bluuebunny/crossref_metadata_2025 huggingface dataset:
PILEA, an Advanced Hybrid Lightweight Algorithm utilizing Logical Mathematical Functions and Chaotic Systems
Authors: Mohammed Zahraa A. , Hussein Khalid Ali
Published: October 2024

In information security, data encryption plays a crucial role in preventing unauthorized access. Traditional methods often fall short when faced with sophisticated cyber threats. This research presents a hybrid encryption technique that integrates a recently devised 5D chaotic system, effectively bolstering data security by encoding information in an intricate, puzzle-like structure. This approach thwarts easy access to sensitive data, thus safeguarding them from potential interception and exploitation. The proposed encryption method combines the Linear Encryption Algorithm (LEA) and the Advanced Encryption Standard (AES) to create the Parallel Improved LEA (PILEA), blending key components of both algorithms to enhance data security. By integrating AES's S-box, Shift Rounds, Mix Columns, and Add Round Key operations, the PILEA significantly raises the complexity of the encrypted data, making them more resistant to unauthorized decryption attempts. A key innovation of this system is the use of a chaotic system for key generation, resulting in a strong, nonlinear, and dynamic key stream. Furthermore, by operating the entire system in a parallel mode, the proposed approach aims to decrease the number of rounds in the encryption process and the overall execution time for encryption and decryption. These enhancements further strengthen the encryption system's resilience against infiltration by malicious entities. Experimental results show that the PILEA method can withstand various types of cryptographic attacks, provides reduced computation times, and produces a highly random keystream, as confirmed by the NIST statistical test suite for randomness.
Analytical Solution for Bending Steel Concrete Composite Plates considering the Shear Deformation Effect
Authors: Tien Dao Ngoc , Tung Nguyen Xuan , Lam Nguyen Ngoc
Published: October 2024

This paper deals with an analytical approach to the static behavior of Steel Concrete Composite (SCC) slabs. The Classical Plate Theory (CPT) and Refined Plate Theory (RPT) are considered when constructing governing equations describing the behavior of steel concrete plates. The displacement fields are represented by trigonometric series to find the exact solution by applying Navier's approach. Various numerical examples have been compared with the considered ones showing their high accuracy. The influence of plate parameters such as the thickness of steel and concrete layers on displacement was investigated in detail.
A Real-Time Charge Predictive Model for Intelligent Networks
Authors: Bartouli Monia , Msolli Amina , Helali Abdelhamid , Fredj Hassen
Published: October 2024

A smart grid is a modern electrical system that uses information technology, including sensors, measurement tools and communication devices, to monitor and improve the efficiency of the power system. However, real-time forecasting remains a challenge due to its complexity.  This paper presents a forecasting framework that combines Convolutional Neural Networks (CNN) and Bidirectional Long-Short Term Memory (BiLSTM) for real-time load forecasting in smart grids. Compared to traditional methods like ARMA and Decision Trees (DTs), the proposed CNN-BiLSTM model demonstrates superior performance in terms of prediction accuracy, reaching up to 99% - higher than Long-Short Term Memory (LSTM) (93%) and Support Vector Machine (SVM) (84%). Additionally, the CNN-BiLSTM model requires fewer computational resources, with 90 Gigaflops (G) and 94 Million (M) parameters, compared to 151 (G) and 120 (G) for ARIMA and CNN-LSTM, respectively. These results indicate the proposed model's ability to accurately predict power system loads in real time with high computational efficiency.
The Performance of Asphalt Concrete Wearing Course Mix using De-οiled Bleaching Earth as Filler reviewed from Marshall Parameters
Authors: Miftah Muhammad Bagus , Rahardjo Boedi , Pranoto .
Published: October 2024

Road construction is essential to support connectivity, mobility, and economic growth. However, road construction can raise economic, social, and environmental issues. Many attempts have been made to develop the concept of sustainable road construction and to minimize environmental impacts by utilizing various materials. This work studies the incorporation of De-Oiled Bleaching Earth (DBE) as a filler into the Asphalt Concrete Wearing Course (AC-WC) mixture. The research objectives were first to analyze the characteristics of the ingredients that make up the AC-WC mixture and second to analyze the performance of AC-WC with DBE as a filler in terms of Marshall parameters. The results revealed that AC-WC with 10% DBE complied with the standard requirements. The highest stability was obtained with 10% DBE content, and the lowest was obtained with 90% DBE. The overall flow values of the specimens met the standards, except for the DBE content of 70% and 100%. The VIM values of the 0% and 10% DBE specimens were 4.76 and 4.87%, respectively, which met the specification standard of 3-5%. All samples exhibited VMA values ≥ 15% in accordance with the specifications. VFA values that met the specifications of ≥ 65% were obtained for DBE levels of 0, 10, 20, and 40%. MQ values that fulfilled the specification of ≥ 250 kg/mm were achieved for 0, 10, 20, 30, 40, 60, and 90% DBE. Thus, DBE can be used as a filler material in AC-WC mixtures for environmentally friendly road construction. The optimum content of DBE in the AC-WC mixtures was found to be 10%.
Delineation of Subsurface Structures using Seismic Refraction Tomographic Inversion in Wadi Al-Dawasir, South Saudi Arabia
Authors: Alotaibi Nouh , Metwally Ahmed
Published: October 2024

