bluuebunny/crossref_metadata_2025 huggingface dataset:
An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles
Authors: Piyakawanich Prot , Phasukkit Pattarapong
Published: December 2024

In the rapidly evolving domain of Unmanned Aerial Vehicles (UAVs), precise altitude estimation remains a significant challenge, particularly for lightweight UAVs. This research presents an innovative approach to enhance altitude estimation accuracy for UAVs weighing under 2 kg without cameras, utilizing advanced AI Deep Learning algorithms. The primary novelty of this study lies in its unique integration of unsupervised and supervised learning techniques. By synergistically combining K-Means Clustering with a multiple-input deep learning regression-based model (DL-KMA), we have achieved substantial improvements in altitude estimation accuracy. This methodology represents a significant advancement over conventional approaches in UAV technology. Our experimental design involved comprehensive field data collection across two distinct altitude environments, employing a high-precision Digital Laser Distance Meter as the reference standard (Class II). This rigorous approach facilitated a thorough evaluation of our model’s performance across varied terrains, ensuring robust and reliable results. The outcomes of our study are particularly noteworthy, with the model demonstrating remarkably low Mean Squared Error (MSE) values across all data clusters, ranging from 0.011 to 0.072. These results not only indicate significant improvements over traditional methods, but also establish a new benchmark in UAVs altitude estimation accuracy. A key innovation in our approach is the elimination of costly additional hardware such as Light Detection and Ranging (LiDAR), offering a cost-effective, software-based solution. This advancement has broad implications, enhancing the accessibility of advanced UAVs technology and expanding its potential applications across diverse sectors including precision agriculture, urban planning, and emergency response. This research represents a significant contribution to the integration of AI and UAVs technology, potentially unlocking new possibilities in UAVs applications. By enhancing the capabilities of lightweight UAVs, we are not merely improving a technical aspect, but revolutionizing the potential applications of UAVs across industries. Our work sets the stage for safer, more reliable, and precise UAVs operations, marking a pivotal moment in the evolution of aerial technology in an increasingly UAV-dependent world.
Optimization Design and Experimental Study of Solid Particle Spreader for Unmanned Aerial Vehicle
Authors: Zhang Linhuan , Zhang Ruirui , Yi Tongchuan , Zhang Danzhu , Ding Chenchen , Wu Mingqi , Noguchi Ryozo
Published: December 2024

This study designed and investigated a solid particle spreader, as well as parameter optimization and experimental for a groove wheel, to mitigate the problems of low uniformity and poor control accuracy of solid particulate material UAV spreading. The discrete element method was used to simulate and analyze the displacement range and stability of each grooved wheel at low speeds. Furthermore, orthogonal regression and response surface analyses were used to analyze the influence of each factor on the stability of the discharge rate and pulsation amplitude. The results showed that the helix angle, sharpness, and length of the groove significantly influenced the application performance, whereas the number of grooves had no significant influence. The groove shape was eccentric, the helix angle was 50°, the length was 35 mm, and the number of grooves was 7. Additionally, the bench test results showed that in the range of 10–60 rpm, the relative deviation of the discharging rate between the simulation and bench test is from 0.47% to 10.39%, and the average relative deviation is 3.93%. Between the groove wheel rotation speed and discharge rate, R2 was 0.991, and the adjustable range of the discharge amount was between 3.68 and 23.43 g/s. The minimum and maximum variation coefficients of the average discharge rate among individual applicators were 1.01% and 2.79%, respectively, whereas the standard deviations were 0.09 and 0.46 g/s, respectively. In conclusion, the discharge stability and adjustable range of the spreader using the optimized groove wheel satisfied the requirements for solid particulate material discharge.
Performance Analysis of Multiple UAV-Based Hybrid Free-Space Optical/Radio Frequency Aeronautical Communication System in Mobile Scenarios
Authors: Zhang Xiwen , Zhao Shanghong , Wang Yuan , Hu Hang , Yang Guangmingzi , Song Xinkang , Li Xin , Li Jianjia
Published: December 2024

Free-space optical (FSO) communication with unmanned aerial vehicles (UAVs) as relays is a promising technology for future aeronautical communication systems. In this paper, a multiple UAV-based aeronautical communication system is proposed, wherein a hybrid FSO/radio frequency (RF) link is established to connect the Airborne Warning and Control System (AWACS) with the mobile ground station (GS). Initially, we consider the velocity variance of both AWACS and the mobile GS, along with the influence of the Doppler effect. Furthermore, four relay selection modes are proposed, and exact closed expressions are derived for the end-to-end outage probability and bit error rate (BER) of the considered system. Numerical simulations demonstrate the impact of velocity variance variation on system performance. Additionally, we analyze the applicability of these four relay modes under different platform mobility characteristics through simulation results, while discussing the optimal numbers and the deployment altitude of UAVs. Finally, effective design guidelines that can be useful for aeronautical communication system designers are presented.
Research on Anti-Submarine Warfare Method of Unmanned Aerial Vehicle Cluster Based on Area Coverage and Distributed Optimization Control
Authors: Yan Yongzhao , Liu Yi , Bi Ying , Wang Qian , Cao Huazhen , Yu Ziping , Li Kangkang , Wang Bo , Ma Xiaoping
Published: December 2024

