bluuebunny/crossref_metadata_2025 huggingface dataset:
The Preliminary Study of Environmental Variations Around the Du-Ku Highway Since 2000
Authors: Mu Yanhu , Niu Fujun , Ding Zekun , Shi Yajun , Li Lingjie , Zhang Lijie , Yang Xiang
Published: November 2024

Highways and their surrounding areas in mountainous and plateau regions are particularly susceptible to environmental changes, which can significantly impact their safety. In the context of global warming, the magnitude of environmental changes around highways has been further amplified. These environmental disturbances pose substantial risks to highway infrastructure in mountainous regions. By using satellite data and remote sensing techniques, this study focused on the environmental variations of the Du-Ku Highway (DKH) in the Tianshan Mountains and the preliminary revealed shifts in surface water, land surface temperature (LST), normalized difference vegetation index (NDVI), and temperature vegetation dryness index (TVDI) since 2000. The quantitative results showed that the water bodies with area between 0.1 and 0.5 ha showing the most significant growth around the DKH. The LST values are primarily distributed between 280 and 285 K, while the NDVI values are mostly below 0.4, and the TVDI is mainly concentrated at the two extremes. In the context of global warming and its amplified impact on mountainous and plateau regions, these findings offer critical insights that can directly support mountainous highway construction and maintenance strategies by identifying environmental indicators, providing a scientific foundation for making data-driven decisions.
Flood Risk Analysis of Urban Agglomerations in the Yangtze River Basin Under Extreme Precipitation Based on Remote Sensing Technology
Authors: Li Haichao , Yang Dawen , Zhu Zhenduo , Wei Yanqi , Zhou Yuliang , Ishidaira Hiroshi , Commey Nii Amarquaye , Cheng Han
Published: November 2024

Flooding is the most pervasive hydrological disaster globally. This study presents a comprehensive analysis of torrential rain and flood characteristics across three major urban agglomerations (CY, MRYR, and YRD) in the Yangtze River Basin from 1991 to 2020. Utilizing satellite-derived microwave SSM/I data and CHIRPS precipitation datasets, this study examines the impacts of urbanization and climate change on flood risk patterns. The results showed: (1) In 1998, the MRYR had the highest flood risk due to heavy rainfall and poor flood control, but by 2020, risk shifted to the CY with rapid urbanization and more rainfall, while the YRD maintained the lowest risk due to advanced flood control. (2) The relationship between impervious surface area and flood risk varied by region. The CY showed a negative correlation (−0.41), suggesting effective flood mitigation through topography and infrastructure; the MRYR had a slight positive correlation (0.12), indicating increased risks from urban expansion; and the YRD’s weak negative correlation (−0.18) reflected strong flood control systems. This research underscores the imperative of strategic urban planning and effective water resource management to mitigate future flood risks and contributes valuable insights to ongoing efforts in flood disaster prevention and control within the Yangtze River Basin.
Successful Precipitation Downscaling Through an Innovative Transformer-Based Model
Authors: Yang Fan , Ye Qiaolin , Wang Kai , Sun Le
Published: November 2024

In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model’s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model.
Incorporating Forest Mapping-Related Uncertainty into the Error Propagation of Wall-to-Wall Biomass Maps: A General Approach for Large and Small Areas
Authors: David Hassan C. , Vibrans Alexander C. , Martins-Neto Rorai P. , Dalla Corte Ana Paula , Péllico Netto Sylvio
Published: November 2024

The sources of uncertainty in wall-to-wall AGB maps propagate from the tree to pixel, but uncertainty due to forest cover mapping is rarely incorporated into the error propagation process. This study aimed to (1) elaborate an analytical procedure to incorporate forest-mapping-related uncertainty into the error propagation from plot and pixel predictions; (2) develop a stratified estimator with a model-assisted estimator for small and large areas; and (3) estimate the effect of ignoring the mapping uncertainty on the confidence intervals (CIs) for totals. Data consist of a subset of the Brazilian national forest inventory (NFI) database, comprising 75 counties that, once aggregated, served as strata for the stratified estimator. On-ground data were gathered from 152 clusters (plots) and remotely sensed data from Landsat-8 scenes. Four major contributions are highlighted. First, we describe how to incorporate forest-mapping-related uncertainty into the CIs of any forest attribute and spatial resolution. Second, stratified estimators perform better than non-stratified estimators for forest area estimation when the response variable is forest/non-forest. Comparing our stratified estimators, this study indicated greater precision for the stratified estimator than for the regression estimator. Third, using the ratio estimator, we found evidence that the simple field plot information provided by the NFI clusters is sufficient to estimate the proportion forest for large regions as accurately as remote-sensing-based methods, albeit with less precision. Fourth, ignoring forest-mapping-related uncertainty erroneously narrows the CI width as the estimate of proportion forest area decreases. At the small-area level, forest-mapping-related uncertainty led to CIs for total AGB as much as 63% wider in extreme cases. At the large-area level, the CI was 5–7% wider.
Estimation of Winter Wheat Stem Biomass by a Novel Two-Component and Two-Parameter Stratified Model Using Proximal Remote Sensing and Phenological Variables
Authors: Chen Weinan , Yang Guijun , Meng Yang , Feng Haikuan , Li Heli , Tang Aohua , Zhang Jing , Xu Xingang , Yang Hao , Li Changchun , Li Zhenhong
Published: November 2024

