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The term Digital Twin (DT) is defined as the virtual demonstration of an object that is represented through real-time datasets. DT is done through artificial intelligence to enhance decision-making techniques. DT includes the process of simulation, amalgamation, observation, analysis, and conservation. The DT is simply the exact reproduction of the physical structures. DT is used in the identification and evaluation of problems through real-time analysis. It is important to have prior analysis and evaluation of the object before existing in the real world. These digital twins help in the manufacturing and implementation of the production line system. DT includes the production line with the station division and the hours needed for the operating conditions for the assembly process. The systems are integrated to reduce the overall cost parameter. The physical simulation model is employed to obtain higher performance with reduced cost. An artificial neural network with a genetic algorithm is used for the optimization process to achieve a production line system using digital twins.

The cropped area of Krishnagiri district is about 37 % of the total geographical area, and due to the utilization of non-agricultural purposes (8.2% of agricultural land) it is mandatory to increase the agricultural production to feed the people. So, this study has been planned to find the agricultural suitability and crop suitability of the Krishnagiri district using AHP-TOPSIS and GIS techniques. In this study, three main limiting factors, such as climate, soil, and topographical parameters (Annual average rainfall, Annual average temperature, slope, pH, Electrical conductivity (EC), Soil organic carbon (SOC), available NPK, and soil texture), were used as criteria for the suitability of crops. All criteria maps were generated using Arc info 10.4 software. The AHP used to estimate the weight and TOPSIS (Technique for Order of Preference by Similarity to an Ideal Solution) approach was assign the rank of each criterion for the suitability analysis. Based on the relative closeness to the ideal solution(pi), the generated crop suitability maps were categorized into five classes, namely “highly suitable (S1), moderately suitable (S2), marginally suitable (S3), and not suitable (N)”. The results revealed that, crop suitability maps finds that that maize, field bean, cluster bean, groundnut, pomegranate, lemon, and lemongrass were highly suitable for the Krishnagiri district. Rice, cotton, sugarcane, sunflower, and cashewnut were moderately suitable, and vanilla was unsuitable. This study will guide farm managers and land policymakers in making decisions and assist farmers in selecting crops that are highly suitable for achieving potential yield and profit.

Physical situations that are duplicated in virtual settings are what the term ''digital twin'' refers to. Utilizing the Internet of Things and sensors to improve microgrid operation, the Microgrid Digital Twin (MGDT) exhibits bidirectional data sharing and sophisticated communication characteristics. Smart grid functionality is enhanced, limits are set in real-time, and data is securely stored by MGDT. It directs the planning and execution phases of microgrid operations. Concurrently, industry and transportation innovation is propelled by the Industrial Revolution. AI-driven ground-dependent controls, communication systems, and sensors are essential for breakthroughs in unmanned vehicle safety. This abstract clarifies how important digital twins more specifically, MGDTs are to system operations optimization. In addition to highlighting technological advancement and the use of AI to ensure the safety of unmanned vehicles, it provides insights into the potentially revolutionary applications of digital twins across a range of industries.

Cloud storage allows individuals to store and access data from remote locations, providing the convenience of on-demand access to high-quality cloud applications. This eliminates the need for individuals to manage local hardware and software. The cloud storage system facilitates the efficient storage of data on cloud servers, allowing users to work with their data seamlessly without encountering resource constraints such as memory or storage limitations. Cloud computing is a technology that shows great promise owing to its ability to provide unlimited resources for computing and data storage services. These services are crucial for effectively managing the data according to specific requirements. In the current system, data is saved in the cloud using dynamic data operations and computations. This study explored the underlying principles of scalable data-partitioning techniques in the context of distributed data processing in cloud environments. The significance of this study lies in the increasing dependence of enterprises on cloud platforms for data-intensive tasks such as machine learning, data analytics, and real-time data processing. This study examines several data-partitioning strategies and methodologies developed to address the unique issues posed by cloud systems. The evaluation included an examination of their influence on the scalability, load distribution, and overall efficiency of the system. The main aim of this study is to enhance the domain of cloud-based data-processing techniques, thereby enabling enterprises to effectively leverage the full potential of the cloud for data-centric projects.

