Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Morphological Features of Sargalu Region in Sulaymaniyah Governorate, North of Iraq, by using Geographic Information System

It is an ideal area of research to examine related indicators to anticipate relative tectonic activities, where there is a broad range of geological formations with elements of different sedimentary rocks. This study includes assessing and evaluating the relative tectonic activities within the Sargalu area by using a morphometric approach, which involved the use of different indices that can explain and help understanding the geometry, development level, lithology, and structural disturbance on a sub-basinal level. The research was accomplished by using ArcGIS 10.5 hydrology tools to design the drainage system of each studied stream. The Advanced Spaceborne Thermal Emission Radiometer (ASTER) satellite imagery data and the Digital Elevation Model (DEM 90m) were used. Moreover, Global Mapper and Statistical Package for the Social Sciences (SPSS) were applied. DEM datasets of ASTER were used for watershed delineation. Also, 14 sub-basins were delineated in the Sargalu area. Morphometric indices used include Shape Related Indices, such as Rc, Bs, Rf, Ls, Re, T, and Af. Basin asymmetry factor (Af) and several geomorphic indices were also utilized. Based on the shape related indices (Rc, Rf, Re, Ls, and Cc), the results were found to be similar and, for most sub-basins, had an elongated nature. The elongated basins are connected with tectonically active areas, while the circulated basins are connected with the tectonically undisturbed environment.
3D-FE Implementation of Evolutionary Cyclic Plasticity Model for Fully Mechanistic (non S-N curve) Fatigue Life Evaluation

Large uncertainties exist in the current methods of fatigue life evolution for nuclear components due to the overdependence on approaches that use empirical stress/strain-life (S~N) curves. Argonne National Laboratory (ANL), under the sponsorship of Department of Energy's Light Water Reactor sustainability (LWRS) program, seeks to develop a fully mechanistic approach for more accurate fatigue life estimation of nuclear components. To this end, ANL has developed evolutionary cyclic plasticity models for reactor steels based on uniaxial fatigue tests to capture the material aging behavior such as stress hardening/softening. In this paper, we introduce an implementation of the evolutionary cyclic plasticity model within the commercial finite element software ABAQUS through the use of an in-house developed user material subroutine. The developed FE model can be used for predicting the time-dependent stress hardening/softening of 3D structures. A strain-controlled variable amplitude fatigue experiment is 3D modeled using the developed ABAQUS based FE modeling framework and verified through the experimental data.
New Strategies in Conformation Dynamics

The aim of conformation dynamics is the identification of metastable sets in the molecular conformation space, and the computation of their statistical weights. Because of the high dimensionality of the state space, coarse graining methods attempt to improve the computational performance by reducing the representation of a molecule’s dynamics without losing relevant details. In this doctoral thesis, a coarse graining method in time, by clustering motions of particles according to a certain criterion, and a coarse graining strategy in space, by revealing a hierarchy in the descriptors of a protein, are developed. To find the global statistical weights of the metastable sets, reweighting strategies are necessary. We present two novel techniques that permit a direct calculation of statistical weights, both based on the estimation of free energy differences. The first method requires an inverse balance condition that leads to a direct calculation of the statistical weights as eigenvalues of a transition matrix. The second approach approximates entropy differences between metastable sets to derive according free energy differences. The performance of the novel strategies is tested on several small examples before applying them to the μ-opioid receptor. The μ-opioid receptor is a transmembrane protein, especially important for the perception and the alleviation of pain. A medication with analgesics like morphine can lead to serious side effects. Therefore, it is desirable to find an opioid that activates the μ-opioid receptor in the inflamed tissue only. While Spahn et al. [94] already found a prototype that binds to the μ-opioid receptor in the inflamed tissue only, the intent of this doctoral thesis is to analyze the μ-opioid receptor in order to find possible reasons for the known better effect of analgesics in inflamed tissue. We model the receptor in a simulation box, embedded in a membrane and solvated with water, both in healthy and inflamed tissue, propose different hypotheses that may cause the better effect of the receptor in inflamed tissue, and investigate electrostatic potentials, Coulomb interactions, and conformational changes for the analysis of the receptor’s behavior.
Effect of Frozen Saline on Postoperative Thirst among Women Undergoing Gynecological surgery

