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The purpose of this paper is to develop the theory of environmentally responsible behavior by extending the theory of planned behavior to empirically examine waste reduction behaviors among young coastal tourists in Bangladesh.

Through review of literature, in-depth interviews and survey, the study is conducted. Initial survey collects 355 data, which is used for exploratory factor analysis to develop constructs and its measurement items. After preparing the formal survey questionnaires, another survey is conducted and collected 403 usable responses. The subsequent valid responses are used for confirmatory factor analysis through structural equation modeling, using Smart PLS 3.3.3.

The results of this study reveal that waste reduction behavior is significantly affected by waste reduction intention, and then subsequently waste reduction intention is significantly affected by environmental concern, climate change concern and healthy environmental norms of young coastal tourists in Bangladesh.

The findings of the study will benefit industry operators and policymakers by understanding the factors that are crucial for influencing young costal tourists’ environmentally responsible behaviors (i.e. waste reduction behaviors). Eventually, the findings may assist industry operators and policymakers to develop strategies to attract more young tourists to the coastal tourist destinations in Bangladesh.

The importance of environmentally responsible behavior has received a significant attention in present decade. Consistent with this phenomenon, this study uniquely develops the theory of environmentally responsible behavior and establishes the link between environmental concern, climate change concern, healthy environmental norms, waste reduction intention and waste reduction behavior in coastal tourism domain in Bangladesh.

The use of waste polyethylene terephthalate (PET) plastics derived from shredded bottles in concrete is not formalized yet, especially in reinforced members such as beams and columns. The disposal of plastic wastes in concrete is a viable alternative to manage those wastes while minimizing the environmental impacts associated to recycling, carbon dioxide emissions and energy consumption.

This paper evaluates the suitability of 2D deterministic and stochastic finite element (FE) modeling to predict the shear strength behavior of reinforced concrete (RC) beams without stirrups. Different concrete mixtures prepared with 1.5%–4.5% PET additions, by volume, are investigated.

Test results showed that the deterministic and stochastic FE approaches are accurate to assess the maximum load of RC beams at failure and corresponding midspan deflection. However, the crack patterns observed experimentally during the different stages of loading can only be reproduced using the stochastic FE approach. This later method accounts for the concrete heterogeneity due to PET additions, allowing a statistical simulation of the effect of mechanical properties (i.e. compressive strength, tensile strength and Young’s modulus) on the output FE parameters.

Data presented in this paper can be of interest to civil and structural engineers, aiming to predict the failure mechanisms of RC beams containing plastic wastes, while minimizing the experimental time and resources needed to estimate the variability effect of concrete properties on the performance of such structures.

The purpose of the article is to identify relevant criteria for decision support in the implementation of waste-to-energy (WtE)-based systems.

The methodology is a simple case study with a qualitative approach. Five experts involved in the project of a thermoelectric power plant qualitatively evaluated, on a Likert scale, a decision model with 15 indicators derived from recent studies. The research object was the first stage of a project to implement a thermoelectric plant employing municipal solid waste (MSW) in southern Brazil.

The study identified 15 criteria supporting the decision-making process regarding WtE implementation for MSW in a mid-sized city in southern Brazil. The study identified that compliance with MSW legislation, compliance with energy legislation, initial investment and public health impact are the most influential criteria. The study offered two models for decision processes: a simplified one and a complete one, with ten and fifteen indicators, respectively.

The study concerns mid-sized municipalities in southern Brazil.

Municipal public managers have now a methodology based on qualitative evaluation that admits multiple perspectives, such as technical, economic, environmental and social, to support decision-making processes on WtE technologies for MSW.

MSW management initiatives can yield jobs and revenues for vulnerable populations and provide a correct destination for MSW, mainly in developing countries.

The main originality is that now municipal public decision-makers have a structured model based on four constructs (technical, economic, environmental and social) deployed in 15 indicators to support decision-making processes involving WtE and MSW management.

