bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of ST2 Receptor in the Regulation of Brucella abortus Oral Infection
Authors: Santos Raiany , Campos Priscila C. , Rungue Marcella , Rocha Victor , Santos David , Mendes Viviani , Marinho Fabio V. , Martins Flaviano , Ricci Mayra F. , dos Reis Diego C. , D. Cassali Geovanni , Alves-Filho José Carlos , Vieira Angelica T. , Oliveira Sergio C.
Published: April 2020

The ST2 receptor plays an important role in the gut such as permeability regulation, epithelium regeneration, and promoting intestinal immune modulation. Here, we studied the role of ST2 receptor in a murine model of oral infection with Brucella abortus, its influence on gut homeostasis and control of bacterial replication. Balb/c (wild-type, WT) and ST2 deficient mice (ST2−/−) were infected by oral gavage and the results were obtained at 3 and 14 days post infection (dpi). Our results suggest that ST2−/− are more resistant to B. abortus infection, as a lower bacterial colony-forming unit (CFU) was detected in the livers and spleens of knockout mice, when compared to WT. Additionally, we observed an increase in intestinal permeability in WT-infected mice, compared to ST2−/− animals. Breakage of the intestinal epithelial barrier and bacterial dissemination might be associated with the presence of the ST2 receptor; since, in the knockout mice no change in intestinal permeability was observed after infection. Together with enhanced resistance to infection, ST2−/− produced greater levels of IFN-γ and TNF-α in the small intestine, compared to WT mice. Nevertheless, in the systemic model of infection ST2 plays no role in controlling Brucella replication in vivo. Our results suggest that the ST2 receptor is involved in the invasion process of B. abortus by the mucosa in the oral infection model.
Psychotropic and Opioid-Based Medication Use among Economically Disadvantaged African-American Older Adults
Authors: Bazargan Mohsen , Cobb Sharon , Wisseh Cheryl , Assari Shervin
Published: April 2020

African-American older adults, particularly those who live in economically deprived areas, are less likely to receive pain and psychotropic medications, compared to Whites. This study explored the link between social, behavioral, and health correlates of pain and psychotropic medication use in a sample of economically disadvantaged African-American older adults. This community-based study recruited 740 African-American older adults who were 55+ yeas-old in economically disadvantaged areas of South Los Angeles. Opioid-based and psychotropic medications were the outcome variables. Gender, age, living arrangement, socioeconomic status (educational attainment and financial strain), continuity of medical care, health management organization membership, sleeping disorder/insomnia, arthritis, back pain, pain severity, self-rated health, depressive symptoms, and major chronic conditions were the explanatory variables. Logistic regression was used for data analyses. Arthritis, back pain, severe pain, and poor self-rated health were associated with opioid-based medications. Pain severity and depressive symptoms were correlated with psychotropic medication. Among African-American older adults, arthritis, back pain, poor self-rated health, and severe pain increase the chance of opioid-based and psychotropic medication. Future research should test factors that can reduce inappropriate and appropriate use and prescription of opioid-based and psychotropic medication among economically disadvantaged African-American older adults.
The Development of an Accreditation Framework for Continuing Education Activities for Pharmacists
Authors: Drumm Sarah , Moriarty Frank , Rouse Michael J. , Croke David , Bradley Catriona
Published: April 2020

Accreditation is the recognition that an educational activity meets certain standards. The processes for accreditation vary considerably depending on the type of activity, and currently there are differing accreditation systems in place for pharmacy continuing education (CE) across different countries. Research was carried out on a selection of these systems with the aim of developing a catalogue of accreditation approaches, and exploring the possibility of developing a common framework for the accreditation of pharmacy CE activities. Accreditation processes from the countries represented by the Global Forum on Quality Assurance of Continuing Education and Continuing Professional Development (GFQACE) were reviewed to explore the themes and patterns in them. This informed the development of a proposed accreditation framework for CE activities for pharmacists. A Delphi method over four rounds involving seven participants from each GFQACE organisation was used as a consensus building technique. Agreement was achieved on including 15 items in the framework within four stages (Input, Process, Output, and Quality Improvement). The GFQACE steering group indicated their intention to use the resultant framework as the basis for the exploration of mutual recognition of accreditation between member countries.
Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain
Authors: Sánchez-Ruiz Sergio , Maselli Fabio , Chiesi Marta , Fibbi Luca , Martínez Beatriz , Campos-Taberner Manuel , García-Haro Francisco Javier , Gilabert María Amparo
Published: April 2020

