Miracle-dz/UniFAR-FTUs huggingface dataset:
ETHNOBOTANICAL NOTES ON THANGMI PLANT NAMES AND THEIR MEDICINAL AND RITUAL USES

Over the past six years, in the course of documenting the grammar of the Thangmi language, I have found the lexicon to be replete with indigenous names for local flora and fauna. Many of these indigenous terms are falling into disuse, or being replaced by Nepali words as fluency in the national language increases. Older Thangmi speakers have encouraged me to document the local botanical terminology, and the medicinal and ritual uses of the plants as a record for younger generations of Thangmi speakers as well as for the international scholarly community. With this goal in mind, the present article offers a list of Thangmi lexical items for flora. Since I am untrained in ethnobotany and taxonomy, the vernacular English terms and Latin names which I have included may in some cases be incorrect and I am unable to ensure accuracy. However, I have crosschecked each term and its associated ritual or medicinal uses with at least three native speakers of Thangmi, and the Nepali and Latin names for plants, alongside the common vernacular English terms, are always provided when known. The list has been ordered according to a modified Roman alphabetical ordering, with aspirates such as /kh/ and /th/ following the unaspirated series. After each Thangmi term, (D) for Dolakhâ and/or (S) for Sindhupâlcok are shown to indicate the Thangmi speech variety in which the word is attested. While many botanical terms are common to both dialects, some terms are noticeably different.
Výchova ke zdraví na základní škole pro sluchově postižené

Tema bakalařske prace Výchova ke zdravi na zakladni skole pro sluchově postižene, jsem si vybrala na zakladě mých předeslých zkusenosti s jedinci se sluchovým postiženim a zaroveň kombinaci toho, že studuji obor výchova ke zdravi. Tato prace je rozdělena na teoretickou cast, kde se věnuji zpracovanim odborne literatury a na cast praktickou, ktera je zpracovana na zakladě kvalitativniho výzkumu prostřednictvim technik jako je analýza pozorovani, rozhovorů a dokumentace. Cilem prace je zjistěni, jakým způsobem je předmět výchova ke zdravi vyucovan na zakladni skole pro sluchově postižene. Dilcim cilem je analyzovat faktory, ktere ovlivňuji výuku tohoto předmětu.
Fast tracking using edge histograms

This paper proposes a new algorithm for tracking objects and objects boundaries. This algorithm was developed and applied in a system used for compositing computer generated images and real world video sequences, but can be applied in general in all tracking systems where accuracy and high processing speed are required. The algorithm is based on analysis of histograms obtained by summing along chosen axles pixels of edge segmented images. Edge segmentation is done by spatial convolution using gradient operator. The advantage of such an approach is that it can be performed in real-time using available on the market hardware convolution filters. After edge extraction and histograms computation, respective positions of maximums in edge intensity histograms, in current and previous frame, are compared and matched. Obtained this way information about displacement of histograms maximums, can be directly converted into information about changes of target boundaries positions along chosen axles.
Validation of a Portable Electronic Visual Acuity System

Background: The use of tablet devices and smartphones in medicine as assessment tools is becoming more widespread. These devices now run mobile applications or ‘‘apps’’ that have traditionally been the domain of desktop computers or more dedicated hardware. It is important that health professionals have confidence in the accuracy of measurements obtained from these new tools. The ‘‘EyeSnellen’’ app for the iPhone/iPad (running Apple Inc’s iOS operating system) allows users to measure visual acuity using a portable Snellen chart installed on a tablet device. Aims: To compare the visual acuity measurements obtained from EyeSnellen iPad app with a standard illuminated Snellen Chart. Methods: Participants were recruited from a tertiary level eye clinic in Western Australia. Visual acuity was measured using the Snellen light box chart and a visual acuity measurement was obtained using EyeSnellen app installed on an Apple iPad mini with the use of an Apple Iphone as a remote that was connected via Bluetooth. Results: 122 eyes were tested. Bland-Altman analysis revealed a mean difference of 0.001 logMAR units between the visual acuity measurements obtained from EyeSnellen app and those taken on the light box chart with 95% limits of agreement of 0.169 to 0.171. Conclusion: The Snellen Chart function on EyeSnellen app is equivalent to the traditional Snellen chart at measuring visual acuity at a test distance of 6 metres.
Thermal-aware memory mapping in 3D designs

