bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Fines on Hydraulic Conductivity of Lawrencepur, Chenab and Ravi Sand
Authors: Khan Tanveer Ahmed , Farooq Khalid , Muhammad Mirza , Khan Mudasser Muneer , Shah Syyed Adnan Raheel , Shoaib Muhammad , Aslam Muhammad Asif , Raza Syed Safdar
Published: November 2019

The amount of fines in sand greatly influence the permeability of sandy soils. Thus, this research was conducted to study the effect of plastic and non-plastic fines on the permeability of three types of sands (Lawrencepur sand, Chenab sand and Ravi sand). For this purpose, plastic and non-plastic fines were collected from different location of Lahore. Samples were prepared by mixing plastic and non-plastic fines into each type of sand separately, in amounts ranging from 0% to 50% with increments of five percent. Overall 63 samples were prepared. Sieve analysis and hydrometric analysis were performed to obtain particle size distribution for each sample. Atterberg’s limits were also determined and each sample was classified according to the Unified Soil Classification System (USCS). Compaction tests were performed on all samples as per the procedure in a standard Proctor test. The test samples were compacted in permeability molds with optimum moisture contents to obtain the density, as per a standard Proctor test. Hydraulic conductivity tests were performed on all sixty-three samples using a constant head permeameter and a falling head permeameter. Permeability results were plotted against the percentage of fines added. It was noted from the curves that the permeability of sand-fine mixtures shows a decreasing trend with the addition of fine contents. A few trials were performed to formulate a correlation. Validation of the correlation was performed with the results of 52 data sets from the field. Finally, the devised correlation was compared with three empirical equations proposed by Mujtaba, Kozeny–Carman and Hazen.
Reliability Evaluation Method Considering Demand Response (DR) of Household Electrical Equipment in Distribution Networks
Authors: Chen Hongzhong , Tang Jun , Sun Lei , Zhou Jiawei , Wang Xiaolei , Mao Yeying
Published: November 2019

The load characteristic of typical household electrical equipment is elaborately analyzed. Considering the electric vehicles’ (EVs’) charging behavior and air conditioning’s thermodynamic property, an electricity price-based demand response (DR) model and an incentive-based DR model for two kinds of typical high-power electrical equipment are proposed to obtain the load curve considering two different kinds of DR mechanisms. Afterwards, a load shedding strategy is introduced to improve the traditional reliability evaluation method for distribution networks, with the capacity constraints of tie lines taken into account. Subsequently, a reliability calculation method of distribution networks considering the shortage of power supply capacity and outages is presented. Finally, the Monte Carlo method is employed to calculate the reliability index of distribution networks with different load levels, and the impacts of different DR strategies on the reliability of distribution networks are analyzed. The results show that both DR strategies can improve the distribution system reliability.
Additive Manufacturing for Repair and Restoration in Remanufacturing: An Overview from Object Design and Systems Perspectives
Authors: Rahito  , Wahab D. , Azman A.
Published: November 2019

Repair and restoration is an important step in remanufacturing as it ensures end-of-life products are returned to as-new condition before entering the subsequent life cycle. Currently, such processes are carried out manually by skilled workers. The advent of additive manufacturing (AM) has encouraged researchers to investigate its potential in automated repair and restoration, thus rendering it as a more effective method for remanufacturing. However, the application of this widespread technology for repair and restoration in remanufacturing is still new. This paper provides an overview of the principles and capabilities offered by the existing metal AM technology for object repair and restoration namely, direct energy deposition, powder bed fusion, and cold spray technology. Their applications in the repair and restoration of remanufacturable components are presented and discussed along with issues requiring attention from the perspectives of object design and process systems capabilities. The study provides a compilation of the challenges in AM repair and restoration, which primarily lie in the aspects of geometrical complexity, geometric dimensioning and tolerancing, material compatibility, and pre-processing requirements since it is critical for remanufacturing to restore end-of-life components to as new-condition. The paper concludes with suggestions for further works in AM restoration to enable product life cycle extension in the circular economy.
The Engineering of Porous Silica and Hollow Silica Nanoparticles to Enhance Drug-loading Capacity
Authors: Nguyen-Thi Ngoc-Tram , Pham Tran Linh Phuong , Le Ngoc Thuy Trang , Cao Minh-Tri , Tran The-Nam , Nguyen Ngoc Tung , Nguyen Cong Hao , Nguyen Dai-Hai , Than Van Thai , Le Quang Tri , Trung Nguyen Quang
Published: November 2019

