Al-Salam Journal for Engineering and Technology

The real concern, which represents the dangerous places in the tanks containing liquid materials, is those where cracks occurs in the walls of those tanks. To determine the zone areas where the maximum precipitation occurs, a study of those walls had to be conducted and the ideal dimensions of those walls had to be searched for in order to control the highest precipitation and thus reduced the possibility of cracks occurring as a result of the large precipitation of the walls and thus avoid being exposed to the risk of the liquids containing in those tanks penetrating into the walls and thus the possibility of the collapse of those and exposure to the loss of liquids and the economic losses and environmental risks that it causes. This paper deals with a new derivation to prediction the elastic behavior of tank wall supported with fixed ended on three edges and freely at the fourth and subjected to hydrostatic liquid triangular distributed load using central finite difference method. The new model was verified by compares with the classical model obtained by Timoshenko and Woinowsky-Krieger based on theory of plates and shells with a good agreement. The present parametric study deals with tank wall with length to depth ratios of 1, 2, 3 and 4 were considered. The results obtained from the analysis showed that the maximum deflection increased by 10.3%, 23.7% and 32.5% for L/h=2, 3, and 4 compares with L/h=1.

Lanthanum oxide (La₂O₃) thin films were successfully synthesized by the pulsed laser deposition (PLD) technique using a sintered La₂O₃ target prepared with a PVA binder. The structural, morphological, vibrational, and optical characteristics were systematically examined to evaluate their suitability for optoelectronic applications. X-ray diffraction confirmed a polycrystalline hexagonal A-type La₂O₃ structure with average crystallite sizes ranging from 20–30 nm and lattice parameters a = 3.94 Å, c = 6.13 Å. Atomic force microscopy revealed a compact granular surface with uniform topography (Sa = 30.17 nm, Sq = 38.34 nm), indicating homogeneous grain growth. FTIR and Raman spectra identified the fundamental La–O stretching vibrations at ~550–600 cm⁻¹, confirming phase purity and the absence of hydroxyl or carbonaceous impurities. Optical characterization demonstrated high transparency (~74 % in the visible range) and strong UV absorption, with a direct optical band gap of 3.81 eV. The extinction coefficient decreased from 5×10⁻¹³ to 1×10⁻¹³ across the visible region, highlighting low optical losses. The integration of structural, morphological, and optical analyses establishes a clear correlation between PLD parameters and film quality. The obtained results underscore the potential of PLD-grown La₂O₃ films as high-k dielectrics, UV-transparent layers, and thermally stable coatings for advanced optoelectronic and photonic devices.

Due to The rapid expansion of Internet of Things (IoT) technologies in many different fields in our lives has led to critical challenges of service security, also while most notably the risky threat of malware. Despite the many methods for detecting this malicious software, the resource constraints of IoT devices remain a fundamental challenge that reduces the effectiveness and operational capacity of these solutions. This study conducts a comprehensive survey to identify effective detection strategies in resource-constrained environments and aims to evaluate the feasibility of integrating Context Awareness with machine learning and deep learning approaches to establish a detection system that adapts to device resources and network conditions. This study highlights the fact that most of today\\'s AI solutions are fundamentally defective: while highly accurate, they are unable to adjust and continuously learn to adapt to modern malicious patterns over a long scope. In this survey, we introduce our view, the main reason for this weakness is the omission of the context-awareness factor. and we will provide insights and criteria for designing lightweight, context-aware AI models to achieve the perfect balance between detection accuracy and model flexibility and incremental learning in resource-constrained IoT environments, to counter advanced threats.

A new selection of input-output variables for a simplex algorithm in linear programming (LP) problems is introduced in this paper. An appropriate pivot rule to find the entering and leaving variable is one of fundamental steps for simplex method. Unfortunately, such a classical pivot does not only fail to bring about any savings in a associated computational cost, it may also lead to cycling problems (which are typically solved by Bland\\'s scheme) or simply it fails to improve the objective function. Combining the perturbation scheme of suboptimality and cycling, with Dantzig\\'s pivot rule we propose a procedure that addresses these issues. The new rule has an improved algorithm effectiveness in the sense of the reduction for the number of iterations. When compared to those of existing pivot rules, calculational results demonstrate that the suggested rule is not only optimal during each iteration but also facilitates improvements in computation time for linear programming problems. The cycle problem of the Dantzig\\'s simplex pivot rule is settled by a new pivot rule proposed, which similarly as much improves the objective function How possible with every iteration. Moreover, it can have the optimal LP value in less iterations.

