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This study aims to investigate the relationship between committee diversity and firm performance among non-financial firms listed on the Pakistan Stock Exchange (PSX).

This research is based on a comprehensive assessment of secondary data retrieved from annual reports of firms listed on the PSX and publications from the State Bank of Pakistan spanning the period from 2012 to 2021. The study used various statistical models, including pooled regression, fixed effects and random effects, to examine the relationship between diversity among committees and firm performance. Firm-specific variables such as return on assets, return on equity and market price per share were used as proxies for firm performance.

The results show that the presence of a female head of the committee on company performance does not show any significant correlation with diversity in board committees. This suggests that the investigation into gender diversity and the appointment of women to leadership positions on these committees is not supported by the findings of this particular sample.

To the best of the authors’ knowledge, this study is the first to investigate the impact of committee diversity on firm performance in Pakistan, one of the Next Eleven countries.

The purpose of this study is to compare the properties and performance of ordinary Portland cement-based self-compacting concrete (OPC-SCC) and pozzolanic-based SCC (pozzolanic-SCC) in concrete applications. The research employs a comparative analysis to examine the workability and strength characteristics of these two types of SCC.

This study involves analyzing and comparing the properties and performance of OPC-SCC and pozzolanic-SCC through a literature review of relevant studies and experiments. The key findings indicate that the use of pozzolanic materials in SCC, such as fly ash, silica fume and metakaolin, can enhance the sustainability and durability of the concrete. The research also reveals that the choice of steel fibers and polypropylene fibers can impact the fire performance and mechanical properties of SCC.

The findings indicate that the inclusion of supplementary cementitious materials enhances the workability, strength and fire resistance of SCC to a greater extent compared to the addition of steel and polypropylene fibers.

The practical implications of this research are significant for selecting and utilizing SCC in concrete applications.

The originality of this research lies in the comparative analysis of OPC-SCC and pozzolanic-SCC, considering their properties, performance and practical implications. The study extends the existing knowledge on the use of SCC and provides insights into best practices for its application. The research contributes to the field of concrete technology and sustainable construction by highlighting the benefits and limitations of different types of SCC and their potential impact on concrete performance.

The current investigation is concerned with the Soret effect along with chemical reaction and radiation on flow of an electrically conductive, viscous fluid through a perpendicular plate, which is porous with oscillatory suction. The aim of this study is to investigate the effects of first-order temperature and chemical reaction and the transverse magnetic field characteristics. The closed form of solutions are obtained using the governing equations for concentration, energy and momentum. The perturbation technique was applied to find the result for the velocity field, temperature profiles and concentration distributions. Furthermore, the impact of various nondimensional parameters on fluid flow variables on the temperature field, velocity field and concentration dispersal was analyzed and the results were depicted graphically. Moreover, the skin friction and the rate of mass transfer (local Sherwood number) were analyzed using tables. In this work, an unsteady 2D flow of a laminar, viscid (Newtonian), electrically conducting fluid across a semi-infinite perpendicular permeable plate under motion in its plane (x-axis) embedded in a constant permeable structure was investigated.

In this work, an unstable 2D flow of a laminar, viscid (Newtonian), electrically conducting fluid across a semi-limitless perpendicular permeable plate under motion in its plane (x-axis) embedded in a constant permeable structure was investigated. The medium is considered to be under a transverse magnetic field with concentrated buoyancy effects. Furthermore, it is considered that no voltage is supplied, which indicates that there is no electrical field. The fluid properties are considered to be uniform. The concentration of the imparting species is considered as C′w at the plate; the concentration of the specimens away from the wall, C′8, is considered to be limitlessly less. The first-order chemical reaction is considered to be seen in the flow. Due to the semi-limitless plane surface considerations, the flow parameters are the functions of y′ and the time t′ only. The oscillatory suction velocity of the fluid at the plate normal to it is v′; initially, the plate relocates with the oscillatory velocity u′, in the direction of x that is in its plane. The pressure gradient is toward the x-axis.

The analytical solutions were obtained using the above analytical method for a few values of the governing parameters, such as the magnetic parameter (M), the permeability parameter (K), Schmidt number (Sc), chemical reaction parameter (Kr), Grashoff number for the concentration (Gm), Radiation parameter (N), Prandtl number (Pr), Chemical reaction parameter (Kr), Grashof number for heat transfer (Gr) and Heat source parameter (s). The influence of M, K, Sc, Kr, Gm, N, Pr, Kr, Gr and s on the fluid velocity, temperature and the concentration over the semi-infinite porous plate was obtained. Furthermore, the numerical computation was carried out using MATLAB.

In this chapter, the analysis of a free convective flow of a viscid compact, electrically conductive fluid was discussed during its flow through a plate in permeable condition with oscillatory suction with first-order temperature and chemical reaction and the transverse magnetic field. The problem formulation and the results were discussed. The following chapter explain the Soret effect of mass transfer and radiation with heat source on magnetohydrodynamics oscillatory viscoelastic fluid in a channel filled with porous medium.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.