bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Complex Well Conditions on the Swelling and Tribological Properties of High-Acrylonitrile Stator Rubber in Screw Pumps
Authors: Liu Xinfu , Shi Xiangzhi , Hao Zhongxian , Wei Songbo , Sun Yi , Niu Xinglong , Liu Chunhua , Li Ming , Li Zunzhao
Published: July 2024

The effects of complex well conditions in shale oil wells on the swelling and tribological properties of high-acrylonitrile stator rubber used in screw pumps were investigated in this study. Tests were conducted considering the combined effects of immersion medium, temperature, and duration. The key parameters measured included mass change rate, volume change rate, hardness, elongation at break, tensile strength, surface micro-morphology of the rubber after thermal expansion and swelling, friction coefficient, and wear quantity. The results indicated that in the actual well fluids, the mass change rate of high-acrylonitrile rubber ranged from −1.08% to 1.29%, with a maximum volume change rate of 2.78%. In diesel oil, the greatest mass change rate of the rubber was 4.68%, and the volume change rate did not exceed ±1%, indicating superior swelling resistance. In both actual well fluids and diesel oil, the maximum decreases in hardness were 8.7% and 9.5%, respectively. Tensile strength and elongation at break decreased with increasing immersion temperature, with elongation at break in 80 °C diesel oil decreasing by over 50%, indicating a significant decline in the tensile properties of the rubber. The average friction coefficient of rubber specimens immersed in actual well fluids at three temperatures, as well as in diesel oil at 25 and 50 °C, decreased compared with the high-acrylonitrile rubber without thermal expansion and swelling. However, the average friction coefficient of rubber specimens immersed in diesel oil at 80 °C increased. The wear quantity of the rubber increased following immersion in both media. Additionally, the friction coefficient and wear quantity of the rubber increased with increasing immersion temperatures. The results of the study can offer valuable insights into assessing the durability of properties in high-acrylonitrile stator rubber under complex well conditions.
Morphological and Structural Characterization of Encapsulated Arginine Systems for Dietary Inclusion in Ruminants
Authors: Contreras-López Germán , Morales-Rodríguez Simón , Vilchis-Néstor Alfredo R. , Rentería-Monterrubio Ana L. , Corral-Luna Agustín , García-Galicia Ivan A. , Carrillo-López Luis M.
Published: July 2024

This research evaluated two methods of arginine encapsulation, melt emulsification and nanoprecipitation, using a lipid matrix of carnauba wax and commercial polymers (Eudragit®) as a protective material. The ratios of wax–arginine were 1:1, 2:1, 3:1, and 4:1, while those of Eudragit® RS:RL were 30:70 and 40:60 in proportions of 1:0.5 and 1:1 Eudragit®–arginine. The microcapsules were morphostructurally characterized by scanning electron microscopy, and a microelement analysis was performed via energy-dispersive X-ray spectroscopy and Fourier transform infrared spectroscopy. Additionally, in vitro digestibility was used to determine the protection efficiency. Both encapsulated systems presented regular (crystals) and spherical (microcapsules) polyhedral morphologies. Qualitative nitrogen decreased significantly as the wax ratio increased in the wax–arginine formulations. The formulations with a 1:1 Eudragit:–arginine ratio (1000 mg arginine) produced a higher nitrogen content in the encapsulated systems than the formulations containing 500 mg of arginine. The 2:1 and 3:1 wax–arginine formulations had the lowest degradability after 5 h of rumen fluid exposure (40.7 and 21.26%, respectively) in comparison with 100% unencapsulated arginine. The 3:1 wax–arginine formulation is an efficient encapsulating system which protects against rumen degradation. The more intense absorption bands at 1738 cm−1 and 1468 cm−1 associated with the C=O and C-H groups in carnauba wax indicate that arginine was more protected than in the other systems.
Effect of Nozzle Quantity on the Flow Field Characteristics and Grinding Efficiency in a Steam Jet Mill
Authors: Huang Shenglong , Zhang Yulu , Yin Xixi , Zhang Mingxing , Li Hong , Wang Zhe , Chen Haiyan , Wang Huan
Published: July 2024

