Search Results (10,225)

This study aims to develop a sustainable, low-cost, and high-performance supercapacitor electrode by valorizing waste date seeds (Phoenix dactylifera) into activated carbon and integrating it with a polymer-based hydrogel electrolyte. Waste date seeds were successfully converted into high-performance activated carbon through hydrothermal carbonization followed by sulfuric acid (H₂SO₄) chemical activation. The obtained date seed activated carbon (DSAC) was applied as an electrode material and incorporated into a hydrogel electrolyte for supercapacitor applications. Structural, thermal, and morphological analyses using SEM, FTIR, XRD, and TGA confirmed the formation of a predominantly microporous carbon framework enriched with oxygen-containing functional groups, indicating effective carbonization and activation. The porous structure and surface chemistry contributed to enhanced electrochemical behavior. The electrochemical behavior of the prepared DSAC electrode was investigated through cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) analyses. The material exhibited a highest specific capacitance of 179 F g⁻¹ at a scan rate of 5 mV s⁻¹ and 159 F g⁻¹ at a current density of 0.2 A g⁻¹, demonstrating reliable and stable capacitive characteristics suitable for biomass-derived carbon-based supercapacitor applications. The device also exhibited excellent cycling stability over 5500 cycles, confirming long-term durability. The results demonstrate a promising and environmentally friendly strategy for advanced energy storage systems. Furthermore, the sustainability and cost-effectiveness of the proposed approach are attributed to the utilization of abundant date seed biomass and the simplicity of the hydrothermal–chemical activation process. The enhanced electrochemical performance is primarily associated with the hierarchical porous structure of the activated carbon and the improved ion transport facilitated by the hydrogel electrolyte, which collectively contribute to stable capacitive behavior and long-term cycling durability. Full article

Green highway networks function as critical linear carbon sinks for sustainable transportation systems, yet the link between their network topological structure and sequestration efficiency remains poorly understood. This research establishes an integrated framework to explore the spatial synergy and mismatch between green highway network structure and carbon sequestration in Shandong Province. We constructed a spatially explicit “node-edge” network at a road corridor scale (250-m buffer) and quantified seasonal Net Primary Productivity (NPP) using the CASA model. Results demonstrate: (1) The green highway network exhibits a highly heterogeneous, heavy-tailed structure with low clustering coefficients (<0.01), characterized by high connectivity efficiency but limited structural redundancy; (2) The network’s NPP shows pronounced spatiotemporal dynamics, peaking in summer (mean: 364.7 gC · ${\mathrm{m}}^{-2}$· ${\mathrm{season}}^{-1}$) and reaching its nadir in winter (mean: 52.2 gC · ${\mathrm{m}}^{-2}$· ${\mathrm{season}}^{-1}$); (3) Statistically significant spatial synergies ($p<0.01,Z>4.00$) exist between green highway topology and NPP, with weighted closeness ($I=0.29$) and weighted degree ($I=0.21$) showing the highest effect sizes; (4) LISA analysis identified specific spatial mismatches, such as “High-Low” clusters (high structural importance but low carbon efficiency) in northern inland regions, which represent priority targets for ecological retrofitting. These outcomes quantify that network topology effectively reflects ecological performance, offering a “topology-guided” strategy to promote climate change mitigation and enhance the long-term sustainability of regional transportation infrastructure. Full article

Sustainable urban logistics requires infrastructure that can support routine low-carbon freight delivery while maintaining emergency supply capacity under disruptions. However, existing underground logistics system studies mainly focus on routine freight efficiency and network feasibility, whereas emergency logistics research is largely based on surface transport systems. Limited attention has been paid to the integrated design and operational validation of dual-use underground logistics networks under uncertain routine and emergency demand. To address this gap, this study proposes a dual-use underground logistics system (DULS) framework that combines robust layout optimization with dynamic simulation. A multi-echelon network consisting of supply centers, primary nodes, secondary nodes, and demand points is constructed. Candidate primary nodes are screened using an entropy-weighted TOPSIS method, and a Wasserstein-based distributionally robust optimization model is formulated to jointly determine node location, resource allocation, and freight paths under demand uncertainty. A hybrid heuristic is developed to solve the model, and an AnyLogic-based discrete-event simulation model is used to evaluate operational performance under different demand-generation patterns and train operation strategies. In the Nanjing case, the optimized DULS includes 19 primary nodes and 72 secondary nodes, achieves an emergency-demand fulfillment rate of 84.84%, and keeps the average end-to-end emergency supply time within 4 h. Cross-station operation performs better than the all-stop mode in both transport time and deprivation cost. An ex-post operational emission comparison further indicates that the DULS can reduce road-based freight emissions by 60.20% under routine operations. The proposed framework provides methodological support for planning sustainable dual-use underground logistics infrastructure serving both routine freight delivery and emergency supply. Full article

