Search Results (27,005)

The timely updating of the spatial distribution of buildings is essential to understanding a city’s development. Deep learning methods have remarkable benefits in quickly and accurately recognizing these changes. Current semi-supervised change detection (SSCD) methods have effectively reduced the reliance on labeled data. However, these methods primarily focus on utilizing unlabeled data through various training strategies, neglecting the impact of pseudo-changes and learning bias in models. When dealing with limited labeled data, abundant low-quality pseudo-labels generated by poorly performing models can hinder effective performance improvement, leading to the incomplete recognition results of changes to buildings. To address this issue, we propose a feature multi-scale information interaction and complementation semi-supervised method based on consistency regularization (MSFG-SemiCD), which includes a multi-scale feature fusion-guided change detection network (MSFGNet) and a semi-supervised update method. Among them, the network facilitates the generation of multi-scale change features, integrates features, and captures multi-scale change targets through the temporal difference guidance module, the full-scale feature fusion module, and the depth feature guidance fusion module. Moreover, this enables the fusion and complementation of information between features, resulting in more complete change features. The semi-supervised update method employs a weak-to-strong consistency framework to achieve model parameter updates while maintaining perturbation invariance of unlabeled data at both input and encoder output features. Experimental results on the WHU-CD and LEVIR-CD datasets confirm the efficacy of the proposed method. There is a notable improvement in performance at both the 1% and 5% levels. The IOU in the WHU-CD dataset increased by 5.72% and 6.84%, respectively, while in the LEVIR-CD dataset, it improved by 18.44% and 5.52%, respectively. Full article

Most large aerospace cylindrical components still adopt a manual aligning method with low automation, large manual intervention, and heavy dependence on operator workers, resulting in the low quality and efficiency of large component aligning, which seriously prolongs the manufacturing time of aerospace products. To cope with this issue, based on closed-loop adaptive control and digital twin (DT) technologies, an adaptive aligning system for large cylindrical components, i.e., the DT aligning system, is proposed in this study. For the DT aligning system, through the DT multi-dimensional modeling, i.e., geometric modeling, physical modeling, functional modeling, and data modeling, it can be divided into a physical space, a virtue space, and twin data. Note that the association, mapping, and interaction between physical space and virtual space of the aligning system can be realized via the twin data, thereby realizing real-time virtual display, monitoring, and control of the large component aligning. In addition, based on the measured pose data, aligning stress, and predicted aligning error, an adaptive force/position control method for large component aligning is proposed, and it can achieve real-time decision-making and precise execution of the aligning process. Finally, through application validation, the DT process system can realize the real-time status perception and process execution decision during the large component aligning. Finally, through experimental validation, it is found that the proposed system, i.e., the DT aligning system, can improve the quality and efficiency of the large aerospace cylindrical component aligning, as well as the automation and intelligent level of the aligning system. Full article

Active inference describes (Bayes-optimal) behaviour as being motivated by the minimisation of surprise of one’s sensory observations, through the optimisation of a generative model (of the hidden causes of one’s sensory data) in the brain. One of active inference’s key appeals is its conceptualisation of precision as biasing neuronal communication and, thus, inference within generative models. The importance of precision in perceptual inference is evident—many studies have demonstrated the importance of ensuring precision estimates are correct for normal (healthy) sensation and perception. Here, we highlight the many roles precision plays in action, i.e., the key processes that rely on adequate estimates of precision, from decision making and action planning to the initiation and control of muscle movement itself. Thereby, we focus on the recent development of hierarchical, “mixed” models—generative models spanning multiple levels of discrete and continuous inference. These kinds of models open up new perspectives on the unified description of hierarchical computation, and its implementation, in action. Here, we highlight how these models reflect the many roles of precision in action—from planning to execution—and the associated pathologies if precision estimation goes wrong. We also discuss the potential biological implementation of the associated message passing, focusing on the role of neuromodulatory systems in mediating different kinds of precision. Full article

