Memory-Adjustable Navigation Piles with Applications to Sorting and Convex Hulls

We consider space-bounded computations on a random-access machine (RAM) where the input is given on a read-only random-access medium, the output is to be produced to a write-only sequential-access medium, and the available workspace allows random reads and writes but is of limited capacity. The length of the input is $N$ elements, the length of the output is limited by the computation, and the capacity of the workspace is $O(S)$ bits for some predetermined parameter $S$. We present a state-of-the-art priority queue---called an adjustable navigation pile---for this restricted RAM model. Under some reasonable assumptions, our priority queue supports $\mathit{minimum}$ and $\mathit{insert}$ in $O(1)$ worst-case time and $\mathit{extract}$ in $O(N/S + \lg{} S)$ worst-case time for any $S \geq \lg{} N$. We show how to use this data structure to sort $N$ elements and to compute the convex hull of $N$ points in the two-dimensional Euclidean space in $O(N^2/S + N \lg{} S)$ worst-case time for any $S \geq \lg{} N$. Following a known lower bound for the space-time product of any branching program for finding unique elements, both our sorting and convex-hull algorithms are optimal. The adjustable navigation pile has turned out to be useful when designing other space-efficient algorithms, and we expect that it will find its way to yet other applications.Comment: 21 page

NUMERICAL INVESTIGATION OF MESH-BASED ENHANCEMENT OF VAPOR BUBBLE CONDENSATION

The aim of this work is to study the vapor bubble condensation process and the enhancement technique via affecting bubbles dynamic using a mesh-based structure. The bubble dynamics and thermal behavior are studied by considering the heat and mass transfer through the bubble’s interface area between the vapor and liquid regions and the resulting condensation effects. The thermal characteristics of bubbles are observed considering the initial and final liquid temperatures. Additionally, the bubble dynamics are studied in terms of several parameters including the relative velocity of the bubble, bubble deformation, interfacial area, and the bubble diameters. The relation between the thermal and dynamic parameters of bubble condensation is analyzed under different operating conditions. A Semi-Empirical simulation is developed to analyze the heat transfer and condensation of a spherical rising bubble. The model includes bubble shrinkage during condensation, and can be used to analyze the total energy loss of the bubble, raising velocity and bubble condensation rate of a single bubble compared to multiple bubbles with the same total thermal energy. A MATLAB code is developed in order to calculate the instantaneous velocity, the radius, and the mass loss of the vapor bubble in each time step. Moreover, the fundamental behavior for a single bubble and multiple bubbles was investigated in various initial conditions under the same total thermal energy. The effects of the initial bubble radius and the temperature difference between the liquid and vapor phases were analyzed for both scenarios in order to examine the condensation rate. To overcome the complexity of the interface between the multi-phases in the flow field, a computational fluid dynamics (CFD) simulation is carried out and adapted using interface capturing methods (Volume of Fluid) with durable time-stepping schemes in ANSYS FLUENT. This study proposes to enhance the bubble condensation by designing a mesh-based structure to be placed in the flow direction so the bubbles will enter the domain through many small holes of the mesh structure, which will force bubble deformation and redirect the bubbles through the liquid domain. The mesh-based structure enhancement method is based on breaking the bubbles into several smaller bubbles realizing a very high surface contact area between the bubble and the liquid. This design arrangement showed an improvement in the condensation and heat transfer. Furthermore, the obtained results from the semi-empirical model of bubbles condensation are examined and compared with experimental data from previous investigations in the literature

Optimizing Meetings-to-Room Assignment

This publication describes a system for improving the efficiency of meetings by automatically matching meetings with the most appropriate meeting room. To perform this matching, the system can calculate a cost for each meeting/room pairing. The system can utilize an integer programming model to evaluate a plurality of configurations, each configuration containing a different combination of meeting/room pairings. The configurations can be scored, and the system can select the configuration with the lowest total cost. The system then updates any meeting with a changed room

SnapCatch: Automatic Detection of Covert Timing Channels Using Image Processing and Machine Learning

