bluuebunny/crossref_metadata_2025 huggingface dataset:
Time and Life in the Relational Universe: Prolegomena to an Integral Paradigm of Natural Philosophy
Authors: Igamberdiev Abir U.
Published: October 2018

Relational ideas for our description of the natural world can be traced to the concept of Anaxagoras on the multiplicity of basic particles, later called “homoiomeroi” by Aristotle, that constitute the Universe and have the same nature as the whole world. Leibniz viewed the Universe as an infinite set of embodied logical essences called monads, which possess inner view, compute their own programs and perform mathematical transformations of their qualities, independently of all other monads. In this paradigm, space appears as a relational order of co-existences and time as a relational order of sequences. The relational paradigm was recognized in physics as a dependence of the spatiotemporal structure and its actualization on the observer. In the foundations of mathematics, the basic logical principles are united with the basic geometrical principles that are generic to the unfolding of internal logic. These principles appear as universal topological structures (“geometric atoms”) shaping the world. The decision-making system performs internal quantum reduction which is described by external observers via the probability function. In biology, individual systems operate as separate relational domains. The wave function superposition is restricted within a single domain and does not expand outside it, which corresponds to the statement of Leibniz that “monads have no windows”.
Functions of Flavonoids in Plant–Nematode Interactions
Authors: Chin Sabrina , Behm Carolyn A. , Mathesius Ulrike
Published: October 2018

Most land plants can become infected by plant parasitic nematodes in the field. Plant parasitic nematodes can be free-living or endoparasitic, and they usually infect plant roots. Most damaging are endoparasites, which form feeding sites inside plant roots that damage the root system and redirect nutrients towards the parasite. This process involves developmental changes to the root in parallel with the induction of defense responses. Plant flavonoids are secondary metabolites that have roles in both root development and plant defense responses against a range of microorganisms. Here, we review our current knowledge of the roles of flavonoids in the interactions between plants and plant parasitic nematodes. Flavonoids are induced during nematode infection in plant roots, and more highly so in resistant compared with susceptible plant cultivars, but many of their functions remain unclear. Flavonoids have been shown to alter feeding site development to some extent, but so far have not been found to be essential for root–parasite interactions. However, they likely contribute to chemotactic attraction or repulsion of nematodes towards or away from roots and might help in the general plant defense against nematodes. Certain flavonoids have also been associated with functions in nematode reproduction, although the mechanism remains unknown. Much remains to be examined in this area, especially under field conditions.
Experimental Investigation on the Law of Grout Diffusion in Fractured Porous Rock Mass and Its Application
Authors: Jiang Donghai , Cheng Xianzhen , Luan Hengjie , Wang Tongxu , Zhang Mingguang , Hao Ruiyun
Published: October 2018

Because of the limitation of mining techniques and economic conditions, large amounts of residual coal resources have been left in underground coal mines around the world. Currently, with mining technology gradually developing, residual coal can possibly be remined. However, when residual coal is remined, caving areas might form, which can seriously affect the safety of coal mining. Hence, grouting technology is put forward as one of the most effective technologies to solve this problem. To study the grouting diffusion in fractured rock mass, this paper developed a visualization platform of grouting diffusion and a three-dimensional grouting experimental system that can monitor the grout diffusion range, diffusion time and grout pressure; then, a grouting experiment is conducted based on this system. After that, the pattern of the grouting pressure variation, grout flow and grout diffusion surface are analyzed. The relationship among some factors, such as the grouting diffusion radius, compressive strength of the grouted gravel, porosity, water-cement ratio, grouting pressure, grouting time, permeability coefficient and level of grout, is quantitatively analyzed by using MATLAB. The study results show that the flow pattern of the grout in fractured porous rock mass has a parabolic shape from the grouting hole to the bottom. The lower the level is, the larger the diffusion range of the grout is. The grouting pressure has the greatest influence on the grouting diffusion radius, followed by the grouting horizon and water-cement ratio. The grouting permeability coefficient has the least influence on the grouting diffusion radius. The grout water-cement ratio has the greatest influence on the strength of the grouted gravel, followed by the grouting permeability. The grouting pressure coefficient has the least amount of influence on the grouting diffusion radius. According to the results, the grouting parameters are designed, and a layered progressive grouting method is proposed. Finally, borehole observation and a core mechanical property test are conducted to verify the application effect. This grouting technology can contribute to the redevelopment and efficient utilization of wasted underground coal resources.
Evaluation of the Phosphoproteome of Mouse Alpha 4/Beta 2-Containing Nicotinic Acetylcholine Receptors In Vitro and In Vivo
Authors: Miller Megan B. , Wilson Rashaun S. , Lam TuKiet T. , Nairn Angus C. , Picciotto Marina R.
Published: October 2018

