bluuebunny/crossref_metadata_2025 huggingface dataset:
Data-Driven Models for Forecasting Failure Modes in Oil and Gas Pipes
Authors: Elshaboury Nehal , Al-Sakkaf Abobakr , Alfalah Ghasan , Abdelkader Eslam Mohammed
Published: February 2022

Oil and gas pipelines are lifelines for a country’s economic survival. As a result, they must be closely monitored to maximize their performance and avoid product losses in the transportation of petroleum products. However, they can collapse, resulting in dangerous repercussions, financial losses, and environmental consequences. Therefore, assessing the pipe condition and quality would be of great significance. Pipeline safety is ensured using a variety of inspection techniques, despite being time-consuming and expensive. To address these inefficiencies, this study develops a model that anticipates sources of failure in oil pipelines based on specific factors related to pipe diameter and age, service (transported product), facility type, and land use. The model is developed using a multilayer perceptron (MLP) neural network, radial basis function (RBF) neural network, and multinomial logistic (MNL) regression based on historical data from pipeline incidents. With an average validity of 84% for the MLP, 85% for the RBF, and 81% for the MNL, the models can forecast pipeline failures owing to corrosion and third-party activities. The developed model can help pipeline operators and decision makers detect different failure sources in pipelines and prioritize the required maintenance and replacement actions.
Simulative Investigation of Different DLD Microsystem Designs with Increased Reynolds Numbers Using a Two-Way Coupled IBM-CFD/6-DOF Approach
Authors: Wullenweber Maike S. , Kottmeier Jonathan , Kampen Ingo , Dietzel Andreas , Kwade Arno
Published: February 2022

Deterministic lateral displacement (DLD) microsystems are suitable for the size fractionation of particle suspensions in the size range of 0.1 to 10 µm. To be able to fractionate real particles beyond a laboratory scale, these systems have to be designed for higher throughputs. High flow resistances and increasing the clogging of the systems impose substantial challenges for industrial operation. Simulative parameter studies are suitable for improving the design of the systems; for example, the position and shape of the posts. A high-resolution, two-way coupled 6-DOF CFD-DEM approach was used to study the flow and particle behavior of different post shapes (circular and triangular) and post sizes at different Reynolds numbers. The results were compared with the classical first streamline width theory. It was shown that the streamline theory does not account for all effects responsible for the separation. Furthermore, a shift in the critical particle diameter to smaller values could be obtained when increasing the Reynolds number and also when using triangular posts with reduced post sizes compared to the post spacing. These findings can help to improve the efficiency of the systems as the post spacing could be extended, thus reducing the flow resistance and the probability of clogging.
Inhibition of Four Inert Powders on the Minimum Ignition Energy of Sucrose Dust
Authors: Zhong Yuankun , Li Xiaoquan , Jiang Juju , Liang Siting , Yang Zhiwen , Soar Jeffrey
Published: February 2022

In order to evaluate the effect of inert powder on the ignition sensitivity of sucrose dust, this study investigated the effects of NaHCO3, NaCl, NH4H2PO4 and Al(OH)3 on the minimum ignition energy (MIE) of sucrose dust. The results showed that all four different inert powders inhibited the MIE of sucrose dust, and all of them showed a trend that the smaller the particle size of the inert powders, the better the inhibition effect. The inhibition effect was ranked as NaHCO3 > NH4H2PO4 > NaCl > Al(OH)3. NaHCO3 and NH4H2PO4 had both physical and chemical inhibition effects, which were better compared to NaCl and Al(OH)3, which had only physical inhibition effects. Analysis of the flame images showed that the inert powder slowed down the combustion of the sucrose dust flame and reduced the flame height. No flame appeared in the region of higher inert powder concentration.
Hydrodynamic Behavior of a Pump as Turbine under Transient Flow Conditions
Authors: Hu Jianxin , Su Xianghui , Huang Xin , Wu Kexin , Jin Yuzhen , Chen Chunguang , Chen Xulai
Published: February 2022

Centrifugal pumps as turbines (PATs) are widely used in chemical engineering for recycling the abundant energy from high-pressure fluid. The operation of PATs is significantly affected by their upstream conditions, which are not steady (i.e., with a constant flow rate) in reality, thus, research on the flow mechanism of PATs under transient conditions should be considered of higher importance. In this study, a numerical model of a PAT was developed by employing the sliding mesh method to describe turbine rotation, and a user-defined function (UDF) for characterizing transient flow conditions. Corresponding experiments were also conducted to provide validation results for the simulation, and the simulation results agreed well with the experimental outcomes. The instantaneous characteristic curves under the current working conditions were obtained for different transient flow rates. The results show that the turbine’s efficiency is significantly affected by transient flow conditions. In particular, a rapid increase (large time derivative) of flow rate results in a large energy dissipation at the turbine outlet, and therefore, the turbine efficiency decreases. In addition, as the flow rate increases, the hydrodynamic force on the impeller, and the pressure fluctuation amplitude in the volute first decrease and then increase, reaching the minimum near the design flow rate. The current study provides a reliable and precise approach for the estimation of hydrodynamic performance of fluid machinery under transient flow conditions.
Mapping Uncertainties of Soft-Sensors Based on Deep Feedforward Neural Networks through a Novel Monte Carlo Uncertainties Training Process
Authors: Costa Erbet A. , Rebello Carine M. , Santana Vinicius V. , Rodrigues Alírio E. , Ribeiro Ana M. , Schnitman Leizer , Nogueira Idelfonso B. R.
Published: February 2022