Plant disease detection and disease classification at initial stages for sensitive commodities like tomatoes and potatoes is highly mandated as the harvest losses have a direct impact on the price fixation of the vegetables. The most identified limitation in the study of plant pathology is the availability of datasets with visual symptoms that covers all the possible diseases of one crop or plant species. Computer Vision systems and advancements in deep learning-based modeling methodologies gained significant attention in smart farming. It is presumed that the implementation of deep learning algorithms demands a large amount of data to learn complex features automatically and this can pose a challenge for applications with lesser data to achieve generalization. In such cases, Transfer Learning with optimum regularization techniques and fine-tuning mechanisms is the solution to overcome the limitations of smaller datasets. The objective of the work is to develop Tomato Disease Classification System using a transfer learning approach for ten tomato disease classes of the PlantVillage dataset downloaded from the Kaggle platform. Inception V3, a pre-trained transfer learning model is used to classify this multi-class, imbalanced, tomato plant disease based on the leaf symptoms such as dark brown lesions, concentric rings, etc. Geometrical data augmentation is used as a regularization technique to expand the size of the dataset. Significant improvement in the performance metrics is observed when the finetuning is optimum. The training accuracy and validation accuracy of the model before and after fine-tuning are 97.08%, 83.52%, and 98.19%, 95.93% respectively. The average accuracy with augmentation and optimal fine-tuning is 98%. In addition, prediction scores in terms of precision, recall, and F1-score are obtained to visualize the rate of mispredictions across the disease classes. It is observed that the misprediction rate is high across the classes early blight, late blight, and Septoria spot due to similar visual symptoms. Further, activations are used to generate an attention map in the form of Heat Maps which are included as a post-processing step before the classification of the output. Plant Leaf Disease Classification- A web application is deployed using Streamlit Python library and Ngrok services.

IrO2 is a current state-of-the-art catalyst for polymer electrolyte membrane water electrolyser (PEMWE) applications due to its high stability during the oxygen evolution reaction (OER). However, its activity needs to be significantly improved to justify the use of such a high-cost material. In this study, the activity of the IrO2 catalyst was improved by optimising and comparing two synthesis methods: the modified Adams fusion method (MAFM) and the molten salt method (MSM). Optimum OER performances of the IrO2 catalysts synthesised with the two synthesis methods were obtained at different temperatures. For the MAFM, a synthesis temperature of 350 °C produced the IrO2 catalyst with an overpotential of 279 mV and the highest OER stability of ~ 82 h at 10 mAcm−2. However, for the MSM, the lowest overpotential of 271 mV was observed for IrO2 synthesised at 350 °C, while the highest stability of ~ 75 h was obtained for the IrO2 synthesised at 500 °C. Significance: IrO2 is still currently a state-of-the-art catalyst in PEMWE due to its high stability in the highly acidic and oxidising conditions of the OER. High-performance IrO2 catalysts were successfully produced via the MAFM and MSM. Both the MAFM and MSM are simple and easily scalable for high-volume production of metal oxide catalysts. This study showed that the physical/structural properties of the IrO2 catalysts can be tailored through synthesis methods and synthesis conditions to improve their OER performance.

Papillary Thyroid Carcinoma (PTC), which is a form of thyroid cancer, originates from follicular epithelial cells and can be identified histopathologically by distinct nuclear characteristics and architecture. It is the most common thyroid neoplasm and is typically found in young females, with a good prognosis. Within the subset of PTC, exists incidentally detected lesions called papillary microcarcinomas (IPMC), having a size ≤1 cm which are usually clinically not suspected, radiologically undetected, and often missed in gross pathological examinations. The present case series highlights five cases (2 males and 3 females) of papillary microcarcinomas that were detected over the course of one year. The patients’ ages ranged from 31 to 60 years, with a mean age of 48.8±10.1 years. The male-to-female ratio was 1:1.5. These patients presented with complaints of neck swelling and were initially diagnosed with benign nodular goitre in 47 cases based on clinical and radiological evaluations. The detection rate of papillary microcarcinomas was 10.6%, with one case showing bilaterality (20%). The authors of the present case series adopted a novel approach of indiscriminate nodule sampling of thyroidectomy specimens to observe changes in detection rates. This approach facilitated a higher yield of papillary microcarcinomas. The authors also considered the dilemma of surgical measures and prognostic outcomes in such cases.

