A Comparative Study of LAMP and PCR in Relation to Time and Cost

Background: The Loop-mediated isothermal amplification (LAMP) represents a very sensitive, easy to use, and less time consuming diagnostic method. Aims: The aim was to establish a simple, cost-effective, molecular technique. Materials and methods: An analytical study was conducted using two hundred acute serum samples using two different molecular techniques; qPCR and LAMP to standardize a costeffective and less time-consuming technique. Results: The cost of in-house LAMP reagents was one-ninth of the cost of commercial qPCR. Consume cost was 23 times less than qPCR besides, lab setup cost was 92 times less than qPCR. More importantly, LAMP requires 5-6 times less time duration than qPCR. Conclusion: Due to its simple short-time operation with low cost, it would be a prevalent molecular technique globally, particularly in Bangladesh. J Shaheed Suhrawardy Med Coll 2020; 12(2): 72-75

Stochastic Computing Emulation of Memristor Cellular Nonlinear Networks

Cellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture capable of massively parallel computation. Since then, CNN have been enhanced by incorporating designs that incorporate memristors to profit from their processing and memory capabilities. In addition, Stochastic Computing (SC) can be used to optimize the quantity of required processing elements; thus it provides a lightweight approximate computing framework, quite accurate and effective, however. In this work, we propose utilization of SC in designing and implementing a memristor-based CNN. As a proof of the proposed concept, an example of application is presented. This application combines Matlab and a FPGA in order to create the CNN. The implemented CNN was then used to perform three different real-time applications on a 512 × 512 gray-scale and a 768 × 512 color image: storage of the image, edge detection, and image sharpening. It has to be pointed out that the same CNN was used for the three different tasks, with the sole change of some programmable parameters. Results show an excellent capability with significant accompanying advantages, such as the low number of needed elements further allowing for a low cost FPGA-based system implementation, something confirming the system’s capacity for real time operation.

Tracking and Monitoring of the Alignment System Used for Nuclear Physics Experiments

The ELI-NP (Extreme Light Intensity—Nuclear Physics) project, developed at the Horia Hulubei National Institute for RD in Physics and Nuclear Engineering (IFIN-HH), has included one component dedicated to the study of interactions between brilliant gamma-ray and matter, with applications in nuclear physics and the science of materials. The paper is focused on the interaction chamber, an important part of the facility which hosts the experiment’s samples. The interaction chamber is endowed with a mobile sample support (holder), which automatically tracks the γ-ray beam. The γ-ray radiation source presents a slight variation of the direction of the emitted radiation in time. The built system ensures the permanent collimation between the γ-ray beam and the sample that is being investigated. This is done with two electric motors, which have a symmetrical movement with respect to the center of a rectangle. The specific measures taken by the design and implementation that permit to reach performances of tracking system are emphasized in the paper. The methodology considers the relative displacement between the detectors with which the laboratory is equipped and the absolute position in space of the sample boundary. The control of this motion is designed to respect the symmetry of the system. Both facets of the project (hardware and software) are detailed, emphasizing the way in which the designers ensured compliance with the system of real-time operation conditions of the tracking and monitoring system.

Efficiency of an up-flow Anaerobic Sludge Blanket reactor coupled with an electrochemical system to remove chloramphenicol in swine wastewater

The application and design of treatment systems in wastewater are necessary due to antibiotics' potential toxicity and resistant genes on residual effluent. This work evaluated a coupled bio-electrochemical system to reduce chloramphenicol (CAP) and chemical oxygen demand (COD) on swine wastewater (SWW). SWW characterization found CAP of <10 μg/L and 17,434 mg/L of COD. The coupled system consisted of preliminary use of an Up-flow Anaerobic Sludge Blanket Reactor (UASB) followed by electrooxidation (EO). UASB reactor (primary stage) was operated for three months at an organic load of 8.76 kg of COD/m3d and 50 mg CAP/L as initial concentration. In EO, we carried out a 22 (time operation and intensity) factorial design with a central composite design; we tried two Ti cathodes and one anode of Ti/PbO2. Optimal conditions obtained in the EO process were 240 min of operation time and 1.51 A of current intensity. It was possible to eliminate 44% of COD and 64.2% of CAP in the preliminary stage. On bio-electrochemical, a total COD and CAP removal were 82.35% and >99.99%, respectively. This coupled system can be applied to eliminate antibiotics and other organic pollutants in agricultural, industrial, municipal, and other wastewaters.

Research on Identification of Power Grid Weakness Based on Bayesian Inference

With the gradual reform and development of the power grid, it is of great significance to study how to effectively identify and evaluate the weak links of the power grid for the actual planning, construction, and operation of the power grid. This paper analyzed the power grid’s historical component data and real-time operation state parameters. We established a weak link identification model based on Bayesian reasoning. Firstly, we constructed the node branch Bayesian network according to the network topology relationship. The power transmission distribution factor is modified according to the historical operation load of the grid components, and the conditional probability table is calculated based on the grid structure; finally, we used the maximum possible explanation algorithm in the Bayesian network. The weakness degree of all components in the network is calculated, and the maximum probability weak link sequence is obtained. The correctness and effectiveness of the proposed method are verified by IEEE 39 bus simulation and regional power grid data.

A numerical typhoon track prediction model and its application on the real-time operation

ABSTRACT. Recently a nested numerical model for typhoon track prediction (MTP) with a higher horizontal resolution and a complex physical package has been developed by the National Meteorological Center (NMC, Beijing). For its improvement of initialization, a modified typhoon bogus scheme (Iwasaki 1987) also has been applied. The design of the M1TP had been determined by the end of 1992 and the forecast capability of this model was tested firstly with a series of experiments for the selected typhoon cases in 1993 (Wang and Li 1994). After it was examined in real-time forecast in the next year, further improvements were made in 1995 including a higher horizontal resolution increased from about 100 to 50 km, a package of complex physics instead of the simple one and a scheme for removal of analyzed vortex. With the improved forecast capability, the MTP run in quasi-operation from the date of 1 June 1995. Its products were also used by the forecasters during the past two years and the results were very encouraging.

Machine Learning and Urban Drainage Systems: State-of-the-Art Review

In the last decade, machine learning (ML) technology has been transforming daily lives, industries, and various scientific/engineering disciplines. In particular, ML technology has resulted in significant progress in neural network models; these enable the automatic computation of problem-relevant features and rapid capture of highly complex data distributions. We believe that ML approaches can address several significant new and/or old challenges in urban drainage systems (UDSs). This review paper provides a state-of-the-art review of ML-based UDS modeling/application based on three categories: (1) operation (real-time operation control), (2) management (flood-inundation prediction) and (3) maintenance (pipe defect detection). The review reveals that ML is utilized extensively in UDSs to advance model performance and efficiency, extract complex data distribution patterns, and obtain scientific/engineering insights. Additionally, some potential issues and future directions are recommended for three research topics defined in this study to extend UDS modeling/applications based on ML technology. Furthermore, it is suggested that ML technology can promote developments in UDSs. The new paradigm of ML-based UDS modeling/applications summarized here is in its early stages and should be considered in future studies.