Search Results (1,773)

Background: Electroencephalography (EEG) offers millisecond-precision measurement of neural oscillations underlying human cognition and emotion. Despite extensive research, systematic frameworks mapping EEG metrics to psychological constructs remain fragmented. Objective: This interdisciplinary scoping review synthesizes current knowledge linking EEG signatures to affective and cognitive models from a neuroscience perspective. Methods: We examined empirical studies employing diverse EEG methodologies, from traditional spectral analysis to deep learning approaches, across laboratory and naturalistic settings. Results: Affective states manifest through distinct frequency-specific patterns: frontal alpha asymmetry (8–13 Hz) reliably indexes emotional valence with 75–85% classification accuracy, while arousal correlates with widespread beta/gamma power changes. Cognitive processes show characteristic signatures: frontal–midline theta (4–8 Hz) increases linearly with working memory load, alpha suppression marks attentional engagement, and theta/beta ratios provide robust cognitive load indices. Machine learning approaches achieve 85–98% accuracy for subject identification and 70–95% for state classification. However, significant challenges persist: spatial resolution remains limited (2–3 cm), inter-individual variability is substantial (alpha peak frequency: 7–14 Hz range), and overlapping signatures compromise diagnostic specificity across neuropsychiatric conditions. Evidence strongly supports integrated rather than segregated processing, with cross-frequency coupling mechanisms coordinating affective–cognitive interactions. Conclusions: While EEG-based assessment of mental states shows considerable promise for clinical diagnosis, brain–computer interfaces, and adaptive technologies, realizing this potential requires addressing technical limitations, standardizing methodologies, and establishing ethical frameworks for neural data privacy. Progress demands convergent approaches combining technological innovation with theoretical sophistication and ethical consideration. Full article

Hearing loss (HL) affects more than 1.5 billion people worldwide and remains a leading cause of disability across the lifespan. While genetic predispositions, otitis media (OM), and cholesteatoma are well-recognized contributors, Eustachian tube dysfunction (ETD) is an underappreciated but pivotal determinant of auditory morbidity. By impairing middle ear pressure (MEP) regulation, ETD drives conductive hearing loss (CHL) through stiffness and mass-loading effects, contributes to sensorineural hearing loss (SNHL) via altered window mechanics and vascular stress, and produces mixed hearing loss (MHL) when these pathways converge. A characteristic clinical trajectory emerges in which conductive deficits often resolve quickly with restored ventilation, whereas sensorineural impairment requires prolonged, physiology-restoring intervention, resulting in transient or persistent MHL. This review integrates mechanistic insights with clinical manifestations, diagnostic approaches, and therapeutic options. Diagnostic frameworks that combine patient-reported outcomes with objective biomarkers such as wideband absorbance, tympanometry, and advanced imaging enable reproducible identification of ETD-related morbidity. Conventional treatments, including tympanostomy tubes and balloon dilation, offer short-term benefit but rarely normalize tubal physiology. In contrast, Eustachian tube catheterization (ETC) has emerged as a promising, mechanism-based intervention capable of reestablishing dynamic tubal opening and MEP regulation. Looking forward, integration of physiology-based frameworks with personalized diagnostics and advanced tools such as artificial intelligence (AI) may help prevent progression from reversible conductive deficits to irreversible SNHL or MHL. Full article

The rapid growth of electric vehicle (EV) charging demand poses significant challenges to distribution networks (DNs), particularly during public holidays when concentrated peaks occur near scenic areas and urban transport hubs. These sudden surges can strain transformer capacity and compromise supply reliability. Fixed soft open points (SOPs) are costly and underutilized, limiting their effectiveness in DNs with multiple transformers and asynchronous peak loads. To address this, from the perspective of power supply companies, this study proposes a mobile soft open point (MSOP)-based approach to enhance the hosting capacity of EV charging stations. The method pre-installs a limited number of fast-access interfaces (FAIs) at candidate transformers and integrates a semi-rolling horizon optimization framework to gradually expand interface availability while scheduling MSOPs daily. An automatic peak period identification algorithm ensures optimization focuses on critical load periods. Case studies on a multi-feeder distribution system coupled with a realistic traffic network demonstrate that the proposed method effectively balances heterogeneous peak loads, matches limited interfaces with MSOPs, and enhances system-level hosting capacity. Compared with fixed SOP deployment, the strategy improves hosting capacity during peak periods while reducing construction costs. The results indicate that MSOPs provide a practical, flexible, and economically efficient solution for power supply companies to manage concentrated holiday charging surges in DNs. Full article

