The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

The emergence of the COVID-19 pandemic led to the need to move educational processes to virtual environments and increase the use of digital tools for different teaching uses. This led to a change in the habits of using information and communication technologies (ICT), especially in higher education. This work analyzes the impact of the COVID-19 pandemic on the frequency of use of different ICT tools in a sample of 950 Latin American university professors while focusing on the area of knowledge of the participating professors. To this end, a validated questionnaire has been used, the responses of which have been statistically analyzed. As a result, it has been proven that participants give high ratings to ICT but show insufficient digital competences for its use. The use of ICT tools has increased in all areas after the pandemic but in a diverse way. Differences have been identified in the areas of knowledge regarding the use of ICT for different uses before the pandemic. In this sense, the results suggest that Humanities professors are the ones who least use ICT for didactic purposes. On the other hand, after the pandemic, the use of ICT for communication purposes has been homogenized among the different knowledge areas.

This research aimed to assess the results of two vendors used by the company in the shipping process of export goods. Two leading suppliers for one similar activity had caused more difficulties in the monitoring and controlling activities of DHL Global Forwarding Indonesia. This research used qualitative and quantitative methods, with the Analytic Hierarchy Process decision-making method using 36 internal staff members as the sample. Through a qualitative calculation method by distributing questionnaires to the existing suppliers, namely Monang Sianipar Kargo and Andima Transportindo, it was found that the weighted score for Monang Sianipar Kargo was 22.84 and for Andima Transportindo was 10.66. Subcriteria and indicators should be prioritized in the criteria of Price and service, significantly to improve the performance of problematic suppliers. This research recommended using the Analytic Hierarchy Process for assessment since it facilitated the research development by the opinion of the company’s experts. Such a finding implied that a policy from the management was needed in the assessment of suppliers. As an implication, it was necessary to assess all suppliers cooperating with DHL Global Forwarding Indonesia by using actual data from the current month.

eGovernment projects are capital intensive and have high probability of failure because of the dynamic and technological laden environment in which they operate. The number of skilled labour and technicalities required are often not available in quantity needed to sustain such project. There is always the need to have in place adequate risk assessment framework to guide the execution and monitoring of eGovernment projects. Several studies have been conducted on the critical success factors relating to risk assessment of eGovernment projects to understand the reasons for the high rate of failure. Therefore, there is need to review these articles and categorize them into different research domain in project risk assessment so as to reveal domain with more or less research and those that need to understand the future research directions in risk assessment for eGovernment projects. Using the positivism paradigm, this study utilized the Systematic Literature Review methodology to collect 147 articles from the following academic databases namely IEEE, Preprints, WorldCat Discovery, ArXiv. Ohio-state University databases, Science Direct, Scopus, ACM, NWU digital library, Usenix, Jise database, Sagepub, MDPI Academia published between 2013 to 2023. Different inclusion and exclusion criteria were applied pruning to 48 articles that were used for the study. The results show the classification of articles in risk assessment for eGovernment projects into those that discusses project analysis, review, framework, maturity and model tools, implementation, and integration, applied methodology and evaluation with the percentage of articles published in each domain with the past 10 years. The various critical success factors that should be considered in the development of a robust risk assessment framework were discussed and future research directions in eGovernment risk assessment were given based on the reviews.

Photocatalysis, an innovative technology, holds promise for addressing industrial pollution issues across aqueous solutions, surfaces, and gaseous effluents. The efficiency of photodegradation is notably influenced by light intensity and duration, underscoring the importance of optimizing these parameters. Furthermore, temperature and pH have a significant impact on pollutant speciation, surface chemistry, and reaction kinetics; therefore, process optimization must consider these factors. Photocatalytic degradation is an effective method for treating water in environmental remediation, providing a flexible and eco-friendly way to eliminate organic contaminants from wastewater. Selectivity in photocatalytic degradation is achieved by a multidisciplinary approach that includes reaction optimization, catalyst design, and profound awareness of chemical processes. To create efficient and environmentally responsible methods for pollution removal and environmental remediation, researchers are working to improve these components.

Dredging and reclamation operations are pivotal aspects of coastal engineering and land development. Within these tasks lie potential hazards for personnel operating dredging machinery and working within reclamation zones. Due to the specialized nature of the work environment, which deviates from conventional workplace settings, the risk of workplace accidents is significantly heightened. The aim of this study is to conduct a comprehensive risk analysis of the safety aspects related to dredging and reclamation activities, with the goal of enhancing safety and minimizing the frequency and severity of potential dangers. This research comprises a thorough risk analysis, integrating meticulous hazard identification from sample projects and literature reviews. It involves risk assessment by gathering insights from experts with direct working experience and aims to assess potential risks. The study focuses on defining effective risk management strategies, exemplified through a case study of a nearshore construction project in Thailand. The study identified numerous high and very high-risk factors in the assessment and analysis of occupational safety in dredging and reclamation work. Consequently, a targeted response was implemented to control and mitigate these risks to an acceptable level. The outcome of this study will provide a significant contribution to the advancement of guidelines and best practices for improving the safety of dredging and reclamation operations.