Miracle-dz/UniFAR-FTUs huggingface dataset:
Short Communication Sprinkler flow rate affects physiological, behavioural and production responses of Holstein cows during heat stress

Heat stress is a major challenge for dairy cows in summer. Sprinklers at feed bunks are commonly used to cool cows. These sprinklers use groundwater, which is becoming limited. There is a need to explore a more efficient use of this precious resource. Eighteen lactating Holstein cows were randomly subjected to three sprinkler flow rates in a crossover design. These rates were 0.5, 1.25, and 2.0 L/min. The average temperature humidity index of the shed was 86.7 during the study. The cows in the 1.25 and 2.0 L/min groups had 0.6 °C and 0.9 °C lower rectal temperatures, respectively, than the 0.5 L/min group. Similarly, the 1.25 and 2.0 L/min groups had lower respiration rates than the 0.5 L/min group. The daily milk yield was approximately 5 kg/d higher in the 1.25 and 2.0 L/min groups, which were not detectably different from each other, than the 0.5 L/min group. The cows in the 0.5 L/min group spent less time feeding than those in the 1.25 and 2.0 L/min groups. These results suggested that sprinkler flow rates greater than 0.5 L/min produced desirable responses by the cows. The flow rate 1.25 L/min appeared to be more efficient as it used 37.5% less groundwater compared with 2.0 L/min. ______________________________________________________________________________________
Microsoft Word-93729031-file00

Affiliations: Division of Pharmacotherapy & Experimental Therapeutics (S.J.S., R.L.H.), and Division of Molecular Pharmaceutics (Z.W., P.C.S.), UNC Eshelman School of Pharmacy, and Division of Gastroenterology and Hepatology, School of Medicine (M.W.F.), University of North Carolina, Chapel Hill, North Carolina; Division of Gastroenterology, University of Pennsylvania (K.R.R.); Department of Biostatistics (A.S.W.), and Department of Epidemiology (S.H.B.), University of Pittsburgh; Liver Center, Beth Israel Deaconess Medical Center (N.H.A.); Division of Gastroenterology and Hepatology, Thomas Jefferson University (V.J.N.); National Center for Complementary and Alternative Medicine (C.M.M.), and Liver Diseases Research Branch, Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases (E.D.), National Institutes of Health, Bethesda, Maryland. DMD Fast Forward. Published on August 24, 2011 as doi:10.1124/dmd.111. 040212
Weakly Supervised Discovery of Semantic Attributes

We consider the problem of extracting semantic attributes, using only classification labels for supervision. For example, when learning to classify images of birds into species, we would like to observe the emergence of features used by zoologists to classify birds. To tackle this problem, we propose training a neural network with discrete features in the last layer, followed by two heads: a multi-layered perceptron (MLP) and a decision tree. The decision tree utilizes simple binary decision stumps, thus encouraging features to have semantic meaning. We present theoretical analysis, as well as a practical method for learning in the intersection of two hypothesis classes. Compared with various benchmarks, our results show an improved ability to extract a set of features highly correlated with a ground truth set of unseen attributes
PaRIS: Polarization-aware Recommender Interactive System

One phenomenon that has been recently observed online is the emergence of polarization among users on social networks, where the population gets divided in groups with opposite opinions. As recommender system algorithms become more selective in filtering what users see and discover, one important question arises: Could recommender system algorithms become more selective in filtering what users see and discover? In this paper, we study this question and propose a new counter-polarization approach for existing Matrix Factorization based recommender systems, that can be tuned by a user-controlled counter-polarization parameter which serves like a voluntary user anti-polarization or discovery dial.
REVIEW ON MACHINE LEARNING ALGORITHM FOR CYBER SECURITY

: Machine learning methodology is being implemented in cybersecurity like never before. Starting with Classification of IP traffic, malicious intrusion detection filtering, ML is one of the promising reactions that may work against zero day attacks. New research is carried out using the techniques of statistical traffic and ML. This paper is a concentrated applying machine learning algorithm on cyber data set and its use in cyber analytics for intrusion detection, classification of network traffic and email filtering applications. Each method has been identified and summarized on the basis of relevance and the number of citations. Since ML approaches are essential, data sets include some well-known datasets. In addition, some suggestions are presented for using a certain algorithm. Various attacks were categorized using the ML-algorithms and each algorithm was eventually assessed for performance.
Sarcasm Analysis Using Machine Learning

: The growth of social media has been exponential within the recent years. Complete information is being put out on the domains directly through social media. Sarcastic tweets can mislead processing activities and end in wrong classification. This paper compares various classification algorithms like Random Forest, SVC, Logistic Regression, Linear SVC and Gaussian Naïve Bayes to detect sarcasm within the input given by the user, the simplest classifier is chosen and paired with various pre-processing and filtering techniques using emojis to supply the simplest possible output. The emojis being the central idea introduced through this paper, the obtained results may be used as an input for other research and applications.
Superstructure Optimization of Multiple Cyclone Arrangements Using Mixed Integer Nonlinear Programming

