A transient current based micro-grid connected power system protection scheme using wavelet approach

Micro-grids comprise Distributed Energy Resources (DER’s) with low voltage distribution networks having controllable loads those can operate with different voltage levels are connected to the micro-grid and operated in grid mode or islanding mode in a coordinated way of control. DER’s provides clear environment-economical benefits for society and consumer utilities. But their development poses great technical challenges mainly protection of main and micro grid. Protection scheme must have to respond to both the main grid and micro-grid faults. If the fault is occurs on main grid, the response must isolate the DER’s from the main grid rapidly to protect the system loads. If the fault ocuurs within the micro-grid, the protection scheme must coordinate and isolates the least priority possible part of the grid to eliminate the fault. In order to deal with the bidirectional energy flow due to large numbers of micro sources new protection schemes are required. The system is simulated using MATLAB Wavelet Tool box and Wavelet based Multi-resolution Analysis is considered. Wavelet based Multi-resolution Analysis is used for detection, discrimination and location of faults on transmission network.  This paper is discussed a transient current based micro-grid connected power system protection scheme using Wavelet Approach described on wavelet detailed-coefficients of Mother Biorthogonal 1.5 wavelet. The proposed algorithm is tested in micro-grid connected power systems environment and proved for the detection, discrimination and location of faults which is almost independent of fault impedance, fault inception angle (FIA) and fault distance of feeder line

Breakthrough Studies of Methyl Salicylate and DMMP Mixed in Methyl Salicylate with Pressure Swing Adsorption Composed of 13X Molecular Sieves

A test procedure for pressure swing adsorption (PSA) was established and elucidated for the air purification using methyl salicylate (MeS) and 5% (v/v) dimethyl methyl phosphonate (DMMP) in MeS containing air stream as feed. The effect of feed flow rate was studied by varying the flow from 5 lpm to 20 lpm, for both the molecules at 25 oC and 4 kg/cm2. The results revealed that the flow rate had a significant influence on the breakthrough time. A method was developed for the determination of feed, purge, and dry air composition, by the solvent extraction method using the XAD-2 and the average concentrations reported. The 13X molecular sieves were characterised for its structural and textural properties such as BET- SA, XRD, and FT-IR. The temperature programmed desorption of DMMP and MeS on 13X clearly demonstrated that it was easily regenerated at ~320 °C after prolonged field operation of PSA. The PSA results obtained with PSA composed molecular sieves appeared to give promising technology for air purification and specifically to the chemical warfare agents simulants

Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET

Mobile ad hoc networks (MANETs) are wireless networks that are perfect for applications such as special outdoor events, communications in areas without wireless infrastructure, crises and natural disasters, and military activities because they do not require any preexisting network infrastructure and can be deployed quickly. Mobile ad hoc networks can be made to last longer through the use of clustering, which is one of the most effective uses of energy. Security is a key issue in the development of ad hoc networks. Many studies have been conducted on how to reduce the energy expenditure of the nodes in this network. The majority of these approaches might conserve energy and extend the life of the nodes. The major goal of this research is to develop an energy-aware, secure mechanism for MANETs. Secure Energy Aware Reinforcement Learning based Decision Making with Hybrid Optimization Algorithm (RL-DMHOA) is proposed for detecting the malicious node in the network. With the assistance of the optimization algorithm, data can be transferred more efficiently by choosing aggregation points that allow individual nodes to conserve power The optimum path is chosen by combining the Particle Swarm Optimization (PSO) and the Bat Algorithm (BA) to create a fitness function that maximizes across-cluster distance, delay, and node energy. Three state-of-the-art methods are compared to the suggested method on a variety of metrics. Throughput of 94.8 percent, average latency of 28.1 percent, malicious detection rate of 91.4 percent, packet delivery ratio of 92.4 percent, and network lifetime of 85.2 percent are all attained with the suggested RL-DMHOA approach

A Novel Cryptography-Based Multipath Routing Protocol for Wireless Communications

