Miracle-dz/UniFAR-FTUs huggingface dataset:
Organizational Trust, Job Satisfaction and Job Performance Among Teachers in Ekiti State, Nigeria

This study examined the influence of organizational trust and job satisfaction on teachers' job performance among teachers in Ekiti State. Two hundred and fifty eight teachers randomly drawn from Ekiti State public secondary schools participated in the study. Organizational trust scale, Job satisfaction scale and Teachers' Sense of Efficacy Scale were used as measures of investigation. Four hypotheses were tested using the independent t-test and the two way Analysis of Variance. Results showed that organizational trust significantly influences job performance among teachers in Ekiti State [F (1,254) = 12.355; P< . 01]. Also, it was revealed that job satisfaction significantly influences job performance among teachers [F (1,254) = 3.529; P< . 05]. However, no significant interaction effect of job satisfaction and organizational trust was found on job performance. Result of data analyses also showed that sex has no significant influence on job performance among teachers in Ekiti State [t (256) = -0.58; p>. 05]. The findings were discussed in line with previous research findings and it was recommended that teachers should be British Open Journal of Psychology Vol. 1, No. 1, March 2015, pp. 110 Available online at http://borpub.com/Journals.php
IN-SILICO MOLECULAR DOCKING AND PHARMACOKINETIC STUDIES OF SOME SELECTED PHYTO-CONSTITUENTS OF BRYOPHYLLUM PINNATUM AS A POTENTIAL SELECTIVE INHIBITOR OF MONOAMINE OXIDASE–B (MAO-B)

Monoamine oxidase (EC, 1.4.3.4) is an important biological enzyme that catalyzes the oxidation of monoamines. It is critical for the inactivation of neurotransmitters. Monoamine oxidase (MAO) exists in two isoforms; A and B. Abnormality in the activity of the monoamine oxidase B has been associated with neurological dysfunctions such as Alzheimer’s disease. An insight into the interaction of monoamine oxidase B binding site with inhibitors is crucial for the development of new pharmaceutical agents. In this study, we examined the inhibitory activity of human MAO-B and a series of bioactive ligands from Bryophyllum pinnatum as potential Monoamine oxidase B inhibitors. Computational docking analysis was performed using extra precision (XP) feature of Glide module, version 5.6. The final products of molecular docking were clustered to specify the binding free energy and Kinetic inhibition (Ki) was calculated. Docking results showed all the compounds tested (patuletin, luteolin, kaempferol and acacetin) had high binding score compared to the standard reference drugs. Specifically, patuletin, an Omethylated flavonol had the optimum binding affinity (-10.105kcal/Mol) compared to the standard drugs selegiline (−7.82 kcal/mol) and rasagiline (-7.82). Hydrogen bond interactions between the 3-OH of patuletin and Tyr-435 and Ile-199 of the active residues were found to have played a critical role in stabilizing the ligand bond at the active site. This present study provides salient information for the rational drug design of more potent and selective Monoamine oxidase B inhibitors in the management of neuro-degenerative disorders. Further in-vitro and in-vivo studies validating the inhibitory activity of Patuletin is highly recommended.
Limnetica, 37 (1): 145-158(2018)

A comparison of modeling techniques to predict hydrological indices in ungauged rivers Predicting the natural flow regime in ungauged rivers is an important challenge in water resource management and ecological research. We developed models to predict 16 hydrological indices in a river network covering the northern third of the Iberian Peninsula. Multiple Linear Regression (MLR), Generalized Additive Models (GAMs), Random Forest (RF) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used and compared according to their prediction accuracy. The results showed that predictive performance varied greatly depending on the modeled hydrological attribute. The magnitude and frequency indices were predicted with excellent accuracy. In contrast, no technique was capable of developing precise models for hydrological indices of timing, duration and rate of change. This is mainly related to the lack of proper environmental databases on the scales on which these flow regime patterns are influenced. In addition, complex modeling techniques did not always outperform linear models and no single approach was optimal for all indices. ANFIS and GAMs provided the best results; however, other issues such as computational cost and the level of knowledge required to apply the method and interpret the results should be taken into account.
The OPPO System for CHiME-6 Challenge