Addressing capacity assessment uncertainties in high-geological-background areas, this study develops a source-oriented risk assessment framework integrating spatial interpolation, source apportionment, and dynamic capacity modeling. Analysis of 39 surface soil samples (As, Cr, Ni, Cu, Zn, Cd, Pb, Se, pH) from ankang basin, China, reveals: Two heavy metal sources were quantitatively identified via APCS-MLR. Natural sources (71.94%) dominate, contributing Se, Cd, Cu, Zn, and Ni, with Cd primarily geogenic. Anthropogenic sources (28.06%) from industrial, transportation, and agricultural activities contribute Cr, As, Pb, and Ni. Static capacity assessment identifies Cd as the primary limiting element (average capacity 0.22). Dynamic model predictions indicate that the 20-year dynamic capacity of all elements is only 14–20% of the theoretical static capacity (Q_i), representing 16–47% of the current existing capacity, and will approach dynamic equilibrium after 40 years. Source-oriented capacity indices reveal natural sources pose the highest comprehensive risk (SPI = 0.916), mainly driving Cd capacity stress (SPICd = 0.34). Anthropogenic sources remain safe (SPI > 1.255) but warrant Pb monitoring. This framework supports precise management of selenium-rich lands, shifting strategies from emission control to avoiding high-risk geological units. Full article

Ningbo City is endowed with abundant mineral water resources. Investigating their chemical characteristics and formation mechanisms is essential for understanding hydrochemical evolution and supporting sustainable resource utilization. Based on hydrochemical data from 12 drinking natural mineral water sources in Ningbo City, this study investigates the hydrochemical features and genesis of mineral water by integrating statistical analysis, hydrochemical diagrams, ionic ratios, and mineral equilibrium modeling. The results indicate that metasilicic acid (as H₂SiO₃) and strontium (Sr) are the principal characteristic components of the drinking natural mineral water in Ningbo City, with concentrations of 32.87–60.8 mg/L and 0.05–4.59 mg/L, respectively. The mineral waters are neutral to slightly alkaline and weakly mineralized, with the pH values ranging from 6.70 to 8.16, and total dissolved solids (TDS) contents of 76.8–767.2 mg/L. The predominant hydrochemical facies are HCO₃-Ca-Na, HCO₃-Ca, HCO₃-Na-Ca. Their chemical composition is mainly governed by rock weathering, whilst also being influenced by cation exchange and mineral dissolution–precipitation equilibrium. H₂SiO₃ is mainly derived from the weathering and hydrolysis of silicate minerals such as plagioclase. Sr enrichment is associated with the dissolution of Sr-bearing silicate minerals and certain sulphate minerals, as well as prolonged water–rock interaction. The Sr- and Si-rich aquifers provide the material basis for the enrichment of Sr and H₂SiO₃ in groundwater. Structural fractures and weathering fractures provide transport pathways and storage spaces for groundwater, facilitating the migration and enrichment of these characteristic components. The mechanism of mineral water emergence can be summarized as of the tectonic fracture-controlled circulation-leaching type. Full article