DRAM is usually used as main memory for program execution. The thermal behavior of a memory block in a 3D SIP is affected not only by the power behavior but also the heat dissipating ability of that block. The power behavior of a block is related to the applications run on the system, while the heat dissipating ability is determined by the number of tier and the position the block locates. Therefore, a thermal-aware memory allocator should consider the following two points. First, the allocator should consider not only the power behavior of a logic block but also the physical location during memory mapping and second, the changing temperature of a physical block during execution of programs. In this article, we will propose a memory mapping algorithm taking into consideration these two points. Our technique can be classified as static thermal management to be applied to embedded software designs. Experiments show that for single-core systems, our method can reduce the temperature of memory system by 17.1°C, as compared to a straightforward mapping in the best case, and 13.3°C on average. For systems with four cores, the temperature reductions are 9.9°C and 11.6°C on average when L1 cache of each core is set to 4KB and 8KB, respectively.
A Data Mining Based Approach for Introducing Products in SaaS (Software as a Service)

– Introducing new products in cloud based environment that may not be reached related customer. To find exact customers who have interest on new product is a crucial activity in the business. So here we proposed Data Mining Approach ‘Ass ociation Rule Learning ’ to create different associations from various products ’ clusters in that cloud. That may reduce the time or effort for advertising products to the customer side. In this paper we have used Market Basket Analysis also for improving business gain by applying the Data Mining concepts on Cloud Computing.
Convolutional neural network-based automatic image recognition for agricultural machinery

An internet of things-based subsoiling operation monitoring system for agricultural machinery is able to identify the type and operating state of a certain machinery by collecting and recognizing its images; however, it does not meet regulatory requirements due to a large image data volume, heavy workload by artificial selective examination, and low efficiency. In this study, a dataset containing machinery images of over 100 machines was established, which including subsoilers, rotary cultivators, reversible plows, subsoiling and soil-preparation machines, seeders, and non-machinery images. The images were annotated in tensorflow, a deep learning platform from Google. Then, a convolutional neural network (CNN) was designed for targeting actual regulatory demands and image characteristics, which was optimized by reducing overfitting and improving training efficiency. Model training results showed that the recognition rate of this machinery recognition network to the demonstration dataset reached 98.5%. In comparison, the recognition rates of LeNet and AlexNet under the same conditions were 81% and 98.8%, respectively. In terms of model recognition efficiency, it took AlexNet 60 h to complete training and 0.3 s to recognize 1 image, whereas the proposed machinery recognition network took only half that time to complete training and 0.1 s to recognize 1 image. To further verify the practicability of this model, 6 types of images, with 200 images in each type, were randomly selected and used for testing; results indicated that the average recognition recall rate of various types of machinery images was 98.8%. In addition, the model was robust to illumination, environmental changes, and small-area occlusion, and thus was competent for intelligent image recognition of subsoiling operation monitoring systems. Keywords: agricultural machinery, monitoring system, automatic image recognition, convolutional neural network DOI: 10.25165/j.ijabe. 20181104.3454 Citation: Yang K, Liu H, Wang P, Meng Z J, Chen J P. Convolutional neural network-based automatic image recognition for agricultural machinery. Int J Agric & Biol Eng, 2018; 11(4): 200-206.
Empirical and Evidence-based Homeopathy: Back to Basics.

That the theme of this issue is Back to Basics is a critical reminder of the problems faced by the Australian homœopathic community. This year, 2013, its practitioners may face political marginalisation and economic asphyxiation. The right of Australian consumers to choose and be rebated for homœopathy may be restricted by the National Health and Medical Research Council, a commission of the Australian Federal Government, as well as by the actions of the Friends of Science in Medicine. Practitioners at the coalface practice a combination of empirical and evidence-based homœopathy. The results presented in this paper are part of a qualitative research study exploring the reasoning and decision making practices of Australian homœopaths. After gaining ethical approval from the University of Sydney in 2009, I observed, interviewed and recorded 12 participants in urban and regional Australia (2009-2012). Using interpretative phenomenological analysis (IPA) to explore and understand the participants’ clinical reasoning behaviours and experiences, this paper asks why homœopathy needs to go back to basics. Their experiences strongly suggest the need for rigorous reproducible methods for the benefit of patients and for the sustainability of homœopathy.
Reference Services Review: content analysis, 2006‐2011

Purpose – The purpose of this paper is to present a retrospective analysis of the content published in Reference Services Review from 2006 through 2011.Design/methodology/approach – The author manually reviewed the content of all journal issues from 2006 through 2011 using the Emerald web site. The information reviewed was number of articles per issue; author affiliations; article format; and article titles, abstracts, and keywords. The type, size, and location of author institutions were confirmed using institutions' official web sites as necessary.Findings – The paper finds that Reference Services Review has published an average of 40 articles per year. The majority of articles focus on topics in information literacy and reference services, with consistent coverage of emerging technologies, working with diverse populations, library space, and management. The majority of contributors are affiliated with large academic institutions in the USA, with some representation from liberal arts colleges, public li.
Development of machine learning‐based real time scheduling systems: using ensemble based on wrapper feature selection approach

