nils-herrmann/clean_scirepeval huggingface dataset:
Promising methane gas sensor synthesized by microwave assisted Co3O4 nanoparticles

The morphology and gas sensing characteristics of Co3O4 nanoparticles prepared using the microwave irradiation were investigated. XRD and TEM are used to analyze the structural and the morphological properties of the prepared nanoparticles. XRD results confirmed the formation of pure phase of these nanoparticles. The gas sensor based on the synthesis Co3O4 nanoparticles reveals faster response and recovery time at low temperature detection toward methane gas. Specifically, for methane concentration of 1% the response and the recovery times at 200 degC are 100 s and 50 s, respectively. Furthermore, the sensing characteristics of Co3O4 nanoparticles were improved by increasing the operating temperatures and gas concentrations as well. The experimental results clearly demonstrate the potential use of Co3O4 nanoparticles as a sensing material in the fabrication of CH4 sensors.
Highly Sensitive, Room Temperature Methane Gas Sensor Based on Lead Sulfide Colloidal Nanocrystals
Venue: IEEE Sensors Journal

A solid state methane gas sensor based on PbS colloidal nanocrystals has been fabricated and tested for the first time. PbS nanoparticles have been synthesized during an all chemical process and its X ray diffraction (XRD) pattern has been analyzed to verify the quality and estimate the particle size of produced nano powder. TEM microscopy and size estimation by XRD analysis both confirm the production of 40 nm PbS nanoparticles. The sensor was fabricated by the drop casting method on interdigitated electrodes and tested at different conditions. The best achieved sensitivity was 47.6% for 5% methane concentration at room temperature. A comprehensive discussion on sensing mechanism, temperature dependence, and sensor's benefits is also provided in this paper. The sensor has the benefit of room temperature detection and being highly sensitive. Moreover, the sensor's detection range is 1% 5% which is of superior importance in air quality monitoring and safety control systems in industrial environments to prevent suffocation and explosion.
Development of a Room Temperature SAW Methane Gas Sensor Incorporating a Supramolecular Cryptophane A Coating
Venue: Sensors

A new room temperature supra molecular cryptophane A (CrypA) coated surface acoustic wave (SAW) sensor for sensing methane gas is presented. The sensor is composed of differential resonator oscillators, a supra molecular CrypA coated along the acoustic propagation path, and a frequency signal acquisition module (FSAM) A two port SAW resonator configuration with low insertion loss, single resonation mode, and high quality factor was designed on a temperature compensated ST X quartz substrate, and as the feedback of the differntial oscillators. Prior to development, the coupling of modes (COM) simulation was conducted to predict the device performance. The supramolecular CrypA was synthesized from vanillyl alcohol using a double trimerisation method and deposited onto the SAW propagation path of the sensing resonators via different film deposition methods. Experiential results indicate the CrypA coated sensor made using a dropping method exhibits higher sensor response compared to the unit prepared by the spinning approach because of the obviously larger surface roughness. Fast response and excellent repeatability were observed in gas sensing experiments, and the estimated detection limit and measured sensitivity are ~0.05% and ~204 Hz/ respectively.
Opportunities for advances in climate change economics
Venue: Science

Target carbon's costs, policy designs, and developing countries There have been dramatic advances in understanding the physical science of climate change, facilitated by substantial and reliable research support. The social value of these advances depends on understanding their implications for society, an arena where research support has been more modest and research progress slower. Some advances have been made in understanding and formalizing climate economy linkages, but knowledge gaps remain [e.g. as discussed in (1, 2) We outline three areas where we believe research progress on climate economics is both sorely needed, in light of policy relevance, and possible within the next few years given appropriate funding: (i) refining the social cost of carbon (SCC) (ii) improving understanding of the consequences of particular policies, and (iii) better understanding of the economic impacts and policy choices in developing economies.
Categorisation of visualisation methods to support the design of Human Computer Interaction Systems.
Venue: Applied ergonomics

During the design of Human Computer Interaction (HCI) systems, the creation of visual artefacts forms an important part of design. On one hand producing a visual artefact has a number of advantages: it helps designers to externalise their thought and acts as a common language between different stakeholders. On the other hand, if an inappropriate visualisation method is employed it could hinder the design process. To support the design of HCI systems, this paper reviews the categorisation of visualisation methods used in HCI. A keyword search is conducted to identify a) current HCI design methods, b) approaches of selecting these methods. The resulting design methods are filtered to create a list of just visualisation methods. These are then categorised using the approaches identified in (b) As a result 23 HCI visualisation methods are identified and categorised in 5 selection approaches (The Recipient,  Type, and The Design Process)
Using research methods in human computer interaction to design technology for resilience

