Multiple Chance Research Articles

Modeling 3E-S sustainable development problem by an ambiguous chance constrained optimization method

The environmental-energy-economic-social (3E-S) sustainable development problem aims to balance the environment, energy, and economy upon which human society critically relies. However, it is further complicated by various ambiguous input parameters about greenhouse gas (GHG) emissions, electricity consumption, gross domestic product (GDP), and unemployment rate. To address this complexity, this paper develops an ambiguous chance constrained optimization model with multiple chance soft constraints for optimizing the 3E-S sustainable development problem by strategically allocating labor across key sectors. Tractable formulations of the proposed model are derived by constructing three types of ambiguity sets for input parameters with ambiguous probability distributions. The first is to construct support-mean ambiguous distribution sets with little known information, the second is to extend support-mean ambiguity sets to mean-covariance ambiguity sets by incorporating interactive relationships among sectors, and the third is to extend support-mean ambiguity sets to inner-outer ambiguity sets with Wasserstein metric from globalized distributionally robust perspective. The availability and computability of the proposed model have been illustrated by planning India’s 3E-S sustainability for the year 2030. The results suggest that (i) the proposed method exhibits the capability to offer flexible policy measures; (ii) India should prioritize the development of the Mining and quarrying sector, the Transport, storage, communication, and services sector, and the Constructions sector; (iii) India needs to make greater efforts for promoting GDP while controlling GHG emissions and energy consumption; (iv) The policy measures and objectives are influenced by tolerance levels, support intervals, interactive relationships among sectors, globalized sensitivity, and Wasserstein ball size.

A Distributionally Robust Optimization Approach to Two-Sided Chance-Constrained Stochastic Model Predictive Control With Unknown Noise Distribution

In this work, we propose a distributionally robust stochastic model predictive control (DR-SMPC) algorithm to address the problem of multiple two-sided chance constrained discrete-time linear systems corrupted by additive noise. The prevalent mechanism to cope with two-sided chance constraints is the so-called risk allocation approach, which conservatively approximates the two-sided chance constraints with two single chance constraints by applying Bool's inequality. In this proposed DR-SMPC framework, an exact second-order cone approach is adopted to abstract the multiple two-sided chance constraints by considering the first and second moments of the noise. With the proposed DR-SMPC algorithm, the worst-case probability of violating safety constraints is guaranteed to be within a pre-specified maximum value. By flexibly adjusting this pre-specified maximum probability, the feasible region of the initial state can be increased for the SMPC problem. The recursive feasibility and convergence of the proposed DR-SMPC are rigorously established by introducing a binary initialization strategy for the nominal state. A simulation study of a single spring and double mass system was conducted to demonstrate the effectiveness of the proposed DR-SMPC algorithm.

Multiple Joint Chance Constraints Approximation for Uncertainty Modeling in Dispatch Problems

Multiple Joint Chance Constraints Approximation for Uncertainty Modeling in Dispatch Problems

A Two-stage Robust Scheduling Optimization of an Energy Hub with Multiple Chance Constraints

A Two-stage Robust Scheduling Optimization of an Energy Hub with Multiple Chance Constraints

