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The purpose of this study is to analyze the effect of foreign direct investment (FDI) on pollution emissions and how environmental regulation affects this relationship.

In the empirical research, the authors selected panel data for 30 provinces in China from 2005 to 2019 as samples. First, the authors used the instrumental variable method to verify the existence of the above hypotheses in China. Then, the authors analyzed the moderating effect of different types of environmental regulations on the environmental effects of FDI. Next, in further discussion, the authors analyzed the difference between the environmental effect and the moderating effect in different time periods and regions, respectively. Finally, the authors discussed whether the different intensities of environmental regulations lead to the transfer effect of FDI in choosing investment destinations.

The result shows that FDI can help reduce pollution emissions and create a “pollution halo” effect, which is enhanced by command-and-control regulation but suppressed by market-based incentives. The heterogeneity analysis reveals that the 18th National Congress of the Communist Party has weakened the pollution halo effect of FDI, while the environmental effect of FDI in the eastern region is not significant, but in the middle and western regions, there is a significant pollution halo effect and a positive moderating effect of environmental regulations. Finally, further analysis reveals that FDI has a transfer effect under command-and-control environmental regulations.

First, the main purpose of this paper is to study the relationship between FDI and pollution emissions from the perspective of heterogeneous environmental regulation. Therefore, there is no detailed discussion on their effect mechanism of them. Second, limited by data, the authors adopt the single index to measure the stringency index of command-and-control and market-based incentive environmental regulations in China. The single index may not be able to fully reflect the intensity of regional environmental regulation, so the construction of a composite indicator is necessary. These shortcomings are the focus of the authors' future research.

Under the guidance of high-quality development, the conclusions above can provide reference for adjusting FDI policies and improving environmental regulation policies.

The innovations in this paper can be summarized as the following four dimensions: First, the authors use the instrumental variable (IV) method to address endogeneity in the relationship between FDI and pollution emission, which can further ensure the robustness of the research results and increases the credibility of the paper. Second, the authors distinguish between two types of environmental regulations to investigate their moderating effect on the environmental impact of FDI. Third, the authors consider the temporal and spatial heterogeneity of both the environmental effects of FDI and the moderating effect of regulation. Last, the authors analyze the spatial spillover of environmental regulation through the study of the transfer effect.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This study aims to fabricate a multifunctional electromagnetic interference (EMI) shielding composite fabric with simultaneous high-efficiency photothermal conversion and Joule heating performances.

A multifunctional polypyrrole (PPy) hydrogel/multiwalled carbon nanotube (MWCNT)/cotton EMI shielding composite fabric (hereafter denoted as PHMC) was prepared by loading MWCNT onto tannin-treated cotton fabric, followed by in situ crosslinking-polymerization to synthesize three-dimensional (3D) conductive networked PPy hydrogel on the surface of MWCNT-coated cotton fabric.

Benefiting from the unique interconnected 3D networked conductive structure of PPy hydrogel, the obtained PHMC exhibited a high EMI-shielding effectiveness vale of 48 dB (the absorbing electromagnetic wave accounted for 84%) within a large frequency range (8.2–12.4 GHz). Moreover, the temperature of the laminated fabric reached 54°C within 900 s under 15 V, and it required more than 100 s to return to room temperature (28.7°C). When the light intensity was adjusted to 150 mW/cm², the PHMC temperature was about 38.2°C after lighting for 900 s, indicating high-efficiency electro-photothermal effect function.

This paper provides a novel strategy for designing a type of multifunctional EMI shielding composite fabric with great promise for wearable smart garments, EMI shielding and personal heating applications.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

Limited research exists on the perceptions of police within specific ethnic minority groups. The primary purpose of this study is to investigate the experiences of Korean and Korean American residents in the Metro Atlanta area regarding their perceptions of cooperation with the police, particularly in relation to hate crimes, along with their perceptions of police legitimacy and other relevant factors. By focusing on this specific population, the study aims to shed light on their unique perspectives and contribute to a deeper understanding of the complex dynamics between ethnic minorities and law enforcement.

The authors’ sample comprised 128 Korean residents who were asked about their demographics, victimization experiences, self-rated English proficiency and police legitimacy. Multiple linear regression analyses were employed to investigate the impact of police legitimacy, victimization experiences and English-speaking skills on the participants' level of cooperation with the police.

Police legitimacy and self-rated levels of English proficiency emerged as the most significant factors in predicting the level of cooperation among residents with the police. Furthermore, individuals who have experienced crime victimization in the past were more willing to cooperate with the police compared to those who have not. Additionally, men showed a higher tendency to cooperate with the police compared to women participants.

The findings of this study suggest important implications for the policies and strategies aimed at enhancing the relationship between the Korean American community and the police. These implications include the need for improved language support for non-English speaking community members and the importance of building trust and fostering mutual understanding to cultivate positive police-community relations. By implementing measures based on these findings, it is recommended to promote a more inclusive and effective approach to policing within the Korean American population.

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.

The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.

The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.

The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.

In the context of the digital age, this study aims to investigate the impact of citizens' digital participation on the scientific and democratic decision-making processes of the government. Specifically, the authors focus on the factors influencing citizens' digital participation, with a particular emphasis on their digital skills.

Exploring the influence of citizens' digital skills on their digital participation is of great practical significance for eliminating the digital divide and for promoting a life characterized by enriched digital interactions with the public. This study selected the social consciousness survey database of Chinese netizens in 2017, used ordered Probit and OLS models, and comprehensively used the instrumental variable method (IV), causal stepwise regression method and bootstrap method to empirically verify and construct a mechanism model of the influence of digital skills on citizens' digital participation.

The empirical findings indicate a noteworthy positive association between citizens' proficiency in digital skills and their active engagement in digital activities. This relationship is positively mediated by factors such as political interest and attention to social issues, underscoring their role in encouraging greater digital participation. Conversely, national identity exhibits a counteractive influence on this mechanism, potentially discouraging digital engagement. Notably, the impact of digital skill mastery on digital participation is more pronounced among non-elderly individuals and those residing in metropolitan areas, highlighting the significance of demographic characteristics in this context.

These research results can help the government and other organizations make better decisions and facilitate improvement of citizens' digital participation by promoting their mastery of digital skills.

This research aims to determine the influence of environmental, social and governance (ESG) factors on market performance. The study shows the perspective of ESG on market performance. The study attempted to test the relationship between ESG and Tobin’s Q and the effect of control variables.

The study used panel data from a sample covering 720 firms and ran a fixed-effects model regression during the 2007–2019 period for eight European countries’ listed companies.

The findings reveal that ESG positively impacts Tobin’s Q. According to the findings, high company ESG performance boosts market performance via the moderator effect of competitive advantage. The results indicate that all control variables are significant. The firm’s leverage has a negative relationship with ESG. The size of the firm impacts ESG positively. Also, the results prove that the firm’s size and industry positively affect Tobin’s Q.

The findings of this study suggest that managers, practitioners and authorities interested in learning about ESG scores (ESGSs), market performance and competitive advantage might draw intriguing conclusions from the data. Managers can identify the appropriate levels of competitive advantage that improve market performance. Practitioners must determine whether fit, size, growth, leverage and industry could enhance market performance. The findings also give authorities and the board of directors information on future growth opportunities for the company and the country.

The research presents a vision of how ESG factors affect market performance. This study aims to identify the positive link between ESGSs and European market performance.

The use of recycled coarse aggregate in concrete structures promotes environmental sustainability; however, performance of these structures might be negatively impacted when it is used as a replacement to traditional aggregate. This paper aims to simulate recycled concrete beams strengthened with carbon fiber-reinforced polymer (CFRP), to advance the modeling and use of recycled concrete structures.

To investigate the performance of beams with recycled coarse aggregate concrete (RCAC), finite element models (FEMs) were developed to simulate 12 preloaded RCAC beams, strengthened with two CFRP strengthening schemes. Details of the modeling are provided including the material models, boundary conditions, applied loads, analysis solver, mesh analysis and computational efficiency.

Using FEM, a parametric study was carried out to assess the influence of CFRP thickness on the strengthening efficiency. The FEM provided results in good agreement with those from the experiments with differences and standard deviation not exceeding 11.1% and 3.1%, respectively. It was found that increasing the CFRP laminate thickness improved the load-carrying capacity of the strengthened beams.

The developed models simulate the preloading and loading up to failure with/without CFRP strengthening for the investigated beams. Moreover, the models were validated against the experimental results of 12 beams in terms of crack pattern as well as load, deflection and strain.