Data-driven sensors are techniques capable of providing real-time information of unmeasured variables based on instrument measurements. They are valuable tools in several engineering fields, from car automation to chemical processes. However, they are subject to several sources of uncertainty, and in this way, they need to be able to deal with uncertainties. A way to deal with this problem is by using soft sensors and evaluating their uncertainties. On the other hand, the advent of deep learning (DL) has been providing a powerful tool for the field of data-driven modeling. The DL presents a potential to improve the soft sensor reliability. However, the uncertainty identification of the soft sensors model is a known issue in the literature. In this scenario, this work presents a strategy to identify the uncertainty of DL models prediction based on a novel Monte Carlo uncertainties training strategy. The proposed methodology is applied to identify a Soft Sensor to provide a real-time prediction of the productivity of a chemical process. The results demonstrate that the proposed methodology can yield a soft sensor based on DL that provides reliable predictions, with precision being proven by its corresponding coverage region.
Toxicological and Nutraceutical Screening Assays of Some Artificial Sweeteners
Authors: Mateo-Fernández Marcos , González-Jiménez Miguel Josué , Celestino Mercedes Del Río , Font Rafel , Alonso-Moraga Ángeles , Merinas-Amo Tania
Published: February 2022

Artificial sweeteners are food additives worldwide used instead of fructose or glucose in many diet beverages. Furthermore, diet beverages intake has been increasing every year. Thus, some food agencies should regulate it based on toxicological studies. Debates and controversial results are demonstrated, and authority can revise its decision on the basis of new data reporting toxicological effects since cyclamate has been forbidden in some countries. Therefore, the aim of this study was to report new data about the toxicity of acesulfame-k, aspartame, and cyclamate, which are useful for authority agencies, determining the toxic potential and nutraceutical capabilities of these compounds. The toxicity, antitoxicity, genotoxicity, antigenotoxicity, and life expectancy assays were carried out in Drosophila as an in vivo model. In addition, in vitro HL-60 line cell was used to evaluate the chemopreventive activity determining the cytotoxic effect and the capability of producing DNA damage due to internucleosomal fragmentation or DNA strand breaks. Furthermore, the methylated status of these cancer cells treated with the tested compounds was assayed as a cancer therapy. Our results demonstrated that all tested compounds were neither toxic nor genotoxic, whereas these compounds resulted in antigenotoxic and cytotoxic substances, except for cyclamate. Aspartame showed antitoxic effects in Drosophila. All tested compounds decreased the quality of life of this in vivo organism model. Acesulfame-k, aspartame, and cyclamate induced DNA damage in the HL-60 cell line in the comet assay, and acesulfame-k generally increased the methylation status. In conclusion, all tested artificial sweeteners were safe compounds at assayed concentrations since toxicity and genotoxicity were not significantly induced in flies. Moreover, Aspartame and Cyclamate showed protective activity against a genotoxin in Drosophila Regarding nutraceutical potential, acesulfame-k and aspartame could be demonstrated to be chemopreventive due to the cytotoxicity activity shown by these compounds. According to DNA fragmentation and comet assays, a necrotic way could be the main mechanism of death cells induced by acesulfame-k and aspartame. Finally, Acesulfame-K hypermethylated repetitive elements, which are hypomethylated in cancer cells resulting in a benefit to humans.
“I Have Apostatized”: Self-Narratives of Catholic Apostasy as Resources for Collective Mobilization in Argentina
Authors: Rabbia Hugo H.
Published: February 2022

Since 2009, the Collective Apostasy Campaign in Argentina has mobilized some people who are opposed to the political interference of the Catholic Church through the formal act of apostatizing. The politicization of sexual and reproductive rights, and, especially, the fight for the legalization of abortion, led to the campaign that acquired great public repercussions between 2018 and 2020. This paper analyzes 13 self-narratives of apostasy publicly available since 2009, digging into its plot, motives to apostatize, and motivation for its publicizing. Through a thematic analysis, the diverse self-narratives show similar motivations (to promote social debate on political secularization in the country), although they differ in the centrality of their personal, sociopolitical, and procedural motives to apostatize. The stories that apostates tell are resources for social mobilization as they seek an increasingly broad audience and serve the pedagogical function of sharing arguments against the political role of the Catholic Church and in favor of personal ideological coherence.
Pillars of Salt: Pastoral Care with Adolescents with a Migration Experience
Authors: Casteel Amy
Published: February 2022

