Miracle-dz/UniFAR-FTUs huggingface dataset:
Hot DA white dwarf model atmosphere calculations: including improved Ni PI cross-sections

To calculate realistic models of objects with Ni in their atmospheres, accurate atomic data for the relevant ionization stages needs to be included in model atmosphere calculations. In the context of white dwarf stars, we investigate the effect of changing the Ni {\sc iv}-{\sc vi} bound-bound and bound-free atomic data has on model atmosphere calculations. Models including PICS calculated with {\sc autostructure} show significant flux attenuation of up to $\sim 80$\% shortward of 180\AA\, in the EUV region compared to a model using hydrogenic PICS. Comparatively, models including a larger set of Ni transitions left the EUV, UV, and optical continua unaffected. We use models calculated with permutations of this atomic data to test for potential changes to measured metal abundances of the hot DA white dwarf G191-B2B. Models including {\sc autostructure} PICS were found to change the abundances of N and O by as much as $\sim 22$\% compared to models using hydrogenic PICS, but heavier species were relatively unaffected. Models including {\sc autostructure} PICS caused the abundances of N/O {\sc iv} and {\sc v} to diverge. This is because the increased opacity in the {\sc autostructure} PICS model causes these charge states to form higher in the atmosphere, moreso for N/O {\sc v}. Models using an extended line list caused significant changes to the Ni {\sc iv}-{\sc v} abundances. While both PICS and an extended line list cause changes in both synthetic spectra and measured abundances, the biggest changes are caused by using {\sc autostructure} PICS for Ni.
Gravitational instabilities in a protosolar-like disc – I. Dynamics and chemistry

To date, most simulations of the chemistry in protoplanetary discs have used 1 + 1D or 2D axisymmetric α-disc models to determine chemical compositions within young systems. This assumption is inappropriate for non-axisymmetric, gravitationally unstable discs, which may be a significant stage in early protoplanetary disc evolution. Using 3D radiative hydrodynamics, we have modelled the physical and chemical evolution of a 0.17 M⊙ self-gravitating disc over a period of 2000 yr. The 0.8 M⊙ central protostar is likely to evolve into a solar-like star, and hence this Class 0 or early Class I young stellar object may be analogous to our early Solar system. Shocks driven by gravitational instabilities enhance the desorption rates, which dominate the changes in gas-phase fractional abundances for most species. We find that at the end of the simulation, a number of species distinctly trace the spiral structure of our relatively low-mass disc, particularly CN. We compare our simulation to that of a more massive disc, and conclude that mass differences between gravitationally unstable discs may not have a strong impact on the chemical composition. We find that over the duration of our simulation, successive shock heating has a permanent effect on the abundances of HNO, CN and NH3, which may have significant implications for both simulations and observations. We also find that HCO+ may be a useful tracer of disc mass. We conclude that gravitational instabilities induced in lower mass discs can significantly, and permanently, affect the chemical evolution, and that observations with high-resolution instruments such as Atacama Large Millimeter/submillimeter Array (ALMA) offer a promising means of characterizing gravitational instabilities in protosolar discs.
Size distribution of submarine landslides and its implication to tsunami hazard in Puerto Rico

We have established for the first time a size frequency distribution for carbonate submarine slope failures. Using detailed bathymetry along the northern edge of the carbonate platform north of Puerto Rico, we show that the cumulative distribution of slope failure volumes follows a power‐law distribution. The power‐law exponent of this distribution is similar to those for rock falls on land, commensurate with their interpreted failure mode. The carbonate volume distribution and its associated volume‐area relationship are significantly different from those for clay‐rich debris lobes in the Storegga slide, Norway. Coupling this relationship with tsunami simulations allows an estimate of the maximum tsunami runup and the maximum number of potentially damaging tsunamis from landslides to the north shore of Puerto Rico.
Improving Salt Tolerance in Barley by Osmopriming and Biopriming

Salt stress hinders the plant growth and productivity by inducing changes in physiological and biochemical processes. This 2years study was conducted to determine the effect of seed priming treatments on tolerance against salinity in barley. Seed priming treatments of two barley genotypes (Haider-93 and Frontier-87) involved hydropriming, osmopriming (1.5% CaCl2) and biopriming (Enterobacter sp. strain FD17); dry seeds were taken as control. Seeds were sown in pots (30 cm diameter and 45 cm depth) containing soil. After stand establishment, salinity treatment i.e. control (50 mM NaCl), moderate salinity (100 mM NaCl) and severe salinity (150 mM NaCl) were imposed. Salt stress hampered growth, yield, chlorophyll content, water relations and cell membrane stability (CMS) whereby increased osmolytes, leaf malondialdehyde (MDA) and Na accumulation in tested barley genotypes. However, seed priming techniques improved the plant height, leaf area, grain yield, harvest index, chlorophyll a and b contents, accumulation of total soluble phenolics and proteins, proline and glycine betaine, K and relative water contents, water, osmotic and pressure potentials and CMS, while decreased leaf MDA and Na contents under each level of salinity. Seed priming induced improvement in yield and related attributes under salinity was ordered as osmopriming > biopriming > hydropriming. Genotype Haider-93 performed better under each level of salinity than Frontier87. In conclusion, seed priming induced salinity tolerance in barley was associated with enhanced osmolytes accumulation, improved water relations and decreased lipid peroxidation and Na accumulation. © 2018 Friends Science Publishers
Preparation and Properties of Kraft Lignin-N-Isopropyl Acrylamide Hydrogel