The main objective of this paper is to delineate the subsurface structures, especially faults, in Al Haddar, Wadi Al-Dawasir area which is a part of the Sulayyimah Quadrangle, South Saudi Arabia using non-linear seismic refraction tomographic inversion. No major structures or faults are observed from the surface geological studies, while most of the area is covered with sand dunes and recent quaternary deposits. The purpose of the current research is to use the seismic method, which has been widely deployed in detecting and mapping subsurface features, to delineate the subsurface structures utilizing the 2-D seismic refraction travel-time tomographic inversion technique. The main advantages of travel time tomography are that it is a nondestructive technique, it provides a velocity model for the subsurface and delineates the subsurface faults with very high accuracy without involving drilling or trenching. The seismic data were acquired employing the most advanced and up-to-date instruments for high-resolution investigation. The system consists of the source, which is a vibrator (Vibroseis), sensors (receivers or geophones), and the acquisition system. A 40 Hz vertical geophone Model GS-20DH was used as the receiver, Strata Visor with geodes was put into service as the acquisition system, and end-on spread was installed by utilizing 112 geophones, while the source is offset 10 m away from the seismic line. The length of the acquired seismic line was 4.5 km. The final velocity tomogram is graphically presented as a 2-D grid of pixels, where each pixel contains the model parameters of interest such as the velocity value or its reciprocal value known as slowness. The travel-time tomography was able to provide a velocity model for the subsurface and delineate the subsurface faults with high accuracy.
The Influence of Horizontal Reinforcement on Punching Shear Strength
Authors: Ameen Ali N. , Al-Sherrawi Mohannad H.
Published: October 2024

The slab is more petite and space-efficient in flat plate buildings since it is supported directly by columns rather than beams or drop panels, allowing additional floors to be added. Despite these benefits, a flat plate slab is vulnerable to brittle punching shear, a catastrophic collapse caused by the abrupt propulsion of a slab piece out from underneath by a column. In this study, Finite Element Analysis (FEA) was provided and carried out in ABAQUS/CAE 2019 to model the effects of punching shear impact on a flat plate reinforced with horizontal steel bars that vary in position, diameter, and number. Concrete was represented in the model by 8-noded hexahedral 3D brick elements and steel reinforcements by 2-noded linear 2D beam elements. The model has been modified according to the results of the experiments. In order to determine how different quantities and sizes of horizontal steel bars placed at different locations in a flat plate affected the slab's performance, parametric analysis was conducted. According to the outcomes, the shear capacity increases between 0.37 and 9.85% as the diameter of bars increases, between 1.2 and 22.9% as the quantity of bars increases, and between 1.99 and 26.1% as the bars shift from the tension side to the compression side.
ECAP: Ensemble Clustering using Affinity Propagation
Authors: Sinha Ankita , Ranjan Rajiv Kumar , Sonu Sankalp , Jha Nitya Nand , Kumar Sanjeet
Published: October 2024

A vast amount of time-series data is generated from multiple fields. Mining these data can uncover hidden patterns and behavior characteristics. The analysis of such data is complex because they are voluminous and have high dimensions. Clustering can provide a preprocessing step to extract insights. However, clustering such data poses challenges, as many existing algorithms are not efficient enough to handle them. In addition, many traditional and modern clustering algorithms need help with parameter-tuning problems. Ensemble clustering, an amalgamation of clustering algorithms, has emerged as a promising method for improving the accuracy, stability, and robustness of clustering solutions. This study presents Ensemble clustering using Affinity Propagation (ECAP). AP is efficient because it does not require the number of clusters to be specified a priori, allowing the data to reveal its structure. When used in an ensemble framework, the inherent strengths of AP are amplified by integrating multiple clustering results. This aggregation mitigates the influence of any single, potentially suboptimal clustering outcome, leading to more stable and reliable clusters. Extensive experiments were performed on four real-world datasets for rand index, homogeneity, completeness, and V-measure to determine the efficacy of the proposed approach. The results show that the proposed method outperforms AP, Kmeans, and spectral clustering.
Computational Simulation and Analysis of Local Thermal Comfort and Indoor Air Quality in Space with Displacement Ventilation
Authors: Kanaan Mohamad , Amine Semaan , Gazo-Hanna Eddie
Published: October 2024

Displacement ventilation has been known for its capacity to lower energy consumption and improve air quality, but it has major thermal comfort limitations. The aim of this paper is to optimize the DV supply conditions by using computational fluid dynamics modeling to achieve acceptable CO2 concentration in the breathing layer at minimum energy cost while preventing local discomfort due to draft and air temperature difference between ankles and head. The results revealed that up to 44% energy savings can be achieved if the selection of supply conditions is optimized. The model can be put into practice to give recommendations on displacement ventilation preliminary design.
Design and Simulation of a Microcantilever Sensor for Precise Detection of Volatile Organic Compounds
Authors: Pancheti Hareesh , Shanmugaraja Pattan , Srinivas Tejomurthy Peddiboina Hanuman
Published: October 2024

