Search Results (4,055)

Remote sensing image retrieval (RSIR) plays a crucial role in remote sensing applications, focusing on retrieving a collection of items that closely match a specified query image. Due to the advantages of low storage cost and fast search speed, deep hashing has been one of the most active research problems in remote sensing image retrieval. However, remote sensing images contain many content-irrelevant backgrounds or noises, and they often lack the ability to capture essential fine-grained features. In addition, existing hash learning often relies on random sampling or semi-hard negative mining strategies to form training batches, which could be overwhelmed by some redundant pairs that slow down the model convergence and compromise the retrieval performance. To solve these problems effectively, a novel Deep Multi-similarity Hashing with Spatial-enhanced Learning, termed DMsH-SL, is proposed to learn compact yet discriminative binary descriptors for remote sensing image retrieval. Specifically, to suppress interfering information and accurately localize the target location, by introducing a spatial enhancement learning mechanism, the spatial group-enhanced hierarchical network is firstly designed to learn the spatial distribution of different semantic sub-features, capturing the noise-robust semantic embedding representation. Furthermore, to fully explore the similarity relationships of data points in the embedding space, the multi-similarity loss is proposed to construct informative and representative training batches, which is based on pairwise mining and weighting to compute the self-similarity and relative similarity of the image pairs, effectively mitigating the effects of redundant and unbalanced pairs. Experimental results on three benchmark datasets validate the superior performance of our approach. Full article

Multi-channel speech enhancement has become an active area of research, demonstrating excellent performance in recovering desired speech signals from noisy environments. Recent approaches have increasingly focused on leveraging spectral information from multi-channel inputs, yielding promising results. In this study, we propose a novel feature integration network that not only captures spectral information but also refines it through shifted-window-based self-attention, enhancing the quality and precision of the feature extraction. Our network consists of blocks containing a full- and sub-band LSTM module for capturing spectral information, and a global–local attention fusion module for refining this information. The full- and sub-band LSTM module integrates both full-band and sub-band information through two LSTM layers, while the global–local attention fusion module learns global and local attention in a dual-branch architecture. To further enhance the feature integration, we fuse the outputs of these branches using a spatial attention module. The model is trained to predict the complex ratio mask (CRM), thereby improving the quality of the enhanced signal. We conducted an ablation study to assess the contribution of each module, with each showing a significant impact on performance. Additionally, our model was trained on the SPA-DNS dataset using a circular microphone array and the Libri-wham dataset with a linear microphone array, achieving competitive results compared to state-of-the-art models. Full article

Background: The aim of the present study was to analyse the association between neuroticism (one of the Big Five personality traits) and the most common secondary sensorimotor complications occurring in patients after spinal cord injury (SCI), i.e., muscle spasticity (hypertonia) and pain, and to investigate the associations between neuroticism and the effects of conventional rehabilitation (dynamic parapodium) and those using robotic-assisted gait training (RAGT) in this group of patients. In addition, the association of neuroticism with self-efficacy, personal beliefs about pain control, and adopted coping strategies among SCI patients was analysed. These data can be used as a reference for designing effective forms of therapy and support dedicated to this group of patients. Methods and procedures: Quantitative analysis included 110 patients after SCI. The participants were divided by simple randomisation into a rehabilitation group with RAGT and a rehabilitation group with dynamic parapodium therapy (DPT). The following survey instruments were used for data collection: Revised NEO Personality Inventory (NEO-PI-R); Ashworth Scale; the Spinal Cord Independence Measure III (SCIM III); the Walking Index for Spinal Cord Injury II (WISCI-II); the American Spinal Injury Association Impairment Scale (AIS); the Pain Coping Strategies Questionnaire—CSQ; and the Beliefs about Pain Control Questionnaire—BPCQ. Outcomes and results: analyses showed a positive association between neuroticism and spastic tension (rho = 0.39; p < 0.001). Conclusions and implications: the study showed that a high level of neuroticism correlates with a higher level of spasticity, but no such correlation was observed for pain. Additionally, the study did not show a significant correlation between neuroticism and rehabilitation outcome depending on the rehabilitation modality (RAGT vs. DPT). The results underline the importance of carrying out a psychological diagnosis of patients to provide therapeutic support in the rehabilitation process. Full article

