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This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.

Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.

The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.

The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

The purpose of this paper is first, to overview the current research situation on job embeddedness (JE), including the theoretical underpinning of JE, the definition and dimensions of JE, its comparisons with similar constructs, and its global and composite measure; second, to intergrate the unfolding model, JE and image theory to better understand voluntary turnover – and indicate future research directions.

An extensive literature search covering several separate electronic databases, including ScienceDirect, EBSCO, Kluwer and Emerald, was conducted. Most of the articles can be acquired online from The University of California Riverside. The validity and reliability are compared between global and composite scales. The authors summarized and categorized the findings of current research.

JE can be differentiated from those similar constructs and measures already in the literature. Almost all of the studies on JE have found that it predicted voluntary turnover better than job attitudes and perceived ease of movement from traditional models of turnover. Along with extended research on it, JE was disaggregated into two major sub‐dimensions, namely, on‐the‐job and off‐the‐job embeddedness, and it has been extended to occupational and career level.

In this paper, the authors use qualitative methods to evaluate the current studies on JE, only. Meta‐analysis, as a reviewing method, should be used in future research on clarifying the relationships between JE and other constructs in organizational behavior.

This research reviews almost all of the studies on JE from 2001 to 2009 and organizes and categorizes them into three kinds: cause, consequence and theoretical extension. The authors also summarize its relationships with other constructs (e.g. turnover, turnover intention, organizational commitment, organizational citizenship behavior) in various settings. Finally, based on discussion, the authors indicate future research directions.

The purpose of this paper is to take advantage of Internet of Things (IoT) for intelligent route programming of crowd emergency evacuation in metro station. It is a novel approach to ensure the crowd safety and reduce the casualties in the emergency context. An evacuation route programming model is constructed to select a suitable evacuation route and support the emergency decision maker of metro station.

The IoT technology is employed to collect and screen information, and to construct an expert decision model to support the metro station manager to make decision. As a feasible way to solve the multiple criteria decision-making problem, an improved multi-attributive border approximation area comparison (MABAC) approach is introduced.

The case study indicates that the model provides valuable suggestions for evacuation route programming and offers practical support for the design of an evacuation route guidance system. Moreover, IoT plays an important role in the process of intelligent route programming of crowd emergency evacuation in metro station. A library has similar structure and crowd characteristics of a metro station, thus the intelligent route programming approach can be applied to the library crowd evacuation.

The highlights of this paper are listed as followings: the accuracy and accessibility of the metro station’s real-time information are improved by integrating IoT technology with the intelligent route programming of crowd emergency evacuation. An improved MABAC approach is introduced to the expert support model. It promotes the applicability and reliability of decision making for emergency evacuation route selection in metro station. It is a novel way to combine the decision-making methods with practice.

With the acceleration of global energy structure transformation, hydrogen has been widely used for its non-pollution and high efficiency, and hydrogen detection is used to guarantee the hydrogen safety. The purpose of this paper is to study the research foundation, trend and hotspots of hydrogen detection field.

A total of 4,076 literature records from 2000 to 2021 were retrieved from the core collection of the Web of Science database selected as data sources. The literature information mining was realized by using CiteSpace software. Bibliometrics was used to analyze information, such as keywords, authors, journals, institutions, countries and cited references, and to track research hotspots.

Since the 21st century, the number of publications in the hydrogen detection field has been in a stable stepped uptrend. In terms of research foundation, the hotspots such as core-shell structures, nano-hybrid materials and optical fiber hydrogen sensors have been studied extensively. In combination with the discipline structure and research frontier, the selectivity, sensitivity, response speed and other performance parameters of hydrogen sensors need further improvement. The establishment of an interdisciplinary knowledge system centered on materials science and electronic science will become a long-term trend in the research of hydrogen detection.

This study presents an overview of research status, hotspots and laws in hydrogen detection field, through the quantitative analysis of much literature in the field and the use of data mining, so as to provide credible references for the research of hydrogen detection technology.

The purpose of this paper is to develop a new structural damage detection technique based on multi-channel empirical mode decomposition (MEMD) of vibrational response data.

Empirical mode decomposition (EMD) is an empirical data-based signal decomposition method which has been applied in many engineering problems. Utilizing classical EMD to reveal the damage-indicating features of structural vibration response encounters some difficulties due to the inconsistency of modes obtained from different data channels. To overcome this problem, MEMD has been employed. To this end, MEMD algorithm has been adopted to impulse response vector of measured DOFs. The proposed method has been carried out concerning both numerical and experimental beam models. Damage has been modeled by reducing the flexural rigidity in some predefined beam sections. The effects of various factors such as measurement grid density, damage severity and damage position are investigated.

The results of both numerical and experimental case studies have been promising. The method could determine the damage location in all cases. The efficiency of method gets better when damage is located far from inflation points of the corresponding mode. In such cases, utilizing higher modes can make up the efficiency.

Since the present research is the first investigation of MEMD in damage localization, just one-dimensional structures have been studied. Extending the method to more complicated geometries needs further attempt.

Although a number of relevant studies have been carried out based on EMD, up to the author’s best knowledge, this is the first attempt to structural damage localization using MEMD.

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 aims to investigate how institutional entrepreneurs in a mature field acquire different forms of legitimacy through various narrative strategies, as revealed by a historical analysis of the Late Qing Dynasty of China. Specifically, the study seeks to illuminate the narrative techniques employed by these entrepreneurs to build multi-level legitimacies for their new systems during the national crises of the late 19th century.

This study employs a qualitative approach based on three historical case studies of Westernization, namely advocates, reformists and revolutionaries during the Late Qing Dynasty of China. It delves into the institutional entrepreneurial activities of these groups, investigating how they navigated and influenced their sociopolitical context through narrative strategies.

The findings reveal that institutional entrepreneurs utilize various narrative techniques to build legitimacy. These include (1) modifying and disseminating narratives at the proper time, (2) ascribing their proposed system to cultural tradition by reinterpreting classical works and (3) offering understandable and concrete promises to stakeholders. The study also identifies an evolving pattern of narratives underpinned by mutual learning and continuous iteration among different factions of institutional entrepreneurs.

This paper adds new insights to the literature on institutional entrepreneurs and, more broadly, to institutional research literature by advancing our understanding of the discursive dynamics underlying institutional changes. It unveils the complex interplay of narrative techniques and legitimacy building in the context of societal and institutional upheaval, providing a nuanced understanding of the role of narratives in institutional entrepreneurship.