bluuebunny/crossref_metadata_2025 huggingface dataset:
Heat Transfer Simulation and Structural Optimization of Spiral Fin-and-Tube Heat Exchanger
Authors: Jiang Huaquan , Jiang Tingting , Tian Hongyang , Wu Qiang , Deng Congying , Zhang Renliang
Published: November 2024

The spiral fin-and-tube heat exchanger is a widely used heat transfer device in heating and cooling applications, and its performance is influenced by multiple structural parameters, including the pitch, thickness, and height of the fins, the diameter and thickness of the base tube, and the transverse and longitudinal tube spacings. This study comprehensively explores how these factors affect the heat transfer performance of the spiral fin-and-tube heat exchanger and aims to determine its optimal configuration of structural parameters. First, orthogonal experiments are arranged based on these factors to conduct the corresponding finite element numerical simulations and to determine the effects of these factors on the heat transfer and resistance performance of the spiral fin-and-tube heat exchanger. Subsequently, support vector regression (SVR) is introduced to predict the heat transfer factor and the resistance factor, with the aim of benefiting the construction of a multi-objective optimization model for optimizing the two factors simultaneously. Then, a comprehensive performance indicator is used to transform the multi-optimization problem to a single optimization problem, and the genetic algorithm is adopted to solve an optimal configuration of the heat exchanger structural parameters. Ultimately, the finite element numerical simulation is utilized to validate the accuracy of the optimization. Case studies are conducted on a specific spiral fin-and-tube heat exchanger. After the optimization, the heat transfer factor is improved by 44.44%, and the resistance factor is increased by 14.19%. However, the comprehensive performance indicator is increased by 38.79%.
Enhancing Security of Automotive OTA Firmware Updates via Decentralized Identifiers and Distributed Ledger Technology
Authors: Kovacevic Ana , Gligoric Nenad
Published: November 2024

The increasing connectivity and complexity of automotive systems require enhanced mechanisms for firmware updates to ensure security and integrity. Traditional methods are insufficient for modern vehicles that require seamless over-the-air (OTA) updates. Current OTA mechanisms often lack robust security measures, leaving vehicles vulnerable to attacks. This paper proposes an innovative approach based on the use of decentralized identifiers (DIDs) and distributed ledger technology (DLT) for secure OTA firmware updates of on-vehicle software. By utilizing DIDs for unique vehicle identification, as well as verifiable credentials (VCs) and verifiable presentations (VPs) for secure information exchange and verification, the solution ensures the integrity and authenticity of software updates. It also allows for the revocation of specific updates, if necessary, thereby improving overall security. The security analysis applied the STRIDE methodology, which enabled the identification of potential threats, including spoofing, tampering, and privilege escalation. The results showed that our solution effectively mitigates these threats, while a performance evaluation indicated low latency during operations.
Adaptive Control of Retrieval-Augmented Generation for Large Language Models Through Reflective Tags
Authors: Yao Chengyuan , Fujita Satoshi
Published: November 2024

While retrieval-augmented generation (RAG) enhances large language models (LLMs), it also introduces challenges that can impact accuracy and performance. In practice, RAG can obscure the intrinsic strengths of LLMs. Firstly, LLMs may become too reliant on external retrieval, underutilizing their own knowledge and reasoning, which can diminish responsiveness. Secondly, RAG may introduce irrelevant or low-quality data, adding noise that disrupts generation, especially with complex tasks. This paper proposes an RAG framework that uses reflective tags to manage retrieval, evaluating documents in parallel and applying the chain-of-thought (CoT) technique for step-by-step generation. The model selects the highest quality content for final output. The key contributions are as follows: (1) reducing hallucinations by focusing on high-scoring documents; (2) improving real-time performance through efficient retrieval; and (3) mitigating negative effects by filtering out irrelevant information using parallel generation and reflective tagging. These innovations aim to optimize RAG for more reliable, high-quality results.
Research on Directional Elements of Two-Terminal Weak-Feed AC Systems with a Negative Sequence Control Strategy
Authors: Li Yan , Yang Wentao , Wu Xiaofang , Cao Runbin , Huang Weihuang , Peng Faxi , Hou Junjie
Published: November 2024

It has become a typical scenario in power systems that renewable energy power supply is connected to an AC system through flexible DC transmission. However, since both sides of the AC line are power electronic converters, the negative sequence suppression strategy will be put into the converters at both ends during the asymmetric fault, which causes fundamental changes in the fault characteristics of the system, which is reflected in the two-terminal weak-feed characteristics, leading to the decline of traditional protection performance and affecting the safe operation of the system. Therefore, this paper presents a directional element of a double-ended weakly fed AC system with a negative sequence control strategy. Firstly, the characteristics of the negative sequence impedance under the negative sequence suppression strategy are analyzed when the AC line has asymmetric faults. Secondly, the difference in negative sequence impedance amplitude is analyzed. Finally, the direction element is constructed by the method of de-wave trend analysis The proposed scheme can realize the rapid identification of fault directions at both ends. The simulation results show that the proposed scheme is suitable for a two-terminal weak-feed AC system and can operate reliably under 300 Ω transition resistance and 20 dB noise interference.
Privacy-Enhanced Sentiment Analysis in Mental Health: Federated Learning with Data Obfuscation and Bidirectional Encoder Representations from Transformers
Authors: Ahsan Shakil Ibne , Djenouri Djamel , Haider Rakibul
Published: November 2024