This study simulates annual net primary production (NPP) of forests over peninsular Spain during the years 2005–2012. The modeling strategy consists of a linked production efficiency model based on the Monteith approach and the bio-geochemical model Biome-BGC. Recently produced databases and data layers over the study area including meteorological daily series, ecophysiological parameters, and maps containing information about forest type, rooting depth, and growing stock volume (GSV), were employed. The models, which simulate forest processes assuming equilibrium conditions, were previously optimized for the study area. The production efficiency model was used to estimate daily gross primary production (GPP), while Biome-BGC was used to simulate growth (RG) and maintenance (RM) respirations. To account for actual forest conditions, GPP, RG, and RM were corrected using the ratio of the remotely-sensed derived actual to potential GSV as an indicator of the actual state of forests. The obtained results were evaluated against current annual increment observations from the Third Spanish Forest Inventory. Coefficients of determination ranged from 0.46 to 0.74 depending on the forest type. A simplified dataset was produced by applying regular increments in air temperature and reductions in precipitation to the original 2005–2012 daily series with the goal of covering the range of variation of the climate projections corresponding to the different climate change scenarios reported in the literature. The modified meteorological series were used to simulate new GPP, RG, and RM through Biome-BGC and corrected using GSV. Precipitation was confirmed as the main limiting factor in the study area. In the regions where precipitation was already a limiting factor during 2005–2012, both the increment in air temperature and the reduction in precipitation contributed to a reduction of NPP. In the regions where precipitation was not a limiting factor during 2005–2012, the increment in air temperature led to an increment of NPP. This study is therefore relevant to characterize the growth of Spanish forests both in current and expected climate conditions.
Drought Sensitivity and Trends of Riparian Vegetation Vigor in Nevada, USA (1985–2018)
Authors: Albano Christine M. , McGwire Kenneth C. , Hausner Mark B. , McEvoy Daniel J. , Morton Charles G. , Huntington Justin L.
Published: April 2020

Dryland riparian areas are under increasing stress due to expanding human water demands and a warming climate. Quantifying responses of dryland riparian vegetation to these pressures is complicated by high climatic variability, which can create strong, transient changes in vegetation vigor that could mask other disturbance events. In this study, we utilize a 34-year archive of Landsat satellite data to (1) quantify the strength and timescales of vegetation responses to interannual variability in drought status and (2) isolate and remove this influence to assess resultant trends in vegetation vigor for riparian areas across the state of Nevada, the driest state in the USA. Correlations between annual late-summer Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation–Evapotranspiration Index (SPEI) were calculated across a range of time periods (varying timing and durations) for all riparian pixels within each of the 45 ecoregions, and the variability of these values across the study area is shown. We then applied a novel drought adjustment method that used the strongest SPEI–NDVI timescale relationships for each ecoregion to remove the influence of interannual drought status. Our key result is a 30 m resolution map of drought-adjusted riparian NDVI trends (1985–2018). We highlight and describe locations where impacts of invasive species biocontrol, mine water management, agriculture, changing water levels, and fire are readily visualized with our results. We found more negatively trending riparian areas in association with wide valley bottoms, low-intensity agricultural land uses, and private land ownerships and more positive trends in association with narrow drainages, public lands, and surrounding perennial water bodies (an indication of declining water levels allowing increased vegetative cover). The drought-adjusted NDVI improved the statistical significance of trend estimates, thereby improving the ability to detect such changes. Results from this study provide insight into the strength and timescales of riparian vegetation responses to drought and can provide important information for managing riparian areas within the study area. The novel approach to drought adjustment is readily transferrable to other regions.
Deep Discriminative Representation Learning with Attention Map for Scene Classification
Authors: Li Jun , Lin Daoyu , Wang Yang , Xu Guangluan , Zhang Yunyan , Ding Chibiao , Zhou Yanhai
Published: April 2020

In recent years, convolutional neural networks (CNNs) have shown great success in the scene classification of computer vision images. Although these CNNs can achieve excellent classification accuracy, the discriminative ability of feature representations extracted from CNNs is still limited in distinguishing more complex remote sensing images. Therefore, we propose a unified feature fusion framework based on attention mechanism in this paper, which is called Deep Discriminative Representation Learning with Attention Map (DDRL-AM). Firstly, by applying Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, attention maps associated with the predicted results are generated in order to make CNNs focus on the most salient parts of the image. Secondly, a spatial feature transformer (SFT) is designed to extract discriminative features from attention maps. Then an innovative two-channel CNN architecture is proposed by the fusion of features extracted from attention maps and the RGB (red green blue) stream. A new objective function that considers both center and cross-entropy loss are optimized to decrease the influence of inter-class dispersion and within-class variance. In order to show its effectiveness in classifying remote sensing images, the proposed DDRL-AM method is evaluated on four public benchmark datasets. The experimental results demonstrate the competitive scene classification performance of the DDRL-AM approach. Moreover, the visualization of features extracted by the proposed DDRL-AM method can prove that the discriminative ability of features has been increased.
A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data
Authors: Nguyen Viet-Nghia , Yariyan Peyman , Amiri Mahdis , Dang Tran An , Pham Tien Dat , Do Minh Phuong , Thi Ngo Phuong Thao , Nhu Viet-Ha , Quoc Long Nguyen , Tien Bui Dieu
Published: April 2020

Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas.
Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds
Authors: Cheng Yi-Ting , Patel Ankit , Wen Chenglu , Bullock Darcy , Habib Ayman
Published: April 2020

Lane markings are one of the essential elements of road information, which is useful for a wide range of transportation applications. Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging (LiDAR) point clouds acquired by mobile mapping systems (MMS). This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction. For comparative evaluation, original intensity thresholding and deep learning using manually established labels strategies are also implemented. A pavement surface-based assessment of lane marking extraction by the four strategies is conducted in asphalt and concrete pavement areas covered by MMS equipped with multiple LiDAR scanners. Additionally, the extracted lane markings are used for lane width estimation and reporting lane marking gaps along various highways. The normalized intensity thresholding leads to a better lane marking extraction with an F1-score of 78.9% in comparison to the original intensity thresholding with an F1-score of 72.3%. On the other hand, the deep learning model trained with automatically generated labels achieves a higher F1-score of 85.9% than the one trained on manually established labels with an F1-score of 75.1%. In concrete pavement area, the normalized intensity thresholding and both deep learning strategies obtain better lane marking extraction (i.e., lane markings along longer segments of the highway have been extracted) than the original intensity thresholding approach. For the lane width results, more estimates are observed, especially in areas with poor edge lane marking, using the two deep learning models when compared with the intensity thresholding strategies due to the higher recall rates for the former. The outcome of the proposed strategies is used to develop a framework for reporting lane marking gap regions, which can be subsequently visualized in RGB imagery to identify their cause.
The Multifrequency Future for Remote Sensing of Sea Surface Salinity from Space
Authors: Le Vine David M. , Dinnat Emmanuel P.
Published: April 2020

Passive microwave remote sensing of sea surface salinity from space is done with measurements in the 27 MHz wide spectral window at 1.413 GHz (L-band) which is protected for passive use only. The frequency, 1.413 GHz, is near the peak in sensitivity to changes in salinity and modern L-band instruments, such as the radiometers on SMOS and Aquarius, have demonstrated the feasibility of monitoring surface salinity from space. They have also demonstrated the need for better accuracy, especially in cold water. Proposals to improve accuracy have largely involved adding more frequencies. For example, adding higher frequencies to improve the correction for sea surface temperature and lower frequencies to improve the sensitivity to salinity in cold water. These strategies involve trade-offs, some obvious such as the effects of interference outside the protected band and loss of spatial resolution at lower frequencies, but some are more subtle because of the interdependence of the measurement on other parameters of the ocean surface, in particular, the interdependence of salinity, water temperature and roughness (wind speed). The objective of this manuscript is to describe these interdependencies in a quantitative way with documented assumptions to support the design of future instruments for remote sensing of salinity.
Application of Hyperspectral Remote Sensing in the Longwave Infrared Region to Assess the Influence of Dust from the Desert on Soil Surface Mineralogy
Authors: Notesco Gila , Weksler Shahar , Ben-Dor Eyal
Published: April 2020

Soil mineralogy can be used to study changes in the environment affecting the soil surface, such as dust from the desert through Aeolian processes, which is one of the sources that determine the mineral nature of the soil. Ground- and field-based hyperspectral longwave infrared images, acquired before and after dust dispersion on the soil surface, were processed and analyzed by applying a procedure for determining soil surface mineralogy from the emissivity spectrum, using two indices―SQCMI (the Soil Quartz Clay Mineral Index) and SCI (the Soil Carbonate Index)―to identify changes in the abundance of quartz, clay minerals and carbonates on the surface, caused by the settling dust particles. Mineralogical changes were identified, depending on the mineral composition of the dust compared to the soil surface mineralogy.
Application of Radio Environment Map Reconstruction Techniques to Platoon-based Cellular V2X Communications
Authors: Roger Sandra , Botella Carmen , Pérez-Solano Juan J. , Perez Joaquin
Published: April 2020

