Search Results (837)

Introduction: Burnout syndrome, long described as an “occupational phenomenon”, now affects 15–20% of the general workforce and more than 50% of clinicians, teachers, social-care staff and first responders. Its precise nosological standing remains disputed. We conducted a mechanistic review of electroencephalography (EEG) studies to determine whether burnout is accompanied by reproducible brain-function alterations that justify disease-level classification. Methods: Following PRISMA-adapted guidelines, two independent reviewers searched PubMed/MEDLINE, Scopus, Google Scholar, Cochrane Library and reference lists (January 1980–May 2025) using combinations of “burnout,” “EEG”, “electroencephalography” and “event-related potential.” Only English-language clinical investigations were eligible. Eighteen studies (n = 2194 participants) met the inclusion criteria. Data were synthesised across three domains: resting-state spectra/connectivity, event-related potentials (ERPs) and longitudinal change. Results: Resting EEG consistently showed (i) a 0.4–0.6 Hz slowing of individual-alpha frequency, (ii) 20–35% global alpha-power reduction and (iii) fragmentation of high-alpha (11–13 Hz) fronto-parietal coherence, with stage- and sex-dependent modulation. ERP paradigms revealed a distinctive “alarm-heavy/evaluation-poor” profile; enlarged N2 and ERN components signalled hyper-reactive conflict and error detection, whereas P3b, Pe, reward-P3 and late CNV amplitudes were attenuated by 25–50%, indicating depleted evaluative and preparatory resources. Feedback processing showed intact or heightened FRN but blunted FRP, and affective tasks demonstrated threat-biassed P3a latency shifts alongside dampened VPP/EPN to positive cues. These alterations persisted in longitudinal cohorts yet normalised after recovery, supporting trait-plus-state dynamics. The electrophysiological fingerprint differed from major depression (no frontal-alpha asymmetry, opposite connectivity pattern). Conclusions: Across paradigms, burnout exhibits a coherent neurophysiological signature comparable in magnitude to established psychiatric disorders, refuting its current classification as a non-disease. Objective EEG markers can complement symptom scales for earlier diagnosis, treatment monitoring and public-health surveillance. Recognising burnout as a clinical disorder—and funding prevention and care accordingly—is medically justified and economically imperative. Full article

Background: The reproducibility of research findings continues to be a challenge in many fields, including neurosciences. It is now required that biological variables such as sex and age be considered in preclinical and clinical research. Rodents are frequently used to model clinical conditions; however, litter information is rarely presented. Some studies utilize entire litters with each animal treated as an independent sample, while others equally assign animals from each litter to different groups/treatments, and others use averaged data. These methods can yield different results. Methods: This study used different analysis methods to evaluate embryo and placenta weights from E17.5 acid-sensing ion channel 2a (ASIC2a) mice with or without seizure exposure. Results: When each embryo was treated as an individual sample, fetal and placental weight significantly differed following seizures in the ASIC2a heterozygous (+/−) and homozygous (−/−) groups. Differences in fetal weight were driven by females in the ASIC2a+/− group and both sexes in the ASIC2a−/− group. These differences were lost when an average per sex/genotype/litter was used. There was no difference in placental weight when treated individually; however, female ASIC2a−/− placentas weighed less following seizures. This difference was lost with averaged data. ASIC2a−/− fetuses from −/− dams had reduced weights post-seizure exposure. Position on the uterine horn influenced embryo and placental weight. Conclusions: Our results indicate that using full litters analyzed as individual data points should be avoided, as it can lead to Type I errors. Furthermore, studies should account for litter effects and be transparent in their methods and results. Full article

Medical image registration is an indispensable preprocessing step to align medical images to a common coordinate system before in-depth analysis. The registration precision is critical to the following analysis. In addition to representative image features, the initial pose settings and multiple poses in images will significantly affect the registration precision, which is largely neglected in state-of-the-art works. To address this, the paper proposes a dual-pose medical image registration algorithm based on improved differential evolution. More specifically, the proposed algorithm defines a composite similarity measurement based on contour points and utilizes this measurement to calculate the similarity between frontal–lateral positional DRR (Digitally Reconstructed Radiograph) images and X-ray images. In order to ensure the accuracy of the registration algorithm in particular dimensions, the algorithm implements a dual-pose registration strategy. A PDE (Phased Differential Evolution) algorithm is proposed for iterative optimization, enhancing the optimization algorithm’s ability to globally search in low-dimensional space, aiding in the discovery of global optimal solutions. Extensive experimental results demonstrate that the proposed algorithm provides more accurate similarity metrics compared to conventional registration algorithms; the dual-pose registration strategy largely reduces errors in specific dimensions, resulting in reductions of 67.04% and 71.84%, respectively, in rotation and translation errors. Additionally, the algorithm is more suitable for clinical applications due to its lower complexity. Full article