“Lot’s wife looked back”. This detail in the migration journey of Lot and his family illustrates being caught in between needing to move forward but wanting to look back. Many adolescents who have migrated to Europe experience in-betweenness. This article begins from their reported practices of lived religion. This interpretive phenomenological analysis study brings together the domains of lived religion, migration theology, and adolescent development to better understand how pastoral care may address this liminal state. Looking at their descriptions of the presence and absence of important relationships, religious practices, and the experience of the divine shows the importance of these three areas working together. In the absence of strong proximal social relationships, many adolescents with a religious identity who have migrated to Europe turn their attention to the divine Godself. Releasing someone caught in between two places may require an awareness of the concepts of grief and loss, post-trauma theology, and skills in orienting and making social connections. One goal of pastoral care for adolescents who have experienced migration can be to provide a path out of the liminal in-between space to a place where there is room to flourish.
Analysis of Regulatory Possibilities and Obstacles to Expand Renewable Energy and Preserve Landscape Quality in the Silesian Voivodship
Authors: Szumilas-Kowalczyk Hanna , Giedych Renata
Published: February 2022

Current international works on strategies for climate change mitigation and adaptation cite energy transition as one of the main challenges of the 21st century. Many social, economic, and ecological aspects have to be addressed, especially in regions which, for decades, relied on coal energy. One of those are changes in spatial planning and land use, which will significantly affect the landscape of those regions. One of these examples is Silesian Voivodship in Poland, where the coal-mining tradition dates back to the 17th century. This research focuses on the question of how and where renewable energy development is planned in the Silesian Voivodship, based on provisions from local spatial polices and to what extent post-mining and industrial sites are planned to be reused and how many other types of landscapes would be transformed into renewable energy landscapes. We argue that permitting development of renewable energy (RE) without appropriate regulations on where and how it should be developed may contribute to irreversible changes in the landscape and, as a result, to its degradation. Methods consisted of query and analyses of available publications, datasets, strategy and planning documents, both at regional and municipal level. The main results show that existing renewable energy and its development is mainly planned away from mining and post-mining industrial areas. In the future, this will have a significant impact on the transformation of, e.g., rural, natural and agricultural landscapes into new industrial energy landscapes, changing views and perception of these places.
Enhancing the Accuracy and Temporal Transferability of Irrigated Cropping Field Classification Using Optical Remote Sensing Imagery
Authors: Gao Zitian , Guo Danlu , Ryu Dongryeol , Western Andrew W.
Published: February 2022

Mapping irrigated areas using remotely sensed imagery has been widely applied to support agricultural water management; however, accuracy is often compromised by the in-field heterogeneity of and interannual variability in crop conditions. This paper addresses these key issues. Two classification methods were employed to map irrigated fields using normalized difference vegetation index (NDVI) values derived from Landsat 7 and Landsat 8: a dynamic thresholding method (method one) and a random forest method (method two). To improve the representativeness of field-level NDVI aggregates, which are the key inputs in our methods, a Gaussian mixture model (GMM)-based filtering approach was adopted to remove noncrop pixels (e.g., trees and bare soils) and mixed pixels along the field boundary. To improve the temporal transferability of method one we dynamically determined the threshold value to account for the impact of interannual weather variability based on the dynamic range of NDVI values. In method two an innovative training sample pool was designed for the random forest modeling to enable automatic calibration for each season, which contributes to consistent performance across years. The irrigated field mapping was applied to a major irrigation district in Australia from 2011 to 2018, for summer and winter cropping seasons separately. The results showed that using GMM-based filtering can markedly improve field-level data quality and avoid up to 1/3 of omission errors for irrigated fields. Method two showed superior performance, exhibiting consistent and good accuracy (kappa > 0.9) for both seasons. The classified maps in wet winter seasons should be used with caution, because rainfall alone can largely meet plant water requirements, leaving the contribution of irrigation to the surface spectral signature weak. The approaches introduced are transferable to other areas, can support multiyear irrigated area mapping with high accuracy, and significantly reduced model development effort.
A Mutual Teaching Framework with Momentum Correction for Unsupervised Hyperspectral Image Change Detection
Authors: Sun Jia , Liu Jia , Hu Ling , Wei Zhihui , Xiao Liang
Published: February 2022

