This research focused on assessing groundwater quality in the Alton Kopri, Kirkuk Province, northern Iraq. Twenty-two samples were selected from twenty-two wells randomly distributed in the study area to assess the subsurface water for drinking purposes. The samples were analyzed for parameters (pH, T.D.S, Na⁺, Mg²⁺, K⁺, Ca²⁺, NO₃^-, SO₄^2-, HCO₃^-, and Cl^-) to compute Water Quality Index (WQI). Pearson's correlation and principal components analysis (PCA) were adopted to study the physicochemical parameters sources in groundwater. The dominant cations were ordered as follows: Na > Ca > Mg > K, and the dominant anions were arranged as follows: SO₃ > Cl > HCO₃ > NO₃. The average concentrations of TDS, Ca, Mg, Na, SO₄, and Cl were 1118.45, 173.54, 132.59, 341.36, 873.63, and 414.50, respectively, surpassing the maximum permissible limits set by WHO. The average concentrations of K, NO₃, HCO₃, and pH were 5.90, 35.02, 172, and, 8.05 respectively, and were within acceptable limits. The WQI ranged from 33.3 to 1024. The findings designated that 23% of the samples were categorized as excellent, 27% as good, 18% as poor, 14% as very poor, and 18% as inappropriate for drinking purposes. The Pearson correlation matrix has been created and analyzed to appraise the important factors impacting groundwater quality. The PCA technique was adopted to analyze water quality parameters, resulting in the extraction of three components that together account for 81.574% of the total variance. The extracted components suggest that the predominant contributors to groundwater contamination include geological characteristics, agricultural practices, precipitation, domestic wastewater, and manufacturing activities. This study stands out from others due to various local factors that impact groundwater quality in the Alton Kopri area. Agricultural practices, including fertilizer and pesticide use, lead to chemical seepage into the aquifer, while pastoral activities contribute organic contaminants. Insufficient sewage infrastructure in some areas results in wastewater infiltration. The region’s geology, dominated by limestone and clay, affects groundwater hardness and heavy metal levels. Additionally, the Little Zab River, which recharges groundwater, can transport pollutants during floods. Minor industrial activities may also introduce trace metals and oils. Understanding these influences is key to interpreting water quality variations and informing sustainable management strategies.

Vehicle tires are subjected to sudden and significant loads when driving at high speeds due to unexpected bumps in the road. To reduce the occurrence of these cracks, this research will address the occurrence of these cracks using various techniques. The Solid Works program will be used to design various wheel models and reinforce the areas where cracks occur. The models will then be loaded into the ANSYS program to determine the various deformations and stresses they experience, as well as the degree of improvement of the wheel models whose designs have been developed. The results demonstrated that the deformation models' values were substantially lower than those of the first model, with the third model showing the biggest percentage decrease (59.56%). The results showed that the Von Mises models' values and the maximum shear stress were considerably lower than those of the first model, with the third model showing the biggest percentage decline at (68.12 and 61.2%), respectively. The fact that these improved percentages are reached in the three models (64.74, 93.12, and 88.72%) indicates that the fatigue damage values of the three improved models in the design are significantly lower than the fatigue damage values of the first model. It is clear that the third model, with a safety factor increase of 93.12%, has the highest increase. This suggests that the third model, which has three collars reinforcing it in the region where cracks are developing, is the best.