To address the issue of missing near-surface air temperature data caused by the uneven distribution of ground meteorological observation stations, we propose a method for near-surface air temperature estimation based on an improved conditional generative adversarial network (CGAN) framework. Leveraging the all-weather coverage advantage of Fengyun meteorological satellites, Fengyun-4A (FY-4A) satellite remote sensing data are utilized as conditional guiding information for the CGAN, helping to direct and constrain the near-surface air temperature estimation process. In the proposed network model of the method based on the conditional generative adversarial network structure, the generator combining a self-attention mechanism and cascaded residual blocks is designed with U-Net as the backbone, which extracts implicit feature information and suppresses the irrelevant information in the Fengyun satellite data. Furthermore, a discriminator with multi-level and multi-scale spatial feature fusion is constructed to enhance the network’s perception of details and the global structure, enabling accurate air temperature estimation. The experimental results demonstrate that, compared with Attention U-Net, Pix2pix, and other deep learning models, the method presents significant improvements of 68.75% and 10.53%, respectively in the root mean square error (RMSE) and Pearson’s correlation coefficient (CC). These results indicate the superior performance of the proposed model for near-surface air temperature estimation. Full article

Background: Access to quality healthcare is essential to the well-being of U.S. veterans. Little is known about what drives veterans’ healthcare decisions. The purpose of this study was to explore factors that drive healthcare choices in veterans, and their experiences in the Veterans Health Administration (VA) and non-VA healthcare settings. Methods: Fifty-nine veterans participated in eight focus groups. Participants were asked to discuss factors that led to their choice of provider and their healthcare experiences. Thematic analysis was conducted to reveal themes around healthcare choices and use. Results: VA and non-VA users described positive experiences with care. VA users reported cost, quality, and ease of care as reasons for use. Non-VA healthcare setting users reported eligibility issues, negative perceptions of the VA, administrative bureaucracy, and lack of continuity of care as reasons they chose not to use VA care. VA users reported difficulty with red tape, continuity of care, limitations to gender specific care, and having to advocate for themselves. Conclusions: Veterans were satisfied with care regardless of where they received it. Experiences with civilian providers indicate that more could be done to provide veterans with choices in the care they receive. Despite positive experiences with the VA, the veterans highlighted needed improvements in key areas. Full article

The capabilities of AUV mutual perception and localization are crucial for the development of AUV swarm systems. We propose the AUV6D model, a synthetic image-based approach to enhance inter-AUV perception through 6D pose estimation. Due to the challenge of acquiring accurate 6D pose data, a dataset of simulated underwater images with precise pose labels was generated using Unity3D. Mask-CycleGAN technology was introduced to transform these simulated images into realistic synthetic images, addressing the scarcity of available underwater data. Furthermore, the Color Intermediate Domain Mapping strategy is proposed to ensure alignment across different image styles at pixel and feature levels, enhancing the adaptability of the pose estimation model. Additionally, the Salient Keypoint Vector Voting Mechanism was developed to improve the accuracy and robustness of underwater pose estimation, enabling precise localization even in the presence of occlusions. The experimental results demonstrated that our AUV6D model achieved millimeter-level localization precision and pose estimation errors within five degrees, showing exceptional performance in complex underwater environments. Navigation experiments with two AUVs further verified the model’s reliability for mutual 6D pose estimation. This research provides substantial technical support for more complex and precise collaborative operations for AUV swarms in the future. Full article

Background: Listening difficulties may frequently occur in school settings, but so far there were no tools to identify them for both hearing and hearing-impaired Italian students. This study performed cross-cultural adaptation and validation of the Listening Inventory for Education-Revised for Italian students (LIFE-R-ITA). Methods: The study procedure followed the stages suggested by the Guidelines for the Process of Cross-cultural Adaptation of Self-Report Measures. For the content validation, six cochlear implanted students (8–18 years old) pre-tested the initial version. Whenever any situation did not occur in Italy, the item was adapted to more typical listening situations in Italy. The final version of LIFE-R-ITA was administered to a sample of 223 hearing students from different school settings and educational degrees in order to collect normative data. Results: For the LIFE-R-ITA, hearing students showed an average score of 72.26% (SD = 11.93), reflecting some listening difficulties. The subscales (LIFE total, LIFE class, and LIFE social) indicated good internal consistency. All items were shown to be relevant. Most challenging situations happened when listening in large rooms, especially when other students made noise. LIFE social scores were significantly worse than those of LIFE class (p < 0.001). Conclusions: The present study provides cross-cultural adaptation and validation for the LIFE-R-ITA along with the normative data useful to interpret the results of students with hearing loss. The LIFE-R-ITA may support teachers and clinicians in assessing students’ self-perception of listening at school. Such understanding may help students overcome their listening difficulties, by planning and selecting the most effective strategies among classroom interventions. Full article

