Search Results (32,891)

Ophiocordyceps sinensis is a precious medicinal material with significant pharmacological and economic value. However, the visual similarity between its wild and cultivated forms poses a challenge for authentication. This study investigates the influence of specimen orientation on the accuracy of hyperspectral identification. Hyperspectral data were systematically acquired from four standard specimen orientations (left lateral, right lateral, dorsal, and ventral) for each sample. Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and Fully Connected Neural Network (FCNN) models were trained and evaluated using both single-orientation and multi-orientation fused data. Results indicate that the LR model achieved superior and stable performance, with an average identification accuracy exceeding 98%. Crucially, for all tested models, no statistically significant difference in identification accuracy was observed across the different specimen orientations. This finding demonstrates that specimen orientation does not significantly influence identification accuracy. The conclusion was further corroborated in experiments using randomly orientation-fused datasets, in which model performance remained consistent and reliable. It is therefore concluded that precise specimen orientation control is unnecessary for the hyperspectral identification of Ophiocordyceps sinensis. This insight substantially simplifies the hardware design of dedicated identification devices by eliminating the need for complex orientation-fixing mechanisms and facilitating the standardization of operational protocols. The study provides a practical theoretical foundation for developing cost-effective, user-friendly, and widely applicable identification instruments for Ophiocordyceps sinensis and offers a reference for similar non-destructive testing applications involving anisotropic medicinal materials. Full article

In mineral processing, visual-based online particle size analysis systems depend on high-precision image segmentation to accurately quantify ore particle size distribution, thereby optimizing crushing and sorting operations. However, due to multi-scale variations, severe adhesion, and occlusion within ore particle clusters, existing segmentation models often exhibit undersegmentation and misclassification, leading to blurred boundaries and limited generalization. To address these challenges, this paper proposes a novel semantic segmentation model named VTC-Net. The model employs VGG16 as the backbone encoder, integrates Transformer modules in deeper layers to capture global contextual dependencies, and incorporates a Convolutional Block Attention Module (CBAM) at the fourth stage to enhance focus on critical regions such as adhesion edges. BatchNorm layers are used to stabilize training. Experiments on ore image datasets show that VTC-Net outperforms mainstream models such as UNet and DeepLabV3 in key metrics, including MIoU (89.90%) and pixel accuracy (96.80%). Ablation studies confirm the effectiveness and complementary role of each module. Visual analysis further demonstrates that the model identifies ore contours and adhesion areas more accurately, significantly improving segmentation robustness and precision under complex operational conditions. Full article

Pilot workload is a critical factor influencing flight safety. This study collects both subjective and objective data on pilot workload using the NASA-TLX questionnaire and electroencephalogram acquisition systems during simulated flight tasks. The raw EEG signals are denoised through preprocessing techniques, and relevant EEG features are extracted using time-domain and frequency-domain analysis methods. One-way ANOVA is employed to examine the statistical differences in EEG indicators under varying workload levels. A fusion model based on CNN-Bi-LSTM is developed to train and classify the extracted EEG features, enabling accurate identification of pilot workload states. The results demonstrate that the proposed hybrid model achieves a recognition accuracy of 98.2% on the test set, confirming its robustness. Additionally, under increased workload conditions, frequency-domain features outperform time-domain features in discriminative power. The model proposed in this study effectively recognizes pilot workload levels and offers valuable insights for civil aviation safety management and pilot training programs. Full article

This study examines reservoir-triggered seismicity (RTS) in Poland and Vietnam. The current state of individual RTS seismic networks necessitates detecting earthquakes from only a few stations. The number of P waves is often inadequate for phase association and event location, which underscores the importance of identifying S waves. Given that individual RTS cases may consist of only hundreds of events, it is crucial for algorithms to be trained on small datasets or to detect effectively using external, global training data. To evaluate this, we compared the efficiency of a deep learning global detection model, transfer learning to the RTS database, a specialized neural network designed for RTS, and manual detection of seismic signals. Transfer learning efficiency was database dependent. Additional interpretation and parametrization of detection results are assumed. Therefore, the emphasis is on phase detection, rather than phase picking accuracy, and detection sensitivity is more important than its specificity. Phase association plays a vital role in detecting seismic signals, facilitating the elimination of most false picks. As a result, the comparisons of detections were based on parameters related to the location of seismic events. The findings indicate that neither the automatic signal detection methods nor the manual methods alone are sufficient. However, their combination significantly enhances detectability. The final catalogs cover up to 30% more events compared to the previous manual. It fulfills the main aim of applying a neural network detector, which is to increase the number of seismic events in the catalog. It may also be further utilized in the research of the triggering process, such as identifying fluid paths and determining fault geometry. Full article

