The objective of the present work is to evaluate the performance of a low-cost tractor equipped with a parallel hybrid engine, which was simulated using AMESim software. The tractor was evaluated with three different farming implements attached to the tractor, and each implement requires a different type of power. The first simulation was executed without any implements attached. The tractor was able to run for 170 s with the electric motor only, which resulted in fuel savings during this period. The first implement, a moldboard plow, was attached for the second round of evaluation, and the electric motor ran by itself for 150 s, which also led to fuel savings during operation. During the third simulation, the tractor was attached to a Bette Harvest, which has a very high-power demand. The obtained results show that both engines were engaged to provide the required energy. During the final round of evaluation, simulations were run for a straw tub grinder. In this simulation, the electric motor ran alone until the battery was fully discharged. Thereafter, the combustion engine was activated in order to facilitate operations and to charge the battery. The results show that the parallel hybrid architecture employed for the low-cost tractor significantly decreased the CO₂ emissions and minimized the consumption of fuel.

In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing.

Throughout the world and particularly in urban areas, population growth can be listed as a direct cause of the uprising use of personal vehicles in cities around the world. Such attitude may lead to dramatic consequences, not only economically, but socially and environmentally. To meet these challenges, and to promote the use of multiple means of public transports by citizens, public authorities and transport operators seek − within the framework of the implementation of connected cities projects and intelligent − to optimize the extraction as well as the exploitation of the multimodal information by developing Interactive Systems of Assistance to the Multimodal Movement (IAMM). However, finding the optimal multimodal path for a given person is far from being a simple matter. Indeed, each potential user may have different or unique preferences regarding the: cost and/or duration of his/her journey, number of mode changes, comfort or safety levels desired. In the present study, we propose a multi-agent system which, based on the parameters entered by each user, proposes the optimal paths in the Pareto sense, including different public transport modes, private cars and parking availability.

Banks are increasingly exposed to liquidity–risk contagion through interbank refinancing links, confidence effects, and balance sheet interactions. This paper develops a fractional‐order extension of a SIR‐type liquidity contagion model previously introduced in Mourad et al. (2022). The main purpose is not merely to replace the classical derivative by a fractional one, but to provide a memory‐dependent framework in which current banking vulnerability depends on past liquidity shortages, delayed refinancing reactions, and persistent confidence shocks. The model is formulated by means of the Caputo derivative, which preserves classical initial conditions and reduces to the integer‐order model when the fractional order is equal to one. We correct the initial shock formulation by allowing a positive initial number of distressed banks, establish well‐posedness, positivity, and boundedness, and analyze the equilibrium manifold through a basic contagion threshold. A Lyapunov comparison argument is used to justify the Mittag–Leffler decay of the distressed compartment under the subthreshold condition. An explicit Adams–Bashforth–Moulton predictor–corrector method for Caputo systems is presented and used to produce numerical simulations calibrated from European banking data. The simulations compare several bankruptcy rates, countries, and fractional orders, and they show that stronger memory slows the adjustment process and prolongs liquidity distress.