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Cataract, an age-related eye disease, poses a significant ophthalmological public health challenge in both developed and developing nations. Tailoring treatment or surgery plans helps accurately categorise the cataract's developmental stage. Precise cataract grading helps in diagnosing cataracts and subsequently scheduling surgical intervention. In this project endeavour, a solution is presented to automate the cataract grading process utilizing slit lamp bio-microscope data sets acquired through smartphones. This innovation is particularly valuable for novice practitioners and non-specialist doctors/experts who may struggle with proficiently interpreting cataract progression, leading to potential misdiagnoses. To address this challenge, a Neural Network model is harnessed to automatically predict the grade of cataracts. The study employs multi-class image classification models, including the Convolutional neural network (CNN) model, the Efficient Net B0 model, and the ResNet50 model, for this purpose. Notably, the ResNet50 model outperforms the other models in terms of accuracy and prediction capability for the provided data set. Achieving an accuracy rate of 0.8611, the ResNet50 model demonstrates superior performance in classifying cataract grades, after augmenting the data set with 544 images. This performance comparison establishes the ResNet50 model as the most robust choice among the considered models and data sets.

Abstract With rapid advancements in machine learning and statistical models, ensuring the reliability of these models through accurate evaluation has become imperative. Traditional evaluation methods often rely on fully labeled test data, a requirement that is becoming increasingly impractical due to the growing size of datasets. In this work, we address this issue by extending existing work on active testing (AT) methods which are designed to sequentially sample and label data for evaluating pre-trained models. We propose two novel estimators: the Actively Improved Levelled Unbiased Risk (AILUR) and the Actively Improved Inverse Probability Weighting (AIIPW) estimators which are derived from nonparametric smoothing estimation. In addition, a model recalibration process is designed for the AIIPW estimator to optimize the sampling probability within the AT framework. We evaluate the proposed estimators on four real-world datasets and demonstrate that they consistently outperform existing AT methods. Our study also shows that the proposed methods are robust to changes in subsample sizes, and effective at reducing labeling costs.

Abstract Background Compared to Bollgard-I, the utilization of Cry2Ab protein in Bollgard-II cotton cultivars enhances insect control. Field and laboratory studies demonstrated reduction in the numbers of bollworm-infested terminals, squares, and bolls in Bollgard-II cotton lines when compared to both Bollgard-I and non-Bollgard cotton cultivars. This indicates that the combination of Cry2Ab with Cry1Ac increased the overall expression of proteins. The expression of Cry protein varied across different plant parts, such as leaves, bracts, squares, and bolls. As the season progresses, the expression of Cry protein decreased in these plant parts. Leaves exhibited the highest levels of Cry protein expression, followed by squares, flowers, and bolls. Variation in the expression levels of delta endotoxins in different plant parts was one of the contributing factors to the survival of pest populations on Bt cotton. Results Using a commercially available QL 96 ELISA plate kit, the concentration of delta endotoxin in various plant parts at different phenological stages was determined in twelve BG-II cotton hybrids, namely Ajeet-155, JKCH-2245, RCH-3863, NCS-866, MRC-7373, JKCH-99, MRC-7387, NCEH-21, ANKR-3324, NCSI-1904, and NCHB 9902. Cry1Ac and Cry2Ab protein levels were determined from samples of flowers and fruiting parts (Rind, locule, seed, locule wall and seed) at 40, 75, 100 and 125 days after sowing (DAS) over two consecutive years 2018–2019 and 2019–2020. Cry1Ac protein content and expression was the highest at 100DAS in locule, seed and rind; followed by rind and locule wall and seed in green bolls at 125 DAS; followed by seed, locule, rind and flowers at 75 DAS; followed by flowers at 40 DAS in all the tested twelve Bollgard-II hybrids. Cry2Ab protein content and expression was the highest at 125DAS in locule wall and seed and rind, followed by seed, locule and rind in green bolls at 100 DAS, followed by seed, locule, rind and flowers at 75 DAS, then by flowers at 40 DAS in all the tested twelve Bollgard-II hybrids. Cry1Ac protein expression was less in comparison with Cry2Ab. Conclusions The research findings indicate that the locule of BG-II cotton plants exhibited the highest expression levels of Cry1Ac and Cry2Ab proteins, followed by the seeds, rind, locule wall, and flowers. These results provide valuable insights into the distribution of Cry protein expression in different plant parts, which can contribute to a better understanding of insect control in Bt cotton cultivars.

