Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet

One of the challenges in the field of remote sensing is how to automatically identify and classify high-resolution remote sensing images. A number of approaches have been proposed. Among them, the methods based on low-level visual features and middle-level visual features have limitations. Therefore, this paper adopts the method of deep learning to classify scenes of high-resolution remote sensing images to learn semantic information. Most of the existing methods of convolutional neural networks are based on the existing model using transfer learning, while there are relatively few articles about designing of new convolutional neural networks based on the existing high-resolution remote sensing image datasets. In this context, this paper proposes a multi-view scaling strategy, a new convolutional neural network based on residual blocks and fusing strategy of pooling layer maps, and uses optimization methods to make the convolutional neural network named RFPNet more robust. Experiments on two benchmark remote sensing image datasets have been conducted. On the UC Merced dataset, the test accuracy, precision, recall, and F1-score all exceed 93%. On the SIRI-WHU dataset, the test accuracy, precision, recall, and F1-score all exceed 91%. Compared with the existing methods, such as the most traditional methods and some deep learning methods for scene classification of high-resolution remote sensing images, the proposed method has higher accuracy and robustness.
A Distributed Computational Implementation Of A Finite Element Code Using Fortran In Windows 9x Environment.

In order to solve large finite element problems, engineers and scientists have used both supercomputers and cluster of workstations. Supercomputers have high acquisition and maintenance costs, and has obsolescence in short period. Clusters of workstations are usually associated with specific network hardware and software, and special implementation techniques are necessary. The aim of this work is to test the viability of obtaining high performance computing using usual low cost PCs connected by low cost network hardware, using neither special operational system nor special programming languages, Only resources usually available in computing laboratories at research centers are used. Specifically, in this work were used PCs with at least 700 MHz processors and 128 Mbytes of RAM, connected by a 100 Mbit/s local network. All machines use windows 9x and the implementation was made using FORTRAN 90 language. An initial application implemented in this computational environment was a Finite Element code for 3D Solid Mechanics analysis in linear elasticity, using 8-nodes 1-integration point bricks with hourglass control, and a conjugated gradient solver using preconditioners. Results of scalability of solution time with the number of PCs used and the size of the problem are shown. Initial results show the viability of this approach, both in solution time and in size of problem that can be solved.
Production and flow of turbulence kinetic energy in convective boundary layers

There are two frameworks within which we can discuss energy in convective boundary layers. The first is the one provided by the Reynolds-averaged Navier Stokes (RANS) equations, as interpreted by Osborne Reynolds in the later 19th Century. The other, much newer framework is that provided by complex dynamical systems. The latter emphasized energy flow through the whole boundary layer rather than the interpretation of local budgets of turbulence kinetic energy. It is argued that the two frameworks constitute two incompatible paradigms, since the first localizes physical causality while the second denies such a simple view. The reasoning applied by Reynolds to his interpretation of the RANS energy equations is examined and found to be faulty. This paper presents a model for energy flow in convective boundary layers from a dynamical systems perspective. This is done both at the whole-system level and, in finer detail, at the level of the individ∗Paper 2A.2 19th Symposium on Boundary Layers and Turbulence, August 1 6, 2010. Keystone CO, American Meteorological Society. †email: keith@mcnaughty.com ual patterns of motion, or eddies, within the system.
RR Lyrae Variables in the Globular Clusters of M31: A First Detection of Likely Candidates

The purpose of this Letter is to show that RR Lyrae variables exist and can be detected in M31 globular clusters. We report on the first tentative identification of RR Lyrae candidates in four globular clusters of the Andromeda galaxy, i.e., G11, G33, G64, and G322. Based on Hubble Space Telescope Wide Field Planetary Camera 2 archive observations in the F555W and F814W filters spanning a total interval of about 5 consecutive hours, we find evidence for two, four, 11, and eight RR Lyrae variables of both ab and c Bailey types in G11, G33, G64, and G322, respectively. Several more candidates can be found by relaxing slightly the selection criteria. These numbers are quite consistent with the horizontal-branch (HB) morphology exhibited by the four clusters, starting from the very blue HB in G11 and progressively moving to redder HBs in G64, G33, and G322.
Quantifying the Relative Importance of Variables and Groups of Variables in Remote Sensing Classifiers Using Shapley Values and Game Theory

