Search Results (4,834)

The rapid expansion of artificial intelligence and cloud computing is straining terrestrial data center infrastructure, motivating exploration of space-based data centers (SBDCs) as a scalable and energy-efficient alternative. While orbital platforms offer unique advantages, including continuous solar energy, radiative cooling, and global coverage, their practical deployment is constrained by unresolved reliability challenges across the mission lifecycle. This study presents a lifecycle-oriented reliability and risk assessment for SBDCs spanning launch, orbital operation, maintenance, and end-of-life phases, using a structured systems-level analysis of failure modes and operational dependencies. This paper focuses on compute-centric SBDC architectures, treating storage solely as a supporting resource. We identify and classify space-environment-specific risks, including launch-induced mechanical stress, radiation-driven degradation, thermal extremes, and single points of failure in power and communication subsystems. By integrating engineering constraints with economic considerations, we develop a unified risk-chain framework that shows how reliability limitations propagate from component design to system cost and operational viability. The analysis reveals a critical trade-off: achieving terrestrial-grade reliability in orbit requires substantial redundancy and radiation hardening, increasing mass and cost and reducing economic feasibility, whereas lower-reliability designs introduce operational and financial risks that challenge sustainability. These findings establish reliability as the central determinant of SBDC viability, providing an applied foundation for fault-tolerant, modular, and lifecycle-aware design strategies essential for transitioning orbital cloud infrastructure from concept to scalable reality. Full article

Poplar (Populus) trees are indispensable to various industries and environmental sustainability efforts. They are widely utilized for paper production, timber, and windbreaks, while also playing a significant role in carbon sequestration. Given their economic and ecological importance, the effective management of diseases is crucial. Convolutional Neural Networks (CNNs), renowned for their ability to process visual data, are pivotal in accurately detecting and classifying plant diseases. This study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, ensuring geographic diversity and broader applicability. The dataset includes four disease classes, i.e., “Parsha (Scab),” “Brown spotting,” “White-Gray spotting,” and “Rust,” which represent common afflictions in these regions. To advance research efforts, this dataset will be made publicly accessible, providing a valuable resource for the scientific community. Leveraging the cutting-edge YOLOv9c model, a state-of-the-art CNN architecture, we applied the Histogram Equalization technique as a preprocessing step to enhance the image quality to increase the accuracy of disease detection. This method not only improves the diagnostic performance of the model but also provides a scalable solution for monitoring and managing poplar diseases. By ensuring the health of poplar trees, this approach supports the sustainability of these critical resources. To our knowledge, this is the first publicly available dataset specifically focused on diseased poplar leaves, making it a significant contribution to global research efforts. It offers an invaluable resource for researchers and practitioners, enabling further advancements in early disease detection and sustainable forestry management. Full article

Deep learning-powered Street View Imagery (SVI) analytics provides a critical mechanism for smart city perception within the framework of Sustainable Development Goal 11 (SDG 11), effectively bridging the gap left by traditional remote sensing in fine-grained street-level observation. Over the years, deep learning-based semantic segmentation of urban streetscapes has become the dominant paradigm. However, when scaling to megacity measurements, current research faces the dual bottlenecks of “computational redundancy” and the “geographical domain shift” caused by the blind application of pre-trained models based on Western datasets. To address these challenges, this study is the first to systematically quantify the performance trade-off between Multi-Task Learning (MTL) and Single-Task Learning (STL) in megacity scenarios. Using this as a baseline, we constructed and validated a “low-computation, high-robustness” framework for streetscape semantic perception and spatial measurement. Relying on an integrated ResNeXt101-FPN MTL architecture and an ultra-low-cost fine-tuning strategy to overcome geographical domain shift, we extracted and analyzed the spatial heterogeneity of five core semantic elements—vegetation, sky, building, road, and vehicle—across the road network within Beijing’s 5th Ring Road. The results indicate the following: (1) We explicitly defined the computation-accuracy trade-off of MTL and STL in megacity perception. While utilizing only 1/5 of the parameters of STL, the MTL framework achieved a 5.34-fold increase in inference speed with a negligible 0.1% loss in overall mean Intersection over Union (mIoU); however, a 27.13% decrease in boundary segmentation accuracy was observed. (2) We established a low-cost, localized correction paradigm to overcome domain shift. Utilizing a minimal annotation cost (only 200 local images) significantly improved cross-domain adaptability, boosting the overall mIoU by 8.92% and significantly mitigating the geographical domain shift problem. (3) Multi-dimensional measurement and spatial analysis revealed a significant spatial decoupling pattern in Beijing’s streetscapes. The visual proportion of vegetation exhibited a pronounced “north-high, south-low” spatial differentiation, whereas built environment elements (e.g., building and road) displayed a typical “center-periphery” concentric gradient. This objectively reflects the spatial inequality of urban street greenery resources and the monocentric development characteristics of the built environment. The proposed framework therefore serves as a low-cost, AI-driven computational paradigm for smart city perception in resource-constrained regions. Furthermore, the revealed spatial heterogeneity offers data-driven insights for formulating sustainable urban renewal policies aligned with SDG 11. Full article

