Molecularly‐Engineered, 4D‐Printed Liquid Crystal Elastomer Actuators

Three‐dimensional structures that undergo reversible shape changes in response to mild stimuli enable a wide range of smart devices, such as soft robots or implantable medical devices. Herein, a dual thiol‐ene reaction scheme is used to synthesize a class of liquid crystal (LC) elastomers that can be 3D printed into complex shapes and subsequently undergo controlled shape change. Through controlling the phase transition temperature of polymerizable LC inks, morphing 3D structures with tunable actuation temperature (28 ± 2 to 105 ± 1 °C) are fabricated. Finally, multiple LC inks are 3D printed into single structures to allow for the production of untethered, thermo‐responsive structures that sequentially and reversibly undergo multiple shape changes.     

Simulation,Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches

Artificial intelligence (AI) is expanding its roots in medical diagnostics. Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications. Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure. High blood pressure, diabetes mellitus, and glomerulonephritis are the root causes of kidney disease. Therefore, the present study is proposed a set of multiple techniques such as simulation, modeling, and optimization of intelligent kidney disease prediction (SMOIKD) which is based on computational intelligence approaches. Initially, seven parameters were used for the fuzzy logic system (FLS), and then twenty-five different attributes of the kidney dataset were used for the artificial neural network (ANN) and deep extreme machine learning (DEML). The expert system was proposed with the assistance of medical experts. For the quick and accurate evaluation of the proposed system, Matlab version 2019 was used. The proposed SMOIKD-FLS-ANN-DEML expert system has shown 94.16% accuracy. Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.     

Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud Computing

Cloud computing has gained significant use over the last decade due to its several benefits, including cost savings associated with setup, deployments, delivery, physical resource sharing across virtual machines, and availability of on-demand cloud services. However, in addition to usual threats in almost every computing environment, cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm. Furthermore, since there are a growing number of attacks directed at cloud environments (including dictionary attacks, replay code attacks, denial of service attacks, rootkit attacks, code injection attacks, etc.), customers require additional assurances before adopting cloud services. Moreover, the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches. In this study, the model based on the root of trust for continuous integration and continuous deployment is proposed, instead of only relying on a single sign-on authentication method that typically uses only id and password. The underlying study opted hardware security module by utilizing the Trusted Platform Module (TPM), which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers. The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.     

A robust and efficient convolutional deep learning framework for age-invariant face recognition

Extensive research has been carried out in the past on face recognition, face detection, and age estimation. However, age-invariant face recognition (AIFR) has not been explored that thoroughly. The facial appearance of a person changes considerably over time that results in introducing significant intraclass variations, which makes AIFR a very challenging task. Most of the face recognition studies that have addressed the ageing problem in the past have employed complex models and handcrafted features with strong parametric assumptions. In this work, we propose a novel deep learning framework that extracts age-invariant and generalized features from facial images of the subjects. The proposed model trained on facial images from a minor part (20–30%) of lifespan of subjects correctly identifies them throughout their lifespan. A variety of pretrained 2D convolutional neural networks are compared in terms of accuracy, time, and computational complexity to select the most suitable network for AIFR. Extensive experimental results are carried out on the popular and challenging face and gesture recognition network ageing dataset. The proposed method achieves promising results and outperforms the state-of-the-art AIFR models by achieving an accuracy of 99%, which proves the effectiveness of deep learning in facial ageing research.     

Environmental exposure to lead and its correlation with biochemical indices in children

