Miracle-dz/UniFAR-FTUs huggingface dataset:
Neonatal Sepsis Overview and Assessment of Mean Platelet Volume for Diagnosis

Background:Neonatal sepsis (NS) is a potentially life-threatening clinical condition that requires early intervention. Initial symptoms are generally nonspecific and may mimic several other medical conditions. NS is an important cause of mortality and morbidity in neonatal populations. There has been constant search of an ideal sepsis biomarker with high sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), so that both the diagnosis and exclusion of neonatal sepsis can be made at the earliest possible and appropriate antibiotics can be started to neonate. Ideal sepsis biomarker will help in guiding us when not to start antibiotics in case of suspect sepsis and total duration of antibiotics course in case of proven sepsis. A combination of increased destruction and inadequate production of platelets during sepsis-induced thrombocytopenia of the neonate may result in release of young platelets into the circulation. An increased proportion of young platelets may result in increased Mean Platelet Volume (MPV). Among platelet indices MPV is the most commonly studied platelet index in neonatal sepsis. During conditions of rapid platelet turnover, increased MPV signifies the release of larger, younger platelets into the circulation. Although MPV varies with gestational age and chronologic age, construction of rigorous normal curves for values of the MPV is difficult in premature infants
Etude de la conduction électrique dans les diélectriques à forte permittivité utilisés en microélectronique

A partir du noeud technologique 45 nm, le remplacement de l'oxyde de grille SiO2 par un dielectrique high-κ est necessaire pour poursuivre la loi de Moore : l'introduction d'un tel materiau permet de maintenir une capacite de grille elevee tout en limitant le courant de fuite parasite a travers la couche dielectrique. Toutefois, les mecanismes physiques a l'origine de la reduction de courant restent meconnus. Ce manuscrit presente une etude complete de la conduction electrique dans un empilement oxyde d'interface - high-κ - grille metallique. Nous presentons dans un premier temps la modelisation Poisson-Schrodinger developpee pour rendre compte de l'effet de confinement a l'interface du Silicium, prenant en compte la penetration de charge dans l'empilement et traitant de la non-parabolicite de la bande de valence. Une etude experimentale etendue met ensuite en evidence les mecanismes physiques a l'origine de la conduction, en s'appuyant sur un ensemble de caracteristiques Ig(Vg) et C(Vg) d'empilements d'epaisseurs variables, mesurees a basse et haute temperature. Des approches originales pour modeliser le transport permettent egalement d'etablir les parametres tunnel et la structure de bande du HfO2, tout en soulignant la nature sous-stoechiometrique de l'oxyde d'interface. Enfin, ces resultats sont appliques a l'etude de la conduction dans des empilements high-κ avec additifs Lanthane et Magnesium, et mettent en evidence la formation d'un dipole contribuant a diminuer la tension de seuil.
Emotional Exhaustion Bottleneck: Analysis of Factors fostering Knowledge Sharing Behavior

The study investigated the relationship between Value Diversity, Social Networks, Information based and Identification based Trust, Emotional Exhaustion, and knowledge-sharing behavior among academicians.The present study investigated knowledge sharing behavior among academic staff of three public sector universities of Quetta City. The survey technique was used to collect the data. A sample of 221 Respondents was obtained and hypotheses were tested by employing Structural equation modeling technique of data analysis. The findings of the study indicate that value diversity had positive association with emotional exhaustion and social network, information based trust and identification based trust had significant and negative effect on emotional exhaustion. Findings also indicated that emotional exhaustion and knowledge sharing behavior were negatively related with each other.The foremost limitation of present research was its focus on academic staff of universities. This limitation can be controlled by considering knowledge sharing behavior of administrative staff in future research. It was also suggested that Emotional Exhaustion can be used as mediator between interpersonal group characteristics and knowledge sharing behavior.This research offer information on how organizations can upsurge the morale of their academic staff to share knowledge with their group members.
Deep orthogonal matrix factorization as a hierarchical clustering technique

