Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.
Effect of Sulfur Levels on Two Sesame (Sesamum indicum L.) Varieties under Climatic Conditions of Pakistan

Present study was carried out at Agronomic Research Area, University of Agriculture Faisalabad during summer season, 2011 to determine the best variety and optimum sulfur level to get higher yield of sesame crop. The experiment was laid out in randomized complete block design (RCBD) with factorial arrangement. The experiment comprised of; two varieties, (TH-6, T-89) and six sulfur levels (0, 10, 20, 30, 40 and 50 kg ha-1). It has been observed that the variety TH-6 was more responsive to the sulfur application than the variety T-89. The collected data showed that variety V1 (TH-6) gave significantly maximum number of seeds plant-1, number of capsules plant-1, 1000-seed weight, seed yield, oil content and protein content. Similarly, the sulfur level S6 (when 50 kg sulfur was applied) gave significantly maximum plant height, number of capsules per plant, number of seeds per capsules, 1000-seed weight, seed yield and oil content of sesame. For obtaining higher yield and good nutritional value, sulfur fertilizer should apply at the rate of Original Research Article Tahir et al.; IJPSS, Article no. IJPSS.2014. 006 282 50 kg ha-1 and the variety TH-6 should grow under the irrigated condition of Faisalabad.
l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences

Image-stitching (or) mosaicing is considered an active research-topic with numerous use-cases in computer-vision, AR/VR, computer-graphics domains, but maintaining homogeneity among the input image sequences during the stitching/mosaicing process is considered as a primary-limitation & major-disadvantage. To tackle these limitations, this article has introduced a robust and reliable image stitching methodology (l,r-Stitch Unit), which considers multiple non-homogeneous image sequences as input to generate a reliable panoramically stitched wide view as the final output. The l,r-Stitch Unit further consists of a pre-processing, post-processing sub-modules & a l,r-PanoED-network, where each sub-module is a robust ensemble of several deep-learning, computer-vision & image-handling techniques. This article has also introduced a novel convolutional-encoder-decoder deep-neural-network (l,r-PanoED-network) with a unique split-encoding-network methodology, to stitch non-coherent input left, right stereo image pairs. The encoder-network of the proposed l,r-PanoED extracts semantically rich deep-feature-maps from the input to stitch/map them into a wide-panoramic domain, the feature-extraction & feature-mapping operations are performed simultaneously in the l,r-PanoED’s encoder-network based on the split-encoding-network methodology. The decoder-network of l,r-PanoED adaptively reconstructs the output panoramic-view from the encoder networks’ bottle-neck feature-maps. The proposed l,r-Stitch Unit has been rigorously benchmarked with alternative image-stitching methodologies on our custom-built traffic dataset and several other public-datasets. Multiple evaluation metrics (SSIM, PSNR, MSE, $L_{\alpha,\beta,\gamma }$ , FM-rate, Average-latency-time) & wild-Conditions (rotational/color/intensity variances, noise, etc) were considered during the benchmarking analysis, and based on the results, our proposed method has outperformed among other image-stitching methodologies and has proved to be effective even in wild non-homogeneous inputs.
Low-temperature plasma treatment of Tencel

Abstract The effect of low-temperature plasma (LTP) on the surface modification of Tencel fabric was studied using the plasma gases of oxygen and argon.The experimental results showed that argon plasma caused higher fabric weight loss. In addition, there was a tremendous decrease in the whiteness of the testing specimens as a result of the long exposure of argon LTP treatment. The results of scanning electron microscopy showed that short exposure of argon or oxygen plasma increased the surface roughness on Tencel fabric that promoted the dye uptake. The contact angle test result showed that the wettability is increased by means of short-exposure LTP treatment. The generation of hydrophilic hydroxyl groups in Tencel during the oxygen or argon LTP treatment was confirmed by the result of Fourier transform infrared spectroscopy, and the increased hydroxyl groups will assist the dye uptake.
Chemical Studies of Marine Invertebrates. XXII. (1) Two Novel Sesquiterpenes from Soft Corals of the Genera Lemnalia and Paralemnalia (Coelenterata, Octocorallia, Alcyonacea)

The structure and absolute configuration of 2‐desoxylemnacarnol (IV) and 2‐desoxy‐12‐oxolemnacarnol (V), two novel sesquiterpenes isolated from soft corals of the genera Lemnalia and Paralemnalia were established by chemical correlation with lemnacarnol [II]. Copyright © 1977 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim
Deep Belief Network Integrating Improved Kernel-Based Extreme Learning Machine for Network Intrusion Detection

