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Polymer filament and its printability, which is strongly influenced by the rheological behavior, can represent a significant hurdle in translating fused deposition modeling (FDM) from the lab to the industrial or clinical settings. The aim of this study is to demonstrate the potential of machine learning (ML) approaches to speed up the development of polymer filaments for FDM. Four types of ML methods; artificial neural network, support vector regression, polynomial chaos expansion (PCE), and response surface model were used to predict the rheological behaivior of polybutylene succinate. In general, all four approaches presented significantly high correlation values with respect to the training and testing data stages. Remarkably, the PCE algorithm repeatedly provided the highest correlation for each response variable in both the training and testing stages. Noteworthy, variation differs between response variables rather than between algorithms. Taken together, these modeling approaches could be used to optimize filament extrusion processes.

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Since the 1970s, Saudi Arabia's Agricultural and Food (A&F) production has grown at an astronomical rate. The Saudi Stock Market (Tadawul) now has several top-ranking agricultural and food processing firms listed, making the country's A&F industry the fourth largest contributor to the local economy. As a result, the A&F sector plays a critical role in maintaining Saudi Arabia's worldwide stock market strength. Any dynamic economy requires longterm sustainability in the A&F industry. To achieve long-term viability, regular evaluations of performance efficiency and comparison are necessary. The study aimed to examine enterprises' financial and operational performance in Saudi Arabia's agriculture and food sectors. Data Envelopment Analysis (DEA) is used in this study to evaluate technological efficacy. A non-parametric analytic approach, the DEA method from one firm, is used to gauge efficiency compared to a productivity unit with the same purpose. According to the findings, the relative efficiency of the examined seven prominent A&F firms significantly varied during the research. According to efficiency-based rankings, financial data may help make more objective decisions. Results of the study indicated potential cost reductions in general administration by 22.63%, owners' equity by 15.15%, and capital expenditures by 10.15%. Implications of this study include providing a reflective understanding of the relative performance of the Saudi A&F companies, which can assist in developing better targeted continuous performance improvement plans and more effective strategies.

Safety is an essential success factor in construction projects. However, due to the complexity of construction projects, accidents typically occur randomly. Thus, efficient leadership based on a systematic approach is vital to reduce the possibility of accidents occurring. A combination of emotional, social, and cognitive competencies aligns with the Systems-Thinking concept. This concept enables safety leaders to influence their followers effectively. Systems-thinking-based leadership enables safety leaders to know how, when, and what leadership behaviors should be acquired and practiced. Therefore, it is essential to understand the interrelationships among those competencies. The main objective of this research study is to model the interpretive structure of critical Systems-Thinking-based leadership competencies as enablers to better construction safety performance. The Interpretive Structural Modeling (ISM) approach was followed to achieve the objective using safety experts’ opinion data collected via questionnaire. The questionnaire was designed using 14 Systems-Thinking-based leadership competencies to collect information on the direction of influence among the competencies. Results of the modeled interrelationships demonstrated that cognitive competencies are the preliminary building blocks to other social and emotional competencies enabling construction safety performance. The developed model provides a better understanding of how safety performance can be improved by building those competencies in construction personnel.

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The roles of all levels of management in influencing safety, particularly in a complex work environment, are crucial. Therefore, safety managers need to develop leadership competencies (i.e., effectiveness in terms of person-oriented behaviours) to reinforce their influencing capabilities through their safety responsibilities. However, practising leadership behaviours without considering how and when these behaviours should be executed is not enough. Therefore, this paper develops a personal leadership competency model by adopting the Systems Thinking approach. The model was developed by conducting exploratory factor analysis and confirmatory factor analysis of three behavioural leadership competencies (emotional, social and cognitive) selected to fulfil the holistic view of Systems Thinking. Data were collected via self-administered questionnaire surveys. A total of 180 valid responses were received from construction managers responsible for overseeing site safety. The statistical results revealed three factors belonging to emotional competency—achievement orientation and adaptability, positive outlook, and emotional self-control. Regarding social competency, four factors represented it—teamwork, organisational awareness, coach and mentor, and conflict management. Finally, cognitive competency was found to be formed by two factors—interaction recognition and pattern recognition. All nine identified factors should, in combination, help safety managers to achieve a better understanding of themselves, of others and of their worksite environments.