bluuebunny/crossref_metadata_2025 huggingface dataset:
Training Graduate Students’ Shaping Skills in an Immersive Virtual Reality Environment
Authors: Owusu Gifty , Luke Nicole
Published: November 2024

Graduate students need a wide range of professional skills, and shaping is one of the critical skills they must learn. This study trained graduate students to acquire shaping skills in an immersive virtual reality environment using the Portable Operant Research Teaching Lab (PORTL). To date, no known study has (a) evaluated the effectiveness of shaping skills training for graduate students or (b) attempted to teach these skills in a virtual environment. We used a single-case A-B design across participants with three graduate students who learned shaping skills in an immersive virtual reality environment using the PORTL curriculum. The shaping skills comprised creating a teaching plan, setting up for a session, delivering reinforcement, and evaluating a session. For all participants, training resulted in improvement in shaping skills. Participants also maintained the shaping skills for a minimum of two weeks. Further, the effect of the training generalized to a novel confederate learner for all participants. Additionally, participants showed high satisfaction with learning shaping skills in an immersive virtual reality (iVR) environment.
Designing Dialogic Peer Feedback in Collaborative Learning: The Role of Thinq Tank
Authors: Van Hoe Amber , Wiebe Joel , Slotta Jim , Rotsaert Tijs , Schellens Tammy
Published: November 2024

As technology continues to reshape education, the integration of peer feedback (PF) is increasingly recognized as a key element in promoting student collaboration and learning. This design study tracks the development and implementation of the Thinq Tank tool, which is aimed at facilitating dialogic PF within computer-supported collaborative-inquiry learning (CSCiL) environments. The research was conducted through three design iterations, comparing two key experimental conditions: one contrasting non-dialogic PF with dialogic PF, and another comparing dialogic PF with scripted dialogic PF featuring dynamic sentence-starters and defined roles. Utilizing a quasi-experimental design, we assessed the overall impact on scientific (collaborative) learning outcomes, students’ perceptions of PF, and the composition and elements of PF dialogues. The findings suggest that the integration of dialogic PF within a CSCiL lesson series helps to boost scientific collaborative learning outcomes and increases student receptivity to PF. The study demonstrates that integrating structured support mechanisms within PF tools promotes PF dialogues in which students critically evaluate each other’s work and collaboratively think about ways to improve quality. These insights illustrate that structured, technology-supported PF can significantly advance inquiry outcomes and foster deeper collaborative learning in secondary STEM education.
Speed-Dedup: A New Deduplication Framework for Enhanced Performance and Reduced Overhead in Scale-Out Storage
Authors: Hamandawana Prince , Cho Da-Jung , Chung Tae-Sun
Published: November 2024

Conventional deduplication systems face critical challenges such as excessive write amplification, high read/write latency, and sub-optimal storage utilization. These limitations often undermine the performance benefits of deduplication by slowing down I/O acknowledgements due to amplified deduplication I/Os, excessive data chunk replication, and strict consistency requirements. To address these issues, we present Speed-Dedup, a novel deduplication framework that employs a deduplicated primary–semi-deduplicated replica object approach. This strategy reduces write amplification by restricting deduplication to the primary object while maintaining a semi-deduplicated replica object used for immediate read/write acknowledgements, thus enhancing I/O latency and storage efficiency. Speed-Dedup also replaces traditional strong consistency models with eventual consistency, allowing for non-blocking read operations and improving overall system throughput. Experimental results demonstrate that Speed-Dedup significantly outperforms traditional methods like GRATE and CAO, showing up to 21% improvement in I/O performance under low deduplication ratios and maintaining 14% or more gains under higher ratios. Additionally, write amplification is substantially reduced and latency improves by over 100% with faster recovery times during system failures. These findings highlight the effectiveness of Speed-Dedup as a scalable and efficient solution.
Research on Joint Forecasting Technology of Cold, Heat, and Electricity Loads Based on Multi-Task Learning
Authors: Han Ruicong , Jiang He , Wei Mofan , Guo Rui
Published: November 2024

The cooperative optimization and dispatch operation of the integrated energy system (IES) depends on accurate load forecasts. A multivariate load, joint prediction model, based on the combination of multi-task learning (MTL) and dynamic time warping (DTW), is proposed to address the issue of the prediction model’s limited accuracy caused by the fragmentation of the multivariate load coupling relationship and the absence of future time series information. Firstly, the MTL model, based on the bidirectional long short-term memory (BiLSTM) neural network, extracts the coupling information among the multivariate loads and performs the preliminary prediction; secondly, the DTW algorithm clusters and splices the load data that are similar to the target value as the input features of the model; finally, the BiLSTM-attention model is used for secondary prediction, and the improved Bayesian optimization algorithm is applied for adaptive selection of optimal hyperparameters. Based on the game-theoretic view of Shapley’s additive interpretation (SHAP), a model interpretation technique is introduced to determine the validity of the liquidity indicator and the asynchronous relationship between the significance of the indicator and its actual contribution. The prediction results show that the joint prediction model proposed in this paper has higher training speed and prediction accuracy than the traditional single-load prediction model.
Secure Dual Network for Reversible Facial Image Anonymization Through the Latent Space Manipulation
Authors: Pan Yi-Lun , Chen Jun-Cheng , Wu Ja-Ling
Published: November 2024

