Search Results (3,222)

Background and objectives: Trismus is a frequent and debilitating complication in people with head and neck cancer (HNC) which leads to significant functional limitations and reduced quality of life. Rehabilitation interventions are commonly recommended to manage or prevent trismus. However, in many randomized controlled trials (RCTs), the theoretical justification for these interventions is poorly articulated, and the underlying biological or physiological mechanisms are not described in detail, limiting our understanding of why certain treatments may (or may not) work. This review aimed to identify and analyze how RCTs report the rationale for rehabilitation interventions and the explanations used to manage this population. Materials and Methods: A scoping review was conducted in accordance with the PRISMA-ScR guidelines. Five databases (PubMed, PEDro, Web of Science, Scopus, and EMBASE) were searched up to May 2025 for RCTs evaluating rehabilitation interventions for the management or prevention of treatment-induced trismus in patients with HNC. Data were extracted and synthesized narratively, focusing on the type of intervention, the rationale for its use, and the proposed mechanisms of action. Results: Of 2215 records identified, 24 RCTs met the inclusion criteria. Thirteen studies focused on preventive interventions—primarily exercise therapy—while the remainder addressed established trismus using exercise, manual therapy, electrotherapy, or combined treatment modalities. The rationales provided for intervention selection were heterogeneous and often lacked depth, with most studies justifying interventions based on their potential to improve mouth opening or reduce fibrosis but rarely grounding these claims in detailed pathophysiological models. Only half of the studies provided any mechanistic explanation for the intervention’s effects, and these were typically generic or speculative. Conclusions: RCTs investigating rehabilitation interventions for treatment-induced trismus in patients with HNC frequently lack comprehensive rationales and mechanistic explanations for their interventions. This gap limits the ability to refine and optimize treatment approaches, as the underlying processes driving clinical improvements remain poorly understood. Future research should be guided by theoretical models and include objective outcomes to better elucidate the mechanisms of action of interventions to inform clinical practice. Full article

Background/Objectives: Taste dysfunction is a highly prevalent yet underrecognized complication among patients with head and neck cancer (HNC), significantly impairing nutritional intake, treatment adherence, and quality of life (QoL). This comprehensive review synthesizes current knowledge on the pathophysiological mechanisms and clinical management of taste dysfunction associated with HNC and its treatments, particularly chemotherapy and radiotherapy. Methods: A structured literature search was performed across PubMed, Scopus, and Cochrane Library for articles published between January 2015 and February 2025. Studies were included if they investigated taste dysfunction related to HNC, focusing on pathophysiological mechanisms and therapeutic interventions. A total of 47 original studies were analyzed through a narrative synthesis due to heterogeneity in study designs and outcomes. Results: Taste dysfunction in HNC patients arises from tumor-related inflammation, cytotoxic injury from chemotherapy, and radiation-induced epithelial and neural damage. Chemotherapy and radiotherapy often exert synergistic negative effects on gustatory function. Management strategies identified include dietary counselling, nutritional supplementation (zinc, lactoferrin, monosodium glutamate, miraculin), pharmacological agents targeting salivary function, and non-pharmacological interventions such as acupuncture, photobiomodulation, and reconstructive surgery. However, the evidence is limited by small sample sizes, methodological variability, and the frequent exclusion of HNC patients from broader dysgeusia trials. Reported prevalence of taste dysfunction ranged from 39% to 97.4%, with higher rates observed among patients treated with radiotherapy and chemoradiotherapy. Conclusions: Taste dysfunction remains a critical yet unmet clinical challenge in HNC patients. High-quality, targeted research is urgently needed to develop standardized assessments and evidence-based management strategies to improve patient outcomes. Full article

