Miracle-dz/UniFAR-FTUs huggingface dataset:
A nuanced study of political conflict using the Global Datasets of Events Location and Tone (GDELT) dataset

As you read this sentence, know that a riot is occurring somewhere in the world. Elsewhere, or perhaps in the same location, government forces are constructing a barricade and a politician is being abducted. These types of politically motivated conflictual events are always occurring. In most places, accounts of these events quickly make their way online in the form of electronic news stories. In this dissertation, I utilize a new datasets called the Global Dataset of Events, Location, and Tone (GDELT) which contains almost 200 million politically relevant events that have been extracted from freely available online news articles. With this data, I analyze the effects of political violence on the Tel Aviv Stock exchange, measure how much civil war effects interstate war, and build an empirical model that generates accurate predictions of future conflict in Afghanistan.
Competition and Challenge on Adopting Cloud ERP

 Abstract —ERP provides businesses flow management and includes manufacturing, accounting, sales and customer relationship management. The new ICT technology, cloud computing, offers an alternative for businesses to conduct ERP. The traditional ERP implementation methodology involves various processes and procedures, which constitute the conditions or means for formulating the actual implementation of ERP projects. It is a time-consuming project. Cloud ERP is then a very good proposition for a start-up, and is simple to deploy, organization need not to bear additional server and other dependent costs. It is also easy and quick to implement an ERP to a business organization. However, on cloud ERP, the challenge of data security, business profit, Internet accessibility, and the total cost become initial issues for businesses to choose a fit one. This study provides a framework for businesses to adopt the cloud ERP.
Coupling Firefly Algorithm and Least Squares Support Vector Regression for Crude Oil Price Forecasting

To improve the prediction accuracy of crude oil price even in current complicated international situation, this paper proposed a novel model linking firefly algorithm (FA) with least squares support vector regression (LSSVR), namely FA-LSSVR. In this hybrid intelligent model, FA is used to find the optimal values of LSSVR parameters (i.e., penalty coefficient and kernel function parameters), in order to achieve fast and accurate prediction results. To evaluate the forecasting ability of FA-LSSVR, its performance is compared with other models, including hybrid intelligent methods (LSSVR models with other popular optimization methods), and single models with given predetermined parameters (i.e., support sector regression (SVR), LSSVR, back-propagation neural network (BPNN), autoregressive integrated moving average (ARIMA)). The empirical results reveal that FA-LSSVR outperforms other benchmarks in terms of prediction accuracy, time saving and robustness, suggesting that the proposed approach is a promising alternative to forecast the crude oil price.
Performance comparison of randomized gossip, broadcast gossip and collection tree protocol for distributed averaging

Gossip and tree-based aggregation algorithms are two popular solutions for distributed averaging in wireless networks. The former uses only local message exchanges and requires no routing structures whereas the latter requires building a spanning tree. In this paper we provide a detailed comparison of their performance in terms of communication overhead, accuracy, latency and energy consumption using the network simulator Castalia. We use randomized gossip, broadcast gossip and the collection tree protocol as practical representatives in each category. Through simulations, we show that broadcast gossip requires, in general, the least communication overhead and lowest latency and energy at the expense of lower accuracy. Randomized gossip requires more transmissions than broadcast gossip, but has higher accuracy. The collection tree protocol requires, in general, the most communication overhead.
Myeloperoxidase Enhances Etoposide and Mitoxantrone-Mediated DNA Damage: A Target for Myeloprotection in Cancer Chemotherapy

Myeloperoxidase is expressed exclusively in granulocytes and immature myeloid cells and transforms the topoisomerase II (TOP2) poisons etoposide and mitoxantrone to chemical forms that have altered DNA damaging properties. TOP2 poisons are valuable and widely used anticancer drugs, but they are associated with the occurrence of secondary acute myeloid leukemias. These factors have led to the hypothesis that myeloperoxidase inhibition could protect hematopoietic cells from TOP2 poison-mediated genotoxic damage and, therefore, reduce the rate of therapy-related leukemia. We show here that myeloperoxidase activity leads to elevated accumulation of etoposide- and mitoxantrone-induced TOP2A and TOP2B-DNA covalent complexes in cells, which are converted to DNA double-strand breaks. For both drugs, the effect of myeloperoxidase activity was greater for TOP2B than for TOP2A. This is a significant finding because TOP2B has been linked to genetic damage associated with leukemic transformation, including etoposide-induced chromosomal breaks at the MLL and RUNX1 loci. Glutathione depletion, mimicking in vivo conditions experienced during chemotherapy treatment, elicited further MPO-dependent increase in TOP2A and especially TOP2B-DNA complexes and DNA double-strand break formation. Together these results support targeting myeloperoxidase activity to reduce genetic damage leading to therapy-related leukemia, a possibility that is enhanced by the recent development of novel specific myeloperoxidase inhibitors for use in inflammatory diseases involving neutrophil infiltration.
New Media in Kenya: Putting Ethnicity in Perspective