This paper presents a practical and communication-independent energy management system (EMS) for a DC microgrid supply within the firezone of a cruise ship. The proposed approach prioritizes operational reliability and fault tolerance under emergency conditions, where communication availability and control complexity should be minimized. The proposed DC microgrid integrates photovoltaic systems (PVs), fuel cell systems (FCs), and lithium-iron-phosphate (LFP) battery energy storage systems (BESSs), coordinated through a rule-based EMS combined with droop-controlled converters. The electrical topology considered in this study is a collaborative development of the project consortium of the publicly funded project Sustainable DC Systems (SuSy), featuring a novel configuration with two independent horizontal busbars for the Cabin Area Distribution (CAD) and Technical Area Distribution (TAD). The EMS can manage two operational scenarios: (i) regular operation, with two decentralized droop controls where power generation is distributed among all generators based on their respective capacities, and a power curtailment strategy is applied to prevent overcharging of BESSs; and (ii) irregular operation, where a fault on one of the vertical busbars triggers the use of reserved battery storage capacity on both sides of the ship and activates load-shedding to ensure continued operation of critical loads and sustain grid functionality. The effectiveness of the proposed architecture is validated through detailed MATLAB/Simulink simulations. Under regular conditions, the EMS achieves stable voltage regulation, balanced power sharing, and efficient energy curtailment. During fault conditions, the battery storage on both sides successfully supports the critical loads. The fuel cells are operated in power-controlled mode effectively up to their full rated 6$\mathrm{k}$$\mathrm{W}$ capacity while the DC bus voltage stabilization is ensured by the battery energy storage systems. These results validate the proposed EMS as a robust and low-complexity solution for maritime DC microgrids, offering stable voltage regulation, effective load prioritization, and resilient operation of critical loads. Full article

Urban traffic congestion presents a complex challenge driven by intricate spatial dependencies and non-stationary temporal dynamics. While Multi-Agent Deep Reinforcement Learning has shown promise for Traffic Signal Control, existing approaches often struggle with partial observability and fail to coordinate effectively across large-scale, heterogeneous road networks. In this paper, we propose HydraLight (HYbrid Deep Reinforcement Learning Architecture for Traffic Lights), a novel spatio-temporal framework that integrates Graph Attention Networks and Temporal Transformers. To overcome the localized myopia of standard graph methods, HydraLight introduces a Global Pooling Context module that broadcasts macroscopic, citywide traffic summaries, enabling agents to proactively mitigate systemic gridlock. Furthermore, to facilitate robust multi-scenario training, we introduce a Unified Prioritized Experience Replay (Unified PER) module that normalizes Temporal-Difference errors, preventing task dominance across diverse topologies. Extensive experiments on the RESCO benchmark across five synthetic and real-world networks demonstrate that HydraLight consistently outperforms state-of-the-art baselines (including X-Light and CoSLight).Byreducing traffic congestion, travel delays, and idle waiting times, the proposed framework also contributes to more sustainable urban mobility through improved traffic flow efficiency, lower fuel consumption, and reduced vehicular carbon emissions. Notably, the proposed architecture excels in structurally irregular environments, achieving up to 13.07% reduction in average travel time on complex arterial networks and consistently improving queue stability and waiting-time minimization across both synthetic and real-world RESCO benchmarks compared to state-of-the-art baselines. Full article

Current challenges in the construction field emphasize the need for compatible and durable materials for heritage interventions. Traditional lime-based mortars often exhibit limitations under environmental exposure, particularly in terms of water absorption and freeze–thaw resistance. This article investigates the performance of hydroxyapatite (HAp)-modified lime mortars applied in a real-scale heritage context, namely a student built micro-museum developed within the Apoș Architecture Summer School. Following the premature degradation of a conventional lime mortar layer applied at roof level, HAp-modified formulations were introduced as a protective and consolidating solution. The experimental approach combines laboratory testing and in situ evaluation, including compressive strength measurements, water absorption, capillarity tests, chromatic analysis, and freeze–thaw assessment. The results indicate a reduction in water absorption from approximately 22% to 12%, an increase in compressive strength from 6.57 MPa to 19.95 MPa and a significant improvement in freeze–thaw resistance, reflected by a decrease in gelivity from 61.2% to 5.73%, compared to traditional lime mortars. In addition, the contact angle increased from 36° to 82°, indicating enhanced hydrophobic behavior. These improvements are associated with pore structure refinement, reduced capillary uptake, and enhanced interfacial bonding within the mortar matrix. The study also highlights the role of real-scale educational environments in validating sustainable material solutions. Full article