Communication in a heterogeneous, dynamic, low-power, and lossy network is dependable and seamless thanks to Mobile Ad-hoc Networks (MANETs). Low power and Lossy Networks (LLN) Routing Protocol (RPL) has been designed to make MANET routing more efficient. For different types of traffic, RPL routing can experience problems with packet transmission rates and latency. RPL is an optimal routing protocol for low power lossy networks (LLN) having the capacity to establish a path between resource constraints nodes by using standard objective functions: OF0 and MRHOF. The standard objective functions lead to a decrease in the network lifetime due to increasing the computations for establishing routing between nodes in the heterogeneous network (LLN) due to poor decision problems. Currently, conventional Mobile Ad-hoc Network (MANET) is subjected to different security issues. Weathering those storms would help if you struck a good speed-memory-storage equilibrium. This article presents a security algorithm for MANET networks that employ the Rapid Packet Loss (RPL) routing protocol. The constructed network uses optimization-based deep learning reinforcement learning for MANET route creation. An improved network security algorithm is applied after a route has been set up using (ClonQlearn). The suggested method relies on a lightweight encryption scheme that can be used for both encryption and decryption. The suggested security method uses Elliptic-curve cryptography (ClonQlearn+ECC) for a random key generation based on reinforcement learning (ClonQlearn). The simulation study showed that the proposed ClonQlearn+ECC method improved network performance over the status quo. Secure data transmission is demonstrated by the proposed ClonQlearn + ECC, which also improves network speed. The proposed ClonQlearn + ECC increased network efficiency by 8-10% in terms of packet delivery ratio, 7-13% in terms of throughput, 5-10% in terms of end-to-end delay, and 3-7% in terms of power usage variation

Pulp Capping Agents in Operative Dentistry: an Updated Review

Background: Pulp capping is a restorative technique that helps preserve the tooth pulp after significant injury during cavity preparation, preventing it from degenerating when it was exposed or nearly exposed. Objective: The aim of this article is to review the literature to assess the current trends and future directions of dental pulp capping materials and it mainly focus on the classification of materials along with their mechanisms. Methods: A comprehensive search was made to identify relevant previous studies in this area in PubMed and Google Scholar databases. Results: The procedure of vital pulp capping primarily depends on the ability of dental pulpal tissue to heal. A wide array of materials has been used for pulp capping. Calcium hydroxide and Mineral Trioxide Aggregates (MTA) are the most commonly used pulp capping materials in dentistry, and they have had significant clinical success. Conclusion: In recent years various other materials like Bone morphogenic protein, Bio dentin, Lasers are also introduced clinically. Further in vitro and in vivo studies are necessary to examine and validate about calcium ion releasing ability, together with the cytotoxic effect and the clinical significance of these next-generation materials

Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)

Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity.  Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput

Establishing inoculum threshold levels for Bean common mosaic virus strain blackeye cowpea mosaic infection in cowpea seed

Bean common mosaic virus strain blackeye cowpea mosaic (BCMV-BlCM) is an important seed-borne virus infecting cowpea and is transmitted both by seeds and aphids. Infected cowpea seeds can act as primary source of inoculum for disease epidemics. Four field experiments were conducted during 2003 - 2006 to assess the role of different amounts of seed-borne inoculum in the dissemination of BCMVBlCM virus in cowpea under field conditions. The identity of BCMV-BlCM was confirmed by ELISA and IC-RT-PCR. Plants infected at an early growth stage appeared to serve as the primary source for subsequent virus spread by aphids. The mean disease incidence during four field experiments reached88-93% in plots sown with 10% infected seed. The disease incidence in plots sown with 5% infected seed recorded 46-63% while for plants raised from 3 and 2% BCMV-BlCM seed infection, disease incidence reached 32-49% and 17-23%, respectively. Mean yield losses in terms of seed yield per plant from four field experiments were 74 and 54% for initial seed infection of 10 and 5%, respectively. Seed infection of 2% BCMV-BlCM incidence resulted in an average of 24% mean seed yield loss/plant-1. The infection appeared to decrease the seed yield in terms of number and size. The BCMV incidence in harvested seed ranged from 0.3 - 19% for the different levels of initial seed infection. The field experiments demonstrated that sowing > 1% BCMV-BlCM infected seed can lead to significant losses in grain yield, while the spread of BCMV-BlCM infection resulting from sowing 1% infected seed did not significantly decrease seed yield. The role of establishing damage or inoculum thresholds from BCMVBlCM seed-borne infections is discussed in the present study.Keywords: Cowpea, potyvirus, seed-borne virus, thresholds, yield los

