bluuebunny/crossref_metadata_2025 huggingface dataset:
Simulation of Advanced Driving Assistance Systems for a Dynamic Vehicle Model
Authors: Ataman Tevfik , Biberci Mehmet Ali , Celik Mustafa Bahattin
Published: October 2024

Advanced Driving Assistance Systems (ADAS), such as collision avoidance systems and adaptive cruise control, are important features of autonomous driving and are gaining importance day by day in terms of increasing road safety. To increase the reliability of the system, virtual simulation environments are used during the design and development stages. This study examines the effect of ADAS features on energy parameters during the driving cycle in a virtual simulation environment. The discussion focuses on the simulation of an electric vehicle and the relationship between energy use and ADAS functions. For ADAS modeling and simulation, a dynamic model was developed in MATLAB and examined throughout the NEDC drive cycle.
Applying Intelligent Algorithms In Short-Term Electrical Load Forecasting
Authors: Le Trong Nghia , Nguyen Ngoc An , Huynh Thi Ngoc Thuong , Le Quang Trung , Huynh Thi Thu Hien , Le Thi Thanh Hoang
Published: October 2024

This study presents short-term electricity load forecasting for the New England area by processing initial data through correlation assessment and data clustering. This method is combined with artificial neural networks to improve accuracy and forecast performance. Data preprocessing focuses on two main issues: identifying correlations between variables to eliminate less relevant factors and retaining highly correlated variables to reduce noise, as well as reducing the data sample size before training the neural network. This evaluation aims to determine the factors that have a significant impact on electricity load. These factors can include previous load values, weather conditions, time, types of electricity usage, and others. This technique ensures that reducing the size in both dimensions of the large dataset does not result in the loss of critical information, maintaining the accuracy of computational programs and the performance of neural network training at high levels. The neural network is trained to classify and cluster data based on previously identified correlated characteristics. As a result, the forecasting model can make more accurate predictions about future electricity loads. Experimental results show that the proposed method achieved more than 97% accuracy, outperforming traditional methods in both speed and load forecasting accuracy. The new dataset had 63% fewer samples compared to the initial dataset.
Quasi-Reflection Learning Arithmetic Firefly Search Optimization with Deep Learning-based Cyberbullying Detection on Social Networking
Authors: Azar Ahmad Taher , Noori Harith Muthanna , Mahlous Ahmed Redha , Al-Khayyat Ahmed , Ibraheem Ibraheem Kasim
Published: October 2024

Social networks are a major medium for communicating, collaborating, and sharing knowledge, data, and ideas. However, due to anonymity preservation, incidents of cyberbullying and hate speech emerge. Cyberbullying is very common on social media, and people end up with depression and do not take action against it. Automatic identification of these situations on many social networking sites requires intelligent systems. Deep learning (DL) methods are preferred for their potential in text classification, with accurate results on various academic benchmark issues. This study develops a Quasi-reflection Learning Arithmetic Firefly Search Optimization with Deep Learning Cyberbullying Detection (QLAFSO-DLCBD) technique to detect accurately cyberbullying on social media. The proposed QLAFSO-DLCBD method undergoes an initial preprocessing stage to convert the raw data into a meaningful format. The Keras embedding layer is used for word embedding purposes. The QLAFSO-DLCBD technique applies the Attention-based Bidirectional Long Short-Term Memory (ABiLSTM) method to detect cyberbullying. The QLAFSO algorithm was employed to select optimal hyperparameters for the ABiLSTM method, enhancing detection performance. Extensive experimental and comparative results suggest a higher efficacy of the proposed QLAFSO-DLCBD method compared to other recent methods.
Evaluation of Glass Powder's Impact on the Atterberg Limits of Anbar Soil
Authors: Al-Mohammedi Amenah Adnan Shakir
Published: October 2024

Soil stabilization is crucial for the construction industry in regions with clayey expanding soil. Adding certain materials can enhance the geotechnical properties of the soil. Ground glass powder is studied herein as an additive to clay soil, by applying the Atterberg Limit Test (ALT). The use of ALT can give a basic assessment of the suitability of the soil, which also determines the optimal ratio of the glass material that must be added to the mixture. Laboratory experiments were conducted utilizing the Casagrande apparatus by adding 4%, 5%, and 6% of ground glass to clay soil, and the impact of these percentages on soil characteristics was evaluated. The results exhibited a noticeable change in the mixture limits and plasticity. Increasing the percentage of added glass leads to a decrease in all three Atterberg limits improving soil stability and reducing soil plasticity.
Critical Success Factors of Agile Software Projects: A Review
Authors: Zhang Fuye , Abdullah Nur Atiqah Sia , Rosli Marshima Mohd
Published: October 2024

