bluuebunny/crossref_metadata_2025 huggingface dataset:
A Comparative Study of the Seismic Response of Different Concrete Slab Systems for a Multistory Building in Madinah
Authors: Laissy Mohamed , Al-Turkmani Zuhair , Atarji Omar , Saud Abdaulaziz
Published: October 2023

Seismic analysis is considered as an important aspect of the design of high-rise buildings, particularly in earthquake prone areas. The structural system choice can have a considerable impact on the building seismic response. The goal of this study is to compare the seismic behavior of multiple slab systems used in a multi-story building in Saudi Arabia's Madinah region. This study's goal is to determine the most effective and efficient slab system performance in a seismic zone. The ETABS V20.3 program was used in this work to model and assess the seismic response of three different types of slab systems: flat, solid, and hollow blocks slab types. Many earthquake aspects, including story displacement, base shear, story drifts, column forces, and bending moments, are estimated for each system. The study examines and assesses each system's seismic response, and the conclusions are given and discussed. According to the findings, the choice of slab system has a considerable impact on the seismic reaction of the building. The hollow block system has the least base shear value and bending moments, while the flat slab system has the greatest. The values in the solid slab system are in the middle. In terms of story displacement and column forces, the study additionally indicates that the hollow block type system performs effectively in terms of story drifts, however, the solid slab system outperforms the others. The study's findings can assist designers and engineers to determine the best slab system for multistory buildings in seismic-prone areas by providing important insight and suggestions.
Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval
Authors: Shazuli Syed Ibrahim Syed Mahamood , Saravanan Arunachalam
Published: October 2023

Several Deep Learning (DL) and medical image Machine Learning (ML) methods have been investigated for efficient data representations of medical images, such as image classification, Content-Based Image Retrieval (CBIR), and image segmentation. CBIR helps medical professionals make decisions by retrieving similar cases and images from electronic medical image databases. CBIR needs expressive data representations for similar image identification and knowledge discovery in massive medical image databases explored by distinct algorithmic methods. In this study, an Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval (IWOADL-RFIGR) approach was developed. The presented IWOADL-RFIGR method mainly focused on retrieving and classifying retinal fundus images. The proposed IWOADL-RFIGR method used the Bilateral Filtering (BF) method to preprocess the retinal images, a lightweight Convolutional Neural Network (CNN) based on scratch learning with Euclidean distance-based similarity measurement for image retrieval, and the Least Square Support Vector Machine (LS-SVM) model for image classification. Finally, the IWOA was used as a hyperparameter optimization technique to improve overall performance. The experimental validation of the IWOADL-RFIGR model on a benchmark dataset exhibited better performance than other models.
Analyzing the Impact of Fly Ash Additive Ratio on Lubricant Properties
Authors: Bui Tuan Anh , Le Duc-Do , Tran Duc-Toan , Nguyen Manh-Toan , Tran Van-Thuc , Bui Ngoc-Tam
Published: October 2023

Preventing surface damage is crucial for optimal machine performance, with lubricants and additives playing a vital role in achieving this objective. This study specifically focuses on evaluating the influence of fly-ash additives on the wear resistance of machine components when incorporated into lubricant oil. The experiments were conducted following ASTM standard operating conditions, utilizing the four-ball wear test to measure the scratch width and weight loss of balls using different lubricant oil formulations, including 0, 0.1%, 0.5%, 0.75%, and 1% additive. The findings demonstrate that the inclusion of 0.5% fly ash additive in the lubricant oil results in a significant reduction in both scratch width and weight loss of the balls. However, it should be noted that higher additive ratios may lead to increased scratch width and weight loss due to the agglomeration of the fly ash particles on the sliding surfaces. To achieve optimal effectiveness in reducing friction and wear, it is recommended to carefully control the content of fly ash within an appropriate range. Furthermore, this study highlights the width of scratches on balls as a reliable indicator for assessing the anti-wear properties of oils. The insights gained from this research offer valuable guidance to manufacturers in the selection of suitable anti-wear oils for specific applications. Further investigations could explore the impact of different lubricants and additive ratios to identify the most appropriate lubrication parameters. Overall, this study contributes to a better understanding of the effects of fly ash additives on the performance of lubricant oil and provides practical guidance for optimizing lubrication strategies in diverse industrial contexts.
Enhancement of Power System Security by the Intelligent Control of a Static Synchronous Series Compensator
Authors: Bhande Sapana Arun , Chandrakar Vinod Kumar
Published: October 2023

