Miracle-dz/UniFAR-FTUs huggingface dataset:
Pedia tric derma tology: a n a udit a t Ha mda rd University Hospita l, Ka ra chi

Background Children up to 15 years of age constitute a major portion of our population and do suffer from a wide variety of skin diseases. Pediatric dermatology is a separate subspecialty by itself hitherto non-existing in Pakistan. The current work focuses on the audit of pediatric dermatological cases presenting in the outpatient department of Dermatology and Pediatrics, Hamdard University Hospital, Karachi, during the calendar year 2005. Objective To determine frequency of dermatoses amongst children in a hospital in Karachi. Patients and methods All patients aged below 15 years, belonging to both sexes suffering from dermatological diseases were included in the study. After a detailed history and clinical examination, a clinical diagnosis was made in each case. The patients enrolled were investigated, as well. Results A total of 830 cases of pediatric dermatology, 539 males (65%) and 291 females (35%) were seen during the calendar year 2005, accounting for 27.2% of all dermatological patients. The age range was 0.1-15 years with a mean age of 8.1 years. Scabies (21.7%) was the most common dermatosis reported followed by atopic eczema (21.4%), fungal infections (15.7%), urticaria (12.1%) and bacterial infections (8.2%). Hair loss (alopecia), pityriasis alba, vitiligo and a few miscellaneous disorders had a comparatively low frequency.
Glucocorticoid Exposure in Late Gestation in the Rat Permanently Programmes Gender Specific Differences in Adult Cardiovascular and Metabolic Physiology

Glucocorticoid overexposure in utero may underlie the association between low birth weight and subsequent development of common cardiovascular and metabolic pathologies. Previously, we have shown that prenatal dexamethasone (DEX) exposure in rat reduces birth weight and programmes the hypothalamic-pituitary-axis (HPA) and fasting and postprandial hyperglycaemia in adult males and hypertension in adult males and females. This study aimed to determine (1) whether there were gender differences in prenatal DEX-programmed offspring, and (2) whether the renin-angiotensin-system (RAS) plays a role in the programming of hypertension. Rats exposed to DEX in utero (100µg.kg-1 .day-1 from embryonic days 14-21), were of lower birth weight (by 12%, p <0.01) and displayed full catch-up growth within the first month of postnatal life. DEX-treated male offspring in adulthood, selectively displayed elevated plasma adrenocorticotrophin (by 221%) and corticosterone (by 188%, p <0.05), postprandial insulin:glucose ratios (by 100%, p <0.05), and hepatic expression of the gluconeogenic enzyme phosphoenolpyruvate carboxykinase (by 38%, p <0.05). Conversely, DEX-programmed females were hypertensive (by 11%, p <0.05) with elevated hepatic angiotensinogen mRNA expression (by 9%, p <0.05), plasma angiotensinogen (by 61%, p <0.05) and renin activity (by 88 %, p <0.05). These findings demonstrate that prenatal glucocorticoids programme adulthood cardiovascular and metabolic physiology in a gender specific pattern, and that an activated RAS may in part underlie the hypertension associated with prenatal dexamethasone programming.
Asymmetric multiscale multifractal analysis of wind speed signals

We develop a new method called asymmetric multiscale multifractal analysis (A-MMA) to explore the multifractality and asymmetric autocorrelations of the signals with a variable scale range. Three numerical experiments are provided to demonstrate the effectiveness of our approach. Then, the proposed method is applied to investigate multifractality and asymmetric autocorrelations of difference sequences between wind speed fluctuations with uptrends or downtrends. The results show that these sequences appear to be far more complex and contain abundant fractal dynamics information. Through analyzing the Hurst surfaces of nine difference sequences, we found that all series exhibit multifractal properties and multiscale structures. Meanwhile, the asymmetric autocorrelations are observed in all variable scale ranges and the asymmetry results are of good consistency within a certain spatial range. The sources of multifractality and asymmetry in nine difference series are further discussed using the corresponding shuffled series and surrogate series. We conclude that the multifractality of these series is due to both long-range autocorrelation and broad probability density function, but the major source of multifractality is long-range autocorrelation, and the source of asymmetry is affected by the spatial distance.
Vulnerability Forecasting: In theory and practice

