Application of soft computing models with input vectors of snow cover area in addition to hydro-climatic data to predict the sediment loads

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R$^{2}$ value of 0.85 and 0.74 during the training and testing period, respectively

Fluctuating rainfall as one of the important cause for desertification in Iraq

Desertification is regarded as one of the most problems over the entire world. There are many reasons lead to this problem. Climatic factors such (rainfall, temperature and evaporation) are some of them. Annual rainfall amount is one of the most important parameters that lead to desertification for this reason it chosen to achieve this research. In this paper rainfall data with the help of Geographic Information System (GIS) to produce maps of Iraqi surface climate. In order to achieve this goal, the available data from a number of climate monitoring stations located on the surface of the country has been were adopted. The annual mean of rainfall for the period from 1990 to 2010 has been used. The Iraqi shape file map was used to overlay the results. The output of the research are presented in GIS maps that demonstrating the variation of rainfall over the study period for the monitoring climate stations

The Effects of TRIZ Theory's Strategies in Improving Creative Teaching Science Skills for Female Student Teachers at Princess Nora Bint AbdulRahman University

The current study aimed to identify the effectiveness of creative problem solving strategies based on TRIZ theory in improving creative teaching skills for student teachers at Princess Nora Bint AbdulRahman University. The sample of the study consisted of (48) female student teachers, which was divided into two groups. Twenty five female student teachers were in the control group, and taught by traditional teaching methods. Twenty Three female student teachers were in the treatment group, and exposed to creative problem solving strategies based on TRIZ theory. Data was collected by using creative teaching skills questionnaire, and statistical analysis such as means, standard deviations, Independent-Sample T-test was conducted. In addition, Eta squared (η2) was computed to determine the effectiveness of creative problem solving strategies on improving creative teaching skills. Results revealed statistically significant differences between the control and treatment groups in favor of the treatment group. This indicated the positive impact of creative problem solving strategies on improving creative teaching skills

Comparative assessment of spatial variability and trends of flows and sediments under the impact of climate change in the upper Indus basin

Extensive research of the variability of flows under the impact of climate change has been conducted for the Upper Indus Basin (UIB). However, limited literature is available on the spatial distribution and trends of suspended sediment concentrations (SSC) in the sub-basins of UIB. This study covers the comparative assessment of flows and SSC trends measured at 13 stations in the UIB along with the variability of precipitation and temperatures possibly due to climate change for the past three decades. In the course of this period, the country’s largest reservoir, Tarbela, on the Indus River was depleted rapidly due to heavy sediment influx from the UIB. Sediment management of existing storage and future planned hydraulic structures (to tap 30,000 MW in the region) depends on the correct assessment of SSC, their variation patterns, and trends. In this study, the SSC trends are determined along with trends of discharges, precipitation, and temperatures using the non-parametric Mann–Kendall test and Sen’s slope estimator. The results reveal that the annual flows and SSC are in a balanced state for the Indus River at Besham Qila, whereas the SSC are significantly reduced ranging from 18.56%–28.20% per decade in the rivers of Gilgit at Alam Bridge, Indus at Kachura, and Brandu at Daggar. The SSC significantly increase ranging from 20.08%–40.72% per decade in the winter together with a significant increase of average air temperature. During summers, the SSC are decreased significantly ranging from 18.63%–27.79% per decade along with flows in the Hindukush and Western–Karakorum regions, which is partly due to the Karakorum climate anomaly, and in rainfall-dominated basins due to rainfall reduction. In Himalayan regions, the SSC are generally increased slightly during summers. These findings will be helpful for understanding the sediment trends associated with flow, precipitation, and temperature variations, and may be used for the operational management of current reservoirs and the design of several hydroelectric power plants that are planned for construction in the UIB

Security Enhancement in Surveillance Cloud Using Machine Learning Techniques

Most industries are now switching from traditional modes to cloud environments and cloud-based services. It is essential to create a secure environment for the cloud space in order to provide consumers with a safe and protected environment for cloud-based transactions. Here, we discuss the suggested approaches for creating a reliable and safe environment for a surveillance cloud. When assessing the security of vital locations, surveillance data is crucial. We are implementing machine learning methods to improve cloud security to more precisely classify image pixels, we make use of Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM). We also extend the conventional two-tiered design by adding a third level, the CloudSec module, to lower the risk of potential disclosure of surveillance data.In our work we  evaluates how well our proposed model (FCM-SVM) performed against contemporary models like ANN, KNN, SVD, and Naive Bayes. Comparing our model to other cutting-edge models, we found that it performed better, with an average accuracy of 94.4%

Human resource management systems for enterprise organizations: A review

Human Resources HRs are one of the most important elements in the organizations, but it is difficult to manage a large number of human resources, especially in large companies. For this purpose, companies have tended to manage these resources taking advantages of the technology and the Internet. In this research, we will focus on human resources management in different companies and different countries and explore the factors that motivate companies to adopt Electronic Human Resource Management E-HRM as well as the factors that affect its adoption. The results showed that HRM plays a vital role in facilitating organizational processes, save cost and time, it also beneficial for competitive advantages. The findings also explored that E-HRM aspects and factors impact E-HRM adoption are varied from firm to another and from country to another. However, the companies moved towards adopting E-HRM because it mitigates the efforts of HR managers to take a decision

Determining the Contribution of Different Structural Layers of Asphalt Pavement System to Rutting Using Transverse Profile Analysis

Abstract Rutting is one of the major distresses which cause hydroplaning and structural failures in a flexible pavement. The extent of damage depends upon the load induced and the strength of each pavement layer. Rutting can occur due to the failure of the subgrade, the base or at the wearing surface/ hot mixasphalt (HMA) layer(s).In order to take remedial measures, it is imperative to be cognizant of the contribution of each layer to ascertain the cause of underlying phenomena of rutting. This research study demonstrates the analysis of the pavement transverse surface profile that could be used to identify the layer responsible for the permanent deformation. Transverse surface profiling techniqueis easier, non-destructive, and economicalas compared to traditional methods of coring and trenching to examine underlying layers. A 300 meter section on National Highway (N-5) was selected exhibiting severe rutting to perform transverse profile analysis. Results of this study suggest that rutting at the selected site is mainly due to the shear failure of HMA layer. These results were also validated by field trenching on the test section. The study concluded that HMA layer should be removed and replaced with appropriately designed high performance mix specifications

Design of Compact Dual-band Fractal Monopole Antenna with Virtually Extended Ground Plane

Achieving a particular response to serve multiple wireless applications is regarded as the primary demand in our modern age because of the considerable development of the communication devices. In this paper, a compact monopole antenna with reduced ground plane has been suggested to meet the requirements of the dual-band WLAN applications. The antenna miniaturization has been carried out in employing two techniques. Initially, the fractal geometry has been applied to the antenna radiating element. Two-sided Koch fractal curves up to the third iteration have been used to increase the path of electrical current on the surface of the radiating element which is in the form of a square with dimensions. To gain more miniaturization, the antenna ground plane has been further reduced by using different lengths of two open-ended parallel stubs to form a virtually extended ground plane. This supportive technique has been adopted as a tuning means to control the path of the electrical currents exciting the resulting resonances. The proposed antenna and has been printed on an FR-4 substrate with a thickness of 1.6 mm and 4.4 relative dielectric constant and is fed by 50-ohm microstrip feed line. The resulting antenna dimensions are of about 19.1 mm × 19.1 mm. A parametric study has been carried out, and the results reveal that the proposed antenna offers a dual-band performance with a considerable ratio of resonant frequencies covering the existing 2.4/5.2/5.8 GHz WLAN applications, besides many other communication services