bluuebunny/crossref_metadata_2025 huggingface dataset:
Ecological Monitoring with Spy Satellite Images—The Case of Red Wood Ants in Romania
Authors: Klimetzek Dietrich , Stăncioiu Petru Tudor , Paraschiv Marius , Niță Mihai Daniel
Published: February 2021

Dynamics of habitat conditions drive important changes in distribution and abundance of animal species making monitoring an important but also a challenging task when data from the past are scarce. We compared the distribution of ant mounds in the 1960s with recent inventories (2018), looking at changes in canopy cover over time, in a managed forest. Both historical and recent sources of information were used. Habitat suitability at present was determined using a Normalized Difference Vegetation Index (NDVI) image as a proxy for stand canopy cover. The NDVI product was obtained using Google Earth Engine and Sentinel 2 repository. For past conditions (no spectral information available), presence of edges and more open canopies was assessed on a Corona spy-satellite image and based on information from old forest management plans. A threshold distance of 30 m was used to assess location of ant nests compared to favorable habitats. Both old and new information sources showed that ants prefer intermediate canopy cover conditions in their vicinity. Nests remained clustered because of the heterogeneous habitat conditions, but spatial distribution has changed due to canopy alteration along time. The analysis on the NDVI was effective for 82% of cases (i.e., nests occurred within 30 m from favorable habitats). For all the remaining nests (18%), the Google Earth high resolution satellite image revealed in their vicinity the presence of small canopy gaps (undetected by the NDVI). These results show that historical satellite images are very useful for explaining the long-term dynamics of ant colonies. In addition, the use of modern remote sensing techniques provides a reliable and expedite method in determining the presence of favorable small-scale habitat, offering a very useful tool for ecological monitoring across large landscapes and in very different areas, especially in the context of ecosystem dynamics driven and exacerbated by climate change.
Assessing Remote Sensing Vegetation Index Sensitivities for Tall Fescue (Schedonorus arundinaceus) Plant Health with Varying Endophyte and Fertilizer Types: A Case for Improving Poultry Manuresheds
Authors: Flynn K. Colton , Lee Trey , Endale Dinku , Franzluebbers Alan , Ma Shengfang , Zhou Yuting
Published: February 2021

Tall fescue (Schedonorus arundinaceus) is a common perennial forage in cattle pastures of the southeastern United States. A mutualistic fungal endophyte normally infects the grass and produces ergot alkaloids toxic to livestock, but fungal biotypes that have no ergot alkaloid production have been developed. Here remote sensing methods were used to assess plant health in 1 ha grazed paddocks with application amongst different combinations of fertilizer sources (inorganic and broiler litter) and endophyte associations (wild, novel–tall fescue MaxQ type with novel endophyte, and free). Broiler litter fertilization is common in the region due to the presence of many chicken farms. Moreover, broiler litter costs are comparable to inorganic fertilizer depending on distance from source to application. Incorporating remote sensing, we tested the sensitivity of three indices: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface water index (LSWI) to assess fescue plant health. Indices were obtained from satellite imagery provided by Landsat 7 ETM+ between the years 2005 and 2009. Sensitivity analytics suggested that LSWI was the optimum index to determine fescue plant health. The year experiencing drought (determined by annual precipitation) showed significant difference between fertilizer types (p = 0.05) and a nearly significant difference between endophyte associations (p = 0.08). There was no significant difference in years with normal or wet precipitation rates due to tall fescue endophyte association or type of fertilization. Limited availability of satellite imagery during parts of the five years of study might have influenced outcomes of statistical analyses. Nevertheless, the data and findings point to the potential use of satellite imagery in assessing grazingland tall fescue health and advancing the concept of poultry manureshed in the region or elsewhere where poultry manure production is extensive.
Investigation of Polar Mesospheric Summer Echoes Using Linear Discriminant Analysis
Authors: Jozwicki Dorota , Sharma Puneet , Mann Ingrid
Published: February 2021

Polar Mesospheric Summer Echoes (PMSE) are distinct radar echoes from the Earth’s upper atmosphere between 80 to 90 km altitude that form in layers typically extending only a few km in altitude and often with a wavy structure. The structure is linked to the formation process, which at present is not yet fully understood. Image analysis of PMSE data can help carry out systematic studies to characterize PMSE during different ionospheric and atmospheric conditions. In this paper, we analyze PMSE observations recorded using the European Incoherent SCATter (EISCAT) Very High Frequency (VHF) radar. The collected data comprises of 18 observations from different days. In our analysis, the image data is divided into regions of a fixed size and grouped into three categories: PMSE, ionosphere, and noise. We use statistical features from the image regions and employ Linear Discriminant Analysis (LDA) for classification. Our results suggest that PMSE regions can be distinguished from ionosphere and noise with around 98 percent accuracy.
Mapping 20 Years of Urban Expansion in 45 Urban Areas of Sub-Saharan Africa
Authors: Forget Yann , Shimoni Michal , Gilbert Marius , Linard Catherine
Published: February 2021

