Search Results (38)

Acute respiratory infections (ARIs) are a leading cause of death in children under five globally. The seasonal trends and profiles of respiratory viruses vary by region and season. Due to limited information and the population’s vulnerability, we conducted the hospital-based surveillance of respiratory viruses in Eastern Uttar Pradesh. Throat and nasal swabs were collected from outpatients and inpatients in the Department of Paediatrics, Baba Raghav Das (BRD) Medical College, Gorakhpur, between May 2022 and April 2023. A total of 943 samples from children aged 1 to 60 months were tested using multiplex real-time PCR for respiratory viruses in cases of ARI and SARI. Out of 943 samples tested, the highest positivity was found for parainfluenza virus [105 (11.13%) PIV-1 (79), PIV-2 (18), PIV-4 (18)], followed by adenovirus [82 (8.7%), RSV-B, [68 (7.21%)], influenza-A [46(4.9%): H1N1 = 29, H3N2 = 14), SARS CoV-2 [28 (3%)], hMPV [13(1.4%), RSV-A [4 (0.42%), and influenza-B (Victoria lineage) 1 (0.10%). The maximum positivity of respiratory viruses was seen in children between 1 to 12 months. The wide variation in prevalence of these respiratory viruses was seen in different seasons. This study enhances understanding of the seasonal and clinical trends of respiratory virus circulation and co-infections in Eastern Uttar Pradesh. The findings highlight the importance of targeted interventions to reduce the burden of respiratory infections in this region. Full article

Background: Signal transducer and activator of transcription 3 (STAT3) is a member of the cytoplasmic inducible transcription factors and plays an important role in mediating signals from cytokines, chemokines, and growth factors. We and others have found that STAT3 directly regulates pro-fibrotic signaling in the kidney. The STAT3 protein–protein interaction plays an important role in activating its transcriptional activity. It is necessary to identify these interactions to investigate their function in kidney disease. Here, we investigated the protein–protein interaction among three species to find crucial interactions that can be targeted to alleviate kidney disease. Method: In this study, we examined common protein–protein interactions leading to the activation or downregulation of STAT3 among three different species: humans (Homo sapiens), mice (Mus musculus), and rabbits (Oryctolagus cuniculus). Further, we chose to investigate the P300 and STAT3 interaction and performed studies of the activation of STAT3 using IL-6 and inhibition of the P300 by its specific inhibitor A-485 in pericytes. Next, we performed immunoprecipitation to confirm whether A-485 inhibits the binding of P300 to STAT3. Results: Using the STRING application from ExPASy, we found that six proteins, including PIAS3, JAK1, JAK2, EGFR, SRC, and EP300, showed highly confident interactions with STAT3 in humans, mice, and rabbits. We also found that IL-6 treatment increased the acetylation of STAT3 and increased histone 3 lysine acetylation (H3K27ac). Furthermore, we found that the disruption of STAT3 and P300 interaction by the P300 inhibitor A-485 decreased STAT3 acetylation and H3K27ac. Finally, we confirmed that the P300 inhibitor A-485 inhibited the binding of STAT3 with P300, which inhibited its transcriptional activity by reducing the expression of Ccnd1 (Cyclin D1). Conclusions: Targeting the P300 protein interaction with STAT3 may alleviate STAT3-mediated fibrotic signaling in humans and other species. Full article

In the recent decade, carbon dots have drawn immense attention and prompted intense investigation. The latest form of nanocarbon, the carbon nanodot, is attracting intensive research efforts, similar to its earlier analogues, namely, fullerene, carbon nanotube, and graphene. One outstanding feature that distinguishes carbon nanodots from other known forms of carbon materials is its water solubility owing to extensive surface functionalization (the presence of polar surface functional groups). These carbonaceous quantum dots, or carbon nanodots, have several advantages over traditional semiconductor-based quantum dots. They possess outstanding photoluminescence, fluorescence, biocompatibility, biosensing and bioimaging, photostability, feedstock sustainability, extensive surface functionalization and bio-conjugation, excellent colloidal stability, eco-friendly synthesis (from organic matter such as glucose, coffee, tea, and grass to biomass waste-derived sources), low toxicity, and cost-effectiveness. Recent advances in the synthesis and characterization of carbon dots have been received and new insight is provided. Presently known applications of carbon dots in the fields of bioimaging, drug delivery, sensing, and diagnosis were highlighted and future applications of these astounding materials are speculated. Full article

