bluuebunny/crossref_metadata_2025 huggingface dataset:
Phaseless Terahertz Coded-Aperture Imaging Based on Deep Generative Neural Network
Authors: Gan Fengjiao , Yuan Ziyang , Luo Chenggao , Wang Hongqiang
Published: February 2021

As a promising terahertz radar imaging technology, phaseless terahertz coded-aperture imaging (PL-TCAI) has many advantages such as simple system structure, forward-looking imaging and staring imaging and so forth. However, it is very difficult to recover a target only from its intensity measurements. Although some methods have been proposed to deal with this problem, they require a large number of intensity measurements for both sparse and extended target reconstruction. In this work, we propose a method for PL-TCAI by modeling target scattering coefficient as being in the range of a generative model. Theoretically, we analyze and model the system structure, derive the matrix imaging equation, and then study the deep phase retrieval algorithm. Numerical tests based on different generative models show that the targets with the different spareness can achieve high resolution reconstruction when the number of intensity measurements are smaller than the number of target grids. Also, we find that the proposed method has good anti-noise and stability.
Three-Dimensional Characterization of a Coastal Mode-Water Eddy from Multiplatform Observations and a Data Reconstruction Method
Authors: Manso-Narvarte Ivan , Rubio Anna , Jordà Gabriel , Carpenter Jeffrey , Merckelbach Lucas , Caballero Ainhoa
Published: February 2021

Coastal mesoscale eddies are important oceanic structures partially responsible for regulating ocean-shelf exchanges. However, their description and characterization are challenging; observations are often too scarce for studying their physical properties and environmental impacts at the required spatio-temporal resolution. Therefore, models and data extrapolation methods are key tools for this purpose. Observations from high-frequency radar, one satellite and two gliders, are used here to better characterize the three-dimensional structure of a coastal mode-water eddy from a multiplatform approach in the southeastern Bay of Biscay in spring 2018. After the joint analysis of the observations, a three-dimensional data reconstruction method is applied to reconstruct the eddy current velocity field and estimate the associated water volume transport. The target eddy is detected by surface observations (high-frequency radar and satellite) for two weeks and presents similar dimensions and lifetimes as other eddies studied previously in the same location. However, this is the first time that the water column properties are also observed for this region, which depicts a mode-water eddy behavior, i.e., an uplift of the isopycnals in the near-surface and a downlift deeper in the water column. The reconstructed upper water column (1–100 m) eddy dynamics agree with the geostrophic dynamics observed by one of the gliders and result in cross-shelf inshore (offshore) volume transports between 0.04 (−0.01) and 0.15 (−0.11) Sv. The multiplatform data approach and the data reconstruction method are here highlighted as useful tools to characterize and three-dimensionally reconstruct coastal mesoscale processes in coastal areas.
Assessing Phytoplankton Bloom Phenology in Upwelling-Influenced Regions Using Ocean Color Remote Sensing
Authors: Ferreira Afonso , Brotas Vanda , Palma Carla , Borges Carlos , Brito Ana C.
Published: February 2021

Phytoplankton bloom phenology studies are fundamental for the understanding of marine ecosystems. Mismatches between fish spawning and plankton peak biomass will become more frequent with climate change, highlighting the need for thorough phenology studies in coastal areas. This study was the first to assess phytoplankton bloom phenology in the Western Iberian Coast (WIC), a complex coastal region in SW Europe, using a multisensor long-term ocean color remote sensing dataset with daily resolution. Using surface chlorophyll a (chl-a) and biogeophysical datasets, five phenoregions (i.e., areas with coherent phenology patterns) were defined. Oceanic phytoplankton communities were seen to form long, low-biomass spring blooms, mainly influenced by atmospheric phenomena and water column conditions. Blooms in northern waters are more akin to the classical spring bloom, while blooms in southern waters typically initiate in late autumn and terminate in late spring. Coastal phytoplankton are characterized by short, high-biomass, highly heterogeneous blooms, as nutrients, sea surface height, and horizontal water transport are essential in shaping phenology. Wind-driven upwelling and riverine input were major factors influencing bloom phenology in the coastal areas. This work is expected to contribute to the management of the WIC and other upwelling systems, particularly under the threat of climate change.
A Lightweight Object Detection Framework for Remote Sensing Images
Authors: Huyan Lang , Bai Yunpeng , Li Ying , Jiang Dongmei , Zhang Yanning , Zhou Quan , Wei Jiayuan , Liu Juanni , Zhang Yi , Cui Tao
Published: February 2021

