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In this paper the aim is that Aramid/alginate blended nonwoven fabrics were prepared, and the flame retardancy of the blended nonwoven fabrics was studied by thermogravimetric analysis, vertical flame test, limiting oxygen index (LOI) and cone calorimeter test.

The advantages of different fibers can be combined by blending, and the defects may be remedied. The study investigates whether incorporating alginate fibers into aramid fibers can enhance the flame retardancy and reduce the smoke production of prepared aramid/alginate blended nonwoven fabrics.

Thermogravimetric analysis indicated that alginate fibers could effectively inhibit the combustion performance of aramid fibers at a higher temperature zone, leaving more residual chars for heat isolation. And vertical flame test, LOI and cone calorimeter test testified that the incorporation of alginate fibers improved the flame retardancy and fire behaviors. When the ratio of alginate fibers for aramid/alginate blended nonwoven fabrics reached 80%, the incorporation of alginate fibers could notably decreased peak-heat release rate (54%), total heat release (THR) (29%), peak-smoke production rate (93%) and total smoke production (86%). What is more, the lower smoke production rate and lower THR of the blends vastly reduced the risk of secondary injury in fires.

This study proposes to inhibit the flue gas release of aramid fiber and enhance the flame retardant by mixing with alginate fiber, and proposes that alginate fiber can be used as a biological smoke inhibitor, as well as a flame retardant for aramid fiber.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

This study examines how contributors with different achievement goals participate under the influence of two common motivators/demotivators on crowdsourcing platforms, namely system design features and task nature.

A free simulation experiment was conducted among undergraduate students with the use of a crowdsourcing platform for two weeks.

The results indicate that contributors with a strong performance-approach goal get better scores and participate in more crowdsourcing tasks. Contributors with a strong mastery-avoidance goal participate in fewer heterogeneous tasks.

Contributors with different achievement goals participate in crowdsourcing tasks to different extents under the influence of the two motivators/demotivators. The inclusion of the approach-avoidance dimension in the performance-mastery dichotomy enables demonstrating the influence of motivators/demotivators more specifically. This article highlights differentiation between the quality and the quantity of heterogeneous crowdsourcing tasks.

Management is advised to approach performance-approach people if a leaderboard and a point system are incorporated into their crowdsourcing platforms. Also, management should avoid offering heterogeneous tasks to mastery-avoidance contributors. System developers should take users' motivational goals into consideration when designing the motivators in their systems.

The study sheds light on habitual achievement goals, which are relatively stable in comparison to contributors' motives and states. The relationships between achievement goals and motivators/demotivators are more persistent across time. This study informs system designers' decisions to include appropriate motivators for sustained contributor participation.

This research aims to update the literature about the importance of reliability in supply chain (SC) and to find out the SC determinants.

This research surveys while contributing to the academic grasp of supply chain reliability (SCR) concepts. The study found 45 peer-reviewed publications using a structured survey technique with a four-step filtering process. The filtering process includes data reduction processes such as an evaluation of abstract and conclusion. The filtered study focuses on SCR and its determinants.

One of the major findings is that most of the study has focused on mathematical and conceptual studies. Also, this study provides the answer to a question like how can reliability be better accepted and evolved within the SC after finding the determinants of SCR.

The observed methodological gap in understanding and development of SCR was identified and classified into three categories: mathematical, conceptual and empirical studies (case studies and survey’s mainly). This research will aid academics in developing and understanding the determinants of SCR.

This paper aims to investigate the effect of characteristic parameters of pits on the mechanical properties and fracture model of cable steel wires.

The tensile test and finite element analysis of steel wires with corrosion damage were carried out. The stress development of corroded steel wire under corrosion morphology was studied by the 3D reverse reconstruction technology. The internal relationship between the stress triaxiality, equivalent plastic strain and pit depth, depth-width ratio of corroded steel wire was discussed.

With the increase of corrosion degree, the neck shrinkage phenomenon of steel wire was not significant, and the crack originated near the pit bottom and expanded to the section inside of specimen. The fiber area of corroded steel wire decreased while the radiation area increased, and the ductile fracture gradually changed to brittle fracture. The pit size significantly changed the triaxial degree and distribution of stress and accelerated the initiation and propagation of internal cracks at the neck shrinkage stage.

The proposed fracture model based on the void growth model could accurately simulate the fracture behavior of steel wires with corrosion damage.

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

Existing literature on algorithmic management practices – defined as autonomous data-driven decision making in people's management by adoption of self-learning algorithms and artificial intelligence – suggests complex relationships with employees' well-being in the workplace. While the use of algorithms can have positive impacts on people-related decisions, they may also adversely influence job autonomy, perceived justice and – as a result – workplace well-being. Literature review revealed a significant gap in empirical research on the nature and direction of these relationships. Therefore the purpose of this paper is to analyse how algorithmic management practices directly influence workplace well-being, as well as investigating its relationships with job autonomy and total rewards practices.

