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In recent years, the development of metaheuristic algorithms for solving optimization problems within a reasonable timeframe has garnered significant attention from the global scientific community. In this work, a new metaheuristic algorithm inspired by the inflection mechanism of the avian influenza virus H5N1 in poultry and humans, taking into account its mutation mechanism, called H5N1.

This algorithm aims to explore optimal solutions for optimization problems by simulating the adaptive behavior and evolutionary process of the H5N1 virus, thereby enhancing the algorithm’s performance for all types of optimization problems. Additionally, a balanced stochastic probability mechanism derived from the infection probability is presented. Using this mechanism, the H5N1 algorithm can change its phrase, including exploitation and exploration phases. Two versions of H5N1, SH5N1 and MH5N1, are presented to solve single-objective optimization problems (SOPs) and multi-objective optimization problems (MOPs).

The performance of the algorithm is evaluated using a set of benchmark functions, including seven unimodal, six multimodal, ten fixed-dimension multimodal to solve SOPs, ZDT functions and CEC2009 has been used to demonstrate its superiority over other recent algorithms. Finally, six optimization engineering problems have been tested. The results obtained indicate that the proposed algorithm outperformed ten algorithms in SOPs and seven algorithms in MOPs.

The experimental findings demonstrate the outstanding convergence of the H5N1 algorithm and its ability to generate solutions of superior quality.

Drought is the primary disaster that negatively impacts agricultural and animal husbandry production. It can lead to crop reduction and even pose a threat to human survival in environmentally sensitive areas of China (ESAC). However, the phases and periodicity of drought changes in the ESAC remain largely unknown. Thus, this paper aims to identify the periodic characteristics of meteorological drought changes.

The potential evapotranspiration was calculated using the Penman–Monteith formula recommended by the Food and Agriculture Organization of the United Nations, whereas the standardized precipitation evaporation index (SPEI) of drought was simulated by coupling precipitation data. Subsequently, the Bernaola-Galvan segmentation algorithm was proposed to divide the periods of drought change and the newly developed extreme-point symmetric mode decomposition to analyze the periodic drought patterns.

The findings reveal a significant increase in SPEI in the ESAC, with the rate of decline in drought events higher in the ESAC than in China, indicating a more pronounced wetting trend in the study area. Spatially, the northeast region showed an evident drying trend, whereas the southwest region showed a wetting trend. Two abrupt changes in the drought pattern were observed during the study period, namely, in 1965 and 1983. The spatial instability of moderate or severe drought frequency and intensity on a seasonal scale was more consistent during 1966–1983 and 1984–2018, compared to 1961–1965. Drought variation was predominantly influenced by interannual oscillations, with the periods of the components of intrinsic mode functions 1 (IMF1) and 2 (IMF2) being 3.1 and 7.3 years, respectively. Their cumulative variance contribution rate reached 70.22%.

The trend decomposition and periods of droughts in the study area were analyzed, which may provide an important scientific reference for water resource management and agricultural production activities in the ESAC. However, several problems remain unaddressed. First, the SPEI considers only precipitation and evapotranspiration, making it extremely sensitive to temperature increases. It also ignores the nonstationary nature of the hydrometeorological water process; therefore, it is prone to bias in drought detection and may overestimate the intensity and duration of droughts. Therefore, further studies on the application and comparison of various drought indices should be conducted to develop a more effective meteorological drought index. Second, the local water budget is mainly affected by surface evapotranspiration and precipitation. Evapotranspiration is calculated by various methods that provide different results. Therefore, future studies need to explore both the advantages and disadvantages of various evapotranspiration calculation methods (e.g. Hargreaves, Thornthwaite and Penman–Monteith) and their application scenarios. Third, this study focused on the temporal and spatial evolution and periodic characteristics of droughts, without considering the driving mechanisms behind them and their impact on the ecosystem. In future, it will be necessary to focus on a sensitivity analysis of drought indices with regard to climate change. Finally, although this study calculated the SPEI using meteorological data provided by China’s high-density observatory network, deviations and uncertainties were inevitable in the point-to-grid spatialization process. This shortcoming may be avoided by using satellite remote sensing data with high spatiotemporal resolution in the future, which can allow pixel-scale monitoring and simulation of meteorological drought evolution.

Under the background of continuous global warming, the climate in arid and semiarid areas of China has shown a trend of warming and wetting. It means that the plant environment in this region is getting better. In the future, the project of afforestation and returning farmland to forest and grassland in this region can increase the planting proportion of water-loving tree species to obtain better ecological benefits. Meanwhile, this study found that in the relatively water-scarce regions of China, drought duration was dominated by interannual oscillations (3.1a and 7.3a). This suggests that governments and nongovernmental organizations in the region should pay attention to the short drought period in the ESAC when they carry out ecological restoration and protection projects such as the construction of forest reserves and high-quality farmland.

The findings enhance the understanding of the phasic and periodic characteristics of drought changes in the ESAC. Future studies on the stress effects of drought on crop yield may consider these effects to better reflect the agricultural response to meteorological drought and thus effectively improve the tolerance of agricultural activities to drought events.

