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Type of the article: Research Article AbstractThis paper investigates the impact of colonial ties, cultural proximity, and host-country market size on outward foreign direct investment from Arab Maghreb Union countries, focusing on both greenfield investments and cross-border mergers and acquisitions. Using the Generalized Method of Moments on a panel dataset of 556 transactions over the period 2004 to 2022, captured by the number of deals, we find that colonial ties and African cultural proximity positively influence both greenfield investments and cross-border mergers and acquisitions. However, Arab cultural proximity and host-country market size influence only greenfield investments. Among the variables studied, colonial ties have the greatest impact, followed by African cultural proximity. The estimated coefficients indicate that the magnitude of these effects is substantially larger for greenfield investments than for cross-border mergers and acquisitions, highlighting important differences in how firms respond to host-country characteristics across entry modes. This pattern is consistently observed across baseline estimations and robustness checks, reinforcing the presence of a clear entry-mode asymmetry in the determinants of outward foreign direct investment from Arab Maghreb Union countries. Taken together, the results integrate cultural proximity and historical ties into international business theories and provide new insights into the outward investment behaviors of emerging-market multinationals. Moreover, the findings reveal the relevance of leveraging shared history and cultural ties as instruments for attracting investment from Arab Maghreb Union countries, while adopting differentiated strategies for greenfield investments and cross-border mergers and acquisitions.

In recent years data-driven machine learning techniques attract the attention of researchers in analyzing many complex systems. This study introduces a novel unsupervised deep neural network approach to predict the temperature and velocity behaviour of electro-magneto-hydrodynamics hybrid nanofluid flow for geothermal pipelines application.The exceptional flow and thermal characteristics of hybrid nanofluids making them ideal for use in geothermal energy extraction applications. The dynamics of hybrid nanofluid flow through a pipe are examined using a third-grade sodium alginate model, which has a lot of potential for geothermal applications. The copper oxide (CuO) and zinc oxide (ZnO) nanoparticles make up the nanofluid. It is also investigated how the flow dynamics are affected by electric and magnetic fields. The energy equation takes into account the effects of Joule heating and viscous dissipation as the fully developed incompressible fluid passes through the pipe. Consequently, an unsupervised deep neural network (DNN) method is used to predict the dynamics of nonlinear differential equations (DEs). The accuracy of the deep neural network ranges from to across different cases. The velocity profile exhibits a clear symmetrical pattern and is found to be significantly influenced by both the electric field and the thermal Grashof number. The overall thermal profile along the pipe’s length decreases as a result of the nanoparticles. Additionally, lowering the pressure has a similar effect on both velocities. This study makes important contributions to the comprehension of the intricate dynamics of electro-magneto-hydrodynamics hybrid nanofluid flow, thereby laying a foundational framework for optimizing thermodynamic systems in geothermal. The findings of this research hold significant practical implications for the design and engineering of systems aimed at energy conservation and improved heat transfer efficiency in geothermal pipelines.

Induction motors (IMs), as essential components in industrial operations, are subject to various operational abnormalities, such as voltage unbalance, harmonic distortions, under/over voltage supply, and ambient temperature variations. These factors necessitate the de-rating of torque to ensure motor reliability, efficiency, and safe operation within rated power loss limits. Traditional methods for estimating de-rated torque often involve complex and time-intensive calculations, creating challenges in real-time applications. To address these limitations, this manuscript introduces the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a robust predictive tool for de-rated torque estimation under abnormal conditions. This study defines and quantifies main de-rating factors (Dfs), including voltage unbalance, harmonic distortions, and temperature rise, employing MATLAB/Simulink simulations for performance analysis. The proposed ANFIS controller integrates neural networks and fuzzy logic, enabling efficient evaluation of de-rated torque by dynamically adjusting to real-time operating conditions. Validation of the ANFIS predictions against Simulink outcomes highlights its reliability and accuracy, with minimal deviations observed. Results reveal the significant impact of DFs on induction motor (IM) performance. Voltage unbalance and harmonic distortions emerged as primary contributors to reduced torque output, while temperature rise exacerbates power losses and thermal stress on IM components. By mitigating the need for extensive calculations, ANFIS simplifies the process of assessing torque de-rating and ensures rapid, precise predictions. ANFIS controller is trained offline to assess the de-rated torque of the IM under different operating conditions. The results from this training have been validated against Simulink outcomes, confirming the reliability and accuracy of the ANFIS technique. This research advances the understanding of IM performance under non-ideal conditions, offering a practical framework for de-rating torque evaluation and management. The integration of ANFIS as a control mechanism not only optimizes motor efficiency but also extends operational longevity, underscoring its potential for real-world industrial applications.

