bluuebunny/crossref_metadata_2025 huggingface dataset:
A Model for Expressing Industrial Information Based on Object-Oriented Industrial Heat Sources Detected Using Multi-Source Thermal Anomaly Data in China
Authors: Ma Caihong , Yang Jin , Xia Wei , Liu Jianbo , Zhang Yifan , Sui Xin
Published: February 2022

Industrial heat sources have made a great contribution to Chinese economic development. However, it has also been found that emissions from industrial heat sources are the main contribution to regional air pollution. Therefore, the detection of industrial heat sources and the expression of related information is becoming important. In this paper, the detection of industrial heat sources was used to express industrial information, thus that the accuracy of the detection of industrial thermal anomalies could be improved and the problems of noise and missing parameters addressed. A model for expressing industrial information based on object-oriented industrial heat sources and using multi-source thermal anomaly data in China was, therefore, proposed. It was a new real-time, objective, and real way to describe the production operation status of industrial heat sources on a large-scale area. First, 4340 working industrial heat sources in mainland China were detected by applying an adaptive k-means algorithm to ACF (NPP VIIRS 375-m active fire/hotspot data) data from the period 19 January 2012 to 31 December 2020. Secondly, several features of working industrial heat sources were extracted from NPP VIIRS 375-m active fire/hotspot data (ACF), VIIRS Nightfire data (VNF), and the Fires product based on Landsat-8 AIRCAS (L8F) data. Areas containing working industrial heat sources were then identified based on these different types of fire data. Light, land-surface temperature, and CO2 and N2O emissions data related to the working industrial heat sources were also extracted. The results show that feature parameters extracted from the multi-source thermal anomaly data mostly have a good positive correlation with the other parameters.
Construction of “Space-Sky-Ground” Integrated Collaborative Monitoring Framework for Surface Deformation in Mining Area
Authors: Yan Yueguan , Li Ming , Dai Linda , Guo Junting , Dai Huayang , Tang Wei
Published: February 2022

Ground deformation measurements in mining areas play a key role in revealing the surface subsidence law, retrieving the subsidence parameters, warning of geological disasters and restoring the surface ecology. With the development of science and technology, there have emerged a great number of monitoring techniques and buildings of diverse protection levels. The diversity of monitoring techniques and the multiplicity of monitoring objects have brought challenges for surface deformation monitoring in the coal industry. Based on the existing deformation monitoring techniques, this paper established a framework of “space-sky-ground” collaborative monitoring system in mining area. We also constructed an AHP-TOPSIS (Analytic Hierarchy Process method- Technique for Order Preference by Similarity to an Ideal Solution) preference model of “space-sky-ground” collaborative monitoring of surface deformation in mining area, and carried out engineering application. Our study shows that the framework of the “space-sky-ground” collaborative monitoring system for surface subsidence in mining areas established in this paper, combined with the AHP-TOPSIS monitoring preference model, which can fully combine the advantages of each monitoring technique, overcome the limitations of a single monitoring technique, comprehensively obtain the surface subsidence data and work out the surface deformation subsidence pattern. This information provides a data and technical support for surface environment management.
Kinematics of Active Landslides in Achaia (Peloponnese, Greece) through InSAR Time Series Analysis and Relation to Rainfall Patterns
Authors: Tsironi Varvara , Ganas Athanassios , Karamitros Ioannis , Efstathiou Eirini , Koukouvelas Ioannis , Sokos Efthimios
Published: February 2022

We studied the kinematic behaviour of active landslides at several localities in the area of Panachaikon Mountain, Achaia (Peloponnese, Greece) using Sentinel (C-band) InSAR time series analysis. We processed LiCSAR interferograms using the SBAS tool, and we obtained average displacement maps for the period 2016–2021. We found that the maximum displacement rate of each landslide is located at about the center of it. The average E-W velocity of the Krini landslide is ~3 cm/year (toward the east) and 0.6 cm/year downward. The line-of-sight (LOS) velocity of the landslide (descending orbit) compares well to a co-located GNSS station within (±) 3 mm/yr. Our results also suggest a correlation between rainfall and landslide motion. For the Krini landslide, a cross-correlation analysis of our data suggests that the mean time lag was 13.5 days between the maximum seasonal rainfall and the change in the LOS displacement rate. We also found that the amount of total seasonal rainfall controls the increase in the displacement rate, as 40–550% changes in the displacement rate of the Krini landslide were detected, following to a seasonal maximum of rainfall values at the nearby meteorological station of Kato Vlassia. According to our results, it seems that large part of this mountainous region of Achaia suffers from slope instability that is manifested in various degrees of ground displacement greatly affecting its morphological features and inhabited areas.
Glacier Monitoring Based on Multi-Spectral and Multi-Temporal Satellite Data: A Case Study for Classification with Respect to Different Snow and Ice Types
Authors: Florath Janine , Keller Sina , Abarca-del-Rio Rodrigo , Hinz Stefan , Staub Guido , Weinmann Martin
Published: February 2022