Sorghum is an important cereal crop. The maintenance of leaf color significantly influences sorghum growth and development. Although the mechanisms of leaf color mutation have been well studied in many plants, those in sorghum remain largely unclear. Here, we identified a sorghum gradient-pale-green leaf mutant (sbgpgl1) from the ethyl methanesulfonate (EMS) mutagenesis mutant library. Phenotypic, photosynthesis-related parameter, ion content, transcriptome, and metabolome analyses were performed on wild-type BTx623 and the sbgpgl1 mutant at the heading stage, revealing changes in several agronomic traits and physiological indicators. Compared with BTx623, sbgpgl1 showed less height, with a smaller length and width of leaf and panicle. The overall Chl a and Chl b contents in sbgpgl1 were lower than those in BTx623. The net photosynthetic rate, stomatal conductance, and transpiration rate were significantly reduced in sbgpgl1 compared to BTx623. The content of copper (Cu), zinc (Zn), and manganese (Mn) was considerably lower in sbgpgl1 leaves than in BTx623. A total of 4469 differentially expressed genes (DEGs) and 775 differentially accumulated metabolites (DAMs) were identified by RNA-seq and UPLC-MS/MS. The results showed that sbgpgl1 primarily influenced sorghum metabolism by regulating metabolic pathways and the biosynthesis of secondary metabolites, especially flavonoids and phenolic acids, resulting in the gradient-pale-green leaf phenotype. These findings reveal key genes and metabolites involved on a molecular basis in physiological variations of the sorghum leaf color mutant. Full article

Underwater navigation remains constrained by technological limitations, driving the exploration of alternative approaches such as polarized light-based systems. This review systematically examines advances in polarized navigation from three perspectives. First, the principles of atmospheric polarization navigation are analyzed, with their operational mechanisms, advantages, and inherent constraints dissected. Second, innovations in bionic polarization multi-sensor fusion positioning are consolidated, highlighting progress beyond conventional heading-direction extraction. Third, emerging underwater polarization navigation techniques are critically evaluated, revealing that current methods predominantly adapt atmospheric frameworks enhanced by advanced filtering to mitigate underwater interference. A comprehensive synthesis of underwater polarization modeling methodologies is provided, categorizing physical, data-driven, and hybrid approaches. Through rigorous analysis of studies, three persistent barriers are identified: (1) inadequate polarization pattern modeling under dynamic cross-media conditions; (2) insufficient robustness against turbidity-induced noise; (3) immature integration of polarization vision with sonar/IMU (Inertial Measurement Unit) sensing. Targeted research directions are proposed, including adaptive deep learning models, multi-spectral polarization sensing, and bio-inspired sensor fusion architectures. These insights establish a roadmap for developing reliable underwater navigation systems that transcend current technological boundaries. Full article

The Content-Centric Network (CCN) presents an alternative to the conventional TCP/IP network, where IP is fundamental for communication between the source and destination. Instead of relying on IP addresses, CCN emphasizes content to enable efficient data distribution through caching and delivery. The increasing demand of graphic-intensive applications requires minimal response time and optimized resource utilization. Therefore, the CCN plays a vital role due to its efficient architecture and content management approach. To reduce data retrieval delays in CCNs, traditional methods improve caching mechanisms through clustering. However, these methods do not address the optimal use of resources, including CPU, memory, storage, and available links, along with the incorporation of social awareness. This study proposes SARAC4N, a socially and resource-aware caching framework for clustered Content-Centric Networks that integrates dual-head clustering and popularity-driven content placement. It enhances caching efficiency, reduces retrieval delays, and improves resource utilization across heterogeneous network topologies. This approach will help resolve congestion issues while enhancing social awareness, lowering error rates, and ensuring efficient content delivery. The proposed Socially and Resource-Aware Caching in Clustered Content-Centric Network (SARAC4N) enhances caching effectiveness by optimally utilizing resources and positioning them with social awareness within the cluster. Furthermore, it enhances metrics such as data retrieval time, reduces computation and memory usage, minimizes data redundancy, optimizes network usage, and lowers storage requirements, all while maintaining a very low error rate. Full article

Background: The mechanisms by which sporadic young-onset neurodegenerative processes develop are uncertain. Methods: We have previously proposed that stochastic processes involving sequence changes at a DNA, RNA, or protein level in critical genes and proteins might be important to this process. Further investigation points to the contribution of probabilistic states in other factors involved in neurodegenerative conditions, such as—in the case of young onset Alzheimer’s disease—head injury, apolipoprotein ε4 alleles and other elements that, by the interaction of conditional probabilities in these variables, influence the evolution of neurodegenerative conditions. Results: This proposal might help to explain why some autosomal dominant neurodegenerative conditions, such as trinucleotide repeat disorder (Huntington’s disease), might have variable ages of onset given the same disease-causing CAG repeat mutation length. Conclusions: The detection of somatic mutations in single brain cells provides some experimental support for these emerging concepts. Full article

