Search Results (4,829)

Facial emotion recognition (FER) plays an important role in healthcare, human–computer interaction, and intelligent security systems. However, despite recent advances, many state-of-the-art FER methods depend on computationally intensive CNN or transformer backbones and large-scale annotated datasets while suffering noticeable performance degradation under cross-dataset evaluation because of domain shift. These limitations hinder practical usage in resource-constrained and real-world environments. To address this issue, we propose Cross-Adaptive Gated Fusion Network (CA-GFNet), a lightweight dual-stream FER framework that explicitly combines shallow structural features with deep semantic representations. The proposed architecture integrates domain-robust gradient-based descriptors with compact deep features extracted from a VGG-based backbone. After face detection and normalization, the structural stream captures fine-grained local appearance cues, whereas the semantic stream encodes high-level facial configurations. The two feature streams are projected into a shared latent space and adaptively fused using a gated fusion mechanism that learns sample-specific weights, allowing the model to prioritize the more reliable feature source under dataset shift. Extensive experiments on KDEF along with zero-shot cross-dataset evaluation on CK+ using a strict train-on-KDEF/test-on-CK+ protocol with subject-independent splits demonstrate the effectiveness of the proposed method. CA-GFNet achieves 99.30% accuracy on KDEF and 98.98% on CK+ while requiring significantly fewer parameters than conventional deep FER models. These results confirm that adaptive gated fusion of shallow and deep features can deliver both high recognition accuracy and strong cross-dataset robustness. Full article

Quantifying river surface flow velocity is essential for hydrodynamic modelling, flood forecasting, and water resource management. Traditional in situ methods provide accurate point measurements but are costly and limited in spatial coverage. Unmanned aerial vehicles (UAVs) offer a flexible, non-contact alternative for high-resolution monitoring. Optical flow is a tracer-independent technique for deriving velocity fields from RGB video, making it well suited to UAV-based surveys. However, its operational use is hindered by the limited availability of annotated datasets and by instability under low-texture or noisy conditions. This study combines a Finite element method (FEM)-based physical flow model with UAV video to generate reference datasets and introduces a modified Recurrent All-Pairs Field Transforms (RAFT) architecture based on multiframe sequences. A Gated Recurrent Unit fusion module (Fuse-GRU) is incorporated prior to correlation computation, improving robustness to illumination changes and surface homogeneity while maintaining computational efficiency. The proposed model delivers stable, physically consistent velocity estimates across multiple rivers and flow conditions. Accuracy improves with higher spatial resolution and moderate temporal spacing. Compared to field measurements, the average angular difference ranged from 8 to 15°. The high error values were mainly caused by inaccuracies in the physical model and by complex river features. These findings confirm that multiframe optical flow can reproduce realistic river flow patterns with accuracy comparable to physically-based simulations, thereby supporting UAV-based hydrometric monitoring and model validation. Full article

The sustainable renewal of old residential communities faces increasing challenges in addressing the diverse environmental needs of older residents while respecting spatial constraints. Conventional approaches often treat older adults as a homogeneous group, overlooking how functional and social heterogeneity shape spatial perception. To address this gap, this study examines perceptual priorities in micro-public spaces of old residential communities in Qingdao, China, by classifying 60 community-dwelling older adults into four profiles using the Successful Aging framework. Participants performed free-viewing tasks using eye-tracking to observe 18 areas of interest (AOIs). Results reveal a clear perceptual hierarchy structured by individual profiles. Older adults with lower functional ability (Q3, Q4) allocate significant visual resources to safety-critical elements as a form of compensatory monitoring. Conversely, a systematic perceptual shift from survival-oriented assessment to quality-oriented evaluation was observed as functional and participatory reserves increased. High-participation groups (Q1, Q3) prioritized comfort facilities, while esthetic features attracted sustained attention primarily among the high-function/high-participation group (Q1). These findings provide empirical evidence for differentiated micro-renewal strategies that prioritize perceptual stress reduction and affordance enrichment in old residential communities. Full article

