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A Hydrogen fuel cell (HFC) broad range associated with Internet of Things (IoT) technologies that require slightly less and constant electricity made possible by remote climate monitoring connections. Novelty demonstrates a miniature HFC based on carbon cloth electrodes and sealing elements manufactured via 3D printing. Cobalt (II) Oxide (Co 3 O 4 )—reduced Graphene Oxide (rGO) and Platinum (Pt) based nanoparticles are coated over carbon cloth to increase the catalytic activity at the anode and cathode. Hydrogen is produced by using an aluminium foil (Al) that is stored in between the filter paper and through capillary action the sodium hydroxide pellets (NaOH) are applied and reacted with Al foil to produce hydrogen. The single HFC device working surface area of 1 × 1 cm 2 effectively generates an open circuit voltage (OCV) of 1.3 V, a current density of 1.602 mA/cm 2 , and a peak power density of 761 mW/cm 2 . The fuel cell stability performance is monitored for up to 10 h. The power obtained from the HFC is stored in a supercapacitor and used to supply energy to the IoT component. The module includes a built-in sensor that monitors the temperature, pressure, and humidity. The measured data is then transmitted to a smartphone via Bluetooth.

Vaccines have significantly reduced the impact of numerous deadly viral infections. However, there is an increasing need to expedite vaccine development in light of the recurrent pandemics and epidemics. Also, identifying vaccines against certain viruses is challenging due to various factors, notably the inability to culture certain viruses in cell cultures and the wide-ranging diversity of MHC profiles in humans. Fortunately, reverse vaccinology (RV) efficiently overcomes these limitations and has simplified the identification of epitopes from antigenic proteins across the entire proteome, streamlining the vaccine development process. Furthermore, it enables the creation of multiepitope vaccines that can effectively account for the variations in MHC profiles within the human population. The RV approach offers numerous advantages in developing precise and effective vaccines against viral pathogens, including extensive proteome coverage, accurate epitope identification, cross-protection capabilities, and MHC compatibility. With the introduction of RV, there is a growing emphasis among researchers on creating multiepitope-based vaccines aiming to stimulate the host's immune responses against multiple serotypes, as opposed to single-component monovalent alternatives. Regardless of how promising the RV-based vaccine candidates may appear, they must undergo experimental validation to probe their protection efficacy for real-world applications. The time, effort, and resources allocated to the laborious epitope identification process can now be redirected toward validating vaccine candidates identified through the RV approach. However, to overcome failures in the RV-based approach, efforts must be made to incorporate immunological principles and consider targeting the epitope regions involved in disease pathogenesis, immune responses, and neutralizing antibody maturation. Integrating multi-omics and incorporating artificial intelligence and machine learning-based tools and techniques in RV would increase the chances of developing an effective vaccine. This review thoroughly explains the RV approach, ideal RV-based vaccine construct components, RV-based vaccines designed to combat viral pathogens, its challenges, and future perspectives.

The advent of long-term implants has increased the urgent need for self-powered biomedical devices. Utilize enzymes to expedite the process of biofuel oxidation. These systems frequently make use of glucose oxidase. A possible solution involves glucose biofuel cells powered by the glucose found in physiological fluids. Biocompatible substances like carbon electrode designs help to transport electrons from the biological reactions to the external circuit as efficiently as possible while maximizing surface area. Despite advances in implantable electrodes, developing miniaturized and flexible electrodes remains challenging. In this work, a metal-coated flexible carbon thread and foam bioelectrode are fabricated and successfully implanted inside a living and freely moving rat. These electrodes are prepared using gold nanostructures as electron enhancers, a negatively charged conducting polymer, a biocompatible redox mediator, and enzymes as biocatalysts. The carbon foam-based enzymatic biofuel cell produces in vitro and in vivo settings, generates a power density of 165 µW/cm 2 and 285 µW/cm 2 , and the carbon thread-based fuel cell produces a power density of 98 µW/cm 2 and 180 µW/cm 2 in vitro and in vivo environments, respectively. This work paves the way for the possible use of inexpensive electrodes for subdermal implantable microsystems.

