Search

Sintering of advanced thermal barrier coatings (TBCs) at high temperatures is key challenge as it can adversely affect service performance and thermal fatigue resistance of TBCs. In this study, sintering behavior of pyrochlore-type La2(Zr0.7Ce0.3)2O7 (LZ7C3) was investigated using experiments and molecular dynamics. Meanwhile, the corresponding dynamic process and behind mechanism were uncovered. Results showed that novel LZ7C3 exhibited significantly higher sintering resistance than host La2Zr2O7 (LZ) and typical 8 wt% yttria-stabilized zirconia (8YSZ) at temperature up to 1773 K, which indicated that pyrochlore-type LZ7C3 is a promising TBC candidate to replace conventional 8YSZ at high temperatures. Further study also revealed that initial stage played crucial role in sintering process, and the sintering mainly occurred at grain boundary (GB) region. Intrinsic sintering activation energy of LZ7C3 GB (695.248 J mol−1) is larger than that of LZ GB (384.171 J mol−1) and 8YSZ GB (173.303 J mol−1), which resulted in outstanding sintering resistance for LZ7C3. Furthermore, no obvious enrichment of second phase was observed at the GB of LZ7C3. This study thus concluded that hindering the atomic diffusion of GB, as well as introducing foreign atom with larger mass and bond energy may act as effective strategy to enhance the sintering resistance of TBCs materials.

Zirconia ceramics has an important position in the field of biomaterials because of excellent mechanical properties. Because of abrasive resistance and hardness, conventional machining of zirconia ceramic is difficult and time-consuming. A nontraditional hybrid laser-assisted grinding (LAG) system combining laser and cubic boron nitride (CBN) grinding wheel was developed for machining zirconia ceramic. This hybrid system utilizes the outstanding thermostability of zirconia ceramic: The material present on the cutting path is rapidly removed by local laser heating and grinding wheel. A combined theoretical and experimental study was performed to evaluate different modes of machining of zirconia ceramic. The machining parameters were predicted by numerical analysis. The surface quality, surface morphology, and subsurface damage of zirconia ceramic specimens were analyzed and compared. The results show that LAG can easily achieve ductile regime grinding in the same machine tool. Compared with conventional grinding, the surface integrity of zirconia ceramic was significantly improved by the LAG process. LAG could achieve ductile regime grinding with a large depth-of-cut. It changes the machinability of zirconia ceramic.

To meet the increasing energy consumption around the world and fight global climate change, there is an urgent need to explore renewable energy crops to replace the traditional energy sources. Duckweed (Spirodela polyrrhiza) is widely distributed in the world and has high starch and low lignin contents, which is perhaps an ideal feedstock for bioenergy production. To investigate the effects of abscisic acid (ABA) on duckweed biomass and starch accumulation, Spirodela polyrrhiza was cultivated at different ABA concentrations. The results showed that the highest starch content in duckweed (21.8% dry weight) was achieved in 1.0 × 10−2 mg L−1 ABA medium, 70.3% higher than that of the control medium without ABA. The number of starch granules in 1.0 × 10−2 mg L−1 ABA medium was far more than that in the control medium. The highest adenosine diphosphate (ADP)-glucose pyrophosphorylase (AGPase) activity was observed in the 1.0 × 10−2 mg L−1 ABA medium, which was caused by the up-regulation expression of ADP-glucose pyrophosphorylase 2 (APL2). Further investigations on cell ultra-structures and stomatal property of the duckweed indicated that ABA increased the number and size of starch granules and stomatal size in duckweed cells. These enhancements lead to a greatly improved energy flow in the aquatic plant from photosynthesis to carbon storage, making duckweed a potential renewable bioenergy crop.

The extensive deployment of wireless infrastructure provides a low-cost approach to tracking of mobile phone users in indoor environments using received signal strength (RSS). Crowdsourcing has been promoted as an efficient way to reduce the labor-intensive site survey process in conventional fingerprint-based localization systems. Despite its stated advantages, use of crowdwourcing for localization has issues of accuracy and reliability in indoor applications, in large part because of multipath propagation. This paper discusses and evaluates a Bayesian approach to localization of mobile users based on a crowdsourced fingerprint, in which environmental constraints as well as dynamic property of the mobile are incorporated as priors. Both a Markov chain and a semi-Markov chain approach are applied for modeling the transition and duration statistics of the mobile user across different location cells. Field test results demonstrate the effectiveness of introducing this additional information for localization in a real-world wireless network.

With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it’s still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use.