Improving and maintaining the stability of a power system is a major focus of modern technology and research. However, due to financial issues, environmental concerns, and health risks associated with electric and magnetic fields, the growth of the current transmission system is constrained. Transmission line problems can be resolved by the effective use of reactive power compensation based on Flexible AC Transmission (FACT) devices. The effectiveness of these devices in regulating active and reactive powers as well as dampening oscillations in the transient phase of the power system is examined using a Static Synchronous Series Compensator (SSSC) with Artificial Neural Network (ANN) control. When compared to the traditional Proportional Integral (PI) controller, the suggested ANN controller offers better dynamic performance. For the suggested test system, this study utilized modeling and simulation using the MATLAB/Simulink software. The observations show that by using ANN in disturbed situations, power oscillations are quickly damped and power flow is enhanced.
Nonsingular Fast Terminal Sliding Mode Controller for a Robotic System: A Fuzzy Approach
Authors: Alnufaie Lafi
Published: October 2023

This study presents a combination of Type-2 fuzzy logic and nonsingular fast sliding mode technique to design a robust controller for a robotic system. The control law is composed of two signals. The first one called equivalent control law is dedicated to maintaining the system on the sliding surface and then converges to zero. Since the system is uncertain, a Type-2 fuzzy nominal model was constructed, deduced from linear local models, which allows a good approximation of the real robotic system. The second signal, whose objective is to force the system to attain the sliding surface, is deduced from stability analysis using Lyapunov theory. Several simulations were conducted to evaluate the efficiency of the proposed approach, showing good tracking performance for different reference signals despite the presence of uncertainties and external disturbances.
A Blockchain-based Conceptual Model to Address Educational Certificate Verification Challenges in Tanzania
Authors: Said Said Hamisi , Dida Mussa Ally , Kosia Efraim Michael , Sinde Ramadhani S.
Published: October 2023

The proliferation of counterfeit educational certificates is an ongoing issue around the world, including Tanzania. The effect of this malpractice is detrimental to the credibility of education. Traditional strategies to prevent fake certificates are abortive, calling for a more sophisticated approach. Blockchain technology has recently emerged as an ideal solution to this problem due to its inherent attributes that ensure disintermediation, immutability, tamper proof, anonymity, transparency, consensus, security, and trust. However, most existing blockchain-based solutions lack crucial functionalities that are pertinent to the Tanzanian education system. This study unveiled the challenges faced by the current verification system in Tanzania and proposed a blockchain-based conceptual model to address them. The proposed model is based on blockchain, smart contracts, and the Interplanetary File System (IPFS). Quantitative and qualitative methods were used to investigate certification problems in Tanzania and modeling techniques were used to construct the conceptual model. The findings showed that the main challenges of the current verification system emanate from manual procedures, unverifiable credentials, susceptibility of centralized storage systems, disintegrated verification systems, revocation problems, difficulties in communication, and high dependency on the issuers. These challenges undermine certificate verification, impose a significant setback in the fight against forgeries, and create loopholes. It was conceptually demonstrated that these issues can be resolved through the proposed blockchain-based solution.
Fault Diagnosis of Rotating Machinery based on the Minutiae Algorithm
Authors: Mogal Shyam , Deshmukh Sudhanshu , Talekar Sopan
Published: October 2023

Rotary machinery plays an important role in industry. Combined faults can be observed in rotating machinery, making fault classification difficult. In this paper, the Minutiae algorithm is used to classify the faults from the frequency domain of a particular fault. This paper provides a fault classification technique based on image processing for fault analysis of rotating machinery, recognizing function extraction automatically. Minutiae algorithm, a rising method within the discipline of image processing for characteristic extraction, is utilized in this paper to classify specific faults from the converted recurrence plot. The results reveal the effectiveness of the proposed method, providing a rather powerful tool for fault diagnosis of rotating machinery. The proposed model achieved an accuracy of 100% for combined faults, 98.33% for loosened faults, and 95% for unbalanced faults proving its applicability.
Development and Application of Linear Variable Differential Transformer (LVDT) Sensors for the Structural Health Monitoring of an Urban Railway Bridge in Vietnam
Authors: Nhung Nguyen Thi Cam , Vu Le Van , Nguyen Huu Quyet , Huyen Dang Thi , Nguyen Duc Binh , Quang Minh Tran
Published: October 2023

Measuring the structure's displacement plays a very important role in ensuring the safe operation of railway bridges in general and urban railway bridges in particular. In Vietnam, traditional methods using high-precision mechanical gauges have been used to measure the displacement of railway bridges. However, these methods need a lot of effort in installation and traffic control during implementation. These methods are based on the static principle: The test loads are placed on the bridge structure, and then the structure's displacement is observed. The safety assessment and analysis results are guaranteed by multiplying the dynamic coefficients, leading to some assessments that may not be close to the actual exploitation of the bridge structure. Therefore, the current study presents a new solution for measuring the displacement of railway bridge structures. This method uses Linear Variable Differential Transformer (LVDT) sensors to record the continuous displacement of the structure during the time the train passes over the bridge. Through field measurements combined with a finite element analysis model, the research focuses on developing and applying LVDT sensors in urban railway bridge structure health monitoring. At the same time, the potential of developing this method in Vietnam in the future is evaluated.
Assessing Window Design's Impact on Daylight Uniformity in Classrooms in Patna, India
Authors: Maurya Alok Kumar , Kumar Ravish , Kumar Ajay
Published: October 2023