Maritime security is vital to national security. The application of unmanned aerial vehicle (UAV) clusters in marine anti-submarine warfare (ASW) presents a new and significant challenge worthy of in-depth study. Based on the anti-submarine principle of geomagnetic anomaly detection and the Find-Fix-Track-Target-Engage-Assess (F2T2EA) framework, this paper divides UAV cluster ASW operations into two stages: regional coverage and cooperative convergence. The regional coverage stage enables the UAV cluster to perform a broad search for submarines, while the cooperative convergence stage facilitates the precise positioning of detected submarines. In simulation, the combat scenario of five UAV clusters against a submarine is carried out. In the given area, the UAV can locate and stalk the target submarine in limited time. Simulation results demonstrate the feasibility of the proposed approach, providing reference for advancing marine ASW capabilities and related research.
Editorial “Thermodynamic Optimization of Industrial Energy Systems”
Authors: Florez-Orrego Daniel , Ribeiro Domingos Meire Ellen , Nogueira Nakashima Rafael
Published: December 2024

Thermodynamic optimization of industrial energy systems is crucial for finding solutions to reduce energy consumption and mitigate losses, leading to environmental and economic benefits [...]
Accurate Evaluation of Commutations of 650 V GaN Power Switches Assisted by Electromagnetic Simulations in a 7 kW Dual Active Bridge Converter for Automotive Battery Charging Applications
Authors: Reali Alessandro , Alemanno Alessio , Rossi Carlo , Florian Corrado
Published: December 2024

The exploitation of high-voltage GaN power switches enables the development of power converters with superior characteristics with respect to components developed with heritage silicon-based technologies. One of the key advantages of GaN switches is their very fast commutation capability due to reduced parasitic capacitance and inductance with respect to silicon devices. The capability of an accurate evaluation of the switch commutations in the design phase is of crucial importance to maximize performance and avoid reliability or electromagnetic compatibility issues in the final converter. In this paper, an accurate evaluation of high-voltage GaN HEMT commutations is performed, exploiting detailed non-linear dynamic models of transistors and electromagnetic simulations of a PCB. A deep insight into the commutation waveforms in the intrinsic device (i.e., conductive drain current and intrinsic node voltages) is proposed to evaluate and explain the mechanisms of almost lossless turn-off and turn-on commutations in a 7 kW DAB converter. The influence on the performance of the PCB parasitics and the driver characteristics are accurately reproduced by simulations, suggesting important guidelines for the optimal design of power converters fully exploiting GaN HEMT’s potential. This detailed simulation/analysis approach for transistor commutation is typically adopted in Radio Frequency amplifier design but also becomes very valuable in power converter design when the very fast commutations of a GaN HEMT at a high switching frequency cannot be fully described and taken under control with conventional approaches used in power electronics design. The simulation results are confirmed by experimental data.
Transforming Personalized Travel Recommendations: Integrating Generative AI with Personality Models
Authors: Aribas Erke , Daglarli Evren
Published: December 2024

Over the past few years, the incorporation of generative Artificial Intelligence (AI) techniques, particularly the Retrieval-Augmented Generator (RAG) framework, has opened up revolutionary opportunities for improving personalized travel recommendation systems. The RAG framework seamlessly combines the capabilities of large-scale language models with retriever models, facilitating the generation of diverse and contextually relevant recommendations tailored to individual preferences and interests, all of which are based on natural language queries. These systems iteratively learn and adapt to user feedback, thereby continuously refining and improving recommendation quality over time. This dynamic learning process enables the system to dynamically adjust to changes in user preferences, emerging travel trends, and contextual factors, ensuring that the recommendations remain pertinent and personalized. Furthermore, we explore the incorporation of personality models like the Myers–Briggs Type Indicator (MBTI) and the Big Five (BF) personality traits into personalized travel recommendation systems. By incorporating these personality models, our research aims to enrich the understanding of user preferences and behavior, allowing for even more precise and tailored recommendations. We explore the potential synergies between personality psychology and advanced AI techniques, specifically the RAG framework with a personality model, in revolutionizing personalized travel recommendations. Additionally, we conduct an in-depth examination of the underlying principles, methodologies, and technical intricacies of these advanced AI techniques, emphasizing their ability to understand natural language queries, retrieve relevant information from vast knowledge bases, and generate contextually rich recommendations tailored to individual personalities. In our personalized travel recommendation system model, results are achieved such as user satisfaction (78%), system accuracy (82%), and the performance rate based on user personality traits (85% for extraversion and 75% for introversion).
High-Quality Instance Mining and Weight Re-Assigning for Weakly Supervised Object Detection in Remote Sensing Images
Authors: Xing Peixu , Huang Mengxing , Wang Chenhao , Cao Yang
Published: December 2024

