bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation
Authors: Grzegorski Michael , Poli Gabriele , Cacciari Alessandra , Jafariserajehlou Soheila , Holdak Andriy , Lang Ruediger , Vazquez-Navarro Margarita , Munro Rosemary , Fougnie Bertrand
Published: December 2021

The Polar Multi-Sensor Aerosol product (PMAp) is based on the synergistic use of three instruments from the Metop platform, GOME-2, AVHRR, and IASI. The retrieval algorithm includes three major steps: a pre-identification of the aerosol class, a selection of the aerosol model, and a calculation of the Aerosol Optical Depth (AOD). This paper provides a detailed description of the PMAp retrieval, which combines information provided by the three instruments. The retrieved AOD is qualitatively evaluated, and a good temporal as well as spatial performance is observed, including for the transition between ocean and land. More quantitatively, the performance is evaluated by comparison to AERONET in situ measurements. Very good consistency is also observed when compared to other space-based data such as MODIS or VIIRS. The paper demonstrates the ability of this first generation of synergistic products to derive reliable AOD, opening the door for the development of synergistic products from the instruments to be embarked on the coming Metop Second Generation platform. PMAp has been operationally distributed in near-real-time since 2014 over ocean, and 2016 over land.
Red Tide Detection Method for HY−1D Coastal Zone Imager Based on U−Net Convolutional Neural Network
Authors: Zhao Xin , Liu Rongjie , Ma Yi , Xiao Yanfang , Ding Jing , Liu Jianqiang , Wang Quanbin
Published: December 2021

Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images.
Detection and 3D Modeling of Potential Buried Archaeological Structures Using WorldView-3 Satellite Imagery
Authors: El-Behaedi Raghda
Published: December 2021

Throughout the world, cultural heritage sites are under the direct threat of damage or destruction due to developing environmental and anthropogenic hazards, such as urban expansion, looting, and rising water levels. Exacerbating this problem is the fact that many of the most vulnerable sites’ exact locations and/or full spatial extents have yet to be uncovered, making any attempts at their protection exceedingly difficult. However, the utilization of earth observation data has recently emerged as an unmatched tool in the exploration and (digital) preservation of endangered archaeological sites. The presented research employs very high-resolution WorldView-3 satellite imagery (~30 cm) for identifying and delineating previously unknown subsurface archaeological structures at the ancient Egyptian site of Hermopolis (el-Ashmunein). A particular emphasis is placed on the application of spectral indices, specifically those looking at vegetation cropmarks and iron oxide levels. Through this analysis, seven promising structures were identified, including three elongated installations, which may have been utilized for storage purposes, and a potential casemate foundation structure. As 2D outlines of structures are often difficult to visualize, the newly identified archaeological features were expanded into a realistic, georeferenced 3D model using the computer programs, SketchUp Pro and Chaos V-Ray. The goal of this 3D model is to ensure that the results derived from this research are more accessible (and tangible) to a wider audience—the scientific community and the public alike. The methodological scheme presented in this article is highly adaptable and with some minor modifications can be replicated for other archaeological sites worldwide.
Building Plane Segmentation Based on Point Clouds
Authors: Su Zhonghua , Gao Zhenji , Zhou Guiyun , Li Shihua , Song Lihui , Lu Xukun , Kang Ning
Published: December 2021

