bluuebunny/crossref_metadata_2025 huggingface dataset:
Cholecalciferol Additively Reduces Serum Parathyroid Hormone Levels in Severe Secondary Hyperparathyroidism Treated with Calcitriol and Cinacalcet among Hemodialysis Patients
Authors: Zheng Cai-Mei , Wu Chia-Chao , Hung Chi-Feng , Liao Min-Tser , Shyu Jia-Fwu , Hsu Yung-Ho , Lu Chien-Lin , Wang Yuan-Hung , Zheng Jing-Quan , Chang Tian-Jong , Lin Yuh-Feng , Lu Kuo-Cheng
Published: February 2018

We evaluated the improvement of intact parathyroid hormone (iPTH) levels and bone parameters by supplementing nutritional vitamin D (cholecalciferol) to combined calcimimetic (cinacalcet) and active vitamin D analog (calcitriol) among severe secondary hyperparathyroidism (SHPT) hemodialysis (HD) patients. A randomized, controlled open-label study was undertaken in 60 HD patients with serum iPTH > 1000 pg/mL or persistently high iPTH ≥ 600 pg/mL even after >3 months of calcitriol (3 μg/week). The study group received oral cholecalciferol (5000 IU/ day) and the control group received a placebo. All patients received fixed dose cinacalcet (30 mg/day, orally) and calcitriol. Calcitriol was reduced if iPTH ≤ 300 pg/mL and cinacalcet was withdrawn if serum iPTH was persistently low (iPTH ≤ 300 pg/mL) for 4 weeks after the reduction of calcitriol. A significantly lower iPTH level was noted from the 20th week in the study group compared to the placebo group, and the target iPTH ≤ 300 pg/mL was achieved at the 24th week in the study group. Most patients achieved serum 25-(OH)D3 ≥ 30 ng/mL in the study group. Nearly 40% of study patients gained >10% improvement in femoral neck (FN) bone mineral density (BMD). We conclude that cholecalciferol additively reduced serum iPTH levels, improved 25-(OH)D3 levels and improved FN BMD when used together with cinacalcet/calcitriol in severe SHPT HD patients.
Compliance with Dietary Guidelines Varies by Weight Status: A Cross-Sectional Study of Australian Adults
Authors: Hendrie Gilly , Golley Rebecca , Noakes Manny
Published: February 2018

Population surveys have rarely identified dietary patterns associated with excess energy intake in relation to risk of obesity. This study uses self-reported food intake data from the validated Commonwealth Scientific and Industrial Research Organisation (CSIRO) Healthy Diet Score survey to examine whether apparent compliance with dietary guidelines varies by weight status. The sample of 185,951 Australian adults were majority female (71.8%), with 30.2%, 35.3% and 31.0% aged between 18–30, 31–50 and 51–70 years respectively. Using multinomial regression, in the adjusted model controlling for gender and age, individuals in the lowest quintile of diet quality were almost three times more likely to be obese than those in the highest quintile (OR 2.99, CI: 2.88:3.11; p < 0.001). The differential components of diet quality between normal and obese adults were fruit (difference in compliance score 12.9 points out of a possible 100, CI: 12.3:13.5; p < 0.001), discretionary foods (8.7 points, CI: 8.1:9.2; p < 0.001), and healthy fats (7.7 points, CI: 7.2:8.1; p < 0.001). Discretionary foods was the lowest scoring component across all gender and weight status groups, and are an important intervention target to improve diet quality. This study contributes to the evidence that diet quality is associated with health outcomes, including weight status, and will be useful in framing recommendations for obesity prevention and management.
Preparation of Protein Molecular-Imprinted Polysiloxane Membrane Using Calcium Alginate Film as Matrix and Its Application for Cell Culture
Authors: Liu Dong , Zhao Kongyin , Qi Meng , Li Shuwen , Xu Guoqing , Wei Junfu , He Xiaoling
Published: February 2018

