Search

A precise land suitability assessment plays a vital role in modern agriculture to sustain production against population explosions worldwide without affecting natural resources or environmental factors. Further, recent urbanization has led to a reduction in arable land and forced the conversion of non-arable land to arable land. Therefore, developing land suitability with its potential and limitations is essential through advanced techniques like multi-criteria Decision Analysis for the particular area. The current research aimed to assess the land suitability of the Manimutha Nadhi watershed, Tamil Nadu, India, using geographical information system (GIS) and Analytic Hierarchy Process (AHP) methods. This study area, Manimutha Nadhi, covers a total area of 263.89 km2. Using a random sampling technique, 57 soil samples were obtained. The parameters for the AHP study were soil pH, EC, Soil organic carbon (SOC), available NPK, soil texture, and soil erosion. The climatic parameters, Annaula average rainfall (AAR) and Annual average temperature (AAT) and topographic parameter slope of the study area are also used as the criteria for generating final agricultural land suitability. The final suitability map through GIS and AHP was created using weighted overlay analysis. The land suitability class on the basis of FAO classification is highly suitable (S1), moderately suitable (S2), marginally suitable (S3), currently not suitable (N1), and permanently not suitable (N2) for agriculture. The results of the final land suitability map by AHP and GIS revealed that 150.58 km2 (57.09%) of the research region is highly suitable for agriculture (S1). The 29.60 km2 (10.08%) area was moderately suitable (S2) for irrigated agriculture. The 53.37 km2 (20.20%) area was marginally suitable (S3) for irrigated agriculture. The 10.56 km2 (7.53%) area was currently unsuitable (N1) for agricultural uses. The area of about 13.45 km2 (5.10%) was permanently unsuitable (N2) for agriculture. The outcomes can be used as baseline information on the land that could strengthen agriculture production in the Manimutha Nadhi watershed. [ABSTRACT FROM AUTHOR]

Pneumonia is an acute respiratory illness caused by viruses or bacteria. Early detection of pneumonia is important to ensure curative treatment and improve survival rates. Pneumonia detection on chest X-rays (CXR) is important for early diagnosis, effective treatment, monitoring patient progress, and managing public health concerns. It plays a vital role in ensuring that individuals with pneumonia receive the appropriate care they need while contributing to research and disease surveillance efforts. However, the examination of CXRs is a difficult process and is prone to subjective variabilities. The use of artificial intelligence (AI) and deep learning (DL) models can perform the detection and classification of pneumonia on CXR images. With this motivation, this study introduces a new Chaotic Sea Horse Optimization with Deep Learning Method for Pneumonia Detection and Classification (CSHODL-PDC) technique on CXR images. The main intention of the CSHODL-PDC algorithm lies in the automated detection and classification of pneumonia on CXR images. The CSHODL-PDC method initially designs a Gaussian filtering (GF) based noise eradication approach to eliminate the noise. In addition, the CSHODL-PDC technique employs the NASNetLarge model to produce a set of feature vectors. Moreover, an improved fuzzy deep neural network (FDNN) model is applied for the automated identification and classification of pneumonia. Finally, the CSHO algorithm selects the optimal hyperparameter values of the improved FDNN model, demonstrating the novelty of the work. A series of simulation analyses were performed on the CXR Pneumonia dataset from the Kaggle repository. The experimental values inferred the improved performance of the CSHODL-PDC method over recent models with a maximum accuracy of 99.22%, precision of 98.96%, and recall of 99.22%. Therefore, the proposed model can be employed for accurate and automated pneumonia detection. [ABSTRACT FROM AUTHOR]

Corrosion is a material removal process in which a refined metal is converted into a chemically stable oxide. This process is the gradual deterioration of materials by chemical or electrochemical reactions with their environment. Corrosion is heavily influenced by environmental factors such as air, moisture, water, and temperature. This study aims to minimize corrosion resistance by applying silicon dioxide nanocoating to mild steel. Silicon dioxide nanomaterials are synthesized with a precursor using the sol–gel method and coated on a mild steel substrate using a spin coater. A corrosion test is conducted on the sample to determine inhibition efficiency and corrosion rate. The nanocoated substrate is tested in a NaCl solution. The characterization techniques of scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) tests are conducted on the substrate. SEM results show the morphology and structure of the coated and uncoated substrates. Nanomaterials are distributed uniformly on the substrate in the third and fourth layers of coating. The absence of cracks in the third and fourth layers of coating can be attributed to the uniform deposition of nanomaterials and the required amount. EDS results indicate the presence of elements or compositions in the nanocoating. The corrosion test result reveals that corrosion rate decreased and inhibition efficiency increased up to three coatings. If more than three coatings are present, then the corrosion rate is increased due to micelle formation. [ABSTRACT FROM AUTHOR]

The requirement and the purpose of the IoT approach have developed substantially over the most recent few years. Here, IoT collects information from actual things, stores it, and then moves it to various organizations. Here, we use a Mobile Ad-Hoc Network (MANET) using IoT. MANET is highly delicate to malware which includes passive and active for the organization. Additionally, this paper shows the security angle-based IoT model utilizing AI. The black hole attack is one among these attacks which drops the entire information traffic and corrupts the organization's execution. In this way, it requires the designing of the novel Adaptive Defense Reinforcement Mountaineering Team Search (ADR-MTS) algorithm that distinguishes and safeguards the organization from the blackhole attack node. The role of ADR-MTS calculation recognizes the source directing and the sum of nodes that are been accomplished by the routing mechanism. This routing method assists with upgrading the course between the both objective node and the source node. The simulation analysis that performs MATLAB shows the improvement with regards to Packet Delivery Ratio (PDR). To improve the system efficiency a similar examination is performed against the current methodologies and from the study, the ADR-MTS calculation gives gainful outcomes concerning the location of black holes in the MANET-based IoT organizations The ADR-MTS method achieved a PDR of 98.7% and scalability of 98.5% and these results demonstrate the efficiency of the ADR-MTS method in comparison to existing methods. [ABSTRACT FROM AUTHOR]

