Early career patterns : a comparison of Great Britain and West Germany

BACKGROUND: When it comes to corporate decision making, the traditional rational model suggests that deliberative analysis yields good results. Thus, when contemplating strategic moves, executives are “required” to conduct deliberative analyses. As today’s business environment is becoming increasingly complex and fast-paced, however, executives often face the dilemma of having to make carefully considered strategic decisions on the one hand and not having enough time on the other hand. Intuition offers an efficient solution in this situation. PURPOSE: The purpose of this study is to investigate how corporate executives employ both rationality and intuition in making strategic decisions under uncertain, complex and time-pressured circumstances. RESEARCH METHOD: We conducted three face-to-face interviews with executives from three companies in Sweden. Each interview lasted around one hour.    RESULTS: Drawing on previous psychological and managerial research, we argue that rationality and intuition are better viewed as being complementary rather than separate. Findings from the study suggest that intuition could serve as an effective and efficient means for managers to make strategic decisions; and that intuition indeed plays a role in strategic decision making under complex, uncertain and time limited contexts

A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations

We propose a model-data asymptotic-preserving neural network(MD-APNN) method to solve the nonlinear gray radiative transfer equations(GRTEs). The system is challenging to be simulated with both the traditional numerical schemes and the vanilla physics-informed neural networks(PINNs) due to the multiscale characteristics. Under the framework of PINNs, we employ a micro-macro decomposition technique to construct a new asymptotic-preserving(AP) loss function, which includes the residual of the governing equations in the micro-macro coupled form, the initial and boundary conditions with additional diffusion limit information, the conservation laws, and a few labeled data. A convergence analysis is performed for the proposed method, and a number of numerical examples are presented to illustrate the efficiency of MD-APNNs, and particularly, the importance of the AP property in the neural networks for the diffusion dominating problems. The numerical results indicate that MD-APNNs lead to a better performance than APNNs or pure data-driven networks in the simulation of the nonlinear non-stationary GRTEs

A coordinated control method of voltage and reactive power for active distribution net-works based on soft open point

The increasing penetration of distributed generators (DGs) exacerbates the risk of voltage violations in active distribu-tion networks (ADNs). The conventional voltage regulation de-vices limited by the physical constraints are difficult to meet the requirement of real-time voltage and VAR control (VVC) with high precision when DGs fluctuate frequently. However, soft open point (SOP), a flexible power electronic device, can be used as the continuous reactive power source to realize the fast voltage regu-lation. Considering the cooperation of SOP and multiple regula-tion devices, this paper proposes a coordinated VVC method based on SOP for ADNs. Firstly, a time-series model of coordi-nated VVC is developed to minimize operation costs and eliminate voltage violations of ADNs. Then, by applying the linearization and conic relaxation, the original nonconvex mixed-integer non-linear optimization model is converted into a mixed-integer sec-ond-order cone programming (MISOCP) model which can be efficiently solved to meet the requirement of voltage regulation rapidity. Case studies are carried out on the IEEE 33-node system and IEEE 123-node system to illustrate the effectiveness of the proposed method

Framework design and optimal bidding strategy for ancillary service provision from a peer-to-peer energy trading community

As an innovative paradigm for electric power systems with a high penetration of distributed energy resources, peer-to-peer (P2P) energy trading enables direct energy trading between end customers, which is able to facilitate local power and energy balance and potentially support the operation of bulk power systems. In this paper, a framework was proposed to enable ancillary service provision from a P2P energy trading community, creating additional value for both customers in the community and power systems. Specifically, an ancillary service provision mechanism was designed along with P2P energy trading and residual balancing mechanisms to enable the power utility to obtain ancillary service from customers in a P2P energy trading community. Furthermore, the optimal bidding strategy of customers was figured out to maximize their benefits in the proposed mechanisms. Simulation studies were conducted based on a residential community in Great Britain. The results show that the proposed ancillary service mechanism can enable the power utility to obtain a significant or required amount of ancillary services of different types. The proposed mechanisms and optimal bidding strategy can achieve Pareto improvement for the revenue of each customer and result in significantly higher social welfare for the whole community. It is also revealed that increasing ancillary service prices and installation rate of electric vehicles can increase the total amount of ancillary service provision and thus bring higher revenue for the customers in the community. By contrast, increasing installation of PV systems does not necessarily increase the amount of service provisio

