bluuebunny/crossref_metadata_2025 huggingface dataset:
Adopting “Difference-in-Differences” Method to Monitor Crop Response to Agrometeorological Hazards with Satellite Data: A Case Study of Dry-Hot Wind
Authors: Wang Shuai , Rao Yuhan , Chen Jin , Liu Licong , Wang Wenqing
Published: January 2021

Rapid changing climate has increased the risk of natural hazards and threatened global and regional food security. Near real-time monitoring of crop response to agrometeorological hazards is fundamental to ensuring national and global food security. However, quantifying crop responses to a specific hazard in the natural environment is still quite challenging, especially over large areas, due to the lack of tools to separate the independent impact of the hazard on crops from other confounding factors. In this study, we present a general difference-in-differences (DID) framework to monitor crop response to agrometeorological hazards at near real-time using widely accessible remotely sensed vegetation indices (VIs). To demonstrate the effectiveness of the DID framework, we applied it in quantifying the dry-hot wind impact on winter wheat in northern China as a case study using the VIs calculated from the MODIS data. The monitoring results for three years with varying severity levels of dry-hot events (i.e., 2007, 2013, and 2014) demonstrated that the framework can effectively detect winter wheat growing areas affected by dry-hot wind hazards. The estimated damage shows a notable relationship (R2 = 0.903, p < 0.001) with the dry-hot wind intensity calculated from meteorological data, suggesting the effectiveness of the method when field data on a large scale is not available for direct validation. The main advantage of this method is that it can effectively isolate the impact of a specific hazard (i.e., dry-hot wind in the case study) from the mixed signals caused by other confounding factors. This general DID framework is very flexible and can be easily extended to other natural hazards and crop types with proper adjustment. Not only can this framework improve the crop yield forecast but also it can provide near real-time assessment for farmers to adapt their farming practice to mitigate impacts of agricultural hazards.
Gap Filling for Historical Landsat NDVI Time Series by Integrating Climate Data
Authors: Yu Wentao , Li Jing , Liu Qinhuo , Zhao Jing , Dong Yadong , Zhu Xinran , Lin Shangrong , Zhang Hu , Zhang Zhaoxing
Published: January 2021

High-quality Normalized Difference Vegetation Index (NDVI) time series are essential in studying vegetation phenology, dynamic monitoring, and global change. Gap filling is the most important issue in reconstructing NDVI time series from satellites with high spatial resolution, e.g., the Landsat series and Chinese GaoFen-1/6 series. Due to the sparse revisit frequencies of high-resolution satellites, traditional reconstruction approaches face the challenge of dealing with large gaps in raw NDVI time series data. In this paper, a climate incorporated gap-filling (CGF) method is proposed for the reconstruction of Landsat historical NDVI time series data. The CGF model considers the relationship of the NDVI time series and climate conditions between two adjacent years. Climate variables, including downward solar shortwave radiation, precipitation, and temperature, are used to characterize the constrain factors of vegetation growth. Radial basis function networks (RBFNs) are used to link the NDVI time series between two adjacent years with variabilities in climatic conditions. An RBFN predicted a background NDVI time series in the target year, and the observed NDVI values in this year were used to adjust the predicted NDVI time series. Finally, the NDVI time series were recursively reconstructed from 2018 to 1986. The experiments were performed in a heterogeneous region in the Qilian Mountains. The results demonstrate that the proposed method can accurately reconstruct and generate continuous 30 m 8-day NDVI time series using Landsat observations. The CGF method outperforms traditional time series reconstruction methods (e.g., the harmonic analysis of time series (HANTS) and Savitzky-Golay (SG) filter methods) when the raw time series is contaminated with large gaps, which widely exist in Landsat images.
Fast Aerial Image Geolocalization Using the Projective-Invariant Contour Feature
Authors: Li Yongfei , Wang Shicheng , He Hao , Meng Deyu , Yang Dongfang
Published: January 2021

We address the problem of aerial image geolocalization over an area as large as a whole city through road network matching, which is modeled as a 2D point set registration problem under the 2D projective transformation and solved in a two-stage manner. In the first stage, all the potential transformations aligning the query road point set to the reference road point set are found by local point feature matching. A local geometric feature, called the Projective-Invariant Contour Feature (PICF), which consists of a road intersection and the closest points to it in each direction, is specifically designed. We prove that the proposed PICF is equivariant under the 2D projective transformation group. We then encode the PICF with a projective-invariant descriptor to enable the fast search of potential correspondences. The bad correspondences are then removed by a geometric consistency check with the graph-cut algorithm effectively. In the second stage, a flexible strategy is developed to recover the homography transformation with all the PICF correspondences with the Random Sample Consensus (RANSAC) method or to recover the transformation with only one correspondence and then refine it with the local-to-global Iterative Closest Point (ICP) algorithm when only a few correspondences exist. The strategy makes our method efficient to deal with both scenes where roads are sparse and scenes where roads are dense. The refined transformations are then verified with alignment accuracy to determine whether they are accepted as correct. Experimental results show that our method runs faster and greatly improves the recall compared with the state-of-the-art methods.
A New Combined Adjustment Model for Geolocation Accuracy Improvement of Multiple Sources Optical and SAR Imagery
Authors: Jiao Niangang , Wang Feng , You Hongjian
Published: January 2021

Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data.
Landscape Similarity Analysis Using Texture Encoded Deep-Learning Features on Unclassified Remote Sensing Imagery
Authors: Malik Karim , Robertson Colin
Published: January 2021

Convolutional neural networks (CNNs) are known for their ability to learn shape and texture descriptors useful for object detection, pattern recognition, and classification problems. Deeper layer filters of CNN generally learn global image information vital for whole-scene or object discrimination. In landscape pattern comparison, however, dense localized information encoded in shallow layers can contain discriminative information for characterizing changes across image local regions but are often lost in the deeper and non-spatial fully connected layers. Such localized features hold potential for identifying, as well as characterizing, process–pattern change across space and time. In this paper, we propose a simple yet effective texture-based CNN (Tex-CNN) via a feature concatenation framework which results in capturing and learning texture descriptors. The traditional CNN architecture was adopted as a baseline for assessing the performance of Tex-CNN. We utilized 75% and 25% of the image data for model training and validation, respectively. To test the models’ generalization, we used a separate set of imagery from the Aerial Imagery Dataset (AID) and Sentinel-2 for model development and independent validation. The classical CNN and the Tex-CNN classification accuracies in the AID were 91.67% and 96.33%, respectively. Tex-CNN accuracy was either on par with or outcompeted state-of-the-art methods. Independent validation on Sentinel-2 data had good performance for most scene types but had difficulty discriminating farm scenes, likely due to geometric generalization of discriminative features at the coarser scale. In both datasets, the Tex-CNN outperformed the classical CNN architecture. Using the Tex-CNN, gradient-based spatial attention maps (feature maps) which contain discriminative pattern information are extracted and subsequently employed for mapping landscape similarity. To enhance the discriminative capacity of the feature maps, we further perform spatial filtering, using PCA and select eigen maps with the top eigen values. We show that CNN feature maps provide descriptors capable of characterizing and quantifying landscape (dis)similarity. Using the feature maps histogram of oriented gradient vectors and computing their Earth Movers Distances, our method effectively identified similar landscape types with over 60% of target-reference scene comparisons showing smaller Earth Movers Distance (EMD) (e.g., 0.01), while different landscape types tended to show large EMD (e.g., 0.05) in the benchmark AID. We hope this proposal will inspire further research into the use of CNN layer feature maps in landscape similarity assessment, as well as in change detection.
Measurement of Rock Joint Surfaces by Using Smartphone Structure from Motion (SfM) Photogrammetry
Authors: An Pengju , Fang Kun , Jiang Qiangqiang , Zhang Haihua , Zhang Yi
Published: January 2021

The measurement of rock joint surfaces is essential for the estimation of the shear strength of the rock discontinuities in rock engineering. Commonly used techniques for the acquisition of the morphology of the surfaces, such as profilometers and laser scanners, either have low accuracy or high cost. Therefore, a high-speed, low-cost, and high-accuracy method for obtaining the topography of the joint surfaces is necessary. In this paper, a smartphone structure from motion (SfM) photogrammetric solution for measuring rock joint surfaces is presented and evaluated. Image datasets of two rock joint specimens were taken under two different modes by using an iPhone 6s, a Pixel 2, and a T329t and subsequently processed through SfM-based software to obtain 3D models. The technique for measuring rock joint surfaces was evaluated using the root mean square error (RMSE) of the cloud-to-cloud distance and the mean error of the joint roughness coefficient (JRC). The results show that the RMSEs by using the iPhone 6s and Pixel 2 are both less than 0.08 mm. The mean errors of the JRC are −7.54 and −5.27% with point intervals of 0.25 and 1.0 mm, respectively. The smartphone SfM photogrammetric method has comparable accuracy to a 3D laser scanner approach for reconstructing laboratory-sized rock joint surfaces, and it has the potential to become a popular method for measuring rock joint surfaces.
Unsupervised Learning of Depth and Camera Pose with Feature Map Warping
Authors: Guo Ente , Chen Zhifeng , Zhou Yanlin , Wu Dapeng Oliver
Published: January 2021

