bluuebunny/crossref_metadata_2025 huggingface dataset:
Linear-Rate Reactions for the Thermal Devolatilization of Wheat Straw Based on Pseudo-Components
Authors: Branca Carmen
Published: February 2022

Thermogravimetric curves are measured in the nitrogen of wheat straw heated up to 773 K with rates between 5–20 K/min. A five-step (or component) parallel reaction scheme was developed for the interpretation of the weight loss characteristics, which makes use of the lumped volatile products based on the volatiles released by the pseudo-macrocomponents. The volumetric rates show the usual Arrhenius dependence on temperature and a linear dependence on the mass fraction of the lumped volatile products. The wheat straw devolatilization mechanism consists of a single step for pseud-ocellulose (activation energies of about 180 kJ/mol) and two steps for the pseudohemicellulose, also including extractives, (activation energies of about 101 and 136 kJ/mol) and pseudo-lignin (activation energies of 189 and 126 kJ/mol). For the first two pseudo-macrocomponents, the activation energies were lower than those obtained through a similar approach for beech wood, owing to the much higher content of alkalis acting as catalysts for the decomposition reactions. These occur at lower temperatures and show an enhanced overlap between the pseudo-components.
Zazen and Self as Environment
Authors: Bowering Scott
Published: February 2022

The teachings of Eihei Dōgen Zenji (1200–1253) emphasize a non-instrumental orientation to zazen as “practice-realization”, which might be described as a single movement of purposeful action and actualization of aims. Yet the question remains, if zazen is not in some sense a set of clearly defined steps toward specific ends, exactly how are its benefits manifested, and why would Dōgen place such an elusive approach at the centre of practice? In the following, I will discuss how Dōgen’s conception of practice does not necessarily function as a prescriptive methodology leading to specified results, but might better be described as an orientation to everyday experience that facilitates a comprehensive integration of physical and perceptual interactions within shared environments. Any sense of the utility or benefit of zazen is inseparable from reference to these relations within specific contexts of practice. Exploring close parallels between Dōgen’s conception of universal self (jiko) and gestalt theory, particularly as it is referenced in ecophilosophy and sociomaterial practices literature, suggests ontological and ethical implications of “practice-realization” from contemporary secular perspectives.
Visualizing Faith and Futility in the Nuclear Apocalypse
Authors: Fields Alison
Published: February 2022

This paper explores the intersection of personal responsibility, futility, and faith in visual representations of nuclear apocalypse. In two films produced during the late Cold War, Testament (1983) and When the Wind Blows (1986), the protagonists attempt to follow public guidance, maintain daily routines as their health and communities break down, and make muddled connections to religious faith. In Testament, a mother is left to care for her children in suburban California for months after an unexplained nuclear attack isolates and contaminates the town. In When the Wind Blows, a retired couple living in the British countryside diligently follow government instructions to “protect and survive”, while quickly succumbing to radiation poisoning. In a contrasting post-Cold War visual representation, the speculative artwork of the artists Erich Berger and Mari Keto imagine the storage of nuclear waste as a personal responsibility. In OpenCare (2016), waste is encased in steel pellets mounted on a bronze disc, and a series of artifacts and instructions assist in determining continued toxicity. While Testament and When the Wind Blows project the futility of personal responsibility and faith in nuclear survival, Berger and Keto’s system envisions a deep nuclear future requiring continued personal management and care.
Conspiracy Theories and Muslim Brotherhood Antisemitism under Sadat
Authors: Santing Kiki
Published: February 2022

This paper highlights how the Muslim Brotherhood instrumentalized antisemitic conspiracies in its journal al-Daʻwa in its bid to strengthen its socio-political authority under Sadat. After discussing theoretical insights on conspiracy theories and (Muslim and Muslim Brotherhood) antisemitism, the paper zooms in on the return of the Muslim Brotherhood under Sadat, focusing on the movement’s internal dynamics and its growing socio-political ambitions, followed by a content analysis of antisemitic conspiracy theories found in al-Daʻwa. The final part of the paper analyses the different dimensions and the functions of these antisemitic conspiracies for the movement. The paper concludes that through the antisemitic conspiracies, the Muslim Brotherhood has positioned itself as a religious, moral and political authority. Although al-Daʻwa promulgated classical (European) antisemitic conspiracies, these were utilized by the movement for purposes other than mere hatred and distrust of the Jews and Jewish–Muslim polemics.
Mea Culpa: A Qualitative Interview Study on the Role of Guilt and Forgiveness with Non-Religious and Multireligious Inmates
Authors: van Herpen Mickey , Kruizinga Renske
Published: February 2022

