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Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

This chapter explored health and safety considerations in stealth construction, emphasising the integration of advanced technologies and innovative practices. It commences with a general introduction, followed by a historical overview of safety practices in the construction industry, highlighting the evolution of a safety culture. The chapter examined various health and safety management techniques, including policy formulation, safety training programs, and job safety analysis. Additionally, it discussed current trends such as wearable technology, IoT, VR/AR, and predictive analytics. The unique requirements of stealth construction are addressed, focusing on building cross-section design, visibility, application of radio frequency emission and countermeasures. Finally, it presents a comprehensive approach to achieving stealth construction, emphasising environmental protection, safety, speed, economy, and aesthetics, and provides practical examples to illustrate these concepts.

This study aims to enhance our understanding of employee emotional competence (EEC) in the context of service failure and recovery. Accordingly, the present study investigates the relationship between perceived EEC and customer emotional attachment (CEA) through the mediating role of service recovery satisfaction (RES). Furthermore, the study examines the moderating impact of service failure severity (SFS) on the relationship between perceived EEC and RES.

A self-administered online survey was carried out to collect data. Using a convenience sampling technique, 195 US consumers were recruited from Prolific Academic. To test the hypotheses, this study employed partial least squares structural equation modeling (PLS-SEM).

According to the analysis, perceived EEC impacts CEA directly and indirectly via RES. Additionally, the study finds that consumers reported feeling more emotionally connected to the restaurant when they were satisfied with service recovery. Finally, the study identified that the connection between perceived EEC and RES increases with service failure severity.

This study emphasizes enhancing EEC through organization-wide training to increase customer satisfaction and emotional attachment to the service organization. Furthermore, it underscores the need for comprehensive employee training to categorize service failure severity and formulate appropriate recovery strategies.

The authors believe this is the first RES study to examine perceived EEC’s effect on CEA. By combining the affect infusion and cognitive appraisal theories to examine recovery satisfaction, this study contributes to the existing body of research on service recovery by shedding light on the relationship between perceived EEC and CEA. Furthermore, the study offers preliminary findings indicating an increase in the impact of perceived EEC on RES during high failure severity (SFS).

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.

This study addresses tourists' post-consumption perspectives on the impact of online destination experiences and animosity on travel decisions. Developing a framework based on the stimulus-organism-response (SOR) theory, we examine the previously unexplored relationship between post-negative events, online destination brand experience (ODBE), tourists' animosity and destination boycott intentions within the domestic tourism context.

Data from 355 actively engaged domestic travelers in Pakistan who follow destination social media pages (i.e. Instagram and Facebook) was analyzed using structural equation modeling.

The findings reveal that post-negative events ODBE significantly stimulate tourists' animosity, which in turn drives destination boycott intentions. The ODBE indirectly affects boycott intentions through animosity, acting as a partial mediator. The analysis highlights the significance of the users' prior experience levels (novice vs experienced). Multigroup analysis shows that novice visitors are more sensitive to negative online experiences, resulting in stronger animosity than experienced visitors. Animosity significantly drives boycott intentions, particularly among experienced visitors.

This study’s novelty lies in its comprehensive examination of post-negative events, focusing on how the ODBE influences tourists' negative emotions and boycott intentions. These findings offer valuable insights for tourism researchers and destination marketers, underscoring the importance of optimizing post-service failure ODBE strategies for brand repair, online reputation management, digital marketing innovation and customized service recovery to mitigate the impact of negative events.

Supply chain risk management (SCRM) is a multi-stage process that handles the adverse impact of disruptions in the supply chain network (SCN), and various SCRM techniques have been widely developed in the literature. As artificial intelligence (AI) techniques advance, they are increasingly applied in SCRM to enhance risk management’s capabilities.

In the current, systematic literature review (SLR), which is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, we analysed the existing literature on AI-based SCRM methods without any time limit to categorise the papers’ focus in four stages of the SCRM (identification, assessment, mitigation and monitoring). Three research questions (RQs) consider different aspects of an SCRM method: interconnectivity, external events exposure and explainability.

