (LCA) method, quantified the green, blue, and grey water footprints of China’s beef cattle industry “from cradle to farm gate” across 31 provinces from 2002 to 2022, covering three farming scales (small-scale, medium-scale, and large-scale) classified according to the proportion of beef cattle slaughter numbers (1–49 head, 50–500 head, and >500 head), and encompassing four stages: feed crop cultivation, beef cattle farming, manure leaching, and transportation and processing, covering three farming scales (small-scale, medium-scale, and large-scale) classified according to the proportion of beef cattle slaughter numbers (1–49 head, 50–500 head, and >500 head), and encompassing four stages: feed crop cultivation, beef cattle farming, manure leaching, and transportation and processing. Furthermore, kernel density estimation and standard deviational ellipse methods were employed to reveal the spatiotemporal evolution characteristics, and a Spatial Durbin Model (SDM) was constructed to identify the driving factors. The findings indicate that the total water footprint of China’s beef cattle industry first decreased and then increased, reaching 918.70 km³ in 2022. The grey water footprint accounted for an average of 90.85% annually, and the manure leaching stage contributed the largest share (averaging 62.24% annually), suggesting that this stage warrants priority attention from the perspective of this indicator. Large-scale farming exhibited the lowest water footprint per unit of beef (averaging 29.39 m³/kg), while small-scale farming had the highest (54.65 m³/kg). The spatial pattern showed a trend of “high in the west and low in the east, rising in the west and declining in the east.” The spatial model results revealed that the water footprint per unit of beef exhibited significant spatial agglomeration and spatial spillover effects. Full article

Land transport infrastructure (LTI) is a core component of sustainable transport systems, shaping mobility, efficiency, and the spatial distribution of development gains. Existing studies of urban–rural income inequality mainly focus on individual transport modes or aggregate infrastructure scale, with limited attention to transport-system composition and its contextual dependence. This study addresses this gap by conceptualizing LTI as a layered system and examining how its internal composition is associated with urban–rural income inequality across different levels of urbanization and economic development. Using a balanced panel of 286 prefecture-level cities from 2013 to 2023, the study constructs ratio-based indicators of compositional shifts within road systems, within rail systems, and between rail and road infrastructure. Two-way fixed-effects models incorporate interactions with urbanization and economic development. Conditional marginal-effect maps are then used to identify how these associations change across development contexts. The results reveal a clear stage-dependent pattern. Urbanization generally attenuates the inequality-widening association of mobility-oriented upgrading, whereas economic development influences whether such upgrading reinforces spatial polarization or supports wider diffusion. When urbanization and development are both sufficiently advanced, the marginal association may shift toward inequality reduction. At earlier stages, accessibility-oriented roads and conventional rail tend to show stronger equalizing associations. Mobility-oriented roads and high-speed rail are more likely to be associated with narrower inequality in more advanced settings. Mechanism-oriented analyses yield evidence consistent with two potential channels: the agricultural–non-agricultural labor-productivity gap and the non-agricultural employment share. The extended analyses and robustness checks broadly support the main findings. These findings indicate that transport infrastructure upgrading should be evaluated not only in terms of efficiency, but also according to whether the resulting infrastructure mix broadens access to opportunities, improves resource allocation, and supports inclusive regional development. Full article

Urban transportation systems face increasing sustainability and resilience challenges due to the growing frequency and intensity of weather extremes. Weather-related congestion may increase travel delays, fuel consumption, and unequal economic costs, thereby undermining urban sustainability. Although previous studies have examined the relationship between weather conditions and traffic congestion, limited attention has been paid to whether street-network design can enhance transportation resilience under extreme weather conditions. This study investigates the relationships among extreme weather, traffic congestion, and street density using daily congestion and meteorological data from 35 major Chinese cities between 2018 and 2024. Fixed-effects regressions estimate the associations between multiple weather extremes and congestion and examine the moderating role of street density. Heavy rainfall, extreme cold, and low visibility are associated with increased congestion, whereas extreme heat is associated with reduced congestion. Street density could buffer congestion under extreme cold and heavy snow cover, suggesting that denser networks may improve resilience to localized road-surface disruptions. Heterogeneity analyses reveal weaker weather-related congestion responses in megacities and clustered cities, and during the COVID-19 period. These findings highlight the potential role of street-network design in supporting sustainable and climate-resilient transportation by reducing vulnerability to weather-related congestion. Full article