This study presents the coating of a thin film of conducting polymer on a silicon cantilever sensor. The mechanical changes in the coated microcantilever, a bimaterial, were investigated when various volatile substances, such as methanol, ethanol, acetone, propanol, dichloroethane, and toluene were added. The results showed that the coated microcantilever is much more sensitive than the uncoated one. The maximum sensitivity observed was 40.14 kHz/ppm, demonstrating a 33.8% improvement over existing systems. Findings revealed that the maximum sensitivity achieved with the proposed sensor was 83%, and the overall improvement was 6.41%. The sensors' responses were found to be reversible, sensible, fast, and proportional to the volatile concentration.
Enhancing the Design of Dynamic Vibration Absorbers through Harmonic Analysis and Lumped Parallel Configuration
Authors: Jabbar Faris A. , Rao Putti Srinivasa , Khafaji Salwan Obaid Waheed
Published: October 2024

This work examines the utilization of negative mass and negative stiffness principles to enhance the effectiveness of Dynamic Vibration Absorbers (DVAs) for vibration attenuation developed in structures. The proposed idealized model aims to reduce the amplitude of resonance and expand the frequency stopband range to achieve vibration reduction by attaching two parallel dynamic vibration absorbers to the primary system. By incorporating stiffness, mass, and damping ratio for each one of the absorbers and in the presence of harmonic excitation, it is possible to expand the frequency range in which vibration may be suppressed, leading to a substantial decrease in the maximum vibration amplitude of the primary system and the stroke length of the absorbers. The Nelder-Mead method is successfully used as an optimization tool to obtain the best selection of the absorbers' design parameters that assure the best vibration attenuation and frequency stopband. The results indicate a strong association between the change in frequency and the decrease in vibration. In the instance of frequency response, the vibration amplitude decreased by an average of 91.46% across all three modes of the whole system. Another optimal selection exhibited the most significant enhancement, achieving an average reduction in vibration amplitude of 97.06% across all three modes. Finally, the results indicate that the suggested lumped parallel architecture of the absorbers with precisely adjusted negative mass and negative stiffness characteristics along with the presented optimization method, can greatly improve the effectiveness of reducing vibrations in various applications. The results illustrate a direct relationship between shifts in frequency and vibration. If the value of the shift is greater than 1 the vibration is decreased, while less than 1 means the worst form of reducing such magnitude. The most notable drop off in amplitude occurred when averaging a simple drop off of up to 91.46% for all three patterns where frequency shifts by Y=2.008.
Decarbonization Pathways: Assessing Life Cycle GHG Emissions in Malaysia's Electricity Generation
Authors: Mohd Nordin Atiqah Hamizah , Sulaiman Shahril Irwan , Mustapa Rijalul Fahmi
Published: October 2024

Electricity generation is a key contributor to global Greenhouse Gases emissions (GHG). The urgent need to mitigate climate change demands a transition to more sustainable ways of electricity generation. However, focusing on operational emissions without assessing their life cycle may lead to less effective decisions on energy policy and technology advancements. This study examines the life cycle of GHG of major electricity generation systems in Malaysia, employing the life cycle assessment approach. The systems are based on energy resources of coal, natural gas, hydro, and solar Photovoltaic (PV). Furthermore, five types of PV systems with different capacity range and module technologies were compared. The present study also compared the scenario of PV installation in capital cities in the country. The results demonstrated that the electricity produced by renewable energy yields substantially lower GHG emissions compared to that generated by fossil fuel energy. Throughout its lifetime, PV and hydroelectricity release GHG emissions at least 5 and 7.4 times, respectively, lower than coal and at least 3.5 and 5.2 times, respectively, lower than natural gas under the worst-case uncertainty scenario. Besides that, the GHG emissions of the PV system installed in the capital city of Malaysia range from 61.4 g to 72.5 g of CO2-eq/kWh. The current study highlights the potential of renewable energy in promoting sustainability within the energy sector, offering a viable pathway towards the decarbonization of the energy sector.
Evaluation of Critical Stress Intensity Factor for Different RSW Joints
Authors: Benchadli Ismail , Benachour Mustapha , Sebaa Fethi , Benachour Nadjia
Published: October 2024

The aim of this paper is to evaluate the critical stress intensity factor (fracture toughness) for a range of Resistance Spot Welding (RSW) joints. The geometry of resistance spot welding joints under investigation comprises lap joints and coach peel joints of 316L stainless steel. The critical stress intensity factor of RSW lap joints and coach peel joints is calculated based on the experimental results of the shearing tensile tests and peel tensile tests, respectively. The welding parameters under investigation are the welding current, welding time, and electrode force. This study employs a fracture mechanics-based approach, to investigate the influence of RSW welding parameters on the critical stress intensity factor. The results demonstrate that the critical stress intensity factor exhibits a relatively decreasing trend with an increase in the welding current, from 8 kA to 16 kA. Moreover, an increase in the nugget diameter is particularly influenced by an increase in the welding current.
Bone Fracture Classification using Convolutional Neural Networks from X-ray Images
Authors: Alshahrani Amal , Alsairafi Alaa
Published: October 2024