Onboard real-time object detection in remote sensing images is a crucial but challenging task in this computation-constrained scenario. This task not only requires the algorithm to yield excellent performance but also requests limited time and space complexity of the algorithm. However, previous convolutional neural networks (CNN) based object detectors for remote sensing images suffer from heavy computational cost, which hinders them from being deployed on satellites. Moreover, an onboard detector is desired to detect objects at vastly different scales. To address these issues, we proposed a lightweight one-stage multi-scale feature fusion detector called MSF-SNET for onboard real-time object detection of remote sensing images. Using lightweight SNET as the backbone network reduces the number of parameters and computational complexity. To strengthen the detection performance of small objects, three low-level features are extracted from the three stages of SNET respectively. In the detection part, another three convolutional layers are designed to further extract deep features with rich semantic information for large-scale object detection. To improve detection accuracy, the deep features and low-level features are fused to enhance the feature representation. Extensive experiments and comprehensive evaluations on the openly available NWPU VHR-10 dataset and DIOR dataset are conducted to evaluate the proposed method. Compared with other state-of-art detectors, the proposed detection framework has fewer parameters and calculations, while maintaining consistent accuracy.
Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin
Authors: Zhou Shilun , Zhang Wanchang , Wang Shuhang , Zhang Bo , Xu Qiang
Published: February 2021

Information about the growth, productivity, and distribution of vegetation, which are highly relied on and sensitive to natural and anthropogenic factors, is essential for agricultural production management and eco-environmental sustainability in the Amur River Basin (ARB). In this paper, the spatial–temporal trends of vegetation dynamics were analyzed at the pixel scale in the ARB for the period of 1982–2013 using remotely sensed data of long-term leaf area index (LAI), fractional vegetation cover (FVC), and terrestrial gross primary productivity (GPP). The spatial autocorrelation characteristics of the vegetation indexes were further explored with global and local Moran’s I techniques. The spatial–temporal relationships between vegetation and climatic factors, land use/cover types and hydrological variables in the ARB were determined using a geographical and temporal weighted regression (GTWR) model based on the observed meteorological data, remotely sensed vegetation information, while the simulated hydrological variables were determined with the soil and water assessment tool (SWAT) model. The results suggest that the variation in area-average annual FVC was significant with an increase rate of 0.0004/year, and LAI, FVC, and GPP all exhibited strong spatial heterogeneity trends in the ARB. For LAI and FVC, the most significant changes in local spatial autocorrelation were recognized over the Sanjiang Plain, and the low–low agglomeration in the Sanjiang Plain decreased continuously. The GTWR model results indicate that natural and anthropogenic factors jointly took effect and interacted with each other to affect the vegetated regime of the region. The decrease in the impact of precipitation to vegetation growth over the Songnen Plain was determined as having started around 1991, which was most likely attributed to dramatic changes in water use styles induced by local land use changes, and corresponded to the negative correlation between pasture areas and vegetation indexes during the same period. The analysis results presented in this paper can provide vital information to decision-makers for use in managing vegetation resources.
Performance Evaluation of Different SAR-Based Techniques on the 2019 Ridgecrest Sequence
Authors: Polcari Marco , Palano Mimmo , Moro Marco
Published: February 2021

We evaluated the performances of different SAR-based techniques by analyzing the surface coseismic displacement related to the 2019 Ridgecrest seismic sequence (an Mw 6.4 foreshock on July 4th and an Mw 7.1 mainshock on July 6th) in the tectonic framework of the eastern California shear zone (Southern California, USA). To this end, we compared and validated the retrieved SAR-based coseismic displacement with the one estimated by a dense GNSS network, extensively covering the study area. All the SAR-based techniques constrained the surface fault rupture well; however, in comparison with the GNSS-based coseismic displacement, some significant differences were observed. InSAR data showed better performance than MAI and POT data by factors of about two and three, respectively, therefore confirming that InSAR is the most consolidated technique to map surface coseismic displacements. However, MAI and POT data made it possible to better constrain the azimuth displacement and to retrieve the surface rupture trace. Therefore, for cases of strike-slip earthquakes, all the techniques should be exploited to achieve a full synoptic view of the coseismic displacement field.
Spatiotemporal Distributions of Ocean Color Elements in Response to Tropical Cyclone: A Case Study of Typhoon Mangkhut (2018) Past over the Northern South China Sea
Authors: Li Junyi , Zheng Quanan , Li Min , Li Qiang , Xie Lingling
Published: February 2021

The ocean color elements refer to total suspended sediment (TSS) and chlorophyll-a (Chl-a), which are important parameters for the marine ecological environment. This study aims to examine the behavior of ocean color elements in response to a tropical cyclone in the case of typhoon Mangkhut (2018), which passed over the northern South China Sea (NSCS) on 16 September 2018, using satellite multi-sensor observations, Argo float profiles, and tidal gauge sea level data. The results indicate that typhoon Mangkhut (2018) resulted in TSS and Chl-a concentrations increasing, with the spatial and timing behavior different in the offshore, shelf, and basin areas. In the offshore area from the coast to isobath 50 m, the mean TSS concentration, i.e., CTSS, reached 13.9 mg/L on 18 September 2018, two days after typhoon landfall, against about 3.5 mg/L before typhoon landfall. In the shelf area with depths from 50 m to 100 m, the mean CTSS reached 2.5 mg/L, against about 0.8 mg/L before typhoon landfall. In the basin area with depths of 100 m and beyond, the mean CTSS had only a little fluctuation. On the other hand, in the offshore area, the mean Chl-a concentration, i.e., CChl-a, was 7.3 mg/m3 on 21 September, five days after typhoon landfall, against 2.4 mg/m3 as the monthly mean value. Furthermore, TSS concentrations favorable for Chl-a bloom range from 6 to 7 mg/L in this area. In the shelf area, the mean CChl-a increased from 0.2 mg/m3 to 0.6 mg/m3 in two days. In the basin area, the CChl-a increased from 0.1 mg/m3 to 0.2 mg/m3 during typhoon passage. Concurrent dynamic condition analysis results indicate that, in the offshore area, typhoon-induced solitary continental waves may play a dominant role in determining the spatial distribution features of the TSS originating from the Pearl River runoff. The Chl-a bloom delayed rather than concurrently occurred with the terrigenous nutrient peak, which is attributed to the nonlinear relation between CChl-a and CTSS. In the shelf and basin areas, typhoon-enhanced vertical mixing and upwelling may play dominant roles in determining the spatiotemporal behavior of the TSS and the Chl-a.
NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau
Authors: Guo Enliang , Wang Yongfang , Wang Cailin , Sun Zhongyi , Bao Yulong , Mandula Naren , Jirigala Buren , Bao Yuhai , Li He
Published: February 2021