This research aims to find an optimal balance between privacy and performance in forecasting mental health sentiment. This paper investigates federated learning (FL) augmented with a novel data obfuscation (DO) technique, where synthetic data is used to "mask" real data points. Bidirectional Encoder Representations from Transformer (BERT) is used for sentiment analysis, forming a new framework, FL-BERT+DO, that addresses the privacy-performance trade-off. With FL, data remains decentralized, ensuring that user-sensitive information is retained on local devices rather than being shared with the FL server. The integration of BERT gives our system an enhanced feature of context sense-making from text conduct, and our model is extremely proficient in emotion categorization tasks. The experiments were performed on combined (real and replica synthetic) datasets containing emotions and showed significant enhancements compared to baseline methods. The proposed FL-BERT+DO framework shows the following metrics: prediction accuracy, 82.74%; precision, 83.30%; recall, 82.74%; F1-score, 82.80%. Further, we assessed its performance in the adversarial setup using membership inference and linkage attacks to ensure the privacy-preserved performance did not suffer deeply. It demonstrates that, even for large datasets, providing privacy-preserving prediction is possible and can significantly improve existing methods of addressing personal issues, like mental health support. Based on the results of our work, we can propose the development of secure decentralized learning systems that are capable of providing high accuracy of sentiment analysis and meeting strict privacy constraints.
Energy Management in a Renewable-Based Microgrid Using a Model Predictive Control Method for Electrical Energy Storage Devices
Authors: Toure Ibrahima , Payman Alireza , Camara Mamadou-Baïlo , Dakyo Brayima
Published: November 2024

In this paper, an energy management strategy is developed in a renewable energy-based microgrid composed of a wind farm, a battery energy storage system, and an electolyzer unit. The main objective of energy management in the studied microgrid is to guarantee a stable supply of electrical energy to local consumers. In addition, it encompasses hydrogen gas production by using part of the available excess energy in the system, which has some economic benefits. Also, energy management can protect the battery bank from damage by preventing the possibility of it being overcharged. These objectives should be achieved by developing a robust and effective control technique for DC-DC converters that are connected to energy storage devices. For this purpose, an advanced control technique based on Model Predictive Control, which is recognized as a popular control technique for industrial and process applications, is developed. This technique has a fast dynamic response and good tracking features and is simple to implement. The simulation results prove the effectiveness of the proposed control strategy and control technique for energy management in the studied renewable energy-based microgrid.
FL-YOLOv8: Lightweight Object Detector Based on Feature Fusion
Authors: Xue Ying , Wang Qijin , Hu Yating , Qian Yu , Cheng Long , Wang Hongqiang
Published: November 2024

In recent years, anchor-free object detectors have become predominant in deep learning, the YOLOv8 model as a real-time object detector based on anchor-free frames is universal and influential, it efficiently detects objects across multiple scales. However, the generalization performance of the model is lacking, and the feature fusion within the neck module overly relies on its structural design and dataset size, and it is particularly difficult to localize and detect small objects. To address these issues, we propose the FL-YOLOv8 object detector, which is improved based on YOLOv8s. Firstly, we introduce the FSDI module in the neck, enhancing semantic information across all layers and incorporating rich detailed features through straightforward layer-hopping connections. This module integrates both high-level and low-level information to enhance the accuracy and efficiency of image detection. Meanwhile, the structure of the model was optimized and designed, and the LSCD module is constructed in the detection head; adopting a lightweight shared convolutional detection head reduces the number of parameters and computation of the model by 19% and 10%, respectively. Our model achieves a comprehensive performance of 45.5% on the COCO generalized dataset, surpassing the benchmark by 0.8 percentage points. To further validate the effectiveness of the method, experiments were also performed on specific domain urine sediment data (FCUS22), and the results on category detection also better justify the FL-YOLOv8 object detection algorithm.
Integrating Social Aspects into Energy System Modelling Through the Lens of Public Perspectives: A Review
Authors: Amin Riasad , Mathur Deepika , Ompong David , Zander Kerstin K.
Published: November 2024