Image analysis for human face recorded a wide range of attention by different authors over the last decades. Despite the significant accuracy yielded by Deep Learning Networks (DLN), Artificial Neural Networks (ANN) still has own contribution. This is due to the obstacle detected in DLN classification, where the almost all control on the type of extracted features is by the adopted DLN. On the other side, previous studies on age estimation showed that each period of human life has its own age-progression signs (features), which differ from others in different age periods. As a result controlling the type of extracted features, using DLN, is a challenging task, which can be controlled using the ANN networks. This paper proposes a pyramidal classification system of two stages, within the first on it uses texture features to classify the human age into four age periods child (0- 8) years, teenager (9- 19) years, adult (20- 49) years, and old (50 and above) years. The number and length these periods are determined due to the changes witnessed the period. In the second stage, the system estimates the age within each period by training and testing the features of the specific period. Due to the wide range of periods, the yielded results recorded high accuracy comparing with other works in age estimation and even with the second stage of this work. Determining the exact ages (±1 year) contains more false positive and false negative cases than the first stage, which means less accuracy. As benchmarking with state of art in the field of age estimation, this paper yielded superior results. The experimental results showed considerable performance yielding (97.75%) for the best accuracy of estimation with (96%) as average of yielded accuracy.

In this paper, we provide an enhanced artificial intelligence (AI) classification strategy for educational achievement prediction. As models that are reusable, Decision Trees (DT) are subject to overfitting and poor generalization. To significantly reduce the number of dimensions, our model combines Principal Component Analysis (PCA) with collaborative learning methods, particularly Random Forest, AdaBoost, and Gradient Boosting.A Python-based experimental framework is implemented using data on actual pupil achievement. The use of PCA and scaling of features greatly increased generalisation achievement, decreased model complexity, and increased training efficiency. Furthermore, GridSearchCV was employed for systematic hyperparameter optimization, leading to noticeable improvements in the performance of the proposed hybrid classifiers. Among the evaluated models, the With 95% accuracy, 89% recall, and a 92% F1-score, the random forest classifier produced the best results. In terms of classification accuracy, the suggested method performs 17.3% better than the baseline Decision Tree model. These results show that the creation of scalable, dependable, and resilient AI-driven instructional decision-making systems can be aided by combining ensemble learning methods with PCA. Future research will solve class imbalance and expand the framework to multi-class categorisation contexts. This study helps provide a modular, comprehensible, and useful approach for academic decision-making.

Box culverts represent one of the most important water conveyance structures. Their hydraulic design requires hydrological information, including available discharges and water levels at the upstream and downstream ends of the structure. The hydraulic performance represented by the box culvert design dimensions was examined using HY-8 software, a versatile tool from the U.S. Federal Highway Administration for analyzing various culvert shapes and configurations. This study investigated the hydraulic design of a culvert by using HY-8 software to design and analyze the box culvert while accounting for the hydraulic behavior of the Al Musayab project surrounding area, Babil City, Iraq. HY-8 can model box culverts in a horizontal path to approximate the behavior of hydraulic structures, according to the available discharges, upstream and downstream water levels of the structure, several box culvert simulations that includes (2x2) m, (2.5x2.5) m, (3x3) m, and (4x4) m were tested to select the optimal box design. The design discharge of 35 m3/s for the culvert's incapacity to carry the design flow, the design considered a 4×4 m box shape, the headwater elevation is 31.9 m, which is below the roadway elevation of 33 m. The results of this study provided an evaluation and selection of the best dimensions for the box culvert according to the available discharges. Furthermore, the results shows how to improve the shortage by using an inflatable weir at the exit of the structure so as to increase the water levels along the structure.

A VANET (as a class of MANET) which is formed by vehicles communication, links moving nodes (vehicles) and infrastructure for intelligent transportation solutions. Dynamic Source Routing (DSR) and Ad-Hoc On-Demand Distance Vector (AODV) are essential for data transfer in these dynamic networks. However, control message exchange greatly impacts these protocols' performance, which impacts network scalability, latency, and packet delivery efficiency. This study examines how control messages overhead affect VANET AODV and DSR protocol efficiency in various mobility situations and traffic volumes. Comprehensive simulations analyze packet delivery ratio, end-to-end delay, and routing overhead. Our findings suggest the use of DSR as a protocol evaluated for comparative analysis (low-density networks) with reduced control overhead while AODV is more adaptable in (high-mobility networks), depending on control message frequency. This work analysis and investigates the impact of control messages overhead on VANET routing protocols (AODV – DSR) for real-world vehicles enhancing efficiency and reliability with simulations under varying network sizes, number of sources, and pause times.

