Reactive point processes: A new approach to predicting power failures in underground electrical systems

Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to handle an important problem within the domain of electrical grid reliability: short-term prediction of electrical grid failures ("manhole events"), including outages, fires, explosions and smoking manholes, which can cause threats to public safety and reliability of electrical service in cities. RPPs incorporate self-exciting, self-regulating and saturating components. The self-excitement occurs as a result of a past event, which causes a temporary rise in vulner ability to future events. The self-regulation occurs as a result of an external inspection which temporarily lowers vulnerability to future events. RPPs can saturate when too many events or inspections occur close together, which ensures that the probability of an event stays within a realistic range. Two of the operational challenges for power companies are (i) making continuous-time failure predictions, and (ii) cost/benefit analysis for decision making and proactive maintenance. RPPs are naturally suited for handling both of these challenges. We use the model to predict power-grid failures in Manhattan over a short-term horizon, and to provide a cost/benefit analysis of different proactive maintenance programs.Comment: Published at http://dx.doi.org/10.1214/14-AOAS789 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Use of dendrochronology to promote understanding of environmental change

The purpose of this research was to determine how dendrochronology can be used in an experiential unit to enhance high school students’ understanding of environmental change. Dendrochronology, the visual examination of tree ring cross sections provides opportunities to relate environmental change to growth patterns of trees and can be used to show the students both how scientists can investigate the past and how the environment can affect trees. Students engaged in a 10-day unit that employed a variety of constructivist learning activities to investigate environmental change, climate change, and tree growth. The culminating activity was student-created experiments that investigated various aspects of the relationship of trees to their environment. This research was a mixed method design and was conducted at a small public high school in the Deep South. The school is a Title One school on a four by four block schedule and is located in a rural area where forestry is one of the major industries. Twenty five juniors and seniors who were members of two environmental science classes were the participants in the research. As evaluated by the Wilcoxon matched-pair signed rank test, students scored significantly higher on the posttest (P \u3c .01) than on the pretest with average scores of 9.52 on the pretest and 18.76 on the posttest. Most of these gains were in questions that evaluated the students understanding of climate change, tree anatomy and statistical analyses of tree growth data. The qualitative components of the research supported that these were the areas of greatest growth and revealed that the students greatly enjoyed participating in investigations of their own

Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions

We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient’s possible future medical conditions given the patient’s current and past history of reported conditions. The core of our technique is a Bayesian hierarchical model for selecting predictive association rules (such as “condition 1 and condition 2 → condition 3”) from a large set of candidate rules. Because this method “borrows strength” using the conditions of many similar patients, it is able to provide predictions specialized to any given patient, even when little information about the patient’s history of conditions is available.National Science Foundation (U.S.) (NSF Grant IIS-10-53407)Google (Firm) (Ph.D. fellowship in statistics

Reactive point processes: A new approach to predicting power failures in underground electrical systems

Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to handle an important problem within the domain of electrical grid reliability: short-term prediction of electrical grid failures (“manhole events”), including outages, fires, explosions and smoking manholes, which can cause threats to public safety and reliability of electrical service in cities. RPPs incorporate self-exciting, self-regulating and saturating components. The self-excitement occurs as a result of a past event, which causes a temporary rise in vulner ability to future events. The self-regulation occurs as a result of an external inspection which temporarily lowers vulnerability to future events. RPPs can saturate when too many events or inspections occur close together, which ensures that the probability of an event stays within a realistic range. Two of the operational challenges for power companies are (i) making continuous-time failure predictions, and (ii) cost/benefit analysis for decision making and proactive maintenance. RPPs are naturally suited for handling both of these challenges. We use the model to predict power-grid failures in Manhattan over a short-term horizon, and to provide a cost/benefit analysis of different proactive maintenance programs.Con EdisonMIT Energy Initiative (Seed Fund)National Science Foundation (U.S.) (CAREER Grant IIS-1053407

Local municipalities and the influence of national networks on city climate governance: Small places with big possibilities

