bluuebunny/crossref_metadata_2025 huggingface dataset:
Studies on the Morphological and Mechanical Properties of TiZrHfNiCuCo Metallic Coatings Deposited by DC Magnetron Sputtering
Authors: Kim Young-Soon , Park Hae-Jin , Seo Jin-Oh , Shin Ji-Woo , Hong Sung-Hwan , Kim Ki-Buem
Published: September 2024

This research aimed to investigate the effects of input power and gas flow rate on the composition, microstructure, and mechanical properties of TiZrHfNiCuCo metallic coatings. These metallic coatings were deposited on a p-type Si wafer using a direct current magnetron sputtering system with varying input powers (100–300 W) and Ar flow rates (5–20 sccm). It was observed that increasing input power and decreasing Ar flow rate led to TiZrHfNiCuCo metallic coatings with higher hardness and smoother surfaces. The experiment resulted in the formation of a metallic amorphous coating. This study describes the mechanism by which the mechanical properties of the TiZrHfNiCuCo coating change according to sputtering parameters. Based on these results, the effects of sputter variables, such as input power and gas flow rate properties, on the properties of coatings are discussed.
Therapeutic Prospects of Abemaciclib for Patients with Endometrial Cancer
Authors: Awada Ahmad , Ahmad Sarfraz
Published: September 2024

Endometrial cancer (EC) is a common gynecologic malignancy with a rising incidence due to obesity, comorbid conditions, and related lifestyle factors. The standard of care for primary disease consists of surgical resection with/without chemotherapy ± radiotherapy for select patients. Recurrence is common in patients with advanced-stage disease and/or high-risk features, who primarily are treated with systemic therapy. The identification of novel targets in malignant EC has led to the development of wide-range inhibitors. Abemaciclib is an orally active unique cyclin-dependent kinase (CDK) inhibitor, selective for the CDK4 and CDK6 cell cycle pathways. This agent has potential anti-neoplastic activity and is indicated in combination with various therapies such as endocrine therapy, aromatase inhibitors, and hormone therapies, primarily in breast cancer (BC). Herein, we sought to summarize the biochemical/pharmacological properties of abemaciclib and its therapeutic potential in EC. While the therapeutic role(s) of abemaciclib was fairly established in a subset of patients with advanced/metastatic BC through the pivotal MONARCH trials, its attributes and clinical utility in EC are limited. Thus, based on some promising pre-clinical/translational insights and a recent phase II study, we highlight abemaciclib’s properties and potential clinical usefulness in patients with EC, particularly in recurrent estrogen-receptor-positive cases.
The Problematic Genus Liodesmus Wagner and a New Genus of Caturoidea (Halecomorphi, Neopterygii) from the Upper Jurassic Solnhofen-Archipelago
Authors: Ebert Martin , López-Arbarello Adriana
Published: September 2024

Our revision of the type material of Liodesmus led to significant adjustments in our understanding of the taxon when the original type specimen of Pholidophorus gracilis, which was later determined as the type species of the genus Liodesmus, was recently found in the collection in Berlin. It had been confused with a specimen that was described as the type specimen of the species Liodesmus gracilis in the collection in Munich. Moreover, the Munich specimen was mistakenly considered by all authors who have studied Liodesmus as a type specimen for the genus Liodesmus. However, the original holotype of Pholidophorus gracilis and this Munich specimen of Liodesmus gracilis belong to two different families. Caturus brevicostatus, whose holotype was recently found in Berlin as well, is described in detail here for the first time. Liodesmus sprattiformis is now described under the new genus Nasrinsotoudehichthys outside Caturidae but within the Caturoidea. Since all specimens that have been described so far under Liodesmus either belong to other, previously described genera, belong to new genera clearly outside the description of the genus Liodesmus, or are nomina dubia (including the type species Pholidophorus gracilis), the genus Liodesmus and the Liodesmidae are nomina dubia as well.
Effects of High-Intensity Training on Complete Blood Count, Iron Metabolism, Lipid Profile, Liver, and Kidney Function Tests of Professional Water Polo Players
Authors: Bukvić Frane , Marijančević Domagoj , Čičak Helena , Šimundić Ana-Maria , Pašalić Daria , Dukić Lora
Published: September 2024

