Search Results (55)

This paper tackles the challenge of disassembly sequence planning (DSP) in energy-efficient remanufacturing by introducing an innovative hybrid optimization framework. The proposed model integrates a Dynamic Time-Varying CRITIC–Entropy (DTVCE) decision-making framework with an Improved Honey Badger Algorithm (IHBA) to optimize disassembly sequences under key operational criteria, including idle rate, line smoothness, and energy consumption. The DTVCE framework constructs a dynamic composite score by normalizing evaluation criteria across time slices and incorporating temporal discounting to capture the evolving importance of each factor. Meanwhile, by establishing a symmetric disassembly constraint matrix to restrict the disassembly sequence and integrating exploration and exploitation mechanisms to enhance the IHBA, the solution process is empowered to efficiently generate feasible disassembly sequences and fulfill task allocation across workstations while satisfying takt time constraints. Experimental validation demonstrates that the proposed framework significantly outperforms traditional disassembly optimization approaches in both energy efficiency and line balance performance. In a case study involving an automotive drive axle, the method achieved a near-optimal configuration using only eight workstations, leading to a marked reduction in both energy consumption and idle times. Sensitivity analysis further verifies the model’s robustness, showing stable convergence and consistent performance under varying takt times and energy parameters. Overall, this study contributes to the advancement of green remanufacturing by offering a scalable, data-driven, and adaptive solution to disassembly optimization—paving the way toward sustainable and energy-aware production environments. Full article

The growing emphasis on sustainable manufacturing has motivated the integration of environmental and social factors into traditional assembly line balancing problems (ALBPs). This study introduces a Sustainable Mixed-Model Assembly Line Balancing Problem (S-MMALBP) that jointly considers task precedence, machine selection, worker allocation, carbon-emission control, and green-rating incentives. An exact optimization model is formulated to minimize total operating cost while satisfying sustainability and capacity constraints. To address the problem’s combinatorial complexity, an Adaptive Large Neighborhood Search (ALNS) metaheuristic is developed, incorporating customized destroy and repair operators, adaptive penalty updating, and a simulated-annealing-based acceptance criterion. An analytical lower bound is derived to evaluate the algorithm’s performance, and an enhanced constructive method, Precedence-Driven Task Grouping (PDTG), is proposed to generate high-quality initial solutions. Computational experiments on benchmark instances confirm that the ALNS achieves near-optimal solutions with deviations below 5% from the lower bound, while solving large instances within seconds. A real-world case study on aircraft assembly involving 166 tasks further validates the model’s applicability, achieving a cost deviation below 4% from the theoretical bound under realistic sustainability constraints. The results demonstrate that the proposed model provides an effective and scalable decision-support tool for designing environmentally and socially responsible production systems. The study is the first to incorporate sustainability and worker–machine decisions into a mixed-model ALB framework solved by a tailored ALNS and lower bound. Full article

This study aimed to evaluate the efficacy of oregano essential oil (OEO) in improving the production performance, health, and welfare of late-phase laying hens raised under commercial farm conditions by analyzing its effect on performance metrics and metabolic and endocrine profiles. Daily performance data for approximately 7884 Hy-Line Brown layers divided into two commercial flocks, one consisting of 96-week-old hens (n = 3849) and the other of 79-week-old hens (n = 4035), were recorded before (Pre-OEO Tx), during (OEO Tx-Week) and one week (Post-OEO Tx Week) following the week of water supplementation with commercial oregano essential oil (5%) of Origanum heracleoticum containing carvacrol (79.75%) as the main component (300 mL of product/1000 L of water). The results show a significant improvement in hen-day egg production (HDEP) during treatment (p < 0.05), a significant decrease in daily mortality one week after the cessation of treatment, mainly in the youngest hens (p < 0.05), and a reduction in feed conversion rate (p < 0.05). The general model (GLM) analysis of data from blood samples collected before and after OEO addition showed a significant decrease in plasma levels of procalcitonin (PCT), calcium, albumin (p < 0.05), and aspartate aminotransferase (AST) (p < 0.01). In contrast, a significant increase in estradiol, total protein globulin (p < 0.01), and phosphorus levels (p < 0.05) was recorded. The changes in endocrine profiles were significantly related to a restoration of calcium–phosphorus balance and a decrease in hepatic activity of AST and gamma glutamyl transferase (GGT). These results reveal the investigative value of PCT, in conjunction with metabolic profiling and reproductive hormones, for evaluating the effectiveness of phytogenic additives. Further studies are suggested to determine whether essential oil components can improve health and production performances of laying hens by a potential concurrent modulation of their metabolism, inflammatory response, and reproductive axis function. Full article

