bluuebunny/crossref_metadata_2025 huggingface dataset:
Neural-Network-Based Nonlinear Model Predictive Control of Multiscale Crystallization Process
Authors: Wang Liangyong , Zhu Yaolong
Published: November 2022

The purpose of this study was to develop an integrated control strategy for multiscale crystallization processes. An image analysis method using a deep learning neural network is used to measure the fine-scale information of the crystallization process, and the mathematical statistical method is adopted to obtain the mean size of the crystal population. A feedforward neural network is subsequently trained and employed in a nonlinear model predictive control formulation to obtain the optimal profile of the manipulated variable. The effectiveness of the proposed nonlinear model predictive control method is evaluated using alum cooling crystallization experiments. Experimental results demonstrate benefits of the proposed combination of feedforward neural network and nonlinear model predictive control method for the multiscale crystallization process.
Impact of Geomagnetic Fields on the Geochemical Evolution of Oil
Authors: Ponomarev Andrey A. , Gafurov Marat R. , Kadyrov Marsel A. , Tugushev Oscar A. , Drugov Denis A. , Vaganov Yuri V. , Zavatsky Mikhail D.
Published: November 2022

Here, we reported on experimental studies related to the exposure of oil to a 50 Hz electromagnetic field (0.81 T strength) and examined the changes in the geochemical characteristics of oil: n-alkane distribution, isotopic composition (δ13C), and concentration of paramagnetic centers. We discovered that electromagnetic fields have impacts on the distribution of n-alkanes and on their individual isotopic composition, with the concentration of paramagnetic centers remaining unchanged. While discussing the results, we looked into the state-of-the-art of research on electromagnetic exposures of the bottom-hole formation zone and into natural electric and geomagnetic fields. We consequently hypothesized that natural geomagnetic fields can influence the geochemical evolution processes of oil. This hypothesis requires further studies to reveal the frequency and strength characteristics of natural geomagnetic fields.
Molecular Dynamics Simulation on the Pyrolysis Process of PODE3-5
Authors: Zhu Qiren , Wang Fang , Lyu Jie-Yao , Li Yang , Chen Dongping , Yang Wenming
Published: November 2022

This paper investigates the pyrolysis of PODEn (n = 3, 4, 5) using ReaxFF molecular dynamics simulation. A large-scale model, which contains 2000 PODEn molecules, is simulated at 3000 K. The higher frequencies of the initial PODEn decomposition reaction at α or β C-O bond show that the α or β C-O bond in PODEn is not easy to break, which is approximately half the number of the other type of C-O bond dissociation. Furthermore, the bond dissociation energies (BDEs) are calculated using the ReaxFF method. The BDE of α or β C-O bond is higher than that of the other C-O bond, ~3–11 kcal/mol, indicating that BDE is one of the factors causing the different proportions of bonds broken. The evolution of pyrolysis products is also investigated. The results reveal that long-chain pyrolysis products from the initial PODEn decomposition are prone to further reaction, while a large amount of CH3O and CH3 remains in the system. This helps explain the difficulty in α and β C-O bond dissociation reactions. The results of the pyrolysis products are consistent with the result in further chemical kinetic simulation. The C2 species in pyrolysis products is relatively low, especially for C2H4 and C2H3, which is around zero. This supports the ability of PODEn to reduce soot emission.
Simultaneous Anaerobic Ammonium Oxidation and Electricity Generation in Microbial Fuel Cell: Performance and Electrochemical Characteristics
Authors: Zhang Jiqiang , Zhang Zaiwang , Rong Kun , Guo Haiying , Cai Jing , Xing Yajuan , Ren Lili , Ren Jiayun , Wu Tao , Li Jialiang , Zheng Ping
Published: November 2022

In this study, a microbial fuel cell (MFC) that can achieve simultaneous anode anaerobic ammonium oxidation (anammox) and electricity generation (anode anammox MFC) by high-effective anammox bacteria fed with purely inorganic nitrogen media was constructed. As the influent concentrations of ammonium (NH4+-N) and nitrite (NO2−-N) gradually increased from 25 to 250 mg/L and 33–330 mg/L, the removal efficiencies of NH4+-N, NO2−-N and TN were over 90%, 90% and 80%, respectively, and the maximum volumetric nitrogen removal rate reached 3.01 ± 0.27 kgN/(m3·d). The maximum voltage and maximum power density were 225.48 ± 10.71 mV and 1308.23 ± 40.38 mW/m3, respectively. Substrate inhibition took place at high nitrogen concentrations (NH4+-N = 300 mg/L, NO2−-N = 396 mg/L). Electricity production performance significantly depended upon the nitrogen removal rate under different nitrogen concentrations. The reported low coulombic efficiency (CE, 4.09–5.99%) may be due to severe anodic polarization. The anode charge transfer resistance accounted for about 90% of the anode resistance. The anode process was the bottleneck for energy recovery and should be further optimized in anode anammox MFCs. The high nitrogen removal efficiency with certain electricity recovery potential in the MFCs suggested that anode anammox MFCs may be used in energy sustainable nitrogen-containing wastewater treatment.
A Study of Performance Evaluation for Textile and Garment Enterprises
Authors: Wang Chia-Nan , Nguyen Phuong-Thuy Thi , Wang Yen-Hui , Dang Thanh-Tuan
Published: November 2022