Among that 100% of the babies < 1000gm have achieved neonatal mortality and 63% babies were born before 31 week of gestation. [...]the study disclosed the early detection, appropriate intervention, proper neonatal care back up facilities can elevate to prevent the preterm labor15. [...]they said that this risk factor had played a major part in preterm deliveries. While periodontitis (ARR, 3.38; 95% CI, 1.6 to 6.9), maternal height <1.50 m (ARR, 2.66; 95% CI, 1.3 to 5.1) gestational hypertension (ARR, 3.70; 95% CI, 1.3 to 10.8) and genital infection at the time of later stages of pregnancy (ARR, 2.79; 95% CI, 1.2 to 6.1) were not correlated to risk factors for LBW. [...]this study figured out that the need of screening for periodontal and genitourinary infections necessary during antenatal care29. Out of 246 5(2%) 95% CI (0.22-3.78%) also gestation diabetes mellitus, education of mother, type of family was statistically significant (p0.005) under univariatant analysis preterm birth had risk in higher the age of father was found to be 7.57 times greater. [...]these high risk factors over loads the adverse pregnancy results of preterm birth, socio demographic chart of antenatal mother31. [...]53.1 % were late preterm birth. [...]the study disclosed the need of improving health care quality of delivery to reduce the preterm birth37.

In diabetes, an increase in adipose tissue lipolysis and hydrolysis of myocardial triglyceride stores is responsible for elevated circulating levels of FFA.23 Metabolism of high levels of FFA requires high oxygen consumption and leads to intracellular accumulation of toxic intermediates, which may negatively influence myocardial performance through reduced availability of ATP.24 Oxidative stress: The NO is inactivated by the superoxide radical and the peroxynitrite anion, which can cause endothelial damage. [...]free radicals, especially superoxide anion, may react NO and can inhibit endothelial nitric acid synthase. Apart from the degree of obesity the risk is also dependent on the distribution of body fat as it has been found that visceral adiposity is more closely linked to CAD than peripheral adiposity.42 Visceral adipocytes are more lipolytically active and release increased amounts of non-esterified fatty acids, glycerol, hormones, proinflammatory cytokines and other factors that cause resistance of the body to the actions of insulin resulting in increased production of this hormone by the pancreas and ensuing hyperinsulinemia. Insulin resistance in obesity and type 2 DM is manifested by decreased insulin-stimulated glucose transport and metabolism in adipocytes and skeletal muscle and by impaired suppression of hepatic glucose output.43 Through this mechanism of insulin resistance, obesity has been found to increase the risk of HT and dyslipidemia in diabetic individuals thus multiplying their overall cardiovascular risk.

The most widespread kind of cancer, affecting millions of lives is skin cancer. When the condition of illness worsens, the chance of survival is reduced, and thus detection of skin cancer is extremely difficult. Hence, this paper introduces a new model, known as Parallel Convolutional Spiking Neural Network (PCSN-Net) for detecting skin cancer. Initially, the input skin cancer image is pre-processed by employing Medav filter to eradicate the noise in image. Next, affected region is segmented by utilizing DeepSegNet, which is formed by integrating SegNet and Deep joint segmentation, where RV coefficient is used to fuse the outputs. Here, the segmented image is then augmented by including process, such as geometric transformation, colorspace transformation, mixing images Pixel averaging (mixup), and overlaying crops (CutMix). Then textural, statistical, Discrete Wavelet Transform (DWT) based Local Direction Pattern (LDP) with entropy, and Local Normal Derivative Pattern (LNDP) features are mined. Finally, skin cancer detection is executed using PCSN-Net, which is formed by fusing Parallel Convolutional Neural Network (PCNN) and Deep Spiking Neural Network (DSNN). In this work, the suggested PCSN-Net system shows high accuracy and reliability in identifying skin cancer. The experimental findings suggest that PCSN-Net has an accuracy of 95.7%, a sensitivity of 94.7%, and a specificity of 92.6%. These parameters demonstrate the model’s capacity to discriminate among malignant and benign skin lesions properly. Furthermore, the system has a false positive rate (FPR) of 10.7% and a positive predictive value (PPV) of 90.8%, demonstrating its capacity to reduce wrong diagnosis while prioritizing true positive instances. PCSN-Net outperforms various complex algorithms, including EfficientNet, DenseNet, and Inception-ResNet-V2, despite preserving effective training and inference times. The results obtained show the feasibility of the model for real-time clinical use, strengthening its capacity for quick and accurate skin cancer detection.

Healthcare is a critical area where blockchain technology (BT) is being heralded as a potential game-changer for facilitating secure and efficient data sharing. The purpose of this review is to examine BT applications, performance challenges, and solutions in healthcare. To begin, This review paper explores popular blockchain networks for data exchange, encompassing both public and permissioned platforms, such as Ethereum and Hyperledger Fabric. This paper analyzes the potential applications of BT’s decentralized, immutable, and smart contract capabilities in healthcare settings, including secure and interoperable health data exchange, patient consent management, drug supply chain oversight, and clinical trial management. The healthcare industry might greatly benefit from the increased privacy, transparency, and accessibility that these technologies provide. Despite BT’s promising medical uses, the technology is not without its drawbacks. High energy consumption, throughput, and scalability are all concerns. We wrapped up by discussing the solutions that have been implemented, including consensus processes, scalability measures like sharding, and off-chain transactions that are designed to mitigate the drawbacks.

