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The integration of renewable energy sources into the power grid is essential for sustainable development, yet it presents significant dependability challenges, particularly in terms of reliability, stability, and robustness due to the inherent variability of these sources. This research introduces a novel hybrid methodology that combines Monte Carlo simulation with Newton-Raphson power flow analysis to enhance the reliability assessment of grid-connected hybrid renewable energy systems. This innovative approach uniquely addresses the limitations of existing methodologies by merging the probabilistic handling of uncertainties with precise deterministic power flow analysis. Our hybrid method significantly reduces the Loss of Load Expectation (LOLE) to 5 h per year and the Loss of Load Energy Expectation (LOEE) to 200 MWh per year, outperforming traditional methods which typically report LOLEs of 2020 h/year and LOEEs of 10001000 MWh/year. Additionally, the hybrid method achieves a reduction in power losses to 1.2%, showcasing its superior efficiency compared to the 2.5% losses seen with standalone Monte Carlo methods. Real-time validation using the IEEE-30 bus model further confirms the practical applicability and robustness of our approach, making it a pivotal tool for enhancing grid stability and optimizing renewable energy integration. This research not only advances the methodology for reliability assessment but also sets a new standard for balancing accuracy and computational efficiency in energy system management. The implications of this work are far-reaching, offering significant contributions to both grid reliability and the sustainable management of renewable energy resources. [ABSTRACT FROM AUTHOR]

Food security is a basic human right that guarantees humans an adequate amount of nutritious food. However, plant viruses and agricultural pests cause real damage to food sources, leading to negative impacts on meeting the human right of obtaining a sufficient amount of food. Understanding infectious disease dynamics can help us to design appropriate control and prevention strategies. Although cassava is among the most produced and consumed crops and greatly contributes to food security, cassava mosaic disease causes a decrease in photosynthesis and reduces cassava yield, resulting in a lack of crops. This paper developed a fractional model for cassava mosaic disease (CMD) dynamics based on the Caputo–Fabrizio (CF) fractional derivative to decrease cassava plant infection. We used fixed-point theory to study the existence of a unique solution in the form of the CMD model. A stability analysis of the model was conducted by using fixed-point theory and the Picard technique. A new numerical scheme was proposed for solving the nonlinear system of a fractional model in the sense of the CF-derivative and applied to obtain numerical simulations for a fractional model of the dynamics of CMD. The obtained results are described using figures that show the dynamics and behaviors of the compartments of CMD, and it is concluded that decreasing the population of whitefly vectors can prevent cassava plants from becoming infected better than increasing the recovery rate of the infected cassava plants. [ABSTRACT FROM AUTHOR]

Effectively managing the delivery of Ready Mixed Concrete (RMC) for diverse construction projects poses a significant challenge for concrete batch plant supervisors. The formulation of an efficient schedule for dispatching concrete truck mixers is paramount, requiring a delicate balance between practicality and adaptability. The concrete batch plant administrator must establish a productive timetable that aligns activities at the concrete batch facility and construction sites. RMC delivery requests inundate the ready mixed concrete batch plant during specific operational hours, necessitating swift decision-making by the concrete batch plant administrator to create a dispatching schedule that meets the varied requirements of different construction projects. The dispatching schedule time for all batch plants is primarily influenced by the dispatcher administrator's experience and preferences. For instance, a ready mixed concrete batch plant administrator may opt to dispatch as many truck mixers as possible to the busiest construction project. However, while the batch plant waits for the RMC trucks to arrive, this strategy may cause truck congestion at the project site. Clearly, an effective dispatching schedule is crucial for enhancing the overall system efficiency of construction projects and batch plants. This paper initially presents various optimization methods and subsequently analyses the ready mixed concrete system and its transportation strategy, delineating the factors impacting the dispatching schedule. The study then introduces a systematic model employing simulation processes and Genetic Algorithms to optimize the dispatching schedule while minimizing the waiting time of RMC truck mixers at construction projects. The goal of this optimisation is to meet the needs for RMC delivery from various building projects while minimising the impact on casting processes. Ultimately, concrete batch plant administrators will find assistance in organising and managing the dispatching procedure from an automated software that was created utilising the C-sharp programming language and included all pertinent modules. Three cases from the available research were examined, and field data from the DECOM batching facility was applied over the course of three working days to evaluate the software's effectiveness. [ABSTRACT FROM AUTHOR]

In construction projects, reduce the cost while keeping the level of quality is an important matter, which defined as Value Engineering (VE). The main concern of Value Engineering (VE) is to search for the most effective type of works in construction projects to save project direct cost by maximizing or keeping target quality for the studied project. It is a difficult challenge to keep a balance between maximizing project quality with corresponding its minimum cost and duration at the same time. When the value-engineering process is used, each factor affecting the cost is taken into consideration all detailed studies as a specific unified factor to determine its suitable alternatives. This Paper investigates ordering the types of works in terms of effectiveness in the cost saving using the value engineering process for construction projects in Saudi Arabia. A questionnaire has been used to obtain results of only valid samples from all 205 responses, which analyzed and discussed. This study concerned with some type of works in a correlation with the four main engineering categories: construction, mechanical, electrical, and finishing work. The results indicate the difference effect of using value engineering between these major categories of construction engineering works. Finally, the importance score index applied to appraise the influence of the tested types of works when applying VE process on the project cost. This paper also serves as a resource and guide for construction managers, contractors, and more efficient management in the construction of Saudi projects to achieve a sustainable level of cost and quality. [ABSTRACT FROM AUTHOR]

This document is a corrigendum, or correction, for an article titled "A data-driven ensemble technique for the detection of false data injection attacks in the smart grid framework." The correction addresses an error in one of the affiliations listed for the authors. The correct affiliation should be "College of Engineering, University of Business and Technology, Jeddah, Saudi Arabia." The authors apologize for the error and state that it does not affect the scientific conclusions of the article. The original article has been updated. The document also includes information about the publisher's note, open access approval, and contact information for the authors. [Extracted from the article]