Miracle-dz/UniFAR-FTUs huggingface dataset:
The effectiveness β-glucan of shiitake mushrooms and Saccharomyces cerevisiae as antidiabetic and antioxidant in mice Sprague Dawley induced alloxan

The shiitake mushrooms (Lentinula edodes) contain biologically active polysaccharides that mostly belong to the group of β-glucans. Beta-glucan was reported as cell wall components of bacteria, fungi, and yeast. Beta-Glucans naturally contain polysaccharides which can prevent Colorectal cancer, coronary heart disease, high blood glucose levels, insulin resistance, high cholesterol levels, and gut microflora. The aim of the study was to examine the antidiabetic and antioxidant activity of β-glucan from Shiitake mushrooms and S. cerevisiae. The experiment used a glucose tolerance test method to measure a decrease in blood glucose. Thirty one white male mice Sprague Dawley’s induced by Alloxan were divided into 7 groups, each group was given 4 replications. Group I was normal control (untreated mice); group II was negative control (mice were given alloxan at a dose of 80 mg/kg bw and CMC Na); group III was positive control of diabetes (mice were given alloxan at a dose of 80 mg/kg bw and a dose of 5 mg/kg bw.
Adaptive Sensor Fault Detection for Rail Vehicle Suspension Systems

This paper develops an adaptive sensor fault detection scheme for rail vehicle suspension systems with uncertain parameters and sensor faults. A parameterized model of the input-to-output description of the rail vehicle suspension systems is developed, based on which different parameterized output estimators are constructed to detect different unknown sensor fault patterns. As a representative study, three fault detection estimators are presented using estimation errors between the sensor output and the output estimates. Robust adaptive parameter update laws are used to ensure desired system performance of the output estimators, for the construction of the fault detection scheme. The proposed adaptive detection scheme not only can handle large parameter uncertainties in rail vehicle suspension system models, but also can check whether sensor fault occurs in rail vehicle suspension system models or not, and identify the patterns of the sensor faults. Simulation study verifies the effectiveness of the developed adaptive sensor fault detection scheme.
TAM and stages of adoption of blended learning in higher education by application of data mining techniques

The purpose of the study is to analyse and identify the stages of adoption of the blended learning (BL or b‐learning) methodology in higher education contexts, and to assess the relationship of these stages with a set of variables related to personal and professional characteristics, attributes perceived on BL and contextual variables. About 980 active academic staff from 43 Spanish public universities participated. The technology acceptance model (TAM) was used as a theoretical framework for the operational definition of the variables. Through the use of data mining techniques (clustering and decision trees), groupings were made based on the b‐learning adoption stage and a subsequent predictive model of these stages. The results show that the intention to use BL is the most important predictor variable in all models applied. In addition, the relationship between higher frequency of use and experience in digital educational environments with higher stages of implementation of the BL methodology was verified. Regarding the socio‐demographic variables included in the study, it was observed that they exert effects that are marginal in all cases. Practitioner NotesWhat is already known about this topic Many universities and higher education institutions are designing strategic plans and diverse actions to implement and spread the use of BL methodologies.TAM has been widely used to study the behaviour and the intention of using a wide variety of specific technologies in the classroom.The study of BL from the perspective of TAM has generally been carried out using inferential analysis techniques.What this paper adds Analysis of complex systems such as the BL methodology from the TAM approach.Groups of adopting teachers can be analysed using the TAM approach according to the stage of use of this innovation.In contrast to traditional inferential techniques, this paper proposes a methodological procedure based on decision trees, which facilitates a more refined analysis of the relationship and interactions between the set of TAM predictor variables (PU, PEOU, BI, etc.) and the criterion variable (stages of adoption). The obtaining of more complete and adjusted trees leads to better prediction and identification of the concrete actions that could be implemented by higher education institutions as protective or motivating factors for the diffusion of the BL methodology.Implications for practice and/or policy Interest in focusing attention on a complex system such as the one represented by the BL methodology from the perspective of models of technological acceptance reinforces an interesting line of research that can enrich the approach and scope of the TAM theory and generates knowledge that helps promote strategic actions to optimise the institutional diffusion of the BL training modality in higher education contexts. [ABSTRACT FROM AUTHOR] uracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Cytotoxicity, antioxidant and phytochemical screening of propolis extracts from four different Malaysian stingless bee species