To address the diverse requirements of accuracy and robustness in integrated navigation for unmanned aerial vehicles, an interactive robust filter algorithm that integrates the interactive multiple model concept and leverages the complementary applicability of the strong tracking filter and the smooth variable structure filter is proposed. The algorithm operates as follows: the strong tracking filter, along with the smooth variable structure filter, operates side by side with distinct models. During the filter process, the likelihood function is utilized to update the filter probabilities and determine the weights for each one of the filters. Input interaction, coupled with output fusion, is then carried out. The results of the experiments validate that the presented interactive filter algorithm significantly reduces estimation errors. When confronted with complex, dynamic noise environments and system uncertainties, it retains high-precision state estimation while demonstrating markedly improved robustness. The proposed interactive robust filter algorithm is compared against the strong tracking filter, smooth variable structure filter, and strong tracking smooth filter. Taking the strong tracking smooth filter, which has the highest accuracy among the three, as the reference baseline, the presented interactive robust filter algorithm achieves over 16% improvement in velocity accuracy and over 40% improvement in position accuracy. Full article

This study aims to address the challenges posed by uneven energy amplitude and a low signal-to-noise ratio (SNR) in the total focus imaging of coarse-crystalline elastic anisotropic materials. A novel method for acoustic field correction vector-coherent total focus imaging, based on the materials’ properties, is proposed. To demonstrate the effectiveness of this method, a test specimen, an austenitic stainless steel nozzle weld, was employed. Seven side-drilled hole defects located at varying positions and depths, each with a diameter of 2 mm, were examined. An ultrasound simulation model was developed based on material backscatter diffraction results, and the scattering attenuation compensation factor was optimized. The acoustic field correction function was derived by combining acoustic field directivity with diffusion attenuation compensation. The phase coherence weighting coefficients were calculated, followed by image reconstruction. The results show that the proposed method significantly improves imaging amplitude uniformity and reduces the structural noise caused by the coarse crystal structure of austenitic stainless steel. Compared to conventional total focus imaging, the detection SNR of the seven defects increased by 2.34 dB to 10.95 dB. Additionally, the defect localization error was reduced from 0.1 mm to 0.05 mm, with a range of 0.70 mm to 0.88 mm. Full article

Objectives: The objective of this study was to evaluate changes in anatomical point position, cranio-cervical posture, and respiratory dimensions following conventional bimaxillary total prosthetic rehabilitation. Methods: A prospective, longitudinal, observational, analytical study was conducted on 12 patients, aged 55 to 75 years, at the Department of Dental Prosthetics at the University of Medicine and Pharmacy in Cluj-Napoca. All patients had complete bimaxillary edentulism and received removable dentures as treatment. Clinical and cephalometric analyses were performed before and after prosthetic treatment to compare changes. The cephalometric analysis was based on the guidelines of Tweed and Rocabado for evaluation. Quantitative data were described using the mean and standard deviation for normal distribution and represented by bar graphs with error bars. A paired samples t-test was used to determine differences between groups, with a significance threshold of 0.05 for the bilateral p-value. Results: When analyzing changes in cranial base inclination, the corresponding angles exhibited an increase, indicating cephalic extension. A statistically significant difference in the anteroposterior diameter of the oropharyngeal lumen with and without bimaxillary complete dentures was identified (p < 0.05). For hyperdivergent patients, modifications in the position of anatomical features on cephalometry slightly reduced the VDO and had a slight compensatory effect on skeletal typology. In contrast, for hypodivergent patients, modifications to the position of anatomical landmarks also had a compensatory effect on skeletal typology, increasing the VDO. Conclusion: Changes in the position of anatomical features on cephalometry generally have a compensatory effect on skeletal typology after complete denture placement. Complete prosthetic treatment with removable dentures can significantly influence respiratory function by reducing the oropharyngeal lumen and body posture by cephalic extension and attenuation of the lordotic curvature of the cervical spine. Full article