Remote sensing techniques are frequently applied for the surveying of remote areas, where the use of conventional surveying techniques remains difficult and impracticable. In this paper, we focus on one of the remote glacier areas, namely the Tyndall Glacier area in the Southern Patagonian Icefield in Chile. Based on optical remote sensing data in the form of multi-spectral Sentinel-2 imagery, we analyze the extent of different snow and ice classes on the surface of the glacier by means of pixel-wise classification. Our study comprises three main steps: (1) Labeled Sentinel-2 compliant data are obtained from theoretical spectral reflectance curves, as there are no training data available for the investigated area; (2) Four different classification approaches are used and compared in their ability to identify the defined five snow and ice types, thereof two unsupervised approaches (k-means clustering and rule-based classification via snow and ice indices) and two supervised approaches (Linear Discriminant Analysis and Random Forest classifier); (3) We first focus on the pixel-wise classification of Sentinel-2 imagery, and we then use the best-performing approach for a multi-temporal analysis of the Tyndall Glacier area. While the achieved classification results reveal that all of the used classification approaches are suitable for detecting different snow and ice classes on the glacier surface, the multi-temporal analysis clearly reveals the seasonal development of the glacier. The change of snow and ice types on the glacier surface is evident, especially between the end of ablation season (April) and the end of accumulation season (September) in Southern Chile.
Unfolded Coprime Linear Array with Three Subarrays for Non-Gaussian Signals: Configuration Design and DOA Estimation
Authors: Yang Meng , Li Jingming , Ye Changbo , Li Jianfeng
Published: February 2022

In this paper, we investigate the problem of sparse array design for the direction of the arrival (DOA) of non-Gaussian signals and exploit the unfolded coprime linear array with three subarrays (UCLATS) to obtain physical sensors location. With the motivation from the large consecutive degree of freedom (DOF), we optimize the process of obtaining physical sensors location from two steps. Specifically, the first is to model the process of obtaining the longest consecutive virtual sum co-array from a given number of physical array elements into a global postage-stamp problem (GPSP), whose solution can be employed to determine the locations of the longest possible consecutive sum co-array (2-SC) and initial physical array. The second step is to multiply the location of the virtual sum co-array by appropriate coprime coefficients to generate UCLATS and then multiply the initial physical array position by the same corresponding coefficients to obtain physical sensors location. Besides, an algorithm is proposed to obtain DOA estimates, which employs the discrete Fourier transform (DFT) method and partial spectrum searching multiple signal classification (PSS-MUSIC) algorithm to obtain initial estimates and fine estimates, respectively, termed as the DFT-MUSIC method. Compared with the traditional total spectrum searching MUSIC (TSS-MUSIC) algorithm, the DFT-MUSIC method performs the same asymptotical performance of DOA estimation with less than 10% complex multiplication times, which can be verified by numerical simulations under the same condition.
GRA-PIN: A Graphical and PIN-Based Hybrid Authentication Approach for Smart Devices
Authors: Kausar Nabeela , Din Ikram Ud , Khan Mudassar Ali , Almogren Ahmad , Kim Byung-Seo
Published: February 2022

In many smart devices and numerous digital applications, authentication mechanisms are widely used to validate the legitimacy of users’ identification. As a result of the increased use of mobile devices, most people tend to save sensitive and secret information over such devices. Personal Identification Number (PIN)-based and alphanumeric passwords are simple to remember, but at the same time, they are vulnerable to hackers. Being difficult to guess and more user-friendly, graphical passwords have grown in popularity as an alternative to all such textual passwords. This paper describes an innovative, hybrid, and much more robust user authentication approach, named GRA-PIN (GRAphical and PIN-based), which combines the merits of both graphical and pin-based techniques. The feature of simple arithmetic operations (addition and subtraction) is incorporated in the proposed scheme, through which random passwords are generated for each login attempt. In the study, we have conducted a comparative study between the GRA-PIN scheme with existing PIN-based and pattern-based (swipe-based) authentications approaches using the standard Software Usability Scale (SUS). The usability score of GRA-PIN was analyzed to be as high as 94%, indicating that it is more reliable and user friendly. Furthermore, the security of the proposed scheme was challenged through an experiment wherein three different attackers, having a complete understanding of the proposed scheme, attempted to crack the technique via shoulder surfing, guessing, and camera attack, but they were unsuccessful.
A Cascade Attention Based Facial Expression Recognition Network by Fusing Multi-Scale Spatio-Temporal Features
Authors: Zhu Xiaoliang , He Zili , Zhao Liang , Dai Zhicheng , Yang Qiaolai
Published: February 2022

The performance of a facial expression recognition network degrades obviously under situations of uneven illumination or partial occluded face as it is quite difficult to pinpoint the attention hotspots on the dynamically changing regions (e.g., eyes, nose, and mouth) as precisely as possible. To address the above issue, by a hybrid of the attention mechanism and pyramid feature, this paper proposes a cascade attention-based facial expression recognition network on the basis of a combination of (i) local spatial feature, (ii) multi-scale-stereoscopic spatial context feature (extracted from the 3-scale pyramid feature), and (iii) temporal feature. Experiments on the CK+, Oulu-CASIA, and RAF-DB datasets obtained recognition accuracy rates of 99.23%, 89.29%, and 86.80%, respectively. It demonstrates that the proposed method outperforms the state-of-the-art methods in both the experimental and natural environment.
Unknown Object Detection Using a One-Class Support Vector Machine for a Cloud–Robot System
Authors: Kabir Raihan , Watanobe Yutaka , Islam Md Rashedul , Naruse Keitaro , Rahman Md Mostafizer
Published: February 2022