AIM: Our goal was to examine the effect of high-intensity physical activity on changes in the lipid profile, complete blood count (CBC), iron metabolism, and kidney and liver function tests of professional water polo players. METHODS: This study included twenty professional male water polo players. Blood sampling was carried out at the beginning of the season and during periods of high-intensity training. CBCs were determined with a Siemens Advia 2120i hematology analyzer. A Beckman CoulterAU680 chemistry analyzer was used to determine the serum concentrations/activities of lipid profiles and liver and kidney function test analytes. The lipid athlete scores were also determined. RESULTS: The mean corpuscular volume (p = 0.006), platelet count (p = 0.008), and mean platelet volume (p < 0.001) significantly decreased during the high-intensity period, compared with the beginning of the season. The total iron-binding capacity increased (p = 0.001), and ferritin concentrations significantly declined (p = 0.017). The lipid profiles revealed a significant difference between phases, with slight increases in serum total (p = 0.025) and LDL cholesterol (p = 0.002) levels and a decrease in triglyceride concentrations (p = 0.040) in the high-intensity period. During the high-intensity period, the liver and kidney function tests showed a substantial positive effect on lactate dehydrogenase levels (p < 0.001), aspartate aminotransferase (p = 0.028) serum activity, and total protein concentrations (p = 0.033), compared with the beginning of the season. CONCLUSIONS: Water polo players might exhibit a decrease in some CBC parameters, an increase in LDL cholesterol, and a decrease in liver function biomarkers due to intense training at the peak of the competitive season. Kidney function biomarkers remain unchanged.
Prediction Models for Perioperative Blood Transfusion in Patients Undergoing Gynecologic Surgery: A Systematic Review
Authors: Pan Zhongmian , Charoenkwan Kittipat
Published: September 2024

This systematic review aimed to evaluate prediction models for perioperative blood transfusion in patients undergoing gynecologic surgery. Given the inherent risks associated with blood transfusion and the critical need for accurate prediction, this study identified and assessed models based on their development, validation, and predictive performance. The review included five studies encompassing various surgical procedures and approaches. Predicting factors commonly used across these models included preoperative hematocrit, race, surgical route, and uterine fibroid characteristics. However, the review highlighted significant variability in the definition of perioperative periods, a lack of standardization in transfusion criteria, and a high risk of bias in most models due to methodological issues, such as a low number of events per variable, inappropriate handling of continuous and categorical predictors, inappropriate handling of missing data, improper methods of predictor selection, inappropriate measurement methods for model performance, and inadequate evaluations of model overfitting and optimism in model performance. Despite some models demonstrating good discrimination and calibration, the overall quality and external validation of these models were limited. Consequently, there is a clear need for more robust and externally validated models to improve clinical decision-making and patient outcomes in gynecologic surgery. Future research should focus on refining these models, incorporating rigorous validation, and adhering to standardized reporting practices.
Applications and Challenges of DNA-Based Electrochemical Biosensors for Monitoring Health: A Systematic Review
Authors: Mondal Himadri Shekhar , Feng Yiwei , Biswas Gitisree , Hossain Md Zakir
Published: September 2024

DNA-based biosensors have emerged as cutting-edge tools with significant potential to revolutionize medical diagnostics and environmental monitoring. These biosensors leverage the specificity and sensitivity of DNA interactions to detect a wide range of biomolecular targets, making them ideal for early disease detection, genetic analysis, and real-time environmental assessment. Despite their promising applications, several challenges impede their widespread adoption. Key issues include the stability of DNA molecules, which are prone to degradation under environmental conditions, and the need for enhanced specificity and sensitivity to accurately detect target molecules in complex samples. Technological hurdles in miniaturizing and integrating these sensors into portable, user-friendly devices, along with ethical concerns regarding data privacy and the misuse of genetic information, also pose significant barriers. This systematic review examines the current state of DNA-based biosensor technology, highlights the main challenges, and discusses potential strategies to overcome these obstacles. By addressing these multifaceted issues through ongoing research and innovation, DNA-based biosensors can be developed into robust tools for various applications, contributing to improved public health outcomes and environmental sustainability.
Time Sequence Deep Learning Model for Ubiquitous Tabular Data with Unique 3D Tensors Manipulation
Authors: Gicic Adaleta , Đonko Dženana , Subasi Abdulhamit
Published: September 2024

Although deep learning (DL) algorithms have been proved to be effective in diverse research domains, their application in developing models for tabular data remains limited. Models trained on tabular data demonstrate higher efficacy using traditional machine learning models than DL models, which are largely attributed to the size and structure of tabular datasets and the specific application contexts in which they are utilized. Thus, the primary objective of this paper is to propose a method to use the supremacy of Stacked Bidirectional LSTM (Long Short-Term Memory) deep learning algorithms in pattern discovery incorporating tabular data with customized 3D tensor modeling in feeding neural networks. Our findings are empirically validated using six diverse, publicly available datasets each varying in size and learning objectives. This paper proves that the proposed model based on time-sequence DL algorithms, which were generally described as inadequate when dealing with tabular data, yields satisfactory results and competes effectively with other algorithms specifically designed for tabular data. An additional benefit of this approach is its ability to preserve simplicity while ensuring fast model training also with large datasets. Even with extremely small datasets, models can be applied to achieve exceptional predictive results and fully utilize their capacity.
Earmarking Taxation and Compliance: Some Evidence from Car Ownership in Italy
Authors: Barile Lory , Grossi Giulio , Lattarulo Patrizia , Pazienza Maria Grazia
Published: September 2024

This study focuses on tax evasion within the framework of earmarking taxation, specifically focusing on the evasion of car ownership taxes. We utilize a unique and extensive micro-database that combines information on regular payments of the tax due, late payments following friendly warnings, and non-payment of vehicle ownership taxes, integrated with fiscal data, individual data, and municipal-level data. The empirical analysis examines individual, socio-economic, and institutional factors related to this issue. Drawing a rich dataset from the 2014 Tuscany car tax, we employ a multilevel logistic model for our empirical investigation. Our findings reveal that tax evasion poses an equity problem, as the inclination to evade vehicle ownership taxes is concentrated among specific demographic categories and types of vehicles. We also suggest that regional-level policies, such as friendly warnings, could be more effective if implemented with greater rigour. Lastly, our results indicate that reinforcing civic responsibility and enhancing institutional and political quality could prove particularly beneficial in enhancing tax compliance.
PAIBoard: A Neuromorphic Computing Platform for Hybrid Neural Networks in Robot Dog Application
Authors: Chen Guang , Cao Jian , Zou Chenglong , Feng Shuo , Zhong Yi , Zhang Xing , Wang Yuan
Published: September 2024

Hybrid neural networks (HNNs), integrating the strengths of artificial neural networks (ANNs) and spiking neural networks (SNNs), provide a promising solution towards generic artificial intelligence. There is a prevailing trend towards designing unified SNN-ANN paradigm neuromorphic computing chips to support HNNs, but developing platforms to advance neuromorphic computing systems is equally essential. This paper presents the PAIBoard platform, which is designed to facilitate the implementation of HNNs. The platform comprises three main components: the upper computer, the communication module, and the neuromorphic computing chip. Both hardware and software performance measurements indicate that our platform achieves low power consumption, high energy efficiency and comparable task accuracy. Furthermore, PAIBoard is applied in a robot dog for tracking and obstacle avoidance system. The tracking module combines data from ultra-wide band (UWB) transceivers and vision, while the obstacle avoidance module utilizes depth information from an RGB-D camera, which further underscores the potential of our platform to tackle challenging tasks in real-world applications.
Byzantine-Robust Multimodal Federated Learning Framework for Intelligent Connected Vehicle
Authors: Wu Ning , Lin Xiaoming , Lu Jianbin , Zhang Fan , Chen Weidong , Tang Jianlin , Xiao Jing
Published: September 2024

In the rapidly advancing domain of Intelligent Connected Vehicles (ICVs), multimodal Federated Learning (FL) presents a powerful methodology to harness diverse data sources, such as sensors, cameras, and Vehicle-to-Everything (V2X) communications, without compromising data privacy. Despite its potential, the presence of Byzantine adversaries–malicious participants who contribute incorrect or misleading updates–poses a significant challenge to the robustness and reliability of the FL process. This paper proposes a Byzantine-robust multimodal FL framework specifically designed for ICVs. Our framework integrates a robust aggregation mechanism to mitigate the influence of adversarial updates, a multimodal fusion strategy to effectively manage and combine heterogeneous input data, and a global optimization objective that accommodates the presence of Byzantine clients. The theoretical foundation of the framework is established through formal definitions and equations, demonstrating its ability to maintain reliable and accurate learning outcomes despite adversarial disruptions. Extensive experiments highlight the framework’s efficacy in preserving model performance and resilience in real-world ICV environments.
VGGIncepNet: Enhancing Network Intrusion Detection and Network Security through Non-Image-to-Image Conversion and Deep Learning
Authors: Chen Jialong , Xiao Jingjing , Xu Jiaxin
Published: September 2024

This paper presents an innovative model, VGGIncepNet, which integrates non-image-to-image conversion techniques with deep learning modules, specifically VGG16 and Inception, aiming to enhance performance in network intrusion detection and IoT security analysis. By converting non-image data into image data, the model leverages the powerful feature extraction capabilities of convolutional neural networks, thereby improving the multi-class classification of network attacks. We conducted extensive experiments on the NSL-KDD and CICIoT2023 datasets, and the results demonstrate that VGGIncepNet outperforms existing models, including BERT, DistilBERT, XLNet, and T5, across evaluation metrics such as accuracy, precision, recall, and F1-Score. VGGIncepNet exhibits outstanding classification performance, particularly excelling in precision and F1-Score. The experimental results validate VGGIncepNet’s adaptability and robustness in complex network environments, providing an effective solution for the real-time detection of malicious activities in network systems. This study offers new methods and tools for network security and IoT security analysis, with broad application prospects.
Stage-by-Stage Adaptive Alignment Mechanism for Object Detection in Aerial Images
Authors: Zhu Jiangang , Jing Donglin , Gao Dapeng
Published: September 2024

Object detection in aerial images has had a broader range of applications in the past few years. Unlike the targets in the images of horizontal shooting, targets in aerial photos generally have arbitrary orientation, multi-scale, and a high aspect ratio. Existing methods often employ a classification backbone network to extract translation-equivariant features (TEFs) and utilize many predefined anchors to handle objects with diverse appearance variations. However, they encounter misalignment at three levels, spatial, feature, and task, during different detection stages. In this study, we propose a model called the Staged Adaptive Alignment Detector (SAADet) to solve these challenges. This method utilizes a Spatial Selection Adaptive Network (SSANet) to achieve spatial alignment of the convolution receptive field to the scale of the object by using a convolution sequence with an increasing dilation rate to capture the spatial context information of different ranges and evaluating this information through model dynamic weighting. After correcting the preset horizontal anchor to an oriented anchor, feature alignment is achieved through the alignment convolution guided by oriented anchor to align the backbone features with the object’s orientation. The decoupling of features using the Active Rotating Filter is performed to mitigate inconsistencies due to the sharing of backbone features in regression and classification tasks to accomplish task alignment. The experimental results show that SAADet achieves equilibrium in speed and accuracy on two aerial image datasets, HRSC2016 and UCAS-AOD.
Performance Analysis of Novel Direct-Condensation Heating Panels Integrated with Air Source Heat Pump System on Thermal Economy and System Efficiencies
Authors: Shao Suola , Xu Chengcheng
Published: September 2024

As an efficient heating terminal, direct-condensation terminals are increasingly being applied. This study proposes novel direct-condensation heating panels with different aspect ratios, which have been optimized in structure and reduced in copper tube length. To quantify their thermal superiority, the proposed panels’ thermal economic performance and system efficiencies are investigated. Compared with previous heating terminals, the vertical temperature difference provided by the proposed panels is reduced by 1.1 ± 0.2 °C, improving thermal comfort. Meanwhile, the system COP is competitive among existing direct-condensation heating terminals. The average heating capacity per cost of the proposed panels reaches 5.4 W/USD, which is 24.1%~46.3% higher than that of previous panels. In addition, the impact of the aspect ratio shows that the panel with a high ratio has advantages in thermal economic performance and system efficiencies. The annual running cost and the system task efficiency of the panel with a ratio of 2.22 are 74.81 USD and 48.5%, respectively, which are 4.7% lower and 0.9% higher than that with a ratio of 0.45, respectively. The developed direct-condensation heating panels help to optimize the heating technology of air source heat pumps, while the evaluation methodology and aspect ratio results are suitable for optimizing other heating terminals.
Essential Features and Torque Minimization Techniques for Brushless Direct Current Motor Controllers in Electric Vehicles
Authors: Tabassum Arti Aniqa , Cho Haeng Muk , Mahmud Md. Iqbal
Published: September 2024

The use of electric automobiles, or EVs, is essential to environmentally conscious transportation. Battery EVs (BEVs) are predicted to become increasingly accepted for passenger vehicle transportation within the next 10 years. Although enthusiasm for EVs for environmentally friendly transportation is on the rise, there remain significant concerns and unanswered research concerns regarding the possible future of EV power transmission. Numerous motor drive control algorithms struggle to deliver efficient management when ripples in torque minimization and improved dependability control approaches in motors are taken into account. Control techniques involving direct torque control (DTC), field orientation control (FOC), sliding mode control (SMC), intelligent control (IC), and model predictive control (MPC) are implemented in electric motor drive control algorithms to successfully deal with this problem. The present study analyses only sophisticated control strategies for frequently utilized EV motors, such as the brushless direct current (BLDC) motor, and possible solutions to reduce torque fluctuations. This study additionally explores the history of EV motors, the operational method between EM and PEC, and EV motor design techniques and development. The future prospects for EV design include a vital selection of motors and control approaches for lowering torque ripple, as well as additional research possibilities to improve EV functionality.
A Power-RPM Reduced-Order Model and Power Control Strategy of the Dual Three-Phase Permanent Magnet Synchronous Motor in a V/f Framework for Oscillation Suppression
Authors: Su Riqing , Wang Yuanze , Deng Hui , Liu Xiong , Guan Yuanpeng
Published: September 2024

The dual three-phase permanent magnet synchronous motor (DTP-PMSM) under a V/f control framework is widely applied in belts, fans, pumps, etc. However, the oscillation in power and rotor speed is difficult to quantify and suppress, due to the higher-order model of the DTP-PMSM. Thus, a power-revolutions per minute (RPM) reduced-order model and power control strategy of the DTP–PMSM are proposed for oscillation description and suppression. Firstly, according to the structure and V/f control framework, the reduced-order model is proposed under a power-RPM scale with coupled performances between sub-PMSMs, and then the decoupled method is employed. Moreover, the oscillated performances of power and rotor speed are detailed in small signals. Secondly, a power control strategy is proposed, including active power feedforward for active damping and reactive power droop control for high power quality and approaching optimal torque per ampere. Compared with the traditional strategies, the proposed method can achieve a stable and efficient operation, with a higher power factor of the DTP–PMSM, less stator current, and lower electromechanical power loss. Finally, an experimental platform of the DTP–PMSM is set up for the correctness and superiority of the proposed method.
Discrete-Time Sliding Mode Control Strategies—State of the Art
Authors: Latosiński Paweł , Adamiak Katarzyna
Published: September 2024

Variable structure control systems are known to provide a high level of robustness to external disturbances and modeling uncertainties with comparably low computational complexity. Thanks to these features, they have found applications in various fields, such as power engineering, electronics, robotics, and aviation. In recent decades, the field of sliding mode control has developed significantly. Therefore, this study aims to discuss the basic concepts and design methodology of such strategies. Although in the 20th century, continuous-time sliding mode control has been the center of the control engineering society’s attention, it has certain major shortcomings. In particular, such control schemes result in undesirable high-frequency oscillations when applied digitally. Therefore, the more recent discrete-time approach to sliding mode control has gained recognition in the 21st century. Since the introduction of discrete-time sliding mode control strategies, the reaching law-based controller design method has been designed, within which two main paradigms may be named: the switching type and the nonswitching type quasi-sliding mode. This paper presents a broad review of the discrete-time sliding mode control strategies, starting from the definition of sliding mode through the controller design procedures and up to potential applications. The aim of this study is to provide an up-to-date state of the art and introduce readers to the newest trends and achievements in the field of sliding mode control.
Key Technologies for the Efficient Development of Thick and Complex Carbonate Reservoirs in the Middle East
Authors: Tong Kaijun , He Juan , Chen Peiyuan , Li Changyong , Dai Weihua , Sun Futing , Tong Yi , Rao Su , Wang Jing
Published: September 2024

In order to enhance the development efficiency of thick and complex carbonate reservoirs in the Middle East, a case study was conducted on M oilfield in Iraq. This study focused on reservoir characterization, injection-production modes, well pattern optimization, and other related topics. As a result, key techniques for the high-efficiency development of thick carbonate reservoirs were established. The research findings include the following: (1) the discovery of hidden “low-velocity” features within the thick gypsum-salt layer, which led to the development of a new seismic velocity model; (2) the differential dissolution of grain-supported limestones is controlled by lithofacies and petrophysical properties, resulting in the occurrence of “porphyritic” phenomena in core sections. The genetic mechanism responsible for reversing petrophysical properties in dolostones is attributed to “big hole filling and small hole preservation” caused by dense brine refluxing; (3) fracture evaluation technology based on anisotropy and dipole shear wave long-distance imaging was developed to address challenges associated with quantitatively assessing micro-fractures; (4) through large-scale three-dimensional physical models and numerical simulations, it was revealed that water–oil displacement mechanisms involving “horizontal breakthrough via hyper-permeability” combined with vertical differentiation due to gravity occur in thick and heterogeneous reservoirs under spatial injection-production modes; (5) a relationship model linking economic profit with well pattern density was established for technical service contracts in the Middle East. Additionally, an innovative stepwise conversion composite well patterns approach was introduced for thick reservoirs to meet production ramp-up requirements while delaying water cut rise; (6) a prediction technology for the oilfield development index, considering asphaltene precipitation, has been successfully developed. These research findings provide robust support for the efficient development of the M oilfield in Iraq, while also serving as a valuable reference for similar reservoirs’ development in the Middle East.
A Literature Review of the Design, Modeling, Optimization, and Control of Electro-Mechanical Inlet Valves for Gas Expanders
Authors: Hossain Md Shazzad , Sultan Ibrahim , Phung Truong , Kumar Apurv
Published: September 2024

The ever-growing concerns about global warming and the rapid depletion of fossil fuels have triggered a rising interest in the research for cleaner, more efficient, and cost-effective energy generation. Organic Rankine cycle systems have immense potential to become a strong alternative to conventional methods of energy generation. However, such systems’ efficiency is limited by the performance of the incorporated gas expanders or prime movers. Conventional gas expanders often utilize ports or cam-operated valves. Ported expanders offer limited efficiency and controllability and produce high emissions, causing a large amount of heated and compressed gas to be wasted. Cam-operated valves, in contrast, increase expansion performance compared to ported expanders; however, they have no variability and adaptability to changes in system parameters such as gas pressure, temperature, dryness fraction, and load variation, to name a few. On a positive note, this issue can be addressed by adopting a variable timing and fast-operating, control valve with an accurate and adaptive control mechanism at the expander inlet. A properly designed and controlled valve can greatly improve the system performance of gas expanders and pave the way for an efficient low-cost alternative energy generation. This manuscript provides a comparative review of recent progress on the design, modeling, optimization, and control aspects of valves for gas expanders. A clear pathway on the scope of further development is also drawn based on the present state of the art.
A Semi-Supervised Approach for Partial Discharge Recognition Combining Graph Convolutional Network and Virtual Adversarial Training
Authors: Zhang Yi , Yu Yang , Zhang Yingying , Liu Zehuan , Zhang Mingjia
Published: September 2024