Most road accidents are caused by drivers engaging in driving practices and being distracted while driving, which contributes to the concern of road safety awareness in society today. Activities like using a phone while driving carelessly and displaying driving habits increase the likelihood of these actions leading to an accident. In this research paper, we introduce a driver behavior detection system based on CNN technology that employs a 22-layer convolutional neural network (CNN) to identify intricate behaviors in real time situations. The proposed method systematically incorporates layers with 3×3 kernels and ReLU activations, along with max pooling layers to classify five main categories: turning movements, using a phone for texting or talking, safe driving practices, and other activities. The system underwent training and testing on a dataset of 10776 RGB images, in 480×640 pixels resolution, depicting driving situations and surroundings. The first test results showed a notable drop in misclassification errors and a notable rise in accuracy rates for classification tasks using a CNN approach could have advantages for enhancing vehicle safety systems by accurately and swiftly detecting driving behaviors to reduce accident risks and enhance road safety overall. The experiment findings were obtained through GPU processing in Matlab. Resulted in a training accuracy of 100% along with a testing accuracy of 100%, achieved within 23.46 seconds. The method suggested for assessing driving habits has been effectively executed.

Multi-modal transportation systems (MMTS) play a critical role in enhancing urban mobility by integrating multiple transport modes to improve efficiency and accessibility. This paper presents a comprehensive review of modelling approaches in MMTS, focusing on optimization techniques, travel behaviour analysis, and network resilience. The study synthesizes a range of methods, including agent-based models, equilibrium approaches, and data-driven simulations, aimed at improving system efficiency, adaptability, and user satisfaction. While significant strengths include real-world data integration and dynamic performance modelling, a thematic analysis reveals recurring limitations across studies, such as model assumptions, data limitations, limited behavioural realism, narrow scope, and high computational complexity. These weaknesses constrain the scalability and applicability of current MMTS models. The review emphasizes the need for frameworks that integrate real-time analytics, support diverse travel behaviours, and incorporate emerging trends like Mobility-as-a-Service (MaaS) and micromobility. It concludes by recommending that future research prioritize cross-regional validation, computational scalability, and dynamic system responsiveness to ensure MMTS can meet evolving urban transport demands. This synthesis serves as a critical reference for researchers, planners, and policymakers aiming to develop resilient and efficient multimodal transit networks.

This work analyzes how inlet tube velocities (2, 4, and 6 m/s) impact the water-ethylene glycol mixture’s flow behavior within the inlet tube in the engine oil heat exchanger cooling system in terms of temperature distribution, oil viscosity, pressure difference, and flow velocity distribution. From simulation findings, oil's viscosity reduced from 0.021 Pa.s at 2 m/s flow velocity to 0.015 Pa.s at 6 m/s flow velocity, suggesting a direct relationship between the thermal and flow rate. Pressure drop rises with the inlet velocity increase, from 2 to 6 m/s, with values of 0.45 and 0.92 Pa. In the tube-end bending investigation, influences on the velocity profile for emulsion were observed. Depending on the velocity gradient in curved tubes at 2 and 1.2 m/s was the maximum velocity at the sharply curved wall, 2.3 m/s, and at the inner wall, 1.7 m/s. The gradient at 4 m/s was 1.6 m/s, whereas at 6 m/s the gradient was 3.0 m/s. Heat transfer coefficient increases with velocity, ranging from 500 W/m²·K at 2 m/s to 950 at 6 m/s. This shows remarkable enhancement in convective heat transfer resulting from increased turbulence. There is also significant fluctuation in the velocity inside the tubes, and while it increases, the velocity towards uniform flow distribution will improve heat transfer within the tubes. This change inside the tubes reduces uneven heat distribution and helps increase the flow rate, especially when temperature differences grow and the main fluid experiences strong heat transfer. Heat transfer rate rises from 15 kW at 2 m/s velocity to 35 kW at 6 m/s velocity, and efficiency increases to 70% due to increasing inlet velocity.