Oceanic whitecaps are caused by wave breaking and are very important in air–sea interactions. Usually, whitecap coverage is considered a key factor in representing the role of whitecaps. However, the accurate identification of whitecap coverage in videos under dynamic marine conditions is a tough task. An EMA-SE-ResUNet deep learning model was proposed in this study to address this challenge. Based on a foundation of residual network (ResNet)-50 as the encoder and U-Net as the decoder, the model incorporated efficient multi-scale attention (EMA) module and squeeze-and-excitation network (SENet) module to improve its performance. By employing a dynamic weight allocation strategy and a channel attention mechanism, the model effectively strengthens the feature representation capability for whitecap edges while suppressing interference from wave textures and illumination noise. The model’s adaptability to complex sea surface scenarios was enhanced through the integration of data augmentation techniques and an optimized joint loss function. By applying the proposed model to a dataset collected by a shipborne camera system deployed during a comprehensive fishery resource survey in the northwest Pacific, the model results outperformed main segmentation algorithms, including U-Net, DeepLabv3+, HRNet, and PSPNet, in key metrics: whitecap intersection over union (IoU_W) = 73.32%, pixel absolute error (PAE) = 0.081%, and whitecap F1-score (F1_W) = 84.60. Compared to the traditional U-Net model, it achieved an absolute improvement of 2.1% in IoU_W while reducing computational load (GFLOPs) by 57.3% and achieving synergistic optimization of accuracy and real-time performance. This study can provide highly reliable technical support for studies on air–sea flux quantification and marine aerosol generation. Full article

This study proposes a fast material parameter evaluation method for multilayer thin-film structures based on machine learning technology to solve the problems of long time and low efficiency in the traditional material parameter inversion process. Nanoindentation experiments are first conducted to establish an experimental basis across film stacks. A two-dimensional elasto-plastic model of the indentation process is then built to generate a large set of load–depth curves, which serve as training data for a machine learning model. Trained on simulated curves and validated against measurements, the model enables fast inverse identification of layer-wise plastic parameters and interfacial cohesive properties. The experimental results show that the method has high accuracy and efficiency in the inversion of interlayer cohesion parameters, and the correlation coefficient R² is 0.99 or more. Compared with traditional methods, the pipeline supports batch analysis of multiple datasets and delivers parameter estimates within 1 h, substantially shortening turnaround time while improving result reliability. This method can not only effectively solve the challenges faced by traditional material evaluation, but also provide a new and effective tool for the performance evaluation and optimization design of multilayer thin-film materials. It has broad application prospects and potential value. Full article

This study presents the OGS-YOLOv8 model for coffee bean maturity identification, designed to enhance accuracy in identifying coffee beans at different maturity stages in complicated contexts, utilizing an upgraded version of YOLOv8. Initially, the ODConv (full-dimensional dynamic convolution) substitutes the convolutional layers in the backbone and neck networks to augment the network’s capacity to capture attributes of coffee bean images. Second, we replace the C2f layer in the neck networks with the CSGSPC (Convolutional Split Group-Shuffle Partial Convolution) module to reduce the computational load of the model. Lastly, to improve bounding box regression accuracy by concentrating on challenging samples, we substitute the Inner-FocalerIoU function for the CIoU loss function. According to experimental results, OGS-YOLO v8 outperforms the original model by 7.4%, achieving a detection accuracy of 73.7% for coffee bean maturity. Reaching 76% at mAP@0.5, it represents a 3.2% increase over the initial model. Furthermore, GFLOPs dropped 26.8%, from 8.2 to 6.0. For applications like coffee bean maturity monitoring and intelligent harvesting, OGS-YOLOv8 offers strong technical support and reference by striking a good balance between high detection accuracy and low computational cost. Full article