Developing bifunctional non-noble metal electrocatalysts with high activity, stability, and cost-effectiveness is essential for large-scale sustainable water splitting, yet remains challenging. Herein, 2P-FeCoNi-MOF was synthesized via hydrothermal reaction of FeCoNi-LDH followed by phosphidation. Its layered structure, integrated with 3D nickel foam, creates a hierarchical porous architecture that increases surface area and accelerates electron transport. Synergistic effects among Fe, Co, Ni in the trimetallic phosphides, together with an amorphous carbon layer, boost catalytic performance. Moreover, superhydrophilic and superaerophobic surfaces enhance mass transfer. In 1 M KOH, 2P-FeCoNi-MOF achieves low overpotentials of 70 mV for HER and 225 mV for OER at 10 mA cm⁻², with excellent stability for 100 h at 100 mA cm⁻². For the overall water splitting, it requires only 1.54 V to reach 10 mA cm⁻² and maintains stability for 100 h at 100 mA cm⁻². Therefore, this study provides a new approach for the preparation of high-performance self-supported non-noble metal-based electrocatalysts for water splitting. Full article

The development of highly efficient and stable photoanodes is essential for advancing photoelectrochemical cathodic protection towards practical applications. Herein, a novel ternary sulfide heterojunction was engineered through the construction of a three-dimensional interlocked architecture of ZnIn₂S₄ on SnIn₄S₈ nanosheets via a sequential hydrothermal synthesis. This unique three-dimensional interlocked configuration creates an intimate interface and continuous charge transfer highways, effectively addressing the slow electron movement and poor interfacial contact that plague conventional photoelectrodes. Spectroscopic and electrochemical analyses verified the formation of a Type-II band alignment, which drives the directional migration of photogenerated electrons from ZnIn₂S₄ to SnIn₄S₈ under an intrinsic built-in electric field. Upon coupling with 304 stainless steel, the ZnIn₂S₄/SnIn₄S₃ heterojunction exhibited outstanding photoelectrochemical cathodic protection performance. It delivered impressive photocurrent densities of 15.22, 19.76, and 72.27 μA·cm⁻² in 3.5 wt% NaCl, 0.1 M Na₂S₂O₃, and 0.1 M Na₂S/NaOH electrolytes, respectively, along with a prominent 720 mV cathodic potential shift in the Na₂S/NaOH system. Most importantly, its good activity and stability in the scavenger-free 3.5 wt% NaCl solution and natural seawater highlight the strong practical potential of this 3D interlocked photoanode for sustainable marine metal corrosion control. Through a strategic multi-electrolyte assessment, the underlying protection mechanisms were decoupled, revealing that the synergy between the heterojunction-induced charge separation enabled by the three-dimensional interlocked structure and electrolyte-specific hole scavenging is key to the enhanced performance. Full article

The Stewart–Gough Platform (SGP) is a spatial parallel manipulator offering high accuracy, rigidity, and adaptability, with applications spanning medical systems, marine engineering, agriculture, manufacturing, entertainment, aerospace, and architectural installations. This paper presents a comparative analytical and computational study of three SGP configurations: the regular SGP, with regular hexagonal base and top platforms; the Irregular-Parallel SGP, derived from the regular SGP by a novel graphical decomposition-and-modification procedure and characterized by similar symmetric hexagonal platforms with limbs preserved parallel; and the Irregular-Skewed SGP, in which the irregular hexagonal platforms of the Irregular-Parallel SGP are retained, but the limbs are connected in an inclined, alternating clockwise (or anticlockwise) topology. The Irregular–Skewed SGP is free from the constraint singularity that persists in the first two configurations and requires the shortest maximum actuator stroke. Static force analysis shows that the regular SGP and the Irregular–Parallel SGP both exhibit a rank-deficient rigidity matrix (rank = 3) across the geometric scaling range tested (radius ratios 1:2 to 1:10; inter-platform distances 100–1000 mm), whereas the Irregular-Skewed SGP achieves full rank (rank = 6) through inclined limb connectivity and is the only configuration capable of sustaining static equilibrium under the loading conditions examined. The forward kinematics of the Irregular-Parallel SGP is verified against a SolidWorks model: under a 9 mm uniform limb extension, the MATLAB and SolidWorks positions of node 7 agree to within 1.27 mm. The rotational workspace volume is equivalent across the three configurations, but the density of valid solution points within that workspace differs. The workspace within joint limits, alternating compression–tension force partition, and asymmetric stroke economy of the Irregular-Skewed SGP indicate applicability to kinetic facades and transformable interiors in architectural-robotics deployment. Full article