Abstract A large and diverse library of glycan-directed monoclonal antibodies (mAbs) was used to determine if plant cell walls are modified by low-gravity conditions encountered during spaceflight. This method called glycome profiling (glycomics) revealed global differences in non-cellulosic cell wall epitopes in Arabidopsis thaliana root extracts recovered from RNA purification columns between seedlings grown on the International Space Station-based Vegetable Production System and paired ground (1-g) controls. Immunohistochemistry on 11-day-old seedling primary root sections showed that ten of twenty-two mAbs that exhibited spaceflight-induced increases in binding through glycomics, labeled space-grown roots more intensely than those from the ground. The ten mAbs recognized xyloglucan, xylan, and arabinogalactan epitopes. Notably, three xylem-enriched unsubstituted xylan backbone epitopes were more intensely labeled in space-grown roots than in ground-grown roots, suggesting that the spaceflight environment accelerated root secondary cell wall formation. This study highlights the feasibility of glycomics for high-throughput evaluation of cell wall glycans using only root high alkaline extracts from RNA purification columns, and subsequent validation of these results by immunohistochemistry. This approach will benefit plant space biological studies because it extends the analyses possible from the limited amounts of samples returned from spaceflight and help uncover microgravity-induced tissue-specific changes in plant cell walls.

Computational methods have driven the rapid identification of tobacco microRNAs (miRNAs) from tissue-specific sequence data and witnessed success in associating miRNAs in response to stress, pollutants, viral infection and resistance, and cigarette smoking. Although tobacco exerted medicinal properties through phytochemicals, the role of its miRNAs in regulating tobacco and human genes and related functional implications is not elucidated thoroughly. In this present study, we have identified new and homologous miRNAs using a rigorous workflow of miRNA derivation and target prediction upon a comprehensive collection of tobacco expressed sequence tags and charted its putative roles in gene regulation via inter and intraspecies relationships. Current, computational approach have identified a total of 38 mature miRNAs comprising 31 tobacco-specific miRNAs from plant homologous families, and 7 new miRNA candidates. These seven new miRNAs were studied for tobacco target gene prediction in which most of them encode innate immunity, defense mechanism, plant development, F-box/Leucine rich-repeat protein and other protein kinases. Two out of these seven miRNAs have passed the updated emphasized criteria namely nta-miR403 and nta-miR8036. Interestingly, the workflow succeeded in establishing an intraspecies relationship by distinguishing the molecular targets already known in tobacco and homologous plants. Interspecies relationship between 38 tobacco miRNAs upon human transcriptome data revealed the most significant target CCDC88c (DAPLE) with perfect seed pairing of miR-156, regulating non-canonical WNT signaling pathways in cancer progression and metastasis. These findings may add to existing knowledge of impacting canonical WNT/β-catenin pathways. These decisive findings hold a strong clue for promoting tobacco miRNAs research and outlined the prediction of conserved miRNAs and their functions in inter and intraspecies relationships.

The development of safe and effective vaccines to respond to COVID-19 pandemic/endemic remains a priority. We developed a novel subunit protein-peptide COVID-19 vaccine candidate (UB-612) composed of: (i) receptor binding domain of SARS-CoV-2 spike protein fused to a modified single-chain human IgG1 Fc; (ii) five synthetic peptides incorporating conserved helper and cytotoxic T lymphocyte (Th/CTL) epitopes derived from SARS-CoV-2 structural proteins (three from S2 subunit, one from membrane and one from nucleocapsid), and one universal Th peptide; (iii) aluminum phosphate as adjuvant. The immunogenicity and protective immunity induced by UB-612 vaccine were evaluated in four animal models: Sprague–Dawley rats, AAV-hACE2 transduced BALB/c mice, rhesus and cynomolgus macaques. UB-612 vaccine induced high levels of neutralizing antibody and T-cell responses, in all animals. The immune sera from vaccinated animals neutralized the SARS-CoV-2 original wild-type strains and multiple variants of concern, including Delta and Omicron. The vaccination significantly reduced viral loads, lung pathology scores, and disease progression after intranasal and intratracheal challenge with SARS-CoV-2 in mice, rhesus and cynomolgus macaques. UB-612 has been tested in primary regimens in Phase 1 and Phase 2 clinical studies and is currently being evaluated in a global pivotal Phase 3 clinical study as a single dose heterologous booster.

Fillers or particulate fillers find a growing utilization as reinforcement material in polymer composites due to their ability to enhance the properties of the ensuing composites. The discarded seed in sapodilla fruit is available in abundant and the shell of the seed can be used as a reinforcing filler. The primary goal of this study is to extract and characterize the sapodilla seed shell powder (SSS) physically and chemically in order to assess its potential for reinforcement as a particulate filler in polymer composites. The sapodilla seed shell filler was characterized experimentally by Physio-chemical analysis, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and Energy dispersive X-ray analysis (EDAX). The morphology and the filler size were determined by Scanning electron microscopy (SEM) and Particle size analysis. The thermal degradation behaviour was evaluated by Thermogravimetric analysis (TGA).

The air compressor holds paramount importance due to its significant energy consumption when compared to other Balance of Plant components of polymer electrolyte membrane (PEM) fuel cells. The air supply system, in turn, plays a critical role in ensuring the stable and efficient operation of the entire fuel cell system. To enhance system efficiency, the impact of varying the stoichiometric ratio of air and air pressure was observed. This investigation was carried out under real loading conditions, replicating the conditions experienced by the power module when fuel cells are in use within a forklift. The air compressor can be operated at different pressure and excess air ratios, which in turn influence both the fuel cell’s performance and the overall efficiency of the power module system. Our research focused on assessing the performance of PEM fuel cells under different load cycles, adhering to the VDI60 requirements for forklift applications. This comprehensive examination encompassed the system’s minimum and maximum load scenarios, with the primary goal of optimizing excess air and pressure ratio parameters, especially under dynamic load conditions. The results revealed that higher air pressures and lower excess air ratios were conducive to increasing system efficiency, shedding light on potential avenues for enhancing the performance of PEM fuel cell systems in forklift applications.

Antibodies provide critical protective immunity against COVID-19, and the Fc-mediated effector functions and mucosal antibodies also contribute to the protection. To expand the characterization of humoral immunity stimulated by subunit protein–peptide COVID-19 vaccine UB-612, preclinical studies in non-human primates were undertaken to investigate mucosal secretion and the effector functionality of vaccine-induced antibodies in antibody-dependent monocyte phagocytosis (ADMP) and antibody-dependent NK cell activation (ADNKA) assays. In cynomolgus macaques, UB-612 induced potent serum-neutralizing, RBD-specific IgG binding, ACE2 binding-inhibition antibodies, and antibodies with Fc-mediated effector functions in ADMP and ADNKA assays. Additionally, immunized animals developed mucosal antibodies in bronchoalveolar lavage fluids (BAL). The level of mucosal or serum ADMP and ADNKA antibodies was found to be UB-612 dose-dependent. Our results highlight that the novel subunit UB-612 vaccine is a potent B-cell immunogen inducing polyfunctional antibody responses contributing to anti-viral immunity and vaccine efficacy.

Obesity has emerged as a risk factor for the Chronic kidney disease (CKD). Recent research propose novel obesity indices like Lipid accumulation product (LAP), Visceral adiposity index (VAI), Cardiometabolic index (CMI) to be associated with CKD risk. We aimed to evaluate the association between novel obesity indices and CKD risk indicators such as estimated glomerular filtration rate (eGFR), urine albumin excretion. Anthropometry measures, plasma lipids, eGFR estimation from serum creatinine and urine albumin creatinine ratio (urine ACR) were performed in 60 apparently healthy adults visiting the health checkup clinic. The subjects were divided based on Body mass index (BMI) as non-obese (n=22) and obese (n=38) groups. Their eGFR and urine ACR were compared using independent t-test or Mann-Whitney test. Spearman correlation test determined the association of obesity and lipid indices with eGFR, urine ACR. Anthropometric measures - BMI, WC (waist circumference), lipid indices - TC/HDLc, LDLc/HDLc ratios, and obesity indices - LAP, CMI were significantly higher in the obese group (p [J Med Allied Sci. 2022; 12(2):58-64]

A live-attenuated COVID vaccine could enrich the current vaccine portfolio. Here, Liu et al. engineer a live-attenuated SARS-CoV-2 vaccine candidate by deleting four viral accessory genes and show immunogenicity and protection in mice and hamsters.

The past 20 years of research has elucidated new innate immune sensing and cell death pathways with disease relevance. Future molecular characterization of these pathways and their crosstalk and functional redundancies will aid in development of therapeutic strategies.

Abstract Respiratory tract vaccination has an advantage of needle-free delivery and induction of mucosal immune response in the portal of SARS-CoV-2 entry. We utilized human parainfluenza virus type 3 vector to generate constructs expressing the full spike (S) protein of SARS-CoV-2, its S1 subunit, or the receptor-binding domain, and tested them in hamsters as single-dose intranasal vaccines. The construct bearing full-length S induced high titers of neutralizing antibodies specific to S protein domains critical to the protein functions. Robust memory T cell responses in the lungs were also induced, which represent an additional barrier to infection and should be less sensitive than the antibody responses to mutations present in SARS-CoV-2 variants. Following SARS-CoV-2 challenge, animals were protected from the disease and detectable viral replication. Vaccination prevented induction of gene pathways associated with inflammation. These results indicate advantages of respiratory vaccination against COVID-19 and inform the design of mucosal SARS-CoV-2 vaccines.