As a promising candidate for expanding the capacity of drug loading in silica nanoplatforms, hollow mesoporous silica nanoparticles (HMSNs) are gaining increasing attention. In this study, porous nanosilica (PNS) and HMSNs were prepared by the sol-gel method and template assisted method, then further used for Rhodamine (RhB) loading. To characterize the as-synthesized nanocarriers, a number of techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), nitrogen absorption-desorption isotherms, dynamic light scattering (DLS), thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR) were employed. The size of HMSN nanoparticles in aqueous solution averaged 134.0 ± 0.3 nm, which could be adjusted by minor changes during synthesis, whereas that of PNS nanoparticles was 63.4 ± 0.6 nm. In addition, the encapsulation of RhB into HMSN nanoparticles to form RhB-loaded nanocarriers (RhB/HMSN) was successful, achieving high loading efficiency (51.67% ± 0.11%). This was significantly higher than that of RhB-loaded PNS (RhB/PNS) (12.24% ± 0.24%). Similarly, RhB/HMSN also possessed a higher RhB loading content (10.44% ± 0.02%) compared to RhB/PNS (2.90% ± 0.05%). From those results, it is suggested that prepared HMSN nanocarriers may act as high-capacity carriers in drug delivery applications.
Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy
Authors: Adams James B. , Vargason Troy , Kang Dae-Wook , Krajmalnik-Brown Rosa , Hahn Juergen
Published: November 2019

Current diagnosis of autism spectrum disorder (ASD) is based on assessment of behavioral symptoms, although there is strong evidence that ASD affects multiple organ systems including the gastrointestinal (GI) tract. This study used Fisher discriminant analysis (FDA) to evaluate plasma metabolites from 18 children with ASD and chronic GI problems (ASD + GI cohort) and 20 typically developing (TD) children without GI problems (TD − GI cohort). Using three plasma metabolites that may represent three general groups of metabolic abnormalities, it was possible to distinguish the ASD + GI cohort from the TD − GI cohort with 94% sensitivity and 100% specificity after leave-one-out cross-validation. After the ASD + GI participants underwent Microbiota Transfer Therapy with significant improvement in GI and ASD-related symptoms, their metabolic profiles shifted significantly to become more similar to the TD − GI group, indicating potential utility of this combination of plasma metabolites as a biomarker for treatment efficacy. Two of the metabolites, sarcosine and inosine 5′-monophosphate, improved greatly after treatment. The third metabolite, tyramine O-sulfate, showed no change in median value, suggesting it and correlated metabolites to be a possible target for future therapies. Since it is unclear whether the observed differences are due to metabolic abnormalities associated with ASD or with GI symptoms (or contributions from both), future studies aiming to classify ASD should feature TD participants with GI symptoms and have larger sample sizes to improve confidence in the results.
Modeling and Thermal Analysis of a Moving Spacecraft Subject to Solar Radiation Effect
Authors: Gadalla Mohamed , Ghommem Mehdi , Bourantas George , Miller Karol
Published: November 2019

The impact of solar radiation on spacecraft can increase the cooling load, degrade the material properties of the structure and possibly lead to catastrophic failure of their missions. In this paper, we develop a computational model to investigate the effect of the exposure to solar radiation on the thermal distribution of a spacecraft with a cylindrical shape which is traveling in low earth orbit environment. This is obtained by the energy conservation between the heat conduction among the spacecraft, the heating from the solar radiation, and the radiative heat dissipation into the surroundings while accounting for the dynamics of the space vehicle (rotational motion). The model is solved numerically using the meshless collocation point method to evaluate the temperature variations under different operating conditions. The meshless method is based on approximating the unknown field function and their space derivatives, by using a set of nodes, sprinkled over the spatial domain of the spacecraft wall and functions with compact support. Meshless schemes bypass the use of conventional mesh configurations and require only clouds of points, without any prior knowledge on their connectivity. This would relieve the computational burden associated with mesh generation. The simulation results are found in good agreement with those reported in previously-published research works. The numerical results show that spinning the spacecraft at appropriate rates ensures low and uniform temperature distribution on the spacecraft, treated as thick-walled object of different geometries. Therefore, this would extend its lifetime and protect all on-board electronic equipment needed to accomplish its mission.
Adaptive Control of Biomass Specific Growth Rate in Fed-Batch Biotechnological Processes. A Comparative Study
Authors: Galvanauskas Vytautas , Simutis Rimvydas , Vaitkus Vygandas
Published: November 2019

