A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by $25.8\%$ and data throughput by $26.86\%$. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.

This paper introduces singular value decomposition (SVD), a major matrix decomposition technique. SVD serves as the underlining computational engine of many other techniques such as principal component analysis (PCA), eigen decomposition, matrix decomposition, Cholesky decomposition and others. SVD is utilized in many applications such as data analysis and dimensionality reduction, image compression, Google's PageRank algorithm, Netflix's recommender system and many more. This paper overviews the mathematics behind SVD in a simple way. It also applies SVD technique in image compression and in dimensionality reduction as the underlining technique of the PCA and data analysis.

Sustainable campuses have a substantial role to play in achieving the Sustainable Development Goals (SDGs). Worldwide, there are many rating schemes to assess universities’ sustainability; UI GreenMetric is one of the most common global rating schemes aimed at encouraging green campuses and university sustainability worldwide. However, none of the existing rating schemes quantitatively measure the contributions of the implementation of its assessment indicators to achieve SDGs. There is a shortage of information on how sustainable campuses contribute to achieving SDGs. Thus, this research aimed to develop a novel index to assess and manage the contributions of sustainable campuses certified by UI GreenMetric to achieve SDGs. This article proposes novel indices, the GreenMetric Indicator Contribution Index (GMICI) and the GreenMetric Sustainable Campus Index (GMSCI), to evaluate the contributions of implementing UI GreenMetric indicators for achieving the SDGs. By implementing questionnaire surveys, we collected the relevant data. Structured questionnaire surveys yielded 35 responses from experts. The contributions of UI GreenMetric indicators to achieving SDGs were evaluated using the Relative Importance Index (RII). The results indicated an important relationship between the GreenMetric indicator and SDGs 3, 4, 6, 7, 8, 9,11, 12, 13 and 15. This research concludes that the proposed GMICI and GMSCI are a rigorous means for evaluating the contribution of UI GreenMetric indicators to UN-SDGs’ achievement.

Sustainable buildings contribute significantly to achieving sustainable development and provide many benefits. However, sustainable building construction projects are riskier than traditional construction projects. Thus, this research aimed to identify and assess the risks in sustainable construction projects in Jordan based on their probabilities of occurrence and impacts on time and cost. For this purpose, a literature review and structured interviews were performed to identify the risks. Additionally, questionnaire surveys were conducted, and the relative relevance index and analytic hierarchy process were utilized to assess the probabilities and impacts of these risks on time and cost. After that, a focus group discussion was conducted. According to the the risks’ probability and impacts on time and cost, the top-scoring risks include changes to the original design, budgeting inaccuracies owing to a lack of experience with sustainable building projects, additional costs incurred as a result of the use of sustainable building construction materials and equipment, inadequate or inaccurate sustainable design information, insufficient funds from clients, inadequate project planning for a sustainable building project, a tight project schedule for a sustainable building project, and insufficient identification of sustainable construction’s scope. This research contributes to the body of knowledge and practice by providing a novel integrated method for assessing risks in sustainable building construction projects in Jordan.

Countries worldwide have implemented several strategies to achieve the Sustainable Development Goals (SDGs), to which sustainable construction projects can contribute significantly. However, an integrated assessment and management method for sustainable construction projects is needed to improve the contributions of such projects to achieving the SDGs. Hence, this research aims to develop an innovative framework that integrates contributions to achieving the SDGs within the assessment and management of sustainable construction projects. We reviewed previous research and used the Delphi method to identify assessment indicators and construct a framework. Next, two new indices were constructed: the Sustainable Construction Project Indicator Contributions Index (SCPICI) and the Integrated Sustainable Construction Project Contributions Index (ISCPCI). Lastly, a focus group discussion was conducted. According to the SCPICI, the top five indicators are energy-efficient management (27.58), the use of renewable energy (27.51), measurements of energy savings during the construction and operation phases (26.64), water savings during construction and operation phases (26.18), and water recycling (25.46). The research findings indicate that sustainable construction projects substantially contribute to achieving SDGs 3, 6, 7, 8, 9, 11, 12, 13, and 15. Policymakers and other stakeholders in the construction sector could use the proposed framework to assess and manage sustainable construction projects. Moreover, researchers worldwide could use the proposed methods to develop new frameworks in other countries.

This research aims to identify and interpret the main traffic characteristics affecting the pavement condition index (PCI) of asphalt within parking areas. The study also examines the effect of severity and type of asphalt pavement distress on parking decisions. Previous literature examines the impact of the environment on asphalt pavement deterioration. This paper will focus on the shape of traffic operations within a parking area. The methodology includes applying a multilevel mixed regression model to reveal the hidden hierarchical relationship between traffic activities and the different types of pavement distresses and then discovering their effect on asphalt PCI. The resulting models showed that parking lots are aging faster than the common traveling roads due to the increased impact of axial loads on their surfaces at low and static speeds and some distinctive traffic characteristics. Moreover, some types of pavement distress, such as alligator cracks, potholes and depressions, appear more widely than others on the parking lot surface and significantly influence the parking decision. The conclusion of the research may significantly impact pavement design and maintenance management for parking lots.