Mircea Boșcoianu

End-of-life electric-vehicle and plug-in hybrid (EV/PHEV) battery packs pose a recurrent decision: refurbish, redeploy in second-life storage, recycle, or reject. Technical condition, safety, economics, regulation, traceability, and environmental benefit interact, making pathway selection a systems-level decision problem. This paper develops a risk-informed multi-criteria framework for the conditional viability of refurbished batteries under data-scarce conditions. Failure mode, effects, and criticality analysis (FMECA) supplies a pathway-specific residual-risk penalty; multi-criteria decision analysis (weighted-sum and the Technique for Order of Preference by Similarity to Ideal Solution, TOPSIS) orders four alternatives on six benefit criteria; and a screening-level avoided-burden indicator, not a life-cycle assessment, positions the environmental criterion. An Integrated Viability Index (IVI) offsets weighted benefits against the risk penalty through one tunable coefficient. All inputs are illustrative and literature-informed; the demonstration tests decision logic, not empirical pathway performance. Preference is conditional: refurbishment leads under economic and technical priority with credible risk mitigation, second-life reuse under environmental priority, and recycling under safety, regulatory, and infrastructure constraints, while rejection never leads. As the risk penalty rises, leadership migrates traceably toward recycling, and IVI–TOPSIS divergence localizes exactly where the risk treatment changes the decision. A proposed Refurbished-Battery Suitability Index (RBSI) would couple measured diagnostics to the IVI; its calibration remains future work.

This study investigates the possibilities for recently listed Romanian Information and Communications Technology (ICT) firms to select optimal sets of financial strategies under adverse macroeconomic conditions, with high and persistent inflation, high volatility, high cost of financing and liquidity constraints that influence investment decisions and financial resilience. Using a stochastic investment model with the Tobin’s Q factor in a dynamic framework equipped with a generalized Wiener process, this study offers an intuitive approach for simultaneously assessing corporate market value under financial constraints and formulating optimal decisions based on liquidity management. In this research, the numerical simulations in Python for a set of 16 scenarios resulting from combining technological and macroeconomic variables were performed, with a series of parameters held constant. The results highlight the decisive role of financial restrictions and macroeconomic volatility in shaping the investment behavior, as well as the importance of adjusting the timing of investments together with liquidity mechanisms capable of improving financial resilience. The main contribution of this study is the simplicity with which one can assess the impact of the risk-free rate and volatility on the main parameters of the set of strategies (earnings dynamics, liquidity risk, cost of capital, liquidation value, opportunity cost associated with holding cash) and to assess the integrated perspective on financial resilience. This procedure is simple and scalable and can also represent a practical tool to support management and investment decisions in volatile and turbulent conditions.

As a medium-sized emerging market, Romania has demonstrated an interesting trend in relation to the transition from internal combustion engine (ICE) vehicles to electric vehicles (EVs). Compared to other emerging markets in the region, the rate of adoption has been higher and the dynamics particularly noteworthy. Customers understood the importance of switching to EVs, while the reduction in financial support provided through the Rabla Plus programs in 2025-2026 was partially offset by the energy crisis of April 2026, which manifested itself in rising fuel prices. A novel aspect addressed in this research concerns the analysis of the feasibility of implementing recycling systems in emerging markets. Romania has entrepreneurial specialists interested in new recycling technologies that would reduce the life-cycle cost of EV products.

This paper proposes and validates a hybrid FMEA-AHP framework designed to quantify the systemic asymmetries inherent in the automotive green transition. By moving beyond traditional component-level failure analysis, this model introduces the Market Maturity Coefficient (k_m), a novel diagnostic tool that benchmarks transition readiness by comparing risk profiles across divergent geoeconomic landscapes. Utilizing France and Romania as proxies for developed and emerging markets, the research demonstrates a significant “RPN Divergence,” where infrastructure and grid risks in emerging contexts are effectively amplified by factors exceeding 8.0× compared to developed counterparts. The results indicate that while software and technological risks show convergence (k_m ≈ 1.3), systemic vulnerabilities regarding grid stability and digital maturity remain the primary barriers to a uniform European transition. This framework provides policymakers with a mathematically rigorous method to move beyond “one-size-fits-all” policies toward targeted resilience funding.

