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With the development of technology, the application scenarios of mobile robots are becoming more and more extensive, accompanied by a variety of application scenarios suitable and safe path planning algorithms are indispensable for mobile robots.

The purpose of this paper to improve the safety performance of your bot during the execution of tasks. The methods are synthesized in three main areas: setting appropriate safety distances based on the actual radius of the robot, turn penalty reduces the number of turns by applying an additional penalty to the number of turns in a heuristic function and path smoothing is used to improve path reliability by reducing the number of right-angle turns.

A suitable safety distance greatly improves the safety of mobile robots and facilitates their development. Optimization of turns in the path of mobile robots improves the travel efficiency of robots. Enhancing the safety of mobile robots has become a research hotspot for path-planning algorithms.

This paper proposes a path planning scheme for mobile robots with safe distances, which provides readers with a comprehensive and systematic progress of path planning research. It helps readers to get inspiration from enhancing the safety of mobile robots.

Triggered by the extensive use of social media brand communities (SMBCs) in interactive marketing, this article aims to explore how brand connectedness (BC) affects consumer engagement behavior (CEB) in SMBCs.

The research model was verified with the partial least squares structural equation modeling applied to the actual data collected from the web crawling largest microblogging platform in China (Sina Weibo).

Results indicate that BC may positively influence consumer emotions (CEs), eventually leading to engagement behavior in SMBCs. In addition, gender and duration of membership act as vital moderators in the model. One of the most interesting findings is the differences between posting and commenting, although both are CEBs. BC has a more significant effect on commenting than posting, and the mediating effect of CEs between BC and posting behavior is not significant.

This research contributes to the literature on interactive marketing by examining BC in the context of SMBCs, which is under-researched in the literature but is highly pertinent to social media contexts. Moreover, we measure BC through social network analysis for the first time, which not only supports the empirical work but also expands the social network theory and social capital theory. This research also extends the body of knowledge on consumer engagement by investigating the differences between posting and commenting behaviors.

This study aims to report the development and experimental evaluation of two kinds of PANI@semiconductor based photocathodic anti-corrosion coating, for application on stainless steel substrates.

PANI was in situ chemical polymerized on TiO₂ and BiVO₄ particles, and FT-IR and SEM/EDS were used to understand the characteristics and elemental distribution of the composite particles. Composite coatings, which consisted of epoxy, PANI@TiO₂ or PANI@BiVO₄ and graphene, were prepared on the 304L stainless steel. Photoelectrochemical response measurement, electrochemical tests and immersion tests were used to assess the anti-corrosion performance of the prepared coatings in 45°C 3.5 wt.% NaCl solution. And the corrosion protection mechanism was further explained by combining with surface observation.

The photoelectrochemical response tests revealed the good photocathodic effect of the coatings, and the reversible oxidation-reduction properties of PANI (pseudocapacitive effect) leading to the repeated usage of the coatings. Consequently, the anti-corrosion mechanism of the composite coating is attributed to the physical barrier effect of the coating, the anodic protection effect of PANI and the photocathodic and energy store effect.

These kind coatings could prevent corrosion from day to night for stainless steel, which has great engineering application prospects on stainless steel corrosion protection.

Smart community construction (SCC) and efficiency require resident participation. This paper aims to explore the determinants of residents’ participation intention (RPI) in the SCC.

Based on the theory of planned behavior (TPB), this study proposed an extended conceptual model to deeply analyze the RPI in the SCC. The relationship between all constructs was verified by processing and analyzing online survey data using confirmatory factor analysis (CFA), structural equation model (SEM), and bootstrapping method.

Participation attitude, perceived behavioral control, subjective norm, and perceived usefulness significantly and positively affected the RPI. Furthermore, intermediary effects in the extended conceptual model had been confirmed.

To fill the critical gap in the research on the determinants of the RPI in the SCC context, this study developed a novel conceptual model by extending the TPB to analyze the effects of self-driven and externally-driven factors on the RPI from the perspectives of residents’ psychology and external environment. The findings not only clarify the complex process of forming the RPI in the SCC but also provide a theoretical foundation for studying the RPI in similar community construction projects. Additionally, several strategies have been proposed to encourage residents’ participation in the SCC and promote the development of smart communities, such as clarifying residents’ participation obligations, improving the convenience services of smart communities, and diversifying residents’ participation approaches.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

This study aims to investigate the influence of economic policy uncertainty (EPU) and geopolitical risk (GPR) on the relationship between internal cash flow and external financing in an emerging market, Saudi Arabia. It also examines the role of asset tangibility and financial crisis in establishing this relationship.