Windows play a vital role in daylight infusion, significantly impacting indoor visual comfort. Various metrics exist for evaluating visual comfort in which the uniformity ratio falls under the distribution category and is as crucial as illuminance levels. This ratio effectively reduces the likelihood of glare and the need for artificial lighting. The primary objective of this research is to assess the impact of window design on daylight uniformity ratio in a classroom setting. In pursuit of this objective, a study investigated the uniformity ratio (Uo) of north-oriented and south-oriented classrooms of Kendriya Vidyalaya (KV) Khagual, Patna. The study considered five common shapes of windows (excluding the existing base cases) at different window-sill levels. Ninety simulations were run in the DesignBuilder software under overcast, intermediate, and clear sky conditions. To assess the uniformity ratio on three dates: March 21st, June 21st, and December 21st, which correspond to the highest, equinox, and lowest solar availability during the year under intermediate and clear sky conditions at three distinct times. The omission of the specific time and date for overcast conditions and the particular year for clear and intermediate sky conditions is justified as the outcome remains consistent throughout all years. The results show that the window design and sill level significantly affect the uniformity ratio. The research findings show that window design in Case 9 at a sill of 1230 mm and lintel of 3050 mm (just below the slab) consistently produces the best uniformity ratio across all sky conditions, independent of classroom orientation. This paper offers valuable design recommendations by comparing the uniformity ratio for five commonly used window designs. This is one of the first studies of window design and position to evaluate the uniformity ratio in the classrooms at Patna.
Mix Design of Fly Ash and GGBS based Geopolymer Concrete activated with Water Glass
Authors: Sangi Rajashekar , Sreenivas Bollapragada Shesha , Shanker Kandukuri
Published: October 2023

Geopolymer Concrete (GPC) has emerged as an alternative to cement concrete due to its reduced carbon footprint and excellent mechanical properties. However, not much emphasis is made on the development of mix designs using industrial waste. The current study focuses on the mix-design considerations for GPC using fly ash and Ground Granulated Blast Furnace Slag (GGBS). The mix design of GPC involves in selecting materials to produce the desired strength. In this investigation, Water Glass (WG) is used as an activator for the activation of the polymerization reaction. The mix design of GPC is the optimization of a group of various parameters, such as the activator to binder ratio, aggregate to binder ratio, coarse aggregate to fine aggregate ratio, activator concentration, and amount of binder content. The activator to binder ratio affects workability and strength, while the activator concentration influences the polymerization reaction and final strength development. The selection of suitable aggregates plays a vital role in achieving a dense and durable GPC matrix. The mix design for GPC requires a holistic approach that considers the selection of appropriate binders, activators, and aggregates. Proper optimization of these factors can result in excellent strength and durability of the GPC and a reduced carbon footprint. Further research is needed to explore alternative binders, evaluate long-term performance, and establish standardized mix design guidelines for the widespread adoption of GPC in construction.
Structural Performance of Lightweight Fiber Reinforced Polystyrene Aggregate Self-Compacted Concrete Beams
Authors: Abbas Rafaa Mahmood , Rakaa Rawah Khalid
Published: October 2023

This study aims to investigate experimentally the flexural behavior of lightweight Self-Compacted Concrete (SCC) beams made by Expanded Polystyrene (EPS) concrete and reinforced with rebars and steel fibers. To achieve the aims of this study, seven simply supported EPS lightweight fiber-reinforced concrete beams were fabricated and tested up to failure to study the effects of EPS content and the volume fraction of the steel fibers on their flexural behavior. The tested specimens were divided into two groups with one additional reference beam to be cast without using EPS or steel fibers. In the first group, three lightweight specimens were constructed using 25% EPS beads and were reinforced with 0%, 0.75%, and 1.5% steel fiber volume fractions. The second group is similar to the first group but was fabricated using 50% EPS beads. The test results showed that the mechanical properties of the hardened concrete were significantly reduced due to polystyrene EPS beads with some enhancement when steel fibers were added to the concrete mix. The flexure strength of EPS-LWT concrete beams was significantly reduced due to the polystyrene EPS beads. Furthermore, the results revealed remarkable enhancement in the flexure strength of the tested beams due to the steel fiber reinforcement.
Optimizing the Power System Operation Problem towards minimizing Generation and Damage Costs due to Load Shedding
Authors: Le Trong Nghia , Nguyen Hoang Minh Vu , Hoang Thi Trang , Nguyen Ngoc Au
Published: October 2023