Deep orthogonal nonnegative matrix factorization (deep ONMF) is a constrained deep low-rank matrix approximation model which decomposes a data matrix through several layers of factorizations. Deep ONMF imposes that each data point is assigned to a single cluster at each layer. In this paper, we first explain why deep ONMF can be interpreted as a bottom-up hiearchical clustering technique. Then our main contribution is to provide a simple yet effective greedy initialization strategy for deep ONMF. We show on synthetic data sets that it performs competitively with other initialization strategies, and apply it on the decomposition of a hyperspectral image into its constitutive materials.
Leakage and Tamper Resilient Permutation-Based Cryptography

Implementation attacks such as power analysis and fault attacks have shown that, if potential attackers have physical access to a cryptographic device, achieving practical security requires more considerations apart from just cryptanalytic security. In recent years, and with the advent of micro-architectural or hardware-oriented attacks, it became more and more clear that similar attack vectors can also be exploited on larger computing platforms and without the requirement of physical proximity of an attacker. While newly discovered attacks typically come with implementation recommendations that help counteract a specific attack vector, the process of constantly patching cryptographic code is quite time consuming in some cases, and simply not possible in other cases. What adds up to the problem is that the popular approach of leakage resilient cryptography only provably solves part of the problem: it discards the threat of faults. Therefore, we put forward the usage of leakage and tamper resilient cryptographic algorithms, as they can offer built-in protection against various types of physical and hardware oriented attacks, likely including attack vectors that will only be discovered in the future. In detail, we present the - to the best of our knowledge - first framework for proving the security of permutation-based symmetric cryptographic constructions in the leakage and tamper resilient setting. As a proof of concept, we apply the framework to a sponge-based stream encryption scheme called asakey and provide a practical analysis of its resistance against side channel and fault attacks.
A Utility-Aware General Framework With Quantifiable Privacy Preservation for Destination Prediction in LBSs

Destination prediction plays an important role as the basis for a variety of location-based services (LBSs). However, it poses many threats to users’ location privacy. Most related work ignores privacy preservation in destination prediction. Few studies focus on specific kinds of privacy-preserving destination prediction algorithms and thus are not applicable to other prediction methods. Furthermore, the third party involved in these studies is a potential privacy threat. Additionally, another line of related work regarding LBSs neither guarantees the utility of the predicted results nor provides quantifiable privacy preservation. To this end, in this paper, we propose a general framework that can provide quantifiable privacy preservation and obtain a trade-off between the privacy and the utility of the predicted results by utilizing differential privacy and a neural network model. Specifically, it first adopts a specially designed differential privacy to construct a data-driven privacy-preserving model that formulates the relationship between injected noise and privacy preservation. Then, it combines a Recurrent Neural Network and Multi-hill Climbing to add fine-grained noise to obtain the trade-off between the privacy preservation and the utility of the predicted results. Our extensive experiments on real-world datasets validate that the proposed framework can be applied to different prediction methods, provide quantifiable location privacy preservation, and guarantee the utility of the predicted results simultaneously.
SDSS catalog of stars in the Draco dwarf spheroidal galaxy

The Sloan Digital Sky Survey (SDSS) has scanned the entire region containing the Draco dwarf spheroidal galaxy to 23 magnitude in g∗. We present a catalog of stars found in a 453 square arcminute, elliptical region centered on the Draco dwarf spheroidal galaxy. Objects in the catalog are matched with five previously published catalogs. The catalog contains SDSS photometry for 5634 individual objects, and also the photometry from matches to any of the other catalogs. A comparison of the photometry between catalogs allows us to identify 142 candidate variable objects. One hundred and twelve of the suspected variables have colors consistent with RR Lyrae variables. Subject headings: catalogs — galaxies: dwarf — galaxies: individual (Draco dwarf spheroidal) — stars: variable – stars: RR Lyrae
Effects of Decision Complexity in Goal-seeking Gridworlds: A Comparison of Instance-Based Learning and Reinforcement Learning Agents

