bluuebunny/crossref_metadata_2025 huggingface dataset:
Co(O)x Particles in Polymeric N-Doped Carbon Nanotube Applied for Photocatalytic H2 or Electrocatalytic O2 Evolution
Authors: Zheng Xiaoxue , Liu Ke , Chen Lichao , He Hengxiao , Chen Lusheng , Sun Chuanzhi
Published: November 2019

A new way for synthesizing porous composite with cobalt species and N-doped carbon nanotubes (NDCNTs) was reported here by using cobalt salts and melamine mixtures as precursor. The Co(O)x/NDCNTs exhibited good activity of electrocatalytic O2 production. Furthermore, after reduced by H2, the Co–NDCNTs showed strong absorption of visible light and high catalytic activity of H2 production, which is 598.5 μmol g−1 h−1 under the visible light (λ > 420 nm). The results suggested that supramolecular preorganization of melamine monomers may be a promising method of synthesizing two-dimensional N-doped carbon nanotube with Co particles in it. The morphologies should be beneficial for the charge transport and separation. This work can encourage further synthesize new efficient noble-metal-free photocatalysts or electrocatalysts.
Dominant Effects of Short-Chain Branching on the Initial Stage of Nucleation and Formation of Tie Chains for Bimodal Polyethylene as Revealed by Molecular Dynamics Simulation
Authors: Hu Yanling , Shao Yunqi , Liu Zhen , He Xuelian , Liu Boping
Published: November 2019

The molecular mechanism of short-chain branching (SCB), especially the effects of methylene sequence length (MSL) and short-chain branching distribution (SCBD) on the initial stage of nucleation, the crystallization process, and particularly the tie chain formation process of bimodal polyethylene (BPE), were explored using molecular dynamics simulation. This work constructed two kinds of BPE models in accordance with commercial BPE pipe resins: SCB incorporated in the long chain or in the short chains. The initial stage of nucleation was determined by the MSL of the system, as the critical MSL for a branched chain to nucleate is about 60 CH2. SCB incorporated in the long chain led to a delay of the initial stage of nucleation relative to the case of SCB incorporated in the short chains. The increase of branch length could accelerate the delay to nucleation. The location of short chain relative to the long chain depended on the MSL of the short chain. As the MSL of the system decreased, the crystallinity decreased, while the tie chains concentration increased. The tie chains concentration of the BPE model with branches incorporated in the long chain was higher than that with branches incorporated in the short chain.
Ultraviolet Light-degradation Behavior and Antibacterial Activity of Polypropylene/ZnO Nanoparticles Fibers
Authors: Zhang Guangyu , Xiao Yao , Yan Jiawei , Xie Ningwei , Liu Rong , Zhang Yu
Published: November 2019

Herein, ZnO nanoparticles (NPs) were synthesized using zinc acetate and an amino hyperbranched polymer. The methods of transmission electron microscope (TEM) and X-ray Diffraction (XRD) were applied to the characterization of ZnO NPs. Polypropylene (PP)/ZnO fiber was prepared using 1–5 wt% ZnO NPs via melt spinning. The ultraviolet light (UV)-degradation behavior, antibacterial activity and mechanical properties of PP fibers were characterized. The PP fiber filled with ZnO NPs presents better mechanical properties and the resistance to UV light degradation. For the best effect, the contents of ZnO NPs were set 4 wt% in PP fiber. FTIR analysis shows significant photo-degradation of PP induced by UV irradiation and a remarkable reduction in the UV degradation of the fiber filled with ZnO NPs. It is also observed that the ZnO NPs-filled fiber has good antibacterial actives against Escherichia coli and staphylococcus aureus.
Side Chains and the Insufficient Lubrication of Water in Polyacrylamide Hydrogel—A New Insight
Authors: Lei Jincheng , Zhou Zidi , Liu Zishun
Published: November 2019

