Latakia University Journal -Engineering Sciences Series
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Enhancing Five-Axis Machining Models Using Advanced Geometric and Differential Techniques
This study aims to develop and analyze the direct and inverse geometric and differential models of a five-axis CNC machine. The research seeks to bridge the gap between theoretical models and practical applications, contributing to improved design and control techniques for programmed machines. The CATIA V5 software was utilized to design and assemble the machine's various components, while mathematical matrices were employed to analyze the relationships among multiple axes. Additionally, the concept of "Jerk" and its significance in enhancing machine performance were explained, with an emphasis on its role in reducing vibrations and improving the quality of machined surfaces. The results demonstrated a strong alignment between the developed models and practical applications, achieving a 15% improvement in coordinate accuracy and reducing machining time. Simulations conducted using MATLAB validated the models' effectiveness in analyzing machine performance and guiding operations. The conclusions emphasize the importance of employing direct and inverse geometric and differential models to enhance five-axis machine operations and advance applications in engineering industries. The study recommends integrating these models with artificial intelligence techniques to achieve further enhancements.
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Behavior Cloning Imitation Learning For Vision Transformer To Predict Steering Angle And Speed Of Self-Driving Vehicles
Controlling a self-driving vehicle on highways is a complex and essential task that involves numerous challenges. Rule-based expert systems and traditional self-driving systems offer limited solutions when dealing with complex real-world scenarios. This has led to a shift towards using learning-based planning, supported by big data, particularly through behavior cloning for End-to-End Architectures. This study proposes the use of transformers that rely on self-attention mechanisms in computer vision, which have proven successful in natural language processing tasks and effective in patch-embedding colored images. The proposed model is trained on a comprehensive dataset that includes driving behaviors, environmental conditions, and vehicle dynamics, with the goal of predicting the steering angle and speed of self-driving vehicles on highways. The results show that the model achieves a significantly lower Mean Absolute Error (MAE) compared to traditional deep learning techniques, by using vision transformers. The paper introduces two models: the first uses a single Multi-Layer Perceptron Head with two output neurons for both the steering angle and vehicle speed, while the second uses two separate prediction heads with one output neuron for each. The mean absolute error for the first model was 0.175 and for the second model 0.37, while the lowest mean absolute error in studies using traditional self-driving systems was 0.491.
The importance of line as an essential element in the art and creating a figurative painting
Great changes arise as a result of scientific and technical progress in the system of performing artistic works for their functions, and the media have also improved, in a way that has shown huge potential for establishing communication between the recipient and the art, and therefore there is a radical change occurring in the relationship between art and the viewer, towards intellectual, cultural, and visual richness, therefore It was necessary to pay attention to the construction of the pictorial painting, its basic components, and its elements that begin with the dot, and the effect resulting from its movement in several directions. It leaves an impact represented by the lines responsible for harvesting the spaces, or the void on the surface of the painting, to compose the vocabulary of the plastic visual text of bodies, shapes, The blocks are composed within purposeful artistic formations, that is, formations that are visually studied to attract the viewer to the artistic work. Studying the formations means understanding the foundations of placing the parts so that the whole is composed. Line is considered an element of composition that has an important and major role in building the artistic work, as hardly any artistic work is devoid of it, although to varying degrees. It is the oldest form of human expression, and a rhythmic journey in a system based on diversity in the shapes of lines and their directions, when they extend and bend, curve or color, thin or thick, rough or smooth…..etc. Due to the strength of the line element in expression and its aesthetics; It was not limited to being an external line defining shapes and blocks, but rather it became an independent value in Itself.
Design of a spider robot to move motors in an industrial environment
This research focuses on the design and development of a spider robot controlled remotely via Bluetooth technology using an Arduino Nano board and SG90 servo motors. The project aims to create a simple educational model that can be utilized to teach students and beginners the fundamentals of programming, electronics, and robotics control. A dedicated mobile application was developed, enabling users to control the robot’s movements effortlessly by sending commands via Bluetooth. The movements include forward, backward, right turn, left turn, and stop. The research adopts a methodology combining engineering design and experimental programming, employing tools such as SolidWorks for the robot’s structural design and Arduino IDE for writing the programming code. The robot was tested in a real educational environment, demonstrating high effectiveness in enhancing students' learning skills. The results also showed the ease of use and cost-effectiveness of the model compared to traditional systems. The study highlights the potential for extending the model to include light industrial applications by integrating advanced sensors to enhance the robot’s performance in various environments. The project offers opportunities for broad applications, including teaching robotics in schools and universities, as well as in industrial settings that require low-cost and easy-to-implement tools. The results present an opportunity to adopt this technology on a wider scale, focusing on improving the programming design and adding intelligent features such as obstacle avoidance and increased operational autonomy for the robot.
