bluuebunny/crossref_metadata_2025 huggingface dataset:
Intratracheally Administered Peptide-Modified Lipid Admixture Containing Fasudil and/or DETA NONOate Ameliorates Various Pathologies of Pulmonary Arterial Hypertension
Authors: Sarkar Tanoy , Moinuddin Sakib M. , Isbatan Ayman , Chen Jiwang , Mann David , Ahsan Fakhrul
Published: November 2023

This study examined the therapeutic potential of a combination therapy using fasudil, a Rho-kinase inhibitor, and DETA NONOate (DN), a nitric oxide donor, delivered as a lipid admixture modified with a cyclic homing peptide known as CAR (CAR-lipid mixture) for the treatment of pulmonary arterial hypertension (PAH). CAR-lipid mixtures were initially prepared via a thin-film hydration method and then combined with fasudil, DN, or a mixture of both. The therapeutic efficacy of this drug-laden lipid mixture was evaluated in a Sugen/Hypoxia (Su/Hx) rat model of PAH by measuring RV systolic pressure (RVSP), mean pulmonary arterial pressure (mPAP), Fulton indices, and assessing right ventricular (RV) functions, as well as evaluating pulmonary vascular morphology. Rats that received no treatment exhibited increases in RVSP, mPAP, Fulton indices, and changes in RV functional parameters. However, the treatment with the CAR-lipid mixture containing either fasudil or DN or a combination of both led to a decline in mPAP, RVSP, and Fulton indices compared to saline-treated rats. Similarly, rats that received these treatments showed concurrent improvement in various echocardiographic parameters such as pulmonary acceleration time (PAT), tricuspid annular plane systolic excursion (TAPSE), and ventricular free wall thickness (RVFWT). A significant decrease in the wall thickness of pulmonary arteries larger than 100 µm was observed with the combination therapy. The findings reveal that fasudil, DN, and their combination in a CAR-modified lipid mixture improved pulmonary hemodynamics, RV functions, and pathological alterations in the pulmonary vasculature. This study underscores the potential of combination therapy and targeted drug delivery in PAH treatment, laying the groundwork for future investigations into the optimization of these treatments, their long-term safety and efficacy, and the underlying mechanism of action of the proposed therapy.
Measurement Method for the Flight Parameters of a Small Flying Object Using a Multi-Dimensional LED Detection Array
Authors: Dong Tao , Li Siqi , Chen Ding , Yang Ziyu
Published: November 2023

This article proposes a measurement method using a multi-dimensional LED detection array, which can be used to obtain the flight parameters (i.e., impact coordinates, flight velocity, and incident angle) of a small flying object. Firstly, the composition of the proposed system and its detection principle are described in detail. Then, a calculation model is derived according to the geometrical relationship between the different LED detection arrays, which can calculate the above flight parameters. Furthermore, numerical simulations are performed to analyze the change trend in the measurement error of the proposed system, and it can be verified that its measurement performance meets the related requirements in theory. Finally, we use the proposed system and a high-speed camera system to carry out comparison experiments with two different reference velocities of 900 m/s and 700 m/s, and the results show that the maximum deviation in the measured absolute velocity is always less than 1 m/s and that the maximum deviations of the measured coordinates of x and y are not more than 2 mm within the effective measurement range. Therefore, the proposed measurement method is feasible and effective, and it can also meet the requirements of the measurement system.
Investigating Mineral Accumulation and Seed Vigor Potential in Bottle Gourd (Lagenaria siceraria) through Crossbreeding Timing
Authors: Malik Anurag , Mor Virender Singh , Punia Himani , Duhan D. S. , Bhuker Axay , Tokas Jayanti , El-Sheikh Mohamed A. , Shah Tariq
Published: November 2023

Bottle gourd (Lagenaria siceraria) is a well-known cucurbit with an active functional ingredient. A two-year field experiment was carried out at the Research Farm of Seed Science and Technology, CCS HAU, Hisar, in a randomized block design during the Kharif season (Kharif is one of the two major cropping seasons in India and other South Asian countries, heavily reliant on monsoon rains with the other being Rabi) and the summer season. Five different crossing periods (CP), viz. CP1, CP2, CP3, CP4, and CP5, were considered to illustrate the effects of agro-climatic conditions on the quality and biochemical components of two bottle gourd parental lines and one hybrid, HBGH-35. The average mean temperature for the Kharif season in 2017 was 31.7 °C, and for the summer season, it was 40.1 °C. Flowers were tagged weekly from the start of the crossing period until the end and harvested separately at different times. The fruits harvested from different crossing periods under different environmental conditions influenced the bottle gourd’s qualitative and biochemical traits and showed significant variations among the five crossing period environments. A positive significance and correlation were observed between weather variables and different biochemical characteristics. Henceforth, the CP4 crossing period at a temperature of 31.7 °C retained high-quality seed development, which may be essential in enhancing agricultural productivity and the national economy.
Predicting the Potential Distribution of Oxalis debilis Kunth, an Invasive Species in China with a Maximum Entropy Model
Authors: Qin Xinsheng , Li Mingxin
Published: November 2023