The timely and precise estimation of stem biomass is critical for monitoring the crop growing status. Optical remote sensing is limited by the penetration of sunlight into the canopy depth, and thus directly estimating winter wheat stem biomass via canopy spectra remains a difficult task. There is a stable linear relationship between the stem dry biomass (SDB) and leaf dry biomass (LDB) of winter wheat during the entire growth stage. Therefore, this study comprehensively considered remote sensing and crop phenology, as well as biomass allocation laws, to establish a novel two-component (LDB, SDB) and two-parameter (phenological variables, spectral vegetation indices) stratified model (Tc/Tp-SDB) to estimate SDB across the growth stages of winter wheat. The core of the Tc/Tp-SDB model employed phenological variables (e.g., effective accumulative temperature, EAT) to correct the SDB estimations determined from the LDB. In particular, LDB was estimated using spectral vegetation indices (e.g., red-edge chlorophyll index, CIred edge). The results revealed that the coefficient values (β0 and β1) of ordinary least squares regression (OLSR) of SDB with LDB had a strong relationship with phenological variables. These coefficient (β0 and β1) relationships were used to correct the OLSR model parameters based on the calculated phenological variables. The EAT and CIred edge were determined as the optimal parameters for predicting SDB with the novel Tc/Tp-SDB model, with r, RMSE, MAE, and distance between indices of simulation and observation (DISO) values of 0.85, 1.28 t/ha, 0.95 t/ha, and 0.31, respectively. The estimation error of SDB showed an increasing trend from the jointing to flowering stages. Moreover, the proposed model showed good potential for estimating SDB from UAV hyperspectral imagery. This study demonstrates the ability of the Tc/Tp-SDB model to accurately estimate SDB across different growing seasons and growth stages of winter wheat.
Spatial and Temporal Characterization of Near Space Temperature and Humidity and Their Driving Influences
Authors: Luo Wenhui , Ma Jinji , Li Miao , Xu Haifeng , Wan Cheng , Li Zhengqiang
Published: November 2024

Near space refers to the atmospheric region 20–100 km above Earth’s surface, encompassing the stratosphere, mesosphere, and part of the thermosphere. This region is susceptible to surface and upper atmospheric disturbances, and the atmospheric temperature and humidity profiles can finely characterize its complex environment. To analyze the relationship between changes in temperature and humidity profiles and natural activities, this study utilizes 18 years of temperature and water vapor data from the TIMED/SABER and AURA/MLS instruments to investigate the variations in temperature and humidity with altitude, time, and spatial distribution. In addition, multiple linear regression analysis is used to examine the impact mechanisms of solar activity, the El Niño–Southern Oscillation (ENSO), and the Quasi-Biennial Oscillation (QBO) on temperature and humidity. The results show that in the mid- and low-latitude regions, temperature and water vapor reach their maxima at an altitude of 50 km, with values of 265 K and 8–9 × 10⁻⁶ ppmv, respectively; the variation characteristics differ across latitudes and altitudes, with a clear annual cycle; the feedback effects of solar activity and the ENSO index on temperature and humidity in the 20–40 km atmospheric layer are significantly different. Among these factors, solar activity is the most significant influence on temperature and water vapor, with response coefficients of −0.2 to −0.16 K/sfu and 0.8 to 4 × 10⁻⁶ ppmv/sfu, respectively. Secondly, in the low-latitude stratospheric region, the temperature response to ENSO is approximately −1.5 K/MEI, while in the high-latitude region, a positive response of 3 K/MEI is observed. The response of water vapor to ENSO varies between −1 × 10⁻⁷ and −4 × 10−⁷ ppmv/sfu. In the low-latitude stratospheric region, the temperature and humidity responses to the QBO index exhibit significant differences, ranging from −1.8 to −0.6 K/10 m/s. Additionally, there are substantial differences in responses between the polar regions and the low-latitude equatorial region. Finally, a three-dimensional model coefficient was constructed to illustrate the influence of solar activity, ENSO, and QBO on temperature and humidity in the near space. The findings of this study contribute to a deeper understanding of the temperature and humidity variation characteristics in near space and provide valuable data and model references for predicting three-dimensional parameters of temperature and humidity in this region.
Comparison of the Distribution of Evapotranspiration on Shady and Sunny Slopes in Southwest China
Authors: Kan Yixi , Shao Huaiyong , Du Chang , Guo Yimeng , Dai Xianglong
Published: November 2024