Estimating the depth of image and egomotion of agent are important for autonomous and robot in understanding the surrounding environment and avoiding collision. Most existing unsupervised methods estimate depth and camera egomotion by minimizing photometric error between adjacent frames. However, the photometric consistency sometimes does not meet the real situation, such as brightness change, moving objects and occlusion. To reduce the influence of brightness change, we propose a feature pyramid matching loss (FPML) which captures the trainable feature error between a current and the adjacent frames and therefore it is more robust than photometric error. In addition, we propose the occlusion-aware mask (OAM) network which can indicate occlusion according to change of masks to improve estimation accuracy of depth and camera pose. The experimental results verify that the proposed unsupervised approach is highly competitive against the state-of-the-art methods, both qualitatively and quantitatively. Specifically, our method reduces absolute relative error (Abs Rel) by 0.017–0.088.
Dynamic Temperature Management of Near-Sensor Processing for Energy-Efficient High-Fidelity Imaging
Authors: Kodukula Venkatesh , Katrawala Saad , Jones Britton , Wu Carole-Jean , LiKamWa Robert
Published: January 2021

Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movement. Many researchers advocate pushing processing close to the sensor to substantially reduce data movement. However, continuous near-sensor processing raises sensor temperature, impairing imaging/vision fidelity. We characterize the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. Our characterization reveals that near-sensor processing reduces system power but degrades image quality. For reasonable image fidelity, the sensor temperature needs to stay below a threshold, situationally determined by application needs. Fortunately, our characterization also identifies opportunities—unique to the needs of near-sensor processing—to regulate temperature based on dynamic visual task requirements and rapidly increase capture quality on demand. Based on our characterization, we propose and investigate two thermal management strategies—stop-capture-go and seasonal migration—for imaging-aware thermal management. For our evaluated tasks, our policies save up to 53% of system power with negligible performance impact and sustained image fidelity.
Experimental Analysis of the Application of Serverless Computing to IoT Platforms
Authors: Benedetti Priscilla , Femminella Mauro , Reali Gianluca , Steenhaut Kris
Published: January 2021

Serverless computing, especially implemented through Function-as-a-Service (FaaS) platforms, has recently been gaining popularity as an application deployment model in which functions are automatically instantiated when called and scaled when needed. When a warm start deployment mode is used, the FaaS platform gives users the perception of constantly available resources. Conversely, when a cold start mode is used, containers running the application’s modules are automatically destroyed when the application has been executed. The latter can lead to considerable resource and cost savings. In this paper, we explore the suitability of both modes for deploying Internet of Things (IoT) applications considering a low resources testbed comparable to an edge node. We discuss the implementation and the experimental analysis of an IoT serverless platform that includes typical IoT service elements. A performance study in terms of resource consumption and latency is presented for the warm and cold start deployment mode, and implemented using OpenFaaS, a well-known open-source FaaS framework which allows to test a cold start deployment with precise inactivity time setup thanks to its flexibility. This experimental analysis allows to evaluate the aptness of the two deployment modes under different operating conditions: Exploiting OpenFaaS minimum inactivity time setup, we find that the cold start mode can be convenient in order to save edge nodes limited resources, but only if the data transmission period is significantly higher than the time needed to trigger containers shutdown.
NMN-VD: A Neural Module Network for Visual Dialog
Authors: Cho Yeongsu , Kim Incheol
Published: January 2021

Visual dialog demonstrates several important aspects of multimodal artificial intelligence; however, it is hindered by visual grounding and visual coreference resolution problems. To overcome these problems, we propose the novel neural module network for visual dialog (NMN-VD). NMN-VD is an efficient question-customized modular network model that combines only the modules required for deciding answers after analyzing input questions. In particular, the model includes a Refer module that effectively finds the visual area indicated by a pronoun using a reference pool to solve a visual coreference resolution problem, which is an important challenge in visual dialog. In addition, the proposed NMN-VD model includes a method for distinguishing and handling impersonal pronouns that do not require visual coreference resolution from general pronouns. Furthermore, a new Compare module that effectively handles comparison questions found in visual dialogs is included in the model, as well as a Find module that applies a triple-attention mechanism to solve visual grounding problems between the question and the image. The results of various experiments conducted using a set of large-scale benchmark data verify the efficacy and high performance of our proposed NMN-VD model.
Segmentation Approaches for Diabetic Foot Disorders
Authors: Arteaga-Marrero Natalia , Hernández Abián , Villa Enrique , González-Pérez Sara , Luque Carlos , Ruiz-Alzola Juan
Published: January 2021

Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred.
Design Study of a Round Window Piezoelectric Transducer for Active Middle Ear Implants
Authors: Shin Dong Ho
Published: February 2021

