bluuebunny/crossref_metadata_2025 huggingface dataset:
Voltage and frequency control of microgrid in presence of micro-turbine interfaced to matrix converter
Authors: Toupchi Khosroshahi Mahdi , Ajami Ali , Sutikno Tole
Published: April 2024

The active and reactive load changes have a significant impact on voltage and frequency. In this paper, in order to stabilize the microgrid (MG) against load variations in islanding mode, the active and reactive power of all distributed generators (DGs), including energy storage (battery), diesel generator, and micro-turbine, are controlled. The micro-turbine generator is connected to MG through a three-phase to three-phase matrix converter, and the droop control method is applied for controlling the voltage and frequency of MG. In addition, a method is introduced for voltage and frequency control of micro-turbines in the transition state from grid-connected mode to islanding mode. A novel switching strategy of the matrix converter is used for converting the high-frequency output voltage of the micro-turbine to the grid-side frequency of the utility system. Moreover, using the switching strategy, the low-order harmonics in the output current and voltage are not produced, and consequently, the size of the output filter would be reduced. In fact, the suggested control strategy is load-independent and has no frequency conversion restrictions. The proposed approach for voltage and frequency regulation demonstrates exceptional performance and favorable response across various load alteration scenarios. The suggested strategy is examined in several scenarios in the MG test systems, and the simulation results are addressed.
Research design and production of ambient atmosphere monitoring control system internet of things technology application
Authors: Chuyen Tran Duc , Hoa Doan Van
Published: April 2024

In this paper, presents solutions to application internet of things (IoT) technology in the field of high-tech agriculture, livestock in Vietnam is currently a new problem. The system includes an application on a smartphone; access the parameters via the web, on the computer. The product has a monitoring function: the ambient atmosphere indicators of the pig farm (temperature, humidity, CO2, NH3, H2S gas, and Biogas pressure), on the website 24/24 hour and control automatic control monitoring system. This is a problem of researching, designing and manufacturing products to automatically control the quality of the ambient atmosphere to improve productivity and quality of pig raising in practice at Bach Khoa Production, Trade and Service Joint Stock Company Bach Phuong (Address: Vinh Hao Commune, Vu Ban District, Nam Dinh Province, Vietnam).
Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach
Authors: Saifullah Shoffan , Dreżewski Rafał
Published: April 2024

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to precisely delineate tumor boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical image analysis and enhance healthcare outcomes. This research paves the way for future exploration and optimization of advanced CNN models in medical imaging, emphasizing addressing false positives and resource efficiency.
Hardware and software co-design for detecting hypertension from photoplethysmogram
Authors: Chowdhury Aditta , Chowdhury Mehdi Hasan , Das Diba , Ghosh Sampad , Chak Chung Cheung Ray
Published: April 2024

Hypertension is one of the leading causes of cardiovascular disease morbidity in the world. If remains untreated, it may cause severe damage like heart attack or even death. Early detection is required to prevent the development of other cardiac abnormalities. Photoplethysmogram (PPG) is a bio signal that can be obtained optically by a sensor. It is studied to monitor the change of volume of blood and detect heart conditions. Previous studies have already applied PPG to detect hypertension at the software level. In this article, along with software-based detection, we have implemented a digital hardware-based system for detecting hypertension from signals recorded using PPG sensor. Xilinx ZedBoard Zynq-7000 field programmable gate array (FPGA) board is utilized for designing the embedded system. The hypertension detection accuracy is 98.02% at the software level while for the digital system, it is 96.05% consuming 0.374 W power. The study can be analyzed for other cardiac disease detection and medical equipment development.
Improved Vigenere approach incorporating pseudorandom affine functions for encrypting color images
Authors: El Bourakkadi Hamid , Chemlal Abdelhakim , Tabti Hassan , Kattass Mourad , Jarjar Abdellatif , Benazzi Abdelhamid
Published: April 2024

This article presents an improvement to the traditional Vigenere encryption method, specifically adapted for the encryption of color images. This enhancement relies on the use of two chaotic maps widely employed in the field of cryptography. After vectorizing the original image and calculating the initialization value, which alters the seeding pixel to trigger the encryption process, our approach integrates two new large substitution tables. These tables are linked to confusion and diffusion functions, incorporating multiple reversible pseudo-random affine functions at the pixel level. Finally, a global permutation is applied to the entire resulting vector to increase the temporal complexity of potential attacks on our system. Simulations conducted on a diverse set of images of various sizes and formats demonstrate the resilience of our approach against any unexpected attacks.
A novel slotted antenna design for future Terahertz applications
Authors: Youssef Amraoui , Halkhams Imane , El Alami Rachid , Ouazzani Jamil Mohammed , Qjidaa Hassan
Published: April 2024

