AUIQ Technical Engineering Science

The construction of large dams has been an essential component of the development of global infrastructure, offering significant advantages such as ensuring water storage, reducing the risk of flooding, and producing about 20% of the world's electricity through hydropower. On the other hand, these structures have significant and varied environmental effects. The environmental effects of large dams are critically examined in this analysis, with an emphasis on four main areas: fish biodiversity, riparian vegetation, sediment deposition, and water quality. The study estimates the degree of these consequences by examining global case studies and evaluating data from over 145 significant rivers worldwide. It finds that dam-induced silt deposition alone reduces the world's freshwater storage capacity by 0.5–1% annually. The research also emphasizes how fish biodiversity has significantly decreased, with species fragmentation seen in more than 70% of rivers affected by dams, and how riparian vegetation and water quality have severely degraded, leading to increasing eutrophication and hypoxia. Even though dams are necessary for meeting basic human needs, their effects on the environment and water resources make it necessary to reassess current dam structures and proceed cautiously with new dam construction. This study's contribution to this discipline is the synthesis of an all-encompassing, global viewpoint that emphasizes the necessity of incorporating environmentally sustainable methods into dam operations. Policymakers, engineers, and environmental scientists can use this assessment as a crucial resource to support the preservation of river ecosystems while balancing the benefits of development.

Head injuries from falls are a major cause of illness and death in children due to trauma. Despite their prevalence and impact, there is limited understanding of how children's heads react during impact events. Infant Post-Mortem-Human-Surrogate (PMHS) testing, a reasonable method for studying impact biomechanics, faces significant restrictions due to emotional, moral, and ethical issues. Computer modeling, though holding significant promise for creating alternative pediatric head surrogates, encounters numerous challenges because of the intricacies of child growth and development. A finite-element (FE) model of an infant head was created from high-resolution CT scans, utilizing published data on tissue material properties. Biofidelity was verified through comprehensive validation against existing PMHS data by simulating experimental impact tests, focusing on the infant head's kinematic response. This surrogate offers a valuable biomechanical engineering insight into how an infant's head behaves under impact loads, which will subsequently aid clinical and forensic management and injury prevention strategies.

There are several essential elements in project construction management to be studied appropriately, and priority to these elements, such as cost and duration, is predominantly interesting to be investigated. In this research, the duration of field canal improvement projects (DFCIP) was predicted using two relatively new machine learning (ML) models - the Multivariate Adaptive Regression Spline (MARS) and Extreme Learning Machine (ELM). The targeted DFCIP was calculated using other dependent parameters, such as the length of the pipe, years of construction, the geographical zone of the network, the supplied area with water, and finally the actual cost of the field canal initiative. The development of the model was established using a dataset collected from the open-source literature. The modeling results indicated that MARS over the testing phase attained the least root mean square error (RMSE =6.1609), maximum determination coefficient (R2= 0.7448), mean absolute error (MAE=4.28), a mean absolute percentage error (MAPE=0.05169) with Nash- Sutcliffe Efficiency (Nash= 0.7418) and agreement index (MD= 0.7632). On the other hand, the ELM model achieved RMSE value of 6.869, R2 value of 0.6831, MAE value of 4.7804, MAPE value of 0.05759 with Nash = 0.68108, and MD = 0.73402. These metrics indicate the better performance of MARS over ELM in terms of accuracy and best precision of prediction using all the predictors. The research concluded that the proposed methodology provides reliable technology for duration prediction. In addition, the introduced model can be considered for reliable and robust field canal management.