Study of the effect of propeller change on the energy efficiency of the JBC bulk carrier ship
In this work, the effect of the propeller type on the energy efficiency of the bulk carrier JBC was studied. The CFD technique was used to achieve this purpose, where the URANS (Unsteady RANS Averaged Navier Stocks Equation) equations and the k-ω SST turbulence model were used to solve the Navier-Stokes equations. The bulk carrier JBC, the MP687 and VP1304 propellers were chosen for this study, due to the availability of a large number of experimental results for this ship and propellers, which helps to verify the quality of the numerical results that will be obtained. At first, the calculations were performed on the ship alone without any appendages, then the open water test was conducted for each of the selected propellers, in order to calibrate the mesh and numerical settings. Then, the effect of changing the propeller on the energy efficiency of the ship was studied. The comparison between the numerical and experimental results helped in choosing the correct mesh and numerical settings that give results very close to reality. The results obtained confirmed that the type of the chosen propeller has a significant impact on the energy efficiency of the ship. The results showed that the MP687 propeller improves the energy efficiency by 38% compared to the VP1304 propeller at the same power provided to the propeller. This amount of improvement is very large and confirms the importance of conducting such a study in choosing the type of propeller for the ship
Effect of the longitudinal trim of KCS container ship on its resistance at different speeds
In this research, CFD technique was used to study the effect of the longitudinal trim of a container ship KCS on its resistance at a constant draft and at different values ​​of ship speed. The URANS method and the k-omega SST turbulence model were used to solve the Navier-Stokes equations. The study was carried out at the following longitudinal trim angles -0.8, -0.6, -0.4, -0.2, -0.1, 0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2 and at each angle the calculations were carried out at three values ​​of Froude number which are 0.108, 0.195, 0.282. Both the ship's draft and its scale were fixed. The study was conducted on the scale model due to the availability of a large number of experimental results for this model, which were used to validate both the grid and the numerical settings. Therefore, the calculations were initially performed for several cases that were completely identical to the experimental cases, and the numerical results were compared with the available experimental results, and the agreement between the results was very good. In the next step, the effect of the longitudinal trim angle on the ship's resistance was studied at different speed values. The results obtained confirmed that at low ship speeds, the effect of the trim angle is negative and does not help in reducing the resistance, and with increasing ship speed, the positive effect of the longitudinal trim angle appears in reducing the ship's resistance. The results also confirmed that the negative trim angle (in which the bow draft is greater than the stern draft) negatively affects the resistance, unlike the positive trim angle, which helps in reducing the ship's resistance.
The Effect Of Joint Offset Between The First And Second Axes On Kinematic Performance Of Anthropomorphic Robots
In this study, we examine a family of manipulators with six revolute joints, where the first joint axis is perpendicular to the subsequent parallel joint axes.This structure is the most widespread among industrial robots and is used in the basis of human-like robots called anthropomorphic. Although the kinematic characteristics of such robots are generally known in terms of the shape of the workspace and the presence of singular points, there remains a gap in knowing which one is the best with the variation of DH parameters. The members of this family are characterized by being non-specific and consequently none of them are cuspidal. Thus they does not have the ability to change their posture without passing through a singularity. This explains why there was almost no interest in classifying such family of robots in terms of their kinematic performance according to DH parameters, especially with the presence of other robots that are more attractive in terms of their stable dynamic performance such as parallel robot, and the existence of orthogonal robots of which families with distinctive features were found in terms of their kinematic properties. In this study, we are interested to select from the design parameters space the optimal DH parameters for the aforementioned family of robots that improves their kinematic properties, namely the offset between the first and the second joint axes. In the design optimization problem, several kinematic criteria were adopted separately, such as manipulability and dexterity. These robots were then divided into classes according to the shape of the worst region in the work space according to the values of one of the DH parameters.
