bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated Methodology for Community-Oriented Energy Investments: Architecture, Implementation, and Assessment for the Case of Nisyros Island
Authors: Papadopoulos Charalampos , Bachoumis Athanasios , Skopetou Niki , Mylonas Costas , Tagkoulis Nikolaos , Iliadis Petros , Mamounakis Ioannis , Nikolopoulos Nikolaos
Published: September 2023

This paper presents an integrated methodology for decision making in smart grid investments that assesses the investment plans of stakeholders in local energy communities (LECs). Considering the energy flow exchanges of the LECs and interpreting them in terms of technical benefits and costs, this methodology indicates the most sustainable and profitable solution covering the LEC energy transition plans. A set of specialized tools capturing the energy, environmental, financial, and social impacts are integrated under a common platform called the IANOS Energy Planning and Transition (IEPT) suite. The tools evaluate a set of well-defined key performance indicators that are gathered using a cost–benefit analysis (CBA) module offering multilateral assessment. By upgrading the functionalities of specialized tools, i.e., the energy modeler INTEMA, the life cycle assessment and costing tool VERIFY, and the smart grid-oriented CBA tool, the IEPT suite evaluates the viability of different smart grid investment scenarios from a multi-dimensional perspective at the LEC level. The functionalities of the proposed suite are validated in the LEC of Nisyros island, Greece, where three smart grid-based investment scenarios of different self-consumption levels are evaluated and ranked in terms of benefits and profitability. The results highlight that for a 20-year horizon of analysis, the investment scenario where a 50% self-consumption target is achieved was more financially viable compared to the 80% and 95% scenarios, achieving values of BCR and NPV equal to EUR 2.12 and EUR 4,400,000, respectively.
Smart Sag Detection and Reactive Current Injection Control for a PV Microgrid under Voltage Faults
Authors: Khan Muhammad Affan , Kim Jaehong
Published: September 2023

Low-power photovoltaic distributed generators are increasing rapidly in number by the grids of the modern era, but they also bring up the concern of grid stability. To maintain grid stability, it is essential for the network operators to update the grid codes at regular intervals due to the case of highly penetrated grid-connected photovoltaic systems (GCPVs). Integration of large-scale electrical grids with renewable energy sources, one being photovoltaic systems, faces the challenge of riding through low-voltage (LVRT) phases. As compared to the previous grid codes for power generation, recent advances require distributed generation resources to provide for such capabilities under grid faults. This work contributes to the ongoing investigation of this specified and destabilizing fault condition. Various simulations of a PV microgrid system are carried out with the ability to ride through low-voltage faults, with the help of a DC-chopper circuit to absorb DC-link over-voltage, and the current is also maintained within acceptable limits according to the required standards. The fundamental contribution of this research is a proposed neural network (NN) control framework. This framework effectively detects voltage sags, comprehends their characteristics, and provides support to the system by injecting reactive current in accordance with the demands imposed by designated grid codes. This NN control model has been systematically developed utilizing data gathered from a vast array of testing simulations conducted under different and dynamic conditions. The proposed strategy, on the other hand, is compared with the RMS fault detection method in combination with the conventional LVRT algorithm. The NN control approach showed better results as compared to the conventional methods in terms of accuracy and robustness, especially when confronted with difficult sag situations.
Fault Diagnosis and Condition Monitoring in Wave Energy Converters: A Review
Authors: Mortazavizadeh Seyed Abolfazl , Yazdanpanah Reza , Gaona David Campos , Anaya-Lara Olimpo
Published: September 2023

The technology used in wave energy conversion systems is still in the early stages of research and development. There are a number of challenges associated with becoming a commercially viable source of renewable energy due to the high operating and maintenance (O&M) costs. A potential solution for increasing the availability of wave energy converters (WECs) and reducing operating and maintenance costs might involve the implementation of condition monitoring and fault-tolerant control systems, because in some reported WEC systems, 57% of total operational expenses go to maintenance activities. The use of condition monitoring techniques in wind energy systems has, for instance, shown the ability to detect failures months in advance, resulting in savings of 15–20% during the operational phase. This paper reviews the methods proposed (and some used) by researchers to monitor WEC’s condition and diagnose faults. Fault-tolerant control methods developed to improve the reliability of WECs and hence their commercial viability are also reviewed and discussed. In addition, a future research plan is provided here.
Multi-Objective Decision Approach for Optimal Real-Time Switching Sequence of Network Reconfiguration Realizing Maximum Load Capacity
Authors: Badran Ola , Jallad Jafar
Published: September 2023

