Search Results (170)

Population aging has made aging in place (AIP) a central goal of sustainable welfare and urban governance, yet older adults’ perceived feasibility of remaining in their current home under conditions of vulnerability remains unevenly distributed. This study conceptualizes AIP intention under anticipated mobility limitation as a stratified condition of social sustainability, asking who expects to remain in the community as a supported and recognized member when mobility declines. Using the 2023 National Survey of Older Koreans (N = 9951), it examines older adults’ stated intention to remain in their current residence under mobility limitation through weighted logistic regression. The results show that this intention is structured most strongly by housing inequality: non-owner tenure reduces the likelihood of intending to remain in place, whereas housing satisfaction increases it. Co-residence with adult children is positively associated with this intention, while activities of daily living limitations are negatively associated with it. Beyond material and health conditions, social participation intention and digital adaptability increase the likelihood of intending to remain in place, whereas age discrimination in public institutions reduces it. Government trust is negatively associated with the intention to remain in place. Because the survey does not directly measure older adults’ awareness, availability, evaluation, or use of alternative residential or care facilities, this association is treated only as a discussion point rather than as an empirically tested mechanism: higher institutional trust may be linked to greater openness to publicly supported alternatives. The findings demonstrate that the perceived feasibility of AIP is not merely an individual preference, but an unevenly distributed possibility shaped by housing security, institutional inclusion, and civic capacity. Sustainable aging policy should integrate housing support, anti-discrimination measures, digital inclusion, and community participation. Full article

Hydrocarbon biodegradation in cold environments is constrained not only by microbial catabolic capacity but also by interfacial access to poorly soluble substrates and by the way remediation materials interact with microbial envelope-related structures. This study presents an uncertainty-aware low-temperature computational screening workflow for prioritizing functionalized oxide surface-patch models that may favor hydrocarbon association while avoiding excessive perturbation of simplified microbial-interface proxies. Twelve finite oxide–ligand candidates derived from TiO₂, ZnO, and CeO₂ patches functionalized with bare, catechol, glycerol, or citric acid states were evaluated against three hydrocarbon probes, hexane, toluene, and naphthalene, and two microbial-interface proxies. The workflow combined GFN2-xTB geometry optimization and relative interaction-energy screening, clean GFN2-xTB/ALPB rescoring with rescue tracking, short xTB-MD perturbation analysis, ORCA refinement of selected candidates, sensitivity analysis of ranking parameters, and integrated evidence classification. The analysis supports interfacial selectivity, rather than maximum adsorption strength, as the central design principle. TiO₂–catechol and TiO₂–glycerol remain experimentally testable primary candidates because their original screening profile combines chemically interpretable hydrocarbon association with comparatively mild microbial-proxy interaction descriptors. ZnO–catechol and ZnO–glycerol emerged as sensitivity-competitive secondary candidates under several scoring assumptions. Completed short xTB-MD trajectories further showed that TiO₂–glycerol produced moderate perturbation against the peptide proxy, whereas TiO₂–glycerol against NAG and ZnO–catechol against the peptide proxy showed very high proxy displacement. Overall, the workflow provides a transparent prioritization framework for experimental validation. Full article

We study dynamic hedging in an incomplete market where the underlying asset follows a stochastic-volatility process and the hedger trades only the stock and the money-market account. The hedging problem is formulated as a multi-stage stochastic control problem with a quadratic terminal-loss objective and is solved through a Hamilton–Jacobi–Bellman framework. For the Heston model, the resulting mean-variance hedge specializes to the Galtchouk–Kunita–Watanabe projection and can be written as the sum of the spot delta and a volatility-risk correction term. We emphasize that this representation is used in the paper as an implementation theorem for our setting, rather than as a new general result. On the numerical side, we compare a finite-difference alternating-direction implicit solver with a Deep Galerkin Method, providing full implementation details for both. The finite-difference solver is the preferred method for the two-state Heston problem because it is faster and more accurate on low-dimensional grids, whereas the neural solver becomes attractive only for higher-dimensional extensions where mesh-based methods become computationally burdensome. In backtests across major S&P 500 market regimes from 2006 to 2022, the stochastic-volatility-aware hedge modestly improves on Black–Scholes hedging during stress episodes, while differences are negligible in calm markets. Across the reported experiments, the PDE-optimal mean-variance hedge is numerically indistinguishable from the recalibrated Heston hedge, indicating that the main value of the framework is theoretical unification and implementation guidance rather than a materially different trading rule in the tested setting. Fixed worst-case robust hedging is overly conservative in the historical sample, although adaptive robustness remains a promising conceptual extension. The main contribution of the paper is therefore a rigorous and implementable unification of multi-stage PDE optimization with stochastic-volatility-aware hedging, together with evidence that the economic value of model sophistication is concentrated in stressed markets. Full article

Decarbonizing heavy-duty logistics requires powertrains that integrate novel topology design, degradation-aware optimization, and robust dynamic performance under real-world operational loads. While solid oxide fuel cells offer high efficiency, their application in transportation is hindered by thermal fatigue. This study proposes a novel hybrid powertrain topology integrating a metal-supported solid oxide fuel cell (SOFC), a micro gas turbine (MGT), and an aluminum–silicon phase change material (PCM) thermal buffer. A high-fidelity dynamic model is developed and coupled with a multi-objective optimization framework to size the PCM buffer and battery pack, balancing capital expenditure and system lifetime. Furthermore, a degradation-aware energy management strategy based on a thermal state-of-charge metric is introduced. Simulations over a 10 h dynamic drive cycle indicate that the optimal configuration (120 kg PCM, 80 kWh battery) extends the SOFC’s simulated remaining useful life to 38,400 h, a 2.5-fold improvement over unbuffered systems. Concurrently, the proposed energy management strategy reduces the MGT mechanical wear index by 98% compared to conventional load-following strategies. The system demonstrates robust performance across ambient temperatures from −20 °C to +45 °C and achieves a 22% reduction in projected capital expenditure compared to standard proton exchange membrane fuel cell powertrains. This topology offers a highly durable and economically viable pathway for next-generation zero-emission heavy-duty vehicles. This work addresses a critical gap in the literature: the lack of integrated thermal buffering and degradation-aware control strategies for high-temperature fuel cell systems in dynamic vehicular applications. By coupling a physical latent heat buffer with a novel Thermal-SOC-proportional Energy Management Strategy, the proposed architecture directly targets the primary degradation mechanisms that have historically impeded SOFC commercialization in heavy-duty transport. Full article

Tree-derived bio-based polymer materials, including natural rubber, raw lacquer, pine resin, and tree gums, are important renewable resources for sustainable forestry and green manufacturing. However, their collection still largely depends on manual operations, which may cause unstable yield, tree damage, and low operational efficiency. This review examines intelligent identification technologies for tree-derived material collection from the perspectives of multimodal perception and machine learning. The collection requirements and recognition targets of typical materials are first analyzed, including trunk localization, tapping line detection, bark feature extraction, tree state assessment, and safe tool–bark interaction. Visual, RGB-D, LiDAR, spectral, force/tactile, and environmental sensing technologies are then reviewed, and their roles in complex forest perception and robotic operation are discussed. Machine learning methods, including traditional classifiers, object detection, image segmentation, point cloud processing, temporal modeling, few-shot learning, transfer learning, and uncertainty-aware evaluation, are further examined. Representative cases in rubber tapping, lacquer collection, and pine resin harvesting are compared to reveal the transition from single-sensor recognition to perception–decision–execution integration. Key challenges are identified in dataset standardization, model generalization, edge deployment, force-aware control, and biological mechanism integration. Future directions are proposed toward autonomous, low-damage, and high-yield intelligent collection systems. Full article

To address challenges such as complex background interference, limited long-range modeling capabilities of CNNs, and poor generalization in steel-concrete cross-material scenarios, this study proposes an enhanced detection framework. This framework integrates a TAPFormer with morphological reasoning rules. The method utilizes TAPFormer as the backbone network. It captures global topological features of cracks through a Task-Aware Query mechanism. This approach compensates for the deficiencies of traditional convolutional operators in modeling the continuity of thin and long cracks. Furthermore, a mask reasoning module based on geometric priors is developed to handle unstructured interferences, such as marker pen marks, welds, and concrete holes. This module defines logical criteria, including edge curvature consistency, axial aspect ratios, and endpoint extension directions. These criteria are used to perform topological repair and filter false positives in the initial segmentation masks. A hybrid dataset containing 4500 cross-material damage images was used for validation. The results show that the proposed method achieves a mean IoU of 86.72% and an F1-score of 90.36%. Notably, the method filters over 91.0% of false positives caused by manual marker pen marks in interference-rich scenarios. Compared to mainstream state-of-the-art models, the IoU improves by at least 5.48%. The results show that the proposed framework improves the robustness and logical self-consistency of crack identification in complex engineering environments. Full article

Combine harvesters in lodged, wet, weedy, uneven, or otherwise heterogeneous fields operate under rapidly changing feed rate, load, and material flow conditions. These disturbances often appear as drum overload, cleaning loss, grain breakage, impurity increase, and unstable travel, whereas conventional fixed-parameter operation still depends heavily on operator experience. This review examines intelligent perception and control technologies for combine harvesters from a mechanism-to-control perspective. The discussion covers dynamic load evolution, cleaning loss and grain damage mechanisms, multivariable coupling, pre-harvest perception, feed rate and internal state sensing, result layer loss and quality monitoring, forward speed control, threshing drum load regulation, adaptive cleaning control, and whole machine integration. The literature shows a clear shift from isolated sensing or single-parameter adjustment toward multimodal perception, state estimation, predictive control, digital twins, and edge deployment. At the same time, field robustness, cross-condition generalization, actuator bandwidth, sensing delay, and the coupling between result layer monitoring and closed-loop control remain the main barriers to deployment. The review, therefore, argues for a whole machine architecture that links environmental preview, internal state estimation, loss quality feedback, actuator-aware control, and cloud–edge–device collaboration for stable, low-loss, and autonomous harvesting in complex agricultural environments. Full article

High-nickel NCA/Si–C 21700 cells exhibit strongly condition-dependent degradation, but the coupled influence of temperature and rate on electrochemical, thermal, and structural evolution remains insufficiently resolved. Here, Samsung INR21700-50E cells were aged under a 3 × 3 matrix of ambient temperatures (0, 23, and 40 °C) and C-rates (0.5C, 1C, and 2C). Periodic reference performance tests were used to track capacity, 10 s direct-current internal resistance, electrochemical impedance, pseudo-open-circuit voltage, differential voltage/incremental capacity behavior, heat generation, and post-mortem morphology. Guided by the hypothesis that temperature and rate history change not only the speed but also the dominant pathway of aging, the results show that both ambient temperature and the charge/discharge rate program govern the aging trajectory. Low-temperature cycling accelerates capacity loss and resistance growth through severe polarization and lithium plating, indicating dominant loss of lithium inventory. High-temperature operation promotes interfacial side reactions, impedance rise, and cathode structural degradation, leading to stronger loss of active material at later stages. An increasing C-rate amplifies these effects by raising overpotential and thermal load. Heat generation power increases markedly with aging and depends strongly on temperature–rate history. Scanning electron microscopy confirms cathode cracking, anode surface film thickening, and separator degradation under severe conditions. These experimental indicators are integrated into a mechanism-aware diagnostic framework that maps capacity retention, DCIR/EIS parameters, ICA/DVA indices, and heat generation metrics to dominant aging modes, supporting BMS state-of-health estimation, lifetime prediction, thermal management, and second-life screening of high-nickel NCA cells. The condition-averaged trajectories are further converted into a semi-empirical aging law that links capacity loss, resistance growth, and heat generation increase for BMS-oriented lifetime prediction. Full article

Real-time physiological state awareness is central to next-generation wearable computing, yet most existing electrophysiological signal acquisition platforms remain limited to single-modality sensing, high component cost, or bulky form factors that hinder everyday deployment. Here, we present a compact, low-cost wearable platform for simultaneous electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG) acquisition. The system integrates an analog front-end, a microcontroller, and a Bluetooth wireless link on a compact single-board platform (5.6 × 3.8 cm, approximately 12.8 g with the selected lithium-polymer battery installed), with an estimated bill-of-materials cost of 67.40 USD. Experimental validation across three healthy subjects, with the ECG channel additionally benchmarked against a commercial clinical-grade ambulatory ECG recorder, demonstrates that the platform captures ECG waveforms with recognizable P-QRS-T morphology under controlled recording conditions, supports reliable R-peak detection and heart rate estimation, records stable resting-state EEG spectral features, and distinguishes EMG activation from resting baseline in both time-domain amplitude and time-frequency structure. Leveraging the real-time wireless data link between the wearable hardware and a PC-hosted MATLAB environment, we further explore application-oriented signal processing scenarios. As an offline algorithm-pipeline compatibility demonstration, a CNN-based seizure detection pipeline is applied to the Bonn EEG benchmark for five-class epileptic state classification, achieving 86.60% mean classification accuracy. The proposed system offers a scalable and affordable foundation for wearable human-state-aware interaction, with potential applications in clinical monitoring, rehabilitation, and brain–computer interfaces. Full article

Magnesium-based hydrides remain among the most intensively studied solid-state hydrogen storage materials because they combine high theoretical hydrogen capacity, elemental abundance, and relatively low cost. Yet their practical behavior often varies far more strongly than nominal composition alone would suggest. Materials described under similar chemical labels may show markedly different activation profiles, sorption kinetics, reversible capacities, and cycling responses, even when they appear compositionally comparable. This Perspective argues that such discrepancies are best understood by recognizing that Mg-based hydrogen storage materials are not fully defined by composition, catalyst identity, and equilibrium thermodynamics alone. Instead, they react from historically written states produced by synthesis, activation, and cycling. These histories generate hidden state variables, including defects, residual strain, metastable structural motifs, interfacial topology, and catalyst transformation states, that reshape the operative hydrogen sorption pathway. The discussion therefore moves from a conventional composition-centered view toward a pathway-centered interpretation of reactivity. First, it examines how hidden state variables are written into Mg-based materials through processing, activation, and repeated use. It then shows how metastability serves as the structural bridge that allows these variables to persist into the reaction window. On that basis, the article argues that hydrogen sorption in Mg-based hydrides is fundamentally pathway-dependent, with history influencing hydrogen entry, transport-network selection, interfacial route construction, and pathway evolution during cycling. This perspective also provides a more coherent explanation for the long-standing reproducibility problem in the field, which is reinterpreted here as a pathway-mismatch problem arising from comparisons among historically different reactive states. Finally, a metadata-aware, pathway-aware, and boundary-aware design framework is proposed as a more realistic basis for cumulative materials development. From this viewpoint, the future of Mg-based solid-state hydrogen storage depends not only on better compositions, but on better-defined, better-constructed, and better-preserved reactive pathways under clearly specified internal and external constraints. Full article

Play is considered the primary activity of children, and toys are their essential tools. However, the toy industry extends beyond children, constituting a significant economic sector with annual revenues exceeding one hundred billion dollars and generating substantial environmental consequences. These include resource consumption, pollution during manufacturing, energy use, consumables during operation, and waste generation at the end of the product’s life cycle. This research presents a study of the state of the art of ecodesign in the toy sector and its potential within this field. Through the analysis of the available scientific literature and the expertise of the Toy Technology Institute (AIJU), experiences from companies in the sector have been identified and classified according to the ecodesign strategy wheel. Simultaneously, a survey of industry stakeholders compared the current situation with that of 30 years ago. The results reveal perceptual progress that is uneven across dimensions, with the strongest advances in materials and production, moderate gains in distribution and end-of-life strategies, and limited improvement in product durability, while innovation in new product concepts shows the highest growth. Correlation analyses indicate that experience and professional background influence how sustainability progress is perceived. Although most improvements have been motivated by cost reduction and regulatory compliance rather than environmental awareness, recent trends reflect a growing corporate commitment to ecological innovation. For consumers, it remains essential to overcome misconceptions about eco-friendly toys, while companies must continue to invest in new materials, technologies, and design strategies that support the transition toward circular and long-lasting toy products. Full article

Accurate service performance evaluation of reinforced concrete (RC) beam structures is crucial for ensuring structural safety and guiding maintenance decisions. However, current practice primarily relies on qualitative visual inspections that fail to quantitatively link apparent defects to internal mechanical behavior. To address this, a novel evaluation framework fusing apparent crack features with static and dynamic responses is proposed. A context-aware grid-based deep learning model (CGDL-Crack) is developed that combines transfer learning with skeleton extraction, achieving crack localization with a maximum validation AP of 96.4% under complex backgrounds. Based on large-scale parametric finite element simulations and Sobol global sensitivity analysis, key state indicators—including static reaction forces, modal frequencies, and crack widths—are identified, and an artificial neural network (ANN) surrogate model is constructed to map multi-source monitoring data to material constitutive parameters. Full-process failure tests on 17 RC beams demonstrate that crack width follows bilinear growth and remains sensitive after stiffness indices saturate. The updated FE model accurately predicts ultimate bearing capacity, demonstrating the effectiveness of the proposed framework and its application potential for RC beam-type components in bridge and building engineering. Full article

Large-span bridges are critical components of transportation networks. Environmental variability, material degradation, and cumulative fatigue continuously affect their long-term performance. Digital Twin (DT) technology has emerged as a promising paradigm for integrating sensing, modeling, and data analytics. Most existing DT implementations in civil infrastructure rely on dense sensor networks, assume near-complete observability, and primarily serve as passive visualization or diagnostic tools, limiting their scalability and practical applicability. This paper proposes a DT framework specifically designed for the monitoring and management of existing large-span bridges under sparse sensing conditions. The framework adopts an information-centric perspective in which limited physical measurements are complemented by full-field state reconstruction through the integration of physics-based modeling, data-driven learning, and uncertainty-aware inference. A synchronized reference configuration, termed State 0, is introduced as the initial basis for tracking structural changes over time, while allowing conditional re-baselining through a Dynamic State 0 (DS0) when verified reassessment justifies it. On this basis, the proposed DT is formulated as an adaptive and decision-oriented cyber–physical system that supports optimization-based recommendations for sensing, inspection, and maintenance planning. Full article

Linear assets (LAs), such as conveyor systems, road networks, pipelines, and power transmission lines, are typically maintained through localized, segment-based interventions. While such approaches effectively address spatially heterogeneous degradation, they often neglect the system-level consequences of repeated local actions. In particular, improvements in segment condition may be accompanied by increased structural complexity, leading to reduced reliability and higher lifecycle costs. This paper proposes a unified engineering framework that integrates segment-level condition assessment with system-level structural effects. The framework is based on a dual representation of asset condition, distinguishing between material state (MS) and structural state (SS), which correspond to material aging (MA) and structural aging (SA), respectively. A key contribution is the introduction of the fragmentation penalty (FP), capturing the negative impact of increasing segmentation and interface density on system performance. The framework incorporates multi-threshold decision logic, enabling differentiation between operational, refurbishment, and replacement regimes, and interprets maintenance actions as transformations affecting both condition and structure. A formal model is developed to represent the asset as a dynamic system of segments and interfaces. It provides a basis for future empirical calibration and structure-aware optimization. Although the model is developed using conveyor belt loops as a reference case, its broader relevance is discussed for other classes of linear assets with repeated local intervention and evolving structural heterogeneity. A simple worked example is included to demonstrate the operational meaning of the proposed fragmentation-aware perspective. The results show that maintenance decisions may change when structural side effects are considered together with local condition improvement, and they provide a basis for future empirical calibration and structure-aware optimization of maintenance strategies. Full article

Along with the enhancement of people’s public health consciousness and the requirement for individual diet arrangement getting more urgent, the meal recommendation method, which is based on artificial intelligence, has hence become an active research domain in the field of intelligent health. One system that makes practical recommendations must deal with the user’s unclear queries, while at the same time, it must satisfy strict nutrient demands. A great number of existing methods at present either do not take into account verifiable food composition data, or they handle implicit dietary restrictions in a not good way. For solving these problems, we put forward CARE (Constraint-Aware Recipe Engine). Beginning from a mixed Retrieval-Augmented Generation (RAG) basic model (CARE v1.0), we have developed CARE v2.0, which is a suggestion engine that unites intention polish, knowledge graph enlargement, and rule-based checking in a unified working line. Instead of depending on huge black-box models, our framework utilizes an effective language model that possesses 1.5 B parameters. User inquiry content are undergone parsing to become structured nutrition targets; a food knowledge graph links abstract health notions to specific cooking materials; and the obtained candidate results are filtered in accordance with strict diet restrictions, with optional checking carried out by an automatic agent-based reviewer. Under a zero-shot cold-start situation, the system attains a semantic recall@5 of 0.825 on 400 k recipes coming from Recipe1M+ and a newly created fuzzy-query benchmark (CAREBench-150), and it thus has a better performance than dense retrieval baselines (0.550) as well as direct zero-shot prompting. The constraint satisfaction rate is located at 85.0% in fast mode, and it rises to 98.5% when the verification module is in the working state; therefore, it supports the safety of recommendations. These findings indicate that structured food knowledge, which matches a compact algorithmic framework, can therefore connect unclear user intentions and accurate nutrition requirements effectively. Full article