This study investigates a bone fracture classification system using deep learning algorithms to determine the best-performing architecture. The primary focus was on training the YOLOv8 model, renowned for its real-time object detection and image segmentation capabilities, as well as the VGG16 model. CNN architectures, known for their effectiveness in image recognition tasks, were chosen for their proven effectiveness in detecting bone fractures from X-ray images. Hyperparameter tuning was used to improve the system's ability to accurately detect and classify bone fractures. The FracAtlas dataset was utilized, which contains 4,083 X-ray images of fractured and non-fractured human bones. Integrating advanced deep learning techniques aims to assist surgeons with more accurate diagnostics. The performance of the developed system was evaluated against existing methods, showcasing its effectiveness in medical diagnostics and fracture treatment. The methodology employed, including data augmentation, extensive model training, and hyperparameter tuning, significantly improved the accuracy of bone fracture detection and classification, demonstrating the potential of deep learning models in aiding medical professionals with more precise and efficient diagnostics.
A Dual-Band, Dual-Pattern Antenna for Body-Centric Communications
Authors: Alqarni Mohammed A. , Masood Rizwan , Alkatheiri Mohammed Saeed , Chauhdary Sajjad Hussain , Saleem Sajid
Published: October 2024

A dual-band, dual-pattern antenna is presented for 1.437 GHz L-band and 2.45 GHz Industrial Scientific and Medical (ISM) band applications. The antenna is based on multilayer circular patches with symmetric slots and two asymmetric cylindrical vias that shorten the multiple layers with the ground plane using Plated Through-Holes (PTHs) to generate the required resonant modes. The first mode is operated at 1.437 GHz, providing an omnidirectional radiation pattern, while the second mode is operated at 2.45 GHz, providing a directional or broadside radiation pattern. The antenna was fabricated to validate the simulation results and excellent agreement was found between the simulation and experimental results. The antenna has a total size of only 6×6×0.443 cm3, which corresponds to an electrical size of 0.5λ0×0.5λ0×0.03λ0 at 2.45 GHz and 0.28λ0×0.28λ0×0.02λ0 at 1.437GHz, thanks to the loading effect of shorting vias. The gain of the antenna is 1.62 dBi at 1.437 GHz and 6.48 dBi at 2.45 GHz, along with quite good radiation efficiencies of 83.4% and 96%, respectively. For body-centric applications, the performance of the antenna was also examined in close proximity to the human body. Nearly stable performance was found in close proximity to the human body, comparable to free space performance in terms of both impedance matching and radiation patterns. The 10g Specific Absorption Rate (SAR) of the antenna was also measured and found to be below the international compliance limits. These characteristics make the antenna a very promising choice for body-centric communications. The antenna also applies to other wireless systems such as MIMO, wireless ad hoc networks, etc. It offers radiation pattern diversity in a single planar package with a highly flexible and adaptable design by providing much more degrees of freedom than classical microstrip antennas.
Precised Cashew Classification Using Machine Learning
Authors: Karnam Sowmya Nag , Vaddagallaiah Veenadevi Siddanahundi , Rangnaik Pradeep Kooganahalli , Kumar Akshaya , Kumar Charan , Vishwanath Bidadi Mahesh
Published: October 2024

This study explores the utilization of deep learning techniques for accurate cashew classification to improve efficiency and accuracy in the cashew industry. YOLOv5, YOLOv9, and a Convolutional Neural Network (CNN) were evaluated in classifying cashews into whole, broken, split-up, split-down, and defect categories. A comprehensive labeled dataset was built to train the models, applying data augmentation to increase robustness. YOLOv5 achieved the highest accuracy of 97.65% and the fastest inference time (0.025 s per image) among the models, making it suitable for real-time applications. Although CNN offered a simpler architecture, YOLOv5's superior performance places it as the most promising candidate for large-scale cashew classification deployment.
Improving PCM Melting Performance using Asymmetric Fin Designs in Rectangular Enclosures
Authors: Mecieb Fatima Zohra , Laouedj Samir
Published: October 2024

This study investigates the enhancement of heat transfer in Latent Heat Thermal Energy Storage (LHTES) systems by using internal fins within a rectangular enclosure. Using lauric acid as the Phase Change Material (PCM), the impact of different fin configurations on the melting process is examined. A numerical model, incorporating the enthalpy-porosity method, is developed to simulate the phase change within the PCM. The model considers the effects of fin geometry, including variations in fin length and positioning. Results indicate that strategically placed fins significantly improve heat transfer efficiency, reducing complete melting time by more than 50% compared to a configuration of uniform-length fins. This enhancement is attributed to increased convective heat transfer facilitated by the longer fin in the lower zone of the enclosure.
A Low Cost Wastewater Reclamation Unit comprising a Lamella Settler for reducing Fresh Water Usage in Carwash Stations
Authors: Syed Naveedul Hasan , Haq Imranul , Ahmad Farooq , Khan Naseer Ahmed , Habib Muddasar , Ahmad Naveed , Rind Imran Khan
Published: October 2024