Notes and Comments

Notes and Comments Martin Menke and Kenneth Pennington Association News The Spring Meeting of the Association will be held at the University of Scranton in Pennsylvania on April 17 and 18, 2020. The deadline for submission of proposals for individual papers, panels, and roundtables is February 3. They should be submitted via the ACHA website (achahistory.org). For further information, please contact Professor Robert Shaffern at robert.shaffern@scranton.edu. The 2021 Annual Meeting will be held in Seattle, Washington, on January 7 to 9, 2021. The ACHA solicits papers and panels on the following topics: Catholicism and missions, Pacific-rim Catholicism, Catholicism in the western United States, Catholicism and the environment, Catholicism and public scholarship, communities traditionally marginalized in the field of Catholic history, and premodern or early modern Catholicism. Proposals for papers, panels, roundtables, book discussions, and site visits should be submitted via the Association's website. For joint AHA-ACHA panels the deadline is February 15, for ACHA panels it is March 16, 2021. Joint panels with affiliated societies such as the American Society of Church History are encouraged. For further information, please contact Professor Anthony B. Smith at asmith1@udayton.edu. At its annual meeting in New York on January 4, 2020 the following awards were announced: 2019 John Gilmary Shea Prize The John Gilmary Shea Prize is given annually to the author of a book, published during a preceding twelve-month period, which is judged by a committee of experts to have made the most original and distinguished contribution to the knowledge of the history of the Catholic Church. Any author who is a citizen or permanent resident of the United States or Canada is eligible. The prize consists of $1500. The Shea Prize committee of the American Catholic Historical Association for 2019 was comprised of three members: Professor Una Cadegan (University of Dayton), Professor Stephen Schloesser, S.J. (Loyola University Chicago), and Professor A. Katie Stirling-Harris (University of California at Davis). Citation: The American Catholic Historical Association awards the 2019 John Gilmary Shea Prize to Karin Vélez for her book The Miraculous Flying House of Loreto: Spreading Catholicism in the Early Modern World (Princeton: Princeton University Press, 2019; published December 2018). [End Page 158] This boldly conceived monograph examines the global spread of early modern Catholicism through the prism of a Marian devotion and by means of an innovative methodology. Vélez initially challenges readers by approaching her topic as mythohistory, a sympathetic position that takes "miracles and religion not as categories of belief but as historical records of reality" (26), a reality of believers' lived experience and memory. Turning away from conventional approaches that require scholars to peel away devotional layers accumulated over time, Vélez instead views religion as a continuous flow of motion, migration, accretion, and hybridization, and fortuitously lands on the Virgin of Loreto's flying house as the portable embodiment of Catholicism's expansion. As a result, Loreto's global Catholicism is "powerfully accretive: it is always in flux and reinvention, never frozen or static" (33). Drawing especially on reports sent to Jesuit authorities in Rome from Spanish colonies in the Americas as well as the published Jesuit Relations from New France, Vélez utilizes a variety of sources including mission accounts, travelogues, sermons, and cultic "histories." However, even while using such sources largely composed by clerics, Vélez expands the category of "author" by blurring boundaries between "authorized" missionaries and less authoritative (usually anonymous) missionized. This lets Vélez reimagine authorship as an organic, fluid process and a complex interweaving of migrants and indigenous: Italian, French, Spanish, Portuguese, Slav, Jesuit, Huron, Moxos, Monquí, Inka—and their invited guests. Avoiding both wholly top-down and bottom-up models of religious transmission and change, Vélez draws attention to multiple voices, haphazard and chance events, and a "jumbled" geography transcending customary center-periphery divisions. As a result, Vélez finds participation and contributions in places that more conventional methodologies might never have identified: "unofficial authors, inadvertent pilgrims, unlicensed architects, unacknowledged artists, and unsolicited cataloguers" (7). In sum, this path-breaking study of the constructions, transformations, and expansions of a Marian devotion in early modern global Catholicism...

Probabilistic Energy Management for Building Climate Comfort in Smart Thermal Grids with Seasonal Storage Systems

This paper presents an energy management framework for building climate comfort (BCC) systems interconnected in a grid via aquifer thermal energy storage (ATES) systems in the presence of two types of uncertainty (private and common). ATES can be used either as a heat source (hot well) or sink (cold well) depending on the season. We consider the uncertain thermal energy demand of individual buildings as a private uncertainty source and the uncertain common resource pool (ATES) between neighbors as a common uncertainty source. We develop a large-scale stochastic hybrid dynamical model to predict the thermal energy imbalance in a network of interconnected BCC systems together with mutual interactions between their local ATES. We formulate a finite-horizon mixed-integer quadratic optimization problem with multiple chance constraints at each sampling time, which is in general a non-convex problem and difficult to solve. We then provide a computationally tractable framework by extending the so-called robust randomized approach and offering a less conservative solution for a problem with multiple chance constraints. A simulation study is provided to compare completely decoupled, centralized and move-blocking centralized solutions. We also present a numerical study using a geohydrological simulation environment (MODFLOW) to illustrate the advantages of our proposed framework.

