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This study, informed by the Situational Crisis Communication Theory, aims to suggest two primary response strategies that can be used for effective internal crisis communication during a pandemic situation, such as COVID-19. The effect of base response strategies on employees' perceptions of communication quality, leadership and relational outcomes were investigated.

An online survey of full-time employees in the United States was conducted.

The findings showed that for an instructing information strategy, not all types of information were equally associated with positive employee responses in terms of perceived quality of internal communication related to the COVID-19 pandemic and transformational leadership. Specific information that employees need to know in order to safely perform daily tasks, such as organizational protocols and thorough preparation, seem to be the most needed and desired information. Adjusting information was positively associated with employee perceptions of internal communication quality and perceptions of CEO leadership. Employees' perceived quality of internal communication affected by the base crisis response strategies were positively correlated with perceptions of transformational leadership and relational outcomes (i.e. employee trust in the organization, employee perceptions of the organization's commitment to relationships with employees, employee support for organizational decision-making related to COVID-19).

This study presents important theoretical and practical insights through an interdisciplinary approach that applies the theoretical framework and relationship-oriented outcomes of public relations to public health crisis situations.

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

The development puts the problem under consideration in a strategic light and gains attention with its wide comprehensiveness on the plane of unification of the activity of several modern scientific fields, which have always had intersections, but in their essence perform rather different roles – human resources management (HRM), public relations (PR), and sustainable development (SD). Examining the possibilities of applying innovative approaches in the research of these areas, in the context of the influence of digital and smart technologies and an entirely new scientific field. In this sense, the theoretical substantiation of the thesis on the synergy between HR, PR, and sustainable development is targeted in the aspect of highlighting contemporary challenges and the relevant response to achieve organizational effectiveness, based on knowledge of the impact of digitization processes and their connection with the development of human capital in the organization.

Emphasis in the present work is placed on the relationship between the management of human capital in the organization and the influence of digital and smart technologies on these processes. Focus in the research is placed in three directions – first of all – the role of digital/smart/technologies on sustainable development. Second, the impact of digital and smart technologies on green human resource management is explored, and third, emphasis is placed on the changed role of strategic communications in the context of the digital revolution.

The development brings out some good practices and ideas in the described areas.

This study examines the extent to which regulatory policy uncertainty, macroeconomic risk, banking industry innovations, etc. influence variability in financial sector development among emerging economies in sub-Sahara Africa (SSA).

Data for the empirical inquiry were compiled from a sample of 25 economies from the subregion from 2010 to 2020. Empirical estimates examining the relationships noted above were carried out using the two-step system generalized method of moments estimation technique.

Results the empirical estimates suggest that regulatory policy uncertainty and macroeconomic risk adversely influence or constrain financial sector development among the economies examined in the study. Banking industry innovations on the other hand is found to positively influence the development of the financial sector in these economies. Furthermore, moderating empirical analysis suggests that effective governance positively moderates the relationship between banking industry innovations and financial development among economies in the subregion.

This study’s approach to the mechanics of financial development among economies in SSA is designed to offer different perspectives to those found in the existing literature on financial development in three fundamental ways. First, although the verification of the role of banking industry innovations in financial development may not be new, it is important to point out that the approach used in this study is based on an index for innovations with different constituents or principal components in its construction; making the variable significantly different from what has been examined in the literature. In addition, the review of regulatory policy uncertainty and macroeconomic risk (both variables are multifaceted constructs using the principal component analysis procedure) further brings into this study’s analysis, a different approach to examining conditions influencing variability in financial development among developing economies.

This study aims to propose a more precise method for robust design optimization of mechanical structures with black-box problems, while also considering the efficiency of uncertainty analysis.

The method first introduces a dual adaptive chaotic flower pollination algorithm (DACFPA) to overcome the shortcomings of the original flower pollination algorithm (FPA), such as its susceptibility to poor accuracy and convergence efficiency when dealing with complex optimization problems. Furthermore, a DACFPA-Kriging model is developed by optimizing the relevant parameter of Kriging model via DACFPA. Finally, the dual Kriging model is constructed to improve the efficiency of uncertainty analysis, and a robust design optimization method based on DACFPA-Dual-Kriging is proposed.