This report describes the design of a new piezoelectric transducer for round window (RW)-driven middle ear implants. The transducer consists of a piezoelectric element, gold-coated copper bellows, silicone elastomer (polydimethylsiloxane, PDMS), metal cylinder (tungsten), and titanium housing. The piezoelectric element is fixed to the titanium housing and mechanical resonance is generated by the interaction of the bellows, PDMS, and tungsten cylinder. The dimensions of PDMS and the tungsten cylinder with output characteristics suitable for compensation of sensorineural hearing loss were derived by mechanical vibrational analysis (equivalent mechanical model and finite element analysis (FEA)). Based on the results of FEA, the RW piezoelectric transducer was implemented, and bench tests were performed under no-load conditions to confirm the output characteristics. The transducer generates an average displacement of 219.6 nm in the flat band (0.1–1 kHz); the resonance frequency is 2.3 kHz. To evaluate the output characteristics, the response was compared to that of an earlier transducer. When driven by the same voltage (6 Vp), the flat band displacement averaged 30 nm larger than that of the other transducer, and no anti-resonance was noted. Therefore, we expect that the new transducer can serve as an output device for hearing aids, and that it will improve speech recognition and treat high-frequency sensorineural hearing loss more effectively.
Research on Secure Debugging Interaction of Sensor Nodes Based on Visible Light Communication
Authors: Yin Yuanchu , Qiu Jiefan , Li Zhiqiang , Cao Mingsheng
Published: February 2021

When a wireless sensor node’s wireless communication fails after being deployed in an inaccessible area, the lost node cannot be repaired through a debugging interaction that relies on that communication. Visible light communication (VLC) as a supplement of radio wave communication can improve the transmission security at the physical layer due to its unidirectional propagation characteristic. Therefore, we implemented a VLC-based hybrid communication debugging system (HCDS) based on VLC using smartphone and sensor node. For the system’s downlink, the smartphone is taken as the VLC gateway and sends the debugging codes to the sensor node by the flashlight. To improve the transmission efficiency of the downlink, we also propose a new coding method for source coding and channel coding, respectively. For the source coding, we analyze the binary instructions and compress the operands using bitmask techniques. The average compression rate of the binary structure reaches 84.11%. For the channel coding, we optimize dual-header pulse interval (DH-PIM) and propose overlapped DH-PIM (ODH-PIM) by introducing a flashlight half-on state. The flashlight half-on state can improve the representation capability of individual symbols. For the uplink of HCDS, we use the onboard LED of the sensor node to transmit feedback debugging information to the smartphone. At the same time, we design a novel encoding format of DH-PIM to optimize uplink transmission. Experimental results show that the optimized uplink transmission time and BER are reduced by 10.71% and 22%, compared with the original DH-PIM.
Comparison of Heating Strategies on Soil Water Measurement Using Actively Heated Fiber Optics on Contrasting Textured Soils
Authors: Vidana Gamage Duminda N. , Vasava Hiteshkumar B. , Strachan Ian B. , Adamchuk Viacheslav I. , Biswas Asim
Published: February 2021

The actively heated fiber optics (AHFO) technique has the potential to measure soil water at high spatial and temporal resolutions, and thus it can bridge the measurement gap from point to large scales. However, the availability of power might restrict its use, since high power is required to heat long fiber optic cables under field conditions; this can be a challenge for long-term soil water monitoring under field conditions. This study investigated the performance of different heating strategies (power intensity and heating duration) on soil water measurement by the AHFO technique on three different textured soils. Different heating strategies: high power–short pulses (20 Wm−1–3 min), low power–short pulses (10 Wm−1–3 min, 5 Wm−1–3 min, 2.5 Wm−1–3 min) and low power–long pulses (10 Wm−1–5 min, 5 Wm−1–10 min, 2.5 Wm−1–15 min) were tested using laboratory soil columns. The study compared the sensitivity of the thermal response, NTcum to volumetric water content (VWC) and the predictive error of different heating strategies and soils. Results of this study showed that the sensitivity of NTcum increased and the predictive error decreased with increasing power intensity, irrespective of the soil type. Low power–short heat pulses such as 5 Wm−1–3 min and 2.5 Wm−1–3 min produced high predictive errors, RMSE of 5–6% and 6–7%, respectively. However, extending the heating duration was effective in reducing the error for both 10 and 5 Wm−1 power intensities, but not for the 2.5 Wm−1. The improvement was particularly noticeable in 5 Wm−1 –10 min; it reduced the RMSE by 1.5% (sand and clay loam) and 2.73% (sandy loam). Overall, the results of this study suggested that extending the heating duration of 10 and 5 Wm−1 power intensities can improve the sensitivity of the thermal response and predictive accuracy of the estimated soil water content (SWC). The results are particularly important for field applications of the AHFO technique, which can be limited by the availability of high power, which restricts the use of 20 Wm−1. For example, 5 Wm−1–10 min improved the predictive accuracy to 3–4%, which has the potential to be used for validating soil water estimations at satellite footprint scales. However, the effects of diurnal temperature variations should also be considered, particularly when using low power intensity such as 5 Wm−1 in surface soils under field conditions.
Single Target SAR 3D Reconstruction Based on Deep Learning
Authors: Wang Shihong , Guo Jiayi , Zhang Yueting , Hu Yuxin , Ding Chibiao , Wu Yirong
Published: February 2021

