Explore the vast range of titles available.

Friction stir forming (FSF) is a solid-state, eco-friendly spot welding technique primarily applied for lap joining dissimilar sheet metals. The details related to the process are scarce in literature. The present work explores the potential of the FSF process for joining dissimilar grade alloys of same metal, namely aluminum. The effect of tool rotational speed on the joint formation and mechanical performance of FSF joints between AA5052-H32 and AA 6061-T6 sheets is studied through systematic experimentation and macrostructure analysis. Hardness distribution across the joint, joint morphology, and failure modes at varying rotational speeds are also presented. The tool rotational speed shows significant effect on the mechanical performance results, zones formed within the joint, and hardness distribution across the joint. Maximum lap shear strength, about 6 kN, obtained in the present work is superior than that of joints fabricated on same material combination with other friction-based joining technologies. The lower and medium tool rotational speeds, between 500 and 1500 rpm, are the best choices for fabricating FSF joints for the materials used. Macrostructure analysis revealed that at lower tool rotational speed (< 1500 rpm), continuous stir zone is observed, at medium tool rotational speed (1500 rpm), partitions within the stir zone are visible, and at higher tool rotational speed (> 1500 rpm), localized stir zones are distributed over the cross-section. Friction stir form samples showed an inverted “W”-shaped hardness profile over the cross-section, and the joint morphological features are independent of the tool rotational speed. Failure modes such as partial bond delamination, tear-off, and pull-out occur randomly and have no systematic correlation with the tool rotational speed.

As the volume of data generated by information systems continues to increase, machine learning (ML) techniques have become essential for the extraction of meaningful insights. However, the sheer volume of data often causes these techniques to become sluggish. To overcome this, feature selection is a vital step in the pre-processing of data. In this paper, we introduce a novel K-nearest neighborhood (KNN)-based wrapper system for feature selection that leverages the iterative improvement ability of the weighted superposition attraction (WSA). We evaluate the performance of WSA against seven well-known metaheuristic algorithms, i.e., differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), flower pollination algorithm (FPA), symbiotic organisms search (SOS), marine predators’ algorithm (MPA) and manta ray foraging optimization (MRFO). Our extensive numerical experiments demonstrate that WSA is highly effective for feature selection, achieving a decrease of up to 99% in the number of features for large datasets without sacrificing classification accuracy. In fact, WSA-KNN outperforms traditional ML methods by about 18% and ensemble ML algorithms by 9%. Moreover, WSA-KNN achieves comparable or slightly better solutions when compared with neural networks hybridized with metaheuristics. These findings highlight the importance and potential of WSA for feature selection in modern-day data processing systems.

The current work investigates the effect of the size of the consumable sheet during friction stir spot welding of AA6063-T6 and CRCA/IS-513 thin sheets through experiments and finite element (FE) simulations. Size ratio, which is defined by shoulder diameter (SD) and consumable diameter (CD), was optimized through bend tests, material flow, and microstructure analysis. Temperature, axial force, torque, effective strain, and strain rate are measured through experiments and simulations. Bendability, joint grain size, intermetallic compounds, and microstructures are revealed following the joining process. FE simulations are performed in DEFORM 3D with calibrated Hollomon and Voce flow stress models. The peak temperature rose 52.7% when the SD was increased from 9 to 15 mm, but only 36% when the SD was increased from 18 to 21 mm. Stir zone with recrystallized grains of 4.3 μm size was observed in Case 3 (15 mm SD and 20 mm CD). Case 5 (21 mm SD and 28 mm CD) had higher hardness (298 HV) because of IMCs at the joint interface (1.8 µm thick AlFe and FeAl 3 ). In case 3, the axial force during FSSW-C peaked at 2835 N, increasing 147% from case 1 before decreasing 38%. In case 1, the torque was 216 N-mm; in case 5, it was 864 N-mm. Root bend shear tests showed higher fracture loads than face bend tests. FE simulated temperatures agreed with experimental values within 6.5%. At the upper surface of the consumable sheet, Case 5 exhibited the highest effective strain of 121.08 mm/mm and strain rate of 115 s −1 . On the other hand, case 1 recorded the lowest values of 47.12 mm/mm and 21.20 s −1 , which is consistent with the literature on the impact of SD. Using the results, a size ratio of 0.75 is recommended for optimal joining of dissimilar sheets.

Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems.

Osteoarthritis (OA) is one of the most commonly occurring forms of arthritis in the world today. It is a debilitating chronic illness causing pain and immense discomfort to the affected individual. Significant research is currently ongoing to understand its pathophysiology and develop successful treatment regimens based on this knowledge. Animal models have played a key role in achieving this goal. Animal models currently used to study osteoarthritis can be classified based on the etiology under investigation, primary osteoarthritis, and post-traumatic osteoarthritis, to better clarify the relationship between these models and the pathogenesis of the disease. Non-invasive animal models have shown significant promise in understanding early osteoarthritic changes. Imaging modalities play a pivotal role in understanding the pathogenesis of OA and the correlation with pain. These imaging studies would also allow in vivo surveillance of the disease as a function of time in the animal model. This review summarizes the current understanding of the disease pathogenesis, invasive and non-invasive animal models, imaging modalities, and pain assessment techniques in the animals.