We develop a method to automatically and stably anonymize and de-anonymize face images with encoder-decoder networks and provide a robust and secure solution for identity protection. Our fundamental framework is a Neural Network (NN)-based encoder-decoder pair with a dual inferencing mechanism. We denote it as the Secure Dual Network (SDN), which can simultaneously achieve multi-attribute face de-identification and re-identification without any pre-trained/auxiliary model. In more detail, the SDN can take responsibility for successfully anonymizing the face images while generating surrogate faces, satisfying the user-defined specific conditions. Meanwhile, SDN can also execute the de-anonymization procedure and visually indistinguishably reconstruct the original ones if re-identification is required. Designing and implementing the loss functions based on information theory (IT) is one of the essential parts of our work. With the aid of the well-known IT-related quantity, Mutual Information, we successfully explained the physical meaning of our trained models. Extensive experiments justify that with pre-defined multi-attribute identity features, SDN generates user-preferred and diverse appearance anonymized faces for successfully defending against attacks from hackers and, therefore, achieves the goal of privacy protection. Moreover, it can reconstruct the original image nearly perfectly if re-identification is necessary.
An Efficient Parallel CRC Computing Method for High Bandwidth Networks and FPGA Implementation
Authors: Zhang Ling , Ye Shanwei , Gou Zhuo , Yang Xuefei , Dai Qilin , Wang Fuqiang , Lin Yingcheng
Published: November 2024

A cyclic redundancy check (CRC) is a widely used technique in data communication for detecting data transmission errors. However, existing FPGA-based parallel CRC hardware implementation schemes often face problems of excessive resource utilization and timing convergence difficulties in high-bandwidth networks. In addition, these problems are further exacerbated by the variable length of the end of the checksum data frame during data transmission. To address these challenges, this paper proposes a parallel CRC computation method based on precomputed seed values for bit-width normalization (named PSV-WN-CRC). The algorithm selects the corresponding primitive seed value according to the length of the data frame tail and converts the CRC computation with arbitrary bit-width to the CRC computation with fixed bit-width, thus adapting to the case of the indefinite length of the data frame tail. Based on this algorithm, this paper designs an efficient parallel CRC circuit on FPGA to reduce the consumption of resources. The experimental results show that the CRC algorithm and circuit proposed in this paper implemented on Virtex UltraScale+ FPGAs with 1024-bit wide CRC consumes only 5981 LUTs and achieves a maximum throughput of 392.2 Gbps. The method effectively reduces the resource consumption and improves the maximum throughput as compared to three advanced works.
Research on Closed-Loop Control of Screen-Based Guidance Operations in High-Speed Railway Passenger Stations Based on Visual Detection Model
Authors: Xu Chunjie , Du Chenao , Li Mengkun , Shi Tianyun , Sun Yitian , Wang Qian
Published: November 2024

Due to adjustments to the operation plan of guided trains at high-speed railway stations, a large amount of information is inevitably displayed, sometimes with delays, omissions, and misalignments. The effective management of guidance information can provide important support for the personnel flow operation of high-speed railway stations. Aiming to meet the requirements of high real-time and high accuracy of guided job control, a closed-loop control method based on a guided job is proposed, which provides enhanced text detection and recognition in a target area. Firstly, using the introduction of the triplet attention mechanism in YOLOv5 and the addition of fusion modules, the feature pyramid network is used to enhance the effective feature and feature interactions between the modules to improve the detection speed of the display. Then, the text on the guide screen is recognized and extracted in combination with the PaddleOCR model, and then, the results are proofread against the original plan to adjust the screen information. Finally, the effectiveness and feasibility of the method are verified by experimental data, with the accuracy of the improved model reaching 90.6% and the speed reaching 1 ms, which meets the requirement of real-time closed-loop control of Screen-Based Guidance Operations.
Enhancing Rate-Splitting-Based Distributed Edge Computing via Multi-Group Information Recycling
Authors: Liang Wanlin , He Xiaofan
Published: November 2024

