bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Bone Glue on the Performance of Traditional Painted Furniture Ground Layers
Authors: Chen Yushu , Xu Wangyu , Chen Tong , Wang Jianan
Published: December 2024

This research investigates how the inclusion of bone glue affects the performance of traditional painted furniture ground layers, particularly under dry–wet cycling conditions. The ground layers, applied to wood substrates in seven different ratios of bone glue to gypsum powder (10%, 20%, 30%, 40%, 50%, and 60%), were tested for mass changes, dimensional stability, adhesion, and surface roughness. The results showed that higher bone glue content (especially 50% and 60%) led to improved stability, reduced mass fluctuations, and better dimensional stability. The 50% bone glue sample exhibited the best overall stability with minimal weight change (<1.6%) and reduced shrinkage. Adhesion strength increased with bone glue content, reaching 3.48 MPa at 60% bone glue. Lower bone glue content resulted in poor adhesion and visible defects such as cracking and blistering. SEM analysis confirmed that higher bone glue content enhanced bonding between the ground layer and the wood substrate.
Optimization of Weight Reduction in Power Transmission Systems Using Bio-Inspired Algorithms and Composite Materials
Authors: Rodriguez-Cabal Miguel Angel , Botero-Gómez Vanessa , Sanin-Villa Daniel
Published: December 2024

Traditional optimization approaches for power transmission systems often optimize gear and shaft designs separately, rely on conventional materials, and face challenges handling mixed-variable problems involving discrete and continuous variables. These limitations hinder the potential for significant weight reduction and performance improvements in modern mechanical systems. This research presents an innovative optimization approach for reducing the gear pitch in a power transmission system comprising a shaft with two gears. By employing bio-inspired metaheuristic algorithms and utilizing materials common in additive manufacturing and composite materials, the study aims to minimize system weight while ensuring mechanical integrity. The optimization integrates gear and shaft design variables, accurately models mechanical constraints through penalty functions, and leverages the unique properties of advanced materials to enhance performance. In this work, we developed an integrated optimization model that overcomes the limitations of previous studies by combining advanced materials with bio-inspired algorithms to achieve significant weight reduction in gear systems. This approach effectively addresses the complexities of mixed-variable optimization and mechanical constraints, providing a practical and innovative solution for modern engineering applications.
Comparative Analysis of Energy Efficiency in Conventional, Modular, and 3D-Printing Construction Using Building Information Modeling and Multi-Criteria Decision-Making
Authors: Al Masri Abdullah , Haddad Assed N. , Najjar Mohammad K.
Published: December 2024

Energy efficiency has become a crucial focus with the growing attention on sustainable development and decreasing energy consumption in the built environment. Different construction methods are being applied worldwide, such as conventional, modular, and 3D-printing methods, to increase energy efficiency in buildings. This study aims to enhance the decision-making process by identifying optimal construction techniques, material selection, and ventilation window dimensions to promote sustainable energy use in buildings. A novel framework combining Building Information Modeling (BIM), computational analysis, and Multi-Criteria Decision-Making (MCDM) approaches is applied to assess the energy use intensity (EUI), annual electric energy consumption, and lifecycle energy cost across multiple sequences for each type of construction. Computational analysis in this research is combined in two main tools. Minitab is utilized for experimental design to determine the number and configurations of sequences analyzed. The Simple Additive Weighting (SAW) method, applied as an MCDM tool, is used to assess and rank the performance of sequences based on equally weighted criteria. Subsequently, 3D models of case study buildings are developed, and energy simulations are conducted using Autodesk Revit and Autodesk Green Building Studio, respectively, as BIM tools to compare the energy performance of various design alternatives. The results revealed that 3D printing surpassed other methods, where Sequence 7 achieved approximately 10.3% higher efficiency than modular methods and 40.5% better performance than conventional methods in the evaluated criteria. The findings underscore the higher energy efficiency of 3D printing, followed by modular construction as a competitive method, while conventional methods lagged significantly.
Development of a National Colorectal Cancer Screening Research Agenda: An Initiative of the Canadian Screening for Colorectal Cancer Research Network (CanSCCRN)
Authors: Kendell Cynthia , Urquhart Robin , Kyei Akua , Heitman Steven J. , Tinmouth Jill
Published: December 2024

The Canadian Screening for Colorectal Cancer Research Network (CanSCCRN) recently set out to develop a national CRC screening research agenda and identify priority research areas. The specific objectives were to (1) identify evidence gaps relevant to CRC screening and the barriers and facilitators to evidence generation and uptake by CRC screening programs, (2) establish high-priority collaborative research ideas to inform best CRC screening practices, and (3) identify one to two research topics for grant development and submission within 12 to 18 months. Three focus groups were conducted with network members and relevant parties (n = 15) to identify evidence gaps, barriers, and facilitators to evidence generation and uptake. Three workshops were subsequently held to discuss focus group findings and develop an action plan for research. An electronic survey was used to prioritize the evidence gaps to be addressed. Overall, five categories of barriers and six categories of facilitators to evidence uptake and generation were identified, as well as 23 evidence gaps to be addressed. Screening participation, post-polypectomy surveillance, and screening age range were identified as research priority research areas. Adequate resourcing and infrastructure, as well as partnerships with knowledge end users, are integral to addressing these research areas and advancing CRC screening programs in Canada and beyond.
A Framework for Current and New Data Quality Dimensions: An Overview
Authors: Miller Russell , Whelan Harvey , Chrubasik Michael , Whittaker David , Duncan Paul , Gregório João
Published: December 2024

