A critical challenge in swarm intelligence is the effective utilization of knowledge gained during the search, a process often confounded by the risk of negative knowledge transfer. To address this, we introduce the Learning-Aided Artificial Bee Colony (LA-ABC), a novel framework guided by a Neural Knowledge Transfer mechanism for global optimization. Our framework establishes a co-evolutionary mechanism between the search process of the ABC algorithm and an online neural knowledge learning engine. LA-ABC operates on a dual-pathway architecture, probabilistically arbitrating between foundational swarm exploration and a knowledge-transfer pathway. In this second pathway, an Artificial Neural Network (ANN) learns a predictive, non-linear model from a dynamic archive of historically successful solutions. This approach enables the model to interpret the complex context of successful moves, thereby preventing the negative knowledge transfer where a beneficial pattern in one region of the search space could be detrimental in another. This learned intelligence is then operationalized through a generative operator that transfers validated positive knowledge to create high-quality candidate solutions. The process transforms the ABC from a memoryless explorer into an intelligent agent that learns to navigate the fitness landscape with high efficacy. The superiority of the LA-ABC framework is demonstrated through comprehensive benchmarking on 23 standard test functions, the competitive IEEE CEC 2019 suite, and a real-world photovoltaic parameter extraction problem. Our proposed neural knowledge transfer approach significantly outperforms 12 state-of-the-art algorithms, including ABC, L-SHADE, JSO, L-DE, L-PSO, KL-variants, and RL variants with the significance of these improvements validated by rigorous statistical tests (Wilcoxon, Bonferroni-Dunn, Friedman, and ANOVA). Ultimately, LA-ABC provides a robust new paradigm for integrating reinforcement learning and knowledge transfer within evolutionary computation.

This study aims to enhance service levels and efficiency in the tourism industry while improving tourism demand forecasting. First, it analyzes the classification of influencing factors based on tourism demand theory. Next, it develops a demand forecasting model using a deep learning framework, employing the stacked auto encoder long short-term memory (SAE-LSTM) as the benchmark structure, with an attention mechanism for optimization. This is combined with the bat algorithm-relevance vector machine (BA-RVM) model to create the BA-RVM-A-SAE-LSTM mixed model, which is then compared to traditional models and advanced mixed models. The results indicate that LSTM performs significantly better in tourism demand forecasting than other traditional models, supporting its use as the benchmark structure in this study. The mean absolute percentage error of the BA-RVM-A-SAE-LSTM mixed model is 2.63, the rank sum ratio is 0.432, the Nash-Sutcliffe efficiency coefficient is 0.992, and the goodness of fit is 0.991, all of which outperform traditional and mixed models. This study highlights the superiority of the BA-RVM-A-SAE-LSTM mixed model in forecasting accuracy and model interpretation, contributing positively to the development of the tourism industry.

Temperature-pressure-flow rate synergy effect on corrosion behavior of supercritical CO2 pipelines: Optimal conditions and FeCO₃ film mechanism

A generative dual-input model based on architectural computational optimization and multi-attention mechanism for remaining useful life prediction

Elucidating the optimization mechanism of oxygen transport at the Pt/ionomer interface in proton exchange membrane fuel cells by the surface-functionalized carbon supports: A molecular dynamics study

To address the challenges of energy-matching uncertainty and precise temperature and humidity control in multi-energy drying systems, this study proposes an adaptive control strategy for a solar-assisted air-source heat pump (SAHP) drying system based on the proximal policy optimization (PPO) algorithm. Due to the intermittent and uncertain nature of solar energy, conventional drying processes struggle to ensure consistent drying quality for alfalfa, often leading to uneven moisture retention and quality degradation. This study developed a synergistic optimization mechanism, integrating the Gym-DryNet simulation environment with a deep reinforcement learning framework, to effectively balance energy input fluctuations and drying process stability. This approach achieved a steady-state control accuracy of 0.3 °C for temperature (relative error of 0.92%) and a humidity regulation accuracy of ± 0.2% RH. Experimental results demonstrate the excellent dynamic anti-interference performance of the intelligent control system. In a 5-h drying cycle, the optimized system produced 160 kg of dried alfalfa (10.3% moisture content) from an initial moisture content of 65.2%, evaporating approximately 250 kg of water at an average rate of 50 kg/h. The total electrical energy consumption was only 3.9 kWh, representing merely 2.3% of the latent heat load associated with the evaporated water. Compared to traditional solar or single heat pump drying systems, this system exhibits significantly reduced temperature fluctuations under full load conditions, demonstrating improved stability and drying efficiency. This fully validates the advantages of the multi-energy synergistic control strategy in optimizing drying efficiency and energy consumption, providing an innovative and intelligent control solution for high-precision drying processes in grass product processing.

