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This study aims to address challenges in the distribution of social donations during epidemic emergencies, focusing on issues such as uneven distribution and material stacking. The goal is to propose optimized strategies that enhance equity and efficiency in the allocation of donated resources.

Firstly, the satisfaction function is constructed from two perspectives of the designated hospital and the Red Cross. On this basis, the fairness perception level of the two is portrayed. Then, we set the time weights, and construct a multi-objective programming model by combining the resource constraints in the social donation distribution process. The combined algorithm of NSGA-II and TOPSIS is also designed for model solving. Finally, an example of social donation distribution of the Red Cross Society of China Wuhan Branch is conducted for numerical analysis.

Numerical analysis reveals that timely transmission of demand information favors a demand-oriented distribution strategy for optimal efficiency. However, in scenarios with poor demand information transmission, an equal distribution of social donations proves to be a more effective strategy. Equal distribution ensures rapid allocation while minimizing perceived unfairness at designated hospitals, ultimately improving overall satisfaction levels and emergency response effectiveness.

The findings provide practical insights for emergency response planners. These include translating the developed methods into guiding principles, establishing real-time monitoring systems, enhancing training for relevant departments, and implementing evaluation mechanisms. Practitioners can utilize this knowledge to optimize the efficiency of social donation distribution during sudden outbreaks.

The equitable distribution of social donations ensures efficient resource allocation and minimizes perceived unfairness, contributing to improved social satisfaction levels. This has broader implications for community resilience and support during emergencies.

This research contributes to the field by proposing a comprehensive model for optimizing social donation distribution in emergencies. The integration of fairness perception, time weights, and a multi-objective planning approach, along with the application of the combined algorithm of NSGA-II and TOPSIS, adds novelty and practical value to the existing literature. The study serves as a decision-making reference for enhancing emergency response theories in sudden event.

Providing quality emergency supplies is crucial to mitigate and respond to emergencies. However, despite government and consumer oversight of emergency supplies’ quality, a troubling trend persists among some enterprises to sacrifice product quality for financial gain. This paper examines the influence of strategy selections among governments, enterprises, and consumers to enhance the quality of emergency supplies.

We develop a tripartite evolutionary game model consisting of three stakeholders: government, enterprises, and consumers, considering factors including subsidies and penalties. After analysing three stakeholders’ strategic choices to ascertain system stability, parametric analyses were conducted.

Excessive or insufficient subsidies are not conducive to encouraging enterprises to adopt an authentic production strategy; excessive subsidies may result in consumers enduring counterfeiting. Furthermore, the government’s supervision strategy can stabilise the system quickly, suggesting that consumer reporting cannot replace government supervision. Additionally, incentivising enterprises to adopt an authentic production strategy can be achieved by increasing penalties and enhancing compensation while reducing consumer reporting, government supervision, and raw materials costs.

We present a preliminary exploration of how to promote the production of qualified emergency supplies in the early stages of an emergency event. The model and findings proposed in this paper can be generalised and applied to various emergency events, including epidemics and earthquakes.

This study aims to investigate the effect of political turmoil on the firm financial performance, particularly in presence of politically affiliated board of directors.

The study applied panel regression analyses on a data set of Pakistan’s listed companies ranged over 14 years, spanning from 2007 to 2021. Political turmoil was first gauged through three determinants, i.e. political protest, government election and constitutional reform, and thereafter, economic uncertainty index was used as a proxy for political turmoil. For the purpose of political connection, the study used political affiliation of the board of directors.

The study finds that political turmoil has deleterious effect on the return on assets and Tobin’s Q. The study further unveils that politically affiliated firms are relatively insulated from the volatility posed by the political uncertainty and exhibit significantly better financial outcomes.

Findings of the study suggest that appropriate composition of the board is imperative in offsetting the risk posed by the political turmoil. Hence, the results are useful for investors, policymakers and regulators to ensure financial soundness of firms in the wake of political turmoil.

To the best of the authors’ knowledge, this is the first study that investigates the moderating impact of political connection on the performance of companies in presence of political turmoil.

Recently, short-form video apps, as a new form of social media, have attracted users and rapidly emerged by virtue of their personalized recommendation algorithms, interesting forms of live interaction, and diverse interactive functions, which may lead to excessive use. From the perspective of IT affordances, this study combines the cognitive-affective-behavioral model and perceived values theory to examine the formation mechanism of the excessive use of short-form video apps.

We surveyed a total of 351 users who have used Tiktok, a typical short-form video app, and used their questionnaires to test the research model.

Searching affordance has a positive impact on perceived information value and perceived entertainment value. Furthermore, meta-voicing, recommending, and livestreaming affordance have a positive impact on perceived information value, perceived entertainment value, and perceived social networking value, which also have a positive impact on negative affect anticipation. In addition, negative affect anticipation is significantly positively correlated with excessive use.

In terms of theory, this study introduces the theory of IT affordances and perceived values into the cognitive-affective-behavioral model, to the best of our knowledge, for the first time. Furthermore, it conducts situational research on the formation mechanism of excessive use of short-form video apps and makes up for the lack of studying of excessive use behavior from the perspective of technical factors.

