bluuebunny/crossref_metadata_2025 huggingface dataset:
Gemcitabine-Loaded Microbeads for Transarterial Chemoembolization of Rabbit Renal Tumor Monitored by 18F-FDG Positron Emission Tomography/X-Ray Computed Tomography Imaging
Authors: Zhang Xiaoli , Li Tingting , Tong Jindong , Zhou Meihong , Wang Zi , Liu Xingdang , Lu Wei , Lou Jingjing , Yi Qingtong
Published: December 2024

Background/Objectives: The purpose of this study was to develop the gemcitabine-loaded drug-eluting beads (G-DEBs) for transarterial chemoembolization (TACE) in rabbit renal tumors and to evaluate their antitumor effect using 2-deoxy-2-[(18)F]fluoro-D-glucose positron emission tomography/X-ray computed tomography (18F-FDG PET/CT). Methods: DEBs were prepared by polyvinyl alcohol-based macromer crosslinked with N-acryl tyrosine and N,N′-methylenebis(acrylamide). Gemcitabine was loaded through ion change to obtain G-DEBs. Their particle size and drug release profile were characterized. VX2 tumors were implanted in the right kidney of rabbits to establish the renal tumor model. The tumor-bearing rabbits received pre-scan by 18F-FDG PET/CT, followed by targeted transarterial injection of G-DEBs under digital subtraction angiography (DSA) guidance. The rabbits received another 18F-FDG PET/CT scan 10 or 14 days after the treatment. The therapeutic effect was further validated by histopathological analysis of the dissected tumors. Results: The average particle size of the microspheres was 58.06 ± 0.50 µm, and the polydisperse index was 0.26 ± 0.002. The maximum loading rate of G-DEBs was 18.09 ± 0.35%, with almost 100% encapsulation efficiency. Within 24 h, GEM was eluted from G-DEBs rapidly and completely, and more than 20% was released in different media. DSA illustrated that G-DEBs were delivered to rabbit renal tumors. Compared with the untreated control group with increased tumor volume and intense 18F -FDG uptake, the G-DEBs group showed significant reductions in tumor volume and maximum standard uptake value (SUVmax) 10 or 14 days after the treatment. Histopathological analysis confirmed that the proliferating area of tumor cells was significantly reduced in the G-DEBs group. Conclusions: Our results demonstrated that G-DEBs are effective in TACE treatment of rabbit VX2 renal tumors, and 18F-FDG PET/CT provides a non-invasive imaging modality to monitor the antitumor effects of TACE in renal tumors.
Measurement of Atmospheric Coherence Length from a Shack–Hartmann Wavefront Sensor with Extended Sources
Authors: Zhang Junrui , Zhao Yuling , Yang Leqiang , Liu Jie , Wang Wenyu , Li Zhengwei , Wang Jianli , Chen Tao
Published: December 2024

Free Space Optical Communication (FSOC) is a wireless communication method that utilizes laser beams for high speed and secure data transmission. Its performance is affected by various factors, among which atmospheric turbulence causes random fluctuations in the atmospheric refractive index, significantly impacting the reliability of communication links. The atmospheric coherence length is a key parameter describing the coherence properties of a laser signal as it propagates through the atmosphere, and accurately measuring it is crucial for assessing the quality of FSOC links. This paper proposes a novel strategy that utilizes extended sources directly as the information sources, combining the wavefront phase variance method with the extended source offset algorithm based on Shack–Hartmann wavefront sensors to directly measure atmospheric coherence length. Existing methods in extended scenarios typically rely on deploying laser beacons to aid in the calibration of atmospheric coherence length but setting up suitable beacons on horizontal communication links is challenging. Additionally, these approaches can be costly in terms of equipment and measurement expenses. Compared to traditional measurement methods, the algorithm proposed in this paper can measure directly based on extended scenarios in horizontal links, thereby effectively reducing system complexity and equipment costs. To verify the feasibility and effectiveness of this method, targeted simulations and experiments were conducted, and the results show that the coherence length measured by the algorithm is highly consistent with that measured by the Differential Image Motion Monitor (DIMM), with a deviation of less than 2% from actual values, effectively demonstrating the algorithm’s feasibility in coherence length assessment.
Impact of Different Land-Use Types on Soil Microbial Carbon Metabolism Function in Arid Region of Alpine Grassland
Authors: Li Keyi , Han Yaoguang , Chen Mo , Yu Guangling , Abulaizi Maidinuer , Hu Yang , Wang Bohao , Yang Zailei , Zhu Xinping , Jia Hongtao
Published: December 2024

