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Environmental sustainability is quickly becoming one of the most critical issues in industry development. This study aims to conduct a systematic literature review through which the author can provide various research areas to work on for future researchers and provide insight into Industry 4.0 and environmental sustainability.

This study accomplishes this by performing a backward analysis using text mining on the Scopus database. Latent semantic analysis (LSA) was used to analyze the corpus of 4,364 articles published between 2013 and 2023. The authors generated ten clusters using keywords in the industrial revolution and environmental sustainability domain, highlighting ten research avenues for further exploration.

In this study, three research questions discuss the role of environmental sustainability with Industry 4.0. The author predicted ten clusters treated as recent trends on which more insight is required from future researchers. The authors provided year-wise analysis, top authors, top countries, top sources and network analysis related to the topic. Finally, the study provided industrialization’s effect on environmental sustainability and the future aspect of automation.

The reliability of the current study may be compromised, notwithstanding the size of the sample used. Poor retrieval of the literature corpus can be attributed to the limitations imposed by the search words, synonyms, string construction and variety of search engines used, as well as to the accurate exclusion of results for which the search string is insufficient.

This research is the first-ever study in which a natural language processing technique is implemented to predict future research areas based on the keywords–document relationship.

This study investigates how federated learning (FL) and human–robot collaboration (HRC) can be used to manage diverse industrial environments effectively. We aim to demonstrate how these technologies not only improve cooperation between humans and robots but also significantly enhance productivity and innovation within industrial settings. Our research proposes a new framework that integrates these advancements, paving the way for smarter and more efficient factories.

This paper looks into the difficulties of handling diverse industrial setups and explores how combining FL and HRC in the mark of Industry 5.0 paradigm could help. A literature review is conducted to explore the theoretical insights, methods and applications of these technologies that justify our proposal. Based on this, a conceptual framework is proposed that integrates these technologies to manage heterogeneous industrial environments.

The findings drawn from the literature review performed, demonstrate that personalized FL can empower robots to evolve into intelligent collaborators capable of seamlessly aligning their actions and responses with the intricacies of factory environments and the preferences of human workers. This enhanced adaptability results in more efficient, harmonious and context-sensitive collaborations, ultimately enhancing productivity and adaptability in industrial operations.

This research underscores the innovative potential of personalized FL in reshaping the HRC landscape for manage heterogeneous industrial environments, marking a transformative shift from traditional automation to intelligent collaboration. It lays the foundation for a future where human–robot interactions are not only more efficient but also more harmonious and contextually aware, offering significant value to the industrial sector.

The Islamic world spans many different languages with different language structures. This paper aims to explore one way in which language structure affects consumer response to the marketing of cobrands.

Two between subject experiments were conducted using samples of participants from Saudi Arabia and the USA. The first manipulated partner brand category similarity and brand name order, along with the structure of the language used to communicate with the market. The data for this study includes Arabic speakers in Saudi Arabia as well as English speakers in the USA. The second study explores how targeting a population fluent in multiple languages of varied structure nullifies the findings from the first study and uses Latino participants in the USA.

This study finds that when brands come from similar product categories, name order did not affect cobrand evaluations, but it did when the brands come from dissimilar product categories. Here, evaluations of the cobrand are enhanced when the invited brand is in the position that adjectives occupy in the participant’s language. The authors also find that being proficient in two languages, each with a different default order for adjectives and nouns, quashes the effect of name order otherwise seen when brands from dissimilar product categories engage in cobranding.

By examining the impact of language structure on the effects of cobrand evaluation and conducting studies among participants with differing dominant languages, this research can rule out simple primacy or recency effects.