The hospitality industry is one of Ghana's key economic contributors. It is an industry that has significant indigenous investment. The sector also brings in foreign exchange for Ghana. In 2019, it generated $325 m through tourist visits. This makes the hospitality industry critical for the attraction of foreign direct investments. The research was therefore aimed at examining the business environment of the hospitality industry for evidence of negative factors that can hamper its greater contribution to the attainment of Goal 8 of the 17 Sustainable Development Goals of the UN such as guest-bullying and the incivility in hospitality occupations.

A convenience sampling method was used to select 346 samples out of the accessible 3,500 targeted population from 38 hotels in the capital city of Ghana, Accra, comprising of junior to senior employees of various departments. The questionnaires were scripted from a paper-based to digital format supported by the Opine software installed on tablets and smartphones, to enable complete adherence to all coronavirus disease 2019 (COVID-19) safety protocols. The study used a regression to ascertain the relationships between the dependent variables and the independent variables.

The study found the “Level of Permissiveness for Guests” positively and significantly “encouraged” guests to bully staff, while “Management and Staff Laxity” negatively but significantly explained guest bullying behaviour.

The study makes the first attempt in context to shed light on workplace bullying which represents one of the main factors that can inhibit or erode any gains or attempts to foster the achievement of Goal 8 of the 17 Sustainable Development Goals of the UN which is to create “Decent Work and Economic Growth”.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

This paper aims to examine the USA's policy toward Myanmar or Burma, analyzing the bilateral relationship between the two countries from the pre-colonial period to the present day. It highlights the implications of political shifts in the system of government in Myanmar for the bilateral relationship with the USA, particularly after the 1962 coup and the emergence of rising regional powers like China. The paper also shows how the economic role of Myanmar has increased, leading to more equal relations with the United States of America.

This paper employs a qualitative research design, analyzing data from both primary and secondary sources. The methodology includes a literature review and document analysis to gain a comprehensive understanding of the USA–Myanmar bilateral relationship. The research also analyzes political and economic developments in Myanmar and the region, placing the USA policy in a broader context.

The research finds that the USA has been an influential player in Myanmar's politics and economy. However, with Myanmar's integration into the Chinese Belt and Road Initiative and its accession to Association of Southeast Asian Nations (ASEAN), its economic role has become more significant, leading to a more balanced relationship with the USA. The paper also shows that the USA's policy toward Myanmar has undergone significant changes, particularly after the 2011 easing of sanctions by former President Barack Obama.

Although many works have traced the history of USA–Myanmar ties, this study provides a fresh perspective by setting past events against the backdrop of contemporary geopolitical upheavals, therefore highlighting the complex evolution of their bilateral dynamics. The paper contributes to the literature on the USA–Myanmar bilateral relationship by providing a comprehensive analysis of the relationship from a historical perspective. The research also adds to the discussion of the implications of political and economic developments in Myanmar for the bilateral relationship with the USA. The findings of this study have important implications for policymakers and scholars interested in the USA's role in Southeast Asia and its relations with rising regional powers like China.

Food supply chains (FSCs) are fast becoming more and more complex. Sustainability is a necessary strategy in FSCs to meet the environmental, economic and societal requirements. Industry 4.0 (I4.0) applications for a circular economy (CE) will play a significant role in sustainable food supply chains (SFSCs). I4.0 applications can be used in for traceability, tracking, inspection and quality monitoring, environmental monitoring, precision agriculture, farm input optimization, process automation, etc. to improve circularity and sustainability of FSCs. However, the factors integrating I4.0 and CE adoption in SFSC are not yet very well understood. Furthermore, despite such high potential I4.0 adoption is also met with several barriers. The present study identifies and analyzes twelve barriers for the adoption of I4.0 in SFSC from an CE context.

A cause-effect analysis and prominence ranking of the barriers are done using Rough-DEMATEL technique. DEMATEL is a widely used technique that is applied for a structured analysis of a complex problems. The rough variant of DEMATEL helps include the uncertainty and vagueness of decision maker related to the I4.0 technologies.

“Technological immaturity,” “High investment,” “Lack of awareness and customer acceptance” and “technological limitations and lack of eco-innovation” are identified as the most prominent barriers for adoption of I4.0 in SFSC.

Successful mitigation of these barriers will improve the sustainability of FSCs through accelerated adoption of I4.0 solutions. The findings of the study will help managers, practitioners and planners to understand and successfully mitigate these barriers.

Permanent magnet synchronous spherical motors can have wide use in robotics and industrial automation. They enable three-DOF omnidirectional motion of their rotor. They are suitable for several applications, such as actuation in robotics, traction in electric vehicles and use in several automation systems. Unlike conventional synchronous motors, permanent magnet synchronous spherical motors consist of a fixed inner shell, which is the stator, and a rotating outer shell, which is the rotor. Their dynamic model is multivariable and strongly nonlinear. The treatment of the associated control problem is important.