Vehicle platoons involve groups of vehicles travelling together at a constant inter-vehicle distance, with different common benefits such as increasing road efficiency and fuel saving. Vehicle platooning requires highly reliable wireless communications to keep the group structure and carry out coordinated maneuvers in a safe manner. Focusing on infrastructure-assisted cellular vehicle to anything (V2X) communications, the amount of control information to be exchanged between each platoon vehicle and the base station is a critical factor affecting the communication latency. This paper exploits the particular structure and characteristics of platooning to decrease the control information exchange necessary for the channel acquisition stage. More precisely, a scheme based on radio environment map (REM) reconstruction is proposed, where geo-localized received power values are available at only a subset of platoon vehicles, while large-scale channel parameters estimates for the rest of platoon members are provided through the application of spatial Ordinary Kriging (OK) interpolation. Distinctive features of the vehicle platooning use case are explored, such as the optimal patterns of vehicles within the platoon with available REM values for improving the quality of the reconstruction, the need for an accurate semivariogram modeling in OK, or the communication cost when establishing a centralized or a distributed architecture for achieving REM reconstruction. The evaluation results show that OK is able to reconstruct the REM in the platoon with acceptable mean squared estimation error, while reducing the control information for REM acquisition in up to 64% in the best-case scenario.
A Multi-Clustering Algorithm to Solve Driving Cycle Prediction Problems Based on Unbalanced Data Sets: A Chinese Case Study
Authors: Wu Yuewei , Zhang Wutong , Zhang Long , Qiao Yuanyuan , Yang Jie , Cheng Cheng
Published: April 2020

Vehicle evaluation parameters, which are increasingly of concern for governments and consumers, quantify performance indicators, such as vehicle performance, emissions, and driving experience to help guide consumers in purchasing cars. While past approaches for driving cycle prediction have been proven effective and used in many countries, these algorithms are difficult to use in China with its complex traffic environment and increasingly high frequency of traffic jams. Meanwhile, we found that the vehicle dataset used by the driving cycle prediction problem is usually unbalanced in real cases, which means that there are more medium and high speed samples and very few samples at low and ultra-high speeds. If the ordinary clustering algorithm is directly applied to the unbalanced data, it will have a huge impact on the performance to build driving cycle maps, and the parameters of the map will deviate considerable from actual ones. In order to address these issues, this paper propose a novel driving cycle map algorithm framework based on an ensemble learning method named multi-clustering algorithm, to improve the performance of traditional clustering algorithms on unbalanced data sets. It is noteworthy that our model framework can be easily extended to other complicated structure areas due to its flexible modular design and parameter configuration. Finally, we tested our method based on actual traffic data generated in Fujian Province in China. The results prove the multi-clustering algorithm has excellent performance on our dataset.
Resource Allocation for Machine-Type Communication of Energy-Harvesting Devices in Wi-Fi HaLow Networks
Authors: Bankov Dmitry , Khorov Evgeny , Lyakhov Andrey , Famaey Jeroen
Published: April 2020

The recent Wi-Fi HaLow technology focuses on adopting Wi-Fi for the needs of the Internet of Things. A key feature of Wi-Fi HaLow is the Restricted Access Window (RAW) mechanism that allows an access point to divide the sensors into groups and to assign each group to an exclusively reserved time interval where only the stations of a particular group can transmit. In this work, we study how to optimally configure RAW in a scenario with a high number of energy harvesting sensor devices. For such a scenario, we consider a problem of device grouping and develop a model of data transmission, which takes into account the peculiarities of channel access and the fact that the devices can run out of energy within the allocated intervals. We show how to use the developed model in order to determine the optimal duration of RAW intervals and the optimal number of groups that provide the required probability of data delivery and minimize the amount of consumed channel resources. The numerical results show that the optimal RAW configuration can reduce the amount of consumed channel resources by almost 50%.
Assessment of an Assistive Control Approach Applied in an Active Knee Orthosis Plus Walker for Post-Stroke Gait Rehabilitation
Authors: Villa-Parra Ana Cecilia , Lima Jessica , Delisle-Rodriguez Denis , Vargas-Valencia Laura , Frizera-Neto Anselmo , Bastos Teodiano
Published: April 2020

The goal of this study is the assessment of an assistive control approach applied to an active knee orthosis plus a walker for gait rehabilitation. The study evaluates post-stroke patients and healthy subjects (control group) in terms of kinematics, kinetics, and muscle activity. Muscle and gait information of interest were acquired from their lower limbs and trunk, and a comparison was conducted between patients and control group. Signals from plantar pressure, gait phase, and knee angle and torque were acquired during gait, which allowed us to verify that the stance control strategy proposed here was efficient at improving the patients’ gaits (comparing their results to the control group), without the necessity of imposing a fixed knee trajectory. An innovative evaluation of trunk muscles related to the maintenance of dynamic postural equilibrium during gait assisted by our active knee orthosis plus walker was also conducted through inertial sensors. An increase in gait cycle (stance phase) was also observed when comparing the results of this study to our previous work. Regarding the kinematics, the maximum knee torque was lower for patients when compared to the control group, which implies that our orthosis did not demand from the patients a knee torque greater than that for healthy subjects. Through surface electromyography (sEMG) analysis, a significant reduction in trunk muscle activation and fatigability, before and during the use of our orthosis by patients, was also observed. This suggest that our orthosis, together with the assistive control approach proposed here, is promising and could be considered to complement post-stroke patient gait rehabilitation.
Crosstalk Reduction Using a Dual Energy Window Scatter Correction in Compton Imaging
Authors: Sakai Makoto , Parajuli Raj Kumar , Kubota Yoshiki , Kubo Nobuteru , Yamaguchi Mitsutaka , Nagao Yuto , Kawachi Naoki , Kikuchi Mikiko , Arakawa Kazuo , Tashiro Mutsumi
Published: April 2020