This study presents a comprehensive spatial analysis of the public transport network across the Almaty agglomeration, encompassing 211 routes within eight urban districts and seven adjacent administrative divisions. Drawing on digitized geodata from 3634 unique bus stops, the research methodology integrates quantitative and qualitative spatial metrics, including distribution density, average stop spacing, elevation gradients, and topological connectivity. The analysis highlights significant territorial imbalances: while 70.53% of all unique stops are concentrated within Almaty’s city limits, the surrounding regional network is highly fragmented, with an average stop spacing of 2579.53 m. Furthermore, topographic assessments confirm substantial operational challenges for north–south routing due to steep elevation changes, reaching 984.48 m within the city and 1243.23 m regionally. A critical evaluation of topological connectivity reveals that 44.63% of suburban bus stops lack transfer intersections, underscoring severe deficits in peripheral public transport provision. By assessing 56 distinct connection types, the study categorizes administrative districts based on their route integration levels. Ultimately, the derived spatial parameters offer a robust evaluation of the current transport framework. These insights establish a crucial scientific and empirical foundation for optimizing route geometries, bridging infrastructural gaps, and guiding sustainable transit planning in alignment with Almaty’s transition toward a polycentric urban model. Furthermore, the developed 3D spatial-topological framework provides a scalable, data-driven blueprint for municipal authorities to prioritize infrastructure investments, deploy multimodal hubs and enhance transit equity in other rapidly growing and topographically complex agglomerations worldwide. Full article

The popularity of electric vehicles (EVs) is growing at a fast pace, creating a need for the strategic deployment of charging stations (CSs) to provide enough coverage, cost effectiveness, and compliance with grid and urban planning regulations. The deployment of large-scale infrastructure under multiple, often conflicting constraints remains a challenging engineering decision-making problem. In this paper, we propose a hybrid optimization framework that combines greedy initialization with reinforcement learning to efficiently explore the charging station deployment problem. The proposed approach employs Q-learning and Deep Q-Network (DQN) agents to iteratively refine the initial deployment while simultaneously optimizing deployment cost, charging demand coverage, and operational utility under practical planning constraints. The constraints include grid capacity limitations, renewable energy utilization, and fairness considerations. The proposed framework is evaluated in realistic urban scenarios. The experimental results demonstrate that the reinforcement learning (RL) approach achieves superior trade-offs among competing objectives compared to baseline heuristic strategies, while maintaining computational scalability for large candidate location sets. The proposed framework demonstrates stable performance across three evaluated deployment scenarios, indicating its potential applicability to increasingly complex charging infrastructure planning problems. The proposed methodology is scalable to other complex engineering planning and resource allocation problems characterized by multi-objective trade-offs and dynamic constraints. Beyond improving optimization performance, the proposed framework contributes to sustainable transportation planning by supporting the efficient deployment of electric vehicle charging infrastructure. Optimized charging station placement promotes greater accessibility to charging services, encourages electric vehicle adoption, reduces unnecessary travel associated with charging activities, and contributes to lower greenhouse gas emissions. Consequently, the proposed methodology provides decision-makers with a scalable and intelligent planning tool that supports the transition toward more sustainable and energy-efficient urban mobility systems. Full article

Energy transitions reshape not only energy supply and demand but also the broader socio-technical, environmental, and economic conditions that influence the resilience of European economies. This study examines whether selected energy and environment-related indicators are associated with employment-rate dynamics in 26 European countries over the period 2005–2022. Harmonised Eurostat indicators from the Sustainable Development Goals monitoring framework are used as empirical proxies for system-level characteristics, rather than as normative measures of SDG implementation. Employment rate by citizenship is treated as an observable indicator of labour-market adjustment within the broader process of sustainable energy-system transformation. The empirical analysis applies a dynamic panel-data model with autoregressive and distributed lag components, estimated using weighted least squares. The results indicate that employment-rate dynamics are associated with energy demand, import dependency, energy productivity, household energy conditions, transport structure, recycling capacity, and environmental pressure. Import dependency shows a negative long-term association, whereas final energy consumption is positively associated with employment-rate dynamics. The study contributes to energy-sustainability research by interpreting labour-market adjustment as one dimension of a resilient and just energy transition. Full article

