Miracle-dz/UniFAR-FTUs huggingface dataset:
A new method to study simultaneous methane oxidation and methane production in soils

Results of laboratory experiments show that 14C‐labeled methane added to soil was consumed faster than atmospheric 12C methane. This implies a source of methane, presumably through methanogenesis, in a soil that is a net consumer of atmospheric methane. The soil was well‐drained forest soil from Ispra, Italy. An undisturbed sample was taken with a steel corer and incubated under oxic conditions in a jar. Headspace samples were taken at time intervals and analyzed for total methane by gas chromatography and analyzed for 14C methane by liquid scintillation counting. Fluxes calculated from the decreasing headspace mixing ratios were, for example, −6.5 and −7.1 μmol m−2 hr−1 for 12C methane and 14C methane, respectively. A simple model is considered which reproduces reasonably well the observed mixing ratios as function of time.
Moderating Effect of Gender on the Relationship between TikTok Usage and Positive Emotion among TikTok Users in China

This study aims to identify the moderating effect of gender on the relationship between TikTok usage and positive emotion among TikTok users in China. As TikTok is a new emerging social media, there are few researches on TikTok usage and its association with positive emotion among TikTok users in China. To fill the gap, this study utilized a quantitative research method by using survey approach. A cross-sectional study was adopted, while the questionnaires were distributed online in this study. The study has collected 244 questionnaires through convenience sampling technique. The results shown that both of the two predictors which are perceived usefulness and perceived ease of use toward using TikTok are significantly associated with positive emotion among TikTok users in China. However, gender does not play a moderating role on the relationship between TikTok usage and positive emotion. It was found that the users of TikTok are mainly young generation. This study suggests that users should use TikTok reasonably and appropriately.
Sex differences in motor performance and anaerobic peak power of Japanese primary school children aged 11 to 12 years

The purpose of this study was to investigate sex differences in motor performance and anaerobic peak power in children. Furthermore, the relationship between power-associated variables and motor performance was also examined. Ninety-four Japanese primary school children aged 11 to 12 years volunteered. Grip strength, repeated lateral jumps, 50-m sprint, and standing broad jump were assessed following the guidelines published by the Japanese Ministry of Education and Science. Anaerobic peak power was assessed based on 6 sec sprint cycling with 4% of body mass as the braking load. The absolute peak power and peak power normalised by body mass were calculated as power-associated variables. The correlation analysis revealed that normalised peak power was moderately and strongly correlated with 50-m sprint time in both sexes, indicating the possibility that the sex difference in sprint performance could be derived from the discrepancy in anaerobic peak power. Sex differences were found in normalised peak power, lateral jumps, and 50-m sprint time. However, absolute peak power and standing broad jump did not differ significantly, and the results were not consistent with those of previous studies that examined participants in a different age range. The non-significant results in terms of absolute peak power and standing broad jump distance suggested the notion that sex differences in some variables fluctuate depending on the participants’ age. Therefore, future studies are required to investigate the sex differences in motor performance and anaerobic peak power in children of different age groups.
Application layer classification of Internet traffic using ensemble learning models

Accurate application layer classification of Internet traffic has been a necessary requirement for various regulatory, control, and operational purposes of Internet service provider (ISP). Due to the dynamic and ever evolving nature of Internet applications generating a diverse mixture of Internet traffic, it has been necessary to apply deep packet inspection (DPI) techniques for traffic classification. DPI methods offer accuracy but degrade overall network throughput and thus cause problems in ensuring quality of service (QoS) and maintaining service‐level agreements. Moreover, Internet traffic is mostly end to end encrypted. This in turn limits the applicability of DPI techniques and renders them useless, unless the encryption tunnel is broken by the service provider which would risk violating user privacy. To address these trade‐offs between classification accuracy, performance, and user privacy, we resort to machine learning (ML)‐based algorithms. In this article, we apply three ensemble ML algorithms and report their performance metrics in the application layer classification of Internet traffic.
Hierarchical Convolutional Features Fusion for Visual Tracking

