The extracted features are used for human activity recognition using Multi-class Support Vector Machine (MCSVM). The proposed model was evaluated by using two benchmark datasets, i.e., the UCI-HAR and DU-MD datasets. This model is compared with other state-of-the-art methods and the model is outperformed.There are several pathologies attacking the central nervous system and diverse therapies for each specific disease. These therapies seek as far as possible to minimize or offset the consequences caused by these types of pathologies and disorders in the patient. Therefore, comprehensive neurological care has been performed by neurorehabilitation therapies, to improve the patients' life quality and facilitating their performance in society. One way to know how the neurorehabilitation therapies contribute to help patients is by measuring changes in their brain activity by means of electroencephalograms (EEG). EEG data-processing applications have been used in neuroscience research to be highly computing- and data-intensive. Our proposal is an integrated system of Electroencephalographic, Electrocardiographic, Bioacoustic, and Digital Image Acquisition Analysis to provide neuroscience experts with tools to estimate the efficiency of a great variety of therapies. The three main axes of this proposal are parallel oent synchronization of sample periods helped isolate the same therapy stimulus and allowed it to be analyzed by tools such as the Power Spectrum or the Fractal Geometry.A planar array of low profile horns fed by a transverse slotted waveguide array in the low millimeter-wave regime (28 GHz) is presented. The array of transverse slots cannot be directly used as antenna as it has grating lobes due to the fact that slot elements must be spaced a guided wavelength. However, these slots can be transformed into low profile horns that with their radiation patterns attenuate the grating lobes. To this aim, low profile horns with less than 0.6λ0 height were designed. The horns include a couple of chips that contribute to further reduce the grating lobes especially in the H-plane. The good performance of the designed array was demonstrated by both simulations and experiments performed on a manufactured prototype. A 5 × 5 array was designed that has a measured realized gain of 26.6 dBi with a bandwidth below 2%, still useful for some applications such as some radar systems. The total electrical size of the array is 6.63λ0× 6.63λ0. The radiation efficiency is very high and the aperture efficiency is above 80%. This all-metal solution is advantageous for millimeter-wave applications where losses sustained by dielectric materials become severe and it can be easily scaled to higher frequencies.With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.The objective of this study was to obtain information about the role of trace element imbalance in the pathogenesis of certain diseases in dogs and to evaluate the suitability of trace element profiling as an additional tool in the diagnosis. Serum trace element concentrations (copper, molybdenum, selenium and zinc) were measured in a cohort of healthy (control) dogs (n = 42) and dogs affected by hepatic (n = 25), gastrointestinal (n = 24), inflammatory/infection (n = 24), and renal (n = 22) diseases. These data were analyzed together with data on basic biochemical parameters (alanine aminotransferase, alkaline phosphatase, blood urea nitrogen, creatinine, albumin, globulin, and glucose) by using chemometric techniques. The chemometric analysis revealed distinctive association patterns between trace elements and biochemical parameters for each clinical disorders. The findings provide clear evidence for the important role of trace elements in disease, particularly in relation to acute phase reactions, with serum copper providing an indirect measurement of ceruloplasmin (positive acute-phase protein) and serum selenium and zinc acting as negative acute phase reactants. Molybdenum may also be a suitable marker of incipient renal disease. Thus, the analysis of trace element profiles, by multielement techniques, in a single serum sample would be a valuable additional tool for the diagnosis of certain diseases.We present here an assessment of a 'real-life' value of automated machine learning algorithm (AI) for examination of immunohistochemistry for fibroblast growth factor receptor-2 (FGFR2) in breast cancer (BC). Expression of FGFR2 in BC (n = 315) measured using a certified 3DHistech CaseViewer/QuantCenter software 2.3.0.  https://www.selleckchem.com/products/ulonivirine.html  was compared to the manual pathologic assessment in digital slides (PA). Results revealed (i) substantial interrater agreement between AI and PA for dichotomized evaluation (Cohen's kappa = 0.61); (ii) strong correlation between AI and PA H-scores (Spearman r = 0.85, p less then 0.001); (iii) a small constant error and a significant proportional error (Passing-Bablok regression y = 0.51 × X + 29.9, p less then 0.001); (iv) discrepancies in H-score in cases of extreme (strongest/weakest) or heterogeneous FGFR2 expression and poor tissue quality. The time of AI was significantly longer (568 h) than that of the pathologist (32 h). This study shows that the described commercial machine learning algorithm can reliably execute a routine pathologic assessment, however, in some instances, human expertise is essential.