One of the most famous methods for minimizing power loss is distribution network reconfiguration (DNR). Accordingly, many researchers have focused their work on finding a network’s optimal configuration in planning mode. However, few address the switching sequence process during operation mode. This paper introduces an innovative approach to minimize power loss in distribution networks. It addresses the often-overlooked real-time switching sequence order (SSO) during network operation, ensuring a smooth transition to the optimal configuration within operational constraints. Simultaneously, it optimizes distribution network reconfiguration (DNR) and distributed generation location and sizing (DG-LS) to maximize load capacity. The primary goal is to reduce power losses, improve voltage profiles, and enhance network efficiency. Utilizing multi-objective decision methods based on AHP, particle swarm optimization (PSO), and firefly algorithm (FA), this study achieves efficient results for SSO, DNR, and DG-LS optimization.
Characterization of Asphaltene Deposition Behavior in Diluted Heavy Oil under High-Pressure Conditions
Authors: Yang Zuguo , Wu Xinpeng , Guo Jixiang , Zhang Jianjun , Xiong Ruiying , Liu Lei , Kiyingi Wyclif
Published: September 2023

Some oil wells in the Tahe oilfield have been reported to produce extremely heavy oil due to asphaltene deposition. To enhance the flow of crude oil through the wellbore, engineers adopted the use of light oil from nearby wells to dissolve the heavy crude in the wells’ sections to maximize recovery from the Tahe oilfield. However, this mixing has led to the problem of accelerated asphaltene deposition, which often blocks the wellbore in the process. In this research, the factors that influence the stability of diluted heavy oil, temperature, and mixing ratio on asphaltene deposition characteristics under high pressure are studied using a high-temperature and high-pressure crude oil flow property experimental device based on the differential pressure method. The results under high pressure show that the initial deposition pressure of asphaltene decreases as the experimental temperature increases. With an increase in the mixing light oil ratio, the initial deposition pressure of diluted heavy oil increases, and the deposition trend of asphaltene strengthens. The asphaltene accumulation and deposition will be aggravated by filling quartz sand and pipe diameter changes. The research here is helpful to understand the deposition characteristics of asphaltene during the production of diluted heavy oil. It offers significant guidance in the prevention and control of asphaltene precipitation in heavy oil wells.
A Low-Level Virtual Machine Just-In-Time Prototype for Running an Energy-Saving Hardware-Aware Mapping Algorithm on C/C++ Applications That Use Pthreads
Authors: Știrb Iulia , Gillich Gilbert-Rainer
Published: September 2023

Low-Level Virtual Machine (LLVM) compiler infrastructure is a useful tool for building just-in-time (JIT) compilers, besides its reliable front end represented by a clang compiler and its elaborated middle end containing different optimizations that improve the runtime performance. This paper specifically addresses the part of building a JIT compiler using an LLVM with the scope of obtaining the hardware architecture details of the underlying machine such as the number of cores and the number of logical cores per processing unit and providing them to the NUMA-BTLP static thread classification algorithm and to the NUMA-BTDM static thread mapping algorithm. Afterwards, the hardware-aware algorithms are run using the JIT compiler within an optimization pass. The JIT compiler in this paper is designed to run on a parallel C/C++ application (which creates threads using Pthreads), before the first time the application is executed on a machine. To achieve this, the JIT compiler takes the native code of the application, obtains the corresponding LLVM IR (Intermediate Representation) for the native code and executes the hardware-aware thread classification and the thread mapping algorithms on the IR. The NUMA-Balanced Task and Loop Parallelism (NUMA-BTLP) and NUMA-Balanced Thread and Data Mapping (NUMA-BTDM) are expected to optimize the energy consumption by up to 15% on the NUMA systems.
LCA of a Proton Exchange Membrane Fuel Cell Electric Vehicle Considering Different Power System Architectures
Authors: Gottardo Gianmarco , Basso Peressut Andrea , Colnago Silvia , Latorrata Saverio , Piegari Luigi , Dotelli Giovanni
Published: September 2023

Fuel cell electric vehicles are a promising solution for reducing the environmental impacts of the automotive sector; however, there are still some key points to address in finding the most efficient and less impactful implementation of this technology. In this work, three electrical architectures of fuel cell electric vehicles were modeled and compared in terms of the environmental impacts of their manufacturing and use phases. The three architectures differ in terms of the number and position of the DC/DC converters connecting the battery and the fuel cell to the electric motor. The life cycle assessment methodology was employed to compute and compare the impacts of the three vehicles. A model of the production of the main components of vehicles and fuel cell stacks, as well as of the production of hydrogen fuel, was constructed, and the impacts were calculated using the program SimaPro. Eleven impact categories were considered when adopting the ReCiPe 2016 midpoint method, and the EF (adapted) method was exploited for a final comparison. The results highlighted the importance of the converters and their influence on fuel consumption, which was identified as the main factor in the comparison of the environmental impacts of the vehicle.
Experimental and Numerical Analysis of the Efficacy of a Real Downhole Heat Exchanger
Authors: Asad Muhammad , Guida Vincenzo , Mauro Alessandro
Published: September 2023

