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This article presents a method to reduce the torque ripple in an 8/6 four-phase switched reluctance motor (SRM). The proposed scheme introduces a nonlinear modulating factor dependent on the rotor position and magnitude of the phase currents. This factor manipulates the currents in two adjacent phases during commutation and reduces the torque ripple effectively. Unlike the conventionally available torque-sharing functions, the proposed method instantaneously modulates every phase current obtained mathematically based on the other phase current in order to maintain the net torque constant. The proposed method requires minimal offline analysis and offers maximum possible torque with a minimal ripple. The method is simple and easy to implement due to a low computational burden. The proposed algorithm is implemented using MATLAB/Simulink software and is also validated experimentally on a 0.6-hp 8/6 SRM using a field-programmable-gate-arrays-based hardware setup developed in the laboratory. Typical results are presented and compared with the existing techniques. A torque ripple of $\approx 8\%$ has been achieved.

Background: The presence of free radicals in the human body is harmful, which may induce many diseases like cardiovascular, diabetes, cancer, inflammation, neurodegenerative disorder, atherosclerosis, cataract, etc. Antioxidants can balance the effect of free radicals. Antioxidant- rich herbs have been identified for the preparation of drugs that can be administered to neutralize the free radicals. In the present context, the selected medicinal plant is “Origanum majorana” (Family- Lamiaceae) widely known as Marwa in the Indian sub-continent. As the medical professionals show their desire towards composite or lateral treatment, the application of drugs with herbal origin has gained its importance. Objective: This review presents various in vitro and in vivo methods used in the antioxidant activity study of O. majorana and highlights its efficacy in reducing the oxidative stress. Methods: Many reliable sources like Research gate, PubMed, Science Direct, Google scholar, Wiley online library, and books have been referred to collect all information regarding the antioxidant activity of the selected plant. Results: Several methods have been used to determine the antioxidant activity of O. majorana, such as superoxide radical scavenging, ferric ion reducing antioxidant potency, thiobarbituric acid reactive substances, hydrogen peroxide scavenging, hydroxyl radical scavenging, lipid peroxidation inhibition, etc. The selected plant contains many phytoconstituents such as gallic acid, ferulic acid, apigenin, catechin, rutin, quercetin, luteolin, linolenic acid, β-sitosterol, and essential oils, which may be responsible for antioxidant activity. Conclusions: The review article provides information for investigating and developing new antioxidant methods and major phytoconstituents from O. majorana for better therapy of oxidative stress-mediated complications.

Green chemistry is also referred to as sustainable technology, which involves the design, synthesis, processing and the use of chemical substances by reducing or eliminating the chemical hazards. This strategy focuses on atom economy, use of safer solvents or chemicals, use of raw materials from renewable resources, consumption of energy and decomposition of the chemical substances to non-toxic material which are eco-friendly. So, this technology is utilized for the sustainable development of novel heterocyclic scaffold like pyrimidine derivatives. Pyrimidine is a six membered heterocyclic aromatic compound with two nitrogen atoms at positions 1 and 3 in the ring system. Among the other heterocyclic compounds, pyrimidine derivatives play a major role due to their diverse promising biological activities, such as antimicrobial, antifungal, anti-viral, anti- tubercular, anti-diabetic, anti-hypertensive, anticancer, anthelmintic, antioxidant, anti-epileptic, antipsychotic, anti-anxiety, antimalarial, antihistaminic, anti-parkinsonian, analgesic and anti-inflammatory etc. The various green methods used for the synthesis of pyrimidine derivatives include microwave assisted synthesis, ultrasound induced synthesis, ball milling technique, grinding technique and photo-catalysis. These processes enhance the rate of the reaction that leads to high selectivity with improved product yields as compared to the conventional synthetic methods. This review is focused on the green synthesis of biologically active pyrimidine derivatives.