As vital material carriers of human civilization, earthen sites are experiencing continuous surface deterioration under the combined effects of weathering and anthropogenic damage. Traditional surface conservation techniques, due to their poor compatibility and limited reversibility, struggle to address the compound challenges of micro-scale degradation and macro-scale deformation. With the deep integration of digital twin technology, spatial information technologies, intelligent systems, and sustainable concepts, earthen site surface conservation technologies are transitioning from single-point applications to multidimensional integration. However, challenges remain in terms of the insufficient systematization of technology integration and the absence of a comprehensive interdisciplinary theoretical framework. Based on the dual-core databases of Web of Science and Scopus, this study systematically reviews the technological evolution of surface conservation for earthen sites between 2000 and 2025. CiteSpace 6.2 R4 and VOSviewer 1.6 were used for bibliometric visualization analysis, which was innovatively combined with manual close reading of the key literature and GPT-assisted semantic mining (error rate < 5%) to efficiently identify core research themes and infer deeper trends. The results reveal the following: (1) technological evolution follows a three-stage trajectory—from early point-based monitoring technologies, such as remote sensing (RS) and the Global Positioning System (GPS), to spatial modeling technologies, such as light detection and ranging (LiDAR) and geographic information systems (GIS), and, finally, to today’s integrated intelligent monitoring systems based on multi-source fusion; (2) the key surface technology system comprises GIS-based spatial data management, high-precision modeling via LiDAR, 3D reconstruction using oblique photogrammetry, and building information modeling (BIM) for structural protection, while cutting-edge areas focus on digital twin (DT) and the Internet of Things (IoT) for intelligent monitoring, augmented reality (AR) for immersive visualization, and blockchain technologies for digital authentication; (3) future research is expected to integrate big data and cloud computing to enable multidimensional prediction of surface deterioration, while virtual reality (VR) will overcome spatial–temporal limitations and push conservation paradigms toward automation, intelligence, and sustainability. This study, grounded in the technological evolution of surface protection for earthen sites, constructs a triadic framework of “intelligent monitoring–technological integration–collaborative application,” revealing the integration needs between DT and VR for surface technologies. It provides methodological support for addressing current technical bottlenecks and lays the foundation for dynamic surface protection, solution optimization, and interdisciplinary collaboration. Full article

The efficient and reliable maintenance and repair of industrial machinery depend critically on circuit diagrams, which serve as essential references for troubleshooting and must be updated when machinery is modified. However, many circuit diagrams are not available in structured, machine-readable format; instead, they often exist as unstructured PDF files, rendered images, or even photographs. Existing digitization methods often address isolated tasks, such as symbol detection, but fail to provide a comprehensive solution. This paper presents a novel pipeline for extracting the underlying graph structures of circuit diagrams, integrating image preprocessing, pattern matching, and graph extraction. A U-net model is employed for noise removal, followed by gray-box pattern matching for device classification, line detection by morphological operations, and a final graph extraction step to reconstruct circuit connectivity. A detailed error analysis highlights the strengths and limitations of each pipeline component. On a skewed test diagram from a scan with slight rotation, the proposed pipeline achieved a device detection accuracy of 88.46% with no false positives and a line detection accuracy of 94.7%. Full article

This study presented an autonomous shield-cutting end-effector for maize surrounding weeding (SEMSW), addressing the challenges of the low weed removal rate (WRR) and high seedling damage rate (SDR) in northern China’s 3–5 leaf stage maize. The SEMSW integrated seedling positioning, robotic arm control, and precision weeding functionalities: a seedling positioning sensor identified maize seedlings and weeds, guiding XYZ translational motions to align the robotic arm. The seedling-shielding anti-cutting mechanism (SAM) enclosed crop stems, while the contour-adaptive weeding mechanism (CWM) activated two-stage retractable blades (TRWBs) for inter/intra-row weeding operations. The following key design parameters were determined: 150 mm inner diameter for the seedling-shielding disc; 30 mm minimum inscribed-circle for retractable clamping units (RCUs); 40 mm ground clearance for SAM; 170 mm shielding height; and 100 mm minimum inscribed-circle diameter for the TRWB. Mathematical optimization defined the shape-following weeding cam (SWC) contour and TRWB dimensional chain. Kinematic/dynamic models were introduced alongside an adaptive sliding mode controller, ensuring lateral translation error convergence. A YOLOv8 model achieved 0.951 precision, 0.95 mAP50, and 0.819 mAP50-95, striking a balance between detection accuracy and localization precision. Field trials of the prototype showed 88.3% WRR and 2.2% SDR, meeting northern China’s agronomic standards. Full article

