Miracle-dz/UniFAR-FTUs huggingface dataset:
On the deterministic and stochastic use of hydrologic models

Environmental simulation models, such as precipitation‐runoff watershed models, are increasingly used in a deterministic manner for environmental and water resources design, planning, and management. In operational hydrology, simulated responses are now routinely used to plan, design, and manage a very wide class of water resource systems. However, all such models are calibrated to existing data sets and retain some residual error. This residual, typically unknown in practice, is often ignored, implicitly trusting simulated responses as if they are deterministic quantities. In general, ignoring the residuals will result in simulated responses with distributional properties that do not mimic those of the observed responses. This discrepancy has major implications for the operational use of environmental simulation models as is shown here. Both a simple linear model and a distributed‐parameter precipitation‐runoff model are used to document the expected bias in the distributional properties of simulated responses when the residuals are ignored. The systematic reintroduction of residuals into simulated responses in a manner that produces stochastic output is shown to improve the distributional properties of the simulated responses. Every effort should be made to understand the distributional behavior of simulation residuals and to use environmental simulation models in a stochastic manner.
Validation of the test for the early detection of learning difficulties in reading and writing

Introducción: la detección temprana de las dificultades en el aprendizaje de la lectoescritura es clave para iniciar cuanto antes programas de intervención. Pero para detectar esas dificultades es necesario disponer de herramientas con probada eficacia. Objetivos: el objetivo de este estudio fue validar un test elaborado con tareas lingüísticas relacionadas con la lectura, aplicado hace tres años a 298 niños prelectores, con el que se pretendía detectar posibles dificultades antes de iniciar el aprendizaje de la lectura.
The Impact of Counseling and Other Psychological Treatments on IBD

INTRODUCTION A lthough the cause of inflammatory bowel disease (IBD) remains unknown; it appears to involve a complex interaction between patients’ genotype and poorly defined environmental factors. Current evidence points to links between anxiety, depression and psychological stress on the one hand, and IBD on the other. Thus, active IBD is itself inherently stressful; life stresses are associated with anxiety and depression, which are both more common in patients with IBD than in the general population; and psychological stress is now recognized as predisposing to relapse in IBD. Here we review the evidence for these assertions and for the possibility that psychological interventions of various types might improve mood disturbances and disease activity in patients with IBD.
SIMULATION OF VARIOUS QoS PARAMETERS IN A HIGH DENSITY MANET SET UP USING AODV nthBR PROTOCOL FOR MULTIMEDIA TRANSMISSION, DATA TRANSMISSION AND UNDER CONGESTION SCENARIO

Mobile ad hoc networks (MANETs) are a setup of several independent nodes that are capable of transmitting any kind of data by themselves. In MANETs, the node themselves act as routers and the setup can either be single hop or multihop depending on the application. These devices have wide and varied applications; like in military deployment, for network set up in meetings, electronic classrooms etc. As the applications and use of MANETs has increased, it has also become imminent for MANETs to support multimedia transmission, high quality data transmission and also to work efficiently in a congestion scenario. To support these parameters it is necessary to have a robust routing protocol that provides good Quality of Service (QoS) parameters under different conditions. In this paper the authors simulate transmission of multimedia traffic with AODV nthBR routing protocol for a high density MANET setup. AODV nthBR routing protocol is also simulated for a congestion scenario and the results are then compared with the existing routing protocols.
Effect of Concomitant Administration of a-tocopherol and ACEI on Cardiovascular Performance and Nucleic Acid Changes in DOCA Hypertensive Rats

