Search

The advanced research on bright 13.5 nm extreme ultraviolet (EUV) light sources with low debris is of crucial importance for the semiconductor industry. The scheme of laser produced colliding plasma holds the potential to significantly improve the conversion efficiency (CE) from the laser energy to the required EUV light sources. A pulsed Nd:YAG laser beam was split into two beams using a polarizing cube and focused on a planar Sn target surface at a distance of 2.5 mm to generate two colliding plasmas. It was found that the optical radiation intensity of the stagnation layer generated in the colliding plasmas region was much higher than that of a single-pulse plasma under the same laser energy. Furthermore, the stagnation layer could exist for a longer time with a higher optical radiation intensity, which meant that the formation of the stagnation layer effectively converted the ion kinetic energy into optical radiation energy. The time-of-flight (TOF) ion signal of the colliding plasma exhibited a much narrower distribution compared to the seed plasma. At the same laser energy, the ion kinetic energy of the colliding plasma was lower than that of the single-pulse plasma. The TOF peak voltage and total charge showed a stronger angular dependence for the colliding plasma. At high laser energy, the colliding plasma could significantly reduce the ion's kinetic energy without changing the CE of the EUV. [ABSTRACT FROM AUTHOR]

Active canopy sensors (ACSs) are great tools for diagnosing crop nitrogen (N) status and grain yield prediction to support precision N management strategies. Different commercial ACSs are available and their performances in crop N status diagnosis and recommendation may vary. The objective of this study was to determine the potential to minimize the differences of two commonly used ACSs (GreenSeeker and Crop Circle ACS-430) in maize (Zea mays L.) N status diagnosis and recommendation with multi-source data fusion and machine learning. The regression model was based on simple regression or machine learning regression including ancillary information of soil properties, weather conditions, and crop management information. Results of simple regression models indicated that Crop Circle ACS-430 with red-edge based vegetation indices performed better than GreenSeeker in estimating N nutrition index (NNI) (R2 = 0.63 vs. 0.50–0.51) and predicting grain yield (R2 = 0.56–0.57 vs. 0.49). The random forest regression (RFR) models using vegetation indices and ancillary data greatly improved the prediction of NNI (R2 = 0.81–0.82) and grain yield (R2 = 0.87–0.89), regardless of the sensor type or the vegetation index used. Using RFR models, moderate degree of accuracy in N status diagnosis was achieved based on either GreenSeeker or Crop Circle ACS-430. In comparison, using simple regression models based on spectral data only, the accuracy was significantly lower. When these two ACSs were used independently, they performed similarly in N fertilizer recommendation (R2 = 0.57–0.60). Hybrid RFR models were established using vegetation indices from both ACSs and ancillary data, which could be used to diagnose maize N status (moderate accuracy) and make side-dress N recommendations (R2 = 0.62–0.67) using any of the two ACSs. It is concluded that the use of multi-source data fusion with machine learning model could improve the accuracy of ACS-based N status diagnosis and recommendation and minimize the performance differences of different active sensors. The results of this research indicated the potential to develop machine learning models using multi-sensor and multi-source data fusion for more universal applications. [ABSTRACT FROM AUTHOR]

Scientific literature, as the major medium that carries knowledge between scientists, exhibits explosive growth in the last century. Despite the frequent use of many tangible measures, to quantify the influence of literature from different perspectives, it remains unclear how knowledge is embodied and measured among tremendous scientific productivity, as knowledge underlying scientific literature is abstract and difficult to concretize. In this regard, there has laid a vacancy in the theoretical embodiment of knowledge for their evaluation and excavation. Here, for the first time, we quantify the knowledge from the perspective of information structurization and define a new measure of knowledge quantification index (KQI) that leverages the extent of disorder difference caused by hierarchical structure in the citation network to represent knowledge production in the literature. Built upon 214 million articles, published from 1800 to 2021, KQI is demonstrated for mining influential classics and laureates that are omitted by traditional metrics, thanks to in-depth utilization of structure. Due to the additivity of entropy and the interconnectivity of the network, KQI assembles numerous scientific impact metrics into one and gains interpretability and resistance to manipulation. In addition, KQI explores a new perspective regarding knowledge measurement through entropy and structure, utilizing structure rather than semantics to avoid ambiguity and attain applicability. [ABSTRACT FROM AUTHOR]

The rapid development of modern science nowadays makes it rather challenging to pick out valuable ideas from massive scientific literature. Existing widely-adopted citation-based metrics are not adequate for measuring how well the idea presented by a single publication is developed and whether it is worth following. Here, inspired by traditional X-ray imaging, which returns internal structure imaging of real objects along with corresponding structure analysis, we propose Scientific X-ray, a framework that quantifies the development degree and development potential for any scientific idea through an assembly of 'X-ray' scanning, visualization and parsing operated on the citation network associated with a target publication. We pick all 71,431 scientific articles of citation counts over 1,000 as high-impact target publications among totally 204,664,199 publications that cover 16 disciplines spanning from 1800 to 2021. Our proposed Scientific X-ray reproduces how an idea evolves from the very original target publication all the way to the up to date status via an extracted 'idea tree' that attempts to preserve the most representative idea flow structure underneath each citation network. Interestingly, we observe that while the citation counts of publications may increase unlimitedly, the maximum valid idea inheritance of those target publications, i.e., the valid depth of the idea tree, cannot exceed a limit of six hops, and the idea evolution structure of any arbitrary publication unexceptionally falls into six fixed patterns. Combined with a development potential index that we further design based on the extracted idea tree, Scientific X-ray can vividly tell how further a given idea presented by a given publication can still go from any well-established starting point. Scientific X-ray successfully identifies 40 out of 49 topics of Nobel prize as high-potential topics by their prize-winning papers in an average of nine years before the prizes are released. Various trials on articles of diverse topics also confirm the power of Scientific X-ray in digging out influential/promising ideas. Scientific X-ray is user-friendly to researchers with any level of expertise, thus providing important basis for grasping research trends, helping scientific policy-making and even promoting social development. [ABSTRACT FROM AUTHOR]

Laser-sustained plasma (LSP), a unique method of obtaining high brightness broadband light sources, has been used for rapid optical inspection of wafer defects and has thus attracted great interest. Then Xe plasma sustained under high pressure by a continuous fiber laser was investigated using optical emission spectroscopy (OES). Emission spectra of spatial integration diagnosis have shown that with the increase of laser power and the decrease of Gaussian beam focusing number F, the electron temperature fitted by Boltzmann plot method increased from 0.45 eV to 0.85 eV, the corresponding electron density was roughly an order of magnitude of 1016-1017 cm-3, and the calculated absorption coefficient of plasma was distributed in 0.9–1.5 cm-1. To further analyze the spatial distribution of plasma parameters, the spatial resolved OES of LSP was acquired, and the two-dimensional electron temperature and electron density were determined. The non-uniform distribution of the plasma parameters was analyzed due to the existence of thermal gravity convection and forced convection. The serious self-reversal of emission lines of 881.94 nm and 823.16 nm was observed and analyzed. [Display omitted] • Optical emission spectroscopy of laser-sustained Xe plasma at high pressure • Spatial integral and spatially resolved electron temperature and electron density • Tight focusing system is beneficial to improve the absorption coefficient • Inhomogeneity of plasma parameters results in self-reversal of spectral lines [ABSTRACT FROM AUTHOR]