With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (CTC) have become an imminent network security risk that continues to grow in both sophistication and utilization. These types of channels utilize inter-arrival times to steal sensitive data from the targeted networks. CTC detection relies increasingly on machine learning techniques, which utilize statistical-based metrics to separate malicious (covert) traffic flows from the legitimate (overt) ones. However, given the efforts of cyber attacks to evade detection and the growing column of CTC, covert channels detection needs to improve in both performance and precision to detect and prevent CTCs and mitigate the reduction of the quality of service caused by the detection process. In this article, we present an innovative image-based solution for fully automated CTC detection and localization. Our approach is based on the observation that the covert channels generate traffic that can be converted to colored images. Leveraging this observation, our solution is designed to automatically detect and locate the malicious part (i.e., set of packets) within a traffic flow. By locating the covert parts within traffic flows, our approach reduces the drop of the quality of service caused by blocking the entire traffic flows in which covert channels are detected. We first convert traffic flows into colored images, and then we extract image-based features for detection covert traffic. We train a classifier using these features on a large data set of covert and overt traffic. This approach demonstrates a remarkable performance achieving a detection accuracy of 95.83% for cautious CTCs and a covert traffic accuracy of 97.83% for 8 bit covert messages, which is way beyond what the popular statistical-based solutions can achieve

Haematological and biochemical observations in four pure breeds of rabbits and their crosses under Egyptian environmental conditions

The present study was conducted to evaluate 16 crosses between 4 breeds of rabbits from a physiological point of view. The breeds tested were Baladi Red (BR), Chinchilla Giganta (ChG), French Giant Papillon (FGP) and Simenwar (S). A total number of 6144 blood samples were collected to detect the effect of crossing, age of kits, month of kindling and sex effects. The traits evaluated were: haematological parameters; red blood cell count (RBCs), haemoglobin concentration (Hb), haematocrit value (Ht%), biochemical parameters of plasma; total protein (TP), albumin (Alb), globulin concentration (Glo), albumin/globulin ratio (Alb/Glo) and triglycerides (TG). BR or its crosses, using BR sires or BR dams, showed the highest value of RBCs, Hb and Ht%. Crossbred rabbits obtained from mating BR and FGP rabbits had the highest Glo values. Rabbits which were born in May-June months had the highest values of TP and its fractions (Alb and Glo). Age of kits had a highly significant effect (P<0.001) on RBCs, Hb, Ht%, TP and TG. Moreover, Glo and Alb/Glo ratio (P<0.01) and Alb (P<0.05) were also significantly affected. Sex had no significant effect on all studied parameters. Significant positive correlations were found between TP and each final body weight, total weight gain, total feed intake, carcass weight and dressing percentage, while significant negative correlation was found with feed conversion.Abdel-Azeem, A.; Abdel-Azim, A.; Darwish, A.; Omar, E. (2010). Haematological and biochemical observations in four pure breeds of rabbits and their crosses under Egyptian environmental conditions. World Rabbit Science. 18(2). doi:10.4995/WRS.2010.18.1318

A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes

Currently, most organizations have a defense system to protect their digital communication network against cyberattacks. However, these defense systems deal with all network traffic regardless if it is from profit or non-profit websites. This leads to enforcing more security policies, which negatively affects network speed. Since most dangerous cyberattacks are aimed at commercial websites, because they contain more critical data such as credit card numbers, it is better to set up the defense system priorities towards actual attacks that come from profit websites. This study evaluated the effect of textual website metrics in determining the type of website as profit or nonprofit for security purposes. Classifiers were built to predict the type of website as profit or non-profit by applying machine learning techniques on a dataset. The corpus used for this research included profit and non-profit websites. Both traditional and deep machine learning techniques were applied. The results showed that J48 performed best in terms of accuracy according to its outcomes in all cases. The newly built models can be a significant tool for defense systems of organizations, as they will help them to implement the necessary security policies associated with attacks that come from both profit and non-profit websites. This will have a positive impact on the security and efficiency of the network