In the last 20 years, NRTK GNSS CORS networks have been designed, developed, and employed in Sicily (Italy) for research purposes. Following a comprehensive description of the CORS networks in this region, this study aims to compare results obtained from multiple reference points with known coordinates. The analysis will not only account for the various CORS networks to which these points belong but also examine the different correction streams applied in NRTK surveys. The assumption of a normal distribution, which is often assumed for the positioning errors, is generally confirmed by applying the areal stream corrections (FKP, MAX, iMAX), but still, a multimodal distribution is evident when punctual corrections (Nearest and VRS) are applied. The representation of the results in a GIS environment allows for showing the different patterns for some differential correction streams. A statistical approach allowed confirming the trends of the different distribution frequencies of the residuals of the coordinates and quantifying separately the correlation with parameters involved in the analysis. The correlation retrieved is predominantly weak and very weak for all CORS, except for one of those analyzed, for which the correlation is moderate. The correlation became strong for the same CORS, if all parameters are included, for both planimetric and plano-altimetric components (r = 0.6 and r = 0.7, respectively). Full article

This study proposes a new method to improve the accuracy of car-following models in predicting the mobility of mixed traffic flow involving trucks and automated vehicles (AVs). A classification is developed to categorize car-following behaviors into eight distinct modes based on vehicle type (passenger car/truck) and autonomy level (human-driven vehicle [HDV]/AV) for parameter calibration and simulation. The car-following model parameters are calibrated based on the HighD dataset, and the models are selected through minimizing statistical error. A cellular-automaton-based simulation platform is implemented in MATLAB (R2023b), and a decision framework is developed for the simulation. Key findings demonstrate that mode-specific parameter calibration improves model accuracy, achieving an average error reduction of 80% compared to empirical methods. The simulation results reveal a positive correlation between the AV penetration rate and traffic flow stability, which consequently enhances capacity. Specifically, a full transition from 0% to 100% AV penetration increases traffic capacity by 50%. Conversely, elevated truck penetration rates degrade traffic flow stability, reducing the average speed by 75.37% under full truck penetration scenarios. Additionally, higher AV penetration helps stabilize traffic flow, leading to reduced speed fluctuations and lower emissions, while higher truck proportions contribute to higher emissions due to increased traffic instability. Full article

The performance of visual SLAM in complex, real-world scenarios is often compromised by unreliable feature extraction and matching when using handcrafted features. Although deep learning-based local features excel at capturing high-level information and perform well on matching benchmarks, they struggle with generalization in continuous motion scenes, adversely affecting loop detection accuracy. Our system employs a Model-Agnostic Meta-Learning (MAML) strategy to optimize the training of keypoint extraction networks, enhancing their adaptability to diverse environments. Additionally, we introduce a coarse-to-fine feature tracking mechanism for learned keypoints. It begins with a direct method to approximate the relative pose between consecutive frames, followed by a feature matching method for refined pose estimation. To mitigate cumulative positioning errors, DK-SLAM incorporates a novel online learning module that utilizes binary features for loop closure detection. This module dynamically identifies loop nodes within a sequence, ensuring accurate and efficient localization. Experimental evaluations on publicly available datasets demonstrate that DK-SLAM outperforms leading traditional and learning-based SLAM systems, such as ORB-SLAM3 and LIFT-SLAM. DK-SLAM achieves 17.7% better translation accuracy and 24.2% better rotation accuracy than ORB-SLAM3 on KITTI and 34.2% better translation accuracy on EuRoC. These results underscore the efficacy and robustness of our DK-SLAM in varied and challenging real-world environments. Full article

In complex environments where satellite signals may be interfered with, it is difficult to achieve precise positioning of high-speed aerial vehicles solely through the inertial navigation system. To overcome this challenge, this paper proposes an NGO-ELM geomagnetic matching-assisted navigation algorithm, in which the Northern Goshawk Optimization (NGO) algorithm is used to optimize the initial weights and biases of the Extreme Learning Machine (ELM). To enhance the matching performance of the NGO-ELM algorithm, three improvements are proposed to the NGO algorithm. The effectiveness of these improvements is validated using the CEC2005 benchmark function suite. Additionally, the IGRF-13 model is utilized to generate a geomagnetic matching dataset, followed by comparative testing of five geomagnetic matching models: INGO-ELM, NGO-ELM, ELM, INGO-XGBoost, and INGO-BP. The simulation results show that after the airborne equipment acquires the geomagnetic data, it only takes 0.27 µs to obtain the latitude, longitude, and altitude of the aerial vehicle through the INGO-ELM model. After unit conversion, the average absolute errors are approximately 6.38 m, 6.43 m, and 0.0137 m, respectively, which significantly outperform the results of four other models. Furthermore, when noise is introduced into the test set inputs, the positioning error of the INGO-ELM model remains within the same order of magnitude as those before the noise was added, indicating that the model exhibits excellent robustness. It has been verified that the geomagnetic matching-assisted navigation algorithm proposed in this paper can achieve real-time, accurate, and stable positioning, even in the presence of observational errors from the magnetic sensor. Full article