By 2050, half of the net increase in the world’s population is expected to reside in sub-Saharan Africa (SSA), driving high urbanization rates and drastic land cover changes. However, the data-scarce environment of SSA limits our understanding of the urban dynamics in the region. In this context, Earth Observation (EO) is an opportunity to gather accurate and up-to-date spatial information on urban extents. During the last decade, the adoption of open-access policies by major EO programs (CBERS, Landsat, Sentinel) has allowed the production of several global high resolution (10–30 m) maps of human settlements. However, mapping accuracies in SSA are usually lower, limited by the lack of reference datasets to support the training and the validation of the classification models. Here we propose a mapping approach based on multi-sensor satellite imagery (Landsat, Sentinel-1, Envisat, ERS) and volunteered geographic information (OpenStreetMap) to solve the challenges of urban remote sensing in SSA. The proposed mapping approach is assessed in 17 case studies for an average F1-score of 0.93, and applied in 45 urban areas of SSA to produce a dataset of urban expansion from 1995 to 2015. Across the case studies, built-up areas averaged a compound annual growth rate of 5.5% between 1995 and 2015. The comparison with local population dynamics reveals the heterogeneity of urban dynamics in SSA. Overall, population densities in built-up areas are decreasing. However, the impact of population growth on urban expansion differs depending on the size of the urban area and its income class.
Hyperspectral Image Classification with Localized Graph Convolutional Filtering
Authors: Pu Shengliang , Wu Yuanfeng , Sun Xu , Sun Xiaotong
Published: February 2021

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.
Framework for Reconstruction of Pseudo Quad Polarimetric Imagery from General Compact Polarimetry
Authors: Yin Junjun , Yang Jian
Published: February 2021

Pseudo quad polarimetric (quad-pol) image reconstruction from the hybrid dual-pol (or compact polarimetric (CP)) synthetic aperture radar (SAR) imagery is a category of important techniques for radar polarimetric applications. There are three key aspects concerned in the literature for the reconstruction methods, i.e., the scattering symmetric assumption, the reconstruction model, and the solving approach of the unknowns. Since CP measurements depend on the CP mode configurations, different reconstruction procedures were designed when the transmit wave varies, which means the reconstruction procedures were not unified. In this study, we propose a unified reconstruction framework for the general CP mode, which is applicable to the mode with an arbitrary transmitted ellipse wave. The unified reconstruction procedure is based on the formalized CP descriptors. The general CP symmetric scattering model-based three-component decomposition method is also employed to fit the reconstruction model parameter. Finally, a least squares (LS) estimation method, which was proposed for the linear π/4 CP data, is extended for the arbitrary CP mode to estimate the solution of the system of non-linear equations. Validation is carried out based on polarimetric data sets from both RADARSAT-2 (C-band) and ALOS-2/PALSAR (L-band), to compare the performances of reconstruction models, methods, and CP modes.
Modelling and Comparing Shading Effects of 3D Tree Structures with Virtual Leaves
Authors: Bohn Reckziegel Rafael , Larysch Elena , Sheppard Jonathan P. , Kahle Hans-Peter , Morhart Christopher
Published: February 2021

Reduced solar radiation brought about by trees on agricultural land can both positively and negatively affect crop growth. For a better understanding of this issue, we aim for an improved simulation of the shade cast by trees in agroforestry systems and a precise estimation of insolation reduction. We present a leaf creation algorithm to generate realistic leaves to be placed upon quantitative structure models (QSMs) of real trees. Further, we couple it with an enhanced approach of a 3D model capable of quantifying shading effects of a tree, at a high temporal and spatial resolution. Hence, 3D data derived from wild cherry trees (Prunus avium L.) generated by terrestrial laser scanner technology formed a basis for the tree reconstruction, and served as leaf-off mode. Two leaf-on modes were simulated: realistic leaves, fed with leaf data from wild cherry trees; and ellipsoidal leaves, having ellipsoids as leaf-replacement. For comparison, we assessed the shading effects using hemispherical photography as an alternative method. Results showed that insolation reduction was higher using realistic leaves, and that the shaded area was greater in size than with the ellipsoidal leaves or leaf-off conditions. All shading effects were similarly distributed on the ground, with the exception of those derived through hemispherical photography, which were greater in size, but with less insolation reduction than realistic leaves. The main achievements of this study are: the enhancement of the leaf-on mode for QSMs with realistic leaves, the updates of the shadow model, and the comparison of shading effects. We provide evidence that the inclusion of realistic leaves with precise 3D data might be fundamental to accurately model the shading effects of trees.
Spatiotemporal Characteristics and Trend Analysis of Two Evapotranspiration-Based Drought Products and Their Mechanisms in Sub-Saharan Africa
Authors: Nooni Isaac Kwesi , Hagan Daniel Fiifi T. , Wang Guojie , Ullah Waheed , Li Shijie , Lu Jiao , Bhatti Asher Samuel , Shi Xiao , Lou Dan , Prempeh Nana Agyemang , Lim Kam Sian Kenny T. C. , Dzakpasu Mawuli , Amankwah Solomon Obiri Yeboah , Zhu Chenxia
Published: February 2021

