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The interferometric synthetic aperture radar (InSAR) imaging technique computes relative distances or surface maps by measuring the absolute phase differences of returned radar signals. The measured phase difference is wrapped in a 2 π cycle due to the wave nature of light. Hence, the proper multiple of 2 π must be added back during restoration and this process is known as phase unwrapping. The noise and discontinuity present in the wrapped signals pose challenges for error-free unwrapping procedures. Separate denoising and unwrapping algorithms lead to the introduction of additional errors from excessive filtering and changes in the statistical nature of the signal. This can be avoided by joint unwrapping and denoising procedures. In recent years, research efforts have been made using deep-learning-based frameworks, which can learn the complex relationship between the wrapped phase, coherence, and amplitude images to perform better unwrapping than traditional signal processing methods. This research falls predominantly into segmentation- and regression-based unwrapping procedures. The regression-based methods have poor performance while segmentation-based frameworks, like the conventional U-Net, rely on a wrap count estimation strategy with very poor noise immunity. In this paper, we present a two-stage phase unwrapping deep neural network framework based on U-Net, which can jointly unwrap and denoise InSAR phase images. The experimental results demonstrate that our approach outperforms related work in the presence of phase noise and discontinuities with a root mean square error (RMSE) of an order of magnitude lower than the others. Our framework exhibits better noise immunity, with a low average RMSE of 0.11. [ABSTRACT FROM AUTHOR]

Globally, researchers have devoted consistent efforts to producing excellent coating properties since coating plays an essential role in enhancing electrochemical performance and surface quality. In this study, TiO2 nanoparticles in varying concentrations of 0.5, 1, 2, and 3 wt.% were added into the acrylic-epoxy polymeric matrix with 90:10 wt.% (90A:10E) ratio incorporated with 1 wt.% graphene, to fabricate graphene/ TiO 2 -based nanocomposite coating systems. Furthermore, the properties of the graphene/ TiO 2 composites were investigated by Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), ultraviolet-visible (UV-Vis) spectroscopy, water contact angle (WCA) measurements, and cross-hatch test (CHT), respectively. Moreover, the field emission scanning electron microscope (FESEM) and the electrochemical impedance spectroscopy (EIS) tests were conducted to investigate the dispersibility and anticorrosion mechanism of the coatings. The EIS was observed by determining the breakpoint frequencies over a period of 90 days. The results revealed that the TiO 2 nanoparticles were successfully decorated on the graphene surface by chemical bonds, which resulted in the graphene/ TiO 2 nanocomposite coatings exhibiting better dispersibility within the polymeric matrix. The WCA of the graphene/ TiO 2 coating increased along with the ratio of TiO 2 to graphene, achieving the highest CA of 120.85° for 3 wt.% of TiO2. Excellent dispersion and uniform distribution of the TiO 2 nanoparticles within the polymer matrix were shown up to 2 wt.% of TiO2 inclusion. Among the coating systems, throughout the immersion time, the graphene/ TiO 2 (1:1) coating system exhibited the best dispersibility and high impedance modulus values ( Z 0.01   Hz ), exceeding 10 10 Ω   cm 2 . [ABSTRACT FROM AUTHOR]

The current paper recovers cubic–quartic optical solitons in fiber Bragg gratings having polynomial law of nonlinear refractive index structures. Lie symmetry analysis is carried out, starting with the basic analysis. Then, it is followed through with improved Kudryashov and generalized Arnous schemes. The parameter constraints are also identified for the existence of such solitons. Numerical surface plots support the adopted applied analysis. [ABSTRACT FROM AUTHOR]

The paper revisits highly dispersive optical solitons that are addressed by the aid of Lie symmetry followed by the implementation of the Riccati equation approach and the improved modified extended tanh-function approach. The soliton solutions are recovered and classified. The conservation laws are also recovered and the corresponding conserved quantities are enlisted. [ABSTRACT FROM AUTHOR]

In this paper, a compact dual circularly-polarized (CP) planar multiple-input-multiple-output (MIMO) antenna is presented for a sub-6 GHz frequency band. The antenna consists of four identical resonating elements, which are placed in a mirrored-image pattern to obtain polarization diversity. Element 2 is a mirror image of element 1, and elements 3 and 4 are mirror images of elements 1 and 2. Each antenna element comprises an elliptical resonator, a 50-Ω microstrip feed line, and a rectangular stub integrated with the feed to increase the surface current path of the antenna, shifting the resonating frequency to the lower side. Additionally, the rectangular stub is lengthened towards the right side (along the +x-axis direction in the antenna element 1), which balances the magnitude and 90° phase variance among the horizontal (Ex) and vertical (Ey) fields. The proposed MIMO antenna supports both types of circular polarization, where radiators 1 and 3 radiate right-hand CP (RHCP) rays and radiators 2 and 4 radiate left-hand CP (LHCP) rays. Developing a compact-size MIMO antenna is a challenging task, especially when the antenna elements share the same ground plane and are placed less than half a wavelength apart. The mutual coupling in the proposed antenna is reduced by increasing the spacing between the elements without the use of any extra decoupling structure. Optimal spacing is maintained to achieve compact geometry with less inter-element correlation. The radiators are closely placed with an edge-to-edge spacing of 0.08λ0, where λ0 is the free space wavelength at 3.6 GHz. A peak gain of 5 dBi, efficiency of 90%, an envelope correlation coefficient (ECC) of less than 0.1, and isolation of more than 18 dB are obtained between different ports of the prototype antenna. The overall size of the antenna element is 17 mm × 17 mm × 1.6 mm, and the MIMO antenna is 40 mm × 40 mm × 1.6 mm. [ABSTRACT FROM AUTHOR]

