bluuebunny/crossref_metadata_2025 huggingface dataset:
Correlation between Mild Cognitive Impairment and Sarcopenia: The Prospective Role of Lipids and Basal Metabolic Rate in the Link
Authors: Wang Xuan , Xiao Rong , Li Hongrui , Li Tiantian , Guan Lizheng , Ding Huini , Li Xiaoying , Zheng Huaguang , Yu Kang , Zhao Ai , Liang Wannian , Xi Yuandi
Published: December 2022

There is evidence of correlation between mild cognitive impairment (MCI) and sarcopenia (SA). However, the influencing factors and the mechanism, such as age-related lipid redistribution, remain unknown. This study aimed to clarify the role of dietary fats and erythrocyte lipids profile combined with basal metabolic rate (BMR) in the link between MCI and SA. A total of 1050 participants aged 65 to 85 were divided into control, MCI, SA and MCI and SA groups. Bioelectrical impedance analysis was used to evaluate appendicular lean mass and BMR. Cognition and dietary nutrition were detected by neuropsychological tests and food frequency questionnaires. UHPLC-QExactive-MS/MS and UHPLC-Qtrap-MS/MS were used to conduct the lipidomics analysis. Lower dietary intake of different phospholipids, unsaturated fatty acids and kinds of choline were significantly associated with MCI and SA. Least absolute shrinkage and selection operator, multivariate logistic regression, receiver operating characteristic curve and validation tests provided evidence that specific phospholipids, unsaturated fatty acids and BMR might be the critical factors in the processing of MCI and SA, as well as in their link. The lipidomic analysis observed a clear discrimination of the lipid profiles in the individuals who are in MCI, SA, or MCI and SA, compared with the control. Lower expressions in certain phospholipid species, such as sphingomyelin and phosphatidylethanolamines, decreased phosphatidylcholine with more unsaturated double bonds, lower level of lipids with C20:5 and C20:4, higher level of lipids with C18:2 and lipids with a remodeled length of acyl chain, might be closely related to the link between MCI and SA. Inadequate dietary intake and lower concentrations of the erythrocyte lipid profile of phospholipids and unsaturated fatty acids with a lower level of BMR might be the key points that lead to progress in MCI and SA, as well as in their link. They could be used as the prospective biomarkers for the higher risk of cognitive decline and/or SA in elderly population.
A Semi-Supervised Method for PatchMatch Multi-View Stereo with Sparse Points
Authors: Zhan Weida , Cao Keliang , Jiang Yichun , Chen Yu , Wang Jiale , Hong Yang
Published: December 2022

Recently, the deep-learning-based PatchMatch method has been rapidly developed in 3D reconstruction, based on which boundary regions are filled with other parts that most closely match edge parts, but limited PatchMatch data hinder the generalization of the method to unknown settings. If various large-scale PatchMatch datasets are generated, the process would require considerable time and resources when performing neighborhood point-matching calculations using random iterative algorithms. To solve this issue, we first propose a new, sparse, semi-supervised stereo-matching framework called SGT-PatchMatchNet, which can reconstruct reliable 3D structures with a small number of 3D points using the ground truth of surface frame values. Secondly, in order to solve the problem of the luminosity inconsistency of some pixels in other views, a photometric similar-point loss function is proposed to improve the performance of 3D reconstruction, which causes the neighborhood information to project the depth value of the predicted depth to meet the same 3D coordinates. Finally, in order to solve the problem of the edge blurring of the depth map obtained using the network model, we propose a robust-point consistency loss function to improve the integrity and robustness of the occlusion and edge areas. The experimental results show that the proposed method not only has good visual effects and performance indicators but can also effectively reduce the amount of computation and improve the calculation time.
Investigation of Catalytic and Photocatalytic Degradation of Methyl Orange Using Doped LaMnO3 Compounds
Authors: Sfirloaga Paula , Ivanovici Madalina-Gabriela , Poienar Maria , Ianasi Catalin , Vlazan Paulina
Published: December 2022

LaMnO3 and 1% Pd-, Ag-, or Y-doped perovskite type nanomaterials were prepared by the sol-gel method, followed by heat treatment at a low temperature (600 °C for 6 h). The investigation through X-ray diffraction and FT-IR spectroscopy indicated that all samples were well crystallized, without secondary phases, and that the transition metal doping changed the crystal structure from the R-3c space group for the undoped LaMnO3 to the Pm-3m space group for the doped perovskite compounds. In this research paper, the efficiencies of the perovskite LaMnO3 materials for methyl orange removal were analyzed, wherein the effect of the doping ions and of the pH on the catalytic activity were studied together with a kinetic approach for the LaMnO3 materials at different values of the pH. Moreover, in the catalytic activity, it should be noted that a slightly better performance was obtained for the Ag-doped materials compared to the Y- and Pd-doped perovskite samples. The results presented for the perovskite LaMnO3 nanomaterials reinforce the interest in these multifunctional materials to be used in industrial applications; e.g., in water treatment.
Research on the Application of Coal Gasification Slag in Soil Improvement
Authors: Zhang Ruimei , Li Xiaonan , Zhang Kai , Wang Pengfei , Xue Peifeng , Zhang Hailong
Published: December 2022