Oxalis debilis Kunth, an invasive plant native to South America, has already spread extensively throughout various regions in China including West China, East China, Central China, and South China. It poses a certain degree of damage to the local ecosystem and demonstrates significant invasive potential. Utilizing distribution information along with environmental variables such as bioclimate, soil factors, elevation, and UV-B radiation, the MaxEnt model combined with ArcGIS was employed to forecast the potential distribution of O. debilis in China. The ROC curve was employed to assess the accuracy of the model, while the jackknife test was utilized to identify dominant environmental variables and determine their optimal values. The simulated AUC value was 0.946 ± 0.004, and the predicted results exhibited a remarkable concordance with the actual outcomes, thereby indicating that the Maxent model demonstrated a high level of confidence in its predictive capabilities. The potential distribution of O. debilis in China spanned 18,914,237 km2, accounting for 19.70% of the total land area. This distribution was primarily observed in East, Central, and South China, with Guangdong, Guangxi, and Guizhou being identified as highly suitable habitats for O. debilis. Furthermore, it was observed that the distribution of O. debilis is primarily influenced by environmental variables such as the precipitation of the driest month, the monthly diurnal range, the mean temperature of the wettest quarter, and the isothermality. The findings can serve as a valuable point of reference for the prevention and monitoring of O. debilis spread, thereby contributing to the protection of China’s agricultural, forestry, and ecological environments. It is imperative to acknowledge the hazards associated with O. debilis, closely monitor its invasion, and prevent uncontrolled dissemination.
Recent Progress of 3D Printing of Polymer Electrolyte Membrane-Based Fuel Cells for Clean Energy Generation
Authors: Golubkov Sergey S. , Morozova Sofia M.
Published: November 2023

This review summarizes recent advances in the application of 3D printing (additive manufacturing) for the fabrication of various components of hydrogen fuel cells with a polymer electrolyte membrane (HFC-PEMs). This type of fuel cell is an example of green renewable energy, but its active implementation into the real industry is fraught with a number of problems, including rapid degradation and low efficiency. The application of 3D printing is promising for improvement in HFC-PEM performance due to the possibility of creating complex geometric shapes, the exact location of components on the substrate, as well as the low-cost and simplicity of the process. This review examines the use of various 3D printing techniques, such as inkjet printing, fused deposition modeling (FDM) and stereolithography, for the production/modification of electrodes, gas diffusion and catalyst layers, as well as bipolar plates. In conclusion, the challenges and possible solutions of the identified drawbacks for further development in this field of research are discussed. It is expected that this review article will benefit both representatives of applied science interested in specific engineering solutions and fundamental science aimed at studying the processes occurring in the fuel cell.
Bead–Spring Simulation of Ionomer Melts—Studying the Effects of Chain-Length and Associating Group Fraction on Equilibrium Structure and Extensional Flow Behavior
Authors: Mohottalalage Supun S. , Saab Andrew P. , Maiti Amitesh
Published: November 2023

Ionomers are associative polymers with diverse applications ranging from selective membranes and high-performance adhesives to abrasion- and chemical-resistant coatings, insulation layers, vacuum packaging, and foamed sheets. Within equilibrium melt, the ionic or associating groups are known to form thermally reversible, associative clusters whose presence can significantly affect the system’s mechanical, viscoelastic, and transport properties. It is, thus, of great interest to understand how to control such clusters’ size distribution, shape, and stability through the designed choice of polymer architecture and the ionic groups’ fraction, arrangement, and interaction strength. In this work, we represent linear associating polymers using a Kremer–Grest type bead–spring model and perform large-scale MD simulations to explore the effect of polymer chain-length (l) and fraction (fs) of randomly placed associating groups on the size distribution and stability of formed clusters. We consider different chain-lengths (below and above entanglement), varying fractions of associating groups (represented by ‘sticky’ beads) between 5 and 20%, and a fixed sticky–sticky nonbond interaction strength of four times that between regular non-associating beads. For all melts containing associating groups the equilibrium structure factor S(q) displays a signature ionomer peak at low wave vector q whose intensity increases with increasing fs and l. The average cluster size Nc increases with fs. However, the effect of chain-length on Nc appears to be pronounced only at higher values of fs. Under extensional flows, the computed stress (and viscosity) is higher at higher fs and l regardless of strain rate. Beyond a critical strain rate, we observe fragmentation of the associative clusters, which has interesting effects on the stress/viscous response.
An Expert System Based on Data Mining for a Trend Diagnosis of Process Parameters
Authors: Wang Zhu , Wang Shaoxian , Zhang Shaokang , Zhan Jiale
Published: November 2023

