Search Results (13,179)

Understanding how flowering phenology varies across spatial scales is essential for assessing plant responses to environmental heterogeneity under climate change. In this study, we investigated the flowering phenology of the plant species Taraxacum officinale across five sites in an agricultural region of Wanju, Republic of Korea. Each site contained five 1 m × 1 m quadrats, where the number of flowering heads was recorded at 1- to 2-day intervals during the spring flowering period (February to May). We applied the nlstimedist package in R to model flowering distributions and to estimate key phenological metrics including flowering onset (5%), peak (50%), and end (95%). The results revealed substantial variation in flowering timing and duration at both the intra-site (quadrat-level) and inter-site (site-level) scales. Across all sites, the mean onset, peak, end, and duration of flowering were day of year (DOY) 89.6, 101.5, 117.6, and 28.0, respectively. Although flowering onset showed relatively small variation across sites (DOY 88 to 92), flowering peak (DOY 97 to 108) and end dates (DOY 105 to 128) exhibited larger differences at the site level. Sites with dry soils and regularly mowed Zoysia japonica vegetation with minimal understory exhibited shorter flowering durations, while those with moist soils, complex microtopography, and diverse slope orientations showed delayed and prolonged flowering. These findings suggest that microhabitat variability—including landform type, slope direction, soil water content, and soil temperature—plays a key role in shaping local flowering dynamics. Recognizing this fine-scale heterogeneity is essential for improving phenological models and informing site-specific climate adaptation strategies. Full article

Background: Surface electromyography (sEMG) enables the non-invasive assessment of muscle activity and is widely used in orthodontics for evaluating masticatory muscles. However, little is known about the dynamic changes in facial expression muscles during orthodontic treatment. This study aimed to investigate alterations in facial muscle tone during the leveling and alignment phase in adult female patients undergoing fixed appliance therapy. Methods: The study included 30 female patients aged 20–31 years who underwent sEMG assessment at four time points: before treatment initiation (T0), at the start of appliance placement (T1), three months into treatment (T2), and six months into treatment (T3). Muscle activity was recorded during four standardized facial expressions: eye closure, nasal strain, broad smile, and lip protrusion. Electrodes were placed on the orbicularis oris, orbicularis oculi, zygomaticus major, and levator labii superioris alaeque nasi muscles. A total of 1440 measurements were analyzed using Friedman and Conover-Inman tests (α = 0.05). Results: Significant changes in muscle tone were observed during treatment. During lip protrusion, the orbicularis oris and zygomaticus major showed significant increases in peak and minimum activity (p < 0.01). Eye closure was associated with altered orbicularis oris activation bilaterally at T3 (p < 0.01). Nasal strain induced significant changes in zygomaticus and levator labii muscle tone, particularly on the right side (p < 0.05). No significant changes were noted during broad smiling. Conclusions: Orthodontic leveling and alignment influence the activity of selected facial expression muscles, demonstrating a dynamic neuromuscular adaptation during treatment. These findings highlight the importance of considering soft tissue responses in orthodontic biomechanics and suggest potential implications for facial esthetics and muscle function monitoring. Full article

Fault detection and remaining useful life (RUL) prediction are critical tasks in self-healing network (SHN) environments and industrial cyber–physical systems. These domains demand intelligent systems capable of handling dynamic, high-dimensional sensor data. However, existing optimization-based approaches often struggle with imbalanced datasets, noisy signals, and delayed convergence, limiting their effectiveness in real-time applications. This study utilizes two benchmark datasets—EFCD and SFDD—which represent electrical and sensor fault scenarios, respectively. These datasets pose challenges due to class imbalance and complex temporal dependencies. To address this, we propose a novel hybrid framework combining Attention-Augmented Convolutional Neural Networks (AACNN) with transformer encoders, enhanced through Enhanced Ensemble-SMOTE for balancing the minority class. The model captures spatial features and long-range temporal patterns and learns effectively from imbalanced data streams. The novelty lies in the integration of attention mechanisms and adaptive oversampling in a unified fault-prediction architecture. Model evaluation is based on multiple performance metrics, including accuracy, F1-score, MCC, RMSE, and score*. The results show that the proposed model outperforms state-of-the-art approaches, achieving up to 97.14% accuracy and a score* of 0.419, with faster convergence and improved generalization across both datasets. Full article