Iraq has experienced record-breaking temperatures, making it one of the hottest places on Earth. It is also ranked among the world\'s top five most climate-vulnerable nations. Climate change is a hazard to Iraq\'s people and may cause societal disintegration, instability, and displacement. Therefore, it is important to assess Iraq\'s decadal climate and landform variables analysis. In the present study, the Climate Hazards Center InfraRed Precipitation with Station (CHIRPS) data in the Google Earth Engine (GEE) platform from 2000 to 2022, as well as rainfall, anomaly, temperature, vegetation, and water, are used to analyse climate change in Iraq. As the land surface temperature (LST) rose by 2.63 °C, the data show that rainfall dropped by 61.45 mm in just 22 years of observation and by 2.79 mm yearly. Additionally, some urban expansion and climatic change have reduced the areas of water bodies and vegetation. The correlation matrix shows a higher negative association between the vegetation and LST indices, with R2 values of -0.58 (2022), -0.56 (2006), -0.60 (2012), -0.55 (2016), and -0.59 (2000), respectively. Iraq, extremely sensitive to climate change, is implementing several adaptation measures, including early warning systems, reforestation and mangrove planting, water management, a national adaptation plan (NAP), and a reforestation program. Due to vulnerabilities in vital areas including water, agriculture, health, and natural resources, Iraq is prioritizing adaptation to climate change.

Due to the advancement of laser material processing and the high-demand of high-quality processing, COMSOL Multiphyiscs emerges as a powerful solution to the problem, offering multiple simulation in virtual environment with both 2D and 3D geometries. In this work, we aim to study the effects of thermal properties on the laser cladding using both 2D and 3D geometries with Nickle and Copper as promising candidates for rapid processing of materials. The work was done on a personal computer using commercially-available package of COMSOL Multiphysics 5.0. Results shows a notable increment of heat affected zone when the laser power increased. Additionally, the time parameter shows distinct and uniform distribution of the heat affected zone.

The rapid growth in demand and environmental concerns in industries like glass manufacturing necessitate the redesign of closed-loop supply chain (CLSC) networks to address both operational inefficiencies and sustainability challenges. Unlike conventional supply chain design, redesigning CLSC networks involves strategic decisions such as opening new facilities, closing existing ones, and managing the cost trade-offs associated with these transitions. Motivated by these challenges, this paper proposes an integrated decision-making framework to tackle the closed-loop supply chain network redesign (CLSCNR) problem. The proposed framework is formulated as a mixed-integer programming (MIP) model, specifically tailored for the glass industry. The forward supply chain includes suppliers, manufacturers, distributors, and customers, while the reverse supply chain comprises collection centers that allocate returned and waste products to recycling, remanufacturing, or disposal centers. This redesign approach addresses critical challenges in facility location, capacity planning, and customer assignment to better align supply chain operations with increasing demand and sustainability goals. Extensive numerical analyses were conducted using 16 test instances, revealing significant improvements through network redesign. For example, the number of open centers decreased by 1 in several instances (such as T5 and T9), while in other instances, up to 3 centers were closed (e.g., T13). The difference in the number of open centers before and after the redesign highlights the ability of the proposed framework to streamline network operations while maintaining service levels. The computational time ranged from 27.48 seconds for smaller instances to 62.26 seconds for larger ones, demonstrating the model's efficiency and scalability. The findings demonstrate the proposed MIP's ability to optimize network configurations, enhancing operational efficiency and demand satisfaction. These insights provide a practical decision-support tool for supply chain designers, enabling companies in high-demand industries to achieve adaptive and sustainable CLSC networks.

In recent years, the demand for efficient and cost-effective 3D printing technology has surged, opening up a plethora
of potential applications across various industries. One promising solution is the design and implementation of a 3D
printer utilizing PLA (Polylactic Acid) filament, an eco-friendly and biodegradable material. This approach not only
promotes sustainability but also offers a reliable printing system for creating accurate 3D models. By harnessing the
power of this innovative technology, users can effectively capture complex geometries and intricate details, ultimately
revolutionizing the way we perceive and interact with the physical world around us. In this work, the design process
takes a practical method in complying with simulations. Several software were used for simulation including fritzing for
electronic circuit design, AutoCAD for overall mechanical layout design and Cura 3D for 3D model simulation and 3D
printing fine operation, The aim of the work is to design and implement a 3D printer for the use in scientific purposes.
This work focuses on the design and implementation of a 3D laser printer that can effectively function with PLA filament,
the principles and benefits of this technology, and potential applications across various fields.

