Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid characterization of biomass using fluorescence spectroscopy coupled with multivariate data analysis. I. Yellow poplar (Liriodendron tulipifera L.)

This is the first of a two-paper series that reports on the use of fluorescence spectroscopy coupled with multivariate data analysis as a potential process analytical tool to develop calibration and prediction models for some physical and chemical properties of yellow poplar (Liriodendron tulipifera L.). Waste streams emanating from the processing of this wood species may potentially serve as feedstock for biofuels and biochemicals With the exception of holocellulose content, all the properties considered in the study were predicted with moderate to strong coefficient of determination (R2). Fluorescence spectra-based prediction model for each property considered in this study was compared with near infrared (NIR) spectra-based prediction models of similar properties from a previous study using the same population. The NIR-based prediction models exhibited slightly superior model strength over the fluorescence spectra-based prediction models of similar properties.
Nitrogen leaching and soil organic carbon sequestration of a Barley crop with improved N use efficiency – A regional case study

ABSTRACT The potential of modified spring barley crops with improved nitrogen (N) use efficiency to reduce nitrogen (N) leaching and to increase soil organic carbon (SOC) storage was assessed at the regional scale. This was done using simulation model applications designed for reporting according to the Helcom (Helsinki Commission) and Kyoto protocols. Using model simulations based on modified crops N dynamics and SOC were assessed for three agro-ecological regions (latitudes ranging 55°20′–60°40′ N) in Sweden over a 20-year period. The modified N use properties of spring barley were implemented in the SOILNDB model (simulating soil C, N, water and heat, and plant N dynamics) by changing the parameters for root N uptake efficiency and plant N demand within a range given by previous model applications to different crops. A doubling of the daily N uptake efficiency and increased N demand (by ca 30%) reduced N leaching by 24%–31%, increased plant N content by 9%–12%, depending on region. The effects of the modified crop on SOC was simulated with the ICBM model, resulting in an increased SOC content (0–25 cm depth) by 57–79 kg C ha−1 y−1. The results suggest that a modified crop might reduce N leaching from spring barley area, in a range similar to the targets of relevant environmental protection directives, a result which held more in the northern than southern regions. The simulated SOC increase on a hectare basis was highest for the central region and least for the most northern region. For the total agricultural area the share of spring barley area was small and more crops would need to be modified to reach the emission reduction targets.
Evaluation antibacterial activity of Biosynthesized Silver Nanoparticles by using extract of Euphorbia Pseudocactus Berger (Euphorbiaceae)

In the present study, silver nanoparticles (Ag NPs) were synthesized by using Euphorbia Pseudocactus Berger (Euphorbiaceae) extract, which played the main role in the formation and stability of nanoparticles. The physic-chemical property of biosynthesized nanoparticles were identified using X-ray diffraction (XRD), Ultraviolet–Visible spectroscopy (UV-Vis), Fourier-Transform Infrared spectroscopy (FT-IR) and Transmission Electron Microscopy (TEM) techniques. UV-Vis results illustrated that maximum plasma resonance absorption of Ag NPs are about 426 nm. Size distribution and spherical morphology was determined by TEM method. The XRD was confirmed face centered cubic (FCC) structure for synthesized nanoparticles.The molecular dynamics (MD) and monte carlo (MC) simulations were used to evaluate the nanoparticles . The antibacterial properties of biosynthesized Ag NPs were studied on E.coli (ATCC 25922), S. aureus (ATCC 2592), P. aeruginosa (ATCC27853) and E. faecalis (ATCC51299) by using micro dilution broth method. The minimum inhibitory concentration (MIC) results of synthesized Ag NPs on S. aureus and E. faecalis obtained 4 and 8 μg/mL and P. aeruginosa and E. coli obtained 16 and 4 μg/mL. So, synthesized nanoparticles can be utilized as an antibacterial agent in medical and industrial devices and tools.
Accelerated Multilevel Monte Carlo With Kernel‐Based Smoothing and Latinized Stratification