A low-cost carwash wastewater reclamation unit comprising a lamella settler and filtration unit was designed and fabricated in the laboratory. A newly designed lamella settler, Reflux Lamella Settler (RLS), consisting of two inclined sections, was incorporated for the first time in the reclamation unit with the objective of enhancing the sedimentation process. Furthermore, organoclay was employed as a component of the filtration unit to remove oil contents. The analysis of the reclaimed water demonstrated a notable reduction in the Total Suspended Solids (TSS), from 821 mg/L to 98 mg/L, in turbidity from 253 Nephelometric Turbidity units (NTU) to 2.70 NTU, and in the oil content from 26 mg/L to zero. This implies a substantial removal of the above substances of 88%, 98.9%, and 100%, respectively. Similarly, the concentration of hardness was reduced by 62.8%, from 321.6 to 120 mg/L, that of Chemical Oxygen Demand (COD) by 65.3%, from 274 mg/L to 95 mg/L, that of total solids by 65%, from 1590 mg/L to 543 mg/L, and that of total dissolved solids by 47.9%, from 769 mg/L to 400 mg/L. These results indicate that the reclaimed water was suitable for car washing. Moreover, a study on the RLS demonstrated a reduction in turbidity from 253 NTU to 175 NTU, 150 NTU, 130 NTU, and 10 NTU, respectively, after 0.5, 1, 1.5, and 24 hours. The RLS is an effective method for the removal of solid particles/sludge as a primary treatment step in carwash reclamation.
Real-Time Liver Tumor Detection with a Multi-Class Ensemble Deep Learning Framework
Authors: Nelaturi Nanda Prakash , Rajesh Vullanki , Syed Inthiyaz
Published: October 2024

Detecting liver tumors in large heterogeneous datasets is vital for accurate diagnosis and treatment to be performed. However, existing segmentation models struggle with multimodal tumor detection, variability in tumor shapes, over-segmentation, and noise in border regions. These issues lead to inconsistent and inaccurate results. The current study introduces a novel multiclass ensemble feature extraction and ranking-based deep learning framework to address these challenges. This framework efficiently identifies key tumor regions with a high true positive rate and maintains runtime efficiency, making it suitable for real-time liver tumor detection. Comparative evaluations using diverse liver imaging databases demonstrate the framework's superiority over existing models in terms of various classification metrics and runtime efficiency. These results highlight the framework's potential for enhancing real-time liver tumor detection applications.
Behavior of GFRP Reinforced-Concrete Bubbled One-Way Slabs by Encased Composite Steel I-Sections
Authors: Abdulkhaliq Mohannad , Al-Ahmed Ali Hussein
Published: October 2024

Bubbled Reinforced Concrete (RC) slabs have gained popularity in recent years as a practical construction method that eliminates unnecessary concrete in the center, thereby reducing the dead weight of the structure. This study provides a systematic framework to compare the performance and capabilities of one-way bubbled concrete slabs reinforced with Glass Fiber Reinforced Polymer (GFRP) bars and embedded steel I-sections. Four one-way concrete slabs, each with a length of 2,600 mm and a rectangular cross-sectional area of 600 mm in width and 150 mm in depth were employed. These slabs were reinforced with Glass Reinforced Plastics (GRP) rebar at the same reinforcement ratio and tested by two-point bending to failure. Different parameters such as specimen type (solid or bubbled slabs) and internal reinforcement were achieved using steel I-sections in two different shapes (2 and 4 pcs of steel I-sections), where 4I-section shape with a cross-sectional area equivalent to 2I-section shape, channel shear connectors, and bent steel bars (10 mm diameter) were used to improve the shear resistance. The results showed that bubbled slabs experienced a higher range of deformations (including deflection, strains, and cracks) by about (28%-88%) and a 15% decrease in ultimate load capacity compared to solid slabs. On the other hand, the use of steel I-sections as internal reinforcement significantly improved the specimen performance compared to unreinforced slabs (Steel Slab (SS) and Bubbled Slab (BS), respectively). Deflection was reduced by approximately 52% and 87% at the same load level, ultimate load capacity increased by approximately 121% and 179%, and flexural stiffness increased by approximately 197% and 272% at the same load level.
Enhanced Convolutional Neural Network for Fashion Classification
Authors: Haji Lailan M. , Mustafa Omar M. , Abdullah Sherwan A. , Ahmed Omar M.
Published: October 2024

Fashion items are hard to classify since there are a million variations in style, texture, and pattern. Image classification is among the noted strengths of convolutional neural networks. This research introduces an improved CNN architecture for fashion classification, utilizing image augmentation and batch normalization to improve model performance and generalization. To make the model more robust, image augmentation techniques like rotation, width and height shift, zoom, and flips were employed. In addition, a Batch Normalization layer is added in the middle, which can help on stabilizing the learning process and accelerating convergence. The proposed model was trained on an augmented dataset, achieving a satisfactory improvement in test accuracy of 91.97% compared to a baseline CNN model, which obtained 88.5% accuracy. According to the results, the image augmentation with the application of Batch Normalization improves the CNN architecture for better effectiveness in fashion classification tasks.
Shear Strength of Conventional and Lightweight Concrete I-Beams with Fibrous Webs
Authors: Raheem Abdullah Basil , Klak Fadya S.
Published: October 2024

