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Nowadays, integration of renewable energy source like solar, wind etc in the grid is encouraged to reduce the losses and meet the demand. However, the integration of these renewable sources, power electronic devices, non-linear and un-balanced loads leads to the power quality issues this motivated power researchers for the development of new controllers and techniques. This paper develops a soccer-league algorithm based optimal tuned hybrid controller for the unified power quality conditioner associated with the solar power and battery-storage systems with the Boost converter and Buck Boost converter. The UPQC simultaneously performs both the functions of Shunt active power filter and series active power filter. The proposed optimally designed controller adapts both the properties of fuzzy logic-controller and SOL algorithm tuned proportional-integral controllers. The Kp, Ki values of shunt and series controllers are treated as control variables, which are optimally tuned by SOL to satisfy the objective function. The key contributions of the proposed work are the reduction of total harmonics in current waveforms thereby enhancing the power factor, quick action to maintain the constant DC-Link capacitor voltage during the solar irradiation variations, elimination of voltage sag/swell/large disturbance, and appropriate compensation for the un-balanced networks and loads. The performance investigation of SLOHC was carried-out with four test studies for different combinations of unbalanced/balanced loads and supply voltage of 3-phase distribution network. Comparative analysis was carried out with those of standard methods like a genetic algorithm, biogeography-based optimization, and proportional-integral controllers. The proposed method reduces the total harmonic distortion to 2.06%, 2.44%, 2.40%, and 2.32% which are much lower than those of existing methods available in literature. The design has been performed on MATLAB/simulink software.

Based on the strong evidence of case histories, this study focused on mineralogical and morphological changes of an artificial kaolinitic soil -Ball clay, when exposed to different concentrations of sodium hydroxide (0.1N, 1N, 4N, and 8N) under different curing periods (7, 28 and 100 days). Sediment volume tests are conducted on Ball clay with all combinations and results are analyzed with the help of analytical techniques. XRD and SEM studies are analyzed to understand the micro-level changes of alkali contaminated Ball clay. Mineralogical and morphological transmutations of Ball clay are investigated for 7, 28, and 100 days curing period. Results revealed new mineral formations like Sodalite under 4N and 8N concentrations of NaOH with 100 days interactions are well observed. The morphological transformation from needle shape to pellet shape is clear evidence of the rate of dissolution and precipitation of minerals under 100 days curing periods.

Abstract Education acts as a soul in the overall societal development, in one way or the other. Aspirants, who gain their degrees genuinely, will help society with their knowledge and skills. But, on the other side of the coin, the problem of fake certificates is alarming and worrying. It has been prevalent in different forms from paper-based dummy certificates to replicas backed with database tampering and has increased to astronomic levels in this digital era. In this regard, an overlay mechanism using blockchain technology is proposed to store the genuine certificates in digital form and verify them firmly whenever needed without delay. The proposed system makes sure that the certificates, once verified, can be present online in an immutable form for further reference and provides a tamper-proof concealment to the existing certification system. To confirm the credibility of the proposed method, a prototype of blockchain-based credential securing and verification system is developed in ethereum test network. The implementation and test results show that it is a secure and feasible solution to online credential management system.

Chronic kidney disease (CKD) affects approximately 14% of adults in the United States and is present in at least 10% of the population worldwide. Blood glucose and blood pressure control are imperative to adequately manage CKD as they are the only primary prevention measures for the condition. Recent changes in CKD evaluation and medication therapies that modify disease progression and aid in managing complications such as anemia of CKD have emerged, including a newly approved mineralocorticoid receptor antagonist and hypoxia-inducible factor–prolyl hydroxylase inhibitor, respectively. This focused update on CKD evaluation and management will review the most recent evidence and approved agents to support patients with CKD, including a review of glomerular filtration rate measurement methods such as CKD-EPI 2021 and utilization of cystatin C, Kidney Disease Improving Global Outcomes (KDIGO) guidelines, American Diabetes Association (ADA) guidelines, and primary literature supporting the use of newer agents in CKD. Checklists for managing blood pressure and blood glucose, CKD-mineral bone disorder, and anemia of CKD targeted for pharmacists are also provided. Additionally, a discussion of Centers for Medicare & Medicaid (CMS) coverage of agents approved for managing complications of CKD is included. [ABSTRACT FROM AUTHOR]