Within monotheistic religions, guilt and forgiveness have long been important themes. In The Netherlands, however, the influence of traditional religions is declining. This qualitative interview study explored how non-religious and multireligious inmates experience guilt and forgiveness, and how the humanistic chaplain could address these issues within counselling. Guilt and forgiveness are complex constructs that are individually and subjectively experienced. Most inmates experience personal guilt and regret for what they have done. Some inmates cope with these feelings in a repressive way, where others have an active coping style. Both multireligious and non-religious inmates have a need for interpersonal forgiveness, especially from close relatives. In addition, multireligious inmates also have a transcendent need for forgiveness from a personal god. The humanistic chaplain may help inmates with an active reflection on guilt and forgiveness, which can contribute to a development on an existential level. Guilt and forgiveness ought to be approached from a counselling perspective and attention should be paid to the multiple meanings of personal responsibility. An active and critical reflection on guilt and forgiveness may lead to new meanings of the (criminal) past and create a positive change in the behaviour of detainees.
Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests
Authors: Pacheco-Pascagaza Ana María , Gou Yaqing , Louis Valentin , Roberts John F. , Rodríguez-Veiga Pedro , da Conceição Bispo Polyanna , Espírito-Santo Fernando D. B. , Robb Ciaran , Upton Caroline , Galindo Gustavo , Cabrera Edersson , Pachón Cendales Indira Paola , Castillo Santiago Miguel Angel , Carrillo Negrete Oswaldo , Meneses Carmen , Iñiguez Marco , Balzter Heiko
Published: February 2022

The commitment by over 100 governments covering over 90% of the world’s forests at the COP26 in Glasgow to end deforestation by 2030 requires more effective forest monitoring systems. The near real-time (NRT) change detection of forest cover loss enables forest landowners, government agencies and local communities to monitor natural and anthropogenic disturbances in a much timelier fashion than the thematic maps that are released every year. NRT deforestation alerts enable the establishment of more up-to-date forest inventories and rapid responses to unlicensed logging. The Copernicus Sentinel-2 satellites provide operational Earth observation (EO) data from multi-spectral optical/near-infrared wavelengths every five days at a global scale and at 10 m resolution. The amount of acquired data requires cloud computing or high-performance computing for ongoing monitoring systems and an automated system for processing, analyzing and delivering the information promptly. Here, we present a Sentinel-2-based NRT change detection system, assess its performance over two study sites, Manantlán in Mexico and Cartagena del Chairá in Colombia, and evaluate the forest changes that occurred in 2018. An independent validation with very high-resolution PlanetScope (~3 m) and RapidEye (~5 m) data suggests that the proposed NRT change detection system can accurately detect forest cover loss (> 87%), other vegetation loss (> 76%) and other vegetation gain (> 71%). Furthermore, the proposed NRT change detection system is designed to be attuned using in situ data. Therefore, it is scalable to larger regions, entire countries and even continents.
Enhanced TabNet: Attentive Interpretable Tabular Learning for Hyperspectral Image Classification
Authors: Shah Chiranjibi , Du Qian , Xu Yan
Published: February 2022

Tree-based methods and deep neural networks (DNNs) have drawn much attention in the classification of images. Interpretable canonical deep tabular data learning architecture (TabNet) that combines the concept of tree-based techniques and DNNs can be used for hyperspectral image classification. Sequential attention is used in such architecture for choosing appropriate salient features at each decision step, which enables interpretability and efficient learning to increase learning capacity. In this paper, TabNet with spatial attention (TabNets) is proposed to include spatial information, in which a 2D convolution neural network (CNN) is incorporated inside an attentive transformer for spatial soft feature selection. In addition, spatial information is exploited by feature extraction in a pre-processing stage, where an adaptive texture smoothing method is used to construct a structure profile (SP), and the extracted SP is fed into TabNet (sTabNet) to further enhance performance. Moreover, the performance of TabNet-class approaches can be improved by introducing unsupervised pretraining. Overall accuracy for the unsupervised pretrained version of the proposed TabNets, i.e., uTabNets, can be improved from 11.29% to 12.61%, 3.6% to 7.67%, and 5.97% to 8.01% in comparison to other classification techniques, at the cost of increases in computational complexity by factors of 1.96 to 2.52, 2.03 to 3.45, and 2.67 to 5.52, respectively. Experimental results obtained on different hyperspectral datasets demonstrated the superiority of the proposed approaches in comparison with other state-of-the-art techniques including DNNs and decision tree variants.
Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems
Authors: Filipponi Federico , Smiraglia Daniela , Agrillo Emiliano
Published: February 2022

