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Debris flow prediction remains a critical yet challenging task due to limitations in accuracy, generalization, and the ability to model the complex nonlinear behavior inherent to debris flow velocity. In response to these challenges, this study presents a novel predictive framework that integrates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) within a deep learning architecture. To further enhance performance, the model is optimized using the Coronavirus Herd Immunity Optimizer (CHIO), a state-of-the-art metaheuristic algorithm designed to fine-tune hyperparameters, thereby improving both predictive accuracy and generalizability. The proposed CHIO-CNN-GRU model was trained and validated using 147 datasets of debris flow dynamics and systematically benchmarked against existing methods. Experimental findings highlight its excellent performance, delivering high predictive accuracy (R 2  = 0.9721, MAPE = 0.0944) and robustness by efficiently capturing the nonlinear physical traits of debris flow velocity. To evaluate its real-world applicability, the model was further validated with 20 additional datasets derived from flume simulation experiments. The results confirmed its strong generalization capability and practical robustness, with improved predictive accuracy (R 2  = 0.9225, MAPE = 0.0545) compared to existing models.This research highlights the efficacy of combining advanced deep learning architectures with intelligent optimization algorithms in solving complex geophysical prediction tasks. The CHIO-CNN-GRU model establishes a novel standard for predicting debris flow velocity, offering a reliable and adaptable tool for real-time disaster prediction and mitigation. The findings provide a robust foundation for further advancements in debris flow research and engineering applications.

This paper proposes an economical-environmental-technical dispatch (EETD) model for adjusted IEEE 30-bus and IEEE 57-bus systems, including thermal and high penetration of renewable energy sources (RESs). Total fuel costs, emissions level, power losses, voltage deviation, and voltage stability are the five objectives addressed in this work. A large set of equality and inequality constraints are included in the problem formulation. Metaheuristic optimization approaches—Coronavirus herd immunity optimizer (CHIO), salp swarm algorithm (SSA), and ant lion optimizer (ALO)—are used to identify the optimal cost of generation, emissions, voltage deviation, losses, and voltage stability solutions. Several scenarios are reviewed to validate the problem-solving competency of the defined optimisation model. Numerous scenarios are studied to verify the proficiency of the optimisation model in problem-solving. The multi-objective problem is converted into a normalized one-objective issue through a weighted sum-approach utilizing the analytical hierarchy process (AHP). Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) is presented for identifying the optimal value of Pareto alternatives. Ultimately, the results achieved reveal that the proposed CHIO performs the other approaches in the EETD problem-solving.

The success of the supervised learning process for feedforward neural networks, especially multilayer perceptron neural network (MLP), depends on the suitable configuration of its controlling parameters (i.e., weights and biases). Normally, the gradient descent method is used to find the optimal values of weights and biases. The gradient descent method suffers from the local optimal trap and slow convergence. Therefore, stochastic approximation methods such as metaheuristics are invited. Coronavirus herd immunity optimizer (CHIO) is a recent metaheuristic human-based algorithm stemmed from the herd immunity mechanism as a way to treat the spread of the coronavirus pandemic. In this paper, an external archive strategy is proposed and applied to direct the population closer to more promising search regions. The external archive is implemented during the algorithm evolution, and it saves the best solutions to be used later. This enhanced version of CHIO is called ACHIO. The algorithm is utilized in the training process of MLP to find its optimal controlling parameters thus empowering their classification accuracy. The proposed approach is evaluated using 15 classification datasets with classes ranging between 2 to 10. The performance of ACHIO is compared against six well-known swarm intelligence algorithms and the original CHIO in terms of classification accuracy. Interestingly, ACHIO is able to produce accurate results that excel other comparative methods in ten out of the fifteen classification datasets and very competitive results for others.

