Zanco Journal of Pure and Applied Sciences

MicroRNAs (miRNAs) have emerged as promising biomarkers in cancer diagnosis and prognosis, including breast cancer (BC). In this study, we examine whether miR-1256 may be regarded as a powerful biomarker for predicting the prognosis of BC. The expression of miR-1256 was detected in 30 formalin-fixed paraffin-embedded (FFPE) tissue pairs of tumoral samples and their non-tumoral tissues using qRT-PCR. The clinicopathological characteristics of patients relative to miR-1256 expression, along with fold change analysis employing the 2-ΔΔCT method, were also investigated. All statistical analyses were conducted using GraphPad Prism and MedCalc. We found that the miR-1256 level in BC FFPE tissues is notably increased with p < 0.006 and a (2.5367)-fold change compared to control tissues. Also, we analyzed the association between the expression level of miR-1256 and the clinicopathological parameters of BC patients. The overexpression of miR-1256 exhibited no significant correlation with age, tumor grade, tumor size, estrogen receptor (ER) status, human epidermal growth factor-2 (Her-2), or tumor, node, metastasis (TNM) status among patients. The progesterone receptor (PR) status suggested a potential trend (P = 0.077), indicating that low miR-1256 expression was more common in PR-positive subjects. Moreover, the p-value, area under the curve (AUC), Std. Error, sensitivity, and specificity are (0.7039, 0.06709, 0.67, and 0.7, respectively), this signifies a moderate capacity of the test to differentiate between tumors with controls. The results of fold change analysis employing the 2-ΔΔCT method indicated a (2.5367)-fold elevation in miR-1256 expression in tumors relative to controls. This suggests that miR-1256 may have an oncogenic role in carcinogenesis, potentially promoting BC development. As a result, we demonstrated that elevated miR-1256 expression correlates with the progression of BC, indicating that miR-1256 may be associated with oncogenic processes in BC tumorigenesis and progression.

Researchers have recently begun to investigate microRNAs (miRNAs) as a potential new class of biomarkers. In this study, we examine whether miR-3652 may be regarded as a powerful biomarker for predicting the prognosis of breast cancer (BC). In this study, the expression level of miR-3652 was detected in 30 FFPE tissue pairs of tumoral samples and their adjacent non-tumoral tissues using qRT-PCR. The clinicopathological characteristics of patients relative to miR-3652 expression level, along with fold change analysis employing the 2-ΔΔCT method, were also investigated. All statistical analyses were conducted using GraphPad Prism version 8.4.3 (686). We found that the miR-3652 level in BC FFPE tissues is notably increased compared to matched non-cancerous specimens. Also, we analyzed the association between the expression level of miR-3652 and the clinicopathological parameters of BC patients. The overexpression of miR-3652 exhibited no significant association with age, tumor stage, ER status, PR status, TNM stage, or calcification. All HER2-negative status patients suggested a potential trend (P = 0.017), indicating that low miR-3652 expression was more common in HER2-negative status subjects. Moreover, the p-value, AUC, Std. Error, sensitivity, and specificity are (p < 0.018, 0.6772, 0.06905, 0.53, and 0.80, respectively), which signifies a moderate capacity of the test to differentiate between patients with tumors and controls. The results of fold change analysis employing the 2-ΔΔCT method indicated a (6.89)-fold elevation in miR-3652 expression in tumors relative to controls. This suggests that miR-3652 may have an oncogenic role in carcinogenesis, potentially promoting BC development. In conclusion, our results demonstrated that elevated miR-3652 expression associates with the progression of BC, and this indicates that miR-3652 may possess an oncogenic function in tumorigenesis, possibly facilitating BC progression. Additional studies are required.

Vertically aligned zinc oxide (ZnO) nanorods (NRs) were grown using a seeded layer-assisted chemical vapor deposition technique. This study examined the impact of altered evaporation crucible-substrate spacing (3, 4, 5, and 6 cm) on the optical behavior, morphological characteristics, and elemental composition of ZnO thin films prepared as a seed layer (buffer layer). Besides, the surface morphology and structural characteristics of the synthesized ZnO NRs was evaluated in the presence and absence of the seed layer. Seed layer samples were evaluated by an ultraviolet-visible spectrophotometer, a field emission scanning electron microscope (FESEM), and energy-dispersive X-ray (EDX) spectroscopy. In contrast, the NRs samples were characterized using FESEM and X-ray diffraction (XRD) technique. The optical energy gap results showed a blue shift from 3.23 eV to 3.28 eV as the separation spacing increased from 3 cm to 6 cm, respectively. FESEM images revealed that the separation spacing strongly influences the morphology of the ZnO films. EDX analysis of the seed layer disclosed the presence of prominent peaks for zinc and oxygen, which confirmed the elemental composition of the grown samples. Furthermore, ZnO NRs synthesized in the absence of a seed layer were observed to be shorter, misaligned, exhibit random growth rates, and possess inferior quality compared to those synthesized with a seed layer. XRD analysis confirmed the high crystalline quality of the grown nanorod samples. Our systematic technique for the synthesis and characterization of the seed layer could play a significant role in the production of superior nanorod arrays.

