China Europe International Business School

This article presents a novel robust internal model-based control (RIMC) method, grounded in output regulation theory, to achieve precise output voltage regulation of dc–dc converters subject to circuit parametric uncertainties and diverse time-varying references and disturbances. The key principle of the method lies in the direct embedding of internal models that capture the dynamics of the exogenous signals into the controller structure. This design, based on the internal model principle, equips the controller with an inherent capability to accurately track and reject a broad class of time-varying signals. Furthermore, the controller is synthesized within a robust framework to ensure stability and performance under circuit parameter variations. The proposed RIMC is applied to the receiver-side buck converter in a wireless power transfer system. Experimental results demonstrate that the proposed method achieves high-precision output voltage tracking under time-varying operating conditions while exhibiting strong robustness against circuit parameter uncertainties.

We advance the springboard perspective by investigating how emerging-market multinational enterprises (EMNEs) evolve into global leaders, uncovering the specific mechanisms for capability development in the post-springboarding phase. We propose a four-stage process model that delineates the transformation of EMNEs, in response to technological paradigm shifts and business opportunities, from followers to leaders: (1) capability acquisition via springboarding from international value-chain partners, (2) capability scaling via co-exploitation with domestic value-chain partners, (3) capability creation via co-exploration with domestic value-chain partners, and (4) capability diffusion via reverse knowledge transfer to international value-chain partners. Using the transition of the automotive supply sector in China from internal combustion engines (ICE) to electric vehicles (EV) as an exemplary case, we illustrate how EMNEs can evolve from learning extant knowledge from, and to contributing new knowledge to, advanced-market multinational enterprises (AMNEs). Our model extends the springboard perspective by detailing the rarely-explored post-springboarding phase, particularly in the context of contemporary geopolitical tensions. More broadly, we contribute to a deeper understanding of the action-based view of dynamic capabilities in international business by specifying the entrepreneurial actions that make EMNE capability development and transformation possible.

Mutation of the tumor suppressor gene TP53 promotes ovarian cancer progression and therapeutic resistance. Whether mutant p53 (mtp53) regulates alternative splicing and how this regulation can be exploited for cancer therapy remain unclear. Here, small nuclear ribonucleoprotein D2 polypeptide (SNRPD2) as a binding partner of mtp53 is identified. SNRPD2 is highly expressed in ovarian cancer and associated with an unfavorable prognosis. The overexpression of SNRPD2 promotes, whereas its depletion inhibits, the growth and migration of ovarian cancer cells. Mechanistically, mtp53 cooperates with SNRPD2 to facilitate the assembly of the Sm/SMN protein complex, an essential component of the spliceosome, modulating alternative splicing of pre-mRNAs. Specifically, the co-depletion of mtp53 and SNRPD2 reduces the level of OTUD3 oncogenic transcripts while increasing its tumor suppressor counterparts through an exon-skipping event. Moreover, therapeutic engineered exosomes are developed with their surfaces decorated with iRGD and their interiors loaded with siRNAs targeting mtp53 and SNRPD2. These exosomes effectively suppress the growth of ovarian cancer cells and enhance their sensitivity to chemotherapy in vivo. Collectively, this study uncovers that mtp53 and SNRPD2 cooperatively regulate alternative splicing to drive ovarian cancer progression, and co-targeting these two molecules via engineered exosomes represents a potential therapeutic strategy for ovarian cancer.

The evolution of Human–Computer Interaction (HCI) has laid the foundation for more immersive and dynamic forms of communication between humans and machines. Building on this trajectory, this work introduces a significant advancement in the domain of Human–Robot Manipulation (HRM), particularly in the remote operation of humanoid robots in complex scenarios. We propose the Advanced Manipulation Assistant System (AMAS), a novel manipulation method designed to be low cost, low latency, and highly efficient, enabling real-time, precise control of humanoid robots from a distance. This method addresses critical challenges in current teleoperation systems, such as delayed response, expensive hardware requirements, and inefficient data transmission. By leveraging lightweight communication protocols, optimized sensor integration, and intelligent motion mapping, our system ensures minimal lag and accurate reproduction of human movements in the robot counterpart. In addition to these advantages, AMAS integrates multimodal feedback combining visual and haptic cues to enhance situational awareness, close the control loop, and further stabilize teleoperation. This transition from traditional HCI paradigms to advanced HRM reflects a broader shift toward more embodied forms of interaction, where human intent is seamlessly translated into robotic action. The implications are far-reaching, spanning applications in remote caregiving, hazardous environment exploration, and collaborative robotics. AMAS represents a step forward in making humanoid robot manipulation more accessible, scalable, and practical for real-world deployment.

This paper examines how analyst coverage shapes the competitive threats firms face from rivals. Using the awareness-motivation-capability (AMC) framework, we argue that analysts act as key information intermediaries, helping rivals understand and interpret a focal firm’s strategy and market position. When analyst coverage decreases due to brokerage house closures or mergers, rivals lose access to critical insights, leading to fewer competitive actions against the firm. We further explore the boundary conditions under which this effect is more pronounced, focusing on two factors: the focal firm’s dynamism and its corporate disclosure volume. Dynamic firms or those with extensive disclosures are particularly impacted when analyst coverage declines, as rivals depend heavily on analysts to make sense of their strategic moves. We leverage a natural experiment combined with a difference-in-difference approach to establish causal relationships. The findings provide robust support to our hypotheses. This study bridges research on analyst coverage and competitive dynamics, highlighting the importance of interpretation in interfirm rivalry and the broader impact of financial analysts.

ABSTRACT This study focuses on the effect of disclosure processing frictions in labor markets. We go back in time 30 years ago and examine whether firms facing strong organized labor strategically responded to the implementation of the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, which substantially reduced labor unions’ information processing costs. Consistent with firms having incentives to maintain an information advantage over unions for bargaining purposes, we find that they reduce financial statement disaggregation, the likelihood and frequency of management forecasts, and the proportion of good news forecasts. Our study is the first to investigate the implications of information processing costs for labor markets and suggests that an SEC mandate intended to reduce disclosure processing costs for investors caused unintended strategic responses by firms facing proprietary cost of disclosures in other markets. Data Availability: Data are available from sources identified in the text. JEL Classifications: M40; M41; J51; J52.