Drought severity still remains a serious concern across Sub-Saharan Africa (SSA) due to its destructive impact on multiple sectors of society. In this study, the interannual variability and trends in the changes of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith (scPDSIPM) and Thornthwaite (scPDSITH) methods for measuring potential evapotranspiration (PET), precipitation (P), normalized difference vegetation index (NDVI), and sea surface temperature (SST) anomalies were investigated through statistical analysis of modeled and remote sensing data. It was shown that scPDSIPM and scPDSITH differed in the representation of drought characteristics over SSA. The regional trend magnitudes of scPDSI in SSA were 0.69 (scPDSIPM) and 0.2 mm/decade (scPDSITH), with a difference in values attributed to the choice of PET measuring method used. The scPDSI and remotely sensed-based anomalies of P and NDVI showed wetting and drying trends over the period 1980–2012 with coefficients of trend magnitudes of 0.12 mm/decade (0.002 mm/decade). The trend analysis showed increased drought events in the semi-arid and arid regions of SSA over the same period. A correlation analysis revealed a strong relationship between the choice of PET measuring method and both P and NDVI anomalies for monsoon and pre-monsoon seasons. The correlation analysis of the choice of PET measuring method with SST anomalies indicated significant positive and negative relationships. This study has demonstrated the applicability of multiple data sources for drought assessment and provides useful information for regional drought predictability and mitigation strategies.
Changes of Corneal Biomechanical Properties upon Exclusive Ytt-/Sr-90 Irradiation of Pterygium
Authors: Rigendinger Fritz , Aebersold Daniel M. , Cvejic Zeljka , Pajic Bojan
Published: February 2021

Background: It is known that pterygia above a certain size cause astigmatism and other aberrations of the human cornea and thus impair the quality of vision. Exclusive Sr-/Ytt-90 beta irradiation is a highly effective treatment for primary pterygia. The aim of this retrospective study is to determine the extent to which higher order corneal aberrations are affected by this treatment. Methods: Evaluation of corneal topographies and wavefront aberration data of 20 primary pterygia patients generated before and at different points in time in the first year after irradiation. Additionally, the size of the pterygium was measured. Results: The study showed a significant increase in coma and triple leaf aberrations in pterygia with a horizontal length of 2 mm and more. It was also found that a pterygium size greater than 2 mm significantly induces astigmatism. Both phenomena reduce visual quality. In none of the patients could a pterygium recurrence be detected after irradiation. Conclusions: If the pterygium size is less than 2 mm, early exclusive Sr/Ytt-90 beta irradiation can be recommended. If the size is more than 2 mm, a pterygium excision 6 months after beta irradiation can be discussed.
A 3D Informational Database for Automatic Archiving of Archaeological Pottery Finds
Authors: Di Angelo Luca , Di Stefano Paolo , Guardiani Emanuele , Morabito Anna Eva
Published: February 2021

From archaeological excavations, huge quantities of material are recovered, usually in the form of fragments. Their correct interpretation and classification are laborious and time-consuming and requires measurement, analysis and comparison of several items. Basing these activities on quantitative methods that process 3D digital data from experimental measurements allows optimizing the entire restoration process, making it faster, more accurate and cheaper. The 3D point clouds, captured by the scanning process, are raw data that must be properly processed to be used in automatic systems for the analysis of archeological finds. This paper focuses on the integration of a shape feature recognizer, able to support the semantic decomposition of the ancient artifact into archaeological features, with a structured database, able to query the large amount of information extracted. Through the automatic measurement of the dimensional attributes of the various features, it is possible to facilitate the comparative analyses between archaeological artifacts and the inferences of the archaeologist and to reduce the routine work. Here, a dedicated database has been proposed, able to store the information extracted from huge quantities of archaeological material using a specific shape feature recognizer. This information is useful for making comparisons but also to improve the archaeological knowledge. The database has been implemented and used for the identification of pottery fragments and the reconstruction of archaeological vessels. Reconstruction, in particular, often requires the solution of complex problems, especially when it involves types of potsherds that cannot be treated with traditional methods.
A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson’s Patients
Authors: Channa Asma , Ifrim Rares-Cristian , Popescu Decebal , Popescu Nirvana
Published: February 2021

