Dayananda Sagar University

Abstract In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients ( F ROH ) for >1.4 million individuals, we show that F ROH is significantly associated ( p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F ROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of F ROH are confirmed within full-sibling pairs, where the variation in F ROH is independent of all environmental confounding.

Wire arc additive manufacturing (WAAM) is a fusion manufacturing process in which the heat energy of an electric arc is employed for melting the electrodes and depositing material layers for wall formation or for simultaneously cladding two materials in order to form a composite structure. This directed energy deposition-arc (DED-arc) method is advantageous and efficient as it produces large parts with structural integrity due to the high deposition rates, reduced wastage of raw material, and low consumption of energy in comparison with the conventional joining processes and other additive manufacturing technologies. These features have resulted in a constant and continuous increase in interest in this modern manufacturing technique which demands further studies to promote new industrial applications. The high demand for WAAM in aerospace, automobile, nuclear, moulds, and dies industries demonstrates compatibility and reflects comprehensiveness. This paper presents a comprehensive review on the evolution, development, and state of the art of WAAM for non-ferrous materials. Key research observations and inferences from the literature reports regarding the WAAM applications, methods employed, process parameter control, optimization and process limitations, as well as mechanical and metallurgical behavior of materials have been analyzed and synthetically discussed in this paper. Information concerning constraints and enhancements of the wire arc additive manufacturing processes to be considered in terms of wider industrial applicability is also presented in the last part of this paper.

This paper proposes a photonic crystal fiber (PCF) based plasmonic biosensor for the detection of various blood compositions like red blood cells (RBCs), hemoglobin (HB), white blood cells (WBCs), plasma, and water. The finite element method (FEM) has been used to simulate and quantitatively evaluate this biosensor. The gold and titanium dioxide coated PCF operates on the surface plasmon resonance (SPR) theory, where the gold layer acts as a plasmonic material, and the titanium dioxide layer improves adhesion between the gold layer and the PCF surface. SPR occurs at the interface between gold and the sensing channel, when the core propagation mode is coupled with the surface plasmon polariton (SPP) mode in the vicinity of the phase-matching point. Due to the occurrence of SPR, the loss peak is noticed in the core propagation mode, and this loss peak is extremely sensitive to the various blood compositions that each have their unique refractive index (RI) poured into the sensing channel of the PCF. The proposed biosensor has maximum wavelength sensitivity of 12400 nm/RIU. However, the maximum amplitude sensitivity is −574.3 RIU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> . Furthermore, with the maximum detection limit of 0.02, the refractive index resolution varies from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.06\times {10}^{-6} {\mathrm {RIU}}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.0\times {10}^{-5} {\mathrm {RIU}}$ </tex-math></inline-formula> . As a result, it is safe to say that this biosensor will work admirably in terms of detecting blood compositions. Thus, the proposed biosensor will explore the broad realms of medical diagnostics.

Bionanotechnology is a branch of science that has revolutionized modern science and technology. Nanomaterials, especially noble metals, have attracted researchers due to their size and application in different branches of sciences that benefit humanity. Metal nanoparticles can be synthesized using green methods, which are good for the environment, economically viable, and facilitate synthesis. Due to their size and form, gold nanoparticles have become significant. Plant materials are of particular interest in the synthesis and manufacture of theranostic gold nanoparticles (NPs), which have been generated using various materials. On the other hand, chemically produced nanoparticles have several drawbacks in terms of cost, toxicity, and effectiveness. A plant-mediated integration of metallic nanoparticles has been developed in the field of nanotechnology to overcome the drawbacks of traditional synthesis, such as physical and synthetic strategies. Nanomaterials' tunable features make them sophisticated tools in the biomedical platform, especially for developing new diagnostics and therapeutics for malignancy, neurodegenerative, and other chronic disorders. Therefore, this review outlines the theranostic approach, the different plant materials utilized in theranostic applications, and future directions based on current breakthroughs in these fields.