This paper presents a new method for detecting phase loss in Ynd transformers by integrating a Backup Interface Unit (BUI). Traditional detection techniques often struggle to reliably distinguish between genuine phase loss events and current imbalances caused by load variations, harmonics, or asymmetrical operating conditions, which can lead to delayed response or false triggering. The proposed method combines Clarke and Park transformations with controlled off-grid transition tests to enhance fault identification and validation. By applying these techniques, the system achieves higher sensitivity to true phase loss while maintaining robustness against normal operating disturbances. Simulation and laboratory experimental results confirm improved detection accuracy, reduced false positives, and faster protection response compared to conventional approaches. In addition, the method ensures continued operation and voltage stability during faults, which is critical for maintaining power quality and equipment safety. These advantages make the approach highly suitable for modern industrial facilities and smart grid applications where reliability and resilience are key requirements. Full article

Achieving high-precision clamping force control is crucial for Electro-Mechanical Braking (EMB) systems but remains challenging due to significant nonlinear friction (e.g., static, Coulomb, and viscous friction) within the transmission mechanism. To address this, a comprehensive model integrating the electrical and mechanical dynamics of the EMB actuator is first established. This pressure-oriented model, which explicitly accounts for the nonlinear frictions, is developed and validated in MATLAB/Simulink 2022b. Furthermore, physical experiments under typical braking scenarios are conducted to investigate the system’s friction characteristics, leading to the identification of a displacement–pressure load curve for the actuator. This curve serves as a key reference for braking force observation. Finally, a braking force observer-based controller is designed, implemented via an Auto-Disturbance Rejection Control (ADRC) algorithm. Experimental results from step and sinusoidal braking force tests demonstrate that the proposed controller not only effectively compensates for nonlinear disturbances but also achieves robust and stable clamping force control. Full article

Background: Basketball performance is highly dependent on morphological and neuromuscular traits, especially during adolescence, when rapid growth and maturation generate marked sex-based differences. However, limited data are available on Latin American players’ performance. This study aimed to compare the anthropometric characteristics, body composition, somatotype, and neuromuscular performance of male and female Colombian U-15 national basketball players. Methods: The sample consisted of thirty-seven players (20 males, 17 females; mean age: 14.8 ± 0.4 years) during the preparatory phase of the 2022 South American U-15 Championships. Anthropometry and body composition were evaluated following ISAK standards, and somatotype was calculated using Carter and Heath’s method. Neuromuscular performance included countermovement and squat jumps, bilateral handgrip strength, and isometric knee extensor and flexor peak torques. Between-sex differences were examined using t-tests, Welch’s tests, or Mann–Whitney U tests. The effects of sex on body composition, somatotype, and neuromuscular outcomes were assessed using MANOVA. Results: Males had higher muscle mass, lower adipose mass, and greater limb lengths than females (p < 0.01). No sex differences were observed in BMI, waist or hip circumference, or quadriceps strength. Regarding neuromuscular performance, males exhibited higher handgrip strength, hamstring torque (absolute and relative), and jump performance than females. Conclusions: Males showed greater muscle mass, strength, and jump performance, whereas females displayed higher fat levels and endomorphy than males. These findings provide useful data for optimizing training load prescriptions, guiding targeted strength programs, and developing sex-specific strategies for injury prevention and talent identification in adolescents. Full article