Computer vision (CV) was developed for computers and other systems to act or make recommendations based on visual inputs, such as digital photos, movies, and other media. Deep learning (DL) methods are more successful than other traditional machine learning (ML) methods in CV. DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization, object detection, and face recognition. In this review, a structured discussion on the history, methods, and applications of DL methods to CV problems is presented. The sector-wise presentation of applications in this paper may be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV. This review will provide readers with context and examples of how these techniques can be applied to specific areas. A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

Patient-generated drug reviews are becoming increasingly available and serve as a rich source for computational drug prioritization. In this study, we developed a Hybrid Particle Swarm-Enhanced African Vulture Optimisation Algorithm (Hybrid PSO-EAVOA) that fosters the development of better balances between the exploration and exploitation of which the framework uses the improved opposition-based learning, Levy flights, and elite preservation approaches. In the framework, multiple evaluation criteria are accommodated, recovering value in the form of an overall single-objective optimization scheme, where effectiveness, side-effects, and consistency of reviews were compiled for clinical significance and combined by a weighted-sum fitness function. To validate the experiment using a large-scale dataset of drug reviews obtained from the Drugs Side Effects and Medical Condition dataset sourced from Drugs.com in Kaggle. Hybrid PSO-EAVOA performed a benchmark comparison against five state-of-the-art metaheuristic algorithms (PSO, EAVOA, WHO, ALO, and HOA) using varying iterations as runs. In each comparison, Hybrid PSO-EAVOA achieved superior or better convergence speed, robustness, and quality of solutions. The specific method of weighted-sum aggregation was used in this study, the framework offered could be easily compatible with other forms of aggregation. Hybrid PSO-EAVOA demonstrates strong potential for broader application in fields such as pharmacovigilance, clinical decision support, and drug re-purposing. The dataset is publicly available on Kaggle Drugs Side Effects and Medical Condition and all source code for parameter settings and preprocessing scripts is publicly available at the GitHub repository https://github.com/suruthi-m/Hybrid_PSO_EAVOA.

In this research, the effectiveness of a novel optimizer dubbed as decomposition-based multi-objective symbiotic organism search (MOSOS/D) for multi-objective problems was explored. The proposed optimizer was based on the symbiotic organisms’ search (SOS), which is a star-rising metaheuristic inspired by the natural phenomenon of symbioses among living organisms. A decomposition framework was incorporated in SOS for stagnation prevention and its deep performance analysis in real-world applications. The investigation included both qualitative and quantitative analyses of the MOSOS/D metaheuristic. For quantitative analysis, the MOSOS/D was statistically examined by using it to solve the unconstrained DTLZ test suite for real-parameter continuous optimizations. Next, two constrained structural benchmarks for real-world optimization scenario were also tackled. The qualitative analysis was performed based on the characteristics of the Pareto fronts, boxplots, and dimension curves. To check the robustness of the proposed optimizer, comparative analysis was carried out with four state-of-the-art optimizers, viz., MOEA/D, NSGA-II, MOMPA and MOEO, grounded on six widely accepted performance measures. The feasibility test and Friedman’s rank test demonstrates the dominance of MOSOS/D over other compared techniques and exhibited its effectiveness in solving large complex multi-objective problems.

The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models.

In recent times, numerous innovative and specialized algorithms have emerged to tackle two and three multi-objective types of problems. However, their effectiveness on many-objective challenges remains uncertain. This paper introduces a new Many-objective Sine–Cosine Algorithm (MaOSCA), which employs a reference point mechanism and information feedback principle to achieve efficient, effective, productive, and robust performance. The MaOSCA algorithm’s capabilities are enhanced by incorporating multiple features that balance exploration and exploitation, direct the search towards promising areas, and prevent search stagnation. The MaOSCA’s performance is evaluated against popular algorithms such as the Non-dominated sorting genetic algorithm-III (NSGA-III), the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) integrated with Differential Evolution (MOEADDE), the Many-objective Particle Swarm Optimizer (MaOPSO), and the Many-objective JAYA Algorithm (MaOJAYA) across various test suites, including DTLZ1-DTLZ7 with 5, 9, and 15 objectives and car cab design, water resources management, car side impact, marine design, and 10-bar truss engineering design problems. The performance evaluation is carried out using various performance metrics. The MaOSCA demonstrates its ability to achieve well-converged and diversified solutions for most problems. The success of the MaOSCA can be attributed to the multiple features of the SCA optimizer integrated into the algorithm.