Electrical distribution systems are essential for the delivery of power to end users. Enhancing the performance of these systems is a strategic approach to meet increasing demands on the electrical grid. Recently, many types of power electronics converters are employed for such enhancement of performance. These converters are either used to alter the topology of the system such as Soft Open Points (SOPs) or are connected directly at buses. For an achievable enhancement of distribution system to occur, an optimization problem must be articulated. In this work a Corona Herd Immunity Optimizer (CHIO), which is inspired by the world wide Corona outbreak, is employed to find the optimal apparent power flow, these converters must condition to achieve a desired objective. In this context two CHIOs are used consecutively to achieve minimal active losses through the connection of two back to back converters and maintain voltage profile within limits using a power converter that condition reactive power flow. Results underscore CHIO effectiveness in minimizing power losses and maintaining voltage stability, demonstrating its potential to enhance overall system efficiency. Simulations conducted in MATLAB, on a standard distribution system, evaluated multiple power electronics converter positions, incorporating Distributed Generation, further affirming the robustness of the CHIO approach.

This study explores the application of the epoxidation process of poly‐β‐myrcene, a constituent of the natural resin from Chios Mastic trees (Pistacia Lentiscus L.), as an educational instrument for teaching Green Chemistry and Engineering to students at various academic levels. The study provides a comprehensive presentation of foundational knowledge essential for interpreting the subsequent experimental data. Consequently, the production process that leads to the production of Mastic Epoxide (MASTEP) stands as an invaluable pedagogical resource, enabling educators to impart crucial principles of Green Chemistry and Engineering to both pre‐graduate and post‐graduate students. By employing MASTEP as a case study, this educational approach actively involves students in a dynamic learning environment. Through this methodology, learners develop a profound comprehension of sustainability, innovation, and good practices. The integration of the MASTEP concept into the curriculum would foster a deeper understanding of responsible methodologies among aspiring chemical engineers and scientists, equipping them to make substantial contributions towards a more sustainable global landscape. This educational model aims to contribute to preparing future generations for a pivotal role in fostering a sustainable world through their professional endeavors. The epoxidation reaction of natural poly‐β‐myrcene serves as a valuable educational tool for teaching Green Chemistry and Engineering to pregraduate and graduate students. This perspective article bridges theory and practice, empowering green teaching methodology, and invites implementation to provoke statistical evaluation.

Τriterpenic acids represent a prominent class of bioactive compounds, with a wide range of biological properties, including anti-inflammatory, antiviral, and anticancer effects. Among them, 24Z-isomasticadienonic acid (IMNA), a major constituent of Chios Mastic Gum, has attracted little attention compared with other well-studied triterpenes such as oleanolic or betulinic acid, largely because its isolation in sufficient purity and quantity was only recently achieved. In this study, a series of IMNA analogs was synthesized through targeted modifications at the A-ring. These included the introduction of heteroatoms at position 2, the incorporation of heterocyclic rings such as an oxazole and a thiazole, and rearrangements of the ring structure. The new compounds were evaluated for their antiproliferative activity against a diverse panel of cancer cell lines (Capan-1, HCT-116, LN-229, NCI-H460, DND-41, HL-60, K-562, Z-138). Among the synthesized analogs, compounds 3, 7 and 9 demonstrated selective anticancer activity toward the Capan-1 cell line, whereas compounds 6 and 10 exhibited broad-spectrum cytotoxic effects across multiple cancer cell lines. Overall, these findings highlight IMNA as a promising scaffold for anticancer drug design and demonstrate the value of A-ring modifications in improving activity and selectivity.

