bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review
Authors: Papalia Giuseppe Francesco , Brigato Paolo , Sisca Luisana , Maltese Girolamo , Faiella Eliodoro , Santucci Domiziana , Pantano Francesco , Vincenzi Bruno , Tonini Giuseppe , Papalia Rocco , Denaro Vincenzo
Published: July 2024

Background: Metastasis commonly occur in the bone tissue. Artificial intelligence (AI) has become increasingly prevalent in the medical sector as support in decision-making, diagnosis, and treatment processes. The objective of this systematic review was to assess the reliability of AI systems in clinical, radiological, and pathological aspects of bone metastases. Methods: We included studies that evaluated the use of AI applications in patients affected by bone metastases. Two reviewers performed a digital search on 31 December 2023 on PubMed, Scopus, and Cochrane library and extracted authors, AI method, interest area, main modalities used, and main objectives from the included studies. Results: We included 59 studies that analyzed the contribution of computational intelligence in diagnosing or forecasting outcomes in patients with bone metastasis. Six studies were specific for spine metastasis. The study involved nuclear medicine (44.1%), clinical research (28.8%), radiology (20.4%), or molecular biology (6.8%). When a primary tumor was reported, prostate cancer was the most common, followed by lung, breast, and kidney. Conclusions: Appropriately trained AI models may be very useful in merging information to achieve an overall improved diagnostic accuracy and treatment for metastasis in the bone. Nevertheless, there are still concerns with the use of AI systems in medical settings. Ethical considerations and legal issues must be addressed to facilitate the safe and regulated adoption of AI technologies. The limitations of the study comprise a stronger emphasis on early detection rather than tumor management and prognosis as well as a high heterogeneity for type of tumor, AI technology and radiological techniques, pathology, or laboratory samples involved.
Garcinia mangostana L. Leaf-Extract-Assisted Green Synthesis of CuO, ZnO and CuO-ZnO Nanomaterials for the Photocatalytic Degradation of Palm Oil Mill Effluent (POME)
Authors: Chan Yu Bin , Aminuzzaman Mohammod , Win Yip Foo , Djearamane Sinouvassane , Wong Ling Shing , Guha Samar Kumar , Almohammadi Hamad , Akhtaruzzaman Md. , Tey Lai-Hock
Published: July 2024

The treatment of palm oil mill effluent (POME) poses a significant challenge for Malaysia’s palm oil industry, necessitating compliance with the Department of Environment (DOE) regulations prior to discharge. This study introduces an eco-friendly synthesis method utilizing mangosteen (Garcinia mangostana L.)-leaf aqueous extract to fabricate copper oxide (CuO), zinc oxide (ZnO) nanoparticles (NPs), and their nanocomposite (CuO-ZnO NCs). The physicochemical properties of these nanomaterials were characterized using various analytical tools and their effectiveness in reducing the chemical oxygen demand (COD) of palm oil mill effluent (POME) was assessed under the illumination of two types of light sources: monochromatic blue- and polychromatic white-light emitting diodes (LEDs). CuO-ZnO NCs demonstrated superior performance, with the lowest energy bandgap (1.61 eV), and achieved a COD removal efficiency of 63.27% ± 0.010 under blue LED illumination, surpassing the DOE’s discharge limit of 100 mg/L. This study offers a cost-effective and environmentally friendly method for synthesizing heterojunction materials, which show great potential as photocatalysts in reducing POME COD to permissible levels for discharge.
Histopathological and Ultrastructural Study of a Canine Langerhans Cell Tumour (Canine Cutaneous Histiocytoma)
Authors: Pires Isabel , Rodrigues Paula , Alves Anabela , Silva Filipe , Lopes Carlos
Published: July 2024

Canine cutaneous histiocytoma (CCH) represents a significant proportion of dog skin tumours, often manifesting as the most common neoplastic skin condition in young animals. Predominantly affecting dogs under four, these tumours appear primarily as solitary lesions that may regress spontaneously. This study, conducted over five years at the University of Trás-os-Montes e Alto Douro, involved a detailed histopathological and ultrastructural examination of 93 CCH cases. Histologically, these tumours showed distinct patterns of lymphoid infiltration, which contributed to their classification into four groups based on the inflammatory response and histological architecture. Most tumours displayed signs of epidermal invasion and frequent mitotic figures, with necrosis present in over half of the cases. Ultrastructurally, the neoplastic cells were characterised by pleomorphism, abundant organelles, and adherens-type junctions. This study offers significant insights into the pathophysiology and morphological characteristics of CCH, underscoring the importance of detailed histological and ultrastructural analysis in accurately diagnosing and understanding this common canine tumour.
In Vivo Approaches to Understand Arrhythmogenic Cardiomyopathy: Perspectives on Animal Models
Authors: Risato Giovanni , Brañas Casas Raquel , Cason Marco , Bueno Marinas Maria , Pinci Serena , De Gaspari Monica , Visentin Silvia , Rizzo Stefania , Thiene Gaetano , Basso Cristina , Pilichou Kalliopi , Tiso Natascia , Celeghin Rudy
Published: July 2024

