Showing papers by "Missouri University of Science and Technology published in 2019"
TL;DR: During the ongoing O3 observation run, squeezed states are improving the sensitivity of the LIGO interferometers to signals above 50 Hz by up to 3 dB, thereby increasing the expected detection rate by 40% and 50% respectively.
Abstract: The Laser Interferometer Gravitational Wave Observatory (LIGO) has been directly detecting gravitational waves from compact binary mergers since 2015. We report on the first use of squeezed vacuum states in the direct measurement of gravitational waves with the Advanced LIGO H1 and L1 detectors. This achievement is the culmination of decades of research to implement squeezed states in gravitational-wave detectors. During the ongoing O3 observation run, squeezed states are improving the sensitivity of the LIGO interferometers to signals above 50 Hz by up to 3 dB, thereby increasing the expected detection rate by 40% (H1) and 50% (L1).
422 citations
18 Jul 2019
TL;DR: Zhang et al. as discussed by the authors proposed a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation, which can be applied when the user~(item) attributes or the social network structure is not available.
Abstract: Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the performance is limited due to the sparseness of user behavior data. With the emergence of online social networks, social recommender systems have been proposed to utilize each user's local neighbors' preferences to alleviate the data sparsity for better user embedding modeling. We argue that, for each user of a social platform, her potential embedding is influenced by her trusted users, with these trusted users are influenced by the trusted users' social connections. As social influence recursively propagates and diffuses in the social network, each user's interests change in the recursive process. Nevertheless, the current social recommendation models simply developed static models by leveraging the local neighbors of each user without simulating the recursive diffusion in the global social network, leading to suboptimal recommendation performance. In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation. For each user, the diffusion process starts with an initial embedding that fuses the related features and a free user latent vector that captures the latent behavior preference. The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues. We further show that our proposed model is general and could be applied when the user~(item) attributes or the social network structure is not available. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model, with more than 13% performance improvements over the best baselines for top-10 recommendation on the two datasets.
308 citations
TL;DR: It is shown that water and related processes of MXene hydrolysis play the main role in the phenomena leading to complete transformations of 2D titanium carbide MXenes into titania in aqueous environments.
Abstract: Although oxidation was deemed as the main factor responsible for the instability of MXenes in aqueous colloids, here we put forward and test a hypothesis about the central role of water as the prim...
225 citations
University of Crete1, University of Central Florida2, Foundation for Research & Technology – Hellas3, United States Army Research Laboratory4, Huawei5, Purdue University6, DePaul University7, United States Department of the Army8, Alibaba Group9, Coventry University10, Cyprus University of Technology11, Intel12, Kyushu University13, Technische Universität Ilmenau14, Open University15, IBM16, Towson University17, San Jose State University18, Tokyo City University19, Missouri University of Science and Technology20, Tsinghua University21, Baidu22, Tokyo University of Science23, Chongqing University24
TL;DR: The Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address are investigated.
Abstract: This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled w...
214 citations
TL;DR: The flexoelectricity describes the contribution of the linear couplings between the electric polarization and strain gradient and between polarization gradient and strain to the thermodynamics of a solid as discussed by the authors.
210 citations
TL;DR: It is found that the high thermocapillary force, induced by the high temperature gradient in the laser interaction region, can rapidly eliminate pores from the melt pool during the LPBF process.
Abstract: Laser powder bed fusion (LPBF) is a 3D printing technology that can print metal parts with complex geometries without the design constraints of traditional manufacturing routes. However, the parts printed by LPBF normally contain many more pores than those made by conventional methods, which severely deteriorates their properties. Here, by combining in-situ high-speed high-resolution synchrotron x-ray imaging experiments and multi-physics modeling, we unveil the dynamics and mechanisms of pore motion and elimination in the LPBF process. We find that the high thermocapillary force, induced by the high temperature gradient in the laser interaction region, can rapidly eliminate pores from the melt pool during the LPBF process. The thermocapillary force driven pore elimination mechanism revealed here may guide the development of 3D printing approaches to achieve pore-free 3D printing of metals.