Breast Cancer Detection Using Machine Learning Techniques
Breast cancer affects approximately 10% of women worldwide at some point in their lives and has emerged as one of the most feared and prevalent cancers among women. The main dilemma arises when cancer cannot be properly detected in its early stages. Machine learning has proven to play a vital role in diagnosing diseases such as cancers. Effective methods for classification and data recognition are particularly essential in the medical field. In this project, classification techniques were employed using the PCA-KNN algorithm on the Wisconsin Breast Cancer Dataset. The main objective was to evaluate the accuracy of data classification concerning the efficiency and effectiveness of the PCA-KNN algorithm in terms of precision, recall, specificity, and the F1 score. The experimental results demonstrated an accuracy of up to 99%.
Bad Data Detection and Identification Using Artificial Neural Networks
This paper explores the capability of the artificial neural networks to detect and identify the single and multiple bad data which can be found within the measurements provided to energy management system centers. A reduced model for state estimation was developed in the MATLAB environment using NN-TOOL. A comparison of the single bad data detection and identification between the proposed state estimator and the Weighted Least Squares state estimator on IEEE 14-bus power systems is provided. The results show that the proposed model is more accurate and faster than the WLS state estimator. Furthermore, the proposed methodology is a great alternative to the conventional techniques and is therefore well suited for smart grid applications. This research presents a different approach for handling bad data compared to the iterative and lengthy statistical methods used previously. It provides a streamlined model for the state estimator that allows for effective and reliable monitoring and operation of the power system with lower computational requirements and the implementation of this procedure in real time, which positively impacts the overall performance of the energy management system. Its main objective is to enhance the functioning of the electric power management system by utilizing artificial neural networks. Typically, a state estimation is performed prior to the process of detecting, identifying, and excluding bad data, where the traditional state estimator relies on a set of redundant measurements to describe the system through a set of over-specified nonlinear equations, followed by a series of iterative numerical mathematical operations aimed at minimizing measurement errors until the optimal value of the system state variables is achieved. The theory of weighted least squares is one of the most commonly used methods for this purpose.
Evaluation of BB84 Distribution Quantum Key protocol performance
Information and communication technology has developed to an integral part of our lives, and has deeply entered into the folds of culture, economy, defense, society, finance, and regional and international organizations. This development was accompanied by the emergence of major security risks and challenges that threaten society, so there was an urgent need to achieve a good level of security. Quantum encryption emerged as a qualitative leap to protect data and information by taking advantage of the scientific renaissance in the world of quantum physics. Quantum encryption is a high-tech method for securing digital communications by taking advantage of quantum principles, which are the cornerstone of this encryption, as it relies on taking advantage of the unique properties of quantum particles, which ensures unbreakable encryption. One of the most famous quantum encryption algorithms is the BB84 algorithm. Therefore, this research aims, on the one hand, to provide a detailed explanation of quantum encryption, and on the other hand, to demonstrate the effectiveness of this algorithm in generating a secure random key to be used in symmetric encryption applications, where distributing the key to users is one of the biggest problems that threaten its safe use. The research also seeks to clarify the effectiveness of this algorithm in detecting any eavesdropper trying to obtain the key.
Using Wood Ash as Superfine Additive and Air Bubble Stabilizer in Foam Concrete
Technological development has led to the emergence of many modern construction techniques in recent decades, which achieve the engineering specifications required in construction with the best quality, lowest cost and greatest durability. As a result of the war that Syria witnessed, which led to the destruction of many buildings, facilities and infrastructure, the demand for building materials and the use of cement has increased, which is this necessitated thinking about finding a solution to secure these resources and searching for alternative building materials, At the same time, focusing on the principle sustainability and achieving its standards, therefor it was necessary to introduce new alternatives to the building materials used in the production of concrete, and an attempt to localize this technology in local industries. One of the common wastes that this research focused on is wood ash which has been used as a cement additive material in the manufacture of foamed concrete with varying mechanical specifications, and a low apparent density that reached to 720Kg/m3 in some compositions, which is considered an important factor in achieving thermal and sound insulation, and a significant reduction of the mechanical load on the structural elements. The preliminary results of this research showed the possibility of manufacturing samples of foamed concrete with an apparent density ranging between 728 and 974Kg/m3, with a homogeneous appearance and mechanical resistance that achieves the values specified by international specifications, with the possibility of increasing them by making modifications to the designs.
