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Stochastic unit commitment is an efficient method for grid operation in the presence of significant uncertainties. An example is an operation during a predicted hurricane with uncertain line out-ages. However, the solution quality comes at the cost of substantial computational burden, which makes its adoption challenging. This paper evaluates some possible ways that machine learning can be used to reduce this computational burden. First, a set of feasibility studies is conducted. Results suggest that using machine learning as an assistant to the stochastic unit commitment solver is more advantageous than using it as a standalone solver. In particular, the machine learning model is trained to facilitate solving the problem by determining the unnecessary constraints that can be removed from the original problem without affecting the final accuracy. The variables that can be used as input features/predictors or outputs for the machine learning model are determined through feasibility studies. Then, an algorithm to train and utilize a machine learning model is proposed. The method is tested on a 500-bus synthetic South Carolina system. Various test cases show an average reduction in solution time by more than 90% by using the trained machine learning model to assist the stochastic unit commitment solver. [ABSTRACT FROM AUTHOR]

The opioid crisis in the United States has had devastating effects on communities across the country, leading many states to pass legislation that limits the prescription of opioid medications in an effort to reduce the number of overdose deaths. This study investigates the impact of South Carolina's prescription limit law (S.C. Code Ann. 44-53-360), which aims to reduce opioid overdose deaths, on opioid prescription rates. The study utilizes South Carolina Reporting and Identification Prescription Tracking System (SCRIPTS) data and proposes a distance classification system to group records based on proximity and evaluates prescription volumes in each distance class. Prescription volumes were found to be highest in classes with pharmacies located further away from the patient. An Interrupted Time Series (ITS) model is utilized to assess the policy impact, with benzodiazepine prescriptions as a control group. The ITS models indicate an overall decrease in prescription volume, but with varying impacts across the different distance classes. While the policy effectively reduced opioid prescription volumes overall, an unintended consequence was observed as prescription volume increased in areas where prescribers were located at far distances from patients, highlighting the limitations of state-level policies on doctors. These findings contribute to the understanding of the effects of prescription limit laws on opioid prescription rates and the importance of considering location and distance in policy design and implementation. [ABSTRACT FROM AUTHOR]

• A novel two-stage formulation for AC optimal transmission switching problems. • A Generalized Benders Decomposition based algorithm to solve the problem. • Be able to handle grid uncertainties in optimal transmission switching problems. • Guaranteed convergence and accuracy for the AC optimal transmission problems. • Numerical results on the IEEE 118-bus system and South Carolina 500-bus system. Optimal transmission switching is proposed in recent years to optimize the power system operational cost in deterministic studies. With the rapid growth of renewable generations, the grid uncertainties have greatly increased, which cannot be ignored in the decision-making of optimal transmission switching problems. This paper proposes a novel two-stage stochastic optimization formulation with a convex relaxation for AC optimal transmission switching problems. A generalized Benders decomposition based algorithm, including an inner loop and an outer loop, is proposed to solve the AC optimal transmission switching problem with grid uncertainties. The optimal switching plan and the expected system cost will be found in the iterative calculation without any sacrifice of accuracy. Numerical studies on the IEEE 118-bus system and the South Carolina 500-bus confirm the effectiveness of the proposed decomposition approach in solving the AC optimal transmission switching problems with grid uncertainties. The scalability analysis shows the proposed approach is efficient in dealing with a large number of scenarios. [ABSTRACT FROM AUTHOR]

Abstract: Redistricting is a complex and challenging spatial optimization problem. It is to group a set of spatial objects (such as counties) into a given number of geographically contiguous districts while satisfying multiple criteria and constraints such as equal population, compact shape, and more. The various criteria are often difficult to optimize and the number of potential solutions is very large. Moreover, many criteria are vaguely defined and may not be measured exactly. Therefore, human judgment and domain knowledge are indispensable and critical in the optimization process. In this paper, we present an interactive and computing-assisted approach to redistricting optimization. Our approach leverages the power of user''s domain knowledge, judgment, and interactive exploration to (1) flexibly define various criteria/constraints, (2) visually and interactively examine alternative plans and achieve a balance among different criteria, and (3) efficiently and iteratively construct a collection of high-quality plans that are difficult to obtain with existing methods. A computational optimization algorithm is integrated to assist optimization under user-provided criteria and constraints. With the visual analytics approach, a user can quickly derive high-quality redistricting plans that satisfy both individual preferences and mandatory requirements. We demonstrate the capability of the approach and system with two case studies, Iowa congressional redistricting and South Carolina congressional redistricting. [Copyright &y& Elsevier]

Early detection of unusual health events depends on the ability to rapidly detect any substantial changes in disease, thus facilitating timely public health interventions. To assist public health practitioners to make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler measure for timely detection of disease outbreaks for small area health data. The detection methods are compared with the surveillance conditional predictive ordinate within the framework of Bayesian hierarchical Poisson modeling and applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties. Properties of the proposed surveillance techniques including timeliness and detection precision are investigated using a simulation study. [ABSTRACT FROM AUTHOR]

Context The problems associated with generating a collaborative ranked preference list represent a common source of dilemma in academic medicine and medical education. Such issues present during the process of choosing among applicants to medical schools, during the selection of postgraduate trainees, and in the course of performance assessments and the prioritising of financial expenditures. Currently, most institutions use pseudo-quantitative methods, such as the averaging of scores awarded on an arbitrary scale. These methods are mathematically problematic and may not accurately reflect group opinion. Methods The present authors developed a novel algorithm for creating a collaborative preference list that generates and sorts a matrix of pairwise comparisons between applicants or choices without placing any reliance on arbitrary Likert scale-type scores. This method achieves equality in influence across individual assessors, as well as transparency and reproducibility. The authors report a case study of their experience using this new algorithm in the 2013 neonatal-perinatal fellowship match. Results When used by this group in the selection of fellowship trainees, the method proposed here allowed for greater efficiency and created a rank-order list that did not require reshuffling or significant debate. A survey of faculty staff and fellows showed much higher levels of satisfaction with the new algorithm and a unanimous desire to use the new algorithm in the future, in preference to a score-based system. Conclusions The algorithm developed and described here may reduce arbitrariness in processes that require the collaborative creation of a preference list. This method may have wide applicability in medical education and training, and beyond. The present authors' experience of using this algorithm during the National Resident Matching Program match showed improved perceptions of fairness, ease of use and efficiency. [ABSTRACT FROM AUTHOR]

A simple and useful technique for prediction of sea breezes, based on readily available wind vector and air temperature predictions from synoptic models in conjunction with observed coastal sea surface temperatures, is presented for evaluation by coastal forecasters. A statistical prediction scheme using the sea-breeze index has been devised and was found to possess significant nowcasting skill, when used with observed synoptic wind vectors and temperatures and with observed coastal sea surface temperatures. The ready availability of data for these variables and the simplicity of the scheme give it the potential of being useful to many coastal forecast offices, once tuned to the area in which it is applied. The technique can be used on simple workstations or even by manual calculation and thus provides a simple method for local tuning of general-area forecasts to coastal areas. For the 1998 sea-breeze season tested, the algorithm, which was built on June data and tested by nowcasting on data from July through October, had a skill score of 31.3% over climatology. [ABSTRACT FROM AUTHOR]