Explore the vast range of titles available.

In recent days, due to potential growth of vehicle usage, the researchers have to concentrate on abnormal vehicle identification areas to provide solutions to avoid accidents. Though many vehicle identification works have been done by applying machine and deep learning approaches, still there is some problem with handling repetition frames and identifying the abnormal vehicles among vehicles in a camera. To overcome these challenges, this paper introduces KFEAVI (Key Frame Extraction based Abnormal Vehicle Identification) technique that uses statistical feature extraction technique and constrained angular second moment technique. The statistical feature extraction technique is used to extract the key frames in a statistical way by using beta distribution estimation. This technique handles well for both gradual and abrupt content changes in frames. The constrained angular second moment method is applied to find the vehicles to identify the abnormal vehicles movement. The experimental results are carried out using Car Accident Detection Dataset (CADP). For evaluating performance of KFEAVI, several algorithms are compared with the KFEAVI. The experimental results reveal that the KFEAVI achieved better results in terms of F-score.

The microbial communities play a crucial role in ecosystem functioning through interactions among individuals and taxonomic groups in a highly dynamic marine ecosystem. The structure and functioning of the microbial communities are often influenced by the changes in the surrounding environment. Monitoring the microbial diversity of the marine ecosystem helps to understand spatial patterns of microbial community and changes due to season, climate, and various drivers of biological diversity. Kuwait is characterized by an arid environment with a high degree of temperature variation during summer and winter. Our understanding of spatial distribution patterns of microbial communities, their diversity, and the influence of human activities on the degree of changes in the diversity of the microbial community in Kuwait territorial waters remain unclear. In this study, we employed 18S rRNA sequencing to explore marine microalgal community composition and dynamics in seawater samples collected from Kuwait waters over two seasonal cycles across six locations. A total of 448,184 sequences across 36 replicates corresponding to 12 samples from six stations were obtained. The quality-filtered sequences were clustered into 1,293 representative sequences, which were then classified into different eukaryotic taxa. This study reveals that the phytoplankton community in Kuwait waters is diverse and shows significant variations among different taxa during summer and winter. Dinoflagellates and diatoms were the most abundant season-dependent microalgae taxa in Kuwait waters. Alexandrium and Pyrophacus were abundant in summer, whereas Gonyaulax was abundant during the winter. The abundance of Coscinodiscus and Navicula , of the diatom genera, were also dependent upon both seasonal and possible anthropogenic factors. Our results demonstrate the effectiveness of a sequencing-based approach, which could be used to improve the accuracy of quantitative eukaryotic microbial community profiles.

Electricity generation with minimal environmental pollution is required for the world’s sustainable future, and wind electric generation is one of them. Brushless doubly fed induction generator (BDFIG), which derives from cascade induction machine technology, has grown in popularity as a wind electric generator due to advantages over doubly fed induction generator (DFIG) and permanent magnet synchronous generator (PMSG) such as the absence of slip rings and brushes and high-cost permanent magnets. Wind energy research is critical for any country’s economic development and long-term sustainability. As a result, an experimental setup in a laboratory is required to replicate the behaviour of a wind turbine in the steady state. This study discusses the emulation of wind turbine characteristics in the laboratory using a prototype of a separately excited DC motor mechanically coupled to a brushless doubly fed induction generator (BDFIG). The wind turbine emulator-brushless doubly fed induction machine (WTE-BDFIM) prototype was tested in the laboratory under high power and low wind speed conditions. As a result, the simulation of the same hardware configuration in MATLAB was performed to investigate the overall performance of the BDFIM-based WECS. To determine the equivalent circuit parameters of the BDFIM, which are required for simulation, tests were performed on a prototype of 3.5 kW, 2/6 pole, 400 V, star/delta-star, and BDFIM in two modes, namely, the simple induction mode and the cascade induction mode. Based on the BDFIM parameters, a MATLAB Simulink model of a BDFIG-based wind electric conversion system (WECS) is created and its performance is investigated. Results of both hardware and simulation show that BDFIG can be used as the wind electric generator over a wider speed range compared to that of DFIG, an important feature that is required to get maximum power extraction from the wind turbine.

The mechanical properties of different polymer matrix composites are discussed in this research study. These composites are multiphase materials in which reinforcing elements and a polymer matrix are suitably combined. The mechanical properties of 18 PMCs, including nylon 66 reinforced with 5, 15, and 25% wt% silicon carbide (SiC) and nylon 66 reinforced with 5, 15, and 25% wt% boron carbide (B4C), were evaluated using an injection moulding technique at three different injection pressures in this study. The optimization of process parameters like reinforcement material, reinforcement quantity, and injection pressure to maximize the tensile and impact strength of nylon 66 composites are the main focus of this study. It is observed that the specimens 25% SiC with an injection pressure of 90 MPa has optimised tensile strength, while the specimen 5% B4C with an injection pressure of 90 MPa has optimised impact strength.