This work recovers cubic-quartic optical solitons with dispersive reflectivity in fiber Bragg gratings and parabolic law of nonlinearity. The Lie symmetry analysis first reduces the governing partial differential equations to the corresponding ordinary differential equations which are subsequently integrated. This integration is conducted using two approaches which are the modified Kudryashov's approach as well as the generalized Arnous' scheme. These collectively yielded a full spectrum of cubic-quartic optical solitons that have been proposed to control the depletion of the much-needed chromatic dispersion. They range from bright, dark, singular to combo solitons. These solitons are considered with dispersive reflectivity, which maintains the necessary balance between chromatic dispersion and nonlinear refractive index structure for an uninterrupted transmission of solitons along intercontinental distances. Their respective surface and contour plots are also exhibited. A few closing words are included with some prospective future avenues of research to extend this topic further. [ABSTRACT FROM AUTHOR]

Automated fruit classification is a stimulating problem in the fruit growing and retail industrial chain as it assists fruit growers and supermarket owners to recognize variety of fruits and the status of the container or stock to increase business profit and production efficacy. As a result, intelligent systems using machine learning and computer vision approaches were explored for ripeness grading, fruit defect categorization, and identification over the last few years. Recently, deep learning (DL) methods for classifying fruits led to promising performance that effectively extracts the feature and carries out an end-to-end image classification. This paper introduces an Automated Fruit Classification using Hyperparameter Optimized Deep Transfer Learning (AFC-HPODTL) model. The presented AFC-HPODTL model employs contrast enhancement as a pre-processing step which helps to enhance the quality of images. For feature extraction, the Adam optimizer with deep transfer learning-based DenseNet169 model is used in which the Adam optimizer fine-tunes the initial values of the DenseNet169 model. Moreover, a recurrent neural network (RNN) model is utilized for the identification and classification of fruits. At last, the Aquila optimization algorithm (AOA) is exploited for optimal hyperparameter tuning of the RNN model in such a way that the classification performance gets improved. The design of Adam optimizer and AOA-based hyperparameter optimizers for DenseNet and RNN models show the novelty of the work. The performance validation of the presented AFC-HPODTL model is carried out utilizing a benchmark dataset and the outcomes report the promising performance over its recent state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This investigation focuses on two novel Kadomtsev–Petviashvili (KP) equations with time-dependent variable coefficients that describe the nonlinear wave propagation of small-amplitude surface waves in narrow channels or large straits with slowly varying width and depth and non-vanishing vorticity. These two variable coefficients, Kadomtsev–Petviashvili (VCKP) equations in (2+1)-dimensions, are the main extensions of the KP equation. Applying the Lie symmetry technique, we carry out infinitesimal generators, potential vector fields, and various similarity reductions of the considered VCKP equations. These VCKP equations are converted into nonlinear ODEs via two similarity reductions. The closed-form analytic solutions are achieved, including in the shape of distinct complex wave structures of solitons, dark and bright soliton shapes, double W-shaped soliton shapes, multi-peakon shapes, curved-shaped multi-wave solitons, and novel solitary wave solitons. All the obtained solutions are verified and validated by using back substitution to the original equation through Wolfram Mathematica. We analyze the dynamical behaviors of these obtained solutions with some three-dimensional graphics via numerical simulation. The obtained variable coefficient solutions are more relevant and useful for understanding the dynamical structures of nonlinear KP equations and shallow water wave models. [ABSTRACT FROM AUTHOR]

Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems and the larger number of videos captured. Thus, an automatic surveillance system that identifies the anomalies has become indispensable for computer vision (CV) researcher workers. The recent advancements in deep learning (DL) algorithms have attracted wide attention for CV processes such as object detection and object classification based on supervised learning that requires labels. The current research study designs the bioinspired Garra rufa optimization-assisted deep learning model for object classification (BGRODL-OC) technique on pedestrian walkways. The objective of the BGRODL-OC technique is to recognize the presence of pedestrians and objects in the surveillance video. To achieve this goal, the BGRODL-OC technique primarily applies the GhostNet feature extractors to produce a set of feature vectors. In addition to this, the BGRODL-OC technique makes use of the GRO algorithm for hyperparameter tuning process. Finally, the object classification is performed via the attention-based long short-term memory (ALSTM) network. A wide range of experimental analysis was conducted to validate the superior performance of the BGRODL-OC technique. The experimental values established the superior performance of the BGRODL-OC algorithm over other existing approaches. [ABSTRACT FROM AUTHOR]

This paper employs Lie symmetry analysis to recover cubic–quartic optical soliton solutions to the Lakshmanan–Porsezian–Daniel model in birefringent fibers. The results are a sequel to the previously reported work on the same model in unpolarized fibers. Dark, singular, and straddled optical solitons that emerged from the scheme are presented. [ABSTRACT FROM AUTHOR]