Remote sensing image classification applications often involve determining which variables are the most important to obtain the best accuracy. Common metrics for assessing variable importance such as a mean decrease in accuracy (MDA) typically provide values in scaled units that are difficult to interpret, and do not easily accommodate user-defined groups of variables. In this letter, an improved method of quantifying the importance of classifier variables is developed and demonstrated in the context of land-cover classification using the random forest algorithm. The proposed method employs concepts from game theory and relies on a metric known as the Shapley value, which allows the importance of variables to be easily interpreted by providing a quantitative assessment of each variable’s contribution to classifier accuracy. Moreover, unlike MDA, the method also applies to arbitrary, user-defined groups of variables. The approach described herein thus provides a robust alternative to single-variable selection using MDA and can be used with any type of classifier.
Exploring the Feasibility of Supporting UK Partners Living Alongside Veterans with PTSD: A Pilot Study of the Together Programme (TTP)

Background Romantic partners living alongside veterans with Post Traumatic Stress Disorder (PTSD) appear at increased risk of secondary traumatic stress (sPTSD) and common mental health difficulties (CMD) compared to the general population. The severity of symptoms implies the need for structured, bespoke and evidence-based interventions. Objective The aim of this study was to explore the feasibility of offering a community support programme (The Together Programme, TTP) for military partners. TTP was developed based upon a number of US programmes and consisted of 10 hours of group-based support delivered over a five-week course. 56 participants engaged in TTP over a year at nine locations across the UK and were followed up three months later. Methods Measures of CMD, sPTSD, alcohol use and relationship satisfaction were used to assess benefits. Data were also collected on attendance and participant feedback. Results Significant reductions were observed for symptoms of sPTSD and CMD at follow up. 51/56 (90.1%) participants completed TTP. The majority of participants reported positive experiences. However, several individuals stated wanting more sessions and that barriers such as work, and family commitments made it difficult to attend. Conclusions Whilst limitations exist, the data presented suggests cautious optimism for the efficacy of offering a structured programme of support to address the needs of military partners living alongside PTSD.
Relativistic self-focusing in interaction of intense laser beam and exponential decay underdense inhomogeneous plasma

In the laser and plasma interaction, Wong et al. (2018) and Fuchs et al. (2000) have found that the plasmas present the approximate exponential or exponential decay variation of density in experiments. In the paper, based on their researched results, we investigated beam relativistic self-focusing in interaction of intense laser beam and axially exponential decay underdense inhomogeneous plasma. The results show that the relativistic nonlinear effect gives rise to obvious self-focusing in homogeneous plasma and inhomogeneous plasma, comparing with beam self-focusing in homogeneous plasma, exponential decay inhomogeneous plasma is more benefit to form relatively steady self-focusing. Moreover, plasma inhomogeneity promotes self-focusing. In addition, when the amplitude of plasma inhomogeneity increases, beam self-focusing would obviously strengthen, while with the increase of the degree of exponential decay, self-focusing would slightly decrease.
VALIDATION OF A QUICK, SUBMAXIMAL HOT OF MAXIMAL OXYGEN INTAKF

