Miracle-dz/UniFAR-FTUs huggingface dataset:
Malware in the Mobile Device Android Environment

Consequent to the world wide increase of smartphone use, the incidence of malware developed to exploit smartphone operating systems has exponentially expanded. Android has become the main target to exploit due to having the largest install base amongst the smartphone operating systems and owing to the open access nature in which application installations are permitted. Many Android users are unaware of the risks associated with a malware infection and to what level current malware scanners protect them. This paper tests how efficient the currently available malware scanners are. To achieve this, ten representative Android security products were selected and tested against a set of 5,560 known and categorized Android malware samples. The tests were carried out using a digital-forensically rigorous testing framework and methodology, which ensures the scientific validity of the results. The detection rates of the tested malware scanners varied widely with half unable to detect any samples at all during initial testing. The malware scanners that were able to detect the samples scored highly with the top four between 97-99% and a fifth scanner scoring 87%. The results emphasise the need for more complex detection mechanisms and protections in future versions of Android and the next generation of malware scanners.
Solomon, Daniel, and the Detective Story: The Social Functions of a Literary Genre

As I have demonstrated elsewhere, 1 the testimony of the two women who appear before Solomon in I Kgs 3:16-27 constitutes a law-court riddle. The king solves this riddle by means of a ruse based on his godlike insight into human nature. Folk riddles can serve social functions, by leading aspiring riddle-solvers to explore cultural boundaries, in order to keep those boundaries flexible and responsive to social change. The boundary explored in the judgment story is that which separates immediate divine knowledge of the human heart, from the inability of ordinary human beings to fathom the true character of their fellows. The ideology of this story, and the kind of comforting message it might have carried to the citizens of ancient Israel, can be better understood by viewing it in terms of a special kind of riddle narrative: the classical detective story. As Hutter points out, detectives are "inevitably concerned with the problem of knowledge" (1983, p. 235). Many critics have described detectives as riddle-solvers, 2 and crime itself has been defined in precisely the same terms as the riddle, namely, as "a question that demands an answer. " 3 The modern detective genre arose at the same time as the first urban Detective Department, and for the same reasons. Both were responses to puzzling and unsettling aspects of life in
The role of station density for predicting daily runoff by top-kriging interpolation in Austria

Abstract Direct interpolation of daily runoff observations to ungauged sites is an alternative to hydrological model regionalisation. Such estimation is particularly important in small headwater basins characterized by sparse hydrological and climate observations, but often large spatial variability. The main objective of this study is to evaluate predictive accuracy of top-kriging interpolation driven by different number of stations (i.e. station densities) in an input dataset. The idea is to interpolate daily runoff for different station densities in Austria and to evaluate the minimum number of stations needed for accurate runoff predictions. Top-kriging efficiency is tested for ten different random samples in ten different stations densities. The predictive accuracy is evaluated by ordinary cross-validation and full-sample crossvalidations. The methodology is tested by using 555 gauges with daily observations in the period 1987-1997. The results of the cross-validation indicate that, in Austria, top-kriging interpolation is superior to hydrological model regionalisation if station density exceeds approximately 2 stations per 1000 km2 (175 stations in Austria). The average median of Nash-Sutcliffe cross-validation efficiency is larger than 0.7 for densities above 2.4 stations/1000 km2. For such densities, the variability of runoff efficiency is very small over ten random samples. Lower runoff efficiency is found for low station densities (less than 1 station/1000 km2) and in some smaller headwater basins.
MALDI Biotyper Characterization of Microorganisms Colonizing Heating Ventilation Air-Conditioning Systems at a South African Hospital

Airborne microbial contaminants can spread from heating ventilation air conditioning (HVAC) systems resulting in exposure to indoor surfaces and building occupants. Rapid identification of airborne contaminants is a necessity especially in healthcare settings. In the current study, samples were collected from a 5X5 surface area of ventilation grills at a public hospital using swabs. In general bacterial counts were above 12cfu/cm -2 while fungal counts were below 5cfu/cm -2 , ranging from clean ( 10). Microbial contaminants were identified and characterized using the MALDI-TOF MS and Scanning Electron Microscopy. Identified genera included Bacillus, Arthrobacter, Rhodotorula and Penicillium. Microorganisms identified in the current study using the MALDI TOF MS have no reported clinical implications; however, some are potential pathogens due to the presence of immune-compromised patients in these settings. The probable aerosolization of these microorganisms from ventilation systems could result in microorganisms settling on hospital surfaces and possible contamination of medical equipment. The current study also demonstrates the MALDI-TOF MS as a rapid, inexpensive and effective method for microbial characterization of microorganisms isolated from HVAC systems. (N. J. Malebo and K. Shale. MALDI-TOF MS Characterization of Microorganisms Colonizing Heating Ventilation Air-Conditioning Systems at a South African Hospital. Life Sci J 2013;10(4):13-17) (ISSN:1097- 8135). http://www.lifesciencesite. com . 54
Seismic Risk Assessment of Masonry Arch Bridges in the United States

