bluuebunny/crossref_metadata_2025 huggingface dataset:
Anatomi Tubuh Manusia Mengenal Sistem-Sistem Organ Manusia menurut Al Quran
Authors: Purwati Fitri
Published: February 2024

Tubuh manusia menurut Islam diciptakan oleh Allah dari bahan berupa saripati tanah. Berdasarkan dalil dalam Al-Quran, mayoritas ulama berpendapat bahwa terdapat perbedaan sumber antara bahan yang digunakan untuk penciptaan tubuh Adam sebagai manusia pertama (Surah Al-Hijr), tubuh Hawa sebagai manusia kedua (Surah Ya Sin), dan tubuh dari keturunan Adam dan Hawa (Surah Al-Mu'minun). Dalam ajaran Islam, tubuh manusia dibentuk oleh Allah dengan bentuk terbaik yang memiliki sifat seimbang dalam susunan dan stuktur organ. Allah menetapkan jiwa sebagai pelengkap dan penyempurna dari tubuh manusia. Karena itu, tubuh manusia memiliki potensi jasmani yang kondisinya terpengaruh secara langsung oleh kondisi jiwa secara individu. Di dalam Al-Quran, tubuh manusia disebut sebagai basyar untuk menyatakan sifat-sifat biologis pada individu manusia. Secara fisik, tubuh manusia mirip dengan tubuh hewan tetapi dengan kondisi kekuatan otot yang lebih lemah. Tubuh manusia juga diciptakan berbeda-beda dari segi jenis kelamin, postur tubuh, dan warna kulit dengan tujuan agar manusia dapat saling mengenal satu sama lain.
Использование систем инвентаризации
Authors: Денисенко В. В. , Гончаров А. М.
Published: February 2024

В данной статье рассмотрена проблема не повсеместного применения различных видов систем инвентаризации в границах информационной инфраструктуры Организации, а также подчеркнут факт эффективности инвентаризационного подхода к контрольным мероприятиям в процессе управления информационными активами. Описан принцип работы и приведена обобщенная схема функционирования указанных систем. Поднята тема владения неполной информацией об инфраструктуре Организации, составляющих элементах системы защиты информации, прикладного программного обеспечения без применения систем инвентаризации специалистами отделов информационных технологий. Проведена классификация различных систем инвентаризации, которые широко применяются на территории Российской Федерации. Авторами приведён пример результата инвентаризации, максимально приближенный к оптимальным условиям для пользователя, так как охватывает различные уровни информационной инфраструктуры (системный, сетевой), а также, в дополнение, отражает функционирующие на объектах инвентаризации средства защиты информации различных классов. Немаловажным дополнением служит отражение в примере результатов инвентаризации используемых протоколов передачи информации, так как при анализе подобных данных опытный сотрудник сможет сделать вывод о применяемых технологиях в конкретном случае. В статье приведены примеры применяемых инструментов для реализации системы контрольных процедур и анализа результатов инвентаризации.
Computation and Evaluation of the Electromagnetic Parameters of Amorphous Core Transformers
Authors: Thanh Bao Doan , Quoc Vuong Dang
Published: February 2024

This paper presents an analytical model for computing and designing a three-phase Amorphous Core Transformer (ACT) with a power rating of 3 kVA, 380/127 V. Based on the main parameters obtained from the analytical model, a Finite Element Method (FEM) was developed to simulate the electromagnetic parameters, such as electric currents, operating voltages, and losses. Finally, an ACT installation was carried out with the steel material of code 2605SA1. The simulation results were compared with the experimental measurements under no-load and full-load operating conditions. Furthermore, the no-load loss of ACT was also compared to that of traditional transformers using silicon steel material. The obtained results prove that the use of ACT can save energy in the fields of education, health services, and industries.
Presenting the Secure Collapsible Makerspace with Biometric Authentication
Authors: Abdul Razak Muhammad Syafiq , Gothandapani Sundaraja Perumal A. , Kamal Noorfazila , Chellappan Kalaivani
Published: February 2024

Traditional product design learning is inefficient, costly, and limited by safety concerns in physical and smaller-scale school workshops. The Secure Collapsible Makerspace is a mobile makerspace with smart sensors and an expandable work area facility. This study aims to develop a smart mobile makerspace platform using edge computing architecture and the biometric security model to compare real-time learners’ engagement time and design performance. The developed platform consists of four blocks that provide service as a makerspace for the learners and monitor their utilization in a secure environment. The edge computing architecture allows real-time analysis of the users’ makerspace utilization. A total of 750 learners were accessed for their product design outcome which presented a positive correlation of 0.72 between the engagement time and the design assessment with a corresponding p-value less than 0.001. This study concludes that makerspaces can be a platform that improves students’ performance in hands-on activity-related learning.
An Artificial Intelligence Framework for Disease Detection in Potato Plants
Authors: Abbas Ahmed , Maqsood Umair , Ur Rehman Saif , Mahmood Khalid , AlSaedi Tahani , Kundi Mahwish
Published: February 2024

