bluuebunny/crossref_metadata_2025 huggingface dataset:
High-Precision Main Shaft Displacement Measurement for Wind Turbines Using an Optimized Position-Sensitive Detector
Authors: Zhang Weitong , Wang Lingyun , Li Guangxi , Zheng Huicheng , Pang Chengwei
Published: December 2024

The main shaft of a wind turbine is a critical component that ensures the normal operation of the turbine, and its axial displacement directly impacts its efficiency and safety. The inaccurate measurement of axial displacement may lead to severe issues such as shaft fractures, causing turbine shutdowns. Correcting measurement errors related to axial displacement is essential to prevent potential accidents. This study proposes an improved error correction method for measuring the axial displacement of wind turbine main shafts. Using a position-sensitive detector (PSD) and laser triangulation, the axial and radial displacements of the main shaft are measured to address environmental interference and cost constraints. Additionally, a Sparrow Search Algorithm- Backpropagation (SSA-BP) model is constructed based on operational data from the wind turbine’s main shaft to correct the system’s nonlinear errors. The Sparrow Search Algorithm (SSA) is employed to optimize the weights and thresholds of the Backpropagation (BP) neural network, enhancing prediction accuracy and model stability. Initially, a main shaft displacement measurement system based on a precision displacement stage was developed, and system stability tests and displacement measurement experiments were conducted. The experimental results demonstrate that the system stability error is ±0.025 mm, which is lower than the typical error of 0.05 mm in contact measurement. After model correction, the maximum nonlinear errors of the axial and radial displacement measurements are 0.83% and 1.29%, respectively, both of which are lower than the typical measurement error of 2% in contact measurements. This indicates that the proposed model can reliably and effectively correct the measurement errors. However, further research is still necessary to address potential limitations, such as its applicability in extreme environments and the complexity of implementation.
Detection of Ethereum Phishing Fraud Nodes Based on Feature Enhancement Strategy and GBM
Authors: Liu Sheng-Zheng , Yu Xin-Yue , Li Ya-Ting , Zhang Hao , Guo Xue-Pin , Ma Cui-Hua , Long Hai-Xia
Published: December 2024

With the rapid development of blockchain technology and the popularity of cryptocurrency, phishing scams pose an increasingly severe threat to the security of cryptocurrency transactions. Existing fraud detection methods have not accurately identified phishing behaviors, especially failing to capture key neighbor information and its impact effectively. To address this problem, we proposed a phishing detection framework based on FAAN-GBM (Feature and Attention Augmented Network with Gradient Boosting Machine), which aims to improve phishing fraud detection effectiveness on the Ethereum platform by further refining the extraction of phishing account features. This framework integrates basic features, transaction features, and interaction features of nodes, optimizes feature aggregation through importance analysis and attention mechanism of neighbor node, and uses autoencoders to deepen the nonlinear expression of node features. Through extensive testing on real Ethereum datasets, FAAN-GBM has demonstrated superior performance over existing methods, effectively improving the identification accuracy of phishing fraud nodes.
Coordinated Volt-Var Control of Reconfigurable Microgrids with Power-to-Hydrogen Systems
Authors: Gholami Khalil , Azizivahed Ali , Arefi Ali , Li Li , Arif Mohammad Taufiqul , Haque Md Enamul
Published: December 2024

The integration of electrolyzers and fuel cells can cause voltage fluctuations within microgrids if not properly scheduled. Therefore, controlling voltage and reactive power becomes crucial to mitigate the impact of fluctuating voltage levels, ensuring system stability and preventing damage to equipment. This paper, therefore, seeks to enhance voltage and reactive power control within reconfigurable microgrids in the presence of innovative power-to-hydrogen technologies via electrolyzers and hydrogen-to-power through fuel cells. Specifically, it focuses on the simultaneous coordination of an electrolyzer, hydrogen storage, and a fuel cell alongside on-load tap changers, smart photovoltaic inverters, renewable energy sources, diesel generators, and electric vehicle aggregation within the microgrid system. Additionally, dynamic network reconfiguration is employed to enhance microgrid flexibility and improve the overall system adaptability. Given the inherent unpredictability linked to resources, the unscented transformation method is employed to account for these uncertainties in the proposed voltage and reactive power management. Finally, the model is formulated as a convex optimization problem and is solved through GUROBI version 11, which leads to having a time-efficient model with high accuracy. To assess the effectiveness of the model, it is eventually examined on a modified 33-bus microgrid in several cases. Through the results of the under-study microgrid, the developed model is a great remedy for the simultaneous operation of diverse resources in reconfigurable microgrids with a flatter voltage profile across the microgrid.
A Strategic Framework for Net-Zero Transitions: Integrating Fuzzy Logic and the DICE Model for Optimizing Ontario’s Energy Future
Authors: Shobeiri Elaheh , Genco Filippo , Hoornweg Daniel , Tokuhiro Akira
Published: December 2024