Cloud computing is a domain that provides the on-demand availability of IT resources, which can be accessed from a remote server over the internet. A widely used cloud service model is Infrastructure as a Service (IAAS). Clients offload their documents, images, and multimedia files to the cloud storage service. Securing the information of the clients is the challenging job of the cloud service providers (CSP). To ensure the Quality of Service (QoS) of the information CSP follows efficient encryption algorithms. To enhance the security of the image files this paper proposes a new Simplified Image Encryption Algorithm (SIEA) using an efficient key generation module, which uses the basic structure of Feistel cipher. The proposed secure algorithm encrypts the images and stores them in the cloud storage to overcome the data attack. The plain image is converted into corresponding pixel values and the converted plaintext is divided into blocks of 128 bits each. The 128-bit cipher key is used to find the sub-keys for 10 rounds. Five sub keys (K0 to K4) are generated with 32-bit each. It also contains 5 keys used in the 10 rounds of the process and after 10 rounds data will be shuffled again in the post-round step to generate the 128-bit cipher text. To ensure Shannon's confusion and diffusion logic the substitution and permutation concepts are used in the proposed algorithm. To show the performance of the algorithm the images of three categories black and white, gray-scale, and color images are taken as input and the algorithm produces encrypted images. The cloud storage devices store the encrypted images. This proposed SIEA algorithm is compared with standard AES algorithms and other recent and competitive algorithms in terms of various statistical analysis methods like key sensitivity, information entropy, correlation, histogram, and differential attack analysis shows that the proposed algorithm outperforms other algorithms in terms of security and robustness with its lightweight process. [ABSTRACT FROM AUTHOR]

The present study focuses on the pathological and molecular characterization of African swine fever virus (ASFV) associated with an outbreak in wild boars in two national parks in southern India in 2022–2023. Significant mortality was observed among free-ranging wild boars at Bandipur National Park, Karnataka, and Mudumalai National Park, Tamil Nadu. Extensive combing operations were undertaken in both national parks, spanning an area of around 100 km2, originating from the reported epicenter, to estimate the mortality rate. Recovered carcasses were pathologically examined, and ASFV isolates was genetically characterized. Our findings suggested spillover infection of ASFV from nearby domestic pigs, and the virus was equally pathogenic in wild boars and domestic pigs. ASFV intrusion was reported in the Northeastern region of the country, which borders China and Myanmar, whereas the current outbreak is very distantly located, in southern India. Molecular data will help in tracing the spread of the virus in the country. [ABSTRACT FROM AUTHOR]

Early detection of lung disease is important for timely intervention and treatment, enhancing patient outcomes and decreasing healthcare cost. Chest X-rays are a widely employed imaging modality to examine the structures within the chest, including the lungs and surrounding tissues. Lung disease detection using chest X-rays is a critical application of medical imaging and artificial intelligence (AI) in healthcare. Recently, lung disease detection using deep learning (DL) becomes a significant research area, which has the potential to improve early detection rate and decrease mortality rate. Therefore, this article introduces a Multi-Feature Fusion Based Deep Transfer Learning with Enhanced Dung Beetle Optimization Algorithm (MFFTL-EDBOA) for lung disease detection and classification. The MFFTL-EDBOA technique aims to recognize the existence of lung diseases on CXR images. At the primary stage, the MFFTL-EDBOA technique uses adaptive filtering (AF) approach to remove the noise level. Besides, a multi-feature fusion-based feature extraction approach is developed based on three DL models namely DenseNet, EfficientNet, and MobileNet. For accurate lung disease detection and classification purposes, the convolutional fuzzy neural network (CFNN) approach is utilized. The hyperparameter tuning of the CFNN model occurs using the EDBOA. To illustrate the enhanced lung disease detection results of the MFFTL-EDBOA technique, a sequence of experiments is carried out on benchmark medical dataset from Kaggle repository. The experimental values highlighted the greater result of the MFFTL-EDBOA system over other recent approaches with maximum accuracy of 98.99%. [ABSTRACT FROM AUTHOR]

In this study, aluminium (Al 5052)/magnesium (AZ31B) composites were fabricated with and without wire mesh (SS 304) and different amounts of silicon carbide particles (1% and 2% SiCp of weight of the flyer plate) as reinforcements through explosive welding at varying loading ratios (R = 0.7–0.9). The melted layer, pore, and crack formed near the interface of the Al 5052/AZ31B weld and wire mesh reinforced weld, whereas no such defects were found in the silicon carbide reinforced weld. AlFe3, FeSi, and Al12Mg17 intermetallics were detected near the interface in wire mesh-reinforced welds. On the contrary, in SiCp-reinforced welds, intermetallic development was suppressed because the addition of reinforcement enhanced the utilization of kinetic energy and inhibited intermetallic development. The maximum microhardness was found close to the interface for attempted conditions. Higher tensile (234 MPa) and shear (135 MPa) strengths were obtained in wire mesh with a 1% SiCp reinforced explosive weld. The corrosion rate of fabricated composites is not significant, so they can be utilized in corrosive environments. [ABSTRACT FROM AUTHOR]

A chitosan (CHT)–Ag nanocomposite was prepared using olive leaf extract (OLE) as the reducing and capping agent and AgNO3 as the precursor. Fourier transform infrared spectroscopy (FTIR), high-resolution transmission electron microscopy (TEM), field emission scanning electron microscopy, X-ray diffraction (XRD), ultraviolet-visible (UV–Vis) spectrophotometry, and energy-dispersive X-ray spectroscopy (EDS) were used to characterize the OLE-mediated chitosan–Ag nanocomposites. The TEM results indicate that the CHT–Ag nanocomposites are spherical and range in diameter from 3.7 to 18.1 nm. The Ag nanoparticles in the nanocomposites are crystalline, as confirmed by XRD and TEM. The Ag nanoparticles are polydispersed in the chitosan matrix and are very stable. The zeta potential of the CHT–Ag nanocomposites fluctuates with pH; the maximum value (+ 50 mV) was achieved at pH 2. The antibacterial activity of the CHT–Ag nanocomposite was tested against gram-positive (B. licheniformis, B. cereus, S. haemolyticus, M. luteus) and gram-negative (E. coli, P. citronellolis, P. aeruginosa, Sphingobacterium sp., B. japonicum, and R. pickettii) bacteria using cup plating. Significant inhibition was reported against the tested bacteria strains. Compared to amoxicillin and ciprofloxacin, the OLE-mediated CHT–Ag nanocomposite inhibits all bacteria strains with MIC and MBC concentrations ranging from 0.001 to 0.1%. S. haemolyticus and P. aeruginosa were unaffected by amoxicillin and ciprofloxacin. In addition, the CHT–Ag nanocomposite prevents the growth of sulfate-reducing bacteria (SRB) and the biocorrosion of steel coupons. The order of biocorrosion mitigation efficacy is CHT2.0–Ag (88.87%) > CHT1.0–Ag (67.74%) > CHT0.5–Ag (51.51%). The CHT–Ag nanocomposites have lower values than QUATS (93.58%), a known biocide. The CHT–Ag nanocomposites exhibit promising antibacterial and biocorrosion-mitigating properties, making them a potential antibacterial agent for biomedical and packaging applications. [ABSTRACT FROM AUTHOR]