A slotted patch antenna operating at 118 GHz is proposed to address challenges in the terahertz (THz) frequency band for wireless communication systems. The antenna design, utilizing a Rogers RO3003 substrate, which has a dielectric constant of 3 and tan 0.001, strategically incorporates slots to enhance key performance parameters. Copper is employed for the ground and radiating patch, and a microstrip feeding method powers the antenna. High frequency structure simulator (HFSS) software is used for design and simulation, revealing resonance at 0.118 THz with a reflection coefficient of -42.41 dB and an impedance bandwidth of 4.42 GHz (115.84–120.26 GHz). At the operating frequency, the antenna exhibits a gain of 7.36 dB, maximum directivity of 7.38 dB, the voltage standing wave ratio (VSWR) of 1.01, and 99.75% radiation efficiency, all within a compact size of 1.5×1.3×0.1 mm³. The suggested antenna outperforms recent counterparts, making it suitable for applications like security screening and wireless communication systems (5G). Future efforts will target bandwidth expansion, gain enhancement, and further size reduction to enhance overall performance.
Optimized decoder for low-density parity check codes based on genetic algorithms
Authors: El Ouakili Hajar , El Ghzaoui Mohammed , El Alami Rachid
Published: April 2024

Low-density parity check (LDPC) codes, are a family of error-correcting codes, their performances close to the Shannon limit make them very attractive solutions for digital communication systems. There are several algorithms for decoding LDPC codes that show great diversity in terms of performance related to error correction. Also, very recently, many research papers involved the genetic algorithm (GA) in coding theory, in particular, in the decoding linear block codes case, which has heavily contributed to reducing the bit error rate (BER). In this paper, an efficient method based on the GA is proposed and it is used to improve the power of correction in terms of BER and the frame error rate (FER) of LDPC codes. Subsequently, the proposed algorithm can independently decide the most suitable moment to stop the decoding process, moreover, it does not require channel information (CSI) making it adaptable for all types of channels with different noise or intensity. The simulations show that the proposed algorithm is more efficient in terms of BER compared to other LDPC code decoders.
Medium access control protocol based on time division multiple access scheme for wireless body area network
Authors: Haszerila Wan Hassan Wan , Mohd Ali Darmawaty , Mohd Sultan Juwita , Kassim Murizah
Published: April 2024

In recent years, the demand for wireless body area network (WBAN) technology has increased, driven by advancements in medical and healthcare applications. WBAN consists of small, low-power, and heterogeneous sensor devices attached inside or outside the body for continuous health monitoring. Medium access control (MAC) is pivotal in addressing WBAN challenges by ensuring reliability and energy efficiency under a dynamic environment caused by body movement. Therefore, to tackle these challenges, this paper presents a MAC protocol based on time division multiple access (TDMA) to enhance the WBAN performance. The proposed TDMA-MAC protocol employs a one-periodic scheduled-based access method to provide reliable data transmission while satisfying the WBAN requirements. The proposed protocol is compared to the IEEE 802.15.6 MAC, enhanced packet scheduling algorithm MAC (EPSA-MAC), and concurrent MAC (C-MAC) protocols based on the performance metrics of packet delivery ratio (PDR), network throughput, energy consumption, and average delay. The simulation results show that the TDMA-MAC protocol outperforms its competitors as it could achieve up to 98% PDR, 30% enhanced throughput, 30% energy optimization, and 20% improvement in average delay.
Peak-to-average power ratio minimization and complexity reduction in MIMO-OFDM systems using spatial circular shifting and temporal interleaving method
Authors: Ramadevi Dubala , Trinatha Rao Polipalli
Published: April 2024

Multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) technological support for the simultaneous and frequent access by a large number of users to radio resources. For 5G cellular systems, this exhaust is not enough to provide physical layer services. An appropriate Peak-to-average power ratio (PAPR) minimization principle, which maximizes data capacity and channel utility, has been used to address this issue. In this paper, mainly focus on minimize the high PAPR of the candidate sequence of the OFDM sub-block using modified enhancement asymmetric arithmetic coding scheme (M-EAAC). According to this, circular shifting of the candidate sequence is established in the spatial circular shifting and temporal interleaving (SCS-TI) form to generated different set of conjugated phases which is multiplied with candidate sequence. Then, the transmitting antenna is identified the best lowest PAPR of the candidate sequence is chosen for entire OFDM data transmission. The simulation results conveys that the proposed SCS-TI method provide acceptable improvement in the PAPR reduction as compared with conventional selective mapping(SLM)and pseudo-random SLM(PR-SLM). Moreover, the complexity evaluation which ensure the proposed method provides better improvement at three important stages includes inverse fast Fourier transform (IFFT) operation, optimization process, and PAPR calculation at each candidate sequence.
Framework for smart contract blockchain in halal traceability, integrity, and transparency
Authors: Munawar Munawar , Mugiono Arif
Published: April 2024