A steam jet mill (SJM), which employs industrial waste heat steam as a gas source, is a widely utilized apparatus for the pulverization of fly ash. To achieve elevated single-machine grinding capacity, efficiency improvement research based on structural optimization should be conducted. In this study, numerical simulations and industrial experiments are carried out on SJMs equipped with three and six nozzles (hereinafter referred to as N3 and N6, respectively) to study the influence of nozzle quantity on the flow field and grinding efficiency. The numerical simulation results indicate that, under the N3 structure, particles can achieve a higher impact velocity in the comminution area and improve the kinetic energy of a single impact. In the conveying area, the airflow diffusion is better, resulting in an upward flow field that is more uniform. The classification area shows an increase in the uniformity of the flow field and a significant reduction in the local vortex structure, which is beneficial for accurate particle classification. In the interim, industrial experiments demonstrate that the N3 structure can markedly enhance the processing capacity and energy efficiency of the system. The smaller the feed particle size, the more pronounced the efficiency improvement.
Application of Principal Component Analysis for the Elucidation of Operational Features for Pervaporation Desalination Performance of PVA-Based TFC Membrane
Authors: Chaouk Hamdi , Obeid Emil , Halwani Jalal , Arayro Jack , Mezher Rabih , Amine Semaan , Gazo Hanna Eddie , Mouhtady Omar , Younes Khaled
Published: July 2024

Principal Component Analysis (PCA) serves as a valuable tool for analyzing membrane processes, offering insights into complex datasets, identifying crucial factors influencing membrane performance, aiding in design and optimization, and facilitating monitoring and fault diagnosis. In this study, PCA is applied to understand operational features affecting pervaporation desalination performance of PVA-based TFC membranes. PCA-biplot representation reveals that the first two principal components (PCs) accounted for 62.34% of the total variance, with normalized permeation with selective layer thickness (Pnorm), water permeation flux (P), and operational temperature (T) contributing significantly to PC1, while salt rejection dominates PC2. Membrane clustering indicates distinct influences, with membranes grouped based on correlation with operational factors. Excluding outliers increases total variance to 74.15%, showing altered membrane arrangements. Interestingly, the adopted strategy showed a high discrepancy between P and Pnorm, indicating the relevance of comparing between PVA membranes with specific layers and those with none. PCA results showed that Pnorm is more important than P in operational features, highlighting its significance in both research and practical applications. Our findings show that even know P remains a key performance property; Pnorm is critical for developing high-performance, efficient, and economically viable pervaporation desalination membranes. Subsequent PCA for membranes without specific layers (M1 to M6) and with specific layers (M7 to M11) highlights higher total variance and influence of variables, aiding in understanding membranes’ behavior and suitability under different conditions. Overall, PCA effectively delineates performance characteristics and potential applications of PVA-based TFC membranes. This study would confirm the applicability of the PCA approach in monitoring the operational efficiency of pervaporation desalination via these membranes.
Dynamic Change Characteristics and Main Controlling Factors of Pore Gas and Water in Tight Reservoir of Yan’an Gas Field in Ordos Basin
Authors: Wan Yongping , Wang Zhenchuan , Wang Meng , Mu Xiaoyan , Huang Jie , Huo Mengxia , Wang Ye , Liu Kouqi , Han Shuangbiao
Published: July 2024

Tight sandstone gas has become an important field of natural gas development in China. The tight sandstone gas resources of Yan’an gas field in Ordos Basin have made great progress. However, due to the complex gas–water relationship, its exploration and development have been seriously restricted. The occurrence state of water molecules in tight reservoirs, the dynamic change characteristics of gas–water two-phase seepage and its main controlling factors are still unclear. In this paper, the water-occurrence state, gas–water two-phase fluid distribution and dynamic change characteristics of different types of tight reservoir rock samples in Yan’an gas field were studied by means of water vapor isothermal adsorption experiment and nuclear magnetic resonance methane flooding experiment, and the main controlling factors were discussed. The results show that water molecules in different types of tight reservoirs mainly occur in clay minerals and their main participation is in the formation of fractured and parallel plate pores. The adsorption characteristics of water molecules conform to the Dent model; that is, the adsorption is divided into single-layer adsorption, multi-layer adsorption and capillary condensation. In mudstone, limestone and fine sandstone, water mainly occurs in small-sized pores with a diameter of 0.001 μm–0.1 μm. The dynamic change characteristics of gas and water are not obvious and no longer change under 7 MPa displacement pressure, and the gas saturation is low. The gas–water dynamic change characteristics of conglomerate and medium-coarse sandstone are obvious and no longer change under 9 MPa displacement pressure. The gas saturation is high, and the water molecules mainly exist in large-sized pores with a diameter of 0.1 μm–10 μm. The development of organic matter in tight reservoir mudstone is not conducive to the occurrence of water molecules. Clay minerals are the main reason for the high water saturation of different types of tight reservoir rocks. Tight rock reservoirs with large pore size and low clay mineral content are more conducive to natural gas migration and occurrence, which is conducive to tight sandstone gas accumulation.
Evaluating the Snow Cover Service Value on the Qinghai–Tibet Plateau
Authors: Gao Xianglong , Feng Qi , Liu Wen , Deng Xiaohong , Zhu Meng , Zhang Baiting , Xue Jian
Published: July 2024

