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The link between interlocking directors and mergers and acquisitions (M&A) efficiency has been analyzed in an information asymmetry environment. Despite an abundance of evidence highlighting that interlocking directors do contribute to M&A efficiency in an acquirer-target binary relationship, the target is embedded in a complex network of supplier-customer relationships, which implies that the acquirer needs to consider the value of suppliers, distributors and retailers in the target’s supply chain in improving M&A efficiency. Through the lenses of acquirer-target multivariate relationships, this paper aims to examine how directors with supply chain experience (DSCs) act as heterogeneous network pipes to affect M&A efficiency.

Using a sample of 311 A-share listed firms on the Shanghai and Shenzhen stock exchanges in China during 2011–2020, this paper investigates the relationship between DSCs and M&A efficiency by using ordinary least squares (OLS) regression.

Through empirical research, we verify a negative relationship between DSCs and M&A duration and an inverted U-shaped relationship between both DSCs and M&A performance, revealing the complexity of the relationship between experience and efficiency. Furthermore, drawing on upper echelon theory, the information value of DSCs will be greatly reduced when executives have overconfident psychological characteristics, which are mainly shown to negatively moderate the relationship between DSCs and M&A performance. We also conduct multiple robustness tests and supplemental analyses to illustrate the robustness and boundaries of our findings. Finally, DSCs are likely more important in environments among growth and mature firms as well as high-growth industries.

We break through the assumption that interlocking directors contribute to M&A efficiency in an acquirer-target binary relationship and examine the impact of DSCs on M&A efficiency based on micro-empirical evidence from the value of target-related upstream or downstream industries, which extends the connotation of interlocking directors and enriches the study related to factors influencing M&A efficiency.

Integrating supplier innovation is considered an effective strategy to reduce uncertainty at the fuzzy front end (FFE). However, the large number of supplier innovation resources and the task environment forces buying firms to precisely identify more valuable or interesting innovation resources for integration. The impact of the interaction between supplier innovation and task environment on FFE performance needs to be further explored. Therefore, this paper aims to propose a contingency framework to examine the relationship between supplier innovation (technology-push and market-pull) and buying firm’s FFE performance in different task environments, with the aim of clarifying which supplier innovation resources should be integrated to create high FFE performance in the face of an uncertain task environment.

Survey data from new product development team leaders in Chinese high-tech firms were collected and analyzed. Furthermore, using multiple regression analysis, the relationship among supplier innovation, task environment and FFE performance was examined.

The results show that although both technology-push and market-pull supplier innovation can improve FFE performance, there are significant differences between the influences of these two types of supplier innovation in the face of different task environments.

Practical guidelines are provided for buyer managers on how to effectively identify the “best” supplier innovation resources to improving the effectiveness and efficiency of supplier involvement in the FFE.

This paper deepens the knowledge of identifying supplier innovation resources in the FFE and enriches the research on supplier-enable FFE innovation.

This chapter uses macro policy analysis conducted at the Center for Teacher Education Research (CTER), Beijing Normal University to analyze the decision-making concerning teacher internship from the perspective of national policy. Internship, a teacher preparation policy initiative in China, is both needed to ease the teacher shortages in rural and poor areas of China and to create new models of teaching practice. Attention is then given to two aspects in policy implementation – student teachers’ learning and teachers’ multiple forms of instruction – from the perspective of teacher preparation. To conclude, the chapter offers some summary statements having to do with the policy debate and the implementation of the internship in China.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

In order to investigate the vibration reduction properties of a three-dimensional elastic metastructure with spherical cavities at low frequencies.

The bandgap characteristics of a three-dimensional elastic metastructure with spherical cavities are studied based on analytical and numerical approaches.

The results of both method revealed that the vibration of the vertexes masses is important for opening bandgaps. The fact that the big sphere cavity radius or short side length of the cube unit leads to a wider bandgap, is noteworthy.

This research provides theoretical guidance for realizing the vibration attenuation application of EMs in practical engineering.

The main purpose of this paper is to investigate the vibration isolation performance of a quasi-zero stiffness (QZS) metastructure by employing the band gap (BG) mechanism.

The metastructure QZS characteristic was investigated through static analysis by numerical simulation. Based on that, the BG mechanism is primarily used in this article to investigate the wave propagation characteristics of this structure. The model's dispersion relation is then examined using theoretical (perturbation method) and finite element techniques. The dynamic response of the finite-size systems and experimental analysis is used to confirm the vibration mitigation property under investigation. Finally, the model's ability to absorb energy was examined and contrasted with a traditional model.

The analytical analysis reveals the dispersion curve and the effect of the nonlinear parameter on the curve shifting. The dispersion curve in the finite element method (FEM) result depicts five complete BGs within the range of 0–1,000 Hz, and the BG width accounted for 67.4% of the frequency concerned (0–1,000 Hz). Eigenmodes of the dispersion curves were analyzed to investigate the BG formation mechanisms. The dependence of BG opening and closure on structure parameters was also studied. Finally, the energy absorption property of the QZS metastructure was evaluated by comparing it with a classical model. The QZS structure absorbs 4.08 J/Kg compared to the 3.69 J/Kg absorbed by the classical model, which reveals that the QZS demonstrates better energy absorption performance. Based on the BG mechanism, it is clear that this model is an excellent vibration isolator, and the study reveals the frequencies at which complete vibration mitigation is achieved. As a result, this model could be a promising candidate for vibration mitigation engineering structures and energy absorption.

The tough vibration issue, which is primarily experienced in mechanical equipment, will be resolved in this study. This study provides a precise understanding of the QZS metastructure's isolation of vibration, including the frequencies at which this isolation occurs.

Based on DNase I and reduced graphene oxide (rGO)-magnetic silicon microspheres (MNPS), a highly sensitive and selective fluorescent probe for the detection of PD-L1 was developed.

Here °C we present a feasibility of biosensor to detection of PD-L1 in lung tumors plasma. In the absence of PD-L1°C the PD-L1 aptamer is absorbed on the surface of graphene oxide modified magnetic nanoparticles °8rGO-MNPS°9 and leading to effective fluorescence quenching. Upon adding PD-L1°C the aptamer sequences could be specifically recognized by PD-L1 and the aptamer/PD-L1 complex is formed°C resulting in the recovery of quenched fluorescence.

This sensor can detect PD-L1 with a linear range from 100 pg mL⁻¹ to 100 ng mL⁻¹, and a detection limit of 10 pg•m⁻¹ was achieved.

This method provides an easy and sensitive method for the detection of PD-L1 and will be beneficial to the early diagnosis and prognosis of tumors.

This paper seeks to propose a new approach for tackling the uncertainty and imprecision of identifying suitable supplier offers, evaluating these offers and choosing the best alternatives in bi‐negotiation. In a build‐to‐order supply chain, the handling of uncertainties is addressed by a real time information sharing system and appropriate supplier selection.

A methodology integrated analytic hierarchy process (AHP) with bi‐negotiation agents based on the multi‐criteria decision‐making approach and software agent technique is then developed to take into account both qualitative and quantitative factors in supplier selection.

During the decision‐making between buyer and suppliers, the AHP process matches product characteristics with supplier characteristics. Next, agents assist the user in the debate to negotiate a joint representation of the supplier chosen and automatically justify proposals with this joint representation.

This study focused on a multi‐attribute negotiation mechanism including qualitative conditions, which enables automated negotiation on multiple attributes. Finally, a fuzzy membership function represented the joint representation's cognition for each condition such as quantity, price, quality, and delivery for the outsourced component. A case study in a high‐end computer manufacturing company is given to demonstrate the potential of the methodology.