Planes are essential features to describe the shapes of buildings. The segmentation of a plane is significant when reconstructing a building in three dimensions. However, there is a concern about the accuracy in segmenting plane from point cloud data. The objective of this paper was to develop an effective segmentation algorithm for building planes that combines the region growing algorithm with the distance algorithm based on boundary points. The method was tested on point cloud data from a cottage and pantry as scanned using a Faro Focus 3D laser range scanner and Matterport Camera, respectively. A coarse extraction of the building plane was obtained from the region growing algorithm. The coplanar points where two planes intersect were obtained from the distance algorithm. The building plane’s optimal segmentation was then obtained by combining the coarse extraction plane points and the corresponding coplanar points. The results show that the proposed method successfully segmented the plane points of the cottage and pantry. The optimal distance thresholds using the proposed method from the uncoarse extraction plane points to each plane boundary point of cottage and pantry were 0.025 m and 0.030 m, respectively. The highest correct rate and the highest error rate of the cottage’s (pantry’s) plane segmentations using the proposed method under the optimal distance threshold were 99.93% and 2.30% (98.55% and 2.44%), respectively. The F1 score value of the cottage’s and pantry’s plane segmentations using the proposed method under the optimal distance threshold reached 97.56% and 95.75%, respectively. This method can segment different objects on the same plane, while the random sample consensus (RANSAC) algorithm causes the plane to become over-segmented. The proposed method can also extract the coplanar points at the intersection of two planes, which cannot be separated using the region growing algorithm. Although the RANSAC-RG method combining the RANSAC algorithm and the region growing algorithm can optimize the segmentation results of the RANSAC (region growing) algorithm and has little difference in segmentation effect (especially for cottage data) with the proposed method, the method still loses coplanar points at some intersection of the two planes.
Rainfall Variability and Trends over the African Continent Using TAMSAT Data (1983–2020): Towards Climate Change Resilience and Adaptation
Authors: Alahacoon Niranga , Edirisinghe Mahesh , Simwanda Matamyo , Perera ENC , Nyirenda Vincent R. , Ranagalage Manjula
Published: December 2021

This study reveals rainfall variability and trends in the African continent using TAMSAT data from 1983 to 2020. In the study, a Mann–Kendall (MK) test and Sen’s slope estimator were used to analyze rainfall trends and their magnitude, respectively, under monthly, seasonal, and annual timeframes as an indication of climate change using different natural and geographical contexts (i.e., sub-regions, climate zones, major river basins, and countries). The study finds that the highest annual rainfall trends were recorded in Rwanda (11.97 mm/year), the Gulf of Guinea (river basin 8.71 mm/year), the tropical rainforest climate zone (8.21 mm/year), and the Central African region (6.84 mm/year), while Mozambique (−0.437 mm/year), the subtropical northern desert (0.80 mm/year), the west coast river basin of South Africa (−0.360 mm/year), and the Northern Africa region (1.07 mm/year) show the lowest annual rainfall trends. There is a statistically significant increase in the rainfall in the countries of Africa’s northern and central regions, while there is no statistically significant change in the countries of the southern and eastern regions. In terms of climate zones, in the tropical northern desert climates, tropical northern peninsulas, and tropical grasslands, there is a significant increase in rainfall over the entire timeframe of the month, season, and year. This implies that increased rainfall will have a positive effect on the food security of the countries in those climatic zones. Since a large percentage of Africa’s agriculture is based only on rainfall (i.e., rain-fed agriculture), increasing trends in rainfall can assist climate resilience and adaptation, while declining rainfall trends can badly affect it. This information can be crucial for decision-makers concerned with effective crop planning and water resource management. The rainfall variability and trend analysis of this study provide important information to decision-makers that need to effectively mitigate drought and flood risk.
Quantifying the Contributions of Climate Change and Human Activities to Water Volume in Lake Qinghai, China
Authors: Yang Guoqing , Zhang Miao , Xie Zhenghui , Li Jiyuan , Ma Mingguo , Lai Peiyu , Wang Junbang
Published: December 2021

Lake Qinghai has shrunk and then expanded over the past few decades. Quantifying the contributions of climate change and human activities to lake variation is important for water resource management and adaptation to climate change. In this study, we calculated the water volume change of Lake Qinghai, analyzed the climate and land use changes in Lake Qinghai catchment, and distinguished the contributions of climate change and local human activities to water volume change. The results showed that lake water volume decreased by 9.48 km3 from 1975 to 2004 and increased by 15.18 km3 from 2005 to 2020. The climate in Lake Qinghai catchment is becoming warmer and more pluvial, and the changes in land use have been minimal. Based on the Soil and Water Assessment Tool (SWAT), land use change, climate change and interaction effect of them contributed to 7.46%, 93.13% and −0.59%, respectively, on the variation in surface runoff into the lake. From the perspective of the water balance, we calculated the proportion of each component flowing into and out of the lake and found that the contribution of climate change to lake water volume change was 97.55%, while the local human activities contribution was only 2.45%. Thus, climate change had the dominant impact on water volume change in Lake Qinghai.
Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning
Authors: Yan Dongchuan , Zhang Hao , Li Guoqing , Li Xiangqiang , Lei Hua , Lu Kaixuan , Zhang Lianchong , Zhu Fuxiao
Published: December 2021