The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities.
Feature Decomposition-Optimization-Reorganization Network for Building Change Detection in Remote Sensing Images
Authors: Ye Yuanxin , Zhou Liang , Zhu Bai , Yang Chao , Sun Miaomiao , Fan Jianwei , Fu Zhitao
Published: February 2022

Building change detection plays an imperative role in urban construction and development. Although the deep neural network has achieved tremendous success in remote sensing image building change detection, it is still fraught with the problem of generating broken detection boundaries and separation of dense buildings, which tends to produce saw-tooth boundaries. In this work, we propose a feature decomposition-optimization-reorganization network for building change detection. The main contribution of the proposed network is that it performs change detection by respectively modeling the main body and edge features of buildings, which is based on the characteristics that the similarity between the main body pixels is strong but weak between the edge pixels. Firstly, we employ a siamese ResNet structure to extract dual-temporal multi-scale difference features on the original remote sensing images. Subsequently, a flow field is built to separate the main body and edge features. Thereafter, a feature optimization module is designed to refine the main body and edge features using the main body and edge ground truth. Finally, we reorganize the optimized main body and edge features to obtain the output results. These constitute a complete end-to-end building change detection framework. The publicly available building dataset LEVIR-CD is employed to evaluate the change detection performance of our network. The experimental results show that the proposed method can accurately identify the boundaries of changed buildings, and obtain better results compared with the current state-of-the-art methods based on the U-Net structure or by combining spatial-temporal attention mechanisms.
Remote Measurement of the Lightning Impulse Charge Moment Change Using the Fast Electric Field Antenna
Authors: Hou Wenhao , Gu Jiaying , Wang Yaojun , Dai Bingzhe , Cui Xun , Wang Hongsheng , Jiao Xue , Li Jie , Zhang Qilin
Published: February 2022

The impulse charge moment change (iCMC) is an important electrical property of cloud-to-ground (CG) lightning. In this paper, a new method of measuring the iCMC at distances of several hundred kilometers is proposed. The method is based on the vertical electric field below 1 kHz measured by the widely used fast electric field antenna with low frequency/very low frequency (LF/VLF) band. The impulse response of Earth-ionosphere waveguide (EIWG) is modeled using a finite difference time domain (FDTD) method considering an anisotropic ionosphere. By comparing the observed waveform with the simulated impulse response, the lightning discharge is classified into the impulsive discharge and the non-impulsive discharge. For the impulsive discharge, its iCMC is obtained directly by comparing the measured ELF waveform to the modeled impulse response at the same distance. For the non-impulsive discharge, its current moment waveform is assumed to be a sum of two Heidler’s functions, and the genetic algorithm is used to search the unknown parameters in the functions. The good agreement between the measured ELF waveform and the simulated waveform implies that the extracted current moments are reasonable. This method can be used to continuously monitor the lightning iCMC in a given time and space.
Assessing Land Cover and Ecological Quality Changes in the Forest-Steppe Ecotone of the Greater Khingan Mountains, Northeast China, from Landsat and MODIS Observations from 2000 to 2018
Authors: Shi Fang , Liu Mingxing , Qiu Jie , Zhang Yali , Su Huiyi , Mao Xupeng , Li Xin , Fan Jiahui , Chen Junsong , Lv Yingying , Xu Wanggu , Wang Zhi , Li Mingshi
Published: February 2022

Land cover changes are the main factors driving the evolution of regional ecological quality. These changes must be considered in the strategic formulation of regional or national ecological policies. The forest-steppe ecotone in the Greater Khingan Mountains is an important ecological barrier in northern China. To measure the effect of ecological protection in recent years, Landsat images, object-oriented image segmentation, and convolutional neural networks were used to create land cover datasets of the forest-steppe ecotone. The Carnegie–Ames–Stanford approach (CASA) and the dimidiate pixel model were used to derive net primary productivity (NPP) and fractional vegetation cover (FVC) to assess the ecological quality of this area. The results showed that only grassland and urban land increased, whereas saline–alkali land and desert areas initially increased and then decreased from 2010 to 2018, indicating that the desertification process was substantially curbed. Total NPP increased by 26.3% (2000–2010) and 10.8% (2010–2018). However, NPP decreased slightly in the center of the study area. FVC first decreased and then increased, and the increased areas were concentrated in the forest-steppe ecotone, saline–alkali land, and desert zone in Xin Barag Left Banner. These observations indicate that the ecological quality has gradually improved due to the strict protection of forest and grassland resources and the suppression of desertification. Our results provide potential insights for land use planning and the development of environmental protection measures in the forest-steppe ecotone.
Estimation of Evapotranspiration in the Yellow River Basin from 2002 to 2020 Based on GRACE and GRACE Follow-On Observations
Authors: Qu Wei , Jin Zehui , Zhang Qin , Gao Yuan , Zhang Pufang , Chen Peinan
Published: February 2022

