Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the descriptive type answers using hyperspace analog to language and self-organizing map

The question papers of many academic institutions contain a significant part of descriptive type questions. Generally, these question papers follow a fixed pattern. Normally, the descriptive type answers are evaluated manually with the possibilities of deviation in the evaluation of the same answer-script by different examiners with almost the same amount of experience and academic qualifications. The present work focuses on the online evaluation of descriptive type answers which will eliminate the discrepancy in manual evaluation. Evaluation of students' answers in online can be performed using Hyperspace Analog to Language (HAL) procedure and Self-Organizing Map (SOM) method. The answer is given as input to HAL. HAL represents a procedure that processes a corpus of text and produce numeric vectors containing information about its meanings. The clustering method of Kohonen Self-Organizing Maps is applied to the vector. SOM is neural based technique. It takes the vectors as inputs and forms a document map in such a way that nearby neurons contain a similar document. The proposed method will be tested with short answers written by learners of our department. This method has a number of benefits like increased reliability of results, reduced time and effort, reduced burden on the faculty and efficient use of resources.
PTEN Gene: A Model for Genetic Diseases in Dermatology

PTEN gene is considered one of the most mutated tumor suppressor genes in human cancer, and it's likely to become the first one in the near future. Since 1997, its involvement in tumor suppression has smoothly increased, up to the current importance. Germline mutations of PTEN cause the PTEN hamartoma tumor syndrome (PHTS), which include the past-called Cowden, Bannayan-Riley-Ruvalcaba, Proteus, Proteus-like, and Lhermitte-Duclos syndromes. Somatic mutations of PTEN have been observed in glioblastoma, prostate cancer, and brest cancer cell lines, quoting only the first tissues where the involvement has been proven. The negative regulation of cell interactions with the extracellular matrix could be the way PTEN phosphatase acts as a tumor suppressor. PTEN gene plays an essential role in human development. A recent model sees PTEN function as a stepwise gradation, which can be impaired not only by heterozygous mutations and homozygous losses, but also by other molecular mechanisms, such as transcriptional regression, epigenetic silencing, regulation by microRNAs, posttranslational modification, and aberrant localization. The involvement of PTEN function in melanoma and multistage skin carcinogenesis, with its implication in cancer treatment, and the role of front office in diagnosing PHTS are the main reasons why the dermatologist should know about PTEN.
Moving equilibria in the public health care sector: A low-quality trap and a resolution

This paper demonstrates the existence, in a particular subset of the Turkish public health care sector, of equilibria moving towards a low-quality trap over time. The dynamics of the movement in question hinges, in part, on the socially necessary but demographically asymmetric burden, on some public health care institutions, of providing affordable health care to certain sections of the population. The paper formulates a policy option that could help the sector to escape the trap, moving the sector towards high quality-high welfare equilibria.
SST-North American Hydroclimate Links in AMIP Simulations of the Drought Working Group Models: A Proxy for the Idealized Drought Modeling Experiments

Abstract The present study assesses the potential of the U.S. Climate Variability and Predictability (CLIVAR) Drought Working Group (DWG) models in simulating interannual precipitation variability over North America, especially the Great Plains. It also provides targets for the idealized DWG model experiments investigating drought origin. The century-long Atmospheric Model Intercomparison Project (AMIP) simulations produced by version 3.5 of NCAR’s Community Atmosphere Model (CAM3.5), the Lamont-Doherty Earth Observatory’s Community Climate Model (CCM3), and NASA’s Seasonal-to-Interannual Prediction Project (NSIPP-1) atmospheric models are analyzed; CCM3 and NSIPP-1 models have 16- and 14-ensemble simulations, respectively, while CAM3.5 only has 1. The standard deviation of summer precipitation is different in AMIP simulations. The maximum over the central United States seen in observations is placed farther to the west in simulations. Over the central plains the models exhibit modest skill in simulating .
Expression and Function of Osteopontin in Vascular Adventitial Fibroblasts and Pathological Vascular Remodeling

Osteopontin is known to play important roles in various diseases including vascular disorders. However, little is known about its expression and function in vascular adventitial fibroblasts. Adventitial fibroblasts have been shown to play a key role in pathological vascular remodeling associating with various vascular disorders. In this study, we measured activation of Osteopontin and its biological functions in cultured adventitial fibroblasts and injured rat carotid injury arteries induced by balloon angioplasty. Our results showed that angiotensin II and aldosterone increased Osteopontin expression in adventitial fibroblasts in a time- and concentration-dependent manner. MAPKs and AP-1 pathways were involved in Osteopontin upregulation. In addition, Adventitial fibroblast migration stimulated by Angiotensin II and aldosterone required OPN expression. Perivascular delivery of antisense oligonucleotide for Osteopontin suppressed neointimal formation post-injury. We concluded that upregulation of Osteopontin expression in adventitial fibroblasts might be important in the pathogenesis of vascular remodeling after arterial injury.
The potential to improve the choice: list conflict-free coloring for geometric hypergraphs