In response to the urgent threat of climate change and the drivers of high greenhouse gas emissions, countries worldwide are adopting policies to reduce their carbon emissions, with net-zero emissions targets. These targets vary by region, with Canada aiming to achieve net-zero emissions by 2050. In response to the Independent Electricity System Operator’s (IESO’s) “Pathways to Decarbonization” report, which evaluates a proposed moratorium on new natural gas generating stations, this study presents a methodology to support energy transitions in Ontario by using a modified Dynamic Integrated Climate-Economy (DICE) model, which focuses on replacing fossil fuel power plants (FFPPs) with clean energy sources, including nuclear, solar, wind, and hydro. This research expands on our prior work that used the DICE model to evaluate the potential for replacing FFPPs with Small Modular Reactors (SMRs) on a global scale. This study includes solar, wind, hydro, and SMRs to provide a diversified clean energy portfolio and integrates fuzzy logic to optimize construction rates and address uncertainties. The study uses Ontario as a case study, aligning with IESO’s objectives for Ontario’s energy transition. The IESO’s projections for net zero by 2050 are applied. The study is extended to 2100 to assess the longer-term implications of sustained energy transition efforts beyond the immediate goals set by the IESO. This approach is scalable to other regions and countries with similar energy transition challenges. The study results indicate that to meet Ontario’s 2050 net-zero target, approximately 183 SMR units, 1527 solar units, 289 wind units, and 449 hydro units need to be constructed. For the 2100 target, the required number of units is slightly higher due to the longer time frame, reflecting a gradual ramp-up in construction. The optimization of construction rates using fuzzy logic shows that the pace of deployment is influenced by critical factors such as resource availability, policy support, and public acceptance. This underscores the need for accelerated clean energy deployment to meet long-term emissions reduction goals. The findings highlight the complexities of transitioning to a low-carbon energy system and the importance of addressing uncertainties in planning. Policymakers are urged to integrate these insights into strategic energy planning to ensure the successful deployment of clean energy technologies. This study provides valuable recommendations for optimizing energy transitions through a robust, flexible framework that accounts for both technological and socio-economic challenges.
A Passive Flow Control Technique of a Small-Scale HAWT and TED Analysis Under Yaw Condition Based on Airfoil Concavity
Authors: Ma Jianlong , Li Qiuyan , Zhao Ming , Chen Yanan , Chai Xiaohui
Published: December 2024

To mitigate the energy loss caused by flow separation of a 300 W small wind turbine, a passive flow control technique based on the airfoil concavity was proposed. The suction surface of the blade was modified with eight different types of concavity, the results showed that the b1 elliptical concavity, with B-spline curves front-and-rear transition, significantly affected the airflow of the airfoil’s suction surface, improving the wind turbine’s aerodynamic performance by 3.26% at maximum. Then, the flow field characteristics of b1, c1, and c4 concave airfoils with typical geometric features under axial flow conditions demonstrated that the b1 airfoil concavity had the greatest impact on flow separation. Moreover, yaw angle was induced, and the wind turbine’s turbulent kinetic energy (TKE) and turbulent energy dissipation (TED) were investigated from the aspects of energy loss. The variation rule of the TED difference between the concave bottom and edge with yaw angle was summarized into an equation that quantitatively explained why the 10° yaw angle was the turning point of the power output, as well as the potential mechanism of concave airfoil-induced power enhancement. These findings provide a foundation for enhancing the aerodynamic performance of large megawatt-class wind turbines.
Partial Transfer Learning from Patch Transformer to Variate-Based Linear Forecasting Model
Authors: Anh Le Hoang , Vu Dang Thanh , Oh Seungmin , Yu Gwang-Hyun , Han Nguyen Bui Ngoc , Kim Hyoung-Gook , Kim Jin-Sul , Kim Jin-Young
Published: December 2024

Transformer-based time series forecasting models use patch tokens for temporal patterns and variate tokens to learn covariates’ dependencies. While patch tokens inherently facilitate self-supervised learning, variate tokens are more suitable for linear forecasters as they help to mitigate distribution drift. However, the use of variate tokens prohibits masked model pretraining, as masking an entire series is absurd. To close this gap, we propose LSPatch-T (Long–Short Patch Transfer), a framework that transfers knowledge from short-length patch tokens into full-length variate tokens. A key implementation is that we selectively transfer a portion of the Transformer encoder to ensure the linear design of the downstream model. Additionally, we introduce a robust frequency loss to maintain consistency across different temporal ranges. The experimental results show that our approach outperforms Transformer-based baselines (Transformer, Informer, Crossformer, Autoformer, PatchTST, iTransformer) on three public datasets (ETT, Exchange, Weather), which is a promising step forward in generalizing time series forecasting models.
The Process of Using Power Supply Technical Solutions for Electronic Security Systems Operated in Smart Buildings: Modelling, Simulation and Reliability Analysis
Authors: Wiśnios Michał , Mazur Michał , Tatko Sebastian , Paś Jacek , Rosiński Adam , Łukasiak Jarosław Mateusz , Koralewski Wiktor , Dyduch Janusz
Published: December 2024

