Miracle-dz/UniFAR-FTUs huggingface dataset:
An Integrated Modelling Approach Inmountainous Watershed of Heihe River Basin, Northwest China

An integrated modelling approach is developed to simulate ecological economics processes of mountainous watershed of Heihe river basin, northwest China. The objectives are 1) a hydrologic unit model HLM_HMU was developed; 2) the impacts of LUCC on the hydrological processes based on different scenarios were simulated respectively; and 3) the Payment for Ecosystem Services (PES) curve of prohibition grazing is given. The hydrological results show that the observed and simulated data have a good fit, in which the Nash–Sutcliffe efficiency, balance error, and explained variance are 0.69, -0.13, and 0.62, respectively; the model can simulate the majority of peaks well; the source code of Spatial Modelling Environment (SME) needs to be deeply understood to improve the algorithms of interpolation and conflux; the PES result shows that prohibition grazing will occur completely when the price of PES reached to 17.42 yuan, and the increased amount of ecosystem services is 2.13 × 108 m3.The main purpose of this paper is to build a better understanding of developing a meaningful integrated model in the study area to solve its ecological and economic problems.
Delay Spread Requirements for Wireless Networks in the 2 . 4 GHz and 5 GHz Bands

This paper starts with a brief overview of radio channel models. The most important parameter in these models is the delay spread, since this is directly related to the complexity of a modem. Various measurement results reported in the literature are summarized, showing that for most office buildings, the delay spread is the range of 40 to 70 ns, while larger delay spreads up to 300 ns can be expected in large buildings like shopping centers and factories.
Predicting Mortality in Diabetic ICU Patients Using Machine Learning and Severity Indices

Diabetes constitutes a significant health problem that leads to many long term health issues including renal, cardiovascular, and neuropathic complications. Many of these problems can result in increased health care costs, as well risk of ICU stay and mortality. To date, no published study has used predictive modeling to examine the relative influence of diabetes, diabetic health maintenance, and comorbidities on outcomes in ICU patients. Using the MIMIC-III database, machine learning and binomial logistic regression modeling were applied to predict risk of mortality. The final models achieved good fit with AUC values of 0.787 and 0.785 respectively. Additionally, this study demonstrated that robust classification can be done as a combination of five variables (HbA1c, mean glucose during stay, diagnoses upon admission, age, and type of admission) to predict risk as compared with other machine learning models that require nearly 35 variables for similar risk assessment and prediction.
Principles and mechanisms of gas sensing by GaN nanowires functionalized with gold nanoparticles

Electrical properties of a chemical sensor constructed from mats of GaN nanowires decorated with gold nanoparticles as a function of exposure to Ar, N2, and methane are presented. The Au nanoparticle decorated nanowires exhibited chemically selective electrical responses. The sensor exhibits a nominal response to Ar and slightly greater response for N2. Upon exposure to methane the conductivity is suppressed by 50% relative to vacuum. The effect is fully reversible and is independent of exposure history. We offer a model by which the change in the current is caused by a change in the depletion depth of the nanowires, the change in the depletion depth being due to an adsorbate induced change in the potential on the gold nanoparticles on the surface of the nanowires.
SPATIAL DIFFUSION MAPS OF DENGUE FEVER EPIDEMICS OCURRING IN SOUTHEASTERN BRAZIL: A METHODOLOGY FOR CARTOGRAPHIC MODELING IN GIS

This study presents a methodology for cartographic modeling of spatial diffusion of dengue fever epidemics, based on spatial analysis techniques and ArcMap and Idrisi32 GIS. Data from 109 counties, organized in epidemiological weeks, related to a dengue fever epidemic that occurred in 2001 in northwestern Sao Paulo state, were used to map the spatial diffusion of cases. The methodology is based on five levels of approaches: four levels that join exact objects/continuous fields models with single/multiple times slices sequences, and a fifth level, which is based on network analysis of connections between counties. With the single time scale/exact objects level, county clusters, spatial contagion of counties and local incidence rates were mapped; and with the multiple time scale/exact objects level, spatial dynamics of the cases were mapped with a spatial&time sequencing model. Using the single time/continuous field level, isoplethic and tendency surface maps were produced. At the multiple times/continuous field level, spatial diffusion maps and spatial-time mobility of mean geographical center of dengue epidemics are arranged using a sequential maps model. At the last level of methodology, urban node connections are spatially analyzed using road network analysis techniques, to map potential for contagion between counties, spatial dispersion of epidemics between counties, and the path of spread of dengue fever over the region as a whole. The results show the spatial diffusion of dengue fever is most efficient in major highway corridors where the daily trips are more frequent, and where there has been no economic impedance to the circulation of merchandise and people.
Polypharmacology Within the Full Kinome: a Machine Learning Approach