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%. Full article

Recommender systems are becoming an integral part of routine life, as they are extensively used in daily decision-making processes such as online shopping for products or services, job references, matchmaking for marriage purposes, and many others. However, these recommender systems are lacking in producing quality recommendations owing to sparsity issues. Keeping this in mind, the present study introduces a hybrid recommendation model for recommending music artists to users which is hierarchical Bayesian in nature, known as Relational Collaborative Topic Regression with Social Matrix Factorization (RCTR–SMF). This model makes use of a lot of auxiliary domain knowledge and provides seamless integration of Social Matrix Factorization and Link Probability Functions into Collaborative Topic Regression-based recommender systems to attain better prediction accuracy. Here, the main emphasis is on examining the effectiveness of unified information related to social networking and an item-relational network structure in addition to item content and user-item interactions to make predictions for user ratings. RCTR–SMF addresses the sparsity problem by utilizing additional domain knowledge, and it can address the cold-start problem in the case that there is hardly any rating information available. Furthermore, this article exhibits the proposed model performance on a large real-world social media dataset. The proposed model provides a recall of 57% and demonstrates its superiority over other state-of-the-art recommendation algorithms. Full article

Renewable energy sources, particularly solar photovoltaic generation, now dominate generation options. Solar generation advancements have resulted in floating solar photovoltaics, also known as FSPV systems. FSPV systems are one of the fastest growing technologies today, providing a viable replacement for ground-mounted PV systems due to their flexibility and low land-space requirement. This paper presents a systematic approach for implementing a proposed FSPV–grid integrated system in Bhilai Steel Plant’s (BSP) subsections. BSP is a steel manufacturing plant located in Bhilai, Chhattisgarh, and the FSPV system has the potential to generate sufficient energy by accessing two of its reservoirs. The system was simulated in HOMER Pro software, which provided the FSPV system power estimations, area requirements, net present cost (NPC), levelized cost of energy (LCOE), production summary, grid purchasing/selling, IRR, ROI, paybacks and pollutant emissions. A sensitivity analysis for a hike in PV prices globally due to a shortage in poly silicone in international markets during the fiscal year 2021–2022 was undertaken for the proposed FSPV–grid system. Here, the authors considered hikes in the PV price of 1%, 9%and 18% respectively, since the maximum percentage increase in PV prices globally is 18%. The authors also compared the proposed FSPV–grid system to the existing grid-only system for two sections of the BSP and the results obtained showed that the NPC and LCOE would be much lower in the case of the FSPV–grid system than the grid-only system. However, with changes in the percentage hike in PV prices, the NPC and LCOE were found to increase due to changes in the proportion of FSPV–grid systems in production. The pollutant emissions were the minimum in the case of the FSPV–grid system, whereas they were the highest in the case of the existing grid-only system. Furthermore, the payback analysis indicated that the minimum ROI for the above-defined construction would be fully covered in 15.81 years with the nominal 1% pricing for FSPV–grid generation. Therefore, the overall results suggest that the FSPV–grid system has the potential to be a perfect alternative solar energy source that can meet the current electrical energy requirements of the steel manufacturing industry with nominal pricing better than the existing grid-only system, as well as addressing economic constraints and conferring environmental benefits. Full article

With the advancement of cyber threats, blockchain technology has evolved to have a significant role in providing secure and reliable decentralized applications. One of these applications is a remote voting system that allow voters to participate in elections remotely. This work proposes a privacy-preserving e-voting system supporting score voting using blockchain technology. The main challenge with score voting compared to the regular yes/no voting approach is that a voter is allowed to assign a score from a defined range for each candidate. To preserve privacy, votes shall be encrypted before submission to the Blockchain, however, a malicious voter can modify the score value before encrypting it to manipulate the elections result for the favor of a certain candidate. To address this challenge, the proposed scheme allows voters to first prove that the submitted score lies in the predefined range before the vote is added to the Blockchain to ensure fairness of the election. The performance of our scheme is evaluated against a set of comprehensive experiments designed to determine optimal bounds for workload and transaction send rates and measure the impact of exceeding these bounds on critical performance metrics. The results of these simulations and their implications therefore indicate that the proposed scheme is secure while being able to handle up to 10,000 transactions at a time. Full article

