bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Active Disturbance Rejection Control with Parameter Autotuning for a Moving Mirror Control System Based on Improved Snake Optimization
Authors: Zhi Liangjie , Huang Min , Qian Lulu , Wang Zhanchao , Wen Qin , Han Wei
Published: April 2024

In order to improve the control of a moving mirror control system and enhance the anti-interference ability of the system, active disturbance rejection control (ADRC) with parameter autotuning is proposed and applied to control a rotary voice coil motor (RVCM). Improved snake optimization (I-SO) was applied to tune and optimize ADRC’s key parameters. To obtain excellent parameters efficiently, in the population initialization phase of SO, the quality and diversity of initial solutions were improved through a chaotic elite opposition learning algorithm. In the local search phase, a sine and cosine (SC) search mode was introduced to enhance the local search ability of SO. The simulation results show that I-SO can effectively find the ideal parameters. I-SO has excellent search capability and stability. The experimental control system of a moving mirror was established, and the effectiveness of the parameters optimized by I-SO was verified. ADRC with parameter autotuning showed excellent control in the moving mirror control system, and the stability of the optical path scanning speed reached 99.2%.
Fault Diagnosis Based on Tensor Computing and Meta-Learning for Smart Grid and Power Communication Network
Authors: Yu Qiusheng , Guan Ti , Tian Anqi , Si Mingyue , Qi Bin , Jiang Yingjie , Zhang Yan , Li Li , Zhang Wensheng
Published: April 2024

Fault diagnosis (FD) is a critical challenge for the smart grid and the power communication network, especially when both heterogeneous networks are exponentially becoming enormous and complicated. Consequently, some conventional FD schemes based on labor seem inefficient, even disabled, because they usually cannot efficiently utilize multi-dimensional and heterogeneous big data from both networks. To deal with this challenging technical problem, a novel FD scheme based on tensor computing and meta-learning is proposed for the smart grid and the power communication network. In the proposed scheme, tensor computing is used to process tensor big data from both networks, and a new data fusion scheme is designed to complete and analyze the incomplete and sparse big data. Based on the fused data, a meta-learning approach is used to construct the FD scheme, especially when the target fault samples are inadequate and sparse. In meta-learning, the convolutional neural network is employed as a base learner to generate an FD training model, and the model-agnostic meta-learning algorithm is utilized to fine-tune and further train the pre-trained model. Simulation results and theoretical analysis indicate that the proposed DF scheme based on tensor computing can efficiently process sparse and heterogeneous big data from both networks. Furthermore, the meta-learning-based FD scheme provides an efficient way to diagnose faults with inadequate target samples. The proposed FD scheme based on tensor computing and meta-learning provides a novel solution to detect and analyze the potential faults for smart grid and power communication networks.
A Modified SVPWM Strategy for Reducing PWM Voltage Noise and Balancing Neutral Point Potential
Authors: Gong Renxi , Wu Hao , Tang Jing , Wan Xingyuan
Published: April 2024

PWM (pulse width modulation) is the most widely applied current conversion technology, but the high-frequency harmonics it causes have a significant negative impact on inverter system performance. This paper focuses on the three-phase T-type three-level inverter as the research object and addresses existing PWM voltage noise and midpoint potential imbalance issues by proposing an improved random SVPWM strategy, named Neutral Point Potential Balance Random Space Vector PWM (NPB–RSVPWM). The NPB–RSVPWM strategy includes three main steps: (1) introducing a midpoint potential balancing control loop to adjust the synthesis timing of the effective vectors to generate pulse signals, optimizing midpoint potential balance; (2) employing a randomly varying carrier frequency in place of the carrier used in the SVPWM strategy to generate the driving signals for switching devices; and (3) controlling the inverter through the driving pulse signals. This strategy optimizes the synthesis sequence of traditional SVPWM strategy vectors and incorporates random frequency modulation techniques. The mathematical model analyzes PWM harmonic expressions corresponding to fixed switching frequencies, and a random frequency carrier is chosen to suppress these PWM harmonics. The effective vector’s equivalent circuit is analyzed, proposing a technique for optimized vector synthesis timing. The simulation and experimental results verify that the NPB–RSVPWM technique can disperse PWM harmonic energy, reduce voltage noise, and optimize midpoint potential balance. Under the NPB–RSVPWM strategy, the line voltage spectrum becomes uniform, the maximum harmonic content is greatly reduced, and the fluctuation in the DC side midpoint potential is significantly improved. Compared with the traditional SVPWM strategy and random PWM strategy, the NPB–RSVPWM strategy has a lower voltage noise, smaller total harmonic distortion, and a more stable midpoint potential. The effectiveness and feasibility of the NPB–RSVPWM strategy are verified by simulation and experimental results.
A Knowledge Graph Completion Algorithm Based on the Fusion of Neighborhood Features and vBiLSTM Encoding for Network Security
Authors: Zhang Wenbo , Wang Mengxuan , Han Guangjie , Feng Yongxin , Tan Xiaobo
Published: April 2024