Hierarchical convolutional features have different impact on the tracking performance, as the higher convolutional layers encode the semantic information of targets and earlier convolutional layers are more precise to localize targets. In this paper, we propose a novel scheme for hierarchical convolutional features fusion for visual tracking. In the proposed scheme, hierarchical convolutional features are first concatenated to form the cascading feature at the feature level, and then a convolutional layer is added to reduce the feature dimension. Discriminative correlation filter (DCF) is finally utilized to obtain the target location, which is treated as a differentiable layer in the neural network. The experimental results demonstrate that our proposed scheme achieves superior performances on the visual tracking benchmark.
Multi-Modal Biometrics based on Data Fusion

With the development of intelligent application, biometrics recognition technology has been widely concerned and applied in many fields of the real world, such as access control and payment. The traditional biometrics are usually based on single modality data of the subjects, but they are limited by the feature information capacity and the bottleneck in recognition accuracy. In this paper, a multi-modal biometric recognition framework is presented, which utilizes a multi-kernel learning algorithm to fuse heterogeneous information of different modal data. In order to extract complementary information from them, we combine the kernel matrix to form the mixed kernel matrix, and then give the final classification results. The experimental results on multiple biometric datasets show that our method can obtain higher recognition accuracy compared with the existing single mode and multi-mode fusion methods.
Calculations Energy of the (nl2) 1Lπ Doubly Excited States of Two-Electron Systems via the Screening Constant by Unit Nuclear Charge Formalism

In this work, the total energies of doubly excited states (ns2) 1Se, (np2) 1De, (nd2) 1Ge, (nf2) 1Ie, (ng2) 1Ke, and (nh2) 1Me of the helium isoelectronic sequence with Z ≤ 10 are calculated in the framework of the variational method of the Screening Constant by Unit Nuclear Charge (SCUNC). These calculations are performed using a new wavefunction correlated to Hylleraas-type. The possibility of using the SCUNC method in the investigation of high-lying Doubly Excited States(DES) in two-electron systems is demonstrated in the present work in the case of the (nl2) 1Lπ doubly excited states, where accurate total energies are tabulated up to n = 20. All the results obtained in this paper are in agreement with the values of the available literature and may be useful for future experimental and theoretical studies on the doubly excited (nl2) 1Lπ states of two-electron systems.
Thin film Li electrolytes for all-solid-state micro-batteries

All-solid-state thin film lithium micro-batteries have attracted more and more interests due to their potential applications as power sources for microelectronic devices. Thin film Li electrolyte, as the key part in the thin film batteries, is very important to the performance of the thin film batteries. This paper reviews a wide range of lithium ion conductors including glassy electrolytes and crystalline electrolytes for potential application in the micro-batteries. The thin film Li electrolytes reported in the literatures are reviewed with a focus on their ionic and electronic conductivity, chemical stability, and compatibility with electrode materials.
Iterative Protein Redesign

An iterative redesign protocol for the transformation of a non-native peptide into a series of nativelike proteins derived from elementary considerations of biological evolution coupled with 1H NMR as an artificial selection criterion is presented. Each of three heptad d position leucines in the helix−helix interfaces of the prototype heme protein maquette, [H10H24]2 or (α-SS-α)2, were replaced in a unit modification per helix by more conformationally restricted β-branched and aromatic amino acids. The secondary structure content (evaluated by circular dichroism and infrared spectroscopies), solvent accessibility of the tryptophan residues (measured by fluorescence spectroscopy), global stability (quantitated by isothermal chemical denaturation), and degree of conformational specificity (determined by 1H NMR spectroscopy) of the resultant peptides were determined. Improvement in the degree of conformational specificity was utilized as a selection criterion to choose three of the nine singly modified pepti.
ANTIMICROBIAL PROPERTIES OF SEVEN BROWN ALGAE HARVESTED FROM THE COAST OF SIDI BOUZID (EL JADIDA-MOROCCO)