The effect of the distance between two bulk carriers on the ship-ship interaction during the overtaking process
The hydrodynamic interaction between two or more ships in ports or inland waterways is a very important research area in marine engineering. During ship maneuvering, the ship captain tries to determine the speed limit and distance between their ship and any other ship passing by, especially in narrow waterways. This requires accurate prediction of the interaction force acting on both ships. Experimental studies have shown that the interaction can lead to a very large transversal forces and these forces are highly time-dependent, which may cause ships to deviate from their original courses during the overtaking process, leading to collisions. Based on the results of experimental measurements, some empirical formulas have been proposed that provide an approximate estimate of the forces acting on ships during the overtaking process, but their results are satisfactory only when the ship speed is very high, and the water depth is shallow and constant. Currently, the Computational Fluid Dynamics (CFD) technique is considered one of the best and most accurate techniques used worldwide to study and calculate all phenomena related to fluids. Accordingly, in this research, the URANS (Unsteady RANS Averaged Navier Stocks Equation) equations and the k-Ꞷ SST turbulence model were used to solve the Navier-Stokes equations. Then, CFD was used to calculate the hydrodynamic interaction between two JBC bulk ships with different values ​​of the transverse distance between the two ships. The interaction between the two ships was studied with different values ​​of the distance between them . At first, a validation study was carried out for the numerical grid and settings by comparing the CFD-results obtained with the available experimental results, and the agreement was very good. The CFD-results confirm that the transverse distance between the two ships greatly affects the interaction between them, and the smaller the distance, the greater the probability of the two ships colliding with each other.
Study of the scale effect on the resistance components and the flow field around the tanker ship
Historically, the field of marine engineering has relied on a combination of model testing and similarity laws, known as the extrapolation approach, which relies on using a set of approximate relationships to predict the power requirements of a real ship. Several problems with the extrapolation approach have been identified as the values ​​obtained through these approximate relationships are not accurate compared to reality, but due to the lack of accurate alternative relationships, it has continued to be used up to the present day. This research focuses on studying the effects of scale on the basic components of the resistance of oil ships (tanker) and the flow field around them using a CFD approach. The study was conducted on the oil tanker KVLCC2 using six different scales at a fixed value of Froude number Fr = 0.142, which is the value corresponding to the nominal speed of the ship. To analyze the effect of scale on resistance, the total resistance components were divided into individual components and the ratio of the individual resistances to the total resistance of each scale was calculated and then compared with each other. The numerical results were compared with experimental data to verify their validity. The comparison demonstrated the accuracy of the setup and the numerical grid used. Finally, we studied and analyzed the effect of the scale on the velocity, pressure and vortices fields around the hull, as these are the basic parameters that must be studied in detail when designing any ship.
Effect of using Nearshore breakwaters on shoreline response south of lattakia city
The problem of beach erosion is one of the serious issues facing coastal areas, especially if those areas are economically and touristically productive. .Alramel alganobi beach is experiencing a noticeable retreat in the shoreline, which necessitates protecting the area using one or more protective measures. Nearshore breakwaters are a means to reduce erosion and increase the stability of the shoreline. Therefore, two different scenarios for the placement of breakwaters were proposed using the JMC computational method. This article aims to analyze the effectiveness of breakwaters and their impact on the shape of the shoreline using the GENCADE numerical model, with a precise calibration of the model to ensure reliable results. The results showed that the breakwaters have had a limited success in reducing erosion due to the absence of an external sediment supply, which negatively affected their effectiveness. Thus, they do not represent a sufficient solution on their own unless additional sand is introduced to the project area (artificial nourishment). The research recommends that when planning coastal management projects, multiple strategies should be integrated to achieve effective results.
Prediction of Flashover Voltage on Contaminated Insulators Using Machine Learning
Electrical insulators are a crucial component of electrical power transmission systems, and the issue of insulator pollution is one of the most significant challenges facing electrical networks. Pollution leads to electrical breakdowns, causing power outages and substantial financial losses. Therefore, accurately predicting the Flashover voltage of polluted insulators is essential for improving insulator design and ensuring high operational efficiency and reliability of power systems. insulator design and ensuring high operational efficiency and reliability of power systems. The use of artificial intelligence techniques significantly contributes to enhancing the accuracy of predicting the Flashover voltage of polluted insulators, reducing associated burdens, and improving the reliability and safety of electrical power systems. In this research, the Random Forest algorithm, one of the most prominent machine learning algorithms, was used to predict the Flashover voltage of polluted insulators. Data was cleaned and processed in a Python environment, where the algorithm demonstrated high accuracy in predicting breakdown voltage, achieving a low Root Mean Square Error (RMSE) compared to previous studies related to this field. These findings highlight the role of artificial intelligence as an effective and reliable tool for estimating Flashover voltage and reducing the need for costly and complex laboratory experiments. This approach contributes to enhancing the reliability of electrical networks, lowering operational costs associated with maintenance and testing, and opening new horizons for applying machine learning techniques to improve the performance of electrical systems and address similar challenges.
