The increasing frequency and complexity of disruptions affecting supply chains, linked to health crises, geopolitical tensions, and technological developments, highlight the limitations of traditional approaches focused primarily on efficiency. This article proposes an integrative decision-making framework aimed at strengthening the resilience of supply chains by structurally linking the assessment of critical indicators, the prioritization of strategic levers, the selection of improvement models, and the implementation of operational actions. Unlike the fragmented approaches observed in the literature, the proposed framework offers a systematic and applicable methodology for translating resilience measures into coherent and effective managerial decisions. The prospects for developing this framework are based on the gradual integration of Industry 4.0 technologies, including the Internet of Things, digital twins, advanced data analysis, and collaborative platforms, in order to improve real-time monitoring, scenario simulation, and the ability to adapt to disruptions. Empirical validation and the integration of advanced optimization techniques, artificial intelligence, and multi-agent simulation could strengthen its potential as a tool to support proactive risk management and strategic planning.
We investigate the slow evaporation of several liquid solutions in a vertical circular tube. This work contributes to the quantitative understanding and modeling of drying and evaporation in complex liquids and suspensions. Evaporation is conducted in a classical Stefan-tube configuration under quiescent conditions, while the sample mass is continuously monitored with an OHAUS Pioneer precision balance (USB-interfaced). Ambient temperature, relative humidity, and total pressure are simultaneously recorded using a BME680 sensor. Based on a mass-transfer analysis of Stefan-tube evaporation, diffusion coefficients in the gas phase (in air) can be quantified under diffusion-controlled conditions. Assuming a quasi-steady concentration field within a stagnant gas column and negligible convective transport, the measured mass-loss kinetics provide direct access to binary diffusion coefficients through a one-dimensional diffusion model. The approach is first validated using reference compounds—water, ethanol, methanol, n-propanol, and n-butanol. For these systems, the experimentally determined diffusion coefficients exhibit only small deviations from tabulated literature values, demonstrating the high accuracy and robustness of the protocol. In addition, the temperature dependence of the evaporation/condensation kinetics of water is analyzed to extract an apparent activation energy, which is found to be consistent with reported values, with relative deviations ranging from 0.06% to 0.85% . Beyond fundamental mass-transfer characterization, quantifying the gas-phase diffusion coefficients of essential oils is of practical interest for applied formulations and processing. In particular, lavender and eucalyptus oils are widely used in cosmetology and pharmacology, where vapor-phase transport governs key phenomena such as evaporation rate, release kinetics, sensory perception, and delivery efficiency. Reliable diffusion data are therefore valuable for predictive modeling of volatilization, controlled release, and mass transfer in packaging, storage, and application conditions. Finally, the methodology is extended to complex multicomponent volatile mixtures, and effective gas-phase diffusion coefficients are reported for selected essential oils (lavender and eucalyptus).
The shift from conventional vehicles to electric vehicles (EVs) is essential for sustainability. Energy management strategies play a crucial role in this transition, as they affect battery performance, extend lifespan, and ensure cost-effectiveness. This study evaluates energy management system (EMS) strategies for EVs using the Proportional Pythagorean Fuzzy Analytical Hierarchy Process (PPF-AHP) to prioritize decision criteria. To achieve this, a decision model is constructed involving four main criteria-sustainability and resilience, cost efficiency, performance and reliability, and competitiveness-as well as sixteen sub-criteria. Then, pairwise evaluations from five experts are collected and aggregated leading to the prioritization of decision criteria. In order to show the robustness of the methodology, the PPF-AHP results are compared with Buckley's Fuzzy AHP and Decomposed Fuzzy AHP.
The transition from internal combustion engines (ICEs) to battery or fuel-cell electric vehicles (EVs) directly impacts the metal casting industry, presenting both opportunities and challenges. Traditional manufacturing processes in foundries have primarily focused on ICE vehicles for decades, while the shift to EV impacts existing infrastructure necessitating new requirements and investments. The demand for lightweight materials in EVs continues to increase, outpacing the requirements of current ICE vehicles. Cast Al alloys play an indispensable role in automotive light weighing, offering an attractive combination of properties including strength and ductility, corrosion resistance and castability. Despite predictions regarding market share, and growth of Al in EVs, relatively few studies have analysed the impacts of casting alloys, manufacturing processes and alloy distribution in EV applications. Therefore, this overview aims to explore the effects of EVs on cast components by focusing on the changes to drive systems and the parts lost or gained due to this transition. The methodologies and manufacturing process are reviewed for design and simulation, microstructure refinement, and mechanical properties. More emphasis is provided on advanced/hybrid methods (mega castings, rheo- and compound casting) and functional designs utilizing metal and sand 3D printing processes. Most research findings indicate the lightweighting, along with structural and functional integration will drive the casting applications for EVs. Secondly, castings for EVs create a surging demand for primary alloys and significantly reduce the market share of secondary alloys in the future. Potential avenues for future research on alloy development, process variables and upgradation, and recyclability issues are highlighted.