For the PRISMA process, 715 journal and conference papers were first found from Scopus and Web of Science (WoS); then, by automatic filtering and screening of the found papers, 72 papers were shortlisted and read thoroughly, our review revealed research gaps, leading to five key recommendations for future studies: (1) Attention to considering the ripple effect of risks, (2) developing methods to explain the AI-based models, (3) capturing the external events impact on the SCN, (4) considering all stages of SCRM holistically and (5) designing user-friendly dashboards.

The current SLR found research gaps in AI-based SCRM and proposed directions for future studies.

This study aims to provide an efficient nanocomposite that might be used to protect deteriorated archaeological stucco.

The current experimental study evaluates the effectiveness of the hydroxyapatite nanoparticles (HA NPs) added to graphite carbon nitride (g-C₃N₄) and mixed with Paraloid (B-72) (B-44) in acetone in consolidating samples. The physicochemical properties of the as-prepared nanopowders have been investigated using Fourier transform infrared (FT-IR). This study involves monitoring the transmission electron microscope (TEM), X-ray diffraction (XRD) and Fourier transform changes in consolidated samples after exposure to various conditions by using the digital microscope and scanning electron microscopy to identify the appearance of the consolidated stucco samples after applying the selected nanocomposites and after their artificial aging procedures. Color change is measured using a colorimeter, and comparisons are made between samples before and after aging. Physical and mechanical properties are determined, and the contact angle is measured to measure hydrophobicity rate.

The obtained results indicate that HA/g-C₃N₄ hybrid nanocomposites with a composition of HA 0.5%/g-C₃N₄ 1%/B-72 3% and HA 0.5%/g-C3N4 1%/B-44 3% achieved the best consolidating results among the proposed mixtures for stucco samples, where the percentage of weight loss was 0.77 with B-72, 0.53 with B-44. Surface identification and characterization of hydroxyapatite HA NPs/g-C₃N₄ hybrid nanocomposites embedded in B72/B44matrix were carried out using Scanning Electron Microscopy coupled with energy-dispersive x-ray spectroscopy (SEM–EDX).

This study provides important findings from the analytical procedures used to evaluate the consolidation materials used in this study. The findings are beneficial for the preservation of archaeological stucco. The investigation findings revealed that the most favorable outcomes were obtained from HA/g-C₃N₄ hybrid nanocomposites containing HA 0.5%, g-C₃N₄ 1% and B-72 3%, as well as HA 0.5%, g-C₃N₄ 1% and B-44 3%. Consequently, it is advised to use this nanocomposite to consolidate archaeological stucco, thus establishing a promising initial stride toward conserving archaeological stucco for future research endeavors. This study introduces a new nanocomposite material (HA NPs/G-C₃N₄) that can be used to protect and improve archaeological plaster. This is very important for preserving cultural heritage. The incorporation of nanotechnology improves the material’s physical and mechanical qualities. The research uses various characterization techniques (including TEM, XRD and FT-IR) to meticulously analyze the physicochemical properties of the nanocomposite material and assess its efficacy in practical applications through artificial aging experiments, offering novel insights and methodologies for future cultural relic preservation studies.

The purpose of the paper is to analyze the impact of involuntary frugality and deliberate frugality on the household intentions to adopt energy-efficient and energy-generating products. Additionally, the study aims to explore the role of motivation to save as a mediating factor between different types of frugality and the adoption of different kinds of energy products.

The study involved a survey of 413 households, gathering information through questionnaires from both tier I and tier II urban areas in India. The investigation used confirmatory factor analysis and structural equation modeling with Amos to explore the impact of frugality and also mediating impacts of motivation to save on the correlation between different forms of frugality (involuntary and deliberate) and the desire to acquire energy-efficient and energy-producing goods. This methodology facilitated a thorough examination of how various levels of frugality impact the uptake of sustainable energy solutions, with a specific emphasis on the fundamental motivational drivers behind these choices.

The study uncovers specific connections between various forms of frugality and the desire to embrace energy-efficient and energy-producing items. Unintentional frugality, characterized by sensitivity to prices, is shown to have a positive correlation with the adoption of energy-efficient devices but a negative association with the intention to adopt energy-generating products. Conversely, intentional frugality, distinguished by deliberate reduction actions, positively impacts the inclination to adopt both energy-efficient and energy-generating products. The results suggest that the mediating impact of motivation for savings varies depending on the type of frugality and the class of energy products being considered, emphasizing the subtle ways in which frugality influences sustainable consumption behaviors.