The proposed study investigates the key factors influencing UAV adoption and proposes an integrated educational–operational framework to enhance implementation in agricultural practice. A case study in Sibiu County, Romania, combined survey-based empirical analysis (n = 80), strategic environmental assessment and the deployment of a demonstration aerial crop monitoring system for educational purposes (ACMS-E). We integrated the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) to examine adoption intentions, revealing perceived usefulness (β = 0.355, p = 0.021) and positive attitudes (β = 0.382, p = 0.005) as the strongest predictors, explaining 44.1% of variance. Based on these findings, a modular training curriculum was designed, combining theoretical instruction, flight operation exercises, remote sensing techniques, data analytics and farm-management integration. ACMS-E provides hands-on training and promotes capacity-building, bridging the gap between technological availability and real-world adoption. By linking technological capabilities with structured training, ACMS-E bridges the gap between UAV availability and effective implementation, offering a scalable model for precision agriculture. This framework provides a pathway to accelerate UAV adoption, optimize field-level monitoring and support evidence-based, resource-efficient farm management in emerging and developed agricultural contexts.

Background: This paper presents an analysis and a structured framework for improving inventory accuracy in an automotive factory, considering the current context of global disruptions. In 2023, the company recorded 20,340 inventory adjustments (1695 per month) and a 0.24% monthly net value discrepancy (EUR 256,594 YTD), with a baseline absolute discrepancy of 2.21% of sales. The project aimed to reduce adjustments to below 700 per month and the net value discrepancy to 0.1%. Methods: The research followed the Six Sigma methodology’s Define, Measure, Analyze, Improve and Control (DMAIC) phases, integrating Root Cause Analysis (RCA) and Failure Mode and Effects Analysis (FMEA) to enhance inventory accuracy in manufacturing operations. Results: Implementation significantly improved inventory accuracy: monthly adjustments decreased from 1695 to 971, the highest RPN was reduced from 576 to 144, and the absolute discrepancy-to-sales ratio stabilized at 0.98% (a 56% improvement). Financial variance was reduced to EUR 1948.10 in Q4 2024, while organizational discipline, role clarity and process control also increased. Conclusions: The integrated DMAIC–RCA–FMEA framework proved effective and replicable, enabling systematic identification of root causes, targeted corrective actions and sustainable KPI-driven improvements. The results demonstrate a scalable approach to inventory optimization that supports operational resilience and supply chain performance.

This study examines cyber vulnerabilities affecting critical infrastructure along NATO’s 2 eastern flank, with a focus on industrial control systems and operational technology. It 3 addresses how hybrid threats exploit legacy protocols and interoperability gaps across 4 mixed-generation IIoT environments, increasing the likelihood of disruptive events. We 5 propose an AI-enabled framework that links cyber resilience engineering to European 6 regulatory and operational requirements through two components: a Unified Compli- 7 ance Framework that maps legal obligations to implementable technical controls, and 8 an AI-enabled Cyber Resilience Index that consolidates detection, operational continuity, 9 governance, and supply-chain risk into a single scoring model. The methodology combines 10 regulatory-control mapping, OT-specific gap analysis, and engineering validation of real- 11 time constraints, supported by a digital-twin testing environment used to evaluate resilience 12 under representative adversarial scenarios. Results from the simulation-based evaluation 13 show consistent improvements in detection and response stability across tested scenar- 14 ios and provide an auditable evidence model for continuous assurance. The framework 15 supports risk-informed governance and investment decisions by translating compliance 16 objectives into measurable service-level targets and operational resilience indicators, while 17 promoting time-deterministic architectures, federated learning, and explainable AI for 18 accountable deployment in industrial settings.

Background: The automotive industry is undergoing a deep transformation driven by 9 the global green transition. This change follows divergent trajectories in developed and 10 emerging markets due to differences in regulation, infrastructure, and economic con- 11 straints. The research methodology is adapted to incorporate different factors of influence 12 and contraints. The research applies a structured Failure Mode and Effects Analysis 13 (FMEA) based on IEC 60812:2018 and AIAG & VDA (2019), and integrates the Analytic 14 Hierarchy Process (AHP) to prioritize corrective measures. Concepts from adaptive risk 15 management, informed by expert consensus and literature-backed data, are also used to 16 interpret dynamic behavior of supply chains and market volatility. The comparative anal- 17 ysis successfully highlights the systematic RPN Divergence between market types, reveal- 18 ing critical differences in failure mode profiles, risk priorities, and capacity to adopt miti- 19 gation strategies. The hybrid FMEA-AHP approach reduces subjectivity and provides 20 transparent prioritization tailored to market maturity. The integrated methodology sup- 21 ports decision-making in electrification programs and offers a robust framework for 22 benchmarking complex transition processes across regions.