Accurate segmentation of nuclei and cytoplasm in cervical cytology images plays a pivotal role in characterizing cellular morphology. The primary challenge is to precisely delineate boundaries within densely clustered cells, which is complicated by low-contrast edges and irregular morphologies. This paper introduces a novel framework combining MaskDino architecture with Hausdorff distance loss, enhanced by a two-phase training strategy. The method begins by employing MaskDino for precise nucleus segmentation. Building on this foundation, the framework then enhances cytoplasmic boundary detection in cellular clusters by incorporating a Hausdorff distance loss, with weight transfer initialization ensuring feature consistency across tasks.. The symmetry between the nucleus and cytoplasm servers as a key morphological indicator for cell assessment, and our method provides a reliable basis for such analysis. Extensive experiments demonstrate that our method achieves state-of-the-art cytoplasm segmentation results on the ISBI2014 dataset, with absolute improvements of 2.9% in DSC, 1.6% in TPRp and 2.0% in FNRo. The performance of nucleus segmentation is better than the average level. These results validate the proposed framework’s effectiveness for improving cervical cancer screening through robust cellular segmentation. Full article

Given the central importance of maritime logistics to global trade, accurate and efficient vessel motion forecasting is essential for strengthening supply chain resilience and improving operational efficiency. However, traditional physical and statistical models often fail to effectively capture the multivariate, noisy, and strongly coupled nature of maritime dynamics. In this manuscript, we adapt the DSformer architecture for ship motion forecasting, leveraging its dual sampling and dual-attention design to address the multi-scale and cross-variable dependencies inherent in maritime data. Across three real-world datasets, the adapted DSformer reduces prediction error by 23% and training time by 70% compared with 13 state-of-the-art (SOTA) baselines. Moreover, we identify a consistent relationship between sampling strategies and sea states, where dense sampling performs best under stable conditions, whereas moderately sparse sampling with multi-head attention improves robustness under turbulent environments. These results apply the algorithm’s new capabilities to the daily management of maritime logistics. By adapting the architecture to real-world operational settings and optimizing its key parameters, the approach enables efficient, real-time vessel forecasting and decision support across global supply chains. Full article

Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease characterized by airflow limitation. Apart from airflow limitation, patients with COPD may also suffer from extra-pulmonary features such as lower limb muscle dysfunction that contribute to an impaired health status. Since the latest statement on lower-limb muscle dysfunction in COPD in 2014, substantial new evidence has emerged with regard to molecular, cellular, and functional mechanisms underlying muscle plasticity. Therefore, this review aims to provide an updated overview of molecular, cellular, and functional mechanisms of lower-limb muscle plasticity in COPD, integrating evidence that has emerged since the 2014 statement on lower limb muscle dysfunction. Additionally, the effects of exercise training on mechanisms of limb muscle dysfunction are explained. From the evidence of the last decade, it can be concluded that limb muscle dysfunction is a multifactorial process driven by both intrinsic alterations and impairments to the muscle as well as extra-pulmonary influences, thereby reinforcing the need for integrated therapeutic strategies. Full article