Marine plastic pollution (MPP) is increasing in recent times because of the high usage of plastic products. Green consumption behaviour (GCB) gaining attention as effective approach to achieving sustainable source reduction of plastic pollution, which negatively affects both human pollution and marine biodiversity and ecosystem. Although, Higher Education (HE) students are key stakeholders in addressing environmental issues, including MPP, there is limited empirical research in Ghana on factors influencing HE students’ GCB. This study, in an endeavour to bridge the gap, used the revised theory of planned behaviour (TPB) framework to investigate the factors influencing higher-education students’ green consumption behaviour in the Ghanaian context. Specifically, the purpose of the study is to examine the interplay of consumer novelty seeking (CNS), environmental concern (EC), perceived behavioural control and social influence on green consumption behaviour among higher-education students in Ghana. The study also explored the moderating role of gender in the relationship between CNS and green consumption behaviour.

This study used quantitative approach to obtain data from a sample of 233 students at the University of Cape Coast and used the partial least squares structural equation modelling approach for the data analysis.

The findings provide valuable insights, highlighting the important role of CNS and ECs in driving higher education students’ green consumption behaviour in Ghana. This study also found a revealing role for gender as a moderator in the relationship between CNS and green consumption behaviour, with females exhibiting a more pronounced response to CNS in influencing green consumption behaviour. On the contrary, the authors found a non-significant impact of perceived behavioural control and social influence.

Although this study presents results that provide valuable insights for policy and practical implications, it has some limitations worth mentioning for future research directions. Firstly, the participants sampled for this study comprised only higher education students from the University of Cape Coast in Ghana, which may limit the applicability of the findings to other student populations at various universities in Ghana and beyond. Moreover, the exclusion of non-students who are considered as “Generation Z” (i.e. born within 1995–2010) may narrow the scope of generalisability in the context of young consumers’ green consumption behaviour in Ghana. To enhance the generalisability of future studies, it is recommended that the scope of this study be extended. Furthermore, it should be noted that this study primarily measured higher education students’ green consumption behaviour based on self-reported data. Therefore, future research could adopt alternative approaches, such as non-self-reported measures or experimental data so to reduce the complexities and the gap that may exist between attitudes and behaviour.

These results provide valuable insights for policymakers, educators and environmental advocates to develop targeted initiatives that resonate with Ghanaian higher education students to foster green consumption practices and contribute to global efforts against marine plastic pollution.

The novelty of this study lies in the decision to propose a TPB model by including variables like CNS and EC that are believed to positively shape attitudes towards green consumption behaviour. The rationale for examining these variables is grounded in the belief that they are appropriate factors that may predict students’ green consumer behaviour, which may serve as a potential solution to marine plastic pollution.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

This study investigates the relationship between female CEOs and SMEs’ financing decisions. The study also examined the moderating role of ownership structure (female, foreign, and state ownership) in female CEO-SMEs’ financing decisions.

The study has applied Generalized Least Square (GLS) and Binomial Logistic Regression. The study has used firm-level data from 2,700 Small and Medium Enterprises (SMEs) in the Chinese economy.

The results suggest that female CEOs use debt financing. However, the financing decision of female CEOs varies if we account for female ownership, foreign ownership, state ownership, firm association with big firms, and the industry in which the firm operates. This study also provides robust evidence that female CEOs utilize debt financing under certain conditions and that female CEOs prefer long-term debt financing to short-term debt financing when considering debt maturity choices.

Recent studies report a negative relationship between female CEOs and financing decisions based on the rationale that females are risk-averse and choose less risky financing compared to their male counterparts. This study posits new evidence that female CEO financing decisions are not always risk averse if we consider female ownership, foreign ownership, state ownership, firm association with big firms, and the industry in which the firm operates. Thus, we contribute to the corporate governance literature, and this study implies a corporate financing policy.

The purpose of this article is to prepare graphene/polyimide composite materials for use as bearing cage materials, improving the friction and wear performance of bearing cages.