Synthetic aperture radar tomography (TomoSAR) is an important 3D mapping method. Traditional TomoSAR requires a large number of observation orbits however, it is hard to meet the requirement of massive orbits. While on the one hand, this is due to funding constraints, on the other hand, because the target scene is changing over time and each observation orbit consumes lots of time, the number of orbits can be fewer as required within a narrow time window. When the number of observation orbits is insufficient, the signal-to-noise ratio (SNR), peak-to-sidelobe ratio (PSR), and resolution of 3D reconstruction results will decline severely, which seriously limits the practical application of TomoSAR. In order to solve this problem, we propose to use a deep learning network to improve the resolution and SNR of 3D reconstruction results under the condition of very few observation orbits by learning the prior distribution of targets. We use all available orbits to reconstruct a high resolution target, while only very few (around 3) orbits to reconstruct a low resolution input. The low-res and high-res 3D voxel-grid pairs are used to train a 3D super-resolution (SR) CNN (convolutional neural network) model, just like ordinary 2D image SR tasks. Experiments on the Civilian Vehicle Radar dataset show that the proposed deep learning algorithm can effectively improve the reconstruction both in quality and in quantity. In addition, the model also shows good generalization performance for targets not shown in the training set.
Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements
Authors: Acosta. Carlos Egas , Gil-Castiñeira Felipe , Costa-Montenegro Enrique , Silva Jorge Sá
Published: February 2021

End-to-end reliability for Wireless Sensor Network communications is usually provided by upper stack layers. Furthermore, most of the studies have been related to star, mesh, and tree topologies. However, they rarely consider the requirements of the multi-hop linear wireless sensor networks, with thousands of nodes, which are universally used for monitoring applications. Therefore, they are characterized by long delays and high energy consumption. In this paper, we propose an energy efficient link level routing algorithm that provides end-to-end reliability into multi-hop wireless sensor networks with a linear structure. The algorithm uses implicit acknowledgement to provide reliability and connectivity with energy efficiency, low latency, and fault tolerance in linear wireless sensor networks. The proposal is validated through tests with real hardware. The energy consumption and the delay are also mathematically modeled and analyzed. The test results show that our algorithm decreases the energy consumption and minimizes the delays when compared with other proposals that also apply the explicit knowledge technique and routing protocols with explicit confirmations, maintaining the same characteristics in terms of reliability and connectivity.
Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy
Authors: Taşkan Aybars Kerem , Alemdar Hande
Published: February 2021

Indoor positioning is getting increased attention due to the availability of larger and more sophisticated indoor environments. Wireless technologies like Bluetooth Low Energy (BLE) may provide inexpensive solutions. In this paper, we propose obstruction-aware signal-loss-tolerant indoor positioning (OASLTIP), a cost-effective BLE-based indoor positioning algorithm. OASLTIP uses a combination of techniques together to provide optimum tracking performance by taking into account the obstructions in the environment, and also, it can handle a loss of signal. We use running average filtering to smooth the received signal data, multilateration to find the measured position of the tag, and particle filtering to track the tag for better performance. We also propose an optional receiver placement method and provide the option to use fingerprinting together with OASLTIP. Moreover, we give insights about BLE signal strengths in different conditions to help with understanding the effects of some environmental conditions on BLE signals. We performed extensive experiments for evaluation of the OASLTool we developed. Additionally, we evaluated the performance of the system both in a simulated environment and in real-world conditions. In a highly crowded and occluded office environment, our system achieved 2.29 m average error, with three receivers. When simulated in OASLTool, the same setup yielded an error of 2.58 m.
A Behavioral Perspective on Community Resilience during the COVID-19 Pandemic: The Case of Paraisópolis in São Paulo, Brazil
Authors: Bento Fabio , Couto Kalliu Carvalho
Published: January 2021

The present article discusses the emergence and dynamics of community resilience by empirically investigating the case of the favela of Paraisópolis in São Paulo, Brazil. The emergence of innovative practices that initially contributed to significantly lower rates of COVID-19 infection and mortality when compared to the city average is described. The analytical framework combines two conceptual perspectives in the study of complex systems. First, resilience in socio-ecological systems highlights the adaptation processes characterized by an interplay of previous experience and emerging new knowledge. Second, the metacontingency framework describes the interplay between a cultural milieu, as a context for cultural practices; an aggregate product; and a selecting environment that embed the acquisition and continuity of interlocking behavioral contingencies. Research methods that combine elements of the descriptive analysis and an exploratory basic qualitative study are employed to understand how the community has self-organized during this period. The findings demonstrate how previous experience with social challenges facilitated self-organization and the emergence of innovative practices in the context of uncoordinated public health measures during the pandemic in Brazil. Furthermore, findings from interviews indicate the existence of positive feedback loops at the community level that facilitated the emergence of innovative practices. This study aims at contributing to the understanding of community resilience by identifying the geographic, psychological, and ecological factors (contextual variables) that facilitate responses to the pandemic.
Is the Environmental Behavior of Farmers Affecting Their Pesticide Practices? A Case Study from Greece
Authors: Karasmanaki Evangelia , Dimopoulou Panagiota , Vryzas Zisis , Karipidis Philippos , Tsantopoulos Georgios
Published: January 2021

