bluuebunny/crossref_metadata_2025 huggingface dataset:
Rare Earth Elements Distribution and Bacteriome to Assess and Characterize the Soil Landscapes of Old Olive Orchards
Authors: Roccotelli Angela , Tommasini Simone , Ceccherini Maria Teresa , Calamai Luca , Ferrari Mattia , Ghiotto Matthias , Riccio Roberto , Bonciani Lisa , Pietramellara Giacomo , Moretti Sandro , Pelacani Samuel
Published: July 2024

The presence of the olive tree in Tuscany, Italy, in its forms that have survived to the present day as an essential component of the landscape dates back many centuries. Global change is now threatening it. Therefore, it is important to find markers to enhance the olive tree environment in terms of its resilience. The aim of the research was to investigate the composition of soil bacteriomes in contrasting geochemical environments using a geochemistry approach based on the behavior of the REEs, inherited from parent rock material. Bacteriome assemblages and REE content were analyzed in 48 topsoils developed in six geochemical Tuscan environments. Combined geochemical, geoinformatic, and bioinformatic techniques highlighted the existence of four bacteriome assemblages depending on Light-REEs. Further results showed that the soil bioavailable fraction of REEs was related to parent rock materials, pH, and bacteriome composition. The most abundant bacteria were Microlunatus in graded fluvio-lacustrine soils, Gaiella in graded arenaceous soils, Bradyrizhobium in pyroclastic soils, and Rubrobacter in soils on gentle slopes of calcareous and carbonatic lithologies. This research represents a starting point to define new indicators able to assess the resilience of the olive trees in the Mediterranean landscape and characterize the territory of extra virgin olive oils.
Agricultural Landscapes: A Pattern-Process-Design Approach to Enhance Their Ecological Quality and Ecosystem Services through Agroforestry
Authors: Vagge Ilda , Sgalippa Nicolò , Chiaffarelli Gemma
Published: July 2024

Agricultural landscapes are currently suffering and generating severe ecological issues. This is especially true in intensively managed alluvial contexts, where biodiversity is declining and ecosystem services (ES) delivery capacity is being depleted. The aim of our study is to set up and test a synthetic analytical methodology that allows us to: understand current agricultural landscape ecological quality drivers (structural and functional traits); identify context-specific strategies to correct current negative trends (landscape ecology design approach); and assess the changes in the landscape ecological behavior provided by design scenarios. The applied methodology is low-cost and low-time-demanding and is based on multi-scale landscape ecology and land-use-based ES assessment; it implements a pattern-process-design approach. Analyses are applied to four northern Italian alluvial agricultural landscape systems. We specifically address landscape biodiversity support functions (landscape ecology indicators) and landscape multifunctionality (ES spatial assessment). We test the agroforestry approach (landscape feature insertions and crop diversification) as a key strategy to enhance ecological quality and ES, and we account for its contributions to context-specific design scenarios. This analytical toolkit might serve for future applications on similar case studies.
Mortality, Length of Stay and Cost of Hospitalization among Patients with Adult-Onset Still’s Disease: Results from the National Inpatient Sample 2016–2019
Authors: Mittal Sushmita , Schroeder Benjamin , Alfaki Musaab
Published: July 2024

We use this study to analyze the trends in in-hospital length of stay, total hospital charges, and mortality among adult patients with a primary diagnosis of adult-onset still’s disease (AOSD). We used the 2016–2019 National Inpatient Sample (NIS) database to conduct a retrospective study on adult AOSD patients (≥18 years old). We analyzed data on baseline patient and hospital characteristics and determined trends in in-hospital mortality, length of stay (LOS), and total hospital charges (TOTCHG). Univariate and multivariate linear and logistic regression analyses were performed to identify factors that independently affected these outcomes. Among the 1615 AOSD hospitalizations, the mean LOS was 7.34 days and the mean TOTCHG was 68,415.31 USD. Macrophage activating syndrome (MAS), disseminated intravascular coagulation (DIC), and a large hospital size were shown to statistically increase the LOS and TOTCHG, while a Native American background was shown to statistically decrease both. The mean in-hospital mortality was 0.929%, with age being the only independent predictor. Our findings reveal an increase in the economic burden of AOSD hospitalizations despite declining admissions and mortality rates. Complications, like MAS and DIC, were found to significantly contribute to this burden despite treatment advancements. Our study indicates the importance of investigating new strategies to prevent these complications.
Is a Pre-Existent Cone-Beam Computed Tomography Able to Detect Metal Dental Posts?
Authors: Solomonov Michael , Hadad Avi , Ben Itzhak Joe , Lvovsky Alex , Azizi Hadas
Published: July 2024