Currently, the halalness of a product may only be determined by the halal certificate or halal label displayed on the product packaging. Simply put, several halal-related issues, like cross-contamination, logistical issues, halal counterfeiting, global halal acceptance, and so forth, keep coming up. To address the numerous issues raised above, it is believed that a framework that can support worldwide halal provisions is necessary. The objective of this research is to create a blockchain smart contract framework for halal transparency, integrity, and traceability. Blockchain technology along with smart contracts can solve classic tracing problems including data leakage, manipulation, and invisible data. Organizational consistency across the halal production process and the availability of raw materials that adhere to sharia laws are two more basic challenges with halal certification that blockchain and smart contracts can resolve. The usual traceability problems, such as data explosion, material cross-contamination between halal and haram, and revelation of private information, can all be resolved by smart contracts that make use of on-chain, off-chain, and electronic product code information services (EPCIS).
Study of the characteristics of broadband matching antennas for fifth-generation mobile communications based on new composite materials
Authors: Nakisbekova Balausa , Yerzhan Assel , Boykachev Pavel , Manbetova Zhanat , Imankul Manat , Shener Anar , Yermekbaev Muratbek , Dunayev Pavel
Published: April 2024

The presented research aims to analyze in detail the characteristics of broadband matching antennas specifically designed for 5G mobile communications applications, with an emphasis on innovative composite materials. The study focuses on a compact planar loop antenna designed for use on smartphones, covering the LTE/WWAN frequency bands 824 to 960 MHz, 1,710 to 2,690 MHz, and 3,300 to 3,600 MHz for full coverage of modern 5G networks. Experimental and numerical methods are used to broadly analyze the frequency range associated with 5G networks. The features of the use of composite materials in the implementation of antenna devices in 5G technologies are noted. A broadband matching circuit (BMC) with elements with lumped parameters and a reduced sensitivity invariant has been synthesized. A 3D model of the adaptive selective surface controller (SSC) was developed using CST Studio. The study results highlight the benefits of new composite materials in improving the performance of 5G antennas. This research makes a significant contribution to the development of 5G technologies by optimizing antenna design for efficient data transmission in modern mobile networks and can be a valuable resource for engineers and designers working in this field.
Performance evaluation of low energy adaptive clustering hierarchy-based cluster routing protocols in wireless sensor networks using a new graphical user interface
Authors: Daanoune Ikram , Baghdad Abdennaceur
Published: April 2024

Wireless sensor network (WSN) is widely used for field data acquisition and monitoring in different domains. To make this type of network functional, efficient routing protocols must be implemented. Nevertheless, WSNs have an energy constraint due to limited batteries. Many clustered protocols are proposed to overcome it. However, the implementation of these protocols would be difficult to understand without a simulation tool, as some problems may arise during their development. Testing real-world applications requires a lot of effort and cost because they often use many nodes in large networks. Therefore, the simulation tool is the most relative way to evaluate these protocols. This paper presents graphical-based cluster protocols simulation interface for WSN(GCPS-WSN), a new interface to simulate some clustered protocols in WSNs. GCPS-WSN allows the user to evaluate the performance of certain low energy adaptive clustering hierarchy (LEACH) enhanced protocols to choose the most appropriate one for his system. The user can simulate protocols without any knowledge of software programming.
Decoding sarcasm: unveiling nuances in newspaper headlines
Authors: D Suma , M Raviraja Holla , M Darshan Holla
Published: April 2024

This study navigates the intricate landscape of sarcasm detection within the condensed confines of newspaper titles, addressing the nuanced challenge of decoding layered meanings. Leveraging natural language processing (NLP) techniques, we explore the efficacy of various machine learning models—linear regression, support vector machines (SVM), random forest, na¨ıve Bayes multinomial, and gaussian na¨ıve Bayes—tailored for sarcasm detection. Our investigation aims to provide insights into sarcasm within the succinct framework of newspaper titles, offering a comparative analysis of the selected models. We highlight the varied strengths and weaknesses of these models. Random forest exhibits superior performance, achieving a remarkable 94% accuracy in accurately identifying sarcasm in text. It is closely trailed by SVM with 90% accuracy and logistic regression with 83% accuracy.
Determinant factors of mobile investment app users among generation Z Indonesia
Authors: Hanif Hidjra , Nadlifatin Reny , Hutama Rizal Risnanda , Ali Achmad Holil Noor , Persada Satria Fadil
Published: April 2024

Generation Z, alternatively referred to as the digital native generation, is distinguished by its profound immersion in technological progress. This study elucidates the determinants of generation Z's technological improvement in mobile investing application usage (MIA). As the instrument for factors analysis, the modified unified theory of acceptance and use of technology-2 (UTAUT-2) technique was implemented. The presented hypotheses were validated through the application of structural equation modeling (SEM) to the data acquired from 280 respondents via online questionnaires. The research revealed that trust, habit, performance expectation, and perceived risk had a substantial impact on the behavioral intention of Generation Z to utilize MIA. Furthermore, actual usage behavior is notably influenced by habit and behavioral intention, whereas gender acts as a substantial moderator in relation to performance expectancy and price value variables.
Holdout based blending approaches for improved satellite image classification
Authors: Kumar Musali Suresh , Janthakal Rajeshwari , Rajasekhar Nuvvusetty
Published: April 2024

