bluuebunny/crossref_metadata_2025 huggingface dataset:
A Hybrid Parallel Computing Architecture Based on CNN and Transformer for Music Genre Classification
Authors: Chen Jiyang , Ma Xiaohong , Li Shikuan , Ma Sile , Zhang Zhizheng , Ma Xiaojing
Published: August 2024

Music genre classification (MGC) is the basis for the efficient organization, retrieval, and recommendation of music resources, so it has important research value. Convolutional neural networks (CNNs) have been widely used in MGC and achieved excellent results. However, CNNs cannot model global features well due to the influence of the local receptive field; these global features are crucial for classifying music signals with temporal properties. Transformers can capture long-range dependencies within an image thanks to adopting the self-attention mechanism. Nevertheless, there are still performance and computational cost gaps between Transformers and existing CNNs. In this paper, we propose a hybrid architecture (CNN-TE) based on CNN and Transformer encoder for MGC. Specifically, we convert the audio signals into mel spectrograms and feed them into a hybrid model for training. Our model employs a CNN to initially capture low-level and localized features from the spectrogram. Subsequently, these features are processed by a Transformer encoder, which models them globally to extract high-level and abstract semantic information. This refined information is then classified using a multi-layer perceptron. Our experiments demonstrate that this approach surpasses many existing CNN architectures when tested on the GTZAN and FMA datasets. Notably, it achieves these results with fewer parameters and a faster inference speed.
Editable Co-Speech Gesture Synthesis Enhanced with Individual Representative Gestures
Authors: Bao Yihua , Weng Dongdong , Gao Nan
Published: August 2024

Co-speech gesture synthesis is a challenging task due to the complexity and uncertainty between gestures and speech. Gestures that accompany speech (i.e., Co-Speech Gesture) are an essential part of natural and efficient embodied human communication, as they work in tandem with speech to convey information more effectively. Although data-driven approaches have improved gesture synthesis, existing deep learning-based methods use deterministic modeling which could lead to averaging out predicted gestures. Additionally, these methods lack control over gesture generation such as user editing of generated results. In this paper, we propose an editable gesture synthesis method based on a learned pose script, which disentangles gestures into individual representative and rhythmic gestures to produce high-quality, diverse and realistic poses. Specifically, we first detect the time of occurrence of gestures in video sequences and transform them into pose scripts. Regression models are then built to predict the pose scripts. Next, learned pose scripts are used for gesture synthesis, while rhythmic gestures are modeled using a variational auto-encoder and a one-dimensional convolutional network. Moreover, we introduce a large-scale Chinese co-speech gesture synthesis dataset with multimodal annotations for training and evaluation, which will be publicly available to facilitate future research. The proposed method allows for the re-editing of generated results by changing the pose scripts for applications such as interactive digital humans. The experimental results show that this method generates more quality, more diverse, and realistic gestures than other existing methods.
A High-Quality and Space-Efficient Design for Memristor Emulation
Authors: Kumar Atul , Chaturvedi Bhartendu
Published: August 2024

The paper presents a new design for a compact memristor emulator that uses a single active component and a grounded capacitor. This design incorporates a current backward transconductance amplifier as the active element, enabling the emulation of both grounded and floating memristors in incremental and decremental modes. The paper provides an in-depth analysis of the circuit, covering ideal, non-ideal, and parasitic factors. The theoretical performance of the memristor emulator is confirmed through post-layout simulations with 180 nm generic process design kit (gpdk) technology, demonstrating its capability to operate at low voltages (±1 V) with minimal power consumption. Additionally, the emulator shows strong performance under variations in process, voltage, and temperature (PVT) and functions effectively at a frequency of 2 MHz. Experimental validation using commercially available integrated circuits further supports the proposed design.
A Deep Learning Approach for Fault-Tolerant Data Fusion Applied to UAV Position and Orientation Estimation
Authors: Saied Majd , Mishi Abbas , Francis Clovis , Noun Ziad
Published: August 2024

This work introduces a novel fault-tolerance technique for data fusion in Unmanned Aerial Vehicles (UAVs), designed to address sensor faults through a deep learning-based framework. Unlike traditional methods that rely on hardware redundancy, our approach leverages Long Short-Term Memory (LSTM) networks for state estimation and a moving average (MA) algorithm for fault detection. The novelty of our technique lies in its dual strategy: utilizing LSTMs to analyze residuals and detect errors, while the MA algorithm identifies faulty sensors by monitoring variations in sensor data. This method allows for effective error correction and system recovery by replacing faulty measurements with reliable ones, eliminating the need for a fault-free prediction model. The approach has been validated through offline testing on real sensor data from a hexarotor UAV with simulated faults, demonstrating its efficacy in maintaining robust UAV operations without resorting to redundant hardware solutions.
Shaping the Engineers of Tomorrow: Integrating Renewable Energies and Advanced Technologies in Electrical and Electronics Engineering Education
Authors: Friman Hen
Published: August 2024

