Fusion System Research Articles

Design of UAV Flight State Recognition System for Multisensor Data Fusion

Design of UAV Flight State Recognition System for Multisensor Data Fusion

Loss of power accident analysis for the HCCR blanket

The blanket system is a crucial element in nuclear fusion power generation, tasked with tritium production and the efficient dissipation of the high thermal flux from the tokamak. Korea is in the process of developing the Helium Cooled Ceramic Reflector (HCCR) blanket, which stands as the forefront design among the nation's blanket systems. This paper presents accident analysis results of LOOP (Loss Of Offsite Power) accident for the HCCR blanket. Loss of power accident is one of the postulated events in the nuclear fusion system. This event may be caused by common failure of the external electrical grids or electrical faults in the site. The objective of this safety analysis is to assess the impact of the activation of an additional power supply within 90 seconds after an accident on the system temperature, and to evaluate the heat load that can be removed by the stagnated coolant in the CCWS (Component Cooling Water System) connected to the heat exchanger of the blanket system.

Research on optimizing the controller configuration for high-power supply control system in nuclear fusion based on hybrid algorithm WOA-IAPSO

With the development of nuclear fusion technology, the high power supply control system plays a crucial role in the operation of nuclear fusion, and the PID controller is widely used in the field of power supply control. In this research, a novel hybrid algorithm is proposed that combines whale optimization algorithm (WOA) and the improved adaptive particle swarm optimization (IAPSO) to tune PID parameters in high-power supply. This organically combines the two methods into a new optimization strategy that is different from the simple weighted average method. By comparing the performance of WOA, IAPSO,SA,GA,SVM, and the hybrid WOA-IAPSO in step response under different working conditions, the simulation results show that the proposed algorithm can achieve more sensitive, stable and efficient response in high-power operation. This research is not only of great significance for the further development nuclear fusion technology, but also provides a useful reference for the application of adaptive optimization methods in other fields.

AV-FDTI: Audio-visual fusion for drone threat identification

In response to the evolving challenges posed by small unmanned aerial vehicles (UAVs), which have the potential to transport harmful payloads or cause significant damage, we present AV-FDTI, an innovative Audio-Visual Fusion system designed for Drone Threat Identification. AV-FDTI leverages the fusion of audio and omnidirectional camera feature inputs, providing a comprehensive solution to enhance the precision and resilience of drone classification and 3D localization. Specifically, AV-FDTI employs a CRNN network to capture vital temporal dynamics within the audio domain and utilizes a pretrained ResNet50 model for image feature extraction. Furthermore, we adopt a visual information entropy and cross-attention-based mechanism to enhance the fusion of visual and audio data. Notably, our system is trained based on automated Leica tracking annotations, offering accurate ground truth data with millimeter-level accuracy. Comprehensive comparative evaluations demonstrate the superiority of our solution over the existing systems. In our commitment to advancing this field, we will release this work as open-source code and wearable AV-FDTI design, contributing valuable resources to the research community.

E-Learning system application in art entrepreneurship teaching based on multimodal feature fusion and neural network

College student entrepreneurship teams can leverage the advantages of mobile internet resource integration and online collaborative production platforms to easily integrate various resources, thereby achieving rapid start-up and development of entrepreneurial projects. This also makes the threshold for college students to start businesses lower and easier to achieve. This article designs an interactive aesthetic education and entrepreneurship teaching system based on multimodal feature fusion and interactive systems. The core concept of the interactive aesthetic education and entrepreneurship teaching system is multimodal feature fusion and interactive system. Multimodal feature fusion can integrate different teaching resources, so that students can better understand the factors that need to be considered in the entrepreneurial process and practice. Interactive systems can enable closer interaction and communication between students and teachers, thereby better understanding and mastering entrepreneurial knowledge and skills. The design and implementation of a systematic system will help enhance the entrepreneurial ability and quality of college students, help them better understand the entrepreneurial process and factors that need to be considered in practice, and provide entrepreneurial practice reference for students in Chinese universities who have the intention of designing entrepreneurship.

Determining the Target Concentration of Propofol for Sedation in Patients Undergoing Endoscopic Retrograde Cholangiopancreatography: A Target-Controlled Infusion Approach.