To address the straggling effect in distributed edge computing, existing methods often introduce extra computation or communication costs. Recently, information recycling has emerged as an efficient solution that avoids such extra overhead. Nonetheless, the performance of the existing information recycling-assisted rate-splitting-based distributed edge computing scheme significantly hinges on the single common stream, whose rate is limited by the edge node (EN) with the worst channel quality. To this end, a multi-group information recycling scheme for rate-splitting-based distributed edge computing is proposed, where the ENs are clustered into multiple groups, each with its own common stream. In this way, the proposed scheme alleviates the communication limitation of a single common stream, thereby boosting the information recycling mechanism for promoted computation collaboration. A K-medoids-based grouping algorithm is designed for the general multi-antenna case, and a more efficient continuous partitioning-based grouping algorithm is proposed for the special case of single-antenna. Besides, a convex–concave procedure-based algorithm is developed to solve the corresponding latency optimization problem. Simulations demonstrate that the proposed scheme offers significant and robust advantages across various communication and computation conditions. In the considered scenarios, the proposed scheme can substantially reduce the processing latency by up to 51.0% compared to the conventional information recycling scheme.
Health Risk Assessment Using Machine Learning: Systematic Review
Authors: Abhadiomhen Stanley Ebhohimhen , Nzeakor Emmanuel Onyekachukwu , Oyibo Kiemute
Published: November 2024

According to the World Health Organization, chronic illnesses account for over 70% of deaths globally, underscoring the need for effective health risk assessment (HRA). While machine learning (ML) has shown potential in enhancing HRA, no systematic review has explored its application in general health risk assessments. Existing reviews typically focus on specific conditions. This paper reviews published articles that utilize ML for HRA, and it aims to identify the model development methods. A systematic review following Tranfield et al.’s three-stage approach was conducted, and it adhered to the PRISMA protocol. The literature was sourced from five databases, including PubMed. Of the included articles, 42% (11/26) addressed general health risks. Secondary data sources were most common (14/26, 53.85%), while primary data were used in eleven studies, with nine (81.81%) using data from a specific population. Random forest was the most popular algorithm, which was used in nine studies (34.62%). Notably, twelve studies implemented multiple algorithms, while seven studies incorporated model interpretability techniques. Although these studies have shown promise in addressing digital health inequities, more research is needed to include diverse sample populations, particularly from underserved communities, to enhance the generalizability of existing models. Furthermore, model interpretability should be prioritized to ensure transparent, trustworthy, and broadly applicable healthcare solutions.
Reconstructing Signals in Millimeter Wave Channels Using Bayesian-Based Fading Models
Authors: Bastos Silva Claudio , Pompilio Pedro E. , Otobo Theoma S. , Tertuliano Filho Horacio
Published: November 2024

Fading in communication channels presents eminently stochastic characteristics and is a significant challenge, especially at millimeter wave (mmW) frequencies, where the need for lines of sight and the high attenuation of obstacles complicate transmission. This article presents a model based on Bayesian fundamentals intended to improve the description and simulation of stochastic fading effects in these channels. It also includes the use of signal processing techniques to simulate and reconstruct the received signal, simulating the communication channel with an FIR filter. The results obtained by simulating the model show its ability to efficiently capture rapid and profound variations in the signal, typical of those that occur in urban and suburban environments and transmissions in the mmW spectrum. It also provides greater uniformity in signal reconstruction compared to the traditional models that are in use. Using Bayesian fundamentals, which allow dynamic adaptation to change in channel behavior, can improve the efficiency and reliability of networks, especially modern smart networks. Compared to traditional models, the proposed model offers improved signal reconstruction and fading mitigation accuracy, with prospects for future integration in smart communication systems. The better capacity in signal reconstruction presents itself as a differentiator of the model, suggesting greater precision in data transmission.
Fixed-Time Consensus Multi-Agent-Systems-Based Speed Cooperative Control for Multiple Permanent Magnet Synchronous Motors with Complementary Sliding Mode Control
Authors: Hou Limin , Lan Xiaoru
Published: November 2024

To improve the tracking performance and robustness of traditional multi-motor speed cooperative control, this paper proposes a speed cooperative control method for multiple permanent magnet synchronous motors (multi-PMSMs) based on the fixed-time consensus protocol for multi-agent systems (MASs) combined with CSMC. Firstly, the speed regulation system of multi-PMSMs is regarded as a MAS. By designing a distributed consensus protocol based on an undirected communication topology, the system achieves fixed-time consensus convergence. Then, a terminal integral sliding mode observer (TISMO) is designed to estimate disturbances, and feedforward compensation is introduced into the consensus protocol to obtain the desired q-axis current. Furthermore, within the framework of the vector control speed cooperative system of PMSMs, a CSMC is designed to track the q-axis reference current. Meanwhile, the stability of the above controllers and observers is theoretically proven using the Lyapunov functions. Finally, comparative experiments are conducted on a multi-PMSM speed regulation experimental platform to verify the proposed control method against the traditional deviation coupling control (DCC) method. The results indicate that under the new control method proposed in this paper, the chattering phenomenon is reduced by about 2 r/min compared to the traditional DCC method. During sudden load and sudden relief load conditions, the speed fluctuation is reduced by approximately 4%, demonstrating good tracking performance and strong robustness.
Intelligent Financial Forecasting with Granger Causality and Correlation Analysis Using Bayesian Optimization and Long Short-Term Memory
Authors: Olaniyan Julius , Olaniyan Deborah , Obagbuwa Ibidun Christiana , Esiefarienrhe Bukohwo Michael , Adebiyi Ayodele A. , Bernard Olorunfemi Paul
Published: November 2024

