bluuebunny/crossref_metadata_2025 huggingface dataset:
Fluorescein- and EGFR-Antibody Conjugated Silica Nanoparticles for Enhancement of Real-time Tumor Border Definition Using Confocal Laser Endomicroscopy in Squamous Cell Carcinoma of the Head and Neck
Authors: Watermann Anna , Gieringer Rita , Bauer Anna-Maria , Kurch Sven , Kiesslich Ralf , Tremel Wolfgang , Gosepath Jan , Brieger Juergen
Published: September 2019

Intraoperative definition of tumor free resection margins in head and neck cancer is challenging. In the current proof-of-principle study we evaluated a novel silica nanoparticle-based agent for its potential use as contrast enhancer. We synthesized silica nanoparticles with an average size of 45 nm and modified these particles with the fluorescence stain fluorescein isocyanate (FITC) for particle detection and with epidermal growth factor receptor (EGFR)-targeting antibodies for enhanced tumor specificity. The nanoparticles exhibited good biocompatibility and could be detected in vitro and in vivo by confocal laser scanning microscopy. Additionally, we show in an ex vivo setting that these modified nanoparticles specifically bind to tumor samples and could be detected using a handheld confocal fluorescence endomicroscope. From a clinical point of view, we believe that this method could be used for tumor border contrast enhancement and for better intraoperative definition of R-0 tumor resection.
Carbamazepine Degradation Mediated by Light in the Presence of Humic Substances-Coated Magnetite Nanoparticles
Authors: Aparicio Francisca , Escalada Juan Pablo , De Gerónimo Eduardo , Aparicio Virginia C. , García Einschlag Fernando S. , Magnacca Giuliana , Carlos Luciano , Mártire Daniel O.
Published: September 2019

The use of iron-based nanomaterials for environmental remediation processes has recently received considerable attention. Here, we employed core-shell magnetite-humic acids nanoparticles as a heterogeneous photosensitizer and iron source in photo-Fenton reaction for the degradation of the psychiatric drug carbamazepine (CBZ). CBZ showed low photodegradation rates in the presence of the magnetic nanoparticles, whereas the addition of hydrogen peroxide at pH = 3 to the system drastically increased the abatement of the contaminant. The measured Fe2+ and Fe3+ profiles point to the generation of Fe3+ at the surface of the nanoparticles, indicating a heterogeneous oxidation of the contaminant mediated by hydroxyl radicals. Products with a higher transformation degree were observed in the photo-Fenton procedure and support the attack of the HO• radical on the CBZ molecule. Promising results encourage the use of the nanoparticles as efficient iron sources with enhanced magnet-sensitive properties, suitable for applications in photo-Fenton treatments for the purification of wastewater.
Facile 3D Boron Nitride Integrated Electrospun Nanofibrous Membranes for Purging Organic Pollutants
Authors: Jiang Dai-Hua , Chiu Pei-Chi , Cho Chia-Jung , Veeramuthu Loganathan , Tung Shih-Huang , Satoh Toshifumi , Chiang Wei-Hung , Cai Xingke , Kuo Chi-Ching
Published: September 2019

Elegant integration of three-dimensional (3D) boron nitride (BN) into the porous structure of a polymer nanofiber (NF) membrane system results in a surface with enhanced absorption capacity for removal. Various BN-based applications were designed and developed successfully, but BN-based absorption systems remain relatively unexplored. To develop a reusable absorption strategy with high removal efficiency, we used a composite of 3D BN and polyacrylonitrile (PAN) to prepare a NF membrane with a porous structure by using electrospinning and spray techniques (BN-PAN ES NFs). The removal efficiency of the 3D BN NF membrane was higher than that of a pure carbon NF membrane. Water pollutants, such as the dyes Congo red (CR), basic yellow 1 (BY), and rhodamine B (Rh B), were tested, and the absorption ratios were 46%, 53%, and 45%, respectively. Furthermore, the aforementioned dyes and pollutants can be completely eliminated and removed from water by heating because of the high heat resistance of 3D BN. The membrane can be recycled and reused at least 10 times. These results indicate that BN-PAN ES NFs have can be used in water purification and treatment for absorption applications, and that they can be reused after heat treatment.
Novel Nanocomposites Based on Functionalized Magnetic Nanoparticles and Polyacrylamide: Preparation and Complex Characterization
Authors: Tanasa Eugenia , Zaharia Catalin , Radu Ionut-Cristian , Surdu Vasile-Adrian , Vasile Bogdan Stefan , Damian Celina-Maria , Andronescu Ecaterina
Published: September 2019