Compact fusion reactors are receiving increasing interest as a promising route for accelerating the path toward commercial fusion, thanks to their reduced size and cost. However, this compactness introduces new technological challenges, including higher radiation loads on critical functional components, such as the magnet system. Neutron shielding is therefore of utmost importance to guarantee the expected lifetime of the device, and its selection must account for the harsh environment imposed by the high radiation flux. Shielding materials should be structurally stable, not melt within the operational temperature windows, and be relatively low-cost. For nuclear reactor applications, binary compounds are typically the preferred choice as they often meet these requirements, particularly in terms of availability and cost. In this work, we present a systematic Monte Carlo analysis of more than 700 binary compounds, exposed to the neutron spectrum at the most loaded position of the vacuum vessel in a simplified model of a compact fusion reactor. Shielding performances were evaluated in a toroidal geometry in terms of neutron attenuation, power deposition, and activation, leading to the identification of several promising compositions for effective neutron shielding in future fusion applications. Full article

Wheat and corn are two major food crops in Xinjiang. However, the spectral similarity between these crop types and the complexity of their spatial distribution has posed significant challenges to accurate crop identification. To this end, the study aimed to improve the accuracy of crop distribution identification in complex environments in three ways. First, by analysing the kNDVI and EVI time series, the optimal identification window was determined to be days 156–176—a period when wheat is in the grain-filling to milk-ripening phase and maize is in the jointing to tillering phase—during which, the strongest spectral differences between the two crops occurs. Second, principal component analysis (PCA) was applied to Sentinel-2 data. The top three principal components were extracted to construct the input dataset, effectively integrating visible and near-infrared band information. This approach suppressed redundancy and noise while replacing traditional RGB datasets. Finally, the Convolutional Block Attention Module (CBAM) was integrated into the U-Net model to enhance feature focusing on key crop areas. An improved Atrous Spatial Pyramid Pooling (ASPP) module based on deep separable convolutions was adopted to reduce the computational load while boosting multi-scale context awareness. The experimental results showed the following: (1) Wheat and corn exhibit obvious phenological differences between the 156th and 176th days of the year, which can be used as the optimal time window for identifying their spatial distributions. (2) The method proposed by this research had the best performance, with its mIoU, mPA, F1-score, and overall accuracy (OA) reaching 83.03%, 91.34%, 90.73%, and 90.91%, respectively. Compared to DeeplabV3+, PSPnet, HRnet, Segformer, and U-Net, the OA improved by 5.97%, 4.55%, 2.03%, 8.99%, and 1.5%, respectively. The recognition accuracy of the PCA dataset improved by approximately 2% compared to the RGB dataset. (3) This strategy still had high accuracy when predicting wheat and corn yields in Qitai County, Xinjiang, and had a certain degree of generalisability. In summary, the improved strategy proposed in this study holds considerable application potential for identifying the spatial distribution of wheat and corn in arid regions. Full article

Reliable finite element models (FEMs) are required in seismic qualification of mechanical structures within a nuclear power plant (NPP). The relative displacement response of such mechanical structures is difficult to measure, and it is costly to extract enough modes. Therefore, the modification of the initial FEM by the common FEM updating method could not always be carried out smoothly. In this paper, a vibration control equation error-based two-stage FEM updating method is proposed to identify uncertain parameters in the FEMs, which is capable of employing seismic response in the form of acceleration. Optimal values of updating parameters are searched by using the Marquardt method, which is effective in avoiding the ill-conditioned iterative equation. Numerical simulation is performed for the steam pipeline to validate the actual feasibility and robustness of the proposed method. The fact that the distribution of damping is independent of the identification of parameters related to the mass and stiffness matrices is also demonstrated. Finally, experimental validation of the method is carried out on a load-bearing pipeline. The damping coefficients of the pipeline are modified independently by back propagation neural network. Full article

