Search Results (19,597)

In response to the significant challenges posed by ice accumulation and contamination from various fluids in complex operating conditions for metallic materials, this study utilises picosecond laser precision machining to develop a ‘slippery surface’ featuring a micro-channel network structure. The core innovation of this study lies in the use of laser-machined micrometre-scale array textures to overcome the limitations of traditional isolated pores. These globally interconnected micro-channels serve as highly efficient reservoirs and dynamic transport channels for lubricants, significantly enhancing the interfacial capillary locking force of the lubricant. Experimental results demonstrate that this unique network geometry endows the surface with exceptional fluid replenishment and self-healing properties, enabling it to exhibit outstanding broad-spectrum hydrophobicity towards various fluids—including water, crude oil and ethanol (surface tension range: 17.9–72.0 mN m⁻¹)—with sliding angles consistently below 12°, whilst effectively slowing the dehydration and solidification processes of biological fluids. At a low temperature of −15 °C, the surface achieved an ice formation delay of up to 286 s, with an ice adhesion strength of only 33.9 kPa, ensuring that accumulated ice could be spontaneously detached under minimal external force. Furthermore, the micro-channel network structure serves as a key protective mechanism against mechanical wear, maintaining robust slippery properties even after three hours of high-pressure water jet scouring (Weber number of 300). This reliable interface, achieved through structural management, provides an efficient and scalable platform for addressing the all-weather anti-icing and antifouling requirements of outdoor infrastructure. Full article

The accelerated aging of the population in vulnerable urban contexts poses significant challenges for architecture, particularly with regard to the quality of life of older adults. Within this framework, the present study aimed to analyze the association between sensory architecture and the quality of life of older adults and to translate this empirical evidence into context-informed design criteria for the development of a comprehensive center for older adults. The study adopted a quantitative approach with a non-experimental, cross-sectional, and correlational design. A structured questionnaire on sensory architecture and quality of life was administered to family members and caregivers acting as proxy respondents, demonstrating high internal consistency (Cronbach’s $\mathit{\alpha }>0.90$). Given the ordinal nature of the data, inferential analysis was conducted using Spearman’s rho coefficient. Within the analyzed dataset, the results revealed a statistically significant and strong association between sensory architecture and the quality of life of older adults ($\rho$ > 0.80). At the dimensional level, visual and tactile stimuli exhibited the highest associations, followed by the social relationships dimension, while therapeutic environments showed a moderate association, allowing the identification of an empirical hierarchy among the analyzed dimensions within this dataset. These findings support the interpretation of sensory architecture as a construct statistically associated with indicators of quality of life, from a non-causal perspective. Based on this hierarchy, the results were articulated into an evidence-based architectural structure, serving as analytical input to inform context-specific criteria for spatial organization, materiality, comfort, orientation, and social interaction derived from the observed statistical associations. The study contributes a methodological approach that systematically connects correlational quantitative findings with architectural design considerations, particularly in urban contexts characterized by limited specialized infrastructure. However, a key limitation is the use of proxy respondents (family members and caregivers), which should be considered when interpreting the results. Full article

Critical minerals are central to industrial strategies in the Global South, but evidence on how such policies reshape domestic production is limited. This paper maps Indonesia’s nickel ecosystem before and after the 2014 export ban using input–output multipliers and labor intensity from the 2010, 2016, and 2020 input–output tables. We provide a descriptive account of nickel’s evolving economic trajectory during the downstreaming push. Three patterns stand out. Forward linkages declined from 16 to 8 and backward linkages moved from 75 to 73, suggesting a narrower structure with greater specialization in higher value, more capital-intensive activities. Output multipliers rose most in sectors that support the electric vehicle supply chain, including professional and technical services, machinery, fabricated metals, transport equipment, energy, and finance. In contrast, the labor multiplier fell from about 6514 to 3366 jobs per IDR 1 trillion of final demand, implying a higher value added alongside lower employment intensity. Overall, downstreaming appears to work through structural concentration and growth in complementary sectors rather than broad-based diversification. Complementary policies in skills, regional development, and energy infrastructure are therefore critical for inclusive industrial transformation. Full article