This article examines the comprehensive changes and adjustments made to the Bachelor’s degree program in Electrical and Electronics Engineering (BSc) to align with the growing need for sustainable energy solutions. The primary objective of these revisions is to equip future engineers with a deep understanding of both traditional fossil energy sources and emerging renewable energy technologies, with a particular emphasis on smart grids. The revamped curriculum integrates advanced content delivery through a unique blend of professional laboratories, in-person lectures, discussions with industry experts, and online courses. By providing a holistic educational experience, the program aims to prepare graduates to lead the transition towards sustainable energy systems, fostering innovation and resilience in the energy sector.
A Two-Stage Multi-Objective Design Optimization Model for a 6 MW Direct-Drive Permanent Magnet Synchronous Generator
Authors: Tian De , Wu Xiaoxuan , Meng Huiwen , Su Yi
Published: August 2024

The design optimization of a direct-drive permanent magnet synchronous generator (DDPMSG) is of great significance for wind turbines because of its unique advantages. This paper proposes a two-stage model to realize multi-objective design optimization for a 6 MW DDPMSG. In the first stage, a surrogate optimized response surface model based on an improved sparrow search algorithm (ISSA) was established for modeling the cogging torque and generator efficiency. In the second-stage model, a multi-objective optimization model is proposed to optimize the cogging torque and generator efficiency of the DDPMSG. Finally, the proposed two-stage model was used for a 6 MW DDPMSG design optimization, and the simulation results demonstrated the superiority and rationality of the proposed model. In the first-stage model, the proposed surrogate model based on the ISSA had a better modeling accuracy and lower errors. Compared with traditional response surface models and correlation analysis models, the proposed optimized surrogate model reduced errors in the cogging torque by 34.63% and 42.97%, respectively, while the errors in the efficiency models were reduced by 12.92% and 60.78%, respectively, which indicates the superiority of the first-stage model. In the second stage, compared with the single-objective optimization model, the multi-objective optimization model achieved a trade-off optimization between the cogging torque and the efficiency. Compared with the cogging torque optimization model, the proposed model optimized the efficiency by 101.41%. Compared with the efficiency optimization model, the proposed model reduced the cogging torque by 16.67%. These results verified the superiority and rationality of the second-stage model.
Revolution in Renewables: Integration of Green Hydrogen for a Sustainable Future
Authors: Zhang Jimiao , Li Jie
Published: August 2024

In recent years, global efforts towards a future with sustainable energy have intensified the development of renewable energy sources (RESs) such as offshore wind, solar photovoltaics (PVs), hydro, and geothermal. Concurrently, green hydrogen, produced via water electrolysis using these RESs, has been recognized as a promising solution to decarbonizing traditionally hard-to-abate sectors. Furthermore, hydrogen storage provides a long-duration energy storage approach to managing the intermittency of RESs, which ensures a reliable and stable electricity supply and supports electric grid operations with ancillary services like frequency and voltage regulation. Despite significant progress, the hydrogen economy remains nascent, with ongoing developments and persistent uncertainties in economic, technological, and regulatory aspects. This paper provides a comprehensive review of the green hydrogen value chain, encompassing production, transportation logistics, storage methodologies, and end-use applications, while identifying key research gaps. Particular emphasis is placed on the integration of green hydrogen into both grid-connected and islanded systems, with a focus on operational strategies to enhance grid resilience and efficiency over both the long and short terms. Moreover, this paper draws on global case studies from pioneering green hydrogen projects to inform strategies that can accelerate the adoption and large-scale deployment of green hydrogen technologies across diverse sectors and geographies.
Simulation Study of Microscopic Seepage in Aquifer Reservoirs with Water–Gas Alternated Flooding
Authors: Yang Zhao , Zhou Ziyu
Published: August 2024

Underground gas storage (UGS) is a beneficial economic method of compensating for the imbalance between natural gas supply and demand. This paper addresses the problem of a lack of research on the two-phase distribution pattern and seepage law during the water–gas alternated flooding in gas storage reservoirs. The study constructed a three-dimensional digital core of the aquifer reservoir based on Computed Tomography (CT) scanning technology, and extracted the connecting pore structure to establish the tetrahedral mesh model. A two-phase microscopic seepage model was established based on the Volume of Fluid (VOF)method, and microscopic gas and gas–liquid two-phase unsaturated microscopic seepage simulation was carried out. The results show that the effective reservoir capacity increases with the increase in the number of alternated flooding cycles. The irreducible water is mainly distributed in the dead-end of the pore space and small pore throats, and the residual gas is mainly distributed as a band in the gas–water interface and the dead-end of the pore space of the previous round. The reservoir capacity can be increased by appropriately increasing the intensity of injection and extracting and decreasing the pressure of the reservoir.
Research on the Heat Transfer Performance of Phase Change Heat Storage Heat Exchangers Based on Heat Transfer Optimization
Authors: Dong Xiaodong , Zhu Chuanhui
Published: August 2024

