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This chapter discusses the extension of legal equality between male and female citizens in four states in North Africa – Tunisia, Egypt, Morocco and Algeria – through one specific lens: A married woman's legal capacity to initiate and obtain divorce without the husband's consent. Building on the works of Stein Rokkan and Reinhard Bendix on the expansion of citizenship to the ‘lower classes’, it is argued that amendments in divorce law by introducing in-court divorce for women, in addition to out-of-court divorce, is a significant institutional change that extends legal equality between men and women. The introduction of in-court divorce expands female citizenship by bolstering woman's juridical autonomy and capacity in state law. Changes in divorce laws are thus part of state centralization by means of standardizing rules that regulate family law through public administrative institutions rather than religious organizations. Two questions are addressed: First, how did amendments in divorce laws occur after independence? Second, in which ways did women's bolstered legal capacity in divorce have a spill over effect on reforms in other patriarchal state laws? Based on observations on sequences of change in four states in North Africa, it is argued that amendments that equalize between men and women in divorce should be seen as a key driver for reforms in other state laws, that reduce legal inequality between male and female citizens. In all four states, women's citizenship was extended in nationality law and criminal law after amendments in divorce law gave women unilateral legal power to exit a marital relationship.

The purpose of variable loading conditions (392 N-785N-392N-785N) with break-in period were used to study interactions between zinc dialkyl dithiophosphate (ZDDP) 0.1 P% (phosphorus) and fine-grade molybdenum disulfide (MoS₂) 3%, in different mixtures of NLGI 2 lithium stearate grease. Four-ball wear tests were used to evaluate the tribological properties of different grease mixtures such as coefficient of friction and wear. ASTM 2266 as reported by earlier studies is useful, but it is not representative of real-life applications where variable loads and speeds and different break-in periods play a role and could change the results and the nature of tribofilms.

In this study, chemical and mechanical properties of tribofilms were examined. Moreover, design of experiment was used to examine the data and shorten experimentation time. Research described here is investigating variable loading conditions for real-life applications by using a break-in period of 2 min at the start to minimize asperities and establish a clean surface. Design expert (DOE) analyzes responses to reveal those variables that are single factor and those that are multifactor whether synergistically or antagonistically.

The results indicated that spectrum loading with break-in period showed reduction in wear when tested in greases with ZDDP/MoS₂ combinations. Ramping up or down the load every 7.5 min for a rotational speed of 1,200 rpm and a total of 36,000 revolutions or 30-min time slowed the wear properties of lithium-based grease under different MoS₂ and ZDDP concentrations. Experiments indicated that wear was largely dependent on the loading condition and ZDDP additives during specific break-in period at 1,200 rotational speed. It is believed that MoS₂ greases perform better under spectrum loading and under constant loading when mixed with ZDDP phosphorus.

This research indicates that there is a synergistic interaction between ZDDP, MoS₂ and variable loading especially when a break-in period is applied. The results indicated that wear was largely dependent on the specific speed used with spectrum loading as presented in the energy dispersive spectroscopy and the Auger electron spectroscopy analysis, and thus a 3% MoS₂ grease with ZDDP (phosphorus: 0.1 Wt.%) are needed to improve the wear resistance and improve the friction characteristics.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0016/

The purpose of this study is to highlight and demonstrate how the study of stress and related responses in construction can best be measured and benchmarked effectively.

A range of perceptual and physiological measures are obtained across different time periods and during different activities in a fieldwork setting. Differences in the empirical results are analysed and implications for future studies of stress discussed.

The results of this study strongly support the use of multiple psychometrics and biosensors whenever biometrics are included in the study of stress. Perceptual, physiological and environmental factors are all shown to act in concert to impact stress. Strong conclusions on the potential drivers of stress should then only be considered when consistent results apply across multiple metrics, time periods and activities.

Stress is an incredibly complex condition. This study demonstrates why many current applications of biosensors to study stress in construction are not up to the task and provides empirical evidence on how future studies can be significantly improved.

