Finally, the device was successfully applied for the determination of QF in pharmaceutical tablets and in human urine, justifying its suitability for routine and on-site analysis.This work describes a new method for determining K+ concentration, [K+], in blood plasma using a smartphone with a custom-built optical attachment. The method is based on turbidity measurement of blood plasma solutions in the presence of sodium tetraphenylborate, a known potassium precipitating reagent. The images obtained by a smartphone camera are analyzed by a custom image-processing algorithm which enables the transformation of the image data from RGB to HSV color space and calculation of a mean value of the light-intensity component (V). Analysis of images of blood plasma containing different amounts of K+ reveal a correlation between V and [K+]. The accuracy of the method was confirmed by comparing the results with the results obtained using commercial ion-selective electrode device (ISE) and atomic absorption spectroscopy (AAS). The accuracy of the method was within ± 0.18 mM and precision ± 0.27 mM in the [K+] range of 1.5-7.5 mM when using treated blood plasma calibration.  https://www.selleckchem.com/products/alantolactone.html  Spike tests on a fresh blood plasma show good correlation of the data obtained by the smartphone method with ISE and AAS. The advantage of the method is low cost and integration with a smartphone which offers possibility to measure [K+] on demand and in remote areas where access to hospitals is limited.Highly sensitive and flexible composite sensors with pressure and temperature sensing abilities are of great importance in human motion monitoring, robotic skins, and automobile seats when checking the boarding status. Several studies have been conducted to improve the temperature-pressure sensitivity; however, they require a complex fabrication process for micro-nanostructures, which are material-dependent. Therefore, there is a need to develop the structural designs to improve the sensing abilities. Herein, we demonstrate a flexible composite with an enhanced pressure and temperature sensing performance. Its structural design consists of a multilayered composite construction with an elastic modulus gradient. Controlled stress concentration and distribution induced by a micropatterned structure between the layers improves its pressure and temperature sensing performance. The proposed composite sensor can monitor a wide range of pressure and temperature stimuli and also has potential applications as an automotive seat sensor for simultaneous human temperature detection and occupant weight sensing.Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain-computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.In this paper, we present a real-time object detection and depth estimation approach based on deep convolutional neural networks (CNNs). We improve object detection through the incorporation of transfer connection blocks (TCBs), in particular, to detect small objects in real time. For depth estimation, we introduce binocular vision to the monocular-based disparity estimation network, and the epipolar constraint is used to improve prediction accuracy. Finally, we integrate the two-dimensional (2D) location of the detected object with the depth information to achieve real-time detection and depth estimation. The results demonstrate that the proposed approach achieves better results compared to conventional methods.In Kalman filter design, the filter algorithm and prediction model design are the most discussed topics in research. Another fundamental but less investigated issue is the careful selection of measurands and their contribution to the estimation problem. This is often done purely on the basis of empirical values or by experiments. This paper presents a novel holistic method to design and assess Kalman filters in an automated way and to perform their analysis based on quantifiable parameters. The optimal filter parameters are computed with the help of a nonlinear optimization algorithm. To determine and analyze an optimal filter design, two novel quantitative nonlinear observability measures are presented along with a method to quantify the dominance contribution of a measurand to an estimate. As a result, different filter configurations can be specifically investigated and compared with respect to the selection of measurands and their influence on the estimation. An unscented Kalman filter algorithm is used to demonstrate the method's capabilities to design and analyze the estimation problem parameters.