This article presents selected issues related to the reliability of the power supply for electronic security systems (ESSs) used in smart buildings (SBs). ESSs operate in diverse environmental conditions and are responsible for the safety of lives, property and the natural environment of SB users. The operational tasks of ESSs in SBs require a continuous power supply from various sources, including renewable energy sources. The authors conducted an analysis of the power supply for selected ESSs used in SBs, which enabled the development of a power supply model. For the proposed model, the authors designed a proprietary graph of the ESS operational process, taking into account power supply implementation. Considering the operational indicators for the analysed ESSs, such as repair and failure rates, a computer simulation was performed. The simulation allowed the determination of the reliability of the ESS power supply within the considered redundancy configuration of additional energy sources, which can be utilised during the design phase. The reliability analysis of the power supply and the determination of rational parameters conducted in the article are crucial for achieving all the functionalities of ESSs in SBs, as envisioned during the design process. The article is divided into six chapters, structured to address the topics sequentially: an introduction to the state of the issue, a critical literature review, an analysis of the power supply for selected ESSs, implementation of renewable energy sources, the development of a proprietary model and operational graph, a computer simulation and conclusions.
Adaptive Neuro-Fuzzy System for Detection of Wind Turbine Blade Defects
Authors: Dubchak Lesia , Sachenko Anatoliy , Bodyanskiy Yevgeniy , Wolff Carsten , Vasylkiv Nadiia , Brukhanskyi Ruslan , Kochan Volodymyr
Published: December 2024

Wind turbines are the most frequently used objects of renewable energy today. However, issues that arise during their operation can greatly affect their effectiveness. Blade erosion, cracks, and other defects can slash turbine performance while also forcing maintenance costs to soar. Modern defect detection applications have significant computing resources needed for training and insufficient accuracy. The goal of this study is to develop the improved adaptive neuro-fuzzy inference system (ANFIS) for wind turbine defect detection, which will reduce computing resources and increase its accuracy. Unmanned aerial vehicles are deployed to photograph the turbines, and these images are beamed back and processed for early defect detection. The proposed adaptive neuro-fuzzy inference system processes the data vectors with lower complexity and higher accuracy. For this purpose, the authors explored grid partitioning and subtractive clustering methods and selected the last one because it uses three rules only for fault detection, ensuring low computational costs and enabling the discovery of wind turbine defects quickly and efficiently. Moreover, the proposed ANFIS is implemented in a controller, which has an accuracy of 91%, that is 1.4 higher than the accuracy of the existing similar controller.
Optimal Control Scheme of Electric Vehicle Charging Using Combined Model of XGBoost and Cumulative Prospect Theory
Authors: Lim Youseok , Bae Sungwoo , Moon Jun
Published: December 2024

In this paper, we propose the XPaC (XGBoost Prediction and Cumulative Prospect Theory (CPT)) model to minimize the operational losses of the power grid, taking into account both the prediction of electric vehicle (EV) charging demand and the associated uncertainties, such as when customers will charge, how much electric energy they will need, and for how long. Given that power utilities supply electricity with limited resources, it is crucial to efficiently control EV charging peaks or predict charging demand during specific periods to maintain stable grid operations. While the total amount of EV charging is a key factor, when and where the charging occurs can be even more critical for the effective management of the grid. Although numerous studies have focused on individually predicting EV charging patterns or demand and evaluating the effectiveness of EV charging control, comprehensive assessments of the actual operational benefits and losses resulting from charging control based on predicted charging behavior remain limited. In this study, we firstly compare the performance of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and decision tree-based XGBoost regression models in predicting hourly charging probabilities and the need for grid demand control. Using the predicted results, we applied the CPT algorithm to analyze the optimal operational scenarios and assess the expected profit and loss for the power grid. Since the charging control optimizer with XPaC incorporates real-world operational data and uses actual records for analysis, it is expected to provide a robust solution for managing the demand arising from the rapid growth of electric vehicles, while operating within the constraints of limited energy resources.
Convolutional Neural Network for the Classification of the Control Mode of Grid-Connected Power Converters
Authors: Ouali Rabah , Legry Martin , Dieulot Jean-Yves , Yim Pascal , Guillaud Xavier , Colas Frédéric
Published: December 2024

With the integration of power converters into the power grid, it becomes crucial for the Transmission System Operator (TSO) to ascertain whether they are operating in Grid Forming or Grid Following modes. Due to intellectual properties, classification can only be performed based on non-intrusive measurements and models, such as admittance at the PCC. This classification poses a challenge as the TSO lacks precise knowledge of the actual control structures and algorithms. This paper introduces a novel classification algorithm based on Convolutional Neural Networks (CNN), capable of detecting patterns in sequential data. The proposed CNN utilizes a new architecture to separate admittances along the d and q axes, and a decision layer allows to determine the correct converter mode. The performance of the proposed CNN model was assessed through two tests and compared to the traditional feedforward model. The proposed CNN architecture demonstrates significant classification capabilities, as it is able to identify the control mode of the converter even when its control structure is not part of the training dataset.
Sigma Delta Modulation Controller and Associated Cybersecurity Issues with Battery Energy Storage Integrated with PV-Based Microgrid
Authors: Saiara Syeda Afra , Ali Mohd. Hasan
Published: December 2024