In recent years, global warming and intense human activity have been responsible for significantly altering vegetation dynamics on the Mongolian Plateau. Understanding the long-term vegetation dynamics in this region is important to assess the impact of these changes on the local ecosystem. Long-term (1982–2015), satellite-derived normalized difference vegetation index (NDVI) datasets were used to analyse the spatio-temporal patterns of vegetation activities using linear regression and the breaks for additive season and trend methods. The links between these patterns and changes in temperature, precipitation (PRE), soil moisture (SM), and anthropogenic activity were determined using partial correlation analysis, the residual trends method, and a stepwise multiple regression model. The most significant results indicated that air temperature and potential evapotranspiration increased significantly, while the SM and PRE had markedly decreased over the past 34 years. The NDVI dataset included 71.16% of pixels showing an increase in temperature and evaporation during the growing season, particularly in eastern Mongolia and the southern border of the Inner Mongolia Autonomous region, China. The proportion indicating the breakpoint of vegetation dynamics was 71.34% of pixels, and the trend breakpoints mainly occurred in 1993, 2003, and 2010. The cumulative effects of PRE and SM in the middle period, coupled with the short-term effects of temperature and potential evapotranspiration, have had positive effects on vegetation greening. Anthropogenic factors appear to have positively impacted vegetation dynamics, as shown in 81.21% of pixels. We consider rapid economic growth, PRE, and SM to be the main driving factors in Inner Mongolia. PRE was the main climatic factor, and combined human and livestock populations were the primary anthropogenic factors influencing vegetation dynamics in Mongolia. This study is important in promoting the continued use of green projects to address environmental change in the Mongolian Plateau.
Boundary-Aware Refined Network for Automatic Building Extraction in Very High-Resolution Urban Aerial Images
Authors: Jin Yuwei , Xu Wenbo , Zhang Ce , Luo Xin , Jia Haitao
Published: February 2021

Convolutional Neural Networks (CNNs), such as U-Net, have shown competitive performance in the automatic extraction of buildings from Very High-Resolution (VHR) aerial images. However, due to the unstable multi-scale context aggregation, the insufficient combination of multi-level features and the lack of consideration of the semantic boundary, most existing CNNs produce incomplete segmentation for large-scale buildings and result in predictions with huge uncertainty at building boundaries. This paper presents a novel network with a special boundary-aware loss embedded, called the Boundary-Aware Refined Network (BARNet), to address the gap above. The unique properties of the proposed BARNet are the gated-attention refined fusion unit, the denser atrous spatial pyramid pooling module, and the boundary-aware loss. The performance of the BARNet is tested on two popular data sets that include various urban scenes and diverse patterns of buildings. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches in both visual interpretation and quantitative evaluations.
The Analysis and Modelling of the Quality of Information Acquired from Weather Station Sensors
Authors: Stawowy Marek , Olchowik Wiktor , Rosiński Adam , Dąbrowski Tadeusz
Published: February 2021

This article explores the quality of information acquired from weather station sensors. A review of literature in this field concludes that most publications concern the analysis of data acquired from weather station sensors and their characteristic properties, estimating the missing values from the data, and assessing the quality of weather information. Despite the large collection of studies devoted to these issues, there is no comprehensive approach that would consider the modelling of information uncertainty. Therefore, the article presents a proprietary method of analysing and modelling the uncertainty of the weather station sensors’ information quality. For this purpose, the structure of a real meteorological station and the measurement data obtained from it were analysed. Next, an information quality model was developed using the certainty factor (CF) of hypothesis calculation. The developed method was verified on an exemplary real meteorological station. It was found that this method enables the improvement of the quality of information obtained and processed in a multi-sensor system. This becomes practical when the influence of individual measurement system elements on the information quality reaching the recipient is determined. An example is furnished by a demonstration of the usage of two sensors to improve the information quality.
Evaluating the Near and Mid Infrared Bi-Spectral Space for Assessing Fire Severity and Comparison with the Differenced Normalized Burn Ratio
Authors: van Gerrevink Max J. , Veraverbeke Sander
Published: February 2021