(1) Background: In this study, the efficacy of cone-beam computed tomography (CBCT) in detecting dental posts was compared to periapical radiography. (2) Methods: A retrospective evaluation of 53 patients’ periapical radiographs and CBCT images was performed. The presence and type of the intra-canal dental post were initially determined on the periapical images (PA) radiographs’ examination and were then compared to the observer’s ability to detect the dental post on a CBCT image. The effect of the post’s type (metal cast or prefabricated) on its detection on CBCT images was determined. (3) Results: 10.5% of teeth that were identified as having a post on a PA radiograph were not identified as having a post on the CBCT examination (p < 0.05). Approximately 17.6% of teeth that were identified as not having a post on a PA radiograph were identified as having a post on the CBCT examination (p < 0.05). Moreover, 16.3% and 50% of teeth with a prefabricated or cast posts on PA radiographs were falsely identified on the CBCT examination, respectively (p < 0.05). (4) Conclusions: A CBCT image is an insufficient tool for the identification of metal prefabricated and cast posts. A PA image is the recommended radiographic tool for achieving information about the post-endodontic restoration status of teeth candidates for endodontic retreatment in patients with a former CBCT scan.
Intelligent Decision-Making Algorithm for UAV Swarm Confrontation Jamming: An M2AC-Based Approach
Authors: He Runze , Wu Di , Hu Tao , Tian Zhifu , Yang Siwei , Xu Ziliang
Published: July 2024

Unmanned aerial vehicle (UAV) swarm confrontation jamming offers a cost-effective and long-range countermeasure against hostile swarms. Intelligent decision-making is a key factor in ensuring its effectiveness. In response to the low-timeliness problem caused by linear programming in current algorithms, this paper proposes an intelligent decision-making algorithm for UAV swarm confrontation jamming based on the multi-agent actor–critic (M2AC) model. First, based on Markov games, an intelligent mathematical decision-making model is constructed to transform the confrontation jamming scenario into a symbolized mathematical problem. Second, the indicator function under this learning paradigm is designed by combining the actor–critic algorithm with Markov games. Finally, by employing a reinforcement learning algorithm with multithreaded parallel training–contrastive execution for solving the model, a Markov perfect equilibrium solution is obtained. The experimental results indicate that the algorithm based on M2AC can achieve faster training and decision-making speeds, while effectively obtaining a Markov perfect equilibrium solution. The training time is reduced to less than 50% compared to the baseline algorithm, with decision times maintained below 0.05 s across all simulation conditions. This helps alleviate the low-timeliness problem of UAV swarm confrontation jamming intelligent decision-making algorithms under highly dynamic real-time conditions, leading to more effective and efficient UAV swarm operations in various jamming and electronic warfare scenarios.
Rapid Initialization Method of Unmanned Aerial Vehicle Swarm Based on VIO-UWB in Satellite Denial Environment
Authors: Wang Runmin , Deng Zhongliang
Published: July 2024

In environments where satellite signals are blocked, initializing UAV swarms quickly is a technical challenge, especially indoors or in areas with weak satellite signals, making it difficult to establish the relative position of the swarm. Two common methods for initialization are using the camera for joint SLAM initialization, which increases communication burden due to image feature point analysis, and obtaining a rough positional relationship using prior information through a device such as a magnetic compass, which lacks accuracy. In recent years, visual–inertial odometry (VIO) technology has significantly progressed, providing new solutions. With improved computing power and enhanced VIO accuracy, it is now possible to establish the relative position relationship through the movement of drones. This paper proposes a two-stage robust initialization method for swarms of more than four UAVs, suitable for larger-scale satellite denial scenarios. Firstly, the paper analyzes the Cramér–Rao lower bound (CRLB) problem and the moving configuration problem of the cluster to determine the optimal anchor node for the algorithm. Subsequently, a strategy is used to screen anchor nodes that are close to the lower bound of CRLB, and an optimization problem is constructed to solve the position relationship between anchor nodes through the relative motion and ranging relationship between UAVs. This optimization problem includes quadratic constraints as well as linear constraints and is a quadratically constrained quadratic programming problem (QCQP) with high robustness and high precision. After addressing the anchor node problem, this paper simplifies and improves a fast swarm cooperative positioning algorithm, which is faster than the traditional multidimensional scaling (MDS) algorithm. The results of theoretical simulations and actual UAV tests demonstrate that the proposed algorithm is advanced, superior, and effectively solves the UAV swarm initialization problem under the condition of a satellite signal rejection.
Reversible Data Hiding in Crypto-Space Images with Polynomial Secret Sharing over Galois Field
Authors: Lu Hao-Wei , Liu Jui-Chuan , Chang Chin-Chen , Horng Ji-Hwei
Published: July 2024

Secret sharing is a data security technique that divides secret information into multiple parts, embeds these parts into various shares, and distributes these shares to different participants. The original secret information can be retrieved only when the number of shares gathered meets a required threshold. This paper proposes a secret sharing method that can hide data in encrypted images with reversibility and allows content owners to add an additional layer of security before uploading data to the cloud. This method enables the independent extraction of images and data, ensuring that the recovered images and extracted data can serve as validation information for each other. The proposed method not only enhances data security but also guarantees the accuracy of the extracted information.
Surface Defect Detection for Aerospace Aluminum Profiles with Attention Mechanism and Multi-Scale Features
Authors: Feng Yin-An , Song Wei-Wei
Published: July 2024

A YOLOv5 aluminum profile defect detection algorithm that integrates attention and multi-scale features is proposed in this paper to address the issues of the low detection accuracy, high false detection rates, and high missed detection rates that are caused by the large-scale variation of surface defects, inconspicuous small defect characteristics, and a lack of concentrated feature information in defect areas. Firstly, an improved CBAM (Channel-Wise Attention Module) convolutional attention module is employed, which effectively focuses on the feature information of defect areas in the aluminum defect dataset with only a small amount of spatial dimension. Secondly, a bidirectional weighted feature fusion network is utilized, incorporating a multi-scale feature fusion network with skip connections to aggregate various high-resolution features, thus enriching the semantic expression of features. Then, new size feature maps that have not been fused are introduced into the detection layer network to improve the detection effect of small target defects. Experimental results indicate that an average detection accuracy (mAP) of 82.6% was achieved by the improved YOLOv5 algorithm on the aluminum surface defect dataset. An improvement of 6.2% over the previous version was observed. The current defect detection requirements of aluminum profile production sites are met by this enhanced algorithm.
Rotating Kernel CNN Optimization for Efficient IoT Surveillance on Low-Power Devices
Authors: Wang Fei , Hou Songwei , Gao Jianbang , Wu Dan
Published: July 2024

This study presents an innovative method to optimize convolutional neural networks (CNNs) tailored for the prevalent low-power platforms in IoT and embedded devices, which are integral to smart city infrastructures. Our approach introduces a novel “Rotating Convolutional Kernel” technique, designed to significantly reduce the computational load of CNNs while maintaining high accuracy, an essential feature for the constrained processing capabilities of devices produced by Hisilicon, MediaTek, and Novatek, among others. By leveraging the intelligent video engine (IVE) capabilities inherent in low-end CPUs, our optimized YOLOv3-Tiny model, with fewer than 100 K parameters and specifically fine-tuned for pedestrian and vehicle detection, demonstrates impressive processing speeds of 52 ms/frame on the HI3516EV200 chip and 66 ms/frame on the MSC313E chip, with minimal accuracy compromise. Despite a substantial reduction in parameter count compared to that in MobileNetV2, our model’s top-1 accuracy only slightly decreases by 2.6%, showcasing the effectiveness of our optimization technique. Our findings highlight the potential and applicability of our method in enhancing the performance and utility of IoT and embedded devices within smart cities. By achieving an optimal balance between computational efficiency and detection accuracy, our approach offers a promising avenue for advancing the capabilities of low-power devices in urban surveillance, traffic management, and other smart city applications, thereby contributing to the development of more intelligent, efficient, and responsive urban environments.
Double-Loop Controller Design of a Single-Phase 3-Level Power Factor Correction Converter
Authors: Han Jun-Hyuk , Kim IL-Song
Published: July 2024

This paper presents a study on the double-loop controller design technique for a single-phase power factor correction (PFC) three-level (TL) boost converter. Designing a double-loop controller using conventional methods is challenging due to the 120 Hz voltage ripple in the output voltage. This study proposes new double-loop control design methods using a band-stop filter and MATLAB SISOTOOL, detailed in a step-by-step sequence. A band-stop filter with a 120 Hz stop band is applied to the double-loop controller design. Modeling based on the state-space equation, applicable to the full duty range, is constructed to obtain the transfer function. The double-loop controller structure is then designed, and optimal gains that satisfy the design requirements are obtained through automatic tuning using the MATLAB SISOTOOL library. The simulation results demonstrate the performance of the proposed method, which is further verified by experimental results.
Study on Thermal Characteristics of Winding under Inter-Turn Fault of Oil-Immersed Transformer
Authors: Hao Yuefeng , Zhang Zhanlong , Xiao Jiayi , Zhao Hui , Yang Yu , Dong Zijian
Published: July 2024

The inter-turn fault is a common fault of oil-immersed transformers. Due to the deterioration of inter-turn insulation, its resistance decreases, which leads to an increase in the winding coil current and loss, causing the winding temperature to rise. Therefore, analyzing the electrothermal characteristics of inter-turn short circuit faults in oil-immersed transformers can provide a theoretical basis for the diagnosis of inter-turn faults in transformers. This paper studies the equivalent circuit under inter-turn faults and establishes a field–circuit coupling model to calculate the current characteristics under different inter-turn short circuit faults of transformers. The distribution of coil losses under faults is calculated as thermal source excitations, and a thermal–fluid-coupled calculation model for oil-immersed transformers is established. After the experimental verification, it is found that both the errors between the winding and tank surface temperatures do not exceed the acceptable limits, validating the effectiveness of this model. Using this model to calculate the temperature distribution for various inter-turn short circuit faults in transformers reveals their hot-spot characteristics. The results indicate that under inter-turn fault conditions, the temperature at faulty turns will gradually increase towards a limit, eventually leading to coil damage. This research provides theoretical support for enhancing transformer detection and diagnostic capabilities.
SL: Stable Learning in Source-Free Domain Adaptation for Medical Image Segmentation
Authors: Wang Yan , Chen Yixin , Yang Tingyang , Zhu Haogang
Published: July 2024