Existing theories cannot predict the mechanical property changes of polyacrylamide hydrogels with different water content because of the absence of side chains. In this study, polyacrylamide hydrogels are prepared and tested to investigate the side chain effect on their mechanical properties. First, the comparison between the effective chain density and total chain density provides proof of the large amount of side chains in the polymer network of PAAm hydrogel. We propose a practical chain density fraction to measure the side chain fraction. Then, the abnormal Young’s moduli-polymer volume fraction relationship reveals that side chains affect the mechanical properties of hydrogel through the insufficient lubrication of water. Water confined in narrow space within a molecular-level size can bear shear force to provide extra deformation resistance. A constitutive mode considering the effect of the insufficient lubrication of water is proposed. Combining this constitutive model with experimental results, we find that this insufficient lubrication of water exists even in equilibrium PAAm hydrogel. Molecular dynamics simulations reveal that this insufficient lubrication of water comes from the constraint of polymer chains. It also demonstrates that when there is insufficient lubrication, the rearrangement of water molecules leads to the persistent energy dissipation in the Mullins effect of PAAm hydrogel.
Green-Based Antimicrobial Hydrogels Prepared from Bagasse Cellulose as 3D-Scaffolds for Wound Dressing
Authors: Pan Yuanfeng , Zhao Xiao , Li Xiaoning , Cai Pingxiong
Published: November 2019

Developing the ideal biomaterials for wound dressing still remains challenging nowadays due to the non-biodegradable features and the lack of antimicrobial activity of conventional synthetic polymer-based dressing materials. To tackle those problems, a novel and green-based antimicrobial hydrogel dressing was synthesized in this work via modifying sugarcane bagasse cellulose with guanidine-based polymer, followed by crosslinking antimicrobial-modified cellulose with unmodified one at various ratios. The resulting hydrogels were comprehensively characterized with swelling measurements, compression test, Fourier transform infrared spectroscopy, and scanning electron microscopy. The results indicated that the dressing possessed the degree of swelling up to 2000% and the compress strength as high as 31.39 Kpa, at 8:2 ratio of pristine cellulose to modified cellulose. The antibacterial activities of the dressing against E. coli were assessed using both shaking flask and ring diffusion methods. The results demonstrated that the dressings were highly effective in deactivating bacterium without leaching effect. Moreover, these hydrogels are biocompatible with live cell viability responses of (NIH3T3) cells above 76% and are very promising as wound dressing.
Extremum Seeking Control for Discrete-Time with Quantized and Saturated Actuators
Authors: Guay Martin , Burns Daniel J.
Published: November 2019

This paper proposes an extremum-seeking controller (ESC) design for a class of discrete-time nonlinear control systems subject to input constraints or quantized inputs. The proposed method implements a proportional-integral ESC design along with a discrete-time anti-windup mechanism. The anti-windup enforces input saturation while preserving the input dither signal. The technique incorporates a mechanism for adjusting the amplitude of the extremum seeking control dither signal. This mechanism ensures that any violation of constraints due to the dither signal is removed while maintaining the probing signal active. An amplitude update routine is also proposed. The amplitude update is coupled with a saturation bias estimation algorithm that correctly accounts for the inherent bias associated with systems operated at or near saturation conditions. The amplitude update is designed to remove the dither signal when the system approaches the optimum. It also ensures that a lower bound of the amplitude is enforced to guarantee that excitation conditions are maintained.
Performance Evaluation Using Multivariate Non-Normal Process Capability
Authors: Alatefi Moath , Ahmad Shafiq , Alkahtani Mohammed
Published: November 2019

Process capability indices (PCIs) have always been used to improve the quality of products and services. Traditional PCIs are based on the assumption that the data obtained from the quality characteristic (QC) under consideration are normally distributed. However, most data on manufacturing processes violate this assumption. Furthermore, the products and services of the manufacturing industry usually have more than one QC; these QCs are functionally correlated and, thus, should be evaluated together to evaluate the overall quality of a product. This study investigates and extends the existing multivariate non-normal PCIs. First, a multivariate non-normal PCI model from the literature is modeled and validated. An algorithm to generate non-normal multivariate data with the desired correlations is also modeled. Then, this model is extended using two different approaches that depend on the well-known Box–Cox and Johnson transformations. The skewness reduction is further improved by applying heuristics algorithms. These two approaches outperform the investigated model from the literature because they can provide more precise results regardless of the skewness type. The comparison is made based on the generated data and a case study from the literature.
Optimal Design of Experiments for Liquid–Liquid Equilibria Characterization via Semidefinite Programming
Authors: Duarte Belmiro P.M. , Atkinson Anthony C. , Granjo José F.O. , Oliveira Nuno M.C.
Published: November 2019

