Miracle-dz/UniFAR-FTUs huggingface dataset:
Toxicity assessment of a new polyherbal medicinal preparation in rats

The tradition of using herbal preparations as a remedy is becoming more pronounced year after year as it is a cheaper alternative medication with lower undesirable effects. Brown algae are a group of seaweeds that has a promising pharmaceutical and biomedical capability in medicine. Cystoseira trinodis (C. trinodis) and Sargassum dentifolium (S. dentifolium) are brown algae belonging to the class Phaeophyceae. Previous studies have demonstrated their hepatoprotective effects and wound healing activities. The current study is a preliminary safety study aiming to investigate any possible toxic effects of an experimental polyherbal preparation (EMP) consisting of two brown algae (S. dentifolium and C. trinodis) plus three plants (garlic, liquorice and ginger) intended to be used orally as a hepatosupportive and immunostimulant supplement. In this study forty Wistar male rats, about two months old, were used to evaluate the acute, subchronic and the chronic adverse effects of the EMP preparation. In the acute study, single oral dose of 5 gm EMP/kg body weight was administered daily for one week. Subchronic and chronic studies were carried out using 250 and 500 mg of the EMP/kg body weight daily for 1 and 3 months, respectively. LD50, haematological, biochemical and redox markers were measured, then histopathological examinations were performed. Results revealed that, administration of EMP in high doses for long duration provoked some adverse effects on liver and kidney; these adverse effects were in doseand time-dependent manners. Further investigations are required to determine the safest dose and duration, as well as the ideal ratio between the EMP constituents.
Borohydrido rare earth based coordinative chain transfer copolymerization: A convenient tool for tuning the microstructure of isoprene/styrene copolymers

A series of lanthanum and neodymium borohydrido complexes comprising the trisborohydrides Nd(BH4)3(THF)3 (1a) and La(BH4)3(THF)3 (1b) and the half-lanthanidocenes Cp*Nd(BH4)2(THF)2 (2a) (Cp* = C5Me5) and Cp*La(BH4)2(THF)2 (2b) has been assessed for the chain transfer copolymerization of isoprene and styrene. A transmetalation process is occurring efficiently with the borohydride complexes in the presence of magnesium dialkyl. The transmetalation is accompanied by (i) a gradual decrease of the 1,4-trans stereoselectivity of the reaction at the benefit of 3,4-selectivity and (ii) an increase in the quantity of styrene inserted in the copolymer. This can be at least partially attributed to a magnesium induced co-oligomerization of isoprene and styrene. By combining dialkylmagnesium and trialkylaluminum, a 1,4-trans stereospecific reversible coordinative chain transfer copolymerization of isoprene and styrene is observed when the half-lanthanocene 2b is used as precatalyst. © 2011 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2011
Undergraduates’ Perception on E-learning Readiness during COVID-19: A case of a Selected University in Sri Lanka

E-learning involves the use of technological advances, tools and digital devices for teaching and learning. In a university context this method of education involves training, delivering and sharing knowledge and ideas. In an extreme situation where teacher and student cannot physically meet, such as the presence of COVID 19 pandemic, the online education is paramount to continue the education. It is crucial to understand the e-learning readiness of users’/learners’ before implementing the e-learning mode. This study involves identifying the readiness of the Sri Lankan undergraduates towards e-learning. Accordingly, this paper mainly explores on three aspects such as perceived self-efficacy, perceived usefulness and technological aspect in relation to e-learning readiness of undergraduates in Sri Lanka and the advantages and disadvantages of e-learning system as per students’ perspective. The survey was carried out through an online questionnaire and was distributed among all the undergraduates in a selected department of one of the universities in Sri Lanka. Altogether 81% of the students responded to the survey out of 224 undergraduates Sri Lankan Journal of Real Estate Department of Estate Management and Valuation University of Sri Jayewardenepura 52 of the department. The analysis explored with descriptive statistics. Overall results of the study indicate that the readiness of students for e-learning activities is positive and above the average in terms of perceived self-efficacy, perceived usefulness and technological aspects. It is recommended to implement a hybrid method according to the nature of the different degree programmes.
Quantifying intrinsic causal contributions via structure preserving interventions

We propose a notion of causal influence that describes the `intrinsic' part of the contribution of a node on a target node in a DAG. By recursively writing each node as a function of the upstream noise terms, we separate the intrinsic information added by each node from the one obtained from its ancestors. To interpret the intrinsic information as a {\it causal} contribution, we consider `structure-preserving interventions' that randomize each node in a way that mimics the usual dependence on the parents and does not perturb the observed joint distribution. To get a measure that is invariant with respect to relabelling nodes we use Shapley based symmetrization and show that it reduces in the linear case to simple ANOVA after resolving the target node into noise variables. We describe our contribution analysis for variance and entropy, but contributions for other target metrics can be defined analogously. The code is available in the package gcm of the open source library DoWhy.
The spring constant calibration of the piezoresistive cantilever based biosensor

