bluuebunny/crossref_metadata_2025 huggingface dataset:
Shade-induced ROS/NO reinforce COP1-mediated diffuse cell growth
Authors: Iglesias María José , Costigliolo Rojas Cecilia , Bianchimano Luciana , Legris Martina , Schön Jonas , Gergoff Grozeff Gustavo Esteban , Bartoli Carlos Guillermo , Blázquez Miguel A. , Alabadí David , Zurbriggen Matias D. , Casal Jorge J.
Published: October 2024

Canopy shade enhances the activity of PHYTOCHROME INTERACTING FACTORs (PIFs) to boost auxin synthesis in the cotyledons. Auxin, together with local PIFs and their positive regulator CONSTITUTIVELY PHOTOMORPHOGENIC 1 (COP1), promotes hypocotyl growth to facilitate access to light. Whether shade alters the cellular redox status thereby affecting growth responses, remains unexplored. Here, we show that, under shade, high auxin levels increased reactive oxygen species and nitric oxide accumulation in the hypocotyl of
Arabidopsis.
This nitroxidative environment favored the promotion of hypocotyl growth by COP1 under shade. We demonstrate that COP1 is S-nitrosylated, particularly under shade. Impairing this redox regulation enhanced COP1 degradation by the proteasome and diminished the capacity of COP1 to interact with target proteins and to promote hypocotyl growth. Disabling this regulation also generated transversal asymmetries in hypocotyl growth, indicating poor coordination among different cells, which resulted in random hypocotyl bending and predictably low ability to compete with neighbors. These findings highlight the significance of redox signaling in the control of diffuse growth during shade avoidance.
Targeting the MAtrix REgulating MOtif abolishes several hallmarks of cancer, triggering antitumor immunity
Authors: Li Chengbei , Kaur Amanpreet , Pavlidaki Alexia , Spenlé Caroline , Rajnpreht Irena , Donnadieu Emmanuel , Salomé Nathalie , Molitor Anne , Carapito Raphael , Wack Fanny , Erne William , Lefebvre Olivier , Averous Gerlinde , Mitrentsi Ioanna , Loustau Thomas , Orend Gertraud
Published: October 2024

Tumor-targeted therapies have often been inefficient due to the lack of concomitant control over the tumor microenvironment. Using an immunocompetent autologous breast cancer model, we investigated a MAtrix REgulating MOtif (MAREMO)-mimicking peptide, which inhibits the protumorigenic extracellular matrix (ECM) molecule tenascin-C that activates several cancer hallmarks. In cultured cells, targeting the MAREMO blocks tenascin-C signaling involved in cell adhesion and immune-suppression by inhibiting tenascin-C interactions with fibronectin, TGFβ, CXCL12, and others, thereby blocking downstream events. Using RNASequencing and various genetic, molecular, in situ, and in vivo assays, we demonstrate that the MAREMO peptide similarly blocks multiple tenascin-C functions in vivo. This includes releasing tumor-infiltrating leukocytes, including CD8+ T cells, from the stroma. The MAREMO peptide also triggers interferon signaling, restoring antitumor immunity, contributing to tumor growth inhibition and reduced dissemination. The MAREMO peptide targets tumor cells directly by promoting growth suppression and inhibiting phenotypic plasticity, subsequently enhancing responsiveness to the endogenous death inducer tumor necrosis factor-related apoptosis-inducing ligand, as shown by a loss-of-function approach. Moreover, the MAREMO peptide largely subdues the tumor bed by depleting fibroblasts, repressing tenascin-C and other ECM molecules, and restoring the function of the few remaining blood vessels. In conclusion, targeting tenascin-C with a MAREMO peptide represents a powerful anticancer strategy with a broad inhibition of several cancer hallmarks.
Toward a metabolic theory of catchments: Scaling of water and carbon fluxes with size
Authors: Bassani Francesca , Fatichi Simone , Rinaldo Andrea , Bonetti Sara
Published: October 2024

Allometric scaling relations are widely used to link biological processes to body size in nature. Several studies have shown that such scaling laws hold also for natural ecosystems, including individual trees and forests, riverine metabolism, and river network organization. However, the derivation of scaling laws for catchment-scale water and carbon fluxes has not been achieved so far. Here, we focus on scaling relations of catchment green metabolism, defined as the set of ecohydrological and biogeochemical processes through which vegetation assemblages in catchments maintain their structure and react to the surrounding environment. By revising existing plant size–density relationships and integrating them across large-scale domains, we show that the ecohydrological fluxes occurring at the catchment scale are invariant with respect to the above-ground vegetation biomass per unit area of the basin, while they scale linearly with catchment size. We thus demonstrate that the sublinear scaling of plant metabolism results in an isometric scaling at catchment and regional scales. Deviations from such predictions are further shown to collapse onto a common distribution, thus incorporating natural fluctuations due to resource limitations into a generalized scaling theory. Results from scaling arguments are supported by hyperresolution ecohydrological simulations and remote sensing observations.
The role of the human-canine bond in recovery from substance use disorder: A scoping review and narrative synthesis
Authors: Dell Colleen , Kosteniuk Brynn , Doi Carolyn , Townsend Courtney , Cook Alexis , Chalmers Darlene , Butt Peter
Published: October 2024