In this study, four types of fall detection systems for seniors living alone using x-y scatter and Doppler range images measured from frequency-modulated continuous wave (FMCW) millimeter-wave (mmWave) sensors were introduced. Despite advancements in fall detection, existing long short-term memory (LSTM)-based approaches often struggle with effectively distinguishing falls from similar activities of daily living (ADLs) due to their uniform treatment of all time steps, potentially overlooking critical motion cues. To address this limitation, an attention mechanism has been integrated. Data was collected from seven participants, resulting in a dataset of 669 samples, including 285 falls and 384 ADLs with walking, lying, inactivity, and sitting. Four LSTM-based architectures for fall detection were proposed and evaluated: Raw-LSTM, Raw-LSTM-Attention, HOG-LSTM, and HOG-LSTM-Attention. The histogram of oriented gradient (HOG) method was used for feature extraction, while LSTM networks captured temporal dependencies. The attention mechanism further enhanced model performance by focusing on relevant input features. The Raw-LSTM model processed raw mmWave radar images through LSTM layers and dense layers for classification. The Raw-LSTM-Attention model extended Raw-LSTM with an added self-attention mechanism within the traditional attention framework. The HOG-LSTM model included an additional preprocessing step upon the RAW-LSTM model where HOG features were extracted and classified using an SVM. The HOG-LSTM-Attention model built upon the HOG-LSTM model by incorporating a self-attention mechanism to enhance the model’s ability to accurately classify activities. Evaluation metrics such as Sensitivity, Precision, Accuracy, and F1-Score were used to compare four architectural models. The results showed that the HOG-LSTM-Attention model achieved the highest performance, with an Accuracy of 95.3% and an F1-Score of 95.5%. Optimal self-attention configuration was found at a 2:64 ratio of number of attention heads to channels for keys and queries. Full article

Weed infestation contributes significantly to global agricultural yield loss and increases the reliance on herbicides, raising both economic and environmental concerns. Effective weed detection in agriculture requires high accuracy and architectural efficiency. This is particularly important under challenging field conditions, including densely clustered targets, small weed instances, and low visual contrast between vegetation and soil. In this study, we propose GTDR-YOLOv12, an improved object detection framework based on YOLOv12, tailored for real-time weed identification in complex agricultural environments. The model is evaluated on the publicly available Weeds Detection dataset, which contains a wide range of weed species and challenging visual scenarios. To achieve better accuracy and efficiency, GTDR-YOLOv12 introduces several targeted structural enhancements. The backbone incorporates GDR-Conv, which integrates Ghost convolution and Dynamic ReLU (DyReLU) to improve early-stage feature representation while reducing redundancy. The GTDR-C3 module combines GDR-Conv with Task-Dependent Attention Mechanisms (TDAMs), allowing the network to adaptively refine spatial features critical for accurate weed identification and localization. In addition, the Lookahead optimizer is employed during training to improve convergence efficiency and reduce computational overhead, thereby contributing to the model’s lightweight design. GTDR-YOLOv12 outperforms several representative detectors, including YOLOv7, YOLOv9, YOLOv10, YOLOv11, YOLOv12, ATSS, RTMDet and Double-Head. Compared with YOLOv12, GTDR-YOLOv12 achieves notable improvements across multiple evaluation metrics. Precision increases from 85.0% to 88.0%, recall from 79.7% to 83.9%, and F1-score from 82.3% to 85.9%. In terms of detection accuracy, mAP:0.5 improves from 87.0% to 90.0%, while mAP:0.5:0.95 rises from 58.0% to 63.8%. Furthermore, the model reduces computational complexity. GFLOPs drop from 5.8 to 4.8, and the number of parameters is reduced from 2.51 M to 2.23 M. These reductions reflect a more efficient network design that not only lowers model complexity but also enhances detection performance. With a throughput of 58 FPS on the NVIDIA Jetson AGX Xavier, GTDR-YOLOv12 proves both resource-efficient and deployable for practical, real-time weeding tasks in agricultural settings. Full article