Training deep learning models is often a time-consuming process, especially when hardware resources are tight. This bottleneck slows down experimentation and increases development costs. The primary goal of this study was to investigate whether distributed computing could cut down on training time while keeping model accuracy intact. To test this, we trained a Convolutional Neural Network (CNN) on the CIFAR-10 dataset using two different configurations: a standard single-device setup and a distributed synchronous setup powered by TensorFlow’s MirroredStrategy. To ensure a fair comparison, both trials used the exact same architecture, hyperparameters, and preprocessing. The results were clear: distributed training reduced the total time by about 19.5%, with validation accuracy remaining nearly identical to the single-device approach. These findings suggest that distributed training is a practical way to accelerate deep learning workflows without sacrificing performance, serving as an efficient solution even in environments with limited hardware.

River water is a fundamental necessity for all aspects of life, especially drinking water. Millions of Iraqis depend primarily on the waters of the Tigris river. The aim of the study is to evaluate the water quality of the Samara Barrage Lake for the period between September 2024 and May 2025. Water samples taken from the lake were analyzed to determine fourteen physical and chemical parameters, namely pH, turbidity, DO, TDS, BOD5, TSS, SO4, NO3, Cl-, PO4, and ions (Ca, Mg, Na, and K). Ten points were taken within the barrage lake, distributed along its edges and center. Geographic Information Systems (GIS) techniques were used to analyze and map the spatial distribution of the selected indicators. The Canadian Water Quality Index was also used to determine the lake\'s water quality, and the results revealed that water quality is subject to seasonal fluctuations. In the autumn season (September 2024), the pH, turbidity, and BOD exceeded the limits permitted for drinking water. In contrast, during the winter (January 2025) and spring (May 2025), turbidity and BOD remained above the World Health Organization\'s permissible limits. This indicates that the Tigris River and Samara lake were subjected to certain conditions, such as erosion, soil degradation, and riverbed disturbance, as well as human activity along the river and even the lake\'s edges, and water pollution from organic matter due to agricultural and animal waste, in addition to inadequately treated sewage discharged into the river. All other parameters remained within recommended limits throughout the study period. According to the Canadian Water Quality Index (CWQI), the lake\'s water quality was generally \"good,\" with CWQI values of 81.38 in the autumn, 82.08 in the winter, and 81.8 in the spring. These results highlight the impact of seasonal variations on lake water quality and demonstrate the effectiveness of integrating environmental indicators with GIS-based spatial analysis for surface water monitoring. The results of this study represent a valuable reference for sustainable water resource management and improving water quality in the Samarra barrage lake.

The photovoltaic (PV) systems operate under Partial Shading Conditions (PSC), which severely degrade the PV system performance as number of local maxima appears in power-voltage (P-V) characteristic curve causing to generate the local maximum points in conventional control algorithms. Considering shading’s profound effect on energy production and system reliability, this paper recommends a new Maximum Power Point Tracking (MPPT) strategy based on the Wave Function Collapse (WFC) algorithm. The proposed approach adopts a probabilistic state-selection scheme, conducive to select the proper navigating steps in the search space for GMPP. The competition between exploration and exploitation is adaptively balanced in this algorithm, which enables it to perform stably at different irradiance levels and levels of noise. Extensive simulation results for a wide range of PSC scenarios show that, in comparison to the classical Perturb and Observe (P&O) and Particle Swarm Optimization (PSO), WFC-based MPPT approach enables a swifter tracking effort as well as a better energy harvesting efficiency. The results show that the proposed approach is strong in solving the drawbacks of metaheuristic and classical trackers under dynamic environmental environment limitations.

The rapid evolution of Large Language Models (LLMs) has enabled the generation of text that is increasingly indistinguishable from human writing. While this advancement benefits various sectors, it raises significant concerns regarding academic integrity, security, and the spread of misinformation. This paper presents a comprehensive systematic review of AI-Generated Text Detection (AIGTD) techniques, evaluating their current efficacy and limitations. We categorized and analyzed various detection methodologies, including statistical and stylometric approaches, transformer-based models, watermarking strategies, and hybrid frameworks. In addition, the analysis covered 16 prominent datasets, such as HC3 and M4 for size, diversity, and limitations and domain bias, along with tools such as GPTZero, Originality.ai, and DetectGPT, which were compared on language support, usability, and detection principles. Our findings reveal that detection accuracy averages 80-99% on in-domain benchmarks but drops to 60-75% against adversarial attacks or cross-domain texts. Datasets often lack multilingual coverage and real-world diversity. Tools show high computational costs and biases toward English, with limited Arabic support. Hybrid methods outperform singles but face scalability issues. Although the field has progressed, developing robust, unbiased, and computationally efficient systems is essential. This review concludes by proposing future research directions to enhance the reliability of detection systems in an era of advancing AI.