By utilizing the combination of multiple dichroic mirrors, droplet detection, synchronization of laser with droplets, and droplet deformation observation in two directions can be realized in a very compact design. A shadowgraph technology is used to investigate the laser-induced tin droplet two-dimensional deformation. Particularly, compressed and propelled targets in side-view and expensed disk-like targets in front-view shadowgraphs are observed at the same time. In addition, the propulsion and expansion processes of the droplet are presented, keeping consistent with the prediction of the theoretical model. All these results highlight the ability of our compact design for droplet dynamic evolution monitoring. • Accurate synchronization of laser and tin droplets. • We realized simultaneous observation of droplet deformation along the laser and its perpendicular direction, and the problems of the backlight arrangement and interference of shadowgraphs from the incident laser were solved. • The relationship of droplet propulsion and expansion with time is analyzed quantitatively. [ABSTRACT FROM AUTHOR]

Experiments involving laser produced tin plasma have been carried out using a CO2 laser with an energy of 800 mJ/pulse and a full width at half maximum (FWHM) of 80 ns in vacuum. Time-integrated extreme ultraviolet spectral measurement showed that the peak of the extreme ultraviolet lithography spectrum was located at 13.5 nm and the spectrum profile's FWHM of the unresolved transition arrays was 1.1 nm. Plasma parameters of the electron temperature and density measurements in both axial and radial directions at later times had been obtained from a two-dimensional time and space resolved image spectra analysis. The axial spatial distribution of the electron density showed a 1/d2.6 decrease profile, and the radial spatial distribution of the electron density showed a 1/r1.1 profile, in which d is the axial distance from the target surface and r is the radial distance. The electron density was found to maintain symmetry across the radial distance at all delay times. Near the plasma plume center, the electron temperature Te varied slightly with increasing axial or radial distance, which was related to collisional decoupling and reheating of the ionized species in the plasma at distances longer than 3 to 4 mm. The space averaged electron temperature was measured in the range of 3.4-1.0 eV, and the space averaged electron density was measured in the range of 2.0 × 1017 to 2.2 × 1016 cm-3, as the time delay varied from 1.6 μs to 3.6 μs with respect to the pulse discharge. Time evolutions of the plasma temperature and density were found to have an apparent rise at a delay time of 2.4 μs in the corresponding time of the laser pulse tail peak. This suggests that plasma parameters and extreme ultraviolet emission intensity can be controlled by a double pulse combined laser. [ABSTRACT FROM AUTHOR]

A core mission of university education is to train students' problem solving abilities, or in some cases, students' research skills. To achieve this, educators assign students certain experiment courses, where students complete a task following preset procedures. However, such courses often employ outdated teaching materials that are far from cutting-edge problems. Meanwhile, a preset experiment procedure cannot evoke students' innovations. In this article we introduce an experimental course we opened at Shanghai Jiao Tong University for junior-year students -- the Research-Oriented Course (ROC). ROC is based on a cutting-edge research project and includes an Introduction -- Demonstration -- Research -- Evaluation four-phase course as well as the T -- W -- R three-dimensional evaluation criteria. Evaluations showed that students finished their research tasks satisfactorily and they improved their research skills through this process. [ABSTRACT FROM PUBLISHER]

Low-density foam targets are expected to be good candidates for efficient laser produced plasma (LPP) and soft X-ray (SXR) sources due to the less energetic debris compared with solid targets. Three Ti targets with different densities were irradiated by a pulsed Nd:YAG laser in vacuum. The average ion energy and the time-of-flight (TOF) peak voltage decreased as the Ti target density decreased. Furthermore, the differences among the three Ti targets increased with increasing laser intensity. The average ion energy of low-density foam Ti targets had a slightly weaker angular dependence compared with solid Ti targets. The plasma plume optical images illustrated that solid Ti plasma expanded faster in the direction of laser incidence with stronger anisotropy compared to foam Ti plasma. In addition, the optical radiation intensity integrated from a wavelength region of 200–850 nm of plasma plume increased with decreasing initial target density, which indicated that more absorbed laser energy was converted to plasma optical radiation energy (internal energy) instead of ion kinetic energy for targets with lower initial densities. The SXR radiation had no significant difference among the three Ti targets. These results demonstrate that appropriately reducing the initial target density can significantly reduce kinetic energy of ion debris without affecting the SXR radiation output. [Display omitted] • The dynamics and soft X-ray radiation characteristics of laser produced solid and low-density foam Ti plasmas were compared. • Reducing the initial target density can significantly reduce the ion kinetic energy. • Reducing target density allowed more laser energy to be converted to plasma radiation energy rather than kinetic energy. • Appropriately reducing the initial target density will not affect the required soft X-ray radiation output. [ABSTRACT FROM AUTHOR]

High-speed optical inspection of wafers requires a high-brightness and broadband light source. The brightness of traditional arc discharge lamps is difficult to increase, which is limited by the cathode current density. Laser sustained plasma (LSP) is a promising method to overcome the limitations of existing light source technologies. Then the characteristics of fiber laser sustained plasma in a xenon bulb were investigated, the absorption coefficient was measured, and the spectrum was analyzed. The results show that with the increase of laser power, the volume of plasma gradually increases and moves towards the direction of laser incident. With the increase of Gaussian beam focusing number F, the threshold laser power required to maintain the stable existence of plasma decreases gradually. There are upper and lower limits for laser power at which the LSP could be sustained. When the laser incident at the optimal incident angle, the laser propagation in the plasma is the optimal path for laser energy deposition, and the plasma has higher absorption coefficient, temperature and brightness. When F ≥ 10, the Xe plasma will show spatial instability due to refraction effect. Compared with the traditional short-arc xenon lamp, the LSP spectrum has a higher proportion in short wavelength. • Second moment method is used to accurately determine the plasma size. • Laser sustained plasma exists stably in high pressure xenon gas. • Plasma instability phenomena and stable existence conditions. • Changes in laser power and focusing parameters improve absorption coefficients. [ABSTRACT FROM AUTHOR]

The spatial distribution of the root system through the soil profile has an impact on moisture and nutrient uptake by plants, affecting growth and productivity. The spatial distribution of the roots, soil moisture, and fertility are affected by tillage practices. The combination of high soil density and the presence of a soil plow pan typically impede the growth of maize (Zea mays L.).We investigated the spatial distribution coordination of the root system, soil moisture, and N status in response to different soil tillage treatments (NT: no-tillage, RT: rotary-tillage, SS: subsoiling) and the subsequent impact on maize yield, and identify yield-increasing mechanisms and optimal soil tillage management practices. Field experiments were conducted on the Huang-Huai-Hai plain in China during 2011 and 2012. The SS and RT treatments significantly reduced soil bulk density in the top 0–20 cm layer of the soil profile, while SS significantly decreased soil bulk density in the 20–30 cm layer. Soil moisture in the 20–50 cm profile layer was significantly higher for the SS treatment compared to the RT and NT treatment. In the 0-20 cm topsoil layer, the NT treatment had higher soil moisture than the SS and RT treatments. Root length density of the SS treatment was significantly greater than density of the RT and NT treatments, as soil depth increased. Soil moisture was reduced in the soil profile where root concentration was high. SS had greater soil moisture depletion and a more concentration root system than RT and NT in deep soil. Our results suggest that the SS treatment improved the spatial distribution of root density, soil moisture and N states, thereby promoting the absorption of soil moisture and reducing N leaching via the root system in the 20–50 cm layer of the profile. Within the context of the SS treatment, a root architecture densely distributed deep into the soil profile, played a pivotal role in plants’ ability to access nutrients and water. An optimal combination of deeper deployment of roots and resource (water and N) availability was realized where the soil was prone to leaching. The correlation between the depletion of resources and distribution of patchy roots endorsed the SS tillage practice. It resulted in significantly greater post-silking biomass and grain yield compared to the RT and NT treatments, for summer maize on the Huang-Huai-Hai plain. [ABSTRACT FROM AUTHOR]

