Fusion Process Research Articles

Research on the Construction of an Evaluation System for the Diversion Effect of Urban Tourist Flow

To address the challenge of assessing real-time urban tourist flow diversion effectiveness, this study incorporates knowledge graph technology, leveraging extensive research results as foundational data. Through the steps of information extraction, knowledge fusion and knowledge processing of the keywords of the article, the index system was preliminarily established by combining the quantitative and qualitative methods of expert consultation. In this study, the Delphi method was used to optimize the index system, and an evaluation index system consisting of 1 standard-level index, 6 system-level indicators and 21 index-level indicators was constructed. Then, the analytic hierarchy process was used to calculate the weights of the index system. Through research and interviews, practical experience and literature to determine the index measurement and scoring rules, combined with the national scientific and technological innovation project based on this institute, the pilot evaluation of tourist flow regulation in Suzhou, Tianshui, Wuhu and other places was carried out, and the evaluation system and method were verified. This comprehensive method, which is based on a knowledge graph and combines qualitative and quantitative approaches, offers a novel idea for constructing an index system, especially in the absence of mature theoretical references and practical experiences. This study effectively solves the problem of the evaluation system of the effectiveness of urban tourist flow control, and has strong theoretical innovation and practical significance.

Use of SPECT/CT in Scintigraphic Imaging Methods

This study aims to acquire images of a human body phantom in two separate gantries (SPECT and CT), perform fusion of these images in a computer environment, determine the necessary criteria for this process, and analyze the clinical validity of the fusion process. To quantify potential fusion errors in imaging for patients with movement restrictions, a unique phantom imaging setup was planned for this study. The data from the six different CT images and the single SPECT image taken at 0 degrees were uploaded to the DICOM system within the PMOD software program. The fusion process was then performed. It was observed that the fusion images exhibited deviations depending on the orientation angles, and these deviations increased proportionally with the angle. The post-processing fusion of SPECT and CT images from separate gantries using a computer-based fusion program has some disadvantages. Patients must move to another department for CT imaging after undergoing SPECT, which can result in misalignment due to body movement. To correct these errors, the use of external markers becomes necessary. Another disadvantage is radiation safety concerns during patient transfers between imaging units due to the presence of radioactive material in the patient’s body.

Dunhuang Culture: A Bridge for Global Cultural Exchange and Integration

Dunhuang culture, as an outstanding representation of the Silk Road's diverse civilizations, exemplifies fundamental patterns of global cultural interaction through its profound historical legacy and unique artistic expressions. Utilizing historical evidence and artistic analysis, this paper examines the pivotal significance of Dunhuang in facilitating dialogue among civilizations. Firstly, its strategic geographical location made Dunhuang a crucial nexus where the agricultural civilization of China's Central Plains, Indian Buddhist philosophy, and nomadic cultures of the Western Regions intersected following the opening of the Silk Road during the Han Dynasty. The evolution of the "Flying Apsaras" (Feitian) imagery depicted in the Mogao Grottoes murals and the coexistence of multiple religious beliefs during the Tibetan (Tubo) period vividly illustrate dynamic processes of cultural fusion, innovation, and reinvention. Secondly, depictions of urban life, characterized by diverse caravan attire and multilingual literature, highlight the historical globalization of material exchange and spiritual interaction. Thirdly, within the contemporary digital context, the "Digital Dunhuang" project transcends physical boundaries to facilitate the global dissemination of cultural resources. In alignment with China's strategic "One Belt, One Road" initiative, the Dunhuang Expo establishes a new mechanism for civilizational dialogue, transforming traditional wisdom into strategic resources to address modern challenges. This inclusive model of cultural integration not only serves as a historical reference for constructing a global community with a shared future but also redefines cultural heritage preservation through technological innovation, thereby continuously expanding the practical boundaries of intercultural dialogue.

LL-37 Inhibits TMPRSS2-Mediated S2' Site Cleavage and SARS-CoV-2 Infection but Not Omicron Variants.