The purpose of this research is to build an automated, robust, intelligent and hybrid system for the early diagnosis and classifying of brain tumor. To serve this purpose, the authors propose the Auto Contrast Enhancer, Tumor Detector and Classifier to efficiently provide on-demand contrast improvement of poor contrast MRI images for the early diagnosis and classification of brain tumors. The classifier accomplishes its task through a two-phase approach. During the initial phase, ODTWCHE is employed to enhance image contrast, facilitating accurate diagnosis of brain tumours. In the subsequent phase, the classifier leverages the power of deep transfer learning, utilizing the pre-trained Inception V3 model to refine the diagnostic process further. tumor classification. Compared to state-of-the-art models, including AlexNet, VGG-16, DenseNet-201, VGG-19, GoogLeNet, and ResNet-50, the proposed system showcased its outstanding performance by achieving the highest accuracy of 98.89% on a public dataset that consists of MRI images with varying contrast and brightness levels. The precise detection and classification achieved on this multicolored dataset prove the system's robustness. The authors of the article address the usage of metrics in a variety of contexts, including academia, as well as the possible problems that may result from their improper application. They emphasize how crucial it is to create measurements that align with the system's objectives and to reduce any negative consequences that can skew the data or allow people to manipulate the system's incentives. The authors provide a thorough process for creating metrics that takes into account design considerations, countermeasures for unfavorable effects, and crucial requirements. The paper provides answers for the creation of metrics and gives examples of metrics' failures in many fields. The authors emphasize the significance of understanding how the goal and the data at hand relate to one another, as well as the necessity of compromise and clarity when goals are contradictory or incoherent. A comparative analysis with existing models further confirms that the proposed system consistently outperforms the competition.

Rapid spread of Coronavirus disease COVID-19 leads to severe pneumonia and it is estimated to create a high impact on the healthcare system. An urgent need for early diagnosis is required for precise treatment, which in turn reduces the pressure in the health care system. Some of the standard image diagnosis available is Computed Tomography (CT) scan and Chest X-Ray (CXR). Even though a CT scan is considered a gold standard in diagnosis, CXR is most widely used due to widespread, faster, and cheaper. This study aims to provide a solution for identifying pneumonia due to COVID-19 and healthy lungs (normal person) using CXR images. One of the remarkable methods used for extracting a high dimensional feature from medical images is the Deep learning method. In this research, the state-of-the-art techniques used is Genetic Deep Learning Convolutional Neural Network (GDCNN). It is trained from the scratch for extracting features for classifying them between COVID-19 and normal images. A dataset consisting of more than 5000 CXR image samples is used for classifying pneumonia, normal and other pneumonia diseases. Training a GDCNN from scratch proves that, the proposed method performs better compared to other transfer learning techniques. Classification accuracy of 98.84%, the precision of 93%, the sensitivity of 100%, and specificity of 97.0% in COVID-19 prediction is achieved. Top classification accuracy obtained in this research reveals the best nominal rate in the identification of COVID-19 disease prediction in an unbalanced environment. The novel model proposed for classification proves to be better than the existing models such as ReseNet18, ReseNet50, Squeezenet, DenseNet-121, and Visual Geometry Group (VGG16).

We present an experimental demonstration of a subwavelength diffraction grating performing first-order differentiation of the transverse profile of an incident optical beam with respect to a spatial variable. The experimental results are in a good agreement with the presented analytical model suggesting that the differentiation is performed in transmission at oblique incidence and is associated with the guided-mode resonance of the grating. According to this model, the transfer function of the grating in the vicinity of the resonance is close to the transfer function of an exact differentiator. We confirm this by estimating the transfer function of the fabricated structure on the basis of the measured profiles of the incident and transmitted beams. The considered structure may find application in the design of new photonic devices for beam shaping, optical information processing, and analog optical computing.