Skin is designed to protect various tissues, and because it is the largest and first human bodily organ to sustain damage, it has an incredible ability to regenerate. On account of extreme injuries or extensive surface loss, the normal injury recuperating interaction might be inadequate or deficient, bringing about risky and disagreeable circumstances that request the utilization of fixed adjuvants and tissue substitutes. Due to their remarkable biocompatibility, biodegradability, and bioactive abilities, such as antibacterial, immunomodulatory, cell proliferative, and wound mending properties, biodegradable polymers, both synthetic and natural, are experiencing remarkable progress. Furthermore, the ability to convert these polymers into submicrometric filaments has further enhanced their potential (e.g., by means of electrospinning) to impersonate the stringy extracellular grid and permit neo-tissue creation, which is a basic component for delivering a mending milieu. Together with natural biomaterial, synthetic polymers are used to solve stability problems and make scaffolds that can dramatically improve wound healing. Biodegradable polymers, commonly referred to as biopolymers, are increasingly used in other industrial sectors to reduce the environmental impact of material and energy usage as they are fabricated using renewable biological sources. Electrospinning is one of the best ways to fabricate nanofibers and membranes that are very thin and one of the best ways to fabricate continuous nanomaterials with a wide range of biological, chemical, and physical properties. This review paper concludes with a summary of the electrospinning (applied electric field, needle-to-collector distance, and flow rate), solution (solvent, polymer concentration, viscosity, and solution conductivity), and environmental (humidity and temperature) factors that affect the production of nanofibers and the use of bio-based natural and synthetic electrospun scaffolds in wound healing. [ABSTRACT FROM AUTHOR]

Pig farming has become a strategically significant and economically important industry across the globe. It is also a potentially vulnerable sector due to challenges posed by transboundary diseases in which viral infections are at the forefront. Among the porcine viral diseases, African swine fever, classical swine fever, foot and mouth disease, porcine reproductive and respiratory syndrome, pseudorabies, swine influenza, and transmissible gastroenteritis are some of the diseases that cause substantial economic losses in the pig industry. It is a well-established fact that vaccination is undoubtedly the most effective strategy to control viral infections in animals. From the period of Jenner and Pasteur to the recent new-generation technology era, the development of vaccines has contributed significantly to reducing the burden of viral infections on animals and humans. Inactivated and modified live viral vaccines provide partial protection against key pathogens. However, there is a need to improve these vaccines to address emerging infections more comprehensively and ensure their safety. The recent reports on new-generation vaccines against swine viruses like DNA, viral-vector-based replicon, chimeric, peptide, plant-made, virus-like particle, and nanoparticle-based vaccines are very encouraging. The current review gathers comprehensive information on the available vaccines and the future perspectives on porcine viral vaccines. [ABSTRACT FROM AUTHOR]

A design method for an ultra-wideband coplanar-stripline-based vertical transition that can be used for ultra-high-speed digital interfaces is proposed. A conventional via structure, based on a differential line (DL), inherently possesses performance limitations (<10 GHz) due to difficulties in maintaining constant line impedance and smooth electric field transformation, in addition to the effects of signal skews, FR4 fiber weave, and unbalanced EM interferences. DL-based digital interfaces may not meet the demands of ultra-high-speed digital data transmission required for the upcoming 6G communications. The use of a coplanar stripline (CPS), a type of planar balanced line (BL), for the vertical transition, along with the ultra-wideband DL-to-CPS transition, mostly removes the inherent and unfavorable issues of the DL and enables ultra-high-speed digital data transmission. The design process of the transition is simplified using the analytical design formulas, derived using the conformal mapping method, of the transition. The characteristic line impedances of the transition are calculated and found to be in close agreement with the results obtained from EM simulations. Utilizing these results, the CPS-based vertical transition, maintaining the characteristic line impedance of 100 Ω, is designed and fabricated. The measured results confirm its ultra-wideband characteristics, with a maximum of 1.6 dB insertion loss and more than 10 dB return loss in the frequency range of DC to 30 GHz. Therefore, the proposed CPS-based vertical transition offers a significantly wider frequency bandwidth, i.e., more than three times that of conventional DL-based via structures. [ABSTRACT FROM AUTHOR]