Nanobubble water (NBW) has been applied in various fields due to the unique properties of nanobubbles (NBs) including long-term stability, negative zeta potential and generation of free radicals. In this study, the performance of four kinds of NBW from different gases (air, N2, H2, and CO2) in addition to deionized water (DW) were investigated and compared in terms of the growth of the probiotic Lactobacillus acidophilus 1028. The NB density, size distribution, zeta potential, pH and dissolved oxygen (DO) of the NBW were firstly investigated. Results indicate that N2-NBW had the highest absolute value of zeta potential and NB density (−25.3 ± 5.43 mV and 5.73 ± 1.0 × 107 particles per mL, respectively), while the lowest was detected in CO2-NBW (−6.96 ± 2.36 mV and 3.39 ± 1.73 × 107 particles per mL, respectively). With the exception of CO2-NBW, all the other types of NBW showed promotion effects on the growth of the strain at the lag and logarithmic phases. Among them, N2-NBW demonstrated the best performance, achieving the highest increase ratio of 51.1% after 6 h cultivation. The kinetic models (Logistic and Gompertz) indicate that the culture with N2-NBW had the shortest lag phase and the maximum specific growth rate when compared to the H2-NBW and DW groups under the same cultivation conditions. Preliminary analysis on the mechanisms suggested that these effects were related to the properties (zeta potential and density) of the NBs, which might affect the transport of substances. This study suggests that NBW has the potential for promoting the production efficiency of probiotics via fermentation.

Vitrified bond CBN grinding wheels are being widely used due to their superior performance. Also, advantages of vitrified grinding wheels are high elastic modulus, stable chemical property, and low thermal expansion coefficient. Brittleness and low strength are key factors restricting the development of vitrified bond CBN grinding wheels. In this paper, the sintering in a high magnetic field was innovatively introduced into the manufacturing of vitrified bond CBN grinding wheels, and the effects of sintering in a high magnetic field on properties on vitrified bond and vitrified CBN composites were systematically investigated. Vitrified bond was characterized using three-point bending, scanning electron microscopy, X-ray diffraction. It was observed that microstructure of vitrified bond could be changed, grain orientation could be controlled and average grain size could be decreased in a high magnetic field, while vitrified bond strength could be simultaneously improved. High quality vitrified bond could be obtained by appropriately adjusting the strength and direction of high magnetic field. Results demonstrated that vitrified bond properties were improved when the magnetic field strength was 6 T. In order to highlight the high magnetic field effect on the vitrified CBN composites, the ordinary CBN abrasives and nickel plated CBN abrasives were used respectively. Microstructures, bending strengths of vitrified CBN composites were compared in different high magnetic fields. When the magnetic field strength was appropriate (less than 6 T), the binding characteristic of vitrified bond CBN composites with nickel plated CBN abrasives was greatly improved. The highest bending strength value of vitrified CBN composites was 79.5 MPa in 6 T high magnetic field.

Absolute localization of a flying UAV on its own in a global-navigation-satellite-system (GNSS)-denied environment is always a challenge. In this paper, we present a landmark-based approach where a UAV is automatically locked into the landmark scene shown in a georeferenced image via a feedback control loop, which is driven by the output of an aerial image registration. To pursue a real-time application, we design and implement a speeded-up-robust-features (SURF)-based image registration algorithm that focuses efficiency and robustness under a 2D geometric transformation. A linear UAV controller with signals of four degrees of freedom is derived from the estimated transformation matrix. The approach is validated in a virtual simulation environment, with experimental results demonstrating the effectiveness and robustness of the proposed UAV self-localization system.

With the development of scientific big data technology, problem-oriented analysis becomes normal case. iAnalysis V1.0, an interactive analysis cloud service system, gives a unified cloud resource management service for scientific data analysis. It can not only be used directly by end-user scientists through the service portal, but also be called by other existing data systems in the form of docker container. In this paper, there are presented the key technical methods of iAnalysis V1.0, including the interactive analysis service solution based on container technology, the interactive analysis components and storage & high-performance computing services for interactive analysis. There are also introduced the specific application services of iAnalysis V1.0 such as lightweight deployment service, management and monitoring services, external application programming interface services and so on. Some applications of iAnalysis V1.0 are also presented. The basic deployment images of iAnalysis V1.0 can be download from https://hub.docker.com/repository/docker/bioinf/ianalysis.