Evapotranspiration (ET) plays a significant role in the surface water cycle, particularly within the unique geographical context of Southwest China. The region’s different topography, driven by mountain uplift and variations in slope direction, alters regional hydrothermal conditions, thereby affecting local ecoclimatic patterns. ET characteristics, shaped by slope orientation, can also serve as important indicators of climate variability in the study area. While most existing ET research on shady and sunny slopes has been conducted at the point scale, this study employed Global Land Surface Satellite (GLASS) ET products to estimate the average ET for shady and sunny slopes across five provinces in Southwest China between 2003 and 2018. The driving factors behind the variation in ET across different regions were also explored. Key results include the following: (1) Annual ET in Southwest China ranges between 200 mm and 800 mm, with Tibet exhibiting the lowest values and Yunnan the highest. (2) ET decreases gradually with increasing altitude in the altitude range of 0 m to 5000 m. The ET is higher on the sunny slopes than on the shady slopes. Notably, when the altitude is higher than 5000 m, ET on shady slopes in Tibet is greater than that on sunny slopes as the altitude increases. (3) ET initially increases with slope inclination before decreasing. Notably, in areas with slopes exceeding 35° in Yunnan, the ET value is found to be significantly higher on shady slopes compared to sunny slopes. (4) The effects of soil moisture, the Normalized Difference Vegetation Index, relative humidity, and land surface temperature on ET are more substantial on shady slopes than sunny slopes, whereas air temperature has a stronger impact on ET on sunny slopes. These results provide valuable data for research on regional climate change and contribute to strategies for ecological and environmental protection.
Multi-Scenario Remote Sensing Image Forgery Detection Based on Transformer and Model Fusion
Authors: Zhao Jinmiao , Shi Zelin , Yu Chuang , Liu Yunpeng
Published: November 2024

Recently, remote sensing image forgery detection has received widespread attention. To improve the detection accuracy, we build a novel scheme based on Transformer and model fusion. Specifically, we model this task as a binary classification task that focuses on global information. First, we explore the performance of various excellent feature extraction networks in this task under the constructed unified classification framework. On this basis, we select three high-performance Transformer-based networks that focus on global information, namely, Swin Transformer V1, Swin Transformer V2, and Twins, as the backbone networks and fuse them. Secondly, considering the small number of samples, we use the public ImageNet-1K dataset to pre-train the network to learn more stable feature expressions. At the same time, a circular data divide strategy is proposed, which can fully utilize all the samples to improve the accuracy in the competition. Finally, to promote network optimization, on the one hand, we explore multiple loss functions and select label smooth loss, which can reduce the model’s excessive dependence on training data. On the other hand, we construct a combined learning rate optimization strategy that first uses step degeneration and then cosine annealing, which reduces the risk of the network falling into local optima. Extensive experiments show that the proposed scheme has excellent performance. This scheme won seventh place in the “Forgery Detection in Multi-scenario Remote Sensing Images of Typical Objects” track of the 2024 ISPRS TC I contest on Intelligent Interpretation for Multi-modal Remote Sensing Application.
Synergistic Coupling of Multi-Source Remote Sensing Data for Sandy Land Detection and Multi-Indicator Integrated Evaluation
Authors: Wu Junjun , Li Yi , Zhong Bo , Zhang Yan , Liu Qinhuo , Shi Xiaoliang , Ji Changyuan , Wu Shanlong , Sun Bin , Li Changlong , Yang Aixia
Published: November 2024