This study was conducted during the 2023-2024 growing season at the Agricultural Technical College, Northern Technical University, Nineveh Governorate, to evaluate the effects of organic and amino acid fertilization on the vegetative growth and yield performance of eggplant (Solanum melongena L.). A randomized complete block design (RCBD) was employed, comprising two fertilizer types (organic and amino acid) and three concentrations (0, 10, and 15 g·L⁻¹), resulting in six treatment combinations, each replicated three times, for a total of 18 experimental units. Statistical analysis was performed using SAS software. Significant improvements were observed in several vegetative and physiological parameters, including plant height, number of branches, stem diameter, and chlorophyll content. Organic fertilization produced the most substantial increases in plant height (43.000 cm), number of branches (5.444 branches·plant⁻¹), stem diameter (16.367 mm·seedling⁻¹), and leaf chlorophyll content (22.723 SPAD), significantly outperforming amino acid fertilization and the control. In contrast, amino acid fertilization resulted in a higher number of fruits per plant (5.888 fruits·plant⁻¹). The interaction between organic fertilization and the 10 g·L⁻¹ concentration yielded the highest plant height (52.667 cm) and number of fruits (6.000 fruits·plant⁻¹). Additionally, the combination of organic fertilization and 15 g·L⁻¹ concentration significantly increased the number of branches (6.666 branches·plant⁻¹) and chlorophyll content (29.417 SPAD). The stem diameter reached its maximum value (20.050 mm·seedling⁻¹) under amino acid fertilization at a concentration of 10 g·L⁻¹. The control treatment consistently produced the lowest values across all evaluated parameters. These findings demonstrate that both organic and amino acid fertilization can significantly enhance specific growth and yield components in eggplant, with organic fertilizers exhibiting superior overall performance in vegetative traits and amino acids promoting reproductive output. The results highlight the potential of integrating amino acid and organic nutrient management strategies to optimize eggplant productivity under field conditions.

This study introduces Chebyshev Metaheuristic Solver Approach (CMSA), a new computational approach, to get approximate solutions with high-accuracy to a vast range of linear and non-linear differential equations (DEs). The main idea is changing the differential problem into a continuous optimization task. First the approximate solution was written as a truncated series of Chebyshev polynomials, where they are chosen due to their numerical stability and optimal approximation properties. The undetermined coefficients of this series turn into the decision variables in an optimization task. The objective function is derived from the residual of the differential equation, integrated with penalty terms to achieve initial or boundary conditions enforcement. Then the Flower Pollination Algorithm (FPA), a nature-inspired metaheuristic algorithm, is used to find the optimal polynomial coefficients via the minimization of this objective function. This hybrid approach symbiotically integrates the spectral method’s exponential convergence properties with the metaheuristic’s powerful global search capabilities. The demonstration of the efficiency and robustness of the approach is done through rigorous computational tests on benchmark problems, involving integro-differential and non-linear boundary value problems. A comparison of the computed results with known exact solutions, validates this optimization-driven spectral technique, showing excellent accordance. The approach is simple to implement and displays outstanding potential for tackling complex DE systems where traditional methods maybe stick.

This study focuses on using bamboo as a substitute for indoor flooring, emphasizing its sustainability, economic benefits, and aesthetic potential. This research systematically reviewed existing literature using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to analyze factors influencing the adoption of bamboo flooring materials as an alternative sustainable material. These factors include cost, durability, sustainability, availability, and aesthetics, positioning bamboo floors as a viable sustainable alternative to other flooring materials. A total of 50 studies were reviewed, with 20 meeting the inclusion criteria, showing a noticeable increase in interest in bamboo flooring in recent years. The findings highlighted bamboo floors' cost-effectiveness, visual appeal, and strong durability when properly processed and treated. The chemical and mechanical properties of bamboo contribute to the durability of bamboo flooring, especially after the manufacturing process. However, some issues persist, like high transportation costs, limited market reach, and low awareness in many regions, particularly outside of tropical areas.