Thermal storage technology has received increasing attention under the policy of encouraging the development of renewable energy and new clean energy. Optimizing the heat exchange system of phase change thermal storage heat exchangers to obtain better performance has become increasingly urgent. This study comprehensively investigated the actual process of heat transfer and assessed the heat transfer correlation laws between the heat transfer fluids, heat exchange tubes, fins, and phase change materials. Taking the heat exchange efficiency of the heat exchanger as a guide, a simulation was conducted on the effect of the presence and quantity of fins as well as the flow rate of the cooling liquid on the heat exchange efficiency of the heat exchanger. The simulation results showed that too many or too few fins were not conducive to improving heat transfer efficiency. In addition, no positive correlation was observed between the flow rate of the cooling liquid in the heat exchanger and the heat transfer efficiency of the heat exchanger. Specifically, models with slightly slower cooling liquid flow rates had a higher heat transfer efficiency. After a sensitivity analysis, it was found that the number of fins had a more significant effect on the heat transfer efficiency of the heat exchanger than the flow rate of the cooling liquid. The heat exchanger with five fins and a cooling liquid flow rate of 0.1 m/s demonstrated the best heat transfer effect, achieving a temperature drop of 14.76% within 5 min.
Storage Regulation Mechanism and Control Strategy of a Hydraulic Wave Power Generation System
Authors: Peng Jianjun , Huang Chenchen , Xue Meng , Feng Run , Zhou Erhao , Zhong Zhidan , Ku Xiangchen
Published: August 2024

Based on a mechanism study, the regulation and control mechanism of the hydraulic energy storage system is elaborated in detail, and the regulation and control strategy is formulated for the hydraulic power generation system under the condition of a stable random wave, and the working mode of the wave power generation system is deeply studied. According to the characteristics of a hydraulic system, a control strategy of a three-position four-way electromagnetic directional valve suitable for adaptive energy storage system is proposed. In order to verify the feasibility of the control strategy, a mathematical model of the hydraulic cylinder displacement control system is designed based on the Matlab/Simulink platform, and a PID control strategy is introduced to build a wave simulation loop. Amesim and Simulink co-simulation is used to verify the performance of the wave simulation circuit and the hydraulic power generation system. The simulation results show that the maximum error rate is only 0.8% after PID control is added to the wave power generation simulation loop, the displacement of the hydraulic cylinder can reach the expected value, and the random wave motion can be simulated effectively. In the hydraulic system of wave energy generation, the proposed adaptive control strategy can accelerate the system stability process, reduce the power overshoot significantly, and convert wave energy into electric energy more effectively.
Evaluating Renewable Energy’s Role in Mitigating CO2 Emissions: A Case Study of Solar Power in Finland Using the ARDL Approach
Authors: Nica Ionuț , Georgescu Irina , Kinnunen Jani
Published: August 2024

This study examines Finland’s increasing investment in solar energy as part of its broader strategy to transition to renewable energy sources. Despite its northern location and limited sunlight during winter months, Finland has effectively harnessed solar power, especially during its long summer days. We conducted a PESTLE analysis, highlighting political commitment to climate agreements, economic benefits of solar energy, public support for clean energy, technological advancements, regulatory support, and environmental benefits. In addition, we applied the autoregressive distributed lag model (ARDL) to study the impact of GDP per capita, urbanization (URB), foreign direct investments (FDI), and the share of primary energy consumption from solar (SOL) on CO2 emissions for Finland during 1990–2022. The long-term findings show that SOL, URB, and FDI negatively impact CO2 emissions, while GDP positively impacts CO2 emissions. Solar energy deployment reflects the effectiveness of solar energy as a cleaner alternative to fossil fuels. Urbanization also shows a negative impact on CO2 emissions due to better infrastructure and more efficient energy use in urban areas. FDI suggests that investments are oriented towards sustainable technologies. Industrial activity associated with economic expansion may indicate the positive effect of GDP in CO2 emissions. Although economic growth has typically resulted in increased emissions, strategic investments in renewable energy and sustainable urban development can greatly reduce this effect. Policy initiatives in Finland should concentrate on promoting green investments and enhancing urban planning with a focus on environmental sustainability.
Analysis of the Restoration of Distribution Substations: A Case Study of the Central–Western Division of Mexico
Authors: Sánchez-Ixta Carlos , Vázquez-Abarca Juan Rodrigo , López-Sosa Luis Bernardo , Golpour Iman
Published: August 2024

The studies on strategies for improving restoration times in electrical distribution systems are extensive. They have theoretically explored the application of mathematical models, the implementation of remotely controlled systems, and the use of digital simulators. This research aims to connect conceptual studies and the implementation of improvements and impact assessment in electrical distribution systems in developing countries, where distribution technologies vary widely, by employing a comprehensive methodology. The proposed research examines the restoration times for faults in substations within general distribution networks in the central–western region of Mexico. The study comprises these stages: (a) diagnosing the electrical supply, demand, and infrastructure; (b) analyzing the electrical restoration time and the restoration index of the substations; and (c) providing recommendations and implementing pilot tests for improvements in the identified critical substations. The results revealed 12 analysis zones, including 120 distribution substations, 150 power transformers, and 751 medium voltage circuits. Among the substations, 73% have ring connections, 15% have TAP connections, and 12% have radial connections. Additionally, 27% of the substations rely on only a single distribution line. The study identified areas with significant challenges in restoring electricity supply, particularly focusing on power transformers: 32 transformers with permanent power line failures requiring load transfer via medium voltage; 67 transformers requiring optimized restoration maneuvers due to specific characteristics; and 4 areas with opportunities to enhance the reliability of the power supply through remote-controlled link systems. The analysis resulted in the installation of 145 remote link systems, which improved restoration rates by over 40%. This approach is expected to be replicated throughout Mexico to identify improvements needed in the national distribution system.
Enhancing Building Energy Efficiency with Innovative Paraffin-Based Phase Change Materials
Authors: Lygerakis Filippos , Gioti Christina , Gournis Dimitris , Yentekakis Ioannis. V. , Karakassides Michalis , Kolokotsa Denia
Published: August 2024