This paper describes our system and experimental results for the 6th CHiME Challenge. We participate in Track1(ASR only) on Category A and B. Category A is system based on conventional acoustic modeling and ofﬁcial language modeling. The outputs of the acoustic model must remain frame-level tied pho-netic (senone) targets and the lexicon and language model must not be changed compared to the conventional ASR baseline. Category B is all other systems. Our system mainly include data preparation, frontend procesing, acoustic modeling, lattice rescoring with RNN Language Model(RNNLM) and system combination. The frontend employs the baseline Guided Source Separation(GSS) [1]. For backend, we use TDNN-F and CNN-TDNNF [2] acoustic models, the systems employs a combination of 8 acoustic models, and ﬁnally apply Minimum Bayes Risk(MBR) [3] decoding for multiple lattices of different acoustic models. Comparing with the ofﬁcal baseline system, our system can get 20.44% and 18.07% relative Word Error Rate(WER) reduction on the dev and eval sets respectively.
Time Granularity-based Privacy Protection for Cloud Metering Systems

Article history: Received: 22 August, 2020 Accepted: 25 September, 2020 Online: 16 December, 2020 Due to the advent of cloud computing and Internet of Things, smart meters have become a crucial part of smart cities. Smart meters generate vast amounts of fine-grained data that can immediately provide useful information to electricity consumers, such as automatic billing, load monitoring, and dynamic time pricing. This will make the electricity consumers more energy-efficient and self-organized in IoT and cloud computing technology. Smart meters also enable two-direction flow of energy and balance the measurement of inflows and outflows of energy between the meter and electric utility's central system through remote reporting. Besides, they help designers integrate renewable energy generation, such as solar rooftops to cover remaining energy needs. However, smart meters entail some severe security challenges. From a security point of view, these meter readings have a potential threat on personal privacy and the collected meter data. They may also reveal some details about consumer's lives. Thus, the data acquisition from those meter readings is expected to incorporate privacy requirements and application demands. In addition, the collected raw data from various meter readings needs to be collected from IoT smart metering sensors and stored in the cloud to be accessed and analyzed by electricity consumers. Subsequently, this considerable information Is prone to privacy threats and security vulnerabilities as the transmission of data to the cloud is most likely to cause a data breaching. In this paper the privacy and confidentiality of meter readings is protected through trusted platform module, which will allow the electricity provider to get the energy consumption at a periodic time granularity.
Modeling the Gradual Degradation of Eventually-Consistent Distributed Graph Databases

: Under the ‘eventual consistency’ approach to updates in a distributed graph database, it is possible that edge information may be corrupted. Errors may then be propagated to other parts of the database by subsequent queries. The process by which this occurs is modeled, with the aim of estimating the time that it takes for a clean database to become degraded to the point of being unusable. A fluid approximation is developed and two solution methods are proposed. The accuracy of those solutions is examined thoroughly, for databases with different sizes, structures and parameter settings, using simulations as a basis of comparison.
Molecular Structure of Substituted Phenylamine α-OMe- and α-OH-p-Benzoquinone Derivatives. Synthesis and Correlation of Spectroscopic, Electrochemical, and Theoretical Parameters