Titanium has a long history of clinical use, but the naturally forming oxide is not ideal for bacterial resistance. Anodization processes can modify the crystallinity, surface topography, and surface chemistry of titanium oxides. Anatase, rutile, and mixed phase oxides are known to exhibit photocatalytic activity (PCA)-driven bacterial resistance under UVA irradiation. Silver additions are reported to enhance PCA and reduce bacterial attachment. This study investigated the effects of silver-doping additions to three established anodization processes. Silver doping showed no significant influence on oxide crystallinity, surface topography, or surface wettability. Oxides from a sulfuric acid anodization process exhibited significantly enhanced PCA after silver doping, but silver-doped oxides produced from phosphoric-acid-containing electrolytes did not. Staphylococcus aureus attachment was also assessed under dark and UVA-irradiated conditions on each oxide. Each oxide exhibited a photocatalytic antimicrobial effect as indicated by significantly decreased bacterial attachment under UVA irradiation compared to dark conditions. However, only the phosphorus-doped mixed anatase and rutile phase oxide exhibited an additional significant reduction in bacteria attachment under UVA irradiation as a result of silver doping. The antimicrobial success of this oxide was attributed to the combination of the mixed phase oxide and higher silver-doping uptake levels.

Abstract Background During field surveillance in Gujarat, India, in 2009, scientists found that pink bollworm was surviving in the first generation of Bollgard cotton, which contains a single protein. This survival was observed in four districts of Gujarat. From 2012 to 2014, surveys revealed that pink bollworm larvae had higher survival rates on Bollgard II cotton in Amreli and Bhavnagar districts. In 2016, a pink bollworm outbreak in Maharashtra caused significant losses in cotton production, leading to damage, yield losses, and increased management costs in several regions. The objective of the present study was to determine the reaction of Pectinophora gossypiella to Cry1Ac and Cry2Ab toxins. Results Neonates of P. gossypiella collected from six distinct locations in Vidarbha were subjected to seven different concentrations of Cry1Ac. The highest LC50 value was recorded in the population collection of Yavatmal (1.362 μg/ml diet) but the lowest LC50 value of 0.600 μg/ml diet in Buldana population. The highest MIC50 value was recorded in the population collected from Yavatmal (0.743 μg/ml diet) and the lowest MIC50 value of 0.413 μg/ml diet in the Buldana population (0.431 μg/ml diet). The highest IC50 value was recorded in the population collected from Yavatmal (0.303 μg/ml diet) but the lowest IC50 value of 0.127 μg/ml diet in the Buldana population. Neonates of P. gossypiella collected from six different locations in Vidarbha were exposed to seven different concentrations (9.0, 3.0, 1.0, 0.333, 0.111, 0.037 and 0.012 for Cry1Ac and 27.0, 9.0, 3.0, 1.0, 0.333, 0.111 and 0.037 μg/ml for Cry2Ab) which revealed that the Yavatmal population had the highest LC50 value of 94.294 μg/ml diet and the lowest LC50 value was recorded in the Amravati population (42.144 μg/ml diet). The highest MIC50 value of 27.258 μg/ml diet was recorded in Buldana population, but the lowest MIC50 value was recorded in the Amravati population (12.881 μg/ml diet). The highest IC50 value of 3.209 μg/ml diet was observed in Yavatmal population, and the lowest one was recorded in the Amravati population (1.574 μg/ml diet). Conclusions According to the LC50 values, all of the P. gossypiella populations in Vidarbha were extremely insensitive and had field-evolved resistance to the Bt proteins found in the transgenic cotton varieties of Bollgard II.