Accurate forecasting of solar photovoltaic (PV) energy generation remains a challenging task due to the strong variability and nonlinear influence of meteorological conditions, which directly affects the reliability of energy management systems. This paper proposed the comparative framework of models for highly accurate forecasting of solar photovoltaic (PV) energy generation, utilizing state-of-the-art machine learning (ML) and deep learning (DL) approaches to substantially boost forecasting reliability under varying environmental conditions. All five models, Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) and families of models, gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) have been implemented and tested using realistic meteorological data and irradiance data directly retrieved from the GRIDouble project, supported in I-NERGY from the HORIZON 2020 program of the European Union. More specifically, the dataset consisted of hourly measurements of such variables as air temperature, cloud opacity, diffuse and direct irradiance, and global horizontal irradiance, coupled with solar energy production reading. To maximize the learning power of time-patterned data and to minimize its variance, a comprehensive pre-processing pipeline was implemented that included temporal features extraction, cyclical encoding, as well as min–max normalization. Experiments confirmed that XGBoost yielded the highest forecasting performance with R² = 0.9478, MSE = 3191.15, RMSE = 56.49, MSRE = 27.59. The remaining models, bidirectional LSTM and MLP, demonstrated R² = 0.8662 and R² = 0.8581, respectively, highlighting their substantial ability to model the temporal and nonlinear environment. In summary, XGBoost can be praised as the top model with the achieved the best performance in this experimental setting. It also presents incredible potential for smart-grid optimization and renewable energy forecasting.

Cement-based materials play a vital role in modern construction, where mechanical performance and durability are of primary importance. This study investigates the microstructural and mechanical properties of Portland cement-based systems using electron paramagnetic resonance (EPR) spectroscopy in combination with compressive strength tests. The results indicate that an increase in the specific surface area of cement particles leads to a higher concentration of spin centers, which influences hydration processes and strength development. The addition of activated Portland cement in the range of 5–15 wt.% resulted in an improvement in compressive strength of up to 15%. However, a further increase beyond this range caused a reduction in strength, which may be attributed to decreased material density and less efficient particle packing. High-strength and rapid-hardening cements exhibited higher concentrations of spin centers and improved mechanical performance. These findings confirm that EPR analysis is a useful tool for evaluating the relationship between microstructural characteristics and the mechanical behavior of cement-based materials.

Lithium aluminum silicate (LAS) glass-ceramics materials are promising or under investigation in the field of dental restorations, but are not currently the most widely used due to their aesthetic performance. However, their materials often exhibit lower hardness under intensive contact conditions compared to alternative prosthetic crown systems. This study aims to determine an optimal temperature–time schedule for the nucleation and crystallization processes of Li–Al–Si glass, as well as clarify the correlation between translucency and surface hardness .The heat treatment protocol was designed based on differential thermal analysis (DTA) results and established thermal protocols. Nucleation was performed at a fixed temperature of 520°C for durations ranging from 0 to 60 minutes. Crystallization stages were conducted at 750°C and 900°C to evaluate the impact of crystal density (nucleation) and crystal growth (crystallization) on the materials properties. Vickers hardness (HV) was measured, with the highest value (~7.66 GPa) observed in samples treated at 750°C. The results indicate that optimal translucency (HTP) is achieved through a fine, homogeneously distributed crystalline phase, whereas uncontrolled crystal growth at higher temperatures leads to a decrease in the translucency parameter (TP), increasing opacity.

