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During the cooperation process between prefabricated building construction enterprises (PBCEs) and Internet platforms (IPs), the sentiments of both parties influence their behavioral strategies. They are the key to improving the informatization and operational efficiency of the prefabricated building industry chain (PBIC).

This paper introduces mental accounting theory and rank-dependent expected utility theory to construct the MA-RDEU game model, exploring the evolutionary mechanism between sentiment and behavioral strategies of PBCEs and IPs.

The study indicates that (1) a mixed strategy equilibrium can be achieved when both parties have no sentiments. (2) PBCEs and IPs are more likely to achieve an optimal equilibrium for cooperation if the latter is optimistic. In contrast, pessimism may lead both parties to prioritize self-interest when only one party has a sentiment. (3) The combined impact of sentiments and behavioral strategies on decision-making is significant: the influence of sentiments from PBCEs or IPs on the optimal strategy for achieving cooperation is contingent upon the behavioral strategies of the other party; different behavioral strategies of IPs or PBCEs can have varying effects on sentiments when both parties have sentiments. (4) The influence of external factors on the sentiments and behavior strategies of PBCEs and IPs is apparent. PBCEs and IPs should concurrently consider the combined influence of external factors and sentiments to contribute to the realization of cooperation between the two parties. Additionally, government supervision is an effective means to restrain “free-riding” behavior.

Finally, based on the above conclusions, the paper proposes measures to improve the construction of service-oriented IPs and establish a mechanism for monitoring and adjusting risk sentiments. Meanwhile, this paper also indicates that under the combined effect of the government, PBCEs and IPs, the influence of external factors on sentiments can be maintained within a controllable scope and the risks of uncertainty can be mitigated.

To systematically characterize and objectively evaluate basic railway safety management capability, creating a closed-loop management approach which allows continuous improvement and optimization.

A basic railway safety management capability evaluation index system based on a comprehensive analysis of national safety management standards, railway safety rules and regulations and existing safety data from railway transport enterprises is presented. The system comprises a guideline layer including safety committee formation, work safety responsibility, safety management organization and safety rules and regulations as its components, along with an index layer consisting of 12 quantifiable indexes. Game theory combination weighting is utilized to integrate subjective and objective weight values derived using AHP and CRITIC methods and further combined using the TOPSIS method in order to construct a comprehensive basic railway safety management capability evaluation model.

The case study presented demonstrates that this evaluation index system and comprehensive evaluation model are capable of effectively characterizing and evaluating basic railway safety management capability and providing directional guidance for its sustained improvement.

Construction of an evaluation index system that is quantifiable, generalizable and accessible, accurately reflects the main aspects of railway transportation enterprises’ basic safety management capability and provides interoperability across various railway transportation enterprises. The application of the game theoretic combination weighting method to derive composite weights which combine experts’ subjective evaluations with the objectivity of data.

The purpose of this study is to improve the accuracy of industrial robot kinematic parameter identification and position accuracy by solving the problem of insufficient consideration of error sources in the kinematic parameter identification model and optimizing the selection of measurement pose set.

In this study, a kinematic calibration method for industrial robots considering multiple error sources is proposed. Based on the Modified Denavit Hartenberg (MD-H) model, a robot kinematics identification model including joint reduction ratio error, target ball installation error and coordinate system transformation error is established. Taking the optimal observability index O1 and the minimum flexible deformation as the optimization objectives, a measurement pose set optimization method based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to obtain a measurement pose set with higher identification accuracy.

Through experiments conducted with the Nantong Zhenkang ZK1400-6 robot as the test subject, the kinematic parameters identified by the optimized measurement pose set are more accurate than the randomly selected measurement pose set, and the positioning accuracy of the robot is improved from 2.11 to 0.31 mm, an increase of 85.3%.

This study introduces a position error model that comprehensively accounts for the error sources causing positioning inaccuracies. Building on this foundation, a novel flexible deformation index is proposed to quantify the flexible deformation in the measurement pose set, thereby reducing the impact of such deformation on the position error in the model. To the best of the authors’ knowledge, for the first time, this study presents an optimization method for the measurement pose set based on NSGA-II, using the flexible deformation index and observability index as objectives for multi-objective optimization, simultaneously optimizing the pose error and Jacobian matrix in the error model.

With burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.

We consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.

Numerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.

Drawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough set analysis. The inclusion of sustainability concept in industrial supply chains has started gaining momentum due to increased environmental protection awareness and social obligations. The selection of sustainable suppliers marks the first step toward accomplishing this objective. The problem of selecting the right suppliers fulfilling the sustainable requirements is a major MCDM problem since various conflicting factors are underplay in the selection process. The decision-makers are often confronted with inconsistent situations forcing them to make imprecise and vague decisions.