Privacy preservation of image data has been a top priority for many applications. The rapid growth of technology has increased the possibility of creating fake images using social media as a platform. However, many people, including researchers, rely on image data for various purposes. In rural areas, lane images have a high level of importance, as this data can be used for analyzing various lane conditions. However, this data is also being forged. To overcome this and to improve the privacy of lane image data, a real-time solution is proposed in this work. The proposed methodology assumes lane images as input, which are further classified as fake or bona fide images with the help of Error Level Analysis (ELA) and artificial neural network (ANN) algorithms. The U-Net model ensures lane detection for bona fide lane images, which helps in the easy identification of lanes in rural areas. The final images obtained are secured by using the proxy re-encryption technique which uses RSA and ECC algorithms. This helps in ensuring the privacy of lane images. The cipher images are maintained using fog computing and processed with integrity. The proposed methodology is necessary for protecting genuine satellite lane images in rural areas, which are further used by forecasters, and researchers for making interpretations and predictions on data. Full article

Images under low-light conditions suffer from noise, blurring, and low contrast, thus limiting the precise detection of objects. For this purpose, a novel method is introduced based on convolutional neural network (CNN) dual attention unit (DAU) and selective kernel feature synthesis (SKFS) that merges with the Retinex theory-based model for the enhancement of dark images under low-light conditions. The model mentioned in this paper is a multi-scale residual block made up of several essential components equivalent to an onward convolutional neural network with a VGG16 architecture and various Gaussian convolution kernels. In addition, backpropagation optimizes most of the parameters in this model, whereas the values in conventional models depend on an artificial environment. The model was constructed using simultaneous multi-resolution convolution and dual attention processes. We performed our experiment in the Tesla T4 GPU of Google Colab using the Customized Raw Image Dataset, College Image Dataset (CID), Extreme low-light denoising dataset (ELD), and ExDark dataset. In this approach, an extended set of features is set up to learn from several scales to incorporate contextual data. An extensive performance evaluation on the four above-mentioned standard image datasets showed that MSR-MIRNeT produced standard image enhancement and denoising results with a precision of 97.33%; additionally, the PSNR/SSIM result is 29.73/0.963 which is better than previously established models (MSR, MIRNet, etc.). Furthermore, the output of the proposed model (MSR-MIRNet) shows that this model can be implemented in medical image processing, such as detecting fine scars on pelvic bone segmentation imaging, enhancing contrast for tuberculosis analysis, and being beneficial for robotic visualization in dark environments. Full article

Wireless sensors and actuator networks (WSNs) are the physical layer implementation used for many smart applications in this decade in the form of the Internet of Things (IoT) and cyber-physical systems (CPS). Even though many research concerns in WSNs have been answered, the evolution of the WSN into an IoT network has exposed it to many new technical issues, including data security, multi-sensory multi-communication capabilities, energy utilization, and the age of information. Cluster-based data collecting in the Internet of Things has the potential to address concerns with data freshness and energy efficiency. However, it may not offer reliable network data security. This research presents an improved method for data sharing and cluster head (CH) selection using the hybrid Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method in conjunction with glowworm swarm optimization (GSO) strategies based on the energy, trust value, bandwidth, and memory to address this security-enabled, cluster-based data aggregation in the IoT. Next, we aggregate the data after the cluster has been built using a genetic algorithm (GA). After aggregation, the data are encrypted and delivered securely using the TIGSO-EDS architecture. Cuckoo search is used to analyze the data and choose the best route for sending them. The proposed model’s analysis of the results is analyzed, and its uniqueness has been demonstrated via comparison with existing models. TIGSO-EDS reduces energy consumption each round by 12.71–19.96% and increases the percentage of successfully delivered data packets from 2.50% to 5.66%. Full article