Bovine serum albumin (BSA) molecular-imprinted polysiloxane (MIP) membrane was prepared by sol-gel technology, using silanes as the functional monomers, BSA as the template and CaAlg hydrogel film as the matrix. The stress-strain curves of wet CaAlg membrane and molecular-imprinted polysiloxane membrane were investigated. We evaluate the adsorption and recognition properties of MIP membrane. Results showed that the adsorption capacity of BSA-imprinted polysiloxane for BSA reached 28.83 mg/g, which was 2.18 times the non-imprinted polysiloxane (NIP) membrane. The adsorption rate was higher than that of the protein-imprinted hydrogel. BSA-imprinted polysiloxane membrane could identify the protein template from competitive proteins such as bovine hemoglobin, ovalbumin and bovine γ-globulin. In order to obtain the biomaterial that can promote cell adhesion and proliferation, fibronectin (FN)-imprinted polysiloxane (FN-MIP) membrane was obtained by using fibronectin as the template, silanes as functional monomers, and CaAlg hydrogel membrane as the substrate or matrix. The FN-MIP adsorbed more FN than NIP. The FN-imprinted polysiloxane membrane was applied to culture mouse fibroblast cells (L929) and the results proved that the FN-MIP had a better effect on cell adhesion than NIP.
Hyperbranched Glycopolymers of 2-(α-d-Mannopyranose) Ethyl Methacrylate and N,N’-Methylenebisacrylamide: Synthesis, Characterization and Multivalent Recognitions with Concanavalin A
Authors: Zhang Yuangong , Wang Bo , Zhang Ye , Zheng Ying , Wen Xin , Bai Libin , Wu Yonggang
Published: February 2018

A series of novel hyperbranched poly[2-(α-d-mannopyranosyloxy) ethyl methacrylate-co-N,N’-methylenebisacrylamide] (HPManEMA-co-MBA) are synthesized via a reversible addition fragmentation polymerization (RAFT). The dosage ratios of linear and branch units are tuned to obtain different degree of branching (DB) in hyperbranched glycopolymers. The DB values are calculated according to the content of nitrogen, which are facilely determined by elemental analysis. The lectin-binding properties of HPManEMA-co-MBA to concanavalin A (ConA) are examined using a turbidimetric assay. The influence of defined DB value and molecular weight of HPManEMA-co-MBA on the clustering rate is studied. Notably, HPManEMA-co-MBAs display a low cytotoxicity in the MTT assay, thus are potential candidates for biomedical applications.
Optimizing Circulating Tumor Cells’ Capture Efficiency of Magnetic Nanogels by Transferrin Decoration
Authors: Biglione Catalina , Bergueiro Julian , Asadian-Birjand Mazdak , Weise Christoph , Khobragade Vrushali , Chate Govind , Dongare Manoj , Khandare Jayant , Strumia Miriam , Calderón Marcelo
Published: February 2018

Magnetic nanogels (MNGs) are designed to have all the required features for their use as highly efficient trapping materials in the challenging task of selectively capturing circulating tumor cells (CTCs) from the bloodstream. Advantageously, the discrimination of CTCs from hematological cells, which is a key factor in the capturing process, can be optimized by finely tuning the polymers used to link the targeting moiety to the MNG. We describe herein the relationship between the capturing efficiency of CTCs with overexpressed transferrin receptors and the different strategies on the polymer used as linker to decorate these MNGs with transferrin (Tf). Heterobifunctional polyethylene glycol (PEG) linkers with different molecular weights were coupled to Tf in different ratios. Optimal values over 80% CTC capture efficiency were obtained when 3 PEG linkers with a length of 8 ethylene glycol (EG) units were used, which reveals the important role of the linker in the design of a CTC-sorting system.
Sikh Self-Sacrifice and Religious Representation during World War I
Authors: Soboslai John
Published: February 2018

This paper analyzes the ways Sikh constructions of sacrifice were created and employed to engender social change in the early twentieth century. Through an examination of letters written by Sikh soldiers serving in the British Indian Army during World War I and contemporary documents from within their global religious, legislative, and economic context, I argue that Sikhs mobilized conceptions of self-sacrifice in two distinct directions, both aiming at procuring greater political recognition and representation. Sikhs living outside the Indian subcontinent encouraged their fellows to rise up and throw off their colonial oppressors by recalling mythic moments of the past and highlighting the plight of colonial subjects of the British Raj. Receiving less discussion are Punjabi Sikhs who fought in British forces during the Great War and who spoke of their potential sacrifice as divinely sanctioned in service to a benevolent state. Both sides utilized religious symbolism in the hope that Sikhs would again enjoy a level of self-rule that had been lost with the arrival of the British Empire.
Cognitive Evolution of a Patient Who Suffered a Subarachnoid Haemorrhage Eight Years Ago, after Being Treated with Growth Hormone, Melatonin and Neurorehabilitation
Authors: Quintana Ana , Agra Carlos , Outeiral Lucía , Devesa Ana , Llorente David , Devesa Jesús
Published: February 2018