This chapter explores how the intersection of disability and family has evolved in the US medical field over the 19th, 20th, and 21st centuries. Through an analysis of the Proceedings of the American Medical Association, this work demonstrates how physicians describe and view the connections between disability and family in bureaucratic activities. The exploration of the Proceedings documents elucidates the changing process around how physicians define the relationship of disability and family in the US through bureaucratic and policy discussions. A qualitative approach of content analysis is employed to evaluate the American Medical Association Proceedings of the House of Delegates from 1846 to 2022. Data collection applies deductive coding focusing on various terms related to the conceptualization of families with analysis exploring themes around disability within the searched terms. Results demonstrate how US physicians describe the relationship between disability and family over time in the US context. The findings highlight cases in which the medical establishment recognizes itself as a potential source of burden, families choosing burdens of supporting or not supporting family members with disabilities, and the medicalization of social phenomena related to disability. Additional findings include discussions of support systems that families with disabled family members can leverage for assistance. This first-of-its-kind longitudinal content analysis study provides insights on the meaning-making processes of physicians in relation to how conceptualizations of disability and family are described in medical proceeding documents. The value of this work lies in both the findings of how physicians describe the intersection of disability and family as well as the viability of medical proceeding documents for analyzing cultural-social phenomena. Additional value is added with the notion that physicians view disability in a familial context as being caught between problems and support.

Continuous fiber-reinforced thermoplastic composites are a class of materials highly valuable for structural applications and modeling of heat transfer within them is critical to the design of their processing methods. However, the fiber reinforcement leads to highly anisotropic thermal conduction. Among a variety of methods to account for anisotropic thermal conductivity, continuum models with effective media approximation thermal conductivity are computationally efficient and require minimal data to begin modeling a specific composite material. The purpose of this study is to evalute the utility of these models.

In this work, six potential effective media approximation models are evaluated against experimental heating data. Thick (>25 mm) glass fiber-reinforced polyethylene terephthalate glycol (PET-G) specimens with 40% fiber volume fraction were heated with embedded resistance heating to produce validation and testing data sets. A two-dimensional finite-difference solver was implemented using each of the six effective media approximation models. The accuracy of each model is compared.

The model developed by Cheng and Vachon was found to predict the experimental results most accurately. Fit statistics were similar in the testing and validation data sets. This model is recommended for simulation of transient heating in continuous fiber-reinforced thermoplastic composites with low-to-moderate fiber volume fractions.

There are a wide variety of mathematical models for effective media approximation thermal conductivity, though very few have been applied to continuous fiber-reinforced thermoplastic composites. This work shows that the simplest methods based on rules of mixtures are well outperformed by more modern and complex models, and should be incorporated for accurate prediction of heating during thermal processing of fiber-reinforced thermoplastic composites.

The main purpose of this study resides essentially in the development of a new tool to quantify the biomass in the bioreactor operating under steady state conditions.

Modeling is the most relevant tool for understanding the functioning of some complex processes such as biological wastewater treatment. A steady state model equation of activated sludge model 1 (ASM1) was developed, especially for autotrophic biomass (XBA) and for oxygen uptake rate (OUR). Furthermore, a respirometric measurement, under steady state and endogenous conditions, was used as a new tool for quantifying the viable biomass concentration in the bioreactor.

The developed steady state equations simplified the sensitivity analysis and allowed the autotrophic biomass (XBA) quantification. Indeed, the XBA concentration was approximately 212 mg COD/L and 454 mgCOD/L for SRT, equal to 20 and 40 d, respectively. Under the steady state condition, monitoring of endogenous OUR permitted biomass quantification in the bioreactor. Comparing XBA obtained by the steady state equation and respirometric tool indicated a percentage deviation of about 3 to 13%. Modeling bioreactor using GPS-X showed an excellent agreement between simulation and experimental measurements concerning the XBA evolution.

These results confirmed the importance of respirometric measurements as a simple and available tool for quantifying biomass.

Social networking sites (SNS) have become popular mediums for individuals to interact with others. However, despite the positive impact of SNS on people’s lives, cyberbullying has become prevalent. Due to this prevalence, substantial research has examined cyberbullying from the perspectives of perpetrators, bystanders, and victims, but little is known about SNS users’ confrontations with cyberbullying. The objectives of this study are to examine confrontation as a victim’s coping response, the effect of blockability affordance on victims’ protection motivation, the impact of a victim’s experiences with cyberbullying perpetration, and social desirability (SD) bias in the context of cyberbullying victimization.

