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.

Data literacy is an important skill for students in studying physics. With data literacy, students have the ability to collect, analyze and interpret data as well as construct data-based scientific explanations and reasoning. However, students’ ability to data literacy is still not satisfactory. On the other hand, various learning strategies still provide opportunities to design learning models that are more directed at data literacy skills. For this reason, in this research a physics learning model was developed that is oriented towards physics objects represented in various modes and is called the Object-Oriented Physics Learning (OOPL) Model. The learning model was developed through several stages and based on the results of the validity analysis; it shows that the OOPL model is included in the valid category. The OOPL model fulfils the elements of content validity and construct validity. The validity of the OOPL model and its implications are discussed in detail in the discussion.

Sanitation challenges are growing at unprecedented rates in the Middle East and North Africa (MENA) region, specifically in the country of Jordan, where more adversities are faced in the provision of inclusive and sustainable sanitation for marginalized communities. The overloaded water supply systems, strained by high population density in the face of political instability manifests itself in poor public health. How countries in the MENA region plan to handle these problems and improve the sanitation infrastructure is the starting point for this work. We aim to develop a comprehensive and multidisciplinary framework between stakeholders, aligned with the Sustainable Development Goals (SDGs), with a specific emphasis on SDG 6, for providing feasible, community-oriented approaches to sanitation issues in disenfranchised communities in Jordan through the Initiative Sanitation and Hygiene Networking in Jordanian Poverty Pockets (ISNJO) project. The findings will be used to formulate strategic guidelines and inform the development and subsequent initiation of innovative and multidisciplinary initiatives to tackle the sanitation and water scarcity challenges at hand.

A salinity gradient solar pond (SGSP) is a large and deep artificial basin of layered brine, that collects and stores simultaneous solar energy for use in various applications. Experimental and theoretical studies have been launched to understand the thermal behavior of SGSPs, under different operating conditions. This article then traces the history of SGSPs, from their natural discovery to their current artificial applications and the progress of studies and research, according to their chronological sequence, in terms of determining their physical and dynamic aspects, their operation, management, and maintenance. It has extensively covered the theoretical and experimental studies, as well as the direct and laboratory applications of this technology, especially the most famous and influential in this field, classified according to the aspect covered by the study, with a comparison between the different results obtained. In addition, it highlighted the latest methods to improve the performance of an SGSP and facilitate its operation, such as the use of a magnetic field and the adoption of remote data acquisition, with the aim of expanding research and enhancing the benefit of this technology.

The PPP scholarly work has effectively explored the material values attached to PPPs such as efficiency of services, value for money and productivity, but little attention has been paid to procedural public values. This paper aims to address this gap by exploring how Enfidha Airport in Tunisia failed to achieve both financial and procedural values that were expected from delivering the airport via the PPP route, and what coping strategies the public and private sectors deployed to ameliorate any resultant value conflicts. Based on the analysis of Enfidha Airport, it is argued that PPP projects are likely to fail to deliver financial and procedural values when the broader institutional context is not supportive of PPP arrangements, and when political and security risks are not adequately counted for during the bidding process.

Currently there is a great acceptance in medicine and dentistry that clinical practice should be “evidence-based” as much as possible. That is why multiple works have been published aimed at decreasing radiation doses in the different types of imaging modalities used in dentistry, since the greater effect of radiation, especially in children, forces us to take necessary measures to rationalize its use, especially with Cone Beam computed tomography (CBCT), the method that provides the highest doses in dentistry. This review was written using such an approach with the purpose of rationalizing the radiation dose in our patients. In order to formulate recommendations that contribute to the optimization of the use of ionizing radiation in dentistry, the SEDENTEXCT project team compiled and analyzed relevant publications in the literature, guidelines that have demonstrated their efficiency in the past, thus helping to see with different perspectives the dose received by patients, and with this, it is recommended taking into account this document so as to prescribe more adequately the complementary examinations that we use on a daily basis.