Exploiting structure of chance constrained programs via submodularity

We introduce a novel approach to reduce the computational effort of solving convex chance constrained programs through the scenario approach. Instead of reducing the number of required scenarios, we directly minimize the computational cost of the scenario program. We exploit the problem structure by efficiently partitioning the constraint function and considering a multiple chance constrained program that gives the same probabilistic guarantees as the original single chance constrained problem. We formulate the problem of finding the optimal partition, a partition achieving the lowest computational cost, as an optimization problem with nonlinear objective and combinatorial constraints. By using submodularity of the support rank of a set of constraints, we propose a polynomial-time algorithm to find suboptimal solutions to this partitioning problem and we give approximation guarantees for special classes of cost metrics. We illustrate that the resulting computational cost savings can be arbitrarily large and demonstrate our approach on a case study from production planning.

Extraction of angular momenta from isomeric yield ratios

.The generation of angular momentum in fission is difficult to model, in particular at higher excitation energies where data are scarce. Isomeric yield ratios (IYR) play an important role in deducing angular momentum properties of fission fragments (FF), albeit this requires some assumptions and simplifications. To estimate FF angular momentum, fission codes can be used to calculate IYRs and compare them to experimental data. Such measurements have systematically been performed at the IGISOL facility using novel experimental techniques. In conjunction, a new method has been developed to infer the angular momentum of the primary FF using the nuclear reaction code TALYS. In this work, we evaluate this new method by comparing our TALYS calculations with values found in the literature and with results from the GEF fission code, for a few well-studied reactions. The overall results show a consistent performance of TALYS and GEF, as well as of many reported literature values. However, some deviations were found, possibly pinpointing the need to re-examine some of the reported literature values. A sensitivity analysis was also performed, in which the role of excitation energy, neutron emission, discrete level structure and level density models were studied. Finally, the role of multiple chance fission, of relevance for the reactions studied at IGISOL, is discussed. Some literature data for this reaction were also re-analyzed using TALYS, revealing significant differences.

Theoretical formulation and numerical simulation of thermal performance enhancements for cascade thermal energy storage systems

Cascaded Thermal Energy Storage (CTES), a term that refers to a thermal energy storage system with multiple phase chance materials (PCMs), has been suggested as a solution for heat transfer reduction through the process of heat exchange by reducing temperature differences. The PCMs used are thus paraffin waxes with different melting temperatures. A numerical simulation was made to determine the optimum length of a CTES system compared with use of a single PCM. The enthalpy-porosity theory was utilised to simulate the phase transition of the PCM, and the simulations then used to mimic the charging and discharging of thermal energy storage of optimum length at different heat transfer fluid flow rates. The results indicated that heat transfer can be greatly enhanced, and melting and solidification time significantly reduced, by using multiple PCMs as compared with using a single PCM.

Evaluations of Energy Spectra of Neutrons Emitted Promptly in Neutron-induced Fission of 235U and 239Pu

The energy spectra of neutrons emitted promptly in the neutron-induced fission reactions of 235U and 239Pu were re-evaluated for ENDF/B-VIII.0. These evaluations are based on a careful modeling of all relevant physics processes, an extensive analysis of experimental data and a detailed quantification of pertinent uncertainties. Energy spectra of neutrons emitted in up to fourth chance fission are considered and both compound and pre-equilibrium processes are included. Also, important nuclear model parameters, such as the average total kinetic energy of the fission fragments and the multiple chance fission probabilities, and their uncertainties are estimated based on experimental knowledge, model information and evaluated data. In addition to experimental information already available for ENDF/B-VII.1, these new evaluations make use of recently published experimental data either of high precision or spanning a broad incident energy range, information on legacy measurements explaining discrepancies and recently measured data of the average total kinetic energy as a function of incident neutron energy. The resulting evaluated data and covariances agree well with the experimental database used for the evaluation. However, the evaluated spectra are softer than the 235U and 239Pu ENDF/B-VII.1, JENDL-4.0 and JEFF-3.2 evaluations for incident neutron energies Einc≤1.5 MeV and Einc≤5 MeV, respectively. For Einc>5 MeV, the evaluated spectra show structures due to the improved modeling which are not present in ENDF/B-VII.1 and JEFF-3.2 but can be observed in JENDL-4.0 evaluations. Part of these new evaluations were adopted for ENDF/B-VIII.0, while the ENDF/B-VII.1 239Pu PFNS was retained for Einc≤5 MeV awaiting more conclusive experimental evidence.