Hydrogen evolution reaction (HER) through electrocatalytic water splitting is regarded as a promising route to produce hydrogen in a large scale. Designing a low-cost, large-scale, and highly active electrolytic hydrogen production catalyst is still a huge challenge. Incorporation of high-loading redox-active materials with small amounts of graphene is a general protocol to achieve high-performance catalysts. Herein, reduced graphene oxide (RGO) and Au NPs cocatalysts-modified NiS hybrid composite photocatalysts were successfully synthesized via a facile hydrothermal method for hydrogen evolution reaction (HER). XRD results suggest the NiS with rhombohedral phase (# JCPDS No. 65-2117). Both SEM and TEM results reveal that Au with NiS sample has clear spherical-shaped nanoparticles sizes in the range of 30–40 nm are coated on the rGO nanosheets. The estimated optical band gap energy is in the order of Au NPs/NiS@rGO (2.36 eV) < Nis@rGO (2.67 eV) < pure NiS (2.91 eV). The surface areas of NiS, NiS@rGO, and Au-NiS@rGO were calculated to be 64.58, 87.6, and 106.35 m2g−1, respectively. The Au-NiS@rGO exhibits significantly enhanced catalytic activity for hydrogen evolution reaction (HER) in both the acid and alkaline electrolytes in comparison with the pristine NiS. The Au-NiS@rGO delivers a striking catalytic kinetic metrics of a small Tafel slope of 54 mV dec−1, a low overpotential of 252 mV at a current density of 10 mA cm−2, and long operation stability of 4 day in the acid electrolyte. The improved HER activity of the Au-NiS@rGO hybrid composite catalyst is attributed to the synergistic effect of the surface plasmon resonance of Au NPs and enhanced electron transfer on RGO. This work could offer a facile and low-cost strategy for the construction of composite photocatalysts with high-efficiency hydrogen generation activity. [ABSTRACT FROM AUTHOR]

A waste biomass is found to be a good source for many value-added products. Shrimp waste is one of the ultimate sources of proteins. The high-value-added protein recovery using aqueous two-phase system (ATPS) from shrimp Litopenaeus vannamei waste (LVW) is investigated. The factors influencing protein extraction were optimized using response surface methodology (RSM) coupled genetic algorithm (GA) and particle swarm optimization (PSO) as two distinct approaches. A three-level, four-factor (CCD) central composite design was employed to evaluate parameters that influence protein extraction yield and determine the suitable conditions for ATPS. Polyethylene glycol (PEG) 2000 concentration (A), trisodium citrate (TSC) concentration (B), pH (C) and temperature (D) are the four factors that influence the protein extraction yield examined by implementing RSM-GA and RSM-PSO. In RSM-GA, the highest protein yield of 94.99% was achieved with optimal extraction conditions of PEG 2000 concentration of 15.8% (w/w), TSC concentration of 16.0% (w/w), pH 8.0, and temperature 35.0 °C. In RSM-PSO, the highest protein yield of 94.16% was achieved from the PEG/TSC ATPS system with optimal extraction conditions of PEG concentration of 16% (w/w), the salt concentration of 15.8% (w/w), pH 8 and temperature 35 °C. The results obtained from RSM-GA and RSM-PSO were compared and experimentally validated. Hence, RSM-PSO was a potential tool compared to RSM-GA for parametric optimization of ATPS for protein extraction from LVW. Using waste biomass in industrial applications could reduce the production cost and increase the industry's circular economy. [ABSTRACT FROM AUTHOR]

In this study, machine learning algorithms viz., k-Nearest Neighbor Regression (KNN), Decision Trees Regression (DTR), Random Forest Regression (RFR), and Extra Tree Regression (ETR) are employed to predict the strength (tensile, shear and impact) of the aluminium 6061-stainless steel 304 explosive clads having different grooves on the base plate. The explosive cladding process parameters, namely the explosive mass to flyer mass ratio (0.6–1.0), distance between flyer and base plate (5–9 mm), predetermined angle (5°–15°), and groove on the base plate (V/Dovetail), were varied in 60 explosive cladding trials. Owing to the prevailing nonlinear relationship between different explosive cladding process parameters, predicting the strength characteristics by analytical techniques is intricate and hence computational machine learning approaches gain significance. The machine learning algorithms were trained in a Python environment using the tensile, shear and impact strengths collected from 80% of the experiments and preliminary experiments, and tested with the remaining results. The four models are capable of aptly predicting the strengths (tensile, shear and impact toughness), as evidenced by the less than 10% deviation from the experimental results. The ETR algorithm exceeds the other three in terms of accuracy (>5%) in predicting the strength of the explosive clads. [ABSTRACT FROM AUTHOR]

Water is an important part of global circulation. Precipitation is the principal input into these cycles, and its measurement and evaluation in spatial and temporal scales are necessary. This paper assessed the performance of seven satellite precipitation products, i.e., Climate Hazards group Infrared Precipitation with Stations, Princeton Global Forcing's, Tropical Rainfall Measuring Mission, Climate Prediction Center, Climate prediction center MORPHing technique, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record, Multi-Source Weighted-Ensemble Precipitation using India Meteorological Department gridded precipitation as a reference from 1998 to 2016 over the Godavari River basin, India, applying continuous and categorical metrics. The Nash–Sutcliffe efficiency, coefficient of determination, and root mean square error for Multi-Source Weighted-Ensemble Precipitation were 0.806, 0.831, and 56.734 mm/mon and for Tropical Rainfall Measuring Mission were 0.768, 0.846, and 57.413 mm/mon. Similarly, categorical metrics, i.e., highest accuracy, Peirce's skill score, and lowest false alarm ratio, were recorded by Multi-Source Weighted-Ensemble Precipitation with 0.844, 0.571, and 0.462, respectively. Cumulative distribution function was also assessed for all the datasets, representing all products that overestimated low to medium precipitation events except Multi-Source Weighted-Ensemble Precipitation and followed a similar pattern of India Meteorological Department except for low precipitation events. All satellite precipitation products were estimated accurately for low-lying areas and inaccurately over low precipitation regions, large forests, and hilly regions. Overall, Multi-Source Weighted-Ensemble Precipitation and Tropical Rainfall Measuring Mission datasets performed better in evaluating precipitation, and these datasets can be used in hydrological applications. [ABSTRACT FROM AUTHOR]

