Search Results (6,731)

Industrial volatile organic compound (VOC) emissions from large-scale petroleum storage represent a persistent environmental challenge, particularly in agricultural perimeters where atmospheric “breathing” cycles drive localized soil loading. This study investigates the thermodynamic and spatial relationship between gasoline storage emissions and chemical contamination in the Constanta South terminal area using a multi-layered analytical approach. By integrating gas chromatography (GC-MS) headspace analysis with an artificial intelligence (AI) framework utilizing high-order polynomial regression, we quantified the source–path–receptor dynamics across a thermal gradient (12 °C to 70 °C). The results reveal a non-linear surge in VOC emissions at temperatures exceeding 37 °C, characterized by a shift toward medium-weight hydrocarbons (C4–C6) that act as carriers for heavier aromatics. The AI risk model identified a significant spatial gradient, identifying a 500 m “critical zone” where the Hazard Quotient (HQ) is elevated, necessitating technological upgrades like Vapor Recovery Units (VRUs) to mitigate ecological risks. Full article

Urban supermarkets are increasingly challenged to design spatial strategies that align with consumers’ demand for convenience, accessibility, and local embeddedness. Despite the growing recognition of spatial behavior in retailing, limited research has examined how different forms of proximity jointly shape consumers’ perceptions of store attractiveness and their subsequent location choices, particularly in emerging urban contexts. This study investigates how four proximity dimensions—access, identity, relational, and process proximity—affect durable and situational attractiveness, which in turn drive consumers’ retail location choices. Data from 567 supermarket shoppers in Tangier, Morocco, were analyzed using a structural model integrating these spatial and behavioral constructs. Results reveal that proximity exerts a strong positive effect on store attractiveness, with access and identity dimensions emerging as the most influential drivers of consumer patronage. This study contributes to the geo-marketing and spatial consumer behavior literature by conceptualizing proximity as a multidimensional construct that bridges spatial accessibility, social attachment, and retail experience, offering new insights for localization strategies in emerging markets. Full article

Plastic pollution from packaging waste is driving the development of biodegradable composites for sustainable packaging. In this work, poly(butylene adipate-co-terephthalate)/poly(3-hydroxybutyrate) (PBAT/PHBH) blends (50/50 wt.%) were reinforced with agro-industrial waste fillers—spent coffee grounds (SCG), brewer’s spent grain (BSG), and cellulose powder (CP)—at 1–15 wt.% loading. The effects of these fillers on tensile properties, impact strength, and thermal stability were examined and supported by scanning electron microscopy (SEM) of fracture surfaces and thermogravimetric analysis (TGA). The neat PBAT/PHBH blend exhibited balanced stiffness and ductility. Low BSG loadings (≤5 wt.%) produced the greatest toughening, with impact strength increasing by ~92% and elongation at break significantly improving over the neat blend. SEM analysis indicated crack deflection and particle pull-out as dominant energy-dissipation mechanisms at low BSG loading. At higher BSG loading (15 wt.%), particle clustering and larger voids acted as stress concentrators, reducing impact performance. SCG improved ductility at low loading (1 wt.%), whereas increasing SCG content led to progressive reductions in tensile strength and elongation due to increased debonding and microvoid formation. In contrast, CP exhibited minimal reinforcement efficiency within the investigated range (1–5 wt.%). Overall, filler addition generally reduced tensile strength and, in several cases, tensile modulus, reflecting limited interfacial compatibility between the hydrophilic lignocellulosic fillers and the hydrophobic polyester matrix. TGA indicated a modest improvement in thermal stability at higher BSG loadings, reflected by shifts in T_5% and T_max1 (PHBH) toward higher temperatures. Overall, this study demonstrates that upcycled coffee and beer waste fillers can impart specific toughness benefits to biodegradable PBAT/PHBH blends, but interfacial incompatibility currently limits their reinforcement efficiency. The findings highlight the potential and challenges of these biocomposites for sustainable packaging applications and suggest that interface engineering (e.g., compatibilizers) will be key to unlocking optimal performance. Full article