Recovery from substance use disorder (SUD) is a personal journey that includes connection with self and others, including animals – known as the human-animal bond (HAB). Research shows that canines are the most common type of animal integrated into animal-assisted interventions to support people with SUD. Yet, to our knowledge, there has been no review of the evidence on the role of canines in SUD recovery. The scoping review’s objective was to examine the literature on the human-canine bond’s role in recovery from SUD among adolescents and adults, including how the bond may help or hinder recovery. The review considered records that described the human-canine bond with respect to recovery in any recovery- or therapy-related setting globally. Eleven databases were searched, and 32 sources met inclusion criteria that involved companion dogs, therapy dogs, service/assistance, dogs and others. The thematic analysis across records identified three key benefits of the human-canine bond in SUD recovery: (1) a source of social connection and a conduit for human-to-human social connection, (2) a calming and comforting effect on individuals with SUD that can reduce stress and anxiety, and (3) the human-canine bond as a motivating factor for positive change. Through these themes, the bond may help divert substance use-related thoughts and reduce cravings, bolster engagement in treatment and recovery, and help to decrease and prevent substance use. However, a few articles found no role or a limited role of the human-canine bond in recovery, and challenges and considerations were reported, particularly for marginalized populations (e.g., related to obtaining and maintaining housing, employment, and SUD treatment). Most of the records discussed canine welfare in some capacity. Calls were also made for improved policy, public awareness, and animal welfare.
Key comparison BIPM.RI(I)-K8 of high dose-rate 192Ir brachytherapy standards for reference air kerma rate of the LNE-LNHB and the BIPM
Authors: Kessler C , Hernandez-Elvira V
Published: October 2024

Main text

An indirect comparison of the standards for reference air kerma rate for 192Ir high dose rate (HDR) brachytherapy sources of the Laboratoire National Henri Becquerel (LNE-LNHB), France, and of the Bureau International des Poids et Mesures (BIPM) was carried out at the LNE-LNHB in May 2024. The comparison result, based on the calibration coefficients for a transfer standard and expressed as a ratio of the LNE-LNHB and the BIPM standards for reference air kerma rate, is 0.9990 with a combined standard uncertainty of 0.0071.

To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.

The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Introduction and evaluation of size-specific DLP for radiation dose estimation in CT examinations
Authors: Dendo Yutaka , Abe Keisuke , Onodera Shu , Kayano Shingo , Ota Hideki , Takase Kei
Published: October 2024

The increased utilization of computed tomography (CT) has raised concerns about patient radiation exposure. Effective dose (ED), which requires precise estimation, is crucial for assessing and managing these risks. Traditional ED estimation methods, which are based on the dose-length product (DLP), often lack accuracy due to variations in patient size and anatomy. This study aims to evaluate the efficacy of size-specific DLP (SS-DLP), a novel metric that combines the size-specific dose estimate (SSDE) with scan length, to provide a more accurate estimation of radiation exposure from CT examinations. Focusing on adult chest-abdomen-pelvis scans, we calculated SSDE and SS-DLP and utilized two simulation tools, Radimetrics and WAZA-ARI, for a detailed analysis. Our findings indicate that SS-DLP is highly correlated with EDs from Monte Carlo simulations, suggesting its reliability. Additionally, SS-DLP showed a moderate reduction in errors based on patient sex and body mass index compared to traditional DLP-based methods. Thus, SS-DLP offers a more accurate and personalized radiation exposure estimate, potentially enhancing patient safety.
Reliable comparison of multi-directional mechanical properties for biomimetic circle arc honeycombs
Authors: Yu Junxian , Zheng Zhaoyang , Yu Wei , Wang Ziyue , Xiao Junhua , Liang Xi
Published: October 2024

Curved honeycombs, inspired by biomimetic strategies, often have better mechanical properties and a wider range of tunability compared to straight honeycombs. Research into these curved honeycombs is growing, but few studies compare multiple designs or consider isotropy. This study replaces straight walls with circle arc walls in five classic honeycomb designs, creating curved honeycombs for analysis. We validated our numerical modeling in ANSYS by testing 3D printed specimens. We evaluated the Young’s modulus, yield strength, Poisson’s ratio, and energy absorption (EA*)of circle arc hexagon (CAH), circle arc square, circle arc triangle, circle arc re-entrant, and circle arc double-V (CAD) under different loading conditions. Results showed CAH has the best EA* and high isotropy, with all isotropic factors below 0.1. CAD shows a negative Poisson’s ratio effect in all directions. These findings offer benchmarks for future curved honeycomb research.
Three-cavity system with multiple magnomechanically induced transparency
Authors: Din Ghaisud , Abbas Muqaddar , Zhang Pei
Published: October 2024