With the digital transformation of the grid, partial discharge (PD) recognition using deep learning (DL) and big data has become essential for intelligent transformer upgrades. However, labeling on-site PD data poses challenges, even necessitating the removal of covers for internal examination, which makes it difficult to train DL models. To reduce the reliance of DL models on labeled PD data, this study proposes a semi-supervised approach for PD fault recognition by combining the graph convolutional network (GCN) and virtual adversarial training (VAT). The approach introduces a novel PD graph signal to effectively utilize phase-resolved partial discharge (PRPD) information by integrating numerical data and region correlations of PRPD. Then, GCN autonomously extracts features from PD graph signals and identifies fault types, while VAT learns from unlabeled PD samples and improves the robustness during training. The approach is validated using test and on-site data. The results show that the approach significantly reduces the demand for labeled samples and that its PD recognition rates have increased by 6.14% to 14.72% compared with traditional approaches, which helps to reduce the time and labor costs of manually labeling on-site PD faults.
Partial Discharge Inception Voltage Monitoring of Enameled Wires under Thermal Stress over Time
Authors: Khan Ishtiaq , Guastavino Francesco , Della Giovanna Laura , Torello Eugenia
Published: September 2024

Electrical insulation is a critical component in electrical machines. The performance of the insulation system can be adversely affected by operating conditions that induce aging. Assessing the impact of environmental stresses is essential for predicting the failure of electrical insulation. Predicting maintenance to prevent service interruptions caused by insulation breakdown is a key objective. For type I insulating systems used in low-voltage and low-power rotating electrical machines, it has been demonstrated that partial discharges (PDs) are a contributing factor to electrical insulation breakdown. In fact, these insulating systems are not able to withstand the action of PD activity. The inception and evolution of PD activity is an indication of the poor conditions of the electrical insulating system, and this activity can be produced by the electronic power supply. The progressive reduction in partial discharge inception voltage (PDIV) is attributed to the deterioration of insulation properties induced by operational stresses. This study aims to evaluate and compare the effects of thermal stress on various types of enameled wires by collecting the PDIV values over time. In this paper, the authors analyze some particular effects of thermal stress as an aging factor. During the tests, an electrical stress was applied, which acted as a conditioning stress rather than one capable of producing degradation phenomena, as it was not high enough to initiate PD activity. In this research study, twisted pairs prepared from copper wires were evaluated. These wires were coated with various types of enamel and belonged to the thermal class of 200 °C. The samples were subjected to thermal aging tests at different temperatures. An electrical conditioning stress was also applied during all the tests and pertained to the same voltage, amplitude and frequency. The PDIV value pertaining to each sample was regularly measured to monitor its evolution over time.
Modeling of Geothermal Energy Recovery from a Depleted Gas Reservoir: A Case Study
Authors: Szott Wiesław , Ruciński Piotr , Łętkowski Piotr , Szpunar Tadeusz , Majkrzak Marcin , Siuda Tomasz , Wojtowicz Robert
Published: September 2024

This paper addresses the problem of the geothermal energy generation process in a depleted gas reservoir with a specific enhanced geothermal system, applying CO2 as an energy transporting medium. Constructed models of the system components are used to perform coupled and dynamic simulation forecasts, taking into account the interdependence of the individual system elements operating in a cyclical fluid flow and the continuous changes in temperature, pressure, and the composition of circulating fluids. The simulation procedure of the geothermal energy generation process is applied to the realistic example of a depleted gas reservoir located in Foresudetic Monocline, Poland. The simulation results are presented in detail and discussed with several conclusions of both case-specific and general characters. Three phases of the energy recovery process can be distinguished, varying in the produced fluid composition and the evolution of the fluid temperature. These phases result in the corresponding behavior of the produced stream power: increasing, stable, and decreasing for the three phases, respectively. Other significant results of the simulation forecasts are also discussed and concluded. In general, the complexity of the obtained results proves the necessity to apply the system’s detailed modeling and simulations to reliably plan and realize a geothermal energy generation project.
Unpacking the Carbon Balance: Biochar Production from Forest Residues and Its Impact on Sustainability
Authors: Voccia Diego , Lamastra Lucrezia
Published: September 2024

Climate change demands urgent action to limit greenhouse gas (GHG) emissions and explore methods for atmospheric carbon removal. Forest residues, a significant biomass resource, represent a readily available solution. With the use of life cycle assessment (LCA), this study investigates the environmental advantages of thermochemical processes utilizing forest residues to produce valuable energy-dense products, like syngas, bio-oil, and biochar, providing a carbon sink. While slow pyrolysis emphasizes biochar production for carbon sequestration, gasification focuses on bioenergy generation. This research evaluates the environmental performance of both techniques in terms of carbon sequestration and GHG emissions mitigation. This comprehensive analysis aims to identify critical factors influencing the environmental sustainability of thermochemical processes in forest management. The findings aim to contribute to achieving sustainable development goals by highlighting the environmental advantages of biochar in mitigating climate change.
Installation Design and Efficiency Evaluation of an EV Transform Powertrain and a 3.3 kW Multi-Charging System Driven by a 30 kW Permanent-Magnet Synchronous Motor
Authors: Techalimsakul Pataphiphat , Niyomphol Arnon
Published: September 2024

