Search Results (8,304)

Numerous conflicting objectives exist in the engineering field, and resolving these conflicts to reduce costs constitutes a problem that demands top-priority consideration. A model for tool wear and a multi-quadratic regression model for milling forces were developed to accurately predict the trends of wear on the rake face of the milling tool and the variations in milling forces. The influence of milling parameters (spindle speed, n; feed rate, v_f; axial milling depth, a_p) on both the wear of the rake face and milling force was analyzed by means of orthogonal experiments. The findings indicated that the impact of these parameters on the wear ranked in the following order: n > v_f > a_p. In contrast, for milling force, F, the ranking was a_p > v_f > n. Utilizing MATLAB’s genetic algorithm, an optimization procedure was conducted with multiple objectives including the wear of the rake face, milling force, and material removal rate; subsequently, a Pareto optimal solution set was generated for milling parameters based on practical processing requirements. Full article

Argania spinosa (L.) Skeels is a unique endemic species in Morocco, renowned for its ecological characteristics and socio-economic importance. In Morocco, recent years have seen an exacerbation of the harmful effects of climate change, leading to an alarming decline in the natural regeneration of this species in its original habitats. It seems that the only viable solution lies in the domestication of this genetic heritage. This study marks the first in-depth investigation of the impact of various climatic and edaphic factors on the morphological and physiological traits of Argania spinosa young plants, assessed in six separate orchards and observed over four seasons (March 2022 (Winter), June 2022 (Summer), November 2022 (Autumn), and March 2023 (Winter)). A climatic assessment was carried out at each site, including measurements of rainfall, maximum and minimum temperatures, mean temperature, air temperature, and wind speed. The soil was analyzed for the pH, electrical conductivity (EC), water content, limestone (CaCO₃), Kjeldahl nitrogen (N), available phosphorus (P₂O₅), organic matter (OM), and carbon/nitrogen ratio (C/N). To gain a better understanding of the morphophysiological characteristics of young argan seedlings, we carried out various observations, such as measuring the height and diameter of aerial parts, and the water content of leaves (WCL) and branches (WCB), quantifying chlorophyll (mg/m²) and leaf area. The results revealed a significant impact of edaphic and climatic factors on the morphophysiological parameters of young argan trees. Results revealed significant correlations of young argan plants between edaphic and climatic factors and morphophysiological parameters. The Tamjloujt site, characterized by protective vegetation cover, showed optimal growth conditions with the highest leaf and branch water content (46.89 ± 4.06% and 37.76 ± 3.51%, respectively), maximum height growth (91.33 ± 28.68 mm), trunk diameter (24.85 ± 3.78 mm), and leaf surface area (69.33 ± 19.28 mm²) during Summer 2022. The Saharan zone of Laqsabi exhibited peak chlorophyll concentrations (506.9 ± 92.25 mg/m²) during Autumn 2022, due to high temperatures. The mountainous environment of Imoulass negatively impacted plant growth (mean height: 52.61 ± 12.37 mm; diameter: 6.46 ± 1.57 mm) due to harsh climatic and edaphic conditions. This research provides vital knowledge regarding the environmental factors influencing the establishment of young argan plants within the Argan Biosphere Reserve. This contributes to the development of more effective domestication strategies and the restoration of agroecosystems. The aim is to use this knowledge to promote the rehabilitation and sustainability of argan agroecosystems. Full article

