bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing battery system identification: nonlinear autoregressive modeling for Li-ion batteries
Authors: Mossaddek Meriem , Laadissi El Mehdi , Ennawaoui Chouaib , Bouzaid Sohaib , Hajjaji Abdelowahed
Published: April 2024

Precisely characterizing Li-ion batteries is essential for optimizing their performance, enhancing safety, and prolonging their lifespan across various applications, such as electric vehicles and renewable energy systems. This article introduces an innovative nonlinear methodology for system identification of a Li-ion battery, employing a nonlinear autoregressive with exogenous inputs (NARX) model. The proposed approach integrates the benefits of nonlinear modeling with the adaptability of the NARX structure, facilitating a more comprehensive representation of the intricate electrochemical processes within the battery. Experimental data collected from a Li-ion battery operating under diverse scenarios are employed to validate the effectiveness of the proposed methodology. The identified NARX model exhibits superior accuracy in predicting the battery's behavior compared to traditional linear models. This study underscores the importance of accounting for nonlinearities in battery modeling, providing insights into the intricate relationships between state-of-charge, voltage, and current under dynamic conditions.
Electrical signal interference minimization using appropriate core material for 3D integrate circuit at high frequency applications
Authors: Kumar Malagonda Siva , Mohanraj Jayavelu
Published: April 2024

As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Embedded machine learning-based road conditions and driving behavior monitoring
Authors: Mosleh Bayan , Hamdan Joud , Sababha Belal H , Alqudah Yazan A
Published: April 2024

Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Design of higher gain linearly polarized 2×2 microstrip patch array antenna for wireless communication
Authors: Gani Prakash G. , Hegde Shriram P
Published: April 2024

The fifth-generation technology is popular and best as far as data rates are concerned for wireless applications. To meet the increased demand of the modern world for faithful communication, this research work is carried out. In this approach, a single patch, 1×2 array, and 2×2 array are designed using a low-cost FR4 dielectric substrate ( =4.4) covering the 3.3–3.8 GHz frequency band. Due to the lack of gain from arrays in the present research, an attempt is made to achieve excellent gain from arrays with a minimum number of patches. First, the gain of a single inset-fed antenna is compared with another normal single patch of the same thickness but with an additional air dielectric medium. Next, the second single patch is extended to obtain a rectangular 1×2 array and a 2×2 array antenna. A second single patch measuring 50×32.5×0.8 mm is designed assuming an infinite ground plane. It has 3mm air gap between the patch strip and the ground. Air acts as a second dielectric layer that reduces power loss. This allows a maximum gain of 15.2 dB with a return loss of -22 dB. Also, antenna efficiency and bandwidth are 91% and 267.69 MHz, respectively.
Machine learning-based lightweight block ciphers for resource-constrained internet of things networks: a review
Authors: Naik Mahendra Shridhar , Mallam Madhavi , Soppinhalli Nataraju Chaitra
Published: April 2024

The increasing number of internet of things (IoT) devices, wearable technologies, and embedded systems has experienced a significant increase in recent years. This surge has brought attention to the necessity for cryptographic algorithms that are lightweight and capable of providing security in resource-constrained environments. The primary objective of lightweight block ciphers is to provide encryption capabilities with minimal computational overhead and decreased power consumption. As a result, they are particularly well-suited for use on devices that have limited resources. At the same time, machine learning methodologies have evolved into powerful mechanisms for the purposes of prediction, categorization, and system optimization. This study introduces a challenges and issues involved in integrating machine learning techniques with the development of lightweight block ciphers.
Enhancing wireless sensor network security with optimized cluster head selection and hybrid public-key encryption
Authors: Puttaswamy Chaya , Kanakapura Shivaprasad Nandini Prasad
Published: April 2024

This paper introduces an integrated methodology that enhances both the efficiency and security of wireless sensor networks (WSNs) against various active attacks. A two-fold strategy is proposed that incorporates an advanced cluster head (CH) selection and a customized, lightweight encryption protocol. The CH selection process is optimized through a multi-phase approach using fuzzy logic, local and global network qualifiers, and a trust index to ensure the election of CHs that are not only energy-efficient but also reliable. To complement the robust CH selection, the study introduces a hybrid yet lightweight encryption scheme customized Rivest-Shamir-Adleman (c-RSA) and customized advanced encryption standard (c-AES) algorithms. This scheme is customized for WSNs with limited computational resources, maintaining strong encryption standards while significantly reducing energy consumption and computational overhead. Experimental results demonstrate that the proposed system substantially enhances network performance, exhibiting a 34.15% improvement in energy efficiency and a 30.95% increase in reliability over existing methods such as LEACH and its modified versions. This comprehensive approach underscores the potential for a synergistic design in WSNs that does not compromise on security while optimizing operational efficiency.
Transforming clinical clerkship processes with integrated information systems
Authors: Septama Hery Dian , Hakim Lukmanul , Muhammad Meizano Ardhi , Rahmayani Fidha , Anggraini Dwi Indria , Lania Siska , Bestak Robert
Published: April 2024