This paper reports the synthesis and complex characterization of nanocomposite hydrogels based on polyacrylamide and functionalized magnetite nanoparticles. Magnetic nanoparticles were functionalized with double bonds by 3-trimethoxysilyl propyl methacrylate. Nanocomposite hydrogels were prepared by radical polymerization of acrylamide monomer and double bond modified magnetite nanoparticles. XPS spectra for magnetite and modified magnetite were recorded to evaluate the covalent bonding of silane modifying agent. Swelling measurements in saline solution were performed to evaluate the behavior of these hydrogels having various compositions. Mechanical properties were evaluated by dynamic rheological analysis for elastic modulus and vibrating sample magnetometry was used to investigate the magnetic properties. Morphology, geometrical evaluation (size and shape) of nanostructural characteristics and the crystalline structure of the samples were investigated by SEM, HR-TEM and selected area electron diffraction (SAED). The nanocomposite hydrogels will be further tested for the soft tissue engineering field as repairing scaffolds, due to their mechanical and magnetization behavior that can stimulate tissue regeneration.
Silk Fibroin as a Functional Biomaterial for Drug and Gene Delivery
Authors: Tomeh Mhd Anas , Hadianamrei Roja , Zhao Xiubo
Published: September 2019

Silk is a natural polymer with unique physicochemical and mechanical properties which makes it a desirable biomaterial for biomedical and pharmaceutical applications. Silk fibroin (SF) has been widely used for preparation of drug delivery systems due to its biocompatibility, controllable degradability and tunable drug release properties. SF-based drug delivery systems can encapsulate and stabilize various small molecule drugs as well as large biological drugs such as proteins and DNA to enhance their shelf lives and control the release to enhance their circulation time in the blood and thus the duration of action. Understanding the properties of SF and the potential ways of manipulating its structure to modify its physicochemical and mechanical properties allows for preparation of modulated drug delivery systems with desirable efficacies. This review will discuss the properties of SF material and summarize the recent advances of SF-based drug and gene delivery systems. Furthermore, conjugation of the SF to other biomolecules or polymers for tissue-specific drug delivery will also be discussed.
Mass Spectrometry Reveals Molecular Structure of Polyhydroxyalkanoates Attained by Bioconversion of Oxidized Polypropylene Waste Fragments
Authors: Johnston Brian , Radecka Iza , Chiellini Emo , Barsi David , Ilieva Vassilka Ivanova , Sikorska Wanda , Musioł Marta , Zięba Magdalena , Chaber Paweł , Marek Adam A. , Mendrek Barbara , Ekere Anabel Itohowo , Adamus Grazyna , Kowalczuk Marek
Published: September 2019

This study investigated the molecular structure of the polyhydroxyalkanoate (PHA) produced via a microbiological shake flask experiment utilizing oxidized polypropylene (PP) waste as an additional carbon source. The bacterial strain Cupriavidus necator H16 was selected as it is non-pathogenic, genetically stable, robust, and one of the best known producers of PHA. Making use of PHA oligomers, formed by controlled moderate-temperature degradation induced by carboxylate moieties, by examination of both the parent and fragmentation ions, the ESI-MS/MS analysis revealed the 3-hydroxybutyrate and randomly distributed 3-hydroxyvalerate as well as 3-hydroxyhexanoate repeat units. Thus, the bioconversion of PP solid waste to a value-added product such as PHA tert-polymer was demonstrated.
Analysis of PLA Geometric Properties Processed by FFF Additive Manufacturing: Effects of Process Parameters and Plate-Extruder Precision Motion
Authors: García Plaza Eustaquio , Núñez López Pedro , Caminero Torija Miguel , Chacón Muñoz Jesús
Published: September 2019

The evolution of fused filament fabrication (FFF) technology, initially restricted to the manufacturing of prototypes, has led to its application in the manufacture of finished functional products with excellent mechanical properties. However, FFF technology entails drawbacks in aspects, such as dimensional and geometric precision, and surface finish. These aspects are crucial for the assembly and service life of functional parts, with geometric qualities lagging far behind the optimum levels obtained by conventional manufacturing processes. A further shortcoming is the proliferation of low cost FFF 3D printers with low quality mechanical components, and malfunctions that have a critical impact on the quality of finished products. FFF product quality is directly influenced by printer settings, material properties in terms of cured layers, and the functional mechanical efficiency of the 3D printer. This paper analyzes the effect of the build orientation (Bo), layer thickness (Lt), feed rate (Fr) parameters, and plate-extruder movements on the dimensional accuracy, flatness error, and surface texture of polylactic acid (PLA) using a low cost open-source FFF 3D printer. The mathematical modelling of geometric properties was performed using artificial neural networks (ANN). The results showed that thinner layer thickness generated lower dimensional deviations, and feed rate had a minor influence on dimensional accuracy. The flatness error and surface texture showed a quasi-linear behavior correlated to layer thickness and feed rate, with alterations produced by 3D printer malfunctions. The mathematical models provide a comprehensive analysis of the geometric behavior of PLA processing by FFF, in order to identify optimum print settings for the processing of functional components.
An Overview of Temperature Issues in Microwave-Assisted Pyrolysis
Authors: Bartoli Mattia , Frediani Marco , Briens Cedric , Berruti Franco , Rosi Luca
Published: September 2019