There are discrepancies that exist in the effects of different land uses on soil organic carbon (SOC) and soil microbial carbon metabolism functions. However, the impact of land-use type changes on soil microbial carbon metabolism in alpine grassland arid areas is not well understood, hindering our understanding of the carbon cycling processes in these ecosystems. Therefore, we chose three types of land use (continuous reclamation of grassland (RG), abandoned grassland (AG), and natural grazing grassland (GG)) to study the microbial carbon metabolism and its driving factors by the Biolog-ECO method. The results showed that the soil organic carbon content decreased by 16.02% in the RG and by 32.1% in the AG compared to the GG in the 0–20 cm soil layer (p < 0.05). Additionally, microorganisms have the highest utilization efficiency of carbohydrate carbon sources, the average values of average well color development (AWCD) were RG (0.26), AG (0.35), and GG (0.26). In the 0–20 cm soil layer, the Shannon–Wiener and the Simpson indices were 3% and 1% higher in the AG compared to the GG, respectively. The soil TOC/TN and soil available phosphorus (AP) were key factors that affected the diversity of soil microbial and carbon metabolism. They were closely related to land-use types. This study holds that abandoning grasslands accelerates the carbon metabolism of microorganisms, leading to the loss of SOC content.
Synthesis and Degradation of the Phytohormone Indole-3-Acetic Acid by the Versatile Bacterium Paraburkholderia xenovorans LB400 and Its Growth Promotion of Nicotiana tabacum Plant
Authors: Vega-Celedón Paulina , Castillo-Novales Diyanira , Bravo Guillermo , Cárdenas Franco , Romero-Silva María José , Seeger Michael
Published: December 2024

Plant growth-promoting bacteria (PGPB) play a role in stimulating plant growth through mechanisms such as the synthesis of the phytohormone indole-3-acetic acid (IAA). The aims of this study were the characterization of IAA synthesis and degradation by the model aromatic-degrading bacterium Paraburkholderia xenovorans LB400, and its growth promotion of the Nicotiana tabacum plant. Strain LB400 was able to synthesize IAA (measured by HPLC) during growth in the presence of tryptophan and at least one additional carbon source; synthesis of anthranilic acid was also observed. RT-PCR analysis indicates that under these conditions, strain LB400 expressed the ipdC gene, which encodes indole-3-pyruvate decarboxylase, suggesting that IAA biosynthesis proceeds through the indole-3-pyruvate pathway. In addition, strain LB400 degraded IAA and grew on IAA as a sole carbon and energy source. Strain LB400 expressed the iacC and catA genes, which encode the α subunit of the aromatic-ring-hydroxylating dioxygenase in the IAA catabolic pathway and the catechol 1,2-dioxygenase, respectively, which may suggest a peripheral IAA pathway leading to the central catechol pathway. Notably, P. xenovorans LB400 promoted the growth of tobacco seedlings, increasing the number and the length of the roots. In conclusion, this study indicates that the versatile bacterium P. xenovorans LB400 is a PGPB.
Colchicine-Induced Tetraploidy in Protocorms of Aerides rosea Lodd. ex Lindl. and Paxton. and Its Identification
Authors: Wang Li , Zheng Pengrui , Ge Hong , Zhao Xin , Kou Yaping , Yang Shuhua , Yu Xiaonan , Jia Ruidong
Published: December 2024

Aerides rosea (Orchidaceae) boasts high ornamental value due to its pleasant aroma, foxtail spike, and elegant floral morphology. Inducing A. rosea to become tetraploid enhances horticultural traits and facilitates fertile intergeneric hybrids through crosses with other market-available tetraploid species. The experimental design involved the application of colchicine at varying concentrations—0.05%, 0.1%, and 0.2%—to a solid medium. Exposure durations were 5, 10, and 15 days, with treatments conducted under sterile conditions on 6-week-old protocorms post-germination. Results indicated that the protocorms were sensitive to colchicine concentrations exceeding 0.05%, with high concentrations leading to a mortality rate exceeding 50%. Flow cytometry (FCM) with 4′,6-diamidino-2-phenylindole (DAPI) staining confirmed a doubling of chromosome numbers in tetraploid plants (2n = 4x = 76) compared to diploid controls (2n = 2x = 38). Induction efficiency was significantly influenced by colchicine concentration and treatment duration. A 10-day treatment with 0.2% colchicine yielded a 70.00% tetraploid induction rate; however, considering protocorm survival, a 5-day treatment with 0.05% colchicine was preferable, achieving a 63.55% survival rate and a 56.67% tetraploid induction rate. Tetraploid plants exhibited distinct morphological traits, such as a more compact growth habit, thicker leaves, and increased stem and root thickness. Leaf morphology changes included larger stomata with reduced density, denser spongy mesophyll, and more pronounced venation. Tetraploids also demonstrated a 1.94-fold increase in genome size compared to diploids. The tetraploid genotypes developed in this study hold significant potential for future Aerides breeding programs.
Effect of Seed Hydropriming on the Elongation of Plumule and Radicle During the Germination Process and Changes in Enzyme Activity Under Water-Deficient Conditions
Authors: Choi Ju-Young , Ju Young-Hwan , Nakamichi Ayaka , Cho Seong-Woo , Woo Sun-Hee , Sakagami Jun-Ichi
Published: December 2024

