Innovative lightweight smart structures incorporating piezoelectric material-based active elements, both as sensors and actuators, have been identified to present manifold advantages over traditional passive systems. Such structures have become intrinsically integrated into smart mechatronic systems, necessitating advanced design, testing, and control techniques. Real-time simulation of shell-type deformable objects, especially when employing the finite element method for non-linear analysis and control, has been challenging due to the extensive computational demand. Presented herein is an efficacious implementation leveraging machine learning with the isogeometric finite element formulation. This implementation focuses on shell-like smart mechatronic structures crafted from composite laminates comprising piezoelectric layers, which are characterised by electro-mechanical coupling. The foundation for the shell kinematics is derived from the Mindlin-Reissner assumptions, effectively incorporating transverse shear effects. While the inclusion of machine learning facilitates real-time efficient operations, the isogeometric finite element analysis (FEA) introduces pronounced advantages over conventional finite element method (FEM), also serving as a valuable source of offline data crucial for the training phases of machine learning algorithms. A piezo-laminated semicircular arch has been analysed to exemplify the effectiveness and performance of the presented methodology. Explorations into further machine learning techniques and intelligent control schemes are also contemplated.

Road transport emerges as a crucial segment of the transportation system, demanding comprehensive analyses of operational performance across passenger and freight domains. This investigation delineates a meticulous multi-criteria analysis of Serbian passenger and freight road transport, relying on data extracted from the Annual Statistical Reports promulgated by the Statistical Office of the Republic of Serbia during 2015-2021. Initially, a compendium of eight pertinent criteria, namely carrying capacity, total number of passenger and tonne-kilometres, employee count, generating power, fuel consumption, and foreign currency receipts, is identified, with a subsequent emphasis placed on six criteria necessitating multi-criteria analysis, applicable cohesively to both passenger and freight transport sectors. Weighting coefficients for each criterion are calculated employing the entropy method, while a multi-criteria ranking of the operational performance of road transport is devised through the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The quintessence of this research lies in the execution of a novel multi-criteria analysis with an aspiration to architect a hierarchy regarding the operational performance within the scrutinised timeframe of road transport in Serbia.

In military operations, the proficient overcoming of water barriers is paramount, with sub-optimal execution potentially leading to significant human and equipment casualties. In this context, global armed forces accord considerable emphasis to the selection of appropriate mechanisms for water obstacle overcoming. This study elucidates the adoption of a Multi-Criteria Decision-Making (MCDM) approach for the selection of optimal pontoon bridge sets for military applications. Criteria identification was undertaken by seven distinguished experts, leading to the determination of weight coefficients using the Defining Interrelationships Between Ranked criteria II (DIBR II) method. Expert assessments were subsequently aggregated utilizing the Normalized Weighted Bonferroni Mean (NWBM) operator. The Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) method, operationalized within the Fermatean Fuzzy (FF) environment, was harnessed for the discernment of the best alternative. An analysis of the sensitivity of the study's findings with respect to variations in criteria weighting, coupled with a comparative exploration, led to the inference that the proposed MCDM model boasts stability. However, it was noted that the model exhibits sensitivity to shifts in criteria weight coefficients, underscoring its utility as a valuable aid for decision-makers, especially in the domain of pontoon bridge set selection.

The burgeoning importance of digitalization and cyber-physical manufacturing systems in the industrial sector is undeniable, yet discussions around viable solutions for small and medium-sized enterprises remain scant. These enterprises often face constraints in replacing extant machinery or implementing extensive IT upgrades, despite the availability of skilled engineering personnel. In response to this gap, an illustrative use case involving the application of Digital Twins (DT) to legacy systems is delineated, encompassing a detailed exploration of necessary hardware and software components, alongside pertinent considerations for implementation design. The establishment of a symbiotic relationship between the physical and digital realms is underscored as imperative, necessitating a granular understanding of the system to uncover opportunities and constraints for intervention. Such understanding is posited as a critical determinant of the DT's utility. This case study, situated within the Cyber-Physical Manufacturing Systems Laboratory at Széchenyi István University, serves to elucidate these principles and contribute to the discourse on smart manufacturing solutions for legacy systems.