This paper presents the characteristics of a weak probe field in a three-connected cavity system. In this system, a microwave cavity contains a yittrium iron garnet sphere that is driven by a strong pump and a weak probe optical fields, and the magnon is driven by a weak microwave source. The other two cavities are empty and are coupled to the first cavity with specific coupling strengths. This setup leads to the observation of multiple magnomechanically induced transparency phenomena by varying quantum parameters g
ma, g
mb, and the coupling strengths between cavities J
1 and J
2. The study of these phenomena in the three coupled cavities can potentially contribute to advancements in quantum transduction and future technologies.
Characters are quantum numbers
Authors: Lemus Renato
Published: October 2024

Character tables represent an invaluable tool for applications of group theory in physics and chemistry. They are useful, among other applications, to carry out the projection of functions which later on constitute the basis to simplify the calculations and establish the selection rules. However, the physical interpretation of the character tables as quantum numbers is not visible. In this contribution we prove that the characters are indeed conserved quantities associated with the discrete symmetry of a molecule. In the process, the eigenfunction method of symmetry projection emerges in natural form, an approach not discussed in traditional textbooks. A non trivial example involving the octahedral group

O

h

is presented.
Simulating the Universe from the cosmological horizon to halo scales
Authors: Montandon Thomas , Hahn Oliver , Stahl Clément
Published: October 2024

Ultra-large scales close to the cosmological horizon will be probed by the upcoming observational campaigns. They hold the promise to constrain single-field inflation as well as general relativity, but in order to include them in the forthcoming analyses, their modelling has to be robust. In particular, general relativistic effects may be mistaken for primordial signals, and no consensus has emerged either from analytical modelling nor from the numerical route, obstructed by the large dynamical range to be simulated. In this work, we present a numerical technique to overcome the latter limitation: we compute the general relativistic displacement field with the N-body relativistic codegevolutionand combine it with the accurate Newtonian simulationGadget-4. This combination leads to an effective simulation reproducing the desired behaviour at the level of the matter power spectrum and bispectrum. We then measure, for the first time in a simulation, the relativistic scale-dependent bias in Poisson gauge; at redshiftz= 0, we findb1GR= -8.1 ± 2.8. Our results at the field level are only valid in the Poisson gauge and need to be complemented with a relativistic ray tracing algorithm to compute the number count observable.
Multi-component decompositions, linear superpositions, and new nonlinear integrable coupled KdV-type systems
Authors: Hao Xiazhi , Lou S Y
Published: October 2024

In the realm of nonlinear integrable systems, the presence of decompositions facilitates the establishment of linear superposition solutions and the derivation of novel coupled systems exhibiting nonlinear integrability. By focusing on single-component decompositions within the potential BKP hierarchy, it has been observed that specific linear superpositions of decomposition solutions remain consistent with the underlying equations. Moreover, through the implementation of multi-component decompositions within the potential BKP hierarchy, successful endeavors have been undertaken to formulate linear superposition solutions and novel coupled KdV-type systems that resist decoupling via alterations in dependent variables.
Hyperbolic map unravels eight regions in temperature volatility regionalization of Mainland China
Authors: Song 宋 Yuxuan 雨轩 , Gu 顾 Changgui 长贵 , Zheng 郑 Muhua 木华 , Feng 冯 Aixia 爱霞 , Xi 席 Yufei 雨菲 , Wang 王 Haiying 海英 , Yang 杨 Huijie 会杰
Published: October 2024

Abrupt temperature volatility has detrimental effects on daily activities, macroeconomic growth, and human health. Predicting abrupt temperature volatility and thus diminishing its negative impacts can be achieved by exploring homogeneous regions of temperature volatility and analyzing the driving factors. To investigate the regionalization of temperature volatility in Mainland China, a network constructed by the cosine similarity of temperature volatility series from Mainland China was embedded in hyperbolic space. Subsequently, we partitioned the network on the hyperbolic map using the critical gap method and then found eight regions in all. Ultimately, a network of communities was constructed while the interaction among communities was quantified. This yields a perspective of temperature volatility regionalization that can accurately reflect factors including altitude, climate type, and the geographic location of mountains. Further analysis demonstrates that the regionalization in the hyperbolic map is distinct from provinces and has a realistic basis: communities in southwest China show strong correlations due to the temperature sensitivity to altitude, and communities in northern China show a convergence in the area of Dingxi, Gansu, mainly owing to the strong temperature sensitivity to climate types. As a consequence, node distributions and community divisions in the hyperbolic map can offer new insights into the regionalization of temperature volatility in Mainland China. The results demonstrate the potential of hyperbolic embedding of complex networks in forecasting future node associations in real-world data.
Analysis of different test techniques of DC circuit breaker
Authors: Zhang Ruike , Xu Feng , Yang Ziyue , Wu Yifei , Wu Wenbo , Wang Song , Huang Xiaoming , Feng Hua
Published: October 2024