200 citations
TL;DR: A unified architectural model and a new taxonomy are presented, by comparing a large number of solutions to support the requirements of IoT applications that could not be met by today’s solutions.
184 citations
TL;DR: In this paper, the effect of steel fiber content and shape on mechanical strength, toughness, and autogenous and drying shrinkage of UHPC was investigated, and three steel fiber shapes, including straight, corrugated, and hooked fibers, with volume fraction ranging from 0 to 3% were employed.
Abstract: Use of steel fibers in ultra-high performance concrete (UHPC) plays a significant role in enhancing strength and toughness and restraining shrinkage. This paper investigates the effect of steel fiber content and shape on mechanical strength, toughness, and autogenous and drying shrinkage of UHPC. Three steel fiber shapes, including straight, corrugated, and hooked fibers, with volume fraction ranging from 0 to 3% were employed. Compressive, flexural, and fiber-matrix bond strengths were evaluated. A statistical quadratic model and the Composite Theory were employed to predict the flexural strength of UHPC. Test results indicated that the increase in fiber volume can enhance the compressive and flexural strengths of UHPC and reduce shrinkage. The optimum fiber content for strength and shrinkage was found at 2%, beyond which the strength was slightly increased and the shrinkage was slightly decreased. For a given fiber content, the use of hooked fibers was most efficient in improving fiber-matrix bond and flexural strengths and reducing shrinkage. The flexural strengths of UHPC made with various fiber contents and shapes can be predicted using the proposed quadratic model and the Composite Theory. The latter considers the primary parameters affecting performance, including bond strength, matrix properties, and fiber characteristics. Finally, several models were used to simulate autogenous shrinkage behavior of UHPC and optimal models were found.
181 citations
TL;DR: The promises, challenges, and future research directions of these transformative technologies are looked at, with a focus on artificial intelligence, machine learning, robotics, and automation.
Abstract: The exponential advancement in artificial intelligence (AI), machine learning, robotics, and automation are rapidly transforming industries and societies across the world. The way we work, the way we live, and the way we interact with others are expected to be transformed at a speed and scale beyond anything we have observed in human history. This new industrial revolution is expected, on one hand, to enhance and improve our lives and societies. On the other hand, it has the potential to cause major upheavals in our way of life and our societal norms. The window of opportunity to understand the impact of these technologies and to preempt their negative effects is closing rapidly. Humanity needs to be proactive, rather than reactive, in managing this new industrial revolution. This article looks at the promises, challenges, and future research directions of these transformative technologies. Not only are the technological aspects investigated, but behavioral, societal, policy, and governance issues are reviewed as well. This research contributes to the ongoing discussions and debates about AI, automation, machine learning, and robotics. It is hoped that this article will heighten awareness of the importance of understanding these disruptive technologies as a basis for formulating policies and regulations that can maximize the benefits of these advancements for humanity and, at the same time, curtail potential dangers and negative impacts.
171 citations
TL;DR: In this paper, an overview of the rheological properties of UHPC, applicable flow models, measurement techniques and errors associated with the interpretation of Rheological measurements are discussed.
163 citations
TL;DR: In this paper, the first report of synthesizing a high-entropy carbide powder using individual transition metal oxides and carbon as precursors was presented, with an average particle size of about 550 nm and an oxygen content of 0.2
TL;DR: This paper systematically surveys recent advances in EH-IoTs from several perspectives, including methods that enable the use of energy harvesting hardware as a proxy for conventional sensors to detect contexts in energy efficient manner and the advancements in efficient checkpointing and timekeeping for intermittently powered IoT devices.