To describe the cognitive evolution of a patient who suffered a subarachnoid haemorrhage resulting in a total loss of his cognitive functions. The patient was initially treated with GH (0.8 mg/day), melatonin (50 mg/day) and neurorehabilitation 1 year after his brain damage, during 3 months. Then continued with GH (0.5 mg/day, 6 months/year, during 2 years) and melatonin treatments and neurorehabilitation (3 days/week). 5 years later the patient came back to our Centre due to the absence of recent memory and personal and spatio-temporal orientation and he received an intensive specific neurorehabilitation, including EINA (Auditory Stimulation and Neurosensory Integration), together with GH (0.8 mg/day) and melatonin, for 6 months. At discharge of his first treatment period cognitive functions showed very poor changes but these had been improved when he came back 5 years later. A review carried out 8 years after SHA demonstrated that the patient significantly recovered in all the cognitive functions and he was able to live an independent life. GH plays a key role on cognition, including its actions on recent memory. Melatonin, in turn, helps as a neuroprotective agent. A specific neurostimulation must be performed so that the effects of GH can be expressed. Within neurostimulation, EINA seems to play a very important role for enhancing the effects of medical and rehabilitative treatments on brain plasticity.
Future Trajectories of Renewable Energy Consumption in the European Union
Authors: Cucchiella Federica , D’Adamo Idiano , Gastaldi Massimo
Published: February 2018

Renewable energy sources (RESs) are able to reduce the European Union (EU)’s dependence on foreign energy imports, also meeting sustainable objectives to tackle climate change and to enhance economic opportunities. Energy management requires a quantitative analysis and the European Commission follows the performance of each Member State (MS) in order to define the corrective measures towards 2020 targets. Starting from historical data reported in the Eurostat database and through a mathematical model, this work proposes future trajectories towards 2020 of the share of energy from renewables (REs) in terms of gross final energy consumption (GFEC). Furthermore, a quantitative analysis based on two indices—(i) the share of REs in GFEC, and (ii) gross final renewable energy consumption (GFREC) per capita—permits a comparison among 28 MSs. The share of REs in GFEC in EU 28 varies from 19.4% to 21.8% in future trajectories towards 2020. Sweden and Finland occupy the top part of the ranking, while six MSs (Belgium, France, Ireland, Luxembourg, The Netherlands, and the United Kingdom) are not able to reach the 2020 targets.
An Efficient Hyperspectral Image Retrieval Method: Deep Spectral-Spatial Feature Extraction with DCGAN and Dimensionality Reduction Using t-SNE-Based NM Hashing
Authors: Zhang Jing , Chen Lu , Zhuo Li , Liang Xi , Li Jiafeng
Published: February 2018

Hyperspectral images are one of the most important fundamental and strategic information resources, imaging the same ground object with hundreds of spectral bands varying from the ultraviolet to the microwave. With the emergence of huge volumes of high-resolution hyperspectral images produced by all sorts of imaging sensors, processing and analysis of these images requires effective retrieval techniques. How to ensure retrieval accuracy and efficiency is a challenging task in the field of hyperspectral image retrieval. In this paper, an efficient hyperspectral image retrieval method is proposed. In principle, our method includes the following steps: (1) in order to make powerful representations for hyperspectral images, deep spectral-spatial features are extracted with the Deep Convolutional Generative Adversarial Networks (DCGAN) model; (2) considering the higher dimensionality of deep spectral-spatial features, t-Distributed Stochastic Neighbor Embedding-based Nonlinear Manifold (t-SNE-based NM) hashing is utilized to make dimensionality reduction by learning compact binary codes embedded on the intrinsic manifolds of deep spectral-spatial features for balancing between learning efficiency and retrieval accuracy; and (3) multi-index hashing in Hamming space is measured to find similar hyperspectral images. Five comparative experiments are conducted to verify the effectiveness of deep spectral-spatial features, dimensionality reduction of t-SNE-based NM hashing, and similarity measurement of multi-index hashing. The experimental results using NASA datasets show that our hyperspectral image retrieval method can achieve comparable and superior performance with less computational time.
Using High-Resolution Airborne Data to Evaluate MERIS Atmospheric Correction and Intra-Pixel Variability in Nearshore Turbid Waters
Authors: Larnicol Morgane , Launeau Patrick , Gernez Pierre
Published: February 2018

The implementation of accurate atmospheric correction is a prerequisite for satellite observation and water quality monitoring in coastal areas. The potential of the fast-line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) was investigated here for the medium resolution imaging spectrometer (MERIS). As the comparison between discrete field sampling points and macro-scale satellite pixels is subject to spatial biases associated with small-scale spatial patchiness in the turbid and highly dynamic nearshore zone, an alternative approach was proposed here using high spatial resolution (1 m) airborne hyperspectral images as radiometric truthing references. While FLAASH was not optimal for moderately turbid offshore waters (suspended particulate matter (SPM) concentration < 50 g∙m−3), it yields satisfactory results in the 50–1500 g∙m−3 range, where MERIS standard atmospheric correction was subject to significant biases and failures. Due to the significant intra-pixel variability of SPM distribution in highly turbid areas, the acquisition of high resolution airborne images should be considered as a consistent strategy for the validation of medium resolution satellite remote sensing in the spatially heterogeneous and optically diverse nearshore waters.
A Genetic Algorithm-Based Urban Cluster Automatic Threshold Method by Combining VIIRS DNB, NDVI, and NDBI to Monitor Urbanization
Authors: Li Kangning , Chen Yunhao
Published: February 2018

