This paper addresses the challenge of material selection for lift-based Wave Energy Converters (WECs) by presenting a novel methodology that integrates fuzzy information representation and Multi-Criteria Decision-Making (MCDM) techniques. The aim is to provide decision-makers with a robust framework for optimizing material choices despite uncertainties and limited field data. The methodology combines a material selection approach tailored for lift-based WECs with a new fuzzy information representation technique, probabilistic picture hesitant fuzzy sets (P-PHFSs). The results underscore the potential of these methods for reducing the risk of machine failures in WEC technology. The proposed method contributes to the development of WEC technology by providing a systematic and flexible framework for decision-makers to optimize material choices and improve the reliability and performance of WEC systems.
Augmented and mixed reality is already considered as needful technology of the modern production systems. It is primarily employed to virtualize proper digital content, mainly related to 3D objects, into the human visual field allowing people to visualize and understand complex spatial shapes, their mutual relations, and positioning. Yet, the huge potential of the technology is waiting to be revealed in its usage for collecting and recording human observations and inferences about the context of the production environment. Its bi-directional interface makes it the most direct and the most efficient knowledge capturing means to date. The paper presents the challenges and benefits that come from the usage of a conceptual interface of an mixed reality application that is designed to collect data, semantics and knowledge about the production context directly from the man-in-process. As a production environment for the development, implementation, and testing of mixed reality applications for this purpose, various processes for the assembly and maintenance of medium-voltage equipment were used.
The existence of a comprehensive and complete model, along with accurate and reliable data, helps to evaluate the performance of the supply chain. Given the different layers and various performances in designing the supply chain, a method that can analyze and evaluate such network structure is required. Moreover, data and conditions’ uncertainty highlight the need for a method that can also include uncertainty in evaluation. In this paper, designing a multi-period network is carried out with rough data to embed in various layers and levels of supply chain. The supply chain performance evaluation is performed using rough network data envelopment analysis. Rough Network Data Envelopment Analysis (RNDEA) is a proper method since it analyzes all the current factors in evaluation; besides, it provides efficiency scores for inefficient decision-making units and boundary forecasting for these units on an efficient border. The study’s outcomes reveal the efficiency of different factors in the designed network. On the other hand, unlike common data envelopment analysis that indicates the maximum of a factor efficiency, the efficiency priority is calculated in the proposed rough network model, and divisional efficiency also is determined in each step.
To investigate the properties of secondary oxidation (SO) and the coal spontaneous combustion (CSC) in submerged coal under varying heating rates, a Programmable Temperature-Controlled (PTC) system was employed. Analysis focused on gas concentrations, oxygen consumption rates (OCR), heat release, apparent activation energy (AAE), and constraint parameters. Results demonstrate that heating rates primarily influence the rapid oxidation stage of coal. Compared to primary oxidation (PO) at identical temperatures, SO exhibits lower oxygen consumption and OCR, but higher CO formation rates, indicating less vigorous and complete coal-oxygen reactions. During PO, AAE reaches its maximum at a heating rate of 0.5 degrees C/min. Both SO and heating rate significantly impact CSC behavior. Their relationship during SO differs slightly from that observed during PO due to rate-dependent effects. Quantitative analysis of AAE and constraint parameters provides a comprehensive explanation of CSC characteristics under varying conditions. Crucially, CO concentration alone proves insufficient for CSC detection. Characteristic temperature points during the slow oxidation phase offer a more reliable indicator.
Reliable fine-pitch interconnects remain a critical challenge for next-generation flexible electronics, as conventional anisotropic conductive films (ACFs) suffer from stochastic particle distribution, unstable electrical contacts, and limited mechanical robustness under deformation. Herein, we report a low-temperature, surface-energy-driven selective soldering strategy that enables defect-free metallurgical interconnect formation on a hole-patterned polyimide (holey PI) platform. By exploiting the intrinsic surface-energy contrast between Cu pillars and the surrounding PI matrix, molten Sn-58Bi eutectic solder spontaneously wets and deposits exclusively on Cu surfaces through a simple dipping process, while solder spreading on PI is effectively suppressed. Thermodynamic analysis based on contact angle measurements, Owens-Wendt surface energy calculations, and Gibbs free wetting enthalpy confirms the strong energetic preference of Sn-58Bi for Cu over PI. Process optimization achieves highly uniform semispherical solder bumps with >98% yield within a few seconds at similar to 200 degrees C. Cross-sectional SEM/EDS reveals a refined eutectic lamellar microstructure and a thin intermetallic layer at the solder/Cu interface, ensuring robust metallurgical bonding. The resulting interconnects exhibit high shear strength up to 16.22 MPa and maintain stable electrical resistance (similar to 0.023 Omega) after thermal aging (85 degrees C/85%RH) and cyclic bending (1,000 cycles). This work provides a scalable and reliable low-temperature interconnection route for flexible and thermally sensitive electronic systems.