Amidst the escalating demand for energy and the proliferation of renewable energy sources, direct current (DC) systems have garnered increasing interest. As pivotal for the safety and reliability of DC systems, testing DC circuit breakers is a critical step to evaluate their performance and dependability. This study focuses on the breaking test method for medium and high-voltage DC circuit breakers. It compares and evaluates the current waveform, recovery voltage, and energy dissipation across various test circuit methodologies during the breaking process of DC circuit breakers and assesses the suitability of test methods across different voltage levels.
Disaster Rescue Drone Based on YOLOv4 Algorithm
Authors: Huang Weicheng , Li Weijie , Yang Liming , Zhang Wenqian , Wang Li
Published: October 2024

With the rapid development of artificial intelligence technology, the application of unmanned aerial vehicles (UAV) in disaster relief is becoming more widespread. This article presents a disaster relief UAV based on the YOLOv4 algorithm, aimed at improving the speed and efficiency of emergency response and rescue. The article designs and implements a UAV integrated with the YOLOv4 object detection algorithm, used for real-time identification and location of people within disaster areas and for deploying rescue materials using a mechanical claw. Through experiments and comparative verification, the system has demonstrated high-efficiency in target detection and tracking in various disaster environments.
A real-time cardiac output monitoring system based on photoplethysmography
Authors: Hu Hepeng , Zhuang Junyue , Zhu Ende , Gao Jiyang , Gao Bo
Published: October 2024

Cardiac output is a critical parameter for assessing the cardiovascular system, and thermodilution is commonly used as CO monitoring in clinical settings. Thermodilution cannot be applied in continuous monitoring due to invasion. Photoplethysmography signal originates from the cardiovascular system. We proposed a CO continuous monitoring system based on photoplethysmography and deep learning method. PPG waveform parameters are extracted during the pre-processing stage, and fed into the deep learning model to calculate cardiac output. The system employs an analog front-end chip and an HPS_FPGA architecture to capture PPG signals. The pre-processing and the deep learning model-driven algorithm are designed in Python on the custom Linux for HPS. It realizes the real-time processing of the PPG signals and the continuous monitoring of cardiac output. Validated by an FPGA, the system reported average cardiac output monitoring results of approximately 5.09 L/min at rest and about 7.39 L/min after exercise.
UAV trajectory planning in NOMA-aided UAV-mounted RIS networks: A budgeted Multi-armed bandit approach
Authors: Hosny Ramez , Hashima Sherief , Hatano Kohei , Zaki Rokaia M. , El Halawany Basem M.
Published: October 2024

Reconfigurable Intelligent Surface (RIS) and Non-Orthogonal Multiple Access (NOMA) can be used as supporting technologies for driving the effective development of Unmanned Aerial Vehicles (UAV) communication systems. Herein, both technologies are combined to widen the millimeter-wave Base Stations (mmWave-BS) coverage range. In this work, we consider UAV Trajectory Planning (UTP) problem to enhance the performance of a NOMA-based UAV-mounted RIS communication network. The problem is formulated using two Budgeted Multi-Armed Bandits (BMAB) schemes. BMAB is a variant of the classical MAB algorithm that considers the random cost of selecting an arm. The UAV performs as a bandit player attempting to maximize its achievable rate (i.e., reward) via accessing different grids in its trajectory. This reward is accomplished simultaneously while minimizing the energy/cost of flying the UAV from one grid to another to preserve its battery lifetime. Hence, two variants of the Budgeted upper confidence bound (BUCB), namely the BUCB1 and BUCB2, are investigated, where the simulations show that both algorithms outperform other benchmarks.
YOLO-EF: A lightweight YOLOv7 PCB defect detection algorithm
Authors: Chen Yu , Song Xudong , Song Liang , Li Changxian
Published: October 2024

This study addresses the challenge of accurate PCB defect detection, considering the computational complexity and excessive model parameters associated with existing methods. To mitigate these issues, we propose an enhanced PCB surface defect detection method called YOLO-EF, which is built upon the YOLOv7 architecture. Firstly, we replace the backbone of YOLOv7 with the lightweight Efficient FormerV2 module. This replacement reduces both the model volume and computational requirements. Secondly, the dynamic head module is introduced to unify the target detection head and improve its performance. Finally, the Wise-IoUv3 function is used to suppress the influence of low-quality samples on the model performance. The experimental results show that the proposed model improves by 2.85% in mAP over the original model. Model computation is reduced from 106.5 GFLOPs to 44.3 GFLOPs. Our YOLO-EF model achieves an optimal balance in terms of precision, computational efficiency, and detection speed.
A miniaturized polarization-insensitive energy absorption metasurface
Authors: Zhen Fajian , Liu Yunqing
Published: October 2024