Battery energy storage systems (BESSs) play a crucial role in integrating renewable energy sources into microgrids. However, robust BESS controllers are needed to carry out this function properly. Existing controllers suffer from overshoots and slow convergence issues. Moreover, as electrical grid networks become increasingly connected, the risk of cyberattacks grows, and traditional physics-based anomaly detection methods face challenges such as reliance on predefined models, high computational demands, and limited scalability for complex, large-scale data. To address the limitations of the existing approaches, this paper first proposes a novel sigma-delta modulation (SDM) controller for BESSs in solar photovoltaic (PV)-connected microgrids. The performance of SDM has been compared with those of the proportional–integral (PI) controller and fuzzy logic controller (FLC). Also, this paper proposes an improved ensemble-based method to detect the false data injection (FDI) and denial-of-service (DoS) attacks on the BESS controller. The performance of the proposed detection method has been compared with that of the traditional ensemble-based method. Four PV-connected microgrid systems, namely the solar DC microgrid, grid-connected solar AC microgrid, hybrid AC microgrid with two BESSs, and hybrid AC microgrid with a single BESS, have been considered to show the effectiveness of the proposed control and detection methods. The MATLAB/Simulink-based results show the effectiveness and better performance of the proposed controller and detection methods. Numerical results demonstrate the improved performance of the proposed SDM controller, with a 35% reduction in AC bus voltage error compared to the conventional PI controller and FLC. Similarly, the proposed SAMME AdaBoost detection method achieves superior accuracy with an F1 score of 95%, outperforming the existing ensemble approaches.
A Comprehensive Overview of Technologies Applied in Hydrogen Valleys
Authors: Bampaou Michael , Panopoulos Kyriakos D.
Published: December 2024

Hydrogen valleys are encompassed within a defined geographical region, with various technologies across the entire hydrogen value chain. The scope of this study is to analyze and assess the different hydrogen technologies for their application within the hydrogen valley context. Emphasizing on the coupling of renewable energy sources with electrolyzers to produce green hydrogen, this study is focused on the most prominent electrolysis technologies, including alkaline, proton exchange membrane, and solid oxide electrolysis. Moreover, challenges related to hydrogen storage are explored, alongside discussions on physical and chemical storage methods such as gaseous or liquid storage, methanol, ammonia, and liquid organic hydrogen carriers. This article also addresses the distribution of hydrogen within valley operations, especially regarding the current status on pipeline and truck transportation methods. Furthermore, the diverse applications of hydrogen in the mobility, industrial, and energy sectors are presented, showcasing its potential to integrate renewable energy into hard-to-abate sectors.
Heat Exchange Analysis of Brushless Direct Current Motors
Authors: Mazur Maciej , Skarka Wojciech , Kobielski Maciej , Kądzielawa Damian , Kubica Robert , Haas Clemens , Unterberger Hubert
Published: December 2024

The brushless DC (BLDC) motor is crucial in a variety of industrial and consumer applications due to its efficiency and precise control. This study investigates the heat transfer and cooling mechanisms in liquid-cooled BLDC motors in dishwashers, which are fundamental to maintaining optimal operating temperatures. Elevated temperatures can reduce operational efficiency, emphasizing the importance of effective heat dissipation. Liquid cooling proves to be very effective and offers advantages over air cooling by providing even temperature distribution and more accurate temperature control. Integrating liquid cooling systems into dishwasher designs provides a viable solution for managing motor temperatures while preheating dishwashing water. Using existing water infrastructure, these systems dissipate heat generated during motor operation, increasing energy efficiency and reliability, as analyzed using computational fluid dynamics (CFDs). The aim of this study is to optimize thermal management strategies in BLDC motors, particularly in dishwashers, by filling a critical gap in the existing literature. The goal of this comprehensive analysis is to develop resistant and efficient cooling solutions tailored to dishwasher environments, ultimately extending the life of BLDC motors in home appliances while using heat transfer to preheat water for wash cycles.
Experimental Assessment of Green Waste HTC Pellets: Kinetics, Efficiency and Emissions
Authors: Garcia Lovella Yaniel , Goel Abhishek , Garin Louis , Blondeau Julien , Bram Svend
Published: December 2024

The combustion of renewable solid fuels, such as biomass, is a reliable option for heat and power production. The availability of biomass resources within urban areas, such as tree leaves, small branches, grass, and other green city waste, creates an opportunity to valorize such resources. The energy densification of such resources using hydrothermal carbonization (HTC) and pelletization of the carbonized material could create a new generation of domestic boiler biofuel. However, combustion efficiency and emission assessments should be carried out for HTC pellets. The primary objective of this study is to assess HTC pellets, provided by a waste upgrade company, in terms of kinetics, combustion efficiency, and emissions, taking as reference base ENplus A1 certified softwood pellets. Therefore, thermogravimetric analysis and combustion tests were conducted for both fuels to achieve this. It was observed that a third peak of the burning rate during the solid carbon oxidation of HTC pellets indicated a high activation energy. Combustion tests showed a 7% increase in boiler efficiency for HTC pellets compared to softwood pellets. However, higher particulate matter (PM), NOx, and CO emissions were recorded during the HTC pellets test. The results suggest that optimizing the air/fuel ratio could further improve the performance of HTC pellets in domestic boilers.
Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode
Authors: Moghimi Seyed Morteza , Gulliver Thomas Aaron , Chelvan Ilamparithi Thirumarai , Teimoorinia Hossen
Published: December 2024

This paper examines Connected Smart Green Buildings (CSGBs) in Burnaby, BC, Canada, with a focus on townhouses with one to four bedrooms. The proposed model integrates sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. These elements improve energy efficiency and promote sustainability. Operating in island mode, CSGBs can function independently of the grid, providing resilience during power outages and reducing reliance on external energy sources. Real data on electricity, gas, and water consumption are used to optimize load management under isolated conditions. Electric Vehicles (EVs) are also considered in the system. They serve as energy storage devices and, through Vehicle-to-Grid (V2G) technology, can supply power when needed. A hybrid Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. The metrics considered include accuracy, efficiency, emissions, and cost. The performance was compared with several well-known models including Linear Regression (LR), CNN, LSTM, Random Forest (RF), Gradient Boosting (GB), and hybrid LSTM–CNN, and the results show that the proposed model provides the best results. For a four-bedroom Connected Smart Green Townhouse (CSGT), the Mean Absolute Percentage Error (MAPE) is 4.43%, the Root Mean Square Error (RMSE) is 3.49 kWh, the Mean Absolute Error (MAE) is 3.06 kWh, and R2 is 0.81. These results indicate that the proposed model provides robust load optimization, particularly in island mode, and highlight the potential of CSGBs for sustainable urban living.
Technological Innovations in Decarbonisation Strategies: A Text-Mining Approach to Technological Readiness and Potential
Authors: Costa Paulo Moisés , Duarte António , Tomé Paulo , Bento Nuno , Fontes Margarida
Published: December 2024

This study presents a novel, multifaceted approach to evaluating decarbonisation technologies by integrating advanced text-mining tools with comprehensive data analysis. The analysis of scientific documents (2011–2021) and mapping 368 technologies from the IEA’s Energy Technology Perspectives identified 41 technology domains, including 20 with the highest relevance and occurrence. Domain readiness was assessed using mean Technology Readiness Levels (TRLs) and linked to six decarbonisation pathways. The “Electrification of uses” pathway ranked highest, demonstrating significant CO2 mitigation potential and high readiness (mean TRL 7.4, with two-thirds of technologies scoring over 7) despite challenges in hard-to-electrify sectors. The findings provide actionable insights for policymakers, highlighting the need for pathway-specific strategies, a deeper understanding of synergies between pathways, and balancing innovation with deployment to accelerate decarbonisation.
Laser Surface Texturing for the Intensification of Boiling Heat Transfer in a Minichannel
Authors: Strąk Kinga , Piasecka Magdalena
Published: December 2024

This study investigates the effects of using laser-textured surfaces in boiling heat transfer during cooling fluid flow in a minichannel. Several laser-textured surfaces, varied in roughness, were created on the heated plate surface that contacted FC-72 during flow in a single minichannel. Infrared thermography was used to measure temperature changes on the untextured side of the plate, while two-phase flow patterns were observed through a glass pane. Three vibration-assisted laser surface textures, previously investigated by the authors, and five novel laser surface textures were tested experimentally. The results were presented as relationships between heated wall temperature, heat transfer coefficient and distance along the minichannel, boiling curves, and flow patterns. The main interest of the authors was to provide a comparative analysis of the heat transfer results at the same value of heat flux supplied to the minichannel heated wall when either a laser-textured surface or a smooth base one was applied. It was noticed that the use of the 90-degree dense grid pattern type 2 (shallow) surface in the research helped achieve the highest local heat transfer coefficient in the subcooled boiling region compared to other surfaces tested. Furthermore, the 90-degree dense grid pattern type 1, characterised by larger maximum depth and height surfaces, performed best in the saturated boiling region. The results obtained for the laser-textured heated plate surface were compared to those collected for the smooth base heated plate surface, generally indicating an intensification of heat transfer processes in boiling heat transfer during FC-72 flow in a minichannel.
Technological Trends for Electrical Machines and Drives Used in Small Wind Power Plants—A Review
Authors: Fodorean Daniel
Published: December 2024

High-power-range wind generators mainly employ classical variants, with the advantages of low cost, high robustness and acceptable energetic performance, while for low-power applications, the available electrical drive solutions are more numerous. This paper investigates the current trend in this field, indicating simple or complex structures, with or without self-excitation and with or without mechanical or magnetic transmission. The discussed variants are compared in terms of complexity, cost, fault-tolerance capability and estimated energetic performances but also the grid connectivity for standard conditions. The review is completed by testing options and conditions, as well as the methods for parameter determination, which have an important effect on the controllability of the entire system.
Planning and Analysis of Microgrids for Fast Charging Stations Considering Net Zero Energy Building Indexes
Authors: da Cruz Matheus Souza , Darui Caroline Beatriz Fucks , Abaide Alzenira da Rosa , Knak Neto Nelson , da Silva Leonardo Nogueira Fontoura , dos Santos Laura Lisiane Callai
Published: December 2024