Propolis is a plant-derived substance collected by stingless bee’s product from various sources, including plant resins with combination of bee’s saliva and wax. Propolis has been used to treat several diseases since ancient times and it is an important source of bioactive natural compound and drug derivatives. The aim of this study was to evaluate biological and chemical profiles of ethanolic extracts from propolis produced by Heterotrigona itama (HI), Geniotrigona thoracica (GT), Lepidotrigona terminate (LT), and Tretrigona apicalis (TA). Cytotoxicity activity was evaluated by using 3-(4,5-Dimethylthiazol-2-YI)-2,5-Diphenyltetrazolium Bromide (MTT) assay against three cancer cell lines. H. itama extracts showed the highest cytotoxicity effect with of 5 µg/mL, 4 µg/mL and 8 µg/mL for MDA-MB-231, SK-UT-1 and HeLa, respectively. Other species only possessed moderate to weak cytotoxicity effect against tested cells. Phytochemical screening was carried out by thin layer chromatography (TLC) analysis and visualized by derivatives agents in order to detect the presence of terpenoids, steroids, saponins, essential oils and phenol. It was found that H. itama (HI) possessed the highest antioxidant activity with the lowest of 30 µg/mL with percetange of inhibition at 85.69 % evaluated by (2,2-diphenyl-1-picryl-hydrazyl-hydrate) DPPH scaveging assay. In conclusion, bee species was considered as important factor in selecting the quality of propolis. It was found that H. itama produced the most active extract compared to other species. The data obtained from this study would be the basis for further investigation on therapeutic application especially for cytotoxic activity, antioxidant and phytochemical screenings for four Malaysian stingless bees of propolis.
Polish Nonword Span (PNWSPAN): A new tool for measuring phonological loop capacity

The phonological loop, which is a component of working memory, is considered to be one of the most significant factors affecting L1 and L2 learning. In order to measure this construct properly, a reliable instrument in the native language of the participants is needed. The purpose of this paper is to present the Polish Nonword Span PNWSPAN , which is a tool constructed to measure verbal working memory, in particular the phonological loop, in the case of adults. The article presents the theoretical framework of the study and the process of construction of the test, namely its structure, scoring and validation procedure.
Cooperative learning : teori & aplikasi paikem

Dunia pendidikan kita ditandai oleh disparitas antara pencapaian academic standard dan performance standard. Faktanya. banyak peserta didik menyajikan tingkat hafalan yang balk terhadap materi ajar yang diterimanya, namun pada kenyataannya mereka tidak memahaminya. Sebagian besar dari peserta didik tidak mampu menghubungkan antara apa yang mereka pelajari dengan bagaimana pengetahuan tersebut akan dipergunakan/dimanfaatkan. Disparitas terjadi karena pembelajaran selama ini hanyalah suatu proses pengondisian-pengondisian yang tidak menyentuh realitas alami. Pembelajaran berlatar realitas artifisial. Aktivitas kegiatan belajar mengajar selama ini merupakan pseudo pembelajaran. Terdapat jarak cukup jauh antara materi yang dipelajari dengan peserta didik sebagai insan yang mempelajarinya. Materi yang dipelajari terpisah dari peserta didik yang mempelajarinya.
Intensity-duration-frequency relationships of rainfall through the technique of disaggregation of daily rainfall

ABSTRACT Rainfall intensity-duration-frequency (IDF) relationships are a tool that can be used in modeling the transformation of rainfall to runoff, required for the design of hydraulic works. The objective of this study was to verify if there is a significant difference between the intensity-duration-frequency relationships generated using pluviographic records and those determined from pluviometric data. Maximum annual rainfall intensity values were obtained from the disaggregation of maximum daily rainfall and rainfall records in the durations of 5, 10, 15, 20, 30, 60, 120, 360, 720 and 1440 min and for the return periods of 2, 5, 10, 20, 25, 50 and 100 years, in the locality of Pelotas, Rio Grande do Sul state, Brazil (31° 46’ 34’’ S; 52° 21’ 34’’ W, altitude of 13.2 m). By Student’s t-test, it was verified that there is no significant difference between the values of maximum rainfall intensity obtained from pluviographic records and those determined from pluviometric data.
Evaluation of chitosan nanoparticles effects with two application methods on wheat under drought stress

Abstract Chitosan and its components have beneficial effects on a wide variety of plant species. Yet, their effects on wheat plants under drought stress are not well known. So, a field experiment was laid out in order to evaluate the effect of chitosan nanoparticles (NPs) on wheat. The wheat seeds were sown in plots. Then, the chitosan NPs were added to them through soil and foliar application at tillering, stem elongation, and heading stages. Results indicated that the drought stress significantly decreased majority of the studied traits compared to the normal irrigation. Application of the NPs especially 90 ppm increased leaf area (LA), relative water content (RWC), chlorophyll content, photosynthesis rate, catalase (CAT), and superoxide dismutase (SOD) activities, yield, and biomass compared to the control. Finally, our results highlight that usage of the chitosan NPs especially 90 ppm can mitigate adverse effects of drought in the wheat under drought stress.
A system for detecting network intruders in real-time

