bluuebunny/crossref_metadata_2025 huggingface dataset:
Event Detection and Classification in Tweets using Deep Learning
Authors: Noui Malika , Lakhfif Abdelaziz , Laouadi Mohamed Amin
Published: February 2025

Online social networks have become important sources of information and contextual data in all areas of life, including finance, elections, social events, health, sports, etc. Recently, the detection and classification of useful events presented in tweets has attracted a lot of interest. However, due to the inherent challenges associated with the nature of the events to be detected or classified, traditional approaches have not yielded satisfactory results. The use of deep learning-based text word embedding representations, such as Word2Vec, GloVe, FastText, and BERT, has shown significant efficacy in improving detection performance by considering the semantic context. This study proposes a model that uses an LSTM stacked on top of BERT representations to effectively detect and classify events in tweets. To this end, a dataset of about 310,000 event-related tweets has been collected and categorized into 50 event types based on a selected set of representative keywords. Multiple experiments were carried out on the collected dataset to evaluate the performance of the proposed model. The proposed model attained an overall accuracy greater than 94.3% and an F1 score of more than 90%, achieving state-of-the-art results in the classification of most of the event categories.
Performance Evaluation of Magnesia-Type Refractory Brick Waste as Complete Replacement for Fine Aggregate and Filler in Asphalt Concrete Wearing Course Mixtures
Authors: Syukuriah Syukuriah , Hustim Muralia , Tjaronge Wihardi , Irmawaty Rita
Published: February 2025

Infrastructure development has been rapidly increasing in recent times, and the use of waste materials as aggregates in this process has positively impacted regional and national economies. This study investigates the use of magnesia-type Refractory Brick (RB) waste as a substitute for fine aggregate and filler in Asphalt Concrete-Wearing Course (AC-WC) mixtures. The RB waste is generated from the kiln walls of nickel smelting furnaces and is used to completely replace natural sand by weight. The study compared Marshall empirical values, such as stability, yield, and Marshall quotient (MQ), volumetric characteristics, such as Void In the Mix (VIM), Void in Mineral Aggregate (VMA), and Void Filled with Bitumen (VFB), and Ultrasonic Pulse Velocity (UPV) of AC-WC mixtures containing natural sand at asphalt percentages of 5.0%, 5.5%, 6.0%, 6.5%, and 7.0%. The findings reveal that the optimum Marshall properties were achieved with RB waste at a 5% asphalt content, compared to 6.0% for natural sand. Furthermore, the AC-WC mixture incorporating RB waste exhibited sufficient strength and durability to withstand traffic loads, suggesting that the complete replacement of natural sand with RB waste significantly influences the properties of AC-WC asphalt, promoting the environmentally friendly and economical reuse of waste materials in the industry.
Punch Force Classification using K Means and a Data Logging System
Authors: Anifah Lilik , Nurhayati . , Rusimamto Puput Wanarti , Zuhrie Muhamad Syarifuddien , Haryanto .
Published: February 2025

Much research has been conducted worldwide on the recognition and monitoring of punches in martial art sports during the training process. The performance of the punching movements can be accurately analyzed based on the collected data. The current study aims to classify the punches on a punching bag using K Means based on a data logging system. Its stages are hardware design, hardware implementation, hardware testing, learning, and testing. The FSR 402 sensor was used to measure the punching force. GY6500 MPU6500 was also utilized to identify and measure the reaction force of these punches. The data were collected utilizing K Means and were subsequently tested. The results revealed that the system exhibited good performance, proven by its accuracy of 93.6%, precision of 0.933, recall of 0.934, and F1 score of 0.934. Based on these results, it seems that the classification of right and left punch forces can be efficiently carried out. This system can help users analyze the punching bag training process, and thus improve their performance.
Diminishing Environmental Impact in the Construction Industry: The Use of Brick Coarse Aggregates Instead of Natural Coarse Aggregates
Authors: Filali Saloua , Nasser Abdelkader , Kerkour-El Miad Abdelhamid
Published: February 2025

The rapid growth of infrastructure, urbanization, and industrialization has increased global concrete demand, putting pressure on natural resources and creating ecological challenges. In response, using Brick Waste (BW) as a substitute for natural aggregates in concrete offers a promising solution to enhance sustainability in construction materials. This study specifically investigates the replacement of Natural Coarse Aggregates (NCA) with Brick Coarse Aggregates (BCA) at substitution rates of 25%, 45%, 65%, and 85%. The experimental results show that replacing 25% of NCA with BCA leads to a 12% decrease in workability and a 2.48% reduction in density compared to a control concrete mix. In its hardened state, this substitution results in a slight decrease of 6.45% in compressive strength (fc). At higher substitution rates, such as 85%, the decrease is intensified, with a 32% reduction in workability, 7.93% in density, and 50.32% in compressive strength, all compared to the control concrete after 56 days. The present study also emphasizes a significant correlation between the measured compressive strength and that estimated by non-destructive methods, such as the Schmidt Rebound Hammer Test. Optimizing substitute materials is crucial for achieving high performance while ensuring environmental benefits. This research proposes an innovative approach to sustainable construction, providing a unique opportunity to reconcile performance and sustainability in the construction sector. The importance of this work lies in its potential to transform waste management practices and promote more ecological construction materials.
Analysis of Factors affecting Construction Project Tender Winning in Small Qualification Contractors
Authors: Arifuddin Rosmariani , Muharram Muh Hanif , Abdurrahman M. Asad
Published: February 2025

