bluuebunny/crossref_metadata_2025 huggingface dataset:
Plasticized Sodium-Ion Conducting PVA Based Polymer Electrolyte for Electrochemical Energy Storage—EEC Modeling, Transport Properties, and Charge-Discharge Characteristics
Authors: Aziz Shujahadeen B. , Nofal Muaffaq M. , Abdulwahid Rebar T. , O. Ghareeb Hewa , Dannoun Elham M. A. , M. Abdullah Ranjdar , Hamsan M. H. , Kadir M. F. Z.
Published: March 2021

This report presents the preparation of plasticized sodium ion-conducting polymer electrolytes based on polyvinyl alcohol (PVA)via solution cast technique. The prepared plasticized polymer electrolytes were utilized in the device fabrication of electrical double-layer capacitors (EDLCs). On an assembly EDLC system, cyclic voltammetry (CV), electrical impedance spectroscopy (EIS), linear sweep voltammetry (LSV), transfer number measurement (TNM) and charge–discharging responses were performed. The influence of plasticization on polymer electrolytes was investigated in terms of electrochemical properties applying EIS and TNM. The EIS was fitted with electrical equivalent circuit (EEC) models and ion transport parameters were estimated with the highest conductivity of 1.17 × 10−3 S cm−1 was recorded. The CV and charge-discharging responses were used to evaluate the capacitance and the equivalent series resistance (ESR), respectively. The ESR of the highest conductive sample was found to be 91.2 Ω at the first cycle, with the decomposition voltage of 2.12 V. The TNM measurement has shown the dominancy of ions with tion = 0.982 for the highest conducting sample. The absence of redox peaks was proved via CV, indicating the charge storing process that comprised ion accumulation at the interfacial region. The fabricated EDLC device is stable for up to 400 cycles. At the first cycle, a high specific capacitance of 169 F/g, an energy density of 19 Wh/kg, and a power density of 600 W/kg were obtained.
Joint Modelling of Wave Energy Flux and Wave Direction
Authors: Soukissian Takvor H. , Karathanasi Flora E.
Published: March 2021

In the context of wave resource assessment, the description of wave climate is usually confined to significant wave height and energy period. However, the accurate joint description of both linear and directional wave energy characteristics is essential for the proper and detailed optimization of wave energy converters. In this work, the joint probabilistic description of wave energy flux and wave direction is performed and evaluated. Parametric univariate models are implemented for the description of wave energy flux and wave direction. For wave energy flux, conventional, and mixture distributions are examined while for wave direction proven and efficient finite mixtures of von Mises distributions are used. The bivariate modelling is based on the implementation of the Johnson–Wehrly model. The examined models are applied on long-term measured wave data at three offshore locations in Greece and hindcast numerical wave model data at three locations in the western Mediterranean, the North Sea, and the North Atlantic Ocean. A global criterion that combines five individual goodness-of-fit criteria into a single expression is used to evaluate the performance of bivariate models. From the optimum bivariate model, the expected wave energy flux as function of wave direction and the distribution of wave energy flux for the mean and most probable wave directions are also obtained.
Co-Production of Aromatics in Biomass and Waste Gasification
Authors: Mourao Vilela Carlos , Boymans Evert , Vreugdenhil Berend
Published: March 2021

Climate changes will have a huge impact on society, one that cannot be truly predicted. However, what is known is that our dependence on fossil feedstock for energy, fuel and chemical production will need to shift towards more biobased and circular feedstock. This paper describes part of an important technology development that uses biogenic and plastic-containing waste streams for the co-production of aromatics with fuels and/or chemicals. This paper captures the first decade of this technology development from idea towards a large Process Demonstration Unit operated and validated within a large gasification R&D infrastructure. The scale-up was successful, with supporting tools to optimize and identify the limits of the technology. Benzene and toluene are directly removed from the product gas with 97% and 99% efficiency, respectively. The next steps will be to include this development in larger piloting and demonstrations for the co-production of aromatics from biomass gasification (biobased chemicals) or aromatics from plastic-containing waste gasification (circular chemicals).
Composition and Antioxidant Activity, Supercritical Carbon Dioxide Extraction Extracts, and Residue after Extraction of Biologically Active Compounds from Freeze-Dried Tomato Matrix
Authors: Urbonavičienė Dalia , Bobinas Česlovas , Bobinaitė Ramunė , Raudonė Lina , Trumbeckaitė Sonata , Viškelis Jonas , Viškelis Pranas
Published: March 2021