The DACFPA outperforms the FPA, particle swarm optimization and gray wolf optimization algorithms in terms of solution accuracy, convergence speed and capacity to avoid local optimal solutions. Additionally, the DACFPA-Kriging model exhibits superior prediction accuracy and robustness contrasted with the original Kriging and FPA-Kriging. The proposed method for robust design optimization based on DACFPA-Dual-Kriging is applied to the motor hanger of the electric multiple units as an engineering case study, and the results confirm a significant reduction in the fluctuation of the maximum equivalent stress.

This study represents the initial attempt to enhance the prediction accuracy of the Kriging model using the improved FPA and to combine the dual Kriging model for uncertainty analysis, providing an idea for the robust optimization design of mechanical structure with black-box problem.

The purpose of this paper is to provide rich, qualitative insights around internal communication in strategic communication agencies, addressing the evolutions in expectations and best practices for agency leadership through COVID-19.

Qualitative interview study with 18 US-based leaders of public relations and advertising agencies to examine their experiences of leading and managing strategic communication teams during COVID-19.

Synthesized findings around changes in leadership values and important facets of ongoing internal crisis communication led to the development of the following five categories—Improvisation and Flexibility, Transparency and Trust, Ownership and Embodiment, Care and Empathy, Relationships and Resilience.

Using a high-value sample, the study is the first (to the best of the authors' knowledge) to focus on the crucial context of agencies and internal communication around COVID-19; diversity, equity, and inclusion (DEI); and other pandemic-era challenges. It provides theoretical implications around ongoing, internal crisis communication and practical implications for agency leaders in crisis.

The study aimed to analyze the influence of network embeddedness on the innovation performance of high-tech enterprises in Guangdong Province.

A conceptual model of the influence of network embeddedness on the innovation performance of high-tech enterprises in Guangdong province is established, which takes the business model as the mediating variable and political association as the moderating variable. Multivariate statistical analysis and the MacKinnon confidence interval method were used to analyze 418 questionnaires.

The results show that both relational embeddedness and structural embeddedness have significant positive effects on the innovation performance of high-tech enterprises in Guangdong Province. The business model has a partial mediating effect between relationship embeddedness, structure embeddedness, and innovation performance of high-tech enterprises in Guangdong Province, respectively. Political relevance has a significant negative moderating effect on the relationship between the relationship embeddedness and innovation performance of high-tech enterprises in Guangdong Province, but the moderating effect on structural embeddedness and innovation performance of high-tech enterprises in Guangdong province has not been verified.

The study of this paper also has some shortcomings: very few data research samples exist; the external factors affecting the performance of high-tech enterprises in Guangdong Province need to be further refined. The research scale needs further improvement.

In this paper, embedding theory, transaction cost theory, resource dependence theory, rent-seeking theory, new institution theory and uncertainty management theory were integrated by system attempt to reveal the mediating and moderating roles of business model and political relevance, respectively, between network embeddedness behavior and entrepreneurial innovation performance of high-tech enterprises. The research conclusions expand the relevant research in the field of entrepreneurial innovation. At the same time, the research results provide theoretical support and reference for the innovative growth of high-tech enterprises and government behavior decision-making in Guangdong province.

Network embeddedness will have a profound impact on the entrepreneurial innovation performance of high-tech enterprises. Existing research has overlooked discussing this issue from the perspective of internal and external influencing factors within the enterprise. Therefore, this study addresses this issue by (1) introducing the business model as the mediating variable from an internal perspective of the enterprise, (2) introducing political association as the moderating variable from an external perspective of the enterprise and (3) 418 original questionnaires of high-tech enterprises in Guangdong Province were used to test the effect of the study variables.

