bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Dissolution Time on the Development of All-Cellulose Composites Using the NaOH/Urea Solvent System
Authors: Delgado Juan Francisco , Salvay Andrés Gerardo , Arroyo Silvana , Bernal Celina Raquel , Foresti María Laura
Published: February 2023

Innovative and sustainable all-cellulose composites (ACCs) can be obtained by partial dissolution of cellulosic fibers and regeneration of the dissolved fraction. Among cellulose solvents, sodium hydroxide/urea solutions are recognized as promising low-environmental impact systems. In this work, filter paper (FP) was dissolved with a 7 wt% NaOH/12 wt% urea aqueous solution, kept at −18 °C for different time intervals, regenerated with distilled water and finally dried under different conditions. The developed films were characterized in terms of morphology, porosity, optical properties, crystalline structure, hydration and mechanical properties. The porosity of the composites decreased with dissolution time due to the progressive filling of voids as the cellulosic fibers’ surface skin layer was dissolved and regenerated. Samples treated for 4 h showed the minimum values of porosity and opacity, high hydration and a substantial change from cellulose I to cellulose II. Hot pressing during drying led to relevant improvements in ACCs stiffness and strength values.
Control-Centric Data Classification Technique for Emission Control in Industrial Manufacturing
Authors: Chen Zihao , Chen Jian
Published: February 2023

Artificial intelligence-based hardware devices are deployed in manufacturing units and industries for emission gas monitoring and control. The data obtained from the intelligent hardware are analyzed at different stages for standard emissions and carbon control. This research article proposes a control-centric data classification technique (CDCT) for analyzing as well as controlling pollution-causing emissions from manufacturing units. The gas and emission monitoring AI hardware observe the intensity, emission rate, and composition in different manufacturing intervals. The observed data are used for classifying its adverse impact on the environment, and as a result industry-adhered control regulations are recommended. The classifications are performed using deep neural network analysis over the observed data. The deep learning network classifies the data according to the environmental effect and harmful intensity factor. The learning process is segregated into classifications and analysis, where the analysis is performed using previous emission regulations and manufacturing guidelines. The intensity and hazardous components levels in the emissions are updated after the learning process for recommending severe lookups over the varying manufacturing intervals.
Study on the Influence of Surface Texture Parameters of Oil Production Screw Motor Rotor on the Tribological Properties of Its Friction Pair
Authors: Peng Shijin , Huang Zhiqiang , Wang Meiling , Xiong Chen , Chen Ke
Published: February 2023

By researching the influence of micro-groove texture on the surface tribological properties of the stator and rotor pair of oil production hydraulic motors, this paper aims to reduce the frictional resistance moment of the spiral pair of hydraulic motors, and further solve the problem of the difficult restart of the pump of a certain type of hydraulic-driven screw pump. According to the spiral pair of screw motors, a metal-rubber flat plate reciprocating friction model is established, and rectangular micro-grooves with different texture angles and depths are machined on the surface of the metal specimen. A combination of finite element simulation and tribological tests is used to carry out a study on the influence of different texture parameters on the friction performance of the hydraulic motor spiral pair. The results showed that at a certain texture angle, the friction coefficient of each specimen basically increases with the texture depth. When the texture depth is constant, the friction coefficient increases first and then decreases with the increase of the texture angle. The texture angle is the main factor affecting the friction coefficient. Under the same test conditions, the friction coefficient of the textured specimen can be reduced by 20.2% compared with the untextured specimen. In the metal-rubber contact pair of the stator and rotor of the hydraulic motor, the friction reduction mechanism of the texture mainly transport the lubricating medium through the micro-grooves to improve the lubricating conditions. Samples with a reasonable design of texture parameters can effectively reduce the friction coefficient of the friction pair without reducing the service life of the hydraulic motor, which is conducive to the smooth restart of the oil production system of the hydraulically driven screw pump.
Ligand-Enhanced Zero-Valent Iron for Organic Contaminants Degradation: A Mini Review
Authors: Chen Qi , Zhou Minghua , Pan Yuwei , Zhang Ying
Published: February 2023

For nearly three decades, zero-valent iron (ZVI) has been used in wastewater treatment and groundwater and soil remediation. ZVI can degrade contaminants by reactions of adsorption, redox, and co-precipitation. It can also react with oxidants like hydrogen peroxide, persulfate, and ozone to produce highly reactive radicals that can rapidly remove and even mineralize organic contaminants. However, the application of ZVI is also limited by factors such as the narrow pH range and surface passivation. The addition of chelating agents such as nitrilotriacetic acid (NTA), ethylenediaminetetraacetic acid (EDTA), or citrate to the ZVI-based processes has been identified to greatly increase the iron stability and improve the efficiency of contaminant degradation. From the perspective of commonly used organic and inorganic chelating agents in ZVI applications, the review addresses the current status of ligand-enhanced ZVI degradation of organic contaminants, illustrates the possible reaction mechanism, and provides perspectives for further research.
A Study on Activity of Coexistent CO Gas during the CO2 Methanation Reaction in Ni-Based Catalyst
Authors: Ahn Jeongyoon , Chung Woojin
Published: February 2023