The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an integrated transverse cutting mechanism and a foldable grasping cavity. Unlike conventional fixed cylindrical cavities, our design utilizes actuated grasping arms and a mechanical linkage system to significantly reduce the operational footprint and enhance maneuverability. Key design parameters were optimized based on broccoli morphological data and experimental measurements of the maximum stem cutting force. Furthermore, dynamic simulations were employed to validate the operational trajectory and ensure interference-free motion. Field tests demonstrated an operational success rate of 93.33% and a cutting success rate of 92.86%. The end effector successfully operated in dense planting environments, effectively avoiding interference with adjacent broccoli heads. This research provides a robust and promising solution that advances the automation of broccoli harvesting, paving the way for the commercial adoption of robotic harvesting technologies. Full article

The article presents a long-term assessment of the surface water quality of six small rivers in the East Kazakhstan region (Breksa, Tikhaya, Ulba, Glubochanka, Krasnoyarka, and Oba) based on hydrochemical monitoring data from the Kazhydromet State Enterprise for the period 2017–2024. A unified water quality classification system was applied, along with statistical methods, including multiple linear regression. The Glubochanka and Krasnoyarka rivers were identified as the most polluted (reaching classes 4–5), with multiple exceedances of Zn (up to 2.96 mg/dm³), Cd (up to 0.8 mg/dm³), and Cu (up to 0.051 mg/dm³). The most stable and highest water quality was recorded in the Oba River, where from 2021 to 2024, water consistently corresponded to Class 2. Regression models of water quality class as a function of time and annual precipitation were constructed to assess the influence of climatic factors. Statistical analysis revealed no consistent linear correlation between average annual precipitation and water quality (correlation coefficients ranging from −0.49 to +0.37), indicating a complex interplay between climatic and anthropogenic factors. Significant relationships were found for the Breksa (R² = 0.903), Glubochanka (R² = 0.602), and Tikhaya (R² = 0.555) rivers, suggesting an influence of temporal and climatic factors on water quality. In contrast, the Oba (R² = 0.130), Ulba (R² = 0.100), and Krasnoyarka (R² = 0.018) rivers exhibited low coefficients, indicating the predominance of other, likely local, sources of pollution. It was found that summer periods are characterized by the highest pollution due to low water flow, while episodes of acid runoff occur in spring. A decrease in pH below 7.0 was first recorded in 2023–2024 in the Ulba and Tikhaya rivers. Forecasts to 2030 suggest relative stability in water quality under current climatic conditions; however, by 2050, the risk of water quality deterioration is expected to rise due to increased precipitation and extreme weather events. This study presents, for the first time, a systematic long-term analysis of small rivers in the East Kazakhstan region, offering deeper insight into the dynamics of surface water quality and providing a scientific foundation for developing adaptive strategies for the protection and sustainable use of water resources under climate change and anthropogenic pressure. The results emphasize the importance of prioritizing rivers with high variability in water quality for regular monitoring and the development of adaptive conservation measures. The research holds strong applied significance for shaping a sustainable water use strategy in the region. Full article

Power hardware-in-the-loop (PHIL) creates a safe test environment to connect simulations with real hardware under test (HuT). Therefore, an interface algorithm (IA) must be chosen. The ideal transformer method (ITM) and the partial circuit duplication (PCD) are popular IAs, where a distinction is made between voltage- (V-) and current-type (C-) IAs. Depending on the sample time of the simulator and further delays, simulation accuracy is reduced and instability can occur due to negative feedback in the V-ITM and C-ITM control loops, which makes PHIL operation impossible. In the case of positive feedback, such as with the V-PCD and C-PCD, the delay causes destructive interference, which results in a phase shift and attenuation of the output signal. In this article, a novel damped Smith predictor (SP) for positive feedback PHIL IAs is presented, which significantly reduces destructive interference while allowing stable operation at low linking impedances at V-PCD and high linking impedances at C-PCD, thus reducing losses in the system. Experimental results show a reduction in phase shift by 21.17° and attenuation improvement of 24.3% for V-PCD at a sample time of 100 µs. The SP transfer functions are also derived and integrated into the listed negative feedback IAs, resulting in an increase in the gain margin (GM) from approximately one to three, which significantly enhances system stability. The proposed methods can improve stability and accuracy, which can be further improved by calculating the HuT impedance in real-time and dynamically adapting the SP model. Stable PHIL operation with SP is also possible with SP model errors or sudden HuT impedance changes, as long as deviations stay within the presented limits. Full article