Deep-learning methods rely on massive labeled data, which has become one of the main impediments in hyperspectral image change detection (HSI-CD). To resolve this problem, pseudo-labels generated by traditional methods are widely used to drive model learning. In this paper, we propose a mutual teaching approach with momentum correction for unsupervised HSI-CD to cope with noise in pseudo-labels, which is harmful for model training. First, we adopt two structurally identical models simultaneously, allowing them to select high-confidence samples for each other to suppress self-confidence bias, and continuously update pseudo-labels during iterations to fine-tune the models. Furthermore, a new group confidence-based sample filtering method is designed to obtain reliable training samples for HSI. This method considers both the quality and diversity of the selected samples by determining the confidence of each group instead of single instances. Finally, to better extract the spatial–temporal spectral features of bitemporal HSIs, a 3D convolutional neural network (3DCNN) is designed as an HSI-CD classifier and the basic network of our framework. Due to mutual teaching and dynamic label learning, pseudo-labels can be continuously updated and refined in iterations, and thus, the proposed method can achieve a better performance compared with those with fixed pseudo-labels. Experimental results on several HSI datasets demonstrate the effectiveness of our method.
Damaged Building Extraction Using Modified Mask R-CNN Model Using Post-Event Aerial Images of the 2016 Kumamoto Earthquake
Authors: Zhan Yihao , Liu Wen , Maruyama Yoshihisa
Published: February 2022

Remote sensing is an effective method of evaluating building damage after a large-scale natural disaster, such as an earthquake or a typhoon. In recent years, with the development of computer vision technology, deep learning algorithms have been used for damage assessment from aerial images. In April 2016, a series of earthquakes hit the Kyushu region, Japan, and caused severe damage in the Kumamoto and Oita Prefectures. Numerous buildings collapsed because of the strong and continuous shaking. In this study, a deep learning model called Mask R-CNN was modified to extract residential buildings and estimate their damage levels from post-event aerial images. Our Mask R-CNN model employs an improved feature pyramid network and online hard example mining. Furthermore, a non-maximum suppression algorithm across multiple classes was also applied to improve prediction. The aerial images captured on 29 April 2016 (two weeks after the main shock) in Mashiki Town, Kumamoto Prefecture, were used as the training and test sets. Compared with the field survey results, our model achieved approximately 95% accuracy for building extraction and over 92% accuracy for the detection of severely damaged buildings. The overall classification accuracy for the four damage classes was approximately 88%, demonstrating acceptable performance.
3D Lightning Location Method Based on Range Difference Space Projection
Authors: Fan Ling , Zhou Changhai
Published: February 2022

Most lightning location networks obtain the position results by optimizing the goodness of fit to determine that all combinatorial time differences of arrivals (TDOAs) are due to a common discharge. This paper proposes a three-dimensional (3D) lightning location method based on range difference (RD) space projection. The proposed method projects all the measurements into the RD space, which has the space-invariant feature of the equivalence cell and can be partitioned soundly. Aiming at the problem of computational cost of the procedure of the projection, the hierarchical strategy is proposed to improve computational efficiency. The performance of the RD space projection based on the hierarchical strategy is analyzed via Monte-Carlo simulations. The results show that the proposed method can locate lightning sources in real time with high accuracy. The results also show that the location accuracy is limited by the level of the inherent time uncertainty, the layout, and the size of the receiver network. Under the fixed layout and size of the receiver network, and the fixed measurement noise uncertainty, the positioning precision cannot be improved more even if the grid step is small enough or the number of receivers is large enough.
Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine
Authors: Guo Yan , Xia Haoming , Pan Li , Zhao Xiaoyang , Li Rumeng
Published: February 2022

Double cropping is an important cropping system in China, with more than half of China’s cropland adopting the practice. Under the background of global climate change, agricultural policies, and changing farming practices, double-cropping area has changed substantially. However, the spatial-temporal dynamics of double cropping is poorly understood. A better understanding of these dynamics is necessary for the northern limit of double cropping (NLDC) to ensure food security in China and the world and to achieve zero hunger, the second Sustainable Development Goal (SDG). Here, we developed a phenology-based algorithm to identify double-cropping fields by analyzing time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images during the period 2000–2020 using the Google Earth Engine (GEE) platform. We then extracted the NLDC using the kernel density of pixels with double cropping and analyzed the spatial-temporal dynamics of NLDC using the Fishnet method. We found that our algorithm accurately extracted double-cropping fields, with overall, user, and producer accuracies and Kappa coefficients of 95.97%, 96.58%, 92.21%, and 0.91, respectively. Over the past 20 years, the NLDC generally trended southward (the largest movement was 66.60 km) and eastward (the largest movement was 109.52 km). Our findings provide the scientific basis for further development and planning of agricultural production in China.
Evaluating the Capability of Satellite Hyperspectral Imager, the ZY1–02D, for Topsoil Nitrogen Content Estimation and Mapping of Farmlands in Black Soil Area, China
Authors: Xu Zhengyuan , Chen Shengbo , Zhu Bingxue , Chen Liwen , Ye Yinghui , Lu Peng
Published: February 2022