Conceptual model of relationships between algorithmic management practices, job autonomy, total rewards and workplace well-being has been formulated on the basis of literature review. Proposed model has been empirically verified through confirmatory analysis by means of structural equation modelling (SEM CFA) on a sample of 21,869 European organisations, using data collected by Eurofound and Cedefop in 2019, with the focus of investigating the direct and indirect influence of algorithmic management practices on workplace well-being.

This research confirmed a moderate, direct impact of application of algorithmic management practices on workplace well-being. More importantly the authors found out that this approach has an indirect influence, through negative impact on job autonomy and total rewards practices. The authors observed significant variation in the level of influence depending on the size of the organisation, with the decreasing impacts of algorithmic management on well-being and job autonomy for larger entities.

While the influence of algorithmic management on various workplace practices and effects is now widely discussed, the empirical evidence – especially for traditional work contexts, not only gig economy – is highly limited. The study fills this gap and suggests that algorithmic management – understood as an automated decision-making vehicle – might not always lead to better, well-being focused, people management in organisations. Academic studies and practical applications need to account for possible negative consequences of algorithmic management for the workplace well-being, by better reflecting complex nature of relationships between these variables.

This study aims to explore the synergistic effect of oxide nanoparticles (ZnO, Fe₂O₃, SiO₂) and cerium nitrate inhibitor on anti-corrosion performance of epoxy coating. First, cerium nitrate inhibitors are absorbed on the surface of various oxide nanoparticles. Thereafter, epoxy nanocomposite coatings have been fabricated on carbon steel substrate using these oxide@Ce nanoparticles as both nano-fillers and nano-inhibitors.

To evaluate the impact of oxides@Ce nanoparticles on mechanical properties of epoxy coating, the abrasion resistance and impact resistance of epoxy coatings have been examined. To study the impact of oxides@Ce nanoparticles on anti-corrosion performance of epoxy coating for steel, the electrochemical impedance spectroscopy has been carried out in 3% NaCl solution.

ZnO@Ce3+ and SiO2@Ce3+ nanoparticles provide more enhancement in the epoxy pore network than modification of the epoxy/steel interface. Whereas, Fe2O3@Ce3+ nanoparticles have more to do with modification of the epoxy/steel interface than to change the epoxy pore network.

Incorporation of both oxide nanoparticles and inorganic inhibitor into the epoxy resin is a promising approach for enhancing the anti-corrosion performance of carbon steel.

The study investigates the linkage between personality traits and firm performance. It examines the role of the pursuit of excellence, perseverance, a proactive mindset and formal education in determining the entrepreneurial success of MSMEs.

Data were collected from 432 MSME entrepreneurs using a structured questionnaire from India's two major industrial towns to analyze the impact of personality traits on firm performance. Structural equation modeling (SEM) was employed to assess the direct and indirect relationships with the help of mediation analysis.

The findings assert that personality traits improve firm performance and determine the success of MSMEs. The results reveal that the need for achievement, a proactive mindset and the pursuit of excellence are crucial to firm performance. In addition, formal education mediates between perseverance and the pursuit of excellence personality attributes on the one side and firm performance on the other.

The research has various theoretical and practical implications for entrepreneurs, financial institutions and policymakers. The results could be productively used to nurture the entrepreneurial ecosystem in India.

Although research on personality traits as a driver of firm performance is growing, the pursuit of excellence, perseverance and proactive mindset attributes as enablers of firm performance have not garnered much attention. The study presents a precise conceptual model by integrating the aforementioned dimensions in the backdrop of an emerging market.

This study is aimed to examine the effects of the economic policy uncertainty (EPU) on carbon dioxide (CO₂) emissions. It further aimed to investigate the moderating role of institutional quality on the impacts of EPU on CO₂ emissions.

The authors apply the two-step system-generalized method of moments (GMM) for 112 emerging economies and low-income developing countries (hereafter, developing countries) for the period 2000–2019.

The findings reveal that the effects of EPU on CO₂ emissions are positive. Specifically, a percent increase in EPU results in a 0.047% increase in CO₂ emissions in developing countries. However, the effects of institutional quality on CO₂ emissions are negative, certifying that strong institutional quality reduces emissions. Also, the results confirm that the positive effect of EPU on CO₂ emissions is weaker in countries with relatively strong institutional quality.

Policymakers should be more vigilant while designing and implementing economic policies. Also, the government should support firms investing in environment-friendly innovations during high EPU. Besides, developing countries should improve institutional quality to mitigate the effect of EPU on CO₂ emissions.

This study is the first in its kind to examine the impacts of EPU on CO₂ emissions in developing countries. It also provides a different viewpoint on the EPU–CO₂ relationship and reinterprets it through the moderating role of institutional quality.