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Human gait recognition (HGR) is the mechanism of biometrics that authors extensively employ to recognize an individuals based on their walking traits. HGR has been prominent for the past few years due to its surveillance capability. In HGR, an individual's walking attributes are utilized for identification. HGR is considered a very effective technique for recognition but faces different problematic factors that degrade its performance. The major factors are variations in clothing, carrying, walking, etc. In this paper, a new hybrid method for the classification of HGR is designed called Stacked-Gait. The system is based on six major steps; initially, image resizing is performed to overcome computation problems. In the second step, these images are converted into grey-scale to extract better features. After that, the dataset division is performed into train and test set. In the next step, the training of the autoencoders and feature extraction of the dataset are performed using training data. In the next step, the stacking of two autoencoders is also performed. After that, the stacked encoders are employed to extract features from the test data. Finally, the feature vectors are given as input to various machine learning classifiers for final classification. The method assessment is performed using the CASIA-B dataset and achieved the accuracy of 99.90, 98.10, 97.20, 97.20, 96.70, and 100 percent on 000, 180, 360, 540, 720, and 900 angles. It is pragmatic that the system gives promising results compared to recent schemes. [ABSTRACT FROM AUTHOR]

Classification and identification of eye diseases using Optical Coherence Tomography (OCT) has been a challenging task and a trending research area in recent years. Accurate classification and detection of different diseases are crucial for effective care management and improving vision outcomes. Current detection methods fall into two main categories: traditional methods and deep learning-based approaches. Traditional approaches rely on machine learning for feature extraction, while deep learning methods utilize data-driven classification model training. In recent years, Deep Learning (DL) and Machine Learning (ML) algorithms have become essential tools, particularly in medical image classification, and are widely used to classify and identify various diseases. However, due to the high spatial similarities in OCT images, accurate classification remains a challenging task. In this paper, we introduce a novel model called "OCTNet" that integrates a deep learning model combining InceptionV3 with a modified multi-scale attention-based spatial attention block to enhance model performance. OCTNet employs an InceptionV3 backbone with a fusion of dual attention modules to construct the proposed architecture. The InceptionV3 model generates rich features from images, capturing both local and global aspects, which are then enhanced by utilizing the modified multi-scale spatial attention block, resulting in a significantly improved feature map. To evaluate the model's performance, we utilized two state-of-the-art (SOTA) datasets that include images of normal cases, Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME). Through experimentation and simulation, the proposed OCTNet improves the classification accuracy of the InceptionV3 model by 1.3%, yielding higher accuracy than other SOTA models. We also performed an ablation study to demonstrate the effectiveness of the proposed method. The model achieved an overall average accuracy of 99.50% and 99.65% with two different OCT datasets. [ABSTRACT FROM AUTHOR]

This study employs a two-dimensional and incompressible flow of Herschel–Bulkley visco-plastic materials in order to investigate the hydrodynamic forces that are acting on a barrier that is located close to the inlet of a channel. As the benchmark configuration, the flow domain that has been selected is a channel that still contains the impediment. The two important parameters of the Herschel–Bulkley Model (HBM) are the yield stress τ y and power law index n. Obtaining special situations within the HBM, such as Newtonian, power-law, and Bingham fluids, can be accomplished by assigning certain values to these parameters at the appropriate times. Utilizing a numerical strategy grounded in the Finite Element Method (FEM), we tackle the nonlinearity of the governing equations as well as the viscosity models. As a result of this nonlinearity, FEM becomes an essential tool. The generation of a refined hybrid mesh is done in order to guarantee accuracy in the computations. The stable finite element pair ( ℙ 2 ∕ ℙ 1 ) has been selected for discretization purposes. The discretized nonlinear system is linearized with Newton's method and subsequently, a direct linear solver PARDISO has been employed in the inner iterations. The pressure, velocity, and viscosity profiles are plotted for various values of n and Bingham number (Bn). In addition, the velocity behavior is observed along the y-direction in a channel through line graphs. Code validation is done as a special case Bn = 0 and a good agreement is found with the results available in the literature. Finally, a correlation analysis has been performed for the drag coefficient C d and lift coefficient C l . [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is pivotal in advancing applications ranging from healthcare monitoring to interactive gaming. Traditional HAR systems, primarily relying on single data sources, face limitations in capturing the full spectrum of human activities. This study introduces a comprehensive approach to HAR by integrating two critical modalities: RGB imaging and advanced pose estimation features. Our methodology leverages the strengths of each modality to overcome the drawbacks of unimodal systems, providing a richer and more accurate representation of activities. We propose a two-stream network that processes skeletal and RGB data in parallel, enhanced by pose estimation techniques for refined feature extraction. The integration of these modalities is facilitated through advanced fusion algorithms, significantly improving recognition accuracy. Extensive experiments conducted on the UTD multimodal human action dataset (UTD MHAD) demonstrate that the proposed approach exceeds the performance of existing state-of-the-art algorithms, yielding improved outcomes. This study not only sets a new benchmark for HAR systems but also highlights the importance of feature engineering in capturing the complexity of human movements and the integration of optimal features. Our findings pave the way for more sophisticated, reliable, and applicable HAR systems in real-world scenarios. [ABSTRACT FROM AUTHOR]

