Dickens, the suspended quotation and the corpus

This article presents a computer-assisted approach to the study of character discourse in Dickens. It focuses on the concept of the ‘suspended quotation’ – the interruption of a character’s speech by at least five words of narrator text. After an outline of the concept of the suspended quotation as introduced by Lambert (1981), the article compares manually derived counts for suspensions in Dickens with automatically generated figures. This comparison shows how corpus methods can help to increase the scale at which the phenomenon is studied. It highlights that quantitative information for selected sections of a novel does not necessarily represent the patterns that are found across the whole text. The article also includes a qualitative analysis of suspensions. With the help of the new tool CLiC, it investigates interruptions of the speech of Mrs Sparsit in Hard Times and illustrates how suspensions can be useful places for the presentation of character information. CLiC is further used to find patterns of the word pause that provide insights into how suspensions contribute to the representation of pauses in character speech

Remeasuring the HDI by Data Envelopement Analysis

The measurement of human development has a potentially strong impact on how the development gap is viewed and on the formulation of new policies. Therefore correct and fair measurement is of great importance. In this paper, we develop an algorithm to compute comprehensive differentiation rules suitable for measuring human development. We used models from Data Envelopment Analysis (DEA) literature to compare performance in a multiple output setting. The models were evaluated by empirically re-estimating the human development index (HDI). The most notable advantages of DEA models are that they endogenously construct a non-linearly arranged set of best practice countries and the weights of each indicator entering the HDI is endogenously determined based on an optimization calculus. These weights are allowed to vary thereby accounting for cross-sectional heterogeneity. While country clusters are identified by their similarity, some interesting outliers can also be singled out using DEA. Such outliers are either best practice frontier countries or countries that are locked in underdevelopment trap

Mind-modelling with corpus stylistics in David Copperfield

We suggest an innovative approach to literary discourse by using corpus linguistic methods to address research questions from cognitive poetics. In this article, we focus on the way that readers engage in mind-modelling in the process of characterisation. The article sets out our cognitive poetic model of characterisation that emphasises the continuity between literary characterisation and real-life human relationships. The model also aims to deal with the modelling of the author’s mind in line with the modelling of the minds of fictional characters. Crucially, our approach to mind-modelling is text-driven. Therefore we are able to employ corpus linguistic techniques systematically to identify textual patterns that function as cues triggering character information. In this article, we explore our understanding of mind-modelling through the characterisation of Mr. Dick from David Copperfield by Charles Dickens. Using the CLiC tool (Corpus Linguistics in Cheshire) developed for the exploration of 19th-century fiction, we investigate the textual traces in non-quotations around this character, in order to draw out the techniques of characterisation other than speech presentation. We show that Mr. Dick is a thematically and authorially significant character in the novel, and we move towards a rigorous account of the reader’s modelling of authorial intention

Impacts to Traffic Behavior from Queue Warning Truck: Current Pilot Project

The Indiana Department of Transportation (INDOT) started deploying queue warning trucks ahead of interstate work zones to alert motorists of queued traffic. Along with visually alerting the motorists, digital alerts were integrated with navigational applications such as Apple Maps, Waze, and the in-vehicle infotainment system of Stellantis vehicles. More than 45,000 hours of alerting was provided to motorists across various interstates in Indiana over a 26-month period. This report evaluated the impact of queue warning trucks on traffic using hard braking events and traffic speeds provided by granular connected trajectory vehicle data. Evaluation of over 370 hours of queuing with the presence of queue trucks and 52 hours of queuing without the queue trucks indicated a decrease in hard braking events by 80% when trucks were present with digital alerts. It was also observed that traffic speeds started to reduce approximately 1,500 to 2,000 ft in advance of deployed queue trucks

Prioritizing Roadway Pavement Marking Maintenance Using Lane Keep Assist Sensor Data