This paper investigates local bifurcations in the Halvorsen system, focusing specifically on transcritical and Hopf bifurcations. The behavior of equilibrium points during bifurcations is studied using Sotomayor's theorem for transcritical bifurcation and normal form theory, which is based on Hassard's formulas, for Hopf bifurcation. When the bifurcation parameter exceeds a critical value, a Hopf bifurcation emerges. By applying normal form theory, we establish the conditions under which a Hopf bifurcation occurs. Furthermore, we discuss the direction of the Hopf bifurcation and the stability of the resulting periodic orbits. Finally, numerical simulations are provided to support the theoretical findings.

The study investigated the influence of genetic strain, generation, and their interaction on reproductive and productive traits in Ukrainian and Jumbo quail breeds. Ukrainian quails showed significantly (P≤0.05) superior reproductive performance, with higher fertility (90.06 vs. 84.43%) hatchability of set eggs (86.59 vs. 81.22%) and hatching weight (9.45 vs. 8.53g) compared to Jumbo quails, while hatchability of fertile eggs was similar. The progeny generation (G2) exhibited relatively improved fertility (89.52 vs. 84.98%) and hatchability of set eggs (85.00 vs. 82.81%) compared to the parent generation (G1), although G1 showed higher hatchability of fertile eggs (97.44 vs. 94.97%). Ukrainian quails reached sexual maturity earlier (35.50 vs. 36.00 days) and attained higher body weight at first egg (278.34 vs. 236.99 g) first egg weight (10.41 vs. 9.79 g) and mean egg weight (12.36 vs. 11.40 g). Phenotypic correlations revealed negative associations between age at maturity and body weight (r = -0.326) or egg weight (r = -0.112), and a positive correlation between body weight and egg weight (r = 0.492). Heritability estimates were higher for productive traits (h² = 0.56–0.62) than for reproductive traits (h² = 0.15–0.22). Strong genetic correlations (r = 0.43–0.57) between body weight and egg weight were observed. These findings provide insights for improving quail productivity through selection.

The present study investigates that seasonal distribution and intensity of infestation of Argulus foliaceus in common carp (Cyprinus carpio) in Taq Taq, Kurdistan Region, Iraq, in relation to ecological factors such as temperature, pH, electrical conductivity (EC), and salinity. A total of 90 fish were taken throughout 2024, and parasitological examinations were conducted to assess infestation rates. The results revealed a clear seasonality, with the highest infestation rates (0.667) in summer and early autumn (June–September) when water temperature rise (8.3–19.2°C) and pH levels were slightly alkaline (pH 8–9). Infestation was lowest in winter (December–February), this was due to the reduced temperatures (3.4–5.3°C), which was not favorable to parasite metabolism and reproduction. Statistical analysis revealed that there was a high correlation (p < 0.01) between intensity of parasite infestation and water temperature. This study indicates the impact of ecology on A. foliaceus distribution and also the potential economic threat it poses to aquaculture. Proper environmental management, regular monitoring, and targeted interventions are essential to mitigate infestations and safeguard fish health in aquaculture settings.

Machine learning technologies have brought significant operational improvements for disease diagnosis-related healthcare activities. Among various conditions, diabetes is particularly suited for prediction through historical and personalized data, which serve as a cornerstone of many machine learning applications. In this study, we present, for the first time, a newly developed, domain-specific diabetes research dataset, called Erbil Diabetes Dataset. The data were collected under the supervision of a medical professional at a laboratory in Erbil, Kurdistan Region of Iraq. The dataset contains twelve key characteristics which were captured from 662 people who visited the laboratory for check-ups. A standard procedure was employed to preprocess the features before presenting them to the diabetes research community for use. The performance evaluation of these algorithms on the specified dataset utilized five algorithms that included Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Xtreme Gradient Boost (XGBoost), and Decision Tree (DT). The evaluation process analyzed the performance results of algorithms through accuracy-recall measurements in addition to precision and F1-score metrics. The result shows KNN and XGBoost reaching outstanding performance values and predictive accuracy measures at 99.25% and 98.80% respectively. The accuracy levels of SVM decrease to 73.68% caused by their sensitivity to hyperparameter optimization. A statistical analysis using a one-way ANOVA test on F1-scores revealed a significant outcome (F = 558.51, p < 0.001), confirming that the differences in model performance were meaningful rather than due to random variation.

