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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.

Intense interest has been shown in creating new and effective biocide agents as a result of changes in bacterial isolates, bacterial susceptibility to antibiotics, an increase in patients with burns and wounds and the difficulty of treating infections and antimicrobial resistance. Woven, nonwoven and knitted materials are used to make dressings; however, nonwoven dressings are becoming more popular because of their softness and high absorption capacity. Additionally, textiles have excellent geometrical, physical and mechanical features including three-dimensional structure availability, air, vapor and liquid permeability, strength, extensibility, flexibility and diversity of fiber length, fineness and cross-sectional shapes. It is necessary to treat every burn according to international protocol and along with it has to focus on particular problems of patients and the best possible results.

The objective of this paper is to conduct a thorough examination of research pertaining to the utilization of textiles, as well as alternative materials and innovative techniques, in the context of burn wound dressings. Through a critical analysis of the findings, this study intends to provide valuable insights that can inform and guide future research endeavors in this field.

In the past years, there have been several dressings such as xeroform petrolatum gauze, silver-impregnated dressings, biological dressings, hydrocolloid dressings, polyurethane film dressings, silicon-coated nylon dressings, dressings for biosynthetic skin substitutes, hydrogel dressings, newly developed dressings, scaffold bandages, Sorbalgon wound dressing, negative pressure therapy, enzymatic debridement and high-pressure water irrigation developed for the fast healing of burn wounds.

This research conducts a thorough analysis of the role of textiles in modern burn wound dressings.

This paper examines the post-COVID-19 travel intentions to Kenya among Hong Kong outbound travelers using the theory of planned behavior (TPB) over three different time horizons of 1, 5, and 10 years.

An extension was made by including two new constructs of perceived destination image and travel constraints. A cross-sectional sample of Hongkongers was surveyed. Data were collected using a self-administrated bilingual (English and Chinese) online survey. Exploratory factor analysis, linear regression and mediation analysis were conducted to test the research model.

The findings from 216 Hongkongers reveal that different combinations of the four constructs, namely, perceived behavioral control, attitude, subjective norms, and destination image, share a positive effect on individuals' travel intention to Kenya over the three different time horizons. Travel constraints act as a significant negative mediator on the four constructs in predicting travel intention to Kenya among Hongkongers.

The results provide useful insight to Kenya's destination marketing organization (DMO) and Hong Kong outbound travel agencies to integrate prominent elements into marketing strategies to arouse travel intention and expand their business prospects, which will also accelerate tourism recovery in the post-pandemic era.

By integrating two extended variables into the TPB model, this study makes a contribution by overcoming the deficiency of the original theory.

As of 2022, the scope of the engagement and interest of debt capital providers in ESG reporting is mainly untapped. However, a vast amount of literature has produced conflicting findings about the importance of debt capital (leverage) as a factor in sustainability reporting (SR). This is the first meta-analysis reconciling the mixed results of 85 single country studies containing 131 effect sizes across 24,482 firms conducted over past three decades (1999–2022) investigating the influence of leverage on SR. The study emphasizes the significance of contextualizing research by identifying the macro-environmental elements modifying debt's impact on SR, through the use of the institutional theory. Eleven country variables were tested on the collected dataset, spread across 36 countries.

Meta-analysis technique for aggregation of existing extant empirical work. Continuous and categorical variable-based moderator analysis to demystify the influence of country characteristics affecting the leverage–SR relationship.

Results show positive significant impact of debt capital providers on SR. Country's level of development, GDP, extent of capital constraints in a country, financial sector development within a nation, country governance factors and corruption levels, country's culture, number of sustainability reporting instruments operational in a country and geographical location proved to be significant moderators.

The study details relevant meaningful research gaps, worthy of uptake by researchers to produce targeted research.

Governments must increasingly go beyond their mandated disclosure role and acknowledge the important institutional factors that have contributed to the expansion of ESG reporting through the creation of nation-specific tools, incentive structures and disclosure-encouraging regulations. To secure a steady flow of funding and prevent negative effects on company value and cost of capital in the midst of prolonged global economic upheaval, businesses must address the information requirements of lenders. The limited total effect size emphasizes the necessity for debt providers to step up their ESG activism and exercise their maximum power and potential in stimulating extensive SR firm-level practices.

The present study is the first meta-analysis reconciling the mixed results of 85 single-country studies containing 131 effect sizes across 24,482 firms conducted over the past three decades (1999–2022) investigating the influence of leverage on SR and demystifying the macro-environmental factors affecting the leverage–SR association.

With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students.

Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers’ responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments.

Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being “messy,” as requiring more work for the student or analyst to pore through than other data sets and as involving computation.

Analysis of teachers’ aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.

This study developed new measures of the routine and non-routine task contents of managerial, professional, technical, and clerical occupations from a workload perspective. Here, we present a comparative analysis of the workload structures of state and industrial sector employees.

Our method involves detailed descriptions of work processes and an element-wise time study. We collected and analysed data to obtain a workload structure that falls within three conceptual task categories: (i) non-routine analytic tasks, (ii) non-routine interactive tasks and (iii) routine cognitive tasks. A total of 2,312 state and industrial sector employees in Kazakhstan participated in the study. The data were collected using a proprietary web application that resembles a timesheet.