With the continuous development of Earth science, soil temperature has received more and more attention in Earth system research as an important parameter. The change of soil temperature (Ts) in different regions and related time series is affected by many factors, which bring certain difficulties to the accuracy of soil temperature prediction and the robustness of the algorithm. In this paper, an embedded network prediction model based on the gated recurrent unit (GRU) model is proposed to learn the local and global features of historical temperature for improving the prediction performance of soil temperature. We input different steps into the GRU model, and the output is weighted to obtain the final prediction result. In order to obtain the global characteristics of soil temperature, we connect the previous steps to the output layer directly, and the local characteristics of soil temperature are obtained through the following steps. This paper uses the soil temperature data from two meteorological stations (Laegern and Fluehli) in Switzerland as the input data to predict the soil temperature for different soil depths (5 cm, 10 cm, and 15 cm) at different time points (6 hrs, 12 hrs, and 24 hrs), using RMSE, MAE, MSE, and R 2 performance indicators as evaluation criteria to verify the accuracy of prediction. As the experimental results show, our method has the best performance compared to the others (artificial neural networks (ANN), extreme learning machine model (ELM), long short-term memory network (LSTM), gated recurrent unit network (GRU)). In particular, we estimated the soil temperature at the soil depth of 10 cm of the Fluehli station in the coming 6 hrs; our method achieved the best performance; and, meanwhile, our model achieved the maximum value of R 2 (0.9914) and the minimum values of RMSE (0.4668), MAE (0.2585), and MSE (0.2214) compared with the other four models. Therefore, our model can not only predict the soil temperature at different depths but also improve the accuracy.

Continual learning has become increasingly important as it enables NLP models to constantly learn and gain knowledge over time. Previous continual learning methods are mainly designed to preserve knowledge from previous tasks, without much emphasis on how to well generalize models to new tasks. In this work, we propose an information disentanglement based regularization method for continual learning on text classification. Our proposed method first disentangles text hidden spaces into representations that are generic to all tasks and representations specific to each individual task, and further regularizes these representations differently to better constrain the knowledge required to generalize. We also introduce two simple auxiliary tasks: next sentence prediction and task-id prediction, for learning better generic and specific representation spaces. Experiments conducted on large-scale benchmarks demonstrate the effectiveness of our method in continual text classification tasks with various sequences and lengths over state-of-the-art baselines. We have publicly released our code at https://github.com/GT-SALT/IDBR., NAACL 2021

This paper presents results of the ongoing development of a crowdsourcing platform for labelling remote sensing images. In order to perform fast spatial and temporal retrieval and read data efficiently, this paper proposes an adaptive grid design method to enable the remote sensing images being segmented into tiles, which supports parallel reading and writing, and uses Ceph cluster for storage. To guarantee crowdsourcing results' quality, a quality control mechanism combining expertsourcing and crowdsourcing is proposed. The online labelling system is designed and a prototype has been developed. Users neither have to install any software, nor download large remote sensing images. They can directly label the images by just selecting tags and drawing polygons. By providing such a platform, a large volume of training dataset for remote sensing classification can be obtained.

Hidden Markov Chains (HMCs) and, more recently, Hidden semi-Markov Chains (HsMCs) have been used by several groups of researchers to provide a model for indoor localization. A homogeneous HMC is completely determined by the state initial probability vector and the state transition probability matrix. This is also true for the HsMC provided the state duration probability is given. These parameters are often chosen heuristically but when sufficient measurement training data are available, they can be learned using the well-known Baum-Welch algorithm. Given the model parameters, approaches such as the forward-only algorithm, the forward-backwards algorithm and the Viterbi algorithm can be applied for state sequence inference under the HMC/HsMC framework. In indoor localization applications, there is often insufficient prior information to specify such parameters in advance of the application and they have to be learned from limited amounts of training data. In this paper, we endeavour to evaluate the parameter learning accuracy of the Baum-Welch algorithm using varying amounts of training data, and evaluate the influence of applying inaccurate model parameters on these typical state estimation algorithms under both the HMC and HsMC frameworks. All of the evaluations are based on received signal strength (RSS) for application to indoor localization.

Nanobubble water (NBW) has been proven to efficiently improve methane production from organic solid wastes. However, the increase in reactor volume due to addition of NBW hinders its practical applications. In this study, anaerobically digested sludge was first pre-augmented by N2-NBW and O2-NBW using corn straw as sole substrate for methane production with electron transfer activity being monitored. 20%, 33% and 38% of cellulose and 29%, 35% and 35% of hemicellulose were reduced respectively from the control, N2-NBW and O2-NBW pre-augmented sludge reactors. N2-NBW and O2-NBW pre-augmented sludge reactors achieved methane yields of 127 and 142 mL/g-VS, about 10% and 22% higher than that from the control. Results show that use of NBW pre-augmented anaerobically digested sludge as inoculum can remarkably enhance methane yield from corn straw, providing a novel concept for NBW-based anaerobic digestion system with no increase of reactor volume and construction cost in practice.