SEM, particle size analysis, and contaminant content of coarse coal gasification slag (CCGS) produced by Shenhua Xinjiang Chemical Co., Ltd. were measured, respectively, and the physicochemical properties of the soil after improvement using gasification slag were investigated in this paper. The results showed that the slag was porous, the particle size was small and the pollutant content was extremely low. Its pollutants were closely related to the pollutants in the raw coal. The coarse slag had a limited effect on soil particle size and texture improvement; the soil water retention performance increased with the increase of proportion of the slag, while pH and conductivity decreased; the improvement effect on soil SOM and available potassium was remarkable; the larger the proportion of the slag, the stronger the effect on maintaining soil alkali-hydrolyzed nitrogen, ammonium nitrogen, and available phosphorus. However, the effect was small, and increased the ion content, especially the cation in soil, and the sum of the eight soil ions before and after evaporation decreased. The results demonstrated that the CCGS generated by the corporation is feasible for soil improvement, and the study has important reference value for the comprehensive utilization of coal gasification slag.
Study of the Dynamic Behavior of an Autonomous Inflow-Control Device Using a Digital Twin
Authors: Cerquera Luis Alfonso Ramirez , Pinilla Jorge Andrés , Ratkovich Nicolás Ríos , Asuaje Miguel
Published: December 2022

Solutions that aim to reduce water production in heavy oil wells have led to the design of devices known as rate-controlled production (RCP) autonomous inflow-control device (AICD) valves, which are placed in well completions and autonomously open with the oil inflow and close with water by choking the flow. These devices, which are based on Bernoulli’s principle, use a levitating disk that chokes the flow of the phase with the lowest flow resistance. This study proposes a numerical model based on computational fluid dynamics (CFD) technology to understand these devices’ operations and propose better designs without experimentation. The numerical model was based on dynamic fluid–body interaction (DFBI) and volume of fluid (VOF) models. The model was found to respond as expected depending on the physical properties of the fluids involved in heavy oil production. Finally, some limitations were found in the numerical study that can be improved in future studies.
Exploration of Cucumber Waste as a Potential Biorefinery Feedstock
Authors: Gao Yang , Briers Hannah K. , Matharu Avtar S. , Fan Jiajun
Published: December 2022

The exploration of cucumber waste as a potential biorefinery feedstock is reported. Initially, extractives (essential oils) were isolated from cucumber waste via vacuum microwave hydro-distillation (VMHD). The yield and quality of the extractive were compared with respect to traditional hydro-distillation (HD). The essential oils were obtained over a range of microwave power (500, 750, 1000 W) and vacuum pressures (100, 200, 300 mbar). The highest quality (0.49 wt %) was obtained at a microwave irradiation power of 500 W and a vacuum of 300 mbar. VMHD is much quicker and more energy-efficient than HD. Within the context of a zero-waste biorefinery, the extractive-free residues were the solid residues from two different extraction methods were compared and characterized by ATR-IR, 13C solid-state NMR spectroscopy, SEM, TGA, and CHN elemental analysis. The resultant residues are cellulosic-rich, and no significant changes were observed with VMHD and HD treatment. The results indicated that the utilization of these residues can provide an efficient, inexpensive, and environment-friendly platform for the production of cellulosic materials.
Washing Post-Consumer Flexible Polyethylene Packaging Waste
Authors: Boz Noyan Ezgi Ceren , Venkatesh Abhijit , Boldizar Antal
Published: December 2022

The mechanical and thermal properties of injection-molded recycled polyethylene were studied, specifically with respect to the influence of large-scale washing and melt-compounding of polyethylene from post-consumer packaging waste. Three types of materials were studied: those taken after sorting, after sorting and washing, and after sorting, washing, and melt-compounding, including melt-filtration, all from a large-scale material flow. The materials were further processed on a laboratory scale and compared. The results showed that large-scale washing significantly reduced thermo-oxidative stability, as well as molar mass and melt viscosity. The degradation during large-scale washing made the material susceptible to further degradation in the subsequent extrusion compounding, as shown by the differences in compounding at 240 and 200 °C using a high-shear screw configuration. The compounding parameters, screw configuration, and compounding temperature did not influence the stiffness and strength of the unwashed and large-scale-washed materials, but the elongation-at-break varied, specifically, with the increased temperature. Washing had an influence on the mechanical properties as well, and the unwashed material provided molded samples with stiffness measurements of approximately 550 MPa, whereas the large-scale-washed material provided stiffness of approximately 400 MPa. The strength measurements were approximately 15 MPa for samples made of both unwashed and large-scale-washed material, and the elongation-at-break measurements were between 50 and 150%. The large-scale-washed and compounded materials had very different mechanical properties, with stiffness measurements of approximately 320 MPa, strength of approximately 20 MPA, and elongation-at-break of approximately 350%. The significantly different mechanical properties of the large-scale-washed and compounded materials were likely due to the melt-filtration included in the compounding through the removal of metal and rubber particles, and they may also have been due to the compatibilizing and stabilizing additive used in the compounding.
Magnetic Separation and Enrichment of Fe–Ti Oxides from Iron Titaniferous Beach Sands: Process Design Applied to Coastal Ecuador
Authors: Trujillo Willam , Cobo Joseph , Vera-Cedeño Dayanna , Palma-Cando Alex , Toro-Álava Jorge , Viloria Alfredo , Ricaurte Marvin
Published: December 2022