Most organizations have begun to adopt agile methods to pursue successful software development. However, the adoption and implementation of agile approaches are facing various challenges. The success of agile software development depends on Critical Success Factors (CSFs), which this study aims to identify and classify based on their relative importance. Through an extensive literature review, these factors are summarized, screened, and categorized into six dimensions. Their evolution is also outlined and analyzed. Then, the factors are illustrated through a bubble chart. Furthermore, this study determines the relevant CFSs that have a significant impact on how effectively can agile software development be implemented in China. The findings suggest certain recommendations to ensure that agile software projects are efficiently implemented in China, maximizing the chances of project success, providing valuable insights and practical guidance.
Reliability of Data obtained by ASTER Satellite for Digital Elevation Models
Authors: Jasim Basheer S. , Mohammed Zainab T. , Mahdi Lamya M. J.
Published: October 2024

The Digital Elevation Model (DEM) is a typical type of topographic data. Data on elevation are often used as a major information source for analysis and modeling by geographic information systems. Modern continuous changes across space are represented by DEM. Particularly, the European Union Digital Elevation Model (EU-DEM), a new dataset derived from the EU's Copernicus Land Monitoring Service, which features contours developed for all DEMs which are compared with the contours of a topographic map. One of the most important sources of topographical information is the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM). Compared to data from the Ground Control Points (GCPs), the vertical differences in ASTER products are utilized to calculate the Root Mean Squared Error (RMSE). The vertical accuracy of this DEM is assessed to be 13.13 m, which is the RMSE that was determined based on the data collected in the field by GNSS. In addition, the Standard Deviation (SDT) error came in at 2.60 m. After considering the effect of correcting bias, the error findings may range from a minimum of -6.47 m to a maximum of 1.998 m. Variations in RMSE and SDT are less than 0.991 m and 0.960 m, respectively.
Control Design of the Quadrotor Aircraft based on the Integral Adaptive Improved Integral Backstepping Sliding Mode Scheme
Authors: Jinlong Zhang , Jianhong Wang , Ruchun Wen , Xi Luo , Yongjun Ding , Azar Ahmad Taher , Ahmed Saim , Hameed Ibrahim A. , Zalzala Ali  Mahdi , Ibraheem Ibraheem Kasim
Published: October 2024

It is known that disturbances reduce tracking accuracy and control effect. To address these issues, in this paper, the Integral Adaptive Improved Integral Backstepping Sliding Mode Control (IAIIBSMC) method for position control of the quadrotor with uncertain disturbances, is proposed. Integrals are introduced into the adaptive reaching law and are extended to the control of virtual variables based on integral backstepping control, enhancing the system's anti-disturbance performance. The final combination with Sliding Mode Control (SMC) further improves system performance. Compared to the traditional Adaptive Integral Backstepping Control (AIBC), the proposed IAIIBSMC demonstrates superior tracking control, faster response, stronger anti-interference ability, and smaller overshoot. Experimental comparisons of different control methods and disturbances during fixed-point hovering and trajectory tracking show that the IAIIBSMC achieves better control. Specifically, the maximum position tracking error using IAIIBSMC is approximately 0.191 m, 22.04% lower than that of the AIBC. The steady-state error of IAIIBSMC is about 3 mm, which is negligible within the allowable range. These results validate the effectiveness and superiority of the proposed controller in achieving precise control under various disturbance conditions.
Hardware Implementation of a Deep Learning-based Autonomous System for Smart Homes using Field Programmable Gate Array Technology
Authors: Tounsi Mohamed , Mahdi Ali Jafer , Ahmed Mahmood Anees , Azar Ahmad Taher , Smait Drai Ahmed , Ahmed Saim , Zalzala Ali Mahdi , Ibraheem Ibraheem Kasim
Published: October 2024

The current study uses Field-Programmable Gate Array (FPGA) hardware to advance smart home technology through a self-learning system. The proposed intelligent three-hidden layer system outperformed prior systems with 99.21% accuracy using real-world data from the MavPad dataset. The research shows that FPGA solutions can do difficult computations in seconds. The study also examines the difficulties of maximizing performance with limited resources when incorporating deep learning technologies into FPGAs. Despite these challenges, the research shows that FPGA-based solutions improve home technology. It promotes the integration of sophisticated learning algorithms into ordinary electronics to boost their intelligence.
Characteristics of Nuclear Radiation Shielding using Natural Bentonitic Shale
Authors: Abd El-Azeem Samah Abdullah , Harpy Nareman M. , Mansour Howaida
Published: October 2024

With the wide use of radioactive materials, it is important to look for locally available and inexpensive materials to serve as efficient absorbers, suitable for shielding against radiation hazards. Due to its widespread availability and affordability, natural bentonitic shale is a viable option for use as a nuclear shielding material. In this study, natural bentonitic samples were cut into cylindrical pellets of varying thicknesses. The chemical composition of the natural bentonitic shale samples was determined through the use of X-Ray Fluorescence (XRF) spectroscopy. The linear and mass attenuation coefficients of bentonitic samples were evaluated using a NaI(Tl) scintillation detector at 662 keV energy of 137Cs, as well as at 1,173 keV and 1,332 keV energies of 60Co, gamma-ray sources. The experimental results indicated that bentonitic samples B3 and M2 exhibited superior shielding parameters compared to other bentonitic samples, attributable to their elevated densities. These two samples are distinguished by a high CaO content and a low Al₂O₃ content. Furthermore, the Half Value Layer (HVL), Tenth Value Layer (TVL), and mean free path thicknesses were calculated at these energies using linear attenuation coefficients. The theoretical mass attenuation coefficient, calculated with the assistance of the XCOM program and experimental estimates, was found to be in good agreement with the theoretical value.
Experimental Evaluation of a System to Control the Incremental Forming of Aluminum Alloy Type 1050
Authors: Ghazi Safaa Kadhim , Salloom Maher Yahya , Bedan Aqeel Sabree
Published: October 2024