The Snow Cover Service Value (SCSV) is an important component of the ecological assets of the Qinghai–Tibet Plateau (QTP). Exploring the SCSVs on the QTP is the key to maintaining the functions of climate regulators and Asian water towers, and it is also an important theoretical basis for maintaining the ecological security of ecological barrier areas. Using multi-source data such as daily and monthly observation data sets and related statistical yearbooks, an evaluation framework for the SCSVs on the QTP was constructed for the first time. The results showed that the average annual SCSV of the QTP from 2001 to 2020 was 6.99 trillion yuan, and the average annual climate regulation value was 5.81 trillion yuan, which was the most important SCSV. The Inner Plateau Basin, the Brahmaputra Basin, and the Yangtze Basin had the highest SCSVs, while the Yellow Basin had the lowest unit SCSV, where it was the most vulnerable area of snow cover resources on the QTP, and the SCSV of the Yellow Basin was significantly correlated with rainfall. The correlation between the SCSV and the temperature in the Indus and Ganges Basins was significant. The freshwater supply value of the snow in the Indus Basin and Tarim Basin was found to be able to reach 30% of the whole year in June, where it would be necessary to pay attention to the risk of flooding caused by snowmelt during the melting period. Finally, this paper discussed the strategies for the protection and development of snow resources in each basin based on the spatial distribution characteristics, seasonal variation characteristics, and influencing factors of the SCSVs. The research can provide reference for the rational allocation of snow resources and ecological protection on the QTP.
Instrument Design and In-Flight Performance of an Airborne Terahertz Ice Cloud Imager
Authors: Lv Rongchuan , Gao Wenyu , Luo Feng , Li Yinan , He Zheng , Wang Congcong , Zhang Yan , Zhang Chengzhen , Sun Daozhong , Shang Jian , Dou Fangli , Wang Xiaodong
Published: July 2024

The Airborne Terahertz Ice Cloud Imager (ATICI) is an airborne demonstration prototype of an ice cloud imager (ICI), which will be launched on the next generation of Fengyun satellites and plays an important role in heavy precipitation detection, typhoon, and medium-to-short-term meteorological/ocean forecasting. At present, it has 13 frequency channels covering 183–664 GHz, which are sensitive to scattering by cloud ice. This paper provides an overview of ATICI and proposes a receiving front-end design scheme using a planar mirror and a quasi-optical feed network which improves the main beam efficiency of each frequency band, with measured values better than 95.5%. It can detect factors such as ice particle size, ice water path, and ice water content in clouds by rotating the circular scanning of the antenna feed system. A high-sensitivity receiver system has been developed and tested for verification. The flight verification results show that the quasi-optical feed network subsystem works well and performs stably under vibration and temperature changes. The system sensitivity is better than 1.5 K, and the domestically produced high-frequency receiver has stable performance, which can meet the conditions of satellite applications. The ATICI performs well and meets expectations, verifying the feasibility of the Fengyun-5 ICI payload.
Water Stress Assessment of Cotton Cultivars Using Unmanned Aerial System Images
Authors: Gu Haibin , Mills Cory , Ritchie Glen L. , Guo Wenxuan
Published: July 2024