Abstract Background After the commercialization of insect-resistant transgenic Bt cotton Bollgard I & II, India ranks first in the world in cotton production. Cotton insecticide consumption was drastically reduced as nearly 95% of the cotton area was replaced with Bollgard II. However, the benefits of transgenic cotton appear to have been diminished as the pink bollworm, Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae) developed field resistance to Cry1Ac expressing Bt cotton in India in 2008. In 2015, there was an unusual survival of P. gossypiella on Bollgard II cotton in some parts of Gujarat and Maharashtra, which is a cause for concern. Results In the present study, PCR analysis and gel visualization of BGII resistant field population of P. gossypiella showed that the presence of r1, r2, r3, r1s, r2s, r3s, and ss mutated cadherin alleles, which produced amplicon sizes of 750 bp, 700 bp, 730 bp, 12,700 bp, 750 bp, 980 bp, 600 bp and 1600 bp, respectively, when seven different types of cadherin allele(s) specific primers were used. The r1 mutant allele was detected in Amaravati, Wardha, Yavatmal, and Nagpur, but not in Akola and Buldhana, using primers r1disfor and Int-540. The r2 mutant allele was detected in Akola, Wardha, and Nagpur, but not in Buldhana, Amaravati, and Yavatmal, using primers r2disback and Cad2366. The r3 mutant allele was detected in all locations using primers r3disback and Cad3221. Amplicons of sizes 750 bp, 700 bp and 730 bp were obtained for r1, r2, and r3 mutant alleles, respectively. The sizes of the amplicons were 1270 bp for r1s, 750 bp for r2s, and 980 bp for r3s.The absence of bands for r1, r2, and r3 cadherin alleles in individuals indicated the possibility of having the ss allele, which was confirmed using primers Cad3324 and Int-651. The presence of ss mutant allele was observed in field-collected P. gossypiella populations from BG II cotton in various locations, with a 600 bp and 1600 bp size amplicons produced using the same primers. Conclusions PCR analysis revealed the presence of r1, r2, r3, r1s, r2s, r3s, and ss mutated cadherin alleles in field-collected populations of Vidarbha which provide evidence to field-evolved resistance of P. gossypiella to BGII cotton.

A primary strategy led by the food industry to improve the sustainability of the agricultural food supply is the development of modern plant-based alternatives. The information provided via marketing and product packaging provides consumers with the expectation that these products provide a similar product experience to conventional products, yet it is not well understood whether these commercial alternative products are comparable to traditional animal-based products. To aid in developing improved plant-based products, this study aimed to compare the quality and physical attributes of commercially available plant-based and dairy yogurts. Using instrumental methods, commercially available yogurt products were analyzed for their pH, titratable acidity, color, water activity, moisture content, and rheology, which included 13 plant-based (almond, cashew, coconut, oat, soy) and 8 whole-milk dairy yogurts. The present study reveals that the plant-based and dairy yogurts had no significant differences in pH, lactic acid, water activity, or moisture content. However, there were significant differences in the color and texture properties between the plant-based and dairy yogurts. Additionally, significant differences were observed across the plant-based yogurt products in terms of their color and texture properties. This highlights the need for additional studies to determine how individual ingredients influence the physical characteristics and textural properties to direct the development of plant-based yogurts. Improving upon the physicochemical properties of plant-based yogurt may encourage more consumers to adopt a more sustainable diet.

Finding periodic-frequent patterns in temporal databases is a prominent data mining problem with bountiful applications. It involves discovering all patterns in a database that satisfy the user-specified minimum support ( $min{\_{}}sup$ ) and maximum periodicity ( $max$ _ $per$ ) constraints. $Min\_{}sup$ controls the least number of transactions in which a pattern must appear in a database. $Max\_{}per$ controls the maximum time interval within which a pattern must reappear in the database. The popular adoption of this task has been hindered by an open problem, which involves setting appropriate $min\_{}sup$ and $max\_{}per$ values for any given database. This paper addresses this open problem by proposing a solution to discover top- $k$ periodic-frequent patterns in a temporal database. Top- $k$ periodic-frequent patterns represent the $k$ number of periodic-frequent patterns having the lowest $periodicity$ value in a database. An efficient depth-first search algorithm, Top- $k$ Periodic-Frequent Pattern Miner ( $k$ -PFPMiner), which takes only $k$ threshold as an input, was presented to find all desired patterns in a database. Experimental results on synthetic and real-world databases demonstrate that our algorithm is efficient and scalable.

