bluuebunny/crossref_metadata_2025 huggingface dataset:
Excellence in Professional Disciplines and Their Importance in Social and Educational Entrepreneurship
Authors: Rocha Alberto , García-Perales Ramón , Cebrián-Martínez Antonio , López-Parra Emilio
Published: April 2024

Knowledge of the potentialities and strengths of individuals is a premise of numerous studies today, even more so when we are in a historical moment characterized by dizzying changes that demand innovation and creativity from society to adapt to such challenges. The aim of this study is to identify and analyse the personal characteristics, together with the role of contextual mediators, of fifteen relevant Portuguese nationals recognised for their excellence. To this end, an interview generalizable to different professional contexts is used, seeking to explain the process of development and manifestation of excellence of the participants in their professional areas. Although the concept of excellence is complex, its approach is essential to favour, among other aspects, social and educational entrepreneurship. Therefore, knowing and understanding the personal and professional trajectories of these subjects may allow us to make an approximation to those distinctive characteristics of excellence, with a view to proposing a conceptual model for action in the social and educational fields. In this way, among the characteristics drawn, it is worth pointing out the necessary specialization in at least one professional area, its manifestation from an early age, the determining influence of the family, teachers or illustrious personalities, or the consideration that its incidence is not a gender issue.
GDCP-YOLO: Enhancing Steel Surface Defect Detection Using Lightweight Machine Learning Approach
Authors: Yuan Zhaohui , Ning Hao , Tang Xiangyang , Yang Zhengzhe
Published: April 2024

Surface imperfections in steel materials potentially degrade quality and performance, thereby escalating the risk of accidents in engineering applications. Manual inspection, while traditional, is laborious and lacks consistency. However, recent advancements in machine learning and computer vision have paved the way for automated steel defect detection, yielding superior accuracy and efficiency. This paper introduces an innovative deep learning model, GDCP-YOLO, devised for multi-category steel defect detection. We enhance the reference YOLOv8n architecture by incorporating adaptive receptive fields via the DCNV2 module and channel attention in C2f. These integrations aim to concentrate on valuable features and minimize parameters. We incorporate the efficient Faster Block and employ Ghost convolutions to generate more feature maps with reduced computation. These modifications streamline feature extraction, curtail redundant information processing, and boost detection accuracy and speed. Comparative trials on the NEU-DET dataset underscore the state-of-the-art performance of GDCP-YOLO. Ablation studies and generalization experiments reveal consistent performance across a variety of defect types. The optimized lightweight architecture facilitates real-time automated inspection without sacrificing accuracy, offering invaluable insights to further deep learning techniques for surface defect identification across manufacturing sectors.
A Voice User Interface on the Edge for People with Speech Impairments
Authors: Mulfari Davide , Villari Massimo
Published: April 2024

Nowadays, fine-tuning has emerged as a powerful technique in machine learning, enabling models to adapt to a specific domain by leveraging pre-trained knowledge. One such application domain is automatic speech recognition (ASR), where fine-tuning plays a crucial role in addressing data scarcity, especially for languages with limited resources. In this study, we applied fine-tuning in the context of atypical speech recognition, focusing on Italian speakers with speech impairments, e.g., dysarthria. Our objective was to build a speaker-dependent voice user interface (VUI) tailored to their unique needs. To achieve this, we harnessed a pre-trained OpenAI’s Whisper model, which has been exposed to vast amounts of general speech data. However, to adapt it specifically for disordered speech, we fine-tuned it using our private corpus including 65 K voice recordings contributed by 208 speech-impaired individuals globally. We exploited three variants of the Whisper model (small, base, tiny), and by evaluating their relative performance, we aimed to identify the most accurate configuration for handling disordered speech patterns. Furthermore, our study dealt with the local deployment of the trained models on edge computing nodes, with the aim to realize custom VUIs for persons with impaired speech.
An Explainable Method for Lung Cancer Detection and Localisation from Tissue Images through Convolutional Neural Networks
Authors: Mercaldo Francesco , Tibaldi Myriam Giusy , Lombardi Lucia , Brunese Luca , Santone Antonella , Cesarelli Mario
Published: April 2024