This study focuses on the transformation of Jaguar XJ40 vehicles to electric power, with the main equipment being a permanent-magnet synchronous motor (PMSM), lithium iron phosphate (LFP) batteries, an on-board charger (OBC) system, and a battery management system (BMS). The process involves integrating the PMSM with the vehicle’s existing transmission system. This research compares the driving range of battery electric vehicles (BEVs) using different testing methods under the same conditions: simulation, dynamometer (dino), and actual on-road testing. Based on Raminthra’s public roads (RITA drive cycle), one drive cycle covers 7.64 km in 11.25 min. The simulation test by MATLAB/SIMULINK R2016a predicts a driving distance of up to 282.14 km. The dino test, using a chassis dynamometer to simulate driving conditions while the vehicle remains stationary, indicates a driving distance of 264.68 km. In contrast, actual on-road tests show a driving distance of 259.09 km, accounting for real-world driving conditions, including variations in speed, road types, weather, and traffic. The motor achieves 95% efficiency at 2400 rpm and 420 Nm torque. The simulated distance differs from the actual road distance by approximately 8.17%, suggesting reasonable accuracy of the model.
Electromagnetic and Mechanical Stress Analysis of a 5 MW High-Pole Non-Overlap Winding Wound-Rotor Synchronous Wind Generator
Authors: Garner Karen S. , Akuru Udochukwu B.
Published: September 2024

Utilizing non-overlap windings has emerged as a favourable choice for minimizing electrical machine manufacturing costs, among other benefits. Nevertheless, it is widely acknowledged that these windings exhibit a notable level of harmonic contents in the resultant magnetomotive force, which can detrimentally impact machine performance, particularly in terms of torque ripple. In the context of wind energy conversion, maintaining low torque ripple is an essential and demanding prerequisite. Medium-speed wind generators present a good trade-off between high energy yield and low gearbox ratios. So far, medium-speed non-overlap winding wound-rotor synchronous generator (WRSG) technologies have been limited to 10/12 and the less common 16/18 pole/slot combinations. In this study, the analysis of a high-pole number combination (24/27 pole/slots) non-overlap WRSG is carried out to theoretically and comparatively predict the electromagnetic and radial force mechanical stress performance analysis with the 16/18 machine with a phase-shifted non-overlap winding (PSW), at 5 MW power level. The study, which is founded on the finite element analysis (FEA) technique, shows that the 24/27 machine exhibits comparable average torque and torque ripple, lower core losses and significantly reduced radial forces compared to the 16/18 PSW-WRSG. However, the 16/18 PSW-WRSG has a 50% reduction in the radial forces compared to the conventional 16/18 non-overlap winding. Experimental vibration analysis of a 3 kW 16/18 WRSG test machine confirms the radial force and vibration reduction in the phase-shifted winding.
Simulation of Battery Thermal Management System for Large Maritime Electric Ship’s Battery Pack
Authors: Jia Fu , Lee Geesoo
Published: September 2024

In recent years, large power batteries have been widely used not only in automobiles and other vehicles but also in maritime vessels. The thermal uniformity of large marine battery packs significantly affects the performance, safety, and longevity of the electric ship. As a result, the thermal management of large power batteries has become a crucial technical challenge with traditional battery management system (BMS) that cannot effectively solve the battery heating problem caused by electrochemical reactions and joule heating during operation. To address this gap, a battery thermal management system (BTMS) has been newly designed. This article presents the design of a large marine battery pack, which features a liquid cooling system integrated into both the bottom and side plates of each pack. The flow plate is constructed from five independent units, each connected by manifold structures at both ends. These connections ensure the formation of a stable and cohesive flow plate assembly. Although research on the BTMS is relatively advanced, there is a notable lack of studies examining the effects of liquid temperature, flow rate, and battery discharge rate on the temperature consistency and uniformity of large marine battery packs. This work seeks to design the cooling system for the battery pack and analyzes the impact of the temperature, flow rate, and battery discharge rate of the liquid fluid on the consistency and uniformity of the battery pack temperature on the overall structure of the battery pack. It was found that, in low discharge conditions, there was good temperature consistency between the battery packs and between the different batteries within the battery pack, and the temperature difference did not exceed 1 °C. However, under high discharge rates, a C-rate of 4C, there might have been a decrease in temperature consistency; the temperature rise rate even exceeded 50% compared to when the discharge rate was low. The flow rate in the liquid flow characteristics had little effect on the temperature consistency between the batteries and the temperature uniformity on the battery surface, and the temperature fluctuation was maintained within 1 °C. Conversely, the liquid flow temperature had little effect on the temperature distribution between the batteries, but it caused discrepancies in the surface temperature of the batteries. In addition, the liquid flow temperature could cause the overall temperature of the battery to increase or decrease, which also occurs under different discharge rates.
Techno-Economic Feasibility Analysis of an Offshore Wave Power Facility in the Aegean Sea, Greece
Authors: Pompodakis Evangelos E. , Orfanoudakis Georgios I. , Katsigiannis Yiannis , Karapidakis Emmanouel
Published: September 2024