This study investigates the behavior of the shear strength of fibrous concrete I-beams made from normal and lightweight concrete that have the same compression strength, of about 30 MPa. Lightweight aggregate concrete was made by replacing 75% of the coarse aggregate with lightweight aggregate (Bonza stone). Fourteen concrete I-beams with dimensions of 1000×210×175 mm were divided into two groups. In the first group, the web area was reinforced with steel fiber added in 0.5%, 1%, and 1.5% of the mix volume. The second group was reinforced with glass fiber added in the same percentage as the steel fiber. The results showed that the shear strength of a Normal Concrete Beam with Steel Fibers (NCSF) is increased by 3.5%, 13.5%, and 13.3% for the addition ratios of 0.5%, 1%, and 1.5%, respectively, compared to the Normal Concrete Beam without Fibers (NC). Webs with glass fibers gain an increase of about 3.7% and 14.05% for the addition ratios of 0.5% and 1%, respectively, while the shear strength decreased by 6.21% for the addition ratio of 1.5%. On the other hand, the Lightweight Concrete Beam with Steel Fibers (LWCBSF) achieved greater shear strength than the Lightweight Concrete Beams without Fibers (LWCB) by 4.8%, 13.5%, and 10.9%; for the three additional percentages, respectively. The shear strength increased by 8.4% and 11.04% for the Lightweight Concrete Beam with Glass Fibers (LWCBGF) at 0.5% and 1% ratios, while the shear strength decreased by 11.9% for the 1.5% glass fibers ratio compared to the Lightweight concrete Beam without Glass Fibers (LWCB). The best performance, according to the ultimate load, was achieved when fibers were added at a ratio of 1% in normal and lightweight concrete compared to other ratios.
A Deep Learning Multimodal Framework for Fake News Detection
Authors: Kumari Shweta , Singh Maheshwari Prasad
Published: October 2024

The swift spread of fake news on social media platforms presents significant challenges to the society, necessitating the development of a more efficient model for fake news detection. Existing fake news detection methods primarily focus on linguistic and compositional characteristics, which may not be able to differentiate between various forms of fake news and impede effective detection. This paper proposes an innovative approach to address such challenges. It introduces a comprehensive framework for detecting fake news, leveraging advanced multimodal techniques to analyze multilingual text and visual data. The proposed framework employs Natural Language Processing (NLP) for text preprocessing, the DeepL translator for language consistency, and vectorization for feature extraction. For the detection models, Long Short-Term Memory (LSTM) networks are used for sequential text analysis, while the Contrastive Language-Image Pretraining (CLIP) model is utilized for image analysis to be performed. The combined features are then processed through a decision-making layer for the news to be classified as real or fake. Results demonstrate the model's high efficacy, with an accuracy of 99.22% for text and 93.12% for text and images, outperforming the existing state-of-the-art techniques.
Design of a Funnel-Shaped MIMO Antenna for RADAR Applications
Authors: Budumuru Srinu , Allu Gayatri , Jenjeti Durgarao , Tanakala Venkata Suri Apparao , Sankranti Srinivasa Rao
Published: October 2024

Achieving a compact-size, high-performance antenna covering the K-Ka bands is not an easy task. A Multi-Input Multi-Ooutput (MIMO) funnel-shaped wideband antenna for RADAR applications is introduced in this paper. One of the flaws of the MIMO antenna is its size. In modern communications, the space given for an antenna is minimal due to the compact size of the communication devices. In the present work, by keeping space occupancy in mind, two element compact antennas, with 76×34×1.6 mm3 size, operating bandwidth of 24.7 GHz, isolation greater than 18.9 dB, and gain of 9.26 dB, were designed. The designed funnel shape patches and defective ground structure provide better gain and isolation, therefore allowing for larger bandwidth. Furthermore, the simulated and measured results are in good agreement.
Multi-Modality Abnormal Crowd Detection with Self-Attention and Knowledge Distillation
Authors: Ho Anh-Dung , Doan Huong-Giang , Pham Thi Thanh Thuy
Published: October 2024

Deep Neural Networks (DNNs) have become a promising solution for detecting abnormal human behaviors. However, building an efficient DNN model in terms of both computational cost and classification accuracy is still a challenging problem. Furthermore, there are limited existing datasets for abnormal behavior detection, and each focuses on a certain context. Therefore, a DNN model trained on a certain dataset will be adaptive for a particular context and not suitable for others. This study proposes a DNN framework with efficient attention and Knowledge Distillation (KD) mechanisms. Attention units capture key information from multiple RGB, optical flow, and heatmap inputs. KD is applied to scale down model size. Experiments were performed on several benchmark datasets, examining both AUC and accuracy. The results show that the proposed framework outperformed other state-of-the-art methods in detection accuracy. Furthermore, the trade-off between detection performance and computational cost was also addressed by the proposed framework with KD.
Design of an Electric Elevator Drive with High Riding Quality under Jerk Control
Authors: Ali Ali Abdulkareem , Ben Salem Fatma , Mohammed Jamal A.-K.
Published: October 2024

Sudden movements during the start and stop of electric elevators, known as jerks, can cause discomfort for passengers and negatively affect the performance and lifespan of their mechanical and electrical subsystems. Modern drives cannot directly regulate jerk, necessitating the development of methods to mitigate its effects. This study focuses on designing optimal S-curve motion profiles to reduce jerk in elevator systems driven by an induction motor. The motor operates according to these profiles when integrated with a TMS320F28335 Digital Signal Processor (DSP) under Direct Torque Control (DTC). Precise motor control is achieved using an incremental rotary encoder, current sensors, and voltage sensors, while an accelerometer sensor connected to an Arduino Nano measures the jerk. This comprehensive approach ensures precise regulation, smooth performance, and minimal sudden changes in acceleration, highlighting the effectiveness of DTC combined with S-curve profiles in enhancing elevator system performance. The results demonstrate that integrating S-curve profiles into the DTC algorithm significantly improves ride comfort by mitigating jerk during acceleration and deceleration phases by up to 57.2% compared to systems without S-curve integration.
Numerical Simulation of Hydraulic Jump in a Compound Channel
Authors: Boudjelal Samia , Fourar Ali , Massouh Fawaz
Published: October 2024

