Background People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear. Methods This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Δage. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Δage, and the models were adjusted for sex and chronological age. Results This study included 44,094 individuals (mean age 64 ± 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Δage (absolute error of biological age and chronological age) was 9.8 ± 7.4 years. Δage was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 ± 0.11 for the healthy diet to 2.37 ± 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 ± 0.29 years of older predicted ECG-age. Conclusion In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.

Tribunals are a major part of administrative law in the United Kingdom. They hear and determine appeals against administrative decisions in areas such as social security, immigration, and tax. This article surveys recent developments in the world of tribunals and their ability to deliver effective administrative justice. It examines the following topics: the 2007 reforms which introduced a coherent tribunal system; the relationship between government and tribunals; jurisdictional issues concerning tribunals and tribunal procedures; the role of the Upper Tribunal; initial administrative and review decision-making; and the current modernisation - or digitisation - of tribunals.

Short-term power system operational planning problems that consider multi-stage uncertainties pose significant challenges, not only in the design of tractable optimization frameworks for implementing them, but also in the testing and benchmarking of such frameworks. This paper presents an implementation using the open-source Matpower Optimal Scheduling Tool (MOST) to study and compare a stochastic day-ahead, security-constrained unit commitment problem with a more traditional deterministic approach. The comparison is based on a testing methodology for day-ahead plans designed to produce expected performance estimates with minimal biases from modeling assumptions. Emphasis is given in the proposed stochastic approach to explicit modeling of the operational characteristics of the technologies available, their spatial and temporal coupling, and the regulatory constraints that assure reliability and adequacy. The problem formulations and testing methodology are described and simulation results from MOST are presented, with discussion of implications for future market design. All of the code and data to replicate the simulations is provided online.

The relevance and applicability of marketing approaches to the design and dissemination of scientific and technical information (STI) systems is hardly debatable. Market segmentation, as one of the fundamental and most applied theories of the marketing discipline, is thus widely accepted conceptually as one of the key building blocks of any marketing strategy for STI systems. The gap between the conceptual acceptance and practical rejection of the segmentation concept can in part be due to the difficulties involved in implementing a segmentation research program and utilizing the results in the design of STI marketing strategies. The chapter reviews some of these difficulties and suggest some possible solutions. The discussion is organized around the five major research phases: segmentation problem definition, research design, data collection, data analysis, and data interpretation and "translation" of results. The segmentation problem definition stage is probably the most crucial and most neglected area of segmentation.

Validation is a critical component in the development of synthetic models, which aims to convince a user that a model achieves its claims of realism. While users of power system test cases are primarily interested in operational results, which could be considered outputs, it is more convenient and feasible to control distributions of data inputs, both structural and otherwise. Validation metrics, are therefore generally focused on input rather than operation features. This paper investigates the link between input and output statistics in power system. Better understanding of this relationship allows validation to continue to focus largely on input statistics, while at the same time offering some assurance about operational behavior.

We consider the decentralized reactive power control of photovoltaic (PV) inverters spread throughout a radial distribution network. Our objective is to minimize the expected voltage regulation error, while guaranteeing the robust satisfaction of distribution system voltage magnitude and PV inverter capacity constraints. Our approach entails the offline design and the online implementation of the decentralized controller. In the offline control design, we compute the decentralized controller through the solution of a robust convex program. Under the restriction that the decentralized controller have an affine disturbance feedback form, the optimal solution of the decentralized control design problem can be computed via the solution of a finite-dimensional conic program. In the online implementation, we provide a method to implement the decentralized controller at a timescale that is fast enough to counteract the fluctuations in the system disturbance process. The resulting trajectories of PV inverter reactive power injections and nodal voltage magnitudes are guaranteed to be feasible for any realization of the system disturbance under the proposed controller. We demonstrate the ability of the proposed decentralized controller to effectively regulate voltage over a fast timescale with a case study of the IEEE 123-node test feeder.

