Identifying Appliances using NIALM with Minimum Features

Government of India has decided to install smart meters in fourteen states. Smart meters are required to identify home appliances to fulfill various tasks in the smart grid environment. Both intrusive and non-intrusive methods have been suggested for identification. However, intrusive method is not suitable for cost and privacy reasons. On the other hand, techniques using non-intrusive appliance load monitoring (NIALM) are yet to result in meaningful practical implementation. Two major challenges in NIALM research are the choice of features (load signatures of appliances), and the appropriate algorithm. Both have a direct impact on the cost of the smart meter. In this paper, we address the two issues and propose a procedure with only four features and a simple algorithm to identify appliances. Our experimental setup, on the recommended specifications of the internal electrical wiring in Indian residences, used common household appliances’ load signatures of active and reactive powers, harmonic components and their magnitudes. We show that these four features are essential and sufficient for implementation of NIALM with a simple algorithm. We have introduced a new approach of ‘multi point sensing’ and ‘group control’ rather than the ‘single point sensing’ and ‘individual control’, used so far in NIALM techniques.DOI:http://dx.doi.org/10.11591/ijece.v4i6.671

Smart scalable ML-blockchain framework for large-scale clinical information sharing

Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results

In silico examination of peptides containing selenium and ebselen Backbone To Assess Their Tumoricidal Potential

Introduction: Cancer has been one of the highest causes of morbidity and mortality in the world for decades. Owing to improved therapeutics along with detection, breast cancer mortality has been slowly reducing. The incidence of breast cancer, on the other hand, has increased gradually. More than 100 types of cancer have been identified with a wide range of treatment protocols comprising of chemotherapy, radiation therapy, hormone therapy, etc. In an attempt to curb the serious deleterious effects caused by the chemotherapeutic drugs, numerous peptide molecules are currently popular as alternatives to the standard chemotherapeutic drugs. Methods: In this study, we have carried out in silico investigations to ascertain the anti-proliferative potential of novel peptides based on selenium and ebselen, i.e. Eb-Trp-Asp, 13, Eb-Trp-Glu, 14, and Eb-Trp-Lys, 15. Analysis of protein-ligand interactions, resulting in protein-ligand complex formation, has been carried out using the AutoDockVina in PyRx aided molecular docking technique, which may be an essential indication of druggability of the test peptides. Results: The molecular docking results revealed that the screened ligands had extraordinarily strong binding interactions and affinity for the target. Conclusion: Findings suggested that novel peptide molecule Eb-Trp-Glu, 14 may be a potent anticancer agent

Artificial intelligence and Machine Learning based Techniques in Analyzing the COVID-19 Gene Expression data: A Review

The novel Coronavirus associated with respiratory illness has become a new threat to human health as it is spreading very rapidly among the human population. Scientists and healthcare specialists throughout the world are still looking for a breakthrough technology to help combat the Covid-19 outbreak, despite the recent worldwide urgency. The use of Machine Learning (ML) and Artificial Intelligence (AI) in earlier epidemics has encouraged researchers by providing a fresh approach to combating the latest Coronavirus pandemic. This paper aims to comprehensively review the role of AI and ML for analysis of gene expressed data of COVID-1

Research Article A new Informatics Framework for Evaluating the Codon Usage Metrics, Evolutionary Models and Phylogeographic reconstruction of Tomato yellow leaf curl virus (TYLCV) in different regions of Asian countries