Deep learning has become a research hotspot in the field of network intrusion detection. In order to further improve the detection accuracy and performance, we proposed an intrusion detection model based on improved deep belief network (DBN). Traditional neural network training methods, like Back Propagation (BP), start to train a model with preset parameters such as the randomly initialized weights and thresholds, which may bring some issues, e.g., attracting the model to the local optimal solutions, or requiring a long training period. We use the Kernel-based Extreme Learning Machine (KELM) with the supervised learning ability to replace the BP algorithm in DBN in a bid to ameliorate the situation. Considering the problem of poor classification performance usually caused by randomly initializing kernel parameters with KELM, an enhanced grey wolf optimizer (EGWO) is designed to optimize the parameters of KELM. In order to improve the search ability and optimization ability of the traditional grey wolf optimizer algorithm, a novel optimization strategy combining the inner and outer hunting is introduced. Experiments on KDDCup99, NSL-KDD, UNSW-NB15 and CICIDS2017 datasets show that the proposed DBN-EGWO-KELM algorithm has greater advantages in terms of its accuracy, precision, true positive rate, false positive rate and other evaluation indices compared with BP, RBF, SVM, KELM, LIBSVM, CNN, DBN-KELM and other intrusion detection models, and can effectively meet the requirements of intrusion detection of complex networks.
On the Anomalous Self-Diffusion in B.C.C. Titanium

Self-diffusion of 44Ti is measured in β-Ti under UHV conditions in the whole range of existence of the b.c.c. phase. The Arrhenius plot shows a strong upward curvature in agreement with previous investigations of Murdock et al. The anomalous diffusion behaviour is related to the experimentally observed softening of the LA ⅔〈111〉 phonon mode and is explained by a monovacancy mechanism with a temperature-dependent migration enthalpy. When separating the phonon related diffusion contribution the analysis yields a frequency factor and an activation enthalpy consistent with normal vacancy diffusion parameters. Die Selbstdiffusion von 44Ti wird unter UHV-Bedingungen in β-Ti im gesamten Existenzbereich der k.r. z. -Phase gemessen. Die Arrheniusdarstellung ergibt eine stark aufwarts gekrummte Kurve in Ubereinstimmung mit fruheren Untersuchungen von Murdock et al. Das anomale Diffusions-verhalten wird mit der experimentell beobachteten Aufweichung der LA ⅔〈111〉-Phononmode verknupft und mit einem Einzelleerstellenmechanismus mit temperaturabhangiger Wanderungsenthalpie erklart. Bei Abspaltung des phononbeeinflusten Diffusionsbeitrages ergibt die Analyse einen Frequenzfaktor und eine Aktivierungsenthalpie, die mit Parametern fur normale Leerstellendiffusion ubereinstimmt.
DESKMatcher

This paper describes DESKMatcher , a label-based ontology matcher. It utilizes background knowledge from the financial services and enterprise domain to better find matches in these domains. The background knowledge utilized for the enterprise domain was in the form of documentation of terms used in SAP software (textual). Therefore, Word2Vec and GloVe were used for these corpora. The Financial Indus-tries Business Ontology ( FIBO ) was used as more specific background knowledge for the financial services domain. Vector space embeddings for this corpus were trained using RDF2Vec and KGloVe . Individual matchers utilizing one set of embeddings (generated from a combination of method and corpus) are pipelined together after a string-based matchers, searching only for matches between entities that have not been assigned to a match in a previous step. Results on the OAEI tracks are expected to be sub-par, because low overlap between corpus and task vocabulary is expected. 3
A new model for the soil‐water retention curve that solves the problem of residual water contents

We present a new model for the soil‐water retention curve, θ(hm), which, in contrast to earlier models, anchors the curve at zero water content and does away with the unspecified residual water content. The proposed equation covers the complete retention curve, with the pressure head, hm, stretching over approximately seven orders of magnitude. We review the concept of pF from its origin in the papers of Schofield and discuss what Schofield meant by the ‘free energy, F ’. We deal with (historical) criticisms regarding the use of the log scale of the pressure head, which, unfortunately, led to the apparent demise of the pF. We espouse the advantages of using the log scale in a model for which the pF is the independent variable, and we present a method to deal with the problem of the saturated water content on the semi‐log graph being located at a pF of minus infinity. Where a smaller range of the water retention is being considered, the model also gives an excellent fit on a linear scale using the pressure head, hm, itself as the independent variable. We applied the model to pF curves found in the literature for a great variety of soil textures ranging from dune‐sand to river‐basin clay. We found the equation for the model to be capable of fitting the pF curves with remarkable success over the complete range from saturation to oven dryness. However, because interest generally lies in the plant‐available water range (i.e. saturation, θs, to wilting point, θwp), the following relation, which can be plotted on a linear scale, is sufficient for most purposes: , where k0, k1 and n are adjustable fitting parameters.
On the Cost of Adaptivity in Graph-Based Games