postoperative thirst affects the quality of sleep and causes great distress and, therefore, leads to negative feelings such as anxiety and irritability, which affect the quality of life and, therefore, increases the concern of medical personnel Aim: Evaluate the effect of frozen saline on postoperative thirst among women undergoing gynecological surgery. Method: Design: Quasiexperimental research design. Setting: The study was accompanied at the inpatient obstetric and gynecological departments in Mansoura University Hospital. Subjects: purposive sampling was used to select study subjects (n=66) women who undergone Gynecological surgery, subjects were divided randomly into two equal groups. Tools: Three tools were used for data collection, structured interviewing schedule, thirst intensity scale and thirst discomfort scale. Results: The study findings revealed that there was a statistically significant difference between control and intervention groups where as the mean level of thirst was statically significant reducing in favor of the intervention group than the control group (<0.001). Also there was statistically significant difference between the pretest and posttest levels of thirst at different time of measurements among the intervention group (9.2 ±0.7 & 7.2 ±0.8 & 6.7 ±1.1&5.9 ±1.4). Conclusion: Postoperative woman who utilized frozen saline express less thirsty than those who don’t utilize it. Recommendations: Utilization of frozen saline as routine care for all obstetrics and gynecological operation.
Changes to manuscript ms-nr acp-2015-903 Modelling and Measurements of Urban Aerosol Processes on the Neighbourhood Scale in Rotterdam, Oslo and Helsinki

This study evaluates the influence of aerosol processes on the particle number (PN) concentrations in three major European cities on the temporal scale of one hour, i.e. on the neighborhood and city scales. We have used selected measured data of particle size distributions from pre5 vious campaigns in the cities of Helsinki, Oslo and Rotterdam. The aerosol transformation processes were evaluated using an aerosol dynamics model MAFOR, combined with a simplified treatment of roadside and urban atmospheric dispersion. We have compared the model predictions of particle 10 number size distributions with the measured data, and conducted sensitivity analyses regarding the influence of various model input variables. We also present a simplified parameterization for aerosol processes, which is based on the more complex aerosol process computations; this simple model 15 can easily be implemented to both Gaussian and Eulerian urban dispersion models. Aerosol processes considered in this study were (i) the coagulation of particles, (ii) the condensation and evaporation of two organic vapors, and (iii) dry deposition. The chemical transformation of gas-phase com20 pounds was not taken into account. By choosing concentrations and particle size distributions at roadside as starting point of the computations, nucleation of gas-phase vapors from the exhaust has been regarded as post tail-pipe emission, avoiding the need to include nucleation in the pro25 cess analysis. Dry deposition and coagulation of particles were identified to be the most important aerosol dynamic processes that control the evolution and removal of particles. The error of the contribution from dry deposition to PN losses due to the uncertainty of measured deposition veloci30 ties ranges from −76 % to +64 %. The removal of nanoparticles by coagulation enhanced considerably when considering the fractal nature of soot aggregates and the combined effect of van der Waals and viscous interactions. The effect of condensation and evaporation of organic vapors emitted 35 by vehicles on particle numbers and on particle size distributions was examined. Under inefficient dispersion conditions, condensational growth might contribute ::: the ::::: model ::::::: predicts ::: that :::::::::::: condensational :::::: growth :::::::::: contributes to the evolution of PN from roadside to the neighborhood scale. The simplified pa40 rameterization of aerosol processes can predict :::::: predicts ::: the :::::: change :: in : particle number concentrations between roadside and the urban background with an inaccuracy of ∼ 10 :::: urban ::::::::: background :::::: within ::: 10 % , compared to :: of :::: that :::::::: predicted :: by the fully size-resolved MAFOR model. 45
Inference in latent factor regression with clusterable features