Iron titaniferous sands, also called black sands, are a source of various magnetic minerals, such as iron and titanium (Fe–Ti) oxides, with countless scientific and industrial applications. Ecuador is deemed a geo-diverse country that contains deposits of black sands in the Andean and coastal regions; therefore, the industrialization of these magnetic sands might be of high interest. This study presents a preliminary industrial design for the magnetic separation process of Fe–Ti oxides from iron titaniferous Ecuadorian beach sands. Four stages are considered for the process, involving collecting, drying, screening, and magnetic separation. This proposal returns the large particles (>150 μm) and the non-magnetic fraction to the original place, generating a minimum environmental impact with the support of natural marine and coastal processes. The process design criteria are based on engineering guidelines, sampling, and characterization of eleven black sand samples. Using conventional techniques, the water content, granulometric distribution, particle size, and semi-quantitative Fe–Ti oxide concentration were determined for the different sand samples. It is estimated that Fe–Ti oxide production may reach 5.835 metric tons per day (5.835 mtpd) with a magnetic content of 97.50%, starting from 100 mtpd of black sands. Based on an economic analysis (Class V), a net profit of USD 835,875.63 is expected during the first year of production. Thus, the magnetic separation and enrichment of Fe–Ti oxides from iron titaniferous coastal sands exploitation should allow the commercial valorization of these resources in an eco-friendly way, i.e., with economic benefits and minimization of environmental impact in the source area.
A Large-Scale Invariant Matching Method Based on DeepSpace-ScaleNet for Small Celestial Body Exploration
Authors: Fan Mingrui , Lu Wenlong , Niu Wenlong , Peng Xiaodong , Yang Zhen
Published: December 2022

Small Celestial Body (SCB) image matching is essential for deep space exploration missions. In this paper, a large-scale invariant method is proposed to improve the matching accuracy of SCB images under large-scale variations. Specifically, we designed a novel network named DeepSpace-ScaleNet, which employs an attention mechanism for estimating the scale ratio to overcome the significant variation between two images. Firstly, the Global Attention-DenseASPP (GA-DenseASPP) module is proposed to refine feature extraction in deep space backgrounds. Secondly, the Correlation-Aware Distribution Predictor (CADP) module is built to capture the connections between correlation maps and improve the accuracy of the scale distribution estimation. To the best of our knowledge, this is the first work to explore large-scale SCB image matching using Transformer-based neural networks rather than traditional handcrafted feature descriptors. We also analysed the effects of different scale and illumination changes on SCB image matching in the experiment. To train the network and verify its effectiveness, we created a simulation dataset containing light variations and scale variations named Virtual SCB Dataset. Experimental results show that the DeepSpace-ScaleNet achieves a current state-of-the-art SCB image scale estimation performance. It also shows the best accuracy and robustness in image matching and relative pose estimation.
Quality Analysis of Small Maritime Target Detection by Means of Passive Radar Reflectors in Different Sea States
Authors: Lisowski Józef , Szklarski Andrzej
Published: December 2022

The paper presents the synthesis and research of a new, more relevant detection model for Small Maritime Targets SMT such as yachts, sailing ships, fishing boats, and fishing cutters. For this purpose, effective reflection surfaces of four types of passive radar reflectors were identified in a special laboratory anechoic chamber. A fluctuation model for small maritime target detection using the Weibull probability distribution was formulated. Analytical and experimental verification of the quality of the developed model was carried out by a comparative assessment of the detection probability of small maritime targets with the use of four types of reflectors for five sea wave states.
Ozone Pollution of Megacity Shanghai during City-Wide Lockdown Assessed Using TROPOMI Observations of NO2 and HCHO
Authors: Xue Ruibin , Wang Shanshan , Zhang Sanbao , Zhan Jingfang , Zhu Jian , Gu Chuanqi , Zhou Bin
Published: December 2022