A novel metasurface structure for energy absorption is proposed. The metasurface structure unit is composed of four bidirectional arrow patches, which are connected by load. In order to achieve unit miniaturization, a humanoid slot structure is proposed, which reduces the unit size to 0.23 λ. The designed metasurface structure can capture more than 90% of the energy at 5.8 GHz, has the characteristics of polarization insensitive, and can maintain 80% of the energy capture efficiency between 0 and 45°. The simulation results show that the structure is nonlinear to the load and can be applied to the design of rectification metasurface structures.
Design and implementation of a multifunctional desktop robot for computer peripherals
Authors: Li Huanzhou , Xu Hongxia , Zhan Minghui
Published: October 2024

As an important component of the intelligent machinery field, desktop robots not only enrich people’s entertainment lives but are also gradually expanding into applications in education, healthcare, and other fields. However, existing products often struggle to meet the increasingly diverse needs due to short battery life and limited computing resources. In response to this challenge, this study designs a multifunctional desktop robot that can be used as a computer peripheral, which significantly improves power supply and computing capabilities. The design uses the STM32F4 series microcontroller as the main control chip, combined with high-performance servos and their self-developed driver boards, to achieve precise motion control. By integrating USB-HS PHY and USB-CDC protocol, the communication efficiency with the computer is greatly improved, and new screen-driving technology is developed using open-source routines, thus optimizing the user interaction experience. In addition, an innovative adaptive algorithm is incorporated into the firmware program, enhancing the robot’s working stability in different environments. Through systematic hardware debugging and software testing, the robot can achieve efficient performance and stable response when executing complex tasks. This research will promote the expansion of desktop robot technology into broader application fields and support future intelligent work and lifestyle.
Global path planning of unmanned ship island region based on improved A* algorithm
Authors: Fu Hongyu , Shao Guoyu , Zhao Lei
Published: October 2024

To solve the path planning problem for unmanned boats in island and reef areas, this paper introduces an adaptive heuristic function specifically for island and reef areas based on the traditional A* algorithm, in order to effectively handle the obstacle avoidance problem. A new cost function is designed to balance the relationship between path length and safety. The sea chart is converted into a path planning map by using the grid method, and the path planning is carried out. The paper compares the performance of the new path-planning algorithm with other path-planning algorithms through experiments, and the improved A* algorithm for global path planning of unmanned boats in island and reef areas shows excellent performance in various complex environments, effectively improving the island and reef area navigation efficiency.
A semantic segmentation network based on CNN and Swin transformer for landslide detection
Authors: Dai Xiangyan , Li Lin
Published: October 2024

To address the issues of poor object localization, difficulty in object recognition, and inadequate segmentation performance of CNN-based semantic segmentation methods in landslide detection, a semantic segmentation model based on the Swin transformer and ResUNet architectures for landslide detection in remote sensing images is proposed in this paper. This method combines the global feature capabilities of the Swin transformer with the local feature extraction abilities of CNN and integrates our proposed RCCT module for landslide segmentation in remote sensing images. We apply this model to the identification and extraction of landslide disasters in Menyuan County and the Sanjiangyuan region of Qinghai Province, China, achieving a promising F1 Score of 81.91. Furthermore, we conducted a comprehensive evaluation of our proposed model on the BiJie Landslide dataset in comparison to four recently developed methods for landslide recognition. The experimental findings indicate the superior performance and effectiveness of our model over the comparative approaches.
Lightweight UAV target detection algorithm
Authors: Zhang Xiaozheng , Bao Dayang , Zhao Haili , Wang Qi
Published: October 2024

Aiming at the public area UAV detection task, the target is easy to occlude, the scale variation is large, and the existing detection algorithm model parameters are large. In this paper, the lightweight network model YOLOv5s-UD is designed for the low-altitude UAV target detection algorithm. In this detection model, the proposed approach employs the streamlined Shufflenetv2 architecture to enhance the core network. To optimize the UAV’s relevant feature data, the CBAM attention module is integrated during the feature amalgamation phase. Furthermore, the GSConv is implemented to upgrade conventional convolution within the neck network, ensuring a reduction in model parameters without compromising detection precision. Experimental outcomes reveal a 36.2% computational reduction compared to the YOLOv5s algorithm, with an average detection accuracy of 84.1%. This demonstrates the algorithm’s capability to efficiently detect UAV targets while minimizing computational loads, thereby exhibiting significant practical significance.
Research on traffic sign detection model with improved YOLOv8s
Authors: Wang Qingsong , Qiao Yanping , Li Jiaen , Wang Ai
Published: October 2024