Given a geometric hypergraph (or a range-space) H=(V,E), a coloring of its vertices is said to be conflict-free if for every hyperedge S ∈ E there is at least one vertex in S whose color is distinct from the colors of all other vertices in S. The study of this notion is motivated by frequency assignment problems in wireless networks. We study the list-coloring (or choice) version of this notion. In this version, each vertex is associated with a set of (admissible) colors and it is allowed to be colored only with colors from its set. List coloring arises naturally in the context of wireless networks. Our main result is a list coloring algorithm based on a new potential method. The algorithm produces a stronger unique-maximum coloring, in which colors are positive integers and the maximum color in every hyperedge occurs uniquely. As a corollary, we provide asymptotically sharp bounds on the size of the lists required to assure the existence of such unique-maximum colorings for many geometric hypergraphs (e.g., discs or pseudo-discs in the plane or points with respect to discs). Moreover, we provide an algorithm, such that, given a family of lists with the appropriate sizes, computes such a coloring from these lists.
Trade Policy Reforms in Pakistan

This paper examines the trade policy reforms in both import and export sectors of Pakistan and provides a critical analysis of the measures undertaken during various regimes to liberalize the economy. The paper discusses the relevant theoretical and empirical research and attempts to evaluate the existing tariff structure under various trade liberalization regimes and substantiate, with the statistics in view of the trade theory, relevant reports and research papers. Alongside it also discusses the tax reforms relevant to trade policy, while briefly discussing key concerns and challenges faced by the policy makers in both formulation and implementation of tariff reforms, such as, lobbying of special interest groups, government revenue constraints and illegal trade across the borders. Finally, it gives an account of export sector and discusses reasons for its weak performance despite government interventions. ii ACKNOWLEDGEMENTS I would like express my sincere gratitude to my research adviser Professor Sajal Lahir for the invaluable guidance before and during the completion of my Master's research paper. I am privileged to having worked under his tutelage and developing a deep appreciation for the subject, which I hope will guide me at every stage in my academic and professional career. Finally, I would like to remember my late father for his love, affection and for everything that he has done for me.
A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWORKS

Wireless sensor network (WSN) is the collection of many micro-sensor nodes, connecting each other by a wireless medium. WSN exhibits different approaches to provide reliable sensing of the environment, detecting and reporting events. In this paper, we have proposed an algorithm for hierarchy based protocols of wireless sensor networks, which consist of two groups of sensor nodes in a single cluster node. Each cluster consists of a three cluster head. The event driven data sensing mechanism is used in this paper and this sensed data is transmitted to the master section head. Hence efficient way of data transmission is possible with larger group of nodes. In this approach, using hierarchy based protocols; the lifetime of the sensor network is increased.
Comparing the Performance of Frequent Pattern Mining Algorithms

Frequent pattern mining is the widely researched field in data mining because of it’s importance in many real life applications. Many algorithms are used to mine frequent patterns which gives different performance on different datasets. Apriori, Eclat and FP Growth are the initial basic algorithm used for frequent pattern mining. The premise of this paper is to find major issues/challenges related to algorithms used for frequent pattern mining with respect to transactional database. General Terms Algorithms.
Seroprevalence and Molecular Evaluation of Toxoplasmosis in Patients Undergoing Chemotherapy for Malignancies in the Bushehr Province, Southwest Iran

Background Toxoplasmosis is a life-threatening infection in organ transplant recipients, people receiving corticosteroid or radiation therapy, people with malignancies, and AIDS patients. Objectives The current study aimed to determine the prevalence of toxoplasmosis in patients receiving chemotherapy for malignancies in the Bushehr province of southwest Iran. Methods Blood samples were taken from 86 patients who were continuously referred to the chemotherapy center in Bushehr province and evaluated by ELISA to determine anti-Toxoplasma IgG and IgM antibodies. Moreover, a blood buffy coat of each sample was assessed by polymerase chain reaction (PCR), targeting a 529 bp gene of T. gondii. PCR products of the positive samples were sequenced to determine the genotype of the parasite. Results Anti-Toxoplasma IgG antibodies were detected in the sera of 21 (24.4%) cases. All of the patients were negative for anti-Toxoplasma IgM antibodies. No statistically significant correlation was found between seropositivity to Toxoplasma and duration of chemotherapy or having contact with cats. PCR detected a 529 bp band of T. gondii in the buffy coats of two out of 86 (2.3%) cases. The sequence analysis demonstrated that both cases were 95% identical to type III (VEG strain) of T. gondii. Conclusions Findings of this study demonstrated the presence of type III T. gondii in the buffy coats of patients undergoing chemotherapy. Given that toxoplasmosis is a life-threatening infection in immunocompromised patients, these patients should be screened for toxoplasmosis before and during chemotherapy to prevent acute toxoplasmosis.
Hybrid Distance Based Document Clustering with Keyword and Phrase Indexing