An unprecedented city-wide lockdown took place in Shanghai from April to May 2022 to curb the spread of COVID-19, which caused socio-economic disruption but a significant reduction of anthropogenic emissions in this metropolis. However, the ground-based monitoring data showed that the concentration of ozone (O3) remained at a high level. This study applied Tropospheric Monitoring Instrument (TROPOMI) observations to examine changes in tropospheric vertical column density (VCD) of nitrogen dioxide (NO2) and formaldehyde (HCHO), which are precursors of O3. Compared with the same period in 2019–2021, VCDs of NO2 and HCHO decreased respectively by ~50% and ~20%. Multiple regression analysis showed that the lockdown effect played a dominant role in this dramatic decline rather than meteorological impacts. Using the exponentially-modified Gaussian method, this study quantified nitrogen oxides (NOX) emission in Shanghai as 32.60 mol/s with a decrease of 50–80%, which was mainly contributed by the transportation and industrial sectors. The significant reduction of NOX emission in Shanghai is much higher than that of volatile organic compounds (VOCs), which led to dramatic changes in formaldehyde-to-nitrogen dioxide ratio (HCHO/NO2, FNR). Thus, when enforcing regulation on NOx emission control in the future, coordinately reducing VOCs emission should be implemented to mitigate urban O3 pollution.
Performance Monitoring of Mast-Mounted Cup Anemometers Multivariate Analysis with ROOT
Authors: Mangano Salvatore , Vega Enrique , Martínez Alejandro , Alfonso-Corcuera Daniel , Sanz-Andrés Ángel , Pindado Santiago
Published: December 2022

This paper analyzes the field performance of two cup anemometers installed in Zaragoza (Spain). Data acquired over almost three years, from January 2015 to December 2017, were analyzed. The effect of the different variables (wind speed, temperature, harmonics, wind speed variations, etc.) on two cup anemometers was studied. Data analysis was performed with ROOT, an open-source scientific software toolkit developed by CERN (Conseil Européen pour la Recherche Nucléaire) for the study of particle physics. The effects of temperature, wind speed, and wind dispersion (as a first approximation to atmospheric turbulence) on the first and third harmonics of the anemometers’ rotation speed (i.e., the anemometers’ output signature) were studied together with their evolution throughout the measurement period. The results are consistent with previous studies on the influence of velocity, turbulence, and temperature on the anemometer performance. Although more research is needed to assess the effect of the anemometer wear and tear degradation on the harmonic response of the rotor’s angular speed, the results show the impact of a recalibration on the performance of an anemometer by comparing this performance with that of a second anemometer.
Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture
Authors: Safarov Furkat , Temurbek Kuchkorov , Jamoljon Djumanov , Temur Ochilov , Chedjou Jean Chamberlain , Abdusalomov Akmalbek Bobomirzaevich , Cho Young-Im
Published: December 2022

Currently, there is a growing population around the world, and this is particularly true in developing countries, where food security is becoming a major problem. Therefore, agricultural land monitoring, land use classification and analysis, and achieving high yields through efficient land use are important research topics in precision agriculture. Deep learning-based algorithms for the classification of satellite images provide more reliable and accurate results than traditional classification algorithms. In this study, we propose a transfer learning based residual UNet architecture (TL-ResUNet) model, which is a semantic segmentation deep neural network model of land cover classification and segmentation using satellite images. The proposed model combines the strengths of residual network, transfer learning, and UNet architecture. We tested the model on public datasets such as DeepGlobe, and the results showed that our proposed model outperforms the classic models initiated with random weights and pre-trained ImageNet coefficients. The TL-ResUNet model outperforms other models on several metrics commonly used as accuracy and performance measures for semantic segmentation tasks. Particularly, we obtained an IoU score of 0.81 on the validation subset of the DeepGlobe dataset for the TL-ResUNet model.
Feasibility of a Real-Time Embedded Hyperspectral Compressive Sensing Imaging System
Authors: Lim Olivier , Mancini Stéphane , Dalla Mura Mauro
Published: December 2022

Hyperspectral imaging has been attracting considerable interest as it provides spectrally rich acquisitions useful in several applications, such as remote sensing, agriculture, astronomy, geology and medicine. Hyperspectral devices based on compressive acquisitions have appeared recently as an alternative to conventional hyperspectral imaging systems and allow for data-sampling with fewer acquisitions than classical imaging techniques, even under the Nyquist rate. However, compressive hyperspectral imaging requires a reconstruction algorithm in order to recover all the data from the raw compressed acquisition. The reconstruction process is one of the limiting factors for the spread of these devices, as it is generally time-consuming and comes with a high computational burden. Algorithmic and material acceleration with embedded and parallel architectures (e.g., GPUs and FPGAs) can considerably speed up image reconstruction, making hyperspectral compressive systems suitable for real-time applications. This paper provides an in-depth analysis of the required performance in terms of computing power, data memory and bandwidth considering a compressive hyperspectral imaging system and a state-of-the-art reconstruction algorithm as an example. The results of the analysis show that real-time application is possible by combining several approaches, namely, exploitation of system matrix sparsity and bandwidth reduction by appropriately tuning data value encoding.
Performance Evaluation of an IoT Sensor Node for Health Monitoring of Artwork and Ancient Wooden Structures
Authors: Fort Ada , Landi Elia , Mugnaini Marco , Parri Lorenzo , Vignoli Valerio
Published: December 2022