During the COVID-19 pandemic, the New Jersey Safe Schools Program (NJSS) surveyed a subset of newer NJ high school (HS) teachers who completed NJSS work-based learning supervisory trainings from October 2021 to June 2023. The purpose of this study was to gain insight on NJ HS teacher perceptions of school provided mental health services, and well-being supports received during the COVID-19 pandemic. Via online surveys, teachers anonymously identified who should be responsible for supporting mental well-being in schools, satisfaction with existing mental health services, and self-care practices implemented during the COVID-19 pandemic. Of the 114 HS teachers surveyed, nearly 70% would recommend existing school mental health services to colleagues, 53% would like an increase in mental health and counseling services available at their school, and 44% would like their schools to improve mental health literacy. This study presents insight into the needs teachers expressed for appropriate school mental health support and services. Data will inform guidance for how to better address identified needs, including employee wellness, and creating positive social and emotional school environments. School districts should prioritize the implementation of suitable and equitable school-based mental health services to teachers and students alike to promote healthy and productive school environments. Full article

Participation in PPG Daljab is essential for improving the performance of Islamic Religious Education (PAI) teachers and promoting internal quality assurance within teacher training institutes (LPTKs). However, how can we effectively assess participation, particularly for individual teachers? This quantitative study investigates the impact of in-service teacher education programs on PAI teachers’ competencies using individual self-assessment. This study involved 255 PAI teachers from three LPTKs under the Ministry of Religious Affairs: UIN Sunan Gunung Djati Bandung, UIN Sunan Kalijaga Yogyakarta, and UIN Syarif Hidayatullah Jakarta. Teachers’ competencies were measured across four dimensions: pedagogical, personality, social, and professional competence. The findings indicate that the PPG program effectively supported the self-development of PAI teachers who have obtained professional certification. The PPG program enhanced teachers’ competencies across all dimensions, with the most significant improvement in personality competence. However, in the professional dimension, areas such as learning evaluation and the use of technology and digital learning require further strengthening post-PPG program. This article provides recommendations for stakeholders to develop continuing professional education programs following the PPG program, taking into consideration PAI teachers’ lack of competence. Full article

Background/Objectives: Previous research has suggested that Western musicians, who generally demonstrate proficiency in reading musical scores, exhibit superior performance in visuospatial working memory tasks compared to non-musicians. Evidence indicates brain activation in regions such as the left inferior parietal lobe and the right posterior fusiform gyrus during music reading, which are associated with visuospatial processing. This study aimed to explore how musical training influences spatial working memory and to examine the relationship between self-assessment accuracy and cognitive performance. Methods: A visuospatial working memory test, the Corsi block-tapping test (CBT), was administered to 70 participants, including 35 musicians with experience in music reading and 35 non-musicians. CBT performances were compared between groups, controlling for sex and age differences using analysis of covariance. Participants were also asked to self-assess their visuospatial capabilities. Results: Musicians performed significantly better than non-musicians in the CBT and demonstrated greater metacognitive accuracy in evaluating their visuospatial memory capacities. A total of 46.34% of musicians who claimed good performance on the CBT did in fact perform well, in comparison with 14.63% of non-musicians. Sex influenced the outcomes of spatial working memory, while age did not significantly affect performance. Conclusions: This self-awareness of visuospatial capabilities reflects a form of metacompetence, encompassing reflective thinking and the ability to assess one’s cognitive skills. Furthermore, while differences in spatial working memory between musicians and non-musicians appear to be related to executive functions associated with general music practice, further investigation is needed to explore other potential influences beyond musical experience. Full article

Reliable pose estimation for non-cooperative spacecraft is a key technology for in-orbit service and active debris removal missions. Utilizing deep learning techniques for processing monocular camera images is effective and is a hotspot of current research. To reduce errors and improve model generalization, researchers often design multi-head loss functions or use generative models to achieve complex data augmentation, which makes the task complex and time-consuming. We propose a pyramid vision transformer spatial-aware keypoints regression network and a stereo-aware augmentation strategy to achieve robust prediction. Specifically, we primarily use the eight vertices of a cuboid satellite body as landmarks and the observable surfaces can be transformed by, respectively, using the pose labels. The experimental results on the SPEED+ dataset show that by using the existing EPNP algorithm and pseudo-label self-training method, we can achieve high-precision pose estimation for target cross-domains. Compared to other existing methods, our model and strategy are more straightforward. The entire process does not require the generation of new images, which significantly reduces the storage requirements and time costs. Combined with a Kalman filter, the robust and continuous output of the target position and attitude is verified by the SHIRT dataset. This work realizes deployment on mobile devices and provides strong technical support for the application of an automatic visual navigation system in orbit. Full article