Three-terminal hybrid transmission lines (TTHTLs) are attractive from both environmental and commercial view. Hybrid transmission lines are growing due to urbanization, connecting an industrial load and renewable integration. A TTHTL comprises sections of both overhead lines and underground/subsea cables or overhead lines with different X/R ratios. Faulted section identification (FSI) is key for defining the adaptive/selective auto-reclosing scheme and estimation of the fault location for TTHTLs. In this paper, FSI and fault location algorithms are proposed without using the parameters of any line section. Novelty of the methodology lies in a two-stage approach to the problem. In the first stage, series impedance parameters of all the sections are calculated using closed loop formulae. These parameters are then utilized in identifying the faulted section. In the second stage, the above calculated line section parameters and faulted section are used to estimate complete line parameters including shunt capacitance and subsequently the fault location. The advantage of the proposed method is that it does not require an initial guess of the line section parameters, is non-iterative in the first stage, and provides correct fault section identification for TTHTLs. These features make it suitable for designing the selective auto-reclosing protection scheme for the TTHTLs within traditional protection relaying hardware. More importantly, the series impedance parameters calculated in the first stage constitute good initialization values for the non-linear problem of estimating the complete line parameters. This results in better convergence of the algorithm and accurate parameter estimation. The developed solution is verified using the PSCAD/EMTDC simulations for TTHTLs connected with conventional and different inverter-based renewable resources. The performance of the proposed solution is compared with commercially available solutions, and it is found to be accurate. This solution is amenable for implementation in line differential protection relays without additional infrastructural changes.

The Analytic visualization suggests representing knowledge during a visual type that may be charts, graphs, lists, or maps. The COVID 19 detection and analysis of spreading is very important for countries. Database management with respect to virus deep analysis is a critical task to the researcher through conventional algorithms. The RNA, DNA, and biological data are helping to the bio-inspired algorithm but its implementation can be complex by software tools. Therefore, an effective technique is required to cross over the above limitations. So that covid 19 pandemic data analysis is performed through FCNN (Fully conventional Neural Network) pre-training network. The dataset is collected from social media, Kaggle, and GitHub databases. At 1st stage, the auto stack encoding process is applied later same data is processed with FCNN deep learning classifier. In this research work, covid-pandemic affects parameters like infected persons, deaths, active cases, and recovering cases. The FCNN is take care of feature extraction, training, testing, and classification. Finally using a confusion matrix accuracy of 98.34%, sensitivity 97.63%, Recall 98.26%, and F measure 98.83% had been estimated.

Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disregarded the occurrences of these itemsets in columnar databases. Furthermore, the naïve approach of transforming a columnar database into a row database and then applying the existing algorithms to find interesting itemsets is not practicable due to computational reasons. With this motivation, this paper proposes a framework to discover stable periodic-frequent itemsets in columnar databases. Our framework employs a novel depth-first search algorithm that compresses a given columnar database into a unified dictionary and mines it recursively to find all stable periodic-frequent itemsets. The dictionary holds the information pertaining to itemsets and their temporal occurrences in a database. Experimental results on six databases demonstrate that the proposed algorithm is computationally efficient and scalable.

