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Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities. Several reviews and surveys on HAR have already been published, but due to the constantly growing literature, the status of HAR literature needed to be updated. Hence, this review aims to provide insights on the current state of the literature on HAR published since 2018. The ninety-five articles reviewed in this study are classified to highlight application areas, data sources, techniques, and open research challenges in HAR. The majority of existing research appears to have concentrated on daily living activities, followed by user activities based on individual and group-based activities. However, there is little literature on detecting real-time activities such as suspicious activity, surveillance, and healthcare. A major portion of existing studies has used Closed-Circuit Television (CCTV) videos and Mobile Sensors data. Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Support Vector Machine (SVM) are the most prominent techniques in the literature reviewed that are being utilized for the task of HAR. Lastly, the limitations and open challenges that needed to be addressed are discussed.

Classification of human actions is an ongoing research problem in computer vision. This review is aimed to scope current literature on data fusion and action recognition techniques and to identify gaps and future research direction. Success in producing cost-effective and portable vision-based sensors has dramatically increased the number and size of datasets. The increase in the number of action recognition datasets intersects with advances in deep learning architectures and computational support, both of which offer significant research opportunities. Naturally, each action-data modality—such as RGB, depth, skeleton, and infrared (IR)—has distinct characteristics; therefore, it is important to exploit the value of each modality for better action recognition. In this paper, we focus solely on data fusion and recognition techniques in the context of vision with an RGB-D perspective. We conclude by discussing research challenges, emerging trends, and possible future research directions.

Large language models (LLMs) are artificial intelligence (AI) based computational models designed to understand and generate human like text. With billions of training parameters, LLMs excel in identifying intricate language patterns, enabling remarkable performance across a variety of natural language processing (NLP) tasks. After the introduction of transformer architectures, they are impacting the industry with their text generation capabilities. LLMs play an innovative role across various industries by automating NLP tasks. In healthcare, they assist in diagnosing diseases, personalizing treatment plans, and managing patient data. LLMs provide predictive maintenance in automotive industry. LLMs provide recommendation systems, and consumer behavior analyzers. LLMs facilitates researchers and offer personalized learning experiences in education. In finance and banking, LLMs are used for fraud detection, customer service automation, and risk management. LLMs are driving significant advancements across the industries by automating tasks, improving accuracy, and providing deeper insights. Despite these advancements, LLMs face challenges such as ethical concerns, biases in training data, and significant computational resource requirements, which must be addressed to ensure impartial and sustainable deployment. This study provides a comprehensive analysis of LLMs, their evolution, and their diverse applications across industries, offering researchers valuable insights into their transformative potential and the accompanying limitations.

In this work, the heat transfer features and stagnation point flow of Magnetohydrodynamics (MHD) hybrid second-grade nanofluid through a convectively heated permeable shrinking/stretching sheet is reported. The purpose of the present investigation is to consider hybrid nanofluids comprising of Alumina Al 2 O 3 and Copper Cu nanoparticles within the Sodium Alginate (SA) as a host fluid for boosting the heat transfer rate. Also, the effects of free convection, viscous dissipation, heat source/sink, and nonlinear thermal radiation are considered. The converted nonlinear coupled fuzzy differential equations (FDEs) with the help of triangular fuzzy numbers (TFNs) are solved using the numerical scheme bvp4c. The numerical results are acquired for various engineering parameters to study the Nusselt number, skin friction coefficient, velocity, and temperature distribution through figures and tables. For the validation, the current numerical results were found to be good as compared to existing results in limiting cases. It is also inspected by this work that with the enhancement of the volume fraction of nanoparticles, the heat transfer rate also increases. So, it may be taken as a fuzzy parameter for a better understanding of fuzzy variables. For the comparison, the volume fraction of nanofluids and hybrid nanofluid are said to be TFN [0, 0.1, 0.2]. In the end, we can see that fuzzy triangular membership functions (MFs) have not only helped to overcome the computational cost but also given better accuracy than the existent results. Finding from fuzzy MFs, the performance of hybrid nanofluids is better than nanofluids.