Accurate and timely information related to quantitative descriptions and spatial distributions of urban areas is crucial to understand urbanization dynamics and is also helpful to address environmental issues associated with rapid urban land-cover changes. Thresholding is acknowledged as the most popular and practical way to extract urban information from nighttime lights. However, the difficulty of determining optimal threshold remains challenging to applications of this method. In order to address the problem of selecting thresholds, a Genetic Algorithm-based urban cluster automatic threshold (GA-UCAT) method by combining Visible-Infrared Imager-Radiometer Suite Day/Night band (VIIRS DNB), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) is proposed to distinguish urban areas from dark rural background in NTL images. The key point of this proposed method is to design an appropriate fitness function of GA by means of integrating between-class variance and inter-class variance with all these three data sources to determine optimal thresholds. In accuracy assessments by comparing with ground truth—Landsat 8 OLI images, this new method has been validated and results with OA (Overall Accuracy) ranging from 0.854 to 0.913 and Kappa ranging from 0.699 to 0.722 show that the GA-UCAT approach is capable of describing spatial distributions and giving detailed information of urban extents. Additionally, there is discussion on different classifications of rural residential spots in Landsat remote sensing images and nighttime light (NTL) and evaluations of spatial-temporal development patterns of five selected Chinese urban clusters from 2012 to 2017 on utilizing this proposed method. The new method shows great potential to map global urban information in a simple and accurate way and to help address urban environmental issues.
Simulation and Analysis of the Topographic Effects on Snow-Free Albedo over Rugged Terrain
Authors: Hao Dalei , Wen Jianguang , Xiao Qing , Wu Shengbiao , Lin Xingwen , Dou Baocheng , You Dongqin , Tang Yong
Published: February 2018

Topography complicates the modeling and retrieval of land surface albedo due to shadow effects and the redistribution of incident radiation. Neglecting topographic effects may lead to a significant bias when estimating land surface albedo over a single slope. However, for rugged terrain, a comprehensive and systematic investigation of topographic effects on land surface albedo is currently ongoing. Accurately estimating topographic effects on land surface albedo over a rugged terrain presents a challenge in remote sensing modeling and applications. In this paper, we focused on the development of a simplified estimation method for snow-free albedo over a rugged terrain at a 1-km scale based on a 30-m fine-scale digital elevation model (DEM). The proposed method was compared with the radiosity approach based on simulated and real DEMs. The results of the comparison showed that the proposed method provided adequate computational efficiency and satisfactory accuracy simultaneously. Then, the topographic effects on snow-free albedo were quantitatively investigated and interpreted by considering the mean slope, subpixel aspect distribution, solar zenith angle, and solar azimuth angle. The results showed that the more rugged the terrain and the larger the solar illumination angle, the more intense the topographic effects were on black-sky albedo (BSA). The maximum absolute deviation (MAD) and the maximum relative deviation (MRD) of the BSA over a rugged terrain reached 0.28 and 85%, respectively, when the SZA was 60° for different terrains. Topographic effects varied with the mean slope, subpixel aspect distribution, SZA and SAA, which should not be neglected when modeling albedo.
Remote Sensing of Landslides—A Review
Authors: Zhao Chaoying , Lu Zhong
Published: February 2018

Triggered by earthquakes, rainfall, or anthropogenic activities, landslides represent widespread and problematic geohazards worldwide. In recent years, multiple remote sensing techniques, including synthetic aperture radar, optical, and light detection and ranging measurements from spaceborne, airborne, and ground-based platforms, have been widely applied for the analysis of landslide processes. Current techniques include landslide detection, inventory mapping, surface deformation monitoring, trigger factor analysis and mechanism inversion. In addition, landslide susceptibility modelling, hazard assessment, and risk evaluation can be further analyzed using a synergic fusion of multiple remote sensing data and other factors affecting landslides. We summarize the 19 articles collected in this special issue of Remote Sensing of Landslide, in the terms of data, methods and applications used in the papers.
SBAS Analysis of Induced Ground Surface Deformation from Wastewater Injection in East Central Oklahoma, USA
Authors: Loesch Elizabeth , Sagan Vasit
Published: February 2018