Knowledge graphs in the field of network security can integrate diverse, heterogeneous, and fragmented network security data, further explore the relationships between data, and provide support for deep analysis. Currently, there is sparse security information in the field of network security knowledge graphs. The limited information provided by traditional text encoding models leads to insufficient reasoning ability, greatly restricting the development of this field. Starting from text encoding, this paper first addresses the issue of the inadequate capabilities of traditional models using a deep learning model for assistance. It designs a vBiLSTM model based on a word2vec and BiLSTM combination to process network security texts. By utilizing word vector models to retain semantic information in entities and extract key features to input processed data into BiLSTM networks for extracting higher-level features that better capture and express their deeper meanings, this design significantly enhances understanding and expression capabilities toward complex semantics in long sentences before inputting final feature vectors into the KGC-N model. The KGC-N model uses feature vectors combined with graph structure information to fuse forward and reverse domain features and then utilizes a Transformer decoder to decode predictions and complete missing information within the network security knowledge map. Compared with other models using evaluation metrics such as MR, MRR demonstrates that employing our proposed method effectively improves performance on completion tasks and increases comprehension abilities toward complex relations, thereby enhancing accuracy and efficiency when completing knowledge graphs.
A Hybrid CNN-LSTM Approach for Precision Deepfake Image Detection Based on Transfer Learning
Authors: Al-Dulaimi Omar Alfarouk Hadi Hasan , Kurnaz Sefer
Published: April 2024

The detection of deepfake images and videos is a critical concern in social communication due to the widespread utilization of deepfake techniques. The prevalence of these methods poses risks to trust and authenticity across various domains, emphasizing the importance of identifying fake faces for security and preventing socio-political issues. In the digital media era, deep learning outperforms traditional image processing methods in deepfake detection, underscoring its significance. This research introduces an innovative approach for detecting deepfake images by employing transfer learning in a hybrid architecture that combines convolutional neural networks (CNNs) and long short-term memory (LSTM). The hybrid CNN-LSTM model exhibits promise in combating deep fakes by merging the spatial awareness of CNNs with the temporal context understanding of LSTMs. Demonstrating effective performance on open-source datasets like “DFDC” and “Ciplab”, the proposed method achieves an impressive precision of 98.21%, indicating its capability to accurately identify deepfake images with a limited false-positive rate. The model’s error rate is 0.26%, emphasizing the challenges and intricacies inherent in deepfake detection tasks. These findings underscore the potential of hybrid deep learning techniques for addressing the urgent issue of deepfake image detection.
Offline Fault Diagnosis for 2-Level Inverter: Short-Circuit and Open-Circuit Detection
Authors: Hyon Byong Jo , Hwang Dae Yeon , Jang Pooreum , Noh Yong-Su , Kim Jin-Hong
Published: April 2024

Fault detection is very important to improve the reliability of power conversion devices. Faults of power semiconductors can be broadly divided into shorts and opens and are further classified into two types depending on whether there is an internal problem with the switch or anti-parallel diode. In this paper, fault-diagnosis methods for short-circuit and open-circuit states are proposed, respectively. A method of classifying and diagnosing faults by applying a gate signal to each switch is proposed to diagnose short-circuit conditions. This method uses only current magnitude information, which reduces the amount of required information and reduces diagnostic failures due to angle errors and current noise. A method is proposed to detect a faulty switch by applying a voltage vector and comparing the current angle with a lookup table to diagnose an open state. An iterative diagnostic algorithm is proposed to prevent diagnostic failure due to angle error and current noise. The effectiveness of the proposed diagnosis method is verified through experiments and simulations.
Exhaustive Study into Machine Learning and Deep Learning Methods for Multilingual Cyberbullying Detection in Bangla and Chittagonian Texts
Authors: Mahmud Tanjim , Ptaszynski Michal , Masui Fumito
Published: April 2024