Gestational diabetes mellitus (GDM) is increasingly recognized as a complex pathology rooted in systemic and organelle-level dysfunction, specifically involving chronic low-grade inflammation (CLGI), mitochondrial impairment, and endoplasmic reticulum (ER) stress. Central to this pathophysiology is mitochondrial dysfunction, characterized by reduced respiration, impaired metabolic flexibility, and dysregulated fission/fusion machinery, which fuels a self-perpetuating cycle of reactive oxygen species (ROS) production. Concurrently, chronic ER stress triggered by hyperglycemia and lipotoxicity activates the unfolded protein response (UPR), further amplifying redox imbalance through the Endoplasmic Reticulum Oxidoreductin 1/Protein Disulfide Isomerase (ERO1/PDI) axis and bridging metabolic toxicity to inflammation via c-Jun N-terminal kinase (JNK) and nuclear factor kappa-light-chain–enhancer of activated B cells (NF-κB) signaling. The Advanced Glycation Endproducts (AGEs) and the Receptor for Advanced Glycation Endproducts (RAGE) axis act as a molecular catalyst that sequester antioxidants and drive pro-inflammatory feedback loops. These converging mechanisms culminate in profound placental maladaptation, including structural abnormalities like chorangiosis and functional defects in nutrient transport mediated by hyperactive mechanistic target of rapamycin complex 1 (mTORC1) signaling. This review article provides insight into recent evidence to elucidate the meta-inflammatory environment of GDM, where modest but sustained elevations in biomarkers like Interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) disrupt redox homeostasis and impair insulin signaling pathways through the activation of stress-sensitive kinases. By integrating these molecular perspectives, the article underscores the necessity of targeting the systemic inflammatory and oxidative continuum spanning pre-conception to the antenatal period through lifestyle interventions and emerging therapeutic strategies to mitigate GDM risk and improve maternal–fetal outcomes. Full article

Critical infrastructure restoration (CIR) is a disaster-management and sustainability challenge because prolonged disruption of energy, water, transport, communications, healthcare, and public-administration services can amplify social, economic, and environmental losses. This PRISMA 2020-reported systematic review synthesizes post-2016 scientific literature and official policy, legal, standards, and technical documents on CIR and AI decision support. The review identified 55 records, removed 1 duplicate, excluded 1 ineligible record, and retained 53 core sources for qualitative synthesis, including 31 scholarly publications and 22 official documents. Manual screening was used; no automated screening or AI-assisted exclusion tools were applied. The results are organized around four research questions covering regulatory frameworks, recovery practices, supporting systems, and AI model families. The synthesis shows that CIR is shaped by layered governance through NIS2, the CER Directive, the AI Act, and national measures; by operational recovery practices such as continuity planning, cyber crisis coordination, interdependency mapping, and model-supported restoration; by digital platforms including SCADA/ICS, IoT sensing, GIS/common operating pictures, decision-support systems, simulation environments, and digital twins; and by AI methods ranging from classical machine learning and computer vision to reinforcement learning and generative assistants. However, evidence maturity remains uneven, with many AI applications still simulation-based, sector-specific, or weakly validated in real restoration settings. The review contributes an integrated CIR-oriented framework showing that AI creates practical value when embedded in interoperable, human-supervised, regulation-aware, and empirically validated restoration architectures that support sustainable service continuity rather than isolated automation. Full article

In extremely cold environments, inhaling frigid, dry air can pose significant health risks, potentially leading to airway inflammation and respiratory injury. While previous studies have examined thermal exchange within lung airways under hot-air inhalation, the majority have focused on localized regions rather than the entire respiratory tract. This study expands the scope of inquiry by simulating airflow and heat transfer throughout a more complete computed tomography (CT)-based respiratory tract, from the nasal cavity to the larynx and trachea and extending down to the 13th generation of the bronchial tree, under two cold-air inhalation scenarios at −5 °C and −20 °C. Using computational fluid dynamics, this study integrates Large Eddy Simulation with the Smagorinsky–Lilly subgrid-scale model to capture the complex interaction of turbulent flow and thermal transport in the human respiratory system. By analyzing temperature distributions, heat flux, heat-transfer coefficients, Nusselt numbers, and mass flux across the airways, the research shows how varying degrees of cold inhalation influence respiratory thermodynamics and associated biomechanical responses. As such, this study establishes a rigorous scientific foundation for the development of more sophisticated and predictive respiratory-tract models in sub-zero environments in future work. Full article