Optimizing the operational parameters and control of the power system in steady-state conditions is a crucial issue in reducing the costs of power generation and operation. In the case of long-term operation of a power system, besides aiming to minimize power generation costs, the cost of damage caused by load shedding also needs to be considered. This paper presents the optimization of the total cost of a power system including minimizing the generation cost function of power plants or power companies and minimizing the damage cost function caused to customers due to load shedding or power outages. At the same time, the objective function must also ensure the constraints on the operating conditions of the power system. This contributes to maintaining the continuity of the power supply to critical loads and minimizing damage. Base loads, priority loads, or loads that are not allowed to be shed are considered as constraints. The optimization problem is addressed by using the Particle Swarm Optimization (PSO) algorithm and the Cuckoo Search Algorithm (CSA). The IEEE 30-bus test system is applied to validate the reduction in total cost. The result comparison shows that when applying the CSA, the total cost is significantly reduced by 3.75% in comparison with the PSO algorithm. The algorithms are implemented in Matlab to demonstrate the efficiency and accuracy of the proposed method.
Bitcoin Price Prediction using the Hybrid Convolutional Recurrent Model Architecture
Authors: Ahmed Omar M. , Haji Lailan M. , Ahmed Ayah M. , Salih Nashwan M.
Published: October 2023

The field of finance makes extensive use of real-time prediction of stock price tools, which are instruments that are put to use in the process of creating predictions. In this article, we attempt to predict the price of Bitcoin in a manner that is both accurate and reliable. Deep learning models, as opposed to more traditional methods, are used to manage enormous volumes of data and to generate predictions. The purpose of this research is to develop a method for predicting stock prices using the Hybrid Convolutional Recurrent Model (HCRM) architecture. This model architecture integrates the advantages of two separate deep learning models: The 1-Dimensional-Convolusional Neural Network (1D-CNN) and the Long-Short Term Memory (LSTM). The 1D-CNN is responsible for the feature extraction, while the LSTM is in charge of the temporal regression. The developed 1D-CNN-LSTM model has an outstanding performance in predicting stock values.
Atmospheric CO2 Level Measurement and Discomfort Index Calculation with the use of Low-Cost Drones
Authors: Kokate Piyush , Sadistap Shashikant , Middey Anirban
Published: October 2023

Unmanned Aerial Vehicle (UAV) platforms are emerging as an essential tool for various studies in environmental engineering. The quadcopters drones have immense potential for sensor interfacing and stable data acquisition. These UAVs can perform critical activities like volcanic eruption monitoring, stack emission monitoring, urban air quality monitoring, identification of pollution levels in 3D space, etc. Carbon dioxide (CO2) and the Discomfort Index (DI) are essential indicators of air quality and climate comfort. Hence, it is critical to monitor them with extreme accuracy. This study demonstrates a novel application of CO2 profiling using low-cost drones at varied altitudes. The drone-aided vertical CO2 profiling was carried out at 60 m AGL (Above Ground Level) during summer and winter, in Nagpur city of India. This study retrieved some exciting data on the DI. It was found that CO2 concentration in the range of 20-70 m AGL was lower than the surface level. The derived DI was maximum at the height range of 40-50 m. Inversion was observed in the range of 30-40 m. A positive correlation between CO2 and temperature was observed in both seasons. The lightweight commercial drones are capable of tethering sensor modules to get accurate results in less cost and effort. This type of novel tethered sensor technique could be applicable in weather forecasting, landfill surface monitoring, volcanic eruption monitoring, and other probable applications with few drone flight limits.
Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification on Surveillance Videos
Authors: Saikrishnan Venkatesan , Karthikeyan Mani
Published: October 2023

Surveillance videos are recordings captured by video recording devices for monitoring and securing an area or property. These videos are frequently used in applications, involving law enforcement, security systems, retail analytics, and traffic monitoring. Surveillance videos can provide valuable visual information for analyzing patterns, identifying individuals or objects of interest, and detecting and investigating incidents. Object detection and classification on video surveillance involves the usage of computer vision techniques to identify and categorize objects within the video footage. Object detection algorithms are employed to locate and identify objects within each frame. These algorithms use various techniques, namely bounding box regression, Convolutional Neural Networks (CNNs), and feature extraction to detect objects of interest. This study presents the Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification (MFODL-RODC) method on surveillance videos. The main aim of the MFODL-RODC technique lies in the accurate classification and recognition of objects in surveillance videos. To accomplish this, the MFODL-RODC method follows a two-step process, consisting of object detection and object classification. The MFODL-RODC method uses the EfficientDet object detector for the object detection process. Besides, the classification of detected objects takes place using the Variational Autoencoder (VAE) model. The MFO algorithm is employed to enrich the performance of the VAE model. The simulation examination of the MFODL-RODC technique is performed on benchmark datasets. The extensive results accentuated the improved performance of the MFODL-RODC method over other existing algorithms with an output of 98.89%.
The Effect of Iron Content on the Thermodynamic Properties of Syndiotactic Polypropylene/Iron Composites
Authors: Ahmad Naveed , Ahmad Farooq
Published: October 2023