Liquid–liquid equilibria (LLE) characterization is a task requiring considerable work and appreciable financial resources. Notable savings in time and effort can be achieved when the experimental plans use the methods of the optimal design of experiments that maximize the information obtained. To achieve this goal, a systematic optimization formulation based on Semidefinite Programming is proposed for finding optimal experimental designs for LLE studies carried out at constant pressure and temperature. The non-random two-liquid (NRTL) model is employed to represent species equilibria in both phases. This model, combined with mass balance relationships, provides a means of computing the sensitivities of the measurements to the parameters. To design the experiment, these sensitivities are calculated for a grid of candidate experiments in which initial mixture compositions are varied. The optimal design is found by maximizing criteria based on the Fisher Information Matrix (FIM). Three optimality criteria (D-, A- and E-optimal) are exemplified. The approach is demonstrated for two ternary systems where different sets of parameters are to be estimated.
Example of a German Free-Float Car-Sharing Company Expansion in East-Central Europe
Authors: Tóth Árpád , Szigeti Cecília
Published: November 2019

This study examines the expansion of a German free-float car-sharing company in Hungary from financial and sustainability perspectives. BMW and Daimler recently created the joint ventures ShareNow, ChargeNow, ReachNow, FreeNow, and ParkNow, which are having a significant global impact, as their services are now available in 14 different countries. We also expect further market development, since ShareNow started to operate in Hungary in May 2019. The whole EU market is just one step away from being covered by the same professional service, and the future might bring a real globally available free-float car-sharing service provider. Our review used a combination of two methodologies: financial statement-based business analysis and sustainability analysis. On the basis of this study, we concluded that these companies are primarily operated for profit and not on a sustainable operation basis. Additionally, it was also found that the current statistical data collection method does not measure precisely these activities. Financial reporting and sustainability reporting are connected, but they cover different areas. As a subject of further research, we suggest examining whether it is possible to establish a clear connection between these methodologies in the foreseeable future.
Obtaining 3D High-Resolution Underwater Acoustic Images by Synthesizing Virtual Aperture on the 2D Transducer Array of Multibeam Echo Sounder
Authors: Wei Bo , Li Haisen , Zhou Tian , Xing Siyu
Published: November 2019

In recent decades, imaging sonar has been the most widely employed remote sensing instruments in the field of underwater detection. The multibeam echo sounder (MBES) plays an important role in obtaining high-accuracy seabed topography. However, the resolution of the MBES substantially decreases with the increasing distance. Synthetic aperture sonar (SAS) achieves constant resolution on the along-track, improving the fineness of the image. However, conventional side-scan SAS usually only achieves 2D images, and gaps always exist. In this modeling and experimental research paper, we propose a novel underwater acoustic imaging scheme to improve the imaging performance of MBES, based on the complementarity of MBES and SAS systems. We design a 2D transducer array to increase the detection efficiency and obtain spatial gain. Moreover, the processing scheme is analyzed to design the working parameters in actual engineering applications. We exploit a target echo simulation approach to establish the research basics of the imaging algorithms, which also reflects the shapes and shadows of targets to match actual situations as realistically as possible. The proposed imaging algorithm synthesizes a virtual aperture receiving array on the along-track and reserves the multi-element manifold on the across-track. This helps to improve the imaging quality of the MBES and achieves high-resolution 3D detection with no gaps. Simulation and tank experimental results demonstrate that the proposed scheme can significantly improve the detection ability of the MBES, especially for small 3D target detection, thus making it suitable for 3D high-resolution underwater detection applications.
Hyperspectral Anomaly Detection via Spatial Density Background Purification
Authors: Tu Bing , Li Nanying , Liao Zhuolang , Ou Xianfeng , Zhang Guoyun
Published: November 2019

In the research of anomaly detection methods, obtaining a pure background without abnormal pixels can effectively improve the detection performance and reduce the false-alarm rate. Therefore, this paper proposes a spatial density background purification (SDBP) method for hyperspectral anomaly detection. First, a density peak clustering (DP) algorithm is used to calculate the local density of pixels within a single window. Then, the local densities are sorted into descending order and the m pixels that have the highest local density are selected from high to low. Therefore, the potential abnormal pixels in the background can be effectively removed, and a purer background set can be obtained. Finally, the collaborative representation detector (CRD) is employed for anomaly detection. Considering that the neighboring area of each pixel will have homogeneous material pixels, we adopt the double window strategy to improve the above method. The local densities of the pixels between the large window and the small window are calculated, while all pixels are removed from the small window. This makes the background estimation more accurate, reduces the false-alarm rate, and improves the detection performance. Experimental results on three real hyperspectral datasets such as Airport, Beach, and Urban scenes indicate that the detection accuracy of this method outperforms other commonly used anomaly detection methods.
Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
Authors: Faria Margarida , Ferreira António Sérgio , Pérez-Leon Héctor , Maza Ivan , Viguria Antidio
Published: November 2019