ABSTRACT Enhancing heat‐transfer efficiency in compact heat exchangers is essential for reducing energy consumption and improving overall system performance. This research investigates the behavior of an innovatively designed thermal and hydraulic energy promoter (EP), mounted on the inner wall of a circular tube, to improve heat transfer and minimize pressure loss. Water was used as the simulation fluid at Reynolds numbers between 4000 and 20,000, employing the Shear Tension Transmission k – ω perturbation model in ANSYS Fluent. The EP generates strong vortices that intensify the fluid mixing near the tube wall. Compared with a smooth tube, the Nusselt number increased by 1.9–3.4, while the coefficient of friction increased by 2.9–4.8. This further indicates an improvement in overall efficiency, exceeding the Performance Evaluation Criterion value of 1 in all cases (1.1–2.6). The promoter height of 1 mm achieved the best performance in terms of both increased heat transfer and reduced pressure. The flow analysis revealed the formation of secondary vortices and high‐kinetic‐energy regions, which are key optimization mechanisms. Compared with traditional passive methods, such as ribs and grooves, the proposed catalyst concept offers an effective solution for improving thermal performance without inducing excessive mechanical stress.

Blockchain technology has emerged as a transformative solution for enhancing security, transparency, and data integrity in healthcare monitoring systems. Central to blockchain’s functionality are hashing algorithms, which ensure data immutability and secure transaction verification. This study presents a comparative analysis of various blockchain hashing algorithms, evaluating their efficiency, security features, computational complexity, and suitability for healthcare monitoring applications. By examining algorithms such as SHA-256, SHA-3, Blake2, and others, the research aims to identify the optimal hashing mechanism that balances performance with robust security requirements in healthcare contexts. The analysis considers factors including speed, resistance to cryptographic attacks, energy consumption, and scalability. Results highlight the trade-offs inherent in selecting hashing algorithms for healthcare monitoring, where real- time data processing and patient privacy are critical. This paper contributes to advancing blockchain adoption in healthcare by guiding the selection of hashing algorithms tailored to the unique demands of healthcare monitoring systems.

The utilization of unmanned aerial vehicle (UAV) in diverse scenarios, including disaster relief and delivery services, is experiencing a daily increase. In these applications, 3D path planning holds substantial research significance as it directly influences the operational efficiency, safety, and adaptability of the UAV. Nevertheless, the challenge of efficient 3D path planning for UAV in complex predefined environments persists due to the computational intractability of exact methods and the susceptibility of metaheuristics to local optima. While recent studies have focused on enhancing planners through multi-strategy fusion, they often rely on static heuristic rules and fixed parameter tuning. In this context, to address such problems more effectively, this paper presents a reinforcement learning-based hybrid algorithm integrating Probabilistic Roadmap (PRM) and Ant Colony Optimization (ACO), namely the PRM-QACO algorithm. Firstly, it employs the PRM method to generate a 3D random graph, thereby simplifying the 3D space and enhancing exploration efficiency. Secondly, it incorporates directional information into the ACO heuristic, enabling the UAV to reach the target more efficiently within the 3D space. Thirdly, and most distinctively, a Q-learning module is embedded as an intelligent controller to dynamically balance exploration and exploitation by rewarding or penalizing the ants' search outcomes, thus optimizing the paths discovered by elite ants. Finally, a path optimization mechanism is introduced to minimize the number of turns in the planned path, which is crucial for the UAV to conserve energy and circumvent obstacles. Simulation experiments conducted in MATLAB and AirSim environments across various 3D terrains demonstrate that PRM-QACO is an effective solution for 3D UAV path planning.