The gas-solid cyclone has been remarkably widely used among all types of industrial gas-cleaning devices. Many studies have been conducted and reported excessive experimental, theoretical, and computational research aimed at understanding and predicting the performance of cyclones. However, the majority of these works have only focused on the development of single cyclones. In the meantime, the use of multiple cyclones can be considered as one solution to the demands of obtaining the best pollution control strategies to achieve a minimum level of pollution reduction. This has motivated the development of effective formulation for the cyclone arrangement problem. In this work a new optimization model of multiple cyclone arrangement is presented. The key idea is to present the capability of General Algebraic Modeling System (GAMS) software in obtaining the optimal number and dimensions of the cyclone, and the best cyclone arrangement for a certain condition with respect to the minimum total cost, including the operating cost and the capital cost. The proposed model of nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) has been successfully applied to different case studies. The NLP model is applied to an NPK (Nitrogen, Phosphorus, and Potassium) fertilizer plant to find the optimal number and dimensions of the 1D3D, 2D2D, and 1D2D cyclones arranged either in parallel or series. In another case study with the total flow rate of 165 m/s of a stream to be processed in a paper mill, the best cyclone arrangement of parallel-series for three different combinations of the 1D3D and 2D2D cyclone is obtained through the use of MINLP modeling. The results show that different types of cyclones, applied in NPK fertilizer plant, result in different optimal numbers of cyclones. Each type of cyclone (i.e., 1D3D, 2D2D, and 1D2D) has an alternative that can be arranged either in parallel or in series configuration. Furthermore, different values used for the upper bound of D and N in the proposed MINLP model, result in a different cyclone arrangement of parallel-series selected as the optimal solution. The cyclone of 2D2D+2D2D arranged in parallel-series is found to be more economical and efficient compared to other arrangements.
Cross-functional big data integration: applying the UTAUT model

To successfully exploit Big Data for competitive advantage, data scientists and domain experts need to work hand in hand. This poses a challenge to most companies as they need to ensure that stakeholders from all functional levels are willed to integrate Big Data into their day-to-day activities and decision-making processes. By adapting the Unified Theory of Use and Acceptance of Technology (UTAUT) to the context of Big Data, this study makes a first approach to explore the factors that drive Big Data acceptance across different functional levels. The results reveal that Performance Expectancy, Business Value Expectancy, and Social Influence have significant positive effects on Big Data adoption intention. Moreover, they indicate that high levels of data-usage experience and data-driven decision-making behavior have a negative moderating effect on Big Data adoption intention. Based on the research findings, first managerial implications are derived and discussed to assist organizations in their initial Big Data implementation phase.
Theoretical aspects of upscale error growth on the mesoscales: Idealised numerical simulations: Upscale error growth on mesoscales: Numerical simulations

Correspondence L. Bierdel, Meteorological Institute, LMU Munich, Theresienstr. 37, 80333 Munich, Germany. Email: lotte.bierdel@lmu.de The mechanism for the upscale growth of small errors through the atmospheric mesoscales has not been conclusively identified, but geostrophic adjustment in response to diabatically generated motions such as cumulus convection is a plausible candidate. In a companion paper, an analytic solution of the linearized, hydrostatic Boussinesq equations to an impulsive, localized heat source that mimics the effect of latent heating within a convective cloud on an unperturbed, rotating environment is found. Three characteristics of the solution are shown to be potentially useful for identifying the geostrophic adjustment process in numerical simulations. The predictions relate to the horizontal gravity wave speed, the Rossby number and the quantitative relationship between a precipitation anomaly and the balanced flow response (i.e. large-scale vorticity). Here these predictions are tested in the framework of error growth experiments in idealized numerical simulations of a convective cloud field. Three different rotation rates are employed in order to identify the geostrophic adjustment mechanism and allow a quantitative comparison with the predictions of the analytic model. The gravity wave speed estimated from the simulations resembles the theoretical value and is independent of the Coriolis parameter, as predicted. The Rossby number resulting from the proposed scaling of temporal and spatial coordinates features a unique shape and the vorticity diagnostic agrees quantitatively with the analytical predictions. Based on these findings, it is proposed that upscale error growth through the atmospheric mesoscales is governed by the geostrophic adjustment process.
Cooperative Perception in the rescue domain: Technical Report

In this work we describe a model-based approach to the executive control of a rescue rover. We show how this control architecture naturally supports human-robot interaction in the diverse activities needed in rescue and search. We illustrate the approach by considering human-robot interaction in the domain of the RoboCup Rescue competition. We discuss the implementation and tests done both during RoboCup contests and in the laboratory, to show performances according to different working modalities such as fully operated, supervised, fully autonomous.
CONSERVATION STANDARDS EFFECTIVENESS & IMPACT