ISSN: 2348 – 8387 www.internationaljournalssrg. org Page 34 A system for detecting network intruders in real-time #1 Dhivya.J, #2 Saritha.A. , PG Student, Department Of Computer Science and Engineering, AP/CSE, Department Of Computer Science and Engineering School of Engineering, Vels University, Chennai, India. Abstract— In this paper, we propose Securitas, a protocol identification system used for network trace, which exploits the semantic information in protocol message formats. LTE first cleans log messages and then clusters the cleaned log messages based on the DBSCAN algorithm. At last it infers message templates by LDA Gibbs sampling algorithm. Experimental results show that LTE approach infers and gets multiple log message formats at the same time with more than 90% accuracy and 100% recall.
Recent developments and comparative study of magnetically geared machines

This paper overviews the recent developments and various topologies of magnetically geared (MGd) machines. Particularly, current design trends and research hotspots of this kind of MGd machines are emphasized, with the aid of statistic summary of the published papers. According to different evolutions from a magnetic gear (MG), four mainstreams of MGd machines are extracted and compared in terms of both mechanical complexity and electromagnetic performance. By virtue of their inherent features, such as high torque density and multi-power port, the feasibility of MGd machines for applications, where continuously variable transmission (CVT) and power split are demanded, is also described.
Trust in automated vehicles: a systematic review

This systematic review investigates literature regarding trust in automated vehicles and follows the PRISMA guidelines. First, the concept of trust is researched, as well as the different levels of automation that exist. Then, the different experiments that were done are being analyzed. The main focus of this analysis is the interest in the subject and the levels of automation, as well as investigating the experiment materials and measuring methods. The analysis on the levels of automation showed that level three and an unspecified level of automation were the most researched levels. Furthermore, the driving simulator was found to be the most used experiment material. Questionnaires were the most used measuring method. However, psychophysiological measurements did seem to be a promising addition. During the analysis, it became clear that some studies lacked some replicability. This was due to missing information on the levels of automation and the type of questionnaire that was used. The conclusion of this study is that it may point in the right direction for finding a reliable measure of trust and that replicability can be improved upon.
Hybrid Model for Cross Project Fault Prediction Using Random Forests and Multi-Objective Ant Lion Optimization

In software development, the software fault is dealing an important task. The faults presence is not reduce the software quality and increase the cost development. The software system has the prediction in the cross project for the software system, the large number of models is presented. The task of fault prediction is difficult because most of them provide the information as inadequate. The proposed method of this paper is hybrid model for predicting the cross project faults using the random forest (RF) technique and multi-objective Ant Lion optimization (MO-ALO) approach in the given software system. By using the eight software projects, the data in PROMISE data repository is used for the experimental results have been done. The performance evaluation of the method is evaluated with the existing techniques which are Support Vector Machine (SVM), RF and the K-nearest Neighbor (KNN). The results for the number of cross project faults prediction shown in the RF and MOALO based model.
Seasonal Variations, Source Apportionment, and Health Risk Assessment of Heavy Metals in PM2.5 in Ningbo, China

In order to assess the seasonal variations, potential sources, and health risks of heavy metals in fine particulate matter (PM2.5), PM2.5 samples (n = 96) were collected between March 2015 and February 2016 in Ningbo, China. Twelve heavy metals (Sb, As, Cd, Cr, Pb, Mn, Ni, Se, Tl, Al, Be, and Hg) found in the PM2.5 were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). We used enrichment factors and principal component analysis/absolute principal component scores (PCA/APCS) to determine the sources of these heavy metals, and models from the United States Environmental Protection Agency (EPA) to assess both the carcinogenic and non-carcinogenic risks to adults and children. Results showed that the average annual mass concentration of the PM2.5 was 62.7 µg m–3, which exceeded the limit specified in the Chinese National Ambient Air Quality Standards (NAAQS). The average annual concentrations of the Pb, Cd, and As were 57.2 ng m–3, 1.5 ng m–3, and 4.7 ng m–3, respectively, which were below the NAAQS limits. The highest total concentrations for the heavy metals occurred in winter, whereas the lowest concentrations were observed in summer. Enrichment factor analysis indicated that the Sb, Cd, Pb, Se, As, and Tl were mainly from anthropogenic sources. Additionally, source apportionment by PCA/APCS identified four major sources for the studied metals: coal combustion and motor vehicles (46.3%), soil and construction dust (37.1%), steelworks (6.9%), and other smelting industries (6.8%). The carcinogenic risk of heavy metals in Ningbo fell within the safe level of exposure for both children and adults. However, the total non-carcinogenic risk exceeded the safe level (HI = 1.38), which warrants further research on sources of air pollution and measures for controlling pollutants in Ningbo, China.
Analysis and Optimization of Random Caching in Multi-Antenna Small-Cell Networks With Limited Backhaul