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) achieves high-value object classification and localization by only using image-level labels. However, two problems limit its performance. Firstly, adjacent instances are often misclassified because their pseudo-labels are determined solely based on the spatial distances between them and their corresponding seed instances. Secondly, most WSOD methods assign the highest weight to the instance that covers the discriminative part of an object, thereby urging WSOD models to focus on the discriminative part rather than the whole object. To handle the first problem, a high-quality instance mining (HQIM) module, which incorporates the feature similarities between instances into the label propagation process, enabling some misclassified adjacent instances to be removed, is proposed. To tackle the second problem, a weight re-assigning (WRA) strategy, which redistributes the loss weights of instances, is proposed. Specifically, the loss weights of instances focusing on the discriminative part are exchanged with those of instances that broadly cover the whole object. Ablation studies demonstrate the effectiveness of HQIM and WRA, while comparisons with popular models on two RSI benchmarks further verify the effectiveness of our model.
Busbar Design for High-Power SiC Converters
Authors: Chen Zibo , Huang Alex Q.
Published: December 2024

Busbars are critical components that connect high-current and high-voltage subcomponents in high-power converters. This paper reviews the latest busbar design methodologies and offers design recommendations for both laminated and PCB-based busbars. Silicon Carbide (SiC) power devices switch at much higher speeds compared to traditional silicon devices, making them more susceptible to parasitic elements within the busbar. In high-frequency SiC converters, using thicker copper offers limited improvement in high-frequency current handling due to the reduced skin depth at such frequencies. PCB busbars, however, provide several advantages, including reduced loop inductance, enhanced high-frequency current capacity, simplified assembly, and lower costs. Additionally, they enable the integration of components such as sensors, capacitors, and resistors, which can further optimize overall system performance. This paper also presents optimized busbar designs for both module-based and discrete device-based SiC high-power converters, comparing various SiC power module packages and offering design insights. Finally, this paper showcases a 75 kW three-phase inverter utilizing a PCB busbar, demonstrating its potential for achieving high power density and cost-effectiveness in discrete SiC device-based high-power converters.
MBSDet: A Novel Method for Marine Object Detection in Aerial Imagery with Complex Background Suppression
Authors: Ji Gaofan , Fan Li , Li Chuanxiang
Published: December 2024

Detecting maritime ship targets in complex aerial images is significantly challenging due to the relatively small size of the ship compared to the image’s predominant background. Current methodologies used to identify maritime targets in aerial imagery are susceptible to disruption from intricate backgrounds, leading to misidentification, overlooked detection, and inaccurate target localization. To address these challenges, we proposed the maritime background suppression network (MBSDet), which facilitates the reliable detection of multi-scale targets in intricate backgrounds. Firstly, we developed a background suppression (BS) module that integrates the Transformer’s context-aware capabilities with local attention features to enhance the model’s ability to identify multi-scale objects. Secondly, we present the multidimensional feature enrichment (MFE) module, which employs feature pyramid-based rearrangement modules and dynamic upsampling operators instead of conventional upsampling methods to enhance feature richness while attenuating background features. The mean average precision (mAP) values can reach 90.54% and 88.89% on HRSC2016 and DOTA v1.0 datasets, respectively. The experimental results indicate that MBSDet exhibits outstanding detection and localization accuracy for objects in difficult background situations.
Selected Aspects of Positioning with the GNSS Galileo
Authors: Džunda Milan , Čikovský Sebastián , Melníková Lucia
Published: December 2024

The quality of Galileo system services is affected by the accuracy of distance determination from the user’s application to the individual satellite. The goal of our research was to find out what influence the accuracy of distance determination between the user’s application of the Galileo system and the cooperating satellites of the Galileo system has on the ability to determine the location of the user’s application. A solution based on Groebner’s algebraic approaches was used to determine the receiver user’s position. When creating distance measurement error models between the Galileo system user’s receiver and cooperating satellites, we assumed that the values of those errors considered all factors that affected the accuracy of those distance measurements. To evaluate the algorithms, we used a statistical set of 500 simulation results to determine the positioning of the user’s application of the Galileo system. If the distances between the user’s application and the individual satellite were measured accurately, then the user’s application coordinate errors had values between 1.86 × 10−9 m and −1.8 × 10−8 m. These errors should be equal to zero. The positioning error was caused by a numerical error in the calculation due to the software used. If the errors of distance determination from the user’s application to the individual satellite varied from −0.05 m to 0.09 m, then the error in determining the positioning of the user’s application of the Galileo system was from 0.0 m to 1.2 m. If the distances of the user’s receiver to the satellites were measured with errors greater than 0.09 m, the errors in determining its position were much larger. The simulation results confirmed the known fact that the satellites’ geometry influences the accuracy of determining the location of the user’s application. In the following research, we will solve the problem of how to reduce the sensitivity of the mentioned algorithms when determining the position of the satellite navigation system receiver due to errors in determining the distance from the user’s application to the individual satellite.
A Review of Hybrid Renewable and Sustainable Power Supply System: Unit Sizing, Optimization, Control, and Management
Authors: Ahmad Shameem , Hasan Sheikh Md. Nahid , Hossain Md. Sajid , Uddin Raihan , Ahmed Tofael , Mustayen A. G. M. B. , Hazari Md. Rifat , Hassan Mahamudul , Parvez Md. Shahariar , Saha Arghya
Published: December 2024

