bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybrid machine learning for stock price prediction in the Moroccan banking sector
Authors: Itri Bouzgarne , Mohamed Youssfi , Omar Bouattane , Latifa El Madani , Lahcen Moumoun , Adil Oualid
Published: April 2024

Analyzing historical stock market data using machine-learning techniques is crucial for data scientists and researchers to optimize stock price prediction models. This study uses machine learning regression algorithms and feature selection methods to optimize a simulated stock price prediction model using real historical data from Bank of Africa, a Moroccan bank. The approach compares multiple supervised regression algorithms, such as linear regression, extreme gradient boosting, ordinary least squared, random forest regressor, a linear least-squares L2-regularized, epsilon-support vector regression, and linear support vector regression. Each of these algorithms is associated with different feature selection algorithms to improve the performance of the prediction model. The analysis results revealed that hybridizing algorithms between the highest score percentiles, univariate linear regression, and linear support vector regression perform better according to the root mean squared error and R2-Score measures. This approach overcomes the problems associated with high-dimensional data by reducing the number of features and improving prediction accuracy.
Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques
Authors: Lestari Sri , Yulmaini Yulmaini , Aswin Aswin , Ma'ruf Singgih Yulizar , Sulyono Sulyono , Fikri Ruki Rizal Nul
Published: April 2024

Recommendation systems are often implemented in e-commerce and micro, small, and medium enterprises (MSMEs) marketplaces to improve consumer services by providing product recommendations according to their interests. However, it still faces problems, namely sparsity and cold start, thus affecting the quality of recommendations. This research proposes clustering and imputation techniques to overcome this problem. The clustering technique used is k-means, while the missing value imputation method uses average values. The imputation results are then implemented in the k-nearest neighbor (KNN) and naïve Bayes algorithms and evaluated based on performance accuracy. Experimental results show an increase in accuracy of 16.48% in the KNN algorithm from 83.52% to 100%. Meanwhile, the naïve Bayes algorithm increased accuracy by 35.30% from 64.70% to 100%.
Comprehensive review of load balancing in cloud computing system
Authors: Oyediran Mayowa O , Ojo Olufemi S. , Ajagbe Sunday Adeola , Aiyeniko Olukayode , Chima Obuzor Princewill , Adigun Matthew O
Published: April 2024

Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems.
Classification of tea leaf disease using convolutional neural network approach
Authors: Hairah Ummul , Septiarini Anindita , Puspitasari Novianti , Tejawati Andi , Hamdani Hamdani , Eka Priyatna Surya
Published: April 2024

Leaf diseases on tea plants affect the quality of tea. This issue must be overcome since preparing tea drinks requires high-quality tea leaves. Various automatic models for identifying disease in tea leaves have been developed; however, their performance is typically low since the extracted features are not selective enough. This work presents a classification model for tea leaf disease that distinguishes six leaf classes: algal spot, brown, blight, grey blight, helopeltis, red spot, and healthy. Deep learning using a convolutional neural network (CNN) builds an effective model for detecting tea leaf illness. The Kaggle public dataset contains 5,980 tea leaf images on a white background. Pre-processing was performed to reduce computing time, which involved shrinking and normalizing the image prior to augmentation. Augmentation techniques included rotation, shear, flip horizontal, and flip vertical. The CNN model was used to classify tea leaf disease using the MobileNetV2 backbone, Adam optimizer, and rectified linear unit (ReLU) activation function with 224×224 input data. The proposed model attained the highest performance, as evidenced by the accuracy value 0.9455.
Feature selection based on chi-square and ant colony optimization for multi-label classification
Authors: Widians Joan Angelina , Wardoyo Retantyo , Hartati Sri
Published: April 2024

Text classification is widely used in organizations with large databases and digital documents. In text classification, there are many features, most of which are redundant. High-dimensional features impact multi-label classification performance. Feature selection is a data processing technique that can overcome this problem. Feature selection techniques have two major approaches: filter and wrapper. This paper proposes a hybrid filter-wrapper technique combining two algorithms: Chi-square (CS) and ant colony optimization (ACO). In the first stage, CS is used to reduce the number of irrelevant features. The ACO method is in the second stage. The ACO is applied to select the efficient features and improve classifier performance. The experiment results show that CS-ACO, CS-grey wolf optimizer (GWO), CS, and without feature selection (FS) have a micro F1-score based multinomial naïve Bayes classifier including 80%, 79.75%, 79.64% and 77.78%. The result indicates that the CS-ACO algorithm is suitable for solving multi-label classification problems.
Hybrid deep learning model for YouTube spam comment detection
Authors: Sam'an Muhammad , Imaddudin Khrisna
Published: April 2024

