Miracle-dz/UniFAR-FTUs huggingface dataset:
The Growth of Health Expenditure in Lesotho

The study analyses the factors behind the growth of health expenditure in Lesotho over the period 1980 to 2011. The cointegration test results reveal that income is one of the important factors explaining the growth of health spending in Lesotho, with public health expenditure being more responsive to changes in income than private health spending. Although the government’s role in disease patterns may be overshadowed by increased external funding, the findings highlight that Lesotho is still committed to improving the overall healthiness of its people. On the other hand, public and private health expenditure are found to follow different paths, with the ability of the citizens to finance their healthcare needs reducing the government’s pressure to offer more health services. External aid programmes also seem to have impacted positively on Lesotho’s public health spending while reducing a burden for privately-financed health services. Keywords : Public Health Expenditure, Private Health Expenditure, Cointegration, Lesotho
Modeling Occurrence of Urban Mosquitos Based on Land Use Types and Meteorological Factors in Korea

Mosquitoes are a public health concern because they are vectors of pathogen, which cause human-related diseases. It is well known that the occurrence of mosquitoes is highly influenced by meteorological conditions (e.g., temperature and precipitation) and land use, but there are insufficient studies quantifying their impacts. Therefore, three analytical methods were applied to determine the relationships between urban mosquito occurrence, land use type, and meteorological factors: cluster analysis based on land use types; principal component analysis (PCA) based on mosquito occurrence; and three prediction models, support vector machine (SVM), classification and regression tree (CART), and random forest (RF). We used mosquito data collected at 12 sites from 2011 to 2012. Mosquito abundance was highest from August to September in both years. The monitoring sites were differentiated into three clusters based on differences in land use type such as culture and sport areas, inland water, artificial grasslands, and traffic areas. These clusters were well reflected in PCA ordinations, indicating that mosquito occurrence was highly influenced by land use types. Lastly, the RF represented the highest predictive power for mosquito occurrence and temperature-related factors were the most influential. Our study will contribute to effective control and management of mosquito occurrences.
Prediction of Drug Indications Based on Chemical Interactions and Chemical Similarities

Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs.
Real-time monitoring and filtering system for mobile SMS

This paper proposed a new method of real-time information monitoring and filtering for mobile short messaging service (SMS) system. This method implements the mobile SMS real-time monitoring and filtering by combining the Pinyin fuzzed keyword matching technology with dynamical adjustment of the userspsila credit-grade. This method firstly classifies the SMS into three categories: legal SMS, doubtful SMS and junk SMS, then creates the keyword dictionary by Bayesian learning and runs parallel real-time filtering on polynuclear hardware platform. It enhances the junk message interception rate and the processing speed of short message service center (SMSC), reduces the misjudgment rate for the monitoring center, and meanwhile gives consideration to interests of both the operators and customers.
Hypermethylation of TUSC5 genes in breast cancer tissue.

AIM Breast cancer (BC) is one of the most common forms of cancer amongst females. Early diagnosis, prognosis and therapy plays crucial role in the survival of patients with breast cancer. The study was aimed on identification of potential markers for early BC diagnostics by means of genome-wide comparative analysis of gene expression in cancer and normal tissue of breast. METHODS The analysis of gene expression in 15 invasive adenocarcinoma specimens and 15 normal breast tissue was conducted using the full-genome microarrays Sentrix HumanWD-6V3 BeadChip (Illumina). Methylation of TP53INK1 and TUSС5 promoters was interrogated by the combined bisulfite restriction analysis (COBRA). RESULTS Analysis of gene expression in the samples of breast adenocarcinoma revealed abnormal expression of more than 2,300 genes. While genes TFF1, S100P, ERBB2, TOP2A, CDF15, HOOK1, DNAJC12, CORO2A were up-regulated in cancer, decreased expression was found for genes TUSC5, SFRP1, PPPQR1B, NTRK4, TIMP4, BARD1, AKR1C2, TP53INK1 and others. Analysis of DNA methylation of TUSC5 by COBRA revealed higher levels of exon 1 methylation (11/12) in samples of breast cancer, whereas the gene was essentially unmethylated in matched normal appearing tissue of breast (2/12). TP53INK1 gene was methylated neither in cancer nor in normalcy. CONCLUSION A total of 149 genes exhibited the highest difference in expression in cancer versus normal appearing tissue of breast. Most prominent down-regulated candidates, TUSC5 and TP53INK1, were reported for the first time in breast cancer and may be considered as potential markers of the disease. Aberrant DNA hypermethylation of TUSC5 suggests epigenetic mechanism of cancer associated down-regulation.
Enhancing Novel Class Detection of Concept Evolving Data Stream