Accurate and timely extraction and evaluation of sandy land are essential for ecological environmental protection; it is urgent to do the research to support the sustainable development goals (SDGs) of Land Degradation Neutrality. This study used Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 8 OLI multispectral data as the main data sources. Combining the rich spectral information from optical data and the penetrating advantages of radar data, a feature-level fusion method was employed to unveil the intrinsic nature of vegetative cover and accurately identify sandy land. Simultaneously, leveraging the results obtained from training with measured data, a comprehensive desertification assessment model was proposed, which combines multiple indicators to achieve a thorough evaluation of sandy land. The results showed that the method based on feature-level fusion achieved an overall accuracy of 86.31% in sandy land detection in Gansu Province, China. The integrated multi-indicator model C22_C/FVC is the ratio of correlation texture features of VH to vegetation cover based on which sandy land can be classified into three categories. When C22_C/FVC is less than 2.2, the pixel is classified as fixed sandy land. Pixels of semi-fixed sandy land have an indicator value between 2.2 and 5.2. Shifting sandy land has values greater than 5.2. Results showed that shifting sandy land and semi-fixed sandy land are the predominant types in Gansu Province, with 85,100 square kilometers and 87,100 square kilometers, respectively. The acreage of fixed sandy land was the least, 51,800 square kilometers. The method presented in this paper is robust for the detection and evaluation of sandy land from satellite imageries, which can potentially be applied for conducting high-resolution and large-scale detection and evaluation of sandy land.
Terahertz Spectroscopy in Assessing Temperature-Shock Effects on Citrus
Authors: Wang Junbo , Zang Ziyi , Li Xiaomei , Tang Dongyun , Xiao Qi , Zhang Mingkun , Yan Shihan
Published: November 2024

Rapid assessment of physiological status is a precondition for addressing biological stress in trees so that they may recover. Environmental stress can cause water deficit in plants, while terahertz (THz) spectroscopy is sensitive to changes in aqueous solutions within organisms. This has given the THz sensor a competitive edge for evaluating plant phenotypes, especially under similar environmental stress, if there are existing differences in the corresponding THz information. In this study, we utilized THz technology in association with traditional weighing methods to explore physiological changes in citrus leaves under different temperature, duration, and stress treatment conditions. It was found that the higher the temperature and the longer the exposure duration, the more severe the reduction in the relative absorption coefficient. There was a positive correlation between the trends and the increase in the ion permeability of cells. In addition, based on the effective medium theory, THz spectral information can be transformed into information on free water and bound water in the leaves. Under different treatment conditions, water content shows different trends and degrees of change on the time scale, and accuracy was verified by traditional weighing methods. These findings revealed that characteristics of THz information can serve as a simple and clear indicator for judging a plant’s physiological status.
Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise
Authors: Lee Jeong-Geun , Kim Kwang Sik , Lee Jang Hyun
Published: November 2024

The influence of environmental noise is generally excluded during research on machine fault diagnosis using acoustic signals. This study proposes a fault diagnosis method using a variational autoencoder (VAE) and domain adaptation neural network (DANN), both of which are based on unsupervised learning, to address this problem. The proposed method minimizes the impact of environmental noise and maintains the fault diagnosis performance in altered environments. The fault diagnosis algorithm was implemented using acoustic signals containing noise, present in the malfunctioning industrial machine investigation and inspection open dataset, and the fault prediction performance in noisy environments was examined based on forklift acoustic data using the VAE and DANN. The VAE primarily learns from normal state acoustic data and determines the occurrence of faults based on reconstruction error. To achieve this, statistical features of Mel frequency cepstral coefficients were extracted, generating features applicable regardless of signal length. Additionally, features were enhanced by applying noise reduction techniques via magnitude spectral subtraction and feature optimization, reflecting the characteristics of rotating equipment. Furthermore, data were augmented using generative adversarial networks to prevent overfitting. Given that the forklift acoustic data possess time-series characteristics, the exponentially weighted moving average was determined to quantitatively track time-series changes and identify early signs of faults. The VAE defined the reconstruction error as the fault index, diagnosing the fault states and demonstrating excellent performance using time-series data. However, the fault diagnosis performance of the VAE tended to decrease in noisy environments. Moreover, applying DANN for fault diagnosis significantly improved diagnostic performance in noisy environments by overcoming environmental differences between the source and target domains. In particular, by adapting the model learned in the source domain to the target domain and considering the domain differences based on signal-to-noise ratio, high diagnostic accuracy was maintained regardless of the noise levels. The DANN evaluated interdomain similarity using cosine similarity, enabling the accurate classification of fault states in the target domain. Ultimately, the combination of the VAE and DANN techniques enabled effective fault diagnosis even in noisy environments.
Synthesis of 2MP-CuNPs Fluorescent Probes and Their Application in Tetracycline Detection
Authors: Dou Qiaoya , Hasanjan Zulpiye , Zhang Hongyan
Published: November 2024