Resource-based cities face unique land use challenges due to resource dependence and path lock-in, yet the driving mechanisms and future trajectories of their land use transitions remain underexplored. This study examines the Huaihai Economic Zone (HEZ), a representative coal-rich region in eastern China, to analyze land use changes from 2000 to 2023 and simulate 2036 scenarios under different development pathways. Using land use transfer matrices, dynamic degree metrics, and the Patch-generating Land Use Simulation (PLUS) model, we systematically identified spatiotemporal evolution patterns, quantified the contributions of driving factors, and projected multi-scenario future land use patterns. Results reveal that land use change in the study area was dominated by the conversion of cultivated land to construction land, alongside spatial restructuring from a monocentric to a polycentric network pattern. Notably, construction land expansion was least evident in the central Mining-Affected Zone, where land use changes remained relatively sluggish compared to other sub-regions. Driving factor analysis indicates that socio-economic factors primarily influenced changes in construction and cultivated land, while natural factors strongly affected ecological land and unused land. Multi-scenario simulations for 2036 demonstrate diverging trajectories: an urban development scenario would accelerate cultivated land loss and unused land expansion; a natural development scenario would maintain current pressures; and an ecological protection scenario would effectively curb urban sprawl while actively promoting ecological land recovery. This study concludes that transcending simple land use control to actively orchestrate “mining-urban-rural-ecological” spatial synergy is critical for achieving a sustainable transition in resource-based regions facing similar transformation pressures. Full article

The treatment of spent drilling fluids generated during the drilling of technological wells for uranium production represents an important engineering and environmental challenge associated with high energy consumption, significant waste generation, and the need for rational water use under arid regional conditions. Conventional phase separation methods based on gravitational settling and chemical–mechanical treatment are characterized by limited process controllability, long processing times, and increased consumption of reagents and energy. This study proposes an energy-efficient and reliable hydrodynamic technology for the treatment of spent drilling fluids based on the formation of controlled turbulent structures without the use of mechanical drives. The research object comprised spent drilling fluids (SDFs) generated during the drilling of technological wells for uranium production in the southern regions of the Republic of Kazakhstan and the Kyzylorda region. Experimental investigations were carried out using a laboratory–pilot hydrodynamic disperser with variations in velocity gradient, treatment time, flocculant dosage, and suspension flow rate. A mathematical model linking hydrodynamic process parameters with phase separation kinetics and energy characteristics was developed. Model calibration by weighted nonlinear least squares yielded a stable parameter set with 95% confidence intervals, and model validation demonstrated good agreement between calculated and experimental data (MAPE 8.4%; maximum relative error 11.8%). It was established that the use of a hydrodynamic disperser provides separation efficiency of up to 90–95% under optimal operating conditions while reducing specific energy consumption and maintaining stable repeated-cycle performance within the investigated operating window. Experimental results confirm that implementation of the hydrodynamic technology enables a reduction in sludge volume by 40–60%, recovery of up to 60–80% of process water, and a significant decrease in waste requiring transportation and disposal. The obtained results demonstrate the high environmental and resource-saving efficiency of the proposed technology and its suitability for scaling and industrial implementation at facilities drilling technological wells for uranium production. The developed hydrodynamic approach can be considered an effective engineering platform for creating energy-efficient and sustainable systems for drilling fluid treatment in regions with limited water resources and remote industrial infrastructure. Full article

Shared autonomous vehicle (SAV) services are gaining attention as an innovative urban transportation paradigm due to their potential to lower travel costs and improve operational efficiency. Unlike manually operated vehicles, SAVs exhibit unique behavioral dynamics, including safe passenger pick-up and drop-off processes, as well as strategic repositioning and autonomous parking to anticipate future travel demands. Consequently, effective and dynamic route planning is paramount to optimizing SAV safety and operational efficiency. This study proposes a novel traffic information, termed Autonomous Driving Stress (ADS), designed to enhance the safety and efficiency of SAV route planning by quantitatively capturing the level of driving challenge encountered during autonomous operation. To predict ADS, a machine learning framework was developed, utilizing microscopic traffic simulation data that incorporates a comprehensive set of 22 input features describing SAV driving behavior, roadway characteristics, and prevailing traffic conditions. Among five machine learning algorithms evaluated, Random Forest exhibited superior predictive performance, achieving an accuracy of 80.9%. The proposed framework enables real-time ADS level prediction by continuously integrating streaming traffic data into the trained model. The dissemination of this real-time ADS information to SAVs supports proactive, informed, and dynamic route planning decisions, thereby enhancing operational safety, traffic flow, and the sustainability of SAV operations within urban mobility systems. Full article

