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Adenosine 5'-triphosphate (ATP) plays a crucial role as an extracellular signaling molecule in the central nervous system and is closely related to various nerve diseases. Therefore, label-free imaging of extracellular ATP dynamics and spatiotemporal analysis is crucial for understanding brain function. To decrease the limit of detection (LOD) of imaging extracellular ATP, we fabricated a redox-type label-free ATP image sensor by immobilizing glycerol-kinase (GK), L-α-glycerophosphate oxidase (LGOx), and horseradish peroxidase (HRP) enzymes in a polymer film on a gold electrode-modified potentiometric sensor array with a 37.3 µm-pitch. Hydrogen peroxide (H2O2) is generated through the enzymatic reactions from GK to LGOx in the presence of ATP and glycerol, and ATP can be detected as changes in its concentration using an electron mediator. Using this approach, the LOD for ATP was 2.8 µM with a sensitivity of 77 ± 3.8 mV/dec., under 10 mM working buffers at physiological pH, such as in in vitro experiments, and the LOD was great superior 100 times than that of the hydrogen ion detection-based image sensor. This redox-type ATP image sensor may be successfully applied for in vitro sensitive imaging of extracellular ATP dynamics in brain nerve tissue or cells.Social interactions significantly impact the quality of life for people with special needs (e.g., older adults with dementia and children with autism). They may suffer loneliness and social isolation more often than people without disabilities. There is a growing demand for technologies to satisfy the social needs of such user groups. However, evaluating these systems can be challenging due to the extra difficulty of gathering data from people with special needs (e.g., communication barriers involving older adults with dementia and children with autism). Thus, in this systematic review, we focus on studying data gathering methods for evaluating socially assistive systems (SAS). Six academic databases (i.e., Scopus, Web of Science, ACM, Science Direct, PubMed, and IEEE Xplore) were searched, covering articles published from January 2000 to July 2021. A total of 65 articles met the inclusion criteria for this systematic review. The results showed that existing SASs most often targeted people with visual impairments, older adults, and children with autism. For instance, a common type of SASs aimed to help blind people perceive social signals (e.g., facial expressions). SASs were most commonly assessed with interviews, questionnaires, and observation data. Around half of the interview studies only involved target users, while the other half also included secondary users or stakeholders. Questionnaires were mostly used with older adults and people with visual impairments to measure their social interaction, emotional state, and system usability. A great majority of observational studies were carried out with users in special age groups, especially older adults and children with autism. We thereby contribute an overview of how different data gathering methods were used with various target users of SASs. Relevant insights are extracted to inform future development and research.This manuscript presents the first measurement program and data collection on the Dinatrans track transition solution after it was installed in a track section in the north of Spain (Galicia). The Dinatrans solution was created to address the limitations of several track transition solutions. This novel solution consists of two inner and outer rails from slab track to ballast track, pads with different stiffness over sleepers of variable lengths installed from ballast track to slab track, and a simple substructure formed by non-structural concrete poured over the natural ground. The main objective of this research was to assess the suitability and the initial performance of the Dinatrans track transition solution. The measured variables for these initial real-world tests were vertical accelerations on sleepers, shear stress on rails, vertical displacements on rails and vertical displacements on sleepers. All measurements of these variables were obtained in an in-situ program by installing vertical accelerometers and LVDTs on the track structure and extensometer gauges on the rails and sleepers. The methodology and the procedures followed are described. The Dinatrans initial solution was compared with the Standard solution used in Spain using these initial measurements. This field analysis provides an initial understanding of the performance of the new track transition. Further measurements will be required to check the track transition performance over the long term; however, no maintenance works have been necessary since construction (2016).The simple and highly sensitive measurement of the refractive index (RI) of liquids is critical for designing the optical instruments and important in biochemical sensing applications. Intensity modulation-based polymer optical fiber (POF) RI sensors have a lot of advantages including low cost, easy fabrication and operation, good flexibility, and working in the visible wavelength. In this review, recent developments of the intensity modulation POF-based RI sensors are summarized. The materials of the POF and the working principle of intensity modulation are introduced briefly. Moreover, the RI sensing performance of POF sensors with different structures including tapered, bent, and side-polished structures, among others, are presented in detail. [https://www.selleckchem.com/products/odm208.html read more] Finally, the sensing performance for different structures of POF-based RI sensors are compared and discussed.The rising use of online media has changed the social customs of the public. Users have become accustomed to