This study investigated the influence of iron concentration on the thermodynamic properties of syndiotactic polypropylene (sPP)/iron composites in the melt phase in a series of samples with varying iron content, ranging from 0 to 10% with a step of 2%. The HYSYS software was used to estimate molecular weight, critical temperature (Tc), critical volume (Vc), and eccentricity (Ec). The results showed that all these properties changed as the iron content varied. The findings of this study confirmed that iron concentration has an impact on the mechanical and chain dynamic (microscopic) properties of polymer composites.
Real-Time Fire and Smoke Detection for Trajectory Planning and Navigation of a Mobile Robot
Authors: Ngoc Pham Van Bach , Hoang Le Huy , Hieu Le Minh , Nguyen Ngoc Hai , Thien Nguyen Luong , Doan Van Tuan
Published: October 2023

Mobile robots have many industrial applications, including security, food service, and fire safety. Detecting smoke and fire quickly for early warning and monitoring is crucial in every industrial safety system. In this paper, a method for early smoke and fire detection using mobile robots equipped with cameras is presented. The method employs artificial intelligence for trajectory planning and navigation, and focus is given to detection and localization techniques for mobile robot navigation. A model of a mobile robot with Omni wheels and a modified YOLOv5 algorithm for fire and smoke detection is also introduced, which is integrated into the control system. This research addresses the issue of distinct objects of the same class by assigning each object a unique identification. The implementation not only detects fire and smoke but also identifies the position of objects in three-dimensional space, allowing the robot to map its environment incrementally for mobile navigation. The experimental results demonstrate the high accuracy achieved by the proposed method in identifying smoke and fire.
The Experimental and Theoretical Effect of Fire on the Structural Behavior of Laced Reinforced Concrete Deep Beams
Authors: Kareem Abbas , Mohammed Shatha D.
Published: October 2023

A Laced Reinforced Concrete (LRC) structural element comprises continuously inclined shear reinforcement in the form of lacing that connects the longitudinal reinforcements on both faces of the structural element. This study conducted a theoretical investigation of LRC deep beams to predict their behavior after exposure to fire and high temperatures. Four simply supported reinforced concrete beams of 1500 mm, 200 mm, and 240 mm length, width, and depth, respectively, were considered. The specimens were identical in terms of compressive strength (  40 MPa) and steel reinforcement details. The same laced steel reinforcement ratio of 0.0035 was used. Three specimens were burned at variable durations and steady-state temperatures (one hour at 500 °C and 600 °C, and two hours at 500 °C). The flexural behavior of the simply supported deep beams, subjected to the two concentric loads in the middle third of the beam, was investigated with ABAQUS software. The results showed that the laced reinforcement with an inclination of 45˚ improved the structural behavior of the deep beams, and the lacing resisted failure and extended the life of the model. The optimal structural response was observed for the specimens. The laced reinforcement improved the failure mode and converted it from shear to flexure-shear failure. The parametric study showed that the lacing bars remarkably improved the strength of the deep beams and they were not affected more by the steady-state temperature and duration. Furthermore, a greater increase in load-carrying capacity was associated with an increase in the flexural diameter of approximately 12 and 16 mm by approximately 24.77% and 87.61%, respectively, compared to the reference LRC deep beams.
The Influence of UHPFRC Jacket Steel Fiber Content on Strengthening Damaged Columns
Authors: Alasmari Hasan A.
Published: October 2023

Steel fiber is a commonly used material to repair damaged concrete, caused by environmental or design issues. This study used various Micro-copper-coated Steel Fiber (MSF) content (0.0, 0.5, 2.0, and 2.5%) with varying aspect ratios (28, 37, and 45) as part of Ultra-High-Performance Fiber-Reinforced Concrete (UHPFRC) mixtures to repair damaged concrete columns using a 30 mm layer jacketing. Twelve columns were prepared and tested at first by loading them with roughly 90% of their ultimate axial load capacity. Damage was caused and the columns were subsequently strengthened and rebuilt using UHPFRC mixtures in 30-mm layer jacketing for a second test, to determine the effect of UHPFRC and MSF content on damaged and reinforced columns. The test results showed that the concrete properties improved as the MSF content increased to 2.0% of the volume fraction, beyond which there was a slight reduction. Additionally, the UHPFRC-strengthened columns with and without MSFs experienced higher load capacities than the corresponding unstrengthened. On the contrary, using 2.5% MSF in the UHPFRC decreased the loading capacity by 14% compared to the UHPFRC with 2.0% MSF. The strengthened column with 2.0% MSF content showed the highest load efficiency (165.7% compared to unstrengthened), along with substantial displacement and ductility.
Inconel 625 Coatings on AISI 304 Steel using Laser Cladding: Microstructure and Hardness
Authors: Vijeesh Vadakke Parambil , Ramesh Motagondanahalli Rangarasaiah , Anoop Aroor Dinesh
Published: October 2023

