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Artificial intelligence (AI) can help in defining preventive strategies in taking decisions in complex situations. This paper aims to research how workers might deal with intervening AI tools, with the goal of improving their daily working decisions and movements. We contribute to deepening how workers might deal with intervening AI tools aiming at improving their daily working decisions and movements. We investigate these aspects within a field, which is growing in importance due to environmental sustainability issues, i.e. waste management (WM).

This manuscript intends to (1) investigate if AI allows better performance in WM by reducing social security costs and by guaranteeing a better continuity of service and (2) examine which structural change is required to operationalize this predictive risk model in the real working context. To achieve these goals, this study developed a qualitative inquiry based on face-to-face interviews with highly qualified experts.

There is a positive impact of AI schemes in helping to detect critical operating issues. Specifically, AI potentially represents a tool for an alignment of operational behaviours to business strategic goals. Properly elaborated information, obtained through wearable digital infrastructures, allows to take decisions to streamline the work organization, reducing potential loss due to waste of time and/or physical resources.

Being a qualitative study, and the limited extension of data, it is not possible to guarantee its replication and generalizability. Nevertheless, the prestige of the interviewees makes this research an interesting pilot, on such an emerging theme as AI, thus eliciting stimulating insights from a deepening of information coming from respondents’ knowledge, skills and experience for implementing valuable AI schemes able to an align operational behaviours to business strategic goals.

The most critical issue is represented by the “quality” of the feedback provided to employees within the business environment, specifically when there is a transfer of knowledge within the organization.

The study focuses on a less investigated context, the role of AI in internal decision-making, particularly, for what regards the interaction between managers and workers as well as the one among workers. Algorithmically managed workers can be seen as the players of summarized results of complex algorithmic analyses offered through simpleminded interfaces, which they can easily use to take good decisions.

Laser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.

Firstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.

Based on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.

This paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.

This study aims to examine magnetohydrodynamic mixed convective phenomena and entropy generation within a semicircular porous channel, incorporating impinging jet cooling and the effects of thermal radiation. The present study analyzes the complex flow dynamics and heat transfer characteristics of a highly diluted 0.1% (volume) concentration Cu–Al₂O₃/water hybrid nanofluid, based on findings from previous studies. The investigation is intended to support the development of effective thermal management systems across diverse industries, such as cooling of electronic devices and enhanced energy system applications.

This study incorporates a heated curved bottom wall and a cooling jet of Cu–Al₂O₃/water hybrid nanofluid impinging from the central top inlet, with two horizontal exit ports along the rectangular duct. Finite element-based simulations are conducted using COMSOL Multiphysics, using a single-phase homogeneous model justified by earlier works. This method uses experimental data of effective thermal conductivity and viscosity, emphasizing the evaluation of thermal performance in scenarios involving intricate geometries and multiphysical conditions. The study analyzes nondimensional variables such as Reynolds number (Re), modified Rayleigh number (Ra_m), Hartmann number (Ha), Darcy number (Da) and radiation parameter while maintaining a constant nanofluid volume fraction. A grid independence study and code validation were performed to ensure numerical accuracy.

The analysis indicates that elevated Re contribute to a lessening in the thermal boundary layer thickness, prompting flow separation and significantly amplifying the average Nusselt number. The mixed convective heat transfer enhancement, coupled with an overall reduction in total entropy generation, diminishes with a rising Ha. However, optimized combinations of higher values for modified Ra_m and Da yield improved heat transfer performance, particularly pronounced with increasing Ha. Radiative heat transfer exerts a detrimental impact on both heat transfer and entropy production.

While the single-phase model captures key macroscopic effects differentiating nanofluids from base fluids, it does not provide insights at the nanoparticle level. Future studies could incorporate two-phase models to capture particle-level dispersion effects. In addition, experimental validation of the findings would strengthen the study’s conclusions.

This work represents innovative perspectives on the development of efficient hydrothermal systems, accounting for the influences of thermal radiation, porous media and hybrid nanofluids within a complex geometry. The results offer critical insights for enhancing heat transfer efficiency in real-world applications, especially in sectors demanding advanced cooling solutions.