This research highlights the effect of the micro polypropylene fibers (mPPF), macro polypropylene fibers (MPPF), and hybrid fibers (combination of mPPF and macro steel fiber at the rate of half) on the flexural strength of the one-way reinforced concrete (RC) slabs. The optimum fiber content is considered based on the ultimate flexural load and mechanical properties. A total of 10 simply supported one-way RC slabs in dimensions 1100 × 400 × 80 mm (length × width × thickness) were prepared and tested under a four-point bending test to investigate their response to first crack load, ultimate load, deflection, and strain. The dosage of the fibers was 0.5%, 1%, and 1.5% by total volume of concrete. Also, the aspect ratios were 375 for mPPFs, 50 for MPPFs, 35 for macro steel fibers. The test results revealed that the flexural stiffness and crack resistance of the one-way RC slabs were improved compared to the control slab. Though the ultimate flexural load increased by 29.73% for the slab with mPPF at 0.5% dosage, 32.46% for the slab with MPPF at 0.5% dosage. The slab with steel fiber + mPPF at 1.5% dosage demonstrated an increased ultimate flexural load of 28.2% compared to the control slab, while the cracks widened. The load-deflection curve of the slab with mPPF at 0.5% dosage displayed the most elastic one-way RC slab among all the slabs, as it enhanced the first crack load by 112%. The load-deflection curve of MPPFRC slabs and the slabs with hybrid fibers displayed the gradual decline in post-cracking behavior.

While Vehicular Fog Computing (VFC) is a hot research area for boosting processing power in connected vehicles (Internet of Vehicles), efficiently assigning the best resources is tricky. This is because new services like augmented reality and self-driving cars are exploding in popularity. These services require lightning-fast responses, powerful computing, and all while dealing with constantly moving vehicles. The biggest difficulty is guaranteeing tasks are completed super-fast (strict latency) as vehicles move in and out of range of roadside stations (RSUs). This research tackles the challenge of scheduling tasks for connected vehicles. It considers both how long a task takes to complete (latency) and how much battery power it uses (energy consumption). The system makes decisions about whether to run the task on the vehicle itself, a nearby roadside station (RSU), or even another RSU further down the road based on the vehicle's planned route. This approach aims to find a balance between getting tasks done quickly and keeping the vehicles running for as long as possible. Initially, we use a special technique called Markov renewal process (MRP) to capture how vehicle moves around over time. This method helps us understand the vehicle's mobility patterns. We then prove some technical properties about our goal of minimizing both latency and energy consumption. This lets us develop a fast and effective method (greedy heuristic) to find a good solution. On top of that, we propose a more powerful approach that combines two existing optimization techniques (Moth-flame and particle swarm) to tackle scheduling tasks in very large networks with moving vehicles. We tested our new scheduling method against existing ones, and computer simulations showed it to be very effective in solving task scheduling problems. The simulation outcomes demonstrate that our suggested MFO-PSO approach reduces average delays in tasks by 23.4% and uses 17.8% less energy than typical scheduling methods.

Nowadays, the usual adjustment methods of the surveying networks are typically addressed through the principle of Least Squares (L.S.) methodologies. One of the most important field data that needs to be adjusted is the observed coordinates of the stations. This kind of adjustment is commonly performed by using the L.S. criterion through solving a set of complex nonlinear functions. Therefore, this research aims to present an approach for implementing position adjustment in surveying networks based on the Artificial Neural Network Backpropagation (ANNB) concept. The proposed method is built on forming one or more geometrical conditions that consider the misclosure error of observations depending on the network’s field measurement circumstances. The initial weights of input data are determined based on their variances, which are then trained and updated. The weights updating are obtained according to the used activation function. Thus, the main advantage of the ANNB method lies in its adoption of a set of linear functions that vanish the misclosure error existing in each geometrical condition according to the network’s situation. The desired residuals are determined based on the updated weights for departures and latitudes of all formed geometrical conditions. Therefore, all misclosure errors in all geometrical conditions will be eliminated perfectly. The evaluation of the proposed ANNB method is done according to the well-known, Least Squares adjustment. The obtained results of the evaluation reveal a logic convergence with minor differences between the outcomes of the ANNB method and those of the parametric L.S. method.