The hybrid method, known as Incremental Sheet Hydro Forming (ISHF), is a combination of the techniques of Incremental Sheet Forming and Sheet Hydro-Forming. The primary concern identified in the incremental sheet forming formation strategy relates to the potential failure of the product due to the thinning of the sheet and subsequent springback. In response to the issue of sheet failure resulting from thinning, a revised iteration of the integrated sheet hydroforming (ISHF) method was proposed. The revised version of the ISHF process has demonstrated notable improvements in the malleability of the material. The ISHF technique entails the movement of a single ball tool along one side of the sheet's surface, while hydraulic support is applied on the opposite side through the use of pressurized hydraulic fluid. The present study sought to investigate the impact of hydraulic support on metal formability and thickness distribution. In addition, a modified variant was also considered. The experimental results are in close agreement with the predictions made by the analytical models. The strain distribution throughout the length of deformation for the sheet has been calculated. The surface quality of the products was found to be satisfactory and a preliminary simulation has been performed. This study examines the influence of process factors, specifically spindle speed (1000/1500/2000 RPM), feed rate (400/600/800 mm/min), tool diameter (8/10/12 mm), and step-down (0.2/0.4/0.6 mm), on the cone-shaped feature at the specimen of aluminum alloy A 5010. The results demonstrated that, through analysis of variance, the most influential factor in the distribution of thickness was speed. Regarding formability, the rate of change was found to be the highest at 50%.
Bonding between New and Substrate Concrete in Composite Beams subjected to the Effect of Repeated Loads
Authors: Ibrahim Tariq Emad , Abdulrazzaq Oday A. , Saleh Samoel Mahdi
Published: October 2024

This study uses experimental methods to assess the bonding strength of aged concrete compared with several types of new concrete under the effect of repeated loads. The major goal of this study is to evaluate several methods for creating bonding behavior. Additionally, it evaluates the influence of altering stiffness and shrinkage rates at the interface between concrete layers poured at different dates. The experimental tests examined the impact of numerous parameters, including surface preparation, bonding agent type, age difference, and the kind of concrete utilized in the new concrete. The flexural test results show that the percent of Pu (repeated) / Pu (static) was about 85%, 95%, 98%, 95%, 97%, 92%, and 95% for the composite beam with the shear connector, SCC, steel fibers, rough surface, SBR, SIKA, and HSC, respectively. The ultimate load increased by 126% for the composite beams with stirrups as shear connectors with respect to the reference beam. So, using stirrups as shear connectors between new and old concrete significantly increased the load-carrying capacity of the beam subjected to repeated loads.
MRAS Speed Estimator for a PMSM Machine: Practice Design
Authors: Elnaggar Mohamed F.
Published: October 2024

Wind energy systems are based on synchronous machines, which can support high-speed rotation cases due to possible high-speed values of incoming winds. The machines used are affiliated with permanent magnet machines, whereas speed detection can become difficult. This complication appears when high incoming wind speed values increase the motor temperature, which in turn can influence motor parameters, and especially the stator resistance. This can impact the proposed speed software estimator robustness. The proposed high-speed estimator algorithm is based on the Model Reference Adaptive System (MRAS) estimation method, which can be used for motor speed estimation. This concept includes the reactive power model, which ensures the robustness of the estimator when facing any possible stator resistance variation, even at very high speeds.
Online Purchase Intention using Social Media: A Systematic Literature Review
Authors: Mubdir Ghaith Abdulridha , Hashim Sharizal , Ayob Abu Hanifah , Rosli Nadzirah
Published: October 2024

Social Media (SM) have become an essential tool for the marketing of products and services, affecting customer Purchase Intention (PI). However, experts have no agreement over the factors or predictors as well as the theories that can explain the variation in PI. This review study attempts to discover the predictors (factors, mediator, moderator) of online PI using an SM platform. A systematic review of the online PI was performed. The findings demonstrated that the number of studies relevant to online PI via SM engagement is growing. The quantitative approach is widely employed in the literature, with most studies, though, not utilizing theories to explain online PI through SM. The findings also displayed that Structural Equation Modeling (SEM) is increasingly being deployed. The frequency analysis exhibited that the most important predictors are Attitude (AT), Subjective Norms (SN), Trust (TR), Perceived Usefulness (PU), Perceived Risk (RS), and Perceived Ease Of Use (PEOU). The findings were discussed, and sellers were urged to enhance information quality and reduce the risk of conducting online transactions by offering various payment methods. Further research employing mixed methods and theoretical frameworks is needed in this domain, especially considering developing nations.
Investigating the Impact of Palm Leaf Fibers onthe Crack Resistance of Hot Asphalt Mixtures
Authors: Kadhim Noor Jawad , Al-Busaltan Shakir
Published: October 2024