Arrhythmogenic cardiomyopathy (AC) is a hereditary cardiac disorder characterized by the gradual replacement of cardiomyocytes with fibrous and adipose tissue, leading to ventricular wall thinning, chamber dilation, arrhythmias, and sudden cardiac death. Despite advances in treatment, disease management remains challenging. Animal models, particularly mice and zebrafish, have become invaluable tools for understanding AC’s pathophysiology and testing potential therapies. Mice models, although useful for scientific research, cannot fully replicate the complexity of the human AC. However, they have provided valuable insights into gene involvement, signalling pathways, and disease progression. Zebrafish offer a promising alternative to mammalian models, despite the phylogenetic distance, due to their economic and genetic advantages. By combining animal models with in vitro studies, researchers can comprehensively understand AC, paving the way for more effective treatments and interventions for patients and improving their quality of life and prognosis.
A Survey of Changes in Grasslands within the Tonle Sap Lake Landscape from 2004 to 2023
Authors: Chea Monysocheata , Fraser Benjamin T. , Nay Sonsak , Sok Lyan , Strasser Hillary , Tizard Rob
Published: July 2024

The Tonle Sap Lake (TSL) landscape is a region of vast natural resources and biological diversity in the heart of Southeast Asia. In addition to serving as the foundation for a highly productive fisheries system, this landscape is home to numerous globally threatened species. Despite decades of recognition by several government and international agencies and the fact that nine protected areas have been established within this region, natural land cover such as grasslands have experienced considerable decline since the turn of the century. This project used local expert knowledge to train and validate a random forest supervised classification of Landsat satellite imagery using Google Earth Engine. The time series of thematic maps were then used to quantify the conversion of grasslands to croplands between 2004 and 2023. The classification encompassed a 10 km buffer surrounding the landscape, an area of nearly 3 million hectares. The average overall accuracy for these thematic maps was 82.5% (78.5–87.9%), with grasslands averaging 76.1% user’s accuracy. The change detection indicated that over 207,281 ha of grasslands were lost over this period (>59.5% of the 2004 area), with approx. 89.3% of this loss being attributed to cropland expansion. The results of this project will inform conservation efforts focused on local-scale planning and the management of commercial agriculture.
Enhanced Pancreatitis Detection: FAPI PET/CT Emerging Utility in Patient with Pancreatic Mass
Authors: Abi Ghanem Alain , El Annan Tamara , El Ghawi Nour , Omran Nadine , Natout Mustafa , Assi Hazem , Shamseddine Ali , Saliba Nina , Haidar Mohamad
Published: July 2024

Fibroblast Activation Protein Inhibitor (FAPI) positron emission tomography (PET) imaging has emerged as a useful method for identifying pancreatic disorders, notably pancreatitis. Unlike Fluorine-18 fluorodeoxyglucose (FDG), FAPI uptake is directly proportional to the degree of fibrosis, making it very useful in separating pancreatic tumors from inflammation. Recent investigations have shown that FAPI positron emission tomography/computer tomography (PET/CT) can identify pancreatic inflammation with great sensitivity, providing vital diagnostic information. In this case study, a 52-year-old male with a history of Ewing sarcoma presented with epigastric pain. Pancreatitis was confirmed on a computer tomography (CT) scan showing mild fat stranding in the pancreatic body and tail, in addition to a significant increase in pancreatic head mass, necessitating further evaluation with FDG PET/CT and FAPI PET/CT, as the patient was known to have metastatic sarcoma. While FDG PET/CT revealed an avid infiltrative lesion in the duodenal/pancreatic head area, FAPI PET/CT showed diffuse uptake in the pancreatic body and tail, indicating fibroblast-mediated inflammation consistent with pancreatitis. This case demonstrates the usefulness of FAPI imaging in discriminating between pancreatic metastasis and pancreatitis, with FAPI PET/CT providing crucial diagnostic information when FDG uptake is ambiguous.
Obstructive Tracheobronchitis in Influenza-Associated Pulmonary Aspergillosis
Authors: Zhang Shuyi , Shang Chen , Tan Zhangjun , Li Wen
Published: July 2024

We report a bronchoscopic image of a 36-year-old with significant airway obstruction from obstructive tracheobronchitis secondary to invasive pulmonary aspergillosis. It is rare to see such a severe form of obstructive tracheobronchitis, likely caused by the patient’sp immunocompromised status and rapid progression nature of influenza-associated pulmonary aspergillosis.
Multiple Object Tracking in Drone Aerial Videos by a Holistic Transformer and Multiple Feature Trajectory Matching Pattern
Authors: Yuan Yubin , Wu Yiquan , Zhao Langyue , Pang Yaxuan , Liu Yuqi
Published: July 2024

Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joint detection and tracking methods often compromise accuracy. We propose a drone multiple object tracking algorithm based on a holistic transformer and multiple feature trajectory matching pattern to overcome these challenges. The holistic transformer captures local and global interaction information, providing precise detection and appearance features for tracking. The tracker includes three components: preprocessing, trajectory prediction, and matching. Preprocessing categorizes detection boxes based on scores, with each category adopting specific matching rules. Trajectory prediction employs the visual Gaussian mixture probability hypothesis density method to integrate visual detection results to forecast object motion accurately. The multiple feature pattern introduces Gaussian, Appearance, and Optimal subpattern assignment distances for different detection box types (GAO trajectory matching pattern) in the data association process, enhancing tracking robustness. We perform comparative validations on the vision-meets-drone (VisDrone) and the unmanned aerial vehicle benchmarks; the object detection and tracking (UAVDT) datasets affirm the algorithm’s effectiveness: it obtained 38.8% and 61.7% MOTA, respectively. Its potential for seamless integration into practical engineering applications offers enhanced situational awareness and operational efficiency in drone-based missions.
Finite Time-Adaptive Full-State Quantitative Control of Quadrotor Aircraft and QDrone Experimental Platform Verification
Authors: Li He , Luo Peng , Li Zhiwei , Zhu Guoqiang , Zhang Xiuyu
Published: July 2024

This paper proposes a novel adaptive finite-time controller for a quadrotor unmanned aerial vehicle (UAV) model with stochastic perturbations and parameter-unknown terms, under the constraints of a state-constrained system. The controller is designed based on full-state quantization, where the error system is defined to be a function of the quantized error signal. An adaptive method is employed to address the quadrotor UAV system model with nonlinear terms and unknown perturbations. The controller utilizes Barrier Lyapunov function (BLF) bounds with adaptive effective time performance to ensure full-state constraint of the system. The stability of the system is proven using Lyapunov’s stability theorem. The effectiveness of the designed full-state constrained controller for quadrotor UAV based on full-state quantization is verified through a physical experimental simulation platform.
Practical Performance Analysis of MDI-QKD with Orbital Angular Momentum on UAV Relay Platform
Authors: Wu Dan , Li Jiahao , Yang Lan , Deng Zhifeng , Tang Jie , Cao Yuexiang , Liu Ying , Hu Haoran , Wang Ya , Yu Huicun , Wei Jiahua , Lun Huazhi , Wang Xingyu , Shi Lei
Published: July 2024

The integration of terrestrial- and satellite-based quantum key distribution (QKD) experiments has markedly advanced global-scale quantum networks, showcasing the growing maturity of quantum technologies. Notably, the use of unmanned aerial vehicles (UAVs) as relay nodes has emerged as a promising method to overcome the inherent limitations of fiber-based and low-Earth orbit (LEO) satellite connections. This paper introduces a protocol for measurement-device-independent QKD (MDI-QKD) using photon orbital angular momentum (OAM) encoding, with UAVs as relay platforms. Leveraging UAV mobility, the protocol establishes a secure and efficient link, mitigating threats from untrusted UAVs. Photon OAM encoding addresses reference frame alignment issues exacerbated by UAV jitter. A comprehensive analysis of atmospheric turbulence, state-dependent diffraction (SDD), weather visibility, and pointing errors on free-space OAM-state transmission systems was conducted. This analysis elucidates the relationship between the key generation rate and propagation distance for the proposed protocol. Results indicate that considering SDD significantly decreases the key rate, halving previous data results. Furthermore, the study identifies a maximum channel loss capacity of 26 dB for the UAV relay platform. This result is pivotal in setting realistic parameters for the deployment of UAV-based quantum communications and lays the foundation for practical implementation strategies in the field.
A Local Path Planning Algorithm for Robots Based on Improved DWA
Authors: Gong Xue , Gao Yefei , Wang Fangbin , Zhu Darong , Zhao Weisong , Wang Feng , Liu Yanli
Published: July 2024

In order to solve the problem whereby the original DWA algorithm cannot balance safety and velocity due to fixed parameters in complex environments with many obstacles, an improved dynamic window approach (DWA) of local obstacle avoidance for robots is proposed. Firstly, to assure the path selection stationarity and enhance the navigation ability of inspection robot, the velocity cost function of the original DWA was improved and the distance cost function of the target point was added. Then, the distances among the inspection robot, observed obstacles, and target points were input into a fuzzy control module, and the fuzzy weights of the velocity and distance cost functions were obtained, by which the motion of the inspection robot can continuously self-adjust and adapt to the unknown environment. Finally, several simulations and experiments were conducted. The results show that the improved DWA algorithm can effectively improve the obstacle avoidance ability of inspection robots in complex environments. The path can be more reasonably selected and the safety of inspection robots can be enhanced, while the safe distance, path length, and the number of samples can also be optimized by the improved DWA compared to the original DWA.
Efficient Processing-in-Memory System Based on RISC-V Instruction Set Architecture
Authors: Lim Jihwan , Son Jeonghun , Yoo Hoyoung
Published: July 2024