The IEEE 802.15.4 standard is known to be widely utilized in Wireless Sensor Networks (WSNs) for their time sensitive applications. However, the default contention-based MAC operations suffer from delays, resource wastage, etc. with heterogeneous dynamic traffic. Though some Guaranteed Time Slots (GTS) are offered, they make little to no use of adaptable allocation GTS strategies to improve the real-time operation of the network. This paper presents GTS Adaptive Priority-Based GTS Allocation Algorithm for the beacon enabled mode of IEEE 802.15.4. It allows better MAC-layer scheduling to improve differentiation and service facilitation for time-sensitive data. The performance of the algorithm was assessed through comparative analysis against the standard CSMA-CA operation in MATLAB simulations with the same topology and traffic distribution. Simulation results prove the predicted GTS allocation algorithm to significantly optimize the network performance across multiple performance metrics. The CSMA-CA algorithm provides data transmission rates of 29.9 and 40.0 (lower and upper bounds respectively). Proposed algorithm offers these same metrics at 92.6 and 94.3, respectively. The method proposed shows low packet loss irrespective of the communication range. On the contrary, CSMA-CA suffers from high packet loss under high traffic and poor channel conditions. With the results confirming that adaptive and priority-aware GTS allocation is a mechanism that improves throughput, reliability, and determinism at the MAC layer, it means that IEEE 802.15.4 networks can be utilized for real-time and high-throughput applications in WSNs.

The research explores impact of compaction and moisture on the California Bearing Ratio (CBR) of residual lateritic soils collected from Kontagora and its surrounding areas in Niger State, Nigeria. 20 disturbed soil samples were used in laboratory testing to exermine the effects of the variables on CBR values. The tests conducted include: Sieve analysis, Atterberg limit, Compaction test and CBR using both British Standard Light (BSL) and British Standard Heavy (BSH) compaction efforts. The results show that soils compacted using heavy compaction effort achieved higher compaction, with an average MDD values of 1.95 g/cc when compared to 1.84 g/cc for BSL. Samples compacted with light compaction effort generally showed higher OMC, averaging 13.91%, eventhough both compaction efforts exhibited good consistency in MDD values. When the samples were soaked, there was a significant reduction on CBR values for both compactive efforts. Under the heavy compaction, the mean CBR decreased from 89.83% (unsoaked) to 69.22% (soaked) with a total reduction of 20.61%. Samples under BSL experienced a more pronounced decline, with the mean CBR dropping from 74.96% to 52.09%, projecting a 22.87% reduction. The statistical analysis indicated that these reductions were significant, reinforcing the critical role of moisture in diminishing soil strength. Although the heavy compacted soil samples consistently showed higher CBR values than BSL compacted samples in both soaked and unsoaked conditions. The high loss in CBR value of the soil samples when soaked in water stresses the importance of adequate moisture control. Based on these findings, the study recommends the adoption of heavy compaction combined with effective moisture control and drainage measures to enhance pavement durability and ensure reliable road performance in the study area.

The worldwide change towards sustainable strength wholes has positioned cosmic photovoltaic (PV) electronics as a cornerstone of future capacity production. However, the efficiency and capacity productivity of PV systems are precariously sensitive to partial shielding, place non-uniform shadow coverage starts disparity deficits, hot spots, and characteristic multi-peak capacity manufacturing that severely compromises strength yield. This inclusive literature review resolves 53 peer-inspected studies (2010-2025) to examine the impact of biased blackening across various PV uses, containing utility-scale methods, construction-joined photovoltaics, agrivoltaics, and electric taxi taxing infrastructure. Our study analyzes that obscuring can cause excessive capacity misfortunes eclipsing 80% however littlest addition, accompanying belongings changing significantly established makeup arrangement and material environments. The review evaluates a range of relief strategies—from not original prevent diodes and progressive array reconfiguration methods to AI-excite listening orders and grown maximum capacity point pursuing algorithms. The verdicts signify that while having to do with analyses answers endure, heavy break wait in long-term stability file, patterned experiment pacts, and techno-business-related studies. The authors resolve that future PV resilience will need complete, perceptive design approaches that are framework distinctive to the individual use and allure air, to stop blame and grant pardon the unification and use of relief procedures. This combining designates valuable information for two together investigators and inventors, and process gods whose challenge search out boost the acting and dependability of solar power blueprints about the field.

Protecting sensitive medical images is essential in modern healthcare systems, where security and robustness are critical. This study presents a hybrid image encryption scheme combining the chaotic Henon map with the RC6 algorithm to ensure secure and efficient image transmission. The method leverages the high sensitivity and unpredictability of the Henon map alongside RC6’s strong diffusion and noise resistance. Experimental results demonstrate strong robustness, evidenced by uniform pixel distributions, high entropy (7.999), and resistance to statistical attacks. Compared with AES, the proposed approach more effectively reduces pixel correlation, exhibits high key sensitivity, and provides strong protection against brute-force attacks due to its large key space.