Thirteen C6 para-substituted anilinebenzoquinones derived from perezone (PZ) (2-(1,5-dimethyl-4-hexenyl)-3-hydroxy-5-methyl-1,4-benzoquinone) were prepared to analyze the effect of the substituents on quinone electronic properties. The effect of a hydrogen bond between the α-hydroxy and carbonyl C4−O4 groups was determined in perezone derivatives by substituting electron-donor and electron-acceptor groups such as −OMe, −Me, −Br, and −CN and comparing the −OH (APZs) and −OMe (APZms) derivatives. Reduction potentials of these compounds were measured using cyclic voltammetry in anhydrous acetonitrile. The typical behavior of quinones, with or without α-phenolic protons, in an aprotic medium was not observed for APZs due to the presence of coupled, self-protonation reactions. The self-protonation process gives rise to an initial wave, corresponding to the irreversible reduction reaction of quinone (HQ) to hydroquinone (HQH2), and to a second electron transfer, attributed to the reversible reduction of perezon.
Supplementary Material of ET-NET: Error Transition Network for Arbitrary Style Transfer

We assemble the non-local block into the bottleneck layer of the proposed framework to capture the long-range dependencies between pixels and make the processed error features more compatible to the current stylized results. The architecture of the revised non-local block is shown in Figure 1. Different from the way used in [8], where all the inputs come from the same image, we feed the top feature of stylized image f in and the full error feature ∆E 4 into the block. Then we measure the similarities between the error feature of one pixel and the features for stylized result at other locations to determine what error information to be transited from one pixel to another. In this way, we find that it is effective to capture long-range dependency with the advantages in respecting texture consistency.
INDOOR BIOMONITORING OF PARTICLE RELATED POLLUTION: TRACE ELEMENT CONCENTRATION IN AN OFFICE ENVIRONMENT

Since particulate pollution is becoming still bigger threat to human health and environment, there is an urgent need of monitoring its degree not only in the outdoors, but indoors, especially in the workplace, as well. Biomonitoring of atmospheric heavy metals and metalloids using bryophytes is rather common procedure. There has been discovered both strong affinity of these elements to the particulate matter and, albeit so far to a lesser extent, a correlation between the amount of the elements in question in the bryophyte material and concentration of the particulate matter in atmosphere. Two distinct ways of biomonitoring techniques were assessed in order to establish their applicability in indoor pollution monitoring. First one was the devitalization of the bryophyte matter (Hylocomium splendens) by drying and the second one was continuous irrigation of live matter with distilled water. Three transplants of both treatments were placed in a standard workplace (office) with the overall period of exposition of 49 days. The samples were taken in a weekly period and analyzed for the contents of Cu, Pb, Sb, Si and Hg by atomic absorption spectroscopy, advanced mercury analyser 254 and inductively coupled plasma – atomic emission spectroscopy. The results are presented in figures and maps with isolines describing the accumulation of elements. It can be concluded that the irrigated moss bag method is more suitable for this kind of experiment and, generally, that active biomonitoring using bryophytes is useful for investigation of particle related pollution in such closed spaces.
Phyllanthus niruri and Chrysanthellum americanum in association with potassium and magnesium citrates are able to prevent symptomatic episode in patients affected by recurrent urinary stones: A prospective study.

OBJECTIVE The aim of this study is to evaluate the efficacy of a food supplement containing Phyllanthus niruri and Chrysanthellum americanum in association with potassium and magnesium citrates in the treatment and prophylaxis of urinary stones. MATERIALS AND METHODS Eighty-two patients (mean age 49.7 ± 11.2) with history of urinary stones received this food supplement, one capsule a day for 6 months. Each administration contained a combination of the following ingredients: 244 mg Potassium citrate, 735 mg Magnesium citrate, Phyllanthus (Phyllantus niruri) herb d.e. 15% mg Tannins 220 mg, Chrysanthellum (Chrysanthellum americanum Vatke) plant d.e. ¼ 55 mg. After 6 months, all patients underwent urologic visit, urinalysis, imaging and quality of life (QoL) questionnaires evaluation. Each patient was also evaluated by computed tomography (CT) scan at baseline and at 6 months. RESULT From January 2018 to March 2019, 82 patients (mean age 49.7 ± 11.2) completed the follow-up period and were analyzed. Fifty patients showed lower stone dimensions (60.9%). The average stone size was 0.9 mm, with a significant reduction in comparison with the baseline (-6.7 mm ± 3 mm) (p < 0.001). Forty-nine patients (59.7%) did not show any symptomatic episode with an improving in QoL (+0.4 ± 0.1) (p < 0.001) in comparison with the baseline. At the end of the follow-up period, 27 patients out of 82 were stone-free (32.9%). Moreover, we report a significant reduction of patients with asymptomatic bacteriuria (ABU) between the baseline and the end of the follow-up evaluation (p < 0.001). CONCLUSIONS In conclusion, this food supplement is able to improve quality of life in patients with urinary stones, reducing symptomatic episodes and the prevalence of ABU.
Combinatorial Evaluation of Antiviral Activity of some Nigerian Medicinal Plants on SARS-CoV-2Combinatorial Evaluation of Antiviral Activity of some Nigerian Medicinal Plants on SARS-CoV-2