A lot of research on deep learning and big data has led to efficient methods for processing large volumes of data and research on conserving computing resources. Particularly in domains like the IoT (Internet of Things), where the computing power is constrained, efficiently processing large volumes of data to conserve resources is crucial. The processing-in-memory (PIM) architecture was introduced as a method for efficient large-scale data processing. However, PIM focuses on changes within the memory itself rather than addressing the needs of low-cost solutions such as the IoT. This paper proposes a new approach using the PIM architecture to overcome memory bottlenecks effectively in domains with computing performance constraints. We adopt the RISC-V instruction set architecture for our proposed PIM system’s design, implementation, and comprehensive performance evaluation. Our proposal expects to efficiently utilize low-spec systems like the IoT by minimizing core modifications and introducing PIM instructions at the ISA level to enable solutions that leverage PIM capabilities. We evaluate the performance of our proposed architecture by comparing it with existing structures using convolution operations, the fundamental unit of deep-learning and big data computations. The experimental results show our proposed structure achieves a 34.4% improvement in processing speed and 18% improvement in power consumption compared to conventional von Neumann-based architectures. This substantiates its effectiveness at the application level, extending to fields such as deep learning and big data.
An Efficient Maximum Entropy Approach with Consensus Constraints for Robust Geometric Fitting
Authors: Hassan Gundu Mohamed , Min Zijian , Kakani Vijay , Jo Geun-Sik
Published: July 2024

Robust geometric fitting is one of the crucial and fundamental problems in computer vision and pattern recognition. While random sampling and consensus maximization have been popular strategies for robust fitting, finding a balance between optimization quality and computational efficiency remains a persistent obstacle. In this paper, we adopt an optimization perspective and introduce a novel maximum consensus robust fitting algorithm that incorporates the maximum entropy framework into the consensus maximization problem. Specifically, we incorporate the probability distribution of inliers calculated using maximum entropy with consensus constraints. Furthermore, we introduce an improved relaxed and accelerated alternating direction method of multipliers (R-A-ADMMs) strategy tailored to our framework, facilitating an efficient solution to the optimization problem. Our proposed algorithm demonstrates superior performance compared to state-of-the-art methods on both synthetic and contaminated real datasets, particularly when dealing with contaminated datasets containing a high proportion of outliers.
Low-Cost Microcontroller-Based System for Condition Monitoring of Permanent-Magnet Synchronous Motor Stator Windings
Authors: Pietrzak Przemyslaw , Wolkiewicz Marcin , Kotarski Jan
Published: July 2024

Permanent-magnet synchronous motors (PMSMs) have played a key role in recent years in both industrial and commercial applications. Despite their many significant advantages, such as high efficiency, very good dynamics, and high power density, these types of motors are prone to various types of faults. This article proposes a low-cost microcontroller-based system for PMSM stator winding condition monitoring and fault diagnosis. It meets the demand created by the use of more and more low-budget solutions in industrial and commercial applications. A printed circuit board (PCB) has been developed to measure PMSM stator phase currents, which are used as diagnostic signals. The key components of this PCB are LEM’s LESR 6-NP current transducers. The acquisition and processing of diagnostic signals using a low-cost embedded system (NUCLEO-H7A3ZI-Q) with an ARM Cortex-M core is described in detail. A machine learning-driven KNN-based fault diagnostic algorithm is implemented to detect and classify incipient PMSM stator winding faults (interturn short-circuits). The effects of the severity of the fault and the motor operating conditions on the symptom extraction process are also investigated. The results of experimental tests conducted on a 2.5 kW PMSM confirmed the effectiveness of the developed system.
Joint Extraction Method for Hydraulic Engineering Entity Relations Based on Multi-Features
Authors: Liu Yang , Wang Xingzhi , Liu Xuemei , Ren Zehong , Wang Yize , Cai Qianqian
Published: July 2024

During the joint extraction of entity and relationship from the operational management data of hydraulic engineering, complex sentences containing multiple triplets and overlapping entity relations often arise. However, traditional joint extraction models suffer from a single-feature representation approach, which hampers the effectiveness of entity relation extraction in complex sentences within hydraulic engineering datasets. To address this issue, this study proposes a multi-feature joint entity relation extraction method based on global context mechanism and graph convolutional neural networks. This method builds upon the Bidirectional Encoder Representations from Transformers (BERT) pre-trained model and utilizes a bidirectional gated recurrent unit (BiGRU) and global context mechanism (GCM) to supplement the contextual and global features of sentences. Subsequently, a graph convolutional network (GCN) based on syntactic dependencies is employed to learn inter-word dependency features, enhancing the model’s knowledge representation capabilities for complex sentences. Experimental results demonstrate the effectiveness of the proposed model in the joint extraction task on hydraulic engineering datasets. The precision, recall, and F1-score are 86.5%, 84.1%, and 85.3%, respectively, all outperforming the baseline model.
Deep Learning Approach for Modeling the Power Consumption and Delay of Logic Circuits Employing GNRFET Technology
Authors: Emir Recep , Yamacli Dilek Surekci , Yamacli Serhan , Tekin Sezai Alper
Published: July 2024