Continual evolution of SARS-CoV-2 spike drives the emergence of Omicron variants that show increased spreading and immune evasion. Understanding how the variants orientate themselves towards host immune defence is crucial for controlling future pandemics. Herein, we demonstrate that human cathelicidin LL-37, a crucial component of innate immunity, predominantly binds to the S2 subunit of SARS-CoV-2 spike protein, occupying sites where TMPRSS2 typically binds. This binding impedes TMPRSS2-mediated priming at site S2' and subsequent membrane fusion processes. The mutation N764K within S2 subunit of Omicron variants reduces affinity for LL-37 significantly, thereby diminishing binding capacity and inhibitory effects on membrane fusion. Moreover, the early humoral immune response enhanced by LL-37 is observed in mice against SARS-CoV-2 spike but not Omicron BA.4/5 spike. These findings reveal the mechanism underlying interactions amongst LL-37, TMPRSS2 and SARS-CoV-2 and VOCs, and highlight the distinct mutation for Omicron variants to evade the fusion activity inhibition by host innate immunity.

Parametric study on hardness and density of Polystyrene (PS200) parts using laser powder bed fusion process for investment casting application

Purpose The laser powder bed fusion (L-PBF) process has gained increasing interest in investment casting (IC) due to its suitability for small-batch production. This study aims to optimize L-PBF process parameters for Polystyrene (PS200), focusing on their effects on hardness and density to produce high-quality investment casting patterns. Design/methodology/approach A comprehensive characterization of PS200 was conducted. A three-factor face-centered central composite design (FC-CCD) was used to analyze the effects of laser power (LP), laser speed (LS) and skin-hatch distance (SHD) on the hardness and density of PS200 components. Findings Powder characterization shows moderate flowability and low ash residue. LP and SHD were identified as the most influential factors. Hardness and density increased with higher LP, whereas LS and SHD showed an inverse effect. Optimized parameters improved hardness by 6.9% and density by 7.5%, with a significant LP−SHD interaction, reducing surface roughness (Ra) from 17 to 14 µm (17.7% improvement) and enabling high-quality PS200 patterns. Research limitations/implications This study is limited to a specific machine-material combination, restricting generalizability to other L-PBF systems. Future work should explore additional process parameters, L-PBF platforms and long-term stability in investment casting. Originality/value Qualifying new materials requires an in-depth understanding of machine-material process parameters. This study introduces a systematic approach to qualifying Polystyrene (PS200) for L-PBF. It establishes its process parameters, contributing to adopting L-PBF to produce high-quality investment casting patterns.

Numerical Optimization of Laser Powder Bed Fusion Process Parameters for High-Precision Manufacturing of Pure Molybdenum

This study presents a comprehensive numerical investigation of the Laser Powder Bed Fusion (LPBF) process for pure molybdenum, focusing on high-precision modeling and process optimization. The powder spreading behavior is simulated using the Discrete Element Method (DEM), while molten pool dynamics are analyzed through Computational Fluid Dynamics (CFD). Optimization of process parameters is performed using FLOW-3D Release 7 software in conjunction with the HEEDS-SHERPA algorithm. A total of 247 simulations are conducted to assess the effects of four critical parameters: laser power (50–400 W), scanning speed (80–300 mm/s), laser spot diameter (40–100 µm), and powder layer thickness (50–100 µm). The optimal parameter set—350 W laser power, 120 mm/s scanning speed, 50 µm spot diameter, and 50 µm layer thickness—results in an 80% laser absorption rate, a 60% reduction in micro-porosity, and over a 30% enhancement in both molten pool volume and surface area. Utilizing a fine 10 µm mesh resolution enables detailed insights into temperature gradients and phase transition behavior. The findings highlight that optimized parameter selection significantly improves the structural integrity of Mo-based components while minimizing manufacturing defects, thus offering valuable guidance for advancing industrial-scale additive manufacturing of refractory metals.