Online health monitoring of thyristor converter valve is very important for the stable operation of high voltage direct current (HVDC) system. However, in practical projects, only offline annual maintenance can be realized. This paper proposes a method for parameter identification of thyristor valve based on wavelet packet key feature extraction and Elman neural network. By preprocessing the measurable characteristic parameters of thyristor valve in practical engineering, such as wavelet packet time‐frequency domain feature extraction, variation coefficient optimization and so on, the characteristic vector that can characterize the health state of thyristor valve is obtained and sent to Elman neural network for training, which realizes the rapid online identification of multiple electrical parameters of thyristor modules in converter valve. Simulation and comparative experiments show that the key feature extraction method proposed in this paper has significant advantages in identification accuracy, and can better identify the damping capacitance and leakage resistance of all thyristor modules. Among them, the deterioration of the damping capacitance is more easily identified, the identification accuracy is the highest and the relative identification error is less than 5%. The parameter identification method proposed in this paper has the advantages of fast identification speed, strong model generalization ability and wide application. This provides a new idea for the online condition monitoring of thyristor valves. [ABSTRACT FROM AUTHOR]

The problem of function computation in large scale heterogeneous wireless sensor networks (WSNs) is studied. Suppose n sensors are placed in a disk network area with radius n^\alpha, where \alpha is a positive constant. The sensors are located heterogeneously around the sink node, i.e., the density of sensors decreases as the distance from the sink node increases. At one instant, each sensor is assigned an input bit. The target of the sink is to compute a function f of the input bits, where f is either a symmetric or the identity function. Energy-efficient algorithms based on inhomogeneous tessellation of the network are designed and the corresponding optimal energy consumption scaling laws are derived. We show that the proposed algorithms are indeed optimal (except for some polylogarithmic terms) by deriving matching lower bounds on the energy consumption required to compute f. At last, based on the results obtained in this paper as well as those obtained by previous works, some discussions and comparisons are presented. We observe that 1) the heterogeneity extent has a great impact on the computation of both symmetric function and identity function, and 2) the energy usage of computing symmetric function can be significantly smaller than that of computing identity function under certain parameter condition, i.e., performing in-network computation helps save energy. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of multicast scaling performance in multichannel multiradio (MC-MR) networks. Under the assumption of both limited bandwidth and node tunability, a total fixed bandwidth (W) is equally split into (c) channels with (0

We study the fundamental lower bound for node buffer size in intermittently connected wireless networks. The intermittent connectivity is caused by the possibility of node inactivity due to some external constraints. We find even with infinite channel capacity and node processing speed, buffer occupation in each node does not approach zero in a static random network where each node keeps a constant message generation rate. Given the condition that each node has the same probability (p) of being inactive during each time slot, there exists a critical value (p_c(\lambda )) for this probability from a percolation-based perspective. When (pp_c(\lambda )), the network is in the subcritical case, and there is a tight lower bound (\Theta (\sqrtn)) for buffer occupation, where $(n)$ is the number of nodes in the network. [ABSTRACT FROM PUBLISHER]

In this paper, we focus on capacity-delay tradeoffs for multicast traffic pattern. Under the assumption that n nodes move in a unit square according to an i.i.d. mobility model, with each serving as a source that sends identical packets to k destinations, we propose four schemes of which the achievable capacity λ and delay D are analyzed: (1) 2-hop noncooperative non-redundancy scheme, (2) 2-hop noncooperative redundancy scheme, (3) 2-hop cooperative non-redundancy scheme, (4) 2-hop cooperative redundancy scheme. Compared with non-cooperative scheme with capacity delay tradeoff λ=O(\frac{{E}[D]}{nk log k}) first developed in , cooperation among destination nodes achieves optimal capacity delay tradeoff λ=O(\frac{{E}[D]}{n log k}) in cell partitioned network. With intelligent cooperation, each destination acts equivalently as relay and helps other destinations get more opportunities of receiving packets with capacity sacrificed to a log{k} factor compared with unicast in under the same delay. In addition, our redundancy schemes also allow delay under Θ(\sqrt{n}) achievable, which is the minimum delay under the schemes of ,. [ABSTRACT FROM PUBLISHER]

Abstract: Tunable third-harmonic and fourth-harmonic generations of pulsed CO2 laser radiation in a GaSe crystal were demonstrated. The optical properties of GaSe and phase-matching conditions in harmonic generations were investigated. The output peak powers for 3.2μm and 2.4μm were measured to be 1500W and 320W, with corresponding conversion efficiency from 9.6μm of 0.1% and 0.02%, respectively. [Copyright &y& Elsevier]

Abstract: In order to improve the cutting quality, a dual-laser-beam method was proposed to cut glass substrates in the current study, where a focused CO2-laser beam was used to scribe a straight line on the substrate, and a defocused CO2-laser beam was used to irradiate on the scribing line to generate a tensile stress and separate the substrate. The finite-element-method (FEM) software ANSYS was applied to calculate the temperature distribution and the resulting thermal stress filed. Through experimental study, it concluded that the glass substrate can be separated along an expected path with dual-laser beams and the cutting quality can be improved comparing with the cutting using a defocused laser beam alone. The relation between the cutting speed and the defocused laser power was also investigated in cutting glass with this method. [Copyright &y& Elsevier]

• The CERES-Maize model performed well for simulating maize yield response to n rate. • Long-term optimal N rate is higher in black soil than in sandy soil. • Maize yield and N uptake could be predicted well using model & weather data fusion. • The EONR could be predicted dynamically using model & weather data fusion. • Model-based N management could increase economic returns and n use efficiency. One challenge in precision nitrogen (N) management is the uncertainty in future weather conditions at the time of decision-making. Crop growth models require a full season of weather data to run yield simulation, and the unknown weather data may be forecasted or substituted by historical data. The objectives of this study were to (1) develop a model-based in-season N recommendation strategy for maize (Zea mays L.) using weather data fusion; and (2) evaluate this strategy in comparison with farmers' N rate and regional optimal N rate in Northeast China. The CERES-Maize model was calibrated using data collected from field experiments conducted in 2015 and 2016, and validated using data from 2017. At two N decision dates - planting stage and V8 stage, the calibrated CERES-Maize model was used to predict grain yield and plant N uptake by fusing current and historical weather data. Using this approach, the model simulated grain yield and plant N uptake well (R2 = 0.85–0.89). Then, in-season economic optimal N rate (EONR) was determined according to responses of simulated marginal return (based on predicted grain yield) to N rate at planting and V8 stages. About 83% of predicted EONR fell within 20% of measured values. Applying the model-based in-season EONR had the potential to increase marginal return by 120–183 $ ha−1 and 0–83 $ ha−1 and N use efficiency by 8–71% and 1–38% without affecting grain yield over farmers' N rate and regional optimal N rate, respectively. It is concluded that the CERES-Maize model is a valuable tool for simulating yield responses to N under different planting densities, soil types and weather conditions. The model-based in-season N recommendation strategy with weather data fusion can improve maize N use efficiency compared with current farmer practice and regional optimal management practice. [ABSTRACT FROM AUTHOR]