Financial forecasting plays a critical role in decision-making across various economic sectors, aiming to predict market dynamics and economic indicators through the analysis of historical data. This study addresses the challenges posed by traditional forecasting methods, which often struggle to capture the complexities of financial data, leading to suboptimal predictions. To overcome these limitations, this research proposes a hybrid forecasting model that integrates Bayesian optimization with Long Short-Term Memory (LSTM) networks. The primary objective is to enhance the accuracy of market trend and asset price predictions while improving the robustness of forecasts for economic indicators, which are essential for strategic positioning, risk management, and policy formulation. The methodology involves leveraging the strengths of both Bayesian optimization and LSTM networks, allowing for more effective pattern recognition and forecasting in volatile market conditions. Key contributions of this work include the development of a novel hybrid framework that demonstrates superior performance with significantly reduced forecasting errors compared to traditional methods. Experimental results highlight the model’s potential to support informed decision-making amidst market uncertainty, ultimately contributing to improved market efficiency and stability.
Multi-Head Attention Affinity Diversity Sharing Network for Facial Expression Recognition
Authors: Zheng Caixia , Liu Jiayu , Zhao Wei , Ge Yingying , Chen Wenhe
Published: November 2024

Facial expressions exhibit inherent similarities, variability, and complexity. In real-world scenarios, challenges such as partial occlusions, illumination changes, and individual differences further complicate the task of facial expression recognition (FER). To further improve the accuracy of FER, a Multi-head Attention Affinity and Diversity Sharing Network (MAADS) is proposed in this paper. MAADS comprises a Feature Discrimination Network (FDN), an Attention Distraction Network (ADN), and a Shared Fusion Network (SFN). To be specific, FDN first integrates attention weights into the objective function to capture the most discriminative features by using the proposed sparse affinity loss. Then, ADN employs multiple parallel attention networks to maximize diversity within spatial attention units and channel attention units, which guides the network to focus on distinct, non-overlapping facial regions. Finally, SFN deconstructs facial features into generic parts and unique parts, which allows the network to learn the distinctions between these features without having to relearn complete features from scratch. To validate the effectiveness of the proposed method, extensive experiments were conducted on several widely used in-the-wild datasets including RAF-DB, AffectNet-7, AffectNet-8, FERPlus, and SFEW. MAADS achieves the accuracy of 92.93%, 67.14%, 64.55%, 91.58%, and 62.41% on these datasets, respectively. The experimental results indicate that MAADS not only outperforms current state-of-the-art methods in recognition accuracy but also has a relatively low computational complexity.
Enhancing YOLOv8’s Performance in Complex Traffic Scenarios: Optimization Design for Handling Long-Distance Dependencies and Complex Feature Relationships
Authors: Li Bingyu , Meng Qiao , Li Xin , Wang Zhijie , Liu Xin , Kong Siyuan
Published: November 2024

In recent years, the field of deep learning and computer vision has increasingly focused on the problem of vehicle target detection, becoming the forefront of many technological innovations. YOLOv8, as an efficient vehicle target detection model, has achieved good results in many scenarios. However, when faced with complex traffic scenarios, such as occluded targets, small target detection, changes in lighting, and variable weather conditions, YOLOv8 still has insufficient detection accuracy and robustness. To address these issues, this paper delves into the optimization strategies of YOLOv8 in the field of vehicle target detection, focusing on the EMA module in the backbone part and replacing the original SPPF module with focal modulation technology, all of which effectively improved the model’s performance. At the same time, modifications to the head part were approached with caution to avoid unnecessary interference with the original design. The experiment used the UA-DETRAC dataset, which contains a variety of traffic scenarios, a rich variety of vehicle types, and complex dynamic environments, making it suitable for evaluating and validating the performance of traffic monitoring systems. The 5-fold cross-validation method was used to ensure the reliability and comprehensiveness of the evaluation results. The final results showed that the improved model’s precision rate increased from 0.859 to 0.961, the recall rate from 0.83 to 0.908, and the mAP50 from 0.881 to 0.962. Meanwhile, the optimized YOLOv8 model demonstrated strong robustness in terms of detection accuracy and the ability to adapt to complex environments.
A Novel Heuristic Algorithm Integrating Battery Digital-Twin-Based State-of-Charge Estimation for Optimized Electric Vehicle Charging
Authors: Iyer Nandini G. , Ponnurangan Sivakumar , Abdul Gafoor Nazar Ali , Rajendran Anand , Lashab Abderezak , Saha Diptish , Guerrero Josep M.
Published: November 2024

