Relationship between perceived occupational stress and psychological well-being among secondary school heads in Khyber Pakhtunkhwa, Pakistan.

The purpose of the study was to examine the relationship between perceived occupational stress and psychological well-being among secondary school heads in Khyber Pakhtunkhwa. A sample of 402 secondary school heads (male n = 260, female n = 142) was selected through multistage sampling technique. A descriptive, quantitative and correlative research design was used. For gathering information from the participants, two standardized tools i.e., "Occupational Stress Index (OSI)" and "Ryff's Psychological Wellbeing Scale (RPWB)" were used for measuring perceived occupational stress and psychological well-being respectively. For statistical analysis, mean, standard deviation, Pearson's product-moment correlation and multiple regression were employed. The findings revealed that there is a strong negative correlation between perceived occupational stress and psychological well-being. Furthermore, moderate negative correlation was found between all the sub-scales of perceived occupational stress and psychological well-being. All the subscales of occupational stress except low status were found significant predictors and have negative effect on psychological well-being of secondary school heads. So, it was suggested that Elementary & Secondary Education Department Khyber Pakhtunkhwa should have a collaboration with policy makers to formulate a comprehensive strategy for stress reduction management for secondary school heads so that they may develop good psychological well-being and perform their duties effectively

Investigating the feasibility of producing sustainable and compatible binder using marble waste, fly ash, and rice husk ash: A comprehensive research for material characteristics and production

This research aims to develop a new marble-based binding material for evaluating its strength blended with rise husk ash and fly ash. Newly developed marble-based cement was prepared by burning waste marble powder and clay. The marble cement was then blended separately with varying amounts of fly ash (20, 30, and 40% by mass of marble cement) and rice husk ash (20, 30, and 40% by mass of marble cement) to find the most suitable combination for mortar in terms of strength gain. The mortar specimens were subjected to various load tests, including compressive and flexural strength, and also to various morphology and microstructural tests including X-Ray diffraction, thermo-gravimetric, and scanning electron microscopy analyses. According to the results, the compressive strength of the marble cement mortar was less than ordinary Portland cement mortar but greater than the M1 mortar (5 MPa, minimum compressive strength of mortar for brick masonry) as per the Building Code of Pakistan 2007 (BCP:2007) and Indian Standards (IS:1905). Blended marble cement mortars displayed improved strengths, yet the early strength of blended mortars was lower than Portland cement mortar due to higher di-calcium silicate content and slow pozzolanic activity, but the later-age strength of mortar prepared with the marble cement blended with 30% rice husk ash was found marginally higher than the ordinary Portland cement mortar. Moreover, incorporating marble waste, rice husk ash, and fly ash as binding materials to manufacture building materials will encourage sustainable growth by reducing environmental issues associated with their disposal

Experimenting the influence of corncob ash on the mechanical strength of slag-based geopolymer concrete

The construction sector has been under growing public attention recently as one of the leading causes of climate change and its detrimental effects on local communities. In this regard, geopolymer concrete (GPC) has been proposed as a replacement for conventional concrete. Predicting the concrete’s strength before pouring is, therefore, quite useful. The mechanical strength of slag and corncob ash (SCA–GPC), a GPC made from slag and corncob ash, was predicted utilizing multi-expression programming (MEP). Modeling parameters’ relative importance was determined using sensitivity analysis. When estimating the compressive, flexural, and split tensile strengths of SCA–GPC with MEP, 0.95, 0.93, and 0.92 R2-values were noted between the target and predicted results. The developed models were validated using statistical tests for error and efficiency. The sensitivity analysis revealed that within the mix proportions, the slag quantity (65%), curing age (25%), and fine aggregate (3.30%) quantity significantly influenced the mechanical strength of SCA–GPC. The MEP models result in distinct empirical equations for the strength characteristics of SCA–GPC, unlike Python-based models, which might aid industry and researchers worldwide in determining optimal mix design proportions, thus eliminating unneeded test repetitions in the laboratory.Validerad;2024;Nivå 2;2024-04-09 (joosat);Funder: Microbial Deposited Calcium Carbonate Reinforced Recycled Fine Aggregate (GZY2021-NEW-06); Najran University (NU/RG/SERC/12/1);Full text license: CC BY</p