This paper presents a new method based on dominance-based rough sets for the selection of right suppliers based on sustainable performance criteria relying on the triple bottom line approach. The method applied has its distinct advantages by providing more transparency in dealing with the preference information provided by the decision-makers and is thus found to be more intuitive and appealing as a performance measurement tool.

The technique is easy to apply using “jrank” software package and devises results in the form of decision rules and ranking that further assist the decision-makers in making an informed decision that increases credibility in the decision-making process.

The novelty of this study of its kind is that uses the dominance-based rough set approach for a sustainable supplier selection process.

Pores are the primary cause of fatigue failure in laser-directed energy deposition (L-DED) titanium alloys, which are largely determined by their location, size and shape. It is crucial for promoting the application of L-DED titanium alloys and ensuring their safety that establishing a fatigue life prediction method induced by pores, resulting in a proposed fatigue life prediction framework for L-DED Ti-6Al-4V based on a physics-informed neural network (PINN) algorithm.

In this study, a novel fatigue life prediction framework for L-DED Ti-6Al-4V based on a PINN algorithm was proposed. The influence patterns of various fatigue-sensitive parameters were revealed. The paper also included validation and analysis of the method, such as hyperparameter analysis of the PINN, efficacy analysis driven by physical information and comparative analysis of different methods.

The proposed method demonstrated high accuracy, with a correlation coefficient of 0.99 with experimental life. The coefficient of determination was 0.95 and the mean squared error was 0.06.

The results indicate that the proposed fatigue life prediction framework was of strong generalization capability and robustness.

Purpose: Industry 5.0 is characterized by a revolution in the industrial field where humans collaborate with machines. This study aims to highlight the role of the concept of the “Digital Twin” (DT) within Industry 5.0, aiming to predict the effects of natural disaster scenarios in advance and to take preventive measures more effectively.

Need for the study: The innovations brought by Industry 5.0 demonstrate the possibility of creating DTs of cities to predict and minimize the effects of natural disasters. This is of great importance in terms of preparation for future natural disasters and risk management.

Methodology: This study was conducted by analyzing the fundamental principles of Industry 5.0 and the concept of DTs. Scientific literature and industry reports were examined to explore how DTs can be used in the field of risk management related to natural disasters.

Findings: The use of DTs has significant potential in simulating natural disaster scenarios in advance and predicting potential damages. For example, through DTs of cities, the effects of disaster scenarios such as earthquakes, tsunamis, and floods can be analyzed in advance, and necessary measures can be taken accordingly.

Practical implications: These findings offer important practical implications for decision-makers working in areas such as urban planning and infrastructure management. The use of DTs can assist in the development of preparation and risk management strategies for natural disasters, thereby minimizing the impact of disasters and ensuring the safety of individuals.

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.

Drawing on social exchange and social comparison theories, the current work aims to examine the direct and indirect effect of sustainable leadership (SL) on employees’ change-oriented organizational citizenship behavior (OCB) through workplace envy (WE).

The authors collected data from multiple sources (311 employees and respective supervisors) of small and medium-sized enterprises (SMEs) in Saudi Arabia. Partial least square-structural equation modeling (PLS-SEM) analysis was conducted to verify the proposed hypotheses.

The current empirical results confirmed that SL positively influences change-oriented OCB – both directly and indirectly (through WE). The negative impact of WE on change-oriented OCB is also concluded in this study.

To the best of authors’ knowledge, this is among pioneer studies which introduced inhibitor as a mediator in the “SL-employee behavioral outcomes” relationship. Limitations and implications have been elaborated at the end of the study.

To improve the surface quality of Mg₂Si/Al composites after solution treatment, the formation mechanism of surface defects under milling machining conditions is investigated to reduce the surface roughness.

This paper analyzes the formation mechanism of surface defects on Mg₂Si/Al composites under micro-milling conditions by establishing a two-dimensional finite element simulation model. Response surface (Box–Behnken) experiments are designed to establish a prediction model for surface roughness, and an analysis of extreme variance is used to investigate the effects of milling depth (ap), spindle speed (vs) and feed rate (vf) on surface quality. NSGA-II multi-objective optimization algorithm is used to optimize the process parameters by considering surface roughness and milling efficiency. Experiments are also applied to verify the relationship between surface defects and particle damage. The effect of depth of cut on surface defects is also investigated.

There are few studies on solid solution treated Mg₂Si/Al composites. Solid solution treated Mg₂Si/Al composites have excellent material properties without changing the original shape of the material, and they are indispensable and critical materials in the fields of aerospace, energy, electronic information and energy transportation.

This study elucidates the formation mechanism of surface damage in Mg₂Si/Al composites, optimizes reasonable process parameters and provides technical guidance for its milling processing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0309/