Evapotranspiration (ET) plays an important role in the hydrological cycle of river basins. Studying ET in the Yellow River Basin (YRB) is greatly significant for the scientific management of water resources. Here, we made full use of the advantages of the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) gravity satellites for monitoring large-scale hydrological changes to calculate the terrestrial water storage anomaly (TWSA) and terrestrial water flux in the YRB from May 2002 to June 2020. Furthermore, combined with terrestrial water flux, precipitation, and runoff data, ET in the YRB was calculated based on the water budget equation and then compared with other traditional ET products. The mutation of annual mean ET was identified by the Mann–Kendall trend test method, and the seasonal and interannual variations of ET were explored. ET was closely related to precipitation. Annual mean ET exhibited a sudden change in 2011, with an insignificant downward trend from 2003 to 2010, followed by an increasing trend from 2011 to 2019, particularly after 2016. Compared with the traditional ET monitoring methods and products, the ET estimated by GRACE/GRACE-FO observations provides a new way to effectively obtain continuous and reliable ET data in a wide range of river basins.
Dynamic Forecast of Desert Locust Presence Using Machine Learning with a Multivariate Time Lag Sliding Window Technique
Authors: Sun Ruiqi , Huang Wenjiang , Dong Yingying , Zhao Longlong , Zhang Biyao , Ma Huiqin , Geng Yun , Ruan Chao , Xing Naichen , Chen Xidong , Li Xueling
Published: February 2022

Desert locust plagues can easily cause a regional food crisis and thus affect social stability. Preventive control of the disaster highlights the early detection of hopper gregarization before they form devastating swarms. However, the response of hopper band emergence to environmental fluctuation exhibits a time lag. To realize the dynamic forecast of band occurrence with optimal temporal predictors, we proposed an SVM-based model with a temporal sliding window technique by coupling multisource time-series imagery with historical locust ground survey observations from between 2000–2020. The sliding window method was based on a lagging variable importance ranking used to analyze the temporal organization of environmental indicators in band-forming sequences and eventually facilitate the early prediction of band emergence. Statistical results show that hopper bands are more likely to occur within 41–64 days after increased rainfall; soil moisture dynamics increasing by approximately 0.05 m³/m³ then decreasing may enhance the chance of observing bands after 73–80 days. While sparse vegetation areas with NDVI increasing from 0.18 to 0.25 tend to witness bands after 17–40 days. The forecast model combining the optimal time lags of these dynamic indicators with other static indicators allows for a 16-day extended outlook of band presence in Somalia, Ethiopia, and Kenya. Monthly predictions from February to December 2020 display an overall accuracy of 77.46%, with an average ROC-AUC of 0.767 and a mean F-score close to 0.772. The multivariate forecast framework based on the lagging effect can realize the early warning of band presence in different spatiotemporal scenarios, supporting early decisions and response strategies for desert locust preventive management.
Design and Evaluation of a Heterogeneous Lightweight Blockchain-Based Marketplace
Authors: Guerra Javier Antonio , Guerrero Juan Ignacio , García Sebastián , Domínguez-Cid Samuel , Larios Diego Francisco , León Carlos
Published: February 2022

The proposal of this paper is to introduce a low-level blockchain marketplace, which is a blockchain where participants could share its power generation and demand. To achieve this implementation in a secure way for each actor in the network, we proposed to deploy it over efficient and generic low-performance devices. Thus, they are installed as IoT devices, registering measurements each fifteen minutes, and also acting as blockchain nodes for the marketplace. Nevertheless, it is necessary that blockchain is lightweight, so it is implemented as a specific consensus protocol that allows each node to have enough time and computer requirements to act both as an IoT device and a blockchain node. This marketplace will be ruled by Smart Contracts deployed inside the blockchain. With them, it is possible to make registers for power generation and demand. This low-level marketplace could be connected to other services to execute matching algorithms from the data stored in the blockchain. Finally, a real test-bed implementation of the marketplace was tested, to confirm that it is technically feasible.
Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
Authors: Kaur Ranpreet , GholamHosseini Hamid , Sinha Roopak , Lindén Maria
Published: February 2022

Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life.
In Situ Flow Cytometer Calibration and Single-Molecule Resolution via Quantum Measurement
Authors: Sabines-Chesterking Javier , Burenkov Ivan A. , Polyakov Sergey V.
Published: February 2022

Fluorescent biomarkers are used to detect target molecules within inhomogeneous populations of cells. When these biomarkers are found in trace amounts it becomes extremely challenging to detect their presence in a flow cytometer. Here, we present a framework to draw a detection baseline for single emitters and enable absolute calibration of a flow cytometer based on quantum measurements. We used single-photon detection and found the second-order autocorrelation function of fluorescent light. We computed the success of rare-event detection for different signal-to-noise ratios (SNR). We showed high-accuracy identification of the events with occurrence rates below 10−5 even at modest SNR levels, enabling early disease diagnostics and post-disease monitoring.
High-Resolution Detection of Rock-Forming Minerals by Permittivity Measurements with a Near-Field Scanning Microwave Microscope
Authors: Gutiérrez-Cano José D. , Catalá-Civera José M. , López-Buendía Angel M. , Plaza-González Pedro J. , Penaranda-Foix Felipe L.
Published: February 2022

The identification of the minerals composing rocks and their dielectric characterization is essential for the utilization of microwave energy in the rock industry. This paper describes the use of a near-field scanning microwave microscope with enhanced sensitivity for non-invasive measurements of permittivity maps of rock specimens at the micrometer scale in non-contact mode. The microwave system comprises a near-field probe, an in-house single-port vectorial reflectometer, and all circuitry and software needed to make a stand-alone, portable instrument. The relationship between the resonance parameters of the near-field probe and the dielectric properties of materials was determined by a combination of classical cavity perturbation theory and an image charge model. The accuracy of this approach was validated by a comparison study with reference materials. The device was employed to determine the permittivity maps of a couple of igneous rock specimens with low-loss and high-loss minerals. The dielectric results were correlated with the minerals comprising the samples and compared with the dielectric results reported in the literature, with excellent agreements.
The Efficient Mobile Management Based on Metaheuristic Algorithm for Internet of Vehicle
Authors: Huang Shih-Yun , Chen Shih-Syun , Chen Min-Xiou , Chang Yao-Chung , Chao Han-Chieh
Published: February 2022

With the low latency, high transmission rate, and high reliability provided by the fifth-generation mobile communication network (5G), many applications requiring ultra-low latency and high reliability (uRLLC) have become a hot research topic. Among these issues, the most important is the Internet of Vehicles (IoV). To maintain the safety of vehicle drivers and road conditions, the IoV can transmit through sensors or infrastructure to maintain communication quality and transmission. However, because 5G uses millimeter waves for transmission, a large number of base stations (BS) or lightweight infrastructure will be built in 5G, which will make the overall environment more complex than 4G. The lightweight infrastructure also has to be considered together. For these reasons, in 5G, there are two mechanisms for handover, horizontal, and vertical handover; hence, it must be discussed how to handle handover to obtain the best performance for the whole network. In this paper, to address handover selection, we consider delay time, energy efficiency, load balancing, and energy consumption and formulate it as a multi-objective optimization (MOO) problem. At the same time, we propose the handover of the mobile management mechanism based on location prediction combined with heuristic algorithms. The results show that our proposed mechanism is better than the distance-based one for energy efficiency, load, and latency. It optimizes by more than about 20% at most.
Connected Vehicles: V2V and V2I Road Weather and Traffic Communication Using Cellular Technologies
Authors: Tahir Muhammad Naeem , Leviäkangas Pekka , Katz Marcos
Published: February 2022