There are two items that significantly enhance the generalisation ability (i.e. classification accuracy) of machine learning‐based classifiers: feature selection (including parameter optimisation) and an ensemble of the classifiers. Accordingly, the objective in this study is to develop an ensemble of classifiers based on a genetic algorithm (GA) wrapper feature selection approach for real time scheduling (RTS). The proposed approach can better enhance the generalisation ability of the RTS knowledge base (i.e. classifier) in comparison with three classical machine learning‐based classifier RTS systems, including the GA‐based wrapper feature selection mechanism, in terms of the prediction accuracy of 10‐fold cross validation as measured according to all the performance criteria. The proposed ensemble classifier RTS also provides better system performance than the three machine learning‐based RTS systems, including the GA‐based wrapper feature selection mechanism and heuristic dispatching rules, under all the performance criteria, over a long period in a flexible manufacturing system (FMS) case study.
Towards a high performance parallel library to compute fluid and flexible structures interactions

Nagar, Prateek. M.S., Purdue University, May 2015. Towards a High Performance Parallel Library to Compute Fluid and Flexible Structures Interactions. Major Professor: Dr. Fengguang Song. LBM-IB method is useful and popular simulation technique that is adopted ubiquitously to solve Fluid-Structure interaction problems in computational fluid dynamics. These problems are known for utilizing computing resources intensively while solving mathematical equations involved in simulations. Problems involving such interactions are omnipresent, therefore, it is eminent that a faster and accurate algorithm exists for solving these equations, to reproduce a real-life model of such complex analytical problems in a shorter time period. LBM-IB being inherently parallel, proves to be an ideal candidate for developing a parallel software. This research focuses on developing a parallel software library, LBM-IB based on the algorithm proposed by [1] which is first of its kind that utilizes the high performance computing abilities of supercomputers procurable today. An initial sequential version of LBM-IB is developed that is used as a benchmark for correctness and performance evaluation of shared memory parallel versions. Two shared memory parallel versions of LBM-IB have been developed using OpenMP and Pthread library respectively. The OpenMP version is able to scale well enough, as good as 83% speedup on multicore machines for ≤ 8 cores. Based on the profiling and instrumentation done on this version, to improve the data-locality and increase the degree of parallelism, Pthread based data centric version is developed which is able to outperform the OpenMP version by 53% on manycore machines. A distributed version using the MPI interfaces on top of the cube based Pthread version has also been designed to be used by extreme scale distributed memory manycore systems.
Once more‐On the extraordinary side effects of drugs

A concise self‐administered questionnaire is described which elicits responses to 40 symptoms usually attributed to psychotropic drugs. The relationship of such symptoms to age, sex, and education leoels in a group of newly admitted psychiatrie outpatients is examined. The symptoms most frequently reported correlate significantly with a standardized self‐rating system form (KDS), suggesting that certain pretreatment symptoms may be somatic concomitants of the patient's illness. It is proposed that the determination of pretreatment incidence of all somatic and psychological symptoms that may be attributed to the administration of drugs is a necessary step in conducting clinical drug trials.
Quantitative and qualitative analysis of collagen in oral submucous fibrosis

Background: Oral submucous fibrosis (OSMF) is considered as a potentially malignant disease, which is associated with betel quid and pan masala chewing. It is thought to be due to the defective collagen synthesis and degradation. The amount of collagen in the diseased varies with the functional stages and histopathological grades of the disease. Aims: The aim of the study was to assess the severity of the disease in oral submucous fibrosis, by correlating the functional staging with the histopathological grading and analyze the collagen distribution in different grades of OSMF using picrosirius red stain under polarizing microscope and also to assess the quantitative changes in collagen with respect to various histopathological grades of OSMF using spectrophotometry. Materials and Methods: The study conducted on 40 subjects, which included 30 subjects with different functional and histopathological grades of oral submucous fibrosis (OSMF), and 10 were in control group. Functional staging and histopathological grading was done based on the definite criteria. A Histopathological and quantitative analysis of collagen was conducted using picrosirius red stain, polarizing microscope and spectrophotometer, respectively. The results were analyzed using chi-square test, and the significance was tabulated. Results: The functional staging and the histopathological grading did not show statistical correlation, but as the histopathological grade of the disease increased; there was a shift in the polarizing color from yellow-green to orange-red. In the colorimetric estimation, the optical densities of the sample decreased as with increased grades of OSMF. Conclusion: In the present study, we observed that the histopathological grading of OSMF is not correlating with the functional staging of OSMF. Therefore, it mandates the proper correlation between the functional staging and the histopathological grading before the commencement of the treatment to render better outcome of the treatment.
Towards an ontology of categories for multimodal annotation