Heterogeneity and a paucity of measurements of key material properties undermine the veracity of quantitative predictions of subsurface flow and transport. For such model forecasts to be useful as a management tool, they must be accompanied by computationally expensive uncertainty quantification, which yields confidence intervals, probability of exceedance, and so forth. We design and implement novel multilevel Monte Carlo (MLMC) algorithms that accelerate estimation of the cumulative distribution functions (CDFs) of quantities of interest, for example, water breakthrough time or oil production rate. Compared to standard non‐smoothed MLMC, the new estimators achieve a significant variance reduction at each discretization level by smoothing the indicator function with a Gaussian kernel or replacing standard Monte Carlo (MC) with the recently developed hierarchical Latinized stratified sampling (HLSS). After validating the kernel‐smoothed MLMC and HLSS‐enhanced MLMC methods on a single‐phase flow test bed, we demonstrate that they are orders of magnitude faster than standard MC for estimating the CDF of breakthrough times in multiphase flow problems.
Control Interactions in the Theory of Planned Behavior: Rethinking the Role of Subjective Norm

Research with the theory of planned behavior (TPB) has typically treated attitude (ATT), subjective norm (SN), and perceived behavioral control (PBC) as independent predictors of intention (INT). However, theoretically, PBC moderates the effects of ATT and SN on intention. In three studies dealing with different behaviors (voting, reducing household waste, and energy consumption) we show that greater PBC tends to strengthen the relative importance of ATT in the prediction of intention, whereas it tends to weaken the relative importance of SN. The latter pattern was observed in relation to injunctive as well as descriptive subjective norms, and it may help explain the relatively weak relation between SN and INT frequently observed in TPB studies.
The Reconstruction Model of Science Learning based PhET-Problem Solving

This study investigates the effects of science learning model reconstruction based on student needs, in order to generate more operational standards and teaching materials. The conceptual understanding through virtual laboratory appears minimal, based on the analysis addressing the demands of junior high school students in several locations in Papua. Hence, there is a necessity to reform the learning structure through the utilization of physics education technology (PhET). Furthermore, the model was reconstructed by the Model Education of Reconstruction with fundamental principles, including an analysis of the education needs based on PhET-Problem solving, to compose context describing energy materials and its changes. Expert validation on the accuracy of illustrations, drawings, tasks, exercises, and questions, indicates the model is eligible. The participants are 60 students control and experiment group for junior high school public 11 Jayapura. The results showed the science learning model has demonstrated increased virtual experiment abilities and conceptual understanding trought pretest and postest based N-Gain. Analysis of data based normality test and independent test (T-test), meanwhile, there is a significant differences between experimental and control group. Outcome of students' responses for a separate learning model reflected positive.
Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Identifying nuclei is often a critical first step in analyzing microscopy images of cells, and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. Besides, large image data sets with ground truth for evaluation have been limiting. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare two deep learning strategies (U-Net and DeepCell) alongside a classical approach implemented in CellProfiler. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments. Our results show that U-Net outperforms both pixel-wise classification networks and classical algorithms. Also, our evaluation framework shows that deep learning improves accuracy and reduces the number of biologically relevant errors by half.
Synthesis, In-silico analysis and Antidepressant activity of New Pyrazoline Analogs