The way the media processes, frames and passes on information either to the government or to the people affects the function of the political system. This chapter discusses the interaction between new media and ethnicity in Kenya, Africa. The chapter investigates ways in which the new media reinforced issues relating to ethnicity prior to Kenya‟s 2007 presidential election. In demonstrating the nexus between new media and ethnicity, the chapter argues that the upsurge of ethnic animosity was chiefly instigated by new media‟s influence. Prior to the election, politicians had mobilized their supporters along ethnic lines, and created a tinderbox situation. Thus there is need for the new media in Kenya to help the citizens to redefine the status of ethnic relationships through the recognition of ethnic differences and the re-discovery of equitable ways to accommodate them; after all, there is more strength than weaknesses in these differences.
Lateral interactions in the visual cortex of patients with schizophrenia and bipolar disorder

Background. Schizophrenia is associated with perceptual organization deficits and abnormal neuronal connectivity has been described in early visual areas. The purpose of this study was to investigate the functional integrity of lateral connections in early visual areas of patients with schizophrenia and type I bipolar disorder with a history of psychosis. Method. Twenty-four out-patients with schizophrenia, 22 out-patients with bipolar disorder, and 20 healthy control subjects participated in the study. Using a computer-assisted psychophysical test, contrast thresholds were measured for centrally presented target stimuli (Gabor patches), which were surrounded by two collinear flankers. Target-to-flanker distances were 0, 1, 2, 3, 4, 6, 9 and 12λ. Psychophysical measures were contrast threshold changes at each target-to-flanker distance compared with baseline thresholds determined for isolated targets with no flankers. Clinical measures included IQ, positive, negative, and depressive symptoms. Results. In patients with schizophrenia, flankers did not facilitate contrast detection for target stimuli at 2–6λ distances compared to controls [effect size (Cohen's d): 1·25–1·42]. The inhibitory effect of flankers (0 and 1λ) and contrast thresholds in the absence of flankers were spared. Patients with bipolar disorder did not differ from the controls. Medicated and non-medicated patients displayed similar performances. Positive and negative symptoms and depression did not correlate with contrast threshold values. Conclusions. Excitatory lateral connections in early visual cortex are specifically impaired in patients with schizophrenia, which may contribute to perceptual disorders such as unclear seeing, partial or skewed sight, disrupted rectilinearity, and abnormal figure-ground segregation.
K-Means Cluster Analysis for Image Segmentation

K-Means reasonably divides the data into k groups is an important question that arises when one works on Image Segmentation? Which color space one should choose and how to ascertain that the k we determine is valid? The purpose of this study was to explore the answers to aforementioned questions. We perform K-Means on a number of 2-cluster, 3- cluster and k-cluster color images (k>3) in RGB and L*a*b* feature space. Ground truth (GT) images have been used to accomplish validation task. Silhouette analysis supports the peaks for given k-cluster image. Model accuracy in RGB space falls between 30% and 55% while in L*a*b* color space it ranges from 30% to 65%. Though few images used, but experimentation proves that K-Means significantly segment images much better in L*a*b* color space as compared to RGB feature space. Keywordsevaluation, L*a*b* Color Space, Precision Recall
Prevalence and natural history of ALK positive non-small-cell lung cancer and the clinical impact of targeted therapy with ALK inhibitors

Improved understanding of molecular drivers of carcinogenesis has led to significant progress in the management of lung cancer. Patients with non-small-cell lung cancer (NSCLC) with anaplastic lymphoma kinase (ALK) gene rearrangements constitute about 4%-5% of all NSCLC patients. ALK+ NSCLC cells respond well to small molecule ALK inhibitors such as crizotinib; however, resistance invariably develops after several months of treatment. There are now several newer ALK inhibitors, with the next generation of agents targeting resistance mutations. In this review, we will discuss the prevalence and clinical characteristics of ALK+ lung cancer, current treatment options, and future directions in the management of this subset of NSCLC patients.
Temozolomide Resistance in Glioblastoma Cell Lines: Implication of MGMT, MMR, P-Glycoprotein and CD133 Expression