Why wait for zero-days when you could predict them in advance? It is possible to predict the volume of CVEs released in the NVD as much as a year in advance. This can be done within 3 percent of the actual value, and different predictive algorithms perform well at different lookahead values. It is also possible to estimate the proportions of that total volumn belonging to specific vendors, software, CVSS scores, or vulnerability types. Strategic patch management should become much easier, with this uncertainty reduction.
Improving Wikipedia's credibility: References and citations in a sample of history articles

This study evaluates how well the authors of Wikipedia history articles adhere to the site’s policy of assuring verifiability through citations. It does so by examining the references and citations of a subset of country histories. The findings paint a dismal picture. Not only are many claims not verified through citations, those that are suffer from the choice of references used. Many of these are from only a few US government Websites or news media and few are to academic journal material. Given these results, one response would be to declare Wikipedia unsuitable for serious reference work. But another option emerges when we jettison technological determinism and look at Wikipedia as a product of a wider social context. Key to this context is a world in which information is bottled up as commodities requiring payment for access. Equally important is the problematic assumption that texts are undifferentiated bearers of knowledge. Those involved in instructional programs can draw attention to the social nature of texts to counter these assumptions and by so doing create an awareness for a new generation of Wikipedians and Wikipedia users of the need to evaluate texts (and hence citations) in light of the social context of their production and use.
The Fractal Dimension of the ρ Ophiucus Molecular Cloud Complex

We estimate the fractal dimension of the ρ Ophiuchus Molecular Cloud Complex, associated with star forming regions. We selected a cube (v, l, b) database, obtained with J = 1−0 transition lines of 12 CO and 13 CO at a resolution of 22′′ using a multibeam receiver system on the 14-m telescope of the Five College Radio Astronomy Observatory. Using a code developed within IRAF, we identified slice-clouds with two threshold temperatures to estimate the fractal dimension. With threshold temperatures of 2.25 K (3σ) and 3.75 K (5σ), the fractal dimension of the target cloud is estimated to be D = 1.52–1.54, where P ∝ A D/2 , which is larger than previous results. We suggest that the sampling rate (spatial resolution) of observed data must be an important parameter when estimating the fractal dimension, and that narrower or wider dispersion around an arbitrary fit line and the intercepts at NP = 100 should be checked whether they relate to rms noise level or characteristic structure of the target cloud. This issue could be investigated by analysing several high resolution databases with different quality (low or moderate sensitivity).
Shedding and interaction of solitons in weakly disordered optical fibers

We study the propagation of a soliton pattern through an optical fiber in the presence of weakly disordered dispersion and birefringence. We show that the solitons, perturbed by the disorder, radiate and as a result, lose the energy. The emergence of a long-range inter-channel interaction between the solitons is also presented. The rigorous theoretical analysis is confirmed by the numerical simulation.
Simultaneous localization and mapping embedded with particle filter algorithm

In this paper, a novel methodology is proposed to solve the simultaneous localization and mapping (SLAM) problem of mobile robot with particle filter (PF) algorithm. Compared with Kalman filter (KF) and extended Kalman filter (EKF), PF has a better performance in non-linear non-Gaussian environments. A close-loop updating scheme is developed in which positions of the robot and landmarks are updated with particle filtering and a weighted averaging algorithm respectively, and are linked through an additional feedback and correction process. An adaptive re-sampling method is used to reduce the computational load. The results of the simulation indicate that the PF-SLAM algorithm can localize the robot and landmarks accurately, and the error of landmarks' estimation converges better than general SLAM algorithms.
Cyclic neutropenia in animals