Document Clustering algorithms group a set of documents into subsets or clusters. Several applications of clustering exist in information retrieval. Our proposed method uses Scatter-Gather approach for clustering group of documents from an entire collection. The selected groupsare merged and the resulting set is again clustered. This process is repeateduntil a cluster of interest is found. This research presents a model for documentclustering that arranges unstructured documents into content-basedhomogeneous groups. The clustering approach uses the popular Cosine similarity measure combined with Euclidian distance measure. To the best of our knowledge, much work has been carried on keyword based clustering and Phrase index based clustering. Our method attempts to combine the two. The method has been applied to standard NewsGroup-20 dataset having documents distributed over 20 different topics. Results have been verified considering fixed number of clusters and different corpora and with variable number of clusters for fixed corpora. Both results indicate a steady increase in the overall purity of clustering compared to the keyword-based clustering method. With Keyword-based clustering, the purity was seen to increase for increasing number of clusters for a fixed corpora, but the purity was observed to decrease with fixed number of clusters and increase in number of corpora. In our method, the increase in purity was more pronounced with increase in number of clusters.
Compact Low-Power Optical/Electrical Devices Enabling Pluggable Coherent Transceivers for Datacom

We devised low-power digital signal processing LSI and compact optical modulators and receivers for 100 Gb/s coherent CFP (100G Form-factor Pluggable) transceivers. We demonstrated error-free operation in an up to 840-km SMF link with one-third the power consumption compared with a conventional 5×7 inch OIF MSA transceiver.
A novel approach for m-representative skyline query

Skyline queries are currently the most notable type of multi-criteria search algorithm. A skyline query returns all of the data points in a given a dataset that are not dominated by other data points. However, this type of query is limited by the fact that the number of results cannot be controlled. In some cases, this can result in an excessive number of results, whereas other cases result in an insufficient number of results. In this study, we propose a scheme referred to as m-representative skyline queries to provide control over the number of results that are returned. We also developed a naive algorithm and a sorted algorithm to provide additional control over the search process. Experiment results demonstrate the efficacy of the proposed approach.
Inquiry into Class Reunions: Nostalgic Stimuli and Preferred Experiences

ABSTRACT This article presents an exploratory study on a class reunion as a nostalgic tourism event. Based on a case study of a college class reunion, this study applied both qualitative and quantitative methods to investigate former classmates’ nostalgic stimuli for attending class reunions and attendees’ preferred nostalgic experiences. Four nostalgia-evoking stimuli are identified, namely, ‘old classmate friendship,’ ‘group identity and sense of “home”,’ ‘commemorative decennials,’ and ‘long time no see coupled with aging. ’ In terms of preferred nostalgic activities, meeting with old classmates is found among the top favorites. Results of the study are discussed from the perspective of DMOs/CVBs about managerial implications. This study provided a conceptual foundation for future studies on class reunions.
Comprehensive analysis of microRNA-Seq and target mRNAs of rice sheath blight pathogen provides new insights into pathogenic regulatory mechanisms

MicroRNAs (miRNAs) are ∼22 nucleotide non-coding RNAs that regulate gene expression by targeting mRNAs for degradation or inhibiting protein translation. To investigate whether miRNAs regulate the pathogenesis in necrotrophic fungus Rhizoctonia solani AG1 IA, which causes significant yield loss in main economically important crops, and to determine the regulatory mechanism occurring during pathogenesis, we constructed hyphal small RNA libraries from six different infection periods of the rice leaf. Through sequencing and analysis, 177 miRNA-like small RNAs (milRNAs) were identified, including 15 candidate pathogenic novel milRNAs predicted by functional annotations of their target mRNAs and expression patterns of milRNAs and mRNAs during infection. Reverse transcription-quantitative polymerase chain reaction results for randomly selected milRNAs demonstrated that our novel comprehensive predictions had a high level of accuracy. In our predicted pathogenic protein-protein interaction network of R. solani, we added the related regulatory milRNAs of these core coding genes into the network, and could understand the relationships among these regulatory factors more clearly at the systems level. Furthermore, the putative pathogenic Rhi-milR-16, which negatively regulates target gene expression, was experimentally validated to have regulatory functions by a dual-luciferase reporter assay. Additionally, 23 candidate rice miRNAs that may involve in plant immunity against R. solani were discovered. This first study on novel pathogenic milRNAs of R. solani AG1 IA and the recognition of target genes involved in pathogenicity, as well as rice miRNAs, participated in defence against R. solani could provide new insights into revealing the pathogenic mechanisms of the severe rice sheath blight disease.
Design of an automatic answering tool for e-learning environment