Supercritical carbon dioxide extraction (SCE-CO2) is an attractive, green technology that is used for the recovery of biologically active compounds from plant material. The antioxidant potential of lipophilic fractions (extract obtained with SCE-CO2) and hydrophilic fractions (extracts obtained from the residue after extraction) obtained from a matrix of freeze-dried tomatoes (cvs. “Admiro” F1, “Jurgiai”, “Vilina”, “Pirmutis”, and “Skariai”) was assessed via different antioxidant activity methods. The total amount of polyphenols, carotenoids, and carotenoid isomers before and after SCE-CO2 extraction was also determined. To investigate the effect of the SCE-CO2 extract on the viability of cancer cells, rat glioblastoma C6 cells were chosen. The SCE-CO2 yielded an average of 800 mg of lipophilic fraction per 100 g of freeze-dried tomatoes. The ABTS•+ scavenging activity of the extract was 251 ± 3.4 µmol TE/g. After SCE-CO2 extraction, the DPPH•-RSA of the freeze-dried tomato matrix was 7 to 12% higher. There was a strong positive correlation (R = 0.84) between the total polyphenolics content and the DPPH•-RSA of the tomato samples. The SCE-CO2 increased the radical scavenging activity of the extraction residue, indicating that a considerable fraction of the hydrophilic compounds with particular antioxidant capacity remain unextracted from the tomato matrix. Our results reveal the cytotoxic effect of lycopene extract rich in cis-isomers (62% cis-isomers of the total lycopene content) on rat glioblastoma C6 cells. The viability of the glioblastoma C6 cells significantly decreased (−42%) at a total lycopene concentration of 2.4 µM after 24 h of incubation.
The Role of 2-[18F]-FDG PET/CT in Detecting Richter Transformation in Chronic Lymphocytic Leukemia: A Systematic Review
Authors: Albano Domenico , Bertagna Francesco , Dondi Francesco , Annunziata Salvatore , Ceriani Luca , Giovanella Luca , Giubbini Raffaele , Treglia Giorgio
Published: March 2021

Richter transformation (RT) is a condition wherein B cell chronic lymphocytic leukemia (CLL) transforms into a more aggressive lymphoma variant. The incidence and the significance of RT detected by 2-[18F]-FDG PET/CT is a clinical challenge and it is not widely investigated in the literature. The aim of this systematic review was to analyze published data about the potential role of 2-[18F]-FDG PET/CT in detecting RT. A comprehensive computer literature search of the PubMed/MEDLINE, Embase and Cochrane library databases was conducted up to December 2020. Thirteen studies (1336 patients with CLL) were selected. The maximum standardized uptake value (SUVmax) was the most common metabolic parameter used to detect RT. An SUVmax of 5 had an average overall sensitivity of 87% (range: 71–96%), an average overall specificity of 49% (range: 4–80%), an average positive predictive value of 41% (range: 16–53%) and an average negative predictive value of 84% (range: 33–97%). Other metabolic variables were only marginally investigated, with promising results. 2-[18F]-FDG PET/CT imaging may play an important role in the detection of RT in CLL, based on the high metabolic activity of the nodal lesions that transformed into aggressive lymphomas. 2-[18F]-FDG PET/CT has high negative predictive value for evaluating RT.
Religious Tourism’s Impact on City Space: Service Zones around Sanctuaries
Authors: Sołjan Izabela , Liro Justyna
Published: March 2021

Pilgrimage centers are important elements of the spatial structure of cities and simultaneously factors influencing their transformations. The pilgrimage function of sanctuaries can lead to development of service zones around them focused mainly on serving visitors, i.e., pilgrims and tourists. They often perform functions complementary to sanctuaries. Here we present the results of studies of sanctuary service zones conducted at twenty six popular Catholic sanctuaries in Europe. In this paper, we discussed the influence of the sanctuary on city space on the macro, meso and micro scales. We proposed a definition of a sanctuary service zone, and developed a model approach to the different types: initial (slightly developed) zones, dispersed zones (integrated into the urban space, with their pilgrimage function coexisting with other urban functions), and compact zones—linear, or pilgrimage districts (with dominating pilgrimage function). The development of sanctuary service zones depends mainly on the rank of the pilgrimage center, as well as on the period in which it was founded, pilgrimage traditions, and the location of the pilgrimage center in the city. This paper is a continuation and extension of research into the impact of pilgrimage centers on city space transformations in the context of socio-cultural changes in the 20th and 21st centuries.
Contemporary Grammars of Meaning Creation: Scientific Creationism and New Atheism
Authors: Sanchez-Sabate Ruben
Published: March 2021