To the editor: Pacheco et al. [1] recently published in these pages an allometric scaling analysis of cyclic neutropenia (CN) in mammals, using as reference points the hereditary disorders in humans and in gray collie dogs [2]. They make a central assumption ‘‘that the biological defect in CN is the same in dogs and in humans. ’’ However, virtually all human cases derive from mutations in the neutrophil elastase (ELA2) gene [3], whereas the disorder in dogs represents a similar hematological phenotype, in association with partial albinism, because of the defects in the adaptor protein complex 3 (AP3) b-subunit, directing trans-Golgi export of transmembrane cargo proteins to lysosomes [4]. Thus, the canine disorder better models Hermansky–Pudlak syndrome Type 2, [5] than human CN. Although the genetic defects in man and dog may share common downstream mechanisms involving elastase trafficking or elicitation of the unfolded protein response, the resultant kinetics of apoptosis induction and recovery in myeloid precursors may not be the same, and thus the assumption may not be correct.
Estrogen Receptor α Gene Variation Is Associated With Risk of Myocardial Infarction in More Than Seven Thousand Men From Five Cohorts

Understanding the mechanisms by which estrogens affect cardiovascular disease risk, including the role of variation in the gene for estrogen receptor α (ESR1), may be key to new treatment strategies. We investigated whether the CC genotype at ESR1 c.454-397T>C is associated with increased risk among men. Study of more than 7000 whites in 5 cohorts from 4 countries provided evidence that genotype CC, present in roughly 20% of individuals, is a risk factor for nonfatal acute myocardial infarction (odds ratio=1.44; P<0.0001), after adjustment for established cardiovascular risk factors. After exclusion of younger subjects from 2 cohorts, because of age interaction, the odds ratio increased (to 1.63).
Optimization of Economic Order Quantity Model on the Boundaries of the Fill Rate

This article investigates an inventory model with partial backordering and correlated demand caused by cross-selling on the boundaries of the fill rate in a fuzzy situation by employing the type of fuzzy numbers which are triangular. The objective is to determine the optimal order lotsize to maximize the total profit. We first propose a model with a fuzzy positive integer (K) on the boundaries of the fill rate (F = 0 and F = 1). For each case, we employ the signed distance, a ranking method for fuzzy numbers, to find the estimate of total profit per unit time in the fuzzy sense and then derive the corresponding optimal order cycle. Numerical examples are provided to illustrate the results of proposed models.
Genetic algorithm approach on pyramidal dielectric absorbers

Array of absorbing pyramid cones are widely used in anechoic chambers. When the period of the array is small compared with wavelength, which is in the low‐frequency limit; the plane wave reflection problem is simplified and can be studied in one‐dimensionally inhomogeneous media. This article deals with the optimal design parameters of pyramid cone absorbers via genetic algorithm for the normal and oblique incidence of plane wave. The optimization parameters are the dielectric permittivity and the length of the absorber over a wide frequency band and incidence angle range. The cost function of the optimization algorithm is the reflection coefficient of the absorber which is to be minimized. The algorithm is applied to different frequency bands and incidence angles. The results show that the minimized reflection coefficients and corresponding relative permittivities and absorber lengths are acceptable for each application. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.
Comparison of two commercially available near-infrared spectroscopy instruments for cerebral oximetry

the skull was first reported by Jobsis.11 After 20 years, this technology was developed for clinical application.8,28 We compared two NIRS instruments that are commercially available for the measurement of tissue HbO2 saturation. These instruments are the INVOS3100A device (Somanetics Corp., Troy, MI), approved by the United States Food and Drug Administration, and the investigational NIRO500 device (Hamamatsu Photonics Corp., Hamamatsu, Japan). Both instruments differ substantially in design. Our aim was to examine simultaneously the responses of these two instruments to isocapnic hypoxia and hyperoxic hypercapnia in healthy volunteers. Clinical Material and Methods
Sharing to learn and learning to share - Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning : A meta review

