Detour Polynomials of Generalized Vertex Identified of Graphs

تعد مسافة الالتفاف من أهم أنواع المسافات التي لها تطبيقات حديثة في الكيمياء وشبكات الكمبيوتر، لذلك حصلنا في هذا البحث على متعددات حدود الالتفاف وأدلتها لـ  nمن البيانات المنفصلة عن بعضها البعض بالنسبة للرؤوس ، n≥3. أيضًا وجدنا متعددات حدود الالتفاف وأدلتها لبعض البيانات الخاصة والتي لها تطبيقات مهمة في الكيمياء.The Detour distance is one of the most common distance types used in chemistry and computer networks today. Therefore, in this paper, the detour polynomials and detour indices of vertices identified of n-graphs which are connected to themselves and separated from each other with respect to the vertices for n≥3 will be obtained. Also, polynomials detour and detour indices will be found for another graphs which have important applications in Chemistry.

Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network

Predicating the required building energy when it is in the design stage and before being constructed considers a crucial step for in charge people. Hence, the main aim of this research is to accurately forecast the needed building cooling energy per hour for educational buildings at University of Technology in Iraq. For this purpose, the feed forward artificial neural network (ANN) has been selected as an efficient technique to develop such a predication system.  Firstly, the main building parameters have been investigated and then only the most important ones were chosen to be used as inputs to the ANN model. However, due to the long time period that is required to collect actual consumed building energy in order to be employed for ANN model training, the hourly analysis program (HAP), which is a building simulation software, has been utilized to produce a database covering the summer months in Iraq. Different training algorithms and range of learning rate values have been investigated, and the Bayesian regularization backpropagation training algorithm and 0.05 learning rate were found very suitable for precise cooling energy prediction. To evaluate the performance of the optimized ANN model, mean square error (MSE) and correlation coefficient (R) have been adopted. The MSE and R indices for the predication results proved that the optimized ANN model is having a high predication accuracy with 5.99*10-6 and 0.9994, respectively

The Comparative Study of Biaxial Bending Analysis of Steel Sections Using AISC and Eurocode Approaches

ABSTRACT This study investigates the capacity of the steel section using both AISC and Eurocode approaches by Robot Structure Analysis software. Three types of steel sections were subject to biaxial bending by applying loads to both main axes and examined by both approaches. The concept of Fisher was also adopted as an approach. The findings suggested that the Eurocode approach is more conservative in the design of steel sections subject to biaxial bending as it takes into account the level at which the load is applied, the type of the section whether rolled or welded and its height-to-width ratio (lateral buckling effect), the effects which are not considered in AISC approach. The AISC approach considers the shear center of the section as the level at which the loads are applied. The conservatism of the results was more pronounced when the section is close to H-section. Fisher`s concept of structural design of biaxial bending of structural steel is more conservative than both AISC and Eurocode approaches of analysis

Al-Hakim's Pygmalion: Emulation and Creation

The use of classical myths is one of the ways that a lot of writers have adopted in composing their literary works. They are regarded as one of the main important sources that provide writers with many topics to explore, themes to depict, characters to portray, and motifs to analyze. However, some writers do not move beyond the events of the myth; they attempt to deal with it without violating its literary elements and events. On the other hand, some adapt its various elements to create a new work. In this paper, Al-Hakim's play Pygmalion is at the centre. It attempts to investigate, explore and analyze Al-Hakim's use of the myth of Pygmalion in creating his play Pygmalion. It also attempts to examine whether he adopts the same version of the Greek myth of Pygmalion or he violates its elements to create a new treatment of the myth

A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm

A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept. The proliferation of smart grids and the concomitant progress made in the development of measuring infrastructure have garnered considerable interest in short-term power consumption forecasting. In reality, predicting future power demands has shown to be a crucial factor in preventing energy waste and developing successful power management techniques. In addition, historical time series data on energy consumption may be considered necessary to derive all relevant knowledge and estimate future use. This research paper aims to construct and compare with original deep learning algorithms for forecasting power consumption over time. The proposed model, LSTM-GRU-PPCM, combines the Long -Short-Term -Memory (LSTM) and Gated- Recurrent- Unit (GRU) Prediction Power Consumption Model. Power consumption data will be utilized as the time series dataset, and predictions will be generated using the developed model. This research avoids consumption peaks by using the proposed LSTM-GRU-PPCM neural network to forecast future load demand. In order to conduct a thorough assessment of the method, a series of experiments were carried out using actual power consumption data from various cities in India. The experiment results show that the LSTM-GRU-PPCM model improves the original LSTM forecasting algorithms evaluated by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for various time series. The proposed model achieved a minimum error prediction of MAE=0.004 and RMSE=0.032, which are excellent values compared to the original LSTM. Significant implications for power quality management and equipment maintenance may be expected from the LSTM-GRU-PPCM approach, as its forecasts will allow for proactive decision-making and lead to load shedding when power consumption exceeds the allowed leve