This article approaches the grammars of meaning creation by Scientific Creationism and New Atheism from an anthropological-communicological perspective. By grammars of meaning creation, we understand the different languages that the human being uses to communicate the meaning of their existence to themself and others. Nowadays, Scientific Creationism is disseminated around the world and has transcended evangelical Christianity by permeating non-Christian religions. On the other hand, New Atheism, headed by Richard Dawkins, has also reached non-Western cultures such as Muslim cultures. Starting from Apelian transcendental semiotics, the hermeneutics of Durand’s symbol, and Lluís Duch’s anthropological study on mythos and logos, we reflect on the horizons of understanding of both movements. Our study shows that, contrary to what one might think given the antagonistic metaphysical positions the two movements seem to profess, Scientific Creationists and New Atheists share the same grammar of meaning creation: positivism. What one could interpret as a new epistemological controversy between science and religion can be better understood as a fight based on positivist science to establish the true origin myth. Thus, creationists and atheists implicitly recognize positivism as the contemporary theological discourse, i.e., the self-evident grammar of meaning creation that allows the Truth to be expressed.
Globally Optimal Redundancy Resolution with Dynamic Programming for Robot Planning: A ROS Implementation
Authors: Ferrentino Enrico , Salvioli Federico , Chiacchio Pasquale
Published: March 2021

Dynamic programming techniques have proven much more flexible than calculus of variations and other techniques in performing redundancy resolution through global optimization of performance indices. When the state and input spaces are discrete, and the time horizon is finite, they can easily accommodate generic constraints and objective functions and find Pareto-optimal sets. Several implementations have been proposed in previous works, but either they do not ensure the achievement of the globally optimal solution, or they have not been demonstrated on robots of practical relevance. In this communication, recent advances in dynamic programming redundancy resolution, so far only demonstrated on simple planar robots, are extended to be used with generic kinematic structures. This is done by expanding the Robot Operating System (ROS) and proposing a novel architecture meeting the requirements of maintainability, re-usability, modularity and flexibility that are usually required to robotic software libraries. The proposed ROS extension integrates seamlessly with the other software components of the ROS ecosystem, so as to encourage the reuse of the available visualization and analysis tools. The new architecture is demonstrated on a 7-DOF robot with a six-dimensional task, and topological analyses are carried out on both its state space and resulting joint-space solution.
3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry
Authors: Grechi Guglielmo , Fiorucci Matteo , Marmoni Gian Marco , Martino Salvatore
Published: March 2021

The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.
Visual Growth Tracking for Automated Leaf Stage Monitoring Based on Image Sequence Analysis
Authors: Bashyam Srinidhi , Choudhury Sruti Das , Samal Ashok , Awada Tala
Published: March 2021

In this paper, we define a new problem domain, called visual growth tracking, to track different parts of an object that grow non-uniformly over space and time for application in image-based plant phenotyping. The paper introduces a novel method to reliably detect and track individual leaves of a maize plant based on a graph theoretic approach for automated leaf stage monitoring. The method has four phases: optimal view selection, plant architecture determination, leaf tracking, and generation of a leaf status report. The method accepts an image sequence of a plant as the input and automatically generates a leaf status report containing the phenotypes, which are crucial in the understanding of a plant’s growth, i.e., the emergence timing of each leaf, total number of leaves present at any time, the day on which a particular leaf ceased to grow, and the length and relative growth rate of individual leaves. Based on experimental study, three types of leaf intersections are identified, i.e., tip-contact, tangential-contact, and crossover, which pose challenges to accurate leaf tracking in the late vegetative stage. Thus, we introduce a novel curve tracing approach based on an angular consistency check to address the challenges due to intersecting leaves for improved performance. The proposed method shows high accuracy in detecting leaves and tracking them through the vegetative stages of maize plants based on experimental evaluation on a publicly available benchmark dataset.
Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle
Authors: Kraft Marek , Piechocki Mateusz , Ptak Bartosz , Walas Krzysztof
Published: March 2021