Artificial light at night (ALAN) data is widely used in urban function analysis and socio-economic activity monitoring, but its application at the micro-scale of cities still faces challenges. This study utilizes high spatial resolution SDGSAT-1 nighttime light data to explore the spatial heterogeneity of ALAN at the street scale in two representative Chinese cities—Beijing and Guangzhou. By integrating multi-source data (such as building vector data, road networks, and point of interest data), a multi-dimensional indicator system covering urban morphology, functional structure, and transportation accessibility is constructed. Based on this, the study employs a Geographically Weighted Random Forest (GWRF) model combined with the Shapley Additive Explanations (SHAP) method to deeply analyze the non-linear relationships between ALAN intensity and multiple driving factors, as well as their spatial variability. Results demonstrate the superiority of the GWRF model over global models in capturing spatial non-stationarity, with R² values of 0.67 for Beijing and 0.74 for Guangzhou, compared to 0.62 and 0.71 for the random forest models, respectively. Road density is the dominant factor influencing nighttime light intensity in both Beijing and Guangzhou. However, the relationship between ALAN and its driving factors varies across these cities. In Beijing, a balanced multi-factor model is observed, whereas in Guangzhou, ALAN intensity is primarily driven by road density, with secondary influences from other factors like sky view factor. This study validates SDGSAT-1 for micro-scale analysis, offering a scientific basis for differentiated urban lighting planning. Full article

Accurate prediction of hard disk drive (HDD) health states is critical for enabling proactive data maintenance and ensuring data reliability in large-scale data centers. However, conventional models often suffer from semantic entanglement among heterogeneous SMART attributes and from the masking of incipient failure signatures by stochastic noise. To address these challenges, we propose SDGR-Net, a spatiotemporally decoupled learning framework designed to model the complex degradation dynamics of HDDs. SDGR-Net introduces three synergistic innovations: (1) a spatiotemporally decoupled dual-branch encoder that disentangles longitudinal temporal evolution from cross-variable correlations via parameter-isolated branches, thereby reducing representational interference; (2) a parsimonious dual-view temporal extraction mechanism that captures early-stage anomalies through forward–reverse sequence concatenation, enabling high-fidelity preservation of non-stationary pre-failure patterns; and (3) a cross-branch dynamic gated residual fusion module that functions as an adaptive information bottleneck to emphasize failure-critical features while suppressing redundant noise. Extensive experiments conducted on three heterogeneous HDD datasets, ST4000DM000, HUH721212ALN604, and MG07ACA14TA, demonstrate that SDGR-Net consistently outperforms six state-of-the-art baselines. In particular, SDGR-Net achieves a peak fault detection rate (FDR) of 0.9898 and a 69.6% relative reduction in false alarm rate (FAR) under high-reliability operating conditions. These results, corroborated by comprehensive ablation studies, indicate that SDGR-Net effectively balances detection sensitivity and operational robustness, offering a practical solution for intelligent HDD health monitoring. Full article

Nanofiltration (NF) is increasingly applied for advanced drinking water treatment, but achieving stable operation at high recovery rates remains challenging for surface waters with high scaling potential. This pilot study investigated the performance and optimization of a three-stage NF270 system (4:2:1 tapered array) for treating coagulated surface water in northern Jiangsu, China, aiming to identify sustainable operating conditions for high-recovery applications. The NF system was operated at recoveries of 80–90% with a feed flux of 20–23 LMH, and the effects of forward flushing frequency, acid dosing location, and concentrate recirculation on fouling behavior were evaluated. The NF270 membrane achieved consistent removal of organic matter (effluent chemical oxygen demand (COD_Mn) < 0.5 mg/L), hardness (40–60% rejection), and alkalinity (~20% rejection), meeting Jiangsu Province drinking water standards. However, operation at 90% recovery resulted in rapid third-stage fouling, with permeate flow declining by >60% within 2.5 h. Osmotic pressure analysis (local concentrate osmotic pressure: 3.8–4.2 bar; net driving pressure: 0.8–2.2 bar) confirmed physical scaling rather than hydraulic limitation as the dominant mechanism. Stage-wise concentration factor calculations (CF₁ = 1.6, CF₂ = 2.9, CF₃ = 4.4) revealed local Langelier Saturation Index (LSI) values of 1.8–2.2 in the third stage, identifying CaCO₃ supersaturation as the primary scaling cause. Reducing recovery to 85% and flux to 20 LMH with 2 h forward flushing extended stable operation. Acid addition effectively mitigated scaling, but dosing location was critical: first-stage addition (pH 8.1 → 7.6) reduced third-stage LSI to 0.7–0.9 and stabilized performance, whereas third-stage addition (pH 8.0 → 7.3) inadvertently promoted Al(OH)₃ precipitation from residual coagulant (feed Al: 0.07–0.11 mg/L). Concentrate recirculation (90% ratio) did not alleviate fouling. These findings demonstrate that for aluminum-rich coagulated surface waters, optimizing recovery, flushing frequency, and acid dosing location is essential for sustainable NF operation, and provide engineering guidance for full-scale applications. Full article