Robust vehicle detection in real-world traffic surveillance remains challenging due to degraded imagery caused by motion blur, adverse weather, and low illumination, which significantly increases detector sensitivity to hyperparameter configurations. This study proposes a “Frugal AI” distributed multi-GPU framework that optimizes hyperparameters via a stochastic simplex-based search coupled with five-fold cross-validation. Utilizing three low-cost NVIDIA GTX 1050 Ti GPUs, the framework performs parallel candidate exploration with an asynchronous model-level exchange mechanism to escape local optima without the overhead of gradient synchronization. Seven CNN backbones—VGG16, VGG19, GoogLeNet, MobileNetV2, ResNet18, ResNet50, and ResNet101—were evaluated within YOLOv2 and Faster R-CNN detectors. To address memory constraints (4 GB VRAM), YOLOv2 was selected for extensive benchmarking. Performance was measured using a harmonic precision–recall-based cost metric to strictly penalize imbalanced outcomes. Experimental results demonstrate that under identical wall-clock time budgets, the proposed framework achieves an average 1.38% reduction in aggregated cost across all models, with the highly sensitive VGG19 backbone showing a 4.00% improvement. Benchmarking against Bayesian optimization, genetic algorithms, and random search confirms that our method achieves superior optimization quality with statistical significance ($p < 0.05$). Under a rigorous IoU = 0.75 threshold, the optimized models consistently yielded F1-scores 0.8444 $\pm$ 0.0346. Ablation studies further validate that the collaborative model exchange is essential for accelerating convergence in rugged loss landscapes. This research offers a practical, scalable, and cost-efficient solution for deploying robust AI surveillance in resource-constrained smart city infrastructure. Full article

While general network dynamics have been extensively modeled using stochastic methods, the emergence of dense Internet of Things (IoT) ecosystems demands a more specialized analytical framework. IoT environments are characterized by extreme non-linearity and sensitivity to initial conditions, where traditional models often fail to account for chaotic latency and packet loss. This paper introduces a specialized approach that integrates Chaos Theory with the innovative paradigm of Vibe Coding—an AI-assisted development and analysis methodology that allows for the `encoding’ and interpretation of the dynamic `vibe’ or signature of network fluctuations in real-time. By categorizing network behavior into four distinct scenarios (quiescent, perturbed, attacked, and perturbed–Attacked), the proposed framework utilizes deep learning to transform chaotic signals into actionable intelligence. Our findings demonstrate that this specialized synergy between chaos analysis and Vibe Coding provides superior classification of adversarial threats, such as DoS and injection attacks, fostering intelligent native security for next-generation IoT infrastructures. Full article

As automated vehicles move from controlled environments to unpredictable real-world roads, scenario-based testing has become the cornerstone of safety validation. In recent years, substantial progress has been made in scenario representation standards and generation methodologies. However, integrating scenario generation, standards-aligned packaging, validation, curation, and structured querying into a reproducible end-to-end lifecycle remains challenging in practice. This work presents a reproducible reference architecture for Scenario Databases (SCDBs) that treats scenario collections as lifecycle-governed data systems rather than static repositories. The proposed architecture unifies the scenario lifecycle within a single workflow. It integrates scenario generation and ingestion, validation and curation, immutable storage, semantic and value-based querying, and reproducible export. Scenario semantics are represented using ASAM OpenX formats (OpenDRIVE and OpenSCENARIO), together with ASAM OpenLABEL metadata, enabling standards-aligned interoperability. Querying is performed over categorical and value-carrying metadata without requiring inspection of raw scenario artifacts at query time. The reference implementation is deployed using Infrastructure-as-Code, supporting reproducibility and low operational overhead. Execution-based metric enrichment is supported as an optional extension, enabling scenarios to be augmented with execution-derived measurements and trace metadata. The contribution is not a centralized database, but a reference architecture and deployment blueprint that supports interoperable and federated scenario ecosystems. By framing SCDBs as reproducible lifecycle systems, this work supports scalable scenario reuse and more transparent safety validation workflows. Full article

