Search Results (426)

The increasing complexity of real-world engineering problems, ranging from manufacturing scheduling to resource optimization in smart grids, has driven demand for adaptive and high-performing heuristic methods. Automatic Heuristic Design (AHD) and neural-enhanced metaheuristics have shown promise in automating strategy development, but often suffer from limited flexibility and scalability due to static operator libraries or high retraining costs. Recently, Large Language Models (LLMs) have emerged as a powerful alternative for exploring and evolving heuristics through natural language and program synthesis. This paper proposes a novel LLM-based memetic framework that synergizes LLM-driven exploration with domain-specific local refinement and memory-aware reflection, enabling a dynamic balance between heuristic creativity and effectiveness. In the experiments, the developed framework outperforms other LLM-based state-of-the-art approaches across the designed AGV-drone scheduling scenario and two benchmark combinatorial problems. The findings suggest that LLMs can serve not only as general-purpose optimizers but also as interpretable heuristic generators that adapt efficiently to complex and heterogeneous domains. Full article

Constrained multi-objective optimization problems (CMOPs) require optimizing multiple conflicting objectives while satisfying complex constraints. These constraints generate infeasible regions that challenge traditional algorithms in balancing feasibility and Pareto frontier diversity. chemical reaction optimization (CRO) effectively balances global exploration and local exploitation through molecular collision reactions and energy management, thereby enhancing search efficiency. However, standard CRO variants often struggle with CMOPs due to the absence of specialized constraint-handling mechanisms. To address these challenges, this paper integrates the CRO collision reaction mechanism with an existing evolutionary computational framework to design a dual-stage and dual-population chemical reaction optimization (DDCRO) algorithm. This approach employs a staged optimization strategy, which divides population evolution into two phases. The first phase focuses on objective optimization to enhance population diversity, and the second prioritizes constraint satisfaction to accelerate convergence toward the constrained Pareto front. Furthermore, to leverage the infeasible solutions’ guiding potential during the search, DDCRO adopts a two-population strategy. At each stage, the main population tackles the original constrained problem, while the auxiliary population addresses the corresponding unconstrained version. A weak complementary mechanism facilitates information sharing between populations, which enhances search efficiency and algorithmic robustness. Comparative tests on multiple test suites reveal that DDCRO achieves optimal IGD/HV values in 53% of test problems. The proposed algorithm outperforms other state-of-the-art algorithms in both convergence and population diversity. Full article

This study addresses the complex challenges associated with flexible mattress (soft mattress) installation in the sharply curved and deep-water sections of the Yangtze River, particularly in the Yaozui revetment reconstruction project. Under extreme hydrodynamic conditions—water depths exceeding 30 m and velocities over 2.5 m/s—the risk of structural failures such as displacement, flipping, or tearing of the mattress becomes significant. To improve construction safety and stability, the study integrates numerical modeling and on-site strain monitoring to analyze the mechanical response of flexible mattresses during deployment. A three-dimensional finite element model based on the catenary theory was developed to simulate stress distributions under varying flow velocities and angles, revealing stress concentrations at the mattress’s upper edge and reinforcement junctions. Concurrently, a real-time monitoring system using high-precision strain sensors was deployed on critical shipboard components, with collected data analyzed through a remote IoT platform. The results demonstrate strong correlations between mattress strain, flow velocity, and water depth, enabling the identification of high-risk operational thresholds. The proposed monitoring and early-warning framework offers a practical solution for managing construction risks in extreme riverine environments and contributes to the advancement of intelligent construction management for underwater revetment works. Full article

In the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designers’ subjective judgments and lack objective data on user cognition. To address these limitations, this study develops a comprehensive methodology grounded in Kansei engineering that combines Extenics-based semantic analysis, eye-tracking experiments, and user imagery evaluation. First, we used web crawlers to harvest user-generated descriptors for industrial floor-cleaning robots and applied Extenics theory to quantify and filter key perceptual imagery features. Second, eye-tracking experiments captured users’ visual-attention patterns during robot observation, allowing us to identify pivotal design elements and assemble a sample repository. Finally, the semantic differential method collected users’ evaluations of these design elements, and correlation analysis mapped emotional needs onto stylistic features. Our findings reveal strong positive correlations between four core imagery preferences—“dignified,” “technological,” “agile,” and “minimalist”—and their corresponding styling elements. By integrating qualitative semantic data with quantitative eye-tracking metrics, this research provides a scientific foundation and novel insights for emotion-driven design in industrial floor-cleaning robots. Full article