This paper studies the phenomenon of hydraulic jump in compound channels using a numerical model and provides remarkable results. Several values of the opening parameter, h1, are utilized to generate the hydraulic jumps. A recirculation zone is detected by studying the RNG-K-Ɛ turbulent Volume Of Fluid (VOF) model, which is distinguished by the modified directions of the velocity vectors. When compared with the experimental values, the numerical simulation demonstrated very good accuracy, with an error of no more than 9.4%. The results underline the reliability and usefulness of the VOF turbulence model for understanding and simulating the hydraulic processes in compound channels.
Research on the Role of Bac Ai Pumped Storage Hydropower in the Operation of Vietnam's Power System in 2030 with a High Proportion of Renewable Energy
Authors: Giap Luong Ngoc , Le Ngo Phuong , Khanh Nguyen Binh , Trung Bui Tien , Tuong An Truong Nguyen , Vinh Tran The , Tu Le Tat
Published: October 2024

Research on solutions to improve the regulation capacity of power systems is essential and urgent in the context of renewable energy sources being highly variable and constituting a significant proportion of Vietnam's power system by 2030. Pumped storage hydropower plants serve as an excellent energy reserve and are widely used to provide peak energy demands for daily and nightly loads. In Vietnam, the Bac Ai hydropower storage project, currently under construction, is the first of its kind and is expected to play a significant role as a large-scale energy storage system. This comes at a time when renewable energy sources are growing rapidly, with many large-scale solar and wind power plants being invested and commissioned. This project will be crucial to addressing surplus and shortage issues in power system load, stabilizing the system, regulating frequency, and ensuring stable, safe, and reliable operation of the national power grid. However, to assess the necessity of this project within the Vietnamese power system, scientific evaluations are required regarding the impact of capacity scale, timing, and operational position of this project within the development scenarios of Vietnam's power system. This study investigates an optimization problem for the operation of the Vietnamese power system, considering the characteristics of generation sources and transmission lines using the PyPSA software. The aim is to calculate and identify the role of the Bac Ai pumped storage hydropower plant in Ninh Thuan province in the 2030 scenario of Vietnam's power system, which includes a high proportion of renewable energy sources.
Harnessing Decision Tree-guided Dynamic Oversampling for Intrusion Detection
Authors: Kaur Ritinder , Gupta Neha
Published: October 2024

Imbalanced datasets present a significant challenge in the realm of intrusion detection, as the rare attacks are often overshadowed by the normal instances. To tackle this issue, it is essential to utilize the various strategies of imbalanced learning that aim to mitigate the effects of class imbalance and improve the performance of intrusion detection systems. One effective approach for dealing with class imbalance is through data augmentation methods like the Synthetic Minority Oversampling Technique (SMOTE). This research presents a novel data resampling approach that performs adaptive synthetic sampling on rare and complex samples by using decision boundaries. The benchmark dataset NSL-KDD was used to evaluate and validate the effectiveness of this approach. The experimental results demonstrated a significant improvement in the detection accuracy of rare classes, achieving 42% for u2r instances and 83% for r2l instances.
Distinguishing Arabic GenAI-generated Tweets and Human Tweets utilizing Machine Learning
Authors: Alghamdi Noura Saad , Alowibdi Jalal Suliman
Published: October 2024

Generative Artificial Intelligence (GenAI) tools, like ChatGPT, have made it easy to create text, music, images, and other types of media. GenAI, a type of AI technology, has rapidly gained fame and popularity for its ability to generate new content. Notably, its applications allow anyone to produce natural conversations and content, making it increasingly challenging to distinguish between human-written and GenAI-generated material. The current research focuses on Arabic content to differentiate GenAI-generated content from authentic human-written content on the X platform (Twitter). Datasets from both real human-written tweets and GenAI-generated tweets were collected. Then, three Machine Learning models were built to predict whether a tweet source is GenAI-generated or human-written. The highest achieved accuracy was 93%.
Optimization of the Suspension System of Passenger Cars using the Vibration Model Multi-Objective Method
Authors: An Tran Thanh , Tuan Nguyen Van
Published: October 2024

The main functions of the suspension system are the provision of comfort and traction. However, in many cases, paying too much attention to the smoothness of the vehicle has led to incorrect determination of suspension parameters and other problems such as rollover and reduced traction. This study aims to present a design method that optimizes vehicle suspension to improve ride comfort and safety. Based on the structure and specifications of a 29-seat passenger car, this study introduces a general oscillation model of eight degrees of freedom of the passenger car in space. The model allows us to analyze the vibrations of the driver, the body, and the wheels simultaneously under the excitation effect of the road surface and the changes in the vehicle's motion modes. In addition, based on the theory of multi-objective optimization, this study optimized the suspension parameters of the passenger car. The optimized values were: cd = 100000 N/m, csr = 154709 N/m, ksf = 7265 Ns/m, csf = 106193 N/m, ksr = 11297 Ns/m, f1 = 0.5906 m/s2, caf = 6711 Nm/rad, f2 = 1171 N, car = 5683 Nm/rad. The main contribution of the research is the provision of a multi-objective optimization method for the suspension system.
The Influence of Cutting Parameters on the Surface Hardness in Turning of 6061 Aluminum Alloy
Authors: Mahdi Basma L. , Ali Abduljabar H. , Hussein Hiba K. , Abdulateef Osamah F.
Published: October 2024