The key objective of the present research is to examine the hybrid magnetohydrodynamics (MHD) nanofluid (Carbon-nanotubes and ferrous oxide–water) CNT – Fe 3 O 4 / H 2 flow into a horizontal parallel channel with thermal radiation through squeezing and dilating porous walls. The parting motion is triggered by the porous walls of the channel. The fluid flow is time-dependent and laminar. The channel is asymmetric and the upper and lower walls are distinct in temperature and are porous. With the combination of nanoparticles of Fe 3 O 4 and single and multi-wall carbon nanotubes, the hybrid nanofluid principle is exploited. By using the similarity transformation, the set of partial differential equations (PDEs) of this mathematical model, governed by momentum and energy equations, is reduced to corresponding ordinary differential equations (ODEs). A very simple numerical approach called the Runge–Kutta system of order four along with the shooting technique is used to achieve the solutions for regulating ODEs. MATLAB computing software is used to create temperature and velocity profile graphs for various emerging parameters. At the end of the manuscript, the main conclusions are summarized. Through different graphs, it is observed that hybrid-nanofluid has more prominent thermal enhancement than simple nanofluid. Further, the single-wall nanotubes have dominated impact on temperature than the multi-wall carbon nanotubes. From the calculations, it is also noted that Fe 2 O 3 – MWCNT – water has an average of 4.84% more rate of heat transfer than the Fe 2 O 3 – SWCNT – water . On the other hand, 8.27% more heat flow observed in Fe 2 O 3 – SWCNT – water than the simple nanofluid. Such study is very important in coolant circulation, inter-body fluid transportation, aerospace engineering, and industrial cleaning procedures, etc.

Anomaly detection in industrial imaging is critical for ensuring quality and reliability in automated manufacturing processes. While recently several methods have been reported in the literature that have demonstrated impressive detection performance on standard benchmarks, they necessarily rely on computationally intensive CNN architectures and post-processing techniques, necessitating access to high-end GPU hardware and limiting practical deployment in resource-constrained settings. In this study, we introduce a novel anomaly detection framework that leverages feature maps from a lightweight convolutional neural network (CNN) backbone, MobileNetV2, and cascaded detection to achieve notable accuracy as well as computational efficiency. The core of our method consists of two main components. First is a PCA-based anomaly detection module that specifically exploits near-zero variance features. Contrary to traditional PCA methods, which tend to focus on the high-variance directions that encapsulate the dominant patterns in normal data, our approach demonstrates that the lower variance directions (which are typically ignored) form an approximate null space where normal samples project near zero. However, the anomalous samples, due to their inherent deviations from the norm, lead to projections with significantly higher magnitudes in this space. This insight not only enhances sensitivity to true anomalies but also reduces computational complexity by eliminating the need for operations such as matrix inversion or the calculation of Mahalanobis distances for correlated features otherwise needed when normal behavior is modeled as Gaussian distribution. Second, our framework consists of a cascaded multi-stage decision process. Instead of combining features across layers, we treat the local features extracted from each layer as independent stages within a cascade. This cascading mechanism not only simplifies the computations at each stage by quickly eliminating clear cases but also progressively refines the anomaly decision, leading to enhanced overall accuracy. Experimental evaluations on MVTec and VisA benchmark datasets demonstrate that our proposed approach achieves superior anomaly detection performance (99.4% and 91.7% AUROC respectively) while maintaining a lower computational overhead compared to other methods. This framework provides a compelling solution for practical anomaly detection challenges in diverse application domains where competitive accuracy is needed at the expense of minimal hardware resources.