Activation of nicotinic acetylcholine receptors containing α4 and β2 subunits (α4/β2* nAChRs) in the mammalian brain is necessary for nicotine reinforcement and addiction. We previously identified interactions between α4/β2* nAChRs and calcium/calmodulin-dependent protein kinase II (CaMKII) in mouse and human brain tissue. Following co-expression of α4/β2 nAChR subunits with CaMKII in HEK cells, mass spectrometry identified 8 phosphorylation sites in the α4 subunit. One of these sites and an additional site were identified when isolated α4/β2* nAChRs were dephosphorylated and subsequently incubated with CaMKII in vitro, while 3 phosphorylation sites were identified following incubation with protein kinase A (PKA) in vitro. We then isolated native α4/β2* nAChRs from mouse brain following acute or chronic exposure to nicotine. Two CaMKII sites identified in HEK cells were phosphorylated, and 1 PKA site was dephosphorylated following acute nicotine administration in vivo, whereas phosphorylation of the PKA site was increased back to baseline levels following repeated nicotine exposure. Significant changes in β2 nAChR subunit phosphorylation were not observed under these conditions, but 2 novel sites were identified on this subunit, 1 in HEK cells and 1 in vitro. These experiments identified putative CaMKII and PKA sites on α4/β2* nAChRs and novel nicotine-induced phosphorylation sites in mouse brain that can be explored for their consequences on receptor function.
Ground Reflectance Retrieval on Horizontal and Inclined Terrains Using the Software Package REFLECT
Authors: Bouroubi Yacine , Batita Wided , Cavayas François , Tremblay Nicolas
Published: October 2018

This paper presents the software package REFLECT for the retrieval of ground reflectance from high and very-high resolution multispectral satellite images. The computation of atmospheric parameters is based on the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) routines. Aerosol optical properties are calculated using the OPAC (Optical Properties of Aerosols and Clouds) model, while aerosol optical depth is estimated using the dark target method. A new approach is proposed for adjacency effect correction. Topographic effects were also taken into account, and a new model was developed for forest canopies. Validation has shown that ground reflectance estimation with REFLECT is performed with an accuracy of approximately ±0.01 in reflectance units (for the visible, near-infrared, and mid-infrared spectral bands), even for surfaces with varying topography. The validation of the software was performed through many tests. These tests involve the correction of the effects that are associated with sensor calibration, irradiance, and viewing conditions, atmospheric conditions (aerosol optical depth AOD and water vapour), adjacency, and topographic conditions.
The AMSU-Based Hydrological Bundle Climate Data Record—Description and Comparison with Other Data Sets
Authors: Ferraro Ralph R. , Nelson Brian R. , Smith Tom , Prat Olivier P.
Published: October 2018

Passive microwave measurements have been available on satellites back to the 1970s, first flown on research satellites developed by the National Aeronautics and Space Administration (NASA). Since then, several other sensors have been flown to retrieve hydrological products for both operational weather applications (e.g., the Special Sensor Microwave/Imager—SSM/I; the Advanced Microwave Sounding Unit—AMSU) and climate applications (e.g., the Advanced Microwave Scanning Radiometer—AMSR; the Tropical Rainfall Measurement Mission Microwave Imager—TMI; the Global Precipitation Mission Microwave Imager—GMI). Here, the focus is on measurements from the AMSU-A, AMSU-B, and Microwave Humidity Sounder (MHS). These sensors have been in operation since 1998, with the launch of NOAA-15, and are also on board NOAA-16, -17, -18, -19, and the MetOp-A and -B satellites. A data set called the “Hydrological Bundle” is a climate data record (CDR) that utilizes brightness temperatures from fundamental CDRs (FCDRs) to generate thematic CDRs (TCDRs). The TCDRs include total precipitable water (TPW), cloud liquid water (CLW), sea-ice concentration (SIC), land surface temperature (LST), land surface emissivity (LSE) for 23, 31, 50 GHz, rain rate (RR), snow cover (SC), ice water path (IWP), and snow water equivalent (SWE). The TCDRs are shown to be in general good agreement with similar products from other sources, such as the Global Precipitation Climatology Project (GPCP) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). Due to the careful intercalibration of the FCDRs, little bias is found among the different TCDRs produced from individual NOAA and MetOp satellites, except for normal diurnal cycle differences.
Evaluating RADARSAT-2 for the Monitoring of Lake Ice Phenology Events in Mid-Latitudes
Authors: Murfitt Justin , Brown Laura C. , Howell Stephen E.L.
Published: October 2018

