Search Results (211)

To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple unmanned aerial vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic costs, delivery delays, and the number of UAVs deployed. The Enhanced Non-Dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The main contributions are threefold: (1) a novel ENSGA-II design for efficient and well-distributed task allocation; (2) an improved PSO-based path planner with chaotic initialization and adaptive parameters; and (3) comprehensive validation demonstrating substantial gains over baseline methods. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved particle swarm optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic costs and 71.4% in UAV deployment quantity while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average inverted generational distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and the diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems. Full article

With the growing popularity of ice sports, indoor ice sports venues are drawing an increasing number of spectators. Maintaining comfort in spectator zones presents a significant challenge for the operational scheduling of climate control systems, which integrate ventilation, heating, and dehumidification functions. To explore economic cost potential while ensuring user comfort, this study proposes a demand response-integrated optimization model for climate control systems. To enhance the model’s practicality and decision-making efficiency, a two-stage optimization method combining multi-objective optimization algorithms with the technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. In terms of algorithm comparison, the performance of three typical multi-objective optimization algorithms—NSGA-II, standard MOEA/D, and Multi-Objective Brown Bear Optimization (MOBBO)—is systematically evaluated. The results show that NSGA-II demonstrates the best overall performance based on evaluation metrics including runtime, HV, and IGD. Simulations conducted in China’s cold regions show that, under comparable comfort levels, schedules incorporating dynamic tariffs are significantly more economically efficient than those that do not. They reduce operating costs by 25.3%, 24.4%, and 18.7% on typical summer, transitional, and winter days, respectively. Compared to single-objective optimization approaches that focus solely on either comfort enhancement or cost reduction, the proposed multi-objective model achieves a better balance between user comfort and economic performance. This study not only provides an efficient and sustainable solution for climate control scheduling in energy-intensive buildings such as ice sports venues but also offers a valuable methodological reference for energy management and optimization in similar settings. Full article

With the increasing complexity of mine ventilation networks, the difficulty of regulating ventilation systems has significantly increased. Lagging regulatory responses are prone to causing problems such as airflow turbulence and insufficient air supply in air-required areas, which seriously threaten the safety of underground operations. To address this challenge, this paper introduces the MOBWO algorithm into the field of ventilation system air volume optimization and proposes a mine air volume optimization and regulation method based on MOBWO. This paper constructs a multi-objective air volume optimization model with the total power of ventilators and the complexity of air pressure regulation as the optimization objectives. Using indicators such as GD and IGD, it compares the performance of the MOBWO algorithm with mainstream optimization algorithms such as NSGA-II and MOPSO and verifies the practicality of the optimization method with the case of the Jinhua Palace Mine. The results show that the MOBWO algorithm has significant advantages over other algorithms in terms of convergence and distribution performance. When applied to the Jinhua Palace Mine, the air volume optimization and regulation using MOBWO can reduce the power of ventilators by 10.3–21.1% compared with that before optimization while reducing the complexity of air volume regulation and the time loss during air volume regulation. This method not only reduces the energy consumption of ventilators but also shortens the regulation timeliness of the ventilation system, which is of great significance for reducing the probability of accidents and ensuring the safety of personnel’s lives and property. Full article

Internet gaming disorder (IGD) has been a prominent social problem throughout the world, causing various physical health issues, and interoceptive sensibility—the ability to perceive internal bodily signals—may be a key factor in this process. However, the relationship between interoceptive sensibility and IGD remains unclear. This study examined how interoceptive sensibility contributes to IGD, the potential mediating roles of positive outcome expectancy and flow experience, as well as the moderating role of refusal self-efficacy. The serial mediation and moderated mediation analyses of data collected from 1733 students (1031 males and 702 females, M_age = 19.56) revealed that interoceptive sensibility was positively associated with IGD, and this connection was serially mediated by positive outcome expectancy and flow experience. Moreover, refusal self-efficacy buffered the positive association between positive outcome expectancy and IGD and between flow experience and IGD. These findings suggest that interoceptive sensibility plays a crucial role in the occurrence of IGD, highlighting the importance of addressing bodily awareness in prevention and intervention strategies. Additionally, enhancing refusal self-efficacy may help mitigate the negative effects of positive outcome expectancy and flow experience, offering potential avenues for reducing IGD risk. Full article