We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. To obtain generated targets that are natural but also faithful to the source table, we introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. We present systematic analyses of our dataset and annotation process as well as results achieved by several state-of-the-art baselines. While usually fluent, existing methods often hallucinate phrases that are not supported by the table, suggesting that this dataset can serve as a useful research benchmark for high-precision conditional text generation., Accepted to EMNLP 2020

NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that are known to be invariant to task labels. For example, in order to attack a model for sentiment classification over product reviews, we can use the product categories as the controllable attribute which would not change the sentiment of the reviews. Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches. We further use our generated adversarial examples to improve models through adversarial training, and we demonstrate that our generated attacks are more robust against model re-training and different model architectures., Comment: 6 pages, accepted to EMNLP 2020

In order to optimize the structure performances of ceramic cubic boron nitride (CBN) grinding wheels, the effects of ZrO2 and Y2O3 addition on the physical and mechanical properties of ceramic bond and ceramic CBN composites were systematically investigated. The basic ceramic bond was composed of B2O3, Al2O3, SiO2, Li2O and Na2O. The additions of ZrO2 and Y2O3 had a certain effect on refractoriness, liquidity, flexural strength of ceramic bonds. The experimental results demonstrated that in the ceramic bonds, both ZrO2 and Y2O3 could promote the formation of glass grid, while the ceramic bond strength would further increase. In addition, the new phase of YBO3 appeared in the ceramic bond, due to the filling of Y2O3. When the contents of ZrO2 and Y2O3 were 4 wt% respectively, the strength of the ceramic bond was highest, increasing by 23% (16.1 Mpa) compared to the basic ceramic bond. The last point required to be pointed out was that the phase transformation toughening of ZrO2 cannot be achieved and it was limited by low temperature sintering.

In this paper, the porosity effects on oxygen ions diffusion was investigated in the yttria-stabilized zirconia (YSZ) by molecular dynamics simulation (MD). Different models were simulated to elucidate the role of yttria (Y2O3) concentration (3 to 8 mol% Y2O3), number of pores and pores density and structure (cubic and tetragonal lattice) on the oxygen ions diffusion in YSZ system. MD simulation results showed that the diffusion coefficient of the tetragonal YSZ was smaller than that of the cubic YSZ, the maximum of oxygen ions diffusion coefficient exhibited in the 6 mol% Y2O3 among all the tetragonal YSZ systems. From MD simulation results, it was found that the pores in the YSZ system were retarding the oxygen ions diffusion. For constant porosity, smaller single pore volume resulted in lower D, while higher total porosity also led to smaller D.

In this study, CH4 production from anaerobic digestion (AD) of refractory cellulose was investigated at a high loading of 3.5 (VScellulose/VSinoculum) under nanobubble water (NBW) addition. A longer proton spin-spin relaxation time (2611–2906 ms) of NBW during 35 days’ storage reflected its high mobility and diffusion of water molecules. Higher volatile fatty acids were yielded at the hydrolysis-acidification stage under NBW addition. Methanogenesis tests showed that Air-NBW and CO2-NBW supplementation accelerated the utilization of crystalline cellulose, achieving methane yields of 264 and 246 mL CH4/g-VSreduced, increasing by 18% and 10% compared to deionized water addition (the control), respectively. In addition, under NBW addition the cellulose crystallinity reduction was enhanced by 14–20% with microbial community being enriched with hydrolytic and methanogenic bacteria. Results from this work suggest that NBW environment with no chemical addition and relatively low energy consumption is advantageous for enhanced AD process of cellulosic biomass.

Properties of vitrified bond with varying Ni doping amounts were extensively investigated. Effects of Ni addition on microstructures and properties of vitrified bond cubic boron nitride (CBN) composites prepared in strong magnetic field were investigated for applications in CBN grinding tools. Vitrified bond was characterized using three-point bending, scanning electron microscopy, X-ray diffraction and other methods. The refractoriness, fluidity, and bending strength of vitrified bond were evaluated. Bending strengths, microstructures, and phase compositions of vitrified bond CBN composites achieved using conventional and strong electromagnetic sintering techniques were compared. Results show that the addition of Ni to vitrified bond CBN composites improved the fluidity and bending strength of the vitrified bond. Strong electromagnetic sintering improved the mechanical strength and pore structure of vitrified bond CBN composites. Moreover, the introduction of the strong magnetic field facilitated Ni migration and aggregation in vitrified bond, rotated abrasives, and formed new substances, thus increasing the stability of vitrified bond CBN composite thermal material. Also, strong magnetic field inhibit grain growth of non-magnetic and ferromagnetic materials with fine-grain effect.

This paper presents structure, thermal expansion coefficient and phase stability of La2(Zr0.7Ce0.3)2O7 (LZ7C3) ceramic by both theoretical and experimental results. It was found out that LZ7C3 powders had a pyrochlore structure after being heat-treated at temperatures higher than 1473 K or higher according to XRD and TEM results. The calculated average thermal expansion coefficient (TEC) was 7.12 × 10−6 K−1, which is a little smaller than experiment result, but changes of calculated average TECs of LZ, YSZ and LZ7C3 had the same trend with experimental results. Finally, the radial distribution function (RDF) was calculated to study the phase stability of LZ7C3.