Social media platforms, including YouTube and Facebook, allow users to interact through comments and videos. However, the openness of these platforms also makes them susceptible to spammers engaging in phishing, malware distribution, and advertisement dissemination. In response, our study introduces an innovative technique for detecting features indicative of spam within comments associated with shared videos. The initial phase involves data collection from the University of California, Irvine (UCI) machine learning repository and preprocessing using tokenization and lemmatization. Subsequently, a rigorous feature selection process is executed, and experiments are conducted with various proposed classification models. The performance evaluation demonstrates outstanding accuracy in identifying spam comments on YouTube: convolutional neural network with gated recurrent unit (CNN-GRU) at 95.92%, convolutional neural network with long short-term memory (CNN-LSTM) at 95.41%, convolutional neural network with bidirectional long short-term memory (CNN-biLSTM) at 96.43%, gated recurrent unit (GRU) at 95.41%, long short-term memory (LSTM) at 94.13%, and bidirectional long short-term memory (biLSTM) at 96.94% and convolutional neural network (CNN) at 94.64%. These results highlight the substantial contribution of our approach to spam detection and the fortification of online security.
Probability distributions in Kerala’s rainfall: implications for hydro energy planning
Authors: Baranitharan Balakrishnan , Chandran Karthik , Subramaniyan Mathan Vaithilingam , Chowdhura Subrata , Nguyen Thi Thu , Tran Duc-Tan
Published: April 2024

Heavy rainfall has consistently acted as the primary catalyst for floods, resulting in numerous casualties and significant economic losses globally. Rainfall forecasting is accomplished by analysing existing rainfall data, which is then used to analyse the hydraulic system’s features. Gaining an understanding of rainfall requirements is a crucial challenge for every location, particularly in the case of India, given its diverse geographical area, population, and other influencing factors that impact various demands. This study evaluated the rainfall data for a span of 1990-2021 in six districts of Kerala State, India. To match the rainfall data from all districts, we utilized both Kaumarasamy-distribution and Dagum-distributions. Various Probabilistic tests, were employed to comparing these distributions. The results revealed that, in Kasargod, the Kumarasamy distribution demonstrates superior goodness-of-fit with the lowest Kolmogorov-Smirnov statistic (0.0597) and Anderson-darling statistic (2.271). However, in Wayanad, Malappuram, Palakkad, Idukki, and Trivandrum, the Dagum distribution consistently exhibits the most accurate fit, evident from its lowest Kolmogorov-Smirnov statistics (0.07447, 0.05435, 0.0556, 0.03636, 0.04291) and favourable Chi-Squared statistics (19.471, 8.4907, 19.239, 5.7318, 7.5297). These results emphasize the regional variation in precipitation data and the suitability of specific distribution models for accurate representation across differentlocations.
Internet of things-based digital scale to detect stunting symptoms in babies under two years of age
Authors: Hutabarat Daniel Patricko , Wijaya Willis , Wijaya Wilbert Devin
Published: April 2024

Given the ongoing global challenge of stunting, characterized primarily by chronic underweight in infants under two years of age, a new approach leveraging digital scale and the internet of things (IoT) has been developed. This innovative system was designed to facilitate the early detection and continual monitoring of stunting symptoms caused by malnourishment. Key features include an IoT-enabled digital scale for precise weight measurement, a robust cloud platform for reliable data storage and comprehensive analysis, and an easy-to-use mobile app for user engagement. This system demonstrates its potential to simplify tracking fluctuations in baby weight and development progress related to stunting over time. Early trials demonstrated an impressive accuracy rate of 99.4% in body weight measurements and provided excellent conclusions in determining the body weight status of the infants. Overall, this IoT-based solution catalyzes the improvement of stunting detection methodologies and early intervention strategies, thus promising a better solution and a significant positive impact on global child health.
Network intrusion detection system by applying ensemble model for smart home
Authors: Amru Malothu , Jagadeesh Kannan Raju , Narasimhan Ganesh Enthrakandi , Muthumarilakshmi Surulivelu , Padmanaban Kuppan , Jeyapriya Jeyaprakash , Murugan Subbiah
Published: April 2024

The exponential advancements in recent technologies for surveillance become an important part of life. Though the internet of things (IoT) has gained more attention to develop smart infrastructure, it also provides a large attack surface for intruders. Therefore, it requires identifying the attacks as soon as possible to provide a secure environment. In this work, the network intrusion detection system, by applying the ensemble model (NIDSE) for Smart Homes is designed to identify the attacks in the smart home devices. The problem of classifying attacks is considered a classification predictive modeling using eXtreme gradient boosting (XGBoosting). It is an ensemble approach where the models are added sequentially to correct the errors until no further improvements or high performance can be made. The performance of the NIDSE is tested on the IoT network intrusion (IoT-NI) dataset. It has various types of network attacks, including host discovery, synchronized sequence number (SYN), acknowledgment (ACK), and hypertext transfer protocol (HTTP) flooding. Results from the cross-validation approach show that the XGBoosting classifier classifies the nine attacks with micro average precision of 94% and macro average precision of 85%.
Sustainability insights on learning-based approaches in precision agriculture in internet-of-things
Authors: Panduranga Kiran Muniswamy , Ranganathasharma Roopashree Hejjaji
Published: April 2024