Under China’s “Rural Revitalisation” strategy, contract farming faces challenges including farmers’ limited access to advanced technologies and high operational risks for agricultural support enterprises. The collaborative involvement of scientific research institutions offers potential solutions but remains underexplored. This study employs Stackelberg game theory to model a contract farming supply chain under two agricultural assistance modes: enterprise-led (EL) and collaborative assistance with scientific research institutions (CI). We further propose two government subsidy mechanisms: subsidies to enterprises and subsidies to scientific research institutions. The models analyze optimal decisions, supply chain performance, and subsidy efficiency, validated through numerical experiments. Key findings reveal the following: (1) The CI mode enhances agricultural output and farmer revenue but may reduce enterprise profits, deterring collaboration. (2) Government subsidies incentivize enterprise–institution collaboration. Subsidizing scientific research institutions typically improves agricultural productivity and economic benefits more effectively than subsidizing enterprises. (3) Synergistic effects exist among the government subsidy coefficient, cost coefficient of technical assistance, consumer preferences for agricultural quality, and profit-sharing ratio. The latter three parameters significantly influence subsidy model selection. This research provides policy insights for enhancing agricultural assistance efficiency and sustainable contract farming development. Full article

During a prolonged domestication and environmental selection, Brassica rapa has formed diverse morphological types during a cultivation process of up to 8000 years, such as root-type turnips (Brassica rapa var. rapa), leaf-type Chinese cabbage (Brassica rapa var. pekinensis), oil-type rapeseed (Brassica rapa L.), and other rich types. China is one of the origins of Brassica rapa L., which is spread all over the east, west, south, and north of China. Studying its origin and evolution holds significant importance for unraveling the cultivation history of Chinese oilseed crops, intraspecific evolutionary relationships, and the utilization value of genetic resources. This article summarizes the cultivation history, evolution, classification research progress, and germplasm resource diversity of Brassica rapa var. oleifera in China. Combining karyotype analysis, genomic information, and wild relatives of Brassica rapa var. oleifera discovered on the Qinghai–Tibet Plateau, it is proposed that Brassica rapa var. oleifera has the characteristic of polycentric origin, and Gansu Province in China is one of the earliest regions for its cultivation. Brassica rapa var. oleifera, originating from the Mediterranean region, was diffused to the East Asian continent through two independent transmission paths (one via the Turkish Plateau and the other via Central Asia and Siberia). Analyzing the genetic diversity characteristics and evolutionary trajectories of these two transmission paths lays a foundation for clarifying the origin and evolutionary process of Brassica rapa var. oleifera and accelerating the breeding of Brassica rapa var. oleifera in China. Despite existing research on the origin of Brassica rapa L., the domestication process of this species remains unresolved. Future studies will employ whole-genome resequencing to address this fundamental question. Full article

User requirements are the core driving force behind the iterative development of complex products. Their comprehensive collection, accurate interpretation, and effective integration directly affect design outcomes. However, current practices often depend heavily on single-source data and designer intuition, resulting in incomplete, biased, and fragile design decisions. Moreover, multi-source heterogeneous user requirements often exhibit inherent asymmetry and imbalance in both structure and contribution. To address these issues, this study proposes a symmetric and balanced optimization method for multi-source heterogeneous user requirements in complex product design. Multiple acquisition and analysis approaches are integrated to mitigate the limitations of single-source data by fusing complementary information and enabling balanced decision-making. Firstly, unstructured text data from online reviews are used to extract initial user requirements, and a topic analysis method is applied for modeling and clustering. Secondly, user interviews are analyzed using a fuzzy satisfaction analysis, while eye-tracking experiments capture physiological behavior to support correlation analysis between internal preferences and external behavior. Finally, a cooperative game-based model is introduced to optimize conflicts among data sources, ensuring fairness in decision-making. The method was validated using a case study of oxygen concentrators. The findings demonstrate improvements in both decision robustness and requirement representation. Full article