The purpose of this study is to investigate the application of non-destructive testing methods in measuring bearing oil film thickness to ensure that bearings are in a normal lubrication state. The oil film thickness is a crucial parameter reflecting the lubrication status of bearings, directly influencing the operational state of bearing transmission systems. However, it is challenging to accurately measure the oil film thickness under traditional disassembly conditions due to factors such as bearing structure and working conditions. Therefore, there is an urgent need for a nondestructive testing method to measure the oil film thickness and its status.

This paper introduces methods for optically, electrically and acoustically measuring the oil film thickness and status of bearings. It discusses the adaptability and measurement accuracy of different bearing oil film measurement methods and the impact of varying measurement conditions on accuracy. In addition, it compares the application scenarios of other techniques and the influence of the environment on detection results.

Ultrasonic measurement stands out due to its widespread adaptability, making it suitable for oil film thickness detection in various states and monitoring continuous changes in oil film thickness. Different methods can be selected depending on the measurement environment to compensate for measurement accuracy and enhance detection effectiveness.

This paper reviews the basic principles and latest applications of optical, electrical and acoustic measurement of oil film thickness and status. It analyzes applicable measurement methods for oil film under different conditions. It discusses the future trends of detection methods, providing possible solutions for bearing oil film thickness detection in complex engineering environments.

Pyrus Pyrifolia (Sand Pear) is one of the most underused pear variety despite its nutraceutical potential. Therefore, this paper aims to explore the Pyrus Pyrifolia in term of origin, distribution and classification, nutritional and bioactive potential, therapeutic potential and valorization along with future prospectus.

A wide variety of publications (88) were identified through electronic databases (Science direct, PubMed, SciELO, Google scholar, Link springer and Research gate) under the umbrella of different keywords such as bioactive compounds, health benefits, nutrition, sand pear, Pyrus and Pyrus pyrifolia.

Pyrus Pyrifolia (Sand Pear) is abundant in nutritional and bioactive compounds such as phenolic acids, flavonoids, terpenoids, vitamins and minerals. It exhibits therapeutic potential as being an antioxidant, anti-obesity, anti-diabetic, anti-inflammatory and anti-cancer agent. However, P. pyrifolia is not much explored by food researchers and industrialists, hence remaining underused. A few attempts have been made toward the use of P. pyrifolia for jam, jelly, candy and wine preparation. However, more research is required for the commercial processing of P. pyrifolia and to enhance its availability outside its growing area.

In this paper, nutritional and bioactive compounds of P. pyrifolia are discussed that provide knowledge to the researchers for its use as a functional ingredient.

In the context of investment decisions, the intricate interplay between exogenous shocks and their influence on investor confidence significantly shapes their behaviors and, consequently, their outcomes. Investment decisions are influenced by uncertainties, exogenous shocks as well as the sentiments and confidence of investors, factors typically overlooked by decision-makers. This study will meticulously examine these multifaceted influences and discern their intricate hierarchical nuances in the sentiments of industrial entrepreneurs during the COVID-19 pandemic.

Employing the robust framework of the generalized linear latent and mixed models (GLLAMM), this research will thoroughly investigate individual and group idiosyncrasies present in diverse data compilations. Additionally, it will delve deeply into the exogeneity of disturbances across different sectors and regions.

Relevant insights gleaned from this research elucidate the adverse influence of exogenous forces, including pandemics and financial crises, on the confidence of industrial entrepreneurs. Furthermore, a significant discovery emerges in the regional analysis, revealing a notable homogeneity in the propagation patterns of industrial entrepreneurs' perceptions within the sectoral and regional context. This finding suggests a mitigation of regional effects in situations of global exogenous shocks.

Within the realm of academic inquiry, this study offers an innovative perspective in unveiling the intricate interaction between external shocks and their significant impacts on the sentiment of industrial entrepreneurs. Furthermore, the utilization of the robust GLLAMM captures the hierarchical dimension of this relationship, enhancing the precision of analyses. This approach provides a significant impetus for data-informed strategic directions.