Parkinson’s disease patients face numerous motor symptoms that eventually make their life different from those of normal healthy controls. Out of these motor symptoms, tremor and bradykinesia, are relatively prevalent in all stages of this disease. The assessment of these symptoms is usually performed by traditional methods where the accuracy of results is still an open question. This research proposed a solution for an objective assessment of tremor and bradykinesia in subjects with PD (10 older adults aged greater than 60 years with tremor and 10 older adults aged greater than 60 years with bradykinesia) and 20 healthy older adults aged greater than 60 years. Physical movements were recorded by means of an AWEAR bracelet developed using inertial sensors, i.e., 3D accelerometer and gyroscope. Participants performed upper extremities motor activities as adopted by neurologists during the clinical assessment based on Unified Parkinson’s Disease Rating Scale (UPDRS). For discriminating the patients from healthy controls, temporal and spectral features were extracted, out of which non-linear temporal and spectral features show greater difference. Both supervised and unsupervised machine learning classifiers provide good results. Out of 40 individuals, neural net clustering discriminated 34 individuals in correct classes, while the KNN approach discriminated 91.7% accurately. In a clinical environment, the doctor can use the device to comprehend the tremor and bradykinesia of patients quickly and with higher accuracy.
Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging
Authors: Naik Kshirasagar , Pandit Tejas , Naik Nitin , Shah Parth
Published: February 2021

In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces.
3D Multiple-Antenna Channel Modeling and Propagation Characteristics Analysis for Mobile Internet of Things
Authors: Zeng Wenbo , He Yigang , Li Bing , Wang Shudong
Published: February 2021

The demand for optimization design and performance evaluation of wireless communication links in a mobile Internet of Things (IoT) motivates the exploitation of realistic and tractable channel models. In this paper, we develop a novel three-dimensional (3D) multiple-antenna channel model to adequately characterize the scattering environment for mobile IoT scenarios. Specifically, taking into consideration both accuracy and mathematical tractability, a 3D double-spheres model and ellipsoid model are introduced to describe the distribution region of the local scatterers and remote scatterers, respectively. Based on the explicit geometry relationships between transmitter, receiver, and scatterers, we derive the complex channel gains by adopting the radio-wave propagation model. Subsequently, the correlation-based approach for theoretical analysis is performed, and the detailed impacts with respect to the antenna deployment, scatterer distribution, and scatterer density on the vital statistical properties are investigated. Numerical simulation results have shown that the statistical channel characteristics in the developed simulation model nicely match those of the corresponding theoretical results, which demonstrates the utility of our model.
On the Use of Movement-Based Interaction with Smart Textiles for Emotion Regulation
Authors: Jiang Mengqi , Nanjappan Vijayakumar , ten Bhömer Martijn , Liang Hai-Ning
Published: February 2021

Research from psychology has suggested that body movement may directly activate emotional experiences. Movement-based emotion regulation is the most readily available but often underutilized strategy for emotion regulation. This research aims to investigate the emotional effects of movement-based interaction and its sensory feedback mechanisms. To this end, we developed a smart clothing prototype, E-motionWear, which reacts to four movements (elbow flexion/extension, shoulder flexion/extension, open and closed arms, neck flexion/extension), fabric-based detection sensors, and three-movement feedback mechanisms (audio, visual and vibrotactile). An experiment was conducted using a combined qualitative and quantitative approach to collect participants’ objective and subjective emotional feelings. Results indicate that there was no interaction effect between movement and feedback mechanism on the final emotional results. Participants preferred vibrotactile and audio feedback rather than visual feedback when performing these four kinds of upper body movements. Shoulder flexion/extension and open-closed arm movements were more effective for improving positive emotion than elbow flexion/extension movements. Participants thought that the E-motionWear prototype were comfortable to wear and brought them new emotional experiences. From these results, a set of guidelines were derived that can help frame the design and use of smart clothing to support users’ emotional regulation.
Social Collective Attack Model and Procedures for Large-Scale Cyber-Physical Systems
Authors: Zhu Peidong , Xun Peng , Hu Yifan , Xiong Yinqiao
Published: February 2021