The energy system model (ESM) predominantly emphasises techno-economic factors and often overlooks the essential social dimensions that are crucial for successful energy transitions. This review investigates the integration of social aspects into energy system models (ESMs) and explores approaches for incorporating public perspectives. Through a systematic literature review utilising the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach (PRISMA), 79 relevant publications were identified. The analysis revealed that while some studies considered socio-economic and socio-environmental elements, these efforts are frequently insufficient to fully comprehend social dynamics. The review highlights the significance of public engagement in ESMs, with 29 studies demonstrating some form of public participation. However, current engagement strategies are often limited to information sharing and consultation, with minimal strong collaboration. This study advocates for the implementation of transparent collaborative approaches in ESMs, including co-creation, active participation, and iterative processes, to enhance the comprehensiveness and societal relevance of models. It also addresses challenges, such as time constraints and the conversion of qualitative inputs into quantitative parameters. The review concludes by calling for further research to develop transparent, iterative frameworks for collaborative approaches in ESMs, emphasising the inclusion of vulnerable population perspectives to ensure equitable energy transitions and more effective, socially acceptable outcomes.
Optimal Operation of Generation Company’s Participating in Multiple Markets with Allocation and Exchange of Energy-Consuming Rights and Carbon Credits
Authors: Yang Hanyu , Ding Mengru , Li Muyao , Wu Shilei , Zhang Ye , Dou Xun
Published: November 2024

The proposal of the energy-consuming right (ECR) market may lead to generation companies (GenCos) facing the risk of being overcharged due to the inaccurate calculation of carbon emission reduction, since it claims the same credit as the carbon market does. To estimate the carbon emission reduction accurately for the GenCos that participate in electricity, carbon, and ECR markets simultaneously, this paper proposes a market framework where a flexible exchange mechanism between the ECR and carbon markets is specially considered. To investigate the influence of the allocation and exchange of ECR and carbon credits on the behavior of GenCos that participate in multi-type markets, a bi-level model based on the leader–follower game theory is proposed. In the upper level of the proposed model, a decision problem for maximizing the profit of GenCos is developed, which is especially constrained to the primary allocation of ECR and carbon credits. While the multi-type market clearing model and an exchange mechanism between the ECR and carbon credits are proposed in the lower level of the model. The bi-level problem is converted into the mathematical program with equilibrium constraints (MPECs) through the Karush–Kuhn–Tucker (KKT) condition to solve. The results illustrate that the interaction between the ECR market and the carbon market can improve the energy efficiency and reduce the carbon emissions of GenCos.
Environmental Impact of Electrification on Local Public Transport: Preliminary Study
Authors: Martini Daniele , Bezzini Pietro , Longo Michela
Published: November 2024

The objective of this study is to provide a comprehensive analysis of the environmental impact of diesel and electric buses, with a focus on pollutant emissions along a mixed urban–rural route in small urban settings. Utilizing a detailed simulation model, this research compares emissions from a diesel bus and an electric bus on a specific route in a small town in central Italy. Key findings reveal that electric buses significantly reduce local exhaust emissions but are not entirely emission-free, considering the full life cycle, including electricity generation. The Well-to-Wheel analysis shows lower CO2 emissions for the electric bus compared with the diesel bus, with a substantial part of the emissions occurring at power generation facilities. Non-exhaust emissions, especially Total Suspended Particles, are similar for both bus types. This study highlights the advantages of adopting electric buses in urban areas to decrease local air pollution and greenhouse gas emissions. However, it also underscores the importance of cleaner electricity generation methods to fully leverage the environmental benefits of electric vehicles. The findings provide valuable insights for decision makers and urban planners in developing sustainable urban transportation systems.
Sensitivities of Geometric Parameters and Inlet Conditions on the Flow-Heat Characteristics of the Precooler in SABRE
Authors: Cui Weiwei , Li Zongming , Xiang Xiaorong , Pang Guoli , Yang Laishun
Published: November 2024

A physical model for the precooler in a SABRE engine was established, and both the influencing degrees and physical mechanisms of inlet conditions and geometric parameters on the flow-heat transfer characteristics of the precooler unit were explored through parametric analysis and sensitivity analysis, respectively. The results demonstrated that the air massflow rate exerted the greatest influence on the total pressure loss coefficient of the precooler unit due to its significant impact on airflow velocity and thermal loading. The inlet total temperature had a dominant effect on heat transfer rate, owing to its substantial influence on air viscosity and thermal loading. Furthermore, both alone and in combination with air massflow rate, inlet total pressure exhibited a greater impact on total pressure loss of precooler unit compared to inlet total temperature and its coupling effects with air massflow rate. In contrast, both air massflow rate and its coupling effects with inlet total temperature significantly affected the heat transfer rate. On the other hand, tube spacing had the most significant impact on both the total pressure loss coefficient and heat transfer rate due to its substantial influence on air throughflow area and acceleration between microtubes. Moreover, the number of tube rows played a predominant role in determining the heat transfer rate of the precooler unit as it caused significant changes in the contact area between the hot air and the microtubes, as well as airflow velocity. However, only two geometric interaction terms (corresponding to interactions between the number of tube rows and tube spacing, as well as between tube spacing and tube diameter) significantly affected the total pressure loss coefficient, and no interaction term was found to be significant for influencing the heat transfer rate. Eventually, two optimal schemes involving inlet conditions and geometric parameters were established to enhance the flow-heat transfer characteristics of the precooler unit.
Research and Application of Characteristic Curve Correction Method for Cascade Hydropower Stations Considering Runoff Inconsistency
Authors: Du Hao , Zhang Zheng , Jiang Zhiqiang , Wang Chao , Qiu Bin , Xu Yichao
Published: November 2024