Compton cameras can simultaneously detect multi-isotopes; however, when simultaneous imaging is performed, crosstalk artifacts appear on the images obtained using a low-energy window. In conventional single-photon emission computed tomography, a dual energy window (DEW) subtraction method is used to reduce crosstalk. This study aimed to evaluate the effectiveness of employing the DEW technique to reduce crosstalk artifacts in Compton images obtained using low-energy windows. To this end, in this study, we compared reconstructed images obtained using either a photo-peak window or a scatter window by performing image subtraction based on the differences between the two images. Simulation calculations were performed to obtain the list data for the Compton camera using a 171 and a 511 keV point source. In the images reconstructed using these data, crosstalk artifacts were clearly observed in the images obtained using a 171 keV photo-peak energy window. In the images obtained using a scatter window (176–186 keV), only crosstalk artifacts were visible. The DEW method could eliminate the influence of high-energy sources on the images obtained with a photo-peak window, thereby improving quantitative capability. This was also observed when the DEW method was used on experimentally obtained images.
Cu2O/PEDOT:PSS/ZnO Nanocomposite Material Biosensor for Esophageal Cancer Detection
Authors: Tseng Kuang-Wen , Hsiao Yu-Ping , Jen Chun-Ping , Chang Tsung-Shun , Wang Hsiang-Chen
Published: April 2020

A highly sensitive photoelectrochemical (PEC) biosensor without external bias was developed in this study. The biosensor was configured with a p-Cu2O and n-ZnO heterostructure. Hexamethylenetetramine (HMTA) and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) was used to improve the crystal structure of Cu2O and ZnO and reduce the defects in the Cu2O/ZnO interface. This fabrication method provided the highly crystallized Cu2O/ZnO structure with excellent electrical property and photoresponse in visible light. The structure was applied to a biosensor for detecting two different cancerous levels of esophageal cells, namely, OE21 and OE21-1, with a high gain in photocurrent (5.8 and 6.2 times, respectively) and a low detection limit (3000 cells in 50 μL). We believe that such a p-n heterojunction PEC biosensor could advance biosensor development and provide a promising candidate for biomedical applications.
Feasibility of Consumer Grade GNSS Receivers for the Integration in Multi-Sensor-Systems
Authors: Kersten Tobias , Paffenholz Jens-André
Published: April 2020

Various GNSS applications require low-cost, small-scale, lightweight and power-saving GNSS devices and require high precision in terms of low noise for carrier phase and code observations. Applications vary from navigation approaches to positioning in geo-monitoring units up to integration in multi-sensor-systems. For highest precision, only GNSS receivers are suitable that provide access to raw data such as carrier phase, code ranges, Doppler and signal strength. A system integration is only possible if the overall noise level is known and quantified at the level of the original observations. A benchmark analysis based on a zero baseline is proposed to quantify the stochastic properties. The performance of the consumer grade GNSS receiver is determined and evaluated against geodetic GNSS receivers to better understand the utilization of consumer grade receivers. Results indicate high similarity to the geodetic receiver, even though technical limitations are present. Various stochastic techniques report normally distributed carrier-phase noise of 2 mm and code-range noise of 0.5–0.8 m. This is confirmed by studying the modified Allan standard deviation and code-minus-carrier combinations. Derived parameters serve as important indicators for the integration of GNSS receivers into multi-sensor-systems.
Effects of Interferometric Radar Altimeter Errors on Marine Gravity Field Inversion
Authors: Wan Xiaoyun , Jin Shuanggen , Liu Bo , Tian Song , Kong Weiya , Annan Richard Fiifi
Published: April 2020