There are still many manual administrative processes involved in clinical clerkships. However, digital technologies and software solutions can significantly improve medical education. This paper aims to utilize extreme programming (XP) as a methodology for developing the information system. A total of 23 user stories have been planned for completion. The priority of each requirement stated in the user stories is determined using the MoSCoW prioritization technique. The results indicate that the software successfully passed all tests among the 23 black box testing scenarios. Developing an integrated information system to streamline clinical clerkships has been successfully achieved. The user experience questionnaire (UEQ) is utilized to gather the perspectives of hospital management and medical faculty members regarding the information system. The average scores for each category are as follows: 1.93 for attractiveness, 2.04 for perspicuity, 2.20 for efficiency, 2.06 for dependability, 2.14 for stimulation, and 1.85 for novelty. Therefore, it can be concluded that all existing user experience scales exhibit an outstanding degree of user satisfaction.
Automated classification of brain tumor-based magnetic resonance imaging using deep learning approach
Authors: Owida Hamza Abu , AlMahadin Ghayth , Al-Nabulsi Jamal I. , Turab Nidal , Abuowaida Suhaila , Alshdaifat Nawaf
Published: April 2024

The treatment of brain tumors poses significant challenges and contributes to a significant number of deaths on a global scale. The process of identifying brain tumors in medical practice involves the visual analysis of photographs by healthcare experts, who manually delineate the tumor locations. However, this approach is characterized by its time-consuming nature and susceptibility to errors. In recent years, scholars have put forth automated approaches to early detection of brain tumors. However, these techniques face challenges attributed to their limited precision and significant false-positive rates. There is a need for an effective methodology to identify and classify tumors, which involves extracting reliable features and achieving precise disease classification. This work presents a novel model architecture that is derived from the EfficientNetB3. The suggested framework has been trained and assessed on a dataset consisting of 7,023 magnetic resonance images. The findings of this study indicate that the fused feature vector exhibits superior performance compared to the individual vectors. Furthermore, the technique that was provided showed superior performance compared to the currently available systems and attained a 100% accuracy rate. As a result, it is viable to employ this technique within a clinical environment for the purpose of categorizing brain tumors based on magnetic resonance images scans.
Automated feature selection using improved migrating birds optimization for enhanced medical diagnosis
Authors: El aboudi Naoual , Riouali Youness , Maminou Ahmed Reda , Jabri Hassane , Benhlima Laila
Published: April 2024

The feature selection task is a crucial phase in data analysis, aiming to identify a minimized set of relevant features for the target class, thereby eliminating irrelevant and redundant attributes used for model training. While population-based feature selection approaches offer prominent solutions for classification performance, their computational time can be prohibitive. To mitigate delays and optimize resource utilization, this study adopts machine learning operations (MLOps). MLOps involves the seamless transition of experimental Machine Learning models into production, serving them to end users and automating the feature selection phase. This paper introduces a novel feature selection method based on improved migrating bird optimization and its automated variant integrated into MLOps. Experiments conducted on six medical datasets validate the effectiveness of our proposed feature selection method in improving the outcomes of medical diagnosis systems. The results showcase satisfactory performance in terms of classification compared to concurrent feature selection algorithms.
An intelligent deep residual learning framework for tomato plant leaf disease classification
Authors: Ezhilarasan Gangadevi , Rani Ranganathan Shoba , Shri Mani Lawanya , Kadry Seifedien
Published: April 2024

Modern agriculture has been fascinated by various advancements in agriculture and the processing of foods and supply management that provision farmers to improve production. The health of plants is essential to improve production and economic growth. Diseases in plants can affect production and create a rigorous impact on the quality and create a hazard to food safety. Hence, detecting and classifying plant leaf diseases is essential to prevent the disease spread across the plants in the agriculture field and to improve productivity. The researchers in existing frameworks utilized artificial intelligence and machine learning techniques to demonstrate noteworthy solutions. However, a few issues exist related to noises in the images, hyperparameter selection problems, and over-fitting problems that influence prediction accuracy. The proposed model jellyfish ResNet(JF-ResNet) works well to achieve a better accuracy level by incorporating jellyfish optimized ResNET for tomato plant leaf disease identification and classification. The performance metrics such as Accuracy, specificity, sensitivity, and F1-Score is used to evaluate the performance of the JF-ResNet model. The proposed model achieves 97.3% accuracy, 95.3% sensitivity, 96.1% specificity, 96.9% recall, 96.4% precision and 97.1% F1-Score.
A new framework to enhance healthcare monitoring using patient-centric predictive analysis
Authors: Madderi Sivalingam Saravanan , Thisin Syed
Published: April 2024

