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Evaporation transpiration is one of the most important results in the water balance equation for any natural area or water body, and it is also a critical component of the hydrological cycle. The relationship between temperature and transpiration was obtained using the Thornthwaite method in three stations along with Babylon Governorate. The data for the three meteorological stations were taken for 10 years. The value of climate change was obtained within the study area, including temperature, evaporation, humidity, and rainfall. The value of potential evapotranspiration was calculated by the Thornthwaite method, and the highest value was obtained in July (421.9), and the lowest value in January (3.66). Also, the increase in water was calculated for a study area. It appeared that the increase in evapotranspiration and decrease was in direct relationship with the temperature intensity of the study area.

Wind turbines deployed in utility-scale wind farms can support and meet future energy desires and also decrease carbon dioxide emissions by reducing energy requirements from fossil fuels. As the air heats up throughout the day, the wind velocity increases due to temperature gradients. This in turn produces a density pressure gradient, inducing air movement that a wind turbine encounters. Depending on ground topography, the wind can encounter and be directed in valleys and between and over hills as it flows and follows the curves of the earth. These topographies produce an increase in wind velocity at summits and ridges. In the current study, a small horizontal wind turbine rotor blade is designed to operate under low wind speed, by using the Q-Blade software. Based on the Blade Element Momentum method (BEM) and airfoil NACA3712, a three-blade rotor and a five-blade rotor are used based on turbine type and rotor size to generate mechanical power from wind power. A comparison and analysis of turbine power, power coefficient, and torque coefficient are carried out at low wind speed 1m/s-8m/s and highly accurate results are obtained. It is found that the best performance is gained when a three-bladed turbine rotor can work with a turbine power of 582W. As for the five-blade rotor, the turbine power obtained is (955W). It is also found that the design of a small horizontal wind turbine with five blades is more efficient than a turbine with three blades, suitable for working in areas with low wind speed and is of high efficiency compared to the size of the turbine.

Due to the scarcity of experimental laboratory research on the effect of repetitive stress resulting from the railway on retaining walls, this study presents an investigation of an experimental laboratory model to study the effect of repetitive loads resulting from the railway on the adjacent retaining wall through several factors. The results of an experimental investigation of horizontal displacements of steel sheet pile walls behind excavations in the sand adjacent to railway tracks are presented. A series of model tests have been carried out in which the effect of sand density, horizontal distance between the railway and the sheet wall, and dynamic load amplitude on the wall horizontal dynamic displacement and its relationship to the railway dynamic vertical settlement were investigated with a load frequency equal to 2 Hz. The study examines various parameters, including different relative densities (30% loose, 55% medium, and 75% dense sand), different distances between the railway and retaining wall (0.5 H, 1 H, and 1.5 H), and variations in burden amplitudes (0.22, 0.44, and 0.66 tons). The most important factor that affects the dynamic displacement ratio (D/H) value is the relative density of the soil. The dynamic displacement ratio inversely correlates with the horizontal distance separating the railway and the retaining wall. The displacement ratio experienced a reduction of approximately 30% and 23% when the railway location relative to the retaining wall shifted from 0.5H to 1H and from 1 H to 1.5 H, respectively. The displacement ratio is directly proportional to the amplitude of cyclic live loading. Ultimately, using dense backfill soil enhances efficiency, resulting in decreased settlement of the railway and improved performance of the nearby retaining walls. It should be noted that there is a direct correlation between the horizontal movement of the wall and the vertical settlement of the rail. In the case of dense soil, these values are closely aligned.

Skirted foundations are one of the solutions proposed to increase the bearing capacity of the soil. They assist in increasing the load and depth of failure in weak ground or soils with low shear resistance and reducing the foundation settlement if a soil improvement method cannot be applied or the cost of implementing deep foundations increases. This study examined and investigated the extent of soil bearing of skirted foundations on sandy soils and studied the effect of soil saturation cases and three cases of water content reduction to measure the matric suction value of unsaturated soil. A physical model was created to simulate the strip foundation and compare these cases (dry-fully saturated-partially saturated). It was found that the soil load carrying capacity in the case of unsaturated soil is the highest, where matric suction is at a depth of 450 mm, followed by the dry case and then the saturated case as it represents the weakest state of the soil.

