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Morocco is an energy-deficient country depending on almost 94% of energy imports to fuel its growing economy. Due to its fast-growing population, Morocco’s energy consumption is projected to increase significantly, adding more pressure on the energy system. On the other hand, the rising tension of scarcity of resources, energy price fluctuations, and environmental issues have all made energy security one of its top priorities. Therefore, Morocco launched the National Energy Strategy (NES) in 2009 to reach 42% renewable generation by 2020, which was renewed to up to 52% by 2050. This study analyzes Morocco’s energy security under the 4-As framework from 2000 to 2016.The 4-As methodology aims to assess and graphically illustrate the changes in Morocco’s energy security by mapping these changes into four key dimensions: the availability of energy resources, the applicability of technology, the acceptability by the environment and society, and the affordability of energy resources. The quantitative analysis shows that Morocco’s energy security performance was at its optimum during the first period of study (2000–2004) but then regressed for the remainder of the study period, as energy imports and prices increased, in addition to the low performance in applicability characterized by low energy efficiency. To improve Morocco’s energy security status and move toward a sustainable energy transition, this study suggests integrating a higher share of renewable energy into the energy mix and boosting efficient technologies through a large scale of green finance and green investment projects.

This work aims to study the time-frequency relationship between the recent COVID-19 pandemic and instabilities in oil price and the stock market, geopolitical risks, and uncertainty in the economic policy in the USA, Europe, and China. The coherence wavelet method and the wavelet-based Granger causality tests are applied to the data (31st December 2019 to 1st August 2020) based on daily COVID-19 observations, oil prices, US-EPU, the US geopolitical risk index, and the US stock price index. The short- and long-term COVID-19 consequences are depicted differently and may initially be viewed as an economic crisis. The results illustrate the reduced industrial productivity, which intensifies with the increase in the pandemic’s severeness (i.e., a 10.57% decrease in the productivity index with a 1% increase in the pandemic severeness). Similarly, indices for oil demand, stock market, GDP growth, and electricity demand decrease significantly with an increase in the pandemic severeness index (i.e., a 1% increase in the pandemic severeness results in a 0.9%, 0.67%, 1.12%, and 0.65% decrease, respectively). However, the oil market shows low co-movement with the stock exchange, exchange rate, and gold markets. Therefore, investors and the government are recommended to invest in the oil market to generate revenue during the sanctions period.

Dense coding is the seminal example of how entanglement can boost qubit communication, from sending one bit to sending two bits. This is made possible by projecting separate particles onto a maximally entangled basis. We investigate more general communication tasks, in both theory and experiment, and show that simpler measurements enable strong and sometimes even optimal entanglement-assisted qubit communication protocols. Using only partial Bell state analysers for two qubits, we demonstrate quantum correlations that cannot be simulated with two bits of classical communication. Then, we show that there exists an established and operationally meaningful task for which product measurements are sufficient for the strongest possible quantum predictions based on a maximally entangled two-qubit state. Our results reveal that there are scenarios in which the power of entanglement in enhancing quantum communication can be harvested in simple and scalable optical experiments. Quantifying communication capabilities produced by sharing an entangled qubit pair is still a subject of debate. Here the authors show that there are communication tasks for which sharing an entangled pair gives higher power than sharing two classical bits, even when there is no entanglement in the measurements.

The nexus between economic growth (EG) and carbon emission has been examined extensively, specifically in consumption-based CO 2 . However, the role of human capital, green energy, and sustainable economic growth in determining the carbon emission is yet to be explored specifically from emerging economies. This study aimed to examine the impact of human capital index, green energy, EG in terms of GDP, and square of GDP on carbon emission for long-short run with the help of CS-ARDL. The data for study variables was collected from 1995 to 2018. The study findings confirmed that there exists CSD, cointegration, and slope heterogeneity among the study variables. In contrast, the output through CS-ARDL indicated that the main reason for higher carbon emission in the targeted economies are economic growth under long-short run estimation. Additionally, the role of green energy and human capital index is also constructive in lowering the environmental degradation for both long-run and short-run estimation. Finally, some policy implications are also convassed at the end of the research.

Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters include cement content, coarse aggregate to fine aggregate ratio, water, and superplasticizer. Moreover, statistical analyses like MAE, RSE, and RRMSE are used to evaluate the performance of models. The RF ensemble model outbursts in performance as it uses a weak base learner decision tree and gives an adamant determination of coefficient R2 = 0.96 with fewer errors. The GEP algorithm depicts a good response in between actual values and prediction values with an empirical relation. An external statistical check is also applied on RF and GEP models to validate the variables with data points. Artificial neural networks (ANNs) and decision tree (DT) are also used on a given data sample and comparison is made with the aforementioned models. Permutation features using python are done on the variables to give an influential parameter. The machine learning algorithm reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model.

Sliding mode control (SMC) is a robust nonlinear control framework that enforces system trajectories onto predefined manifolds, providing strong robustness guarantees against uncertainties. However, SMC inherently introduces unwanted transients or chattering in system state trajectories, which may cause issues especially for sensitive applications such as regulation of drug administration. This paper proposes a multi-input smooth sliding mode control (MISSMC) strategy that combines radiotherapy and chemotherapy for a nonlinear tumor–immune dynamical system described by ordinary differential equations. The closed-loop system is first analyzed to establish key qualitative properties: all state variables remain positive and bounded, the sliding surfaces exhibit asymptotic convergence, and explicit analytical upper bounds on the cumulative therapy doses are derived under clinically motivated constraints. On this basis, a smooth hyperbolic-tangent sliding manifold and associated control law are designed to regulate the radiation and drug infusion rates. While the use of a hyperbolic-tangent smoothing function effectively suppresses chattering, it introduces a small steady-state error due to the presence of a boundary layer. To address this limitation, integral action is incorporated into the sliding surfaces, ensuring asymptotic convergence of tumor state and reducing residual steady-state error, while enhancing robustness against model uncertainties and parameter variations. Numerical simulations, based on a brain-tumor case study, show that the proposed smooth SMC markedly suppresses transient overshoots in both states and control inputs, while preserving effective tumor reduction. Compared with a conventional (non-smooth) SMC scheme, the MISSMC controller reduces baseline radiation and chemotherapy intensities on average by roughly 70%. Similarly, MISSMC lowers the overall cumulative doses on average by about 40%, without degrading the therapeutic outcome. The resulting integral smooth SMC framework therefore offers a rigorous nonlinear-systems approach to designing combined radio-chemotherapy protocols with guaranteed positivity, boundedness, and asymptotic stabilization of the closed-loop system, together with explicit bounds on the control inputs.

The aim of the study is to estimate the nexus between energy insecurity and energy poverty with the role of climate change and other environmental concerns. We used DEA like WP methods and properties of MCDA, a most common form of data envelopment analysis (DEA) to estimate the nexus between constructs. This paper presents a measurement and analysis of G7 countries’ energy, economic, social, and environmental performance associated with energy poverty indexes. The study used the multiple, comprehensive, and relevant set of indicators, including energy economics and environmental consideration of energy poverty. The net energy consumption of al G7 economies is equal to 34 percent of the entire world along with the net estimate GDP score of around 50 percent. Using DEA modelling and estimation technique, our research presented valuable insights for readers, theorists and policy makers on energy, environment, energy poverty and climate change mitigation. For this reasons, all these indicators combined in a mathematical composite indicator to measure energy, economic, social, and environmental performance index (EPI). Results show that Canada has the highest EPII score, which shows that Canada’s capacity to deal with energy self-sufficiency, economic development, and environmental performance is greater than the other G7 countries. France and Italy rank second and third. Japan comes next with 0.50 EPI scores, while the USA has the lowest average EPI score environment vulnerable even though have higher economic development among the G7 group countries. We suggest a policy framework to strengthen the subject matter of the study.