Introduction Endoscopic retrograde cholangiopancreatography (ERCP) is vital for diagnosing and treating biliary and pancreatic diseases, necessitating deep sedation typically achieved through total intravenous anesthesia. Propofol, with its favorable pharmacokinetic profile, is the preferred sedative, but conventional administration methods of mg/kg boluses or infusion rates pose challenges. Target-controlled infusion (TCI) systems offer a solution that ensures precise dose delivery of propofol. Despite its widespread use, the literature lacks specific guidance on the target plasma concentration (Cp) of propofol for sedation in patients undergoing ERCP. Methods A prospective interventional study was conducted at the Institute of Liver and Biliary Sciences, Delhi, India to determine the target Cp of propofol for sedation during ERCP. The study enrolled 86 American Society of Anesthesiologists (ASA) grade I and II patients aged 18-70 years. The primary objective was to establish the optimal propofol concentration for sedation as guided by a bispectral index (BIS) value of 60-70. Secondary outcomes included induction time, recovery time, total propofol consumption, and the occurrence of adverse events (if any). The Marsh pharmacokinetic model guided the TCI pump, adjusting Cp until the target sedation was achieved. Results The mean Cp of propofol to maintain the BIS value 60-70 was 2.21 ± 0.42 µg/ml. Age-wise analysis revealed variations, emphasizing the need for individualized dosing. Induction time was 4.21 ± 0.68 minutes; recovery times were seven minutes (median, IQR: 5-10 minutes) for BIS >80 and seven minutes (median, IQR: 5-10 minutes) for achieving a Modified Observer's Assessment of Alertness/Sedation score of ≥5. The mean propofol consumption was 6.24 mg/kg/hr. Side effects were minimal, with 1.16% experiencing transient hypoxia and hypotension. Conclusion The study establishes a mean target propofol concentration of 2.21 ± 0.42 µg/ml for sedation in ASA I and II patients undergoing ERCP.

Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion.

The rapid advancements in Artificial Intelligence of Things (AIoT) are pivotal for the healthcare sector, especially as the world approaches an aging society which will be reached by 2050. This paper presents an innovative AIoT-enabled data fusion system implemented at the CMUH Respiratory Intensive Care Unit (RICU) to address the high incidence of medical errors in ICUs, which are among the top three causes of mortality in healthcare facilities. ICU patients are particularly vulnerable to medical errors due to the complexity of their conditions and the critical nature of their care. We introduce a four-layer AIoT architecture designed to manage and deliver both real-time and non-real-time medical data within the CMUH-RICU. Our system demonstrates the capability to handle 22 TB of medical data annually with an average delay of 1.72 ms and a bandwidth of 65.66 Mbps. Additionally, we ensure the uninterrupted operation of the CMUH-RICU with a three-node streaming cluster (called Kafka), provided a failed node is repaired within 9 h, assuming a one-year node lifespan. A case study is presented where the AI application of acute respiratory distress syndrome (ARDS), leveraging our AIoT data fusion approach, significantly improved the medical diagnosis rate from 52.2% to 93.3% and reduced mortality from 56.5% to 39.5%. The results underscore the potential of AIoT in enhancing patient outcomes and operational efficiency in the ICU setting.

Optical properties and concentration optimization of Nd: PbF2 crystal for high-energy pulsed laser

Appropriate saturation flux and long fluorescence lifetime are essential requirements for the gain medium in high-energy solid-state laser systems. A novel laser crystal Nd:PbF2 was grown successfully, and the optical performances were evaluated in detail. A theoretically relatively practical method revealing the relationship between the excited state population and doping concentration was developed to guide the optimization of Nd3+ doping concentration. The optical properties of different doping concentrations of Nd:PbF2 single crystals were studied in detail and compared with our theory to demonstrate the accuracy and feasibility of the proposed method. Furthermore, we found that the optimal doping concentration of Nd3+ in the PbF2 crystal was 1 %, with a fluorescence lifetime of 395.3 μs at ∼ 1.06 μm and a saturation flux of 5.8 J/cm2, which is close to the current one is comparable to phosphate glass widely used in nuclear fusion systems, indicating that the 1 % Nd: PbF2 crystal can be used as an excellent gain medium for high-energy lasers.

