Search Results (493)

A modified Johnson–Mehl–Avrami–Kolmogorov (JMAK) model, including the heating rates, was proposed in this study to improve the accuracy of isothermal austenite formation kinetics prediction. Since the ultrafast heating process affects the behavior of ferrite recrystallization and austenite formation before the isothermal process, which in turn influences the subsequent isothermal austenite formation kinetics, the effects of varying austenitization temperatures and heating rates on isothermal austenite formation in cold-rolled quenching and partitioning (Q&P) steel, which remain insufficiently understood, were systematically investigated. Under a constant heating rate, the austenite formation rate initially increases and subsequently decreases as the austenitization temperature rises from formation start temperature A_c1 to finish temperature A_c3, and complete austenitization is achieved more quickly at elevated temperatures. At a given austenitization temperature, an increased heating rate was found to accelerate the isothermal transformation kinetics and significantly reduce the duration required to achieve complete austenitization. The experimental results revealed that both the transformation activation energy ($Q$) and material constant ($k_0$) decreased with increasing heating rates, while the Avrami exponent ($n$) showed a progressive increase, leading to the development of the heating-rate-dependent modified JMAK model. The model accurately characterizes the effect of varying heating rates on isothermal austenite formation kinetics, enabling kinetic curves prediction under multiple heating rates and austenitization temperatures and overcoming the limitation of single heating rate prediction in existing models, with significantly broadened applicability. Full article

This research investigates the integration of green cores as central biophilic elements in residential architecture, proposing a climate-responsive design methodology grounded in architectural optimization. The study begins with the full-scale refurbishment of a compact urban apartment, wherein interior partitions, fenestration and material systems were reconfigured to embed vegetated zones within the architectural core. Light exposure, ventilation potential and spatial coherence were maximized through data-driven design strategies and structural modifications. Integrated planting modules equipped with PAR-specific LED systems ensure sustained vegetation growth, while embedded environmental infrastructure supports automated irrigation and continuous microclimate monitoring. This plant-centered spatial model is evaluated using quantifiable performance metrics, establishing a replicable framework for optimized indoor ecosystems. Photosynthetically active radiation (PAR)-specific LED systems and embedded environmental infrastructure were incorporated to maintain vegetation viability and enable microclimate regulation. A programmable irrigation system linked to environmental sensors allows automated resource management, ensuring efficient plant sustenance. The configuration is assessed using measurable indicators such as daylight factor, solar exposure, passive thermal behavior and similar elements. Additionally, a post-occupancy expert assessment was conducted with several architects evaluating different aspects confirming the architectural and spatial improvements achieved through the refurbishment. This study not only demonstrates a viable architectural prototype but also opens future avenues for the development of metabolically active buildings, integration with decentralized energy and water systems, and the computational optimization of living infrastructure across varying climatic zones. Full article

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article

We study the dynamics of von Neumann entropy driven by the Gorini–Kossakowski–Sudarshan–Lindblad (GKSL) equation, focusing on its camel-like behavior—a hump-like entropy evolution reflecting the system’s adaptation to its environment. Within this framework, we analyze quantum correlations under decoherence and environmental interaction for three sets of quantum states. Our results show that the sign of the entanglement entropy’s derivative serves as an indicator of the system’s drift toward either classical or quantum information exchange—an insight relevant to quantum error correction and dissipation in quantum thermal machines. We parameterize quantum states using both single-parameter and Bloch-sphere representations, where the angle $\theta$ on the Bloch sphere corresponds to the state’s position. On this sphere, we construct gradient and basin maps that partition the dynamics of quantum states into stable and unstable regions under decoherence. Notably, we identify a Braiding ring of decoherence-unstable states located at $\theta ={\displaystyle \frac{3\pi }{4}}$; these states act as attractors under a constructed Lyapunov function, illustrating the topological and dynamical complexity of quantum evolution. Finally, we propose a testable experimental setup based on camel-like entropy and discuss its connection to the theoretical framework of this entropy behavior. Full article