Policymakers often assume that farmers with pro-environmental behavior are more likely to follow proper pesticide practices and thus, in order to improve the safety of their pesticide practices, they implement strategies and programs designed to raise environmental awareness among the general public. The aim of this paper is to examine whether pro-environmental behavior can instigate proper pesticide practices among farmers. According to our results, farmers’ environmental behavior does not affect their pesticide practices and thus strategies aiming at raising environmental awareness among the general public would not prompt them to follow proper pesticide practices. In addition, the respondents reported following overall proper practices such as wearing masks and appropriate clothes during sprayings as well as rinsing the empty containers by performing the triple-rinse method. However, they did not wear gloves during applications, and many disposed the remaining pesticide concentrate to non-arable land. To conclude, in order to improve farmers’ pesticide practices, strategies and programs specifically designed for farmers must be developed because those addressed to the general public would not be effective. Moreover, certain improper practices found in this study ought to be addressed by policymakers and actors involved in the agricultural sector.
Barriers and Enablers to Buying Biodegradable and Compostable Plastic Packaging
Authors: Allison Ayşe Lisa , Lorencatto Fabiana , Michie Susan , Miodownik Mark
Published: January 2021

Biodegradable and compostable plastic packaging (BCPP) has the potential to reduce a global plastic waste problem. We aimed to identify influences on buying BCPP as a basis for designing strategies that enable BCPP’s environmental benefits. Using a UK-focused citizen science dataset, we thematically analysed 610 survey responses to a question exploring reasons for BCPP purchase. Themes are categorised as barriers and enablers and according to the components of the Capability, Opportunity, Motivation, and Behaviour (COM-B) model of behaviour. Key barriers concerned: psychological capability (not understanding terminology used to label packaging, not taking notice of packaging, and preferring other types of packaging and product qualities); reflective motivation (negative beliefs about BCPP’s environmental impacts and skepticism over decomposition claims), and physical opportunity (no access to appropriate waste management). Key enablers concern: reflective motivation (positive beliefs about BCPP’s environmental impact and resolve to behave pro-environmentally) and physical opportunity (access to appropriate waste management). Reducing ambiguity concerning the labels of biodegradable and compostable may reduce skepticism over environmental claims of packaging. Interventions should improve information about the source of the packaging material, how the packaging waste is processed, and how to dispose of the packaging. This will not be sufficient unless facilities for local BCPP waste collection and processing are increased.
HOG-ESRs Face Emotion Recognition Algorithm Based on HOG Feature and ESRs Method
Authors: Zhong Yuanchang , Sun Lili , Ge Chenhao , Fan Huilian
Published: January 2021

As we all know, there are many ways to express emotions. Among them, facial emotion recognition, which is widely used in human–computer interaction, psychoanalysis of mental patients, multimedia retrieval, and other fields, is still a challenging task. At present, although convolutional neural network has achieved great success in face emotion recognition algorithms, it has a rising space in effective feature extraction and recognition accuracy. According to a large number of literature studies, histogram of oriented gradient (HOG) can effectively extract face features, and ensemble methods can effectively improve the accuracy and robustness of the algorithm. Therefore, this paper proposes a new algorithm, HOG-ESRs, which improves the traditional ensemble methods to the ensembles with shared representations (ESRs) method, effectively reducing the residual generalization error, and then combining HOG features with ESRs. The experimental results on the FER2013 dataset show that the new algorithm can not only effectively extract features and reduce the residual generalization error, but also improve the accuracy and robustness of the algorithm, the purpose of the study being achieved. The application of HOG-ESRs in facial emotion recognition is helpful to solve the symmetry of edge detection and the deficiency of related methods in an outdoor lighting environment.
H-Irregularity Strengths of Plane Graphs
Authors: Bača Martin , Hinding Nurdin , Javed Aisha , Semaničová-Feňovčíková Andrea
Published: January 2021

Graph labeling is the mapping of elements of a graph (which can be vertices, edges, faces or a combination) to a set of numbers. The mapping usually produces partial sums (weights) of the labeled elements of the graph, and they often have an asymmetrical distribution. In this paper, we study vertex–face and edge–face labelings of two-connected plane graphs. We introduce two new graph characteristics, namely the vertex–face H-irregularity strength and edge–face H-irregularity strength of plane graphs. Estimations of these characteristics are obtained, and exact values for two families of graphs are determined.
On (2-d)-Kernels in the Tensor Product of Graphs
Authors: Bednarz Paweł
Published: January 2021