Predicting stroke events with a proactive fusion system: a comprehensive study on imbalance class handling in computational biomechanics

Stroke, as a critical global health concern and the second leading cause of death, occurs when blood flow to the brain is interrupted. Although machine learning has advanced in medical safety, there is limited research on stroke prediction using information fusion systems. This study presents a fusion framework that combines multiple base classifiers and a Meta classifier to improve stroke prediction performance. The research utilizes Grid Search optimized models, such as Random Forest, Support Vector Machine, K Nearest Neighbors, AdaBoost, Gradient Boosting, Light Gradient Boosting, Categorical Boosting, and eXtreme Gradient Boosting for in-depth stroke analysis. Since stroke events are rare and unpredictable, classification outcomes can be deceptive due to imbalanced data. This article examines stroke probability by comparing three data balancing methods: over-sampling, under-sampling, and tomek-link synthetic minority over-sampling (SMOTE-TL) to enhance prediction accuracy. The findings reveal that AdaBoost as a meta-classifier attains the highest performance in the fusion framework, with a peak of 88.09% Recall and 83.66% F1 score. This innovative approach provides crucial insights into stroke prediction and can be a valuable resource for strengthening intervention efforts in advanced healthcare systems.

Digital Virtual Simulation for Cultural Clothing Restoration: Case Study of Tang Dynasty Mural ‘Diplomatic Envoys’ from Crown Prince Zhang Huai’s Tomb

The advent of 3D virtual presentation technology for clothing has led to the gradual popularisation of digital virtual clothing in the modern fashion industry. However, there remains a gap between the application of this technology and the integration of cultural attributes in the field of digital communication of traditional cultural clothing. Consequently, the objective of this paper is to propose the establishment of a fusion system integrating archaeological research on traditional culture with emerging virtual presentation technology. This paper draws inspiration from the replicability and easy dissemination of digital products to combine cultural archaeology and digital technology. The aim is to provide ideas for the diversity of dissemination of cultural heritage. The research object is Diplomatic Envoys, a Chinese mural painting of the Tang Dynasty that depicts friendly exchanges between countries. The research is divided into two research stages. A CLO3D software-based digital restoration test was conducted to reproduce the costumes of officials and foreign envoys depicted in the Tang Dynasty mural. The FAHP model was employed to verify the accuracy of the restoration results. The experiment demonstrated that the digitally reconstructed clothing exhibited a high degree of similarity to the unearthed mural figure clothing object. Furthermore, the restoration result passed the credibility verification, resulting in a ‘credible’ outcome. The application of digital virtual simulation clothing restoration methods offers two key advantages. Firstly, in comparison with traditional clothing restoration methods, digital restoration enables the rapid assessment of the resulting clothing effect, thereby reducing the likelihood of secondary damage to cultural relics due to manual errors. Secondly, the benefits of digital technology facilitate the convenient storage, replication, and dissemination of clothing data information. Data can not only be extended to online exhibition halls but also to game animation, clothing production, and other fields for the purposes of creative redesign and information dissemination. Furthermore, these benefits can penetrate the education industry to disseminate information to the public through all-round display models and explanations.

Earthquake prediction using satellite data: Advances and ahead challenges

Due to the considerable loss of life and damages caused by powerful Earthquakes, along with the strengthening of structures and disaster management systems, many researches are being conducted associated with the possibility of creating Earthquake warning systems. However, no reliable scientific report on successful Earthquake prediction has been published so far. Although there are a couple of published scientific papers indicating the detection of unusual changes in physical and chemical parameters obtained from ground stations, but due to the limitations of in-situ measurement in terms of number, location, time and cost, there was no noticeable progress in Earthquake warning. After the advent of remote sensing satellites, the number of statistical studies related to Earthquake precursors increased significantly, and most of them emphasized the existence of abnormal behaviors in physical and chemical precursors around the time (about 1–30 days before) and locations of powerful Earthquakes. In this study, an attempt has been made to address the advances and challenges in the way of Earthquake prediction using satellite data and provide a perspective of the future of these researches. It should be noted that the increase in the number of satellites designed and launched specially for Earthquake studies, the variety of available Earthquake precursors (multi-precursor analysis), the development of classic and intelligent anomaly detection and predictor algorithms (multi-method analysis), the provision of intelligent systems for the integration of various precursors (fusion and decision systems), the creation of cloud storing and processing data services (Google Earth Engine, Giovanni, etc.) and the development of intelligent user interfaces for public, have made researchers more hopeful for the appearance of low-uncertainty Earthquake warning systems in the near future.