Abstract: With the growing number of deployments of Internet of Things (IoT) infrastructure for a wide variety of applications, the battery maintenance has become a major limitation for the sustainability of such infrastructure. To overcome this problem, energy harvesting offers a viable alternative to autonomously power IoT devices, resulting in a number of battery-less energy harvesting IoTs (or EH-IoTs) appearing in the market in recent years. Standards activities are also underway, which involve wireless protocol design suitable for EH-IoTs as well as testing procedures for various energy harvesting methods. Despite the early commercial and standards activities, IoT sensing, computing and communications under unpredictable power supply still face significant research challenges. This paper systematically surveys recent advances in EH-IoTs from several perspectives. First, it reviews the recent commercial developments for EH-IoT in terms of both products and services, followed by initial standards activities in this space. Then it surveys methods that enable the use of energy harvesting hardware as a proxy for conventional sensors to detect contexts in energy efficient manner. Next it reviews the advancements in efficient checkpointing and timekeeping for intermittently powered IoT devices. We also survey recent research in novel wireless communication techniques for EH-IoTs, such as the applications of reinforcement learning to optimize power allocations on-the-fly under unpredictable energy productions, and packet-less IoT communications and backscatter communication techniques for energy impoverished environments. The paper is concluded with a discussion of future research directions.
TL;DR: In this article, the effect of silica fume content, ranging from 0 to 25%, by mass of cementitious materials, on rheological, fiber-matrix bond, and mechanical properties of non-fibrous UHPC matrix and uHPC made with 2% micro-steel fibers was investigated.
TL;DR: This paper presents an approach that combines conventional image processing with deep learning by fusing the features from the individual techniques, hypothesizing that the two techniques, with different error profiles, are synergistic.
Abstract: This paper presents an approach that combines conventional image processing with deep learning by fusing the features from the individual techniques. We hypothesize that the two techniques, with different error profiles, are synergistic. The conventional image processing arm uses three handcrafted biologically inspired image processing modules and one clinical information module. The image processing modules detect lesion features comparable to clinical dermoscopy information—atypical pigment network, color distribution, and blood vessels. The clinical module includes information submitted to the pathologist—patient age, gender, lesion location, size, and patient history. The deep learning arm utilizes knowledge transfer via a ResNet-50 network that is repurposed to predict the probability of melanoma classification. The classification scores of each individual module from both processing arms are then ensembled utilizing logistic regression to predict an overall melanoma probability. Using cross-validated results of melanoma classification measured by area under the receiver operator characteristic curve (AUC), classification accuracy of 0.94 was obtained for the fusion technique. In comparison, the ResNet-50 deep learning based classifier alone yields an AUC of 0.87 and conventional image processing based classifier yields an AUC of 0.90. Further study of fusion of conventional image processing techniques and deep learning is warranted.
TL;DR: It is shown that epitaxial films of inorganic materials such as cesium lead bromide (CsPbBr3), lead(II) iodide (PbI2), zinc oxide (ZnO), and sodium chloride (NaCl) can be deposited onto a variety of single-crystal and single- Crystalline substrates by simply spin coating either solutions of the material or precursors to the material.
Abstract: Spin-coated films, such as photoresists for lithography or perovskite films for solar cells, are either amorphous or polycrystalline. We show that epitaxial films of inorganic materials such as cesium lead bromide (CsPbBr3), lead(II) iodide (PbI2), zinc oxide (ZnO), and sodium chloride (NaCl) can be deposited onto a variety of single-crystal and single-crystal-like substrates by simply spin coating either solutions of the material or precursors to the material. The out-of-plane and in-plane orientations of the spin-coated films are determined by the substrate. The thin stagnant layer of supersaturated solution produced during spin coating promotes heterogeneous nucleation of the material onto the single-crystal substrate over homogeneous nucleation in the bulk solution, and ordered anion adlayers may lower the activation energy for nucleation on the surface. The method can be used to produce functional materials such as inorganic semiconductors or to deposit water-soluble materials such as NaCl that can serve as growth templates.