A large-scale Cyber-Physical System (CPS) such as a smart grid usually provides service to a vast number of users as a public utility. Security is one of the most vital aspects in such critical infrastructures. The existing CPS security usually considers the attack from the information domain to the physical domain, such as injecting false data to damage sensing. Social Collective Attack on CPS (SCAC) is proposed as a new kind of attack that intrudes into the social domain and manipulates the collective behavior of social users to disrupt the physical subsystem. To provide a systematic description framework for such threats, we extend MITRE ATT&CK, the most used cyber adversary behavior modeling framework, to cover social, cyber, and physical domains. We discuss how the disinformation may be constructed and eventually leads to physical system malfunction through the social-cyber-physical interfaces, and we analyze how the adversaries launch disinformation attacks to better manipulate collective behavior. Finally, simulation analysis of SCAC in a smart grid is provided to demonstrate the possibility of such an attack.
Structural Health Monitoring Using Ultrasonic Guided-Waves and the Degree of Health Index
Authors: Cantero-Chinchilla Sergio , Aranguren Gerardo , Royo José Manuel , Chiachío Manuel , Etxaniz Josu , Calvo-Echenique Andrea
Published: February 2021

This paper proposes a new damage index named degree of health (DoH) to efficiently tackle structural damage monitoring in real-time. As a key contribution, the proposed index relies on a pattern matching methodology that measures the time-of-flight mismatch of sequential ultrasonic guided-wave measurements using fuzzy logic fundamentals. The ultrasonic signals are generated using the transmission beamforming technique with a phased-array of piezoelectric transducers. The acquisition is carried out by two phased-arrays to compare the influence of pulse-echo and pitch-catch modes in the damage assessment. The proposed monitoring approach is illustrated in a fatigue test of an aluminum sheet with an initial notch. As an additional novelty, the proposed pattern matching methodology uses the data stemming from the transmission beamforming technique for structural health monitoring. The results demonstrate the efficiency and robustness of the proposed framework in providing a qualitative and quantitative assessment for fatigue crack damage.
Carbon Nanotube Field-Effect Transistor-Based Chemical and Biological Sensors
Authors: Yao Xuesong , Zhang Yalei , Jin Wanlin , Hu Youfan , Cui Yue
Published: February 2021

Chemical and biological sensors have attracted great interest due to their importance in applications of healthcare, food quality monitoring, environmental monitoring, etc. Carbon nanotube (CNT)-based field-effect transistors (FETs) are novel sensing device configurations and are very promising for their potential to drive many technological advancements in this field due to the extraordinary electrical properties of CNTs. This review focuses on the implementation of CNT-based FETs (CNTFETs) in chemical and biological sensors. It begins with the introduction of properties, and surface functionalization of CNTs for sensing. Then, configurations and sensing mechanisms for CNT FETs are introduced. Next, recent progresses of CNTFET-based chemical sensors, and biological sensors are summarized. Finally, we end the review with an overview about the current application status and the remaining challenges for the CNTFET-based chemical and biological sensors.
An Optical Chiral Sensor Based on Weak Measurement for the Real-Time Monitoring of Sucrose Hydrolysis
Authors: Li Dongmei , Weng Chaofan , Ruan Yi , Li Kan , Cai Guoan , Song Chenyao , Lin Qiang
Published: February 2021

A chiral sensor with optical rotation detection based on weak measurement for the kinetic study of sucrose hydrolysis is presented. Based on the polarization modulation to the pre-selection state, the optical rotation of chiral sample was accurately determined through the central wavelength shift of the output spectrum. With this approach, the concentration response curves of sucrose and its hydrolysis products, i.e., fructose and glucose, were experimentally obtained for the hydrolysis analysis. By collecting the output spectrum with a frequency of 100 Hz and fitting the central wavelength shift synchronously during the measurement, the sucrose hydrolysis process was monitored in real time. Different hydrolysis conditions with varied concentration of invertase enzyme and citrate were implemented in this work. As a consequence, the real-time hydrolysis curves of the hydrolysis process with distinct velocities was achieved and analyzed. Such a kinetic monitoring about sucrose hydrolysis with optical rotation detection technology played a critical role in the researches involving sucrose, and also revealed the great potential of weak measurement in intersections, such as food safety inspection and chemical analysis.
Implementation of Non-Linear Non-Parametric Persistent Scatterer Interferometry and Its Robustness for Displacement Monitoring
Authors: Ogushi Fumitaka , Matsuoka Masashi , Defilippi Marco , Pasquali Paolo
Published: February 2021