Distributed Energy Resources (DERs) aggregation increases the sustainability of the Electric Vehicles (EVs) market. For example, Fast Charging Stations (FCSs) associated with distributed generation and storage systems in a microgrid infrastructure may be beneficial in increasing self-consumption and peak-shaving strategies and mitigating impacts on the grid. However, microgrid sizing planning is a complex challenge, mainly due to numerous factors related to EV market growth and user behavior. This work defines a methodology focusing on sizing planning and analysis of microgrids for FCSs based on quantitative indices formulated according to the Net Zero Energy Building (NZEB) concept, optimizing self-sufficiency and limiting impacts on the primary electrical grid. The methodology is applied to a real case study considering the growth of EVs in southern Brazil. The developed analyses demonstrate that the proposed microgrid meets the energy needs of the FCS and presents the best NZEB indexes within the considered study horizon. Additionally, representative profiles were characterized for different load and generation conditions, complementing the analyses. It was shown that the storage promotes a delay and reduction in the reverse peak power flow, further enhancing the NZEB indexes.
Classification Model of Site Quality for Mixed Forests Based on the TWINSPAN Method and Site Form in Southwestern Zhejiang
Authors: Wu Ruoyun , Dong Chen , Zhang Chengwei , Gao Weifang , Zheng Xinyu , Lou Xiongwei
Published: December 2024

The evaluation of site quality for mixed forests is a comprehensive approach to analyzing forest site conditions and tree species growth performance. Accurate site quality assessment is crucial for understanding and enhancing the ecological functions and productivity potential of forests. This study focuses on mixed forests in Lishui City, Zhejiang Province. Using the Two-way Indicator Species Analysis (TWINSPAN) method, coniferous mixed forest, broadleaved mixed forest, and mixed coniferous–broadleaved forests in the region were classified into 15 forest types. Site form models for each type were then constructed using the Algebraic Difference Approach (ADA) to categorize site quality levels. Subsequently, a site quality classification model was developed by integrating site and climatic factors, employing four machine learning algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost. This model effectively facilitated the evaluation of site quality in mixed forests. The results showed that, across the 15 forest types, the site form models based on the ADA method achieved R2 values greater than 0.634, indicating accuracy in capturing tree height growth trends in mixed forests. For site quality classification, all four models (RF, KNN, SVM, and XGBoost) achieved overall accuracies above 0.77. Among these, the machine learning models ranked in effectiveness for site quality classification as follows: XGBoost > RF > SVM > KNN. These findings suggest that the site form model is a suitable criterion for classifying site quality in mixed forests in Lishui City, Zhejiang Province, and that the XGBoost-based model demonstrates strong classification accuracy. This study provides a scientific basis for site-adapted tree selection and advances information on mixed forest management.
Study on the Surface Properties of Hot-Waxed Wood with MCC Filled Fischer-Tropsch Synthetic Wax
Authors: Zhang Luoyuan , Song Kuiyan
Published: December 2024

This study aims to enhance the mechanical properties of hot-waxed wood by incorporating microcrystalline cellulose (MCC), thereby addressing the issue of inadequate surface durability. We investigated the effects of varying mass fractions (0%, 5%, 10%, 15%) of MCC on multiple surface properties of hot-waxed wood modified with natural wax and maleic anhydride-ethyl acetate copolymer-grafted Fischer-Tropsch wax. These properties encompass adhesion, hardness, abrasion resistance, impact resistance, surface roughness, gloss, and hydrophobicity. To gain deeper insights into the reinforcing role of MCC, analytical techniques such as Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) were employed to comprehensively characterize the chemical structure, crystalline structure, and surface morphology of the samples. The results reveal that, upon the addition of 5% MCC, the hot-waxed wood treated with modified Fischer-Tropsch wax demonstrates a 41% increase in surface adhesive strength, an improvement in hardness from 2H to 3H, a reduction in surface impact resistance from grade 5 to grade 2, and a 72% decrease in wear mass. Additionally, it exhibits enhanced surface hydrophobicity and thermal stability, while preserving its decorative appearance. These findings endorse the use of MCC in hot-waxed wood, offering significant potential in fields like wooden architecture, indoor wooden decorative panels, and furniture.
Isolation and Identification of Novel Non-Dairy Starter Culture Candidates from Plant Matrix Using Backslopping Propagation
Authors: Andreson Maret , Kazantseva Jekaterina , Kallastu Aili , Jakobson Taaniel , Sarand Inga , Kütt Mary-Liis
Published: December 2024

The majority of non-dairy starter cultures on the market are originally isolated from milk and therefore do not provide the most optimal fermentation for plant matrices. Developing plant-derived starter cultures is essential for creating high-quality, tasty dairy alternatives. This study aims to isolate and characterize bacterial strains with the potential to be used as non-dairy starters from plant sources via backslopping evolution. A natural consortium of macerated plants was inoculated into two oat and two pea commercial drinks and backslopped for seventeen cycles to evolve the bacterial consortium at 25 °C, 34 °C, and 42 °C. The results showed that the initial natural consortium contained less than 1% lactic acid bacteria, and after the seventeenth cycle, lactic acid bacteria dominated in all investigated consortia. Oat Od1-25 and Od2-42 and pea Pd1-34 and Pd1-42 samples were selected for strain isolation based on amplicon-based metagenetic analysis of 16S rRNA gene sequencing and sensory properties. The strain isolation was performed using an out-plating technique, and colonies were identified by MALDI-TOF mass spectrometry. Altogether, eleven lactic acid bacteria species of plant origin were obtained. The strains belonged to the Leuconostoc, Enterococcus, Lactobacillus, and Lactococcus genera.
IoT-Based LPG Level Sensor for Domestic Stationary Tanks with Data Sharing to a Filling Plant to Optimize Distribution Routes
Authors: Morales-Caporal Roberto , Pérez-Loaiza Rodolfo Eleazar , Bonilla-Huerta Edmundo , Hernández-Pérez Julio , Rangel-Magdaleno José de Jesús
Published: December 2024