This paper presents a comprehensive exploration of data quality terminology, revealing a significant lack of standardisation in the field. The goal of this work was to conduct a comparative analysis of data quality terminology across different domains and structure it into a hierarchical data model. We propose a novel approach for aggregating disparate data quality terms used to describe the multiple facets of data quality under common umbrella terms with a focus on the ISO 25012 standard. We introduce four additional data quality dimensions: governance, usefulness, quantity, and semantics. These dimensions enhance specificity, complementing the framework established by the ISO 25012 standard, as well as contribute to a broad understanding of data quality aspects. The ISO 25012 standard, a general standard for managing the data quality in information systems, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of digital systems across a multitude of domains. In contrast, frameworks such as ALCOA+, which were originally developed for specific regulated industries, can be applied more broadly but may not always be generalisable. Ultimately, the model we propose aggregates and classifies data quality terminology, facilitating seamless communication of the data quality between different domains when collaboration is required to tackle cross-domain projects or challenges. By establishing this hierarchical model, we aim to improve understanding and implementation of data quality practices, thereby addressing critical issues in various domains.
Sepsis-Associated Acute Kidney Injury: What’s New Regarding Its Diagnostics and Therapeutics?
Authors: Kounatidis Dimitris , Tzivaki Ilektra , Daskalopoulou Stavroula , Daskou Anna , Adamou Andreas , Rigatou Anastasia , Sdogkos Evangelos , Karampela Irene , Dalamaga Maria , Vallianou Natalia G.
Published: December 2024

Sepsis-associated acute kidney injury (SA-AKI) is defined as the development of AKI in the context of a potentially life-threatening organ dysfunction attributed to an abnormal immune response to infection. SA-AKI has been associated with increased mortality when compared to sepsis or AKI alone. Therefore, its early recognition is of the utmost importance in terms of its morbidity and mortality rates. The aim of this review is to shed light on the pathophysiological pathways implicated in SA-AKI as well as its diagnostics and therapeutics. In this review, we will elucidate upon serum and urinary biomarkers, such as creatinine, cystatin, neutrophil gelatinase-associated lipocalin (NGAL), proenkephalin A 119–159, interleukin-6, interleukin-8 and interleukin-18, soluble toll-like receptor 2 (sTLR2), chemokine ligand 2 (CCL2) and chemokine C-C-motif 14 (CCL14). In addition, the role of RNA omics as well as machine learning programs for the timely diagnosis of SA-AKI will be further discussed. Moreover, regarding SA-AKI treatment, we will elaborate upon potential therapeutic agents that are being studied, based on the pathophysiology of SA-AKI, in humans and in animal models.
Comparison of Three-Bag Method Acetylcysteine Versus Two-Bag Method Acetylcysteine for the Treatment of Acetaminophen Toxicity: An Updated Systematic Review and Meta-Analysis
Authors: Alrashed Mohammed , Alyousef Abdulrahman , Badreldin Hisham A. , Bin Saleh Khalid , Al Harbi Shmeylan , Albekairy Abdulkareem M. , Alghamdi Abrar , Al-Nahdi Amal , Alonazi Dhay , Alnuhait Mohammed , Alshammari Abdullah , Alqahtani Tariq
Published: December 2024

Background: Acetaminophen is generally considered safe when used according to the recommended guidelines. Consumption in excessive doses can lead to severe liver damage and, in critical cases, may even result in death. To reduce the effects of acetaminophen overdose, N-acetylcysteine (NAC) has been established as the preferred intervention to prevent liver damage. Objectives: The purpose of this updated systematic review and meta-analysis is to evaluate the potential benefits of a two-bag N-acetylcysteine (NAC) dosing regimen compared to the traditional three-bag protocol in the treatment of acetaminophen-induced liver toxicity. Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The research team utilized the PubMed and Cochrane databases to perform a thorough and comprehensive search of the relevant literature from the inception of these databases up until January 2024. Results: Nine studies were included. The overall use of two-bag NAC was associated with lower anaphylactic reactions and gastrointestinal symptoms compared to the three-bag method. The rate of liver toxicity resolution was the same between the two treatment groups. Conclusions: The two-bag NAC regimen can be considered a safe and effective method for managing acetaminophen toxicity.
Effects of Different Crystallization Protocols on Marginal Gap of Lithium Disilicate Single Crowns: SEM Analysis
Authors: Shadur Alon , Nissan Joseph , Lugassy Diva , Umansky Ariana , Zenziper Eran , Ben-Izhack Gil
Published: December 2024