Nanoparticles are frequently used to enhance the thermal performance of numerous materials. This study has many practical applications for activities that have to minimize losses of energy due to several impacts. This study investigates the inclusion of ternary hybrid nanoparticles in a partially ionized hyperbolic tangent liquid passed over a stretched melting surface. The fluid motion equation is presented by considering the rotation effect. The thermal energy expression is derived by the contribution of Joule heat and viscous dissipation. Flow equations were modeled by using the concept of boundary layer theory, which occurs in the form of a coupled system of partial differential equations (PDEs). To reduce the complexity, the derived PDEs (partial differential equations) were transformed into a set of ordinary differential equations (ODEs) by engaging in similarity transformations. Afterwards, the converted ODEs were handled via a finite element procedure. The utilization and effectiveness of the methodology are demonstrated by listing the mesh-free survey and comparative analysis. Several important graphs were prepared to show the contribution of emerging parameters on fluid velocity and temperature profile. The findings show that the finite element method is a powerful tool for handling the complex coupled ordinary differential equation system, arising in fluid mechanics and other related dissipation applications in applied science. Furthermore, enhancements in the Forchheimer parameter and the Weissenberg number are necessary to control the fluid velocity.

Although the global prevalence of neurological disorders such as Parkinson’s disease, Alzheimer’s disease, glioblastoma, epilepsy, and multiple sclerosis is steadily increasing, effective delivery of drug molecules in therapeutic quantities to the central nervous system (CNS) is still lacking. The blood brain barrier (BBB) is the major obstacle for the entry of drugs into the brain, as it comprises a tight layer of endothelial cells surrounded by astrocyte foot processes that limit drugs’ entry. In recent times, intranasal drug delivery has emerged as a reliable method to bypass the BBB and treat neurological diseases. The intranasal route for drug delivery to the brain with both solution and particulate formulations has been demonstrated repeatedly in preclinical models, including in human trials. The key features determining the efficacy of drug delivery via the intranasal route include delivery to the olfactory area of the nares, a longer retention time at the nasal mucosal surface, enhanced penetration of the drugs through the nasal epithelia, and reduced drug metabolism in the nasal cavity. This review describes important neurological disorders, challenges in drug delivery to the disordered CNS, and new nasal delivery techniques designed to overcome these challenges and facilitate more efficient and targeted drug delivery. The potential for treatment possibilities with intranasal transfer of drugs will increase with the development of more effective formulations and delivery devices.

The silver, magnesium oxide and gyrotactic microorganism-based hybrid nanofluid flow inside the conical space between disc and cone is addressed in the perspective of thermal energy stabilization. Different cases have been discussed between the spinning of cone and disc in the same or counter wise directions. The hybrid nanofluid has been synthesized in the presence of silver Ag and magnesium oxide MgO nanoparticulate. The viscous dissipation and the magnetic field factors are introduced to the modeled equations. The parametric continuation method (PCM) is utilized to numerically handle the modeled problem. Magnesium oxide is chemically made up of Mg 2+ and O 2- ions that are bound by a strong ionic connection and can be made by pyrolyzing Mg(OH) 2 (magnesium hydroxide) and MgCO 3 (magnesium carbonate) at high temperature (700–1500 °C). For metallurgical, biomedical and electrical implementations, it is more efficient. Similarly, silver nanoparticle's antibacterial properties could be employed to control bacterial growth. It has been observed that a circulating disc with a stationary cone can achieve the optimum cooling of the cone-disk apparatus while the outer edge temperature remains fixed. The thermal energy profile remarkably upgraded with the magnetic effect, the addition of nanoparticulate in base fluid and Eckert number.

Satellite communication (SATCOM) is crucial for global connectivity, particularly in remote far-flung areas. Typically, these links are established by connecting a Satcom Remote Terminal to a Satcom Hub Station. However, relying on a single Hub Station does not offer redundancy, leaving remote terminals isolated in case of a Hub Station failure. To provide redundancy, the remote terminals are shifted to a secondary Hub Station. However, several limitations and challenges are associated with the available methodologies for shifting of remotes to the secondary Hub Station. This research addresses the challenges associated with the implementation of redundancy/ Disaster Recovery (DR) mode in a Satcom Network to improve reliability and efficiency of Satcom links. An optimized model has been developed for Satcom Remote Modems to automatically switch to a secondary Hub Station during a primary Hub Station failure. The technique was tested in a real Satellite Communication Network, showing improved results compared to traditional methods.