Rare plasma cell disorders—including IgD, IgE, and IgM multiple myeloma, non-secretory myeloma (NSMM), plasma cell leukemia (PCL), and heavy chain disease (HCD)—are biologically heterogeneous and often present with atypical features and aggressive behavior. This review synthesizes current evidence on their epidemiology, pathophysiology, diagnosis, and treatment. Advances in proteasome inhibitors, immunomodulatory agents, and autologous transplantation have improved outcomes in select subtypes. However, challenges persist in distinguishing IgM myeloma from Waldenström macroglobulinemia, monitoring non-secretory disease, and treating highly aggressive forms such as IgE myeloma and PCL. Standardized diagnostic criteria and prospective trials are essential to guide future management. Full article

Recombinantly produced monoclonal antibodies (mabs) belong to the fastest growing class of biotherapeutics. In humans, antibodies are classified into five different classes: IgA, IgD, IgE, IgG and IgM. Most of the therapeutic mabs used in the clinic belong to the IgG class, albeit other antibody classes, e.g., IgM, have been evaluated in clinical stages. Antibodies are composed of heavy chains paired with a light chain. In IgM and IgA, an additional chain, the J-chain, is present. Two types of light chains exist in humans: the κ-light chain and the λ-light chain. The κ-light chain predominates in humans and is used in the vast majority of therapeutic IgG. The reason for the preference of the κ-light chain in humans is not known. Our study investigates whether light-chain selection influences the productivity of the clinically validated mabs adalimumab and trastuzumab. Both mabs were expressed as IgG and IgM with a κ- or a λ-light chain in HEK293 cells. Besides comparing the expression levels of the different mabs, we also evaluated whether the passage number of the cell line has an impact on product yield. In addition, the expressions of adalimumab, trastuzumab, an anti-CD38 and an anti-PD-L1-antibody were analyzed in HEK293 and CHO cells when both the κ- and λ-light chains are present. In summary, IgG outperformed IgM variants in expression efficacy, while light-chain selection had minimal impact on the overall expression levels. The yields of all mab variants were higher in fresh cells, despite cell cultures with a high cell passage number having higher cell densities and cell numbers at the time of harvest. The incorporation of a particular light chain occurred at similar rates in HEK293 and CHO cells. Full article

Gaming is a popular leisure activity with an increasing number of participants worldwide. It has positive aspects as well as a problematic side—Internet Gaming Disorder (IGD). This behavior attracts concern among mental health and education professionals because of possible negative psycho-emotional factors. This study aimed to assess IGD among Japanese and Israeli university students and other young adults. We explored the association of culture and IGD based on gender, burnout, and loneliness. It was hypothesized that IGD would differentiate based on the respondent country (i.e., Japan or Israel) and gender, with males reporting higher levels. Furthermore, IGD would be associated with higher levels of loneliness and burnout, regardless of country. Qualtrics and Excel platforms were used to collect responses to the Internet Gaming Disorder Scale–Short Form, De Jong Gierveld Loneliness Scale, and Short Burnout Measure (SBM). Data was gathered from a cross-cultural sample of 1318 male and female university students and other young adults in Japan and Israel, between 2022 and 2023. Japanese gamers showed less IGD (p < 0.05); and males evidenced higher levels regardless of their country (p < 0.001). IGD was significantly associated with loneliness (p < 0.001) and burnout (p < 0.001). However, multiple regression analysis showed that IGD is predicted only by burnout and gender (p < 0.001), Adjusted R² = 0.234. This study provides information for policy, prevention, and intervention purposes targeting burnout particularly among males who are a high-risk group. Additionally, this study contributes to possible joint online program development to reduce IGD among Japanese and Israeli gamers. Further research should examine the association between IGD and loneliness, controlling gender and other factors such as substance use, religiosity, eating behavior, depression, game genre, and motivation to play. Full article