Objective: This work aims at the screening of the antimicrobial activity of the seven brown marine algae of theCoast of Sidi Bouzid (El Jadida-Morocco). Methods: The aim of this study was to evaluate the antimicrobial activity of seven brown marine algae against three Gram-positive bacteria (Staphylococcus epidermidis, Staphylococcus aureus and Streptococcus pyogenes). Three Gram-negative bacteria (Escherichia coli, Pseudomonas aeruginosa and Klebsiella pneumonia) and two fungi (Aspergillus Niger and Candida tropicalis). Thus, 35 algal extracts were prepared with five organic solvents methanol/water, methanol, dichloromethane/methanol, dichloromethane and ethyl acetate. The antibacterial activity was evaluated through the disk diffusion method. Results: Data revealed that the Staphylococcus aureus bacteria was the most sensitive pathogen by showing the highest zone of inhibitions of 20 mm with the lowest Minimum Inhibitory Concentration (MIC) of 2 μgmL⁻¹ methanol/water extract of Cystoseira tamariscifolia. Whereas, antifungal activity, the highest zone of inhibitions of 21 mm and 22 mm with the lowest Minimum Inhibitory Concentration (MIC) of 5 μgmL-¹ was respectively shown in the methanol/water extract of Laminaria ochroleuca against Candida tropicalis and in the dichloromethanolic extract of Sargassum vulgare against Aspergillus niger. Conclusion: The results indicate that these algal extracts can further be analyzed and purified for relevant antibacterial and antifungal compounds which can be used in therapeutics and other applications.
Evaluating Deep Learning EEG-Based Anxiety Classification in Adolescents with Autism for Breathing Entrainment BCI

Anxiety is one of the most common comorbidities in youth with autism spectrum disorder (ASD), severely limiting academic opportunities and overall quality of life. In the present study, we compared several machine learning classifiers, namely support vector machine (SVM) and deep learning methods, in order to evaluate the feasibility of an EEGbased BCI for the real-time assessment and mitigation of anxiety through a closed-loop adaptation of respiration entrainment. We trained a total of eleven subject-dependent models—four with conventional BCI methods and seven with deep learning approaches—on the EEG of neurotypical (n=5) and ASD (n=8) participants performing alternating blocks of mental arithmetic stress induction, guided breathing, and unguided breathing. We propose a multiclass two-layer LSTM RNN deep learning classifier capable of identifying anxious states from ongoing EEG with an overall accuracy of 93.27%. Our study is the first to successfully apply an LSTM RNN classifier to identify anxious states from EEG in both neurotypical adolescents and adolescents with ASD.
Use of enzymes to detoxify pesticide-contaminated soils and waters

Applying enzymes to transform or degrade pesticides is an innovative treatment technique for removal of these chemicals from polluted environments. Enzyme-catalyzed degradation of a pollutant by a parathion hydrolase may be more effective than existing chemical methods. Removal of certain pollutants (for instance phenols and aromatic amines) from wastewater may be achieved by applying phenoloxidases that can convert these chemicals to water-insoluble polymers that can then be removed by filtration or sedimentation. Another decontamination method involves the enzyme-catalyzed incorporation of pesticides into organic matter. This procedure reduces the mount of leachable pesticides as well as the toxicity and bioavailability of the chemicals. Pesticide-detoxifying enzymes must be immobilized before they can be used in the environment. In spite of promising potential applications, very few enzymes have been tested as possible tools in the detoxification of pesticides. Research in this domain can contribute greatly to developing new methods for pollution control. Contribution from the Lab. of Soil Biochemistry, Pennsylvania State Univ., University Park.
Automatic History Matching Using the Integration of Response Surface Modeling with a Genetic Algorithm