In highly automated driving systems (ADSs), drivers’ ability to resume manual driving remains a road safety issue. However, to the best of our knowledge, there is no existing computational model to predict which drivers require more than the 4 seconds mandated by United Nations Regulation No. 157 to regain manual control. To address this challenge, we developed a Random Forest model that predicts takeover time using measurable human factors. Three controlled driving simulator experiments were conducted in which participants engaged in distinct tasks—texting, drinking, and traffic monitoring—before responding to a takeover request. During the experiments, we collected human factor features, including gaze behavior, age, and scores, from the self-reported driving behavior questionnaire (K-DBQ). The Random Forest classifier achieved 77% accuracy. Recursive feature elimination selected 10 dominant predictors; notably, engaging in non-driving-related tasks, reduced on-road gaze, and older age were significantly associated with longer takeover times. Although K-DBQ scores were not directly correlated with takeover time, their inclusion improved model robustness, consistent with ensemble learning from weak yet complementary signals. The proposed model can be integrated into advanced driver assistance systems (ADASs) to proactively identify drivers likely to exceed the 4-second takeover window, support targeted interventions, and enhance human-centered transition safety in ADSs. Full article

The 2025 approval of the selective NaV1.8 blocker suzetrigine for acute pain marked a pivotal advance in analgesic drug development. Yet the subsequent failure of Vertex’s next-generation NaV1.8 inhibitor VX993 to demonstrate clinical analgesia underscores enduring challenges in translating mechanistic promise into patient benefit. This review examines why promising targets and compounds, spanning NaV and TRP channels, often falter and outlines a path toward more reliable target selection and validation. I first summarize the pain pathway, from nociceptor transduction through spinal processing to cortical perception, emphasizing how inflammation and peripheral sensitization reshape excitability. Historically serendipitous, pain drug discovery now prioritizes molecular precision. Most approved chronic pain therapies act in the CNS and are limited by modest efficacy and adverse effects. Nociceptor-enriched targets (NaV1.7/1.8/1.9; TRP channels) remain attractive, yet redundancy among NaV subtypes and the necessity of blocking targets at the correct anatomical sites complicate translation. Human genetics and multi-omics provide a powerful, unbiased engine for target discovery. Rare high-impact variants offer strong causal hypotheses, while common polygenic contributions illuminate broader susceptibility. Large biobanks increasingly reveal a mismatch between legacy pain targets and genetically supported candidates across neuronal and non-neuronal cells. Human DRG transcriptomics highlight NaV channel redundancy. Human in vitro electrophysiology and PK/PD analyses show suzetrigine achieves ~90–95% NaV1.8 engagement, yet neurons can still fire unless additional channels are blocked. Species differences and drug distribution (including BBB/PNS penetration and P-gp efflux) critically influence efficacy; centrally accessible blockade (e.g., for NaV1.7 or TRPA1) may be necessary to achieve robust analgesia, challenging peripherally restricted strategies. Osteoarthritis illustrates how obesity-driven metabolic inflammation, synovial immune activation, subchondral bone remodeling, and specific nociceptor subtypes converge to drive mechanical pain. Multi-omic integration across diseased human tissues can pinpoint causal processes and cell types, enabling more selective and safer target choices. I propose a practical framework for target validation that integrates: (i) rigorous human genetic support; (ii) cell-type and site-of-action mapping; (iii) human-relevant electrophysiology and PK/PD with verified target engagement; (iv) species-appropriate models; (v) consideration of modality (small molecule, biologic, RNA, targeted protein degradation). Advancing genetically and anatomically aligned targets, tested at the right sites and exposures, offers the best path to genuinely effective, better-tolerated pain therapeutics. Full article

This review examines the transformative interplay between computing chips and Artificial Intelligence (AI), driving a revolution across various industries. First, the broader artificial intelligence and semiconductor ecosystem is analyzed, including hardware manufacturers, software frameworks, and system integration. Next, the development prospects are examined, revealing current challenges such as power consumption, manufacturing complexity, supply chain constraints, and ethical considerations. Further discussion focuses on cloud-edge collaboration in relation to system architecture and workload allocation strategies. Then, cutting-edge AI technologies are analyzed, and key insights are summarized. Finally, the overall trends in artificial intelligence and the chip industry are summarized, clearly presenting the findings for the future and making a unique contribution to this review. Full article