Primarily constructed at the turn of the 20th century, over 1600 masonry arch bridges continue to serve as part of the U.S. transportation infrastructure. The preservation of these bridges is important for not only their role in the transportation network but also their cultural and architectural value. For successful preservation, the stewards of these historical bridges must be equipped with tools to evaluate the bridge vulnerability to extreme events and to make risk-aware decisions about their preservation and maintenance. To this end, focusing specifically on earthquakes, the current study presents a framework to calculate seismic risk indices for masonry arch bridges in the U.S. The risk indices are estimated first by developing non-linear finite element models and next by using those models to conduct fragility analysis under seismic excitation. The framework presented herein, if implemented for all site-appropriate hazard scenarios, can aid in the development of retrofitting strategies that will minimize risk, reduce financial loss, and ensure the preservation of the historically valuable bridges.
Assessment of Spironolactone Effects on the Prevention of Progression of Proteinuria in Chronic Kidney Diseases

AbstractBackground: Chronic kidney disease is a persistent disorder in kidney function. This is a progressive disorder characterized byarterial hypertension, glomerular hypertension, proteinuria and some other signs; controlling any of them can reduce the progressionof chronic kidney disease. In chronic kidney disease, proteinuria is used as a measure for monitoring nephron injuries andits response to treatment. Angiotensin converting enzyme inhibitors and Angiotensin receptor blockers can reduce the progressionof chronic kidney disease by inhibition of Renin-Angiotensin-Aldosterone system and reduction of glomerular pressure andcontrolling proteinuria. However, none of them can control plasma aldosterone level appropriately. Aldosterone produces Transforminggrowth factor-b (TGF-b), which induces proliferation of fibroblasts and glomerulosclerosis and accelerates chronic kidneydisease. Aldosterone antagonist can increase useful effects of angiotensin-converting-enzyme inhibitor (ACEI) and Angiotensin receptorblockers (ARB) Drugs.Objectives: The study was designed to assess the effects of spironolactone as the aldosterone antagonist combined with ACEI orARB drugs to reduce proteinuria in chronic kidney disease to prevent its progression.Patients and Methods: This was a semi-experimental without control study. Eighty patients treated for at least two months withACEI or ARB with uncontrolled proteinuria above 0.9g/dL were treated with 25mg/d spironolactone for two months. 24-hour urineprotein and some other variables were measured at the beginning of the study, after two months treatment and one month afterdiscontinuing the treatment.Results: Administration of 25 mg/d spironolactone combined with ACEI or ARB for two months led to mean reduction of 24 h-urineprotein from 2796.1 to 1857.4. No hyperkalemia or change in glomerular filtration rate occurred. One month after discontinuationof spironolactone, proteinuria returned to baseline level. Persistence of reduction in proteinuria in patients receiving ARBs wasmore than those taking ACEIs.Conclusions: Spironolactone combined with ACEIs or ARBs leads to reduction of proteinuria in chronic kidney disease and thereforereduction of progression of the disease.Keywords: Spironolactone, Proteinuria, Angiotensins, Aldosterone, Chronic Kidney Disease1. BackgroundChronic kidney disease (CKD) is a persistent disorder inthe kidney function so that the kidney cannot maintain thenormal values of protein metabolic products such as urea,blood pressure, hematocrit and acid-base equilibrium. Atlast, it leads to end stage renal disease (ESRD), which needskidney replacement treatment such as dialysis or kidneytransplantation (1). These patients are often symptom-freeuntil the final stages of disease, but have numerous laboratorychanges such as proteinuria (1). Risk factors of chronickidney disease
The application of random regret minimization on commuter's mode choice behaviour: Model-Fit comparisons with Rum-Modelling (case: comparison between Matsuyama and Yogyakarta)

Modelling the mode choice behaviours of travellers is a key to design effective transport management policies, particularly in shifting travellers to public transport. Abundant studies have analysed the impact of level of services on mode choice preferences through its Random Utility Maximization (RUM), but the possibility of minimalize the regret have been overlooked. This paper will discusses the possibility of using generalised Random Regret Minimization (G-RRM) model on choosing transportation modes. The study is performed in two cities for comparison: Jogjakarta in Indonesia and Matsuyama in Japan. A stated preference (SP) survey isconducted, in which respondents choose Bike or Bus under hypothetical situations. As the result of RUM revealed that travellers prefer the transportation mode with more ensuring level of service. While an empirical proof of concept, the G-RRM model is estimated on a stated mode choice dataset, and its outcomes are compared with RUM and RRM counterparts.
A layered-analysis of the features in higher education data set