Greenhouse gases, the main cause of global warming, are generated largely in the energy sector. As the need for technology that has reduced greenhouse gas emissions while producing energy is on an increase, CCU technology, which uses CO2 to produce CH4 (SNG energy, synthetic natural gas), is drawing attention. Thus, the reaction for converting CO2 to CH4 at a specific temperature using a catalyst is CO2 methanation. The field of CO2 methanation has been actively studied, and many studies have been conducted to enhance the activity of the catalysts. However, there is a lack of research on the variables that may appear when CO2 methanation is attempted using emissions containing CO2 generated from industrial fields and bio-plants. According to previous studies, it is reported that realistic feed gases from gasification or biomass plants contain a significant amount of CO. this study is a follow-up study focused on the application of CO2 methanation in various real processes. In the CO2 methanation reaction, a study was conducted on the catalyst efficiency and durability of CO gas that can coexist in the inlet gas rather than CO2 and H2 gas. The CO2 methanation activity was observed at 200–350 °C when 0–15% CO coexisted using the Ni-Ce-Zr catalyst, and the operating variables were set for optimal SNG production. As a result of adjusting the ratio of inlet gas to increase the yield of CH4 in the produced gas, the final CO2 conversion of 83% and CO conversion of 97% (with 15% CO gas at 280 °C) were obtained. In addition, catalytic efficiency and catalyst surface analysis were performed by exposing CO gas during the CO2 methanation reaction for 24 h. It showed high activity and excellent stability. The results of this study can be used as the basic data when applying an actual process.
Sunflower Residues-Based Biorefinery: Circular Economy Indicators
Authors: Havrysh Valerii , Kalinichenko Antonina , Pysarenko Pavlo , Samojlik Maryna
Published: February 2023

Fossil fuel price increases, their uneven distribution, environmental issues from their incineration, and lack of guarantees of their energy security are the main drivers for the development of green energy. Agricultural waste is an abundant resource for energy bioprocessing, which improves the functioning of the circular economy. In this study, the following were used as the main indicators: the share of renewable energy and the benefit from it, the coefficient of cyclical use of biomass, and the reduction in carbon dioxide emissions. The ways in which sunflower waste is applied for energy purposes are emphasized. The highest comprehensive ecological and economic effects are shown to be achieved in the production of biogas from sunflower residues with the incineration of this biogas in cogeneration plants. The residues from the biogas plant that are left after fermentation should be used as a biofertilizer. Such a cyclic system allows not only the full processing of all biomass waste that significantly reduces carbon dioxide emissions during the cultivation and processing of sunflower, but also an increase in the share of renewable energy used in technological processes up to 70%.
Characteristics of European Universities That Participate in Library Crowdfunding Initiatives for Open Access Monographs
Authors: Roncevic Mirela
Published: February 2023

The aim of the study was to identify the traits of 100 European universities across 26 countries that did or did not support one particular library crowdfunding initiative for open access (OA) monographs over the past few years. By relying on the rankings of four sources, including THE, ARWU, QS, and Leiden, the study identifies some of the traits of the universities that have shown strong interest in the model by already taking part in an established library crowdfunding initiative, as well as those that may play a vital role in its sustainability. The study’s results show that the institutions that are likely to participate in library crowdfunding initiatives for OA monographs may be defined as highly ranked and produce research in quantity, quantity, relevance, and timeliness. The study’s key revelation is the high academic standing of the institutions that rarely participate in one crowdfunding initiative. These institutions may not be as “international” in their outlooks, but they stand out for their high-quality and significant research output. As such, they may accelerate the model’s adoption with more consistent participation in library crowdfunding.
Initial Steps in the Reaction of H2O2 with Fe2+ and Fe3+ Ions: Inconsistency in the Free Radical Theory
Authors: Kremer Mordechai L.
Published: February 2023

Consideration of the changes in free energy shows that the assumed initial steps in reactions of H2O2 with Fe2+ and Fe3+ in the free radical theory are not consistent. The free radical theory is unable to account for the Fe3+-initiated decomposition of H2O2 or for oxidations by it. In reactions with Fe2+ ions at high [H2O2], where O2 evolution reaches a limit, such limit is not foreseen by the free radical model. At lower [H2O2], because of a disallowed substitution in the equation used, the interpretation is not valid. It appears, therefore, that free radicals derived from H2O2 do not provide a suitable basis for constructing models for these reactions. Non-radical models are more successful in interpreting experimental results.
Making Sense of the Missionary Life of Adele Fielde, Woman of Religious Belief, Science, and Activism
Authors: Andrilenas Nadia
Published: February 2023