In order to diagnose abnormal trends in the process parameters of industrial production, the Expert System based on rolling data Kernel Principal Component Analysis (ES-KPCA) and Support Vector Data Description (ES-SVDD) are proposed in this paper. The expert system is capable of identifying large-scale trend changes and abnormal fluctuations in process parameters using data mining techniques, subsequently triggering timely alarms. The system consists of a rule-based assessment of process parameter stability to evaluate whether the process parameters are stable. Also, when the parameters are unstable, the rolling data-based KPCA and SVDD methods are used to diagnose abnormal trends. ES-KPCA and ES-SVDD methods require adjusting seven threshold parameters during the offline parameter adjustment phase. The system obtains the adjusted parameters and performs a real-time diagnosis of process parameters based on the set diagnosis interval during the online diagnosis phase. The ES-KPCA and ES-SVDD methods emphasize the real-time alarms and the first alarm of process parameter abnormal trends, respectively. Finally, the system validates the experimental data from UniSim simulation and a chemical plant. The results show that the expert system has an outstanding diagnostic performance for abnormal trends in process parameters.
Study on Cavitation Bubble Characteristics in Centrifugal Pump Based on Image Recognition
Authors: Cui Yanyu , Cheng Bin , Ding Qingmiao , Li Xiaoman , Zhao Manjun
Published: November 2023

In this paper, a cavitation bubble in the centrifugal pump cavitation phenomenon was observed in the analysis and research to explore the characteristics of a cavitation bubble in a centrifugal pump. Through the construction of a visualization centrifugal pump test platform and the observation of a high-speed camera, an image processing method was used to extract the characteristics of the cavitation bubble in the captured results, and the characteristics of the cavitation bubble in the centrifugal pump were analyzed and studied in a quantitative way. The results show that the shapes of the cavitation bubbles in a centrifugal pump can be simplified approximately into an ellipsoid shape, more than 75% of the bubbles have a length–diameter ratio between 1 and 2, and the distribution is relatively uniform. Different working conditions affect the size of the cavitation bubble but have little effect on the shape. The average size of the cavitation bubble under different working conditions was calculated by data fitting. This method, which combines high-speed imaging technology and image processing technology, is capable of observing the behavioral characteristics of cavitation bubbles in centrifugal pump cavitation flow both in detail and intuitively. The new method is provided to describe quantitatively the shapes and sizes of bubbles. It is of great significance in understanding the movement characteristics and manifestations of bubbles during a centrifugal pump’s operation and in the further study of the micro mechanisms of the negative effects of cavitation on equipment performance.
Rheological Characterization of Gofio and Aloe Vera Blended Juice
Authors: Rubio-Hernández Francisco José , Rubio-Merino Julia , García-López Elvira
Published: November 2023

Considering that gofio (Gf) and aloe vera juice (AVJ) have very good nutritional qualities, their combination is proposed because it is indicated as an easy and fast source of basic bio-elements. The texture of a food must be accepted by customers. This means that the rheological characteristics of the product must be known and controlled. Therefore, the influence of Gf concentration on the rheological behavior of Gf/AVJ suspensions must be determined. With continuous shear experiments, the purely viscous response of a material can be obtained. AVJ and Gf/AVJ suspensions showed shear-thinning behavior. The ability of Gf particles and aggregates to distort the flow field was quantified determining the intrinsic viscosity η of the suspensions at several shear rates using Krieger–Dougherty equation. The results indicated that the shape and size of Gf aggregates is not affected by the mechanical action due to shear. The power law (Ostwald–de Waele) model fitted the experimental steady viscosity versus shear rate values (steady viscosity curves). The flow index was less than 1, which corresponded to shear-thinning behavior. It was obtained that the flow index of AVJ maintained unaltered despite the presence of Gf particles. However, the viscosity value increased with the increasing amount of Gf as it was expected. The viscoelastic behavior of the microstructure at rest of the AVJ and Gf/AVJ suspensions was studied using oscillatory shear tests. First, linear viscoelastic response was confirmed in the relatively low amplitude shear region γ0<0.001 using an amplitude sweep shear test. After that, frequency sweep shear tests were conducted in the region where Gf/AVJ suspensions showed linear viscoelastic behavior. Varying the frequency, the response of the microstructure at rest of the suspensions when the mechanical action lasts from short to long time interval can be characterized. Jeffreys mechanical model was used for the analysis of the LVE response of Gf/AVJ suspensions. Using small amplitude oscillatory shear (SAOS) tests, it was obtained that Gf/AVJ suspensions are viscoelastic liquids that change their texture from chewy to creamy when the Gf concentration increases.
SPIONs Magnetophoresis and Separation via Permanent Magnets: Biomedical and Environmental Applications
Authors: Wu Xian , Ciannella Stefano , Choe Hyeon , Strayer Jacob , Wu Kai , Chalmers Jeffrey , Gomez-Pastora Jenifer
Published: November 2023