Abstract: In the flat panel display (FPD) industry, lasers may be used to cut glass plates. In order to reduce the possibility of fracture in the process of cutting glass by lasers, the thermal stress has to be less than the critical rupture strength. In this paper, a dual-laser-beam method is proposed, where an off-focus CO2-laser beam was used to preheat the glass sample to reduce the thermal gradients and a focused CO2-laser beam was used to machine the glass. The distribution of the thermal stress and the temperature was simulated by using finite element analysis software, Ansys. The thermal stress was studied both when the glass sample was machined by a single CO2-laser beam and by dual CO2-laser beams. It was concluded that the thermal stress can be reduced by means of the dual-laser-beam method. [Copyright &y& Elsevier]

With the advent of big data science, the field of geoscience has undergone a paradigm shift toward data-driven scientific discovery. However, the abundance of geoscience data distributed across multiple sources poses significant challenges to researchers in terms of data compilation, which includes data collection, collation, and database construction. To streamline the data compilation process, we present GeoKnowledgeFusion, a publicly accessible platform for the fusion of text, visual, and tabular knowledge extracted from the geoscience literature. GeoKnowledgeFusion leverages a powerful network of models that provide a joint multimodal understanding of text, image, and tabular data, enabling researchers to efficiently curate and continuously update their databases. To demonstrate the practical applications of GeoKnowledgeFusion, we present two scenarios: the compilation of Sm-Nd isotope data for constructing a domain-specific database and geographic analysis, and the data extraction process for debris flow disasters. The data compilation process for these use cases encompasses various tasks, including PDF pre-processing, target element recognition, human-in-the-loop annotation, and joint multimodal knowledge understanding. The findings consistently reveal patterns that align with manually compiled data, thus affirming the credibility and dependability of our automated data processing tool. To date, GeoKnowledgeFusion has supported forty geoscience research teams within the program by processing over 40,000 documents uploaded by geoscientists. [ABSTRACT FROM AUTHOR]

• Corn NNI was accurately predicted using proximal sensor and ancillary data with machine learning methods. • Corn yield was accurately predicted by combining sensor data and ancillary data. • An innovative RFR model-based in-season N management strategy was developed. • Sensor data should be used together with soil, weather and management information. Reliable and efficient in-season nitrogen (N) status diagnosis and recommendation methods are crucially important for the success of crop precision N management (PNM). The accuracy of these methods has been found to be influenced by soil properties, weather conditions, and crop management practices. It is important to effectively incorporate these variables to improve in-season N management. Machine learning (ML) methods are promising due to their capability of processing different types of data and modeling both linear and non-linear relationships. The objectives of this study were to (1) determine the potential improvement of in-season prediction of corn N nutrition index (NNI) and grain yield by combining soil, weather and management data with active sensor data using random forest regression (RFR) as compared with Lasso linear regression (LR) using similar data and simple regression (SR) models only using crop sensor data; and (2) to develop a new in-season side-dress N fertilizer recommendation strategy at eighth to ninth leaf stage (V8-V9) of corn developement using the RFR model. Twelve site-year experiments examining corn N rates and planting densities were conducted in Northeast China. The GreenSeeker sensor data and corn NNI were collected at V8-V9 stage, and grain yield was determined at the harvest stage (R6). The soil information was obtained at planting and the weather data was measured throughout the growing season. The results indicated that corn NNI and grain yield were better predicted by combining soil, weather and management information with GreenSeeker sensor data using RFR model (R2 = 0.86 and 0.79) and LR model (R2 = 0.85 and 0.76) as compared with only using GreenSeeker sensor data (R2 = 0.66 and 0.62–63) based on the test dataset. An innovative in-season side-dress N recommendation strategy was developed using the RFR grain yield prediction model to simulate corn grain yield responses to a series of side-dress N rates at V8-V9 stage. Based on these response curves, site-, and year-specific optimum side-dress N rates can be determined. The scenario analysis results indicated that this RFR model-based in-season N recommendation strategy could recommend side-dress N rates similar to those based on measured agronomic optimum N rate (AONR) or economic optimum N rate (EONR), with root mean square error (RMSE) of 17 kg ha−1 and relative error (RE) of 14–15 %. It is concluded that combining soil, weather and management information with crop sensor data using RFR can significantly improve both in-season corn NNI and grain yield prediction and N management, compared with the approach based only on crop sensor data. More studies are needed to further improve and evaluate this approach under diverse on-farm conditions. [ABSTRACT FROM AUTHOR]

Rapid methods allowing for non-destructive crop monitoring are imperative for accurate in-season nitrogen (N) status assessment and precision N management. The objectives of this paper were to (1) compare the performance of a leaf fluorescence sensor Dualex 4 and an active canopy reflectance sensor Crop Circle ACS-430 for estimating maize (Zea mays L.) N status indicators across growth stages; (2) evaluate the potential of N status prediction across growth stages using the reflectance parameters acquired from the canopy sensor at an early growth stage; and, (3) investigate the prospect of combining the active canopy sensor and leaf fluorescence sensor data to estimate N nutrition index (NNI) indirectly using a general model across growth stages. The results indicated that data from both sensors were closely related to NNI across stages. However, using the direct NNI estimation method, among the tested indices, only the N balance index (NBI) could diagnose N status satisfactorily, based on the Kappa statistics. The effect of growth stages on proximal sensing was reduced by incorporating the information of days after sowing. It was found that the leaf fluorescence sensor performed relatively better in estimating plant N concentration whereas the canopy reflectance sensor performed better in aboveground biomass estimation. Their combination significantly improved the reliability of N diagnosis, including NNI prediction. In addition, the study confirmed that N status can be assessed by predicting aboveground biomass at the later stages using the canopy reflectance measurements at an early stage. Furthermore, the integrated NBI was verified to be a more robust and sensitive N status indicator than the chlorophyll concentration index. It is concluded that combining active canopy sensor data, of an early growth stage (e.g. V8), with leaf fluorescence sensor data, modified using days after sowing, can improve the accuracy of corn N status diagnosis across growth stages. [ABSTRACT FROM AUTHOR]

The dynamic interactions between soil, weather and crop management have considerable influences on crop yield within a region, and should be considered in optimizing nitrogen (N) management. The objectives of this study were to determine the influence of soil type, weather conditions and planting density on economic optimal N rate (EONR), and to evaluate the potential benefits of site-specific N management strategies for maize production. The experiments were conducted in two soil types (black and aeolian sandy soils) from 2015 to 2017, involving different N rates (0 to 300 kg ha−1) with three planting densities (55,000, 70,000, and 85,000 plant ha−1) in Northeast China. The results showed that the average EONR was higher in black soil (265 kg ha−1) than in aeolian sandy soil (186 kg ha−1). Conversely, EONR showed higher variability in aeolian sandy soil (coefficient of variation (CV) = 30%) than in black soil (CV = 10%) across different weather conditions and planting densities. Compared with farmer N rate (FNR), applying soil-specific EONR (SS-EONR), soil- and year-specific EONR (SYS-EONR) and soil-, year-, and planting density-specific EONR (SYDS-EONR) would significantly reduce N rate by 25%, 30% and 38%, increase net return (NR) by 155 $ ha−1, 176 $ ha−1, and 163 $ ha−1, and improve N use efficiency (NUE) by 37–42%, 52%, and 67–71% across site-years, respectively. Compared with regional optimal N rate (RONR), applying SS-EONR, SYS-EONR and SYDS-EONR would significantly reduce N application rate by 6%, 12%, and 22%, while increasing NUE by 7–8%, 16–19% and 28–34% without significantly affecting yield or NR, respectively. It is concluded that soil-specific N management has the potential to improve maize NUE compared with both farmer practice and regional optimal N management in Northeast China, especially when each year's weather condition and planting density information is also considered. More studies are needed to develop practical in-season soil (site)-specific N management strategies using crop sensing and modeling technologies to better account for soil, weather and planting density variation under diverse on-farm conditions. [ABSTRACT FROM AUTHOR]