TL;DR: A comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF based on 60 financial and economic features and results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset, as well as several other hybrid machine learning algorithms.
Abstract: Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY) based on 60 financial and economic features. DNNs and traditional artificial neural networks (ANNs) are then deployed over the entire preprocessed but untransformed dataset, along with two datasets transformed via principal component analysis (PCA), to predict the daily direction of future stock market index returns. While controlling for overfitting, a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000. Moreover, a set of hypothesis testing procedures are implemented on the classification, and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset, as well as several other hybrid machine learning algorithms. In addition, the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested, including in a comparison against two standard benchmarks.
TL;DR: The results show that well-known ANNs such as multilayer perceptron and radial basis function can predict membrane fouling with an R2 equal to 0.99 and an error approaching zero, demonstrating that hybrid intelligent models utilizing intelligent optimization methods such as GA and PSO for adjusting their weights and functions perform better than single models.
TL;DR: In this article, the authors report the direct observation and quantification of melt pool variation during the laser powder bed fusion (LPBF) additive manufacturing process under constant input energy density by in-situ high-speed high-energy x-ray imaging.
Abstract: Size and shape of a melt pool play a critical role in determining the microstructure in additively manufactured metals. However, it is very challenging to directly characterize the size and shape of the melt pool beneath the surface of the melt pool during the additive manufacturing process. Here, we report the direct observation and quantification of melt pool variation during the laser powder bed fusion (LPBF) additive manufacturing process under constant input energy density by in-situ high-speed high-energy x-ray imaging. We show that the melt pool can undergo different melting regimes and both the melt pool dimension and melt pool volume can have orders-of-magnitude change under a constant input energy density. Our analysis shows that the significant melt pool variation cannot be solely explained by the energy dissipation rate. We found that energy absorption changes significantly under a constant input energy density, which is another important cause of melt pool variation. Our further analysis reveals that the significant change in energy absorption originates from the separate roles of laser power and scan speed in depression zone development. The results reported here are important for understanding the laser powder bed fusion additive manufacturing process and guiding the development of better metrics for processing parameter design.
TL;DR: In this article, the authors explored the mechanisms of imbibition enhanced oil recovery (IEOR) using three of the most commonly used chemical systems (surfactant, brine based nano-silica, and surfactant based nano silica solutions).
TL;DR: A unique type of 3D Janus plasmonic helical nanoaperture with direction-controlled polarization sensitivity is reported, which is simply fabricated via the one-step grayscale focused ion beam milling method.
Abstract: Helical structures have attracted considerable attention due to their inherent optical chirality. Here, we report a unique type of 3D Janus plasmonic helical nanoaperture with direction-controlled polarization sensitivity, which is simply fabricated via the one-step grayscale focused ion beam milling method. Circular dichroism in transmission of as large as 0.72 is experimentally realized in the forward direction due to the spin-dependent mode coupling process inside the helical nanoaperture. However, in the backward direction, the nanoaperture acquires giant linear dichroism in transmission of up to 0.87. By encoding the Janus metasurface with the two nanoaperture enantiomers having specified rotation angles, direction-controlled polarization-encrypted data storage is demonstrated for the first time, where a binary quick-response code image is displayed in the forward direction under the circularly polarized incidence of a specified handedness, while a distinct grayscale image is revealed in the backward direction under linearly polarized illumination with a specified azimuthal angle. We envision that the proposed Janus helical nanoapertures will provide an appealing platform for a variety of applications, which will range from multifunctional polarization control, enantiomer sensing, data encryption and decryption to optical information processing.
TL;DR: It is proposed that a trans-lithospheric continuum exists whereby post-subduction magmas transporting metal-rich sulfide cargoes play a fundamental role in fluxing metals into the crust from metasomatised lithospheric mantle.