The traditional altimetry satellite, which is based on pulse-limited radar altimeter, only measures ocean surface heights along tracks; hence, leads to poorer accuracy in the east component of the vertical deflections compared to the north component, which in turn limits the final accuracy of the marine gravity field inversion. Wide-swath altimetry using radar interferometry can measure ocean surface heights in two dimensions and, thus, can be used to compute vertical deflections in an arbitrary direction with the same accuracy. This paper aims to investigate the impact of Interferometric Radar Altimeter (InRA) errors on gravity field inversion. The error propagation between gravity anomalies and InRA measurements is analyzed, and formulas of their relationship are given. By giving a group of possible InRA parameters, numerical simulations are conducted to analyze the accuracy of gravity anomaly inversion. The results show that the accuracy of the gravity anomalies is mainly influenced by the phase errors of InRA; and the errors of gravity anomalies have a linear approximation relationship with the phase errors. The results also show that the east component of the vertical deflections has almost the same accuracy as the north component.
Performance Analysis for SWIPT Cooperative DF Communication Systems with Hybrid Receiver and Non-Linear Energy Harvesting Model
Authors: Yuan Tianwen , Liu Mingang , Feng Yizhi
Published: April 2020

In this paper, we study the outage and throughput performance for the simultaneous wireless information and power transfer (SWIPT) cooperative decode-and-forward (DF) communication systems. The hybrid receiver that uses both time switching (TS) and power splitting (PS) methods for energy harvesting (EH) and information decoding (ID), and the piece-wise linear EH model that captures the non-linear input-output characteristic of the EH circuit, are considered. We present exact analytical expressions of the outage probability (OP) and throughput, which are expressed as single definite integral on finite interval and can be easily evaluated, for the systems in Rayleigh fading channel. For further simplicity of calculation, we derive novel and closed-form approximate expressions of the OP and throughput. The impact of different system parameters on the system performance is investigated. Numerical results show the high accuracy of the proposed closed-form approximate expressions especially in the region of higher signal-to-noise ratio (SNR). It is also shown that the system performance is greatly overestimated when the ideal linear EH model is used instead of the practical non-linear EH model. A different result to the non-hybrid receiver with both linear EH model and non-linear EH model that there exists an optimal location to minimize the OP for the hybrid receiving relay node with non-linear EH model is also demonstrated.
On the Capability of QoE Improvement Based on the Adjustment of RLC Parameters
Authors: Mendoza Jessica , de-la-Bandera Isabel , Palacios David , Herrera-García Ana , Barco Raquel
Published: April 2020

The constant evolution in mobile communications networks have led operators to seek new techniques to optimize their mobile networks with the objective of satisfying the expectations of the users. In this way, traditional optimization techniques based on improving radio indicators, have given way to new techniques based on improving the quality of experience (QoE) perceived by users. This paper is focused on analyzing the impact of the adjustment of radio link control (RLC) layer configuration parameters on the QoE perceived by the users of two different types of services. Firstly, an evaluation of the QoE experienced by the user of a real-time video streaming service with respect to the transmission buffer size of the RLC layer in unacknowledged mode (UM) has been carried out. Secondly, the QoE perceived by the user of a file transfer service in relation to the variation of the configuration parameters of the RLC layer in acknowledged mode (AM) has been evaluated. The study, which has been carried out in a simulated cellular environment, has been performed for different system bandwidth values, thus proving the relationship between the QoE perceived by the users, the optimal RLC configuration parameters values and the available bandwidth.
A Doorway Detection and Direction (3Ds) System for Social Robots via a Monocular Camera
Authors: Othman Kamal M. , Rad Ahmad B.
Published: April 2020

In this paper, we propose a novel algorithm to detect a door and its orientation in indoor settings from the view of a social robot equipped with only a monocular camera. The challenge is to achieve this goal with only a 2D image from a monocular camera. The proposed system is designed through the integration of several modules, each of which serves a special purpose. The detection of the door is addressed by training a convolutional neural network (CNN) model on a new dataset for Social Robot Indoor Navigation (SRIN). The direction of the door (from the robot’s observation) is achieved by three other modules: Depth module, Pixel-Selection module, and Pixel2Angle module, respectively. We include simulation results and real-time experiments to demonstrate the performance of the algorithm. The outcome of this study could be beneficial in any robotic navigation system for indoor environments.
Trunk Range of Motion Is Related to Axial Rigidity, Functional Mobility and Quality of Life in Parkinson’s Disease: An Exploratory Study
Authors: Cano-de-la-Cuerda Roberto , Vela-Desojo Lydia , Moreno-Verdú Marcos , Ferreira-Sánchez María del Rosario , Macías-Macías Yolanda , Miangolarra-Page Juan Carlos
Published: April 2020