Stress-strain modelling of reinforced concrete membrane sructures

In this study, a nonlinear finite element (FE) model is proposed to investigate the behaviour and failure mechanism of reinforced concrete membrane structures. Proven accurate stress-strain relation is incorporated in the model to describe the stress-strain behaviour of the concrete under compression for uniaxial and biaxial stress system. The nonlinearity behaviour of the materials in the compressive stress field is considered for the concrete in the orthogonal directions. The effect of micro cracking confinement and softening on the stress-strain relationship under biaxial stresses are included by employing the equivalent uniaxial strain concept. Tension stiffening effect by concrete in tension is modelled in the ascending and descending parts. The model allows for the progressive local failure of the reinforced concrete materials. The applicability of the proposed FE model is investigated by demonstrating the nonlinear structural response and failure mechanism of a simple deep beam and validated with published experimental work. Good agreement is achieved between the developed FE model and the experimental test results which gives confidence that the approach is fundamentally correct

Modeling of combined thermal and mechanical action in roller compacted concrete dam by three-dimensional finite element method

A combined thermal and mechanical action in roller compacted concrete (RCC) dam analysis is carried out using a three-dimensional finite element method. In this work a numerical procedure for the simulation of construction process and service life of RCC dams is presented. It takes into account the more relevant features of the behavior of concrete such as hydration, ageing and creep. A viscoelastic model, including ageing effects and thermal dependent properties is adopted for the concrete. The different isothermal temperature influence on creep and elastic modulus is taken into account by the maturity concept, and the influence of the change of temperature on creep is considered by introducing a transient thermal creep term. Crack index is used to assess the risk of occurrence of crack either at short or long term. This study demonstrates that, the increase of the elastic modulus has been accelerated due to the high temperature of hydration at the initial stage, and consequently stresses are increased

Sponge media drying using a swirling fluidized bed dryer

Surface preparation today has seen the introduction of sponge media as an alternative product against the traditionally used abrasive materials. Being soft and elastic, the sponge media reduces air borne emission significantly during surface preparation with capability to be re-used. However the environmental conditions limit the sponge media usage whereby wet surroundings prohibit the re-use of the sponge without being dried properly. This study proposes the swirling fluidized bed dryer as a novel drying technique for sponge media. Batch experiments were conducted to study the bed’s hydrodynamics followed by drying studies for three bed loadings of 0.5 kg, 0.75 kg and 1.0 kg at three drying temperatures of 80°C, 90°C and 100°C. It was found that, minimum fluidization velocities for the wet sponge particles were found to be 1.342, 1.361 and 1.382 m/s with minimum swirling velocities of 1.400, 1.469 and 1.526 m/s. Drying times were recorded between 6 to 16 minutes depending on bed loading and drying temperature. Smaller bed weights exhibits faster drying with constant-rate drying period while higher drying temperature and larger bed load resulted in falling-rate drying period. Thin layer modelling for the falling-rate region indicates that Verma et. al model provides the best fit for the present experimental data with coefficient of determination, R2 = 0.98773, root mean square error, RMSE = 0.05048, residuals = 0.3442 and reduced chi-square, χ2 = 0.00254. The effective diffusivity, Deff, for 0.5 kg bed load was found to be 3.454 x 10-9 m2/s and 1.751 x 10-9 m2/s for 0.75 kg bed load. In conclusion, SFBD was found to be a viable and efficient method in drying of sponge media for various industrial applications particularly surface preparation

AN IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SPECTRUM ALLOCATION IN COGNITIVE RADIO NETWORKS

The seriousness of the spectrum scarcity has increased dramatically due to the rapid increase of wireless services. The key enabling technology that can be viewed as a novel approach for utilizing the spectrum more efficiently is known as Cognitive Radio. Therefore, assigning the spectrum opportunistically to the unlicensed users without interfering with the licensed users, concurrently with maximizing the spectrum utilization is addressed as a major challenge problem in cognitive radio networks. In this paper, an improved metaheuristic optimization algorithm has been proposed to solve this problem that contingent on a graph coloring model. The proposed approach is a hybrid algorithm composed of a Particle Swarm Optimization algorithm with Random Neighborhood Search. The key objective function is maximizing the spectrum utilization in the cognitive radio networks with the subjected constraints. MATLAB R2021a was used for conducting the simulation. The proposed hybrid algorithm improved the system utilization by 1.23% compared to Particle Swarm Optimization algorithm, 5.57% compared to Random Neighborhood Search, 7.9% compared to Color Sensitive Graph Coloring algorithm, and 27.33% compared to Greedy algorithm. Moreover, the system performance was evaluated with various deployment scenarios of the primary users, secondary users, and channels for investigating the impact of varying these parameters on the system performance