In the context of the coordinated development of high-proportion renewable energy integration and alternating current/direct current (AC/DC) hybrid transmission, the sending-end power grid faces challenges such as decreased system strength, contracted stability boundaries, and difficulties in covering high-risk operating conditions. This paper proposes a new renewable energy scale impact analysis method that integrates “typical scenario construction-scale ladder comparison–prediction-driven time series injection” in response to the operational constraints of AC/DC transmission. In terms of method implementation, firstly, a two-layer typical scenario system is constructed under unified transmission constraints and fixed grid boundaries: A regular benchmark scenario covers the main operating range, and a set of high-risk scenarios near the boundaries is obtained through multi-objective intelligent search, which is then refined through clustering to form a computable stress-test scenario library. Here, the boundary scenarios are generated by a multi-objective search that simultaneously drives multiple key section load rates towards their limits, subject to AC power-flow feasibility and operational constraints, and the resulting Pareto candidates are reduced into a compact stress-test library by clustering. Secondly, a ladder scenario with increasing renewable energy scale is constructed, and cross-scale comparisons are carried out within the same scenario system to extract the scale effect and critical laws of key safety indicators. Finally, data resampling and Gated Recurrent Unit multi-step prediction are introduced to generate wind power output time series, enabling the temporal mapping of prediction results to scenario injection quantities, and constructing a closed-loop input interface of “prediction–scenario–grid indicators”. The results demonstrate that the proposed hierarchical framework, under unified AC/DC export constraints, can effectively construct a compact stress-test scenario library with enhanced boundary-risk coverage and can reveal how transient voltage security evolves across renewable expansion scales. By coupling boundary-oriented scenario construction, cross-scale comparable assessment, and forecasting-driven time series injection, the framework improves engineering interpretability and practical applicability compared with conventional scenario sampling/reduction workflows. For the forecasting module, the Gated Recurrent Unit (GRU) model achieves MAPE = 8.58% and RMSE = 104.32 kW on the test set, outperforming Linear Regression (LR)/Random Forest (RF)/Support Vector Regression (SVR) in multi-step ahead prediction. Full article

Cells rely heavily on DNA repair networks to survive genomic damage. For repairing double-strand breaks, Non-Homologous End Joining (NHEJ) remains the primary pathway, which is largely controlled by the DNA-dependent protein kinase catalytic subunit (DNA-PKcs). Researchers have long studied how phosphorylation drives this kinase. However, recent data point to an important additional layer of control. Drawing on evidence accumulated over the past two decades, we propose a “Spatiotemporal Logic Circuit” model for DNA-PKcs regulation. In this model, SUMO-associated interactions may help stabilize synaptic assembly, HUWE1-mediated neddylation may facilitate kinase activation at Lys4007, and K48-linked ubiquitination—potentially involving RNF144A—may contribute to the turnover of persistent repair complexes. Importantly, we frame these UBL-mediated events within the broader autophosphorylation-driven conformational cycle of DNA-PKcs, which remains central to NHEJ progression. Additionally, we highlight the structural interface where activation and degradation signals may converge and the extraction barrier posed by the massive DNA-PKcs scaffold. From a translational perspective, we argue that the exceptional size of DNA-PKcs (~470 kDa) and its topological entrapment on DNA render it an unusually challenging PROTAC target—one that may require p97/VCP-assisted extraction before proteolysis can proceed. We also highlight the underappreciated risk that E3 ligase loss-of-function, already documented in BET-PROTAC resistance, may similarly undermine DNA-PKcs degrader strategies. Full article

Aquaporins (AQPs) are transmembrane channel proteins that transport water and small solutes. Their dysregulation in cancer reveals functions beyond maintaining osmotic balance. This review summarizes that AQPs drive tumor progression through three core mechanisms: metabolic reprogramming, enhanced motility, and remodeling of the immune microenvironment. Specifically, AQP3, AQP7, and AQP9 serve as metabolic hubs for glycerol, while AQP3 and AQP8 help maintain redox homeostasis. AQP1 and AQP4 facilitate cell migration via hydrodynamic mechanisms, and AQP5 promotes invasion through signaling pathways such as Ras/NF-κB. In immune regulation, AQP9 and AQP3 modulate immune cell function by transporting metabolites, and AQP1 influences angiogenesis. Other isoforms, including AQP0, AQP2, AQP6, AQP10, and AQP11, also play roles in malignancy. Collectively, AQPs form a multifunctional network linking tumor metabolism, physical properties, and immunity, offering insights for novel diagnostic and therapeutic strategies. However, tissue-specific functions, complex regulatory mechanisms, and challenges in developing targeted therapies remain significant hurdles in translational medicine. Full article