The sample was taken from non-financial sector companies listed on the Saudi Stock Exchange between 2002 and 2019. The data were analyzed using panel data regression analysis, including ordinary least squares and fixed effects model. The author addresses potential endogeneity through the generalized method of moments.

This study found that both EPU and GPR reduce the sensitivity of external financing to internal cash flow. This implies that firms depend more on internally generated funds during periods of increased EPU and GPR. Besides, this study found that the influence of EPU and GPR on the sensitivity of external financing to internal cash flow is more (less) negative for more tangible firms (during the financial crisis period). This result implies that Saudi firms boasting a higher level of tangibility are more flexible when it comes to seeking external financing. However, the presence of uncertainty during the crisis period makes the external financing costly, and therefore, firms will be less likely to raise funds from external sources.

This study has important implications for managers, policymakers and regulators. First, the paper findings provide insights for corporate decision-makers in helping them to focus on internal funds to finance their investment during uncertain times. Second, the findings help managers to understand the role of asset tangibility in raising external funding when firms face financial constraints due to uncertainty. Third, this study also helps corporates to focus on internal funds to finance their investment during the crisis period because EPU and GPR increase the cost of external finance. Finally, the results provide guidelines for policymakers and regulators to make appropriate policy measures to increase the easy availability of external finance during periods of increased EPU and GPR.

This paper is the first to shed light on the impact of internal funds on external financing while paying close attention to the role of EPU and GPR.

This paper attempts to document the effect of economic uncertainty on financing constraints faced by private firms.

Ordered logistic regression is used to analyze the data of private firms from 101 developing countries. The data was provided by the World Bank's Enterprise Surveys and was gathered during the period between 2006 and 2019.

The findings show that firms headquartered in countries with high economic uncertainty face more financing constraints than firms headquartered in countries with low economic uncertainty. The authors argue that the increase in economic uncertainty allows capital providers to adjust their lending decisions by reducing the provision of capital to firms. The paper also shows that firms headquartered in countries with strong institutional infrastructure and well-functioning firms are less likely to be affected by economic uncertainty while accessing finance.

The findings would help managers, investors, regulators, and policymakers better understand the implication of economic policy uncertainty on the real economy. This study also sheds the light on the importance of minimizing volatility, ambiguity, and randomness in governmental decisions and policies. Regardless of the pertinence of these policies, arbitrariness surrounding their development and communication can limit their effectiveness and produce unwanted effects.

This paper is closely related to prior literature that documents the behavior of credit providers and investors (the supply side) during the periods of economic uncertainty. The authors differ from this strand of literature by taking the perspective of firms – the demand side.

New business density (NBD) is the ratio of the number of newly registered liability corporations to the working-age population per year. NBD is critical to assessing a country's business environment. The present work endeavors to discover and gauge the contribution of 28 potential socio-economic enablers of NBD for 2006–2021 across developed and developing economies separately and to make a comparative assessment between those two regions.

Using World Bank data, the study first performs exploratory data analysis (EDA). Then, it deploys a deep learning (DL)-based regression framework by utilizing a deep neural network (DNN) to perform predictive modeling of NBD for developed and developing nations. Subsequently, we use two explainable artificial intelligence (XAI) techniques, Shapley values and a partial dependence plot, to unveil the influence patterns of chosen enablers. Finally, the results from the DL method are validated with the explainable boosting machine (EBM) method.

This research analyzes the role of 28 potential socio-economic enablers of NBD in developed and developing countries. This research finds that the NBD in developed countries is predominantly governed by the contribution of manufacturing and service sectors to GDP. In contrast, the propensity for research and development and ease of doing business control the NBD of developing nations. The research findings also indicate four common enablers – business disclosure, ease of doing business, employment in industry and startup procedures for developed and developing countries.

NBD is directly linked to any nation's economic affairs. Therefore, assessing the NBD enablers is of paramount significance for channelizing capital for new business formation. It will guide investment firms and entrepreneurs in discovering the factors that significantly impact the NBD dynamics across different regions of the globe. Entrepreneurs fraught with inevitable market uncertainties while developing a new idea into a successful new business can momentously benefit from the awareness of crucial NBD enablers, which can serve as a basis for business risk assessment.

DL-based regression framework simultaneously caters to successful predictive modeling and model explanation for practical insights about NBD at the global level. It overcomes the limitations in the present literature that assume the NBD is country- and industry-specific, and factors of the NBD cannot be generalized globally. With DL-based regression and XAI methods, we prove our research hypothesis that NBD can be effectively assessed and compared with the help of global macro-level indicators. This research justifies the robustness of the findings by using the socio-economic data from the renowned data repository of the World Bank and by implementing the DL modeling with validation through the EBM method.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.