In this paper, a three-dimensional (3D) numerical model based on the finite element method (FEM) is developed to determine the fluid flow and heat transfer phenomena in a real multi-tube downhole heat exchanger (DHE), designed ad hoc for the present application, considering natural convection inside a geothermal reservoir. The DHE has been effectively installed and tested on the island of Ischia, in southern Italy, and the measurements have been used to validate the model. In particular, the authors analyze experimentally and numerically the behavior of the DHE based on the outlet temperature of the working fluid, thermal power, overall heat transfer coefficient, and efficiency. Furthermore, the influence of the degree of salinity on the performance of the DHE has been studied, observing that it degrades with the increase in the degree of salinity. The results show that the DHE allows to exchange more than 40 kW with the ground, obtaining overall heat transfer coefficient values larger than 450 W/m2 K. At the degree of salinity of 180 ppt, a decrease in the efficiency of the DHE of more than 8% is observed.
Research on Cavitation Performance of Bidirectional Integrated Pump Gate
Authors: Lai Huahuang , Wang Haoshu , Zhou Zhen , Zhu Rongsheng , Long Yun
Published: September 2023

A pump gate is a device that controls the flow of water. It can stop the flood when it comes, drain the ponding gathered in the city, and improve the water circulation of the city. Traditional pumping stations require a large land area, and their pump houses and gates need to be designed separately. Furthermore, the construction period of traditional pumping stations is lengthy, and the maintenance costs are high. It can no longer meet the needs of modern cities for water environment management. Therefore, it is imperative to design a new type of pump gate. The integrated pump gate introduced in this paper is an integrated construction of gates and pumps to achieve automatic control and bidirectional operation. The research mainly consists of three parts: design of pumping station, theoretical analysis, and numerical calculation. By studying the unstable flow inside the integrated pump, the characteristics and the degree of cavitation occurrence are predicted. This can provide a reference basis for the optimal design and stability operation of the integrated pump gate. To investigate cavitation in an integrated pump gate, numerical simulations were performed for multiple operating conditions using the SST turbulence model. Constant numerical simulations of cavitation through numerical calculation, the characteristic curves of the integrated pump gate under forward and reverse operation at different flow points were obtained, and flow field analysis was performed for the model pump at 1.0 Q. The location and degree of cavitation occurrence were predicted. In this study, a preliminary analysis was conducted to investigate the influence of cavitation on the internal flow characteristics of integrated gate pumps. The research collected data related to cavitation characteristics, streamline patterns, and blade pressures. Additionally, the study explored the characteristics of cavitation phenomena, laying the foundation for the optimization of the design of bidirectional operation in integrated sluice gate pumps for future practical engineering applications.
Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network
Authors: Yan Junshuai , Liu Yongqian , Ren Xiaoying , Li Li
Published: September 2023

Gearbox fault deterioration can significantly impact the safety, reliability, and efficiency of wind turbines, resulting in substantial economic losses for wind farms. However, current condition monitoring methods face challenges in effectively mining the hidden spatio-temporal features within SCADA data and establishing reasonable weight allocations for model input variables. To tackle these issues, we proposed a novel condition monitoring method for wind turbine gearboxes called HBCE, which integrated a feature-time hybrid attention mechanism (HA), the bidirectional convolutional long short-term memory networks (BiConvLSTM), and an improved exponentially weighted moving-average (iEWMA). Specifically, utilizing historical health SCADA data acquired through the modified Thompson tau data-cleaning algorithm, a normal behavior model (HA-BiConvLSTM) of gearbox was constructed to effectively extract the spatio-temporal features and learn normal behavior patterns. An iEWMA-based outlier detection approach was employed to set dynamic adaptive thresholds, and real-time monitor the prediction residuals of HA-BiConvLSTM to identify the early faults of gearbox. The proposed HBCE method was validated through actual gearbox faults and compared with conventional spatio-temporal models (i.e., CNN-LSTM and CNN&LSTM). The results illustrated that the constructed HA-BiConvLSTM model achieved superior prediction precision in terms of RMSE, MAE, MAPE, and R2, and the proposed method HBCE can effectively and reliably identify early anomalies of a wind turbine gearbox in advance.
The Multi-Objective Optimal Scheduling of the Water–Wind–Light Complementary System Based on an Improved Pigeon Flock Algorithm
Authors: Wang Kangping , Ge Pengjiang , Duan Naixin , Wang Jili , Lv Jinli , Liu Meng , Wang Bin
Published: September 2023

The output of wind power and photovoltaic power is random, fluctuating and intermittent, and a direct grid connection will result in the reduction of power generation income and a great fluctuation in the power grid’s connection. The addition of hydropower stations can reduce the above problems. Therefore, this paper first introduces and analyzes a typical application scenario of a water–wind–light combined power generation system. Then, a multi-objective optimization model is established, considering the two objectives of maximizing the joint generation and minimizing the system’s power fluctuation. Third, the adaptive fractional order calculus strategy is introduced, and a multi-objective pigeon swarm algorithm, which can adaptively adjust the fractional order according to the location information of a flock, is proposed. Finally, an optimization simulation is carried out. The simulation results show that the improved multi-objective pigeon swarm algorithm has better optimization accuracy. It provides a reference for the future implementation of hydropower stations, and the surrounding wind and photoelectric field joint dispatching strategy.
A Simple and Effective Model for Predicting the Thermal Energy Requirements of Greenhouses in Europe
Authors: Dimitropoulou Anna-Maria N. , Maroulis Vasileios Z. , Giannini Eugenia N.
Published: September 2023