In the context of the rapid development of the new energy vehicle industry, how to achieve the mixed production of fuel vehicles and electric vehicles has become an important issue for the transformation and flexible manufacturing of automotive production lines. This paper addresses the balance problem of the mixed assembly line for electric vehicles and fuel vehicles and proposes a mathematical modeling method based on the product structure differences and workstation sharing. An improved genetic algorithm is designed for optimization. The established optimization model includes mathematical models of process priority relationships, cycle time constraints, synchronization constraints, and exclusive process co-placement constraints, with the optimization goals of minimizing workstation quantity and balancing workstation load. To solve such models, the decoding process of the genetic algorithm is redesigned in the algorithm design. The improved genetic algorithm can be well used to solve the workstation-sharing model. A case study of the chassis assembly line of an automotive manufacturing enterprise is used for verification. The results show that the method considering workstation sharing can effectively reduce the number of workstations, improve the distribution of workstation loads, and increase the utilization rate of the production line, while ensuring the cycle time constraints. The conclusions of this study expand the theoretical framework of the balance problem of mixed assembly lines and provide practical references for the transformation of fuel vehicle production lines into new energy vehicles. Full article

Generative Artificial Intelligence (GAI) technologies are revolutionizing productivity and creativity across educational and engineering contexts. This study addresses a critical gap by examining the key factors influencing the successful integration of GAI tools to enhance faculty and student productivity, with a focus on higher education and its role in advancing sustainable development. Specifically, it investigates challenges, opportunities, and essential conditions for effective GAI adoption that support not only academic excellence but also the preparation of engineers capable of addressing global sustainability challenges in line with the United Nations Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 12 (Responsible Consumption and Production). A preliminary literature review identified significant factors requiring attention, further refined through interviews with 14 students and 13 faculty members, and expanded upon via a survey involving 54 students and 42 faculty members. Participants rated the significance of various factors on a five-point Likert scale, allowing for the calculation of the Relative Importance Index (RII). The findings reveal that while compliance with ethical standards and bias mitigation emerged as the most significant concerns, mid-level considerations such as institutional support, training, and explainability are critical for fostering GAI adoption in sustainable learning environments. Foundational elements, including robust technical infrastructure, data security, and scalability, are vital for long-term success and alignment with responsible and sustainable innovation. Notably, this study highlights a divergence in perspectives between faculty and students regarding GAI’s impact on productivity, with faculty emphasizing ethical considerations and students focusing on efficiency gains. This study offers a comprehensive set of considerations and insights for guiding GAI integration in educational and engineering settings. It emphasizes the need for multidisciplinary collaboration, continuous training, and strong governance to balance innovation, responsibility, and sustainability. The findings advance theoretical understanding and provide practical insights for academia, policymakers, and technology developers aiming to harness GAI’s full potential in fostering sustainable engineering education and development. Full article

On large-scale product assembly lines, such as those used in aircraft manufacturing, multiple assembly positions and devices often coexist within a single workstation, leading to complex task interactions. As a result, the problem of parallel task execution within workstations must be effectively addressed. This study focuses on positional and equipment conflicts within workstations. To manage positional and equipment conflicts, a multi-objective optimization model is developed that integrates assembly sequence planning with the first type of assembly line balancing problem. This model aims to minimize the number of workstations, balance task loads, and reduce equipment procurement costs. An improved NSGA-II algorithm is proposed by incorporating artificial immune algorithm concepts and neighborhood search. A selection strategy based on dominance rate and concentration is introduced, and crossover and mutation operators are refined to enhance search efficiency under restrictive parallel constraints. Case studies reveal that a chromosome concentration weight of about 0.6 yields superior search performance. Compared with the traditional NSGA-II algorithm, the improved version achieves the same optimal number of workstations but provides a 5% better workload balance, 2% lower cost, a 76% larger hyper-volume, and a 133% increase in Pareto front solutions. The results demonstrate that the proposed algorithm effectively handles assembly line balancing with complex parallel constraints, improving Pareto front quality and maintaining diversity. It offers an efficient, practical optimization strategy for scheduling and resource allocation in large-scale assembly systems. Full article