To derive surface displacement, interferometric stacking with synthetic aperture radar (SAR) data is commonly used, and this technique is now in the implementation phase in the real world. Persistent scatterer interferometry (PSI) is one of the most universal approaches among in- terferometric stacking techniques, and non-linear non-parametric PSI (NN-PSI) was proposed to overcome the drawbacks of PSI approaches. The estimation of the non-linear displacements was successfully conducted using NN-PSI. However, the estimation of NN-PSI is not always stable with certain displacements because wider range of the velocity spectrum is used in NN-PSI than the conventional approaches; therefore, a calculation procedure and parameter optimization are needed to consider. In this paper, optimized parameters and procedures of NN-PSI are proposed, and real data processing with Sentinel-1 in the Kanto region in Japan was conducted. We confirmed that the displacement estimation was comparable to the measurement of the permanent global positioning system (GPS) stations, and the root mean square error between the GPS measurement and NN-PSI estimation was less than 3 mm in two years. The displacement over 2π ambiguity, which the conventional PSI approach wrongly reconstructed, was also quantitatively validated and successfully estimated by NN-PSI. As a result of the real data processing, periodical displacements were also reconstructed through NN-PSI. We concluded that the NN-PSI approach with the proposed parameters and method enabled the estimation of several types of surface displacements that conventional PSI approaches could not reconstruct.
Semi-Supervised Joint Learning for Hand Gesture Recognition from a Single Color Image
Authors: Xu Chi , Jiang Yunkai , Zhou Jun , Liu Yi
Published: February 2021

Hand gesture recognition and hand pose estimation are two closely correlated tasks. In this paper, we propose a deep-learning based approach which jointly learns an intermediate level shared feature for these two tasks, so that the hand gesture recognition task can be benefited from the hand pose estimation task. In the training process, a semi-supervised training scheme is designed to solve the problem of lacking proper annotation. Our approach detects the foreground hand, recognizes the hand gesture, and estimates the corresponding 3D hand pose simultaneously. To evaluate the hand gesture recognition performance of the state-of-the-arts, we propose a challenging hand gesture recognition dataset collected in unconstrained environments. Experimental results show that, the gesture recognition accuracy of ours is significantly boosted by leveraging the knowledge learned from the hand pose estimation task.
Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments
Authors: Yin Yuqing , Yang Xu , Li Peihao , Zhang Kaiwen , Chen Pengpeng , Niu Qiang
Published: February 2021

Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.
Robust Data Recovery in Wireless Sensor Network: A Learning-Based Matrix Completion Framework
Authors: Kortas Manel , Habachi Oussama , Bouallegue Ammar , Meghdadi Vahid , Ezzedine Tahar , Cances Jean-Pierre
Published: February 2021

In this paper, we are interested in the data gathering for Wireless Sensor Networks (WSNs). In this context, we assume that only some nodes are active in the network, and that these nodes are not transmitting all the time. On the other side, the inactive nodes are considered to be inexistent or idle for a long time period. Henceforth, the sink should be able to recover the entire data matrix whie using the few received measurements. To this end, we propose a novel technique that is based on the Matrix Completion (MC) methodology. Indeed, the considered compression pattern, which is composed of structured and random losses, cannot be solved by existing MC techniques. When the received reading matrix contains several missing rows, corresponding to the inactive nodes, MC techniques are unable to recover the missing data. Thus, we propose a clustering technique that takes the inter-nodes correlation into account, and we present a complementary minimization problem based-interpolation technique that guarantees the recovery of the inactive nodes’ readings. The proposed reconstruction pattern, combined with the sampling one, is evaluated under extensive simulations. The results confirm the validity of each building block and the efficiency of the whole structured approach, and prove that it outperforms the closest scheme.
Software for Matching Standard Activity Enzyme Biosensors for Soil Pollution Analysis
Authors: Kratasyuk Valentina A. , Kolosova Elizaveta M. , Sutormin Oleg S. , Lonshakova-Mukina Viktoriya I. , Baygin Matvey M. , Rimatskaya Nadezhda V. , Sukovataya Irina E. , Shpedt Alexander A.
Published: February 2021

This work is dedicated to developing enzyme biosensor software to solve problems regarding soil pollution analysis. An algorithm and specialised software have been developed which stores, analyses and visualises data using JavaScript programming language. The developed software is based on matching data of 51 non-commercial standard soil samples and their inhibitory effects on three enzyme systems of varying complexity. This approach is able to identify the influence of chemical properties soil samples, without toxic agents, on enzyme biosensors. Such software may find wide use in environmental monitoring.
Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning
Authors: Yang Shengluo , Xu Zhigang , Wang Junyi
Published: February 2021

Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep reinforcement learning (DRL). A system architecture for solving dynamic PFSP using DRL is proposed, and the mathematical model to minimize total tardiness cost is established. Additionally, the intelligent scheduling system based on DRL is modeled, with state features, actions, and reward designed. Moreover, the advantage actor-critic (A2C) algorithm is adapted to train the scheduling agent. The learning curve indicates that the scheduling agent learned to generate better solutions efficiently during training. Extensive experiments are carried out to compare the A2C-based scheduling agent with every single action, other DRL algorithms, and meta-heuristics. The results show the well performance of the A2C-based scheduling agent considering solution quality, CPU times, and generalization. Notably, the trained agent generates a scheduling action only in 2.16 ms on average, which is almost instantaneous and can be used for real-time scheduling. Our work can help to build a self-learning, real-time optimizing, and intelligent decision-making scheduling system.
Framework Development of an Asset Manager Selection Based on Risk Management and Performance Improvement Competences
Authors: Maletič Damjan , Pačaiová Hana , Nagyová Anna , Gomišček Boštjan , Maletič Matjaž
Published: February 2021

This research focuses on proposing a framework based on an analytic hierarchy process (AHP) for the selection/evaluation of managers in the field of asset management. A hierarchical structure is constructed, encompassing the set of competences under the role of risk management and performance improvement of the Institute of Asset Management’s (IAM’s) competences framework. It also describes the AHP implementation and illustrates the entire process with an example that uses IAM competences as model criteria. A sensitivity analysis is also carried out to confirm the robustness of the proposed methodology. As per the findings, the AHP was proven to be a usable and reliable method in selecting the most appropriate asset manager. Therefore, it can help organizations to plan and develop the competences they need to meet current and future needs. This study is among the few studies that focus on competence requirements for people working in asset management. As such, a novel approach for selecting managers in the field of asset management is proposed by this study.
Vascular Adhesion Protein 1 Mediates Gut Microbial Flagellin-Induced Inflammation, Leukocyte Infiltration, and Hepatic Steatosis
Authors: Toivonen Raine , Vanhatalo Sanja , Hollmén Maija , Munukka Eveliina , Keskitalo Anniina , Pietilä Sami , Elo Laura , Huovinen Pentti , Jalkanen Sirpa , Pekkala Satu
Published: February 2021

Toll-like receptor 5 ligand, flagellin, and vascular adhesion protein 1 (VAP-1) are involved in non-alcoholic fatty liver disease. This study aimed to determine whether VAP-1 mediates flagellin-induced hepatic fat accumulation. The effects of flagellin on adipocyte VAP-1 expression were first studied in vitro. Then, flagellin (100 ng/mouse) or saline was intraperitoneally injected into C57BL/6J (WT) and C57BL/6-Aoc3-/- (VAP-1 KO) mice on a high-fat diet twice a week every 2 weeks for 10 weeks. After that, the effects on inflammation, insulin signaling, and metabolism were studied in liver and adipose tissues. Hepatic fat was quantified histologically and biochemically. Because flagellin challenge increased VAP-1 expression in human adipocytes, we used VAP-1 KO mice to determine whether VAP-1 regulates the inflammatory and metabolic effects of flagellin in vivo. In mice, VAP-1 mediated flagellin-induced inflammation, leukocyte infiltration, and lipolysis in visceral adipose tissue. Consequently, an increased release of glycerol led to hepatic steatosis in WT, but not in KO mice. Flagellin-induced hepatic fibrosis was not mediated by VAP-1. VAP-1 KO mice harbored more inflammation-related microbes than WT mice, while flagellin did not affect the gut microbiota. Our results suggest that by acting on visceral adipose tissue, flagellin increased leukocyte infiltration that induced lipolysis. Further, the released glycerol participated in hepatic fat accumulation. In conclusion, the results describe that gut microbial flagellin through VAP-1 induced hepatic steatosis.
Building of the Al-containing Secondary Raw Materials Registry for the Production of Low CO2 Mineral Binders in South-Eastern European Region
Authors: Žibret Gorazd , Teran Klemen , Žibret Lea , Šter Katarina , Dolenec Sabina
Published: February 2021

The bottleneck in the process for increasing production of low CO2 mineral binders, based on BCSA (belite sulfoaluminate) clinkers, is the availability of Al-rich raw materials. For that purpose, a new registry of Al-containing secondary mineral residues (industrial and mine waste) has been developed and is presented in this paper. The methodology of creating the registry consists of three main steps: Gathering ideas, consolidation of ideas, and implementation. In order to achieve this, the following methodology was adopted: Analysis of similar registries by potential end-users and seeking potential solutions and tools to be used, and conducting 3 rounds of stakeholder consultations via workshops in order to determine crucial parameters and features the registry needs to contain. The key discussion points were about which data the registry needs to contain, who shall be the potential users, and what are the stakeholder’s expectations from the registry’s portal. Potential individual registry variables were identified as being relevant/irrelevant or available/unavailable, and potential solutions for the registry’s sustainability were explored. Each Al-rich waste/residue data entry is divided into 10 slots, describing legal status, location, quantities, chemical (REE included), mineralogical, physical and radiological properties, life-cycle assessment, additional data, and data relevancy. The registry will act as a matchmaking tool between producers/holders of Al-rich secondary raw materials and potential producers of cement clinkers.
Municipal Solid Waste Landfill Site Selection Based on Fuzzy-AHP and Geoinformation Techniques in Asir Region Saudi Arabia
Authors: Mallick Javed
Published: February 2021

One of the main issues with solid waste management is finding appropriate sites for landfill. Non-scientific and inappropriate disposal practices have a negative impact on the environment which affects the quality of life. The study provides an integrated framework with a focus on structuring the decision-making process for the landfill suitability site map. This could be determined by the use of proper data collection, criterion weighting and normalization. In order to understand the procedures that affect the suitability of landfill sites, the integrated GIS-based fuzzy-AHP-MCDA method was implemented to appropriate landfill site for Abha-Khamis-Mushyet located in Aseer region Following the extensive literature review and expert opinion, 10 themes were selected for this study such as drainage density, land use/land cover (LULC), slope, elevation, lineament density, normalized difference vegetation index (NDVI), rainfall, distance from the airport, distance from road, and geology. These themes have been developed through RS (remote sensing) and conventional data. Subsequently, potential landfill sites were identified and divided into five classes: very low suitable (fuzzy value 0.20–0.45), low suitable (0.46–0.55), moderately suitable (0.56–0.65), high suitable (0.66–0.75), and very high suitable (0.76–0.92). According to the statistical analysis, 23.91% and 3.67% of the total area were within a very good and good landfill area, while 38.14% and 22.84% accounted for the moderate and poor suitable zone, respectively. As a quality-based site, the existing two landfill sites were located over a very low suitable and low suitable potential area while one landfill site was located over the high suitable∙ The spatial variance of high and very high potential landfill site zones found in the north-eastern, east-central and south-eastern parts of the watershed. The sensitivity analysis was performed to determine the efficacy of each parameter and reveals that the effective weights for each theme differ slightly from the theoretical weight assigned to the landfill site suitability zone. This technique and its findings can provide an appropriate guideline to assist hydrogeologists, engineers, regional planners, and decision-makers in selecting an optimal landfill site in the future.
Sustainable Waste Management for a City Multifloor Manufacturing Cluster: A Framework for Designing a Smart Supply Chain
Authors: Dzhuguryan Tygran , Deja Agnieszka
Published: February 2021

This study focuses on integrated sustainable waste management (ISWM) within a city multifloor manufacturing (MFM) cluster. Manufacturing activities in residential areas of the urban environment and the associated generation of municipal production waste (MPW) are serious problems. The primary goal of this study is to design smart supply chain (SSC) scenarios for the shipment of MPW from a city MFM cluster under uncertainty. This paper presents a new model of the finite MPW generation capacity for a city MFM cluster on the basis of an analysis of its finite production capacity using the material flow analysis (MFA) methodology. The proposed model allows us to determine the number of transport fleet units needed for the implementation of various supply chain (SC) scenarios of MPW. To select the best scenario for MPW shipment in real time, the application of SSC and SSC management (SSCM) technologies is proposed. SSCM performance indicators are proposed which allow us to evaluate the efficiency of using vehicles for cluster MPW transportation. The numerical values of the SSCM performance indicators for various options regarding the handling of city MFM buildings using trucks are obtained. These evaluations form the basis for the decision-making and planning associated with the SSCs of MPW.
Effect of the Flow Rate on the Relative Permeability Curve in the CO2 and Brine System for CO2 Sequestration
Authors: Jeong Gu Sun , Ki Seil , Lee Dae Sung , Jang Ilsik
Published: February 2021

The relative permeabilities of CO2 and brine are important parameters that account for two-phase flow behavior, CO2 saturation distribution, and injectivity. CO2/brine relative permeability curves from the literature show low endpoint CO2 permeability values and high residual brine saturation values. These are the most distinguishing aspects of the CO2/brine relative permeability from oil/water and gas/oil. In this study, this aspect is investigated experimentally by employing a wide range of CO2 injection flow rates. As a result, all the measurements align with previous studies, having low endpoint relative permeability and high residual brine saturation values. They have obvious relationships with the changes in CO2 flow rates. As the CO2 flow rate increases, the endpoint relative permeability increases, the residual brine saturation decreases, and they converge to specific values. These imply that a high CO2 injection flow rate results in high displacement efficiency, but the improvement in efficiency decreases as the flow rate increases. The reasons are identified with the concept of the viscous and capillary forces, and their significance in the CO2 injection into a reservoir is analyzed.