Vietnam’s textile and garment enterprises make an important contribution to the country with the second largest export turnover. The existence and development of textile and garment enterprises have a significant influence on the socioeconomic development of Vietnam. Currently, Vietnam’s textile and garment industry is facing difficulties caused by the COVID-19 pandemic, along with competition from foreign direct investment (FDI) enterprises. Therefore, it is imperative for managers to assess competitiveness by measuring their past and current performance indicators. This study assesses the performance of Vietnam’s 10 textile and garment enterprises from 2017 to 2020 by combining the DEA–Malmquist productivity index (MPI) and epsilon-based measure (EBM) model. The proposed model considered three inputs (total assets, cost of goods sold, and liabilities) and two outputs (total revenue and gross profit). In addition to showing the best-performing companies from certain aspects during the period (2017–2020), the results show that the EBM method combined with the Malmquist model in the field can be successfully applied. This study is a reference for companies in the textile and garment industry to identify their position to improve their operational efficiency and overcome their weaknesses.
Effects of Container Design on the Temperature and Moisture Content Distribution in Pork Patties during Microwave Heating: Experiment and Numerical Simulation
Authors: Jung Hwabin , Lee Myeong Gi , Yoon Won Byong
Published: November 2022

Effects of the container design on the heat transfer rate and food quality during microwave heating were explored and validated with numerical simulations and experiments. The uniformity of moisture content and temperature was investigated, and to describe microwave heating patterns, a simulation model was created. Pork patties with different moisture and salt contents were heated in three different containers (center and edge-perforated lid as well as without lid) to achieve 80 °C using a domestic microwave oven. Compared to the center or mid-way positions, the temperatures at the edge of the patties rose quickly. By containing the evaporated vapor from the heated pork patties inside the container, the container with a center-perforated lid decreased the heating rate and non-uniformity in temperature and moisture content. A simplified numerical model for the electromagnetics, heat, and momentum transfer coupling simulation was developed to understand the moisture and temperature distribution of the pork patties after microwave heating. Heating uniformity and the final quality of the pork patties could be improved by a container with a center-perforated lid. The proposed model was able to describe the microwave warming process for ready-to-eat products; thus, it is a useful tool for designing microwavable ready meals.
Kinetics of Polymer Network Formation by Nitroxide-Mediated Radical Copolymerization of Styrene/Divinylbenzene in Supercritical Carbon Dioxide
Authors: Jaramillo-Soto Gabriel , Sarracino-Silva Samuel Alejandro , Vivaldo-Lima Eduardo
Published: November 2022

The kinetics of nitroxide-mediated dispersion copolymerization with crosslinking of styrene (STY) and divinylbenzene (DVB) in supercritical carbon dioxide (scCO2) is addressed experimentally. 2,2,6,6-Tetramethylpiperidinyl-1-oxy (TEMPO) and dibenzoyl peroxide (BPO) were used as nitroxide controller and initiator, respectively. A high-pressure cell with lateral sapphire windows at 120 °C and 207 bar was used to carry out the polymerizations. The nitroxide-mediated homopolymerization (NMP) of STY, as well as the conventional radical copolymerization (FRC) of STY/DVB, at the same conditions were also carried out as reference and for comparison purposes. The effect of nitroxide content on polymerization rate, evolution of molecular weight averages, gel fraction, and swelling index was studied.
Modelling and Simulation of Dissolution/Reprecipitation Technique for Low-Density Polyethene Using Solvent/Non-Solvent System
Authors: Zein Sharif H. , Hussain Ali A. , Yansaneh Osman Y. , Jalil A. A.
Published: November 2022

The global production and consumption of plastics have continued to increase. Plastics degrade slowly, causing persistent environmental pollution Developed waste plastic recycling methods are discussed in this report, with a focus on the dissolution/reprecipitation technique to restore low-density polyethene (LDPE) wastes. Aspen HYSYS is used to simulate the recycling of waste LDPE. Turpentine/petroleum ether (TURP/PetE) is chosen as solvent/non-solvent with fractions proved efficient through laboratory experiments. PetE is selected to be the non-solvent used for the precipitation of pure LDPE. The feedstock is assumed to be LDPE products containing additives such as dye. The simulation model developed estimated a pure LDPE precipitate recovery with a composition of 99% LDPE with a flowrate of 1024 tonnes per year. In addition, Aspen HYSYS could approximate a rough cost estimate that includes utility cost, installation cost and other factors. Technical challenges were eliminated, and several assumptions were taken into consideration to be able to simulate the process.
Decomposition of Heavy Diesel SCR Urea Fluid Adsorbed in Cu/HZSM-5 SCR Catalysts Studied by FTIR Spectroscopy at Ambient Conditions
Authors: Tempelman Christiaan , el Arkoubi Brahim , Spaan Jochem , Slevani Ronny , Degirmenci Volkan
Published: November 2022