The purpose of this study was to validate a submaximal treadmill test for prediction of maximal oxygen consumption in fifteen male college students. This objective was accomplished by comparing maximal oxygen intake values recorded during a maximal treadmill test with those predicted from the submaximal test. Comparisons were also made among and between the true and predicted values and those estimated from the AstrandRhyming nomogram. Application of the analysis of variance techniques indicated that no significant difference existed between the criterion and the two predictors. The average error of prediction; however, was 8 percent for the submaximal test and 15 percent for the nomogram. The results of the study indicated that the submaximal test yielded a valid estimate of maximal oxygen intake and is more accurate than several of the commonly used submaximal tests. Ilnrl agsi f\pA jrcutit* Clmific«tion DOCUMENT CONTROL DATA R&D f$*9*Htf < tmmUi »lion ml till*, bmdy ot *6«fMt f mnd indtMtng mnotmtlcn mutt 6# mr\90r*4 m on Iff uvarmll f#poff I» tlmaailltäj I ORlOlNATINS ACTIVIT* (Colpoimf tulhor) Center of Biotechnology and Human Performance Texas Tech University Lubbock, Texas 79409 *«. RIPOKT StCuniTV CL/tMIFICJkTION Unclassified lb CROUP NA I «t^OKT TltL« Validation of a Quick, Submaximal Test of Maximal Oxygen Intake x 4 DttCNirTlvt NOTItfTVp« <Wr«pofl and /nclu'fr« <<•>••) Submitted to Research Quarterly April, 1972 • >u TMOniii (Urn IMM. mIMIm Mlitol. Immi Ham») Alfred E. Coleman • «PORT D«TI April. 1972 H. CONTR/tCT OR OP4NT NO. DAAD05-69-C-0102 ». rnojccr NO. 1TO14501B81A <. Program Element 61102A * THEMIS Proposal No. 603 7«. TOTAL NO OF PAGII 16 76. NO. OF KIFt 15
Fast Induction Logging Modeling With Hierarchical Sudoku Meshes Based on DGFD

This letter extends the discontinuous Galerkin frequency domain (DGFD)-based domain decomposition method (DDM) to model the open borehole environment accurately with a newly developed hierarchical sudoku mesh. Both the borehole and mud invasion effects can be modeled by this framework efficiently. Furthermore, deep reading measurements can be modeled conveniently by inserting arbitrarily shaped objects into the hierarchical sudoku mesh; the capability to distinguish a cavity saturated with either oil or water with different borehole-object distances are then studied for a deep reading tool in an open borehole carbonate environment. The DGFD with the hierarchical sudoku mesh shows six times faster speed than the traditional finite-element method (FEM) for the deep reading case.
Optimized short text embedding for bilingual similarity using Probase and BabelNet

Most existing methodologies for text classiﬁcation represent text as vectors of words, to be specific "bag-of-words. " This content portrayal results in a high dimensionality of feature space and much of the time experiences surface jumbling. When it comes to short texts, these become even more serious because of their shortness and sparsity and with the bilingual similarity of text it gets more difficult. This paper proposes an approach to deal with both sparsity and computational complexity of bilingual similarity of short text. English short text is mapped with Probase and Hindi short text is mapped with BabelNet a knowledge base with coverage of words and concepts for 248 languages. A semantic network is created to manipulate the word to word and concept to concept correlation. Unlike the earlier approaches of embedding, words and concepts from both English and Hindi short texts are treated separately to yield word embedding (Word2Vec) and concept embedding (Concept2Vec) respectively. The similarity between bilingual short texts is computed using the skip-gram based word embedding and concept embedding. When evaluated with Pilot and STSS 131 short text benchmark datasets, the proposed optimized bilingual short text embedding gives better similarity score.
Control-Induced Time-Scale Separation for Multiterminal High-Voltage Direct Current Systems Using Droop Control

This paper introduces a control induced time-scale separation scheme for a multiterminal high-voltage direct current system used for large scale integration of renewable energy sources. The main idea is to provide a detailed theoretical analysis to the long stand practice that consists of the empirical design of two control loops for the terminals. Experience has shown that such loops, i.e., current and voltage control loops, when heuristically tuned, often display very different dynamics. In this paper, singular perturbation theory is applied to give explanation and fundamental analysis on why and how the two control loops work and how to achieve the timescale separation between various state variables. Mathematical analysis is also carried out to illustrate a clear tradeoff between system performance (actuator constraint) and the size of the region of attraction of the controller. Numerical simulations for a system with four terminals are presented to evaluate the system performance and illustrate the theoretical analysis.
Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text