Soil nitrogen (N) content plays a vital role in agriculture and biogeochemical processes, ranging from the N fertilization management for intensive agricultural production to the patterns of N cycling in agroecological systems. While proximal sensing in laboratory settings can achieve ideal soil N estimation accuracy, the estimation and mapping by using remote sensing methods in a large spatial scale diplays low ability. A new hyperspectral imager with 166 spectral channels, the ZY1-02D, makes possible the detection of subtle but important spectral features of soil. This study aimed at exploring the capability of the ZY1-02D to estimate and map the topsoil N content of the black soil-covered farmlands in northeast China. To this aim, 646 soil samples from study sites were collected, processed, spectrally and geochemically measured for the soil N sensitive bands detection and partial least squares regression (PLSR) calibration and validation. The sensitive bands detection results showed an appealing regularity of the variability and stable tendency of the soil N sensitive spectral bands with the change of the sample size. Based on this, we compared the estimation capacity of the models developed with the full wavelength spectra and the models developed with the sensitive bands. The estimation based on ZY1-02D full wavelength spectral reflectance were robust, with R2 of 0.64 in validation. Further, the results of model developed with the sensitive bands showed better validation accuracy with R2 of 0.66 and were applied to create a map of topsoil N content of farmlands in the northeast China black soil area. The results demonstrated that sensitive bands modelling could enhance the accuracy of the estimation and simplify model, and what is more, showed the ideal capability of ZY1-02D for soil N content estimation at the regional scale.
A Two-Stage Seismic Damage Assessment Method for Small, Dense, and Imbalanced Buildings in Remote Sensing Images
Authors: Wang Yu , Cui Liangyi , Zhang Chenzong , Chen Wenli , Xu Yang , Zhang Qiangqiang
Published: February 2022

Large-scale optical sensing and precise, rapid assessment of seismic building damage in urban communities are increasingly demanded in disaster prevention and reduction. The common method is to train a convolutional neural network (CNN) in a pixel-level semantic segmentation approach and does not fully consider the characteristics of the assessment objectives. This study developed a machine-learning-derived two-stage method for post-earthquake building location and damage assessment considering the data characteristics of satellite remote sensing (SRS) optical images with dense distribution, small size, and imbalanced numbers. It included a modified You Only Look Once (YOLOv4) object detection module and a support vector machine (SVM) based classification module. In the primary step, the multiscale features were successfully extracted and fused from SRS images of densely distributed buildings by optimizing the YOLOv4 model toward the network structures, training hyperparameters, and anchor boxes. The fusion improved multi-channel features, optimization of network structure and hyperparameters have significantly enhanced the average location accuracy of post-earthquake buildings. Thereafter, three statistics (i.e., the angular second moment, dissimilarity, and inverse difference moment) were further discovered to effectively extract the characteristic value for earthquake damage from located buildings in SRS optical images based on the gray level co-occurrence matrix. They were used as the texture features to distinguish damage intensities of buildings, using the SVM model. The investigated dataset included 386 pre- and post-earthquake SRS optical images of the 2017 Mexico City earthquake, with a resolution of 1024 × 1024 pixels. Results show that the average location accuracy of post-earthquake buildings exceeds 95.7% and that the binary classification accuracy for damage assessment reaches 97.1%. The proposed two-stage method was validated by its extremely high precision in respect of densely distributed small buildings, indicating the promising potential of computer vision in large-scale disaster prevention and reduction using SRS datasets.
Interannual Variability of the Congo River Plume-Induced Sea Surface Salinity
Authors: Martins Meike Sena , Stammer Detlef
Published: February 2022

Based on satellite surface salinity (SSS) observations from the SMOS, Aquarius and SMAP missions, we investigate the interannual SSS variability during the period from 2010 to 2020 in the Gulf of Guinea, impacted by the Congo River run-off. Combined with in situ data, the available 11 years of satellite salinity data suggest that the plume of Congo run-off primarily spreads into western directions, leading to reduced SSS. A fraction of it also shows a coastal southward extent subject to interannual variability influenced by coastal trapped waves. The low-salinity water is associated with high values of net primary production, confirming the riverine origin of the nutrient rich plume. No correlation can be found between the plume patterns and the different upwelling strengths in the subsequent upwelling months, nor could a correlation be found with the occurrence of the Benguela Niños. Linking the occurrence of a barrier layer to the occurrence of low-salinity plumes remains difficult, mainly because of the sparseness of in situ data. However, the influence of the low-salinity layer is evident in its stronger stratification and an increased available potential energy.
Stepwise Fusion of Hyperspectral, Multispectral and Panchromatic Images with Spectral Grouping Strategy: A Comparative Study Using GF5 and GF1 Images
Authors: Huang Leping , Hu Zhongwen , Luo Xin , Zhang Qian , Wang Jingzhe , Wu Guofeng
Published: February 2022

Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping over urban and coastal areas. The fusion of HSIs with high spatial resolution multispectral images (MSIs) and panchromatic (PAN) images could be a solution. To address the challenging work of fusing HSIs, MSIs and PAN images, a novel easy-to-implement stepwise fusion approach was proposed in this study. The fusion of HSIs and MSIs was decomposed into a set of simple image fusion tasks through spectral grouping strategy. HSI, MSI and PAN images were fused step by step using existing image fusion algorithms. According to different fusion order, two strategies ((HSI+MSI)+PAN and HSI+(MSI+PAN)) were proposed. Using simulated and real Gaofen-5 (GF-5) HSI, MSI and PAN images from the Gaofen-1 (GF-1) PMS sensor as experimental data, we compared the proposed stepwise fusion strategies with the traditional fusion strategy (HSI+PAN), and compared the performances of six fusion algorithms under three fusion strategies. We comprehensively evaluated the fused results through three aspects: spectral fidelity, spatial fidelity and computation efficiency evaluation. The results showed that (1) the spectral fidelity of the fused images obtained by stepwise fusion strategies was better than that of the traditional strategy; (2) the proposed stepwise strategies performed better or comparable spatial fidelity than traditional strategy; (3) the stepwise strategy did not significantly increase the time complexity compared to the traditional strategy; and (4) we also provide suggestions for selecting image fusion algorithms using the proposed strategy. The study provided us with a reference for the selection of fusion strategies and algorithms in different application scenarios, and also provided an easy-to-implement solution and useful references for fusing HSI, MSI and PAN images.
Sentinel-2 Data and Unmanned Aerial System Products to Support Crop and Bare Soil Monitoring: Methodology Based on a Statistical Comparison between Remote Sensing Data with Identical Spectral Bands
Authors: Dubbini Marco , Palumbo Nicola , De Giglio Michaela , Zucca Francesco , Barbarella Maurizio , Tornato Antonella
Published: February 2022

The growing need for sustainable management approaches of crops and bare soils requires measurements at a multiple scale (space and time) field system level, which have become increasingly accurate. In this context, proximal and satellite remote sensing data cooperation seems good practice for the present and future. The primary purpose of this work is the development of a sound protocol based on a statistical comparison between Copernicus Sentinel-2 MIS satellite data and a multispectral sensor mounted on an Unmanned Aerial Vehicle (UAV), featuring spectral deployment identical to Sentinel-2. The experimental dataset, based on simultaneously acquired proximal and Sentinel-2 data, concerns an agricultural field in Pisa (Tuscany), cultivated with corn. To understand how the two systems, comparable but quite different in terms of spatial resolution and atmosphere impacts, can effectively cooperate to create a value-added product, statistical tests were applied on bands and the derived Vegetation and Soil index. Overall, as expected, due to the mentioned impacts, the outcomes show a heterogeneous behavior with a difference between the coincident bands as well for the derived indices, modulated in the same manner by the phenological status (e.g., during the canopy developments) or by vegetation absence. Instead, similar behavior between two sensors occurred during the maturity phase of crop plants.
Determination of Drugs in Clinical Trials: Current Status and Outlook
Authors: Tavana Babak , Chen Aicheng
Published: February 2022

All pharmaceutical drugs, vaccines, cosmetic products, and many medical breakthroughs must first be approved through clinical research and trials before advancing to standard practice or entering the marketplace. Clinical trials are sets of tests that are required to determine the safety and efficacy of pharmaceutical compounds, drugs, and treatments. There is one pre-phase and four main clinical phase requirements that every drug must pass to obtain final approval. Analytical techniques play a unique role in clinical trials for measuring the concentrations of pharmaceutical compounds in biological matrices and monitoring the conditions of patients (or volunteers) during various clinical phases. This review focuses on recent analytical methods that are employed to determine the concentrations of drugs and medications in biological matrices, including whole blood, plasma, urine, and breast milk. Four primary analytical techniques (extraction, spectroscopy, chromatography, and electrochemical) are discussed, and their advantages and limitations are assessed. Subsequent to a survey of evidence and results, it is clear that microelectromechanical system (MEMS) based electrochemical sensor and biosensor technologies exhibit several notable advantages over other analytical methods, and their future prospects are discussed.
Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization
Authors: Liu Qixin , Gu Xingfa , Chen Xinran , Mumtaz Faisal , Liu Yan , Wang Chunmei , Yu Tao , Zhang Yin , Wang Dakang , Zhan Yulin
Published: February 2022

Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.
Development of an Electrochemical Sensor Using a Modified Carbon Paste Electrode with Silver Nanoparticles Capped with Saffron for Monitoring Mephedrone
Authors: Papaioannou Georgios Christos , Karastogianni Sophia , Girousi Stella
Published: February 2022

Mephedrone, also known as 4-methylmethcathinone, is growing into a prominent recreational drug for young people. When it came to detecting mephedrone, limited efforts were made using electrochemical sensors. As a result, this application depicts the fabrication of a new, sensitive, selective, and economical electrochemical sensor capable of detecting mephedrone by using silver nanoparticles capped with saffron produced through electropolymerization to modify carbon paste electrodes (CPEs). Silver nanoparticles (AgNPs) were capped with saffron (AgNPs@Sa) using a green method. AgNPs@Sa were studied using electron scanning microscopy (SEM) and UV-vis spectroscopy. The sensor was evaluated under the optimum condition to determine its analytical features. The results showed that this procedure had a wide linear range, low detection limit and sufficient reproducibility. Furthermore, the sensor posed sufficient stability. Moreover, it was applied in the determination of mephedrone in urine samples, showing the potential applicability of this electrochemical sensor in real sample analysis.
Deep Q-Learning-Based Transmission Power Control of a High Altitude Platform Station with Spectrum Sharing
Authors: Jo Seongjun , Yang Wooyeol , Choi Haing Kun , Noh Eonsu , Jo Han-Shin , Park Jaedon
Published: February 2022

A High Altitude Platform Station (HAPS) can facilitate high-speed data communication over wide areas using high-power line-of-sight communication; however, it can significantly interfere with existing systems. Given spectrum sharing with existing systems, the HAPS transmission power must be adjusted to satisfy the interference requirement for incumbent protection. However, excessive transmission power reduction can lead to severe degradation of the HAPS coverage. To solve this problem, we propose a multi-agent Deep Q-learning (DQL)-based transmission power control algorithm to minimize the outage probability of the HAPS downlink while satisfying the interference requirement of an interfered system. In addition, a double DQL (DDQL) is developed to prevent the potential risk of action-value overestimation from the DQL. With a proper state, reward, and training process, all agents cooperatively learn a power control policy for achieving a near-optimal solution. The proposed DQL power control algorithm performs equal or close to the optimal exhaustive search algorithm for varying positions of the interfered system. The proposed DQL and DDQL power control yields the same performance, which indicates that the actional value overestimation does not adversely affect the quality of the learned policy.
Requirements, Limitations and Recommendations for Enabling End-to-End Quality of Context-Awareness in IoT Middleware
Authors: Jagarlamudi Kanaka Sai , Zaslavsky Arkady , Loke Seng W. , Hassani Alireza , Medvedev Alexey
Published: February 2022

Satisfying a context consumer’s quality of context (QoC) requirements is important to context management platforms (CMPs) in order to have credibility. QoC indicates the contextual information’s quality metrics (e.g., accuracy, timeliness, completeness). The outcomes of these metrics depend on the functional and quality characteristics associated with all actors (context consumers (or) context-aware applications, CMPs, and context providers (or) IoT-data providers) in context-aware IoT environments. This survey identifies and studies such characteristics and highlights the limitations in actors’ current functionalities and QoC modelling approaches to obtain adequate QoC and improve context consumers’ quality of experience (QoE). We propose a novel concept system based on our critical analysis; this system addresses the functional limitations in existing QoC modelling approaches. Moreover, we highlight those QoC metrics affected by quality of service (QoS) metrics in CMPs. These recommendations provide CMP developers with a reference system they could incorporate, functionalities and QoS metrics to maintain in order to deliver an adequate QoC.
Estimation and Sharpening of Blur in Degraded Images Captured by a Camera on a Moving Object
Authors: Hayashi Toshiyuki , Tsubouchi Takashi
Published: February 2022

In this research, we aim to propose an image sharpening method to make it easy to identify concrete cracks from blurred images captured by a moving camera. This study is expected to help realize social infrastructure maintenance using a wide range of robotic technologies, and to solve the future labor shortage and shortage of engineers. In this paper, a method to estimate parameters of motion blur for Point Spread Function (PSF) is mainly discussed, where we assume that there are two main degradation factors caused by the camera, out-of-focus blur and motion blur. A major contribution of this paper is that the parameters can properly be estimated from a sub-image of the object under inspection if the sub-image contains uniform speckled texture. Here, the cepstrum of the sub-image is fully utilized. Then, a filter convoluted PSF which consists of convolution with PSF (motion blur) and PSF (out-of focus blur) can be utilized for deconvolution of the blurred image for sharpening with significant effect. PSF (out-of-focus blur) is a constant function unique to each camera and lens, and can be confirmed before or after shooting. PSF (motion blur), on the other hand, needs to be estimated on a case-by-case basis since the amount and direction of camera movement varies depending on the time of shooting. Previous research papers have sometimes encountered difficulties in estimating the parameters of motion blur because of the emphasis on generality. In this paper, the main object is made of concrete, and on the surface of it there are speckled textures. We hypothesized that we can narrow down the candidates of parameters of motion blur by using these speckled patterns. To verify this hypothesis, we conducted experiments to confirm and examine the following two points using a general-purpose camera used in actual bridge inspections: 1. Influence on the cepstrum when the isolated point-like texture unique to concrete structures is used as a feature point. 2. Selection method of multiple images to narrow down the candidate minima of the cepstrum. It is novel that the parameters of motion blur can be well estimated by using the unique speckled pattern on the surface of the object.
A Novel Method for Intelligibility Assessment of Nonlinearly Processed Speech in Spaces Characterized by Long Reverberation Times
Authors: Kurowski Adam , Kotus Jozef , Odya Piotr , Kostek Bozena
Published: February 2022