Background/Objectives: Nursing competence encompasses the integration of knowledge, skills, and attitudes essential for comprehensive and safe patient care. This study aimed to compare self-assessment and tutor evaluation of nurses’ competencies in a hospital emergency department before and after a training intervention. Methods: A quasi-experimental design was employed, involving 63 newly hired nurses who participated in a mentorship program. The intervention included theoretical and practical sessions on critical care skills. Data were collected through self-assessment questionnaires and objective evaluations by tutors using validated rubrics. Results: The results indicated significant differences between self-assessment and tutor evaluations in pre- and post-intervention phases, particularly in competencies related to orotracheal intubation and fibrinolytic therapy for ischemic stroke. Post-intervention, discrepancies between self-assessment and tutor evaluations decreased, suggesting improved self-awareness and competence among participants. Conclusions: This study highlights the importance of combining self-assessment and external evaluation to ensure accurate competency assessment and effective educational interventions, ultimately enhancing the quality of patient care. Full article

The application value of low-quality photos taken in foggy conditions is significantly lower than that of clear images. As a result, restoring the original image information and enhancing the quality of damaged images on cloudy days are crucial. Commonly used deep learning techniques like DehazeNet, AOD-Net, and Li have shown encouraging progress in the study of image dehazing applications. However, these methods suffer from a shallow network structure leading to limited network estimation capability, reliance on atmospheric scattering models to generate the final results that are prone to error accumulation, as well as unstable training and slow convergence. Aiming at these problems, this paper proposes an improved end-to-end convolutional neural network method based on the denoising self-encoder-DenseNet (DAE-DenseNet), where the denoising self-encoder is used as the main body of the network structure, the encoder extracts the features of haze images, the decoder performs the feature reconstruction to recover the image, and the boosting module further performs the feature fusion locally and globally, and finally outputs the dehazed image. Testing the defogging effect in the public dataset, the PSNR index of DAE-DenseNet is 22.60, which is much higher than other methods. Experiments have proved that the dehazing method designed in this paper is better than other algorithms to a certain extent, and there is no color oversaturation or an excessive dehazing phenomenon in the image after dehazing. The dehazing results are the closest to the real image and the viewing experience feels natural and comfortable, with the image dehazing effect being very competitive. Full article

This study looks into how companies react to and adjust to shifting social and environmental factors. A comprehensive model is put forth and empirically tested using data from the pharmaceutical business, utilizing the dynamic capabilities theory perspective. An investigation is conducted into the factors that explain and influence the relationship between environmental performance (EP) and green transformational leaders (GTLs). Green empowerment and efficacy are suggested as potential explanators and green training is regarded as a prerequisite. A total of 247 managers employed by pharmaceutical companies provided data for the analysis of the suggested model. The analysis methods employed were PROCESS Macro and Structure Equation Modeling (SEM). The findings show that green transformational leaders have an insignificant direct influence on environmental performance but a significant indirect impact. This relationship is significantly mediated by green empowerment and self-efficacy and moderated by green training. Full article

Self-paced exercise protocols have gained attention for their potential to optimize performance and manage fatigue by allowing individuals to regulate their efforts based on perceived exertion. This pilot study aimed to investigate the neural and physiological responses during a self-paced $\dot{\mathrm{V}}$O₂max (SPV) and incremental exercise tests (IET). Six trained male cyclists (mean age 39.2 ± 13.3 years; $\dot{\mathrm{V}}$O₂max 54.3 ± 8.2 mL·kg⁻¹·min⁻¹) performed both tests while recording their brain activity using electroencephalography (EEG). The IET protocol involved increasing the power every 3 min relative to body weight, while the SPV allowed participants to self-regulate the intensity using ratings of perceived exertion (RPE). Gas exchange, EEG, heart rate (HR), stroke volume (SV), and power output were continuously monitored. Statistical analyses included a two-way repeated measures ANOVA and Wilcoxon signed-rank tests to assess differences in alpha and beta power spectral densities (PSDs) and the EEG/$\dot{\mathrm{V}}$O₂ ratio. Our results showed that during the SPV test, the beta PSD initially increased but stabilized at around 80% of the test duration, suggesting effective management of effort without further neural strain. In contrast, the IET showed a continuous increase in beta activity, indicating greater neural demand and potentially leading to an earlier onset of fatigue. Additionally, participants maintained similar cardiorespiratory parameters ($\dot{\mathrm{V}}$O₂, HR, SV, respiratory frequency, etc.) across both protocols, reinforcing the reliability of the RPE scale in guiding exercise intensity. These findings suggest that SPV better optimizes neural efficiency and delays fatigue compared to fixed protocols and that individuals can accurately control exercise intensity based on perceived exertion. Despite the small sample size, the results provide valuable insights into the potential benefits of self-paced exercise for improving adherence to exercise programs and optimizing performance across different populations. Full article