An essential component of remote sensing, image analysis, and pattern recognition is image categorization. The classification of land use using remotely sensed data creates a map-like representation as the final form of the investigation. With its ability to effectively categorize satellite images, machine learning (ML) algorithms have gained significant traction in a number of fields, including land-use planning, disaster response, and natural resource management. Ensemble learning is also a widely used technique for enhancing the precision of satellite image categorization, which combines multiple models to get more precise predictions. Holdout is an ensemble technique, where multiple ML algorithms are used for training on the same dataset. The primary goal of this study is to create a holdout model for classifying satellite images. Initially, this study explores the usage of ML algorithms namely support vector machines (SVM), k-nearest neighbor (KNN), decision trees (DT), gradient boosting classifier (GBC), histogram-based GBC (HGBC), random forest classifier (RF), bagging classifier (BC), XGBoost classifier for classifying satellite images. Later, GBC, HGBC, RF, BC, and XGBoost are combined to build a stacking model. The bagging ensemble model outperforms all other methods and reaches an accuracy of 88.90%. Finally, blending models with holdout approach were developed and achieved accuracy of 93.70%, 94.14%, and 93.87% which outperformed all previous algorithms.
Feature selection techniques and classification algorithms for student performance classification: a review
Authors: Alias Muhamad Aqif Hadi , Hambali Najidah , Abdul Aziz Mohd Azri , Taib Mohd Nasir , Jailani Rozita
Published: April 2024

The process of categorizing students’ performance based on input data, encompassing demographic information and final exam results, is recognized as student performance classification. Educational data mining has gained traction in assessing students’ performance. However, this study entails the need to analyze the diverse attributes of students’ information within an educational institution by using data mining techniques. This study thoroughly examines both previous and current methodologies presented by researchers, addressing two main aspects: data preprocessing and classification algorithms applied in student performance classification. Data preprocessing specifically delves into the exploration of feature selection techniques, encompassing three types of feature selection and search methods. These techniques aim to identify the most significant features, eliminate unnecessary ones, and reduce data dimensionality. In addition, classification algorithms play a crucial role in categorizing or predicting student performance. Models such as k-nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANN), and linear models (LR) were scrutinized based on their performance in prior research. Ultimately, this study highlights the potential for further exploration of feature selection techniques like information gain, Chi-square, and sequential selection, particularly when applied to new datasets such as students’ online learning activities, utilizing a variety of classification algorithms.
Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network
Authors: Budiarsa Rahmat , Wardoyo Retantyo , Musdholifah Aina
Published: April 2024

Face recognition entails detecting and identifying facial attributes. Mask region convolutional neural network (R-CNN) method is a prominent approach, while prior research predominantly delved into refining loss functions and perfecting object and face detection, recognizing, and identifying faces using imperfect data remained relatively unexplored. This study focuses on an occluded dataset comprising Indonesian faces, wherein 'occluded' denotes facial data that lacks complete visibility-encompassing instances where objects obscure faces or are partially cropped. This investigation involves a deliberate experiment that tailors the intersection over union (IoU) of the region proposal network (RPN) to suit the nuances of occluded Indonesian faces, thereby augmenting accuracy in recognition and segmentation tasks. The innovation IoU in the strategic utilization of Anchors, which involves the exclusion of anchors falling beyond the image borders to optimize computational efficiency. The outcomes of this research are striking; it showcases a remarkable 14.75%, 10.9%, and 12.97% surge based on mean average precision (mAP), mean average recall (mAR), and F1-Scores compared to the conventional Mask R-CNN approach. Notably, our proposed model elevates the average accuracy by 10% to 15% and decreases running time by 21%, a noteworthy enhancement compared to the preceding model. This progress is substantiated by validation utilizing 300 instances dataset, reinforcing the robustness of our approach.
Fine tuning attribute weighted naïve Bayes model for detecting anxiety disorder levels of online gamers
Authors: Latubessy Anastasya , Wardoyo Retantyo , Musdholifah Aina , Kusrohmaniah Sri
Published: April 2024