Efficiently monitoring and quantifying the response of genotypes to water stress is critical in developing resilient crop cultivars in water-limited environments. The objective of this study was to assess water stress in cotton (Gossypium hirsutum L.) using high-resolution unmanned aerial system (UAS) images and identify water-stress-resistant cultivars in plant breeding. Various vegetation indices (VIs) and the crop water stress index (CWSI) derived from UAS images were applied to assess water stress in eight cotton cultivars under four irrigation treatments (90%, 60%, 30%, and 0% ET). The enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), normalized difference vegetation index (NDVI), and crop water stress index (CWSI) were effective in detecting the effects of the irrigation treatments during the growing season. These VIs effectively differentiated cultivars in the middle and late seasons, while the CWSI detected cultivar differences in the mid–late growing season. The NDVI, GNDVI, NDRE, and EVI had a strong positive relationship with cotton yield starting from the mid-growing season in two years (R2 ranged from 0.90 to 0.95). Cultivars under each irrigation treatment were clustered into high-, medium-, and low-yielding groups based on the VIs at the mid–late growing seasons using hierarchical cluster analysis (HCA). The EVI derived from UAS images with high temporal and spatial resolutions can effectively screen drought-resistant cotton varieties under 30% and 60% irrigation treatments. The successful classification of cultivars based on UAS images provides critical information for selecting suitable varieties in plant breeding to optimize irrigation management based on water availability scenarios. This technology enables the targeted selection of water-stress-resistant cotton cultivars and facilitates site-specific crop management and yield prediction, ultimately contributing to precision irrigation and sustainable agriculture in water-limited environments.
Evidence of Dextral Strike-Slip Movement of the Alakol Lake Fault in the Western Junggar Based on Remote Sensing
Authors: Yi Wenxing , Li An , Xu Liangxin , Hu Zongkai , Li Xiaolong
Published: July 2024

The NW-SE-trending dextral strike-slip faults on the north side of the Tian Shan, e.g., the Karatau fault, Talas–Fergana fault, Dzhalair–Naiman fault, Aktas fault, Dzhungarian fault, and Chingiz fault, play an important role in accommodating crustal shortening. The classic viewpoint is that these strike-slip faults are an adjustment product caused by the difference in the crustal shortening from west to east. Another viewpoint attributes the dextral strike-slip fault to large-scale sinistral shearing. The Alakol Lake fault is a typical dextral strike-slip fault in the north Tian Shan that has not been reported. It is situated along the northern margin of the Dzhungarian gate, stretching for roughly 150 km from Lake Ebinur to Lake Alakol. Our team utilized aerial photographs, satellite stereoimagery, and field observations to map the spatial distribution of the Alakol Lake fault. Our findings provided evidence supporting the assertion that the fault is a dextral strike-slip fault. In reference to its spatial distribution, the Lake Alakol is situated in a pull-apart basin that lies between two major dextral strike-slip fault faults: the Chingiz and Dzhungarian faults. The Alakol Lake fault serves as a connecting structure for these two faults, resulting in the formation of a mega NW-SE dextral strike-slip fault zone. According to our analysis of the dating samples taken from the alluvial fan, as well as our measurement of the displacement of the riser and gully, it appears that the Alakol Lake fault has a dextral strike-slip rate of 0.8–1.2 mm/a (closer to 1.2 mm/a). The strike-slip rate of the Alakol Lake fault is comparatively higher than that of the Chingiz fault in the northern region (~0.7 mm/a) but slower than that of the Dzhungarian fault in the southern region (3.2–5 mm/a). The Chingiz–Alakol–Dzhungarian fault zone shows a gradual decrease in deformation towards the interior of the Kazakhstan platform.
Cell-Resolved PV Soiling Measurement Using Drone Images
Authors: Winkel Peter , Wilbert Stefan , Röger Marc , Krauth Julian J. , Algner Niels , Nouri Bijan , Wolfertstetter Fabian , Carballo Jose Antonio , Alonso-Garcia M. Carmen , Polo Jesus , Fernández-García Aránzazu , Pitz-Paal Robert
Published: July 2024

The maintenance of photovoltaic (PV) power plants is of central importance for their yield. To reach higher efficiencies in PV parks, it is helpful to detect soiling such as dust deposition and to apply this information to optimize cleaning strategies. Furthermore, a periodic inspection of the PV modules with infrared (IR) imagery is of advantage to detect and potentially remove faulty PV modules. Soiling can be erroneously interpreted as PV module defects and hence spatially resolved soiling measurements can improve the results of IR-based PV inspection. So far, soiling measurements are mostly performed only locally in PV fields, thus not supporting the above-mentioned IR inspections. This study presents a method for measuring the soiling of PV modules at cell resolution using RGB images taken by aerial drones under sunny conditions. The increase in brightness observed for soiled cells under evaluation, compared to clean cells, is used to calculate the transmission loss of the soiling layer. Photos of a clean PV module and a soiled module for which the soiling loss is measured electrically are used to determine the relation between the brightness increase and the soiling loss. To achieve this, the irradiance at the time of the image acquisitions and the viewing geometry are considered. The measurement method has been validated with electrical measurements of the soiling loss yielding root mean square deviations in the 1% absolute range. The method has the potential to be applied to entire PV parks in the future.
Sea Ice Detection from GNSS-R Data Based on Local Linear Embedding
Authors: Hu Yuan , Hua Xifan , Yan Qingyun , Liu Wei , Jiang Zhihao , Wickert Jens
Published: July 2024