With the rapid advancement of intelligent driving technologies such as autonomous driving and driver assistance, the significance of traffic sign recognition in road and traffic safety has become increasingly prominent. Addressing the challenges posed by the current mainstream traffic sign detection models, which include complex background structures and the detection of small targets, this study proposes an enhanced network for detecting traffic signs based on YOLOv8s. Firstly, we modify the convolutional component of the backbone by replacing the initial convolutions with the Alterable Kernel Convolution (AKConv) network, thereby enhancing feature extraction efficiency and network performance. Secondly, we introduce the Global Attention Mechanism (GAM) within the network to effectively aggregate features, improve reconstruction performance, and maintain low computational and storage costs. Lastly, we integrate the Bi-directional Feature Pyramid Network (BiFPN) into the YOLOv8s framework, enabling the model to capture multi-scale features efficiently and enhance recognition accuracy. Experimental results conducted on the CCTSDB-2021 show the efficacy of the study improved YOLOv8s model. Precision, recall, and mAP@50% achieve impressive values of 96.0%, 79.4%, and 88.3%, respectively. These metrics represent a 4.6% improvement in precision, a 0.4% enhancement in the recall, and a 2% enhancement in mAP@50%, compared with the original YOLOv8s model. Moreover, this improved model exhibits greater precision when compared with other existing models.
Design and experiment of Beidou satellite navigation railway tunnel coverage enhancement system
Authors: Pan Peifen , Zhang Jifeng , Tao Tao , Liu Yangxue , Feng Boqing
Published: October 2024

In areas of satellite navigation signals being obstructed, coveraging the interior of the tunnel with Satellite navigation signals based on navigation signals real-time regeneration technology is an important way to achieve the applications of satellite navigation in railway mobile equipment positioning and auxiliary protection for operators. We analyzed the application requirements on navigation positioning and time synchronization, and technical requirements on universality and electromagnetic compatibility, etc. The article designed the composition and logical architecture of the Beidou satellite navigation railway tunnel coverage enhancement system, proposed the design of system functions and performance parameters, and verified the system’s functionality and performance through static mode testing and dynamic mode testing. The design and implementation of the Beidou satellite navigation railway tunnel coverage enhancement system have important practical value for the application in mobile equipment positioning and auxiliary protection for operators in railway tunnel.
A speech separation model improved based on Conv-TasNet network
Authors: Shi Hongwei , Wu Shiqi , Ye Minghao , Ma Changda
Published: October 2024

In the field of single-channel speech separation, the extraction and separation of features from mixed audio have always been the focus and difficulty of research. Currently, mainstream methods mainly suffer from poor generalization ability and issues such as inadequate feature extraction, which leads to the models’ inferior separation capability. This paper proposes an improved DConv-TasNet network model, focusing on the optimization of the encoder/decoder modules and separation modules and utilizing deep dilated encoders/decoders to extract features from mixed speech signals. It enhances feature extraction capability and generalization compared to conventional encoders/decoders. In terms of the separation module, improvements were made to the convolutional blocks within the module by enhancing feature extraction in the channel dimension, leading to improved performance of the separation network. Validation of the model’s performance was conducted using the WSJ0-Mix2 dataset, demonstrating superior performance compared to the Conv-TasNet network.
Disentangled traffic forecasting via efficient graph neural network
Authors: Zhang Wenyu , Zheng Kaidong
Published: October 2024

Traffic prediction is important to intelligent transportation systems, but it poses significant challenges due to the complex spatio-temporal correlation of traffic data. To address these challenges, we decompose complex traffic time series into events and trends using discrete wavelet transform. However, the high-frequency information decomposed by wavelet transform contains noise, so we designed a module to denoise the high-frequency events. We model the spatio-temporal correlations of trends and events separately using two mutually independent spatio-temporal networks. We use causal dilation convolution with local receptive fields and attention with global receptive fields to capture the correlation of fluctuating time series and the correlation of stable time series, respectively. We capture spatial correlation using a GNN optimized by an attention mechanism. Finally, we merge useful information from volatile events into predictable trends using adaptive event fusion. Our model significantly outperforms nine existing traffic prediction methods across four real datasets.
An enhanced seagull algorithm for multi-threshold image segmentation based on Kapur entropy
Authors: He Hanyue
Published: October 2024

To address the shortcomings of the Seagull Optimization Algorithm (SOA), such as poor distribution of individuals’ positions and low population diversity leading to susceptibility to local optima after the collision avoidance phase, an Enhanced Seagull Algorithm (ESOA) is proposed. Firstly, a chaos-guided mechanism is utilized to replace the original individual positions after collision avoidance with the mean of individual optima and current positions, effectively improving the distribution of the population. Secondly, a circular-topology neighborhood structure with random weights is introduced, allowing interaction between the current individual’s position and the information from its two adjacent optimal positions, thereby improving population clustering and enhancing diversity. Thirdly, a dynamic lens mapping strategy is employed to direct the optimal seagull individuals toward their newly generated mapped points, enhancing their ability to avoid local optima. ESOA is applied to the problem of multi-threshold image segmentation based on Kapur entropy, and experimental results demonstrate its effectiveness in improving segmentation performance.
Human behavior recognition method based on wearable devices
Authors: Zhang Wei , Yu Guibo , Deng Shijie
Published: October 2024