Interval joint-probabilistic chance-constrained programming with two-side multi-randomness: an application to energy-environment systems management

Regional energy-environment systems management become more and more focused on greenhouse gas emission control through improving energy efficiency and efficiently managing energy activities. Inexact linear programming models are developed for supporting the management. Due to the weather/climatic variations in the future, electricity demands and renewable power generations (in the right/left hand sides of constraints) have random characteristics. Moreover, an overall satisfactory level needs to be quantified based on multiple chance constraints. Therefore, this study improved upon traditional chance-constrained programming and interval linear programming, and developed an interval joint-probabilistic two-side chance-constrained programming (IJTCP) approach. A sufficient but non-equivalent linearization form of the model was proposed so that the inexact model could be solved through the two-step solution algorithm. The IJTCP was then applied to an integrated energy-environment systems management under dual uncertainties. The application demonstrated that the IJTCP can effectively address the uncertainties presented as not only interval numbers and two-side multi-randomness but also the reliability of satisfying the entire system constraints. The application implicated that the IJTCP approach can be applied to other energy-environment management problems under dual uncertainties.

Chance-Constrained Optimization Approach Based on Density Matching and Active Subspaces

Chance-constrained optimization has recently been receiving much attention from the engineering community. Uncertainties are being incorporated in increasingly large numbers to ensure reliability and robustness. However, the efficiency and accuracy of chance-constrained optimization under multiple uncertainties remains challenging. In this study, a constrained density-matching optimization methodology is established to address these pressing issues in chance-constrained optimization. The methodology employs an alternative objective metric between a designer-given target and system response, enables more uncertainties in design variables and random parameters to be handled, and accommodates multiple chance constraints with an adaptive penalty function. An active subspace identification strategy and a dynamic response surface are given to overcome the curse of uncertainty dimensionality and to guarantee sufficient samples for kernel density estimation in an uncertainty analysis. The efficacy is demonstrated on three optimization examples: a response function problem, a standard NASA test, and a practical application in the conceptual design of a satellite system. Different quadrature points, penalty functions, and target forms are discussed, respectively. The methodology exhibits high accuracy and strong adaptability at considerably reduced computational cost, thus providing a potential template for tackling a wide variety of chance-constrained optimization problems.

Energy Management for Building Climate Comfort in Uncertain Smart Thermal Grids with Aquifer Thermal Energy Storage

In this paper, we present an energy management framework for building climate comfort systems that are interconnected in a grid via aquifer thermal energy storage (ATES) systems in the presence of two types of uncertainty namely private and common uncertainty sources. The ATES system is considered as a large-scale storage system that can be a heat source or sink, or a storage for thermal energy While the private uncertainty source refers to uncertain thermal energy demand of individual buildings, the common uncertainty source describes the uncertain common resource pool (ATES) between neighbors. To this end, we develop a large-scale uncertain coupled dynamical model to predict the thermal energy imbalance in a network of interconnected building climate comfort systems together with mutual interactions between the local ATES systems. A finite-horizon mixed-integer quadratic optimization problem with multiple chance constraints is formulated at each sampling time, which is in general a non-convex problem and hard to solve. We then provide a computationally tractable framework based on an extension to the so-called robust randomized approach which offers a less conservative solution for a problem with multiple chance constraints. A simulation study is provided to compare two different configurations, namely: completely decoupled, and centralized solutions.