The state of Oklahoma has experienced a dramatic increase in the amount of measurable seismic activities over the last decade. The needs of a petroleum-driven world have led to increased production utilizing various technologies to reach energy reserves locked in tight formations and stimulate end-of-life wells, creating significant amounts of undesirable wastewater ultimately injected underground for disposal. Using Phased Array L-band Synthetic Aperture Radar (PALSAR) data, we performed a differential Synthetic Aperture Radar Interferometry (InSAR) technique referred to as the Small BAseline Subset (SBAS)-based analysis over east central Oklahoma to identify ground surface deformation with respect to the location of wastewater injection wells for the period of December 2006 to January 2011. Our results show broad spatial correlation between SBAS-derived deformation and the locations of injection wells. We also observed significant uplift over Cushing, Oklahoma, the largest above ground crude oil storage facility in the world, and a key hub of the Keystone Pipeline. This finding has significant implications for the oil and gas industry due to its close proximity to the zones of increased seismicity attributed to wastewater injection. Results southeast of Drumright, Oklahoma represent an excellent example of the potential of InSAR, identifying a fault bordered by an area of subduction to the west and uplift to the east. This differentiated movement along the fault may help explain the lack of any seismic activity in this area, despite the large number of wells and high volume of fluid injected.
When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature
Authors: Wang Cong , Zhang Lei , Wei Wei , Zhang Yanning
Published: February 2018

When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery classification. However, these methods have limitations in generalizing well in challenging cases due to the limited representative capacity of the shallow feature learning model, as well as the insufficient robustness of the classifier which only depends on the supervision of labelled samples. To address these two problems simultaneously, we present an effective low-rank representation-based classification framework for hyperspectral imagery. In particular, a novel unsupervised segmented stacked denoising auto-encoder-based feature learning model is proposed to depict the spatial-spectral characteristics of each pixel in the imagery with deep hierarchical structure. With the extracted features, a low-rank representation based robust classifier is then developed which takes advantage of both the supervision provided by labelled samples and unsupervised correlation (e.g., intra-class similarity and inter-class dissimilarity, etc.) among those unlabelled samples. Both the deep unsupervised feature learning and the robust classifier benefit, improving the classification accuracy with limited labelled samples. Extensive experiments on hyperspectral imagery classification demonstrate the effectiveness of the proposed framework.
An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery
Authors: De Castro Ana , Torres-Sánchez Jorge , Peña Jose , Jiménez-Brenes Francisco , Csillik Ovidiu , López-Granados Francisca
Published: February 2018

Accurate and timely detection of weeds between and within crop rows in the early growth stage is considered one of the main challenges in site-specific weed management (SSWM). In this context, a robust and innovative automatic object-based image analysis (OBIA) algorithm was developed on Unmanned Aerial Vehicle (UAV) images to design early post-emergence prescription maps. This novel algorithm makes the major contribution. The OBIA algorithm combined Digital Surface Models (DSMs), orthomosaics and machine learning techniques (Random Forest, RF). OBIA-based plant heights were accurately estimated and used as a feature in the automatic sample selection by the RF classifier; this was the second research contribution. RF randomly selected a class balanced training set, obtained the optimum features values and classified the image, requiring no manual training, making this procedure time-efficient and more accurate, since it removes errors due to a subjective manual task. The ability to discriminate weeds was significantly affected by the imagery spatial resolution and weed density, making the use of higher spatial resolution images more suitable. Finally, prescription maps for in-season post-emergence SSWM were created based on the weed maps—the third research contribution—which could help farmers in decision-making to optimize crop management by rationalization of the herbicide application. The short time involved in the process (image capture and analysis) would allow timely weed control during critical periods, crucial for preventing yield loss.
An Information Entropy-Based Sensitivity Analysis of Radar Sensing of Rough Surface
Authors: Liu Yu , Chen Kun-Shan
Published: February 2018

We apply Shannon entropy, an information content measure, in sensitivity analysis (SA), stemming from the fact that the essence of SA is to preserve the maximum information content of the parameters of interest that are inverted from the radar response. Then, the sensitivity to the observation configuration and surface parameters is subsequently analyzed. Attempts are also made to explore advantages, by maximizing the information content, of dual-polarization and multi-angle in improving the parameter retrieval from radar sensing of rough surface. Simulation results show that the entropy is a good indicator of the sensitivity of the radar response to the surface parameter, as it contains information on not only the probability distribution of the scattering coefficient but also on its deviation. By information entropy, richer details, to large extent, on the scattering behavior are offered through quantitatively predicting the scattering signal saturation, evaluating the effect of using multi-polarization and multi-angle observation configuration, and identifying non-significant variables. It is found that Shannon entropy, compared to Renyi entropy, appears to better represent the sensitivity with respect to monotonic variation and narrower parameter ranges. The proposed entropy-based SA method helps to deepen our understanding of the microwave scattering behavior in response to surface parameters.
A Real-Time Wireless Sweat Rate Measurement System for Physical Activity Monitoring
Authors: Brueck Andrew , Iftekhar Tashfin , Stannard Alicja , Yelamarthi Kumar , Kaya Tolga
Published: February 2018