Abstract Novel Immunological and Mass Spectrometry Methods for Comprehensive Analysis of Recalcitrant Oligosaccharides in AFEX Pretreated Corn Stover. Lignocellulosic biomass is a sustainable alternative to fossil fuel and is extensively used for developing bio-based technologies to produce products such as food, feed, fuel, and chemicals. The key to these technologies is to develop cost competitive processes to convert complex carbohydrates present in plant cell wall to simple sugars such as glucose, xylose, and arabinose. Since lignocellulosic biomass is highly recalcitrant, it must undergo a combination of thermochemical treatment such as Ammonia Fiber Expansion (AFEX), dilute acid (DA), Ionic Liquid (IL) and biological treatment such as enzyme hydrolysis and microbial fermentation to produce desired products. However, when using commercial fungal enzymes during hydrolysis, only 75–85% of the soluble sugars generated are monomeric sugars, while the remaining 15–25% are soluble recalcitrant oligosaccharides that cannot be easily utilized by microorganisms. Previously, we successfully separated and purified the soluble recalcitrant oligosaccharides using a combination of charcoal and celite-based separation followed by size exclusion chromatography and studies their inhibitory properties on enzymes. We discovered that the oligosaccharides with higher degree of polymerization (DP) containing methylated uronic acid substitutions were more recalcitrant towards commercial enzyme mixtures than lower DP and neutral oligosaccharides. Here, we report the use of several complementary techniques that include glycome profiling using plant biomass glycan specific monoclonal antibodies (mAbs) to characterize sugar linkages in plant cell walls and enzymatic hydrolysate, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) using structurally-informative diagnostic peaks offered by negative ion post-secondary decay spectra, gas chromatography followed by mass spectrometry (GC–MS) to characterize oligosaccharide sugar linkages with and without derivatization. Since oligosaccharides (DP 4–20) are small, it is challenging to mobilize these molecules for mAbs binding and characterization. To overcome this problem, we have applied a new biotin-coupling based oligosaccharide immobilization method that successfully tagged most of the low DP soluble oligosaccharides on to a micro-plate surface followed by specific linkage analysis using mAbs in a high-throughput system. This new approach will help develop more advanced versions of future high throughput glycome profiling methods that can be used to separate and characterize oligosaccharides present in biomarkers for diagnostic applications.

Background: Dementia is a neurocognitive disorder that contributes to a high caregiver burden. This article describes caregivers' experiences in providing care at home for persons with dementia during the COVID-19 pandemic. The pandemic had brought the world to a standstill that no one alive had experienced before. Methods: Cross-sectional focus group discussion with 17 caregivers of persons living with Alzheimer's disease was conducted using video conferencing software. Results: The significant issues reported by the caregivers were changing routine, an increase in demanding behavior, an increase in the tendency of wandering behavior, increased frustration among caregivers, intolerance, and overall burden. However, some caregivers were also able to adapt to emerging situations and design a new set of daily living activities. Conclusion: Understanding caregiving experiences and guiding caregivers to build their resilience would prevent caregivers' burnout.

Purpose: This study aimed to evaluate the knowledge, awareness and attitude of eye donation among non-clinical staff of tertiary eye hospitals and to convey a positive attitude toward eye donation by enhancing their awareness and knowledge. Methods: An online cross-sectional study was conducted among the non-clinical staff from all centers of a tertiary eye care hospital across Tamil Nadu. Quiz link was emailed to non-clinical staff of all the centers. On completion of the quiz, the participants viewed their respective scores and the correct answers to all questions. This activity was presumed to subsequently improve their knowledge and clear up the myths on eye donation. Results: Two hundred twenty-eight non-clinical staff from 11 hospitals participated in the quiz. Mean age was 35.3 ± 9.8 years and 130 were female staff (57.05%). One hundred eighty-one participants (79.39%) scored over 50% of the total 17 queries. One hundred eighty-six (81.58%) and 142 (62.28%) participants scored over 50% in the awareness section and knowledge section, respectively. Eye bank volunteers (73, 32.02%) were the main source of information. Twenty-four (10.53%) had already taken pledge for eye donation and 175 (76.75%) were willing to pledge, 29 (12.72%) were not willing to pledge. Twenty-two out of these 29 (75.86%) had no specific reason for not pledging. Family, religious reasons, lack of clarity and fear were least cited reasons (13.79%). Conclusion: Non-clinical staff of an eye hospital are easily approachable and are expected to be more knowledgeable by the general public around them. They might act as primary motivators in raising awareness within their family, friends, relatives and neighbors.