The rapid evolution of artificial intelligence has far outpaced the traditional 3–5-year revision cycles of university curricula, resulting in outdated instructional materials and limiting students’ ability to understand emerging technologies. To address this challenge, this paper proposes a dynamic update mechanism for AI literacy course resources based on a knowledge graph. The mechanism integrates three core components: a technology-driven agile response system, an industry–academia collaborative injection mechanism, and a data-driven feedback and optimization loop. The agile response system employs frontier-technology tracking and multi-level content alerts to identify outdated knowledge in real time. The collaborative injection mechanism incorporates up-to-date industrial cases, datasets, and application scenarios, ensuring the practical relevance of teaching materials. The data-driven optimization mechanism leverages learning analytics to prioritize revisions, detect anomalies, and support rapid, atomic-level updates. By establishing an institutionalized workflow covering knowledge tracking, priority setting, content co-creation, review, publication, and effectiveness evaluation, the proposed framework transforms teaching resources from static repositories into dynamic knowledge services. This approach significantly enhances the timeliness, accuracy, and utilization efficiency of instructional materials while strengthening industry–education integration and improving students’ engineering competencies. The dynamic update mechanism provides a sustainable pathway for AI literacy education to adapt to fast-changing technological landscapes and to cultivate future-ready talent.

HighlightsWhat are the main findings?Y doping reduces WC/Co interfacial energy and enhances bonding strength. Y-W orbital hybridization forms strong covalent bonds at the atomic interface.Material achieves dual improvement: Hardness (1454 HV) and toughness (9.84 MPa·m1/2).Optimal 0.5 wt.% Y refines grains and uniformly distributes the Co binder phase. Wear resistance is significantly improved, with the lowest wear rate at 0.5 wt.% Y.What are the implications of the main findings?Provides an atomic-scale mechanism for rare earth-enhanced cemented carbides and offers a design strategy for co-optimizing hardness and toughness in composites;Validates the integration of first-principles calculations with experimental validation;Identifies an optimal Y doping threshold for superior comprehensive properties.This study aims to clarify the optimization mechanism of yttrium (Y) doping on the interfacial bonding and macroscopic properties of WC/Co cemented carbides, with the goal of achieving materials that combine high hardness, high toughness, and excellent wear resistance through interfacial regulation. Combining first-principles calculations and experimental verification, the interfacial energy, density of states, and charge density of WC/Co and WC/CoY interfaces were systematically investigated. Three alloys (WC-10Co, WC-10Co-0.5Y, and WC-10Co-1Y) were prepared, and the effects of Y addition were quantitatively evaluated through microstructural characterization, mechanical testing, and tribological experiments. The calculation results indicate that Y doping reduces interfacial energy, enhances interfacial bonding, and increases surface energy, which contributes to improved toughness. At the atomic scale, the orbital hybridization between Y and W promotes the formation of strong covalent bonds at the interface, thereby enhancing interfacial bonding strength. The experimental results show that the introduction of Y significantly improves the overall performance of the material, with the alloy containing 0.5 wt.% Y exhibiting the best performance. Its Vickers hardness reaches (1454 ± 1.3) HV, fracture toughness is (9.84 ± 0.15) MPa·m1/2, and the wear rate is as low as 0.794 × 10−5 mm3·N−1·m−1.

Enhancing Resource Efficiency: Utilizing Maize Stalk in Ganoderma lingzhi Substrates: Formula Optimization and Degradation Mechanism

Perioperative periods are often accompanied by significant anxiety, pain, and stress responses, which impact patient recovery and treatment adherence. Music, as a low-cost, non-pharmacological adjunctive intervention, demonstrates positive effects in alleviating preoperative anxiety, aiding intraoperative analgesia, and promoting postoperative recovery. Addressing current limitations in intervention models — including insufficient standardization, poor individual adaptability, and lack of interdisciplinary integration — this study proposes multidimensional improvement strategies. These include process optimization, precision prescribing, and collaborative mechanism development to advance music therapy toward routine clinical implementation.