Hydropriming rice seeds effectively improve the germination percentage, shortens the germination period, and promotes seedling growth. The impact of seed hydropriming is to speed up growth under dry soil conditions, thereby avoiding drought damage. This study analyzes the effect of hydropriming on morpho-physiological changes in the water uptake of rice seeds using “Kasalath” and “Nipponbare” under water-deficit conditions. Upon exposure to osmotic stress, both varieties showed delays in the time to reach germination. In addition, all germination phases exhibited reductions in the activity of alpha-amylase and total soluble sugar by osmotic stress; however, in all germination phases of the hydroprimed seeds, the activity and contents of those were significantly increased, resulting in increased size of the coleoptile, plumule, and radicle. In hydroprimed seeds, “Kasalath” was superior to “Nipponbare” in the ratio of the water-deficit-to-well-watered conditions for all traits related to germination, which may have been attributable to hydropriming having a greater effect on “Kasalath”. Interestingly, Primed “Kasalath” had a lower level of α-amylase, despite the having a higher content of total soluble sugars than primed “Nipponbare”.
Impact Resistance of Layered Aramid Fabric: A Numerical Study on Projectile-Induced Damage
Authors: Titire Larisa , Muntenita Cristian , Chivu Mariana
Published: December 2024

The aim of this work is to comparatively analyze, using numerical simulation, the impact behavior of aramid fabric. A layered panel was impacted by two projectiles specific to the NIJ protection level HG1. The protection level in this study is based on NIJ Standard 0123.00. This standard is used to establish protection levels. The two projectiles specific to the NIJ HG1 protection level are 9 mm Luger and .357 Mag FMJ. Law enforcement personnel use body armor designed to protect the torso. With the help of numerical simulation, the mechanisms of destruction of the aramid fabric on impact are identified. The protection performance is analyzed as a function of the influence of the number of layers and the projectile velocity variation. The fabric is modeled at the yarn level, with each yarn consisting of hundreds or even thousands of fibers. Simulations are performed at the yarn level, since fiber-level modeling is difficult to implement due to high computational resource requirements. The material properties for the yarn, as well as for the projectiles, are selected from the literature. The results show that only the 20-layer fabric panel impacted by the 9 mm Luger FMJ RN 9 mm FMJ RN projectile at 398 m/s meets the protection requirements of the NIJ standard (NIJ HG1 protection level). In contrast, panels impacted at 436 m/s, or those with fewer layers, show rapid stress wave propagation, severe deformation, and complete perforation, indicating insufficient energy dissipation. This study highlights the critical role of impact velocity, projectile geometry, and number of layers in determining ballistic resistance. These findings contribute to the development of more effective ballistic protective equipment, highlighting the need for optimized layer configurations and improved material properties to meet NIJ standards under different impact conditions.
Chess-like Pieces Realized by Selective Laser Sintering of PA12 Powder: 3D Printing and Micro-Tomographic Assessment
Authors: Colucci Giovanna , Fontana Luca , Barberi Jacopo , Vitale Brovarone Chiara , Messori Massimo
Published: December 2024

The paper highlights the realization of 3D-printed parts with complex geometries, such as chess-like pieces, using polyamide 12 (PA12) as polymeric powder via selective laser sintering (SLS). The research activity focuses on the study of the powder printability, the optimization of the printing parameters, and the tomographic evaluation of the printed objects. Morphological analyses were carried out to study the PA12 powder microstructure considering that SLS required specific particle size distribution and shape, able to guarantee a good flowability necessary to take part in a sintering process. DSC and TG analyses were performed to determine the sintering window and the crystallinity degree, and to evaluate the thermal stability of the PA12 powder due to the importance of the powder processability for the SLS process. The novelty lies in the realization of chess-like pieces very challenging to print via SLS due to their different and highly detailed structures, and the in-depth analysis of the dimensional accuracy evaluated by micro-tomography. The 3D-printed samples obtained show high printing quality and dimensional stability. The μ-CT analysis also confirms the key role of the object shape and section changes on the final porosity of the chess-like pieces.
Effect of Side Substituent on Comb-like Polysiloxane Membrane Pervaporation Properties During Recovery of Alcohols C2-C4 from Water
Authors: Grushevenko Evgenia , Chechenov Islam , Rokhmanka Tatyana , Anokhina Tatiana , Bazhenov Stepan , Borisov Ilya
Published: December 2024

The pervaporation properties of membranes based on comb-like polysiloxanes when C2-C4 alcohols are removed from water were studied for the first time. It was established that membranes based on comb-like polysiloxanes with linear aliphatic and organosilicon substituents have increased permeability selectivity for C3+ alcohols. The obtained results were interpreted from the point of view of the solubility of the components of the separated mixture in polysiloxanes. It was shown that membranes based on polysiloxanes with linear substituents have increased butanol/water permeability selectivity (2.5–3.7). The achieved selectivity values correspond to the level of highly selective zeolite membranes, which allows for a reduction in energy consumption for the pervaporation removal of butanol by more than two times.
Creation of Flexible Heterogeneously-Doped Carbon Nanotube Paper PN Diodes to Enhance Thermoelectric and Photovoltaic Effects
Authors: Liu Jih-Hsin , Yen Chen-Yu
Published: December 2024