The rapid adoption of electric vehicles (EVs) has driven the continuous evolution of traction systems, necessitating efficiency, reliability, and performance improvements. Conventional motor designs, such as single-stator permanent magnet synchronous machines (PMSMs) and induction motors, often suffer from limited torque density, inefficient thermal dissipation, high torque ripple, and reduced fault tolerance. These challenges hinder optimal EV performance, particularly under varying load conditions. Dual-stator machines (DSMs) have emerged as a promising alternative. They offer higher torque density, improved power distribution, enhanced thermal management, and increased redundancy, making them more resilient to faults. This paper presents a comprehensive mathematical modelling and operational analysis of DSMs, emphasizing their advantages over traditional motor architectures. Furthermore, the Field-Oriented Control (FOC) strategy, a widely adopted method for high-performance motor control, is explored in depth for its suitability in DSM applications. The challenge here is to designate one stator as the primary induction system, ensuring its power is effectively controlled. When the first stator reaches its maximum power capacity, the second stator compensates accordingly. The control design was specifically developed to achieve this objective. MATLAB-based simulations are conducted to assess efficiency, torque response, and fault-tolerant capability, demonstrating the superior performance of DSMs in EV traction systems. The findings highlight the potential of DSMs to redefine next-generation EV propulsion by enhancing power efficiency, reliability, and operational stability.

Accurate assessment of transformer insulating paper is vital for reliable operation and optimal transformer management, with the Degree of Polymerization (DP) serving as a primary indicator of insulation health. Direct DP measurement is often impractical, prompting this study to explore machine learning models for predicting DP using 2-Furfuraldehyde (2-FAL), a cellulose degradation byproduct measurable in transformer oil. This approach classifies insulation into four categories—Fresh (DP: 700–1200), Lightly Aged (DP: 450–700), Moderately Aged (DP: 250–450), and Worstly Aged (DP < 250)—based on DP values, offering a streamlined alternative to conventional multi-gas diagnostic methods. Supervised machine learning algorithms were developed using IEEE C57.104-2019 standard data, employing regression (Linear Regression, Polynomial Regression, Random Forest Regressor) to predict continuous DP and classification (Logistic Regression, Support Vector Machine with RBF kernel, Random Forest Classifier) to categorize insulation condition. Model performance was evaluated using regression metrics (Mean Squared Error, Mean Absolute Error, R² Score) and classification metrics (accuracy, precision, recall, F1-score). The Random Forest Regressor (R²: 0.894) and Classifier (accuracy: 0.925) demonstrated superior performance, enabling precise, non-invasive DP estimation and condition assessment. These findings highlight the efficacy of 2-FAL-based machine learning models for transformer health monitoring, facilitating predictive maintenance and enhancing operational reliability.

This paper deals with an energy management problem to ensure the best performance of the recharging tools used in electric vehicles. The main objective of this work is to find the optimal condition for controlling a hybrid recharging system by regrouping the photovoltaic cells and fuel cells. The photovoltaic and fuel cell systems were connected in parallel via two converters to feed either a lithium battery bank or the main traction motor. This combination of energy sources resulted in a hybrid recharging system. The mathematical model of the overall recharging system and the designed power management loop was developed, taking into account multiple aspects, including vehicle loading, the stepwise mathematical modelling of each component, and a detailed discussion of the required electronic equipment. Finally, a simplistic management loop was designed and implemented. Multiple case studies were simulated, statistical approaches were used to quantify the contribution of each recharging method, and the benefits of the combination of the two sources were evaluated. The energetic performance of an electric vehicle with the proposed hybrid recharging tool under various conditions, including static and dynamic modes, was simulated using the MATLAB/Simulink tool. The results suggest that despite the additional weight of PV panels, the combination of the PV and FC systems improves the vehicle’s energetic performance and provides a higher charging capacity instead of using an FC alone. A comparison with similar studies revealed that the proposed model has a higher efficiency. Finally, the benefits and drawbacks of each solution are discussed to emphasise the significance of the hybrid recharging system.