With the advancement of forestry modernization, the research and development of forestry vehicles provide solid technical support for the efficiency and sustainability of forest operations. This study aims to reduce the mass of the forest-use tri-axle unmanned vehicle frame through structural optimization design, improve its static and dynamic characteristics, and enhance vehicle mobility and environmental adaptability while maintaining or enhancing its structural strength and stability. Initially, the finite element model of the vehicle frame was established using the finite element software Hypermesh (2022), and its static and dynamic characteristics were analyzed using OptiStruct (2022) software. The accuracy of the finite element calculations was verified through experiments. Subsequently, a sensitivity analysis method was employed to screen the design variables of the thin-walled beam structure of the forest-use tri-axle unmanned vehicle. Response surface models were created using least squares regression (LSR) and radial basis function network (RBF). Considering indicators such as frame mass, modal frequency, and maximum bending and torsional stresses, the multi-objective genetic algorithm (MOGA) was applied to achieve a multi-objective lightweight design of the vehicle frame. This comprehensive optimization method is rarely reported in forestry vehicle design. By employing the proposed optimization approach, a weight reduction of 10.1 kg (a 7.44% reduction) was achieved for the vehicle frame without compromising its original static and dynamic performance. This significant lightweighting result demonstrates considerable practical application potential in the field of forestry vehicle lightweight design. It responds to the demand for efficient and environmentally friendly forestry machinery under forestry modernization and holds important implications for reducing energy consumption and operational costs. Full article

Fuel blending plays a very important role in petroleum refineries, because it directly affects the quality of the end products, as well as the overall profitability of the refinery. This process of blending involves a combination of various hydrocarbon streams to make fuels that meet specific performance standards and comply with regulatory guidelines. For many decades, most refineries have been dependent on linear programming (LP) models for developing recipes for blending optimization. However, most LP models normally fail to capture the complex nonlinear interaction of blend components with fuel properties, leading to off-specification products that may necessitate re-blending. This work discusses a case study of a hybrid artificial intelligence (AI)-based method for gasoline blending based on a genetic algorithm (GA) combined with an artificial neural network (ANN). AI-based blending systems are more flexible and will enable the refineries to meet the product specifications regularly and result in cost reduction owing to the fall in quality giveaways. The AI-powered process discussed can predict, with much better accuracy, critical combustion properties of gasoline such as the Research Octane Number (RON), Motor Octane Number (MON), and Antiknock Index (AKI), compared to the classical LP models, with the added advantage of optimization of the blend ratio in real time. The results showed that the AI-integrated fuel blending system was able to produce fuel recipes with a mean absolute error (MAE) of 1.4 for the AKI. The obtained MAE is close to the experimental uncertainty of 0.5 octane. A high coefficient of determination (R²) of 0.99 was also obtained when the system was validated with a new set of 57 fuels comprising primary reference fuels and real gasoline blends. The study highlights the potential of AI-integrated systems in transforming traditional fuel blending practices towards sustainable and economically viable refinery operations. Full article

Stop planning is aimed to provide proper services for passenger demand, but diverse stop patterns lead to differences in stop density and travel speeds, impacting the utilization of line capacity. This paper incorporates capacity utilization into stop planning in the strategic line planning stage to trade off the matching between supply and demand and the stop distribution balance among trains. A bi-level programming model is established to formulate the Stackelberg game relation between supply and demand, where the stop distribution imbalance and the passenger travel inefficiency are measured. An adaptive hybrid solving algorithm combined with Genetic Algorithm and Simulated Annealing Algorithm is proposed, with several adaptive operations according to the problem characteristics and optimization degree to improve searching efficiency. A case study on the local network of Beijing–Shanghai High-speed Railway Line demonstrates that the proposed approach can not only mitigate the stop distribution imbalance, but also improve the travel efficiency of passengers, indicating that it can benefit the simultaneous improvement of capacity utilization and service level. Full article