In this work, a Lie group reduction for a (2 + 1) dimensional fractional Kadomtsev-Petviashvili (KP) system is determined by using the Lie symmetry method with Riemann Liouville derivative. After reducing the system into a two-dimensional nonlinear fractional partial differential system (NLFPDEs), the power series (PS) method is applied to obtain the exact solution. Further the obtained power series solution is analyzed for convergence. Then, using the new conservation theorem with a generalized Noether's operator, the conservation laws of the KP system are obtained. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer is considered the most significant public health issue which severely threatens people's health. The occurrence and mortality rate of breast cancer have been growing consistently. Initial precise diagnostics act as primary factors in improving the endurance rate of patients. Even though there are several means to identify breast cancer, histopathological diagnosis is now considered the gold standard in the diagnosis of cancer. However, the difficulty of histopathological image and the rapid rise in workload render this process time-consuming, and the outcomes might be subjected to pathologists' subjectivity. Hence, the development of a precise and automatic histopathological image analysis method is essential for the field. Recently, the deep learning method for breast cancer pathological image classification has made significant progress, which has become mainstream in this field. Therefore, in this work, we focused on the design of metaheuristics with deep learning based breast cancer classification process. The proposed model is found to be an effective tool to assist physicians in the decision making process. Breast cancer is the major cause behind the death of women worldwide and is responsible for several deaths each year. Even though there are several means to identify breast cancer, histopathological diagnosis is now considered the gold standard in the diagnosis of cancer. However, the difficulty of histopathological image and the rapid rise in workload render this process time-consuming, and the outcomes might be subjected to pathologists' subjectivity. Hence, the development of a precise and automatic histopathological image analysis method is essential for the field. Recently, the deep learning method for breast cancer pathological image classification has made significant progress, which has become mainstream in this field. This study introduces a novel chaotic sparrow search algorithm with a deep transfer learning-enabled breast cancer classification (CSSADTL-BCC) model on histopathological images. The presented CSSADTL-BCC model mainly focused on the recognition and classification of breast cancer. To accomplish this, the CSSADTL-BCC model primarily applies the Gaussian filtering (GF) approach to eradicate the occurrence of noise. In addition, a MixNet-based feature extraction model is employed to generate a useful set of feature vectors. Moreover, a stacked gated recurrent unit (SGRU) classification approach is exploited to allot class labels. Furthermore, CSSA is applied to optimally modify the hyperparameters involved in the SGRU model. None of the earlier works have utilized the hyperparameter-tuned SGRU model for breast cancer classification on HIs. The design of the CSSA for optimal hyperparameter tuning of the SGRU model demonstrates the novelty of the work. The performance validation of the CSSADTL-BCC model is tested by a benchmark dataset, and the results reported the superior execution of the CSSADTL-BCC model over recent state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This article presents the Reliability Assessment (RA) of renewable energy interfaced Electrical Distribution System (EDS) considering the electrical loss minimization (ELM). ELM aims at minimizing the detrimental effect of real power and reactive power losses in the EDS. Some techniques, including integration of Renewable Energy Source (RES), network reconfiguration, and expansion planning, have been suggested in the literature for achieving ELM. The optimal RES integration (also referred to as Distributed Generation (DG)) is one of the globally accepted techniques to achieve minimization of electrical losses. Therefore, first, the locations to accommodate these DGs are obtained by implementing two indexes, namely Index-1 for single DG and Index-2 for multiple DGs. Second, a Constriction Factor-based Particle Swarm Optimization (CF-PSO) technique is applied to obtain an optimal sizing(s) of the DGs for achieving the ELM. Third, the RA of the EDS is performed using the optimal location(s) and sizing(s) of the RESs (i.e., Solar photovoltaic (SPV) and Wind Turbine Generator (WTG)). Moreover, a Battery Storage System (BSS) is also incorporated optimally with the RESs to further achieve the ELM and to improve the system's reliability. The result analysis is performed by considering the power output rating of WTG-GE's V162-5.6MW (IECS), SPV-Sunpower's SPR-P5-545-UPP, and BSS-Freqcon's BESS-3000 (i.e., Battery Energy Storage System 3000), which are provided by the corresponding manufacturers. According to the outcomes of the study, the results are found to be coherent with those obtained using other techniques that are available in the literature. These results are considered for the RA of the EDS. RA is further analyzed considering the uncertainties in reliability data of WTG and SPV, including the failure rate and the repair time. The RA of optimally placed DGs is performed by considering the electrical loss minimization. It is inferred that the reliability of the EDS improves by contemplating suitable reliability data of optimally integrated DGs. [ABSTRACT FROM AUTHOR]