Fiber-reinforced polymer composites are widely used in various applications because of their mechanical properties and ease of manufacture. Fiber-reinforced plastics are being developed using synthetic fiber and natural fibers of bagasse, palm biomass, etc. In this study, the mechanical strength of bagasse fiber-reinforced epoxy composite was investigated using a Design of Experiment technique. The parameters of fiber volume percentage, alkali concentration, and treatment time with three levels were considered, and an L27 design matrix was developed using the Taguchi orthogonal array. The bagasse was first treated with sodium hydroxide solution (NaOH); subsequently, 27 specimens were developed for experimental investigation. The mechanical strength of newly developed bagasse fiber-reinforced epoxy composite was investigated using a three-point bending testing machine. The flexural strength was calculated using three-point bending strength for length versus load combination. The analysis and optimization was done using the Taguchi method, and a second-order mathematical model was developed using response surface methodology (RSM). A significant performance improvement in the flexural strength of the newly developed composite was found.
COLLISION AVOIDANCE APPROACH FOR EXAMPLE-BASED CROWD SIMULATION

Collision avoidance plays an essential role in crowd simulation and determines the simulation quality to some extent. We propose a novel method to improve the collision avoidance performance based on the existing crowd simulation methods. The example-based method is chosen as the basic method by reasons of the realistic simulation result. Considering collision avoidance in the example-based method relies heavily on data captured from the real world, velocity obstacle (VO) is introduced to calculate an action of an agent when collision-free example cannot be found. We finally optimize the performance of collision avoidance by performing the novel collision fixing (CF) algorithm. The CF takes advantage of obtained actions to detect potential collisions and employs repulsive force to avoid the collisions. Experiments are conducted to evaluate the performance of the proposed method, and the results show that the method greatly reduces collision times, especially in complex simulation scenarios, while keeping the scenarios otherwise realistic.
Alterations of blood indices associated with low-dose administration of methomyl insecticide for short times to albino rats

The study aimed to investigate haematological and biochemical alterations associated with short-term intraperitoneal sublethal (1/10 LD 50 , 2 mg/kg body weight) methomyl administration to male albino rats over two and five days, and also the effects of a two-week recovery period. Methomyl administration over five but not two days decreased mean corpuscular hemoglobin and mean corpuscular hemoglobin concentration. A number of blood biochemical parameters increased (serum glucose, cholesterol, triglycerides, urea, uric acid, creatinine) or decreased (globulin); a recovery period returned many entirely or nearly to control levels. Enzyme activities (ALP, ALT, AST) and bilirubin levels increased following methomyl treatment, but again returned to control levels following a recovery period. We conclude that short low doses of methomyl can harm the blood, kidney and liver of rats. Keywords : methomyl insecticide; hematology and biochemical alternations; male rats.
Visualizing Armed Groups: The Democratic Republic of the Congo’s M23 in Focus

New information technologies can help unpack the relationships and tendencies that per- sist in complex networks. We present an interactive, web-based visualization that captures interlocking networks related to the Mouvement du 23 Mars (M23) rebel group in the Democratic Republic of the Congo (DRC) to demonstrate the utility of visually summariz- ing relationships in a multi-dimensional dataset. We draw exclusively from the findings the UN Group of Governmental Experts report S/2012/843 documenting the procurement of weapons and ammunition by the M23 network. The visualization was built using a hierar- chical edge bundling algorithm (Holten 2006) implemented in Data-Driven Documents (d3) (Bostock 2011), an open-source JavaScript-based library for interactive web visualizations. We contend that such visualizations can provide improved understanding of complex social networks both within and beyond the confines of war.
Label-Space Dimensionality Reduction and a Similarity-Based Representation for Protein Function Prediction