AIoT-informed digital twin communication for bridge maintenance

Digital twin (DT) has been moving progressively from concept to practice for bridge operation and maintenance (O&M), but its issues of data synchronization and fault tolerance remain problematic. This paper investigates the time delay of bridge DT services according to communication and computation complexity, revealing the distinct impact of their sequence, and proposes an AIoT-informed DT communication framework to solve the above issues. The information hierarchy and two-way communication can be leveraged to minimize communication complexity in the framework. Meanwhile, the data flow and resilience of the proposed framework are demonstrated using a Petri net. Moreover, the framework is developed into a prototypical DT through cross-platform integration and validated with different cases. The results demonstrate that compared with other existing bridge DTs, the proposed framework has high efficiency, low-latency, and excellent fault tolerance, which can contribute to the efficiency and safety of bridge O&M, especially under communication-constraint circumstances. The framework is also promising for federated learning to protect the AI-model privacy of different stakeholders and has the potential to support agent-based intelligent bridge management in the future with little human intervention

Effects of Nutritional Level of Concentrate‐Based Diets on Meat Quality and Expression Levels of Genes Related to Meat Quality in Hainan Black Goats

The present study investigated the effects of the nutritional levels of diets on meat quality and related gene expression in Hainan black goat. Twenty-four goats were divided into six dietary treatments and were fed a concentrate-based diet with two levels of crude protein (CP) (15% or 17%) and three levels of digestive energy (DE) (11.72, 12.55 or 13.39 MJ/kg DM) for 90 days. Goats fed the concentrate-based diet with 17% CP had significantly (P \u3c 0.05) higher average daily gains (ADG) and better feed conversion rates (FCR). The pH 24h value tended to decrease (P \u3c 0.05) with increasing DE levels. The tenderness of Longissimus dorsi muscle (LD) and Semimembranosus muscle (SM) reduced with increasing CP levels (P \u3c 0.05). With increasing DE levels, tenderness was increased (P \u3c 0.05). The heart fatty acid-binding protein (H-FABP) mRNA expression levels in LD and SM increased with increasing DE levels (P \u3c 0.05), but decreased with increasing CP levels (P \u3c 0.05). The calpastatin (CAST) and μ-calpain mRNA expressions levels in LD and SM were affected significantly (P \u3c 0.05) by CP and DE levels in the diet. Therefore, the nutritional levels of diets affect meat quality and expression levels of genes associated with meat quality in Hainan black goats

Optimization of Microwave Vacuum Drying and Pretreatment Methods for Polygonum cuspidatum

This study was conducted to optimize the drying process of Polygonum cuspidatum slices using an orthogonal experimental design. The combined effects of pretreatment methods, vacuum pressure and temperature of inner material, drying kinetics, color value, and retention of the indicator compounds were investigated. Seven mathematical models on thin-layer drying were used to study and analyze the drying kinetics. Pretreatment method with blanching for 30 s at 100°C increased the intensity of the red color of P. cuspidatum slices compared with other pretreatment methods and fresh P. cuspidatum slices. P. cuspidatum slices dried at 60°C retained more indicator compounds. Furthermore, microwave pretreatment methods, followed by microwave vacuum for 200 mbar at 50°C, resulted in high concentration of indicator compounds, with short drying time and less energy. This optimized condition for microwave vacuum drying and pretreatment methods would be useful for processing P. cuspidatum. The Newton, Page, and Wang and Singh models slightly fitted the microwave vacuum drying system. The logarithmic, Henderson and Pabis, two-term, and Midilli et al. models can be used to scale up the microwave vacuum drying system to a commercial scale. The two-term and Midilli et al. models were the best fitting mathematical models for the no-pretreatment case at 600 mbar and 60°C