Agricultural products are a fundamental necessity for every country. When plants are afflicted with diseases, it influences the country's agricultural productivity, as well as its economic resources. Diseases are an important problem for potato plants, causing potatoes to be rejected and resulting in financial losses. Viruses and diseases in potatoes and field plants can be missed with the naked eye, particularly in the early stages of cultivation. The use of modern instruments and technology at an early stage of disease diagnosis dramatically reduces costs. This study used deep learning techniques to categorize and detect plant leaf diseases in photos taken from the Plant Village dataset. The dataset consists of 20,636 photos of plants and their diseases. This study focused on potato plants because it is the most common type of plant in the world, particularly in Pakistan. Convolutional Neural Network (CNN) methods were used to categorize plant leaf diseases into 15 classes, including three classes for healthy leaves and classes for several plant diseases such as fungal and bacterial infections, among others. The proposed models were trained and tested, achieving 98.29 and 98.029% accuracy, respectively.
The Impact of Injection/Pumping Wells on the Pollution Transport in Groundwater
Authors: Khalifa Walid Mohamed Abdel-Samad , Achour Belkacem , Butt Tayyab , Mirza Cirrus , Salah Heba , El-Didy Sherif
Published: February 2024

The natural quality of groundwater tends to be degraded by industry, agriculture, and wastewater. There are several alternatives to prevent migration and the spread of pollution in groundwater. Some alternatives are physical such as grouting, or slurry walls. Others could be hydrodynamic containment by injection or pumping wells. Injection wells are used to confine a pollutant in place or dilute its concentration by injecting clean water into the aquifer. Pumping wells are used to discharge the pollutants out of the groundwater reservoir or act as interceptors. In this research, the hydraulic characteristics and behavior of the hydrodynamic methods are investigated by using numerical simulation. In this investigation, the numerical model MT3D has been integrally used with the flow model MODFLOW. Injection/pumping rate, screen length and layer, and the number of wells are considered. The results have shown that increasing the rate or the number of injection/pumping wells permits less pollution spread. Changing the screen length of the injection/pumping wells is not effective in preventing pollution from spreading in the long term. Changing the number of wells has more effect on a containment spread. Injection wells can prevent the spread of contaminants more than pumping wells.
A Resonator Noise Reduction Solution for a Centrifugal Gas Compressor
Authors: Deaconu Marius , Cican Grigore , Dragasanu Luminita , Cristea Laurentiu
Published: February 2024

The purpose of this work is to reduce the noise generated by a compressor that conveys methane gas. After certain measurements were conducted a high level of noise was observed in the 2000-3000 Hz range, therefore a solution for noise reduction at the source is addressed and presented in this paper. The research method is based on designing resonators to be applied on the stator of a centrifugal compressor used in a natural gas distribution station. First, the calculations are made on resonators with air as the working fluid and then are validated through real measurements in a Kundt tube. After validation, the working fluid is changed with gas, calculations are made once again, and acoustic simulations are performed. To facilitate acoustic simulations and reduce computational time, a simplified stator geometry was employed. This simplified model encompassed the region starting from the rotor's gas exit, where the resonators were deployed. The purpose of the acoustic simulation was to validate the frequency range influenced by the resonators and to estimate the overall noise reduction. Depending on the operating regime of the compressor, the rotor fundamental can vary within the frequency domain of 2000 – 3000 Hz. This broadband domain requires the usage of several resonators with different resonant frequencies. The proposed solution obtained an average value of attenuation, excluding the peaks of the attenuation, in the frequency domain of 2000 - 3000 Hz, of 9 dBA. If the fundamental frequency coincides with a resonance of the resonator, higher attenuation can occur. Also, fundamental attenuation can lead to attenuation of the harmonics.
Effects of Geometry Design Parameters on the Fatigue Failure of a Drive Axle Housing using Finite Element Analysis
Authors: Pervan Nedim , Trobradovic Mirsad , Muminovic Adis J. , Lulic Haris , Metovic Sadjit , Hadziabdic Vahidin
Published: February 2024

The current paper investigates the effects of geometric design parameters on the fatigue failure of the drive axle housing using the Finite Element Method (FEM). The study examines the effects of various factors on the fatigue life of the drive axle housing, such as axle housing wall thickness, housing cross-sectional rounding radius, and rounding radius of the central part of the housing. Based on the known material properties and dynamic loads, a CAD/FEM model of the drive axle housing was developed, and a structural analysis was carried out. Based on the results of the structural analysis, critical places on the housing were determined, and fatigue analysis and lifetime prediction were performed. Through a series of simulations, the study reveals that increasing housing wall thickness can significantly improve fatigue performance. Similarly, increasing the rounding radius at the housing cross-section, as well as the rounding radius at the central part of the housing can also lead to improved fatigue performance. However, the effect of increasing the value of these two radii is not as significant as the effect of the wall thickness. These findings give useful information regarding the design and manufacture of drive axle housings for vehicles, intending to reduce the likelihood of fatigue failure.
Prediction of Agricultural Commodity Prices using Big Data Framework
Authors: Rana Humaira , Farooq Muhammad Umer , Kazi Abdul Karim , Baig Mirza Adnan , Akhtar Muhammad Ali
Published: February 2024