A deep learning model to recognize human movement by using auto coders
In the recent years, there has been great interest in identifying human movement using deep learning and skeletal data for its effective performance compared to models that depend on images or depth data. In this research, we presented a model that solves the problem of the size and inconsistency of data that used on deep learning model and enables these results to works in environments poor with hardware. In this paper, we proposed to use a model consisting of two stages: the first stage of the feature extraction process using autoencoder, and we made use of reducing the size of the features extracted from the training groups in order to reduce the size and complexity of the classification model represented by the second stage. The proposed model provided good results in comparison with the size and complexity of the classification model.
Optimization of power flow in a local electrical network using phase shift transformers
The use of phase shift transformer to control the active power flow in three-phase power transmission network is an alternative to new investment in high-cost overhead lines. The control of the active power flow is achieved by adjusting the phase angle of the voltage at the transformer terminals, In this research, we chose a local network within the coastal area of ​​230 Kv to control the flow of actual power after the Zeizoun station went out of service, as this led to an increase in the load of the Baniyas-Jableh line from 78% to 96% of its nominal load limit, using a phase shift transformer to ensure the operation of the electric power transmission lines within the permissible load limits and to ensure the continuity of the electric supply in a reliable manner. The case of installing a phase shift transformer on the Baniyas-Lattakia line was studied, after the Zizoun station was taken out of service. It was found that by controlling the transmission voltage angle, optimum power flow was achieved, represented by loading all electrical power transmission lines within their nominal load limits. where the actual power percentage transmitted through the Baniyas-Jableh line was reduced from 96% to 91% at the expense of increasing the load of the Baniyas-Lattakia line from 80% to 91% of its nominal load value, by adjusting the phase angle between the input and output voltages of the transformer at a value of 2.3°, The network operation simulation with and without phase shift transformer is performed using Power World Simulator software, according to the simulation results, the busbar voltages and angles, active power flow values ​​in the lines and total losses are calculated.
"Effect of prying forces and contact stresses on steel joint components subjected to tension"
Hanger connections are important steel connections in which bolts and/or welding are used to connect steel plates together. They are similar, according to many references, to what is called the Equivalent T-stub. they are found in most connections, for example, connections using partial end plates. Like other types of connections, they are subjected to different types of forces and stresses in their components depending on the design forces transmitted between the connected elements. One of the main challenges when transferring tensile forces in these connections is the emergence of lifting or prying forces that negatively affect the bolts by increasing the contact stresses between the connected plates, which may lead to deformation of the plates near the bolts. These deformations, whether in the form of bending or rotation, may cause increasing the design axial loads of the bolts, affecting the strength of the connection and increasing the risk of premature failure. In this research paper, the differences in the methods of calculating additional tensile forces (prying forces) and their results according to some references will be highlighted, in addition to helping in understanding how they develop and the effect of the most important factors on them, such as the thickness of the front plate, the diameters of the bolts, bolts grade, and the way they are distributed (the spacing between them) through comparison with a parametric analytical study using the component-based finite element method (CBFEM) for a beam-column with partial end plate bolted connection.
Improving the thermal performance of flat-plat solar collector using nanofluid as a working fluid
The research aims to improve the thermal performance of a flat-plate solar collector by using nanofluids as a heat transfer medium, contributing to an increase in the efficiency of solar systems. Two identical solar collectors were fabricated using aluminum and copper components, insulated with transparent glass. The collectors were tested under identical climatic conditions in Latakia city during the summer and autumn of 2023. The results demonstrated that the nanofluid, primarily composed of copper oxide nanoparticles (CuO) at a concentration of 0.2%, improved the outlet fluid temperature by 13% compared to using water alone. The tests included monitoring solar radiation intensity and temperature changes at the optimal flow rate, which was determined to prevent energy loss due to excessive or insufficient flow. The results also showed that the outlet temperature of the nanofluid was higher than that of the traditional fluid, enhancing the thermal performance of the solar collectors. The conclusions indicated that adding copper oxide at a specified concentration significantly improved thermal conductivity and efficiency. However, increasing the concentration of nanomaterials leads to agglomeration, reducing the thermal transport efficiency. The study recommended conducting future research to explore other types of nanofluids to further enhance performance. This research provides a scientific model for improving the thermal efficiency of solar collectors using nanotechnology, contributing to the promotion of solar energy as a sustainable energy source.