The study results are consistent with the general trend reported by previous studies: the higher the job level, the lower the occupation’s routine task content. In addition, the routine cognitive task contents of managerial, professional, technical, and clerical occupations in the industrial sector are higher than those in local governments. The work of women is also more routinary than that of men. Finally, vthe routine cognitive task contents of occupations in administrative units are higher than those of occupations in substantive units.

Our study sought to address the challenges of using the task-based approach associated with measuring tasks by introducing a new measurement framework. The main advantage of our task measures is a direct approach to assessing workloads consisting of routine tasks, which allows for an accurate estimation of potential staff reductions due to the automation of work processes.

The purpose of this study was to ascertain the relationship between EFL teachers' perception of the intended and unintended consequences of formative assessment (FA) decisions and their sense of self-efficacy and anxiety toward data-driven decision-making (DDDM).

A correlational research design and correlational/regression analysis was utilized to conduct this study. In addition, a thematic analysis was conducted of participants' responses to the open-ended questions.

Descriptive statistics suggest that most EFL teachers perceived both intended and unintended consequential validity of their FA decisions with a moderate level of DDDM self-efficacy and a low level of anxiety. The results of the correlational analysis indicated a strong positive relationship indicating that those with higher teachers' perceptions of consequential validity of formative assessment (TPCVFA) scores tended to report higher DDDM efficacy. The results of the correlational analysis also indicated that a significant relationship did not exist between TPCVFA scores and DDDM anxiety. Finally, multiple regression analyses revealed that TPCVFA was a significant predictor of DDDM efficacy; however, TPCVFA was not a significant predictor of DDDM anxiety.

Data collected from 114 Iranian EFL teachers using the Data-Driven Decision-Making Efficacy and Anxiety Inventory (3D-MEA, Dunn et al. 2013a) and the TPCVFA questionnaire, which was self-developed and validated for the current study.

This study aims to, firstly, develop a red flag checklist for cryptocurrency Ponzi schemes and, secondly, to test this red flag checklist against publicly available marketing material for Mirror Trading International (MTI). The red flag checklist test seeks to establish if MTI’s marketing material posted on YouTube^® (in the form of a live video presentation) exhibits any of the red flags from the checklist.

The study uses a structured literature review and qualitative analysis of red flags for Ponzi and cryptocurrency Ponzi schemes.

A research lacuna was discovered with regard to cryptocurrency Ponzi scheme red flags. By means of a structured literature review, journal papers were identified that listed and discussed Ponzi scheme red flags. The red flags from the identified journal papers were subsequently used in a qualitative analysis. The analyses and syntheses resulted in the development of a red flag checklist for cryptocurrency Ponzi schemes, with five red flag categories, containing 18 associated red flags. The red flag checklist was then tested against MTI’s marketing material (a transcription of a live YouTube presentation). The test resulted in MTI’s marketing material exhibiting 88% of the red flags contained within the checklist.

The inherent limitations in the design of using a structured literature review and the lack of research regarding the cryptocurrency Ponzi scheme red flags.

The study provides a red flag checklist for cryptocurrency Ponzi schemes. The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags.

The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags.

The study provides a red flag checklist for cryptocurrency Ponzi schemes.

The purpose of this study is to systematically examine the existing literature published on the circulation methods used in academic libraries and to discuss a proposed model for the self-check-in and check-out methods using quick response (QR) codes.

A systematic literature review (SLR) provided a complete overview of circulation systems used in academic libraries for the last more than 140 years. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) method for SLR was used. Several databases such as ProQuest, Emerald, Library and Information Science Abstracts, EBSCO and Google Scholar were searched. Out of 277 papers retrieved in the search, 43 most relevant papers were taken up for the SLR. These were classified into four themes based on the systems of circulation used, namely, manual (5), mechanized (13), automated (5) and networked system (20). Based on the findings of the SLR, a model of circulation system in which QR code technology has been used.

The result of SLR identified 33 systems used in above mentioned four groups. Among this, a large number 48.48% of mechanized systems were found. Each system has limitations either because of devices used or of technology. The present study proposes a QR code-based model at the circulation desk, for which a prototype of Android app has been designed. This would help to initiate the new “self-services” facility to users at the circulation desk.

The proposed model, after its successful implementation, can be adopted by academic libraries. Guidelines and a graphical representation of this study can be used by any researcher for further experimentation.

To the best of the authors’ knowledge, this is the first kind of study in which a QR code-based Android app model has been proposed for library circulation records.

Identifying the criteria that effectively drive innovation in universities is critical to assessing their innovation maturity level, and hence, planning for the improvements required to reach a target level. This paper aims to propose a three-phase approach to develop a multidimensional maturity assessment framework used by university decision-makers to determine their level of innovation readiness.

First, a systematic collection of evaluation criteria from the literature is conducted. The results are mapped into different categories in a hierarchical and multidimensional way, and validated by experts. The second phase aims to identify the critical factors and their priorities, which are determined using analytic network process (ANP). To facilitate that, a panel of thirteen experts is formed and questionnaires are sent to rank the importance of the criteria and their elements. Finally, a maturity assessment tool is developed to complement the framework, allowing decision-makers to determine the level of innovation maturity with respect to each dimension and the overall position.

Results revealed three clusters, eight criteria and 26 subcriteria related to innovation in universities. The findings about the relative importance of the various attributes are reflected in the developed assessment tool and taken into consideration in the maturity indices computation approach.

To the best of the authors’ knowledge, this is the first attempt to develop a comprehensive list of innovation success drivers in universities and to use this list to design an innovation maturity assessment framework