Lake ice is an important component in understanding the local climate as changes in temperature have an impact on the timing of key ice phenology events. In recent years, there has been a decline in the in-situ monitoring of lake ice events in Canada and microwave remote sensing imagery from synthetic aperture radar (SAR) is more widely used due to the high spatial resolution and response of backscatter to the freezing and melting of the ice surface. RADARSAT-2 imagery was used to develop a threshold-based method for determining lake ice events for mid-latitude lakes in Central Ontario from 2008 to 2017. Estimated lake ice phenology events are validated with ground-based observations and are compared against the Moderate Resolution Imaging Spectroradiometer (MODIS band 2). The threshold-based method was found to accurately identify 12 out of 17 freeze events and 13 out of 17 melt events from 2015–2017 when compared to ground-based observations. Mean absolute errors for freeze events ranged from 2.5 to 10.0 days when compared to MODIS imagery while the mean absolute error for water clear of ice (WCI) ranged from 1.5 to 7.1 days. The method is important for the study of mid-latitude lake ice due to its unique success in detecting multiple freeze and melting events throughout the ice season.
Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard
Authors: Arellano-Pérez Stéfano , Castedo-Dorado Fernando , López-Sánchez Carlos Antonio , González-Ferreiro Eduardo , Yang Zhiqiang , Díaz-Varela Ramón Alberto , Álvarez-González Juan Gabriel , Vega José Antonio , Ruiz-González Ana Daría
Published: October 2018

Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). However, there is often a lack of quantitative data about these variables, especially at the landscape scale. Methods: In this study, data from 123 sample plots established in pure, even-aged, Pinus radiata and Pinus pinaster stands in northwest Spain were analyzed. In each plot, an intensive field inventory was used to characterize surface and canopy fuels load and structure, and to estimate SFL, FSG, CBH, and CBD. Equations relating these variables to Sentinel-2A (S-2A) bands and vegetation indices were obtained using two non-parametric techniques: Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS). Results: According to the goodness-of-fit statistics, RF models provided the most accurate estimates, explaining more than 12%, 37%, 47%, and 31% of the observed variability in SFL, FSG, CBH, and CBD, respectively. To evaluate the performance of the four equations considered, the observed and estimated values of the four fuel variables were used separately to predict the potential type of wildfire (surface fire, passive crown fire, or active crown fire) for each plot, considering three different burning conditions (low, moderate, and extreme). The results of the confusion matrix indicated that 79.8% of the surface fires and 93.1% of the active crown fires were correctly classified; meanwhile, the highest rate of misclassification was observed for passive crown fire, with 75.6% of the samples correctly classified. Conclusions: The results highlight that the combination of medium resolution imagery and machine learning techniques may add valuable information about surface and canopy fuel variables at large scales, whereby crown fire potential and the potential type of wildfire can be classified.
Sparsity-Based Spatiotemporal Fusion via Adaptive Multi-Band Constraints
Authors: Ying Hanchi , Leung Yee , Cao Feilong , Fung Tung , Xue Jie
Published: October 2018

Remote sensing is an important means to monitor the dynamics of the earth surface. It is still challenging for single-sensor systems to provide spatially high resolution images with high revisit frequency because of the technological limitations. Spatiotemporal fusion is an effective approach to obtain remote sensing images high in both spatial and temporal resolutions. Though dictionary learning fusion methods appear to be promising for spatiotemporal fusion, they do not consider the structure similarity between spectral bands in the fusion task. To capitalize on the significance of this feature, a novel fusion model, named the adaptive multi-band constraints fusion model (AMCFM), is formulated to produce better fusion images in this paper. This model considers structure similarity between spectral bands and uses the edge information to improve the fusion results by adopting adaptive multi-band constraints. Moreover, to address the shortcomings of the ℓ 1 norm which only considers the sparsity structure of dictionaries, our model uses the nuclear norm which balances sparsity and correlation by producing an appropriate coefficient in the reconstruction step. We perform experiments on real-life images to substantiate our conceptual augments. In the empirical study, the near-infrared (NIR), red and green bands of Landsat Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) are fused and the prediction accuracy is assessed by both metrics and visual effects. The experiments show that our proposed method performs better than state-of-the-art methods. It also sheds light on future research.
Global Fractional Vegetation Cover Estimation Algorithm for VIIRS Reflectance Data Based on Machine Learning Methods
Authors: Liu Duanyang , Yang Linqing , Jia Kun , Liang Shunlin , Xiao Zhiqiang , Wei Xiangqin , Yao Yunjun , Xia Mu , Li Yuwei
Published: October 2018