Due to thermally reactive hazards, guanidine nitrate (GN) has caused numerous serious accidents involving manufacturing, storing, and transporting processes. Differential scanning calorimetry and thermogravimetry (TG) were used to study the thermal reactive hazards of GN and the influence of adding water, nitric acid, ammonium nitrate (AN), and urea on GN thermal decomposition in air. The results indicated that the exothermal onset temperature and peak temperature rose with the heating rates. According to the Kissinger method, Ozawa method, as well as Friedman method, the apparent activation energy of GN in the peak temperature is 121.1, 123.0, and 126.0 kJ mol−1, respectively. The thermal decomposition of GN consisted of four stages. The mass loss rate of GN reached the maximum at 300 °C, and this temperature was in line with the exothermic peak temperature. The TG curve of GN in nitrogen atmosphere has a certain hysteresis compared to the air atmosphere. The thermal hazard of GN was inversely proportional to the size of the particles. Water had little effect on the decomposition of GN. Furthermore, nitric acid can promote the decomposition of GN more vigorously and increase the hazards of thermal runaway of GN. With the growth of the amount of AN and urea, the decomposition reaction of GN was more likely to initiate and more difficult to govern. These results could be used as a reference guide to the actual manufacturing, storing, and transporting processes for GN.

We describe a method for processing radar signals, combining the outputs of two – a Weighted average Doppler algorithm and an existing Binominal Design algorithm via a point-wise minimum processor. The method renders a better performance in range sidelobe suppression for the detection of multiple moving targets using Golay complementary waveforms than the existing Binominal Design algorithm. The effectiveness of the method is justified by analyzing the corresponding ambiguity functions. We also show the performance of the new signal processing method by simulations under a realistic scenario.

This paper presents thermal properties in both yttria-stabilized zirconia (YSZ) and the coupled system based on lanthanum zirconate (LZ) and YSZ through molecular dynamics (MD) simulation. Mean square displacement (MSD), thermal transport behavior and thermal conductivity of both YSZ and LZ/YSZ systems were calculated at temperatures ranging from 1273 K to 1473 K. Typical oxygen ion vibration in both YSZ and LZ/YSZ coupled system were studied. Radial distribution functions (RDFs) of Y ions were investigated in both systems. The oxygen ion hopping in YSZ explains the movements of oxygen ions among vacancies in the YSZ easier than in LZ/YSZ coupled system. The vibrational density of state (VDOS) of scattered phonons explains the trends in thermal conductivity for both systems. In the case of LZ/YSZ coupled-system, LZ keeps more ions vibrated on their original position and reduces the hopping of oxygen ions among vacancies in the YSZ unit, while also providing thermal resistance to ensure lower thermal conductivity of the coupled system.

Image dehazing is a fast-growing research area in image processing and computer vision. Due to the extreme fog, haze, and air dispersion within an environment, the hazy image raises several challenges in retrieving original image information type. However, past techniques endure massive computation complexity and even the distortion of original images such as halos and over-saturation. In this research work, a new wavelet Hybrid (Local-Global Combined) Network is proposed for single image dehazing using a convolution neural network (CNN) in the wavelet domain (WH-Net). It is observed that low-level features such as edges are more important than high-level features such as texture. So we have used 2-DWT to decompose the single image model into the frequency subbands, which performs more quickly. It is demonstrated that the estimation of wavelet sub-bands reformulates the trainable end-to-end learning with a special architecture where DWT and IDWT are the feature extraction layers instead of Conv and Deconv, distinguishing it from classical CNN networks. The WHNet method is designed to achieve multi-level representations of hazy images to provide local and global information. The proposed network prominent features are designed with fewer convolution layers without decreasing performance relative to the commonly observed deeper learning models. Compared to several state-of-the-art algorithms, our proposed WHNet neural network surpasses in visual and quantitative performances on three public datasets.

Distance-based hierarchical clustering (HC) methods are widely used in unsupervised data analysis but few authors take account of uncertainty in the distance data. We incorporate a statistical model of the uncertainty through corruption or noise in the pairwise distances and investigate the problem of estimating the HC as unknown parameters from measurements. Specifically, we focus on single linkage hierarchical clustering (SLHC) and study its geometry. We prove that under fairly reasonable conditions on the probability distribution governing measurements, SLHC is equivalent to maximum partial profile likelihood estimation (MPPLE) with some of the information contained in the data ignored. At the same time, we show that direct evaluation of SLHC on maximum likelihood estimation (MLE) of pairwise distances yields a consistent estimator. Consequently, a full MLE is expected to perform better than SLHC in getting the correct HC results for the ground truth metric., Comment: 21 pages, 6 figures