Decisions under uncertainty are often made by weighing the expected costs and beneﬁts of the available options. The costs-beneﬁts tradeoffs may make decisions easy or difﬁcult, particularly given uncertainty of these costs and rewards. In this research, we evaluate how a cognitive model based on Instance-Based Learning Theory (IBLT) and two well-known reinforcement learning (RL) algorithms learn to make better choices in a goal-seeking gridworld task under uncertainty and on increasing degrees of decision complexity. We also use a random agent as a base level comparison. Our results suggest that IBL and RL models are comparable in their accuracy levels on simple settings, although the RL models are more efﬁcient than the IBL model. However, as decision complexity increases, the IBL model is not only more accurate but also more efﬁcient than the RL models. Our results suggest that the IBL model is able to pursue highly rewarding targets even when the costs increase; while the RL models seem to get “distracted” by lower costs, reaching lower reward targets.
A Study of Asymptomatic Bacteriuria among Pregnant Women with and Without Gestational Diabetes Mellitus in Al-Jalla Gynecology Hospital, Tripoli, Libya

This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). Background. The infection of the urinary tract is one of the most significant reasons for seeking medical attention among individuals, and is one of the most widely common nosocomial infections. Gestational diabetes mellitus during pregnancy is considered an important risk factor for urinary tract infections. This study aims to investigate the prevalence of asymptomatic bacteriuria and the incidence of urinary tract infections in pregnant women with and without gestational diabetes mellitus and their antibiotic sensitivity patterns. Methods. Total of one hundred consecutive pregnant women with a clinical diagnosis of gestational diabetes mellitus (n=50) and non-diabetic pregnant women (n=50) were included in the study at Gynecology Hospital Tripoli, Libya. Mid-stream urine samples were obtained for a full urine examination. All samples and isolates were investigated by standard laboratory procedures. Asymptomatic bacteriuria was described as at least 105 CFU / ml growth isolated from a woman's urine without complaints about urinary tract infections. Result. A total of 60 (60%) samples showed significant growth. No significant difference among the culture positivity rate was noted between gestational diabetic and non-diabetic patients (30% Vs 30%). Escherichia coli was the most frequent organism (50% in gestational diabetes mellitus and 43% non-diabetic) followed by staphylococcus aureus (27% in diabetic and 40% in nondiabetic). Ciprofloxacin (80%) was highly sensitive to infected bacteria. Conclusion. No significant differences in the prevalence of asymptomatic bacteriuria or incidence of urinary tract infections were found between pregnant women with and without gestational diabetes mellitus.
Grouping 3D Structure Conformations using Network Analysis on 2D Cryo-EM Projection Images

Cryogenic Electron microscopy (cryo-EM) is one of the most promising instruments for determining the structures of macromolecular protein complexes in near-atomic resolution. Nevertheless, there are still open challenges unsolved and here we addressed the heterogeneity problem inherent in cryo-EM data set. Originally, single particle analysis assumes the projection images come from the same molecule with the same structure conformation, but in fact, some molecules have various conformation states in the solution even after purification. Tradition approaches address this problem at 3D level. Thus, they require the information of 3D orientations and a consensus 3D structure before starting analyze the 3D variability which are computationally expensive. Therefore, there is a need to develop a new algorithm that preserves the accuracy while improving speed and scalability with the growing dataset size.
Stock Market Prediction using CNN and LSTM

Starting with a data set of 130 anonymous intra-day market features and trade returns, the goal of this project is to develop 1-Dimensional CNN and LSTM prediction models for high-frequency automated algorithmic trading. Two novelties are introduced, ﬁrst, rather than trying to predict the exact value of the return for a given trading opportunity, the problem is framed as a binary classiﬁcation with the positive class selected as the trades resulting in returns in the top ten percentile of all returns in the training set. Furthermore, the 130 anonymous features are augmented with a logical matrix to reﬂect the missing data values at each time step, thus preserving any relevant information from the fact that a given feature is missing from a given record. The models are compared using both machine learning accuracy measures and investment risk and return metrics. Two CNN and three LSTM candidate models differing in architecture and number of hidden units are compared using rolling cross-validation. Out-of-sample test results are reported showing high average return per trade and low overall risk.
Aggression subtyping for improved insight and treatment innovation in psychiatric disorders

