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This study aims to investigate the use of CdO nanoparticles with recycled aggregates (RAs) and its effect on the structural behavior of reinforced concrete (RC) slab elements.

The study has been conducted through three phases: in the first phase, the structure of lab-synthesized CdO nanoparticles was investigated and then cement was partially replaced by CdO nanoparticles to estimate the optimum dose. The second phase focused on the properties of the RA collected from demolition wastes. In the third phase, RC slabs with different concrete mixes using RA and CdO nanoparticles were experimentally tested.

The results indicated good effect of using CdO nanoparticles with RA to improve the RC slab specimens’ behavior compared with the control specimen.

In present times, nanoparticles have a promising importance in the construction field. The influence of nanoparticles on the compressive strength of the concrete has been investigated by many researchers, but using it with RA is considered a new topic.

The “Germanwings” air crash in 2015 in which 150 people were killed highlighted the challenges pilots working in the aviation industry face. Pilots regularly work for extensive periods in inhospitable and high-pressure operational conditions, exposing them to considerable work-related stress. This has raised calls for a more systemic cultural change across the aviation industry, championing a more holistic perspective of pilot health and well-being. The study aims to explore how peer coaching (PC) can promote an inclusive psychosocial safety climate enhancing pilot well-being and can mitigate hazardous attitudes and dysfunctional behaviours.

Adopting an interpretative phenomenological analysis (IPA), semi-structured interviews and questionnaires were conducted with military and civilian peer coach/coachee pilots and key industry stakeholders, totalling 39 participants. The research provided significant insights into the perceived value of PC in promoting both pilot health and mental well-being (MW) and flight safety across the aviation industry.

The study highlights four key PC superordinate themes, namely, coaching skills, significance of well-being, building of peer relationships and importance of confidentiality and autonomy. Such combined themes build reciprocal trust within peer conversations that can inspire engagement and effectively promote personal well-being. The contagious effect of such local interventions can help stimulate systemic cultural change and promote a positive psychosocial safety climate throughout an organisation and, in this case, across the aviation industry. This study provides a PC conceptual framework “Mutuality Equality Goals Autonomy Non-evaluative feedback, Skill Confidentiality Voluntary Supervisory (MEGANS CVS),” highlighting the salient features of PC in promoting MW.

The study highlights the salient features of PC and its role in promoting peer conversations that enable personal transition, openness and acceptance. This study also highlights how PC and well-being can be used to encourage inclusivity and engagement, thereby strengthening institutional resilience.

This study highlights how PC that can assist HRM/HRD professionals to embed a more inclusive and salutogenic approach to MW that can reshape organisational cultures. This study highlights the significance and link of workplace stress to hazardous attitudes and dysfunctional behaviours. It further notes that whilst the MEGANS CVS peer coaching framework has been applied to pilots, it can also be applied across all sectors and levels.

This study highlights the value of PC as an inexpensive means to engage at the grassroots level, which not only improves personal performance, safety and well-being but by building peer relationships can also act as a catalyst for positive and deep organisational cultural change.

This study offers the MEGANS CVS framework that exposes insights into PC practice that can assist HRM/HRD professionals embed a more inclusive and salutogenic approach to health and well-being that can reshape organisational cultures. This study highlights the significance and link of workplace stress to hazardous attitudes and dysfunctional behaviours, and whilst this framework has been applied to pilots, it can also have relevance across all sectors and levels. This study calls for a “salutogenic turn,” employing MW and PC to transform organisational capabilities to be more forward-thinking and solution-focused, promoting an inclusive “just culture” where leaders positively lead their people.

The engineering courses offered in Somali universities attract many students, ranging between 300 and 500 every semester, making the management and delivery of the course challenging. The increasing popularity of massive open online courses (MOOCs) has led to rapid growth in enrollment, posing difficulties in effectively managing and delivering content to large volumes of learners. To this end, this study aimed to explore the influence of MOOC implementation factors on learners’ continuance intention and satisfaction to provide insights that can enhance the learning experience and ensure long-term engagement.

The study utilized a survey approach based on an extensive literature review to collect data on the challenges faced by Somali universities in managing and delivering engineering courses. The survey included a series of questions, and 148 responses were collected from students enrolled in different programs. The collected data were analyzed using partial least squares-structural equation modeling and deep neural network approaches.

The result demonstrated that MOOC implementation factors, including course design quality, instructor reputation, self-paced flexibility, information relevance, platform usability and student support services, significantly affect students’ continuance intention and satisfaction. Therefore, the study recommends universities should enhance MOOC implementation factors to improve the quality of teaching and increase students’ continuance intention to study in a MOOC environment.

The study provides empirical evidence on how MOOC implementation factors affect the level of satisfaction and continuance intention of engineering students. It suggests that the findings could be useful for university management and lecturers to increase teaching and learning quality in the course and develop new strategies and approaches that suit modern-day learners. The study also aims to enhance the efficiency and effectiveness of class delivery and improve student engagement in the learning process.

The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.

This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R²], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.

The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R², RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.

This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.

The stormwater industry grapples with numerous environmental challenges resulting from producing and using storm materials. Green building materials (GBMs) offer a more ecologically friendly alternative to conventional construction materials. However, establishing criteria for selecting GBMs and assessing their sustainability has proven to be a complex endeavor. Therefore, this paper aims to assess the suitability of GBMs in stormwater management projects.