Fire severity, defined as the degree of environmental change caused by a fire, is a critical fire regime attribute of interest to fire emissions modelling and post-fire rehabilitation planning. Remotely sensed fire severity is traditionally assessed by the differenced normalized burn ratio (dNBR). This spectral index captures fire-induced reflectance changes in the near infrared (NIR) and short-wave infrared (SWIR) spectral regions. This study evaluates a spectral index based on a band combination including the NIR and mid infrared (MIR) spectral regions, the differenced normalized difference vegetation index with mid infrared (dNDVIMID), to assess fire severity. This evaluation capitalized upon the unique opportunity stemming from the pre- and post-fire airborne acquisitions over the Rim (2013) and King (2014) fires in California with the MODIS/ASTER Airborne Simulator (MASTER) instrument. The field data consist of 85 Geometrically structured Composite Burn Index (GeoCBI) plots. In addition, six different index combinations, respectively three with a NIR–SWIR combination and three with a NIR–MIR combination, were evaluated based on the optimality of fire-induced spectral displacements. The optimality statistic ranges between zero and one, with values of one representing pixel displacements that are unaffected by noise. The results show that the dNBR demonstrated a stronger relationship with GeoCBI field data when field measurements over the two fire scars were combined than the dNDVIMID approaches. The results yielded an R2 of 0.68 based on a saturated growth model for the best performing dNBR index, whereas the performance of the dNDVIMID indices was lower with an R2 = 0.61 for the best performing dNDVIMID index. The dNBR also outperformed the dNDVIMID in terms of spectral optimality across both fires. The best performing dNBR index yielded median optimality statistics of 0.56 over the Rim and 0.60 over the King fire. The best performing dNDVIMID index recorded optimality values of 0.49 over the Rim and 0.46 over the King fire. We also found that the dNBR approach led to considerable differences in the form of the relationship with the GeoCBI between the two fires, whereas the dNDVIMID approach yielded comparable relationships with the GeoCBI over the two fires. This suggests that the dNDVIMID approach, despite its slightly lower performance than the dNBR, may be a more robust method for estimating and comparing fire severity over large regions. This premise needs additional verification when more air- or spaceborne imagery with NIR and MIR bands will become available with a spatial resolution that allows ground truthing of fire severity.
Potential of P-Band SAR Tomography in Forest Type Classification
Authors: Ho Tong Minh Dinh , Ngo Yen-Nhi , Lê Thu Trang
Published: February 2021

Forest type classification using spaceborne remote sensing is a challenge. Low-frequency Synthetic Aperture Radar (SAR) signals (i.e., P-band, ∼0.69 m wavelength) are needed to penetrate a thick vegetation layer. However, this measurement alone does not guarantee a good performance in forest classification tasks. SAR tomography, a technique employing multiple acquisitions over the same areas to form a three-dimensional image, has been demonstrated to improve SAR’s capability in many applications. Our study shows the potential value of SAR tomography acquisitions to improve forest classification. By using P-band tomographic SAR data from the German Aerospace Center F-SAR sensor during the AfriSAR campaign in February 2016, the vertical profiles of five different forest types at a tropical forest site in Mondah, Gabon (South Africa) were analyzed and exploited for the classification task. We demonstrated that the high sensitivity of SAR tomography to forest vertical structure enables the improvement of classification performance by up to 33%. Interestingly, by using the standard Random Forest technique, we found that the ground (i.e., at 5–10 m) and volume layers (i.e., 20–40 m) play an important role in identifying the forest type. Together, these results suggested the promise of the TomoSAR technique for mapping forest types with high accuracy in tropical areas and could provide strong support for the next Earth Explorer BIOMASS spaceborne mission which will collect P-band tomographic SAR data.
Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar
Authors: Wang Binbin , Cha Hao , Zhou Zibo , Tian Bin
Published: February 2021

Clutter cancellation and long time integration are two vital steps for global navigation satellite system (GNSS)-based bistatic radar target detection. The former eliminates the influence of direct and multipath signals on the target detection performance, and the latter improves the radar detection range. In this paper, the extensive cancellation algorithm (ECA), which projects the surveillance channel signal in the subspace orthogonal to the clutter subspace, is first applied in GNSS-based bistatic radar. As a result, the clutter has been removed from the surveillance channel effectively. For long time integration, a modified version of the Fourier transform (FT), called long-time integration Fourier transform (LIFT), is proposed to obtain a high coherent processing gain. Relative acceleration (RA) is defined to describe the Doppler variation results from the motion of the target and long integration time. With the estimated RA, the Doppler frequency shift compensation is carried out in the LIFT. This method achieves a better and robust detection performance when comparing with the traditional coherent integration method. The simulation results demonstrate the effectiveness and advantages of the proposed processing method.
Wearable Inertial Measurement Unit Sensing System for Musculoskeletal Disorders Prevention in Construction
Authors: Zhao Junqi , Obonyo Esther , G. Bilén Sven
Published: February 2021