Public littering and discarded trash are, despite the effort being put to limit it, still a serious ecological, aesthetic, and social problem. The problematic waste is usually localised and picked up by designated personnel, which is a tiresome, time-consuming task. This paper proposes a low-cost solution enabling the localisation of trash and litter objects in low altitude imagery collected by an unmanned aerial vehicle (UAV) during an autonomous patrol mission. The objects of interest are detected in the acquired images and put on the global map using a set of onboard sensors commonly found in typical UAV autopilots. The core object detection algorithm is based on deep, convolutional neural networks. Since the task is domain-specific, a dedicated dataset of images containing objects of interest was collected and annotated. The dataset is made publicly available, and its description is contained in the paper. The dataset was used to test a range of embedded devices enabling the deployment of deep neural networks for inference onboard the UAV. The results of measurements in terms of detection accuracy and processing speed are enclosed, and recommendations for the neural network model and hardware platform are given based on the obtained values. The complete system can be put together using inexpensive, off-the-shelf components, and perform autonomous localisation of discarded trash, relieving human personnel of this burdensome task, and enabling automated pickup planning.
A Novel Approach to Estimating Time-Averaged Volcanic SO2 Fluxes from Infrared Satellite Measurements
Authors: Hyman David M.R. , Pavolonis Michael J. , Sieglaff Justin
Published: March 2021

Long-term continuous time series of SO2 emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged SO2 plume from which to estimate the SO2 source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional SO2 datasets from ultraviolet (UV) sensors lack altitude information, which must be assumed, to correctly calibrate the SO2 data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded SO2 data. We then fit a solution to the general time-averaged dispersion of SO2 from a point source. We demonstrate these techniques using SO2 data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 satellites. This SO2 data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR SO2 data can fill an important data gap in the UV-based record: estimating SO2 emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used.
Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression
Authors: Crusiol Luís Guilherme Teixeira , Nanni Marcos Rafael , Furlanetto Renato Herrig , Sibaldelli Rubson Natal Ribeiro , Cezar Everson , Sun Liang , Foloni José Salvador Simonetto , Mertz-Henning Liliane Marcia , Nepomuceno Alexandre Lima , Neumaier Norman , Farias José Renato Bouças
Published: March 2021

Soybean grain yield has regularly been impaired by drought periods, and the future climatic scenarios for soybean production might drastically impact yields worldwide. In this context, the knowledge of soybean yield is extremely important to subsidize government and corporative decisions over technical issues. This paper aimed to predict grain yield in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least square regression (PLSR). Field experiments were undertaken at Embrapa Soja (Brazilian Agricultural Research Corporation) in the 2016/2017, 2017/2018 and 2018/2019 cropping seasons. The data collected were analyzed following a split plot model in a randomized complete block design, with four blocks. The following water conditions were distributed in the field plots: irrigated (IRR), non-irrigated (NIRR) and water deficit induced at the vegetative (WDV) and reproductive stages (WDR) using rainout shelters. Soybean genotypes with different responses to water deficit were distributed in the subplots. Soil moisture and weather data were monitored daily. A total of 7216 leaf reflectance (from 400 to 2500 nm, measured by the FieldSpec 3 Jr spectroradiometer) was collected at 24 days in the three cropping seasons. The PLSR (p ≤ 0.05) was performed to predict soybean grain yield by its leaf-based reflectance spectroscopy. The results demonstrated the highest accuracy in soybean grain yield prediction at the R5 phenological stage, corresponding to the period when grains are being formed (R2 ranging from 0.731 to 0.924 and the RMSE from 334 to 403 kg ha−1—7.77 to 11.33%). Analyzing the three cropping seasons into a single PLSR model at R5 stage, R2 equal to 0.775, 0.730 and 0.688 were obtained at the calibration, cross-validation and external validation stages, with RMSE lower than 634 kg ha−1 (13.34%). The PLSR demonstrated higher accuracy in plants submitted to water deficit both at the vegetative and reproductive periods in comparison to plants under natural rainfall or irrigation.
Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion
Authors: Mouazen Abdul Mounem , Shi Zhou
Published: March 2021

Recent advances in remote and proximal sensing technologies provide a valuable source of information for enriching our geo-datasets, which are necessary for soil management and the precision application of farming input resources [...]
Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform
Authors: Xiao Wu , Xu Suchen , He Tingting
Published: March 2021