Background: People with Parkinson’s disease (PD) present deficits of the active range of motion (ROM), prominently in their trunk. However, if these deficits are associated with axial rigidity, the functional mobility or health related quality of life (HRQoL), remains unknown. The aim of this paper is to study the relationship between axial ROM and axial rigidity, the functional mobility and HRQoL in patients with mild to moderate PD. Methods: An exploratory study was conducted. Non-probabilistic sampling of consecutive cases was used. Active trunk ROM was assessed by a universal goniometer. A Biodex System isokinetic dynamometer was used to measure the rigidity of the trunk. Functional mobility was determined by the Get Up and Go (GUG) test, and HRQoL was assessed with the PDQ-39 and EuroQol-5D questionnaires. Results: Thirty-six mild to moderate patients with PD were evaluated. Significant correlations were observed between trunk extensors rigidity and trunk flexion and extension ROM. Significant correlations were observed between trunk flexion, extension and rotation ROM and GUG. Moreover, significant correlations were observed between trunk ROM for flexion, extension and rotations (both sides) and PDQ-39 total score. However, these correlations were considered poor. Conclusions: Trunk ROM for flexion and extension movements, measured by a universal goniometer, were correlated with axial extensors rigidity, evaluated by a technological device at 30°/s and 45°/s, and functional mobility. Moreover, trunk ROM for trunk flexion, extension and rotations were correlated with HRQoL in patients with mild to moderate PD.
Study on a New Electromagnetic Flow Measurement Technology Based on Differential Correlation Detection
Authors: Ge Liang , Chen Junxian , Tian Guiyun , Zeng Wen , Huang Qi , Hu Ze
Published: April 2020

Under the conditions of low flow rate and strong noise, the current electromagnetic flowmeter (EMF) cannot satisfy the requirement for measurement or separate the actual flow signal and interference signal accurately. Correlation detection technology can reduce the bandwidth and suppress noise effectively using the periodic transmission of signal and noise randomness. As for the problem that the current anti-interference technology cannot suppress noise effectively, the noise and interference of the electromagnetic flowmeter were analyzed in this paper, and a design of the electromagnetic flowmeter based on differential correlation detection was proposed. Then, in order to verify the feasibility of the electromagnetic flow measurement system based on differential correlation, an experimental platform for the comparison between standard flow and measured flow was established and a verification experiment was carried out under special conditions and with flow calibration measurements. Finally, the data obtained in the experiment were analyzed. The research result showed that an electromagnetic flowmeter based on differential correlation detection satisfies the need for measurement completely. The lower limit of the flow rate of the electromagnetic flowmeter based on the differential correlation principle could reach 0.084 m/s. Under strong external interferences, the electromagnetic flowmeter based on differential correlation had a fluctuation range in output value of only 10 mV. This shows that the electromagnetic flowmeter based on the differential correlation principle has unique advantages in measurements taken under the conditions of strong noise, slurry flow, and low flow rate.
A Review of IoT Sensing Applications and Challenges Using RFID and Wireless Sensor Networks
Authors: Landaluce Hugo , Arjona Laura , Perallos Asier , Falcone Francisco , Angulo Ignacio , Muralter Florian
Published: April 2020

Radio frequency identification (RFID) and wireless sensors networks (WSNs) are two fundamental pillars that enable the Internet of Things (IoT). RFID systems are able to identify and track devices, whilst WSNs cooperate to gather and provide information from interconnected sensors. This involves challenges, for example, in transforming RFID systems with identification capabilities into sensing and computational platforms, as well as considering them as architectures of wirelessly connected sensing tags. This, together with the latest advances in WSNs and with the integration of both technologies, has resulted in the opportunity to develop novel IoT applications. This paper presents a review of these two technologies and the obstacles and challenges that need to be overcome. Some of these challenges are the efficiency of the energy harvesting, communication interference, fault tolerance, higher capacities to handling data processing, cost feasibility, and an appropriate integration of these factors. Additionally, two emerging trends in IoT are reviewed: the combination of RFID and WSNs in order to exploit their advantages and complement their limitations, and wearable sensors, which enable new promising IoT applications.
A Review on Electrochemical Sensors and Biosensors Used in Phenylalanine Electroanalysis
Authors: Dinu Ancuta , Apetrei Constantin
Published: April 2020

Phenylalanine is an amino acid found in breast milk and in many foods, being an essential nutrient. This amino acid is very important for the human body because it is transformed into tyrosine and, subsequently, into catecholamine neurotransmitters. However, there are individuals who were born with a genetic disorder called phenylketonuria. The accumulation of phenylalanine and of some metabolites in the body is dangerous and may cause convulsions, brain damage and mental retardation. Determining the concentration of phenylalanine in different biologic fluids is very important because it can provide information about the health status of the individuals envisaged. Since such determinations may be made by using electrochemical sensors and biosensors, numerous researchers have developed such sensors for phenylalanine detection and different sensitive materials were used in order to improve the selectivity, sensitivity and detection limit. The present review aims at presenting the design and performance of some electrochemical bio (sensors) traditionally used for phenylalanine detection as reported in a series of relevant scientific papers published in the last decade.
A Novel Point Cloud Encoding Method Based on Local Information for 3D Classification and Segmentation
Authors: Song Yanan , Gao Liang , Li Xinyu , Shen Weiming
Published: April 2020