Sea ice plays a critical role in the Earth’s climate system, and its variations affect ecosystem stability. This study introduces a novel method for detecting sea ice in the Arctic Ocean using bidirectional radar reflections from the Global Navigation Satellite System (GNSS). Utilizing delay-Doppler maps (DDM) from the UK TechDemoSat-1 (TDS-1) satellite mission and surface data from the U.S. National Oceanic and Atmospheric Administration (NOAA), we employ the local linear embedding (LLE) algorithm for feature extraction. This approach notably reduces training costs and enhances real-time performance, while maintaining a high accuracy and robust noise immunity level. Focusing on the region above 70° north latitude throughout 2018, we aimed to distinguish between sea ice and seawater. The extracted DDM features via LLE are input into a support vector machine (SVM) for classification. The results indicate that our method achieves an accuracy of over 99% for selected low-noise data and a monthly average accuracy of 92.74% for data containing noise, while the CNN method has a monthly average accuracy of only 77.31% for noisy data. A comparative analysis between the LLE-SVM approach and the convolutional neural network (CNN) method demonstrated the superior anti-interference capabilities of the former. Additionally, the impact of the sea ice melting period on detection accuracy was analyzed.
Two-Tier Efficient QoE Optimization for Partitioning and Resource Allocation in UAV-Assisted MEC
Authors: He Huaiwen , Yang Xiangdong , Huang Feng , Shen Hong
Published: July 2024

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a traveling salesman problem (TSP). Subsequently, we develop a coordinate descent (CD)-based method and an alternating direction method of multipliers (ADMM)-based method for network bandwidth and computing resource allocation in the MEC system. Extensive simulations demonstrate that the CD-based method is simple to implement and highly efficient in large-scale UAV-MEC networks, reducing the time complexity by three orders of magnitude compared to convex optimization methods. Meanwhile, the ADMM-based joint optimization method achieves approximately an 8% reduction in shrinkage ratio optimization compared to baseline methods.
Day-to-Day Variability in Measurements of Respiration Using Bioimpedance from a Non-Standard Location
Authors: Goyal Krittika , Shah Dishant , Day Steven W.
Published: July 2024

Non-invasive monitoring of pulmonary health may be useful for tracking several conditions such as COVID-19 recovery and the progression of pulmonary edema. Some proposed methods use impedance-based technologies to non-invasively measure the thorax impedance as a function of respiration but face challenges that limit the feasibility, accuracy, and practicality of tracking daily changes. In our prior work, we demonstrated a novel approach to monitor respiration by measuring changes in impedance from the back of the thigh. We reported the concept of using thigh–thigh bioimpedance measurements for measuring the respiration rate and demonstrated a linear relationship between the thigh–thigh bioimpedance and lung tidal volume. Here, we investigate the variability in thigh–thigh impedance measurements to further understand the feasibility of the technique for detecting a change in the respiratory status due to disease onset or recovery if used for long-term in-home monitoring. Multiple within-session and day-to-day impedance measurements were collected at 80 kHz using dry electrodes (thigh) and wet electrodes (thorax) across the five healthy subjects, along with simultaneous gold standard spirometer measurements for three consecutive days. The peak–peak bioimpedance measurements were found to be highly correlated (0.94 ± 0.03 for dry electrodes across thigh; 0.92 ± 0.07 for wet electrodes across thorax) with the peak–peak spirometer tidal volume. The data across five subjects indicate that the day-to-day variability in the relationship between impedance and volume for thigh–thigh measurements is smaller (average of 14%) than for the thorax (40%). However, it is affected by food and water and might limit the accuracy of the respiratory tidal volume.
Novel Wearable System to Recognize Sign Language in Real Time
Authors: Umut İlhan , Kumdereli Ümit Can
Published: July 2024

The aim of this study is to develop a practical software solution for real-time recognition of sign language words using two arms. This will facilitate communication between hearing-impaired individuals and those who can hear. We are aware of several sign language recognition systems developed using different technologies, including cameras, armbands, and gloves. However, the system we propose in this study stands out for its practicality, utilizing surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. We address the drawbacks of other methods, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have limited their practical application. Our software can run on different operating systems using digital signal processing and machine learning methods specific to this study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The random forest algorithm emerged as the most successful, achieving a remarkable 99.875% accuracy, while the naïve Bayes algorithm had the lowest success rate with 87.625% accuracy. The new system promises to significantly improve communication for people with hearing disabilities and ensures seamless integration into daily life without compromising user comfort or lifestyle quality.
RADAR-IoT: An Open-Source, Interoperable, and Extensible IoT Gateway Framework for Health Research
Authors: Ranjan Yatharth , Chang Jiangeng , Sankesara Heet , Conde Pauline , Rashid Zulqarnain , Dobson Richard J. B. , Folarin Amos
Published: July 2024