Precision agriculture (PA) is meant to automate the complete agricultural processes with the sole target of enhanced crop yield with reduced cost of operation. However, deployment of PA in internet of things (IoT) based architecture demands solutions towards addressing various challenges where most are related to proper and precise predictive management of agricultural data. In this perspective, it is noted that learning-based approaches have made some contributory success towards addressing different variants of issues in PA; however, such methods suffer from certain loopholes, primarily related to the non-inclusion of practical constraints of IoT infrastructure in PA and lack of emphasis towards bridging the trade-off between higher accuracy and computational burden that is eventually associated with this. This paper contributes towards highlighting the strengths and weaknesses of recent learning approaches and contributes towards novel findings.
Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience
Authors: Idouglid Lahcen , Tkatek Said , Elfayq Khalid , Guezzaz Azidine
Published: April 2024

In the dynamic landscape of Industry 4.0, characterized by the integration of smart technologies and the industrial internet of things (IIoT), ensuring robust security measures is imperative. This paper explores advanced security solutions tailored for the IIoT, focusing on the integration of intrusion detection systems (IDS) with advanced machine learning (ML) and deep learning (DL) techniques. In this paper, we present a novel intrusion detection model to fortify to fortify Industry 4.0 systems against evolving cyber threats by leveraging ML an DL algorithms for dynamic adaptation. To evaluate the performances and effectiveness of our proposed model, we use the improved Coburg intrusion detection data sets (CIDDS) and BoT-IoT datasets, showcasing notable performance attributes with an exceptional 99.99% accuracy, high recall, and precision scores. The model demonstrates computational efficiency, with rapid learning and detection phases. This research contributes to advancing next-gen security solutions for Industry 4.0, offering a promising approach to tackle contemporary cyber.
Enhancing energy efficiency in tyre pressure and temperature monitoring systems
Authors: Kulkundri Praveen Uday , Desai Veena , Uday Kalkundri Ravi
Published: April 2024

This study addresses the pivotal challenge of enhancing power efficiency in tyre pressure and temperature monitoring systems (TPMS) for heavy vehicles and trailers. Employing field-programmable gate arrays (FPGA) and adaptive channel frequency hopping in bluetooth low energy (BLE) communication, the research focuses on mitigating power consumption issues specific to heavy vehicles with multiple tyres. The proposed solution incorporates strategic BLE channel blocking and adaptive frequency hopping on the FPGA platform to alleviate channel congestion and interference, ultimately reducing TPMS power consumption. The FPGA's adaptability tailors frequency hopping strategies to automotive TPMS nuances, optimizing channel selection and minimizing energy-intensive processes. Empirical results showcase a significant reduction in power consumption, with the TPMS operating at 100 MHz during active mode consuming 66 mW, dropping to 11 mW in sleep mode, and reaching 0 mW in hibernate mode for the majority of operational time. This research establishes a practical FPGA-based approach for power optimization in commercial TPMS, promising heightened reliability, safety improvements, and environmental impact reduction in the automotive sector.
Enactment of HBMMC five-level inverter based D-STATCOM using robust controllers
Authors: Ramakrishna Elluru , Jayakrishna Gadhamappagari , Peddakotla Sujatha
Published: April 2024

This paper provides the results of a concerted investigation into developing a robust voltage controller for the modular multilevel converter (MMC) used in the D-STATCOM. The simplicity of the half bridge (HB) sub-module, along with the fact that it has minimal conduction losses, led to its adoption in the MMC. For the purpose of controlling the HB MMC switches, an enhanced modulation strategy based on the phase disposition (PD) scheme has been implemented. In the presence of nonlinear load conditions, the converters are experiencing difficulties with the DC voltage balancing. The sliding mode controller (SMC) is utilized so that the DC voltage can be maintained in a balanced state. Performance characteristics including active power, reactive power, dc voltage control and THD are shown and compared. For the purpose of simulating the indicated methodology, MATLAB/Simulink is the tool of choice.
Design of a high-speed MCML D-Latch at 0.6 V in 45 nm CMOS technology
Authors: Madheswaran Sivasakthi , Panneerselvam Radhika
Published: April 2024

Metal oxide semiconductor (MOS) current mode logic (MCML) is generally preferred for high-speed circuit design. In this paper, a novel low voltage folded (LVF) MCML D-Latch is designed. The existing topologies of the MCML D-Latch consume more power and operate at 1 V. The proposed D-Latch can operate at 0.6V with better delay and power management. MCML circuits minimize delay and perform fast operations, hence it can be used in high-frequency applications. The proposed LVF MCML D–Latch is analyzed with the parameters such as power, delay, power delay product and output noise using cadence virtuoso in 45 nm complementary metal oxide semiconductor (CMOS) technology at a voltage of 0.6 V and a temperature of 27 °C. The proposed technique achieves 62.11% of power reduction, transient response speed improved by 51.23% and noise cancellation becomes 26.13% improvement over the existing circuit. It also achieves 96% of output swing which is more efficient compared to others. Finally, the parametric analysis is performed with different temperatures to verify the stability of the proposed circuit. From the simulated results, it is clear that the proposed LVF MCML D-Latch provides better performance in high-speed phase locked loop (PLL) applications.
Design and development of model for proton exchange membrane fuel cell
Authors: Aher Ankush Babaji , Patil Sudhir Madhav , Sutikno Tole
Published: April 2024