Airborne LiDAR technology is a popular technique to quickly acquire high-precision information of the ground and the objects above it. Moreover, filtering the data is one of the necessary core processing steps. In recent years, many algorithms have been derived from traditional filtering algorithms. However, when applied to LiDAR data obtained from mountainous regions, most algorithms generate numerous omissions and errors when attempting to retain steep terrain features and filter vegetation information. This paper aims to quickly and accurately extract the digital terrain model (DTM) in mountainous regions. To accomplish this goal, a filtering algorithm based on a regular mesh generation strategy is proposed. The proposed algorithm initially divides the huge amount of point cloud data into strips and selects the appropriate spacing to subdivide each strip into equidistant grid data. The grid data is used as the input to an iterative polynomial fitting process, after which the point cloud is classified based on a controlled threshold. The experimental results show that the proposed method can quickly and efficiently classify data of mountains with different characteristics while retaining terrain feature information better than other algorithms. The average accuracy of recognition is greater than 92%. In addition, this algorithm also applies to mountains with lush vegetation.

Spirodela polyrrhiza, a fast-growing duckweed with high starch and low lignin content, shows promise as a feedstock for bioenergy. Abscisic acid (ABA) is a biological hormone that controls plant growth and stress response. The effects of different ABA concentrations (0, 1.0 × 10−5, 1.0 × 10−4, 1.0 × 10−3, 1.0 × 10−2, and 1.0 × 10−1 mg/L) on duckweed biomass growth, carbon dioxide fixation, formation of photosynthetic pigments (Chlorophyll a (Chla), Chlorophyll b (Chlb), and carotenoids), the activities of soluble starch synthase (SSS) and starch branching enzyme (SBE), and the starch content of biomass were investigated in this study. ABA at concentrations lower than 1.0 × 10−3 mg/L promoted carbon dioxide fixation, whereas it inhibited carbon dioxide fixation at concentrations over 1.0 × 10−3 mg/L. ABA enhanced SSS and SBE activities at concentrations lower than 1.0 × 10−2 mg/L. ABA treatment increased the content of Chla, Chlb, and carotenoids and resulted in the enhancement of starch content. Chla content gradually increased with the increasing concentration of ABA (1.0 × 10−5 to 1.0 × 10−2 mg/L). After culturing for 10 days, starch content in 1.0 × 10−2 mg/L ABA medium reached 35.3% of dry weight (DW), which was the highest level in this study. This suggests that there is a great potential to develop a technology to increase starch accumulation in duckweed which can be used as an alternative to corn, sugarcane, or other food crops as a starch source.

Thermal properties of yttria stabilized zirconia (YSZ) were investigated using molecular dynamics (MD) simulation. Several YSZ samples were designed to elucidate the effect of porosity on thermal conductivity of the designed YSZ systems. Mean square displacement (MSD), vibrational density of state (VDOS) and atomic movement in designed YSZ and other samples were also investigated. Results of MD simulation indicated the key role of pores in preventing thermal transport behavior. For same volume of a single pore, samples with larger pore volume showed smaller thermal conductivity ( κ ), while for same amount of total porosity, samples with small-sized pores displayed a smaller thermal conductivity. The latter was due to the presence of relatively larger number of smaller-sized pores as compared to larger-sized ones. Samples with smaller-sized pores in greater numbers also exhibited lower vibration frequency and lower amplitude of single atoms, suggesting smaller thermal conductivity ( κ ) values. For constant pore volume, YSZ samples were observed to have a decrease in thermal conductivity with increasing number of pores. Among YSZ samples with same levels of porosity, those with smaller pores displayed smaller thermal conductivity. Similar levels of thermal conductivity could be acquired in samples with smaller pore amounts and/or smaller porosity as compared to those with larger pores.

Nano-bubble water (NBW) refers to water with a large number of nanoscale particle bubbles. The aim of this work was to study the mechanism of NBW addition into the anaerobic digestion (AD) of swine manure (SM). The results showed that the cumulative methane production from the NBW added reactor was 192–225 mL/g-VS and 19–39% higher than the control group (without NBW addition). Based on the analysis of soluble organics, NBW addition not only accelerated hydrolysis rates of proteins and carbohydrates, but also enhanced the production of VFAs. Moreover, mechanism analysis reveals that NBW with higher spin–spin relaxation time and absolute value of zeta potential might promote enzyme activity and the hydrolysis of organic solids. Simultaneously, the electron transport system activity of the methanogenic communities and electric conductivity were enhanced by NBW addition. This work implies that NBW addition is promising for enhancing AD for enhancement of methane production.