Background: Evidence for cardiovascular oxidative stress in animals with hypertension has been accumulated. Antioxidant agents have been used to treat endothelial dysfunction and arterial hypertension. Aim: The aim of present study is to elucidate the effect of concomitant administration of α-tocopherol (antioxidant) and angiotensin converting enzyme inhibitor (ACEI; anti-hyptertensive) on cardiac smooth muscle DNA and RNA changes in DOCA induced hypertension in rats. Methods: 48 male Sprague-Dawley rats were used in this study and were divided into 6 groups (n=8): C control rats; CE control rats received α-tocopherol (60 mg/kg) intragastrically (IG); DOCA rats were made hypertensive by SC injection of DOCA (50 mg/kg once weekly for 4 weeks); DOCA+E; DOCA hypertensive rats received α-tocopherol (60 mg/kg) IG; DOCA+ACEI; DOCA hypertensive rats received ACEI (captopril; 15 mg/kg) IG; and DOCA+E+ACEI; DOCA hypertensive rats received both α-tocopherol and captopril in the same doses as DOCA+E and DOCA+ACEI groups respectively. All IG treatments were given to the animals 6 days weekly for 6 weeks. At the end of experimental protocol, systolic blood pressure (MSP), renal vascular flow velocity (RFV), renal vascular resistance (RVR), myocardial contractility (MC) and heart rate (HR) were measured in rats of all groups. Also, serum sodium (Na + ), potassium (K + ) and total plasma antioxidant capacity (TOC) were measured. DNA and RNA analysis were done for the myocardial tissue homogenate. Results: While DOCA administration in rats produced significant increase in serum Na + , MSP, RVR, and RNA levels; it reduced significantly serum K + , TOC, MC and DNA levels compared with the control group. Concomitant administration of both α-tocopherol and ACEI resulted in significant reduction of serum Na + ,
An End-to-End Sparse Coding

Learned ISTA (LISTA) is a recently proposed method to achieve sparse codes by unfolding the iterative hard-thresholding algorithm (ISTA) into a simple recurrent neural network (RNN). LISTA’s performance, however, depends on the efficiency of precomputed sparse code, as they are prerequisite for loss computation in LISTA. To overcome this limitation, we propose to unfold ISTA into a newly designed Sparse LSTM (SLSTM) network. And it bridges LSTM and sparse coding. Different from traditional sparse coding approaches, SLSTM recasts the optimization procedure as a neural network, thus enables efficient inference both on seen/unseen data. Unlike LISTA, a simple RNN, SLSTM is a LSTM network, which is not an approximation to existing sparse coding methods. SLSTM also differs from traditional LSTMs in the unit structure. SLSTM is specifically designed to sparse coding, whereas LSTMS cannot give sparse representation.
ECOLOGICAL ORGANIZATION, BIOGEOGRAPHY, AND THE PHYLOGENETIC STRUCTURE OF TROPICAL FOREST TREE COMMUNITIES

The assembly of local tropical forest tree communities is influenced by abiotic filters from a larger regional species pool (e.g., habitat differentiation, mass effects, dispersal limitation) and local biotic interactions (e.g., density dependence, resource competition; summarized in Figure 6.1). These assembly processes are mediated by the phenotypic similarities or differences of individuals, which are the outcome of evolutionary change in historical communities, and ultimately the composition of taxa in a regional species pool is the outcome of biogeographic processes. Given the great diversity of tropical tree species, we are unlikely ever to know enough about the ecologically important phenotypes or precise spatial ranges of species to be able to predict local community species composition based on detailed attributes of every species. However, we suggest in this chapter that because species similarity and difference are strongly influenced by common ancestry, as is the presence or absence of a taxonomic clade in a geographic area, a phylogenetic approach may be most effective for understanding and predicting local community composition. In this chapter, we briefly review current understanding of abiotic and biotic controls of local species composition, and of evolutionary patterns in ecological characters. We then describe phylogenetic analyses that explore the outcomes of neighborhood interactions, habitat filtering, climatic gradients, and biogeographic history by analyzing the phylogenetic patterns of species composition at nested spatial scales (Figure 6.1). We test these methods with data from forests in Southeast Asia. Finally, we discuss the association of ecological and biogeographic characteristics with internal nodes of plant phylogenies and the creation of predictive models for the general taxonomic and ecological composition of communities.
Repair of enamel by using hydroxyapatite nanoparticles as the building blocks