Inter-robot communication and high computational power are challenging issues for deploying indoor mobile robot applications with sensor data processing. Thus, this paper presents an efficient cloud-based multirobot framework with inter-robot communication and high computational power to deploy autonomous mobile robots for indoor applications. Deployment of usable indoor service robots requires uninterrupted movement and enhanced robot vision with a robust classification of objects and obstacles using vision sensor data in the indoor environment. However, state-of-the-art methods face degraded indoor object and obstacle recognition for multiobject vision frames and unknown objects in complex and dynamic environments. From these points of view, this paper proposes a new object segmentation model to separate objects from a multiobject robotic view-frame. In addition, we present a support vector data description (SVDD)-based one-class support vector machine for detecting unknown objects in an outlier detection fashion for the classification model. A cloud-based convolutional neural network (CNN) model with a SoftMax classifier is used for training and identification of objects in the environment, and an incremental learning method is introduced for adding unknown objects to the robot knowledge. A cloud–robot architecture is implemented using a Node-RED environment to validate the proposed model. A benchmarked object image dataset from an open resource repository and images captured from the lab environment were used to train the models. The proposed model showed good object detection and identification results. The performance of the model was compared with three state-of-the-art models and was found to outperform them. Moreover, the usability of the proposed system was enhanced by the unknown object detection, incremental learning, and cloud-based framework.
A Smart System for the Contactless Measurement of Energy Expenditure
Authors: Sprowls Mark , Victor Shaun , Mora Sabrina Jimena , Osorio Oscar , Pyznar Gabriel , Destaillats Hugo , Wheatley-Guy Courtney , Johnson Bruce , Kulick Doina , Forzani Erica
Published: February 2022

Energy Expenditure (EE) (kcal/day), a key element to guide obesity treatment, is measured from CO2 production, VCO2 (mL/min), and/or O2 consumption, VO2 (mL/min). Current technologies are limited due to the requirement of wearable facial accessories. A novel system, the Smart Pad, which measures EE via VCO2 from a room’s ambient CO2 concentration transients was evaluated. Resting EE (REE) and exercise VCO2 measurements were recorded using Smart Pad and a reference instrument to study measurement duration’s influence on accuracy. The Smart Pad displayed 90% accuracy (±1 SD) for 14–19 min of REE measurement and for 4.8–7.0 min of exercise, using known room’s air exchange rate. Additionally, the Smart Pad was validated measuring subjects with a wide range of body mass indexes (BMI = 18.8 to 31.4 kg/m2), successfully validating the system accuracy across REE’s measures of ~1200 to ~3000 kcal/day. Furthermore, high correlation between subjects’ VCO2 and λ for CO2 accumulation was observed (p < 0.00001, R = 0.785) in a 14.0 m3 sized room. This finding led to development of a new model for REE measurement from ambient CO2 without λ calibration using a reference instrument. The model correlated in nearly 100% agreement with reference instrument measures (y = 1.06x, R = 0.937) using an independent dataset (N = 56).
Bio-Interface on Freestanding Nanosheet of Microelectromechanical System Optical Interferometric Immunosensor for Label-Free Attomolar Prostate Cancer Marker Detection
Authors: Maeda Tomoya , Kanamori Ryoto , Choi Yong-Joon , Taki Miki , Noda Toshihiko , Sawada Kazuaki , Takahashi Kazuhiro
Published: February 2022

Various biosensors that are based on microfabrication technology have been developed as point-of-care testing devices for disease screening. The Fabry–Pérot interferometric (FPI) surface-stress sensor was developed to improve detection sensitivity by performing label-free biomarker detection as a nanomechanical deflection of a freestanding membrane to adsorb the molecules. However, chemically functionalizing the freestanding nanosheet with excellent stress sensitivity for selective molecular detection may cause the surface chemical reaction to deteriorate the nanosheet quality. In this study, we developed a minimally invasive chemical functionalization technique to create a biosolid interface on the freestanding nanosheet of a microelectromechanical system optical interferometric surface-stress immunosensor. For receptor immobilization, glutaraldehyde cross-linking on the surface of the amino-functionalized parylene membrane reduced the shape variation of the freestanding nanosheet to 1/5–1/10 of the previous study and achieved a yield of 95%. In addition, the FPI surface-stress sensor demonstrated molecular selectivity and concentration dependence for prostate-specific antigen with a dynamic range of concentrations from 100 ag/mL to 1 µg/mL. In addition, the minimum limit of detection of the proposed sensor was 2,000,000 times lower than that of the conventional nanomechanical cantilevers.
Quantitative Evaluation of Hypomimia in Parkinson’s Disease: A Face Tracking Approach
Authors: Pegolo Elena , Volpe Daniele , Cucca Alberto , Ricciardi Lucia , Sawacha Zimi
Published: February 2022