Regression models, in which the observed features X ∈ R p and the response Y ∈ R depend, jointly, on a lower dimensional, unobserved, latent vector Z ∈ R K , with K (cid:3) p , are popular in a large array of applications, and mainly used for predicting a response from correlated features. In contrast, methodology and theory for inference on the regression coefﬁcient β ∈ R K relating Y to Z are scarce, since typically the un-observable factor Z is hard to interpret. Furthermore, the determination of the asymptotic variance of an estimator of β is a long-standing problem, with solutions known only in a few particular cases. To address some of these outstanding questions, we develop inferential tools for β in a class of factor regression models in which the observed features are signed mixtures of the latent factors. The model speciﬁcations are both practically desirable, in a large array of applications, render interpretability to the components of Z , and are sufﬁcient for parameter identiﬁability. Without assuming that the number of latent factors K or the structure of the mixture is known in advance, we construct computationally efﬁcient estimators of β , along with estimators of other important model parameters. We benchmark the rate of convergence of β by ﬁrst establishing its (cid:3) 2 -norm minimax lower bound, and show that our proposed estimator (cid:2) β is minimax-rate adaptive. Our main contribution is the provision of a uniﬁed analysis of the component-wise Gaussian asymptotic distribution of (cid:2) β and, especially, the derivation of a closed form expression of its asymptotic variance, together with consistent variance estimators. The resulting inferential tools can be used when both K and p are independent of the sample size n , and also when both, or either, p and K vary with n , while allowing for p > n . This complements the only asymptotic normality results obtained for a particular case of the model under consideration, in the regime K = O( 1 ) and p → ∞ , but without a variance estimate. As an application, we provide, within our model speciﬁcations, a statistical platform for inference in regression on latent cluster centers, thereby increasing the scope of our theoretical results. We benchmark the newly developed methodology on a recently collected data set for the study of the effectiveness of a new SIV vaccine. Our analysis enables the determination of the top latent antibody-centric mechanisms associated with the vaccine response.
GAMIFICATION AND INTERDISCIPLINARY SCIENTIFIC RESEARCH: SCIENTIFIC TEXT MINING

This study investigates the issues of forming the terminological base of interdisciplinary areas of scientific research to discover new knowledge, to make the prediction, such as to identify the public discourse effects on the dynamics of scientific interests. The goals of this paper are to highlight gamification as interdisciplinary area and to represent the complex approach to contextual knowledge research with scientific text mining on the example of “The gamification in the main spheres of digital society” topic. In the work, we use the capabilities of contextual search and extraction of metadata from texts, describing the use of gamification in various fields of activity, followed by quantitative and qualitative processing of the textual arrays obtained. The knowledge gained was used to expand the capabilities of the synthetic method in the part of the text mining analysis as well as the construction and interpretation technique of subject-specific trends. The conclusion of the research consists in the establishment of the sustainable dependence amongst scientific and public discourse: socio-political initiatives form the modern public life which term-concepts identify not only applying technologies in the life of society as such but the scientific discourse directions.
L 1 -Regulated Feature Selection and Deep Learning based Classification for Intrusion Detection System in MANET