This study aimed to evaluate the use of a by-product, olive cake silage (OCS), as a forage replacement in sheep diets for the improvement of fatty acid (FA) content of milk and thus, the lipids of the ovine halloumi cheese produced. Sixty second-parity purebred Chios ewes in mid-lactation were assigned to three diet treatments (2 lots of 10 animals per treatment) receiving 0%, 10%, and 20% of OCS on dry matter basis for 3 weeks (treatments S0, S10, and S20, respectively). Halloumi cheese was manufactured from fresh raw milk of ewes fed the three different diets. Inclusion of OCS in the diets increased linearly the concentration in milk of unsaturated FA up to 20%, monounsaturated FA up to 23%, polyunsaturated FA up to 11%, rumenic acid (CLA cis-9, trans-11) up to 61%, and consequently reduced the atherogenicity and thrombogenicity milk indices by 31% and 27%, for the S10 and S20 treatments, respectively, compared with the control treatment. Moreover, these differences were carried over to the lipid profile of ovine halloumi cheese showing, on average, more than 25% increase of unsaturated, polyunsaturated, and monounsaturated FA, with particularly enhanced oleic and rumenic acid content. These changes resulted in reduced atherogenicity by 29% and 45% and thrombogenicity by 23% and 24% of ovine halloumi cheese made from milk of S10 and S20 diets, respectively. Milk yield, milk fat, or protein content was not affected by S10 or S20 feeding treatments compared to control. Overall, the applied ensiling method of olive cake produces a by-product that can be included as a forage replacement up to 20% of DM intake in Chios sheep without adversely affecting the lactating performance. Furthermore, the present study showed that such substitution improves the lipid quality of milk and related halloumi cheese enriching these ovine dairy products with beneficial to human health fatty acids.

Hybrid renewable energy systems (HRESs) that merge wind and solar power with energy storage offer a trustworthy and affordable alternative for remote consumers. Energy storage integrates variable wind and solar energy, while energy management enhances system reliability, reduces costs, and minimizes environmental impact. This paper proposes a novel methodology called the coronavirus herd immunity optimizer (CHIO) for modeling and sizing HRESs. The CHIO algorithm uniquely balances exploration and exploitation phases inspired by herd immunity principles, setting it apart from traditional optimization methods. It addresses the optimization problem of minimizing the system's overall net present cost, aiming to reduce the cost of energy (COE) while improving system reliability. We investigate the efficacy of the CHIO method in solving hybrid system design issues and compare its performance to other popular optimization strategies, such as cuckoo search (CS) and particle swarm optimization (PSO). The results demonstrate that CHIO achieves superior solutions to the optimization problem, producing energy with a lower COE and higher reliability compared to PSO and CS.

Chios mastic gum, the product of the tree Pistacia lentiscus var. Chia, has been used for more than 2500 years in traditional Greek medicine for treating several diseases, thanks to the anti-inflammatory and antioxidant properties of its components. Despite the long-time use of mastic in gastroenterology and in particular in chronic-inflammation-associated diseases, to date, the literature lacks reviews regarding this topic. The aim of the present work is to summarize available data on the effects of P. lentiscus on inflammatory bowel disease. A comprehensive review of this topic could drive researchers to conduct future studies aimed at deeply investigating P. lentiscus effects and hypothesizing a mechanism of action. The present review, indeed, schematizes the possible bioactive components of mastic gum. Particular care is given to P. lentiscus var. Chia medicaments’ and supplements’ chemical compositions and their pharmacological action in inflammatory bowel disease.

Pistacia lentiscus L. (P. lentiscus) is an evergreen shrub (Anacardiaceae family) primarily found in the Mediterranean region. The plant has been thoroughly characterized, resulting in a high concentration of bioactive compounds as flavonoids and phenolics. Moreover, P. lentiscus was revealed to possess a great nutritional and industrial importance because of its variety of biological activities, including antibacterial, anti-inflammatory, anti-atherogenic and antioxidant properties. Many of its beneficial health properties and applications date back to antiquity, and the European Medicines Agency officially acknowledged it as an herbal medicinal product. Indeed, it is widely employed in conventional medicine to treat several diseases, including type 2 diabetes (T2D). On this basis, this review aims to summarize and describe the chemical composition of different parts of the plant and highlight the potential of P. lentiscus, focusing on its antidiabetic activities. The plant kingdom is drawing increasing attention because of its complexity of natural molecules in the research of novel bioactive compounds for drug development. In this context, P. lentiscus demonstrated several in vitro and in vivo antidiabetic effects, acting upon many therapeutic T2D targets. Therefore, the information available in this review highlighted the multitarget effects of P. lentiscus and its great potential in T2D treatment.