This paper studies the supervisory control of discrete event systems (DESs) from an event observation perspective and addresses the problem of supervisor design with minimal observation. In supervisory control, a supervisor enables or disables controllable events based on its observation of the system trajectory to guarantee controllability and nonblocking behavior with respect to a given specification, while the number of observed events critically affects the implementation complexity and cost of the control logic. Rather than minimizing the state space of the supervisor, which is the focus of classical supervisor reduction, this paper is dedicated to the minimization of observable events. Specifically, it aims to reduce the observation alphabet while preserving control equivalence with the original supremal supervisor. By analyzing the consistency of disabling decisions between event-enabled and event-disabled states, necessary and sufficient distinguishability conditions are derived and represented using Parikh vectors, which enables their formulation as linear separation constraints. In addition, event-enabled circles are introduced to capture intrinsic structural observability requirements induced by cyclic behaviors of the supervisor. These results lead to a mixed-integer linear programming (MILP) formulation that minimizes the observation alphabet while preserving control equivalence with the original supremal supervisor, together with an E-closure-based construction that synthesizes an executable event-minimal supervisor. Illustrative examples demonstrate that the proposed method can significantly reduce observation requirements even when state-minimal supervisors are already available, thereby improving implementation efficiency in resource-constrained DES applications. Full article

Background: Extracellular vesicles (EVs) are increasingly explored as nanocarriers in drug delivery; however, selecting an appropriate loading strategy for a given small-molecule cargo still relies largely on empirical, resource-intensive parallel screening within EV formulation workflows. Despite the widespread application of passive incubation, electroporation, saponin-mediated permeabilization, freeze–thaw cycling, and sonication, there is currently no mechanistically grounded, descriptor-informed framework that enables rational prioritization of loading methods during the early design stage of EV-based dosage forms, leading to inefficient trial-and-error experimentation. Methods: We assembled a chemically diverse dataset of 21 compounds with experimentally determined loading efficiencies across five EV loading methods and calculated seven mechanistically motivated physicochemical descriptors (LogP, molecular weight, aqueous solubility, hydrogen bond donors/acceptors, polar surface area, and formal charge) for each drug. Separate Elastic Net regression models were trained for each loading strategy. Model performance was evaluated using leave-one-out cross-validation, a predefined external validation set (n = 4), and 50 repeated random train–test splits. The analysis emphasized decision-level ranking of loading methods rather than the precise prediction of absolute efficiencies. The applicability domain was assessed via leverage analysis to define the supported chemical space for prospective implementation in EV-based formulation development. Results: As anticipated for biologically heterogeneous EV systems, continuous regression performance remained modest (LOOCV R² = 0.06–0.41). In contrast, decision-level accuracy for identifying the experimentally optimal loading method was consistently high across validation schemes (internal: 76.5%; predefined external: 75%; repeated random validation: 80.5 ± 16.8%). Mechanical disruption methods (freeze–thaw and sonication) demonstrated comparatively greater predictive stability, while misclassification patterns suggested potential nonlinear behavior for highly polar, ionizable cargos. All compounds resided within the leverage-defined applicability domain, confirming adequate descriptor-space representation. Conclusions: This study establishes a mechanistically interpretable, descriptor-based decision-support framework capable of reliably prioritizing EV loading strategies for small-molecule cargos beyond empirical chance without altering standard protocols. By reframing the modeling objective from high-precision efficiency prediction to robust ranking of candidate methods, the approach offers a practical tool to triage between commonly used techniques, thereby reducing experimental burden in early-stage EV formulation development. The framework provides a quantitative basis for integrating molecular-descriptor-guided method selection into rational EV-based drug delivery design and can be expanded with membrane-specific descriptors and larger datasets. Full article

Climate hazards increasingly disrupt schooling, revealing the limits of preparedness models that treat teachers only as implementers. This study reframes teacher empowerment as a climate-resilience capability and examines how governance arrangements enable (or constrain) hazard-ready education systems. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), searches of Web of Science, Scopus, and Google Scholar (2000–2025) identified 53 eligible studies. Across diverse hazards and settings, the evidence converges on a governance-to-capability pathway: empowerment becomes resilient performance only when the delegated decision space is matched with financed capacity (time, training, contingency resources), timely risk information and functional communication/digital infrastructure, institutionalized cross-sector coordination (education–DRR–health–protection–local government), and learning-oriented accountability (after-action review and adaptive revision rather than punitive compliance). Reported outcomes include higher preparedness quality, earlier protective action, improved learning continuity and safeguarding, and more sustainable teacher well-being/retention. Predictable failure modes include mandate–resource mismatch, accountability overload, unstable centralization–autonomy dynamics, and inequitable empowerment distribution affecting rural schools, women, and contract teachers, and disability inclusion. The evidence gaps remain pronounced for chronic hazards (especially heat and wildfire smoke), high-vulnerability contexts (fragile/conflict settings and informal settlements), and standardized measures of equity, burden distribution, governance performance, and cost-effectiveness. Policies should prioritize integrated governance packages with explicit protection and equity safeguards. Full article