The situation wherein a hydrological series does satisfy the assumption of “independent and identically distributed” is called runoff inconsistency. “Flow Inversion Phenomenon” is also a kind of runoff inconsistency in space that reflects errors in the hydrological modeling process and can provide a basis for curve correction. In this paper, the causes of the flow inversion phenomenon are analyzed, and a characteristic curve correction method for hydropower stations based on runoff inconsistency is proposed. “Interval Fallacy Rate” is proposed to quantify the flow inversion phenomenon, and with the goal of minimizing the interval fallacy rate, several characteristic curve correction methods, such as single-point discrete optimization, multi-point discrete optimization, overall translation optimization and partial translation optimization, are constructed to correct the turbine characteristic curve, discharge curve, and capacity curve of a hydropower station. In the case study, we take a six-reservoir cascade reservoir system in China as an example. After curve correction, the interval fallacy rate of each interval is reduced to varying degrees. This study provides a new idea for the correction of hydropower stations’ characteristic curves in basins lacking measured flow data.
Hierarchical Modeling of the Thermal Insulation Performance of Novel Plasters with Aerogel Inclusions
Authors: Skouras Eugene D. , Tsolou Georgia , Kalarakis Alexandros N.
Published: November 2024

Silica aerogel possesses a significantly lower thermal conductivity compared to still air at room temperature, thanks to its high porosity and advanced thermal and physical properties. It is extensively investigated for its potential use as an insulation material, usually being incorporated into other matrix materials, such as cement plasters, to enhance the overall thermal performance with minimal weight load. The development of lightweight thermal insulation materials is a key step in reducing energy consumption in hot and cold environments during construction and in thermal equipment. The superior insulation capabilities of aerogels stem from their nanostructured SiO2 framework, which induces nanoscale rarefaction effects on the enclosed air near the SiO2 structure. This study reconstructed the nanostructured SiO2 network of modern aerogels using microscopy imaging and the literature data and integrated it into sophisticated heat transfer simulations at a microscopic level to predict its thermal performance. The simulation assumed conduction as the primary energy dissipation mechanism, incorporating local rarefaction effects based on kinetic theory approaches. SiO2 aggregates were modeled as interconnected strings of spherical beads, with variations in the aggregate size explored in a parametric study. Nanoscale rarefaction phenomena, such as slip wall and Knudsen diffusion, prevalent at these grain sizes and structures, were incorporated to refine the modeling approach. The degree of the aerogel content relative to the effective properties of the multiphasic material was then investigated systematically along the multilayered mortar thickness and on a representative multiphasic layer at the mesoscopic level. The results quantify the significant decrease in the thermal conductivity of the heterogeneous material as the porosity of the aerogel increased. The insulation performance of this aerogel incorporated into cement plasters was assessed with this hierarchical approach and validated against experimental data, providing insights for the optimization of the fabrication process and potential applications in construction.
Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration
Authors: Chen Pin , Wang Xiyue , Yang Zexia , Shi Changfeng
Published: November 2024

Urban agglomerations with a high economic activity and population density are key areas for carbon emissions and pioneers in achieving carbon peaking and the Sustainable Development Goals (SDGs). This study combines machine learning with an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model to uncover the mechanisms driving carbon peaking disparities within these regions. It forecasts carbon emissions under different scenarios and develops indices to assess peaking pressure, reduction potential, and driving forces. The findings show significant carbon emission disparities among cities in the Yangtze River Delta, with a fluctuating downward trend over time. Technological advancement, population size, affluence, and urbanization positively impact emissions, while the effects of industrial structure and foreign investment are weakening. Industrially optimized cities lead in peaking, while others—such as late-peaking and economically radiating cities—achieve peaking only under the ER scenario. Cities facing population loss and demonstration cities fail to peak by 2030 in any scenario. The study recommends differentiated carbon peaking pathways for cities, emphasizing tailored targets, pathway models, and improved supervision. This research offers theoretical and practical insights for global urban agglomerations aiming to achieve early carbon peaking.
Empirical Analysis of Inter-Zonal Congestion in the Italian Electricity Market Using Multinomial Logistic Regression
Authors: Hosseini Imani Mahmood
Published: November 2024