Alzheimer’s is one of the fast-growing diseases among people worldwide leading to brain atrophy. Neuroimaging reveals extensive information about the brain’s anatomy and enables the identification of diagnostic features. Artificial intelligence (AI) in neuroimaging has the potential to significantly enhance the treatment process for Alzheimer’s disease (AD). The objective of this study is two-fold: (1) to compare existing Machine Learning (ML) algorithms for the classification of AD. (2) To propose an effective ensemble-based model for the same and to perform its comparative analysis. In this study, data from the Alzheimer’s Diseases Neuroimaging Initiative (ADNI), an online repository, is utilized for experimentation consisting of 2125 neuroimages of Alzheimer’s disease (n = 975), mild cognitive impairment (n = 538) and cognitive normal (n = 612). For classification, the framework incorporates a Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (K-NN) followed by some variations of Support Vector Machine (SVM), such as SVM (RBF kernel), SVM (Polynomial Kernel), and SVM (Sigmoid kernel), as well as Gradient Boost (GB), Extreme Gradient Boosting (XGB) and Multi-layer Perceptron Neural Network (MLP-NN). Afterwards, an Ensemble Based Generic Kernel is presented where Master-Slave architecture is combined to attain better performance. The proposed model is an ensemble of Extreme Gradient Boosting, Decision Tree and SVM_Polynomial kernel (XGB + DT + SVM). At last, the proposed method is evaluated using cross-validation using statistical techniques along with other ML models. The presented ensemble model (XGB + DT + SVM) outperformed existing state-of-the-art algorithms with an accuracy of 89.77%. The efficiency of all the models was optimized using Grid-based tuning, and the results obtained after such process showed significant improvement. XGB + DT + SVM with optimized parameters outperformed all other models with an efficiency of 95.75%. The implication of the proposed ensemble-based learning approach clearly shows the best results compared to other ML models. This experimental comparative analysis improved understanding of the above-defined methods and enhanced their scope and significance in the early detection of Alzheimer’s disease. Full article

To avoid dire situations, the medical sector must develop various methods for quickly and accurately identifying infections in remote regions. The primary goal of the proposed work is to create a wearable device that uses the Internet of Things (IoT) to carry out several monitoring tasks. To decrease the amount of communication loss as well as the amount of time required to wait before detection and improve detection quality, the designed wearable device is also operated with a multi-objective framework. Additionally, a design method for wearable IoT devices is established, utilizing distinct mathematical approaches to solve these objectives. As a result, the monitored parametric values are saved in a different IoT application platform. Since the proposed study focuses on a multi-objective framework, state design and deep learning (DL) optimization techniques are combined, reducing the complexity of detection in wearable technology. Wearable devices with IoT processes have even been included in current methods. However, a solution cannot be duplicated using mathematical approaches and optimization strategies. Therefore, developed wearable gadgets can be applied to real-time medical applications for fast remote monitoring of an individual. Additionally, the proposed technique is tested in real-time, and an IoT simulation tool is utilized to track the compared experimental results under five different situations. In all of the case studies that were examined, the planned method performs better than the current state-of-the-art methods. Full article

Machine Translation (MT) systems are now being improved with the use of an ongoing methodology known as Neural Machine Translation (NMT). Natural language processing (NLP) researchers have shown that NMT systems are unable to deal with out-of-vocabulary (OOV) words and multi-word expressions (MWEs) in the text. OOV terms are those that are not currently included in the vocabulary that is used by the NMT system. MWEs are phrases that consist of a minimum of two terms but are treated as a single unit. MWEs have great importance in NLP, linguistic theory, and MT systems. In this article, OOV words and MWEs are handled for the Punjabi to English NMT system. A parallel corpus for Punjabi to English containing MWEs was developed and used to train the different models of NMT. Punjabi is a low-resource language as it lacks the availability of a large parallel corpus for building various NLP tools, and this is an attempt to improve the accuracy of Punjabi in the English NMT system by using named entities and MWEs in the corpus. The developed NMT models were assessed using human evaluation through adequacy, fluency and overall rating as well as automated assessment tools such as the bilingual evaluation study (BLEU) and translation error rate (TER) score. Results show that using word embedding (WE) and MWEs corpus increased the accuracy of translation for the Punjabi to English language pair. The best BLEU score obtained was 15.45 for the small test set, 43.32 for the medium test set, and 34.5 for the large test set, respectively. The best TER rate score obtained was 57.34% for the small test set, 37.29% for the medium test set, and 53.79% for the large test set, repectively. Full article

Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds all other female cancers, including ovarian cancer. Researchers can use access to healthcare records to find previously unknown healthcare trends. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The novelty of our work is to develop an optimized stacking ensemble learning (OSEL) model capable of early breast cancer prediction. A dataset from the University of California, Irvine repository was used, and comparisons to modern classifier models were undertaken. The implementation analyses reveal the unique approach’s efficacy and superiority when compared to existing contemporary categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). In every classification task, predictive models may be used to predict the class level, and the current research explores a range of predictive models. It is better to integrate multiple classification algorithms to generate a set of prediction models capable of predicting each class level with 91–99% accuracy. On the breast cancer Wisconsin dataset, the suggested OSEL model attained a maximum accuracy of 99.45%, much higher than any single classifier. Thus, the study helps healthcare professionals find breast cancer and prevent it from happening. Full article