This study investigates and identifies the green storm drainage materials criteria based on previous literature and an extensive survey involving 140 stakeholders from the Egyptian industry, including facilities managers, asset managers, engineers and policymakers. A comprehensive model employing partial least squares structural equation modeling and artificial neural network is developed to assess the suitability of GBMs in stormwater management projects.

The study’s findings emphasize the pivotal role of social factors in the practical implementation of green material selection criteria. Understanding the intricate interplay among economic, environmental and social dimensions becomes crucial as stakeholders in the stormwater industry navigate the transition toward sustainable storm materials.

This research highlights the importance of integrating social factors into decision-making, contributing to more holistic and effective strategies for sustainable stormwater management. The study’s originality lies in its innovative approach to assessing the suitability of GBMs in stormwater management projects and its novel insights into the complex dynamics of sustainable materials selection, addressing a significant research gap in the field.

This paper aims to examine the factors affecting the implementation of corporate social responsibility (CSR) sustainably in mobile operators in the kingdom of Bahrain.

The research relies on the existing literature as a secondary data source. The primary data was collected through questionnaires distributed to three leading mobile operators in the Kingdom of Bahrain. The research's population numbered 1,689, and the sample size was 313; the simple random sampling method was used for data collection with a response rate of 87.2% out of a total sample size of 273 respondents. In addition, the responses were analyzed using statistical package for the social sciences (SPSS) software version 24.0; specifically, Spearman's rank correlation was used to test the hypotheses. The research design was quantitative, so a nonparametric procedure was applied to test the hypotheses.

The research produced positive relationships between the independent variables (economic, legal, ethical, philanthropic and environmental responsibilities) and the dependent variable: CSR implementation by Bahrain mobile operators; therefore, the five proposed hypotheses were accepted; furthermore, the highest positive correlation coefficient was 0.735 for environmental responsibility, and the lowest correlation coefficient was 0.533 for economic responsibility.

The research produced positive relationships between the independent variables (economic responsibility, legal responsibility, ethical responsibility, philanthropic responsibility and environmental responsibility) and the dependent variable: CSR implementation by Bahrain mobile operators; therefore, the five proposed hypotheses were accepted; furthermore, the highest positive correlation coefficient was 0.735 for environmental responsibility, and the lowest correlation coefficient was 0.533 for economic responsibility.

The outcomes of the research mainly suggest that mobile operators assign employees who have been working with the company for more than 7 years to implement CSR; due to their engagement to implement CSR as a sustainable practice more than others with less than 3 years or 3-6 years in Bahrain mobile operators. Besides, the research provides a starting point by which other researchers could investigate CSR in other sectors in the Kingdom of Bahrain.

The research provided a framework for Bahrain mobile operators to assist them in enhancing the implementation of CSR in a sustainable manner, which are economic, legal, ethical, philanthropic and environmental responsibilities.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

High surface area-to-volume ratios make nanoparticles ideal for cancer heat therapy and targeted medication delivery. Moreover, ternary nanofluids (TNFs) may possess superior thermophysical properties compared to mono- and hybrid nanofluids due to their synergistic effects. In light of this information, the objective of this article is to examine the blood-based TNF flow within convergent/divergent channels under velocity slip and temperature jump.

Leading partial differential equations corresponding to the problem are transformed into a system of nonlinear ordinary differential equations by using similarity variables. The bvp4c code that uses the finite difference method is used to obtain a numerical solution.

The effect of nanoparticles may change depending on the characteristics of flow near the wall. The properties and proportions of the used nanoparticles become important to control the flow. When TNF was used, an increase in the Nusselt number between 4.75% and 6.10% was observed at low Reynolds numbers. At high Reynolds numbers, nanoparticles reduce the Nusselt number and skin friction coefficient values under some special flow conditions. Importantly, the effects of second-order slip on engineering parameters were also investigated. Furthermore, the Nusselt number increases with increasing shape factor.

Obtained results of the study can be beneficial in both nature and engineering, especially blood flow in veins.

The main innovations of this study are the usage of blood-based TNF and the examination of the effect of shape factor in convergent/divergent channels with second-order velocity slip.

This study aims to analyze the two-dimensional ferrofluid flow in porous media. The effects of changes in parameters such as permeability parameter, buoyancy parameter, Reynolds and Prandtl numbers, radiation parameter, velocity slip parameter, energy dissipation parameter and viscosity parameter on the velocity and temperature profile are displayed numerically and graphically.

By using simplification, nonlinear differential equations are converted into ordinary nonlinear equations. Modeling is done in the Cartesian coordinate system. The finite element method (FEM) and the Akbari-Ganji method (AGM) are used to solve the present problem. The finite element model determines each parameter’s effect on the fluid’s velocity and temperature.

The results show that if the viscosity parameter increases, the temperature of the fluid increases, but the velocity of the fluid decreases. As can be seen in the figures, by increasing the permeability parameter, a reduction in velocity and an enhancement in fluid temperature are observed. When the Reynolds number increases, an increase in fluid velocity and temperature is observed. If the speed slip parameter increases, the speed decreases, and as the energy dissipation parameter increases, the temperature also increases.

When considering factors like thermal conductivity and variable viscosity in this context, they can significantly impact velocity slippage conditions. The primary objective of the present study is to assess the influence of thermal conductivity parameters and variable viscosity within a porous medium on ferrofluid behavior. This particular flow configuration is chosen due to the essential role of ferrofluids and their extensive use in engineering, industry and medicine.