In this paper, an IoT sensor node, based on smart Bluetooth low energy (BLE), for the health monitoring of artworks and large wooden structures is presented. The measurements from sensors on board the node are collected in real-time and sent to a remote gateway. The sensor node allows for the monitoring of environmental parameters, in particular, temperature and humidity, with accurate and robust integrated sensors. The developed node also embeds an accelerometer, which also allows other mechanical quantities (such as tilt) to be derived. This feature can be exploited to perform structural monitoring, exploiting the processing of data history to detect permanent displacements or deformations. The node is triggered by acceleration transients; therefore, it can also generate alarms related to shocks. This feature is crucial, for instance, in the case of transportation. The developed device is low-cost and has very good performance in terms of power consumption and compactness. A reliability assessment showed excellent durability, and experimental tests proved very satisfactory robustness against working condition variations. The presented results confirm that the developed device allows for the realization of pervasive monitoring systems, in the context of the IoT paradigm, with sensor nodes devoted to the monitoring of each artwork present in a museum or in a church.
Multi-Task Learning Model for Kazakh Query Understanding
Authors: Haisa Gulizada , Altenbek Gulila
Published: December 2022

Query understanding (QU) plays a vital role in natural language processing, particularly in regard to question answering and dialogue systems. QU finds the named entity and query intent in users’ questions. Traditional pipeline approaches manage the two mentioned tasks, namely, the named entity recognition (NER) and the question classification (QC), separately. NER is seen as a sequence labeling task to predict a keyword, while QC is a semantic classification task to predict the user’s intent. Considering the correlation between these two tasks, training them together could be of benefit to both of them. Kazakh is a low-resource language with wealthy lexical and agglutinative characteristics. We argue that current QU techniques restrict the power of the word-level and sentence-level features of agglutinative languages, especially the stem, suffixes, POS, and gazetteers. This paper proposes a new multi-task learning model for query understanding (MTQU). The MTQU model is designed to establish direct connections for QC and NER tasks to help them promote each other mutually, while we also designed a multi-feature input layer that significantly influenced the model’s performance during training. In addition, we constructed new corpora for the Kazakh query understanding task, namely, the KQU. As a result, the MTQU model is simple and effective and obtains competitive results for the KQU.
Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis
Authors: Amrouni Nadia , Benzaoui Amir , Bouaouina Rafik , Khaldi Yacine , Adjabi Insaf , Bouglimina Ouahiba
Published: December 2022

In recent years, palmprint recognition has gained increased interest and has been a focus of significant research as a trustworthy personal identification method. The performance of any palmprint recognition system mainly depends on the effectiveness of the utilized feature extraction approach. In this paper, we propose a three-step approach to address the challenging problem of contactless palmprint recognition: (1) a pre-processing, based on median filtering and contrast limited adaptive histogram equalization (CLAHE), is used to remove potential noise and equalize the images’ lighting; (2) a multiresolution analysis is applied to extract binarized statistical image features (BSIF) at several discrete wavelet transform (DWT) resolutions; (3) a classification stage is performed to categorize the extracted features into the corresponding class using a K-nearest neighbors (K-NN)-based classifier. The feature extraction strategy is the main contribution of this work; we used the multiresolution analysis to extract the pertinent information from several image resolutions as an alternative to the classical method based on multi-patch decomposition. The proposed approach was thoroughly assessed using two contactless palmprint databases: the Indian Institute of Technology—Delhi (IITD) and the Chinese Academy of Sciences Institute of Automatisation (CASIA). The results are impressive compared to the current state-of-the-art methods: the Rank-1 recognition rates are 98.77% and 98.10% for the IITD and CASIA databases, respectively.
A Comparison of Deep Neural Networks for Monocular Depth Map Estimation in Natural Environments Flying at Low Altitude
Authors: Romero-Lugo Alexandra , Magadan-Salazar Andrea , Fuentes-Pacheco Jorge , Pinto-Elías Raúl
Published: December 2022

Currently, the use of Unmanned Aerial Vehicles (UAVs) in natural and complex environments has been increasing, because they are appropriate and affordable solutions to support different tasks such as rescue, forestry, and agriculture by collecting and analyzing high-resolution monocular images. Autonomous navigation at low altitudes is an important area of research, as it would allow monitoring parts of the crop that are occluded by their foliage or by other plants. This task is difficult due to the large number of obstacles that might be encountered in the drone’s path. The generation of high-quality depth maps is an alternative for providing real-time obstacle detection and collision avoidance for autonomous UAVs. In this paper, we present a comparative analysis of four supervised learning deep neural networks and a combination of two for monocular depth map estimation considering images captured at low altitudes in simulated natural environments. Our results show that the Boosting Monocular network is the best performing in terms of depth map accuracy because of its capability to process the same image at different scales to avoid loss of fine details.
Coherent Signal DOA Estimation for MIMO Radar under Composite Background of Strong Interference and Non-Uniform Noise
Authors: Lin Bin , Hu Guoping , Zhou Hao , Zheng Guimei
Published: December 2022