Sustainability attracts high interest in various fields. Over 95% of roads are paved using Hot Mix Asphalt (HMA), consequently, employing one sustainable material adds significant value. In this work, modified dry methods are suggested to generate modified balls including Palm Leaf Fibers (PLFs). This study aims to evaluate the extent of improvement in HMA crack resistance using these balls. Measurements of volumetric characteristics, mixture sensitivity to moisture damage, and crack-related tests, namely, the Ideal-CT test, the Indirect Tensile Strength test (ITS), the fracture energy (Gf), the Cracking Resistance Index (CRI), the Flexibility Index (FI), and the Toughness Index (TI) were performed to assess the tensile strength of the mixtures. The results showed that the method deployed to create HMA using PLFs was effective in preventing cracks. Moreover, the results suggest that utilizing ITS test alone is insufficient in capturing all phases of mixture behavior since this test resistance to tensile cracking is largely dependent on the peak load whereas there are numerous characteristic indices, such as the CT-index, which provide a more accurate assessment. Therefore, this study offers a significant sustainable approach by modifying the mixing and improving the cracking resistance.
Detecting Remote Access Trojan (RAT) Attacks based on Different LAN Analysis Methods
Authors: Rashid Salar Jamal , Baker Shatha A. , Alsaif Omar I. , Ahmad Ali I.
Published: October 2024

Cyberattacks aim to access confidential information or disrupt system functionality. These days, they can take the form of attacks that give the attacker complete control over the victim's computer. Remote Access Trojans (RAT) are malware designed for these purposes. RAT gives an attacker direct access to a victim's computer and allows him to interact with the victim to steal confidential information, spy on him in real time, or interact directly with him through a dialogue box. RATs are used for information theft, surveillance, and extortion of victims. This study installed multiple virtual machines as a prototype for both the attacker and the victim, interconnected on a Local Area Network (LAN). RAT installations were explored using Mega RAT version 1.5 Beta. Ultimately, various RAT attacks were executed on target machines, and a range of static and dynamic analysis tools were employed to identify RAT. The scenarios implemented on the LAN demonstrated that RATs can be built and used with ease. Furthermore, their attacks can be identified through static or dynamic analysis using various freely available tools. The findings show that the static detection approach to identify RAT malware is more user-friendly compared to dynamic methods. However, dynamic detection can be easily performed using cost-free software.
Drained Bearing Capacity of Strip Footings on Two-Layered Sand Soil Slope
Authors: Benali Redha , Mazouz Badis , Belkadi Ahmed Abderraouf , Mansouri Tarek , Goudjil Kamel
Published: October 2024

The objective of this study is to investigate the drained bearing capacity of a strip footing on a two-layered sand slope through the use of numerical analysis. The analysis is conducted using Plaxis, a two-dimensional finite element software. The behavior of the sand is modeled utilizing the non-linear Mohr-Coulomb criteria. The research examines the influence of various parameters on the footing's behavior, including the normalized thickness of the top layer h1/B, slope angle β, and friction angles of both loose and dense sand, φ1 and φ2, respectively. The findings indicate that for the scenarios with a denser top layer φ1/φ2 > 1, the bearing capacity increases with rising h1/B. Conversely, for the cases with a looser top layer φ1/φ2 < 1, the bearing capacity declines with increasing h1/B. Moreover, irrespective of the slope angle β, the bearing capacity rises with a higher sand friction angle φ.
Emotional Facial Expression Detection using YOLOv8
Authors: Alshammari Aadil , Alshammari Muteb E.
Published: October 2024

Emotional facial expression detection is a critical component with applications ranging from human-computer interaction to psychological research. This study presents an approach to emotion detection using the state-of-the-art YOLOv8 framework, a Convolutional Neural Network (CNN) designed for object detection tasks. This study utilizes a dataset comprising 2,353 images categorized into seven distinct emotional expressions: anger, contempt, disgust, fear, happiness, sadness, and surprise. The findings suggest that the YOLOv8 framework is a promising tool for emotional facial expression detection, with a potential for further enhancement through dataset augmentation. This research demonstrates the feasibility and effectiveness of using advanced CNN architectures for emotion recognition tasks.
Ventilation in Small-Compartment Fires: The Potential of Fire Retardancy
Authors: Al-Waqfi Mohammed S. El-Ali , Al-Jahmany Yarub , Al-Jarrah Jawdat , Rbehat Diana , Al-Qudah Omar Ayed
Published: October 2024

Modern architecture expands building openings to take advantage of natural light, which may have detrimental effects in case of fire occurrence. The falling panes by the high temperatures may cause fire to spread and endanger lives and property. Based on that and by using Pyrosim software, the current study investigated the effect of natural ventilation on small-compartment fire development and on the latter reaching the flashover phase. The simulated enclosure was a 3.6 m × 3.6 m × 2.9 m room with a closed door of 0.90 m × 2.10 m. The study analyzed the impact of the ventilation opening size and dimensions on fire development and the neutral plane height. The results showed that the ventilation area, rather than its dimensions, was the factor with the most significant impact. Higher ventilation areas reduced the temperatures well below the flashover conditions. The current study reveals the potential of employing natural ventilation to help prevent compartment fires from reaching the flashover stage as well as its consequences on lives and property.
Enhancing of Material Removal Rate and Surface Roughness in Wire EDM Process using Grey Relational Analysis
Authors: Abdullah Mostafa Adel , Ahmed Baqer A. , Ghazi Safaa Kadhim
Published: October 2024