Construction workers executing manual-intensive tasks are susceptible to musculoskeletal disorders (MSDs) due to overexposure to awkward postures. Automated posture recognition and assessment based on wearable sensor output can help reduce MSDs risks through early risk-factor detection. However, extant studies mainly focus on optimizing recognition models. There is a lack of studies exploring the design of a wearable sensing system that assesses the MSDs risks based on detected postures and then provides feedback for injury prevention. This study aims at investigating the design of an effective wearable MSDs prevention system. This study first proposes the design of a wearable inertial measurement unit (IMU) sensing system, then develops the prototype for end-user evaluation. Construction workers and managers evaluated a proposed system by interacting with wearable sensors and user interfaces (UIs), followed by an evaluation survey. The results suggest that wearable sensing is a promising approach for collecting motion data with low discomfort; posture-based MSDs risk assessment has a high potential in improving workers’ safety awareness; and mobile- and cloud-based UIs can deliver the risk assessment information to end-users with ease. This research contributes to the design, development, and validation of wearable sensing-based injury prevention systems, which may be adapted to other labor-intensive occupations.
Growth Mechanisms of ZnO Micro-Nanomorphologies and Their Role in Enhancing Gas Sensing Properties
Authors: Fioravanti Ambra , Marani Pietro , Morandi Sara , Lettieri Stefano , Mazzocchi Mauro , Sacerdoti Michele , Carotta Maria Cristina
Published: February 2021

Zinc oxide (ZnO) is one of the main functional materials used to realize chemiresistive gas sensors. In addition, ZnO can be grown through many different methods obtaining the widest family of unique morphologies. However, the relationship between the ZnO morphologies and their gas sensing properties needs more detailed investigations, also with the aim to improve the sensor performances. In this work, seven nanoforms (such as leaves, bisphenoids, flowers, needles, etc.) were prepared through simple wet chemical synthesis. Morphological and structural characterizations were performed to figure out their growth mechanisms. Then, the obtained powders were deposited through screen-printing technique to realize thick film gas sensors. The gas sensing behavior was tested toward some traditional target gases and some volatile organic compounds (acetone, acetaldehyde, etc.) and compared with ZnO morphologies. Results showed a direct correlation between the sensors responses and the powders features (morphology and size), which depend on the specific synthesis process. The sensors can be divided in two behavioral classes, following the two main morphology kinds: aggregates of nanocrystals (leaves and bisphenoids), exhibiting best performances versus all tested gases and monocrystal based (stars, needle, long needles, flowers, and prisms).
Rapid Analysis for Staphylococcus aureus via Microchip Capillary Electrophoresis
Authors: Chen Jin , Sun Yu , Peng Xiaogai , Ni Yi , Wang Fengchao , Dou Xiaoming
Published: February 2021

Staphylococcus aureus (S. aureus) is one of the most common pathogens for nosocomial and community infections, which is closely related to the occurrence of pyogenic and toxic diseases in human beings. In the current study, a lab-built microchip capillary electrophoresis (microchip CE) system was employed for the rapid determination of S. aureus, while a simple-to-use space domain internal standard (SDIS) method was carried out for the reliable quantitative analysis. The precision, accuracy, and reliability of SDIS were investigated in detail. Noted that these properties could be elevated in SDIS compared with traditional IS method. Remarkably, the PCR products of S. aureusnuc gene could be identified and quantitated within 80 s. The theoretical detection limit could achieve a value of 0.066 ng/μL, determined by the using SDIS method. The current work may provide a promising detection strategy for the high-speed and highly efficient analysis of pathogens in the fields of food safety and clinical diagnosis.
Detection of an IL-6 Biomarker Using a GFET Platform Developed with a Facile Organic Solvent-Free Aptamer Immobilization Approach
Authors: Khan Niazul I. , Song Edward
Published: February 2021

Aptamer-immobilized graphene field-effect transistors (GFETs) have become a well-known detection platform in the field of biosensing with various biomarkers such as proteins, bacteria, virus, as well as chemicals. A conventional aptamer immobilization technique on graphene involves a two-step crosslinking process. In the first step, a pyrene derivative is anchored onto the surface of graphene and, in the second step, an amine-terminated aptamer is crosslinked to the pyrene backbone with EDC/NHS (1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride/N-hydroxysuccinimide) chemistry. However, this process often requires the use of organic solvents such as dimethyl formamide (DMF) or dimethyl sulfoxide (DMSO) which are typically polar aprotic solvents and hence dissolves both polar and nonpolar compounds. The use of such solvents can be especially problematic in the fabrication of lab-on-a-chip or point-of-care diagnostic platforms as they can attack vulnerable materials such as polymers, passivation layers and microfluidic tubing leading to device damage and fluid leakage. To remedy such challenges, in this work, we demonstrate the use of pyrene-tagged DNA aptamers (PTDA) for performing a one-step aptamer immobilization technique to implement a GFET-based biosensor for the detection of Interleukin-6 (IL-6) protein biomarker. In this approach, the aptamer terminal is pre-tagged with a pyrene group which becomes soluble in aqueous solution. This obviates the need for using organic solvents, thereby enhancing the device integrity. In addition, an external electric field is applied during the functionalization step to increase the efficiency of aptamer immobilization and hence improved coverage and density. The results from this work could potentially open up new avenues for the use of GFET-based BioMEMS platforms by broadening the choice of materials used for device fabrication and integration.
Porous Silicon Optical Devices: Recent Advances in Biosensing Applications
Authors: Moretta Rosalba , De Stefano Luca , Terracciano Monica , Rea Ilaria
Published: February 2021