There is a rising demand for energy-efficient and low-carbon buildings that is driven by the energy consumption in the building sector, global population growth, and high standards of comfort. Integrating contemporary energy-efficient technologies is crucial for tackling this issue. In this study, thermal energy storage (TES) technologies are investigated, particularly phase change materials (PCMs), by using them in buildings and in order to improve energy efficiency. Paraffin-based PCMs are the main focus and are known for their advanced thermal storage capacity and compatibility with building materials. The work focuses on embedding these PCMs into building components such as roofs and walls in order to maximize energy efficiency. Key data, including thermal conductivity (varying from 0.063 W/mK to 0.175 W/mK) and solar reflectance (ranging from 42.7% to 70.31%), were taken with a Hot Disc TPS1500 and a UV-Vis-NIR spectrophotometer and used as inputs for EnergyPlus calculations. The results show that PCM-enhanced materials greatly increase thermal regulation and energy efficiency. Gypsum boards 30% PCM-enhanced used in buildings achieved up to 12.8% annual energy consumption reductions (106.1 kWh/m2) and 22.3% net annual energy consumption savings (52.2 kWh/m2) when compared to baseline scenarios. The study indicates that PCM integration can significantly cut energy usage while improving indoor thermal comfort, underlining its potential for widespread use in sustainable building design.
Review of Construction Technology of Advanced Energy Infrastructure
Authors: Han Jianyong , Tian Li
Published: August 2024

Energy is crucial to the development of human civilization. Energy infrastructure, such as oil and gas pipelines, power generation systems, and storage facilities, provide core support for the exploitation and utilization of various types of energy. Thus, energy infrastructure is vital to the economic sustainable development of a country. This paper provides the motivations and a brief introduction to the Special Issue entitled “Frontiers in Construction Technology of Advanced Energy Infrastructure”, which aims to present advanced technologies and theories for energy infrastructure. The primary challenges in the current construction technology of energy infrastructure are described. Furthermore, the focus of current research in this field addressed in this Special Issue is presented. A comparison of the articles included or considered for inclusion in this Special Issue with other available literature on this topic is performed, which proves the prospects and relevance of this Special Issue. Finally, perspectives on future directions of energy utilization and energy infrastructure construction are provided.
Dual Effect of Hydrothermal Fluid on Shale Oil Reservoir in Gulong Sag, Songliao Basin: Constrained by C-O Isotope and Geochemistry
Authors: Li Junhui , Fu Xiuli , Bai Yue , Zhang Haixin , Liu Zongbao , Zhao Rongsheng
Published: August 2024

Hydrothermal activity is widespread in sedimentary basins, but its dual effects (chemistry and temperature) on shale reservoirs are rarely discussed. In this research, we systematically collected 33 well core samples from Q1 to Q9 units in Gulong Sag, Songliao Basin, and analyzed them using a variety of analytical techniques, including a field emission scanning electron microscopy (FE-SEM), an energy-dispersive spectrometer (EDS), X-ray diffraction (XRD), and stable C-O isotopes. Combined with the collected vitrinite reflectance (Ro), total organic carbon (TOC), and soluble hydrocarbon content data, which is the sum of free oil (pyrolysis S1) and sorb oil content (pyrolysis S2), the results show that (1) Q4 and Q8 units have large amounts of hydrothermal minerals, and its C-O isotope obviously shifts to negative, which implied those units are the main hydrothermal fluid influence area; (2) the hydrothermal activity occurred in the late depositional period of Q1–Q9 units such that its geochemistry has little effect on the proliferation of algae blooms, but its high temperature calculated by δ18O temperature formulas (around 208 °C) promoted the organic matter maturation process around Q4 and Q8; and (3) the overpressure caused by hydrothermal activity protected the shale reservoir and minimized the decrease in mineral reservoir brittleness index caused by hydrothermal fluid influence. We suggest that the shale reservoir affected by hydrothermal fluid will become a good geology “dessert”, and its upper and/or lower bounds can form an engineering “dessert” due to the precipitation of large amounts of brittle carbonate minerals.
Applications of the Internet of Things in Renewable Power Systems: A Survey
Authors: Jia Laura , Li Zhe , Hu Zhijian
Published: August 2024

The integration of the Internet of Things (IoT) with renewable energy technologies is revolutionizing modern power systems by enhancing efficiency, reliability, and sustainability. This paper examines the role of the IoT in optimizing the integration and management of renewable energy sources, such as solar and wind power, into the electrical grid. The IoT enables real-time monitoring, data analysis, and automation, facilitating advanced load management, demand response, and energy storage solutions. Key advancements in IoT technologies, including smart grids and energy management systems, are discussed, highlighting their impact on improving grid stability and promoting the use of renewable energy. The paper also finds some challenges such as data security, privacy, and the need for standardized communication protocols. Furthermore, it finds how the IoT optimizes electric vehicle performance through advanced battery management, real-time energy consumption monitoring, and improved interaction with the electrical grid. Future research directions emphasize the potential of the IoT to further enhance renewable energy integration through artificial intelligence and machine learning, driving the transition towards a more sustainable and resilient energy future.
Review of Uncertainty Sources in Optical Current Sensors Used in Power Systems
Authors: Costa Marcelo M. , Martinez Maria A. G. , Costa João C. W. A.
Published: August 2024

Optical current sensors have been developed and improved over the past few decades, and they have been increasingly employed in power systems, including smart and high-voltage grids. This is due to their many advantages over conventional electromagnetic current sensors, such as reduced size and weight, greater operational safety, and electromagnetic immunity. Like any measuring instrument or system, their quality and reliability are associated with measurement uncertainty, which quantifies their precision. This measurement uncertainty depends on a series of influencing quantities, such as the wavelength of light used in the sensor, the birefringence of the optical material used in the construction of the sensor, and environmental conditions, such as temperature and vibration. This article presents a review of the main influences that affect the quality and performance of optical current sensors and how these influences can be used to estimate measurement uncertainty. The main objective is to serve as a guide or reference for the identification and evaluation of uncertainty sources in optical current sensors used in power systems.
Research on the Characteristics of Oscillation Combustion Pulsation in Swirl Combustor
Authors: Liu Chongyang , Ge Xinkun , Zhang Xiang , Yang Chen , Liu Yong
Published: August 2024

This study focuses on the center-staged swirl model combustion chamber, conducting experiments and numerical simulations to investigate the unstable combustion characteristics of diffusion flames under different Reynolds numbers and air–fuel ratios. The results were analyzed using methods such as Empirical Mode Decomposition (EMD), Fast Fourier Transform (FFT), and Proper Orthogonal Decomposition (POD). The research found that the first three intrinsic mode functions (IMFs) of the combustion chamber pressure fluctuation signal (DP) correspond to different physical fluctuation characteristics. Specifically, the 1st IMF represents the fluctuation characteristics of the heat release rate, corresponding to the flame shear region in the heat release rate field; the 2nd IMF represents the fluctuation characteristics of airflow swirl, corresponding to the swirl vortex structure region in the vorticity field; the 3rd IMF represents the flame detachment fluctuation characteristics, corresponding to the flame detachment region in the heat release rate field. Using the same experimental and numerical calculation methods to study another swirl model combustion chamber, the results also showed the aforementioned correspondence, further demonstrating the accuracy of the experimental results and the universality of this conclusion.
Thermostratigraphic and Heat Flow Assessment of the South Slave Region in the Northwest Territories, Canada
Authors: Rajaobelison Mirah , Thibault Michaël , Comeau Félix-Antoine , Raymond Jasmin , Smejkal Emily , Terlaky Viktor
Published: August 2024

Despite the elevated heat flow known in the Western part of the South Slave Region (Northwest Territories, Canada), a continuous and equilibrium geothermal gradient was never measured in boreholes below the communities where geothermal energy could be developed. This paper aims to predict the geothermal gradient and assess the Earth’s natural heat flow below the communities of Fort Providence, Kakisa, Hay River, and Enterprise. Temperatures from drill-stem tests and bottom well logs were corrected for drilling disturbance and paleoclimate. The thermal conductivity and heat generation rate of the geological formations were determined from the literature and with new laboratory measurements. Original 1D models were developed to evaluate subsurface temperature through the sedimentary formations based on a thermostratigraphic assessment. The results indicate a geothermal gradient that varies from 44.1 ± 10.6 °C km−1 to 59.1 ± 14.9 °C km−1 and heat flow that varies from 105.5 mW m−2 to 160.2 mW m−2 below the communities. These estimates were in agreement with the equilibrium geothermal gradients measured in Cameron Hills, south of the four communities, and were used to verify our predictions. The highest geothermal gradient (59.1 ± 14.9 °C km−1) was estimated at Hay River, which, therefore, has the most favorable geological conditions for geothermal development.
Predicting the Remaining Life of Centrifugal Pump Bearings Using the KPCA–LSTM Algorithm
Authors: Zhu Rongsheng , Zhang Xinyu , Huang Qian , Li Sihan , Fu Qiang
Published: August 2024

This paper proposes a data-driven prediction scheme for the remaining life of centrifugal pump bearings based on the KPCA–LSTM network. A centrifugal pump bearing fault experiment bench is built to collect data, and the performance of time domain, frequency domain, and time-frequency domain characteristics under different working conditions is analyzed. Time domain characteristics, frequency domain characteristics, wavelet packet decomposition energy characteristics, and CEEMDAN energy features are found to be able to capture fault information under different working conditions. Therefore, 43 sensitive features are determined from the time domain, frequency domain, and time-frequency domain. Through the analysis of XJTU-SY bearing life cycle data and based on the weighted scores of monotonicity, robustness, and trend indicators, twelve outstanding characteristics of the bearing in the whole life cycle are selected, and a one-dimensional feature quantity that can characterize the life-degradation process of the centrifugal pump bearing is constructed after KPCA dimension reduction processing. The LSTM network, sensitive to time series, is selected to predict and analyze the constructed one-dimensional feature trend, and the prediction effects of the BP network and the CNN network are compared. The results show that this method has advantages in prediction accuracy and model training time.
Thermoeconomic Evaluation of a High-Performance Solar Biogas Polygeneration System
Authors: Moreira José Luciano Batista , Marques Adriano da Silva , Lago Taynara Geysa Silva do , Arruda Victor Carlos de Lima , Carvalho Monica
Published: August 2024

Because of the higher efficiencies achieved by polygeneration systems compared with conventional generation systems, they have been increasingly adopted to reduce the consumption of resources and consequent environmental damage. Heat dissipated by equipment can be harnessed and reused in a cascade manner. This study applies the Theory of Exergetic Cost (TEC), a thermoeconomic approach, to a high-performance polygeneration system. The system includes a biogas-fueled internal combustion engine, a water–ammonia absorption refrigeration system driven by the engine’s exhaust gases, and a set of photovoltaic panels with a cooling system coupled to solar panels and a hot water storage tank. The pieces of equipment are dimensioned and selected according to the energy demands of a hotel. Then, the temperature, pressure, and energy flows are established for each point of the system. Mass, energy, and exergy balances are developed to determine exergy flows and efficiencies. The main component in terms of exergy and operation costs is the engine, which consumes 0.0613 kg/s of biogas, produces 376.80 kW of electricity, and provides thermal energy for the refrigeration system (101.57 kW) and the hot water tank (232.55 kW), considering the average operating regime throughout the day. The levelized costs are 2.69 USD/h for electricity, 1.70 USD/h for hot water (thermal energy tank), and 1.73 USD/h for chilled water (absorption chiller). The thermoeconomic diagnosis indicated that the hot water tank and the engine are the most sensitive to changes in the maintenance factor. Reducing operating expenses by 20% for the tank and engine lowers energy costs by 10.75% for the tank and 9.81% for the engine.
Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting
Authors: Bui Duy Linh , Nguyen Quang Ninh , Doan Van Binh , Riva Sanseverino Eleonora , Tran Thi Tu Quynh , Le Thi Thuy Hang , Le Quang Sang , Le Cong Thinh , Cu Thi Thanh Huyen
Published: August 2024

This article presents a research approach to enhancing the quality of short-term power output forecasting models for photovoltaic plants using a Long Short-Term Memory (LSTM) recurrent neural network. Typically, time-related indicators are used as inputs for forecasting models of PV generators. However, this study proposes replacing the time-related inputs with clear sky solar irradiance at the specific location of the power plant. This feature represents the maximum potential solar radiation that can be received at that particular location on Earth. The Ineichen/Perez model is then employed to calculate the solar irradiance. To evaluate the effectiveness of this approach, the forecasting model incorporating this new input was trained and the results were compared with those obtained from previously published models. The results show a reduction in the Mean Absolute Percentage Error (MAPE) from 3.491% to 2.766%, indicating a 24% improvement. Additionally, the Root Mean Square Error (RMSE) decreased by approximately 0.991 MW, resulting in a 45% improvement. These results demonstrate that this approach is an effective solution for enhancing the accuracy of solar power output forecasting while reducing the number of input variables.
A Thermodynamic-Based Black-Box Modeling Approach for the Comprehensive Analysis of Vortex Tube Applications
Authors: Sager Robert , Petersen Nils Hendrik , Wirsum Manfred
Published: August 2024

To combat climate change successfully, enhancing existing processes is imperative alongside exploring new regenerative technologies. For this purpose, new components must be considered to improve the efficiency of thermodynamic processes. A promising candidate is the Ranque–Hilsch vortex tube due to its low investment cost and maintenance. Previous research has highlighted the thermodynamic advantages of employing a vortex tube in various applications, such as Brayton cycles or as a replacement for conventional expansion valves. However, to assess the potential of the vortex tube within a thermodynamic process, a computationally efficient but precise model of the vortex tube is required. Existing modeling approaches often fail to accurately predict experimental trends or require information such as geometry data that are not available for potential analyses. Thus, the present study proposes a novel thermodynamic-based black-box modeling approach: the vortex tube efficiency is introduced by incorporating operating and geometrical conditions into a single parameter. The vortex tube efficiency is systematically investigated for different operating conditions and various fluids and compared with available experimental results. The resulting modeling approach allows the qualitative and quantitative prediction of vortex tube behavior for air at various operating pressures and cold gas fractions. Further experimental investigations are required for a comprehensive quantitative description of vortex tubes with different geometries and working fluids.
Study on Flow Heat Transfer and Particle Deposition Characteristics in a Kettle Reboiler
Authors: Liu Xue , Sun Qi , Tang Hui , Peng Wei , Zhang Mingbao , Zhao Gang , Fu Tairan
Published: August 2024