The application of calcium phosphates and their nanoparticles have been received great attention. However, hydroxyapatite (HAP) is not suggested in dental therapy to repair the damaged enamel directly although this compound has a similar chemical composition to enamel. We note that the size-effects of HAP are not taken into account in the previous studies as these artificial particles frequently have sizes of hundreds of nanometres. It has recently been revealed that the basic building blocks of enamel are 20–40 nm HAP nanoparticles. We suggest that the repair effect of HAP can be greatly improved if its dimensions can be reduced to the scale of the natural building blocks. Compared with conventional HAP and nano amorphous calcium phosphate (ACP), our in vitro experimental results demonstrate the advantages of 20 nm HAP in enamel repairs. The results of scanning electron microscopy, confocal laser scanning microscopy, quantitative measurement of the adsorption, dissolution kinetics, and nanoindentation, show the strong affinity, excellent biocompatibility, mechanical improvement, and the enhancement of erosion-free by using 20 nm particles as the repairing agent. However, these excellent in vitro repair effects cannot be observed when conventional HAP and ACP are applied. Clearly, nano HAP with a size of 20 nm shares similar characteristics to the natural building blocks of enamel so that it may be used as an effective repair material and anticaries agent. Our current study highlights the analogues of nano building blocks of biominerals during biomedical applications, which provide a novel pathway for biomimetic repair.
Title : Influence of counselor characteristics and behaviors on the efficacy of a brief motivational intervention for heavy drinking in young men-- a randomized controlled trial

Background: Brief motivational intervention (BMI) has shown promising results to reduce alcohol use in young adults. Knowledge on mechanisms that predict BMI efficacy could potentially improve treatment effect sizes through data that optimizes clinical training and implementation. Particularly, little attention has been given to counselor influence on treatment mechanisms. Methods: We investigated the influence of counselors on BMI efficacy in reducing alcohol use among non-treatment seeking young men (age 20) screened as hazardous drinkers. Participants were randomly allocated to a) a group receiving a single BMI from one of eighteen counselors selected to maximize differences in several of their characteristics (gender, professional status, clinical and MI experience), or b) a control group receiving assessment only. Drinking at 3-month follow-up was first compared between the BMI and control groups to assess efficacy. Then, the influence of counselors’ characteristics (i.e., gender, professional status, clinical experience, motivational interviewing (MI) experience, BMI attitudes and expectancies) and within-session behaviors (i.e., measured by the Motivational Interviewing Skill Code) on outcome was tested in regression analyses. Results: There was a significant (p=0.02) decrease in alcohol use among the BMI group compared to the control group. Counselors that were male, more experienced, that had more favorable BMI attitudes and expectancies, higher MI skills, but surprisingly less MI-consistent behaviors, had significantly better outcomes than the control group while their counterparts did not. Conclusions: The current study demonstrated BMI efficacy on alcohol use reduction within a sample of non-treatment seeking young adult males. Moreover, BMI effect was related to inter-individual differences among counselors, and results therefore provide recommendations for BMI training and implementation with similar populations.
Bc Optical efficiency of Ag and Au nanoparticles

In this paper, we present the results of calculations aimed at the optical radiation efficiency of Ag and Au nanoparticles, which is defined by the ratio of the scattering cross-section to the extinction one. The calculations were performed using Mie theory formalism for surrounding medium of various refractive indexes. It has been shown that silver nanoparticles exhibit substantially larger optical efficiency in a broad spectral range as compared to gold nanoparticles. The optical efficiency for silver nanoparticles with diameters over 90 nm exceeds 90%.
The Effects of Ph and Chelating Agent on Characterization of Ni Mo / γ-Alumina Heavy Oil