: In mobile adhoc network (MANET), the network efficiency and the lifetime of network partly depends upon the competence of the robust intrusion detection system to handle the attacks. The intrusion detection system (IDS) is one of the major component to be integrated with any of the routing protocols so that the data packets reach the intended destination. Due to the infrastructure less architecture of the MANET, it is quite complex to develop an accurate and robust intrusion detection system. In this context, we have made an attempt to come out with an accurate IDS based on L 1 -regulated feature selection, and deep learning is applied for classification. The L 1 -regulated feature selection is based on Linear Support Vector Machine (LSVM) which is characterized by adding a penalty term to the prediction error in order to reduce the weight of the irrelevant features and to make the relevant features having nonzero weights. For classification purpose, deep learning neural network is initialized with sigmoid activation function in the input and hidden layers. In order to accommodate multiclass classification, the softmax activation function is used in the output layer. In order to demonstrate the suitability of the proposed approach, experiments are conducted on the standard datasets and to argue the predictive capability of the proposed approach. Comparative study is also provided with state-of-the-art approaches and the results are presented considering classification accuracy, precision, recall, f1-scoreand detection rate metrics to exhibit the performance of the proposed approach.
1 AN EVENT-CODING ACCOUNT OF ATTITUDES

Attitudes (or opinions, preferences, biases, stereotypes) can be considered bindings of the perceptual features of the attitudes’ object to affective codes with positive or negative connotations, which effectively renders them “event files” in terms of the Theory of Event Coding. We tested a particularly interesting implication of this theoretical account: that affective codes might “migrate” from one event file to another (i.e., effectively function as component of one while actually being part of another), if the two files overlap in terms of other features. We tested this feature-migration hypothesis by having participants categorize pictures of fictitious outer space characters as members of two fictitious races by pressing a left or right key, and to categorize positive and negative pictures of the International Affective Picture System (IAPS) as positive and negative by using the same two keys. When the outer space characters were later rated for likability, members of the race that was categorized by means of the same key as positive IAPS pictures were significantly liked better than members of the race that was categorized with the same key as negative IAPS pictures—suggesting that affective feature codes from the event files for the IAPS pictures effectively acted as an ingredient of event files for the outer space characters that shared the same key. These findings were fully replicated in a second experiment in which the two races were replaced by two unfamiliar fonts. These outcomes are consistent with the claim that attitudes, opinions, and preferences are represented in terms of event files and created by feature binding.
The CSP-CFP missing link : complementarity between environmental , social and governance practices ? ∗

This article analyzes the relationship between corporate social performance (CSP) and corporate financial performance (CFP) by proposing a theoretical model and by testing its main predictions through an econometric study on a matched CSP-CFP panel data for the biggest European listed firms over the 2002-2007 period. Our matched micro-economic dataset gathers two sources of information: environmental, social and governance ratings from the Vigeo database and economic and financial performance data from the Orbis database. Using panel data technique, we examine how the complementarity between various corporate social responsibility practices is likely to increase performance, as predicted by our theoretical model.
HDL in the 21st Century

Low high-density lipoprotein cholesterol (HDL-C) characterizes an atherogenic dyslipidemia that reflects adverse lifestyle choices, impaired metabolism, and increased cardiovascular risk. Low HDL-C is also associated with increased risk of inflammatory disorders, malignancy, diabetes, and other diseases. This epidemiologic evidence has not translated to raising HDL-C as a viable therapeutic target, partly because HDL-C does not reflect high-density lipoprotein (HDL) function. Mendelian randomization analyses that have found no evidence of a causal relationship between HDL-C levels and cardiovascular risk have decreased interest in increasing HDL-C levels as a therapeutic target. HDLs comprise distinct subpopulations of particles of varying size, charge, and composition that have several dynamic and context-dependent functions, especially with respect to acute and chronic inflammatory states. These functions include reverse cholesterol transport, inhibition of inflammation and oxidation, and antidiabetic properties. HDLs can be anti-inflammatory (which may protect against atherosclerosis and diabetes) and proinflammatory (which may help clear pathogens in sepsis). The molecular regulation of HDLs is complex, as evidenced by their association with multiple proteins, as well as bioactive lipids and noncoding RNAs. Clinical investigations of HDL biomarkers (HDL-C, HDL particle number, and apolipoprotein A through I) have revealed nonlinear relationships with cardiovascular outcomes, differential relationships by sex and ethnicity, and differential patterns with coronary versus noncoronary events. Novel HDL markers may also have relevance for heart failure, cancer, and diabetes. HDL function markers (namely, cholesterol efflux capacity) are associated with coronary disease, but they remain research tools. Therapeutics that manipulate aspects of HDL metabolism remain the holy grail. None has proven to be successful, but most have targeted HDL-C, not metrics of HDL function. Future therapeutic strategies should focus on optimizing HDL function in the right patients at the optimal time in their disease course. We provide a framework to help the research and clinical communities, as well as funding agencies and stakeholders, obtain insights into current thinking on these topics, and what we predict will be an exciting future for research and development on HDLs.
Conceptual analysis methods in nursing : a theoretical reflection