Fuel prices are rising, and fossil fuel resources are depleting. This fuel is consumed by conventional fuel vehicles, which contributes to increased greenhouse gas emissions and air pollution. Various vehicle technologies have been introduced in modern times. Among these innovations, fuel cell vehicle technology stands out due to its ease of use, robustness, and simplicity. The primary energy source for this system is hydrogen. Fuel cells use hydrogen to generate electricity through a chemical process that does not involve combustion. The purpose of this paper is to describe the design and development of a simulation model for a fuel cell (proton exchange membrane type) vehicle using mathematical equations and the MATLAB/Simulink R2020a software, taking into account all voltage losses and temperature variations of the proton exchange membrane (PEM) type fuel cell. The designed model is validated in two ways: first by generating V-I characteristics for the designed PEM-type fuel cell, and then by performing a performance test in Simulink using the driving cycle on one of the fuel cell vehicles. The obtained simulated results are nearly identical to the expected results, and the designed PEM-type fuel cell performed well in all real-time driving cycle test conditions.
Fast synchronization with enhanced switching control for grid-tied single-phase square wave inverter using FPGA
Authors: Sithananthan Tharnisha , Bakar Afarulrazi Abu , Sannasy Balarajan , Utomo Wahyu Mulyo , Taufik Taufik
Published: April 2024

Research on grid synchronization has been conducted worldwide by researchers in conjunction with the development of innovative technologies, such as dedicated short-range communication (DSRC) and cellular vehicle-to-everything (C-V2X). However, grid-connected inverters face several challenges, mainly the mismatch in voltage amplitude, frequency, and phase angle, as well as grid voltage disturbance and grid faults. Thus, the control algorithm of this research mainly focused on a half-cycle algorithm to design an enhanced digital switching control for fast synchronization using an FPGA. The control algorithm was developed based on zero-crossing detection (ZCD) and digital phase-locked loop (PLL) modeling techniques using the hardware description language (HDL) and a combination of digital logic blocks in Quartus II software, where the proposed switching was applied using the square-wave switching technique through a 300-watt full-bridge experimental prototype. The performance of the proposed technique was studied, where the total harmonic distortion (THD) for voltage and current resulted in a percentage reduction of 89.29% and 78.05% for voltage and current, respectively, after filter implementation. Also, the resulting signal synchronized in every half cycle and matched the voltage amplitude, frequency, and phase angle of the grid signal in 10 ms.
Techno-economic study of a hybrid photovoltaic-diesel system for a remote area in southwest Algeria
Authors: Touahri Tahar , Berbaoui Brahim , Aoun Nouar , Laribi Slimane , Maouedj Rachid , Benmedjahed Miloud
Published: April 2024

Exploiting natural sun and wind resources in remote and isolated areas is undoubtedly an excellent decision to generate electrical energy due to their availability and cleanliness. Various systems were used to generate this energy, such as photovoltaics (PV), wind turbine sand other energy systems. Moreover, for optimum energy use, some of these systems are combined either with each other or with other conventional systems, such as diesel generators with PV systems (i.e., hybrid systems). This work aims to present a technical-economic study of PV/diesel autonomous hybrid systems to supply electrical power for an isolated house located in a hot desert climate, Adrar. For optimizing the hybrid systems, hourly input data of solar radiation and load were used according to two configurations, where the annual load is 11.2 kWh/day. The findings showed that the diesel system had a high cost, with a cost for energy of $0.407/kWh and a fuel price of $0.140/l. Among the hybrid power systems but with significant pollution, the proposed hybrid system 2 kw photovoltaic and diesel generator with 2.3 kW has important economic feasibility, where the energy cost amounted to $0.172/kWh. In addition, CO2 emissions are reduced by approximately 5 tons every year compared to an independent diesel generator system.
Analysis of nickel oxide as a counter electrode for dye-sensitized solar cells using OghmaNano software
Authors: Senin Nur Afiqah Hani , Rummaja Iskandar Dzukarnain , Idris Muhammad Idzdihar , Napiah Zul Atfyi Fauzan Mohammed , Ramlee Radi Husin , Rashid Marzaini , Bradley Luke
Published: April 2024

Dye-sensitized solar cells (DSSCs), a promising green technology, convert solar energy into electricity more cost-effectively than traditional solar cells. While platinum (Pt) is commonly used in DSSCs, its high cost and toxicity limit practical applications. Recent research aims to develop low-cost counter electrodes with high efficiency. Nickel oxide (NiO), a p-type semiconductor with a wide bandgap, good transmittance, and suitable work function, emerges as a potential alternative for counter electrode of DSSCs. In this work, DSSCs with NiO of thicknesses varying from 100 nm to 1000 nm were simulated to determine its influence on photovoltaic performance using OghmaNano software. The structure of simulated solar cells consists of NiO as counter electrode, zinc oxide (ZnO) as photoanode, N719 as dyes, electrolyte as charge carrier transport, and fluorine-doped tin oxide (FTO) as a contact layer. There are five data of NiO used as an active layer. From the simulation results, NiO-doped gold exhibits the highest power conversion efficiency (PCE) of 15.95% at a thickness of 700 nm, while pure NiO shows the lowest PCE with 4.53% at a thickness of 600 nm. These results have demonstrated that NiO can replace Pt as a counter electrode for DSSCs and doping plays a vital role in increasing efficiency.
Novel control of PV-wind-battery powered standalone power supply system based LSTM based ANN
Authors: Hazarathaiah Yerragolla , Reddy B. Venkata Rami
Published: April 2024