A method is presented to study the decomposition of urea deposited on Cu/HZSM-5 SCR catalysts and therewith the ability of the Cu/HZSM-5 SCR catalyst to be regenerated when being overdosed with SCR urea fluids during operation. This straightforward laboratory method could speed up calibration of exhaust gas aftertreatment systems. As an example, the removal of adsorbed urea to the SCR substrate due to dosage malfunction is studied. To study the removal of adsorbed urea on the catalyst substrate, FTIR experiments have been conducted to investigate the state of the catalyst. Besides Cu/HZSM-5 also HZSM-5 and CuOx were studied as model compounds to provide more inside on the processes occurring at the Cu/HZSM-5 surface upon urea decomposition. To simulate exposure of the SCR catalyst to overdosing of the urea solution, samples were impregnated with a 32 wt% urea solution, which correlates to that of commercial heavy duty diesel urea solutions. After impregnation, the samples were heated at various temperatures in the 133–400 °C temperature region, typically the operation window of a SCR catalyst. After heating, the samples were cooled to room temperature and measured in FTIR. The obtained spectra were compared with various literature reports to correlate the observed absorption bands to urea, urea related compounds and decomposition compounds. The concentration of these adsorbed species decreases at increased thermolysis temperature and is no longer visible at temperatures >250 °C. Extended heat treatment at 200 °C revealed only minor loss of adsorbents after 6 h and were still observable in the FTIR spectra after 24 h. Urea derived adsorbents were completely removed when simulating catalyst regeneration under SCR operation conditions under continuous air flow with a humidity of 10% and at elevated temperatures (400 °C).
Issues of War and Peace: Is Religion More of the Problem and What Are Mahatma Gandhi’s Insights?
Authors: Allen Douglas
Published: November 2022

When examining the history of religions and dominant religious narratives, institutions, cultures, ideologies, and practices in the contemporary world, one is tempted to conclude that religion is more of the problem in relating to diverse issues of war and peace. Dominant religions and religious cultures seem overwhelmingly to be causes, express systemic structures, and provide ideological, theological, and philosophical justifications for violence, war, militarism, intolerance, divisiveness, oppression, injustice, hatred, environmental destruction, and anti-democratic hierarchical domination. Can religious culture also be a positive force for nonviolence, peace, love, compassion, justice, tolerance and mutual respect, and harmonious and sustainable relations with human and nonhuman life, nature, and the cosmos? A universal, phenomenological, structural model of the dialectic of the sacred and the profane allows us to understand how and why religious culture has been such a negative force, but also how it can develop as a positive force. In that regard, Mahatma Gandhi, the best known and most influential proponent of nonviolence, offers a complex and insightful approach to religious culture in ways that are most significant for relating issues of war, peace, and religious culture today. What I propose to show, by focusing on the phenomenology of religion and the insights of Mahatma Gandhi, is that the full picture of religious culture, violence, war, and peace is complex, nuanced, and contradictory, and there are structural and contextualized openings for understanding ways that religious culture can be a positive force for nonviolence and peace.
Presenting the Spatio-Temporal Model for Predicting and Determining Permissible Land Use Changes Based on Drinking Water Quality Standards: A Case Study of Northern Iran
Authors: Allahdad Zahra , Malmasi Saeed , Montazeralzohour Morvarid , Sadeghi Seyed Mohammad Moein , Khabbazan Mohammad M.
Published: November 2022

Quantifying the effect of non-point source pollution from different land use types (e.g., agricultural lands, pastures, orchards, and urban areas) on stream water quality is critical in determining the extent and type of land use. The relationship between surface water quality as the primary source of drinking water and land use patterns in suburban areas with an accelerated pace of industrial development and progressive growth of population has drawn much attention recently. This study aims to determine the type and portion of the land use changes over three-time intervals from 2000 to 2015 in the Jajrood River Catchment (Tehran metropolis, north of Iran). We used satellite images of Landsat TM and ETM for 2005, 2010, and 2015 to analyze land use changes as a spatiotemporal model. According to the image processing and analysis, we classified the land uses of the study area into irrigated farming, orchards, pastures, and residential areas. In addition, we used temporal data from sampling stations to identify the relationship between land use and water quality based on a multivariate regression model. The analysis shows a significant correlation between the type and extent of land use and water quality parameters, including pH, Na+, Ca+, Mg+, Cl−, SO42−, NO3−, and TDS. Pastures and residential areas had the highest impact on water quality parameters among all land use types. Besides, we have used the regression analysis results to determine the maximum permissible areas of each land use type. Consequently, effective management strategies such as land use optimization in catchment scale for this catchment and similar areas will help to consciously protect and manage the quality of drinking water resources.
Quantifying the Impact of Dust Sources on Urban Physical Growth and Vegetation Status: A Case Study of Saudi Arabia
Authors: Alsubhi Yazeed , Qureshi Salman , Assiri Mazen E. , Siddiqui Muhammad Haroon
Published: November 2022