Abstract: Coherent terahertz pulses have been generated at a range of 236.3–1104.5μm (0.27–1.3THz) by one CO2 laser with dual-wavelength output based on collinearly phase-matched different frequency generation (DFG) in a GaSe crystal. This source has the advantages of compact and simplicity for tuning. The output power of the THz pulse and phase-matching conditions were investigated. The maximum single pulse energy of 11nJ was generated at a frequency of 1.23THz (243.6μm), corresponding to a peak output power 182mW. [Copyright &y& Elsevier]

Increasing planting densities and nitrogen (N) application rates are two practices commonly used in high-yield maize (Zea mays L.) production systems to increase crop yield, but have resulted in lower N use efficiency, increased lodging, and negative environmental problems. Crop sensing-based precision N management (PNM) strategies have been developed to optimize maize yield, N use efficiency, and reduce environmental footprints, however, PNM strategies to balance grain yield and lodging risks are still very limited. The objectives of this study were to: (1) propose a N nutrition index (NNI)-based algorithm for in-season estimation of maize N demand; and (2) develop a sensor-based PNM strategy to balance grain yield and lodging risk for maize. Field experiments were conducted in Northeast China from 2017 to 2019, using a split-plot design with three planting densities (5.5, 7.0 and 8.5 plants m−2) as main plots and six N rates (0–300 kg ha−1) as subplots. Based on previous studies, a leaf fluorescence sensor Dualex 4 good for estimating plant N concentration and a canopy reflectance sensor Crop Circle ACS 430 good for estimating plant aboveground biomass were used to estimate maize NNI and predict lodging risk. Total N rates to achieve low lodging risk were determined based on wind velocity causing maize stalk lodging and historical actual natural wind speed, as well as the response of a lodging risk indicator (stem failure moment, Bs) to N supply. In-season side-dress N rates were determined based on theoretical amount of preplant N fertilizer estimated using NNI and a target total N rate. The final recommended sidedress N rates were adjusted based on the sensor-predicted lodging risk. The results indicated that NNI could be used for estimating the theoretical amount of preplant N fertilizer required to reach the current N status. It's feasible to estimate maize side-dress N demand based on the difference of a target total N rate (to achieve an optimal grain yield or low lodging risk) and the current theoretical N supply. Total N rate to ensure low lodging risk was suggested to be adopted under low and medium planting densities. Medium planting density of 70,000 plants ha−1 matched with the corresponding optimal N rate would be recommended for the study area to balance economic return and lodging risk. In general, high planting density is not recommended because it has high lodging risk. More studies are needed to further improve the developed crop sensing-based PNM strategy with more site-years of data and multi-source data fusion using machine learning models for practical on-farm applications. [Display omitted] • A sensor-based PNM strategy was developed to balance grain yield and lodging risk. • Optimal N rate to achieve low lodging risk was determined. • NNI was used to calculate theoretical preplant N amount. • Medium planting density matched with the corresponding optimal N rate was recommended. • The adoption of denser planting density for high yield relies on reliable lodging risk prediction and weather forecast. [ABSTRACT FROM AUTHOR]

This article studies the problem of RFID-based gesture recognition, which is practically important in various human-computer interaction scenarios, for example, smart homes, intelligent logistics, and smart cities. However, the existing solutions normally suffer from two major limitations: the model-driven methods are sensitive to specific environmental factors, and usually do not adapt well to a complex scenario that is full of multipath; the data-driven methods normally need the collection of massive RFID training data, and deploying the model in the remote cloud leads to long response delay. To overcome the above limitations, this article proposes a cross-domain augmentation-based AI learning (CAL) framework in the context of cloud-edge computing. In the CAL framework, we can simulate massive RFID phase profiles by converting the computer vision data that contains the gesture movement information, instead of costing lots of manpower to actually collect RFID training data. The simulated RFID phase profiles are used to train an AI model in the high-performance cloud. Note that since many sources of this kind of computer vision data are available online, we actually do not even need any manpower to collect training data. To achieve time-efficient recognition, knowledge distillation is applied to get a light and accurate model, which is deployed at the edge side. Thus, recognition response delay can be significantly reduced because the edge server where the AI model is actually deployed is much closer to users than the cloud server. We use commercial off-the-shelf RFID, Kinect, a high-performance server, and a laptop to implement the CAL framework. Extensive experiments are conducted to evaluate the performance of CAL. The results reveal that gesture recognition accuracy of CAL can reach nearly 90 percent without collection of any RFID training data. [ABSTRACT FROM AUTHOR]

Geoscience knowledge graph (GKG) can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related services. As a result, it has gained significant attention and become a frontier in geoscience. Geoscience knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales, granularities, and dimensions. Therefore, establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of GKG. However, existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and relationships. To address this issue, this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience knowledge. On this basis, an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and relationships. Under the constraint of a unified spatiotemporal ontology, this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal correlation. This model can efficiently represent geoscience knowledge, thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge retrieval. It can further enable the alignment, transformation, computation, and reasoning of spatiotemporal information through a spatiotemporal ontology. [ABSTRACT FROM AUTHOR]

With the rapid development of big data science, the research paradigm in the field of geosciences has also begun to shift to big data‐driven scientific discovery. Researchers need to read a huge amount of literature to locate, extract and aggregate relevant results and data that are published and stored in PDF format for building a scientific database to support the big data‐driven discovery. In this paper, based on the findings of a study about how geoscientists annotate literature and extract and aggregate data, we proposed GeoDeepShovel, a publicly available AI‐assisted data extraction system to support their needs. GeoDeepShovel leverages state‐of‐the‐art neural network models to support researcher(s) easily and accurately annotate papers (in the PDF format) and extract data from tables, figures, maps, etc., in a human–AI collaboration manner. As a part of the Deep‐Time Digital Earth (DDE) program, GeoDeepShovel has been deployed for 8 months, and there are already 400 users from 44 geoscience research teams within the DDE program using it to construct scientific databases on a daily basis, and more than 240 projects and 50,000 documents have been processed for building scientific databases. [ABSTRACT FROM AUTHOR]