Research in human computer interaction (HCI) covers both technological and human behavioural concerns. As a consequence, the contributions made in HCI research tend to be aware to either engineering or the social sciences. In HCI the purpose of practical research contributions is to reveal unknown insights about human behaviour and its relationship to technology. Practical research methods normally used in HCI include formal experiments, field experiments, field studies, interviews, focus groups, surveys, usability tests, case studies, diary studies, ethnography, contextual inquiry, experience sampling, and automated data collection. In this paper, we report on our experience using the evaluation methods focus groups, surveys and interviews and how we adopted these methods to develop artefacts: either interface's design or information and technological systems. Four projects are examples of the different methods application to gather information about user's wants, habits, practices, concerns and preferences. The goal was to build an understanding of the attitudes and satisfaction of the people who might interact with a technological artefact or information system. Conversely, we intended to design for information systems and technological applications, to promote resilience in organisations (a set of routines that allow to recover from obstacles) and user's experiences. Organisations can here also be viewed within a system approach, which means that the system perturbations even failures could be characterized and improved. The term resilience has been applied to everything from the real estate, to the economy, sports, events, business, psychology, and more. In this study, we highlight that resilience is also made up of a number of different skills and abilities (self awareness, creating meaning from other experiences, self efficacy, optimism, and building strong relationships) that are a few foundational ingredients, which people should use along with the process of enhancing an organisation's resilience. Resilience enhances knowledge of resources available to people confronting existing problems.
Climate change effects and adaptation strategies in the wine sector: a quantitative literature review

This paper presents a quantitative literature review focusing on how scientific research analysed climate change impact on wine chain as well as potential adaptation strategies. The work is based on content analysis and text mining and takes into account researches from 1990 to 2015. A particular emphasis was given to the evaluation of suggested or implemented adaptation strategies at both global and national levels. Data were analysed using cluster analysis, multidimensional scaling and specificity evaluation. Results show that the study of climate change impacts on the wine sector is a recently emerging research topic. Adaptation strategies have not yet been explored thoroughly in the literature, and in depth uncertainty quantification is also needed. Finally, additional research gaps and potential future issues are suggested.
3D Reconstructed Cyto Muscarinic M2 Receptor, and Fiber Architecture of the Rat Brain Registered to the Waxholm Space Atlas
Venue: Front. Neuroanat.

High resolution multiscale and multimodal 3D models of the brain are essential tools to understand its complex structural and functional organization. Neuroimaging techniques addressing different aspects of brain organization should be integrated in a reference space to enable topographically correct alignment and subsequent analysis of the various datasets and their modalities. The Waxholm Space (http:/software.incf.org/software/waxholm space) is a publicly available 3D coordinate based standard reference space for the mapping and registration of neuroanatomical data in rodent brains. This paper provides a newly developed pipeline combining imaging and reconstruction steps with a novel registration strategy to integrate new neuroimaging modalities into the Waxholm Space atlas. As a proof of principle, we incorporated large scale high resolution cyto muscarinic M2 receptor, and fiber architectonic images of rat brains into the 3D digital MRI based atlas of the Sprague Dawley rat in Waxholm Space. We describe the whole workflow, from image acquisition to reconstruction and registration of these three modalities into the Waxholm Space rat atlas. The registration of the brain sections into the atlas is performed by using both linear and non linear transformations. The validity of the procedure is qualitatively demonstrated by visual inspection, and a quantitative evaluation is performed by measurement of the concordance between representative atlas delineated regions and the same regions based on receptor or fiber architectonic data. This novel approach enables for the first time the generation of 3D reconstructed volumes of nerve fibers and fiber tracts, or of muscarinic M2 receptor density distributions, in an entire rat brain. Additionally, our pipeline facilitates the inclusion of further neuroimaging datasets, e.g. 3D reconstructed volumes of histochemical stainings or of the regional distributions of multiple other receptor types, into the Waxholm Space. Thereby, a multiscale and multimodal rat brain model was created in the Waxholm Space atlas of the rat brain. Since the registration of these multimodal high resolution datasets into the same coordinate system is an indispensable requisite for multi parameter analyses, this approach enables combined studies on receptor and cell distributions as well as fiber densities in the same anatomical structures at microscopic scales for the first time.
A Programmable Five Qubit Quantum Computer Using Trapped Atomic Ions