—Integrating knowledge across different domains is an essential feature of human learning. Learning paradigms like transfer learning, meta learning, and multi-task learning reﬂect the human learning process by exploiting the prior knowledge for new tasks, encouraging faster learning and good generalization for new tasks. This article gives a detailed view of these learning paradigms along with a comparative analysis. The weakness of a learning algorithm turns out to be the strength of another, and thereby merging them is a prevalent trait in the literature. This work delivers a literature review of the articles, which fuses two algorithms to accomplish multiple tasks. A global generic learning network, an ensemble of meta learning, transfer learning, and multi-task learning, is also introduced here, along with some open research questions and directions for future research.
Spectral Ageing in the Most Powerful Radio Galaxies

Radio-loud AGN represent the most energetic and influential large-scale structures in the Universe. Through physical processes such as AGN feedback, they are suggested to play a major role in the evolution of galaxies. The key physical parameter in these studies is the jet power, which requires an accurately determined age for the source. In this thesis I present a detailed investigation of the dynamics and energetics of two powerful radio galaxies found in cluster environments, in order to probe the well known spectral age/dynamical age discrepancy. Through the use of the high-resolution and newly improved radio interferometer, the JVLA, I present the highest fidelity radio maps of 3C320 and 3C444 to date, which I then use to determine their spectral ages. I also present analysis of the deep and sensitive Chandra observation of the cluster shock surrounding 3C320, used to determine its dynamical age. Applying the most widely used models of spectral ageing I find that, although all models do not provide a reasonable fit to the data, the spectral and dynamical ages can be brought into agreement. I find that the low frequency spectral index (injection index) is relatively flat for these sources, agreeing with previously held assumptions although in contrast to recent work on spectral ageing. There is some suggestion to reject the Kardashev-Pacholczyk model as a valid ageing model for powerful radio galaxies, although this is not robust. I suggest that, since the Tribble model provides consistent fits compared to the other models while providing a more physically realistic description of spectral ageing in the lobes of radio galaxies, it may proceed to be the ageing model of choice for future studies. I argue that the combination of broad-bandwidth data at low and high frequencies with a lobe magnetic field that has a large departure from equipartition estimates can give an agreement between spectral and dynamical ages. I also argue that projection effects and a robust determination of the Mach number of the driven shock by the radio galaxy into its environment are also important factors which may cause a discrepancy in ages. I find no evidence for the conclusions drawn from previous work on powerful radio galaxies in richcluster environments, where it was suggested that their lobes are apparently under-pressured with respect to their environment and therefore require a significant contribution from non-radiating particles for pressure-balance. I therefore suggest that conclusions cannot be made regarding the effect of the environment on lobe particle content of powerful radio galaxies in these rich environments until further samples are studied.
Integrated Power Anomaly Defense: Towards Oversubscription-Safe Data Centers

— Energy storage devices (e.g., batteries) are critical components for high-availability data center infrastructure today. Without resilient energy management of these devices, existing power-hungry data centers are largely unguarded targets for cyber criminals. Particularly for some of today’s scale-out data centers, power infrastructure oversubscription unavoidably taxes the data center’s backup energy resources (i.e., UPS), leaving very little room for dealing with power emergency. As a result, an attacker could manipulate the computing system to generate peak power demand and disrupt power-constrained server racks. This study aims at protecting data centers from malicious loads that seek to drain precious energy backup, overload server racks and compromise workload performance. We term such load as Elusive Power Peak (EPP) and demonstrate its basic three - phase attacking model. To defend against EPP, we propose IPAD, a remediation solution build on integrated software and hardware mechanisms. IPAD not only increases the attacking cost considerably by hiding vulnerable server racks from visible power peaks, but also strengthens the last line of defense against hidden power spikes with fine - grained power control strategy. We show that IPAD can effectively raise the bar of power - related attack, with reasonable design overhead.
Smart Health: Natural Language Processing based Question and Answering Retrieval System in Healthcare