This study investigates the fabrication and characterization of flexible PN diode devices using phosphorus- and boron-doped carbon nanotube (CNT) paper, also known as Buckypaper (BP). The BP substrate is fabricated from multi-walled carbon nanotubes (MWCNTs) and doped with phosphorus and boron to form N-type and P-type semiconductors, respectively. Various experimental techniques, including Raman spectroscopy, Hall effect measurements, and scanning electron microscopy (SEM), are employed to analyze the properties of the doped BP. The results reveal that the current-voltage (I-V) and capacitance-voltage (C-V) characteristics preliminarily exhibit the basic electrical properties of a diode after doping with P-type and N-type carriers. Subsequently, optimized vertical stacking combined with parallel electrode configurations for the BP diode devices demonstrates that vertical series stacking gradually enhances the thermoelectric voltage, while horizontal parallel connections approximately scale up the thermoelectric and photovoltaic voltages proportionally. The findings underscore the critical role of creating heterogeneously doped CNT-paper PN junction electric fields in improving the performance of carbon-based semiconductor devices. Furthermore, we demonstrate that these directionally oriented energy devices, when stacked, can form modular systems with enhanced efficiency. This work highlights the potential of flexible carbon material-based devices for advanced thermoelectric and photovoltaic applications.
Efficient Synthesis of Novel Triazolo[5,1-b]purines by Diacetoxyiodobenzene-Mediated Oxidative Cyclization of Schiff Bases
Authors: Neymash Artyom O. , Fedotov Victor V. , Ulomsky Evgeny N. , Lyapustin Daniil N. , Aminov Semen V. , Rusinov Vladimir L.
Published: December 2024

In this work, we have developed a method for synthesizing new 8-substituted triazolo[5,1-b]purines using diacetoxyiodobenzene as an oxidizing agent with good yields (59–67%). The advantages of this approach include mild reaction conditions and removing the need to use transition metals. Based on the results obtained, a plausible reaction pathway was proposed. The developed approach opens new possibilities for the preparation of previously inaccessible condensed purine derivatives, which are of interest for the development of biomolecules with a variety of pharmacological applications. The structures of the compounds were confirmed by the data of 1H, 13C NMR spectroscopy, IR spectroscopy, and an elemental analysis.
Spatiotemporal Evolution of Air–Sea CO2 Flux in the South China Sea and Its Response to Environmental Factors
Authors: Chen Ying , Zhao Hui , Gao Hui
Published: December 2024

Increasing atmospheric carbon dioxide (CO2) from human activities underscores the need to understand air–sea CO2 flux in marine environments, particularly in marginal seas like the South China Sea (SCS). This research analyzes the spatial and temporal patterns of air–sea CO2 flux across four typical regions of the SCS: the northern SCS, western SCS, SCS basin, and northeastern SCS. Our results show that the SCS serves as a carbon source from spring to autumn and shifts to a carbon sink in winter. The northern SCS exhibits strong carbon sink behavior during winter, transitioning to a source in warmer months, while the western SCS and SCS basin consistently act as carbon sources year-round, with summer peaks. The northeastern SCS acts as a source in warmer months, becoming a weak sink in winter. Partial correlation analysis reveals that temperature and wind speed significantly influence air–sea CO2 flux, though regional differences exist. Notably, chlorophyll-a in the northern SCS is negatively correlated with air–sea CO2 flux, indicating that high primary productivity enhances CO2 absorption, whereas other regions show contrasting relationships. These findings provide valuable insights into the complex carbon cycle mechanisms in the SCS.
An Evaluation of the Performance of Remote Sensing Indices as an Indication of Spatial Variability and Vegetation Diversity in Alpine Grassland
Authors: Sang Yanan , Gu Haibin , Meng Qingmin , Men Xinna , Sheng Jiandong , Li Ning , Wang Ze
Published: December 2024

Vegetation diversity is a crucial indicator for evaluating grassland ecosystems. Remote sensing technology has great potential in assessing grassland vegetation diversity. In this study, the relationship between remote sensing indices and species diversity was investigated at varying spatial and temporal scales in Bayanbulak Grassland National Nature Reserve, China. Spectral variation, defined as the coefficient of variation in vegetation indices, was used as a proxy for species diversity, which was quantified using species diversity indices. The “spectral diversity-species diversity” relationship was validated across diverse spatial scales and between different years using Sentinel-2 images and ground investigation data. This study found that Kendall’s τ coefficients showed the best performance in evaluating the relationship between the coefficient of variation in VIs (CVVIs) and species diversity index. The highest τ value was observed for CVNDVI in 2017 (τ = 0.660, p < 0.01), followed by the Shannon index in 2018 (τ = 0.451, p < 0.01). In addition, CVEVI demonstrated a significant positive correlation with the Shannon-Wiener Index at the 50 m scale (τ = 0.542), and the highest relationship τ between CVNDVI and the Shannon-Wiener Index was observed at the 100 m scale (τ = 0.660). The Shannon-Wiener Index in relation to CVVIs performs better in representing changes in grassland vegetation. Spatial scales and vegetation indices influence the assessment of grassland vegetation diversity. These findings underscore the critical role of remote sensing technology in assessing grassland vegetation diversity across various scales, offering valuable support tools for measuring regional grassland vegetation diversity.
Enhanced Flood Monitoring in the Pearl River Basin via GAIN-Reconstructed GRACE Terrestrial Water Storage Anomalies
Authors: Wang Jing , Li Haiyang , Wu Shuguang , Nie Guigen , Wang Yawei
Published: December 2024