The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.
Determination of the Geometric Parameters of Electrode Systems for Electrical Impedance Myography: A Preliminary Study
Authors: Briko Andrey , Kapravchuk Vladislava , Kobelev Alexander , Tikhomirov Alexey , Hammoud Ahmad , Al-Harosh Mugeb , Leonhardt Steffen , Ngo Chuong , Gulyaev Yury , Shchukin Sergey
Published: December 2021

The electrical impedance myography method is widely used in solving bionic control problems and consists of assessing the change in the electrical impedance magnitude during muscle contraction in real time. However, the choice of electrode systems sizes is not always properly considered when using the electrical impedance myography method in the existing approaches, which is important in terms of electrical impedance signal expressiveness and reproducibility. The article is devoted to the determination of acceptable sizes for the electrode systems for electrical impedance myography using the Pareto optimality assessment method and the electrical impedance signals formation model of the forearm area, taking into account the change in the electrophysical and geometric parameters of the skin and fat layer and muscle groups when performing actions with a hand. Numerical finite element simulation using anthropometric models of the forearm obtained by volunteers’ MRI 3D reconstructions was performed to determine a sufficient degree of the forearm anatomical features detailing in terms of the measured electrical impedance. For the mathematical description of electrical impedance relationships, a forearm two-layer model, represented by the skin-fat layer and muscles, was reasonably chosen, which adequately describes the change in electrical impedance when performing hand actions. Using this model, for the first time, an approach that can be used to determine the acceptable sizes of electrode systems for different parts of the body individually was proposed.
Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
Authors: Xhafa Alda , del Peral-Rosado José A. , López-Salcedo José A. , Seco-Granados Gonzalo
Published: December 2021

Accurate and reliable positioning solution is an important requirement for many applications, for instance, emergency services and vehicular-related use cases. Positioning using cellular signals has emerged as a promising solution in Global Navigation Satellite System (GNSS) challenging environments, such as deep urban canyons. However, harsh working conditions of urban scenarios, such as with dense multipath and Non-Line of Sight (NLoS), remain as one of the key factors causing the detriment of the positioning estimation accuracy. This paper demonstrates that the use of joint Uplink Time Difference of Arrival (UTDoA) and Angle of Arrival (AoA) gives a significant improvement in the position accuracy thanks to the use of antenna arrays. The new advances of this technology enable more accurate user locations by exploiting angular domains of propagation channel in combination with time measurements. Moreover, it is shown that a better localization is achieved by combining the joined UTDoA and AoA with a base-station selective exclusion method that is able to detect and eliminate measurements affected by NLoS. The proposed approach has been tested through simulations based on a deep urban deployment map, which comes with an experimental data file of the user’s position. A sounding reference signal of 5G new radio operating in the centimeter-wave band is used. The obtained results add value to the use of advance antennas in 5G positioning. In addition, they contribute towards the fulfillment of high-accuracy positioning requirements in challenging environments when using cellular networks.
Fabrication of AuNPs/MWCNTS/Chitosan Nanocomposite for the Electrochemical Aptasensing of Cadmium in Water
Authors: Rabai Selma , Teniou Ahlem , Catanante Gaëlle , Benounis Messaoud , Marty Jean-Louis , Rhouati Amina
Published: December 2021