Located in northeastern Anatolia, Çıldır and Ardahan serve as a gateway to the Caucasus for political entities such as the state and mobile groups such as the tribe. Due to this geopolitical characteristic, the region has fallen under the dominion of numerous states and civilizations throughout history. With its fertile highlands, Lake Çıldır, and natural water resources like the Kura River, the area constitutes an attractive living space for hem settled agriculturalists and nomadic tribe groups subsisting on animal husbandry. These features have profoundly influenced the ethnic, demographic, socio-economic, and cultural fabric of the region. Following the establishment of Ottoman sovereignty in the 16th century, Çıldır and Ardahan assumed a vital role in the state’s Caucasian and Eastern policies. This research addresses the Turkmen tribe and other ethnic communities residing around the eyalet of Çıldır and the sanjak of Ardahan. It further examines the banditry activities carried out by these groups, the attitudes of central and local administrators toward such activities, migration and settlement patterns, and the economic and political pressures exerted by the Russian State upon these tribes. The political and economic pressures exerted by the Russian State on these tribes reflect a broader imperial strategy of frontier making, as discussed by Khodarkovsky in the context of Russia’s expansion into its southern borderlands. By positioning the region as a negotiated frontier, this study moves beyond a descriptive narrative to analyze how tribal mobility and settlement functioned as tools of sovereignty and resistance within the broader context of Ottoman state formation and trans-imperial rivalry. The methodology employed in this study is the Qualitative Research Method; accordingly, documents from the Presidential Ottoman Archives (BOA) were transcribed, and the relevant sections were interpreted and incorporated into the study. The archival findings are contextualized within recent historiographical debates concerning the shifting definition of the state versus nomadic agency during the transition from the 18th to the 19th century. While existing literature contains academic studies aiming to elucidate the archaeological, geographical, economic, and administrative structures of Çıldır and Ardahan, it has been determined that no academic research has been conducted to analyze the ethno-socio-demographic structure of the region specifically focusing on the 18th and 19th centuries in a historical sense. With this focus on the interplay between imperial frontiers and tribal identity, this study provides a critical analysis of how local dynamics shaped the grand strategies of the Ottoman and Russian Empires. Full article

Structural symmetry appears as a natural feature in both optimal solution landscapes and hospital scheduling behaviors, representing an inherent balance that can be deliberately leveraged to improve how quickly algorithms converge and how reliably systems perform in intricate healthcare optimization contexts. Managing hospital resources is a multifaceted challenge that requires simultaneously addressing several competing goals, such as reducing costs, improving patient experiences, making the most of available resources, distributing staff workload fairly, and strengthening readiness for emergencies. Traditional optimization approaches frequently struggle to cope with the complexity and ever-changing nature of modern healthcare environments. To address this gap, this study introduces a novel Multi-Objective Dream Optimization Algorithm (MO-DOA) tailored for smart healthcare resource management, which adapts a biologically inspired optimization framework to meet the specific demands of healthcare settings. The MO-DOA is built around three core mechanisms: a foundational memory component that retains high-quality solutions, a forgetting-supplementation component that maintains a productive balance between exploration and exploitation, and a dream-sharing component that promotes diversity among candidate solutions. Rigorous testing across realistic hospital environments confirms MO-DOA’s outstanding effectiveness, with results showing a 21.86% gain in resource utilization, a 30.95% decrease in patient waiting times, a 19.06% boost in patient satisfaction, and a 29.56% improvement in how evenly staff workloads are distributed. The algorithm’s emergency response capabilities are especially noteworthy, achieving bed assignments within 4.23 min and an equipment deployment success rate of 94.56%. Computationally, the algorithm proves highly efficient, with an average response time of 18.87 s and strong scalability across different operational scales. Collectively, these findings position MO-DOA as a powerful and practical tool for optimizing hospital operations in real time. Full article