In this paper, a simple model is proposed for predicting the thermal energy requirements of greenhouses in Europe. The model estimates the annual heating requirements and the maximum required heating power, along with the corresponding heating and zero-energy operating periods. It is based on the greenhouse technical data (the overall heat loss coefficient, cover transmission, sensible absorbance), the cultivation conditions (temperature range), and the meteorological data (solar radiation and ambient temperature) according to the site characteristics (longitude and latitude). The model results can be obtained using a hand calculator. The model results are compared with those of a detailed model simulating a greenhouse’s thermal performance and they agreed with real data from the literature. Moreover, a model sensitivity analysis revealed the effect of various factors on the greenhouse’s energy requirements. The results proved that the most significant factor affecting heating requirements, the maximum heating power, and heating periods is the latitude of the greenhouse site, whereas zero-energy periods are primarily influenced by the plant cultivation temperature range and then by the latitude. According to our findings, in lower latitudes (40 to 50 degrees), heating requirements range from 250 to 430 kWh/m2/y, whereas, in higher latitudes (50 to 60 degrees), heating needs range from 430 to 650 kWh/m2/y.
Synthesis of 1-Hexanol/Hexyl hexanoate Mixtures from Grape Pomace: Insights on Diesel Engine Performances at High Bio-Blendstock Loadings
Authors: Frigo Stefano , Raspolli Galletti Anna Maria , Fulignati Sara , Licursi Domenico , Bertin Lorenzo , Martinez Gonzalo Agustin , Pasini Gianluca
Published: September 2023

The production of oxygenated bio-additives for traditional fuels represents a key challenge due to their depletion in the near-future and their positive contribution to the reduction in environmental pollution. The present study considers the synthesis of 1-hexanol/hexyl hexanoate mixtures, two oxygenated Diesel bio-additives produced through the hydrogenation of hexanoic acid, obtainable from the fermentation of a wide variety of waste biomasses. In our case, crude hexanoic acid was produced through the fermentation of grape pomace, an abundant Italian agrifood waste. Commercial 5 wt% Re/γ-Al2O3 was adopted for the catalytic hydrogenation of crude hexanoic acid, and the support acidity allowed the tuning of the reaction selectivity toward the formation of hexyl hexanoate, instead of 1-hexanol, reaching yields of 40 and 25 mol%, respectively. The effects of each bio-additive on Diesel engine performance and exhaust emissions (soot, nitrogen oxides, carbon monoxide, unburned hydrocarbons) were evaluated, highlighting noteworthy positive effects especially on the reduction in carbon monoxide and soot emissions, if compared with those of Diesel fuel alone. Similar promising performances were achieved by employing Diesel blend mixtures of 1-hexanol/hexyl hexanoate, mimicking typical compositions of the rhenium-catalyzed post-hydrogenation mixtures. Even in such cases, 1-hexanol/hexyl hexanoate mixtures can be blended with commercial Diesel fuel, up to high loadings currently not yet investigated (20 vol%), without altering the engine performances and, again, significantly lowering soot and carbon monoxide emissions by more than 40%. This work highlights the possibility of obtaining such oxygenated bio-additives starting from waste through to a fully sustainable process and proves their beneficial effects on the reduction in exhaust emissions with no changes in engine performance.
Indoor Climate Monitoring in Office Buildings—Comparative Analysis of Two Office Buildings without Air Conditioning
Authors: Voss Karsten , Voß Tjado , Kaliga Marvin
Published: September 2023

Against the background of climate protection and the rising costs of a fossil-fuel-based energy supply, the interest in the energy performance and indoor climate of buildings in real operation is rising. This paper, therefore, deals with the indoor climate investigation of two medium-sized office buildings in Germany by taking measurements over a whole year. These relate to one new building and one refurbished building. Sensors of various types were installed and operated in a large number of office rooms, so that in total results are available for over 100 rooms, typically occupied by one or two persons. The analysis focuses on the indoor temperature in summer and the air quality in winter based on the CO2 concentration. The comfort classes according to DIN EN 16798 including the adaptive comfort approach are used as a basis to cluster the results. Both buildings have movable sun protection and openable windows but no facilities for active cooling. They, thus, represent a large number of existing ‘low tech’ office buildings in Germany and central Europe. The results reflect the respective building concepts but also show a wide range between the rooms due to the user preferences and behaviour. The refurbished building shows better results, especially in terms of air quality but also in terms of summer room temperatures. This underlines the benefit of the targeted measures as a result of an analysis of the deficits in the existing building before the refurbishment. The additional measures for decentralised mechanical ventilation and passive cooling are having positive effects. As part of the projects, further measures to improve the indoor climate were investigated in both buildings. In one case, this involved CO2 traffic lights to stimulate personal window ventilation in winter, and in the other, the use of newly developed individual ceiling fans supports convective heat dissipation on the human body during hot spells in summer. The positive effect could be demonstrated for both measures.
Isobaric Expansion Engines–Compressors: Thermodynamic Analysis of Multistage Vapor Driven Compressors
Authors: Kronberg Alexander , Glushenkov Maxim , Roosjen Sander , Kersten Sascha
Published: September 2023