Cadmium (Cd2+) is one of the most toxic heavy metals causing serious health problems; thus, designing accurate analytical methods for monitoring such pollutants is highly urgent. Herein, we report a label-free electrochemical aptasensor for cadmium detection in water. For this, a nanocomposite combining the advantages of gold nanoparticles (AuNPs), carbon nanotubes (CNTs) and chitosan (Cs) was constructed and used as immobilization support for the cadmium aptamer. First, the surface of a glassy carbon electrode (GCE) was modified with CNTs-CS. Then, AuNPs were deposited on CNTs-CS/GCE using chrono-amperometry. Finally, the immobilization of the amino-modified Cd-aptamer was achieved via glutaraldehyde cross-linking. The different synthesis steps of the AuNPs/CNTs/CS nano assembly were characterized by cyclic voltammetry (CV). Electrochemical impedance spectroscopy (EIS) was employed for cadmium determination. The proposed biosensor exhibited excellent performances for cadmium detection at a low applied potential (−0.5 V) with a high sensitivity (1.2 KΩ·M−1), a detection limit of 0.02 pM and a wide linear range (10−13–10−4 M). Moreover, the aptasensor showed a good selectivity against the interfering ions: Pb2+; Hg2+ and Zn2+. Our electrochemical biosensor provides a simple and sensitive approach for Cd2+ detection in aqueous solutions, with promising applications in the monitoring of trace amounts of heavy metals in real samples.
Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review
Authors: Quezada-Gaibor Darwin , Torres-Sospedra Joaquín , Nurmi Jari , Koucheryavy Yevgeni , Huerta Joaquín
Published: December 2021

Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios.
Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise
Authors: Esmaiel Hamada , Xie Dongri , Qasem Zeyad A. H. , Sun Haixin , Qi Jie , Wang Junfeng
Published: December 2021

Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.
Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation
Authors: Zi Jiali , Lv Danju , Liu Jiang , Huang Xin , Yao Wang , Gao Mingyuan , Xi Rui , Zhang Yan
Published: December 2021

In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has the problem of incomplete separation, as not all the signal sources can be separated. An improved algorithm for BSS with SI based on signal cross-correlation (SI-XBSS) is proposed in this paper. Our method created a candidate separation pool that contains more separated signals than the traditional SI-BSS does; it identified the final separated signals by the value of the minimum cross-correlation in the pool. Compared with the traditional SI-BSS, the SI-XBSS was applied in six SI algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Sine Cosine Algorithm (SCA), Butterfly Optimization Algorithm (BOA), and Crow Search Algorithm (CSA)). The results showed that the SI-XBSS could effectively achieve a higher separation success rate, which was over 35% higher than traditional SI-BSS on average. Moreover, SI-SDR increased by 14.72 on average.
Development and Evaluation of BenchBalance: A System for Benchmarking Balance Capabilities of Wearable Robots and Their Users
Authors: Bayón Cristina , Delgado-Oleas Gabriel , Avellar Leticia , Bentivoglio Francesca , Di Tommaso Francesco , Tagliamonte Nevio L. , Rocon Eduardo , van Asseldonk Edwin H. F.
Published: December 2021

Recent advances in the control of overground exoskeletons are being centered on improving balance support and decreasing the reliance on crutches. However, appropriate methods to quantify the stability of these exoskeletons (and their users) are still under development. A reliable and reproducible balance assessment is critical to enrich exoskeletons’ performance and their interaction with humans. In this work, we present the BenchBalance system, which is a benchmarking solution to conduct reproducible balance assessments of exoskeletons and their users. Integrating two key elements, i.e., a hand-held perturbator and a smart garment, BenchBalance is a portable and low-cost system that provides a quantitative assessment related to the reaction and capacity of wearable exoskeletons and their users to respond to controlled external perturbations. A software interface is used to guide the experimenter throughout a predefined protocol of measurable perturbations, taking into account antero-posterior and mediolateral responses. In total, the protocol is composed of sixteen perturbation conditions, which vary in magnitude and location while still controlling their orientation. The data acquired by the interface are classified and saved for a subsequent analysis based on synthetic metrics. In this paper, we present a proof of principle of the BenchBalance system with a healthy user in two scenarios: subject not wearing and subject wearing the H2 lower-limb exoskeleton. After a brief training period, the experimenter was able to provide the manual perturbations of the protocol in a consistent and reproducible way. The balance metrics defined within the BenchBalance framework were able to detect differences in performance depending on the perturbation magnitude, location, and the presence or not of the exoskeleton. The BenchBalance system will be integrated at EUROBENCH facilities to benchmark the balance capabilities of wearable exoskeletons and their users.
A Sensitive Electrochemical Sensor Based on Sonogel-Carbon Material Enriched with Gold Nanoparticles for Melatonin Determination
Authors: Lete Cecilia , López-Iglesias David , García-Guzmán Juan José , Leau Sorina-Alexandra , Stanciu Adina Elena , Marin Mariana , Palacios-Santander José Maria , Lupu Stelian , Cubillana-Aguilera Laura
Published: December 2021