Nickel-base super alloys such as Inconel 625 are preferred in high-temperature and corrosive environments. Since Inconel 625 is expensive and often difficult to machine, it is advantageous to deposit a protective coating of this alloy on a less costly and easily machinable substrate material such as stainless steel. In the present work, coatings were produced on AISI 304 steel substrate by depositing Inconel 625 powder using the laser cladding technique. As-received powder particles of Inconel 625 alloy were characterized using X-Ray Diffraction (XRD) and Field Emission Scanning Electron Microscopy (FESEM). After laser cladding, it becomes important to carry out the microstructural analysis of the cross-sectional areas of the coating and the substrate/coating interface region, for further understanding of the structure-property correlations. In this study, the microstructural features of the coatings and substrate/coating interface were examined using an FESEM equipped with X-ray elemental analysis. The phase analysis of the coating was carried out using XRD. In the coating region, the growth of planar, cellular, columnar dendritic, and equiaxed grains was noticed. It was observed that small amounts of Laves phase were precipitated. Furthermore, the laser-clad Inconel 625 coating showed superior microhardness over the stainless steel substrate.
A Recommendation Engine Model for Giant Social Media Platforms using a Probabilistic Approach
Authors: Alshammari Aadil , Alshammari Mohammed
Published: October 2023

Existing recommender system algorithms often find it difficult to interpret and, as a result, to extract meaningful recommendations from social media. Because of this, there is a growing demand for more powerful algorithms that are able to extract information from low-dimensional spaces. One such approach would be the cutting-edge matrix factorization technique. Facebook is one of the most widely used social networking platforms. It has more than one billion monthly active users who engage with each other on the platform by sharing status updates, images, events, and other types of content. Facebook's mission includes fostering stronger connections between individuals, and to that end, the platform employs techniques from recommender systems in an effort to better comprehend the actions and patterns of its users, after which it suggests forming new connections with other users. However, relatively little study has been done in this area to investigate the low-dimensional spaces included within the black box system by employing methods such as matrix factorization. Using a probabilistic matrix factorization approach, the interactions that users have with the posts of other users, such as liking, commenting, and other similar activities, were utilized in an effort to generate a list of potential friends that the user who is the focus of this work may not yet be familiar with. The proposed model performed better in terms of suggestion accuracy in comparison to the original matrix factorization, which resulted in the creation of a recommendation list that contained more correct information.
Effective Feature Prediction Models for Student Performance
Authors: Alsubhi Bashayer , Alharbi Basma , Aljojo Nahla , Banjar Ameen , Tashkandi Araek , Alghoson Abdullah , Al-Tirawi Anas
Published: October 2023

The ability to accurately predict how students will perform has a significant impact on the teaching and learning process, as it can inform the instructor to devote extra attention to a particular student or group of students, which in turn prevents those students from failing a certain course. When it comes to educational data mining, the accuracy and explainability of predictions are of equal importance. Accuracy refers to the degree to which the predicted value was accurate, and explainability refers to the degree to which the predicted value could be understood. This study used machine learning to predict the features that best contribute to the performance of a student, using a dataset collected from a public university in Jeddah, Saudi Arabia. Experimental analysis was carried out with Black-Box (BB) and White-Box (WB) machine-learning classification models. In BB classification models, a decision (or class) is often predicted with limited explainability on why this decision was made, while in WB classification models decisions made are fully interpretable to the stakeholders. The results showed that these BB models performed similarly in terms of accuracy and recall whether the classifiers attempted to predict an A or an F grade. When comparing the classifiers' accuracy in making predictions on B grade, the Support Vector Machine (SVM) was found to be superior to Naïve Bayes (NB). However, the recall results were quite similar except for the K-Nearest Neighbor (KNN) classifier. When predicting grades C and D, RF had the best accuracy and NB the worst. RF had the best recall when predicting a C grade, while NB had the lowest. When predicting a D grade, SVM had the best recall performance, while NB had the lowest.
Arabic Sentiment Analysis for Student Evaluation using Machine Learning and the AraBERT Transformer
Authors: Alamoudi Huda , Aljojo Nahla , Munshi Asmaa , Alghoson Abdullah , Banjar Ameen , Tashkandi Araek , Al-Tirawi Anas , Alsaleh Iqbal
Published: October 2023

Recently, Sentiment Analysis (SA) has become a crucial area of research as it enables us to gauge people's opinions from various sources such as student evaluations, social media posts, product reviews, etc. This paper aims to create an Arabic dataset derived from student satisfaction surveys conducted at the University of Jeddah regarding their subjects and instructors. In addition, this study presents an evaluation of classical machine learning models such as Naive Bayes, Support Vector Machine, Logistic Regression, Decision Tree, and Random Forest classifier for Arabic SA, whereas the results are compared using various metrics. Furthermore, AraBERT was used for the pre-trained transformer to improve the performance, achieving an accuracy of 78%. The paper fills the lack of SA research in the education domain in the Arabic language.
Hybrid Neural Network Models for the Optimization of Induction Hardening Processes
Authors: Lari Salman , Kim Jong-Moon , Kwon Hyock Ju
Published: October 2023