The interest in alternative logic technologies is continuously increasing for short nanometer designs. From this viewpoint, logic gates, full adder and D-latch designs based on graphene nanoribbon field effect transistors (GNRFETs) at 7 nm technology nodes were presented, considering that these structures are core elements for digital integrated circuits. Firstly, NOT, NOR and NAND gates were implemented using GNRFETs. Then, 28T full adder and 18T D-latch circuits based on CMOS logic were designed using GNRFETs. As the first result of this work, it was shown through HSPICE simulations that the average power consumption of the considered logic circuits employing GNRFETs was 78.6% lower than those built using classical Si-based MOSFETs. Similarly, the delay advantage of the logic circuits employing GNRFETs was calculated to be 53.2% lower than those using Si-based MOSFET counterparts. In addition, a deep learning model was developed to model both the power consumption and the propagation delay of GNRFET-based logic inverters. As the second result, it was demonstrated that the developed deep learning model could accurately represent the power consumption and delay of GNRFET-based logic circuits with the coefficient of determination (R2) values in the range of 0.86 and 0.99.
Proposal of an External Remote Sensing Circuitry for Switching-Mode Power Supplies
Authors: Salavarin George Catalin , Placinta Vlad-Mihai , Ravariu Cristian
Published: July 2024

This work proposes a low-cost and easy-to-implement solution for a remote voltage-sensing circuitry that can be used to ensure stability and good voltage regulation in applications with power supplies driving currents over long paths. The circuitry is implemented with general-purpose electronic components and is provided as an external feature to be attached to power supplies without remote sensing. To demonstrate its capabilities, a test bench was put together alongside a power supply module which has local sensing only. With the test bench, the output voltage from the power supply was delivered in various scenarios to an electronic load. Multiple parameters were monitored in order to assess the power supply performance to which the proposed remote voltage-sensing circuitry is attached. The measurements include stability and transient response tests, and the results were compared with the ones obtained with the standard local sensing. The tests reveal that the power supply module kept its output voltage stable with less than +/−10% voltage variation, and the average transient recovery time was less than 100 µs. The results are given for two different output voltage values of 1.2 V and 3.3 V and at high current capabilities of 3.5 A.
Photocatalytic Hydrogen Production Enhancement of NiTiO3 Perovskite through Cobalt Incorporation
Authors: Quispe Cohaila Alberto Bacilio , Sacari Sacari Elisban Juani , Lanchipa Ramos Wilson Orlando , Tamayo Calderón Rocío María , Medina Salas Jesús Plácido , Gamarra Gómez Francisco , Viswanathan Mangalaraja Ramalinga , Rajendran Saravanan
Published: July 2024

In this study, we synthesized pure and cobalt-doped NiTiO3 perovskite nanostructures using a sol–gel method and characterized them to investigate the impact of cobalt incorporation on their photocatalytic hydrogen production under UV light. XRD analysis confirmed the formation of the hexagonal ilmenite structure, with lattice parameters increasing with cobalt doping, indicating the substitution of larger Co2+ ions onto smaller Ni2+ sites. Raman spectroscopy revealed a decrease in the intensity of active modes, suggesting crystal structure distortion and oxygen vacancy generation. UV-vis spectroscopy showed a decrease in bandgap energy from 2.24 to 2.16 eV with cobalt doping up to 5%, enhancing UV light absorption. SEM and TEM images revealed nanoparticle agglomeration, while cobalt doping did not significantly alter particle size up to 5% doping. Photoluminescence spectroscopy revealed an initial increase in PL intensity for NiTiO3-1%Co, followed by a systematic decrease with higher cobalt concentrations, with NiTiO3-10%Co exhibiting the lowest intensity. Photocatalytic experiments demonstrated a remarkable improvement in hydrogen evolution rate with increasing cobalt doping, with NiTiO3-10%Co exhibiting the highest rate of 940 μmol∙g−1·h−1, a 60.4% increase compared to pure NiTiO3. This enhanced performance is attributed to the substitution of Co2+ on Ni2+ sites, the modification of electronic structure, the suppression of electron–hole recombination, and the creation of surface catalytic sites induced by cobalt incorporation. The proposed mechanism involves the introduction of Co2+/Co3+ energy levels within the NiTiO3 bandgap, facilitating charge separation and transfer, with the Co+/Co2+ redox couple aiding in suppressing electron–hole recombination. These findings highlight the potential of cobalt doping to tune the properties of NiTiO3 perovskite for efficient hydrogen production under UV light.
Can Aqueous Na2SO4-Based Neutral Electrolyte Increase Energy Density of Monolithic Wood Biochar Electrode Supercapacitor?
Authors: Ye Long , Feng Tianjie , Kirk Donald W. , Jia Charles Q.
Published: July 2024

The performance of supercapacitors is significantly influenced by both the nature and the concentration of the electrolyte employed. This study investigates the impact of a neutral electrolyte on the electrochemical properties of the maple-derived monolithic wood biochar (MWB)–sodium sulfate (Na2SO4) supercapacitor. The goal is to determine if a neutral electrolyte, in this case Na2SO4, can increase the supercapacitor energy density compared to a previous study employing a KOH electrolyte. Starting from examining the ion sizes and conductivities of salt species in KOH and Na2SO4 electrolytes, the difference in voltage window, measured specific capacitance, and resistance are discussed. By switching the electrolyte from 4 M KOH to 0.5/1 M Na2SO4, the voltage stability window was extended from 0.8 V to 1.4 V. For 1 M Na2SO4, the supercapacitor attains a specific capacitance of 46 F/g at 5 mA/g, accompanied by an energy density of 12.5 Wh/kg and a maximum power density of 300 W/kg. The MWB electrode, derived from naturally abundant wood, when combined with the non-toxic Na2SO4 electrolyte, offers an environmentally friendly and cost-effective energy storage solution. With a prolonged lifetime and minimal maintenance requirements, MWB-Na2SO4 supercapacitors emerge as a promising choice for diverse applications.
Short-Term Power Load Forecasting Method Based on Feature Selection and Co-Optimization of Hyperparameters
Authors: Liu Zifa , Zheng Siqi , Li Kunyang
Published: July 2024