Cyberbullying is a serious problem in online communication. It is important to find effective ways to detect cyberbullying content to make online environments safer. In this paper, we investigated the identification of cyberbullying contents from the Bangla and Chittagonian languages, which are both low-resource languages, with the latter being an extremely low-resource language. In the study, we used both traditional baseline machine learning methods, as well as a wide suite of deep learning methods especially focusing on hybrid networks and transformer-based multilingual models. For the data, we collected over 5000 both Bangla and Chittagonian text samples from social media. Krippendorff’s alpha and Cohen’s kappa were used to measure the reliability of the dataset annotations. Traditional machine learning methods used in this research achieved accuracies ranging from 0.63 to 0.711, with SVM emerging as the top performer. Furthermore, employing ensemble models such as Bagging with 0.70 accuracy, Boosting with 0.69 accuracy, and Voting with 0.72 accuracy yielded promising results. In contrast, deep learning models, notably CNN, achieved accuracies ranging from 0.69 to 0.811, thus outperforming traditional ML approaches, with CNN exhibiting the highest accuracy. We also proposed a series of hybrid network-based models, including BiLSTM+GRU with an accuracy of 0.799, CNN+LSTM with 0.801 accuracy, CNN+BiLSTM with 0.78 accuracy, and CNN+GRU with 0.804 accuracy. Notably, the most complex model, (CNN+LSTM)+BiLSTM, attained an accuracy of 0.82, thus showcasing the efficacy of hybrid architectures. Furthermore, we explored transformer-based models, such as XLM-Roberta with 0.841 accuracy, Bangla BERT with 0.822 accuracy, Multilingual BERT with 0.821 accuracy, BERT with 0.82 accuracy, and Bangla ELECTRA with 0.785 accuracy, which showed significantly enhanced accuracy levels. Our analysis demonstrates that deep learning methods can be highly effective in addressing the pervasive issue of cyberbullying in several different linguistic contexts. We show that transformer models can efficiently circumvent the language dependence problem that plagues conventional transfer learning methods. Our findings suggest that hybrid approaches and transformer-based embeddings can effectively tackle the problem of cyberbullying across online platforms.
Using Feature Selection Enhancement to Evaluate Attack Detection in the Internet of Things Environment
Authors: Harahsheh Khawlah , Al-Naimat Rami , Chen Chung-Hao
Published: April 2024

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects of supervised classification, including feature selection, model training, and evaluation methodologies, to comprehensively evaluate their impact on attack detection effectiveness. The key features selected to improve IDS efficiency and reduce dataset size, thereby decreasing the time required for attack detection, are drawn from the extensive network dataset. This paper introduces an enhanced feature selection method designed to reduce the computational overhead on IoT resources while simultaneously strengthening intrusion detection capabilities within the IoT environment. The experimental results based on the InSDN dataset demonstrate that our proposed methodology achieves the highest accuracy with the fewest number of features and has a low computational cost. Specifically, we attain a 99.99% accuracy with 11 features and a computational time of 0.8599 s.
Comparative Study on Schottky Contact Behaviors between Ga- and N-Polar GaN with SiNx Interlayer
Authors: Yu Zhehan , Dai Yijun , Tang Ke , Luo Tian , Qi Shengli , Singh Smriti , Huang Lu , Ye Jichun , Sarkar Biplab , Guo Wei
Published: April 2024

We conducted a comparative study on the characterization of Ga-polar and N-polar GaN metal–insulator–semiconductor (MIS) Schottky contact with a SiNx gate dielectric. The correlation between the surface morphology and the current–voltage (I–V) characteristics of the Ga- and N-polar GaN Schottky contact with and without SiNx was established. The insertion of SiNx helps in reducing the reverse leakage current for both structures, even though the leakage is still higher for N-polar GaN, consistent with the Schottky barrier height calculated using X-ray photoelectron spectroscopy. To optimize the electric property of the N-polar device, various substrate misorientation angles were adopted. Among the different misorientation angles of the sapphire substrate, the GaN MIS Schottky barrier diode grown on 1° sapphire shows the lowest reverse leakage current, the smoothest surface morphology, and the best crystalline quality compared to N-polar GaN grown on 0.2° and 2° sapphire substrates. Furthermore, the mechanism of the reverse leakage current of the MIS-type N-polar GaN Schottky contact was investigated by temperature-dependent I–V characterization. FP emissions are thought to be the dominant reverse conduction mechanism for the N-polar GaN MIS diode. This work provides a promising approach towards the optimization of N-polar electronic devices with low levels of leakage and a favorable ideality factor.
Measurement and Assessment of Reactive, Unbalanced and Harmonic Line Losses
Authors: Zhou Qun , Dian Yulin , Liu Xueshan , Leng Minrui , Chen Canyu , Liu Haibo
Published: April 2024

This study investigates the feasibility of utilizing the line loss power factor to assess the reactive, unbalanced, and harmonic line losses in low-voltage distribution networks and explores the method of calculating decoupled line loss values based on this factor. To achieve this objective, we establish preliminary definitions of single-phase and three-phase reactive, unbalanced, and harmonic line loss power factors, drawing upon the principles of electrical theory outlined in IEEE Standard 1459. These power factors serve as crucial indicators for evaluating the severity of line losses caused by reactive power, unbalance, and harmonic problems. Subsequently, the values of line loss attributed to reactive, unbalanced, and harmonic components are decoupled and quantified using the line loss power factor as a fundamental parameter. The effectiveness and accuracy of the proposed method were verified in Matlab simulation and physical experiments.
Nonlinear Modeling and Control Strategy Based on Type-II T-S Fuzzy in Bi-Directional DC-AC Converter
Authors: Chen Zhihua , Huang Ruochen , Lin Qiongbin , Yu Xinhong , Dan Zhimin
Published: April 2024

