Miracle-dz/UniFAR-FTUs huggingface dataset:
Biological Control of Damping off and Foot Rot of Chilli Using an Indigenous Trichoderma harzianum

Quick Response Code Abstract: Damping off and foot rot caused by Sclerotium rolfsii and Fusarium oxysporum, respectively are the two deadly diseases in chilli occur at the seedling stage. In the present study, the formulated indigenous Trichoderma harzianum and its spore suspension alone or in combinations were used to control damping off and foot rot diseases of chilli in net house conditions. The combined application of chickpea based formulated T. harzianum and its spore suspension was found to reduce damping off by 75.02-86.96% and foot rot by 74.29-89.28% over control caused by both the fungi. The combined application of the treatment was also found to increase seed germination by 66.23-90.57% over control. Furthermore, the combined application of formulated T. harzianum and its spore suspension also resulted higher shoot length (9.33 cm and 7.88 cm), root length (4.03 cm and 3.95 cm), and vigor index (1097% and 1010%) at 14 days after sowing over S. rolfsii and F. oxysporum inoculated plants, respectively. Based on the present investigation, the chickpea bran-based Trichoderma harzianum formulation and suspension might be exploited for the management of damping off and foot rot diseases of chilli.
Package wsbackfit for Smooth Backfitting Estimation of Generalized Structured Models

A package is introduced that provides the weighted smooth backfitting estimator for a large family of popular semiparametric regression models. This family is known as generalized structured models, comprising, for example, generalized varying coefficient model, generalized additive models, mixtures, potentially including parametric parts. The kernel based weighted smooth backfitting belongs to the statistically most efficient procedures for this model class. Its asymptotic properties are well understood thanks to the large body of literature about this estimator. The introduced weights allow for the inclusion of sampling weights, trimming, and efficient estimation under heteroscedasticity. Further options facilitate an easy handling of aggregated data, prediction, and the presentation of estimation results. Cross-validation methods are provided which can be used for model and bandwidth selection.
A P ] 2 O ct 2 01 5 THE RIGOROUS DERIVATION OF THE 2 D CUBIC FOCUSING NLS FROM QUANTUM MANY-BODY EVOLUTION

Abstract. We consider a 2D time-dependent quantum system of N -bosons with harmonic external confining and attractive interparticle interaction in the Gross-Pitaevskii scaling. We derive stability of matter type estimates proving that the k-th power of the energy controls the H Sobolev norm of the solution over k particles. This estimate is new and more difficult for attractive interactions than repulsive interactions. For the proof, we use a version of the finite-dimensional quantum de Finetti theorem from [49]. A high particle-number averaging effect is at play in the proof, which is not needed for the corresponding estimate in the repulsive case. This a priori bound allows us to prove that the corresponding BBGKY hierarchy converges to the GP limit as was done in many previous works treating the case of repulsive interactions. As a result, we obtain that the focusing nonlinear Schrödinger equation is the mean-field limit of the 2D time-dependent quantum many-body system with attractive interatomic interaction and asymptotically factorized initial data. An assumption on the size of the L-norm of the interatomic interaction potential is needed that corresponds to the sharp constant in the 2D Gagliardo-Nirenberg inequality though the inequality is not directly relevant because we are dealing with a trace instead of a power.
When discs hit-and-stick, nature plays a weird trick

We present a computer-based experiment that simulates the accretion process and generates fluffy aggregates with fractal nature. The Box Counting is performed without the aid of an image processing tool by using the coordinate information of the constituents of the aggregate. A simple algorithm to create aggregate and calculate box-dimension will introduce fractals to students in universities and schools.
A GPU-accelerated particle-detection algorithm for real-time volumetric particle-tracking velocimetry under non-uniform illumination