Integrated wind-photovoltaic (PV) based standalone electric power supply systems are widely used for various applications. A battery storage system is needed to provide continuous power supply to loads despite changes in loads, wind speed, and solar irradiance. Power quality is crucial in these hybrid systems, as the battery needs to charge from surplus power when generation exceeds the load and discharge to meet load demand. A bidirectional DC to DC converter is used to connect the battery to the network, and maximum power point tracking devices with proper algorithms are incorporated for optimal utilization of PV and wind turbines. Multiple PV systems and wind turbines are considered for proper power supply system ratings. Long short-term memory (LSTM) based artificial neural network (ANN) controllers are implemented for various control units in the hybrid standalone power system. The proposed control techniques improve power quality under various situations. Results are presented using MATLAB/Simulink to evaluate the performance of the proposed method.
Multi-objective algorithm for hybrid microgrid energy management based on multi-agent system
Authors: Tyass Ilham , Bellat Abdelouahed , Raihani Abdelhadi , Mansouri Khalifa
Published: April 2024

In the dynamic landscape of renewable energies, microgrid systems emerge as a promising avenue for fostering sustainable local energy generation. However, the effective management of energy resources holds the key to unlocking their full potential. This study assumes the task of creating a multi-objective optimization algorithm for microgrid energy management. At its core, the algorithm places a premium on seamlessly integrating renewable energy sources and orchestrating efficient storage coordination. Leveraging the prowess of a multi-agent system, it allocates and utilizes energy resources. Through the combination of renewable sources, storage mechanisms, and variable loads, the algorithm promotes energy efficiency and ensures a steady power supply. This transformative solution is underscored by the algorithm's remarkable performance in practical simulations and validations across diverse microgrid scenarios, offering a prevue into the future of sustainable energy utilization.
Validation and test of a novel multi-input converter in extreme conditions using PSOPIC in hybrid power generation environment
Authors: Jayaprakash Supriya , Siddamallaiah Rajashekar Jangam
Published: April 2024

The need for renewable energy resources increases due to the day-by-day increase in load demand. Still, there is a need for new technology to operate the existing power system optimally. Distributed generation (DGENs) are helpful in meeting the demand of power due to its lower cost compared to the construction of the complete power system. But this DGEN is constructed using renewable resources, which are intermittent in nature. So, hybrid power generation, which uses multiple sources, is used to satisfy the power need. In this paper, validation, and test of a new multi-input single ended primary inductor converter (MI-SEPIC) is proposed. The performance of the MI-SEPIC converter is tested by connecting photovoltaic (PV), wind, and fuel cells. The proposed system is connected to the grid, and the power transients are analyzed. The DQ control of grid synchronization is discussed in this paper. The conventional PI controller is replaced with a hybrid particle swarm optimization tuned proportional integral control (PSOPIC), and the results are compared. Verification is done using MATLAB software. Validation and test with different test cases to prove the sturdiness of the complete system is explained.
IoT-based smart net energy meter with advanced billing feature for residential buildings including solar PV system
Authors: Hassan Tonmoy , Das Debraj , Rhyme Shahir Islam , Bal Supratik , Hazari Md. Rifat , Jahan Effat , Hossain Chowdhury Akram , Mannan Mohammad Abdul
Published: April 2024

Electricity consumption is rising across all industries. Residential electricity use dominates the sector. Solar photovoltaic (PV) systems on residential roofs are increasing quickly, notably in Dhaka, Bangladesh. PV power generation is high at peak sun irradiance. Due to light loads, residential structures use less electricity. PV system surplus electricity may be transmitted to the national grid. Residential customers may sell power to the government, lowering their electricity expense. Traditional energy meters make it difficult to calculate PV system consumption by load and grid injection. This is possible with net metering. Thus, this study presents an internet of things (IoT)-based smart net energy meter for home users to provide surplus solar PV power and consume grid electricity when needed. With the government's new power tariff rate, the net bill will be calculated automatically. A dedicated mobile application is used to monitor all the activities. The billing statement will be generated automatically, and the payment of that bill will be payable using a redirect link with the same mobile application. The suggested smart net energy meter will inform SMS/mobile app users of gas, smoke, and tempering. The suggested meter's performance and efficacy were evaluated using software simulation and hardware analysis.
Mitigation of voltage sag and voltage swell by using dynamic voltage restorer
Authors: Mattar El-Zahraa M. , Mahmoud Ekramy S. , El-Sayed Mohammed Ibrahim
Published: April 2024