In this work, the development of an electrochemical sensor for melatonin determination is presented. The sensor was based on Sonogel-Carbon electrode material (SNGCE) and Au nanoparticles (AuNPs). The low-cost and environmentally friendly SNGCE material was prepared by the ultrasound-assisted sonogel method. AuNPs were prepared by a chemical route and narrow size distribution was obtained. The electrochemical characterization of the SNGCE/AuNP sensor was carried out by cyclic voltammetry in the presence of a redox probe. The analytical performance of the SNGCE/AuNP sensor in terms of linear response range, repeatability, selectivity, and limit of detection was investigated. The optimized SNGCE/AuNP sensor displayed a low detection limit of 8.4 nM melatonin in synthetic samples assessed by means of the amperometry technique. The potential use of the proposed sensor in real sample analysis and the anti-matrix capability were assessed by a recovery study of melatonin detection in human peripheral blood serum with good accuracy.
Design of a Capacitance-to-Digital Converter Based on Iterative Delay-Chain Discharge in 180 nm CMOS Technology
Authors: Cicalini Mattia , Piotto Massimo , Bruschi Paolo , Dei Michele
Published: December 2021

The design of advanced miniaturized ultra-low power interfaces for sensors is extremely important for energy-constrained monitoring applications, such as wearable, ingestible and implantable devices used in the health and medical field. Capacitive sensors, together with their correspondent digital-output readout interfaces, make no exception. Here, we analyse and design a capacitance-to-digital converter, based on the recently introduced iterative delay-chain discharge architecture, showing the circuit inner operating principles and the correspondent design trade-offs. A complete design case, implemented in a commercial 180 nm CMOS process, operating at 0.9 V supply for a 0–250 pF input capacitance range, is presented. The circuit, tested by means of detailed electrical simulations, shows ultra-low energy consumption (≤1.884 nJ/conversion), excellent linearity (linearity error 15.26 ppm), good robustness against process and temperature corners (conversion gain sensitivity to process corners variation of 114.0 ppm and maximum temperature sensitivity of 81.9 ppm/°C in the −40 °C, +125 °C interval) and medium-low resolution of 10.3 effective number of bits, while using only 0.0192 mm2 of silicon area and employing 2.93 ms for a single conversion.
Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh
Authors: Chy Md. Kalim Amzad , Masum Abdul Kadar Muhammad , Sayeed Kazi Abdullah Mohammad , Uddin Md Zia
Published: December 2021

The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.
Continuous Delivery of Blockchain Distributed Applications
Authors: Górski Tomasz
Published: December 2021