Fusogenic Lipid Nanovesicles as Multifunctional Immunomodulatory Platforms for Precision Solid Tumor Therapy.

Although immunotherapy demonstrates considerable prospect in overcoming solid tumors, its clinical efficacy is limited by several factors, such as poor tumor immunogenicity, inadequate immune activation, and immunosuppressive tumor microenvironment (TME). To overcome these challenges, a versatile and universal immune modulation platform should be developed, and lipid nanovesicles with membrane fusion capabilities (LNV-Fs) have attracted great attention for this purpose. By mimicking natural membrane fusion processes, LNV-Fs enable the precise presentation of immunogenic components on tumor cell membranes, effectively activating anti-tumor immune surveillance. Similarly, LNV-Fs can equip multiple functionalities on autologous and adoptive effector cells for enhanced cell therapies. Additionally, LNV-Fs function as vaccines that elicit robust autologous anti-tumor immunity while promoting long-term immune memory. Furthermore, different LNV-Fs with powerful ability in reprogramming TME have been reported. Given the recent advancements and the absence of comprehensive reviews on this topic, a comprehensive analysis of LNV-F systems, including their structural classifications, membrane fusion mechanisms, and recent applications in cancer immunotherapy is provided. Furthermore, the future prospects of LNV-Fs, with particular emphasis on artificial intelligence-assisted design are explored. This review is intended to engage researchers from diverse interdisciplinary fields and provide valuable insights for advancing precision immunotherapy.

YOLO-HPSD: A high-precision ship target detection model based on YOLOv10.

Ship target detection is crucial in maritime traffic management, smart ports, autonomous ship systems, environmental monitoring, and ship scheduling. Accurate detection of various ships on the water can significantly enhance maritime traffic safety, reduce accidents, and improve the efficiency of port and waterway management. This study proposes a high-precision ship target detection algorithm based on YOLOv10, named YOLO-HPSD (High-precision Ship Target Detection). To meet the high-precision requirements in practical applications, several precision-enhancement strategies are introduced based on YOLOv10. To optimize the feature fusion process, the Iterative Attentional Feature Fusion (iAFF) is integrated with the C2F module in the backbone, resulting in the development of a novel C2F_iAFF module that utilizes a multi-scale channel attention mechanism. Meanwhile, the Mixed Local Channel Attention (MLCA) is introduced after the C2F module at the network neck, which improves the model's ability to integrate both local and global information. Additionally, the BiFPN module is incorporated after the connection operation at the network neck, utilizing learnable weights to optimize the importance of different input features, thereby further enhancing multi-scale feature fusion. The experimental results demonstrate that YOLO-HPSD achieves excellent detection performance on the ship dataset, with an F1-score of 97.88% and mAP@0.5 of 98.86%. Compared to YOLOv10n, the F1-score, and mAP@0.5 have improved by 1.22% and 0.31%, respectively. Furthermore, the detection time for a single image is only 20.6 ms. These results indicate that the model not only ensures high detection speed but also delivers high-accuracy ship target detection. This study provides technical support for real-time ship target detection and the development of edge computing devices.

Hybrid Mode Sensor Fusion for Accurate Robot Positioning.

Robotic systems are becoming increasingly crucial in applications requiring high precision. While a robot can operate using basic sensor feedback under controlled conditions, achieving micro-level accuracy requires more comprehensive data integration, especially in dynamic environments. The fusion of data from a variety of sensors is necessary for improving the positioning accuracy of a robot because the accuracy of one type of sensor is insufficient. The field of micro-positioning presents new challenges and tasks that have been gradually explored in the recent literature published from 2015 to 2025. Micro-positioning is a complex operation that involves factors such as mechanical drift, environmental effects, and sensor signal errors. Hybrid fusion is a sensor fusion technique that combines elements of fusion at different levels. For the effective deployment of robots in such contexts, it is essential to integrate multiple sensors and ensure reliable data fusion between them. This involves the use of different sensors, advanced fusion algorithms, and accurate calibration methods through sensor fusion and sophisticated data processing techniques. This literature review presents an analysis of the sensor data fusion methods for precise robot micro-positioning. The focus is on the investigated sensors, the applied synthesis methods, and the developed algorithms and their practical application to identify the existing gaps for future system improvements. Finally, discussions and conclusions based on the collected ideas are presented.