Recent power quality (PQ) research shows that the most common types of power quality disturbances are voltage sags and swells in medium and low-voltage distribution grids. This paper shows how to improve two significant power quality disturbances: sags and swells voltage. To find out what effect these two PQ issues have in real life, a study case based on real nonlinear loads data from large induction motors at Beshai Company in Sadat City in Egypt is investigated. The dynamic voltage restorer (DVR) has been suggested as a solution to the voltage swell and voltage sag issues. The point of common coupling (PCC) is linked with the dynamic voltage restorer to mitigate the PQ problems that have been found. The power network, loads, and DVR may all be modeled using the MATLAB/Simulink platform. The jellyfish search optimizer (JFS) is used to get the gain settings of the proportional and integral (PI) controller for the proposed DVR. MATLAB/Simulink's results show that the proposed device is effective, reliable, and has low latency.
Impact of grading capacitor on transient recovery voltage due to shunt reactor de-energization for different values of current chopping
Authors: Al-Tak Mazyed A. , Ain Mohd Fadzil , Al-Yozbaky Omar Sh. , Jamil Mohamad Kamarol Mohd
Published: April 2024

This paper investigates the impact of grading capacitors on transient recovery voltage (TRV) during shunt reactor switching in high voltage systems, considering different levels of current chopping. Shunt reactor de-energization can result in voltage surges and instability due to current chopping effects. The study utilizes simulation models using ATP-Draw software to assess the effectiveness of grading capacitors in mitigating TRV under various operating conditions as well as of using a proposed method to mitigate the excessive TRV across the circuit breaker. The findings provide valuable insights into managing TRV during shunt reactor switching, enhancing power system stability and reliability. The results obtained showed that the TRV across the circuit breaker decreased by 61.5% by using circuit modification as well as adding a grading capacitor.
Coordination of hybrid vehicles strategies to improve fuel consumption and reduce the economic cost
Authors: Dawood Basima Taresh , Ahmad Yasser , Kadhim Amjed Alwan
Published: April 2024

To reduce fuel consumption and environmental pollution, this study focused on coordinating the use of a hybrid vehicle, that is, a bus type, powered by a diesel and electric engine. Solar cells have been added to the roof of the bus lighting, heating, and cooling, and fuzzy logic is applied to control the battery charge. Adding solar energy charge the battery by 25% of the total battery charge, thereby reducing fuel costs. The time was also calculated with the battery charge, and the battery is charge more. The aim of the research is to reduce environmental pollution and to use artificial intelligence to control the hybrid vehicle battery charge and speed.
Analysis of magnetic saturation effects in the squirrel cage induction generators
Authors: Hachelaf Redouane , Kouchih Djilali , Tadjine Mohamed , Boucherit Mohamed Seghir
Published: April 2024

This work concerns an investigation on the analysis of the magnetic saturation effects in the three-phase squirrel cage induction generator which is considered as a main device in wind energy conversion systems. The magnetic saturation is considered as an important factor causing destructive effects on power qualities such as harmonics and distortion. Several approaches have been presented in the literature for the modeling of the electric machines considering magnetic saturation. The widely used and precise approach is the finite elements method which is particularly characterized by its high computational time. The novelty in this paper is that a state model has been developed for the healthy conditions of the three-phase squirrel cage induction generators considering the experimental variation of the magnetizing inductance in terms of the magnetizing current. Simulation and experimental tests are provided to extract some important signatures on stator voltages and currents. It will be deduced that the spectrum analysis of stator currents contains useful information on magnetic saturation. Experimental and theoretical results illustrate the consistency of this approach for the modeling and analysis of the squirrel cage induction generators considering the magnetic saturation.
A review on soft computing techniques used in induction motor drive application
Authors: Durgasukumar Gadwala , Prasad Repana Ramanjan , Gorantla Srinivasa Rao
Published: April 2024

In this paper, hybrid models based on fuzzy systems and neural networks are reviewed. A fuzzy inference system is explicitly represented by expertise for induction motor drives, incorporating the learning capability of artificial neural networks. Researchers have been attracted to neuro-fuzzy techniques for training and inference in induction motor drives due to their efficiency. According to the classification of research articles from 2000 to 2020, this article presents a review of different artificial neural network techniques, fuzzy and neuro-fuzzy systems. The main objective is to provide a concise overview of current neuro-fuzzy research and to enable readers to identify appropriate methods according to their research interests.

 
A differential current based protection scheme for DC microgrid
Authors: Giddalur Eswaraiah , Laxmi Askani Jaya
Published: April 2024

A continuous increase in greenhouse emissions has led to more frequent use of renewable energy resources. The increased emergence of DC loads in day-to-day lives has further led to conversion to DC distribution lines. In prospect of getting more environmentally friendly, economical, and reliable power delivery a DC micro grid was developed and has become more common in recent years. The disaster management cell located at the Department of Electrical and Electronics Engineering has designed a DC micro grid which consists of sources provided from the grid, a battery bank, and an array of solar cells supplying to a set of nine loads, which are segregated into three sets of three loads each. This paper presents a protection scheme for the buses present in a micro grid that is based on the differential current principle. It is done with the help of a centralized protection controller that enables fault identification and fault isolation. This protection scheme is further extended to a DC ring-bus micro grid, where the centralized protection controller enables fault identification, fault location, and fault isolation. MATLAB/Simulink is used to obtain the simulation and verify the results.
Improving the protection system in the distribution networks integrated with photovoltaic cells by changing the ratio of X/R
Authors: Ali Muhanad N. , Alsmadi Othman M. K. , Baniyounes Ali M.
Published: April 2024