Tomato yellow leaf curl virus (TYLCV) is a major devastating viral disease, majorly affecting the tomato production globally. The disease is majorly transmitted by the Whitefly. The Begomovirus (TYLCV) having a six major protein coding genes, among them the C1/AC1 is evidently associated with viral replication. Owing to immense role of C1/AC1 gene, the present study is an initial effort to elucidate the factors shaping the codon usage bias and evolutionary pattern of TYLCV-C1/AC1 gene in five major Asian countries. Based on publically available nucleotide sequence data the Codon usage pattern, Evolutionary and Phylogeographic reconstruction was carried out. The study revealed the presence of significant variation between the codon bias indices in all the selected regions. Implying that the codon usage pattern indices (eNC, CAI, RCDI, GRAVY, Aromo) are seriously affected by selection and mutational pressure, taking a supremacy in shaping the codon usage bias of viral gene. Further, the tMRCA age was 1853, 1939, 1855, 1944, 1828 for China, India, Iran, Oman and South Korea, respectively for TYLCV-C1/AC1 gene. The integrated analysis of Codon usage bias, Evolutionary rate and Phylogeography analysis in viruses signifies the positive role of selection and mutational pressure among the selected regions for TYLCV (C1/AC1) gene

Genetic diversity and differentiation among populations of the Indian eri silkworm, Samia cynthia ricini, revealed by ISSR markers

Samia cynthia ricini (Lepidoptera:Saturniidae), the Indian eri silkworm, contributes significantly to the production of commercial silk and is widely distributed in the Brahmaputra river valley in North-Eastern India. Due to over exploitation coupled with rapid deforestation, most of the natural populations of S. cynthia ricini are dwindling rapidly and its preservation has become an important goal. Assessment of the genetic structure of each population is a prerequisite for a sustainable conservation program. DNA fingerprinting to detect genetic variation has been used in different insect species not only between populations, but also between individuals within a population. Since, information on the genetic basis of phenotypic variability and genetic diversity within the S. cynthia ricini populations is scanty, inter simple sequence repeat (ISSR) system was used to assess genetic diversity and differentiation among six commercially exploited S. cynthia ricini populations. Twenty ISSR primers produced 87% of inter population variability among the six populations. Genetic distance was lowest between the populations Khanapara (E5) and Mendipathar (E6) (0.0654) and highest between Dhanubhanga (E4) and Titabar (E3) (0.3811). Within population, heterozygosity was higher in Borduar (E2) (0.1093) and lowest in Titabar (E3) (0.0510). Highest gene flow (0.9035) was between E5 and E6 and the lowest (0.2172) was between E3 and E5. Regression analysis showed positive correlation between genetic distance and geographic distance among the populations. The high GST value (0.657) among the populations combined with low gene flow contributes significantly to the genetic differentiation among the S. cynthia ricini populations. Based on genetic diversity, these populations can be considered as different ecotypes and in situ conservation of them is recommended

Conditioned Medium Reconditions Hippocampal Neurons against Kainic Acid Induced Excitotoxicity: An In Vitro

Stem cell therapy is gaining attention as a promising treatment option for neurodegenerative diseases. The functional efficacy of grafted cells is a matter of debate and the recent consensus is that the cellular and functional recoveries might be due to “by-stander” effects of grafted cells. In the present study, we investigated the neuroprotective effect of conditioned medium (CM) derived from human embryonic kidney (HEK) cells in a kainic acid (KA) induced hippocampal degeneration model system in in vitro condition. Hippocampal cell line was exposed to KA (200 µM) for 24 hrs (lesion group) whereas, in the treatment group, hippocampal cell line was exposed to KA in combination with HEK-CM (KA + HEK-CM). We observed that KA exposure to cells resulted in significant neuronal loss. Interestingly, HEK-CM cotreatment completely attenuated the excitotoxic effects of KA. In HEK-CM cotreatment group, the cell viability was ~85–95% as opposed to 47% in KA alone group. Further investigation demonstrated that treatment with HEK-CM stimulated the endogenous cell survival factors like brain derived neurotrophic factors (BDNF) and antiapoptotic factor Bcl-2, revealing the possible mechanism of neuroprotection. Our results suggest that HEK-CM protects hippocampal neurons against excitotoxicity by stimulating the host’s endogenous cell survival mechanisms

Therapeutic efficacy of artemether–lumefantrine for the treatment of uncomplicated Plasmodium falciparum malaria from three highly malarious states in India

Additional file 1. Flowchart showing site-wise enrolment and follow-up profile