Solid-state Nmr Analysis Of Unlabeled Fungal Cell Walls From Aspergillus And Candida Species

Fungal infections cause high mortality in immunocompromised individuals, which has emerged as a significant threat to human health. The efforts devoted to the development of antifungal agents targeting the cell wall polysaccharides have been hindered by our incomplete picture of the assembly and remodeling of fungal cell walls. High-resolution solid-state nuclear magnetic resonance (ss NMR) studies have substantially revised our understanding of the polymorphic structure of polysaccharides and the nanoscale organization of cell walls in Aspergillus fumigatus and multiple other fungi. However, this approach requires 13C/15N-enrichment of the sample being studied, severely restricting its application. Here we employ the dynamic nuclear polarization (DNP) technique to compare the unlabeled cell wall materials of A. fumigatus and C. albicans prepared using both liquid and solid media. For each fungus, we have identified a highly conserved carbohydrate core for the cell walls of conidia and mycelia, and from liquid and solid cultures. Using samples prepared in different media, the recently identified function of α-glucan, which packs with chitin to form the mechanical centers, has been confirmed through conventional ss NMR measurements of polymer dynamics. These timely efforts not only validate the structural principles recently discovered for A. fumigatus cell walls in different morphological stages, but also open up the possibility of extending the current investigation to other fungal materials and cellular systems that are challenging to label

Recognition and Classification of Leaf Disease in Potato Plants

Farming is one of the most important lifelines of the country. A nation’s growth majorly depends on how advanced and effective their agricultural practices are in improving the crop yield. When a crop is grown many at times, farmers are unable to identify the health and wellbeing of the plant; they only recognize the problems when it becomes too late hence losing out on that year's expected yield. In this study, we have introduced a recognition and classification technique which is able to detect any ailments that the plant is suffering from at an early stage itself thus enabling the farmers to do the needful at a recoverable stage itself. To make the system as user-friendly as possible, we have provided a feature where the farmers are able to assess the health of the plant by providing a picture of the potato plants’ leaf

Isolation, identification and molecular characterization of Ralstonia solanacerum isolates collected from Southern Karnataka

Bacterial wilt caused by Ralstonia solanacearum, is the major threat to tomato cultivation in all tomato growing areas of Karnataka.  R. solanacearum was isolated from the infected host plants collected from different locations of southern Karnataka. The identity of the isolates was established using morphological, biochemical, and molecular analysis using species specific PCR primers. The race and biovar specificity of pathogen was determined through pathogenicity test on different host plants and the ability of isolates to use carbohydrates, respectively. Phylotype classification was done by phylotype specific multiplex PCR using phylotype specific primers. All the bacterial isolates showed the characteristic creamy white fluidal growth with pink centre on the Tetrazolium chloride medium. Further, the isolates amplified at 280 bp, which confirmed the identity of pathogen as Ralstonia solanacearum. Our results showed that all isolates belonged to Race 1 of the pathogen. Among different isolates obtained, four isolates each were identified to be Biovar III and Biovar IIIA, repectively, while two isolates were identified as Biovar IIIB. All the ten isolates were affiliated to Phylotype I of Ralstonia solanaceraum species complex. These findings may help in devising the management practices for bacterial wilt of tomato in southern Karnataka