Objective: This study is undertaken to design and synthesize new pyrazoline derivatives and evaluate their anti-depressant efficacy by In-silico and In-vivo studies. Methodology: Pyrazoline derivatives (PY1-PY9) were yielded by the cyclization of chalcones(C1-C9) with phenylhydrazine.HCl in glacial acetic acid. FT-IR, 1H-NMR, and Mass spectral data established the structures of the newly synthesized compounds. In-silico analysis was carried out using Schrodinger 2018-3 suite device Maestro and docked to the binding site of the Human MAO-A enzyme(2Z5X). In-vivo anti-depressant study was performed by Tail suspension method and Forced swimming test. Results: The confirmation of the probable mechanism by which the compounds exhibit neuropharmacological interactions resulted from the interaction of the compounds with the residues of the binding sites, namely, ASP 46. This shows that the compounds have a profound affinity towards MAO-A target protein. In addition, the non-toxic nature of the compounds was established by the LAZAR program. Compounds PY3 and PY2 depicted the best docking scores (-9.894 and -9.766 respectively), and subsequent In-vivo analysis established that substantial anti-depressant efficacy upon comparison to the standard was demonstrated by compounds PY2 and PY8 in FST and compounds PY2 and PY3 in the TST. Conclusion: The data indicated that among the synthesized pyrazoline derivatives, 4-(1-ethyl-5-(phenyl)-4,5-dihydro-1H-pyrazol-3-yl)phenol (PY8) , 4-(1-ethyl-5-(4-chlorophenyl)-4,5-dihydro-1H-pyrazol-3-yl)phenol(PY2) and 4-(1-ethyl-5-(4-fluorophenyl)-4,5-dihydro-1H-pyrazol-3-yl)phenol (PY3) showed good anti-depressive activity with potent inhibitory activity against MAO-A target protein.
SENTIMENT ANALYSIS USING MACHINE LEARNING TECHNIQUES ON TWITTER: A CRITICAL REVIEW

A BSTRACT . Social web for Social applications are developing an exponential rate over the internet with the growing Internet communities. Users are increasing every second to use the social applications on Social platforms like Twitter, Facebook, etc. Users are sharing their feeling or opinion about any person, product in the form of images or text on the social networks. Sentiment analysis has gained a lot of popularity in the research ﬁeld of Natural language processing (NLP). Through it, the hidden sentiment in the text can be well extracted. This can assist companies, organizations, or users to make a useful conclusion to achieve their objective. The millions of data on the social network which has shared by the users will get certainly brings more opportunities and challenges to the sentiment analysis. In this paper, we will analyze the various Existing methods, techniques, and approaches for sentiment analysis, like Support Vector Machine, Naive Bayes, and KNN. In this paper, we have adopted a step by step literature review process to identify areas well focused by researchers for sentiment analysis in different ﬁelds. We have also tried to identify the best-used sentiment analysis techniques to ﬁnd research gaps from the previous research, which can further be extended in future research work. We will also discuss and explore new development trends of sentiment analysis.
A Trust-Management-Based Intrusion Detection System for Routing Protocol Attacks in Internet of Things

Abstract— The Internet of things is a pool of on-demand and configurable resources and services that are delivered across the usage of the internet. Providing privacy and security to protect their resources is considered a very challenging issue since the distributed architecture of the cloud makes it vulnerable to the intruders. To mitigate this issue, intrusion detection system plays an important role in detecting the attacks in the network. Intrusion detection system is a software or hardware component that implements monitoring and analysis processes of the system events or network activities. Once detecting any intrusion, an alert is raised to the administrator in order to take appropriate actions against such these intrusive events. In this paper an intrusion detection system is proposed for routing protocol for lossy and low power network attacks. The objective of the proposed system is to detect a variety of routing attacks namely sinkhole, selective forward and blackhole attacks. The detection algorithm uses trust management strategies that are based on a set of trust properties each of which is used for the detection of a specific type of routing attacks. The proposed attack detection algorithm was simulated using the Contiki Cooja simulator with centralized intrusion detection system placement strategy. The evaluation results show that in the proposed algorithm was able to detect the simulated attacks with 100% true positive detection rate in some scenarios.
Microscale CFD Simulations of a Wind Energy Test Site in the Swabian Alps with Mesoscale Based Inflow Data