Bi-directional DC-AC converters are widely used in the field of electric vehicle-to-grid. However, the inductance of the grid-side interface filter is affected by the length of the grid connection and the power level, which presents nonlinear characteristics. This poses challenges for high-performance grid waveform control. In this paper, a modeling method for bi-directional DC-AC grid-connected converters based on type-II T-S fuzzy models is proposed, and the corresponding type-II T-S fuzzy control strategy is designed to address the parameter uncertainty and non-linearity issues. Simulation results show that type-II T-S fuzzy control offers superior control performance and better current waveform quality compared to type-I T-S fuzzy control under uncertainty parameter conditions. The effectiveness of the proposed strategy is further validated through a 1 kW prototype of a bi-directional DC-AC converter.
Field Plate Integration for Mitigating Partial Discharge Activity in PCB-Embedded Power Electronic Modules
Authors: Bruyere Paul , Vagnon Eric , Avenas Yvan
Published: April 2024

This paper proposes a concept based on field plate (FP) integration inside printed circuit board (PCB)-embedded power modules. The goal is to reduce the electric field at their surface and thus increase the partial discharge inception voltage (PDIV). Electrostatic simulations are first carried out to analyze the electric field reduction induced by the use of FPs. Then, dedicated experiments are proposed to demonstrate that the actual PDIV increases in AC sinus 50 Hz when FPs are implemented. More specifically, it is observed that an optimal FP length exists. Several analyses based on simulations and experiments are thus proposed to explain this phenomenon. Finally, an assessment of PD activity and PD location is presented to support the analysis. AC sinus 50 Hz characterizations indicate that PDIV can be increased by 178% compared to PCBs without FPs with a proper definition of equipotential prolongation and PCB length.
Low-Friction and -Knocking Diesel Engine Cylindrical-Tapered Bore Profile Design
Authors: Zhang Junhong , Wang Ning , Wang Jian , Wang Hui , Zhang Xueling , Dai Huwei , Lin Jiewei
Published: April 2024

To reduce the friction loss and the piston-knocking noise from the perspective of the design of the cylinder bore profile, the piston-ring cylinder bore (PRCB) dynamic model of an L6 diesel engine was developed using AVL-Excite-Piston & Rings. Based on the full-scale test method, the effects of bore taper, starting height of tapered profile, and ellipticity on the friction power and knocking energy of the PRCB system were investigated, and the optimization of the design of the bore profile was carried out with the objectives of minimizing the system’s friction power and the peak knocking kinetic energy. The results showed that the taper of the cylinder bore has the greatest influence on the system’s friction power and the peak knocking kinetic energy, followed by the starting height of the conical profile. For the peak knocking kinetic energy of the piston, there was an obvious interaction between the taper and the starting height of the conical profile. When the taper was 35 μm and 45 μm, the peak knocking kinetic energy showed a decreasing and then increasing trend with the increase in the starting height of the profile, and when the taper was 55 μm the peak knocking kinetic energy monotonically was decreased with the increase in the starting height of the conical profile. The optimization results showed that the system’s friction power was decreased by 15.05% and the peak knocking kinetic energy was decreased by 21.41% for a taper degree of 55 μm, a tapered profile starting height of 31 mm, and an ellipticity of 50 μm compared to the initial cylindrical cylinder bore.
Not All about the Money: The Role of Financial Information in Promoting Residential Rooftop Photovoltaics
Authors: Boaz David , Palatnik Ruslana Rachel , Ayalon Ofira
Published: April 2024

This study investigates the role of financial information in shaping the intention to adopt (ITA) Residential Rooftop Photovoltaic (RPV) systems in Israel—a country with a surprisingly low RPV adoption rate given the high solar irradiance, and significant governmental support for RPV. We used an online survey of 899 respondents to examine how different presentations of RPV revenues impact both RPV deal comprehension and ITA. Our results align with existing literature on the roles of early technology adopters and pro-environmental characteristics while introducing two novel findings. First, households value their roofs—especially flat ones used for leisure. This negatively impacts ITA. Second, we found a positive correlation between trust in government entities and ITA, underscoring the importance of trust in institutions. Although financial information does enhance RPV deal comprehension, it does not significantly influence ITA. This study concludes that RPV penetration should not be promoted solely by financial incentives, but also by other motivations like environmental concerns, novelty seeking, and improved institutional trust.
Performance Characteristics and Optimization of a Single-Stage Direct Air Capture Membrane System in Terms of Process Energy Intensity
Authors: Niesporek Kamil , Kotowicz Janusz , Baszczeńska Oliwia , Maj Izabella
Published: April 2024