Executive Summary: The ‘Aggression subtyping for improved insight and treatment innovation in psychiatric disorders’ (Aggressotype) project was a five year project funded in the context of the HEALTH.2013. 2.2. 1-3 call for projects addressing paediatric conduct disorders characterised by aggressive traits and/or social impairment: from preclinical research to treatment. The consortium consisted of 27 partners from multiple disciplines, forming a team to address aggression research in a highly interdisciplinary manner. Aggressotype was aimed at understanding the mechanisms underlying impulsive and aggressive behaviour in individuals with childhood the psychiatric disorders attention-deficit/hyperactivity disorder
Learning to Fly: A Distributed Deep Reinforcement Learning Framework for Software-Defined UAV Network Control

Control and performance optimization of wireless networks of Unmanned Aerial Vehicles (UAVs) require scalable approaches that go beyond architectures based on centralized network controllers. At the same time, the performance of model-based optimization approaches is often limited by the accuracy of the approximations and relaxations necessary to solve the UAV network control problem through convex optimization or similar techniques, and by the accuracy of the channel network models used. To address these challenges, this article introduces a new architectural framework to control and optimize UAV networks based on Deep Reinforcement Learning (DRL). Furthermore, it proposes a virtualized, ‘ready-to-fly’ emulation environment to generate the extensive wireless data traces necessary to train DRL algorithms, which are notoriously hard to generate and collect on battery-powered UAV networks. The training environment integrates previously developed wireless protocol stacks for UAVs into the CORE/EMANE emulation tool. Our ‘ready-to-fly’ virtual environment guarantees scalable collection of high-fidelity wireless traces that can be used to train DRL agents. The proposed DRL architecture enables distributed data-driven optimization (with up to 3.7 $\times$ throughput improvement and 0.2 $\times$ latency reduction in reported experiments), facilitates network reconfiguration, and provides a scalable solution for large UAV networks.
Multiscale, radially anisotropic shear wave imaging of the mantle underneath the contiguous United States through joint inversion of USArray and global data sets

EGU2013-2658. Levander, A. , Schmandt, B. , Miller, M.S., Liu, K. , Karlstrom, K.E. , Crow, R.S. , Lee, C. -T.A. & Humphreys, E.D. , 2011. Continuing Colorado plateau uplift by delamination-style convective lithospheric downwelling, Nature, 472(7344), 461–465. Li, C. , van der Hilst, R.D. , Engdahl, E.R. & Burdick, S. , 2008. A new global model for P wave speed variations in Earth’s mantle, Geochem. Geophys. Geosyst. , 9(5), doi:10.1029/2007GC001806. Liu, L. , 2015. The ups and downs of North America: evaluating the role of mantle dynamic topography since the Mesozoic, Rev. Geophys. , 53(3),
In vivo Anti Inflammation Studies of Novel 1, 2, 5 Oxadiazole Sulfonamide Hybrids

Hafiz Adnan Ahmad1,2*, Muhammad Aslam2, Salman Gul2, Tariq Mehmood2 and Munawar Ali Munawar2 1Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of Ministry of Education, Department of Chemistry and Materials Science, Northwest University, Xi’an 710069, People’s Republic of China 2Institute of Chemistry, University of the Punjab, Lahore 54590, Pakistan Article Information Received 01 June 2020 Revised 30 July 2020 Accepted 12 January 2021 Available online 24 June 2021
Guidelines for the use of Smart Home Technology for Elderly People