Ensuring a production-ready state of the application under development is the imminent feature of the Continuous Delivery (CD) approach. In a blockchain network, nodes communicate and store data in a distributed manner. Each node executes the same business application but operates in a distinct execution environment. The literature lacks research focusing on continuous practices for blockchain and Distributed Ledger Technology (DLT). Specifically, it lacks such works with support for both design and deployment. The author has proposed a solution that takes into account the continuous delivery of a business application to diverse deployment environments in the DLT network. As a result, two continuous delivery pipelines have been implemented using the Jenkins automation server. The first pipeline prepares a business application whereas the second one generates complete node deployment packages. As a result, the framework ensures the deployment package in the actual version of the business application with the node-specific up-to-date version of deployment configuration files. The Smart Contract Design Pattern has been used when building a business application. The modeling aspect of blockchain network installation has required using Unified Modeling Language (UML) and the UML Profile for Distributed Ledger Deployment. The refined model-to-code transformation generates deployment configurations for nodes. Both the business application and deployment configurations are stored in the GitHub repositories. For the sake of verification, tests have been conducted for the electricity consumption and supply management system designed for prosumers of renewable energy.
Plasmonic Metasurfaces for Medical Diagnosis Applications: A Review
Authors: Wang Zhenbiao , Chen Junjie , Khan Sayed Ali , Li Fajun , Shen Jiaqing , Duan Qilin , Liu Xueying , Zhu Jinfeng
Published: December 2021

Plasmonic metasurfaces have been widely used in biosensing to improve the interaction between light and biomolecules through the effects of near-field confinement. When paired with biofunctionalization, plasmonic metasurface sensing is considered as a viable strategy for improving biomarker detection technologies. In this review, we enumerate the fundamental mechanism of plasmonic metasurfaces sensing and present their detection in human tumors and COVID-19. The advantages of rapid sampling, streamlined processes, high sensitivity, and easy accessibility are highlighted compared with traditional detection techniques. This review is looking forward to assisting scientists in advancing research and developing a new generation of multifunctional biosensors.
An Occupancy Mapping Method Based on K-Nearest Neighbours
Authors: Miao Yu , Hunter Alan , Georgilas Ioannis
Published: December 2021

OctoMap is an efficient probabilistic mapping framework to build occupancy maps from point clouds, representing 3D environments with cubic nodes in the octree. However, the map update policy in OctoMap has limitations. All the nodes containing points will be assigned with the same probability regardless of the points being noise, and the probability of one such node can only be increased with a single measurement. In addition, potentially occupied nodes with points inside but traversed by rays cast from the sensor to endpoints will be marked as free. To overcome these limitations in OctoMap, the current work presents a mapping method using the context of neighbouring points to update nodes containing points, with occupancy information of a point represented by the average distance from a point to its k-Nearest Neighbours. A relationship between the distance and the change in probability is defined with the Cumulative Density Function of average distances, potentially decreasing the probability of a node despite points being present inside. Experiments are conducted on 20 data sets to compare the proposed method with OctoMap. Results show that our method can achieve up to 10% improvement over the optimal performance of OctoMap.
Wheat Yellow Rust Disease Infection Type Classification Using Texture Features
Authors: Shafi Uferah , Mumtaz Rafia , Haq Ihsan Ul , Hafeez Maryam , Iqbal Naveed , Shaukat Arslan , Zaidi Syed Mohammad Hassan , Mahmood Zahid
Published: December 2021

Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20–30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Patterns (LBP) texture features were extracted from grayscale images of the collected dataset. In order to classify wheat yellow rust disease into its three classes (healthy, resistant, and susceptible), Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and CatBoost were used with (i) GLCM, (ii) LBP, and (iii) combined GLCM-LBP texture features. The results indicate that CatBoost outperformed on GLCM texture features with an accuracy of 92.30%. This accuracy can be further improved by scaling up the dataset and applying deep learning models. The development of the proposed study could be useful for the agricultural community for the early detection of wheat yellow rust infection and assist in taking remedial measures to contain crop yield.
Online Donation Attitude and Satisfaction with Simple Mobile Payments: A Case of the Korean Red Cross
Authors: Kim Dong-Hyuk , Kim Bo-Young
Published: December 2021