A transition toward organic fertilizers has increasingly been adopted as a key strategy to support sustainable agriculture, particularly in highland farming systems. In Sumber Brantas Village, Batu City, East Java—one of Indonesia's major highland potato-producing regions—potato (Solanum tuberosum) cultivation plays a critical role due to its high market value, adaptability to altitude, and importance as a carbohydrate source. This study investigated the effects of Tithonia diversifolia-derived organic fertilizer and varying plant densities on potato growth and productivity. Four fertilizer application rates (0, 120, 175, and 230 kg N/ha) and three plant densities (35,000, 47,000, and 71,000 plants/ha) were evaluated using a randomized block design arranged in a split-plot layout. Results indicated that the application of Tithonia diversifolia organic fertilizer significantly enhanced plant height, tuber biomass, and nitrogen (N) uptake. The highest fertilizer dose (230 kg N/ha) was associated with a 25% increase in N absorption and a 28% improvement in tuber yield relative to the unfertilized control. However, plant density did not exert a statistically significant effect on measured agronomic parameters. These findings underscore the agronomic value of Tithonia diversifolia as an organic fertilizer capable of improving nutrient use efficiency and tuber productivity under highland cultivation conditions. The results support the integration of this bioresource into sustainable nutrient management strategies for potato production, particularly in regions where agroecological conditions favor organic inputs.

The rise of distributed applications and cloud computing has created a demand for scalable, high-performance key-value storage systems. This paper presents a performance evaluation of three prominent NoSQL key-value stores: Redis, Aerospike, and Dragonfly, using the Yahoo! Cloud Serving Benchmark (YCSB) framework. We conducted extensive experiments across three distinct workload patterns (read-heavy, write-heavy), and balanced while systematically varying client concurrency from 1 to 32 clients. Our evaluation methodology captures both latency, throughput, and memory characteristics under realistic operational conditions, providing insights into the performance trade-offs and scalability behaviour of each system.

Takaful is an alternative Shariah compliant insurance product which is being offered by more than fifty takaful companies in Pakistan. Currently takaful market is facing low penetration due to many challenges including regulatory or compliance, payment efficiency, fraud prevention, transparency. Blockchain technology, a decentralized, transparent and trust-based system, which could address these issues efficiently and effectively by offering smart contracts.

This paper examines Blockchain's feasibility and its impact on Pakistan’s insurance market in general and takaful sector in particular, using a systematic literature review (SLR) and case studies from Malaysia, the UAE, and Indonesia. In Malaysia and the UAE, the success of using Blockchain in Islamic finance highlights potential efficiency and security benefits. However, in Pakistan's regulatory ambiguity, lack of Shariah-compliant frameworks, limited human expertise, and low industry readiness are few factors which needs to look at, by the Government of Pakistan, and this could lead to sustainable growth in Pakistan’s digital financial sector including takaful industry. The Policymakers, Ministry of science and technology and State of bank of Pakistan could benefits from this study by creating a regulatory sandbox and offer current takaful operators full IT and regulatory support to develop Shariah-compliant smart contracts. The results reveal that, Takaful operators should develop and test pilot digital projects focusing on cost reduction, fraud prevention, automation of standards claims where possible, streamline the insurance industry and takaful operations and this leads to not only increase takaful penetration but also help Pakistani takaful market to align with global digital trends.

Solar energy for power generation has increased significantly due to population growth and economic expansion. Solar irradiance is a primary determinant of solar photovoltaic (PV) technology. However, high-quality ground-based solar irradiance measurements remain scarce. Accurate prediction of global solar irradiance has become essential for grid distribution, financial planning, performance assurance, operational efficiency, and safety in solar PV systems. In this study, various Machine Learning (ML) algorithms—including Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), Decision Tree (DT), Multilayer Perceptron (MLP), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Linear Regression (LR), were employed to predict solar irradiance in Jerusalem, Palestine. Data was collected from a meteorological station in Jerusalem over a one-year period, from January 1, 2023, to December 31, 2023. Eight critical features influencing solar irradiance prediction were collected and analyzed, including diffuse irradiance, direct irradiance, mean temperature, pressure, relative humidity, wind speed, and wind direction. The models' accuracy was assessed using the coefficient of determination (R2), Root Mean Square Error (RMSE), relative Root Mean Square Error (rRMSE), and Mean Absolute Error (MAE). The results indicate that the RF model achieved the highest accuracy in predicting solar irradiance, with metrics of R2=0.90, RMSE=104.58 W/m2, rRMSE=0.24, and MAE=63.29 W/m2.