Superparamagnetic iron oxide nanoparticles (SPIONs) have emerged as cutting-edge materials, garnering increasing attention in recent years within the fields of chemical and biomedical engineering. This increasing interest is primarily attributed to the distinctive chemical and physical properties of SPIONs. Progress in nanotechnology and particle synthesis methodologies has facilitated the fabrication of SPIONs with precise control over parameters such as composition, size, shape, stability, and magnetic response. Notably, these functionalized materials exhibit a remarkable surface-area-to-volume ratio, biocompatibility, and, most importantly, they can be effectively manipulated using external magnetic fields. Due to these exceptional properties, SPIONs have found widespread utility in the medical field for targeted drug delivery and cell separation, as well as in the chemical engineering field, particularly in wastewater treatment. Magnetic separation techniques driven by magnetophoresis have proven to be highly efficient, encompassing both high-gradient magnetic separation (HGMS) and low-gradient magnetic separation (LGMS). This review aims to provide an in-depth exploration of magnetic field gradient separation techniques, alongside a comprehensive discussion of the applications of SPIONs in the context of drug delivery, cell separation, and environmental remediation.
Evaluation of Bio-Oils in Terms of Fuel Properties
Authors: Stelmach Sławomir , Ignasiak Karina , Czardybon Agata , Bigda Joanna
Published: November 2023

In response to the global climate challenge and the increasing demand for energy, exploring renewable energy alternatives has become crucial. Bio-oils derived from biomass pyrolysis are emerging as potential replacements for fossil fuel-based liquid fuels. This paper shares findings from the Institute of Energy and Fuel Processing Technology on the quality of crude biomass pyrolysis bio-oil samples. These findings highlight their potential as motor liquid fuels. The article details the results of tests on the physicochemical properties of four distinct bio-oil samples. Additionally, it presents preliminary test results on the hydrodeoxygenation of bio-oils in a batch reactor. The production of homogeneous, stable mixtures using other fuel additives, such as diesel oil, rapeseed methyl ester (RME), and butanol, is also discussed.
An Alternative Way to Produce High-Density Graphite from Carbonaceous Raw Materials
Authors: Mirea Radu
Published: November 2023

In this study, graphite, the most stable form of carbon, was examined for its hexagonal crystalline structure with specific dimensions (ao = 2.46 Ǻ; co = 6.70 Ǻ). Its framework comprises parallel carbon atom planes, forming regular hexagons (side length 1.415 Ǻ) and 120° angles between adjacent atoms. Two structural variations exist: hexagonal symmetry (1-2-1-2-1-2 planes) and rhomboidal symmetry (1-2-3-1-2-3 planes). The aim of this research was to produce high-density graphite utilizing carbonaceous raw materials. Graphite-based materials often exhibit high porosity, necessitating additional treatment. In this study, we successfully obtained mesophase tar pitch (yield: 45%), a pivotal raw material, and high-density graphite. The resulting graphite underwent characterization for physical properties (apparent and real density, porosity, and compression strength), demonstrating conformity with the existing literature data.
Prehistoric Cult Sites along the Desert Roads
Authors: Avner Uzi
Published: November 2023

The Negev desert, the southern half of Israel, is an arid-to-hyper-arid region. Despite that, some 13,000 ancient sites have been recorded here to date, and many were excavated. One characteristic of the Negev (as well as of other deserts) is the abundance of prehistoric and early historic cult sites, dated ca. 8000–2000 BCE. Another is the many ancient roads. The roads, the main types of cult sites and the connection between them are described and discussed in the following sections.
Assessing Rice Sheath Blight Disease Habitat Suitability at a Regional Scale through Multisource Data Analysis
Authors: Zhang Jingcheng , Li Huizi , Tian Yangyang , Qiu Hanxiao , Zhou Xuehe , Ma Huiqin , Yuan Lin
Published: November 2023

Extensive occurrence of rice sheath blight has been observed in China in recent years due to agricultural practices and climatic conditions, posing a serious threat to rice production. Assessing habitat suitability for rice sheath blight at a regional scale can provide important information for disease forecasting. In this context, the present study aims to propose a regional-scale habitat suitability evaluation method for rice sheath blight in Yangzhou city using multisource data, including remote sensing data, meteorological data, and disease survey data. By combining the epidemiological characteristics of the crop disease and the Relief-F algorithm, some habitat variables from key stages were selected. The maximum entropy (Maxent) and logistic regression models were adopted and compared in constructing the disease habitat suitability assessment model. The results from the Relief-F algorithm showed that some remote sensing variables in specific temporal phases are particularly crucial for evaluating disease habitat suitability, including the MODIS products of LAI (4–20 August), FPAR (9–25 June), NDVI (12–20 August), and LST (11–27 July). Based on these remote sensing variables and meteorological features, the Maxent model yielded better accuracy than the logistic regression model, with an area under the curve (AUC) value of 0.90, overall accuracy (OA) of 0.75, and a true skill statistics (TSS) value of 0.76. Indeed, the results of the habitat suitability assessment models were consistent with the actual distribution of the disease in the study area, suggesting promising predictive capability. Therefore, it is feasible to utilize remotely sensed and meteorological variables for assessing disease habitat suitability at a regional scale. The proposed method is expected to facilitate prevention and control practices for rice sheath blight disease.
Application of a Multifractal Model for Identification of Lithology and Hydrothermal Alteration in the Dasuji Porphyry Mo Deposit in Inner Mongolia, China
Authors: Xi Mingjie , Zhang Wanchang , Tang Jiakui , Gao Huiran , Shalamzari Masoud Jafari
Published: November 2023