Finding employment in the construction sector almost always involves a tender process, which is essential for the construction entrepreneurs because the company's continuity depends on it. Tender qualifications and requirements must fulfill several stages, namely the administrative, qualification, technical, and price evaluation. The purpose of this research is to predict the relationship between certain variables. The Partial Least Square (PLS) method is deployed since it can directly analyze the latent variables, indicator variables, and measurement errors and determine the complexity of the relationship between several variables and their indicators. Eight main factors were successfully obtained: Regulations, Company Qualifications, Administration, Equipment Resources, Human Resources, Construction Safety Plan, Financial, and Technology and Information Systems. Using the SEM-PLS also resulted in estimating the significance value of the factors influencing the highest tender winner determinants: Human Resources obtained a value of 84.5% and the Financial variable attained a value of 82.3%. This research is expected to assist contractors in getting acquainted with the level of significance of the tender winners’ determinants for the construction projects. Contractors can also prepare and improve the Human Resources factor by conducting advanced training, certification, and competency development that supports its technical capabilities. Contractors need to obtain a deep understanding of the tender requirements, and with a high experience level they can make more appropriate and competitive offers.
An Enhanced Document Source Identification System for Printer Forensic Applications based on the Boosted Quantum KNN Classifier
Authors: Mashhadani Shahlaa , Abdulsalam Wisal Hashim , Alhakam Iptehaj , Hassen Oday Ali , Darwish Saad M.
Published: February 2025

Document source identification in printer forensics involves determining the origin of a printed document based on characteristics such as the printer model, serial number, defects, or unique printing artifacts. This process is crucial in forensic investigations, particularly in cases involving counterfeit documents or unauthorized printing. However, consistent pattern identification across various printer types remains challenging, especially when efforts are made to alter printer-generated artifacts. Machine learning models are often used in these tasks, but selecting discriminative features while minimizing noise is essential. Traditional KNN classifiers require a careful selection of distance metrics to capture relevant printing characteristics effectively. This study proposes leveraging quantum-inspired computing to improve KNN classifiers for printer source identification, offering better accuracy even with noisy or variable printing conditions. The proposed approach uses the Gray Level Co-occurrence Matrix (GLCM) for feature extraction, which is resilient to changes in rotation and scale, making it well-suited for texture analysis. Experimental results show that the quantum-inspired KNN classifier captures subtle printing artifacts, leading to improved classification accuracy despite noise and variability.
Effect of Iron Loading on Quiescent Crystallization of Syndiotactic Polypropylene/Iron Composites
Authors: Ahmad Naveed
Published: February 2025

The present study investigates the crystallization kinetics of the syndiotactic polypropylene/iron (sPP/Fe) composites using the rheological and Differential Scanning Calorimetry (DSC) techniques to evaluate the impact of varying iron content. Rheology, which is particularly sensitive under slow crystallization kinetics, was employed to complement the widely used DSC method. The current study aimed to integrate the aforementioned approaches to provide a comprehensive understanding of how the iron content influences the crystallization behavior of the sPP composites. Non-isothermal and isothermal crystallization experiments revealed that the increasing iron content significantly enhanced the crystallization and melting temperatures, indicating improved thermal stability and crystallinity. The rheological measurements, carried out using an Atomic Rheometric Expansion System (ARES), demonstrated higher sensitivity than the DSC at low iron concentrations, providing a more precise detection of crystallization kinetics. The results showed excellent agreement between the two techniques, confirming the robustness of rheology as a complementary method. This study underscores the importance of the iron content in tailoring the thermal and mechanical properties of sPP composites and highlights the value of integrating rheological methods with traditional thermal analysis for polymer characterization.
Exploring LDoS Attack Detection in SDNs using Machine Learning Techniques
Authors: Salih Ali Osman Mohammed
Published: February 2025

This study investigates the application of machine learning algorithms for detecting Low-Rate Denial-of-Service (LDoS) attacks within Software-Defined Networks (SDNs). LDoS attacks are challenging to detect due to their similarity to normal network behavior. This study evaluates the performance of algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), and BIRCH clustering in this challenge. The results show that the LR and BIRCH algorithms outperformed other approaches, achieving a detection accuracy of 99.96% with minimal false positive and negative rates. The models demonstrated a fast detection time of 0.03 seconds, highlighting the potential of machine learning to improve SDN security. The study recommends future work to validate these findings in real-world environments to strengthen security systems.
Enhanced Variational Graph Convolutional Networks with Multi-Scale Convolutions and Attention Mechanisms for Dynamic Network Analysis
Authors: Mir Aabid Ahmad , Zuhairi Megat F. , Musa Shahrulniza , Alanazi Fuhid , Namoun Abdallah , Alrehaili Ahmed
Published: February 2025