This paper addressed the challenge of exploring large, unknown, and unstructured industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system is that all the algorithms relied on the multi-resolution of the octomap for the world representation. We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements of the capability of the open-source system to run online and on-board the UAV in real-time. Our approach is compared to different reference heuristics under this simulation environment showing better performance in regards to the amount of explored space. With the proposed approach, the UAV is able to explore 93% of the search space under 30 min, generating a path without repetition that adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstacles.
Light-Emitting-Diode-Based Multispectral Photoacoustic Computed Tomography System
Authors: Agrawal Sumit , Fadden Christopher , Dangi Ajay , Yang Xinyi , Albahrani Hussain , Frings Neilesh , Heidari Zadi Sara , Kothapalli Sri-Rajasekhar
Published: November 2019

Photoacoustic computed tomography (PACT) has been widely explored for non-ionizing functional and molecular imaging of humans and small animals. In order for light to penetrate deep inside tissue, a bulky and high-cost tunable laser is typically used. Light-emitting diodes (LEDs) have recently emerged as cost-effective and portable alternative illumination sources for photoacoustic imaging. In this study, we have developed a portable, low-cost, five-dimensional (x, y, z, t, λ ) PACT system using multi-wavelength LED excitation to enable similar functional and molecular imaging capabilities as standard tunable lasers. Four LED arrays and a linear ultrasound transducer detector array are housed in a hollow cylindrical geometry that rotates 360 degrees to allow multiple projections through the subject of interest placed inside the cylinder. The structural, functional, and molecular imaging capabilities of the LED–PACT system are validated using various tissue-mimicking phantom studies. The axial, lateral, and elevational resolutions of the system at 2.3 cm depth are estimated as 0.12 mm, 0.3 mm, and 2.1 mm, respectively. Spectrally unmixed photoacoustic contrasts from tubes filled with oxy- and deoxy-hemoglobin, indocyanine green, methylene blue, and melanin molecules demonstrate the multispectral molecular imaging capabilities of the system. Human-finger-mimicking phantoms made of a bone and blood tubes show structural and functional oxygen saturation imaging capabilities. Together, these results demonstrate the potential of the proposed LED-based, low-cost, portable PACT system for pre-clinical and clinical applications.
Greener, Energy-Efficient and Sustainable Networks: State-Of-The-Art and New Trends
Authors: Lorincz Josip , Capone Antonio , Wu Jinsong
Published: November 2019

Although information and communications technologies (ICTs) have the potential of enabling powerful social, economic and environmental benefits, ICT systems give a non-negligible contribution to world electricity consumption and carbon dioxide (CO2) footprint. This contribution will sustain since the increased demand for user′s connectivity and an explosion of traffic volumes necessitate continuous expansion of current ICTs services and deployment of new infrastructures and technologies which must ensure the expected user experiences and performance. In this paper, analyses of costs for the global annual energy consumption of telecommunication networks, estimation of ICT sector CO2 footprint contribution and predictions of energy consumption of all connected user-related devices and equipment in the period 2011–2030 are presented. Since presented estimations of network energy consumption trends for main communication sectors by 2030 shows that highest contribution to global energy consumption will come from wireless access networks and data centres (DCs), the rest of the paper analyses technologies and concepts which can contribute to the energy-efficiency improvements of these two sectors. More specifically, different paradigms for wireless access networks such as millimetre-wave communications, Long-Term Evolution in unlicensed spectrum, ultra-dense heterogeneous networks, device-to-device communications and massive multiple-input multiple-output communications have been analysed as possible technologies for improvement of wireless networks energy efficiency. Additionally, approaches related to the DC resource management, DCs power management, green DC monitoring and thermal management in DCs have been discussed as promising approaches to improvement of DC power usage efficiency. For each of analysed technologies, future research challenges and open issues have been summarised and discussed. Lastly, an overview of the accepted papers in the Special Issue dedicated to the green, energy-efficient and sustainable networks is presented.
A Sensing and Tracking Algorithm for Multiple Frequency Line Components in Underwater Acoustic Signals
Authors: Luo Xinwei , Shen Zihan
Published: November 2019