Human behavior recognition remains a prevalent subject of inquiry in contemporary scientific studies. Behavior recognition technology has penetrated into every aspect of our lives, mainly in video surveillance, health monitoring, and smart homes. Aiming at some people who need behavioral monitoring to prevent danger, a human behavior recognition method based on wearable devices is proposed, which first acquires the three-axis acceleration data of behavioral activities of such people through wearable devices and then performs sliding window segmentation and feature extraction on the acquired data and finally inputs the obtained features into the dual-directional long and short term memory framework (BiLSTM) model to complete the identification of human behavior. To affirm the method’s reliability, we performed activity detection tests on a dataset from UCI featuring a non-powered wearable sensor used by the senior population. The outcomes indicate that the method is proficient in identifying the routine daily activities of this demographic, demonstrating its practical utility.
Diffusion-Cap: A diffusion model for image captioning
Authors: Xiao Tingxuan , Yang Yizhe , Gao Yang
Published: October 2024

While autoregressive image caption generation models have achieved remarkable success, they are still constrained in generation speed, which may become a bottleneck in practical applications. To address this challenge, our research introduces the Diffusion Image Captioning Model (Diffusion-Cap). This innovative, non-autoregressive framework conceptualizes image captioning as a process that unifies continuous and discrete diffusion. By exploring two distinct architectural designs, our research investigates optimal strategies for the image captioning diffusion process. Comprehensive assessments on the MSCOCO dataset confirm the superior performance of our Diffusion-Cap, surpassing existing non-autoregressive benchmarks in crafting accurate captions.
An entity alignment method with attribute augmentation and contrastive learning
Authors: Yang Cheng , Zhang Chunxia , Chen Yihao
Published: October 2024

The entity alignment(EA) task is to identify entities with the same semantics in the knowledge graph(KG), an essential issue in KG fusion and big data mining. Existing entity alignment methods mainly adopt graph embedding-based methods. However, they still have some shortcomings. First, they heavily rely on high-quality alignment seed and external semantic information. Secondly, the present attention mechanism focuses on the entire graph information, neglecting the noise of attribute information. This paper proposes an EA approach based on Attribute Augmentation and Contrastive Learning (AACL). Our method introduces attribute augmentation to enhance the structure information of knowledge graphs and reduce dependence on alignment seed. A masked attention mechanism is developed to emphasize important attribute information and mask out invalid attributes to better capture semantic dependencies in the KG. Experimental results on three public datasets indicate that our AACL outperforms the present entity alignment approaches.
Divergent future trends in global land greening and carbon sequestration under climate change scenarios
Authors: Bian Chenyu , Xia Jianyang
Published: October 2024

Over the past four decades, global land greening has promoted carbon (C) storage in terrestrial ecosystems. However, whether the future trajectory of this positive greening effect on ecosystem C sequestration is sustainable under various climate scenarios remains uncertain. Here, using projections from ten Earth system models, we found divergent trends in the relationship between global land greening and C storage among three distinct shared socioeconomic pathways (SSPs). We identified global transition times for their positive relationships, which will occur in 2034, 2038, and 2048 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. We found a widespread decoupling trend of vegetation greening and ecosystem C storage under medium to high emission scenarios, particularly in the tundra, boreal forest, and grassland regions, by delving deeper into six transition modes. These findings underscore the highly uncertain role of vegetation greening in land C sequestration under prospective climate change scenarios.
Management Effectiveness Evaluation using the Bayesian Belief Network Approach: A case study of Cu Lao Cham Marine Protected Area, Vietnam
Authors: Khuu D T
Published: October 2024

The Kunming-Montreal Global Biodiversity Framework set an ambitious target of safeguarding 30% of the Earth’s land and sea through well-managed Protected Areas (PAs) by 2030. Despite 196 countries committing to expand PA coverage, a lack of evidence on existing PA effectiveness and limited data on human-ecosystem interactions pose formidable challenges to implementation and prioritising conservation funding. Within this context, this paper aims to test a simple and user-friendly model, which is based on Bayesian Belief Networks, in evaluating the management effectiveness of Cu Lao Cham Marine Protected Area (CLC-MPA) in Vietnam. It focuses on assessing whether the MPA is effective in achieving its conservation objectives and if the target resources can endure the current pace of economic development. The study underscores the importance of prioritising eco-tourism and sustainable local economic models amid the intense forces of mass tourism development. It argues that tourism carrying capacity for CLC should be estimated at the outset of sustainable tourism development plan. Besides, there is a need for an integrated approach to MPA management to include legal provisions to address the impacts of tourism and pollution. Also, given the increasing pressure from incoming fishers, it is essential for increasing trans-provincial coordination for better enforcement of MPA regulations.
Understanding sustainable tourist behaviours of Vietnamese visitors in Ho Chi Minh City, Vietnam
Authors: Tung N T T , Thu T N M , San N D , Ngoc P T B
Published: October 2024