Fractional vegetation cover (FVC) is an essential input parameter for many environmental and ecological models. Recently, several global FVC products have been generated using remote sensing data. The Global LAnd Surface Satellite (GLASS) FVC product, which is generated from Moderate Resolution Imaging Spectroradiometer (MODIS) data, has attained acceptable performance. However, the original MODIS operation design lifespan has been exceeded. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) satellite was designed to be the MODIS successor. Therefore, developing an FVC estimation algorithm for VIIRS data is important for maintaining continuous FVC estimates in case of MODIS failure. In this study, a global FVC estimation algorithm for VIIRS surface reflectance data was proposed based on machine learning methods, which investigated the performances of back propagating neural networks (BPNNs), general regression networks (GRNNs), multivariate adaptive regression splines (MARS), and Gaussian process regression (GPR). The training samples were extracted from the GLASS FVC product and corresponding reconstructed VIIRS surface reflectance in 2013 over the global sampling locations. The VIIRS reflectances of red and near infrared (NIR) bands were the input variables for these machine learning methods. The theoretical performances and independent validation results indicated that the four machine learning methods could achieve similar and reliable FVC estimates. Regarding the FVC estimation accuracy, the GPR method achieved the best performance (R2 = 0.9019, RMSE = 0.0887). The MARS method had the obvious advantage of computational efficiency. Furthermore, the FVC estimates achieved good spatial and temporal continuities. Therefore, the proposed FVC estimation algorithm for VIIRS data can potentially generate reliable global FVC data for related applications.
Nondestructive Inspection of Reinforced Concrete Utility Poles with ISOMAP and Random Forest
Authors: Ullah Saeed , Jeong Minjoong , Lee Woosang
Published: October 2018

Reinforced concrete poles are very popular in transmission lines due to their economic efficiency. However, these poles have structural safety issues in their service terms that are caused by cracks, corrosion, deterioration, and short-circuiting of internal reinforcing steel wires. Therefore, they must be periodically inspected to evaluate their structural safety. There are many methods of performing external inspection after installation at an actual site. However, on-site nondestructive safety inspection of steel reinforcement wires inside poles is very difficult. In this study, we developed an application that classifies the magnetic field signals of multiple channels, as measured from the actual poles. Initially, the signal data were gathered by inserting sensors into the poles, and these data were then used to learn the patterns of safe and damaged features. These features were then processed with the isometric feature mapping (ISOMAP) dimensionality reduction algorithm. Subsequently, the resulting reduced data were processed with a random forest classification algorithm. The proposed method could elucidate whether the internal wires of the poles were broken or not according to actual sensor data. This method can be applied for evaluating the structural integrity of concrete poles in combination with portable devices for signal measurement (under development).
Error Budget for Geolocation of Spectroradiometer Point Observations from an Unmanned Aircraft System
Authors: Gautam Deepak , Watson Christopher , Lucieer Arko , Malenovský Zbyněk
Published: October 2018

We investigate footprint geolocation uncertainties of a spectroradiometer mounted on an unmanned aircraft system (UAS). Two microelectromechanical systems-based inertial measurement units (IMUs) and global navigation satellite system (GNSS) receivers were used to determine the footprint location and extent of the spectroradiometer. Errors originating from the on-board GNSS/IMU sensors were propagated through an aerial data georeferencing model, taking into account a range of values for the spectroradiometer field of view (FOV), integration time, UAS flight speed, above ground level (AGL) flying height, and IMU grade. The spectroradiometer under nominal operating conditions (8 ∘ FOV, 10 m AGL height, 0.6 s integration time, and 3 m/s flying speed) resulted in footprint extent of 140 cm across-track and 320 cm along-track, and a geolocation uncertainty of 11 cm. Flying height and orientation measurement accuracy had the largest influence on the geolocation uncertainty, whereas the FOV, integration time, and flying speed had the biggest impact on the size of the footprint. Furthermore, with an increase in flying height, the rate of increase in geolocation uncertainty was found highest for a low-grade IMU. To increase the footprint geolocation accuracy, we recommend reducing flying height while increasing the FOV which compensates the footprint area loss and increases the signal strength. The disadvantage of a lower flying height and a larger FOV is a higher sensitivity of the footprint size to changing distance from the target. To assist in matching the footprint size to uncertainty ratio with an appropriate spatial scale, we list the expected ratio for a range of IMU grades, FOVs and AGL heights.
A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN)
Authors: Jiang Changhui , Chen Shuai , Chen Yuwei , Zhang Boya , Feng Ziyi , Zhou Hui , Bo Yuming
Published: October 2018

Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions. The GPS receiver is able to generate stable and precise position and time information in open sky environment. However, under signal challenging conditions, for instance dense forests, city canyons, or mountain valleys, if the GPS signal is weak and even is blocked, the GPS receiver will fail to output reliable positioning information, and the integration system will fade to an INS standalone system. A number of effects have been devoted to improving the accuracy of INS, and de-nosing or modelling the random errors contained in the MEMS IMU have been demonstrated to be an effective way of improving MEMS INS performance. In this paper, an Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. A MEMS IMU (MSI3200, manufactured by MT Microsystems Company, Shijiazhuang, China) was employed to test the proposed method, a 2 min raw gyroscope data with 400 Hz sampling rate was collected and employed in this testing. The results show that the standard deviation (STD) of the gyroscope data decreased by 60.3%, 37%, and 44.6% respectively compared with raw signals, and on the other way, the three-axis attitude errors decreased by 15.8%, 18.3% and 51.3% individually. Further, compared with an Auto Regressive and Moving Average (ARMA) model with fixed parameters, the STD of the three-axis gyroscope outputs decreased by 42.4%, 21.4% and 21.4%, and the attitude errors decreased by 47.6%, 42.3% and 52.0%. The results indicated that the de-noising scheme was effective for improving MEMS INS accuracy, and the proposed LSTM-RNN method was more preferable in this application.
Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain
Authors: Wu Yuan , Chen Xiangxu , Shi Jiajun , Ni Kejie , Qian Liping , Huang Liang , Zhang Kuan
Published: October 2018

Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs’ social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain.
Joint Detection and DOA Tracking with a Bernoulli Filter Based on Information Theoretic Criteria
Authors: Zhang Guangpu , Zheng Ce , Sun Sibo , Liang Guolong , Zhang Yifeng
Published: October 2018

In this paper, we study the problem of the joint detection and direction-of-arrival (DOA) tracking of a single moving source which can randomly appear or disappear from the surveillance volume. Firstly, the Bernoulli random finite set (RFS) is employed to characterize the randomness of the state process, i.e., the dynamics of the source motion and the source appearance. To increase the performance of the detection and DOA tracking in low signal-to-noise ratio (SNR) scenarios, the measurements are obtained directly from an array of sensors and allow multiple snapshots. A track-before-detect (TBD) Bernoulli filter is proposed for tracking a randomly on/off switching single dynamic system. Secondly, since the variances of the stochastic signal and measurement noise are unknown in practical applications, these nuisance parameters are marginalized by defining an uninformative prior, and the likelihood function is compensated by using the information theoretic criteria. The simulation results demonstrate the performance of the filter.
Electro-Active Paper as a Flexible Mechanical Sensor, Actuator and Energy Harvesting Transducer: A Review
Authors: Khan Asif , Khan Faisal Raza , Kim Heung Soo
Published: October 2018

Electro-active paper (EAPap) is a cellulose-based smart material that has shown promising results in a variety of smart applications (e.g., vibration sensor, piezo-speaker, bending actuator) with the merits of being flexible, lightweight, fracture tolerant, biodegradable, naturally abundant, cheap, biocompatible, and with the ability to form hybrid nanocomposites. This paper presents a review of the characterization and application of EAPap as a flexible mechanical vibration/strain sensor, bending actuator, and vibration energy harvester. The working mechanism of EAPap is explained along with the various parameters and factors that influence the sensing, actuation, and energy harvesting capabilities of EAPap. Although the piezoelectricity of EAPap is comparable to that of commercially available polyvinylidene fluoride (PVDF), EAPap has the preferable merits in terms of natural abundance and ample capacity of chemical modification. The article would provide guidelines for the characterization and application of EAPap in mechanical sensing, actuation, and vibration energy scavenging, along with the possible limitations and future research prospects.
A LN/Si-Based SAW Pressure Sensor
Authors: Nicolay Pascal , Chambon Hugo , Bruckner Gudrun , Gruber Christian , Ballandras Sylvain , Courjon Emilie , Stadler Matthias
Published: October 2018