The primary design property necessary to ensure the longevity and durability of manufactured materials is the material hardness. The primary objective of this study was to investigate the effect of cutting parameters, namely feed rate, cutting speed, and depth of cut, on the surface hardness generated during the turning process of aluminum alloy 6061. The turning experiments were conducted using a Taguchi L27 orthogonal array arranged for three-level cutting parameters. The Analysis of Variance (ANOVA) was employed to determine the relative importance of each parameter on surface hardness. Additionally, an Artificial Nural Network (ANN) predictive model using the back-propagation learning algorithm was created to predict surface hardness levels at each level of the cutting parameters. The results revealed that increasing the values of all the turning parameters resulted in an increase in hardness, and it was concluded that the feed rate was the most critical factor (53.41%) in achieving high surface hardness, followed by the depth of cut (27.89%), whereas cutting speed had a lower impact (18.7%). This study also suggests a simple equation for estimating the surface hardness from the cutting parameters. The ANN model could accurately estimate the surface hardness with a coefficient of correlation (R) higher than 0.98 between the predicted and experimental values. The predicted values of hardness by ANN were more precise (R2 =0.973839) than those predicted by ANOVA (R2=0.893).
Link Slab Behavior in Continuous Bridge Systems: A Comparative Study of Finite Element and Analytical Approach
Authors: Suangga Made , Megasari Olivia , Suwondo Riza
Published: October 2024

Performance and longevity of bridge structures are usually compromised by the use of expansion joints, which are prone to material deterioration and are costly to maintain. To address these drawbacks, partially continuous systems with link slabs are increasingly adopted. This study aims to provide a detailed understanding of the behavior of link slabs. A numerical model was developed using finite element analysis to simulate the behavior of the link slabs. The results of the model were compared to those of the analytical solution. The study also examined the effects of various parameters, including the Debonding Length Ratio (DLR) and the span length on link slab performance. The results indicate that the numerical model tends to overpredict the link-slab rotations when compared to the analytical solution. However, the moments in the link slab showed a decrease with increasing span length in the numerical analysis, contrary to the analytical solution which indicated an increase. In addition, higher DLRs resulted in reduced rotations and increased moments in the numerical model, whereas the analytical solution showed no effect on rotation and a decrease in moments with higher DLR. These results highlight the necessity of using detailed numerical models to accurately analyze and optimize the design of link slabs.
Brain Tumor Classification using Deep Learning: A State-of-the-Art Review
Authors: Rasool Mohammed , Noorwali Abdulfatah , Ghandorh Hamza , Ismail Nor Azman , Yafooz Wael M. S.
Published: October 2024

Given that the number of available brain tumor images has grown, Deep Learning (DL) plays a critical role in brain tumor classification in terms of accurately diagnosing and predicting such tumors. Regarding the classification of several large-scale images, DL-driven techniques, such as convolutional neural networks, have not only shown significant results, but have also demonstrated that they can progressively learn features from data at multiple levels. As the use of medical imaging for analysis and education grows in popularity and the same occurs with the unstructured multi-faceted nature of the data, a state-of-the-art review of brain tumor classification is important. This study provides a systematic review of the state-of-the-art techniques and approaches utilized to classify massive Magnetic Resonance Imaging (MRI) data, especially for cancerous brain tissues. Thorough research was conducted on the subject of DL utilization in brain tumor classification based on studies between 2020 and 2023 derived from a variety of scholarly databases. Of the 142 studies retrieved, 20 were included to investigate the proposed or applied DL techniques for the recognition and categorization of brain tumors using MRI. A meta-analysis of current DL classification techniques, algorithms, and their validation was introduced. Overall, DL techniques should receive more attention due to their automatic and accurate feature extraction capacity.
Comparative Analysis of YOLOv8 and YOLOv9 Models for Real-Time Plant Disease Detection in Hydroponics
Authors: Tripathi Abhishek , Gohokar Vinaya , Kute Rupali
Published: October 2024

Plant diseases are a significant threat to modern agricultural productivity. Hydroponic systems are also affected for various reasons. Reliable and efficient detection methods are essential for early intervention and management of diseases in hydroponics. This study investigates the use of You Only Look Once (YOLO) models, namely YOLOv8 and YOLOv9, for the detection of plant diseases in a hydroponic environment. A diverse dataset was prepared, comprising images from a hydroponics system setup and the New Plant Disease Image Dataset from Kaggle. Custom annotated images were used to train and test the models and compare their accuracy, processing speed, and robustness in hydroponic systems. The results showed that YOLOv9 is slightly better than YOLOv8 in terms of detection accuracy, as it achieved 88.38% compared to 87.22%, respectively. YOLOv8 requires less computational resources and takes relatively less time than YOLOv9 for real-time plant disease detection. Therefore, it is recommended for portable devices.