Background The use of temozolomide (TMZ) has improved the prognosis for glioblastoma multiforme patients. However, TMZ resistance may be one of the main reasons why treatment fails. Although this resistance has frequently been linked to the expression of O6-methylguanine-DNA methyltransferase (MGMT) it seems that this enzyme is not the only molecular mechanism that may account for the appearance of drug resistance in glioblastoma multiforme patients as the mismatch repair (MMR) complex, P-glycoprotein, and/or the presence of cancer stem cells may also be implicated. Methods Four nervous system tumor cell lines were used to analyze the modulation of MGMT expression and MGMT promoter methylation by TMZ treatment. Furthermore, 5-aza-2’-deoxycytidine was used to demethylate the MGMT promoter and O(6)-benzylguanine to block GMT activity. In addition, MMR complex and P-glycoprotein expression were studied before and after TMZ exposure and correlated with MGMT expression. Finally, the effect of TMZ exposure on CD133 expression was analyzed. Results Our results showed two clearly differentiated groups of tumor cells characterized by low (A172 and LN229) and high (SF268 and SK-N-SH) basal MGMT expression. Interestingly, cell lines with no MGMT expression and low TMZ IC50 showed a high MMR complex expression, whereas cell lines with high MGMT expression and high TMZ IC50 did not express the MMR complex. In addition, modulation of MGMT expression in A172 and LN229 cell lines was accompanied by a significant increase in the TMZ IC50, whereas no differences were observed in SF268 and SK-N-SH cell lines. In contrast, P-glycoprotein and CD133 was found to be unrelated to TMZ resistance in these cell lines. Conclusions These results may be relevant in understanding the phenomenon of TMZ resistance, especially in glioblastoma multiforme patients laking MGMT expression, and may also aid in the design of new therapeutic strategies to improve the efficacy of TMZ in glioblastoma multiforme patients.
Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach

This study analyzes the political agenda of the European Parliament (EP) plenary, how it has evolved over time, and the manner in which Members of the European Parliament (MEPs) have reacted to external and internal stimuli when making plenary speeches. To unveil the plenary agenda and detect latent themes in legislative speeches over time, MEP speech content is analyzed using a new dynamic topic modeling method based on two layers of Non-negative Matrix Factorization (NMF). This method is applied to a new corpus of all English language legislative speeches in the EP plenary from the period 1999 to 2014. Our findings suggest that two-layer NMF is a valuable alternative to existing dynamic topic modeling approaches found in the literature, and can unveil niche topics and associated vocabularies not captured by existing methods. Substantively, our findings suggest that the political agenda of the EP evolves significantly over time and reacts to exogenous events such as EU Treaty referenda and the emergence of the Euro Crisis. MEP contributions to the plenary agenda are also found to be impacted upon by voting behavior and the committee structure of the Parliament.
A Systematic Review With Consensus Expert Opinion of Best Reconstructive Techniques After Osseous En Bloc Spinal Column Tumor Resection

Study Design. Systematic literature review and consensus expert opinion. Objective. To provide recommendations on reconstructive constructs for large tumor resections of the spinal column. Four questions were studied: (1) What are the best reconstructive options for single versus multilevel resections? (2) Should short segment fixation be considered in primary tumor reconstruction? (3) How should reconstructive techniques differ at various regions of the spine? (4) Does planned postoperative radiation change the fusion strategy? Summary of Background Data. Primary spinal tumors requiring en bloc resection are rare. Most studies focus on disease-free survival and local recurrence rates. Few studies focus on reconstructive options and outcomes with respect to fusion rates and need for revision. Methods. A literature search was performed from January 1990 to December 2013. Data were combined and construct survivorship summarized. A survey was administered to international spine tumor surgeons, evaluating reconstructive preferences. Results. The search yielded 381 articles, 12 included in the final analysis. Revision rates for anterior reconstruction were similar for autogenous strut grafts (10%), cages (7.7%), and allograft strut grafts (8.3%). There were two reports of revision from short to long segment constructs and three reports of broken pedicle screws, one requiring revision. Expert survey results revealed that most surgeons preferred cages packed with morcelized allograft and autograft (75%) for anterior reconstruction of single-level vertebrectomies, and strut bone grafting at the cervicothoracic junction (65%) and when more than one vertebrae was resected in the mid-thoracic spine (75%). Surgeons may alter their fusion technique if postoperative radiation is planned. Conclusion. Posterior reconstruction with at least two vertebral levels above and below is recommended. Cages should be used for single-level defects and structural bone graft alone, or in combination with a cage, should be used when spanning a defect greater than two vertebral bodies. Planned postoperative radiation may affect fusion strategy. Level of Evidence: N/A
Strategies for Workplace Learning Used by Entry‐Level Physician Assistants

Purpose: Physician assistants (PAs) play an increasingly important role in medicine. While PAs have strong basic science and clinical training in school, typically they don't participate in postgraduate training and are expected to expand their generalists’ competencies through continuing professional education. The purpose of this study was to investigate the methods PAs use during their initial phase of workplace learning and determine which methods are deemed most effective by PAs. Methods: This large, cross‐sectional study of PAs within the first 2 years of practice consisted of a survey of PAs investigating the methods used for assessment of educational needs, instruction, and assessment of learning. Results: The results revealed that PAs generally found that employers are well aware of their learning needs and their supervising physicians play an essential role in their learning. PAs themselves often recognized their learning needs based on perceived gaps in competencies noted with patient encounters. Though many PAs reported having an orientation period, most PAs said that this orientation was not well adapted to their unique learning needs. A variety of methods were used during workplace learning, with those related to real patient problems deemed most helpful by PAs. Although PAs reported that their learning was assessed, review of patient outcome data was uncommon. Conclusions: Consistent with principles of adult learning theory, these findings suggest that PAs find active forms of learning most valuable. Employers and supervising physicians should consider these findings and the available literature on adult learning in developing an environment that is supportive of continuing professional development.
Integrating Heterogeneous Multidimensional Databases