This research presents the design and implementation of an Internet of Things (IoT)-based solution to measure the percentage of Liquefied Petroleum Gas (LPG) inside domestic stationary tanks. The IoT-based sensor, in addition to displaying the percentage of the LPG level in the tank to the user through a mobile application (app), has the advantage of simultaneously sharing the acquired data with an LPG filling plant via the Internet. The design process and calculations for the selection of the electronic components of the IoT-based sensor are presented. The methodology for obtaining and calibrating the measurement of the tank filling percentage from the magnetic level measurement system is explained in detail. The operation of the developed software, and the communication protocols used are also explained so that the data can be queried both in the user’s app and on the gas company’s web platform safely. The use of the Clark and Wright savings algorithm is proposed to sufficiently optimize the distribution routes that tank trucks should follow when serving different home refill requests from customers located in different places in a city. The experimental results confirm the functionality and viability of the hardware and software developed. In addition, by having the precise location of the tank, the generation of optimized gas refill routes for thirty customers using the heuristic algorithm and a visualization of them on Google Maps is demonstrated. This can lead to competitive advantages for home gas distribution companies.
A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
Authors: AlJamal Mahmoud , Alquran Rabee , Alsarhan Ayoub , Aljaidi Mohammad , Alhmmad Mohammad , Al-Jamal Wafa’ Q. , Albalawi Nasser
Published: December 2024

As the Internet of Things (IoT) expands rapidly and 5G networks become more widespread, the need for strong cybersecurity measures in IoT systems has become increasingly critical. Traditional security methods are no longer sufficient due to the shear volume, diversity, and limited processing capabilities of IoT devices. The high data transmission speeds of 5G networks further intensify the potential risks, making it essential to implement effective security measures. One of the major threats to IoT systems is Cross-Site Scripting (XSS) attacks. To address this issue, we introduce a new machine learning (ML) approach designed to detect and predict XSS attacks on IoT systems operating over 5G networks. By using ML classifiers, particularly the Random Forest classifier, our approach achieves a high classification accuracy of 99.89% in identifying XSS attacks. This research enhances IoT security by addressing the emerging challenges posed by 5G networks and XSS attacks, ensuring the safe operation of IoT devices within the 5G ecosystem through early detection and prevention of vulnerabilities.
Soil Burn Severity Assessment Using Sentinel-2 and Radiometric Measurements
Authors: Llorens Rafael , Sobrino José Antonio , Fernández Cristina , Fernández-Alonso José M. , Vega José Antonio
Published: December 2024

The objective of this article is to create soil burn severity maps to serve as field support for erosion tasks after forest fire occurrence in Spain (2017–2022). The Analytical Spectral Device (ASD) FieldSpec and the CIMEL CE-312 radiometers (optical and thermal, respectively) were used as input data to establish relationships between soil burn severity and reflectance or emissivity, respectively. Spectral indices related to popular forest fire studies and soil assessment were calculated by Sentinel-2 convolved reflectance. All the spectral indices that achieve the separability index algorithm (SI) were validated using specificity, sensitivity, accuracy (ACC), balanced accuracy (BACC), F1-score (F1), and Cohen’s kappa index (k), with 503 field plots. The results displayed the highest overall accuracy results using the Iron Oxide ratio (IOR) index: ACC = 0.71, BACC = 0.76, F1 = 0.63 and k = 0.50, respectively. In addition, IOR was the only spectral index with an acceptable k value (k = 0.50). It is demonstrated that, together with NIR and SWIR spectral bands, the use of blue spectral band reduces atmospheric interferences and improves the accuracy of soil burn severity mapping. The maps obtained in this study could be highly valuable to forest agents for soil erosion restoration tasks.
Effect of Sheep’s Whey Edible Coatings with a Bioprotective Culture, Kombucha Tea or Oregano Essential Oil on Cheese Characteristics
Authors: Pereira Carlos D. , Varytskaya Hanna , Łydzińska Oliwia , Szkolnicka Katarzyna , Gomes David , Pires Arona
Published: December 2024