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user’s health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability.
Advanced Techniques for Liver Fibrosis Detection: Spectral Photoacoustic Imaging and Superpixel Photoacoustic Unmixing Analysis for Collagen Tracking
Authors: Sultan Laith R. , Grasso Valeria , Jose Jithin , Al-Hasani Maryam , Karmacharya Mrigendra B. , Sehgal Chandra M.
Published: July 2024

Liver fibrosis, a major global health issue, is marked by excessive collagen deposition that impairs liver function. Noninvasive methods for the direct visualization of collagen content are crucial for the early detection and monitoring of fibrosis progression. This study investigates the potential of spectral photoacoustic imaging (sPAI) to monitor collagen development in liver fibrosis. Utilizing a novel data-driven superpixel photoacoustic unmixing (SPAX) framework, we aimed to distinguish collagen presence and evaluate its correlation with fibrosis progression. We employed an established diethylnitrosamine (DEN) model in rats to study liver fibrosis over various time points. Our results revealed a significant correlation between increased collagen photoacoustic signal intensity and advanced fibrosis stages. Collagen abundance maps displayed dynamic changes throughout fibrosis progression. These findings underscore the potential of sPAI for the noninvasive monitoring of collagen dynamics and fibrosis severity assessment. This research advances the development of noninvasive diagnostic tools and personalized management strategies for liver fibrosis.
Opportunistic Sensor-Based Authentication Factors in and for the Internet of Things
Authors: Saideh Marc , Jamont Jean-Paul , Vercouter Laurent
Published: July 2024

Communication between connected objects in the Internet of Things (IoT) often requires secure and reliable authentication mechanisms to verify identities of entities and prevent unauthorized access to sensitive data and resources. Unlike other domains, IoT offers several advantages and opportunities, such as the ability to collect real-time data through numerous sensors. These data contains valuable information about the environment and other objects that, if used, can significantly enhance authentication processes. In this paper, we propose a novel idea to building opportunistic sensor-based authentication factors by leveraging existing IoT sensors in a system of systems approach. The objective is to highlight the promising prospects of opportunistic authentication factors in enhancing IoT security. We claim that sensors can be utilized to create additional authentication factors, thereby reinforcing existing object-to-object authentication mechanisms. By integrating these opportunistic sensor-based authentication factors into multi-factor authentication schemes, IoT security can be substantially improved. We demonstrate the feasibility and effectivenness of our idea through illustrative experiments in a parking entry scenario, involving both mobile robots and cars, achieving high identification accuracy. We highlight the potential of this novel method to improve IoT security and suggest future research directions for formalizing and comparing our approach with existing techniques.
Exploring Spectrogram-Based Audio Classification for Parkinson’s Disease: A Study on Speech Classification and Qualitative Reliability Verification
Authors: Jeong Seung-Min , Kim Seunghyun , Lee Eui Chul , Kim Han Joon
Published: July 2024

Patients suffering from Parkinson’s disease suffer from voice impairment. In this study, we introduce models to classify normal and Parkinson’s patients using their speech. We used an AST (audio spectrogram transformer), a transformer-based speech classification model that has recently outperformed CNN-based models in many fields, and a CNN-based PSLA (pretraining, sampling, labeling, and aggregation), a high-performance model in the existing speech classification field, for the study. This study compares and analyzes the models from both quantitative and qualitative perspectives. First, qualitatively, PSLA outperformed AST by more than 4% in accuracy, and the AUC was also higher, with 94.16% for AST and 97.43% for PSLA. Furthermore, we qualitatively evaluated the ability of the models to capture the acoustic features of Parkinson’s through various CAM (class activation map)-based XAI (eXplainable AI) models such as GradCAM and EigenCAM. Based on PSLA, we found that the model focuses well on the muffled frequency band of Parkinson’s speech, and the heatmap analysis of false positives and false negatives shows that the speech features are also visually represented when the model actually makes incorrect predictions. The contribution of this paper is that we not only found a suitable model for diagnosing Parkinson’s through speech using two different types of models but also validated the predictions of the model in practice.
Discrete-Time Visual Servoing Control with Adaptive Image Feature Prediction Based on Manipulator Dynamics
Authors: Liu Chenlu , Ye Chao , Shi Hongzhe , Lin Weiyang
Published: July 2024

