SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter

Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words’ sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure

Closed-Loop Scheduling for Cost Minimization in HVAC Central Plants

In this paper, we examine closed-loop operation of an HVAC central plant to demonstrate that closed-loop receding-horizon scheduling provides robustness to inaccurate forecasts, and that economic performance is not seriously impaired by shortened prediction horizons or inaccurate forecasts when feedback is employed. Using a general mixed-integer linear programming formulation for the scheduling problem, we show that optimization can be performed in real time. Furthermore, we demonstrate that closed-loop operation with a moderate prediction horizon is not significantly worse than a long-horizon implementation in the nominal case, and that closed-loop operation can correct for inaccurate long-term forecasts without significant cost increase. In addition, we show that terminal constraints can be employed to ensure recursive feasibility. The end result is that forecasts of demand need not be extremely accurate over long times, indicating that closed-loop scheduling can be implemented in new or existing central plants

Design and Application of Distributed Economic Model Predictive Control for Large-Scale Building Temperature Regulation

Although recent research has suggested model predictive control as a promising solution for minimizing energy costs of commercial buildings, advanced control systems have not been widely deployed in practice. Large-scale implementations, including industrial complexes and university campuses, may contain thousands of air handler units each serving a multiplicity of zones. A single centralized control system for these applications is not desirable. In this paper, we propose a distributed control system to economically optimize temperature regulation for large-scale commercial building applications. The decomposition strategy considers the complexities of thermal energy storage, zone interactions, and chiller plant equipment while remaining computationally tractable. One of the primary benefits of the proposed formulation is that the low-level airside problem can be decoupled and solved in a distributed manner; hence, it can be easily extended to handle large applications. Peak demand charges, a major source of coupling, are included. The interactions of the airside system with the waterside system are also considered, including discrete decisions, such as turning chillers on and off. To deploy such a control scheme, a system model is required. Since using physical knowledge about building models can greatly reduce the number of parameters that must be identified, grey-box models are recommended to reduce the length of expensive identification testing. We demonstrate the effectiveness of this control system architecture and identification procedure via simulation studies

Characterization of Singlet Ground and Low-Lying Electronic Excited States of Phosphaethyne and Isophosphaethyne

The singlet ground _X˜ 1_+_ and excited _1_− , 1__ states of HCP and HPC have been systematically investigated using ab initio molecular electronic structure theory. For the ground state, geometries of the two linear stationary points have been optimized and physical properties have been predicted utilizing restricted self-consistent field theory, coupled cluster theory with single and double excitations _CCSD_, CCSD with perturbative triple corrections _CCSD_T__, and CCSD with partial iterative triple excitations _CCSDT-3 and CC3_. Physical properties computed for the global minimum _X˜ 1_+HCP_ include harmonic vibrational frequencies with the cc-pV5Z CCSD_T_ method of _1=3344 cm−1, _2=689 cm−1, and _3=1298 cm−1. Linear HPC, a stationary point of Hessian index 2, is predicted to lie 75.2 kcal mol−1 above the global minimum HCP. The dissociation energy D0_HCP_X˜ 1_+_→H_2S_+CP_X 2_+__ of HCP is predicted to be 119.0 kcal mol−1, which is very close to the experimental lower limit of 119.1 kcal mol−1. Eight singlet excited states were examined and their physical properties were determined employing three equation-of-motion coupled cluster methods _EOM-CCSD, EOM-CCSDT-3, and EOM-CC3_. Four stationary points were located on the lowest-lying excited state potential energy surface, 1_− →1A_, with excitation energies Te of 101.4 kcal mol−1_1A_ HCP_, 104.6 kcal mol−1_1_− HCP_, 122.3 kcal mol−1_1A_ HPC_, and 171.6 kcal mol−1_1_− HPC_ at the cc-pVQZ EOM-CCSDT-3 level of theory. The physical properties of the 1A_ state with a predicted bond angle of 129.5° compare well with the experimentally reported first singlet state _A˜ 1A__. The excitation energy predicted for this excitation is T0=99.4 kcal mol−1_34 800 cm−1 , 4.31 eV_, in essentially perfect agreement with the experimental value of T0=99.3 kcal mol−1_34 746 cm−1 ,4.308 eV_. For the second lowest-lying excited singlet surface, 1_→1A_, four stationary points were found with Te values of 111.2 kcal mol−1 _21A_ HCP_, 112.4 kcal mol−1 _1_ HPC_, 125.6 kcal mol−1_2 1A_ HCP_, and 177.8 kcal mol−1_1_ HPC_. The predicted CP bond length and frequencies of the 2 1A_ state with a bond angle of 89.8° _1.707 Å, 666 and 979 cm−1_ compare reasonably well with those for the experimentally reported C ˜ 1A_ state _1.69 Å, 615 and 969 cm−1_. However, the excitation energy and bond angle do not agree well: theoretical values of 108.7 kcal mol−1 and 89.8° versus experimental values of 115.1 kcal mol−1 and 113°

Delayed maximum northern European summer temperatures during the Last Interglacial as a result of Greenland Ice Sheet melt

This is the author accepted manuscript. The final version is available from the Geological Society of America via the DOI in this record.Here we report a new quantitative mean July temperature reconstruction using non-biting midges (chironomids) from the Danish Last Interglacial (LIG) site Hollerup (spanning 127–116 ka). We find that peak mean July temperatures of 17.5 °C, similar to those of the present day (1961–1990 CE), were reached shortly before the onset of the regional Carpinus pollen zone. Through comparison to terrestrial and marine sequences we demonstrate that peak summer warmth took place some three millennia after the onset of LIG warming in Europe, a marked delay in line with records from the North Atlantic. Crucially, the warmest northern European summer temperatures appear to follow maximum Greenland Ice Sheet mass loss, implying that meltwater substantially reduced Atlantic Meridional Overturning Circulation and depressed European temperatures during the early part of the interglacial.Turney and Fogwill thank the Australian Research Council (grants FL100100195, FT120100004, LP120200724). Thanks to Bjørn Buchardt for providing the C:N data, Angela Self for help with statistical analysis, David Campbell and Alan Bedford for laboratory work, and three reviewers for their constructive comments