Diagnosis technology of bearing with variable working conditions based on generalized sidelobe canceller and multi-domain acoustic fusion

Abstract The early stage of rolling bearing failure is often accompanied by detectable weak signals, which can be detected and recognized to a certain extent by non-contact acoustic emission (NAE) and acoustic emission (AE) detection. This paper presents a bearing diagnostic technique that combines generalized sidelobe canceller (GSC) and Multi-Domain Acoustic Fusion (MDAF), which combines the advantages of easy detection in the audible domain and high sensitivity and anti-interference capability in the ultrasonic domain. First, Wavelet Threshold Denoising (WTD) is applied to preprocess the signal matrix, then the microphone array signals are beamformed using GSC based on Linearly constrained minimum variance (LCMV). Subsequently, the beamformed signals are integrated with the ultrasonic domain signals of the acoustic emission through a multi-domain acoustic fusion process. Finally, multiple high-dimensional time-frequency feature representations of the fused signals are used as model inputs using a lightweight RepViT network, enabling their use in mobile devices in the experiment. The results show that IGSC-MDAF-RepViT achieved an accuracy rate of 99.98% on the used features, which was 1.12% and 4.3% higher than that when only using a single sound domain signal, respectively. It can more accurately characterize the operating status of the bearing.

Advanced phase diversity method for RSA: wavefront detection and image fusion.

Rotating synthetic aperture (RSA) represents an emerging large-aperture imaging technology characterized by a rectangular aperture with a high aspect ratio. This configuration enables enhanced imaging resolution along the long-axis direction. During target observation, the aperture undergoes rotational maneuvers to acquire multi-angle imaging data, where subsequent fusion processing techniques facilitate high-quality image reconstruction. Current RSA image fusion methodologies primarily address high-frequency errors with statistical regularity, such as tremor and noise, yet remain ineffective against systematic wavefront aberrations. This paper proposes an advanced phase diversity (PD) algorithm based on aperture rotation to calculate the wavefront characteristics of the RSA system, combined with a multi-frame Richardson-Lucy algorithm to achieve superior image restoration performance.

A feasibility study on the circular manufacturing of sustainable metal additive manufacturing powders from machining chips

Abstract The holistic vision of the research is to develop a circular hybrid manufacturing framework to achieve ‘Net Zero’ for additive manufacturing sector by producing sustainable powders from production scrap/machining chips. This paper reports an initial feasibility study results from the foundation stage of the circular hybrid manufacturing framework that centres on generating additive manufacturing powders via solid-state crushing/ball milling of machining chips at room temperature. Here, the viability of the ball milling process to produce additive powders from three readily available chip materials is evaluated, viz. a low carbon steel (AISI 1020), and two aluminium alloy chips (AA6082-T6 and AA5083-H111). The ball-milled powders were characterised in terms of their morphology, size distribution, flowability and phase analysis. The morphology/size distributions were found to be influenced by the chip materials and their length scale. Single-track laser melting of pre-placed AA6082 ball-milled powder particles was subsequently performed to emulate the laser powder bed fusion process. Cross-sectional micrographs demonstrated melting and bonding of the ball-milled particles to the AA6082 substrate. A further feasibility trial was undertaken to fabricate cubes from the ball-milled AA5083 powders via the powder bed fusion process. The microhardness (71–88 HV0.01) and microstructure of the specimens were comparable to rolled AA5083-H111 plates. Graphical Abstract

Method for solving spatial pose of hydraulic support based on autonomous AR measurement by inspector