In this paper, a practical discrete-time control method with adaptive image feature prediction for the image-based visual servoing (IBVS) scheme is presented. In the discrete-time IBVS inner-loop/outer-loop control architecture, the time delay caused by image capture and computation is noticed. Considering the dynamic characteristics of a 6-DOF manipulator velocity input system, we propose a linear dynamic model to describe the motion of a robot end effector. Furthermore, for better estimation of image features and smoothing of the robot’s velocity input, we propose an adaptive image feature prediction method that employs past image feature data and real robot velocity data to adopt the prediction parameters. The experimental results on a 6-DOF robotic arm demonstrate that the proposed method can ensure system stability and accelerate system convergence.
Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements
Authors: Haghbin Masoud , Chiachío Juan , Muñoz Sergio , Escalona Franco Jose Luis , Guillén Antonio J. , Crespo Marquez Adolfo , Cantero-Chinchilla Sergio
Published: July 2024

This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model’s performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails’ corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways.
A Wideband High Gain Differential Patch Antenna Featuring In-Phase Radiating Apertures
Authors: Zhang Honglin , Ye Jianhao
Published: July 2024

Communication systems need antennas with wide bandwidths to provide large throughput, while imaging radars benefit from high gain for increased range and wide bandwidths for high-resolution imaging. This paper presents the design and evaluation of a wideband, high-gain antenna that achieves an average gain of 9.7 dBi over a bandwidth of 1.49 GHz to 3.92 GHz by using multiple in-phase radiating apertures. The antenna has a unique structure with a central rectangular short-circuited patch sandwiched between two back-to-back U-shaped radiating patches and two flanking H-shaped short-circuited patches. Each of the U-shaped patches employs a coplanar waveguide as feeding to achieve ultra-wideband impedance matching. Benefiting from design arrangement, in-phase electrical field distributions appear at the gaps between the patches that result in equivalent radiating magnetic currents in the same direction. Theory analysis shows that the close-spaced, same-direction magnetic currents created by the radiating apertures intensify the radiation and increase antenna gain within its impedance bandwidth. Simulated data show that the use of the coplanar waveguide feeding and short-circuited patches increase the bandwidth from 65 MHz to 2.43 GHz. Moreover, the short-circuited patches increase the gain by 3.45 dB at 2.4 GHz. Simulation and measurement results validate the design and show that the antenna features a maximum gain of 11.3 dBi and an average gain of 9.7 dBi in a fractional bandwidth of 89.8%. Because of the high gain values and the wide bandwidth, the antenna is particularly suited for long-range communication systems and high-resolution radar applications.
Biases in Ecoacoustics Analysis: A Protocol to Equalize Audio Recorders
Authors: Potenza Andrea , Zaffaroni-Caorsi Valentina , Benocci Roberto , Guagliumi Giorgia , Fouani Jalal M. , Bisceglie Alessandro , Zambon Giovanni
Published: July 2024

Eco-acoustic indices allow us to rapidly evaluate habitats and ecosystems and derive information about anthropophonic impacts. However, it is proven that indices’ values and trends are not comparable between studies. These incongruences may be caused by the availability on the market of recorders with different characteristics and costs. Thus, there is a need to reduce these biases and incongruences to ensure an accurate analysis and comparison between soundscape ecology studies and habitat assessments. In this study, we propose and validate an audio recording equalization protocol to reduce eco-acoustic indices’ biases, by testing three soundscape recorder models: Song Meter Micro, Soundscape Explorer Terrestrial and Audiomoth. The equalization process aligns the signal amplitude and frequency response of the soundscape recorders to those of a type 1 level meter. The adjustment was made in MATLAB R2023a using a filter curve generated comparing a reference signal (white noise); the measurements were performed in an anechoic chamber using 11 audio sensors and a type 1 sound level meter (able to produce a .WAV file). The statistical validation of the procedure was performed on recordings obtained in an urban and Regional Park (Italy) assessing a significant reduction in indices’ biases on the Song Meter Micro and Audiomoth.
Enhancing Human Activity Recognition through Integrated Multimodal Analysis: A Focus on RGB Imaging, Skeletal Tracking, and Pose Estimation
Authors: Rehman Sajid Ur , Yasin Aman Ullah , Ul Haq Ehtisham , Ali Moazzam , Kim Jungsuk , Mehmood Asif
Published: July 2024