Objective: In everyday dentistry, lithium disilicate is a valid option for single-fix partial dentures, and this material crystallization process is available with two protocols: long and short. This study’s aim was to assess the effects of these two different crystallization protocols, long and short, on the marginal gap of lithium disilicate single crowns. Methods: A total of 24 abutment plastic teeth were scanned using an intra-oral scanner. For each plastic tooth, an identical pair of lithium disilicate crowns was milled (a total of 48 crowns) by a four-axis machine. Each paired sample was categorized into two groups: long crystallization (24 crowns) and short crystallization (24 crowns). To assess precision, each unit’s marginal gap (including abutments and crowns) was meticulously measured at four specified regions using a scanning electron microscope. A Kolmogorov–Smirnov test performed on the study variables indicated a normal distribution (p > 0.05), and it was followed by independent t-tests (α = 0.05). Results: For the long crystallization group, the mean total marginal gap values were 42.91 ± 9.67 μm, and for the short crystallization group, the values were 43.25 ± 8.14 μm, with no significant difference between the groups (p = 0.894). In addition, no significant differences were found between the groups regarding the mean marginal gap measurements for all four surfaces (distal (p = 0.310), mesial (p = 0.732), palatal (p = 0.655), and buccal (p = 0.535)). Conclusions: Both the long and short crystallization methods used for lithium disilicate single crowns demonstrated marginal gap values of less than 120 μm, which are within the clinically acceptable range, with no significant differences across any parameters between the two groups. Regarding the marginal gap value, it is recommended to use the short crystallization protocol as it is more time-efficient.
A Rule-Based Agent for Unmanned Systems with TDGG and VGD for Online Air Target Intention Recognition
Authors: Chen Li , Yang Jing , Zhou Yuzhen , Ling Yanxiang , Zhang Jialong
Published: December 2024

Air target intention recognition (ATIR) is critical for unmanned systems in modern air defense operations. Through the analysis of typical air defense combat scenarios, first, the paper defines the intention space and intention parameters of air units based on military experience and domain knowledge. Then, a rule-based agent for unmanned systems for online intention recognition is proposed, with no training, no tagging, and no big data support, which is not only for intention recognition and parameter prediction, but also for formation identification of air targets. The most critical point of the agent is the introduction and application of a thermal distribution grid graph (TDGG) and virtual grid dictionary (VGD), where the former is used to identify the formation information of air targets, and the latter is used to optimize the storage space and simplify the access process for the large-scale and real-time combat information. Finally, to have a performance evaluation and application analysis for the algorithm, we carried out a data instance analysis of ATIR for unmanned systems and an air defense warfare simulation experiment based on a Wargame platform; the comparative experiments with the classical k-means, FCNIRM, and the sector-based forward search method verified the effectiveness and feasibility of the proposed agent, which characterizes it as a promising tool or baseline model for the battlefield situational awareness tasks of unmanned systems.
Securing Authentication and Detecting Malicious Entities in Drone Missions
Authors: Constantinescu Nicolae , Ticleanu Oana-Adriana , Hunyadi Ioan Daniel
Published: December 2024

This study proposes a hierarchical communication framework for drone swarms designed to enhance security and operational efficiency. Leveraging elliptic curve cryptography and space quanta concepts, the model ensures continuous authentication and risk assessment of participating entities. Experimental results demonstrate the framework’s effectiveness in mitigating security risks, achieving reliable communication even in adverse conditions. Key findings include significant improvement in threat detection accuracy and reduced computational overhead, validating the model’s applicability for real-world drone swarm operations. These contributions establish a robust foundation for secure and resilient drone coordination.
Engendering Playful Purpose in Pre-Service Early Childhood Educator Preparation: Why Community-Engaged Courses Matter
Authors: Wiltshire Cynthia A. , Pinilla Robyn K. , Garcia Heriberto J.
Published: December 2024

Play is often called the work of children, but questions abound about how early childhood educators are prepared to support children’s learning through play. In this study, we investigated undergraduate pre-service teachers’ perception of community-engaged coursework in the early childhood and elementary educator preparation program of a U.S.-based Hispanic-Serving Institution and how such a course could support student training and capacity for planning playful lessons. We analyzed data collected by our institutional Center for Community Engagement to understand the impact of field-based engagement on student experiences. Findings indicate that despite initially mixed perceptions, students developed professionally and felt positively about engaging children in various play types in the classroom. Practical implications for teacher educators and directions for future research are discussed.
Supporting In-Service and Pre-Service Teachers in Rethinking Formative Literacy Assessments
Authors: Ward Alessandra , Hattan Courtney
Published: December 2024

