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Continuous monitoring of diabetic patients improves their quality of life. The use of multiple technologies such as the Internet of Things (IoT), embedded systems, communication technologies, artificial intelligence, and smart devices can reduce the economic costs of the healthcare system. Different communication technologies have made it possible to provide personalized and remote health services. In order to respond to the needs of future intelligent e-health applications, we are called to develop intelligent healthcare systems and expand the number of applications connected to the network. Therefore, the 5G network should support intelligent healthcare applications, to meet some important requirements such as high bandwidth and high energy efficiency. This article presents an intelligent architecture for monitoring diabetic patients by using machine learning algorithms. The architecture elements included smart devices, sensors, and smartphones to collect measurements from the body. The intelligent system collected the data received from the patient, and performed data classification using machine learning in order to make a diagnosis. The proposed prediction system was evaluated by several machine learning algorithms, and the simulation results demonstrated that the sequential minimal optimization (SMO) algorithm gives superior classification accuracy, sensitivity, and precision compared to other algorithms.Two types of single-photon avalanche diodes (SPADs) with different diameters are investigated regarding their avalanche behavior. SPAD type A was designed in standard 0.35-µm complementary metal-oxide-semiconductor (CMOS) including a 12-µm thick p- epi-layer with diameters of 50, 100, 200, and 400 µm; and type B was implemented in the high-voltage (HV) line of this process with diameters of 48.2 and 98.2 µm. Each SPAD is wire-bonded to a 0.35-µm CMOS clocked gating chip, which controls charge up to a maximum 6.6-V excess bias, active, and quench phase as well as readout during one clock period. Measurements of the cathode voltage after photon hits at SPAD type A resulted in fall times (80 to 20%) of 10.2 ns for the 50-µm diameter SPAD for an excess bias of 4.2 V and 3.45 ns for the 200-µm diameter device for an excess bias of 4.26 V. For type B, fall times of 8 ns for 48.2-µm diameter and 5.4-V excess bias as well as 2 ns for 98.2-µm diameter and 5.9-V excess bias were determined. In measuring the whole capacitance at the cathode of the SPAD with gating chip connected, the avalanche currents through the detector were calculated. This resulted in peak avalanche currents of, e.g., 1.19 mA for the 100-µm SPAD type A and 1.64 mA for the 98.2-µm SPAD type B for an excess bias of 5 and 4.9 V, respectively.Obtaining zinc coatings by the batch hot-dip galvanizing process currently represents one of the most effective and economical methods of protecting steel products and structures against corrosion. The batch hot-dip galvanizing process has been used for over 150 years, but for several decades, there has been a dynamic development of this technology, the purpose of which is to improve the efficiency of zinc use and reduce its consumption and improve the quality of the coating. The appropriate selection of the chemical composition of the galvanizing bath enables us to control the reactivity of steel, improve the drainage of liquid zinc from the product surface, and reduce the amount of waste, which directly affects the quality of the coating and the technology of the galvanizing process. For this purpose, the effect of many alloying additives to the zinc bath on the structure and thickness of the coating was tested. The article reviews the influence of various elements introduced into the bath individually and in different configurations, discusses the positive and negative effects of their influence on the galvanizing process. PLX5622 The current development in the field of the chemical composition of galvanizing baths is also presented and the best-used solutions for the selection and management of the chemical composition of the bath are indicated.Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient-light compensation methods that successively built upon each other were investigated. The default method used a variable integration time that was determined based on a previous measurement to maximize intensity of the spectral signature (M1). The next method divided the spectral signature by the integration time to normalize the spectrum and reveal relative differences in ambient light intensity (M2). The third method divided the normalized spectrum by the ambient light spectrum on a wavelength basis (M3). Spectral data were classified using a two-step process. First, raw spectral data were preprocessed using a partial least squares (PLS) regression method to compress highly correlated wavelengths and to avoid overfitting. Next, an ensemble of machine learning algorithms was trained, validated, and tested to determine the overall classification accuracy of each algorithm. Results showed that simply maximizing sensitivity led to the best prediction accuracy when classifying known targets. Average prediction accuracy across all spectrometers and compensation methods exceeded 93%.The decline of skeletal muscle mass and strength that leads to sarcopenia is a pathology that might represent an emergency healthcare issue in future years. Decreased muscle mass is also a condition that mainly affects master athletes involved in endurance physical activities. Skeletal muscles respond to exercise by reshaping the biochemical, morphological, and physiological state of myofibrils. Adaptive responses involve the activation of intracellular signaling pathways and genetic reprogramming, causing alterations in contractile properties, metabolic status, and muscle mass. One of the mechanisms leading to sarcopenia is an increase in reactive oxygen and nitrogen species levels and a reduction in enzymatic antioxidant protection. The present review shows the recent experimental models of sarcopenia that explore molecular mechanisms. Furthermore, the clinical aspect of sport sarcopenia will be highlighted, and new strategies based on nutritional supplements, which may contribute to reducing indices of oxidative stress by reinforcing natural endogenous protection, will be suggested.
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