Many of the most pressing current research problems (e.g., public health, food security, or climate change) require multi-disciplinary collaborations. In order to facilitate this process, we propose a system that incorporates multi-domain extractions of causal interactions into a single searchable knowledge graph. Our system enables users to search iteratively over direct and indirect connections in this knowledge graph, and collaboratively build causal models in real time. To enable the aggregation of causal information from multiple languages, we extend an open-domain machine reader to Portuguese. The new Portuguese reader extracts over 600 thousand causal statements from 120 thousand Portuguese publications with a precision of 62%, which demonstrates the value of mining multilingual scientific information.
Relationship between inflammatory biomarkers and oxidative stress with uterine health in dairy cows with different dry period lengths1

Abstract Earlier studies indicated that the inflammatory status of dairy cows in early lactation could not be fully explained by the negative energy balance (NEB) at that moment. The objective of the present study was to determine relationships between inflammatory biomarkers and oxidative stress with uterine health in dairy cows after different dry period lengths. Holstein–Friesian dairy cows were assigned to one of three dry period lengths (0-, 30-, or 60-d) and one of two early lactation rations (glucogenic or lipogenic ration). Cows were fed either a glucogenic or lipogenic ration from 10-d before the expected calving date. Part of the cows which were planned for a 0-d dry period dried themselves off and were attributed to a new group (0 → 30-d dry period), which resulted in total in four dry period groups. Blood was collected (N = 110 cows) in weeks −3, −2, −1, 1, 2, and 4 relative to calving to determine biomarkers for inflammation, liver function, and oxidative stress. Uterine health status (UHS) was monitored by scoring vaginal discharge (VD) based on a 4-point scoring system (0, 1, 2, or 3) in weeks 2 and 3 after calving. Cows were classified as having a healthy uterine environment (HU, VD score = 0 or 1 in both weeks 2 and 3), nonrecovering uterine environment (NRU, VD score = 2 or 3 in week 3), or a recovering uterine environment (RU, VD score = 2 or 3 in week 2 and VD score= 0 or 1 in week 3). Independent of dry period length, cows with NRU had higher plasma haptoglobin (P = 0.05) and lower paraoxonase levels (P < 0.01) in the first 4 weeks after calving and lower liver functionality index (P < 0.01) compared with cows with HU. Cows with NRU had lower plasma albumin (P = 0.02) and creatinine (P = 0.02) compared with cows with a RU, but not compared with cows with HU. Independent of UHS, cows with a 0 → 30-d dry period had higher bilirubin levels compared with cows with 0-, 30-, or 60-d dry period (P < 0.01). Cows with RU and fed a lipogenic ration had higher levels of albumin in plasma compared with cows with NRU and fed a lipogenic ration (P < 0.01). In conclusion, uterine health was related to biomarkers for inflammation (haptoglobin and albumin) and paraoxonase in dairy cows in early lactation. Cows which were planned for a 0-d dry period, but dried themselves off (0 → 30-d dry period group) had higher bilirubin levels, which was possibly related to a more severe NEB in these cows. Inflammatory biomarkers in dairy cows in early lactation were related to uterine health in this period.
Managing business model innovation for relocalization in the process and manufacturing industry

More and more industrial activities are performed outside of Europe. INSPIRE is a project, that is driven by the European process industry in order to facilitate the relocalization of process industry value chain activities to Europe. Within the project four business model archetypes (BMA) that facilitate this relocalization were identified: decentralization and modularization, mass customization, servitization and product service systems (PSS), circular business model, by name Re-use, Recycle and Sustainability (RR&S). For companies that want to adopt these business models, we have developed four INSPIRE Tools to integrate flexibilization into process industry business models concepts: Technologies Dashboard for the 5 INSPIRE BMA’s1, Business Model Innovation (BMI) Game, BMI Decision Support Tool for each BMA, and Business Model Archetype Revenue Pattern Map. This article presents the main results and partly reprints other relevant aspects from the INSPIRE deliverable D 4.4. It aims to provide recommendations for decision makers to choose the right business model given their specific context and key parameter.
Molecular Screening of Sideropohore Genes in Extraintestinal Pathogenic Escherichia coli Isolated From Clinical and Escherichia coli Isolated Food Samples in Turkey