Stock price forecasting remains a persistent challenge in time series analysis due to complex inter-stock relationships and dynamic textual signals such as financial news. While Graph Neural Networks (GNNs) can model relational structures, they often struggle with capturing higher-order dependencies and are sensitive to noise. Moreover, sentiment signals are typically aggregated using fixed time windows, which may introduce temporal bias. To address these issues, we propose DASF-Net (Diffusion-Aware Sentiment Fusion Network), a multimodal framework that integrates structural and textual information for robust prediction. DASF-Net leverages diffusion processes over two complementary financial graphs—one based on industry relationships, the other on fundamental indicators—to learn richer stock representations. Simultaneously, sentiment embeddings extracted from financial news using FinBERT are aggregated over an empirically optimized window to preserve temporal relevance. These modalities are fused via a multi-head attention mechanism and passed to a temporal forecasting module. DASF-Net integrates daily stock prices and news sentiment, using a 3-day sentiment aggregation window, to forecast stock prices over daily horizons (1–3 days). Experiments on 12 large-cap S&P 500 stocks over four years demonstrate that DASF-Net outperforms competitive baselines, achieving up to 91.6% relative reduction in Mean Squared Error (MSE). Results highlight the effectiveness of combining graph diffusion and sentiment-aware features for improved financial forecasting. Full article

With the rapid growth of urban populations and the expansion of metro networks, accurate energy consumption prediction has become a critical task for optimizing metro operations and supporting low-carbon city development. Traditional statistical and machine learning methods often struggle to model the complex, nonlinear, and time-varying nature of metro energy data. To address these challenges, this paper proposes MTMM, a novel hybrid model that integrates the multi-head attention mechanism of the Transformer with the efficient, state-space-based Mamba architecture. The Transformer effectively captures long-range temporal dependencies, while Mamba enhances inference speed and reduces complexity. Additionally, the model incorporates multivariate energy features, leveraging the correlations among different energy consumption types to improve predictive performance. Experimental results on real-world data from the Guangzhou Metro demonstrate that MTMM significantly outperforms existing methods in terms of both MAE and MSE. The model also shows strong generalization ability across different prediction lengths and time step configurations, offering a promising solution for intelligent energy management in metro systems. Full article

Accurate heat load forecasting is essential for the efficiency of District Heating Systems (DHS). Still, it is challenged by the need to model long-term temporal dependencies and nonlinear relationships with weather and other factors. This study proposes a hybrid deep learning framework combining an attention-enhanced Neural Basis Expansion Analysis for Time Series (N-BEATS) model and eXtreme Gradient Boosting (XGBoost). The N-BEATS component, with a multi-head self-attention mechanism, captures temporal dynamics, while XGBoost models non-linear impacts of external variables. Predictions are integrated using an optimized weighted averaging strategy. Evaluated on a dataset from 103 heating units, the model outperformed 13 baselines, achieving an MSE of 0.4131, MAE of 0.3732, RMSE of 0.6427, and R² of 0.9664. This corresponds to a reduction of 32.6% in MSE, 32.0% in MAE, and 17.9% in RMSE, and an improvement of 5.1% in R² over the best baseline. Ablation studies and statistical tests confirmed the effectiveness of the attention mechanism and ensemble strategy. This model provides an efficient solution for DHS load forecasting, facilitating optimized energy dispatch and enhancing system performance. Full article

Compared to traditional ad hoc networks, self-organizing networks of unmanned aerial vehicle (UAV) are characterized by high node mobility, vulnerability to interference, wide distribution range, and large network scale, which make network management and routing protocol operation more challenging. Cluster structures can be used to optimize network management and mitigate the impact of local topology changes on the entire network during collaborative task execution. To address the issue of cluster structure instability caused by the high mobility and vulnerability to interference in UAV networks, we propose a capacity-constrained weighted clustering algorithm for UAV self-organizing networks under interference. Specifically, a capacity-constrained partitioning algorithm based on K-means++ is developed to establish the initial node partitions. Then, a weighted cluster head (CH) and backup cluster head (BCH) selection algorithm is proposed, incorporating interference factors into the selection process. Additionally, a dynamic maintenance mechanism for the clustering network is introduced to enhance the stability and robustness of the network. Simulation results show that the algorithm achieves efficient node clustering under interference conditions, improving cluster load balancing, average cluster head maintenance time, and cluster head failure reconstruction time. Furthermore, the method demonstrates fast recovery capabilities in the event of node failures, making it more suitable for deployment in complex emergency rescue environments. Full article