Among the several threats to humanity by anthropogenic activities, contamination of the environment by heavy metals is of great concern. Upon entry into the food chain, these metals cause serious hazards to plants and other organisms including humans. Use of microbes for bioremediation of the soil and stress mitigation in plants are among the preferred strategies to provide an efficient, cost-effective, eco-friendly solution of the problem. The current investigation is an attempt in this direction where fungal strain PH1 was isolated from the rhizosphere of Parthenium hysterophorus which was identified as Aspergillus niger by sequence homology of the ITS 1 and ITS 4 regions of the rRNA. The strain was tested for its effect on growth and biochemical parameters as reflection of its potential to mitigate Pb stress in Zea mays exposed to 100, 200 and 500 µg of Pb/g of soil. In the initial screening, it was revealed that the strain has the ability to tolerate lead stress, solubilize insoluble phosphate and produce plant growth promoting hormones (IAA and SA) and other metabolites like phenolics, flavonoids, sugar, protein and lipids. Under 500 µg of Pb/g of soil, Z. mays exhibited significant growth retardation with a reduction of 31% in root length, 30.5% in shoot length, 57.5% in fresh weight and 45.2% in dry weight as compared to control plants. Inoculation of A. niger to Pb treated plants not only restored root and shoot length, rather promoted it to a level significantly higher than the control plants. Association of the strain modulated the physio-hormonal attributes of maize plants that resulted in their better growth which indicated a state of low stress. Additionally, the strain boosted the antioxidant defence system of the maize there by causing a significant reduction in the ascorbic acid peroxidase (1.5%), catalase (19%) and 1,1-diphenyl-2 picrylhydrazyl (DPPH) radical scavenging activity (33.3%), indicating a lower stress condition as compared to their non-inoculated stressed plants. Based on current evidence, this strain can potentially be used as a biofertilizer for Pb-contaminated sites where it will improve overall plant health with the hope of achieving better biological and agricultural yields. [ABSTRACT FROM AUTHOR]

This communication is aimed at presenting the flow behavior of viscoplastic materials in a channel driven-cavity by utilizing the Bingham constitutive equation in conjunction with the modification proposed by Papanastasiou. The whole system has been represented by a model consisting of coupled nonlinear partial differential equations. In addition, the governing equations were nondimensionalized by making use of the appropriate set of variables and the computations were carried out using the finite element method. A finite element space including quadratic polynomial ℙ 2 is chosen for the approximation of velocity profiles whilst a space containing linear polynomials is used for the estimation of pressure ℙ 1 . The hydrodynamics forces like drag and lift values on cylindrical obstacle whose center is located at (1.5, 1.5) are computed against various values of Bingham number. Moreover, pressure drop values between the back and front of the square obstacle have also been tabulated. [ABSTRACT FROM AUTHOR]