Wire Electrical Discharge Machining (WEDM) is a non-conventional machining technique that utilizes electrical discharges to remove material from a workpiece. This study investigates the effects of three WEDM parameters: feed rate (500, 600, and 700 m/min), pulse-on time (20, 25, and 30 μs), and pulse-off time (10, 30, and 50 μs) on the Material Removal Rate (MRR) and surface roughness (Ra) of stainless-steel workpieces. Experiments were conducted based on an L9 orthogonal array, and the results were analyzed using Analysis of Variance (ANOVA) and Grey Relational Analysis (GRA). ANOVA revealed that the pulse-on time had the most significant influence on both MRR and Ra. GRA was employed to determine the optimal combination of machining parameters, which were found to be a pulse-on time of 25 μs, pulse-off time of 10 μs, and feed rate of 600 m/min. Under these conditions, the highest MRR (12.444 g/min) and best surface finish (2.169 μm) were achieved. The findings of this study demonstrate the effectiveness of using GRA in conjunction with ANOVA to optimize the WEDM process for improving the MRR and Ra in stainless steel machining.
A Multi-Head Self-Attention Mechanism for Improved Brain Tumor Classification using Deep Learning Approaches
Authors: Reddi Prasadu , Srinivas Gorla , Prasad Reddy P. V. G. D. , Krihsna Dasari Siva
Published: October 2024

One of the most common life-threatening diseases, the brain tumor is a condition characterized by the rapid proliferation of abnormal cells that leads to the destruction of healthy brain cells. Its aggressive nature can result in a patient succumbing to the disease before an accurate diagnosis is achieved. Timely detection is crucial to effective treatment and patient survival. Similarly, early detection plays a pivotal role in the case of brain tumors, where swift identification is vital to providing optimal care and increasing the chances of patient recovery. Streamlining the complex process of brain tumor detection is a significant undertaking that aims to simplify and expedite the procedure, ultimately contributing to saving valuable time and enhancing patient outcomes. The proposed model, a modified VGG-16, facilitates faster and more accurate identification of abnormal brain cells, leading to early detection of brain tumors. A novel multihead self-attention mechanism is used in the modified VGG-16 architecture to improve tumor detection performance. The proposed model performs better than other state-of-the-art models, such as normal VGG-16, ResNet-50, and EfficientNet.
Experimental Investigation of Composite Circular Encased GFRP I-Section Concrete Columns under Different Load Conditions
Authors: Ahmed Hiba Shihab , Allawi Abbas , Hindi Riyadh
Published: October 2024

Pultruded materials made of Fiber-Reinforced Polymer (FRP) come in a broad range of shapes, such as bars, I-sections, C-sections, etc. FRP materials are starting to compete with steel as structural materials owing to their great resistance, low self-weight, and cheap maintenance costs, especially in corrosive conditions. This study aims to evaluate the effectiveness of a novel concrete Composite Column (CC) using Encased I-Section (EIS) as a reinforcement in contrast to traditional steel bars by using Glass Fiber-Reinforced Polymer (GFRP) as I-section (CC-EIS) to evaluate the effectiveness of the hybrid columns which have been built by combining GFRP profiles with concrete columns. To achieve the aims of this study, nine circular columns with a diameter of 150 mm and a height of 1000 mm were cast with compression strength equal to 42.4 MPa at the test day. The research involved three different types of reinforcement: Hybrid circular columns with GFRP I-section and 1% reinforcement ratio of steel bars, Hybrid circular columns with steel I-section and 1% reinforcement ratio of steel bars (the cross-section area of the I-section was the same for GFRP and for steel), and a reference column without an I-section. This study investigates the ultimate capacity, axial and lateral deformation, and failure mode of the circular columns under different loading conditions: concentric, eccentric (with eccentricities of 25 mm), and flexural loading. The results showed that the ultimate capacity of the composite columns using either encased steel I-section or GFRP I-section was higher than the traditional columns under all loading conditions. The concentric tested specimens, with steel I-section and with GFRP I-section, exceeded the ultimate strength of the reference specimen by 8.9% and 2.9%, respectively. Specimens with steel I-section and GFRP I-section achieved 11.9% and 9.7% higher ultimate strength than the reference specimens under a compression load of 25 mm eccentricity. Specimens with steel I-section and the specimens with GFRP I-section achieved ultimate strengths of 114.3% and 36.6% under flexural loading testing.
Enhancement of the Rutting Resistance of Asphalt Mixtures Modified by Nano Clay and Crumb Rubber
Authors: Hassan Farah Salam , Ismael Mohammed Qadir
Published: October 2024