In this paper we present a number of techniques that can be at the basis of a practical integration tool for multidimensional databases. We start by addressing the basic issue of matching heterogeneous dimensions and provide a number of general properties that a dimension matching should fulfill. We then propose two different approaches to the problem of integration that try to enforce matchings satisfying these properties. The first approach refers to a scenario of loosely coupled integration, in which we just need to identify the common information between sources and perform drill-across queries over the original sources. The goal of the second approach is the derivation of a materialized view built by merging the sources, and refers to a scenario of tightly coupled integration in which queries are performed against the view. We finally show how these techniques can be actually used to perform drill-across operations over heterogeneous multidimensional information sources.
Anxiety Predicts Increases in Struggles with Religious/Spiritual Doubt Over Two Weeks, One Month, and One Year

ABSTRACT Struggles with religious/spiritual (r/s) doubt occur when uncertainty, questions, and hesitancy about aspects of r/s beliefs and experiences become a source of conflict. Cross-sectional research suggests that doubt-related struggles correlate with anxiety, yet no studies have investigated the temporal patterning of the associations between these constructs. We employed three large samples (Ns = 268, 527, 292) to examine concurrent and prospective associations between struggles with r/s doubt and anxiety over three time frames: 2 weeks, 1 month, and 1 year. Analyses revealed that (a) each construct exhibited high cross-temporal stability; (b) there were moderate, positive concurrent associations between anxiety and doubt-related struggles; (c) anxiety predicted increases in doubt-related struggles over each time frame; and (d) there was mixed evidence regarding whether doubt-related struggles predicted changes in anxiety over time. The discussion focuses on how anxiety may begin to undermine confidence in one’s r/s worldview, thus destabilizing one’s r/s beliefs.
AGILE RESEARCH IN INFORMATION SYSTEMS FIELD: ANALYSIS FROM KNOWLEDGE TRANSFORMATION PERSPECTIVE

Due to the relative success of agile methods in software development, the idea of having agile processes started to be tested in other areas, for example, agile business process development. This trend already reached the research community and there have appeared some materials that suggest using agility in research projects. Analysis of these suggestions, however, shows that they do not go beyond finding superficial analogy between the concepts of the software development and research projects. The paper presents a deeper analysis of the concept of agile research in Information Systems (IS) based on the analysis of the research projects from the knowledge transformation perspective. As a basis for analysis, the SECI model of Nonaka is used. Based on this analysis, several suggestions are made on how to conduct agile research in IS, e.g. prioritize relevance over vigor, test early for a practical purpose, use own experience and reflections, etc. It is also shown that some research types, like action research and design science, are more suitable for conducting agile research than others. The paper also gives analysis of risks of non-agile research, and presents an example where they are revealed.
Long-Term CPU Load Prediction

In the past few decades, large-scale distributed computing systems such as grids have been widely used to serve a growing number of applications in a time-shared manner. In such environment, resources should be strictly assigned to achieve high performance. Hence, resource monitoring and usage prediction are required for the scheduling. Among these resources, CPU load has a significant effect on the performance. So prediction of CPU load plays an important role in the scheduling. In recent years, some research has been carried out in the field of CPU load prediction. Many prediction models were developed, such as Network Weather Service, the most popular performance prediction system. However, most of them adopt one-step-ahead or short-term prediction strategies, which cannot meet the requirement of the applications with much longer execution time. In this paper, we present a new long-term prediction model applying Fourier transform to exploit the periods of the CPU waves and using tendency-based methods to predict the variation.
THE SHADOWING PROPAGATION MODEL IN UNMANNED AERIAL VEHICLE NETWORKS

Unmanned aerial vehicles (UAVs) have become very popular recently for both civil uses and potential commercial uses, such as law enforcement, crop survey, grocery delivery, and photographing, although they were mainly used for military purposes before. Researchers need the help of simulations when they design and test new protocols for UAV networks because simulations can be done for a network of a size that a test bed can hardly approach. In the simulation of an UAV network it is important to choose a radio propagation model for the links in the network. We study the shadowing radio propagation model in this paper and compare it with the free space model, both of which are available in the ns2 network simulation package. We also show how the choice of the parameters of the shadowing model would impact on the network performance of a UAV network.