Piezoresistive microcantilevers are widely applied to measurements of low forces, masses and viscosity [1]. After surface functionalization they might be used as a biochemical sensors being capable of the intermolecular force investigation. The problem is that such sensors change its mechanical properties in the environment they operate. Therefore there is a need for a high accuracy technique being capable of measuring of mechanical properties of functionalized cantilevers operating in the target environment. We suppose that such conditions meet the analysis of thermomechanical oscillation noise. In this paper the analysis of two types of cantilevers, that might be used in bioelectrochemical measurements, was performed. We determined the cantilever deflection and force sensitivity. The spring constant was measured by three different methods: the Cleveland methods [2] and the thermomechanical noise analysis. The obtained results indicate that analysis of thermomechanical excitation noise is the simplest and the most reliable method for spring constant calibration of piezoresistive cantilever based sensors. © 2010 Published by Elsevier Ltd.
HEAVY METALS AND ITS IMPACT ON TEA CULTIVATED SOIL AND TEA LEAF IN SIVASAGAR DISTRICT OF ASSAM, INDIA

Tea is the most popular beverage in the world and contains several essential nutrients, which are beneficial for human health. Tea leaves also contains heavy metals which are harmful for human health. In this study, the concentration of heavy metals in tea cultivated soil and tea leaf were determined by atomic absorption spectroscopy (AAS). The concentration of Cd, Cr, Ni and Pb in all the soil samples ranged from 1.28-2.58, 48.26-78.42, 43.12-65.28 and 22.74-49.85 mg/kg respectively. The concentration of heavy metals in leaves samples were ranged from 2.02-2.67, 60.2884. 34, 56.18-79.20 and 30.96-50.12 mg/kg respectively. The concentration of heavy metals in the tea soil and tea leaves can be arranged in the following order, with regards to their total contents: Cr > Ni > Pb > Cd. The concentration of heavy metals in tea leaf is slightly higher than the tea cultivated soil. The concentration of heavy metals are within the tolerable level.
A race between SARS-CoV-2 variants and vaccination: The case of the B.1.1.7 variant in France

The SARS-CoV-2 pandemic has entered an uncertain race between the emergence of variants that are more transmissible and vaccine roll-out. Here, we developed a mathematical model to evaluate how the interplay of variants, vaccines and non-pharmaceutical interventions might shape the pandemic dynamics, using the rise of the B.1. 1.7 variant in metropolitan France as a case study. Our analysis highlights the challenges ahead for the management of the SARS-CoV-2 pandemic and shows how the quick roll-out of vaccines to at-risk individuals and non-pharmaceutical interventions are needed to mitigate the impact of the emerging variants.
562 KANE : GEOMAGNETIC , INTERPLANETARY , AND COSMIC RAY STORMS ( a )

Major geomagnetic storms (Dst change exceeding 60 7) and cosmic ray storms (changes exceeding 3% for the Alert neutron monitor) are compared for the period November 1965 through August 1973. The parameters of the two phenomena show virtually no intercorrelation. Interplanetary disturbances, characterized by enhanced number density and speed of solar wind and enhanced magnetic field, are responsible for both these phenomena. For cosmic ray storms, only an interplanetary magnetic blob seems in general to be necessary. However, the quantitative relationship between the total magnetic field and cosmic ray storm magnitudes is poor, and occasionally large blobs produce very small cosmic ray effects or vice versa, often not simultaneously. It is surmised that the blobs as recorded by the present day space probes (confined near the ecliptic plane) are only crude indicators of the main configurations (perhaps magnetic bottles) that cause the cosmic ray changes, with their cores not always hitting the earth directly. For geomagnetic storms the most important parameter seems to be the Bz (southward) component of the interplanetary magnetic field, which may or may not have any relation to the other characteristics of the blob. Several other details are discussed.
Nanotechnology and Chitosan Nanoparticles in Veterinary Medicine

Nanotechnology is the new interesting field of science that enables us to use agents of extremely tiny sizes below 100 nm in novel applications. Although advent of nanotechnology in veterinary medicine isn’t similar as in human medical settings, but this field has been a recently attracted veterinary researcher. Here, we review the recent papers dealt with the uses of nanoparticles in variety of applications in particular chitosan nanoparticles.
Therapeutic Effect of Chitosan Nanoparticles and Metronidazole in Treatment of Experimentally Giardiasis Infected Hamsters