Global energy demand relies heavily on fossil fuels, which produce greenhouse gas emissions. Additionally, municipal solid waste, driven by population growth, represents another source of emissions. In Mexico, organic waste contributes 61 million tons of CO₂eq annually due to inadequate disposal. In Mérida, Yucatan, over 231,000 tons of organic waste are generated yearly, including Urban Tree Pruning (UTP) from 760 public spaces—a significant, undervalued lignocellulosic resource. This study presents a comprehensive, year-round compositional characterization of Mérida’s UTP to establish its chemical profile and assess its seasonal stability as a precursor for bio-based products (i.e., bioethanol). Characterizing local and stable feedstocks, such as UTP, is a fundamental step to enabling Mexico’s compliance with biofuel policies like the 5.8% gasoline blend mandate (NOM-016-CRE) and the Alcohol-to-Jet strategy, supporting progress toward SDGs 7, 11, and 13. Based on a stratified random sampling, monthly analysis (May 2024–April 2025) revealed a consistent biochemical profile with mean annual contents of 23.32% lignin and 62.46% holocellulose. Statistical analysis (Tukey’s test) confirmed its structural homogeneity throughout the year. This uniformity is a key operational attribute, as it allows for the use of standardized industrial pretreatment parameters. Furthermore, the characterized composition supports a theoretical ethanol yield of 170 g/kg of dry biomass, a value competitive with traditional feedstocks like sugarcane bagasse. Consequently, Mérida’s UTP is characterized as a reliable and consistent biomass resource, supporting a transition from linear waste disposal to a circular bioeconomy model. Full article

The large-scale grid integration of distributed renewable energy enhances the flexible regulation capacity of the power system. However, the inherent randomness and volatility of its output, coupled with weak coupling access characteristics, pose severe challenges to the safe and stable operation of the power system. To address these issues, this paper proposes a power system planning method suitable for urban power grids. To accurately characterize the uncertainty of renewable energy output, the method incorporates the concept of multi-scenario stochastic optimization and introduces a dynamic scenario generation method for wind and solar power based on nonparametric kernel density estimation and standard multivariate normal distribution sequence sampling. This method generates a set of typical daily dynamic output scenarios for wind and solar power that closely match actual output characteristics. Considering the spatiotemporal response characteristics of flexible resources, the Soft Open Point (SOP) DC link enables flexible cross-node power transmission and spatiotemporal coupling regulation of flexible resources. Therefore, this paper constructs a mathematical model for the grid integration of flexible resources based on the SOP DC link. By integrating operational constraints such as power flow constraints in the power grid and source-load uncertainty constraints, a power system planning model is established. However, traditional convex optimization methods require approximate simplifications of the model, which can easily lead to a loss of accuracy. Although the Particle Swarm Optimization (PSO) algorithm is suitable for nonlinear optimization, it is prone to getting trapped in local optima. Therefore, this paper introduces an improved PSO algorithm based on refraction opposite learning, which enhances the algorithm’s global optimization capability by expanding the particle search space and increasing population diversity. Finally, simulation verification is conducted based on an improved IEEE-39 bus test system, and the results show that the proposed scenario generation method achieves a sum of squared errors of only 4.82% and a silhouette coefficient of 0.94, significantly improving accuracy compared to traditional methods such as Monte Carlo sampling. Full article

The intellectual exchanges between late-Ming Jesuits and Chinese literati have long been interpreted primarily as a process of cultural accommodation aimed at “harmonizing with Confucianism” (合儒), and scholarship has tended to focus on missionary strategies, social networks, or individual conversion histories. By contrast, the question of how resources within Confucian thought made ethical dialogue with Catholicism possible—especially why the practical-learning strand (實學派) of Wang Yangming’s School (陽明學) exhibited such pronounced receptivity to Catholic ideas among late-Ming literati—remains insufficiently theorized at the level of conceptual structure. This study, therefore, shifts the analytical focus from “historical narratives of converts” to an explanation of the mechanisms that enabled Sino-Jesuit dialogue. It argues that Augustine and Wang Yangming display a notable convergence in their conceptions of good and evil (善惡論), and that this convergence created an intellectual space for engagement between Jesuits and later Yangming scholars. The Jesuits’ deliberate promotion of doctrines concerning the punishment of evil (懲惡) further facilitated the practical-learning Yangmingists’ reception of Catholic resources regarding ultimate judgment and retributive justice, especially as they confronted the problem of inadequate means to restrain or punish wrongdoing. This article situates late-Ming Sino-Western intellectual exchange within an analytical framework of “theories of good and evil—mechanisms for punishing evil—pathways for supplementing Confucianism (補儒),” thereby offering a mechanism-based explanation, grounded in theories of good and evil, for the historical interaction between Chinese Confucian thought and the ethical systems of incoming religions. Full article