Deep learning techniques for medical image analysis often encounter domain shifts between source and target data. Most existing approaches focus on unsupervised domain adaptation (UDA). However, in practical applications, many source domain data are often inaccessible due to issues such as privacy concerns. For instance, data from different hospitals exhibit domain shifts due to equipment discrepancies, and data from both domains cannot be accessed simultaneously because of privacy issues. This challenge, known as source-free UDA, limits the effectiveness of previous UDA medical methods. Despite the introduction of various medical source-free unsupervised domain adaptation (MSFUDA) methods, they tend to suffer from an over-fitting problem described as “longer training, worse performance”. To address this issue, we proposed the Stable Learning (SL) strategy. SL is a method that can be integrated with other approaches and consists of weight consolidation and entropy increase. Weight consolidation helps retain domain-invariant knowledge, while entropy increase prevents over-learning. We validated our strategy through experiments on three MSFUDA methods and two public datasets. For the abdominal dataset, the application of the SL strategy enables the MSFUDA method to effectively address the domain shift issue. This results in an improvement in the Dice coefficient from 0.5167 to 0.7006 for the adaptation from CT to MRI, and from 0.6474 to 0.7188 for the adaptation from MRI to CT. The same improvement is observed with the cardiac dataset. Additionally, we conducted ablation studies on the two involved modules, and the results demonstrated the effectiveness of the SL strategy.
Eye Tracking Based on Event Camera and Spiking Neural Network
Authors: Jiang Yizhou , Wang Wenwei , Yu Lei , He Chu
Published: July 2024

An event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, and addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye-tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of a spiking neural network (SNN) with an event camera, a near-range eye-tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081 ms, enhancing tracking stability while maintaining accuracy.
A Method for Predicting Transformer Oil-Dissolved Gas Concentration Based on Multi-Window Stepwise Decomposition with HP-SSA-VMD-LSTM
Authors: Chen Tie , Guo Shinan , Zhang Zhifan , Yuan Yimin , Gao Jiaqi
Published: July 2024

Predicting the concentration of dissolved gases in transformer oil is a critical activity for the early detection of potential faults. To address the prevalent issue of data leakage in current prediction methods, this paper proposes a prediction method that completely avoids data leakage. First, the Hodrick Prescott (HP) filter is used for stepwise decomposition to obtain the long-term trend and high-frequency periodic component. The high-frequency periodic component is further decomposed using singular spectrum analysis (SSA) to extract periodic features. Dispersion entropy (DE) and fuzzy entropy (FE) are utilized alongside the HP and SSA methods to determine the optimal decomposition windows during the process, enhancing the ability of the model to acquire time series features. Then, variational mode decomposition (VMD) is applied to remove noise from the high-frequency component. Finally, the long short-term memory network (LSTM) is employed to predict each decomposed component, and the network parameters undergo optimization through the sparrow search optimization algorithm (SSOA). The two case studies in this work verify that the proposed model excels over other prediction means, providing strong support for subsequent fault prediction.
Covert Communications in Active-RIS-Aided NOMA Systems: Element Grouping or Not?
Authors: Kang Xueyu , Lu Feng , Zhu Miaomiao , Liu Hongwu , Pang Xiyu , Yang Hai , Zeng Qingsheng
Published: July 2024

This paper investigates the impacts of element grouping on the covert communication performance of an active reconfigurable intelligent surface (ARIS)-aided uplink non-orthogonal multiple access (NOMA) system. Through element grouping, each element of the ARIS works in either the reflecting mode to reflect the information signal or the jamming mode to generate a jamming signal. Optimizing the NOMA transmit power, ARIS beamforming, and receive beamforming jointly is necessary to maximize the covert communication rate. To tackle the unsolvable covert communication rate maximization problem, we decouple the original problem into three sub-problems of optimizing the NOMA transmit power, ARIS beamforming, and receive beamforming, respectively. To tackle the mixed-integer non-linear programming for the element grouping, we introduce the arithmetic- and geometric-mean-based penalty term and apply the Dinkelbach transform to reformulate the optimization problem. Next, we propose an alternating optimization algorithm to optimize the system parameters. The numerical results demonstrate the effectiveness of the element grouping in improving the covert communication rate. However, the element grouping scheme achieves a lower covert communication rate performance compared to the scheme without element grouping, indicating that using element grouping for covert communications in the ARIS-aided uplink NOMA system is not the preferred option.
Proposing an Efficient Deep Learning Algorithm Based on Segment Anything Model for Detection and Tracking of Vehicles through Uncalibrated Urban Traffic Surveillance Cameras
Authors: Shokri Danesh , Larouche Christian , Homayouni Saeid
Published: July 2024

