Search results

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

This study aims to explore the internationalization of multinational enterprises (MNEs) from China and aims to examine the relationship between Chinese MNEs’ duration of internationalization and export intensity, and the contingent roles of the home country government.

By extending the springboard theory with institutional and cost-benefit analyses, the authors elaborate a two-phase framework of internationalization to explain how Chinese MNEs develop their international business under the influences of the home country government. Furthermore, the authors apply the Heckman two-stage method based on a panel data set of 19,994 firm-year observations of Chinese listed firms in 2008–2018 to test the hypotheses.

The research findings demonstrate an inverted U-shape relationship between the duration of internationalization and the export intensity of MNEs from China. The export intensity of MNEs from China increases during the initial phase of internationalization, and decreases during the subsequent. A further study reveals that the inverted U-shape of Chinese non-SOEs is steeper than that of SOEs, and this moderating effect is more salient after the Belt and Road Initiative. These results highlight the influence of the home government through state ownership and policies on the inverted U-shaped relationship.

This study helps to refine the understanding of Chinese MNEs’ global expansion by addressing time as an explicit dimension and revealing the mechanism of state ownership and the home country governmental policy in the dynamic internationalization process.

The present work aims to prepare biocomposites blend based on linear low density polyethylene/ starch without using harmful chemicals to improve the adhesion between two phases. Also, the efficiency of essential oils as green plasticizers and natural antimicrobial agents were evaluated.

Barrier properties and biodegradation behavior of linear low density polyethylene/starch (LLDPE/starch) blends plasticized with different essential oils including moringa oleifera and castor oils wereassessed as a comparison with traditional plasticizer such as glycerol. Biodegradation behavior forLLDPE/starch blends was monitored by soil burial test. The composted samples were recovered then washed followed by drying, and weighting samples after 30, 60, and 90 days to assess the change in weight loss. Also, mechanical properties including retention values of tensile strength and elongation at break were measured before and after composting. Furthermore, scanning electron microscope (SEM) was used to evaluate the change in the morphology of the polymeric blends. In addition to, the antimicrobial activity of plasticized LLDPE/starch blends films was evaluated using a standard plate counting technique.

The results illustrate that the water vapor transition rate increases from 2.5 g m⁻² 24 h⁻¹ for LLDPE/5starch to 4.21 g m⁻² 24 h⁻¹ and 4.43 g m⁻² 24 h⁻¹ for castor and moringa oleifera respectively. Also, the retained tensile strength values of all blends decrease gradually with increasing composting period. Unplasticized LLDPE/5starch showed highest tensile strength retention of 91.6% compared to the other blends that were 89.61, 88.49 and 86.91 for the plasticized LLDPE/5starch with glycerol, castor and M. oleifera oils respectively. As well as, the presence of essential oils in LLDPE/ starch blends increase the inhibition growth of escherichia coli, candida albicans and staphylococcus aureus.

The objective of this work is to develop cost-effective and environmentally-friendly methods for preparing biodegradable polymers suitable for packaging applications.

This study aims to validate the Bolton Forgiveness Scale (BFS) created by Amanze and Carson (2019) for the Indian population.

The data for the validation of the BFS was collected (Total N = 813) in two phases (Phase-I, N1 = 613 and Phase-II, N2 = 200) through online surveys. SPSS 26 and AMOS were used to establish the psychometric properties of the scale through internal consistency and confirmatory factor analysis.

The results indicated the validation of the BFS in the Indian context, with a high internal consistency (a = 0.847). Confirmatory factor analysis validated the factor structure and items, along with face validity.

This study offers comprehensive suggestions on the approaches to forgiveness, addresses biases, advocates for qualitative exploration and emphasizes rigour for the future research on forgiveness.

The present study validates the BFS for future use for the Indian population. The authors offer comprehensive suggestions on the approaches to forgiveness, address biases, advocate for qualitative exploration and emphasize rigour for future research on forgiveness.

Service-learning (SL) is a widely accepted pedagogy that can enrich the learning experience for students in higher education while they apply their skills in a meaningful community service. This research is part of a larger project that aimed to motivate educational achievement among youths living in a priority neighborhood through SL. Toward this goal, this study investigated the impact of SL on the college students from a college information technology programmer-analyst (ITPA) program, who were deployed as role models to youths in a priority neighborhood on the east coast of Canada.

The overall project used the design-based methodology. Seven college students were deployed in two phases to a community center as role models for the delivery of science, technology, engineering, art and mathematics (or STEAM) programs to youths living in a priority neighborhood. Data were collected using open-ended survey, journal entries and focus groups and was qualitatively analyzed by drawing on two frameworks: the experiential-learning framework by Kolb (1984) and the conceptual SL framework by Ash and Clayton (2009).

The findings describe the outcome of the college students' experiences in SL with respect to the development of skills and capacities needed by employers. Specifically, their experiences mirrored all aspects of the two frameworks applied. Therefore, the study validates the use of SL pedagogy in higher education. In addition, the study identified the role of SL as an integration strategy for international students. While the research contributes to the wider SL conversation for policymakers, faculty and administrators of higher education, it also promotes development opportunities for college students.

The integration of SL pedagogy is widespread among programs in higher education. However, there are no common SL frameworks used in literature. The study is novel in that it combines two theoretical frameworks – Kolb (1984) and Ash and Clayton (2009) in explaining the outcomes. In addition, it uses two high-impact educational practices – SL and role modeling to improve educational attainment for college students.

Cuenca, a World Heritage City, faces urban expansion as residents move to the outskirts, leaving the historic center abandoned and deteriorating. The challenge now is to relocate these spaces into sustainable and cohesive nodes. This research aims to identify cultural facility oversupply in the city center and understand the required usage for heritage buildings to promote a habitable, sustainable and cohesive historic center.