Explosive welding is a solid-state joining procedure that involves the propulsion of a flyer plate by the explosion of an explosive to produce welds of two or more similar or dissimilar materials. The development of molten intermetallic compounds at the interface degrades the mechanical properties of welded alloys. However, the employment of an interlayer in explosive welding significantly increases the kinetic energy dissipation and prevents the formation of molten intermetallic compounds at the interface, thereby increasing the bonding strength. Earlier researchers employed interlayers having different values of the thickness, yield strength, ductility, and density. The influence of the interlayer on the microstructure and mechanical properties of explosively welded similar and dissimilar alloys is thoroughly reviewed in this study. In addition, the significance of explosive welding in different environments, such as helium, underwater, and gelatin media, is also summarized. Correspondingly, future advancements in joining of materials through explosive welding are forecasted. [ABSTRACT FROM AUTHOR]

In the present study, we report the synthesis of Au nanoparticles (Au NPs) on molybdenum sulfide@reduced graphene oxide (MoS2@rGO)-based hetero-photocatalytic nanohybrids through a one pot hydrothermal method. Variety of characterization was performed to support this hypothesis including X-ray diffraction (XRD), transmission electron microscope (TEM), X-ray photoelectron spectra (XPS), Raman, Ultra violet, photoluminescence and N2 adsorption analysis. BET surface area and BJH pore size of Au/MoS2@rGO was found to be 128 m2g−1 and 10.5 nm, which is higher than bare MoS2 (43 m2/g and 3.5 nm). Electrochemical impedance spectroscopy (EIS) results indicate that Au/MoS2@rGO performed a faster charge-transfer in comparison to MoS2@rGO hybrid. The calculated optical bandgap values are 2.78, 2.51 and 2.23 eV for MoS2, MoS2@rGO and Au/MoS2@rGO composite samples, respectively. Moreover, the lowest PL intensity of Au/MoS2@rGO indicates that less recombination of electron-hole pair and can be useful for the generation of H2 activity under light illumination. Furthermore, the resulting Au/MoS2@rGO catalyst has a reduced overpotential of 60 mV and a low Tafel slope of 32 mV decade−1, indicating increased electrocatalytic activity towards HER. The improved performance is attributed to the increased active sites and a synergistic effect between Au and molybdenum, leading to electronic structure change and charge redistribution of MoS2. The results provide a low-cost countermeasure for the preparation of rGO-supported MoS2 catalysts, and have broad application prospects. [ABSTRACT FROM AUTHOR]

Bimodal size SiC particles reinforced Al-Si alloy composites were fabricated by powder metallurgy route. Three different bimodal reinforcements with various volume fractions of micro and nano sized SiC particles were dispersed in Al-11.5%Si-1%Mg matrix by mechanical alloying for 10 h. The composite powders were hot pressed at 580 °C, 600 °C and 620 °C in a vacuum atmosphere with different cooling rates (10 °C/min, 15 °C/min and 20 °C/min). The microstructural changes of bimodal SiC(m–n) in the matrix material were carried out using OM, SEM and TEM with energy dispersive spectroscopy (EDS). The sample experiments were implemented based on Taguchi L9 OA with three parameters of bimodal reinforcement, sintering temperature and rate of cooling respectively. The measurement of mechanical properties specifies that hardness and tensile strength of Al-11.5%Si-1%Mg composite increases with the decrease of nano-SiC concentration in bimodal reinforcement. Moreover, the fine precipitation of Al-Si rich compound as observed at higher cooling rate (20 °C/min) also contributed for the strength improvement of developing composite. [ABSTRACT FROM AUTHOR]

Aluminum alloy (Al 5052) and magnesium alloy (AZ31B) plates were welded at different loading ratios (mass of explosive/mass of flyer plate, R = 0.7, 0.8, and 0.9) through explosive welding. The interface microstructure revealed a wavy interface for the attempted loading ratios, whereas, for a higher loading ratio (R = 0.9), cracks and pores were observed in the aluminum alloy (Al 5052). X-ray diffraction detected the presence of the Al12Mg17 compound at the interface for all the attempted conditions. The interface microhardness is directly proportional to the loading ratio. The maximum ram tensile strength (167 MPa) and shear strength (103 MPa) were obtained at a lower loading ratio (R = 0.7). Therefore, the satisfactory weld of the Al 5052/AZ31B composite plate was achieved at a lower loading ratio (R = 0.7). [ABSTRACT FROM AUTHOR]

Groundwater remains the primary source of freshwater for essential agricultural practice. Two billion people depend on groundwater as their essential source of water. Thus, fifty percent of the groundwater is used to produce food for the globe. Groundwater serves as the main strategic asset in periods of drought. Determining the groundwater potential zones highly facilitates the effective management of available water resources. The current study attempts to find out the potential zones for groundwater using remote sensing and geographical information system. This tool has become deliberate for groundwater potentiality modeling and mapping as it is highly efficient, accurate, and cost-effective. Satellite and conventional data sources include a total of nine parameters, such as rainfall, geology, geomorphology, lineament density, drainage density, soil, land use, slope, and topographic wetness index, which are considered for the identification of groundwater potential by using multicriteria decision-making approaches such as analytical hierarchy process (AHP), frequency ratio (FR), and multi-influencing factor (MIF). The study is applied to Tanjore District where the proposed methodology has not been attempted in earlier studies. The potential groundwater zones obtained were categorized into five classes: very low, low, moderate, high, and very high. The receiver operating characteristic curve was plotted, and the area under the curve was estimated for results, and the precision showed FR 88%, AHP 75%, and MIF 71%. The results will be valuable for controlling water supplies, land-use planning, and environmental management purposes for planners and engineers. [ABSTRACT FROM AUTHOR]

From the dairy herds (n = 16) reared in few localities of South India with the history of reproductive inefficiency and incidental abortion, 176 sera samples from Jersey (n = 108) and Holstein Friesian (n = 68) crossbred cows were collected to detect prevalence of bovine neosporosis antibodies induced by Neospora caninum (N. caninum) through competitive enzyme linked immuno-sorbent assay (cELISA). The overall true prevalence was found as 23.5% whereas 7.7, 19.1, 25.7 and 40.5% was observed in cows of less than 1 year, 1 to 3 years, 3 to 6 years and above 6 years of age, respectively, denoting that increase in prevalence of N. caninum antibody correlated directly with the age. Among the cattle with and without abortion, 41.1 and 20.6% of true prevalence was found, respectively. The breed-wise true prevalence was 24.3 and 22.2% in Jersey and Holstein Friesians crossbred cows, respectively, indicating that crossbred cows of both breeds are equally susceptible to neosporosis. The prevalence of N. caninum antibody might be attributed to coexistence of dogs resulting in contamination of feed with dog faeces. The presence of dogs with the cattle herd predisposed the herd 3.59 times more to acquire neosporosis than the herd without dogs. The annual estimated economic loss in an aborted herd of having 11 animals was 0.23 million INR due to loss of both calf and milk yield. [ABSTRACT FROM AUTHOR]