The agriculture sector plays a crucial role in the economy of Pakistan, contributing significantly to the Gross Domestic Product (GDP) and the employment rate. However, this sector faces challenges such as climate change, water scarcity, and low productivity, which have a direct impact on agricultural commodity prices. Accurate forecasting of commodity prices is essential for farmers, traders, and policymakers to make informed decisions and improve economic outcomes. This paper explores the use of a big data framework for agricultural commodity price forecasting in Pakistan, using a historical dataset on commodity prices in various Pakistani cities from 2007 to 2022 and Apache Spark to preprocess and clean the data. Based on historical spinach prices in Vehari City, the machine learning models Auto-Regressive Moving Average (ARIMA), Random Forest, and Long-Short-Term Memory (LSTM) were applied to price trends, and their performance was compared using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and squared correlation coefficient (R2). LSTM outperformed ARIMA and Random Forest with a higher R2 value of 0.8 and the lowest MAE of 125.29. Such predictions can help farmers to effectively plan crop cultivation and traders to make well-informed decisions.
Forecasting Tariff Rates and Enhancing Power Quality in Microgrids: The Synergistic Role of LSTM and UPQC
Authors: Sahoo Satyabrata , Swain Sarat Chandra , Dash Ritesh , Samal Padarbinda
Published: February 2024

The current paper presents an original approach into the microgrid control framework by incorporating LSTM-based optimization with specific emphasis on refining the gain parameters of a Proportional-Integral-Derivative (PID) controller. This integration represents a significant advancement in improving the overall efficiency of microgrid control systems. By creatively applying LSTM optimization, the paper achieves dynamic adjustments of the PID controller's parameters, resulting in more precise regulation of output power quality. Through the utilization of the Unified Power Quality Conditioner (UPQC) in conjunction with LSTM-based optimization, the paper establishes a compelling link between improved power quality and the resultant tariff rates. This highlights their combined influence on enhancing power quality and calibrating tariff rates, providing a fresh perspective on optimizing microgrid operations.
Finite Element Analysis of a Double Beam connected with Elastic Springs
Authors: Hang Do Thi , Tung Nguyen Xuan , Tu Doan Van , Lam Nguyen Ngoc
Published: February 2024

This paper develops a finite element method for double beams subjected to static loading. The double beam consists of two Euler–Bernoulli beams connected continuously by an elastic spring connection. The finite element for a double beam is formulated with eight degrees of freedom based on the Euler-Bernoulli beam theory. The finite element method is implemented in MATLAB software to analyze the behavior of the double beams. The MATLAB code calculates the displacements of both the upper and lower beams. Numerical examples are compared with the analytical solution to demonstrate the high accuracy of the proposed method.
Assessment of a Hybrid (Wind-Solar) System at High-Altitude Agriculture Regions for achieving Sustainable Development Goals
Authors: Kassem Youssef , Camur Huseyin , Ghoshouni Ehsan Golzar
Published: February 2024

Power generation from hybrid renewable energy systems is gaining popularity worldwide, especially in developing countries suffering from electricity crises. Small-scale hybrid wind and solar systems, especially in high-altitude agriculture regions, which may experience electricity shortages during extreme weather conditions, can be critical to achieving sustainability goals and objectives. The latter will be reached by providing clean energy and addressing economic concerns. Accordingly, the main aim of the current paper is to evaluate the techno-economic feasibility of a grid-connected hybrid (vertical axis wind turbine – 2-axis photovoltaic) system at high-altitude agriculture regions (Ardal and Faridan) in Iran for the production of clean energy. To this aim, the wind speed and solar radiation data were analyzed statistically using 13 distribution functions. The results indicate that Generalized Extreme Value produced the best fit for the wind speed and solar radiation data. Furthermore, the purpose of the current work is to evaluate the technical and economic aspects of grid-connected hybrid vertical axis wind turbines as well as PV tracking systems using RETScreen software. The results demonstrate that implementing the proposed system could generate significant amounts of electricity in order to meet the demand for domestic and agricultural applications while ensuring clean energy in line with sustainable development goals. Besides, this study can help integrate renewable energy into the grid and help policymakers facilitate the installation of rooftop small-scale hybrid systems in the future.
Classified Volatile Organic Compound Detection using Data Classification Algorithms
Authors: Chennoju Jaya Prakash , Siddiah Nalluri
Published: February 2024