Cloud resource brokerage is a fundamental challenge in cloud computing, requiring the efficient selection and allocation of services from multiple providers to optimize performance, sustainability, and cost-effectiveness. Traditional approaches often struggle with balancing conflicting objectives, such as minimizing the response time, reducing energy consumption, and maximizing broker profits. This paper presents NSGA-III-GKM++, an advanced multi-objective optimization model that integrates the NSGA-III evolutionary algorithm with an enhanced K-means++ clustering technique to improve the convergence speed, solution diversity, and computational efficiency. The proposed framework is extensively evaluated using Deb–Thiele–Laumanns–Zitzler (DTLZ) and Unconstrained Function (UF) benchmark problems and real-world cloud brokerage scenarios. Comparative analysis against NSGA-II, MOPSO, and NSGA-III-GKM demonstrates the superiority of NSGA-III-GKM++ in achieving high-quality tradeoffs between performance and cost. The results indicate a 20% reduction in the response time, 15% lower energy consumption, and a 25% increase in the broker’s profit, validating its effectiveness in real-world deployments. Statistical significance tests further confirm the robustness of the proposed model, particularly in terms of hypervolume and Inverted Generational Distance (IGD) metrics. By leveraging intelligent clustering and evolutionary computation, NSGA-III-GKM++ serves as a powerful decision support tool for cloud brokerage, facilitating optimal service selection while ensuring sustainability and economic feasibility. Full article

This study proposes a novel effective improved whale optimization algorithm via angle penalized distance (IWOA-APD) for automatic train operation (ATO) to effectively improve the ATO quality. Specifically, aiming at the high-quality target speed curve of urban rail trains, a target speed curve multi-objective optimization model for ATO is established with energy saving, punctuality, accurate stopping, and comfort as the indexes; and the comprehensive evaluation strategy utilizing angle-penalized distance as the evaluation index is proposed to enhance the assessment’s rationality and applicability. On this basis, the IWOA-APD is proposed using strategies of non-linear decreasing convergence factor, solutions of out-of-bounds eliminating via combination of reflection and refraction, mechanisms of genetic evolution with variable probability, and elite maintenance based on fusion distance and crowding degree distance. In addition, the detailed design scheme of IWOA-APD is given. The test results show that the proposed IWOA-APD achieves significant performance improvements compared to traditional MOWOA. In the optimization scenario from Lvshun New Port Station to Tieshan Town Station of Dalian urban rail transit line No.12, the IGD value shows a remarkable 69.1% reduction, while energy consumption decreases by 12.5%. The system achieves a 64.6% improvement in punctuality and a 76.5% enhancement in parking accuracy. Additionally, comfort level improves by 15.9%. Full article

Understanding the mechanisms of immune activation and deactivation is paramount. A host must initiate effective immunity against pathogenic infections while avoiding triggering immunity against self-antigens, which can lead to detrimental autoimmune disorders. Host immunological pathways can be categorized as Immunoglobulin (Ig)G-dominant eradicable immune reactions and IgA-dominant tolerable immune reactions. Eradicable immune reactions include Th1, Th2, Th22, and Thαβ immune responses against four different types of pathogens. Tolerable immune reactions include Th1-like, Th9, Th17, and Th3 immune responses against four different types of pathogens. Here, we try to determine the mechanisms of activation and deactivation of host immune reactions. The spleen and liver play contrasting roles in mediating immune responses: the spleen is primarily involved in immune activation, whereas the liver is responsible for immune deactivation. Similarly, the sympathetic and parasympathetic nervous systems have opposing functions in immune modulation, with the sympathetic system promoting pro-inflammatory responses and the parasympathetic system facilitating anti-inflammatory processes. Furthermore, adrenocorticotropic hormone (ACTH) and glucocorticosteroids exhibit contrasting effects on immune regulation: ACTH is involved in activating adaptive immunity while inhibiting innate immunity, whereas glucocorticosteroids activate natural IgM antibody associated with innate immunity while inhibiting adaptive immunity. Heat shock proteins, particularly molecular chaperones induced by fever, play pivotal roles in immune activation. Conversely, IgD B cells and gamma/delta T cells contribute to immune deactivation through mechanisms such as clonal anergy. Understanding these mechanisms provides insights into immunological pathways, aiding in the better management of infectious diseases and autoimmune disorders. Full article