This paper proposes a new narrative of the life of nineteenth-century American Baptist missionary, activist, and scientist Adele Fielde. In the common historical narrative, her separation from the American Baptist Missionary Union (ABMU) after over twenty years of mission service in Siam and China marks her shift towards careers devoid of religious beliefs, in suffrage, activism, and science. Rather than perpetuating this deconversion narrative, I propose that she demonstrated continuity in her beliefs and interests, exercised through diverse careers and starting as a missionary. By looking to biographical accounts by her friends, colleagues, and later historians alongside her writing and life, I highlight her unorthodox Christian beliefs that motivated not only her missionary life but her later careers in science and activism in the US. Reframing Fielde’s life in this way offers a more realistic model of the intertwined beliefs and motivations of female missionaries, activists, and scientists in the nineteenth century.
The Variable “Catholic” Influence on US Presidential and Abortion Politics
Authors: Calfano Brian Robert , Ponder Daniel E.
Published: February 2023

We demonstrate that, in comparison to religious groups showing reliable, contemporary voting tendencies (e.g., white evangelical Protestants voting Republican, Jews and Muslims voting Democratic), Roman Catholics show far less consistency in supporting one major party over the other. After reviewing relevant literature Catholic public political preferences and behavior, we delve into a basic overview of the history of the Catholic Church in the United States. We then analyze historical periods when the impact of the church seems consequential, such as effects of the “Catholic vote”. We summarize scholarship and opinion surveys concerning Catholic political views and behavior over the last several decades, focusing on attitudes toward abortion in the wake of the Dobbs decision. We then highlight differences and similarities between Catholic rank-and-file and the church clergy and hierarchy, some of which are well known in the religion and politics literature. In sum, we find that unlike past or more contemporaneous takes on the impact of Catholics and Catholicism on politics and policy, there is no longer (if there ever was) a single, identifiable Catholic impact, even as the Catholic vote remains a demographic for which politicians compete.
Greenhouse Gases Monitoring Instrument on GaoFen-5 Satellite-II: Optical Design and Evaluation
Authors: Luo Haiyan , Li Zhiwei , Wu Yang , Qiu Zhenwei , Shi Hailiang , Wang Qiansheng , Xiong Wei
Published: February 2023

The Greenhouse gases Monitoring Instrument on GaoFen-5 satellite-II (GMI-II) uses spatial heterodyne spectroscopy (SHS) for quantitative monitoring of atmospheric greenhouse gases (GHG). Unlike the traditional SHS, the interferometer component of the GMI-II was designed with zero optical path difference offset, effectively improving spectral resolution while maintaining the same detector specifications. The secondary imaging system with non-isometric scaling of spatial and spectral dimensions was designed to decrease the integration time of a frame image or improve the spectral signal-to-noise ratio (SNR) under the same integration time. This paper introduces the design, manufacture, adjustment methods, and test results of the main performance indexes of the GMI-II that indicate that the spectral resolution of the O2 A-band detection channel is better than 0.6 cm−1 and other channels are better than 0.27 cm−1. Under the typical radiance of other carbon monitors’ on-orbit statistics, the spectral SNR of the GMI-II is more than 300. These test results demonstrate that the GMI-II can be well adapted to quantitative remote sensing monitoring of atmospheric GHG.
Editorial for the Special Issue “Latest Development in 3D Mapping Using Modern Remote Sensing Technologies”
Authors: Habib Ayman F.
Published: February 2023

Recent advances in remote sensing technologies have provided the research community with unprecedented geospatial data characterized by high geometric, radiometric, spectral, and temporal resolution [...]
CRBeDaSet: A Benchmark Dataset for High Accuracy Close Range 3D Object Reconstruction
Authors: Gabara Grzegorz , Sawicki Piotr
Published: February 2023

This paper presents the CRBeDaSet—a new benchmark dataset designed for evaluating close range, image-based 3D modeling and reconstruction techniques, and the first empirical experiences of its use. The test object is a medium-sized building. Diverse textures characterize the surface of elevations. The dataset contains: the geodetic spatial control network (12 stabilized ground points determined using iterative multi-observation parametric adjustment) and the photogrammetric network (32 artificial signalized and 18 defined natural control points), measured using Leica TS30 total station and 36 terrestrial, mainly convergent photos, acquired from elevated camera standpoints with non-metric digital single-lens reflex Nikon D5100 camera (ground sample distance approx. 3 mm), the complex results of the bundle block adjustment with simultaneous camera calibration performed in the Pictran software package, and the colored point clouds (ca. 250 million points) from terrestrial laser scanning acquired using the Leica ScanStation C10 and post-processed in the Leica Cyclone™ SCAN software (ver. 2022.1.1) which were denoized, filtered, and classified using LoD3 standard (ca. 62 million points). The existing datasets and benchmarks were also described and evaluated in the paper. The proposed photogrammetric dataset was experimentally tested in the open-source application GRAPHOS and the commercial suites ContextCapture, Metashape, PhotoScan, Pix4Dmapper, and RealityCapture. As the first experience in its evaluation, the difficulties and errors that occurred in the software used during dataset digital processing were shown and discussed. The proposed CRBeDaSet benchmark dataset allows obtaining high accuracy (“mm” range) of the photogrammetric 3D object reconstruction in close range, based on a multi-image view uncalibrated imagery, dense image matching techniques, and generated dense point clouds.
Physical Mechanism and Parameterization for Correcting Radar Wave Velocity in Yellow River Ice with Air Temperature and Ice Thickness
Authors: Li Zhijun , Li Chunjiang , Yang Yu , Zhang Baosen , Deng Yu , Li Guoyu
Published: February 2023