Raman spectroscopy is a widely practicable technique in gas analysis, but the application in simultaneous detection of multiple‐trace gas is still a challenge for its weak signal level. To extend the application of Raman gas analysis to detection of trace gas constituents, a closed parabolic sample cell proposed in our previous work is optimized by optical analysis and experimental verification. The optical analysis shows that for a parabolic reflector with rim diameter 6p (p, semi‐latus rectum), the collecting solid angle can be as large as 90% of full space (4π) and the power‐collecting efficiency can be as high as 80%. The variation of the Raman signal level with the position of collecting aperture and the parameter p of parabolic reflector was verified by the experimental results. At the optimized structure parameters, we get a signal‐to‐background ratio of 122 and a signal‐to‐noise ratio of 305 in the Raman spectra of ambient air with exposure time of 1 s. From the results of standard analytic gaseous samples, the limits of detection of 33 ppm‐bar for H2, 38 ppm‐bar for CO, 19 ppm‐bar for CH4, 27 ppm‐bar for C2H6, 23 ppm‐bar for C2H4, and 20 ppm‐bar for C2H2 were obtained with exposure time of 200 s. These results indicate that the parabolic sample cell is a competitive Raman collector, which showing good balance in signal enhancement and background level. [ABSTRACT FROM AUTHOR]

Caching plays an important role in reducing the backbone traffic when serving high-volume multimedia content. Recently, a new class of coded caching schemes have received significant interest, because they can exploit coded multi-cast opportunities to further reduce backbone traffic. Without considering file popularity, prior works have characterized the fundamental performance limits of coded caching through a deterministic worst-case analysis. However, when heterogeneous file popularity is considered, there remain open questions regarding the fundamental limits of coded caching performance. In this paper, for an arbitrary popularity distribution, we first derive a new information-theoretic lower bound on the expected transmission rate of any coded caching schemes. We then show that a simple coded-caching scheme attains an expected transmission rate that is at most a constant factor away from the lower bound. Unlike other existing studies, the constant factor that we derived is independent of the popularity distribution. [ABSTRACT FROM PUBLISHER]

Reasonable canopy structure and leaf physiological characteristics are considered as important factors for improving canopy nitrogen (N) distribution by matching the available light resources and thus increasing the grain yield of maize (Zea mays L.). However, the determinants of different maize varieties in light–N matching and grain yields with specific canopy structures and leaf physiological characteristics, as well as the response to the N application rate, remain poorly understood. In this study, we analyzed the relationships between different canopy structures and the enzyme activity and light utilization of spring maize in the field. Two maize varieties (XY335 and ZD958) with different canopy structures were used as the experimental material in a 2-year field experiment from 2014 to 2015, grown under different N inputs of 0, 100, 200, and 300 kg N ha−1 (N0, N1, N2, and N3) at a planting density of 90,000 plants ha−1 in Jilin Province on the Northeast China Plain. The results show that XY335 combined with N3 had a greater leaf angle, upper internode length and number, and upper leaf area index of the upper layer compared with ZD958. Higher N assimilatory enzyme (glutamine synthase (GS), glutamate synthase (GOGAT), and nitrate reductase (NR)) activities in the upper and middle leaves were observed in XY335 compared to ZD958. Furthermore, the light interception and light utilization efficiency of the upper leaves of XY335 increased, especially at higher N application rates, which significantly affected the N translocation post-silking and its distribution in different populations. As a result, the photosynthetic N use efficiency (PNUE) values of the upper leaves (10.4%) and middle leaves (5.2%) of XY335 were higher than those of ZD958, coordinating the canopy light and N distributions and being positively correlated with the maize grain yield. This suggested that the superior canopy structure of the upper layer and N assimilatory enzymes of the upper and middle leaves of this maize variety significantly increased the light interception of the canopy, while the synchronization of light and the N of the upper and middle leaves increased the light and N utilization efficiency of maize, which ultimately increased the grain yield at a high plant density. [ABSTRACT FROM AUTHOR]

Debris mitigation using an ambient gas is the easiest way for laser-produced plasma extreme ultraviolet light source. The debris mitigation power of hydrogen, helium and argon buffer gases against CO2 laser produced tin plasma plumes was quantitatively estimated by means of the visualization imaging system as well as the optical emission spectroscopy technique. The debris mitigation power of hydrogen buffer gas was assessed under ambient pressure ranging from 30 to 104 Pa. The debris mitigation power of the hydrogen buffer gas was determined as 150 eV mm−1 for energetic particles of 400 eV under a pressure of 104 Pa, which remained as high as 40 eV mm−1 at a pressure of 100 Pa. The maximum stopping power and collision scattering cross section of argon were measured to be almost three times larger in comparison with hydrogen and one and a half times larger than helium atmosphere at a pressure of 2000 Pa. Time-resolved optical emission spectroscopy showed that thermalizing collisions were responsible for slowing down the fast energetic ions and atoms towards a thermal equilibrium. [ABSTRACT FROM AUTHOR]

Subsoiling is an important management practice for improving maize yield, especially for maize planted at high plant density. However, the affected physiological processes have yet to be specifically identified. In this study, field experiments with two soil tillage (CK: no-tillage, SS: subsoiling) and three planting densities (low: 45000 plants ha−1, medium: 67500plants ha−1, and high: 90000 plants ha−1) were conducted from 2010 to 2012 at Xinxiang, Henan province. Yield, canopy function, and root system were investigated to determine the associated physiological processes for improving maize production affected by soil tillage and plant density. Subsoiling significantly increased the grain yield of the low-, medium-, and high-planting densities by 6.21%, 8.92%, and 10.09%, respectively. Yield increase in the SS plots was mainly attributed to greater post-anthesis DMA and improved grain filling compared to CK plots. Greater green leaf area, leaf net photosynthetic rate, FV/Fm and ΦPSII in the SS plots were mainly contributed to enhanced dry matter production post-anthesis. This is mainly because subsoiling increased density of root dry weight in deep soil and root bleeding sap amount due to decreased the bulk density of the 0–30 cm soil profile layer. Density of root dry weight at 10–50 cm depth with SS increased by 40.68%, 32.17%, and 20.14% at low, medium, and high planting densities compared to CK, respectively, while the root bleeding sap amount increased by 17.41%, 15.82%, and 20.91%. These results indicate that subsoiling could change the root distribution and improve soil layer environment for root growth, thus maintaining a higher canopy photosynthetic capacity post-anthesis and in turn promoting DMA and yield, particularly at higher planting densities. [ABSTRACT FROM AUTHOR]

The performance of throughput, delay, and their tradeoff in mobile ad hoc networks (MANETs) has been investigated on different assumptions. Nevertheless, few papers consider the supportive infrastructure in MANETs. With the help of supportive infrastructure, i.e., cellular networks, the performance of throughput and delay in MANETs can be improved. The impact of supportive infrastructure on the throughput–delay tradeoff in MANETs is an open research point. In this paper, we are interested in the optimal throughput–delay tradeoff in MANETs with supportive infrastructure. We investigate the supportive infrastructure that only provides transmission pipes between distant nodes. We study the optimal throughput–delay tradeoff in MANETs on different assumptions such as different densities of nodes and base stations (BSs), discrete/continuous mobility models with various velocities, and the fast/slow mobility assumption. We obtain the asymptotically optimal throughput–delay tradeoff and propose transmission policies based on random linear coding (RLC) to achieve the optimal throughput–delay tradeoff asymptotically. From the obtained optimal throughput–delay tradeoff, we find that supportive infrastructure reduces the delay bound from velocity, which is a lower bound of delay caused by nodes' velocity. In particular, if supportive infrastructure can cover the entire network, then the delay bound from velocity vanishes when per-node throughput does not exceed a threshold. Moreover, we find that there also exists a delay bound from the transmission range of the instant transmission. In addition, we observe that, for the optimal throughput–delay tradeoff in the large throughput region, the case in the continuous mobility model outperforms the case in the discrete mobility model, and the case on the slow mobility assumption outperforms the case on the fast mobility assumption. [ABSTRACT FROM PUBLISHER]