In this study, we present a novel approach leveraging the segment anything model (SAM) for the efficient detection and tracking of vehicles in urban traffic surveillance systems by utilizing uncalibrated low-resolution highway cameras. This research addresses the critical need for accurate vehicle monitoring in intelligent transportation systems (ITS) and smart city infrastructure. Traditional methods often struggle with the variability and complexity of urban environments, leading to suboptimal performance. Our approach harnesses the power of SAM, an advanced deep learning-based image segmentation algorithm, to significantly enhance the detection accuracy and tracking robustness. Through extensive testing and evaluation on two datasets of 511 highway cameras from Quebec, Canada and NVIDIA AI City Challenge Track 1, our algorithm achieved exceptional performance metrics including a precision of 89.68%, a recall of 97.87%, and an F1-score of 93.60%. These results represent a substantial improvement over existing state-of-the-art methods such as the YOLO version 8 algorithm, single shot detector (SSD), region-based convolutional neural network (RCNN). This advancement not only highlights the potential of SAM in real-time vehicle detection and tracking applications, but also underscores its capability to handle the diverse and dynamic conditions of urban traffic scenes. The implementation of this technology can lead to improved traffic management, reduced congestion, and enhanced urban mobility, making it a valuable tool for modern smart cities. The outcomes of this research pave the way for future advancements in remote sensing and photogrammetry, particularly in the realm of urban traffic surveillance and management.
Joint Scheduling Method for Pumped Storage Units and Renewable Energy Sources
Authors: Lin Boyuan , Huang Ying , Xu Ling , Xu Zheng , Wang Guoteng , Zhao Le
Published: July 2024

With the integration of renewable energy sources, how we can improve the stability of the new energy power system has become an urgent issue pursued by scholars. In this paper, a joint scheduling method for pumped storage units (PSUs) and renewable energy sources (RESs) considering frequency deviation and voltage stiffness constraints is proposed. First, the analytical expression of the frequency deviation constraint is derived based on the primary frequency regulation model. Then, the voltage stiffness constraint is constructed to describe the system’s capability of maintaining the voltage magnitude. Finally, to minimize the renewable energy abandonment caused by stability requirements, the joint scheduling model for PSUs and RESs is established. The application of the method ensures that the frequency deviation of the modified IEEE 39 bus system is less than 0.5 Hz, while the voltage stiffness remains above 0.9.
Capacitance Evaluation of Metallized Polypropylene Film Capacitors Considering Cumulative Self-Healing Damage
Authors: Du Guoqiang , Zhang Jie
Published: July 2024

Self-healing (SH) in metallized polypropylene film capacitors (MPPFCs) can lead to irreversible damage to electrode and dielectric structures, resulting in capacitance loss and significant stability degradation, especially under cumulative SH conditions. To enhance the reliability assessment of MPPFCs post-SH, this study conducted SH experiments on MPPFCs, examined the damage patterns of the electrodes and dielectric films, and proposed a novel capacitance evaluation method for MPPFCs under cumulative SH conditions. The results reveal that with increasing SH voltage, the number of dielectric layers experiencing single SH breakdowns rises, SH energy significantly escalates, and the loss area of the electrode due to high-temperature evaporation expands. Under cumulative SH conditions, the number of SH events is linearly correlated with the total number of SH-breakdown film layers and shows an exponential decay with the average single SH energy. By utilizing a Support Vector Machine (SVM) to classify the SH condition and damage features within the capacitor based on the correlation and distribution patterns of SH feature parameters, this study introduces an advanced method for evaluating the capacitance of MPPFCs under cumulative SH conditions. This method promises to improve the predictive maintenance and reliability of power electronic systems utilizing MPPFCs.
Ambient Temperature Effects on Energy Consumption and CO2 Emissions of a Plug-in Hybrid Electric Vehicle
Authors: Ansari Amir , Abediasl Hamidreza , Shahbakhti Mahdi
Published: July 2024

The ambient temperature affects the operation of different powertrain systems, including electric, hybrid electric, and internal combustion engines. This study investigated the effect of the ambient temperature on the energy consumption and CO2 emissions of a plug-in hybrid electric vehicle running in different powertrain modes. The vehicle was driven for 4150 km following a selected route 199 times in different powertrain modes and in different ambient temperatures ranging from −24 °C to 32 °C. Instantaneous and cumulative fuel consumptions were measured using a fuel flow meter, and the battery energy usage was determined from the vehicle telematics during each test. The total energy consumption and total CO2 emissions were affected by the ambient temperature in all powertrain modes, including electric, hybrid electric (charge-depleting and charge-sustaining), and conventional internal combustion engine modes. The highest increase was associated with the charge-depleting hybrid electric mode, with 350% and 290% increases in energy consumption and CO2 emissions when the ambient temperature dropped from 29 °C to −24 °C. The conventional internal combustion engine mode was the least affected, with only 7% and 8% increased in energy consumption and CO2 emissions, respectively.
Joint Modeling of Wind Speed and Power via a Nonparametric Approach
Authors: de Aquino Ferreira Saulo Custodio , Maçaira Paula Medina , Cyrino Oliveira Fernando Luiz
Published: July 2024