Microwave-assisted pyrolysis is a promising thermochemical technique to convert waste polymers and biomass into raw chemicals and fuels. However, this process involves several issues related to the interactions between materials and microwaves. Consequently, the control of temperature during microwave-assisted pyrolysis is a hard task both for measurement and uniformity during the overall pyrolytic run. In this review, we introduce some of the main theoretical aspects of the microwaves–materials interactions alongside the issues related to microwave pyrolytic processability of materials.
Gaussian Process Methodology for Multi-Frequency Marine Controlled-Source Electromagnetic Profile Estimation in Isotropic Medium
Authors: Mohd Aris Muhammad Naeim , Daud Hanita , Dass Sarat Chandra , Mohd Noh Khairul Arifin
Published: September 2019

The marine controlled-source electromagnetic (CSEM) technique is an application of electromagnetic (EM) waves to image the electrical resistivity of the subsurface underneath the seabed. The modeling of marine CSEM is a crucial and time-consuming task due to the complexity of its mathematical equations. Hence, high computational cost is incurred to solve the linear systems, especially for high-dimensional models. Addressing these problems, we propose Gaussian process (GP) calibrated with computer experiment outputs to estimate multi-frequency marine CSEM profiles at various hydrocarbon depths. This methodology utilizes prior information to provide beneficial EM profiles with uncertainty quantification in terms of variance (95% confidence interval). In this paper, prior marine CSEM information was generated through Computer Simulation Technology (CST) software at various observed hydrocarbon depths (250–2750 m with an increment of 250 m each) and different transmission frequencies (0.125, 0.25, and 0.5 Hz). A two-dimensional (2D) forward GP model was developed for every frequency by utilizing the marine CSEM information. From the results, the uncertainty measurement showed that the estimates were close to the mean. For model validation, the calculated root mean square error (RMSE) and coefficient of variation (CV) proved in good agreement between the computer output and the estimated EM profile at unobserved hydrocarbon depths.
Water-Gas Two-Phase Flow Behavior of Multi-Fractured Horizontal Wells in Shale Gas Reservoirs
Authors: Li Lei , Sheng Guanglong , Su Yuliang
Published: September 2019

Hydraulic fracturing is a necessary method to develop shale gas reservoirs effectively and economically. However, the flow behavior in multi-porosity fractured reservoirs is difficult to characterize by conventional methods. In this paper, combined with apparent porosity/permeability model of organic matter, inorganic matter and induced fractures, considering the water film in unstimulated reservoir volume (USRV) region water and bulk water in effectively stimulated reservoir volume (ESRV) region, a multi-media water-gas two-phase flow model was established. The finite difference is used to solve the model and the water-gas two-phase flow behavior of multi-fractured horizontal wells is obtained. Mass transfer between different-scale media, the effects of pore pressure on reservoirs and fluid properties at different production stages were considered in this model. The influence of the dynamic reservoir physical parameters on flow behavior and gas production in multi-fractured horizontal wells is studied. The results show that the properties of the total organic content (TOC) and the inherent porosity of the organic matter affect gas production after 40 days. With the gradual increase of production time, the gas production rate decreases rapidly compared with the water production rate, and the gas saturation in the inorganic matter of the ESRV region gradually decreases. The ignorance of stress sensitivity would cause the gas production increase, and the ignorance of organic matter shrinkage decrease the gas production gradually. The water film mainly affects gas production after 100 days, while the bulk water has a greater impact on gas production throughout the whole period. The research provides a new method to accurately describe the two-phase fluid flow behavior in different scale media of fractured shale gas reservoirs.
Buongiorno’s Nanofluid Model over a Curved Exponentially Stretching Surface
Authors: Alblawi Adel , Malik Muhammad Yousaf , Nadeem Sohail , Abbas Nadeem
Published: September 2019

We considered the steady flow of Buongiorno’s model over a permeable exponentially stretching channel. The mathematical model was constructed with the assumptions on curved channels. After applying the boundary layer approximation on the Navier–Stocks equation, we produced nonlinear partial differential equations. These equations were converted into a system of non-dimensional ordinary differential equations through an appropriate similarity transformation. The dimensionless forms of the coupled ordinary differential equations were elucidated numerically through boundary value problem fourth order method. This method gains fast convergence as compared to other method such as the shooting method and the Numerical Solution of Differential Equations Mathematica method. The influence of the governing parameters which are involved in ordinary differential equations are highlighted through graphs while R e s 1 / 2 C f , R e s 1 / 2 N u s , and R e s − 1 / 2 S h s are highlighted through the tables. Our interest of study was to analyze the heat transfer rate of nanofluids. Surprisingly, for momentum boundary layer thickness, thermal boundary layer thickness and solutal boundary layer thickness became larger when λ > 0 , as compared to the case when λ < 0 .
A Feasibility Study of Cellulosic Isobutanol Production—Process Simulation and Economic Analysis
Authors: Roussos Avraam , Misailidis Nikiforos , Koulouris Alexandros , Zimbardi Francesco , Petrides Demetri
Published: September 2019

Renewable liquid biofuels for transportation have recently attracted enormous global attention due to their potential to provide a sustainable alternative to fossil fuels. In recent years, the attention has shifted from first-generation bioethanol to the production of higher molecular weight alcohols, such as biobutanol, from cellulosic feedstocks. The economic feasibility of such processes depends on several parameters such as the cost of raw materials, the fermentation performance and the energy demand for the pretreatment of biomass and downstream processing. In this work, two conceptual process scenarios for isobutanol production, one with and one without integrated product removal from the fermentor by vacuum stripping, were developed and evaluated using SuperPro Designer®. In agreement with previous publications, it was concluded that the fermentation titer is a crucial parameter for the economic competitiveness of the process as it is closely related to the energy requirements for product purification. In the first scenario where the product titer was 22 g/L, the energy demand for downstream processing was 15.8 MJ/L isobutanol and the unit production cost of isobutanol was $2.24/L. The integrated product removal by vacuum stripping implemented in the second scenario was assumed to improve the isobutanol titer to 50 g/L. In this case, the energy demand for the product removal (electricity) and downstream processing were 1.8 MJ/L isobutanol and 10 MJ/L isobutanol, respectively, and the unit production cost was reduced to $1.42/L. The uncertainty associated with the choice of modeling and economic parameters was investigated by Monte Carlo simulation sensitivity analysis.
Satellite Monitoring of Thermal Performance in Smart Urban Designs
Authors: Mullerova Daniela , Williams Meredith
Published: September 2019

Climate change amplified by rapidly increasing urbanization is resulting in rising temperatures within urban environments. In recent years, to mitigate this the design and construction of new buildings has emphasized “smart” methods and materials for individual buildings rather than landscape-level planning and evaluation of new developments. Remote Sensing potentially offers a cost-effective means to monitor effectiveness of landscape-level urban design and guide developers to improve thermal regimes. This paper focuses on satellite monitoring of thermal variation in the area of London most affected by construction in 2010–2015. Split-window Land Surface Temperature (LST) models were applied to ASTER and Landsat 8 satellite imagery, requiring separate investigation of thermal trends due to temporal inconsistency. Getis-Ord-Gi* cluster analysis of the ASTER image identified three main thermal hot spots: Eastern, Stratford railway/underground station, and Stratford High Street. It is assumed that increased thermal stress within these areas is mainly from anthropogenic heat. However, local thermal variations for Eastern Olympic Village are attributed to changing meteorological conditions, facade materials, canyon morphology and orientation, or insufficient shading and ventilation. Future development of a new cultural hub at this location will significantly affect distribution of green spaces and influence their cooling ability.
Automatic Inundation Mapping Using Sentinel-2 Data Applicable to Both Camargue and Doñana Biosphere Reserves
Authors: Kordelas Georgios A. , Manakos Ioannis , Lefebvre Gaëtan , Poulin Brigitte
Published: September 2019

Flooding periodicity is crucial for biomass production and ecosystem functions in wetland areas. Local monitoring networks may be enriched by spaceborne derived products with a temporal resolution of a few days. Unsupervised computer vision techniques are preferred, since human interference and the use of training data may be kept to a minimum. Recently, a novel automatic local thresholding unsupervised methodology for separating inundated areas from non-inundated ones led to successful results for the Doñana Biosphere Reserve. This study examines the applicability of this approach to Camarque Biosphere Reserve, and proposes alternatives to the original approach to enhance accuracy and applicability for both Camargue and Doñana wetlands in a scientific quest for methods that may serve accurately biomes at both protected areas. In particular, it examines alternative inputs for automatically estimating thresholds while applying various algorithms for estimating the splitting thresholds. Reference maps for Camargue are provided by local authorities, and generated using Sentinel-2 Band 8A (NIR) and Band 12 (SWIR-2). The alternative approaches examined led to high inundation mapping accuracy. In particular, for the Camargue study area and 39 different dates, the alternative approach with the highest overall Kappa coefficient is 0.84, while, for the Doñana Biosphere Reserve and Doñana marshland (a subset of Doñana Reserve) and 7 different dates, is 0.85 and 0.94, respectively. Moreover, there are alternative approaches with high overall Kappa for all areas, i.e., at 0.79 for Camargue, over 0.91 for Doñana marshland, and over 0.82 for Doñana Reserve. Additionally, this study identifies the alternative approaches that perform better when the study area is extensively covered by temporary flooded and emergent vegetation areas (i.e., Camargue Reserve and Doñana marshland) or when it contains a large percentage of dry areas (i.e., Doñana Reserve). The development of credible automatic thresholding techniques that can be applied to different wetlands could lead to a higher degree of automation for map production, while enhancing service utilization by non-trained personnel.
Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study
Authors: Castaño Fernando , Strzelczak Stanisław , Villalonga Alberto , Haber Rodolfo E. , Kossakowska Joanna
Published: September 2019

Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.
Structural 3D Reconstruction of Indoor Space for 5G Signal Simulation with Mobile Laser Scanning Point Clouds
Authors: Cui Yang , Li Qingquan , Dong Zhen
Published: September 2019

3D modelling of indoor environment is essential in smart city applications such as building information modelling (BIM), spatial location application, energy consumption estimation, and signal simulation, etc. Fast and stable reconstruction of 3D models from point clouds has already attracted considerable research interest. However, in the complex indoor environment, automated reconstruction of detailed 3D models still remains a serious challenge. To address these issues, this paper presents a novel method that couples linear structures with three-dimensional geometric surfaces to automatically reconstruct 3D models using point cloud data from mobile laser scanning. In our proposed approach, a fully automatic room segmentation is performed on the unstructured point clouds via multi-label graph cuts with semantic constraints, which can overcome the over-segmentation in the long corridor. Then, the horizontal slices of point clouds with individual room are projected onto the plane to form a binary image, which is followed by line extraction and regularization to generate floorplan lines. The 3D structured models are reconstructed by multi-label graph cuts, which is designed to combine segmented room, line and surface elements as semantic constraints. Finally, this paper proposed a novel application that 5G signal simulation based on the output structural model to aim at determining the optimal location of 5G small base station in a large-scale indoor scene for the future. Four datasets collected using handheld and backpack laser scanning systems in different locations were used to evaluate the proposed method. The results indicate our proposed methodology provides an accurate and efficient reconstruction of detailed structured models from complex indoor scenes.
Coupling Waveguide-Based Micro-Sensors and Spectral Multivariate Analysis to Improve Spray Deposit Characterization in Agriculture
Authors: Taleb Bendiab Anis , Ryckewaert Maxime , Heran Daphné , Escalier Raphaël , Kribich Raphaël K. , Vigreux Caroline , Bendoula Ryad
Published: September 2019

The leaf coverage surface is a key measurement of the spraying process to maximize spray efficiency. To determine leaf coverage surface, the development of optical micro-sensors that, coupled with a multivariate spectral analysis, will be able to measure the volume of the droplets deposited on their surface is proposed. Rib optical waveguides based on Ge-Se-Te chalcogenide films were manufactured and their light transmission was studied as a response to the deposition of demineralized water droplets on their surface. The measurements were performed using a dedicated spectrophotometric bench to record the transmission spectra at the output of the waveguides, before (reference) and after drop deposition, in the wavelength range between 1200 and 2000 nm. The presence of a hollow at 1450 nm in the relative transmission spectra has been recorded. This corresponds to the first overtone of the O–H stretching vibration in water. This result tends to show that the optical intensity decrease observed after droplet deposition is partly due to absorption by water of the light energy carried by the guided mode evanescent field. The probe based on Ge-Se-Te rib optical waveguides is thus sensitive throughout the whole range of volumes studied, i.e., from 0.1 to 2.5 μL. Principal Component Analysis and Partial Least Square as multivariate techniques then allowed the analysis of the statistics of the measurements and the predictive character of the transmission spectra. It confirmed the sensitivity of the measurement system to the water absorption, and the predictive model allowed the prediction of droplet volumes on an independent set of measurements, with a correlation of 66.5% and a precision of 0.39 μL.
High-Throughput Chemometric Quality Assessment of Extra Virgin Olive Oils Using a Microtiter Plate Reader
Authors: He Huihui , Lu Weiying
Published: September 2019

A commercially available microtiter plate reader was applied as a high-throughput counterpart of ultraviolet-visible (UV–Vis) spectrophotometer to identify the producing location of extra virgin olive oils (EVOOs). Multiplicative scatter correction and the first derivative was used to denoise the UV–Vis spectra and eliminate the effects of background drift. The spectra were analyzed using chemometrics methods including the principal component analysis (PCA) and the partial least squares-discriminant analysis (PLS-DA). The PLS-DA model on full spectra using 5 latent variables showed a classification accuracy of 97.92% by cross-validation. The overall results demonstrated that the use of a UV–Vis spectrophotometer based on the microtiter plate reader combined with chemometrics can be applied to the quality assessment of EVOOs. It is demonstrated that the microtiter plate reader can be a high-throughput tool in the quality assessment of food ingredients.
Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles
Authors: Min Kyunghan , Sim Gyubin , Ahn Seongju , Sunwoo Myoungho , Jo Kichun
Published: September 2019

The connected powertrain control, which uses intelligent transportation system information, has been widely researched to improve driver convenience and energy efficiency. The vehicle state prediction on decelerating driving conditions can be applied to automatic regenerative braking in electric vehicles. However, drivers can feel a sense of heterogeneity when regenerative control is performed based on prediction results from a general prediction model. As a result, a deceleration prediction model which represents individual driving characteristics is required to ensure a more comfortable experience with an automatic regenerative braking control. Thus, in this paper, we proposed a deceleration prediction model based on the parametric mathematical equation and explicit model parameters. The model is designed specifically for deceleration prediction by using the parametric equation that describes deceleration characteristics. Furthermore, the explicit model parameters are updated according to individual driver characteristics using the driver’s braking data during real driving situations. The proposed algorithm was integrated and validated on a real-time embedded system, and then, it was applied to the model-based regenerative control algorithm as a case study.
Special Issue “Internet of Things for Smart Homes”
Authors: You Ilsun , Pau Giovanni , Salerno Valerio Mario , Sharma Vishal
Published: September 2019

Smart homes represent one of the principal points in the new ecosystem of the Internet of Things (IoT), both for the centrality of the home in the life of individuals and the significant potential concerning the diffusion of smart objects and innovative services. While IoT-oriented smart homes can revise how inhabitants interact with the domestic environment, each well-defined piece of technology necessitates precise network performance and distinct levels of security based on the sensitivity of the controlled system and the information it handles. This editorial presents a review of the papers accepted in the special issue. The issue has focused at obtaining high-quality papers aimed at solving well-known technical problems and challenges typical of IoT-oriented smart homes.
EKF-Based Parameter Identification of Multi-Rotor Unmanned Aerial VehiclesModels
Authors: Munguía Rodrigo , Urzua Sarquis , Grau Antoni
Published: September 2019

This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied.
Research on Small Square PCB Rogowski Coil Measuring Transient Current in the Power Electronics Devices
Authors: Jiao Chaoqun , Zhang Juan , Zhao Zhibin , Zhang Zuoming , Fan Yuanliang
Published: September 2019

With the development of China’s electric power, power electronics devices such as insulated-gate bipolar transistors (IGBTs) have been widely used in the field of high voltages and large currents. However, the currents in these power electronic devices are transient. For example, the uneven currents and internal chip currents overshoot, which may occur when turning on and off, and could have a great impact on the device. In order to study the reliability of these power electronics devices, this paper proposes a miniature printed circuit board (PCB) Rogowski coil that measures the current of these power electronics devices without changing their internal structures, which provides a reference for the subsequent reliability of their designs.
The Influence of Temperature and Community Structure on Light Absorption by Phytoplankton in the North Atlantic
Authors: Brewin Robert J. W. , Ciavatta Stefano , Sathyendranath Shubha , Skákala Jozef , Bruggeman Jorn , Ford David , Platt Trevor
Published: September 2019

We present a model that estimates the spectral phytoplankton absorption coefficient ( a p h ( λ ) ) of four phytoplankton groups (picophytoplankton, nanophytoplankton, dinoflagellates, and diatoms) as a function of the total chlorophyll-a concentration (C) and sea surface temperature (SST). Concurrent data on a p h ( λ ) (at 12 visible wavelengths), C and SST, from the surface layer (<20 m depth) of the North Atlantic Ocean, were partitioned into training and independent validation data, the validation data being matched with satellite ocean-colour observations. Model parameters (the chlorophyll-specific phytoplankton absorption coefficients of the four groups) were tuned using the training data and found to compare favourably (in magnitude and shape) with results of earlier studies. Using the independent validation data, the new model was found to retrieve total a p h ( λ ) with a similar performance to two earlier models, using either in situ or satellite data as input. Although more complex, the new model has the advantage of being able to determine a p h ( λ ) for four phytoplankton groups and of incorporating the influence of SST on the composition of the four groups. We integrate the new four-population absorption model into a simple model of ocean colour, to illustrate the influence of changes in SST on phytoplankton community structure, and consequently, the blue-to-green ratio of remote-sensing reflectance. We also present a method of propagating error through the model and illustrate the technique by mapping errors in group-specific a p h ( λ ) using a satellite image. We envisage the model will be useful for ecosystem model validation and assimilation exercises and for investigating the influence of temperature change on ocean colour.
Technologies for Monitoring Lifestyle Habits Related to Brain Health: A Systematic Review
Authors: Moreno-Blanco Diego , Solana-Sánchez Javier , Sánchez-González Patricia , Oropesa Ignacio , Cáceres César , Cattaneo Gabriele , Tormos-Muñoz Josep M. , Bartrés-Faz David , Pascual-Leone Álvaro , Gómez Enrique J.
Published: September 2019