Investigating the pore structure and understanding the relationship between pore characteristics and mechanical properties are crucial to research in the study of cement mortar. At present, the segmentation of large-scale concrete pores is mainly conducted using traditional algorithms or software, which are time-consuming and operate in a semi-automated manner. However, the application of these methods faces challenges when analyzing large-scale rock pores due to factors such as a lack of data, artifacts, and inconsistent contrast. In this study, six series of cement mortars were subjected to real-time CT scanning under uniaxial loading (RT-CT) to collect real-time three-dimensional data on the evolution of pore structures during loading. To address issues such as artifacts and inconsistent contrast, a new augmentation method was proposed to overcome artifacts and enhance contrast consistency. Finally, the augmented dataset was utilized for training, and the Fast R-CNN algorithm served as the framework for developing the pore recognition model. The results indicate that the improved algorithm demonstrates enhanced convergence and greater accuracy in pore segmentation. A mathematical model is developed to relate uniaxial compressive strength (UCS) to pore fractal dimension and porosity, based on pore segmentation analysis. The fractal dimensions evolution of each specimen is consistent with the progressive failure indicated by the strain-stress curve. Under uniaxial loading, specimens with a 4:1 cement–sand ratio exhibited peak strength. The incorporation of fractals improved particle contact, thereby facilitating the formation of the skeletal structure. These efforts contribute to improving the identification of the deformation of cement mortars. Full article

The Yangtze sturgeon (Acipenser dabryanus) is designated as critically endangered in the IUCN Red List and is a first-class protected species in China. During the summer of 2024, it suffered lethal disease outbreaks. Comprehensive pathological and microbiological analyses were conducted to clarify the etiology. Clinically, infected sturgeon exhibited systemic manifestations including cutaneous ulcers, hemorrhagic septicemia, and diffuse necrosis in liver, kidney and heart tissues. Histopathologically, infected sturgeon showed liver hepatocyte vacuolation/necrosis, renal glomerular atrophy, and cardiac epicardial thickening with lymphocyte/eosinophil infiltration; Gram staining revealed co-localized Gram-positive/negative bacteria in lesions, and TEM identified diverse bacterial morphotypes. Through isolation and molecular identification, four bacterial pathogens were characterized: Streptococcus iniae, Klebsiella pneumoniae, Edwardsiella tarda, and Bacillus cereus. Bacterial load detection revealed the presence of these pathogens in lesion tissues. Antimicrobial susceptibility testing indicated multidrug resistance to florfenicol, tetracycline, and ampicillin (commonly used antibiotics in aquaculture), while high sensitivity to ceftazidime, ceftriaxone, and ciprofloxacin was observed. Thus, we infer that sustained high-temperature stress triggered bacterial co-infection is closely related to this large-scale death incident. This is the first evidence of polymicrobial infection in the Yangtze sturgeon, emphasizing the significance of shifting from a single-pathogen perspective to a multi-pathogen framework, and highlighting the urgency of implementing ecological prevention strategies for this species. Full article

Power electronic devices for AC/DC and AC/AC conversion are, nowadays, widely distributed in electrified transportation and industrial applications, which can determine significant deviation in supply voltage waveform from the AC sinusoidal and promote insulation extrinsic aging mechanisms as partial discharges (PDs). PDs are one of the most harmful processes as they are able to cause accelerated extrinsic aging of electrical insulation systems and are the cause of premature failure in electrical asset components. PD phenomenology under pulse width modulated (PWM) voltage waveforms has been dealt with in recent years, also through some IEC/IEEE standards, but less work has been performed on PD harmfulness under AC distorted waveforms containing voltage harmonics and notches. On the other hand, these voltage waveforms can often be present in electrical assets containing conventional loads and power electronics loads/drives, such as for ships or industrial installations. The purpose of this paper is to provide a contribution to this lack of knowledge, focusing on PD sensing and phenomenology. It has been shown that PD patterns can change considerably with respect to those known under sinusoidal AC when harmonic voltages and/or notches are present in the supply waveform. This can impact PD typology identification, which is based on features related to PD pattern-based physics. The adaptation of identification AI algorithms used for AC sinusoidal voltage as well as distorted AC waveforms is discussed in this paper, showing that effective identification of the type of defects generating PD, and thus of their harmfulness, can still be achieved. Full article