Research evolution on self-healing asphalt: A scientometric review for knowledge mapping

This study employed a novel approach by analyzing the self-healing asphalt literature based on scientometric analysis. The most difficult components of modern study are the mapping and analysis of knowledge, co-citations, and co-occurrences. Scopus was searched to find the necessary data for the analysis, which consisted of keywords, abstracts, citations, and bibliographic information. Throughout the data assessment process, the most prolific research locations, the most often referenced articles, and the most influential authors in the field of self-healing asphalt were analyzed, along with their correlations. The need for self-repairing asphalt was also emphasized, along with the main problems of using it. The keyword analysis showed that researchers have studied self-healing asphalt for crack repair in asphalt road pavements as a sustainable maintenance method. The literature study showed that heating and encapsulating rejuvenating chemicals are two techniques of self-healing asphalt. Encapsulation uses rejuvenating capsules, whereas the heating technique uses induction heating and microwave radiation. Researchers have also developed hybrid asphalt self-healing methods as enhanced self-healing for asphalt. Academics may benefit from the quantitative assessment of regions and researchers as well as the scientific description of these areas in order to form joint initiatives and spread new ideas and approaches

Promoting the suitability of rice husk ash concrete in the building sector via contemporary machine intelligence techniques

Eco-friendly concrete is in great demand, and as a consequence, the necessity to find sustainable alternatives to ordinary cement has become critical. In recent years, there has been considerable interest in the potential benefits of utilizing rice husk ash (RHA) as a cement substitute in concrete. Evaluating concrete properties in a laboratory setup like compressive strength (CS) is a time-consuming and expensive process. The development of trustworthy and precise models for CS estimation might be a better strategy. To determine the CS of RHA concrete, this research used boosting ensemble algorithms, namely gradient boosting (GB), AdaBoost regressor (ABR), and extreme gradient boosting (XGB), over a vast literature database. The estimation models were validated using statistical measures, error distribution by plotting violin graphs, and k-fold analysis. Furthermore, SHapley Additive exPlanations (SHAP) analysis was utilized to assess the importance of contributing elements. The results showed that XGB had the greatest performance in estimating the CS of RHA concrete out of the three methods tested. While the GB and ABR models both had R2 values of 0.90 and 0.94, respectively, the XGB model achieved a value of 0.96. The violin graphs indicated that the average absolute error values for the GB, ABR, and XGB were 5.82, 4.65, and 3.53 MPa, implying the higher precision of the XGB model in estimating the CS of RHA concrete. The SHAP study demonstrated that the three most influential factors in increasing the strength were cement, specimen age, and rice husk ash. The construction sector may benefit from the application of such technologies by facilitating the development of quick and low-cost methods for identifying material qualities and the influence of input parameters

A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis

This research used gene expression programming (GEP) and multi expression programming (MEP) to determine the compressive strength (CS) of alkali-activated material (AAM) to compare and develop more reliable genetic algorithm-based prediction models. To learn more about how raw ingredients affect and interact with the CS of AAM, a SHapley Additive exPlanations (SHAP) analysis was conducted. A comprehensive dataset containing 676 points with fifteen influential parameters was formulated from the previously published literature. According to this study, considering the impact of 15 input variables, both genetic algorithms produced results close to the experimental CS (retrieved from the literature). When the performance of the GEP and MEP models were compared, it was found that the MEP model, with an R2 of 0.86, performed better than the GEP model, with an R2 of 0.82. The assessment of the statistical parameters of generated models revealed that the MEP model was more effective. Additionally, SHAP analysis revealed that slag content, followed by the specimen's age, sodium silicate, and curing temperature, showed a positive correlation with CS of AAM, which were the most important parameters. The results also revealed the importance of chemical contents, i.e., CaO, SiO2, Al2O3, of FA and slag on the CS of AAM. The built models might be used to compute the CS of AAMs with varying input parameter values, minimizing the effort, time, and cost of unnecessary lab tests. Furthermore, the outcomes of the SHAP study might help researchers and the industry determine the quantity or composition of raw ingredients when producing AAMs