Metal binding feature of copper‒induced metallothionein from freshwater crab Sinopotamon henanense reveals its Cu‒thionein character

Sinopotamon Henanense expresses two metal‒induced metallothioneins (MTs), Cd‒induced MT and Cu‒induced MT (ShCuMT). The Cd‒induced MT has been characterized as a Cd‒thiolate MT. However, it is unknown whether ShCuMT is a Cu‒thiolate MT. In the present study, ShCuMT was expressed heterologously in Escherichia coli and purified by Ni‒NTA column and superdex‒75 column. And its metal‒binding feature was evaluated by DTNB reaction, circular dichroism spectroscopy (CD), isothermal microtitration (ITC), electrospray flight mass spectrometry (ESI‒TOF‒MS), and matrix‒assisted laser desorption ionization flight mass spectrometry (MALDI‒TOF‒MS). Bioinformatics analysis demonstrated that ShCuMT possessed the cysteine‒triplet motif of a Cu‒specific MT. Expression and purification of ShCuMT illustrated that SUMO tag used as the production system for ShCuMT resulted in a high production yield. The stability order of ShCuMT binding metal ions were Cu (Ⅰ) > Cd (Ⅱ) > Zn (Ⅱ). The CD spectrum indicated that ShCuMT binding with Cu (I) exhibited a compact thiol metal clusters structure. Besides, there emerged no a visible nickel‒thiol absorption after Ni‒NTA column affinity chromatography. The ITC results implied that Cu‒ShCuMT possessed the optimal thermodynamic conformation and the highest stoichiometric number of Cu (Ⅰ). Overall, the results suggested that SUMO fusion system is a robust and inexpensive approach for ShCuMT expression and Ni‒NTA column had no influence on metal binding of ShCuMT and Cu(Ⅰ) was considered its cognate metal ion, and ShCuMT possessed canonical Cu‒thiolate characteristics. The metal binding feature of ShCuMT reported here contributes to elucidating the structure‒function relationship of ShCuMT in S. Henanense.

Bi-metallic lattice structures manufactured via an intralayer multi-material powder bed fusion method

The laser powder bed fusion technology is widely utilized for manufacturing intricate metallic components, overcoming design limitations inherent in traditional subtractive methods. Recent advancements in powder bed fusion have extended to the creation of metal multi-material structures, allowing for functionally graded and tailored properties within a single component. While functionally graded lattice structures are commonly achieved by spatially varying the relative density and/or topology of the lattice, multi-material lattices are rarely studied particularly via metal additive manufacturing where multi-material methods are a relatively recent development. This study applies an accessible enhancement to a standard powder bed fusion system for multi-material additive manufacturing, and investigates printed and heat-treated bi-metallic lattices containing regions of both 316L and 17–4PH stainless steel. The bi-material interface is examined in lattice and bulk samples, and the compression behaviour of bi-metallic lattices is investigated experimentally and computationally. The material interface is shown to be robust as built and under loading, and the bi-metallic lattices exhibit greater energy absorption than single-material samples. The inclusion of the ductile 316L in bi-metallic lattices also delays the propagation of local failure and shear bands initiated in the more brittle 17–4PH. Finite element analysis simulates the lattice compression which includes strut fracture and as-built diameters informed by computed tomography, enabling further investigation and analysis of multi-material lattice designs. This work demonstrates the benefit of multi-material additive manufacturing by providing improved performance over single-material designs.

Abnormal Traffic Detection System Based on Feature Fusion and Sparse Transformer

This paper presents a feature fusion and sparse transformer-based anomalous traffic detection system (FSTDS). FSTDS utilizes a feature fusion network to encode the traffic data sequences and extracting features, fusing them into coding vectors through shallow and deep convolutional networks, followed by deep coding using a sparse transformer to capture the complex relationships between network flows; finally, a multilayer perceptron is used to classify the traffic and achieve anomaly traffic detection. The feature fusion network of FSTDS improves feature extraction from small sample data, the deep encoder enhances the understanding of complex traffic patterns, and the sparse transformer reduces the computational and storage overhead and improves the scalability of the model. Experiments demonstrate that the number of FSTDS parameters is reduced by up to nearly half compared to the baseline, and the success rate of anomalous flow detection is close to 100%.