There is a lot of work is done for handling the challenges in data stream mining. Because of some practical aspects it is more challenging job. Main problem of data stream is its temporal behavior. Likewise, infinite length and concept drift are also two aspects. One important term is concept evolution. It is occur when new classes are invoking in data stream. As we know the exiting challenges of infinite length and concept drift, we address concept evolution detection in this paper. In this, enhance approach is used for detection of unseen classes in data stream using adaptive outlier detection, discrete Gini coefficient and multiple unseen classes detection.
Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing

In this paper, we present an algorithm for multidimensional vector regression on data that are highly uncertain and nonlinear, and then apply it to the problem of indoor location estimation in a wireless local area network (WLAN). Our aim is to obtain an accurate mapping between the signal space and the physical space without requiring too much human calibration effort. This location estimation problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. In contrast, our algorithm adopts kernel canonical correlation analysis (KCCA) to build a nonlinear mapping between the signal-vector space and the physical location space by transforming data in both spaces into their canonical features. This allows the pairwise similarity of samples in both spaces to be maximally correlated using kernels. We use a Gaussian kernel to adapt to the noisy characteristics of signal strengths and a Matern kernel to sense the changes in physical locations. By using real data collected in an 802.11 wireless LAN environment, we achieve accurate location estimation for pervasive computing while requiring a much smaller set of labeled training data than previous methods
Standard Business Reporting in Australia: Efficiency, Effectiveness, or Both?

The benefits of using eXtensible Business Reporting Language (XBRL) as a business reporting standard have been widely canvassed in the extant literature, in particular, as the enabling technology for standard business reporting tools. One of the key benefits noted is the ability of standard business reporting to create significant efficiencies in the regulatory reporting process. Efficiency-driven cost reductions are highly desirable by data and report producers. However, they may not have the same potential to create long-term firm value as improved effectiveness of decision making. This study assesses the perceptions of Australian business stakeholders in relation to the benefits of the Australian standard business reporting instantiation (SBR) for financial reporting. These perceptions were drawn from interviews of persons knowledgeable in XBRL-based standard business reporting and submissions to Treasury relative to SBR reporting options. The combination of interviews and submissions permit insights into the views of various groups of stakeholders in relation to the potential benefits. In line with predictions based on a transaction-cost economics perspective, interviewees who primarily came from a data and report-producer background mentioned benefits that centre largely on asset specificity and efficiency. The interviewees who principally came from a data and report-consumer background mentioned benefits that centre on reducing decision-making uncertainty and decision-making effectiveness. The data and report consumers also took a broader view of the benefits of SBR to the financial reporting supply chain. Our research suggests that advocates of SBR have successfully promoted its efficiency benefits to potential users. However, the effectiveness benefits of SBR, for example, the decision-making benefits offered to investors via standardised reports, while becoming more broadly acknowledged, remain not a priority for all stakeholders.
Major-Histocompatibility-Complex Variation in Two Species of Cichlid Fishes from Lake Malawi 1

Lake Malawi in eastern Africa harbors >500 endemic species of cichlid fishes, all of which are believed to have emerged from a single founding population in the past 2 Myr. Molecular characterization of differences among the species could provide important information about the nature of speciation in the period of adaptive radiation. Because of the close relationship, however, molecular variation among the species has been difficult to ascertain. In this communication, we provide evidence for extensive differences, in major-histocompatibility-complex (Mhc) class II genes, between two related species, Pseudotropheus zebra and Melanochromis auratus. We used specific primers to amplify and sequence intron 1 and exon 2 of the class II genes from 18 individuals. Although we found 20 different sequences among the 42 that we produced, there was not a single sequence shared by the two species. Thus the study suggests that different cichlid species of Lake Malawi have different profiles of class II alleles, presumably because the polymorphism present in the ancestral founding population segregated differentially into the various species. These results make Mhc genes an important tool for elucidating speciation.
Estimating Product Characteristics and Spatial Competition in the Network Television Industry