Machine Learning (ML) is used in many bioinformatics problems, but it is not so widely applied in Automatic Function Prediction (AFP). One of the reasons for this is that ML techniques require a feature representation of proteins which is not trivial to obtain. We propose a novel feature representation of a protein based on its sequence similarity to a set of annotated training proteins. Additionally, the CAFA challenges (1) have pointed out that BLAST performs on par with, or even better than, most of the more elaborate participating methods in the Biological Process (BP) and Molecular Function subcategories of the Gene Ontology (GO). We argue that this apparent “failure” of many AFP algorithms is partly due to the nature of the GO itself. Although very intuitive, GO terms seem to be hard for computers to learn. A reason for this might be that GO contains many terms, in particular in the BP ontology (>29,000), which is also the “hardest” category (1). We propose to use Label-Space Dimensionality Reduction (LSDR) techniques to transform GO terms into a more compact latent representation that is easier to predict.
Local Government and Rural Infrastructural Delivery in Nigeria

In the contemporary world today, the need to decentralize administration to facilitate efficiency, effectiveness and good governance has become the rule rather than the exception. Both developed and developing countries ensure that services are delivered to the people at the grassroots. It is no doubt that these services are delivered via a structure put in place by these countries, which are given different names. In Nigeria, it is called local government. Larger percentage of people in Nigeria live in the rural areas, where most of the local foods are produced, and bulk of votes also reside. It is however unfortunate that larger percentage of local government in Nigeria lack basic rural infrastructure needed to engender development. It is against this background that this paper assessed the level of rural infrastructure at the grassroots level with a view to identify the problems militating infrastructural development. The paper relied on both primary and secondary data to source its data. The paper submitted that for genuine development to take place in the rural areas necessary infrastructure must be put in place.
Towards a Simulation−based Framework for the Security Testing of Autonomous Vehicles

Autonomous cars depend on data collection and processing in order to operate safely. Attacks against sensors can lead to an incorrect perception of the environment. Testing the impact of sensor attacks on the road is costly and dangerous. Simulation tools for autonomous driving research enable the simulation of the urban environment, thus providing a cheap and risk-free platform for the testing of perception systems. In this paper, we extend an open-source autonomous driving simulator with models of attacks to camera sensors. These attacks are modelled as computer graphics effects, which are subsequently added to the simulated environment. We also present a formal model, developed in the process algebra CSP, by which systematic testing of all possible sensorattack combinations may be yielded. Although our results are at a preliminary stage, we are mindful of its contributions to the security — and therefore, safety — of autonomous driving.
The Bubble Game : An experimental Study of Speculation (An earlier version of this paper was circulated under the title "The Rational and Irrational Bubbles : an Experiment")

This paper proposes a theory of rational bubbles in an economy with finite trading opportunities. Bubbles arise because agents are never sure to be last in the market sequence. This theory is used to design an experimental setting in which bubbles can be made rational or irrational by varying one parameter. This complements the experimental literature on irrational bubbles initiated by Smith, Suchanek and Williams (1988). Our experimental results suggest that it is pretty difficult to coordinate on rational bubbles even in an environment where irrational bubbles flourish. Maximum likelihood estimations show that these results can be reconciled within the context of Camerer, Ho, and Chong (2004)'s cognitive hierarchy model, and Mc Kelvey and Palfrey (1995)'s quantal response equilibrium.
Evaluation of Genomic Instability in the Abnormal Prostate

Abstract : The aim of this study is to investigate the field effect in prostate cancer, the relationship between tumor and nearby histologically normal tissues compared to truly disease free prostate tissue. Identification of changes within tumor adjacent tissues has two possible clinical implications: prognosis and diagnosis. Several tools are being used to investigate this effect, specifically the assessment of telomere length, allelic imbalance, and methylation status, all markers of genomic instability. Microarray studies will be used to aid in the identification of additional gene expression changes occurring between tumor and histologically normal tissues compared to truly disease free tissue. While telomere length and allelic imbalance have been shown to correlate with outcome, it is expected that, when compared with truly normal tissue from disease-free prostates, several progressive changes will be seen, as has been found in prostate cancer cell lines. The proposed study will allow for interaction with other scientists, exposure to new technologies, teaching and continued patient interaction, all of which are important to the physician scientist.
Levy Processes and the Financial Crisis: Can We Design a More Effective Deposit Protection?

Deposit Guarantee Schemes (DGSs) are institutions whose main aim is to provide a safety net for depositors. If a bank fails, depositors will recover their bank deposits up to a certain limit. During the recent financial crisis, DGSs were brought at the centre of the political and financial debate, especially due to the fact that the some DGSs resulted incapable to react to the crisis because of the lack of funds set aside. In this paper we propose to use Levy processes to simulate the loss distribution of deposits insured by a DGS in case of banks’ failure. The simulated distribution can be used to design an effective DGS. By simulating banks’ default and the corresponding losses, our model allows defining a target level for the funds to be collected in order to promptly and effectively respond to financial turmoil and protect the citizens. The proposed approach is applied to a sample of Italian banks.
A New Approach on Restructuring and Reranking of User Search Results by using Feedback Sessions