Oral reading assessments provide teachers with valuable information about children’s reading abilities, which can then inform instruction. However, the structure of some oral reading assessments (e.g., running records) have been critiqued, especially when they are used to provide students with a prescribed “reading level”. The purpose of the current study was to support in-service teachers (ISTs) and pre-service teachers (PSTs) in implementing a highly supportive oral reading assessment, the Listening to Reading–Watching While Writing Protocol (LTR—WWWP), which guides teachers towards specific instructional targets for individual children instead of a score or level. We were curious about the ISTs’ and PSTs’ perceptions of the protocol, including its affordances and limitations. Data sources were the ISTs’ feedback responses and the researcher fieldnotes after five professional learning sessions, as well as the PSTs’ survey responses after learning about the protocol during literacy method courses. Results revealed that both the ISTs and PSTs found the protocol to be useful, and they appreciated the concrete information it provides regarding multiple components of literacy (e.g., decoding, comprehension monitoring). However, they also noted logistical concerns regarding assessment implementation and selecting authentic texts. Results also revealed misconceptions about the assessment, which subsequently informed revisions of the LTR—WWWP and the related professional learning.
Video-Wise Just-Noticeable Distortion Prediction Model for Video Compression with a Spatial–Temporal Network
Authors: Liu Huanhua , Liu Shengzong , Xiao Jianyu , Xu Dandan , Fan Xiaoping
Published: December 2024

Just-Noticeable Difference (JND) in an image/video refers to the maximum difference that the human visual system cannot perceive, which has been widely applied in perception-guided image/video compression. In this work, we propose a Binary Decision-based Video-Wise Just-Noticeable Difference Prediction Method (BD-VW-JND-PM) with deep learning. Firstly, we model the VW-JND prediction problem as a binary decision process to reduce the inferring complexity. Then, we propose a Perceptually Lossy/Lossless Predictor for Compressed Video (PLLP-CV) to identify whether the distortion can be perceived or not. In the PLLP-CV, a Spatial–Temporal Network-based Perceptually Lossy/Lossless predictor (ST-Network-PLLP) is proposed for key frames by learning the spatial and temporal distortion features, and a threshold-based integration strategy is proposed to obtain the final results. Experimental results evaluated on the VideoSet database show that the mean prediction accuracy of PLLP-CV is about 95.6%, and the mean JND prediction error is 1.46 in QP and 0.74 in Peak-to-Noise Ratio (PSNR), which achieve 15% and 14.9% improvements, respectively.
TransSMPL: Efficient Human Pose Estimation with Pruned and Quantized Transformer Networks
Authors: Kim Yeonggwang , Yoo Hyeongjun , Ryu Je-Ho , Lee Seungjoo , Lee Jong Hun , Kim Jinsul
Published: December 2024

Existing Transformers for 3D human pose and shape estimation models often struggle with computational complexity, particularly when handling high-resolution feature maps. These challenges limit their ability to efficiently utilize fine-grained features, leading to suboptimal performance in accurate body reconstruction. In this work, we propose TransSMPL, a novel Transformer framework built upon the SMPL model, specifically designed to address the challenges of computational complexity and inefficient utilization of high-resolution feature maps in 3D human pose and shape estimation. By replacing HRNet with MobileNetV3 for lightweight feature extraction, applying pruning and quantization techniques, and incorporating an early exit mechanism, TransSMPL significantly reduces both computational cost and memory usage. TransSMPL introduces two key innovations: (1) a multi-scale attention mechanism, reduced from four scales to two, allowing for more efficient global and local feature integration, and (2) a confidence-based early exit strategy, which enables the model to halt further computations when high-confidence predictions are achieved, further enhancing efficiency. Extensive pruning and dynamic quantization are also applied to reduce the model size while maintaining competitive performance. Quantitative and qualitative experiments on the Human3.6M dataset demonstrate the efficacy of TransSMPL. Our model achieves an MPJPE (Mean Per Joint Position Error) of 48.5 mm, reducing the model size by over 16% compared to existing methods while maintaining a similar level of accuracy.
DGA Domain Detection Based on Transformer and Rapid Selective Kernel Network
Authors: Tang Jisheng , Guan Yiling , Zhao Shenghui , Wang Huibin , Chen Yinong
Published: December 2024

Botnets pose a significant challenge in network security by leveraging Domain Generation Algorithms (DGA) to evade traditional security measures. Extracting DGA domain samples is inherently complex, and the current DGA detection models often struggle to capture domain features effectively when facing limited training data. This limitation results in suboptimal detection performance and an imbalance between model accuracy and complexity. To address these challenges, this paper introduces a novel multi-scale feature fusion model that integrates the Transformer architecture with the Rapid Selective Kernel Network (R-SKNet). The proposed model employs the Transformer’s encoder to couple the single-domain character elements with the multiple types of relationships within the global domain block. This paper proposes integrating R-SKNet into DGA detection and developing an efficient channel attention (ECA) module. By enhancing the branch information guidance in the SKNet architecture, the approach achieves adaptive receptive field selection, multi-scale feature capture, and lightweight yet efficient multi-scale convolution. Moreover, the improved Feature Pyramid Network (FPN) architecture, termed EFAM, is utilized to adjust channel weights for outputs at different stages of the backbone network, leading to achieving multi-scale feature fusion. Experimental results demonstrate that, in tasks with limited training samples, the proposed method achieves lower computational complexity and higher detection accuracy compared to mainstream detection models.
The Role of Artificial Intelligence and Big Data Analytics in Shaping the Future of Professions in Industry 6.0: Perspectives from an Emerging Market
Authors: Deliu Delia , Olariu Andrei
Published: December 2024