Isobaric expansion (IE) engines can directly convert heat into mechanical energy, making them particularly attractive for applications such as vapor-driven pumps and compressors. A recent initial assessment investigating the utilization of IE engines as vapor-driven reciprocating compressors has revealed that the vapor use efficiency is inherently low in the case of the simplest direct-acting compressor designs. Based on this analysis, it was anticipated that multistage compression can offer significant advantages for vapor-driven compressors. Therefore, this paper aims to conduct a comprehensive analytical thermodynamic analysis of direct vapor-driven multistage reciprocating compressors. The analysis considers processes without intercooling and processes with intercooling of the compressed gas between stages. The findings demonstrate that, for vapor-driven compression, the benefits of multistage compression are superior to those known for conventional compression processes. Multistage vapor-driven compression not only reduces compression work and temperature elevation but, more importantly, mitigates the adverse effects on vapor compression of the driving vapor, thereby enabling a substantial improvement in vapor utilization efficiency. Furthermore, the total volume of the IE engine compressor experiences a significant decrease with an increasing number of stages. Consequently, under specific process parameters, the overall dimensions of the engine-compressor system may also decrease as the number of stages increases. The results offer significant opportunities for energy savings in energy-intensive compression processes by replacing electrical energy with readily available low-grade heat sources (<100 °C). Such processes include hydrogen, air, and ethylene compression at high pressure.
Parameter-Free Model Predictive Current Control for PMSM Based on Current Variation Estimation without Position Sensor
Authors: Luo Laiwu , Yu Feng , Ren Lei , Lu Cheng
Published: September 2023

To remove parameter dependence in existing sensorless control strategies, a parameter-free model predictive current control is proposed for permanent magnet synchronous motor without any position sensor. First, the current variation during one sampling period is analyzed and divided into two elements: natural attenuation and forced response. Second, recursive least squares algorithm is utilized to estimate the future current variation so that the model predictive current control can be successfully executed paying no attention to motor parameters. Meanwhile, the position information is obtained by the arc tangent function according to the estimated forced response of current variation. At last, experimental results verify that the estimation errors of rotor position are reduced to around 0.1 rad with smaller current prediction error even at low speed where no motor parameters are required.
Advances in Selective Hydrogenation of 5-Hydroxymethylfurfural over Heterogeneous Metal Catalysts
Authors: Xia Haihong , Li Jing , Zhou Minghao
Published: September 2023

Biomass is an excellent renewable organic energy in nature. 5-hydroxymethylfurfural (HMF) is a significant platform chemical derived from biomass. It can be obtained from biomass and has the potential to produce high value-added derivatives. For the past few years, the chemocatalysis pathway has been extensively studied and is the main pathway of HMF transformation. In this paper, the influence factors and reaction mechanisms of different catalyst types on HMF hydrogenation processes were discussed. The latest progress on the efficient catalytic system using hydrogen, alcohol and other hydrogen sources to catalyze HMF was introduced. Future research prospects of catalytic hydrogenation of HMF were also prospected.
The Effect of Dust Deposition on the Performance of Photovoltaic Panels
Authors: Rusănescu Carmen Otilia , Rusănescu Marin , Istrate Irina Aura , Constantin Gabriel Alexandru , Begea Mihaela
Published: September 2023

Given the energy crisis and climate change due to pollution, and given that the largest emissions of greenhouse gases are produced by the energy industry, we must turn our attention to the efficient use of solar energy, which is the cleanest and most abundant of all renewable energies. In this paper, based on an analysis of the specialized literature, we studied the effect of dust accumulation on the surface of photovoltaic modules on some performance characteristics and on the efficiency of these panels and modules compared to the efficiency of clean modules. We analyzed the cause of dust accumulation and the influence of the tilt angles of the photovoltaic panels on the dust deposition rate. We highlighted the influence of atmospheric temperature, solar radiation, wind speed, and relative humidity depending on the density of the dust deposited on the surface of the photovoltaic panel, and we found a decrease in the efficiency of the panel based on the increase in dust density for slightly high values of solar radiation, wind speed, and relative humidity. We highlighted the reduction in CO2 emissions by replacing electricity from fossil fuels with solar energy. The efficient use of solar energy is a solution for the decarbonization of the energy sector.
A Novel Magnetic Suspension Flywheel Battery with a Multi-Function Air Gap
Authors: Zhang Weiyu , Lv Shengyan
Published: September 2023