Reaching the 1.5°C target of the Paris Agreement not only requires ambitious goals from national governments, but also the active participation of local municipalities. It is in cities where climate actions need to be implemented to reduce greenhouse gas emissions and reach international and national climate goals. While the importance of cities and their participation in networks has been well-researched, studies have systematically neglected the committed individual agents in small and medium-sized cities and overlooked the importance of national networks. To address these research gaps, this article looks at how local climate managers use their municipality's membership in national networks to increase action and implementation. This article is based on 12 semi-structured interviews with seven municipal representatives and five representatives of two national city networks, and four informal discussions. Through comparative content analysis, it was identified that the main functions derived from network participation are direct exchanges between the climate managers, mobilization of others in the municipality, accounting of greenhouse gas emissions, and project support. These functions helped overcome key limitations that the actors often faced within the municipality related to a lack of legal competences, administrative resources and internal support for climate work and financial resources. This has implications for city networks which have been focusing on larger cities and not including smaller cities who have less capacity and who can benefit the most from the functions provided by them

A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction

In many healthcare settings, patients visit healthcare professionals periodically and report multiple medical conditions, or symptoms, at each encounter. We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient’s possible future symptoms given the patient’s current and past history of reported symptoms. The core of our technique is a Bayesian hierarchical model for selecting predictive association rules (such as “symptom 1 and symptom 2 → symptom 3 ”) from a large set of candidate rules. Because this method “borrows strength” using the symptoms of many similar patients, it is able to provide predictions specialized to any given patient, even when little information about the patient’s history of symptoms is available

Development and validation of an Opioid Attractiveness Scale: a novel measure of the attractiveness of opioid products to potential abusers

BACKGROUND: The growing trends in opioid abuse, assessment of the abuse liability of prescription opioid products, and growing efforts by the pharmaceutical industry to develop 'abuse-resistant' formulations highlight a need to understand the features that make one product more 'attractive' than another to potential abusers. We developed a scale to measure the 'attractiveness' of prescription opioids to potential abusers, and used the scale to measure the relative attractiveness of 14 opioid analgesic products. METHODS: First, the concept of attractiveness was empirically defined with a group of prescription opioid abusers and experts in opioid abuse using a process called Concept Mapping. Abuse liability consisted of two components: factors intrinsic to the drug formulation (e.g., speed of onset, duration) and factors extrinsic to drug formulation (e.g., availability, availability of alternatives, cost). A 17-item Opioid Attractiveness Scale (OAS) was constructed, focusing on factors intrinsic to the drug product. RESULTS: A total of 144 individuals participated in tests of validity and reliability. Internal consistency was excellent (Cronbach's α = 0.85–0.94). Drug rankings based on OAS scores achieved good inter-rater agreement (Kendall's W 0.37, p < 0.001). Agreement on drug OAS scores between the developmental sample and a confirmation sample was good (IntraClass Correlations [ICC] of 0.65–0.69). Global ratings of overall attractiveness of the 14 selected opioid products by substance abuse counselors corresponded with the rankings based on OAS ratings of the abuser group. Finally, substance abuse counselors completed the OAS, yielding a high level of correspondence with ratings by the abuser group (ICC = 0.83, p = 0.002). The OAS differentiated attractiveness among 14 selected pharmaceutical opioid products. OxyContin, Dilaudid, and Percocet were ranked highest (most attractive); Talwin NX and Duragesic were ranked lowest (least attractive). CONCLUSION: An initial examination of the psychometric properties of the OAS suggests that it is a valid and reliable scale. The OAS may be useful in providing important guidance on product features that are attractive to potential abusers

Analytics for Power Grid Distribution Reliability in New York City

We summarize the first major effort to use analytics for preemptive maintenance and repair of an electrical distribution network. This is a large-scale multiyear effort between scientists and students at Columbia University and the Massachusetts Institute of Technology and engineers from the Consolidated Edison Company of New York (Con Edison), which operates the world’s oldest and largest underground electrical system. Con Edison’s preemptive maintenance programs are less than a decade old and are made more effective with the use of analytics developing alongside them. Some of the data we used for our projects are historical records dating as far back as the 1880s, and some of the data are free-text documents typed by Con Edison dispatchers. The operational goals of this work are to assist with Con Edison’s preemptive inspection and repair program and its vented-cover replacement program. This has a continuing impact on the public safety, operating costs, and reliability of electrical service in New York City