Reliable and efficient sensing and tracking of multiple weak or time-varying frequency line components in underwater acoustic signals is the topic of this paper. We propose a method for automatic detection and tracking of multiple frequency lines in lofargram based on hidden Markov model (HMM). Instead of being directly subjected to frequency line tracking, the whole lofargram is first segmented into several sub-lofargrams. Then, the sub-lofargrams suspected to contain frequency lines are screened. In these sub-lofargrams, the HMM-based method is used for detection of multiple frequency lines. Using image stitching and statistical model method, the frequency lines with overlapping parts detected by different sub-lofargrams are merged to obtain the final detection results. The method can effectively detect multiple time-varying frequency lines of underwater acoustic signals while ensuring the performance under the condition of low signal-to-noise ratio (SNR). It can be concluded that the proposed algorithm can provide better multiple frequency lines sensing ability while greatly reducing the amount of calculations and providing potential techniques for feature sensing and tracking processing of unattended equipment such as sonar buoys and submerged buoys.
The Correlation between Vehicle Vertical Dynamics and Deep Learning-Based Visual Target State Estimation: A Sensitivity Study
Authors: Weber Yannik , Kanarachos Stratis
Published: November 2019

Automated vehicles will provide greater transport convenience and interconnectivity, increase mobility options to young and elderly people, and reduce traffic congestion and emissions. However, the largest obstacle towards the deployment of automated vehicles on public roads is their safety evaluation and validation. Undeniably, the role of cameras and Artificial Intelligence-based (AI) vision is vital in the perception of the driving environment and road safety. Although a significant number of studies on the detection and tracking of vehicles have been conducted, none of them focused on the role of vertical vehicle dynamics. For the first time, this paper analyzes and discusses the influence of road anomalies and vehicle suspension on the performance of detecting and tracking driving objects. To this end, we conducted an extensive road field study and validated a computational tool for performing the assessment using simulations. A parametric study revealed the cases where AI-based vision underperforms and may significantly degrade the safety performance of AVs.
The Enhanced Sensitivity of a Porous Silicon Microcavity Biosensor Based on an Angular Spectrum Using CdSe/ZnS Quantum Dots
Authors: Zhou Rui , Jia Zhenhong , Lv Xiaoyi , Huang Xiaohui
Published: November 2019

To improve the detection sensitivity of porous silicon microcavity biosensors, CdSe/ZnS quantum dots are used to label complementary DNA molecules for the refractive index amplification and angular spectrum method for detection. In this method, the TE mode laser is used as the detection light and the light source is changed into a parallel beam by collimating and expanding the beam, which illuminates the PSM surface and receives the reflected light from the PSM surface through the detector. The angle corresponding to the weakest reflected light intensity before and after the biological reaction between probe DNA and complementary DNA of different concentrations labeled by quantum dots was measured by the detector, and the relationship between the angle change before and after the biological reaction and the complementary DNA concentration labeled by quantum dots was obtained. The experimental results show that the angle change increases linearly with increasing complementary DNA concentration. The detection limit of the experiment, as determined by fitting, is approximately 36 pM. The detection limit of this method is approximately 1/300 of that without quantum dot labeling. Our method has a low cost because it does not require the use of a reflectance spectrometer, and it also demonstrates high sensitivity.
A Review: Evolution and Diversity of Optical Fibre Plasmonic Sensors
Authors: Allsop Thomas , Neal Ron
Published: November 2019

The purpose of this review is to bring to the attention of the wider research community how two quite different optical sensory techniques were integrated resulting in a sensor device of exceptional sensitivity with wide ranging capability. Both authors have collaborated over a 20 year period, each researching initially surface plasmon resonance (SPR) and optical fibre Bragg grating devices. Our individual research, funded in part by EPSRC and industry into these two areas, converged, resulting in a device that combined the ultra-sensitive working platform of SPR behavior with that of fibre Bragg grating development, which provided a simple method for SPR excitation. During this period, they developed a new approach to the fabrication of nano-structured metal coatings for plasmonic devices and demonstrated on fibre optic platform, which has created an ultra-sensitive optical sensing platform. Both authors believe that the convergence of these two areas will create opportunities in detection and sensing yet to be realised. Furthermore, giving the reader “sign-post” research articles to help to construct models to design sensors and to understand their experimental results.
Laser Resonance Frequency Analysis: A Novel Measurement Approach to Evaluate Acetabular Cup Stability During Surgery
Authors: Kikuchi Shunsuke , Mikami Katsuhiro , Nakashima Daisuke , Kitamura Toshiyuki , Hasegawa Noboru , Nishikino Masaharu , Kanaji Arihiko , Nakamura Masaya , Nagura Takeo
Published: November 2019