The dynamic and constantly evolving landscape of cyber threats demands innovative methods capable of adapting to the complex relationships and structures inherent in network data. Traditional methods often struggle to adequately capture the intricacies of dynamic networks, especially in terms of evolving temporal dynamics and multiscale dependencies. The proposed solution, Enhanced V-GCN, combines the structural insights of Graph Convolutional Networks (GCNs) with the temporal modeling capabilities of Variational Autoencoders (VAEs), further augmented by multiscale convolutions and attention mechanisms. Multiscale convolutions enable the model to aggregate information across broader neighborhood ranges, while attention mechanisms prioritize the most critical nodes and edges, dynamically adapting to changes within the network. This enhanced approach allows V-GCN to effectively capture both nodal and structural patterns, significantly improving performance in node classification tasks. The Enhanced V-GCN model has demonstrated superior performance in node classification, outperforming baseline models with an accuracy of 98.00%, precision of 97.93%, recall of 98%, and an F1-score of 97.92%, indicating robust classification capabilities and exceptional generalization across diverse network structures.
Enhancing Cloud Data Center Security through Deep Learning: A Comparative Analysis of RNN, CNN, and LSTM Models for Anomaly and Intrusion Detection
Authors: Ahmed Shimaa A. , Khalifa Entisar H. , Nawaz Majid , Abdalla Faroug A. , Mahmoud Ashraf F. A.
Published: February 2025

Cloud data centers form the backbone of modern digital ecosystems, enabling critical operations for businesses, governments, and individuals around the world. However, their high connectivity and complexity make them prime targets for cyberattacks, leading to service disruptions and data breaches. This paper investigates the use of deep learning techniques, namely Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, to enhance cloud data center security. By employing these models for anomaly detection and intrusion prevention, the study performs a comparative analysis. The results indicate that the LSTMs achieved the highest ROC AUC score (0.90), demonstrating better detection of persistent threats. These findings highlight the potential of deep learning to revolutionize cloud security by providing scalable, accurate, and proactive measures against evolving cyber threats.
Α Study on the Possibility of Replacing Roller Bearings in CM 120L Concrete Mixers with Necuron 1050 Sliding Bearings
Authors: Romanet Mirela , Zisopol Dragos Gabriel , Roth Mihail Bogdan , Iacob Dragos Valentin
Published: February 2025

This paper presents the results of the study on the possibility of replacing the roller bearings of CM 120L concrete mixers with sliding bearings made of Necuron 1050. In order to carry out the study, the radial stresses located in planes normal to the axis of symmetry of a sliding bearing in the cantilever of the material Necuron 1050, were determined using two analytical methods, the finite element method and the electro-resistive tensometry method. An experimental stand was constructed to determine the mechanical and tribological characteristics of the sliding bearings in static and dynamic regimes. The difference between the maximum stress values determined by the two analytical methods is 1.057 MPa, and the difference between the results obtained by Finite Element Analysis (FEA) for static and dynamic stress is 3.093 MPa. The results of the study show that it is recommended to replace a roller bearing with a sliding bearing made of Necuron 1050 polyurethane material.
DTXG-RF-based Intrusion Detection System for Artificial IoT Cyber Attacks
Authors: Nourildean Shayma Wail , Mefteh Wafa , Frihida Ali Mouhsin
Published: February 2025

The swift advancement of networking technology and the rising incidence of cyber-attacks have made effective cybersecurity a critical priority. The primary concern with IoT networks is their susceptibility to vulnerabilities. IoT security necessitates the substantial involvement of artificial intelligence as a security technology to mitigate these challenges. Cyberattacks are evolving in sophistication, consequently posing greater obstacles in the precise detection of intrusions. An Intrusion Detection System (IDS) is a device or software application that monitors the activities of network systems for malicious actions or policy breaches and produces reports. The primary objective of an IDS is to efficiently identify attacks. Moreover, it is imperative to identify attacks at an early stage to mitigate their effects. Machine learning models have become increasingly popular in IDSs due to their capacity to process substantial data volumes and identify patterns in real time. Machine learning involves building an algorithm to identify consistent patterns within a dataset. This study aimed to build an IDS using an ensemble machine learning (DTXG-RF) model and compare it with DT, XGBoost, KNN, RF, NB, and CatBoost on the CIC-IoT-2023 and a Ransomware dataset. The results showed that the proposed DTXG-RF outperformed other machine learning models with accuracy reaching 95.06%.
Performance Analysis of a Swastika shaped MIMO Antenna for Wireless Communication Applications
Authors: Chowdary Polavarapu Sushma , Panda Sampad Kumar
Published: February 2025

A novel wideband 4-port Multiple-Input Multiple-Output (MIMO) circular patch antenna is presented for operation in a resonant frequency band from 1.76 to 9.06 GHz used for PCs, Wireless Local Area Networks (WLANs) and satellite applications. The structure of the design consists of microstrip fed slot antenna considered on the four corners of the FR-4 substrate. To reduce mutual coupling and enhance the parameters of the MIMO antenna, a swastika-shaped ground is embedded. The simulated and experimental results show that the design achieves good performance, isolation >15 dB and radiation efficiency greater than 85%. The radiation patterns, surface currents, gain and channel capacity are also investigated. This circular shaped patch antenna is applicable to WLAN and satellite communication applications. Each circular patch on the four corners is connected to a 50 Ω microstrip feedline. A ring slot is etched on the circular patch and a small rectangular slot is embedded to the etched circular patch. The design provides good radiation patterns and the operating bands are obtained at 1.76 GHz, 2.4 GHz, 3.5 GHz and 9.06 GHz attaining reflection loss of -16 dB, -22 dB, -24 dB, -15 dB. The structure of the proposed design is simulated and evaluated in High-Frequency Structure Simulator (HFSS) software.  The MIMO structure is fabricated on the FR-4 substrate. The minimum proximity between simulated and measured results is observed.
Design of a Miniaturized Dual-Band Antenna using Slotted Techniques for 2.45/5.8 GHz Microwave Band RFID Utilizations
Authors: El Hachimi Younes , Louragli El Mustapha , Arockiam Sahaya Anselin Nisha , Subramanian Varalakshmi , Das Sudipta , Farchi Abdelmajid
Published: February 2025

In this study, a compact dual-band antenna for Radio Frequency Identification (RFID) readers operating at 2.45 GHz and 5.8 GHz is proposed. It is mounted on a FR4 substrate with a dielectric permittivity of 4.3. The proposed structure occupies a total area of 728 mm², with dimensions of 28×26 mm². The antenna features a rectangular radiating patch with integrated slots to enhance performance. A precisely tuned rectangular transmission line feeds the antenna, ensuring efficient signal coupling. The ground plane design incorporates a geometry derived from the slot technique, consisting of a rectangular loop, a square loop, and an attached rectangular element. This slot-based approach facilitates antenna miniaturization and enables the generation of two distinct resonant frequencies, significantly improving overall performance. The antenna was designed, simulated, and analyzed using the CST Microwave Studio (CST MWS) tool. The resulting structure is compact and straightforward, exhibiting suitable gain, coherent radiation patterns, and efficient impedance matching. Its design allows easy integration into handheld RFID readers operating in the microwave band.
Determination of Best Input Parameters for Internal Grinding SKD11 Tool Steel using MCDM
Authors: Nguyen Anh Tuan , Vu Duc Binh , Nguyen Van Trang , Le Xuan Hung , Nguyen Manh Cuong
Published: February 2025

This article presents the results of an optimization study on the determination of the best input parameters for the internal grinding process when processing cylindrical-shaped parts of SKD11 tool steel. For this purpose, three Multi-Criteria Decision Making (MCDM) methods including the Evaluation based on Distance from Average Solution (EDAS), Multi-Attributive Border Approximation Area Comparison (MABAC), and Multi Attribute Utility Theory (MAUT) were used to solve the MCDM problem, while the entropy method was applied to find the criteria weights. Three objectives, namely the Surface Roughness (SR), Material Removal Rate (MRR), and wheel life (Tw), were also investigated. Six input factors, involving the coarse dressing depth (ar), coarse dressing times (nr), fine dressing depth (af), fine dressing times (nf), non-feeding dressing (n0), and dressing feed rate (Sd), were examined. Additionally, the Taguchi method with the L16 (44+22) design and the Minitab R19 program were deployed to design this study’s experiment and investigate its outcomes. The MCDM work was successfully solved and the best process factors are proposed.
Sentiment and Emotion Modeling in Text-based Conversations utilizing ChatGPT
Authors: Mullangi Pradeep , Nagajyothi Dimmita  , Supriya M. , Murty Patnala S. R. Chandra , Nirmala Gera Vijaya , Palagan C. Anna , Rao Komati Thirupathi , Rajeswaran N.
Published: February 2025

Emotional Intelligence (EI) constitutes a vital element of human communication, and its integration into text-based dialogues has gained great significance in the modern digital era. The present paper proposes an innovative method for modeling sentiment and emotion within text-based conversations using the ChatGPT language model. The advancements in sentiment and emotion recognition are centered on the role of EI in text-based conversational models. The study underscores the significance of diverse datasets, including Interactive Emotional Dyadic Motion Capture (IEMOCAP), MELD, EMORYNLP, and DAILYDIALOG, for training and evaluating emotion detection algorithms. IEMOCAP and MELD offer detailed emotional annotations, EMORYNLP emphasizes sensitive dialogue scenarios, and DAILYDIALOG encompasses a wide range of everyday interactions, providing distinct advantages for capturing emotional subtleties. The proficiency of different emotion categorization models, including ChatGPT and models with four levels of detail, is demonstrated through their capacity to understand and respond to emotions aptly. The crucial role of conversational AI with sophisticated EI in fostering empathy and context-sensitive interactions is emphasized.
Enhancing 5G Performance: A Study of an MIMO Antenna with Elliptical Ring Polarization
Authors: Ramya P. , Kumar G. Sunil , Prasad G. Venkata Hari , Stanley Berakhah F. , C. Anna Palagan  , Kesav V. Silpa , Kumar A. Pradeep , Rajeswaran N.
Published: February 2025

This paper discusses the design and evaluation of a four-element Multiple-Input, Multiple-Output (MIMO) antenna array with Circular Polarization (CP), tailored explicitly for sub-6 GHz 5G wireless systems. The proposed antenna utilizes an elliptical ring slot with a variable width in the ground plane and an asymmetric feed line layout to achieve orthogonal CP radiation in both the forward and backward directions. Three interconnected strip lines are incorporated into the ground plane for enhanced performance, increasing isolation and ensuring effective linkage between the antenna components and their respective grounds. The antenna exhibits strong isolation, a low Envelope Correlation Coefficient (ECC), and minimal Channel Capacity Loss (CCL), making it ideal for MIMO applications. The analysis confirms the antenna's stable CP gain and operational efficiency throughout the sub-6 GHz band, meeting the rigorous standards of contemporary 5G networks.
Enhanced Image Tampering Detection using Error Level Analysis and CNN
Authors: Gorle Ramesh , Guttavelli Anitha
Published: February 2025

This paper introduces a novel approach to image tampering detection by integrating Error Level Analysis (ELA) with a Convolutional Neural Network (CNN). Traditional forensic methods, such as ELA and Residual Pixel Analysis (RPA), often struggle to detect subtle or advanced manipulations in digital images. To address these limitations, this method leverages ELA to highlight compression-induced variations and CNN to extract and classify spatial features indicative of tampering. The dataset, consisting of both authentic and tampered images, was preprocessed to generate ELA representations, which were then used to train a CNN model designed to distinguish between authentic and manipulated regions. Extensive experimentation was performed on the CASIA v2.0 dataset, demonstrating significant improvements in detection accuracy, precision, and recall. The proposed framework achieved a detection accuracy of 96.21%, outperforming established deep learning models such as VGG16, VGG19, and ResNet101. These results underscore the potential of combining ELA and CNN in advancing image forensics, offering a robust solution to ensure the integrity of digital content in an era of sophisticated digital manipulation.
A Multidimensional Approach for Formal Modeling and Analyzing Medical Cyber-Physical Systems
Authors: Bouheroum Ayoub , Benmerzoug Djamel , Hemam Sofiane Mounine , Belala Faiza
Published: February 2025

The combination of integrated software controlling devices, networking capabilities, and sensing/actuation technologies in Medical Cyber-Physical Systems (M-CPS) highlights some specific research challenges. The major challenge is to formally ensure the confidentiality of the data or resources they handle. This study tackles this problem by proposing a formal approach that combines CA-BRS (Control Agent and Bigraphical Reactive Systems) and BPMN (Business Process Model Notation) to specify and analyze CPS in general, while respecting several dimensions. The structural dimension of the CPS, representing the space (physical and cyber entities) in which agents exist and interact, is defined with BRS. Control agents constitute the virtual dimension and observe and control the physical and cyber entities of their environment. The complex and adaptive behavior of CPS (behavioral dimension) is defined through several types of rules, each managing a possible evolution of a CPS component (physical, cyber, or virtual). Two distinctive perspectives are associated with the semantic interpretation of these rules: the states perspective and the activities perspective. This study focuses on the activities perspective that specifies the behavior of control agents with a BPMN activity diagram. This highlights how these two models (CA-BRS and BPMN) complement each other to assist the designer in defining formal models for CPS. Additionally, it reveals how to provide the CA-BRS model with means to control unauthorized access to an electronic health record system.
Design and Implementation of a ROLAP Cube in Scalable Distributed Data Structure
Authors: Mechri Amel , Bouaita Bilal , Zegour Djamel Eddine , Hidouci Walid Khaled
Published: February 2025

The Scalable Distributed Data Structure (SDDS) is a data model specifically designed for distributed environments. An SDDS file comprises records that are dynamically distributed across servers using an SDDS algorithm. A notable feature of SDDS is the removal of a centralized addressing component, simplifying client-server communication and reducing both the message count and data access time in distributed systems. This work also explores a Data Warehouse (DW) within a decision support system, where multidimensional data are represented as a cube and managed through Relational Online Analytical Processing (ROLAP). Although extensive research has been conducted in both the data warehousing and SDDS fields, no prior studies have combined these two areas. This paper introduces a novel approach to implementing a ROLAP cube within an SDDS using the Linear Hashing algorithm (LH*), which eliminates centralized addressing, enabling direct client-server communication and improving performance by reducing inter-site message exchanges. This work demonstrates the feasibility of this method and its positive impact on data processing efficiency in distributed systems.
Impact of Variable Speed Limits on Crash Frequency and Crash Rate: Stimulation by Flow Rate and Percentage of Heavy Vehicles
Authors: Al-Nuaimi Atheer N. , Jameel Abeer K. , Alsadik Samer M.
Published: February 2025

The current transportation system has faces a high number of crashes on road networks, often resulting in damages and human losses. According to WHO, these incidents cost most countries approximately 3% of their GDP. This underscores the need to address the weaknesses in road networks and provide efficient countermeasures to reduce these incidents and consequent losses. This study focuses on Variable Speed Limits (VSL) as a crucial factor that could influence road safety. To model the impact of VSL on the Crash Frequency (CF) and Crash Rate (CR), the researchers also considered other relevant traffic characteristics, such as the Percentage of Heavy Vehicles (PHV) and Average Daily Traffic (ADT). For the purposes of this research, the Mandali-Baqubah highway in Iraq was selected as a case study for the potential implementation of VSL. The statistical regression approach was utilized to model the Safety Performance Functions (SPF) for the road crash count. The findings of the current study revealed that the VSL are positively and significantly associated with CF and CR.
Assessing the Mechanical Properties of Open-Graded Asphalt Mixtures
Authors: Ahmed Shams Ali , Tayh Sady A. , Jasim Nidaa Adil
Published: February 2025

In consideration of the escalating vehicular intensity and the substandard material properties of pavements in Iraq, particularly with regard to their impact on wet-weather accident rates and noise pollution in urban areas, there is an urgent need for an analysis of Open-Graded Asphalt (OGA) mixes to address the environmental and safety concerns. While OGA mixtures offer the dual advantages of reducing stormwater runoff and enhancing wet skid resistance, they are also more prone to raveling due to their high porosity. To enhance the performance of OGA mixes, various methods have been employed, including the incorporation of recycled polymers. The primary objective of this research is to evaluate the durability and strength properties of OGA mixes through laboratory testing using the Recycled Polyvinyl Chloride (RPVC) polymer. Laboratory tests were conducted on OGA mixes to ascertain the Marshall stability, resistance to abrasion, permeability, tensile strength, and moisture-induced damage. The mix designs were executed in accordance with the design procedure proposed by the National Cooperative Highway Research Program (NCHRP) for a range of 5.5%–7.0% asphalt content. RPVC was used in various proportions (2%, 4%, 6%, and 8%) by the weight of the base binder. The experimental findings demonstrated that the incorporation of RPVC led to enhanced Marshall stability and Indirect Tensile Strength (ITS) in porous asphalt concrete, surpassing the performance of conventional asphalt mixes. Additionally, the OGA mixture exhibited significant improvements in raveling resistance and moisture susceptibility. The study concluded that the Optimal Binder Content (OBC) of RPVC could enhance the pertinent engineering properties of OGA mixtures without compromising their permeability.
Advancing Sentiment Analysis: Evaluating RoBERTa against Traditional and Deep Learning Models
Authors: Pookduang Pongsathon , Klangbunrueang Rapeepat , Chansanam Wirapong , Lunrasri Tassanee
Published: February 2025

This research evaluates the performance of various sentiment analysis models, including traditional machine learning approaches (Naive Bayes, KNN, CART), a deep learning model (LSTM), and the transformer-based model RoBERTa using an Amazon book reviews dataset. ROBERTa outperformed all other models, achieving an accuracy of 96.30% and an F1-score of 98.11%, underscoring its superior ability to process complex and semantically diverse textual data. Traditional models, while computationally efficient, demonstrated limitations in capturing nuanced textual relationships, and the LSTM model, although competitive, faced scalability challenges and overfitting issues. These results demonstrate how transformer-based architectures such as RoBERTa offer advantages in real-world applications, particularly in e-commerce and social media sentiment analysis. This study underscores the superior capabilities of RoBERTa for sentiment analysis, particularly in processing semantically diverse and context-rich textual data that traditional models struggle to capture. Future work will explore optimizing RoBERTa's computational efficiency and expanding its applications to multilingual and cross-domain sentiment analysis tasks.
The Optimization of Rotary Bending Die Process: Criteria for the Metal Sheet Angles and Springback Effects
Authors: Quang Vu Duc
Published: February 2025

Rotary die bending enables the precise fabrication of sheet metal components across various bending angles, offering high dimensional accuracy, structural stability, and reduced springback. This study employs explicit and implicit numerical simulations using the Abaqus software to analyze the rotary die bending process and springback behavior of SUS304 stainless steel sheets. Five key criteria are investigated: desired angle post-springback (asi), springback factor (ksi), forming stress (Von Mises) at the required bending angle (Sr), residual stress (Von Mises) after springback (Sb), and equivalent plastic strain (PEEQ). These criteria enable accurate predictions of material behavior during rotary die bending, including elastic-plastic deformation and stress distribution. The insights gained support a more flexible design process, enhance the precision of sheet metal bending, and ensure that the final product meets the specified requirements. This research serves as a valuable reference for professionals working with sheet metal components made from various metals and bimetal sheets. Additionally, it informs strategies to mitigate or eliminate residual stress in bent parts, improving reliability and manufacturability.
Enhancement of the Shear Capacity of RC Deep Beams with Ultra-High Performance Fiber-reinforced Concrete
Authors: Elghany Ahmed Abd , Elsayed Mahmoud , Elsayed Alaa , Shaheen Ayman
Published: February 2025

This research investigates the shear behavior of Reinforced Concrete (RC) deep beams strengthened with Ultra-High-Performance Fiber-Reinforced Concrete (UHPFRC). For this purpose, eight RC deep beams were fabricated and tested for failure. One beam served as a control beam (un-strengthened), while the remaining seven deep beams were strengthened utilizing various strengthening schemes. This experimental study primarily focused on the thickness of the UHPFRC layer, Steel Fiber (SF) volume fraction, and strengthening schemes (jacketing, bilateral layers, and strips exclusively in the shear zone). The experimental findings demonstrated that UHPFRC significantly enhanced the shear capacity, toughness, and stiffness of the RC deep beams. The performance of the strengthened beams exhibited improvements in ultimate shear strength, stiffness, and toughness of about 43.6%, 102.2%, and 171.3%, respectively, higher than that of the un-strengthened deep beam. UHPFRC U-jacketing is a highly effective method for strengthening the RC deep beams. Incorporating SF into the UHPFRC mixture improved the shear properties of the strengthened specimens and delayed fracture propagation. Finally, the shear capacity of the strengthened specimens was compared to the values predicted by the analytical approaches presented in earlier research.
Determination of Zea Mays Plant Fertility Level in Automatic Fodder Systems using Supervised Learning based on GLCM and Physical Feature
Authors: Haryanto . , Kuswanto Dwi , Purnamasari Dian Neipa , Anifah Lilik
Published: February 2025

The problem during the dry season is the availability of animal feed, especially for cattle. One of the efforts made is to use fermented feed and corn fodder. Automated feedstock monitoring and control is one of the technologies that has been developed. This study proposes a method to determine the fertility of Zea May sp plants in automatic fodder using supervised learning based on Self-Organizing Map (SOM), Gray Level Co-occurrence Matrix (GLCM), and physical features. The results showed that the system worked satisfactorily, where both methods achieved an accuracy of 93.5% on 3-day Zea Mays fodder using SOM and the highest on 12-day Zea Mays fodder using both methods with an accuracy of 96%. Although this system has shown good performance using both SOM and K-means, in some conditions, K-means achieved higher performance. These contributions are expected to help farmers provide animal feed.
Harnessing Deep Learning and Technical Indicators for Enhanced Stock Predictions of Blue-Chip Stocks on the Indonesia Stock Exchange (IDX)
Authors: Dwiandiyanta Bernadectus Yudi , Hartanto Rudy , Ferdiana Ridi
Published: February 2025

Given the limitations of existing models in accurately predicting stock prices, particularly in emerging markets such as Indonesia, this study aimed to evaluate the effectiveness of deep learning models in forecasting stock prices using blue-chip company shares traded on the Indonesia Stock Exchange (IDX). The main focus lies in combining historical stock data with a series of existing technical indicators, optimizing their integration to improve prediction accuracy. The accuracy of this method is reflected in a comprehensive evaluation of model performance using robust metrics, including R2, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Empirical results show the superiority of models integrating technical indicators compared to models relying only on historical data. The LSTM model showed the most significant improvement, with R2 for ASII stock jumping by 14.59% after incorporating technical indicators. The prediction accuracy of the GRU model for BBCA shares increased significantly, as shown by a decrease of 45.16% in MSE. These findings underscore the critical role of feature selection in developing prediction models. Integrating technical indicators with historical stock data increases prediction accuracy and provides additional tools for informed decision-making.
Fruit Export and Advisory App using Deep Learning
Authors: Prof. Shegar. S. R.  , Wakchaure Siddhant Sanjay  , Bhor Pradyumna Machhindra  , Somvanshi Harish Ambadas
Published: February 2025

Because of the agriculture sector’s vital role in global food production and sustainability, fruit quality is critical. Traditional methods of fruit quality evaluation are time consuming and subjective, resulting in inefficiencies in the supply chain and significant food loss. Our research provides a novel solution. By evaluating fruit photos, our algorithm detects fresh versus sub optimal quality fruits using cutting-edge machine learning techniques. Our model correctly identifies ripeness, flaws, and deformities across multiple fruit varieties using deep learning with CNNs at its heart and transfer learning. Implementing this technology reduces food waste and promotes sustainable agriculture by streamlining fruit quality testing. It helps farmers, distributors, and consumers by guaranteeing that only the best fruits make it to market. Our study provides a cost effective, environmentally friendly, and long-term solution
The Influence of Brand Image and Social Media Information on Purchase Intention Through Perceived Value
Authors: Junianto Ario , Ruswanti Endang
Published: February 2025

The aim of this study is to explore brand image and social media information on purchase intention through perceived value related to gaming smartphone purchase intention. The data collection taken from 150 respondents with the criteria are the consumers of smartphone gaming products who lives in West Jakarta, Indonesia and being active social media users in the last six months. Purposive method sampling is used for data collection. The finding is brand image has an influence on social media information. Then, Brand image and social media information have a positive influence on perceived value and purchase intention. Perceived value also has positive influence on purchase intention. The managerial implication of this research is companies can develop information that aligns with their brand image. Then, companies should pay attention to information such as reviews and testimonials. Finally, companies must innovate to create products that have great value and a strong brand image in order to increase purchase intention.
The effect of adding hip strengthening exercises to lumbar stabilizing exercises on gait for the treatment of non-specific low back pain: a randomized controlled trial
Authors: Saleh Amir Morsy , Abdel-Aal Nabil Mahmoud , Abo Elatta Ahmed Bayuomy
Published: February 2025

Introduction

To evaluate the impact of adding hip strength exercises to lumbar stabilizing exercises on pain, disability, and spatio-temporal parameters of gait in the treatment of non-specific low back pain (NSLBP).

Methods

In this randomized controlled trial, 60 patients diagnosed with NSLBP were randomized into two equal groups. The subjects were randomly allocated to either group A, the control group (n = 30), performing lumbar stabilizing exercises, or group B, the study group (n = 30), receiving lumbar stabilizing exercises and a progressive hip strengthening exercise program. The frequency of intervention was 3 sessions per week for six weeks. In this trial, the Visual Analogue Scale for pain intensity, Modified Oswestry Disability Index for disability, and Kinovea 2D motion analysis for spatio-temporal parameters were evaluated pre-treatment and after six weeks.

Results

There were statistically significant differences between groups in changes in pain scores (p = 0.035), the disability index (p = 0.012), and COG vertical displacement (p = 0.017). There were no statistically significant differences in stride length, stride time, stride speed, or cadence between the control and study groups (p > 0.005).

Conclusions

Adding hip strengthening exercises to a lumbar stabilization exercise program can reduce pain and disability and decrease COG vertical displacement more than a lumbar stabilization exercise program alone in NSLBP patients. However, no differences were found in gait speed, cadence, stride length, and stride time.
Upaya Meningkatkan Hasil Belajar Matematika dengan Menggunakan Metode Demonstrasi Pada Kelas II di SDN
Authors: Santy Maria , Supriyanto Manurung Alberth
Published: February 2025

This research comes from observations at SD Negeri Setia Asih 05, where it was found that students tended to enjoy learning more on their own or showed less interest in Mathematics lessons delivered through the lecture method. Meanwhile, one useful learning method is the demonstration method. This method can help engage students in learning so that they do not feel stuck or bored. With this method, students can see the teaching aids shown during the learning session. The aim of this research is to improve the learning outcomes of class II students at SDN Setia Asih 05. This research was conducted in the form of classroom action research (PTK) by utilizing data collection techniques through observation, examiners and interviews. Increase in student learning outcomes from the pre test by 72% to 84%. With an increase of 12%. After that, in their activities, the percentage of teacher participation increased from 83% to 84%. This had an impact on increasing student participation from 65% to 83%, an increase of 18%.
Pengaruh Penerapan Metode Jarimatika terhadap Hasil Belajar Matematika pada Materi Operasi Hitung di Kelas III
Authors: Kamila Raihanah Fairuz , Supriyanto Manurung Alberth
Published: February 2025

This research is based on the low average mathematics learning outcomes of class III students at SDN Sriamur 04, this can be seen when the class teacher only uses lecture and demonstration methods with stick pieces which take a long time to calculate. Apart from that, it was also seen that some students had difficulty memorizing multiplications of 6 and above. The purpose of this research is to find out whether the Jarimatics method influences the mathematics learning outcomes of students in class III at SDN Sriamur 04 on multiplication arithmetic operations. This research uses a quantitative approach using the form of Pre-Experimental Design, One-Group Pretest-Posttest Design. In this study, the population was all class III students at SDN Sriamur 04, Bekasi district with a total of 55 students. The research sample was class III A students, totaling 28 students. The average pretest result was 51.00. After the pretest, treatment was given in the form of applying the Jarimatika method to improve mathematics learning outcomes in multiplication calculation operations. During the treatment, there was an improvement which was visible from the posttest results, which obtained an average of 79.57. To test significance using the paired sample t-test, the sig value is known. (2-tailed) of 0.000. According to the basis for decision making in the paired sample t-test, the sig. (2-tailed) < 0.05 or 0.000 < 0.05. This means that the hypothesis H0 is rejected and Ha is accepted because there is a significant difference between the pretest and posttest learning outcomes. So it can be concluded that there is an influence of the application of the Jarimatics method on mathematics learning outcomes in class III multiplication calculation operations at SDN Sriamur 04.
Mengaplikasikan Konsep Kriteria Kebenaran Koherensi, Korespomdensi dan Paragmatis dalam Menyusun Tesis
Authors: Hadi Khairul , Abrar Ragil Wahyuda , Alvionita Icha , Ulfa Mariam
Published: February 2025

Kegiatan penelitian membutuhkan jawaban akhir yang harus dapat dipercaya kebenarannya. Untuk mendapatkan kebenaran ilmiah, perlu pengkajian ilmu pengetahuan yang berkenaan dengan kejelasan obyek materi sesuai metode yang sesuai dengan kaidah yang relevan dan tersistem. Dalam penulisan ini, pendekatan penelitian menggunakan metode kualitatif dengan penelitian pustaka (library research). Hasil penelitian menunjukkan bahwa ada tiga teori untuk menentukan kebenaran, yaitu teori koherensi, korespondensi dan paragmatisme. Teori koherensi adalah konsisten atau tidak akan bertentangan suatu pernyataan yang baru dengan pernyataan sebelumnya. Teori korespondensi adalah teori yang berdasarkan pada fakta yang obyektif, yaitu sebuah pertanyaan akan benar jika pernyataan tersebut berhubungan dengan fakta obyektif yang ada. Teori pragmatisme dinyatakan sebagai pernyataan yang dianggap benar jika pernyataan ataupun konsekuensinya akan memberikan manfaat praktis bagi manusia. Ketiga teori-teori kebenaran tersebut cenderung untuk saling menyempurnakan daripada saling bertentangan, dalam menentukan suatu kesimpulan kegiatan penelitian.