Brain health refers to the preservation of brain integrity and function optimized for an individual’s biological age. Several studies have demonstrated that our lifestyles habits impact our brain health and our cognitive and mental wellbeing. Monitoring such lifestyles is thus critical and mobile technologies are essential to enable such a goal. Three databases were selected to carry out the search. Then, a PRISMA and PICOTS based criteria for a more detailed review on the basis of monitoring lifestyle aspects were used to filter the publications. We identified 133 publications after removing duplicates. Fifteen were finally selected from our criteria. Many studies still use questionnaires as the only tool for monitoring and do not apply advanced analytic or AI approaches to fine-tune results. We anticipate a transformative boom in the near future developing and implementing solutions that are able to integrate, in a flexible and adaptable way, data from technologies and devices that users might already use. This will enable continuous monitoring of objective data to guide the personalized definition of lifestyle goals and data-driven coaching to offer the necessary support to ensure adherence and satisfaction.
Realtime Tracking of Passengers on the London Underground Transport by Matching Smartphone Accelerometer Footprints
Authors: Nguyen Khuong An , Wang You , Li Guang , Luo Zhiyuan , Watkins Chris
Published: September 2019

Passengers travelling on the London underground tubes currently have no means of knowing their whereabouts between stations. The challenge for providing such service is that the London underground tunnels have no GPS, Wi-Fi, Bluetooth, or any kind of terrestrial signals to leverage. This paper presents a novel yet practical idea to track passengers in realtime using the smartphone accelerometer and a training database of the entire London underground network. Our rationales are that London tubes are self-driving transports with predictable accelerations, decelerations, and travelling time and that they always travel on the same fixed rail lines between stations with distinctive bumps and vibrations, which permit us to generate an accelerometer map of the tubes’ movements on each line. Given the passenger’s accelerometer data, we identify in realtime what line they are travelling on and what station they depart from, using a pattern-matching algorithm, with an accuracy of up to about 90% when the sampling length is equivalent to at least 3 station stops. We incorporate Principal Component Analysis to perform inertial tracking of passengers’ positions along the line when trains break away from scheduled movements during rush hours. Our proposal was painstakingly assessed on the entire London underground, covering approximately 940 km of travelling distance, spanning across 381 stations on 11 different lines.
Lossy Mode Resonance Generation on Sputtered Aluminum-Doped Zinc Oxide Thin Films Deposited on Multimode Optical Fiber Structures for Sensing Applications in the 1.55 µm Wavelength Range
Authors: Prieto-Cortés Patricia , Álvarez-Tamayo Ricardo I. , García-Méndez Manuel , Durán-Sánchez Manuel
Published: September 2019

In this work, we demonstrated lossy mode resonance (LMR) generation in optical fiber structures based on multimode fibers coated with aluminum-doped zinc oxide (AZO) films. AZO thin films were deposited by using radio frequency magnetron sputtering. In order to exhibit the usefulness of the LMR effect for sensing applications in optical fiber based systems, the deposition conditions of the AZO film coatings were set to obtain the second LMR order within the 1.55 µm wavelength range. An optical transmission configuration setup was used to investigate the LMR effect on fiber structures based on the use of no-core and cladding-removed multimode fibers coated with AZO films synthesized from metallic sputtering targets with different proportions of Zn:Al, 92:8% and 98:2%, at atomic concentrations. The optical and electrical/chemical features of the AZO films were characterized with UV–vis and XPS spectroscopy, respectively. The optical response of the proposed sensing configuration to refractive index (RI) variations was experimentally demonstrated. For the best approach, the sensitivity of wavelength displacement to RI variations on the liquid surrounding media was found to be 1214.7 nm/RIU.
Cryptographic Considerations for Automation and SCADA Systems Using Trusted Platform Modules
Authors: Tidrea Alexandra , Korodi Adrian , Silea Ioan
Published: September 2019

The increased number of cyber threats against the Supervisory Control and Data Acquisition (SCADA) and automation systems in the Industrial-Internet-of-Things (IIoT) and Industry 4.0 era has raised concerns in respect to the importance of securing critical infrastructures and manufacturing plants. The evolution towards interconnection and interoperability has expanded the vulnerabilities of these systems, especially in the context of the widely spread legacy standard protocols, by exposing the data to the outside network. After gaining access to the system data by launching a variety of attacks, an intruder can cause severe damage to the industrial process in place. Hence, this paper attempts to respond to the security issue caused by legacy structures using insecure communication protocols (e.g., Modbus TCP, DNP3, S7), presenting a different perspective focused on the capabilities of a trusted platform module (TPM). Furthermore, the intent is to assure the authenticity of the data transmitted between two entities on the same (horizontal interoperation) or different (vertical interoperation) hierarchical levels communicating through Modbus TCP protocol based on functionalities obtained by integrating trusted platform modules. From the experimental results perspective, the paper aims to show the advantages of integrating TPMs in automation/SCADA systems in terms of security. Two methods are proposed in order to assure the authenticity of the messages which are transmitted, respectively the study presents the measurements related to the increased time latency introduced due to the proposed concept.
A Robust Balance-Control Framework for the Terrain-Blind Bipedal Walking of a Humanoid Robot on Unknown and Uneven Terrain
Authors: Joe Hyun-Min , Oh Jun-Ho
Published: September 2019

Research on a terrain-blind walking control that can walk stably on unknown and uneven terrain is an important research field for humanoid robots to achieve human-level walking abilities, and it is still a field that needs much improvement. This paper describes the design, implementation, and experimental results of a robust balance-control framework for the stable walking of a humanoid robot on unknown and uneven terrain. For robust balance-control against disturbances caused by uneven terrain, we propose a framework that combines a capture-point controller that modifies the control reference, and a balance controller that follows its control references in a cascading structure. The capture-point controller adjusts a zero-moment point reference to stabilize the perturbed capture-point from the disturbance, and the adjusted zero-moment point reference is utilized as a control reference for the balance controller, comprised of zero-moment point, leg length, and foot orientation controllers. By adjusting the zero-moment point reference according to the disturbance, our zero-moment point controller guarantees robust zero-moment point control performance in uneven terrain, unlike previous zero-moment point controllers. In addition, for fast posture stabilization in uneven terrain, we applied a proportional-derivative admittance controller to the leg length and foot orientation controllers to rapidly adapt these parts of the robot to uneven terrain without vibration. Furthermore, to activate position or force control depending on the gait phase of a robot, we applied gain scheduling to the leg length and foot orientation controllers, which simplifies their implementation. The effectiveness of the proposed control framework was verified by stable walking performance on various uneven terrains, such as slopes, stone fields, and lawns.
Analysis of the Influence of Different Settings of Scan Sequence Parameters on Vibration and Noise Generated in the Open-Air MRI Scanning Area
Authors: Přibil Jiří , Přibilová Anna , Frollo Ivan
Published: September 2019

A system of gradient coils of the magnetic resonance imaging (MRI) device produces significant vibration and noise. Energetic relations of these phenomena are analyzed depending on MRI scan parameters (sequence type, repetition time (TR), echo time (TE), slice orientation, body weight). This issue should be investigated because of negative physiological and psychological effects on a person exposed to vibration and acoustic noise. We also measured the sound pressure level in the MRI scanning area and its vicinity in order to minimize these negative impacts, depending on intensity and time duration of exposition. From the recorded vibration and noise signals, the energy parameters were determined and statistically analyzed, and the obtained results were visually and numerically compared. Finally, subjective evaluation by a listening test method was used to analyze the influence of the generated MRI noise on the human psyche.
Conditional Artificial Potential Field-Based Autonomous Vehicle Safety Control with Interference of Lane Changing in Mixed Traffic Scenario
Authors: Gao Kai , Yan Di , Yang Fan , Xie Jin , Liu Li , Du Ronghua , Xiong Naixue
Published: September 2019

Car-following is an essential trajectory control strategy for the autonomous vehicle, which not only improves traffic efficiency, but also reduces fuel consumption and emissions. However, the prediction of lane change intentions in adjacent lanes is problematic, and will significantly affect the car-following control of the autonomous vehicle, especially when the vehicle changing lanes is only a connected unintelligent vehicle without expensive and accurate sensors. Autonomous vehicles suffer from adjacent vehicles’ abrupt lane changes, which may reduce ride comfort and increase energy consumption, and even lead to a collision. A machine learning-based lane change intention prediction and real time autonomous vehicle controller is proposed to respond to this problem. First, an interval-based support vector machine is designed to predict the vehicles’ lane change intention utilizing limited low-level vehicle status through vehicle-to-vehicle communication. Then, a conditional artificial potential field method is used to design the car-following controller by incorporating the lane-change intentions of the vehicle. Experimental results reveal that the proposed method can estimate a vehicle’s lane change intention more accurately. The autonomous vehicle avoids collisions with a lane-changing connected unintelligent vehicle with reliable safety and favorable dynamic performance.