The Dasuji giant porphyry molybdenum deposit is one of the largest ore deposits recently discovered along the Yinshan–Yanshan–Liaoning molybdenum belt in China. Using this deposit along the Yinshan–Yanshan–Liaoning molybdenum belt as the study area, the present study proposed a two-stage approach aimed at marking out the hydrothermally altered anomalies in the study area for the guidance of future prospecting in other regions. First of all, the Principal Component Analysis (PCA) and specific Band Ratio methods were applied to the ASTER images from different acquisition dates to extract ferric oxides and hydroxyl alterations related to the porphyry molybdenum deposit. Then, the Fractal-Aided Anomaly-Overlaying Selection model was adopted to recognize two ferric and hydroxyl alteration layers for separating anomalies from the interferences caused by geology and random noise from the data. Furthermore, for lithological differentiation in the previously marked off area, the Random Forest Classifier (RFC) was applied to the composite data obtained via the ASTER, ETM, and DEM, and it is demonstrated that the DEM can significantly improve lithological mapping in areas with complex vegetation cover and topography. Based on field verification and comparison with geological maps, the research revealed that the suggested two-stage approach may effectively reduce erroneously recognized anomalies produced during the first stage while retaining ore-related anomalies for gigantic porphyry molybdenum deposit prospecting in the Dasuji area, which showed the good application potential of the proposed model to extract actual hydrothermally altered anomalies adopted for lithological discrimination and mapping.
Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle
Authors: Shabestari Zahra Badamchi , Hosseininaveh Ali , Remondino Fabio
Published: November 2023

Motorcycle detection and collision warning are essential features in advanced driver assistance systems (ADAS) to ensure road safety, especially in emergency situations. However, detecting motorcycles from videos captured from a car is challenging due to the varying shapes and appearances of motorcycles. In this paper, we propose an integrated and innovative remote sensing and artificial intelligence (AI) methodology for motorcycle detection and distance estimation based on visual data from a single camera installed in the back of a vehicle. Firstly, MD-TinyYOLOv4 is used for detecting motorcycles, refining the neural network through SPP (spatial pyramid pooling) feature extraction, Mish activation function, data augmentation techniques, and optimized anchor boxes for training. The proposed algorithm outperforms eight existing YOLO versions, achieving a precision of 81% at a speed of 240 fps. Secondly, a refined disparity map of each motorcycle’s bounding box is estimated by training a Monodepth2 with a bilateral filter for distance estimation. The proposed fusion model (motorcycle’s detection and distance from vehicle) is evaluated with depth stereo camera measurements, and the results show that 89% of warning scenes are correctly detected, with an alarm notification time of 0.022 s for each image. Outcomes indicate that the proposed integrated methodology provides an effective solution for ADAS, with promising results for real-world applications, and can be suitable for running on mobility services or embedded computing boards instead of the super expensive and powerful systems used in some high-tech unmanned vehicles.
Fully Autonomous Active Self-Powered Point-of-Care Devices: The Challenges and Opportunities
Authors: Crivillé-Tena Laura , Colomer-Farrarons Jordi , Miribel-Català Pere Ll.
Published: November 2023

Quick and effective point-of-care (POC) devices have the chance to revolutionize healthcare in developed and developing countries since they can operate anywhere the patient is, with the possibility of obtaining and sending the results to the doctor without delay. In recent years, significant efforts have focused on developing new POC systems that can screen for biomarkers continuously and non-invasively in body fluids to prevent, diagnose, and manage diseases. However, one of the critical challenges left to address is how to power them effectively and sufficiently. In developing countries and rural and remote areas, where there are usually no well-established electricity grids or nearby medical facilities, and using batteries is unreliable or not cost-effective, alternative power sources are the most challenging issue for stand-alone and self-sustained POC devices. Here, we provide an overview of the techniques for used self-powering POC devices, where the sample is used to detect and simultaneously generate energy to power the system. Likewise, this paper introduced the state-of-the-art with a review of different research projects, patents, and commercial products for self-powered POCs from the mid-2010s until present day.
Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks
Authors: El Abbaoui Amal , Sodoyer David , Elbahhar Fouzia
Published: November 2023

The measurement and analysis of vital signs are a subject of significant research interest, particularly for monitoring the driver’s physiological state, which is of crucial importance for road safety. Various approaches have been proposed using contact techniques to measure vital signs. However, all of these methods are invasive and cumbersome for the driver. This paper proposes using a non-contact sensor based on continuous wave (CW) radar at 24 GHz to measure vital signs. We associate these measurements with distinct temporal neural networks to analyze the signals to detect and extract heart and respiration rates as well as classify the physiological state of the driver. This approach offers robust performance in estimating the exact values of heart and respiration rates and in classifying the driver’s physiological state. It is non-invasive and requires no physical contact with the driver, making it particularly practical and safe. The results presented in this paper, derived from the use of a 1D Convolutional Neural Network (1D-CNN), a Temporal Convolutional Network (TCN), a Recurrent Neural Network particularly the Bidirectional Long Short-Term Memory (Bi-LSTM), and a Convolutional Recurrent Neural Network (CRNN). Among these, the CRNN emerged as the most effective Deep Learning approach for vital signal analysis.
Failure of Digital Device Performance in Monitoring Physical Exercise in a Pilot Study in Sedentary Persons with HIV
Authors: Bonato Matteo , Marmondi Federica , Turrini Filippo , Albergoni Andrea , Pennacchi Maddalena , Cerizza Camilla , Piacentini Maria Francesca , Castagna Antonella , Galli Laura , Sartor Francesco , Cinque Paola
Published: November 2023