Unfrozen free and non-free water between ice crystals in flat and hummock ice in the Yellow River exists as water films with varying contents based on ice temperature. These contents can affect the radar wave velocity of the ice despite its theoretical dependence on the crystal structure and ice body components. The unfrozen water content in ice depends on the ice temperature, which is controlled by the air temperature, solar radiation, and ice thickness. Winter air temperature and radar-detected ice thickness data observed at the Shisifenzi bend in the Yellow River from 2020 to 2021 were analyzed. The unfrozen water content in the ice was the primary factor influencing the accuracy of flat ice thickness detection. The heat flux at the ice–water interface in the Yellow River was determined. The evolution of ice thickness and temperature were simulated using a one-dimensional (1D) ice thermodynamic model forced by the local weather station data (i.e., air temperature, solar radiation, wind speed, and cloud cover). On this basis, the measured ice thickness data of 13 drill holes were combined to calculate 1251 thermodynamically simulated ice thicknesses consistent with the ice thickness detection time of the radar; therefore, statistical relationships regarding the influence of air temperature and the combined action of air temperature and ice thickness on the radar wave velocity in granular and columnar ice during air temperature increases and decreases were determined. Finally, the statistical relationship between the combined influence of air temperature and ice thickness on radar wave velocity was selected as a parameterization scheme to dynamically correct the radar wave velocity of flat ice. To enhance the radar detection accuracy for flat ice thickness, the radar wave velocity of ice was parameterized as a function. Given the presence of unfrozen frazil ice and accumulated broken ice blocks in the Yellow River, radar is suggested to detect the thickness of different types of ice in future research.
Construction of Urban Thermal Environment Network Based on Land Surface Temperature Downscaling and Local Climate Zones
Authors: Zhang Xueling , Kasimu Alimujiang , Liang Hongwu , Wei Bohao , Aizizi Yimuranzi , Zhao Yongyu , Reheman Rukeya
Published: February 2023

It has become undeniable that global land surface temperature (LST) has continued to rise in recent years. The threat of extreme heat to humans has become self-evident, especially in arid regions. Many studies have clarified the temperature rise/fall mechanism of LST from the perspective of influencing factors. However, there are few studies on mitigating LST from the standpoint of regional networks. This paper first combines Landsat 8 with Sentinel-2 imagery for LST downscaling based on the Google Earth engine as a way to match local climate zone (LCZ) with 17 classification types. Then, the thermal environment resistance surface is constructed according to LCZ, and the essential cold sources are identified using morphological spatial pattern analysis (MSPA) and circuit theory to form the thermal environment green corridor and obtain the pinch point and barrier point areas. The results show that (1) The downscaling of LST based on random forest (RF) for the Urumqi–Changji–Wujiaqu metropolitan area has an R2 of 0.860 and an RMSE of 3.23, with high downscaling accuracy. (2) High temperature (HT), medium temperature (MT), and low temperature (LT) have the largest proportions in the study area; HT dominates in Urumqi, LT in Changji, and MT in Wujiaqu. (3) The natural types (LCZ-D, LCZ-C, and LCZ-F) in the LCZ classification occupy a large area, and the building types are mainly concentrated in Urumqi; LCZ-D, LCZ-G, and LCZ-A contribute the most to the cooling of LST, and LCZ-F, LCZ-C, and LCZ-10 contribute the most to the warming of LST. (4) After identifying critical cold source patches according to MSPA to arrive at 253 green corridors, subsensitive corridors and sensitive corridors need to take certain measures to prevent corridor blockage; pinch point areas, as well as barrier point areas, need to be protected and repaired according to their respective characteristics. In summary, corresponding cooling measures to specific areas can improve the connectivity between cooling sources and slow down the temperature increase of the whole area. This study and experimental approach can provide new insights for urban planners and climate researchers.
SAR and Optical Data Applied to Early-Season Mapping of Integrated Crop–Livestock Systems Using Deep and Machine Learning Algorithms
Authors: Toro Ana P. S. G. D. D. , Bueno Inacio T. , Werner João P. S. , Antunes João F. G. , Lamparelli Rubens A. C. , Coutinho Alexandre C. , Esquerdo Júlio C. D. M. , Magalhães Paulo S. G. , Figueiredo Gleyce K. D. A.
Published: February 2023

