Miracle-dz/UniFAR-FTUs huggingface dataset:
Dosage, Efficacy and Safety of Cannabidiol Administration in Adults: A Systematic Review of Human Trials

Considering data from in vitro and in vivo studies, cannabidiol (CBD) seems to be a promising candidate for the treatment of both somatic and psychiatric disorders. The aim of this review was to collect dose(s), dosage schemes, efficacy and safety reports of CBD use in adults from clinical studies. A systematic search was performed in PubMed, Embase and Cochrane library for articles published in English between January 1, 2000 and October 25, 2019. The search terms used were related to cannabis and CBD in adults. We identified 25 studies (927 patients; 538 men and 389 women), of which 22 studies were controlled clinical trials (833 patients) and three were observational designs (94 patients) from five countries. Formulations, dose and dosage schemes varied significantly between studies. Varying effects were identified from the randomized controlled trials (RCTs), more apparent effects from non-RCTs and minor safety issues in general. From the controlled trials, we identified anxiolytic effects with acute CBD administration, and therapeutic effects for social anxiety disorder, psychotic disorder and substance use disorders. In general, studies were heterogeneous and showed substantial risks of bias. Although promising results have been identified, considerable variation in dosage schemes and route of administration were employed across studies. There was evidence to support single dose positive effect on social anxiety disorder, short medium-term effects on symptomatic improvement in schizophrenia and lack of effect in the short medium-term on cognitive functioning in psychotic disorders. Overall, the administration was well tolerated with mild side effects.
Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Cancer is one of the leading causes of death in the world. It is the main reason why research in this field becomes challenging. Not only for the pathologist but also from the view of a computer scientist. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image. The use of full high-resolution histopathology images will take a longer time for the extraction of all information due to the huge amount of data. This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch. We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a mean-shift filter as the data pre-processing of the images. The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images. The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking. For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c. According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72±0.48 for four cross-validations. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.
IoT based Biometric Access Control System

: In this paper, a Biometric Access control system based on IoT is designed and implemented. These systems can be used for security of an environment so that only the authorized persons are allowed to pass or also for attendance measuring purposes. Biometric authentication is the best among security systems because it’s unique and personal. These systems are comprised of biometrics such as fingerprint, iris, etc. Fingerprint based biometric system is a good combination of low cost and high accuracy. Evaluation of person’s authentication is done by updating time, attendance and all related information to a Web server.
Evidence-based retention: Where are we now?

Relapse is unpredictable. Retention is used at the end of orthodontic treatment to resist tooth movements back toward the original malocclusion, but it is also required to resist unwanted tooth movements and disruptions in the occlusion that occur as a result of normal age changes. The approaches to retention vary significantly across the world. This article explores why we need retention and our current understanding of the best evidence-based approach to reducing relapse. There is a lack of high-quality evidence to support several aspects of retention, so the implications for clinical practice are discussed.
Creating Evidence to Advocate the Validity of Results of Clinical Performance in the Undergraduate Surgery Clerkship

Background/ Purpose: Evidential bases were not performed en masse to validate assessment results in the undergraduate Surgery clerkship in King Abdulaziz University (KAU). This study aimed at producing a comprehensive package of evidence to prove validity of students’ clinical performance assessment results (as defined by Messick’s framework). Method: Guided by Messick’s conceptual framework, the problem was analyzed. Hands-on faculty development on creating an exam blueprint was done: 1. Learning objectives (LOs) revised; 2. Alignment secured; 3. Weight of (LOs) determined; 4. Number of items/topic/domain calculated; and 5. Appropriate assessment methods selected.Quantitative evidences as reliability and correlation coefficients of various validity components were calculated. The underlying values that scaffold validity evidences were explored via a Focus Group Discussion and the results analyzed by content analysis. Results: 1. The weight of different domains in the test equally reflected their weight in the curriculum (content validity); 2. Positive unintended consequences resulted from the new assessment approach (consequential validity); 3. There was a statistically significant correlation among various assessment methods that provided evidence for concurrent and predictive validity; 4. Success rates and grades distribution alone could not provide evidence to advocate an argument on validity of results. Conclusion: A newly introduced assessment plan with new tools had to be validated by pursuing a comprehensive, unified approach to create evidence from multiple sources of data in order to support the argument of advocating the assessment results. [Omayma A.E. Hamed; Husain Hamza Jabbad; Hebatullah Alsayed; Asim Alshareef; Mohannad Alzain; Omar I. Saadah; Fatin M. Al-Sayes; Rani Ghazi Ahmad. Creating Evidence to Advocate the Validity of Results of Clinical Performance in the Undergraduate Surgery Clerkship. J Am Sci 2016;12(3):59-71]. ISSN 1545-1003 (print); ISSN 2375-7264 (online). http://www.jofamericanscience. org. 9. doi:10.7537/marsjas12031609.
Development of a photopletysmography based method for investigating changes in blood volume pulsations