This work proposes a behavior-aware caching architecture that improves cache hit rates by up to 10.8% over LRU and 36% over LFU in large-scale web applications, reducing redundant traffic and alleviating backend server load. The architecture partitions the cache into three sections—static, dynamic, and burst—according to query reuse patterns derived from user behavior. Static queries remain permanently stored, dynamic queries have time-bound validity, and burst queries are detected in real time using a statistical monitoring mechanism to prioritize sudden, high-demand requests. The proposed architecture was evaluated through simulation experiments using real-world query logs (a one-month trace of 1.5 billion queries from a commercial search engine) under multiple cache capacity configurations ranging from 1000 to 100,000 entries and in combination with the Least Recently Used (LRU) and Least Frequently Used (LFU) replacement policies. The results show that the proposed architecture consistently achieves higher performance than the baselines, with the largest relative gains in smaller cache configurations and applicability to distributed and hybrid caching environments without fundamental design changes. The integration of user-behavior modeling and burst-aware segmentation delivers a practical and reproducible framework that optimizes cache allocation policies in high-traffic and distributed environments. Full article

Open-mouth panting in broiler chickens is a visible and critical indicator of heat stress and compromised welfare. However, detecting this behavior in densely populated cages is challenging due to the small size of the target and frequent occlusions and cluttered backgrounds. To overcome these issues, we proposed an enhanced object detection method based on the lightweight YOLOv8n framework, incorporating four key improvements. First, we add a dedicated P2 detection head to improve the recognition of small targets. Second, a space-to-depth grouped convolution module (SGConv) is introduced to capture fine-grained texture and edge features crucial for panting identification. Third, a bidirectional feature pyramid network (BIFPN) merges multi-scale feature maps for richer representations. Finally, a squeeze-and-excitation (SE) channel attention mechanism emphasizes mouth-related cues while suppressing irrelevant background noise. We trained and evaluated the method on a comprehensive, full-cycle broiler panting dataset covering all growth stages. Experimental results show that our method significantly outperforms baseline YOLO models, achieving 0.92 mAP@50 (independent test set) and 0.927 mAP@50 (leakage-free retraining), confirming strong generalizability while maintaining real-time performance. The initial evaluation had data partitioning limitations; method generalizability is now dually validated through both independent testing and rigorous split-then-augment retraining. This approach provides a practical tool for intelligent broiler welfare monitoring and heat stress management, contributing to improved environmental control and animal well-being. Full article

Special-shaped concrete-filled steel tube (CFST) frames can be embedded in partition walls to improve space utilization, but their frame-level seismic behavior across joint types remains under-documented. This study examines six two-story, single-bay frames with cruciform, T-, and L-shaped CFST columns and three joint configurations: external hoops with vertical ribs, fully bolted joints, and fully bolted joints with replaceable flange plates. Low-cycle reversed loading tests were combined with validated ABAQUS and OpenSees models to interpret mechanisms and conduct parametric analyses. All frames exhibited stable spindle-shaped hysteresis with minor pinching; equivalent viscous damping reached 0.13–0.25, ductility coefficients 3.03–3.69, and drift angles 0.088–0.126 rad. Hooped-and-ribbed joints showed the highest capacity and energy dissipation, while replaceable joints localized damage for rapid repair. Parametric results revealed that increasing the steel grade and steel ratio (≈5–20%) improved seismic indices more effectively than raising the concrete strength. Recommended design windows include axial load ratio < 0.4–0.5, slenderness ≤ 30, stiffness ratio ≈ 0.36, and flexural-capacity ratio ≈ 1.0. These findings provide symmetry-based, repair-oriented guidance for transportation buildings requiring rapid post-earthquake recovery. Full article