A fluorescent probe composed of 2-mercaptopyridine–copper nanoparticles (2MP-CuNPs) was synthesized through a hydrothermal method utilizing CuCl2 and 2-mercaptopyridine (2MP). The experimental results indicate that the 2MP-CuNPs probe exhibited an excellent fluorescence emission peak at 525 nm with an excitation wavelength of 200 nm. Furthermore, this emission peak was accompanied by a substantial Stokes shift of 325 nm, which effectively minimized the overlap between the excitation and emission spectra, thereby enhancing detection sensitivity. In tetracycline (TC) detection, the dimethylamino group on TC undergoes protonation in acidic conditions, resulting in a H+ ion. Consequently, the nitrogen atom within the pyridine moiety of the 2MP-CuNPs probe forms a coordination complex with H+ via multi-toothed n-bonding interactions, leading to a significant reduction in fluorescence intensity at 525 nm. Based on this mechanism, a quantitative detection method for TC was successfully established with a linear range spanning from 0.1 to 240 µM and an impressive detection limit of 120 nM. Furthermore, during actual sample analyses involving milk and chicken feed, this analysis method based on the 2MP-CuNPs probe achieved absolute recovery rates ranging from 94% to 98%, underscoring its considerable potential for practical applications.
Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment
Authors: Procházka Aleš , Martynek Daniel , Vitujová Marie , Janáková Daniela , Charvátová Hana , Vyšata Oldřich
Published: November 2024

Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes and further medical problems. Recent technological and artificial intelligent advancements have equipped healthcare professionals with innovative tools, such as sensor systems and telemedicine equipment, offering groundbreaking opportunities to monitor and analyze patients’ physical activity. This paper investigates the potential applications of mobile accelerometers in evaluating the symmetry of specific rehabilitation exercises using a dataset of 1280 tests on 16 individuals in the age range between 8 and 75 years. A comprehensive computational methodology is introduced, incorporating traditional digital signal processing, feature extraction in both time and transform domains, and advanced classification techniques. The study employs a range of machine learning methods, including support vector machines, Bayesian analysis, and neural networks, to evaluate the balance of various physical activities. The proposed approach achieved a high classification accuracy of 90.6% in distinguishing between left- and right-side motion patterns by employing features from both the time and frequency domains using a two-layer neural network. These findings demonstrate promising applications of precise monitoring of rehabilitation exercises to increase the probability of successful surgical recovery, highlighting the potential to significantly enhance patient care and treatment outcomes.
Knee4Life: Empowering Knee Recovery After Total Knee Replacement Through Digital Health Protocol
Authors: Mansoubi Maedeh , Leveridge Phaedra , Smith Matthew , Fox Amelia , Massey Garry , Lamb Sarah E. , Keene David J. , Newell Paul , Jacobs Elizabeth , Kalson Nicholas S. , Haron Athia , Dawes Helen
Published: November 2024

Pain and knee stiffness are common problems following total knee replacement surgery, with 10–20% of patients reporting dissatisfaction following their procedure. A remote assessment of knee stiffness could improve outcomes through continuous monitoring, facilitating timely intervention. Using machine learning algorithms, computer vision can extract joint angles from video footage, offering a method to monitor knee range of motion in patients’ homes. This study outlines a protocol to provide proof of concept and validate a computer vision-based approach for measuring knee range of motion in individuals who have undergone total knee replacement. The study also explores the feasibility of integrating this technology into clinical practice, enhancing post-operative care. The study is divided into three components: carrying out focus groups, validating the computer vision-based software, and home testing. The focus groups will involve five people who underwent total knee replacement and ten healthcare professionals or carers who will discuss the deployment of the software in clinical settings. For the validation phase, 60 participants, including 30 patients who underwent total knee replacement surgery five to nine weeks prior and 30 healthy controls, will be recruited. The participants will perform five tasks, including the sit-to-stand test, where knee range of motion will be measured using computer vision-based markerless motion capture software, marker-based motion capture, and physiotherapy assessments. The accuracy and reliability of the software will be evaluated against these established methods. Participants will perform the sit-to-stand task at home. This will allow for a comparison between home-recorded and lab-based data. The findings from this study have the potential to significantly enhance the monitoring of knee stiffness following total knee replacement. By providing accurate, remote measurements and enabling the early detection of issues, this technology could facilitate timely referrals to non-surgical treatments, ultimately reducing the need for costly and invasive procedures to improve knee range of motion.
Robustness of Deep-Learning-Based RF UAV Detectors
Authors: Elyousseph Hilal , Altamimi Majid
Published: November 2024