Background: Healthcare policy in the United Kingdom recognizes that teenagers and young adults (TYAs: 16–24 years at diagnosis) require specialist care. In England, Principal Treatment Centers (PTCs) exist, delivering enhanced care exclusively within the PTC or as ‘joint care’ with designated hospitals (DHs). Central to this is the TYA multidisciplinary team (MDT) and an outreach model coordinating care between hospitals. We previously reported similar outcomes regardless of care location. Aims: To compare TYA experiences of care with healthcare professionals’ perspectives of the service they deliver. Methods: Mixed methods across England and Wales were used. The TYA-MDT identified TYAs who then received a postal invite to a cross-sectional survey capturing experiences of places of care, treatment, healthcare professional support (HCP), mental health, sexuality/fertility, clinical trials and care coordination. Comparisons were made based on exposure to care in a specialist TYA environment within 6 months of diagnosis: all-TYA-PTC (all care in the TYA-PTC, n = 70, 28%), no-TYA-PTC (no care in the TYA-PTC (n = 87, 35%): care delivered in a children/adult unit only), and joint care (care in a TYA-PTC and in a children’s/adult unit, n = 91, 36%). HCP perspectives were captured by rapid ethnography. Results: A total of 250/1056 (24%) TYAs participated. Overall, 200 (80%) rated their teams as excellent/good for helping them prepare for treatment. No evidence of significant differences existed between categories of care for proportions receiving support from key TYA-related professionals: TYA cancer nurse specialists (all-TYA-PTC n = 58, 91%; joint care n = 71, 88%; no-TYA-PTC n = 64, 82%) and social workers (all-TYA-PTC n = 30, 55%; joint care n = 36, 48%; no-TYA-PTC n = 28, 38%). A trend of diminishing support from youth support co-coordinators existed (all-TYA-PTC 63%; joint care 49%; no-TYA-PTC 40%, p = 0.069). This may explain why few differences in patient experiences existed across categories of care. Forty-nine HCPs participated. They were more critical in their interpretation of care, highlighting inequity in resources and challenges in some pathways and coordination. Conclusions: Similar access to age-appropriate support across care settings is likely to reflect recruitment methods. When TYAs are known to the MDT, age-appropriate care can be mobilized beyond TYA units, which could explain the equitable outcomes observed across different care locations in young people who responded to the survey. Nevertheless, gaps persist in communication and coordination, particularly within joint care models, and in the involvement of allied health professionals such as dieticians and physiotherapists, whose input is essential for rehabilitation and return to normal life. Strengthening these areas will require continued investment in workforce capacity and digital infrastructure to support genuinely coordinated, developmentally appropriate TYA cancer care. Full article

Over six decades, satellite remote sensing of water resources has evolved from manual interpretation of weather photographs to AI systems that learn hydrologic predictions directly from satellite imagery. This review traces that evolution through the observation-to-inference arc—a framework for the progressively tightening coupling between what satellites observe and what hydrologists infer. Using illustrative applications in precipitation, evapotranspiration, soil moisture, snow, surface water, and groundwater, we show how early observations (1960–1985) remained disconnected from operational hydrology; how calibrated retrieval algorithms (1985–2000) established a one-way pipeline from satellites to models; how operational infrastructure (2000–2015), anchored by MODIS, GRACE, GPM, and Sentinel, achieved assimilative coupling through computational feedback between models and observations; and how deep learning (2015–present) is beginning to collapse this pipeline. Multi-source data fusion has been a recurring enabler at each stage. We articulate a four-level AI vision and research trajectory, from AI-assisted interpretation through AI-native retrieval and AI-driven inference to autonomous Earth observation intelligence. Persistent challenges in mission continuity, calibration, equity of access, and translating satellite-derived information into operational water management decisions provide essential context for evaluating both the promise and limits of this trajectory. Full article

The rapid growth of cities and expansion of impervious surfaces have intensified surface runoff problems and urban flooding risk. This scenario, exacerbated by the effects of climate change, demands sustainable and integrated solutions. Thus, this study evaluates the pre-feasibility of implementing sustainable urban drainage systems (SUDS) in the Monteverde neighborhood in Montería, Colombia; an area that is critically affected by floods during rainfall events. Using the storm water management model (SWMM) and hydrological simulations based on design hyetographs for different return periods, the performance of a conventional drainage system was compared with five scenarios using SUDS. To determine the modeling scenarios, a decision-making method through the analytic hierarchy process, AHP, was used to select the most appropriate SUDS. The results showed that implementing storage tanks reduces peak flows at outlets 1 and 2 up to 50%, while bioretention zones and rain gardens in isolation showed reduced effectiveness (<6%). Combining strategies slightly improves overall efficiency, although the impact keeps being dominated by tanks. This study demonstrates that the incorporation of SUDS in vulnerable urban areas lessens water risks, strengthens urban resilience, promotes rainwater harvesting, and eases the transition to a more sustainable infrastructure. In addition, it proposes a methodology that can be replicated in other similar Latin American cities. Full article