Lead is a global concern because of its ubiquity in the environment and known to be associated with abnormal neurobehavioral and cognitive development of young children. There is no study from India to describe a composite profile of blood lead and its biochemical influences in children. The present study was aimed at determining the proportion of children with >10 mug/dL blood lead levels (BLLs), association between BLLs, and sociodemographic characteristics, if any, and alterations in biochemical indices in the blood as an underlying mechanism of lead intoxication. A total of 62 children (4--12 y) of Lucknow and nearby areas were recruited to determine BLLs, delta-amimolevulinic acid dehydratase (delta-ALAD) activity, catalase (CAT) activity, and malondialdehyde (MDA) and glutathione (GSH) levels in the blood. Mean level of blood lead was 7.47+/-3.06 microg/dL (2.78--15.0) and 29%-exceeded 10 microg/dL, CDC intervention level. The BLLs were found to be significantly influenced by social status, area of residence, source of water supply, maternal educational status (p<0.001), type of house, and proximity to traffic density (p<0.01). delta-ALAD was significantly lower in the group of children with BLLs 11.39+/-1.39 microg/dL when compared to children with BLLs 7.11+/-1.25 microg/dL and 3.93+/-0.61 microg/dL (p=0.0007, 0.0005, respectively). However, CAT activity was higher in the groups of children with higher blood levels than with lower BLLs (p=0.0159, 0.0001, respectively). There was an increase in MDA level with a concomitant decrease of GSH in children with BLLs 11.39+/-1.39 microg/dL compared with those of children with BLLs 7.11+/-1.25 microg/dL and 3.93+/-0.61 microg/dL (p=0.0001, 0.0002, and p=0.0001, respectively). There was statistically significant correlation of BLLs with delta-ALAD (r=-0.44, p=0.00035), MDA (r=0.46, p=0.00018), GSH (r=-0.62, p=0.00001), and CAT (r=0.44, p=0.00035). Significantly, CAT activity, MDA, and GSH levels were in turn, found to be correlated with delta-ALAD (r=-0.45, p=0.00024; r=-0.43, p=0.00053; r=0.43, p=0.00053, respectively). Results of the present study indicate a declining trend of BLLs in children when compared with those reported from metropolitan cities of India when leaded gasoline was in practice and that the BLLs were significantly associated with biochemical indices in the blood which have the potential to be used as biomarkers of lead intoxication.     

Chemotactic migration triggers IL-8 generation in neutrophilic leukocytes

Neutrophils recovered from inflammatory exudates possess increased levels of IL-8, but exposure of neutrophils to chemoattractants results in only a modest stimulation of IL-8 generation. This study was undertaken to explore the hypothesis that IL-8 generation in these cells is dependent upon the process of migration. Neutrophils synthesized up to 30 times as much IL-8 during migration in response to a gradient of diverse chemoattractants than they did when stimulated directly by the attractants in the absence of a gradient. This IL-8 response was dependent on migration since it was not observed in cells exposed to concentration gradients of chemoattractants under conditions that prevented cell movement. While actinomycin-D (1 microg/ml) had little influence on the generation of IL-8 during chemotaxis, the protein synthesis inhibitor cycloheximide (10 microg/ml) markedly blunted the accumulation of cell-associated IL-8, suggesting that new protein synthesis from preexisting mRNA was responsible for the effect. Consistent with this interpretation, migrating cells incorporated over 10 times as much [3H]leucine into IL-8 as did nonmotile neutrophils exposed to chemoattractants. A substantial portion of the IL-8 generated during chemotaxis was released upon subsequent metabolic stimulation. Thus, the IL-8 synthesized during chemotaxis is uniquely positioned to exert a regulatory influence on inflammatory processes governed by neutrophilic leukocytes responding to inflammatory and infectious stimuli.     

Numerical studies on heat and fluid flow of nanofluid in a partially heated vertical annulus

A numerical investigation on natural convective heat transfer of nanofluid (Al₂O₃+water) inside a partially heated vertical annulus of high aspect ratio (352) has been carried out. The computational fluid dynamics solver Ansys Fluent is used for simulation and results are presented for various volume fraction of nanoparticles (0‐0.04) at different heat flux values (3‐12 kW/m²). Two well‐known correlations for evaluating thermal conductivity and viscosity have been used. Thus different combinations of the available correlations have been set to form four models (I, II, III, and IV). Therefore, a detailed analysis has been executed to identify effects of thermophysical properties on heat transfer and fluid flow of nanofluids using different models. The results show enhancement in heat transfer coefficient with volume fraction of nanoparticles. Highest enhancement achieved is found to be 14.17% based on model III, while the minimum is around 7.27% based on model II. Dispersion of nanoparticles in base fluid declines the Nusselt number and Reynolds number with different rates depending on various models. A generalized correlation is proposed for Nusselt number of nanofluids in the annulus in terms of volume fraction of nanoparticles, Rayleigh number, Reynolds number, and Prandtl number.