The oil absorption and discharge tests were conducted to evaluate the oil content properties of the materials, while the mechanical properties were analyzed through cross-sectional morphology examination. Investigation into the tribological behavior and wear mechanisms encompassed characterization and analysis of wear trace morphology in PPI-based materials. Consequently, the influence of varied graphene nanoplatelets (GN) concentrations on the oil content, mechanical and tribological properties of PPI-based materials was elucidated.

The composites exhibit excellent oil-containing properties due to the increased porosity of PPI-GN composites. The robust formation of covalent bonds between GN and PPI amplifies the adhesive potency of the PPI-GN composites, thereby inducing a substantial enhancement in impact strength. Notably, the PPI-GN composites showed enhanced lubrication properties compared to PPI, which was particularly evident at a GN content of 0.5 Wt.%, as evidenced by the minimization of the average coefficient of friction and the width of the abrasion marks.

This paper includes implications for elucidating the wear mechanism of the polyimide composites under frictional wear conditions and then to guide the optimization of oil content and tribological properties of polyimide bearing cage materials.

In this paper, homogeneously dispersed PPI-GN composites were effectively synthesized by introducing GN into a polyimide matrix through in situ polymerization, and the lubrication mechanism of the PPI composites was compared with that of the PPI-GN composites to illustrate the composites’ superiority.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0415

The purpose of this paper was to investigate how variations in the geometry of silicon chips and the presence of surface defects affect their static bending properties. By comparing the bending radius and strength across differently sized and treated chips, the study sought to understand the underlying mechanics that contribute to the flexibility of silicon-based electronic devices. This understanding is crucial for the development of advanced, robust and adaptable electronic systems that can withstand the rigors of manufacturing and everyday use.

This study explores the impact of silicon chip geometry and surface defects on flexibility through a multifaceted experimental approach. The methodology included preparing silicon chips of three distinct dimensions and subjecting them to thinning processes to achieve a uniform thickness verified via scanning electron microscopy (SEM). Finite element method (FEM) simulations and a series of four-point bending tests were used to analyze the bending flexibility theoretically and experimentally. The approach was comprehensive, examining both the intrinsic geometric factors and the extrinsic influence of surface defects induced by manufacturing processes.

The findings revealed a significant deviation between the theoretical predictions from FEM simulations and the experimental outcomes from the four-point bending tests. Rectangular-shaped chips demonstrated superior flexibility, with smaller dimensions leading to an increased bending strength. Surface defects, identified as critical factors affecting flexibility, were analyzed through SEM and atomic force microscopy, showing that etching processes could reduce defect density and enhance flexibility. Notably, the study concluded that surface defects have a more pronounced impact on silicon chip flexibility than geometric factors, challenging initial assumptions and highlighting the need for defect minimization in chip manufacturing.

This research contributes valuable insights into the design and fabrication of flexible electronic devices, emphasizing the significant role of surface defects over geometric considerations in determining silicon chip flexibility. The originality of the work lies in its holistic approach to dissecting the factors influencing silicon chip flexibility, combining theoretical simulations with practical bending tests and surface defect analysis. The findings underscore the importance of optimizing manufacturing processes to reduce surface defects, thereby paving the way for the creation of more durable and flexible electronic devices for future technologies.

This study aims to identify the perception of people in a Portuguese company regarding the main barriers to the effectiveness of the existing performance measurement system (PMS) and whether those perceptions are dependent of people’s hierarchical levels, education levels, work shifts, gender and department.

Primary data was collected through structured interviews (adapted to three levels of interviewees in the company hierarchy) and Likert scale questionnaires. Descriptive statistical analysis of the collected data was performed as well as a chi-square test.

The results provide an insight on the perception of barriers to the PMS effectiveness in the company. After performing interviews and questionnaires it was possible to identify that the main perceived barriers were: poor communication system and issues on target definition, lack of trained resources, employee involvement, indicators understanding and use for improvement.

This study is the starting point to develop actions aiming to eliminate, or at least mitigate, the impact of the barriers on the PMS effectiveness.

PMSs play an essential role in an organization, so it is essential to identify what hinders its effectiveness. This study opens the discussion by diagnosing the company’s perception of the barriers to PMS effectiveness.