The increase in emissions and concentration of carbon dioxide in the atmosphere necessitates the implementation of direct carbon dioxide capture technologies. The article presents the characteristics of a single-stage membrane unit for the direct capture of carbon dioxide from the air. A membrane with a selectivity of αCO2/N2=70 and permeability PCO2=108m3(STP)(m2·h·bar) is chosen as the reference variant. It is demonstrated that increasing the pressure difference in the system by reducing the pressure of the permeate stream results in an improvement of all analyzed parameters. Manipulating both the membrane surface and its CO2 permeability yields similar results. With an increase in permeability or membrane surface area, the proportion of CO2 in the retentate and permeate decreases, while the degree of carbon dioxide recovery increases. However, the energy intensity of the process is a complex issue due to the presence of a local minimum in the obtained characteristics. Therefore, a relationship between the constants of energy intensity values for the separation process on the surface area field and CO2 membrane permeability is presented. The minimum energy intensity of the process obtained is 22.5 kWh/kgCO2. The CO2 content in the retentate for all analyses did not exceed 280 ppm.
CO2 Capture Cost Reduction Potential of the Coal-Fired Power Plants under High Penetration of Renewable Power in China
Authors: He Song , Zheng Yawen
Published: April 2024

With a significant share of renewable power generation integrated into the power supply, it is crucial to timely adjust the regulating peak load for coal-fired power plants equipped with CO2 capture to ensure the stable operation of the multi-energy supply system. In this paper, the effects of varying boiler loads on the techno-economic performance of the retrofitted power plant were studied. Furthermore, the potential for reducing the cost of CO2 capture was investigated, and early opportunities for demonstration were discussed. Results showed that when the boiler load decreased from 100% turbine heat acceptance condition to 50% turbine heat acceptance condition, the cost of CO2 capture increased from 37.0 $/t CO2 to 57.0 $/t CO2, cost contribution of energy penalty and extra capital investment also increased from 20.6 $/t-CO2 to 25.7 $/t-CO2, and from 16.4 $/t-CO2 to 31.3 $/t-CO2, respectively. Furthermore, by improving separation efficiency from 0.15 to 0.5, a 25% to 30% reduction in CO2 capture cost can be achieved. The cost of CO2 capture could decrease by 42.2–50.5% when the cumulative capacity reaches 250 GW under the high investment learning rate scenario. According to the distribution of coal prices and renewable energy sources in China, the early demonstration projects of multi-energy supply systems should prioritize the northern region. The results of this work can provide informative references for making roadmaps and policies for CO2 emission reduction toward carbon neutrality.
Multilevel Aircraft-Inverter Design Based on Wavelet PWM for More Electric Aircraft
Authors: Catalbas Nurbanu , Pakfiliz Ahmet Gungor , Soysal Gokhan
Published: April 2024

This paper proposes a comprehensive power system designed for the use of a more electric aircraft power distribution system. Instead of traditional Nicad battery solutions as the energy source of the aircraft power system, lithium battery structures, which are a recent and promising solution in the field of aviation power systems, are modeled and analyzed. In this study, a WPWM-based, single-phase, multi-level pure sine wave static aircraft-inverter system is designed and integrated to improve the performance of conventional aircraft power systems. In the designed power system, a boost converter structure is proposed that boosts 28 VDC-to-270 VDC voltage coming from the lithium–ion battery pack and can reach a steady state in 0.032 s. The performance of the modeled WPWM-based aircraft-inverter system, compared to SPWM Bipolar and Unipolar switching techniques commonly used in single-phase inverter designs, reveals a THD reduction of approximately 27% with WPWM, resulting in a THD value below 2% for both load current and load voltage. As a result of the study, a power system that will enable the aircraft avionics, ventilation, and navigation systems to perform better than conventional power systems and comply with aircraft electric-power characteristic standards has been designed and detailed.
Analysis of the Energy, Environmental and Economic Efficiency of Multi-Family Residential Buildings in Poland
Authors: Alsabry Abdrahman , Szymański Krzysztof , Backiel-Brzozowska Beata
Published: April 2024

Improving the energy efficiency of buildings is one of the main challenges facing Europe in the context of climate neutrality policy. In this article, the authors decided to investigate the impact of thermal insulation of building envelopes and the type of ventilation on the energy efficiency of multi-family buildings located in different Polish cities. In accordance with EU directives, economic analyses of the costs incurred during the lifecycle and calculations of CO2 emissions were carried out for the analyzed facilities. It was determined what measures need to be taken under Polish climatic conditions in order to meet the requirements for improving the energy efficiency of multi-family buildings. The multi-criteria analyses presented in this article provide a voice in the discussion of the issues related to the achievement of the energy performance of buildings with almost zero primary energy demand (nZEB). Based on the conclusions of the research conducted, it was possible to develop general recommendations for investors and designers of multi-family residential buildings characterized by energy efficiency and reduced environmental impact.
Offshore Energy Development in Poland—Social and Economic Dimensions
Authors: Chomać-Pierzecka Ewa
Published: April 2024