Background: Alcohol-associated liver disease (ALD) is characterized by gut–liver axis dysfunction and metabolic dysregulation, yet the therapeutic potential of probiotics remains underexplored. This study aimed to investigate the protective effects and mechanisms of Lacticaseibacillus paracasei 36 (LP36) against ethanol-induced ALD in mice. Methods: Mice were pretreated with LP36 prior to ethanol exposure. Liver injury was assessed through serum ALT/AST levels, hepatic steatosis (TC/TG content), and ethanol detoxification capacity (ADH/ALDH activity). Intestinal barrier integrity was evaluated via Mucin2 and ZO-1 expression, and gut microbiota alterations were analyzed by 16S rRNA sequencing. Hepatic transcriptomics (RNA-seq) was performed to identify key regulatory pathways. Results: LP36 significantly attenuated ethanol-induced liver injury, evidenced by reduced ALT/AST, improved hepatic steatosis (lower TC/TG), and enhanced ADH/ALDH activity. Mechanistically, LP36 restored intestinal barrier function (upregulated Mucin2 and ZO-1), modulated gut microbiota (suppressed Parasutterella, Romboutsia, and Christensenellaceae_R-7_group; enriched Faecalibaculum and Tuzzerella), and reduced systemic inflammation. Transcriptomics revealed LP36-mediated rescue of AMPK signaling, involving regulation of Stk11, Prkag3, lipid synthesis genes (Fasn, Acaca), and metabolic modulators (Creb3l3, G6pc3, mTOR, Rps6kb2).Conclusions: LP36 ameliorates ethanol-induced ALD by enhancing intestinal barrier integrity, reshaping gut microbiota, and restoring AMPK-dependent metabolic homeostasis. These findings highlight LP36 as a promising probiotic candidate for ALD prevention. Full article

Arsenic (As) and cadmium (Cd) in rice grains are major global food safety concerns. Iron (Fe) can help reduce both, but current Fe treatments suffer from poor stability, low leaf absorption, and fast soil immobilization, with unclear underlying mechanisms. To address these issues, an Fe-based metal–organic framework (MIL-88) was modified with sodium alginate (SA) to form MIL-88@SA. Its stability as a foliar inhibitor and its leaf absorption were tested, and its effects on As and Cd accumulation in rice were compared with those of soluble Fe (FeCl₃) and chelating Fe (HA + FeCl₃) in a field study on As–Cd co-contaminated rice paddies. Compared with the control, MIL-88@SA outperformed or matched the other Fe treatments. A single foliar spray during the tillering stage increased the rice yield by 19% and reduced the inorganic As and Cd content in the grains by 22.8% and 67.8%, respectively, while the other Fe treatments required two sprays. Its superior performance was attributed to better leaf affinity and thermal stability. Laser ablation inductively coupled plasma–mass spectrometry (LA–ICP–MS) and confocal laser scanning microscopy (CLSM) analyses revealed that Fe improved photosynthesis and alleviated As–Cd stress in leaves, MIL-88@SA promoted As and Cd redistribution, and Fe–Cd co-accumulation in leaf veins enhanced Cd retention in leaves. Full article

This study evaluated the effects of Yupingfeng polysaccharides (YPF-P) on production performance, immune response, and intestinal health in goslings. A total of 240 one-day-old healthy male goslings were randomly assigned to four groups, each with six replicates of ten goslings. The Control group (Control) was fed a basal diet, while the experimental groups received the basal diet supplemented with 200 mg/kg (YPFPI), 400 mg/kg (YPFPII), and 600 mg/kg (YPFPIII) of YPF-P. The results demonstrated that supplementation with 400 mg/kg of YPF-P significantly decreased the final body weight at 21 days and the feed conversion ratio (FCR) from days 1 to 14 (p < 0.05). Plasma activities of glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD) were significantly elevated, while malondialdehyde (MDA) levels were reduced in the 400 and 600 mg/kg groups (p < 0.05). Both dosages significantly increased thymus and bursa of Fabricius indices, as well as plasma IL-1β concentration (p < 0.05), with IL-6 levels further elevated in the 600 mg/kg group (p < 0.05). Duodenal and ileal villus height and the villus height to crypt depth ratio were significantly improved in the 400 and 600 mg/kg groups (p < 0.05). In the cecum, acetate and isobutyrate concentrations were increased in the 400 and 600 mg/kg groups, while propionate concentration was significantly higher in the 600 mg/kg group (p < 0.05). The 600 mg/kg group also exhibited a significant increase in the relative abundance of beneficial bacteria such as Akkermansia and Alistipes, alongside a marked reduction in harmful pathogens, including Rickettsia (p < 0.05). In summary, dietary supplementation with YPF-P enhanced antioxidant capacity, immune response, and gut microbiota composition in goslings, with the most pronounced effects observed at 600 mg/kg. Full article