The security of cryptographic primitives and protocols against adversaries that are allowed to make adaptive choices (e.g., which parties to corrupt or which queries to make) is notoriously difficult to establish. A broad theoretical framework was introduced by Jafargholi et al. [Crypto’17] for this purpose. In this paper we initiate the study of lower bounds on loss in adaptive security for certain cryptographic protocols considered in the framework. We prove lower bounds that almost match the upper bounds (proven using the framework) for proxy re-encryption and generalized selective decryption, a security game that captures the security of certain group messaging and broadcast encryption schemes. The security games used to model these protocols involve an underlying graph that can be adaptively built by the adversary. Some of our lower bounds only apply to a certain class of black-box reductions, which we term “oblivious”. (We do however show one lower bound on proxy re-encryption that applies to general fully black-box reductions.) The fact that our lower bounds crucially rely on “obliviousness” hints to the possibility that the existing upper bounds can be improved by using more sophisticated reductions. As the main technical contribution, we introduce a two-player multi-stage game called the Builder-Pebbler Game and then analyse strategies for this game to establish bounds on success probability of its players. Finally, using oracle separation techniques, we translate these bounds into cryptographic lower bounds. ∗Funded by Charles University project PRIMUS/17/SCI/9. Part of the work was done while the author was at Northeastern University, supported by the IARPA grant IARPA/2019-19-020700009. †Funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (682815 TOCNeT).
Bioassay‐directed fractionation for analyzing estrogens in surface waters of the German Baltic Sea

A reversed-phase fractionation method with subsequent biological and chemical analysis has been developed to estimate the contributions of the most potent estrogens to observed estrogenic effect potentials. Surface water samples were taken in the German Baltic Sea (Inner Wismar Bay and Darss Peninsula, sampling campaign July 2003) and were separated into seven individual fractions. Three fractions showed significant estrogenic activities and clear dose-dependant responses were obtained in the yeast estrogen screen (YES). In the 2nd fractions liquid chromatographic-electrospray-tandem mass spectrometric (LC-ESI-MS-MS) analyses showed the presence of bisphenol A (Inner Wismar, Bay: 4.8 ng L -1 and 6 ng L -1 ; Darss Peninsula: 0.91 ng L -1 and 1.7 ngL -1 ) and ethinylestradiol (Inner Wismar Bay: 2.0 ng L -1 and 6.0 ng L -1 ; Darss Peninsula: < MDL and 1.7 ng L -1 ), whereas estrogenic activities in the YES were only around 10% of the positive control E2. Although not identified prior in the total extract the natural hormones estradiol (Inner Wismar Bay: 0.13 ng L -1 and 0.19 ng L -1 ; Darss Peninsula: 0.12 ng L -1 and 0.16 ng L -1 ) and estriol (Inner Wismar Bay: < MDL and 0.33 ng L -1 ; Darss Peninsula: < MDL) could be detected in the 3rd fractions, where high estrogenic potentials could be observed. The 4th fractions showed high responses as well and estrone were herein quantified with concentrations of 0.16 ng L -1 and 0.18 ng L -1 (Darss Peninsula) up to 0.37 ng L -1 (Inner Wismar Bay). Measured and calculated estradiol equivalents for individual fractions correlated very well (R 2 = 0.78), when disregarding results of the 2nd fraction, where high deviations occurred.
Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications

In recent years information and communication technologies (ICT) have played a significant role in all aspects of modern society and have impacted socioeconomic development in sectors such as education, administration, business, medical care and agriculture. The benefits of such technologies in agriculture can be appreciated only if farmers use them. In order to predict and evaluate the adoption of these new technological tools, the technology acceptance model (TAM) can be a valid aid. This paper identifies the most commonly used external variables in e-learning, agriculture and virtual reality applications for further validation in an e-learning tool designed for EU farmers and agricultural entrepreneurs. Starting from a literature review of the technology acceptance model, the analysis based on Quality Function Deployment (QFD) shows that computer self-efficacy, individual innovativeness, computer anxiety, perceived enjoyment, social norm, content and system quality, experience and facilitating conditions are the most common determinants addressing technology acceptance. Furthermore, findings evidenced that the external variables have a different impact on the two main beliefs of the TAM Model, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). This study is expected to bring theoretical support for academics when determining the variables to be included in TAM extensions.
DRAWING DOWN RETIREMENT SAVINGS—DO PENSIONS, TAXES AND GOVERNMENT TRANSFERS MATTER MUCH FOR OPTIMAL DECISIONS?

Abstract This paper examines the importance of pensions (employment and social security), taxes and government transfers for alternative retirement savings drawdown strategies (DS), compared to the conventional approach in published literature of using a gross income concept obtainable from retirement savings alone. Using a lifetime utility framework, our longitudinal dynamic micro-simulation model incorporates risk aversion, stochastic markets, stochastic mortality and the interactions among sources of retirement income within the complex Canadian tax and social benefit system, enabling us to rank commonly advocated DS and to ask whether incorporating pensions, taxes and transfers alters those rankings. Our findings show the importance of treating the evaluation of alternative DS as a comprehensive and integrated problem by including all sources of income — including pensions, taxes and government transfers. Using restricted income measures can potentially lead to simplistic, and possibly misleading, conclusions.
Allelic Diversity, Structural Analysis, and Genome-Wide Association Study (GWAS) for Yield and Related Traits Using Unexplored Common Bean (Phaseolus vulgaris L.) Germplasm From Western Himalayas

The north-western Indian Himalayas possesses vast diversity in common bean germplasm due to several years of natural adaptation and farmer’s selection. Systematic efforts have been made for the first time for the characterization and use of this huge diversity for the identification of genes/quantitative trait loci (QTLs) for yield and yield-contributing traits in common bean in India. A core set of 96 diverse common bean genotypes was characterized using 91 genome-wide genomic and genic simple sequence repeat (SSR) markers. The study of genetic diversity led to the identification of 691 alleles ranging from 2 to 21 with an average of 7.59 alleles/locus. The gene diversity (expected heterozygosity, He) varied from 0.31 to 0.93 with an average of 0.73. As expected, the genic SSR markers detected less allelic diversity than the random genomic SSR markers. The traditional clustering and Bayesian clustering (structural analysis) analyses led to a clear cut separation of a core set of 96 genotypes into two distinct groups based on their gene pools (Mesoamerican and Andean genotypes). Genome-wide association mapping for pods/plant, seeds/pod, seed weight, and yield/plant led to the identification of 39 significant marker–trait associations (MTAs) including 15 major, 15 stable, and 13 both major and stable MTAs. Out of 39 MTAs detected, 29 were new MTAs reported for the first time, whereas the remaining 10 MTAs were already identified in earlier studies and therefore declared as validation of earlier results. A set of seven markers was such, which were found to be associated with multiple (two to four) different traits. The important MTAs will be used for common bean molecular breeding programs worldwide for enhancing common bean yield.
Identification of New Soluble Factors Correlated With the Development of Graft Failure After Haploidentical Hematopoietic Stem Cell Transplantation

Graft failure is a severe complication of allogeneic hematopoietic stem cell transplantation (HSCT). The mechanisms involved in this phenomenon are still not completely understood; data available suggest that recipient T lymphocytes surviving the conditioning regimen are the main mediators of immune-mediated graft failure. So far, no predictive marker or early detection method is available. In order to identify a non-invasive and efficient strategy to diagnose this complication, as well as to find possible targets to prevent/treat it, we performed a detailed analysis of serum of eight patients experiencing graft failure after T-cell depleted HLA-haploidentical HSCT. In this study, we confirm data describing graft failure to be a complex phenomenon involving different components of the immune system, mainly driven by the IFNγ pathway. We observed a significant modulation of IL7, IL8, IL18, IL27, CCL2, CCL5 (Rantes), CCL7, CCL20 (MIP3a), CCL24 (Eotaxin2), and CXCL11 in patients experiencing graft failure, as compared to matched patients not developing this complication. For some of these factors, the difference was already present at the time of infusion of the graft, thus allowing early risk stratification. Moreover, these cytokines/chemokines could represent possible targets, providing the rationale for exploring new therapeutic/preventive strategies.