Digital technologies are fundamentally transforming professions by altering roles and redefining competencies across all sectors. The progression from computerization to digitization, digitalization, and now digital transformation has been driven by the widespread integration of artificial intelligence (AI) and big data analytics (BDA). Industry 4.0 introduced smart automation and connectivity, Industry 5.0 emphasized human–machine collaboration and personalization, and Industry 6.0 now integrates advanced technologies with sustainability and ethical considerations, exerting a profound influence on many professions. This transformation is especially significant in emerging markets, where AI and BDA are overhauling traditional practices and enhancing efficiency but also introducing new challenges. Focusing on the accounting profession, this paper examines AI’s and BDA’s dual impact on the roles and skill sets of professional accountants (PAs). Specifically, it addresses how these technologies shape the activities, interactions, roles, and competencies of PAs in an Industry 6.0 context, as well as the opportunities and challenges that arise. Given the public interest role of PAs in ensuring accuracy and transparency in financial reporting, understanding their perceptions and experiences of digital transformation is essential. The findings reveal that while AI and BDA drive efficiency gains and open strategic pathways, they also risk eroding core traditional accounting competencies, reducing client engagement, and raising ethical concerns such as data security and privacy—all of which can undermine service quality and, ultimately, public trust. These insights underscore the need for responsible AI and BDA integration, particularly in emerging markets, where digital literacy gaps and regulatory limitations may slow adoption. This study offers actionable recommendations for policymakers, educators, and organizations, highlighting the importance of ethical standards, targeted training, and sustainable practices to preserve the relevance and integrity of the accounting profession in an increasingly technology-driven era.
Knowledge Extraction from LLMs for Scalable Historical Data Annotation
Authors: Celli Fabio , Mingazov Dmitry
Published: December 2024

This paper introduces a novel approach to extract knowledge from large language models and generate structured historical datasets. We investigate the feasibility and limitations of this technique by comparing the generated data against two human-annotated historical datasets spanning from 10,000 BC to 2000 CE. Our findings demonstrate that generative AI can successfully produce historical annotations for a wide range of variables, including political, economic, and social factors. However, the model’s performance varies across different regions, influenced by factors such as data granularity, historical complexity, and model limitations. We highlight the importance of high-quality instructions and effective prompt engineering to mitigate issues like hallucinations and improve the accuracy of generated annotations. The successful application of this technique can significantly accelerate the development of reliable structured historical datasets, with a potentially high impact on comparative and computational history.
Low-Cost Hardware Analog and Digital Real-Time Circuit Simulators for Developing Power Electronics Control Circuits
Authors: Sozański Krzysztof
Published: December 2024

The paper describes low-cost hardware-based analog and digital real-time circuit simulators for the development of power electronics control circuits. During the process of designing and developing digital control circuits for power electronics systems, preliminary verification of control algorithms is required. For this purpose, software simulators such as Pspice, Psim, Matlab-Simulink, and many others are commonly used. Afterward, the developed control algorithm is implemented in the digital control system. For further verification of the implemented control algorithms, a hardware-based analog or digital simulator can be utilized. The paper presents the author’s proposed analog simulators. In the digital version of the simulator, TMS320F28388D microcontroller with 200 MHz clock was used. These simulators have demonstrated their usefulness in the development of power electronics systems.
Simple Energy Model for Hydrogen Fuel Cell Vehicles: Model Development and Testing
Authors: Ahn Kyoungho , Rakha Hesham A.
Published: December 2024

Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the energy modeling of HFCVs. To address this gap, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. Our HFCV energy model leverages real-time vehicle speed, acceleration, and roadway grade data to determine instantaneous power exertion for the computation of hydrogen fuel consumption, battery energy usage, and overall energy consumption. The results suggest that the model’s forecasts align well with real-world data, demonstrating average error rates of 0.0% and −0.1% for fuel cell energy and total energy consumption across all four cycles. However, it is observed that the error rate for the UDDS drive cycle can be as high as 13.1%. Moreover, the study confirms the reliability of the proposed model through validation with independent data. The findings indicate that the model precisely predicts energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Furthermore, the model is compared to FASTSim, which was developed by the National Renewable Energy Laboratory (NREL), and the difference between the two models is found to be around 2.5%. Additionally, instantaneous battery state of charge (SOC) predictions from the model closely match observed instantaneous SOC measurements, highlighting the model’s effectiveness in estimating real-time changes in the battery SOC. The study investigates the energy impact of various intersection controls to assess the applicability of the proposed energy model. The proposed HFCV energy model offers a practical, versatile alternative, leveraging simplicity without compromising accuracy. Its simplified structure reduces computational requirements, making it ideal for real-time applications, smartphone apps, in-vehicle systems, and transportation simulation tools, while maintaining accuracy and addressing limitations of more complex models.
An Evidential Solar Irradiance Forecasting Method Using Multiple Sources of Information
Authors: Mroueh Mohamed , Doumiati Moustapha , Francis Clovis , Machmoum Mohamed
Published: December 2024