We examine how multimodal data collections, resulting mainly from (psycho)linguistic experiments, can be expressed in standardized metadata description formats. We summarize how such data collections differ structurally from the traditional concept of corpora, and we list thoughts, problems, and solutions that occurred when we designed and collected ISOcat data categories and CMDI components for the metadata representation of these data collections. As a result we present plans for an ontology of modalities and related concepts and data units, which we consider a more appropriate enviroment for the kind of multimodal data we are dealing with.
A Review of: “PSYCHOTROPIC DRUG PRESCRIBER'S SURVIVAL GUIDE: ETHICAL MENTAL HEALTH TREATMENT IN THE AGE OF BIG PHARMA”

Steven Dubovsky is a professor and Chair in the Department of Psychiatry, The State University of New York at Buffalo and a clinical professor of Psychiatry and Medicine at the University of Colorado. He is the author of Clinical Guide to Psychotropic Medications (2005). Amelia Dubovsky is a medical student at New York University School of Medicine and, along with Professor Dubovsky, author of Concise Guide to Mood Disorders (2002). Psychotropic Drug Prescriber’s Survival Guide: Ethical Mental Health Treatment In The Age Of Big Pharma provides an excellent overview of the influences of pharmaceutical marketing on psychotropic prescribing, with emphasis on how clinicians can manage marketing interactions and make informed decisions about the strength of evidence presented. The book is divided into two parts: Part I provides background information and Part II contains practical suggestions. Part I begins with the presentation of a brief review describing the interface of the drug industry with the discipline of psychiatry. Next is a thorough, easy to understand overview of research design, including
A Low Noise Low Power OTA with Adjustable Gain PID Feedback Network for EEG SoC Arrays

Standard electroencephalogram (EEG) exams are subject to noises and interferences that may mask or corrupt signals and may cause a wrong medical evaluation. The noise from the environment is more relevant for discrete topologies, in which the components are placed far apart from other. That problem is more relevant on the neurological amplifiers, where the signals are in the range of tens of microvolts. Thus, the use of integrated circuits for the neurological signal amplification is essential to reduce that interference.
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Estimation of Hurricane Parameters using Artificial Neural Network

In this study, an Artificial Neural Network (ANN) model has been developed and applied to estimate hurricane central pressure CP and Radius to Maximum Winds (RMax). The network has been trained and verified using historical dataset collected from the National Hurricane Centre (NHC). Promising correlation has been observed in data during model training and validation. The successful application of the neural network provides an efficient tool to estimate important hurricane parameters typically used in storm surge calculations. For the application and demonstration of the model, the historical hurricanes which influenced on the Gulf of Mexico (GOM) coast have been used. However, this methodology can be adopted easily to study storm surge in other area also.
MR-Guided Laser Interstitial Thermal Therapy for the Treatment of Medically Refractory Epilepsy

Resective epilepsy surgery is highly effective in the treatment of medically refractory epilepsy. However, it involves an open craniotomy and carries the risks of permanent functional deficits and cosmetic concerns. Multiple minimally invasive surgical approaches have been attempted to destroy seizure foci with minimal damage to surrounding normal tissue. Here we review the currently available minimally invasive surgical approaches for epilepsy, with an emphasis on MR-guided laser interstitial thermal therapy (MRgLITT). The results from multiple centers are reviewed and our center’s experiences with MRgLITT are reported to highlight the advantages and limitations of this new technology.
Adaptation of large vocabulary recognition system parameters

The authors report on a series of experiments in which the hidden Markov model baseforms and the language model probabilities were updated from spontaneously dictated speech captured during recognition sessions with the IBM Tangora system. The basic technique for baseform modification consisted of constructing new fenonic baseforms for all recognized words. To modify the language model probabilities, a simplified version of a cache language model was implemented. The word error rate across six talkers was 3.7%. Baseform adaptation reduced the average error rate to 3.5%, and using the cache language model reduced the error rate to 3.2%. Combining both techniques further reduced the error rate to 3.1%-a respectable improvement over the original error rate, especially given that the system was speaker-trained prior to adaptation. <<ETX>>