Since rising worldwide energy consumption is anticipated with increasing rapid industrialization and urbanization, green energy sources have become the ineluctable choice among energy engineers, power engineers, and researchers for carbon-free and sustainable electric power generation. By integrating several energy sources, a hybrid renewable and sustainable power supply system (HRSPSS) has been created to solve the global warming problem. HRSPSS aims to develop contemporary electricity grids that benefit society, the environment, and the economy. However, there is a need for thorough assessment of these complex HRSPSSs for making the most use of renewable energy potential and carefully crafting suitable solutions. This paper provides a thorough investigation of the most effective methods for sizing, optimizing, controlling, and managing energy, as well as how to combine different renewable energy sources to create a hybrid sustainable power supply system. Information on several software simulation tools and optimization methods that have been used to support HRSPSS development, research, and planning is presented in this study. Additionally, this study covers energy management and control strategies that have been used to ensure efficient and optimal operation of HRSPSS. Furthermore, this article presents an extensive comparison among various strategies utilized in each area (sizing, optimizing, controlling, and managing energy) to provide conclusive remarks on the suitable strategies for respective applications. The outcome of this study will help various stakeholders in the energy sector to make appropriate decisions during the design, development, and implementation phases of a hybrid sustainable power supply system.
Power Quality Control Using Superconducting Magnetic Energy Storage in Power Systems with High Penetration of Renewables: A Review of Systems and Applications
Authors: Arsénio Costa António J. , Morais Hugo
Published: December 2024

The increasing deployment of decentralized power generation based on intermittent renewable resources to reach environmental targets creates new challenges for power systems stability. Several technologies and approaches have been proposed in recent years including the use of superconducting magnetic energy storage. This study focuses on the review of existing superconducting magnetic energy storage systems for power quality control purposes. Such systems can supply and absorb the rated power level within seconds, promoting fast power quality regulation. Systems for power quality services such as frequency regulation, power oscillation damping, power fluctuation suppression, and active power filtering are identified and described. First, the physical characterization of superconducting magnets concerning geometries, materials, associated inductances, and nominal magnetic energy storage capacities is conducted. Then, the functional description of several current conversion circuits and systems used as interfaces for superconducting magnets is performed. The existing methodologies and systems to perform the control of current converters for different power control services and applications are also identified and described. Finally, the results regarding the number of different systems identified for each power quality control service are presented, and their applicability is discussed based on the adopted control approach. Challenges concerning the development of new systems to improve the power quality on grids with high penetration of decentralized energy resources from intermittent renewables are also identified.
Towards Bio-Crude Refinery Integration: Hydrodeoxygenation and Co-Hydroprocessing with Light Cycle Oil
Authors: Dimitriadis Athanasios , Bezergianni Stella
Published: December 2024

Hydrothermal liquefaction of solid waste has been gaining more and more attention over the last few years. However, the properties of the HTL product, i.e., biocrude, are limiting its direct utilization. As a result, HTL biocrude upgrading is essential to improve its quality. The main objective of the current research is to study the hydrotreatment stabilization of HTL biocrude, produced from spent coffee grounds, utilizing commercial hydrotreated catalysts, and also to investigate the integration of the stabilized biocrude into a light cycle oil (LCO) hydrotreatment plant for coprocessing to target hybrid fuel production. The results have shown that hydrotreatment is a very promising technology that can successfully remove the oxygen content from raw biocrude by hydrodeoxygenation, decarbonylation and decarboxylation reactions, leading to a stabilized product. The stabilized product can be easily blended with the LCO stream of a typical refinery, leading to the production of jet and diesel boiling range hydrocarbons, favoring at the same time the hydrogen consumption of the process. The findings of this manuscript set the basis for future research targeting the production of renewable advanced biofuels from HTL biocrude from municipal waste.
Evaluation and Analysis of the Energy Potential of Grapevine Peduncles of PIWI Group Varieties
Authors: Klimek Kamila E. , Kapłan Magdalena , Maj Grzegorz , Borkowska Anna , Buczyński Kamil , Sotolář Radek , Danko Richard
Published: December 2024