The contemporary marketing environment is marked by high complexity, rapid market changes, intensive information flows, and the need for timely decision-making. Under these conditions, effective management of marketing operational processes becomes a key factor in maintaining enterprise competitiveness. The purpose of this study is to substantiate and analyze the impact of modern self-management trends on the optimization of marketing operational processes and to identify mechanisms for improving their efficiency and strategic outcomes. The purpose of the research is to analyze key marketing operational processes, including market research, segmentation and targeting, planning and budgeting, marketing communications, interaction with target audiences, sales and logistics management, monitoring, and performance evaluation, as well as to examine contemporary self-management trends such as Slow Productivity, Energy Management, Monk Mode, the use of Artificial Intelligence as a «Second Brain» and asynchronous work. Particular attention is given to the integration of these practices into marketing operations in order to enhance their efficiency. The methodology is based on theoretical analysis, systematization of scientific literature, and comparative evaluation of the influence of self-management trends on marketing activities. Secondary data from empirical studies and modern operational marketing frameworks were synthesized, while analytical tools and tables were used to illustrate optimization mechanisms and practical outcomes. The results demonstrate that the application of modern self-management approaches reduces cognitive overload, improves decision quality, optimizes resource allocation, and increases the adaptability of marketing processes. The integration of Slow Productivity, energy-based task distribution, focused work modes, AIassisted automation, and asynchronous interaction enhances operational performance, creativity, and alignment with strategic goals. The practical significance of the study lies in the proposed framework for implementing self-management tools in marketing operations, enabling enterprises to increase productivity, reduce operational risks, and strengthen competitive advantage. The findings also provide a basis for further empirical research on the measurable effects of self-management practices on marketing efficiency and business performance.

Abstract The Li–CO 2 battery emerges as a transformative technology for simultaneous CO 2 utilization, high‐energy‐density storage, offering a dual solution to carbon neutrality, renewable energy integration. However, its practical deployment is hindered by high thermodynamic energy barriers, kinetic limitations associated with the conventional 4e − /3CO 2 pathway, which generates insulating Li 2 CO 3 discharge products, leading to severe overpotentials, parasitic reactions, and poor cyclability. This review comprehensively analyzes current polarization‐reducing tactics to mitigate these limitations through two complementary approaches: 1) enhancing reversibility of the 4e − /3CO 2 pathway through advanced catalyst design, electrolyte engineering, external field assistance, discharge product morphology control; 2) switching to the kinetically favorable 2e − /2CO 2 pathway by stabilizing Li 2 C 2 O 4 as the final discharge product via strategies including chemical bonding modulation, non‐covalent interactions, redox mediator‐, solvent‐mediated reaction pathway reconstruction, charge–discharge protocol optimization. By synthesizing pioneering studies, persistent challenges are identified, targeted research directions for advancing low‐polarization, long‐lifetime Li–CO 2 battery are proposed. This review is intended to help readers understand the pathway‐specific optimization mechanisms to drive innovation in the next‐generation carbon‐neutral energy storage.

The increasing use of vanadium causes environmental concern about the toxic effluents. In this study, Fe- pillared bentonite (Fe-PB) was synthesized from a domestic clay sample. Characterization ofiron- pillared bentonite by XRF, XRD, FTIR, BET and SEM, indicated the success of the pillaring operation. According to the results, the Fe-PB adsorbent has better properties compared to the NB. In removal experiments, the vanadium removal percentage has been investigated by varying different main parameters including pH (2-10), initial vanadium ion concentration (50-200mg/L) and adsorbent dosage (1-6g/L). A combination of Design-Expert software and response surface methodology was applied to determine the influence of each operational parameter on the vanadium removal percentage, optimum condition to reach maximum vanadium removal. Based on the predicted model, maximum vanadium removal onto Fe-PB was 59.96% on the optimum operational condition as pH = 5.82, initial vanadium concentration = 50 ppm and adsorbent dosage = 6g/L at 3h contact time. Equilibrium data were best described by Langmuir model, and analysis of kinetic data demonstrated that the pseudo-second-order model accurately represents the kinetic data.

The subject. The article examines the basic approaches to the regulatory framework for informing and campaigning in the context of the growing influence of global digitalization on electoral law, the use of previously unknown digital technologies and the transfer of electoral legal relations to the Internet space.The aim of the article is to identify the specific features of legal regulation of informing and campaigning in digital era.The study of informing and campaigning was conducted with the methodology of a systems approach, formal legal interpretation of legislative acts and comparative legal analysis.Main results. The search for an optimal mechanism for information support for elections in the context of changes in the content of the subjective rights of participants in the electoral process actualizes the need to improve the traditionally understood procedure for observing, implementing and using the law. The increasing complexity of the process of interaction between participants in legal relations and the expanding list of legal liability measures emphasize the importance of resolving law enforcement issues. The establishment of an expanded list of entities entitled to campaign in social networks, the inclusion of various information resources on the Internet (including the so-called "new media") in the list of sources of information dissemination, the possibility of recognizing the informational nature of materials distributed in the media by those entities that, by law, are subjects of campaigning activities – all this leads to the erasure of the boundaries between informing and campaigning and, in fact, to the creation of a new information electoral space for the preparation and holding of elections, requiring the establishment of new requirements and principles for its organization.Conclusions The dissemination of campaigning in the field of the Internet space (digital services) transforms the rules for conducting campaigning. They become more unified, strictly ranked, which brings campaigning closer to informing and transfers the freedom of use of the right to the plane of compliance with the specified rules.