Uptake and Transport of Selenium in a Soil–Tea Plant–Tea Infusion System: A Study of Typical Tea Plantations in a Selenium-Rich Area of China

Selenium is an important indicator for the evaluation of tea quality. However, the relationship between selenium uptake by tea plants (Camellia sinensis) and soil properties, as well as selenium transport and distribution in a tea plantation soil–tea plant–tea infusion production system, remain unclear. In this study, 12 tea plantations situated in a typical selenium-rich area of China were selected, and the characteristics and crucial factors influencing the uptake and transport of selenium were analyzed using a plantation soil–tea plant–tea infusion production system. The soil total selenium content ranged from 1.12 to 6.67 mg kg−1, with an average of 2.57 mg kg−1. The average available selenium content was 53.56 µg kg−1, and the activation rate of soil selenium was 2.27%. Soil-available selenium was significantly positively correlated with total selenium, available potassium, and soil organic matter contents, and was significantly negatively correlated with soil pH. The selenium content in old leaves ranged from 0.29 to 2.73 mg kg−1, which met the standard for selenium-rich tea, whereas only 33% of young leaves met this standard. The selenium enrichment factor was highest in the fibrous root and lowest in the young leaves. The average selenium transport factors from fibrous roots to main roots, from main roots to main stems, from main stems to lateral stems, from lateral stems to young leaves, and from lateral stems to old leaves were 0.53, 0.92, 0.67, 0.97, and 2.30, respectively. The selenium concentration of tea infusion ranged from 1.88 to 12.49 μg L−1, and the average selenium dissolution rate was 22.62% after one brewing. This study identified critical factors that influence soil-selenium availability. The selenium content in tea plant organs is indicated to be strongly associated with the selenium content in the main roots.

TALEN-mediated homologous-recombination-based fibroin light chain in-fusion expression system in Bombyx mori.

Silkworm was the first domesticated insect and has important economic value. It has also become an ideal model organism with applications in genetic and expression studies. In recent years, the use of transgenic strategies has made the silkworm silk gland an attractive bioreactor for the production of recombinant proteins, in particular, piggyBac-mediated transgenes. However, owing to differences in regulatory elements such as promoters, the expression levels of exogenous proteins have not reached expectations. Here, we used targeted gene editing to achieve site-specific integration of exogenous genes on genomic DNA and established the fibroin light chain (FibL) in-fusion expression system by TALEN-mediated homology-directed recombination. First, the histidine-rich cuticular protein (CP) was successfully site-directed inserted into the native FibL, and the FibL-CP fusion gene was correctly transcribed and expressed in the posterior silk gland under the control of the endogenous FibL promoter, with a protein expression level comparable with that of the native FibL protein. Moreover, we showed based on molecular docking that the fusion of FibL with cuticular protein may have a negative effect on disulfide bond formation between the C-terminal domain of fibroin heavy chain (FibH) and FibL-CP, resulting in abnormal spinning and cocoon in homozygotes, indicating a significant role of FibL in silk protein formation and secretion. Our results demonstrate the feasibility of using the FibL fusion system to express exogenous proteins in silkworm. We expect that this bioreactor system will be used to produce more proteins of interest, expanding the application value of the silk gland bioreactor.

Spatio-temporal data fusion techniques for modeling digital twin City

ABSTRACT The digital twin city technique maps the massive city environmental and social data on a three-dimensional virtual model. It presents the operational status of physical world and supports intelligent city governance. However, the inefficient utilization of distributed data resources, and the lack of sharing and collaboration among multiple departments restrict the data formulation of digital twin city construction. This research proposes a new cross-domain spatio-temporal data fusion framework for supporting complex urban governance. It integrates the heterogeneous urban information generated and stored by different government departments using multiple-information techniques. A specified geographic base reflecting the real city status is established, using geographical entities with unified address as identifiers to encapsulate the urban elements information. We introduce a comprehensive urban spatio-temporal data center construction process, which has already supported multiple urban governance projects. The two distinct advantages in using this data fusion system are: 1) The proposed Bert+PtrNet+ESIM-based address mapping method associates the urban elements information to their corresponding geographic entities with 99.3% F1-Score on real-world dataset. 2) The Wuhan spatio-temporal data center operation illustrates the capability of our framework for complex urban governance, which significantly improves the efficiency of urban management and services. This integrated system engineering provides reference and inspiration for further spatio-temporal data management, which contributes to the future social governance in digital twin city platform.