The decarbonization goals of each country necessitate the utilization of renewable resources, with photovoltaic (PV) and wind turbine (WT) generators being the most common forms. However, spatial constraints, especially on islands, can hinder the expansion of PV and WT installations. In this context, wave energy emerges as a viable supplementary renewable source. Islands are candidate regions to accommodate wave power resources due to their abundant wave potential. While previous studies have explored the wave energy potential of the Aegean Sea, they have not focused on the electricity production and techno-economic aspects of wave power facilities in this area. This paper aims to fill this knowledge gap by conducting a comprehensive techno-economic analysis to evaluate the feasibility of deploying an offshore wave power facility in the Aegean Sea, Greece. The analysis includes a detailed sensitivity assessment of CAPEX and OPEX variability, calculating key indicators like LCOE and NPV to determine the economic viability and profitability of wave energy investments in the region. Additionally, the study identifies hydraulic efficiency and CAPEX thresholds that could make wave power more competitive compared with traditional energy sources. The techno-economic analysis is conducted for a 45 MW offshore floating wave power plant situated between eastern Crete and Kasos—one of the most wave-rich areas in Greece. Despite eastern Crete’s promising wave conditions, the study reveals that with current techno-economic parameters—CAPEX of 7 million EUR/MW, OPEX of 6%, a 20-year lifetime, and 25% efficiency—the wave energy in this area yields a levelized cost of energy (LCOE) of 1417 EUR/MWh. This rate is significantly higher than the prevailing LCOE in Crete, which is between 237 and 300 EUR/MWh. Nonetheless, this study suggests that the LCOE of wave energy in Crete could potentially decrease to as low as 69 EUR/MWh in the future under improved conditions, including a CAPEX of 1 million EUR/MW, an OPEX of 1%, a 30-year lifetime, and 35% hydraulic efficiency for wave converters. It is recommended that manufacturing companies target these specific thresholds to ensure the economic viability of wave power in the waters of the Aegean Sea.
Climate Change Adaptation through Renewable Energy: The Cases of Australia, Canada, and the United Kingdom
Authors: Eitan Avri
Published: September 2024

In recent years, climate change has escalated significantly, with forecasts indicating that this trend will further accelerate in the future. Renewable energy systems play a crucial role in global efforts to mitigate climate change due to their minimal greenhouse gas emissions. These systems also have the potential to facilitate the energy sector’s adaptation to climate change, given their decentralized nature, which enhances the resilience of energy infrastructure to extreme climate events. Nevertheless, existing literature predominantly focuses on their role in global mitigation efforts, often overlooking their significant adaptation capacity, particularly as reflected in national policies. This study seeks to bridge this gap through a qualitative examination of how renewable energy is incorporated into climate change adaptation policies in three countries: Australia, Canada, and the United Kingdom. It highlights a growing awareness of the role of renewable energy within these countries’ adaptation policies. However, while there is consensus on the importance of policy factors such as local focus, research initiatives, and risk assessment in utilizing renewable energy for adaptation, this study reveals that the actual deployment of renewable energy remains largely centered on mitigation efforts, partly neglecting crucial adaptation needs in the energy sector, such as geographical and technological diversification.
Predicted Responses of Genetically Improved Populations to Climate Changes Based on Second-Cycle Douglas-Fir Progeny Tests
Authors: Ye Terrance Z. , Jayawickrama Keith J. S.
Published: September 2024

The current planting of economically important timber species, such as Douglas-fir, mainly relies on genetically improved seeds from seed orchards. However, published research on the effects of climate change has largely focused on natural populations. To bridge this gap, data from 80 cooperative second-cycle coastal Douglas-fir progeny tests across eight breeding zones in western Washington and Oregon were analyzed. Climate transfer functions for age-12 growth were derived, showing significant results for the US Pacific Northwest. Region-specific transfer functions (Coast, Inland, and Cascade) displayed stronger correlations. Mean annual temperature and mean coldest month temperature were the most important climatic variables explaining growth. The study found that populations from slightly warmer areas tended to grow better but moving populations from colder to warmer areas by 2 °C (analogous to projected global warming) would result in an 8% genetic loss in age-12 height and a 25% genetic loss in age-12 volume. However, substantial diversity in climatic response was found among full-sib families within large breeding zones, suggesting that breeding and selecting suitable families for future climatic conditions within breeding zones is feasible. The study discusses potential strategies to adapt current breeding programs to address the impacts of future climate change while maintaining high population growth rates in Douglas-fir breeding programs.