This work is concerned with finding nonlinear stability limits of a thermally stratified horizontal layer governed by the Darcy–Lapwood–Brinkman equations. Time periodic modulation is imposed on either the boundary temperatures or the gravitational field permeating the layer. Unlike the classical theory of porous media, thermodynamic non-equilibrium is assumed within the layer in the form of non-negligible temperature differences. The solid and fluid constituents may undergo their own temperature variations and the heat flow is studied using the two-temperature model. The generalized energy method is employed to determine the relevant limits that suffices the system stability. The non-equilibrium and modulational parameters are found to affect the stability limits that are based on a higher-order Galerkin solution. Moreover, it is established that the stability limits so obtained are unconditional in the Euclidean measure. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disorder that primarily affects the elderly for over 55 years. PD can be characterised by patients exhibiting various non-motor and motor symptoms. It is significant to note that even though modern-day medical technology has grown exponentially over the years, there is still no cure for Parkinson's disease. Hence, it is a scientifically exciting proposal to develop technologies that diagnose Parkinson's disease earlier. Early diagnosis of PD can enhance the patient's quality of life to a reasonable extent, as the disease's nature is progressive, and it may take years to cripple the patient. It is also essential to observe that the symptoms will get intensified over time. Early diagnosis can also predict other types of neurodegenerative diseases, as the symptoms are pretty similar. The idea of Artificial Intelligence (AI) techniques is recently getting significant medical diagnosis attention, as these technologies can process massive data and come up with good statistical predictions. This study presents a detailed review of various machine learning and deep learning-based AI techniques applied to PD diagnosis and their impact in opening up newer research avenues. Furthermore, this paper explores the possible opportunities of data-driven AI technologies in PD diagnosis and its current status. [ABSTRACT FROM AUTHOR]

Infectious laryngotracheitis (ILT) is an acute respiratory disease in chickens that is a serious threat to poultry-producing countries worldwide. In the present study, we isolated and characterized infectious laryngotracheitis (ILTV) virus isolates by sequencing and restriction fragment length polymorphism analysis of PCR-amplified products (PCR-RFLP). A total of 26 ILTV outbreaks were investigated that occurred between 2019 and 2020 in flocks that had not been vaccinated against ILTV. ILTV was isolated by cultivating tracheal samples in embryonated chicken eggs, which showed multiple opaque pock lesions and thickening of the chorioallantoic membrane after 120 hours of infection. The ILTV isolates were identified and characterized by PCR and sequencing a portion of the ICP4 and TK genes. Phylogenetic analysis based on the ICP4 region showed that the sequences clustered with chicken-embryo-origin vaccine-like strains. Sequence analysis of the ICP4 region differentiated chicken-embryo-origin (CEO), tissue-culture-origin (TCO), and field ILTV strains, with significant differences in nucleotide and amino acid sequences. Furthermore, PCR–RFLP analysis of the TK gene showed that the patterns were identical to those obtained with low-virulence and vaccine strains. In conclusion, sequencing of a portion of the ICP4 region of ILTV allowed differentiation of ILTV field, CEO, and TCO vaccine strains. In this study, CEO-vaccine-like strains were found to be the cause of ILTV outbreaks between 2019 and 2020 in Tamil Nadu in southern India. [ABSTRACT FROM AUTHOR]

Now a day, privacy-preserved data mining is an important research topic that finds significant information from the database while hiding sensitive information. Numbers of algorithms are presented for concealing sensitive information. This paper presents the privacy preserved association rule mining by constraint-based objective function and the genetic algorithm (GA). The proposed method uses the Frequent Pattern (FP)-Growth algorithm for mining the association rules from the database. At first, the FP-Growth algorithm extracts the FPs from the database by applying the support threshold and then, generates the association rules from the FPs by applying the confidence threshold. Then, the generated association rules are processed by the GA for producing the privacy preserved association rules while preserving the sensitive rules. Finally, the privacy preserved association rules are provided to the third party users. The performance of the proposed method is evaluated with the existing Particle Swarm Optimization (PSO) for the evaluation metrics' privacy and utility. The experimental results show that the proposed method mines the privacy preserved association rules from the database with the maximum privacy of 0.124 and the minimum utility of 0.0989 when compared to the existing method. [ABSTRACT FROM AUTHOR]

This article discusses a transformer-free, high-efficiency DC–DC converter besides renewable energy applications. The traditional buck–boost, classic Zeta, Sepic, and Cuk converter does have the benefits of a simple design, low cost, as well as the capacity to execute voltage step-up and step-down. Conversely, because of the detrimental consequences of the parasitic constraints of the device, the voltage conversion gain of the traditional DC–DC converter is much more restricted, and the efficiency is also significantly smaller, whereas this proposed converter does have a higher voltage gain and efficiency because it is used in a single power switch, resulting in reduced switching losses and voltage stress. The said converter's design is very simple, which simplifies the operation control and reduces switching and conduction losses, leading to an efficiency of 97.4%. This converter seems to have the same capabilities as the Zeta converter, including continuous desired output current and desired buck–boost operation. Such an article offers the operation principle and steady evaluation, as well as a comparison with other existing high step-up configurations. The proposed converter employs a fuzzy logic controller, which improves the voltage level as well as reduces the time taken to set the voltage output of a conventional PI and ANN controller, especially in comparison to the FLC controller. For deployment, experimental result and MATLAB/Simulink have been used, and the modeling results indicate that the proposed controller performance has improved. The proposed approach delivers better performance with an output power of 248 W and an efficiency of 97.4%, which is comparatively better than the other converters. [ABSTRACT FROM AUTHOR]

This study deals with the application of soft computing techniques viz., response surface methodology, artificial neural network, radial basis function network and support vector regression in analyzing and predicting the ram tensile and shear strengths of aluminium 5052–stainless steel 316 explosive clads, having different interlayer. 60 explosive cladding experiments were conducted, based on central composite design of experiments, by varying the process parameters viz., loading ratio (mass of the explosive/mass of the flyer plate: 0.6–1.0), distance of separation (6–10 mm), preset angle (6°–8°) and interlayer (aluminium 1100/pure copper/stainless steel 304). The responses viz., ram tensile and shear strengths obtained from 90% of the experiments and trial experiments are used for training artificial neural network, radial basis function network and support vector regression in a Matlab environment, altering training algorithms and number of neurons in the hidden layer. The remaining 10% of the experimental outcome is used for testing the developed models. Likewise in RSM, regression equations are generated for the responses, based on analysis of variance. All the four models are capable of predicting the ram tensile and shear strength effectively, as the average percentage deviation with the experimental outcome are less than 10%. Of the three models, artificial neural network model predicts the ram tensile strength and shear strength in a better manner. [ABSTRACT FROM AUTHOR]

