Showing papers by "Illinois Institute of Technology published in 2017"
TL;DR: It is shown that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deepLearning.
Abstract: The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. “Deep learning”, or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.
623 citations
TL;DR: This data indicates that cancer and its treatment lead to increased financial distress for patients, and no standardized patient‐reported outcome measure has been validated to assess this distress.
Abstract: BACKGROUND
Cancer and its treatment lead to increased financial distress for patients. To the authors’ knowledge, to date, no standardized patient-reported outcome measure has been validated to assess this distress.
METHODS
Patients with AJCC Stage IV solid tumors receiving chemotherapy for at least 2 months were recruited. Financial toxicity was measured by the COmprehensive Score for financial Toxicity (COST) measure. The authors collected data regarding patient characteristics, clinical trial participation, health care use, willingness to discuss costs, psychological distress (Brief Profile of Mood States [POMS]), and health-related quality of life (HRQOL) as measured by the Functional Assessment of Cancer Therapy: General (FACT-G) and the European Organization for Research and Treatment of Cancer (EORTC) QOL questionnaires. Test-retest reliability, internal consistency, and validity of the COST measure were assessed using standard-scale construction techniques. Associations between the resulting factors and other variables were assessed using multivariable analyses.
RESULTS
A total of 375 patients with advanced cancer were approached, 233 of whom (62.1%) agreed to participate. The COST measure demonstrated high internal consistency and test-retest reliability. Factor analyses revealed a coherent, single, latent variable (financial toxicity). COST values were found to be correlated with income (correlation coefficient [r] = 0.28; P<.001), psychosocial distress (r = -0.26; P<.001), and HRQOL, as measured by the FACT-G (r = 0.42; P<.001) and by the EORTC QOL instruments (r = 0.33; P<.001). Independent factors found to be associated with financial toxicity were race (P = .04), employment status (P<.001), income (P = .003), number of inpatient admissions (P = .01), and psychological distress (P = .003). Willingness to discuss costs was not found to be associated with the degree of financial distress (P = .49).
CONCLUSIONS
The COST measure demonstrated reliability and validity in measuring financial toxicity. Its correlation with HRQOL indicates that financial toxicity is a clinically relevant patient-centered outcome. Cancer 2016. © 2016 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.
467 citations
09 May 2017
TL;DR: It is concluded that networked microgrids in particular provide a universal solution for improving the resilience against extreme events in Smart Cities.
Abstract: This paper focuses on the role of networked microgrids as distributed systems for enhancing the power system resilience against extreme events. Resilience is an intrinsically complex property which requires deep understanding of microgrid operation in order to respond effectively in emergency conditions. The paper first introduces the definition and offers a generic framework for analyzing the power system resilience. The notion that large power systems can achieve a higher level of resilience through the deployment of networked microgrids is discussed in detail. In particular, the management of networked microgrids for riding through extreme events is analyzed. In addition, the merits of advanced information and communication technologies (ICTs) in microgrid-based distributed systems that can support the power system resilience are presented. The paper also points out the challenges for expanding the role of distributed systems and concludes that networked microgrids in particular provide a universal solution for improving the resilience against extreme events in Smart Cities.
393 citations
TL;DR: MicroBooNE as discussed by the authors is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics.
Abstract: This paper describes the design and construction of the MicroBooNE liquid argon time projection chamber and associated systems. MicroBooNE is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics. In this document details of design specifications, assembly procedures, and acceptance tests are reported.
341 citations
TL;DR: In this paper, a robust co-optimization scheduling model was proposed to study the coordinated optimal operation of the two energy systems, while considering power system key uncertainties and natural gas system dynamics.