Protein kinases generate nearly a thousand different protein products and regulate the majority of cellular pathways and signal transduction. It is therefore not surprising that the deregulation of kinases has been implicated in many disease states. In fact, kinase inhibitors are the largest class of new cancer therapies. Understanding polypharmacology within the full kinome, how drugs interact with many different kinases, would allow for the development of safer and more efficacious cancer therapies. A full understanding of these interactions is not experimentally feasible making highly accurate computational predictions extremely useful and important. This work aims at making a machine learning model useful for investigating the full kinome. We evaluate many feature sets for our model and get better performance over molecular docking with all of them. We demonstrate that you can achieve a nearly 60% increase in success rate at identifying binding compounds using our model over molecular docking scores.
Overview of Caregiver Distress : Why Do We Need Interventions for Caregivers ?

Families remain the most common source of assistance for older adults with physical and/or cognitive limitations (National Alliance for Caregiving and the American Association of Retired Persons, 1997; Schulz & Martire, 2004). A recent national phone survey found that nearly one in four US households included at least one selfidentified caregiver who gave unpaid assistance to an impaired or physically frail relative over the age of 50 (NAC/AARP, 1997). The term “caregiving” encompasses a wide range of activities, from management of medications and appointments to bathing, dressing, and toileting the individual. Typically, tasks change over time as the conditions in question either become more stable, deteriorate, or in some instances, improve. Family and friends often experience considerable stress when trying to provide extensive caregiving in addition to the other demands of their everyday lives – they are often referred to as the “sandwich generation” since many are caught in the middle between employment and the responsibilities of caring for their husbands and children (and perhaps grand-children), as well as the responsibilities of caring for an impaired parent or parent-in-law. Spouses also experience considerable distress, typically focusing on their sense of loss as changes occur in the marital relationship (Davidson, 2006). Although it is true that caregiving can occur in the context of any significant physical and/or emotional disorder, most of the intervention research has been conducted with relatives of older adults with Alzheimer’s disease or another form of dementia. In addition, studies have found that these dementia family caregivers are generally more distressed than caregivers of physically impaired elders (Ory, Yee, Tennstedt, & Schulz, 2000). Given space limitations, this chapter will focus on interventions with dementia family caregivers.
The mitochondrial genomes of two walnut pests, Gastrolina depressa depressa and G. depressa thoracica (Coleoptera: Chrysomelidae), and phylogenetic analyses

In this study, the mitochondrial genomes (mitogenomes) of two walnut leaf insect pests, Gastrolina depressa depressa and G. depressa thoracica, were sequenced by Sanger sequencing technology. The mitogenome of G. depressa thoracica was complete at 16,109 bp in length, while the mitogenome of G. depressa depressa (14,277 bp) was partial. The genomic analyses indicated that both mitogenomes have the typical gene content and arrangement. The formerly identified elements, ‘TAGTA’ between trnSer(UCN) and nad2, and ‘ATGATAA’ between atp8 and atp6, were more conserved than that between nad4L and nad4, which was ‘ATGTTAA’ in Coleoptera excluding Polyphaga. Phylogenetic analyses of the 13 protein-coding genes from 36 coleopteran species well supported a close affinity between the subfamily Chrysomelinae including G. depressa thoracica and G. depressa depressa and Galerucinae, as well as a sister relationship of ((Eumolpinae + Cryptocephalinae) + Cassidinae) within Chrysomelidae.
Automating Installation of the Integrating Biology and the Bedside (i2b2) Platform

Informatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at more than 150 institutions for querying patient data. An i2b2 installation (called hive) comprises several i2b2 cells that provide different functionalities. Given the complex architecture of i2b2 installation, creating a working installation of the platform is challenging for new users. This is despite the availability of extensive documentation for i2b2 and access to a large and active mailing list community of i2b2 users. To address this problem, we have created an automated installation package, called i2b2-quickstart, which automatically downloads the latest i2b2 source code and dependencies, and compiles and configures the i2b2 cells to create a functional i2b2 hive installation. This package will serve as a convenient starting point and reference implementation that will facilitate researchers in the installation and exploration of the i2b2 platform.
Minimum Weekly Temperature Forecasting using ANFIS

Temperature changes had a direct effect on crops. In the present study an adaptive neuro-fuzzy inference system (ANFIS) has been used to model the relationship between maximum and minimum temperature data. Time series data of weekly maximum temperature at a location is analyzed to predict the maximum temperature of the next week at that location based on the weekly maximum temperatures for a span of previous n week referred to as order of the input. Mean weekly maximum and mean weekly minimum temperature data of 10 years 1997 to 2006 (520 weeks) taken from regional center of Indian Meteorological Department at Dehradun, India. The objectives of this paper are to develop prediction model and validate its ability to provide weekly temperature data. Keywords: Minimum weekly temperature, ANFIS, forecasting
Geometry-Aware Scene Text Detection with Instance Transformation Network