Plastic waste is currently a major environmental issue but also plays a key role in the circular economy. Recycled plastics have become suitable for use in several applications, especially in construction, where they can improve the properties of conventional materials to enable sustainable development. This study designed new eco-gypsum composites containing recycled fishing net (FN) fibers and evaluated their mechanical, hygrothermal, fire and environmental performances. All the developed composites achieved the minimum standardized strengths. Regarding the impact hardness test, the composite with 40% recycled FN fibers (FN40%) reached a five times higher energy of rupture than the reference gypsum sample. Indeed, FN40% presented better properties in general, e.g., 33% less water absorption by capillarity, 17% lower thermal conductivity and 40% less environmental impacts. Moreover, the use of these FN40% gypsum composites was modeled in an LSF partition wall, and it was predicted that they increased the thermal resistance by 4.4%, taking traditional gypsum plasterboards (Ref.) with the same thickness as a reference. These promising results allow us to conclude that it is possible to obtain eco-friendly gypsum composite panels by incorporating recycled FN fibers, satisfying the mechanical resistance requirements (flexural and compressive) and even improving their impact hardness, as well as their functional performance regarding their hygrothermal behavior. Full article

With the advancement of new power system construction, thermostatically controlled loads represented by regional air conditioning systems are being extensively integrated into the grid, leading to a surge in the number of user nodes. This large-scale integration of new loads creates challenges for the grid, as the resulting load data exhibits strong periodicity and randomness over time. These characteristics are influenced by factors like temperature and user behavior. At the same time, spatially adjacent nodes show similarities and clustering in electricity usage. This creates complex spatiotemporal coupling features. These complex spatiotemporal characteristics challenge traditional forecasting methods. Their high model complexity and numerous parameters often lead to overfitting or the curse of dimensionality, which hinders both prediction accuracy and efficiency. To address this issue, this paper proposes a load forecasting method based on spatiotemporal partitioning and collaborative cross-regional attention. First, a spatiotemporal similarity matrix is constructed using the Shape Dynamic Time Warping (ShapeDTW) algorithm and an adaptive Gaussian kernel function based on the Haversine distance. Spectral clustering combined with the Gap Statistic criterion is then applied to adaptively determine the optimal number of partitions, dividing all load nodes in the power grid into several sub-regions with homogeneous spatiotemporal characteristics. Second, for each sub-region, a local Spatiotemporal Graph Convolutional Network (STGCN) model is built. By integrating gated temporal convolution with spatial feature extraction, the model accurately captures the spatiotemporal evolution patterns within each sub-region. On this basis, a cross-regional attention mechanism is designed to dynamically learn the correlation weights among sub-regions, enabling collaborative fusion of global features. Finally, the proposed method is evaluated on a multi-node load dataset. The effectiveness of the approach is validated through comparative experiments and ablation studies (that is, by removing key components of the model to evaluate their contribution to the overall performance). Experimental results demonstrate that the proposed method achieves excellent performance in short-term load forecasting tasks across multiple nodes. Full article

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by heterogeneous behavioral and neurological patterns, complicating timely and accurate diagnosis. Behavioral datasets are commonly used to diagnose ASD. In clinical practice, it is difficult to identify ASD because of the complexity of the behavioral symptoms, overlap of neurological disorders, and individual heterogeneity. Correct and timely identification is dependent on the presence of skilled professionals to perform thorough neurological examinations. Nevertheless, with developments in deep learning techniques, the diagnostic process can be significantly improved by automatically identifying and automatically classifying patterns of ASD-related behaviors and neuroimaging features. Method: This study introduces a novel multimodal diagnostic paradigm that combines structured behavioral phenotypes and structural magnetic resonance imaging (sMRI) into an interpretable and personalized framework. A Generalized Additive Model with Interactions (GAMI-Net) is used to process behavioral data for transparent embedding of clinical phenotypes. Structural brain characteristics are extracted via a hybrid CNN–GNN model, which retains voxel-level patterns and region-based connectivity through the Harvard–Oxford atlas. The embeddings are then fused using an Autoencoder, compressing cross-modal data into a common latent space. A Hyper Network-based MLP classifier produces subject-specific weights to make the final classification. Results: On the held-out test set drawn from the ABIDE-I dataset, a 20% split with about 247 subjects, the constructed system achieved an accuracy of 99.40%, precision of 100%, recall of 98.84%, an F1-score of 99.42%, and an ROC-AUC of 99.99%. For another test of generalizability, five-fold stratified cross-validation on the entire dataset yielded a mean accuracy of 98.56%, an F1-score of 98.61%, precision of 98.13%, recall of 99.12%, and an ROC-AUC of 99.62%. Conclusions: These results suggest that interpretable and personalized multimodal fusion can be useful in aiding practitioners in performing effective and accurate ASD diagnosis. Nevertheless, as the test was performed on stratified cross-validation and a single held-out split, future research should seek to validate the framework on larger, multi-site datasets and different partitioning schemes to guarantee robustness over heterogeneous populations. Full article