A kettle reboiler uses the latent heat from the condensation of high-temperature and high-pressure steam in the tube to produce low-pressure saturated steam in the outer shell. The deposition of particles on the tube may change the boiling heat transfer mode from nucleate boiling to natural convection, thereby deteriorating the heat transfer performance of the kettle reboiler. Therefore, it is very important to explore the deposition characteristics of particles in the kettle reboiler. In this study, the RPI boiling model based on the Euler–Euler method is used to analyze the water boiling process on the surface of the tube bundle. The DRW model and critical adhesion velocity model based on the Euler–Lagrangian method are used to calculate the motion of particles during the boiling process and the deposition (rebound) behavior. The results show that the boiling of liquid water enhances the local flow velocity of the fluid, so that the maximum flow velocity appears around the near-wall region. The local high-speed flow disperses the particles in the wake flow of the tube bundle, which inhibits the impact of particles on the wall. As the particle size increases, the wall adhesion and fluid drag on the particles are weakened, and the gravity effect gradually becomes dominant, increasing the residence time of the particles in the tube bundle and the frequency of particle impact on the wall. The particle deposition ratio first decreases and then increases. Ultimately, most particles will be deposited in the low-speed area at the end of the tube bundle.
Machine Learning in Cartel Screening—The Case of Parallel Pricing in a Fuel Wholesale Market
Authors: Bejger Sylwester
Published: August 2024

The detection and deterrence of collusive agreements among firms, such as price-fixing cartels, remain pivotal in maintaining market competition. This study investigates the application of machine learning methodologies in the behavioral screening process for detecting collusion, with a specific focus on parallel pricing behaviors in the wholesale fuel market. By employing unsupervised learning techniques, this research aims to identify patterns indicative of collusion—referred to as collusion markers—within time series data. This paper outlines a comprehensive screening research plan based on the CRISP-DM model, detailing phases from business understanding to monitoring. It emphasizes the significance of machine learning methods, including distance measures, motifs, discords, and semantic segmentation, in uncovering these patterns. A case study of the Polish wholesale fuel market illustrates the practical application of these techniques, demonstrating how anomalies and regime changes in price behavior can signal potential collusion. The findings suggest that unsupervised machine learning methods offer a robust alternative to traditional statistical and econometric tools, particularly due to their ability to process large and complex datasets without predefined models. This research concludes that these methods can significantly enhance the detection of collusive behaviors, providing valuable insights for antitrust authorities.
Loss Model Control for Efficiency Optimization and Advanced Sliding Mode Controllers with Chattering Attenuation for Five-Phase Induction Motor Drive
Authors: Moussa Hassen , Krim Saber , Kesraoui Hichem , Mansouri Majdi , Mimouni Mohamed Faouzi
Published: August 2024

This paper proposes firstly a Second Order Sliding Mode Control (SOSMC) based on a Super Twisting Algorithm (STA) (SOSMC-STA) combined with a Direct Field-Oriented Control (DFOC) strategy of a Five-Phase Induction Motor (FPIM). The SOSMC-STA is suggested for overcoming the shortcomings of the Proportional Integral Controller (PIC) and the Conventional Sliding Mode Controller (CSMC). Indeed, the main limitations of the PIC are the slower speed response, the tuning difficulty of its parameters, and the sensitivity to changes in system parameters, including variations in process dynamics, load changes, or changes in setpoint. It is also limited to linear systems. Regarding the CSMC technique, its limitation is the chattering phenomenon, characterized by the rapid switching of the control signal. This phenomenon includes high-frequency oscillations which induce wear and tear on mechanical systems, adversely affecting performance. Secondly, this paper also proposes a Loss Model Controller (LMC) for FPIM energy optimization. Thus, the suggested LMC chooses the optimal flux magnitude required by the FPIM for each applied load torque, which consequently reduces the losses and the FPIM efficiency. The performance of the optimized DFOC-SOSMC-STA based on the LMC is verified using numerical simulation under the Matlab environment. The analysis of the simulation results shows that the DFOC-SOSMC-STA guarantees a high dynamic response, chattering reduction, good precision, and robustness in case of external load or parameter disturbances. Moreover, the DFOC-SOSMC-STA, combined with the LMC, reduces losses and increases efficiency.
A Review on Key Technologies and Developments of Hydrogen Fuel Cell Multi-Rotor Drones
Authors: Shen Zenan , Liu Shaoquan , Zhu Wei , Ren Daoyuan , Xu Qiang , Feng Yu
Published: August 2024