Although there is a broad assumption that using an adaptive management framework in conservation leads to improved conservation efficiency, effectiveness, and impact, there is little robust and consolidated evidence supporting this hypothesis. The Conservation Standards Effectiveness & Impact Learning Initiative aimed to investigate what evidence exists to verify that increased adoption of “good” conservation practice leads to more successful conservation projects. We developed a theory of change documenting the intermediate results we were expecting to be achieved following the successful adoption of an adaptive management framework. Data collected in an evidence library was linked to key results along the theory of change to assess the quality and quantity of evidence that exists, and generic indicators, possible methods and tools associated with results along the theory of change were identified. Through this initiative, we have an increased understanding of the positive outcomes that may result following the adoption of an adaptive management framework. Although the availability of empirical evidence is limited, we recognize the value of anecdotal data and continue to support the sharing of personal observations and testimonies to build the evidence base. However, considering the most-commonly identified barriers, such as the time and financial investment, associated with transitioning to a framework like the Conservation Standards, there is an urgency to collect robust, empirical evidence to prove that the use of an adaptive management framework is likely to lead to increased conservation impact.
Hazard/Risk Assessment DYNAMIC ENERGY BUDGET AS A BASIS TO MODEL POPULATION-LEVEL EFFECTS OF ZINC-SPIKED SEDIMENTS IN THE GASTROPOD VALVATA PISCINALIS VIRGINIE DUCROT,*†‡ ALEXANDRE R.R. PÉRY,† RAPHAËL MONS,† HERVÉ QUÉAU,† SANDRINE CHARLES,§ and JEANNE GARRIC†

This paper presents original toxicity test designs and mathematical models that may be used to assess the deleterious effects of toxicants on Valvata piscinalis (Mollusca, Gastropoda). Results obtained for zinc, used as a reference toxicant, are presented. The feeding behavior, juvenile survival, growth, age at puberty, onset of reproduction, number of breedings during the life cycle, and fecundity were significantly altered when the snails were exposed to zinc-spiked sediments. Dynamic energy budget models (DEBtox) adequately predicted the effects of zinc on the V. piscinalis life cycle. They also provided estimates for lifecycle parameters that were used to parameterize a demographic model, based on a Z-transformed life-cycle graph. The effect threshold for the population growth rate ( ) was estimated at 259 mg/kg dry sediment of zinc, showing that significant changes in abundance may occur at environmental concentrations. Significant effects occurring just above this threshold value were mainly caused by the severe impairment of reproductive endpoints. Sensitivity analysis showed that the value of depended mainly on the juvenile survival rate. The impairment of this latter parameter may result in extinction of V. piscinalis. Finally, the present study highlights advantages of the proposed modeling approach in V. piscinalis and possible transfer to other test species and contaminants. Keywords—Metal-contaminated sediments No-observed-effect concentration Life-history traits Bioenergetics Demographic models
Application of SRRs in conventional waveguide slot array to achieve performance improvement and dual-band characteristics

This paper presents application of split ring resonators (SRR) in conventional waveguide slot array antenna to achieve performance improvement and multiband characteristics. Three SRRs have been placed on the transverse plane of a conventional slotted waveguide and has been simulated and measured. The measured results show that the antenna has dual band response with respective 10 dB return loss bandwidth 8.23–9.23 GHz (11.45%) and 9.68–11.01 GHz (12.87%). The measured gain and radiation pattern reveal that the proposed antenna has higher gain and front to back radiation ratio as compared with a conventional slotted waveguide antenna. Equivalent circuit of the proposed antenna also has been presented.
Efficient wideband digital front-end transceivers for software radio systems

Software radios (SWR) have been proposed for wireless communication systems to enable them to operate according to incompatible wireless communication standards by implementing most analog functions in the digital section on software-reprogrammable hardware. However, this significantly increases the required computations for SWR functionality, mainly because of the digital front-end computationally intensive filtering functions, such as sample rate conversion (SRC), channelization, and equalization. For increasing the computational efficiency of SWR systems, two new SRC methods with better performance than conventional SRC methods are presented. In the first SRC method, we modify the conventional CIC filters to enable them to perform SRC on slightly oversampled signals efficiently. We also describe a SRC method with high efficiency for SRC by factors greater than unity at which SRC in SWR systems may be computationally demanding. This SRC method efficiently increases the sample rate of wideband signals, especially in SWR base station transmitters, by applying Lagrange interpolation for evaluating output samples hierarchically using a low-rate signal that is computed with low cost from the input signal. A new channelizer/synthesizer is also developed for extracting/combining frequency multiplexed channels in SWR transceivers. The efficiency of this channelizer/synthesizer, which uses modulated perfect reconstruction (PR) filter banks, is higher than polyphase filter banks (when applicable) for processing few channels, and significantly higher than discrete filter banks for processing any number of variable-bandwidth channels where polyphase filter banks are inapplicable. Because the available methods for designing modulated PR filter banks are inapplicable due to the required number of subchannels and stopband attenuation of the prototype filters, a new design method for these filter banks is introduced. This method is reliable and significantly faster than the existing methods. Modulated PR filter banks are also considered for implementing a frequency-domain block blind equalizer capable of equalizing SWR signals transmitted though channels with long impulse responses and severe intersymbol interference (ISI). This blind equalizer adapts by using separate sets of weights to correct for the magnitude and phase distortion of the channel. The adaptation of this blind equalizer is significantly more reliable and its computational requirements increase at a lower rate compared to conventional time-domain equalizers making it efficient for equalizing long channels that exhibit severe ISI.
Torque ripple reduction of induction motor based on a hybrid method of model predictive torque control and particle swarm optimization