Regenerative agricultural practices are a suitable path to feed the global population. Integrated Crop–livestock systems (ICLSs) are key approaches once the area provides animal and crop production resources. In Brazil, the expectation is to increase the area of ICLS fields by 5 million hectares in the next five years. However, few methods have been tested regarding spatial and temporal scales to map and monitor ICLS fields, and none of these methods use SAR data. Therefore, in this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested. As a result, we found that all proposed algorithms and sensors could correctly map both study sites. For Study Site 1(SS1), we obtained an overall accuracy of 98% using the random forest classifier. For Study Site 2, we obtained an overall accuracy of 99% using the long short-term memory net and the random forest. Further, the early-season experiments were successful for both study sites (with an accuracy higher than 90% for all time windows), and no significant difference in accuracy was found among them. Thus, this study found that it is possible to map ICLSs in the early-season and in different latitudes by using diverse algorithms and sensors.
SAR-to-Optical Image Translation and Cloud Removal Based on Conditional Generative Adversarial Networks: Literature Survey, Taxonomy, Evaluation Indicators, Limits and Future Directions
Authors: Xiong Quan , Li Guoqing , Yao Xiaochuang , Zhang Xiaodong
Published: February 2023

Due to the limitation of optical images that their waves cannot penetrate clouds, such images always suffer from cloud contamination, which causes missing information and limitations for subsequent agricultural applications, among others. Synthetic aperture radar (SAR) is able to provide surface information for all times and all weather. Therefore, translating SAR or fusing SAR and optical images to obtain cloud-free optical-like images are ideal ways to solve the cloud contamination issue. In this paper, we investigate the existing literature and provides two kinds of taxonomies, one based on the type of input and the other on the method used. Meanwhile, in this paper, we analyze the advantages and disadvantages while using different data as input. In the last section, we discuss the limitations of these current methods and propose several possible directions for future studies in this field.
MMCAN: Multi-Modal Cross-Attention Network for Free-Space Detection with Uncalibrated Hyperspectral Sensors
Authors: Fang Feiyi , Zhou Tao , Song Zhenbo , Lu Jianfeng
Published: February 2023

Free-space detection plays a pivotal role in autonomous vehicle applications, and its state-of-the-art algorithms are typically based on semantic segmentation of road areas. Recently, hyperspectral images have proven useful supplementary information in multi-modal segmentation for providing more texture details to the RGB representations, thus performing well in road segmentation tasks. Existing multi-modal segmentation methods assume that all the inputs are well-aligned, and then the problem is converted to fuse feature maps from different modalities. However, there exist cases where sensors cannot be well-calibrated. In this paper, we propose a novel network named multi-modal cross-attention network (MMCAN) for multi-modal free-space detection with uncalibrated hyperspectral sensors. We first introduce a cross-modality transformer using hyperspectral data to enhance RGB features, then aggregate these representations alternatively via multiple stages. This transformer promotes the spread and fusion of information between modalities that cannot be aligned at the pixel level. Furthermore, we propose a triplet gate fusion strategy, which can increase the proportion of RGB in the multiple spectral fusion processes while maintaining the specificity of each modality. The experimental results on a multi-spectral dataset demonstrate that our MMCAN model has achieved state-of-the-art performance. The method can be directly used on the pictures taken in the field without complex preprocessing. Our future goal is to adapt the algorithm to multi-object segmentation and generalize it to other multi-modal combinations.
Aboveground Forest Biomass Estimation by the Integration of TLS and ALOS PALSAR Data Using Machine Learning
Authors: Singh Arunima , Kushwaha Sunni Kanta Prasad , Nandy Subrata , Padalia Hitendra , Ghosh Surajit , Srivastava Ankur , Kumari Nikul
Published: February 2023

Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest Range, Uttarakhand, India. The integration was performed to overcome the AGB saturation issue in ALOS PALSAR L-band SAR data for the high biomass density forest of the study area using 13 plots. Various parameters, namely, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, Yamaguchi decomposition components, polarimetric parameters, and backscatter values of HH and HV band intensity, were derived from the ALOS SAR data. However, TLS was used to obtain the diameter at breast height (dbh) and tree height for the sample plots. A total of 23 parameters was retrieved using TLS and SAR data for integration with the LiDAR footprint. The integration was performed using Random Forest (RF) and Artificial Neural Network (ANN). The statistical measures for RF were found to be promising compared with ANN for AGB estimation. The R2 value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha−1 for the predicted biomass value. The RMSE% was 15.92, while the RMSECV was 0.15. The R2 value for ANN was 0.77, with an RMSE of 98.46 ton ha−1. The RMSE% was 26.0, while the RMSECV was 0.26. RF performed better in estimating the biomass, which ranged from 122.46 to 581.89 ton ha−1, while uncertainty ranged from 15.75 to 85.14 ton ha−1. The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data.
Testing Thermostatic Bath End-Scale Stability for Calibration Performance with a Multiple-Sensor Ensemble Using ARIMA, Temporal Stochastics and a Quantum Walker Algorithm
Authors: Besseris George
Published: February 2023