The current power load exhibits strong nonlinear and stochastic characteristics, increasing the difficulty of short-term prediction. To more accurately capture data features and enhance prediction accuracy and generalization ability, in this paper, we propose an efficient approach for short-term electric load forecasting that is grounded in a synergistic strategy of feature optimization and hyperparameter tuning. Firstly, a dynamic adjustment strategy based on the rate of the change of historical optimal values is introduced to enhance the PID-based Search Algorithm (PSA), enabling the real-time adjustment and optimization of the search process. Subsequently, the proposed Improved Population-based Search Algorithm (IPSA) is employed to achieve the optimal adaptive variational mode decomposition of the load sequence, thereby reducing data volatility. Next, for each load component, a Bi-directional Gated Recurrent Unit network with an attention mechanism (BiGRU-Attention) is established. By leveraging the interdependence between feature selection and hyperparameter optimization, we propose a synergistic optimization strategy based on the Improved Population-based Search Algorithm (IPSA). This approach ensures that the input features and hyperparameters for each component’s predictive model achieve an optimal combination, thereby enhancing prediction performance. Finally, the optimal parameter prediction model is used for multi-step rolling forecasting, with the final prediction values obtained through superposition and reconstruction. The case study results indicate that this method can achieve an adaptive optimization of hybrid prediction model parameters, providing superior prediction accuracy compared to the commonly used methods. Additionally, the method demonstrates robust adaptability to load forecasting across various day types and seasons. Consequently, this approach enhances the accuracy of short-term load forecasting, thereby supporting more efficient power scheduling and resource allocation.
Application of an Improved Harmony Search Algorithm on Electric Vehicle Routing Problems
Authors: Minanda Vanny , Liang Yun-Chia , Chen Angela H. L. , Gunawan Aldy
Published: July 2024

Electric vehicles (EVs) have gained considerable popularity, driven in part by an increased concern for the impact of automobile emissions on climate change. Electric vehicles (EVs) cover more than just conventional cars and trucks. They also include electric motorcycles, such as those produced by Gogoro, which serve as the primary mode of transportation for food and package delivery services in Taiwan. Consequently, the Electric Vehicle Routing Problem (EVRP) has emerged as an important variation of the Capacitated Vehicle Routing Problem (CVRP). In addition to the CVRP’s constraints, the EVRP requires vehicles to visit a charging station before the battery level is insufficient to continue service. EV battery consumption is linearly correlated to their weight. These additional constraints make the EVRP more challenging than the conventional CVRP. This study proposes an improved Harmony Search Algorithm (HSA), with performance validated by testing 24 available benchmark instances in the EVRP. This study also proposes a novel update mechanism in the improvement stage and a strategy to improve the routes with charging stations. The results show that in small and large instances, the proposed HSA improved the number of trips to the charging stations by 24% and 4.5%, respectively. These results were also verified using the Wilcoxon signed-rank significant test.
Modeling and Transmission Characteristics Study of a Resonant Underwater Wireless Electric Power Transmission System
Authors: Hu Qiong , Qin Yu , Li Zhenfu , Zheng Meiling , Huang Junqiang , Ou Yujia
Published: July 2024

Compared to the traditional wet-mate underwater power supply method, Magnetic Coupling Resonant Wireless Power Transfer (MCR-WPT) technology boasts advantages such as excellent insulation, high safety, and convenient operation, showing promising application prospects in the field of power supply for underwater vehicles and other mobile underwater devices. In order to explore the transmission characteristics of this technology underwater, this article first establishes a traditional mathematical model, and then modifies the underwater model through analysis of changes in coil self-inductance and mutual inductance, as well as the impact of eddy current losses. Using the modified mathematical model of the underwater MCR-WPT system, the transmission characteristics are analyzed, and simulations and experimental validations are performed using MATLAB R2022a software. In the study of frequency characteristics, it is found that the system operates optimally when both ends of the circuit work at the resonant state; that is, when finput = fresonance = 100 kHz, the output performance is at its best, and the optimal resonant frequency significantly improves power and transmission efficiency. When the input frequency is less than 87.3 kHz or greater than 122.9 kHz, the output power decreases to less than half of the maximum power. In the investigation of load effects, the optimal load for maximizing system output power was identified, but the load that maximizes transmission efficiency is different from this optimal load. This study provides strong theoretical support and guidance for improving the performance of underwater wireless power transmission systems.
Predictive Heating Control and Perceived Thermal Comfort in a Norwegian Office Building
Authors: Lolli Nicola , Gorantonaki Evgenia , Clauß John
Published: July 2024