New technology and smart homes have the potential to enhance standard of living, safety, and take care of older ones in house. The elderly and disabled can be monitored with various intelligent devices. Sensors can be ingrained into their home for continuous quality help and non-obtrusive malady hindrance. Sensor-embedded homes, or smart homes, cannot solely assist individuals with reduced physical functions, however they facilitate resolving the social isolation they face. However, we do not yet know how older people’s perceptions of these technologies may vary, in particular how views based on experience of actual use may differ from those related to anticipated use. We also do not know how older people living independently might view technology that may be of future rather than current value to them. The paper has explored older people’s views of smart home monitoring technology and compares between people with direct experience and people without. Support that, what guidelines they may want to follow while using it. From this study, one will ascertain totally different understandings by mature individuals, acceptance will increase over time and with use. Also relating to privacy, trust, usability, and more concerns about utility. This study concluded the views about the technical feasibility, affordability, impact on relationships, and about the engagement and competencies of those who would view the monitoring data to use into the design field for future use specially for elderly people.
J ul 2 02 1 Mean field games with absorption and common noise with a model of bank run Matteo Burzoni

We consider a mean field game describing the limit of a stochastic differential game ofN -players whose state dynamics are subject to idiosyncratic and common noise and that can be absorbed when they hit a prescribed region of the state space. We provide a general result for the existence of weak mean field equilibria which, due to the absorption and the common noise, are given by random flow of sub-probabilities. We first use a fixed point argument to find solutions to the mean field problem in a reduced setting resulting from a discretization procedure and then we prove convergence of such equilibria to the desired solution. We exploit these ideas also to construct ε-Nash equilibria for the N -player game. Since the approximation is two-fold, one given by the mean field limit and one given by the discretization, some suitable convergence results are needed. We also introduce and discuss a novel model of bank run that can be studied within this framework.
Emergency Department Visits after Childbirth : A Need to Reassess Postpartum Care ?

Objective: To describe the reasons for emergency department (ED) visits and hospital readmissions in the immediate postpartum period (defined as six weeks after delivery) and to assess whether the utilization of ED services could have been prevented. Methods: We conducted a retrospective chart review of women who presented to the ED within six weeks after delivery. Pregnancy data was collected and analyzed for type of delivery and complications in the antepartum, intrapartum and postpartum periods. ED visit data was collected on timing, reason for visit, and re-admitting diagnosis. Descriptive and comparison analysis were performed. Results: During the study period, 401 patients presented to the ED within the 6-week postpartum period. Of those, only 17.7% required hospital readmission. The re-admission rate was significantly higher in women presenting with hypertensive disorders of pregnancy (18.3%), shortness of breath (15.5%), and vaginal bleeding (19.7%) compared to those without those diagnoses (p < 0.05). Two hundred and forty-three (60.6%) visits occurred during the first week after discharge and 321 (80.0%) within two weeks. In the first 2 weeks postpartum, edema (7.8%; P = 0.007) and wound breakdown (20.6%, P = 0.056) were the most common presenting complaints within our population. Conclusion: The majority of ED visits occurred within the first two weeks after childbirth and did not require hospital readmission. These findings suggest that earlier postpartum visits (rather than the traditional 6 weeks after delivery), thorough discharge counseling, and improved clinical support after discharge may decrease unnecessary ED visits in the postpartum period.
Robust Predictive Power of the Electrostatic Term at Shortened Intermolecular Distances

At distances shorter than equilibrium, electrostatic interactions seem to be a more robust indicator of relative molecular dimer stability than more accurate electronic structure approaches. We arrive at this conclusion by investigating the nonparametric correlation between reference interaction energies at equilibrium geometries (coupled cluster with singles, doubles, and perturbative triples at the complete basis set limit, ΔECCSD(T)CBS,ref) and its various approximate values obtained at a range of distances for a training set of 22 biologically relevant dimers. The reference and other costly methods start to fail to reproduce the equilibrium ranking of dimer stabilities when the intermolecular distance is shortened by more than 0.2 A, but the full electrostatic component (includes penetration) maintains a high success rate. Such trends provide a new perspective for any applications where inaccurate structures are used out of necessity, such as the scoring of ligands docked to enzyme active sites.