To address the problems of low accuracy and low robustness of the conventional algorithm in estimating the direction of arrival (DOA) of coherent signals against a composite background of strong interference and non-uniform noise, in this paper, a coherent signal DOA estimation algorithm based on fixed projection blocking is proposed in conjunction with a multi-input multi-output (MIMO) radar. The covariance matrix of the received signal is first decomposed by eigenvalues, and a fixed projection matrix orthogonal to the interference guidance vector is constructed as the interference blocking matrix. Then, the received array signal is pre-processed to re-form the covariance matrix, and this matrix is rendered decoherent through a Toeplitz reconstruction. Finally, the reconstructed covariance matrix is estimated by DOA using the propagation operator algorithm to reduce the complexity. The simulation verifies that the proposed algorithm has a better robustness and higher accuracy than conventional algorithms for the DOA estimation of coherent signals in composite backgrounds.
Self-Calibrating Magnetometer-Free Inertial Motion Tracking of 2-DoF Joints
Authors: Laidig Daniel , Weygers Ive , Seel Thomas
Published: December 2022

Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, optical motion capture, but at lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientations in which the sensors are attached to the body segments. This knowledge is commonly obtained via time-consuming and error-prone anatomical calibration based on precisely defined poses or motions. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints in the angular rates and the relative orientations to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.
Bandwidth Optimization of MEMS Accelerometers in Fluid Medium Environment
Authors: Xu Xiang , Wu Shuang , Fang Weidong , Yu Zhe , Jia Zeyu , Wang Xiaoxu , Bai Jian , Lu Qianbo
Published: December 2022

There is a constraint between the dynamic range and the bandwidth of MEMS accelerometers. When the input acceleration is comparatively large, the squeeze film damping will increase dramatically with the increase in the oscillation amplitude, resulting in a decrease in bandwidth. Conventional models still lack a complete vibration response analysis in large amplitude ratios and cannot offer a suitable guide in the optimization of such devices. In this paper, the vibration response analysis of the sensing unit of an accelerometer in large amplitude ratios is first completed. Then, the optimal design of the sensing unit is proposed to solve the contradiction between the dynamic range and the bandwidth of the accelerometer. Finally, the results of the vibration experiment prove that the maximum bandwidth can be achieved with 0~10g external acceleration, which shows the effectiveness of the design guide. The new vibration analysis with the complete model of squeeze film damping is applicable to all sensitive structures based on vibration, not limited to the MEMS accelerometer studied in this thesis. The bandwidth optimal scheme also provides a strong reference for similar structures with large oscillation amplitude ratios.
Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal
Authors: Islam Mohammed Saidul , Hussain Iqram , Rahman Md Mezbaur , Park Se Jin , Hossain Md Azam
Published: December 2022

State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. However, most AI models are considered “black boxes,” because there is no explanation for the decisions made by these models. Users may find it challenging to comprehend and interpret the results. Explainable AI (XAI) can explain the machine learning (ML) outputs and contribution of features in disease prediction models. Electroencephalography (EEG) is a potential predictive tool for understanding cortical impairment caused by an ischemic stroke and can be utilized for acute stroke prediction, neurologic prognosis, and post-stroke treatment. This study aims to utilize ML models to classify the ischemic stroke group and the healthy control group for acute stroke prediction in active states. Moreover, XAI tools (Eli5 and LIME) were utilized to explain the behavior of the model and determine the significant features that contribute to stroke prediction models. In this work, we studied 48 patients admitted to a hospital with acute ischemic stroke and 75 healthy adults who had no history of identified other neurological illnesses. EEG was obtained within three months following the onset of ischemic stroke symptoms using frontal, central, temporal, and occipital cortical electrodes (Fz, C1, T7, Oz). EEG data were collected in an active state (walking, working, and reading tasks). In the results of the ML approach, the Adaptive Gradient Boosting models showed around 80% accuracy for the classification of the control group and the stroke group. Eli5 and LIME were utilized to explain the behavior of the stroke prediction model and interpret the model locally around the prediction. The Eli5 and LIME interpretable models emphasized the spectral delta and theta features as local contributors to stroke prediction. From the findings of this explainable AI research, it is expected that the stroke-prediction XAI model will help with post-stroke treatment and recovery, as well as help healthcare professionals, make their diagnostic decisions more explainable.
A Reinforcement Learning-Based Strategy of Path Following for Snake Robots with an Onboard Camera
Authors: Liu Lixing , Guo Xian , Fang Yongchun
Published: December 2022

For path following of snake robots, many model-based controllers have demonstrated strong tracking abilities. However, a satisfactory performance often relies on precise modelling and simplified assumptions. In addition, visual perception is also essential for autonomous closed-loop control, which renders the path following of snake robots even more challenging. Hence, a novel reinforcement learning-based hierarchical control framework is designed to enable a snake robot with an onboard camera to realize autonomous self-localization and path following. Specifically, firstly, a path following policy is trained in a hierarchical manner, in which the RL algorithm and gait knowledge are well combined. On this basis, the training efficiency is sufficiently optimized, and the path following performance of the control policy is greatly improved, which can then be implemented on a practical snake robot without any additional training. Subsequently, in order to promote visual self-localization during path following, a visual localization stabilization item is added to the reward function that trains the path following strategy, which endows a snake robot with smooth steering ability during locomotion, thereby guaranteeing the accuracy of visual localization and facilitating practical applications. Comparative simulations and experimental results are illustrated to exhibit the superior performance of the proposed hierarchical path following the control method in terms of convergence speed and tracking accuracy.
Bioprocessing of Agro-Industrial Waste for Maximization of Pectinase Production by a Novel Native Strain Aspergillus cervinus ARS2 Using Statistical Approach
Authors: Shet Anil R. , Muhsinah Abdullatif Bin , Alhazmi Abdulfattah Y. , Achappa Sharanappa , Desai Shivalingsarj V. , Mahnashi Mater H. , Muddapur Uday M. , Khan Aejaz Abdullatif , Shaikh Ibrahim Ahmed
Published: December 2022