Building optimisation techniques provide a rigorous framework for exploring new optimal design solutions. In this study, a genetic algorithm (GA) was used to investigate the energy efficiency of a vernacular architectural element (Rawshan) in Saudi Arabia. Two objectives were optimised using a GA simulation enhanced: energy consumption optimisation and useful daylight illuminance (UDI) optimisation. A calibrated simulation model of a typical house in Saudi Arabia was used in the study. Several metrics, such as light interference from shadows or other windows, were considered to indicate the importance of the Rawshan. Computational studies were performed using different climatic conditions, and the results were compared with and without a Rawshan element using the weather data of Mecca, Jeddah, Riyadh, and Al-Baha. In this study, the blind thicknesses on the front and sides of the Rawshan were used as optimisation variables. The results showed that using a GA with energy consumption as an objective can reduce energy consumption. One of the methods proposed in the paper can reduce energy consumption by 3.6%, 3.6%, and 16.6% for Mecca, Riyadh, and Al-Baha, respectively. The single-objective optimisation method demonstrated that Rawshan provided sufficient UDI in four cities: Mecca, Jeddah, Riyadh, and Al-Baha. The research provided optimised values for Rawshan blind thicknesses on the front and lateral sides under different optimisation constraints. The results showed that using Rawshans in modern building architecture can reduce energy consumption and improve useful daylight illuminance. Full article

Sargassum fusiforme, an edible seaweed, plays a crucial role in our daily lives by providing essential nutrients, including minerals, to the human body. The detection of mineral content during different growth stages of S. fusiforme benefits the goals of ensuring product quality, meeting diverse consumer needs, and achieving quality classification. Currently, the determination of minerals in S. fusiforme primarily relies on inductively coupled plasma mass spectrometry and other methods, which are time-consuming and labor-intensive. Thus, a rapid and convenient method was developed for the determination of six minerals (i.e., Na, Mg, Ca, Cu, Fe, and K) in S. fusiforme via near-infrared (NIR) spectroscopy based on chemometrics. This study investigated the variations in minerals in S. fusiforme from different growth stages. The effects of four spectral pretreatment methods and three wavelength selection methods, including the synergy interval partial least squares (SI-PLS) algorithm, genetic algorithm (GA), and competitive adaptive reweighted sampling method (CARS) on the model optimization, were evaluated. Superior CARS-PLS models were established for Na, Mg, Ca, Cu, Fe, and K with root mean square error of prediction (RMSEP) values of 0.8196 × 10³ mg kg⁻¹, 0.4370 × 10³ mg kg⁻¹, 1.544 × 10³ mg kg⁻¹, 0.9745 mg kg⁻¹, 49.88 mg kg⁻¹, and 7.762 × 10³ mg kg⁻¹, respectively, and coefficient of determination of prediction (R_P²) values of 0.9787, 0.9371, 0.9913, 0.9909, 0.9874, and 0.9265, respectively. S. fusiforme demonstrated higher levels of Mg and Ca at the seedling stage and lower levels of Cu and Fe at the maturation stage. Additionally, S. fusiforme exhibited higher Na and lower K at the growth stage. NIR combined with CARS-PLS is a potential alternative for monitoring the concentrations of minerals in S. fusiforme at different growth stages, aiding in the convenient evaluation and further grading of the quality of S. fusiforme. Full article

Direct somatic embryogenesis represents a fundamental tool for obtaining genetically homogeneous clones; however, its commercial scaling faces critical challenges at various stages of the process. In this study, a protocol is standardized for the induction and germination of somatic embryos from leaf segments, rooting, and acclimatization of four Coffea arabica hybrids: Casiopea, Excelencia, H3, and Milenio. The results show that the Casiopea and Excelencia hybrids achieve the highest induction rates (71.64% and 74.43%) and embryo production (8.74 and 10) per explant in the M1 medium, while these values are significantly lower for H3 and Milenio. In addition, the germination and conversion of embryos into plantlets are more efficient in the woody plant medium (WPM), while rooting is optimized using indole-3-butyric acid (IBA) concentrations between 1 mg L⁻¹ and 3 mg L⁻¹, regardless of the hybrid. During the acclimatization phase, plantlets treated with mycorrhizae exhibit improved morphological, physiological, and nutritional indicators, achieving a superior quality according to the Dickson index. These findings significantly reduce production times by establishing precise standards for each genotype, thereby overcoming existing gaps in production protocols and providing a solid foundation for industrial growth. Full article