In this study, we report on a preliminary investigation into the use of headspace SPME (solid phase microextraction) coupled with a novel, field-portable GC-MS system (Torion T-9, PerkinElmer Inc, Shelton, CT) for the on-site screening of VOCs and SVOCs prior to shipping and upon receipt of raw materials by the cocoa manufacturer. The ability to quickly fingerprint raw materials for the presence of mould indicator compounds at various points in the supply chain can reduce costs by allowing the procurer (shipper, buyer, broker, etc.) to rapidly evaluate the quality of a shipment before it is purchased. In addition to cocoa beans, prescreening of raw materials and end-products using this technology can be broadly applied to numerous other foodstuffs to determine the quality and safety of food products and materials.

The Electric Power Track activities at HICSS began twenty years ago. This is an account of its history, its focus, and its impact over those years.

In This article, We Relate events leading up to the creation of the Power Systems Engineering Research Center (PSERC ) and the problems associated with convincing people that a group of universities can function as a single center. The keys to this achievement are industrial support, university collaboration, and effective management. The article continues with a discussion of how PSERC has evolved over the past 20 years.

The short-term forecasting of real-time locational marginal price (LMP) and network congestion is considered from a system operator perspective. A new probabilistic forecasting technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability distribution of the real-time LMP/congestion is obtained. The proposed method incorporates load/generation forecast, time varying operation constraints, and contingency models. By shifting the computation associated with multiparametric programs offline, the online computational cost is significantly reduced. An online simulation technique by generating critical regions dynamically is also proposed, which results in several orders of magnitude improvement in the computational cost over standard Monte Carlo methods.

This chapter provides a comprehensive study on the topological and electrical characteristics of a power grid transmission network based on a number of synthetic and real-world power systems. The D. J. Watts–S. H. Strogatz small-world model is generated starting from a regular ring lattice, then, using a small probability, rewiring some local links to an arbitrary node chosen uniformly at random from the entire network. The chapter explores an algorithm that is able to generate random topology power grids featuring the same topology and electrical characteristics as those found in the real data. It draws on the topological and electrical characteristics of a sample medium-voltage power distribution network. The chapter focuses on the high-voltage transmission network of a power grid. The smart grid is also positioned to adopt new technologies, such as smart metering, synchronous phasor measurement units, plug-in hybrid electric vehicles, various forms of distributed generation, solar and wind energy, electric load management systems and distribution automation.

In this paper we review and expand our previous work on developing random topology networks as a way to generate synthetic power grid data. We first present an algorithm that is able to generate the small-world "eletric" topology appropriate for a transmission network. Next we re-examine the definitions of the proposed numerical measure called "Bus Type Entropy", to characterize the correlated assignment of generation, load, and connection buses in a real power grid, using the IEEE test cases and newly obtained realistic grid data. In order to study the scaling property of bus type assignment versus network work, we revise the entropy definition for better statistical property and improved numerical stability. We then develop an more efficient search algorithm for the best bus type assignments with the help of the derived scaling function. Finally we introduce some most recent work to generate other electrical parameters such as generation capacities and load settings.

Introduction to Electric Energy System Track.

This study investigates the technological and economic relationships of integrating wind power, plug-in electric vehicles (PEVs) and mixtures of Level 1/Level 2 charger infrastructures in New York Independent System Operator’s (NYISO’s) two-settlement wholesale electric energy market. Using 7560 scenarios constructed from various PEV penetrations, Level 2 charging and wind dispatch policies, this study reports findings that substantiate and challenge aspects of the previously envisioned synergy between wind power, PEVs and charging infrastructure. An econometric model based on historical market data, including system-level costs of load ramps, was used to study resource integration and to avoid data fidelity issues that plague traditional fundamentals-based models. Results show: (1) the existence of time-series correlation between PEV charging and wind dispatch depends on curtailment policy, (2) PEV charging with wind over-forecast nearly triples the rate of reduction in curtailed wind energy compared to under-forecast, (3) using wholesale energy cost as metric, PEVs can be adversely coupled to curtailable wind, and decoupled with must-take wind, and (4) PEV penetration, Level 2 charging and wind power may be economic substitutes in the energy market.