Fission modelling with FIFRELIN

The nuclear fission process gives rise to the formation of fission fragments and emission of particles $ (n,\gamma , e^{-})$ . The particle emission from fragments can be prompt and delayed. We present here the methods used in the FIFRELIN code, which simulates the prompt component of the de-excitation process. The methods are based on phenomenological models associated with macroscopic and/or microscopic ingredients. Input data can be provided by experiment as well as by theory. The fission fragment de-excitation can be performed within Weisskopf (uncoupled neutron and gamma emission) or a Hauser-Feshbach (coupled neutron/gamma emission) statistical theory. We usually consider five free parameters that cannot be provided by theory or experiments in order to describe the initial distributions required by the code. In a first step this set of parameters is chosen to reproduce a very limited set of target observables. In a second step we can increase the statistics to predict all other fission observables such as prompt neutron, gamma and conversion electron spectra but also their distributions as a function of any kind of parameters such as, for instance, the neutron, gamma and electron number distributions, the average prompt neutron multiplicity as a function of fission fragment mass, charge or kinetic energy, and so on. Several results related to different fissioning systems are presented in this work. The goal in the next decade will be i) to replace some macroscopic ingredients or phenomenological models by microscopic calculations when available and reliable, ii) to be a support for experimentalists in the design of detection systems or in the prediction of necessary beam time or count rates with associated statistics when measuring fragments and emitted particle in coincidence iii) extend the model to be able to run a calculation when no experimental input data are available, iv) account for multiple chance fission and gamma emission before fission, v) account for the scission neutrons. Several efforts have already been made to replace macroscopic ingredients and phenomenology by microscopic ingredients provided in various nuclear parameter libraries such as electric dipole photon strength functions or HFB level densities. First results relative to theses aspects are presented in this work.

Application of data screening to drug exposure in large risk factor studies of birth defects.

Birth defects are the leading cause of infant death. While causes of most are unknown, those that might be due to medication use are among the most preventable. This study describes an approach to identifying those medications that most warrant attention by using a "screen" program that calculates odds ratios for pairs of exposures and specific birth defects. We discuss the development of this tool and illustrate its application to two large risk factor studies, the Slone Epidemiology Center's Birth Defects Study and the Centers for Disease Control and Prevention's National Birth Defects Prevention Study, ideal settings for the systematic study of risks and relative safety of drugs in relation to birth defects while recognizing the inherent limitations of such an approach. Suggestions for establishing criteria for exposures and outcomes that balance the need for specific details with the practical considerations of sample size and volume of output are presented. Selection of appropriate exposure reference categories and control groups is also discussed, as well as the need to address potential confounding. An example that motivated a detailed investigation of possible associations between a medication (butalbital) and selected specific birth defects is provided. While screening programs such as the one described can be a valuable tool for exploring potential associations in large data bases, they must be applied with caution. The issue of multiple testing and chance findings is a major concern. While statistics are a necessary component, human judgment must be an integral part of the process.

Construction and Initial Validation of the Planned Happenstance Career Inventory

This study was conducted to develop and initially evaluate the psychometric properties of the Planned Happenstance Career Inventory (PHCI), which aims to assess skill in using chance events to develop career opportunities. After item generation and exploratory factor analysis, 130 original items were reduced to 25 items across 5 factors (5 items for each corresponding factor). Multigroup confirmatory factor analysis confirmed the validity of this 5‐factor structure, and the goodness of fit showed an adequate fit to the observed data for both women and men. Results showed that PHCI subscales correlated moderately and in expected directions with career preparation, career decision‐making self‐efficacy, and career stress. These findings suggest that the PHCI, which assesses 5 dimensions of career‐related planned happenstance skill, shows promise as a useful tool for facilitating understanding of the influence of chance events on career choice behavior.

