Goal interdependencies and opportunism for supply chain partnership in China

The possibility of opportunistic behavior is an important barrier to the collaboration between partners in the supply chain as partners pursue their self-interests with guile. Opportunistic behavior threatens the partners’ relationships, influences their work accomplishment and prevents future collaboration. This study hypothesizes that opportunism is not just the result of people’s self-interests pursuit but depends on how they think their self-interests are related. Opportunism in organizational partnerships could be understood in terms of how partners perceive their goals are related to each other. When partners believe that their goals are competitively or dependently rather than cooperatively related, they are more likely to pursue their self-interests opportunistically. Altogether 86 face-to-face interviews were carried out in Beijing, Nanchang and Guangzhou, China to explore the links and relations among goal interdependencies, opportunism and the outcomes. Participants who work in a supply chain partnership were asked to describe an incident regarding their collaboration with their partners. It included the people involved, the reasons, what occurred, and the consequences. Structural equation modeling explored the proposed model that goal interdependencies could affect the levels of opportunism and thus influence the partnerships. Results suggest that cooperative goals are important foundations for effective organizational partnerships

Automatic Data Transformation Using Large Language Model: An Experimental Study on Building Energy Data

Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To bridge this gap, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions.Comment: 10 pages, 7 figure

Multiple Precast Component Orders Acceptance and Scheduling

Precast components manufacturer generally operates under limited production capacity and produces products of one order which may delay another. This paper develops a precast component order acceptance and scheduling model that aims to maximize the total profit in a stochastic multiple orders environment. In that model, the increasing of the overall profit of the precast component manufacturer is achieved by using a heuristic algorithm and a dynamic order acceptance heuristic. Results of numerical examples indicate the proposed model realizes the increasing total profit in most cases comparing to accept all of the orders. Besides, this study tested three order acceptance criteria and found that the profit-based criterion is to be more stable in terms of maximum total profit. This approach is anticipated to provide support to precast component manufacturers when faced with multiple orders in long-term production

Study on Foundation Pit Construction Cost Prediction Based on the Stacked Denoising Autoencoder

To accurately predict the construction costs of foundation pit projects, a model based on the stacked denoising autoencoder (SDAE) is constructed in this work. The influencing factors of foundation pit project construction costs are identified from the four attributes of construction cost management, namely, engineering, the environment, the market, and management. Combined with Chinese national standards and the practice of foundation pit project management, a method of the quantization of the influencing factors is presented. 60 deep foundation pit projects in China are selected to obtain 13 main characteristic factors affecting these project construction cost by using the rough set. Then, considering the advantages of the SDAE in dealing with complex nonlinear problems, a prediction model of foundation pit project construction costs is created. Finally, this paper employs these 60 projects for a case analysis. The case study demonstrates that, compared with the actual construction costs, the calculation error of the proposed method is less than 3%, and the average error is only 1.54%. In addition, three error analysis tools commonly used in machine learning (the determination coefficient, root mean square error, and mean absolute error) emphasize that the calculation accuracy of the proposed method is notably higher than those of other methods (Chinese national code, the multivariate return method, the BP algorithm, the BP model optimized by the genetic algorithm, the support vector machine, and the RBF model). The relevant research results of this paper provide a useful reference for the prediction of the construction costs of foundation pit projects

Carbon emission reduction and profit distribution mechanism of construction supply chain with fairness concern and cap-and-trade.

Fairness concern behavior is extremely common in social life, and many scholars are beginning to pay attention to this behavior. In this study, we investigate a two-echelon construction supply chain that consists of a general contractor and a subcontractor under cap-and-trade policy. We study the carbon emission reduction decisions and profit distribution mechanism in the construction supply chain with fairness concern and cap-and-trade. We use the Nash bargaining model to describe the fairness concerns of the construction supply chain members and use the co-opetition model to portray the profit distribution. We show that the fairness concern can impose an adverse influence on firms' profits and decrease the magnitude of their carbon emission reductions. The subcontractor's fairness concern causes greater losses to the construction supply chain's profit. We further demonstrate the impact of fairness concern on the optimal decisions of the general contractor and the subcontractor through numerical analysis