In this paper, a much more detailed representation of the nation's electricity system than has been traditionally used in policy models is employed. This detailed representation greatly increases the computational difficulty of obtaining optimal solutions, but is necessary to accurately model the location of new investment in generation. Given the proposed regulation of CO2 emissions from US power plants, an examination of economically efficient policies for reducing these emissions is warranted. The model incorporates realistic physical constraints, investment and retirement of generation, and price-responsive load to simulate the effects of policies for limiting CO2 emissions over a twenty-year forecast horizon. Using network reductions for each of the three electric system regions in the U.S. And Canada, an optimal economic dispatch, that satisfies reliability criteria, is assigned for 12 typical hour-types in each year. Three scenarios are modeled that consider subsidies for renewables and either CO2 emissions regulation on new investment or cap-and-trade. High and low gas price trends are also simulated and have large effects on prices of electricity but small impacts on CO2 emissions. Low gas prices with cap-and-trade reduce CO2 emissions the most, large subsidies for renewables alone do not reduce carbon emissions much below existing levels. Extensive retirement of coal-fired power plants occurs in all cases.

The problem of short-term probabilistic forecast of real-time locational marginal price (LMP) is considered. A new forecast technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability mass function of the real-time LMP is estimated using Monte Carlo techniques. The proposed methodology incorporates uncertainty models such as load and stochastic generation forecasts and system contingency models. With the use of offline computation of multiparametric linear programming, online computation cost is significantly reduced.

Data attacks on state estimation modify part of system measurements such that the tempered measurements cause incorrect system state estimates. Attack techniques proposed in the literature often require detailed knowledge of system parameters. Such information is difficult to acquire in practice. The subspace methods presented in this paper, on the other hand, learn the system operating subspace from measurements and launch attacks accordingly. Conditions for the existence of an unobservable subspace attack are obtained under the full and partial measurement models. Using the estimated system subspace, two attack strategies are presented. The first strategy aims to affect the system state directly by hiding the attack vector in the system subspace. The second strategy misleads the bad data detection mechanism so that data not under attack are removed. Performance of these attacks are evaluated using the IEEE 14-bus network and the IEEE 118-bus network.

In order to demonstrate and test new concepts and methods for the future grids, power engineers and researchers need appropriate randomly generated grid network topologies for Monte Carlo experiments. If the random networks are truly representative and if the concepts or methods test well in this environment they would test well on any instance of such a network as the IEEE model systems or other existing grid models. Our previous work [1] proposed a random topology power grid model, called RT-nested-small world, based on the findings from a comprehensive study of the topology and electrical properties of a number of realistic grids. The proposed model can be utilized to generate a large number of power grid test cases with scalable network size featuring the same small-world topology and electrical characteristics found from realistic power grids. On the other hand, we know that dynamics of a grid not only depend on its electrical topology but also on the generation and load settings, and the latter closely relates with an accurate bus type assignment of the grid. Generally speaking, the buses in a power grid test case can be divided into three categories: the generation buses (G), the load buses (L), and the connection buses (C). In [1] our proposed model simply adopts random assignment of bus types in a resulting grid topology, according to the three bus types' ratios. In this paper we examined the correlation between the three bus types of G/L/C and some network topology metrics such as node degree distribution and clustering coefficient. We also investigated the impacts of different bus type assignments on the grid vulnerability to cascading failures using IEEE 300 bus system as an example. We found that (a) the node degree distribution and clustering characteristic are different for different type of buses (G/L/C) in a realistic grid, (b) the changes in bus type assignment in a grid may cause big differences in system dynamics, and (c) the random assignment of bus types in a random topology power grid model should be improved by using a more accurate assignment which is consistent with that of realistic grids.