In the contemporary healthcare landscape, various intelligent automated approaches are revolutionizing healthcare tasks. Learning concepts are pivotal for activities like comprehending acquired data and monitoring patient behavior. Among patient-centric concerns, addressing data heterogeneity, extraction, and prediction challenges is crucial. To enhance patient monitoring using care indicators like cost and length of stay at healthcare centers, many researchers found a model for automated tools, but do not have the artificial intelligence (AI) based models as of now. Therefore, this research study will propose an AI and internet of things (IoT) integrated automated approach with smart sensors called the “PatientE” framework with heterogeneity and patient data. Employing certain rules for data extraction to form a distinct representation, our model integrates pre-treatment information and employs a modified combined random forest, long-short term memory (LSTM), and bidirectional long-short term memory (BiLSTM) algorithm for predictive post-treatment monitoring. This framework, synergizing AI, IoT, and advanced neural networks, facilitates real-time health monitoring, especially focusing on breast cancer patients. Embracing pre-treatment, in-treatment, and post-treatment phases, our model aims for accurate diagnosis, improved cost-efficiency, and extended stays. The evaluation underscores scalability, reliability enhancement, and validates the framework's efficacy in transforming healthcare practices.
Half mirror algorithm: a metaheuristic that hybridizes swarm intelligence and evolution-based system
Authors: Kusuma Purba Daru , Hasibuan Faisal Candrasyah
Published: April 2024

This paper promotes a new metaheuristic called the half mirror algorithm (HMA). As its name suggests, HMA offers a new kind of mirroring search. HMA is developed by hybridizing swarm intelligence and the evolution system. Swarm intelligence is adopted by constructing several autonomous agents called swarms. On the other hand, the evolution system is adopted using arithmetic crossover based on a particular reference called a mirror. Four mirrors are used in HMA: the best swarm member, a randomly selected swarm member, the central point of the space, and the corresponding swarm member. During the confrontative assessment, HMA is confronted with average and subtraction-based optimization (ASBO), total interaction algorithm (TIA), walrus optimization algorithm (WaOA), coati optimization algorithm (COA), and clouded leopard optimization (CLO). The result shows that HMA is superior to ASBO, TIA, WaOA, COA, and CLO in 20, 19, 19, 20, and 20 out of 23 functions, respectively. Moreover, HMA has found the global optimal of eight functions. It means the superiority of HMA occurs in almost entire functions. In the future, the mirroring search can be combined with the guided and neighborhood search to construct a more powerful metaheuristic.
Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network
Authors: Fadzail Noor Fazliana , Zali Samila Mat , Mid Ernie Che
Published: April 2024

The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.
A bibliometric analysis of the advance of artificial intelligence in medicine
Authors: Andrade-Arenas Laberiano , Yactayo-Arias Cesar
Published: April 2024

This bibliometric study analyzes the evolution of research in artificial intelligence (AI) applied to medicine from 2015 to September 2023. Using the Scopus database and keywords related to AI, machine learning, and deep learning in medicine, tools such as VOSviewer and Bibliometrix were used to explore publication trends, subject areas, co-authorship networks, and the most productive countries, among others. 2,064 articles were analyzed, and a significant increase in global academic production has been evident in the last five years. International collaboration was notable, with China and the United States leading in knowledge contribution. The keyword analysis highlights the breadth of topics and applications of AI in medicine, with particular emphasis on cancer detection, dengue diagnosis, and medical image analysis, among others. In conclusion, this study highlights the growing academic interest in the application of AI in medicine and the need for collaborative research. The findings underscore the relevance of these technologies in key areas of health care, contributing significantly to advances in medical diagnosis and prognosis.
The social media sentiment analysis framework: deep learning for sentiment analysis on social media
Authors: Rangarjan Prasanna Kumar , Gurusamy Bharathi mohan , Muthurasu Gayathri , Mohan Rithani , Pallavi Gundala , Vijayakumar Sulochana , Altalbe Ali
Published: April 2024

Researching public opinion can help us learn important facts. People may quickly and easily express their thoughts and feelings on any subject using social media, which creates a deluge of unorganized data. Sentiment analysis on social media platforms like Twitter and Facebook has developed into a potent tool for gathering insights into users' perspectives. However, difficulties in interpreting natural language limit the effectiveness and precision of sentiment analysis. This research focuses on developing a social media sentiment analysis (SMSA) framework, incorporating a custom-built emotion thesaurus to enhance the precision of sentiment analysis. It delves into the efficacy of various deep learning algorithms, under different parameter calibrations, for sentiment extraction from social media. The study distinguishes itself by its unique approach towards sentiment dictionary creation and its application to deep learning models. It contributes new insights into sentiment analysis, particularly in social media contexts, showcasing notable advancements over previous methodologies. The results demonstrate improved accuracy and deeper understanding of social media sentiment, opening avenues for future research and applications in diverse fields.
Association rules forecasting for the foreign exchange market
Authors: EL Mahjouby Mohamed , Bennani Mohamed Taj , Lamrini Mohamed , El Far Mohamed
Published: April 2024