We describe a simple hybrid methodology to simulate an induction heating process that combines observational (black-box) and physics-based (white-box) methodologies. This method uses a neural network to predict the process' physical characteristics, which were previously unknown. A primary emphasis is placed on monitoring temperature variations within a subsurface layer of a bolt sample. The hybrid model incorporates an ordinary differential equation for the heating rate, leading to improved data accuracy compared to a standalone black-box model. This innovative approach not only improves predictive precision but also simplifies interpretability, ultimately serving as a pivotal instrument for the effective management and advancement of induction heating operations.
Isolation of Peroxidase from Watermelon Rinds Using Three Phase Partitioning
Authors: Al-Madhagi Haitham , Yazbik Valantina
Published: October 2023

Background and Objective: Peroxidase is an oxidoreductase that uses different compounds as substrate and thus can be utilized for different applications. The goal of this work is to isolate peroxidase from watermelon rinds using three phase portioning (TPP).
Methods: TPP was set by adding varying amounts of salts and alcohol and the enzyme activity recovery was measured for each variable. Different parameters were optimized successively in order to achieve the highest enzyme activity recovery including salt type, salt concentration, pH, alcohol/crude extract ratio and type of alcohol and then, combining all optimized conditions together.
Results: Salt that gave maximal recovery was sodium potassium tartarate, optimal salt concentration was 17%, optimal pH was 8, optimal alcohol/crude extract ratio was 2 and 1-butanol was preferred to t-butanol. Efficiently, upon combining all optimized factors, activity recovery of 116% was obtained.
Heavy Metal Contents and Proximate Analysis of Telfairia occidentalis (fluted pumpkin) Brought from Dagbolu along Ikirun, Olorunda Local Government and Irepodun Village Osogbo Local Government, Osun State.
Authors: OLAOYE ADEKUNLE , Olatunji Fakorede Kasali
Published: October 2023

In this study, the leaves of pumpkin purchased from two different markets in Osun State, Nigeria - Dagbolu along Ikirun Olorunda Local Government and Irepodun Village Osogbo Local Government Osun State, were analyzed for proximate and heavy metal content. The results of the proximate analysis revealed that the leaves contained essential nutrients like moisture, ash, crude fiber, fat, protein, and carbohydrate, which makes them a good source of food supplement. And their percentage composition were (2.64 % and 2.15%), (13.33% and 12.98%), (2.32 % and 2.15 %),(11.85 % and 11.20%),(24.54 % and 25.40 %) and(45.32 % and 46.12 %) for A and B respectively. Analysis also showed the concentration of heavy metals like Zn, Cu, Fe, and Mn, which were found to be relatively high in the leaves with following concentration (3.12 mg/g and 2.56 mg/g), (1.95 mg/g and 1.64 mg/g), (6.98 mg/g and 5.89 mg/g), and (0.43 mg/g and 0.36 mg/g) respectively for A and B. Despite this, the high concentration of Zn and Fe makes pumpkin leaves an excellent source of energy and protein for both humans and animals.
Online Assessment in the Subject of Applied Mathematics as one of the Instruments during the Formative Assessment
Authors: Orhani Senad
Published: October 2023

Taking assessments these days doesn't have to be stressful as technology has revolutionized the entire education system. Assessments are the most important part of the applied mathematics course, as they give students an accurate background of where they stand. It acts as a catalyst and positive reinforcer for students encouraging them to perform better. Most importantly, teaching and assessing students are two actions that should not be confined within the walls of a classroom but can be done anytime, anywhere with the help of technology. Therefore, in this study we have tried to analyze an assessment process developed through online tests in the subject of applied mathematics, as one of the instruments of formative assessment. To formalize this study, it was decided to use quantitative research methodology as a research strategy. The sample number was 110 students and it was a purposive sample. The empirical data collected during this research study involved a variety of data collection techniques. The methods used consist of questionnaires. The results from this research are important because online assessment is one of the formative assessment tools needed for students. The conclusion from this study is that students enjoyed taking online tests in the subject of applied mathematics. Therefore, the online assessment had a great positive impact on the formative assessment process in this subject. This paper has shown that online assessment as one of the instruments used in the formative assessment process affects the appearance of special assessment strategies in the subject of applied mathematics.
Emirati University Students’ Use of the Request Speech Act in Arabic in Email Communication with Faculty
Authors: Deveci Tanju , Abbas Elrashid Yousif
Published: October 2023