In recent years the increased traffic, axle load, tire pressure, and hot weather have hastened the spread of rutting in flexible pavements. Recent research indicates that nanomaterials and crumb rubber considerably alter asphaltic mixture characteristics. This research aims to examine the impact of Nano Clay (NC) combined with Crumb Rubber (CR) on the Marshall characteristics and the rutting resistance of HMA. It involves determining the optimal asphalt content, by using the method of Marshall design, as well as the rutting depth for asphalt mixes with varying amounts of NC (1%, 3%, and 5%) and CR (10%, 20%, and 30%) as a percentage of the asphalt binder. The optimal content of asphalt was 4.93% for the control mix. The Marshall stability was enhanced by the inclusion of NC and CR, with the combination of 5% NC and 30% CR exhibiting the most significant increase of 20.9%. Marshall flow was decreased by adding NC and CR. The control mix had a Marshall flow of 3.30 mm, but when using 3% of NC and 30% of CR, the flow decreased to 2.88 mm, which was the greatest reduction. The ideal proportion of NC and CR was 5% and 30%, respectively. This resulted in a 40.85% reduction in rut depth compared to the control mixture.
Flexural and Abrasion Performance of High Volume GGBS Concrete Pavements
Authors: Patel Vikram J. , Juremalani Jayesh , Kumavat Hemraj R. , Patel Jaymik
Published: October 2024

Ground Granulated Blast Furnace Slag (GGBS) is an effective supplementary cementitious material because of its pozzolanic properties and positive environmental impact. This research explores the flexural strength of concrete pavements by analyzing several high-volume formulations of GGBS concrete against conventional mixes. The study aims to assess the viability of incorporating high-volume GGBS mixes in pavement construction, with a particular emphasis on their flexural performance. The study employs various GGBS content levels (60%, 65%, 70%, and 75%) in concrete mixes, with the control mix containing ordinary Portland cement. Specimens are prepared according to standard procedures, and flexural testing is conducted to evaluate their performance. Statistical analysis is performed to compare the flexural strength and behavior of GGBS mixes with the control mix. The results obtained from the experiments revealed that the GGBS concrete mixes had better flexural performance than the control mix, with more strength and less cost. The results provide the performance of large-volume GGBS concrete mixes under flexural loading circumstances, which helps to understand whether these mixes are suitable for use in concrete pavements. Ultimately, this research sheds light on the possibilities of utilizing large-volume GGBS concrete mixes as long-term substitutes for traditional concrete pavements, which may help in the promotion of greener and more long-lasting infrastructure solutions.
Parallel Discrete Harmony Search Algorithm for the Graph Coloring Problem
Authors: Chemaa Sofiane , Kout Akram , Djelloul Halima , Harrag Nassir
Published: October 2024

Graph coloring is an NP-hard combinatorial optimization problem with significant implications in both theoretical and practical contexts due to its complexity and extensive applicability. In this work, a novel approach is proposed to address the graph coloring problem through a parallel discrete Harmony Search algorithm, termed PDHSCol. By harnessing the robustness of the Harmony Search algorithm and integrating parallel processing, our algorithm enhances performance by concurrently generating and evaluating multiple solutions. Implemented in MATLAB, PDHSCol was evaluated using a variety of DIMACS benchmark instances. The experimental results demonstrate that the algorithm performs effectively and yields promising improvements over various other methods, highlighting its potential to deliver high-quality solutions.
Smart City Feasibility Study using IoT and Machine Learning
Authors: Ali Rowedah Hussien , Alazawy Suha Falih Mahdi , Mustafa Ali , Erzaij Kadhim Raheim
Published: October 2024

Complexity and resource constraints accompany urban growth. According to UN figures, cities currently use 75% of the global energy, with 70% of the greenhouse gas emissions being mostly generated from transportation and residence buildings. Furthermore, city residents are susceptible to the consequences of climate change. Therefore, a feasibility study for the possibility of implementing a smart city was conducted in this paper. The results show that the current situation of the cities is/renders them far from being smart, while the environmental aspect needs to be controlled by the use of IoT sensors. The utilized Hyperd algorithm gave highly accurate prediction results.
Harnessing Wind Vibration, a Novel Approach towards Electric Energy Generation - Review
Authors: Shraddha S Magar  , Archana S Sugandhi  , Shweta H Pawar  , Suhas B Khadake  , H. M. Mallad
Published: October 2024

Vortex-Bladeless is a Spanish SME whose objective is to develop a new concept of wind turbine without blades called Vortex or vorticity wind turbine. This design represents a new paradigm in wind energy and aims to eliminate or reduce many of the existing problems in conventional generators. The bladeless vortex turbine is one such concept that uses the principle of aero-elasticity and thereby the variations produced by it to generate electricity. Project work will include the design and development of a vortex wind bladeless turbine and a gyro action based e-generator to be coupled to it to generate the electricity. Prototype development will be done using 3-D printing for the vortex turbine and the e-generator to make a scaled working model that will demonstrate electricity generation and testing will be done on the same to determine the effect of wind speed on , turbine speed , voltage , current and power generated by the model
Participatory Writing in the Remote ESOL Classroom Space: Critical Learnings from a Pandemic
Authors: Metz-Matthews Kelly , McConnell Michele
Published: October 2024