Object detection is an important process in surveillance system to locate objects and it is considered as major application in computer vision. The Convolution Neural Network (CNN) based models have been developed by many researchers for object detection to achieve higher performance. However, existing models have some limitations such as overfitting problem and lower efficiency in small object detection. Object detection in remote sensing hasthe limitations of low efficiency in detecting small object and the existing methods have poor localization. Cascade Object Detection methods have been applied to increase the learning process of the detection model. In this research, the Additive Activation Function (AAF) is applied in a Faster Region based CNN (RCNN) for object detection. The proposed AAF-Faster RCNN method has the advantage of better convergence and clear bounding variance. The Fourier Series and Linear Combination of activation function are used to update the loss function. The Microsoft (MS) COCO datasets and Pascal VOC 2007/2012 are used to evaluate the performance of the AAF-Faster RCNN model. The proposed AAF-Faster RCNN is also analyzed for small object detection in the benchmark dataset. The analysis shows that the proposed AAF-Faster RCNN model has higher efficiency than state-of-art Pay Attention to Them (PAT) model in object detection. To evaluate the performance of AAF-Faster RCNN method of object detection in remote sensing, the NWPU VHR-10 remote sensing data set is used to test the proposed method. The AAF-Faster RCNN model has mean Average Precision (mAP) of 83.1% and existing PAT-SSD512 method has the 81.7%mAP in Pascal VOC 2007 dataset.

Ag/AgCl). A remarkable percentage increase (134%) in achievable current density is realised by the former over that of the latter. Tafel analysis and turn over frequency is reported with a likely underlying mechanism for the Ni/NiO nanocomposites towards the OER proposed. In the former case, Tafel analysis is overviewed for general multi-step overall electrochemical reaction processes, which can be used to assist other researchers in determining mechanistic information, such as electron transfer and rate determining steps, when exploring the OER. The optimal Ni/NiO nanocomposite exhibits promising stability at the potential of +231 mV, retaining near 100% of its achievable current density after 28 hours. Due to the facile and rapid fabrication methodology of the Ni/NiO nanocomposites, such an approach is ideally suited towards the mass production of highly active and stable electrocatalysts for application within the anodic catalyst layers of commercial alkaline electrolysers.

Cocrystallization is a well-established technique to improve the solubility, bioavailability, and stability of active pharmaceutical ingredients (APIs) but permeability and diffusion rate control via cocrystals is relatively less well studied, and the exact role of coformers in influencing the diffusion rate of drug cocrystals is still not fully understood. The aqueous solubility and permeability diffusion of Entacapone, ETP, a Biopharmaceutical Classification System (BCS) Class IV drug of low solubility and low permeability, with Generally Regarded as Safe (GRAS) coformers has been studied. Fixed stoichiometry cocrystals of ETP with acetamide (ACT, 1:1), nicotinamide (NAM, 1:1), isonicotinamide (INAM, 1:1), pyrazinamide (PYZ, 1:1), and isoniazid (INZ), 1:1) were prepared by solvent-assisted grinding. Theophylline (THP) resulted in a cocrystal hydrate (ETP-THP-HYD 1:1:1). The cocrystals were structurally characterized by single crystal and powder X-ray diffraction, DSC and TGA thermal measurements, and IR and NMR spectroscopy. Solubility and dissolution rate showed that there is a correlation between cocrystal stability and solubility governed by the heteromeric N–H···O, O–H···N, and O–H···O hydrogen bonds and conformational changes of ETP in cocrystal structures. ETP-THP-HYD and ETP-PYZ exhibit faster dissolution rate and high solubility and they are stable in phosphate buffer medium compared to the other cocrystals which dissociate partially during solubility experiments. Diffusion rates in a Franz cell showed that the stable and high solubility ETP-THP-HYD cocrystal has good permeability. Given that stability, solubility, and permeability are in general inversely correlated, the entacapone–theophylline hydrate cocrystal is a unique example of the thermodynamically stable cocrystal exhibiting high solubility and high permeability.