Lung cancer, a prevalent and life-threatening condition, necessitates early detection for effective intervention. Considering the recent advancements in deep learning techniques, particularly in medical image analysis, which offer unparalleled accuracy and efficiency, in this paper, we propose a method for the automated identification of cancerous cells in lung tissue images. We explore various deep learning architectures with the objective of identifying the most effective one based on both quantitative and qualitative assessments. In particular, we assess qualitative outcomes by incorporating the concept of prediction explainability, enabling the visualization of areas within tissue images deemed relevant to the presence of lung cancer by the model. The experimental analysis, conducted on a dataset comprising 15,000 lung tissue images, demonstrates the effectiveness of our proposed method, yielding an accuracy rate of 0.99.
Multi-Scale Adaptive Feature Network Drainage Pipe Image Dehazing Method Based on Multiple Attention
Authors: Li Ce , Tang Zhengyan , Qiao Jingyi , Su Chi , Yang Feng
Published: April 2024

Drainage pipes are a critical component of urban infrastructure, and their safety and proper functioning are vital. However, haze problems caused by humid environments and temperature differences seriously affect the quality and detection accuracy of drainage pipe images. Traditional repair methods are difficult to meet the requirements when dealing with complex underground environments. To solve this problem, we researched and proposed a dehazing method for drainage pipe images based on multi-attention multi-scale adaptive feature networks. By designing multiple attention and adaptive modules, the network is able to capture global features with multi-scale resolution in complex underground environments, thereby achieving end-to-end dehazing processing. In addition, we also constructed a large drainage pipe dataset containing tens of thousands of clear/hazy image pairs of drainage pipes for network training and testing. Experimental results show that our network exhibits excellent dehazing performance in various complex underground environments, especially in the real scene of urban underground drainage pipes. The contributions of this paper are mainly reflected in the following aspects: first, a novel multi-scale adaptive feature network based on multiple attention is proposed to effectively solve the problem of dehazing drainage pipe images; second, a large-scale drainage pipe data is constructed. The collection provides valuable resources for related research work; finally, the effectiveness and superiority of the proposed method are verified through experiments, and it provides an efficient solution for dehazing work in scenes such as urban underground drainage pipes.
Optimal Design for New Rotary Engine with Geometric Shape Functions on Combustion Chamber and Ports
Authors: Kim Young-Jic , Park Tae-Joon , Yang Ji-Hyuck , Lee Chang-Eon
Published: April 2024

This study presents an optimal design for a new rotary engine. Instantaneous shape functions were applied to the combustion chamber, and intake and exhaust ports were derived based on the rotor and housing design functions; then optimized design parameters were derived for a rotary engine. The three main parameters were shape distance, eccentric distance and rotor thickness. The design process for the optimized internal shape was subsequently defined considering target specifications for the rotary engine, and a prototype engine was designed and fabricated with a 336 cm3 intake volume. The compression pressure for the prototype engine was compared with the motoring test and calculated. These outcomes confirmed the new engine’s feasibility, and the derived geometric shape functions will be helpful to ensure optimal design for rotary engines using one- or three-dimensional computational fluid dynamics.
Thermodynamic Analysis of an Increasing-Pressure Endothermic Power Cycle Integrated with Closed-Loop Geothermal Energy Extraction
Authors: Yu Hao , Lu Xinli , Zhang Wei , Liu Jiali
Published: April 2024

The thermodynamic analysis of an increasing-pressure endothermic power cycle (IPEPC) integrated with closed-loop geothermal energy extraction (CLGEE) in a geothermal well at a depth from 2 km to 5 km has been carried out in this study. Using CLGEE can avoid some typical problems associated with traditional EGS technology, such as water contamination and seismic-induced risk. Simultaneous optimization has been conducted for the structural parameters of the downhole heat exchanger (DHE), the CO2 mixture working fluid type, and the IPEPC operating parameters. The CO2-R32 mixture has been selected as the optimal working fluid for the IPEPC based on the highest net power output obtained. It has been found that, when the DHE length is 4 km, the thermosiphon effect is capable of compensating for 53.8% of the pump power consumption. As long as the DHE inlet pressure is higher than the critical pressure, a lower DHE inlet pressure results in more power production. The power generation performance of the IPEPC has been compared with that of the organic Rankine cycle (ORC), trans-critical carbon dioxide cycle (t-CO2), and single-flash (SF) systems. The comparison shows that the IPEPC has more net power output than other systems in the case that the DHE length is less than 3 km, along with a DHE outer diameter of 0.155 m. When the DHE outer diameter is increased to 0.22 m, the IPEPC has the highest net power output for the DHE length ranging from 2 km to 5 km. The application scopes obtained in this study for different power generation systems are of engineering-guiding significance for geothermal industries.
Optimization Techniques and Evaluation for Building an Integrated Lightweight Platform for AI and Data Collection Systems on Low-Power Edge Devices
Authors: Cho Woojin , Lee Hyungah , Gu Jae-hoi
Published: April 2024

Amidst an energy crisis stemming from increased energy costs and the looming threat of war, there has been a burgeoning interest in energy conservation and management worldwide. Industrial complexes constitute a significant portion of total energy consumption. Hence, reducing energy consumption in these complexes is imperative for energy preservation. Typically, factories within similar industries aggregate in industrial complexes and share similar energy utilities. However, they often fail to capitalize on this shared infrastructure efficiently. To address this issue, a network system employing a virtual utility plant has been proposed. This system enables proactive measures to counteract energy surplus or deficit through AI-based predictions, thereby maximizing energy efficiency. Nevertheless, deploying conventional server systems within factories poses considerable challenges. Therefore, leveraging edge devices, characterized by low power consumption, high efficiency, and minimal space requirements, proves highly advantageous. Consequently, this study focuses on constructing and employing data collection and AI systems to utilize edge devices as standalone systems in each factory. To optimize the AI system for low-performance edge devices, we employed the integration-learning AI modeling technique. Evaluation results demonstrate that the proposed system exhibits high stability and reliability.
Anisotropic Mechanical Behaviors of Shale Rock and Their Relation to Hydraulic Fracturing in a Shale Reservoir: A Review
Authors: Yin Peng-Fei , Yang Sheng-Qi , Ranjith Pathegama Gamage
Published: April 2024

Shale gas is an important supplement to the supply of natural gas resources and plays an important role on the world’s energy stage. The efficient implementation of hydraulic fracturing is the key issue in the exploration and exploitation of shale gas. The existence of bedding structure results in a distinct anisotropy of shale rock formation. The anisotropic behaviors of shale rock have important impacts on wellbore stability, hydraulic fracture propagation, and the formation of complex fracture networks. This paper briefly reviews previous work on the anisotropic mechanical properties of shale rock and their relation to hydraulic fracturing in shale reservoirs. In this paper, the research status of work addressing the lithological characteristics of shale rock is summarized first, particularly work considering the mineral constituent, which determines its physical and mechanical behavior in essence. Then the anisotropic physical and mechanical properties of shale specimens, including ultrasonic anisotropy, mechanical behavior under uniaxial and triaxial compression tests, and tensile property under the Brazilian test, are summarized, and the state of the literature on fracture toughness anisotropy is discussed. The concerns of anisotropic mechanical behavior under laboratory tests are emphasized in this paper, particularly the evaluation of shale brittleness based on mechanical characteristics, which is discussed in detail. Finally, further concerns such as the effects of bedding plane on hydraulic fracturing failure strength, crack propagation, and failure pattern are also drawn out. This review study will provide a better understanding of current research findings on the anisotropic mechanical properties of shale rock, which can provide insight into the shale anisotropy related to the fracture propagation of hydraulic fracturing in shale reservoirs.
Hybrid Power System Design and Dynamic Modeling for Enhanced Reliability in Remote Natural Gas Pipeline Control Stations
Authors: Waqas Muhammad , Jamil Mohsin , Khan Ashraf Ali
Published: April 2024

The most rapid and efficient method to transport natural gas from its source to its destination is through a pipeline network. The optimal functioning of control stations for natural gas pipelines depends on the use of electrical devices, including data loggers, communication devices, control systems, surveillance equipment, and more. Ensuring a reliable and consistent power supply proves to be challenging due to the remote locations of these control stations. This research article presents a case study detailing the design and dynamic modeling of a hybrid power system (HPS) to address the specific energy needs of a particular natural gas pipeline control station. The HOMER Pro 3.17.1 software is used to design an optimal HPS for the specified location. The designed system combines a photovoltaic (PV) system with natural gas generators as a backup to ensure a reliable and consistent power supply for the control station. Furthermore, it provides significant cost savings, reducing the cost of energy (COE) by USD 0.148 and the annual operating costs by USD 87,321, all while integrating a renewable energy fraction of 79.2%. Dynamic modeling of the designed system is performed in MATLAB/Simulink R2022a to analyze the system’s response, including its power quality, harmonics, voltage transients, load impact, etc. The experimental results are validated using hardware in the loop (HIL) and OPAL-RT Technologies’ real-time OP5707XG simulator.
A Stochastic Methodology for EV Fast-Charging Load Curve Estimation Considering the Highway Traffic and User Behavior
Authors: Silva Leonardo Nogueira Fontoura da , Capeletti Marcelo Bruno , Abaide Alzenira da Rosa , Pfitscher Luciano Lopes
Published: April 2024

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations’ load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations’ operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs.
Dynamic Comprehensive Evaluation of a 660 MW Ultra-Supercritical Coal-Fired Unit Based on Improved Criteria Importance through Inter-Criteria Correlation and Entropy Weight Method
Authors: Yuan Haotian , Ma Xiaojing , Cheng Zening , Kari Tusongjiang
Published: April 2024

To address the issue of traditional static evaluation models being unable to comprehensively analyze the performance of ultra-supercritical coal-fired units under varying loads, we propose a dynamic comprehensive evaluation model based on the improved Criteria Importance Through Inter-criteria Correlation (CRITIC) method and entropy weight method (EWM). The comprehensive performance evaluation index system of ultra-supercritical coal fired units is constructed by examining the boiler performance, turbine performance, plant power performance, environmental performance, and flexible performance of coal-powered units. The CRITIC and EWM methods are used to calculate the weights of the indicators, which are then combined with the static evaluation results. Using a dynamic comprehensive evaluation model, we analyze ultra-supercritical coal-fired units, taking into account time weight. This allows us to obtain the comprehensive dynamic real-time evaluation value of the units under different loads. The research indicates that the weight of the evaluation index is changed when using the dynamic comprehensive evaluation model of the improved CRITIC and EWM. The index with lower weight is increased by 6.2%, while the index with higher weight is decreased by 0.22%. This alteration in weight range can provide a more objective reflection of the relationship between evaluation indicators. This model offers significant advantages in improving evaluation accuracy, weight balance distribution, and generality.
Thermodynamic Analysis of a Cogeneration System Combined with Heat, Cold, and Electricity Based on the Supercritical CO2 Power Cycle
Authors: Zhang Rujun , Wang Xiaohe , Yang Shuang , Shen Xin
Published: April 2024

The supercritical CO2 power cycle driven by solar as a new generation of solar thermal power generation technology has drawn significant attention worldwide. In this paper, a cogeneration system derived from a supercritical CO2 recompression Brayton cycle is proposed, by considering the recovery of waste heat from the turbine outlet. The absorption refrigeration cycle is powered by the medium-temperature waste heat from the turbine outlet, while the low-temperature waste heat is employed for heating, achieving the cascaded utilization of the heat from the turbine outlet. As for the proposed combined cooling, heating, and power (CCHP) system, a dynamic model was built and verified in MATLAB R2021b/Simulink. Under design conditions, values for the energy utilization factor (EUF) and exergy efficiency of the cogeneration system were obtained. Moreover, the thermodynamic performances of the system were investigated in variable cooling/heating load and irradiation conditions. Compared with the reference system, it is indicated that the energy utilization factor (EUF) and exergy efficiency are 84.7% and 64.8%, which are improved by 11.5% and 10.3%. The proposed supercritical CO2 CCHP system offers an effective solution for the efficient utilization of solar energy.
Enhancing Thermal Performance of Thermodynamic Cycle through Zeotropic Mixture Composition Regulation: An Overview
Authors: Huang Kunteng , Xu Weicong , Deng Shuai , Zhang Jianyuan , Chen Ruihua , Zhao Li
Published: April 2024

Composition regulation of zeotropic mixture working fluid for a thermodynamic cycle is an effective way to improve energy conversion efficiency, which offers the potential to construct efficient, flexible and intelligent cycles. Current research on cycle construction of zeotropic mixture composition regulation still heavily relies on construction methods using pure working fluids, where the characteristics of flexible composition variations fail to be utilized. In this paper, the research progress of cycle construction methods and composition regulated structures are comprehensively reviewed, aiming to clarify the potential for enhancing a thermodynamic cycle based on composition regulation. The characteristics of different cycle construction methods are firstly summarized and compared. Then, the composition-regulated structures of a physical-based method and chemical-based method are introduced, and the composition regulation performance are also concluded. Finally, a future outlook on the cycle design and structure design is provided. The review results show that the combination of 3D construction method and superstructure/intelligences construction method has the potential to maximize the cycle performance, where the improvement of each thermal process and the optimization of complex cycles can be considered simultaneously. The composition regulation based on a passive physical method has the advantage of being readily applicable; however, the composition regulation range is limited. In addition, the distillation and hydrate method have a wider regulation range through extra energy input, where the trade-off between energy consumption and cycle performance improvement should be considered in the future. This study greatly assists in the design of thermodynamic cycles involving zeotropic mixture composition regulation and the corresponding composition regulation structures.
A Capacitance Monitoring Strategy Based on Offset Error Compensation for Modular Multilevel Converters
Authors: Jiang Huijie , Deng Fujin , Li Huailong , Tian Jie , Lu Yu , Li Gang
Published: April 2024

The modular multilevel converter (MMC) is a research hotspot in medium-voltage and high-voltage applications. The measurement offset error would cause an increase in the monitoring error of the submodule (SM) capacitance of the MMC, affecting the estimation accuracy of the SM capacitance monitoring. This paper proposes a capacitor monitoring strategy based on the offset error compensation, where two reasonable capacitor monitoring periods are selected in one fundamental period under the proposed voltage-balancing control (VBC) based on the virtual capacitor voltage (VCV) to compensate for the offset error impact on the capacitance monitoring. The proposed strategy can effectively eliminate the offset error impact on the capacitance monitoring, which ensures the accuracy of the SM capacitance monitoring in the MMCs. The effectiveness of the proposed monitoring strategy is confirmed by the simulations and experiments.
Sustainable Energy Development and Sustainable Economic Development in EU Countries
Authors: Jędrzejczak-Gas Janina , Wyrwa Joanna , Barska Anetta
Published: April 2024

Sustainable development is the subject of many economic analyses, but so far no attempt has been made to identify the main mechanism of interdependence between sustainable energy development and sustainable economic development in the second decade of the 21st century. The particular role of energy in achieving the Sustainable Development Goals is due to the fact that the production, supply and use of energy underpin economic growth. The article fills this research gap and spawns both a better understanding of the essence of sustainable development as well as practical conclusions. The aim is to assess sustainable energy development and sustainable economic development in EU member states and to determine the correlation between the two in the EU. Substantive and formal methods were used to select diagnostic variables, including: the parametric method, the standardized sums method, and correlation analysis. The analysis period covers the years 2014–2021. The conducted research demonstrated a significant variation in the level of sustainable energy development and sustainable economic development among EU countries.
Numerical Analysis of Flow-Induced Transverse Vibration of a Cylinder with Cubic Non-Linear Stiffness at High Reynolds Numbers
Authors: Sadasivan Sreeja , Litak Grzegorz , Gęca Michał Jan
Published: April 2024

Numerical calculations were performed to study the vortex-induced vibration (VIV) of a circular cylinder, which was elastically supported by springs of linear and cubic terms. These simulations were conducted at high Reynolds numbers ranging from 4200 to 42,000. To simulate the cylinder’s motion and the associated aerodynamic forces, Computational Fluid Dynamics were employed in conjunction with dynamic mesh capabilities. The numerical method was initially verified by testing it with various grid resolutions and time steps, and subsequently, it was validated using experimental data. The response of cubic nonlinearities was investigated using insights gained from a conventional linear vortex-induced vibration (VIV) system. This 2D study revealed that both the amplitude and frequency of vibrations are contingent on the flow velocity. The highest output was achieved within the frequency lock-in region, where internal resonance occurs. In the case of a hardening spring, the beating response was observed from the lower end of the initial branch to the upper end of the initial branch. The response displacement amplitude obtained for the linear spring case was 27 mm, whereas in the cubic nonlinear case, the value was 31.8 mm. More importantly, the results indicate that the inclusion of nonlinear springs can substantially extend the range of wind velocities in which significant energy extraction through vortex-induced vibration (VIV) is achievable.
Towards Energy Transformation: A Case Study of EU Countries
Authors: Manowska Anna , Bluszcz Anna , Chomiak-Orsa Iwona , Wowra Rafał
Published: April 2024

The decarbonization of European economies is an established reality that has been accelerating in recent years. The focus of EU policy is on the dynamic transformation of the energy balances of Member States, which most significantly impacts economies reliant on coal. In the context of emerging megatrends, this study sets out to determine the extent of changes occurring in the economies of European Union countries in relation to the Green Deal paradigm. The objective of this article is to introduce a comprehensive method developed by the authors for assessing the dynamics of energy transformation in the European Union countries under study. This method is divided into two phases. Initially, countries are classified according to the energy transformation dynamics matrix. Subsequently, the actual assessment of energy transformation dynamics is conducted using a novel composite indicator, the ETPI (Energy Transition Progress Index), based on analyses for 2022 and 2013 using Eurostat data. The results identify leaders in energy transformation, such as Sweden, Germany, Denmark, France, Italy, Spain, Austria, Finland, and the Netherlands, while highlighting significant challenges facing Poland and Bulgaria.
Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model
Authors: Boucetta Lakhdar Nadjib , Amrane Youssouf , Chouder Aissa , Arezki Saliha , Kichou Sofiane
Published: April 2024

Renewable energies have become pivotal in the global energy landscape. Their adoption is crucial for phasing out fossil fuels and promoting environmentally friendly energy solutions. In recent years, the energy management system (EMS) concept has emerged to manage the power grid. EMS optimizes electric grid operations through advanced metering, automation, and communication technologies. A critical component of EMS is power forecasting, which facilitates precise energy grid scheduling. This research paper introduces a deep learning hybrid model employing convolutional neural network–long short-term memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting. The proposed method integrates the variational mode decomposition (VMD) algorithm with the CNN-LSTM model to predict PV power output from a solar farm in Boussada, Algeria, spanning 1 January 2019, to 31 December 2020. The performance of the developed model is benchmarked against other deep learning models across various time horizons (15, 30, and 60 min): variational mode decomposition–convolutional neural network (VMD-CNN), variational mode decomposition–long short-term memory (VMD-LSTM), and convolutional neural network–long short-term memory (CNN-LSTM), which provide a comprehensive evaluation. Our findings demonstrate that the developed model outperforms other methods, offering promising results in solar power forecasting. This research contributes to the primary goal of enhancing EMS by providing accurate solar energy forecasts.
Positive Rail Voltage Rise Behavior and Inhibition Analysis of Regenerative Braking of Medium–Low-Speed Maglev Train
Authors: Huang Ke
Published: April 2024

When a medium–low-speed (MLS) maglev train is braking, part of its regenerative braking (RB) power consumption may cause a significant rise in the positive rail (PR) voltage. For RB energy re-utilization, an RB energy feedback system (RBEFS) is a promising application, but there is still no specific research in the field of MLS maglev trains. From this perspective, this article focuses on identifying the PR voltage rise behavior and investigating the application of an RBEFS on the over-voltage inhibition. Some development trends of the MLS maglev train, including the DC 3 kV traction grid system and the speed being raised to 160~200 km/h, are also considered in the analyzed scenarios. At first, a modeling scheme of a detailed vehicle–grid electrical power model with the RBEFS is established. On this basis, the PR voltage rise characteristics are analyzed with consideration of three pivotal influencing factors: RB power, PR impedance and supply voltage level. Subsequently, to stabilize the PR voltage fluctuations, the influence rules of the RBEFS on the voltage rise and the mutual transient voltage influences under the operating status switching for multiple vehicles running on the same power supply section are analyzed.
Dynamic Management of Flexibility in Distribution Networks through Sensitivity Coefficients
Authors: Knez Klemen , Herman Leopold , Blažič Boštjan
Published: April 2024

Due to a rising share of renewable energy sources on the production side and electrification of transport and heating on the consumption side, the efficient management of flexibility in distribution networks is crucial for ensuring optimal operation and utilization of resources. Nowadays, the sensitivity-based approach is mainly used in medium-voltage (MV) networks for regulating voltage profiles with reactive power of distributed energy resources (DER). The main disadvantage of the simplified sensitivity-based method is its inaccuracy in case of a high deviation of the network voltage from the nominal values. Furthermore, it was also noted that despite the fact that the method is well described in the literature, there is a lack of systematic approach to its implementation in real-life applications. Thus, the main objective of this paper is to address this disadvantage and to propose an algorithm designed to calculate required consumer flexibility in near real-time to ensure distribution grid operation within operational criteria. In the first part of the paper, network state, including line loading and node voltages, is assessed to determine distribution network node capacity. By analyzing the sensitivity of network busbars to changes in consumption and production, our algorithm effectively identifies the most efficient nodes and facilitates strategic decision-making for resource allocation. We demonstrate the effectiveness of our approach through simulations of real-world distribution network data, highlighting its ability to enhance network flexibility and improve resource utilization. Leveraging sensitivity coefficients, the algorithm enables flexible consumption and production management across various scenarios, supporting the transition to a more dynamic and efficient power system.
Technical Language Processing of Nuclear Power Plants Equipment Reliability Data
Authors: Wang Congjian , Mandelli Diego , Cogliati Joshua
Published: April 2024

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well.
The Port Sector in Italy: Its Keystones for Energy-Efficient Growth
Authors: Nigro Marialisa , De Domenico Massimo , Murgia Tiziana , Stimilli Arianna
Published: April 2024

Italy has been defined as the “logistics platform” of the Mediterranean Sea. The Italian port system, with 11.6 million TEUs handled and 61.4 million passengers in 2022 (Assoporti data January–December 2022), is the key to fulfilling this title through adequate levels of reliability, safety, and sustainability. This contribution addresses port logistics and shipping, focusing on primary issues related to the energy sector with a specific focus on what can be observed in the Italian context. Specifically, the decarbonization of the maritime sector and related infrastructural problems (e.g., cold ironing or alternative fuels, where the uncertainty about resource availability and related costs do not allow for easy strategic planning by both the ship owner and the port authority), as well as policies such as the Emission Trading System (ETS), will be analyzed. All these issues, hereafter addressed with a systematic critical review of the existing literature and other relevant sources, could represent the driving force of the growth of the national port sector toward its competitiveness at a global scale. The review was performed through a wide search and analysis of studies published in well-known online research databases (Scopus, Web of Science, IEEE Xplore, ScienceDirect), sector studies, or specialized technical magazines. The review focuses on the results of each analyzed contribution rather than on the analysis method adopted with the final aim to identify useful hints and innovative ideas for further studies on the topic.
Comparison of Cut-to-Length Harvesting Methods in Tree Plantations in Brazil
Authors: Palander Teijo , Pasi Antti , Laurèn Annamari , Ovaskainen Heikki
Published: April 2024

The aim of this research was to determine the most productive tree-cutting methods, and the factors influencing them, in flat and sloping terrains in tree plantations in Brazil. The study utilized drone-captured video material from harvesting operations in eucalyptus and pine plantations. In both terrains, two cutting methods were compared, differing in the felling to the side method used: either at the edge or inside of the harvesting front. In addition, on flat terrain, the efficiency of forward felling was studied in relation to the aforementioned cutting methods. In sloping terrain, the machines were also equipped with a winch assistance system. The time study data of the harvesting work were processed using a video analysis tool developed for the research. The output data of the cut trees were collected with the automatic measuring system of the harvester. Statistical tests were used to determine the most productive cutting methods by analyzing differences in productivity. With an average tree size of 0.3 m3, cutting productivity was 45 m3/E0h in pine cuttings and 55 m3/E0h in eucalyptus cuttings. The average cutting productivity on flat terrain was about 11 m3/E0h higher than on sloping terrain, mainly due to the time spent attaching the winch assistance system, which was a necessary phase of the work on sloping terrain. The research results suggest that it would be most productive to use sideways felling inside the harvesting front method. However, the need for further research is evident, if we want to precisely identify the factors and work phases in the tree-cutting cycle affecting differences in the productivity of the harvesting chain in tree plantations.
The Spatiotemporal Variation Characteristics and Influencing Factors of Green Vegetation in China
Authors: Zhang Xiaodong , Han Haoying , Dai Anran , Xie Yianli
Published: April 2024

Green vegetation is one of the main objects of ecological environment restoration and protection, objectively reflecting the quality of regional ecological environments. Studying its spatial distribution characteristics is of great significance to the formulation of ecological environment restoration policies. Based on data on urban green vegetation in China from 2000 to 2022, this study attempts to analyze the destruction and protection patterns of urban green vegetation in China from the perspectives of total changes in green vegetation contraction and growth and spatial evolution characteristics and trends, and it explores the driving factors affecting the change in green vegetation area. The results show the following: (1) Green vegetation growth and contraction occurred alternately in China from 2000 to 2022. Vegetation contraction showed a “point–line–plane” evolution pattern, forming a contraction stage of point-like aggregation, linear series, and planar spread. Vegetation growth has always presented a frontal pattern. (2) The growth and contraction of green vegetation in China showed a north–south differentiation phenomenon. The vegetation contraction phenomenon spread in the Central Plains urban agglomeration and its surrounding areas and showed an expanding trend. The growth trend is obviously moving northward, mainly concentrated in Inner Mongolia, Ningxia, Gansu, Xinjiang, and other northern provinces, which also coincides with the key ecological restoration policies in northern China in recent years. (3) City scale, economic level, population scale, agro-industrial structure, and water resources content have significant effects on the spatial distribution of green vegetation.
Electrical Properties at Multi-Frequencies for Analysis of Physical and Anatomical Properties of Fast-Growing Standing Teak Trees at Various Ages
Authors: Agustiningrum Dyah Ayu , Siregar Iskandar Zulkarnaen , Damayanti Ratih , Taruno Warsito Purwo , Nugraha Harisma , Rohmadi  , Karlinasari Lina
Published: April 2024