Students’ academic performance is greatly affected by their communication skills, a significant one of which is email writing. Due to technological advances and the distance education imposed by the COVID-19 pandemic, a great deal of communication between students and professors is in the form of email communication. Students’ inattention to the rules of this particular genre may result in face-threatening situations. Prompted by the lack of research on university students’ utilization of the request speech act in Arabic emails, this study investigated 96 Emirati first-year students’ request behaviors in Arabic in communication with university professors. Data were collected using a discourse-completion task and an evaluation form filled in by two judges. Results showed that the students mostly produced a direct request speech act. However, a significant number of them failed to follow the expected discourse structure often omitting the subject line, not thanking or writing their names at the end. The judges thought the emails were generally respectful. However, they felt they were not appropriate, which was mainly caused by the frequent mistakes with spelling, punctuation, and vocabulary. There were also instances of informal language use and grammatical errors. Based on these results, recommendations are provided focusing on awareness-raising and practice activities.
School Principals’ Attitudes and Level of Technology Integration in Moroccan Public Schools
Authors: Laouni Nour-eddine
Published: October 2023

This study investigates the relationship between school principals’ attitudes and the impact this has on the level of technology integration in Moroccan public schools. Data were collected by surveying 167 school principals via two different instruments. The computer attitude scale (CAS) (Gressard and Loyd, 1986) measures attitude toward computers and computer use and the level of technology integration (LoTi) Moersch (1999) measures the various levels of technology integration.  Findings suggest that there is a negative correlation between school principals’ attitudes and their level of technology integration at the Computer Attitude Scale (CAS) with (r = -.32, n = 167, p< .0005) and at the four sub-scales as well. The results obtained at this level accentuate another negative correlation existing between Computer confidence with (r = -.23, n = 167, p < .0005), computer liking (r = - .40, n = 166, p< .0005), computer usefulness (r = - .25, n = 167, p < .0005) and finally, computer anxiety with (r = - .08, n = 167, p < 0005) and level of technology integration. Recommendations for research and practice are provided.
Effective Methods of Teaching Foreign Languages Online: A Global View
Authors: Kianinezhad Neda
Published: October 2023

Foreign language acquisition has been a key issue in the field of language education. In recent years, the integration of Artificial Intelligence (AI) into language learning has brought about a transformative shift in the teaching and acquisition of the English language. This article delves into the tasks associated with implementing these actions and provides recommendations that form the methodological foundation for cultivating linguistic personalities in ELT. The research is dedicated to the analysis of prevalent and frequently employed methods and competency-based approaches to online foreign language instruction, grounded in both national and international experiences, illuminating the progress made in this burgeoning field. This comprehensive review of effective strategies for teaching foreign languages in the context of distance online learning offers valuable insights into contemporary language teaching.
Stand-Up Comedy as Media to Improve Students' Speaking Skills in The Second Academic Year of SMA Negeri 1 Sumbul
Authors: Tampubolon Surya Kelana Tua , Sinaga Nenni Triana , Tampubolon Sahlan
Published: October 2023

A Thesis, English Departement Teacher’s Training Faculty of HKBP Nommensen University This study aims to determine the results of using a communicative approach to improve students’ speaking skills. The researcher conducted the research at SMA Negeri 1 Sumbul. This study was carried out by Experimental quantitative research. The sample of this study was class XI-3 (the experimental class) and class XI-4 (the control class), with a total of 20 students for each group. The research conducted experimental teaching and speaking tests with stand-up comedy personal experience to collect data. The result from this research can be seen as follows: t-coun t> t-table (p = 0.1) with the degree of freedom (df) = 38. This means 6,665 > 1.21 (p=0.1) with df 38. This means that the use of a communicative approach has a significant effect on students’ speaking skills. Using the communicative approach as a teaching method through stand-up comedy in class activities improved students’ speaking skills.
Development Literacy Module Media In Indonesian Language To Improve Reading Skills Of Grade Ii Elementary School Students
Authors: Fitriyani Mega Dewi Sukmawati Hasyim,
Published: October 2023

Reading and writing are early literacy that was first recognized in human life. The reading difficulties experienced by some students make students have minimal reading literacy skills. This can affect students' ability in writing skills, because a lack of reading makes students minimal information and minimal vocabulary. Therefore, teachers must be smart in managing learning that takes place in class. The literacy literacy module is a book which contains elements of reading and practice questions. With the practice questions associated in order to train students, after reading the module. In this study, researchers used a design design and development research approach(Research and Development atau RnD). Researchers focused on grade 2 students, using 27 students. Data collection using a questionnaire, analyzed with qualitative descriptive data and quantitative data. Based on the results of product feasibility reached an average of 85.25%. From the practicality test results by teachers and students in the average module, it is 94.2%. Based on the results of the effectiveness test, the average was 81.4% while the students' reading skills test averaged 87.96%. So that the development of the Media Literacy Module produces products that are very valid, effective and practical to use in the learning process on Indonesian content for poetry material as well as expressions of apology and help for class II SD Negeri Mekar Mukti 01.