Hydrogen energy systems rely extensively on polymeric materials for storage, sealing, transport, and tribological applications; however, their long-term reliability is strongly influenced by hydrogen–polymer interactions. This review presents a comparative analysis of polymers with and without hydrogen bonding, focusing on how molecular architecture governs hydrogen compatibility, transport behavior, and degradation mechanisms under high-pressure environments. Hydrogen-bonded polymers, such as polyamides, polyurethanes (PU), and polyimides, exhibit high mechanical strength and thermal stability due to strong intermolecular interactions but are susceptible to hydrogen-assisted chemical degradation and embrittlement. In contrast, non-hydrogen-bonded polymers, including polyethylene, polypropylene (PP), polytetrafluoroethylene (PTFE), and Polyether ether ketone (PEEK), demonstrate excellent chemical inertness and low hydrogen reactivity, yet experience diffusion-driven damage such as blistering and fatigue softening. This study establishes a unified framework linking molecular structure, hydrogen transport, and failure mechanisms, revealing a fundamental trade-off between mechanical integrity and chemical stability. Advanced strategies, including polymer blending, nanofiller reinforcement, and multilayer composites, are proposed to optimize durability, permeability, and overall hydrogen compatibility. Full article

Hypoxia is one of the most important factors limiting the effectiveness of modern anticancer therapies, particularly photodynamic therapy (PDT). The hypoxia of the tumor microenvironment results from abnormal angiogenesis and the high metabolic demand of cancer cells, which leads to reduced oxygen availability necessary for generating reactive oxygen species (ROS). Consequently, conventional therapeutic approaches, mainly based on the type II PDT mechanism, show limited effectiveness under hypoxic conditions. In response to these limitations, strategies are being developed to increase oxygen availability within the tumor. Of particular importance are nanocarriers based on perfluorocarbons (PFCs), which, due to their high gas solubility, can effectively transport and release oxygen in the tumor microenvironment. Research indicates that the use of such systems leads to improved PDT efficiency by increasing the production of singlet oxygen and enhancing cancer cell damage. Parallelly, alternative approaches independent of high oxygen concentration, including type I photosensitizers, are being developed. Unlike classical type II mechanisms, they generate free radicals through electron transfer reactions, which allows effective action even under conditions of significant hypoxia. This approach significantly expands the possibilities of using PDT in the treatment of tumors with low oxygen levels. Current research directions focus on integrating various therapeutic strategies to achieve a synergistic effect. Hybrid systems combining oxygen delivery (e.g., using PFCs) with the use of type I photosensitizers and other treatment methods, such as chemotherapy or immunotherapy, show the greatest clinical potential. Such multifunctional approaches simultaneously allow improving tumor oxygenation and increasing the efficiency of ROS generation, which makes them a promising strategy for the future of anticancer therapies. Full article

The expansion of the transport sector in Thailand has resulted in a continuous increase in greenhouse gas emissions and air pollution. Therefore, promoting the adoption of commercial electric vehicles (EVs) has become an important approach to mitigating environmental impacts and enhancing sustainability. This study integrates the TAM, TPB, and 7Ps frameworks to examine factors influencing the intention to adopt EVs among freight transport operators in Thailand. A total of 876 freight operators were surveyed, and the data were analyzed using a random parameters probit model with heterogeneity in means. The results indicate that environmental motivation, perceived safety, ease of use, reductions in operational costs, social benefits, dealership credibility, and perceived quality-of-life improvement positively influence the intention to adopt EVs. In contrast, gaps between EV attitudes and purchasing readiness, along with over-reliance on promotional and online channels, negatively affect EV adoption intention. Furthermore, perceptions of price appropriateness show heterogeneous effects across respondents, reflecting hidden costs and operational uncertainties. Based on these findings, the study proposes an integrated set of policy measures to support a sustainable transition toward EV adoption in the freight transport sector. These results provide useful guidance for policymakers and freight transport operators in developing strategies and policies that encourage the long-term adoption of electric vehicles in freight transportation. Full article

Urban expressway projects are critical components of modern transportation infrastructure, yet their construction quality is often threatened by multi-source, latent, and dynamic risks. Traditional expert-driven risk identification methods frequently suffer from subjective bias and low efficiency, failing to meet the rigorous management requirements of complex engineering environments. To address these challenges, this study proposes a robust risk assessment framework integrating Large Language Models (LLMs) and the Delphi method within a Bayesian Network (BN) structure. First, LLM technology is leveraged to perform semantic mining on extensive engineering texts, including construction specifications and project reports, to pre-identify potential risk factors. Second, the Delphi method is applied through multiple rounds of expert consultation to refine a comprehensive inventory comprising 32 risk factors across five dimensions: personnel, machinery, materials, methods, and environment. Finally, a BN-based evaluation model is developed, utilizing forward inference, backward diagnosis, and sensitivity analysis to quantify risk levels and pinpoint critical risk drivers. The framework was empirically validated using the T Expressway Project in Hangzhou as a case study. Results demonstrate that the model effectively transforms empirical management into precise, data-driven diagnosis, providing project managers with a quantitative tool for optimizing construction quality control and decision making in complex urban bridge projects. Full article