Title of dissertation: A Programmable Five Qubit Quantum Computer Using Trapped Atomic Ions Shantanu Debnath, Doctor of Philosophy, 2016 Dissertation directed by: Professor Christopher Monroe Joint Quantum Institute, University of Maryland Department of Physics and National Institute of Standards and Technology Quantum computers can solve certain problems more efficiently compared to conventional classical methods. In the endeavor to build a quantum computer, several competing platforms have emerged that can implement certain quantum algorithms using a few qubits. However, the demonstrations so far have been done usually by tailoring the hardware to meet the requirements of a particular algorithm implemented for a limited number of instances. Although such proof of principal implementations are important to verify the working of algorithms on a physical system, they further need to have the potential to serve as a general purpose quantum computer allowing the flexibility required for running multiple algorithms and be scaled up to host more qubits. Here we demonstrate a small programmable quantum computer based on five trapped atomic ions each of which serves as a qubit. By optically resolving each ion we can individually address them in order to perform a complete set of single qubit and fully connected two qubit quantum gates and also perform efficient individual qubit measurements. We implement a computation architecture that accepts an algorithm from a user interface in the form of a standard logic gate sequence and decomposes it into fundamental quantum operations that are native to the hardware using a set of compilation instructions that are defined within the software. These operations are then effected through a pattern of laser pulses that perform coherent rotations on targeted qubits in the chain. The architecture implemented in the experiment therefore gives us unprecedented flexibility in the programming of any quantum algorithm while staying blind to the underlying hardware. As a demonstration we implement the Deutsch Jozsa and Bernstein Vazirani algorithms on the five qubit processor and achieve average success rates of 95 and 90 percent, respectively. We also implement a five qubit coherent quantum Fourier transform and examine its performance in the period finding and phase estimation protocol. We find fidelities of 84 and 62 percent, respectively. While maintaining the same computation architecture the system can be scaled to more ions using resources that scale favorably (O(N2) with the number of qubits N A Programmable Five Qubit Quantum Computer Using Trapped Atomic Ions
Tracing Media Piracy: Current and Future Trends

This chapter assesses the current and future trends and trajectory of the optical media piracy in the Philippines and Vietnam as well as in the world. Media piracy is an evolving cybercrime which can easily adapt to the current technological and global environment. This chapter also traces briefly the development of media piracy from the VCR technology using the cassette format to the current digital and nanotechnology using the Internet and file sharing protocols. Optical disc piracy will be a transitory phenomenon in developing countries with underdeveloped IP and Internet culture. As a country increases in Internet penetration, the locus of media piracy shifts from the temporal space of the sidewalk stalls selling pirated DVDs or illegal CD DVD shops to the cyberspace of the Internet. The use of discs becomes less popular as online media piracy becomes more convenient, easier, and cheaper with direct illegal downloading, peer to peer sharing, and other evasive techniques with the latest sophisticated hardware and software technologies. This chapter then examines some popular and current online digital piracy such as peer to peer (P2P) file sharing, cyberlocker, media box, anti circumvention technology, digital spying and hacking, and other online piracy supported by digital, cloud, and nanotechnology. It also makes projections on how media piracy would develop with the advent of quantum computing and technology. Media piracy follows technological advancement and innovation, thus making it difficult for authorities to curb. It uses the same technology used by copyright holders which provides them with a variety of options to respond to regulation. Finally, this chapter examines the difficulty of regulating the Internet and the role and future involvement of China in counterfeiting and media piracy. With the rise of online media piracy, regulating the Internet would then be the main challenge of law enforcement and copyright owners as regulating the Web to curb piracy would be difficult with legality and illegality becoming more intimately connected with the growing sophistication of technology and with the conflicting business interests between service providers and content providers and between copyright and top IT and ICT industries.
Open Domain Audio Visual Speech Recognition: A Deep Learning Approach
Venue: INTERSPEECH

Automatic speech recognition (ASR) on video data naturally has access to two modalities: audio and video. In previous work, audio visual ASR, which leverages visual features to help ASR, has been explored on restricted domains of videos. This paper aims to extend this idea to open domain videos, for example videos uploaded to YouTube. We achieve this by adopting a unified deep learning approach. First, for the visual features, we propose to apply segment(utterance level features, instead of highly restrictive frame level features. These visual features are extracted using deep learning architectures which have been pre trained on computer vision tasks, e.g. object recognition and scene labeling. Second, the visual features are incorporated into ASR under deep learning based acoustic modeling. In addition to simple feature concatenation, we also apply an adaptive training framework to incorporate visual features in a more flexible way. On a challenging video transcribing task, audio visual ASR using our proposed approach gets notable improvements in terms of word error rates (WERs) compared to ASR merely using speech features.
A speaker dependent deep learning approach to joint speech separation and acoustic modeling for multi talker automatic speech recognition
Venue: 2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP)