The increasing need for effective electric vehicle (EV) charging solutions in the context of transportation electrification has become a significant challenge. This system introduces an innovative algorithm, named Energy Distribution and Node Allocation using Evolutionary and Resourceful Optimization (ENDEAVOR), designed to elevate the efficiency of EV charging through the integration of a battery’s digital twin. This cutting-edge algorithm offers precise estimations of EV charging time, seamlessly updating both the State of Charge (SOC) via the Unscented Kalman Filter (UKF) and the internal battery resistance using parameterization, while sending this information to the cloud. ENDEAVOR optimizes charging-node allocation and intelligently distributes energy among incoming EVs based on their specific charging requests, all within the context of renewable-energy-sourced charging stations. The incorporation of a digital twin for the battery confers several benefits, including highly accurate SOC and charging-time estimates that ultimately enhance the overall efficiency of the charging process. This algorithm further optimizes energy distribution, resulting in significantly improved charging-time predictions, reduced wait times for users, and an enhanced overall experience for the user. The day-to-day implications of these enhancements are remarkable, culminating in substantial annual energy savings of approximately 180 units. ENDEAVOR has the potential to revolutionize the landscape of EV charging.
An Efficient Framework for Finding Similar Datasets Based on Ontology
Authors: Sultana Tangina , Qudus Umair , Umair Muhammad , Hossain Md. Delowar
Published: November 2024

Governments are embracing an open data philosophy and making their data freely available to the public to encourage innovation and increase transparency. However, the number of available datasets is still limited. Finding relationships between related datasets on different data portals enables users to search the relevant datasets. These datasets are generated from the training data, which need to be curated by the user query. However, relevant dataset retrieval is an expensive operation due to the preparation procedure for each dataset. Moreover, it requires a significant amount of space and time. In this study, we propose a novel framework to identify the relationships between datasets using structural information and semantic information for finding similar datasets. We propose an algorithm to generate the Concept Matrix (CM) and the Dataset Matrix (DM) from the concepts and the datasets, which is then used to curate semantically related datasets in response to the users’ submitted queries. Moreover, we employ the proposed compression, indexing, and caching algorithms in our proposed scheme to reduce the required storage and time while searching the related ranked list of the datasets. Through extensive evaluation, we conclude that the proposed scheme outperforms the existing schemes.
Optimized YOLOv5 Architecture for Superior Kidney Stone Detection in CT Scans
Authors: Abdimurotovich Khasanov Asliddin , Cho Young-Im
Published: November 2024

The early and accurate detection of kidney stones is crucial for effective treatment and improved patient outcomes. This paper proposes a novel modification of the YOLOv5 model, specifically tailored for detecting kidney stones in CT images. Our approach integrates the squeeze-and-excitation (SE) block within the C3 block of the YOLOv5m architecture, thereby enhancing the ability of the model to recalibrate channel-wise dependencies and capture intricate feature relationships. This modification leads to significant improvements in the detection accuracy and reliability. Extensive experiments were conducted to evaluate the performance of the proposed model against standard YOLOv5 variants (nano-sized, small, and medium-sized). The results demonstrate that our model achieves superior performance metrics, including higher precision, recall, and mean average precision (mAP), while maintaining a balanced inference speed and model size suitable for real-time applications. The proposed methodology incorporates advanced noise reduction and data augmentation techniques to ensure the preservation of critical features and enhance the robustness of the training dataset. Additionally, a novel color-coding scheme for bounding boxes improves the clarity and differentiation of the detected stones, facilitating better analysis and understanding of the detection results. Our comprehensive evaluation using essential metrics, such as precision, recall, mAP, and intersection over union (IoU), underscores the efficacy of the proposed model for detecting kidney stones. The modified YOLOv5 model offers a robust, accurate, and efficient solution for medical imaging applications and represents a significant advancement in computer-aided diagnosis and kidney stone detection.
Using Deep Learning, Optuna, and Digital Images to Identify Necrotizing Fasciitis
Authors: Tsai Ming-Jr , Lin Chung-Hui , Lai Jung-Pin , Pai Ping-Feng
Published: November 2024

Necrotizing fasciitis, which is categorized as a medical and surgical emergency, is a life-threatening soft tissue infection. Necrotizing fasciitis diagnosis primarily relies on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound scans, surgical biopsy, blood tests, and expert knowledge from doctors or nurses. Necrotizing fasciitis develops rapidly, making early diagnosis crucial. With the rapid progress of information technology and systems, in terms of both hardware and software, deep learning techniques have been employed to address problems in various fields. This study develops an information system using convolutional neural networks (CNNs), Optuna, and digital images (CNNOPTDI) to detect necrotizing fasciitis. The determination of the hyperparameters in convolutional neural networks plays a critical role in influencing classification performance. Therefore, Optuna, an optimization framework for hyperparameter selection, is utilized to optimize the hyperparameters of the CNN models. We collect the images for this study from open data sources such as Open-i and Wikipedia. The numerical results reveal that the developed CNNOPTDI system is feasible and effective in identifying necrotizing fasciitis with very satisfactory classification accuracy. Therefore, a potential future application of the CNNOPTDI system could be in remote medical stations or telemedicine settings to assist with the early detection of necrotizing fasciitis.
Evaluation of Organic Waste Long-Term Effects on Cellulose, Hemicellulose and Lignin Content in Energy Grass Species Grown in East-Central Poland
Authors: Malinowska Elżbieta , Torma Stanislav
Published: November 2024