Randomized Solutions to Convex Programs with Multiple Chance Constraints

The scenario-based optimization approach (`scenario approach') provides an intuitive way of approximating the solution to chance-constrained optimization programs, based on finding the optimal solution under a finite number of sampled outcomes of the uncertainty (`scenarios'). A key merit of this approach is that it neither assumes knowledge of the uncertainty set, as it is common in robust optimization, nor of its probability distribution, as it is usually required in stochastic optimization. Moreover, the scenario approach is computationally efficient as its solution is based on a deterministic optimization program that is canonically convex, even when the original chance-constrained problem is not. Recently, researchers have obtained theoretical foundations for the scenario approach, providing a direct link between the number of scenarios and bounds on the constraint violation probability. These bounds are tight in the general case of an uncertain optimization problem with a single chance constraint. However, this paper shows that these bounds can be improved in situations where the constraints have a limited `support rank', a new concept that is introduced for the first time. This property is typically found in a large number of practical applications---most importantly, if the problem originally contains multiple chance constraints (e.g. multi-stage uncertain decision problems), or if a chance constraint belongs to a special class of constraints (e.g. linear or quadratic constraints). In these cases the quality of the scenario solution is improved while the same bound on the constraint violation probability is maintained, and also the computational complexity is reduced.

Mrs. Malaprop’s neighborhood: Using word errors to reveal neighborhood structure

Many theories of language production and perception assume that in the normal course of processing a word, additional non-target words (lexical neighbors) become active. The properties of these neighbors can provide insight into the structure of representations and processing mechanisms in the language processing system. To infer the properties of neighbors, we examined the non-semantic errors produced in both spoken and written word production by four individuals who suffered neurological injury. Using converging evidence from multiple language tasks, we first demonstrate that the errors originate in disruption to the processes involved in the retrieval of word form representations from long-term memory. The targets and errors produced were then examined for their similarity along a number of dimensions. A novel statistical-simulation procedure was developed to determine the significance of the observed similarities between targets and errors relative to multiple chance baselines. The results reveal that in addition to position-specific form overlap (the only consistent claim of traditional definitions of neighborhood structure) the dimensions of lexical frequency, grammatical category, target length and initial segment independently contribute to the activation of non-target words in both spoken and written production. Additional analyses confirm the relevance of these dimensions for word production showing that, in both written and spoken modalities, the retrieval of a target word is facilitated by increasing neighborhood density, as defined by the results of the target–error analyses.

Chance events in career development: Influence, control and multiplicity

This article reports three studies on the nature and impact of chance events. The first study investigated chance events in terms of the dimensions of influence and control. The second and third studies investigated the effects of multiplicity of chance events on career development are in terms of respondents’ own careers and then in terms of career scenarios. Results indicated that chance events can be characterized by the interactive influence of influence and control. High influence and low control chance events had the biggest impact on career development. When there is a connection between multiple chance events, the impact is greater than when unrelated chance events occur. The finding was confirmed regardless of whether individuals rated their own experiences or those presented in scenarios. However, it was also found that negative outcome chance events had the greatest impact regardless of whether such events were single or multiple influences on individuals’ careers. The results provide further support for the integration of chance events into the Chaos Theory of Careers.

Biases on Initial Mass Function Determinations. II. Real Multiple Systems and Chance Superpositions1

When calculating stellar initial mass functions (IMFs) for young clusters, one has to take into account that (1) most massive stars are born in multiple systems, (2) most IMFs are derived from data that cannot resolve such systems, and (3) multiple chance superpositions between members are expected to happen if the cluster is too distant. In this article I use numerical experiments to model the consequences of those phenomena on the observed color-magnitude diagrams and the IMFs derived from them. Real multiple systems affect the observed or apparent massive-star MF slope little but can create a significant population of apparently ultramassive stars. Chance superpositions produce only small biases when the number of superimposed stars is low but, once a certain number threshold is reached, they can affect both the observed slope and the apparent stellar upper mass limit. I apply these experiments to two well known massive young clusters in the Local Group, NGC 3603 and R136. In both cases I show that the observed population of stars with masses above 120 M☉ can be explained by the effects of unresolved objects, mostly real multiple systems for NGC 3603 and a combination of real and chance-alignment multiple systems for R136. Therefore, the case for the reality of a stellar upper mass limit at solar or near-solar metallicities is strengthened, with a possible value even lower than 150 M☉. An IMF slope somewhat flatter than Salpeter or Kroupa with γ between –1.6 and –2.0 is derived for the central region of NGC 3603, with a significant contribution to the uncertainty arising from the imprecise knowledge of the distance to the cluster. The IMF at the very center of R136 cannot be measured with the currently available data but the situation could change with new HST observations.