The aim of this thesis is to develop and evaluate a prototype for measuring volumetric changes of the blood with PPG. The prototype consist of a sensor plate controlled in Labview and algorithms for signal processing and analysis of results developed in Matlab. A study divided into three parts is performed, where the collected data is used for further development and alternations of the prototype between the study parts. A measurement in the study is divided into three stages before, during and after pressure, where the subject is changing body position between each stage in order to either apply or relieve pressure from the sensor plate. The amplitude changes of the recorded signals are analysed and the results from the stable parts of the measurements are presented as the ratio between before and during pressure. A ratio separated from 1 either show a decrease or an increase of pulsating blood volume as a response to the applied pressure. The results from the study show that there are both large spatial variations and large variations over time in the measurements. Today the prototype does not give repeatable results and there are several uncertainties in the measurement method. An optimal sensor plate would be flexible and have several LEDs over a larger area in order to give reliable result despite spatial variations. Abbreviations and Notations Abbreviations AC signal Alternating Current signal bpm Beats Per Minute DC signal Direct Current signal DAQ Data Acquisition FWHM Full width at half maximum HP filter High-Pass filter I/O Input/Output LED Light Emitting Diode LP filter Low-Pass filter mmHg Millimeters of Mercury PPG Photoplethysmography PIV Pressure-Induced Vasodilation RMS Root mean square SNR Signal to noise ratio SD standard deviation UV Ultraviolet VI Virtual Instrument Notations g Anisotropy m f p Mean free path n Refractive index p Significance r Correlation coefficient r2 Coefficient of determination μa Absorption coefficient μs Scattering coefficient μt Total attenuation coefficient
Performance evaluation of Classification algorithms based on Weather Forecasting Dataset

— Data Mining is the process of extracting hidden knowledge from a large volume of data [7]. In this paper different types of classification algorithms including Naive Bayes, Logistic Regression, Random Forest and Support Vector Machine (SVM) algorithms have been comparatively tested to find the optimum algorithm for classification. Commonly a classification technique follows three approaches Machine Learning [9][10], Neural Network and Statistical for classification. This research analyzed the performance evaluation of popular classification algorithms used by machine learning systems. The main aim of the classification algorithm is to produce a good classification model which classifies the data accurately based on the training data set. Each classifier was tested with respect to accuracy, performance and execution time using the measured values. The experiments were carried out using the WEKA data mining tool, which includes the implementation of machine learning algorithms.
The pricing of environmental risks with non-pecuniary benefits

Investors may accept financial returns from environmental assets that are lower than those justified by their risk, if additional benefits are present. We show this using a novel consumption-based asset pricing model that allows for risk-free assets and equities along with environmental assets. We assume that the later produce non-pecuniary utility dividends in addition to financial returns. Under realistic parameter assumptions, our model predicts discount rates for the environmental assets that are close those of the risk-free asset although their risk profile resembles that of equities. The results lend support to the much debated practice of adopting low discount rates in the cost-benefit analysis of long-term environmental damages. JEL classification: G12; H43; Q51; Q57
Application Note Designing micropillar strain rate jump tests to study time dependent plasticity

Nanocrystalline and ultra-fine grained materials show enhanced strain rate sensitivity (SRS) in comparison to their coarse grained counterparts. Conventionally, SRS of these materials has been measured on bulk specimens, either in compression or in tension. With the emergence of electrodeposition and magnetron sputtering as promising methods for deposition of controlled microstructures of nanocrystalline and twinned materials, it has become necessary to adapt bulk mechanical tests to smaller samples. Due to inherent difficulty in micro-sample preparation and testing, majority of the micro and nano-mechanical efforts to measure strain rate sensitivity involve approaches based on nanoindentation [1-3]. The current study aims to extend the repertoire of nanomechanical testing approaches for strain rate sensitivity measurements by designing micropillar strain rate jump tests on nanocrystalline nickel. The results from these tests will be compared directly with more established methods like tensile and nanoindentation strain rate jump tests. For maximum comparability, all tests will be performed on the same sample.
Back-propagation through STL Speciﬁcations: Infusing Logical Structure into Gradient-Based Methods