In tropical/subtropical monsoon regions, accurate rice mapping is hampered by the following factors: (1) The frequent occurrence of clouds in such areas during the rice-growing season interferes strongly with optical remote sensing observations; (2) The agro-landscape in such regions is fragmented and scattered. Rice maps produced using low spatial resolution data cannot well delineate the detailed distribution of rice, while pixel-based mapping using medium and high resolutions has significant salt-and-pepper noise. (3) The cropping system is complex, and rice has a rotation schedule with other crops. Therefore, the Phenology-, Object- and Double Source-based (PODS) paddy rice mapping algorithm is implemented, which consists of three steps: (1) object extraction from multi-temporal 10-m Sentinel-2 images where the extracted objects (fields) are the basic classification units; (2) specifying the phenological stage of transplanting from Savitzky–Golay filtered enhanced vegetation index (EVI) time series using the PhenoRice algorithm; and (3) the identification of rice objects based on flood signal detection from time-series microwave and optical signals of the Sentinel-1/2. This study evaluated the potential of the combined use of the Sentinel-1/2 mission on paddy rice mapping in monsoon regions with the Hangzhou-Jiaxin-Huzhou (HJH) plain in China as the case study. A cloud computing approach was used to process the available Sentinel-1/2 imagery from 2019 and MODIS images from 2018 to 2020 in the HJH plain on the Google Earth Engine (GEE) platform. An accuracy assessment showed that the resultant object-based paddy rice map has a high accuracy with a producer (user) accuracy of 0.937 (0.926). The resultant 10-m paddy rice map is expected to provide unprecedented detail, spatial distribution, and landscape patterns for paddy rice fields in monsoon regions.
Clustering of Rainfall Types Using Micro Rain Radar and Laser
Disdrometer Observations in the Tropical Andes
Authors: Urgilés Gabriela , Célleri Rolando , Trachte Katja , Bendix Jörg , Orellana-Alvear Johanna
Published: March 2021

Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some rainfall characteristics such as intensity and velocity. However, these thresholds highly depend on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Thus, this study aims to analyze rainfall-event types by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics of each rainfall type. It was carried out using three years of data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The results show two main rainfall types (convective and stratiform) in the area which highly differ in their rainfall features. In addition, a mixed type was found as a subgroup of the stratiform type. The stratiform type was found more frequently throughout the year. Furthermore, rainfall events of short duration (less than 70 min) were prevalent in the study area. This study will contribute to analyze the rainfall formation processes and the vertical profile.
Vertical Variability of Total and Size-Partitioned Phytoplankton Carbon in the South China Sea
Authors: Zheng Wendi , Zhou Wen , Cao Wenxi , Liu Yupeng , Wang Guifen , Deng Lin , Li Cai , Zhang Yu , Zeng Kai
Published: March 2021

The standing stock of phytoplankton carbon is a basic and essential property for understanding oceanic ecosystems, biogeochemical cycles, and regional climates. However, current related algorithms mainly focus on remote-sensed application, which cannot describe the vertical profile of phytoplankton carbon throughout the whole euphotic zone. In this study, we modified a previous absorption-based bio-optical algorithm to acquire vertical variabilities of the total and size-partitioned phytoplankton carbon based on field data from the South China Sea (SCS). The mean absolute errors and the biases between estimated and field picophytoplankton carbon were <2.14 and 0.6–2.0, respectively. The results showed that the vertical profile of total phytoplankton carbon displayed a Gaussian distribution in the stratified SCS basin. The picophytoplankton carbon was always the fundamental component of the total phytoplankton carbon within the whole euphotic zone. The dominant picophytoplankton species changed from Synechococcus-like cyanobacteria at the sea surface to pico-sized haptophytes at the phytoplankton carbon maximum layer. The strong covariation between total phytoplankton carbon and chlorophyll-a concentration suggested that they can be converted into each other through an accurate carbon-to-chlorophyll ratio in the open SCS. These results provide essential information that can be used to decipher the three-dimensional structure of total and size-partitioned phytoplankton carbon in the open SCS.
When Small Is Not Beautiful: The Unexpected Impacts of Trees and Parcel Size on Metered Water-Use in a Semi-Arid City
Authors: Rasmussen Shaundra , Warziniack Travis , Neel Abbye , O’Neil-Dunne Jarlath , McHale Melissa
Published: March 2021

Colorado’s water supply is under threat due to climate change pressures and population growth, however Colorado has been recognized to have some of the most progressive water conservation programs in the country. Limiting outdoor water consumption is an increasingly popular approach to conserving water in semi-arid cities, yet in order to implement effective water reduction and conservation policies, more utilities and city managers need a firm understanding of the local drivers of outdoor water consumption. This research explores the drivers of outdoor water consumption in a semi-arid, medium-sized Colorado city that is projected to undergo significant population growth. We used a combination of correlation and linear regression analyses to identify the key descriptive variables that predict greater water consumption at the household scale. Some results were specific to the development patterns of this medium-sized city, where outdoor water use increased 7% for each additional mile (1.6 km) a household was located from the historic urban center. Similarly, more expensive homes used more water as well. Surprisingly, households with a higher ratio of vegetation cover to parcel size tended toward less water consumption. This result could be because parcels that are shaded by their tree canopy require less irrigation. We discuss these results to assist city managers and policymakers in creating water-efficient landscapes and provide information that can be leveraged to increase awareness for water conservation in a growing, semi-arid city.
D2D Mobile Relaying Meets NOMA—Part II: A Reinforcement Learning Perspective
Authors: Driouech Safaa , Sabir Essaid , Ghogho Mounir , Amhoud El-Mehdi
Published: March 2021

Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. Such a communication paradigm requires a certain level of intelligence at the device level, thereby allowing it to interact with the environment and make proper decisions. However, decentralizing decision-making may induce paradoxical outcomes, resulting in a drop in performance, which sustains the design of self-organizing yet efficient systems. We propose that each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. In the first part of this article, we describe a biform game framework to analyze the proposed self-organized system’s performance, under pure and mixed strategies. We use two reinforcement learning (RL) algorithms, enabling devices to self-organize and learn their pure/mixed equilibrium strategies in a fully distributed fashion. Decentralized RL algorithms are shown to play an important role in allowing devices to be self-organized and reach satisfactory performance with incomplete information or even under uncertainties. We point out through a simulation the importance of D2D relaying and assess how our learning schemes perform under slow/fast channel fading.
Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion
Authors: Li Liangliang , Ma Hongbing
Published: March 2021

The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is utilized to decompose the source images into low-frequency sub-bands and high-frequency sub-bands. Low-frequency sub-bands reflect the contrast and brightness of the source images, while high-frequency sub-bands reflect the texture and details of the source images. Using this information, the contrast saliency map and SML fusion rules are introduced into the corresponding sub-bands. Finally, the inverse NSST reconstructs the fusion image. Experimental results demonstrate that the proposed multisource remote image fusion technique performs well in terms of contrast enhancement and detail preservation.
Validity and Reliability of an Instrumented Treadmill with an Accelerometry System for Assessment of Spatio-Temporal Parameters and Impact Transmission
Authors: Encarnación-Martínez Alberto , Pérez-Soriano Pedro , Sanchis-Sanchis Roberto , García-Gallart Antonio , Berenguer-Vidal Rafael
Published: March 2021

Running retraining programs focused on concurrent feedback of acceleration impacts have been demonstrated to be a good strategy to reduce running-related injuries (RRI), as well as to improve running economy and reduce acceleration impacts and injury running incidence. Traditionally, impacts have been registered by mean of accelerometers attached directly to the athletes, which is inaccessible to the entire population, because it requires laboratory conditions. This study investigated the validity and reliability of a new device integrated directly into the treadmill, compared to a traditional acceleration impact system. Thirty healthy athletes with no history of RRI were tested on two separate days over the instrumented treadmill (AccTrea) and simultaneously with an acceleration impact system attached to the participant (AccAthl). AccTrea was demonstrated to be a valid and reliable tool for measuring spatio-temporal parameters like step length (validity intraclass correlation coefficient (ICC) = 0.94; reliability ICC = 0.92), step time (validity ICC = 0.95; reliability ICC = 0.96), and step frequency (validity ICC = 0.95; reliability ICC = 0.96) during running. Peak acceleration impact variables showed a high reliability for the left (reliability ICC = 0.88) and right leg (reliability ICC = 0.85), and peak impact asymmetry showed a modest validity (ICC = 0.55). These results indicated that the AccTrea system is a valid and reliable way to assess spatio-temporal variables, and a reliable tool for measuring acceleration impacts during running.
An 8.72 µW Low-Noise and Wide Bandwidth FEE Design for High-Throughput Pixel-Strip (PS) Sensors
Authors: Jérôme Folla Kamdem , Evariste Wembe Tafo , Bernard Essimbi Zobo , Crespo Maria Liz , Cicuttin Andres , Reaz Mamun Bin Ibne , Bhuiyan Mohammad Arif Sobhan
Published: March 2021

