National Economics University

The rapid growth of e-commerce has intensified interest in using drones for last-mile delivery; however, existing systems remain constrained by limited drone range and customer unavailability. To overcome these challenges, we introduce the Parallel Drone Scheduling Traveling Salesman Problem with Lockers (PDSTSP-L), where a truck and a fleet of drones deliver parcels either directly to customers or to strategically placed lockers equipped with battery-swapping stations. This integration offers key benefits, including extended drone range through battery swapping, consolidation via multi-parcel locker trips, and greater routing flexibility by providing alternative delivery points, which collectively improve overall efficiency. We propose two Mixed Integer Linear Programming (MILP) formulations strengthened with valid inequalities and develop branch-and-cut (B&C) and branch-and-cut-and-check (BCC) algorithms to solve small-sized instances. For larger instances, we develop an Iterated Local Search (ILS) metaheuristic incorporating four novel neighborhood operators and a Ruin-and-Recreate perturbation mechanism. Extensive computational experiments on newly generated benchmark instances validate the effectiveness of the proposed methods. The component analysis underscores the critical role of the designed local search operators and perturbation mechanisms, while the sensitivity analysis shows that incorporating battery-swap lockers can significantly enhance delivery efficiency and yield substantial cost savings compared to the systems without lockers.

Engineering solutions for sustainable energy systems require supportive innovation ecosystems, efficient financial systems, and sustained investment to achieve long-term economic and environmental objectives. However, limited evidence exists on how these factors jointly relate to economic growth in Malaysia. This study investigates the relationship among innovation, financial development, foreign direct investment (FDI), renewable energy, and real GDP per capita in Malaysia using annual data from 1990 to 2023. The Autoregressive Distributed Lag (ARDL) bounds testing approach is employed because it accommodates variables integrated at mixed orders and estimates both long-run equilibrium relationships and short-run dynamics. The bounds test confirms cointegration among the variables (F-statistic = 5.84). Long-run ARDL estimates indicate that innovation (β = 0.118, p = 0.002), financial development (β = 0.092, p = 0.032), and renewable energy (β = 0.067, p = 0.028) exhibit statistically significant positive relationships with real GDP per capita. Foreign direct investment also has a positive coefficient (β = 0.031), but the relationship provides only weak evidence at the 10% significance level (p = 0.095). The findings indicate that innovation, financial development, and renewable energy consumption have positive long-run relationships with real GDP per capita. These results provide evidence on the macroeconomic drivers of economic growth in Malaysia and offer policy insights relevant to the country's broader energy transition. However, environmental sustainability is not directly estimated in this study. Beyond macroeconomic policy implications, the findings provide engineering insights for renewable-energy infrastructure planning, smart-grid modernization, energy-storage deployment, and technology investment supporting Malaysia's National Energy Transition Roadmap. The findings provide several evidence-based policy implications. Malaysia should increase investment in research and development and in renewable energy innovation, expand green financial instruments and sustainable infrastructure financing, and accelerate renewable energy deployment through grid modernization and energy storage investment under the National Energy Transition Roadmap (NETR). Given the weaker evidence for aggregating FDI, policies should prioritize technology-intensive and clean-energy foreign investment rather than focusing solely on increasing FDI inflows. These findings provide empirical evidence to support renewable energy infrastructure planning and long-term economic growth in Malaysia.

CONTEXT Low-carbon rice farming is widely recognized as a key strategy for agricultural decarbonization, yet adoption remains limited among smallholder farmers despite expanding AI-enabled advisory and increasing awareness of climate risks. Existing adoption-based models provide limited explanation for why improved information and awareness often fail to translate into low-carbon transition. OBJECTIVE This study explains how farmers translate information into transition intention under real-world constraints by conceptualizing low-carbon agriculture as a constrained system transition. It develops a process-based cognitive decision pathway linking risk perception, perceived feasibility, and decision confidence, while positioning AI-enabled advisory as cognitive infrastructure rather than a direct transition intention. METHODS Survey data were collected from 398 smallholder rice farmers in Vietnam's Mekong Delta. Partial least squares structural equation modelling (PLS-SEM) was employed to estimate a framework integrating sequential mediation and moderated mediation, with government support and market access modelled as system-level enabling conditions. RESULTS AND CONCLUSIONS AI-enabled advisory significantly enhances risk perception, perceived feasibility, and decision confidence but has no direct effect on transition intention. Perceived feasibility emerges as the critical implementation bottleneck, while decision confidence functions as the final cognitive mechanism translating favourable feasibility assessments into transition intention. In contrast, risk perception increases awareness but does not significantly improve feasibility, revealing a fundamental tension between recognizing climate risks and judging transition to be practically achievable. Government support and market access further strengthen this decision pathway by reinforcing the translation of cognitive readiness into transition intention. SIGNIFICANCE This study makes three principal contributions. First, it reconceptualizes low-carbon agriculture as a constrained system transition rather than a discrete adoption decision. Second, it advances a process-based cognitive decision pathway that explains how farmers progress from implementation feasibility to confidence to act under uncertainty. Third, it repositions AI-enabled advisory as cognitive infrastructure, demonstrating that its effectiveness depends on enabling institutional and market conditions rather than information provision alone. These findings provide a more integrated theoretical framework for understanding low-carbon agricultural transition and offer practical guidance for designing AI-enabled and system-oriented transition policies.

In this paper, we introduce two self-adaptive subgradient extragradient methods for solving non-monotone variational inequalities involving non-Lipschitz mappings in real Hilbert space. Under weaker conditions than the existing conditions, we investigate and present strong convergence theorems of the proposed algorithms. Our results extend and improve the studied results in the literature. Finally, we give an application to image restoration and some numerical experiments to demonstrate the efficiency of our proposed algorithms.

This paper examines the dynamic and long-term relationships between real gross domestic product (GDP), foreign direct investment (FDI), renewable energy consumption (RE), trade openness (TO), innovation (INN), and carbon dioxide emissions (CO2) in Slovakia. Using the Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration and error correction modelling, we explore whether clean energy and innovation can decouple growth from emissions in a small open European economy integrated into global value chains. Annual data are modelled with careful attention to lag selection, structural breaks, persistence, and endogeneity. We complement baseline ARDL with robustness checks (dynamic ARDL simulations, FMOLS/DOLS, and Toda–Yamamoto causality). The results template indicates: (i) a cointegrating relationship among the variables; (ii) in the long run, RE and INN are associated with lower CO2 intensity, while TO and FDI exert mixed effects depending on composition and technological spillovers; and (iii) short-run dynamics are dominated by adjustment toward equilibrium with moderate speed of correction. We discuss the policy implications for Slovakia's green transition in light of its EU climate targets.