Power output from wind turbines is influenced by wind speed, but the traditional theoretical power curve approach introduces uncertainty into wind energy forecasting models. This is because it assumes a consistent power output for a given wind speed. To address this issue, a new nonparametric method has been proposed. It uses K-means clustering to estimate wind speed intervals, applies kernel density estimation (KDE) to establish the probability density function (PDF) for each interval and employs Monte Carlo simulation to predict power output based on the PDF. The method was tested using data from the MERRA-2 database, covering five wind farms in Brazil. The results showed that the new method outperformed the conventional estimation technique, improving estimates by an average of 47 to 49%. This study contributes by (i) proposing a new nonparametric method for modeling the relationship between wind speed and power; (ii) emphasizing the superiority of probabilistic modeling in capturing the natural variability in wind generation; (iii) demonstrating the benefits of temporally segregating data; (iv) highlighting how different wind farms within the same region can have distinct generation profiles due to environmental and technical factors; and (v) underscoring the significance and reliability of the data provided by the MERRA-2 database.
Switching Current Predictive Control of a Permanent Magnet Synchronous Motor Based on the Exponential Moving Average Algorithm
Authors: Yu Fengming , Liu Jun
Published: July 2024

To improve the dynamic and steady-state control performance of permanent magnet synchronous motors under the three-vector model predictive current control method, this study proposes a switching current predictive control method based on the exponential moving average algorithm, which evaluates the magnitude of the change of the q-axis current slope in real time to discriminate the motor’s operating conditions and selects the optimal control method for different operating conditions. Meanwhile, the traditional three-vector model predictive current control method is improved by introducing a comparison mechanism for the q-axis current slope to select the second effective voltage vector, avoiding the secondary optimization calculation of the value function and reducing the computational complexity of the traditional method. By comparing the proposed method with the traditional three-vector model predictive current control method, the experimental results prove that the proposed method improves the system’s dynamic response and steady-state performance.
Control of Grid-Connected and Standalone Microhydraulic Turbine Using a Six-Phase Induction Generator
Authors: Ouédraogo Marius , Yazidi Amine , Betin Franck
Published: July 2024

Microhydraulic turbines offer a promising solution for decentralized energy production, suitable for both grid-connected and standalone applications, due to their compactness and high efficiency. This paper introduces a control approach for such systems employing microhydraulic turbines as distributed generators (DGs), utilizing six-phase induction generators for electricity production. This study emphasizes control strategies for both grid-connected and standalone modes utilizing proportional-integral (PI) controllers. An integrated energy storage system based on Li-Ion battery technology is also implemented to store the excess energy and compensate for production deficits to meet demand. The results obtained using MATLAB/Simulink demonstrate efficient and reliable power management among production sources, the grid and the local load, highlighting the unique contribution of employing a six-phase induction generator with the energy storage system.
A Gap Nonlinearity Compensation Strategy for Non-Direct-Drive Servo Systems
Authors: Wang Bo , Ji Runze , Zhou Chengpeng , Sohel Rana M. , Liu Kai , Hua Wei , Ye Hairong
Published: July 2024

In this paper, a gap nonlinear compensation strategy is proposed for the full closed-loop control structure of non-direct-drive servo motor systems. Firstly, an improved deadband model containing the initial value of the gap is proposed, and two gap amplitude identification methods, namely, incremental torque and velocity difference integral, are compared. Then, for the full closed-loop structure, based on the describing function and the stability theory of the nonlinear system, the limit-loop oscillating frequency and the influencing factors are predicted, which are related to the system control stiffness and independent of the gap amplitude; finally, the state-feedback control is proposed, and the feedback coefficients are designed by using the pole configuration, making the system a pseudo-linear system. Simulation and experimental verification show that the method can suppress the limit loop oscillation, attenuate the system shock, and have a certain robustness.
A Study on the Problem of AC Corrosion of Power Umbilical Cables Caused by Electromagnetic Induction Phenomena
Authors: Shao Pengjin , Li Haijun , Pan Pan , Shao Qibing , Li Zhen , Yang Jiaming
Published: July 2024

During the normal laying and operation of a three-core umbilical cable, AC current can easily lead to AC electrochemical corrosion on the outer surface of the steel tube. To explore the electrochemical corrosion mechanism and the factors affecting the three-core umbilical cable, this paper optimizes the internal induced potential calculation method for three-core umbilical cables. It analyzes the changes in the characteristics of the induced potential and explores the variations in the density of induced current under different conditions. The research results show that by optimizing the calculation method for the induction potential of the umbilical cable’s steel pipe, for the electromagnetic significance of the smallest repeating unit, the induction potential on the steel pipe’s surface exhibited a cyclic change. The peak part of the induction potential is most likely to experience electrochemical corrosion. Additionally, reducing the radius of the outer insulation aperture of the steel pipe and improving the conductivity of seawater will increase the density of the induced current in the insulation aperture, thereby increasing the risk of electrochemical corrosion. As the cable pitch and AC frequency increase, the current density in the steel pipe pores will also rise.
Performance Analysis of an Ejector-Enhanced Heat Pump System for Low-Temperature Waste Heat Recovery Using UHVDC Converter Valves
Authors: Jin Menghan , Zhang Xingjuan , Zhou Jianhui , Zhang Limin
Published: July 2024