Surface Acoustic Wave (SAW) sensors are small, passive and wireless devices. We present here the latest results obtained in a project aimed at developing a SAW-based implantable pressure sensor, equipped with a well-defined, 30 μm-thick, 4.7 mm-in-diameter, Lithium Niobate (LN) membrane. A novel fabrication process was used to solve the issue of accurate membrane etching in LN. LN/Si wafers were fabricated first, using wafer-bonding techniques. Grinding/polishing operations followed, to reduce the LN thickness to 30 μm. 2.45 GHz SAW Reflective Delay-Lines (R-DL) were then deposited on LN, using a combination of e-beam and optical lithography. The R-DL was designed in such a way as to allow for easy temperature compensation. Eventually, the membranes were etched in Si. A dedicated set-up was implemented, to characterize the sensors versus pressure and temperature. The achieved pressure accuracy is satisfactory (±0.56 mbar). However, discontinuities in the response curve and residual temperature sensitivity were observed. Further experiments, modeling and simulations were used to analyze the observed phenomena. They were shown to arise essentially from the presence of growing thermo-mechanical strain and stress fields, generated in the bimorph-like LN/Si structure, when the temperature changes. In particular, buckling effects explain the discontinuities, observed around 43 °C, in the response curves. Possible solutions are suggested and discussed.
A Transmission-Based Dielectric Property Probe for Clinical Applications
Authors: Meaney Paul , Rydholm Tomas , Brisby Helena
Published: October 2018

We have developed a transmission-based, open-ended coaxial dielectric probe that can be used in clinical situations and overcomes many of the limitations related to the typical reflection-based dielectric probes. The approach utilizes the low profile, open-ended coaxial cables enabling clinicians to still probe relatively compact spaces. The sensing depth can be extended to as large as 1.5 to 2 cm compared with the more typical range of 0.3 mm for conventional probes and is dramatically less affected by measurement technique variability including poor sample contact and cable bending. As a precursor to an actual clinical implementation, we study the technique in a range of homogeneous liquids with substantially varying dielectric properties. The initial results demonstrate good agreement between the transmission-based probe and commercial, reflection-based probes and pave the way for more substantial clinical implementation.
A Normal Sensor Calibration Method Based on an Extended Kalman Filter for Robotic Drilling
Authors: Chen Dongdong , Yuan Peijiang , Wang Tianmiao , Cai Ying , Tang Haiyang
Published: October 2018

To enhance the perpendicularity accuracy in the robotic drilling system, a normal sensor calibration method is proposed to identify the errors of the zero point and laser beam direction of laser displacement sensors simultaneously. The procedure of normal adjustment of the robotic drilling system is introduced firstly. Next the measurement model of the zero point and laser beam direction on a datum plane is constructed based on the principle of the distance measurement for laser displacement sensors. An extended Kalman filter algorithm is used to identify the sensor errors. Then the surface normal measurement and attitude adjustments are presented to ensure that the axis of the drill bit coincides with the normal at drilling point. Finally, simulations are conducted to study the performance of the proposed calibration method and experiments are carried out on a robotic drilling system. The simulation and experimental results show that the perpendicularity of the hole is within 0.2°. They also demonstrate that the proposed calibration method has high accuracy of parameter identification and lays a basis for high-precision perpendicularity accuracy of drilling in the robotic drilling system.
Exploring Risks Transferred from Cloud-Based Information Systems: A Quantitative and Longitudinal Model
Authors: Bouaynaya Wafa , Lyu Hongbo , Zhang Zuopeng (Justin)
Published: October 2018

With the growing popularity of Internet of Things (IoT) and Cyber-Physical Systems (CPS), cloud- based systems have assumed a greater important role. However, there lacks formal approaches to modeling the risks transferred through information systems implemented in a cloud-based environment. This paper explores formal methods to quantify the risks associated with an information system and evaluate its variation throughout its implementation. Specifically, we study the risk variation through a quantitative and longitudinal model spanning from the launch of a cloud-based information systems project to its completion. In addition, we propose to redefine the risk estimation method to differentiate a mitigated risk from an unmitigated risk. This research makes valuable contributions by helping practitioners understand whether cloud computing presents a competitive advantage or a threat to the sustainability of a company.
Business Lobbying: Mapping Policy Networks in Brazil in Mercosur
Authors: Benítez Galia J.
Published: October 2018

In the creation of trade policy, business actors have the most influence in setting policy. This article identifies and explains variations in how economic interest groups use policy networks to affect trade policymaking. This article uses formal social network analysis (SNA) to explore the patterns of articulation or a policy network between the government and business at the national level within regional trade agreements. The empirical discussion herein focuses on Brazil and the setting of exceptions list to Mercosur’s common external tariff. It specifically concentrates on the relations between the Brazilian executive branch and ten economic subsectors. The article finds that the patterns of articulation of these policy networks matter and that sectors with stronger ties to key government decision-makers have a structural advantage in influencing trade policy and obtaining and/or maintaining their desired, privileged trade policies, compared with sectors that are connected to government actors with weak decision-making power, but might have numerous and diversified connections. Therefore, sectors that have a strong pluralist–clientelist policy structure with connections to government actors with decision-making power have greater potential for achieving their target policies compared with more corporatist policy networks.
Sustainable Development of the Economic Circle around Beijing: A View of Regional Economic Disparity
Authors: Chu Zhaopeng , Liu Changxin , Li Gang , Guan Kaixuan
Published: October 2018