Sign language is a complex and dynamic visual language that requires the coordinated movement of various body parts, such as the hands, arms, and limbs—making it an ideal application domain for sensor networks to capture and interpret human gestures accurately. To address the intricate task of precise and expedient SLR from raw videos, this study introduces a novel deep learning approach by devising a multimodal framework for SLR. Specifically, feature extraction models are built based on two modalities: skeleton and RGB images. In this paper, we firstly propose a Multi-Stream Spatio-Temporal Graph Convolutional Network (MSGCN) that relies on three modules: a decoupling graph convolutional network, a self-emphasizing temporal convolutional network, and a spatio-temporal joint attention module. These modules are combined to capture the spatio-temporal information in multi-stream skeleton features. Secondly, we propose a 3D ResNet model based on deformable convolution (D-ResNet) to model complex spatial and temporal sequences in the original raw images. Finally, a gating mechanism-based Multi-Stream Fusion Module (MFM) is employed to merge the results of the two modalities. Extensive experiments are conducted on the public datasets AUTSL and WLASL, achieving competitive results compared to state-of-the-art systems. Full article

The recirculating aquaculture system (RAS) has been rapidly adopted worldwide in recent years due to its high productivity, good stability, and good environmental controllability (and therefore friendliness to environment and ecology). Nevertheless, the effluent from seawater RAS contains a high level of ammonia nitrogen which is toxic to fish, so it is necessary to overcome the salinity conditions to achieve rapid and efficient nitrification for recycling. The moving bed biofilm reactor (MBBR) has been widely applied often by using PE fillers for efficient wastewater treatment. However, the start-up of MBBR in seawater environments has remained a challenge due to salinity stress and harsh inoculation conditions. This study investigated a new PE-filler surface modification method towards the enhanced start-up of mariculture MBBR by combining liquid-phase oxidation and maifanstone powder. The aim was to obtain a higher porous surface and roughness and a strong adsorption and alkalinity adjustment for the MBBR PE filler. The hydrophilic properties, surface morphology, and chemical structure of a raw polyethylene filler (an unmodified PE filler), liquid-phase oxidation modified filler (LO-PE), and liquid-phase oxidation combined with a coating of a maifanstone-powder-surface-modified filler (LO-SCPE) were first investigated and compared. The results showed that the contact angle was reduced to 45.5° after the optimal liquid-phase oxidation modification for LO-PE, 49.8% lower than that before modification, while SEM showed increased roughness and surface area by modification. Moreover, EDS presented the relative content of carbon (22.75%) and oxygen (42.36%) on the LO-SCPE surface with an O/C ratio of 186.10%, which is 177.7% higher than that of the unmodified filler. The start-up experiment on MBBRs treating simulated marine RAS wastewater (HRT = 24 h) showed that the start-up period was shortened by 10 days for LO-SCPE compared to the PE reactor, with better ammonia nitrogen removal observed for LO-SCPE (95.8%) than the PE reactor (91.7%). Meanwhile, the bacterial community composition showed that the LO-SCPE reactor had a more diverse and abundant AOB and NOB. The Nitrospira has a more significant impact on nitrification because it would directly oxidize NH₄⁺-N to NO₃⁻-N (comammox pathway) as mediated by AOB and NOB. Further, the LO-SCPE reactor showed a higher NH₄⁺-N removal rate (>99%), less NO₂⁻-N accumulation, and a shorter adaption period than the PE reactor. Eventually, the NH₄⁺-N concentrations of the three reactors (R1, R2, and R3) reached <0.1 mg/L within 3 days, and their NH₄⁺-N removal efficiencies achieved 99.53%, 99.61%, and 99.69%, respectively, under ammonia shock load. Hence, the LO-SCPE media have a higher marine wastewater treatment efficiency. Full article