This paper presents an analysis of the energy potential of grape stalk biomass from PIWI varieties, namely ‘Seyval Blanc’, ‘Muscaris’, ‘Hibernal’, and ‘Regent’, during the combustion process. Biometric, technical, and elemental analyses of the grape stalk biomass were conducted. We evaluated the mass, length, and width of the stalks and their contribution to the total cluster mass. The higher and lower heating values, moisture content, volatile compounds, ash, fixed carbon content, and elemental composition were analysed. Emissions of carbon monoxide, nitrogen oxides, carbon dioxide, sulphur, and particulates were also measured. A significant influence of the cultivar on the assessed biometric and technical parameters was found. ‘Muscaris’ exhibited the highest calorific value (HHV 16.44 MJ·kg−1) and the lowest ash content (9.99%). The highest carbon content (45.51%) was recorded for ‘Seyval Blanc’, and the highest hydrogen content (6.74%) for ‘Muscaris’. Nitrogen oxide emissions were the lowest for ‘Seyval Blanc’, making it more environmentally friendly. The biomass of grape stalks from PIWI varieties, particularly ‘Muscaris’ and ‘Seyval Blanc’, shows high energy potential and can be effectively utilised as a renewable energy source. Our results could be summarised as ‘sustainable energy production and reduced greenhouse gas emissions from grape stalks’.
Review of the Coupled System of Solar and Air Source Heat Pump
Authors: Meng Xin , Zhou Xin , Li Zhenyu
Published: December 2024

The coupled operation of solar energy and air source heat pump (ASHP) can effectively solve the intermittent problem of solar energy systems running alone and the problem of performance degradation of ASHP systems running alone in winter. The coupled system of solar energy and ASHP can be divided into direct expansion type and indirect expansion type according to the structure form, and the indirect expansion type can be divided into series type, parallel type, and hybrid type. Various architectural forms of the solar-air source heat pump coupled system (S–ASHP) have achieved enhanced energy efficiency by means of a series of strategies, including the optimization of collectors, the refinement of evaporator structures, and the regulation of the temperature within hot water storage tanks. Choosing the appropriate architecture needs to comprehensively consider factors such as the external environment and load demand. In this paper, a variety of S–ASHP are summarized in order to provide some guidance for the future application of S–ASHP systems in the field of heating.
Heat Transfer in Annular Channels with the Inner Rotating Cylinder and the Radial Array of Cylinders
Authors: Hayrullin Aidar , Sinyavin Alex , Haibullina Aigul , Khusnutdinova Margarita , Bronskaya Veronika , Bashkirov Dmitry , Gilmutdinov Ilnur , Ignashina Tatyana
Published: December 2024

Numerical investigations of heat transfer for forced, mixed, and natural convection conditions within an annular channel are carried out. The main objective was to investigate, for the first time, the effect of the radial cylinder array on heat transfer in the annular channel with the rotating cylinder. The governing equations for velocity and temperature with the Boussinesq approximation were solved using the finite-volume method. The heat transfer quantities were obtained for different Rayleigh numbers (104–106), the radius ratios (1.4–2.6), the radial cylinder spacing, and for different rotating velocities in the form of the Richardson number (10−2–104). The Prandtl number was 0.7. It has been shown that radial cylinders do not influence significantly the intensity and the local distribution of heat transfer on the inner rotating cylinder. The Nusselt number was 1.4–2.0 times higher on the radial cylinder array for all convection modes relative to the outer flat surface. For all annuli gaps with radial cylinders, the maximal values of the Nusselt number were observed with an increase of the radial spacing of cylinders.
Dynamic Relationship Between Environmental Policy and Technological Innovation: Evidence from Firms in Korean Emission Trading Scheme
Authors: Mo Jung Youn , Jeon Wooyoung
Published: December 2024

To cope with radical global environmental regulation, it is necessary to find efficient ways to decrease carbon emissions with a low burden on firms and to design valid policies for a sustainable economy. This study investigates the link between the carbon emissions market and firms’ innovation activities and examines the impact of R&D investment caused by the carbon emissions market on the financial performance of firms participating in the Korean Emission Trading Scheme (KETS). This study assumes that a well-designed carbon emissions market can amplify the effect of R&D investment on improving the financial performance of firms and tests the efficiency of this market based on the KETS data. The results show that the indirect R&D elasticity that affects financial performance is simultaneously negative in the short and long run, except in Phase 1 (2015–2018). From this result, this study concludes that the carbon emissions market cannot induce technological innovation to increase financial performance in Korea, and the dynamic efficiency of the market is low. The industry comparison results confirm that the emissions market has a statistically significant effect on financial performance only in carbon-intensive industries. This study thus shows that the impact of technological innovation induced by the carbon emissions market varies depending on industrial characteristics.
Compensated Neural Network Training Algorithm with Minimized Training Dataset for Modeling the Switching Transients of SiC MOSFETs
Authors: Wang Ruwen , Chen Yu , Tong Siyu , Cheng Congzhi , Kang Yong
Published: December 2024

Accurate modeling of the switching transients of SiC MOSFETs is essential for overvoltage evaluation, EMI prediction, and other critical applications. Due to the fast switching speed, the switching transients of SiC MOSFETs are highly sensitive to parasitic parameters and nonlinear components, making precise modeling challenging. This paper proposes a hybrid model for SiC MOSFET, in which the analytical model is treated as the basis to provide the fundamental waveforms (knowledge-driven), while the neural network (NN) is utilized to fit the high-order and nonlinear features (data-driven). An NN training method with augmented data is proposed to minimize the training datasets. Verification results show that, even though the NN is trained with the data from a single operating condition, the model can accurately predict switching transients of other operating conditions. The proposed methodology has the potential to co-work with the “black-box” or “grey-box” models to enhance the model accuracy.
Multidimensional Index of Energy Poverty of Households in Poland—Measurement Concept
Authors: Gromada Arkadiusz , Wiluk Magdalena , Trocewicz Anna , Siedlecka Agnieszka , Płonka Aleksandra , Bórawski Piotr , Bełdycka-Bórawska Aneta
Published: December 2024