Analgesia for Donor Hepatectomy: Recent Perspectives

Background: Donor hepatectomy is a complex surgical procedure associated with significant postoperative pain, which can impact both short-term recovery and long-term outcomes. Adequate pain management plays a crucial role in ensuring the well-being of the living liver donor and optimising their overall experience. This review aims to provide a comprehensive overview of current practices and emerging strategies in analgesic management for donor hepatectomy. Methodology: To find relevant material, searches were conducted on PubMed and Google Scholar. The evaluation took into consideration review articles, clinical trials, retrospective studies, observational studies, and case-control studies. Results: The conventional approach to pain control involves a multimodal strategy combining opioids, non-steroidal anti-inflammatory drugs, and regional anaesthesia techniques. However, concerns regarding opioid-related side effects and potential complications have prompted a re-evaluation of analgesic protocols. Alternative methods such as thoracic epidural analgesia, transversus abdominis plane blocks and continuous wound infusion systems have gained attention for their potential to minimise opioid requirements and enhance recovery. Recent advancements in the field of pain management, including the utilisation of enhanced recovery, after surgery protocols, personalised analgesic regimens, and novel pharmaceutical agents, are explored in this review. Additionally, the impact of psychological factors and patient-centred care on postoperative pain experiences is discussed. Conclusion: The review concludes by emphasising the importance of tailoring analgesic strategies to individual patient needs and characteristics. It highlights the potential benefits of incorporating innovative approaches to enhance pain control, reduce opioid consumption and ultimately improve the overall outcome and satisfaction of living liver donors undergoing hepatectomy. Future directions in research and clinical practice are also suggested to further refine and optimise analgesic management in the context of donor hepatectomy.

A novel usage of rough sets in design of data fusion systems

Design changes for crewless vessels are unexplored compared to the maritime design processes that have been utilized and updated for hundreds of years. This paper presents an exploration into how autonomous and unmanned systems can impact maritime design, specifically focusing on how well they can fuse multiple types of information. Currently, formal and informal communication onboard crewed vessels between various departments is critical in constructing a view of the vessel’s current health and future capability. Amajor focus area is determining whether utilizing data classification techniques can replace these human centered decision processes, and what the design implications of losing the human synthesis will be. This paper proposes a mechanical spring-mass-damper system with base excitation using real-world ocean data to be used to perform analyses. Rough Set Theory (RST) is a data classification technique that can be used for the characterization of a set of objects, finding dependency between attributes, and creating rules formaking decisions. RST is compared with other data classification techniques to determine where each classifier succeeds and how they can generate information useful in design. By integrating the results of these analyses, this paper identifies ways to begin fusing multiple information types and how this will impact marine design in the future of crewless systems.

Compositionally graded titanium to aluminum processed by laser powder bed fusion process: Microstructure evolution and mechanical properties

A compositionally graded Ti–Al alloy with a designed composition range from pure Ti to pure Al was successfully produced by a laser powder bed fusion (L-PBF) system equipped with two powder feeders. The compositionally graded Ti–Al profile is confirmed by scanning electron microscopy, X-ray diffraction analysis, electron backscatter diffraction microscopy, and transmission microscopy. The graded material is free of macro-horizontal cracks, but micro-cracks appear at the center of the sample. Varying the Ti and Al ratio along the build direction induces microstructural changes, transforming α+β-Ti into solid solution, creating intermetallics (Ti and Al), and yielding an Al phase with diverse morphologies. Additionally, the Ti–Al ratio's influence on microstructure formation and phase evolution results in varying mechanical properties among the graded material's layers. The results demonstrate that the L-PBF process can achieve continuous compositional grading of Ti–Al materials in a single run. Moreover, this study hold the potential to facilitate the design and production of numerous compositional variants for high entropy alloys and multi-materials, enabling future investigation into microstructural evolution, phase transformation, and physical and mechanical properties.