Objective: Echinacea purpurea, an herb with diverse pharmacological activities, has its roots widely used for anti-inflammatory and immunomodulatory purposes. Interestingly, its aerial parts, which are also rich in bioactive compounds, remain underutilized. This study aims to optimize the extraction and purification processes to obtain the aerial part extract of Echinacea purpurea (APE-EP) to enhance the content of active constituents and improve its anti-inflammatory and immunomodulatory effects. Methods: We analyzed the chemical composition of APE-EP using HPLC-MS. The intestinal absorption characteristics of APE-EP were evaluated using an ex vivo everted gut sac assay. Furthermore, the anti-inflammatory and immunomodulatory effects of APE-EP were validated using a DSS-induced colitis mouse model. Results: Several phenolic acids were identified, including chicoric acid and caffeic acid, which have significant antioxidant and anti-inflammatory activities. The everted gut sac assay revealed concentration-dependent absorption of chicoric acid in the gut. Results from the mouse model showed that APE-EP promoted macrophage polarization from pro-inflammatory M1 to anti-inflammatory M2 macrophages at the lesion sites, effectively suppressing inflammation and alleviating colitis-related pathological damage. Conclusions: This study enhances the medicinal value of the E. purpurea, provides new insights for the efficient utilization of plant resources, and offers a potential natural drug candidate for inflammatory bowel disease treatment.

In customized production environments featuring multi-task parallelism, the efficient adaptability of edge intelligent models is essential for ensuring the stable operation of production lines. However, rapidly generating deployable lightweight models under conditions of frequent task changes and constrained hardware resources remains a major challenge for current edge intelligence applications. This paper proposes an adaptive lightweight artificial intelligence (AI) model compression method for edge nodes in customized production lines to overcome the limited transferability and insufficient flexibility of traditional static compression approaches. First, a task requirement analysis model is constructed based on accuracy, latency, and power-consumption demands associated with different production tasks. Then, the hardware information of edge nodes is structurally characterized. Subsequently, a compression-strategy candidate pool is established, and an adaptive decision engine integrating ensemble reinforcement learning (RL) and Bayesian optimization (BO) is introduced. Finally, through an iterative optimization mechanism, compression ratios are dynamically adjusted using real-time feedback of inference latency, memory usage, and recognition accuracy, thereby continuously enhancing model performance in edge environments. Experimental results demonstrate that, in typical object-recognition tasks, the lightweight models generated by the proposed method significantly improve inference efficiency while maintaining high accuracy, outperforming conventional fixed compression strategies and validating the effectiveness of the proposed approach in adaptive capability and edge-deployment performance.

3D Gaussian Splatting (3D GS), known for its efficient and explicit radiance field representation, demonstrates considerable potential for modeling complex 3D scenes. However, its geospatial applicability remains limited, especially for areas such as multi-level scene parsing, heterogeneous 3D data extraction and fusion, and task-driven structured representations. As a preliminary step to address these challenges, this paper proposes a novel 3D GS framework that jointly optimizes geometry and semantics. First, a radiance field optimization mechanism that integrates multi-view 2D semantic labels with monocular depth priors is developed. This mechanism generates Gaussian representations with rich geospatial semantic attributes while substantially improving geometric accuracy. For complex urban environments, a geometry–semantics co-optimization module, comprising a semantics-guided adaptive densification strategy and a depth-weighted semantic propagation method, is further introduced. These strategies effectively suppress cross-view semantic noise and optimize memory efficiency. Experimental results demonstrate that, compared to Light Detection and Ranging (LiDAR)-scanned ground truth, the proposed framework achieves a mean Intersection over Union (mIoU) of 81.2% for semantic segmentation and a mean geometric error of 0.093 m, all while preserving high-fidelity rendering quality. Overall, this work provides a practical pathway for integrating 3D GS into GIS ecosystems and establishes the groundwork for advanced geospatial applications.