This paper presents a technique, named stlcg , to compute the quantitative semantics of Signal Temporal Logic (STL) formulas us-ing computation graphs. This provides a platform which enables the incorporation of logic-based speciﬁcations into robotics problems that beneﬁt from gradient-based solutions. Speciﬁcally, STL is a powerful and expressive formal language that can specify spatial and temporal properties of signals generated by both continuous and hybrid systems. The quantitative semantics of STL provide a robustness metric, i.e., how much a signal satisﬁes or violates an STL speciﬁcation. In this work we devise a systematic methodology for translating STL robustness formulas into computation graphs. With this representation, and by leveraging oﬀ-the-shelf auto-diﬀerentiation tools, we are able to back-propagate through STL robustness formulas and hence enable a natural and easy-to-use integration with many gradient-based approaches used in robotics. We demonstrate, through examples stemming from various robotics applications, that the technique is versatile, computationally eﬃcient, and capable of injecting human-domain knowledge into the problem formulation.
SUPPLEMENT TO “NONLINEAR TAX INCIDENCE AND OPTIMAL TAXATION

As in the partial-equilibrium environment with exogenous wages, the uniqueness of the solution to the individual first-order condition allows us to apply the IFT. The condition |p(y(θ))e(θ)|< 1 ensures that the elasticities ε r (θ) ε w(θ) are well-defined. Specifically, the condition p(y(θ))e(θ) > −1 ensures that the second-order condition of the individual problem is satisfied. The condition p(y(θ))e(θ) < 1 ensures the convergence of the labor supply responses toward the fixed point that characterizes the elasticities along the nonlinear budget constraint. Finally, the condition |εS w(θ)/εD w(θ)| < 1 ensures that the equilibrium labor elasticities εw(θ) introduced in Lemma 1 are well-defined. One-to-one Map Between Skills, Wages, and Incomes. Without loss of generality, we order skills θ so that wages w(θ) are strictly increasing in θ in the initial equilibrium. Next, note that the individual first-order condition (1) implies that the elasticity of income with
Impression Materials and Techniques used by Practitioners in Saveetha Dental College, Chennai- A Survey

Aim: The aim of this article is to conduct a questionnaire based survey on the various impression materials and techniques used by practitioners in Saveetha Dental College, Chennai. Background: Prosthodontics, as a speciality, has evolved abundantly in the past few years. Various impression materials such as elastomeric impression materials, irreversible hydrocolloids, etc and techniques have come into use today and all of them have some advantages and disadvantages based on the situation for use. A questionnaire will be prepared and 50 practitioners in Saveetha Dental College, Chennai will be asked to fill it in order to assess the same. Result: Irreversible hydrocolloid was the most commonly used material for primary impressions. Most practitioners adopted the selective pressure theory while making the impressions. Majority of the practitioners use a spacer covering only the secondary stress bearing and relief areas. The thickness of the spacer is decided based on the amount of relief by most dentists. A spacer is used along with light body addition silicone by the majority. Green stick compound is the material of choice for border molding the custom tray. Polyvinylsiloxane has taken over eugenol pastes as the material for making final impressions. Consideration for excessive movable tissue is provided by making relief holes. The cost of the material does not significantly influence the choice of material. Reason: As a wide range of products are now available for use, this survey will help provide a better understanding about the most convenient and preferred materials by practitioners.
ISSN 2320-5407 International Journal of Advanced Research (2015), Volume 3, Issue 7, 363-374

Karuna Joshi This paper draws on a case study of women involvement in the water and forest management in lesser Himalayan region of Nainital District, India and provides insights into the characteristics of Communities of Practice (CoPs) prevailing in the hill women community for water and forest management of the region. Social participation in formal and informal communities by the women indicated that more number of women participates in informal community practices. Majority of the women were not the member of any formal organization. Though, most of the women were member of the one society, the office bearers were least, majority of the women were peripheral members in the CoPs of village community and least were active members. As nature of participation of the women, they considered knowledge features as very important giving I rank to “Having informal meetings, fun and nonwork-related activities”. Mediocre sense of belongingness in community of practices was shown by majority of women. The percentage of attitude of women towards Community of Practice as measured by people‟s participation index was highly favorable. Lesser Himalayan region of Nainital district has demonstrated that Communities of Practice and smallholder‟s tacit knowledge can work for each other complementarily and therefore CoPs through smallholder‟s tacit knowledge is highly operational. The CoPs model for natural resource management is of great relevance to other fragile parts of Asia, which are losing natural resources in terms of the twin goals of national and food security.
StepRoute - A MultiRoute Variant Based on Congestion Intervals