Decarbonizing the transportation sector depends not only on the scale of mitigation programs but also on whether financing systems are capable of generating measurable emission reductions. In Indonesia, climate finance allocation remains substantially below the level required to achieve the transportation-sector target under the Enhanced Nationally Determined Contribution (ENDC). At the same time, mitigation planning rarely establishes a clear relationship between financial expenditure and verified greenhouse gas (GHG) reduction outcomes, making policy effectiveness difficult to assess. This study examines the relationship between climate finance and mitigation outcomes in Indonesia’s transportation sector using verified emission reduction data and realized mitigation expenditures during 2018–2022. A cost-based assessment approach was applied to estimate the financing required to reduce one ton of CO₂ equivalent (tCO₂-e) across direct and indirect mitigation actions. The analysis identified 33 mitigation actions categorized under the Avoid–Shift–Improve (ASI) framework and evaluated their contribution to sectoral emission reduction. The results indicate substantial variation in mitigation costs among intervention types. Direct mitigation actions, particularly mass public transportation expansion, are linked to larger emission reductions at relatively lower costs than enabling or indirect measures. On average, reducing 1 tCO₂-e in Indonesia’s transportation sector requires approximately USD 184–1000 (IDR 3–16.4 million), using a standardized exchange rate of approximately IDR 16,400 per USD. Based on the transportation-sector ENDC target, the estimated financing requirement by 2030 ranges from USD 2.5–13.8 billion (IDR 42–226 trillion). The findings suggest that climate finance policies should move beyond expenditure-oriented approaches toward financing frameworks that explicitly connect investment allocation with verified mitigation performance. Full article

The catalytic valorization of biomass-derived 5-hydroxymethylfurfural (HMF) provides an important route toward sustainable chemical production. Herein, we report a bimetallic Pt₁Au₁ catalyst supported on Vulcan XC-72R carbon (Pt₁Au₁/XC) that enables efficient aerobic oxidation of HMF to FDCA using NaHCO₃ as a weak base. Under optimized conditions (100 °C, 1 MPa O₂, NaHCO₃/HMF = 3:1, 12 h), the catalyst achieves near-quantitative HMF conversion (>99%) with an FDCA selectivity of 94.2%, substantially outperforming monometallic Pt/XC and Au/XC as well as their physical mixtures. The enhanced performance is associated with the beneficial combination of Pt and Au, together with the favorable surface and transport properties imparted by the carbon support. Kinetic and intermediate-trapping experiments indicate a dual-pathway oxidation mechanism, wherein Au facilitates aldehyde oxidation (via 5-hydroxymethyl-2-furancarboxylic acid (HMFCA)) and Pt promotes alcohol oxidation (via furan-2,5-dicarbaldehyde (DFF)), with both routes converging at 2-formyl-5-furancarboxylic acid (FFCA) for subsequent conversion to FDCA. The catalyst exhibited only a marginal decrease in catalytic activity after five consecutive cycles. Overall, this work provides insight into the roles of bimetallic effects and support properties in HMF oxidation and offers a useful strategy for designing efficient catalysts for biomass-derived furan oxidation. Full article

Electric Vehicle Charging Systems (EVCSs) have become a critical component of the global transition toward sustainable and intelligent transportation. However, their tight integration with heterogeneous cyber–physical, vehicular, and cloud-based infrastructures exposes them to an expanding attack surface, including data poisoning, malware injection, denial-of-service, and man-in-the-middle (MITM) attacks. Existing security solutions largely rely on isolated detection mechanisms and lack a unified semantic representation of EVCS assets, attack propagation paths, and mitigation dependencies, limiting their effectiveness in complex and evolving threat scenarios. To address these challenges, this paper proposes EVCS-SecOnt, an ontology-driven cybersecurity framework for modeling, reasoning, and mitigating security threats in EVCS infrastructures. The proposed ontology formalizes relationships across four core modules, namely Attack Surface, Attack Classification, Protection Mechanisms, and Risk and Mitigation, enabling holistic threat representation and TARA-based risk assessment. EVCS-SecOnt incorporates standard semantic namespaces (em:, seas:, uiote:, sch:, and time:) to ensure interoperability and is instantiated using the CICEVSE2024 dataset to support observation-level security reasoning. A unified SPARQL-based analytical workflow is employed to perform global ontology validation, attack–risk–severity correlation, mitigation prioritization, and observation-level inference using statistical feature vectors. Experimental results demonstrate that the ontology captures multiple attack classes, risk levels, severity categories, and mitigation strategies, enabling automated identification of critical attack scenarios and context-aware defense recommendations. The validation demonstrates logical consistency, semantic traceability, and query-based coverage of the ontology across attack classes, risk levels, severity categories, and mitigation strategies. EVCS-SecOnt enhances the interpretability, reusability, and explainability of EVCS cybersecurity management by bridging operational data with semantic intelligence. The proposed framework supports adaptive protection, risk-aware decision-making, and ontology-driven security analytics, providing a semantic foundation for next-generation e-mobility and smart charging infrastructures. Full article