Recently, dust has created many problems, including negative effects on health, and environmental and economic costs, for people who live both near to and far from sources of dust. The aim of this study is to evaluate and quantify the impact of dust sources located inside Saudi Arabia on the physical growth and vegetation status of cities. In order to do so, satellite data sets, simulated surface data, and soil data for Saudi Arabia from 2000 to 2021 were used. In the first step, a dust sources map of the study area was generated using multi-criteria decision analysis. Land surface temperature (LST), vegetation cover, soil moisture, precipitation, air humidity, wind speed, and soil erodibility factors were considered as effective criteria in identifying dust sources. In the second step, built-up land and vegetation status maps of major cities located at different distances from dust sources were generated for different years based on spectral indicators. Then, the spatiaotemporal change of built-up land and vegetation status of the study area and major cities were extracted. Finally, impacts of major dust sources on urban physical growth and vegetation were quantified. The importance degrees of soil erodibility, wind speed, soil moisture, vegetation cover, LST, air humidity, and precipitation to identify dust sources were 0.22, 0.20, 0.16, 0.15, 0.14, 0.07, and 0.05, respectively. Thirteen major dust sources (with at least 8 years of repetition) were identified in the study area based on the overlap of the effective criteria. The identified major dust sources had about 300 days with Aerosol Optical Depth (AOD) values greater than 0.85, which indicates that these dust sources are active. The location of the nine major dust sources identified in this study corresponds to the location of the dust sources identified in previous studies. The physical growth rates of cities located <400 km or >400 km from a major dust source (DMDS) are 46.2% and 95.4%, respectively. The reduction rates of average annual normalized difference vegetation index (NDVI) in these sub-regions are 0.006 and 0.002, respectively. The reduction rate of the intensity of vegetation cover in the sub-region close to dust sources is three times higher than that of the sub-region farther from dust sources. The coefficients of determination (R2) between the DMDS and urban growth rate and the NDVI change rate are 0.52 and 0.73, respectively, which indicates that dust sources have a significant impact on the physical growth of cities and their vegetation status.
Time Series Analysis of Land Cover Change Using Remotely Sensed and Multisource Urban Data Based on Machine Learning: A Case Study of Shenzhen, China from 1979 to 2022
Authors: Ding Kai , Huang Yidu , Wang Chisheng , Li Qingquan , Yang Chao , Fang Xu , Tao Ming , Xie Renping , Dai Ming
Published: November 2022

Shenzhen has experienced rapid urbanization since the establishment of the Special Economic Zone in 1978. However, it is rare to witness high-speed urbanization in Shenzhen. It is important to study the LUCC progress in Shenzhen (regarding refusing multisource data), which can provide a reference for governments to solve the problems of land resource shortages and urban expansion spaces. In this paper, nine Landsat images were used to retrieve land cover maps in Shenzhen, China, from 1979 to 2022. The classification method is based on support vector machines with assistance from visual interpretation. The results show that the urban area increased by 756.84 km2, the vegetation area decreased by 546.27 km2, the water area decreased by 132.95 km2, and the bare area decreased by 77.62 km2 in the last 43 years of our research region. Urban sprawl starts from the Luohu district, then propagates to Futian, Nanshan, and Yantian districts, and finally expands to other outlying districts (Baoan, Longgang, Guangming, Dapeng, and Pingshan). The spatial–temporal characteristics and the impact factors of urbanization were further analyzed. The visualization of land cover changes based on a complex network approach reveals that the velocity of urban expansion is growing. The coastline distributions were retrieved from nine observation times from 1979 to 2022; the results show that the west coastline changed more dramatically than the east and most of the east coastline remained stable, except for the parts near Yantian port and Mirs Bay, which experienced some changes. The impact factors of coastline changes are further discussed. Through a correlation analysis using urban data, such as transportation and socioeconomic factors, it was found that elevation and roads have strong constraints on the spatial patterns of a city’s expansion. There is exponential decay in the urban land increase against the distance to the roads, implying that traffic factors greatly determine urban land expansion. The turning point of the exponential decay is a distance of around 150 m. Time and population are highly correlated with land use development, indicating that urban land grows linearly with time and the population, which are important driving forces of urban land development. Compared with secondary and tertiary industries, the primary industry is less related to urban land use in Shenzhen.
Effects of Forest Fire Prevention Policies on Probability and Drivers of Forest Fires in the Boreal Forests of China during Different Periods
Authors: Zhou Qing , Zhang Heng , Wu Zhiwei
Published: November 2022

Fire prevention policies during different periods may lead to changes in the drivers of forest fires. Here, we use historical fire data and apply the boosted regression tree (BRT) model to analyze the spatial patterns and drivers of forest fires in the boreal forests of China from 1981 to 2020 (40 years). We divided the fire data into four periods using the old and new Chinese Forest Fire Regulations as a dividing line. Our objectives here were: to explore the influence of key historical events on the drivers of forest fires in northern China, establish a probability model of forest fire occurrence, and draw a probability map of forest fire occurrence and a fire risk zone map, so as to interpret the differences in the drivers of forest fires and fire risk changes over different periods. The results show that: (1) The model results from 1981 to 2020 (all years) did not improve between 2009 and 2020 (the most recent period), indicating the importance of choosing the appropriate modeling time series length and incorporating key historical events in future forest fire modeling; (2) Climate factors are a dominant factor affecting the occurrence of forest fires during different periods. In contrast with previous research, we found that here, it is particularly important to pay attention to the relevant indicators of the autumn fire prevention period (average surface temperature, sunshine hours) in the year before the fire occurrence. In addition, the altitude and the location of watchtowers were considered to have a significant effect on the occurrence of forest fires in the study area. (3) The medium and high fire risk areas in our three chosen time periods (1981–14 March 1988; 15 March 1988–2008; 2009–2020) have changed significantly. Fire risks were higher in the east and southeast areas of the study area in all periods. The northern primeval forest area had fewer medium-risk areas before the new and old regulations were formulated, but the medium-risk areas increased significantly after the old regulations were revised. Our study will help understand the drivers and fire risk distribution of forest fires in the boreal forests of China under the influence of history and will help decision-makers optimize fire management strategies to reduce potential fire risks.
Effect of Snow Cover on Detecting Spring Phenology from Satellite-Derived Vegetation Indices in Alpine Grasslands
Authors: Wang Yiting , Chen Yuanyuan , Li Pengfei , Zhan Yinggang , Zou Rui , Yuan Bo , Zhou Xiaode
Published: November 2022