In this paper, we study the existence, construction and number of (2-d)-kernels in the tensor product of paths, cycles and complete graphs. The symmetric distribution of (2-d)-kernels in these products helps us to characterize them. Among others, we show that the existence of (2-d)-kernels in the tensor product does not require the existence of a (2-d)-kernel in their factors. Moreover, we determine the number of (2-d)-kernels in the tensor product of certain factors using Padovan and Perrin numbers.
Thirteen Years of Phleboviruses Circulation in Lombardy, a Northern Italy Region
Authors: Percivalle Elena , Cassaniti Irene , Calzolari Mattia , Lelli Davide , Baldanti Fausto
Published: January 2021

Phleboviruses transmitted by phlebotomine sandflies are endemic in the Mediterranean basin. Toscana phlebovirus (TOSV), Sicilian phlebovirus (SFSV), and Naples phlebovirus (SFNV) are responsible of summer fever, with well-known pathogenic potential for humans ranging from asymptomatic to mild fever, in addition to neuro-invasive infections during summer. Although TOSV, in particular, is a significant and well-known human pathogen, SFVs remain neglected, with many gaps in the relevant knowledge. Sero-epidemiological studies and case reports recently showed a geographical wider distribution than previously considered, although the real incidence of phleboviruses infections in the Mediterranean area is still unknown. Here we retrospectively evaluated the circulation of phleboviruses during summer seasons between 2007 and 2019 in 649 patients showing neurological symptoms using both molecular and serological approaches. We found that 42/649 (6.5%) subjects experienced phlebovirus infection and only 10/42 cases were detected by molecular assays, whereas the other 32/42 were identified using serological approaches, including neutralization assays. During the 2013 summer, an outbreak in the Lombardy region is described because the prevalence of phlebovirus infection reached 37.2% (19/51 subjects). Interestingly, only 5/19 (26.5%) reported traveling in endemic areas. Of note, no cross-neutralization was observed between different strains tested, showing the possibility to be reinfected by newly discovered phlebovirus strains. In conclusion, phlebovirus infections are still inadequately considered by physicians and are generally underestimated. However, based on our results, sandfly fever viruses should be routinely included in diagnostic panels during summer period, including in Northern Italy.
Hepatitis B Virus DNA Integration and Clonal Expansion of Hepatocytes in the Chronically Infected Liver
Authors: Mason William S. , Jilbert Allison R. , Litwin Samuel
Published: January 2021

Human hepatitis B virus (HBV) can cause chronic, lifelong infection of the liver that may lead to persistent or episodic immune-mediated inflammation against virus-infected hepatocytes. This immune response results in elevated rates of killing of virus-infected hepatocytes, which may extend over many years or decades, lead to fibrosis and cirrhosis, and play a role in the high incidence of hepatocellular carcinoma (HCC) in HBV carriers. Immune-mediated inflammation appears to cause oxidative DNA damage to hepatocytes, which may also play a major role in hepatocarcinogenesis. An additional DNA damaging feature of chronic infections is random integration of HBV DNA into the chromosomal DNA of hepatocytes. While HBV DNA integration does not have a role in virus replication it may alter gene expression of the host cell. Indeed, most HCCs that arise in HBV carriers contain integrated HBV DNA and, in many, the integrant appears to have played a role in hepatocarcinogenesis. Clonal expansion of hepatocytes, which is a natural feature of liver biology, occurs because the hepatocyte population is self-renewing and therefore loses complexity due to random hepatocyte death and replacement by proliferation of surviving hepatocytes. This process may also represent a risk factor for the development of HCC. Interestingly, during chronic HBV infection, hepatocyte clones detected using integrated HBV DNA as lineage-specific markers, emerge that are larger than those expected to occur by random death and proliferation of hepatocytes. The emergence of these larger hepatocyte clones may reflect a survival advantage that could be explained by an ability to avoid the host immune response. While most of these larger hepatocyte clones are probably not preneoplastic, some may have already acquired preneoplastic changes. Thus, chronic inflammation in the HBV-infected liver may be responsible, at least in part, for both initiation of HCC via oxidative DNA damage and promotion of HCC via stimulation of hepatocyte proliferation through immune-mediated killing and compensatory division.
Imaging Viral Infection by Fluorescence Microscopy: Focus on HIV-1 Early Stage
Authors: Mukherjee Soumajit , Boutant Emmanuel , Réal Eleonore , Mély Yves , Anton Halina
Published: January 2021

