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In recent years, the poultry breeding industry has been converted into a large-scale, intensive, and intelligent production mode. The Internet of Things (IoT) is under rapid development, which promotes the development of precision livestock farming. In this study, we developed an intelligent service platform for a facility environment based on the IoT structure, utilizing the capabilities of Platform as a Service (PaaS). The platform consists of four layers, including an information perception layer, network layer, management service layer, and application layer. By using the cloud service model with a distributed network architecture, asynchronous data transmission, and a distributed file system, the platform can centrally manage multiple farm's data. The intelligent service platform includes the following functions: displaying environmental data, water and electricity consumption, data analysis, and managing production data. Over a 500-day trial period in a live poultry house, the platform demonstrated high data integrity (>87%) and resilience against network disruptions and power outages. The data validity of each environmental element exceeded 94%, among which the validity of the temperature, humidity, and carbon dioxide concentration exceeded 99%. The overall accuracy of the dataset remained relatively high, providing a robust data foundation for further research. Key features included audio analysis, environmental monitoring, and production data management. The platform's operational status was efficiently communicated via data statistics and email alerts, facilitating timely system recovery. The demonstrated modules included sound recognition, psychrometric charts for visual alerts, and financial analysis tools, offering versatile solutions for integrating PLF models and advanced analytics. [ABSTRACT FROM AUTHOR]

The traditional optimal design of a transverse magnetic field motor mainly uses the electromagnetic relational formulas and the finite element analysis to empirically select its structural parameters, therefore making it difficult to obtain the global optimal solution of each parameter and meeting the requirements for multi-objective optimization. This paper suggests a hybrid algorithm of harmony search and differential evolution and introduces the elite retention strategy to improve the hybrid algorithm. It presents the principles of the hybrid algorithm, and then the algorithm performance test is used to verify the superiority of the hybrid algorithm. The electromagnetic analysis demonstrates the influence of the main dimensional parameters of the transverse magnetic field motor on its maximum torque density, therefore providing the basis for its optimal design. Jointly with the hybrid algorithm, the maximum torque density and the production cost are used as the objective function to optimize the transverse magnetic field motor under two groups of weighted coefficients. The simulation results show that the maximum torque density of the transverse magnetic field motor optimized with the hybrid algorithm is larger compared with the prototype motor but its production cost is lower.

Texas electricity market saw a recent integration of electricity transmission as a part of Competitive Renewable Energy Zones (CREZ). Exploiting the commissioning date of CREZ based transmission integration as an exogenous shock, we analyze the effect of transmission expansion on market clearing prices of Congestion Revenue Rights (CRR). Reduced form estimates suggest that excess transmission led to a lowering of CRR prices for contracts at all Times of Use. We find strong evidence of spatial, distributional, and firm specific heterogeneity. The paper shows that transmission expansion enhanced efficiency of the CRR market in terms of a spatial convergence in prices and a decrease in aggregate auction expenditure of approximately $260 million over a period of 4.5 years post CREZ. Keywords: Congestion Revenue Rights, CREZ, Market Integration, Transmission Expansion, 1. INTRODUCTION Texas electricity market is marked by a substantial wind energy penetration that accounted for about 17 percent of energy use in 2017, (1) up from 9 percent in [...]

Financing constraints present a significant challenge for enterprises. Given China's push for the integration of digital technology and the real economy, it is crucial to examine the impact of corporate digital transformation on financing. Using data from Chinese A-share listed companies from 2010 to 2019, we construct the digital transformation indicator employing a machine learning approach and find that corporate digital transformation can significantly alleviate financing constraints. The mechanism analysis shows that digital transformation improves corporate financing by reducing information asymmetry and transaction costs on both sides of financing and enhancing corporate performance. Combined with the mechanism analysis, we reveal that digital transformation has a significant effect on the alleviation of financing constraints among firms with private ownership, higher levels of interaction with investors, more developed financial environments and more intermediaries marketed in their locations, higher proportions of highly skilled workers, and better matching of digital technologies used for transformation. We explore micro-level measurement methods of corporate digital transformation, contributes to the literature on the economic consequences of digital transformation, and enriches the research on enterprise responses to financing constraints. The findings have important implications for promoting corporate digital transformation practices and for understanding coping strategies of different firms. [ABSTRACT FROM AUTHOR]