Parkinson’s disease (PD) is a neurological disorder that mainly affects the motor system. Among other symptoms, hypomimia is considered one of the clinical hallmarks of the disease. Despite its great impact on patients’ quality of life, it remains still under-investigated. The aim of this work is to provide a quantitative index for hypomimia that can distinguish pathological and healthy subjects and that can be used in the classification of emotions. A face tracking algorithm was implemented based on the Facial Action Coding System. A new easy-to-interpret metric (face mobility index, FMI) was defined considering distances between pairs of geometric features and a classification based on this metric was proposed. Comparison was also provided between healthy controls and PD patients. Results of the study suggest that this index can quantify the degree of impairment in PD and can be used in the classification of emotions. Statistically significant differences were observed for all emotions when distances were taken into account, and for happiness and anger when FMI was considered. The best classification results were obtained with Random Forest and kNN according to the AUC metric.
Hardware-in-the-Loop-Based Real-Time Fault Injection Framework for Dynamic Behavior Analysis of Automotive Software Systems
Authors: Abboush Mohammad , Bamal Daniel , Knieke Christoph , Rausch Andreas
Published: February 2022

A well-known challenge in the development of safety-critical systems in vehicles today is that reliability and safety assessment should be rigorously addressed and monitored. As a matter of fact, most safety problems caused by system failures can lead to serious hazards and loss of life. Notwithstanding the existence of several traditional analytical techniques used for evaluation based on specification documents, a complex design, with its multivariate dynamic behavior of automotive systems, requires an effective method for an experimental analysis of the system’s response under abnormal conditions. Simulation-based fault injection (FI) is a recently developed approach to simulate the system behavior in the presence of faults at an early stage of system development. However, in order to analyze the behavior of the system accurately, comprehensively and realistically, the real-time conditions, as well as the dynamic system model of the vehicle, should be considered. In this study, a real-time FI framework is proposed based on a hardware-in-the-loop (HiL) simulation platform and a real-time electronic control unit (ECU) prototype. The framework is modelled in the MATLAB/Simulink environment and implemented in the HiL simulation to enable the analysis process in real time during the V-cycle development process. With the objective of covering most of the potential faults, nine different types of sensor and actuator control signal faults are injected programmatically into the HiL system as single and multiple faults without changing the original system model. Besides, the model of the whole system, containing vehicle dynamics with the environment system model, is considered with complete and comprehensive behavioral characteristics. A complex gasoline engine system is used as a case study to demonstrate the capabilities and advantages of the proposed framework. Through the proposed framework, transient and permanent faults are injected in real time during the operation of the system. Finally, experimental results show the effects of single and simultaneous faults on the system performance under a faulty mode compared to the golden running mode.
Single-Layer-Graphene-Coated and Gold-Film-Based Surface Plasmon Resonance Prism Coupler Sensor for Immunoglobulin G Detection
Authors: Yang Zhe-Wei , Pham Thi-Thu-Hien , Hsu Chin-Chi , Lien Chi-Hsiang , Phan Quoc-Hung
Published: February 2022

A graphene-based surface plasmon resonance (SPR) prism coupler sensor is proposed for the rapid detection of immunoglobulin G (IgG) antibodies. The feasibility of the proposed sensor is demonstrated by measuring the IgG concentration in phantom mouse and human serum solutions over the range of 0–250 ng/mL. The results show that the circular dichroism and principal fast axis angle of linear birefringence increase in line with increases in IgG concentration over the considered range. Moreover, the proposed device has a resolution of 5–10 ng/mL and a response time of less than three minutes. In general, the sensor provides a promising approach for IgG detection and has significant potential for rapid infectious viral disease testing applications.
Soil Water Content Prediction Using Electrical Resistivity Tomography (ERT) in Mediterranean Tree Orchard Soils
Authors: Acosta José A. , Gabarrón María , Martínez-Segura Marcos , Martínez-Martínez Silvia , Faz Ángel , Pérez-Pastor Alejandro , Gómez-López María Dolores , Zornoza Raúl
Published: February 2022

Water scarcity in arid and semiarid regions poses problems for agricultural systems, awakening special interest in the development of deficit irrigation strategies to improve water conservation. Toward this purpose, farmers and technicians must monitor soil water and soluble nutrient contents in real time using simple, rapid and economical techniques through time and space. Thus, this study aimed to achieve the following: (i) create a model that predicts water and soluble nutrient contents in soil profiles using electrical resistivity tomography (ERT); and (ii) apply the model to different woody crops under different irrigation regimes (full irrigation and regulated deficit irrigation (RDI)) to assess the efficiency of the model. Simple nonlinear regression analysis was carried out on water content and on different ion contents using electrical resistivity data as the dependent variable. A predictive model for soil water content was calibrated and validated with the datasets based on exponential decay of a three-parameter equation. Nonetheless, no accurate model was achieved to predict any soluble nutrient. Electrical resistivity images were replaced by soil water images after application of the predictive model for all studied crops. They showed that under RDI situations, soil profiles became drier at depth while plant roots seemed to uptake more water, contributing to reductions in soil water content by the creation of desiccation bulbs. Therefore, the use of ERT combined with application of the validated predictive model could be a sustainable strategy to monitor soil water evolution in soil profiles under irrigated fields, facilitating land irrigation management.
Denoising of Fluorescence Image on the Surface of Quantum Dot/Nanoporous Silicon Biosensors
Authors: Liu Yong , Sun Miao , Jia Zhenhong , Yang Jie , Kasabov Nikola K.
Published: February 2022