In the present study, AZ91 Mg alloy was heat treated at 410 °C for 6, 12 and 24 h to investigate the influence of heat treatment on machinability and corrosion behavior. The effect of soaking time on the amount and distribution of Mg17Al12 (β – phase) was analyzed under the optical microscope. Microhardness measurements demonstrated the increased hardness with increased heat treatment soaking time, which can be attributed to the solid solution strengthening. The influence of super saturated α-grains on reducing the cutting force (Fz) with respect to increased cutting speed was observed as prominent. The corrosion behavior of the heat treated specimens was studied by conducting electrochemical tests. Surprisingly, corrosion rate of heat treated samples was observed as increased compared with the base material. From the results, it is evident that the machinability of AZ91 Mg alloy can be improved by producing super saturated α-grains through heat treatment but at the cost of losing corrosion resistance. Keywords: AZ91 Mg alloy, Solid solution, Turning, Corrosion, Machinability

The microorganisms that have developed resistance to available therapeutic agents are threatening the globe and multidrug resistance among the bacterial pathogens is becoming a major concern of public health worldwide. Bacteria develop protective mechanisms to counteract the deleterious effects of antibiotics, which may eventually result in loss of growth-inhibitory potential of antibiotics. ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) pathogens display multidrug resistance and virulence through various mechanisms and it is the need of the hour to discover or design new antibiotics against ESKAPE pathogens. In this article, we have discussed the mechanisms acquired by ESKAPE pathogens to counteract the effect of antibiotics and elaborated on recently discovered secondary metabolites derived from bacteria and plant sources that are endowed with good antibacterial activity towards pathogenic bacteria in general, ESKAPE organisms in particular. Abyssomicin C, allicin, anthracimycin, berberine, biochanin A, caffeic acid, daptomycin, kibdelomycin, piperine, platensimycin, plazomicin, taxifolin, teixobactin, and thymol are the major metabolites whose antibacterial potential have been discussed in this article.

Abstract Today, due to the modern life style people have joined technology life and using more technology for shopping as well as financial transactions in their cyber space. At the same time, safeguarding of knowledge has become increasingly difficult. In addition, the heavy use and growth of social media, online crime or cybercrime has increased. In the world of information technology, data security plays a significant role. The information security has become one of today’s main challenges. Whenever we think of cyber security, we first of all think of ‘cybercrimes,’ which expand tremendously every day. Different government and businesses take various steps to avoid this form of cybercrime. In addition to numerous cyber protection initiatives, many people are also very worried about it. This paper focuses primarily on cyber security concerns related to the new technology. It also concentrates on the new technologies for cyber security, ethics and developments that impact cyber security.

Breast cancer is one of the deadly cancer types that causes high mortality among women globally. Meanwhile, Deep Learning (DL) emerges as the most frequently utilized and rapidly developing branch of classical machine learning. The study examines a modern Computer-Aided Diagnosis (CAD) framework that uses DL to extract features and classify them for aiding radiologists in breast cancer diagnosis. This is accomplished through four distinct experimentations aimed at identifying the most optimal method of effective classification. Here, the first uses Deep CNNs that are pre-trained, such as AlexNet, GoogleNet, ResNet50, and Dense-Net121. The second is based on experiments using Deep CNNs to extract features and applying them onto a Support Vector Machine algorithm using three different kernels. The next one involves the fusion of different deep features for demonstrating the classification improvement by fusion of these deep features. The final experiment involves Principal Component Analysis (PCA) for reducing the computational cost and for decreasing the larger feature vectors created during fusion. The abovesaid experimentations are carried out in two different mammogram datasets namely MIAS and INbreast. The classification accuracy attained for both datasets through the fuzing of deep features (97.93% for MIAS and 96.646% for INbreast) is the highest compared with the state-of-the-art frameworks. In contrast, the classification performance did not enhance while applying the PCA on combined deep features; but the decrease in execution time provides a reduced computational cost.Abbreviations: CAD: Computer Aided Diagnosis; CNN: Convolution Neural Network; CSI: Classification Success Index; DCNN: Deep Convolution Neural Network; DICOM: Digital Imaging and Communications in Medicine; DL: Deep Learning; FC layer: Fully Connected layer; FFDM: Full-Field Digital Mammograms; FN: False Negative; FP: False Positive; ICSI: Individual Classification Success Index; MIAS: Mammographic Image Analysis Society; ML: Machine Learning; MLO: Medio-Lateral Oblique; PCA: Principal Component Analysis; PGM: Portable Gray Map; PPV: Positive Predictive Value; RBF: Radial Basis Function; SGDM: Stochastic Gradient Descent with Momentum; SVM: Support Vector Machine; TN: True Negative; TP: True Positive; TPR: True Positive Rate; UK: United Kingdom