Due to the increased demand for electrical energy, many countries were prompted nowadays to search for new sources. One important source is the photovoltaic energy. However, despite the great benefits of this source, some defects where observed when linking to distribution networks (negative impact on the protection system). Hence, an approach of improving the protection system in distribution networks integrated with photovoltaic cells is presented in this paper. The protection system improvement is proposed by changing the impedance to resistance ratio (X/R) values of the networks cables, which leads to increasing the overcurrent relays' response speed when a fault occurs. A comparative analysis with different cable reactance values was conducted. The electrical transient analyzer program (ETAP) software was used to investigate and validate the effects on the relay’s response times. Amriya Fallujah power station, located in Iraq, was investigated as a case study. Simulation results show that it is possible to increase the response speed of the overcurrent relay by reducing X/R of the network cables. For more validation, the distance relay was used to compare the best improvement results of changing the X/R ratio with the results of changing the type of relay, which also shows the strength of the proposed method.
Improve the voltage profile of the grid-integrated induction motor with a fuzzy-based voltage source converter
Authors: Jyothiraman Neelagandan Virchuly , Sivachidambaranathan Viraduchalam
Published: April 2024

This article suggests installing a series compensator on grid-connected induction motors that allows for the ride-through of unbalanced voltage sag. Fuzzy logic controls the motor voltages in a standard three-phase voltage source inverter or voltage source inverter (VSI). An open-ended three-phase machine and the grid form a series connection for the VSI. The proposed system is well suited for uses where frequency variation is unnecessary, such as with large pumps or fans. There is no requirement for a direct current (DC) source or injection transformer when conducting an electric mutual coupling The severity of the sag in the grid voltage that will prevent EMC from shutting down is proportional to the load on the motors. An increase in the voltage of the DC link may be necessary if the voltage is not balanced. A 1.5 hp four-pole induction motor was used alongside grid voltage disturbances to test the proposed compensator's ride-through capability. Grid's current analysis demonstrates that the proposed system does not require the addition of a passive filter to achieve low levels of total harmonic distortion (THD). Additionally, the control strategy, component ratings, and analysis of the converter's output voltage operating principle and pulse width modulation technique are covered. The system's viability is shown via simulation and experimental results.
Performance analysis for induction motor fed by reduced switch symmetrical multilevel inverter topology
Authors: Gajula Ujwala , Manivannan Kalpanadevi , Reddy N. Malla
Published: April 2024

Due to its capacity to supply more voltage levels than conventional 2-level inverters, multilevel inverters have attracted a lot of attention in recent years. Multilevel inverters may produce output waveforms that nearly approximate sinusoidal waveforms because to this characteristic, which also significantly lowers harmonic distortion. In many different power conversion systems, the introduction of reduced switch symmetrical multilevel inverter topologies has drawn significant interest. Numerous benefits, such as higher output voltage quality, reduced harmonic distortions, and increased power conversion efficiency, are provided by these novel topologies. The inclusion of these inverters in the feeding of induction motors is one of their many prominent uses. In this paper, a generalized topology is presented that generates nine levels using nine switching devices. To reduce complexity, switching pulses are generated using a low frequency pulse width modulation technique. MATLAB/Simulink is used to analyze the performance assessment of a unique three-phase symmetric cascaded multilevel inverter-based reduced switch symmetrical inverter fed induction motor drive, brushless DC (BLDC) motor and grid has been verified. According to the findings the total harmonic distortion (THD) is found to be 15% for a three-phase system and as the number of levels increases to 17 level the THD is 7.10%.
Power converter for battery charger of electric vehicle with controllable charging current
Authors: Suroso Suroso , Prasetijo Hari , Winasis Winasis , Siswantoro Hari , Nugroho Daru Tri , Utomo Wahyu Mulyo , Abu Bakar Afarulrazi
Published: April 2024

Research on electric vehicle chargers primarily focuses on improving charging technology to enhance the convenience, quality, efficiency, and affordability of charging electric vehicles. This paper discusses a different AC-DC power converter topology proposed as a potential candidate for electric vehicle battery charger. Using the proposed converter circuit, the charging process of electric vehicle battery can be simply adjusted to meet the battery requirement, and even for increasing the charging speed while avoiding overcharging of battery. During the charging process, if the voltage of battery approaches to the targeted battery voltage level, the charging current can be reduced gradually to zero. Hence it will prevent the battery from overcharging condition. Moreover, the ability of working at high power factor operation, low distortion of alternating current (AC) input current, and low direct current (DC) output current ripple are other features of the proposed charger circuits. Some test results of the proposed power converter were presented. The test results revealed potential features of the converter for battery charger of electric vehicle with power factor 0.9996, total harmonic distortion (THD) of AC current 2.73%, and low charging current ripple, i.e. 3.91%. Therefore, it minimizes the negative effects caused by ripples of charging current especially for lithium-ion batteries widely used in electric vehicles.
Depoimento de Sam Kornhauser
Authors: Kornhauser Sam , Ruggiero Amanda Saba
Published: April 2024