The temporal stability of alpine plant α-diversity remains poorly understood, constraining predictions of biodiversity dynamics. Here, this study examined spatiotemporal patterns in the temporal stability of plant α-diversity (species richness, Shannon, Simpson, and Pielou) across the Tibetan grasslands from 2000 to 2020. The temporal stability of plant α-diversity was more sensitive to changes in elevation compared to longitude and latitude. The greater the temporal stability of a plant species’ Shannon, the higher its rate of increase under the combined effects of climate change and human activities. The spatial average temporal stability of plant α-diversity declined by 8.83–16.40% across all the grasslands of the Qinghai-Xizang Plateau, while 39.34–43.77% of the region exhibited increasing trends under the combined effects of climate change and human activities. Climate change and human activities dominated 44.12–48.71% and 51.24–55.84% of grassland areas of the change of temporal stability of plant α-diversity, respectively. Radiation variability exerted some exclusive effects on the temporal stability of plant α-diversity. The relative change in plant α-diversity did not exhibit simple linear relationships with the relative change in its temporal stability. Therefore, climate change and human activities resulted in the spatial heterogenization of the temporal stability of plant α-diversity. While the overall temporal stability of plant α-diversity declined, some areas experienced local increases. Human activities drove changes in temporal stability across a broader area than climate change. In addition to climate warming and precipitation changes, attention should also be paid to the impact of radiation variability on the temporal stability of plant α-diversity. The relationships between plant α-diversity and its temporal stability were not always characterized by a trade-off or synergy. In future grassland biodiversity conservation efforts, it is essential to consider the potential influence of global dimming on the temporal stability of plant α-diversity. Simultaneously monitoring both α-diversity and its temporal stability, especially in areas where both are declining, should be a priority. Full article

This paper proposes a comprehensive Mixed-Integer Linear Programming (MILP) formulation for the simultaneous scheduling of machines and Automated Guided Vehicles (AGVs) within a partitioned Flexible Manufacturing System (FMS). The main objective is to numerically optimize the simultaneous scheduling of machines and AGVs while considering various workshop layouts and operational constraints. Three different workshop layouts are analyzed, with varying numbers of machines in partitioned workshop areas A and B, to evaluate the performance and effectiveness of the proposed model. The model is tested in multiple scenarios that combine different layouts with varying numbers of workpieces, followed by an extension to consider dynamic initial conditions in a more generalized MILP framework. Results demonstrate that the proposed MILP formulation efficiently generates globally optimal solutions and consistently outperforms a greedy algorithm enhanced by A*-inspired heuristics. Although computationally intensive for large scenarios, the MILP’s optimal results serve as an exact benchmark for evaluating faster heuristic methods. In addition, the study provides practical insight into the integration of AGVs in modern manufacturing systems, paving the way for more flexible and efficient production planning. The findings of this research are expected to contribute to the development of advanced scheduling strategies in automated manufacturing systems. Full article

This research describes a multi-objective trajectory planning method for robotic arms based on time, energy, and impact. The quintic Non-Uniform Rational B-Spline (NURBS) curve was employed to interpolate the trajectory in joint space. The quintic NURBS interpolation curve can make the trajectory become constrained within the kinematic limits of velocity, acceleration, and jerk while also satisfying the continuity of jerk. Then, based on the Parrot Optimization (PO) algorithm, through improvements to reduce algorithmic randomness and the introduction of appropriate multi-objective strategies, the algorithm was extended to the Multi-Objective Parrot Optimization (MOPO) algorithm, which better balances global search and local convergence, thereby more effectively solving multi-objective optimization problems and reducing the impact on optimization results. Subsequently, by integrating interpolation curves, the multi-objective optimization of joint trajectories could be performed under robotic kinematic constraints based on time–energy-jerk criteria. The obtained Pareto optimal front can provide decision-makers in industrial robotic arm applications with flexible options among non-dominated solutions. Full article