There is a continuous need to design and develop wireless technologies to meet the increasing demands for high-speed wireless data transfer to incorporate advanced intelligent transport systems. Different wireless technologies are continuously evolving including short-range and long-range (WiMAX, LTE, and 5G) cellular standards. These emerging technologies can considerably enhance the operational performance of communication between vehicles and road-side infrastructure. This paper analyzes the performance of cellular-based long-term evolution (LTE) and 5GTN (5G Test Network) in pilot field measurements (i.e., vehicle-to-vehicle and vehicle-to-infrastructure) when delivering road weather and traffic information in real-time environments. Measurements were conducted on a test track operated and owned by the Finnish Meteorological Institute (FMI), Finland. The results showed that 5GTN outperformed LTE when exchanging road weather and traffic data messages in V2V and V2I scenarios. This comparison was made by mainly considering bandwidth, throughput, packet loss, and latency. The safety critical messages were transmitted at a transmission frequency of 10 Hz. The performance of both compared technologies (i.e., LTE and 5GTN) fulfilled the minimum requirements of the ITS-Assisted Road weather and traffic platform to offer reliable communication for enhanced road traffic safety. The field measurement results also illustrate the advantage of cellular networks (LTE and 5GTN) with a clear potential to use it heterogeneously in future field tests with short-range protocols, e.g., IEEE 802.11p.
Capacitive Photodetector Thin-Film Cells of Cu-As2S3-Cu as Revealed by Dielectric Spectroscopy
Authors: Ganea Paul , Socol Gabriel , Zamfira Sorin , Creţu Nicolae , Matei Elena , Lőrinczi Adam
Published: February 2022

The As2S3-Cu interface was studied by dielectric spectroscopy measurements on Cu-As2S3-Cu thin film heterostructure samples to assess the charge carriers’ contribution to the electrical properties of such an interface. Three-dimensional printed masks ensured good reproducibility during the PLD deposition of heterostructure samples. The samples were tested for electrical conductivity and AC photoconductivity by dielectric spectroscopy measurements. DC bias voltages and light were applied to the samples. The electrical capacity of the thin film heterostructure can be modified electrically and optically. We observed long-term photoconductivity with a time dependency that was not exponential, and a quick change of the electrical capacity, indicating the potential of the heterostructure cells as photodetector candidates.
A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography
Authors: Tsai Kuen-Jang , Chou Mei-Chun , Li Hao-Ming , Liu Shin-Tso , Hsu Jung-Hsiu , Yeh Wei-Cheng , Hung Chao-Ming , Yeh Cheng-Yu , Hwang Shaw-Hwa
Published: February 2022

Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.
Study on Machine Learning Models for Building Resilience Evaluation in Mountainous Area: A Case Study of Banan District, Chongqing, China
Authors: Zhang Chi , Wen Haijia , Liao Mingyong , Lin Yu , Wu Yang , Zhang Hui
Published: February 2022

‘Resilience’ is a new concept in the research and application of urban construction. From the perspective of building adaptability in a mountainous environment and maintaining safety performance over time, this paper innovatively proposes machine learning methods for evaluating the resilience of buildings in a mountainous area. Firstly, after considering the comprehensive effects of geographical and geological conditions, meteorological and hydrological factors, environmental factors and building factors, the database of building resilience evaluation models in a mountainous area is constructed. Then, machine learning methods such as random forest and support vector machine are used to complete model training and optimization. Finally, the test data are substituted into models, and the models’ effects are verified by the confusion matrix. The results show the following: (1) Twelve dominant impact factors are screened. (2) Through the screening of dominant factors, the models are comprehensively optimized. (3) The accuracy of the optimization models based on random forest and support vector machine are both 97.4%, and the F1 scores are greater than 94.4%. Resilience has important implications for risk prevention and the control of buildings in a mountainous environment.
SP-WVD with Adaptive-Filter-Bank-Supported RF Sensor for Low RCS Targets’ Nonlinear Micro-Doppler Signature/Pattern Imaging System
Authors: Kumawat Harish C. , Raj A Arockia Bazil
Published: February 2022