The front-end electronics (FEE) of the Compact Muon Solenoid (CMS) is needed very low power consumption and higher readout bandwidth to match the low power requirement of its Short Strip application-specific integrated circuits (ASIC) (SSA) and to handle a large number of pileup events in the High-Luminosity Large Hadron Collider (LHC). A low-noise, wide bandwidth, and ultra-low power FEE for the pixel-strip sensor of the CMS has been designed and simulated in a 0.35 µm Complementary Metal Oxide Semiconductor (CMOS) process. The design comprises a Charge Sensitive Amplifier (CSA) and a fast Capacitor-Resistor-Resistor-Capacitor (CR-RC) pulse shaper (PS). A compact structure of the CSA circuit has been analyzed and designed for high throughput purposes. Analytical calculations were performed to achieve at least 998 MHz gain bandwidth, and then overcome pileup issue in the High-Luminosity LHC. The spice simulations prove that the circuit can achieve 88 dB dc-gain while exhibiting up to 1 GHz gain-bandwidth product (GBP). The stability of the design was guaranteed with an 82-degree phase margin while 214 ns optimal shaping time was extracted for low-power purposes. The robustness of the design against radiations was performed and the amplitude resolution of the proposed front-end was controlled at 1.87% FWHM (full width half maximum). The circuit has been designed to handle up to 280 fC input charge pulses with 2 pF maximum sensor capacitance. In good agreement with the analytical calculations, simulations outcomes were validated by post-layout simulations results, which provided a baseline gain of 546.56 mV/MeV and 920.66 mV/MeV, respectively, for the CSA and the shaping module while the ENC (Equivalent Noise Charge) of the device was controlled at 37.6 e− at 0 pF with a noise slope of 16.32 e−/pF. Moreover, the proposed circuit dissipates very low power which is only 8.72 µW from a 3.3 V supply and the compact layout occupied just 0.0205 mm2 die area.
Utilising Flow Aggregation to Classify Benign Imitating Attacks
Authors: Hindy Hanan , Atkinson Robert , Tachtatzis Christos , Bayne Ethan , Bures Miroslav , Bellekens Xavier
Published: March 2021

Cyber-attacks continue to grow, both in terms of volume and sophistication. This is aided by an increase in available computational power, expanding attack surfaces, and advancements in the human understanding of how to make attacks undetectable. Unsurprisingly, machine learning is utilised to defend against these attacks. In many applications, the choice of features is more important than the choice of model. A range of studies have, with varying degrees of success, attempted to discriminate between benign traffic and well-known cyber-attacks. The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour. To overcome this barrier, in this manuscript, we introduce new features based on a higher level of abstraction of network traffic. Specifically, we perform flow aggregation by grouping flows with similarities. This additional level of feature abstraction benefits from cumulative information, thus qualifying the models to classify cyber-attacks that mimic benign traffic. The performance of the new features is evaluated using the benchmark CICIDS2017 dataset, and the results demonstrate their validity and effectiveness. This novel proposal will improve the detection accuracy of cyber-attacks and also build towards a new direction of feature extraction for complex ones.
Sensors Cybersecurity
Authors: Gritzalis Dimitris A. , Pantziou Grammati , Román-Castro Rodrigo
Published: March 2021

At present, sensors are increasingly used in all kinds of platforms, manned or unmanned, particularly in view of the emerging Internet of Things (IoT) [...]
Evaluation of Concurrent Validity between a Smartphone Self-Test Prototype and Clinical Instruments for Balance and Leg Strength
Authors: Mansson Linda , Bäckman Pernilla , Öhberg Fredrik , Sandlund Jonas , Selling Jonas , Sandlund Marlene
Published: March 2021

The evolving use of sensors to objectively assess movements is a potentially valuable addition to clinical assessments. We have developed a new self-test application prototype, MyBalance, in the context of fall prevention aimed for use by older adults in order to independently assess balance and functional leg strength. The objective of this study was to investigate the new self-test application for concurrent validity between clinical instruments and variables collected with a smartphone. The prototype has two test procedures: static standing balance test in two positions, and leg strength test performed as a sit-to-stand test. Thirty-one older adults were assessed for balance and functional leg strength, in an outpatient physiotherapy setting, using seven different clinical assessments and three sensor-tests. The results show that clinical instruments and sensor measurements correlate to a higher degree for the smartphone leg strength test. For balance tests, only a few moderate correlations were seen in the Feet Together position and no significant correlations for the Semi Tandem Stance. This study served as a first step to develop a smartphone self-test application for older adults to assess functional balance at home. Further research is needed to test validity, reliability, and user-experience of this new self-test application.
Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)
Authors: González-Enrique Javier , Ruiz-Aguilar Juan Jesús , Moscoso-López José Antonio , Urda Daniel , Deka Lipika , Turias Ignacio J.
Published: March 2021

This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model’s performance, especially for t + 4 (ρ ≈ 0.68 to ρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases.
Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks
Authors: Teshome Eden , Deac Diana , Thielemans Steffen , Carlier Matthias , Steenhaut Kris , Braeken An , Dobrota Virgil
Published: March 2021