(1) Background: Drug–drug interactions (DDIs) are a frequent cause of medication-related harm, particularly in ambulatory care. Community pharmacists are uniquely positioned to identify and manage these risks. This study assessed DDI knowledge, practices, and barriers among community pharmacists in the Jazan Region, Saudi Arabia. (2) Methods: A structured, self-administered questionnaire was distributed to community pharmacists. The survey assessed DDI knowledge using 26 clinically relevant drug pairings and included questions on professional behavior, training exposure, software use, and educational needs. Descriptive and inferential statistics were applied to identify associations between knowledge scores and demographic or practice-related variables. (3) Results: A total of 219 pharmacists participated in the study. The mean knowledge score was (9.63 ± 4.58) out of 26, reflecting suboptimal to moderate awareness. Female pharmacists demonstrated significantly higher DDI knowledge scores than males (10.74 ± 5.4 vs. 9.08 ± 4.2; p = 0.016). Knowledge scores also differed significantly by academic qualification (p < 0.001), with PharmD holders scoring higher than B. Pharm and postgraduate degree holders. Pharmacists with less than 10 years of experience had significantly higher scores compared with those with longer practice duration (p = 0.002). Additionally, pharmacists who graduated from Saudi institutions scored higher than those trained outside Saudi Arabia (10.22 ± 4.7 vs. 8.44 ± 4.2; p = 0.005). Pharmacists who had received professional development training and those who attended workshops regularly also scored significantly higher. Familiarity with guidelines showed a positive trend. Reported barriers to effective DDI counseling included time constraints, limited patient understanding, and poor collaboration with prescribers. Self-rated awareness of DDIs was positively associated with actual knowledge scores. Pharmacists expressed strong preferences for workshops, online courses, and webinars as future training formats. (4) Conclusions: Pharmacists in the Jazan Region demonstrate moderate awareness of DDIs, with variation influenced by training, experience, and qualifications. Enhancing access to structured professional development and integrating clinical decision support tools could strengthen pharmacists’ role in preventing DDIs in community practice. Full article

During rotary tillage operations, the draft force is jointly affected by operating parameters and soil conditions, exhibiting pronounced nonlinearity, time-varying behavior, and historical dependence, which all impose higher requirements on tractor operating parameter matching and traction performance analysis. A draft force prediction method that is based on a long short-term memory (LSTM) neural network jointly optimized by a genetic algorithm (GA) and the bald eagle search (BES) algorithm, termed GABES-LSTM, is proposed to address the limited prediction accuracy and stability of traditional empirical models and single data-driven approaches under complex field conditions. First, on the basis of the mechanical characteristics of rotary tillage operations, a time-series mathematical description of draft force is established, and the prediction problem is formulated as a multi-input single-output nonlinear temporal mapping driven by operating parameters such as travel speed, rotary speed, and tillage depth. Subsequently, an LSTM-based draft force prediction model is constructed, in which GA is employed for global hyperparameter search and BES is integrated for local fine-grained optimization, thereby improving the effectiveness of model parameter optimization. Finally, a dataset is established using measured field rotary tillage data to train and test the proposed model, and comparative analyses are conducted against LSTM, GA-LSTM, and BES-LSTM models. Experimental results indicate that the GABES-LSTM model outperforms the comparison models in terms of mean absolute percentage error, mean relative error, relative analysis error, and coefficient of determination, effectively capturing the dynamic variation characteristics of draft force during rotary tillage operations while maintaining stable prediction performance under repeated experimental conditions. This method provides effective data support for draft force prediction analysis and operating parameter adjustment during rotary tillage operations. Full article

Airway management is central to the care of critically ill patients, yet it remains one of the most challenging interventions in emergency departments and intensive care units. Patients often present with severe physiological instability, limited cardiopulmonary reserve, and high acuity, while clinicians often work under constraints related to time for preparation, equipment availability, trained workforce, monitoring, and access to advanced rescue techniques. These challenges are particularly pronounced in low- and middle-income countries and other resource-limited or austere environments, where the margin for error is narrow and delays or repeated attempts in airway management may rapidly precipitate hypoxemia, hemodynamic collapse, or cardiac arrest. Although contemporary airway guidelines emphasize structured preparation and rescue pathways, many assume resources that are not consistently available in such settings. This narrative review discusses pragmatic, context-adapted strategies for airway management in constrained environments, with emphasis on physiology-first preparation, appropriate oxygenation and induction techniques, simplified rapid-sequence intubation, and the judicious use of basic airway adjuncts, supraglottic devices, and video laryngoscopy, where available. Adapted difficult airway algorithms, front-of-neck access in the absence of surgical backup, human factors, team training, and ethical considerations are also addressed. This review aims to support safer and effective airway management for critically ill patients in resource-limited emergency and intensive care settings. Full article