Assessing the demand for products with characteristics that are unobservable or difficult to measure is becoming increasingly important with the growing proliferation and value of such products. Analyzing industry performance and firm competition in these sectors is hindered by the failure of traditional empirical methods to estimate demand for the products of these sectors. This paper focuses on the network television industry to present: (a) an empirical analysis of spatial competition, and (b) a structural approach to estimating product characteristics and consumer preferences in such industries, and (c) optimal network programming and scheduling given the estimated demand system. We use maximum simulated likelihood to estimate a structural model of viewer choice, yielding estimates of the latent characteristics of each show, the distribution of consumers' preferences for these characteristics, and the state dependence of choices. Results indicate the attribute space spans four dimensions of horizontal differentiation and one vertically differentiated dimension. Interpretations of these dimensions reflect the traditional show labels. For example, one of the dimensions represents the degree of realism in a show. Furthermore, the clustering of shows based on the estimated characteristics corresponds to traditional show labels. We identify four clusters --- sitcoms for mature viewers, sitcoms for younger viewers, reality based dramas, and fictional dramas. Regarding strategic behavior, our model suggests the networks should use counter-programming (i.e., differentiated products) within each time slot and homogeneous programming through each night. The estimated show locations reveal an extensive use of these strategies, as well as a limited degree of branding. Nonetheless, by unilaterally changing their schedules to increase both counter-programming and homogeneity, ABC, CBS, and NBC are able to increase their weekly ratings by 16%, 12%, and 15%, respectively. In a Nash equilibrium of the static scheduling game, these gains are reduced to 15%, 6%, and 12% increases.
Additive regularization: fusion of training and validation levels in kernel methods

In this paper the training of Least Squares Support Vector Machines (LS-SVMs) for classification and regression and the determination of its regularization constants is reformulated in terms of additive regularization. In contrast with the classical Tikhonov scheme, a major advantage of this additive regularization mechanism is that it enables to achieve computational fusion of the training and validation levels leading to the solution of one single set of linear equations that characterizes the training and validation at once. The problem of avoiding overfitting on validation data is approached by restricting explicitly the degrees of freedom of the regularization constants. Different restriction schemes are investigated, including an ensemble model approach. The link between the Tikhonov scheme and additive regularization is explained and an efficient cross-validation method with additive regularization is proposed. The new methods are illustrated with several examples on synthetic and real-life data sets.
Exploring the density-dependent structure of blowfly populations by nonparametric additive modeling

Abundances of 12 laboratory populations of the greenbottle blowfly (Lucilia sericata) were recorded every two days for 776 d, with separate counts for larvae, pupae, and adults. Half of the populations were exposed to sublethal dosages of the toxic compound cadmium acetate; the remaining populations were considered controls. Initial density was low for half of the populations in each group, and high for the other half. In all populations, the adult abundance underwent sustained fluctuations. However, cadmium-exposed populations had smaller mean larval and adult densities, and fluctuations in adult abundance were less regular than for controls. Data from the first and the second half of the experimental period were analyzed separately in order to assess the effects of possible long-term changes in the dynamics on the estimates. Nonparametric (generalized) additive modeling (GAM) was used to investigate time series dynamics and, in particular, to explore the density-independent components and the structure of the density-dependent components of the system. Overall, cadmium populations had larger larva-to-adult survival rate and smaller adult survival rate than control populations, and for the second half of the experimental period the reproductive rate was smaller for cadmium populations than for control populations. Estimation of the density-dependent components suggested that survival from larva to adult depended nonlinearly on larval density and that increased larval density had a positive effect on larval survival at low densities. Furthermore, cadmium generally decreased vital rates. However, the analysis suggested that most of the observed differences in dynamical behavior between control and cadmium populations are related to differences in the density-independent components of the demographic rates, rather than differences in the density-dependent structure.
An improved extraction method of individual building wall points from mobile mapping system data

Nowadays, since urban planning and landscape simulations are required to get the "characteristics" of target buildings, especially in structures, the reconstruction of 3D building models, necessary for visualization on a computer, becomes crucial. In 3D landscape modeling, a 3D building model that can well represent reality is always the upmost goal. We have already proposed an automatic extraction method of individual building wall points from a Mobile Mapping System (MMS) data. During the process called "plane extraction" to extract multiple planes, a method called RANSAC has been used. However, it is time-consuming to ensure that planes of all walls are extracted. The reason of this problem is the inlier ratio becomes lower as the process goes on. In this paper, we propose a more efficient method which groups the point cloud of a building into "wall groups" based on building structure to increase the inlier ratio. We show that the execution time of our grouping method is about seven times faster than the previous one.
A homogeneous sample of sub-damped Lyman α systems- I. Construction of the sample and chemical abundance measurements

