1,782 research outputs found

    ARSENIC REMOVAL FROM GROUNDWATER BY IRON CO PRECIPITATION IN CONTACT FILTER

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    Joint Research on Environmental Science and Technology for the Eart

    Imaging spontaneous currents in superconducting arrays of pi-junctions

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    Superconductors separated by a thin tunneling barrier exhibit the Josephson effect that allows charge transport at zero voltage, typically with no phase shift between the superconductors in the lowest energy state. Recently, Josephson junctions with ground state phase shifts of pi proposed by theory three decades ago have been demonstrated. In superconducting loops, pi-junctions cause spontaneous circulation of persistent currents in zero magnetic field, analogous to spin-1/2 systems. Here we image the spontaneous zero-field currents in superconducting networks of temperature-controlled pi-junctions with weakly ferromagnetic barriers using a scanning SQUID microscope. We find an onset of spontaneous supercurrents at the 0-pi transition temperature of the junctions Tpi = 3 K. We image the currents in non-uniformly frustrated arrays consisting of cells with even and odd numbers of pi-junctions. Such arrays are attractive model systems for studying the exotic phases of the 2D XY-model and achieving scalable adiabatic quantum computers.Comment: Pre-referee version. Accepted to Nature Physic

    Attitudes toward Self-Disclosure on Facebook: A Review of Perception, Emotion and Behavior in University Students

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    A number of social networking platforms have emerged as a result of the development of information and communication technology which have become increasingly user-friendly and full with valuable features. The social networking site with most users is Facebook. Teenagers, particularly college students use Facebook most frequently to study, gain information, entertain themselves and connect with others through self-disclosing personal information on the Facebook profile page. This quantitative study aimed to analyze the attitude of pedagogical students regarding self-disclosure on Facebook as represented through cognition, emotion and behavior concerning academic achievement. The survey was completed by 535university student’s majority in pedagogy. There were 41 students who used it for less than three years between three to five years by 218 students and 276 students who use it more than five years. The questionnaire was self-reported by participants to assess university students' attitudes toward self-disclosure on Facebook. The results indicate that pedagogical students with excellent academic achievement and more than five years of Facebook experience had the highest-level attitude toward self-disclosure on Facebook. The results indicate a positive relationship between cognition, emotion and influence factors students' Facebook attitudes. Future research on methods that enhance student positive disclosure can benefit from this study. Future research should examine how self-disclosure on Facebook relates to other aspects, such as Facebook usage time, financial state and perception of advantages and its disadvantages of Facebook in order to evaluate students' attitudes objectively

    Unlocking the capabilities of explainable fewshot learning in remote sensing

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    Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. To overcome this challenge, fewshot learning has emerged as a valuable approach for enabling learning with limited data. While previous research has evaluated the effectiveness of fewshot learning methods on satellite based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from UAVs, which are increasingly used in remote sensing studies. In this review, we provide an up to date overview of both existing and newly proposed fewshot classification techniques, along with appropriate datasets that are used for both satellite based and UAV based data. Our systematic approach demonstrates that fewshot learning can effectively adapt to the broader and more diverse perspectives that UAVbased platforms can provide. We also evaluate some SOTA fewshot approaches on a UAV disaster scene classification dataset, yielding promising results. We emphasize the importance of integrating XAI techniques like attention maps and prototype analysis to increase the transparency, accountability, and trustworthiness of fewshot models for remote sensing. Key challenges and future research directions are identified, including tailored fewshot methods for UAVs, extending to unseen tasks like segmentation, and developing optimized XAI techniques suited for fewshot remote sensing problems. This review aims to provide researchers and practitioners with an improved understanding of fewshot learnings capabilities and limitations in remote sensing, while highlighting open problems to guide future progress in efficient, reliable, and interpretable fewshot methods.Comment: Under review, once the paper is accepted, the copyright will be transferred to the corresponding journa

    Context-aware Knowledge-based Systems: A Literature Review

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    Context awareness systems, a subcategory of intelligent systems, are concerned with suggesting relevant products/services to users' situations as smart services. One key element for improving smart services’ quality is to organize and manipulate contextual data in an appropriate manner to facilitate knowledge generation from these data. In this light, a knowledge-based approach, can be used as a key component in context-aware systems. Context awareness and knowledge-based systems, in fact, have been gaining prominence in their respective domains for decades. However, few studies have focused on how to reconcile the two fields to maximize the benefits of each field. For this reason, the objective of this paper is to present a literature review of how context-aware systems, with a focus on the knowledge-based approach, have recently been conceptualized to promote further research in this area. In the end, the implications and current challenges of the study will be discussed

    WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images

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    Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV's ability to promptly comprehend the crisis and optimally utilize its limited power and processing resources to narrow down search areas is crucial. Therefore, developing an efficient and lightweight method for scene classification is of utmost importance. However, current approaches tend to prioritize accuracy on benchmark datasets at the expense of computational efficiency. To address this shortcoming, we introduce the Wider ATTENTION EfficientNet (WATT-EffNet), a novel method that achieves higher accuracy with a more lightweight architecture compared to the baseline EfficientNet. The WATT-EffNet leverages width-wise incremental feature modules and attention mechanisms over width-wise features to ensure the network structure remains lightweight. We evaluate our method on a UAV-based aerial disaster image classification dataset and demonstrate that it outperforms the baseline by up to 15 times in terms of classification accuracy and 38.3%38.3\% in terms of computing efficiency as measured by Floating Point Operations per second (FLOPs). Additionally, we conduct an ablation study to investigate the effect of varying the width of WATT-EffNet on accuracy and computational efficiency. Our code is available at \url{https://github.com/TanmDL/WATT-EffNet}.Comment: This paper is accepted in IEEE Trans. GRS

    Search for squarks and gluinos with the ATLAS detector in final states with jets and missing transverse momentum using √s=8 TeV proton-proton collision data

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    A search for squarks and gluinos in final states containing high-p T jets, missing transverse momentum and no electrons or muons is presented. The data were recorded in 2012 by the ATLAS experiment in s√=8 TeV proton-proton collisions at the Large Hadron Collider, with a total integrated luminosity of 20.3 fb−1. Results are interpreted in a variety of simplified and specific supersymmetry-breaking models assuming that R-parity is conserved and that the lightest neutralino is the lightest supersymmetric particle. An exclusion limit at the 95% confidence level on the mass of the gluino is set at 1330 GeV for a simplified model incorporating only a gluino and the lightest neutralino. For a simplified model involving the strong production of first- and second-generation squarks, squark masses below 850 GeV (440 GeV) are excluded for a massless lightest neutralino, assuming mass degenerate (single light-flavour) squarks. In mSUGRA/CMSSM models with tan β = 30, A 0 = −2m 0 and μ > 0, squarks and gluinos of equal mass are excluded for masses below 1700 GeV. Additional limits are set for non-universal Higgs mass models with gaugino mediation and for simplified models involving the pair production of gluinos, each decaying to a top squark and a top quark, with the top squark decaying to a charm quark and a neutralino. These limits extend the region of supersymmetric parameter space excluded by previous searches with the ATLAS detector

    Search for direct production of charginos and neutralinos in events with three leptons and missing transverse momentum in √s = 7 TeV pp collisions with the ATLAS detector

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    A search for the direct production of charginos and neutralinos in final states with three electrons or muons and missing transverse momentum is presented. The analysis is based on 4.7 fb−1 of proton–proton collision data delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in three signal regions that are either depleted or enriched in Z-boson decays. Upper limits at 95% confidence level are set in R-parity conserving phenomenological minimal supersymmetric models and in simplified models, significantly extending previous results

    Measurement of the production of a W boson in association with a charm quark in pp collisions at √s = 7 TeV with the ATLAS detector

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    The production of a W boson in association with a single charm quark is studied using 4.6 fb−1 of pp collision data at s√ = 7 TeV collected with the ATLAS detector at the Large Hadron Collider. In events in which a W boson decays to an electron or muon, the charm quark is tagged either by its semileptonic decay to a muon or by the presence of a charmed meson. The integrated and differential cross sections as a function of the pseudorapidity of the lepton from the W-boson decay are measured. Results are compared to the predictions of next-to-leading-order QCD calculations obtained from various parton distribution function parameterisations. The ratio of the strange-to-down sea-quark distributions is determined to be 0.96+0.26−0.30 at Q 2 = 1.9 GeV2, which supports the hypothesis of an SU(3)-symmetric composition of the light-quark sea. Additionally, the cross-section ratio σ(W + +c¯¯)/σ(W − + c) is compared to the predictions obtained using parton distribution function parameterisations with different assumptions about the s−s¯¯¯ quark asymmetry
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