How can we build recommender systems to take into account fairness? Real-world recommender systems are often composed of multiple models, built by multiple teams. However, most research on fairness focuses on improving fairness in a single model. Further, recent research on classification fairness has shown that combining multiple "fair" classifiers can still result in an "unfair" classification system. This presents a significant challenge: how do we understand and improve fairness in recommender systems composed of multiple components? In this paper, we study the compositionality of recommender fairness. We consider two recently proposed fairness ranking metrics: equality of exposure and pairwise ranking accuracy. While we show that fairness in recommendation is not guaranteed to compose, we provide theory for a set of conditions under which fairness of individual models does compose. We then present an analytical framework for both understanding whether a real system's signals can achieve compositional fairness, and improving which component would have the greatest impact on the fairness of the overall system. In addition to the theoretical results, we find on multiple datasets -- including a large-scale real-world recommender system -- that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components., Comment: WSDM 2021

The launch of the new Google News in 2018 (https://www.blog.google/products/news/new-google-news-ai-meets-human-intelligence/.) introduced the Frequently asked questions feature to structurally summarize the news story in its full coverage page. While news summarization has been a research topic for decades, this new feature is poised to usher in a new line of news summarization techniques. There are two fundamental approaches: mining the questions from data associated with the news story and learning the questions from the content of the story directly. This paper provides the first study, to the best of our knowledge, of a learning based approach to generate a structured summary of news articles with question and answer pairs to capture salient and interesting aspects of the news story. Specifically, this learning-based approach reads a news article, predicts its attention map (i.e., important snippets in the article), and generates multiple natural language questions corresponding to each snippet. Furthermore, we describe a mining-based approach as the mechanism to generate weak supervision data for training the learning based approach. We evaluate our approach on the existing SQuAD dataset (https://rajpurkar.github.io/SQuAD-explorer/.) and a large dataset with 91K news articles we constructed. We show that our proposed system can achieve an AUC of 0.734 for document attention map prediction, a BLEU-4 score of 12.46 for natural question generation and a BLEU-4 score of 24.4 for question summarization, beating state-of-art baselines.

We propose an iterative nonlinear estimator based on the technique of variational Bayesian optimization. The posterior distribution of the underlying system state is approximated by a solvable variational distribution approached iteratively using evidence lower bound optimization subject to a minimal weighted Kullback-Leibler divergence, where a penalty factor is considered to adjust the step size of the iteration. Based on linearization, the iterative nonlinear filter is derived in a closed-form. The performance of the proposed algorithm is compared with several nonlinear filters in the literature using simulated target tracking examples.

The high costs involved in sewage sludge treatment and disposal in wastewater treatment plants (WWTPs) not only bring about improper sludge disposal and thus environmental pollutions, but also limit the investment on construction of WWTPs, especially in rural areas or low-income regions. This comparative study examined the effect of biogas recirculation coupled with chemical addition in a semi-continuous anaerobic digester for sludge treatment, which was proven to achieve biogas upgrading, phosphorus conservation and sludge conditioning simultaneously, largely reducing the sludge treatment cost. Results show that FeCl3 addition coupling biogas recirculation can improve sludge dewaterability by 94% in comparison to 75% by equivalent MgCl2 addition, and 97% phosphorus in digestate can be conserved in solid with formation of vivianite-like crystals. Biogas recirculation can enhance CH4 yield and content by 13% and 11%, respectively, likely attributable to the increased relative abundances of both hydrogenotrophic Methanomicrobiales and acetoclastic Methanosarcinales.

Nanobubble water (NBW) contains 106–108 gas bubbles per milliliter with diameter

This paper investigates the effects of interface layer on thermal transport behavior of LZ/YSZ coupled system at various temperatures. Thermal transport behavior of LZ/YSZ coupled system was studied at atomic level. Trajectories of atoms near the interface layer in both LZ and YSZ unit were simulated at various temperatures between 1273 and 1473 K. Spatial distribution of atoms near the interface layer was illustrated at elevated temperatures. Radial distribution function of Y3+ ions was used to analyze changes in this spatial distribution, as it was affected by thermal transport inside the coupled system. Distances of typical atomic pairs close to the interface layer were used to describe the vibrational intensity in coupled system. Simulation results show that thermal transport behavior was influenced by the interface layer in LZ/YSZ systems with different LZ:YSZ ratio. The vibrational intensity of atoms and distance between atoms reflected heat flow inside the system from an atomic perspective.

The mutual effects of heat transfer and ions distribution on YSZ with porous structures have been investigated in this paper. Various of porous YSZ microstructures were simulated to elucidate the relationships of ions spacial distribution and heat transfer from atomic perspective. Typical ion movement and the phonon vibrational density of state were investigated in various porous systems. The simulation results show that thermal transport behavior bonds close to the ions distribution in porous systems. The ions distribution nearby pores in the system have a predictable reduction effects on thermal transport behavior, which could lead to a decrease in thermal conductivity.