In black gram, yellow mosaic virus (YMV) disease is a serious problem causing severe yield reduction. In the present study, F2 and RIL Population (F4:5) of the cross IC436656 (YMV susceptible) x KKB14045 (YMV resistant) were evaluated for YMV resistance through bulked segregant analysis (BSA) using SSR markers. Out of 91 SSR markers studied, thirteen were polymorphic between IC436656 and KKB14045 which showed 14.3% of genome diversity. The F2 population segregated as 3:1 for susceptibility and resistance in chi-square test which indicated monogenic recessive gene control the YMV resistance. From single marker analysis (SMA), CEDG141 and CEDG008 were strongly associated to YMV resistance. From BSA, CEDG141 differentiated IC436656 (200 bp) and KKB14045 (280 bp) in F2 population. CEDG141, CEDG 264 and CEDG 008 differentiated the RILs of the same population in BSA. The identified SSR markers would be valuable in black gram breeding programs for YMV resistance studies. [ABSTRACT FROM AUTHOR]

Polyacetal is widely used in thin walled injection moulding due to its high mechanical properties. But, its widespread application is limited due to volumetric shrinkage and warpage. The investigations on reducing warpage on Polyacetal is attempted by incorporating nano fumed silica of 1 wt% to 5 wt% as a nucleating agent. The crystallinity study shows that the crystallization rate is increased with the addition of fumed silica up to 4 wt%, beyond which it is reduced. The increased warpage and tensile strength are observed with increase in crystallinity. The tensile strength of PA/5 wt% FS nanocomposite is 1.79% lesser than PA/4 wt% FS nanocomposite, but still 2.5% higher than neat PA. The effect of the filler on surface roughness and optical properties such as haze and gloss are also studied. The micro voids are steadily increased as filler loading increases. Hence, the surface roughness increases uniformly upon increase of filler and it does not depend on crystallinity but depends on growth of micro voids. Furthermore, the microscopic analysis is carried out through FTIR, XRD and SEM and discussed. It is concluded from the above analysis that 5 wt% of FS loading into PA reduces warpage of the thin walled parts with a gain of mechanical strength than neat PA. [ABSTRACT FROM AUTHOR]

Free convective onset in a bottom heated porous medium under the influence of transverse magnetic field is studied within the framework of nonlinear theory. Simple harmonic time modulation is imposed on either the bounding surface temperature or the gravitational field acting on the medium. The Darcy–Lapwood–Brinkman model of flow through porous media is employed with two basic types of physical boundary conditions. The generalized energy approach coupled with the variational principles is followed. Higher order Galerkin method is then used to determine the unconditional and sharp stability limits for arbitrary values of modulational parameters. The increase in the Hartmann number is found to inhibit instability, thereby delaying the onset of two dimensional rolls. Moreover the initiation of instability mechanism can be well adjusted through a good knowledge of the parameters involved. [ABSTRACT FROM AUTHOR]

In recent days, cancer is a deadly disease because of its spreading nature to other cells, and this disease is not identified at an early detection stage. Generally, the cancer is detected with the help of a biopsy method, which is a painful approach. Due to the development of technology, nowadays, it is identified with the help of image processing methods. Here, the image processing approach is used for identifying and classifying the skin cancer types, namely melanoma, common and atypical nevi. The methods used earlier for the detection and classification are artificial skin leison merging, Raman spectroscopy and back-propagation networks. Cancer is classified into many types like blood cancer, bone, colon, and stomach and skin cancer. Among these cancer types, skin cancer can be a dreadful disease, which is detected and then treated at the starting stage of the disease. Hence, this paper proposed an optimized neural and fuzzy approach for skin cancer classification. The fuzzy c-means segmentation is used for the detection of the cancer region. Firefly optimization determines the dominant feature for the training of the neural network. The dominant feature is determined by reducing the error rate of the classifier. The overall process is evaluated with the help of evaluation metrics like accuracy, specificity and sensitivity. In this proposed method, the best result is achieved for the pattern net by improving its accuracy by 4.9% from its previous Moth-Flame Optimization based classification in its evaluation. [ABSTRACT FROM AUTHOR]

Among rice (Oryza sativa L.) bacterial leaf blight (BB), which is caused by Xanthomonas oryzae pv. oryzae, plays a pivotal role in decreasing rice yield. It is reported to be the most serious constraint to improving rice yield. The present investigation assessed the potentiality of BB resistance and molecular characterization of 100 rice accessions for four major BB resistance genes, viz., Xa4, xa5, xa13, and Xa21. Disease screening was carried out under glasshouse conditions using a BB culture isolated from BB-infected rice fields through the leaf clipping method. Analyses of 13 BB resistance genes linked with polymorphic microsatellites markers indicated the presence of single-, two-, three-, and four-gene combinations of xa5, xa13, Xa4, and Xa21. We found four accessions to be resistant, 34 accessions to be moderately resistant, 49 accessions to be moderately susceptible, and 13 accessions to be susceptible. Among the resistant lines, IR12L110, Namcheonbyeo, Dhalaheera, and SahbhagiDhan recorded a minimum lesion length of 3.70, 4.20, 4.67, and 8.30 cm, respectively. Phylogenetic tree, constructed using molecular data, grouped the rice germplasm into four major clusters. Resistant genes xa5 and Xa4 contributed 14 positive compatible resistant genes, each belonging to 28 germplasm for the BB resistance. The potential genetic resources identified as resistant to the BB can be used as donors for the improvement of rice bacterial blight resistance in rice breeding programs. [ABSTRACT FROM AUTHOR]

Challenge of an early prediction of customer churn is a major demand among research community. To understand an intention of a customer on reasons to make a churn as well time taken by a customer to churn is always an unknown mystery. Though good numbers of research works have suggested works on customer churn an exact measure of accurate churn and approaches to suggest on retention is the major discussion of this paper. Traditional approaches such as ACO, PSO are supports on appreciable churn prediction but consider more time to converge, whereas GWO algorithm supports with minimal time to converge as well improved accuracy of 89.26% along with actual churn match compared to PSO and ACO approaches. CUPGO also focuses on customer retention of 34.81% to retain valuable customers. CUPGO works on a large dataset collected over two consistent years. [ABSTRACT FROM AUTHOR]