COVID-19 and the fourth industrial revolution have rapidly changed our society into an overall contactless one. As smartphones become more popularized, donation methods are shifting to online activities that are beyond the traditional methods. In such a contactless society, mobile payment services are emerging as an innovative payment method. However, donation consolidation and persistence are lacking in online donation marketing and other online situations. This study empirically examines the effects of personal factors (unselfishness, self-esteem, and social norms) and technical factors (perceived usefulness, ease of use, and perceived behavioral control) on donation trust, attitude, and satisfaction if donations were made through a simple mobile payment system. To this end, an online questionnaire survey was carried out on donors using the Korean Red Cross’ simple mobile payment service. By collecting 250 data samples, this study verified hypotheses. As a result of the analysis, social norms under the personal factors were significant, but unselfishness and self-esteem did not affect donors. The perceived usefulness and ease of use, which are technical factors, positively affected trust in donation, but the perceived behavioral control was not significant. Consequently, intrinsic behavioral influence factors such as personal unselfishness, self-esteem, or behavioral control did not significantly affect donation behavior, in contrast with traditional donation methods.
High-Speed Rail Network Expansion and Its Impact on Regional Economic Sustainability in the Yangtze River Delta, China, 2009–2018
Authors: Sun Wei , Wang Chen , Liu Chonggang , Wang Lei
Published: December 2021

The rapid expansion of high-speed rail (HSR) has significantly improved spatial accessibility and connectivity efficiency, and affected the reallocation of spatial resources and regional economic sustainability. This study examined 40 prefecture-level (or above) cities in the Yangtze River Delta (YRD) region, and explored the evolution process of the HSR service network and its impact on the sustainability of economic development. The research results show that: (1) From the perspective of intercity travel time and service connections from 2009 to 2018, the rapid development of HSR has increased the city’s rail accessibility by about 50%, leading to closer intercity connections. (2) There are obvious regional differences in the effect of HSR on urban functional levels and the intensity of inter-city connections. Compared with 2009, the central cities play a greater role as transportation hubs in 2018, creating a significant Matthew effect of accumulated advantage. (3) The distribution pattern of regional urban intensity index is uneven, and the difference in urban intensity index in 2018 is significantly greater than that in 2009. (4) The evolution of the HSR network has significantly affected regional economic development, especially the development of tertiary industry, and increased the polarization of economic development in the YRD. This research can provide a certain reference for regional sustainable development.
Chemical and Microbiological Air Quality in a Municipal Solid Waste Landfill and Its Surroundings, in South-Eastern Romania
Authors: Pop Mirel , Bucur Iulia , Zoldan Dan , Imre Kálmán , Nichita Ileana , Cristina Gașpar , Tîrziu Andreea , Tîrziu Emil
Published: December 2021

The aim of this study was to assess the microbiological and chemical air quality in a municipal solid waste landfill and its inhabited surroundings, in a particular context in which Romania struggles with the incapacity to comply with its environmental commitments. The research was conducted on a landfill near the capital Bucharest between November 2018 and September 2019. To evaluate the chemical (oxygen, carbon dioxide, methane, hydrogen sulfide, ammonia and carbon monoxide–MX6 iBrid™ Détector multigas) and microbiological (airborne bacteria and fungi–aspiration method) parameters, eight sampling points were established, located both on the perimeter of the landfill and within its surroundings. CO and CH4 were not detected in any of the sampling points, during the study period; O2 was in normal values 20.09–21.05%; CO2 had a maximum average concentration of 620 ± 215; H2S had values between 0.1 and 5.0 ppm only in the sampling points inside the landfill; NH3 was present only once in a single sampling point with values between 1.0 and 3.0 ppm. The microbiological results provide an overview of the total plate count and total fungal count, with no significant differences between the level of contamination inside the landfill and within its surroundings (p > 0.05). Ten bacterial species and fungi from six genera have been identified. It was also found that the number of microorganisms in the air was significantly lower during the winter, spring and early summer months compared with the late summer and autumn months (p < 0.05).
Management and Organizational Research: Structural Topic Modeling for a Better Understanding of Theory Application
Authors: Mishra Rohit Bhuvaneshwar , Jiang Hongbing
Published: December 2021