Biomass can be used for electricity generation, especially in developing countries, but also in developed ones, where the utilization of renewable energy sources is being integrated into a sustainable economy. There are considerable differences in the scale of biomass use and in the technology of its processing. One of the most important sources of biofuel is the biomass of grass. This research aimed to determine the long-term effects of organic fertilizers on cellulose, hemicellulose, and lignin content in the biomass of three grass species: giant miscanthus (Miscanthus × giganteus), prairie cordgrass (Spartina pectinata), and switchgrass (Panicum virgatum L.) in the first three years of growth. The experiment was established in four replications on microplots of 2 m2 in April 2018. Before planting grass rhizomes, municipal sewage sludge (SS) and spent mushroom substrate (SMS) were introduced into the soil in various combinations. Biomass is harvested in December every year. The content of structural polysaccharides in the grass species statistically significantly varied in response to organic waste. Compared to other fertilizer combinations, SS application increased the content of cellulose in the biomass of Miscanthus giganteus (43.66% of DM) and Spartina pectinata (37.69% of DM) and hemicellulose in Spartina pectinata (27.80% of DM) and Panicum virgatum (23.64% of DM). Of the three species of grass, the chemical composition of Miscanthus giganteus cell walls was the most favorable for biofuel production, with the most cellulose and hemicellulose and the least lignin compared to other grass species. The content of lignin in the biomass of Miscanthus × giganteus and Spartina pectinata was the greatest on the plot with SMS and amounted to 7.79% of DM and 12.32% of DM, respectively. In the case of Panicum virgatum, the average content of lignin was similar across all fertilized plots, with 15.42% DM.
An Electromagnetic Field Concentrator for Process Chambers
Authors: Paplicki Piotr , Strobejko Piotr
Published: November 2024

This paper describes selected results of the design and optimization of an electromagnetic field concentrator (MFC) for process chambers. On the basis of 2D/3D FE analysis, it was confirmed that the presented new structure for an electromagnetic field concentrator with three-phase windings and an additional DC central coil excites, in the working space of the device, a steady or time-varying magnetic field with adjustable peak value, pulsation amplitude and vector magnetic field orientation. The presented field concentrator can be used for process chambers carrying out chemical, biological, thermal and measurement processes, among others.
A Comprehensive Review of CO2 Mineral Sequestration Methods Using Coal Fly Ash for Carbon Capture, Utilisation, and Storage (CCUS) Technology
Authors: Uliasz-Bocheńczyk Alicja
Published: November 2024

CO2 emissions from fossil fuel combustion are the main source of anthropogenic greenhouse gases (GHGs). A method of reducing CO2 emissions is CCUS (carbon capture, utilisation, and storage) technology. One part of CCUS technology involves mineral sequestration as its final stage, utilisation, which can be carried out using natural raw materials or waste. This is a particularly interesting option for power and CHP plants that use coal as their primary fuel. Combustion processes produce fly ash as a waste by-product, which has a high potential for CO2 sequestration. Calcium fly ash from lignite combustion and fly ash from fluidised bed boilers have particularly high potential due to their high CaO content. Fly ash can be used in the mineral sequestration of CO2 via direct and indirect carbonation. Both methods use CO2 and flue gases. Studies conducted so far have analysed the influence of factors such as temperature, pressure, and the liquid-to-solid (L/S) ratio on the carbonation process, which have shown different effects depending on the ash used and the form of the process. Due to the large differences found in the properties of fly ash, related primarily to the type of fuel and boiler used, the process of mineral CO2 sequestration requires much research into its feasibility on an industrial scale. However, the method is promising for industrial applications due to the possibility of reducing CO2 emissions and, at the same time, recovering waste.
Fossil Fuel Prospects in the Energy of the Future (Energy 5.0): A Review
Authors: Zhironkin Sergey , Abu-Abed Fares
Published: November 2024

Achieving the energy and climate goals of sustainable development, declared by the UN as imperative and relevant for the upcoming Society 5.0 with its human-centricity of technological development, requires ensuring a “seamless” Fourth Energy Transition, preserving but at the same time modifying the role of fossil fuels in economic development. In this regard, the purpose of this review is to analyze the structure of publications in the field of technological platforms for the energy of the future (Energy 5.0), with digital human-centric modernization and investment in fossil fuel extraction in the context of the Fourth Energy Transition. To achieve this goal, this review presents a comprehensive overview of research in the field of determining the prospects of fossil fuels within Energy 5.0, characterized not only by the dominance of renewable energy sources and the imperative of zero CO2 emissions, but also by the introduction of human-centric technologies of Industry 5.0 (the Industrial Internet of Everything, collaborative artificial intelligence, digital triplets). It was concluded that further research in such areas of Energy 5.0 development as the human-centric vector of modernization of fossil fuel extraction and investment, achieving energy and climate goals for sustainable development, reducing CO2 emissions in the mineral extractive sector itself, and developing CO2 capture and utilization technologies is important and promising for a “seamless” Fourth Energy Transition.
Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods
Authors: Ma Minghui , Pektezel Oguzhan , Ballerini Vincenzo , Valdiserri Paolo , Rossi di Schio Eugenia
Published: November 2024