Federated learning is a distributed machine learning technique that allows multiple devices to collaborate on learning a shared model without exchanging data. It can be used to improve model accuracy while protecting user privacy. However, traditional federated learning is vulnerable to attacks from generative adversarial networks (GANs). As a new privacy protection method, differential privacy enhances privacy protection capabilities by sacrificing some data accuracy. To optimize the privacy budget allocation scheme in traditional differential privacy, we propose a differential privacy method called ADP-FL, which dynamically adjusts the privacy budget based on Newton’s Law of Cooling. While maintaining the overall privacy budget, it dynamically tunes adaptive parameters to improve training accuracy. Additionally, we propose an asynchronous federated learning aggregation scheme that combines privacy budget with data freshness, thereby reducing the impact of differential privacy on accuracy. We conducted extensive experiments on differential privacy algorithms based on Gaussian mechanisms and Laplace mechanisms. The experimental results show that, under the same privacy budget, our algorithm achieves higher accuracy and lower communication overhead compared to the baseline algorithm. Full article

The Republican era of China witnessed three primary positions regarding Buddhist monastic robe reform. Taixu advocated preserving canonical forms (法服) for ritual garments while adapting regular robes (常服) to contemporary needs; Dongchu proposed diminishing ritual distinctions by establishing a tripartite hierarchical system—virtue-monk robes (德僧服), duty-monk robes (職僧服), and scholar-monk robes (學僧服); and Lengjing endorsed the full secularization of monastic robes. As a reformist leader, Taixu pursued reforms grounded in both doctrinal authenticity and contextual responsiveness. His initial advocacy for robe modifications, however, rendered him a target for traditionalists like Cihang, who conflated his measured approach with the radicalism of Dongchu’s faction. Ultimately, the broader Buddhist reform collapsed, with robe controversies serving as a critical lens into its failure. The reasons for its failure include not only wartime disruption and inadequate governmental support, but also the structural disadvantages of the reformists compared to the traditionalists, which proved decisive. This was due to the fact that the traditionalists mostly controlled monastic economies, wielded institutional authority, and commanded discursive hegemony, reinforced by lay Buddhist alignment. These debates crystallize the core tension in Buddhist modernization—the dialectic between “disenchantment” and “preservation of monastic discipline”. This dynamic of negotiated adjustment offers a vital historical framework for navigating contemporary Buddhism’s engagement with modernity. Full article

Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article

This paper proposes a hybrid methodology for one-step-ahead torsional torque estimation in an electric drive with an elastic connection. The approach integrates Nonlinear Autoregressive Neural Networks with Exogenous Inputs (NARX NNs) and model-based feedback. The NARX model uses real-time and historical motor speed and torque signals as inputs while leveraging physics-derived torsional torque as a feedback input to refine estimation accuracy and robustness. While model-based methods provide insight into system dynamics, they lack predictive capability—an essential feature for proactive control. Conversely, standalone NARX NNs often suffer from error accumulation and overfitting. The proposed hybrid architecture synergises the adaptive learning of NARX NNs with the fidelity of physics-based feedback, enabling proactive vibration damping. The method was implemented and evaluated on a two-mass drive system using an IP controller and additional torsional torque feedback. Results demonstrate high accuracy and reliability in one-step-ahead torsional torque estimation, enabling effective proactive vibration damping. MATLAB 2024a/Simulink and dSPACE 1103 were used for simulation and hardware-in-the-loop testing. Full article

Introduction: Assessing the quality of patient care within healthcare systems remains a multifaceted challenge due to varying definitions of “quality” and the complexity of care delivery structures worldwide. Patient-centeredness, institutional responsiveness, and contextual adaptability are increasingly recognized as core pillars in quality assessment. Objective: This narrative literature review aims to explore conceptual models and practical frameworks for evaluating healthcare quality, emphasizing tools that integrate technical, functional, and emotional dimensions and proposing a comprehensive model adaptable to diverse health system contexts. Methodology: A systematic literature search was conducted in the PubMed, Scopus, and Cochrane Library databases, covering the years 2000 to 2024. Studies were selected based on relevance to quality assessment models, patient satisfaction, accreditation, and strategic improvement methodologies. The review followed a thematic synthesis approach, integrating structural, process-based, and outcome-driven perspectives. Results: Core frameworks such as Donabedian’s model and balancing measures were reviewed alongside evaluation tools like the Dutch Consumer Quality Index, SERVQUAL, and Importance–Performance Analysis (IPA). These models revealed significant gaps between patient expectations and actual service delivery, especially in functional and emotional quality dimensions. This review also identified limitations related to contextual generalizability and bias. A novel integrative model is proposed, emphasizing the dynamic interaction between institutional structure, clinical processes, and patient experience. Conclusions: High-quality healthcare demands a multidimensional approach. Integrating conceptual frameworks with context-sensitive strategies enables healthcare systems to align technical performance with patient-centered outcomes. The proposed model offers a foundation for future empirical validation, particularly in resource-limited or hybrid settings. Full article