Abstract History matching is the process of updating a petroleum reservoir model using its production history. It is a required step before a model is accepted for forecasting production. This work aims at integrating response surface modeling with a genetic algorithm to realize a global optimization method for the history matching process. To achieve this, a proxy model was constructed for the flow simulator outputs for all measurements that enter a global objective function. A genetic algorithm was applied to minimize the proxy model. Then a reservoir model was built based on this minimized proxy model. Construction of a good proxy model requires only a limited number of simulation runs and its simulation time is a couple of seconds. Therefore, the evolution of proxy model using a genetic algorithm takes a short time. Compared to other global optimization techniques, this method can perform the history matching process in a quick, low-cost manner. The method was validated by a field case study. The simulation model used contains 41 years of production history. During the history matching process, a limited number of simulation runs (79) was used to construct a high-quality proxy model and by application of genetic algorithm, the global objective function was reduced from 581.362 to 9.347. The observed oil rate, shut-in pressure, repeated formation test pressure, and water cut (must be zero) were matched using the proposed method.
Precollaborative validation of an amperometric immunosensor for Salmonella

The method of Salmonella detection recommended is cultural, but it is laborious, presents a high consumption of material, and requires about five days for presumptive results. Immunosensor is an alternative tool that has shown promising and rapid results, although many devices have their performance evaluated only under buffering conditions and few achieve the validation stage. The objective was to perform a pre-collaborative validation of an electrochemical immunosensor assembled on screen-printed electrodes for the detection of Salmonella sp. in milk. The antibodies were immobilized by cysteamine self-assembled monolayer. The sandwich-type amperometric immunosensor was evaluated for contaminated raw and whole UHT milk and compared to performance with a gold standard reference method (BAM) according to AOAC recommendations for a single laboratory. A binary response (positive/negative) of the immunosensor was used based on a cut off established from current electric obtained for the absence of the pathogen. There was no significant difference for the results of the biosensor and the reference method, in the absence and the levels from 101 to 103 CFU mL−1 of Salmonella Typhimurium for the two types of milk. This result indicates the efficiency of the biosensor in detecting the pathogen into a complex matrix.
Characterization of High Purity bcc Metals by Mechanical Spectroscopy

The mechanical loss (internal friction) spectra of bcc metals (Fe,Nb,Ta) indicate the presence of interstitial foreign atoms by Snoek peaks at distinct, characteristic temperatures. The magnitude of the peaks is proportional to the contents of interstitial solute atoms. Experiments with single crystals allow determination of the dipole tensor components |λ 1 -λ 2 |. Equipment for low frequency measurements (10 -4 Hz - 20 Hz) is demonstrated.
Dual-Branch Feature Fusion Network for Single Image Super-Resolution

Rcently, the research of single image super-resolution (SISR) based on deep learning has made great progress. However, most of the methods of super-resolution (SR) study use a simple chain structure to obtain higher super-resolution performance. Additionally, most methods do not fully utilize the hierarchical features generated in the middle of the network, thereby cannot achieve relatively high performance. In this paper, we propose a novel dual-branch feature fusion network (DBFFN) to address this two problems in image SR. The backbone of DBFFN is composed of multiple dual-branch feature fusion block (DBFFB) cascaded. The DBFFB has a dual-branch structure which mainly contains two parallel sub-networks, one extracts image fine features via densely connected convolution layers, the other one sub-network extracts image more contextual features by stacking dilated convolution layers. The features extracted from each hierarchical of the DBFFB are cascaded, and then feature fusion is performed. A skip connection is introduced to learn residual between input and output of the DBFFB. Especially, we use the deconvolution layer at the end of the network to enlarge the image. The results of the experiment are exciting, when the scale factor is 3, the performance of our network has 0.56dB, 0.6dB, and 0.79dB improvement on the set5, set14, and urban100 benchmark datasets compared with RDN.
The same cloze for all occasions?