Liquid distributors are critical internals in packed columns, whose distribution uniformity directly governs the column’s hydrodynamic performance, mass transfer efficiency, and operational stability. To address the poor liquid distribution uniformity of trough distributors under high liquid loads, this study proposes a novel trough distributor integrated with a resistance–guidance synergistic composite unit. Combining numerical simulations and experimental validation, the core synergistic mechanism of the unit was systematically investigated. The horizontal baffle serves as a secondary throttling point, which converts axial kinetic energy into static pressure energy to supplement the driving force for transverse energy redistribution and physically suppresses the generation and development of large-scale vortices. Meanwhile, vertical guide vanes guide liquid flow, constrain the expansion of harmful secondary flows, and construct a controllable transverse pressure gradient. The resistance–guidance unit collaboratively realizes two-stage energy conversion and redistribution, reconstructs the liquid momentum transfer path, and restores the static pressure gradient-dominated transverse energy transport mechanism. This study clarifies the intrinsic mechanism of resistance–diversion synergy for liquid distribution control, laying a theoretical foundation for the structural optimization of trough liquid distributors under high-liquid-load conditions. Full article

Rough-walled fractures in conglomerate reservoirs promote near-wellbore proppant deposition, nonuniform flow, and insufficient distal support, making proppant-schedule screening difficult using small-scale smooth-slot tests alone. This study develops a benchmark-constrained and cost-aware hierarchical screening workflow by integrating a 20 m rough-wall physical experiment, transient Fluent simulations, and archived short-time EDEM sensitivity records. The benchmark experiment used a 20 m × 4.5 m × 10 mm artificial rough-wall fracture and ten operating conditions involving pumping rate, fluid viscosity, proppant size, and sand concentration. In the Fluent model, wall roughness was treated as a regularized roughness representation, and the carrier fluids were modeled using Newtonian constant viscosities measured from laboratory calibration. The experimental effective propped area ranged from 25.5% to 65.1%. Within single-factor comparison subsets, medium viscosity improved support continuity, pumping-rate gains became limited near 0.20 m³/min, particle size affected the balance between distal coverage and bed stability, and 300 kg/m³ sand concentration caused blockage. Image-segmentation-based comparison showed that Fluent captured the main wedge-shaped deposition morphology and screening-level geometric trends. The archived EDEM records indicated that grid-resolution refinement and mixed particle-size representation substantially increased computational cost. A Case 10 mesh-sensitivity check further confirmed that mesh refinement did not alter the first-order deposition morphology. The proposed workflow uses Fluent for whole-domain rapid screening and reserves EDEM/CFD-DEM for targeted short-time sensitivity checks. Full article

Accurate characterization of coherent vortex structures in high-speed turbulent boundary layers presents a persistent challenge due to the flow’s high dimensionality and nonlinear dynamics. This study investigates an optimized decomposition framework that integrates modal decomposition techniques with a novel vortex identification strategy to extract dynamically significant features. The numerical solution from a previously conducted high-fidelity simulation of MVG-controlled supersonic flow serves as the testbed. Principal Component Decomposition and Non-negative Matrix Factorization are applied across multiple flow variables to evaluate their effectiveness in isolating coherent structures. The results show that, across the velocity-based cases, 3–4 modes capture 70% of the TKE with MSE about 0.1, while the Liutex case requires 14 modes but achieves a lower MSE of about 0.04. Overall, using the same number of modes yields similar reconstruction performance across all cases. The influence of various normalization and rescaling methods on decomposition performance is also examined. Optimization is guided by two primary criteria: the interpretability of spatial modes and MSE in reconstructing vortex structures. By employing low-rank matrix representations, this optimization study aims to enhance interpretability and reduce computational costs. This approach establishes a mathematically rigorous and efficient platform for analyzing vortex dynamics, achieving significant dimensionality reduction while preserving key features of turbulent transport. Full article