Cryptocurrency has emerged as a revolutionary innovation that has been replacing traditional finances and enthralling the worldwide technology landscape. This has gained a lot of popularity worldwide for its potential to enable peer-to-peer transactions and offer opportunities for investment and novelty. Nevertheless, it gives rise to issues concerning regulatory adherence, instability, and security apprehensions, turning them into a topic of continuous evaluation and investigation within the fields of finance and technology. This research paper presents a comprehensive exploration of the historical evolution of “Ethereum” as one of the leading blockchain platforms, with a primary focus on price prediction using a long-short-term memory (LSTM) machine learning model. The study includes various critical aspects of Ethereum, starting from its historical evolution to its potential future scope in scaling solutions and payments, and also covering the insights of Ethereum’s tokenomics, utility, and beyond. In addition, the methodology involves using the LSTM model to analyze data from Ethereum. The accuracy of price predictions is assessed by evaluating error metrics and further improved by visualizing the data through graphs that show indicators. This paper gives an in-depth perspective for anyone who is seeking a holistic understanding of cryptocurrencies, mainly concentrated on Ethereum, and also provides valuable guidance to investors, developers, and enthusiasts, encouraging them to make knowledgeable decisions in the ever-changing blockchain ecosystem.

ABSTRACT Over the past 150 years, vaccines have revolutionized the relationship between people and disease. During the COVID-19 pandemic, technologies such as mRNA vaccines have received attention due to their novelty and successes. However, more traditional vaccine development platforms have also yielded important tools in the worldwide fight against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A variety of approaches have been used to develop COVID-19 vaccines that are now authorized for use in countries around the world. In this review, we highlight strategies that focus on the viral capsid and outwards, rather than on the nucleic acids inside. These approaches fall into two broad categories: whole-virus vaccines and subunit vaccines. Whole-virus vaccines use the virus itself, in either an inactivated or an attenuated state. Subunit vaccines contain instead an isolated, immunogenic component of the virus. Here, we highlight vaccine candidates that apply these approaches against SARS-CoV-2 in different ways. In a companion article (H. M. Rando, R. Lordan, L. Kolla, E. Sell, et al., mSystems 8:e00928-22, 2023, https://doi.org/10.1128/mSystems.00928-22), we review the more recent and novel development of nucleic acid-based vaccine technologies. We further consider the role that these COVID-19 vaccine development programs have played in prophylaxis at the global scale. Well-established vaccine technologies have proved especially important to making vaccines accessible in low- and middle-income countries. Vaccine development programs that use established platforms have been undertaken in a much wider range of countries than those using nucleic acid-based technologies, which have been led by wealthy Western countries. Therefore, these vaccine platforms, though less novel from a biotechnological standpoint, have proven to be extremely important to the management of SARS-CoV-2. IMPORTANCE The development, production, and distribution of vaccines is imperative to saving lives, preventing illness, and reducing the economic and social burdens caused by the COVID-19 pandemic. Vaccines that use cutting-edge biotechnology have played an important role in mitigating the effects of SARS-CoV-2. However, more traditional methods of vaccine development that were refined throughout the 20th century have been especially critical to increasing vaccine access worldwide. Effective deployment is necessary to reducing the susceptibility of the world’s population, which is especially important in light of emerging variants. In this review, we discuss the safety, immunogenicity, and distribution of vaccines developed using established technologies. In a separate review, we describe the vaccines developed using nucleic acid-based vaccine platforms. From the current literature, it is clear that the well-established vaccine technologies are also highly effective against SARS-CoV-2 and are being used to address the challenges of COVID-19 globally, including in low- and middle-income countries. This worldwide approach is critical for reducing the devastating impact of SARS-CoV-2.