The accurate estimation of phenological metrics from satellite data, especially the start of season (SOS), is of great significance to enhance our understanding of trends in vegetation phenology under climate change at regional or global scales. However, for regions with winter snow cover, such as the alpine grasslands on the Tibetan Plateau, the presence of snow inevitably contaminates satellite signals and introduces bias into the detection of the SOS. Despite recent progress in eliminating the effect of snow cover on SOS detection, the mechanism of how snow cover affects the satellite-derived vegetation index (VI) and the detected SOS remains unclear. This study investigated the effect of snow cover on both VI and SOS detection by combining simulation experiments and real satellite data. Five different VIs were used and compared in this study, including four structure-based (i.e., NDVI, EVI2, NDPI, NDGI) VIs and one physiological-based (i.e., NIRv) VI. Both simulation experiments and satellite data analysis revealed that the presence of snow can significantly reduce the VI values and increase the local gradient of the growth curve, allowing the SOS to be detected. The bias in the detected SOS caused by snow cover depends on the end of the snow season (ESS), snow duration parameters, and the snow-free SOS. An earlier ESS results in an earlier estimate of the SOS, a later ESS results in a later estimate of the SOS, and an ESS close to the snow-free SOS results in small bias in the detected SOS. The sensitivity of the five VIs to snow cover in SOS detection is NDPI/NDGI < NIRv < EVI2 < NDVI, which has been verified in both simulation experiments and satellite data analysis. These findings will significantly advance our research on the feedback mechanisms between vegetation, snow, and climate change for alpine ecosystems.
AQE-Net: A Deep Learning Model for Estimating Air Quality of Karachi City from Mobile Images
Authors: Ahmed Maqsood , Shen Yonglin , Ahmed Mansoor , Xiao Zemin , Cheng Ping , Ali Nafees , Ghaffar Abdul , Ali Sabir
Published: November 2022

Air quality has a significant influence on the environment and health. Instruments that efficiently and inexpensively detect air quality could be extremely valuable in detecting air quality indices. This study presents a robust deep learning model named AQE-Net, for estimating air quality from mobile images. The algorithm extracts features and patterns from scene photographs collected by the camera device and then classifies the images according to air quality index (AQI) levels. Additionally, an air quality dataset (KARACHI-AQI) of high-quality outdoor images was constructed to enable the model’s training and assessment of performance. The sample data were collected from an air quality monitoring station in Karachi City, Pakistan, comprising 1001 hourly datasets, including photographs, PM2.5 levels, and the AQI. This study compares and examines traditional machine learning algorithms, e.g., a support vector machine (SVM), and deep learning models, such as VGG16, InceptionV3, and AQE-Net on the KHI-AQI dataset. The experimental findings demonstrate that, compared to other models, AQE-Net achieved more accurate categorization findings for air quality. AQE-Net achieved 70.1% accuracy, while SVM, VGG16, and InceptionV3 achieved 56.2% and 59.2% accuracy, respectively. In addition, MSE, MAE, and MAPE values were calculated for our model (1.278, 0.542, 0.310), which indicates the remarkable efficacy of our approach. The suggested method shows promise as a fast and accurate way to estimate and classify pollutants from only captured photographs. This flexible and scalable method of assessment has the potential to fill in significant gaps in the air quality data gathered from costly devices around the world.
Tree Species Classification Using Ground-Based LiDAR Data by Various Point Cloud Deep Learning Methods
Authors: Liu Bingjie , Huang Huaguo , Su Yong , Chen Shuxin , Li Zengyuan , Chen Erxue , Tian Xin
Published: November 2022

