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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.

Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.

Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.

The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.

This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.

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 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.

The textile industry is evolving toward nanotechnology, which provides materials with self-cleaning properties. This paper aims to provide a thorough explanation of the green synthesis and mechanism of ZnO nanoparticles, with prospective applications of zinc oxide nanoparticles (ZnO NPs) in self-cleaning textiles.

This review introduces a green mechanism for the synthesis of ZnO NPs using plant extracts, their self-cleaning properties and the mechanisms of physical, chemical and biological self-cleaning actions for textile applications.

ZnO NPs are among the several nanoparticles that are beneficial for self-cleaning textiles because of their exceptional physical and chemical properties, although review publications addressing the use of ZnO NPs in textiles for self-cleaning are uncommon. These results indicate that the plant-synthesized ZnO NPs display excellent biological, physical and chemical self-cleaning properties, the mechanism of which involves photocatalysis, surface roughness and interactions between ZnO NPs and bacterial surfaces.

Nanoformulations of plant-synthesized ZnO have been reviewed to achieve promising self-cleaning textile properties and have not been reviewed earlier.

The effect of spinning has been studied and analysed for different projectile shapes such as ogive, blunt, cylindrical and conical by using numerical simulations.

Projectile shape is one of the important parameters in the penetration mechanism. The present study deals with the failure mechanisms and ballistic evaluation for different nose-shaped projectiles undergoing normal impact with spinning. Materials characterization has been made by Johnson–Cook strength and failure models, and LS-DYNA simulations are used to analyse the impact of steel projectiles on an Al 7075-T651 target at different impact velocities under normal impact conditions. The experimental results from the literature are used to validate the model. Based on the residual velocity values, the Recht-Ipson model has been curve-fitted and approximate ballistic limit velocity has been evaluated. The approximated ballistic limit velocity is found to be 3.4% higher than the experimental results and compared well with the experimental results. Subsequently, the validated model conditions are used to study and analyse the effect of spinning for different nose-shaped projectiles undergoing normal impact conditions.

The ductile hole failure is observed for the ogive nose projectile, petals are formed and fragmented for the conical projectile, and plugging is observed for cylindrical projectiles. A Recht-Ipson curve is presented for each spinning condition for each projectile shape and the ballistic limit has been evaluated for each condition.

The proposed research outputs are original and innovative and, have a lot of importance in defence applications, particularly in arms and ammunition.

This paper aims to produce lightweight concrete by combining aerated concrete with expanded polystyrene beads concrete to create structural aerated-polystyrene lightweight concrete that satisfies the criteria of sustainability for thermal and sound insulation properties and the structural criteria of having satisfactory compressive strength for structural elements.

The experimental study was carried out to reach the largest compressive strength while maintaining the lowest possible density by preparing nine mixes of concrete, involving different ratios of aluminum waste powder and polystyrene beads as 0%, 0.2% and 0.3% and 0%, 0.1% and 0.2%, respectively, by weight of cement to produce the lightweight concrete with different densities. The performance of mechanical properties, thermal conductivity, ultrasonic pulse velocity, density, modulus of elasticity, acoustic impedance and scanning electron microscopy were studied and discussed.

Results showed that aerated-expended polystyrene beads concrete had the most suitable properties when the proportions of aluminum waste powder and expanded polystyrene beads were 0.2% and 0.1%, respectively. The compressive strength, density, thermal conductivity and acoustic impedance were 38.5 MPa, 1,768 Kg/m³, 0.358 W/(m.k) and 4.91 Kg/m² s, respectively.

The experimental work was done using aluminum scrap waste powder as an expanding agent to produce aerated concrete and combining it with expanded polystyrene bead concrete to produce structural aerated-polystyrene concrete, which contains fine materials (silica fume and local natural raw limestone) and superplasticizers.