Artificial joint acetabular cup stability is essential for successful total hip arthroplasty. However, a quantitative evaluation approach for clinical use is lacking. We developed a resonance frequency analysis (RFA) system involving a laser system that is fully contactless. This study aimed to investigate the usefulness of laser RFA for evaluating acetabular cup stability. First, the finite element method was performed to determine the vibration mode for analysis. Second, the acetabular cup was press-fitted into a reamed polyurethane cavity that replicated the human acetabular roof. The implanted acetabular cup was vibrated with pulse laser irradiation and the induced vibration was detected with a laser Doppler vibrometer. The time domain signal from the vibrometer was analyzed by fast Fourier transform to obtain the vibration frequency spectrum. After laser RFA, the pull-down force of the acetabular cup was measured as conventional implant fixation strength. The frequency of the first highest amplitude between 2 kHz and 6 kHz was considered as the resonance peak frequency, and its relationship with the pull-down force was assessed. The peak frequency could predict the pull-down force (R2 = 0.859, p < 0.000). Our findings suggest that laser RFA might be useful to measure acetabular cup stability during surgery.
Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework
Authors: Yahya Norashikin , Musa Huwaida , Ong Zhong Yi , Elamvazuthi Irraivan
Published: November 2019

In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.
Permissible Area Analyses of Measurement Errors with Required Fault Diagnosability Performance
Authors: Jiang Dong-Nian , Li Wei
Published: November 2019

Fault diagnosability is the basis of fault diagnosis. Fault diagnosability evaluation refers to whether there is enough measurable information in the system to support the rapid and reliable detection of a fault. However, due to unavoidable measurement errors in a system, a quantitative evaluation index of system fault diagnosability is inadequate. In order to overcome the adverse effects of measurement errors, improve the accuracy of the quantitative evaluation of fault diagnosability, and improve the safety level of the system, a method for a permissible area analysis of measurement errors for a quantitative evaluation of fault diagnosability is proposed in this paper. Firstly, in order for the residuals obey normal distribution, a design method of the permissible area of measurement errors based on the Kullback–Leibler divergence (KLD) is given. Secondly, two key problems in calculating the KLD are solved by sparse kernel density estimation and the Monte Carlo method. Finally, the feasibility and validity of the method are analyzed through a case study.
QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis
Authors: Terroso-Saenz Fernando , Muñoz Andres , Cecilia José
Published: November 2019

Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8.
Trajectory Planning for Data Collection of Energy-Constrained Heterogeneous UAVs
Authors: Qin Zhen , Dong Chao , Wang Hai , Li Aijing , Dai Haipeng , Sun Weihao , Xu Zhengqin
Published: November 2019

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Since the battery capacity is limited, sensors cannot transmit over a long distance. It is necessary for designing efficient sensor data collection mechanisms to prolong the lifetime of the IoT and enhance data collection efficiency. In this paper, we consider a UAV-enabled data collection scenario, where multiple heterogeneous UAVs with different energy constraints are employed to collect data from sensors. The value of data depends on the importance of the monitoring area of the sensor and the freshness of collected data. Our objective is to maximize the data collection utility by jointly optimizing the communication scheduling and trajectory of each UAV. The data collection utility is determined by the amount and value of the collected data. This problem is a variant of multiple knapsack problem, which is a classical NP-hard problem. First, we transform the initial problem into a submodular function maximization problem under energy constraints, and then we design a novel trajectory planning algorithm to maximize the data collection utility, while accounting for different values of data and different energy constraints of heterogeneous UAVs. Finally, under different network settings, the performance of the proposed trajectory planning algorithm is evaluated via extensive simulations. The results show that the proposed algorithm can obtain maximum data collection utility.
Wearable Fall Detector Using Recurrent Neural Networks
Authors: Luna-Perejón Francisco , Domínguez-Morales Manuel Jesús , Civit-Balcells Antón
Published: November 2019

Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls’ risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time.
Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices
Authors: Krausz Nili E. , Hu Blair H. , Hargrove Levi J.
Published: November 2019

Significant research effort has gone towards the development of powered lower limb prostheses that control power during gait. These devices use forward prediction based on electromyography (EMG), kinetics and kinematics to command the prosthesis which locomotion activity is desired. Unfortunately these predictions can have substantial errors, which can potentially lead to trips or falls. It is hypothesized that one reason for the significant prediction errors in the current control systems for powered lower-limb prostheses is due to the inter- and intra-subject variability of the data sources used for prediction. Environmental data, recorded from a depth sensor worn on a belt, should have less variability across trials and subjects as compared to kinetics, kinematics and EMG data, and thus its addition is proposed. The variability of each data source was analyzed, once normalized, to determine the intra-activity and intra-subject variability for each sensor modality. Then measures of separability, repeatability, clustering and overall desirability were computed. Results showed that combining Vision, EMG, IMU (inertial measurement unit), and Goniometer features yielded the best separability, repeatability, clustering and desirability across subjects and activities. This will likely be useful for future application in a forward predictor, which will incorporate Vision-based environmental data into a forward predictor for powered lower-limb prosthesis and exoskeletons.
Disabled Families: The Impacts of Disability and Care on Family Labour and Poverty in Rural Guatemala
Authors: Grech Shaun
Published: November 2019

An increasing body of literature has started to look at how disability impacts and shifts poverty in the global South in and through a range of areas, including health, education, and livelihoods. However, much of this research is limited to disabled individuals, while qualitative research focusing on and articulating the circumstances, needs and demands of rural families remains scarce, especially research focusing on Latin America. This paper reports on a qualitative study looking at how disability affects family labouring patterns in rural Guatemala, with a special focus on women carers of people with acquired physical impairments, in the bid to contribute to a more inclusive understanding of the disability and poverty relationship and its gendered dimensions. Findings highlight how in rural communities already living in dire poverty, the fragmentation of labour input of the disabled person, costs (notably health care) and intensified collective poverty, push fragile families with no safety nets into a set of dynamic responses in the bid to ensure survival of the family unit. These include harder and longer work patterns, interruption of paid labour, and/or induction into exploitative and perilous labour, not only for women, but also children. These responses are erosive and have severe personal, social, cultural and economic consequences, strengthening a deep, multidimensional, chronic and intergenerational impoverishment, transforming these families into ‘disabled families’, among the poorest of the poor. This paper concludes that research, policy and services need to move beyond the disabled individual to understand and address the needs and demands of whole families, notably women, and safeguard their livelihoods, because ultimately, these are the units that singlehandedly care for and ensure the well-being and survival of disabled people. It is also within these units that disability is constructed, shaped, and can ultimately be understood.
Consumer Perception of Remanufactured Automotive Parts and Policy Implications for Transitioning to a Circular Economy in Sweden
Authors: Milios Leonidas , Matsumoto Mitsutaka
Published: November 2019

Promoting longer lifetimes and efficient re-use of products has a significant potential to save resources and reduce adverse environmental impacts, especially for products that have large resource footprints related to extraction and production processes, as for instance automobiles. Remanufacturing is a product life extension strategy promoting the effective and efficient re-use of products by replacing worn-out components with used or end-of-life parts restored to “like-new” condition and functionality. For developing remanufacturing processes, it is important to take into account the consumers’ perception of remanufactured products, together with factors such as technology and operations management. Previous research has concluded that the perception of a product or service constitutes a critical factor in consumers’ decision-making. However, there is a lack of studies exploring consumers’ perceptions of remanufactured parts, especially in Europe. This contribution analyses the results of an on-line survey (n = 203) of Swedish consumers, concerning their familiarity with remanufactured auto parts and their perceptions on associated benefits and risks. The survey revealed that Swedish car owners have limited knowledge about remanufactured parts. On the other hand, they do recognise the benefits of using such parts, without showing a significant risk aversion in their purchase decision. The survey also explored consumers’ opinion on potential measures to bridge the identified gap in knowledge, revealing that although they would trust a quality certification scheme for remanufactured auto parts—preferably set up by an industry association—that would not be the most critical factor in their purchase decision. Concluding, the article points out the potential of policy interventions to raise consumers’ perception of remanufactured parts to create a market pull for expanding their uptake, and thus increasing the overall resource efficiency in the automotive sector.
Consumption-Based Carbon Emissions of Tianjin Based on Multi-Scale Input–Output Analysis
Authors: Xu Peiqi , Shao Ling , Geng Zihao , Guo Manli , Wei Zijun , Wu Zi
Published: November 2019

Cities are a major source of carbon emissions and should play an important role in reducing carbon emissions. This study applies the method of multi-scale input–output analysis (MSIO) to analyze the consumption-based carbon emissions of Tianjin in 2012. This method can estimate the carbon emissions embodied in imported products. The results reveal that the production-based carbon emissions of Tianjin were 1.52 × 108 tonnes CO2 in 2012, which had increased over 50% since 2007. Meanwhile, the consumption-based carbon emissions of Tianjin city were 2.55 × 108 tonnes CO2, 1.71 times higher than those in 2007 and 1.67 times the amount of production-based carbon emissions in 2012. Regarding the total embodied carbon emissions involved in the Tianjin economy in 2012, about 6% were from foreign countries, over 60% were from other regions of China, and only one-third were territorial-based or production-based carbon emissions. Correspondingly, Tianjin respectively exported 11% and 34% of the total embodied carbon emissions to foreign countries and other regions in China, while over half were embodied in the local final demand. Tianjin was a carbon budget importer in domestic trade and an exporter in international trade in both 2007 and 2012. However, when both domestic and international trades are considered, Tianjin had shifted from a carbon budget exporter in 2007 to an importer in 2012. Since 2007, the carbon nexus between Tianjin and other regions in China had become much closer (carbon emissions embodied in domestic trade increased 103.47%), but the connection with foreign countries became looser (carbon emissions embodied in international trade decreased 21.96%). Compared to Beijing in 2012, it is evident that there were less carbon emission transfer issues for Tianjin city.
Servant Leadership and Innovative Behaviour: An Empirical Analysis of Ghana’s Manufacturing Sector
Authors: Opoku Mavis Agyemang , Choi Suk Bong , Kang Seung-Wan
Published: November 2019

In the competitive global market, innovation is vital to a firm’s longevity. To this end, organisations seek new and alternative ways to motivate employee innovation. This study examines the role of servant leadership as an antecedent to innovation. Drawing on the social identity model, this study examines the effect of servant leadership, team-member exchange (TMX) and perceived insider status on employee innovative behaviour. Primary data were collected from six manufacturing companies in Ghana. Using a sample of 213 employees and their immediate supervisors, a confirmatory factor analysis was conducted to test the discriminant validity of our measurement model. Hierarchical multiple regression was then used to determine direct and interaction effects, followed by bootstrapping tests to identify mediation and moderated mediation effects. The results showed that servant leadership and TMX are significantly related to perceived insider status. The bootstrapping indirect test and Sobel test demonstrated that perceived insider status mediates the relationship between servant leadership and innovative work behaviour. Moreover, the mediated relationship is only significant when TMX is low. This study empirically validated servant leadership as an antecedent to employee innovative behaviour. The findings demonstrated that perceived insider status is a mediating mechanism in this relationship, with TMX as its boundary condition.
Partial Node Failure in Shortest Path Network Problems
Authors: Ye Qian , Kim Hyun
Published: November 2019

This paper investigates the impact of partial node failure from the perspective of shortest path network problems. We propose a network model that we call shortest path network problems for partial node failure, designed to examine the influence of partial node failures in a flow-based network using a set of indicators. The concept of partial node failure was applied to a special type of hub station, a mandatory transfer in subway or railway systems where multiple lines are arranged for the transfer of passengers. Numerical experiments were carried out on the Washington Metropolitan Area Transit Authority network (WMATA). The results or analysis detail how changes in flow distribution in the network were measured when a station partially failed, as well as ways of identifying heavily impacted stations with respect to different indicators. Various partial node failure scenarios were simulated for origin–destination (OD) flows by days, providing comprehensive information with which to evaluate plans for partial node failures, such as those related to scheduling maintenance, along with insights with which to make contingent plans for potential closure of stations. A major finding emphasizes that the rankings of station criticality are highly sensitive to the different OD flows by days when partial node failures are assumed in network modeling.