Real-time volumetric particle tracking velocimetry (VPTV) equipped with field-programmable gate array (FPGA) cameras has been used for open-space, low particle density, and large-scale airflow measurements with long measurement periods. However, the particle detection accuracy of FPGA cameras is inevitably hindered by non-uniform illumination, resulting in a reduction in the particle detection ratio and positional accuracy. In this article, we propose to use both synchronized FPGA and grayscale cameras in a VPTV system, where grayscale cameras utilize a new algorithm based on two-frame centroid and corner extraction (TFCCE) under non-uniform white-light illumination. To keep the frame rate of the FPGA cameras the same, the TFCCE algorithm was accelerated by a graphics processing unit (GPU). The simulation results showed that the 2D particle detection ratio of TFCCE was enhanced to approximately 80% with a positional accuracy of 0.57 pixels, compared to 30% and 0.94 pixels for the single-frame centroid extraction used in the FPGA. The GPU version of TFCCE was 15.09 times faster than the CPU version, resulting in a calculation time of 4.55 ms per image, compared to 68.70 ms when using the CPU. This system was also validated by the measurement of a turbulent jet flow in real-time at 120 fps. The experimental results correspond well with data published in the literature. Therefore, this new algorithm can improve VPTV systems in terms of particle detection ratio and positional accuracy in real time under conditions of non-uniform illumination.
BPS monopoles and dyons in generalized BPS Lagrangian method

We generalize BPS Lagrangian method and rederive BPS equations for monopoles and dyons from the Lagrangian of SU(2) Maxwell-Higgs model in four dimensional spacetime. We show that in the BPS Lagrangian method the Gausss’s law constraint does not necessary to be imposed beforehand. We find that the BPS Lagrangian has non-boundary terms which in turn gives us some constraint equations. These constraint equations can be reduced by taking the standard BPS equations for Monopoles and Dyons, which turns out to be the Gausss’s law constraint in the BPS limit.
Trend Analysis and Periodicity Analysis of Annual Precipitation in Dongxi River Basin of Xiamen

Precipitation is an important part of hydrology, which is of great significance to local water resources allocation and farmland irrigation. This study uses the Dongxi River basin in Xiamen City as the research area. Based on the precipitation series data from 1956 to 2015, the Mann-Kendall test, linear regression method, Sen’s slope estimation and wavelet analysis are used to analyze the trend and periodicity of precipitation. The results show that precipitation generally followed a small rise over a period of 60 years. The Mann-Kendall test detected several possible change points, but they can only indicate that the precipitation in the basin changes frequently. In addition, wavelet analysis reveals that the annual precipitation series have main periods of 4a, 12a, 30a, and 49 a, and on the time scale of the first main period, the precipitation has undergone two wet and dry transitions between 1956 and 2015. Our results reveal the trend and periodicity of precipitation in Dongxi River basin, which is conducive to the optimization of local water resources allocation, and provides scientific support for subsequent agricultural water conservancy project application and ecological construction.
Spray measurements with the time-shift technique

The time-shift technique is a well-documented technique for the size and velocity measurement of individual drops passing through one or two tightly focused laser beams. It is a counting technique, nominally applicable for pure liquid drops, but with potential to also characterize drops with embedded particles or drops containing a second dispersed phase. In the present study a novel approach to signal processing is introduced in which the signal detection and validation phase is eliminated. This extends the capabilities of the time-shift technique in two manners. For one, size and velocity estimates are made possible for drops exhibiting very poor signal structure or signal-to-noise ratio. Such signals are commonly expected when measuring complex drops, either drops with embedded nano/micro-particles (dispersions) or emulsions. Second, the size and velocity distributions are estimated not by processing of signals from individual drops (single realization counting technique), but from a large ensemble of drop signals, improving both computational speed and reducing the influence of outliers in final statistics. These capabilities are achieved without sacrificing accuracy of mean and variance estimates of size and velocity of drop ensembles. To demonstrate the advantages of this new approach, measurements of a paint spray are presented, processed using both standard processing routines and the new approach. Limitations concerning the application of this new approach are discussed in detail.
Safe-by-design strategies applied to scaffold hybrid manufacturing