This study focuses on the properties and process parameters dictating behavioural aspects of friction stir welded Aluminium Alloy AA6061 metal matrix composites reinforced with varying percentages of SiC and B4C. The joint properties in terms of mechanical strength, microstructural integrity and quality were examined. The weld reveals grain refinement and uniform distribution of reinforced particles in the joint region leading to improved strength compared to other joints of varying base material compositions. The tensile properties of the friction stir welded Al-MMCs improved after reinforcement with SiC and B4C. The maximum ultimate tensile stress was around 172.8 ± 1.9 MPa for composite with 10% SiC and 3% B4C reinforcement. The percentage elongation decreased as the percentage of SiC decreases and B4C increases. The hardness of the Al-MMCs improved considerably by adding reinforcement and subsequent thermal action during the FSW process, indicating an optimal increase as it eliminates brittleness. It was seen that higher SiC content contributes to higher strength, improved wear properties and hardness. The wear rate was as high as 12 ± 0.9 g/s for 10% SiC reinforcement and 30 N load. The wear rate reduced for lower values of load and increased with B4C reinforcement. The microstructural examination at the joints reveals the flow of plasticized metal from advancing to the retreating side. The formation of onion rings in the weld zone was due to the cylindrical FSW rotating tool material impression during the stirring action. Alterations in chemical properties are negligible, thereby retaining the original characteristics of the materials post welding. No major cracks or pores were observed during the non-destructive testing process that established good quality of the weld. The results are indicated improvement in mechanical and microstructural properties of the weld.

A 5G ultra dense network architecture makes use of a high density of micro cell base stations to provide increased coverage, capacity, and performance for 5G communication systems. This is accomplished by the utilization of a very densely packed network. A 5G ultradense network is another name for this particular category of network. Ultradense networks are built to overcome the limitations of ordinary cellular networks. This is accomplished by increasing the number of base stations that are located inside a given area. This is performed by reducing the coverage area provided by each base station, which ultimately leads to an increase in the capacity of the entire network. The maximum data rate or capacity that may be obtained through a certain wireless channel is referred to as the “channel capacity” in a 5G ultradense network, when the phrase “channel capacity” is used. One possibility is that this represents the maximum quantity of data that can be transmitted across the channel. These criteria, which represent the capabilities of the channel, can be used to evaluate the performance of channels and equipment on 5G networks. This article presents for further research a model for improving the channel capacity of the 5G ultradense network. The model can be found at the end of the article. This change will result in an increase in both the usefulness of the channel and the amount of bandwidth that it consumes; both of these aspects will improve. The fact that it uses a very small quantity of energy and power in compared to other methods is the major trait that sets it apart from other methods. This technology is superior to others in terms of both the amount of data it can communicate and the amount of electricity it consumes in the process.