Digital devices have gained popularity in the last 10 years as a tool for exercise prescription, the monitoring of daily physical activity, and nutrition for the management of a health-related parameter. Therefore, the aim of this study was to assess the effectiveness of the use of digital devices to monitor exercise data in sedentary persons with HIV who exercise following an individualized activity pacing (AP) protocol on cardiorespiratory fitness body composition, blood lipid profile, and psychological parameters. Twenty-four PLWH were enrolled in an 18-week randomized, open-label, pilot AP exercise protocol. All participants were monitored by a Health Band connected to a mobile app that transmitted the data to a server. At week 3, they were randomized either in an experimental group (EG), in which an open device configuration enabled them to receive training data feedback (n = 12), or continued with no data feedback (control group, n = 12). The primary endpoint was improvement from the baseline of 15% of steady-state oxygen consumption (V˙O2) during a 6-min walking test. Technical issues occurred when pairing the health band with the app, which prevented EG participants from regularly receiving data feedback, and with data transmission to the server, which enabled only 40% monitoring of the total training days. Consequently, the study outcomes could not be compared between the two groups, and participants also lost confidence in the study. However, 19 out of 24 participants completed the AP program. Overall, only 6 (32%) improved steady-state V˙O2, with no significant changes at W18 from the baseline. Significant reductions were observed of BMI (p = 0.040), hip circumference (p = 0.027), and total-(p = 0.049) and HDL-cholesterol (p = 0.045). The failure of digital device performance substantially affected study procedures, monitoring, and participants’ engagement, and likely limited the potential benefits of the AP exercise program.
Embedded Processing for Extended Depth of Field Imaging Systems: From Infinite Impulse Response Wiener Filter to Learned Deconvolution
Authors: Fontbonne Alice , Trouvé-Peloux Pauline , Champagnat Frédéric , Jobert Gabriel , Druart Guillaume
Published: November 2023

Many works in the state of the art are interested in the increase of the camera depth of field (DoF) via the joint optimization of an optical component (typically a phase mask) and a digital processing step with an infinite deconvolution support or a neural network. This can be used either to see sharp objects from a greater distance or to reduce manufacturing costs due to tolerance regarding the sensor position. Here, we study the case of an embedded processing with only one convolution with a finite kernel size. The finite impulse response (FIR) filter coefficients are learned or computed based on a Wiener filter paradigm. It involves an optical model typical of codesigned systems for DoF extension and a scene power spectral density, which is either learned or modeled. We compare different FIR filters and present a method for dimensioning their sizes prior to a joint optimization. We also show that, among the filters compared, the learning approach enables an easy adaptation to a database, but the other approaches are equally robust.
Cost-Sensitive Decision Support for Industrial Batch Processes
Authors: Mählkvist Simon , Ejenstam Jesper , Kyprianidis Konstantinos
Published: November 2023

In this work, cost-sensitive decision support was developed. Using Batch Data Analytics (BDA) methods of the batch data structure and feature accommodation, the batch process property and sensor data can be accommodated. The batch data structure organises the batch processes’ data, and the feature accommodation approach derives statistics from the time series, consequently aligning the time series with the other features. Three machine learning classifiers were implemented for comparison: Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Machine (SVM). It is possible to filter out the low-probability predictions by leveraging the classifiers’ probability estimations. Consequently, the decision support has a trade-off between accuracy and coverage. Cost-sensitive learning was used to implement a cost matrix, which further aggregates the accuracy–coverage trade into cost metrics. Also, two scenarios were implemented for accommodating out-of-coverage batches. The batch is discarded in one scenario, and the other is processed. The Random Forest classifier was shown to outperform the other classifiers and, compared to the baseline scenario, had a relative cost of 26%. This synergy of methods provides cost-aware decision support for analysing the intricate workings of a multiprocess batch data system.
Adaptive Impact Mitigation Based on Predictive Control with Equivalent Mass Identification
Authors: Graczykowski Cezary , Faraj Rami
Published: November 2023