This study addresses the critical challenge of real-time identification of tomato leaf diseases using edge computing. Traditional plant disease detection methods rely on centralized cloud-based solutions that suffer from latency issues and require substantial bandwidth, making them less viable for real-time applications in remote or bandwidth-constrained environments. In response to these limitations, this study proposes an on-the-edge processing framework employing Convolutional Neural Networks (CNNs) to identify tomato diseases. This approach brings computation closer to the data source, reducing latency and conserving bandwidth. This study evaluates various pre-trained models, including MobileNetV2, InceptionV3, ResNet50, and VGG19 against a custom CNN, training and validating them on a comprehensive dataset of tomato leaf images. MobileNetV2 demonstrated exceptional performance, achieving an accuracy of 98.99%. The results highlight the potential of edge AI to revolutionize disease detection in agricultural settings, offering a scalable, efficient, and responsive solution that can be integrated into broader smart farming systems. This approach not only improves disease detection accuracy but can also provide actionable insights and timely alerts to farmers, ultimately contributing to increased crop yields and food security. [ABSTRACT FROM AUTHOR]

In the present investigation, a comparative assessment of Kachanov-Rabotnov (KR) and Liu-Murakami (LM) Creep Damage Models is presented for India-Specific Reduced Activation Ferritic Martensitic (IN-RAFM) steel, in the framework of Continuum Damage Mechanics (CDM) approach using ABAQUS finite element analysis software. The assessment is made based on the prediction of entire creep curve and creep rupture life of IN-RAFM steel at 823 K under uniaxial tensile loading at the stress levels of 200–260 MPa. Results disclosed that LM model accurately predicts the rupture life and creep deformation in comparison to KR model and the corresponding R-square values are 0.999 (LM model) and 0.993 (KR model), respectively. The material constant q of LM model controlled the damage evolution in tertiary creep stage. In contrast, the high value of constant ∅ in damage rate equation of KR model, led to over prediction of creep strain owing to high damage rate evolution. [ABSTRACT FROM AUTHOR]

Biodegradable films have been developed to tackle the problem of environmental pollution caused by food packaging waste. However, microbial growth in these films can lead to food spoilage and pose a threat to health. This study aimed to create antibacterial edible films using tragacanth gum, carrageenan, and clove essential oil (CEO) to prevent microbial growth. The influence of various concentrations of CEO (10%, 20%, and 30 w/w%) on the physical, optical, mechanical, antimicrobial, biodegradability properties and packaging of these films was examined. The results indicated that the addition of CEO enhanced film elongation, whereas it reduced tensile strength and Young's modulus. Fourier transform infrared and X‐ray diffraction analyses revealed an interaction between the CEO and tragacanth gum/carrageenan. Incorporating CEO reduced the films' moisture content and water vapor permeability, while increasing their antimicrobial and antioxidant effects. The films containing 30% CEO exhibited a strong inhibitory effect against both Escherichia coli and Bacillus. The incorporation of CEO into tragacanth gum/carrageenan enhanced the film's properties, making it suitable for packaging edible food items. This study fills a gap by providing a sustainable solution to mitigate microbial growth in biodegradable food packing materials. [ABSTRACT FROM AUTHOR]

Image-stitching (or) mosaicing is considered an active research-topic with numerous use-cases in computer-vision, AR/VR, computer-graphics domains, but maintaining homogeneity among the input image sequences during the stitching/mosaicing process is considered as a primary-limitation & major-disadvantage. To tackle these limitations, this article has introduced a robust and reliable image stitching methodology (l,r-Stitch Unit), which considers multiple non-homogeneous image sequences as input to generate a reliable panoramically stitched wide view as the final output. The l,r-Stitch Unit further consists of a pre-processing, post-processing sub-modules & a l,r-PanoED-network, where each sub-module is a robust ensemble of several deep-learning, computer-vision & image-handling techniques. This article has also introduced a novel convolutional-encoder-decoder deep-neural-network (l,r-PanoED-network) with a unique split-encoding-network methodology, to stitch non-coherent input left, right stereo image pairs. The encoder-network of the proposed l,r-PanoED extracts semantically rich deep-feature-maps from the input to stitch/map them into a wide-panoramic domain, the feature-extraction & feature-mapping operations are performed simultaneously in the l,r-PanoED's encoder-network based on the split-encoding-network methodology. The decoder-network of l,r-PanoED adaptively reconstructs the output panoramic-view from the encoder networks' bottle-neck feature-maps. The proposed l,r-Stitch Unit has been rigorously benchmarked with alternative image-stitching methodologies on our custom-built traffic dataset and several other public-datasets. Multiple evaluation metrics (SSIM, PSNR, MSE, Lα,β,γ, FM-rate, Average-latency-time) & wild-Conditions (rotational/color/intensity variances, noise, etc) were considered during the benchmarking analysis, and based on the results, our proposed method has outperformed among other image-stitching methodologies and has proved to be effective even in wild non-homogeneous inputs.