The simulation of human sensory functions is a key trend in the field of sensor development. In taste sensing, taste biosensors emulate taste perception using biorecognition elements that participate in taste transduction, such as taste receptors, cells, tissues, etc. This approach obtains high selectivity and a wide detection range of human taste perception, making taste biosensors widely used in food analysis and taste perception studies. By combining biorecognition elements with suitable data processing and analysis techniques, the taste information generated during the process of taste transduction, obtained by the sensing elements of the sensor, can be accurately captured. In this paper, we explore current available solutions to stability and sensitivity, and other challenges in taste biosensors using taste receptors, cells, and tissues as sensing elements. We also outline the applied signal processing techniques based on the signal characteristics from different types of taste biosensors. Finally, it is proposed that the development of taste biosensing sensors will further promote the application of intelligent sensory evaluation and human perception analysis systems in food, medicine, and other fields. Full article

Overactive bladder (OAB) has a significant impact on the quality of life; thus, it requires treatment that can be adhered to over a long period without undue side effects. The current treatment which uses an anticholinergic or β-3 agonist may fail to improve symptoms and has side effects, leading to high discontinuation rates. OnabotulinumtoxinA (OnabotA) detrusor injection has been approved for idiopathic OAB as a second-line treatment with good effectiveness and tolerability. This study used a visual analog scale (VAS) to assess the impact of the type of needle used for OnabotA detrusor injections under local anesthesia on the pain levels after each injection. This study included 68 female patients. We used three different needles with thicknesses ranging from 22 to 27 gauge, lengths between 4 and 5 mm, and different cuts. The sensation of pain was rated at each standardized injection location. Regardless of the needle used, the patients’ perceptions of pain at the beginning of the procedure were rated as being less than the subsequent injections. Most pain sensations were rated as low to moderate. The mean pain sensation on the VAS was 2.5 ± 0.3 overall, i.e., for all patients and needles used. Statistically significant differences in pain sensation were rated only at some locations of the bladder (on the back wall and the right side of the bladder). The single needles averaged the following pain scores: 2.8 ± 0.3 for needle A (20 G, 4 mm), 2.1 ± 0.3 for needle B (27 G, 5 mm), and 2.6 ± 0.4 for needle C (20 G, 4 mm, sharp cut 15°). The 27-gauge needle caused significantly less pain, and it had no negative impact due to its length, which was 1 mm longer than the other needles. Thus, the needle thickness was a decisive factor in the patients’ perceptions of pain. Full article

Sugar kelp (Saccharina latissma) has many nutritional benefits and has been identified as a rich source of fibre, vitamins, and antioxidants. However, it is not regularly consumed in the Western world, and the sensory perception of foods containing sugar kelp must be investigated to increase acceptance in North America. This study evaluated consumers’ (n = 99) sensory perception of couscous with increasing amounts of sugar kelp (0% (control), 4%, 6%, 8%, and 10% wt/wt). Furthermore, consumers’ purchase intent, liking, and emotional response to couscous with added sugar kelp was evaluated with and without nutritional information. Sugar kelp at 6% incorporation did not impact the consumers’ liking scores (“Like Slightly” on the hedonic scale), but at 8% the consumers’ liking significantly decreased (“Neither Like nor Dislike”). The 8% and 10% levels of sugar kelp addition led to astringency, bitter, hard, brackish, fishy, and chewy attributes being perceived by the consumers. The consumers identified they preferred samples that had soft, savoury, salty, and bland flavours and disliked samples that were brackish and gritty. The nutritional information did not increase overall liking scores, purchase intent, or emotional response. However, the inclusion of sugar kelp in the couscous did lead to an increased selection of positive emotions like happy, joyful, pleasant, and enthusiastic. Overall, the consumers were interested in foods containing seaweed and believed they were nutritious. The results indicated that sugar kelp could be added to couscous up to 6% wt/wt without impacting overall liking. Full article