The economic circle around Beijing is a very important area that supports the development of Beijing-Tianjin-Hebei region in China. The economic growths of the economic circle around Beijing are deeply affected by the regional economic disparity. Besides the resources and environment problems, economic factors may be more prominent of regional sustainability. The objective of this paper is: (1) To find out what caused the regional economic disparity of Beijing-Tianjin-Hebei region; and (2) to assess the economic sustainable development status of the cities around Beijing, and give suggestions to narrow the regional disparity from the economic growth view. We used an assessment method based on the economic sustainable index system—the order relation method named G1 method, revised by standard deviation. The results showed that the fundamental reason for the differences lies in the disparity in production efficiency. The disparity in the regional economy increases the flow of talent to Beijing and Tianjin, so the labor productivity of Hebei Province will not improve. Thus, the economic gap among Hebei, Beijing, and Tianjin will continue to grow. This negative feedback will further exacerbate the economic growth problem. In terms of narrowing regional disparity, some implications are highlighted. Rational industrial structure, a high level of development in the manufacturing industry, and a high proportion of strategic emerging industries are important for the cities around Beijing to achieve high-level industry transfer, and the technological progress needed to promote economic growth.
A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets
Authors: Khan Umair , Aadil Farhan , Ghazanfar Mustansar Ali , Khan Salabat , Metawa Noura , Muhammad Khan , Mehmood Irfan , Nam Yunyoung
Published: October 2018

Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges—New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies—Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
Daylight Performance of Classrooms in a Mediterranean School Heritage Building
Authors: Nocera Francesco , Lo Faro Alessandro , Costanzo Vincenzo , Raciti Chiara
Published: October 2018

In school buildings, natural light has considerable physiological benefits, and increases energy efficiency while reducing the operational energy consumption of buildings. It is thus crucial to maximize the amount of daylight, as well as to improve its quality, in educational premises. In Italy and other European countries, many historic buildings are reused as school buildings, changing their original function. This process of adaptive reusing is one method for conserving heritage buildings, however sometimes this process sacrifices the quality of daylight and well being of pupils. It has therefore become apparent that it is difficult to reconcile the cultural value of historic buildings with comfort standards. This study aimed to investigate the natural lighting performance of a school located in an historic building, and proposes different technological solutions to improve the visual comfort in classrooms whilst also respecting the cultural value of built heritage. Daylight performance was carried out in a representative classroom in the ‘Caserma Gaetano Abela’, an historical building located in Siracusa (Italy). A daylight model built in Radiance was first validated against an illuminance measurement campaign, and was then used to run detailed dynamic simulations. Climate Based Daylight Modelling (CBDM) metrics were used to show the achievable improvements in visual comfort conditions by means of proposed retrofit interventions. These interventions may also be used in other areas of southern Europe with similar climatic and constructive/distributive characteristics of architectural heritage.
Nodal Farmers’ Motivations for Exchanging Sorghum Seeds in Northwestern Ethiopia
Authors: Rodier Christophe , Struik Paul C.
Published: October 2018

One of the main challenges of Ethiopian agriculture is the shortage of certified seeds of improved varieties, which results in uneven dispersal of quality seed amongst farmers. In a context where 80% to 90% of the seed requirement is covered by the informal seed sector, understanding how and why seeds are exchanged through informal channels is crucial. This study aims to describe why nodal farmers disseminate seeds at a higher rate than other farmers in their network. Following a social network analysis, in-depth surveys were conducted with identified nodal and connector sorghum farmers in order to determine the main social characteristics that differentiate them from other farmers in a western lowlands community of the Tigray Region of Ethiopia. We examined empirically the main factors that motivate them, the main drawbacks they experience, and the behavioral decisions that could potentially speed up or slow down the adoption of newly released improved varieties of sorghum. The study showed that, in this district, few significant socio-demographic differences exist between nodal and non-nodal farmers. The seed exchange network was hyper localized, as the majority of exchanges took place within village boundaries. Focus group discussions showed that a nodal position should not be taken for granted, as the network is dynamic and in constant evolution. In-depth interviews revealed that it was unlikely for accessing farmers to be consistently denied seeds due to a deeply rooted social norm insisting that one should not, under any circumstances, be turned down when asking for seeds. However, in practice, chronic seed insecure farmers suffering from poor performances may find themselves unable to access quality seeds, as automatic support should not be assumed. In terms of motivation, nodal farmers ranked maintaining friendships and relationships as the two most important. Thus, beyond the risk-sharing mechanism underlying much of the seed exchange, it is a mix of personal and community interests that motivates nodal farmers to have more exchange partners and thus disseminate more seeds on average than other farmers in the seed networks. This indicates that their social capital is the major driver to exchange seeds.
Analyzing Urban Residents’ Appraisal of Ridepooling Service Attributes with Conjoint Analysis
Authors: König Alexandra , Bonus Tabea , Grippenkoven Jan
Published: October 2018