The increasing integration of renewable energy sources (RESs) into the Italian electricity market has heightened inter-zonal congestion challenges as power flows vary across importing and exporting zones. Utilizing a Multinomial Logistic Regression model as an empirical approach, this study investigates the key factors driving inter-zonal congestion between zonal pairs from 2021 to 2023, focusing on how local and neighboring zones’ RES generation (wind, solar, and hydropower) and demand dynamics impact congestion probabilities. The findings reveal that increased local RES generation generally reduces the likelihood of congestion for importing regions but increases it for exporting zones. Specifically, higher wind and solar production in importing zones like CNOR and CSUD alleviates congestion by reducing the need for imports, while in exporting zones, such as NORD and CALA, increased RES generation can exacerbate congestion due to higher export volumes. Hydropower production shows similar trends, with local production mitigating congestion in importing zones but increasing it in exporting zones. In addition to the effects of local generation and demand within each zonal pair, the generation and demand from neighboring zones also have a notable and statistically significant impact. Although their marginal effects tend to be smaller, the contributions from neighboring zones are essential for comprehending the overall congestion dynamics. These insights underscore the need for strategic RES placement to enhance market efficiency and minimize congestion risks across the Italian zonal electricity market.
Cooperative Control Strategy of Optical Storage System Based on an Alternating Sequence Filter
Authors: Han Zifen , Zhang Yun , Tian Biao , Fan Yi , Zhang Chao , Wu Huijuan
Published: November 2024

Due to photovoltaic (PV) power generation depending on the environment, its output power is volatile, and effectively dealing with its power fluctuation has become a key concern. Aiming at this problem, this article presents an optical storage cooperative control technology based on an Alternating Sequence Filter (ASF), which controls the power management of the Energy Storage System (ESS) consisting of a vanadium redox battery, battery, and supercapacitor. Firstly, an ASF is designed to stabilize the PV power generation by alternating sequence and improve system response speed. Secondly, according to the output signal of the filter, the charge and discharge of the three energy storage units are dynamically adjusted, and the power fluctuation is compensated in real-time to improve the system stability and conversion efficiency. Finally, the simulation results of actual illumination show that the control strategy calls the ESS to stabilize the power fluctuation, so that the power of the direct current bus is stabilized at about 15 kw, and the fluctuation is maintained between −4.48% and 4.05%. The strategy significantly reduces power fluctuation and improves the dynamic response ability and energy storage utilization of the system.
Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks
Authors: El-Hendawi Mohamed , Wang Zhanle , Paranjape Raman , Fick James , Pederson Shea , Kozoriz Darcy
Published: November 2024

Achieving transportation decarbonization and reducing carbon emissions are global initiatives that have attracted a lot of effort. The use of electric vehicles (EVs) has experienced a significant increase lately, which will have a considerable impact on current power systems. This study develops a framework to evaluate/mitigate the negative impact of increasing EV charging on urban power distribution systems. This framework includes data analytics of actual residential electrical load and EV charging profiles, and the development of optimal EV charging management and AC load flow models using an actual residential power distribution system in Saskatchewan, Canada. We use statistical methods to identify a statistically-extreme situation for a power system, which a power utility needs to prepare for. The philosophy is that if the power system can accommodate this situation, the power system will be stable 97.7% of the time. Simulation results show the house voltage and transformer loading at various EV penetration levels under this statistically-extreme situation. We also identify that the particular 22-house power distribution system can accommodate a maximum number of 11 EVs (representing 50% EV penetration) under this statistically-extreme situation. The results also show that the proposed optimal EV charging management model can reduce the peak demand by 43%. Since we use actual data for this study, it reflects the current real-world situation, which presents a useful reference for power utilities. The framework can also be used to evaluate/mitigate the impact of EV charging on power systems and optimize EV infrastructure development.
AVR Fractional-Order Controller Based on Caputo–Fabrizio Fractional Derivatives and Integral Operators
Authors: Lozynskyy Andriy , Kozyra Jacek , Kutsyk Andriy , Łukasik Zbigniew , Kuśmińska-Fijałkowska Aldona , Kasha Lidiia , Lishchuk Andriy
Published: November 2024

The application of a fractional-order controller (FOC) using the Caputo–Fabrizio representation in the automatic voltage regulation (AVR) system of a synchronous generator is shown in this paper. The mathematical model of the system is created and the adequacy of the model is confirmed. The efficiency of the proposed regulator in different operating regimes is demonstrated. In particular, the proposed controller improves voltage regulation in a wide range of changes in the coordinates that characterize the power system operation mode, and it increases the system’s robustness to both uncertainties and nonlinearities that often occur in power systems. The synthesized fractional-order regulator provides higher response and control accuracy compared to traditional regulators used in automatic voltage regulation (AVR) systems.
10 MW FOWT Semi-Submersible Multi-Objective Optimization: A Comparative Study of PSO, SA, and ACO
Authors: Drabo Souleymane , Lai Siqi , Liu Hongwei , Feng Xiangheng
Published: November 2024