Background/Objectives: To explore how Public Health Nurses (PHNs) in Fukushima perceived their roles and activities as necessary or inadequate from the immediate aftermath through the long-term recovery of the nuclear disaster. Methods: We conducted a qualitative study using a self-administered questionnaire with open-ended questions to capture the perceptions of PHNs across three disaster phases: peacetime; nuclear emergency; and recovery. Responses were analyzed through qualitative content analysis. Results: PHNs’ needs and perceived inadequacies varied across the disaster phases. In peacetime, the emphasis was on education for disaster preparedness for both nurses and residents. During the nuclear emergency, the focus shifted to the need for PHN deployment and radiation screening systems, highlighting a significant gap in radiation knowledge. In the recovery phase, the importance of ongoing resident support, rumor management, and trust-building was emphasized, alongside an increased need for radiation education. Conclusions: This study highlights the critical need for phase-specific support systems and educational programs to enhance PHNs’ disaster response capabilities. It underscores the importance of preparedness plans and continuous training to improve PHNs’ effectiveness in addressing public health challenges during nuclear disasters. This study was not registered. Full article

To address the problem of low accuracy in joint point estimation in hand pose estimation methods due to the self-similarity of fingers and easy self-obscuration of hand joints, a hand pose estimation method based on coordinate correction and graph convolution is proposed. First, the standard coordinate encoding is improved by generating an unbiased heat map, and the distribution-aware method is used for decoding coordinates to reduce the error in decoding the coordinate encoding of joints. Then, the complex dependency relationship between the joints and the relationship between pixels and joints of the hand are modeled by using graph convolution, and the feature information of the hand joints is enhanced by determining the relationship between the hand joints. Finally, the skeletal constraint loss function is used to impose constraints on the joints, and a natural and undistorted hand skeleton structure is generated. Training tests are conducted on the public gesture interaction dataset STB, and the experimental results show that the method in this paper can reduce errors in hand joint point detection and improve the estimation accuracy. Full article

Salmonellosis is the second-most commonly reported foodborne gastrointestinal infection in the European Union and a major contributor to foodborne outbreaks globally. Salmonella serotyping differentiates typhoidal strains requiring antibiotic therapy (e.g., serovars Typhi, Paratyphi A, Paratyphi B-d-tartrate negative, Paratyphi C) from typically self-limiting non-typhoidal Salmonella (NTS) strains, making precise identification essential for appropriate treatment and epidemiological tracking. At the same time, the ability to identify the serogroup of Salmonella, regardless of which of the above two groups it belongs to, provides an important initial epidemiological indication that is useful for case management by competent health authorities. This study evaluates the effectiveness of ATR-FTIR spectroscopy coupled with a machine learning algorithm to identify four key Salmonella enterica serogroups (B, C1, D1—including typhoidal strains such as S. Typhi—and E1) directly from solid monomicrobial cultures without sample pretreatment. The system was paired with I-dOne software v2.2 already able to detect Salmonella spp., possibly leading to the characterisation of both the species and serotype from one colony. The multivariate classification model was trained and validated with 248 strains, with an overall accuracy of >98% over 113 samples. This approach offers a potential rapid alternative for clinical labs without serotyping facilities. Full article

(1) Background: Youths with multiple risks and severe disadvantages experience poorer health and educational outcomes than less disadvantaged peers. To address problems with coping and self-regulation in this group, mental skills training (MST) approaches more commonly used in sport are an emerging intervention approach. (2) Methods and results: this narrative review synthesizes literature to explain the need for MST, how it works, and evidence to support it works by focusing on two well evaluated programs: LifeMatters and My Strengths Training for Life™. (3) Conclusions: To support positive youth development, MST is a strengths-based, flexible, and adaptable approach to help fill the shortage of available evidence-based programs for those youths facing multiple disadvantages. The findings of this review may facilitate policy makers, commissioners, program planners, and researchers in the uptake of MST or similar psychoeducational approaches in future. Full article