This review summarizes the leading advancements in porous silicon (PSi) optical-biosensors, achieved over the past five years. The cost-effective fabrication process, the high internal surface area, the tunable pore size, and the photonic properties made the PSi an appealing transducing substrate for biosensing purposes, with applications in different research fields. Different optical PSi biosensors are reviewed and classified into four classes, based on the different biorecognition elements immobilized on the surface of the transducing material. The PL signal modulation and the effective refractive index changes of the porous matrix are the main optical transduction mechanisms discussed herein. The approaches that are commonly employed to chemically stabilize and functionalize the PSi surface are described.
Design, Implementation, and Measurement Procedure of Underwater and Water Surface Antenna for LoRa Communication
Authors: Dala Aliyu , Arslan Tughrul
Published: February 2021

There is an increasing interest in water bodies, which make up more that seventy percent of our planet. It is thus imperative that the water environment should be remotely monitored. Radio frequency (RF) signals have higher bandwidth and lower latency compared to acoustic signals. However, water has high permittivity and conductivity which presents a challenge for the implementation of RF technology. In this work, we undertook a novel design, fabrication, measurement and implementation of an antenna for a sensor node with dual ability of underwater and water surface long range (LoRa) communication at 868 MHz. It was observed that the antenna’s performance deteriorated underwater without −10 dB effective bandwidth between 668 MHz and 1068 MHz. The introduction of an oil-impregnated paper buffer around the antenna resulted in an effective 400 MHz bandwidth within the same frequency span. The sensor node with the buffered antenna was able to achieve a distance of 6 m underwater and 160 m over water surface communication to a data gateway node. The sensor node without the buffered antenna was only able to achieve 80 m over water surface communication. These experimental results show the feasibility of using the LoRa 868 MHz frequency in underwater and water surface communication.
Instantaneous Disturbance Index for Power Distribution Networks
Authors: Borrás-Talavera María Dolores , Bravo Juan Carlos , Álvarez-Arroyo César
Published: February 2021

The stability of power systems is very sensitive to voltage or current variations caused by the discontinuous supply of renewable power feeders. Moreover, the impact of these anomalies varies depending on the sensitivity/resilience of customer and transmission system equipment to those deviations. From any of these points of view, an instantaneous characterization of power quality (PQ) aspects becomes an important task. For this purpose, a wavelet-based power quality indices (PQIs) are introduced in this paper. An instantaneous disturbance index (ITD(t)) and a Global Disturbance Ratio index (GDR) are defined to integrally reflect the PQ level in Power Distribution Networks (PDN) under steady-state and/or transient conditions. With only these two indices it is possible to quantify the effects of non-stationary disturbances with high resolution and precision. These PQIs offer an advantage over other similar because of the suitable choice of mother wavelet function that permits to minimize leakage errors between wavelet levels. The wavelet-based algorithms which give rise to these PQIs can be implemented in smart sensors and used for monitoring purposes in PDN. The applicability of the proposed indices is validated by using a real-time experimental platform. In this emulated power system, signals are generated and real-time data are analyzed by a specifically designed software. The effectiveness of this method of detection and identification of disturbances has been proven by comparing the proposed PQIs with classical indices. The results confirm that the proposed method efficiently extracts the characteristics of each component from the multi-event test signals and thus clearly indicates the combined effect of these events through an accurate estimation of the PQIs.
Human Mesh Reconstruction with Generative Adversarial Networks from Single RGB Images
Authors: Gao Rui , Wen Mingyun , Park Jisun , Cho Kyungeun
Published: February 2021

Applications related to smart cities require virtual cities in the experimental development stage. To build a virtual city that are close to a real city, a large number of various types of human models need to be created. To reduce the cost of acquiring models, this paper proposes a method to reconstruct 3D human meshes from single images captured using a normal camera. It presents a method for reconstructing the complete mesh of the human body from a single RGB image and a generative adversarial network consisting of a newly designed shape–pose-based generator (based on deep convolutional neural networks) and an enhanced multi-source discriminator. Using a machine learning approach, the reliance on multiple sensors is reduced and 3D human meshes can be recovered using a single camera, thereby reducing the cost of building smart cities. The proposed method achieves an accuracy of 92.1% in body shape recovery; it can also process 34 images per second. The method proposed in this paper approach significantly improves the performance compared with previous state-of-the-art approaches. Given a single view image of various humans, our results can be used to generate various 3D human models, which can facilitate 3D human modeling work to simulate virtual cities. Since our method can also restore the poses of the humans in the image, it is possible to create various human poses by given corresponding images with specific human poses.
Reduced Graphene Oxide (rGO)-Loaded Metal-Oxide Nanofiber Gas Sensors: An Overview
Authors: Majhi Sanjit Manohar , Mirzaei Ali , Kim Hyoun Woo , Kim Sang Sub
Published: February 2021