This article presents a comparative study on the development and application of two distinct adaptive control algorithms for biomass specific growth rate control in fed-batch biotechnological processes. A typical fed-batch process using Escherichia coli for recombinant protein production was selected for this research. Numerical simulation results show that both developed controllers, an adaptive PI controller based on the gain scheduling technique and a model-free adaptive controller based on the artificial neural network, delivered a comparable control performance and are suitable for application when using the substrate limitation approach and substrate feeding rate manipulation. The controller performance was tested within the realistic ranges of the feedback signal sampling intervals and measurement noise intensities. Considering the efforts for controller design and tuning, including development of the adaptation/learning algorithms, the model-free adaptive control algorithm proves to be more attractive for industrial applications, especially when only limited knowledge of the process and its mathematical model is available. The investigated model-free adaptive controller also tended to deliver better control quality under low specific growth rate conditions that prevail during the recombinant protein production phase. In the investigated simulation runs, the average tracking error did not exceed 0.01 (1/h). The temporary overshoots caused by the maximal disturbances stayed within the range of 0.025–0.11 (1/h). Application of the algorithm can be further extended to specific growth rate control in other bacterial and mammalian cell cultivations that run under substrate limitation conditions.
Energy Saving and Low-Cost-Oriented Design Processes of Blank’s Dimensions Based on Multi-Objective Optimization Model
Authors: Xiao Yongmao , Gong Qingshan , Chen Xiaowu
Published: November 2019

The blank’s dimensions are an important focus of blank design as they largely determine the energy consumption and cost of manufacturing and further processing the blank. To achieve energy saving and low cost during the optimization of blank dimensions design, we established energy consumption and cost objectives in the manufacturing and further processing of blanks by optimizing the parameters. As objectives, we selected the blank’s production and further processing parameters as optimization variables to minimize energy consumption and cost, then set up a multi-objective optimization model. The optimal blank dimension was back calculated using the parameters of the minimum processing energy consumption and minimum cost state, and the model was optimized using the non-dominated genetic algorithm-II (NSGA-II). The effect of designing blank dimension in saving energy and costs is obvious compared with the existing methods.
A Dispatching Optimization Model for Park Power Supply Systems Considering Power-to-Gas and Peak Regulation Compensation
Authors: Qin Yunfu , Lin Hongyu , Tan Zhongfu , Yan Qingyou , Li Li , Yang Shenbo , De Gejirifu , Ju Liwei
Published: November 2019

To ensure the stability of park power supply systems and to promote the consumption of wind/photovoltaic generation, this paper proposes a dispatching optimization model for the park power supply system with power-to-gas (P2G) and peak regulation via gas-fired generators. Firstly, the structure of a park power system with P2G was built. Secondly, a dispatching optimization model for the park power supply system was constructed with a peak regulation compensation mechanism. Finally, the effectiveness of the model was verified by a case study. The case results show that with the integration of P2G and the marketized peak regulation compensation mechanism, preferential power energy storage followed by gas storage had the best effect on the park power supply system, which minimized the clean energy curtailment to 11.18% and the total cost by approximately $120.190 and maximized the net profit by approximately $152.005.
A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems
Authors: López-Estrada Francisco-Ronay , Rotondo Damiano , Valencia-Palomo Guillermo
Published: November 2019

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).
Search for the Theological Grounds to Develop Inclusive Islamic Interpretations: Some Insights from Rationalistic Islamic Maturidite Theology
Authors: Zhussipbek Galym , Satershinov Bakhytzhan
Published: November 2019

Inclusive religious interpretations accept that a salvation beyond their teachings can be found. Whether Islam accepts inclusive religious interpretations or not, constitutes one of the most debated issues related to Islam in our days. In this paper it is argued that, although al-Maturidi’s views can hardly be described as inclusive, the dynamic rationalistic Maturidite theology (so-called Maturidite “software”) may help produce inclusive Islamic interpretations. In addition to the key two principles of rationalistic Maturidite theology, especially the Maturidite understanding of the fate of people not exposed to divine mission, may be understood as accepting that the people may reach faith which is similar to valid through their reason, and they may be saved hereafter, although they do not believe in Islamic teachings in a strict sense. This Maturidite position can also be used to justify the inclusive understanding of Quranic verses. By and large, Maturidite theological views analyzed in this article can be seen as factors laying the grounds to develop inclusive Islamic interpretations.
Market Risk Analysis of Energy in Vietnam
Authors: Tran Ngoc Phu , Nguyen Thang Cong , Vo Duc Hong , McAleer Michael
Published: November 2019

The purpose of this paper is to evaluate and estimate market risk for the ten major industries in Vietnam. The focus of the empirical analysis is on the energy sector, which has been designated as one of the four key industries, together with services, food, and telecommunications, targeted for economic development by the Vietnam Government through to 2020. The oil and gas industry is a separate energy-related major industry, and it is evaluated separately from energy. The data set is from 2009 to 2017, which is decomposed into two distinct sub-periods after the Global Financial Crisis (GFC), namely the immediate post-GFC (2009–2011) period and the normal (2012–2017) period, in order to identify the behavior of market risk for Vietnam’s major industries. For the stock market in Vietnam, the website used in this paper provided complete and detailed data for each stock, as classified by industry. Two widely used approaches to measure and analyze risk are used in the empirical analysis, namely Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). The empirical findings indicate that Energy and Pharmaceuticals are the least risky industries, whereas oil and gas and securities have the greatest risk. In general, there is strong empirical evidence that the four key industries display relatively low risk. For public policy, the Vietnam Government’s proactive emphasis on the targeted industries, including energy, to achieve sustainable economic growth and national economic development, seems to be working effectively. This paper presents striking empirical evidence that Vietnam’s industries have substantially improved their economic performance over the full sample, moving from relatively higher levels of market risk in the immediate post-GFC period to a lower risk environment in a normal period several years after the end of the calamitous GFC.
Multiple Source Localization in a Shallow Water Waveguide Exploiting Subarray Beamforming and Deep Neural Networks
Authors: Huang Zhaoqiong , Xu Ji , Gong Zaixiao , Wang Haibin , Yan Yonghong
Published: November 2019

Deep neural networks (DNNs) have been shown to be effective for single sound source localization in shallow water environments. However, multiple source localization is a more challenging task because of the interactions among multiple acoustic signals. This paper proposes a framework for multiple source localization on underwater horizontal arrays using deep neural networks. The two-stage DNNs are adopted to determine both the directions and ranges of multiple sources successively. A feed-forward neural network is trained for direction finding, while the long short term memory recurrent neural network is used for source ranging. Particularly, in the source ranging stage, we perform subarray beamforming to extract features of sources that are detected by the direction finding stage, because subarray beamforming can enhance the mixed signal to the desired direction while preserving the horizontal-longitudinal correlations of the acoustic field. In this way, a universal model trained in the single-source scenario can be applied to multi-source scenarios with arbitrary numbers of sources. Both simulations and experiments in a range-independent shallow water environment of SWellEx-96 Event S5 are given to demonstrate the effectiveness of the proposed method.
Frequency Dependency of the Delta-E Effect and the Sensitivity of Delta-E Effect Magnetic Field Sensors
Authors: Spetzler Benjamin , Golubeva Elizaveta V. , Müller Cai , McCord Jeffrey , Faupel Franz
Published: November 2019

In recent years the delta-E effect has been used for detecting low frequency and low amplitude magnetic fields. Delta-E effect sensors utilize a forced mechanical resonator that is detuned by the delta-E effect upon application of a magnetic field. Typical frequencies of operation are from several kHz to the upper MHz regime. Different models have been used to describe the delta-E effect in those devices, but the frequency dependency has mainly been neglected. With this work we present a simple description of the delta-E effect as a function of the differential magnetic susceptibility χ of the magnetic material. We derive an analytical expression for χ that permits describing the frequency dependency of the delta-E effect of the Young’s modulus and the magnetic sensitivity. Calculations are compared with measurements on soft-magnetic ( Fe 90 Co 10 ) 78 Si 12 B 10 thin films. We show that the frequency of operation can have a strong influence on the delta-E effect and the magnetic sensitivity of delta-E effect sensors. Overall, the delta-E effect reduces with increasing frequency and results in a stiffening of the Young’s modulus above the ferromagnetic resonance frequency. The details depend on the Gilbert damping. Whereas for large Gilbert damping the sensitivity continuously decreases with frequency, typical damping values result in an amplification close to the ferromagnetic resonance frequency.
Prediction of Sea Level Nonlinear Trends around Shandong Peninsula from Satellite Altimetry
Authors: Zhao Jian , Cai Ruiyang , Fan Yanguo
Published: November 2019

Sea level change is a key indicator of climate change, and the prediction of sea level rise is one of most important scientific issues. In this paper, the gridded sea level anomaly (SLA) data from satellite altimetry are used to analyze the sea level variations around Shandong Peninsula from 1993 to 2016. Based on the Complete Ensemble Empirical Mode Decomposition (CEEMD) method and Radial Basis Function (RBF) network, the paper proposes an improved sea level multi-scale prediction approach, namely, CEEMD-RBF combined model. Firstly, the multi-scale frequency oscillatory modes (intrinsic mode functions (IMFs)) representing different oceanic processes are extracted by CEEMD from the highest frequency to the lowest frequency oscillating mode. Secondly, RBF network is used to establish prediction models for various IMF components to predict their future trends, and each IMF is used as an input factor of the RBF network separately. Finally, the prediction results of each IMF component with RBF network are reconstructed to obtain the final predictions of sea level anomalies. The results shows that CEEMD is particularly suitable for analyzing nonlinear and non-stationary time series and RBF network is applicable for regional sea level prediction at different scales.
Fully Coupled Model for Frequency Response Simulation of Miniaturized Cantilever-Based Photoacoustic Gas Sensors
Authors: Zhou Sheng
Published: November 2019

To support the development of miniaturized photoacoustic gas sensors, a fully coupled finite element model for a frequency response simulation of cantilever-based photoacoustic gas sensors is introduced in this paper. The model covers the whole photoacoustic process from radiation absorption to pressure transducer vibration, and considers viscous damping loss. After validation with experimental data, the model was further applied to evaluate the possibility of further optimization and miniaturization of a previously reported sensor design.
Internet of Unmanned Aerial Vehicles—A Multilayer Low-Altitude Airspace Model for Distributed UAV Traffic Management
Authors: Samir Labib Nader , Danoy Grégoire , Musial Jedrzej , Brust Matthias R. , Bouvry Pascal
Published: November 2019

The rapid adoption of Internet of Things (IoT) has encouraged the integration of new connected devices such as Unmanned Aerial Vehicles (UAVs) to the ubiquitous network. UAVs promise a pragmatic solution to the limitations of existing terrestrial IoT infrastructure as well as bring new means of delivering IoT services through a wide range of applications. Owning to their potential, UAVs are expected to soon dominate the low-altitude airspace over populated cities. This introduces new research challenges such as the safe management of UAVs operation under high traffic demands. This paper proposes a novel way of structuring the uncontrolled, low-altitude airspace, with the aim of addressing the complex problem of UAV traffic management at an abstract level. The work, hence, introduces a model of the airspace as a weighted multilayer network of nodes and airways and presents a set of experimental simulation results using three UAV traffic management heuristics.
Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness Weighting
Authors: Kim Yong Hun , Choi Min Jun , Kim Eung Ju , Song Jin Woo
Published: November 2019

This research proposes an algorithm that improves the position accuracy of indoor pedestrian dead reckoning, by compensating the position error with a magnetic field map-matching technique, using multiple magnetic sensors and an outlier mitigation technique based on roughness weighting factors. Since pedestrian dead reckoning using a zero velocity update (ZUPT) does not use position measurements but zero velocity measurements in a stance phase, the position error cannot be compensated, which results in the divergence of the position error. Therefore, more accurate pedestrian dead reckoning is achievable when the position measurements are used for position error compensation. Unfortunately, the position information cannot be easily obtained for indoor navigation, unlike in outdoor navigation cases. In this paper, we propose a method to determine the position based on the magnetic field map matching by using the importance sampling method and multiple magnetic sensors. The proposed method does not simply integrate multiple sensors but uses the normalization and roughness weighting method for outlier mitigation. To implement the indoor pedestrian navigation algorithm more accurately than in existing indoor pedestrian navigation, a 15th-order error model and an importance-sampling extended Kalman filter was utilized to correct the error of the map-matching-aided pedestrian dead reckoning (MAPDR). To verify the performance of the proposed indoor MAPDR algorithm, many experiments were conducted and compared with conventional pedestrian dead reckoning. The experimental results show that the proposed magnetic field MAPDR algorithm provides clear performance improvement in all indoor environments.
Kullback–Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information
Authors: Gao Ruofei , Zhang Jie , Xiao Wendong , Li Yanjiao
Published: November 2019

Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices or sensors to assist the localization process, whereas the latter has no such requirement, which merely requires the wireless network to be deployed around the environment to sense the target, rendering it much more challenging. Channel State Information (CSI)—a kind of information collected in the physical layer—is composed of multiple subcarriers, boasting highly fined granularity, which has gradually become a focus of indoor localization applications. In this paper, we propose an approach to performing DFL tasks by exploiting the uncertainty of CSI. We respectively utilize the CSI amplitudes and phases of multiple communication links to construct fingerprints, each of which is a set of multivariate Gaussian distributions that reflect the uncertainty information of CSI. Additionally, we propose a kind of combined fingerprints to simultaneously utilize the CSI amplitudes and phases, hoping to improve localization accuracy. Then, we adopt a Kullback–Leibler divergence (KL-divergence) based kernel function to calculate the probabilities that a testing fingerprint belongs to all the reference locations. Next, to localize the target, we utilize the computed probabilities as weights to average the reference locations. Experimental results show that the proposed approach, whatever type of fingerprints is used, outperforms the existing Pilot and Nuzzer systems in two typical indoor environments. We conduct extensive experiments to explore the effects of different parameters on localization performance, and the results demonstrate the efficiency of the proposed approach.
SensorTalk: An IoT Device Failure Detection and Calibration Mechanism for Smart Farming
Authors: Lin Yi-Bing , Lin Yun-Wei , Lin Jiun-Yi , Hung Hui-Nien
Published: November 2019

In an Internet of Things (IoT) system, it is essential that the data measured from the sensors are accurate so that the produced results are meaningful. For example, in AgriTalk, a smart farm platform for soil cultivation with a large number of sensors, the produced sensor data are used in several Artificial Intelligence (AI) models to provide precise farming for soil microbiome and fertility, disease regulation, irrigation regulation, and pest regulation. It is important that the sensor data are correctly used in AI modeling. Unfortunately, no sensor is perfect. Even for the sensors manufactured from the same factory, they may yield different readings. This paper proposes a solution called SensorTalk to automatically detect potential sensor failures and calibrate the aging sensors semi-automatically. Numerical examples are given to show the calibration tables for temperature and humidity sensors. When the sensors control the actuators, the SensorTalk solution can also detect whether a failure occurs within a detection delay. Both analytic and simulation models are proposed to appropriately select the detection delay so that, when a potential failure occurs, it is detected reasonably early without incurring too many false alarms. Specifically, our selection can limit the false detection probability to be less than 0.7%.
Outage Probability Performance Prediction for Mobile Cooperative Communication Networks Based on Artificial Neural Network
Authors: Wang Han , Xu Lingwei , Wang Xianpeng
Published: November 2019

This paper investigates outage probability (OP) performance predictions using transmit antenna selection (TAS) and derives exact closed-form OP expressions for a TAS scheme. It uses Monte-Carlo simulations to evaluate OP performance and verify the analysis. A back-propagation (BP) neural network-based OP performance prediction algorithm is proposed and compared with extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), and BP neural network methods. The proposed method was found to have higher OP performance prediction results than the other prediction methods.
Vision-Based Multirotor Following Using Synthetic Learning Techniques
Authors: Rodriguez-Ramos Alejandro , Alvarez-Fernandez Adrian , Bavle Hriday , Campoy Pascual , How Jonathan P.
Published: November 2019

Deep- and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or motion-control strategies. On this subject, the research community lacks robust approaches to overcome unavailable real-world extensive data by means of realistic synthetic-information and domain-adaptation techniques. In this work, synthetic-learning strategies have been used for the vision-based autonomous following of a noncooperative multirotor. The complete maneuver was learned with synthetic images and high-dimensional low-level continuous robot states, with deep- and reinforcement-learning techniques for object detection and motion control, respectively. A novel motion-control strategy for object following is introduced where the camera gimbal movement is coupled with the multirotor motion during the multirotor following. Results confirm that our present framework can be used to deploy a vision-based task in real flight using synthetic data. It was extensively validated in both simulated and real-flight scenarios, providing proper results (following a multirotor up to 1.3 m/s in simulation and 0.3 m/s in real flights).
Gas Biosensor Arrays Based on Single-Stranded DNA-Functionalized Single-Walled Carbon Nanotubes for the Detection of Volatile Organic Compound Biomarkers Released by Huanglongbing Disease-Infected Citrus Trees
Authors: Wang Hui , Ramnani Pankaj , Pham Tung , Villarreal Claudia Chaves , Yu Xuejun , Liu Gang , Mulchandani Ashok
Published: November 2019

Volatile organic compounds (VOCs) released by plants are closely associated with plant metabolism and can serve as biomarkers for disease diagnosis. Huanglongbing (HLB), also known as citrus greening or yellow shoot disease, is a lethal threat to the multi-billion-dollar citrus industry. Early detection of HLB is vital for removal of susceptible citrus trees and containment of the disease. Gas sensors are applied to monitor the air quality or toxic gases owing to their low-cost fabrication, smooth operation, and possible miniaturization. Here, we report on the development, characterization, and application of electrical biosensor arrays based on single-walled carbon nanotubes (SWNTs) decorated with single-stranded DNA (ssDNA) for the detection of four VOCs—ethylhexanol, linalool, tetradecene, and phenylacetaldehyde—that serve as secondary biomarkers for detection of infected citrus trees during the asymptomatic stage. SWNTs were noncovalently functionalized with ssDNA using π–π interaction between the nucleotide and sidewall of SWNTs. The resulting ssDNA-SWNT hybrid structure and device properties were investigated using Raman spectroscopy, ultraviolet (UV) spectroscopy, and electrical measurements. To monitor changes in the four VOCs, gas biosensor arrays consisting of bare SWNTs before and after being decorated with different ssDNA were employed to determine the different concentrations of the four VOCs. The data was processed using principal component analysis (PCA) and neural net fitting (NNF).
A Joint Power and Channel Scheduling Scheme for Underlay D2D Communications in the Cellular Network
Authors: Lin Zefang , Song Hui , Pan Daru
Published: November 2019

Device-to-device (D2D) communication, as one of the promising candidates for the fifth generation mobile network, can afford effective service of new mobile applications and business models. In this paper, we study the resource management strategies for D2D communication underlying the cellular networks. To cater for green communications, our design goal is to the maximize ergodic energy efficiency (EE) of all D2D links taking into account the fact that it may be tricky for the base station (BS) to receive all the real-time channel state information (CSI) while guaranteeing the stability and the power requirements for D2D links. We formulate the optimization problem which is difficult to resolve directly because of its non-convex nature. Then a novel maximum weighted ergodic energy efficiency (MWEEE) algorithm is proposed to solve the formulated optimization problem which consists of two sub-problems: the power control (PC) sub-problem which can be solved by employing convex optimization theory for both cellular user equipment (CUE) and D2D user equipment (DUE) and the channel allocation (CA) sub-problem which can be solved by obtaining the weighted allocation matrix. In particular, we shed light into the impact on EE metric of D2D communication by revealing the nonlinear power relationship between CUE and DUE and taking the QoS of CUEs into account. Furthermore, simulation results show that our proposed algorithm is superior to the existing algorithms.
Advancements in Non-Invasive Biological Surface Sampling and Emerging Applications
Authors: Nalbant Atakan Arda , Boyacı Ezel
Published: November 2019

Biological surfaces such as skin and ocular surface provide a plethora of information about the underlying biological activity of living organisms. However, they pose unique problems arising from their innate complexity, constant exposure of the surface to the surrounding elements, and the general requirement of any sampling method to be as minimally invasive as possible. Therefore, it is challenging but also rewarding to develop novel analytical tools that are suitable for in vivo and in situ sampling from biological surfaces. In this context, wearable extraction devices including passive samplers, extractive patches, and different microextraction technologies come forward as versatile, low-invasive, fast, and reliable sampling and sample preparation tools that are applicable for in vivo and in situ sampling. This review aims to address recent developments in non-invasive in vivo and in situ sampling methods from biological surfaces that introduce new ways and improve upon existing ones. Directions for the development of future technology and potential areas of applications such as clinical, bioanalytical, and doping analyses will also be discussed. These advancements include various types of passive samplers, hydrogels, and polydimethylsiloxane (PDMS) patches/microarrays, and other wearable extraction devices used mainly in skin sampling, among other novel techniques developed for ocular surface and oral tissue/fluid sampling.
Estimation of Energy Activity and Flexibility Range in Smart Active Residential Building
Authors: Stepaniuk Viktor , Pillai Jayakrishnan , Bak-Jensen Birgitte , Padmanaban Sanjeevikumar
Published: November 2019

The smart active residential buildings play a vital role to realize intelligent energy systems by harnessing energy flexibility from loads and storage units. This is imperative to integrate higher proportions of variable renewable energy generation and implement economically attractive demand-side participation schemes. The purpose of this paper is to develop an energy management scheme for smart sustainable buildings and analyze its efficacy when subjected to variable generation, energy storage management, and flexible demand control. This work estimate the flexibility range that can be reached utilizing deferrable/controllable energy system units such as heat pump (HP) in combination with on-site renewable energy sources (RESs), namely photovoltaic (PV) panels and wind turbine (WT), and in-house thermal and electric energy storages, namely hot water storage tank (HWST) and electric battery as back up units. A detailed HP model in combination with the storage tank is developed that accounts for thermal comforts and requirements, and defrost mode. Data analytics is applied to generate demand and generation profiles, and a hybrid energy management and a HP control algorithm is developed in this work. This is to integrate all active components of a building within a single complex-set of energy management solution to be able to apply demand response (DR) signals, as well as to execute all necessary computation and evaluation. Different capacity scenarios of the HWST and battery are used to prioritize the maximum use of renewable energy and consumer comfort preferences. A flexibility range of 22.3% is achieved for the scenario with the largest HWST considered without a battery, while 10.1% in the worst-case scenario with the smallest HWST considered and the largest battery. The results show that the active management and scheduling scheme developed to combine and prioritize thermal, electrical and storage units in buildings is essential to be studied to demonstrate the adequacy of sustainable energy buildings.
An Evaluation of Heart Rate Variability in Female Youth Soccer Players Following Soccer Heading: A Pilot Study
Authors: Harriss Alexandra B. , Abbott Kolten , Kimpinski Kurt , Holmes Jeffrey D. , Johnson Andrew M. , Walton David M. , Dickey James P.
Published: November 2019

Most head impacts in soccer occur from purposeful heading; however, the link between heading and neurological impairment is unknown. Previous work suggests concussion may result in an uncoupling between the autonomic nervous system and cardiovascular system. Accordingly, heart rate variability (HRV) may be a sensitive measure to provide meaningful information regarding repetitive heading in soccer. The purpose of this pilot study assesses the feasibility of measuring HRV to evaluate autonomic function following soccer heading. Sixteen youth female participants underwent heart rate monitoring during a heading and footing condition. Participants completed a five minute resting supine trial at the start and end of each testing session. Standard 450 g soccer balls were projected at 6 m/s towards participants. Participants performed five headers, for the header condition, and five footers for the footer condition. The HRV for resting supine trials, pre- and post-header and footer conditions were assessed for both time and frequency domains. HRV effect sizes were small when comparing conditions, except absolute low frequency (d = 0.61) and standard deviation of the normal-normal (NN) intervals (d = 0.63). Participant retention and adherence were high, without adverse events. Findings suggest HRV is a feasible measure for evaluating the effects of heading on autonomic function.
Risk Assessment of Shunting Derailment Based on Coupling
Authors: Zhang Huafeng , Sun Quanxin
Published: November 2019

Shunting operation is risky and complex because of the nonlinear coupling effect of personnel, equipment, environment and management, which reflects symmetry, can easily cause shunting derailment accidents. Traditional railway safety risk assessment techniques seldom consider the nonlinear coupling relationships of hazard factors in the shunting process,. The Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP), which are applicable to the coupling analysis of complex systems, have been used in many areas to overcome this shortcoming. Thus, this paper built a hybrid model, developed by integrating DEMATEL and ANP methodologies, to determine the multifarious interdependencies’ relative strengths among the hazard factors and to assess the relative importance of the hazard factors with Super Decision (SD) software. By constructing the “frequency-consequence-vulnerability” three-dimensional risk parameter model, the risk magnitude of shunting derailment could be obtained through risk aggregation process. Finally, the model was applied to the risk assessment of shunting derailment in Huangyangcheng station, the results revealed that this model was effective and efficient, which could yield more reliable and realistic solutions to the shunting safety.
Cytotoxicity of Deoxynivalenol after Being Exposed to Gaseous Ozone
Authors: Ren Dongliang , Diao Enjie , Hou Hanxue , Xie Peng , Mao Ruifeng , Dong Haizhou , Qian Shiquan
Published: November 2019

In this study, deoxynivalenol (DON) in aqueous solution was exposed to gaseous ozone for periods ranging from 0 to 20 min. The degradation efficiency and cytotoxicity of DON were investigated after being treated by ozone. The results showed that DON was rapidly degraded from 10.76 ± 0.09 mg/L to 0.22 ± 0.04 mg/L within 15 min (P < 0.05), representing a reduction of 97.95%, and no DON was detected after being exposed to 14.50 mg/L of ozone at a flow rate of 80 mL/min for 20 min. The degradation of DON depended on the ozone exposure time, and followed the first-order kinetic model (R2 = 0.9972). Human hepatic carcinoma (HepG2) and Henrietta Lacks (Hela) cells were used to evaluate the cytotoxicity of DON treated by ozone using the 3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay. The half-maximal inhibitory concentrations (IC50) values of DON on HepG2 and Hela cells were 2.10 and 1.33 mg/L after 48 h of exposure, respectively, and showed a dose-dependent manner. The cell vitalities of HepG2 and Hela cells on DON were both evidently improved after being exposed to ozone for 15 min, and there were no significant differences between the negative control and that treated at 20 min of ozone exposure. Gaseous ozone can potentially be used as a new method to detoxify DON in agricultural products.
Field Experiments and Numerical Analysis of the Ground Vibration Isolation of Shock Wave Propagation under Explosion Shock Loading
Authors: Wang Iau-Teh
Published: November 2019

Because blast effects can jeopardize the safety of facilities, controlling blast hazards is critical in engineering design and construction. The attenuation and amplification effects generated by blast waves are affected by the topography and terrain of the blast area. This study examined the effects of topography on the propagation of seismic waves induced by explosions. From the perspective of explosion control, this study adopted explosion mechanics theories and conducted in situ explosion tests to verify finite element numerical simulation results. This study employed the finite element analysis program, to construct a 3D solid structural model to examine fluid–solid coupling, and the Multi-Material Arbitrary Lagrangian–Eulerian algorithm was adopted to develop a dynamic numerical analysis model. By analyzing the propagation of blast waves and ground vibration effects, this study examined the impact of topographical differences on blast effects. The study results may provide a reference for controlling vibration hazards subject to shock waves from explosions, in order to reduce vibrations.
Effect of Visual Feedback on the Eye Position Stability of Patients with AMD
Authors: González Esther G. , Mandelcorn Mark S. , Mandelcorn Efrem D. , Tarita-Nistor Luminita
Published: November 2019

The sources of the reduced fixation stability exhibited by patients with central vision loss in the light are relatively well understood, but we have no information on how they control eye position in complete darkness, in the absence of visual error signals. We therefore explored the effect of visual feedback on eye position stability by testing patients with age-related macular degeneration (AMD) and controls with normal vision in the light and in complete darkness. Nine patients (ages 67 to 92 years) and 16 controls (ages 16 to 74 years) were tested binocularly in the light and in complete darkness while remembering the location of a now invisible target. Binocular eye position was recorded with a video-based eye tracker. Results show that eye position stability both in the light and in the dark is worse for patients than for controls and that, for the two groups, eye position stability in the dark is, on average, 5.9 times worse than in the light. Large instability of fixation in patients with AMD was found even in absolute darkness when the scotoma cannot impair vision. These data reflect permanent changes in the oculomotor reference of patients with AMD.