A broadband compact-sized planar four-port multiple-input–multiple-output (MIMO) antenna with polarization diversity is presented. The proposed dual circularly polarized (CP) MIMO antenna consists of four G-shaped monopole elements, two of which are left-hand CP and the other two are right-hand CP. A vertical line strip in the G-shaped radiating element acts in balancing the vertical and horizontal electric field components to obtain 90° phase difference between them for circular polarization. Also, an I-shaped strip is incorporated between the ground planes of the G-shaped antenna elements to obtain equal voltage level in the proposed MIMO configuration. The dual circular polarization mechanism of the proposed MIMO/diversity antenna is analysed from the vector current distributions. The impedance bandwidth (S11 ≤ –10 dB) of the MIMO antenna is 105.9% (4–13 GHz) and the 3 dB axial ratio bandwidth (ARBW) is 67.7% (4.2–8.5 GHz), which is suitable for C-band applications. The overall size of the MIMO antenna is 70 × 68 × 1.6 mm3, and the minimum isolation between the resonating elements is 18 dB. The envelope correlation coefficient is less than 0.25, and the peak gain within the resonating band is 6.4 dBi. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are of prominent use in unmanned surveillance applications. This peculiar trait of WSNs is actually the underlying technology of various applications of the Internet of Things (IoT) such as smart homes, smart cities, smart shopping complexes, smart traffic, smart health, and much more. Over time, WSNs have evolved as a strong base for laying the foundations of IoT infrastructure. In order to address the scenario in which a user wants to access the real-time data directly from the sensor node in wireless sensor networks (WSNs), Das recently proposed an anonymity-preserving three-factor authentication protocol. Das's protocol is suitable for resource-constrained sensor nodes because it only uses lightweight cryptographic primitives such as hash functions and symmetric encryption schemes as building blocks. Das's protocol is claimed to be secure against different known attacks by providing formal security proof and security verification using the Automated Validation of Internet Security Protocols and Applications tool. However, we find that Das's protocol has the following security loopholes: (1) By using a captured sensor node, an adversary can impersonate a legal user to the gateway node, impersonate other sensor nodes to deceive the user, and the adversary can also decrypt all the cipher-texts of the user; (2) the gateway node has a heavy computational cost due to user anonymity and thus the protocol is vulnerable to denial of service (DoS) attacks. We overcome the shortcomings of Das's protocol and propose an improved protocol. We also prove the security of the proposed protocol in the random oracle model. Compared with the other related protocols, the improved protocol enjoys better functionality without much enhancement in the computation and communication costs. Consequently, it is more suitable for applications in WSNs [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we aimed to address the critical need for a prognostic biomarker in the treatment of GBM. Various approaches and treatments have been examined in the recent literature; however, their effectiveness is limited due to the highly invasive, heterogeneous, and resistant nature of GBM tumors. TCGA, which is a database available online, was used to assess the role of guanine nucleotide-binding protein G(i) subunit alpha 3 (GNAI3), with a focus on analyzing its impact across different WHO grades. The results revealed that GNAI3 is associated with a poor prognosis and is involved in significantly important pathways, such as macrophage maturation and cytoskeleton arrangements. These findings suggest that GNAI3 may serve as a valuable prognostic biomarker for the GBM microenvironment and could provide actionable information for the treatment of GBM. Glioblastoma multiforme (GBM) is one of the most aggressive cancers with a low overall survival rate. The treatment of GBM is challenging due to the presence of the blood–brain barrier (BBB), which hinders drug delivery. Invasive procedures alone are not effective at completely removing such tumors. Hence, identifying the crucial pathways and biomarkers for the treatment of GBM is of prime importance. We conducted this study to identify the pathways associated with GBM. We used The Cancer Genome Atlas (TCGA) GBM genomic dataset to identify differentially expressed genes (DEGs). We investigated the prognostic values of the guanine nucleotide-binding protein G(i) alpha subunit (GNAI) family of genes in GBM using a Chinese Glioma Genome Atlas (CGGA) dataset. Within this dataset, we observed the association in the tumor microenvironment between the gene expression of GNAI subunit 3 (GNAI3) and a poor prognosis. MetaCore and gene ontology (GO) analyses were conducted to explore the role of GNAI3 in co-expressed genes and associated signaling pathways using a transcript analysis. Notable pathways included "Cytoskeleton remodeling regulation of actin cytoskeleton organization by the kinase effectors of Rho GTPases" and "Immune response B cell antigen receptor (BCR) pathway". A single-cell analysis was used to assess GNAI3 expression in GBM. The results demonstrated that GNAI family genes, specifically GNAI3, were significantly associated with carcinogenesis and malignancy in GBM patients. Our findings suggest that the GNAI3 gene holds potential as a prognostic biomarker for GBM. [ABSTRACT FROM AUTHOR]

Genomic regions governing grain protein content (GPC), 1000 kernel weight (TKW), and normalized difference vegetation index (NDVI) were studied in a set of 280 bread wheat genotypes. The genome-wide association (GWAS) panel was genotyped using a 35K Axiom array and phenotyped in three environments. A total of 26 marker-trait associations (MTAs) were detected on 18 chromosomes covering the A, B, and D subgenomes of bread wheat. The GPC showed the maximum MTAs (16), followed by NDVI (6), and TKW (4). A maximum of 10 MTAs was located on the B subgenome, whereas, 8 MTAs each were mapped on the A and D subgenomes. In silico analysis suggest that the SNPs were located on important putative candidate genes such as NAC domain superfamily, zinc finger RING-H2-type, aspartic peptidase domain, folylpolyglutamate synthase, serine/threonine-protein kinase LRK10, pentatricopeptide repeat, protein kinase-like domain superfamily, cytochrome P450, and expansin. These candidate genes were found to have different roles including regulation of stress tolerance, nutrient remobilization, protein accumulation, nitrogen utilization, photosynthesis, grain filling, mitochondrial function, and kernel development. The effects of newly identified MTAs will be validated in different genetic backgrounds for further utilization in marker-aided breeding. [ABSTRACT FROM AUTHOR]

Simple Summary: The reported global incidences of skin cancer led to the development of automated clinical aids for making proper clinical decision models. Correctly classifying the skin lesions during the early stage may increase the chances of being cured before cancer. However, the skin lesion dataset images pose many critical challenges related to available features to develop classification models with cross-domain adaptability and robustness. This paper made an attempt to select important features from skin lesion datasets for proper skin cancer classification by proposing some feature fusion strategies. Three pre-trained models were utilized to select the important features and then an adaptive weighted mechanism of choosing important features was explored to propose model-based and feature-based optimized feature fusion strategies by optimally and adaptively choosing the weights using a meta-heuristic artificial jellyfish algorithm. The empirical evidence shows that choosing the weights of the pre-trained networks adaptively in an optimized way gives a good starting point for initialization to mitigate the chances of exploding or vanishing gradients. This study mainly focuses on pre-processing the HAM10000 and BCN20000 skin lesion datasets to select important features that will drive for proper skin cancer classification. In this work, three feature fusion strategies have been proposed by utilizing three pre-trained Convolutional Neural Network (CNN) models, namely VGG16, EfficientNet B0, and ResNet50 to select the important features based on the weights of the features and are coined as Adaptive Weighted Feature Set (AWFS). Then, two other strategies, Model-based Optimized Weighted Feature Set (MOWFS) and Feature-based Optimized Weighted Feature Set (FOWFS), are proposed by optimally and adaptively choosing the weights using a meta-heuristic artificial jellyfish (AJS) algorithm. The MOWFS-AJS is a model-specific approach whereas the FOWFS-AJS is a feature-specific approach for optimizing the weights chosen for obtaining optimal feature sets. The performances of those three proposed feature selection strategies are evaluated using Decision Tree (DT), Naïve Bayesian (NB), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) classifiers and the performance are measured through accuracy, precision, sensitivity, and F1-score. Additionally, the area under the receiver operating characteristics curves (AUC-ROC) is plotted and it is observed that FOWFS-AJS shows the best accuracy performance based on the SVM with 94.05% and 94.90%, respectively, for HAM 10000 and BCN 20000 datasets. Finally, the experimental results are also analyzed using a non-parametric Friedman statistical test and the computational times are recorded; the results show that, out of those three proposed feature selection strategies, the FOWFS-AJS performs very well because its quick converging nature is inculcated with the help of AJS. [ABSTRACT FROM AUTHOR]