—Congestion-aware routing protocols require network statistics which are timely and as precise as possible. Consequently, there is a need to represent congestion information efﬁciently and in a scalable manner. Based on our previous work, we propose a routing protocol, called StepRoute, which achieves these objectives, while retaining the functionality of routing according to local and remote network conditions. By classifying congestion values into different categories, we are able to deliver timely information to routers while retaining a meaningful estimate of the original statistical value. We compare our results with our previous work (MultiRoute) as well as shortest path only routing. We show that our new variant outperforms both shortest path and MultiRoute in terms of throughput. The protocol itself is media independent, but for test purposes we have employed Ethernet.
PUBLIC HEALTH MONITORING ON SOCIAL NETWORS

With the movement of life online, an impressive number of individuals routinely give their behaviors, inclinations and wellordered battles with mental success problems through methods for electronic media frames in the media stages of the organization such as Twitter. In no way, as the standard observer-observer evaluators conducted through self-developed surveys and surveys, did we investigate the reliable revelation of the clinical misery of subtly collected tweets. In the context of the examination of tweets from clients with problematic autodemolent signs in their Twitter profiles, we show the potential to see clinical sadness responses that duplicate the PHQ-9 scheme currently used by physicians. Our survey uses a genuine semi-guided model to study how the reach of these signs and their appearance on Twitter (the extent to which wordplans and topical propensities) are aligned with point-by-point support revelations using techniques for the PHQ-9. Our proactive and adjusted detection instrument can perceive problematic clinical responses with an accuracy of 68% and a precision of 72%.
Fine tuning of MapReduce jobs using parallel K Map clustering

: Advancement and development of information technology has led to huge growth in data, which pose huge challenge into data storage and analysis to conclude meaningful information from data. Size of data ranges from petabyte or exabyte range, to mine information out of large data set will increase the computing capacity. Thus to address and manage this high velocity data growth an advanced processing algorithms and methods are required for data analysis. To achieve same MapReduce word count program and P-KMeans a parallel clustering algorithm used in Hadoop . Through experiment, it has been concluded that execution time reduce when nodes count increases in a cluster, but also some of the important points has been observed while conducting experiment. Various performance change, and plotted results on different performance charts. This paper aims to study MapReduce applications and its performance verification and improvement recorded for Parallel KMeans algorithm on four nodes Hadoop Cluster.
Understanding and reinterpreting Piaget

I am not a psychologist. I am an epistemologist”, Piaget once declared to Bringuier in an interview, admitting that he was generally misperceived. In order to understand him better, his research work is biographically unfolded. Following this, Piaget’s data are reinterpreted within the framework of Holzkamp’s ontogenetic categories. It is shown that Piaget’s hypotheses regarding the developmental dynamics fail to recognize human subjectivity.
Web Documents Clustering Approach Base on Improvise Fuzzy Clustering using Cosine Similarity and Name Entity Recognition Method

—Recent advances in computers and technology have resulted in a growing body of documents. The need is to classify the set of documents according to type. Placing related documents together is convenient for making decisions. Researchers conducting interdisciplinary research acquire repositories on different topics. The classification of the repositories according to the theme is a real need to analyze the research work. The experiments are tested on different sets of real and artificial data, such as NEWS 20, Reuters, emails, research on different topics. The term frequency inverse document frequency algorithm is used together with the fuzzy hierarchical algorithm and K-means. Initially, the experiment is being carried out in small data sets and cluster analysis was performed. The best algorithm applies to the extended data set. Together with the different groups of related documents, the resulting coefficient and the trend of measure F are presented to show the behavior of the algorithm for each data set. Our model combines two components: a mixing component used to discover latent groups in the collection of documents and a theme model component used to mine multigrain issues, including cluster-specific local issues and global topics shared between clusters. We use the variational inference to approximate the posterior part of the hidden variables and learn the parameters of the model. The experiments in two data sets demonstrate the effectiveness of our model.
Does Taking Care of Grandchildren Affect Grandparents' Cognition?

Using the China Health and Retirement Longitudinal Study (CHARLS) baseline data, we explore to what extent grandparental care influences grandparents’ cognition, by considering two dimensions of cognitive ability. In order to address the endogeneity of providing childcare, we adopt an instrumental variable approach. We find no evidence to suggest that care of grandchildren has a positive effect on grandparents cognitive ability. On the contrary, for one of the considered dimensions, episodic memory, we find a substantial negative effect.