ABSTRACT In the 21st century, several emergent viruses have posed a global threat. Each pathogen has emphasized the value of rapid and scalable vaccine development programs. The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has made the importance of such efforts especially clear. New biotechnological advances in vaccinology allow for recent advances that provide only the nucleic acid building blocks of an antigen, eliminating many safety concerns. During the COVID-19 pandemic, these DNA and RNA vaccines have facilitated the development and deployment of vaccines at an unprecedented pace. This success was attributable at least in part to broader shifts in scientific research relative to prior epidemics: the genome of SARS-CoV-2 was available as early as January 2020, facilitating global efforts in the development of DNA and RNA vaccines within 2 weeks of the international community becoming aware of the new viral threat. Additionally, these technologies that were previously only theoretical are not only safe but also highly efficacious. Although historically a slow process, the rapid development of vaccines during the COVID-19 crisis reveals a major shift in vaccine technologies. Here, we provide historical context for the emergence of these paradigm-shifting vaccines. We describe several DNA and RNA vaccines in terms of their efficacy, safety, and approval status. We also discuss patterns in worldwide distribution. The advances made since early 2020 provide an exceptional illustration of how rapidly vaccine development technology has advanced in the last 2 decades in particular and suggest a new era in vaccines against emerging pathogens. IMPORTANCE The SARS-CoV-2 pandemic has caused untold damage globally, presenting unusual demands on but also unique opportunities for vaccine development. The development, production, and distribution of vaccines are imperative to saving lives, preventing severe illness, and reducing the economic and social burdens caused by the COVID-19 pandemic. Although vaccine technologies that provide the DNA or RNA sequence of an antigen had never previously been approved for use in humans, they have played a major role in the management of SARS-CoV-2. In this review, we discuss the history of these vaccines and how they have been applied to SARS-CoV-2. Additionally, given that the evolution of new SARS-CoV-2 variants continues to present a significant challenge in 2022, these vaccines remain an important and evolving tool in the biomedical response to the pandemic.

Introduction Serious illness communication in oncology increases goal concordant care. Factors associated with the frequency of serious illness conversations are not well understood. Given prior evidence of the association between suboptimal decision-making and clinic time, we aimed to investigate the relationship between appointment time and the likelihood of serious illness conversations in oncology. Methods We conducted a retrospective study of electronic health record data from 55 367 patient encounters between June 2019 to April 2020, using generalized estimating equations to model the likelihood of a serious illness conversation across clinic time. Results Documentation rate decreased from 2.1 to 1.5% in the morning clinic session (8am-12pm) and from 1.2% to .9% in the afternoon clinic session (1pm-4pm). Adjusted odds ratios for Serious illness conversations documentation rates were significantly lower for all hours of each session after the earliest hour (adjusted odds ratios .91 [95% CI, .84-.97], P = .006 for overall linear trend). Conclusions Serious illness conversations between oncologists and patients decrease considerably through the clinic day, and proactive strategies to avoid missed conversations should be investigated.

Introduction Competency-based medical education (CBME) approach in the medical curriculum has been introduced globally with the goal of providing flexibility, accountability, and learner-centeredness among medical learners. Traditional surgical skill training in most places has relied on “see one, do one, teach one model,” while simulation model-based training has been shown to improve competencies in surgical trainees. We wanted to assess the usefulness of a hydrophilic barrier adhesive foam wound dressing as a novel skin simulation model for learning biomechanics and practice of cutaneous flaps among plastic surgical resident trainees at our institute. Materials and Methods An absorbent, soft polyurethane foam pad located centrally upon a larger polyurethane membrane, coated with a hydrocolloid adhesive, forming an island dressing, was used as a simulation model for this study. It was obtained from the hospital store either after or nearing their expiry dates of clinical use. Plastic surgery residents in different years of training were invited to participate in a simulation workshop, using this novel model, and give their feedback. Results Seventeen residents in different plastic surgery training levels participated in the workshop and gave their feedback on the skin flap simulation model. The simulation model received extremely high (100%) scores on two parameters, namely, utility for flap and suture practice and high scores (88%–94%) for texture, ability to mark, and improving confidence among trainees. Conclusions Adhesive bilayer polyurethane foam can be used as a novel cutaneous skin flap simulation model for understanding the biomechanics of skin flaps and cutaneous flap practice.