Temporal domain generalization is crucial for the temporal forecasting of sensor data due to the non-stationary and evolving nature of most sensor-generated time series. However, temporal dynamics vary in scale, semantics, and structure, leading to distribution shifts that a single model cannot easily generalize over. Additionally, conflicts between temporal domain-specific patterns and limited model capacity make it difficult to learn shared parameters that work universally. To address this challenge, we propose an ensemble learning framework that leverages multiple domain-specific models to improve temporal domain generalization for sensor data forecasting. We first segment the original sensor time series into distinct temporal tasks to better handle the distribution shifts inherent in sensor measurements. A meta-learning strategy is then applied to extract shared representations across these tasks. Specifically, during meta-training, a recurrent encoder combined with variational inference captures contextual information for each task, which is used to generate task-specific model parameters. Relationships among tasks are modeled via a self-attention mechanism. For each query, the prediction results are adaptively reweighted based on all previously learned models. At inference, predictions are directly generated through the learned ensemble mechanism without additional tuning. Extensive experiments on public sensor datasets demonstrate that our method significantly enhances the generalization performance in forecasting across unseen sensor segments. Full article

The ability to collaborate with new teammates, adapt to unfamiliar environments, and engage in effective planning is essential for multi-drone agents within unmanned combat systems. This paper introduces DETEAMSK (Model-based Reinforcement Learning by Decoupling the Identification of Teammates and Tasks), a model-based reinforcement learning method in intelligent top-level planning and decisions designed for ad hoc teamwork among multi-drone agents. It specifically addresses integrated reconnaissance and strike missions in urban combat scenarios under varying conditions. DETEAMSK’s performance is evaluated through comprehensive, multidimensional experiments and compared with other baseline models. The results demonstrate that DETEAMSK exhibits superior effectiveness, robustness, and generalization capabilities across a range of task domains. Moreover, the model-based reinforcement learning approach offers distinct advantages over traditional models, such as the PLASTIC-Model, and model-free approaches, like the PLASTIC-Policy, due to its unique “dynamic decoupling identification” feature. This study provides valuable insights for advancing both theoretical and applied research in model-based reinforcement learning methods for multi-drone systems. Full article

Modeling accident investigation reports is crucial for elucidating accident causation mechanisms, analyzing risk evolution processes, and formulating effective accident prevention strategies. However, such reports are typically long, hierarchically structured, and information-dense, posing unique challenges for existing language models. To address these domain-specific characteristics, this study proposes SAFE-Transformer, a Structure-Aware and Format-Enhanced Transformer designed for long-document modeling in the emergency safety context. SAFE-Transformer adopts a dual-stream encoding architecture to separately model symbolic section features and heading text, integrates hierarchical depth and format types into positional encodings, and introduces a dynamic gating unit to adaptively fuse headings with paragraph semantics. We evaluate the model on a multi-label accident intelligence classification task using a real-world corpus of 1632 official reports from high-risk industries. Results demonstrate that SAFE-Transformer effectively captures hierarchical semantic structure and outperforms strong long-text baselines. Further analysis reveals an inverted U-shaped performance trend across varying report lengths and highlights the role of attention sparsity and label distribution in long-text modeling. This work offers a practical solution for structurally complex safety documents and provides methodological insights for downstream applications in safety supervision and risk analysis. Full article

This study explores the design and implementation of a control system integrating the anti-lock braking system (ABS) with frequency-modulated continuous wave (FMCW) radar technology to enhance safety and performance in autonomous vehicles. The proposed system employs a hybrid fuzzy logic controller (FLC) and proportional-integral-derivative (PID) controller to improve braking efficiency and vehicle stability under diverse driving conditions. Simulation results showed significant enhancements in stopping performance across various road conditions. The integrated system exhibited a marked improvement in braking performance, achieving significantly shorter stopping distances across all evaluated surface conditions—including dry concrete, wet asphalt, snowy roads, and icy roads—compared with scenarios without ABS. These results highlight the system’s ability to dynamically adapt braking forces to different conditions, significantly improving safety and stability for autonomous vehicles. The limitations are acknowledged, and directions for real-world validation are outlined to ensure system robustness under diverse environmental conditions. Full article