The proliferation of low-cost, small radar cross-section UAVs (unmanned aerial vehicles) necessitates innovative solutions for countering them. Since these UAVs typically operate with a radio control link, a promising defense technique involves passive scanning of the radio frequency (RF) spectrum to detect UAV control signals. This approach is enhanced when integrated with machine-learning (ML) and deep-learning (DL) methods. Currently, this field is actively researched, with various studies proposing different ML/DL architectures competing for optimal accuracy. However, there is a notable gap regarding robustness, which refers to a UAV detector’s ability to maintain high accuracy across diverse scenarios, rather than excelling in just one specific test scenario and failing in others. This aspect is critical, as inaccuracies in UAV detection could lead to severe consequences. In this work, we introduce a new dataset specifically designed to test for robustness. Instead of the existing approach of extracting the test data from the same pool as the training data, we allowed for multiple categories of test data based on channel conditions. Utilizing existing UAV detectors, we found that although coefficient classifiers have outperformed CNNs in previous works, our findings indicate that image classifiers exhibit approximately 40% greater robustness than coefficient classifiers under low signal-to-noise ratio (SNR) conditions. Specifically, the CNN classifier demonstrated sustained accuracy in various RF channel conditions not included in the training set, whereas the coefficient classifier exhibited partial or complete failure depending on channel characteristics.
Prediction of Margin of Gait Stability by Using Six-DoF Motion of Pelvis
Authors: Kuroda Tomohito , Okamoto Shogo , Akiyama Yasuhiro
Published: November 2024

Unstable gait increases the risk of falls, posing a significant danger, particularly for frail older adults. The margin of stability (MoS) is a quantitative index that reflects the risk of falling due to postural imbalance in both the anterior-posterior and mediolateral directions during walking. Although MoS is a reliable indicator, its computation typically requires specialized equipment, such as motion capture systems, limiting its application to laboratory settings. To address this limitation, we propose a method for estimating MoS using time-series data from the translational and angular velocities of a single body segment—the pelvis. By applying principal motion analysis to process the multivariate time-series data, we successfully estimated MoS. Our results demonstrate that the estimated MoS in the mediolateral direction achieved an RMSE of 0.88 cm and a correlation coefficient of 0.72 with measured values, while in the anterior-posterior direction, the RMSE was 0.73 cm with a correlation coefficient of 0.87. These values for the mediolateral direction are better than those obtained in previous studies using only the three translational velocity components of the pelvis, whereas the values for the anterior direction are comparable to previous approaches. Our findings suggest that MoS can be reliably estimated using six-axial kinematic data of the pelvis, offering a more accessible method for assessing gait stability.
Human Daily Breathing Monitoring via Analysis of CSI Ratio Trajectories for WiFi Link Pairs on the I/Q Plane
Authors: Zhuang Wei , Lu Yuhang , Shen Yixian , Su Jian
Published: November 2024

The measurement of human breathing is crucial for assessing the condition of the body. It opens up possibilities for various intelligent applications, like advanced medical monitoring and sleep analysis. Conventional approaches relying on wearable devices tend to be expensive and inconvenient for users. Recent research has shown that inexpensive WiFi devices commonly available in the market can be utilized effectively for non-contact breathing monitoring. WiFi-based breathing monitoring is highly sensitive to motion during the breathing process. This sensitivity arises because current methods primarily rely on extracting breathing signals from the amplitude and phase variations of WiFi Channel State Information (CSI) during breathing. However, these variations can be masked by body movements, leading to inaccurate counting of breathing cycles. To address this issue, we propose a method for extracting breathing signals based on the trajectories of two-chain CSI ratios on the I/Q plane. This method accurately monitors breathing by tracking and identifying the inflection points of the CSI ratio samples’ trajectories on the I/Q plane throughout the breathing cycle. We propose a dispersion model to label and filter out CSI ratio samples representing significant motion interference, thereby enhancing the robustness of the breathing monitoring system. Furthermore, to obtain accurate breathing waveforms, we propose a method for fitting the trajectory curve of the CSI ratio samples. Based on the fitted curve, a breathing segment extraction algorithm is introduced, enabling precise breathing monitoring. Our experimental results demonstrate that this approach achieves minimal error and significantly enhances the accuracy of WiFi-based breathing monitoring.
WO3-Based Thin Films Grown by Pulsed Laser Deposition as Gas Sensors for NO2 Detection
Authors: Bellucci Alessandro , De Bonis Angela , Curcio Mariangela , Santagata Antonio , Pace Maria Lucia , Bolli Eleonora , Mastellone Matteo , Polini Riccardo , Salerno Raffaella , Valentini Veronica , Trucchi Daniele M.
Published: November 2024

Thin films based on tungsten oxide (WO3) were grown by nanosecond pulsed laser deposition on alumina printed-circuit boards to fabricate electrochemical sensors for nitrogen dioxide (NO2) detection. Samples exposed to thermal annealing (400 °C for 3 h) were also produced to compare the main properties and the sensor performance. Before gas testing, the morphology and structural properties were investigated. Scanning electron microscopy and atomic force microscopy showed the formation of granular films with a more compact structure before the thermal treatment. Features of the main WO3 phases were identified for both as-deposited and annealed samples by Raman spectroscopy, whereas X-ray diffraction evidenced the amorphous nature of the as-deposited samples and the formation of crystalline phases after thermal annealing. The as-deposited samples showed a higher W/O ratio, as displayed by energy-dispersive X-ray spectroscopy. An Arrhenius plot revealed a lower activation energy (0.11 eV) for the as-deposited thin films, which are the most electrically conductive samples, presenting a better gas response (30% higher than the response of the annealed ones) in the investigated NO2 concentration range of 5–20 ppm at the moderate device operating temperature of 75 °C. This behavior is explained by a larger quantity of oxygen vacancies, which enhances the sensing mechanism.
An Optimization Method for PCB Surface Defect Detection Model Based on Measurement of Defect Characteristics and Backbone Network Feature Information
Authors: Liu Huixiang , Zhao Xin , Liu Qiong , Chen Wenbai
Published: November 2024

Printed Circuit Boards (PCBs) are essential components in electronic devices, making defect detection crucial. PCB surface defects are diverse, complex, low in feature resolution, and often resemble the background, leading to detection challenges. This paper proposes the YOLOv8_DSM algorithm for PCB surface defect detection, optimized based on the three major characteristics of defect targets and feature map visualization. First, to address the complexity and variety of defect shapes, we introduce CSPLayer_2DCNv3, which incorporates deformable convolution into the backbone network. This enhances adaptive defect feature extraction, effectively capturing diverse defect characteristics. Second, to handle low feature resolution and background resemblance, we design a Shallow-layer Low-semantic Feature Fusion Module (SLFFM). By visualizing the last four downsampling convolution layers of the YOLOv8 backbone, we incorporate feature information from the second downsampling layer into SLFFM. We apply feature map separation-based SPDConv for downsampling, providing PAN-FPN with rich, fine-grained shallow-layer features. Additionally, SLFFM employs the bi-level routing attention (BRA) mechanism as a feature aggregation module, mitigating defect-background similarity issues. Lastly, MPDIoU is used as the bounding box loss regression function, improving training efficiency by enhancing convergence speed and accuracy. Experimental results show that YOLOv8_DSM achieves a mAP (0.5:0.9) of 63.4%, representing a 5.14% improvement over the original model. The model’s Frames Per Second (FPS) reaches 144.6. To meet practical engineering requirements, the designed PCB defect detection model is deployed in a PCB quality inspection system on a PC platform.
A Novel and Powerful Dual-Stream Multi-Level Graph Convolution Network for Emotion Recognition
Authors: Hou Guoqiang , Yu Qiwen , Chen Guang , Chen Fan
Published: November 2024

Emotion recognition enables machines to more acutely perceive and understand users’ emotional states, thereby offering more personalized and natural interactive experiences. Given the regularity of the responses of brain activity to human cognitive processes, we propose a powerful and novel dual-stream multi-level graph convolution network (DMGCN) with the ability to capture the hierarchies of connectivity between cerebral cortex neurons and improve computational efficiency. This consists of a hierarchical dynamic geometric interaction neural network (HDGIL) and multi-level feature fusion classifier (M2FC). First, the HDGIL diversifies representations by learning emotion-related representations in multi-level graphs. Subsequently, M2FC integrates advantages from methods for early and late feature fusion and enables the addition of more details to final representations from EEG samples. We conducted extensive experiments to validate the superiority of our model over numerous state-of-the-art (SOTA) baselines in terms of classification accuracy, the efficiency of graph embedding and information propagation, achieving accuracies of 98.73%, 95.97%, 72.74% and 94.89% for our model as well as increases of up to 0.59%, 0.32%, 2.24% and 3.17% over baselines on the DEAP-Arousal, DEAP-Valence, DEAP and SEED datasets, respectively. Additionally, these experiments demonstrated the effectiveness of each module for emotion recognition tasks.
High-Sensitivity Electrical Admittance Sensor with Regression Analysis for Measuring Mixed Electrolyte Concentrations
Authors: Chen Chun-Chi , Hung Chih-Hung , Zhu Han-Xiang , Chen Ji-Zun
Published: November 2024

Electrolyte balance is essential for the proper functioning of the body, and imbalances can lead to various health issues, some of which may be life-threatening. Therefore, measuring electrolyte concentrations is becoming increasingly important, particularly for athletes engaged in high-intensity and prolonged physical activity. In this project, we developed a highly sensitive sensing device capable of accurately and rapidly measuring electrolyte concentrations in mixed solutions, providing precise analysis of trace electrolyte levels. The sensor device requires no complex operational procedures and can quickly complete measurements, making it well-suited for point-of-care applications. Integration of regression models further enhances the device’s ability to estimate concentrations in mixed electrolyte solutions. The test results demonstrate that the device can detect subtle concentration variations, with a precision as low as 0.5 mM. This proposed sensing device offers a cost-effective and efficient solution for real-time monitoring of electrolyte levels in healthcare.
Unveiling the Circular Behavior of Product Appreciation: An Exploratory Study
Authors: Ali Mohammed I. M. , Choe Pilsung
Published: November 2024