Objective assessment of speech intelligibility is a complex task that requires taking into account a number of factors such as different perception of each speech sub-bands by the human hearing sense or different physical properties of each frequency band of a speech signal. Currently, the state-of-the-art method used for assessing the quality of speech transmission is the speech transmission index (STI). It is a standardized way of objectively measuring the quality of, e.g., an acoustical adaptation of conference rooms or public address systems. The wide use of this measure and implementation of this method on numerous measurement devices make STI a popular choice when the speech-related quality of rooms has to be estimated. However, the STI measure has a significant drawback which excludes it from some particular use cases. For instance, if one would like to enhance speech intelligibility by employing a nonlinear digital processing algorithm, the STI method is not suitable to measure the impact of such an algorithm, as it requires that the measurement signal should not be altered in a nonlinear way. Consequently, if a nonlinear speech enhancing algorithm has to be tested, the STI—a standard way of estimating speech transmission cannot be used. In this work, we would like to propose a method based on the STI method but modified in such a way that it makes it possible to employ it for the estimation of the performance of the nonlinear speech intelligibility enhancement method. The proposed approach is based upon a broadband comparison of cumulated energy of the transmitted envelope modulation and the received modulation, so we called it broadband STI (bSTI). Its credibility with regard to signals altered by the environment or nonlinear speech changed by a DSP algorithm is checked by performing a comparative analysis of ten selected impulse responses for which a baseline value of STI was known.
An Energy-Efficient Routing Algorithm Based on Greedy Strategy for Energy Harvesting Wireless Sensor Networks
Authors: Hao Sheng , Hong Yong , He Yu
Published: February 2022

Energy harvesting wireless sensor network (EH-WSN) is considered to be one of the key enabling technologies for the internet of things (IoT) construction. Although the introduced EH technology can alleviate the energy limitation problem that occurs in the traditional wireless sensor network (WSN), most of the current studies on EH-WSN fail to adequately consider the relationship between energy state and data buffer constraint, and thereby they do not address well the issues of energy efficiency and long end-to-end delay. In view of the above problems, a brand new greedy strategy-based energy-efficient routing protocol is proposed in this paper. Firstly, in the system modeling process, we construct an energy evaluation model, which comprehensively considers the energy harvesting, energy consumption and energy classification factors, to identify the energy state of node. Then, we establish a channel feature-based communication range judgment model to determine the transmission area of nodes. Combining these two models, a reception state adjustment mechanism is designed. It takes the buffer occupancy and the MAC layer protocol into account to adjust the data reception state of nodes. On this basis, we propose a greedy strategy-based routing algorithm. In addition, we also analyze the correctness and computational complexity of the proposed algorithm. Finally, we conduct extensive simulation experiments to show that our algorithm achieves optimum performance in energy consumption, packet delivery ratio, average hop count and end-to-end delay and acceptable performance in energy variance.
Digital Twins-Based Impact Response Prediction of Prestressed Steel Structure
Authors: Liu Zhansheng , Yuan Chao , Sun Zhe , Cao Cunfa
Published: February 2022

Civil infrastructure O&M requires intelligent monitoring techniques and control methods to ensure safety. Unfortunately, tedious modeling efforts and the rigorous computing requirements of large-scale civil infrastructure have hindered the development of structural research. This study proposes a method for impact response prediction of prestressed steel structures driven by digital twins (DTs) and machine learning (ML). The high-fidelity DTs of a prestressed steel structure were constructed from the perspective of both a physical entity and virtual entity. A prediction of the impact response of prestressed steel structure’s key parts was established based on ML, and a structure response prediction of the parts driven by data was realized. To validate the effectiveness of the proposed prediction method, the authors carried out a case study in an experiment of a prestressed steel structure. This study provides a reference for fusion applications with DTs and ML in impact response prediction and analysis of prestressed steel structures.