Thermostatic bath calibration performance is usually checked for uniformity and stability to serve a wide range of industrial applications. Particularly challenging is the assessment at the limiting specification ends where the sensor system may be less effective in achieving consistency. An ensemble of eight sensors is used to test temperature measurement stability at various topological locations in a thermostatic bath (antifreeze) fluid at −20 °C. Eight streaks of temperature data were collected, and the resulting time-series were processed for normality, stationarity, and independence and identical distribution by employing regular statistical inference methods. Moreover, they were evaluated for autoregressive patterns and other underlying trends using classical Auto-Regressive Integrated Moving Average (ARIMA) modeling. In contrast, a continuous-time quantum walker algorithm was implemented, using an available R-package, in order to test the behavior of the fitted coefficients on the probabilistic node transitions of the temperature time series dataset. Tracking the network sequence for persistence and hierarchical mode strength was the objective. The quantum walker approach favoring a network probabilistic framework was posited as a faster way to arrive at simultaneous instability quantifications for all the examined time-series. The quantum walker algorithm may furnish expedient modal information in comparison to the classical ARIMA modeling and in conjunction with several popular stochastic analyzers of time-series stationarity, normality, and data sequence independence of temperature end-of-scale calibration datasets, which are investigated for temporal consistency.
Pavement Roughness Grade Recognition Based on One-dimensional Residual Convolutional Neural Network
Authors: Xu Juncai , Yu Xiong
Published: February 2023

A pavement’s roughness seriously affects its service life and driving comfort. Considering the complexity and low accuracy of the current recognition algorithms for the roughness grade of pavements, this paper proposes a real-time pavement roughness recognition method with a lightweight residual convolutional network and time-series acceleration. Firstly, a random input pavement model is established by the white noise method, and the pavement roughness of a 1/4 vehicle vibration model is simulated to obtain the vehicle vibration response data. Then, the residual convolutional network is used to learn the deep-level information of the sample signal. The residual convolutional neural network recognizes the pavement roughness grade quickly and accurately. The experimental results show that the residual convolutional neural network has a robust feature-capturing ability for vehicle vibration signals, and the classification features can be obtained quickly. The accuracy of pavement roughness classification is as high as 98.7%, which significantly improves the accuracy and reduces the computational effort of the recognition algorithm, and is suitable for pavement roughness grade classification.
Knowledge Extraction and Discovery about Web System Based on the Benchmark Application of Online Stock Trading System
Authors: Borowiec Marcin , Piszko Rafał , Rak Tomasz
Published: February 2023

Predicting workload characteristics could help web systems achieve elastic scaling and reliability by optimizing servers’ configuration and ensuring Quality of Service, such as increasing or decreasing used resources. However, a successful analysis using a simulation model and recognition and prediction of the behavior of the client presents a challenging task. Furthermore, the network traffic characteristic is a subject of frequent changes in modern web systems and the huge content of system logs makes it a difficult area for data mining research. In this work, we investigate prepared trace contents that are obtained from the benchmark of the web system. The article proposes traffic classification on the web system that is used to find the behavior of client classes. We present a case study involving workload analysis of an online stock trading application that is run in the cloud, and that processes requests from the designed generator. The results show that the proposed analysis could help us better understand the requests scenario and select the values of system and application parameters. Our work is useful for practitioners and researchers of log analysis to enhance service reliability.
High-Resolution Estimation of Suspended Solids and Particulate Phosphorus Using Acoustic Devices in a Hydrologically Managed Canal in South Florida, USA
Authors: Onwuka Ikechukwu S. , Scinto Leonard J. , Fugate David C.
Published: February 2023

Conventional methods of measuring total suspended sediments (TSS) and total particulate phosphorus (TPP) are typically low-resolution and miss critical processes that impact their exports in aquatic environments. To create high-resolution TSS and TPP estimates, echo intensity (EI), a biproduct of velocity measurements from acoustic devices, was utilized. An acoustic Doppler velocimeter (ADV) and an acoustic Doppler current profiler (ADCP) were deployed in three locations in the L-29 Canal in South Florida, USA, to obtain estimates near the canal bed and in the water column, respectively. Corrections for transmission losses from the ADCP proved unnecessary due to the low vertical variability in the measured EI. EI calibrations were performed using artificially created TSS obtained from bed sediments (ADV) and gravimetrically measured TSS from water samples that matched the depths and times of the ADCP deployments. The measured TSS values were then analyzed for total phosphorus and converted to TPP estimates. The results showed that high TSS and TPP were caused by the rapid discharge releases typical of managed canals. This work demonstrates that high-resolution estimates are imperative for assessing the effects of such swift hydrologic changes on the potential export of sediments and nutrients to delicate ecosystems downstream.
Closed-Form Capacity Reliability Analysis of Multiuser MIMO System in the Presence of Generalized Multipath Fading
Authors: Gvozdarev Aleksey S. , Alishchuk Aleksandra M. , Kazakova Marina A.
Published: February 2023