The development of green technology in the world is progressing extremely rapidly. New possibilities for obtaining energy from renewable sources are constantly being sought and existing solutions are being improved. The multifaceted potential of the seas and oceans is an important aspect being taken into account in the development of the energy systems of a number of economies. One dimension of action in this area is the orientation towards offshore wind energy and the construction of offshore wind farms for this purpose. The purpose of this article is to analyse the importance of offshore wind farms in Poland’s energy system and to assess public perception of the changes taking place in this dimension. The article is based on research and critical analysis of the available literature, legal regulations and industry reports, as well as on the results of our own surveys, the scientific findings of which were developed with the application of statistical instruments using PQstat software, ensuring the expected quality of results. The findings of the article indicate the significant importance of offshore wind farms in the creation of Poland’s energy mix, with differing public attitudes towards their construction. Furthermore, the results of the research indicate a differentiated attitude of society towards the construction of offshore wind farms. The main motivation for majority support of the measure in question are economic reasons, which are connected with the expectation of a real price reduction per 1 kW of energy, as well as increased attractiveness of the region due to investments in this area. The main concern with the measure relates to environmental aspects, with concerns about the functioning of ecosystems in light of the construction and subsequent operation of wind farms. Negative public opinion is also signalled in relation to the potential risk of landscape change in a direction that is undesirable for the studied developed coastal tourist region in Poland.
Thermophysical Properties of Compressed Earth Blocks Incorporating Natural Materials
Authors: Turco Chiara , Abedi Mohammadmahdi , Teixeira Elisabete , Mateus Ricardo
Published: April 2024

Building materials are responsible for significant CO2 emissions and energy consumption, both during production and operational phases. Earth as a building material offers a valuable alternative to conventional materials, as it naturally provides high hygrothermal comfort and air quality even with passive conditioning systems. However, disadvantages related to high density, conductivity, and wall thickness prevent its effective inclusion in the mainstream. This research explores enhancing the thermophysical properties of compressed earth blocks (CEBs) by using locally sourced natural materials. The study is framed in the Portuguese context and the natural materials involved are wheat straw (WS) as a by-product of wheat harvesting, cork granules (CGs) from bottle caps, and ground olive stone (GOSs) residues from olive oil production. Blocks were produced with different mixtures of these materials and the thermal response was examined in a hot box apparatus. Best results include a 20 and 26% reduction in thermal conductivity for mixtures with 5v.% CG and 10v.% GOS, respectively, and an associated reduction in bulk density of 3.8 and 5.4%. The proposed approach therefore proves to be effective in improving the key thermophysical characteristics of CEBs. The article includes a comparative analysis of the experimental data from this study with those from the literature. The study contributes to the growing knowledge of sustainable materials, providing insights for researchers and practitioners looking for innovative solutions for low-carbon and energy-efficient materials.
Study on the Influence of Circulating Water Bypass on the Thermal and Anti-Freezing Characteristics of High-Level Wet Cooling Tower
Authors: Wang Zhonghua , Yue Zenggang , Wang Wei , Ma Chenghui , Li Xiaoguang , Guo Changmin , Zhao Yuanbin
Published: April 2024

When heating units are operated in winter, the extreme conditions, such as deep peak regulation and large extraction, can easily lead to a low unit load and severe icing in the wet cooling tower, which threatens the safe operation of the unit. Therefore, it is necessary to study the anti-freezing characteristics of the wet cooling tower. In this paper, a three-dimensional numerical model of a high-level, natural draft wet cooling tower is developed based on the constant heat load method. The influence of withdrawing a certain percentage of circulating water into the bypass on the cooling performance and anti-freezing characteristics of the high-level, natural draft wet cooling tower is investigated. The results show that as the percentage of circulating water bypass extraction increases, the temperature drop of circulating water in the tower continues to increase, but the lowest and the average water temperatures at the bottom of the packing continue to decrease. At the same time, the amount of circulating water entering the tower decreases, the pressure difference between the inside and outside of the tower under the same environmental conditions decreases, and the pumping force of the cooling tower decreases. If the circulating water bypass extraction percentage is less than 10%, it can prevent the circulating water from freezing at the bottom of the packing and, at the same time, try to reduce the temperature of the circulating water entering the condenser to ensure the efficiency of the unit.
Performance Analysis of Wave Rotor Combustor Integration into Baseline Engines: A Comparative Study of Pressure-Gain and Work Cycles
Authors: Zheng Renchuan , Gong Erlei , Li Jianzhong , Yao Qian , Nie Zhaolong
Published: April 2024

This study presents two concepts for integrating a wave rotor combustor (WRC) into a baseline engine: the wave rotor pressure-gain cycle (WRPGC) and the wave rotor work cycle (WRWC). Performance parameters were calculated under different thermodynamic cycles, and a comparative analysis of the thermodynamic cycles was conducted, considering both the ideal- and actual-loss conditions. Furthermore, the impact of the WRC precompression ratio, turbine inlet temperature, and fixed peak cycle temperature on the thermodynamic-cycle performance was investigated. The results indicate that embedding a WRC into a baseline engine with a compressor pressure ratio higher than 24.0 does not lead to an improvement in the thermal efficiency. However, under a baseline engine pressure ratio of 3.6, the actual-loss WRC cycle achieves efficiency improvements of 40.5% and 49.5% in the WRPGC and WRWC, respectively, compared to the baseline engine cycle. Increasing the wave rotor precompression ratio or the turbine inlet temperature ratio results in greater performance improvements for the WRWC compared to the WRPGC. When the peak cycle temperature of the wave rotor is fixed, there exists a narrow pressure ratio range wherein the WRPGC outperforms the WRWC. Therefore, the WRPGC is more suitable for embedment in baseline engines with lower pressure ratios.
A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity
Authors: Suchithra Jude , Robinson Duane A. , Rajabi Amin
Published: April 2024

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization.
Environmental Impact of Enhanced Geothermal Systems with Supercritical Carbon Dioxide: A Comparative Life Cycle Analysis of Polish and Norwegian Cases
Authors: Strojny Magdalena , Gładysz Paweł , Andresen Trond , Pająk Leszek , Starczewska Magdalena , Sowiżdżał Anna
Published: April 2024

Low-carbon electricity and heat production is essential for keeping the decarbonization targets and climate mitigation goals. Thus, an accurate understanding of the potential environmental impacts constitutes a key aspect not only for the reduction in greenhouse gas emissions but also for other environmental categories. Life cycle assessment allows us to conduct an overall evaluation of a given process or system through its whole lifetime across various environmental indicators. This study focused on construction, operation and maintenance, and end-of-life phases, which were analyzed based on the ReCiPe 2016 method. Within this work, authors assessed the environmental performance of one of the renewable energy sources—Enhanced Geothermal Systems, which utilize supercritical carbon dioxide as a working fluid to produce electricity and heat. Heat for the process is extracted from hot, dry rocks, typically located at depths of approximately 4–5 km, and requires appropriate stimulation to enable fluid flow. Consequently, drilling and site preparation entail significant energy and material inputs. This stage, based on conducted calculations, exhibits the highest global warming potential, with values between 5.2 and 30.1 kgCO2eq/MWhel, corresponding to approximately 65%, 86%, and 94% in terms of overall impacts for ecosystems, human health, and resources categories, respectively. Moreover, the study authors compared the EGS impacts for the Polish and Norwegian conditions. Obtained results indicated that due to much higher electricity output from the Norwegian plant, which is sited offshore, the environmental influence remains the lowest, at a level of 11.9 kgCO2eq/MWhel. Polish cases range between 38.7 and 54.1 kgCO2eq/MWhel of global warming potential in terms of electricity production. Regarding power generation only, the impacts in the case of the Norwegian facility are two to five times lower than for the installation in the Polish conditions.
Deciphering the Diversity in Bacterial Transporters That Salvage Queuosine Precursors
Authors: Quaiyum Samia , Yuan Yifeng , Kuipers Paul J. , Martinelli Maria , Jaroch Marshall , de Crécy-Lagard Valérie
Published: April 2024

Queuosine (Q) is a modification of the wobble base of tRNA harboring GUN anticodons with roles in decoding accuracy and efficiency. Its synthesis is complex with multiple enzymatic steps, and several pathway intermediates can be salvaged. The only two transporter families known to salvage Q precursors are QPTR/COG1738 and QrtT/QueT. Analyses of the distribution of known Q synthesis and salvage genes in human gut and oral microbiota genomes have suggested that more transporter families remain to be found and that Q precursor exchanges must occur within the structured microenvironments of the mammalian host. Using physical clustering and fusion-based association with Q salvage genes, candidate genes for missing transporters were identified and five were tested experimentally by complementation assays in Escherichia coli. Three genes encoding transporters from three different Pfam families, a ureide permease (PF07168) from Acidobacteriota bacterium, a hemolysin III family protein (PF03006) from Bifidobacterium breve, and a Major Facilitator Superfamily protein (PF07690) from Bartonella henselae, were found to allow the transport of both preQ0 and preQ1 in this heterologous system. This work suggests that many transporter families can evolve to transport Q precursors, reinforcing the concept of transporter plasticity.
A Study of the Effects of Stimulants on Resin Yield, Resin Duct and Turpentine Chemical Composition in Pinus kesiya var. langbianensis
Authors: Yang Huanxin , Shi Junjie , Chen Lin , Yang Chunwang , Li Changzhao , Huang Yuxi , Qiu Jian
Published: April 2024