This study examines how default bias, driven by the default positive review (DPR) rule – which automatically classifies reviews not provided by consumers within a specified period as positive – and rebate bias, associated with the conditional rebate strategy (CRS), where sellers offer rebates exclusively to consumers who submit positive reviews, distort the distribution of online product reviews over time and impact consumer satisfaction.

A key aspect of our method lies in developing latent variable models that capture the relationship between biased online reviews and consumer satisfaction levels. By applying our models to a panel dataset from Taobao – a leading Chinese e-commerce platform – and using insights from online consumer feedback surveys, we assess the extent of the biases introduced by DPR and CRS in a given feedback system. A hierarchical regression model was employed to investigate the impact of the proposed biases on consumer satisfaction.

Consumers who have previously written online reviews experience satisfaction outcomes 72.9% of the time with DPR and up to 81.3% when CRS is included. Implementing DPR may boost product sales to some extent, but it would significantly amplify consumer dissatisfaction, whereas offering a rebate could effectively alleviate consumer discontent, even though the rebate is conditional.

Our findings reveal the extent of biases introduced by CRS and DPR in online reviews and inform the consumer satisfaction debate regarding the phenomenon of excessive positive reviews resulting from these practices.

Recently, due to the practicability in several domains, generative adversarial network (GAN) has successfully been adopted in the field of natural language generation (NLG). The purpose of this paper focuses on improving the quality of text and generating sequences similar to human writing for several real applications.

A novel model, GAN², is developed based on a GAN with dual adversarial architecture. We train the generator by an internal discriminator with a beam search technique to improve the quality of generated sequences. Then, we enhance the generator with an external discriminator to optimize and strengthen the learning process of sequence generation.

The proposed GAN² model could be utilized in widespread applications, such as chatbots, machine translation and image description. By the proposed dual adversarial structure, we significantly improve the quality of the generated text. The average and top-1 metrics, such as NLL, BLEU and ROUGE, are used to measure the generated sentences from the GAN² model over all baselines. Several experiments are conducted to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on numerous evaluation metrics.

Generally, reward sparsity and mode collapse are two main challenging issues when adopt GAN to real NLG applications. In this study, GAN² exploits a dual adversarial architecture which facilitates the learning process in the early training stage for solving the problem of reward sparsity. The occurrence of mode collapse also could be reduced in the later training stage with the introduced comparative discriminator by avoiding high rewards for training in a specific mode. Furthermore, the proposed model is applied to several synthetic and real datasets to show the practicability and exhibit great generalization with all discussed metrics.

Tacit knowledge in frontline operations is primarily reflected in the holders’ intuition about dynamic systems. Despite the implicit nature of tacit knowledge, the understanding of complex systems it encapsulates can be displayed through formalization methods. This study seeks to develop a methodology for formalizing tacit knowledge in a dynamic delivery system.

This study employs a structured survey to gather experiential knowledge from dispatchers engaged in last-mile delivery operations. This knowledge is then formalized using a value function approximation approach, which transforms tacit insights into structured inputs for dynamic decision-making. We apply this methodology to optimize delivery operations in an online-to-offline pharmacy context.

The raw system feature data are not strongly correlated with the system’s development trends, making them ineffective for guiding dynamic decision-making. However, the system features obtained through preprocessing the raw data increase the predictiveness of dynamic decisions and improve the overall effectiveness of decision-making in delivery operations.

This research provides a foundational framework for studying sequential dynamic decision problems, highlighting the potential for improved decision quality and system optimization through the formalization and integration of tacit knowledge.

This approach proposed in this study offers a method to preserve and formalize critical operational expertise. By embedding tacit knowledge into decision-making systems, organizations can enhance real-time responsiveness and reduce operational costs.

This study presents a novel approach to integrating tacit knowledge into dynamic decision-making frameworks, demonstrated in a real-world last-mile delivery context. Unlike previous research that focuses primarily on explicit data-driven methods, our approach leverages the implicit, experience-based insights of operational staff, leading to more informed and effective decision-making strategies.

This paper aims to improve the selection process of heavy machinery in construction projects by developing an advanced optimization technique using the Improved Particle Swarm Optimization algorithm (IPSOM). The main objectives of such a study are to optimize the key parameters of time and cost while ensuring adherence to a predefined quality benchmarks, thereby facilitating more informed and balanced decision-making in construction management.

A rigorous methodology was applied to identify the relevant optimization parameters, combining a comprehensive literature review with consultations with industry experts. This approach identified the most influential factors affecting machinery selection, ensuring the model’s applicability and relevance across different project scales and complexities. Unique to this study, the model’s novelty lies in its advanced application of the IPSOM tailored to the construction industry’s specific needs, offering a systematic approach to balancing time, cost and quality considerations.

IPSOM was validated through a detailed case study, which provided empirical evidence of the model’s effectiveness in a real-world application. The study introduces a groundbreaking approach to optimizing equipment selection in highway construction, with the dual aims of minimizing costs and project duration while maintaining high-quality standards. The model proposed in the study saved 53% time, reduced costs by 30.8% and increased quality by 26.3%, outperforming traditional equipment selection methods.