This study reports the synthesis of mixed oxides of Mo, and Ni nanostructures on γAlumina as support, by utilizing ammonium hepta molybdate(AHM), nickel nitrate, as precursors, and organic additives such as citric acid, ethylene diamine tetra acetic acid (EDTA), and urea were used as chelating agent and different pH, in order to investigate the effects of additive structure and pH variation on chemical and physical properties, morphology, size of nanocatalyst and interaction of active metals with support. Also, this study evaluated the activity of catalysts and compare with commercial catalyst. The metal oxide nanoparticles were prepared and impregnated using wetness impregnation method on γAlumina in-situ. The supported nanocatalyst was calcined to remove the volatile material and gases. The nanocatalyst prepared using organic additive,EDTA as surfactant showed enhanced catalytic activity for the oil residue hydrodesulphurization which contained 21000 ppm sulfur. The sulfur content of the feed was reduced to 3000 ppm at 380°C and LHSV 1.5h showing a good catalytic activity.
Asking for a Friend: Evaluating Response Biases in Security User Studies

The security field relies on user studies, often including survey questions, to query end users' general security behavior and experiences, or hypothetical responses to new messages or tools. Self-report data has many benefits -- ease of collection, control, and depth of understanding -- but also many well-known biases stemming from people's difficulty remembering prior events or predicting how they might behave, as well as their tendency to shape their answers to a perceived audience. Prior work in fields like public health has focused on measuring these biases and developing effective mitigations; however, there is limited evidence as to whether and how these biases and mitigations apply specifically in a computer-security context. In this work, we systematically compare real-world measurement data to survey results, focusing on an exemplar, well-studied security behavior: software updating. We align field measurements about specific software updates (n=517,932) with survey results in which participants respond to the update messages that were used when those versions were released (n=2,092). This allows us to examine differences in self-reported and observed update speeds, as well as examining self-reported responses to particular message features that may correlate with these results. The results indicate that for the most part, self-reported data varies consistently and systematically with measured data. However, this systematic relationship breaks down when survey respondents are required to notice and act on minor details of experimental manipulations. Our results suggest that many insights from self-report security data can, when used with care, translate to real-world environments; however, insights about specific variations in message texts or other details may be more difficult to assess with surveys.
Immunohistochemical and Genetic Study of Breast Cancer in Iraqi Women

The book sheds light on some profiles (histopathological classification, demographic presentation, ABO blood group polymorphism, cytogenetic analysis of cultured blood lymphocytes, serum level of estradiol, immunohistochemical evaluation of estrogen, progesterone, HER-1 and HER-2 receptors, tumor-infiltrating immune cells defined by CD4, CD8 and CD68 markers, tumor expression of T-bet, GATA3, IL-17A and FOXP3, and mutational change in exon 3 and exon 7 of estrogen-β gene) of breast tumor (benign and malignant) in a sample of Iraqi female patients.
Beyond Weight Tying: Learning Joint Input-Output Embeddings for Neural Machine Translation

Tying the weights of the target word embeddings with the target word classifiers of neural machine translation models leads to faster training and often to better translation quality. Given the success of this parameter sharing, we investigate other forms of sharing in between no sharing and hard equality of parameters. In particular, we propose a structure-aware output layer which captures the semantic structure of the output space of words within a joint input-output embedding. The model is a generalized form of weight tying which shares parameters but allows learning a more flexible relationship with input word embeddings and allows the effective capacity of the output layer to be controlled. In addition, the model shares weights across output classifiers and translation contexts which allows it to better leverage prior knowledge about them. Our evaluation on English-to-Finnish and English-to-German datasets shows the effectiveness of the method against strong encoder-decoder baselines trained with or without weight tying.
Effect of Feeding System on Enteric Methane Emissions from Individual Dairy Cows on Commercial Farms