Current electrical self-sensing methods for composite structural health monitoring face significant limitations. Firstly, they often require complicated electrode layouts. Secondly, accurately determining both the location and amplitude of external loads remains a significant challenge. In this study, a deep learning-based self-sensing method is developed to identify the location and amplitude of external mechanical loads in resin-based conductive composites with a simple electrode layout. First, conductive filler-filled resin composites are prepared, and three-dimensional conductive networks are constructed within them. Subsequently, four electrodes are installed at the edges of the composite plate, and boundary electrical resistance responses are collected when applying mechanical loads at various positions on the composite plate. Finally, a residual learning-based CNN model is proposed for the accurate localization and amplitude identification of the applied loads. Research results demonstrate that the trained CNN model can accurately and effectively determine both the load amplitude and position. The obtained localization error and amplitude error are 0.91 mm and 0.13 N, respectively, surpassing the reported error values in previous studies. The research presented here opens a new avenue for achieving highly accurate and efficient prediction of load location and amplitude, which can be widely applied in composite structural health monitoring. Full article

Deep reinforcement learning (DRL) with self-play has emerged as a promising paradigm for solving combinatorial optimization (CO) problems. The recently proposed Gumbel AlphaZero Plan-to-Play (GAZ PTP) framework adopts a competitive training setup between a learning agent and an opponent to tackle classical CO tasks such as the Traveling Salesman Problem (TSP). However, in complex and multi-constrained environments like the Electric Vehicle Routing Problem (EVRP), standard self-play often suffers from opponent mismatch: when the opponent is either too weak or too strong, the resulting learning signal becomes ineffective. To address this challenge, we introduce Two-Stage Self-Play GAZ PTP (TSS GAZ PTP), a novel DRL method designed to maintain adaptive and effective learning pressure throughout the training process. In the first stage, the learning agent, guided by Gumbel Monte Carlo Tree Search (MCTS), competes against a greedy opponent that follows the best historical policy. As training progresses, the framework transitions to a second stage in which both agents employ Gumbel MCTS, thereby establishing a dynamically balanced competitive environment that encourages continuous strategy refinement. The primary objective of this work is to develop a robust self-play mechanism capable of handling the high-dimensional constraints inherent in real-world routing problems. We first validate our approach on the TSP, a benchmark used in the original GAZ PTP study, and then extend it to the multi-constrained EVRP, which incorporates practical limitations including battery capacity, time windows, vehicle load limits, and charging infrastructure availability. The experimental results show that TSS GAZ PTP consistently outperforms existing DRL methods, with particularly notable improvements on large-scale instances. Full article

Teacher education represents a global strategic priority for improving educational systems and fostering inclusive, high-quality processes. Recent studies highlight the need for systematic and replicable education models capable of addressing the challenges of contemporary complexity and bridging the gap between theory and practice. Teaching professionalism is increasingly recognized as a key driver of change, requiring a balance of pedagogical, relational, and technological competences, along with strong reflective capacity. Within this framework, practicum programs play a crucial role for the development of professional identity and authentic teaching skills. Methods: This contribution adopts a theoretical–argumentative approach grounded in a critical analysis of the international scientific literature on teacher education, with specific focus on the role of practicums. The aim is to present the model implemented by Pegaso University in the context of practicum activities within initial teacher education programs to outline an interpretative framework and provide pedagogical reflections in light of the results arising from critical reflection and systematic monitoring (not covered in this specific contribution) of the effectiveness of the model implemented in the first two training cycles (academic years 23–24 and 24–25), with the involvement of 5 regions and a total of 2834 teachers in the first cycle and 10 regions and a total of 5551 teachers in the second cycle. Convenience sampling based on a non-probabilistic method was adopted, using the entire sample of teachers admitted to the training program who met the requirements of Article 7 of the Decree of the President of the Council of Ministers (DPCM). Results: This paper outlines the theoretical and methodological trajectories of the model, offering interpretative frameworks and pedagogical reflections in light of the outcomes achieved during the initial implementation phase. Conclusions: In accordance with recent national and European regulatory frameworks, the Pegaso teaching model is presented as an example of good practice for initial teacher education. It aims to foster a reflective, situated, and responsible teaching professionalism, moving beyond traditional approaches toward a continuous and transformative learning process. Full article