Floods are a significant and pervasive threat globally, exacerbated by climate change and increasing extreme weather events. The Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) provide crucial insights into terrestrial water storage anomalies (TWSA), which are vital for understanding flood dynamics. However, the observational gap between these missions presents challenges for flood monitoring, affecting the estimation of long-term trends and limiting the analysis of interannual variability, thereby impacting overall analysis accuracy. Reconstructing the missing data between GRACE and GRACE-FO is essential for systematically understanding the spatiotemporal distribution characteristics and driving mechanisms of interannual changes in regional water reserves. In this study, the Generative Adversarial Imputation Network (GAIN) is applied to improve the monitoring capability for flood events in the Pearl River Basin (PRB). First, the GRACE/GRACE-FO TWSA data gap is imputed with GAIN and compared with long short-term memory (LSTM) and k-Nearest Neighbors (KNN) methods. Using the reconstructed data, we develop the Flood Potential Index (FPI) by integrating GRACE-based TWSA with precipitation data and analyze key characteristics of FPI variability against actual flood events. The results indicate that GAIN effectively predicts the GRACE/GRACE-FO TWSA gap, with an average improvement of approximately 50.94% over LSTM and 68.27% over KNN. The reconstructed FPI proves effective in monitoring flood events in the PRB, validating the reliability of the reconstructed TWSA. Additionally, the FPI achieves a predictive accuracy of 79.7% for real flood events, indicating that short-term flood characteristics are better captured using TWSA. This study demonstrates the effectiveness of GAIN in enhancing data continuity, providing a reliable framework for large-scale flood risk assessment and offering valuable insights for flood management in vulnerable regions.
A Hierarchical Machine Learning-Based Strategy for Mapping Grassland in Manitoba’s Diverse Ecoregions
Authors: Mousavi Mirmajid , Biney James Kobina Mensah , Kishchuk Barbara , Youssef Ali , Cordeiro Marcos R. C. , Friesen Glenn , Cattani Douglas , Namous Mustapha , Badreldin Nasem
Published: December 2024

Accurate and reliable knowledge about grassland distribution is essential for farmers, stakeholders, and government to effectively manage grassland resources from agro-economical and ecological perspectives. This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed in the province of Manitoba, Canada. The grassland classification process involved three stages: (1) to distinguish between vegetation and non-vegetation covers, (2) to differentiate grassland from non-grassland landscapes, and (3) to identify three specific grassland classes (tame, native, and mixed grasses). Initially, this study investigated different satellite data, such as Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 and 9, individually and combined, using the random forest (RF) method, with the best performance at the first two steps achieved using a combination of S1 and S2. The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). In step 3, after filtering out non-grassland pixels, the performance of RF, SVM, and GTB classifiers was evaluated with combined S1 and S2 data to distinguish different grassland types. Eighty-nine multitemporal raster-based variables, including spectral bands, SAR backscatters, and digital elevation models (DEM), were input for ML models. RF had the highest classification accuracy at 69.96% overall accuracy (OA) and a Kappa value of 0.55. After feature selection, the variables were reduced to 61, increasing OA to 72.62% with a Kappa value of 0.58. GTB ranked second, with its OA and Kappa values improving from 67.69% and 0.50 to 72.18% and 0.58 after feature selection. The impact of raster data quality on grassland classification accuracy was assessed through multisensor image fusion. Grassland classification using the Hue, Saturation, and Value (HSV) fused images showed higher OA (59.18%) and Kappa values (0.36) than the Brovey Transform (BT) and non-fused images. Finally, a web map was created to show grassland results within the Soil Landscapes of Canada (SLC) polygons, relating soil landscapes to grassland distribution and providing valuable information for decision-makers and researchers. Future work may include extending the current methodology by considering other influential variables, like meteorological parameters or soil properties, to create a comprehensive grassland inventory across the whole Prairie ecozone of Canada.
Pulse Shape Discrimination of n/γ in Liquid Scintillator at PMT Nonlinear Region Using Artificial Neural Network Technique
Authors: Yun Eungyu , Choi Ji Young , Kim Sang Yong , Joo Kyung Kwang
Published: December 2024

Reactor-emitted electron antineutrinos can be detected via the inverse beta decay reaction, which produces a characteristic signal: a two-fold coincidence between a prompt positron event and a delayed neutron capture event within a specific time frame. While liquid scintillators are widely used for detecting neutrinos reacting with matter, detection is difficult because of the low interaction of neutrinos. In particular, it is important to distinguish between neutron (n) and gamma (γ) signals. The principle of the interaction of neutrons with matter differs from that of gamma rays with matter, and hence the detection signal’s waveform is different. Conventionally, pulse shape discrimination (PSD) is used for n/γ separation. This study developed a machine learning method to see if it is more efficient than the traditional PSD method. The possibility of n/γ discrimination in the region beyond the linear response limits was also examined, by using 10- and 2-inch photomultiplier tubes (PMTs) simultaneously. To the best of our knowledge, no study has attempted PSD in a PMT nonlinear region using artificial neural networks. Our results indicate that the proposed method has the potential to distinguish between n and γ signals in a nonlinear region.
Characterization of an Inorganic Powder-Based Scintillation Detector Under a UHDR Electron Beam
Authors: Tho Daline , Beddar Sam
Published: December 2024