Background: Global water scarcity, intensified by climate change, demands optimization of irrigation systems consuming 70% of freshwater resources. Despite significant investments in modernizing irrigation infrastructure from open channels to pressurized networks, performance often falls below expectations. Objective: This study develops an integrated diagnostic and simulation framework for evaluating and improving large-scale pressurized irrigation systems by adapting the Mapping System and Services for Pressurized Irrigation (MASSPRES) methodology. Methods: The framework integrates three components: (1) demand flow dynamics determination using stochastic modelling; (2) hydraulic performance simulation incorporating multiple flow regimes; and (3) performance analysis using relative pressure deficit and reliability indicators. The methodology combines deterministic soil water balance calculations with stochastic farmer behaviour modelling. Results: Application to the Sinistra Ofanto irrigation scheme revealed localized pressure deficits during peak demand periods. The rehabilitation strategy restored full hydraulic feasibility of the network, increasing the proportion of hydraulically satisfied operating configurations from 62% to 100% under peak demand conditions and ensuring adequate pressure at all 317 hydrants across the system. Conclusions: The methodology provides robust decision support for cost-effective rehabilitation, ensuring reliable water delivery while promoting water-energy efficiency. Full article

Transmission tower climbing is a critical specialized operation in ultra-high-voltage power maintenance and communication infrastructure servicing. However, existing lower-limb exoskeletons used for tower climbing still suffer from insufficient motion intention recognition accuracy under complex operational environments. To address this issue, this study proposes an inertial measurement unit (IMU)-based bidirectional temporal deep learning method for motion intention recognition. First, a one-dimensional convolutional neural network (1D-CNN) is employed to extract local temporal features from multi-channel IMU signals. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) is introduced to model the forward and backward temporal dependencies of motion sequences. Furthermore, a temporal attention mechanism is incorporated to emphasize discriminative features at critical movement phases, enabling the precise recognition of short-duration and transitional motions. Experimental results demonstrate that the proposed method outperforms traditional machine learning approaches and unidirectional temporal models in terms of accuracy, F1-score, and other evaluation metrics. In particular, this method demonstrates significant advantages in identifying the flexion/extension phases and transitional states. This study provides an offline method for analyzing movement intentions in lower-limb exoskeleton control for power transmission tower climbing scenarios and offers a reference for developing assistive control strategies for assisted climbing tasks in this specific context. Full article

Large-scale surface subsidence induced by extra-thick seam longwall top-coal caving (LTCC) is strongly amplified by thick unconsolidated overburden, posing serious serviceability risks to overlying linear infrastructure. Taking the S103 Provincial Highway above Panel 6118 in Inner Mongolia, China, as the engineering background, this study integrates theoretical analysis, numerical simulation, and in situ monitoring to investigate the subsidence-control mechanism of directional presplitting roof cutting. The results show that roof cutting mitigates surface subsidence by reconstructing the overburden structural system and weakening the stress-transfer chain, thereby transforming key-stratum deformation from integral bending to segmented block movement and narrowing the subsidence-affected zone. An equivalent mining-depth model for subsidence-boundary convergence is proposed to characterize the inward migration of the subsidence-basin boundary under thick unconsolidated cover, and a segmented probability-integral model is developed to explain the kink-like high-gradient feature in the post-cut subsidence profile. Parametric simulations of roof-cutting positions (p = 0, 2, 4, …, 32 m) show that the most effective mitigation occurs in the range p = 4–12 m; using minimum–maximum highway subsidence together with profile flattening as the optimization criteria, the representative optimum is identified at p ≈ 10 m, for which the maximum highway subsidence is approximately 57 mm, about 76% lower than that in the non-cutting case. The results further indicate that, although roof cutting significantly reduces subsidence and deformation gradients, fissure localization and possible discontinuous deformation near the pre-split weak plane still require careful field monitoring. Full article