Abiotic stressors are major constraints that affect agricultural plant physio-morphological and biochemical attributes, resulting in a loss of normal functioning and, eventually, a severe decline in crop productivity. The co-occurrence of different abiotic stresses, rather than a specific stress situation, can alter or trigger a wide range of plant responses, such as altered metabolism, stunted growth, and restricted development. Therefore, systematic and rigorous studies are pivotal for understanding the impact of concurrent abiotic stress conditions on crop productivity. In doing so, this review emphasizes the implications and potential mechanisms for controlling/managing combined abiotic stresses, which can then be utilized to identify genotypes with combined stress tolerance. Furthermore, this review focuses on recent biotechnological approaches in deciphering combined stress tolerance in plants. As a result, agronomists, breeders, molecular biologists, and field pathologists will benefit from this literature in assessing the impact of interactions between combined abiotic stresses on crop performance and development of tolerant/resistant cultivars. [ABSTRACT FROM AUTHOR]

With an ever-increasing number of electric vehicles (EVs) on the roads, there is a high demand for EV charging infrastructure. The present charging infrastructure in the market requires a lot of space and sometimes leads to traffic congestion, increasing the risk of accidents and obstruction of emergency vehicles. As the current infrastructure requires ample space, the cost of setting up this charging infrastructure becomes very high in metropolitan cities. In addition, there are a lot of adverse effects on the power grid due to the integration of EVs. This paper discusses a space-efficient charging infrastructure and multi-agent system-based power grid balance to overcome these issues. The proposed multi-level EV charging station can save a lot of space and reduce traffic congestion as more vehicles can be accommodated in the space. Depending on the size, capacity, and type of multi-level vehicle charging system, it can serve as a reliable charging solution at sites with medium and high daily footfall. We integrated the EV charging station with IEEE 33 bus test system and analyzed the grid and charging stations. The proposed scheme is exhaustively tested by simulation in a discrete-time event simulator in MATLAB and analyzed with varying EV arrival rates, time periods, etc. [ABSTRACT FROM AUTHOR]

This paper proposed a short-term two-stage hybrid algorithmic framework for trade and trend analysis of the Forex market by augmenting the currency pair datasets with transformed attributes using a few technical indicators and statistical measures. In the first phase, an optimized deep predictive coding network (DPCN) based on a meta-heuristic reptile search algorithm (RSA) inspired by the intelligent hunting activities of the crocodiles is exploited to develop this RSA-DPCN predictive model. The proposed model has been compared with optimized versions of extreme learning machine (ELM) and functional link artificial neural network (FLANN) with genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) along with the RSA optimizers. The performance of this model has been evaluated and validated through several statistical tests. In the second phase, the up and down trends are analyzed using the Higher Highs Higher Lows, and Lower Highs Lower Lows (HHs/HLs and LHs/LLs) trend analysis tool. Further, the observed trends are compared with the actual trends observed on the exchange price of real datasets. This study shows that the proposed RSA-DPCN model accurately predicts the exchange price. At the same time, it provides a well-structured platform to discern the directions of the market trends and thereby guides in finding the entry and exit points of the Forex market. [ABSTRACT FROM AUTHOR]

Class imbalance is one of the significant challenges in classification problems. The uneven distribution of data samples in different classes may occur due to human error, improper/unguided collection of data samples, etc. The uneven distribution of class samples among classes may affect the classification accuracy of the developed model. The main motivation behind this study is the design and development of methodologies for handling class imbalance problems. In this study, a new variant of the synthetic minority oversampling technique (SMOTE) has been proposed with the hybridization of particle swarm optimization (PSO) and Egyptian vulture (EV). The proposed method has been termed SMOTE-PSOEV in this study. The proposed method generates an optimized set of synthetic samples from traditional SMOTE and augments the five datasets for verification and validation. The SMOTE-PSOEV is then compared with existing SMOTE variants, i.e., Tomek Link, Borderline SMOTE1, Borderline SMOTE2, Distance SMOTE, and ADASYN. After data augmentation to the minority classes, the performance of SMOTE-PSOEV has been evaluated using support vector machine (SVM), Naïve Bayes (NB), and k-nearest-neighbor (k-NN) classifiers. The results illustrate that the proposed models achieved higher accuracy than existing SMOTE variants. [ABSTRACT FROM AUTHOR]