Machine learning is applied on high education for analyzing the interaction between the students and electronic learning systems. This type of analysis serves in predicting of the student scores, in alerting students-at-risk, and in managing the degree of student engagement to educational system. The approaches in this work implements the divide and conquer algorithm on feature set of an educational data set to enhance the analysis and prediction accuracy. It divides the feature set into a number of logical subgroups based on the problem domain. Each subgroup is analyzed separately and the final result is the combination of the results of the analysis of these subgroups. The classifier that shows the best prediction accuracy is dependent on the logical non-statistical nature of the features in each group. This approach provides the possibility to apply a brute force algorithm in the selection of the best feature subgroups with a low computational complexity. The experimental work shows a high prediction accuracy of the students-at-risk relative to the current research, and provides a list of new important features in the field of electronic learning systems.
Accuracy of one‐step integration schemes for damped/forced linear structural dynamics

This paper proposes an energy‐based measure for the evaluation of the local truncation error of two‐level one‐step integration schemes. The measure applies to multiple degree of freedom systems and does not necessarily require modal reduction to a scalar model; it naturally handles the structural damping and external forcing terms that are generally and mistakenly neglected in error analyses, and it segregates the error associated with the free and forced response components of the problem. To illustrate the approach, two examples associated with the application of the trapezoidal scheme and of a high‐order scheme proposed in the literature are analyzed. The latter reveals the shortcomings of the standard approach that is based on the undamped/unforced linear oscillator and therefore highlights the need for the proposed framework. Indeed, the scheme order of accuracy is below expectation when structural damping or external forcing is considered, in the numerically dissipative setting. Developments on the basis of the time discontinuous Galerkin (TDG) method are then proposed to recover the scheme high‐order accuracy. Additionally, they show the similarity that exists between schemes related to the TDG method and the ones obtained by integration by parts of the equation of motion. Copyright © 2014 John Wiley & Sons, Ltd.
Annual variation in food densities and factors affecting wetland use by waterfowl in the Mississippi Alluvial Valley

Spatial and temporal heterogeneity in habitat quantity and quality, weather and other variables influence the production of food and the distribution of waterfowl, making it difficult to predict carrying capacity accurately. Food densities for waterfowl, which are key parameters of energetic carrying capacity models, were examined in managed moist-soil wetlands and bottomland hardwood forests in or near the Mississippi Alluvial Valley (MAV) of the southern United States of America, to determine variation in those densities across wetlands and years. Secondly, the relationship between migratory waterfowl density in managed wetlands and local and mid-latitude factors north of the study area was examined to identify mechanisms influencing waterfowl density at latitudes used during winter. At individual wetlands and within years, food densities were highly variable, but coefficients of variation (CV) at the scale of the MAV and nearby areas across years were relatively low (moist-soil CV = 21%, bottomland hardwood forest CV = 11%). Local precipitation was inversely related to waterfowl density in managed moist-soil wetlands, and this relationship was stronger than other local and mid-latitude factors including weather severity and temperature. Our data suggest that simplistic daily ration models may reasonably incorporate fixed estimates of food density for managed moist-soil wetlands and bottomland hardwood forests to predict energetic carrying capacity of waterfowl habitat at the scale of the MAV across multiple years. However, substantial variation in food densities among locations and time periods likely limits the utility and accuracy of these models when scaled down temporally or spatially. Therefore, the challenge in predicting annual carrying capacity for waterfowl in the MAV likely depends less on precisely estimating food densities at the scale of individual wetlands and more on determining spatial and temporal availability of habitats that contain food resources for waterfowl.
Maximal Oxygen Uptake in Children of Di erent Body Mass

Background: Maximal oxygen uptake ( V̇ O2max ) has important implications for current and future health in children. There is ongoing debate on how to express V̇ O2max in children and adolescents. Objectives: Are scaling factors di erent in obese and normal weight children and between genders? How does percentage fat in uence aerobic tness? Methods: 57 obese (30 girls) and 79 children of normal weight (38girls) in the age 7-17 years were include in the present study. V̇ O2max were measured during a treadmill running test, were both the incline and speed increased until subjects were exhausted. Body compositions were measured with dual-energy X-ray absorptiometry (DXA) or a Harpenden fat caliper. Allometric scaling factors were calculated using linear regression of log-transformed data. Results: Allometric scaling factors were 0.51 for obese girls, 0.53 for normal weight girls, 0.47 for obese boys and 0.27 for normal weight boys. Obese boys had almost the same allometric scaling factor (0.47) as girls, and normal weight boys (0.27) had the lowest scaling factor of all subjects. Conclusion: ndings in the present study demonstrate that use of mass exponent when comparing V̇ O2max in obese and normal weight children. It seems that percentage fat doesn't have a great in uence on children's V̇ O2max . The di erence between obese and normal weight children has to be due to other factors than percentage fat.
Preparation and Free Radical Detection of Doped ZnO Nanomaterials