Root morphology is an important factor determining nitrogen (N) uptake by plants, which might be affected by the extent of N application. The processes associated with root morphogenesis of spring maize in response to N application rates remain poorly understood. In this study, both field and pot experiments were conducted to explore the effect of zero-N (N0), optimized-N (N180), and high-N (N360) on root morphology, anatomical structure, and N accumulation in spring maize. N application rates affected root length and surface area, and its endogenous hormone contents. The largest difference in total root length and surface area among the three N rates was found at the silking stage: the total root length and surface increased by 51.36% and 42.58% under N180 and by 7.8% and 30.14% under N360, respectively, compared with N0, and the root/shoot ratio and root bleeding sap significantly increased under N180 and N360 compared with N0. The auxin and jasmonic acid levels of roots under N180 and N360 were higher than N0. N application rates also affected root microstructure and ultrastructure. Compared with N0, the proportions of root aerating tissue under N180 and N360 were decreased by 32.42% and 11.92% at silking. The root tip cell structure was damaged under N0, and intact under N180 and N360. Moreover, the 15N allocation proportions to root and grain under N180 and N360 were increased compared to N0. Grain yields under N180 and N360 increased by 20.44% and 16.6% compared with N0, respectively. It can be concluded that optimized-N application decreased root aerated tissue and thus improved root length and root surface area through regulating auxin and jasmonic acid levels and affected N uptake and grain yield of N-efficient spring maize variety. [ABSTRACT FROM AUTHOR]

In this paper, we study a unified mobility model for mobile multicast (MotionCast) with n nodes, and k destinations for each multicast session. This model considers nodes which can either serve in a local region or move around globally, with a restricted speed R. In other words, there are two particular forms: Local-based Speed-Restricted Model (LSRM) and Global-based Speed-Restricted Model (GSRM). We find that there is a special turning point when mobility speed varies from zero to the scale of network. For LSRM, as R increases, the delay-capacity trade-off ratio decreases iff R is greater than the turning point \Theta (\sqrt1\over k ); For GSRM, as R increases, the trade-off ratio decreases iff R is smaller than the turning point, where the turning point is located at \Theta (k^0.25\over \sqrtn ) when k=o(n^2\over 3 ), and at \Theta (k\over n ) when k=\omega (n^2\over 3 ). As k increases from 1 to n-1, the region that mobility can improve delay-capacity trade-off is enlarged. When R=\Theta (1), the optimal delay-capacity trade-off ratio is achieved. This paper presents a general approach to study the performance of wireless networks under more flexible mobility models. [ABSTRACT FROM AUTHOR]

We study the effect of various geometries on the capacity of wireless networks via percolation, which was not considered much before. Percolation theory was first applied to derive an achievable rate \frac1\sqrtn in [5] by constructing a highway system, in contrast with the previous result Θ(\frac1\sqrtn\log n) in [6], where n is the number of the nodes. While a highway system that consists of both horizontal and vertical edge-disjoint paths exists in a square network, B. Liu et al. in [1] pointed out that the horizontal paths will disappear if the width of a strip network is increasing more slowly than \log n. In this paper, first we take a deeper look at the percolation in a strip network. We discover that when a highway system exists, the capacity is restricted by the maximum length of the sides. Moreover, a sub-highway system is still in presence when the highway system disappears. Secondly, we consider the situations in a triangle network. Conditions that percolation highway exists in it, and the achievable rate for a triangle network are discussed. We find that corner effect can be a bottleneck of the capacity. By combining the achievable rate of the former networks, we attribute the variance between them to their symmetry discrepancies. Finally, we turn to the capacity of three dimensional (3D) networks via percolation. The whole study shows that geometric symmetry plays a significant role in the percolation and the capacity, thereby shedding a light on the network design and the scheduling. [ABSTRACT FROM PUBLISHER]

Neural networks have achieved great success in many tasks, including data classification and pattern recognition. However, how neural networks work and what representations they learn are still not fully understood. For any data sample fed into a neural network, we wondered how its corresponding vectors expanded by activated neurons change throughout the layers and why the final output vector could be classified or clustered. To formally answer these questions, we define the data sample outputs of each layer as activation vectors and the space expanded by them as the activation space. Then, we investigate the geometric structure of the high-dimensional activation spaces of neural networks by studying the geometric characters of the massive activation vectors through approximated convex hulls. We find that the different layers of neural networks have different roles, where the former and latter layers can disperse and gather data points, respectively. Moreover, we also propose a novel classification method based on the geometric structures of activation spaces, called nearest convex hull (NCH) classification, for the activation vectors in each layer of a neural network. The empirical results show that the geometric structure can indeed be utilized for classification and often outperforms original neural networks. Finally, we demonstrate that the relationship among the convex hulls of different classes could be a good metric to help us optimize neural networks in terms of over-fitting detection and network structure simplification. [ABSTRACT FROM AUTHOR]

• The cross-domain network can weaken the spectral differences of the same feature. • Zero sample extraction of winter wheat acreage was realized. • Comparison loss ensures the diversity and reliability of the generated samples. • Transformer has good modeling ability on time series data. Accurate extraction of winter wheat area is essential for wheat yield estimation. Remotely sensed images have limited coverage, are taken at different times, from different angles and in different geographical areas, and the spectral information of the same feature varies from image to image. Machine learning methods are very popular in extracting winter wheat planting area, there are differences between different images, namely the distribution differences between source domain data and target domain data, the results obtained by these methods directly applied to other areas are not satisfactory. To achieve cross regional extraction of winter wheat area, we propose generative adversarial cross-domain networks for image classification under the same image type. The cross-domain network proposed comprises a generative network and a feature extractor. The generative network creates diverse and acceptable samples from the initial input data, constrained by the contrast loss. The feature extractor converts the initial input data and the generated data into a high-level representation. Two time series datasets were constructed and a series of experiments were conducted on these datasets, obtaining high-precision and optimal classification results. Sample training was performed using 70%, 50%, 30% and 10% of the data on both datasets. The study results demonstrated the high performance and cross-domain capability of our network. Furthermore, the winter wheat cultivation area of the entire Zhoukou city was extracted, yielding results with an accuracy of 94.23%. Extensive experiments and applications on the dataset demonstrate that our method is feasible and reliable. [ABSTRACT FROM AUTHOR]

As a potential high-energy laser, optically pumped metastable rare gas laser (OPRGL) has drawn attention worldwide. In this article, a stack-pumped metastable argon laser was demonstrated at 800 mbar of Ar/He mixture. The metastable particles were produced by repetitively pulsed discharge in the gas mixture, and laser oscillation was realized in a planar-concave cavity with the gain medium longitudinally or transversely pumped by the pumping source of a diode-stack with an external cavity of gratings. Maximum averaged power of 750 mW was obtained with transverse pumping in a repetition rate of 30 kHz of pulsed discharge. Compared to longitudinal pumped OPRGLs, extraction ability and pumping uniformity would be improved at transverse pumping configuration. And it is found that the average output power similar to that of longitudinal pumping can be achieved at a shorter gain length with transverse pumping, which is linearly proportional to the discharge repetition rate. This demonstration implies that power scaling of OPRGL is feasible by combining diode-stacks and multi-pass transverse pumping. • Transversely pumped metastable Ar laser with measurable output power (750 mW) was demonstrated. • Home-made Narrow linewidth continuous-wave diode stack was applied as pumping source. • Potential of realizing higher output power were analyzed at different discharge repetition rates. • Relation between output power of OPRGL and gas temperature was experimentally analyzed. [ABSTRACT FROM AUTHOR]

Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to user’s privacy concern, overwhelming network scale etc., it is hard to target any user in the network as one wishes. Instead, only a small subset of users is initially accessible. Such access limitation would significantly impair the influence spread, since IM often relies on seeding high degree users, which are particularly rare in such a small subset due to the power-law structure of social networks. In this paper, we attempt to solve the limited IM in real-world scenarios by the adaptive approach with seeding and diffusion uncertainty considered. Specifically, we consider fine-grained discounts and assume users accept the discount probabilistically. The diffusion process is depicted by the independent cascade model. To overcome the access limitation, we prove the set-wise friendship paradox (FP) phenomenon that neighbors have higher degree in expectation, and propose a two-stage seeding model with the FP embedded, where neighbors are seeded. On this basis, for comparison we formulate the non-adaptive case and adaptive case, both proven to be NP-hard. In the non-adaptive case, discounts are allocated to users all at once. We show the monotonicity of influence spread w.r.t. discount allocation and design a two-stage coordinate descent framework to decide the discount allocation. In the adaptive case, users are sequentially seeded based on observations of existing seeding and diffusion results. We prove the adaptive submodularity and submodularity of the influence spread function in two stages. Then, a series of adaptive greedy algorithms are proposed with constant approximation ratio. Extensive experiments on real-world datasets show that our adaptive algorithms achieve larger influence spread than non-adaptive and other adaptive algorithms (up to a maximum of 116 percent). [ABSTRACT FROM AUTHOR]

Fiber‐enhanced Raman spectroscopy is applied actively for gas analysis. Hollow‐core anti‐resonant fibers (HC‐ARFs) with low loss and low spatial overlap between core and cladding modes show the potential of low background. We integrated an HC‐ARF into the Raman system, collected the forward scattering radiation with a lens‐coupled imaging spectrometer, and filtered most of the background through selection of region of interest. The limits of detection (LODs) of this system were determined as 1.2 ppm·bar for CH4, 2.9 ppm·bar for C2H6, 1.6 ppm·bar for C2H4, 2.7 ppm·bar for C2H2, 13.8 ppm·bar for H2, and 16.7 ppm·bar for CO with the excitation laser of 200 mW and the exposure time of 60 s. In addition, the LOD was better than 2.5 ppm·bar for H2S. By the detection of gas samples with the critical detectable concentration, it was proven that the estimated values of LOD from the preceding experimental results are reliable and the system had practical detectability. The gas samples we tested are relevant for multiple fields such as industrial process control and environment monitoring, showing the diversified application prospects of the Raman gas analysis system based on HC‐ARF. [ABSTRACT FROM AUTHOR]

In this paper, we study the impact of human mobility on throughput and delay for people-centric applications in mobile ad hoc networks (MANETs). We consider a general human mobility model for MANETs, which can capture important features of human mobility, such as time correlation, node correlation, location heterogeneity, and node heterogeneity. Multiple unicasts with general arrival processes are delivered, and nodes are equipped with infinite buffers. Under our system model, we first characterize the network stability region in terms of the probability of each node set visiting each location and the amount of transmission resources at each location. We show that the node correlation and heterogeneity of locations' popularity usually decrease the size of the network stability region, whereas the diversity of locations visited by a node usually increases the size of the network stability region. Then, by solving a stability-related optimization problem, we develop a throughput-optimal policy based on the obtained optimal solution. We obtain the upper and lower bounds of the delay performance under the proposed policy. Finally, using simulations based on a theoretical model and some real traces, we verify the analytical results and compare the performance of the proposed policy with some existing policies. [ABSTRACT FROM PUBLISHER]

This paper characterizes an evolving network with growing number of nodes, which are associated with each other through social relations but employ transmission via wireless communications. The network exhibits social relations between nodes by attaching a new arriving node to a sample node already existing as well as some nodes the sample directly links to. We study capacity in this evolving network in terms of both geographic distribution of nodes and traffic patterns. Our results show that in heterogeneous geographic distribution where nodes having strong social relations tend to locate more closer, the corresponding capacity is significantly impacted by both social relations and network evolution. In particular, with appropriate control on the initial number of nodes as well as links between them, it is even possible to achieve almost constant per-node capacity except for a poly-logarithmic factor. For homogeneous geographic topology where nodes' positions exhibit uniform distribution, social relations among nodes is useless for improving capacity, which is mainly affected by network evolution and therefore degrades sharply with time. To our best knowledge, this is the first work studying capacity from the perspective of social evolving network. [ABSTRACT FROM AUTHOR]

In this paper, we use the Lyapunov optimization technique to develop scheduling policies for multiradio multichannel cognitive networks to optimize jointly the throughput and the average delay of a secondary network, with limited interference to primary users (PUs)' transmission. In particular, we consider three scenarios, including a static single-hop secondary network, a static multihop secondary network, and a mobile secondary network. For these cases, with the consideration of general mobility and interference models, we design efficient scheduling and channel allocation algorithms based on the concept of Lyapunov drift and give the corresponding performance bound. Furthermore, our theoretical analysis demonstrates that the proposed algorithms can achieve a flexible tradeoff between network throughput and average backlogs by using the Lyapunov optimization technique. Finally, the efficiency of the proposed algorithms is verified by the numerical simulations. [ABSTRACT FROM PUBLISHER]

Both compact planting and selecting superior maize (Zea mays L.) hybrids can greatly optimize the source–sink relationship and enhance maize productivity. However, the underlying physiological mechanism for regulating carbon (C) assimilate transport and influencing grain yield between maize cultivars has remained unclear under contrasting plant densities. A two-year field experiment was conducted to investigate grain yield, vascular bundle character, grain filling, C allocation in grains and other tissues, and hormone level and enzyme activity in grains under 60,000 (ND) and 90,000 plants ha−1 (HD) densities using Xianyu 335 (XY335) and Zhengdan958 (ZD958) hybrids. Compared to the ZD958, XY335 increased grain yield, kernel number per plant (KNP), and sink capacity by 11.4%, 15.7%, and 7.4%, respectively. Moreover, XY335 performed higher net photosynthetic rate and sucrose synthase activities in grains than those in ZD958, and higher levels of sucrose phosphate synthase and soluble acid invertase activity were mainly exhibited in the middle of the grain filling stage, which contributed to increasing the proportion of grain in total dry matter, grain C content and leaf C transport efficiency by 4.3%, 12.2%, and 52.9%, respectively, under HD conditions. Additionally, a greater area and number of small vascular bundle in ear of XY335 resulted in 21.3% higher matter transport efficiency and 4.8% higher maximum grain filling rate than ZD958 under HD conditions. In addition, grains of XY335 exhibited generally higher levels of indole acetic acid (IAA) and abscisic acid (ABA), as well as ABA/GA3 ratio after maize pollination relative to those from ZD958, conducive to regulating C translocation from leaves to grains. Overall, our study illustrates that stronger source activity, sink characteristics, and matter transport channels for maize hybrids are significant for C assimilate transport to grain for achieving high grain yield under higher plant density. [ABSTRACT FROM AUTHOR]

Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84–0.93) and the most accurate diagnostic result. [ABSTRACT FROM AUTHOR]