: To enhance the significance of web search engine, it is very essential to meet the user’s requirement of results. These user search results can be generated with the help of feedback sessions. The framework in this approach firstly creates Feedback session out of user click-through logs. The click-through data will later be preprocessed for precise information retrieval. The original search results will then be restructured and they would be re ranked according to user’s domain of interest. Lastly, to evaluate the performance of search results, Average Precision will be used. The main advantage of this framework is that it will provide the most optimum search result in short period of time. Also it will reduce those noisy URLs that degrade the performance of web search engines on large scale.
Applying Social Network Analysis to Discover Service Innovation within Agile Service Networks

The literature indicates that there is urgent need to address the significant gap in our ability to value the contributory interaction of service networks in organisational performance. This paper is primarily concerned with exploring how service (re)configuration is utilised to optimise network performance. The paper will summarise the literature review over the past year in the quest to document how we can understand the contributory value of service innovation networks. It identifies some interesting overlaps in business process management, service science, and network innovation literature. This paper will discuss how failing to account for the value of service networks inhibits our capability to discover and monitor service performance and how this complements the evolvement of service science. This prevents managers from transforming information on network activity and infrastructural capabilities into strategic knowledge. This paper demonstrates how social network analysis (SNA) can be a powerful tool for managers to understand organisational network performance and service interaction. [ Service Science , ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3. ]
Lipid and Lipoprotein Abnormalities in Chronic Renal Insufficiency: Review

1.1 Kidney disease and cardiovascular mortality Cardiovascular disease is a leading case of mortality not only in the whole population but also in groups with different, noncardiovascular chronic conditions. Kidney disease is one of these and many patients with kidney disease paradoxically do not die from end stage kidney failure but from cardiovascular causes. Already mild or moderate renal impairment represents a considerable excess risk of cardiovascular mortality. The probability of premature death is even more striking in some subgroups of kidney patients – e.g. the cardiovascular mortality rate of young end stage renal disease (ESRD) patients is 500 times higher as compared with an age-matched control group. The situation is moreover complicated by the fact that traditional risk factors (the “Framingham factors”) are of lesser predictive value in kidney disease than in general population (Foley et al, 1998, Magnus and Beaglehole, 2001, Go et al, 2004). To explain this dismal picture it is not sufficient to disclose that chronic kidney disease (CKD) and chronic renal insufficiency (CRI) is associated with accelerated atherosclerosis and abnormal lipid/lipoprotein metabolism (Felstrom et al., 2003, Lacquaniti, 2010). One should keep in mind that “CKD”, “CRI” and “ESRD” are collective terms and the actual diseases and conditions behind them are manifold (Table 1). Some of them (e.g. diabetes mellitus and hypertension) have a profound effect on lipid metabolism and atherosclerosis independently from kidney function already before the manifestation of renal impairment. From the data in Table 1 is evident that diabetes and hypertension is behind ESRD in one quarter (Great Britain) or even two thirds (USA) of cases. The natural history of each underlying disease is dependent on a wide range of factors and although the K/DOQI classification based on glomerulal filtration rate is a very useful one from practical point of view it does not reveal anything about the pathogenesis of the particular condition.
Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning

Deep learning has become the standard model for object detection and recognition. Recently, there is progress on using CNN models for geometric vision tasks like depth estimation, optical flow prediction or motion segmentation. However, Visual SLAM remains to be one of the areas of automated driving where CNNs are not mature for deployment in commercial automated driving systems. In this paper, we explore how deep learning can be used to replace parts of the classical Visual SLAM pipeline. Firstly, we describe the building blocks of Visual SLAM pipeline composed of standard geometric vision tasks. Then we provide an overview of Visual SLAM use cases for automated driving based on the authors' experience in commercial deployment. Finally, we discuss the opportunities of using Deep Learning to improve upon state-of-the-art classical methods.
STCN: Stochastic Temporal Convolutional Networks

Convolutional architectures have recently been shown to be competitive on many sequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs), while providing computational and modeling advantages due to inherent parallelism. However, currently there remains a performance gap to more expressive stochastic RNN variants, especially those with several layers of dependent random variables. In this work, we propose stochastic temporal convolutional networks (STCNs), a novel architecture that combines the computational advantages of temporal convolutional networks (TCN) with the representational power and robustness of stochastic latent spaces. In particular, we propose a hierarchy of stochastic latent variables that captures temporal dependencies at different time-scales. The architecture is modular and flexible due to the decoupling of the deterministic and stochastic layers. We show that the proposed architecture achieves state of the art log-likelihoods across several tasks. Finally, the model is capable of predicting high-quality synthetic samples over a long-range temporal horizon in modeling of handwritten text.