Caching at base station is a promising technology to reduce the backhaul loads in future wireless networks. To exploit the content diversity gain and multiple antenna transmission, in this paper, we consider the analysis and optimization of random caching in multi-antenna small-cell networks with limited backhaul. We analyze the performance of the success probability, which is defined as the probability that the requested file is not only obtained from the cache or the backhaul at the small base station (SBS), but also successfully delivered via the wireless link from the SBS to the user. Using tools from stochastic geometry, we first derive an exact expression and an upper bound of the success probability. Based on the analytical results, we then consider network throughput maximization by optimizing the design parameters in the cache-enabled multi-antenna small-cell networks. The optimization problem is a complicated mixed-integer optimization problem. After analyzing the optimal properties, we obtain a local optimal solution with low complexity. To further simplify the optimization, we solve an asymptotic optimization problem in the high user density region, using the upper bound as the objective function. Numerical simulations show that our proposed scheme improves the performance by up to $14\%$ when the Zipf parameter is 0.4 and the antenna number is 8.
Passage of a bubble through the interface between a shear-thinning heavier liquid and a Newtonian lighter liquid

Abstract Numerical simulations are carried out to study the phenomenon of passage of a bubble through the interface between two initially quiescent liquids. The heavier liquid is a viscous shear-thinning liquid whereas the lighter liquid is a viscous Newtonian liquid. The computational model, which is based on axisymmetric computational domain and VOF (volume of fluid) method, is validated by using the data reported in literature. The validated computational model is then used to perform parametric analysis to understand the effects of bubble diameter, interfacial tension between the two liquids and flow index of the heavier liquid on the drainage time, drainage height, retention time and retention height of the bubble at the liquid–liquid interface. Finally, correlations for retention time and retention height are proposed.
Continual Learning with Deep Architectures

Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of Artificial Intelligence (AI) is building an artificial “continual learning” agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex knowledge and skills. However, despite early speculations and few pioneering works, very little research and effort has been devoted to address this vision. Current AI systems greatly suffer from the exposure to new data or environments which even slightly differ from the ones for which they have been trained for. Moreover, the learning process is usually constrained on fixed datasets within narrow and isolated tasks which may hardly lead to the emergence of more complex and autonomous intelligent behaviors. In essence, continual learning and adaptation capabilities, while more than often thought as fundamental pillars of every intelligent agent, have been mostly left out of the main AI research focus. In this dissertation, we study the application of these ideas in light of the more recent advances in machine learning research and in the context of deep architectures for AI. We propose a comprehensive and unifying framework for continual learning, new metrics, benchmarks and algorithms, as well as providing substantial experimental evaluations in different supervised, unsupervised and reinforcement learning tasks.
Universal and Dynamic Locally Repairable Codes With Maximal Recoverability via Sum-Rank Codes

Locally repairable codes (LRCs) are considered with equal or unequal localities, local distances, and local field sizes. An explicit two-layer architecture with a sum-rank outer code is obtained, having disjoint local groups and achieving maximal recoverability (MR) for all families of local linear codes (MDS or not) simultaneously, up to a specified maximum locality <inline-formula> <tex-math notation="LaTeX">$r $ </tex-math></inline-formula>. Furthermore, the local linear codes (thus the localities, local distances, and local fields) can be efficiently and dynamically modified without global recoding or changes in architecture or outer code, while preserving the MR property, easily adapting to new configurations in storage or new hot and cold data. In addition, local groups and file components can be added, removed or updated without global recoding. The construction requires global fields of size roughly <inline-formula> <tex-math notation="LaTeX">$g^{r} $ </tex-math></inline-formula>, for <inline-formula> <tex-math notation="LaTeX">$g $ </tex-math></inline-formula> local groups and maximum or specified locality <inline-formula> <tex-math notation="LaTeX">$r $ </tex-math></inline-formula>. For equal localities, these global fields are smaller than those of previous MR-LRCs when <inline-formula> <tex-math notation="LaTeX">$r \leq h $ </tex-math></inline-formula> (global parities). For unequal localities, they provide an exponential field size reduction on all previous best known MR-LRCs. For bounded localities and a large number of local groups, the global erasure-correction complexity of the given construction is comparable to that of Tamo–Barg codes or Reed–Solomon codes with local replication, while local repair is as efficient as for the Cartesian product of the local codes. Reed–Solomon codes with local replication and Cartesian products are recovered from the given construction when <inline-formula> <tex-math notation="LaTeX">$r=1 $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$h = 0 $ </tex-math></inline-formula>, respectively. The given construction can also be adapted to provide hierarchical MR-LRCs for all types of hierarchies and parameters. Finally, subextension subcodes and sum-rank alternant codes are introduced to obtain further exponential field size reductions, at the expense of lower information rates.