(1) Background: Ultra-high dose rate (UHDR) radiation therapy needs a reliable dosimetry solution and scintillation detectors are promising candidates. In this study, we characterized an inorganic powder-based scintillation detector under a 9 MeV UHDR electron beam. (2) Methods: A mixture of ZnS:Ag powder and optic glue was coupled to an 8 m Eska GH-4001-P polymethyl methacrylate (PMMA) optical fiber. We evaluated the dependence of the detector on dose per pulse (DPP), pulse repetition frequency (PRF), and pulse width (PW). Additionally, we determined the stability and the reproducibility of the detector. (3) Results: The signal ratio between the PMMA clear optical fiber and the ZnS:Ag scintillator was around 210. ZnS:Ag produced a signal yield 54 times greater than that of a BCF-12 plastic scintillator. Signal variation with PRF changes was under 0.5%. The signal was linear to the integrated dose up to the maximum deliverable dose, 180 Gy. The variation in signal was linear to the change in both PW and DPP. Regarding stability, the standard deviation of 10 consecutive irradiations was 0.83%. For the reproducibility, all daily measurements varied within ±1.5%. (4) Conclusions: These findings show that the ZnS:Ag detector can be used for accurate dosimetry with UHDR beams.
GeometryFormer: Semi-Convolutional Transformer Integrated with Geometric Perception for Depth Completion in Autonomous Driving Scenes
Authors: Su Siyuan , Wu Jian
Published: December 2024

Depth completion is widely employed in Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SfM), which are of great significance to the development of autonomous driving. Recently, the methods based on the fusion of vision transformer (ViT) and convolution have brought the accuracy to a new level. However, there are still two shortcomings that need to be solved. On the one hand, for the poor performance of ViT in details, this paper proposes a semi-convolutional vision transformer to optimize local continuity and designs a geometric perception module to learn the positional correlation and geometric features of sparse points in three-dimensional space to perceive the geometric structures in depth maps for optimizing the recovery of edges and transparent areas. On the other hand, previous methods implement single-stage fusion to directly concatenate or add the outputs of ViT and convolution, resulting in incomplete fusion of the two, especially in complex outdoor scenes, which will generate lots of outliers and ripples. This paper proposes a novel double-stage fusion strategy, applying learnable confidence after self-attention to flexibly learn the weight of local features. Our network achieves state-of-the-art (SoTA) performance with the NYU-Depth-v2 Dataset and the KITTI Depth Completion Dataset. It is worth mentioning that the root mean square error (RMSE) of our model on the NYU-Depth-v2 Dataset is 87.9 mm, which is currently the best among all algorithms. At the end of the article, we also verified the generalization ability in real road scenes.
Light-Emitting Diode Array with Optical Linear Detector Enables High-Throughput Differential Single-Cell Dielectrophoretic Analysis
Authors: Kovacs Emerich , Arzang Behnam , Salimi Elham , Butler Michael , Bridges Greg E. , Thomson Douglas J.
Published: December 2024

This paper presents a lens-free imaging approach utilizing an array of light sources, capable of measuring the dielectric properties of many particles simultaneously. This method employs coplanar electrodes to induce velocity changes in flowing particles through dielectrophoretic forces, allowing the inference of individual particle properties from differential velocity changes. Both positive and negative forces are detectable. The light source utilized in this system is composed of LEDs with a wavelength of 470 nm, while detection is performed using a 256-element optical array detector. Measurements with 10 μm polystyrene beads demonstrate this method can resolve changes equivalent to a Clausius–Mossotti factor of 0.18. Simulations in this work, using values from the literature, predict that Clausius–Mossotti factor differences of 0.18 are sufficient to differentiate viable from nonviable cells and cancerous from multidrug-resistant cancerous cells. We demonstrate that for Chinese hamster ovary (CHO) cells, the method can collect a dielectric response spectrum for a large number of cells in several minutes. We demonstrate that for CHO cells, Clausius–Mossotti factor differences of 0.18 can be discriminated. Due to its simple detection apparatus and the utilization of high-throughput, wide, clog-resistant channels, this method holds promise for a wide range of applications.
The Application and Optimisation of a Neural Network PID Controller for Trajectory Tracking Using UAVs
Authors: Siwek Michał , Baranowski Leszek , Ładyżyńska-Kozdraś Edyta
Published: December 2024

This paper considers the problem of flying a UAV along a given trajectory at speeds close to the speed of sound and above. A novel pitch channel control system is presented using the example of a trajectory with rapid and large changes in flight height. The control system uses a proportional–integral–differential (PID) controller, whose gains were first determined using the Ziegler-Nichols II method. The determined gains were then optimised to minimise height error using a recurrent back-propagation neural network (PIDNN), with which new controller gains were determined, which is also a novelty of this study. Simulations were carried out for flights at subsonic speeds close to the speed of sound and supersonic speeds, at low and high altitudes. The simulations showed that determining controller gains using a recurrent neural network significantly minimises height errors and increases the flexibility of the PID controller.
Force Feedback Method for Statically Indeterminate Steel Structure Construction Based on Staged Temperature Measurement
Authors: Lu Wei , Fang Xianwei , Liu Liming , Ao Chunfeng , Hu Weihua , Teng Jun , Huo Zhongcheng
Published: December 2024