Crude oil market analysis has become one of the emerging financial markets and the volatility effect of the market is paramount and has been considered as an issue of utmost importance. This study examines the dynamics of this volatile market of crude oil by employing a hybrid approach based on an extreme learning machine (ELM) as a regressor and the improved grey wolf optimizer (IGWO) for prophesying the crude oil rate for West Texas Intermediate (WTI) and Brent crude oil datasets. The datasets are augmented using technical indicators (TIs) and statistical measures (SMs) to obtain better insight into the forecasting ability of this proposed model. The differential evolution (DE) strategy has been used for evolution and the survival of the fittest (SOF) principle has been used for elimination while implementing the GWO to achieve better convergence rate and accuracy. Whereas, the algorithmic simplicity, use of less parameters, and easy implementation of DE efficiently decide the evolutionary patterns of wolves in GWO and the SOF principle updates the wolf pack based on the fitness value of each wolf, thereby ensuring the algorithm does not fall into local optimum. Furthermore, the comparison and analysis of the proposed model with other models, such as ELM–DE, ELM–Particle Swarm Optimization (ELM–PSO), and ELM–GWO shows that the predictability evidence obtained substantially achieves better performance for ELM–IGWO with respect to faster error convergence rate and mean square error (MSE) during training and testing phases. The sensitivity study of the proposed ELM–IGWO provides better results in terms of the performance measures, such as Theil's U, mean absolute error (MAE), average relative variance (ARV), mean average percentage error (MAPE), and minimal computational time. [ABSTRACT FROM AUTHOR]

Brain tumors are most common in children and the elderly. It is a serious form of cancer caused by uncontrollable brain cell growth inside the skull. Tumor cells are notoriously difficult to classify due to their heterogeneity. Convolutional neural networks (CNNs) are the most widely used machine learning algorithm for visual learning and brain tumor recognition. This study proposed a CNN-based dense EfficientNet using min-max normalization to classify 3260 T1-weighted contrast-enhanced brain magnetic resonance images into four categories (glioma, meningioma, pituitary, and no tumor). The developed network is a variant of EfficientNet with dense and drop-out layers added. Similarly, the authors combined data augmentation with min-max normalization to increase the contrast of tumor cells. The benefit of the dense CNN model is that it can accurately categorize a limited database of pictures. As a result, the proposed approach provides exceptional overall performance. The experimental results indicate that the proposed model was 99.97% accurate during training and 98.78% accurate during testing. With high accuracy and a favorable F1 score, the newly designed EfficientNet CNN architecture can be a useful decision-making tool in the study of brain tumor diagnostic tests. [ABSTRACT FROM AUTHOR]

Neutrophil extracellular traps (NETs) are associated with multiple disease pathologies including sepsis, asthma, rheumatoid arthritis, cancer, systemic lupus erythematosus, acute respiratory distress syndrome, and COVID-19. NETs, being a disintegrated death form, suffered inconsistency in their identification, nomenclature, and quantifications that hindered therapeutic approaches using NETs as a target. Multiple strategies including microscopy, ELISA, immunoblotting, flow cytometry, and image-stream-based methods have exhibited drawbacks such as being subjective, non-specific, error-prone, and not being high throughput, and thus demand the development of innovative and efficient approaches for their analyses. Here, we established an imaging and computational algorithm using high content screening (HCS)—cellomics platform that aid in easy, rapid, and specific detection as well as analyses of NETs. This method employed membrane-permeable and impermeable DNA dyes in situ to identify NET-forming cells. Automated algorithm-driven single-cell analysis of change in nuclear morphology, increase in nuclear area, and change in intensities provided precise detection of NET-forming cells and eliminated user bias with other cell death modalities. Further combination with Annexin V staining in situ detected specific death pathway, e.g., apoptosis, and thus, discriminated between NETs, apoptosis, and necrosis. Our approach does not utilize fixation and permeabilization steps that disturb NETs, and thus, allows the time-dependent monitoring of NETs. Together, this specific imaging-based high throughput method for NETs analyses may provide a good platform for the discovery of potential inhibitors of NET formation and/or agents to modulate neutrophil death, e.g., NETosis-apoptosis switch, as an alternative strategy to enhance the resolution of inflammation. [ABSTRACT FROM AUTHOR]

Cool season grain legumes occupy an important place among the agricultural crops and essentially provide multiple benefits including food supply, nutrition security, soil fertility improvement and revenue for farmers all over the world. However, owing to climate change, the average temperature is steadily rising, which negatively affects crop performance and limits their yield. Terminal heat stress that mainly occurred during grain development phases severely harms grain quality and weight in legumes adapted to the cool season, such as lentils, faba beans, chickpeas, field peas, etc. Although, traditional breeding approaches with advanced screening procedures have been employed to identify heat tolerant legume cultivars. Unfortunately, traditional breeding pipelines alone are no longer enough to meet global demands. Genomics-assisted interventions including new-generation sequencing technologies and genotyping platforms have facilitated the development of high-resolution molecular maps, QTL/gene discovery and marker-assisted introgression, thereby improving the efficiency in legumes breeding to develop stress-resilient varieties. Based on the current scenario, we attempted to review the intervention of genomics to decipher different components of tolerance to heat stress and future possibilities of using newly developed genomics-based interventions in cool season adapted grain legumes. [ABSTRACT FROM AUTHOR]

A quad-element multiple-input-multiple-output (MIMO) antenna with ultra-wideband (UWB) performance is presented in this paper. The MIMO antenna consists of four orthogonally arranged microstrip line-fed hexagonal monopole radiators and a modified ground plane. In addition, E-shaped and G-shaped stubs are added to the radiator to achieve additional resonances at 1.5 GHz and 2.45 GHz. The reliability of the antenna in the automotive environment is investigated, with housing effects taken into account. The housing effects show that the antenna performs consistently even in the presence of a large metal object. The proposed MIMO antenna has potential for various automotive applications, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-everything (V2X), intelligent transport system (ITS), automatic vehicle identifier, and RFID-based electronic toll collection. [ABSTRACT FROM AUTHOR]