In this study, a novel magnetic suspension flywheel battery with a multi-function air gap is proposed. Based on the unique multi-function air gap, the degrees of freedom between the control magnetic circuits can be independent of each other, reducing the coupling effect between degrees of freedom. The proposed flywheel battery system topology inherits the unique advantages of the magnetic suspension flywheel battery. However, due to the breakthrough of the overall topology in the air gap, flywheel, and protection measures, the system has more advantages in the precise design of the magnetic circuit, decoupling, high-stability control, and energy storage. First, the advantages, geometric characteristics, and magnetic circuit of the flywheel battery system are introduced and analyzed. Then, the position and length of the multi-function air gap and conventional air gap in the magnetic circuit are optimized to ensure the best magnetic performance of the entire magnetic circuit. Then, the flywheel, motor, and protection settings are introduced in detail. Finally, performance tests are conducted, and the good experimental results show that the proposed flywheel battery has good anti-interference ability and decoupling ability.
Evolution and Multi-Scenario Prediction of Land Use and Carbon Storage in Jiangxi Province
Authors: Huang Yue , Xie Fangting , Song Zhenjiang , Zhu Shubin
Published: September 2023

In recent years, escalating global warming and frequent extreme weather events have caused carbon emission reduction to become a pressing issue on a global scale. Land use change significantly impacts ecosystem carbon storage and is a crucial factor to consider. This study aimed to examine the evolutions in land use and their impact on carbon storage in Jiangxi Province, China. Using the coupled PLUS-InVEST model, we analyzed the spatial patterns alterations of both land use and carbon storage from 2000 to 2020 and set four scenarios for 2040. Our findings indicated the following: (1) From 2000 to 2020, the area of cropland, forest, grassland, and unused land declined, whereas the area of water and built-up land increased, with changes mainly occurring in 2010–2020. (2) From 2000 to 2020, due to the land use change, carbon storage in Jiangxi Province demonstrated a decreasing trend, with a total reduction of 2882.99 × 104 t. (3) By 2040, under the dual protection scenario for cropland and ecology, the expansion of built-up land will be most restricted among the four scenarios, and the largest projected carbon storage was foreseen. This suggests that carbon loss can be minimized by focusing on cropland and ecological conservation, especially forests. Our research findings can facilitate policy decisions to balance economic development and environmental protection in Jiangxi Province in the future.
Taxonomy, Phylogeny, and Life Cycle of Four Neophysopella Species on Meliosma myriantha and M. oldhamii in Korea
Authors: Na Dong Hwan , Lee Jae Sung , Shin Hyeon-Dong , Choi Young-Joon
Published: September 2023

The present study performed an in-depth investigation of rust diseases affecting Meliosma myriantha and Meliosma oldhamii trees (Sabiaceae) in Korea. The analysis identified four distinct species of the genus Neophysopella (Pucciniales) as the causal agents. Among these, N. hornotina was found to infect only M. oldhamii, whereas three Neophysopella species (N. meliosmae, N. meliosmae-myrianthae, and N. vitis) were parasitic on M. myriantha. To our knowledge, this is the first report of the former two species (N. hornotina and N. meliosmae) in Korea. In addition, we specified their alternate host plants for two heteroecious species (N. meliosmae-myrianthae and N. vitis) in Korea, completing the life cycles of the four rust species, and provided detailed morphological descriptions at each stage of their life cycles. Phylogenetic relationships of these rust species were uncovered using a comprehensive sample size, and we have constructed a phylogenetic tree for Neophysopella using the cytochrome c oxidase subunit III (cox3) gene sequences, demonstrating an effective approach for species delineation within this genus. The findings contribute to identifying and managing rust diseases affecting Meliosma species.
Complex Three-Dimensional Mathematical Model of the Ignition of a Coniferous Tree via a Cloud-to-Ground Lightning Discharge: Electrophysical, Thermophysical and Physico-Chemical Processes
Authors: Baranovskiy Nikolay Viktorovich
Published: September 2023

Thunderstorms are the main natural source of forest fires. The ignition mechanism of trees begins with the impact of cloud-to-ground lightning discharge. A common drawback of all predicting systems is that they ignore the physical mechanism of forest fire as a result of thunderstorm activity. The purpose of this article is to develop a physically based mathematical model for the ignition of a coniferous tree via cloud-to-ground lightning discharge, taking into account thermophysical, electrophysical, and physicochemical processes. The novelty of the article is explained by the development of an improved mathematical model for the ignition of coniferous trees via cloud-to-ground lightning discharge, taking into account the processes of soot formation caused by the thermal decomposition phase of dry organic matter. Mathematically, the process of tree ignition is described by a system of non-stationary nonlinear differential equations of heat conduction and diffusion. In this research, a locally one-dimensional method is used to solve three-dimensional partial differential equations. The finite difference method is used to solve one-dimensional heat conduction and diffusion equations. Difference analogues of the equations are solved using the marching method. To resolve nonlinearity, a simple iteration method is used. Temperature distributions in a structurally inhomogeneous trunk of a coniferous tree, as well as distributions of volume fractions of phases and concentrations of gas mixture components, are obtained. The conditions for tree trunk ignition under conditions of thunderstorm activity are determined. As a result, a complex three-dimensional mathematical model is developed, which makes it possible to identify the conditions for the ignition of a coniferous tree trunk via cloud-to-ground lightning discharge.
Importance Values of Mycorrhizal Fungal Types and Species Diversity Driving Variations in Fungi- and Bacteria-Derived Residues in Planted Forests in Northeast China
Authors: Huang Haiyan , Song Xiaoqian , Jin Lu , Shi Yu , He Haisheng , Zhang Zhonghua , Tang Zhonghua , Wang Wenjie
Published: September 2023