Degraded industrial sites in arid and semi-arid regions often suffer from loose surface substrates, weak water-retention capacity, high wind-erosion risk, and poor early vegetation establishment. Combining photovoltaic (PV) deployment with ecological utilization may improve near-surface habitats by shading, reducing wind speed, and regulating soil heat and moisture. This study investigated an ash storage yard of a coal-fired power plant in Ordos, Inner Mongolia, China, by comparing soil temperature, soil moisture, and near-surface wind-speed responses under three representative fixed PV tilt angles of 36°, 43°, and 50°, together with the corresponding early plant-growth suitability. A multi-physics model coupling near-surface airflow, water-vapor transport, and porous-media hydrothermal migration was established. A Gaussian suitability function combined with AHP-CRITIC weighting was used to construct a model-based comprehensive growth index (CGI) from soil temperature and moisture, while short-term field monitoring was used to validate afternoon soil hydrothermal trends. Among the three scenarios, the 36° configuration produced the widest horizontal heat–moisture-affected zone and the highest CGI values for alfalfa and Elymus nutans, reaching 0.7741 and 0.6875, respectively. Relative to the outside reference area, the rear PV zone reduced the near-surface wind speed by 33–40% and increased the plant heights of alfalfa and Elymus nutans by 49.4% and 37.8%, respectively. A first-order PVsyst assessment showed that the 43° configuration achieved the highest specific energy yield of 1814 kWh kWp⁻¹ year⁻¹, whereas the annual grid-connected output at 36° was only 0.59% lower. These findings indicate that the 36° configuration may provide a favorable compromise between early vegetation establishment and photovoltaic electricity generation among the tested scenarios. By linking renewable-energy production with microenvironment regulation and early vegetation establishment, the proposed framework provides a decision basis for the multifunctional and sustainable reuse of degraded industrial land. Nevertheless, the results represent a site-specific, single-season assessment and should not be interpreted as a universal optimum. Full article

The National Transport Safety Board found that Wheels-Up Landing was the second highest defining event for aircraft accidents from 2008 to 2022. The increasing push for sustainable propulsion and accompanying novel airframe architectures present new integration and safety challenges for aircraft design and development, among which is the structural integrity and crashworthiness of the aircraft under such extreme events. This paper examines the regulations and design requirements governing aircraft emergency Wheels-Up Landing scenarios, emphasising their implications for aircraft safety and structural integrity. It consolidates standards from aviation authorities, such as the FAA and EASA, which identify and define key requirements relating to occupant safety and fire prevention and protection during such events. The paper then considers the Wheels-Up Landing scenario and its design requirements within the context of future novel aircraft employing sustainable propulsion systems, from higher bypass turbofan to electric- and hydrogen-based technologies. The unique characteristics and challenges of these emerging propulsion technologies are described, highlighting how alternative structural configurations, weight distributions and powerplant architectures may influence the aircraft response under a Wheels-Up Landing event. Finally, an exploration of predictive modelling strategies and methods currently used in Wheels-Up Landing analysis was conducted. While reviewing the breadth of accurate, high-fidelity modelling methods targeting fuselage impact, it also highlighted the gap in both considering the increasingly relevant and frequent powerplant impact scenarios, and the provision of lightweight modelling approaches necessary to rapidly and adequately address the emergency Wheels-Up Landing response early in the aircraft design process. Full article

Background: Generative artificial intelligence (GenAI) has attracted significant attention in supply chain management (SCM) due to its potential to improve data-driven decision-making and operational performance. However, existing studies mainly focus on individual GenAI models or specific supply chain applications, lacking a comprehensive understanding of how different GenAI architectures support decision-making across the supply chain. Methods: This study conducts a systematic literature review using the PRISMA framework to examine the applications of Generative Adversarial Networks (GANs), Transformers, Variational Autoencoders (VAEs), and flow-based models within a six-level supply chain decision-making framework. A total of 692 peer-reviewed publications were analyzed using bibliometric methods, including keyword co-occurrence, temporal and density analyses, and Supervised Embedding Visualization. Results: Current research is concentrated on Transformer and GAN applications, particularly in data analytics, optimization, forecasting, manufacturing, transportation, logistics, and quality management. The analyses also reveal major research themes, the evolution of GenAI in SCM, and limited attention to sustainability, cybersecurity, resilience, and reverse logistics. Conclusions: This study provides a comprehensive overview of GenAI applications in SCM, identifies key research gaps, and offers a foundation for future research while helping practitioners evaluate opportunities and limitations of GenAI for supply chain decision-making. Full article