This article presents a quad-band multiple-input-multiple-output (MIMO) antenna for the Internet of Things (IoT) applications. The proposed antenna consists of four quarter-wavelength asymmetrical meandered radiators, microstrip feed lines, and modified ground planes. The antenna elements are arranged in a chiral pattern to improve isolation between them, with two radiators and two ground planes placed on the front side of the substrate and the other two on the back side. The MIMO antenna has an operating bandwidth (S11 ≤ −10 dB) of 1.76–1.84 GHz, 2.37–2.56 GHz, 3.23–3.68 GHz, and 5.34–5.84 GHz, covering GSM, WLAN, WiMAX, and 5G frequency bands. The isolation between the radiating elements is greater than 18 dB in the operating bands. The peak gain of the antenna is 3.6 dBi, and the envelope correlation coefficient (ECC) is less than 0.04. Furthermore, the proposed antenna is validated for IoT-based smart home (SH) applications. The prototype MIMO antenna is integrated with a commercially available ZigBee device, and the measured values are found to be consistent with the expected results. The proposed MIMO antenna could be a good candidate for IoT systems/modules due to its low profile, compact size, lightweight, and easy integration with wireless communication devices. [ABSTRACT FROM AUTHOR]

Worldwide, bacterial resistance to beta-lactam antibiotics is the greatest challenge in public health care. To overcome the issue, metal-based nanoparticles were extensively used as an alternative to traditional antibiotics. However, their unstable nature limits their use. In the present study a very simple, environmentally friendly, one-pot synthesis method that avoids the use of organic solvents has been proposed to design stable, novel nanocomposites. Formulation was done by mixing biogenic copper oxide (CuO) nanomaterial with glycerol and phospholipids isolated from egg yolk in an appropriate ratio at optimum conditions. Characterization was done using dynamic light scattering DLS, Zeta potential, high performance liquid chromatography (HPLC), and transmission electron microscopy (TEM). Further, its antibacterial activity was evaluated against the extended-spectrum beta-lactamase strains based on zone of inhibition and minimal inhibitory concentration (MIC) indices. Results from this study have demonstrated the formulation of stable nanocomposites with a zeta potential of 34.9 mV. TEM results indicated clear dispersed particles with an average of 59.3 ± 5 nm size. Furthermore, HPLC analysis of the egg yolk extract exhibits the presence of phospholipids in the sample and has significance in terms of stability. The newly formed nanocomposite has momentous antibacterial activity with MIC 62.5 μg/mL. The results suggest that it could be a good candidate for drug delivery in terms of bactericidal therapeutic applications. [ABSTRACT FROM AUTHOR]

Computer-aided diagnosis permits biopsy specimen analysis by creating quantitative images of brain diseases which enable the pathologists to examine the data properly. It has been observed from other image classification algorithms that the Extreme Learning Machine (ELM) demonstrates superior performance in terms of computational efforts. In this study, to classify the brain Magnetic Resonance Images as either normal or diseased, a hybridized Salp Swarm Algorithm-based ELM (ELM-SSA) is proposed. The SSA is employed to optimize the parameters associated with ELM model, whereas the Discrete Wavelet Transformation and Principal Component Analysis have been used for the feature extraction and reduction, respectively. The performance of the proposed "ELM-SSA" is evaluated through simulation study and compared with the standard classifiers such as Back-Propagation Neural Network, Functional Link Artificial Neural Network, and Radial Basis Function Network. All experimental validations have been carried out using two different brain disease datasets: Alzheimer's and Hemorrhage. The simulation results demonstrate that the "ELM-SSA" is potentially superior to other hybrid methods in terms of ROC, AUC, and accuracy. To achieve better performance, reduce randomness, and overfitting, each algorithm has been run multiple times and a k-fold stratified cross-validation strategy has been used. [ABSTRACT FROM AUTHOR]

This paper presents the design of a multi-objective tool for sizing shell and tube heat exchangers (STHX), developed under a University/Industry collaboration. This work aims to show the feasibility of implementing artificial intelligence tools during the design of Heat Exchangers in industry. The design of STHX optimisation tools using artificial intelligence algorithms is a visited topic in the literature, nevertheless, the degree of implementation of this concept is uncommon in industrial companies. Thus, the challenge of this research consists of the development of a tool for the design of STHX using artificial intelligence algorithms that can be used by industrial companies. The approach is implemented using a simulated dataset contrasted with ARA TT, the company taking part in the project. The given dataset to develop a theoretical STHX calculator was modeled using MATLAB. This dataset was used to train seven neural networks (NNs). Three of them were mono-objective, one per objective to predict, and four were multi-objective. The last multi-objective NN was used to develop an inverse neural network (INN), which is used to find the optimal configuration of the STHXs. In this specific case, three design parameters, the pressure drop on the shell side, the pressure drop on the tube side and heat transfer rate, were jointly and successfully optimised. As a conclusion, this work proves that the developed tool is valid in both terms of effectiveness and user-friendliness for companies like ARA TT to improve their business activity. [ABSTRACT FROM AUTHOR]