Currently, COVID-19 due to emergence of various variants shows no signs of slowing down and has affected every aspect of life with significant negative impact on economic and energy structures around the world. As a result, the governments around the world have introduced policy responses to help businesses and industrial units to overcome the consequences of compliance with COVID-19 strategies. Our analysis indicates that global energy sector is one of the most severely affected industries as energy price mechanisms, energy demand, and energy supply have shown great uncertainty under these unprecedented economic and social changes. In this regard, we provide brief overview of demand, supply, and pricing structure of energy products as well as policy mechanisms to provide better outlook about how industrial sector can cope with energy consumption in the post pandemic era. We further propose changes in the existing policy mechanisms so that transition towards renewable energy sources under different environmental agreements can be achieved. Moreover, as a reference, we outline major challenges and policy recommendations to ease energy transition from fossil fuels to environmental friendly energy mix.

Trehalose is a natural non-reducing sugar that is found in the vast majority of organisms such as bacteria, yeasts, invertebrates and even in plants. Regarding its features, it is considered as a unique compound. It plays a key role as a carbon source in lower organisms and as an osmoprotectant or a stabilizing molecule in higher animals and plants. Although in plants it is present in a minor quantity, its levels rise upon exposure to abiotic stresses. Trehalose is believed to play a protective role against different abiotic stressful cues such as temperature extremes, salinity, desiccation. Moreover, it regulates water use efficiency and stomatal movement in most plants. Detectable endogenous trehalose levels are vital for sustaining growth under stressful cues. Exogenously applied trehalose in low amounts mitigates physiological and biochemical disorders induced by various abiotic stresses, delays leaf abscission and stimulates flowering in crops. External application of trehalose also up-regulates the stress responsive genes in plants exposed to environmental cues. The genetically modified plants with trehalose biosynthesis genes exhibit improved tolerance against stressful conditions. An increased level of trehalose has been observed in transgenic plants over-expressing genes of microbial trehalose biosynthesis. However, these transgenic plants display enhanced tolerance to heat, cold, salinity, and drought tolerance. Due to multiple bio-functions of this sugar, it has gained considerable ground in various fields. However, exogenous use of this bio-safe sugar would only be possible under field conditions upon adopting strategies of low-cost production of trehalose. In short, trehalose is a unique chemical that preserves vitality of plant life under harsh ecological conditions. Certainly, the new findings of this disaccharide will revolutionize a wide array of new avenues.

The novelty and growing sophistication of cyber threats mean that high accuracy and interpretable machine learning models are needed more than ever before for Intrusion Detection and Prevention Systems. This study aims to solve this challenge by applying Explainable AI techniques, including Shapley Additive explanations feature selection, to improve model performance, robustness, and transparency. The method systematically employs different classifiers and proposes a new hybrid method called Hybrid Bagging-Boosting and Boosting on Residuals. Then, performance is taken in four steps: the multistep evaluation of hybrid ensemble learning methods for binary classification and fine-tuning of performance; feature selection using Shapley Additive explanations values retraining the hybrid model for better performance and reducing overfitting; the generalization of the proposed model for multiclass classification; and the evaluation using standard information metrics such as accuracy, precision, recall, and F1-score. Key results indicate that the proposed methods outperform state-of-the-art algorithms, achieving a peak accuracy of 98.47% and an F1 score of 96.19%. These improvements stem from advanced feature selection and resampling techniques, enhancing model accuracy and balancing precision and recall. Integrating Shapley Additive explanations-based feature selection with hybrid ensemble methods significantly boosts the predictive and explanatory power of Intrusion Detection and Prevention Systems, addressing common pitfalls in traditional cybersecurity models. This study paves the way for further research on statistical innovations to enhance Intrusion Detection and Prevention Systems performance.