Tree species information is an important factor in forest resource surveys, and light detection and ranging (LiDAR), as a new technical tool for forest resource surveys, can quickly obtain the 3D structural information of trees. In particular, the rapid and accurate classification and identification of tree species information from individual tree point clouds using deep learning methods is a new development direction for LiDAR technology in forest applications. In this study, mobile laser scanning (MLS) data collected in the field are first pre-processed to extract individual tree point clouds. Two downsampling methods, non-uniform grid and farthest point sampling, are combined to process the point cloud data, and the obtained sample data are more conducive to the deep learning model for extracting classification features. Finally, four different types of point cloud deep learning models, including pointwise multi-layer perceptron (MLP) (PointNet, PointNet++, PointMLP), convolution-based (PointConv), graph-based (DGCNN), and attention-based (PCT) models, are used to classify and identify the individual tree point clouds of eight tree species. The results show that the classification accuracy of all models (except for PointNet) exceeded 0.90, where the PointConv model achieved the highest classification accuracy for tree species classification. The streamlined PointMLP model can still achieve high classification accuracy, while the PCT model did not achieve good accuracy in the tree species classification experiment, likely due to the small sample size. We compare the training process and final classification accuracy of the different types of point cloud deep learning models in tree species classification experiments, further demonstrating the advantages of deep learning techniques in tree species recognition and providing experimental reference for related research and technological development.
Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
Authors: Ioannidou Maria , Koukos Alkiviadis , Sitokonstantinou Vasileios , Papoutsis Ioannis , Kontoes Charalampos
Published: November 2022

Crop classification is an important remote sensing task with many applications, e.g., food security monitoring, ecosystem service mapping, climate change impact assessment, etc. This work focuses on mapping 10 crop types at the field level in an agricultural region located in the Spanish province of Navarre. For this, multi-temporal Synthetic Aperture Radar Polarimetric (PolSAR) Sentinel-1 imagery and multi-spectral Sentinel-2 data were jointly used. We applied the Cloude–Pottier polarimetric decomposition on PolSAR data to compute 23 polarimetric indicators and extracted vegetation indices from Sentinel-2 time-series to generate a big feature space of 818 features. In order to assess the relevance of the different features for the crop mapping task, we run a number of scenarios using a Support Vector Machines (SVM) classifier. The model that was trained using only the polarimetric data demonstrates a very promising performance, achieving an overall accuracy over 82%. A genetic algorithm was also implemented as a feature selection method for deriving an optimal feature subset. To showcase the positive effect of using polarimetric data over areas suffering from cloud coverage, we contaminated the original Sentinel-2 time-series with simulated cloud masks. By incorporating the genetic algorithm, we derived a high informative feature subset of 120 optical and polarimetric features, as the corresponding classification model increased the overall accuracy by 5% compared to the model trained only with Sentinel-2 features. The feature importance analysis indicated that apart from the Sentinel-2 spectral bands and vegetation indices, several polarimetric parameters, such as Shannon entropy, second eigenvalue and normalised Shannon entropy are of high value in identifying crops. In summary, the findings of our study highlight the significant contribution of Sentinel-1 PolSAR data in crop classification in areas with frequent cloud coverage and the effectiveness of the genetic algorithm in discovering the most informative features.
Identifying Biomarkers for Accurate Detection of Stress
Authors: Jambhale Kiran , Mahajan Smridhi , Rieland Benjamin , Banerjee Nilanjan , Dutt Abhijit , Kadiyala Sai Praveen , Vinjamuri Ramana
Published: November 2022

Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one’s brain and behaviour. Stress is an established risk factor in SUD’s development of addiction and in reinstating drug seeking. Despite this expanding epidemic and the potential for its grave consequences, there are limited options available for management and treatment, as well as pharmacotherapies and psychosocial treatments. To this end, there is a need for new and improved devices dedicated to the detection, management, and treatment of SUD. In this paper, the negative effects of SUD-related stress were discussed, and based on that, a few significant biomarkers were selected from a set of eight features collected by a chest-worn device, RespiBAN Professional, on fifteen individuals. We used three machine learning classifiers on these optimal biomarkers to detect stress. Based on the accuracies, the best biomarkers to detect stress and those considered as features for classification were determined to be electrodermal activity (EDA), body temperature, and a chest-worn accelerometer. Additionally, the differences between mental stress and physical stress, as well as different administrations of meditation during the study, were identified and analysed. Challenges, implications, and applications were also discussed. In the near future, we aim to replicate the proposed methods in individuals with SUD.
Application of Edge Computing in Structural Health Monitoring of Simply Supported PCI Girder Bridges
Authors: Lin Yi-Ching , Hsiao Chin-Yu , Tong Jian-Hua , Liao Chih-Pin , Song Shin-Tai , Tsai Hsin-Chu , Wang Jui-Lin
Published: November 2022

This study proposes an innovative method for structural health monitoring of simply supported PCI girder bridges based on dynamic strain and edge computing. Field static and dynamic load tests were conducted on a bridge consisting of a span with newly replaced PCI girders and numerous spans with old PCI girders. Both the static and dynamic test results showed that the flexural rigidity of the old PCI girders decreased significantly due to deterioration. To improve the efficiency of on-site monitoring data transmission and data analysis, this study developed a smart dynamic strain gauge node with the function of edge computing. Continuous data with a sampling frequency of 100 Hz were computed at the sensor node. Among the computed results, only the maximum dynamic strain data caused by the passage of the heaviest vehicle within 1 min were transmitted. The on-site monitoring results indicated that under routine traffic conditions, the dynamic strain response of the new PCI girder was smaller than that of the deteriorated PCI girder. When the monitored dynamic strain response has a tendency to magnify, attention should be paid to the potential prestress loss or other deterioration behaviors of the bridge.
Intelligent Reflecting Surfaces Enhanced Mobile Edge Computing: Minimizing the Maximum Computational Time
Authors: Sarfraz Mubashar , Alshahrani Haya Mesfer , Tarmissi Khaled , Alshahrani Hussain , Elfaki Mohamed Ahmed , Hamza Manar Ahmed , Nauman Ali , Khurshaid Tahir
Published: November 2022

Intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) have recently attracted significant attention in academia and industry. Without consuming any external energy, IRS can extend wireless coverage by smartly reconfiguring the phase shift of a signal towards the receiver with the help of passive elements. On the other hand, MEC has the ability to reduce latency by providing extensive computational facilities to users. This paper proposes a new optimization scheme for IRS-enhanced mobile edge computing to minimize the maximum computational time of the end users’ tasks. The optimization problem is formulated to simultaneously optimize the task segmentation and transmission power of users, phase shift design of IRS, and computational resource of mobile edge. The optimization problem is non-convex and coupled on multiple variables which make it very complex. Therefore, we transform it to convex by decoupling it into sub-problems and then obtain an efficient solution. In particular, the closed-form solutions for task segmentation and edge computational resources are achieved through the monotonical relation of time and Karush–Kuhn–Tucker conditions, while the transmission power of users and phase shift design of IRS are computed using the convex optimization technique. The proposed IRS-enhanced optimization scheme is compared with edge computing nave offloading, binary offloading, and edge computing, respectively. Numerical results demonstrate the benefits of the proposed scheme compared to other benchmark schemes.
Efficient Asynchronous Federated Learning for AUV Swarm
Authors: Meng Zezhao , Li Zhi , Hou Xiangwang , Du Jun , Chen Jianrui , Wei Wei
Published: November 2022

The development of automatic underwater vehicles (AUVs) has brought about unprecedented profits and opportunities. In order to discover the hidden valuable data detected by an AUV swarm, it is necessary to aggregate the data detected by AUV swarm to generate a powerful machine learning model. Traditional centralized machine learning generates a large number of data exchanges and faces problems of enormous training data, large-scale models, and communication. In underwater environments, radio waves are strongly absorbed, and acoustic communication is the only feasible technology. Unlike electromagnetic wave communication on land, the bandwidth of underwater acoustic communication is extremely limited, with the transmission rate being only 1/105 of the electromagnetic wave. Therefore, traditional centralized machine learning cannot support underwater AUV swarm training. In recent years, federated learning could only interact with model parameters without interacting with data, which greatly reduced communication costs. Therefore, this paper introduces federated learning into the collaboration of an AUV swarm. In order to further reduce the constraints of underwater scarce communication resources on federated learning and alleviate the straggler effect, in this work, we designed an asynchronous federated learning method. Finally, we constructed the optimization problem of minimizing the weighted sum of delay and energy consumption, relying on jointly optimizing the AUV CPU frequency and signal transmission power. In order to solve this complex optimization problem of high-dimensional non-convex time series accumulation, we transformed the problem into a Markov decision process (MDP) and use the proximal policy optimization 2 (PPO2) algorithm to solve this problem. The simulation results demonstrate the effectiveness and superiority of our method.
Sensor Fusion for Social Navigation on a Mobile Robot Based on Fast Marching Square and Gaussian Mixture Model
Authors: Mora Alicia , Prados Adrian , Mendez Alberto , Barber Ramon , Garrido Santiago
Published: November 2022

Mobile robot navigation has been studied for a long time, and it is nowadays widely used in multiple applications. However, it is traditionally focused on two-dimensional geometric characteristics of the environments. There are situations in which robots need to share space with people, so additional aspects, such as social distancing, need to be considered. In this work, an approach for social navigation is presented. A multi-layer model of the environment containing geometric and topological characteristics is built based on the fusion of multiple sensor information. This is later used for navigating the environment considering social distancing from individuals and groups of people. The main novelty is combining fast marching square for path planning and navigation with Gaussian models to represent people. This combination allows to create a continuous representation of the environment from which smooth paths can be extracted and modified according to dynamically captured data. Results prove the practical application of the method on an assistive robot for navigating indoor scenarios, including a behavior for crossing narrow passages. People are efficiently detected and modeled to assure their comfort when robots are around.
Novel Generalized Low-Pass Filter with Adjustable Parameters of Exponential-Type Forgetting and Its Application to ECG Signal
Authors: Petráš Ivo
Published: November 2022

In this paper, a novel form of the Gaussian filter, the Mittag–Leffler filter is presented. This new filter uses the Mittag–Leffler function in the probability-density function. Such Mittag–Leffler distribution is used in the convolution kernel of the filter. The filter has three parameters that may adjust the curve shape due to the filter-forgetting factor. Illustrative examples present the main advantages of the proposed filter compared to classical Gaussian filtering techniques, as well as real ECG-signal denoising. Some implementation notes, along with the Matlab function, are also presented.
A Modified Spectroscopic Approach for the Real-Time Detection of Pollen and Fungal Spores at a Semi-Urban Site Using the WIBS-4+, Part I
Authors: Markey Emma , Hourihane Clancy Jerry , Martínez-Bracero Moisés , Neeson Finnian , Sarda-Estève Roland , Baisnée Dominique , McGillicuddy Eoin J. , Sewell Gavin , O’Connor David J.
Published: November 2022