The EU-project FAST (GA 685825) has developed a 3D printer machine prototype for the manufacture of bone implants (scaffolds), by merging masterbatches of biodegradable polymer poly(ethylene oxide)terephthalate/poly(butylene terephthalate) [PEOT/PBT] doped with nanofillers [reduced graphene oxide (rGO), hydroxyapatite (HA) and magnesium aluminium hydroxide ciprofloxacin hydrotalcite (LDH-CFX)], and atmospheric plasma technology. This paper focus on the safe design strategies identified by FAST to address the risk to health resulting from the potential airborne emission of nano-objects and their aggregates and agglomerates (NOAAs) by the 3D printer prototype, which might result in occupational exposures by inhalation. The work also includes measurements of airborne emissions and occupational exposures carried out during the verification stage of the prototype design. Nanofillers particles (rGO, n-HA, LDH-CFX) were not observed, neither at source nor in the working area, suggesting no release of free nanofillers to the air one they have been embedded in the polymer masterbatch. Additionally, the exposure in the workplace was far below the selected Occupational Exposure Levels (OELs), for total particle number concentration (PNC), dust, elemental carbon (EC) and volatile organic compounds (VOCs). The results showed that, when working with the current prototype in normal operation (for its intended use) and with controls enabled [enclosure with the doors closed and Local Exhaust Ventilation (LEV) activated], the emission from the machine and the worker’s exposure to NOAAs are well controlled.
The effect of oxygen admixture on the properties of microwave generated plasma in Ar–O2: a modelling study

This work presents results of a self-consistent modelling analysis on microwave plasma generated in Ar–O2 mixtures at a frequency of 2.45 GHz at atmospheric pressure. The study focuses on how the plasma properties are influenced by the increase of the oxygen fraction in the gas mixture. The oxygen admixture is increased from 1% up to 95% in mass for values of the input microwave power of 1 and 1.5 kW. The results show that for a power of 1 kW and gradually increasing the oxygen admixture from 1% to 25% the electron density drops by a factor of more than four due to the energy lost by the electrons due to dissociation of oxygen molecules and the gas heating. An analysis of the number densities of species produced in the Ar–O2 plasma is presented. Oxygen admixtures of above 50% are considered along with an increase of the input microwave power in order to supply the discharge with electron number density values of the order of 1019 m−3. Gas temperatures above 3700 K are obtained in the plasma core along with a strong production of oxygen atoms with a number density of the order of 1023 m−3.
Reservoir ecological operation by quantifying outflow disturbance to aquatic community dynamics

Reservoir operation causes spatiotemporal variations in outflow, which influence the dynamics of downstream aquatic communities. However, empirical evidence of community responses to hydrological alteration remains limited for dam-regulated rivers. This study focused on quantifying the streamflow disturbance to multi-population dynamics in downstream of the China’s Danjiangkou Reservoir. First, the stochastic population dynamics model (PDM) was used to simulate aquatic community dynamics. Then, the flow–ecology relationship was established to identify community response to reservoir outflow. Third, two novel ecological indicators, stable time (ST) and coefficient of variation at stable time (CVST), were proposed to evaluate the resilience and resistance of multi-population systems, respectively. Finally, the reservoir operating rule curves were optimized by considering tradeoffs between socioeconomic and ecological objectives. The coevolution processes of multi-population systems (fish, phytoplankton, zooplankton, zoobenthos, and macrophytes) were simulated by stochastic PDMs. The population densities of stable states showed continuous downward trends with increasing degree of hydrological alteration for multi-population systems, and aquatic community systems could be destroyed when alteration reached its acceptable maximum. The greater the degree of hydrological alteration, the longer the recovery time from an unstable to a stable state, and the weaker resistance for each population system. The resilience and resistance of downstream multi-population systems were enhanced by optimizing reservoir outflow. The optimization results illustrated that the performances of the multiple objectives of water supply, hydropower generation, and ST were improved by 2.37%, 2.40%, and 2.67%, respectively, whereas the performance of CVST was the same as the conventional operation. The flow–ecology relationship provided an approach to quantify the impacts of reservoir outflow on an aquatic community, which is helpful in guiding ecological flow strategies.
Density functional study on the interaction of Graphene-supported Nickel cluster with CO2 molecule