Depoimento do arquiteto Sam Kornhauser sobre seu contato com Jorge Caron.
Single-Cell RNA Sequencing Reveals an Immune Landscape of CD4
+
T Cells in Coronary Culprit Plaques With Acute Coronary Syndrome in Humans
Authors: Takeda Shintaro , Emoto Takuo , Yamashita Tomoya , Yamamoto Hiroyuki , Takaya Tomofumi , Sawada Takahiro , Yoshida Takeshi , Inoue Masatoshi , Suzuki Yuya , Hamana Tomoyo , Inoue Taishi , Taniguchi Masayuki , Sasaki Naoto , Otake Hiromasa , Ohkawa Takenao , Furuyashiki Tomoyuki , Kawai Hiroya , Hirata Ken-ichi
Published: April 2024

BACKGROUND:

Acute coronary syndrome (ACS) involves plaque-related thrombosis, causing primary ischemic cardiomyopathy or lethal arrhythmia. We previously demonstrated a unique immune landscape of myeloid cells in the culprit plaques causing ACS by using single-cell RNA sequencing. Here, we aimed to characterize T cells in a single-cell level, assess clonal expansion of T cells, and find a therapeutic target to prevent ACS.

METHODS:

We obtained the culprit lesion plaques from 4 patients with chronic coronary syndrome (chronic coronary syndrome plaques) and the culprit lesion plaques from 3 patients with ACS (ACS plaques) who were candidates for percutaneous coronary intervention with directional coronary atherectomy. Live CD45
+
immune cells were sorted from each pooled plaque samples and applied to the 10× platform for single-cell RNA sequencing analysis. We also extracted RNA from other 3 ACS plaque samples and conducted unbiased TCR (T-cell receptor) repertoire analysis.

RESULTS:

CD4
+
T cells were divided into 5 distinct clusters: effector, naive, cytotoxic, CCR7
+
central memory, and FOXP3 (forkhead box P3)
+
regulatory CD4
+
T cells. The proportion of central memory CD4
+
T cells was higher in the ACS plaques. Correspondingly, dendritic cells also tended to express more HLAs (human leukocyte antigens) and costimulatory molecules in the ACS plaques. The velocity analysis suggested the differentiation flow from central memory CD4
+
T cells into effector CD4
+
T cells and that from naive CD4
+
T cells into central memory CD4
+
T cells in the ACS plaques, which were not observed in the chronic coronary syndrome plaques. The bulk repertoire analysis revealed clonal expansion of TCRs in each patient with ACS and suggested that several peptides in the ACS plaques work as antigens and induced clonal expansion of CD4
+
T cells.

CONCLUSIONS:

For the first time, we revealed single cell–level characteristics of CD4
+
T cells in patients with ACS. CD4
+
T cells could be therapeutic targets of ACS.

REGISTRATION:

URL:
https://www.umin.ac.jp/ctr
; Unique identifier: UMIN000040747.
Impact of Renal Denervation on Urinary Peptide-Based Biomarkers in Hypertension
Authors: Lauder Lucas , Siwy Justyna , Mavrogeorgis Emmanouil , Keller Felix , Kunz Michael , Wachter Angelika , Emrich Insa E. , Böhm Michael , Mischak Harald , Mahfoud Felix
Published: April 2024

BACKGROUND:

Catheter-based renal denervation (RDN) reduces blood pressure in hypertension. Urinary peptides are associated with cardiovascular and renal disease and provide prognostic information. We aimed to investigate the effect of RDN on urinary peptide-based classifiers associated with chronic kidney and heart disease and to identify urinary peptides affected by RDN.

METHODS:

This single-arm, single-center study included patients undergoing catheter-based RDN. Urine samples were collected before and 24 months after RDN and were analyzed using capillary electrophoresis coupled with mass spectrometry. Predefined urinary peptide-based classifiers for chronic kidney disease (CKD273), coronary artery disease (CAD238), and heart failure (HF1) were applied.

RESULTS:

This study included 48 patients (33% female) with uncontrolled hypertension. At 24 months after RDN, systolic blood pressure (165±17 versus 148±20 mm Hg;
P
<0.0001), diastolic blood pressure (90±17 versus 81±13 mm Hg;
P
<0.0001), and mean arterial pressure (115±15 versus 103±13 mm Hg;
P
<0.0001) decreased significantly. A total of 103 urinary peptides from 37 different proteins, mostly collagens, altered following RDN. CAD238, a 238 coronary artery-specific polypeptide-based classifier, significantly improved following RDN (Cohen’s
d
, −0.632;
P
=0.0001). The classification scores of HF1 (
P
=0.8295) and CKD273 (
P
=0.6293) did not change significantly.

CONCLUSIONS:

RDN beneficially affected urinary peptides associated with coronary artery disease.

REGISTRATION:

URL:
https://www.clinicaltrials.gov
; Unique identifier: NCT01888315.