Background: In recent years, the healthcare field has experienced significant advancements. New diagnostic techniques, treatments, and insights into the causes of various diseases have emerged. Despite these progressions, cancer remains a major concern. It is a widespread illness affecting individuals of all ages and leads to one out of every six deaths. Lung and colon cancer alone account for nearly two million fatalities. Though it is rare for lung and colon cancers to co-occur, the spread of cancer cells between these two areas—known as metastasis—is notably high. Early detection of cancer greatly increases survival rates. Currently, histopathological image (HI) diagnosis and appropriate treatment are key methods for reducing cancer mortality and enhancing survival rates. Digital image processing (DIP) and deep learning (DL) algorithms can be employed to analyze the HIs of five different types of lung and colon tissues. Methods: Therefore, this paper proposes a refined DL model that integrates feature fusion for the multi-classification of lung and colon cancers. The proposed model incorporates three DL architectures: ResNet-101V2, NASNetMobile, and EfficientNet-B0. Each model has limitations concerning variations in the shape and texture of input images. To address this, the proposed model utilizes a concatenate layer to merge the pre-trained individual feature vectors from ResNet-101V2, NASNetMobile, and EfficientNet-B0 into a single feature vector, which is then fine-tuned. As a result, the proposed DL model achieves high success in multi-classification by leveraging the strengths of all three models to enhance overall accuracy. This model aims to assist pathologists in the early detection of lung and colon cancer with reduced effort, time, and cost. The proposed DL model was evaluated using the LC25000 dataset, which contains colon and lung HIs. The dataset was pre-processed using resizing and normalization techniques. Results: The model was tested and compared with recent DL models, achieving impressive results: 99.8% for precision, 99.8% for recall, 99.8% for F1-score, 99.96% for specificity, and 99.94% for accuracy. Conclusions: Thus, the proposed DL model demonstrates exceptional performance across all classification categories. [ABSTRACT FROM AUTHOR]

Background: Cancer survival prediction is vital in improving patients' prospects and recommending therapies. Understanding the molecular behavior of cancer can be enhanced through the integration of multi-omics data, including mRNA, miRNA, and DNA methylation data. In light of these multi-omics data, we proposed a graph attention network (GAT) model in this study to predict the survival of non-small cell lung cancer (NSCLC). Methods: The different omics data were obtained from The Cancer Genome Atlas (TCGA) and preprocessed and combined into a single dataset using the sample ID. We used the chi-square test to select the most significant features to be used in our model. We used the synthetic minority oversampling technique (SMOTE) to balance the dataset and the concordance index (C-index) to measure the performance of our model on different combinations of omics data. Results: Our model demonstrated superior performance, with the highest value of the C-index obtained when we used both mRNA and miRNA data. This demonstrates that the multi-omics approach could be effective in predicting survival. Further pathway analysis conducted with KEGG showed that our GAT model provided high weights to the features that are associated with the viral entry pathways, such as the Epstein–Barr virus and Influenza A pathways, which are involved in lung cancer development. From our findings, it can be observed that the proposed GAT model leads to a significantly improved prediction of survival by exploiting the strengths of multiple omics datasets and the findings from the enriched pathways. Our GAT model outperforms other state-of-the-art methods that are used for NSCLC prediction. Conclusions: In this study, we developed a new model for the survival prediction of NSCLC using the GAT based on multi-omics data. Our model showed outstanding predictive values, and the KEGG analysis of the selected significant features showed that they were implicated in pivotal biological processes underlying pathways such as Influenza A and the Epstein–Barr virus infection, which are linked to lung cancer progression. [ABSTRACT FROM AUTHOR]

Most of the studies of laterally loaded piles focused on the behavior for piles subjected to static or cyclic lateral loading embedded within dry or saturated soils, few studies investigate the behavior of piles embedded within partially saturated soils and subjected to dynamic loads. In this research, an experimental study presents an aluminum pile model embedded within dry, fully saturated and partially saturated soils, subjected to dynamic load with the El Centro 1940 NS acceleration data (0.05g, 0.15g, and 0.32g) accelerations. Three different lowering levels of the water table for fully saturated soils model is achieved to get partially saturated soils of three different values of matrix suction. During an earthquake model, a liquefaction phenomenon is observed by boiling of sand and completed collapse in the soil as shown in the results. It is concluded that the resisting to the bending moment reduced by 22%, 50%, and 57% after 1st, 2nd and 3rd lowering of water level respectively, than that of the saturated condition. This reduction approaches to 28% for completely dry soil. It is worth to mention, that, the deflection of the pile reduced as the lowering of water level increased. The soil resistance increases with the increasing of dynamic load acceleration. The soil resistance increases about 35% when the acceleration increase from 0.05g to 0.15g and an increase of about 22% when the acceleration increases from 0.15g to 0.32g.

