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This paper aims to investigate the prepared modified alkyd and poly(ester-amide) (PEA) resins as antimicrobial and insecticide binders for surface coating applications.

Salicylic diethanolamine and 4-(N, N-dimethylamino) benzylidene glutamic acid were prepared and used as new sources of polyol and dibasic acid for PEA and alkyd resins, then confirmed by: acid value, FT-IR and 1H-NMR. The coating performance of the resins was determined using measurements of physico-mechanical properties. The biological and insecticide activities of the prepared resins were investigated.

The tests carried out revealed that the modified PEA and alkyd enhanced both phyisco-mechanical and chemical properties in addition to the biological and insecticide activities. The results of this paper illustrate that the introduction of salicylic diethanolamine and 4-(N, N-dimethylamino) benzylidene glutamic acid within the resin structure improved the film performance and enhanced the antimicrobial activity performance of PEA and alkyd resins.

The modified alkyd and PEA organic resins can be used as biocidal binders when incorporated into paint formulations for multiple surface applications, especially those that are exposed to several organisms.

Modified alkyd and PEA resins based on newly synthesized modifiers have a significant potential to be promising in the production and development of antimicrobial and insecticide paints, allowing them to function to restrict the spread of insects and microbial infection.

This study aims to prepare some disperse dyes based on pyrazole derivatives and fully evaluate their use as azo-disperse dyes linked with pyrazolo[1,5-a] pyrimidine. This study aims to prepare dye polyester fabric at different conditions (pH and time) at a depth of 4% using a high-temperature pressure process in water.

The disperse dyes were created by dissolving enaminone and pyrazole derivatives in glacial acetic acid and fusing sodium acetate at reflux for 6 h. The chemical structure of the produced dye was studied using elemental analysis, Fourier-transform infrared spectroscopy, 1H NMR and 13C NMR spectroscopic analytical methods. The dyeing of Dispersed dyes 14–16 on polyester under diverse conditions was comprehensively investigated in this work.

The prepared Organic dyes 14–16 were found to be highly functional and suitable for this type of dyeing technique. High color strength is possessed by the materials dyed with Disperse dyes 14–16. Even in low alkaline conditions, these dyes exhibited a strong affinity for polyester fabric, changing just little in response to pH changes. The hue of the dyed polyester samples varied from beige to reddish brown and yellowish brown because of the coupler moieties.

This study is important because it offers novel dyes that may be used to dye polyethylene terephthalate fibers that exhibit remarkable brightness and levelness. It also offers a viable solution for creating a variety of colors in polyester fabrics.

The paper aims to the preparation of novel disperse dye based on azo salicylaldehyde derivatives TF-A [2-hydroxy-5-((3-(trifluoromethyl)phenyl)diazenyl)benzaldehyde] and full evaluation of their use as disperse dye TF-ASC [bis 2-hydroxy-5-((3-(trifluoromethyl)phenyl)diazenyl)benzaldehyde Schiff base with 4,4'-methylenedianiline] for dyeing polyester fabric at various conditions.

The dispersed dye was synthesized via Schiff base condensation in the presence of ceric ammonium nitrate cerium ammonium nitrate 10 mmole% as an eco-friendly catalyst at room temperature. The chemical structure of the prepared dye was characterized via elemental analysis, Fourier-transform infrared spectroscopy, 1H- and 13 C-NMR spectroscopic analysis tools. This study thoroughly examined the dyeing of disperse dye TF-ASC on polyester at various conditions. The characteristics of dyed polyester fabric were measured by colour measurements, as well as light, washing, crock fastness and finally, colour strength. The discrete fourier transform (DFT) theoretical studies, including E_HOMO, E_LUMO and optimized geometrical structure, were assumed and discussed in detail.