This study explores the role of populist leaders as gatekeepers on social media, seeking to understand how André Ventura, president of Chega!, uses news to legitimize his political discourse. The methodology involved collecting 90 tweets containing legacy media news features, posted by Ventura on the social media platform X. These tweets cover key political events such as the resignation of Portugal’s Prime Minister, the dissolution of the Portuguese Parliament, and European elections. Quantitative analysis using Voyant Tools identified key terms related to Ventura’s ideological stance, while Critical Discourse Analysis (CDA) examined how these terms support his political narrative. The findings reveal a strategic use of news to promote themes like nationalism, immigration control, corruption and social dichotomy between “us” and “them”. Ventura’s tweets leverage news headlines to enhance his persuasive appeal, acting as heuristic shortcuts to reinforce his political messages. This study highlights the relevance of understanding social media’s role in promoting populism and suggests avenues for future research, including comparative analyses of other populist leaders and the impact of these narratives on voter behavior and perceptions. Full article

As digital content increasingly moves away from free access, microtransactions may provide an alternative. There is a need for research on microtransactions as a general digital content payment mechanism. Businesses can capitalize on this technological advancement by expanding their paid digital content offerings without resorting to subscription-based services. This study examines a taxonomy for microtransactions based on consumer buying behavior, presents business models for microtransaction payment processing, and examines issues with its implementation. Data collected from 180 consumers were analyzed using quantitative and qualitative analyses to assess the receptiveness and perception of microtransactions. The results indicate that consumers are receptive to the concept of microtransactions and are willing to spend on five distinct digital content categories. This study concludes with a discussion of issues that consumers foresee with digital content microtransactions and implications for interested businesses. Full article

Dating violence (DV) is a form of intentional abuse carried out in young couples, which over the years has increasingly gained attention for its pervasiveness and high frequency. The present study represents an effort to expand and deepen the literature on factors associated with DV perpetration. The sample consisted of 225 Italian young people who completed an online survey. A model was tested, in which DV perpetration is affected by the presence of gender stereotypes and personal values oriented to power and dominance (i.e., self-enhancement) and to universalism and interest in others (i.e., self-transcendence) through the mediation of the perception of the romantic relationship quality, controlling for age. Sex was also considered in the model. The findings showed that self-enhancement and self-transcendence were, respectively, linked positively and negatively to a negative perception of the relationship quality, which in turn explained DV perpetration. The results also suggested that hostile sexism, self-enhancement and being male were directly linked to DV perpetration, thus representing potential risk factors for it. The study suggests the importance of working on gender role beliefs and personal orientation to power, which guide the way people perceive their romantic relationships and behave toward their partners, providing interesting insights for the implementation of DV prevention programs. Moreover, the findings highlight the importance of working with families and other educational agencies to foster a change in cultural terms. Full article

Corruption is a global problem that affects the fair distribution of wealth of every country to different degrees and represents a problem to be solved to prevent the diversion and waste of resources. Among the different efforts to first measure it and later reduce it by proposing strategies, there exist a variety of indices, such as the corruption perception index, and other related issues, such as the global impunity index, the laxness of anticorruption policies, etc., which are computed for different countries worldwide. Based on these indices, we propose a model for corruption using a system of ordinary differential equations, considering anticorruption policies. Those three factors were identified after analyzing the phenomenon and available data, particularly for Mexico. Also, we fit it to the reported data of this country and perform simulations expecting to predict the short term, and performed a sensitivity analysis. The model is capable of reproducing the observed oscillatory behavior of the phenomenon. The model fit can still be improved by including the data for the anticorruption policies, which were only studied for different scenarios. Moreover, the model is susceptible to application in other countries, as long as data are available, and then provides a computational tool to predict and visualize the effect of appropriate public policies to fight corruption. Full article

The advance in the development of augmented reality technologies has attracted interest in their applicability in rescue scenarios. The characteristics of the different missions covered by First Responders, as well as the different objectives they can cover in a rescue operation, condition the importance of the additional information they can receive in these rescue processes through technology. This white paper aims to analyze the difficulties encountered when converging on the design of an interface that is adaptable to the professional and contextual circumstances in a rescue task. Full article