Sensors are becoming smaller and less expensive, sparking interest in assessing vast volumes of sensor data. Meanwhile, the emergence of machine learning has led to the development of technologies that have a substantial impact on our lives. Machine learning models are often used to produce accurate, real-time predictions even in the presence of noisy sensed data. In this study, a Volatile Organic Compound (VOC) categorization system based on sensor data collected from a sensor array was developed. The most difficult challenge posed in the sensor array was the detection of the type of VOC. It is feasible to categorize VOCs brought on by applying data classification algorithms to data collected from sensor devices. In this work, we used data from the classification algorithms Decision Tree (DT), Naive Bayes (NB), and Linear Regression (LR) on a developed linear sensor array and their classification accuracy was compared. Four different VOCs were evaluated: acetone (C3H6O), benzene (C6H6), ethanol (C2H5OH), and toluene (C6H5CH3). The acquired classification accuracy reached 95.65% with the LR algorithm.
Experimental Investigation of Heat Transfer and Pressure Drop Performance of a Circular Tube with Coiled Wire Inserts
Authors: Kapse Arvind A. , Shewale Vinod C. , Barahate Sanjay D. , Kakade Amol B. , Surywanshi Satish J.
Published: February 2024

This paper evaluates the thermo-hydraulic performance of a coiled wire passive insert for internal turbulent flow through a circular copper tube test section in an in-tube exchanger. Experiments were carried out using water as the working fluid with Reynolds number ranging from 8000 to 32000. The experimental setup was validated for Nusselt number and friction factor with well-established equations for plain tubes. The average Nusselt number ratios (Nua/Nup) and the friction factor ratios (fa/fp) for the augmented tube case over the plain tube case are reported to range from 1.55 to 1.38 and from 1.513 to 1.583, respectively. The average performance ratios considering equal pumping power criteria are also reported and found in the range of 0.846 to 0.921. The study concludes that coiled wire inserts are suitable for heat transfer augmentation applications where pumping power is of minor concern.
Performance Analysis of a New Vertical Axis Turbine Design for Household Usage
Authors: Stratila Sergiu , Glasberg Dan , Mălăel Ion
Published: February 2024

The popularity of small wind turbines intended for domestic use has significantly increased during the recent years, and it is reasonable to assume that this trend will continue given the present political and economic environment. There is a greater need for clean, pollution-free energy due to worries about climate change. In this study, a 1.5 KW vertical-axis Darrieus helix wind turbine for residential use was designed and its performance was mathematically evaluated under typical wind speed circumstances of 12 m/s. The study is split into two sections: In the first, we examined a standard wind turbine design with three identical blades, whereas in the second, the blades were different, each with a unique airfoil with a varying chord, even though they shared the same rotor diameter. For each case, 5 CFD simulations were performed in order to determine the power characteristics of the wind turbines. To correctly set up the computational domain, the number of elements and the minimum element size were taken into account whereas mesh dependency analysis was performed. In order to compare the results, the vorticity magnitude was measured at 4 different blade locations in each boundary condition. The results showed that when the power coefficient of the turbines is considered, such geometry adjustments are possible. Furthermore, the evolution of the torque coefficient over a full 360-degree rotation was studied. A summary of the improvements in performance resulting from the geometry adjustment is provided.
Optimal Valley-Filling Algorithm for Electric Two-wheeler Charging Stations
Authors: Ngoc Van Nguyen , Huu Duc Nguyen
Published: February 2024

In Vietnam, comprehensive measures are required to accommodate and encourage the potential development of electric two-wheelers in urban traffic and the rooftop solar power potential. In the case of PV-integrated electric two-wheeler charging stations, numerous vehicles charging simultaneously may trigger very high peak loads which adversely impacts other loads and the distribution grid. In this study, an optimal valley-filling algorithm for electric two-wheeler charging stations is proposed. The proposed algorithm can update the variation of available vehicles as well as the dynamic changes in the energy level of individual E2Ws at each time slot. The simulation results proved that the proposed method can effectively perform valley filling, significantly improving the total load profile compared to uncontrolled charging and average charging schemes.
Investigation of the Experimental Shear Resistance of RC T-beams after Strengthening with Carbon Fiber-Reinforced Polymer (CFRP) Bars
Authors: Hasan Hussain , Al-Farttoosi Mahdi Hameed
Published: February 2024

This study investigates the stiffness and shear resistance of T-reinforced concrete beams that have been strengthened against shear using the embedded through-section approach. The beams were exposed to a monotonic one-point load until failure. The experimental methodology included the investigation of 12 T beams made of reinforced concrete, consisting of 2 reference beams that were not subjected to any strengthening measures and 10 reinforced beams. The 12 beams were classified into two primary categories: those with stirrups and those without stirrups. The primary factors from each group encompassed the spacing and angle of inclination pertaining to the Carbon Fiber-Reinforced Polymer (CFRP) bars placed along the central axis of the section. Various configurations were used, including varied spacing intervals and degrees of inclination. The results showed that the ratio of the CFRP shear resistance to the total section (Vf / Vsec) ranged between 10 to 21% in group one (with stirrups). This means that the Embedded Through Section (ETS) technique with CFRP bars is useful in increasing the shear resistance of reinforced concrete beams. For group two (without stirrups), this ratio ranged between 56 to 58.5%. That is, ETS with CFRP bars significantly increases the shear resistance of reinforced concrete beams without stirrups.
Enhancing Road Holding and Vehicle Comfort for an Active Suspension System utilizing Model Predictive Control and Deep Learning
Authors: Tu Do Trong
Published: February 2024

Active Suspension Systems (ASS) with control are gaining traction as researchers strive for optimal system performance. They are significant in diverse commercial vehicle applications, catering to user demands. This study employs the advanced Model Predictive Control (MPC) technique to enhance the smoothness and safety of a half-car model. The simulation results showed the prowess of MPC controllers under varied control force signal constraints, demonstrating superiority in curtailing vehicle chassis rotation angle and speed by up to 46.93% and 43.34%, respectively. The controller was compared with an artificial neural network controller utilizing only two state signals of the system, trained from MPC data, demonstrating high accuracy with R2 reaching 0.97024 and mean squared error at 7.3557×10-5. This study contributes to the refinement of ASS by focusing on practical implementation and performance enhancement.
Human Activity Recognition through Smartphone Inertial Sensors with ML Approach
Authors: Alanazi Munid , Aldahr Raghdah Saem , Ilyas Mohammad
Published: February 2024

Human Activity Recognition (HAR) has several applications in healthcare, security, and assisted living systems used in smart homes. The main aim of these applications or systems is to classify body movement read from the built in sensors such as accelerometers and gyroscopes. Some actions could be performed in response to the output of these HAR systems. The number of smartphone users increases, whereas the sensors are widely available in different sizes and shapes (internal or external sensors). Recent advances in sensor technology and machine learning have led researchers to conduct studies on sensor technology such as HAR. HAR systems typically use a combination of sensors, such as accelerometers, gyroscopes, and cameras, to collect images or signal data that can be classified by machine learning algorithms. HAR research has focused on several key challenges including dealing with variability in sensor data, handling missing data or noise, and dealing with large amounts of sensor-generated data. In this work, several machine learning algorithms were tested in predefined settings using the KU-HAR dataset in a series of experiments. Subsequently, various performance metrics were calculated to assess the chosen algorithms’ performance. The experimental findings showed that the LightGBM classifier surpassed the other machine learning algorithms in performance metrics, such as accuracy, F1 score, precision, and recall. Although Gradient Boosting has lengthy training time, the other classifiers complete their training in an acceptable time period.
Design, Modeling, and Control of a Brushless Permanent Magnet Motor for Electric Vehicles
Authors: Khlifi Mohamed Arbi , Ben Slimene Marwa
Published: February 2024

Electric Vehicles (EVs) are becoming more and more common. In addition to their ecological advantages, EVs have demonstrated their efficiency in curtailing travel expenses by transitioning from gasoline to electricity, which is significantly less costly. This study proposes a Brushless Permanent Magnet Motor (BPMM) design for EVs, models the influence of average and ripple torque, and evaluates its efficiency compared to other designs. The validity of the proposed design was supported by simulation results.
Computational and Experimental Investigation of Thermal Generation in CNC Milling Machine Spindle Βearing with the Oil-Air Lubrication Method
Authors: Le Duc-Do , Pham Van-Hung , Bui Tuan-Anh
Published: February 2024

This study investigates the thermal generation dynamics in the spindle unit of a CNC milling machine. The main objective is to analyze the heat transfer from the bearings to the housing during operation, using simulations and experiments. An air-oil-cooled lubrication system is employed, which enables the airflow to dissipate some of the heat produced. Before conducting the experiment, heat generation and transfer are accurately predicted. The air-oil mixture, formed by pressurized air atomizing oil droplets, is effective in cooling high-temperature regions. A spindle ER16-80SK 24k is integrated with a CNC machine for real-time data acquisition, including parameters such as spindle speed and temperature. The results show significant temperature rises in all bearings, which reach a steady state after an hour of operation. There are noticeable differences in range, from 6.1% to 43.4%, between the experimental and simulated maximum temperatures, indicating possible real factors not considered in the simulation. The environmental impacts of oil particle emission are also discussed, which require proper ventilation management during operations. This research provides valuable insight into the thermal dynamics of CNC milling machine spindles and establishes a solid basis for further research and development in this important field of machining technology.
Experimental Investigation of Polymer and Nanomaterial modified Asphalt Binder
Authors: Emmaima Ali Mohamed , Ali Shaban Ismael Albrka , Gallouz Khalifa Salem
Published: February 2024