This research applies the fine tuning attribute weighted naïve Bayes (FTAWNB) model using ordinal data. It is known that in previous research, the FTAWNB model outperformed its competitors on the dataset used. However, the FTAWNB model has not been applied in the mental health domain that uses ordinal data. Therefore, this research used the anxiety gamers dataset to test the fine-tuning attribute weighted Naïve Bayes (FTAWNB) model. Anxiety disorders are mental health disorders that can indicate the emergence of a gaming disorder. Gamers can experience anxiety disorders classified into four classes, namely minimal, mild, moderate, and severe anxiety. Then compare the results by FTAWNB obtained with three other naïve Bayes algorithms, namely Gaussian naïve Bayes, multinomial naïve Bayes, and categorical naïve Bayes, using the same dataset. Model performance is measured based on accuracy, precision, recall, and processing time. The test results show that the FTAWNB outperforms the other three models' accuracy, precision, and recall, with an accuracy value of 99.22%. While the accuracy of Gaussian NB is 91.132%, Categorical is 91.592%, and multinomial naïve Bayes is 61.104%. However, the FTAWNB takes slightly longer than the other three models' processing time. The FTAWNB takes 0.07 seconds to build the model and 0.05 seconds to test the model on training data.
A multimodal machine learning approach to generate news articles from geo-tagged images
Authors: Gotmare Abhay , Thite Gandharva , Bewoor Laxmi
Published: April 2024

Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by leveraging cutting-edge developments in machine learning, image captioning, and advanced text generation technologies. Employing a multimodal approach that integrates machine learning and transformer algorithms, such as visual geometry group network16 (VGGNet16), convolutional neural network (CNN) and a long short-term memory (LSTM) based system, the algorithm initiates by extracting the location from exchangeable image file format (Exif) data from the image. The features are extracted from the image and corresponding news headline is generated. The headlines are used for generating a comprehensive article with contemporary large language model (LLM). Further, the algorithm generates the news article big-science large open-science open-access multilingual language model (BLOOM). The algorithm was tested on real time photographs as well as images from the internet. In both the cases the news articles generated were validated with ROUGE and BULE score. The proposed work is found to be successful attempt in journalism field.
Bi-directional DC-AC using BLDC motor for electric and hybrid electric vehicles applications with reduced number of switches
Authors: Arokianathan Nishalini Delcy , Josh Francis Thomas , Brindha Evelyn , Joseph Jeyaraj Jency , Das Raman Mohan , Kannadhasan Suriyan
Published: April 2024

Electric scooters often have a single design and are difficult to produce at a greater level than other forms of transportation. A possible cut electric scooter is built with the optimization design of the vehicle combustible wealth and the pollution characteristics in order to arrange to a correct solution by resolving each and every challenge. The engines' electrical systems are comprised of lead acid batteries. For the sake of a straightforward simulation procedure, the engine operates in two states ON and OFF while the vehicle's speed is regulated in three ranges roughly corresponding to high, medium, and low. The planetary speed ratio and the final drive speed ratio are gathered in the computation method's general outline. Utilizing simulation MATLAB, the findings of optimization strategies are resolved. This technique improves the fuel plenty and discharge quality of the possibility cut electric automobiles.
High-reliability uninterruptible power supply manager for critical virtualization cluster
Authors: Piumatti Davide , Galletto Andrea , Castagno Flavio
Published: April 2024

Information technology servers are complex, delicate, and expensive systems; power grid interruption is one of the possible causes that can lead to hardware damage or logical disk structure damage, with severe consequences. Moreover, a power grid drop causes a sudden interruption of the services managed by a server. Uninterruptible power supplies (UPS) provide backup power when the regular power grid source drops. UPSs can only maintain a server for a short time. Therefore, it is necessary to introduce a blackout manager who can correctly shut down the server activities. Moreover, the manager must be able to restore the servers' status when the electrical grid power returns to its normal state. This paper proposes a possible UPS power manager able to manage servers during a prolonged electrical blackout. The UPS power manager identifies the blackout and keeps the servers safe by saving their state and shutting them down properly. Following the power grid restoration, the UPS power manager restarts the servers and restores their state. The proposed approach has been evaluated on a virtualization cluster used for critical activities at the Politecnico di Torino.
Primary side control technique for capacitive power transfer system without any wireless feedback
Authors: Nutwong Supapong , Hatchavanich Nattapong , Sangswang Anawach , Mujjalinvimut Ekkachai , Konghirun Mongkol
Published: April 2024

The output voltage of capacitive power transfer (CPT) system will change if the load resistance is varied. This paper presents a method to regulate the output voltage using a controller that is located on the primary side, known as the primary side control technique. It does not require any additional components and wireless feedback, which lowers the cost and complexity of CPT system compared to the conventional control technique. Instead of directly measuring the output voltage on secondary side, it is estimated through the measured capacitor voltage on primary side. Modified sine wave control of the full-bridge inverter is adopted to regulate the output voltage. The proposed control technique is validated by the simulation via PSIM software using the practical parameters of capacitive coupler presented in the literature. Simulation results of the output voltage control against the step change in desired output voltage and load resistance indicate the performance of proposed control technique.
The optimization of a GaN-based current aperture vertical electron transistor
Authors: Hadjem Dalila , Kourdi Zakarya , Kerai Salim
Published: April 2024