This paper explores the ways ESOL writing instructors implement and assess participatory writing practices in the classroom using digital technologies during the COVID-19 pandemic. Participatory writing practices are largely sociocultural in nature and thereby resist the notion of standardized and individualized practices to focus on co-creating a shared culture around writing (Jenkins et al., 2016). In other words, they require that students voluntarily enculturate themselves into broader, co-created discourse communities (Johns, 1997). Participatory writing practices and any subsequent assessment of them are complicated by inequitable access to and varying levels of comfort with educational and other digital technologies—a fact which is particularly salient considering that a substantial majority of ESOL courses in California shifted to remote instruction in early 2020. Using several remotely taught post-secondary ESOL writing courses in California as critical entry points for this work, we examine our collective understanding of participation in light of the shift to remote teaching and learning while also pushing back against traditional western notions of participatory writing implementation and assessment to offer a more expansive and inclusive model in which remote students are encouraged to go beyond “pseudotransactional” forms of collaboration (Wardle & Downs, 2020). With these remote ESOL writing courses as examples, we argue that there are innate challenges to supporting students in gaining a new language through participatory writing practices while simultaneously grappling with new technologies and remote learning, but we also suggest that it can be accomplished given appropriate training, tools, and attention to power dynamics.
Using Google Docs for Collaborative Writing Feedback With International Students
Authors: Andrade Christina , Roshay Amber
Published: October 2024

Giving effective writing feedback can be a challenge for any English instructor. Teaching students how to provide peer feedback can be problematic as well. Both these challenges may seem even more apparent when teaching online during a pandemic. Using Google Docs for collaborative writing feedback is one effective method for addressing both these concerns in a university-based Intensive English Program (IEP). This critical perspective examines how to scaffold collaborative writing feedback remotely using free and widely available platform Google Docs and looks at future use, post-pandemic. In particular, it will share how the authors used Google Docs to track feedback and corrections from instructors to students, set up interactive writing exercises in synchronous courses, and engage in peer-to-peer editing during a pandemic.
HyperDocs, GIFs, and Collaboration Boards: Online Writing Instruction Supports for English Learners
Authors: DeCamillis Kelsey
Published: October 2024

During traditional, in-person writing instruction, teachers can quickly model tasks for students, students have clear directions for following lesson sequences step-by-step, and partner or group collaboration can begin by simply asking students to turn to whomever is sitting next to them. Online instruction poses challenges to these typical classroom dynamics, especially for teaching writing. This article highlights some tools (HyperDocs, GIFs, and collaboration boards, including Jamboard and Padlet) that I have integrated into the National Writing Project’s “College, Career, and Community Writer’s Program” (C3WP), an argument writing curriculum. In addition to making the units of instruction generally effective for online instruction, these specific adaptations are also designed to support English learners (ELs) as they learn writing skills in an online setting. In each section, I discuss the given tool, provide a rationale for why the tool fits the task originally outlined in the writing program, and explain the effectiveness of the tool for ELs.
Analyzing the Use of AI Writing Assistants in Generating Texts with Standard American English Conventions: A Case Study of ChatGPT and Bard
Authors: Lee Sue-Jin
Published: October 2024

The emergence of AI writing assistants has raised concerns about their potential impact on language diversity, preservation, and education. This paper examines the capabilities and limitations of AI writing assistants in generating dialectic text in response to academic and professional writing prompts. The study uses a concordance tool to conduct N-gram and keyword analyses on texts generated by AI writing assistants to examine the collocational patterns and linguistic conventions in AI-generated text productions. The results suggest that AI writing assistants rely heavily on Standard American English (SAE) conventions. Pedagogical implications include integrating language technology to promote language diversity and preservation and utilizing register-diversified corpora to enhance students’ understanding of language beyond SAE. This study emphasizes the importance of critically evaluating and revising AI-generated content and contributes to a better comprehension of the potential role of AI writing assistants in academic and professional writing.
Elementary Computing for All: A Computational Thinking Curriculum for Multilingual Students
Authors: Jacob Sharin , Warschauer Mark
Published: October 2024

Over the last decade, there has been an explosion of national interest in computer science (CS) education. In response to this, several organizations and initiatives have emerged in recent years to expand the CS pipeline. However, within these broad and laudable efforts, one important area has been largely overlooked—the instruction of CS to multilingual students, including the large and growing number of students designated as English learners in K-12 schools. These are one of the most underserved and understudied groups in CS education. In this article, we draw on existing research, as well as our own and others’ theoretical and empirical work to date, to put forth both a framework and curriculum for teaching CS to multilingual students.
AI Literacy for Multilingual Learners: Storytelling, Role-playing, and Programming
Authors: Ojeda-Ramirez Santiago , Ritchie Daniel , Warschauer Mark
Published: October 2024