In the process of biological detection of porous silicon photonic crystals based on quantum dots, the concentration of target organisms can be indirectly measured via the change in the gray value of the fluorescence emitted from the quantum dots in the porous silicon pores before and after the biological reaction on the surface of the device. However, due to the disordered nanostructures in porous silicon and the roughness of the surface, the fluorescence images on the surface contain noise. This paper analyzes the type of noise and its influence on the gray value of fluorescent images. The change in the gray value caused by noise greatly reduces the detection sensitivity. To reduce the influence of noise on the gray value of quantum dot fluorescence images, this paper proposes a denoising method based on gray compression and nonlocal anisotropic diffusion filtering. We used the proposed method to denoise the quantum dot fluorescence image after DNA hybridization in a Bragg structure porous silicon device. The experimental results show that the sensitivity of digital image detection improved significantly after denoising.
Blockchain-Based Distributed Information Hiding Framework for Data Privacy Preserving in Medical Supply Chain Systems
Authors: EL Azzaoui Abir , Chen Haotian , Kim So Hyeon , Pan Yi , Park Jong Hyuk
Published: February 2022

Medical supply chain communication networks engender critical information and data. Notably in the COVID era, inner personal and private information is being shared between healthcare providers regarding the medical supply chain. In recent years, multiple cyber-attacks have targeted medical supply chain communication networks due to their lack of security measures. In the era where cyber-attacks are cheaper and easier due to the computational power and various algorithms available for malicious uses, security, and data privacy requires intensive and higher measures. On the other hand, Information Hiding Techniques (IHT) compromise various advanced methods to hide sensitive information from being disclosed to malicious nodes. Moreover, with the support of Blockchain, IHT can bring higher security and the required privacy levels. In this paper, we propose the implementation of Blockchain and smart contract with the information hiding technique to enhance the security and privacy of data communication in critical systems, such as smart healthcare supply chain communication networks. Results show the feasibility of the framework using Hyperledger smart contract along with the desired security level.
Analysis on the Subdivision of Skilled Mowing Movements on Slopes
Authors: Wu Bo , Wu Yuan , Nishimura Shoji , Jin Qun
Published: February 2022

Owing to the aging of the rural population in the hilly and mountainous areas of Japan, mowing on narrow ridges and steep slopes is done manually by the elderly—individuals over 65 years of age. Studies have shown that many accidents that occurred during mowing were caused by workers’ unstable posture, especially when mowing on steep surfaces where there is a high risk of falling. It is necessary to analyze the body movements of mowing workers to elucidate the elements related to the risk of falls. Therefore, in this study, based on a high-precision motion-capture device and a series of experiments with elderly, skilled mowing workers, we focused on the movements of mowing. We sought to identify effective and safe mowing patterns and the factors that lead to the risk of falls. In various mowing styles, compared to the stride (S) and downward (D) mowing patterns, the basic (B) and moving (M) patterns were the most efficient; however, the risk of falls was also the highest among these patterns. While mowing, workers need to pay more attention to their arm strength and take appropriate measures to reduce the risk of falls according to their age and physique. The results can be used as data for the development of fall-detection systems and offer useful insights for the training of new mowing workers.
Comparing Sampling Strategies for Tackling Imbalanced Data in Human Activity Recognition
Authors: Alharbi Fayez , Ouarbya Lahcen , Ward Jamie A
Published: February 2022