ABSTRACT We investigated the temporal profile of multiple components of the serological response after asymptomatic or mildly symptomatic SARS-CoV-2 infection, in a cohort of 67 previously SARS-CoV-2 naive young adults, up to 8.5 months after infection. We found a significant decrease of spike IgG and neutralization antibody titers from early (11 to 56 days) to late (4 to 8.5 months) time points postinfection. Over the study period, S1-specific IgG levels declined significantly faster than that of the S2-specific IgG. Further, serum antibodies from PCR-confirmed participants cross-recognized S2, but not S1, of the betacoronaviruses HKU1 and OC43, suggesting a greater degree of cross-reactivity of S2 among betacoronaviruses. Antibody-Dependent Natural Killer cell Activation (ADNKA) was detected at the early time point but significantly decreased at the late time point. Induction of serum Antibody-Dependent Monocyte Phagocytosis (ADMP) was detected in all the infected participants, and its levels remained stable over time. Additionally, a reduced percentage of participants had detectable neutralizing activity against the Beta (50%), Gamma (61 to 67%), and Delta (90 to 94%) variants, both early and late postinfection, compared to the ancestral strain (100%). Antibody binding to S1 and RBD of Beta, Gamma, Delta (1.7 to 2.3-fold decrease), and Omicron (10 to 16-fold decrease) variants was also significantly reduced compared to the ancestral SARS-CoV-2 strain. Overall, we found variable temporal profiles of specific components and functionality of the serological response to SARS-CoV-2 in young adults, which is characterized by lasting, but decreased, neutralizing activity and antibody binding to S1, stable ADMP activity, and relatively stable S2-specific IgG levels. IMPORTANCE Adaptive immunity mediated by antibodies is important for controlling SARS-CoV-2 infection. While vaccines against COVID-19 are currently widely distributed, a high proportion of the global population is still unvaccinated. Therefore, understanding the dynamics and maintenance of the naive humoral immune response to SARS-CoV-2 is of great importance. In addition, long-term responses after asymptomatic infection are not well-characterized, given the challenges in identifying such cases. Here, we investigated the longitudinal humoral profile in a well-characterized cohort of young adults with documented asymptomatic or mildly symptomatic SARS-CoV-2 infection. By analyzing samples collected preinfection, early after infection and during late convalescence, we found that, while neutralizing activity decreased over time, high levels of serum S2 IgG and Antibody-Dependent Monocyte Phagocytosis (ADMP) activity were maintained up to 8.5 months after infection. This suggests that a subset of antibodies with specific functions could contribute to long-term protection against SARS-CoV-2 in convalescent unvaccinated individuals.

The Internet of Things (IoT) offers users a wide variety of facilities because it interconnects billions of smart devices. However, when connected to wireless connections, unlimited access to IoT gadgets poses potential risks. As it eases cost constraints on sensor nodes, the cloud service with IoT networks has received greater attention. In addition, the high complexity of the distribution and networking of IoT makes them vulnerable to attacks. Intrusion detection systems (IDSs) are selected to ensure the security of reliable information and operations. IDS successfully detects anomalies in complex network situations and guarantees network security. Deep Convolution Network (DCN) IDS have a slow learning curve and poor categorization precision. Deep Learning (DL) methods are often used in a wide range of safety data processing, imaging, and signal processing like Poor transfer learning ability, reusability of modules, and integration. To overcome the constraints of Machine Learning (ML) IDS is intended to provide a comprehensive mechanism to learn the detection mechanism for multicloud IoT environments. The proposed IDS approach increases training efficiencies while increasing detection accuracy. Experimental investigations of the proposed system using the considered database confirms that the performance of the proposed system is capable and in the range of acceptance with relative to existing methods. Further, achieving detection capability, reliability, and accuracy of 97.51, 96.28, and 94.41% respectively are achieved.

Abstract Novel SARS-CoV-2, an etiological factor of Coronavirus disease 2019 (COVID-19), poses a great challenge to the public health care system. Among other druggable targets of SARS-Cov-2, the main protease (Mpro) is regarded as a prominent enzyme target for drug developments owing to its crucial role in virus replication and transcription. We pursued a computational investigation to identify Mpro inhibitors from a compiled library of natural compounds with proven antiviral activities using a hierarchical workflow of molecular docking, ADMET assessment, dynamic simulations and binding free-energy calculations. Five natural compounds, Withanosides V and VI, Racemosides A and B, and Shatavarin IX, obtained better binding affinity and attained stable interactions with Mpro key pocket residues. These intermolecular key interactions were also retained profoundly in the simulation trajectory of 100 ns time scale indicating tight receptor binding. Free energy calculations prioritized Withanosides V and VI as the top candidates that can act as effective SARS-CoV-2 Mpro inhibitors.