The remanufacturing of end-of-life products is an effective approach to alleviating resource shortages, environmental pollution, and global warming. As the initial step in the remanufacturing process, the quality and efficiency of disassembly have a decisive impact on the entire workflow. However, the complexity of product structures poses numerous challenges to practical disassembly operations. These challenges include not only conventional precedence constraints among disassembly tasks but also sequential dependencies, where interference between tasks due to their execution order can prolong operation times and complicate the formulation of disassembly plans. Additionally, the inherent uncertainties in the disassembly process further affect the practical applicability of disassembly plans. Therefore, developing reliable disassembly plans must fully consider both sequential dependencies and uncertainties. To this end, this paper employs a chance-constrained programming model to characterise uncertain information and constructs a multi-objective sequence-dependent disassembly line balancing (MO-SDDLB) problem model under uncertain environments. The model aims to minimise the hazard index, workstation time variance, and energy consumption, achieving a multi-dimensional optimisation of the disassembly process. To efficiently solve this problem, this paper designs an innovative multi-objective adaptive large neighbourhood search (MO-ALNS) algorithm. The algorithm integrates three destruction and repair operators, combined with simulated annealing, roulette wheel selection, and local search strategies, significantly enhancing solution efficiency and quality. Practical disassembly experiments on a lithium-ion battery validate the effectiveness of the proposed model and algorithm. Moreover, the proposed MO-ALNS demonstrated a superior performance compared to other state-of-the-art methods. On average, against the best competitor results, MO-ALNS improved the number of Pareto solutions (NPS) by approximately 21%, reduced the inverted generational distance (IGD) by about 21%, and increased the hypervolume (HV) by nearly 8%. Furthermore, MO-ALNS exhibited a superior stability, providing a practical and feasible solution for disassembly optimisation. Full article

Rapid socio-economic development has intensified the conflict between supply and demand for regional water resources, necessitating optimized water resource allocation to enhance water security. This study establishes a multi-objective water resource optimization model by comprehensively considering economic, social, and ecological benefits. Based on the Non-dominated Sorting Genetic Algorithm-III (NSGA-III), we propose the I-NSGA-III algorithm by integrating reference point improvement strategies, dynamic retention of high-quality solutions, and optimized selection strategies to solve the multi-objective optimization model. A multi-system coupling coordination evaluation model is constructed to assess the final allocation schemes. Compared with some commonly used multi-objective algorithms and tested using the DTLZ series functions, the proposed algorithm demonstrates improved overall performance. Specifically, the IGD indicator decreases by 5.17–50.22%, and the HV indicator increases by 2.71–25.51% compared to NSGA-III. The proposed model is applied to Jinzhong City, China, with four scenarios set for the years 2030 and 2035 at P = 50% and P = 75% to derive reasonable water resource allocation schemes. The results show that the economic benefits range from 161.94 × 10⁸ to 212.74 × 10⁸ CNY, the water shortage rate is controlled between 1.38% and 10.86%, and COD emissions are maintained between 6.03 × 10⁴ and 6.91 × 10⁴ tons. Except for the 2030 drought scenario (P = 75%) with a coordination degree of 0.7847, classified as a medium coordination level, all other scenarios have coordination degrees greater than 0.8, indicating a good coordination level. The optimized allocation scheme can serve as a reference for the rational allocation of water resources in Jinzhong City. Moreover, the method proposed in this paper is a general approach that can be extended to other similar water-scarce cities with appropriate parameter adjustments, contributing to the sustainable development of urban water resources. Full article

To address the multi-objective flexible job shop scheduling problem in rolling production mode (FJSP-RPM), this study proposes a Multi Objective Improved of Salp Swarm Algorithm (MISSA) that simultaneously optimizes equipment utilization and total tardiness. The MISSA generates initial population through various heuristic strategies to improve the initial population quality. The exploitation capability of the algorithm is enhanced through the global crossover strategy and variety of local search strategies. In terms of improvement strategies, the MISSA (using all three strategies) outperforms other incomplete variant algorithms (using only two strategies) in three metrics: Generational Distance (GD), Inverted Generational Distance (IGD), and diversity metric, achieving superior results in 9 test cases, 8 test cases, and 4 test cases respectively. When compared with NSGA2, NSGA3, and SPEA2 algorithms, the MISSA demonstrates advantages in 8 test cases for GD, 8 test cases for IGD, and 7 test cases for the diversity metric. Additionally, the distribution of the obtained solution sets is significantly better than that of the comparative algorithms, which validats the effectiveness of the MISSA in solving FJSP-RPM. Full article