Several association rule mining algorithms exist, and among them, Apriori is one of the most commonly used methods for extracting frequent item sets from vast databases and generating association rules to gain insights. In this research, we have applied a data mining technique to implement association rules and explore frequent item sets. Our study introduced a model that employs association rules to uncover associations between the foreign exchange market, the gold commodity, and the National Association of Securities Dealers automated quotations (NASDAQ). We suggested a method that used data mining to identify the good points of buying and selling in the foreign exchange market by utilizing technical indicators such as moving average convergence divergence (MACD) and the stochastic indicator to create association rules. The experimental findings indicate that the proposed model successfully generates strong association rules.
Enhancing routing efficiency in social internet of things: R-OPTICS and vEBT based congestion free model
Authors: D Bhavya , Vinod D. S. , Prakash S P Shiva , Krinkin Kirill
Published: April 2024

The emergence of the social internet of things (SIoT) network has brought forth distinctive challenges, including node mobility and varying densities, leading to congestion and hampered network efficiency. To overcome these issues, a congestion-free routing model for SIoT is proposed. This model combines the relationship-ordering points to identify the clustering structure (R-OPTICS) algorithm for intelligent node clustering based on relationships and ordering,along with a van emde boas tree (vEBT) for efficient path selection. R-OPTICS enables effective network management by clustering nodes appropriately. The model’s performance is evaluated using metrics such as Rand-Index (1.5765),Davies-Bouldin (-0.4305), and Silhouette Coefficient (1.71685) to assess average goodness values. vEBT identifies optimal paths between clusters, facilitating smart routing decisions. The primary objective of the model is to enhance network efficiency and alleviate congestion by intelligently routing data between clusters. Through extensive simulations, the proposed model outperforms existing routing methods, resulting in improved efficiency and congestion reduction. This congestion-free routing model presents a promising solution to address the unique challenges of SIoT networks, ensuring optimal performance and effective resource management.
Low-power body-coupled transceiver for miniaturized body area networks
Authors: Nataraju Chaitra Soppinahally , Karanam Sreekantha Desai , V. S. S. S. S.Sairam Kanduri
Published: April 2024

As wearable devices continue to proliferate, seamlessly integrating them into wireless body-area networks (WBANs) becomes increasingly crucial. Body-coupled communication (BCC) emerges as a promising WBAN technology, utilizing the human body itself as a transmission channel. This paper presents a novel BCC transceiver designed for efficiency and miniaturization. The proposed transceiver prioritizes reliable data transmission with a convolutional encoder. It leverages a simple direct digital synthesizer (DDS) for frequency shift keying (FSK) modulation, minimizing chip area. At the receiver, a Viterbi decoder (VD) ensures accurate data recovery. This design shines in its resource efficiency. It occupies less than 1% of an Artix-7 FPGA, operates at 268.77 MHz with a mere 111 mW power consumption, and achieves a remarkable data rate of 13.78 Mbps. This translates to a hardware efficiency of 44.46 Kbps/slice, surpassing existing transceivers. Moreover, the BCC transceiver exhibits a stellar bit error rate (BER) of over 10⁻⁷ under realistic body channel conditions. Overall, this work presents a highly efficient BCC transceiver with significant improvements in chip area, power consumption, and data rate compared to existing designs. This paves the way for practical and miniaturized WBAN solutions for future wearable applications.
Investigation on implementing the swarm nano grid system for effective utilization of solar-powered agro-industries
Authors: Karthikeyan N. , Nanthagopal S. , Dharmaprakash R. , Ravikumar R.
Published: April 2024

The agro-industry is the backbone of the global economy, even in the twenty-first century. The agro-industry would not be what it is now without irrigation. The production and use of renewable energy in this sector of the agricultural economy have also expanded rapidly in recent years. Base-load power production and conversions dominate the literature. This research examines user concerns. The swarm nano grid system fixes this. The pulse width modulation (PWM) sinusoidal inverter converted stable DC power from the bidirectional DC-DC converter into sinusoidal AC voltage for the irrigation pump induction motor. Solar panels, batteries, and converters are costly, but they pay off. The nano grid distributes excess power generated during low demand to local loads. This technique works well when the load can be disconnected from the power grid. MATLAB is used to keep an eye on the reliability and efficiency of the induction motor. In the first simulation, solar power generation is modeled using the MATLAB Simulink software in two distinct modes. A PWM sinusoidal inverter that is driven by solar energy is what provides power to the 5.67 kW induction submersible motor. The simulation result provides a conceptual model of how induction motors powered by renewable energy sources function in practice.
Design of actuator motor acceleration model in dual axis tracker movement for stand-alone PV system
Authors: Satria Habib , Syah Rahmad B.Y , Silviana Nukhe Andri , Syafii Syafii
Published: April 2024

Stand-alone photovoltaic system or PV is a power generation technology with potential that is environmentally friendly and also one of the solutions for saving high electricity rates today. However, problems that often occur due to weather fluctuations that are always changing, especially North Sumatra, Indonesia result in the conversion produced by solar cells not being optimal. Therefore, it is necessary to do a new model with a dual tracker system and the development of accelerator motor actuators so that the resulting energy conversion is more optimal. The result of optimizing the reliability of the polycrystalline type solar panel which is designed with an additional photovoltaic tracker system to maximize the conversion of solar energy to solar panels is to obtain an output power of 303.72 volts DC and 267.52 volts DC in the position where the tracker is not used. Then the percentage increase in energy reached 29.80%. Dual axis tracker technology is able to maximize energy conversion in improving PV usage performance. The implementation of a stand-alone PV system will be beneficial if the installation is in Indonesian territory, especially in disadvantaged, frontier and outermost areas.
Economic optimization of hybrid renewable energy resources for rural electrification
Authors: Adebayo Isaiah , Sun Yanxia , Awal Umar
Published: April 2024

In rural areas, grid expansions and diesel generators are commonly used to provide electricity, but their high maintenance costs and CO2 emissions make renewable energy sources (RES) a more practical alternative. Traditional methods such as analytical, statistical, and numerical-based techniques are inadequate for designing an energy-efficient RES. Therefore, this study utilized the bat algorithm (BA) to optimize the use of hybrid RES for rural electrification. A feasibility study was conducted in the village of Kalema to assess energy consumption, and a diesel-only system was modeled to serve the entire community. The BA was used to determine the optimal size and cost-effectiveness of the hybrid RES, with MATLAB R (2021a) utilized for simulation. The BA's performance was compared with diesel only and GA using cost of energy (COE) and CO2 emissions as metrics. Diesel generators only produced a COE of $6,562,000 and 1679.6 lb/hr of CO2 emissions. COE with BA was $356,9781.37 (a 45.6% reduction) and CO2 emissions were 635.29 lb/hr (a 62.2% drop). Genetic algorithm (GA) resulted in $364,3122.46 COE and 652.69 lb/hr CO2 emissions, indicating 61.1% and 44.5% decreases, respectively. BA significantly reduced COE and CO2 emissions over GA, according to the analysis.
Optimising power of PV module using modified MPPT for standalone load
Authors: Behera Siddhartha , Sahu Manoj Kumar , Dash Saroja Kumar , Swain Sarat Kumar
Published: April 2024

The maximum power point tracking (MPPT) has been common terminology among the researchers for those who deal with solar energy. A lot of techniques involving MPPTs have been investigated, evolved and proposed by researchers. But those techniques have got some advantages and suffer from limitation to some extent. The gravity of limitations varies from case to case. In current work, it has been investigated the MPPT based Boost Converter without and with MPPT which is perturb and observe (P&O) type. After observing its limitation, this method of MPPT is modified so as to make it suitable for extracting the maximum power from PV unit at all different load conductance’s. In order to validate this concept, the mathematical model is developed using a MATLAB/Simulink environment and this Modified MPPT technique is implemented. The whole model is simulated and compared with conventional method. This concept is quite new and the proposed one exhibits better performance as compared to conventional methods.
Multi-objective economic load dispatch using hybrid NSGA-II and PVDE techniques
Authors: Chandrashekhar Mothala , Dhal Pradyumna Kumar
Published: April 2024

Over decades, numerous methods have been used to optimize objective functions. Where cost and emissions clash. The improved non-dominated sorting genetic algorithm (NSGA-II) employs elitism to discover the optimum value and speed convergence in multi-objective optimization problems. Population variant differential evolution algorithm alters differential evolution (DE). The main distinction between DE and population variant differential evolution algorithm (PVDE) is population replenishment. NSGA-II and PVDE are combined in the suggested hybrid approach. The hybrid technique solves multi-objective optimization problems efficiently by combining two or more methods. The hybrid technique solves multi-objective optimization problems well. This optimization problem pits cost vs pollution. The hybrid approach exposes half the population to the NSGA-II algorithm and half to the PVDE algorithm. In optimization problems with opposing aims, such as minimizing costs and emissions, a hybrid technique is utilized to find the optimal solution. Elitist diversity-preserving strategies avoid optimization issues becoming converging too soon. A 10-generator IEEE 39 bus test system was validated using this method. The hybrid NSGA-II and PVDE methodology achieves global optimal solutions with more durability, simplicity, and optimization performance than existing methods.
Virtual voltage control to redistribute reactive power of generators in a microgrid
Authors: Viloria Eder Alexander Molina , Becerra John E. Candelo , Durán Dario Enrique Soto
Published: April 2024