We propose a novel speaker dependent (SD) approach to joint training of deep neural networks (DNNs) with an explicit speech separation structure for multi talker speech recognition in a single channel setting. First, a multi condition training strategy is designed for a SD DNN recognizer in multi talker scenarios, which can significantly reduce the decoding runtime and improve the recognition accuracy over the approaches that use speaker independent DNN models with a complicated joint decoding framework. In addition, a SD regression DNN for mapping the acoustic features of mixed speech to the speech features of a target speaker is jointly trained with the SD recognition DNN for acoustic modeling. Our experiments on the Speech Separation Challenge (SSC) task show that the proposed SD recognition system under multi condition training achieves an average word error rate (WER) of 3.8% yielding a relative WER reduction of 65.1% from the proposed DNN preprocessing approach under clean condition training [1] Furthermore, the jointly trained DNN system generates a relative WER reduction of 13.2% from the state of the art systems under multi condition training.
A unified approach to transfer learning of deep neural networks with applications to speaker adaptation in automatic speech recognition
Venue: Neurocomputing

In this paper, we present a unified approach to transfer learning of deep neural networks (DNNs) to address performance degradation issues caused by a potential acoustic mismatch between the training and testing conditions due to inter speaker variability in state of the art connectionist (a.k.a. hybrid) automatic speech recognition (ASR) systems. Different schemes to transfer knowledge of deep neural networks related to speaker adaptation can be developed with ease under such a unifying concept as demonstrated in the three frameworks investigated in this study. In the first solution, knowledge is transferred between homogeneous domains, namely the source and the target domains. Moreover the transfer takes place in a sequential manner from the target to the source speaker to boost the ASR accuracy on spoken utterances from a surprise target speaker. In the second solution, a multi task approach is adopted to adjust the connectionist parameters to improve the ASR system performance on the target speaker. Knowledge is transferred simultaneously among heterogeneous tasks, and that is achieved by adding one or more smaller auxiliary output layers to the original DNN structure. In the third solution, DNN output classes are organised into a hierarchical structure in order to adjust the connectionist parameters and close the gap between training and testing conditions by transferring prior knowledge from the root node to the leaves in a structural maximum a posteriori fashion. Through a series of experiments on the Wall Street Journal (WSJ) speech recognition task, we show that the proposed solutions result in consistent and statistically significant word error rate reductions. Most importantly, we show that transfer learning is an enabling technology for speaker adaptation, since it outperforms both the transformation based adaptation algorithms usually adapted in the speech community, and the multi condition training (MCT) schemes, which is a data combination methods often adopted to cover more acoustic variabilities in speech when data from the source and target domains are both available at the training time. Finally, experimental evidence demonstrates that all proposed solutions are robust to negative transfer even when only a single sentence from the target speaker is available. HighlightsA paradigm to transfer learning of deep neural networks in automatic speech recognition systems is presented.Three different transfer learning solutions for deep neural network are developed and tested on large datasets.Experimental evidence shows that the proposed solutions outperform the state of the art and avoid negative transfer.
Physicochemical Properties, Minerals, Trace Elements, and Heavy Metals in Honey of Different Origins: A Comprehensive Review.
Venue: Comprehensive reviews in food science and food safety

Honey is a popular natural food product with a very complex composition mainly consisting of both organic and inorganic constituents. The composition of honey is strongly influenced by both natural and anthropogenic factors, which vary based on its botanical and geographical origins. Although minerals and heavy metals are minor constituents of honey, they play vital role in determining its quality. There are several different analytical methods used to determine the chemical elements in honey. These methods are typically based on spectroscopy or spectrometry techniques (including atomic absorption spectrometry, atomic emission spectrometry, inductively coupled plasma mass spectrometry, and inductively coupled plasma optical emission spectrometry) This review compiles available scientific information on minerals and heavy metals in honey reported from all over the world. To date, 54 chemical elements in various types of honey have been identified and can be divided into 3 groups: major or macroelements (Na, K, Ca, Mg, P, S, Cl) minor or trace elements (Al, Cu, Pb, Zn, Mn, Cd, Tl, Co, Ni, Rb, Ba, Be, Bi, U, V, Fe, Pt, Pd, Te, Hf, Mo, Sn, Sb, La, I, Sm, Tb, Dy, Sd, Th, Pr, Nd, Tm, Yb, Lu, Gd, Ho, Er, Ce, Cr, As, B, Br, Cd, Hg, Se, Sr) and heavy metals (trace elements that have a specific gravity at least 5 times higher than that of water and inorganic sources) Chemical elements in honey samples throughout the world vary in terms of concentrations and are also influenced by environmental pollution.