Deep learning is robust to the perturbation of a point cloud, which is an important data form in the Internet of Things. However, it cannot effectively capture the local information of the point cloud and recognize the fine-grained features of an object. Different levels of features in the deep learning network are integrated to obtain local information, but this strategy increases network complexity. This paper proposes an effective point cloud encoding method that facilitates the deep learning network to utilize the local information. An axis-aligned cube is used to search for a local region that represents the local information. All of the points in the local region are available to construct the feature representation of each point. These feature representations are then input to a deep learning network. Two well-known datasets, ModelNet40 shape classification benchmark and Stanford 3D Indoor Semantics Dataset, are used to test the performance of the proposed method. Compared with other methods with complicated structures, the proposed method with only a simple deep learning network, can achieve a higher accuracy in 3D object classification and semantic segmentation.
On Singular Perturbation of Neutron Point Kinetics in the Dynamic Model of a PWR Nuclear Power Plant
Authors: Chen Xiangyi , Ray Asok
Published: April 2020

This short communication makes use of the principle of singular perturbation to approximate the ordinary differential equation (ODE) of prompt neutron (in the point kinetics model) as an algebraic equation. This approximation is shown to yield a large gain in computational efficiency without compromising any significant accuracy in the numerical simulation of primary coolant system dynamics in a PWR nuclear power plant. The approximate (i.e., singularly perturbed) model has been validated with a numerical solution of the original set of neutron point-kinetic and thermal–hydraulic equations. Both models use variable-step Runge–Kutta numerical integration.
A Research on the Combination of Oblique Photography and Mobile Applications Based on the Sustainable Development of Tourism
Authors: Lin Mengyi , Li Fu-Yuan , Zhou Haibin
Published: April 2020

Tourism is one of the world’s fastest driving forces of economic development, playing an important role in achieving sustainable development goals. In modern society, mobile social media is a communication and decision-making platform for users and a source of big data information about travel. Obtaining and analyzing travel data can provide customer-oriented information about travel destinations and comprehensive services for both tourism operators and tourists. It has a positive impact on the sustainable development of society, economy, environment, and humanities. Starting with theoretical analysis and empirical research, this study combines social media and oblique photography, conducts a case study of the Pingtan comprehensive experimental area in China, and develops an app about online travelling to provide corresponding information for consumers’ decisions. This study also discusses the potential value of the app, i.e., assisting the development of smart travel in city, achieving sustainable development of tourism, and contributing to tourism globally.
Reinventing Mobility Paradigms: Flying Car Scenarios and Challenges for Urban Mobility
Authors: Postorino Maria Nadia , Sarné Giuseppe M. L.
Published: April 2020

Flying vehicles are receiving more and more attention and are becoming an opportunity to start a new urban mobility paradigm. The most interesting feature of flying cars is the expected opportunity they could offer to reduce congestion, traffic jams and the loss of time to move between origin/destination pairs in urban contexts. In this perspective, urban air mobility might meet the concept of “sustainable mobility”, intended as the ideal model of a transport system that minimizes the environmental impacts by maximizing efficiency and travel speed. For transport engineering planning issues, further knowledge is required in this field to understand the effects that a possible urban air mobility system, including the ground traffic component, could have in terms of sustainable mobility in the above meaning. This paper contributes to this topic by providing an analysis of different urban flying car scenarios by using an agent-based approach with different traffic conditions. The preliminary results obtained on some test networks and focusing on travel cost effects suggest that the expected advantages the flying car will depend on trip origin/destination points, average distances travelled in the urban contexts and the location of transition nodes, which are introduced as interchange nodes between aerial and ground mode.
Contradictions of Transit-Oriented Development in Low-Income Neighborhoods: The Case Study of Rosengård in Malmö, Sweden
Authors: Derakhti Laleh , Baeten Guy
Published: April 2020

Transit-oriented development (TOD) has become a leading model of urban planning worldwide that promises to meet a broad range of local and regional objectives: improving mobility, expanding ridership, attracting investment, reducing urban poverty, improving quality of life, making affordable housing and fostering urban integration. At the same time, the implementation of TOD in many cities has raised concerns about gentrification, displacement, re-segregation, and more polarization. This article aims to shed light on these issues by bringing together previously disparate literature that mentions these contradictions and discusses policymakers’ hopes and critics’ concern for the implementation of a newly started TOD project in a universal housing system in Rosengård—a segregated, low-income neighborhood in Malmö, Sweden. Although policy advocates view the project as a significant development strategy for a more sustainable Malmö, there are also real concerns about gentrification and the potential displacement of low-income residents. Furthermore, the mixed-methods study showes how integration might be achieved, but concerns have arisen about the possible exclusion of the current low-income residents, which brings up issues of inequality, representation of poverty, and marginalization.