Underwater visible light communication (UVLC) has recently come to light as a viable wireless carrier for signal transmission in risky, uncharted, and delicate aquatic environments like seas. Despite the potential of UVLC as a green, clean, and safe alternative to conventional communication methods, it is challenged by significant signal attenuation and turbulent channel conditions compared to long-distance terrestrial communication. To address linear and nonlinear impairments in UVLC systems, this paper presents an adaptive fuzzy logic deep-learning equalizer (AFL-DLE) for 64 Quadrature Amplitude Modulation-Component minimal Amplitude Phase shift (QAM-CAP)-modulated UVLC systems. The proposed AFL-DLE is dependent on complex-valued neural networks and constellation partitioning schemes and utilizes the Enhanced Chaotic Sparrow Search Optimization Algorithm (ECSSOA) to improve overall system performance. Experimental outcomes demonstrate that the suggested equalizer achieves significant reductions in bit error rate (55%), distortion rate (45%), computational complexity (48%), and computation cost (75%) while maintaining a high transmission rate (99%). This approach enables the development of high-speed UVLC systems capable of processing data online, thereby advancing state-of-the-art underwater communication.

Abstract The development of effective countermeasures against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the agent responsible for the COVID-19 pandemic, is a priority. We designed and produced ConVac, a replication-competent vesicular stomatitis virus (VSV) vaccine vector that expresses the S1 subunit of SARS-CoV-2 spike protein. We used golden Syrian hamsters as animal models of severe COVID-19 to test the efficacy of the ConVac vaccine. A single vaccine dose elicited high levels of SARS-CoV-2 specific binding and neutralizing antibodies; following intranasal challenge with SARS-CoV-2, animals were protected from weight loss and viral replication in the lungs. No enhanced pathology was observed in vaccinated animals upon challenge, but some inflammation was still detected. The data indicate rapid control of SARS-CoV-2 replication by the S1-based VSV-vectored SARS-CoV-2 ConVac vaccine.

The use of Phyllanthus emblica (gooseberry) leaf extract to synthesize Boron-doped zinc oxide nanosheets (B-doped ZnO-NSs) is deliberated in this article. Scanning electron microscopy (SEM) shows a network of synthesized nanosheets randomly aligned side by side in a B-doped ZnO (15 wt% B) sample. The thickness of B-doped ZnO-NSs is in the range of 20–80 nm. B-doped ZnO-NSs were tested against both gram-positive and gram-negative bacterial strains including Staphylococcus aureus, Pseudomonas aeruginosa, Klebsiella pneumonia, and Escherichia coli. Against gram-negative bacterium (K. pneumonia and E. coli), B-doped ZnO displays enhanced antibacterial activity with 26 and 24 mm of inhibition zone, respectively. The mass attenuation coefficient (MAC), linear attenuation coefficient (LAC), mean free path (MFP), half-value layer (HVL), and tenth value layer (TVL) of B-doped ZnO were investigated as aspects linked to radiation shielding. These observations were carried out by using a PTW® electron detector and VARIAN® irradiation with 6 MeV electrons. The results of these experiments can be used to learn more about the radiation shielding properties of B-doped ZnO nanostructures.

Background: Older adults are likely to experience psychological and social problems due to decline in physical strength, increased health issues, inadequate financial resources, and family dynamics. In this context, they need psychosocial care services to handle such challenges. Particularly, inmates of old-age homes are more vulnerable to experience such challenges as they are away from the family care. In this background, knowing their views on psychosocial care needs while living in old-age homes is significant to improve old-age home-based care. The aim of this study is to explore the psychosocial care needs expressed by the inmates of old-age homes. Materials and Methods: Inmates of old-age homes were selected using purposive sampling. A total of twenty residents out of four old-age homes of Bengaluru Metropolitan City were interviewed using a semi-structured interview guide. The qualitative content analysis has been carried out to identify core psychosocial care needs out of the text data. Results: Three themes, namely “health-care needs,” “concerns regarding the behavior of the staff,” and “mental health needs,” are emerged. Conclusion: The current study findings provide insiders' views and highlight the importance of psychosocial care services to address their needs at every old-age home.