The healthcare system may be a data-driven trade that consists of large and growing volumes of narrative data obtained from discharge summaries/reports, physicians case notes, pathologists similarly as radiologists reports. This data is typically held on in unstructured and non-standardized formats in electronic healthcare systems that create it tough for the systems to grasp {the data the knowledge the data} contents of the narrative information. Thus, access to valuable and pregnant attention data for deciding may be a challenge. all the same, natural language processing (NLP) techniques are accustomed structure narrative data inattention. Thus, NLP techniques have the potential to capture unstructured healthcare data, analyze its grammatical structure. Question and Answering System is one of the major researches is in Natural Language. Main challenges of Question and Answer system gives an exact answer of question which gives by Patient (User). Question and Answering system can be classified into three categories are an open domain, closed domain, and restricted domain. Using advanced Natural Language Processing tool, we will be developed a framework for question answering system. In this paper, we work on the restricted domain question answering system. Proposed system works on keyword and question matching and returns a precise answer to the question. Index Terms Smart Healthcare Systems, Healthcare, Natural Language processing, information retrieval, semantic similarity, restricted domain, answer extraction, answer ranking.
Graph Regularized Nonnegative Tensor Ring Decomposition for Multiway Representation Learning

Tensor ring (TR) decomposition is a powerful tool for exploiting the low-rank nature of multiway data and has demonstrated great potential in a variety of important applications. In this paper, nonnegative tensor ring (NTR) decomposition and graph regularized NTR (GNTR) decomposition are proposed, where the former equips TR decomposition with local feature extraction by imposing nonnegativity on the core tensors and the latter is additionally able to capture manifold geometry information of tensor data, both significantly extend the applications of TR decomposition for nonnegative multiway representation learning. Accelerated proximal gradient based methods are derived for NTR and GNTR. The experimental result demonstrate that the proposed algorithms can extract parts-based basis with rich colors and rich lines from tensor objects that provide more interpretable and meaningful representation, and hence yield better performance than the state-of-the-art tensor based methods in clustering and classification tasks.
Enhancing 3D range image measurement density via dynamic Papoulis–Gerchberg algorithm

As one of the most popular range detection methods, lidar is commonly used in various robotic applications. Although most robotic platforms easily adopt 2D lidar for range sensing, 3D lidar is rarely used in mobile robots, owing to its high cost. Some methods reported in the literature obtain 3D range information by rotating a single 2D lidar device. However, for most of these methods, there is a trade-off between 3D scan frequency and measurement density. Existing methods discussed in the literature for increasing the measurement density in high-frequency lidar have high time complexity and require certain conditions on data distribution. In a previous work, we showed the usability of an image super-resolution method, the Papoulis–Gerchberg (P–G) algorithm, on range data represented in the form of a greyscale image. However, the low convergence rate of the original P–G algorithm impedes its use for online applications. In this study, we advanced the P–G algorithm to drastically reduce the convergence time and improve performance by utilizing previous range images. The proposed algorithm now supports application on a mobile robot with online measurement density enhancement for 3D range images collected by rotating a 2D lidar device around its pitch axis with a high 3D scan frequency. We show illustrative examples for different scenarios to present the effectiveness of the proposed method on a 3D range sensor mounted on a mobile robot.
Springer MRW: [AU:0, IDX:0]

Subgoal scaffolding theory was proposed as a more nuanced account of the processes of the behavioral approach system (BAS), which is one of the major components of the well-known reinforcement sensitivity theory (RST) of personality. Subgoal scaffolding theory delineates the separate, and sometimes opposing, BAS aspects which contain both heterogeneity and complexity of goals and processes. It is especially concerned with the cascade of processes along the temporospatial gradient from start goal state (i.e., exploration of a potentially rewarding environment) to the final goal state (i.e., attaining the end reward). As operationalized in the Reinforcement Sensitivity Theory Personality Questionnaire (RST-PQ; Corr and Cooper 2016), this cascade entails reward interest, goal-drive persistence, reward reactivity, and impulsivity, where behavioral caution is more appropriate at the early states and impulsive reacting at the later stages of the cascade. Introduction