Public ridepooling systems could contribute to the reduction of traffic volume and emissions in cities by decreasing the number of rides by private car while increasing the average number of passengers per vehicle. Yet, it is unknown how urban travelers value different attributes of the ridepooling’s operational concept. Which characteristics of ridepooling concepts are most important to the users? In order to obtain a deeper understanding of travelers’ preferences concerning a ridepooling system, choice-based Conjoint Analysis was performed. Based on a literature review and a focus group, six relevant attributes of the operational concept of ridepooling systems were determined: fare, walking distance, time of booking, shift of departure time, travel time, and information provision. Data from 237 German city dwellers were analyzed with the help of Cox regression. Except for time of booking, all service attributes significantly affected the respondents’ choice. Besides the high relevance of fare, the results underline the particular importance of the attribute walking distance to the pick-up point for elderly. The results give guidance for the creation of user-centered public transport systems that meet the requirements of the prospective passengers and thus might contribute to the development of shared passenger transport systems for sustainable urban mobility.
Perceptions, Risk Attitude and Organic Fertilizer Investment: Evidence from Rice and Banana Farmers in Guangxi, China
Authors: Chen Xinjian , Zeng Di , Xu Ying , Fan Xiaojun
Published: October 2018

Overuse of chemical fertilizer has led to severe land degradation and environmental pollution in China. Switching to organic fertilizer may improve soil quality and reduce pollution, which is meaningful to the sustainable development of Chinese agriculture. This study examines how farmers’ perceptions and risk preference affect their organic fertilizer investment using a representative rural household survey from Guangxi, a major agricultural region in China. Tobit and double-hurdle models are used to empirically test their impacts on organic fertilizer adoption and investment. An ordinary least squares model is used to regress chemical fertilizer use on the same set of explanatory variables to compare and contrast farmers’ different fertilizer investment behaviors. It is found that both organic fertilizer perceptions and risk attitude significantly affect organic fertilizer investment. Perceived yield-increasing and quality-improving effects encourage organic fertilizer investment, while perceived cost increases discourage it. Moreover, risk-averse farmers are more likely to invest in organic fertilizers. Most of the perceptions affecting organic fertilizer investment have an opposite impact on chemical fertilizer investment, which suggests substitutability between organic and chemical fertilizer. Interventions that aim to improve farmers’ perceptions of organic fertilizer and illustrate its risk-reduction effect could be effective in promoting organic fertilizer use, which can help achieve China’s sustainable development of agriculture.
Fault Diagnosis of Mine Shaft Guide Rails Using Vibration Signal Analysis Based on Dynamic Time Warping
Authors: Wu Bo , Li Wei , Jiang Fan
Published: October 2018

Guide rails are amongst the most important components of mine hoist systems, and faults in them must be detected as early as possible to avoid fatal breakdowns in mine production. Presently, guide rail inspection is performed visually in most situations. In this paper, we examine a more efficient approach based on multi-time scale and dynamic time warping (DTW) to diagnose guide rail faults including embossment, bumps, and clearance. Firstly, vibration signals collected from operational conveyance under different fault categories are analyzed and the corresponding characteristic waveforms (CWs) are extracted. Embossment faults are identified with priority according to visible disparities in CW patterns on a large time scale. Then, templates for bump and clearance faults are established through processing the CWs on a small time scale. Subsequently, the distances of DTW (DDTWs) between test samples and the selected templates are calculated. Finally, the remaining fault conditions are classified according to the DDTW results since the same fault category has the smallest distance. Experiments are conducted on a guide rail fault simulator to demonstrate the reliability of the proposed method. The resultant diagnosis accuracies are 100%, 90.40%, and 84.53%, respectively, for embossment, bump, and clearance faults, which indicates that the proposed approach is feasible and effective for diagnosing guide rail faults under variable operating conditions and different fault severities.