The present study aims to carry out a comparative Multi-Objective Optimization (MOO) of a 10 MW FOWT semi-submersible using three different metaheuristic optimization techniques and a sophisticated approach for optimizing a floating platform. This novel framework enables highly efficient 3D plots, an optimization loop, and the automatic and comparative output of solutions. Python, the main interface, integrated PyMAPDL and Pymoo for intricate modeling and simulation tasks. For this case study, the ZJUS10 Floating Offshore Wind Turbine (FOWT) platform, developed by the state key laboratory of mechatronics and fluid power at Zhejiang University, was employed as the basis. Key criteria such as platform stability, overall structural mass, and stress were pivotal in formulating the objective functions. Based on a preliminary study, the three metaheuristic optimization algorithms chosen for optimization were Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO). Then, the solutions were evaluated based on Pareto dominance, leading to a Pareto front, a curve that represents the best possible trade-offs among the objectives. Each algorithm’s convergence was meticulously evaluated, leading to the selection of the optimal design solution. The results evaluated in simulations elucidate the strengths and limitations of each optimization method, providing valuable insights into their efficacy for complex engineering design challenges. In the post-processing phase, the performances of the optimized FOWT platforms were thoroughly compared both among themselves and with the original model, resulting in validation. Finally, the ACO algorithm delivered a highly effective solution within the framework, achieving reductions of 19.8% in weight, 40.1% in pitch, and 12.7% in stress relative to the original model.
Use of a Carbon Density Growth Model to Assess the Potential Carbon Sink Function of a Mongolian Pine Plantation in Heilongjiang Province, Northeast China
Authors: Dong Jiyang , Li Guochun , Liu Dandan , Wang Weifang , Jiang Lichun
Published: November 2024

Accurate estimation of the potential increase in the carbon (C) sink function of forests is required for climate mitigation and C neutrality assessments. Also, accurate forest carbon density estimates are critical for understanding national- and global-level carbon cycling and storage and can inform climate change mitigation. This study established a stand C density growth model to further analyze the C sink potential of planted Mongolian pine (Pinus sylvestris var. mongolica) forests. Samples (390) from fixed plots of Mongolian pine were collected in Heilongjiang Province, Northeast China. The site index (SCI) and stand density index (SDI) were introduced to a constructed stand C density growth model, with an optimal model selected through model fitting. The effect of SDI on stand C density in different SCI grouping intervals was assessed. Total C sequestration of Mongolian pine was calculated using the established C density model. Sample plots with higher C density in each forest age stand were selected to establish a model of potential C sequestration for Mongolian pine, and the difference between this rate and the average was compared to obtain the potential increase in C sink capacity of the forest stand. Slightly different fitting accuracies among the different C density growth models were observed, with the Richards model showing the best performance, which improved through the introduction of the SCI and SDI. Stand C density was associated with an increasing trend in SCI, which within each SCI subgroup was related to the increasing SDI trend. The potential C sequestration rate of the stand was close to the average between years 5 and 13. The average C sequestration rate peaked at 3.86 Mg·ha−1·year−1 at year 13, whereas the potential C sequestration rate peaked at 4.42 Mg·ha−1·year−1 in year 15. A gap between the potential and average C sequestration rate existed between ages 13 and 45, indicating the possibility for an increased C sink function in this forest age range. The Richards growth model incorporating SCI and SDI provided a better reflection of the C density of the Mongolian pine plantation, and the established stand C sequestration rate model showed that the optimal increment in the plantation C sink function can be obtained between years 13 and 45. The results of this study can guide C sink management in the Mongolian pine plantation.
Monitoring the Degree of Gansu Zokor Damage in Chinese Pine by Hyperspectral Remote Sensing
Authors: Hu Yang , Aba Xiaoluo , Ren Shien , Yang Jing , He Xin , Zhang Chenxi , Lu Yi , Jiang Yanqi , Wang Liting , Chen Yijie , Mi Xiaoqin , Nan Xiaoning
Published: November 2024

Chinese pine has been extensively planted in the Loess Plateau, but it faces significant threats from Gansu zokor. Traditional methods for monitoring rodent damage rely on manual surveys to assess damage rates but are time-consuming and often underestimate the actual degree of damage, particularly in mildly affected pines. This study proposes a remote sensing monitoring method that integrates hyperspectral analysis with physiological and biochemical parameter models to enhance the accuracy of rodent damage detection. Using ASD Field Spec 4, we analyzed spectral data from 125 Chinese pine needles, measuring chlorophyll (CHC), carotenoid (CAC), and water content (WAC). Through correlation analysis, we identified sensitive vegetation indices (VIs) and red-edge parameters (REPs) linked to different levels of damage. We report several key results. The 680 nm spectral band is instrumental in monitoring damage, with significant decreases in CHC, CAC, and WAC corresponding to increased damage severity. We identified six VIs and five REPs, which were later predicted using stepwise regression (SR), support vector machine (SVM), and random forest (RF) models. Among all models, the vegetation index-based RF model exhibited the best predictive performance, achieving coefficient of determination (R2) values of 0.988, 0.949, and 0.999 for CHC, CAC, and WAC, with root mean square errors (RMSEs) of 0.115 mg/g, 0.042 mg/g, and 0.007 mg/g, and mean relative errors (MREs) of 8.413%, 9.169%, and 1.678%. This study demonstrates the potential of hyperspectral remote sensing technology for monitoring rodent infestations in Chinese pines, providing a reliable basis for large-scale assessments and effective management strategies for pest control.
Effects of Urban Tree Species and Morphological Characteristics on the Thermal Environment: A Case Study in Fuzhou, China
Authors: Luo Tao , Jia Jia , Qiu Yao , Zhang Ying
Published: November 2024