The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization

The prediction of construction cost of metro shield engineering is of great significance to project management. In this study, we used the rough set theory, a backpropagation (BP) neural network, and quantum particle swarm optimization (QPSO) to establish a prediction model for predicting the metro shield construction costs. The model accounts for the complexity of metro shield construction and the nonlinear relationship between the construction cost factors. First, the factors affecting the construction cost were determined by referring to the Chinese National Standards and analysing the engineering practice of typical metro shield projects. The rough set theory was used to simplify the system of influencing factors to extract the dominant influencing factors and reduce the number of input variables in the BP neural network. Since the BP neural network easily falls into a local minimum and has a slow convergence speed, QPSO was used to optimize the weights and thresholds of the BP neural network. This method combined the strong nonlinear analysis capabilities of the BP and the global search capabilities of the QPSO. Finally, we selected 50 projects in China for a case analysis. The results showed the dominant factors affecting the construction cost of these projects included ten indicators, such as the type of tunnelling machine and the geological characteristics. The determination coefficient, mean absolute percentage error, root mean square error, and mean absolute error, which are frequently used error analysis tools, were used to analyse the calculation errors of different models (the proposed model, a multiple regression method, a traditional BP model, a BP model optimized by the genetic algorithm, and the BP model optimized by the particle swarm optimization). The results showed that the proposed method had the highest prediction accuracy and stability, demonstrating the effectiveness and excellent performance of this proposed method

CTLA4-Ig Abatacept Ameliorates Proteinuria by Regulating Circulating Treg/IL-17 in Adriamycin-Induced Nephropathy Rats

Objective. This study is aimed at investigating the efficacy of CTLA4-Ig abatacept in normalizing proteinuria and its possible mechanism in adriamycin-induced nephropathy (AIN) rats. Methods. A total of 32 healthy male Sprague-Dawley rats were randomly divided into a normal group, an AIN group, an abatacept group, and a prednisone group. Adriamycin (6.5 mg/kg) was injected once via the tail vein of rats to induce nephrotic syndrome. After adriamycin treatment, the abatacept group rats were given abatacept (0.5 mg/kg) once by intraperitoneal injection on day 14. In addition, the prednisone group rats were given prednisone (12.5 mg/kg) daily consecutively by gavage from day 14 to day 21. Blood, urine, and kidney tissue specimens were collected when sacrificed on day 21. The 24-hour urinary protein, serum albumin, cholesterol, creatinine, and urea nitrogen were then detected. An enzyme-linked immunosorbent assay was used to determine the level of urine CD80 and serum IL-17. Flow cytometry was used to investigate the prevalence of circulating Treg. Hematoxylin-eosin staining and electron microscopy were used for a renal histological study. Immunofluorescence staining was performed to confirm the CD80 expression of renal tissue. Results. The 24-hour urinary protein of the abatacept group was significantly lower than that of the prednisone group and the AIN group. The level of urine CD80 of the abatacept group was significantly lower than that of the AIN group. Compared with the AIN group and the prednisone group, the circulating Treg prevalence of the abatacept group was significantly higher, while the level of serum IL-17 was lower. A negative kidney staining of CD80 expression was demonstrated in each group in this study. The 24-hour urinary protein had a negative correlation with the circulating Treg prevalence and Treg/IL-17 and a positive correlation with the urine CD80 and serum IL-17. Urinary CD80 had a positive correlation with serum IL-17 and no correlation with the circulating Treg prevalence. Conclusions. CTLA4-Ig abatacept can reduce proteinuria of adriamycin-induced nephropathy rats, possibly at least partially as a result of regulating circulating Treg/IL-17. CTLA4-Ig abatacept could be a promising regimen for idiopathic nephrotic syndrome

The Rain-Induced Urban Waterlogging Risk and Its Evaluation: A Case Study in the Central City of Shanghai

Waterlogging induced by rain in urban areas has a potential risk impact on property and safety. This paper focuses on the impact of rain on waterlogging and evaluates the waterlogging risk in the central city of Shanghai. A simplified waterlogging depth model is developed in different areas with different drainage capacity and rainfall in consumption of simplifying the effect of complex terrain characteristics and hydrological situation. Based on urban waterlogging depth and its classification collection, a Rain-induced Urban Waterlogging Risk Model (RUWRM) is further established to evaluate waterlogging risk in the central city. The results show that waterlogging depth is closely linked with rainfall and drainage, with a linear relationship between them. More rainfall leads to higher waterlogging risk, especially in the central city with imperfect drainage facilities. Rain-induced urban waterlogging risk model can rapidly gives the waterlogging rank caused by rainfall with a clear classification collection. The results of waterlogging risk prediction indicate that it is confident to get the urban waterlogging risk rank well and truly in advance with more accurate rainfall prediction. This general study is a contribution that allows the public, policy makers and relevant departments of urban operation to assess the appropriate management to reduce traffic intensity and personal safety or strategy to lead to less waterlogging risk