Background: Mounting evidence suggests that nutritional exposures during pregnancy influence the fetal epigenome, and that these epigenetic changes can persist postnatally, with implications for disease risk across the life course. Methods: We review human intergenerational studies using a three-part search strategy. Search 1 investigates associations between preconceptional or pregnancy nutritional exposures, focusing on one-carbon metabolism, and offspring DNA methylation. Search 2 considers associations between offspring DNA methylation at genes found in the first search and growth-related, cardiometabolic and cognitive outcomes. Search 3 isolates those studies explicitly linking maternal nutritional exposure to offspring phenotype via DNA methylation. Finally, we compile all candidate genes and regions of interest identified in the searches and describe their genomic locations, annotations and coverage on the Illumina Infinium Methylation beadchip arrays. Results: We summarize findings from the 34 studies found in the first search, the 31 studies found in the second search and the eight studies found in the third search. We provide details of all regions of interest within 45 genes captured by this review. Conclusions: Many studies have investigated imprinted genes as priority loci, but with the adoption of microarray-based platforms other candidate genes and gene classes are now emerging. Despite a wealth of information, the current literature is characterized by heterogeneous exposures and outcomes, and mostly comprise observational associations that are frequently underpowered. The synthesis of current knowledge provided by this review identifies research needs on the pathway to developing possible early life interventions to optimize lifelong health.

This experimental study investigates the mechanical properties of polymer matrix composites containing nanofiller developed by fused deposition modelling (FDM). A novel polymer nanocomposite was developed by amalgamating polycarbonate-acrylonitrile butadiene styrene (PC-ABS) by blending with graphene nanoparticles in the following proportions: 0.2, 0.4, 0.6, and 0.8 wt %. The composite filaments were developed using a twin-screw extrusion method. The mechanical properties such as tensile strength, low-velocity impact strength, and surface roughness of pure PC-ABS and PC-ABS + graphene were compared. It was observed that with the addition of graphene, tensile strength and impact strength improved, and a reduction in surface roughness was observed along the build direction. These properties were analyzed to understand the dispersion of graphene in the PC-ABS matrix and its effects on the parameters of the study. With the 0.8 wt % addition of graphene to PC-ABS, the tensile strength increased by 57%, and the impact resistance increased by 87%. A reduction in surface roughness was noted for every incremental addition of graphene to PC-ABS. The highest decrement was seen for the 0.8 wt % addition of graphene reinforcement that amounted to 40% compared to PC-ABS.

Invertase or β-D-fructofuranoside fructohydrolase (EC 3.2.1.26) was one of the foremost enzyme biocatalysts and established the primary concepts of most enzyme-kinetic principles. Invertases are glycoside hydrolases and occur mostly in microorganisms. Among microbial strains, for many decades yeast species have been extensively researched for invertase production, characterization, and applications in industries. Besides, limited literature is available on invertases from bacterial strains. The enzymic and molecular biological reports from bacterial invertases are scarce. In this minireview, occurrence, production, biochemical properties, and significance of transfructosylation of bacterial invertases are reported.

Abstract The present work's target is to study the impact of fly ash or TiC nanoparticles or both on the characteristics of coir fiber epoxy hybrid composites. Mechanical characteristics like tensile, flexural, inter‐laminar shear strength, impact strength, shore D hardness, and thermal stability or degradation characteristics were determined. The microstructure of the samples was observed from a scanning electron microscopy. It was observed that the addition of coir fiber, fly ash, and TiC nanoparticles in the epoxy polymer enhanced the mechanical and thermal characteristics of composites. It can be assigned to the better interaction and uniform distribution between the fillers and the epoxy polymer. Fillers acted a critical role in enhancing the characteristics of epoxy hybrid composites. Additionally, water absorption characteristics were also investigated for all specimens. A comparative examination was performed between various fabricated composite specimens. Results exhibited that, the water absorption of the composites improves considerably with the addition of reinforcements. Also, thermogravimetric analysis exhibited that the fabricated epoxy hybrid composites were stable thermally.