Human activity recognition (HAR) using wearable sensors is an increasingly active research topic in machine learning, aided in part by the ready availability of detailed motion capture data from smartphones, fitness trackers, and smartwatches. The goal of HAR is to use such devices to assist users in their daily lives in application areas such as healthcare, physical therapy, and fitness. One of the main challenges for HAR, particularly when using supervised learning methods, is obtaining balanced data for algorithm optimisation and testing. As people perform some activities more than others (e.g., walk more than run), HAR datasets are typically imbalanced. The lack of dataset representation from minority classes hinders the ability of HAR classifiers to sufficiently capture new instances of those activities. We introduce three novel hybrid sampling strategies to generate more diverse synthetic samples to overcome the class imbalance problem. The first strategy, which we call the distance-based method (DBM), combines Synthetic Minority Oversampling Techniques (SMOTE) with Random_SMOTE, both of which are built around the k-nearest neighbors (KNN). The second technique, referred to as the noise detection-based method (NDBM), combines SMOTE Tomek links (SMOTE_Tomeklinks) and the modified synthetic minority oversampling technique (MSMOTE). The third approach, which we call the cluster-based method (CBM), combines Cluster-Based Synthetic Oversampling (CBSO) and Proximity Weighted Synthetic Oversampling Technique (ProWSyn). We compare the performance of the proposed hybrid methods to the individual constituent methods and baseline using accelerometer data from three commonly used benchmark datasets. We show that DBM, NDBM, and CBM reduce the impact of class imbalance and enhance F1 scores by a range of 9–20 percentage point compared to their constituent sampling methods. CBM performs significantly better than the others under a Friedman test, however, DBM has lower computational requirements.
Hyperconnected Openings Codified in a Max Tree Structure: An Application for Skull-Stripping in Brain MRI T1
Authors: Paredes-Orta Carlos , Mendiola-Santibañez Jorge Domingo , Ibrahimi Danjela , Rodríguez-Reséndiz Juvenal , Díaz-Florez Germán , Olvera-Olvera Carlos Alberto
Published: February 2022

This article presents two procedures involving a maximal hyperconnected function and a hyperconnected lower leveling to segment the brain in a magnetic resonance imaging T1 weighted using new openings on a max-tree structure. The openings are hyperconnected and are viscous transformations. The first procedure considers finding the higher hyperconnected maximum by using an increasing criterion that plays a central role during segmentation. The second procedure utilizes hyperconnected lower leveling, which acts as a marker, controlling the reconstruction process into the mask. As a result, the proposal allows an efficient segmentation of the brain to be obtained. In total, 38 magnetic resonance T1-weighted images obtained from the Internet Brain Segmentation Repository are segmented. The Jaccard and Dice indices are computed, compared, and validated with the efficiency of the Brain Extraction Tool software and other algorithms provided in the literature.
Evaluation of a New Lightweight UAV-Borne Topo-Bathymetric LiDAR for Shallow Water Bathymetry and Object Detection
Authors: Wang Dandi , Xing Shuai , He Yan , Yu Jiayong , Xu Qing , Li Pengcheng
Published: February 2022

Airborne LiDAR bathymetry (ALB) has proven to be an effective technology for shallow water mapping. To collect data with a high point density, a lightweight dual-wavelength LiDAR system mounted on unmanned aerial vehicles (UAVs) was developed. This study presents and evaluates the system using the field data acquired from a flight test in Dazhou Island, China. In the precision and accuracy assessment, the local fitted planes extracted from the water surface points and the multibeam echosounder data are used as a reference for water surface and bottom measurements, respectively. For the bathymetric performance comparison, the study area is also measured with an ALB system installed on the manned aerial platform. The object detection capability of the system is examined with placed small cubes. Results show that the fitting precision of the water surface is 0.1227 m, and the absolute accuracy of the water bottom is 0.1268 m, both of which reach a decimeter level. Compared to the manned ALB system, the UAV-borne system provides higher resolution data with an average point density of 42 points/m2 and maximum detectable depth of 1.7–1.9 Secchi depths. In the point cloud of the water bottom, the existence of a 1-m target cube and the rough shape of a 2-m target cube are clearly observed at a depth of 12 m. The system shows great potential for flexible shallow water mapping and underwater object detection with promising results.
On-Chip Structures for Fmax Binning and Optimization
Authors: Zhang Dongrong , Ren Qiang , Su Donglin
Published: February 2022

Process variations during manufacturing lead to differences in the performance of the chips. In order to better utilize the performance of the chips, it is necessary to perform maximum operation frequency (Fmax) tests to place the chips into different speed bins. For most Fmax tests, significant efforts are put in place to reduce test cost and improve binning accuracy; e.g., our conference paper published in ICICM 2017 presents a novel binning sensor for low-cost and accurate speed binning. However, by promoting chips placed at the lower bins, because of conservative binning, into higher bins, the overall profit can greatly increase. Therefore, this paper, extended based on a conference paper, presents a novel and adaptive methodology for speed binning, in which the paths impacting the speed bin of a specific IC are identified and adapted by our proposed on-chip Binning Checker and Binning Adaptor. As a result, some parts at a bin margin can be promoted to higher bins. The proposed methodology can be used to optimize the Fmax yield of a digital circuit when it has redundant timing in clock tree, and it can be integrated into current Fmax tests with low extra cost. The proposed adaptive system has been implemented and validated on five benchmarks from ITC, ISCAS89, and OpenSPARCT2 core on 28 nm Altera FPGAs. Measurement results show that the number of higher bin chips is improved by 7–16%, and our cost analysis shows that the profit increase is between 1.18% and 3.04%.
Enabling Secure Data Exchange through the IOTA Tangle for IoT Constrained Devices
Authors: Carelli Alberto , Palmieri Andrea , Vilei Antonio , Castanier Fabien , Vesco Andrea
Published: February 2022