This study examines the effect of blockability affordance on SNS users’ protection motivation. It also investigates the relationships among perceived threat, perceived coping efficacy, and use of confrontation. Furthermore, this investigation analyzes the effect of SNS users’ experiences as perpetrators on their decision to confront cyberbullies. Finally, this study assesses and controls SD bias in SNS users’ confrontation behavior. To test the research model, we used an online vignette study to collect 314 data points.

Blockability affordance, perceived threat, perceived coping efficacy, and cyberbullying perpetration experiences are essential factors in explaining use of confrontation. This study also finds SD bias in the context of cyberbullying victimization.

This is one of the first studies in information systems research to empirically examine the effect of blockability affordance in the context of cyberbullying.

The purpose of this study is to investigate the complexity of how demands and stress are mitigated to enhance employee performance in remote working arrangements.

A time-lagged snowball sample of 223 full-time remote working adults in the United States participated in an online survey. Data were analyzed using R 4.0.2 and structural equation modeling.

Results suggest remote job resources involving organizational trust and work flexibility increase performance via serial mediation when considering information communication technology (ICT) demands and work–life interference (WLI). The findings provide insights into counterbalancing the negative aspects of specific demands and stress in remote work arrangements.

This study provides insights for managers to understand how basic job resources may shape perspectives on demands and WLI to impact performance. Specific to remote working arrangements, establishing trust with the employees and promoting accountability with their work flexibility can play an important part in people and their performance.

This study contributes theoretically to the literature by evidencing how components of the E-Work Life (EWL) scale can be used with greater versatility beyond the original composite measurement because of the job-demand resource (JD-R) framework and conservation of resources theory (COR). This study answers several calls by research to investigate how ICT demands and WLI play a complex role in work performance.

This study aims to identify critical determinants of sovereign credit risk by examining the influence of oil prices, gold prices, geopolitical risk, market volatility, exchange rates, inflation and non-performing loans on Türkiye’s credit default swap (CDS) spreads. This analysis provides a comprehensive understanding of how economic uncertainty and political risk impact Türkiye’s financial stability, as reflected in its CDS market. This study investigates the importance of ex ante proxies in explaining changes in CDS spread by financial and economic indicators in Türkiye.

This research explores the connections between critical financial and economic indicators and the credit risk of Türkiye between 2009 and 2022 by using advanced econometric techniques such as ARDL bound tests, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), Johansen co-integration tests and VECM Granger causality analyses.

ARDL bound test results reveal significant negative impacts of BIST and non-performing loans on CDS, and positive associations with inflation, VIX and geopolitical risk on CDSs. The short-term results show that BIST, INFL, NPL, USD, VIX and GPRT have negative coefficients. Johansen co-integration, FMOLS and DOLS results reinforce the ARDL findings. Moreover, BIST is a significant Granger cause of CDS.

This study is significant, as it jointly considers economic and political risk factors, thereby integrating multiple econometric models to provide more robust, meaningful and comparable results. By examining these factors together, the analysis offers a more comprehensive understanding of risk dynamics, yielding insights relevant to Türkiye. Although the findings are specific to Türkiye, they have broader implications, enriching the understanding of emerging economies. Türkiye’s status as a key representative of emerging markets strengthens the study’s value, as the results can serve as a reference point for other countries with similar economic structures. The importance of this study is also underscored by its potential to inform risk management strategies, guide policy decisions and offer insights to investors and financial analysts. By elucidating the intricate relationships among a broad spectrum of macroeconomic variables, this research contributes to a more comprehensive risk assessment framework. It equips stakeholders with a more informed perspective on the factors influencing credit risk in Türkiye’s economic landscape.