Localizing text in the wild is challenging in the situations of complicated geometric layout of the targets like random orientation and large aspect ratio. In this paper, we propose a geometry-aware modeling approach tailored for scene text representation with an end-to-end learning scheme. In our approach, a novel Instance Transformation Network (ITN) is presented to learn the geometry-aware representation encoding the unique geometric configurations of scene text instances with in-network transformation embedding, resulting in a robust and elegant framework to detect words or text lines at one pass. An end-to-end multi-task learning strategy with transformation regression, text/non-text classification and coordinates regression is adopted in the ITN. Experiments on the benchmark datasets demonstrate the effectiveness of the proposed approach in detecting scene text in various geometric configurations.
Evolution of lithium abundance in the Sun and solar twins

Evolution of the 7Li abundance in the convection zone of the Sun during different stages of its life time is considered to explain its low photospheric value in comparison with that of the solar system meteorites. Lithium is intensively and transiently burned in the early stages of evolution (pre-main sequence, pMS) when the radiative core arises, and then the Li abundance only slowly decreases during the main sequence (MS). We study the rates of lithium burning during these two stages. In a model of the Sun, computed ignoring pMS and without extra-convective mixing (overshooting) at the base of the convection zone, the lithium abundance does not decrease significantly during the MS life time of 4.6 Gyr. Analysis of helioseismic inversions together with post-model computations of chemical composition indicates the presence of the overshooting region and restricts its thickness. It is estimated to be approximately half of the local pressure scale height 0.5Hp) which corresponds to 3.8 % of the solar radius. Introducing this extra region does not noticeably deplete lithium during the MS stage. In contrast, at the pMS stage, an overshooting region with a value of approximately 0.18Hp is enough to produce the observed lithium depletion. If we conclude that the dominant lithium burning takes place during the pMS stage, the dispersion of the lithium abundance in solar twins is explained by different physical conditions, primarily during the early stage of evolution before the MS.
Recent Progress in Antitumoral Nanotechnology

Citation: Méndez FB, Medrano FJE, Pariona N, Gómez RDL, Romero JLO (2015) Recent Progress in Antitumoral Nanotechnology. Int J Nanoparticles Nanotech 1:005 Copyright: © 2015 Méndez FB. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. *Corresponding author: José Luis Olivares Romero, Red de Estudios Moleculares Avanzados, Clúster Científico y Tecnológico Biomimic®, Instituto de Ecología. Carretera antigua a Coatepec 351, El Haya, Xalapa 91070, Veracruz, México, E-mail: jose.olivares@inecol.mx Méndez et al. Int J Nanoparticles Nanotech 2015, 1:1
SARCASM DETECTOR ( TSD ) USING TOPIC MODELING ON USER DESCRIPTION

Internet users go to different social media platforms to read about reviews or comments about a product before they buy or invest in one. Thus, for many companies it is very important to keep track of sentiments of their customers review, respond to them in-time, and to keep the brand value high. There is no dearth of models being created for mining sentiments from twitter data but these models fail when sarcasm is involved in tweets. Thus, sarcasm detection (when expressed implicitly, in contrary to being expressed using explicit words) can help in gaining better insight of customer sentiments on their opinion or review about a product or company. In order to fulfil the above mentioned objective of detecting sarcasm, this paper engineers features from tweets and their description in order to capture the sarcasm expressed in tweets. Each registered author on twitter has the opportunity to selfdescribe oneself. We utilize this selfdescription to extract extra information about personality traits of twitter authors. This additional knowledge about the author of a tweet, helps in classifying sarcastic tweets from nonsarcastic ones with a high degree of accuracy. We engineer and test a range of different feature-sets from tweet-text and it’s author’s self-description using LinearSVM and Logistic Regression predictive models. We achieve reliable results (an F-score of 0.78 and AUC of 0.88), which are comparable to the other sarcasm detection models reported in literature.
High-Level Synthesis of Loops Using the Polyhedral Model The MMAlpha Software

High-level synthesis (HLS) of loops allows efficient handling of intensive computations of an application, e.g. in signal processing. Unrolling loops, the classical technique used in most HLS tools, cannot produce regular parallel architectures which are often needed. In this Chapter, we present, through the example of the MMAlpha testbed, basic techniques which are at the heart of loop analysis and parallelization. We present here the point of view of the polyhedral model of loops, where iterative calculations are represented as recurrence equations on integral polyhedra. Illustrated from an example of string alignment, we describe the various transformations allowing HLS and we explain how these transformation can be merged in a synthesis flow.
Localization corrections for mobile laser scanner using local support-based outlier filtering