Abstract Target language proficiency assessment has become an integral part of Second Language Acquisition (SLA) research design, with cloze tests frequently serving this purpose for reasons of practicality. Assumptions underlying the interpretation of such cloze test scores, however, are often not examined. With the goal of providing researchers with better means for drawing inferences from cloze test scores, we present an analysis of a combined dataset comprised of scores from 1,724 test takers on a frequently used English cloze test (Brown 1980). We examine variation in score distributions and reliability estimates among L2 groups, between L2 and native-speaker (NS) examinees, and for different scoring methods, and investigate the degree to which different sets of items were effective for classifying low- vs high-proficiency L2 examinees and L2 vs NS test takers. Standardized scores are provided for each scoring method so that future researchers can reference their scores to this larger set.
A Distributed Control Architecture for Cascaded H-Bridge Converter With Integrated Battery Energy Storage

Cascaded H-bridge topology has been used in grid-tied converters for battery energy storage system due to its modular structure. To fully utilize the converter's modularity, this article proposes a hierarchical distributed control architecture that consists of primary control, secondary control, and battery state-of-charge (SOC) balancing control. Primary control ensures accurate current tracking, whereas a distributed secondary control based on consensus algorithm is presented to regulate power sharing among modules and is proved to be stable theoretically. A distributed SOC balancing control is further introduced to improve energy efficiency of battery energy storage system. Finally, the hierarchical distributed control strategy is implemented using hardware controllers and a software platform. Besides, a carrier phase shift control is also implemented to achieve multilevel output voltage and harmonic reduction. The experimental results demonstrate the performance of the proposed control scheme effectively.
SYNTHESIS AND STRUCTURAL STUDIES OF SYMMETRIC AND UNSYMMETRIC ADAMANTYLMETHYLENEAZINES

The synthesis and spectroscopic properties (1H, 13C and 15N NMR, in solution and in the solid state) of six new 1-adamantylmethylene azines are reported. The crystal and molecular structures of 1-adamantylcarbaldehyde azine and 1-adamantyl methyl ketone azine, which exist in the solid state in the E,E-configuration, were determined by x-ray analysis. The geometric characteristics of the azine central bridge and the preferred configuration with regard to it (E,E, E,Z or Z,Z) were investigated by means of the crystallographic data retrieved from the Cambridge Structural Database and ab initio quantum chemical calculations. Copyright © 1999 John Wiley & Sons, Ltd.
Deep Neural Network Trained on Surface ECG Improves Diagnostic Accuracy of Prior Myocardial Infarction Over Q Wave Analysis

Clinical screening of myocardial infarction is important for preventative treatment and risk stratification in cardiology practice, however current detection by electrocardiogram Q-wave analysis provides only modest accuracy for assessing prior cardiac events. We set out to evaluate the ability of a deep neural network trained on the electrocardiogram to identify patients with clinical history of myocardial infarction. We assessed 608 patients at two academic centers with adjudicated history of myocardial infarction. Surface electrocardiograms were used to train a neural network-based model that classifies patients with and without a history of infarction. Endpoints were assessed by clinical record review and accuracy of the model was compared against the manual assessment of pathologic Q waves. The neural network outperformed the accuracy of pathologic Q waves (62%). In training, the model accuracy converged to >98%. Validation was performed by cross-validation (k=5) with validation accuracy 71 ± 5%. Receiver-operator characteristics analysis resulted in a c-statistic of 0.730. Deep learning of a 12-lead ECG can identify features of prior myocardial injury more accurately than clinical Q-wave analysis and may serve as a valuable clinical screening tool.
Effects of blocked versus interleaved presentation on children’s vocabulary learning: A pilot study

In some educational contexts, it has been shown that interleaved presentation (i.e. alternating different tasks/skills) results in better learning than blocked presentation (i.e. practising one task/skill at a time). However, this interleaving effect has been studied in a limited range of contexts. Here, we examined whether blocked versus interleaved presentation influenced vocabulary learning in 56 children aged from 6 to 10 years old. We devised a novel online game involving two tasks. The first was a vocabulary learning task, in which children matched spoken words to images of novel animals. The second was a comprehension task, where the children arranged objects to match the spatial relations described in a spoken sentence. Stimuli were presented in a between-subjects design, such that trials from each task either occurred in blocks of 10 or were fully interleaved. Our analysis revealed vocabulary learning in both conditions, but the rate of learning did not differ for participants assigned to the blocked vs interleaved conditions. We did not, however, use posttest assessment means, so we cannot rule out any benefit of interleaving that may emerge after the initial learning phase.