Smart buildings benefit from IEEE 802.15.4e time slotted channel hopping (TSCH) medium access for creating reliable and power aware wireless sensor and actuator networks (WSANs). As in these networks, sensors are supposed to communicate to each other and with actuators, IPv6 multicast forwarding is seen as a valuable means to reduce traffic. A promising approach to multicast, based on the Routing Protocol for Low Power and Lossy Networks (RPL) is Bidirectional Multicast RPL Forwarding (BMRF). This paper aimed to analyze the performance of BMRF over TSCH. The authors investigated how an adequate TSCH scheduler can help to achieve a requested quality of service (QoS). A theoretical model for the delay and energy consumption of BMRF over TSCH is presented. Next, BMRF’s link layer (LL) unicast and LL broadcast forwarding modes were analyzed on restricted and realistic topologies. On topologies with increased interference, BMRF’s LL broadcast on top of TSCH causes high energy consumption, mainly because of the amount of energy needed to run the schedule, but it significantly improves packet delivery ratio and delay compared to ContikiMAC under the same conditions. In most cases, the LL unicast was found to outperform the LL broadcast, but the latter can be beneficial to certain applications, especially those sensitive to delays.
Motion-Tolerant Non-Contact Heart-Rate Measurements from Radar Sensor Fusion
Authors: Rong Yu , Dutta Arindam , Chiriyath Alex , Bliss Daniel W.
Published: March 2021

Microwave radar technology is very attractive for ubiquitous short-range health monitoring due to its non-contact, see-through, privacy-preserving and safe features compared to the competing remote technologies such as optics. The possibility of radar-based approaches for breathing and cardiac sensing was demonstrated a few decades ago. However, investigation regarding the robustness of radar-based vital-sign monitoring (VSM) is not available in the current radar literature. In this paper, we aim to close this gap by presenting an extensive experimental study of vital-sign radar approach. We consider diversity in test subjects, fitness levels, poses/postures, and, more importantly, random body movement (RBM) in the study. We discuss some new insights that lead to robust radar heart-rate (HR) measurements. A novel active motion cancellation signal-processing technique is introduced, exploiting dual ultra-wideband (UWB) radar system for motion-tolerant HR measurements. Additionally, we propose a spectral pruning routine to enhance HR estimation performance. We validate the proposed method theoretically and experimentally. Totally, we record and analyze about 3500 seconds of radar measurements from multiple human subjects.
Capacity of a Radio Vortex Communication System Using a Partial Angular Aperture Receiving Scheme under the Horizontal Non-Kolmogorov Model
Authors: Ma Qian , Zhao Hengkai
Published: March 2021

A partial receiving scheme based on limited angular aperture multi-beam receiving and demultiplexing can solve the difficulty caused by the divergence of the vortex beam in the conventional whole beam receiving scheme and realize the long-distance transmission of the vortex wave. The propagation of the radio vortex beam in atmospheric turbulence is of significant importance in theoretical study and practical applications. In this paper, the influence of atmospheric turbulence on the performance of a radio vortex (RV) communication system based on a partial angular aperture receiving (PAAR) scheme under the horizontal non-Kolmogorov channel model is studied. The spiral spectrum of the PAAR scheme and the channel capacity of the RV communication system using the PAAR scheme are derived. Simulation results demonstrate that the selected transmission frequency range has a great influence on the RV communication system based on the PAAR scheme, and the choice of the orbital angular momentum (OAM) mode number L has an influence on the propagation distance. The capacity of RV communication systems based on the PAAR scheme increases with the increase of the transmission frequency in the selected transmission frequency range of 10 GHz–60 GHz. When the number of orbital angular momentum (OAM) modes L is small, we can improve the signal-to-noise ratio (SNR) to obtain a larger capacity of the RV communication system based on the PAAR scheme over a longer propagation distance.
Temporal and Spatial Nearest Neighbor Values Based Missing Data Imputation in Wireless Sensor Networks
Authors: Deng Yulong , Han Chong , Guo Jian , Sun Lijuan
Published: March 2021

Data missing is a common problem in wireless sensor networks. Currently, to ensure the performance of data processing, making imputation for the missing data is the most common method before getting into sensor data analysis. In this paper, the temporal and spatial nearest neighbor values-based missing data imputation (TSNN), a new imputation based on the temporal and spatial nearest neighbor values has been presented. First, four nearest neighbor values have been defined from the perspective of space and time dimensions as well as the geometrical and data distances, which are the bases of the algorithm that help to exploit the correlations among sensor data on the nodes with the regression tool. Next, the algorithm has been elaborated as well as two parameters, the best number of neighbors and spatial–temporal coefficient. Finally, the algorithm has been tested on an indoor and an outdoor wireless sensor network, and the result shows that TSNN is able to improve the accuracy of imputation and increase the number of cases that can be imputed effectively.