This research studies the problem of a joint capacity/capacity reliability analysis of the multiuser multi-input multioutput (MIMO) system functioning in the presence of generalized multipath fading. The study presents the derived results of the closed-form analytical statistical description of the ergodic sum-rate capacity, the capacity reliability and the capacity’s higher-order statistics in the case of complex Nakagami-m distributed channel transmission coefficients. A numerical verification of the derived expressions was performed, and it demonstrated excellent correspondence with the simulation. The system performance was evaluated with the help of a numerical analysis of the joint first- and second-order statistics description, depending on the channel and system parameters. The results demonstrated several peculiarities, e.g., the existence of a specific extremum of the capacity reliability for small-sized MIMO systems, its opposite behavior (in terms of the varying number of antenna elements) for heavy and light fading, and the existing asymptotic regions of the system and the channel parameters.
Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison
Authors: Costantini Giovanni , Cesarini Valerio , Di Leo Pietro , Amato Federica , Suppa Antonio , Asci Francesco , Pisani Antonio , Calculli Alessandra , Saggio Giovanni
Published: February 2023

Parkinson’s Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis is achieved clinically on the basis of different symptoms with considerable delays from the onset of neurodegenerative processes in the central nervous system. In this study, we investigated early and full-blown PD patients based on the analysis of their voice characteristics with the aid of the most commonly employed machine learning (ML) techniques. A custom dataset was made with hi-fi quality recordings of vocal tasks gathered from Italian healthy control subjects and PD patients, divided into early diagnosed, off-medication patients on the one hand, and mid-advanced patients treated with L-Dopa on the other. Following the current state-of-the-art, several ML pipelines were compared usingdifferent feature selection and classification algorithms, and deep learning was also explored with a custom CNN architecture. Results show how feature-based ML and deep learning achieve comparable results in terms of classification, with KNN, SVM and naïve Bayes classifiers performing similarly, with a slight edge for KNN. Much more evident is the predominance of CFS as the best feature selector. The selected features act as relevant vocal biomarkers capable of differentiating healthy subjects, early untreated PD patients and mid-advanced L-Dopa treated patients.
A Wavelet Derivative Spectrum Length Method of TFBG Sensor Demodulation
Authors: Cięszczyk Sławomir , Skorupski Krzysztof , Wawrzyk Martyna , Panas Patryk
Published: February 2023

Fibre optic sensors using tilted fibre Bragg grating (TFBG) have high sensitivity for refractive index measurements. In order to achieve good metrological parameters of the measurement, an appropriate method of spectrum demodulation must be used. The method proposed in the article is an improvement of the spectral length algorithm. The spectral length parameter is treated as the sum of the derivative filter responses. In the original version, the first difference of spectrum elements was used, while this article proposes to use the wavelet transform to calculate the numerical derivative approximation. The advantage of this solution is an easy way to select the level of smoothing filtration by changing the scale parameter. The derivation is appropriate even for a relatively low signal-to-noise level. The approximation of the spectral length by the derivative calculated using the wavelet transform eliminates the high-frequency noise of the optical signal. The absolute value of determined spectral derivatives after significant smoothing can be used to estimate the wavelength of the decay of modes. After analyzing experimental data and performing calculations, it turns out that this is a linear method with better resolution than the contour length algorithm.
Near-Infrared Spectroscopy for the In Vivo Monitoring of Biodegradable Implants in Rats
Authors: Hassan Hafiz Wajahat , Mota-Silva Eduarda , Grasso Valeria , Riehakainen Leon , Jose Jithin , Menichetti Luca , Mirtaheri Peyman
Published: February 2023

Magnesium (Mg) alloys possess unique properties that make them ideal for use as biodegradable implants in clinical applications. However, reports on the in vivo assessment of these alloys are insufficient. Thus, monitoring the degradation of Mg and its alloys in vivo is challenging due to the dynamic process of implant degradation and tissue regeneration. Most current works focus on structural remodeling, but functional assessment is crucial in providing information about physiological changes in tissues, which can be used as an early indicator of healing. Here, we report continuous wave near-infrared spectroscopy (CW NIRS), a non-invasive technique that is potentially helpful in assessing the implant–tissue dynamic interface in a rodent model. The purpose of this study was to investigate the effects on hemoglobin changes and tissue oxygen saturation (StO2) after the implantation of Mg-alloy (WE43) and titanium (Ti) implants in rats’ femurs using a multiwavelength optical probe. Additionally, the effect of changes in the skin on these parameters was evaluated. Lastly, combining NIRS with photoacoustic (PA) imaging provides a more reliable assessment of tissue parameters, which is further correlated with principal component analysis.
Optimization of Gradient Descent Parameters in Attitude Estimation Algorithms
Authors: Sever Karla , Golušin Leonardo Max , Lončar Josip
Published: February 2023