The contrasting effects of involuntary and voluntary frugality on the adoption of energy-efficient versus energy-generating products highlight the need to explore the underlying psychological and economic mechanisms. Future research should investigate the factors influencing the preferences of price-sensitive and deliberate frugal consumers towards this energy-efficient and energy-generating products.

Policymakers should develop specific subsidies and financial strategies for low-income households and incentive programs for conscientious consumers. Educational campaigns emphasizing the benefits of energy-generating goods and creating incentive structures with tax advantages, refunds and financial aid are essential. Companies should continue to emphasize cost savings for energy-efficient appliances and consider leasing or instalment plans for energy-generating products to appeal to price-sensitive consumers.

Literature shows that 82% of Indians prefer frugality to conserve energy through reduced consumption. However, consumer motivations for frugality vary. This study analyses the distinct impacts of involuntary and voluntary frugality on adopting energy-efficient and energy-generating products, offering a nuanced understanding of consumer behavior in sustainability—a topic underexplored in existing research. Additionally, this study investigates the role of the motivation to save as a mediator between frugality and energy product adoption, providing a novel perspective on how different frugality motivations influence different category of energy products.

Agriculture innovation systems (AIS) examine the complex socio-technical and institutional aspects affecting sustainable agriculture. However, it is predominantly constrained to the formal sector activities in the high-income countries (HICs). The informal sector actors play a major role in the agricultural sector of low- and middle-income countries (LMICs), such as India, by innovating and disseminating grassroots innovations (GI). This study aims to explore the role of different GI, both by the informal and formal sectors, within an emerging AIS focused on seabuckthorn in Ladakh, India.

This study used a qualitative methodology, using semi-structured interviews and focused group discussions to gather data from the stakeholders involved in seabuckthorn value chain. The data was analysed using the AIS framework’sa priori themes and was validated through data triangulation with secondary sources.

This study reveals the existence of GI, by both the formal and informal sector actors, and their complex interaction within the seabuckthorn value chain. It highlights the importance of co-existence of these GI to make it a sustainable seabuckthorn AIS.

This study offers noteworthy perspectives for governments, policymakers and agricultural practitioners with respect to the assimilation of GI into AIS. These insights could help improve agricultural sustainability and viability, particularly in LMICs where the informal sector plays a significant role.

To the best of the authors’ knowledge, this study is one of the first to explore the role of GI within AIS and opens up research avenues for further inquiry in both LMICs and HICs.

The purpose of the current work is to present an intuitionistic fuzzy (IF) concept-based structured framework for selecting the optimal maintenance policy in a sugar mill.

The proposed framework utilizes IF concept based multi-criteria decision making (MCDM) approaches, specifically IF-analytic hierarchy process (IF-AHP) and IF-Technique for Order of Preference by Similarity to Ideal Solution (IF-TOPSIS). IF theory based MCDM approaches utilises the hesitation present in the maintenance experts to encounter the uncertainties/vagueness to much higher degree of accuracy in decision-making. The suggested framework is used to evaluate and select the optimal maintenance policy based on six different criteria namely safety factor, cost factor, maintenance factor, reliability, risk and added values.

The IF-AHP approach has been employed to calculate the weights of the criteria and sub-criteria, while the IF-TOPSIS approach was utilized to rank the maintenance strategies. Based on the results, with a relative coefficient value of 0.7204, corrective maintenance (CM) is determined to be the best maintenance policy. For certifying the consistency of the recommended structured framework, sensitivity analysis (SA) has been also conducted.

The ranking results obtained from the analysis are provided to the maintenance management of the considered sugar mill for its further implementation and validation. The findings of this work are also applicable to all other sugar mill industries which are installed globally.

The analysis results has been supplied to the maintenance manager of considered sugar mill industry. The implementation policy of the results will be shared with the higher management and hence once implemented the results could be tested and verified.

The developed framework so implemented to the considered sugar mill industry is original in nature. Also, consideration of hesitation effect in the collected raw data under the developed framework provide more authenticated decision results which proves to be useful in achieving higher availability and profitability of the industry.