Responsible management of water resources is critical owing to its effects on the environment and society. This study aims to address customer perceptions of a water utility during a severe environmental crisis that affected northern Italy and aims to deepen the understanding of the relationship between corporate social responsibility (CSR), perceived crisis response and corporate reputation.

This study draws on legitimacy theory and attribution theory, adopting a quantitative design. In detail, a moderated mediation model is used to investigate the direct effect of CSR on reputation, the mediating effect of perceived crisis response on the relationship between CSR and reputation and the moderating effect of blame attribution on the relationship between CSR and perceived crisis response. In addition, the evolution of the crisis event and its management is traced through the analysis of the water utilities’ sustainability reports published since the beginning of the crisis.

The findings show that CSR affects corporate reputation directly and via perceived crisis response. In addition, CSR improves perceived crisis response, especially when an organization is held responsible for a crisis. The analysis of the CSR report allows for understanding the evolution of CSR policies of water utilities, shifting attention from a merely informative role of sustainability disclosure to a more comprehensive approach to perfluoroalkyl substances risks in the struggle of contributing to sustainable development. Theoretical and managerial implications are also discussed.

The findings suggest some managerial implications about the usefulness of adopting CSR for crisis management and, furthermore, the importance of communicating CSR policies to all stakeholders overall – the customers of public utilities.

This paper focuses on the relationship between CSR, reputation and blame attribution. Literature on this topic is still scarce overall in the field of public utilities. Furthermore, this study is relevant because it faces one of the major European environmental crises that affected the water sector and provides helpful insights for all public utility sectors and, more generally, for environmental crisis management.

This paper aims to create a comprehensive framework for selecting alternative materials in construction projects, integrating building information modeling (BIM) and the particle swarm optimization (PSO) algorithm. Materials comprise 60%–65% of the total project cost, and current methods require significant time and human resources.

A prototype framework is developed that considers multiple criteria to optimize the material selection process, addressing the significant investment of time and resources required in current methods. The study uses surveys and interviews with construction professionals to collect primary data on alternative materials selection.

The results show that integrating BIM and the PSO algorithm improves cost optimization and material selection outcomes.

This comprehensive tool enhances decision-making capabilities and resource utilization, improving project outcomes and resource utilization. It offers a systematic approach to evaluating and selecting materials, making it a valuable resource for construction professionals.

This paper aims to identify the variables responsible for classifying the investors into risk takers (RT) and risk avoiders (RA) across their economic perspectives.

The research offers a novel and unobtrusive measure of classifying investors into RT and RA based on a set of financial risk tolerance (FRT) questions. The authors have investigated the causes of discrimination across economic perspectives over a sample of 552 investors exposed to market risk.

The authors identify that out of the total of 11 risk assessment variables, only three are responsible for classifying investors into RA and RT. The variables are risk return trade-off, comfort level dealing with risk, and understanding short-term volatility. Financial literacy is considered as an emerging cause of discrimination. Further, the authors highlight the most striking finding to be the discriminating factors across wealth and source of income of the investors.

Existing research on FRT can be loosely segregated into three groups: the relationship between an individual's financial and non-FRT, estimation of FRT score (FRTS), and perceived self-assessed FRTS. The current research roughly falls into the third category of study where the authors have not only studied the self-assessed risk tolerance but also evaluated the predictors. Most of the studies have focussed on estimating self-assessed FRT with the help of one direct question to the respondent. However, the uniqueness of this study is that the researchers have used an instrument comprising a series of direct and indirect questions that can easily estimate the self-assessed risk perception and also discriminate the role of the economic factors that have any impact on self-assessed FRTS.

This study aims to assess the risk profile of millennial investors residing in the Delhi NCR region. In addition, the relationship between the risk profile and demographic traits of millennial investors was also analyzed.