Developing a reliable analytical model using ANSYS software to demonstrate the structural and thermal behavior of flat slabs under fire exposure.
This study presents a clear, reliable, and experimentally consistent method for developing an analytical model for flat slabs using the finite element method. A numerical platform was established using ANSYS software, enabling the analysis of additional parameters specific to flat slabs, such as fire exposure side, concrete characteristic strength, and support conditions. The study also demonstrated a good agreement between the analytical results obtained using the finite element method and the experimental results. Based on the assumptions of the completed analytical research, a reliable and accurate numerical model was developed in comparison to experimental models. This approach helps save time and costs while enabling the investigation of numerous variables with a high degree of reliability in the results.
Improving Reliability Calculations In Cellular Communications System Stations Using Fuzzy Logic
Reliability is one of the most important parameters studied in many modern engineering applications, especially critical applications (spacecraft - nuclear reactors - medical applications - communications...). Traditional methods for reliability calculations have suffered from difficulties related to collecting information and monitoring, as these methods require quantitative information about malfunctions and failures of the system (equipment) studied in order to calculate its reliability. In most of the targeted applications, collecting information is a difficult task due to the difficulty of monitoring. Accordingly, we relied on Artificial Intelligence techniques to avoid the aforementioned difficulties, and in our research, we relied on Fuzzy Logic technology (one of the artificial intelligence techniques) to find a general model for improving reliability calculations in electronic equipment in general, including communications system equipment. To ensure the effectiveness of this model, we conducted a realistic study of applying this model to cellular broadcast stations (Base Stations) and compared the results with results obtained in a previous study by studying the reliability of the same broadcast stations, but according to traditional methods for calculating reliability (Weibull Distribution).
Designing a Fuel Measurement System for Validating Dispensed Quantities in Fuel Stations"
In this study, we designed and developed a comprehensive system for measuring fuel flow rates at filling stations with the aim of improving measurement accuracy and providing a reliable and user-friendly solution. The system consists of a turbine flow meter equipped with a turbine containing permanent magnets, a Hall Effect sensor that converts the turbine’s motion into electrical pulses, an Arduino UNO microcontroller that receives these pulses and calculates the flow rate using a predetermined correction factor, an LCD screen for data display, and a Bluetooth module for real-time data transmission to a mobile app. The design presented an innovative solution that achieved high precision, demonstrated by the experiment showing that the system provides readings with an error margin of up to 1% and a repeatability error of no more than 0.75% at a flow rate of 45 litters per minute. The system effectively displayed results and shared them via Bluetooth, facilitating real-time monitoring of data from a mobile device. The results confirmed that the system delivers reliable measurements and performs effectively in the environment of fuel stations. Recommendations include further development of the device for use with liquids of varying densities, testing under different pressure and temperature conditions for enhanced accuracy, modifying the mechanical design to better match fuel dispensing standards, exploring new measurement techniques, and adding features such as a fuel level sensor for vehicles. This system represents a significant advancement towards developing sophisticated and effective measurement solutions for fuel dispensing applications.
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Suggesting a method for managing expected health and safety risks from the design phase of construction projects
The construction sector is characterized by its dynamic nature, long working hours, and heavy reliance on manual labor. Despite the fact that safety procedure costs are significantly lower than the costs of accidents and injuries, the construction industry consistently ranks among the top industries with the highest rates of workplace injuries and fatalities. Safety planning is critical in construction; however, safety management on construction sites is typically limited to measures implemented during the execution phase, separate from project design and planning stages. This necessitates the adoption of a proactive safety management strategy instead of traditional methods. Research has demonstrated that making sound decisions during the design phase significantly enhances safety levels on construction sites. This study introduces a methodology for proactive safety management aimed at early hazard control and the reduction of injuries and fatalities. It involves calculating the risk levels associated with construction elements based on twelve major hazards present in projects. By determining the overall risk value during the design phase, the methodology enables effective control of project risks and design modifications to achieve the lowest possible risk level for each project day. Furthermore, it identifies optimal mitigation measures for high-risk factors and incorporates proactive design strategies in early stages to minimize risks.