The study consisted of two phases. Firstly, a georeferenced spatial analysis and monthly usage frequency of each facility is proposed. Secondly, interviews explored the criteria for designating heritage buildings as cultural facilities. Additionally, a survey assessed urban habitability in three historic center parishes, measuring aspects like coverage, satisfaction and security from residents' perspectives.

The underutilization of cultural facilities demonstrates both inefficient heritage management and a lack of resident interest in cultural activities and neighborhood decision-making. Thus, ensuring collective ownership of heritage assets becomes crucial. Additionally, the municipality's approach to heritage must be reconsidered. While implementing a cultural program may seem faster and cheaper, the long-term cost-benefit of maintaining a cohesive historical center outweighs that of a dispersed city.

This paper calls for a fundamental reimagining of the concept of built heritage, emphasizing the need for a more inclusive and integrated approach that goes beyond museum and tourism-driven strategies. This perspective recognizes the importance of social, cultural and environmental sustainability in revitalizing the historic center, considering the broader context of the city and its diverse inhabitants.

Green, lean and six sigma (GLSS) practices are widely used and well accepted techniques that have the capability to improve the economic, social and environmental performance of Pakistani small and medium enterprises (SMEs). However, implementation of these practices in an integrated approach has not yet been witnessed in Pakistani SMEs due to a variety of challenges. To overcome the implementation challenges, this study has analyzed the impact of GLSS implementation over the sustainability in Pakistani SMEs.

This study consists of two phases. In phase 1, interviews were conducted to scrutinize the elements of GLSS implementation to simplify the model and in phase 2, a questionnaire survey was conducted to collect the data from the SMEs. The partial least squares structural equation modeling (PLS-SEM) approach is used to analyze the relationships among the latent variables and constructs.

Results showed that, leadership for GLSS, understanding GLSS techniques and technology upgradation are considered the most important elements for GLSS implementation in Pakistani SMEs. The environmental and social perspectives have been given more weightage compared to economical perspective. This inferred that there is a need to focus more on environmental and social perspectives in SMEs as compared to economic perspectives to achieve sustainable growth. Moreover, the results of the hypothesis testing revealed that GLSS implementation has a significant positive impact over the sustainability in SMEs considering the Pakistani scenario (β = 0.529, STDEV = 0.078, t = 6.81, p = < 0.001).

This study is the first of its kind for Pakistani SMEs. The structural model developed in this study for Pakistani SMEs will help practitioners to understand the important elements of GLSS implementation and sustainability dimensions.

The present research aimed to examine the role of FDP in enhancing the teachers' competencies through modern technological tools and innovative teaching methods. And also, to evaluate the impact of FDPs on teachers' attitudes and motivation to integrate modern teaching innovations into their classroom practices.

Using a pre-post study design, the study was conducted in two phases, Phase I was conducted one week before conducting FDP (14th to 20th August 2023) and Phase II was conducted after one week of FDP (15th to 21st September 2023). The data were gathered through structured questionnaires from the participants in FDP and collected data were processed and analyzed through a statistical package for social sciences.

The study found that undergoing FDP by teachers significantly influences the adoption of advanced pedagogical approaches and technology integration during their teaching practice. This indirectly helps in promoting quality education at large and contributes to the UNSDG-4. The study also revealed that the effective FDP not only helps in advancing teaching methods but also influences instructors' philosophies and attitudes towards students' overall learning and development.

These findings have several key implications for higher education institutions, policymakers and teachers at large. Firstly, HEIs should prioritize undertaking FDPs that promote in adopting advanced pedagogical approaches. This indirectly equips the instructors with the knowledge and skills necessary to integrate modern teaching methods into their real classroom practices. Further, by adopting FDP as a strategic initiative to improve the quality of education and work by aligning these programs with broader SDG goals.

Advancing education for enhancing the overall quality of learning depends on the competencies of instructors. The competencies of instructors rely upon the involvement of instructors in life-long learning, level of adoption of technology, innovative approaches used, etc. This study may help higher education institutions adopt FDP as a strategic initiative to improve the quality of education and work by aligning these programs with broader SDG goals.

This study focuses on developing a scale that encompasses aspects of readiness to change by library management in dealing with the next normal scenario in libraries. It investigates measuring change readiness in terms of library service operations, workflows, administration, programs and spaces, which can give an overview of a library institution's preparedness to meet new roles and expectations.

The descriptive quantitative research was employed using a two-phase approach. It attempts to design an instrument referred to as change-readiness instrument for library managers (CRILM), which was derived from an original self-developed questionnaire consisting of a 20-item readiness-for-change attributes. Validity and reliability of CRILM were tested using Exploratory Factor Analysis and Cronbach’s Alpha. Sample size calculation was employed to determine the required number of subjects to participate in the study. Library managers who are head librarians from different types of libraries were the target samples. Content validation of the items was conducted for suitability and context.

CRILM was tested both valid and reliable. The initial results yielded from the respondent sample indicated that CRILM is deemed suitable for measuring change-readiness among library managers and libraries. The factor-analyzed items were grouped into three subscales based on their characteristics, namely, flexibility in work and services, learning support readiness and adaptive to changing service environments.

CRILM covered the major domains and constructs of change readiness in the context of a library setting. However, the scale should be subjected to further testing and use by a wider target group to strengthen its usability and confidence level. Item analysis was not employed.

The results of the study offer help to library managers in assessing the readiness of their organizations to effectively manage change and be future-ready.

This study attempts to bridge the gap in readiness-for-change instruments by introducing a scale in the library service environment as a potential contribution in the practice of library management.