The coronavirus disease COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) has presented unprecedented challenges to the healthcare systems in the world. There are no definite effective therapeutic agents or vaccines against the virus currently. However clinical management of the infection includes prevention, control measures, supportive care and repurposed drug therapy based on pathophysiology of the virus and manifestation of the disease condition thereby using antiviral agents such as remdesivir, lopinavir and favipiravir. Herbal preparations are being promoted for the management of Covid-19. Some selected Nigerian medicinal plants are hereby investigated by In-silico studies of the plant constituents. When compared with the listed therapeutic agents, the phytochemical constituents of the selected plants have better binding affinity to several Covid-19 viral target proteins. Also they were found to be safe for human use with LD50 of >2000 mg/Kg for the plant extracts. Some of the plants also contained phytochemicals that can be employed for the symptoms of covid-19.
BOARD DIVERSITY & FINANCIAL PERFORMANCE – EVIDENCE FROM LISTED INDIAN

The essence of board diversity has been imbibed in the legislature with the advent of new Companies Act, 2013 and revised clause 49 of the SE BI listing agreement of stock exchange in India, mandating to include at least one women dire ctor and specifying the minimum number of independent directors in the board of companies lis ted in India. The outcome of increasing research from developed economies have indicated th benefits of having a diversity balanced board in the company, which includes better compreh ension of the market, business growth, increased innovation among few. Rather than restric ting to gender diversity this study has examined the statutory aspect of the diversity also that is, board independence. The present study undertakes a sample of NIFTY 500 index companies li sted in India in the National stock exchange belonging to different sectors – informati on technology, healthcare, pharmaceutical, and consumer goods. This finds that gender diversit y is positively related with Tobin’s Q (proxy for company performance) and board independence is not related with Tobin’s Q.
Thermodynamic models for water sorption by grape skin and pulp

ABSTRACT The enthalpy-entropy compensation theory was applied to water sorption for grapes of Italy variety. The moisture sorption isotherms were analyzed using the static gravimetric method at 35, 40, 50, 60, 70 and 75° C. For isotherms construction, the skin and pulp of the grape were used separately and it was possible to observe significant differences. The GAB equation was fitted to the experimental data, using direct nonlinear regression analysis; the agreement between experimental and calculated values was satisfactory. The net isosteric heat or enthalpy of water sorption, determined from the equilibrium sorption data, showed a different behavior when compared with other works, as it was obtained for skin and pulp separately. Plots of Ah vs AS for skin and pulp provided the isokinetic temperatures TBS = 423.2 ± 27.6 K and TBP = 424.5 ± 25.3 K, respectively, indicating an enthalpy-controlled desorption process over the whole range of moisture content considered.
Tool for estimating the probability of having COVID-19 with one or more negative RT-PCR results

Early case detection and isolation of infected individuals are critical to controlling COVID-19. RT-PCR is considered the diagnosis gold standard, but false-negatives occur. Based on previous work, we built a user-friendly online tool to estimate the probability of having COVID-19 with negative RT-PCR results and thus avoid preventable SARS-CoV-2 transmission.
AS-Net: Fast Photoacoustic Reconstruction With Multi-Feature Fusion From Sparse Data