The main objective of this paper is to simulate and optimize a current aperture vertical electron transistor (CAVET) based on gallium nitride (GaN), which combines both a two-dimensional electron gas (2DEG) and a vertical structure using the SILVACO-TCAD simulator. The dimensions of the structure were reduced by 45% to minimize the size and improve the performances of the proposed device; also, a part of aluminum nitride (AlN)was added to the current blocking layer (CBL) to modulate the conduction band profile. The results obtained from the simulation of our structure demonstrated a maximum drain current of 1.8 A/mm, Pinch-off voltage (VP) of -6 V, drain induced barrier lowering (DIBL) of 166 mV/V, maximum transconductance (gm) of 570 mS/mm, gate-leakage of 7.10-7 A, cut-off frequency (ft) of 200 GHz, maximum oscillation frequency (fMax) of 400 GHz. The proposed device exhibited outstanding performance while consuming low power, making it well-suited for use as a low-noise amplifier (LNA) in satellite reception applications.
Increase the operational reliability of the electric drive of the weaving machine
Authors: Yulbarsovich Usmonov Shukurillo , Ugli Sultonov Ruzimatjon Anvarjon , Ugli Mamadaliev Musulmonkul Imomali , Saleem Adeel , Toptiyevna Kuchkarova Dilnoza
Published: April 2024

The main purpose of this research work is to analyze malfunctions, power consumption, engine overheating and vibrations based on the loadings of electrical circuits through artificial neural networks. The reliability of artificial intelligence systems was proven on the basis of a model-based system depending on the task, and the obtained values were experimentally compared in the electrical operation of existing equipment in general industrial enterprises. An imitation model of the real object was developed. A concept of increasing productivity was set up to identify malfunctions, in contrast to the existing annular method. The article developed an algorithm for increasing the operational reliability of the electrical operation of the weaving machine on the basis of integrated indicators of excitation to determine the probability of failure of electrical operation. The article proposes the possibility of directly processing the diagnostic energy parameters through artificial neural networks. An experimental combination of signals resulted in a model based on input power and torque, and was based on an asynchronous motorized electrical circuit. It has been proven that intellectual reliability can be increased by 3-5% compared to operational reliability in traditional methods.
An innovative dry-lab test rig for mechanical-hydraulic power take-off of wave energy conversion system
Authors: Jusoh Mohd Afifi , Yusop Zulkifli Mohd , Albani Aliashim , Daud Muhamad Zalani , Ibrahim Mohd Zamri
Published: April 2024

A dry-lab test rig is a powerful means to reduce costs in the design process of a wave energy conversion system (WECs). A dry-lab test rig technique allows the use of real components inside a simulation of a mathematical model. This paper presents the development of an innovative dry-lab test rig for the mechanicalhydraulic power take-off (MHPTO) unit of WECs. The development of a drylab test rig of MHPTO involves several processes, such as three-dimension (3D) modelling, component purchasing, structure fabrication, component installation, and operational testing. The developed dry-lab test rig consists of two main parts, such as the simulated wave emulator plant and the real MHPTO unit plant. The simulated wave emulator plant was developed in this test rig to replicate the interaction motion between the ocean wave motion and the wave absorber device. The developed dry-lab test rig was tested using five different irregular wave input conditions to ensure it could perform under the five different wave input conditions. The overall results demonstrate that the developed dry-lab test rig was successfully performed in all sea states. From the results, the profile of electrical power produced by the real MHPTO unit can be clearly obtained in each sea state.
Optimizing photovoltaic systems performance under partial shading using an advanced cuckoo search algorithm
Authors: Benlafkih Abdessamad , El Moujahid Yassine , Hadjoudja Abdelkader , El Harfaoui Nadia , Said El-Bot , El Idrissi Mohamed Chafik
Published: April 2024

Partial shading negatively impacts power output in photovoltaic systems (PVs), causing multiple local maximum power points (LMPP) instead of a single global maximum power point (GMPP). The cuckoo search (CS) technique utilizes the maximum power point tracking (MPPT) technique to extract the global maximum power (GMP) from shaded PVs. CS is a metaheuristic technique that has gained widespread recognition. Moreover, the CS algorithm is associated with several challenges, including a failure rate, long response time, and noticeable oscillations during steady-state operation. To address these limitations, our proposed advanced cuckoo search (ACS) algorithm is designed to overcome the shortcomings of the standard CS algorithm. The algorithm iteratively evaluates individual solar panels and collectively explores the solution space using levy flight operations. Persistent variables are used to store and track the current state and previous iterations. Where the duty cycles of the solar panels are optimally set to enhance the overall power generation efficiency. We also evaluate and analyze the results obtained from the performance of our proposed technique and compare them to the performance of the four most recent CS optimization techniques. for all test cases, the tracking efficiency was improved to 99.98% with a fast-settling time of <44 ms.
Performance of grid-connected photovoltaic systems in Northern and Southern Hemispheres under equatorial climate
Authors: Abdul Rahim Yang Ilya Akila , Zainuddin Hedzlin , Setiawan Eko Adhi , Madsuha Alfian Ferdiansyah , Hussin Mohamad Zhafran , Sulaiman Shahril Irwan , Ibrahim Siti Nor Nadhirah
Published: April 2024