Understanding long-term movement ecology is critical for conserving endangered species; however, comprehensive spatiotemporal analyses remain limited. In this study, we leveraged a decade-long GPS tracking dataset (2014–2024) of 31 endangered Crested Ibis (Nipponia nippon) individuals to elucidate their spatiotemporal behavioral patterns. The study focused on three key aspects: (1) fidelity to nesting, foraging, and roosting sites; (2) movement patterns and their ecological drivers; and (3) foraging habitat preferences across regions and activity periods. The results revealed exceptional fidelity to nesting, foraging (mean value = 0.253), and roosting sites (mean value = 0.261), underscoring the species’ pronounced spatial memory. Temporal factors emerged as the primary drivers of movement patterns, demonstrated by a significant annual reduction in home range size (p < 0.01) and a decline in daily flight distance in 2019 (β = −1890 ± 772 m, p < 0.05) and 2022 (p = 0.052). Behavioral factors also significantly influenced daily flight distance, with notable variations across different activity periods. Foraging habitat selection exhibited considerable spatial heterogeneity (14.2% constrained variance, p < 0.01). Cultivated lands, particularly paddy fields (Yangxian population) and drylands (Tongchuan population), served as core foraging zones. In contrast, spatiotemporal variables such as age had limited effects (<5% variance). This study provides the first empirical evidence of long-term site fidelity and habitat partitioning in the Crested Ibis, emphasizing the importance of landscape-level conservation planning. To this end, we propose two targeted strategies: establishing habitat corridors to enhance connectivity and safeguarding stable foraging areas within agricultural landscapes. These findings contribute to movement ecology theory while offering actionable frameworks for endangered species management. Full article

Background/Objectives: Alzheimer’s disease (AD) is a devastating neurodegenerative disorder that progressively impairs cognitive, neurological, and behavioral functions, severely affecting quality of life. The current diagnostic process relies on expert interpretation of extensive clinical assessments, often leading to delays that reduce the effectiveness of early interventions. Given the lack of a definitive cure, accelerating and improving diagnosis is critical to slowing disease progression. Electroencephalography (EEG), a widely used non-invasive technique, captures AD-related brain activity alterations, yet extracting meaningful features from EEG signals remains a significant challenge. This study introduces a machine learning (ML)-driven approach to enhance AD diagnosis using EEG data. Methods: EEG recordings from 36 AD patients, 23 Frontotemporal Dementia (FTD) patients, and 29 healthy individuals (HC) were analyzed. EEG signals were processed within the 0.5–45 Hz frequency range using the Welch method to compute the Power Spectral Density (PSD). From both the time-domain signals and the corresponding PSD, a total of 342 statistical and spectral features were extracted. The resulting feature set was then partitioned into training and test datasets while preserving the distribution of class labels. Feature selection was performed on the training set using Spearman and Pearson correlation analyses to identify the most informative features. To enhance classification performance, hyperparameter tuning was conducted using Bayesian optimization. Subsequently, classification was carried out using Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN) the optimized hyperparameters. Results: The SVM classifier achieved a notable accuracy of 96.01%, outperforming previously reported methods. Conclusions: These results demonstrate the potential of machine learning-based EEG analysis as an effective approach for the early diagnosis of Alzheimer’s Disease, enabling timely clinical intervention and ultimately contributing to improved patient outcomes. Full article