Abstract. Following the pioneering work introduced in [Lehtola et al., ISPRS J. Photogramm. Remote Sens. 99, 2015, pp. 25–29], we extend the state-of-the-art intrinsic localization solution for a single two-dimensional (2D) laser scanner from one into (quasi) three dimensions (3D). By intrinsic localization, we mean that no external sensors are used to localize the scanner, such as inertial measurement devices (IMU) or global navigation satellite systems (GNSS). Specifically, the proposed method builds on a novel concept of local support-based filtering of outliers, which enables the use of six degrees-of-freedom (DoF) simultaneous localization and mapping (SLAM) for the purpose of enacting appropriate trajectory corrections into the previous one-dimensional solution. Moreover, the local support-based filtering concept is platform independent, and is therefore likely to be widely generalizable. The here presented overall method is yet limited into quasi-3D by its inability to recover trajectories with steep curvature, but in the future, it may be further extended into full 3D.
Holographic Declarative Memory: Using Distributional Semantics within ACT-R

We explore replacing the declarative memory system of the ACT-R cognitive architecture with a distributional semantics model. ACT-R is a widely used cognitive architecture, but scales poorly to big data applications and lacks a robust model for learning association strengths between stimuli. Distribu-tional semantics models can process millions of data points to infer semantic similarities from language data or to infer product recommendations from patterns of user preferences. We demonstrate that a distributional semantics model can account for the primacy and recency effects in free recall, the fan effect in recognition, and human performance on iterated decisions with initially unknown payoffs. The model we propose provides a ﬂexible, scalable alternative to ACT-R’s declarative memory at a level of description that bridges symbolic, quantum, and neural models of cognition.
Brain MRI Segmentation for Focal Cortical Dysplasia Lesion Detection

Focal cortical dysplasia (FCD), a malformation of the cortical development in the brain, is a major cause of refractory epilepsy. In clinical treatments, FCD lesions often have to be removed by surgery and before this can be done, it is necessary to detect and delineate the lesions. However, identification of FCD lesions is a very challenging task and standard radiological MRI evaluation still fails in many cases, because of the complexity of the cortex and subtle behavior of the lesions. On T1-weighted MRI sequence, FCD is usually characterized by increased cortical thickness and blurring between cortex and white matter junction. Therefore, to automatically detect FCD lesion, it is necessary to calculate cortical thickness and intensity map, which often require accurate MRI cortex segmentation. To date, the most popular methods for brain MRI segmentation are the histogram-based method with automated threshold (HTR), Functional MRI of the Brain (FMRIB) Automated Segmentation Tool (FAST) [1] and Statistical Parametric Mapping (SPM) [2]. Although all of these techniques are fast, reproducible and require minimum human intervention, their outcome may be influenced by image noise, bias field and partial volume effects. Thus, to improve the accuracy of cortex segmentation, in this work we propose a 3D brain MRI segmentation technique based on graph cuts algorithm [3]. The novelty of our approach is automatic initialization of the voxel probabilities using Gaussian Mixture Model (GMM) and a 3D instead of 2D segmentation using three labels: gray matter, white matter and cerebrospinal fluid (CSF).
Modelling Herschel observations of infrared-dark clouds in the Hi-GAL survey

We demonstrate the use of the 3D Monte Carlo radiative transfer code phaethon to model infrared-dark clouds (IRDCs) that are externally illuminated by the interstellar radiation field (ISRF). These clouds are believed to be the earliest observed phase of high-mass star formation, and may be the high-mass equivalent of lower mass pre-stellar cores. We model three different cases as examples of the use of the code, in which we vary the mass, density, radius, morphology and internal velocity field of the IRDC. We show the predicted output of the models at different wavelengths chosen to match the observing wavebands of Herschel and Spitzer. For the wavebands of the long-wavelength Spectral and Photometric Imaging Receiver (SPIRE) photometer on Herschel, we also pass the model output through the SPIRE simulator to generate output images that are as close as possible to the ones that would be seen using SPIRE. We then analyse the images as if they were real observations, and compare the results of this analysis with the results of the radiative transfer models. We find that detailed radiative transfer modelling is necessary to accurately determine the physical parameters of IRDCs (e.g. dust temperature, density profile). This method is applied to study G29.55+00.18, an IRDC observed by the Herschel Infrared Galactic Plane Survey (Hi-GAL), and in the future it will be used to model a larger sample of IRDCs from the same survey.