Antenatal dexamethasone effect on Doppler blood flow velocity in women at risk for preterm birth: prospective case series

Background: Maternal administration of corticosteroids is essential to improve fetal lung surfactant production and hasten the fetal lung maturity in women at risk for preterm birth.Objectives: The current study aims to evaluate the effects of dexamethasone on fetal and uteroplacental circulation in pregnancies at risk for preterm birth after 24 hours of its administration.Methods: A prospective cross-sectional study was carried out in a tertiary University Hospital and included 52 pregnant women with singleton pregnancies. Doppler studies were performed on maternal uterine arteries, umbilical artery, fetal middle cerebral artery (MCA) and fetal descending aorta and just before dexamethasone administration and repeated 24 hours after completion of the course.Results: There was a statistically significant difference between all Doppler indices in the umbilical artery (PI= 1.09±0.4 and 1.05±0.39, RI= 0.66±0.14 and 0.63±0.14; p=0.001), fetal MCA (RI= 0.86±0.12 and 0.83±0.13, PI= 2.19±0.72 and 2.15±0.72; p=0.001) and aorta (RI= 0.9±0.55 and 0.87±0.55; p=0.001, PI= 1.91±0.44 and 1.89±0.44; p=0.040) in comparison before and 24 hours after maternal dexamethasone administration respectively. Also uterine artery PI was significantly different (0.9±0.27 and 0.87±0.26; p=0.001).Conclusion: Antenatal dexamethasone for women at risk of preterm birth improves the fetal and uteroplacental blood flow at 24 hours after its administration.Keywords: Doppler; preterm birth; corticosteroids; dexamethasone

An explainable machine learning framework for lung cancer hospital length of stay prediction

This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3–100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.3–100%, and 97%, CI 95%: 93.7–100% SMOTE-Tomek, and SMOTE-ENN respectively). Under-sampling methods reported the least important AUC results (50%, with CI 95%: 40.2–59.8%) for both (ENN and Tomek- Links). Using ML explainable technique called SHAP, we explained the outcome of the predictive model (RF) with SMOTE class balancing technique to understand the most significant clinical features that contributed to predicting lung cancer LOS with the RF model. Our promising framework allows us to employ ML techniques in-hospital clinical information systems to predict lung cancer admissions into ICU

Re-annotation of the woodland strawberry (Fragaria vesca) genome

Fragaria vesca is a low-growing, small-fruited diploid strawberry species commonly called woodland strawberry. It is native to temperate regions of Eurasia and North America and while it produces edible fruits, it is most highly useful as an experimental perennial plant system that can serve as a model for the agriculturally important Rosaceae family. A draft of the F. vesca genome sequence was published in 2011 [Nat Genet 43:223,2011]. The first generation annotation (version 1.1) were developed using GeneMark-ES+[Nuc Acids Res 33:6494,2005]which is a self-training gene prediction tool that relies primarily on the combination of ab initio predictions with mapping high confidence ESTs in addition to mapping gene deserts from transposable elements. Based on over 25 different tissue transcriptomes, we have revised the F. vesca genome annotation, thereby providing several improvements over version 1.1. The new annotation, which was achieved using Maker, describes many more predicted protein coding genes compared to the GeneMark generated annotation that is currently hosted at the Genome Database for Rosaceae (http://www.rosaceae.org/). Our new annotation also results in an increase in the overall total coding length, and the number of coding regions found. The total number of gene predictions that do not overlap with the previous annotations is 2286, most of which were found to be homologous to other plant genes. We have experimentally verified one of the new gene model predictions to validate our results. Using the RNA-Seq transcriptome sequences from 25 diverse tissue types, the re-annotation pipeline improved existing annotations by increasing the annotation accuracy based on extensive transcriptome data. It uncovered new genes, added exons to current genes, and extended or merged exons. This complete genome re-annotation will significantly benefit functional genomic studies of the strawberry and other members of the Rosaceae.https://doi.org/10.1186/s12864-015-1221-