Abstract: The significant growth of gas-fired power plants and emerging power-to-gas (PtG) technology has intensified the interdependency between electricity and natural gas systems. This paper proposes a robust co-optimization scheduling model to study the coordinated optimal operation of the two energy systems. The proposed model minimizes the total costs of the two systems, while considering power system key uncertainties and natural gas system dynamics. Because of the limitation on exchanging private data and the challenge in managing complex models, the proposed co-optimization model is tackled via alternating direction method of multipliers (ADMM) by iteratively solving a power system subproblem and a gas system subproblem. The power system subproblem is solved by column-and-constraint generation (C&CG) and outer approximation (OA), and the nonlinear gas system subproblem is solved by converting into a mixed-integer linear programming model. To overcome nonconvexity of the original problem with binary variables, a tailored ADMM with a relax-round-polish process is developed to obtain high-quality solutions. Numerical case studies illustrate the effectiveness of the proposed model for optimally coordinating electricity and natural gas systems with uncertainties.
323 citations
TL;DR: It is concluded that goal-setting theory is valuable to understand the success of leaderboards, and further exploration of existing psychological theories, including goal- Setting theory, are recommended to better explain the effects of gamification.
264 citations
TL;DR: It is shown that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg=−0.155), and these findings suggest novel biological pathways through which human genetic variation influences hippocampus volume and risk for neuropsychiatric illness.
Abstract: The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer’s disease (rg=−0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.
256 citations
TL;DR: The objective of this paper is to demonstrate that controllable and islandable microgrids can help improve the resiliency of power grids in extreme conditions.
Abstract: This paper presents a framework for analyzing the resilience of an electric power grid with integrated microgrids in extreme conditions. The objective of this paper is to demonstrate that controllable and islandable microgrids can help improve the resiliency of power grids in extreme conditions. Four resilience indices are introduced to measure the impact of extreme events. Index $ {K}$ measures the expected number of lines on outage due to extreme events. Index loss of load probability measures the probability of load not being fully supplied. Index expected demand not supplied measures the expected demand that cannot be supplied. Index $ {G}$ measures the difficulty level of grid recovery. The mechanism of extreme events affecting power grid operation is analyzed based on the proposed mesh grid approach. The relationship among transmission grid, distribution grid, and microgrid in extreme conditions is discussed. The Markov chain is utilized to represent the state transition of a power grid with integrated microgrids in extreme conditions. The Monte Carlo method is employed to calculate the resilience indices. The proposed power grid resilience analysis framework is demonstrated using the IEEE 30-bus and 118-bus systems assuming all loads are within microgrids.
240 citations
TL;DR: It is demonstrated that the patient skin microbial communities were shaped by a diversity of clinical and environmental factors during hospitalization, and that patients initially acquired room-associated taxa that predated their stay but that their own microbial signatures began to influence the room community structure over time.
Abstract: The microorganisms that inhabit hospitals may influence patient recovery and outcome, although the complexity and diversity of these bacterial communities can confound our ability to focus on potential pathogens in isolation To develop a community-level understanding of how microorganisms colonize and move through the hospital environment, we characterized the bacterial dynamics among hospital surfaces, patients, and staff over the course of 1 year as a new hospital became operational The bacteria in patient rooms, particularly on bedrails, consistently resembled the skin microbiota of the patient occupying the room Bacterial communities on patients and room surfaces became increasingly similar over the course of a patient's stay Temporal correlations in community structure demonstrated that patients initially acquired room-associated taxa that predated their stay but that their own microbial signatures began to influence the room community structure over time The α- and β-diversity of patient skin samples were only weakly or nonsignificantly associated with clinical factors such as chemotherapy, antibiotic usage, and surgical recovery, and no factor except for ambulatory status affected microbial similarity between the microbiotas of a patient and their room Metagenomic analyses revealed that genes conferring antimicrobial resistance were consistently more abundant on room surfaces than on the skin of the patients inhabiting those rooms In addition, persistent unique genotypes of Staphylococcus and Propionibacterium were identified Dynamic Bayesian network analysis suggested that hospital staff were more likely to be a source of bacteria on the skin of patients than the reverse but that there were no universal patterns of transmission across patient rooms
232 citations
TL;DR: In this paper, an integrated electricity and natural gas transportation system planning algorithm is proposed for enhancing the power grid resilience in extreme conditions, where a variable uncertainty set is developed to describe the interactions among power grid expansion states and extreme events.