Aims: Microbial residue deposition is considered an important part of soil carbon sequestration. However, there is still a lack of understanding of the link between tree species composition and diversity and microbial carbon deposition, which hampers the rational selection and allocation of tree species for artificial carbon sequestration afforestation in northern China. Methods: In this study, plots from temperate planting forests (>60 years) were examined for the importance values of tree species, mycorrhizal types, tree diversity, and soil properties. Soil amino sugar was used as the biomarker to indicate the accumulation of fungi- and bacteria-derived carbon. Results: We found that tree species diversity and the importance values of tree species and mycorrhizal types were significantly positively correlated with soil microbial residual carbon. Hierarchical partitioning modeling showed that three groups of variables significantly affected soil microbial residual carbon, accounting for a total of 26.75% of the variation. Among them, tree species diversity accounted for the largest proportion (11.5%), and tree species diversity and importance values had a high joint impact (9.74%). The importance values of all AM-associated species constituted one of the most significant individual factors and could independently account for 10.9% of the variation in microbial residues. The findings of piecewise structural equation modeling showed that the importance of tree species had a large direct impact on GluN, GalN, and the GluN/MurN ratio. By influencing soil properties, the importance values of tree species also had indirect effects on soil microbial residual carbon. Conclusions: We suggest that an increase in the importance values of AM-associated tree species, such as Acer negundo L., will be accompanied by an increase in the total importance value of AM-associated tree species, which can significantly increase soil microbial residual carbon.
Documenting Two Centuries of Change in Longleaf Pine (Pinus palustris) Forests of the Coastal Plain Province, Southeastern USA
Authors: Hanberry Brice B. , Stober Jonathan M. , Bragg Don C.
Published: September 2023

While many tree species occur across the Coastal Plain of the southeastern United States, longleaf pine (Pinus palustris C. Lawson) savannas and woodlands once dominated this region. To quantify longleaf pine’s past primacy and trends in the Coastal Plain, we combined seven studies consisting of 255,000 trees from land surveys, conducted between 1810 and 1860 with other descriptions of historical forests, including change to the present day. Our synthesis found support that Pinus palustris predominantly constituted 77% of historical Coastal Plain trees and upland oaks (Quercus) contributed another 8%. While Pinus still dominates these forests today (58% of all trees), most are now either planted loblolly (Pinus taeda L.) or slash (Pinus elliottii Engelm.) pines. Water oak (Quercus nigra L.), live oak (Quercus virginiana Mill.), sweetgum (Liquidambar styraciflua L.), and red maple (Acer rubrum L.) have increased their proportions compared to historical surveys; both longleaf pine and upland oaks have declined to ≤5% of all trees. Our work also supports previous estimates that longleaf pine originally dominated over 25–30 million ha of Coastal Plain forests. As late as the early 1900s, longleaf pine may still have covered 20 million ha, but declined to 7.1 million ha by 1935 and dropped to 4.9 million ha by 1955. Longleaf pine’s regression continued into the mid-1990s, reaching a low of about 1.3 million ha; since then, restoration efforts have produced a modest recovery to 2.3 million ha. Two centuries of overcutting, land clearing, turpentining for chemicals, fire exclusion followed by forest densification by fire-sensitive species, and other silvicultural influences, including widespread loblolly and slash pine plantations, have greatly diminished the Coastal Plain’s once extensive open longleaf pine forests.
Linking Forest Management Practices to the Functional Composition of Plant Communities
Authors: Ma Yu-Qi , Wang Chun-Jing , Chen Zhi , Yu Fei-Hai , Wan Ji-Zhong
Published: September 2023

The functional composition of plant communities (FCPC) makes a significant contribution to ecosystem properties, functions, and services. Here, we used 18 plant functional traits from the sPlot database v2.1 and the global forest management type dataset to explore the links between forest management and the FCPC. We used the post hoc Tukey test to explore the differences in the community-weighted mean (CWM) and community-weighted variance (CWV) among different forest management types [i.e., intact forests, managed forests with natural regeneration, planted forests, plantation forests (with up to a 15-year rotation), and agroforestry]. We found that different forest management types can result in significant variability in plant communities’ functional composition. Plantation forests could result in significantly higher CWM and CWV compared to intact forests, and significant differences could occur between natural and managed forests with natural regeneration. Furthermore, the relationship between forest management practices and the FCPC depends on ecozone type changes. There were significant differences between natural and plantation forests for CWM and CWV in temperate forests. Our study provides an effective reference for applying plant functional traits to regulate and optimize the functions and services of forest ecosystems.
Assessing the Effects of Conservation Measures on Soil Erosion in Arasbaran Forests Using RUSLE
Authors: Sasanifar Samira , Alijanpour Ahmad , Banj Shafiei Abbas , Eshaghi Rad Javad , Molaei Morteza , Álvarez-Álvarez Pedro
Published: September 2023