In the context of global warming, renewable energy sources, particularly wind and solar power, have garnered increasing attention in recent decades. Accurate forecasting of the energy output in microgrids (MGs) is essential for optimizing energy management, reducing maintenance costs, and prolonging the lifespan of energy storage systems. This study proposes an innovative approach to solar irradiance forecasting based on the theory of belief functions, introducing a novel and flexible evidential method for short-to-medium-term predictions. The proposed machine learning model is designed to effectively handle missing data and make optimal use of available information. By integrating multiple predictive models, each focusing on different meteorological factors, the approach enhances forecasting accuracy. The Yager combination method and pignistic transformation are utilized to aggregate the individual models. Applied to a publicly available dataset, the method achieved promising results, with an average root mean square error (RMS) of 27.83 W/m2 calculated from eight distinct forecast days. This performance surpasses the best reported results of 30.21 W/m2 from recent comparable studies for one-day-ahead solar irradiance forecasting. Comparisons with deep learning-based methods, such as long short-term memory (LSTM) networks and recurrent neural networks (RNNs), demonstrate that the proposed approach is competitive with state-of-the-art techniques, delivering reliable predictions with significantly less training data. The full potential and limitations of the proposed approach are also discussed.
Solar Energy in Buildings: Feasibility Analysis of Integrated and Conventional Photovoltaic Panels
Authors: Becerra-González Francisco Javier , Vera-Dimas José Gerardo , Cisneros-Villalobos Luis , Martínez-Oropeza Alina
Published: December 2024

The feasibility study is crucial for decision-making in the investment stage of photovoltaic systems projects. A cost–benefit analysis for a project should not be evaluated solely in terms of money in-flows and outflows; it is important to consider other characteristics such as climate, solar irradiation, and the hours of sunshine in different spaces, as well as the electricity rates of the electricity supply company. Therefore, analyzing and simulating the performance conditions, both technical and economic, of photovoltaic systems is key. The objective of this study was to analyze the investment models in two types of photovoltaic systems: one integrated into the construction and the other conventional in a building in the Mexican Republic, considering ideal conditions, thus evaluating the energy efficiency in cities, as they consume around two-thirds of the world’s energy and are responsible for 70% of global greenhouse gas emissions. The methodological proposal was to select a location, determine the predominant type of climate and collect data on solar radiation, the electricity supplier rates, the profitability for a cost–benefit analysis, and the inflation rates to determine the viability of a project that comprehensively covers the variables for decision-making.
An Inherent Decoupled Triple-Active Bridge Converter for All-Electric Aircraft DC Power Systems
Authors: Bossi Giuseppe , Campagna Nicola , Boi Mauro , Miceli Rosario , Damiano Alfonso
Published: December 2024

This paper focuses on a power conditioning system for an all-electric aircraft (AEA) powered by a single battery pack. The research project aims to identify a multi-port DC/DC converter topology that adequately supplies the two DC buses connected to the propulsion system and auxiliary equipment, respectively. To achieve this, a triple-active bridge (TAB) in its inherently decoupled configuration has been investigated, prototyped, and experimentally verified. The TAB voltage control system was designed, simulated, and experimentally validated. Specifically, start-up, steady-state and step-load performances were evaluated by the simulation study and then experimentally validated on a scaled prototype. The results assess the feasibility of using an inherently decoupled TAB as a power conditioning system for interconnecting the AEA battery pack with the electric propulsion and auxiliary systems. In particular, the developed TAB configuration secures the decoupled power transfer between the two output ports providing at the same time good dynamic performance in terms of voltage control during step-load variation.
Estimating Greenhouse Gas Emissions from Hydrogen-Blended Natural Gas Networks
Authors: Paglini Roberto , Minuto Francesco Demetrio , Lanzini Andrea
Published: December 2024