The results showed that the synthesized organic dye TF-ASC was highly functional and appropriate for this kind of dyeing method. The dyeing fabrics obtained from disperse dye TF-ASC, properties possess high colour strength as well as good overall fastness properties. These dyes had a high affinity for polyester fabric, with just a tiny change in dye affinity when the pH was changed, even under alkaline circumstances. The dye levelness and shade depth of the colour results were good, and there were a variety of hues from light brownish yellow to deep brownish yellow. The results obtained from DFT computational studies such as E_HOMO, E_LUMO, optimized structure, diploe moment µ and electrophilicity index deduced that prepared organic dye TF-ASC is more applicable as a dispersed dye.

This research is significant because it provides a new dye for dyeing polyethylene terephthalate fibres with exceptional brightness and levelness; the method of preparation is a useful pathway due to its being known as a green chemistry method.

This paper aims to enlighten the importance of the risk management process which is considered as a major procedure to effectively handle the potential inherent risks in the construction industry. However, most traditional risk analysis techniques are based on theories that deal with each risk factor as an independent, which does not take into consideration the causal relationships between risk factors.

This study aspires to identify the overall risk of the administrative construction projects in Egypt and to recognize the most influencing risk factors through the project life cycle by using Bayesian belief networks (BBN). Through a review of the literature, 27 risk factors were identified and categorized as the most common risk factors in the construction industry. A structured questionnaire was performed to estimate the probability and severity of these risks. Through site visits and interviews with experts in the construction field, 200 valid questionnaires were collected. A risk analysis model was developed using BBNs, then the applicability of this model was verified using a case study in Egypt.

However, the outcome showed that critical risks that manipulate administrative construction projects in Egypt were corruption and bribery, contractor financial difficulties, force majeure, damage to the structure and defective material installation.

The proposed study presents the possibilities available to the project parties to obtain a better forecast of the project objectives, including the project duration, total project cost and the target quality by examining the causal relationships between project risks and project objectives.

This study aspires to identify the overall risk of the administrative construction projects in Egypt and to recognize the most influencing risk factors through the project life cycle by using BBNs.

This chapter explores the pioneering integration of generative artificial intelligence (GenAI) into teaching and assessment within a module at a UK university. This initiative represents a significant step in advancing AI literacy in higher education, aiming to equip students with essential employability skills. The innovative approach empowered students to use GenAI tools effectively, critically, ethically, and responsibly. The collaborative effort with students provided valuable insights into GenAI’s potential to enhance student learning and future career prospects. Key recommendations for educators include dispelling the notion that GenAI use equates to cheating and that its ethical and critical application should be promoted. Encouraging transparency in GenAI usage can mitigate student engagement issues, while continuous feedback from students ensures the module remains responsive to their needs and experiences. By making GenAI use explicit and teaching effective prompt engineering, the module fostered a transition from covert use to responsible application. Hands-on experiential learning sessions were pivotal in developing students’ GenAI proficiency, enhancing their engagement and skill development.

Environmental risks (ERs) are critical to any highway construction project (HCP). One of the main contracting parties responsible for ERs is the contractor. Hence, it has been crucial to look into ways to control ERs in HCPs from the contractor’s perspective. This study aims to investigate how ERs can be managed in HCP in Sri Lanka.

A quantitative research approach with three rounds of Delphi was used. Statistical techniques were used to analyse and validate the ERs, the parties to whom the risks were to be allocated, and risk management measures identified from the empirical data collection.

The study reveals the 11 most significant ERs for HCP. Further, the most significant ERs in HCP were mainly found to be the responsibility of contractors in Sri Lanka. Twenty-four most appropriate risk response measures were determined; 13 were found to be common measures that could be used to manage two or more risks, while the remaining 11 were unique to specific risks.

Overall, this research determines the most significant ERs in HCP, the best risk allocation among the parties and appropriate risk-handling strategies and measures for each significant ERs. Additionally, the study addresses the demand for ERs management in HCP.

Integrating social comparison and social identity theories, this study aims to examine students’ emotional and behavioral responses to the use of ChatGPT in academic settings, focusing on intrinsic motivation, dissonance, envy, schadenfreude and artificial intelligence (AI) usage intentions.

The research design consisted of two sequential survey-based studies with undergraduate business students. Study 1, analyzed with SmartPLS, measured students’ intrinsic motivation, cognitive engagement, dispositional envy, emotional dissonance and schadenfreude experienced in response to academic dishonesty related to ChatGPT. Study 2 explored the motivations behind students’ future use of AI tools, examining ethical considerations and emotional responses.