This work constitutes a new component to be incorporated in e-learning environments. Its objective is to answer automatically the students by exploiting cumulated experiences in past e-learning’ sessions. For instance, this tool will be added to the system we are developing since several years that we have called PERSO. The main objectives of PERSO is to introduce intelligence into e-learning environments by automating a set of its features (e-mail return, student profile determination, dynamic course generation, online assessment and generation of course material at the student level,.)
An Improved CV Level Set Image Segmentation Model Based on Rapid Narrow-Band Method

In this paper, according to the traditional C-V level set model image segmentation of slow speed, low efficiency of initialization and weak ability to capture edge, puts forward a kind of improved C-V level set image segmentation model added edge information of image in model; in the numerical experiment, using narrow band method improved, thereby reducing the amount of computation, improve the efficiency of image segmentation. Experiments show that this algorithm is better than the traditional C-V model compared with the segmentation speed, and the algorithm has low complexity and high accuracy.
Cluster Tendency Assessment for Fuzzy Clustering of Incomplete Data

The quality of results for partitioning clustering algorithms depends on the assumption made on the number of clusters presented in the data set. Applying clustering methods on real data missing values turn out to be an additional challenging problem for clustering algorithms. Fuzzy clustering approaches adapted to incomplete data perform well for a given number of clusters. In this study, we analyse dierent cluster validity functions in terms of applicability on incomplete data on the one hand. On the other hand we analyse in experiments on several data sets to what extent the clustering results produced by fuzzy clustering methods for incomplete data reect the distribution structure of data.
Genotoxicological Safety Assessment of a New Antiparkinsonian Substance ((1r,2r,6s)-3-methyl-6-(Prop-1-en-2-yl)cyclohex-3-ene-1,2-diol)

12 Abstract: Parkinson's disease (PD) is a neurodegenerative condition of unknown etiology. This article is devoted to special genotoxicological testing of a new substance with antiparkinsonian activity. It was assessed using Allium cepa-test system. As a result of study of the effect (1R, 2R, 6S)-3-methyl-6-(prop-1-en-2-yl) cyclohex-3-ene-1,2-diol in cell model object (Allium cepa) we revealed two distinctive features of the effects of substance-itotoxic effect and no increase in the frequency of genetic disorders with increasing concentration of the substance. In general, the tested substance seems safe.
A mixed-binomial model for Likert-type personality measures

Personality measurement is based on the idea that values on an unobservable latent variable determine the distribution of answers on a manifest response scale. Typically, it is assumed in the Item Response Theory (IRT) that latent variables are related to the observed responses through continuous normal or logistic functions, determining the probability with which one of the ordered response alternatives on a Likert-scale item is chosen. Based on an analysis of 1731 self- and other-rated responses on the 240 NEO PI-3 questionnaire items, it was proposed that a viable alternative is a finite number of latent events which are related to manifest responses through a binomial function which has only one parameter—the probability with which a given statement is approved. For the majority of items, the best fit was obtained with a mixed-binomial distribution, which assumes two different subpopulations who endorse items with two different probabilities. It was shown that the fit of the binomial IRT model can be improved by assuming that about 10% of random noise is contained in the answers and by taking into account response biases toward one of the response categories. It was concluded that the binomial response model for the measurement of personality traits may be a workable alternative to the more habitual normal and logistic IRT models.
Phase space properties of local observables and structure of scaling limits

A toute algebre d'observables locales d'une theorie de champs relativistes, est associee une algebre d'echelles permettant d'introduire des transformations de groupe de renormalisation et de construire la limite d'echelle a courte distance de la theorie. Sur la base de ce resultat, nous discutons comment les proprietes d'espace des phases de la theorie determinent sa limite d'echelle. Nous donnons des bornes sur le nombre des degres de liberte locaux apparaissant dans cette limite, lesquelles permettent de discerner entre theories ayant des limites d'echelle classiques et quantiques. Ces resultats peuvent aussi etre utilises pour etablir les proprietes algebriques physiquement significatives des theories de limite d'echelle, comme par exemple la « propriete de separation » (split property).