During the last two decades, progresses in bioimaging and the development of various strategies to fluorescently label the viral components opened a wide range of possibilities to visualize the early phase of Human Immunodeficiency Virus 1 (HIV-1) life cycle directly in infected cells. After fusion of the viral envelope with the cell membrane, the viral core is released into the cytoplasm and the viral RNA (vRNA) is retro-transcribed into DNA by the reverse transcriptase. During this process, the RNA-based viral complex transforms into a pre-integration complex (PIC), composed of the viral genomic DNA (vDNA) coated with viral and host cellular proteins. The protective capsid shell disassembles during a process called uncoating. The viral genome is transported into the cell nucleus and integrates into the host cell chromatin. Unlike biochemical approaches that provide global data about the whole population of viral particles, imaging techniques enable following individual viruses on a single particle level. In this context, quantitative microscopy has brought original data shedding light on the dynamics of the viral entry into the host cell, the cytoplasmic transport, the nuclear import, and the selection of the integration site. In parallel, multi-color imaging studies have elucidated the mechanism of action of host cell factors implicated in HIV-1 viral cycle progression. In this review, we describe the labeling strategies used for HIV-1 fluorescence imaging and report on the main advancements that imaging studies have brought in the understanding of the infection mechanisms from the viral entry into the host cell until the provirus integration step.
Lassa Virus Vaccine Candidate ML29 Generates Truncated Viral RNAs Which Contribute to Interfering Activity and Attenuation
Authors: Johnson Dylan M. , Cubitt Beatrice , Pfeffer Tia L. , de la Torre Juan Carlos , Lukashevich Igor S.
Published: January 2021

Defective interfering particles (DIPs) are naturally occurring products during virus replication in infected cells. DIPs contain defective viral genomes (DVGs) and interfere with replication and propagation of their corresponding standard viral genomes by competing for viral and cellular resources, as well as promoting innate immune antiviral responses. Consequently, for many different viruses, including mammarenaviruses, DIPs play key roles in the outcome of infection. Due to their ability to broadly interfere with viral replication, DIPs are attractive tools for the development of a new generation of biologics to target genetically diverse and rapidly evolving viruses. Here, we provide evidence that in cells infected with the Lassa fever (LF) vaccine candidate ML29, a reassortant that carries the nucleoprotein (NP) and glycoprotein (GP) dominant antigens of the pathogenic Lassa virus (LASV) together with the L polymerase and Z matrix protein of the non-pathogenic genetically related Mopeia virus (MOPV), L-derived truncated RNA species are readily detected following infection at low multiplicity of infection (MOI) or in persistently-infected cells originally infected at high MOI. In the present study, we show that expression of green fluorescent protein (GFP) driven by a tri-segmented form of the mammarenavirus lymphocytic choriomeningitis virus (r3LCMV-GFP/GFP) was strongly inhibited in ML29-persistently infected cells, and that the magnitude of GFP suppression was dependent on the passage history of the ML29-persistently infected cells. In addition, we found that DIP-enriched ML29 was highly attenuated in immunocompetent CBA/J mice and in Hartley guinea pigs. Likewise, STAT-1-/- mice, a validated small animal model for human LF associated hearing loss sequelae, infected with DIP-enriched ML29 did not exhibit any hearing abnormalities throughout the observation period (62 days).
Spatio-Temporal Variations in Phytoplankton Communities in Sediment and Surface Water as Reservoir Drawdown—A Case Study of Pengxi River in Three Gorges Reservoir, China
Authors: Ouyang Wenjuan , Li Zhe , Yang Jixiang , Lu Lunhui , Guo Jinsong
Published: January 2021

The resting stages of phytoplankton are usually regarded as the seed bank and source of harmful algal blooms because of the recruitment of phytoplankton from sediment to the water column under suitable environmental conditions. Information about resting stages of phytoplankton is abundant in shallow lakes and littoral sea; yet, studies on river–reservoir systems are rare. The river–reservoir continuum shows a unique structuring of longitudinal gradients of hydrological and hydrodynamic conditions. We hypothesized that the seed bank and algal blooms in reservoirs are influenced by the hydrodynamic conditions of each reservoir. We used Illumina Miseq sequencing to examine the spatio-temporal variation in the phytoplankton community in the sediment as reservoir drawdown and in surface water during algal blooms in Pengxi River, a tributary of China’s Three Gorges Reservoir. The results show that the cyanobacteria community in sediment is significantly influenced by temperature, total carbon, maximum flow velocity, and total phosphorous, the eukaryotic phytoplankton community in sediment is significantly influenced by total phosphorous, temperature, total carbon, maximum flow velocity, and total nitrogen. Additionally, the dominant species in sediment is significantly different from that in surface water during algal blooms. Our results suggest that the dominant species in surface water during algal blooms is more influenced by the environmental factors and hydrodynamic conditions in the water column than the seeds in the sediment. These findings are fundamental for further research on the influence of hydrodynamic conditions on algal blooms in artificially regulated river-reservoir systems.