With the rapid development of intelligent transportation, accurately predicting vehicle trajectories is crucial for ensuring road safety and enhancing traffic efficiency. This paper proposes a trajectory prediction model that integrates a diffusion model framework with trajectory features of target and neighboring vehicles, as well as driving intentions. The model uses historical trajectories of the target and adjacent vehicles as input, employs Long Short-Term Memory (LSTM) networks to extract temporal features, and dynamically captures the interaction between the target and neighboring vehicles through a multi-head attention mechanism. An intention module regulates lateral offsets, and the diffusion framework selects the most probable trajectory from various possible predictions, thereby improving the model’s ability to handle complex scenarios. Experiments conducted on real traffic data demonstrate that the proposed method outperforms several representative models in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE), without sacrificing efficiency. Notably, it exhibits higher robustness and predictive accuracy in high-interaction and uncertain scenarios, such as lane changes and overtaking. To the best of our knowledge, this is the first application of the diffusion framework in vehicle trajectory prediction. This study provides an efficient solution for vehicle trajectory prediction tasks. The average ADE within 1 to 5 s reached 0.199 m, while the average FDE within 1 to 5 s reached 0.437 m. Full article

To ensure the normal water status of apple rootstock softwood cuttings during the initial stage of cutting, a drought stress monitoring model was designed. The model is optimized based on the YOLOv11n-seg instance segmentation model, using the leaf curl degree of cuttings as the classification basis for drought-stress grades. The backbone structure of the IMYOLOv11n-seg model is improved by the C3K2_CMUNeXt module and the multi-head self-attention (MHSA) mechanism module. The neck part is optimized by the KFHA module (Kalman filter and Hungarian algorithm model), and the head part enhances post-processing effects through HIoU-SD (hierarchical IoU–spatial distance filtering algorithm). The IMYOLOv11-seg model achieves an average inference speed of 33.53 FPS (frames per second) and the mean intersection over union (MIoU) value of 0.927. The average recognition accuracies for cuttings under normal water status, mild drought stress, moderate drought stress, and severe drought stress are 94.39%, 93.27%, 94.31%, and 94.71%, respectively. The IMYOLOv11n-seg model demonstrates the best comprehensive performance in ablation and comparative experiments. The automatic humidification system equipped with the IMYOLOv11n-seg model saves 6.14% more water than the labor group. This study provides a design approach for an automatic humidification system in protected agriculture during apple rootstock cutting propagation. Full article

Accurate and real-time detection of rice shelling rate (SR) and brown rice breakage rate (BR) is crucial for intelligent hulling sorting but remains challenging because of small grain size, dense adhesion, and uneven illumination causing missed detections and blurred boundaries in traditional YOLOv8n. This paper proposes a high-precision, lightweight solution based on an enhanced YOLOv8n with improvements in network architecture, feature fusion, and attention mechanism. The backbone’s C2f module is replaced with C2f-Faster-CGLU, integrating partial convolution (PConv) local convolution and convolutional gated linear unit (CGLU) gating to reduce computational redundancy via sparse interaction and enhance small-target feature extraction. A bidirectional feature pyramid network (BiFPN) weights multiscale feature fusion to improve edge positioning accuracy of dense grains. Attention mechanism for fine-grained classification (AFGC) is embedded to focus on texture and damage details, enhancing adaptability to light fluctuations. The Detect_Rice lightweight head compresses parameters via group normalization and dynamic convolution sharing, optimizing small-target response. The improved model achieved 96.8% precision and 96.2% mAP. Combined with a quantity–mass model, SR/BR detection errors reduced to 1.11% and 1.24%, meeting national standard (GB/T 29898-2013) requirements, providing an effective real-time solution for intelligent hulling sorting. Full article