Films and coatings based on biopolymers have been extensively studied in recent years since they have less impact on the environment, can be obtained from renewable sources, have good coating and film-forming capacity, are biodegradable and can have interesting nutritional properties. In the present study, sheep’s cheese whey powder (SCWP) was used to produce edible cheese coatings. Six types of cheese samples were produced: without coating (CON); treated with natamycin (NAT); with SCWP coating without antimicrobials (WCO); with SCWP coating with a commercial bioprotective culture (WFQ); with SCWP coating with kombucha tea (WKO); and with SCWP coating with oregano essential oil (WEO). At the end of the ripening period, all the cheeses were classified as full-fat and semihard, according to the Portuguese standard. The higher hardness and adhesiveness values of samples CON, WFQ and WKO were in line with the lower moisture in defatted cheese observed in these samples, indicating that future work should address the improvement of water vapor barrier properties of the whey-based coating. The samples treated with natamycin and with oregano essential oil presented significantly lower values for hardness. Differences were also observed on titratable acidity and aw, both between samples and because of ripening time. The color parameters of cheese samples also presented differences, chiefly in the rind, but the highest differences observed resulted from ripening time rather than between samples. In all cases, the counts of lactobacilli and lactococci surpassed log 7 CFU/g by the end of ripening. Regarding yeast and mold counts, the samples CON and WCO presented the highest values by the end of the ripening period (>log 4 CFU/g), while sample NAT presented the lowest value (ca. log 3 CFU/g). Samples WFQ, WKO and WEO presented values which were ca. 0.5 log cycles lower than samples CON and WCO. Hence, the use of SCWP alongside bioprotective culture, kombucha tea or oregano essential oil had a positive impact in the reduction of mold counts on cheese surfaces. Future work should also evaluate the joint use of different antimicrobials.
Amelioration of LPS-Induced Jejunum Injury and Mucus Barrier Damage in Mice by IgY Embedded in W/O/W Emulsion
Authors: Wang Zhaohui , Ye Ruihua , Zhang Shidi , Liu Chuanming , Chen Ke , Zhu Kongdi , Wang Pengjie , Wang Fuqing , Huang Jiaqiang
Published: December 2024

Chicken yolk immunoglobulin (IgY) is a natural immunologically active antibody extracted from egg yolk and can be used as a natural dietary supplement for the treatment of inflammation and damage to the intestines. In our study, IgY was embedded in a double emulsion (W/O/W; DE) to explore the therapeutic effect of the embedded IgY on Lipopolysaccharide (LPS)-induced jejunal injury in mice. The results showed that W/O/W-embedded IgY as a dietary supplement (IgY + DE) attenuated LPS-induced damage to mouse small intestinal structures and protected the integrity of the jejunal mucosal barrier. IgY + DE increased the amount of related transcription factors (Math1, Spdef, Elf3, and Klf4) and promoted thrush cell differentiation. IgY + DE ameliorated LPS-induced reduction in mucin quantity and markers. It promoted the expression of Muc1 and Muc2 and increased the mRNA expression levels of Muc1, Muc2, Muc3, Muc4, Muc13, and Agr2 (p < 0.05). IgY + DE increased the expression of several glycosyltransferases involved in mucin glycosylation. IgY + DE also neutralized the LPS attack on the expression of jejunal inflammatory factors IL-1β, IL-6, IL-4, and TNF-α. In conclusion, the IgY-embedded double emulsion can be used as a dietary supplement for immunotherapy to prevent LPS-induced jejunal injury in mice.
Variety Screening and Characterization Analysis of Storage Stability of Eating Quality of Rice
Authors: Tian Jinyu , Ji Guangmei , Zhang Jiafeng , Luo Danqiu , Zhang Fang , Li Lijiang , Jiang Mingjin , Zhu Dawei , Li Min
Published: December 2024

To screen rice varieties with high storage stability for eating quality and elucidate their traits, 34 widely grown rice varieties were selected to examine the changes in the eating quality of their grains after natural storage for one year. A hierarchical analysis, normalization method, and cluster analysis were used to identify the rice varieties that maintained their eating quality during storage. Meanwhile, the yield and its components, panicle traits, grain size, grain major component content, physiological indicators (such as antioxidant enzyme activity), and key growth stages were analyzed at rice maturity. The results showed that after storage, the values of the appearance, texture, and taste of the cooked rice decreased by 18.7%, 19.1%, and 14.2%, respectively. The storage stability of the eating quality of rice was evaluated using a hierarchical analysis based on the storage stability scores of the appearance, texture, and taste of the cooked rice. A judgment matrix was established, where the corresponding weights of the appearance, texture, and taste of the cooked rice were identified to be 0.105, 0.259, and 0.637, respectively. Based on a cluster analysis and the normalization method, these varieties were classified into three categories regarding the storage stability of their eating quality: high storage stability, intermediate storage stability, and low storage stability, accounting for 14.7%, 52.9%, and 32.4%, respectively. Finally, five rice varieties with high storage stability were screened. These varieties exhibited storage stability scores of the appearance, texture, and taste of cooked rice that were 215%, 219%, and 340% higher than those of the low storage stability type, respectively. The correlation analysis revealed that the storage stability of the eating quality of the rice was significantly and negatively correlated with amylose starch content, peroxidase activity, and malondialdehyde content. The amylose starch content, peroxidase activity, and malondialdehyde content of the high storage stability type were 12.4%, 35.9%, and 6.42% lower than those of the low-storage-stability variety, respectively. Therefore, the major features of rice varieties with high storage stability included low amylose starch content, diminished peroxidase activity, and low malondialdehyde content. This study provides valuable theoretical insights into the safe storage of rice grains and the selection and breeding of rice varieties with high storage stability.