The paper presents the concept of equivalent parameter predictive control (EPPC) elaborated for semi-active fluid-based (hydraulic and pneumatic) shock absorbers equipped with controllable valves and subjected to impact excitation. The undertaken problem concerns the absorption and dissipation of the impact energy with the requirement to minimize the generated reaction force and corresponding impacting object deceleration. The development of a control strategy for a challenging problem with unknown impacting object mass and unknown changes of external and disturbance forces is proposed and discussed in detail. The innovative solution utilizes the paradigm of model predictive control supplemented by the novel concept of equivalent system parameters identification. The EPPC is based on the online measurement of system response, the computation of the equivalent mass of the impacting object, and the repetitive solution of the optimal control problem with various prediction intervals and constraints imposed on valve opening. The presented method is proven to operate robustly for unknown excitations, including double-impact conditions, and it has similar efficiency to control methods developed previously for known impact parameters.
Novel Sweat-Based Wearable Device for Advanced Monitoring of Athletic Physiological Biometrics
Authors: Aguilar-Torán Javier , Rabost-Garcia Genis , Toinga-Villafuerte Samantha , Álvarez-Carulla Albert , Colmena-Rubil Valeria , Fajardo-Garcia Andrea , Cardona-Bonet Andrea , Casals-Terré Jasmina , Muñoz-Pascual Xavier , Miribel-Català Pere , Punter-Villagrasa Jaime
Published: November 2023

Blood testing has traditionally been the gold standard for the physiological analysis and monitoring of professional athletes. In recent years, blood testing has moved out of the laboratory thanks to portable handheld devices, such as lactate meters. However, despite its usefulness and widespread use, blood testing has several drawbacks and limitations, such as the need for the athlete to stop exercising for blood extraction and the inability to have data continuously collected. In this scenario, sweat has become an alternative to blood testing because of its rich content of electrolytes and metabolites, as well as small quantities of sugars, proteins, and ions. Nevertheless, there are few devices capable of analyzing this biofluid and providing useful information to users. In this paper, an electronic system designed for the autonomous analysis of sweat electrolytes and metabolites along with heart rate dynamics is presented. This system is part of a novel wearable device tailored for athletes that offers to the user a real-time assessment of their physiological status and performance.
Thermal Load Model of a Proportional Solenoid Valve Based on Random Load Conditions
Authors: Liu Chenyu , Wang Anlin , Li Xiaotian , Li Xiaoxiang
Published: November 2023

Drastic changes in the random load of an electromechanical system bring about a reliability problem for the proportional solenoid valve based on a thermal effect. In order to accurately and effectively express the thermal load of a proportional solenoid valve under random load conditions and to meet the requirements of online acquisition, adaptive anomaly detection, and the missing substitution of thermal load data, a thermal load prediction model based on the Kalman filter algorithm is proposed. Taking the compound operation process of an excavator as the object and based on the field testing of an excavator and the independent testing experiment of a proportional solenoid valve in a non-installed state, a method of obtaining historical samples of the proportional solenoid valve’s power and thermal load is given. The k-means clustering algorithm is used to cluster the historical samples of the power and thermal load corresponding to the working posture of a multi-tool excavator. The Grubbs criterion is used to eliminate the outliers in the clustering samples, and unbiased estimation is performed on the clustering samples to obtain the prediction model. The results show that the cross-validation of the sample data under the specific sample characteristics of the thermal load model was carried out. Compared with other methods, the prediction accuracy of the thermal load model based on the Kalman filter is higher, the adaptability is strong, and the maximum prediction deviation percentage is stable within 5%. This study has value as a reference for random cycle thermal load analyses of low-frequency electromechanical products.
Remote Sensing Image Ship Matching Utilising Line Features for Resource-Limited Satellites
Authors: Li Leyang , Cao Guixing , Liu Jun , Cai Xiaohao
Published: November 2023

The existing image matching methods for remote sensing scenes are usually based on local features. The most common local features like SIFT can be used to extract point features. However, this kind of methods may extract too many keypoints on the background, resulting in low attention to the main object in a single image, increasing resource consumption and limiting their performance. To address this issue, we propose a method that could be implemented well on resource-limited satellites for remote sensing images ship matching by leveraging line features. A keypoint extraction strategy called line feature based keypoint detection (LFKD) is designed using line features to choose and filter keypoints. It can strengthen the features at corners and edges of objects and also can significantly reduce the number of keypoints that cause false matches. We also present an end-to-end matching process dependent on a new crop patching function, which helps to reduce complexity. The matching accuracy achieved by the proposed method reaches 0.972 with only 313 M memory and 138 ms testing time. Compared to the state-of-the-art methods in remote sensing scenes in extensive experiments, our keypoint extraction method can be combined with all existing CNN models that can obtain descriptors, and also improve the matching accuracy. The results show that our method can achieve ∼50% test speed boost and ∼30% memory saving in our created dataset and public datasets.
Semi-Supervised Image Stitching from Unstructured Camera Arrays
Authors: Nghonda Tchinda Erman , Panoff Maximillian Kealoha , Tchuinkou Kwadjo Danielle , Bobda Christophe
Published: November 2023