Diverse storage formats, archive dispersal, and inconsistent naming make it difficult for researchers and the general public to find and access remote sensing data. To facilitate the use of remote sensing data, this paper provides an integrated framework for direct reading remote sensing data in a widely compatible and analysis-ready format, NumPy ndarray. The framework is composed of two main components. One is the raster data processing and storage model. All the operational gridded remote sensing data are split into tiles, and reorganized in n-dimensional array. Then the N-Dimensional data array is serialized into netCDF and stored into distributed file system. The other is the spatiotemporal filter to achieve parallel query, and it has been encapsulated into Internet-accessible application programming interfaces (APIs). The scenario of calculating NDVI of a specified spatiotemporal range given at last illustrate the efficiency and convenience of our platform provided for remote sensing data analysis.

This paper considers the joint recursive estimation of the dynamic state and the time-varying process noise covariance for a linear state space model. The conjugate prior on the process noise covariance, the inverse Wishart distribution, provides a latent variable. A variational Bayesian inference framework is then adopted to iteratively estimate the posterior density functions of the dynamic state, process noise covariance and the introduced latent variable. The performance of the algorithm is demonstrated with simulated data in a target tracking application.

In this paper, we present a method for estimating the location of a WiFi transmitter by a receiver using the radio resource and knowledge of the indoor room structure. We derive a three-ray path propagation model for the received radio signal in a known indoor environment. We show that the position of the transmitter could be localized using the received radio signal measurements. The likelihood under this model exhibits multiple local peaks when only few frequencies are used, which leads to the location ambiguities under the Maximum Likelihood criterion. We observed in simulation that the ambiguous locations under the Maximum Likelihood estimation vary with the WiFi radio frequency used but the ground truth location is always presented as a peak. Therefore, we use multiple WiFi frequency bands to resolve the localization ambiguity. A subspace based method is applied in combination with Maximum Likelihood method utilizing the same set of measurements to improve localization efficiency. Simulation using commercial ray tracing software presents promising result.

We study the problem of maximizing a monotone non-decreasing function $$f$$f subject to a matroid constraint. Fisher, Nemhauser and Wolsey have shown that, if $$f$$f is submodular, the greedy algorithm will find a solution with value at least $$\frac{1}{2}$$12 of the optimal value under a general matroid constraint and at least $$1-\frac{1}{e}$$1-1e of the optimal value under a uniform matroid $$(\mathcal {M} = (X,\mathcal {I})$$(M=(X,I), $$\mathcal {I} = \{ S \subseteq X: |S| \le k\}$$I={S⊆X:|S|≤k}) constraint. In this paper, we show that the greedy algorithm can find a solution with value at least $$\frac{1}{1+\mu }$$11+μ of the optimum value for a general monotone non-decreasing function with a general matroid constraint, where $$\mu = \alpha $$μ=?, if $$0 \le \alpha \le 1$$0≤?≤1; $$\mu = \frac{\alpha ^K(1-\alpha ^K)}{K(1-\alpha )}$$μ=?K(1-?K)K(1-?) if $$\alpha > 1$$?>1; here $$\alpha $$? is a constant representing the "elemental curvature" of $$f$$f, and $$K$$K is the cardinality of the largest maximal independent sets. We also show that the greedy algorithm can achieve a $$1 - (\frac{\alpha + \cdots + \alpha ^{k-1}}{1+\alpha + \cdots + \alpha ^{k-1}})^k$$1-(?+?+?k-11+?+?+?k-1)k approximation under a uniform matroid constraint. Under this unified $$\alpha $$?-classification, submodular functions arise as the special case $$0 \le \alpha \le 1$$0≤?≤1.

Temperature, solar radiation and water are major important variables in ecosystem models which are measurable via wireless sensor networks (WSN). Effective data analysis is necessary to extract significant spatial and temporal information. In this work, information regarding the long term variation of seasonal field environment conditions is explored using Hilbert-Huang transform (HHT) based analysis on the wireless sensor network data collection. The data collection network, consisting of 36 wireless nodes, covers an area of 100 square kilometres in Yanqing, the northwest of Beijing CBD, in China and data collection involves environmental parameter observations taken over a period of three months in 2011. The analysis used the empirical mode decomposition (EMD/EEMD) to break a time sequence of data down to a finite set of intrinsic mode functions (IMFs). Both spatial and temporal properties of data explored by HHT analysis are demonstrated. Our research shows potential for better understanding the spatial-temporal relationships among environmental parameters using WSN and HHT.

Use of the phase of a signal to measure distance carries an inherent ambiguity. The problem is typically addressed by the use of several different frequencies and the Chinese Remainder Theorem or lattice methods, but these methods result in computational complexity issues. The difficulties are increased by the presence of noise. This paper presents a lattice-based algorithm to resolve phase ambiguity more efficiently and under more relaxed constraints than existing approaches. Simulations are presented to illustrate the performance of the proposed algorithm and compared with existing methods.