The aggregation behavior of typical aromatic pollutants in the n-octanol phase and its influence on the n-octanol–air partition coefficient (K_OA) were investigated using molecular dynamics simulation. The aggregate proportion of selected aromatic pollutants gradually increased with increasing simulation time and then reached a dynamic equilibrium state. It is interesting to find that the higher the concentration of aromatic pollutants, the more aggregates formed in the n-octanol phase. Log K_OA values of these aromatic pollutants were subsequently estimated based on the percentages of aggregates and the solvation free energy from the gas phase to the n-octanol phase. The log K_OA values were also found to gradually increase with increasing concentration. Therefore, the effect of concentration on K_OA should be taken into consideration during the analysis of the environmental behavior and transport of these aromatic pollutants. In addition, it was found that π–π interactions drive the formation of different numbers of aggregates for different aromatic pollutants, a phenomenon that affects the K_OA values of aromatic pollutants. The above results shed some light on the effects of aggregates and concentration on the partition behavior of aromatic pollutants and provide a theoretical basis for the correction of K_OA of aromatic pollutants in the environment. Full article

An innovative ultrasound-assisted deep eutectic solvent-based three-phase partitioning (UA-DES-TPP) system was developed for the sustainable extraction of Ficus carica polysaccharide (FCP). Using a hydrophobic DES composed of dodecanoic acid and octanoic acid (1:1 molar ratio), a phase behavior-driven separation mechanism was established. The system was systematically optimized through single-factor experiments and response surface methodology (RSM), achieving a maximum FCP yield of 9.22 ± 0.20% under optimal conditions (liquid–solid ratio 1:24.2 g/mL, top/bottom phase volume ratio 1:1.05 v/v, ammonium sulfate concentration 25.8%). Structural characterization revealed that FCP was a heteropolysaccharide primarily composed of glucose and mannose with α/β-glycosidic linkages and a loose fibrous network. Remarkably, the DESs demonstrated excellent recyclability over five cycles. Furthermore, FCP exhibited significant concentration-dependent antioxidant activities: 82.3 ± 3.8% DPPH radical scavenging at 8 mg/mL, 76.8 ± 0.8% ABTS⁺ scavenging, and ferric ion reducing power of 45.53 ± 1.07 μmol TE/g. This study provides a new path for the efficient and sustainable extraction of bioactive macromolecules. Full article

To ensure sustainable marine resource utilization, advanced monitoring methods are urgently needed to mitigate overfishing and ecological imbalances. Conventional fishing activity detection methods, including speed threshold-based approaches and Gaussian Mixture Models, often fail to accurately handle complex vessel trajectories, resulting in imprecise quantification of fishing effort and hindering effective monitoring of illegal, unreported, and unregulated (IUU) fishing activities. To address these limitations, we propose a spatiotemporal adaptive clustering and segmentation (STACS) framework for recognizing fishing activities. First, ST-DBSCAN clustering distinguishes concentrated fishing operations from transit movements. Second, an adaptive segmentation algorithm that incorporates heading stability and local density dynamically partitions trajectories into coherent segments, using spatiotemporal clusters as the basic units. Third, multiple features capturing temporal dynamics and spatial patterns are extracted to characterize fishing behaviors. Finally, an XGBoost classifier with run-length encoding post-processing converts point-level predictions to continuous fishing episodes. Experiments on fishing vessel trajectory datasets demonstrate that STACS outperforms conventional methods and advanced segmentation approaches, improving both point-level classification and segment-level coherence across diverse fishing scenarios. By enhancing IUU fishing detection and reducing classification inconsistencies, STACS provides valuable insights for marine conservation, policymaking, and fisheries management, bridging local behavioral dynamics with global trajectory analysis. Full article