The accurate prediction of thermogravimetric properties is critical for evaluating the suitability of natural materials like Dawakin Tofa clay for heat storage applications, but traditional linear models often fail to capture the complex, non-linear relationships inherent in such datasets. This study develops a hybrid intelligence framework integrating Bilateral Neural Network (BNN), Kernel Support Vector Machine (KSVM), Step-Wise Linear Regression (SWLR), and Robust Linear Regression (RLR) to predict the derivative weight of Dawakin Tofa clay based on 5,030 experimentally obtained instances. Comprehensive data preprocessing, including normalization, feature selection, and dataset splitting (80% training and 20% testing), ensured high-quality inputs for the models. The results demonstrated that non-linear models significantly outperformed linear approaches, with BNN achieving a coefficient of determination R² of 0.999, a Mean Absolute Error (MAE) of 0.004377, and a Mean Absolute Percentage Error (MAPE) of 9.6% on the testing dataset. Similarly, KSVM achieved an R² of 0.999, MAE of 0.012134, and MAPE of 26.7%, indicating its robust predictive capabilities. In contrast, linear models performed poorly, with SWLR and RLR yielding R² values of 0.03 and -0.41, respectively, and unacceptably high MAPE values of 612% and 53.5%. The findings underscore the limitations of linear models in predicting complex thermogravimetric behaviors and highlight the transformative potential of advanced machine learning techniques like BNN and KSVM. Furthermore, these results align with global sustainability efforts, including SDG 7 and 12, by optimizing the use of locally available, eco-friendly materials for energy storage. This study provides a replicable framework for leveraging artificial intelligence to enhance material characterization, offering a significant step toward developing sustainable energy solutions.

Analysis of major watershed basins in a global vision provides a crucial information on sustainable management of water resource throughout the Earth. In this review, a globally significant watersheds were reviewed including the Murray-Darling Basin (Australia), Yangtze River basin (Asia), Volga River Basin (Europe), Nile River Basin (Africa), Hudson Bay Watershed & Mississippi Basin (Canada/North America), and the Amazon Basin (South America). A detailed overview was performed on emphasizing climate, agriculture, hydrology, groundwater, and ecological aspects of the watershed basins, providing holistic knowledge in terms of their environmental and socio-economic impacts. This review explores the challenges which are encountered by these watershed basins, involving over-allocation of water, loss of biodiversity because of water deficiency, deforestation, and advancement of hydroelectric power. Also, it examines the fundamental strategies for sustainable management of water, including climate adaptation, improvement of water quality control, and incorporation of ecosystem health principles. The result of review suggests that future research should emphasize advancing basin management, with specific attention to the Mississippi and Nile basins, to balance human demands with sustainability of ecology. Further, the review presents critical insights and guidelines for protecting these essential watersheds basins, supporting additional effective decision-making and sustainable management practices that can certify their long-term sustainability in the face of growing environmental impacts.

Blockchain is a digital recording technology where each block contains encrypted data linked to previous blocks, forming an immutable and transparent sequence. This review systematically analyzes the applications and evolution of blockchain technology across multiple sectors from 2021–2023. peer-reviewed articles focused on key areas such as finance, healthcare, energy, and supply chain management. The findings highlight blockchain's transformative potential, including its ability to enhance security through encryption and decentralization, improve trust and data integrity, and eliminate intermediaries, enabling faster transactions and cost reduction. However, challenges such as scalability issues, high energy consumption in cryptocurrency mining, privacy concerns due to the immutability of records, and the need for regulatory adaptation remain significant barriers to widespread adoption. This review provides a comprehensive overview of blockchain's opportunities and limitations, offering valuable insights for researchers and practitioners aiming to leverage this technology in various fields.