Fast-growing teak trees are cultivated extensively in Indonesia to meet the growing demand for teak wood. However, it is necessary to assess the conditions of teak stands throughout their growth period. The nondestructive testing of wood utilizing dielectric spectroscopy approaches based on electrical properties is currently under development, particularly for evaluating tree stands. This study aimed to analyze the dielectric values of fast-growing teak tree stands within a frequency range of 250 kHz to 60 MHz and to understand the relationship between their physical and anatomical properties. A capacitance measurement system was employed to collect dielectric spectroscopy data directly from trees aged 4, 5, and 7 years. Simultaneously, physical and anatomical samples were obtained using a 0.5 cm diameter increment borer. The results revealed significant differences in the fiber length, lumen diameter, and wall thickness at each age. The optimal dielectric frequency for distinguishing wood properties in standing trees was identified to be within a range of 18 MHz to 23 MHz. In the linear model, a moderate relationship was observed with a correlation coefficient of (r)0.403, although the coefficient of determination (r2) was weak at 0.162 for green density. However, a robust relationship was observed in the linear model for specific gravity with r = 0.826 and r2 = 0.682. A weak but significant relationship was also identified with r = 0.2, a coefficient of determination of r2 = 0.04, and a significance level < 0.05 in the predictive model of wood anatomy properties (vessel diameter and fiber wall thickness). Models with low r2 but high significance indicate that the independent variables still noticeably contribute to explaining the dependent variable. Further analysis and data processing can be enhanced by identifying the crucial variables in the capacitance measurement system.
Transcriptome Analysis Reveals the Impact of Arbuscular Mycorrhizal Symbiosis on Toona ciliata var. pubescens Seedlings
Authors: Jiang Xue-Ru , Pan Jian-Feng , Zhao Ming , Guo Xiao-Yan , Wang Qiong , Zhang Lu , Liu Wei
Published: April 2024

Toona ciliata var. pubescens, known as “Chinese mahogany”, has high commercial value and is classified as a level II priority protected wild plant in China. However, due to overexploitation and its poor natural regeneration capacity, natural T. ciliata var. pubescens forests show varying degrees of decline in habitat adaptability. Arbuscular mycorrhizal fungi (AMF) symbiosis presents a potential strategy to enhance its regeneration. In this study, T. ciliata var. pubescens seedlings were inoculated with Septoglomus viscosum, followed by RNA-Seq analysis to compare gene expression differences between AMF-inoculated (AMI) and non-mycorrhizal (NM) treatments three months post-inoculation. A total of 16,163 differentially expressed genes (DEGs) were upregulated by AMF colonization, constituting 96.46% of the total DEGs. Specifically, 14,420 DEGs were exclusively expressed in the AMI treatment, while 35 DEGs were completely silenced. Most of the upregulated DEGs were located on the cell membrane, nucleus, and cytoskeleton and functioned in protein binding, S-adenosylmethionine-dependent methyltransferase activity, and lipid binding during cellular/macromolecule/protein localization, intracellular/protein transport, the cell cycle, and signal transduction. Additionally, lots of key genes related to oxidative stress responses, nutrient transport, and small GTPase-mediated signal transduction were found to be upregulated. These results suggest that AMF inoculation may enhance root cell growth by activating genes involved in nutrient uptake, stress responses, signal transduction, and substance transportation. This study elucidates the molecular mechanisms underlying the growth promotion of T. ciliata var. pubescens through AMF symbiosis, laying a foundation for the future application of AMF in its natural forest regeneration.
Scaling Landscape Fire History: Wildfires Not Historically Frequent in the Main Population of Threatened Gunnison Sage-Grouse
Authors: Baker William L.
Published: April 2024

The main population of ~5000 threatened Gunnison sage-grouse (GUSG; Centrocercus minimus) in Colorado depends on sagebrush plants that are killed by wildfires, with recovery taking decades, so frequent fire is a threat, but did it occur historically? Early land surveys showed that the historical (preindustrial) fire rotation (FR), the expected period to burn area equal to a focal land area, was 90–143 years in GUSG ranges, which is not classed as frequent fire (≤25 years). However, recent research, based on fire scars on trees at ten sites near sagebrush, suggested some frequent fire historically in the main population. That study was not spatial, essential to estimate FR, so spatial data were created in GIS with land-survey reconstructions, survey dates, fire-scar sites, mapped sagebrush, and Thiessen polygons around sites. The previous study assumed fires that burned 2+ sites likely burned across sagebrush. Historical FRs were calculated several ways over a common period. A recovery estimate of FR was 90–135 years, a land-survey estimate was 82–131 years, and three spatial scar-based estimates were 93–107 years, showing agreement. However, the comparison found that only 8.8% of the land-survey fire area was detected at fire-scar sites. Detailed analysis showed that 10 fire-scar sites were insufficient to detect historical fire sizes and distributions across the large 168,753 ha sagebrush area. Adequate fire reconstruction could require ~45–60 fire-scar sites, making it feasible to study only ~30,000 ha of sagebrush. Using the two remaining methods, which cross-validate, showed frequent fire did not occur historically in the study area, as historical FRs were 82–135 years.