Evaluation of Temporal and Spatial Changes in Al-Sin Lake Water Quality Parameters
Al-Sin Lake, which supplies the cities of Lattakia and Tartous governorates with drinking water, is exposed to pollution from human activities in its basin, which causes a deterioration in its water quality. The research aims to evaluate Al-Sin Lake water quality and the need for the water treatment before using it for drinking. The aims were conducted through studying the temporal and spatial changes of several physicochemical and microbial parameters of water quality, which were measured three times monthly in 2021 from four different locations according to the program approved by the concerned staff to monitor and protect the lake. One-Way ANOVA was used to determine whether there is significantly spatial difference in water quality parameters among the four monitoring points. The research results showed significant spatial differences between the four monitoring points in some significant water quality parameters, including fecal coliforms counts in the water, water turbidity, and the concentrations of ammonia, nitrite, nitrate and phosphate in the water. They were also showed that the water characteristics in feeding springs area of Al-Sin Lake, where the Tartous drinking water intake is located, are better than those at the Lattakia drinking water intake, as the water from Tartus drinking water intake only needs to be sterilized before pumping it to Tartus, unlike the water from Lattakia drinking water intake which needs to be treated and sterilized. The results confirm the necessity of preventing the growth of floating plants and algae on the edges and bottom of the lake, to reduce the organic load in the lake, especially since the purification system in the Lattakia drinking water purification plant and the pumping system in Tartous drinking water pumping plant are not qualified to remove it from the water, as well as the sterilization process in both plants is currently performed using chlorine.
Design of Piled-Raft using the PLAXIS 3D
The traditional design of piles in the foundations of piled-raft buildings assumes that the total loads of the building are distributed only to the piles, and the role of the raft as a structural element that transfers the load to the piles is limited only, as the method adopted in the Syrian Arab code. Therefore, many piles increase the economic cost of piled-raft foundations.In this research, the pilled-raft of a 16-story residential building in Latakia was modeled using the PLAXIS 3D program to determine the percentage of the contribution of both the raft and the piles from the total loads of the building and compared with the proposed simplified method of calculating the contribution of the raft, which is based on the principle of the additional effect of the piles as raft columns, which reduces their settlement, the raft's contribution reached 21.11%, thus reducing the number of piles, which became 99 piles with 2m spacing instead of 120 piles with 1.8m spacing in the case of neglecting the raft's contribution, It is possible to consider this method safe to use compared to the Poulos method, which gave a higher percentage of the raft contribution, reaching 36.8%, compared to the analytical model of PLAXIS 3D program, which reached 28.36%.
Applying Machine Learning to Detect DDoS Attacks in SD-VANET Networks
Vehicular Ad Hoc Network (VANET) is a modern technology that integrates next-generation wireless network capabilities into vehicles. Integrating Software-Defined Networking (SDN) with VANETs is a significant step towards developing intelligent vehicular networks. This new architecture offers innovative solutions to VANET challenges in various fields, especially in security.Using machine learning to detect Distributed Denial of Service (DDoS) attacks in Software-Defined VANETs (SD-VANET) is a modern and effective technique. This approach relies on analyzing network traffic patterns and detecting abnormal behaviors that indicate DDoS attacks. Machine learning algorithms can quickly and accurately identify these patterns, helping to take preventive measures to protect the network. In this research, an SDN-VANET environment was created using the Mininet-WiFi emulator, with the Ryu controller providing software components and APIs that facilitate developers in creating new applications for network management and control. Statistical information extracted from flow tables was used to detect DDoS attacks targeting controllers in the SDN-VANET environment. The dataset used to train the machine learning model was obtained from the experimentally designed SD-VANET architecture, and the best-performing parameters for the SVM classifier were used during the training phase, resulting in high accuracy.
Effectiveness Evaluation of Smartphone Based Structure from Motion (SfM) Photogrammetry in Calculating the Volume of Regular Elements
In this research, the effectiveness of the Structure from Motion (SfM) technique in calculating the volume of regular objects was verified by comparing the calculation results with the reference volume and the volume calculated using traditional topographic methods. In the proposed methodology, photos and video recordings were obtained using a smartphone and a digital camera. Then, 25 samples were formed by varying the parameters (geometric resolution, processing level, and number of photos) and repeating the calculation process. The models were then evaluated visually and geometrically. The results showed that when using high and medium processing levels with photos of high geometric resolution, the values were almost identical to those obtained from traditional methods, while differences increased with photos extracted from video recordings. Changing the number of photos did not affect the volume accuracy, as all values were close to each other. The overall accuracy of the photo triangulation ranged between 5mm and 24mm, with all values within acceptable limits. Additionally, the results indicated an increase in the number of points clouds and faces with higher resolution and a greater number of photos, with the difference values in relative comparison of point clouds ranging between 0mm and 9mm.