This article aimed to assess the level of energy poverty of households in Poland based on the multidimensional index proposed by the authors. Surveys conducted in July 2022 on a sample of 1112 respondents were used. The questionnaire used the computer-assisted web interview (CAWI) method. The variables needed to build the index and various socio-economic characteristics describing households were defined during the research. The energy poverty index was calculated for individual households and was analyzed for a correlation with variables describing the characteristics of households. The analysis showed correlations, among others, with the place of residence and income variable and the energy poverty index. Statistical, descriptive, and comparative methods were used.
Type-B Energetic Processes: Introduction and Invitation to Special Issue of Energies
Authors: Lee James Weifu , Sheehan Daniel P.
Published: December 2024

Currently, there is substantial scientific evidence for the existence of Type-B energetic processes on Earth, such as the naturally occurring transmembrane-electrostatically localized protons (TELP) thermotrophic properties in life [...]
Optimal Variable Frequency Soft Switching for Interleaved Grid Tied Inverters in Electric Vehicle Charging Applications
Authors: Fahmy Youssef A. , Jahnes Matthew , Preindl Matthias
Published: December 2024

Synchronized variable frequency soft-switching is analyzed and implemented in a 3-phase bidirectional grid-tied inverter. The common-mode connected topology and control allow for independent analysis of a single phase leg before six are combined into two interleaved, 3-phase inverters. Effective operation is enabled by discretizing the variable switching frequencies before synchronizing them with a control signal. The resulting inverter can operate at any power factor at power levels up to 50 kVA while maintaining zero-voltage switching (ZVS) throughout the grid cycle. Formal conditions for soft-switching and methods for achieving ZVS while maintaining global synchronization are presented. These conditions are then verified in a simulation. Finally, results for different power factors with and without interleaving are demonstrated in a prototype that achieves >98.1% efficiency when converting all real power.
Control Strategy for Power Fluctuation Smoothing at Distribution Network Substations Considering Multiple Types of Adjustment Resources
Authors: Yang Shaobo , Hu Xuekai , Meng Liang , Xue Shiwei , Zhou Hao , Shi Fengming , Liao Siyang
Published: December 2024

With the proposal of the dual carbon target, the distributed photovoltaic (PV) industry has rapidly developed in recent years. However, the randomness and volatility of photovoltaic energy can be transmitted to the main grid through distribution network substations, posing challenges to the stable operation of the power system. Therefore, this paper considers tapping into the regulation potential of feeder loads on the distribution network side, as well as distributed energy storage and distributed PV resources, to enhance the grid’s control methods. A power fluctuation smoothing control strategy for substations in distribution networks, accounting for multiple types of regulation resources, is proposed. In the day-ahead stage, traditional voltage regulation devices such as the OLTC (on-load tap changer) and CB (circuit breaker) are pre-dispatched based on source–load forecasts, optimizing the fluctuation range of substation power and the number of device operations. This provides optimal substation power values for day-to-day optimization. During the intraday phase, fast regulation devices such as PV (photovoltaic), SVC (static var compensator), and energy storage systems are coordinated, and an optimization model is established with the goal of reducing power curtailment while closely tracking substation trends. This model quickly calculates the active power regulation and device operations of various adjustable resources, improving the economic efficiency of the distribution network system while achieving power fluctuation smoothing at the substation level. Finally, the feasibility and effectiveness of the power fluctuation smoothing control model are verified through simulations on an improved standard distribution system.
Modelling of Biomass Gasification Through Quasi-Equilibrium Process Simulation and Artificial Neural Networks
Authors: Marcantonio Vera , De Falco Marcello , Di Paola Luisa , Capocelli Mauro
Published: December 2024

In the past two decades, advancements in thermochemical technologies have improved biomass gasification for distributed power generation, enhancing efficiency, scalability, and emission control. This study aims to optimize syngas production from biomass gasification by comparing two computational models: a quasi-equilibrium thermodynamic model implemented in Aspen Plus and an artificial neural network (ANN) model. Operating at 850 °C with varying steam-to-biomass (S/B) ratios, both models were validated against experimental data. Results show that hydrogen concentration in syngas increased from 19.96% to 43.28% as the S/B ratio rose from 0.25 to 0.5, while carbon monoxide concentration decreased from 24.6% to 19.1%, consistent with the water–gas shift reaction. The ANN model provided rapid predictions, showing a mean absolute error of 3% for hydrogen and 2% for carbon monoxide compared to experimental data, though it lacks thermodynamic constraints. Conversely, the Aspen Plus model ensures mass and energy balance compliance, achieving a cold gas efficiency of 95% at an S/B ratio of 0.5. A Multivariate Statistical Analysis (MVA) further clarified correlations between input and output variables, validating model reliability. This combined modelling approach reduces experimental costs, enhances gasification process control and offers practical insights for improving syngas yield and composition.
Experimental Study on the Alternate Oil Displacement Mechanism of CO2 and Modified Water in Low-Permeability Oil Layers
Authors: Zhao Shupei , Fu Meilong , Chen Jun , Li Zhaoxing , Lin Jiayi , Zhang Shuo , Wang Pengju
Published: December 2024