This study investigated the effects of feeding system on diurnal enteric methane (CH4) emissions from individual cows on commercial farms. Data were obtained from 830 cows across 12 farms, and data collated included production records, CH4 measurements (in the breath of cows using CH4 analysers at robotic milking stations for at least seven days) and diet composition. Cows received either a partial mixed ration (PMR) or a PMR with grazing. A linear mixed model was used to describe variation in CH4 emissions per individual cow and assess the effect of feeding system. Methane emissions followed a consistent diurnal pattern across both feeding systems, with emissions lowest between 05:00 and 08:59, and with a peak concentration between 17:00 and 20:59. No overall difference in emissions was found between feeding systems studied; however, differences were found in the diurnal pattern of CH4 emissions between feeding systems. The response in emissions to increasing dry matter intake was higher for cows fed PMR with grazing. This study showed that repeated spot measurements of CH4 emissions whilst cows are milked can be used to assess the effects of feeding system and potentially benchmark farms on level of emissions.
Open-Schema Event Profiling for Massive News Corpora

With the rapid growth of online information services, a sheer volume of news data becomes available. To help people quickly digest the explosive information, we define a new problem - schema-based news event profiling - profiling events reported in open-domain news corpora, with a set of slots and slot-value pairs for each event, where the set of slots forms the schema of an event type. Such profiling not only provides readers with concise views of events, but also facilitates various applications such as information retrieval, knowledge graph construction and question answering. It is however a quite challenging task. The first challenge is to find out events and event types because they are both initially unknown. The second difficulty is the lack of pre-defined event-type schemas. Lastly, even with the schemas extracted, to generate event profiles from them is still essential yet demanding. To address these challenges, we propose a fully automatic, unsupervised, three-step framework to obtain event profiles. First, we develop a Bayesian non-parametric model to detect events and event types by exploiting the slot expressions of the entities mentioned in news articles. Second, we propose an unsupervised embedding model for schema induction that encodes the insight: an entity may serve as the values of multiple slots in an event, but if it appears in more sentences along with the same set of more entities in the event, its slots in these sentences tend to be similar. Finally, we build event profiles by extracting slot values for each event based on the slots' expression patterns. To the best of our knowledge, this is the first work on schema-based profiling for news events. Experimental results on a large news corpus demonstrate the superior performance of our method against the state-of-the-art baselines on event detection, schema induction and event profiling.
Detection and Localization of Multiple Spoofing Attackers for Mobile Wireless Networks

The openness nature of wireless networks allows adversaries to easily launch variety of spoofing attacks and causes havoc in network performance. Recent approaches used Received Signal Strength (RSS) traces, which only detect spoofing attacks in mobile wireless networks. However, it is not always desirable to use these methods as RSS values fluctuate significantly over time due to distance, noise and interference. In this paper, we discusses a novel approach, Mobile spOofing attack DEtection and Localization in WIireless Networks (MODELWIN) system, which exploits location information about nodes to detect identity-based spoofing attacks in mobile wireless networks. Also, this approach determines the number of attackers who used the same node identity to masquerade as legitimate device. Moreover, multiple adversaries can be localized accurately. By eliminating attackers the proposed system enhances network performance. We have evaluated our technique through simulation using an 802.11 (WiFi) network and an 802.15. 4 (Zigbee) networks. The results prove that MODELWIN can detect spoofing attacks with a very high detection rate and localize adversaries accurately.
P4ML: A Phased Performance-Based Pipeline Planner for Automated Machine Learning

While many problems could beneﬁt from recent advances in machine learning, signiﬁcant time and expertise are required to design customized solutions to each problem. Prior attempts to automate machine learning have focused on generating multi-step solutions composed of primitive steps for feature engineering and modeling, but using already clean and featurized data and carefully curated primitives. However, cleaning and featurization are often the most time-consuming steps in a data science pipeline. We present a novel approach that works with naturally occurring data of any size and type, and with diverse third-party data processing and modeling primitives that can lead to better quality solutions. The key idea is to generate multi-step pipelines (or workﬂows) by factoring the search for solutions into phases that apply a diﬀerent expert-like strategy designed to improve performance. This approach is implemented in the P4ML system, and demonstrates superior performance over other systems on a variety of raw datasets.