This article proposes a heating method based on heat pump technology to address the large amount of low-grade waste heat generated by a certain type of ultra-high voltage direct current (UHVDC) converter valve. Thermal performance calculations for two systems, a basic vapor compression heat pump system (BVCHPS) based on thermal expansion valve throttling and an ejector-enhanced heat pump system (EEHPS) are analyzed. The research results show that the EEHPS exhibits superior COP and exergy efficiency when generating hot water above 80 °C using a heat source below 50 °C. Additionally, mathematical modeling analysis identifies optimal structural parameters such as nozzle throat diameter, throat area ratio, and nozzle outlet diameter for the ejector in its design state. The low-temperature waste heat recovered from the UHVDC converter valves can be further used in engineering applications such as heating, refrigeration, seawater desalination, and sewage treatment.
The Effect of Hydrogen as a Coolant on the Characteristics of Humidification-Dehumidification Desalination Systems
Authors: Abdala Antar M. M. , Elwekeel Fifi N. M. , Taccani Rodolfo
Published: July 2024

The air humidification-dehumidification (HDH) technique for water desalination can be useful in many water production applications. Researchers from all around the world have examined various implementations of this technology to improve it. The present research investigates the effect of three dehumidifier coolants on the system. These coolants include water, helium, and hydrogen. The impact of these coolants on the parameters of the humidification-dehumidification desalination system will be discussed. The investigation’s parameters are tested at various mass ratios, air flow rates, and air outlet heaters. The results show that when hydrogen is employed as a dehumidifier coolant, the gained output ratio (GOR) achieves its peak of 6.37 in the considered mass ratio range of 2.1 to 3. On the other hand, when hydrogen is utilized as a dehumidifier coolant, the system produces the maximum entropy, with the dehumidifier contributing the most. When the mass ratio changes from 2 to 3, the average entropy generation for the system using hydrogen in the dehumidifier increases by 3.8 and 2.9 times, respectively, compared to the average entropy generation for the system using water and helium. However, when hydrogen is used as a dehumidifier coolant, safety concerns must be addressed, as well as the size and cost of heat exchangers in comparison to water.
A Review of Exploration and Development Technologies for Coal, Oil, and Natural Gas
Authors: Feng Gan , Wang Guifeng , Xie Hongqiang , Hu Yaoqing , Meng Tao , Li Gan
Published: July 2024

Energy is the fundamental prerequisite for human survival and development, as well as the driving force behind the progress of human civilization [...]
Effects of Natural Vegetation Restoration on Soil Physicochemical Properties in Tropical Karst Areas, Southwestern China
Authors: Zhang Bei , Hu Gang , Xu Chaohao , Hu Cong , Zhong Chaofang , Chen Siyu , Zhang Zhonghua
Published: July 2024

Natural soil and vegetation recovery following human disturbance is the primary means of restoring degraded ecosystems globally. However, it remains unclear how vegetation recovery in the tropical karst areas of China affects the soil physicochemical properties. Here, we investigated the impacts of natural vegetation recovery on soil physicochemical properties at different soil depths in tropical karst areas in southwestern China, using a space–time substitution method. We found that with the natural vegetation recovery, soil bulk density (SBD) decreased. Soil pH initially decreased and then increased, reaching its lowest value during the shrubland stage. There was a significant increase in other soil physicochemical factors. In the soil profiles, SBD tended to increase with depth. The pH, total potassium (TK), total phosphorus (TP), and exchangeable calcium remained relatively stable across the different soil layers. TK, TP, available phosphorus, SBD, total nitrogen, pH, exchangeable magnesium, and available potassium significantly contributed to the soil physicochemical properties. Soil physicochemical properties were predominantly directly affected by litter and biological factors, albeit indirectly influenced by topographic factors. Our study provides crucial insights into karst soils and their relationship with vegetation recovery, which are pivotal for steering vegetation restoration and soil amelioration in karst areas.
Tree-Ring δ13C and Intrinsic Water-Use Efficiency Reveal Physiological Responses to Climate Change in Semi-Arid Areas of North China
Authors: Lu Weiwei , Wu Bo , Yu Xinxiao , Jia Guodong , Gao Ying , Wang Lili , Lu Anran
Published: July 2024