We try to use small size Ni cluster supported on graphitic material as an alternative for CO 2 hydrogenation catalyst. As an initial step, we study the ability of the supported Ni 4 cluster to interact with CO 2 molecule by means of density functional theory-based calculations. We find that strong CO2 adsorption can only be formed on top of the Ni 4 cluster site. Due to the strong Ni 4-CO 2 interaction, the CO 2 molecule is always adsorbed with a bidentate adsorption configuration on the Ni 4 cluster. The graphene surface and Ni 4-graphene interface cannot accommodate CO 2 chemisorption due to the weak graphene-CO 2 interaction.
Study on Hot Spot Detection Algorithm based on K-Means

Solar energy is a widely used clean energy at present, and it is inexhaustible. As the main power generation device, photovoltaic modules play an important role in solar power generation. Among the various abnormalities of photovoltaic modules, the hot spot effect will not only reduce the power generation efficiency of photovoltaic modules, but even cause fires due to high temperatures. In this paper, k-means algorithm is adopted for photovoltaic hot spot detection. K-means is an unsupervised machine learning clustering algorithm, which clusters the pixel points of the image, and extracting the connected domain with the points with the shape similar to rectangular rectangle in the clustering, that is, obtaining the hot spot area. At the same time, this paper also uses the traditional computer vision MSER algorithm for experiments, compares the MSER algorithm with the K-means algorithm, and draws the conclusion that the traditional computer vision algorithm has low accuracy but high speed. Machine learning algorithms are accurate but fast.
Application of Error Correction model with monetary function factors in the return of bitcoin

Bitcoin is a kind of virtual currency based on block chain technology. In many scenarios, it shows the function of currency to a certain extent. However, many believe that the value of Bitcoin is hollow, speculative and unable to undertake monetary functions, which prevents it to circulate as a real currency. This paper assumes that Bitcoin can undertake monetary functions, which can verify the relationship between monetary functions and Bitcoin earnings. According to the availability of the data and the previous research results, the monthly value of the indexes to measure the monetary function from 2010 to 2020 are selected to form a regression equation with the monthly return data of Bitcoin in the corresponding period. ADF test shows that the explained variable is a first-order simple integer, and the explanatory variables have both first-order simple integer and stationary sequence, and there is a cointegration relationship between them. The result of error correction model shows that the relationship between Bitcoin rate of return and the relevant indicators to measure monetary function is not significant, which rejects the original assumption that Bitcoin can assume monetary function, indicating that Bitcoin does not have the ability and potential to assume monetary function. This may be due to the speculative nature of Bitcoin and the lack of powerful credit support. Therefore, investment in Bitcoin needs to be cautiously thought, but the blockchain technology used in Bitcoin needs to be vigorously developed, which could lead to a technological revolution; in addition, it is momentous for countries to develop digital currencies of central bank to meet the challenge.
Into the Animal Mind: Perceptions of Emotive and Cognitive Traits in Animals

ABSTRACT As incidences of human–wildlife interaction escalate, it is useful to increase understanding of the perceptions that might underpin these interactions or explain human behavior so associated. This study sought to identify public perceptions of the animal mind across wildlife species and to examine how states or qualities such as conscious thinking and feeling are perceived. We also aimed to evaluate whether people anthropomorphize species as readily as is often postulated. Using an online survey of 2,342 participants from the United States, we characterized perceptions of 36 wildlife species. In doing so, we also sought to stabilize inconsistent terminology in previous animal mind studies, by characterizing and measuring attributions of two specific traits, which we categorized as “cognitive” and “emotive. ” We found that people differentiate between cognitive traits (intellectual traits) and emotive traits (experiential, emotional states). Contrary to some past studies as well as popular assumptions, cognitive traits were ascribed more frequently than emotive traits for all animals. In addition, different animal classes were perceived as having varying levels of capacity of both traits. Mammals were ranked highest on qualities that defined both traits, followed by birds, reptiles, amphibians, and fish. The ranges within class also varied. Our findings provide new insights on how the public view the mental capabilities of wildlife species. The study further suggests that perceptions regarding the cognitive ability of animals may be higher than previously believed and that emotive traits may not be as notable as traditionally assumed. Elucidating these points may contribute to further progress in wildlife discussions and conservation strategies.
The Study of TSV-Induced and Strained Silicon-Enhanced Stress in 3D-IC

In this work, we discuss the influences of strained silicon technology on transistors in the context of TSV thermal stress. An accurate thermal stress distribution around a single TSV is firstly obtained by finite element analysis. Then we simulate the transistors using strained silicon technology and apply the TSV stress to the structure to study their magnitudes and mutual influences. It is demonstrated that the stress distribution combination of these two stress sources of planar transistors can be viewed as the superposition of the separate results. Finally, based on the updated stress distribution, the mobility variations of transistors around the TSV are studied.
EFFECTS OF FORM AND LEVEL OF ORTHOPHOSPHATE ON GROWTH, UPTAKE AND DISTRIBUTION OF SOME ELEMENTS IN MAIZE (ZEA MAYS L.CV.KSC700)

In this study, the plants of maize (Zea mays L.cv.ksc700) were grown in Hoagland solution containing 1, 3 and 5 mM KH2PO4 or K2HPO4 under controlled conditions. At similar levels of mono and diphosphate, plants supplied with monophosphate have produced higher content of dry matter and characterized with higher total chlorophyll content and longer and well proliferated root system than those treated with similar levels of diphosphate. Plants fed with monophosphate exported more than 50% of the total produced substances toward root system whereas, for the plant supplied with diphosphate, it was less than 20% approximately. In addition, there were a positive and significant correlation between the rate of the uptake and the content of the measured elements such as P, N, Ca, Mg, K, Na and monophosphate level; however, the same elements except for K negatively correlated with diphosphate. In both groups, the major proportion of the absorbed elements was more or less accumulated in leaves in relation to that of other parts of the plants. At the end of experiment, while pH decreased in the root medium supplied with monophosphate, an increase was observed in pH of those containing diphosphate. Differences between means values related to the same parameters in most of the cases were significant at P<0.05. Keyword: Zea mays; growth; macroelement; absorption; distribution; pH
基於語境特徵及分群模型之中文多義詞消歧(Using Contextual Information in Clustering Methods for Chinese Word Disambiguation)

We present preliminary results for searching for useful sentences for learning ambiguous words with clustering methods. First, we search for sentences that contain an ambiguous word (the target word, henceforth). To make the extracted sentences useful for learning the target word, we attempt to guide the clustering methods to separate the sentences that carry different senses of the target word into different clusters. We influence the functioning of a clustering method
Composition Characteristics of Soil Humus of Light-sweet Flavor Style of Flue-Cured Tobacco in Wumeng Tobacco-Growing Areas

Soil and tobacco leave samples were collected from 31 filed sites in Wumeng tobacco-growing areas and the composition characteristics of soil humus of light-sweet flavor style of flue-cured tobacco were analyzed. The results indicated that the average content of the total humus carbon was 12.24 g kg-1in Wumeng tobacco-growing areas. The average contents of Humin carbon(HM), humic acid carbon(HA) and fulvic acid carbon(FA) were 8.49 g kg-1,0.74 g kg-1and 3.02 g kg-1, respectively. Of humus components, the proportions of humin were highest, while humic acid(HE) accounted for 31.83% averagely. The coefficient of variation of each humus component was relatively large.Carbon contents of humus components of different subtypes showed that weak light-sweet flavor style was significantly higher than typical light-sweet flavor style and light-sweet flavor style. The percentage of HA of typical light-sweet flavor style was significantly higher than light-sweet flavor style and weak light-sweet flavor style. The percentage of HE of weak light-sweet flavor style was lowest while its percentage of HM was highest. The overall level of soil humus of weak light-sweet flavor style was higher, of which the average content of the total humus carbon was 16.31 g kg-1.The soil samples which carbon contents of humus of weak light-sweet flavor style over 15 g kg-1accounted for 50%.