An office building in Trondheim, Norway, was used as a case study to test the influence of Predictive Control (PC) for the optimization of energy use on the employees’ thermal comfort. A predictive control was implemented in the Building Energy Management System (BEMS) by operating on the supply temperature of the radiator circuit. A questionnaire was given to the employees to evaluate to what extent the operation of the predictive control influenced their perceived thermal comfort. Several factors known to influence employees’ satisfaction (such as office type, perceived noise level, level of control, perceived luminous environment, perceived indoor air quality, adaptation strategies, well-being) were investigated in the questionnaire. The evaluation shows that the occupants rated the perceived thermal comfort as equally good compared to the business-as-usual operation. This is an important finding toward the user acceptance of such predictive control schemes.
Multi-Criteria Decision-Making for Selecting Solar Window Film Sheets for Energy Saving in Buildings
Authors: Alzarooni Mohamed , Olabi Abdul Ghani , Mahmoud Montaser
Published: July 2024

Recently, there have been several advancements in the field of sustainable energy solutions, particularly in the selection of solar window film sheets. In this research, a multi-criteria decision-making approach was applied to compare three different types of window film sheets, Silver 35, TrueVue 15, and Sterling 40, to aid in selecting the most suitable window film based on the United Arab Emirates market. The primary aim of this work is to provide decision-makers with a structured approach to enhance their choices for selecting window film sheets. The methodology employed involves evaluating various criteria, including visible light transmittance, solar energy rejected, energy transmittance, energy absorptance, cost, glare reduction, visible light reflectance interior, and fade reduction. These criteria are assessed using the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results demonstrate that Sterling 40 is the best choice followed by Silver 35. Based on the final TOPSIS results, the difference between the scores of these two window film sheets was not significant, while they were far from the score of TrueVue 15.
Optimization of Practicality for Modeling- and Machine Learning-Based Framework for Early Fault Detection of Induction Motors
Authors: Benninger Moritz , Liebschner Marcus
Published: July 2024

This paper addresses the further development and optimization of a modeling- and machine learning-based framework for early fault detection and diagnosis in induction motors. The goal behind the multi-level framework is to provide a pragmatic and practical approach for the autonomous monitoring of electrical machines in various industrial applications. The main contributions of this paper include the elimination of a fingerprint measurement in the processing of the framework and the development of a generalized model for fault detection and diagnosis. These aspects allow the training of neural networks with a simulated data set before even knowing the specific induction motor to be monitored. The pre-trained feed-forward neural networks enable the detection of several electrical and mechanical faults in a real induction motor with an overall accuracy of 99.56%. Another main contribution is the extension of the methodology to a larger operating range. As a result, various faults in a real induction motor can be detected under different load conditions with accuracies of over 92%. As a further part of the paper, a concept for a prototype is presented, which enables the autonomous and practice-friendly application of the framework.
Properties of Chemically Stabilized Methanol–HVO Blends
Authors: Wang-Alho Huaying , Sirviö Katriina , Balogun Fatimoh , Kaivosoja Jonna , Nuortila Carolin , Mikulski Maciej , Niemi Seppo
Published: July 2024

Approximately 25% of global carbon emissions come from food production. Renewable fuels are crucial for curbing greenhouse gas (GHG) emissions from vehicles, non-road machines, and agricultural machinery. Tractors, key to modern farming, are central to these efforts. As agriculture strives for sustainability, alternative fuels like methanol and hydrotreated vegetable oil (HVO) are arousing interest because they are renewable and offer potential for blending for use in diesel engines. Methanol and HVO have limited solubility in direct mixing, so the addition of a co-solvent is essential. This study addresses the research gap regarding the properties of HVO and methanol blends with co-solvents. It investigated the impact of three co-solvents, 1-dodecanol, 1-octanol, and methyl butyrate, on the miscibility of HVO and methanol. The experimental measurements cross-varied the co-solvent type with different blending ratios (MeOH5 and MeOH10). Investigated parameters include fuel density, kinematic viscosity, distillation properties, and surface tension. The co-solvents enabled the formation of a singular, clear, and homogeneous phase in methanol-HVO blends. The co-solvent 1-dodecanol demonstrated the highest solubilizing capacity for MeOH5 and MeOH10 blends, followed by 1-octanol. Adding co-solvents led to increased fuel density, decreased kinematic viscosity, and small changes in surface tension. These findings contribute to the optimization of methanol–HVO fuel blends for efficient and environmentally friendly use in vehicles, non-road machinery, and agricultural machinery.
Experimental Study of Thermal Performance of Pulsating-Heat-Pipe Heat Exchanger with Asymmetric Structure at Different Filling Rates
Authors: Liu Jianhong , Liu Dong , Shang Fumin , Yang Kai , Zheng Chaofan , Cao Xin
Published: July 2024