There has been significant research on the physiology of sweat in the past decade, with one of the main interests being the development of a real-time hydration monitor that utilizes sweat. The contents of sweat have been known for decades; sweat provides significant information on the physiological condition of the human body. However, it is important to know the sweat rate as well, as sweat rate alters the concentration of the sweat constituents, and ultimately affects the accuracy of hydration detection. Towards this goal, a calorimetric based flow-rate detection system was built and tested to determine sweat rate in real time. The proposed sweat rate monitoring system has been validated through both controlled lab experiments (syringe pump) and human trials. An Internet of Things (IoT) platform was embedded, with the sensor using a Simblee board and Raspberry Pi. The overall prototype is capable of sending sweat rate information in real time to either a smartphone or directly to the cloud. Based on a proven theoretical concept, our overall system implementation features a pioneer device that can truly measure the rate of sweat in real time, which was tested and validated on human subjects. Our realization of the real-time sweat rate watch is capable of detecting sweat rates as low as 0.15 µL/min/cm2, with an average error in accuracy of 18% compared to manual sweat rate readings.
Performance Analysis of Diversity-Controlled Multi-User Superposition Transmission for 5G Wireless Networks
Authors: Yeom Jeong , Chu Eunmi , Jung Bang , Jin Hu
Published: February 2018

In this paper, we propose a novel low-complexity multi-user superposition transmission (MUST) technique for 5G downlink networks, which allows multiple cell-edge users to be multiplexed with a single cell-center user. We call the proposed technique diversity-controlled MUST technique since the cell-center user enjoys the frequency diversity effect via signal repetition over multiple orthogonal frequency division multiplexing (OFDM) sub-carriers. We assume that a base station is equipped with a single antenna but users are equipped with multiple antennas. In addition, we assume that the quadrature phase shift keying (QPSK) modulation is used for users. We mathematically analyze the bit error rate (BER) of both cell-edge users and cell-center users, which is the first theoretical result in the literature to the best of our knowledge. The mathematical analysis is validated through extensive link-level simulations.
An IoT-Based Gamified Approach for Reducing Occupants’ Energy Wastage in Public Buildings
Authors: Papaioannou Thanasis , Dimitriou Nikos , Vasilakis Kostas , Schoofs Anthony , Nikiforakis Manolis , Pursche Fabian , Deliyski Nikolay , Taha Amr , Kotsopoulos Dimosthenis , Bardaki Cleopatra , Kotsilitis Sarantis , Garbi Anastasia
Published: February 2018

Conserving energy amenable to the activities of occupants in public buildings is a particularly challenging objective that includes associating energy consumption to particular individuals and providing them with incentives to alter their behavior. This paper describes a gamification framework that aims to facilitate achieving greater energy conservation in public buildings. The framework leverages IoT-enabled low-cost devices, to improve energy disaggregation mechanisms that provide energy use and—consequently—wastage information at the device, area and end-user level. The identified wastages are concurrently targeted by a gamified application that motivates respective behavioral changes combining team competition, virtual rewards and life simulation. Our solution is being developed iteratively with the end-users’ engagement during the analysis, design, development and validation phases in public buildings located in three different countries: Luxembourg (Musée National d’Histoire et d’Art), Spain (EcoUrbanBuilding, Institut Català d’Energia headquarters, Barcelona) and Greece (General Secretariat of the Municipality of Athens).
A COTS-Based Portable System to Conduct Accurate Substance Concentration Measurements
Authors: Aznar-Poveda Juan , Lopez-Pastor Jose , Garcia-Sanchez Antonio-Javier , Garcia-Haro Joan , Otero Toribio
Published: February 2018