This work studied the actual and simulated technical performance between two grid-connected photovoltaic (GCPV) systems representing opposite latitudes. The system with a capacity of 5.4 kWp installed in Kelantan, Malaysia represents the northern equator, and the 183.6 kWp system installed in Cikarang, Indonesia, denotes the southern equator. The performance was simulated using PVsyst software, which included the energy output (E_outt), reference yield (Y_r), final yield 〖(Y〗_f), performance ratio (PR), and capacity factor (CF). The mean bias error (MBE) between the actual and simulated technical performance were as follows; for system A, the yearly MBE for the E_out, Y_r, Y_f, PR, and CF were -0.4%, 17.1%, -1.4%, -15.8%, and 1.4%, respectively, and for system B, the E_out, Y_r, 〖 Y〗_f, PR, and CF values were 9.80%, 18.3%, 10.0%, -7.2%, and 10.0% respectively. The results have proven that PVsyst has successfully simulated the yearly E_out, 〖 Y〗_f and CF for both systems including PR, for system B, with MBE less than 10%. However, it is noteworthy to highlight that PVsyst significantly overestimated the Y_r of both systems up to 18.3% and conversely underestimated the PR for system A by 15.8%, which highly likely caused by the Meteonorm imported weather data.
Comparing fuzzy logic and backstepping control for a buck boost converter in electric vehicles
Authors: Ennajih Elmehdi , Allali Hakim , Jarmouni Ezzitouni , Ennaoui Ilias
Published: April 2024

Significant advances have been made in the control of DC/DC converters, owing to the effective combination of linear and nonlinear approaches. The above-mentioned approaches have greatly improved the efficiency as well as stability of the converter, even when faced with changing conditions. Nevertheless, the emergence of artificial intelligence has introduced new perspectives in the domain. The objective of the article is to examine two separate methodologies for controlling a boost-buck converter: using a nonlinear approach, especially backstepping, and utilizing artificial intelligence through fuzzy logic. The main aim of this work is to illustrate the inherent stability and robustness of fuzzy logic controllers compared to backstepping control in managing effectively various variations and challenges encountered in converter control.
Performance evaluation of bridgeless isolated SEPIC-Luo converter for EV battery charging using PI and ANN controller
Authors: Dhandapani Meena , Ravichandran Padmathilagam , Shanmugam Arulvizhi , Pachaivanan Nammalvar
Published: April 2024

Electric vehicle (EV) rechargeable battery packs that employ traditional power factor correction (PFC) circuit design have performance limitations due to their substantial conductivity loss that ensures at the input of a diode bridge rectifier (DBR). This study suggests a bridgeless (BL) isolated single ended primary inductance converter (SEPIC) - Luo converter to address the problem. As a result, the input current exhibits a power factor operation of unity throughout the charging process. DBR elimination and current conduction through a remarkably small number of circuits both significantly reduce conduction losses. The use of an artificial neural network (ANN) and proportional integral (PI) controller enhances the converter's performance with a stable DC link voltage. The suggested converter overall operation is thoroughly described in terms of variety of operating modes and simulation-based effectiveness. Here, with the assistance of the hysteresis current controller (HCC), the input current disruptions are reduced. Constant current and voltage management is used to successfully charge the EV battery, resulting in improved efficacy and inherent PFC. By utilizing simulation outcomes achieved from MATLAB, the performance of proposed BL isolated SEPIC-Luo in boosting the power quality of EV charger system is examined.
Multicarrier PWM techniques for three phase modular multilevel converter application in MRI systems
Authors: Devendiren Shobana , Babu Samuel Rajesh
Published: April 2024

This research paper presents about the performance analysis of three phase modular multilevel converter with level shifted (phase disposition pulse width modulation) and phase shifted pulse width modulation (PWM) method. This research not only discusses the significance of different modulation procedures in the context of modular multilevel converters but also carefully evaluates each of their unique performance characteristics. This research is based on the fundamental creation of pulses and patterns that can only be acquired by comparing sinusoidal reference signals with triangular carrier signal. Both levels shifted (phase disposition) and phase shifted PWM procedures are used to calculate the phase output voltage of a converter at 2N+1 level. The output waveform is crucially shaped by these modulation techniques, which also ensure effective energy conversion and lower harmonic content. On the basis of the output voltage waveform's quality, the comparative research is conducted and simulated results were presented.
“Fico triste por você, que prefere me seguir ao invés de me acompanhar...”
Authors: Franco Caius Marcellus Cortese
Published: April 2024