Structural design usually adopts uniform temperature action. However, during the actual construction of the structure, the temperature field acting on the structure is inhomogeneous. Therefore, the simulation of the construction of statically indeterminate steel structures considering only the uniform temperature field cannot truly reflect the temperature action after structural molding and the evolution of the stress performance of the temporary stress system of structural construction. This paper proposes a force feedback method for the construction of statically indeterminate steel structures based on staged temperature measurements. In this method, the construction sequence and the measured temperature are taken into account, and the structural response under the action of temperature is obtained by using the first processing method in the matrix displacement method and the deformation coordination formula to obtain the deformation and stress values of the structure. The application results show that the structural forces calculated by the method proposed in this paper are closer to the measured data in the field, and the error is reduced by 10~40%. This paper provides a reference for the calculation of construction forces in statically indeterminate steel structures considering the effect of temperature inhomogeneity and provides a basis for construction safety assessment and schedule management.
Reanalysis on Performance of Microwave Phase Detector for Multisignals
Authors: Deng Jing , Wang Hongxun , Xiang Xin
Published: December 2024

Microwave phase detectors (MPDs) are key components of instantaneous frequency measurement (IFM) receivers and phase interferometer direction finding (PIF-DF) receivers. In conventional analyses, there is seldom a major quantitative discussion of MPD characterization when multiple signals arrive at the same time, which is often the case in complex and noisy electromagnetic environments. We have reanalyzed the characteristics of MPDs with respect to filter effects acting on more than two RF signals and differential amplifiers, which are not considered in conventional analyses. First, a step-by-step mathematical model of the signal flow is developed, which creates a cross term between the two signals and naturally introduces intermodulation effects. Second, the new response characteristics of the MPD are evaluated by simulation. Finally, the intermodulation effects of zero-forcing and extreme-forcing were found simultaneously in the crosspoint frequency and near-frequency regions of multiple signals, which led to significant deviations and errors in the output of the MPD. This effect may have significant implications for IFM and PIF-DF receivers.
Joint Fault Diagnosis of IGBT and Current Sensor in LLC Resonant Converter Module Based on Reduced Order Interval Sliding Mode Observer
Authors: Zha Xi , Feng Wei , Zhang Xianfeng , Cao Zhonghua , Chen Xinyang
Published: December 2024

LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter’s ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability. This article proposes a fault diagnosis strategy for LLC resonant converters utilizing a reduced-order interval sliding mode observer. Initially, an augmented generalized system for the LLC resonant converter is developed to convert current sensor faults into generalized state vectors. Next, the application of matrix transformations plays a critical role in decoupling open-circuit faults from the inverter system’s state and current sensor faults. To achieve accurate estimation of phase currents and detection of current sensor faults, a reduced-order interval sliding mode observer has been designed. Building upon the estimation results generated by this observer, a diagnostic algorithm featuring adaptive thresholds has been introduced. This innovative algorithm effectively differentiates between current sensor faults and open switch faults, enhancing fault detection accuracy. Furthermore, it is capable of localizing faulty power switches and estimating various types of current sensor faults, thereby providing valuable insights for maintenance and repair. The robustness and effectiveness of the proposed fault diagnosis algorithm have been validated through experimental results and comparisons with existing methods, confirming its practical applicability in real-world inverter systems.
Cross-Field Road Markings Detection Based on Inverse Perspective Mapping
Authors: Lu Eric Hsueh-Chan , Hsieh Yi-Chun
Published: December 2024

With the rapid development of the autonomous vehicles industry, there has been a dramatic proliferation of research concerned with related works, where road markings detection is an important issue. When there is no public open data in a field, we must collect road markings data and label them by ourselves manually, which is huge labor work and takes lots of time. Moreover, object detection often encounters the problem of small object detection. The detection accuracy often decreases when the detection distance increases. This is primarily because distant objects on the road take up few pixels in the image and object scales vary depending on different distances and perspectives. For the sake of solving the issues mentioned above, this paper utilizes a virtual dataset and open dataset to train the object detection model and cross-field testing in the field of Taiwan roads. In order to make the model more robust and stable, the data augmentation method is employed to generate more data. Therefore, the data are increased through the data augmentation method and homography transformation of images in the limited dataset. Additionally, Inverse Perspective Mapping is performed on the input images to transform them into the bird’s eye view, which solves the “small objects at far distance” problem and the “perspective distortion of objects” problem so that the model can clearly recognize the objects on the road. The model testing on the front-view images and bird’s eye view images also shows a remarkable improvement of accuracy by 18.62%.
Blade Vibration Difference-Based Circumferential Fourier Fitting Algorithm for Synchronous Vibration Parameter Identification of Rotation Blades
Authors: Fan Zhenfang , Li Hongkun , Huang Jinying , Liu Siyuan
Published: December 2024