An Economic Model Predictive Control Framework for Distributed Embedded Battery Applications

Since building heating, ventilation, and air conditioning (HVAC) systems are significant consumers of primary energy, considerable efforts are being made to improve energy efficiency and decrease energy costs in these applications. Notably, substantial opportunities in the area of HVAC control exist for decreasing energy costs by shifting loads from peak periods to off-peak periods in the presence of time-varying utility prices. This load shifting is also beneficial for power companies since it results in a more constant total load allowing them to operate more efficiently. Economic model predictive control (MPC) has been shown to significantly decrease the energy costs of commercial HVAC systems via load shifting. Typically, thermal energy storage (TES) is used for this purpose by running HVAC equipment at higher rates during periods of low power prices to charge TES and at lower rates during periods of higher prices while discharging TES to meet building demand loads. However, with batteries becoming less expensive to manufacture, electrical energy storage in batteries is becoming a viable option for load shifting. Batteries can be used for both load shifting to decrease costs and revenue generation if the incentives on the electricity market are appropriate. In this work, embedded battery applications are considered. In embedded battery applications, the batteries are directly packaged with airside equipment such as air handler units (AHUs), roof-top units (RTUs), and variable refrigerant flow systems (VRFs). In this arrangement, the batteries are accessible only to the local unit and not to other units. In this paper, we propose a hierarchical control system framework for the economic optimization of distributed embedded battery units. The architecture considers both building mass storage as well as the electrical energy storage of the battery units. A high-level problem performs an economic optimization over the entire system using aggregate models. The low-level layer is broken into subsystems, each optimizing its local decisions with higher fidelity models. Advantages of this framework include no iterative communication required between subsystems, decreased computational complexity in the high-level problem allowing for real-time online implementation, and management of total demand across the entire system to reduce peak demand charges. We conclude with a simulation study demonstrating the benefits of the proposed control architecture

A Case Study of Economic Optimization of HVAC Systems based on the Stanford University Campus Airside and Waterside Systems

Commercial buildings account for $200 billion per year in energy expenditures, with heating, ventilation, and air conditioning (HVAC) systems accounting for most of these costs. In energy markets with time-varying prices and peak demand charges, a significant potential for cost savings is provided by using thermal energy storage to shift energy loads. Since most implementations of HVAC control systems do not optimize energy costs, they have become a primary focus for new strategies aimed at economic optimization. Model predictive control (MPC) has emerged as one popular method to achieve this load shifting, while respecting system constraints. MPC uses a model of the system to make predictions and to solve an optimization problem. Much research has shown the benefits of MPC over alternative strategies for HVAC control [1]. However, some industrial applications, such as large research centers or university campuses, are too large to be solved in a single MPC instance. Decompositions have been proposed in the literature, but it is difficult to evaluate and to compare decompositions against one another when using different systems. In this paper, we present a large-scale relevant case study where solving a single MPC optimization problem is neither desirable nor feasible for real-time implementations. The study is modeled after the Stanford University campus, consisting of both an airside and waterside system [2]. The airside system includes 500 zones spread throughout 25 campus buildings along with the air handler units and regulatory building automation system used for temperature regulation. The waterside system includes the central plant equipment, such as chillers, that is used to meet the load from the buildings. Active thermal energy storage is available to the campus in addition to the passive thermal energy storage present in the form of building mass. The airside models describe the temperature dynamics in each of the 500 zones, and the waterside models describe the power consumption of the central plant equipment. The aim of the control system is to minimize costs in the presence of time-varying electricity prices and a peak demand charge as well as environmental disturbances such as weather while meeting constraints on comfort and equipment. We perform an economic optimization of the entire campus using a hierarchical system with distributed airside controllers to demonstrate the potential savings. The models from this case study are made publicly available for other researchers interested in designing alternative control strategies for managing chilled water production to meet airside loads. The aim of the case study release is to provide a standardized problem for the research community. A benchmark is provided for evaluating performance. References [1] A. Afram and F. Janabi-Sharifi. Theory and applications of HVAC control systems—A review of model predictive control (MPC). Building and Environment, 72:343–355, February 2014. [2] J. B. Rawlings, N. R. Patel, M. J. Risbeck, C. T. Maravelias, M. J. Wenzel, and R. D. Turney. Economic MPC and real-time decision making with application to large-scale HVAC energy systems. Computers & Chemical Engineering, 2017. In Press

Fluid mechanical modeling of the upper urinary tract

The upper urinary tract (UUT) consists of kidneys and ureters, and is an integral part of the human urogenital system. Yet malfunctioning and complications of the UUT can happen at all stages of life, attributed to reasons such as congenital anomalies, urinary tract infections, urolithiasis and urothelial cancers, all of which require urological interventions and significantly compromise patients' quality of life. Therefore, many models have been developed to address the relevant scientific and clinical challenges of the UUT. Of all approaches, fluid mechanical modeling serves a pivotal role and various methods have been employed to develop physiologically meaningful models. In this article, we provide an overview on the historical evolution of fluid mechanical models of UUT that utilize theoretical, computational, and experimental approaches. Descriptions of the physiological functionality of each component are also given and the mechanical characterizations associated with the UUT are provided. As such, it is our aim to offer a brief summary of the current knowledge of the subject, and provide a comprehensive introduction for engineers, scientists, and clinicians who are interested in the field of fluid mechanical modeling of UUT

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes