bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing the Geotechnical Properties of Expansive Soils through Coconut Shell Ash Treatment: An Experimental Investigation
Authors: Suhendra Andryan , Suwondo Riza , Ryan Benjamin
Published: December 2024

Expansive Soils (ES) present a significant challenge to civil engineering projects worldwide due to their propensity to undergo volumetric changes in response to fluctuations in the moisture content. This study examined the potential of Coconut Shell Ash (CSA) as a soil stabilizer to mitigate the adverse effects of ES. The objective was to conduct a systematic evaluation of the impact of CSA on a range of soil properties, including the plasticity index, compressive strength, shear strength, swelling potential, and compaction characteristics, across a diverse array of soil types. This study adopted a comprehensive methodology, which involved the laboratory testing of soil samples with varying proportions of CSA. The tests included the determination of the Atterberg limits, the evaluation of the compaction properties, unconfined compression tests, and swelling tests. The findings revealed significant variations in the soil properties in response to the CSA content. The plasticity index responses exhibited a range of subtle changes, with a downward trend at lower CSA concentrations and more complex behaviors at higher concentrations. The compaction characteristics exhibited alterations in the optimum moisture content and maximum dry unit weight, indicating changes in soil density and stability. Similarly, the compressive and shear strength properties exhibited fluctuations with varying CSA content, underscoring the necessity for a comprehensive assessment of soil stability under CSA-treated conditions. Additionally, swelling tests demonstrated the potential of CSA to mitigate soil expansiveness, with lower swelling percentages observed in treated soils. This study highlights the importance of considering the soil type, CSA content, and engineering requirements to optimize the effectiveness of CSA in soil stabilization applications.
An Adaptive Neural Fuzzy Inference System for the Estimation of the Atmospheric Corrosion Rate of Steel
Authors: Nguyen Trong-Ha , Nguyen Kieu-Vinh T. , Phan Van-Long , Nguyen Duy-Duan
Published: December 2024

This paper aims to develop a practical Adaptive Neural Fuzzy Inference System (ANFIS) for estimating carbon steel's Atmospheric Corrosion Rate (ACR). The ANFIS model is developed using 125 datasets. The input variables of the ANFIS model include average Temperature (T), average Relative Humidity (RH), total Rainfall (Rf), Time of Wetness (TOW), and average Chloride Ion (Cl-). The output variable of the Machine Learning (ML) model is the ACR value. The results of the proposed model are compared to those of the literature. The comparisons reveal that the ANFIS model established in this study outperforms the existing equations in predicting ACR. Furthermore, a Graphical User Interface (GUI) is developed for practical use in predicting the ACR of carbon steel.
A Customized Method for Recovery of Gaussian Beam Profile Emerging from Optical Fibers
Authors: Chughtai Muhammad Tajammal
Published: December 2024

Loss of the Gaussian beam profile is frequently observed when lasers are combined with either classical or modern optics. This alteration in the beam profile affects the coherence length of the beam and produces an unfavorable output in laser applications. Poor cleaving of the optical fiber end face is the main cause of this problem, especially when cleaving is performed using low-precision equipment or nonstandard methods. This profile deformation prevents the intended output, which leads to an unanticipated leap in the laser beam profile from one Transverse Electromagnetic Mode (TEM) to another. In this work a method is proposed to mitigate this effect by attaching an optically flat glass piece to the end face of the fiber and using index matching gel. By guaranteeing a uniform distribution of the index matching gel, this technique enhances the consistency of the laser beam and successfully restores the Gaussian beam profile. Laboratory test results show that this technology is a viable substitute for conventional fiber-cleaving techniques and is rapid, easy, inexpensive, and dependable. While successful in controlled situations, other improvements, such as optical adhesives, are needed to achieve stable performance in settings that are prone to vibration.
Investigation of Acoustic Waves generated by the Detonation Gun of a Zenith-M modernized Powerful Antihail Station
Authors: Ayvazyan Gagik , Vardanyan Arman
Published: December 2024

In this paper, a detonation gun of an antihail station was used to study acoustic waves as they propagated upward. The station's power was enhanced by placing a Hartmann resonance tube into the conical nozzle of the gun. Propagation speed, pressure-time waveform, and acoustic spectrum of the acoustic waves were determined. The propagation speed was approximately 890 m/s (Mach number ~2.6), and the initial (near-ground) intensity was 130-160 dB in the low-frequency range up to 200 Hz, where the energy distribution is relatively uniform. The results show that the antihail station can effectively influence atmospheric processes, either promoting rain precipitation or preventing hail formation.
Research on the Influence of Genetic Algorithm Parameters on XGBoost in Load Forecasting
Authors: Tran Thanh-Ngoc , Nguyen Quoc-Dai
Published: December 2024

Electric load forecasting is crucial in a power system comprising electricity generation, transmission, distribution, and retail. Due to its high accuracy, the ensemble learning method XGBoost has been widely applied in load forecasting. XGBoost's performance depends on its hyperparameters and the Genetic Algorithm (GA) is a commonly used algorithm in determining the optimal hyperparameters for this model. In this study, we propose a flowchart algorithm to investigate the impact of GA parameters on the accuracy of XGBoost models over the hyperparameter grid for load forecasting. The maximum load data of Queensland, Australia, are used for the research. The analysis of the results indicates that the accuracy of the XGBoost model significantly depends on the values of its hyperparameters. Using default hyperparameter values may lead to substantial errors in load forecasts, while selecting appropriate values for the GA to determine the optimal hyperparameters for the XGBoost model can significantly improve its accuracy.
Multi-Objective Optimization of a Two-stage Helical Gearbox with Double Gears in the First Stage using MARCOS
Authors: Bao Le Duc , Binh Vu Duc , Thanh Dinh Van , Nguyen Khac Minh , Hung Le Xuan
Published: December 2024

This study demonstrates the solution of the Multi-Objective Optimization Problem (MOOP) of  a two-stage helical gearbox with double gears at the first stage, following the MARCOS methodology. The goal of this work is to identify the most effective essential design factors to reduce the bottom area of the gearbox while maximizing its efficiency, which constituted a significant novel finding. For this purpose, three crucial design parameters were selected, the first stage gear ratio and the wheel face width (Xba) coefficients for the first and second stage. Furthermore, the Multi-Criteria Decision Making (MCDM) issue was chosen to be handled by the MARCOS method, and the weight criterion for solving the MOOP was determined by the MEREC method. The drawn conclusions are useful in developing a two-stage helical gearbox with double gears at the first stage by helping to identify the ideal values for the three important design parameters.
Advancements and Challenges: A Comprehensive Review of GAN-based Models for the Mitigation of Small Dataset and Texture Sticking Issues in Fake License Plate Recognition
Authors: Habeeb Dhuha , Alhassani A. H. , Abdullah Lili N. , Der Chen Soong , Alasadi Loway Kauzm Qata
Published: December 2024

This review paper critically examines the recent advancements in refining Generative Adversarial Networks (GANs) to address the challenges posed by small datasets and the persisting issue of texture sticking in the domain of fake license plate recognition. Recognizing the limitations posed by insufficient data, the survey begins with an exploration of various GAN architectures, including pix2pix_GAN, CycleGAN, and SRGAN, that have been employed to synthesize diverse and realistic license plate images. Notable achievements include high accuracy in License Plate Character Recognition (LPCR), advancements in generating new format license plates, and improvements in license plate detection using YOLO. The second focal point of this review centers on mitigating the texture sticking problem, a crucial concern in GAN-generated content. Recent enhancements, such as the integration of StyleGAN2-ADA and StyleGAN3, aim to address challenges related to texture dynamics during video generation. Additionally, adaptive data augmentation mechanisms have been introduced to stabilize GAN training, particularly when confronted with limited datasets. The synthesis of these findings provides a comprehensive overview of the evolving landscape in mitigating challenges associated with small datasets and texture sticking in fake license plate recognition. The review not only underscores the progress made but also identifies emerging trends and areas for future exploration. These insights are vital for researchers, practitioners, and policymakers aiming to bolster the effectiveness and reliability of GAN-based models in the critical domain of license plate recognition.
Investigation of the Brittleness and Sensitivity of the Gypseous Sand Improved by Nano-clay
Authors: Abrahim Mustafa Jamal , Ibrahim Rula Fuad , Salih Bilal Muiassar M. , Mahmood Mohammed Shakir , Aldarraji Suha A. H.
Published: December 2024

This study examines the brittleness index and sensitivity ratio of two gypseous sand soils under saturation conditions improved by nano-clay. The soil samples were obtained from the cities of Al-Najaf and Tikrit containing 29% and 55% gypsum, respectively. The tests were performed on remolded specimens in a direct shear box. The soil specimens were examined mainly under saturated conditions for both different soil and nano-clay contents (0, 2, 5, and 7 %) under three normal stress levels: 25, 50, and 100 kPa. Additional tests were performed under dry soil conditions for comparison. The calculations of the brittleness index and sensitivity ratio of the saturated soil specimens were dependent on the newly suggested definitions of the peak values of the shear stress. The tP is the peak value in the dry condition, whereas the tR is the peak value in the saturation condition. The results emphasize that the values of the brittleness index and sensitivity ratio require more attention to the possibility that the soil is brittle owing to increased gypsum dissolution and the demolition of the soil structure. The brittleness index and sensitivity ratio increased with increasing gypsum content and decreased with increasing nano-clay content and average stress levels. The optimum percentage of nano-clay for both soil specimens was found to be 5%.
A Hybrid Metaheuristic Aware Enhanced Deep Learning Approach for Software Effort Estimation
Authors: Bbadana Mahesh , Kiran Mandava Kranthi
Published: December 2024

Software Effort Estimating (SEE) is a fundamental task in all software development lifecycles and procedures. Therefore, when deciding how to anticipate effort in a variety of project types, the comparative assessment of effort prediction methods has emerged as a standard strategy. Unfortunately, these studies include a range of sample techniques and error metrics, making a comparison with other work challenging. To overcome these drawbacks, this study proposes a deep learning model to effectively estimate software effort. The estimation is mainly focused on minimizing the cost and time consumption. The input data is taken from the dataset and preprocessing is performed to remove the noise content. Then the required features are extracted using the preprocessed data with the help of the simple and higher-order statistical features. A novel Modified Chaotic Enriched Jaya with Moth Flame Optimization (MCEJMO) algorithm is introduced for feature selection to enhance SEE accuracy. The estimation is performed using Multilayer Long Short-Term Memory (M-LSTM). The proposed method achieved a Mean Square Error (MSE) of 0.2825 for dataset 1 and 0.2285 for dataset 2.
A Robust Control Strategy for Effective Field-Oriented Control of PMSMs
Authors: Le Thanh-Lam
Published: December 2024

Field-Oriented Control (FOC) is widely recognized as a standard framework for Permanent Magnet Synchronous Motor (PMSM) drives. Linear control techniques are commonly employed in designing controllers for this strategy. However, traditional control methods often exhibit performance limitations and reduced robustness, particularly under harsh operating conditions, which makes the FOC structure less appealing and less effective. To address and overcome these challenges, this study proposes a Second-order Non-singular Terminal Sliding (SNTS) mode approach to achieve fast, accurate, and robust tracking for the FOC control structure applied to PMSM drives. The SNTS method combines the benefits of non-singular terminal sliding mode and second-order control laws. This approach ensures rapid and precise tracking while minimizing steady-state errors by using a nonlinear terminal sliding mode surface instead of a linear one. Furthermore, the system state transitions smoothly along the sliding mode surface with continuous functions, which reduces chattering around the sliding surface. The second-order control law incorporated into this method helps mitigate chattering and achieve fast convergence. The Lyapunov stability theory is employed to verify the stability of the SNTS technique designed for the PMSM system. Simulation and experimental validation on a hardware platform confirm the effectiveness and superiority of the proposed SNTS method, demonstrating its capability to enhance the performance of speed controllers for PMSM drives.
Simulation of a New Well SAGD Configuration based on the example of an Oil Field in Kazakhstan
Authors: Logvinenko Alexandr
Published: December 2024

Steam-Assisted Gravity Drainage (SAGD) method is recognized as one of the most effective methods for the recovery of heavy oil and natural bitumen. This technology has received several modifications throughout its history designed to improve it. One of the promising modifications is the Single Well-SAGD (SW-SAGD), which allows significantly reducing the CAPEX for drilling a well. However, this method has several disadvantages such as steam breakthrough into the production part and the uneven development of the steam chamber along the well. This article presents the concept of a Single-Well Cyclic SAGD (SWC-SAGD), which allows preventing the breakthrough of the injected steam into the production section and the uniform development of the steam chamber along the well. The comparison analysis of the developed modification of SWC-SAGD was carried out using the classical method of 3D hydrodynamic modeling of both options using the example of one of the fields of the Republic of Kazakhstan. The results of the work show the efficiency of the proposed technology in terms of field oil total production.
A Customized CNN Architecture with CLAHE for Multi-Stage Diabetic Retinopathy Classification
Authors: Phimphisan Songgrod , Sriwiboon Nattavut
Published: December 2024

This paper presents a customized Convolutional Neural Network (CNN) architecture for multi-stage detection of Diabetic Retinopathy (DR), a leading cause of vision impairment and blindness. The proposed model incorporates advanced image enhancement techniques, particularly Contrast Limited Adaptive Histogram Equalization (CLAHE), to improve the visibility of critical retinal features associated with DR. By integrating CLAHE with a finely tuned CNN, the proposed approach significantly enhances accuracy and robustness, allowing for more precise detection across various stages of DR. The proposed model was evaluated against several state-of-the-art techniques, with the customized CNN alone achieving an overall accuracy of 97.69%. The addition of CLAHE further boosts the performance, achieving an accuracy of 99.69%, underscoring the effectiveness of combining CLAHE with CNN for automated DR detection. This approach provides an efficient, scalable, and highly accurate solution for early and multistage DR detection, which is crucial for timely intervention and prevention of vision loss.
A Fine Tuned-based Framework to Predict Salesforce Data using Machine Learning in Business Analytics
Authors: Kumar Naveen
Published: December 2024

Sales forecasting is one of the critical areas in business analytics where business organizations aim to enhance efficiency and, therefore, revenues. An excellent example of a CRM program is Salesforce, which produces massive amounts of sales data that are essential for forecasting and decision-making. Data analysis involves the use of complex and effective tools for its processing. This study proposes a framework based on the following classification algorithms: Support Vector Machines (SVM), Decision Trees (DT), and Random Forests (RF). The proposed framework follows a fine-tuned approach to improve the prediction of sales data. Regarding the fine-tuning of these algorithms, it was observed that specific changes were required within the hyperparameters to better relate to the inherent patterns and other factors that exist in the sales data. The optimization process was very crucial in improving the performance of the model. The proposed framework was used on a sales dataset and evaluated in terms of accuracy, precision, data loss, and F1 score. Fine-tuned algorithms had higher accuracy and lower data loss.
Comparative Assessment of Hash Functions in Securing Encrypted Images
Authors: Al-Asad Ghayth , Al-Husainy Mohammed , Bani-Hani Mohammad , Al-Zu’bi Ala’eddin , Albatienh Sara , Abuoliem Hazem
Published: December 2024

Different encryption methods have been developed to securely transmit confidential images over the Internet and combat the increasing cybercrime. Many of these methods use hash functions to enhance encryption strength. Due to the lack of a comprehensive evaluation of how different hash functions affect image encryption, this study presents a comparative analysis of the performance of various hash functions as encryption keys and analyzes their security, speed, and efficiency. The source image is first processed as a series of bytes. The bytes are divided into byte vectors, each with a length that matches the length of the hash value of a specified hash function. An XOR operation is performed between the hash value bytes and the associated byte vector. The bytes are reordered in each vector according to the ascending order of the associated hash value. Several metrics, such as Normalized Mean Absolute Error (NMAE), Peak Signal to Noise Ratio (PSNR), entropy, key size, and hash time, were used to evaluate the performance of different hash functions in image encryption. The results showed a clear variation in using various hash functions in terms of security, speed, and efficiency. With NMAE>72%, PSNR<6.62 dB, and Entropy>7.999 bpp, the use of the SHA family and MD5 is recommended in applications that need to achieve a high level of distortion in encrypted images. To resist brute-force attacks on the key, Blake2b, SHA512, and Whirlpool are the best choices with a key size of 512 bits. The Tiger is the fastest hash function, requiring the least average time of 0.372 seconds to complete the encryption process, making it the best choice for real-time applications. These findings help to choose the appropriate hash function in developing cryptographic techniques for a particular area.
Optimized Multi-Level Security for Content Contribution and Retrieval in Online Social Networks using a Content Visualization Mechanism
Authors: Tabassum S. Nasira , Kancherla Gangadhara Rao
Published: December 2024

Online social networks have become an integral part of modern communication, providing platforms for users to share personal information, media, and opinions. However, these platforms face significant challenges in preserving user privacy while ensuring efficient data retrieval and maintaining data integrity. Existing privacy preservation methods, such as PPK-MEANS, CFCAF, and CLDPP, are limited in their ability to handle the growing complexity and scale of user data, often leading to inefficiencies such as high Content Retrieval Time (CRT), increased Information Loss (IL), and compromised data accuracy. These inefficiencies are crucial to address, as they can degrade the user experience by causing delays, compromising data integrity, and limiting system scalability. High CRT frustrates users, while increased IL reduces data accuracy, undermining trust and system reliability. The primary issue addressed in this study is the need for an advanced privacy-preserving mechanism that can provide multilevel security while maintaining optimal system performance. To overcome these limitations, the Layered Secure Online Collaborative Verification (LSOCV) algorithm is proposed, designed to offer a scalable solution with tiered privacy controls based on user requirements. LSOCV enhances Privacy Retrieval Accuracy (PRA), significantly reduces CRT, and minimizes IL. The experimental results show that LSOCV achieved a PRA of 91.97%, reduced CRT to 7ms, and decreased IL by up to 8% for 500KB files, outperforming existing approaches. This method provides robust privacy protection and efficient data handling on social networks, with the potential for future application in big data environments, such as Hadoop, to ensure scalable, secure, and efficient privacy-preserving solutions.
A Quantum Encryption Algorithm based on the Rail Fence Mechanism to Provide Data Integrity
Authors: Ali Arshad , Farquad M. A. H. , Atheeq C. , Altaf C.
Published: December 2024

The rapid development of quantum computer technology poses an increasing threat to conventional encryption algorithms, and accordingly, more advanced security practices need to be developed. The current paper presents an innovative quantum cryptographic mechanism that combines classical encryption techniques with quantum principles such as superposition, entanglement, and uncertainty to enhance data security in digital communication. The proposed scheme, defined as Enhanced Quantum Key Distribution (EQKD), demonstrates superior performance in key metrics, including Quantum Bit Error Rate (QBER), fidelity, key distribution rate, and resilience to eavesdropping. In particular, EQKD achieves lower QBER and higher fidelity over longer distances while also enhancing key generation efficiency and increasing the probability of detecting eavesdropping attempt. These findings highlight the effectiveness of EQKD in improving the security and reliability of quantum cryptographic systems.
Feature Selection using Improved Nomadic People Optimizer in Intrusion Detection
Authors: Aboud Zinah Sattar Jabbar , Tawil Rami , Salam Kadhm Mustafa
Published: December 2024

Intrusion Detection (ID) in network communication and Wireless Sensor Networks (WSN) is a big challenge that has grown with the rapid development of these technologies. Various types of intrusion attacks may occur to the transferred data of such networks and various ID methods and algorithms have been proposed. One powerful tool used in this field is Machine Learning (ML), which has achieved satisfied detection results. However, these results with the available ID datasets can be further improved. This paper proposes an accurate approach for ID in the network and WSN using ML methods including chaotic map, Nomadic People Optimizer (NPO), and Support Vector Machine (SVM). The proposed approach has five main stages which are: data collection, pre-processing, feature selection, classification, and evaluation. An improved version of NPO based on chaotic map and Cauchy mutation called CNPO is proposed. The proposed scheme uses chaotic maps to initialize the population and Cauchy mutation for solution distribution. Besides, the proposed fitness function based on SVM is proposed. The CNPO is employed for the feature selection task. The proposed approach was evaluated in two datasets, NSL-KDD, and WSN-DS, with accuracy of 99.97% and 99.99, respectively.
Nature-Based Solutions for Flood Mitigation in Metropolitan Areas
Authors: Juliastuti . , Wijayanti Yureana , Gunawan Alexander Agung Santoso , Irwansyah Edy , Wulandari Sri
Published: December 2024

Flooding is a globally common problem in metropolitan areas including Jakarta, Indonesia. The increased intensity and frequency of rainfall caused by climate change and rapid urbanization have raised the risk of flooding in urban areas. One solution is to implement polders to mitigate flooding in coastal metropolitan areas. Regrettably, the current polder system is inadequate for managing flooding due to rapid land-use changes and regional expansion. This study analyzes flood control in the Jakarta region using the East Sunter Polder System, which experienced flooding in both 1990 and 2020 despite the implementation of the polder system. The polder system, consisting of four catchment areas—Petukangan, KBN 1/Sukapura, KBN 2, and Kebantenan—faces drainage challenges exacerbated by rainfall. To mitigate flood risks, Nature-Based Solutions (NBSs) have been implemented, including retention ponds and long storage systems. Hydrological and hydraulic analyses were conducted using HEC-HMS and HEC-RAS, and ArcGIS was employed for floodplain integration. This study underscores the significance of incorporating NBSs in urban flood management, demonstrating how they enhance resilience and mitigate flood risks. By integrating NBSs into the urban planning framework, the findings suggest that flood risk management can be significantly improved, leading to better preparation and long-term sustainability for managing natural hazards.
Enhanced Deep Learning Techniques for Real-Time Speech Emotion Recognition in Multilingual Contexts
Authors: Badawood Donia Y. , Aldosari Fahd M.
Published: December 2024

Emotion recognition from speech is crucial for advancing human-computer interactions, enabling more natural and empathetic communication. This study proposes a novel Speech Emotion Recognition (SER) framework that integrates Convolutional Neural Networks (CNNs) and transformer-based architectures to capture local and contextual speech features. The model demonstrates strong classification performance, particularly for prominent emotions such as anger, sadness, and happiness. However, challenges persist in detecting less frequent emotions, such as surprise and calm, highlighting areas for improvement. The limitations of current datasets, such as limited linguistic diversity, are discussed. The findings underscore the model's robustness and identify avenues for future enhancement, such as incorporating more diverse datasets and employing techniques such as transfer learning. Future work will explore multimodal approaches and real-time implementation on edge devices to improve the system's adaptability in real-world scenarios.
Enhanced Intrusion Detection in Software-Defined Networking using Advanced Feature Selection: The EMRMR Approach
Authors: Basfar Raed , Dahab Mohamed Y. , Ali Abdullah Marish , Eassa Fathy , Bajunaied Kholoud
Published: December 2024

Most traditional IP networks face serious security and management challenges due to their rapid increase in complexity. SDN resolves these issues by the separation of control and data planes, hence enabling programmability for centralized management with flexibility. On the other hand, its centralized architecture makes SDN very prone to DDoS attacks, hence necessitating the use of advanced and efficient IDSs. This study focuses on improving IDS performance in SDN environments through the integration of deep learning techniques and novel feature selection methods. This study presents an Enhanced Maximum Relevance Minimum Redundancy (EMRMR) approach that incorporates a Mutual Information Feature Selection (MIFS) strategy and a new Contextual Redundancy Coefficient Upweighting (CRCU) strategy to optimize feature selection for early attack detection. Experiments on the inSDN dataset showed that EMRMR achieved better precision, recall, F1-score, and accuracy compared to the state-of-the-art approaches, especially when fewer features are selected. These results highlight the efficiency of the proposed EMRMR approach in the selection of relevant features with minimal computational overhead, which enhances the real-time capability for IDS in SDN environments.
Enhancing the Mechanical and Fire-Resistant Properties of GFRP Composite using Boric Acid and Sodium Silicate Fillers
Authors: Wibawa Tri , Diharjo Kuncoro , Ariawan Dody , Raharjo Wijang Wisnu , Wibowo Cahyo Hadi , Saputro Fathony Nada , Rakhman Andry , Muharam Aam , Kaleg Sunarto , Hapid Abdul , Zulkefly Mohd
Published: December 2024

This study investigates the effects of Boric Acid (BA) , H3BO3, and Sodium Silicate (SS), Na2SiO3, as single fillers on the mechanical and fire-resistant properties of Glass Fiber Reinforced Polymer (GFRP) composites. Various compositions of BA and SS were incorporated into the GFRP matrix, and the resulting composites were analyzed using X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Thermo-Gravimetric Analysis (TGA), and Differential Thermal Analysis (DTA). The results demonstrate that BA significantly enhances the amorphous structure and mechanical strength of GFRP composites, with optimal performance at 10% BA content. In contrast, SS improves thermal stability but reduces mechanical strength at higher concentrations due to agglomeration. Fire resistance testing revealed that both fillers increase the ignition time and decrease the burning rate, with BA exhibiting superior performance. These findings suggest that BA is a more effective filler for improving the mechanical and fire-resistant properties of GFRP composites, while SS can serve as a complementary additive to enhance thermal stability.
Comparative Study Between Friction Stir Welding and Tungsten
Inert Gas Welding Processes
Authors: Al-Jarrah Jawdat
Published: December 2024

This study compares the properties of the aluminium alloy welded joints prepared by friction stir welding (FSW) to that of
tungsten inert gas (TIG). It concentrated on the microstructure, hardness, corrosion resistance, and wear resistance. It has
been found that the joints prepared by FSW have fine microstructure while coarse grains microstructure was obtained for
joints produced by TIG welding. Also, the lowest Rockwell hardness measured at the centre of the welding line is 52 for
joints prepared by FSW and 46 for ones produced by TIG welding. For corrosion resistance the reslt of tests indicated that
the welded joints of the TIG process had higher corrosion resistance, the mass loss after 72 hours is 0.2519 grams from
TIG joints and it is about double in the case of FSW joints (0.5263 grams). However, TIG joints have lower wear resistance
compared to joints produced by the FSW process. It seems that the grain size of the welded joints, in these two considered
welding processes, has a great effect on the investigated properties.
G7 ülkeleri sürdürülebilir kalkınma performansının ölçülmesi
Authors: Kahreman Yusuf
Published: December 2024

Ekonomik, sosyal ve çevresel kavramları ele alan ve bu üç boyut arasında uzun dönemde denge sağlamayı hedefleyen bir olgu olan sürdürülebilir kalkınma olgusuna daha sonraki yıllarda finansal ve yönetişim kavramları da eklenerek beş alt boyuttan oluşan bir yapıya dönüşmüştür. Çalışmada G7 ülkelerinin küresel kriz dönemini de kapsayan 2007-2015 dönemini kapsayan sürdürülebilir kalkınma performansının 5 alt boyutu ele alınarak performans değerlendirmesi amaçlanmıştır. Bu amaç doğrultusunda ÇKKV yöntemlerinden yararlanılmıştır. MEREC-MAIRCA bütünleşik modeli ele alınarak ekonomik performans endeksi elde edilirken, MAIRCA yöntemi ile yönetişimsel gelişim endeksi elde edilmiş ve sürdürülebilir kalkınma performansı elde edilmiştir. Ayrıca yapılan çalışmanın ve kullanılan modelin sağlamlığı test edilmek amacıyla çeşitli duyarlılık analizleri yapılmıştır. Çalışmanın sonucuna göre, 2007-2015 dönemi için ortalama değerler ele alındığında sürdürülebilir kalkınma performansında en iyi performans gösteren ülkeden en kötü performansı gösteren ülkeye göre sıralanacak olursa İngiltere, Kanada, Almanya, Fransa, ABD, Japonya ve İtalya olmuştur. Yapılan duyarlılık testlerine göre önerilen modelin ve çalışmanın sağlamlığını ortaya koymuştur.
Fatores associados a acidentes de trânsito em pedestres: Rondônia, Amazônia Ocidental Brasileira, 1998-2022
Authors: Ribeiro Alves Mario
Published: December 2024

Embora tema relevante para a saúde pública nacional, há poucos trabalhos que analisem no tempo e no espaço os Acidentes de Trânsito no país. Este estudo teve como objetivo analisar o tema, fornecendo subsídios para planejamento vinculado à prevenção do agravo em questão. Estudo ecológico e descritivo, uso de dados secundários coletados no DataSUS, organizados por ano e por município de internação em Rondônia. Variáveis socioeconômicas foram obtidas e analisadas em autocorrelações espaciais com a taxa média de internações. Foram observadas 3.812 internações durante o período de análise, sendo observado aumento de valores de taxas por municípios. Os municípios das partes norte, oeste e leste do estado apresentaram as maiores taxas durante o período de estudo. Houve presença de rodovias nos municípios com maiores taxas. Foram observadas autocorrelações entre distorção de idade-série, percentual de vulneráveis, internações por condições sensíveis e internações por doenças relacionadas a inadequado saneamento e taxas médias. Os Acidentes de Trânsito referem-se a questões individuais e coletivas, além de contextuais. É importante a conscientização de todos os atores e agentes de trânsito como fator de proteção de acidentes.
Segregação socioespacial, insegurança hídrica e saneamento: um olhar interseccional sobre a cidade de São Paulo
Authors: Gantus-Oliveira Talita , De Souza Gabi Barcellos Jéssica
Published: December 2024

O artigo analisa a relação entre interseccionalidade de gênero, raça e classe e insegurança hídrica, evidenciando a segregação socioespacial e ambiental na cidade de São Paulo. A justificativa reside no fato de que as desigualdades socioespaciais, a atomização do cuidado e a privatização dos serviços públicos vêm tornando o acesso ao saneamento ambiental básico cada vez mais precário, e, portanto, um debate urgente. Foi utilizada uma metodologia quali-quantitativa que faz uso de métodos estatísticos e de geoprocessamento. Como as mulheres são responsabilizadas pelos trabalhos reprodutivo e de cuidados na rotina familiar, acabam sendo, também, responsáveis pela gestão hídrica doméstica, tendo em vista a necessidade de água para a realização dessas tarefas. Assim, a segregação socioespacial e a ausência de saneamento básico, marcadas pelos fatores étnico-raciais e de classe, afetam principalmente as mulheres, revelando o caráter racista e patriarcal das desigualdades socioambientais presentes no espaço urbano em São Paulo.
O lugar e seus processos por entre tempos e espaços - uma reflexão sobre uma possível des-lugarização e re-lugarização
Authors: Cidreira Jefferson Henrique , Jesus Morais Maria de
Published: December 2024

O presente artigo busca analisar e conceituar relações e/ou acontecimentos nas constituições e inserções dos/nos lugares, numa acepção fenomenológica, a partir das interlocuções com os estudos de Martin Heidegger (1889-1976), de Eric Dardel (1899-1967), de Yi-Fu Tuan (1930-2022), Gilles Deleuze (1925-1995), Haesbaert (1958) e o trajeto de vida de um migrante paranaense ao Norte do Brasil, para, assim, desaguarmos no processo de re-lugarização e des-lugarização. Recentemente, refletindo sobre alguns acontecimentos na trajetória humana, como diásporas, ou simples mudanças de local, de perdas, de traumas etc., uma questão nos sobressaiu, será que há espécies de episódios, processos de des-re-lugarização perante a esses acontecimentos? Nesse pensamento, é possível haver uma conexão afetiva do ser com um novo espaço? Mediante a essas perguntas e/ou reflexões, analisamos trânsitos, fluxos e experiências particulares, pessoas que passaram por perdas, mudanças etc., fazendo com que emergissem discursos, pistas, respostas e aberturas para um possível processo de des-re-lugarização do lugar fenomenológico, sua transmutação e/ou aparição dentro da ciência geográfica, através das interlocuções entre a geografia, filosofia e história.
Estudo sistemático da matriz do índice de dissecação e aplicação no município de Caldas - MG.
Authors: Silva Vieira Marcelo , Alvisi de Oliveira Thomaz
Published: December 2024

O Índice de Dissecação do Relevo (IDR) desempenha um papel importante em estudos geoambientais, fornecendo informações sobre o desgaste das superfícies, contribuindo para a compreensão da dinâmica das paisagens e para a formulação de estratégias de planejamento ambiental. Este artigo é um estudo do IDR para aplicação no município de Caldas - MG. O método incluiu correções nos modelos digitais de elevação, a extração de dados morfométricos e um estudo de aplicação das matrizes de dissecação do relevo propostas por Ross (1992, 1994, 2006), Nunes et al. (1994) e Guimarães et al. (2017) com posterior calibração e ajustes para a composição de uma matriz de dissecação específica para a área em apreço. Nesse contexto, foram comparadas as matrizes de dissecação do relevo e apresentada uma proposta adaptada, no intuito de obter resultados mais condizentes à realidade da área em análise. Os resultados revelaram que as propostas existentes quando aplicadas a áreas e escalas diversas daquelas para o qual foram elaboradas tendem a generalizar algumas classes de dissecação, revelando a necessidade de adaptações para refletir com precisão as características locais. A proposta adaptada abrangeu as classes de dissecação com maior precisão.
desafios para o manejo multiprofissional do paciente obeso em unidade de terapia intensiva:
Authors: De Oliveira Soares Juliano , Silva dos Santos Thaísa , Fonseca Ferreira Rita de Cássia , Luza Cararo Giane , Marques de Lima Santos Bruna , Dal Ongaro Rodrigues Pamela Natali , Vieira da Silva Tailane , De Santana Ferreira Thalles Marciano , Carretta Diniz Marisa
Published: December 2024

INTRODUÇÃO: A obesidade, definida pelo acúmulo excessivo de gordura corporal prejudicial à saúde, é classificada pelo Índice de Massa Corporal (IMC) igual ou superior a 30 kg/m². Seus fatores de risco envolvem hábitos de vida, insegurança alimentar e contexto social. OBJETIVO: Relatar o caso de uma paciente obesa crítica, destacando a atuação multiprofissional e a importância do cuidado integral. RELATO DE CASO: Mulher, 50 anos, IMC 57,7kg/m², admitida na UTI após hernioplastia umbilical. Apesar dos intensos manejos, desenvolveu lesão por pressão grave e quadro neurológico, vindo a óbito durante novo desbridamento. DISCUSSÃO: Os desafios terapêuticos, como a dificuldade de mudança de decúbito e agravamento da lesão por pressão, impactaram negativamente no desfecho clínico. A abordagem familiar foi crucial para gerir o sofrimento e desdobramentos emocionais, além de informar sobre os direitos decorrentes da hospitalização CONCLUSÃO: O manejo do obeso crítico requer singularidade e uma abordagem integral, incluindo a família, considerando as questões sociais que transcendem a UTI e contribuem para fatores de risco.
Анализ динамических параметров метеорного комплекса Канкрид и его дрейфового движения
Authors: М.В. Сергиенко , Ю.А. Нефедьев , А.О. Андреев
Published: December 2024

Работа посвящена исследованию динамических параметров метеороидного комплекса Канкриды и его дрейфового движения. Согласно современным представлениям, малые тела Солнечной системы благодаря своей малой массе, не изменили свои физико-химические свойства и состав. Изучение астрофизических характеристик малых небесных тел, в особенности метеороидов и связанных с ними метеорных потоков, дают ценные сведения об эволюции Солнеч- ной системы. Отдельно для метеорных баз данных, созданных по разным каталогам метеорных орбит, определены радианты, площадь радиации, уточнены параметры суточного смещения. Определены резонансы с Юпитером. По- строено и проанализировано распределение координат радиантов метеороидного комплекса Канкриды в зависимости от долготы Солнца. Анализ распределения показывает, что северная и южная ветви метеороидного комплекса Кан- криды наблюдаются в схожие даты, наблюдается хорошее согласие по данным скоростей для разных метеорных баз и с литературными источниками, локализация радиантов происходит по склонению и прямому восхождению. Сделан вывод, что эволюционный механизм образования ветвей комплекса Канкрид, разный. Показано, что южная и северная ветвь комплекса Канкриды образовались при различных эволюционных условиях, как, например, при вторичном распаде родительского тела.

The work is devoted to the study of the dynamic parameters of the Cancrids meteoroid complex and its drift motion. According to modern concepts, small bodies of the Solar System, due to their low mass, have not changed their physical and chemical properties and composition. The study of the astrophysical characteristics of small celestial bodies, especially meteoroids and associated meteor showers, provides valuable information about the evolution of the solar system. Separately for meteor databases created using different meteor orbit catalogues, radiants and radiation areas were determined, and the parameters of daily shift were refined. Resonances with Jupiter were determined. The distribution of radiant coordinates of the Cancrids meteoroid complex was constructed and analyzed depending on the longitude of the Sun. The distribution analysis shows that the northern and southern branches of the Cancrids meteoroid complex are observed on similar dates, there is good agreement in the velocity data for different meteor bases and with literary sources, the localization of radiants occurs by declination and right ascension. It is concluded that the evolutionary mechanism of formation of branches of the Cancrids complex is different. It is shown that the southern and northern branches of the Cancrids complex were formed under different evolutionary conditions, such as the secondary disintegration of the parent body.
Малые тела Солнечной системы: научные и практические вопросы проекта BITDN
Authors: Б.М. Шустов
Published: December 2024

Тема малых тел Солнечной системы — это гигантская область научных исследований. Малые тела несут инфор- мацию обо всех уголках Солнечной системы. Важно, что многие фундаментальные исследования малых тел имеют очень тесную связь с практическими вопросами нашей жизни. В частности, происхождение и эволюция популя- ции ОСЗ (объектов, сближающихся с Землей) тесно связаны с проблемой астероидно-кометной опасности. Ранее в проектe OTN (Optical Transient Network) [1], получившего затем название BITDN (BRICS Intelligent Telescope and Data Network), предагалось создать сеть из нескольких широкоугольных телескопов 1-метрового класса и включить в список задач сети помимо наблюдений транзиентных астрофизических явлений также и тематику ОСЗ. В данной работе анализируются перспективы инструментов для наблюдений ОСЗ и делается вывод, что наряду с 1-м теле- скопами необходима широкая сеть малоапертурных инструментов, позволяющих очень быстро (за часы) провести обзор всего неба для обнаружения малых (декаметровых) астероидов в околоземном пространстве. Предлагается мультиапертурный вариант такого инструмента.

The topic of the Solar System small bodies (SSSB) is a gigantic field of scientific research. Small bodies carry information about all corners of the Solar system. It is important that many fundamental studies of small bodies have significant connections with the practical issues of our lives. In particular, the origin and evolution of the population of NEOs (near- Earth objects) are closely related to the problem of asteroid-comet hasard. Earlier, the OTN (Optical Transient Network) project [1], now called BITDN (BRICS Intelligent Telescope and Data Network), proposed to create a network of several 1-meter wide-angle telescopes and include in the list of network tasks, in addition to observations of transient astrophysical phenomena, also the subject of NEOs. In this paper, the prospects of instruments for observing NEOSs are analyzed and it is concluded that, along with 1-m telescopes, a wide network of low-aperture instruments is needed, allowing very quickly (in hours) to survey the entire sky and detect small (decameter) asteroids in the near-Earth space. A multi-aperture version of such a telescope is proposed.
terrível descoberta do drama da fome
Authors: Amorim Helder Remigio de , Santos Neto Francisco Ferreira dos
Published: December 2024

O presente artigo busca analisar a circulação internacional do romance Homens e Caranguejos, de Josué de Castro, bem como as representações da fome feitas em suas páginas. O livro foi publicado simultaneamente em outubro de 1966 na França, em Portugal e na Espanha. Além disso, a obra conta com 9 traduções e 11 edições. Em abril de 1964, Josué de Castro foi exilado pela ditadura civil-militar brasileira, portanto, o momento de escrita do romance foi durante o seu exílio em Paris. A narrativa da história é contada a partir das vivências de um menino chamado João Paulo, que, fugindo da seca do sertão com sua família, busca melhores condições de vida nos mangues do Recife, para compor o ciclo do caranguejo. Na obra, também estão presentes as críticas do autor às estruturas agrárias e capitalistas exploratórias e produtoras da fome, bem como relatos de memória de sua infância nos alagados do Recife. O romance é visto como uma denúncia, um grito de alerta em prol dos famintos. O renome internacional de Josué de Castro, atrelado às redes de sociabilidade que este construiu, auxiliou na ampliação da capacidade de circulação do romance, sobre a qual percebemos ainda que tem um alcance internacional amplo, com diversas críticas elogiosas na imprensa de periódicos franceses, norte-americanos, espanhóis, português, entre outros.
HYBRID PELATIHAN PENGEMBANGAN SOFT SKILLS DAN HARD SKILLS UNTUK MENDUKUNG KARIR MAHASISWA DI ERA DIGITAL
Authors: Jafar Jafar , Sumardi Sumardi , Aziz Firman , Anirwan Anirwan , Buang Ariyani , Ishak Pertiwi , Gunawan Resky Nuralisa , Abasa Sustrin , Nasir Norma
Published: December 2024

Pengabdian kepada mahasiswa merupakan bagian dari Tridharma Perguruan Tinggi yang bertujuan untuk meningkatkan kualitas sumber daya manusia melalui pengembangan keterampilan akademik dan non-akademik. Kegiatan pengabdian ini dilaksanakan dengan memberikan pelatihan pengembangan soft skills dan hard skills kepada mahasiswa, guna mempersiapkan mereka dalam menghadapi tantangan di dunia kerja yang semakin kompetitif, terutama di era digital. Pelatihan ini mencakup penguasaan teknologi dasar, keterampilan pemrograman, serta pengembangan keterampilan interpersonal seperti komunikasi efektif dan kerja tim. Metode yang digunakan dalam kegiatan ini adalah seminar, workshop, dan simulasi yang interaktif. Hasil dari kegiatan menunjukkan bahwa mahasiswa memiliki peningkatan signifikan dalam pemahaman dan keterampilan yang dibutuhkan di dunia kerja. Selain itu, mahasiswa memberikan umpan balik positif terhadap kegiatan ini, yang dinilai bermanfaat dalam mempersiapkan karir mereka. Pengabdian ini diharapkan dapat terus berlanjut dengan menyesuaikan kebutuhan industri dan tantangan yang dihadapi mahasiswa di masa depan.