The coefficient of performance (COP) is a crucial metric for evaluating the efficiency of heat pump systems. Real-time monitoring of heat pump system performance necessitates continuously collecting and processing data from various components utilizing multiple sensors and controllers. This process is inherently complex and presents significant challenges. In recent years, artificial intelligence (AI) models have increasingly been applied in refrigeration, heat pump, and air conditioning systems due to their capability to identify and analyze complex patterns and data relationships, demonstrating higher accuracy and reduced computation time. In this study, multilayer perceptron (MLP), support vector machines (SVM), and random forest (RF) are used to develop COP prediction models for solar-assisted heat pumps. By comparing the predictive accuracy and modeling time of the three models built, the results demonstrate that the random forest model achieves the best prediction performance, with a mean absolute error (MAE) of 2.42% and a root mean squared error (RMSE) of 4.01% on the train set. On the test set, the MAE was 2.35% and the RMSE was 3.84%. The modeling time for the RF model was 6.57 s.
Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory
Authors: Bian Jiang , Wang Yang , Dang Zhaoshuai , Xiang Tianchun , Gan Zhiyong , Yang Ting
Published: November 2024

In the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon measurement across all operational aspects of distribution networks, reduce their carbon emissions through controlling unit operations, and ensure stable and safe operation. First, we propose a method for measuring carbon emission intensity on the source and network sides of active distribution networks with network losses, allowing for the calculation of total carbon emissions throughout the operation of networks and their equipment. Next, based on the carbon flow distribution of distribution networks, we construct a low-carbon dispatch model and formulate its optimization problem within a Markov Decision Process framework. We improve the Soft Actor–Critic (SAC) algorithm by adopting a Gaussian-distribution-based reward function to train and deploy agents for optimal low-carbon dispatch. Finally, the effectiveness of the proposed model and the superiority of the improved algorithm are demonstrated using a modified IEEE 33-bus distribution network test case.
Charging Profile Modeling of Electric Trucks at Logistics Centers
Authors: Walz Kathrin , Rudion Krzysztof
Published: November 2024

The future charging requirements of electric trucks will lead to new demands on the power grid. In order not to slow the expansion of the charging infrastructure for electric trucks, the power grid must be strengthened for this purpose. However, due to the limited penetration of electric trucks in fleets to date, grid planners lack information on their time- and location-dependent charging demand. The question arises as to how the charging demand of electric trucks can be realistically taken into account in power grid simulations. This paper therefore presents a methodology that makes it possible to quantify the charging demand of electric trucks at typical charging locations and derives initial parameters for power system planning with electric trucks. For location-based charging demand modeling, the arrival and departure behavior of trucks at representative logistics centers is combined with mobility data and vehicle parameters. This allows the determination of time series-based charging demand. A charging demand analysis at five different logistics center types shows that that energy demand, peak load, and temporal behavior vary greatly depending on the center type. It is therefore advisable to take these different charging location types into account when designing the electricity grids.
A Novel Cooling System for High-Speed Axial-Flux Machines Using Soft Magnetic Composites
Authors: Meier Matthew , Strangas Elias G.
Published: November 2024

Demand is high for small, lightweight, and power-dense machines. However, as power increases and size decreases, rejecting losses becomes more difficult. Many novel cooling systems have been developed, which have allowed machines to be made smaller while increasing power. This paper proposes a cooling system making use of soft magnetic composite (SMC) cores to improve cooling specifically in a high-speed axial-flux machine via the use of an integrated cooling channel in the SMC core. A series of experiments on a prototype machine are performed and the experimental data are used to determine a set of parameters for the FEA thermal model. Using the thermal FEA model, a comparison is completed with a traditional closed cooling system using laminated steels and an attached cooling plate.The SMC machine is then simulated at speeds up to 160 krpm and currents up to 8 A. To achieve the same coil temperature between the two designs, the laminated steel model required 4 MPa contact pressure at 10 krpm and 5 MPa contact pressure at 20 krpm. At the same time, the novel design removed approximately 20% more heat per shear air gap surface area and approximately 15% more heat per total machine surface area than the version with the attached cooling plate. Extending the operating range of the model to 160 krpm demonstrated that the maximum temperature rise remained below 180 °C.
Numerical Investigations on the Enhancement of Convective Heat Transfer in Fast-Firing Brick Kilns
Authors: Unterluggauer Julian , Schieder Manuel , Gutschka Stefan , Puskas Stefan , Vogt Stefan , Streibl Bernhard
Published: November 2024