Underground hydrogen storage in aquifers is a promising solution to address the imbalance between energy supply and demand, yet its practical implementation requires optimized strategies to ensure high efficiency and economic viability. To improve the storage and production efficiency of hydrogen, it is essential to select the appropriate cushion gas and to study the influence of reservoir and process parameters. Based on the conceptual model of aquifer with single-well injection and production, three potential cushion gas (carbon dioxide, nitrogen and methane) were studied, and the changes in hydrogen recovery for each cushion gas were compared. The effects of temperature, initial pressure, porosity, horizontal permeability, vertical to horizontal permeability ratio, permeability gradient, hydrogen injection rate and hydrogen production rate on the purity of recovered hydrogen were investigated. Additionally, the impact of different well pattern on the purity of recovered hydrogen was studied. The results indicate that methane is the most effective cushion gas for improving hydrogen recovery in UHS. Different well patterns have significant impacts on the purity of recovered hydrogen. The mole fractions of methane in the produced gas for the single-well, line-drive pattern and five-spot pattern were 16.8%, 5%, and 3.05%, respectively. Considering the economic constraints, the five-spot well pattern is most suitable for hydrogen storage in aquifers. Reverse rhythm reservoirs with smaller permeability differences should be chosen to achieve relatively high hydrogen recovery and purity of recovered hydrogen. An increase in hydrogen production rate leads to a significant decrease in the purity of the recovered hydrogen. In contrast, hydrogen injection rate has only a minor effect. These findings provide actionable guidance for the selection of cushion gas, site selection, and operational design of aquifer-based hydrogen storage systems, contributing to the large-scale seasonal storage of hydrogen and the balance of energy supply and demand. Full article

Breeding improvement in alfalfa (Medicago sativa L.) is often constrained by the complexity of agronomic traits and trade-offs among yield-related characteristics. Conventional single-trait selection rarely captures the full range of phenotypic variation or the interactions among traits. To address this, we developed a principal component analysis (PCA)-based framework for multivariate selection in hybrid breeding. Six yield-related traits—plant height, branch number, fresh/hay yield ratio (FHR), leaf/stem ratio (LSR), multifoliolate leaf frequency, and dry weight per plant—were quantified in two parental lines and their F₁/F₂ generations. PCA identified three principal components (PC1–PC3) with eigenvalues >1, explaining 71.14% of the total phenotypic variance: PC1 (32.43% variance) was predominantly loaded with positive contributions from dry weight per single plant, height, and branches, biologically representing overall plant vigor and biomass accumulation; PC2 (21.77% variance) showed strong negative loadings for LSR, capturing architectural trade-offs between stem dominance and leaf production; PC3 (16.94% variance) had positive loadings on multifoliolate leaf rate and fresh/dry ratio, embodying quality and physiological resilience traits. Based on PCA scores, a composite selection index was constructed, and the top 31.1% of F1 hybrids were selected. Their F₂ progeny showed significant improvements in dry weight (+15.56%, p < 0.01), multifoliolate leaf frequency (+74.78%, p < 0.001), and reduced FHR (–8.2%, p < 0.05), accompanied by lower yield decline (−7.2% versus −14.1% in controls). These results show that PCA-based multivariate selection effectively balances trait trade-offs, enhances intergenerational stability, and improves selection efficiency. This framework offers a practical tool for alfalfa breeding. Full article