With the increasing competition in elite sports, pre-competition anxiety has become increasingly prevalent among high-level competitive athletes, and high levels of such anxiety may impair sports performance and threaten athletes’ psychological health. Traditional psychological interventions (e.g., cognitive-behavioral therapy) are often poorly accepted and costly; however, pre-competition anxiety in these athletes may be alleviated through multiple pathways of traditional mind–body exercises like Tai Chi. Yet, the psychological mechanism by which mind–body exercises such as Tai Chi training influence pre-competition anxiety remains insufficiently explored, particularly the chain-mediating effect of the “flow experience → mental toughness” pathway. This study thus aimed to investigate the impact of Tai Chi training on pre-competition anxiety in high-level competitive athletes and verify the chain-mediating role of the “flow experience → mental toughness” pathway, thereby providing a theoretical basis and practical reference for sports psychology interventions. Using a randomized controlled experimental design, 86 high-level competitive athletes were randomly divided into an experimental group (n = 43) and a control group (n = 43). The experimental group received standardized Tai Chi training for 8 weeks, while the control group maintained their regular training regimen. Data were collected at baseline, week 4, and week 8 of the intervention using the Competition State Anxiety Inventory-2 (CSAI-2), Flow State Scale-2 (FSS-2), and Sport Mental Toughness Questionnaire (SMTQ), and chain-mediating effects were tested via hierarchical regression analysis and the bootstrap method with 5000 resamples. The results indicated that Tai Chi training could reduce pre-competition anxiety levels (β = −0.30, p < 0.5), and both flow experience (β = 0.38, p < 0.5) and mental toughness (β = 0.21, p < 0.5) exerted significant mediating effects. The chain mediation model further revealed that Tai Chi training alleviated pre-competition anxiety by enhancing flow experience and improving mental toughness sequentially (β = 0.01, 95% CI [0.00, 0.03]), accounting for 78.9% of the total mediated effect. In conclusion, Tai Chi training is associated with reduced pre-competition anxiety in high-level competitive athletes, and this relationship is statistically mediated by the sequential pathway of flow experience and mental toughness. These findings offer a new theoretical basis and practical direction for mind–body interventions in sports psychology. It should be noted that future research could further optimize and refine the intervention protocol, and explore the underlying mechanism of mind–body interventions at the neurobiological level. Full article

Accurate downhole depth measurement is essential for oil and gas well operations, directly influencing reservoir contact, production efficiency, and operational safety. Collar correlation using a casing collar locator (CCL) is fundamental for precise depth calibration. While neural network has achieved significant progress in collar recognition, preprocessing methods for such applications remain underdeveloped. Moreover, the limited availability of real well data poses substantial challenges for training neural network models that require extensive datasets. This paper presents a system integrated into a downhole toolstring for CCL log acquisition to facilitate dataset construction. Comprehensive preprocessing methods for data augmentation are proposed, and their effectiveness is evaluated using baseline neural network models. Through systematic experimentation across diverse configurations, the contribution of each augmentation method is analyzed. Results demonstrate that standardization, label distribution smoothing (LDS), and random cropping are fundamental prerequisites for model training, while label smoothing regularization (LSR), time scaling, and multiple sampling significantly enhance model generalization capabilities. Incorporating the proposed augmentation methods into the two baseline models results in maximum F1 score improvements of 0.027 and 0.024 for the TAN and MAN models, respectively. Furthermore, applying these techniques yields F1 score gains of up to 0.045 for the TAN model and 0.057 for the MAN model compared to prior studies. Performance evaluation on real CCL waveforms confirms the effectiveness and practical applicability of our approach. This work addresses the existing gaps in data augmentation methodologies for training casing collar recognition models under CCL data-limited conditions, and provides a technical foundation for the future automation of downhole operations. Full article