Reduced graphene oxide (rGO) is a reduced form of graphene oxide used extensively in gas sensing applications. On the other hand, in its pristine form, graphene has shortages and is generally utilized in combination with other metal oxides to improve gas sensing capabilities. There are different ways of adding rGO to different metal oxides with various morphologies. This study focuses on rGO-loaded metal oxide nanofiber (NF) synthesized using an electrospinning method. Different amounts of rGO were added to the metal oxide precursors, and after electrospinning, the gas response is enhanced through different sensing mechanisms. This review paper discusses rGO-loaded metal oxide NFs gas sensors.
Self-Organized Crowd Dynamics: Research on Earthquake Emergency Response Patterns of Drill-Trained Individuals Based on GIS and Multi-Agent Systems Methodology
Authors: Sun Hai , Hu Lanling , Shou Wenchi , Wang Jun
Published: February 2021

Predicting evacuation patterns is useful in emergency management situations such as an earthquake. To find out how pre-trained individuals interact with one another to achieve their own goal to reach the exit as fast as possible firstly, we investigated urban people’s evacuation behavior under earthquake disaster coditions, established crowd response rules in emergencies, and described the drill strategy and exit familiarity quantitatively through a cellular automata model. By setting different exit familiarity ratios, simulation experiments under different strategies were conducted to predict people’s reactions before an emergency. The corresponding simulation results indicated that the evacuees’ training level could affect a multi-exit zone’s evacuation pattern and clearance time. Their exit choice preferences may disrupt the exit options’ balance, leading to congestion in some of the exits. Secondly, due to people’s rejection of long distances, congestion, and unfamiliar exits, some people would hesitant about the evacuation direction during the evacuation process. This hesitation would also significantly reduce the overall evacuation efficiency. Finally, taking a community in Zhuhai City, China, as an example, put forward the best urban evacuation drill strategy. The quantitative relation between exit familiar level and evacuation efficiency was obtained. The final results showed that the optimized evacuation plan could improve evacuation’s overall efficiency through the self-organization effect. These studies may have some impact on predicting crowd behavior during evacuation and designing the evacuation plan.
HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints
Authors: Büker Linda Christin , Zuber Finnja , Hein Andreas , Fudickar Sebastian
Published: February 2021

With approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images like robustness to light variation or color- and texture invariance. Correspondingly, we introduce High- Resolution Depth Net (HRDepthNet)—a machine learning driven approach to detect human joints (body, head, and upper and lower extremities) in purely depth images. HRDepthNet retrains the original HRNet for depth images. Therefore, a dataset is created holding depth (and RGB) images recorded with subjects conducting the timed up and go test—an established geriatric assessment. The images were manually annotated RGB images. The training and evaluation were conducted with this dataset. For accuracy evaluation, detection of body joints was evaluated via COCO’s evaluation metrics and indicated that the resulting depth image-based model achieved better results than the HRNet trained and applied on corresponding RGB images. An additional evaluation of the position errors showed a median deviation of 1.619 cm (x-axis), 2.342 cm (y-axis) and 2.4 cm (z-axis).
Evidence of Preferential Flow Activation in the Vadose Zone via Geophysical Monitoring
Authors: De Carlo Lorenzo , Perkins Kimberlie , Caputo Maria Clementina
Published: February 2021

Preferential pathways allow rapid and non-uniform water movement in the subsurface due to strong heterogeneity of texture, composition, and hydraulic properties. Understanding the importance of preferential pathways is crucial, because they have strong impact on flow and transport hydrodynamics in the unsaturated zone. Particularly, improving knowledge of the water dynamics is essential for estimating travel time through soil to quantify hazards for groundwater, assess aquifer recharge rates, improve agricultural water management, and prevent surface stormflow and flooding hazards. Small scale field heterogeneities cannot be always captured by the limited number of point scale measurements collected. In order to overcome these limitations, noninvasive geophysical techniques have been widely used in the last decade to predict hydrodynamic processes, due to their capability to spatialize hydrogeophysical properties with high resolution. In the test site located in Bari, Southern Italy, the geophysical approach, based on electrical resistivity tomography (ERT) monitoring, has been implemented to detect preferential pathways triggered by an artificial rainfall event. ERT-derived soil moisture estimations were obtained in order to quantitatively predict the water storage (m3m−3), water velocity (ms−1), and spread (m2) through preferential pathways by using spatial moments analysis.
ESI–LC–MS/MS for Therapeutic Drug Monitoring of Binary Mixture of Pregabalin and Tramadol: Human Plasma and Urine Applications
Authors: Almalki Atiah H. , Ali Nesma A. , Elroby Fadwa A. , El Ghobashy Mohamed R. , Emam Aml A. , Naguib Ibrahim A.
Published: February 2021