Retrotransposons exhibit increased activity in cancer cells. One possible approach to anticancer therapy is to use this activity to influence the energy balance in cells. Abnormal distribution of retrotransposons in the genome requires additional energy consumption, which can lead to a significant decrease in the total amount of free ATP molecules in the cell. A decrease in ATP levels below a certain threshold can in turn trigger a cell death program. To investigate the possibility of such a scenario, we developed a mathematical model of the cellular energy balance that describes the dynamics of energy consumption by the main cellular processes, including costs of retrotransposon activity. The model considers changes in the concentrations of ATP, active retrotransposons (LINE-1 and SINE) in the human genome, as well as mRNAs and proteins that are expression products of retrotransposon and constitutive genes. We estimated the parameter values in the model based on literature data and numerical optimization. We found a single stable stationary solution, characterized by low retrotransposon activity, and used it as the reference steady state for further analysis. Parametric sensitivity analysis revealed the parameters whose changes had the greatest impact on cellular ATP levels. The LINE-1 deactivation rate constant and the maximum LINE-1 transcription rate were the most sensitive among the transposon-related parameters. Perturbation of these parameters led to a decrease in the number of free ATP to 30% of the reference value and below. Transcription of retrotransposons under perturbed parameters became comparable to the translation of constitutive genes in terms of energy costs. The presented results indicate that cancer cell death can be initiated by increasing the load on the energy balance due to the activation of transposons. Full article

The growing demand for effective water treatment solutions, particularly in smaller communities in Brazil, highlights the potential of ozonation. However, implementing this technology at a smaller scale presents challenges, including the need to adapt it for compact systems and optimize processes for both efficiency and feasibility. This study investigates the use of a corona discharge ozone generator operating at 60 Hz in compact systems. Experiments evaluated ozone production at different gas flow rates (0.2 to 1.0 L of ozone-containing gas per minute), with the total flow divided between two lines, A (60%) and C (40%), for simultaneous treatment applications. Mass balance tests were performed using caffeine (CAF) and atenolol (ATL) as model compounds to assess molecular interactions. The results highlight the need to stabilize ozone generation to ensure consistent production and process efficiency, confirming ozone’s effectiveness in degrading emerging compounds (ECs), CAF and ATL, by approximately 80%, after process optimization using the compact ozonation unit. Key factors such as the position and diameter of the flow divider, diffuser type, and pollutant characteristics were shown to affect gas distribution, head loss, and ozone transfer efficiency. Thus, this work underscores the critical role of system configuration in optimizing ozonation, offering insights to enhance its feasibility for providing safe potable water during water crises and emergencies in Brazil. Full article

The objective of the study was to investigate the effects of low-protein amino acid-balanced (LPAB) diets supplemented with Astragalus polysaccharides (APSs) on the production performance, antioxidants, immunity, and biochemical index of laying hens in an elevated-temperature environment. Fifty-two-week-old Hy-Line Brown chickens (n = 768) were randomly divided into four groups, with eight replicates of 24 hens each. The control group was kept at 24 °C with a basal diet (CON), while the treatment groups were exposed to 32 °C and given the following diets: basal (HB), LPAB (HL), and LPAB with 0.5% APSs (HLA). Under heat stress, APSs increased the egg production rate and number of small white follicles, improved the yolk color, and lowered the feed conversion ratio. LPAB diets increased follicle-stimulating hormone, antioxidant enzyme activities, and anti-inflammatory cytokine activity and up-regulated related genes, whereas they reduced stress-related hormones, malondialdehyde concentrations, and triglyceride concentrations and down-regulated related genes. The addition of APSs enhanced immunoglobulin concentrations and cholesterol recovery and altered the expression of related genes. The study found that the adverse effects of high temperatures are directly related to oxidative stress. LAPB diets and APSs relatively alleviate these adverse effects. Therefore, the importance of feeding strategies such as LPAB diets and APSs for laying hens under heat stress conditions has been identified. Full article