Internet-of-Things (IoT) and sensor technologies have enabled the collection of data in a distributed fashion for analysis and evidence-based decision making. However, security concerns regarding the source, confidentiality and integrity of the data arise. The most common method of protecting data transmission in sensor systems is Transport Layer Security (TLS) or its datagram counterpart (DTLS) today, but exist an alternative option based on Distributed Ledger Technology (DLT) that promise strong security, ease of use and potential for large scale integration of heterogeneous sensor systems. A DLT such as the IOTA Tangle offers great potential to improve sensor data exchange. This paper presents L2Sec, a cryptographic protocol which is able to secure data exchanged over the IOTA Tangle. This protocol is suitable for implementation on constrained devices, such as common IoT devices, leading to greater scalability. The first experimental results evidence the effectiveness of the approach and advocate for the integration of an hardware secure element to improve the overall security of the protocol. The L2Sec source code is released as open source repository on GitHub.
Challenging “Citizen Science”: Liminal Status Students and Community-Engaged Research
Authors: Syeed Esa , Rosas Abigail , Hammam Farah , Shen Sherry , Zeferino Fatima
Published: February 2022

The problematic term “citizen science” continues to circulate in scholarly circles and points to challenges with how researchers may conceptualize who takes part in community-engaged inquiry. Emerging from experiences with a research team intentionally comprised of students who are undocumented, political asylees, and those belonging to mixed status families, we seek to center how immigration status can inform justice-oriented research processes. By focusing on students experiencing liminal status, we note both the structural barriers they face as well as their agency. Through a critical reflexive process, we outline four key tensions that address skills, authenticity, inclusivity, and possibilities relevant to mixed status teams conducting community-engaged research. By exploring how citizenship status impacts research at epistemological and applied levels, we arrive at more inclusive and just possibilities for community-engaged research.
Minimum Rainfall Inter-Event Time to Separate Rainfall Events in a Low Latitude Semi-Arid Environment
Authors: Brasil José Bandeira , Guerreiro Maria Simas , Andrade Eunice Maia de , de Queiroz Palácio Helba Araújo , Medeiros Pedro Henrique Augusto , Ribeiro Filho Jacques Carvalho
Published: February 2022

Water scarcity in dry tropical regions is expected to intensify due to climate change. Characterization of rainfall events is needed for a better assessment of the associated hydrological processes, and the proposition of adaptation strategies. There is still no consensus on the most appropriate method to separate rainfall events from a continuous database, although the minimum inter-event time (MIET) is a commonly used criterion. Semi-arid regions of low latitudes hold a distinct rainfall pattern compared to their equivalent at higher latitudes; these seasonally dry tropical forests experience strong spatial–temporal variability with intense short-duration rainfall events, which, in association with high energy surplus and potential evaporation, leads to an atmospheric water deficit. In this study, we identified the most adequate MIET based on rainfall data continuously measured at 5-min intervals over the last decade (2009–2020) in the semi-arid northeast of Brazil. The rainfall events were grouped according to different MIETs: 15 min, 1 h, 2 h, 3 h, 6 h, 12 h, and 24 h to determine rainfall depth, duration and intensity at intervals of 5, 30, and 60 min, time between events, and respective temporal distribution, with and without single tip events. Including single tip events in the dataset affected the number of rainfall events and respective characteristics up to a MIET of 3 h. A MIET of 6 h is the most appropriate to characterize the rainfall distribution in this tropical semi-arid region. Three classes were defined based on rainfall depth, duration, and intensity: I-small events (77% below 40 mm and 32 mm/h), II-high intensity events (3% between 36 and 76 mm/h), III-longer events of higher depth (20%). This study is useful for understanding how the MIET relates to other ecohydrological processes and provides more precise information on the rainfall characteristics at the event scale.
Chilean University Teachers’ Social Representations and Attitudes about Students’ Sexual Diversity
Authors: Soria-Barreto Karla , Yáñez-Galleguillos Luz María , Zuniga-Jara Sergio
Published: February 2022

This study seeks to explore social representations and attitudes of university teachers in relation to sexual diversity. The study has a qualitative approach through content analysis. The research was collected through 62 critical incidents with university professors, with at least one year of experience in undergraduate teaching, in five universities located in the northern macro zone of Chile. The results highlight that there are teachers who take on three different types of roles when faced with situations of gender diversity.. Some are in favour of the inclusion of the sexual minority community, others are more like external observers and a third group, with little interest in inclusion, practice for the students belonging to sexual minorities, within the diversity of the university classrooms. Many teachers tend to contain and compensate for the scarce support that sexually diverse young people have within their families. Teachers demand more training than they currently have in order to be able to carry out adequate interventions in the classroom. Finally, the number of feelings and emotions described by teachers in the face of events and experiences related to sexual diversity in their university institutions is highly significant.
Systematic Multiscale Models to Predict the Compressive Strength of Cement Paste as a Function of Microsilica and Nanosilica Contents, Water/Cement Ratio, and Curing Ages
Authors: Rahimzadeh Chiya Y. , Salih Ahmed , Barzinjy Azeez A.
Published: February 2022