In this first paper of a series, we report on the use of quasar spectra obtained with the high-resolution Ultraviolet-Visual Echelle Spectrograph (UVES) and available through the European Southern Observatory (ESO) Very Large Telescope (VLT) archive to build the first homogeneous and unbiased sample of sub-damped Lyman a (sub-DLA) systems, absorbers with Hi column densities >10 1 9 cm - 2 but lower than the classical definition of damped Lyman a systems (2 x 10 2 0 cm - 2 ). A systematic investigation of the properties of these systems and a comparison with those of the DLAs (Paper II of this series) is expected to provide new clues on the association of high column density absorbers with galaxies and on the overall evolution of the neutral hydrogen gas mass and metal content in the Universe. In the spectra of 22 quasars which were found suitable for a sub-DLA search, we identified 12 sub-DLAs and one borderline case between the DLA and sub-DLA systems in the redshift interval z = 1.8-4.3. We measured the column densities of H I and of up to 16 additional ions of low, intermediate and high ionization: O I, C II, C IV, Si II, Si iv, N I, S II, Mg I, Mg II, Al II, Al III, Fe II, Fe III, Ni II, Zn II and Cr II. We further investigated the significance of the ionization corrections in the determination of the chemical abundances from the low-ionization ions in the sub-DLA HI column density range. Using the predictions of different ion ratios as a function of the ionization parameter computed using the CLOUDY software package, we have estimated that with the exception of one case, the ionization corrections to the abundances of nine systems for which we were able to constrain the ionization parameter are lower than 0.2 dex for all of the elements except Al II and Zn n down to H I column densities of log N(HI)=19.3 cm - 2 . Finally, we present the first sub-DLA chemical abundance data base, which contains the abundance measurements of 11 different elements (O, C, Si, N, S, Mg, Al, Fe, Ni, Zn and Cr). We took advantage of the lower H I column densities in sub-DLAs to measure, in particular, the O and C abundances using lines that are normally saturated in DLAs.
Pharmacological Inhibition of Glucosylceramide Synthase Enhances Insulin Sensitivity

A growing body of evidence implicates ceramide and/or its glycosphingolipid metabolites in the pathogenesis of insulin resistance. We have developed a highly specific small molecule inhibitor of glucosylceramide synthase, an enzyme that catalyzes a necessary step in the conversion of ceramide to glycosphingolipids. In cultured 3T3-L1 adipocytes, the iminosugar derivative N-(5′-adamantane-1′-yl-methoxy)-pentyl-1-deoxynojirimycin (AMP-DNM) counteracted tumor necrosis factor-α–induced abnormalities in glycosphingolipid concentrations and concomitantly reversed abnormalities in insulin signal transduction. When administered to mice and rats, AMP-DNM significantly reduced glycosphingolipid but not ceramide concentrations in various tissues. Treatment of ob/ob mice with AMP-DNM normalized their elevated tissue glucosylceramide levels, markedly lowered circulating glucose levels, improved oral glucose tolerance, reduced A1C, and improved insulin sensitivity in muscle and liver. Similarly beneficial metabolic effects were seen in high fat–fed mice and ZDF rats. These findings provide further evidence that glycosphingolipid metabolites of ceramide may be involved in mediating the link between obesity and insulin resistance and that interference with glycosphingolipid biosynthesis might present a novel approach to the therapy of states of impaired insulin action such as type 2 diabetes.
Video forgery detection and localization using normalized cross-correlation of moment features

Digital technology enabled tampering of digital videos much easier using sophisticated image/video editing software. As a result, the integrity of image/video content can no longer be taken for granted and a number of forensic related issues arise paving the way for many security concerns. So detection of video forgery has become a critical requirement to ensure integrity of video data. A video forgery detection and localization method based on statistical moment features and normalized cross correlation factor is proposed. The features from prediction-error array are calculated for each frame block (set of a certain number of continuous frames in the video). The normalized cross correlation of those features between duplicated frame blocks will be high as compared to other non-duplicated ones. By using calculated threshold, based on mean-squared error, the duplication is confirmed. The location of duplicated block is also found using the algorithm. Compared to existing video forgery detection results, better true positive rates are attained.