Objective: to reflect on different conceptual analysis methods used in nursing. Method: The methods were identified with the criteria: level of detail, distinctions with other proposals and use in nursing studies. It was elaborated a chart with eight methods of concept analysis, in synthesizing them, analyzing them and highlighting their central aspects. Results: Two ‘lines’ of analysis methodologies were identified. The first one is structured, with objective and not very flexible steps. The second, in turn, makes the research path more flexible, which is built/modified in the process. Conclusion and implication for practice: Conceptual analysis is useful to assess how a concept has been used in nursing, what changes are processed with its use over time, to which situations it applies. The choice for a method depends on the level of development of the concept, reinforcing the proposition that there is a gradation, that is, there are concepts already being widely used by nursing and others that are appearing in the discipline’s speeches and texts.
Structure and Dynamics Properties at Interphase Region in the Composite of Polystyrene and Cross-Linked Polystyrene Soft Nanoparticle

It is crucial to understand how much and how far the structure and dynamics properties of the polymer melt can be affected by incorporating nanoparticles (NPs) in the system. Here, we show that in an athermal all-polymer nanocomposites prepared from mixtures of linear polystyrene chains and cross-linked polystyrene NPs, the influence range of the NP on the melt polymer properties, namely interphase thickness, depends on the properties investigated. The local segment conformations can be affected in the range of an NP radius. However, if the interphase thickness is defined by the influence range of the NP on the radius of gyration (Rg) of the melt chain, it can be 1.5Rg for short-chain melts where the chain has a Rg smaller than particle radius Ra. For intermediate chain length polymer melts (Rg > Ra), the influence range of the NP does not change with the chain length. With the chain length further increasing, melt chains cannot feel the existence of the NP; the influence of the NP on the Rg tensors of th.
ICLASS 06-104 A Coupled Interface-capturing and Multi-fluid Model Method for Computing Liquid Jet from Nozzle Internal Flow

We present a coupled interface capturing and multi-fluid model (CIM) method for computing fuel injection behavior from a nozzle internal flow to a liquid jet seamlessly. This method is a kind of CFD algorithm for multi-phase and multi-scale flow simulation. In a computational cell, a relatively large-scale gas/liquid interface captured by the computational mesh is handled by the PLIC-VOF method, and small-scale dispersed gas bubbles and liquid droplets are handled by the four-fluid model. The MF-ICE method is employed for coupling of pressure and velocity, where the compressibility of the gas phase is taken into account. A cavitation model and the CSF model are implemented into the four-fluid model and the VOF method, respectively. The present method demonstrated an ability to handle the multi-phase and multi-scale flows in which different size scales of the gas/liquid interfaces are coexisting. 2-D axisymmetrical calculations of a liquid jet from a cylindrical nozzle were performed. The transition from a cavitating flow regime to a hydraulic flip within a nozzle orifice was simulated well. Moreover the influence of the cavitation bubbles at the nozzle orifice exit and of the hydraulic flip on the subsequent liquid jet was also simulated by the uninterrupted calculation from the nozzle internal flow to the liquid jet. The predicted discharge coefficient of the nozzle and transition of the flow regime by the increase of the pressure difference were corresponding to measurement data.