Nowadays, ultra-high hard armor (UHA) steels are employed in armor tracked vehicle (ATV) construction because of their high hardness, high strength to weight ratio, and excellent toughness. UHA steels are usually welded using austenitic stainless steel (ASS) welding consumables, to avoid hydrogen-induced cracking (HIC). The use of ASS consumables to weld the above steel was the only available remedy because of higher solubility of hydrogen in the austenitic phase. In this investigation, an attempt was made to investigate the effect of ASS consumables (with different Creq/Nieq ratio) on solidification mode, impact toughness and microstructural characteristics of shielded metal arc (SMA) welded UHA steel joints. The welded joints were characterised based on impact toughness properties, hardness, and microstructural features. As the ferrite number increases with an increase in Creq/Nieq ratio result in different solidification mode (A, FA, F). It is also found that ferrite number of weld metal has appreciable influence on impact toughness and has inversely proportional relationship with impact toughness of the welded joints.

Tobacco has been implicated as the strongest risk factor for oral malignancies and common potentially malignant disorders. Recent trend shows an increase in the prevalence of these lesions in non tobacco population. Studies suggest that clinicopathological features of leukoplakia differ in tobacco users and non users. Also, leukoplakia without known risk factors is at a higher risk of malignant transformation. Preliminary studies on carcinogenesis of non tobacco-associated head and neck cancers have identified a difference in the key genes involved in the pathogenesis of cancers associated with and without tobacco. While the genetic characterisation of non tobacco oral cancers is gaining focus in the literature, there is a paucity of studies on non tobacco leukoplakia. An understanding of the non tobacco leukoplakia, possibly a distinct subgroup, may provide an insight into the inception of non tobacco oral cancers, leading to avenues for prevention, early diagnosis and precision medicine-led treatment approaches. Through this narrative review, authors revisit and summarise the existing literature on leukoplakia among non tobacco population.

Young adults infected with SARS-CoV-2 are frequently asymptomatic or develop only mild disease. Because capturing representative mild and asymptomatic cases require active surveillance, they are less characterized than moderate or severe cases of COVID-19. However, a better understanding of SARS-CoV-2 asymptomatic infections might shed light into the immune mechanisms associated with the control of symptoms and protection. To this aim, we have determined the temporal dynamics of the humoral immune response, as well as the serum inflammatory profile, of mild and asymptomatic SARS-CoV-2 infections in a cohort of 172 initially seronegative prospectively studied United States Marine recruits, 149 of whom were subsequently found to be SARS-CoV-2 infected. The participants had blood samples taken, symptoms surveyed and PCR tests for SARS-CoV-2 performed periodically for up to 105 days. We found similar dynamics in the profiles of viral load and in the generation of specific antibody responses in asymptomatic and mild symptomatic participants. A proteomic analysis using an inflammatory panel including 92 analytes revealed a pattern of three temporal waves of inflammatory and immunoregulatory mediators, and a return to baseline for most of the inflammatory markers by 35 days post-infection. We found that 23 analytes were significantly higher in those participants that reported symptoms at the time of the first positive SARS-CoV-2 PCR compared with asymptomatic participants, including mostly chemokines and cytokines associated with inflammatory response or immune activation (i.e., TNF-α, TNF-β, CXCL10, IL-8). Notably, we detected 7 analytes (IL-17C, MMP-10, FGF-19, FGF-21, FGF-23, CXCL5 and CCL23) that were higher in asymptomatic participants than in participants with symptoms; these are known to be involved in tissue repair and may be related to the control of symptoms. Overall, we found a serum proteomic signature that differentiates asymptomatic and mild symptomatic infections in young adults, including potential targets for developing new therapies and prognostic tests.

The advent of machine learning potentials (MLPs) provides a unique opportunity to access simulation time scales and to directly compute physicochemical properties that are typically intractable using density functional theory (DFT). In this study, we use an active learning curriculum to train a generalizable MLP using the DeepMD-kit architecture. By using sufficiently long MLP-based molecular dynamics (MD) simulations, which provide DFT-level accuracy, we investigate the diffusion of key surface-bound adsorbates on a Ag(111) facet. Detailed analysis of the MLP/MD-calculated diffusivities sheds light on the potential shortcomings of using DFT-based nudged elastic band to estimate surface diffusion barriers. More generally, while this study is focused on a specific system, we anticipate that the underlying workflows and the resulting models can be extended to other adsorbates and other materials in the future.