Precision thickness control in new energy electrode coatings is a critical determinant of battery performance characteristics. This study presents a non-destructive inspection methodology employing terahertz time-domain spectroscopy (THz-TDS) to achieve high-precision coating thickness measurement in lithium iron phosphate (LFP) battery manufacturing. Industrial THz-TDS systems mostly adopt fixed threshold filtering or Fourier filtering, making it disssssfficult to balance noise suppression and signal fidelity. The developed approach integrates three key technological advancements. Firstly, the refractive index of the material is determined through multi-peak amplitude analysis, achieving an error rate control within 1%. Secondly, a hybrid signal processing algorithm is applied, combining an optimized Savitzky–Golay filter for high-frequency noise suppression with an enhanced sinc function wavelet threshold technique for signal fidelity improvement. Thirdly, the time-of-flight method enables real-time online measurement of coating thickness under atmospheric conditions. Experimental validation demonstrates effective thickness measurement across a 35–425 μm range, achieving a 17.62% range extension and a 2.13% improvement in accuracy compared to conventional non-filtered methods. The integrated system offers a robust quality control solution for next-generation battery production lines. Full article

Biopolymer hydrogel-based scaffold materials have received a lot of interest in tissue engineering and regenerative medicine because of their unique characteristics, which include biocompatibility, biodegradability, and the ability to replicate the natural extracellular matrix (ECM). These hydrogels are three-dimensional biopolymer networks that are highly hydrated and provide a supportive, wet environment conducive to cell growth, migration, and differentiation. They are especially useful in applications involving wound healing, cartilage, bone, and soft tissue regeneration. Natural biopolymers such as collagen, chitosan, hyaluronic acid, and alginate are frequently employed as the foundation for hydrogel fabrication, providing benefits such as low toxicity and improved cell adherence. Despite their potential, biopolymer hydrogel scaffolds have various difficulties that prevent broad clinical implementation. Key difficulties include the challenge of balancing mechanical strength and flexibility to meet the needs of various tissues, managing degradation rates to line up with tissue regeneration, and assuring large-scale manufacturing while retaining scaffold uniformity and quality. Furthermore, fostering appropriate vascularization and cell infiltration in larger tissues remains a significant challenge for optimal tissue integration and function. Future developments in biopolymer hydrogel-based scaffolds are likely to concentrate on addressing these obstacles. Strategies such as the creation of hybrid hydrogels that combine natural and synthetic materials, smart hydrogels with stimulus-responsive features, and 3D bioprinting technologies for accurate scaffold production show significant potential. Furthermore, integrating bioactive compounds and growth factors into hydrogel matrices to promote tissue regeneration is critical for enhancing therapeutic results. Full article

This paper introduces an optimization method for multi-robot automated control welding based on a Particle Swarm Genetic Algorithm (PSGA), aiming to address issues such as high costs, large footprint, and excessive production cycles in multi-robot welding production lines. The method first constructs a multi-axis robotic kinematic model to provide constraint conditions. Then, the PSO (particle swarm optimization) algorithm, which integrates penalty functions into the fitness evaluation, is used to determine the optimal welding path by simulating collective behavior within a group. The GA (genetic algorithm) encodes the position of the welding robot bases into chromosomes to find the optimal layout for coordinated control of multiple robots. The entire process is optimized according to welding standards and requirements. Additionally, a comprehensive production line performance estimation model was used to quantitatively analyze the new scheme. The results show that the optimized production line’s balance rate increased by 10%, the balance loss rate decreased by 10%, the smoothness index increased by 37.8%, the space costs reduced by 44.4%, the equipment demand reduced by 41.1%, the labor demand reduced by 50%, the total costs reduced by 10%, and the average product cycle time was reduced by 5.07 s. Finally, we tested the algorithm in various complex scenarios and compared its performance against mainstream algorithms within the context of this study. The results demonstrated that the optimized production line significantly improved efficiency while maintaining safety standards. Full article

This paper addresses the multi-operator human–robot collaborative disassembly line balancing problem aimed at minimizing the number of workstations, workstation idle time, and disassembly costs, considering the diversity of end-of-life products and the characteristics of their components. A hybrid artificial fish swarm algorithm (HAFSA) is designed in accordance with the problem characteristics and applied to a disassembly case of a hybrid refrigerator. Comparative experiments with the non-dominated sorting genetic algorithm II (NSGA-II) and teaching–learning-based optimization (TLBO) algorithms demonstrate the superiority of the proposed algorithm. Finally, the performance of the three algorithms is evaluated based on non-dominated rate (NR), generational distance (GD), and inverted generational distance (IGD) metrics. Full article