The quality of torque of induction motors plays an important role in many electrical drive systems. Model predictive torque control has been a good alternative to conventional direct torque control for improving the torque qualities. Recently, some researchers tried to modify the model predictive torque control with other principles such as minimize torque ripple, optimize duty cycle, in an effort to get smaller torque ripples. However, according to the reported results the torque ripples are still significant. In this article, model predictive torque control based on particle swarm optimization is proposed to modify the model predictive torque control in order to improve the control qualities, especially the steady-state torque ripples. The key idea of this approach is using particle swarm optimization to minimize the cost function. The optimal voltage vector is implemented by space vector modulation technique. In addition, the control performance of model predictive torque control–particle swarm optimization combination is also compared with that of model predictive torque control–genetic algorithm to justify the effectiveness of our method. The presented simulations prove the better torque and phase currents quality at steady state of this approach.
Current and future low-k dielectrics for Cu interconnects

Low-k/Cu integration with low-cost process is a key to future multilevel Cu damascene. This paper describes current status and future direction of low-k dielectrics for Cu interconnects. A novel direct patterning of low-k dielectric films by photochemical reaction is proposed as a future low cost multilevel interconnect technology. It is demonstrated that the smallest feature size of 50 nm damascene lines and via holes can be directly patterned in methylsilsesquiazane low-k dielectric films by electron beam lithography for the first time. In addition, a porous methylsilsesquiazane film is developed and evaluated as a candidate for future ultra-low-k dielectrics.
Power dividers based simplified design topology for planar microwave I-Q mixers

ABSTRACT A simplified design topology for realising planar microwave I-Q mixers is proposed in this article. Conventional I-Q mixers utilise hybrids, vias, cross over or wire-bonds for realisation. The reported design is based on a unique arrangement of six radio frequency (RF) power dividers with simpler routing for attaining similar performances for 10% RF bandwidth and fixed local oscillator (LO) frequency. The proposed topology is demonstrated by realising a mixer at 13.5 GHz in microwave integrated circuit (MIC) technology. With +10 dBm LO power, the measured maximum RF-I and RF-Q conversion loss is 13 dB and amplitude and phase imbalances are 0.1 dB root mean square (RMS) and 4.7° RMS over 1000 MHz bandwidth. The design topology is generic and can be used for any frequency band and technology (MIC, monolithic microwave integrated circuit, RF complementary metal oxide semiconductor etc.).
Sea temperature and cloudiness obtained with NOAA-14 AVHRR imagery

This work mainly deal with the processing of remote sensed images gathered from a radiometer flown aboard an environmental satellite. In this work some NOAA/AVHRR images have been used with focus on the southern Europe and the Mediterranean Basin. The images are navigated by resolving the satellite motion and the sensor's scanning laws and calibrated in order to obtain true radiance or temperature values. The first and second band of the AVHRR radiometer are useful to distinguish between land and sea pixels. While the two thermal infrared channels (4 and 5) proved to be useful to find out the cloud affected pixels and to estimate the value of sea surface temperature on the cloud free pixels. The presented method has been tested on an Intel Pentium 90 MHz based computer. It took 95 sec to perform the following steps: select sea pixel on the basis of radiance on channels 1 and 2; select cloud pixels on the basis of the temperature of channel 4; estimate on sea, cloudy-free, pixels the sea surface temperature by the means of a split window algorithm on a multispectral image. This performance allows this estimation to be done in real time.
A fully coupled element‐free Galerkin model for hydro‐mechanical analysis of advancement of fluid‐driven fractures in porous media

Hydraulic fracturing (HF) of underground formations has widely been used in different fields of engineering. Despite the technological advances in techniques of in situ HF, the industry uses semi‐analytical tools to design HF treatment. This is due to the complex interaction among various mechanisms involved in this process, so that for thorough simulations of HF operations a fully coupled numerical model is required.