The demand for microbial pectinase has increased due to its vast applications in different industries. The current study dealt with the synthesis of pectinase by a novel native strain Aspergillus cervinus ARS2 using agro-industrial waste. Comparative studies conducted on pectinase production by submerged fermentation (SmF) and solid-state fermentation (SSF) showed that pectinase activity was more increased in SSF (44.51 ± 1.33 IU/mL) than in SmF (40.60 ± 1.15 IU/mL) when using orange peel as a substrate. Using SSF, one-factor-at-a-time (OFAT) studies were conducted, considering different process variables such as inoculum size, initial pH, incubation time, moisture content, incubation temperature, and substrate particle size, all of which affected the pectinase activity. OFAT results showed the highest pectinase activity at an inoculum size of 106 spores/mL (43.11 ± 1.06 U/mL), an incubation time of 6 days (43.81 ± 1.21 U/mL), a moisture content of 100% (44.30 ± 1.69 U/mL), a substrate particle size of 1.7 mm (42.06 ± 1.20 U/mL), an incubation temperature of 37 °C (45.90 ± 1.33 U/mL), and an initial pH of 4 (43.31 ± 0.89 U/mL). The identified significant process variables were then optimized by response surface methodology (RSM)-central composite design (CCD). The results showed optimum pectinase activity of 107.14 ± 0.71 IU/mL for a substrate particle size of 2 mm, an incubation temperature of 31.5 °C, an initial pH of 4.9, and a moisture content of 107%, which was obtained from the Minitab optimizer. By using statistical optimization, the pectinase production from the isolated novel fungal strain A. cervinus ARS2 was increased 2.38-fold. Therefore, the A. cervinus ARS2 strain can be further explored for large-scale pectinase production which could meet the growing industrial demands.
An Improved d-MP Algorithm for Reliability of Logistics Delivery Considering Speed Limit of Different Roads
Authors: Yeh Wei-Chang , Huang Chia-Ling , Wu Haw-Sheng
Published: December 2022

The construction of intelligent logistics by intelligent wireless sensing is a modern trend. Hence, this study uses the multistate flow network (MFN) to explore the actual environment of logistics delivery and to consider the different types of transportation routes available for logistics trucks in today’s practical environment, which have been neglected in previous studies. Two road types, namely highways and slow roads, with different speed limits are explored. The speed of the truck is fast on the highway, so the completion time of the single delivery is, of course, fast. However, it is also because of its high speed that it is subject to many other conditions. For example, if the turning angle of the truck is too large, there will be a risk of the truck overturning, which is a quite serious and important problem that must be included as a constraint. Moreover, highways limit the weight of trucks, so this limit is also included as a constraint. On the other hand, if the truck is driving on a slow road, where its speed is much slower than that of a highway, it is not limited by the turning angle. Nevertheless, regarding the weight capacity of trucks, although the same type of trucks running on slow roads can carry a weight capacity that is higher than the load weight limit of driving on the highway, slow roads also have a load weight limit. In addition to a truck’s aforementioned turning angle and load weight capacity, in today’s logistics delivery, time efficiency is extremely important, so the delivery completion time is also included as a constraint. Therefore, this study uses the improved d-MP method to study the reliability of logistics delivery in trucks driving on two types of roads under constraints to help enhance the construction of intelligent logistics with intelligent wireless sensing. An illustrative example in an actual environment is introduced.
A 10-Year Statistic Study on the Tornadoes That Occurred in Jiangsu and Zhejiang Province: Composite Background Environment and Linear Trends
Authors: Li Danyu , Liu Jinghua , Liu Bin , Jiang Wendong , Zhou Xiaoyu , Gao Chao , Wang Zhenguo , Bai Cang
Published: December 2022