With the emergence of the Internet of Things (IoT), deep neural networks (DNNs) are widely used in different domains, such as computer vision, healthcare, social media, and defense. The hardware-level architecture of a DNN can be built using an in-memory computing-based design, which is loaded with the weights of a well-trained DNN model. However, such hardware-based DNN systems are vulnerable to model stealing attacks where an attacker reverse-engineers (REs) and extracts the weights of the DNN model. In this work, we propose an energy-efficient defense technique that combines a ferroelectric field effect transistor (FeFET)-based reconfigurable physically unclonable function (PUF) with an in-memory FeFET XNOR to thwart model stealing attacks. We leverage the inherent stochasticity in the FE domains to build a PUF that helps to corrupt the neural network’s (NN) weights when an adversarial attack is detected. We showcase the efficacy of the proposed defense scheme by performing experiments on graph-NNs (GNNs), a particular type of DNN. The proposed defense scheme is a first of its kind that evaluates the security of GNNs. We investigate the effect of corrupting the weights on different layers of the GNN on the accuracy degradation of the graph classification application for two specific error models of corrupting the FeFET-based PUFs and five different bioinformatics datasets. We demonstrate that our approach successfully degrades the inference accuracy of the graph classification by corrupting any layer of the GNN after a small rewrite pulse.

Electric vehicles (EVs) are widely recognized for their environmentally friendly attributes and superior performance. They offer considerable potential for managing energy in low-voltage distribution networks through the use of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies. This article provides a detailed investigation into the management of energy in distribution networks using a combination of EVs, solar photovoltaic (PV), and diesel generators (DG). The water filling algorithm (WFA) is utilized to distribute the storage of EVs in each energy zone in an optimal manner, thereby achieving load flattening, minimizing energy costs, and reducing grid reliance. A multiobjective genetic algorithm (MOGA) is employed to solve a formulated multiobjective optimization problem for load flattening and voltage regulation, with optimal power transaction (OPT) serving as the decision variable. An adaptive neuro-fuzzy inference system (ANFIS) based EV ranking technique is employed to prioritize EVs based on their ability to provide the required services and determine optimal energy distribution (OED) for different scenarios. This study investigates the impact of OED in several scenarios and examines the influence of ANFIS prioritization on overall EV power availability and cost of charging (CoC). The findings of this study are crucial for developing effective energy management strategies that minimize energy costs and reduce grid reliance.

Salvia hispanica L. (Chia) seeds are good source of proteins with diverse health benefits. The seed protein was extracted through alkaline solubilisation followed by acid precipitation to separate fibres and are digested sequentially by pepsin and pancreatin. Enzyme-substrate ratio, temperature and contact time had high impact on degree of hydrolysis affecting their chelating ability. Maximum degree of hydrolysis (14.06%) and maximum copper chelation (74.98%) was obtained at 4% w/w enzyme-substrate ratio at 37 °C for 4 h. Copper chelating enzymatic hydrolysate was isolated by HiTrap chelating column and purified further by rpHPLC. Out of nine fractions obtained by rpHPLC the sixth fraction with 93.09 ± 0.16% of copper chelating activity and 82.91 ± 0.52% of antioxidant activity was further characterized as Copper chelating Chia Protein Hydrolysate (CCPH). Ultraviolet spectroscopy and fluorescence spectroscopic studies revealed the interaction of the major chelating sites of the CCPH with the copper divalent ion. The purified CCPH was subjected to LC-MS/ESI-TOF analysis from which six major intense peaks obtained with m/z value ranging from 0.4 kDa to 2.5 kDa were identified and sequenced using Mascot database. The functional behaviour and the binding capacity of these peptides were analysed by their amino acid composition. The CCPH was stable in a simulated gastric condition and its chelating ability remained unaltered. These results explored an informative bioactive peptides with varied activity and one valuable among is the copper chelating with antioxidant property. Furthermore, these Chia seed protein hydrolysates can be useful as dietary supplements to enhance mineral bioavailability.