Data was collected using a structured questionnaire segregated into two sections. In the first section, millennials were asked questions on socio-demographic factors, and the second section contained ten Likert-type statements to cover the multidimensionality of financial risk. Factor analysis and one-way ANOVA were used to analyze the primary data collected for this study.

The findings indicate that the risk profile of millennials is mainly affected by three factors: risk-taking capacity, risk attitude and risk propensity. Except for educational qualification and occupation, all other demographic features, such as age, gender, marital status, income and family size, seem to significantly influence the factors defining millennials' risk profile.

Uncertainty is inherent in any financial decision, and an investor’s willingness to deal with these variations determines their investment risk profile. To make sound financial decisions, it is mandatory to understand one’s risk profile. The awareness of millennials' distinctive risk profile will come in handy to financial stakeholders because they account for one-third of India’s population, and their financial decisions will shape the financial world for the decades to come.

The economic significance of self-help groups (SHGs), a critical community-based initiative based on social capital, is well encapsulated in generating employment opportunities, financial inclusion, empowerment of marginalized communities and economic development. However, these SHGs face multiple operating obstacles and sustainability challenges that have drawn the attention of policymakers and academicians alike. The landscape of SHGs has undergone a significant unfavorable transformation after the post-COVID-19 pandemic, which poses an existential crisis for SHGs. This study aims to explore the sustainability challenges in the post-pandemic landscape of SHGs and identify the factors that drive or deter individuals from joining SHGs.

Initially, six National Cooperative Union of India representatives, the apex body of cooperative societies in India, were approached to understand the current policy framework and probable concerns of SHGs. Based on the interaction, the research agenda was modified to examine the sustainability of SHGs during and after the pandemic. An in-depth semistructured interview of 13 SHG leaders/coordinators and 52 individual members from different regions of India is conducted.

Lack of digital inclusion, restrictions in mobility, impact on health and well-being and infrastructural ecosystem are identified as fundamental (and novel) operational challenges that hinder SHG sustainability post-pandemic. At the member level, entrepreneurial aspirations, a sense of belongingness, social networks and corporate and nongovernment organization (NGO) initiatives are the critical drivers for SHG participation. In contrast, the key deterrents are mistrust, lack of coordination and customer perception.

Although many studies present mixed findings on women’s empowerment through SHGs, much of the research primarily emphasizes individual factors and the challenges faced by women. To the best of the authors’ knowledge, this study is among the first to expand the discussion beyond individual experiences to address broader operational challenges, particularly in the post-pandemic context. In doing so, this research aims to assist SHGs in overcoming these challenges and to guide government and nongovernment organizations (NGOs) in supporting the sustainable growth of SHGs.

The purpose of this paper is to examine the mechanical characteristics and optimization of wear parameters of hybrid (TiO₂ + Y₂O₃) nanoparticles with Al matrix using squeeze casting technique.

The hybrid aluminium matrix nanocomposites (HAMNCs) were fabricated with varying concentrations of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃), from 2.5 to 10 Wt.% in 2.5 Wt.% increments. Dry sliding wear test variables were optimized using the Taguchi method.

The introduction of hybrid nanoparticles in the aluminium (Al) matrix was evenly distributed in contrast to the base matrix. HAMNC6 (5 Wt.% TiO₂ + 5 Wt.% Y₂O₃) reported the maximum enhancement in mechanical properties (tensile strength, flexural strength, impact strength and density) and decrease in porosity% and elongation% among other HAMNCs. The results showed that the optimal combination of parameters to achieve the lowest wear rate was A3B3C1, or 15 N load, 1.5 m/s sliding velocity and 200 m sliding distance. The sliding distance showed the greatest effect on the dry sliding wear rate of HAMNC6 followed by applied load and sliding velocity. The fractured surfaces of the tensile sample showed traces of cracking as well as substantial craters with fine dimples and the wear worn surfaces were caused by abrasion, cracks and delamination of HAMNC6.

Squeeze-cast Al-reinforced hybrid (TiO₂+Y₂O₃) nanoparticles have been investigated for their impact on mechanical properties and optimization of wear parameters.