Abstract: Ore deposits are loci on Earth where energy and mass flux are greatly enhanced and focussed, acting as magnifying lenses into metal transport, fractionation and concentration mechanisms through the lithosphere. Here we show that the metallogenic architecture of the lithosphere is illuminated by the geochemical signatures of metasomatised mantle rocks and post-subduction magmatic-hydrothermal mineral systems. Our data reveal that anomalously gold and tellurium rich magmatic sulfides in mantle-derived magmas emplaced in the lower crust share a common metallogenic signature with upper crustal porphyry-epithermal ore systems. We propose that a trans-lithospheric continuum exists whereby post-subduction magmas transporting metal-rich sulfide cargoes play a fundamental role in fluxing metals into the crust from metasomatised lithospheric mantle. Therefore, ore deposits are not merely associated with isolated zones where serendipitous happenstance has produced mineralisation. Rather, they are depositional points along the mantle-to-upper crust pathway of magmas and hydrothermal fluids, synthesising the concentrated metallogenic budget available.
TL;DR: It is shown that adhesion of MXenes depends on their monolayer thickness and, in contrast to graphene, does not show number-of-monolayers dependency.
Abstract: Two-dimensional transition metal carbides (MXenes) have attracted a great interest of the research community as a relatively recently discovered large class of materials with unique electronic and optical properties. Understanding of adhesion between MXenes and various substrates is critically important for MXene device fabrication and performance. We report results of direct atomic force microscopy (AFM) measurements of adhesion of two MXenes (Ti3C2Tx and Ti2CTx) with a SiO2 coated Si spherical tip. The Maugis-Dugdale theory was applied to convert the AFM measured adhesion force to adhesion energy, while taking into account surface roughness. The obtained adhesion energies were compared with those for mono-, bi-, and tri-layer graphene, as well as SiO2 substrates. The average adhesion energies for the MXenes are 0.90 ± 0.03 J m−2 and 0.40 ± 0.02 J m−2 for thicker Ti3C2Tx and thinner Ti2CTx, respectively, which is of the same order of magnitude as that between graphene and silica tip. The adhesion of two-dimensional transition metal carbides (MXenes) is important for potential MXene device fabrication and performance. Here, the authors show that adhesion of MXenes depends on their monolayer thickness and, in contrast to graphene, does not show number-of-monolayers dependency.
TL;DR: In this paper, a cyclone separator is simulated by means of DEM-CFD and the effects of inlet angle and particle size on separation efficiency are quantified. But the results of the simulation are limited to two regions: the loss-free vortex region and forced vortex region.
TL;DR: The proposed model and an efficient implementation of the value iteration algorithm are tested and results show that the optimal routing policy improves average unit profit and occupancy rate by 23.0% and 8.4% over the random walk and local hotspot heuristic respectively.
Abstract: The optimal routing of a vacant taxi is formulated as a Markov Decision Process (MDP) problem to account for long-term profit over the full working period. The state is defined by the node at which a vacant taxi is located, and action is the link to take out of the node. State transition probabilities depend on passenger matching probabilities and passenger destination probabilities. The probability that a vacant taxi is matched with a passenger during the traversal of a link is calculated based on temporal Poisson arrivals of passengers and spatial Poisson distributions of competing vacant taxis. Passenger destination probabilities are calculated directly using observed fractions of passengers going to destinations from a given origin. The MDP problem is solved by value iteration resulting in an optimal routing policy, and the computational efficiency is improved by utilizing parallelized matrix operations. The proposed model and an efficient implementation of the value iteration algorithm are tested in a case study with parameters derived from GPS trajectories of over 12,000 taxis in Shanghai, China for a study period of 5:30 - 11:30 am on a typical weekday. The optimal routing policy is compared with three heuristics based on simulated trajectories. Results show that the optimal routing policy improves average unit profit by 23.0% and 8.4% over the random walk and local hotspot heuristic respectively; and improves occupancy rate by 23.8% and 8.3% respectively. The improvement is larger during higher demand periods.
TL;DR: In this paper, the authors focused on the promising surfactant formulas and their corresponding mechanisms under different reservoir conditions, especially harsh conditions, and provided clues to N2/CO2 foam EOR design and also promoted the development of harsh reservoirs.
01 Mar 2019
TL;DR: A research framework that integrates consideration of usefulness voting and the collaboration tool of commenting to explain variation in individuals' online knowledge contribution behaviors is proposed and it is found that comments moderate the relationships between voting and knowledge contribution.
Abstract: Participants are increasingly using online knowledge communities to access and share information and to collaboratively solve problems. However, in online communities that address technical questions (i.e., utilitarian, rather than hedonic or supportive, in nature), a key problem is encouraging sufficient, ongoing knowledge contribution. While many may use the community to post and obtain information periodically, fewer take the time to consistently contribute knowledge to the community. Interestingly, research has yet to comprehensively consider the impact of motivational affordances, in the forms of voting and commenting features, to address this challenge of under-contribution in online knowledge communities. To fill this research gap, this study proposes a research framework that integrates consideration of usefulness voting and the collaboration tool of commenting to explain variation in individuals' online knowledge contribution behaviors. The research model is estimated with a fixed-effects Poisson regression applied to a longitudinal panel dataset collected from a technical online question and answer community. We find that positive votes motivate participants' knowledge contributions, while received negative votes significantly decrease their knowledge contributions. Thus, while maintaining content quality is important, negative motivational affordances (i.e., down voting) can reduce sustained contributions. We also find commenting to be an important motivator for online knowledge contribution and, interestingly, find that comments moderate the relationships between voting and knowledge contribution. Overall, this research not only extends our current understandings of knowledge contribution in online communities, but also sheds light on improving online community knowledge exchange.
TL;DR: In this paper, the authors investigated the factors that enable total quality management (TQM) implementation in the readymade garment (RMG) sector of Bangladesh and found that structural, strategic, contextual and human resource-enabling factors are significant to TQM implementation.
Abstract: The purpose of this paper is to investigate the factors that enable total quality management (TQM) implementation in the readymade garment (RMG) sector of Bangladesh. More specifically, the present study is a supplement of the previous call from research to investigate the TQM-enabling factors from a broader aspect of organizational change.,This study was conducted through an online survey, followed by phone calls. Data were collected using a questionnaire survey with 256 respondents of the Bangladeshi RMG sector. The TQM-enabling factors were divided into five distinct groups, based on strategic or overall changes required within an organization for TQM implementation. A theoretical research model was created to investigate the contingency of various TQM-enabling factors. Structural equation modeling (SEM) was applied to confirm the factor that enabled TQM implementation in the RMG sector of Bangladesh.,The main finding of this study shows that structural, strategic, contextual and human resource-enabling factors are significant to TQM implementation in the Bangladeshi RMG sector.,This study has been completed in single time frame. Therefore, consideration of the time factor is completely ignored in this research. Furthermore, understanding of TQM-enabling factors in this research relied on quantitative findings only. Also, this study was limited to one industry and one geographic region. However, this study could determine whether data triangulation will provide a good perception on enabling factors and the methodology can be extended to other industries and regions.,This study provides a research methodology for other manufacturing industries that are planning to implement TQM in their organization. This research will contribute to the existing literature by examining the contingency of various TQM-enabling factors in the context of the Bangladeshi RMG sector, and it, therefore, provides direction to increase the success rate of TQM implementation. Furthermore, the research methodology can be used in other studies for variation of contextual variables such as size of the industry, developed or underdeveloped country and manufacturing or service industry.,The methodology used in this study can lead the way for other industries in the RMG sector that implements TQM in their organization. Also, this research further contributes to the existing literature by investigating the contingency of various TQM enabling factors in the context of the Bangladeshi RMG sector and developing associated strategies to raise success rate of TQM implementation.