Traditionally, electrochemical procedures aimed at determining substance concentrations have required a costly and cumbersome laboratory environment. Specialized equipment and personnel obtain precise results under complex and time-consuming settings. Innovative electrochemical-based sensors are emerging to alleviate this difficulty. However, they are generally scarce, proprietary hardware and/or software, and focused only on measuring a restricted range of substances. In this paper, we propose a portable, flexible, low-cost system, built from commercial off-the-shelf components and easily controlled, using open-source software. The system is completed with a wireless module, which enables the transmission of measurements to a remote database for their later processing. A well-known PGSTAT100 Autolab device is employed to validate the effectiveness of our proposal. To this end, we select ascorbic acid as the substance under consideration, evaluating the reliability figure and obtaining the calibration curves for both platforms. The final outcomes are shown to be feasible, accurate, and repeatable.
Monitoring Walker Assistive Devices: A Novel Approach Based on Load Cells and Optical Distance Measurements
Authors: Viegas Vítor , Dias Pereira J. , Postolache Octavian , Girão Pedro
Published: February 2018

This paper presents a measurement system intended to monitor the usage of walker assistive devices. The goal is to guide the user in the correct use of the device in order to prevent risky situations and maximize comfort. Two risk indicators are defined: one related to force unbalance and the other related to motor incoordination. Force unbalance is measured by load cells attached to the walker legs, while motor incoordination is estimated by synchronizing force measurements with distance data provided by an optical sensor. The measurement system is equipped with a Bluetooth link that enables local supervision on a computer or tablet. Calibration and experimental results are included in the paper.
Plane Wave SH0 Piezoceramic Transduction Optimized Using Geometrical Parameters
Authors: Boivin Guillaume , Viens Martin , Belanger Pierre
Published: February 2018

Structural health monitoring is a prominent alternative to the scheduled maintenance of safety-critical components. The nondispersive nature as well as the through-thickness mode shape of the fundamental shear horizontal guided wave mode (SH 0 ) make it a particularly attractive candidate for ultrasonic guided wave structural health monitoring. However, plane wave excitation of SH 0 at a high level of purity remains challenging because of the existence of the fundamental Lamb modes (A 0 and S 0 ) below the cutoff frequency thickness product of high-order modes. This paper presents a piezoelectric transducer concept optimized for plane SH 0 wave transduction based on the transducer geometry. The transducer parameter exploration was initially performed using a simple analytical model. A 3D multiphysics finite element model was then used to refine the transducer design. Finally, an experimental validation was conducted with a 3D laser Doppler vibrometer system. The analytical model, the finite element model, and the experimental measurement showed excellent agreement. The modal selectivity of SH 0 within a 20 ∘ beam opening angle at the design frequency of 425 kHz in a 1.59 mm aluminum plate was 23 dB, and the angle of the 6 dB wavefront was 86 ∘ .
On the Temporal Stability of Analyte Recognition with an E-Nose Based on a Metal Oxide Sensor Array in Practical Applications
Authors: Kiselev Ilia , Sysoev Victor , Kaikov Igor , Koronczi Ilona , Adil Akai Tegin Ruslan , Smanalieva Jamila , Sommer Martin , Ilicali Coskan , Hauptmannl Michael
Published: February 2018

The paper deals with a functional instability of electronic nose (e-nose) units which significantly limits their real-life applications. Here we demonstrate how to approach this issue with example of an e-nose based on a metal oxide sensor array developed at the Karlsruhe Institute of Technology (Germany). We consider the instability of e-nose operation at different time scales ranging from minutes to many years. To test the e-nose we employ open-air and headspace sampling of analyte odors. The multivariate recognition algorithm to process the multisensor array signals is based on the linear discriminant analysis method. Accounting for the received results, we argue that the stability of device operation is mostly affected by accidental changes in the ambient air composition. To overcome instabilities, we introduce the add-training procedure which is found to successfully manage both the temporal changes of ambient and the drift of multisensor array properties, even long-term. The method can be easily implemented in practical applications of e-noses and improve prospects for device marketing.
Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors
Authors: Zhang Chongxing , Dong Ming , Ren Ming , Huang Wenguang , Zhou Jierui , Gao Xuze , Albarracín Ricardo
Published: February 2018

Metal-enclosed switchgear, which are widely used in the distribution of electrical energy, play an important role in power distribution networks. Their safe operation is directly related to the reliability of power system as well as the power quality on the consumer side. Partial discharge detection is an effective way to identify potential faults and can be utilized for insulation diagnosis of metal-enclosed switchgear. The transient earth voltage method, an effective non-intrusive method, has substantial engineering application value for estimating the insulation condition of switchgear. However, the practical application effectiveness of TEV detection is not satisfactory because of the lack of a TEV detection application method, i.e., a method with sufficient technical cognition and analysis. This paper proposes an innovative online PD detection system and a corresponding application strategy based on an intelligent feedback distributed TEV wireless sensor network, consisting of sensing, communication, and diagnosis layers. In the proposed system, the TEV signal or status data are wirelessly transmitted to the terminal following low-energy signal preprocessing and acquisition by TEV sensors. Then, a central server analyzes the correlation of the uploaded data and gives a fault warning level according to the quantity, trend, parallel analysis, and phase resolved partial discharge pattern recognition. In this way, a TEV detection system and strategy with distributed acquisition, unitized fault warning, and centralized diagnosis is realized. The proposed system has positive significance for reducing the fault rate of medium voltage switchgear and improving its operation and maintenance level.
An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms
Authors: Ding Kai , Li Qingquan , Zhu Jiasong , Wang Chisheng , Guan Minglei , Chen Zhipeng , Yang Chao , Cui Yang , Liao Jianghai
Published: February 2018