sharing daily experiences and publishing personal opinions on social networks. Social data carrying emotion and attitude has provided significant decision support for numerous tasks in sentiment analysis. Conventional methods for sentiment classification only concern textual modality and are vulnerable to the multimodal scenario, while common multimodal approaches only focus on the interactive relationship among modalities without considering unique intra-modal information. A hybrid fusion network is proposed in this paper to capture both inter-modal and intra-modal features. Firstly, in the stage of representation fusion, a multi-head visual attention is proposed to extract accurate semantic and sentimental information from textual contents, with the guidance of visual features. Then, multiple base classifiers are trained to learn independent and diverse discriminative information from different modal representations in the stage of decision fusion. The final decision is determined based on fusing the decision supports from base classifiers via a decision fusion method. To improve the generalization of our hybrid fusion network, a similarity loss is employed to inject decision diversity into the whole model. Empiric results on five multimodal datasets have demonstrated that the proposed model achieves higher accuracy and better generalization capacity for multimodal sentiment analysis.The COVID-19 pandemic has spread to almost all countries of the World and affected people both mentally and economically. The primary motivation of this research is to construct a model that takes reviews or evaluations from several people who are affected with COVID-19. As the number of cases has accelerated day by day, people are becoming panicked and concerned about their health. A good model may be helpful to provide accurate statistics in interpreting the actual records about the pandemic. In the proposed work, for sentimental analysis, a unique classifier named the Sentimental DataBase Miner algorithm (SADBM) is used to categorize the opinions and parallel processing, and is applied on the data collected from various online social media websites like Twitter, Facebook, and Linkedin. The accuracy of the proposed model is validated with trained data and compared with basic classifiers, such as logistic regression and decision tree. The proposed algorithm is executed on CPU as well as GPU and calculated the acceleration ratio of the model. The results show that the proposed model provides the best accuracy compared with the other two models, i.e., 96% (GPU).U-V disparity is a technique that is commonly used to detect obstacles in 3D scenes, modeling them as a set of vertical planes. In this paper, the authors describe the general lines of a method based on this technique for fully reconstructing 3D scenes, and conduct an analytical study of its performance and sensitivity to errors in the pitch angle of the stereoscopic vision system. The equations of the planes calculated for a given error in this angle yield the deviation with respect to the ideal planes (with a zero error in the angle) for a large test set consisting of planes with different orientations, which is represented graphically to analyze the method's qualitative and quantitative performance. The relationship between the deviation of the planes and the error in the pitch angle is observed to be linear. Two major conclusions are drawn from this study first, that the deviation between the calculated and ideal planes is always less than or equal to the error considered in the pitch angle; and second, that even though in some cases the deviation of the plane is zero or very small, the probability that a plane of the scene deviates from the ideal by the greatest amount possible, which matches the error in the pitch angle, is very high.Sea waves constitute a natural phenomenon with a great impact on human activities, and their monitoring is essential for meteorology, coastal safety, navigation, and renewable energy from the sea. Therefore, the main measurement techniques for their monitoring are here reviewed, including buoys, satellite observation, coastal radars, shipboard observation, and microseism analysis. For each technique, the measurement principle is briefly recalled, the degree of development is outlined, and trends are prospected. The complementarity of such techniques is also highlighted, and the need for further integration in local and global networks is stressed.Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method to deal with multi-target tracking (MTT). However, the traditional GM-PHD filter cannot form a continuous track in the tracking process, and it is easy to produce a large number of redundant invalid likelihood functions in a dense clutter environment, which reduces the computational efficiency and affects the update result of target probability hypothesis density, resulting in excessive tracking error. Therefore, based on the GM-PHD filter framework, the target state space is extended to a higher dimension. By adding a label set, each Gaussian component is assigned a label, and the label is merged in the pruning and merging step to increase the merging threshold to reduce the Gaussian component generated by dense clutter update, which reduces the computation in the next prediction and update. After pruning and merging, the Gaussian components are further clustered and optimized by threshold separation clustering, thus as to improve the tracking performance of the filter and finally realizing the accurate formation of multi-target tracks in a dense clutter environment. Simulation results show that the proposed algorithm can form a continuous and reliable track in dense clutter environment and has good tracking performance and computational efficiency.