This paper presents a straightforward power management algorithm that supervises the contribution of more than one energy source for charging a vehicle, even if the car is in motion. The system is composed of a wireless charging system, photovoltaic (PV) generator, fuel cell (FC), and a battery system. It also contains a group of power converters associated with each energy resource to make the necessary adaptation between the input and output electrical signals. The boost converter relates to the PV/FC, and the boost–buck converter is connected with the battery pack. In this work, the wireless charging, FC, and PV systems are connected in parallel via a DC/DC converter for feeding the battery bank when the given energy is in excess. Therefore, for each of these elements, the mathematical model is formulated, then the corresponding power management loop is built, which presents the significant contribution of this paper. The efficient power management methodology proposed in this work was verified on Matlab/Simulink platforms. The battery state of charge and the hydrogen consumption obtained results were compared to show the effectiveness of this multi-source system.

In the era of renewable energy integration, precise solar energy modeling in power systems is crucial for optimized generation planning and facilitating sustainable energy transitions. The present research proposes a comprehensive framework for assessing the operational reliability of solar integrated systems, validated using the IEEE RTS 96 test system. A robust uncertainty model has been developed to characterize variations in solar irradiance to address the uncertainties in solar panel output, followed by a multi-state modeling approach to account for the dynamic nature of solar panel output. The research introduces a time series-based ‘non-linear autoregressive neural network’ (NAR-Net) to forecast the solar irradiance levels five days ahead to optimize solar power efficiency. A comparative analysis has been conducted of three other state-of-the-art approaches, such as auto-regressive (AR), auto-regressive with moving average, and multi-layer perceptron, for predicting solar irradiance. Performance metrics, including mean square error, regression, and computational time, were evaluated to demonstrate the efficacy of the NAR-Net. The proposed prediction-based approach enhances the reliability of power generation planning by integrating modeling, which is based on forecasting. It is found that the proposed method achieves an accuracy of 98% w.r.t its counterpart. Moreover, the assessment to optimize the operational reliability of solar-integrated systems and improve generation planning for a sustainable energy future is achieved.

This study investigates the dynamic behavior of the linear quadratic model (LQM), a fundamental framework in radiation biology that describes cellular response to radiation, particularly in the context of DNA damage and cancer progression. The LQM was originally developed to quantify radiation-induced cell death and repair mechanisms, with a focus on double-stranded DNA breaks, the most critical type of radiation damage. Despite advances in tracking tumor cell dissemination, the mechanisms underlying cancer invasion remain poorly understood. Mathematical modeling, particularly through partial differential equations, has become an essential tool for simulating tumor growth and optimizing therapeutic strategies, bridging the gap between theoretical biology and clinical applications. In this work, we employ advanced analytical techniques, including the generalized Arnous method, modified F-expansion method, and generalized exponential rational function approaches to solve the model for the first time. By transforming the governing PDE into an ordinary differential equation using -derivative and wave transformations, we derive exact solutions in the form of dark, bright, singular, mixed, complex, and combined soliton waves. These solutions, visualized through 2D and 3D plots, reveal the system’s behavior under varying parameters, demonstrating the computational power and effectiveness of the applied methods. The results not only validate the proposed techniques but also enhance our understanding of the model’s nonlinear dynamics. The novel findings presented here are expected to advance future research in radiation biology and cancer treatment optimization.

This manuscript presents an innovative control strategy for the Hybrid Excitation Permanent Magnet Synchronous Motor (HEPMSM) designed for electric vehicle (EV) applications. The strategy combines Maximum Torque Point Tracking (MTPT) and Maximum Torque Per Ampere (MTPA) techniques to track the ideal torque-speed profile, ensuring maximum torque at low speeds for starting and climbing, and high power at higher speeds for cruising. A novel unidirectional excitation current method is proposed to replace traditional bidirectional field current control, eliminating the risk of permanent magnet demagnetization, reducing copper losses, and increasing efficiency. This approach extends the constant power (CP) region by a 4.2:1 ratio. The manuscript also introduces a detailed mathematical model, considering both iron core losses and their impact on the EV profile. Additionally, the Multi-Objective Ant Lion Optimizer (MOALO) algorithm is used in two stages: first to optimize the hybridization ratio (HR) and base speed ( N b ), and second to analyze the effect of varying the hybridization ratio while maintaining constrained output power. The proposed strategy is validated through MATLAB simulations, demonstrating its effectiveness in achieving high acceleration, efficiency, and reliability for EV applications.