Trees and their morphology can mitigate the urban heat island (UHI) effect, but the impacts of tree species and their two-dimensional (2D) and three-dimensional (3D) morphological characteristics on the thermal environment of residential spaces at the building scale have not been effectively evaluated. This research extracted the data of trees in the spatial range of a 50 m radius of the sampling sites located in a subtropical humid city’s residential area based on unmanned aerial vehicle (UAV) imagery and field measurements. It included Ficus microcarpa L. f., Cinnamomum camphora (L.) J. Presl, and Alstonia scholaris (L.) R. Br. as three typical evergreen tree species and six quantitative indicators of trees, with the number of trees (N) serving as fundamental indicator and mean canopy width (MCW), mean canopy height (MCH), mean tree height (MTH), canopy biomass (CV), and mean canopy biomass (MCV) as morphological characteristic indicators. We analyzed the impact of the six indicators above on two thermal environment parameters: Air temperature (AT) and relative humidity (RH), by correlation analysis and multiple linear regression analysis. Results showed that: (1) F. microcarpa, as a dominant local species, provided more than 65% of the tree canopy volume within the study area (50 m radius buffer zones), and its contribution to cooling and humidification effects was superior to those of C. camphora and A. scholaris. (2) The MTH and CV of F. microcarpa are the key factors influencing daytime AT and RH, respectively, with temporal fluctuation in impact intensity during the spring (May) daytime. (3) The MTH and N of F. microcarpa show the best cooling effect (adjusted R2 = 0.731, p < 0.05) during midday (13:00–14:00 p.m.), while its CV and MTH have the best humidification effect (adjusted R2 = 0.748, p < 0.05) during the morning (9:00–10:00 a.m.) among three typical tree species. The 2D and 3D morphological characteristic indicators effectively describe the impact and variation of tree species on the spring microclimate within small-scale residential spaces. This work provides new insights into the thermal benefits brought by the spatial growth features of trees at the building scale and offers reference for urban residential areas in the planning and management related to tree species selection, canopy maintenance, and the improvement of thermal comfort for inhabitants.
WPS-Dataset: A Benchmark for Wood Plate Segmentation in Bark Removal Processing
Authors: Wang Rijun , Zhang Guanghao , Liang Fulong , Mou Xiangwei , Wang Bo , Chen Yesheng , Sun Peng , Wang Canjin
Published: November 2024

Wood plate bark removal processing is critical for ensuring the quality of wood processing and its products. To address the issue of lack of datasets available for the application of deep learning methods to this field, and to fill the research gap of deep learning methods in the application field of wood plate bark removal equipment, a benchmark for wood plate segmentation in bark removal processing is proposed in this study. Firstly, a costumed image acquisition device is designed and assembled on bark removal equipment to capture wood plate images in real industrial settings. After data filtering, enhancement, annotation, recording, and partitioning, a benchmark dataset named the WPS-dataset containing 4863 images was constructed. The WPS-dataset is evaluated by training six typical semantic segmentation models. The experimental results show that the models effectively learn and understand the WPS-dataset characteristics during training, resulting in high performance and accuracy in wood plate segmentation tasks. The WPS-dataset can lay a solid foundation for future research in bark removal processing and contribute to advancements in this field.
Finding Spalting Fungi in the Peruvian Tropical Premontane Cloud Forest on Peruvian Native Wood Species
Authors: Illescas Guevara Javier F. , Candiotti Martinez Kevin P. , Vega Gutierrez Patricia T. , Araujo Flores Martin , Vega Gutierrez Sarath M.
Published: November 2024

Tropical montane and premontane forests are diverse, including fungi. However, little is known about spalting fungi (decay fungi that change the color of wood) in tropical regions despite the economic importance they could bring by enhancing wood esthetics. To increase the knowledge of the diversity of spalting fungi, a sampling of fallen logs, branches (exposing xylem to identify potential pigmenting and zone line-producing fungi), and fruiting bodies (on wood) was conducted in the premontane moist forest in the district of San Ramon, Junín, Peru. The fungi were collected, cultured, isolated, and sequenced. Also, the identified species were used in a novel test to confirm they were producing spalting on Guazuma crinita. The species found belong to the Ascomycota orders Xylariales and Diaporthales and the Basidiomycota orders Agaricales, Polyporales, and Russulales. The fungi collected produced bleaching, different colors of zone lines, and pigmentation in laboratory conditions. The results increase the database of spalted fungi in Peru, and the test used in this research could be the basis for a quick test to identify spalting fungi.
Weighing Policy Effectiveness Through Recent Forest Fire Status
Authors: Wu Guangming , Yao Qichao , Bai Maowei , Shi Lamei , Wang Zhou , Fang Keyan , Guo Futao , Pei Yanyan
Published: November 2024