The case study analysis demonstrated the model’s adaptability and potential as a crucial tool for decision-making in construction projects.

The aim of this study is to investigate the effects of the laser cladding process on the microstructure, hardness and corrosion resistance properties of high-entropy alloys (HEA).

Laser cladding technology was used, using AlCoCrFeNiCu HEA powder as the cladding material. HEA coatings were prepared on the surface of 45 steel using a coaxial powder feeding method. The microstructure, phase composition, hardness and corrosion resistance properties of the HEA cladding layer were analyzed using optical microscopy (OM), X-ray diffractometer, digital microhardness tester and electrochemical workstation.

Laser power affects the coating surface; lower power reveals more visible unmelted powder particles. Higher power results in increased melt width and height, a brighter, smoother surface. Phase structure remains consistent, but the coating hardness is significantly higher than the substrate. The hardness of the melted zone in the substrate peaks at approximately 890.5 HV. The cladding zone hardness is about 60 HV higher than the substrate zone. Electrochemical corrosion parameters of the cladding show that, compared to the substrate, Ecor shifts positively by 113 mV, Icor decreases by one order of magnitude and Rp increases by one order of magnitude. These results indicate that the cladding has superior corrosion resistance to the substrate. The bonding strength between the coating and the substrate is greater than 93.6 MPa.

First, based on preliminary pilot experiments, nine sets of single-factor experiments were designed. Through these experiments, a specimen with relatively favorable cross-sectional morphology was observed. This specimen was then subjected to coating research, revealing that its microstructure and properties had significantly improved compared to the substrate. This enhancement holds remarkable significance for prolonging the service life of components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0413/

Salt rock from salt lakes can serve as a cost-effective material for subgrade filling, as demonstrated in projects like the Qarhan Salt Lake section of the Qinghai-Tibet Railway and the Qarhan Salt Lake section of the G215 Highway. This state-of-the-art paper aims to summarize the engineering properties of salt rock filling and present the advances of its utilization.

This paper collects and analyzes laboratory and field data of salt rock filling from previous studies to present a comprehensive analysis of the engineering properties and utilization of salt rock fillings.

Salt rock primarily contains minerals such as halite and glauberite, which contribute to its unique phase-changing behavior under varying environmental conditions, impacting its mechanical properties. Salt rock filling shrinks when in contact with vapor or unsaturated brine and expands under cooling or evaporation. Its use is particularly recommended for arid regions, with specific restrictions depending on the structure type. This paper discusses suggested countermeasures to mitigate these issues, as well as key quality acceptance indices for salt rock filling compaction. Moisture content after air-drying is recommended as a crucial parameter for construction quality control.

This review aims to support future research and engineering practices in salt rock subgrade applications.

This paper aims to develop an optimal maintenance and spare parts policy for an urban micro wind power system, focusing on two urban micro wind farms (UMWF). The reliability and efficiency of these systems are sought to be enhanced by considering the relationship between urban wind parameters and wind turbine degradation.

A proportional hazards (PH) model is utilized to describe how urban wind conditions impact turbine degradation. The maintenance strategy includes preventive maintenance (PM), corrective maintenance (CM) and opportunistic maintenance (OM). A multi-objective optimization algorithm is developed to optimize the joint policy of OM plans and spare parts resource allocation.

The proposed maintenance and spare parts policy effectively balances the trade-offs between PM, CM and OM strategies. Numerical experiments demonstrate that the policy improves the reliability of UMWF, reducing downtime and maintenance costs while ensuring the availability of spare parts when needed. The results show a significant enhancement in system performance compared to traditional maintenance approaches.

A novel maintenance policy and spare parts management approach for urban micro wind power systems is proposed. A multi-objective optimization algorithm is developed to optimize the OM schedule and maintenance spare parts resource management strategy for wind farms in urban wind environments.

This study aims to improve the positioning accuracy of a large-scale parallel pose alignment mechanism by calibration and error compensation.

The dynamic modelling of the parallel pose alignment mechanism is achieved using the Newton Euler method. Combined with a deformation compatibility analysis, the support force at the spherical hinge and the friction of the follow-up prismatic pair are calculated. The deformation of the moving platform in multi-pose space is analysed by the integral method, and a corresponding deformation model is established. Based on the calculated support force, friction and deformation, the deformation error is analysed. Combined with the calculated deformation error, kinematics calibration and positioning error compensation are carried out.

The simulation results show that the deviation of structural error identification is decreased from 3.03 × 10^–1 mm to 6.8 × 10^–2 mm. The experimental results show that the maximum pose errors after compensation are reduced from 2.77 mm to 6.5 × 10^–1° to 3.9 × 10^–1 mm and 3.7 × 10^–1°, which verifies the effectiveness of the proposed method.

This method can be used in the field of aircraft assembly for the calibration and error compensation of a large-scale parallel pose alignment mechanism based on positioners.