Accepted 8th, April 2019 Abstract Iron is an essential element for Escherichia coli growth and survival in the host and the external environment, but its availability is generally low due to the poor solubility of its ferric form in aqueous environments and the presence of iron-withholding proteins in the host. Most E. coli can enhance access to iron by excreting siderophores such as enterobactin, which has a very strong affinity for Fe3+. A smaller proportion of isolates appeared to generate up to 3 additional siderophores associated with pathogenesis; aerobactin, salmochelin, and yersiniabactin. The aim of this study was to compare the presence of siderophore genes in E. coli strains isolated from clinical and food samples. Sixty five and thirty five of E. coli strains were isolated from the clinical and food samples, respectively. The prevalence of the siderophore-related genes, which was determined in the E. coli strains by using PCR method, was as follow: enterobactin receptor (Fep A), 18%; enterobactin biosynthesis (Ent A), 18 % ; yersiniabactin receptor (Fyu A), 17%; heme receptor (Chu A) 16%; aerobactin receptor (iut A),12%; aerobactin biosynthesis (iuc A), 15%; and salmochelin receptor (iro N), 3%, and iron-responsive element (ire A), 1%. The siderophore-related genes are considered as main factors in the growth and survival of the clinical strains of E. coli and adaptation to their low-iron environment. Our results strongly supported that and found the prevalence of siderophore genes is significantly higher among clinical isolates than among food isolates.
Heated Metal Mark Attribute Recognition Based on Compressed CNNs Model

This study considered heated metal mark attribute recognition based on compressed convolutional neural networks (CNNs) models. Based on our previous works, the heated metal mark image benchmark dataset was further expanded. State-of-the-art lightweight CNNs models were selected. Technologies of pruning, compressing, weight quantization were introduced and analyzed. Then, a multi-label model training method was devised. Moreover, the proposed models were deployed on Android devices. Finally, comprehensive experiments were evaluated. The results show that, with the fine-tuned compressed CNNs model, the recognition rate of attributes meta type, heating mode, heating temperature, heating duration, cooling mode, placing duration and relative humidity were 0.803, 0.837, 0.825, 0.812, 0.883, 0.817 and 0.894, respectively. The best model obtained an overall performance of 0.823. Comparing with traditional CNNs, the adopted compressed multi-label model greatly improved the training efficiency and reduced the space occupation, with a relatively small decrease in recognition accuracy. The running time on Android devices was acceptable. It is shown that the proposed model is applicable for real time application and is convenient to implement on mobile or embedded devices scenarios.
Neighborhood Qualities and Parenting Among Mothers With Young Children: Variation by Relationship Status

This study investigates associations between neighborhood social processes and parenting qualities among mothers of young children and tests variation according to relationship status. Data come from Year 3 of the Fragile Families and Child Wellbeing Study (n = 3,535). Aim 1 is to examine associations between neighborhood collective efficacy and social disorder and parenting qualities (parenting stress, parental efficacy, harsh discipline, and neglect). Aim 2 is to test relationship status (married, cohabiting, or single) as a moderator of links between neighborhood social processes and parenting. Results showed significant associations in expected directions; collective efficacy was related to more positive parenting qualities, and social disorder had negative implications for parenting. Interactions by relationship status revealed that neighborhood social processes had stronger implications for single relative to married or cohabiting mothers. Discussion centers on the importance of identifying neighborhood risk and protective factors for parenting qualities among mothers with young children.