The elevated death rate associated with colorectal cancer (CRC) continues to impact human life worldwide. It helps prevent disease and extend human life by being detected early. CRC is frequently diagnosed and detected through histopathological examination. The decision is based on clinicians' subjective perceptions and daily image analyses. Histological image (HI) classification is difficult because HIs contain multiple tissue types and characteristics. Therefore, deep learning (DL) models are employed to classify different kinds of CRC HIs. Therefore, to increase the efficiency of the CRC diagnostic procedure from HIs, we propose a fine-tuning model for the CRC diagnosis process with the EfficientNet-B0 DL model. The proposed model performs a multi-classification for HIs. It uses an adaptive learning rate (ALR) to overcome the overfitting problem caused by using the static learning rate (SLR) and to enhance the performance of detecting the CRC. The ALR compares the training loss value at the beginning of each epoch. If it is smaller, we increase the ALR; if it is larger, we decrease it. Our proposed model speeds diagnosis, reduces diagnostic costs, and reduces medical errors; hence, it enhances the diagnostic procedure from the patient's perspective. We trained and evaluated the proposed model over the two datasets (NCT-CRC-HE-100K and CRC-VAL-HE-7K). Normalization and scaling methods were used to pre-process the NCT-CRC-HE-100K dataset. The EfficientNet-B0 model attained accuracy, sensitivity, specificity, precision, and an F1-score of 99.87%, 99.64%, 99.95%, 99.62%, and 99.63%, respectively when applied to the NCT-CRC-HE-100K dataset. On the CRC-VAL-HE-7K dataset, the EfficientNet-B0 model achieved 99%, 94.52%, 99.45%, 94.41%, and 94.36% for accuracy, sensitivity, specificity, precision, and F1-score, respectively. As a result, the EfficientNet-B0 model outperforms the state of the art in this field. [ABSTRACT FROM AUTHOR]

In this paper, we introduce k-directional Darboux curves of a given timelike curve α lying on a timelike surface in Minkowski 3-space E3 1 as the integral curves of some vector fields generated by the Darboux frame along the curve. The relationships of such associate curves are given including the relationship of their curvatures. By using these curves, we give a new approach to classifying some special curves such as helices, and slant helices. [ABSTRACT FROM AUTHOR]

There are many methods to measure the suction and saturation point of any soil and the most used is Tensiometer. The objective of this research is to investigate a new trend to measure the saturation point and suction by using soil moisture content sensors (SMCS) and compare the procedure with tensiometer for the granular soil. The soil in this study has been prepared at three relative densities (5%, 35% and 75%) used. The results show that the readings of SMCS become stable when the water content is more than 20% with different relative densities. The relative density did not affect the SMCS reading when approximately reaching the saturation point.

In this paper, we introduce a new type of curve called the k-directional Darboux curve. These curves are generated by vector fields that are constructed using the Darboux frame of a given spacelike curve α lying on a timelike surface in Minkowski 3-space E³1 We give the relationships between these curves and their curvatures. In particular, we show how k-directional Darboux curves can be used to classify some special curves, such as helices and slant helices. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new type of curve called the k-directional Darboux curve. These curves are generated by vector fields that are constructed using the Darboux frame of a given spacelike curve α lying on a timelike surface in Minkowski 3-space E³1 We give the relationships between these curves and their curvatures. In particular, we show how k-directional Darboux curves can be used to classify some special curves, such as helices and slant helices. [ABSTRACT FROM AUTHOR]

The trend towards smart greenhouses stems from various factors, including a lack of agricultural land area owing to population concentration and housing construction on agricultural land, as well as water shortages. This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers. The proposed model uses a one-dimensional convolutional neural network (CNN) deep learning model to control the growth of strategic crops, including cucumber, pepper, tomato, and bean. The proposed model uses the Internet of Things (IoT) to collect data on agricultural operations and then uses this data to control and monitor these operations in real time. This helps to ensure that crops are getting the right amount of fertilizer, water, light, and temperature, which can lead to improved yields and a reduced risk of crop failure. Our dataset is based on data collected from expert farmers, the photovoltaic construction process, agricultural engineers, and research centers. The experimental results showed that the precision, recall, F1-measures, and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%, 98.2%, 97.25%, and 97.56%, respectively. The new smart greenhouse automation system was also evaluated on four crops with a high turnover rate. The system has been found to be highly effective in terms of crop productivity, temperature management and water conservation. [ABSTRACT FROM AUTHOR]