Nanomaterials play an important role in the field of optics and medicine, and rare earth cerium can act as a catalyst and a dopant to increase improve its optical properties. Used nanometer zinc oxide as a carrier, the Ag-ZnO and Ag-Ce-ZnO samples were prepared with the sol-gel method and characterized by X-ray diffractometer. The nanometer zinc oxide materials can generate the free hydroxyl radicals, so the methylene blue solution (MB) was used as the capture agent, and the free radicals produced by the doped nanometer zinc oxide were detected by UV-spectrophotometry. The results showed that the preparation particle size of the Ag-ZnO sample was about 75nm, particle size of the Ag-Ce-ZnO sample was about 70nm. The concentration of two samples treated with the dispersant PAAS generated hydroxyl radicals is much larger than the two samples treated with the dispersant SDBS. The doping of Ce ions not only improved the optical activity of the samples, but also increased the content of hydroxyl radicals. Rare-earth doped effe ctively improved the optical properties of the ZnO nanoparticles, and the effect of sample Ag-Ce-ZnO was better than Ag-ZnO samples. Discussion and study on the nature of both the nanometer zinc oxide and doped nanometer zinc oxide, as well as the method of preparation and the theory of produce hydroxyl radicals. In the experiment, doped nano-ZnO was used as carrier and methylene blue (MB) as capture reagent. This study provided a new method for the detection of free radicals drugs.
Implementation of Image Quality and Design Time for Block-based Lossy VQ Image Compression using K-Means and K-Medoids Algorithm in Spatial Domain

Data Mining (DM) technologies are one of the vast and hot topics in todayrsquos era. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. It is increasing significantly in bio-medical, bio-informatics, engineering and health-care research. Large amount of biological and clinical data have been generated and collected at an unprecedented speed and scale. It seems to be a big disadvantage during storage and transmission. It raises the problem of reducing the memory size of a digital image. This research has focused on the lossy Vector Quantization (VQ) image compression to reduce the data volume in the spatial domain. We are using two unsupervised clustering algorithm for lossy VQ technique such as K-Means and K-Medoids. These two algorithms are proposed to analyze the performance in data mining and to other applications. To evaluate the clustering quality in image compression, the distance between two data points are taken for analysis. For two unsupervised clustering techniques, different block size are used as 4x4 (16) and 8x8 (64) with the codebook size of 25 and 50 in this research paper.nbsp In spatial domain, K-Medoids was compared with K-Means in terms of execution time and quality (PSNR). The research results show that the K-Means algorithm yields the best results compared with K-Medoids algorithm. nbsp
Anomalously strong observations of PKiKP/PcP amplitude ratios on a global scale

The inner core boundary marks the phase transition between the solid inner core and the fluid outer core. As the site of inner core solidification, the boundary provides insight into the processes generating the seismic structures of the inner core. In particular, it may hold the key to understanding the previously observed hemispherical asymmetry in inner core seismic velocity, anisotropy, and attenuation. Here we use a large PKiKP‐PcP amplitude ratio and travel time residual data set to investigate velocity and density contrast properties near the inner core boundary. Although hemispherical structure at the boundary has been proposed by previous inner core studies, we find no evidence for hemispheres in the amplitude ratios or travel time residuals. In addition, we find that the amplitude ratios are much larger than can be explained by variations in density contrast at the inner core boundary or core‐mantle boundary. This indicates that PKiKP is primarily observed when it is anomalously large, due to focusing along its raypath. Using data in which PKiKP is not detected above the noise level, we calculate an upper estimate for the inner core boundary (ICB) density contrast of 1.2 kg m−3. The travel time residuals display large regional variations, which differ on long and short length scales. These regions may be explained by large‐scale velocity variations in the F layer just above the inner core boundary, and/or small‐scale topography of varying magnitude on the ICB, which also causes the large amplitudes. Such differences could arise from localized freezing and melting of the inner core.
Two-directional two-dimensional principal component analysis based on wavelet decomposition for high-dimensional biomedical signals classification

Here, we present a multi-scale two-directional two-dimensional principal component analysis (MS2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify twenty hand motions using 89-channel EMG signals recorded in stroke survivors, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis. With this multi-scale two-directional two-dimensional principal component analysis for high-dimensional signal classification, spatial-time-frequency discriminant information from high-dimensional EMG electrode array can be effectively extracted and reduced using the proposed method. Compared with the time domain feature extraction in conjunction with PCA, MS2D2PCA performed better with higher classification accuracy and less PCs in EMG classification. The efficiency and effectiveness of the method can be further validated by using high-dimensional EEG, MEG, fMRI signals. Although the present study focuses on high-dimensional signal pattern classification, based on the PCs obtained at multiple scales, it is relatively straightforward to expand MS2D2PCA for high-dimensional signal compression, denoising, component extraction, and other related tasks.