The current study describes analyses of the WINSENT wind energy test site located in complex terrain in Southern Germany by highly resolved numerical simulations. The resolved atmospheric turbulence is simulated with Delayed Detached Eddy Simulations by the flow solver FLOWer without consideration of the research wind turbines. The mean inflow and wind direction of the analysed time period is provided by precursor simulations of project partners. The simulation model chain consists of three codes with different time scales and resolutions. The model chain provides a data transfer from mesoscale WRF simulations to OpenFOAM. As a next step OpenFOAM provides inflow data in the valley of the terrain site for the present FLOWer simulations, the code with the highest resolution in space and time. The mean velocity field provided by OpenFOAM is superimposed with fluctuations that are based on measurements to obtain the small turbulent scales within the FLOWer simulations, which the previous tools of the model chain can not resolve. Comparisons with the two already installed met masts clarify that the current FLOWer simulations provide an adequate agreement with measured data. The results are verified with the application of a second simulation, in which a homogeneous velocity profile is superimposed with turbulence. Thus, comparisons with measured data showed that the benefit of using the inflow data of this model chain is especially evident near the ground.
Spatial, biogeographic and environmental predictors of diversity in Amazonian Zygoptera

Our objectives were to assess how turnover and nestedness contribute to beta‐diversity patterns of the Zygoptera in Amazonian streams, and to relate these components of beta diversity to environmental, spatial and biogeographic predictors. Our first hypothesis is that the turnover is the most important component of beta‐diversity patterns due to the historical isolation of all or part of the areas located in the interfluves of the major Amazonian rivers. Our second hypothesis is that the interaction between environmental conditions and the area of endemism (biogeography) would be the most important predictor of beta‐diversity patterns. To test these hypotheses, we compiled data on the Zygoptera communities from 172 Amazonian streams. We used three sets of predictor variables: (i) environmental variables, (ii) area of endemism (biogeographic) and (iii) spatial filters. The turnover explained 99.36% of the beta diversity, corroborating our first hypothesis. Together, environmental and biogeographic variables were the best predictors of beta‐diversity patterns. For turnover, however, the biogeographic variables were the best predictors, contrary to our second hypothesis. We found high gamma diversity, but low alpha diversity in the Zygoptera communities. This paradox is explained by the high turnover among sites within the study landscape. This pattern of diversity is related to both historical biogeographic factors and the spatial structuring of environmental conditions in the Amazon region. In the light of our results (high turnover and beta diversity), and their correspondence with areas of endemism, adequate conservation of Amazonian Zygoptera diversity will depend on the establishment of so‐called mega‐reserves throughout the major Amazonian interfluves and, whenever possible, in the areas with adequate environmental conditions for the greatest possible number of species, otherwise, most species may be at a constant risk of extinction.
Modelling the effects of osmotic stress on tomato fruit development

Whereas most high-tech tomato greenhouses focus primarily on high production yields, consumers prefer a higher quality product. Dry matter content is one of the key factors determining fruit quality, and is known to be substantially influenced by altering the salinity of the nutrient solution. While this imposed osmotic stress can improve fruit quality, this often goes hand in hand with a decrease in production due to less water accumulation in the fruit. A more thorough insight in the underlying mechanisms might contribute to a better understanding and eventually steering of this delicate balance. To achieve this deeper knowledge, we combined intensive monitoring of plant and fruit physiological variables with a model-based approach. An experiment on tomato (Solanum lycopersicum L. 'Dirk') was set up in a greenhouse, where two different water treatments were imposed by altering the salinity (Electric Conductivity, EC) of the substrate. Besides plant variables such as sap flow, stem diameter variation and stem water potential, fruit growth and quality parameters were measured as well. These data were then used in a recently developed virtual tomato plant and fruit model, which is capable of modelling both plant and fruit growth as well as fruit quality (sugars and acids) and xylem and phloem contribution to fruit growth, but which has not been tested under salt stressed conditions. Results did not only show that the model can be used to predict fruit growth during salt stress conditions, but also which model parameters and related plant traits are affected most. This is an important step towards a better understanding of the underlying mechanisms controlling fruit development under osmotic stress.
New Insights into the Systematics of the Schoenoxiphium Clade (Carex, Cyperaceae)