In management and organization research, theory development is often linked with developing a new theory. However, regardless of the number of existing theories, most theories remain empirically untested, and the progress in understanding the application of theories has been scarce. This article discusses how theories are applied in existing management and organization research studies. This study applies the Structural Topic Model to 4636 research papers from the S2ORC dataset. The results reveal twelve research themes, establish correlations, and document the evolution of themes over time. The findings of this study reveal that the theoretical application is not consistent across research themes, theories are primarily used for descriptive and communicative properties, and most research themes in management and organization research are more concerned with discovering phenomena rather than with understanding and forecasting them.
A Study on the Influence of the Next Generation Colored Inorganic Geopolymer Material Paint on the Insulation Measurement of Concrete Building Shell
Authors: Li Yeou-Fong , Xie Ya-Xuan , Syu Jin-Yuan , Huang Chih-Hong , Tsai Hsin-Hua , Cheng Ta-Wui , Chen Yen-Chun , Lee Wei-Hao
Published: December 2021

Many studies have shown that paint with reflective heat can effectively reduce the temperature of the building envelope and reduce the future energy consumption of the building. This study inspired the next-generation inorganic geopolymer material (IGM) color paint without volatile matter, which could be applied on concrete surfaces to reduce energy consumption in warm seasons. In this study, a total of five insulating IGM paints, white, red, green, blue, and yellow, were applied to a 50 cm × 50 cm × 12 cm concrete slab top surface. The highest average light reflectance of all the paints was 87.5% of white IGM paint, which was higher than plain concrete (36.4%). The heat flux and surface temperature were examined in the laboratory, and those test results were verified outdoor. The results showed that the IGM paints could effectively reduce the surface temperature and heat flux of the upper and lower surfaces of concrete slabs, and the white colored IGM paint was the best performer among all five colors, whereas the heat storage coefficient (Sf) of red, white, yellow, blue, and green IGM painted concrete slabs were 0.57, 0.53, 3.62, 2.95, and 1.91 W·m−2·K−1, respectively, lower than plain concrete (24.40 W·m−2·K−1). This coefficient was presented to externalize the thermal admittance. The overall measurement results showed that the concrete slab with colored IGM paints had better heat insulation ability than the plain concrete slab, especially in white IGM paint.
Determination of Critical Limit of Zinc for Rice (Oryza sativa L.) and Potato (Solanum tuberosum L.) Cultivation in Floodplain Soils of Bangladesh
Authors: Rahman Mamunur , Jahangir Mohammad Mofizur Rahman , Kibria Mohammad Golam , Hossain Mahmud , Hosenuzzaman Md , Solaiman Zakaria M. , Abedin Md Anwarul
Published: December 2021

The critical limit for zinc (Zn) varies from 0.38 to 2 µg/g soil depending on the crop and soil type. However, the critical limit for Zn was not explored recently for rice and potato cultivation in the floodplain soils of Bangladesh. A pot experiment was conducted to determine the critical limits of Zn in soil and plants for rice and potato cultivation in two agro-ecological zones (AEZs) of Bangladesh. The soil samples were collected from 20 different locations of Old Brahmaputra and Active Ganges Floodplains with low (<0.9 µg/g), medium (0.91–1.80 µg/g) and high (>1.80 µg/g) Zn status. The experiment was laid out in a factorial and completely randomized design with two levels of Zn (Zn0 and Zn1 (0 and 4.87 kg/ha as Zn sulphate)) applied to 20 different soil samples for rice and potato cultivation using three replications. The critical limit of Zn was determined through a graphical and statistical approach and crops were harvested at the stem elongation (for rice) and tuber filling (for potato) stage. The critical limit of Zn in soil for rice was found to be 0.8 and 0.85 µg/g by graphical and statistical methods, respectively, and both methods revealed the same value (0.73 µg/g) for potato. The critical limit of Zn in rice plants was 23.9 and 24.32 µg/g, whereas in potato plants it was 27.1 and 26.61 µg/g, determined by graphical and statistical methods, respectively. The added Zn supply in soil significantly increased the dry matter accumulation in rice (by 5.6%) and potato (by 10%) compared to no Zn supply. Therefore, a significant positive response to added Zn could be observed on crop growth and yield when the Zn concentration remained below the mentioned critical level for rice and potato cultivation in floodplain soils of Bangladesh.