The paper examines a strategy for managing voltage control in a microgrid by redistributing reactive power among its distributed generators. Unlike traditional droop control, the new control approach can provide a more accurate reactive power response based on a virtual impedance that helps calculate a virtual voltage. In addition, this virtual impedance is employed for the current controller inverter to improve the results. The adaptive virtual voltage control works well to provide active and reactive power. The proposed control works effectively by balancing the active and reactive power of the grid and maintains the fundamental frequency. The control technique assists the new microgrid (MG) in adapting the operation effectively and redistributing the active and reactive power.
Steady state stability assessment of voltage source converter in a DC microgrid under weak grid conditions
Authors: Viswanathan Ganeshan , Jayaramaiah Govindanayakanapalya Venkatagiriyappa
Published: April 2024

Traditionally, steady-state assessment involves analyzing numerous variables using Eigen analysis. This paper presents a decision support application for diagnosing the steady-state assessment of droop-controlled voltage source inverters in islanded microgrid operations or weak grid operations with reduced input attributes. This paper proposes an approach using feature extraction from the state space variables of the droop-controlled voltage source inverter (VSI). Photovoltaic (PV) and wind energy sources are considered with their stipulated power-delivering capability considered. To improve the generalization of the predictive model, preprocessing techniques are employed to eliminate data distortions. Dimensionality reduction is achieved through principal component analysis (PCA) applied to the steady-state variables. The evaluation of the VSI's steady-state stability is conducted utilizing support vector classification algorithm. To ascertain the reliability of the steady-state stability classification, an assessment of the support vector machine (SVM) model's performance is carried out, which includes the examination of metrics like the area under the curve (AUC) and the receiver operating characteristics (ROC) curve. The findings from the assessment of VSI's steady-state stability indicate a commendable level of performance, achieving an accuracy rate of 93.5%.
Simulation and optimization of EMI filter of conducted emission for high voltage gain DC-DC converter
Authors: Ouhammam Abdelaali , Mahmoudi Hassane , El Hachimi Youssef , Daghouri Amina
Published: April 2024

In the continuity of the work on design of high voltage gain DC-DC converter, used for feeding the CubeSat electrospray thrusters with a variable output voltage according to the maneuver size, this paper introduces a simulation of the converter model with MATLAB/Simulink to determine its conducted emission level. By referring to the MIL-STD-461 standard, a required passive electromagnetic interference (EMI) filter parameters are calculated to reduce, under thresholds, the converter noise. With consideration of the available volume and power constraints in this kind of satellite, the design of common-mode (CM) choke is optimized with proposed procedure optimization, so as to reduce its volume and electrical losses. Also, this optimization procedure can be generalized and applied for any passive EMI filter design.
Power converters analyzed in energy storage systems to enhance the performance of the smart grid application
Authors: Rahman Md Jahidur , Tafticht Tahar , Doumbia Mamadou Lamine
Published: April 2024

This paper implements and compares the existing power supply converters for an energy storage system to determine the best suited for the smart grid application. A survey of different DC-DC converters is carried out to analyze the battery's overall performance. The main objective is to identify this application's most appropriate energy storage device. The advantages of this technology have high efficiency and reliability, which can connect various energy sources and reduce conduction losses in the power converters. Through the converter control, reference currents are imposed to charge the battery. The battery nominal voltage needs to change to see which type of converter is the most suitable and robust. Simulation results show that the operating ranges of boost-buck, buck-boost, and buck-boost converters with negative output voltage enhance the efficiency of battery and renewable energy sources and compared the DC converters to know the functional voltage for the energy storage system. The power converter's efficiency and control facility will allow us to link the energy storage system with the power grid. The overall installation is established using MATLAB/Simulink software.
Review of DC-DC boost converter derived topologies for renewable energy applications
Authors: Thulasiraman Nivethaa , Viswanathan Lavanya , Sriramalakshmi Palanidoss
Published: April 2024

This paper deals with three different power converter topologies for boosting the available dc input voltage. The converters considered for the study are conventional DC-DC boost converter, quasi switched boost DC-DC converter (qSBC) and quasi Z source converter (qZSC). The converters are designed for an input voltage of 24 V to deliver a power of 200 W to a resistive load. The steady-state analysis of all three topologies is discussed to determine the key characteristics of the proposed topologies. All the converters are simulated in MATLAB/Simulink environment and the outcomes are explained in detail. The performance comparison of the converters such as switch stress, diode stress and boost factor versus duty ratio are presented. Thus, this comparison helps to choose a suitable boost converter topology for a specific application.
A bidirectional power converter connecting electric vehicle battery and DC microgrid
Authors: Wijanarko Sulistyo , Hasyim Gifari Iswandi , Furqani Jihad , Rizqiawan Arwindra , Dahono Pekik Argo , Muqorobin Anwar
Published: April 2024

One way to increase electric vehicle (EV) battery utilization is to connect it to a dc microgrid. The EV battery can assume the role of an energy storage from the grid point of view. A bidirectional DC-DC converter will be needed to transfer power between them back and forth. This paper proposes the converter design considering its functional objective, including interleaved phase number determination. Efficiency performance evaluation is presented by power loss analysis with the parasitic-parameters consideration of the components. Finding optimum switching frequency based on power loss analysis is performed independently between input and output sides of the converter. Finally, experiments using a scaled-down prototype are shown to verify the analytical analysis of the converter. The experimental results properly validate the power loss analytic analysis carried out in this paper with a maximum error of 2.04% at 1131-watt, 60 V battery voltage, and 140 V grid voltage. Maximum efficiency 96.97% is obtained at 301-watt, 130 V battery voltage, and 151 V grid voltage. Overall, the converter has a simple structure, capable to be operated in various levels of input and output voltages with a minimum battery side current ripple.
Monumento aos ex-combatentes
Authors: Caron Jorge Osvaldo
Published: April 2024

Sem resumo.
Caron e o ensino de arquitetura e urbanismo: a experiência do curso da Belas Artes
Authors: Negrelos Eulalia Portela
Published: April 2024

Sem resumo.
Resenha do livro Joaquim Guedes de Mônica Junqueira de Camargo (2000)
Authors: Fujioka Paulo Yassuhide
Published: April 2024

Resenha do livro Joaquim Guedes de Mônica Junqueira de Camargo por Paulo Yassuhide Fujioka.
Teatro Kit: elementos pré-projetados componíveis
Authors: Caron Jorge Osvaldo
Published: April 2024

Sem resumo.
A responsabilidade no quadro cultural
Authors: Caron Jorge Osvaldo
Published: April 2024

Sem resumo.
Thrombectomy in Patients With Ischemic Stroke Without Salvageable Tissue on CT Perfusion
Authors: Broocks Gabriel , McDonough Rosalie V. , Bechstein Matthias , Klapproth Susan , Faizy Tobias D. , Schön Gerhard , Kniep Helge C. , Bester Maxim , Hanning Uta , Kemmling André , Zeleñák Kamil , Fiehler Jens , Meyer Lukas
Published: April 2024

BACKGROUND:

Computed tomography perfusion (CTP) imaging is regularly used to guide patient selection for mechanical thrombectomy (MT). However, the effect of MT in patients without salvageable tissue on CTP has not been investigated. The purpose of this study was to assess the effect of MT in patients with stroke without perfusion mismatch profiles.

METHODS:

This observational study analyzed patients with ischemic stroke consecutively treated between March 1, 2015, and January 31, 2022, triaged by multimodal-computed tomography undergoing MT. CTP lesion-core mismatch profiles were defined using a mismatch volume/ratio of ≥10 mL/1.2, respectively. The primary end point was the rate of functional independence at 90 days, defined as the modified Rankin Scale score of 0 to 2. Recanalization was evaluated with the modified Thrombolysis in Cerebral Infarction scale. The effect of baseline variables on functional outcome was assessed using multivariable logistic regression analysis. Outcomes of patients with and without CTP-mismatch profiles were compared using 1:1 propensity score matching.

RESULTS:

Of 724 patients who met the inclusion criteria of this retrospective observational study, 110 (15%) patients had no CTP mismatch and were analyzed. The median age was 74 (interquartile range, 62–80) years and 53% were women. Successful recanalization (modified Thrombolysis in Cerebral Infarction score, ≥2b) was achieved in 66% (73) and associated with functional independence at 90 days (adjusted odds ratio, 7.33 [95% CI, 1.22–43.70];
P
=0.03). A significant interaction was observed between recanalization and age, as well as the extent of infarction, indicating MT to be most effective in patients <70 years and with a baseline Alberta Stroke Program Early Computed Tomography Score range between 3 and 7. These findings remained stable after propensity score matching, analyzing 152 matched pairs with similar rates of functional independence between patients with and without CTP-mismatch profiles (17% versus 23%;
P
=0.42).

CONCLUSIONS:

In patients without CTP-mismatch profiles defined according to the EXTEND (Extending the Time for Thrombolysis in Emergency Neurological Deficits) criteria, recanalization was associated with improved functional outcomes. This effect was associated with baseline Alberta Stroke Program Early Computed Tomography Score and age, but not with the time from onset to imaging.
Genetically Determined Plasma Hepatocyte Growth Factor Levels Are Associated With the Risk and Prognosis of Ischemic Stroke
Authors: Xu Qingyun , Guo Daoxia , Shi Mengyao , Wang Yinan , Yang Pinni , Jia Yiming , Sun Lulu , Liu Yi , Chang Xinyue , He Yu , Hui Li , Zhang Yonghong , Zhu Zhengbao
Published: April 2024

BACKGROUND:

Observational studies suggest that hepatocyte growth factor (HGF) is associated with the risk and prognosis of ischemic stroke, but the causality of these associations remains unclear. Therefore, we conducted Mendelian randomization (MR) analyses to explore the associations of genetically determined plasma HGF levels with the risk and prognosis of ischemic stroke.

METHODS:

A total of 13 single-nucleotide polymorphisms associated with plasma HGF were selected as genetic instruments based on the data from a genome-wide association study with 21 758 European participants. Summary data about the risk of ischemic stroke were obtained from the MEGASTROKE (Multiancestry Genome-Wide Association Study of Stroke) Consortium with 34 217 ischemic stroke cases and 406 111 controls of European ancestry, and summary data about the prognosis of ischemic stroke were obtained from the GISCOME study (Genetics of Ischaemic Stroke Functional Outcome) with 6165 European patients with ischemic stroke. We conducted an inverse-variance weighted Mendelian randomization analysis followed by a series of sensitivity analyses to evaluate the associations of genetically determined plasma HGF with the risk and prognosis of ischemic stroke.

RESULTS:

The primary analyses showed that genetically determined high HGF was associated with an increased risk of ischemic stroke (odds ratio per SD increase, 1.11 [95% CI, 1.04–1.19];
P
=1.10×10
−3
) and poor prognosis of ischemic stroke (odds ratio per SD increase, 2.43 [95% CI, 1.76–3.52];
P
=6.35×10
−8
). In the secondary analysis, genetically determined plasma HGF was associated with a high risk of large atherosclerotic stroke (odds ratio per SD increase, 1.39 [95% CI, 1.18–1.63];
P
=5.08×10
−5
) but not small vessel stroke and cardioembolic stroke. Mendelian randomization-Egger regression showed no directional pleiotropy for all associations, and the sensitivity analyses with different Mendelian randomization methods further confirmed these findings.

CONCLUSIONS:

We found positive associations of genetically determined plasma HGF with the risk and prognosis of ischemic stroke, suggesting that HGF might be implicated in the occurrence and development of ischemic stroke.
Catullus 32: Fututiones with Ipsitilla
Authors: Ferriss-Hill Jennifer
Published: April 2024

Catullus poem 32 has traditionally been read as describing an afternoon assignation with a prostitute. The reinterpretation offered here reads the poem not as a one-off piece about a figure mentioned nowhere else in Catullus, but rather as a poem that connects with those in the Lesbia-cycle as well as with Catullus’ metapoetic project more broadly. Ipsitilla, I argue, acts as a Muse-like figure analogous to Lesbia, and the fututiones she prepares for Catullus at his request represent his neoteric poetry: erotic, clever, and about the process of writing.
Taking Texts Seriously
Authors: Godzimirska Zuzanna , Kjaer Anne Lise
Published: April 2024

Many international law scholars have found inspiration in quantitative methods of text analysis in recent years. Such methods enable scholars to identify patterns in large quantities of text and challenge pre-existing assumptions about various aspects of international law, how it is developed, practiced, interpreted and applied. Yet, subjecting legal texts to quantitative methods of analysis also comes with notable shortcomings. When treating texts as abstract data in accordance with the logic of data science, researchers often fail to appreciate texts as means of human interaction and communication, thereby diverting attention from terms and meaning to numbers and statistics. The aim of this article is threefold: to offer a lay of the land of the burgeoning empirical legal scholarship that adopts text analytical approaches in its study of international law, exhibit what gets lost when insights from linguistics are ignored, and demonstrate what is gained when they are not.
The Blind Men and the Elephant
Authors: Steininger Silvia , Hamilton Byrne William , Oidtmann Raphael
Published: April 2024

What is the role of the social sciences in international law? This article maps how international law interacts with the social sciences, including its concepts, findings, methods, and epistemologies. It provides a first encompassing genealogy of social science references in six renowned international law journals, including the American, Asian, European, Leiden, and Nordic Journal of International Law as well as the British International and Comparative Law Quarterly, by using a corpus linguistic approach that encompasses more than 15,000 documents from 1907 to 2022. Moreover, it explores how structural factors related to the institutionalization and funding of certain strands of social science-inspired international law scholarship have influenced regional and temporal patterns in Europe, Germany, and Australia.