Premise of research. The Schoenoxiphium clade (Carex, Cyperaceae) exhibits a high species diversity in South Africa and a complex taxonomy. Previous phylogenetic studies did not resolve the species relationships within the Schoenoxiphium clade due to the lack of informative characters in DNA markers used. Our aim is to resolve the species relationships within the Schoenoxiphium clade by adding information from more markers and more samples to information from previous studies. Methodology. We sampled 19 out of 20 recognized species in the former genus Schoenoxiphium. Four DNA regions (two nuclear ribosomal: internal transcribed spacer, external transcribed spacer; two plastid: matK and rps16) were sequenced for 134 samples. Phylogenetic reconstruction was performed using Bayesian inference and maximum likelihood analyses. Pivotal results. The monophyly of the Schoenoxiphium clade was confirmed. Five main clades with strong support were retrieved in congruence with a previous phylogeny. Although species relationships within these clades are still partially unresolved, our phylogeny highlights the need for the description of at least two new species in this group. Conclusions. The monophyly of the former genus Schoenoxiphium, taken together with its morphological synapomorphies and the recent phylogenetic studies and subsequent recircumscription of the genus Carex to include all nested genera, advises the consideration of this clade as a section (Carex sect. Schoenoxiphium Baillon). Additional investigations based on genomic sequencing are needed to fully resolve the species relationships within each of the five main clades.
Oncurrent-voltage and capacitance-voltage characteristics of metal-semiconductor contacts

: It is expected the fact that the current following across metal-semiconductor (MS) rectifying contact named as Schottky barrier diode (SBDs) and effect of sample temperature on their electrical properties obey thermionic emission (TE) current model. But, it has been seen that the abnormal behaviors in the measured electrical characteristics cannot be exactly understood by the classical TE transport theory. In the literature, the observed abnormal behaviors have been successfully explained by a Gaussian distribution function and by the pinch-off model being interaction of the neighbor patches suggested by Tung and coworkers, the named discrete regions model as “patches” with low barrier placed in a higher uniform barrier area.
Civil Law Agreement and Its Implication on Regulation for Prevention of Corruption within Covid-19 Pandemic

The purpose of this study is to determine the social impact of the learning process of agreements and regulations in the Civil Law regarding the procurement of goods and services during the Covid-19 pandemic, its implications for preventing corruption in Indonesia. The research method used is normative using a theory-in-use approach. The results of the study found three crucial points. First, the law of goods and services agreements gives freedom to people who do not have restrictions in the code for that. The contract for the procurement of products and services during the Covid-19 pandemic was categorized as a relatively temporary force majeure so that it could renegotiate to rearrange the implementation schedule. Second, good faith is the key to success in resolving frustrating agreement problems to save the agreement to provide benefits and benefits to both parties. Third, in the end, civil law provides an essential lesson that in transactions based on good faith where there are values of decency, honesty, and fair wisdom is the prevention of corruption in the procurement of goods. Good faith is the key to success in resolving frustrating agreement problems to save the agreement to provide benefits and benefits to both parties. And service in difficult times like today.
DESCRIPTION OF TYLENCHORHYNCHUS IRANENSIS SP. N. (NEMATODA TELOTYLENCHIDAE) FROM IRAN

A new species of stunt nematodes, Tylenchorhynchus iranensis sp. n. is described from the rhizosphere of faba bean ( Vicia faba L.), from material collected in the Khuzestan province, southwestern Iran. The new species is characterized by the following combination of features: lateral fields with four non areolated incisures, cephalic region continuous to slightly offset and conformed by 6-7 fine annuli, stylet 15-18 µm long, post-anal intestinal sac extends into the entire tail cavity, epiptygma present, tail sub-cylindrical with a hemispherical to sub-hemispherical smooth terminus, 46-65 μm long and composed by 46-49 annuli. Molecular analysis based on 28S rRNA gene sequences placed T . iranensis sp. n. within a clade that contained representatives of the genus Tylenchorhynchus with high support.