In this paper, the multivariable dynamic model of permanent magnet synchronous spherical motors is analysed, and a nonlinear optimal (H-infinity) control method is developed for it. Differential flatness properties are proven for the spherical motors’ state-space model. Next, the motors’ state-space description undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization process takes place at each sampling instance around a time-varying operating point, which is defined by the present value of the motors’ state vector and by the last sampled value of the control input vector. For the approximately linearized model of the permanent magnet synchronous spherical motors, a stabilizing H-infinity feedback controller is designed. To compute the controller’s gains, an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis. Finally, the performance of the nonlinear optimal control method is compared against a flatness-based control approach implemented in successive loops.

Due to the nonlinear and multivariable structure of the state-space model of spherical motors, the solution of the associated nonlinear control problem is a nontrivial task. In this paper, a novel nonlinear optimal (H-infinity) control approach is proposed for the dynamic model of permanent magnet synchronous spherical motors. The method is based on approximate linearization of the motor’s state-space model with the use of first-order Taylor series expansion and the computation of the associated Jacobian matrices. Furthermore, the paper has introduced a different solution to the nonlinear control problem of the permanent magnet synchronous spherical motor, which is based on flatness-based control implemented in successive loops.

The presented control approaches do not exhibit any limitations, but on the contrary, they have specific advantages. In comparison to global linearization-based control schemes (such as Lie-algebra-based control), they do not make use of complicated changes of state variables (diffeomorphisms) and transformations of the system's state-space description. The computed control inputs are applied directly to the initial nonlinear state-space model of the permanent magnet spherical motor without the intervention of inverse transformations and thus without coming against the risk of singularities.

The motion control problem of spherical motors is nontrivial because of the complicated nonlinear and multivariable dynamics of these electric machines. So far, there have been several attempts to apply nonlinear feedback control to permanent magnet-synchronous spherical motors. However, due to the model’s complexity, few results exist about the associated nonlinear optimal control problem. The proposed nonlinear control methods for permanent magnet synchronous spherical motors make more efficient, precise and reliable the use of such motors in robotics, electric traction and several automation systems.

The treated research topic is central for robotic and industrial automation. Permanent magnet synchronous spherical motors are suitable for several applications, such as actuation in robotics, traction in electric vehicles and use in several automation systems. The solution of the control problem for the nonlinear dynamic model of permanent magnet synchronous spherical motors has many industrial applications and therefore contributes to economic growth and development.

The proposed nonlinear optimal control method is novel compared to past attempts to solve the optimal control problem for nonlinear dynamical systems. Unlike past approaches, in the new nonlinear optimal control method, linearization is performed around a temporary operating point, which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector and not at points that belong to the desirable trajectory (setpoints). Besides, the Riccati equation which is used for computing the feedback gains of the controller is new, and so is the global stability proof for this control method. Compared to nonlinear model predictive control, which is a popular approach for treating the optimal control problem in industry, the new nonlinear optimal (H-infinity) control scheme is of proven global stability, and the convergence of its iterative search for the optimum does not depend on initial conditions and trials with multiple sets of controller parameters. It is also noteworthy that the nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state dependent Riccati equations (SDRE). The SDRE approaches can be applied only to dynamical systems which can be transformed into the linear parameter varying form. Besides, the nonlinear optimal control method performs better than nonlinear optimal control schemes, which use approximation of the solution of the Hamilton–Jacobi–Bellman equation by Galerkin series expansions. Furthermore, the second control method proposed in this paper, which is flatness-based control in successive loops, is also novel and demonstrates substantial contribution to nonlinear control for robotics and industrial automation.

The purpose of this study is to explore the interfacial adhesion between superhydrophobic coatings FC-X (X = 1%, 2%, 3%, 4% and 5%) and the concrete substrate, along with the impact of FC-X on the water repellency characteristics of the concrete substrate.

One synthetic step was adopted to prepare novel F-SiO₂ NP hybrid fluororesin coating. The impact of varying mass fractions of F-SiO₂ NPs on the superhydrophobicity of FC-X was analyzed and subsequently confirmed through water contact angle (WCA) measurements. Superhydrophobic coatings were simply applied to the concrete substrate using a one-step spraying method. The interfacial adhesion between FC-X and the concrete substrate was analyzed using tape pasting tests and abrasion resistance measurements. The influence of FC-X on the water repellency of the concrete substrate was investigated through measurements of water absorption, impermeability and electric flux.

FC-4% exhibits excellent superhydrophobicity, with a WCA of 157.5° and a sliding angle of 2.3°. Compared to control sample, FC-X exhibits better properties, including chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

This study offers a thorough investigation into the practical implications of enhancing the durability and water repellency of concrete substrates by using superhydrophobic coatings, particularly FC-4%, which demonstrates exceptional superhydrophobicity alongside remarkable chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

Through the examination of the interfacial adhesion between FC-X and the concrete substrate, along with an assessment of FC-X’s impact on the water repellency of the concrete, this paper provides valuable insights into the practical application of superhydrophobic coatings in enhancing the durability and performance of concrete materials.