Underground mine inspection is a critical operation for safety and resource management. It presents unique challenges, including confined spaces, harsh environments, and the lack of reliable positioning systems. This paper presents the design, development, and evaluation of an Unmanned Aerial Vehicle (UAV) specifically engineered for supervised autonomous inspection in subterranean scenarios. Key technical contributions include mechanical adaptations for collision tolerance, an optimized sensor-actuator selection for navigation, and the deployment of a mission-governing state machine for seamless autonomous acquisition. Furthermore, we detail the data treatment workflow, employing a multi-modal point cloud registration technique that successfully integrates high-resolution visual-depth scans of critical mine pillars into a comprehensive, globally referenced map derived from Light Detection and Ranging (LiDAR) data of the entire workspace. We show experiments that illustrate and validate our approach in two real-world scenarios, a simulated coal mine used to train mine rescue teams and an operating Limestone mine. Full article

With the growing significance of renewable energy resources and energy storage systems, the number of grid-connected inverters has been rising at an increasingly rapid pace. Generally, these inverters are directly integrated with the distribution network by synchronizing with the grid voltage at the point of common coupling. However, the low grid strength and varying R/X ratios, as the common characteristics of most distribution networks or weak grids, can lead to dynamic interactions that comprise stability and limit the power transfer capacity of grid-connected inverters. To ensure stable operation of the inverters, researchers must determine the stability boundary, described as the maximum power transfer capacity of grid-connected inverters under the premise of maintaining system small-signal stability. For this purpose, we propose to formulate a state-space model of the system in the synchronously rotating dq-frame of reference and perform eigenvalue analysis to determine the stability boundary. With a detailed model of the control structure and parameters of the grid-connected inverters, the stability boundary is identified as a surface with respect to different grid strengths and R/X ratios. Case study results of proposed eigenvalue analysis are compared with those of admittance model-based stability analysis as well as time-domain simulation using a switching model in Matlab/Simulink, validating the effectiveness and accuracy of the proposed eigenvalue analysis for stability boundary identification. Full article

Sustainable urban development increasingly depends on understanding how digital activity is distributed across space and time, yet the spatiotemporal dynamics of the urban digital landscape remain poorly mapped by conventional data sources. This study uses Beijing as an empirical testbed, applying a multi-dimensional analytical framework to massive mobile network traffic data to decode the metabolic rhythms, distributional laws, and functional organization of the urban digital landscape. The results reveal three findings. First, the urban digital landscape exhibits a sleepless trapezoidal temporal rhythm characterized by continuous saturation without a midday trough and a quantifiable weekend activation lag, indicating that digital metabolism is structurally decoupled from physical mobility patterns. Second, digital traffic follows a skew-normal distribution consistent with a 20/70 rule of spatial polarization, in which the top 20% of super-connector nodes sustain approximately 70% of total urban digital flow, yielding a Gini coefficient of 0.68 as a measurable indicator of infrastructure inequality and systemic vulnerability. Third, four distinct functional prototypes are identified—ranging from continuously active metropolitan cores to inverse-tidal ecological peripheries—empirically validating Beijing’s polycentric transformation through the lens of digital flows. These findings demonstrate that large-scale mobile network traffic data offers a replicable and structurally distinct lens for sustainable urban digital governance, supporting resilient network planning, equitable allocation of digital resources, and evidence-based monitoring of urban functional transformation in rapidly growing megacities. Full article

The development and execution of prospective inner and outer space missions require focusing on the use of many small space vehicles operating in swarms with multiple informational, navigational, and mission-oriented interactions among themselves. Such missions involve providing communication and surveillance services, facilitating distributed material production in space, and conducting research expeditions to explore the resources and environments of new worlds. The cornerstone technology for operating distributed space systems is propulsion. Among a range of propulsion technologies—from using pressurized cold gases to implementing laser beams to generate thrust—certain methods stand out for application in small spacecraft. This paper provides a summary of space-operated propulsion, emphasizing the reasons for the more frequent adoption of one technology over another. The discussion on propulsion trends is complemented by examining the physical, engineering, production, operational, and societal rationale behind these choices. The findings reinforce the trend toward transitioning to fully electric satellites. This review serves as a means for reevaluating global propulsion trends and guiding the future development of inner and outer space propulsion-assisted economies effectively. Full article