Feature selection is a crucial part of data mining applications in the diversity area of data analysis. Most of the feature selection methods issue features analysis for classification, which is not sufficient for classification by feature selection techniques. Classification is involved in traditional classes with redundant feature data, which is not helpful in the database for further analysis. There is a more negligible effect on feature analysis by taking an existing class. So, it proposed to obtain a new class or subclass using the analysis of feature's tiny data named subfeature (SF) data of corresponding instances from a traditional class. These data are involved in the limited number of significant instances where it generates the new class. It is a challenging task to find such instances with subfeatures from any database. Thus, this article proposed the optimization model based on the Lagrangian multiplier to find such data and analyze for a new class from a traditional class. Several algorithms have been taken to explain subfeature data. The theoretical approaches such as domain- and variance-based subfeatures and the convergence of subfeature are used to select subfeatures with the effectiveness of the proposed model. Furthermore, several classifiers with searching methods and statistical methods (i.e., local and global variances) analyze the classification through subfeature data. Result analysis in the experiment on different datasets shows that the proposed model is beneficial toward novel classes based on selected subfeature data. IEEE

In this paper, a profitable strategy of utilizing solar and bio-energy available in a rural location for meeting the energy deficit is discussed. The proposed model can provide 24-hour continual power supply to the vicinity without the need for any battery. The land requirement is minimal in the proposed model making it cost-effective for implementation. The paper provides a step by step procedure to optimally size the solar and bio-power plants and schedule their operation for maximum utility. A programmable smart meter connected with a central system having a user-friendly control algorithm has also been proposed at every home for end-user profitability. The smart meter will indicate energy consumption in local home, its solar energy generation, vicinity power demand, current tariff of power usage, energy imported/exported and other required details. The proposed model is discussed in detail for comprising of two adjacent villages named Gaddikheri and Tajamajra in Rohtak (Haryana), North India (having 768 houses). The technical and financial analysis at this location is done using RETScreen expert software and the results are presented. The system is found to provide 24-hr power supply and also be profitable with the capital cost being recovered in less than 8 years time. Apart from this, the proposed system also provides other benefits like reduction in greenhouse gas emission (by 93%), auto power theft control, bio-fertilizers, and a clean environment.

Demand response modeling in smart grids plays a significant role in analyzing and shaping the load profiles of consumers. This approach is used in order to increase the efficiency of the system and improve the performance of energy management. The use of demand response analysis in determining the load profile enhances the scheduling approach to the user profiles in the residential sector. In accordance with the behavioral pattern of the user’s profile, incentive-based demand response programs can be initiated in the residential sector. In modeling the behavioral pattern of the user’s profile, the machine learning approach is used to analyze the profile patterns. The incentive-based demand response is demonstrated in order to show the importance of maintaining the privacy of residential users, during interactions between demand- and load-profile patterns. In this work, real-time demand response modeling for residential consumers, with incentive schemes, are analyzed. The incentive schemes are proposed in order to show how the privacy of the residential units may be considered, as a result the model is developed with a two-step analysis approach. In the first step, the demand response modeling is performed with the scheduling of appliances on the residential side, by forming hubs in a cloud–fog-based smart grid environment. This process, with an incentive demand response scheme and scheduling of appliances, is performed using an optimal demand response strategy that uses a discounted stochastic game. In the second step, the privacy concerns of the demand response model from the strategy analysis are addressed using a generative adversarial network (GAN) Q-learning model and a cloud computing environment. In this work, the DR strategy model with privacy concerns for residential consumers, along with EV management, is performed in a two-step process and arrives at an optimal strategy. The efficiency and real time analysis proposed in this model are validated with real-time data analysis in simulation studies and with mathematical analysis of the proposed model.

India is moving towards electric vehicles. However, the road to the progress of EV adoption does not seem to be smooth and may need effort from the people and the government. With regard to the growth of Electric Vehicles in India, there is a need to find the factors that could hasten its progress. In this paper, we on the basis of the review of the relevant literature from the scientific database are suggesting a range of factors which requires the attention of policymakers and other stakeholders that could contribute to improving electric vehicle adoption in India. The factors are explained in detail along with its implication on electric vehicle adoption.