Modifying the asphalt binder and mixture becomes one of the best ways to mitigate pavement distress and increase the service life of constructed road networks. This study aimed to evaluate the influence of modified asphalt binders with the best different percentages of polymer and nanoparticles. Typical asphalt binder (penetration, softening point, and viscosity) and frequency sweep tests were used to evaluate the physical and rheological properties of modified asphalt binders with 5% Acrylonitrile Styrene Acrylate (ASA), 5% aluminum oxide nanoparticles (Al2O3), and 5% calcium carbonate (CaCO3). The results showed that the physical properties of all modified blends improved compared to those of the base asphalt binder. The improvement in softening point was up to 19%, the penetration reduction was nearly 69%, and the sensitivity to elevated temperatures was reduced by up to 13%. Evaluation of the rheological properties showed that modified asphalt with 5% Al2O3 binder had the highest permanent deformation resistance, followed by 5% ASA. The 5% CaCO3 binder showed a small improvement compared to the other samples. The results showed that the 5% Al2O3 binder had the highest complex modulus and the lowest phase angle, which means that it has the best viscoelastic properties. Therefore, it can be recognized as the best asphalt binder among the modified binders in this study.
A Study on the Influence of aging of the Butt-welded PE100 SDR11 on Shore A Hardness and Tensile Strength
Authors: Zisopol Dragos Gabriel , Minescu Mihail , Iacob Dragos Valentin
Published: February 2024

This paper presents the results of an experimental study on the influence of aging on the shore A hardness and tensile strength of butt-welded PE100 SDR11 pipeline joints with a nominal diameter of 125 mm and wall thickness of 11.40 mm used in natural gas distribution transportation. For the experimental determination, 12 samples were taken from the body of the pipe, 9 of which were taken from the area of the butt-welded joint. The test tubes were divided into 3 groups of 4 pieces each (1 unwelded test tube and 3 welded test tubes). Using the Arrhenius method, the test tubes in 2 groups were given artificial aging treatments of 10 and 20 years. Subsequently, all 12 test tubes were tested for shore A hardness and tensile strength. For the welded samples, an increase in tensile strength was observed with increasing aging time by 6.5% for the 10-year aged samples and by 6.16% for the 20-year aged samples. For the unwelded samples, the tensile strength decreased by 3.57% for 10-year aging and increased by 5.84% for 20-year aging. Artificial aging of 10 and 20 years of natural gas transmission and distribution pipelines did not considerably influence the Shore A hardness values, as they were in the medium/soft hardness range.
Advanced Fraud Detection in Blockchain Transactions: An Ensemble Learning and Explainable AI Approach
Authors: Taher Shimal Sh. , Ameen Siddeeq Y. , Ahmed Jihan A.
Published: February 2024

In recent years, cryptocurrencies have experienced rapid growth and adoption, revolutionizing the financial sector. However, the rise of digital currencies has also led to an increase in fraudulent transactions and illegal activities. In this paper, we present a comprehensive study on the detection of fraudulent transactions in the context of cryptocurrency exchanges, with a primary focus on the Ethereum network. By employing various Machine Learning (ML) techniques and ensemble methods, including the hard voting ensemble model, which achieved a remarkable 99% accuracy, we aim to effectively identify suspicious transactions while maintaining high accuracy and precision. Additionally, we delve into the importance of eXplainable Artificial Intelligence (XAI) to enhance transparency, trust, and accountability in AI-based fraud detection systems. Our research contributes to the development of reliable and interpretable models that can significantly improve the cryptocurrency ecosystem security and integrity.
Mitigating Reflection Cracking in Asphalt Concrete Overlays with ECC and Geotextile
Authors: Albayati Amjad H. , Oukaili Nazar K. , Obaidi Hadel , Alatta Bahaa M.
Published: February 2024

The rehabilitation of deteriorated pavements using Asphalt Concrete (AC) overlays consistently confronts the reflection cracking challenge, where inherent cracks and joints from an existing pavement layer are mirrored in the new overlay. To address this issue, the current study evaluates the effectiveness of Engineered Cementitious Composite (ECC) and geotextile fabric as mitigation strategies. ECC, characterized by its tensile ductility, fracture resistance, and high deformation capacity, was examined in interlayer thicknesses of 7, 12, and 17 mm. Additionally, the impact of geotextile fabric positioning at the base and at 1/3 depth of the AC specimen was explored. Utilizing the Overlay Testing Machine (OTM) for evaluations, the research demonstrated that ECC17 significantly mitigated reflection cracking, showing a notable 764% increase in the number of load cycles to failure (Nf) compared to the Geotextile Base (GB) specimen. Against the Reference Specimen (RS), ECC17 exhibited a remarkable 1307% enhancement in Nf values, underscoring its effectiveness. Geotextile fabric, particularly at 1/3 depth, demonstrated notable resistance but was overshadowed by the performance of ECC interlayers. The results clearly indicate that ECC, especially ECC17, stands out as an effective solution for mitigating reflection cracking, including joints, in AC overlays.
A Human Face Detector for Big Data Analysis of Pilgrim Flow Rates in Hajj and Umrah
Authors: Alharbey Riad , Banjar Ameen , Said Yahia , Atri Mohamed , Abid Mohamed
Published: February 2024

