Preventive conservation is conductive to the long-term preservation of works of art. In order to realize the avoidance of damages in advance, risk management as well as foresighted thinking is required. The application of the method of engineering mechanics for preventive conservation is at the very beginning of its development. This article is a contribution to this still very young field. Generally, sensitive artworks combine all properties of complex mechanical structures. Oil paintings on canvas, for instance, are asymmetric, multiple curvilinear structures made of aged anisotropic compound materials with cracks and other damages. Due to their popularity, some artworks travel a lot, and during the exhibition and storage, they are always exposed to shocks and vibrations, therefore the protection of sensitive paintings is a demanding task, the solution of which requires the multidisciplinary cooperation especially in the context of engineering mechanics with its many specializations. The subject of the presented research is an artificial aged painting dummy in the simplest conceivable composition. This paper aims to describe the mechanical behavior of this test object, which is the basic requirement for the development of technological protective measures. The concept of the digital twin, known from Industry 4.0, is used to solve this task. This article focuses on the design of a virtual painting dummy that has the same static and dynamic behavior as the investigated real test object. Therefore, the deflection of the real dummy in lying position as well as the curvature of its standing position without and with dynamic excitations have been measured. The advantage of the analytical and Finite Element Analysis (FEA) models presented are their practicability and quick realizability at fair correlation. The concept presented offers a potential way to assess and finally reduce the risk for original paintings during various transport, exhibition, and storage situations with the help of virtual objects.

Choosing a battery supplier is a vital decision-making problem, for which it is essential to obtain stable evaluations. For such sustainable supplier evaluation, multi-criteria decision analysis (MCDA) methods are often used, as their ability to handle uncertain data gives experts more significant opportunities to consider a broader range of cases. However, given the great number of MCDA approaches, it is challenging to find out which approach is the most appropriate. Therefore, this paper presents a sensitivity analysis on evaluating battery suppliers by ARAS, EDAS, MAIRCA, TOPSIS, and VIKOR methods in a fuzzy environment. The provided study presented similar results for the considered MCDA methods confirmed by the WS similarity measure of rankings and the weighted Spearman correlation . On the other hand, the sensitivity analysis conducted on the considered methods indicated that the most relevant criteria for this problem are transportation cost, delivery time, and warranty period.

Affected by new trends, automobile companies have altered stakeholder requirements on their main product - the automobile. With enactment of new regulations concerning sustainability, new features appeared quickly, such as electrification, sharing services, autonomous mobility and so on. In this study, we present sustainability as a stakeholder and analyze the method of its realization in Systems Engineering (SE) based product development. Formula SAE provides a validated setting to conduct experiments on integrating sustainability into the classical product requirement architectures. By taking into consideration the use of SE or adding other methodological frameworks, findings can establish a new setting in sustainability research. The results of this study may be enlightening for scholars and practitioners and calls for further research on embedding sustainability requirements in automotive product development by using SE.

In the global economy, plastics are considered a versatile and ubiquitous material. It can reach to marine ecosystems through diverse channels, such as road runoff, wastewater pathways, and improper waste management. Therefore, rapid mitigation and reduction are required for this ever-growing problem. The marine habitats are believed to be the highest emitters and absorbers of O₂ and CO₂ respectively. As such, every day, the prominence of managing the litter in the ocean is growing effectively and efficiently. One of the most significant challenges in oceanography is creating a comprehensive meshless algorithm to handle the mathematical representation of waste plastic management in the ocean. This research dedicates to studying the dynamics of waste plastic management model governed by a mathematical representation depending on three components viz. Waste plastic (W), Marine litter (M) and Recycling of debris (R), i.e., WMR model. In this regard, an unsupervised machine learning approach, namely Mexican Hat Wavelet Neural Network (M_hWNN) refined by the efficient Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm (L-BFGS), i.e., M_hWNN-LBFGS model has been implemented for handling the non-linear phenomena of WMR models. Besides, the obtained solution is meshfree and compared with the state-of-art numerical result to establish the precision of the M_hWNN-LBFGS model. Furthermore, different global statistical measures (MAPE, TIC, RMSE, and ENSE) have been computed at twenty testing points to validate the stability of the proposed algorithm.

Aircraft maintenance is an expansive multidisciplinary field which entails robust design and optimization of extensive maintenance operations and procedures; encompassing the fault identification, detection and rectification, and overhauling, repair or modification of aircraft systems, subsystems, and components, as well as the scheduling for various maintenance operations, in compliance with the aviation standards; in order to predict, pre-empt and prevent failures and thus ensure the continual reliability of aircraft. Advances in Big Data Analytics (BDA) and artificial intelligence techniques have revolutionized predictive maintenance operations. Predictive maintenance is making big strides in the aerospace sector accompanied by a variety of prognostic health management options. Artificial intelligence algorithms have recently been extensively applied to optimize aircraft maintenance systems and operations. Several researchers have proposed, analysed, and investigated the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) based data analytics for predictive maintenance of aircraft systems, subsystems, and components. This paper provides a comprehensive review of the ML techniques like Multilayer Perceptron (MLP), Logic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), Linear Regression (LR), and other common ML techniques for their present implementation and potential future applications in aircraft maintenance.