The Intelligent Transport System (ITS) is very prominent due to its connection with the Internet of Things (IoT), which enhances passenger security and comfort. The Vehicular Ad-hoc Network (VANET) is a component of ITS. It manages the techniques used for routing and privacy in autonomous cars. The increasing number of autonomous cars has exceeded the capacity of current wireless networks for transmission. It is expected that the proposed 6G wireless network can meet VANET criteria. Very little research has investigated the privacy concerns of VANETs in 6G networking connections. This work presents a method for dealing with authentic and privacy concerns for automobiles in VANETs. Our solution strengthens the vehicle's connectivity system by detecting malicious attacks like replay attacks, DoS attacks, and impersonification attacks. The proposed system uses batch authentication to reduce traffic and workload on the network. The proposed system employs both ID-based authentication and deep learning methods. Where the role of ID-based authentication is to check for access in the network, deep learning takes on the role of identifying all the malicious packets in the system. Our result also shows that the proposed system can identify malicious packets with an accuracy of 98.25% and works successfully in 6G networking communication.

The extreme variability of the tropical tropopause may serve as an important factor in understanding the critical conditions of dynamical and radiative coupling between the troposphere and stratosphere. The extreme variability of the cold point tropopause (CPT) temperature (T CPT ) and height (H CPT ) are examined over a tropical station, Gadanki (13.45° N, 79.2° E), using high-resolution radiosonde data during 2006–2014. We identified the extremely cold, warm, high, and low tropopauses. The extremely cold (warm) tropopause is defined as the T CPT lesser (greater) than the lower (upper) limit of the two standard deviations of its climatological monthly mean. While the extremely high (low) tropopause is defined as the H CPT greater (lesser) than the upper (lower) limit of the two standard deviations of its climatological monthly mean. In total, 161 cases comprising extremely cold (52), warm (30), high (57), and low (22) are observed. The extremely cold (187.2 ± 1.60 K, 17.3 ± 0.52 km), warm (194.2 ± 1.78 K, 16.9 ± 0.89 km), high (191.7 ± 1.78 K, 18.2 ± 0.55 km), and low (191.8 ± 2.11 K, 16.2 ± 0.38 km) tropopauses occur without preference of season. However, these extreme tropopause cases occur independently and show distinct thermal structures. The thermal structures of the extremely cold tropopause cases are often sharper, whereas the extremely warm, high, and low tropopause cases are broader. Water vapor and ozone concentrations are found to be high for the extremely warm tropopause and low for the extremely cold tropopause. Under the shallow convection, extreme temperature profiles, in general, show prominent warming between 8 and 14 km while anomalous cooling (warming) just below (above) the CPT. Plausible mechanisms responsible for extreme tropopause variabilities such as deep convection, equatorial wave propagation, transports of water vapor and ozone, and potential vorticity intrusions are examined.

For smart cities, predicting traffic flow is crucial to lower traffic jams and enhancing transportation efficiency. The smart city needs effective models, highly dependable networks, and data privacy for traffic flow prediction (Traff-FP). The majority of current research uses a central training mode and ignores privacy issue conveyed by distributed traffic data. In this paper, an effective traffic flow prediction (ETraff-FP) is proposed to forecast traffic flow using actual historical traffic data. Initially, pre-processing is carried out using data normalization and handling missing value. The three major components of Traff-FP framework for each local Traff-FP model are recurrent long short-term capture network (RLSCN), federated gated graph attentive network (FGAN) and semantic connection relationship capture network (SCRCN). The long-term spatio and temporal information in each location has been captured by RLSCN, which encompasses constituents like fully connected (FC) layers, convolution, and bidirectional long short term memory (BiLSTM) to collect short-term information. FGAN, which incorporates bi-directional gated recurrent unit (Bi-GRU), exchanges short-term spatio-temporal hidden information while it trains local Traff-FP model using elliptic curve diffie-hellman (ECDiff-H) algorithm. Accordingly, the hyper parameters of ETraff-FP are tuned using extended remora optimization algorithm (EReOA). The ETraff-FP framework is trained and tested with TaxiNYC and TaxiBJ datasets. For simulation, python platform is utilized and various evaluation metrics are analysed. Accordingly, the ETraff-FP framework has reached better improvements with MSE of 8.98% and 10.57%, RMSE of 8.62% and 18.65%, MAE of 2.11% and 10.57%, R2-score of 0.959% and 0.913%, and MAPE of 21.12% and 24.89% against the existing methods using TaxiNYC and TaxiBJ datasets. Overall, the proposed work not only advances the state-of-the-art in traffic flow prediction but also proves the value of enabling effective and efficient traffic management systems in urban and smart city environments.