Tramadol (TRM) and pregabalin (PGB) are frequently used in combination for neuropathic pain management. Accordingly, a selective and sensitive high-performance liquid chromatography–electrospray ionization–mass/mass spectrometric (ESI–LC–MS/MS) method is presented for determination of TRM and PGB, whether in pure forms or human biological fluids (plasma/urine), using gabapentin (GBP) (IS) as the internal standard. Chromatographic separation was effected in total run time of 2.5 min, on Phenomenex Luna® Omega 1.6 um polar C18 (LC 150 × 2.1 mm) column with a mobile phase of methanol/water (70:30, v/v), 0.1% (v/v) formic acid at a flow rate of 0.3 mL/min. Ionization of the analytes was obtained using electrospray in the positive ion mode (ESI+). The MS/MS detection was performed by monitoring the fragments for TRM, PGB and GBP on a triple quadrupole mass spectrometer. Assay calibration was over the range of 10–1000 ng mL−1 for TRM and PGB with the correlation coefficients over 0.999 in pure form, human plasma and urine spiked with the studied compounds. Validation data showed the inter-run relative standard deviations (RSDs) were less than 4.3% for TRM and 3.8% for PGB, whereas the intra-run RSDs were less than 3.7% for TRM and 3.6% for PGB. The mean extraction recoveries for TRM and PGB were in the ranges of 86.51–93.38% and 86.20–92.42%. This method was successfully performed on real plasma and urine samples taken from neuropathic patients and proved to be an applicable method for routine therapeutic drug monitoring of the proposed drug combination.
Dismantling the Deadlock: Australian Muslim Women’s Fightback against the Rise of Right-Wing Media
Authors: Giotis Chrisanthi
Published: February 2021

In Australia, as in other multicultural countries, the global Islamophobic discourse linking Muslims to terrorists to refugees results in the belief of an “enemy within”, which fractures the public sphere. Muslim minorities learn to distrust mainstream media as the global discourse manifests in localised right-wing discussion. This fracturing was further compounded in 2020 with increased media concentration and polarisation. In response, 12 young Australian Muslim women opened themselves up to four journalists working for the Australian Broadcasting Corporation (ABC). They engaged in critical journalism research called Frame Reflection Interviews (FRIs). The process gave journalists important knowledge around the power dynamics of Islamophobia and empowered participants to help shape new media discourses tackling Islamophobia. This paper proposes that the FRIs are one method to rebuild trust in journalism while redistributing risk towards the journalists. These steps are necessary to build a normatively cosmopolitan global public sphere capable of breaking the discursive link between refugees and terrorism and fighting back against the rise of the far right.
COVID-19 and Higher Education: First-Year Students’ Expectations toward Distance Learning
Authors: Cicha Karina , Rizun Mariia , Rutecka Paulina , Strzelecki Artur
Published: February 2021

The article deals with distance education, which as a teaching method had to be suddenly introduced in schools and higher education institutions as a result of the global pandemic situation. The paper captures the second wave of Poland’s pandemic situation in relation to global circumstances and the methods of conducting distance learning used across the globe. The purpose of this study was to investigate first-year students’ expectations about the education shift to distance learning. GETAMEL, which is the adapted General Extended Technology Acceptance Model for E-Learning, was used in the study. The study analyzed the influence of Experience, Subjective Norms, Enjoyment, Computer Anxiety, and Self-Efficacy on students’ expectations in the context of distance learning during the COVID-19 pandemic. To test the research model presented during the research, The Partial Least Squares method of Structural Equation Modeling was used. An online survey was created to conduct the research, which collected data from 670 Polish first-year undergraduate students. The acquired data were analyzed using the SmartPLS 3 software. The results of the research indicated that the most important factors that influence the feelings of students and can convince them to change from teaching in the classroom to teaching in the distance learning model are the feeling of pleasure in this form of education and a sense of self-efficacy. The results of this study may be of particular interest to education practitioners, including teachers, and a starting point for further research on e-learning models, including, in particular, the understanding of students’ expectations regarding distance learning.
Large-Area Empirically Based Visual Landscape Quality Assessment for Spatial Planning—A Validation Approach by Method Triangulation
Authors: Roth Michael , Hildebrandt Silvio , Walz Ulrich , Wende Wolfgang
Published: February 2021

Large area visual landscape quality assessment, especially at the national level is needed to answer the demand from strategic planning. In our paper, we describe and compare two recent modelling approaches for this task regarding their theoretical and empirical basis, resolution, model configuration and results. To compare the outcomes of the two methods, both correlation measures and a visual overlay analysing the inversions are used. The results show, that despite the different methodological approaches, in over 90% of the area of Germany there are only minor deviations between the resulting scenic quality maps (less or equal one step on a five-step scale). The main differences occur due to a different relative weight given to terrain and water indicators in the respective methods. We conclude that a methodologically valid scenic quality evaluation using geodata of homogenous quality is possible also at the national level. By triangulating between different methods, for both, the validity could be proven. The datasets elaborated can also be used as a benchmark for regional landscape assessments and for an upcoming monitoring of changes in visual landscape quality.