Background: The present study aimed to assess the therapeutic effect of chitosan nanoparticles and metronidazole against Giardia lamblia as well as evaluate the efficacy of loading metronidazole on chitosan nanoparticles. Methods: This study was carried out at medical Parasitology Department, Faculty of Medicine, Zagazig University and Theodor Bilharz Research institute (TBRI) from February 2019 to February 2020 on 45 hamsters. They were divided into 5 groups 9 hamsters each: Group A non-infected hamsters, Group B infected control group, Group C, D and E infected with G. lamblia and treated with Chitosan nanoparticles (CsNPs), metronidazole (MTZ) and metronidazole-loaded chitosan nanoparticles (MTZ-CsNPs) respectively. Results: The highest percentage of reduction in the Giardia cyst and trophozoite counts were in group that received MTZ-CsNPs (94.69%, 94.29%). Lower percentages of reduction were recorded for MTZ treated group (90.15%, 89.52%) and CsNPs treated group (63.64%, 75.24%). Histopathological examination showed marked healing of intestinal mucosa after treatment with MTZ-CsNPs. Conclusion: CsNPs showed a therapeutic effect against Giardia infection in hamsters. Loading of metronidazole on chitosan nanoparticles enhanced therapeutic effect of both CsNPs as well as metronidazole.
Load Balancing and Resource Allocation in Smart Cities using Reinforcement Learning

Technology is being adopted by many municipalities to improve their services and operations through so-called “smart city applications”. Development of these smart city applications are increasingly looking to leverage edge and cloud computing to support these initiatives. This approach brings a number of challenges, including the allocation of tasks to that process data from sensors to computing systems, dealing with the data that may be time sensitive, handling frequently starting and finishing tasks, dynamically changing resource utilization of computing elements, etc. This paper focuses on the challenge of allocating tasks to resources to try to ensure balanced loads on processing elements in dynamic environments. A reinforcement learning approach is introduced to address this problem. The reinforcement learning agent uses a novel Multi-Observation Single-State model based on observed features from multiple sources at a single step. A model of a smart city computational infrastructure is introduced and is used to define our reinforcement learning algorithm for task allocation. We illustrate the agent behavior through simulations.
Comparison of Deep and Traditional Learning Methods for Email Spam Filtering

Electronic mail, or email, is a method for com-municating using the internet which is inexpensive, effective, and fast. Spam is a type of email where unwanted messages, usually unwanted commercial messages, are distributed in large quantities by a spammer. The objective of such behavior is to harm email users; these messages need to be detected and prevented from being sent to users in the first place. In order to filter these emails, the developers have used machine learning methods. This paper discusses different methods which are used deep learning methods such as a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models with(out) a GloVe model in order to classify spam and non-spam messages. These models are only based on email data, and the extraction set of features is automatic. In addition, our work provides a comparison between traditional machine learning and deep learning algorithms on spam datasets to find out the best way to intrusion detection. The results indicate that deep learning offers improved performance of precision, recall, and accuracy. As far as we are aware, deep learning methods show great promise in being able to filter email spam, therefore we have performed a comparison of various deep learning methods with traditional machine learning methods. Using a benchmark dataset consisting of 5,243 spam and 16,872 not-spam and SMS messages, the highest achieved accuracy score is 96.52% using CNN with the GloVe model.
Robots in Groups and Teams

Autonomous robots are increasingly placed in contexts that require them to interact with groups of people rather than just a single individual. Interactions with groups of people introduce nuanced challenges for robots, since robots? actions influence both individual group members and complex group dynamics. We review the unique roles robots can play in groups, finding that small changes in their nonverbal behavior and personality impacts group behavior and, by extension, influences ongoing interpersonal interactions.
Formulating a Crowd State Prediction Problem for Application to Crowd Control

Formulating a Crowd State Prediction Problem for Application to Crowd Control Brooks A. Butler Department of Computer Science, BYU Master of Science This project considers a new application of crowd control, namely, keeping the public safe during large scale demonstrations. This problem is difficult for a variety of reasons, including limited access to informative sensing and effective actuation mechanisms, as well as limited understanding of crowd psychology and dynamics. This project takes a first step towards solving this problem by formulating a crowd state prediction problem in consideration of recent work involving crowd behavior identification, crowd movement modeling, and crowd psychology modeling. We build a non-linear crowd behavior model incorporating components of personality modeling, human emotion modeling, group opinion dynamics, and group movement modeling. This model is then linearized and used to build a state observer whose effectiveness is then tested on system outputs from both non-linear and linearized models. We show that knowledge of the crowd emotion equilibrium is necessary for zero-error convergence; however, other parameters, such as individual personality parameters of crowd agents, may be approximated and zero-error convergence still achieved given the crowd equilibrium point and sign of agent opinions. We conclude that using this model class to predict live crowd emotion may be impractical due to the need for knowledge of individual agent personality parameters to simulate the crowd equilibrium. Directions for future work are discussed.
Ultrafast electron crystallography of graphite