In digital image processing, image segmentation is the key methodology which is to be used frequently. In digital image processing, noise reduction and enhancement techniques are plays as a vital role. Brain is major and major organ of the human body which is to be controlled by the nervous system. In this paper, we proposed a brain tumor image enhancement technique with the help of the ICA-LDA (independent component analysis-linear discriminate analysis algorithm with ARHE (adaptive region based histogram enhancement) model. Image fusion technique is to apply for combination of the two or more input image. In this paper, the weighted average technique is to be used for image fusion techniques. The noise reduction and enhancement techniques are to be applied in preprocessing stage. The adaptive median filter is to be used for preprocessing stage. The ARHE (adaptive region based histogram enhancement) model is to be used for enhancement present in the preprocessing stage. The feature extraction and the feature optimization have to be utilized with the ICA (independent component analysis). The LDA (linear discriminate analysis) is to be used for the classification techniques. Using this classifier which is to separate the abnormal and normal stages. When the brain tumor is denoted as abnormal case then the morphological based segmentation is to be done. The simulation and result shows the analysis of various parameters such as specificity, sensitivity, positive predictive value, negative predictive value, accuracy, precision, and recall. [ABSTRACT FROM AUTHOR]

The security of the data becomes the main concern due to the quick rise in the exchange of data over the open networks and the Internet. The cryptographic techniques based on chaos theory reveal some novel and effectual orders to develop secure image encryption approaches. Images are the most attractive kinds of data in the encryption domain. In current years, the chaos-based cryptographic techniques have provided an efficient way to expand secure image encryption frameworks. This proposal temps to develop an optimized HCM for promoting novel image encryption. The proposed image encryption model involves 4 steps, such as image pre-processing, key generation, image encryption using optimized HCM, and image decryption. In the pre-processing, the RGB image is converted into a grayscale image, and the key is generated by the SHA-256 cryptographic hash algorithm. Further, the encryption of the image is performed by HCM with the integration of 2DLCM, and PWLCM. While hybridizing the two chaotic maps for image encryption, parameter tuning or optimization is performed for improving its performance. Moreover, Information entropy is considered as the objective model that has to be maximized while tuning the parameters, and the maximum value of information entropy will mean the best performance. An improvement in the well-known optimization algorithms termed as CI-WOA is adopted for performing the parameter optimization of HCM. Hence, the optimized HCM-based image encryption can be confirmed as an efficient and secure way for all types of image transmissions. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are group of spatially distributed atomic sensors which are deployed widely in unattended environments. Each sensor node is capable of monitoring environmental parameters like temperature, pressure, humidity, vibration etc. And WSNs has wide range of applications starting from home automation to military surveillance. However, one of the noticeable issues in WSNs is its efficient energy usage because sensor nodes are battery powered, which cannot be replaced or recharged once deployed in target area. Since communication module consumes most of the node power, energy efficient topology construction and routing algorithm design plays the vital role for energy conservation. For energy efficient optimized topology construction selected nodes are being chosen to construct a virtual backbone. One of the principle practices followed for virtual backbone construction for optimized topology is Dominating Set (DS) of graph theory. However, generating a virtual backbone by DS or Connected Dominating Set (CDS) is NP-hard problem because of larger network size. To overcome this issue, in this paper an energy aware Total Edge Domination based semigraph model (TEDS) is proposed. The performance ratio of proposed TEDS algorithm is measured as 3 + l n Δ ′ + 1 Δ ′ opt , where |opt| represents size of the network constructed using proposed model. The time complexity of the proposed model is measured as O(m) and message complexity O m n + m Δ ′ , where Δ ′ is the maximum edge degree of the network. In addition, the performance of the network is simulated using the NS-2 simulator, and the performance metrics were studied. [ABSTRACT FROM AUTHOR]

Globally in recent days, the potential risk for patients with diabetes mellitus is the prevalence of diabetic retinopathy, which is a silent disease with no early symptoms and is the imperative cause of vision loss. An early diagnosis can be used to prevent for visual loss and blindness. In the regular screening process, assistance of computerized diagnosis can considerably minimize an ophthalmologists work and improve inter and intra viewer variability. A generalized method of semi automatic exudates characterization to diagnose diabetic retinopathy with exudates screening system of retinal image is presented in this paper. This system uses morphological processing based retinal blood vessel suppression, Semi automatic masking of optic disc structure and morphological component analysis based texture enhancement followed by segmentation and Adaptive Neuro-Fuzzy Inference System (ANFIS) based classification method to discriminate between normal and pathological retinal structures. The novelty of this system relies on the appropriate sequential application of exclusive image processing techniques in synergy with ANFIS classifier to improve the accuracy of exudate lesions characterization. The performance of the system has been evaluated by comparing it with various state of the art existing methods in terms of several performance metrics such as Accuracy, Average error rate, F-Score and Kappa value. The obtained numerical results prove that the proposed system with ANFIS classifier demonstrated superior performance in identification of exudate lesions. [ABSTRACT FROM AUTHOR]

This study is concerned about the stabilization for delayed fuzzy neutral-type system (DFNTS) with uncertain parameters under intermittent control. Firstly, by constructing the augmented Lyapunov-Krasovskii functional (LKF) about different time delays along with single and double auxillary function-based integral inequalities (SAFBII, and DAFBII, respectively), a new class of delay-dependent adequate conditions are proposed, so that the robust fuzzy neutral-type system under consideration is guaranteed to be globally asymptotically stable (GAS). Secondly, the intermittent control (IC) is introduced to stabilize the system with mixed time-varying delays. In the view of inferred adequate conditions, the IC parameters are determined as for the arrangement of linear matrix inequalities (LMIs). It is noted that the strategies exploited in this work are apart from the other methods engaged in the literature, and the proposed conditions are less conservative. Finally, numerical examples are given to demonstrate the effectiveness of the developed techniques in this work. One of the practical applications is single-link robot arm (SLRA) model to show the viability and benefits of the structured intermittent control. [ABSTRACT FROM AUTHOR]

In this proposed work, we examined the interaction properties of alcohol viz. methanol and ethanol molecules on stair phosphorene nanosheet (stair-PNS) based on the first-principles study. Moreover, the adsorption property of chief molecules on the stair-PNS base substrate is explored based on the binding energy, average energy gap variation, and Bader charge transfer. The methanol and ethanol molecules are physisorbed on stair-PNS, which is validated with regard to the lower scale of binding energy. Besides, the band gap of stair-PNS gets changed upon the adsorption of alcohol vapors. Thus, we suggest from the results that stair-PNS can be facilitated as the chemi-resistive sensor to probe alcohol vapors. [ABSTRACT FROM AUTHOR]