Numerical weather prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are increasingly used over the Indian region to forecast extreme rainfall events. However, studies which explore the application of high-resolution rainfall simulations obtained from the WRF model in urban hydrology are limited. In this paper, the utility of a model coupling framework to predict urban floods is explored through the case study of Bangalore city in India. This framework is used to simulate multiple extreme events that occurred over the city for the monsoons of years 2020 and 2021. To address the uncertainty from the WRF model, a 12-member convection permitting ensemble is used. Model configurations using Kain Fritsch and WSM6 parameterization schemes could simulate the spatial and temporal pattern of the selected event. The city is easily flooded with rainfall events above a threshold of 60 mm/day and to capture the response of the urban catchment, the Personal Computer Storm Water Management Model (PCSWMM) is used in this study. Flood forecasts are created using the outputs from the WRF ensemble and the Global Forecasting System (GFS). The high temporal and spatial resolution of the rainfall forecasts (

How the development of the cochlear epithelium is regulated is unclear. Here, the authors use single cell RNAseq analysis to provide insight into the transcriptional changes arising during development of the murine cochlear inner and outer hair cells.

Organ transplant has, arguably, been the pinnacle of the advancement in medical science so far and vascularized composite allotransplantation has been the most recent addition to it. The scope of vascularized tissue allotransplantation has been widening with each passing day and more and more reports regarding the safety and efficacy of the procedure have been described. Due to the limited available literature on the management of the complications and implications of the hand transplant, the importance of each report on the procedure is paramount. A mid-arm allotransplant is a challenge in terms of expected motor recovery, and many controversies exist over the efficacy of the procedure altogether. Moreover, our case was complicated by a long ischemia time due to logistic reasons. We share our experience of a transhumeral upper limb allotransplantation, the complications associated with it along with the early postoperative results at 10 months follow up.

Cryptocurrency has emerged as a revolutionary innovation that has been replacing traditional finances and enthralling the worldwide technology landscape. This has gained a lot of popularity worldwide for its potential to enable peer-to-peer transactions and offer opportunities for investment and novelty. Nevertheless, it gives rise to issues concerning regulatory adherence, instability, and security apprehensions, turning them into a topic of continuous evaluation and investigation within the fields of finance and technology. This research paper presents a comprehensive exploration of the historical evolution of “Ethereum” as one of the leading blockchain platforms, with a primary focus on price prediction using a long-short-term memory (LSTM) machine learning model. The study includes various critical aspects of Ethereum, starting from its historical evolution to its potential future scope in scaling solutions and payments, and also covering the insights of Ethereum’s tokenomics, utility, and beyond. In addition, the methodology involves using the LSTM model to analyze data from Ethereum. The accuracy of price predictions is assessed by evaluating error metrics and further improved by visualizing the data through graphs that show indicators. This paper gives an in-depth perspective for anyone who is seeking a holistic understanding of cryptocurrencies, mainly concentrated on Ethereum, and also provides valuable guidance to investors, developers, and enthusiasts, encouraging them to make knowledgeable decisions in the everchanging blockchain ecosystem.

Cyberbullying is the use of technology to harass, intimidate, or harm another person by making hurtful comments, sending threatening messages to humiliate someone in social media. It is important to recognize the signs of cyberbullying activities and takes steps to prevent it. Automated Machine Learning algorithms and Text mining concepts for detecting and classifying bullying messages in social media environment. The abusive texts are clustered using Multinomial Naïve Bayes, LinearSVC, Logistic Regression, K-Nearest neighbour to build a classifier from training datasets. Implementation uses Suspicious-communications-on-social-platforms dataset.

A food delivery business must be able to accurately forecast demand on a daily and weekly basis since it deals with a lot of perishable raw components. A warehouse that keeps too much inventory runs the danger of wasting items, whereas a warehouse that maintains too little inventory runs the risk of running out of stock, which might lead consumers to switch to your competitors. Planning for purchasing is essential because most raw materials are perishable and delivered on a weekly basis. For this issue to be resolved, demand forecasting is crucial. With the aid of historical data-driven predictive research, demand forecasting determines and forecasts future consumer demand for a good or service. By predicting future sales and revenues, demand forecasting assists the organisation in making more educated supply decisions. Regression methods like linear regression, decision trees, and Xgboost are used to overcome this issue.