In today's digital world, some crowded venues still rely on outdated methods, such as counting people using counters or sensors at the entrance. These techniques generally fail in areas where people move randomly. Crowd management is an important challenge for ensuring human safety. This paper focuses on developing a crowd management system for Hajj and Umrah duty. Motivated by the recent artificial intelligence techniques and the availability of large-scale data, a crowd management system was established and is presented in this paper. Utilizing the most recent Deep Learning techniques, the proposed crowd management system will be charged with detecting human faces, face identification, tracking, and human face counting tasks. Face counting and detection will be achieved by computing the number of people in a given area. Face detection and tracking will be carried out for person identification, flow rate estimation, and security. The suggested crowd management system is composed of three key components: (1) face detection, (2) assignment of a specific identifier (ID) to each detected face, (3) each detected face will be compared to the stored faces in the dataset. If the detected face is identified, it will be assigned to its ID, or a new ID will be assigned. The crowd management system has been developed to improve the Cross-Stage Partial Network (CSPNet) with attention module integration. An attention module was employed to address object location challenges and a channel-wise attention module for determining the objects of focus. Extensive experiments on the WIDER FACE dataset proved the robustness of the proposed face detection module, which allows for building reliable crowd management and flow rate estimation systems through detecting, tracking, and counting human faces. The reported results demonstrated the power of the proposed method while achieving high detection performance in terms of processing speed and detection accuracy.
Automated Skin Cancer Detection and Classification using Cat Swarm Optimization with a Deep Learning Model
Authors: Rajendran Vijay Arumugam , Shanmugam Saravanan
Published: February 2024

The application of Computer Vision (CV) and image processing in the medical sector is of great significance, especially in the recognition of skin cancer using dermoscopic images. Dermoscopy denotes a non-invasive imaging system that offers clear visuals of skin cancers, allowing dermatologists to analyze and identify various features crucial for lesion assessment. Over the past few years, there has been an increasing fascination with Deep Learning (DL) applications for skin cancer recognition, with a particular focus on the impressive results achieved by Deep Neural Networks (DNNs). DL approaches, predominantly CNNs, have exhibited immense potential in automating the classification and detection of skin cancers. This study presents an Automated Skin Cancer Detection and Classification method using Cat Swarm Optimization with Deep Learning (ASCDC-CSODL). The main objective of the ASCDC-CSODL method is to enforce the DL model to recognize and classify skin tumors on dermoscopic images. In ASCDC-CSODL, Bilateral Filtering (BF) is applied for noise elimination and U-Net is employed for the segmentation process. Moreover, the ASCDC-CSODL method exploits MobileNet for the feature extraction process. The Gated Recurrent Unit (GRU) approach is used for the classification of skin cancer. Finally, the CSO algorithm alters the hyperparameter values of GRU. A wide-ranging simulation was performed to evaluate the performance of the ASCDC-CSODL model, demonstrating the significantly improved results of the ASCDC-CSODL model over other approaches.
„Peace of eat“
Authors: Filko Nadine
Published: February 2024

Goslar. Wer in der EU über die Proteinwende spricht, der blickt auch nach Dänemark. Das Land hat als einer der größten Exporteure von Schweinefleisch in Europa 2023 den weltweit ersten nationalen Aktionsplan für pflanzenbasierte Produkte veröffentlicht.
Ecological Consciousness in Puranas: A Comprehensive Review
Authors: Dobriyal Kusum , Kumar Mithlesh , Smriti Smriti , Dwivedi Deepak Kumar , Anshul Anshul
Published: February 2024

This article explores the ecological consciousness ingrained in the Puranas, a category of ancient Indian literature, and examines its significance in the present-day context. The Puranas depict nature as divine, personifying elements like rivers and mountains, fostering a profound spiritual and ecological perspective. The concept of "Nature as Divine" is explored in the Padma Purana and Matsya Purana, emphasizing the
interconnected web of life and encouraging reverence for the environment. Conservation ethics and sustainable living practices are evident in narratives from the Bhagavata Purana, Brahma Purana, Padma Purana, and Matsya Purana. These texts advocate for responsible stewardship of natural resources, aligning human existence
with cosmic rhythms and promoting a harmonious relationship between humanity and the environment. The article highlights the theme of "Cosmological Harmony" in Hindu cosmology, emphasizing the interconnectedness of cosmic elements and divine forces to maintain balance. It also discusses environmental crises depicted in certain Puranas, cautioning against ecological imbalance and advocating responsible stewardship. Rituals and eco-friendly practices, such as yajnas and vratas, are explored as indirect suggestions for sustainable living. The Puranas also promote animal welfare and ahimsa, with examples from the Vishnu Purana and other texts emphasizing compassion towards all living beings.While acknowledging the contemporary relevance of Puranic wisdom, the article discusses challenges in interpreting and applying these teachings in the modern world. Balancing literal and symbolic interpretations, addressing societal changes, preventing cultural appropriation, and reconciling scientific knowledge with religious beliefs pose challenges. Despite these hurdles, the ongoing relevance of the Puranas lies in their ability to offer guidance and inspiration
for sustainable living and responsible environmental stewardship in the face of current environmental challenges.
Understanding Menstruation Material in Fiqh Learning for VII Grade Female Students at SMP Syubbanul Wathon 2 Bandongan Magelang
Authors: Fitria Nur Azizah Dwi , Siswanto Siswanto
Published: February 2024