Climate change has had a widespread and profound impact on global temperature and precipitation patterns, especially in semi-arid areas. Plant δ13C and iWUE indicate the trade-off between carbon uptake and water loss, which is pivotal for understanding plant responses to climate change. Information about the long-term responses of the physiological and ecological processes of different tree species to climate change is also required. To investigate the impact of different forest stand structures and site conditions on long-term growth and physiological processes of coniferous and broad-leaved trees in the mountainous area of Beijing, we analyzed the tree-ring δ13C variation of four tree species (Platycladus orientalis, Pinus tabuliformis, Quercus variabilis, Robinia pseudoacacia) sampled from 64 plots with varying site and stand conditions. We found that the tree-ring δ13C of the four tree species varied from each other and was mainly affected by density and slope aspect, followed by slope and age. Both tree-ring δ13C and iWUE of the four tree species showed increasing trends over time, mechanistically linked to long-term changes in global CO2 concentration. This indicates the four native tree species have adapted well to climate change, and the risk of decline is relatively low. The increased iWUE translated into different growth patterns which varied with tree species, site, and stand condition. Different tree species have varying sensitivities to environmental factors. The iWUE of coniferous tree species is more sensitive to climate change than that of broad-leaved tree species, especially to temperature (T), the Standardized Precipitation Evapotranspiration Index (SPEI), and vapor pressure deficit (VPD).
Evaluation of the Potentials of Tobacco Waste Extract as Wood Preservatives against Wood Decay Fungi
Authors: Liu Lin , Tian Junkai , Zhou Ziyao , Yang Chunwang , Yang Susu , Zhang Kangkang , Yang Yushan , Qiu Jian
Published: July 2024

Utilizing conventional wood preservatives poses potential risks to ecosystems and human health. Therefore, the wood protection industry must develop alternatives that are both efficient and environmentally friendly. In this paper, industrial tobacco waste extracts were used as eco-friendly wood preservatives against wood decay fungi. Three major constituents in the extracts were identified via gas chromatography-mass spectrometry (GC-MS) and included nicotine, neophytadiene, and 2,7,11-cembratriene-4,6-diol. The antifungal activities of waste tobacco extracts and these three major constituents against wood decay fungi were tested. At a concentration of 40 mg/mL, the tobacco waste extract treated with 50% ethanol significantly inhibited the activity of wood decay fungi. This was because the extract contained nicotine as the primary active component and neophytadiene as a synergistic active component. Wood decay resistance tests were conducted on Pinus yunnanensis and Hevea brasiliensis treated with a 50% ethanol extract of tobacco waste at a concentration of 40 mg/mL. The mass losses of Pinus yunnanensis exposed to G. trabeum and T. versicolor were 4.11% and 5.03%, respectively, while the mass losses in Hevea brasiliensis exposed to G. trabeum and T. versicolor were 7.85% and 9.85%, respectively, which were classified as highly resistant. The acute ecotoxicity of the tobacco waste extract was assessed using a kinetic luminescent bacteria test with Aliivibrio fischeri, which revealed significantly lower acute toxicity than a commercial copper-based wood preservative. This study offers insights into high-value utilization of tobacco waste and advancement of natural wood preservatives.
The Effect of Hardwood Veneer Densification on Plywood Density, Surface Hardness, and Screw Withdrawal Capacity
Authors: Kallakas Heikko , Akkurt Tolgay , Scharf Alexander , Mühls Fred , Rohumaa Anti , Kers Jaan
Published: July 2024

Increasing environmental awareness and the carbon-storing capability of wood have amplified its relevance as a building material. The demand for high-quality wood species necessitates exploring alternative, underutilized wood sources due to limited forest areas and premium wood volume. Consequently, the veneer-based industry is considering lower-value hardwood species like grey alder (Alnus Incania), black alder (Alnus glutinosa), and aspen (Populus tremula) as substitutes for high-quality birch (Betula pendula). Initially less appealing due to their lower density and mechanical properties, these species show promise through densification, which enhances their density, strength, and hardness. This study aims to enhance plywood screw withdrawal capacity and surface hardness by densifying low-density wood species and using them in plywood face-veneer layers, or in all layers. The relationship between the wood density, surface hardness, and screw withdrawal capacity of plywood made of low-value species like aspen and black alder is examined. Experimental work with a pilot-scale veneer and plywood production line demonstrates improved surface hardness (65% and 93% for aspen and black alder, respectively) and screw withdrawal capacity (16% and 35% for aspen and black alder, respectively) in densified face veneer plywood. This research highlights the potential of densified low-value wood species to meet construction requirements, expanding their practical applications.
Trade Creation or Diversion?—Evidence from China’s Forest Wood Product Trade
Authors: Gao Lei , Pei Taowu , Tian Yu
Published: July 2024

In recent years, trade protectionism and unilateralism have prevailed, and countries around the world have imposed restrictions on log exports. It has also become more difficult for China to import wood resources and export deep-processed wood forest products. Based on panel data from 2000 to 2019, this study uses social network analysis to measure the level of the Chinese wood forest product trade network, takes the Chinese free trade agreements (FTAs) as the natural experiment, and uses the multi-stage double-difference method to investigate the impact of the signed FTAs on China’s wood forest product trade. The study finds that the trade network of Chinese wood forest products is becoming increasingly complex, and the central position of China and the Association of Southeast Asian Nations (ASEAN) in the network is increasing year by year. The signing of FTAs has had a significant positive impact on the trade of wood forest products in China and a significant trade creation effect. This finding remains true after conducting the placebo test and propensity score-matched regression control. At the same time, the import of wood forest products in China will have a significant trade transfer effect due to the signing of FTAs, and this will not affect exports. Although FTAs show significant trade creation and trade transfer effects in China’s wood forest product trade, they also increase, to a certain extent, the mismatch of forest resources worldwide.