Artificial Intelligence technologies are becoming ubiquitous, transforming the workforce by altering or creating jobs and influencing decisions that affect minority communities. The necessity of AI literacy, comprising knowledge and skills for critical interaction with AI, is increasingly important. Multilingual learners, engaging with both every day and domain-specific vocabulary and syntax, must acquire AI literacy, necessitating tailored pedagogical practices. This article outlines three strategies implemented for multilingual middle school students during a summer camp in Southern California. We argue for a mutually beneficial relationship between acquiring second language proficiency and AI literacy. Supporting these processes, we present three pedagogical strategies: (1) storytelling to model AI decision-making, (2) role-playing as an AI to demonstrate programmability and learning from data, and (3) programming text-to-speech-to-text AI to illustrate sensor functionality and action-reaction concepts. Additionally, we discuss their alignment with AI competencies. These strategies potentially foster linguistic scaffolding and translanguaging, aiding multilingual learners in acquiring new literacies.
Examining Curriculum through a Critical Antiracist Lens
Authors: Schmier Stephanie , Grant Rachel
Published: October 2024

Informed by a critical antiracist perspective for teacher education, we describe a year-long research collaboration we engaged to interrogate how White allyship can be reimagined to support the design of critical antiracist initiatives in language teacher education. We were inspired to write this article because we see the realities, possibilities, and problems inherent in addressing the historic and entrenched ways that race and racism still operate in America, within teacher education, and within our institution, to sustain white privilege and power. This research is grounded methodologically in the notion of inquiry as stance, which recognizes the capacity of teacher educators to work collaboratively to reform education in democratizing ways. We conclude that within our institution and throughout teacher education barriers and resistance remain to taking antiracist actions to change the way we prepare teachers for guiding students who increasingly are not White and whose first language is not English.
Positioning Radical Love through Narrative Inquiry to Foster Transformative Language Identities in the Multilingual Classroom
Authors: Young RAsheda , Sánchez-Martín Cristina
Published: October 2024

Multilingual students learning English are situated in systems of oppression that require a critical understanding of the role that language plays in discrimination. We describe how we create opportunities to develop and implement critical pedagogies that aim to challenge white mainstream English (Baker-Bell, 2020) as the default language in our multilingual classroom spaces and detail how our pedagogies can contribute to decentering “whiteness” in English Language Teaching (Motha, 2014). Through narrative inquiry, we demonstrate how teachers can develop antiracist pedagogies in ELT by actively listening to each other’s experiences, forming hush-harbors (Kynard, 2010), and integrating radical love (hooks, 2001). Additionally, exposing students to counternarratives from marginalized writers diversifies and enriches their learning experience. These pedagogical practices create opportunities for students’ growth by centering love, humanizing education, and deliberately creating a sense of belonging.
Developing Autonomous Self-Editors: An Alternative Approach to Written Corrective Feedback
Authors: Maamuujav Undarmaa
Published: October 2024

Written corrective feedback has been a long-standing practice among second language writing instructors, yet the efficacy of this practice for long-term development of students’ writing remains uncertain. In th field of writing research, error correction in second language writing continues to be a topic of much controversy. While numerous studies have investigated the long-term effect of written corrective feedback, no consensus has been reached. Challenging the deep-rooted conviction that instructors’ correction is beneficial, this article (a) argues that the role of a writing instructor is not to serve as an editor but to help students to become autonomous self-editors of their own work, and (b) proposes an alternative approach that is designed to develop students’ self-editing skills. Through effective scaffolding and strategy training, writing instructors can develop in second language writers a habit of mind to critically read and edit their own work.
Educating Students of Refugee Backgrounds: Critical Language Teacher Education in TESOL
Authors: Baecher Laura , Kasper Julie , Mincin Jenny
Published: October 2024

Critical language teacher education (Hawkins & Norton, 2009) describes an approach to TESOL teacher preparation that explicitly attends to the structures of schooling and society that privilege some groups over others. More than presenting methods or theories of TESOL in isolation, the intention of a critical approach is to foster awareness of local policies and practices in relation to possible teacher action for equity—essential for those in the position of welcoming and supporting English learners in US schools. This article explores how this approach was enacted in a domestic travel course for ESL teachers focused on the education of students of refugee backgrounds, an area underdeveloped in teacher education. Implications for TESOL teacher preparation in general, and for TESOL teacher professional learning about refugees specifically, are discussed.
AB 705: Where We’ve Been, and Where We’re Headed
Authors: Shaw Leigh Anne , Rice Sydney , Wada Kathy
Published: October 2024

Between 2012 and 2018, the California Community Colleges system underwent significant changes in policy and practice to address student success rates; this article chronicles policies affecting ESL students and how ESL faculty worked to ensure equitable, appropriate implementation for students enrolled in credit ESL courses. The 2018 California law, AB 705 (Irwin) and its partner bill, AB 1805 (Irwin), changed placement processes; impact was greatest in the disciplines of English and mathematics, which were mandated to primarily use high school information for direct placement into transfer-level course work. Credit ESL students were not included in the original text of either bill. However, advocacy by three community college ESL faculty led to language amendments in both laws on behalf of ESL students. Further advocacy resulted in an ESL subcommittee to ensure that the needs of ESL students would still be met in the implementation of AB 705.