This article proposes an optimization framework for bus elimination in power system networks using the Kron Reduction Method (KRM), aimed at reducing system complexity while maintaining computational accuracy. Using the IEEE 14-bus system as a testbed, we evaluate seven sequential reduction scenarios, reducing the network from 14 to 7 buses. To improve the quality of reduction, the study integrates Kron’s Loss Equation (KLE) with electrical centrality measures to prioritize passive bus elimination based on loss sensitivity and network topology. The results demonstrate that indiscriminate bus removal can cause substantial deviations in voltage profiles and power loss estimations, whereas the proposed loss-aware approach achieves improved accuracy and stability in reduced models. Visualizations of Y_-bus matrix transformations and voltage deviation metrics illustrate the trade-offs between model simplification and fidelity. The proposed methodology supports real-time system modelling and is scalable for larger grid applications. Future extensions include automated reduction strategies leveraging machine learning and applications in dynamic grid optimization.

In real-life conditions, rubber components in truck tires are exposed to fluctuating loads, often leading to failure from the formation and growth of cracks-an issue especially common in retreaded tires. Tire retreading is one of the first methods of recycling tires by extending their life cycle, but the lack of knowledge and extensive research on quality tire retreading is putting the lives of road users at stake. The main objective of this research work is to study the spliced pre-cured treaded liners (PTLs) by its mechanical properties, and their endurance life cycle under variable stresses. Two types of PTL rubber compounds were studied: Compound 1, designed for steering axle tires, and Compound 2, designed for driving axle tires. These positions on a truck typically bear the highest loads, requiring materials with strong mechanical properties. The study evaluated these compounds using three tests: hardness, tensile strength, and endurance. The hardness test measured the resistance of the rubber to indentation using the Shore A scale, a standard method for rubber materials. Compound 1 (for steering tires) showed a Shore A hardness value of 62, while Compound 2 (for driving tires) had a value of 66. Both values fall within the industry standard range of 50–70 for tire rubber, indicating that the splicing process did not negatively affect the curing or hardness of the PTL materials. The tensile test demonstrated that the spliced joints maintained strong performance, with only a minor reduction in maximum load and elongation compared to unsliced material. The endurance test further confirmed the durability of the spliced PTL under simulated real-world conditions. Overall, the results show that the splicing and use of pre-cured treaded liners-incorporating recycled tread waste-can maintain the necessary mechanical properties for demanding truck tire applications, while also supporting more sustainable and efficient production practices.

The overall task of this study is to determine an efficient control strategy and optimize the operation of the sequencing batch reactor (SBR) plant for treating the domestic wastewater of Al-Nasiriya City. During this research, a pilot-scale SBR unit was constructed to treat real domestic wastewater. The constructed SBR unit comprised: a collection tank of (250 L); an SBR reactor of (150 L); mixing and aeration units, PVC pipes, an Air flowmeter, influent pumps, effluent pumps, a programmable control panel (PCP), and other accessories. The raw wastewater characteristics of COD, TKN, NH4-N, and NO3-N ranged between230-627mg/l, 29-55mg/l, 19-36mg/l, and 0.14-0.57 mg/l, respectively. The results showed that the SBR system can be successfully used for treating domestic wastewater of Al-Nasiriya city and achieving high removal rates for pollutants which were 83%, 86%, and 66% for COD, NH4-N, and TN, respectively, and the effluent matching with the Iraqi standard limitations for the effluent of WWTP. The results showed that the optimal scenario is three steps of Anoxic/Oxic/Anoxic. The reaction phase is achieved in 2/6/1 hr for Anoxic/Oxic/Anoxic conditions respectively, and a sludge age of 10 days to achieve an optimum removal rate for COD and TN components.