Pulsating heat pipes (PHPs) are widely used in the heat dissipation of electronic components, waste heat recovery, solar energy utilization, etc., relying on the pulsating flow of the work material in the pipe and the heat transfer by phase change, and they have the advantages of high heat-transfer efficiency, simple structure, and low cost. In this paper, an experimental method is used to adjust the length of local pipes in the PHP structure, so that the PHP forms a high- and low-staggered asymmetric structure, and to study the effects of different liquid charging rates and heat-source temperatures on the vibration, startup, and operation of the PHP in the asymmetric structure. We found the following: it is difficult to start up and operate the workpiece at 10%, 68%, and 80% liquid charging rates; the effect of the oscillating impact is worse; the temperature difference between the evaporation section of the pulsating heat pipe and condensation section is larger; and the temperature difference between the evaporation section and condensation section is larger. The temperature difference between the evaporation section and condensation section of the pulsating heat pipe is large, the temperature difference is between 10~25 °C, and it is difficult to achieve a small temperature difference in heat transfer. When the liquid charging rate is 30% and 50%, the pulsating heat pipe oscillates better; the pulsation frequency is relatively high; and the temperature difference between the end of the cold and hot sections is small, the temperature difference is between 3 and 7 °C, and the performance of heat transfer is better. However, when the liquid charging rate is 30% and the heat source is 70 °C, the thermal resistance is increased to 0.016 K/W, and the equivalent thermal conductivity is reduced. When the performance of heat transfer is changed to 0.016 K/W and the equivalent thermal conductivity is reduced, the coefficient decreases, and the heat-transfer performance becomes weaker.
Robust Secondary Controller for Islanded Microgrids with Unexpected Electrical Partitions under Fault Conditions
Authors: Pompodakis Evangelos E. , Orfanoudakis Georgios I. , Yiannis Katsigiannis , Karapidakis Emmanuel S.
Published: July 2024

This paper proposes a sophisticated, fault-tolerant, and centralized secondary controller that is designed for inverter-based, islanded microgrids. The proposed controller enhances system resilience to unexpected network partitions, which typically occur due to the tripping of protective devices under fault conditions. In typical radially configured MGs, a line fault can cause protective devices to isolate the faulted line, thereby splitting the MG into two electrically independent sub-microgrids (SMGs), while retaining the existing communication and control framework. In contrast to traditional centralized and distributed secondary controllers, which often fail to restore the frequency to the nominal value (50 Hz) in split SMGs, the proposed controller exhibits exceptional performance. Through simulation studies on 6-bus and 13-bus islanded MG setups, the controller has not only demonstrated its ability to swiftly restore the nominal frequency in both SMGs within a few seconds (specifically 5 s), but also to ensure fair power distribution among the distributed generators (DGs) supplying the SMGs. This rapid frequency stabilization underscores the controller’s effectiveness in maintaining stable frequency levels immediately following a fault. In contrast, the use of traditional centralized and consensus controllers typically results in a frequency deviation of about 3 Hz from the nominal value in one of the SMGs during the microgrid’s partition.
Unmanned Aerial Vehicle (UAV)-Assisted Damage Detection of Wind Turbine Blades: A Review
Authors: Zhang Zengyi , Shu Zhenru
Published: July 2024

The wind energy sector is experiencing rapid growth, marked by the expansion of wind farms and the development of large-scale turbines. However, conventional manual methods for wind turbine operations and maintenance are struggling to keep pace with this development, encountering challenges related to quality, efficiency, and safety. In response, unmanned aerial vehicles (UAVs) have emerged as a promising technology offering capabilities to effectively and economically perform these tasks. This paper provides a review of state-of-the-art research and applications of UAVs in wind turbine blade damage detection, operations, and maintenance. It encompasses various topics, such as optical and thermal UAV image-based inspections, integration with robots or embedded systems for damage detection, and the design of autonomous UAV flight planning. By synthesizing existing knowledge and identifying key areas for future research, this review aims to contribute insights for advancing the digitalization and intelligence of wind energy operations.
Application and Challenge of High-Speed Pumps with Low-Temperature Thermosensitive Fluids
Authors: Zhang Beile , Niu Ben , Zhang Ze , Chen Shuangtao , Xue Rong , Hou Yu
Published: July 2024

The rapid development of industrial and information technology is driving the demand to improve the applicability and hydraulic performance of centrifugal pumps in various applications. Enhancing the rotational speed of pumps can simultaneously increase the head and reduce the impeller diameter, thereby reducing the pump size and weight and also improving pump efficiency. This paper reviews the current application status of high-speed pumps using low-temperature thermosensitive fluids, which have been applied in fields such as novel energy-saving cooling technologies, aerospace, chemical industries, and cryogenic engineering. Due to operational constraints and thermal effects, there are inherent challenges that still need to be addressed for high-speed pumps. Based on numerical simulation and experimental research for different working fluids, the results regarding cavitation within the inducer have been categorized and summarized. Improvements to cavitation models, the mechanism of unsteady cavity shedding, vortex generation and cavitation suppression, and the impact of cavitation on pump performance were examined. Subsequently, the thermal properties and cavitation thermal effects of low-temperature thermosensitive fluids were analyzed. In response to the application requirements of pump-driven two-phase cooling systems in data centers, a high-speed refrigerant pump employing hydrodynamic bearings has been proposed. Experimental results indicate that the prototype achieves a head of 56.5 m and an efficiency of 36.1% at design conditions (n = 7000 rpm, Q = 1.5 m3/h). The prototype features a variable frequency motor, allowing for a wider operational range, and has successfully passed both on/off and continuous operation tests. These findings provide valuable insights for improving the performance of high-speed refrigerant pumps in relevant applications.