This study presents a comprehensive examination of Pinus kesiya var. langbianensis (Pinus kesiya var. langbianensis), the primary resin-extraction tree species in Yunnan Province, China. In this study, we formulated different concentration gradients of 0.25%, 0.5%, 1%, and 2% of diquat solution as tapping stimulant to test the effect of different concentrations on the resin gain rate of Pinus kesiya, and analyzed the relationship between anatomical structure, major chemical composition of turpentine and resin yield by methods such as wood anatomy and chemical composition analysis of turpentine. The primary focus of the investigation was on exploring the interrelationships among resin-tapping stimulants, anatomical structures, turpentine components, and resin yield. Research findings demonstrate a significant enhancement in resin production due to the application of stimulants, with the highest increase rate reaching 55% in a specific group, while others achieved approximately 30% increments. Moreover, measurement data about resin duct dimensions indicate a noteworthy increase in resin duct area for the stimulant-treated group compared to the control group. However, it should be noted that the impact on resin duct area by varying stimulant concentrations was relatively minor. Furthermore, continuous observation of resin extraction from different resin-yield classes of P. kesiya revealed insignificant variation in resin yield over time for the low and moderate resin-yield groups. In contrast, the high resin-yield group exhibited a gradual increase in resin production. Interestingly, the high resin-yield group exhibited the smallest resin duct area, but the highest resin duct density, indicating an interconnectedness of resin duct-related data that influences resin yield. Additionally, correlative investigations between anatomical structures and resin yield demonstrate a positive correlation between resin duct area and resin yield, total resin production, and average resin yield. This underscores the importance of resin duct area as a significant factor in resin production. On the other hand, the influence of stimulant concentrations on the turpentine components was found to be negligible. Overall, the correlation results suggest that turpentine components cannot reliably predict or differentiate between high and low resin-yield trees. This study provides a comprehensive analysis of the interrelationships among stimulants, anatomical structures, and turpentine components, offering a theoretical foundation for the resin extraction and resin processing industries in Yunnan Province.
Seasonal and Depth Dynamics of Soil Moisture Affect Trees on the Tibetan Plateau
Authors: Li Qian , Jiao Liang , Xue Ruhong , Che Xichen , Zhang Peng , Wang Xuge , Yuan Xin
Published: April 2024

The soil moisture (SM) influences tree growth with climate change. However, the spatial and temporal dynamics of tree water use strategies in climate-sensitive areas remain uncertain. Therefore, we collected the tree-ring oxygen isotope (δ18OTR) chronologies and divided the wet–dry gradients according to the precipitation on the Tibetan Plateau (TP). Further, the relationship between the δ18OTR and environmental factors was analyzed across different gradients. We found the following: (1) The SM during the growing season was the most important factor for δ18OTR. (2) The response of the δ18OTR to the SM had a lag in arid areas than in humid areas. (3) Trees absorbed the SM on the surface in humid areas (r = −0.49 to −0.41, p < 0.01), while trees absorbed the SM from deep in the soil in arid areas (r = −0.48 to −0.29, p < 0.01). The results demonstrated that trees were better able to cope with drought stress in arid regions because they used more stable deep soil water than in humid regions. Therefore, the findings will provide a scientific basis for water use of trees using the δ18OTR in complex environmental contexts. Trees with single water use strategies should be given more attention to keep ecosystems healthy.
Effects of Illumination Conditions on Individual Tree Height Extraction Using UAV LiDAR: Pilot Study of a Planted Coniferous Stand
Authors: Li Tianxi , Lin Jiayuan , Wu Wenjian , Jiang Rui
Published: April 2024

Tree height is one of the key dendrometric parameters for indirectly estimating the timber volume or aboveground biomass of a forest. Field measurement is time-consuming and labor-intensive, while unmanned aerial vehicle (UAV)-borne LiDAR is a more efficient tool for acquiring tree heights of large-area forests. Although individual tree heights extracted from point cloud data are of high accuracy, they are still affected by some weather and environment factors. In this study, taking a planted M. glyptostroboides (Metasequoia glyptostroboides Hu & W.C. Cheng) stand as the study object, we preliminarily assessed the effects of various illumination conditions (solar altitude angle and cloud cover) on tree height extraction using UAV LiDAR. The eight point clouds of the target stand were scanned at four time points (sunrise, noon, sunset, and night) in two consecutive days (sunny and overcast), respectively. The point clouds were first classified into ground points and aboveground vegetation points, which accordingly produced digital elevation model (DEM) and digital surface model (DSM). Then, the canopy height model (CHM) was obtained by subtracting DEM from DSM. Subsequently, individual trees were segmented based on the seed points identified by local maxima filtering. Finally, the individual tree heights of sample trees were separately extracted and assessed against the in situ measured values. As results, the R2 and RMSEs of tree heights obtained in the overcast daytime were commonly better than those in the sunny daytime; the R2 and RMSEs at night were superior among all time points, while those at noon were poorest. These indicated that the accuracy of individual tree height extraction had an inverse correlation with the intensity of illumination. To obtain more accurate tree heights for forestry applications, it is best to acquire point cloud data using UAV LiDAR at night, or at least not at noon when the illumination is generally strongest.