This research was conducted to determine the factors that influence female students' understanding of menstruation material, how they learn menstrual material, and the extent to which class VII female students understand menstruation material at SMP Syubbanul Wathon 2 Bandongan. This research was conducted at Syubbanul Wathon 2 Bandongan Middle School with a focus on female students' understanding of menstruation material. This research was conducted using qualitative research methods with a descriptive approach and various data analysis, collection and validity techniques to support optimal results. In this research, learning about menstruation material for class VII female students uses several learning stages: learning planning, menstrual learning material, learning methods, learning media, learning evaluation and assessment. The level of understanding of class VII students' material on menstruation at Syubbanul Wathon 2 Middle School is quite good. This can be seen from their interviews and written test results, although some students don't understand it and some of the material is difficult for them.

Keywords: Understanding Menstruation Material, Fiqh, Syubbanul Wathon
THE EFFECTIVENESS OF ANTENATAL HYPNOTHERAPY ON ANXIETY IN FACING THE CHILDBIRTH PROCESS
Authors: Octavia Yunida Turisna , Hasibuan Eva Kartika , Siahaan Julia Mahdalena
Published: February 2024

The Maternal Mortality Rate (MMR) in Indonesia is still around 305 per 100,000 Live Births, which has not yet reached the set target of 183 per 100,000 LB in 2024. One of the causes of this high rate is attributed to the anxiety experienced during pregnancy, reaching 373,000 cases. Antenatal hypnotherapy is an effort aimed at reducing maternal anxiety with the goal of preparing for a normal pregnancy and facilitating the labor process. Hypnotherapy in Antenatal care focuses on providing comfort to mothers by inducing deep relaxation. The purpose of this study is to determine the effectiveness of antenatal hypnotherapy in reducing anxiety during the labor process at PMB Pera Medan. The research employed a quantitative approach with a Quasi-Experimental Design (pseudo-experiment). The study utilized a One Group Pre-Test and Post-Test design. The population consisted of all third-trimester pregnant women at PMB Pera Medan, totaling 61 pregnant women, with a sample size of 16 selected through purposive sampling. The research findings indicate that the average anxiety level among third-trimester pregnant women at the Independent Practice of Midwife Pera Medan before receiving antenatal hypnotherapy was 30.1875 (moderate anxiety), which decreased to 13.8750 (mild anxiety) after receiving antenatal hypnotherapy. There is a significant influence of antenatal hypnotherapy on reducing anxiety during labor at PMB Pera Medan with a p-value of 0.000 < 0.05. Therefore, it is advisable to continue antenatal hypnotherapy classes for pregnant women during each ANC visit and at home to reduce anxiety, thereby facilitating a normal labor process.
Application of Implicit One-step Third Derivative Method for the Solution of Temperature Gradient in a Fish Chunk Placed in a Smoking Kiln
Authors: K. Olumurewa O.
Published: February 2024

This paper described numerical solution of temperature gradient in a fish chunk placed in a smoking kiln, using an implicit one-step third-derivative numerical method which was developed by introducing more derivative properties into the conventional linear multistep method. The heat transferred from the furnace generated by the biomass through the metal plate to the drying chamber by conduction, to the fish chunk in the smoking kiln was evaluated by the Fourier first heat flow equation. Comparison of the numerical result with the exact solution showed that the implicit one–step third- derivative method was accurate and thus recommended for solving Engineering problems resolving in differential equations.
Examining professionals’ opinions on effective educational management and supervision for the gifted education
Authors:   Dr., Çukurova Bilim ve Sanat Merkezi, Türkiye, Şahin Çiğdem
Published: February 2024

The aim of this research is to find out the characteristics of effective educational management and supervision in gifted education. Qualitative research method was used in the study. The data collected from supervisors, managers, assistant managers, lecturers, teachers, coordinators, specialists, consultants in 2020-2021 academic term were analyzed using content analysis technique. As a result of the research, the themes and contents for the effective educational management and supervision were defined under the following titles: Educational program management, communication, human resource management, leadership, organizational targets, organizational behaviors (Educational Management); accountability and efficiency, professional development, organizational development, technology-based development, qualifications of supervision and supervisors (Educational Supervision).