Photoacoustic (PA) imaging is a biomedical imaging modality capable of acquiring high-contrast images of optical absorption at depths much greater than traditional optical imaging techniques. However, practical instrumentation and geometry limit the number of available acoustic sensors surrounding the imaging target, which results in the sparsity of sensor data. Conventional PA image reconstruction methods give severe artifacts when they are applied directly to the sparse PA data. In this paper, we firstly propose to employ a novel signal processing method to make sparse PA raw data more suitable for the neural network, concurrently speeding up image reconstruction. Then we propose Attention Steered Network (AS-Net) for PA reconstruction with multi-feature fusion. AS-Net is validated on different datasets, including simulated photoacoustic data from fundus vasculature phantoms and experimental data from in vivo fish and mice. Notably, the method is also able to eliminate some artifacts present in the ground truth for in vivo data. Results demonstrated that our method provides superior reconstructions at a faster speed.
Enriching Non-Autoregressive Transformer with Syntactic and Semantic Structures for Neural Machine Translation

The non-autoregressive models have boosted the efficiency of neural machine translation through parallelized decoding at the cost of effectiveness, when comparing with the autoregressive counterparts. In this paper, we claim that the syntactic and semantic structures among natural language are critical for non-autoregressive machine translation and can further improve the performance. However, these structures are rarely considered in the existing non-autoregressive models. Inspired by this intuition, we propose to incorporate the explicit syntactic and semantic structure of languages into a non-autoregressive Transformer, for the task of neural machine translation. Moreover, we also consider the intermediate latent alignment within target sentences to better learn the long-term token dependencies. Experimental results on two real-world datasets (i.e., WMT14 En-De and WMT16 En- Ro) show that our model achieves a significantly faster speed, as well as keeps the translation quality when compared with several state-of-the-art non-autoregressive models.
Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification?

Text classification is a fundamental problem in the field of natural language processing. Text classification mainly focuses on giving more importance to all the relevant features that help classify the textual data. Apart from these, the text can have redundant or highly correlated features. These features increase the complexity of the classification algorithm. Thus, many dimensionality reduction methods were proposed with the traditional machine learning classifiers. The use of dimensionality reduction methods with machine learning classifiers has achieved good results. In this paper, we propose a hybrid feature selection method for obtaining relevant features by combining various filter-based feature selection methods and fastText classifier. We then present three ways of implementing a feature selection and neural network pipeline. We observed a reduction in training time when feature selection methods are used along with neural networks. We also observed a slight increase in accuracy on some datasets.
Graphical models for financial time series and portfolio selection

We examine a variety of graphical models to construct optimal portfolios. Graphical models such as PCA-KMeans, autoencoders, dynamic clustering, and structural learning can capture the time varying patterns in the covariance matrix and allow the creation of an optimal and robust portfolio. We compared the resulting portfolios from the different models with baseline methods. In many cases our graphical strategies generated steadily increasing returns with low risk and outgrew the S&P 500 index. This work suggests that graphical models can effectively learn the temporal dependencies in time series data and are proved useful in asset management.
CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media Text

Code-mixing(CM) is a frequently observed phenomenon that uses multiple languages in an utterance or sentence. CM is mostly practiced on various social media platforms and in informal conversations. Sentiment analysis (SA) is a fundamental step in NLP and is well studied in the monolingual text. Code-mixing adds a challenge to sentiment analysis due to its non-standard representations. This paper proposes a meta embedding with a transformer method for sentiment analysis on the Dravidian code-mixed dataset. In our method, we used meta embeddings to capture rich text representations. We used the proposed method for the Task:"Sentiment Analysis for Dravidian Languages in Code-Mixed Text", and it achieved an F1 score of $0.58$ and $0.66$ for the given Dravidian code mixed data sets. The code is provided in the Github https://github.com/suman101112/fire-2020-Dravidian-CodeMix.