Study 1 determined that higher levels of cognitive engagement reduce dissonance and envy among highly motivated students. Nevertheless, driven by cognitive engagement, dissonance and envy, it was established that highly motivated students experience schadenfreude when others are caught misusing ChatGPT. In contrast, low-motivated students only feel schadenfreude as a product of dissonance and envy. The focus of Study 2 was on the adoption of ChatGPT. Results indicate that future usage is driven by ethical considerations for highly motivated students, whereas less dissonance is key for low-motivated students.

The study’s originality lies in its exploration of schadenfreude in the context of AI use among students, highlighting how cognitive engagement and motivation influence emotional responses. Drawing on social comparison and social identity theories, it sheds new light on the dynamics of academic integrity and the emotional landscape surrounding AI tools like ChatGPT, thus filling a research gap in understanding student behavior and perceptions in higher education.

The purpose of this paper is to predict the concrete pouring production rate by considering both construction and supply parameters, and by using a more stable learning method.

Unlike similar approaches, this paper considers not only construction site parameters, but also supply chain parameters. Machine learner fusion-regression (MLF-R) is used to predict the production rate of concrete pouring tasks.

MLF-R is used on a field database including 2,600 deliveries to 507 different locations. The proposed data set and the results are compared with ANN-Gaussian, ANN-Sigmoid and Adaboost.R2 (ANN-Gaussian). The results show better performance of MLF-R obtaining the least root mean square error (RMSE) compared with other methods. Moreover, the RMSEs derived from the predictions by MLF-R in some trials had the least standard deviation, indicating the stability of this approach among similar used approaches.

The size of the database used in this study is much larger than the size of databases used in previous studies. It helps authors draw their conclusions more confidently and introduce more generalised models that can be used in the ready-mixed concrete industry.

Introducing a more stable learning method for predicting the concrete pouring production rate helps not only construction parameters, but also traffic and supply chain parameters.

In recent decades, professionals in the architecture, engineering and construction industry have come to recognize building information modeling (BIM) as one of the most powerful technologies available to ensure successful project outcomes. The purpose of this paper is to explore the benefits of BIM on design defect prevention during the design phase of building projects.

The authors qualitatively analyzed 160 design defect leading indicators (LIs) to identify key themes for design defect prevention. Then, by matching appropriate BIM functionalities to each key LI theme, they identified BIM-supported key LI themes.

The result of this paper served as the foundation of a BIM-based key design processes framework, which identifies the necessary data, project parties, actions and applicable BIM functions for preventing particular design defects. In addition, the authors found that BIM implementation can benefit 71.2% of the LIs of the design defects associated with problematic deliverables.

This study establishes the current state of BIM use for design defect prevention and also gives practitioners precisely targeted guidelines for using BIM functions during the design phase for better quality management.

In recent years, the demand for road traffic has continued to increase, but the casualties and economic losses caused by traffic accidents have also remained high. Therefore, the use of social service robots to manage, supervise and warn real-time traffic information has become an inevitable trend of traffic safety management.

In order to explore the inherent objective development law of road traffic accidents, in this paper, the factor analysis (FA) is used to explore the main influencing factors of traffic accidents, then the random forest algorithm is applied to build an FA–RF-based road traffic accident severity prediction model to predict two- and three-category accidents.

By comprehensively comparing the classification results of the two- and the three-category accident prediction, it also finds that due to the intersection between injuries and fatalities and the lack of necessarily external environmental information, the FA–RF model has a large degree of misjudgment for injuries and fatalities. Therefore, it is recommended to establish a real-time autonomous information communication mechanism between different kinds of social robots, which can improve the prediction of traffic accidents.

(1) A fusion model of FA–RF is considered to predict traffic accidents, which can be applied in traffic service robot. (2) It is recommended to establish a real-time autonomous information communication mechanism between different kinds of social robots, which can improve the prediction of traffic accidents.