Alternating carbon dioxide and water flooding can not only seal greenhouse gases, but also combine the advantages of water flooding and carbon dioxide flooding, and can well control mobility and stabilize the displacement front, thereby greatly improving the macro-replacing efficiency. In order to further improve the development effect of water–carbon dioxide alternating flooding, this paper, based on sufficient collection of the literature, research, and analysis, pre-uses modified water instead of water, and deeply explores and studies the impact of modified water–carbon dioxide alternating flooding on the improvement of development effect and the mechanism of enhancing oil recovery in low-permeability reservoirs. The main work completed is as follows: (1) A comparative experiment of multiple groups of sand-filled tubes with different displacement media, modified water concentrations, and injection plug sizes was conducted under the conditions of simulating reservoir formation temperature of 70 °C and formation pressure of 18 MPa, and the optimal scheme and injection parameters of alternating modified water and carbon dioxide flooding were rationally selected. The results show that the alternating flooding of modified water and carbon dioxide in low-permeability reservoirs can significantly improve the development effect. The optimal injection parameters are a formulation concentration of 0.3% and an injection method of alternating a 0.1 PV slug injection of carbon dioxide and modified water. (2) Using Berea cores instead of sand-fill tubes, a comparative experiment of alternating oil displacement using carbon dioxide and modified water was carried out under the same experimental conditions. Nuclear magnetic resonance measurements were performed on five of the cores to analyze the microscopic oil displacement mechanisms of different displacement media. The results show the following: nuclear magnetic resonance testing shows that carbon dioxide displacement can greatly improve the oil recovery efficiency in tiny pores (about 47.43%); alternating injection can further improve the oil recovery efficiency in tiny pores (about 70.6%); and modified water can improve the oil recovery efficiency in larger pores (about 56.47%).
Identification of the Parameters of the Highly Saturated Permanent Magnet Synchronous Motor (PMSM): Selected Problems of Accuracy
Authors: Gierczynski Michal , Jakubowski Rafal , Kupiec Emil , Niewiara Lukasz Jan , Tarczewski Tomasz , Grzesiak Lech M.
Published: December 2024

Machines with highly saturated magnetic circuits are utilized to maximize drive efficiency. However, their control is non-trivial due to highly non-linear characteristics, and therefore, an accurate parameter identification procedure is crucial. This paper presents and validates a comprehensive flux linkage identification procedure. Several steps needed for accurate parameter identification and their influence on the estimation results are described. The impact of all main parts of the electrical drive, i.e., dead times introduced between switching off and switching on the power transistors, phase delay from the current low-pass filters, delay from the control system, and misalignment of the d–axis angle due to the machine asymmetries is considered and partially eliminated. An improved identification procedure with the look-up table-based dead time compensation and estimation of equivalent circuit resistance is applied to estimate the parameters of the highly saturated PMSM traction machine from the fourth generation of the Toyota Prius, and the obtained results are compared with a Finite Element Method-based model of this machine. Finally, dynamic test results are conducted to prove the proposed approach’s accuracy.
Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
Authors: Chajia Meryem , Nfaoui El Habib
Published: December 2024

Nowadays, predicting customer churn is essential for the success of any company. Loyal customers generate continuous revenue streams, resulting in long-term success and growth. Moreover, companies are increasingly prioritizing the retention of existing customers due to the higher costs associated with attracting new ones. Consequently, there has been a growing demand for advanced methods aimed at enhancing customer loyalty and satisfaction, as well as predicting churners. In our work, we focused on building a robust churn prediction model for the telecommunications industry based on large embeddings from large language models and logistic regression to accurately identify churners. We conducted extensive experiments using a range of embedding techniques, including OpenAI Text-embedding, Google Gemini Text Embedding, bidirectional encoder representations from transformers (BERT), Sentence-Transformers, Sent2vec, and Doc2vec, to extract meaningful features. Additionally, we tested various classifiers, including logistic regression, support vector machine, random forest, K-nearest neighbors, multilayer perceptron, naive Bayes, decision tree, and zero-shot classification, to build a robust model capable of making accurate predictions. The best-performing model in our experiments is the logistic regression classifier, which we trained using the extracted feature from the OpenAI Text-embedding-ada-002 model, achieving an accuracy of 89%. The proposed model demonstrates a high discriminative ability between churning and loyal customers.
Effects of Prescribed Burns on Soil Respiration in Semi-Arid Grasslands
Authors: De la Cruz Domínguez Juan Carlos , Alfaro Reyna Teresa , Aguirre Gutierrez Carlos Alberto , Rodríguez Moreno Víctor Manuel , Delgado Balbuena Josué
Published: December 2024