With the rise of Industry 5.0, “people-oriented” has become a core concept. However, the current degree of intelligence in coal mines is not high, and the inspector is only limited to simple inspection, but cannot measure the synthesized mining equipment autonomously. Therefore, this paper proposes a method based on the autonomous AR measurement by inspector, which introduces the augmented reality (AR) measurement technology into the synthesized mining face in coal mines. By allowing inspector to wear HoloLens2 devices, while using multi-modal interaction methods to match the reality of the hydraulic support components, accurate measurement of the pose of the hydraulic support, and at the same time fully utilize the “autonomy” of the inspector, in line with the development trend of intelligent inspection. This paper mainly consists of four parts, namely, “key technology of virtual reality matching-virtual reality matching of single support-virtual reality matching between supports-virtual reality matching between supports-verification experiment of matching measurement.” Firstly, the key technology of virtual reality matching is elaborated, the unified coordinate system of hydraulic support group is constructed and the virtual reality matching method of multi-modal control with gesture interaction + voice fine-tuning is proposed. Secondly, it describes the virtual and real matching technology of single hydraulic support, puts forward the method of matching measurement by the feature block of support components with the idea of “local replacing the whole,” and constructs the standard model library of feature block, and at the same time, it applies the box diagram method to the measurement data for fusion processing, so as to improve the accuracy of the data. Then the key technology of virtual-reality matching between supports is discussed, the virtual-reality matching of external components and internal components between supports is investigated, and the verification method of AR matching accuracy based on the intra-intra relationship is proposed. Finally, the accuracy of the AR measurement method was validated under laboratory conditions. The experimental results show that the error of AR measurement of support distance is within 1 mm, and the error of measurement angle is within 0.5°, which confirms the accuracy and feasibility of AR measurement method. It can provide a new method and technical support for the measurement of spatial pose data of hydraulic support group and other comprehensive mining equipment group.

Mechanistic study of modulating mitochondrial fission and fusion to ameliorate neuropathic pain in mice

The emergence of neuropathic pain is significantly influenced by the impairment of mitochondrial processes. Ensuring the stability of mitochondrial activity requires a delicate equilibrium between the processes of mitochondrial fission and fusion. However, the specific alterations in mitochondrial activity across different models of neuropathic pain and the underlying mechanisms remain largely unclear. We developed a persistent compression injury (CCI) model targeting the sciatic nerve in mice. CCI induced pain like behaviors in mice, which were associated with increased levels of dynamin related protein 1 (Drp1) and decreased expression of the fusion protein OPA1 and an increase in the percentage of DRG nerve cell mitochondria in the fission form, and a decrease in percentage in the fusion form. Ultrastructural analysis showed that mitochondria in CCI mice were smaller in perimeter and area, adopting a more circular shape. Overexpression of OPA1 mediated by AAV attenuated pain hypersensitivity, lowered oxidative stress, and expanded mitochondrial circumference and area. Mdivi-1 treatment reduced pain, whereas blocking fusion with MYLS22 augmented pain and oxidative stress and further led to increased mitochondrial fragmentation. Our results illustrate that Mitochondria in DRG nerve cell are highly sensitive to neuropathic pain. Modulating mitochondrial fission and fusion through targeted gene overexpression and pharmacological inhibitors restores mitochondrial dynamics, reduces oxidative stress, and alleviates neuropathic pain in mice. These findings position mitochondrial dynamics as promising therapeutic targets for pain management.

Identification of a nanobody able to catalyze the destruction of the spike-trimer of SARS-CoV-2.