Blades are the core components of rotating machinery, and the blade vibration status directly impacts the working efficiency and safe operation of the equipment. The blade tip timing (BTT) technique provides a solution for blade vibration monitoring and is currently a prominent topic in research on blade vibration issues. Nevertheless, the non-stationary factors present in actual engineering applications introduce inaccuracies in the BTT technique, resulting in blade vibration measurement errors. The theory of blade vibration difference offers a new perspective for high-precision BTT techniques. This paper optimizes the traditional circumferential Fourier fitting (CFF) algorithm. According to the blade departure time measurement mechanism, four sets of BTT signals are obtained by two probes, six sets of blade vibration differences are established, and, then, a blade vibration difference-based circumferential Fourier fitting (BVD-CFF) algorithm for blade synchronous vibration parameter identification is proposed. Simulation studies demonstrate that the BVD-CFF algorithm exhibits superior anti-noise performance. Moreover, experimental investigations on a high-speed rotation blade vibration test rig and a large-scale centrifugal compressor test rig display that the engine order of blade synchronous vibrations obtained by the BVD-CFF algorithm are essentially the same as those obtained by the strain gauge method.
Evaluation of WIMU Sensor Performance in Estimating Running Stride and Vertical Stiffness in Football Training Sessions: A Comparison with Smart Insoles
Authors: Pinelli Salvatore , Mandorino Mauro , Lacome Mathieu , Fantozzi Silvia
Published: December 2024

Temporal parameters are crucial for understanding running performance, especially in elite sports environments. Traditional measurement methods are often labor-intensive and not suitable for field conditions. This study seeks to provide greater clarity in parameter estimation using a single device by comparing it to the gold standard. Specifically, this study aims to investigate how the temporal parameters and vertical stiffness (Kvert) of running stride exerted by IMU sensors are related to the parameters of the smart insole for outdoor acquisition. Ten healthy male subjects performed four 60-meter high-speed runs. Data were collected using the WIMU PRO™ device and smart insoles. Contact time (CT) and flight time (FT) were identified, and Kvert was calculated using Morin’s method. Statistical analyses assessed data normality, correlations, and reliability. WIMU measured longer CT, with differences ranging from 26.3% to 38.5%, and shorter FT, with differences ranging from 27.3% to 54.5%, compared to smart insoles, across different running speeds. Kvert values were lower with WIMU, with differences ranging from 23.96% to 45.01% depending on the running activity, indicating significant differences (p < 0.001). Using these results, a multiple linear regression model was developed for the correction of WIMU’s Kvert values, improving the accuracy. The improved accuracy of Kvert measurements has significant implications for athletic performance. It provides sports scientists with a more reliable metric to estimate player fatigue, potentially leading to more effective training regimens and injury prevention strategies. This advancement is particularly valuable in team sports settings, where easy-to-use and accurate biomechanical assessments of multiple athletes are essential.
Integrating Paths: Enhancing Deliberative Democracy Through Collective Intelligence Insights
Authors: Rask Mikko , Shin Bokyong
Published: December 2024

This literature review critically examines the potential of collective intelligence (CI) to enhance theories of deliberative democracy and participatory governance through academic discourse. We employed PRISMA guidelines for systematic article selection, complemented by a narrative approach for in-depth thematic analysis and supplemented by quantitative methodologies such as Sankey diagrams and keyness analysis. Reviewing 61 scholarly articles focusing on CI within the public sector, this study identifies theoretical insights that could significantly impact the field of democratic innovations and participatory governance. Our analysis reveals that CI methodologies can make governance more inclusive and dynamic by integrating advanced digital tools that foster broader and more effective citizen participation. We conclude that integrating CI with deliberative democracy and participatory governance theories holds substantial promise for developing more responsive and adaptive governance models. Future research should focus on measuring deliberative quality in real time, deploying CI tools to empower underrepresented groups and address specific governance challenges, and examining CI’s ethical and social implications, especially concerning privacy, security, and power dynamics in technology-driven public decision-making.
Perception and Ethical Challenges for the Future of AI as Encountered by Surveyed New Engineers
Authors: Khogali Hisham O. , Mekid Samir
Published: December 2024

Our extensive history of embracing AI technological advances demonstrates that AI may be a useful tool if humans learn to use it intelligently, and that concerns about it replacing human occupations may be unwarranted. Indeed, a range of remarkable new AI approaches are fast transforming diverse human experiences and fundamentally disrupting our lives, but not without some drawbacks. This study reflects on how new engineers view AI’s influence on trust and ethical attitudes. Data-driven perceptions drive educated debates, education initiatives, and legislative decisions aimed at effectively addressing non-scientific AI concerns. This contributes to improving the future of AI-based learning through transdisciplinary research that considers the evidence of ethical challenges raised by AI misapplication. Our analysis of quantitative data from a survey of 715 recently graduated engineers from diverse fields, who often use information technologies, reveals that many believed AI-related difficulties were scientifically uncertain. According to this study’s findings, the observed variance in the trend relating to reduced fear of job losses due to AI (R2 = 0.1121) suggests that specialties heavily impacted by crucial decision making have a lower level of fear. This provides strong evidence for an optimistic path to AI breakthroughs boosting the level of confidence in and acceptance of AI across many industries.