Vegetation cover is known as one of the most dominant parameters in soil erosion that can considerably affect soil erosion drivers. This study aimed to assess the effects of vegetation cover on soil conservation in Arasbaran Forests, Iran. A part of Arsbaran forests has been protected for 45 years. The other part has not been under protection during these years. This study was carried out in order to investigate the effects of forest protection management on the changes in the amount of soil erosion and compare it with the non-protection sector. To this end, 66 samples were grouped in the two selected elevation classes. Out of every three sample plots, one plot was randomly selected for collecting soil samples. Landsat 8 images and a Digital Elevation Model were utilized for sample collection via ENVI (Environment for Visualizing Images) and GIS (Geographic Information System), respectively. Then, the Revised Universal Soil Loss Equation (RUSLE) was employed to estimate the annual soil loss in the studied sites. The results showed the annual soil erosion of 9.84 and 10.06 tons per hectare/year for protected and non-protected areas, respectively. Moreover, the average annual soil erosion of 9.95 tons per hectare/year was calculated for the whole Arasbaran Forests. The results of the statistical test revealed no significant difference between protected and non-protected sites in terms of erosion rates (p > 0.05). Based on the findings, despite the non-significant and slightly lower soil loss per unit area in the protected site, there is a notable soil loss throughout the entire non-protected area. It appears necessary to conduct a thorough review of existing conservation laws and to closely monitor their effective implementation. This step is crucial for enhancing the effectiveness of forest conservation management in mitigating soil erosion. The results show that absolute forest protection alone cannot make a big difference in preventing soil erosion. In this regard, there is a need to carry silviculture measures to manage protected forest stands to increase the sustainability of the forest. Obviously, in the case of proper management along with protection, it is possible to have a greater effect in preventing soil erosion.
Plant Population Classification Based on PointCNN in the Daliyabuyi Oasis, China
Authors: Li Dinghao , Shi Qingdong , Peng Lei , Wan Yanbo
Published: September 2023

Populus euphratica and Tamarix chinensis hold significant importance in wind prevention, sand fixation, and biodiversity conservation. The precise extraction of these species can offer technical assistance for vegetation studies. This paper focuses on the Populus euphratica and Tamarix chinensis located within Daliyabuyi, utilizing PointCNN as the primary research method. After decorrelating and stretching the images, deep learning techniques were applied, successfully distinguishing between various vegetation types, thereby enhancing the precision of vegetation information extraction. On the validation dataset, the PointCNN model showcased a high degree of accuracy, with the respective regular accuracy rates for Populus euphratica and Tamarix chinensis being 92.106% and 91.936%. In comparison to two-dimensional deep learning models, the classification accuracy of the PointCNN model is superior. Additionally, this study extracted individual tree information for the Populus euphratica, such as tree height, crown width, crown area, and crown volume. A comparative analysis with the validation data attested to the accuracy of the extracted results. Furthermore, this research concluded that the batch size and block size in deep learning model training could influence classification outcomes. In summary, compared to 2D deep learning models, the point cloud deep learning approach of the PointCNN model exhibits higher accuracy and reliability in classifying and extracting information for poplars and tamarisks. These research findings offer valuable references and insights for remote sensing image processing and vegetation study domains.
An Enhanced Minimax Loss Function Technique in Generative Adversarial Network for Ransomware Behavior Prediction
Authors: Gazzan Mazen , Sheldon Frederick T.
Published: September 2023

Recent ransomware attacks threaten not only personal files but also critical infrastructure like smart grids, necessitating early detection before encryption occurs. Current methods, reliant on pre-encryption data, suffer from insufficient and rapidly outdated attack patterns, despite efforts to focus on select features. Such an approach assumes that the same features remain unchanged. This approach proves ineffective due to the polymorphic and metamorphic characteristics of ransomware, which generate unique attack patterns for each new target, particularly in the pre-encryption phase where evasiveness is prioritized. As a result, the selected features quickly become obsolete. Therefore, this study proposes an enhanced Bi-Gradual Minimax (BGM) loss function for the Generative Adversarial Network (GAN) Algorithm that compensates for the attack patterns insufficiency to represents the polymorphic behavior at the earlier phases of the ransomware lifecycle. Unlike existing GAN-based models, the BGM-GAN gradually minimizes the maximum loss of the generator and discriminator in the network. This allows the generator to create artificial patterns that resemble the pre-encryption data distribution. The generator is used to craft evasive adversarial patterns and add them to the original data. Then, the generator and discriminator compete to optimize their weights during the training phase such that the generator produces realistic attack patterns, while the discriminator endeavors to distinguish between the real and crafted patterns. The experimental results show that the proposed BGM-GAN reached maximum accuracy of 0.98, recall (0.96), and a minimum false positive rate (0.14) which all outperform those obtained by the existing works. The application of BGM-GAN can be extended to early detect malware and other types of attacks.