A novel wideband bandpass frequency-selective surface functioning at X, Ku, and Ka bands is proposed in this article. The designed FSS has a metallic square loop and a circular ring, and they are printed on both sides of the FR4 substrate. The proposed design FR4-based single-layer FSS is operating from 11.075 GHz to 22.075 GHz with a fractional bandwidth of 66.36%. The parameters of the square loop and circular ring regulate the characteristics of the passband. The optimum dimension of these parameters is obtained with parametric analysis. The proposed structure is measured and fabricated. However, the measured results strongly agree with the simulated results, which authenticate the proposed design performance.

In this paper, we propose an analytic solution of a boundary value problem which models a steady, laminar, two dimensional, boundary layer flow of an incompressible and viscous micropolar fluid over a moving wedge. The governing non-linear partial differential equations are converted into highly non-linear ordinary differential equations using similarity transformations. An analytical exact solution obtained for particular values of parameters are then extended to obtain an exact solution for more general values of the parameters involved. We also propose asymptotic solution of the Micropolar boundary layer flow. The results thus obtained are compared with those of direct numerical solutions, which show a good agreement. The results are discussed in terms of velocity profiles and wall shear stresses for various physical parameters.

The combination of artificial intelligence (AI) and machine learning (ML) in drug discovery has significantly transformed traditional pharmaceutical research by enabling data‐driven decision‐making, accelerating the identification of hits, and improving the efficiency of lead optimization. This review provides a comprehensive overview of AI/ML models, including supervised, unsupervised, semisupervised, deep learning, and reinforcement learning approaches and their applications across various stages of drug development, from target identification and virtual screening to de novo molecule design and ADME/T prediction. We highlight widely used ML algorithms, performance evaluation metrics, and AI‐driven tools that have become instrumental in modern drug discovery pipelines. Despite rapid advancements, challenges such as limited data availability, heterogeneity, bias, lack of model interpretability, reproducibility concerns, clinical translational barriers, and regulatory uncertainties continue to hinder full‐scale adoption. The review also discusses emerging trends, including explainable AI, federated learning, and integration with high‐throughput experimental platforms, which offer promising directions for overcoming current limitations. Emphasis is placed on the importance of interdisciplinary collaboration to bridge computational predictions with experimental validation, ensuring robust, ethical, and clinically translatable AI applications in drug development.

In the field of agriculture Internet of Things (IoT) plays a vital role for the farmers in a high range with the help of these technologies soil moisture is measured and its accuracy is computed for further validation. IOT Technology has paved the way to gather knowledge about conditions such as wind, humidity, water, temperature, soil fertility and online cultivation tracking to identify weeds, the level of farmers connecting to the farm from everywhere. Wireless sensor network is used to track farms and to manage and automate the harvested farm using microcontrollers. An intelligent computer, namely a cell phone, can allow farmers, at all times and everywhere in the world, to keep up to date with the present conditions in the agricultural field. The use of IOT costs can be minimised and high precision efficiency increased in conventional farming.

Garbage management has become the most pressing challenge in today’s world, particularly in countries with significant population growth rates. Excess of gas can be formed in the environment which causes production of greenhouse gas in the environment. We employed this technology to monitor the level of waste in the garbage, as well as fire and gas emissions. When there is a fire or a significant volume of hazardous gas in the waste. The location will be updated in the web and the buzzer will give an alert. The whole process used to reduce greenhouse gas in the environment. And also, it detects the level of garbage, when the level is high it shares the location to IOT.

Brushless doubly fed induction machine has acquired relevance as a wind electric generator because of its relative merits over doubly fed induction generator. However, the main drawback of brushless doubly fed induction machine is the presence of torque ripples due to spatial and time harmonics caused by two stator windings and complex rotor structure. In this article, a special design of brushless doubly fed induction machine using delta-star connection in one of the two stator windings is proposed to reduce the torque ripples. Simulation of the new brushless doubly fed induction machine design is performed in ANSYS Maxwell software, and the results when compared with the conventional winding design validated the effectiveness of the new design in minimizing the torque ripples. Prototype of the new brushless doubly fed induction machine has been fabricated and tested in laboratory. Tests have been conducted in both synchronous and asynchronous modes of brushless doubly fed induction machine. Simple induction and cascade connections have been tested in asynchronous motoring mode. Motoring as well as generating conditions have been tested in synchronous mode. Test results show that the new brushless doubly fed induction machine has not only the desired characteristics for wind turbine generator but also capabilities suited for variable torque–variable speed motor applications as well as constant speed applications.