A Study Of The Effect Of Heat Treatment On Some Mechanical Properties Of Unsaturated Polyester Composites Filled With Particles Of Aluminum Oxide And Carbon Black
This research aims to study the effect of heat treatment on some mechanical properties (such as frictional properties, wear and surface hardness) of unsaturated polyester composites filled with aluminum oxide (AL2O3) and carbon black (CB) particles. To achieve this goal, samples were prepared by mixing the fillers in different weight fractions and separately with the matrix (unsaturated polyester) using an electric mixer, then the hardener MEPK (methyl ethyl ketone peroxide) was added at a rate of 1.5 % until homogeneity of the mixture mass. After the good mixing process, the composite polymer material was poured into prepared cylindrical molds and left to solidify at room temperature. Then, a heat treatment process was carried out for the composite material samples (UPR composites containing AL2O3 and containing CB) at two temperatures of 60 °C and 80 °C and at different heat treatment times (2, 4, 6, 24) h. The results of the heat treatment showed that a decrease in the volumetric loss values when both the heat treatment time and temperature were increased. In order to verify the validity of the results of the heat treatment effect, the change of surface hardness was studied according to Shore D in terms of the treatment regime followed.
Improving the performance of software-defined networks (SDN) with indefinite architectures based on hybrid edge nodes
Software-defined networking (SDN) architectures and applications have revolutionized the way wired networks are built and operated. However, their implementation in wireless environments still faces many obstacles. While we can implement centralized control in wired networks, wireless networks suffer from the difficulties of accessing and managing resources under centralized control. In reality, this is even more difficult in networks with constantly changing structures. Networks with changing architectures depend on diverse that differ in their structure, operation, and capabilities, including mobile devices of various types. At the same time, these networks need to provide flexibility in providing resources and services to end users. Since some links between end nodes are wireless in nature, unstable, and have a low rate. Reconnecting components to the controller and thus to each other, when these links are interrupted, especially when the path is multi-hop, will be difficult, if not impossible, because collecting the necessary statistical data to reconnect will be slow. This research seeks to use SDN to solve the problem of networks with changing architectures that are exposed to the possibility of frequent failure of network links. Given the increasing growth of the capabilities of the end devices and the network's use of these capabilities provided by the end devices, the interest in spreading the functions of the network control plane at its edges, gives it a great potential to control and invest its resources better. In this context, a method has been proposed to make SDN more suitable for the environment of these networks by combining the central control of SDN and the distributed control that uses traditional protocols, in a hybrid design that takes the best of both models. That is, providing a general view of the network and the possibility of programming SDN on the one hand and using the robustness of traditional routing protocols on the other hand. Therefore, this research has been interested in proposing a mechanism for the operation of the hybrid node so that it can work as an SDN node or a traditional node, depending on the network condition and the availability of communication between its nodes. This takes into account the improvement of performance according to the parameters generally used in such cases.
Study and Analysis the effect of changing the distance between the elements of a (1x2) Patch Antenna Array on antenna performance
Microstrip patch antennas are the most widespread and widely used antennas due to their ease of manufacturing, light weight and small size, which makes them suitable for applications in wireless communications systems such as GPS, Wi-Fi, Bluetooth, satellite communications and medical applications. This research presents a model of a (1x2) array of inset-feed patch ​​antennas which connected to each other by a tree feed network with a distance r separating them. The array is on top of a FR4_epoxy substrate. The HFSS program was used to simulate the antenna array model and compare it with the single element model. The radiation patterns and scattering matrix S11 were obtained after verifying the convergence curves in the HFSS program to ensure accuracy in the results. It turns out that for a distance separating the centers of the two patches r =82mm, we obtain an antenna operating at the frequency of 2.43GHZ with a value of reflection coefficient S11=-41.16dB and bandwidth BW=102MHZ, and for a stand wave ratio VSWR<2 the antenna can operate on a very wide range of frequencies such as the X-band.