Image stitching involves combining multiple images of the same scene captured from different viewpoints into a single image with an expanded field of view. While this technique has various applications in computer vision, traditional methods rely on the successive stitching of image pairs taken from multiple cameras. While this approach is effective for organized camera arrays, it can pose challenges for unstructured ones, especially when handling scene overlaps. This paper presents a deep learning-based approach for stitching images from large unstructured camera sets covering complex scenes. Our method processes images concurrently by using the SandFall algorithm to transform data from multiple cameras into a reduced fixed array, thereby minimizing data loss. A customized convolutional neural network then processes these data to produce the final image. By stitching images simultaneously, our method avoids the potential cascading errors seen in sequential pairwise stitching while offering improved time efficiency. In addition, we detail an unsupervised training method for the network utilizing metrics from Generative Adversarial Networks supplemented with supervised learning. Our testing revealed that the proposed approach operates in roughly ∼1/7th the time of many traditional methods on both CPU and GPU platforms, achieving results consistent with established methods.
Multi-UAV Redeployment Optimization Based on Multi-Agent Deep Reinforcement Learning Oriented to Swarm Performance Restoration
Authors: Wu Qilong , Geng Zitao , Ren Yi , Feng Qiang , Zhong Jilong
Published: November 2023

Distributed artificial intelligence is increasingly being applied to multiple unmanned aerial vehicles (multi-UAVs). This poses challenges to the distributed reconfiguration (DR) required for the optimal redeployment of multi-UAVs in the event of vehicle destruction. This paper presents a multi-agent deep reinforcement learning-based DR strategy (DRS) that optimizes the multi-UAV group redeployment in terms of swarm performance. To generate a two-layer DRS between multiple groups and a single group, a multi-agent deep reinforcement learning framework is developed in which a QMIX network determines the swarm redeployment, and each deep Q-network determines the single-group redeployment. The proposed method is simulated using Python and a case study demonstrates its effectiveness as a high-quality DRS for large-scale scenarios.
Urban Advanced Mobility Dependability: A Model-Based Quantification on Vehicular Ad Hoc Networks with Virtual Machine Migration
Authors: Silva Luis Guilherme , Cardoso Israel , Brito Carlos , Barbosa Vandirleya , Nogueira Bruno , Choi Eunmi , Nguyen Tuan Anh , Min Dugki , Lee Jae Woo , Silva Francisco Airton
Published: November 2023

In the rapidly evolving urban advanced mobility (UAM) sphere, Vehicular Ad Hoc Networks (VANETs) are crucial for robust communication and operational efficiency in future urban environments. This paper quantifies VANETs to improve their reliability and availability, essential for integrating UAM into urban infrastructures. It proposes a novel Stochastic Petri Nets (SPN) method for evaluating VANET-based Vehicle Communication and Control (VCC) architectures, crucial given the dynamic demands of UAM. The SPN model, incorporating virtual machine (VM) migration and Edge Computing, addresses VANET integration challenges with Edge Computing. It uses stochastic elements to mirror VANET scenarios, enhancing network robustness and dependability, vital for the operational integrity of UAM. Case studies using this model offer insights into system availability and reliability, guiding VANET optimizations for UAM. The paper also applies a Design of Experiments (DoE) approach for a sensitivity analysis of SPN components, identifying key parameters affecting system availability. This is critical for refining the model for UAM efficiency. This research is significant for monitoring UAM systems in future cities, presenting a cost-effective framework over traditional methods and advancing VANET reliability and availability in urban mobility contexts.
Experimental Study on Flexural Fatigue Resistance of Recycled Fine Aggregate Concrete Incorporating Calcium Sulfate Whiskers
Authors: Zhong Chuheng , Chen Xiaoyu , Mao Weiqi , Xing Sijia , Chen Jinhui , Zhou Jinzhi
Published: November 2023

In order to study the flexural fatigue resistance of calcium sulfate whisker-modified recycled fine aggregate concrete (RFAC), flexural fatigue cyclic loading tests at different stress levels (0.6, 0.7, and 0.9) considering a calcium sulfate whisker (CSW) admixture as the main influencing factor were designed. Furthermore, the fatigue life was analyzed, and fatigue equations were established using the three-parameter Weibull distribution function theory. In addition, the micro-morphology of CSW-modified recycled fine aggregate concrete was observed and analyzed through Scanning Electron Microscopy (SEM), and the strengthening and toughening mechanisms of CSW on recycled fine aggregate concrete were further explored. The test results demonstrate that the inclusion of recycled fine aggregate reduces the fatigue life of concrete, while the incorporation of CSW can effectively improve the fatigue life of the recycled fine aggregate concrete, where 1% of CSW modification can extend the fatigue life of recycled fine aggregate concrete by 56.5%. Furthermore, the fatigue life of concrete under cyclic loading decreases rapidly as the maximum stress level increases. Fatigue life equations were established with double logarithmic curves, and P-S-N curves considering different survival probabilities (p = 0.5, 0.95) were derived. Microscopic analyses demonstrate that the CSW has a “bridging” effect at micro-seams in the concrete matrix, delaying the generation and enlargement of micro-cracks in the concrete matrix, thus resulting in improved mechanical properties and flexural fatigue resistance of the recycled fine aggregate concrete.