Many researchers have been interested in the study of torsion in concrete members during the last decades. Most of these studies focused on the reinforced beams under torsion and not deeply researched the case of slab. This paper presents a study on the behavior of reinforced concrete slabs under torsion by using finite element analysis (FEA). Twenty-one slabs with different reinforcement and concrete compressive strength have been included in the study. To apply torsional moment on the slabs, vertical displacement at three corners was constrained and the load was applied to the fourth corner. According to the results, steel bar size had more influence on the torsional yielding moment compared to the torsional cracking moment. Using steel bar size of 12 mm instead of 8 mm increased the torsional cracking and yielding moments by 4% and 19% respectively, for the slab with compressive strength of concrete of 50 MPa. Furthermore, both the torsional cracking and yielding moments substantially increased with the increase in compressive strength of concrete. Raising the compressive strength of concrete from 21 MPa to 50 MPa increased the torsional cracking and yielding moments by 54% and 44% respectively. Torsional stiffness of reinforced concrete slabs in the uncracked stage is about 20-28 times that in the cracked stage.

Standard deep-learning (DL) and machine learning (ML) approaches are valuable frameworks in computer vision that improve the accuracy of medical image diagnosis and classification, including the identification of microscopic blood cells. This study examines the detection and categorization of acute leukemia in detail. Improving patient prognosis and treatment options requires early diagnosis, categorization, and accurate detection of white blood cells. Developing a precise and effective model for identifying and classifying malignancies in white blood cell (WBC) images remains challenging despite the widespread use of microscopes for blood cell examination and advancements in AI-based detection techniques. To address these challenges, the authors utilized a total of 48,000 images, comprising both public and private sources, after augmenting three classes of white blood cell (WBC) cells. This study aims to evaluate whether Vision Transformers can match or surpass the performance of convolutional neural networks (CNNs) for WBC classification using large-scale datasets, and to determine whether spatial inductive biases inherent in CNNs offer a measurable advantage, and achieved accuracies of 95.07%, 95.27%, 82.66%, 92.00%, and 83.59%., also creating an Ensemble model that combine three models(VGG19, Xception, and U-Net) running on gpu.v2-4090x4 server with RAM 384 GB because of volume of data which achieving accuracy of 92%, Also study proposed an architecture for deep learning that automatically recognizes and classifies WBC images, categorizing them into five types. The models for detection and classification techniques were also assessed using accuracy, F1 score, recall, and precision for each class of WBC.

Securing Internet of Things (IoT) networks is an ongoing challenge. As more devices connect to the internet with limited resources, these systems have become more vulnerable to cyberattacks. Many attacks continually evolve and become more sophisticated. This highlights the need for scalable, efficient anomaly detection deployable close to IoT devices to minimize latency, while maintaining high accuracy with low memory and computational demands. Many solutions have been applied for enhancing the problem area, either they are heavy models unsuitable for edge devices or they lack generalizability with recent datasets and current attack traffic patterns. Our research suggests a lightweight anomaly detection model that combines Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) model, to recognize patterns across both spatial and temporal dimensions, as well as identify significant relationships among an interpretable selected set of features. with SHapley Additive exPlanations (SHAP) for feature selection and Synthetic Minority Oversampling Technique - Edited Nearest Neighbors (SMOTE-ENN) for balancing the distribution of classes in the datasets. The model’s performance was evaluated using accuracy, precision, recall, and F1 parameters. Following the study, an accuracy rate of 99.12% for multiclassification is achieved in the CICIoT2023 dataset. In the TON_IoT dataset, a multiclassification success rate of 99.08% is reached. The model with 10 features selected achieved 99.0%, 98.85% in the CICIoT2023 and TON_IoT dataset. With just 43,406 trainable parameters and Top 10 features selected proposed framework offers a lightweight, explainable model that is effective for edge IoT devices with limited resources.

Kurdistan Region of Iraq (KRI) faced significant challenges related to air quality due to the rapid urbanization and industrial activities, which necessitate the air quality assessments. The advanced techniques of remote sensing, Geographic Information Systems (GIS), and the Analytic Hierarchy Process (AHP) were combined in this research to perform air quality zonation for the KRI. The Ozone (O3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Carbon Monoxide (CO), and UV Aerosol Index, were the selected air pollutants from Sentinel 5P as average of 2024 to use as a critical parameters for air quality assessments. The considered parameters were weighted using pairwise comparison, and thematic maps were created to calculate air quality zonation map for KRI. The results were categorized into five pollution levels: very low, low, medium, high, and very high. The findings showed that 2,815 km² (6.1%) of KRI identified as region with very high air pollution, suggesting the critical need for targeted mitigation strategies. This study aid notably in sustainable environment and offers important information for policymakers to enhance public safety.