In this work, bimetallic phosphide NiCoP was synthesized by a simple hydrothermal method using red phosphorus as the P source. Then, NiCoP was anchored on the surface of g-C3N4 nanosheets via physical grinding followed by calcination. XRD and TEM results suggest that NiCoP has hexagonal crystalline structure with spherical-shaped nanoparticles (25–30 nm), which is uniformly wrapped on the 2D-g-C3N4 nanosheets. The NiCoP/g-C3N4 hybrid composite shows high surface area (105.46 m2/g) and porous nature (11.25 nm) than compared with bare NiCoP (58.51 m2/g and 34.71 nm). UV–Vis spectroscopy shows that the enhancement of absorption spectrum in the UV region with red shift was observed in the composite samples. Furthermore, the energy level difference between NiCoP and g-C3N4 generated a potential barrier that prevented the recombination of the electrons in the NiCoP conduction band with the I3− ions in the electrolyte. The optimal composite photoanode (NCPG-3) exhibits outstanding photoelectric conversion efficiency (PCE) of 11.24%, which exceeds the single NiCoP (4.54%) and is on par with the standard Pt CE (7.12%) under same conditions. This work provides new insights into the utilization of NiCoP/g-C3N4–based photoanode materials for high-performance DSSC–based applications. [ABSTRACT FROM AUTHOR]

Sesame (Sesamum indicum L.) also known as Queen of oilseeds is being cultivated worldwide for its oil content for nutritional and medicinal value. Low productivity is one of the major constraints of sesame cultivation. Systematic efforts are made using induced mutagenesis to increase its productivity and realize earliness and alteration in genetic architecture. Sodium Azide was used as a chemical mutagen in the present study. Nine sesame varieties viz., TMV 3, TMV 4, TMV 5, TMV 6, TMV (Sv) 7, CO 1, VRI 1, VRI(Sv) 2 and Paiyur 1 released from Tamil Nadu Agricultural University, Coimbatore, India were subjected to mutation at three different concentrations of Sodium Azide viz., 5 mM, 10 mM and 15 mM. An extra early mutant that matured on 70 DAS was identified in M4 generation of TMV 5 at 15 mM. This mutant (TVS 1401) was forwarded to subsequent generations with confirmed earliness. After confirming the superior performance of the mutant TVS 1401 for yield attributes in station trials, it was evaluated in MLT for two seasons. During Rabi 2017 MLT, it recorded 521 kg/ha of seed yield with the duration of 72 days. During Kharif 2018, it recorded 683 kg/ha of seed yield which was 21.96% higher yield than the popular check TMV 7 and matured 16 days earlier. This extra early mutant has white seed coat colour with 1000 seed weight of 3.22 g. Dissection of fatty acid profile of this mutant also confirmed its superiority. Distinctness of TVS1401 in molecular level was also established through RAPD markers viz., OPC03550 and OPA04550. [ABSTRACT FROM AUTHOR]

Aluminum alloy plates were explosively cladded to stainless steel plates with trapezoidal grooves on the mating surface. The process parameters viz, loading ratio, standoff distance and flyer plate thickness were varied based on the Taguchi analogy. The variation in the process parameters alters the kinetic energy dissipation and the deformation work performed at the interface, and dictates the interfacial wave amplitude and the mechanical strength of the dissimilar explosive clad. The optimum level of process parameters for attaining higher tensile and shear strength is computed by signal-to-noise ratio. Further, a mathematical model is developed for calculating tensile and shear strength of the clad, based on the regression analysis using statistical software Minitab-16, and the level of fit is determined by analysis of variance. [ABSTRACT FROM AUTHOR]

A hybrid on-chip approach based on the variants of soft computing techniques is proposed in this work to evaluate the performance metrics of a 6T static random access memory (SRAM) cell in 45-nm technology. The performance metric evaluated for the SRAM cell is the data retention voltage (DRV) with optimal memory requirements. Each of the SRAM memory cells intends the chip to possess low density but operates at high speed. This paper formulates a hybrid soft computing framework comprising a deep backpropagation neural network which trains the fuzzy inference system to determine the performance metrics of 6T SRAM cell. The weight and bias parameters of the deep learning neural framework are optimized through a cat swarm optimization algorithm so as to reduce the elapsed convergence time of the new hybrid soft computing model. Evaluation process is executed based on the driven outputs from deep learning model to the fuzzy inference system (FIS) module so as to achieve the best values of DRV. Data retention voltage plays a major role in reducing the substantial leakage current and static noise margin intends to retain the data without losing them. Deep backpropagation neural network gets trained with deep learning procedures and optimizes the rule parameters and membership parameters of the FIS design structure. The performance metric DRV under various constraints prove to be better and effective in comparison with the solutions from the existing literature works for the same configuration of 6T SRAM cell using 45-nm technology. [ABSTRACT FROM AUTHOR]

Internet speed is important in many applications. Mobile signal strength of − 110 to − 120 dBm provides poor internet speed by mobile phones. Mobile makes less signal strength and less internet speed from mobile towers. Signal strength ranges between − 30 and − 65 dBm must be maintained to achieve high-speed internet through mobile phones. Good antenna design is necessary to maintain good signal strength and to improve high-speed internet from weak internet speed in required applications. In this work, an FM radio receiver antenna and parabolic dish antenna with a winding helical coil is proposed for the design of an antenna (FMDH antenna) in order to improve weak internet signals to enable high-speed internet signal. Cell tower radiation is normally reduced at a distance of 1000 m, with a weak signal resulting in slow internet speeds. The reduced radiation signal density from the cell tower causes slower internet speeds in mobile. The winding coil of a helical antenna receives the weaker internet signal from the cell towers to increase gain and signal strength between − 30 and − 65 dBm, improving internet to high-speed performance. The FM radio receiver antenna length is increased, so the gain in the main lobe radiation pattern improves internet speed. High-speed internet is needed for computers through mobile internet. Most computers work through mobile internet connections. Wi-Fi connection signal strength should be − 30 to − 60 dBm for maintaining high-speed internet. The FMDH antenna design is proposed in this paper improves mobile Wi-Fi internet, increasing internet signal strength and internet speed. [ABSTRACT FROM AUTHOR]

The study focuses on the analysis of the surface mounted components of the printed circuit boards (PCB) under random vibration loading. The surface mount technology used for the study involves ball grid arrays. In order to predict the fatigue life of the component under certain defense and automotive applications, modal analysis is done by using the finite element method. The results are verified experimentally using JEDEC standards to validate the finite element design used for the study, and random vibration experiments were performed with finite element analysis. The fatigue life of PCB under different random vibration environments is predicted and compared. The fatigue life of PCB is least with jet cargo environment and is maximum with rail cargo environment. The analysis reveals that under the jet cargo environment, more numbers of natural frequency excitations are present. It was found that upon loading, maximum stress was found to be induced at the corner of solder balls for packages mounted in the center of the PCB. Hence, it is suggested to incorporate design modification such that the central area of the PCB must not contain any packaging designs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)