Attitude estimation methods provide modern consumer, industrial, and space systems with an estimate of a body orientation based on noisy sensor measurements. The gradient descent algorithm is one of the most recent methods for optimal attitude estimation, whose iterative nature demands adequate adjustment of the algorithm parameters, which is often overlooked in the literature. Here, we present the effects of the step size, the maximum number of iterations, and the initial quaternion, as well as different propagation methods on the quality of the estimation in noiseless and noisy conditions. A novel figure of merit and termination criterion that defines the algorithm’s accuracy is proposed. Furthermore, the guidelines for selecting the optimal set of parameters in order to achieve the highest accuracy of the estimate using the fewest iterations are proposed and verified in simulations and experimentally based on the measurements acquired from an in-house developed model of a satellite attitude determination and control system. The proposed attitude estimation method based on the gradient descent algorithm and complementary filter automatically adjusts the number of iterations with the average below 0.5, reducing the demand on the processing power and energy consumption and causing it to be suitable for low-power applications.
Multiband Photogrammetry and Hybrid Image Analysis for the Investigation of a Wall Painting by Paolo de San Leocadio and Francesco Pagano in the Cathedral of Valencia
Authors: Rahrig Max , Herrero Cortell Miguel Ángel , Lerma José Luis
Published: February 2023

A workflow for the photogrammetric combination of non-invasive multispectral imaging techniques ranging from ultraviolet (UV) and visible (VIS) to near infrared (NIR) for the investigation of wall paintings is presented. Hereby, different methods for image analysis and visualisation techniques are discussed. This includes the combination of spectral bands in hybrid false-colour images and image analysis by applying NDVI/NDPI and PCA. The aim of the research is to generate a high-resolution photogrammetric image set, providing information on underdrawings, material differences, damages, painting techniques and conservation measures. The image data are superimposed with pixel accuracy in a geographic information system (GIS) for further analysis, tracing of observations and findings and the annotation of further information. The research is carried out on the ‘Adoration of the Shepherds’, an early Spanish Renaissance wall painting created in 1472 by Paolo de San Leocadio and Francesco Pagano in the Cathedral of Valencia. The wall painting is preserved in an unfinished condition, and half of it is represented by the initial plaster and preparation layers. This gives the possibility to compare and evaluate the observations of the finished areas as well as carry out an in-depth study of the working techniques.
Zero-Shot Image Classification Method Based on Attention Mechanism and Semantic Information Fusion
Authors: Wang Yaru , Feng Lilong , Song Xiaoke , Xu Dawei , Zhai Yongjie
Published: February 2023

The zero-shot image classification (ZSIC) is designed to solve the classification problem when the sample is very small, or the category is missing. A common method is to use attribute or word vectors as a priori category features (auxiliary information) and complete the domain transfer from training of seen classes to recognition of unseen classes by building a mapping between image features and a priori category features. However, feature extraction of the whole image lacks discrimination, and the amount of information of single attribute features or word vector features of categories is insufficient, which makes the matching degree between image features and prior class features not high and affects the accuracy of the ZSIC model. To this end, a spatial attention mechanism is designed, and an image feature extraction module based on this attention mechanism is constructed to screen critical features with discrimination. A semantic information fusion method based on matrix decomposition is proposed, which first decomposes the attribute features and then fuses them with the extracted word vector features of a dataset to achieve information expansion. Through the above two improvement measures, the classification accuracy of the ZSIC model for unseen images is improved. The experimental results on public datasets verify the effect and superiority of the proposed methods.
An Igor Pro 8.01 Procedure to Analyze Pulse Oximetry during Acute Hypoxia Test in Aircrews
Authors: Alvear-Catalán Manuel , Montiglio Claudio , Perales Ignacio , Viscor Ginés , Araneda Oscar F.
Published: February 2023

The recognition of hypoxia symptoms is a critical part of physiological training in military aviation. Acute exposure protocols have been designed in hypobaric chambers to train aircrews to recognize hypoxia and quickly take corrective actions. The goal of the acute hypoxia test is to know the time of useful consciousness and the minimal arterial oxygen saturation tolerated. Currently, there is no computer system specifically designed to analyze the physiological variables obtained during the test. This paper reports the development and analytical capabilities of a computational tool specially designed for these purposes. The procedure was designed using the Igor Pro 8.01 language, which processes oxygen saturation and heart rate signals. To accomplish this, three functional boards are displayed. The first allows the loading and processing of the data. The second generates graphs that allow for a rapid visual examination to determine the validity of individual records and calculate slopes on selected segments of the recorded signal. Finally, the third can apply filters to generate data groups for analysis. In addition, this tool makes it possible to propose new study variables that are derived from the raw signals and can be applied simultaneously to large data sets. The program can generate graphs accompanied by basic statistical parameters and heat maps that facilitate data visualization. Moreover, there is a possibility of adding other signals during the test, such as the oxygenation level in vital organs, electrocardiogram, or electroencephalogram, which illustrates the test’s excellent potential for application in aerospace medicine and for helping us develop a better understanding of complex physiological phenomena.