Cell lysis is a process in which the outer cell membrane is broken to release intracellular constituents in a way that important information about the DNA or RNA of an organism can be obtained. This article is a thorough review of reported methods for the achievement of effective cellular boundaries disintegration, together with their technological peculiarities and instrumental requirements. The different approaches are summarized in six categories: chemical, mechanical, electrical methods, thermal, laser, and other lysis methods. Based on the results derived from each of the investigated reports, we outline the advantages and disadvantages of those techniques. Although the choice of a suitable method is highly dependent on the particular requirements of the specific scientific problem, we conclude with a concise table where the benefits of every approach are compared, based on criteria such as cost, efficiency, and difficulty.

Air pollution is both an environmental and a social problem, as it leads to a multitude of adverse effects on human health, ecosystems and the climate. Air pollution is one of the largest environmental health risks in Europe today. Quality of the air in city and urban areas is the most important factor that directly influences the incidence of diseases and decreases the quality of life. Taking appropriate decisions in a timely period depends on the measurement and analysis of the parameters of the air, which creates the need for the development of real time air quality monitoring. The use of multi-parameter air quality monitoring systems makes it possible to do a detailed level analysis of major pollutants and their sources. These monitoring systems are important components in many smart city projects for monitoring air quality and for controlling the main pollutant concentrations in urban areas. In this paper we present an approach for cost-effective measurement of relevant environmental parameters, based on a scalable sensor array with integrated amperometric and infrared gas sensors. The device has been tested in the city and the measurement was compared with the output data of the local environmental control authority stations. The preliminary results show that this approach can be used as an economical alternative to the professional grade systems.

In this work, we introduce and use an innovative approach for adaptive piecewise linear interval approximation of sensor characteristics, which are differentiable functions. The aim is to obtain a discreet type of inverse sensor characteristic, with a predefined maximum approximation error, with minimization of the number of points defining the characteristic, which in turn is related to the possibilities for using microcontrollers with limited energy and memory resources. In this context, the results from the study indicate that to overcome the problems arising from the resource constraints of smart devices, appropriate “lightweight” algorithms are needed that allow efficient connectivity and intelligent management of the measurement processes. The method has two benefits: first, low-cost microcontrollers could be used for hardware implementation of the industrial sensor devices; second, the optimal subdivision of the measurement range reduces the space in the memory of the microcontroller necessary for storage of the parameters of the linearized characteristic. Although the discussed computational examples are aimed at building adaptive approximations for temperature sensors, the algorithm can easily be extended to many other sensor types and can improve the performance of resource-constrained devices. For prescribed maximum approximation error, the inverse sensor characteristic is found directly in the linearized form. Further advantages of the proposed approach are: (i) the maximum error under linearization of the inverse sensor characteristic at all intervals, except in the general case of the last one, is the same; (ii) the approach allows non-uniform distribution of maximum approximation error, i.e., different maximum approximation errors could be assigned to particular intervals; (iii) the approach allows the application to the general type of differentiable sensor characteristics with piecewise concave/convex properties.

This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.

The precise localisation of rail vehicles is fundamental to the development and employment of more efficient train control systems in security, logistics and disposition applications. A key element of this is the exact and reliable velocity and distance estimation. The popular satellite navigation (global navigation satellite systems) and visual odometry, based on optical systems, tend to fail in the rough environment of rail application scenarios. This study describes an approach to making a precise distance estimate using an innovative eddy current sensor system. This is achieved through the combination of an augmented cross‐correlation approach to highly accurate velocity estimation. That is subsequently employed to determine the travelled distance based on counting rail clamps in a spatial transformed space. This approach relies on recursive state estimation of the dynamic states and a statistical assessment of the counted rail clamps for the purpose of outlier rejection. The proposed methods are compared and evaluated in a quantitative way on the basis of experimental data. The results prove that the proposed approach can improve the accuracy and reliability of velocity measurement and relative position detection.

The paper presents the noise analysis of different sensor types using Allan Variance (AV). Compared to the conventional variance that assesses the variation around the mean value of the aggregate data surveyed, AV estimates variations by averaging measurements for different periods. This approach often leads to the possibility of directly distinguishing the different noise types and to better convergence of the process of assessing their levels. An important advantage of this method is that there is no need for any further transformations. According to IEEE recommendations, the AV approach is the preferred method for identifying stochastic error and for determining the type of noise in different types of inertial sensors. The purpose of this work is to study the applicability of the AV analysis method for efficient noise analysis for other types of sensors such as CO 2 and MEMS pressure sensors.