With the increase in local energy generation from Renewable Energy Sources (RESs), the concept of decentralized peer-to-peer Local Energy Market (LEM) is becoming popular. In this paper, a blockchain-based LEM is investigated, where consumers and prosumers in a small community trade energy without the need for a third party. In the proposed model, a Home Energy Management (HEM) system and demurrage mechanism are introduced, which allow both the prosumers and consumers to optimize their energy consumption and to minimize electricity costs. This method also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. The proposed solution shows how energy consumption and electricity cost are optimized using HEM and demurrage mechanism. It also provides economic benefits at both the community and end-user levels and provides sufficient energy to the LEM. The simulation results show that electricity cost is reduced up to 44.73% and 28.55% when the scheduling algorithm is applied using the Critical Peak Price (CPP) and Real-Time Price (RTP) schemes, respectively. Similarly, 65.15% and 35.09% of costs are reduced when CPP and RTP are applied with demurrage mechanism. Moreover, 51.80% and 44.37% electricity costs reduction is observed when CPP and RTP are used with both demurrage and scheduling algorithm. We also carried out security vulnerability analysis to ensure that our energy trading smart contract is secure and bug-free against the common vulnerabilities and attacks.
A secure, smart and peer-to-peer transactions framework can be developed by Blockchain. Blockchain has enormous potential to turn health care systems as a horizontal technology, which has changed many fields of industry. The aim of this article is to critically review 50 papers published between 2015 and 2020 on blockchain-based health systems. 36 of these were journal papers; 7 were from conferences, 4 were from various symposiums; 3 were from seminars and 1 chapter was written in the book. Three key questions will be answered in this report. Firstly, what are the emerging trends of blockchain application development in healthcare from a technical perspective? Secondly, in what ways will the systematic analysis presented here contribute to a better understanding of the potential for incorporating blockchain-based technologies into the healthcare domain? Third, what are the important challenges in adopting blockchain as a solution in the healthcare domain? The descriptive analysis contains in this article shows the statistical statistics on the strategies of these 50 papers reveal that many of the blockchain systems proposed are using privately held blockchain and Ethereum platforms. We also address possible emerging trends of blockchain application such as blockchain integration with artificial intelligence, cloud based solutions and parallel block chain architecture.
The emergence of the novel coronavirus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the late months of 2019 had the officials to declare a public health emergency leading to a global response. Public measurements rely on an accurate diagnosis of individuals infected with the virus by using real-time reverse transcriptase-polymerase chain reaction (RT-PCR). The aim of our study is to relate the fundamental clinical and analytical performance of SARS-CoV-2 (RT-PCR) commercial kits. A total of 94 clinical samples were selected. Generally, 400 µl of each respiratory specimen was subjected to extraction using ExiPrep 96 Viral RNA Kit. All kits master mix preparation, cycling protocol, thermocycler, and results interpretation were carried out according to the manufacturer's instructions of use and recommendations. The performance of the kits was comparable except for the LYRA kit as it was less sensitive (F = 67, p < .001). Overall, four kits scored a sensitivity of 100% including: BGI, IQ Real, Sansure, and RADI. For specificity, all the tested kits scored above 95%. The performance of these commercial kits by gene target showed no significant change in CT values which indicates that kits disparities are mainly linked to the oligonucleotide of the gene target. We believe that most of the commercially available RT-PCR kits included in this study can be used for routine diagnosis of patients with SARS-CoV-2. We recommend including kits with multiple targets in order to monitor the virus changes over time.
This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients’ lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein–protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.
We have investigated graphene-based three various refractive index sensors (split ring resonator (SRR), split ring resonator with thin wire (SRRTW), and thin wire (TW) refractive index sensors) for the encoding and sensing-based applications. The sensors are designed to detect the presence of hemoglobin biomolecules with high sensitivity. The results are analyzed in the form of transmittance, and electric field, and detailed sensitivity analysis is also carried out for the proposed graphene-based refractive index sensors for four various concentrations of hemoglobin biomolecules. We have also investigated the sensor's performance in terms of quality factor, Q, and figure of merit (FOM). The encoding of '0' and '1' is attained by varying the graphene chemical potential fulfilling the one-digit coding. An array of these sensors can then be used for encoding-based applications. The detailed analysis of reported sensors is also carried out by checking the effect of varying physical parameters such as substrate thickness, split ring gap, and thin wire width on tunability. These sensors can be applied in biomedical or encoding-based applications. Experiments are performed using XGBoost regressor to determine, whether simulation time and resources can be reduced by using regression analysis to predict the transmittance values of intermediate frequency or not. Experimental results prove that regression analysis using XGBoost Regressor can reduce the simulation time and resources by at least 70 percent.
This study proposes two tellurite-based PCFs whose are OSOC-PCF (Octa-Spiral with Octagonal Cladding PCF) and DSHC-PCF (Dodeca-Spiral with Hexagonal Cladding PCF) designs with higher nonlinearity and higher birefringence. Result comparisons of proposed PCFs are mentioned and demonstrated in this research study. By applying the finite element method with the assistance of COMSOL Multiphysics, we have investigated and simulated guiding properties like nonlinear coefficient, birefringence, dispersion, bending effect, confinement loss, power fraction, effective material loss. Comparing both designs, the highest nonlinearity is gained by OSOC-PCF reaches up to 7333 W −1 Km −1 around 1.55 μm of wavelength and obtained birefringence of 5.85×10 −2 wherein, a slight confinement loss occurs. The alluded PCFs possess an upstanding structure that associates operating an easy fabrication process. The convenience of propounded PCFs has plenty of purposes in polarization maintenance, optical communication, faster data communication, and super-continuum generation.
This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large. The available virus detection techniques take a long time, are costly, and complex and some of them generates false negative or false positive that might cost patients their lives. The biosensor technique is one of the best qualified to address this difficult challenge. In this systematic review, we have summarized recent advancements in biosensor-based detection of these pandemic viruses including COVID-19. Biosensors are emerging as efficient and economical analytical diagnostic instruments for early-stage illness detection. They are highly suitable for applications related to
Over the last decades, load forecasting is used by power companies to balance energy demand and supply. Among the several load forecasting methods, medium-term load forecasting is necessary for grid’s maintenance planning, settings of electricity prices, and harmonizing energy sharing arrangement. The forecasting of the month ahead electrical loads provides the information required for the interchange of energy among power companies. For accurate load forecasting, this paper proposes a model for medium-term load forecasting that uses hourly electrical load and temperature data to predict month ahead hourly electrical loads. For data preprocessing, modified entropy mutual information-based feature selection is used. It eliminates the redundancy and irrelevancy of features from the data. We employ the conditional restricted Boltzmann machine (CRBM) for the load forecasting. A meta-heuristic optimization algorithm Jaya is used to improve the CRBM’s accuracy rate and convergence. In addition, the consumers’ dynamic consumption behaviors are also investigated using a discrete-time Markov chain and an adaptive k-means is used to group their behaviors into clusters. We evaluated the proposed model using GEFCom2012 US utility dataset. Simulation results confirm that the proposed model achieves better accuracy, fast convergence, and low execution time as compared to other existing models in the literature.
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