An Arduino microcontroller based RLC meter

An RLC meter is a single electronic instrument or device which is capable to measure the Resistance (R), Inductance (L) and Capacitance (C). This instrument has wide applications in electrical and electronics laboratory, industry and engineering research works. Nowadays, a large variety of RLC meter is available. The high precision RLC meter is slow responding, bulky size, higher operational power and expensive. However, many applications do not need very high accuracy measurement, for this reason, this paper has proposed a simple and moderate precision RLC meter based on Arduino microcontroller which would overcome the existing issues. The proposed design has been verified by simulation and experimentally. The results show good compliance with theory and experiment; in addition, it shows moderate accuracy

Conductive textiles for signal sensing and technical applications

Conductive textiles have found notable applications as electrodes and sensors capable of detecting biosignals like the electrocardiogram (ECG), electrogastrogram (EGG), electroencephalogram (EEG), and electromyogram (EMG), etc; other applications include electromagnetic shielding, supercapacitors, and soft robotics. There are several classes of materials that impart conductivity, including polymers, metals, and non-metals. The most significant materials are Polypyrrole (PPy), Polyaniline (PANI), Poly(3,4-ethylenedioxythiophene) (PEDOT), carbon, and metallic nanoparticles. The processes of making conductive textiles include various deposition methods, polymerization, coating, and printing. The parameters, such as conductivity and electromagnetic shielding, are prerequisites that set the benchmark for the performance of conductive textile materials. This review paper focuses on the raw materials that are used for conductive textiles, various approaches that impart conductivity, the fabrication of conductive materials, testing methods of electrical parameters, and key technical applications, challenges, and future potential

Reaching the poor with health interventions: Programme-incidence analysis of seven randomised trials of women's groups to reduce newborn mortality in Asia and Africa

Background Efforts to end preventable newborn deaths will fail if the poor are not reached with effective interventions. To understand what works to reach vulnerable groups, we describe and explain the uptake of a highly effective community-based newborn health intervention across social strata in Asia and Africa. Methods We conducted a secondary analysis of seven randomised trials of participatory women's groups to reduce newborn mortality in India, Bangladesh, Nepal and Malawi. We analysed data on 70 574 pregnancies. Socioeconomic and sociodemographic differences in group attendance were tested using logistic regression. Qualitative data were collected at each trial site (225 focus groups, 20 interviews) to understand our results. Results Socioeconomic differences in women's group attendance were small, except for occasional lower attendance by elites. Sociodemographic differences were large, with lower attendance by young primigravid women in African as well as in South Asian sites. The intervention was considered relevant and interesting to all socioeconomic groups. Local facilitators ensured inclusion of poorer women. Embarrassment and family constraints on movement outside the home restricted attendance among primigravid women. Reproductive health discussions were perceived as inappropriate for them. Conclusions Community-based women's groups can help to reach every newborn with effective interventions. Equitable intervention uptake is enhanced when facilitators actively encourage all women to attend, organise meetings at the participants' convenience and use approaches that are easily understandable for the less educated. Focused efforts to include primigravid women are necessary, working with families and communities to decrease social taboos

Improved EEG-based emotion recognition through information enhancement in connectivity feature map

Abstract Electroencephalography (EEG), despite its inherited complexity, is a preferable brain signal for automatic human emotion recognition (ER), which is a challenging machine learning task with emerging applications. In any automatic ER, machine learning (ML) models classify emotions using the extracted features from the EEG signals, and therefore, such feature extraction is a crucial part of ER process. Recently, EEG channel connectivity features have been widely used in ER, where Pearson correlation coefficient (PCC), mutual information (MI), phase-locking value (PLV), and transfer entropy (TE) are well-known methods for connectivity feature map (CFM) construction. CFMs are typically formed in a two-dimensional configuration using the signals from two EEG channels, and such two-dimensional CFMs are usually symmetric and hold redundant information. This study proposes the construction of a more informative CFM that can lead to better ER. Specifically, the proposed innovative technique intelligently combines CFMs’ measures of two different individual methods, and its outcomes are more informative as a fused CFM. Such CFM fusion does not incur additional computational costs in training the ML model. In this study, fused CFMs are constructed by combining every pair of methods from PCC, PLV, MI, and TE; and the resulting fused CFMs PCC + PLV, PCC + MI, PCC + TE, PLV + MI, PLV + TE, and MI + TE are used to classify emotion by convolutional neural network. Rigorous experiments on the DEAP benchmark EEG dataset show that the proposed CFMs deliver better ER performances than CFM with a single connectivity method (e.g., PCC). At a glance, PLV + MI-based ER is shown to be the most promising one as it outperforms the other methods

Halal Food Safety: PCR Based Detection of Porcine DNA in Imported Chocolate

Halal food and food products consumption is a major part of living in Muslim community. Pork meat or meat items are not considered halal in Muslim countries and consumers. Ensuring pork-free food items is a challenge for the food industry and exporters to Muslim nations. This study aims to detect porcine DNA in imported chocolate products in Bangladesh for halal safety assurance. The imported chocolate samples were collected from various multi shops in Dhaka. Polymerase chain reaction (PCR) method is used in our research to detect the porcine DNA. Two primer sets are used for the detection of porcine mitochondrial cyt-b (cytochrome-b) gene fragments in chocolate samples. To visualize the amplified DNA, agarose gel (1%) was used. After electrophoresis, DNA band in agarose gel indicated that the gene fragments are amplified properly. In our research, out of 42 chocolate samples, only 2 samples were found positive. The chocolate samples were branded as Wild Berry Flavor Chocolate and Cadbury Milk Tray Chocolate. In comparison with the positive pork sample, these two samples also containing the 165bp and 359bp fragment of the porcine cytochrome b gene. We reported that chocolate products contain the pork contamination were not labeled as halal. While other samples that did not have any halal logo originated from outside Bangladesh and imported also showed negative result. The present study established the DNA-based porcine detection system based on mitochondrial cyt-b that is viable in highly processed products. It can be used in the halal certification process to determine the pork items presence in food and halal safety. Our research also reported that imported chocolates should halal certify before release into the market

Global Burden of Lumpy Skin Disease, Outbreaks, and Future Challenges

Lumpy skin disease (LSD), a current global concern, causes economic devastation in livestock industries, with cattle and water buffalo reported to have higher morbidity and lower mortality rates. LSD is caused by lumpy skin disease virus (LSDV), a member of the Poxviridae family. It is an enzootic, rapidly explorative and sometimes fatal infection, characterized by multiple raised nodules on the skin of infected animals. It was first reported in Zambia in 1929 and is considered endemic in Africa south of the Sahara desert. It has gradually spread beyond Africa into the Middle East, with periodic occurrences in Asian and East European countries. Recently, it has been spreading in most Asian countries including far East Asia and threatens incursion to LSD-free countries. Rapid and accurate diagnostic capabilities, virus identification, vaccine development, vector control, regional and international collaborations and effective biosecurity policies are important for the control, prevention, and eradication of LSD infections. This review critically evaluates the global burden of LSD, the chronological historical outbreaks of LSD, and future directions for collaborative global actions