Bike sharing is an established component of urban mobility infrastructure, offering a low-emission alternative to motorized transport for short trips in cities worldwide. Accurate demand forecasting is essential for efficient system operation: it enables better bike redistribution, reduces user wait times, and lowers the operational costs associated with rebalancing. This study evaluated multiple ensemble strategies for hourly bike-sharing demand prediction, comparing bagging methods (Random Forest, Extra Trees), boosting methods (AdaBoost, Gradient Boosting Regressor, Histogram-based Gradient Boosting Regressor), and a Voting ensemble, while systematically investigating the impact of hyperparameter optimization. A repeated hold-out protocol was used, in which the dataset was randomly divided into 80% training and 20% test subsets across 10 random splits; 5-fold cross-validation was applied within each training fold exclusively for hyperparameter tuning, ensuring the test set remained unseen during model selection. Random Search and Bayesian Optimization were compared under identical budgets of 60 configurations per model. Results show that optimization substantially improves all models, with the most pronounced gains for AdaBoost (58% RMSE reduction) and Gradient Boosting Regressor (45% RMSE reduction). A Voting ensemble combining a Random Search-tuned Gradient Boosting Regressor and a Bayesian-optimized Histogram-based Gradient Boosting Regressor achieves the best overall performance (RMSE of 38.48, R² of 0.955) with the lowest variance among all repeated splits. Feature importance analysis confirms that hour of day and temperature are the dominant demand drivers, consistent with the operational patterns of urban bike-sharing systems. The performance difference between Random Search and Bayesian Optimization is negligible for most models, suggesting that well-designed search spaces allow simpler strategies to achieve competitive results. A controlled comparison conducted under identical experimental conditions shows that the Voting ensemble is statistically equivalent to XGBoost and nominally better than LightGBM, while CatBoost achieves a statistically significant advantage, highlighting it as a strong individual alternative. Full article

The translaminar fracture toughness of pultruded Glass Fiber Reinforced Polymers (GFRP) is influenced by several factors, including the type of matrix, fiber, the fiber volume ratio, the proportion of plies at each angle and the size of the test specimens. Conventional test approaches tend to overestimate the fracture toughness of GFRP composites due to imperfect specimen fabrication. This paper introduces an anisotropic two-dimensional adaptation of phase field theory to evaluate the translaminar fracture toughness of pultruded GFRP in conjunction with the size effect. It is found that the fracture toughness is linearly correlated with the fiber volume ratio when the proportion of 0° plies ranges from 30% to 60%. Additionally, it was found that at the same fiber volume ratio, the fracture toughness increases with the increase of 0° plies by 5%. Five machine learning algorithms, i.e., BP, RF, SVR, GA-BP, and PSO-BP, are employed to predict the fracture toughness of pultruded GFRP laminates. It has been found that the PSO-BP algorithm is robust in predicting the fracture toughness of pultruded GFRP laminates, with the correlation coefficient R² being 0.987 and 0.994 in the test and training set, respectively and the prediction error in fracture toughness being less than 4 kJ/m². The trained machine learning method can accurately predict GFRP fracture toughness. When the proportion of 0° plies is larger than 50%, the increase in the fracture toughness is approximately twice that of those taking up a proportion of 30–50%. Fracture toughness predictions are provided using the developed machine learning model for pultruded GFRP profiles, which are commonly used in infrastructure construction with fiber volume ratios range of 60–70% and 0° layup percentages of 60–75%. Full article

The sustainable conservation of linear industrial heritage corridors remains challenged by a limited understanding of their formation mechanisms and driving forces. Addressing this gap, this study develops a transferable analytical framework to explain the spatio-temporal evolution of such systems. Using Jiangsu Province (China) as a case study and a dataset of 344 industrial heritage sites, we apply an integrated spatial-analytical approach to examine distribution patterns and underlying drivers. The results reveal an evolving dual-axis spatial structure shaped by transportation networks and regional development dynamics, with railway density emerging as a key influencing factor. Furthermore, the interaction of infrastructural, demographic, and institutional variables highlights a synergistic mechanism underpinning corridor formation. Building on these findings, the study proposes a “corridor-as-process” framework, conceptualizing industrial heritage corridors as dynamic socio-spatial products of long-term interactions between institutions, networks, and economic activities. This perspective advances beyond static, descriptive approaches by offering a process-oriented and explanatory understanding of heritage systems. This study contributes to sustainability by providing a spatially explicit basis for adaptive reuse, vulnerability assessment, and differentiated conservation strategies, supporting the integration of heritage preservation within broader regional sustainability transitions. The proposed framework offers a transferable methodological reference for analyzing industrial heritage corridors in comparable global contexts. Full article