The motivation behind this study is to simplify the complex mathematical formulations and reduce the time-consuming processes involved in traditional numerical methods for solving differential equations. This study develops a computational intelligence approach with a Morlet wavelet neural network (MWNN) to solve the nonlinear Van der Pol–Mathieu–Duffing oscillator (Vd-PM-DO), including parameter excitation and dusty plasma studies. The proposed technique utilizes artificial neural networks to model equations and optimize error functions using global search with a genetic algorithm (GA) and fast local convergence with an interior-point algorithm (IPA). We develop an MWNN-based fitness function to predict the dynamic behavior of nonlinear Vd-PM-DO differential equations. Then, we apply a novel hybrid approach combining WCA and ABC to optimize this fitness function, and determine the optimal weight and biases for MWNN. Three different variants of the Vd-PM-DO model were numerically evaluated and compared with the reference solution to demonstrate the correctness of the designed technique. Moreover, statistical analyses using twenty trials were conducted to determine the reliability and accuracy of the suggested MWNN-GA-IPA by utilizing mean absolute deviation (MAD), Theil’s inequality coefficient (TIC), and mean square error (MSE). Full article

To establish a finite element model that accurately represents the dynamic characteristics of actual super high-rise building and improve the accuracy of the finite element simulation results, a finite element model updating method for super high-rise building is proposed based on the response surface method (RSM). Taking a 120 m super high-rise building as the research object, a refined initial finite element model is firstly established, and the elastic modulus and density of the main concrete and steel components in the model are set as the parameters to be updated. A significance analysis was conducted on 16 parameters to be updated including E1–E8, D1–D8, and the first 10 natural frequencies of the structure, and 6 updating parameters are ultimately selected. A sample set of updating parameters was generated using central composite design (CCD) and then applied to the finite element model for calculation. The response surface equations for the first ten natural frequencies were obtained through quadratic polynomial fitting, and the optimal solution of the objective function was determined using a genetic algorithm. The results of the engineering case study indicate that the errors in the first ten natural frequencies of the updated finite element model are all within 5%. The updated model accurately reflects the current situation of the super high-rise building and provides a basis for super high-rise building health monitoring, damage detection, and reliability assessment. Full article

Obtaining the mechanical parameters of SiC_f/SiC composites quickly and accurately is crucial for the performance evaluation and optimal design of novel turbine disc structures. A representative volume element (RVE) model of 2D woven SiC_f/SiC composites was developed using CT scanning and machine learning-driven image reconstruction techniques. The stress-strain curve was obtained by uniaxial tensile test, and the anisotropic mechanical parameters were obtained by inverse analysis using a non-dominated sorting genetic algorithm (NSGA-II). Subsequently, the uniaxial tension simulation was carried out based on the RVE model and mechanical parameters. The results show that the simulation curve is in good agreement with the test, and the errors of initial modulus and peak stress were 3.98% and 2.75%, respectively. Finally, the finite element models of the turbine disc with two braiding schemes were established to simulate the damage of the turbine disc. And the simulation results were verified by a centrifugal test. The failure modes of the two kinds of turbine discs are similar to the centrifugal test results, and the maximum rotating speed was close to the test results. The findings of this study provide a novel solution for obtaining the anisotropic mechanical parameters of SiC_f/SiC composites with different woven schemes. Full article

The increasing demand for renewable energy sources has led to significant interest in second-generation biofuels derived from lignocellulosic biomass and waste materials. This review underscores the pivotal role of lignocellulosic biomass valorization in meeting global energy needs, mitigating greenhouse gas emissions, and fostering a circular bioeconomy. Key pretreatment methods—including steam explosion, pressure treatment, and chemical pretreatment—are analyzed for their ability to enhance the accessibility of cellulose and hemicellulose in enzymatic saccharification. Advances in cellulolytic enzyme development and fermentation strategies, such as the use of genetically engineered microorganisms capable of fermenting both hexoses and pentoses, are discussed in detail. Furthermore, the potential of biorefinery systems is explored, highlighting their capacity to integrate biomass valorization into biofuel production alongside high-value bioproducts. Case studies and recent trends in bioethanol and biogas production are examined, providing insights into the current state of research and its industrial applications. While lignocellulosic biofuels hold considerable promise for sustainable development and emissions reduction, challenges related to cost optimization, process scalability, and technological barriers must be addressed to enable large-scale implementation. This review serves as a comprehensive foundation for bridging the gap between laboratory research and industrial application, emphasizing the need for continued innovation and interdisciplinary collaboration in biofuel technologies. Full article