Sem resumo.
Cenografias de Jorge O. Caron
Authors: Ruggiero Amanda Saba , Andrade Carlos Roberto Monteiro de
Published: April 2024

Sem resumo.
Glycoursodeoxycholic Acid Alleviates Arterial Thrombosis via Suppressing Diacylglycerol Kinases Activity in Platelet
Authors: Yang Wenchao , Feng Ruijia , Peng Guiyan , Wang Zhecun , Cen Meifeng , Jing Yexiang , Feng Weiqi , Long Ting , Liu Yunchong , Li Zilun , Huang Kan , Chang Guangqi
Published: April 2024

BACKGROUND:

Glycoursodeoxycholic acid (GUDCA) has been acknowledged for its ability to regulate lipid homeostasis and provide benefits for various metabolic disorders. However, the impact of GUDCA on arterial thrombotic events remains unexplored. The objective of this study is to examine the effects of GUDCA on thrombogenesis and elucidate its underlying mechanisms.

METHODS:

Plasma samples from patients with arterial thrombotic events and diet-induced obese mice were collected to determine the GUDCA concentrations using mass spectrometry. Multiple in vivo murine thrombosis models and in vitro platelet functional assays were conducted to comprehensively evaluate the antithrombotic effects of GUDCA. Moreover, lipidomic analysis was performed to identify the alterations of intraplatelet lipid components following GUDCA treatment.

RESULTS:

Plasma GUDCA level was significantly decreased in patients with arterial thrombotic events and negatively correlated with thrombotic propensity in diet-induced obese mice. GUDCA exhibited prominent suppressing effects on platelet reactivity as evidenced by the attenuation of platelet activation, secretion, aggregation, spreading, and retraction (
P
<0.05). In vivo, GUDCA administration robustly alleviated thrombogenesis (
P
<0.05) without affecting hemostasis. Mechanistically, GUDCA inhibited DGK (diacylglycerol kinase) activity, leading to the downregulation of the phosphatidic acid–mediated signaling pathway. Conversely, phosphatidic acid supplementation was sufficient to abolish the antithrombotic effects of GUDCA. More importantly, long-term oral administration of GUDCA normalized the enhanced DGK activity, thereby remarkably alleviating the platelet hyperreactivity as well as the heightened thrombotic tendency in diet-induced obese mice (
P
<0.05).

CONCLUSIONS:

Our study implicated that GUDCA reduces platelet hyperreactivity and improves thrombotic propensity by inhibiting DGKs activity, which is a potentially effective prophylactic approach and promising therapeutic agent for arterial thrombotic events.
Iron Deficiency, Anemia, and Iron Supplementation in Patients With Heart Failure: A Population-Level Study
Authors: Wahid Muizz , Islam Sunjidatul , Sepehrvand Nariman , Dover Douglas C. , McAlister Finlay A. , Kaul Padma , Ezekowitz Justin A.
Published: April 2024

BACKGROUND:

Studies have shown an association between iron deficiency (ID) and clinical outcomes in patients with heart failure (HF), irrespective of the presence of ID anemia (IDA). The current study used population-level data from a large, single-payer health care system in Canada to investigate the epidemiology of ID and IDA in patients with acute HF and those with chronic HF, and the iron supplementation practices in these settings.

METHODS:

All adult patients with HF in Alberta between 2012 and 2019 were identified and categorized as acute or chronic HF. HF subtypes were determined through echocardiography data, and ID (serum ferritin concentration <100 μg/L, or ferritin concentration between 100 and 300 μg/L along with transferrin saturation <20%), and IDA through laboratory data. Broad eligibility for 3 clinical trials (AFFIRM-AHF [Study to Compare Ferric Carboxymaltose With Placebo in Patients With Acute HF and ID], IRONMAN [Intravenous Iron Treatment in Patients With Heart Failure and Iron Deficiency], and HEART-FID [Randomized Placebocontrolled Trial of Ferric Carboxymaltose as Treatment for HF With ID]) was determined.

RESULTS:

Among the 17 463 patients with acute HF, 38.5% had iron studies tested within 30 days post-index-HF episode (and 34.2% of the 11 320 patients with chronic HF). Among tested patients, 72.6% of the acute HF and 73.9% of the chronic HF were iron-deficient, and 51.4% and 49.0% had IDA, respectively. Iron therapy was provided to 41.8% and 40.5% of patients with IDA and acute or chronic HF, respectively. Of ID patients without anemia, 19.9% and 21.7% were prescribed iron therapy. The most common type of iron therapy was oral (28.1% of patients). Approximately half of the cohort was eligible for each of the AFFIRM-AHF, intravenous iron treatment in patients with HF and ID, and HEART-FID trials.

CONCLUSIONS:

Current practices for investigating and treating ID in patients with HF do not align with existing guideline recommendations. Considering the gap in care, innovative strategies to optimize iron therapy in patients with HF are required.