Carbon fluxes are valuable indicators of soil and ecosystem health, particularly in the context of climate change, where reducing carbon emissions from anthropogenic activities, such as forest fires, is a global priority. This study aimed to evaluate the impact of prescribed burns on soil respiration in semi-arid grasslands. Two treatments were applied: a prescribed burn on a 12.29 ha paddock of an introduced grass (Eragostis curvula) with 11.6 t ha−1 of available fuel, and a simulation of three fire intensities, over 28 circular plots (80 cm in diameter) of natural grasslands (Bouteloua gracilis). Fire intensities were simulated by burning with butane gas inside an iron barrel, which represented three amounts of fuel biomass and an unburned treatment. Soil respiration was measured with a soil respiration chamber over two months, with readings collected in the morning and afternoon. Moreover, CO2 emissions by combustion and productivity after fire treatment were quantified. The prescribed burns significantly reduced soil respiration: all fire intensities resulted in a decrease in soil respiration when compared with the unburned area. Changes in albedo increased the soil temperature; however, there was no relationship between changes in temperature and soil respiration; in contrast, precipitation highly stimulated it. These findings suggest that fire, under certain conditions, may not lead to more CO2 being emitted into the atmosphere by stimulating soil respiration, whereas aboveground biomass was reduced by 60%. However, considering the effects of fire in the long-term on changes in nutrient deposition, aboveground and belowground biomass, and soil properties is crucial to effectively quantify its impact on the global carbon cycle.
The Effects of Fire Intensity on the Biochemical Properties of a Soil Under Scrub in the Pyrenean Subalpine Stage
Authors: Alfaro-Leranoz Andoni , Badía-Villas David , Martí-Dalmau Clara , Escuer-Arregui Marta , Quintana-Esteras Silvia
Published: December 2024

Fire causes changes in many soil attributes, depending on multiple factors which are difficult to control in the field, such as maximum temperature, heat residence time, charred material incorporation, etc. The objective of this study is to evaluate the effect of a gradient of fire intensities on soils at the cm scale. Undisturbed topsoil monoliths were sampled under scrubs in the subalpine stage in the Southern Pyrenees (NE Spain). They were burned, under controlled conditions in a combustion tunnel, to obtain four charring intensities (CIs), combining two temperatures (50 and 80 °C) and two residence times (12 and 24 min) reached at 1 cm depth from the soil. Unburned soil samples were used as a control. All soils were sampled, cm by cm, up to 3 cm deep. The following soil properties were measured: soil respiration (basal, bSR and normalized, nSR), β-D-glucosidase (GLU), microbial biomass carbon (MBC), glomalin-related soil proteins (GRSPs), soil organic carbon (SOC), labile carbon (DOC), recalcitrant organic carbon (ROC), total nitrogen (TN), soil pH, electrical conductivity (EC) and soil water repellency (SWR). Even at low intensities, GLU, SOC and total GRSP were significantly reduced and, conversely, SWR was enhanced. At the higher CIs, additional soil properties were significantly reduced (MBC and C/N) or increased (DOC, ROC, nSR, easily extractable GRSP). This study demonstrates that there is a differential degree of thermal sensitivity in the measured biochemical soil properties. Furthermore, these properties are more affected at 0–1 cm than at 1–2 and 2–3 cm soil thicknesses.
Hepatic Gene Expression Changes of Zebrafish Fed Yeast Prebiotic, Yeast Probiotic, Black Soldier Fly Meal, and Butyrate
Authors: Gao Nancy , Zhang Junyu , Shandilya Umesh K. , Lumsden John S. , Barzrgar Amir Behzad , Huyben David , Karrow Niel A.
Published: December 2024

As global fish consumption rises, improving fish health through immunomodulatory feed ingredients shows promise while also supporting growth performance. This study investigated the effects of yeast prebiotics, probiotics, a postbiotic (butyrate), and black soldier fly larvae (BSFL) meal on fish immune responses. Zebrafish were fed diets containing these ingredients for 63 days and then exposed to either Pseudomonas aeruginosa lipopolysaccharide (LPS) or live Flavobacterium psychrophilum to assess hepatic candidate gene expression and weight gain. No mortalities were observed post-immune challenges, and weight gains were not significantly different across treatments. Liver samples were collected for mRNA analysis, and real-time qPCR was used to evaluate the expression of immune-related genes such as TNF-α, IL-1β, hepcidin, and NF-κB/p65. NF-κB/p65 was upregulated in response to immune challenges, indicating a reaction to both LPS and pathogen exposure. Fish on the BSFL diet showed decreased NF-κB/p65 expression after the pathogen challenge, while probiotic-fed fish had reduced angiopoietin-like 4 (angptl4) levels following LPS exposure. Butyrate supplementation had the most significant impact, downregulating pro-inflammatory cytokines and other immune-related genes, suggesting a protective effect. These findings support the health benefits of BSFL and sodium butyrate during an immune challenge.