In order to reduce CO2 emissions in the brick manufacturing process, the effectiveness of the energy-intensive firing process needs to be improved. This can be achieved by enhancing the heat transfer in order to reduce firing times. As a result, current development of tunnel kilns is oriented toward fast firing as a long-term goal. However, a struggling building sector and complicated challenges, such as different requirements for product quality, have impeded developments in this direction. This creates potential for the further development of oven designs, such as improved airflow through the kiln. In this article, numerical flow simulations are used to investigate two different reconstruction measures and compare them to the initial setup. In the first measure, the kiln height is reduced, while in the second measure, the kiln cars are adjusted to alternate the height of the bricks so that every other pair of bricks is elevated, creating a staggered arrangement. Both measures are investigated to determine the effect on the heating rate compared to the initial configuration. A transient grid independence study is performed, ensuring numerical convergence and the setup is validated by experimental results from measurements on the initial kiln configuration. The simulations show that lowering the kiln height improves the heat transfer rate by 40%, while the staggered arrangement of the bricks triples it. This leads to an average brick temperature after two hours which is around 130 °C higher compared to the initial kiln configuration. Therefore, the firing time can be significantly reduced. However, the average pressure loss coefficient rises by 70% to 90%, respectively, in the staggered configuration.
Examining the Potential of Biogas: A Pathway from Post-Fermented Waste into Energy in a Wastewater Treatment Plant
Authors: Michalski Krzysztof , Kośka-Wolny Magdalena , Chmielowski Krzysztof , Bedla Dawid , Petryk Agnieszka , Guzdek Paweł , Dąbek Katarzyna Anna , Gąsiorek Michał , Grübel Klaudiusz , Halecki Wiktor
Published: November 2024

Biogas has improved due to technological advancements, environmental awareness, policy support, and research innovation, making it a more cost-effective and environmentally friendly renewable energy source. The Generalized Linear Model (GLM) was employed to examine the relationship between purchased and generated energy from 2007 to 2023. Metrics such as deviance, log likelihood, and dispersion phi were examined to assess model fit. The Mann–Kendall test was utilized to detect trends in energy datasets. Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) reduction was significant, exceeding 97% from 2014 to 2023. However, treated sewage displayed limited susceptibility to biological degradation, with COD to BOD5 ratios increasing from 2.28 to 6.59 for raw sewage and from 2.33 to 7.05 for treated sewage by 2023. Additionally, the efficiency of sewage purification processes was calculated, and multivariate regression analysis was conducted on gas composition data. Principal Coordinate Ordination (PCO) and k-means clustering were used for dimensionality reduction and biogas component clustering, respectively. This research showed that biogas from the waste water treatment process can be used, particularly in methane production. Technological advancements have made biogas production more efficient, enhancing energy generation within a circular economy framework.
The Analysis of Geospatial Factors Necessary for the Planning, Design, and Construction of Agricultural Biogas Plants in the Context of Sustainable Development
Authors: Kochanek Anna , Ciuła Józef , Generowicz Agnieszka , Mitryasova Olena , Jasińska Aleksandra , Jurkowski Sławomir , Kwaśnicki Paweł
Published: November 2024

This spatial analysis focuses on the relevant elements regarding the use of renewable energy sources, in particular, biogas, taking into account sustainability. The characteristics of the processes of biogas formation are presented, including the technological and biological aspects of its production. The issues of agricultural biogas plant profitability are discussed using examples from Poland, analyzing the economic and financial aspects of investing in this type of plant. Geospatial analysis supported by geographic information systems (GISs) looked at factors affecting the relief of the land and also the analysis of local plans or zoning decisions for the selected site. In addition, distance relationships and geological aspects taking into account terrain, landslides, and forestation, as well as hydrological aspects relating to flood and flooding hazards, were examined. It analyzed the availability of utility networks and proximity to road infrastructure. It was also examined that the proposed project will not have a negative impact on environmental protection. The analyzed site meets favorable conditions for the construction of the proposed project, and the conclusion drawn from this study is to emphasize the importance of integrating renewable energy sources with information systems to achieve sustainable development.
Model Predictive Control for Three-Phase, Four-Leg Dynamic Voltage Restorer
Authors: Liu Decan , Zhang Huaying , Liang Xiaorui , Deng Shicong
Published: November 2024

Dynamic voltage restores (DVRs) are usually used to mitigate the effect of voltage sag and guarantee sufficient power supply for sensitive loads. However, three-phase voltage cannot be compensated to the desired balance voltage under unbalanced three-phase loads by traditional DVRs with a three-phase, three-leg inverter. To address this problem, a three-phase, four-leg inverter-based DVR is first introduced in this paper, and then the state space model in its continuous form and discrete form are established, respectively. A two-step predictive method is proposed for the prediction of the output voltage in each switching state by the established voltage prediction model. Finite-control-set model predictive control (MPC) is developed to be used in the three-phase, four-leg inverter-based DVR. Its dynamic response is effectively improved by the proposed MPC method under various voltage sag conditions. The proposed DVR control strategy is validated via MATLAB/Simulink-R2022b simulations, which demonstrate its effectiveness in voltage compensation under various sag conditions.