Forest fires pose a great threat to the global ecological environment as well as human life and the safety of property. Formulating effective policies for preventing forest fires is not only a scientific concern but also an urgent need for government management. Canada and China both have extensive forest areas but have different fire management strategies. Canada primarily focuses on fire suppression while China pays more attention on fire prevention. This difference led to significant discrepancies in the forest fire regimes between the two countries, providing an opportunity to explore the impact of fire management policies on forest fire. By analyzing the fire occurrences in Canada and China since 1990, combining the fire prevention funds and fire management strategies, this paper discussed the influence of different policies on fire occurrence. Previously, Canada’s forest suppression strategy has been widely recognized internationally, but recent widespread fires indicate that its fire management policy may still require further improvement to cope with future global warming. Although China’s fire prevention strategy can effectively control current forest fires, the lack of fundamental theories on forest fires and the backwardness of fire prevention technology and equipment may increase the likelihood of major forest fires in the future. As global warming continues to intensify in the future, the length of the forest fire season and the intensity of fires will increase, making it urgent to develop more effective forest fire prevention and suppression policies to achieve sustainable development.
Potential Probiotic Bacillus Strains with Antioxidant and Antimutagenic Activity Increased Weight Gain and Altered hsp70, cxc, tnfα, il1β, and lysC Gene Expression in Clarias gariepinus
Authors: Skripnichenko Radomir Viktorovich , Chelombitskaya Daria Sergeevna , Prazdnova Evgeniya Valer’evna , Kulikov Maxim Pavlovich , Neurov Alexey Mikhailovich , Zaikina Anna Andreevna , Grigoryev Vadim Alekseevich , Sorokina Marina Nikolaevna , Chistyakov Vladimir Anatolievich , Chikindas Michael Leonidas , Rudoy Dmitriy Vladimirovich
Published: November 2024

The potential probiotic properties of three Bacillus strains were studied. A probiotic supplement for the African catfish Clarias gariepinus was produced via the solid-state fermentation protocol and incorporated into the fish feed for a period of seven weeks. Since the 36th day of the experiment, all experimental groups had a statistically significant increase in their weight gain than the control group. The maximum weight gain observed in fish fed the probiotic-supplemented feed was 29.16% higher than that of the control group, and the maximum feed conversion rate improvement was 24%. Cell-free extracts from these strains showed antioxidant (11.55–27.40%) and DNA-protective (45.33–61.83%) activity in a series of in vitro biosensor tests. Further investigation into the antimutagenic activity of the strains revealed that two of them reduced the level of induced mutagenesis in an Escherichia coli model (by 33.58% and 54.35%, respectively). We also assessed the impact of probiotic strains on the expression of several key genes in the host (C. gariepinus), including hsp70, cxc, tnfα, il1β, and lysC. More than a 10-fold increase in expression rates was observed for hsp70 in gonads and liver; for cxc in muscles and gonads; for tnfα in brain, gills, and liver; for il1β in the brain, gills, gonads, and liver; and for lysC in gills, gonads, liver, and muscles. This study provides evidence that probiotics exhibiting antioxidant and antimutagenic properties can provide significant benefits in vivo within aquaculture systems. The molecular effects of these probiotics appear to be complex and tissue-specific, with both upregulation and downregulation of immune system genes observed. Nevertheless, at the organismal level, the impact was unequivocally positive in terms of aquaculture objectives, manifested as enhanced body weight gain in the fish. Consequently, these Bacillus strains warrant serious consideration as potential probiotics for this species.
The Potential of Machine Learning Methods for Separated Turbulent Flow Simulations: Classical Versus Dynamic Methods
Authors: Heinz Stefan
Published: November 2024

Feasible and reliable predictions of separated turbulent flows are a requirement to successfully address the majority of aerospace and wind energy problems. Existing computational approaches such as large eddy simulation (LES) or Reynolds-averaged Navier–Stokes (RANS) methods have suffered for decades from well-known computational cost and reliability issues in this regard. One very popular approach to dealing with these questions is the use of machine learning (ML) methods to enable improved RANS predictions. An alternative is the use of minimal error simulation methods (continuous eddy simulation (CES), which may be seen as a dynamic ML method) in the framework of partially or fully resolving simulation methods. Characteristic features of the two approaches are presented here by considering a variety of complex separated flow simulations. The conclusion is that minimal error CES methods perform clearly better than ML-RANS methods. Most importantly and in contrast to ML-RANS methods, CES is demonstrated to be well applicable to cases not involved in the model development. The reason for such superior CES performance is identified here: it is the ability of CES to properly account for causal relationships induced by the structure of separated turbulent flows.