Neutralizing antibodies have been designed to specifically target and bind to the receptor binding domain (RBD) of spike (S) protein to block severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus from attaching to angiotensin converting enzyme 2 (ACE2). This study reports a distinctive nanobody, designated as VHH21, that directly catalyzes the S-trimer into an irreversible transition state through postfusion conformational changes. Derived from camels immunized with multiple antigens, a set of nanobodies with high affinity for the S1 protein displays abilities to neutralize pseudovirion infections with a broad resistance to variants of concern of SARS-CoV-2, including SARS-CoV and BatRaTG13. Importantly, a super-resolution screening and analysis platform based on visual fluorescence probes was designed and applied to monitor single proteins and protein subunits. A spontaneously occurring dimeric form of VHH21 was obtained to rapidly destroy the S-trimer. Structural analysis via cryogenic electron microscopy revealed that VHH21 targets specific conserved epitopes on the S protein, distinct from the ACE2 binding site on the RBD, which destabilizes the fusion process. This research highlights the potential of VHH21 as an abzyme-like nanobody (nanoabzyme) possessing broad-spectrum binding capabilities and highly effective anti-viral properties and offers a promising strategy for combating coronavirus outbreaks.

Comparison of corrosion behavior of high strength aluminum alloy welded using various fusion and solid-state welding processes

The investigation of post welding corrosion behavior of aluminum 7004 alloy joined using Friction Stir Welding (FSW), Tungsten Inert Gas welding (TIG), and Metal Inert Gas welding (MIG) was carried out. GTAW welding was carried out at 220 A, 28 V, and 1 mm/s speed. Whereas GMAW was carried out at 320 A, 32 V, and 1.5 mm/s. FSW was carried out at rotational speed of 320 and 960 rpm, and tool travel speed of 0.5, 1, 1.5 mm/s. The average grain size of TIG and MIG samples were observed as 70 ± 5 µm and 75 ± 5 µm respectively, whereas FSW samples resulted in average grain size ranging from 6 to 22 ± 2 µm. Fine grains with an average size of 6 ± 2 µm were observed in the sample with a low rotational speed of 320 rpm and tool travel speed of 0.5 mm/s. Corrosion rates of TIG and MIG samples were 12.63 × 10−3 mm/year and 7.16 × 10−3 mm/year, respectively. Corrosion rate of base metal was 1.88 × 10−3 mm/year, whereas FSW with 320 rpm at 0.5 mm/s was 0.042 × 10−3 mm/year, indicating that the FSW joints show better corrosion resistance among all the samples studied.

MLFusion: Multilevel Data Fusion using CNNs for atrial fibrillation detection.

MLFusion: Multilevel Data Fusion using CNNs for atrial fibrillation detection.

Does Adding a Modality Really Make Positive Impacts in Incomplete Multi-Modal Brain Tumor Segmentation?

Previous incomplete multi-modal brain tumor segmentation technologies, while effective in integrating diverse modalities, commonly deliver under-expected performance gains. The reason lies in that the new modality may cause confused predictions due to uncertain and inconsistent patterns and quality in some positions, where the direct fusion consequently raises the negative gain for the final decision. In this paper, considering the potentially negative impacts within a modality, we propose multi-modal Positive-Negative impact region Double Calibration pipeline, called PNDC, to mitigate misinformation transfer of modality fusion. Concretely, PNDC involves two elaborate pipelines, Reverse Audit and Forward Checksum. The former is to identify negative regions impacts of each modality. The latter calibrates whether the fusion prediction is reliable in these regions by integrating the positive impacts regions of each modality. Finally, the negative impacts region from each modality and miss-match reliable fusion predictions are utilized to enhance the learning of individual modalities and fusion process. It is noted that PNDC adopts the standard training strategy without specific architectural choices and does not introduce any learning parameters, and thus can be easily plugged into existing network training for incomplete multi-modal brain tumor segmentation. Extensive experiments confirm that our PNDC greatly alleviates the performance degradation of current state-of-the-art incomplete medical multi-modal methods, arising from overlooking the positive/negative impacts regions of the modality. The code is released at PNDC.

The insight into the fusion process and sintering behaviour of blended ash from co-gasification of coal and biomass feedstock

The insight into the fusion process and sintering behaviour of blended ash from co-gasification of coal and biomass feedstock

Herpes simplex virus 1 fusion glycoprotein B H516P prefusion mutation had no effect on vaccine immunogenicity.

Herpes simplex virus 1 fusion glycoprotein B H516P prefusion mutation had no effect on vaccine immunogenicity.