Multi-rotor drones, a kind of unmanned equipment which is widely used in the military, commercial consumption and other fields, have been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell multi-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural light weight, hydrogen storage methods, energy management strategies, thermal management, etc. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.
Industrial Production of Bioactive Nutrient-Enhanced Extra Virgin Olive Oil under Continuous-Flow Ultrasound and Pulsed Electric Field Treatment
Authors: Boffa Luisa , Calcio Gaudino Emanuela , Grillo Giorgio , Binello Arianna , Capaldi Giorgio , Rego Duarte , Pereira Marcos , Cravotto Giancarlo
Published: August 2024

Extra virgin olive oil (EVOO) is a cornerstone of the Mediterranean diet. Many studies have highlighted its crucial preventive role against cardiovascular disease, neurodegenerative disorders, metabolic syndrome and cancer, with these effects being due to the synergistic anti-inflammatory and antioxidant activities of minor components, such as polyphenols and tocols. The aim of the present study is to implement new technologies for olive oil mills and develop an efficient large-sized industrial process for the continuous extraction of healthier EVOOs that are enriched with these bioactive compounds. Non-thermal technologies, namely ultrasound (US) and pulsed electric field (PEF), have been tested, separately and in combination, to eliminate the need for traditional malaxation. There is extensive literature to support the efficacy of ultrasound-assisted extraction (UAE) and PEF treatments in EVOO production. A newly designed US device and a PEF industrial chamber have been combined into a single, integrated continuous-flow setup, the performance of which in the extraction of EVOO from green Coratina olives has been evaluated herein. Extraction yields, physico-chemical and organoleptic characteristics, and polyphenol and tocol contents were monitored throughout the trials, and the last three were measured at accelerated aging times (AAT) of 15 and 30 days. The US and combined US-PEF processes not only increased daily oil production (ton/day, by nearly 45%), but also eliminated the need for kneading during malaxation, resulting in significant energy savings (approximately 35%). In addition, these innovations enriched the resulting EVOO with nutritionally relevant minor components (8–12% polyphenols, 3–5% tocols), thereby elevating its quality and market value, as well as overall stability. The introduction of continuous-flow US and PEF technologies is a remarkable innovation for the EVOO industry, as they offer benefits to both producers and consumers. The EVOO resulting from non-thermal continuous-flow production meets the growing demand for healthier, nutrient-enriched products.
The Galloyl Group Enhances the Inhibitory Activity of Catechins against LPS-Triggered Inflammation in RAW264.7 Cells
Authors: Peng Jinming , Chen Guangwei , Guo Shaoxin , Lin Ziyuan , Li Jun , Yang Wenhua , Xiao Gengsheng , Wang Qin
Published: August 2024

The galloyl group in catechins was confirmed to be crucial for their health benefits. However, whether the catechins’ galloyl group had a contribution to their anti-inflammation remains unclear. This study investigated the anti-inflammation properties and mechanisms of catechins in RAW264.7 cells by using ELISA, fluorometry, flow cytometer, Western blot, and molecular docking. Results showed that the galloyl group enhanced the inhibitory abilities of catechins on inflammatory cytokines (NO, PGE2, IL-1β, and TNF-α) and ROS release in LPS-induced cells. This suppression was likely mediated by delaying cells from the G0/G1 to the S phase, blocking COX-2 and iNOS via the TLR4/MAPK/NF-κB pathway with PU.1 as an upstream target. The research proved that the existence of galloyl groups in catechins was indispensable for their anti-inflammatory capacities and offered a theoretical basis for the anti-inflammatory mechanism of galloylated catechins. Future research is needed to verify the anti-inflammatory effects of catechins in various sources of macrophages or the Caco-2/RAW264.7 cell co-culture system.
Hydrogen Sulfide Improves Postharvest Quality of Okra (Abelmoschus esculentus (L.) Moench) Pods by Enhancing Antioxidant Capacity and Delaying Lignification
Authors: Luo Weihua , Chen Tinghui , Gong Xiao , Chen Jingjing , Zhou Wei , Li Jihua
Published: August 2024

Okra (Abelmoschus esculentus (L.) Moench) pod storage is challenging due to its high water content and tendency to lignify. Sodium hydrosulfide (NaHS) served as an H2S donor in this investigation. Compared with the control group, the group treated with 0.5 mmol/L NaHS solution effectively maintained the appearance quality, and its weight loss was only 6.21% at 20 days. The H2S treatment not only preserved tissue nutrients but also significantly enhanced catalase (CAT), ascorbic acid peroxidase (APX), and superoxide dismutase (SOD) activities while decreasing oxidant damage. In addition, H2S slowed down lignin synthesis by inhibiting the activities of key enzymes such as phenylalanine ammonialyase (PAL), cinnamate 4-hydroxylase (C4H), and cinnamyl alcohol dehydrogenase (CAD) in the lignin biosynthesis pathway. Transcriptome analysis revealed that H2S affects 34 genes in the phenylpropanoid biosynthesis pathway, such as AePAL, Ae4CL1, AeCCOAOMT1, AePOD, etc., which inhibit lignin synthesis of okra pods. All in all, moderate H2S can improve postharvest quality and extend the shelf-life of okra pods by enhancing antioxidant capacity and delaying lignification; the results will provide an overview of its application in the preservation of okra pods.