Product appreciation is defined as valuing and sustaining the use of a functional, energy-efficient product. This study adopted a user perspective to understand the circular behavior of product appreciation and explore the factors influencing it. Employing a qualitative exploratory approach, in-depth, cross-language (English and Arabic) interviews were conducted with participants from different nationalities. The collected data were then subjected to an adapted and augmented version of reflexive thematic analysis, allowing for a thorough examination of the underlying motivations and barriers to product appreciation. Our analysis revealed 60 motivation factors and 30 barrier factors. More importantly, six motivation themes emerged: attachment due to sentimental value, user satisfaction, holistic positive engagement, product ingenuity and enduring value, economic mindfulness, and retention and status quo tendency. In contrast, four barrier themes were identified: changes in user preferences, user dissatisfaction, craving newness, and free upgrades. This study accentuates the delineation of the newly identified circular behavior of product appreciation and highlights its significance in extending product life cycles and slowing resource loops. The findings provide valuable insights for designers, researchers, business strategists, and policymakers aiming to foster sustainable consumption and production patterns through product design, sustainable value propositions, and extended producer responsibility policies. Furthermore, the proposed adapted version of the reflexive thematic analysis method is expected to be a significant contribution to the qualitative research literature.
Enhancing Construction Management Digital Twins Through Process Mining of Progress Logs
Authors: Wang Yongzhi , Liao Shaoming , Gong Zhiqun , Deng Fei , Yin Shiyou
Published: November 2024

Large-scale infrastructure projects involve numerous complex processes, and even small construction management (CM) deficiencies can lead to significant resource waste. Digital twins (DTs) offer a potential solution to the management side of the problem. The current DT models focus on real-time physical space mapping, which causes the fragmentation of process data in servers and limits lifecycle algorithm implementation. In this paper, we propose a DT framework that integrates process twins to achieve process discovery through process mining and that serves as a supplement to DTs. The proposed framework was validated in a highway project. Based on BIM, GIS, and UAV physical entity twins, construction logs were collected, and process discovery was performed on them using process mining techniques, achieving process mapping and conformance checking for the process twins. The main conclusions are as follows: (1) the process twins accurately reflect the actual construction process, addressing the lack of process information in CM DTs; (2) process variants can be used to analyze abnormal changes in construction methods and identify potential construction risks in advance; (3) sudden changes in construction nodes during activities can affect resource allocation across multiple subsequent stages; (4) process twins can be used to visualize construction schedule risks, such as lead and lag times. The significance of this paper lies in the construction of process twins to complement the existing DT framework, providing a solution to the lost process relationships in DTs, enabling better process reproduction, and facilitating prediction and optimization. In future work, we will concentrate on conducting more in-depth research on process twins, drawing from a wider range of data sources and advancing intelligent process prediction techniques.
The Key Role of Employee Commitment in the Relationship Between Flexible Work Arrangements and Employee Behavior
Authors: Gašić Dimitrije , Berber Nemanja , Slavić Agneš , Strugar Jelača Maja , Marić Slobodan , Bjekić Radmila , Aleksić Marko
Published: November 2024

The research’s main objective is to examine the mediating role of Employee Commitment (EC) in the relationship between Flexible Work Arrangements (FWAs) and employee behavior (Innovative Work Behavior (IWB) and Employee Performance (EP)) among employees in the Republic of Serbia. The research consists of a theoretical part (review of the literature on previous theoretical and empirical findings) and an empirical part (Partial least squares structural equation modeling (PLS-SEM) analysis conducted on a sample of 582 employees in Serbia). The main findings have determined that there is full mediation, as the indirect effect of FWAs on Innovative Work Behavior through employee commitment is significant, and partial mediation, as the indirect effect of FWAs on Employee Performance through Employee commitment. The flexibility provided by FWAs not only increases employee satisfaction and loyalty but also motivates them to reciprocate through improved behavior and employee performance. In this way, employee commitment becomes a key factor that links organizational flexibility policies with positive outcomes in employee behavior. Flexible work arrangements are key to HR sustainability by enabling a better work-life balance, reducing stress, increasing employee commitment, and fostering long-term innovation and productivity. The mediating role of employee commitment in the relationship between flexible work arrangements and employee behaviors, such as innovative work behavior and employee performance, is particularly important. A high level of commitment, which stems from flexible work conditions, significantly contributes to innovative practices and improved performance, further strengthening the sustainability of organizations.