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The current study examines an essential type of vehicle routing problem (VRP). This type is one where customers are served by limited-capacity vehicles from multiple depots and is known as Multi-Depot Capacitated Vehicle Routing Problem (MDCVRP). The novelty of this study is that in the present case, cars, after Leaving the Depot, can go back to any other depot. Those issues seem to occur in most real-world issues where information, messages, or news are sent electronically from somewhere. The purpose of the problem is to minimize the costs associated with routing. Regarding the literature on such issues, it has been proven in previous studies and research that these problems are among the hard-NP problems, and to solve them using the metaheuristic method, the exact methods justify it. After changing the basic model, this study developed a Tabu Search (TS) algorithm. The study results demonstrate that if the equipment can return to any depot, the cost is significantly reduced in contrast to the case where equipment is forced to return to their depot.

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly. [ABSTRACT FROM AUTHOR]

In this study, energy harvesting using a two-layer piezoelectric sensor in non-linear single-mode mode was investigated, and the optimal performance conditions for power extraction were investigated. Non-linear equations or non-linear electric enthalpy proposal were obtained using Lagrange's method. In addition, the model was identified with the help of perturbation methods and based on experimental results. The results indicate the presence of second-order damping and third-order stiffness with magnitudes of 2.8 × 106 and −3.9 × 1021. Finally, non-linear energy harvesting was investigated, and the electrical resistance for an optimal electrical power of 185.2 was obtained. [ABSTRACT FROM AUTHOR]

Background: The societal challenges presented by fear related to the coronavirus disease (COVID-19) pandemic may present unique challenges for an individual's mental health. However, the moderating role of compassion in the relationship between fear of COVID-19 and mental health has not been well-studied. The present study aimed to explore the association between fear of COVID-19 and mental health, as well as test the buffering role of compassion in this relationship. Methods: The participants in this study were 325 Iranian undergraduate students (228 females), aged 18-25 years, who completed questionnaires posted on social networks via a web-based platform. Results: The results showed that fear of COVID-19 was positively related with physical symptoms, social function, depressive symptoms, and anxiety symptoms. The results also showed that compassion was negatively associated with physical symptoms, social function, depressive symptoms, and anxiety symptoms. The interaction-moderation analysis revealed that compassion moderated the relationship between fear of COVID-19 and subscale of mental health. Conclusion: Results highlight the important role of compassion in diminishing the effect of fear of COVID-19 on the mental health (physical symptoms, social function, depressive symptoms, and anxiety symptoms) of undergraduate students. [ABSTRACT FROM AUTHOR]

Pulsed laser deposition (PLD) is a commonly utilized technology for growing thin films in academia and industry. Compared to alternative deposition processes, the PLD offers more excellent benefits such as adaptability, control over the growth rate, stoichiometric transfer, and an infinite degree of freedom in the ablation geometry. This investigation collected data from five reputable academic databases, including Science Direct, IEEE Xplore, Scopus, Web of Science, and Google Scholar. In this review, we analyzed and summarized 20 empiricals on the impact of pulsed laser deposition on the production nanostructure, including laser wavelength, laser fluence, repetition rate and pulse length of the laser pulse, pulse shaping of the laser spot, plasma generation, distance between substrates and target, angular position of the material, substrate temperature, gas composition, and target material properties. Finally, we show this field's advantages, challenges, and viewpoints and focus on the strengths and weaknesses that can improve the deposition of nanostructure properties for various applications. Therefore, provide fascinating insights into the interaction of these processes in different fields. [ABSTRACT FROM AUTHOR]