To the best of the author’s knowledge, this is the first study to focus explicitly on demonstrating the need for multiple research instruments and settings when studying stress or related conditions in construction.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

Perspiration and heat are produced by the body and must be eliminated to maintain a stable body temperature. Sweat, heat and air must pass through the fabric to be comfortable. The cloth absorbs sweat and then releases it, allowing the body to chill down. By capillary action, moisture is driven away from fabric pores or sucked out of yarns. Convectional air movement improves sweat drainage, which may aid in body temperature reduction. Clothing reduces the skin's ability to transport heat and moisture to the outside. Excessive moisture makes clothing stick to the skin, whereas excessive heat induces heat stress, making the user uncomfortable. Wet heat loss is significantly more difficult to understand than dry heat loss. The purpose of this study is to provided a good compilation of complete information on wet thermal comfort of textile and technological elements to be consider while constructing protective apparel.

This paper aims to critically review studies on the thermal comfort of textiles in wet conditions and assess the results to guide future research.

Several recent studies focused on wet textiles' impact on comfort. Moisture reduces the fabric's thermal insulation value while also altering its moisture characteristics. Moisture and heat conductivity were linked. Sweat and other factors impact fabric comfort. So, while evaluating a fabric's comfort, consider both external and inside moisture.

The systematic literature review in this research focuses on wet thermal comfort and technological elements to consider while constructing protective apparel.

Reacting to the calls in the contemporary literature to further examine the relationship between board attributes and firms’ decisions to obtain corporate social responsibility assurance (CSRA) through the use of pioneering techniques, this study aims to analyse the influence of such attributes together with the existence of a corporate social responsibility (CSR) committee on the adoption of CSRA using fuzzy set qualitative comparative analysis (Fs-QCA).

Fs-QCA was performed on a sample of nonfinancial European companies listed on the STOXX Europe 600 index over the period 2016–2018.

The study findings indicate that the decision to obtain a CSRA report depends on a complex combination of the influence of the CSR committee and certain board attributes, such as size, experience, independence, meeting frequency, gender and CEO separation. These attributes play essential contributing roles and, if suitably combined, stimulate the adoption of CSRA.

The study findings are important for policymakers, professionals, organisations and regulators in forming and modifying the rules and guidelines related to CSR committees and board composition.

To the best of the authors’ knowledge, this study represents the first examination of the impact of board attributes and CSR committees on the adoption of CSRA using Fs-QCA method. It also offers a novel methodological contribution to the board-CSRA literature by combining traditional statistical (logistic regression) and Fs-QCA methods. This study emphasises the benefits of Fs-QCA as an alternative to logistic regression analysis. Through the use of these methods, the research illustrates that Fs-QCA offers more detailed and informative results when compared to those obtained through logistic regression analysis. This finding highlights the potential of Fs-QCA to enhance our understanding of complex phenomena in academic research.

The purpose of this study is to address the relationship between multinational corporations (MNCs) and grand challenges. Stressing the moderating impact of stakeholders and governments, it frames and introduces the six contributions of the special issue, equally divided into those illustrating how MNCs contribute to the existence of grand challenges and those exploring how MNCs contribute to addressing grand challenges.

Based on a review of the existing literature on the relationship between MNCs and grand challenges and recent developments in mainstream international business, the viewpoint emphasizes the need to move beyond a one-sided focus on the positive contributions of MNCs to grand challenges.

The special issue contributions reveal that even established MNCs are actively engaged in strategic efforts to perpetuate unsustainable practices and minimize the impact of societal rules and stakeholders. The contributions also highlight the complications when MNCs aim to tackle grand challenges.

Displaying positive practices of how MNCs contribute to the solution of grand challenges should not be considered a functional substitute for regulatory action, contrary to the frequent assertion of MNCs and their political representatives.

This special issue is the first one in IB to address the relationship between MNCs and grand challenges from an empirical vantage point.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

Traditionally, life cycle assessment (LCA) has focused on environmental aspects, but integrating social aspects in LCA has gained traction among scholars and practitioners. This study aims to review key social life cycle assessment (SLCA) themes, namely, drivers and barriers of SLCA implementation, methodology and measurement metrics, classification of initiatives to improve SLCA and customer perspectives in SLCA.

A total of 148 scientific papers extracted from the Web of Science database were used and analyzed using bibliometric and content analysis.

The findings suggest that the existing research ignores several aspects of SCLA, which impedes positive growth in topical scholarship, and the study proposes a classification of SLCA research paths to enrich future research. This study contributes positively to SLCA by further developing this area, and as such, this research is a primer to gain deeper knowledge about the state-of-the-art in SLCA as well as to foresee its future scope and challenges.

The study provides an up-to-date review of extant research pertaining to SLCA.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.