Methane is a significant contributor to anthropogenic greenhouse gas emissions. Blending hydrogen with natural gas in existing networks presents a promising strategy to reduce these emissions and support the transition to a carbon-neutral energy system. However, hydrogen’s potential for atmospheric release raises safety and environmental concerns, necessitating an assessment of its impact on methane emissions and leakage behavior. This study introduces a methodology for estimating how fugitive emissions change when a natural gas network is shifted to a 10% hydrogen blend by combining analytical flowrate models with data from sampled leaks across a natural gas network. The methodology involves developing conversion factors based on existing methane emission rates to predict corresponding hydrogen emissions across different sections of the network, including mainlines, service lines, and facilities. Our findings reveal that while the overall volumetric emission rates increase by 5.67% on the mainlines and 3.04% on the service lines, primarily due to hydrogen’s lower density, methane emissions decrease by 5.95% on the mainlines and 8.28% on the service lines. However, when considering the impact of a 10% hydrogen blend on the Global Warming Potential, the net reduction in greenhouse gas emissions is 5.37% for the mainlines and 7.72% for the service lines. This work bridges the gap between research on hydrogen leakage and network readiness, which traditionally focuses on safety, and environmental sustainability studies on methane emission.
Multi-Objective Coordinated Optimization Method of Active and Reactive Power Considering Power Characteristics of Renewable Energy Converters
Authors: Wang Xuebin , Fu Guobin , Chen Yanbo , Song Rui , Sun Haibin , Ma Jiahao , Huang Tao
Published: December 2024

In new power systems with a high proportion of renewable energy, optimization criteria based solely on economic efficiency or system stability may lead to a reduction in the static stability domain of the system or lead to long-term deviations from economic operation, thus reducing the overall applicability of such methods. This paper proposes a multi-objective active–reactive power coordinated optimization model that considers both economic efficiency and static stability indicators. The goal of the model is to minimize operating costs while optimizing static stability margins. It combines the reactive power support capabilities of converters and other reactive power compensation equipment to ensure safe and economical dispatch of the system. The proposed method is verified through a case study, which shows that this method can make full use of the potential reactive power regulation capability of the converter. At the same time, the economics and stability of the system are significantly improved by using this method. The overall improvement is about is 12.3%.
Deep Symbolic Regression-Based MPPT Control for a Standalone DC Microgrid System
Authors: Khan Muhammad Affan , Usama Muhammad , Kim Jaehong
Published: December 2024

Enhancing the performance of maximum power point tracking (MPPT) methods is essential for optimizing the operation of solar systems under any weather condition. This improvement is crucial because MPPT methods serve as the controllers for power converters, directly influencing the ability to maintain a steady and efficient energy output from solar installations. Therefore, we propose a deep symbolic regression (DSR)-based MPPT method in standalone direct current microgrids, which is particularly useful under drastically shifted meteorological conditions. This study aimed to improve the significant performance parameters of the MPPT approach, such as the rate of convergence during transient periods, tracking accuracy, steady-state stability, and overshoot/undershoot reduction, creating more reliable and efficient solar microgrid systems. To evaluate the proposed method, performance comparisons were made with those of existing advanced algorithms, namely particle swarm optimization-, artificial neural network-, and adaptive neuro-fuzzy inference system-based MPPT methods. The comparative results demonstrate a significant improvement with the proposed method. Moreover, the simple mathematical expression obtained by the DSR process minimizes the computational complexity of the control.
The Detection of an Inter-Turn Short-Circuit Fault in a Brushless Permanent Magnet Motor with Different Winding Configurations
Authors: Korkosz Mariusz , Prokop Jan , Ryłło Karol
Published: December 2024

This article is about the detection of a partial inter-turn short-circuit fault in a brushless motor with permanent magnets (BLPMM). The detection of a single inter-turn short circuit is a difficult issue. The authors of this article tested the sensitivity of the tested powertrain damage detection method. The diagnostic method developed for BLPMM allows for any configuration of the motor winding. A number of analysed configurations have been applied for the combined star–delta connection (YΔ). For the combined star–delta connection Y/Δ, the problem of partial short circuit at two locations was analysed. In the first case, this was the short-circuit winding phenomena in the star part (SC1). In the second case, the short circuit was connected in part to the delta (SC2). A mathematical model has been developed that takes into account both the type of connection and the chance of a partial short circuit of the coil. Based on numerical calculations, the sensitivity of diagnostic methods is designated for both cases. Furthermore, the impact of partial short circuits on motor performance was also examined. The effect of a partial inter-turn short-circuit fault on motor parameters was also determined. Laboratory verification was carried out.
Synthesis of Waste-Derived Geopolymer–Zeolite Composite with Enhanced CO2 Adsorption Capacity
Authors: da Silveira Andresa , Freire Alisson , Elyseu Fábio , de Fátima Peralta Muniz Moreira Regina , Peterson Michael , Doyle Aidan , Pergher Sibele , Hotza Dachamir , De Noni Agenor
Published: December 2024