Traditional wooden granaries in rural Türkiye are disappearing at an accelerating rate due to agricultural abandonment, rural depopulation, and the absence of systematic documentation and conservation frameworks. In the Sındırgı district of Balıkesir, one of the richest concentrations of vernacular granary architecture in the Marmara Region, these structures remain largely unprotected and unstudied within a sustainable design framework, constituting an urgent conservation challenge. This study aims to assess the current preservation status of Sındırgı granaries, classify their typological diversity, and evaluate their sustainability performance against a defined set of ecological design criteria. A mixed methods approach was employed, combining a systematic literature review with extensive fieldwork across 33 neighborhoods. In total, 1411 granaries were identified and grouped into five typologies: evli, Simav, kabak, sandık, and üstü örtülü sandık. These typologies were systematically compared to five parameters: spatial distribution across neighborhoods, plan and section geometry, construction system and structural elements, material selection and condition, and preservation status. This comparison revealed that typological variation is not incidental but directly reflects differences in land ownership, agricultural production capacity, topography, and distance from the district center. Representative examples from each typology were documented through onsite measurements, photogrammetry, technical drawings, and interviews with local craftsmen. The sustainability performance of the granaries was then assessed across seven ecological design criteria: spatial organization, building form design, structural element design, material use and conservation, design with nature, urban design area planning, and nature interaction. The findings demonstrate that the long-term durability of these structures depends on an interrelated system of climate-responsive design decisions rather than any single factor. The study concludes by proposing a holistic conservation model comprising typology-based inventory, roof water moisture-focused intervention, periodic monitoring, and transmission of vernacular building knowledge, a framework applicable to comparable rural granary heritage across the region. Full article

Preservation and revitalization of historic districts are critical for quality urban development and renewal. Accurately assessing what drives district vitality is essential for sustainable historic area development. Current research often uses cross-sectional data and single models, limiting understanding. This study uses Xigong District, Luoyang, and integrates multi-source data—street view imagery, points of interest, road networks, and nighttime lighting—from 2014 to 2021. MGWR and XGBoost models create a dynamic framework for analyzing how the built environment affects street vitality over time. Results: (1) Spatial effects: Physically, green exposure, functional mix, and road network access are highly spatially sensitive. Morphological indicators—commercial frontage, street continuity, complexity, and building texture—show reduced local variation over time. Perceptually, the influence of abstract color narrows each year, and subjective preference broadens. (2) Nonlinear effects: Green exposure and openness dominate but show negative inhibition and diminishing returns. Morphological, functional, and road network indicators have moderate explanatory power with clear thresholds. Perceptual importance shifts from abstract color to architectural texture, which now rises while color influence steadies. Renewal should go beyond basic greening and surface color. Instead, focus on refined, threshold-based control of form and function, and preserve authentic historic texture. This approach enables scientific, sustainable vitality. Full article

The concept of architecturally mediated allostatic overload has established that chronic exposure to stress-inducing built environments can elicit stress responses within the body, overwhelming regulatory systems and contributing to adverse health outcomes through sustained activation of stress response pathways. Recent advances in epigenetics, combined with emerging evidence of environmental stress-induced epigenetic modifications, suggest that the health impacts of chronic built environment stress may extend far beyond previously understood physiological consequences. This paper introduces the theoretical concept of “built environment-modulated epigenetics” (BEME), extending the framework of architecturally mediated allostatic overload to consider how chronic exposure to stress-inducing built environments may create lasting epigenetic modifications with potential transgenerational implications. We propose that prolonged activation of the hypothalamic–pituitary–adrenal (HPA) and sympathetic-adreno-medullary (SAM) axes by built environment stressors may result in maladaptive DNA methylation and histone modifications affecting stress-responsive genes, similar to documented effects of environmental toxins, air pollution, and psychosocial stressors. Given robust evidence that environmental stressors can create transgenerational epigenetic effects, this theoretical framework suggests that stress-inducing built environments may impact not only current occupants, but future generations through heritable epigenetic modifications. This extension of architecturally mediated allostatic overload theory fundamentally challenges traditional approaches to architectural design and urban planning, positioning the built environment as a potential determinant of long-term epigenetic programming. Full article