Solar power is free from environmental pollution and also offers economic benefits to the investors. In order to optimize at most solar power, tracking schemes are employed and traditional schemes from the family of perturbation suffer from slow response, due to specific step size disruption and oscillations around operating point. Therefore, this paper proposes an effective design and implementation of a hybrid Maximum Power Point Tracking (MPPT) scheme based on Linear Tangents - Neville Interpolation (LT-NI) techniques. Two tangents are drawn on the P-V characteristics at 75% and 90% of open circuit voltage (VOC).The chosen two tangents intersect and gives rise to a third voltage (N) point. In continuation, the three voltage points (0.75VOC, N & 0.9VOC) are computed using Neville Interpolation. The performance of the proposed LT-NI scheme is compared with Perturb & Observe (P&O) and advanced Divide and Conquer (DC) algorithms.

PURPOSEEarlier imaging techniques for coronary artery disease (CAD) focused primarily on either morphological or functional assessment of CAD. However, dual-energy computed tomography (DECT) can be used to assess myocardial blood supply both morphologically and functionally. We aimed to evaluate the diagnostic accuracy of DECT in detecting morphological and functional components of CAD, using invasive coronary angiography (ICA) and single photon emission computed tomography (SPECT) as reference standards.METHODSTwenty-five patients with known or suspicious CAD and scheduled for ICA were investigated by DECT and SPECT. DECT was performed during the resting state using retrospective electrocardiography (ECG) gating. CT coronary angiography and perfusion images were generated from the same raw data. All patients were evaluated for significant stenosis (≥50%) on both ICA and DECT coronary angiography, and for myocardial perfusion defects on SPECT and DECT perfusion. Comparison was done between ICA and DECT coronary angiography for detection of significant stenosis and between SPECT and DECT perfusion for detecting myocardial perfusion defects.RESULTSUsing ICA as reference standard, sensitivity, specificity, and accuracy of DECT coronary angiography in detecting ≥50% stenosis of coronary artery lumen were 81.6%, 97.8%, and 95.0%, respectively, by segment-based analysis and 92.1%, 96.1%, and 93.7%, respectively, by vessel-based analysis. Using SPECT as the reference standard, the sensitivity, specificity, and accuracy of DECT perfusion in detecting myocardial perfusion defects were 70.4%, 86.4%, and 80.6%, respectively, on per-segment analysis and 90.7%, 66.6%, and 84.7%, respectively, on per-territorial basis.CONCLUSIONDECT accurately detected coronary artery stenosis and myocardial ischemia using ICA and SPECT as reference standards. In the same scan, DECT can accurately provide integrative imaging of coronary artery morphology and myocardial perfusion.

The usage of transportation systems is inevitable; any assistance module which can catalyze the flow involved in transportation systems, parallelly improving the reliability of processes involved is a boon for day-to-day human lives. This paper introduces a novel, cost-effective, and highly responsive Post-active Driving Assistance System, which is "Adaptive-Mask-Modelling Driving Assistance System" with intuitive wide field-of-view modeling architecture. The proposed system is a vision-based approach, which processes a panoramic-front view (stitched from temporal synchronous left, right stereo camera feed) & simple monocular-rear view to generate robust & reliable proximity triggers along with co-relative navigation suggestions. The proposed system generates robust objects, adaptive field-of-view masks using FRCNN+Resnet-101_FPN, DSED neural-networks, and are later processed and mutually analyzed at respective stages to trigger proximity alerts and frame reliable navigation suggestions. The proposed DSED network is an Encoder-Decoder-Convolutional-Neural-Network to estimate lane-offset parameters which are responsible for adaptive modeling of field-of-view range (157o-210o) during live inference. Proposed stages, deep-neural-networks, and implemented algorithms, modules are state-of-the-art and achieved outstanding performance with minimal loss(L{p, t}, Lδ, LTotal) values during benchmarking analysis on our custombuilt, KITTI, MS-COCO, Pascal-VOC, Make-3D datasets. The proposed assistance-system is tested on our custom-built, multiple public datasets to generalize its reliability and robustness under multiple wild conditions, input traffic scenarios & locations.

In our study, we aim to detect various weapons through image processing using a combination of traditional machine learning algorithms and deep learning techniques. This would prove beneficial in anticipating unusual occurrences. The utilization of Closed-Circuit Televisions (CCTVs) has become widespread in the security and surveillance industry; however, it requires constant human monitoring to extract useful information, which is both tedious and prone to errors. The base paper deals with identifying only firearms. We intend to utilize machine learning and deep learning methods to recognize different weapons automatically, not only firearms and alert the CCTV monitor to that specific area.