Sustainable tourism is an important goal within the global sustainable development framework. In sustainable tourism, tourists are expected to behave in an environmentally friendly manner and respect the local culture and economy. However, the level of sustainable behaviour among tourists varies across different regions and countries. This study aims to understand the sustainable tourism behaviour of Vietnamese tourists visiting Ho Chi Minh City, Vietnam. It focuses on (1) the sustainable behaviour of tourists, (2) the correlation between access to sustainable tourism information and sustainable behaviour, as well as participant characteristics and the behaviour; and (3) initial suggestions for promoting changes towards sustainable behaviour among tourists. The study uses a mixed-methods approach, combining both quantitative and qualitative methods. Quantitative data were collected through direct questionnaires from 240 tourists visiting Ho Chi Minh City. Qualitative data were obtained through semi-structured, in-depth interviews with 47 participants. The results indicate three common groups of behaviour related to sustainable tourism during travel: saving electricity and water, choosing public transportation, and food-related behaviour. Specifically, the study found a correlation between access to sustainable tourism information and sustainable behaviour, including the choice of transportation (sig = 0.040) and food-related behaviour (sig = 0.006). Surprisingly, the study did not find a correlation between access to sustainable tourism information and behaviour related to conserving electricity and water at destination accommodations. The findings also revealed that well-educated young Vietnamese travellers (aged 18-35) earning high salaries, living in urban areas, and currently employed by international companies have a better understanding of sustainability, leading to increased sustainable practices within the tourism industry. Finally, the study suggests that strategies should shift towards changing behaviour related to saving electricity and water through improvements in hotel infrastructure and management practices rather than focusing solely on engaging sustainable tourist behaviour through providing sustainable information.
Impact of the Urbanization Process on the Subsidence Phenomena Using Remote Sensing in the Inner Districts of Ho Chi Minh City, Vietnam
Authors: Ha T C , Nguyen T P C , Tran H P , Do V T
Published: October 2024

Subsidence in Ho Chi Minh city (HCM city) occurs in many places, the current reason no longer comes from natural disasters but mainly from human activities (groundwater exploitation, ground improvement,…). Especially in innercity districts, during high tide or heavy rain, many areas are locally flooded, concentrated in places with high construction density. To monitor the subsidence phenomenon in urban areas, the advent of remote sensing technology is an effective tool, helping researchers and managers capture information quickly, accurately and continuously. In this study, the DInSAR method was used to determine the rate of subsidence in urban districts of HCM city. DInSAR is a popular method for estimating land displacement based on the principles of Interferometric Synthetic Aperture Radar (InSAR) using different time series data. We used C-band SAR Sentinel 1 data in the period 2015 - 2023 to analyze subsidence, the results showed that subsidence hot spots were concentrated in District 7, Binh Thanh District and part of Go Vap District at high speeds from 2 - 3 cm/year. Over time, the phenomenon of subsidence with a value of > 2cm/year was distributed in Binh Tan and Tan Phu districts following the trend of new urban expansion to the West of HCM city. Additionally, Sentinel 2 imaging data was also used in this study to demonstrate that areas with high construction density have high subsidence rates. From there, it can be seen that the urbanization process is one of the causes of subsidence in HCM city.
Particle migration due to non-uniform laminar flow
Authors: Curt Koenders M A , Petford Nick
Published: October 2024

Using methods of granular mechanics in the quasi-static limit, with inter-particle interactions derived from the lubrication limit, the intensity of velocity fluctuations in the slurry is associated with fluctuations in the local distribution of inter-particle distances. These are shown to consist of a vector intensity and a scalar intensity; the former couples to the first velocity gradient, the latter (which is associated with solidosity fluctuations) couples to the second velocity gradient. Rheologies for both are presented, as is the rheology that links the particle pressure to the intensity of the velocity fluctuations (also known as the ‘granular temperature’) to the dispersive pressure. The rheologies are informed by experimental results. The granular temperature profile, modified from previous work, is responsible for axial particle migration (Bagnold effect). Two broad categories are assessed: symmetrical vertical and non-symmetrical lateral flow. For the latter the roughness of the boundary walls and a non-zero density contrast are important; this case is studied for a system in which flow effects are confined to the immediate vicinity of the boundary. Sensitivity analysis reveals several key variables including the parameters that control a slipping boundary condition and the mean solidosity in the conduit. For lateral flow, a sedimentary deposit with a solidosity profile may develop near the upper or lower boundary. The theory predicts an approximate relation between the fluid-particle density contrast and sediment thickness as a function of the mean flow rate, conduit width, the mean particle diameter and fluid viscosity that has utility in a range of engineering and geological situations where particulate matter is transported in the laminar flow regime.
Quantum Onsager relations
Authors: Tsang Mankei
Published: October 2024

Using quantum information geometry, I derive quantum generalizations of the Onsager rate equations, which model the dynamics of an open system near a steady state. The generalized equations hold for a flexible definition of the forces as well as a large class of statistical divergence measures and quantum-Fisher-information metrics beyond the conventional definition of entropy production. I also derive quantum Onsager–Casimir relations for the transport tensors by proposing a general concept of time reversal and detailed balance for open quantum systems. The results establish a remarkable connection between statistical mechanics and parameter estimation theory.