In this study, the authors present the accurate imaging of the behavior of simultaneous operations of multiple low radar cross-section (RCS) aerial targets. Currently, the popularity of low RCS targets is increasing day by day, and detection and identification of these targets have become critical issues. Micro-Doppler signatures are key components for detecting and identifying these low RCS targets. For this, an innovative approach is proposed along with the smooth pseudo-Wigner–Ville distribution (SP-WVD) and adaptive filter bank to improve the attenuation of cross-term interferences to generate more accurate images for the micro-Doppler signatures/patterns of simultaneous multiple targets. A C-band (5.3 GHz) radio-frequency (RF) sensor is designed and used to acquire the micro-Doppler signatures of aerial rotational, flapping, and motional low RCS targets. Digital pipelined-parallel architecture is designed inside the Xilinx field-programable gate array (FPGA) for fast sensor data collection, data preprocessing, and interface to the computer (imaging algorithm). The experimental results of the proposed approach are validated with the results of the classical short-term Fourier transform (STFT), continuous wavelet transform (CWT), and smooth pseudo-Wigner Ville distribution (SP-WVD). Realistic open-field outdoor experiments are conducted covering different simultaneous postures of (i) two-/three-blade propeller/roto systems, (ii) flapping bionic bird, and (iii) kinetic warhead targets. The associated experimental results and findings are reported and analyzed in this paper. The limitations and possible future research studies are also discussed in the conclusion.
Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data
Authors: Jaber Mustafa Musa , Alameri Thamer , Ali Mohammed Hasan , Alsyouf Adi , Al-Bsheish Mohammad , Aldhmadi Badr K. , Ali Sarah Yahya , Abd Sura Khalil , Ali Saif Mohammed , Albaker Waleed , Jarrar Mu’taman
Published: February 2022

Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient’s health condition remotely. Every layer has specific functionality in the COVID-19 symptoms’ monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis.
Correlation Coefficient Based Optimal Vibration Sensor Placement and Number
Authors: Shin Geon-Ho , Hur Jang-Wook
Published: February 2022

Vibration sensors are mostly used for fault diagnoses of machines or structures. If more sensors are applied, more accurate fault diagnosis is possible. However, it will obviously cost more. There are many approaches to optimize the number and installation location/point of vibration sensors for more efficient fault diagnosis. Existing methods require a great deal of computational throughput for optimization when considering many mode frequencies with points where vibration sensors are likely to be installed. This paper proposes a practical way of optimizing the sensor installation point considering many mode frequencies with lots of places for sensor installation. FEA was conducted to identify displacement values of each frequency in the candidate points. Then, correlation coefficients were applied to the FEA result to optimize the installation location and number of vibration sensors. Taking into account cases where the number of sensors has been set by users, FIM was applied. The correlation coefficient optimized the candidate points where 24,252 vibration sensors were to be installed and reduced this to 10 points. FIM, which was not suitable for directly optimizing sensor locations because it required a lot of computational throughput and was usually applied to evaluate other methods, is now applicable to candidate points that have been reduced by the correlation coefficient. This paper does not draw the best optimal sensor location but presents a way to apply to large-scale or complicated forms with a little computational throughput.
Telehealth Secure Solution to Provide Childhood Obesity Monitoring
Authors: Jiménez-García Elitania , Murillo-Escobar Miguel Ángel , Fontecha-Diezma Jesús , López-Gutiérrez Rosa Martha , Cardoza-Avendaño Liliana
Published: February 2022

Childhood obesity causes not only medical and psychosocial problems, it also reduces the life expectancy of the adults that they will become. On a large scale, obese adults adversely affect labor markets and the gross domestic product of countries. Monitoring the growth charts of children helps to maintain their body weight within healthy parameters according to the World Health Organization. Modern technologies allow the use of telehealth to carry out weight control programs and monitoring to verify children’s compliance with the daily recommendations for risk factors that can be promoters of obesity, such as insufficient physical activity and insufficient sleep hours. In this work, we propose a secure remote monitoring and supervision scheme of physical activity and sleep hours for the children based on telehealth, multi-user networks, chaotic encryption, and spread spectrum, which, to our knowledge, is the first attempt to consider this service for safe pediatric telemedicine. In experimental results, we adapted a recent encryption algorithm in the literature for the proposed monitoring scheme using the assessment of childhood obesity as an application case in a multi-user network to securely send and receive fictitious parameters on childhood obesity of five users through the Internet by using just one communication channel. The results show that all the monitored parameters can be transmitted securely, achieving high sensitivity against secret key, enough secret key space, high resistance against noise interference, and 4.99 Mb/sec in computational simulations. The proposed scheme can be used to monitor childhood obesity in secure telehealth application.
Exhaustive Search of Correspondences between Multimodal Remote Sensing Images Using Convolutional Neural Network
Authors: Uss Mykhail , Vozel Benoit , Lukin Vladimir , Chehdi Kacem
Published: February 2022