Abstract: Power systems are exceedingly faced with extreme events such as natural disasters and deliberate attacks. In comparison, the underground natural gas system is considered less vulnerable to such extreme events. We consider that the overhead power grid can be hardened by replacing segments of electric power grid with underground natural gas pipelines as an energy transportation system to countereffect extreme events which can damage interdependent infrastructures severely. In this paper, an integrated electricity and natural gas transportation system planning algorithm is proposed for enhancing the power grid resilience in extreme conditions. A variable uncertainty set is developed to describe the interactions among power grid expansion states and extreme events. The proposed planning problem is formulated as a two-stage robust optimization problem. First, the influence of extreme events representing natural disasters is described by the proposed variable uncertainty set and the proposed robust model for the integrated planning is solved with the grid resilience represented by a set of constraints. Second, the investment decisions are evaluated iteratively using the conditional events. The integrated electricity and natural gas planning options are analyzed using the modified IEEE-RTS 1979 for enhancing the power grid resilience. The numerical results point out that the proposed integrated planning is an effective approach to improving the power grid resilience.
224 citations
TL;DR: The numerical results demonstrate that the proposed interconnection planning methodology will determine an optimal topology accurately and efficiently for a cluster of microgrids, and show that the suggested adaptive planning methodology can easily be applied to practical microgrid applications.
Abstract: The optimal planning of the interconnected network of multimicrogrids is discussed in this paper. The interconnection planning will enhance the reliability and the economic operation of a community of microgrids. The proposed approach will apply a probabilistic minimal cut-set-based iterative methodology for the optimal planning of interconnection among microgrids with variable renewable energy sources. The optimal planning takes into account various factors including the economics, reliability, and variability of renewables, network- and resource-based uncertainties, and adaptability to accommodate the prevailing operating concerns. A clustering-based method is considered for analyzing the variable data concerning the potential deployment of renewable energy in microgrids. The proposed interconnection planning methodology is applied to a six-microgrid system and the planning results are discussed. The numerical results demonstrate that the proposed interconnection planning methodology will determine an optimal topology accurately and efficiently for a cluster of microgrids, and show that the proposed adaptive planning methodology can easily be applied to practical microgrid applications.
TL;DR: It is shown that an attacker can construct an undetectable attack vector against ac state estimation based on a few measurements in the attacking region associated with boundary buses without knowing the full topology and parameter information of the entire power network.
Abstract: Power systems are being exposed to cyber-attacks due to the high integration of information technology and the vulnerability of communication networks. Existing false data attacks research focus on dc state estimation. In this paper, we show that an attacker can construct an undetectable attack vector against ac state estimation based on a few measurements in the attacking region associated with boundary buses without knowing the full topology and parameter information of the entire power network. An iteration approach is adopted to obtain the attack vector. The simulations on the IEEE 14-bus and 118-bus systems are used to demonstrate the correctness and effectiveness of the proposed attack scheme. This paper provides a basis to study the attack behaviors under the ac case, and a theoretical guide to develop protection strategies and detection methods.
TL;DR: In this article, a state variable-based linear energy hub model is developed, which avoids the introduction of dispatch factor variables applied traditionally to the optimal power flow problem, and a multidimensional piecewise linear approximation method is proposed for representing nonconvex natural gas transmission constraints in which the approximation error is further analyzed.
Abstract: In this paper, an mixed integer linear programming (MILP) method is proposed for calculating the optimal power flow in a multicarrier energy system. A state variable-based linear energy hub model is developed, which avoids the introduction of dispatch factor variables applied traditionally to the optimal power flow problem. The multidimensional piecewise linear approximation method is proposed for representing nonconvex natural gas transmission constraints in which the approximation error is further analyzed. Accordingly, the optimal power flow is reformulated as an MILP problem. Compared with the nonlinear models, the proposed model can be solved by the existing optimization techniques, which can be easily implemented in the optimal power system planning problem. The proposed method is verified by case studies applied to the modified six-bus and the IEEE-118 systems. The test results show that the proposed method can provide a fast solution for the optimal power flow which can be applied to large scale hub systems with sufficient accuracy. The results also demonstrate that the proposed method outperforms the existing MILP methods in calculation time especially in large scale hub applications.
TL;DR: Three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs.
Abstract: Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.
TL;DR: While measurements of the evolution in the IBD spectrum show general agreement with predictions from recent reactor models, the measured evolution in total IBD yield disagrees with recent predictions at 3.1σ, indicating that an overall deficit in the measured flux with respect to predictions does not result from equal fractional deficits from the primary fission isotopes.
Abstract: The Daya Bay experiment has observed correlations between reactor core fuel evolution and changes in the reactor antineutrino flux and energy spectrum. Four antineutrino detectors in two experimental halls were used to identify 2.2 million inverse beta decays (IBDs) over 1230 days spanning multiple fuel cycles for each of six 2.9 GWth reactor cores at the Daya Bay and Ling Ao nuclear power plants. Using detector data spanning effective ^(239)Pu fission fractions F_(239) from 0.25 to 0.35, Daya Bay measures an average IBD yield σ_f of (5.90±0.13)×10^(-43) cm^2/fission and a fuel-dependent variation in the IBD yield, dσ_f/dF_(239), of (-1.86±0.18)×10^(-43) cm^2/fission. This observation rejects the hypothesis of a constant antineutrino flux as a function of the ^(239)Pu fission fraction at 10 standard deviations. The variation in IBD yield is found to be energy dependent, rejecting the hypothesis of a constant antineutrino energy spectrum at 5.1 standard deviations. While measurements of the evolution in the IBD spectrum show general agreement with predictions from recent reactor models, the measured evolution in total IBD yield disagrees with recent predictions at 3.1σ. This discrepancy indicates that an overall deficit in the measured flux with respect to predictions does not result from equal fractional deficits from the primary fission isotopes ^(235)U, ^(239)Pu, ^(238)U, and ^(241)Pu. Based on measured IBD yield variations, yields of (6.17±0.17) and (4.27±0.26)×10^(-43) cm^2/fission have been determined for the two dominant fission parent isotopes ^(235)U and ^(239)Pu. A 7.8% discrepancy between the observed and predicted ^(235)U yields suggests that this isotope may be the primary contributor to the reactor antineutrino anomaly.
TL;DR: In this article, a multistage active distribution network (ADN) planning model that is integrated with the application of energy storage system (ESS) is presented, where both the long-term investment cost and short-term operation conditions of ADN are considered in the proposed model.
Abstract: A multistage active distribution network (ADN) planning model that is integrated with the application of energy storage system (ESS) is presented in this paper. Both the long-term investment cost and short-term operation conditions of ADN are considered in the proposed model. The power supply reliability improvement brought by ESS is also analyzed. At each planning stage, the operation conditions are divided into several typical day scenarios and an extreme condition scenario which are based on the load forecast data. The long-term expansion planning decisions including those for replacing and adding circuits, introducing ESS, and considering ADN short-term operation strategies for the ESS charging and discharging are optimized together in the proposed model. The ESS benefits pertaining to peak load shaving and power reliability enhancement are demonstrated using numerical cases, in which the centralized and the distributed ESS are considered as options for the ADN implementation. The effectiveness of the proposed model is demonstrated through various discussions in the paper.
TL;DR: An optimal traffic-power flow model is proposed, which is a mixed integer nonlinear program with traffic UE constraints and further reformulated as a mixedinteger second-order cone program, whose global optimal solution is accessible with reasonable computation effort.
Abstract: This paper conducts an interdisciplinary study on the coordinated operation of both transportation system and power system. We consider an electrified transportation network enabled by wireless power transfer technology and coupled with a power distribution network (PDN) in the future city. The independent system operator, which is a public entity, is eligible to manage generation assets and charge congestion tolls (CTs) on electrified roads with the purpose of minimizing social cost. The route choices of electric vehicles are amenable to the Wardrop user equilibrium (UE) principle, such that no one can reduce his travel cost by changing route unilaterally. The traffic UE pattern further influences the spatial distribution of the electrical loads of the PDN. The power flow of the PDN is modeled through Dist-Flow equations. To find out the best generation schedule and CTs, we propose an optimal traffic-power flow model, which is a mixed integer nonlinear program with traffic UE constraints and further reformulated as a mixed integer second-order cone program, whose global optimal solution is accessible with reasonable computation effort. Case studies corroborate the benefits from the joint operation of the coupled networks, and demonstrate that ignoring the interdependency between the two critical infrastructures may lead to an insecure operation.
TL;DR: MTANNs can achieve desired performance with a smaller training dataset than do the CNNs, and would be a suitable end-to-end machine-learning model for detection and classification of focal lesions that do not require high-level semantic features.
TL;DR: Numerical simulations demonstrate the effectiveness of the proposed reliability-based planning approach to interconnect energy hubs with multiple energy infrastructures.
Abstract: This paper presents a reliability-based optimal planning model for an interconnection of energy hubs with multiple energy infrastructures. Energy hub represents a coupling among various energy infrastructures for supplying electricity and natural gas loads. The proposed planning problem determines a least-cost network of transmission lines and natural gas pipelines for interconnecting energy hubs from a given set of candidate paths that satisfy probabilistic reliability criteria. The minimal cut-maximal flow algorithm is applied for network flow analyses and calculating transfer capabilities of a multiple energy system. So, in contrast to a single energy infrastructure, the proposed hub planning model enables a synergetic strategy to design multiple energy networks for optimizing the supply economics and satisfying the reliability criteria. Numerical simulations demonstrate the effectiveness of the proposed reliability-based planning approach to interconnect energy hubs with multiple energy infrastructures.
TL;DR: A lateral architecture based on the synchronization mechanism of synchronous machines (SM), which has underpinned the growth and operation of power systems for over 100 years, is proposed to unify the integration and interaction of these players with the grid by operating power electronic converters to behave like cyber synchronous Machines (CSM), which paves the way for autonomous operation of future power systems.
Abstract: Power systems are going through a paradigm change from centralized generation to distributed generation and further on to smart grids. In this paper, it is shown that future power systems will be power electronics based, instead of electric machines based, with a huge number of incompatible players and that the fundamental challenge behind this paradigm change is how to make sure these players could work together and maintain system stability. Then, a lateral architecture based on the synchronization mechanism of synchronous machines (SM), which has underpinned the growth and operation of power systems for over 100 years, is proposed to unify the integration and interaction of these players with the grid by operating power electronic converters to behave like virtual synchronous machines (VSM), which are coined cyber synchronous machines (CSM) here. Thus, all the suppliers and the majority of loads can follow the same mechanism to regulate system stability. This paves the way for autonomous operation of future power systems. Moreover, two technical routes, one based on the synchronverter technology and the other based on the robust droop control technology, are proposed to implement the architecture. Real-time simulation results are presented to illustrate the operation of such a system.
TL;DR: In this article, conformal, ultrathin aluminum oxide coatings on lithium were used to stabilize Li anodes for high performance energy storage devices such as Li-S batteries, and the results indicated that ALD Al2O3 coatings are a promising strategy to stabilize lithium anodes.
Abstract: Lithium metal is a highly desirable anode material for lithium batteries due to its extremely high theoretical capacity (3860 mA h g−1), low potential (−3.04 V versus standard hydrogen electrode), and low density (0.534 g cm−3). However, dendrite growth during cycling and low coulombic efficiency, resulting in safety hazards and fast battery fading, are huge barriers to commercialization. Herein, we used atomic layer deposition (ALD) to prepare conformal, ultrathin aluminum oxide coatings on lithium. We investigated the growth mechanism during Al2O3 ALD on lithium by in situ quartz crystal microbalance and found larger growth than expected during the initial cycles. We also discovered that the ALD Al2O3 enhances the wettability of the Li surface towards both carbonate and ether electrolytes, leading to uniform and dense SEI formation and reduced electrolyte consumption during battery operation. Scanning electron microscopy verified that the bare Li surfaces become rough and dendritic after electrochemical cycling, whereas the ALD Al2O3 coated Li surfaces remain smooth and uniform. Analysis of the Li surfaces after cycling using X-ray photoelectron spectroscopy and in situ transmission electron microscopy revealed that the ALD Al2O3 coating remains intact during electrochemical cycling, and that Li ions diffuse through the coating and deposit on the underlying Li. Coin cell testing demonstrated more than two times longer cycling life for the ALD Al2O3 protected Li, and a coulombic efficiency as high as ∼98% at a practical current rate of 1 mA cm−2. More significantly, when the electrolyte volume was reduced from 20 to 5 μL, the stabilizing effect of the ALD coating became even more pronounced and the cycling life was around four times longer. These results indicate that ALD Al2O3 coatings are a promising strategy to stabilize Li anodes for high performance energy storage devices such as Li–S batteries.
TL;DR: This work presents the largest computational database of electronic transport properties based on a large set of 48,000 materials originating from the Materials Project database, and presents the workflow to generate the data, the data validation procedure, and the database structure.
Abstract: Electronic transport in materials is governed by a series of tensorial properties such as conductivity, Seebeck coefficient, and effective mass. These quantities are paramount to the understanding of materials in many fields from thermoelectrics to electronics and photovoltaics. Transport properties can be calculated from a material's band structure using the Boltzmann transport theory framework. We present here the largest computational database of electronic transport properties based on a large set of 48,000 materials originating from the Materials Project database. Our results were obtained through the interpolation approach developed in the BoltzTraP software, assuming a constant relaxation time. We present the workflow to generate the data, the data validation procedure, and the database structure. Our aim is to target the large community of scientists developing materials selection strategies and performing studies involving transport properties.
23 May 2017
TL;DR: It is concluded that cybersecurity could play a significant role in managing microgrid operations as microgrids strive for a higher degree of resilience as they supply power services to customers.
Abstract: This paper presents the application of cybersecurity to the operation and control of distributed electric power systems. In particular, the paper emphasizes the role of cybersecurity in the operation of microgrids and analyzes the dependencies of microgrid control and operation on information and communication technologies for cybersecurity. The paper discusses common cyber vulnerabilities in distributed electric power systems and presents the implications of cyber incidents on physical processes in microgrids. The paper examines the impacts of potential risks attributed to cyberattacks on microgrids and presents the affordable technologies for mitigating such risks. In addition, the paper presents a minimax-regret approach for minimizing the impending risks in managing microgrids. The paper also presents the opportunities provided by software-defined networking technologies to enhance the security of microgrid operations. It is concluded that cybersecurity could play a significant role in managing microgrid operations as microgrids strive for a higher degree of resilience as they supply power services to customers.
TL;DR: A new measurement of the reactor antineutrino flux and energy spectrum by the Daya Bay reactor neutrino experiment is reported in this article, where an excess of events in the region of 4−6 MeV was found in the measured spectrum, with a local significance of 4.4σ.
Abstract: A new measurement of the reactor antineutrino flux and energy spectrum by the Daya Bay reactor neutrino experiment is reported. The antineutrinos were generated by six 2.9 GWth nuclear reactors and detected by eight antineutrino detectors deployed in two near (560 m and 600 m flux-weighted baselines) and one far (1640 m flux-weighted baseline) underground experimental halls. With 621 days of data, more than 1.2 million inverse beta decay (IBD) candidates were detected. The IBD yield in the eight detectors was measured, and the ratio of measured to predicted flux was found to be 0.946±0.020 (0.992±0.021) for the Huber+Mueller (ILL+Vogel) model. A 2.9σ deviation was found in the measured IBD positron energy spectrum compared to the predictions. In particular, an excess of events in the region of 4–6 MeV was found in the measured spectrum, with a local significance of 4.4σ. A reactor antineutrino spectrum weighted by the IBD cross section is extracted for model-independent predictions.
TL;DR: The proposed EGTran model could be utilized by grid operators for the short-term commitment and dispatch of power systems in highly interdependent conditions with relatively large natural gas-fired generating units.
Abstract: This paper proposes a coordinated stochastic model for studying the interdependence of electricity and natural gas transmission networks (referred to as EGTran). The coordinated model incorporates the stochastic power system conditions into the solution of security-constrained unit commitment problem with natural gas network constraints. The stochastic model considers random outages of generating units and transmission lines, as well as hourly forecast errors of day-ahead electricity load. The Monte Carlo simulation is applied to create multiple scenarios for the simulation of the uncertainties in the EGTran model. The nonlinear natural gas network constraints are converted into linear constraints and incorporated into the stochastic model. Numerical tests are performed in a six-bus system with a seven-node gas transmission network and the IEEE 118–bus power system with a ten-node gas transmission network. Numerical results demonstrate the effectiveness of EGTran to analyze the impact of random contingencies on power system operations with natural gas network constraints. The proposed EGTran model could be utilized by grid operators for the short-term commitment and dispatch of power systems in highly interdependent conditions with relatively large natural gas-fired generating units.
TL;DR: validation options developed for fresh-cut leafy vegetables may serve as examples for validating processes that prevent cross-contamination during washing of other fresh produce commodities.
TL;DR: This work uses Density Functional Perturbation Theory as a way to screen for the dielectric constant and refractive index of materials in a fast and computationally efficient way and results constitute the largest dielectrics database to date, containing 1,056 compounds.
Abstract: Dielectrics are an important class of materials that are ubiquitous in modern electronic applications. Even though their properties are important for the performance of devices, the number of compounds with known dielectric constant is on the order of a few hundred. Here, we use Density Functional Perturbation Theory as a way to screen for the dielectric constant and refractive index of materials in a fast and computationally efficient way. Our results constitute the largest dielectric tensors database to date, containing 1,056 compounds. Details regarding the computational methodology and technical validation are presented along with the format of our publicly available data. In addition, we integrate our dataset with the Materials Project allowing users easy access to material properties. Finally, we explain how our dataset and calculation methodology can be used in the search for novel dielectric compounds.
Fermilab1, Yale University2, Illinois Institute of Technology3, University of Texas at Arlington4, University of Bern5, University of Oxford6, Scientific and Technological Research Council of Turkey7, Brookhaven National Laboratory8, Lancaster University9, Kansas State University10, Massachusetts Institute of Technology11, Columbia University12, Pacific Northwest National Laboratory13, University of Manchester14, SLAC National Accelerator Laboratory15, University of Pittsburgh16, University of Cambridge17, Syracuse University18, University of Chicago19, Los Alamos National Laboratory20, University of Cincinnati21, Virginia Tech22, New Mexico State University23, University of Michigan24, University of Minnesota25, Otterbein University26
TL;DR: In this paper, convolutional neural networks are applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC), for particle identification or event detection on simulated neutrino interactions.
Abstract: We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
TL;DR: In this article, the authors explored the relationship between a construction company's organizational culture and delay and found that the percentage of delay relative to project duration is lower in the U.S. compared to India.
TL;DR: The authors traces emergent discussions around posthumanism from across a range of disciplines and perspectives, and considers examples from emerging design practices that emphasize the interrelations between human and nonhuman actors.