Human activity recognition (HAR) is pivotal in advancing applications ranging from healthcare monitoring to interactive gaming. Traditional HAR systems, primarily relying on single data sources, face limitations in capturing the full spectrum of human activities. This study introduces a comprehensive approach to HAR by integrating two critical modalities: RGB imaging and advanced pose estimation features. Our methodology leverages the strengths of each modality to overcome the drawbacks of unimodal systems, providing a richer and more accurate representation of activities. We propose a two-stream network that processes skeletal and RGB data in parallel, enhanced by pose estimation techniques for refined feature extraction. The integration of these modalities is facilitated through advanced fusion algorithms, significantly improving recognition accuracy. Extensive experiments conducted on the UTD multimodal human action dataset (UTD MHAD) demonstrate that the proposed approach exceeds the performance of existing state-of-the-art algorithms, yielding improved outcomes. This study not only sets a new benchmark for HAR systems but also highlights the importance of feature engineering in capturing the complexity of human movements and the integration of optimal features. Our findings pave the way for more sophisticated, reliable, and applicable HAR systems in real-world scenarios.
Optimizing the Steering of Driverless Personal Mobility Pods with a Novel Differential Harris Hawks Optimization Algorithm (DHHO) and Encoder Modeling
Authors: Reda Mohamed , Onsy Ahmed , Haikal Amira Y. , Ghanbari Ali
Published: July 2024

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods.
Half-Space Sound Field Reconstruction Based on the Combination of the Helmholtz Equation Least-Squares Method and Equivalent Source Method
Authors: Jiang Laixu , Xi Yingqi , Hu Yingying , Wang Guo , Liu Jingqiao
Published: July 2024

In practical conditions, near-field acoustic holography (NAH) requires the measurement environment to be a free sound field. If vibrating objects are located above the reflective ground, the sound field becomes non-free in the presence of a reflecting surface, and conventional NAH may not identify the sound source. In this work, two types of half-space NAH techniques based on the Helmholtz equation least-squares (HELS) method are developed to reconstruct the sound field above a reflecting plane. The techniques are devised by introducing the concept of equivalent source in HELS-method-based NAH. Two equivalent sources are tested. In one technique, spherical waves are used as the equivalent source, and the sound reflected from the reflecting surface is regarded as a linear superposition of orthogonal spherical wave functions of different orders located below the reflecting surface. In the other technique, some monopoles are considered equivalent sources, and the reflected sound is considered a series of sounds generated by simple sources distributed under the reflecting surface. The sound field is reconstructed by matching the pressure measured on the holographic surface with the orthogonal spherical wave source in the vibrating object and replacing the reflected sound with an equivalent source. Therefore, neither technique is related to the surface impedance of the reflected plane. Compared with the HELS method, both methods show higher reconstruction accuracy for a half-space sound field and are expected to broaden the application range of HELS-method-based NAH techniques.
Driving Sustainable Tourism Villages: Evaluating Stakeholder Commitment, Attitude, and Performance: Evidence from West Sumatra, Indonesia
Authors: Ferdian Feri , Mohd Zahari Mohd Salehuddin , Abrian Youmil , Wulansari Nidia , Azwar Hendri , Adrian Arif , Putra Trisna , Wulandari Dwi Pratiwi , Suyuthie Hijriyantomi , Pasaribu Pasaribu , Susanti Dessi , Aisiah Aisiah , Yulfa Arie , Pratama Vischa Mansyera , Harmawan Violintikha , Fadilah Rahmi , Donie Donie , Waryono Waryono
Published: July 2024

This research paper evaluates the commitment, attitude, and performance of various stakeholders, particularly the local government authorities, academic institutions, local businesses, media, and private sector, in the sustainable development of tourism villages. Quantitative data were collected through a survey questionnaire to gather perceptions and feedback from 384 tourism awareness group leaders in the province of West Sumatra, Indonesia. This study employs Partial Least Squares–Structural Equation Modeling (PLS-SEM) to test the hypotheses. The findings indicate that stakeholders’ commitment, attitude, and performance exert a strong influence on sustainable tourism villages. In addition, community culture moderates the relationship between the attitudes of stakeholders and sustainable tourism villages as opposed to the stakeholders’ commitment and performance. These findings provide valuable insights and far-reaching consequences for stakeholders, tourism village heads, and the community. There is a need to continuously enhance a comprehensive plan for sustainable tourism, one that goes beyond cultural implication and incorporates a broader range of influencing factors.