Sustainable construction requires high-strength cement materials that additives with silica content could provide the requirements as well. In this study, the effect of the micro and nano-size of silica on the compressive strength of cement paste using different mathematical approaches is investigated. This study compares the strength of preferentially replaced cement pastes with microsilica (MS) and nanosilica (NS) incorporation by proposing several mathematical models. In this study, 205 data were extracted from the literature and analyzed. The modeling processes considered the most significant variables as input variables that influence the compression strength, such as curing time, which ranged between 3 and 90 days, the water-cement ratio, which varied between 0.4 and 0.85, and NS ranged between 0 and 15%. MS ranged between 0 and 40% based on the weight of cement. In this process, the compressive strength of cement paste modified with NS and MS was modeled using four different models, including the Linear Regression Model (LR), Nonlinear Model (NLR), Multi-Logistic Regression Model (MLR), and artificial neural network (ANN). The efficiency of the suggested models was evaluated using different statistical assessments, such as the Root Mean Squared Error (RMES), the Mean Absolute Error (MAE), Scatter Index (SI), Objective value (OBJ), and coefficient of determination (R2). The findings revealed that the ANN model conducted better performance for predicting compressive strength for cement paste than the other models based on the statistical assessment. In addition, based on the statistical assessment of the sensitivity of parameters, NS had more of an effect on the compressive strength of cement paste, with 6.3% more than MS.
Circular Economy for Cities and Sustainable Development: The Case of the Portuguese City of Leiria
Authors: Antunes João Cesar Cadima , Eugénio Teresa , Branco Manuel Castelo
Published: February 2022

The principles of the circular economy play a central role in the global economy, and its application is suggested in the context of business and urban development for creating cities capable of reinventing themselves in the long term. The main objective of this study is to assess the perception of the inhabitants of the city of Leiria (Portugal) regarding the concept of the circular economy and the acceptance of circular actions and projects, gathering their opinions and motivations, and understanding the challenges they perceive for implementing the circular economy. This study is relevant because, to date, no work has analyzed the circular practices of citizens and consequent projects for a city. The adopted methodology consisted of a mixed qualitative and quantitative approach, which was materialized in the administration of an online questionnaire to the inhabitants, workers, or students of the Municipality of Leiria. We obtained a sample of 547 answers. These results underwent statistical analysis, and it was concluded that the familiarization of study participants with the circular economy is low, but that they have a positive predisposition to engage in rental, reuse, article repair, and recycling activities. The municipality should continue to invest in the extension and promotion of circular economy initiatives in the city, since there is a predisposition of respondents towards using them, which may lead to the conclusion that they would support their implementation. This is an initial step that should promote further research into understanding the perception of citizens regarding circular economy initiatives and actions, followed by how it might be supported to achieve its full potential in cities.
Traffic Accident Severity Prediction Based on Random Forest
Authors: Yan Miaomiao , Shen Yindong
Published: February 2022

The prediction of traffic accident severity is essential for traffic safety management and control. To achieve high prediction accuracy and model interpretability, we propose a hybrid model that integrates random forest (RF) and Bayesian optimization (BO). In the proposed model, BO-RF, RF is adopted as a basic predictive model and BO is used to tune the parameters of RF. Experimental results show that BO-RF achieves higher accuracy than conventional algorithms. Moreover, BO-RF provides interpretable results by relative importance and a partial dependence plot. We can identify important influential factors for traffic accident severity by relative importance. Further, we can investigate how the influential factors affect traffic accident severity by the partial dependence plot. These results provide insights to mitigate the severity of traffic accident consequences and contribute to the sustainable development of transportation.
Artificial Intelligence (AI) and Sustainable Development Goals (SDGs): Exploring the Impact of AI on Politics and Society
Authors: Visvizi Anna
Published: February 2022

In popular discourse, artificial intelligence (AI) has turned into one of the most inexplicable issues It has turned into an artifact that people do not dare to question [...]
The Impacts of COVID-19 and Policies on Spatial and Temporal Distribution Characteristics of Traffic: Two Examples in Beijing
Authors: Guo Weiwei , Feng Yan , Luo Wenxiu , Ren Yilong , Tan Jiyuan , Jiang Xiaobei , Xue Qingwan
Published: February 2022

The global closure policy to limit the spread of the new coronavirus (COVID-19) in 2020 was based on public safety and health considerations. In the implementation of arrangements to prevent the epidemic, the function of the transportation system as a basis for securing cities has been severely affected. After summarizing the domestic and international literature on epidemic policies and travel, this study analyzes the changes of the spatial and temporal distribution characteristics of people’s travel and the impacts in the context of the two epidemic phases in Beijing and abroad. During the epidemic, traffic volume into and out of Beijing showed a downward trend. In our study, we found that total travel volume in Beijing during the Spring Festival in 2020 was down by about 70% year-on-year, the distribution of daily traffic trips during the day was not affected by the outbreak, and six urban areas in the center of Beijing experienced greater declines in travel volume compared to other urban areas. The conclusions of the study can provide a reference for the sustainability and recovery of urban areas and formulation of policies in the subsequent pandemic era in terms of the relationship between public travel and epidemic control.