Background: Although clinical studies have indicated a possible association between dyslipidemia and osteoporosis, the underlying genetic basis and mechanistic pathways remain insufficiently defined. Most prior research has concentrated on conventional lipid markers, which are prone to confounding and limit causal inference. Exploring lipidomic profiles offers a more comprehensive view of lipid metabolism and may reveal novel genetic links beyond traditional lipid traits. Additionally, alterations in immune cell function, often triggered by metabolic disturbances, may contribute to osteoporosis development; however, the potential mediating role of immune cells in the lipid–bone axis has not been systematically investigated. Methods: A total of 179 lipid species across 13 lipid classes were analyzed in 7174 Finnish individuals from the GeneRISK cohort. Genome-Wide Association Study (GWAS) summary statistics for osteoporosis and 731 immune cell immunophenotypes were sourced from the GWAS Catalog. A two-step, two-sample Mendelian randomization analysis, using inverse variance weighting (IVW), was conducted to explore the potential causal effects of lipids on osteoporosis and the mediating role of immune cells in the relationship between lipids and osteoporosis. Results: Mendelian randomization analysis indicated that triacylglycerol levels of 48:0 were possibly associated with an increased risk of osteoporosis (IVW: odds ratio [OR] 1.1320, 95% CI 1.0401–1.2321; p = 0.004), while triacylglycerol levels of 48:3 appeared to be associated with a reduced risk of osteoporosis (IVW: OR 0.9053, 95% CI 0.8364–0.9800; p = 0.014). Two statistically significant mediating effects were identified: First, IgD− CD38dim %B cells appeared to partially negatively mediate the association between triacylglycerol levels of 48:3 and osteoporosis, with a negative mediating effect of −0.00669 (95% CI: −0.0214, 0.00805), which accounted for 6.73% of the total effect. That is, the protective effect of triacylglycerol levels of 48:3 against osteoporosis was attenuated by IgD− CD38dim %B cells. Second, HLA DR++ monocytes% leukocytes also partially negatively mediated this relationship, with a mediating effect of −0.023 (95% CI: −0.0434, −0.00266), accounting for 23.2% of the total effect. This indicates that other immune cells, HLA DR++ monocytes %leukocytes, resisted the protective effect of triacylglycerol levels of 48:3 against osteoporosis, with a weakening effect stronger than that of IgD− CD38dim %B cells. Conclusions: Our findings contribute to the growing understanding of the potential causal relationships and shared pathogenic mechanisms between dyslipidemia and osteoporosis. The results suggest that the potential genetic effects of plasma lipid metabolites on osteoporosis may be partially down-regulated by specific kinds of immune cells. Full article

This paper introduces an improved multi-objective grey wolf optimizer (IMOGWO) and demonstrates its application to the aerodynamic optimization of an axial cooling fan. Building upon the traditional multi-objective grey wolf optimizer (MOGWO), several improvement strategies were adopted to enhance its performance. Firstly, the IMOGWO started population initialization based on the Bloch coordinates of qubits to ensure a high-quality initial population. Additionally, it employed a nonlinear convergence factor to facilitate global exploration and integrated the inspiration of Manta Ray Foraging to enhance the information exchange between populations. Finally, associative learning was leveraged for archive updating, allowing for perturbative mutation of solutions in crowded regions of the archive to increase solution diversity and improve the algorithm’s search capability. The proposed IMOGWO was applied to five multi-objective benchmark functions, comprising three two-objective and two three-objective problems, and experimental results were compared with three well-known multi-objective algorithms: the non-dominated sorting genetic algorithm II (NSGA II), MOGWO, and the multi-objective multi-verse optimizer (MOMVO). It is demonstrated that the proposed algorithm had advantages in convergence accuracy and diversity of solutions, which were quantified by the performance metrics (generational distance (GD), inverted generational distance (IGD), Spacing (SP), and Hypervolume (HV)). Furthermore, a multi-objective optimization process coupled with the IMOGWO algorithm and Computational Fluid Dynamics (CFD) was proposed. By optimizing the design parameters of an axial cooling fan, a set of non-dominated solutions was obtained within limited iteration steps. Consequently, the IMOGWO also presented an effective and practical approach for addressing multi-objective optimization challenges with respect to engineering problems. Full article