The real-time monitoring of primary biological aerosol particles (PBAP) such as pollen and fungal spores has received much attention in recent years as a result of their health and climatic effects. In this study, the Wideband Integrated Bioaerosol Sensor (WIBS) 4+ model was evaluated for its ability to sample and detect ambient fungal spore and pollen concentrations, compared to the traditional Hirst volumetric method. Although the determination of total pollen and fungal spore ambient concentrations are of interest, the selective detection of individual pollen/fungal spore types are often of greater allergenic/agricultural concern. To aid in this endeavour, modifications were made to the WIBS-4 instrument to target chlorophyll fluorescence. Two additional fluorescence channels (FL4 and FL5 channels) were combined with the standard WIBS channels (FL1, FL2, FL3). The purpose of this modification is to help discriminate between grass and herb pollen from other pollen. The WIBS-4+ was able to successfully detect and differentiate between different bioaerosol classes. The addition of the FL4 and FL5 channels also allowed for the improved differentiation between tree (R2 = 0.8), herbaceous (R2 = 0.6) and grass (R2 = 0.4) pollen and fungal spores (R2 = 0.8). Both grass and herbaceous pollen types showed a high correlation with D type particles, showing strong fluorescence in the FL4 channel. The additional fluorescent data that were introduced also improved clustering attempts, making k-means clustering a comparable solution for this high-resolution data.
Analysis of the Refined Mean-Field Approximation for the 802.11 Protocol Model
Authors: Ispizua Begoña , Doncel Josu
Published: November 2022

Mean-field approximation is a method to investigate the behavior of stochastic models formed by a large number of interacting objects. A new approximation was recently established, i.e., the refined mean-field approximation, and its high accuracy when the number of objects is small has been shown. In this work, we consider the model of the 802.11 protocol, which is a discrete-time model and show how the refined mean-field approximation can be adapted to this model. Our results confirm the accuracy of the refined mean-field approximation when the model with N objects is in discrete time.
Luminescent Silicon Nanowires as Novel Sensor for Environmental Air Quality Control
Authors: Morganti Dario , Faro Maria José Lo , Leonardi Antonio Alessio , Fazio Barbara , Conoci Sabrina , Irrera Alessia
Published: November 2022

Air quality monitoring is an increasingly debated topic nowadays. The increasing spillage of waste products released into the environment has contributed to the increase in air pollution. Consequently, the production of increasingly performing devices in air monitoring is increasingly in demand. In this scenario, the attention dedicated to workplace safety monitoring has led to the developing and improving of new sensors. Despite technological advancements, sensors based on nanostructured materials are difficult to introduce into the manufacturing flow due to the high costs of the processes and the approaches that are incompatible with the microelectronics industry. The synthesis of a low-cost ultra-thin silicon nanowires (Si NWs)-based sensor is here reported, which allows us the detection of various dangerous gases such as acetone, ethanol, and the ammonia test as a proof of concept in a nitrogen-based mixture. A modified metal-assisted chemical etching (MACE) approach enables to obtain ultra-thin Si NWs by a cost-effective, rapid and industrially compatible process that exhibit an intense light emission at room temperature. All these gases are common substances that we find not only in research or industrial laboratories, but also in our daily life and can pose a serious danger to health, even at small concentrations of a few ppm. The exploitation of the Si NWs optical and electrical properties for the detection of low concentrations of these gases through their photoluminescence and resistance changes will be shown in a nitrogen-based gas mixture. These sensing platforms give fast and reversible responses with both optical and electrical transductions. These high performances and the scalable synthesis of Si NWs could pave the way for market-competitive sensors for ambient air quality monitoring.
LMGAN: Linguistically Informed Semi-Supervised GAN with Multiple Generators
Authors: Cho Whanhee , Choi Yongsuk
Published: November 2022

Semi-supervised learning is one of the active research topics these days. There is a trial that solves semi-supervised text classification with a generative adversarial network (GAN). However, its generator has a limitation in producing fake data distributions that are similar to real data distributions. Since the real data distribution is frequently changing, the generator could not create adequate fake data. To overcome this problem, we present a novel approach for semi-supervised learning for text classification based on generative adversarial networks, Linguistically Informed SeMi-Supervised GAN with Multiple Generators, LMGAN. LMGAN uses trained bidirectional encoder representations from transformers (BERT) and the discriminator from GAN-BERT. In addition, LMGAN has multiple generators and utilizes the hidden layers of BERT. To reduce the discrepancy between the distribution of fake data and real data distribution, LMGAN uses fine-tuned BERT and the discriminator from GAN-BERT. However, since injecting fine-tuned BERT could induce incorrect fake data distribution, we utilize linguistically meaningful intermediate hidden layer outputs of BERT to enrich fake data distribution. Our model shows well-distributed fake data compared to the earlier GAN-based approach that failed to generate adequate high-quality fake data. Moreover, we can get better performances with extremely limited amounts of labeled data, up to 20.0%, compared to the baseline GAN-based model.