Carbon dioxide levels in the atmosphere are related to global warming and climate change. Materials to be used for CO2 capture are an important factor in assisting humanity in overcoming this challenge. The goals of this study are to look into the synthesis of adsorbents from red mud (RM), fly ash (FA), and metakaolin (MK). The initial composition was chosen to induce in situ crystallization of zeolites dispersed together with a geopolymer matrix. Two aging steps were used, which combined temperature (25; 95 °C) and atmosphere (air; water). The MK + FA system crystallized zeolite sites dispersed throughout the geopolymer matrix. These crystals were identified as faujasite-Na. They were responsible for the surface area ranging from 23.2 to 238.4 m2.g−1, and CO2 adsorption from 0.83 to 2.32 mmol.g−1 at 35 °C and 1 atm. The best results were obtained by first aging at 95 °C for 120 h, followed by water aging at 25 °C for 120 h.
Thermodynamic and Kinetic Analysis of a CO2 Hydrogenation Pilot Scale Reactor for Efficient Methanol Production
Authors: Peppas Antonis , Politi Chrysa , Charalampopoulos Konstantinos , Kontou Vasiliki , Karellas Sotirios
Published: December 2024

Decarbonization of hard-to-abate industrial sectors, namely the extractive industries, has become an imperative, and thus, processes such as carbon capture and utilization (CCU) have been explored thoroughly and seem to be a promising solution. Carbon dioxide (CO2) catalytic hydrogenation employing green hydrogen (H2) to produce synthetic methanol (MeOH) aims to utilize industrial-captured carbon. A thermodynamic and kinetic analysis of a pilot scale methanol synthesis reactor was conducted by modeling the process using Aspen Plus V12 software. The methanol synthesis model consists mainly of a multi-tubular packed-bed reactor with a thermal oil heat recovery system, a product separator, and an internal recycle loop for optimal efficiency. The reactor has a 5 kg h−1 methanol production capacity, and its heat recovery system achieves an overall heat reduction of 64.1% and can retrieve 1.293 kWh per kg of methanol produced. The overall carbon conversion achieved is 80.6%. Valuable information concerning the design and profile of the reactor is provided in this study.
Seedling Production in Eco-Friendly Tubes Manufactured with Beeswax and Cashew Nutshell
Authors: Paula Yara Lemos de , Melo Rafael Rodolfo de , Paula Edgley Alves de Oliveira , Morais Elis Regina Costa de , Rodolfo Junior Francisco , Pimenta Alexandre Santos , Rusch Fernando , Scatolino Mário Vanoli , Pedrosa Talita Dantas
Published: December 2024

The disposal of polypropylene plastic tubes generated by producing seedlings of forest species promotes negative impacts on the environment and human health. These factors have motivated the search for biodegradable and environmentally friendly materials. Thus, the present study aims to evaluate the efficiency and quality of tubes made from particulate waste from cashew nut shells and beeswax in the development of Mimosa caesalpiniifolia Benth. The sustainable tubes were produced using a natural beeswax matrix with proportions of 10, 20, 30, 40, and 50% cashew nut particles. The chemical characterization of the material was performed. The tubes were made in a handmade cardboard mold and P80 wood sandpaper, 35 mm × 125 mm (diameter × length). The quality of the seedlings was evaluated using the Dickson quality index (DQI). The results showed that the cashew nutshell particles present in their chemical composition have values of 6.83 g kg−1 of nitrogen (N), 0.60 g kg−1 of phosphorus (P), and 1.93 g kg−1 of potassium (K). The quality assessment found that all biodegradable tubes had higher DQI values than polypropylene tubes, emphasizing the treatment with 40% of cashew peel particles, which showed a DQI of 0.14, while the polypropylene tube presented a value of 0.09. Therefore, biodegradable tubes seem a sustainable and efficient alternative for replacing polypropylene tubes in cultivating forest seedlings.
Synergistic Promotion of Direct Interspecies Electron Transfer by Biochar and Fe₃O₄ Nanoparticles to Enhance Methanogenesis in Anaerobic Digestion of Vegetable Waste
Authors: Ma Hongruo , Chen Long , Guo Wei , Wang Lei , Zhang Jian , Zhang Dongting
Published: December 2024

When vegetable waste (VW) is used as a sole substrate for anaerobic digestion (AD), the rapid accumulation of volatile fatty acids (VFAs) can impede interspecies electron transfer (IET), resulting in a relatively low biogas production rate. In this study, Chinese cabbage and cabbage were selected as the VW substrates, and four continuous stirred tank reactors (CSTRs) were employed. Different concentrations of biochar-loaded nano-Fe3O4(Fe3O4@BC) (100 mg/L, 200 mg/L, 300 mg/L) were added, and the organic loading rate (OLR) was gradually increased during the AD process. The changes in biogas production rate, VFAs, and microbial community structure in the fermentation tanks were analyzed to identify the optimal dosage of Fe3O4@BC and the maximum OLR. The results indicated that at the maximum OLR of 3.715 g (VS)/L·d, the addition of 200 mg/L of Fe3O4@BC most effectively promoted an increase in the biogas production rate and reduced the accumulation of VFAs compared to the other treatments. Under these conditions, the biogas production rate reached 0.658 L/g (VS). Furthermore, the addition of Fe3O4@BC enhanced both the diversity and abundance of bacteria and archaea. At the genus level, the abundance of Christensenellaceae_R-7_group, Sphaerochaeta, and the archaeal genus Thermovirga was notably increased.