A primary cell culture derived from the gill tissues of largemouth bass (Micropterus salmoides) was successfully established and characterized, providing a physiologically relevant model for virological research. Gill tissues were enzymatically dissociated, and their cells were cultured in M199 supplemented with 20% fetal bovine serum at 25 °C, yielding optimal growth. Viral replication within these primary cells was confirmed by transmission electron microscopy, and further qRT-PCR demonstrated the upregulation of antiviral genes (IFN1, Mx1, ISG15, and Viperin). These primary gill cells of spindle-like morphology exhibited significantly higher susceptibility to Micropterus salmoides rhabdovirus (MSRV) compared to established cell lines, as evidenced by higher viral titers, thus establishing their suitability for studying host–virus interactions. Furthermore, these cells were amenable to genetic manipulation, with the successful transfection of an mCherry reporter gene using commercially available reagents. These findings highlight the utility of the largemouth bass gill-derived primary cell culture as an alternative in vitro system for investigating MSRV pathogenesis and host immune responses, which serves as a stepping stone for improved antiviral strategies in largemouth bass aquaculture. Full article

A novel hybrid Severe Plastic Deformation (SPD) method called Vortex Extrusion–Equal-Channel Angular Pressing (Vo-CAP) was developed and applied to AA6082 workpieces in this study. Before experimental application, a comprehensive optimization of the die design was performed considering effective strain, strain inhomogeneity, and pressing load parameters. The optimization process utilized an integrated approach combining Finite Element Analysis (FEA), artificial neural networks (ANNs), and the non-dominated sorting genetic algorithm II (NSGA-II). The optimized die successfully achieved a balance between maximizing effective strain while minimizing pressing load and strain inhomogeneity. The Vo-CAP process incorporates a unique conical die design that enables assembly without traditional fasteners. Moreover, this novel die combines VE and ECAP advantages in a single-pass operation. While VE has been previously studied, experimental work was limited to specific configurations, and its integration with ECAP had not been explored. Through the development of Vo-CAP, this research gap has been addressed. The results showed substantial enhancements in hardness values, ultimate tensile strength, and strain homogeneity. These findings demonstrate that Vo-CAP represents a significant advancement in SPD, offering an efficient single-pass process for improving the mechanical properties of aluminum alloys through grain refinement. Full article

The integration of small interfering RNA (siRNA) with traditional cancer therapies represents a promising frontier in oncology aimed at enhancing treatment effectiveness, reducing side effects, and overcoming drug resistance. This review highlights the potential of siRNA to selectively silence genes that are overexpressed or uniquely expressed in cancer cells, thereby disrupting critical pathways that support tumor growth and survival. Key target genes discussed include survivin, VEGF, EGFR, c-MET, HER2, MUC1, and Bcl-2, all of which play vital roles in tumor proliferation, angiogenesis, and resistance to therapies. Clinical trials investigating various siRNA candidates, such as EZN-3042 and ALN-VSP, indicate that these therapies are generally well-tolerated; however, significant challenges persist, including the effective delivery and stability of siRNA. Recent advancements in nanoparticle-based delivery systems have shown promise in addressing these issues. Future research will focus on optimizing siRNA delivery methods, personalizing therapies based on individual genetic profiles, and establishing clearer regulatory guidelines for approval. As the field evolves, siRNA-based combination therapies are poised to become an integral part of precision oncology, offering new therapeutic options and hope for patients with difficult-to-treat cancers. Full article