According to a 10-year (from 2007 to 2016) statistical analysis on the tornadoes in China, Jiangsu Province and Zhejiang Province, which share many similar geographical characteristics, experienced 159 (ranked first) and 59 (ranked fifth) tornadoes, respectively. Tornadoes within Jiangsu [i.e., the Jiangsu type (JST)] and those within Zhejiang [i.e., the Zhejiang type (ZJT)] featured notable annual, monthly and diurnal variations, which were remarkably different from each other. Both JST and ZJT showed the largest occurrence frequency (~50% on average) in the afternoon of summer, and the background environments before the formation of these tornadoes were composited to focus on their respective universal features. For the JST, it is found that, the upper-tropospheric divergence and positive geopotential-height anomaly, the middle-tropospheric shortwave trough, warm temperature advection and positive temperature anomaly, and the lower-tropospheric strong southwesterly wind, convergence and cyclonic vorticity all acted as favorable conditions for the convective activities within Jiangsu. For the ZJT, their background environment differed from that of the Jiangsu notably, and the conditions for the tornadoes’ formation were overall more favorable for the JST. Linear trend analyses indicated that, both the annual and seasonal (summer) occurrence of the JST tended to decrease significantly, whereas, those of the ZJT were not significant. For the JST, its occurrence frequency showed a low linear correlation to the variation of the surface temperature, instead, its decreasing frequency was more closely related to the significant weakening in the intensity of the vertical shear of the zonal wind and the updraft helicity within Jiangsu.
Data Stream Approach for Exploration of Droughts and Floods Driving Forces in the Dongting Lake Wetland
Authors: Zhai Yeqing , Liang Jie , An Zhenyu , Li Xin , Zhu Ziqian , Wang Wanting , Yi Yuru , Yang Suhang
Published: December 2022

Wetlands are important environmental resources that are vulnerable to droughts and floods. Studying drought-flood events and their driving factors is essential for wetland resource planning and management. However, climate change and human activities present dynamic challenges that traditional approaches are unable to simulate dynamically in a rapidly changing environment. This makes quantitative analysis difficult. Our research focused on the innovative use of the data stream model, namely online bagging of Hoeffding adaptive trees, to quantify drought and flood drivers in response to climate change and human activity. The proposed approach was applied to a river-lake system, the Dongting Lake wetland. The frequency and duration characteristics of drought-flood events were analyzed. In addition, the cyclical changes of droughts and floods were analyzed by wavelet analysis. Then, drought-flood indicators as well as climatic and hydrological factors were entered into a dynamic data stream model for quantitative calculations. The results showed that the water conservancy projects largely reduced flood events while aggravating droughts. The frequency of floods decreased by 4.91% and the frequency of droughts increased by 6.81% following the construction of the Gezhouba Hydro-project and the Three Gorges Dam. Precipitation and Sankou streamflow were two dominant factors in the Dongting Lake drought and flood events, both of which had a feature importance value of approximately 0.3. This research showed how the data stream model can be used in a changing environment and the applicability of the conclusions reached through real-world instances. Moreover, these quantitative outputs can help in the sustainable utilization of Dongting Lake wetland resources.
Functional Properties of Egg White Protein and Whey Protein in the Presence of Bioactive Chicken Trachea Hydrolysate and Sodium Chloride
Authors: Pramualkijja Teeda , Pirak Tantawan , Euston Stephen Robert
Published: December 2022

The interactions of chicken trachea collagen hydrolysate (CTH) obtained from the enzymatic hydrolysis of by-products from a chicken slaughterhouse, with two common proteins (egg white (E) and whey (W) proteins) was studied with and without sodium chloride (NaCl). The treatments with two protein types (E and W at 10% w/w) and different CTH concentrations (0, 0.25, and 0.5% w/w) and NaCl concentrations (0 and 1.5% w/w) were conducted. The addition of CTH reduced the emulsifying and foaming properties of those proteins due to their fibrillar structure, while the addition of NaCl promoted the foaming capacity. Moreover, CTH and NaCl promoted the gelling properties of those proteins, as revealed by the shift-up of the storage modulus (G′) value. The rapid shifting at 60–70 °C indicated that the heat-set gelation was observed. The strong gel strength was exhibited with the mixture containing W. The addition of 1.5% w/w NaCl improved the antioxidant and antihypertensive activities of the mixture of 0.5% w/w CTH and 10% w/w W. The best DPPH, ABTS, and FRAP radical-scavenging activities (40.00, 180.95, and 46.00 TEAC µM/mL, respectively) and the lowest IC50 value of the ACE inhibitor (30.05 mg/mL) was revealed. This mixture exhibits the highest inhibitory activity and is suited for improving the functionalities of high-protein products.
Experimental Study of Cyclist’ Sensitivity When They Are Overtaken by a Motor Vehicle: A Pilot Study in a Street without Cycle Lanes
Authors: Seriani Sebastian , Perez Vicente , Aprigliano Vicente , Fujiyama Taku
Published: December 2022

The objective of this work is to study the sensitivity of cyclists who are overtaken by a motor vehicle in a street route without cycling lanes. To achieve this, an experimental method is proposed in which 17 cyclists were recruited and classified into two groups: non-experienced users and experienced ones. In each run, the motor vehicle generated a stimulus presented on the route, considering different speeds and distances in the overtaken maneuver of the motor vehicle. The sensitivity was reported by audiovisual records to capture the stimuli to which they were subjected. The results obtained show that an experienced cyclist reacts 1.27 times faster than a non-experienced one. This advantage gives experienced users greater safety and a level of confidence on the road, since being able to go faster, reduces their relative speed difference with motor vehicles and gives such cyclists a greater feeling of comfort during their trip. As future research, it is proposed to carry out studies with different types of cyclists and group size to be able to compare the perceived sensitivities of making the trip individually versus one made collectively for different types of road infrastructure such as dedicated cycling pathways.