Three-dimensional (3D) bioprinting has rapidly evolved into a controlling platform for the fabrication of patient-specific biomedical implants, with growing importance in advanced drug delivery systems. Beyond structural tissue engineering, bioprinted constructs now function as programmable therapeutic depots capable of localized, sustained, and stimuli-responsive drug release. This review focuses on recent biomaterial design strategies that enable precise control over drug encapsulation, retention, and release kinetics within 3D bioprinted architectures. The physicochemical and mechanical properties of bioinks, including crosslinking density, porosity, degradation behavior, viscoelasticity, and swelling characteristics, directly influence drug loading efficiency and release dynamics under physiological conditions. The rational tuning of these parameters allows the development of constructs that provide spatially controlled and temporally regulated therapeutic delivery. Recent advances in predictive modeling, such as finite element modeling (FEM), data-driven machine learning approaches, and ML, have significantly improved the ability to correlate material composition, printing parameters, and structural geometry with drug diffusion and degradation-mediated release mechanisms. These tools facilitate the optimization of printing variables including extrusion pressure, nozzle diameter, and layer resolution to ensure structural fidelity while maintaining therapeutic functionality. Emerging strategies incorporating multi-material printing, gradient architectures, and stimuli-responsive biomaterials have expanded the potential of 3D bioprinting for combination therapies and personalized medicine. This review discusses key challenges in translating bioprinted drug delivery systems into clinical applications, including the standardization of drug release characterization methods, and long-term stability assessment. Full article

The rational configuration of electronic band structures through deep-seated structural disorder remains a formidable challenge in sustainable solar-to-fuel conversion. Herein, we report a transformative kinetic strategy to “freeze” an extraordinary density of non-equilibrium structural defects within an integrated Cu₄MgO₅/ZnO nanocomposite. Synthesized via a chitosan-assisted coordination-combustion route followed by rapid thermal quenching, the material preserves a record crystallographic dislocation density of 1.09 × 1015 m⁻² and significant lattice microstrain (1.04 × 10⁻³). This engineered structural disorder induces a profound reconfiguration of the electronic landscape, generating a continuous manifold of sub-bandgap “tail states” that narrow the optical bandgap to a remarkable 1.34 eV. Consequently, the defect-rich architecture facilitates unprecedented dual-channel photocatalytic performance under simulated solar irradiation in an aqueous solution containing 5 vol% triethanolamine (TEOA) as a sacrificial electron donor; the catalyst achieved a hydrogen evolution rate of 17,700.0 µmol g⁻¹ h⁻¹ and a methane production rate of 172.50 µmol g⁻¹ h⁻¹—representing a 36.3-fold and 43.1-fold enhancement over commercial ZnO, respectively. With an apparent quantum yield of 8.42% at 420 nm and robust photostability—maintaining 95.3% of its activity over five consecutive cycles (25 h total)—this noble-metal-free ternary system bypasses the limitations of traditional heterojunctions. Our findings establish a new benchmark for defect-engineered catalysts, providing a scalable blueprint for high-efficiency carbon neutrality and solar fuel production. Full article

Driven by global energy transformation and the progress of artificial intelligence technology, traditional automotive engineering is undergoing profound changes. Transportation is rapidly advancing toward electrification and intelligence. Against this background, this paper identifies three emerging paradigms for the development of electric vehicles: Heart Revolution, Brain Evolution, and Network Integration. This paper points out that automobiles are evolving from traditional one-way energy consumers to dynamic energy nodes in smart grids. With the support of artificial intelligence technology, the role of automobiles is also shifting from a simple means of transportation to an intelligent mobile terminal. At the same time, this paper focuses on analyzing the application of the integration theory of “Four Networks and Four Flows” in automobile upgrading. The theory does not focus on the optimization of a single node unit but emphasizes a systematic perspective to improve overall performance and support sustainable development. This paper suggests that the development of the automobile industry must be deeply integrated with the humanity world, information world and physical world. By building a five-in-one architecture of “Human–Vehicle–Road–Cloud–Satellite”, the automobile industry could follow a practical pathway toward coordinated development. At the same time, breakthroughs in core technologies such as solid-state batteries and wide-bandgap semiconductors are also imminent. This paper aims to provide a sustainable and high-performance automobile development path and integrate the concept of human-oriented design into it. Meanwhile, China’s new energy vehicle industry is used as a representative context to illustrate its engineering and industrial implementation. Full article