Zinc selenium phosphate glasses added with the traces of Au2O3 were prepared and heat-treated. XRD, TEM, DSC, FT-IR and optical absorption (OA) spectroscopy methods were used for the structural assessment of these samples. Later, diferent dielectric parameters viz., dielectric constant (ε′), electric moduli (M), impedance (Z) and a.c. conductivity (σac) were measured over wide regions of frequency (ω) and temperature (T) as functions of Au2O3 concentration. Dielectric breakdown strength (DBS) of these samples was also measured at ambient conditions. The characterization studies indicated that multiple crystal grains were entrenched in the residual amorphous phase of the samples. The X-ray difraction studies revealed that the bulk glasses consisted of Au2(SeO3)3 crystal phase and Au0 metallic particles (MPs). As Au2O3 content was raised, the intensity of the XRD peak related to Au3+ crystal phase indicated a growing trend, whereas that of Au0 MPs showed a decreasing trend. The results of XPS studies revealed similar inferences. The FT-IR results pointed out an improved degree of augmentation of the glass network as the content of Au2O3 was increased. Optical absorption (OA) - spectra of the glasses indicated a broad absorption band in the spectral range of 500–600 nm due to the surface plasmon resonance (SPR) of Au0 MPs. The intensity of the band was observed to decrease as Au2O3 content was increased and indicated decreasing propor- tion of Au0 metallic particles in the samples. The dielectric parameters exhibited a decreasing trend, whereas the dielectric breakdown strength (DBS) and electrical impedance indicated a substantial hike due to the increasing concentration of Au2O3. Overall, the presence of Au2O3 caused a signifcant improvement in the insulating strength of the ZnO added SeO2- P2O5 glasses and hence such glasses may be considered as insulating layers in the display panel devices.

New technology is often met with zeal by engineers. When friction stir welding (FSW) was first introduced as feed-less welding technique initially for non ferrous metals like aluminum, it was widely adopted and its applicability was studied using different approaches. Extensive research focused on optimizing the process has been published and still are under study. Similar enthusiasm was observed upon the introduction of the particulate reinforced metal matrix composites (PRMMC). As the PRMMCs were considered as an upgrade to existing aluminum, a plethora of researchers tried to extend the FSW studies for Al towards later. This precipitations approach has led to nebulous assumptions and exclusions to meet the numerical and computational criteria. Recent advances in micro mechanical analysis has opened a completely new pathway of experimentation and also exposed the downside of generalizations made previously. With use of modern techniques like machine learning and Representative volume element (RVE), these inquiries can be further expanded to provide an inclusive solution with better focus on unique morphological nature of PRMMCs. One of the main setbacks in such studies is lack of available datasets for evaluation. Current paper aims to present a new methodology using Cycle generative adversarial networks (cycle GAN) to generate synthetic two dimensional databases for microscopic images of Al-SiC MMC. Keywords: FSW; Al-SiC MMC; Cycle GAN; Representative Volume Element.

WSNs find valuable application in monitoring purposes, but they suffer from energy-efficiency issues that affect the network lifetime. The energy-efficiency problem is addressed using the cluster head (CH) formation, data aggregation, and routing techniques. Therefore, an energy-aware routing algorithm named protruder optimization algorithm is proposed, which boosts the network lifetime through finding the optimal routing path. The proposed protruder optimization is developed with the hybridization of the wave propagator characteristics and weed characteristics in such a way that the global optimal convergence is boosted while selecting the optimal routing path. Moreover, the communication in the network through the optimal path is progressed through the optimal CHs selection based on fractional artificial bee colony optimization (FABC), and in turn, the energy minimization problem is aided with data aggregation process using sliding window approach that avoids retransmission of the data. The results of the proposed method are compared with the existing methods on the basis of its performance measures, such as energy, alive nodes, and throughput.

PurposeThe process of supply chain digitalization (SCD) through enterprise and inter-enterprise information systems (EIIS) is a challenging task. Understanding and studying barriers of SCD adopting EIIS are essential for better management of supply chains (SCs) in the long run. This paper analyses the key EIIS adoption barriers affecting the process of SCD.Design/methodology/approach Fifteen key EIIS barriers of SCD in the context of an electronic SC were identified from existing literature and in consultation with experts from industry and academia. Grey-based decision-making trial and evaluation laboratory (DEMATEL) methodology is used to identify cause–effect relationship and extent of the strength of relationship amongst EIIS barriers. Sensitivity analysis is done to verify the robustness of the study.Findings EIIS barriers were classified into seven major cause factors and eight effect factors. This study provides a structural framework of key EIIS adoption barriers to decision makers by classifying barriers into cause and effect groups for effective SCD and better management.Originality/value This study contributes to SC literature by exploring key EIIS barriers affecting the process of SCD. These findings will also help the practitioners in effective decision making for digitalization.