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
A survey on anti-phishing techniques: From conventional methods to machine learning

Phishing is a deceptive technique to steal conﬁdential information like user credentials and bank account details of web users. Employing technical and social engineering skills phishers make huge ﬁnancial loss to web users and large organizations alike, and it has become one of the serious cybercrime today. This paper discusses different types of phishing techniques, their impacts, common indicators of phishing attacks, and analyses various anti-phishing solutions from conventional methods implementing blacklist, white list, heuristics, fuzzy logic, visual similarity, etc. to machine learning methods. The study provides gap analysis of conventional anti-phishing techniques, and points out the challenges facing machine learning based approaches including proper feature selection, diversity in data sets, imbalanced scenarios, and differences in evaluation metrics. This investigation outlines the need for serious researches in this area since there is no foolproof solution to phishing as phishers change their tactics very often to bypass anti-phishing detection systems.
A Comparison of Methods for OOV-Word Recognition on a New Public Dataset

A common problem for automatic speech recognition systems is how to recognize words that they did not see during training. Currently there is no established method of evaluating different techniques for tackling this problem.We propose using the CommonVoice dataset to create test sets for multiple languages which have a high out-of-vocabulary (OOV) ratio relative to a training set and release a new tool for calculating relevant performance metrics. We then evaluate, within the context of a hybrid ASR system, how much better subword models are at recognizing OOVs, and how much benefit one can get from incorporating OOV-word information into an existing system by modifying WFSTs. Additionally, we propose a new method for modifying a subword-based language model so as to better recognize OOV-words. We showcase very large improvements in OOV-word recognition and make both the data and code available.
Cloning and transcriptional analysis of RuBisCO hybrid promoter

Two variants of RuBisCO promoter from zea mays were selected to generate hybrid promoter by using bioinformatics tools. Sequence analysis of both RuBisCO promoter variants revealed several critical cis regulatory elements and transcription factors binding sites within the promoter region. Various regulatory motifs related to constitutive expression were located in RuBisCO promoter fragments. Analysis of cis-regulatory regions has paved way to design synthetic promoters. Both variants were separately cloned in TA vector (pTZ57R/T) and then joined to get the complete hybrid promoter (Rub-H). Hybrid RuBisCO promoter was further cloned in expression vector pGR1. Transient GUS assay revealed that hybrid promoter exhibited endosperm specific expression in wheat. From the study it is demonstrated that hybrid promoter (Rub-H) may be used to derive constitutive expression in monocots. The present work provides an important insight in the designing of hybrid monocot promoters to improve various traits in crops without facing IPR issues. It is expected that complete understanding of the regulatory regions and transcription factors in the regulatory regions would help in designing new synthetic/ hybrid promoters for tissue specific or constitutive expression of transgenes.
CNN-Based Acoustic Scene Classification System

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). This presents the solution to Task 1 of the DCASE 2020 challenge submitted by the Chung-Ang University team. Task 1 addressed two challenges that ASC faces in real-world applications. One is that the audio recorded using different recording devices should be classified in general, and the other is that the model used should have low-complexity. We proposed two models to overcome the aforementioned problems. First, a more general classification model was proposed by combining the harmonic-percussive source separation (HPSS) and deltas-deltadeltas features with four different models. Second, using the same feature, depthwise separable convolution was applied to the Convolutional layer to develop a low-complexity model. Moreover, using gradient-weight class activation mapping (Grad-CAM), we investigated what part of the feature our model sees and identifies. Our proposed system ranked 9th and 7th in the competition for these two subtasks, respectively.
State of the ‘Art’: A Taxonomy of Artistic Stylization Techniques for Images and Video (cid:63)

—This paper surveys the ﬁeld of non-photorealistic rendering (NPR), focusing on techniques for transforming 2D input (images and video) into artistically stylized renderings. We ﬁrst present a taxonomy of the 2D NPR algorithms developed over the past two decades, structured according to the design characteristics and behavior of each technique. We then describe a chronology of development from the semi-automatic paint systems of the early nineties, through to the automated painterly rendering systems of the late nineties driven by image gradient analysis. Two complementary trends in the NPR literature are then addressed, with reference to our taxonomy. First, the fusion of higher level computer vision and NPR, illustrating the trends toward scene analysis to drive artistic abstraction and diversity of style. Second, the evolution of local processing approaches toward edge-aware ﬁltering for real-time stylization of images and video. The survey then concludes with a discussion of open challenges for 2D NPR identiﬁed in recent NPR symposia, including topics such as user and aesthetic evaluation.