Finding putative correspondences between a pair of images is an important prerequisite for image registration. In complex cases such as multimodal registration, a true match could be less plausible than a false match within a search zone. Under these conditions, it is important to detect all plausible matches. This could be achieved by an exhaustive search using a handcrafted similarity measure (SM, e.g., mutual information). It is promising to replace handcrafted SMs with deep learning ones that offer better performance. However, the latter are not designed for an exhaustive search of all matches but for finding the most plausible one. In this paper, we propose a deep-learning-based solution for exhaustive multiple match search between two images within a predefined search area. We design a computationally efficient convolutional neural network (CNN) that takes as input a template fragment from one image, a search fragment from another image and produces an SM map covering the entire search area in spatial dimensions. This SM map finds multiple plausible matches, locates each match with subpixel accuracy and provides a covariance matrix of localization errors for each match. The proposed CNN is trained with a specially designed loss function that enforces the translation and rotation invariance of the SM map and enables the detection of matches that have no associated ground truth data (e.g., multiple matches for repetitive textures). We validate the approach on multimodal remote sensing images and show that the proposed “area” SM performs better than “point” SM.
Deep Multi-Feature Transfer Network for Fourier Ptychographic Microscopy Imaging Reconstruction
Authors: Wang Xiaoli , Piao Yan , Yu Jinyang , Li Jie , Sun Haixin , Jin Yuanshang , Liu Limin , Xu Tingfa
Published: February 2022

Fourier ptychographic microscopy (FPM) is a potential imaging technique, which is used to achieve wide field-of-view (FOV), high-resolution and quantitative phase information. The LED array is used to irradiate the samples from different angles to obtain the corresponding low-resolution intensity images. However, the performance of reconstruction still suffers from noise and image data redundancy, which needs to be considered. In this paper, we present a novel Fourier ptychographic microscopy imaging reconstruction method based on a deep multi-feature transfer network, which can achieve good anti-noise performance and realize high-resolution reconstruction with reduced image data. First, in this paper, the image features are deeply extracted through transfer learning ResNet50, Xception and DenseNet121 networks, and utilize the complementarity of deep multiple features and adopt cascaded feature fusion strategy for channel merging to improve the quality of image reconstruction; then the pre-upsampling is used to reconstruct the network to improve the texture details of the high-resolution reconstructed image. We validate the performance of the reported method via both simulation and experiment. The model has good robustness to noise and blurred images. Better reconstruction results are obtained under the conditions of short time and low resolution. We hope that the end-to-end mapping method of neural network can provide a neural-network perspective to solve the FPM reconstruction.
Geodesics of a Static Charged Black Hole Spacetime in f(R) Gravity
Authors: Sharma Prateek , Nandan Hemwati , Nashed Gamal G. L. , Giri Shobhit , Abebe Amare
Published: February 2022

In recent years, the modification of general relativity (GR) through f(R) gravity is widely used to study gravity in a variety of scenarios. In this article, we study various physical properties of a black hole (BH) that emerged in the linear Maxwell f(R) gravity to constrain the values of different BH parameters, i.e., c and α. In particular, we study those values of the defining α and c for which the particles around the above-mentioned BH behave like other astrophysical BH in GR. The main motivation of the present research is to study the geodesics equations and discuss the possible orbits for c=0.5 in detail. Furthermore, the frequency shift of a photon emitted by a timelike particle orbiting around the BH is studied given different values of α and c. The stability of both timelike and null geodesics is discussed via Lyapunov’s exponent.
An Efficient Variant of Pollard’s p − 1 for the Case That All Prime Factors of the p − 1 in B-Smooth
Authors: Somsuk Kritsanapong
Published: February 2022

Due to the computational limitations at present, there is no efficient integer factorization algorithm that can break at least 2048 bits of RSA with strong prime factors in polynomial time. Although Shor’s algorithm based on a quantum computer has been presented, the quantum computer is still in its early stages of the development. As a result, the integer factorization problem (IFP) is a technique that is still being refined. Pollard’s p − 1 is an integer factorization algorithm based on all prime factors of p − 1 or q − 1, where p and q are two distinct prime factors of modulus. In fact, Pollard’s p − 1 is an efficient method when all prime factors of p − 1 or q − 1 are small. The aim of this paper is to propose a variant of Pollard’s p − 1 in order to decrease the computation time. In general, the proposed method is very efficient when all prime factors of p − 1 or q − 1 are the members of B-smooth. Assuming this condition exists, the experimental results demonstrate that the proposed method is approximately 80 to 90 percent faster than Pollard’s p − 1. Furthermore, the proposed technique is still faster than Pollard’s p − 1 for some values of modulus in which at least one integer is a prime factor of p − 1 or q − 1 while it is not a member of B-smooth. In addition, it is demonstrated that the proposed method’s best-case running time is O(x),where x is represented as bits length of n.