In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What’s more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.
Chemical Sensor Systems and Associated Algorithms for Fire Detection: A Review
Authors: Fonollosa Jordi , Solórzano Ana , Marco Santiago
Published: February 2018

Indoor fire detection using gas chemical sensing has been a subject of investigation since the early nineties. This approach leverages the fact that, for certain types of fire, chemical volatiles appear before smoke particles do. Hence, systems based on chemical sensing can provide faster fire alarm responses than conventional smoke-based fire detectors. Moreover, since it is known that most casualties in fires are produced from toxic emissions rather than actual burns, gas-based fire detection could provide an additional level of safety to building occupants. In this line, since the 2000s, electrochemical cells for carbon monoxide sensing have been incorporated into fire detectors. Even systems relying exclusively on gas sensors have been explored as fire detectors. However, gas sensors respond to a large variety of volatiles beyond combustion products. As a result, chemical-based fire detectors require multivariate data processing techniques to ensure high sensitivity to fires and false alarm immunity. In this paper, we the survey toxic emissions produced in fires and defined standards for fire detection systems. We also review the state of the art of chemical sensor systems for fire detection and the associated signal and data processing algorithms. We also examine the experimental protocols used for the validation of the different approaches, as the complexity of the test measurements also impacts on reported sensitivity and specificity measures. All in all, further research and extensive test under different fire and nuisance scenarios are still required before gas-based fire detectors penetrate largely into the market. Nevertheless, the use of dynamic features and multivariate models that exploit sensor correlations seems imperative.
Active Electro-Location of Objects in the Underwater Environment Based on the Mixed Polarization Multiple Signal Classification Algorithm
Authors: Xu Yidong , Shang Wenjing , Guo Lili , Qi Junwei , Li Yingsong , Xue Wei
Published: February 2018

This article proposes a novel active localization method based on the mixed polarization multiple signal classification (MP-MUSIC) algorithm for positioning a metal target or an insulator target in the underwater environment by using a uniform circular antenna (UCA). The boundary element method (BEM) is introduced to analyze the boundary of the target by use of a matrix equation. In this method, an electric dipole source as a part of the locating system is set perpendicularly to the plane of the UCA. As a result, the UCA can only receive the induction field of the target. The potential of each electrode of the UCA is used as spatial-temporal localization data, and it does not need to obtain the field component in each direction compared with the conventional fields-based localization method, which can be easily implemented in practical engineering applications. A simulation model and a physical experiment are constructed. The simulation and the experiment results provide accurate positioning performance, with the help of verifying the effectiveness of the proposed localization method in underwater target locating.
An Improved Pansharpening Method for Misaligned Panchromatic and Multispectral Data
Authors: Li Hui , Jing Linhai , Tang Yunwei , Ding Haifeng
Published: February 2018

Numerous pansharpening methods were proposed in recent decades for fusing low-spatial-resolution multispectral (MS) images with high-spatial-resolution (HSR) panchromatic (PAN) bands to produce fused HSR MS images, which are widely used in various remote sensing tasks. The effect of misregistration between MS and PAN bands on quality of fused products has gained much attention in recent years. An improved method for misaligned MS and PAN imagery is proposed, through two improvements made on a previously published method named RMI (reduce misalignment impact). The performance of the proposed method was assessed by comparing with some outstanding fusion methods, such as adaptive Gram-Schmidt and generalized Laplacian pyramid. Experimental results show that the improved version can reduce spectral distortions of fused dark pixels and sharpen boundaries between different image objects, as well as obtain similar quality indexes with the original RMI method. In addition, the proposed method was evaluated with respect to its sensitivity to misalignments between MS and PAN bands. It is certified that the proposed method is more robust to misalignments between MS and PAN bands than the other methods.
Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact
Authors: Khanna Shruti , Santos Maria , Ustin Susan , Shapiro Kristen , Haverkamp Paul , Lay Mui
Published: February 2018

Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills.