Showing papers by "Illinois Institute of Technology published in 2016"

Subcortical brain alterations in major depressive disorder : findings from the ENIGMA Major Depressive Disorder working group

Abstract: The pattern of structural brain alterations associated with major depressive disorder (MDD) remains unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. To address this, we meta-analyzed three-dimensional brain magnetic resonance imaging data from 1728 MDD patients and 7199 controls from 15 research samples worldwide, to identify subcortical brain volumes that robustly discriminate MDD patients from healthy controls. Relative to controls, patients had significantly lower hippocampal volumes (Cohen's d=-0.14, % difference=-1.24). This effect was driven by patients with recurrent MDD (Cohen's d=-0.17, % difference=-1.44), and we detected no differences between first episode patients and controls. Age of onset ⩽21 was associated with a smaller hippocampus (Cohen's d=-0.20, % difference=-1.85) and a trend toward smaller amygdala (Cohen's d=-0.11, % difference=-1.23) and larger lateral ventricles (Cohen's d=0.12, % difference=5.11). Symptom severity at study inclusion was not associated with any regional brain volumes. Sample characteristics such as mean age, proportion of antidepressant users and proportion of remitted patients, and methodological characteristics did not significantly moderate alterations in brain volumes in MDD. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared with controls. This currently largest worldwide effort to identify subcortical brain alterations showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status.

The Economics of Privacy

Abstract: This article summarizes and draws connections among diverse streams of theoretical and empirical research on the economics of privacy. We focus on the economic value and consequences of protecting and disclosing personal information, and on consumers' understanding and decisions regarding the trade-offs associated with the privacy and the sharing of personal data. We highlight how the economic analysis of privacy evolved over time, as advancements in information technology raised increasingly nuanced and complex issues associated with the protection and sharing of personal information. We find and highlight three themes that connect diverse insights from the literature. First, characterizing a single unifying economic theory of privacy is hard, because privacy issues of economic relevance arise in widely diverse contexts. Second, there are theoretical and empirical situations where the protection of privacy can both enhance, and detract from, individual and societal welfare. Third, in digital economies, consumers' ability to make informed decisions about their privacy is severely hindered, because consumers are often in a position of imperfect or asymmetric information regarding when their data is collected, for what purposes, and with what consequences. We conclude the article by highlighting some of the ongoing issues in the privacy debate of interest to economists.

Combined Heat and Power Dispatch Considering Pipeline Energy Storage of District Heating Network

Abstract: The regional integration of variable wind power could be restricted by a strong coupling of electric power generation dispatch and heat supply of combined heat-and-power (CHP) units. The coupling in cold seasons precludes CHPs from providing the necessary flexibility for managing the wind power dispatch. The lack of flexibility problem can be tackled by exploiting the energy storage capability of a district heating network (DHN) which decouples the strong linkage of electric power and heat supplies. In this paper, a combined heat and power dispatch (CHPD) is formulated to coordinate the operation of electric power system (EPS) and district heating system (DHS). The proposed CHPD model which is solved by an iterative method considers the temperature dynamics of DHN for exploiting energy storage as an option for managing the variability of wind energy. The simulation results are discussed for several test systems to demonstrate the potential benefits of the proposed method in terms of operation economics, wind power utilization, as well as the potential benefits for real systems.

Silicon as a potential anode material for Li-ion batteries: where size, geometry and structure matter

Abstract: Silicon has attracted huge attention in the last decade because it has a theoretical capacity ∼10 times that of graphite. However, the practical application of Si is hindered by three major challenges: large volume expansion during cycling (∼300%), low electrical conductivity, and instability of the SEI layer caused by repeated volume changes of the Si material. Significant research efforts have been devoted to addressing these challenges, and significant breakthroughs have been made particularly in the last two years (2014 and 2015). In this review, we have focused on the principles of Si material design, novel synthesis methods to achieve such structural designs, and the synthesis-structure-performance relationships to enhance the properties of Si anodes. To provide a systematic overview of the Si material design strategies, we have grouped the design strategies into several categories: (i) particle-based structures (containing nanoparticles, solid core-shell structures, hollow core-shell structures, and yolk-shell structures), (ii) porous Si designs, (iii) nanowires, nanotubes and nanofibers, (iv) Si-based composites, and (v) unusual designs. Finally, our personal perspectives on outlook are offered with an aim to stimulate further discussion and ideas on the rational design of durable and high performance Si anodes for the next generation Li-ion batteries in the near future.

Small-Signal Modeling and Parameters Design for Virtual Synchronous Generators

Abstract: The concept of the virtual synchronous generator (VSG) is emerging as an attractive solution for controlling the grid-connected inverter when the renewable energy has a high penetration level into the grid. This paper focuses on the small-signal modeling and parameters design of the power loop of the VSG, and points out that the bandwidth of the power loop should be far less than twice the line frequency for the purpose of avoiding the VSG output voltage to be severely distorted. Consequently, the line-frequency-averaged small-signal model of the VSG is derived for system analysis and parameters design. Based on the model, the decoupling conditions between the active power loops (APLs) and the reactive power loops (RPLs) of the VSG are given. Finally, a step-by-step parameters design method is proposed to facilitate the design of the control parameters of the VSG. A 10-kVA prototype is built and tested in the laboratory, and the experimental results are given to verify the effectiveness of the theoretical analysis and the proposed parameters design method.

PETSc Users Manual Revision 3.7

Abstract: This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that may be used in application codes written in Fortran, C, C++, Python, and MATLAB (sequential). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++ or Fortran and experience using a debugger such as gdb or dbx, it may require more » a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself; For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. PETSc also has a MATLAB interface, so portions of your code can be written in MATLAB to “try out” the PETSc solvers. The resulting code will not be scalable however because currently MATLAB is inherently not scalable; and PETSc should not be used to attempt to provide a “parallel linear solver” in an otherwise sequential code. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect any kind of reasonable performance. Do not expect to generate your matrix sequentially and then “use PETSc” to solve the linear system in parallel. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc is supported; see the web site http://www.mcs.anl.gov/petsc for information on contacting support. A http://www.mcs.anl.gov/petsc/publications may be found a list of publications and web sites that feature work involving PETSc. We welcome any reports of corrections for this document. « less

Perching and takeoff of a robotic insect on overhangs using switchable electrostatic adhesion.

Abstract: For aerial robots, maintaining a high vantage point for an extended time is crucial in many applications. However, available on-board power and mechanical fatigue constrain their flight time, especially for smaller, battery-powered aircraft. Perching on elevated structures is a biologically inspired approach to overcome these limitations. Previous perching robots have required specific material properties for the landing sites, such as surface asperities for spines, or ferromagnetism. We describe a switchable electroadhesive that enables controlled perching and detachment on nearly any material while requiring approximately three orders of magnitude less power than required to sustain flight. These electroadhesives are designed, characterized, and used to demonstrate a flying robotic insect able to robustly perch on a wide range of materials, including glass, wood, and a natural leaf.

Emissions of Ultrafine Particles and Volatile Organic Compounds from Commercially Available Desktop Three-Dimensional Printers with Multiple Filaments

Abstract: Previous research has shown that desktop 3D printers can emit large numbers of ultrafine particles (UFPs, particles less than 100 nm) and some hazardous volatile organic compounds (VOCs) during printing, although very few filament and 3D printer combinations have been tested to date. Here we quantify emissions of UFPs and speciated VOCs from five commercially available filament extrusion desktop 3D printers utilizing up to nine different filaments by controlled experiments in a test chamber. Median estimates of time-varying UFP emission rates ranged from ∼108 to ∼1011 min–1 across all tested combinations, varying primarily by filament material and, to a lesser extent, bed temperature. The individual VOCs emitted in the largest quantities included caprolactam from nylon-based and imitation wood and brick filaments (ranging from ∼2 to ∼180 μg/min), styrene from acrylonitrile butadiene styrene (ABS) and high-impact polystyrene (HIPS) filaments (ranging from ∼10 to ∼110 μg/min), and lactide from polylactic ac...

Hourly Electricity Demand Response in the Stochastic Day-Ahead Scheduling of Coordinated Electricity and Natural Gas Networks

Abstract: This paper studies the role of hourly economic demand response in the optimization of the stochastic day-ahead scheduling of electric power systems with natural gas transmission constraints. The proposed coordinated stochastic model (referred to as EGTran) considers random outages of generating units and transmission lines, and random errors in forecasting the day-ahead hourly loads. The Monte Carlo simulation is applied to create multiple scenarios for representing the coordinated system uncertainties. The nonlinear natural gas network constraints are linearized and incorporated into the stochastic model. Numerical results demonstrate the benefits of applying the hourly economic demand response for enhancing the scheduling coordination of natural gas and electricity networks. It is demonstrated that electricity demand response would offer a less volatile hourly load profile and locational marginal prices, and less dependence on natural gas constraints for the optimal operation of electric power systems. The proposed model for EGTran could be applied by grid operators for the hourly commitment and dispatch of power system units.

Microgrid Scheduling With Uncertainty: The Quest for Resilience

Abstract: In recent years, natural disasters around the world have underscored the need for operative solutions that can improve the power grid resilience in response to low-probability high-impact incidents. The advent of microgrids (MGs) in modern power systems has introduced promising measures that can fulfil the power network resiliency requirements. This paper presents a two-stage stochastic programing approach to the optimal scheduling of a resilient MG. The impact of natural disasters on the optimal operation of MGs is modeled using a stochastic programming process. Other prevailing uncertainties associated with wind energy, electric vehicles, and real-time market prices are also taken into account. The proposed hourly scheme attempts to mitigate damaging impacts of electricity interruptions by effectively exploiting the MG capabilities. Incorporating AC network constraints in the proposed model offers a better solution to the security-constrained operation of MGs. The proposed model is linearized which offers robustness, simplicity, and computational efficiency in optimizing the MG operation. The effectiveness of proposed approach is illustrated using a large-scale MG test bed with a realistic set of data.

Microgrid stability: Classification and a review

Abstract: Microgrid is becoming an attractive concept to meet the increasing demands for energy and deal with air pollutions. Distributed energy sources (DERs) in Microgrid are usually interfaced with the utility grid by inverters, so the characteristics of Microgrid stability are much different from that of a traditional grid. However, the classifications, guidelines, and analysis method of Microgrid stability are well behind of the Microgrid development. In this paper, a Microgrid stability classification methodology is proposed on the basis of the of Microgrid characteristics investigation, which considers the Microgrid operation mode, types of disturbance and time frame. Then a comprehensive review of the body of research on Microgrid stability is presented in order to identify and advance the field. Finally, some challenges and suggestions of Microgrid stability for further researches are discussed.

Virtual Synchronous Machines: A unified interface for grid integration

Abstract: Power systems are going through a paradigm change from centralized generation to distributed generation and further on to smart grids. More and more renewable-energy sources, electric vehicles, energy storage systems, and so forth are being connected to power systems through power electronic converters. Moreover, the majority of loads are expected to connect to the grid through power electronic converters as well. This article shows that these converters, either on the supply side or on the load side, can all be controlled to behave like virtual synchronous machines (VSMs) and possess the dynamics of synchronous machines, providing a unified interface for smart grid integration. Synchroconverter technology and its developments are the focus of this article because the mathematical model of synchronous machines is embedded in the controller of synchronverters to provide close imitation.

The NuMI neutrino beam

Abstract: This paper describes the hardware and operations of the Neutrinos at the Main Injector (NuMI) beam at Fermilab. It elaborates on the design considerations for the beam as a whole and for individual elements. The most important design details of individual components are described. Beam monitoring systems and procedures, including the tuning and alignment of the beam and NuMI long-term performance, are also discussed.

Bilevel Model for Analyzing Coordinated Cyber-Physical Attacks on Power Systems

Abstract: This paper analyzes a coordinated cyber–physical attack on power systems, which could lead to undetectable line outages. Coordinated with physical attacks that cause line outages, the two-step cyberattacks comprising topology preserving and load redistribution attacks could mask and potentially exasperate outages to trigger cascading failures. These coordinated cyber–physical attacks are analyzed in a proposed bilevel model, which aims at identifying the most damaging and undetectable physical attacks constrained by attackers’ total budget. After being transformed into a mixed-integer linear programming problem, the proposed bilevel model is solved by a rigorous two-stage solution approach. This paper also discusses the relevant countermeasure strategies. The proposed model, the solution algorithm, and the effectiveness of countermeasures are examined by case studies based on the IEEE 14- and 118-bus test systems.

Corporate Environmental Responsibility and Firm Risk

Abstract: In this study, we examine the relation between corporate environmental responsibility (CER) and risk in U.S. public firms. We develop and test the risk-reduction, resource-constraint, and cross-industry variation hypotheses. Using an extensive U.S. sample during the 1991–2012 period, we find that for U.S. industries as a whole, CER engagement inversely affects firm risk after controlling for various firm characteristics. The result remains robust when we use firm fixed effect or an alternative measure of CER using principal component analysis or downside risk measures. To address the concern of endogeneity bias, we use a system equations approach and dynamic system generalized methods of moment regressions, and continue to find that environmentally responsible firms experience lower risk. These findings support the risk-reduction hypothesis, but not the resource-constraint hypothesis, along with the notion that the top management in U.S. firms is generally risk averse and that their CER engagement facilitates their risk management efforts. Our cross-industry analysis further reveals that the inverse CER-risk association mainly comes from the manufacturing sector, whereas in the service sector, CER tends to increase firm risk.

Fruit Polyphenols: A Review of Anti-inflammatory Effects in Humans

Abstract: Underlying etiological factors in the development of obesity-related chronic diseases are long-term imbalances of oxidative and inflammatory stress leading to tissue dysfunction, damage, and ultimately failure. Poor dietary quality contributes significantly to the oxidative and inflammatory status of an individual. Conversely, various dietary approaches, including specific dietary factors can mitigate or prevent the occurrence of these risk-conferring imbalances brought about by modern lifestyle. Plant-derived polyphenolic compounds are well known for their antioxidant properties. Recent evidence indicates these compounds may confer anti-inflammatory and/or inflammatory response stabilizing activities, which would have important implications in health maintenance and disease risk reduction. Commonly consumed fruits, such as grapes, berries, and oranges/orange juice, contain polyphenolic compounds that have been studied for their effects on inflammation, but the nature and extent of their effects in humans remain unclear. Therefore, this article aims to provide a comprehensive overview of human clinical trials investigating the acute and chronic (feeding) effect of polyphenols from commonly consumed fruits or their derived products on inflammation.

Measurement of the Reactor Antineutrino Flux and Spectrum at Daya Bay

Abstract: This Letter reports a measurement of the flux and energy spectrum of electron antineutrinos from six 2.9 GWth nuclear reactors with six detectors deployed in two near (effective baselines 512 and 561 m) and one far (1579 m) underground experimental halls in the Daya Bay experiment. Using 217 days of data, 296 721 and 41 589 inverse β decay (IBD) candidates were detected in the near and far halls, respectively. The measured IBD yield is (1.55±0.04) ×10(-18) cm(2) GW(-1) day(-1) or (5.92±0.14) ×10(-43) cm(2) fission(-1). This flux measurement is consistent with previous short-baseline reactor antineutrino experiments and is 0.946±0.022 (0.991±0.023) relative to the flux predicted with the Huber-Mueller (ILL-Vogel) fissile antineutrino model. The measured IBD positron energy spectrum deviates from both spectral predictions by more than 2σ over the full energy range with a local significance of up to ∼4σ between 4-6 MeV. A reactor antineutrino spectrum of IBD reactions is extracted from the measured positron energy spectrum for model-independent predictions.

Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression

Abstract: Compressive strength of concrete, recognized as one of the most significant mechanical properties of concrete, is identified as one of the most essential factors for the quality assurance of concrete. In the current study, three different data-driven models, i.e., Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC). Recycled aggregate is the current need of the hour owing to its environmental pleasant aspect of re-using the wastes due to construction. 14 different input parameters, including both dimensional and non-dimensional parameters, were used in this study for predicting the 28 days compressive strength of concrete. The present study concluded that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANN and ANFIS in comparison to MLR. In other words, comparing the test step of all the three models, it can be concluded that the MLR model is better to be utilized for preliminary mix design of concrete, and ANN and ANFIS models are suggested to be used in the mix design optimization and in the case of higher accuracy necessities. In addition, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated. Finally, the effect of number of input parameters on 28 days compressive strength of concrete is examined.

Spectrum Occupancy Measurements: A Survey and Use of Interference Maps

Abstract: In order to provide meaningful data about spectrum use, occupancy measurements describing the utilization rate of a specific frequency band should be conducted over a specific area instead of a single location. This paper presents a comprehensive methodology for the measurement and analysis of spectrum occupancy. This paper surveys spectrum measurement campaigns and associated interference maps, introducing the latter as a tool for spectrum analysis and management based on measurement data. An interference map characterizes the spectrum use by defining the level of interference over an area of interest in a certain frequency band. Building on findings from practical measurement studies, guidelines for spectrum occupancy measurements are given. While many scientific spectrum occupancy measurement papers tend to be too optimistic about the significance and generality of the results, we propose a cautionary perspective on drawing strong conclusions based on the often limited amount of data gathered. The different phases of the spectrum occupancy measurement and analysis process are described and a thorough discussion of interpolation methods is provided. Means to improve the measurement accuracy are discussed, especially regarding spatial domain considerations and the impact of the sampling interval on the results. A practical example of an improved measurement system design covering all the phases of the measurement process and used at the Turku, Finland; Blacksburg, VA, USA; and Chicago, IL, USA, spectrum observatories is given. Using the improved design, more realistic spectrum occupancy data can be obtained to lay the foundation for spectrum management decisions.

Mining Brand Perceptions from Twitter Social Networks

Abstract: Consumer perceptions are important components of brand equity and therefore marketing strategy. Segmenting these perceptions into attributes such as eco-friendliness, nutrition, and luxury enable a fine-grained understanding of the brand’s strengths and weaknesses. Traditional approaches towards monitoring such perceptions (e.g., surveys) are costly and time consuming, and their results may quickly become outdated. Extant data mining methods are unsuitable for this goal, and generally require extensive hand-annotated data or context customization, which leads to many of the same limitations as direct elicitation. Here, we investigate a novel, general, and fully automated method for inferring attribute-specific brand perception ratings by mining the brand’s social connections on Twitter. Using a set of over 200 brands and three perceptual attributes, we compare the method’s automatic ratings estimates with directly-elicited survey data, finding a consistently strong correlation. The approach provides a rel...

Understanding thermoelectric properties from high-throughput calculations: trends, insights, and comparisons with experiment

Abstract: We present an overview and preliminary analysis of computed thermoelectric properties for more than 48 000 inorganic compounds from the Materials Project (MP). We compare our calculations with available experimental data to evaluate the accuracy of different approximations in predicting thermoelectric properties. We observe fair agreement between experiment and computation for the maximum Seebeck coefficient determined with MP band structures and the BoltzTraP code under a constant relaxation time approximation (R2 = 0.79). We additionally find that scissoring the band gap to the experimental value improves the agreement. We find that power factors calculated with a constant and universal relaxation time approximation show much poorer agreement with experiment (R2 = 0.33). We test two minimum thermal conductivity models (Clarke and Cahill–Pohl), finding that both these models reproduce measured values fairly accurately (R2 = 0.82) using parameters obtained from computation. Additionally, we analyze this data set to gain broad insights into the effects of chemistry, crystal structure, and electronic structure on thermoelectric properties. For example, our computations indicate that oxide band structures tend to produce lower power factors than those of sulfides, selenides, and tellurides, even under the same doping and relaxation time constraints. We also list families of compounds identified to possess high valley degeneracies. Finally, we present a clustering analysis of our results. We expect that these studies should help guide and assess future high-throughput computational screening studies of thermoelectric materials.

Hierarchically Structured Nanomaterials for Electrochemical Energy Conversion.

Abstract: Hierarchical nanomaterials are highly suitable as electrocatalysts and electrocatalyst supports in electrochemical energy conversion devices. The intrinsic kinetics of an electrocatalyst are associated with the nanostructure of the active phase and the support, while the overall properties are also affected by the mesostructure. Therefore, both structures need to be controlled. A comparative state-of-the-art review of catalysts and supports is provided along with detailed synthesis methods. To further improve the design of these hierarchical nanomaterials, in-depth research on the effect of materials architecture on reaction and transport kinetics is necessary. Inspiration can be derived from nature, which is full of very effective hierarchical structures. Developing fundamental understanding of how desired properties of biological systems are related to their hierarchical architecture can guide the development of novel catalytic nanomaterials and nature-inspired electrochemical devices.

Novel genetic loci underlying human intracranial volume identified through genome-wide association

Abstract: Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρgenetic = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (Ncombined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.

A Game Theoretic Approach to Risk-Based Optimal Bidding Strategies for Electric Vehicle Aggregators in Electricity Markets With Variable Wind Energy Resources

Abstract: This paper proposes a stochastic optimization model for optimal bidding strategies of electric vehicle (EV) aggregators in day-ahead energy and ancillary services markets with variable wind energy. The forecast errors of EV fleet characteristics, hourly loads, and wind energy as well as random outages of generating units and transmission lines are considered as potential uncertainties, which are represented by scenarios in the Monte Carlo Simulation (MCS). The conditional value at risk (CVaR) index is utilized for measuring EV aggregators risks caused by the uncertainties. The EV aggregators optimal bidding strategy is formulated as a mathematical programming with equilibrium constraints (MPEC), in which the upper level problem is the aggregators CVaR maximization while the lower level problem corresponds to the system operation cost minimization. The bi-level problem is transformed into a single-level mixed integer linear programming (MILP) problem using the prime-dual formulation with linearized constraints. The progressive hedging algorithm (PHA) is utilized to solve the resulting single-level MILP problem. A game theoretic approach is developed for analyzing the competition among the EV aggregators. Numerical cases are studied for a modified 6-bus system and the IEEE 118-bus system. The results show the validity of the proposed approach and the impact of the aggregators bidding strategies on the stochastic electricity market operation.

Universal Droop Control of Inverters With Different Types of Output Impedance

Abstract: Droop control is a well-known strategy for the parallel operation of inverters. However, the droop control strategy changes its form for inverters with different types of output impedance, and so far, it is impossible to operate inverters with inductive and capacitive output impedances in parallel. In this paper, it is shown that there exists a universal droop control principle for inverters with output impedance having a phase angle between $- ({\pi }/{2})$ rad and $({\pi }/{2})$ rad. It takes the form of the droop control for inverters with resistive output impedance ( $R$ -inverters). Hence, the robust droop controller recently proposed in the literature for $R$ -inverters actually provides one way to implement such a universal droop controller that can be applied to all practical inverters without the need of knowing the impedance angle. The small-signal stability of an inverter equipped with the universal droop controller is analyzed, and it is shown to be stable when the phase angle of the output impedance changes from $- ({\pi }/{2})$ rad to $({\pi }/{2})$ rad. Both real-time simulation results and experimental results from a test rig consisting of an $R$ -inverter, an $L$ -inverter, and a $C$ -inverter operated in parallel are presented to validate the proposed strategy.

Outcome Measures for Artificial Pancreas Clinical Trials: A Consensus Report.

Abstract: Research on and commercial development of the artificial pancreas (AP) continue to progress rapidly, and the AP promises to become a part of clinical care. In this report, members of the JDRF Artificial Pancreas Project Consortium in collaboration with the wider AP community 1) advocate for the use of continuous glucose monitoring glucose metrics as outcome measures in AP trials, in addition to HbA1c, and 2) identify a short set of basic, easily interpreted outcome measures to be reported in AP studies whenever feasible. Consensus on a broader range of measures remains challenging; therefore, reporting of additional metrics is encouraged as appropriate for individual AP studies or study groups. Greater consistency in reporting of basic outcome measures may facilitate the interpretation of study results by investigators, regulatory bodies, health care providers, payers, and patients themselves, thereby accelerating the widespread adoption of AP technology to improve the lives of people with type 1 diabetes.

What is the impact of self-stigma? Loss of self-respect and the “why try” effect

Abstract: Background: The “Why Try” phenomenon, a consequence of self-stigma, is a sense of futility that occurs when people believe they are unworthy or incapable of achieving personal goals because they apply the stereotypes of mental illness to themselves.Aims: This study examines a four-stage model of self-stigma (aware, agree, apply, and self-stigma harm) and examines the “why try” effect as a result. We do that by testing a measure of “why try.”Method: Two hypothetical path models were tested. In the first, applying stereotypes to oneself leads to diminished self-respect and a sense of “why try”. In the second, the effect of applying stereotypes on “why try” is mediated by diminished self-respect. Participants completed the “why try” measure along with measures of self-stigma, public stigma, recovery, and empowerment.Results: Results show application of stereotypes to oneself predicts diminished self-respect and “why try”. “Why try” was significantly associated with agreement with public stigma, depre...

Real-Time Charging Station Recommendation System for Electric-Vehicle Taxis

Abstract: Electric vehicle (EV) taxis have been introduced into the public transportation systems to increase EV market penetration. Different from regular taxis that can refuel in minutes, EV taxis' recharging cycles can be as long as one hour. Due to the long cycle, the bad decision on the charging station, i.e., choosing one without empty charging piles, may lead to a long waiting time of more than an hour in the worst case. Therefore, choosing the right charging station is very important to reduce the overall waiting time. Considering that the waiting time can be a nonnegligible portion to the total work hours, the decision will naturally affect the revenue of individual EV taxis. The current practice of a taxi driver is to choose a station heuristically without a global knowledge. However, the heuristical choice can be a bad one that leads to more waiting time. Such cases can be easily observed in current collected taxi data in Shenzhen, China. Our analysis shows that there exists a large room for improvement in the extra waiting time as large as 30 min/driver. In this paper, we provide a real-time charging station recommendation system for EV taxis via large-scale GPS data mining. By combining each EV taxi's historical recharging events and real-time GPS trajectories, the current operational state of each taxi is predicted. Based on this information, for an EV taxi requesting a recommendation, we can recommend a charging station that leads to the minimal total time before its recharging starts. Extensive experiments verified that our predicted time is relatively accurate and can reduce the cost time of EV taxis by 50% in Shenzhen.

Modeling and Analysis of SiC MOSFET Switching Oscillations

Abstract: SiC MOSFETs exhibit extremely fast switching characteristics, which are unfortunately accompanied by undesirable switching oscillations. In this paper, equivalent circuit models incorporating all parasitic elements are developed for the turn-ON and turn-OFF of a SiC MOSFET. Simple mathematical formulas are derived to provide the theoretical analysis of the switching oscillation phenomenon, and to guide the snubber or damping circuit design. Both circuit simulation and experimental measurement are carried out to validate these simple equivalent circuit models.

Unraveling Anthocyanin Bioavailability for Human Health

Abstract: This review considers the bioavailability of health-protective anthocyanin pigments from foods, in light of the multiple molecular structures and complicated traffic patterns taken by anthocyanins both as flavonoid metabolites and as phenolic acid metabolites within the body. Anthocyanins have generally been considered to have notoriously poor bioavailability, based on the very low levels typically detected in routine human blood draws after ingestion. Although some investigations have assessed anthocyanin bioavailability solely based on the measurement of parent anthocyanins or phenolic acid breakdown products, more recent research has increasingly revealed the presence, qualitative diversity, relatively high concentrations, and tenacity of molecular intermediates of anthocyanins that retain the unique flavonoid C6-C3-C6 backbone structure. We argue that the persistence of anthocyanin metabolites suggests enterohepatic recycling, leading to prolonged residence time, and supports the notion that anthocyanins are far more bioavailable than previously suggested.