This study examines the impact of geomagnetic disturbances quantified by the Kp and Dst indices on the accuracy of single-frequency GPS positioning across mid-latitudes and the equatorial zone, with a focus on temporal and spatial positioning errors variability. GNSS data from a globally distributed network of 14 IGS stations were analyzed for September 2017, featuring significant geomagnetic activity. The selection of stations encompassed equatorial and mid-latitude regions (approximately ±45°), strategically aligned with the distribution of the Dst index during geomagnetic storms. Satellite navigation data were processed using RTKLIB software in standalone mode with standardized atmospheric and orbital corrections. The GPS was chosen over GLONASS following preliminary testing, which revealed a higher sensitivity of GPS positional accuracy to variations in geomagnetic indices such as Kp and Dst, despite generally lower total error magnitudes. The ECEF coordinate system calculates the total GPS error as the vector sum of deviations in the X, Y, and Z axes. Statistical evaluation was performed using One-Way Repeated Measures ANOVA to determine whether positional error variances across geomagnetic activity phases were significant. The results of the variance analysis confirm that the variation in the total GPS positioning error is non-random and can be attributed to the influence of geomagnetic storms. However, regression analysis reveals that the impact of geomagnetic storms (quantified by Kp and Dst) displays spatiotemporal variability, with no consistent correlation to GPS positioning error dynamics. The findings, as well as the developed methodology, have qualitative implications for GNSS-dependent operations in sensitive sectors such as navigation, timing services, and geospatial monitoring. Full article

Objectives/Background: With increasing awareness of the association between physical activity and mental health, promoting youth health has gained prominence. For this, education and support are needed. As psychological school-based factors could be key to affecting this behavior, this study investigates middle school students’ trust in their physical education teachers and their grit, analyzing their effects on health-promoting behaviors that could follow these adolescents through adulthood. Methods: Middle school students, aged 12–14, were recruited from three schools in Sejong City, Korea, in May 2025; 420 survey questionnaires were distributed and 390 were returned (response rate: 92.86%). After eliminating those with insincere responses, 369 valid questionnaires (boys = 186, girls = 183) were analyzed. The analysis covered the descriptive statistics, Pearson’s correlation, and structural equation modeling, with grit, trust in physical education teachers, and health-promoting behaviors as variables. Results: The correlation analysis verified multicollinearity between trust in physical education teachers (closeness, fairness, teaching method, and physical ability), grit (effort, perseverance, and interest consistency), and health-promoting behaviors (self-actualization, health management, and stress management). A positive significant correlation was found between all subfactors (p < 0.05). The research model’s fit was confirmed through several fit indices; specifically, normed χ² = 4.138, goodness-of-fit-index = 0.942, root mean square residual = 0.033, root mean square error of approximation = 0.092, incremental fit index = 0.965, Tucker–Lewis index = 0.947, and comparative fit index = 0.965, and all values were judged acceptable. The standardized coefficients of each latent variable explaining the measurement variables were 0.707 or higher. Therefore, the explanatory power of the measurement variables was also satisfactory; thus, the research model was appropriate and could be used for analysis. The model findings revealed that trust in physical education teachers had a positive effect on student grit (β = 0.505, p < 0.001) and that grit had a positive effect on health-promoting behaviors (β = 0.743, p < 0.001); however, trust in physical education teachers did not have a direct effect on health-promoting behaviors (statistically insignificant [β = 0.103, p > 0.05]). Thus, grit had a mediating effect between trust in physical education teachers and health-promoting behaviors (β = 0.375, p < 0.01). Conclusions: This study highlights the educational implications for physical education teachers of building trust and strengthening student grit as key factors in achieving sustainable health-promoting behaviors among adolescents. Full article