There are over four million miles of roads in the United States, and the prioritization of locations to perform maintenance activities typically relies on human inspection or semi-automated dedicated vehicles. Pavement markings are used to delineate the boundaries of the lane the vehicle is driving within. These markings are also used by original equipment manufacturers (OEM) for implementing advanced safety features such as lane keep assist (LKA) and eventually autonomous operation. However, pavement markings deteriorate over time due to the fact of weather and wear from tires and snowplow operations. Furthermore, their performance varies depending upon lighting (day/night) as well as surface conditions (wet/dry). This paper presents a case study in Indiana where over 5000 miles of interstate were driven and LKA was used to classify pavement markings. Longitudinal comparisons between 2020 and 2021 showed that the percentage of lanes with both lines detected increased from 80.2% to 92.3%. This information can be used for various applications such as developing or updating standards for pavement marking materials (infrastructure), quantifying performance measures that can be used by automotive OEMs to warn drivers of potential problems with identifying pavement markings, and prioritizing agency pavement marking maintenance activities

Implementation of Enhanced Probe Data (CANBUS) for Tactical Workzone and Winter Operations Management

For over a decade, segment-based probe data has been extensively used by transportation stakeholders for monitoring mobility on Indiana roadways. However, enhanced probe data from connected vehicles includes a richer dataset that can provide more detailed real-time and after-action reviews. This enhanced data includes detailed vehicle trajectories, at 3s resolution, and “event data.” This event data is near real-time and includes hard-braking events, hard-acceleration events, weather-related data, including wiper activations and some seat belt usage data. This project developed a set of methodologies and resulting visualizations that enables the use of emerging connected vehicle data in operational decision-making on work zone management and winter operations activities. Each month approximately 13 billion connected vehicle records are ingested for Indiana. During peak periods, approximately 625,000 records per minute are ingested. Without substantial processing, this large data set is “data-rich, information-poor.” This study developed techniques to rapidly assign relevant data to interstate segments so that visual graphics could be efficiently generated. This provided the ability for both real-time monitoring as well as after action assessment to identify opportunities to improve both work zone operations and winter operation activities. The summaries derived from these datasets have helped promote effective actionable dialog among agencies, contractors, and public safety colleagues towards the overarching goal of improving interstate safety and mobility

Crowdsourcing/Winter Operations Dashboard Upgrade

INDOT has recently completed the deployment of Parsons telematics-based dash-cameras, automatic vehicle locator (AVL) positions, and spreader rate monitoring across their winter operations fleet. The motivation of this study was to develop dashboards that integrate connected vehicle data into the real-time monitoring and after-action review of winter storms. Each month approximately 13 billion connected vehicle records are ingested for the state of Indiana and almost 99 billion weather data records are ingested nationwide in 15-minute intervals. This study developed techniques to utilize this connected vehicle data and weather data to monitor real-time mobility of interstates and post storm after-action assessments to identify improvement opportunities of winter operations activities. In multiple instances, these agile reviews have influenced operational changes in snow removal and maintenance around the state, leading to a marked improvement in observed mobility and safety

Nightly treatment of primary insomnia with prolonged release melatonin for 6 months: a randomized placebo controlled trial on age and endogenous melatonin as predictors of efficacy and safety

<p>Background: Melatonin is extensively used in the USA in a non-regulated manner for sleep disorders. Prolonged release melatonin (PRM) is licensed in Europe and other countries for the short term treatment of primary insomnia in patients aged 55 years and over. However, a clear definition of the target patient population and well-controlled studies of long-term efficacy and safety are lacking. It is known that melatonin production declines with age. Some young insomnia patients also may have low melatonin levels. The study investigated whether older age or low melatonin excretion is a better predictor of response to PRM, whether the efficacy observed in short-term studies is sustained during continued treatment and the long term safety of such treatment.</p> <p>Methods: Adult outpatients (791, aged 18-80 years) with primary insomnia, were treated with placebo (2 weeks) and then randomized, double-blind to 3 weeks with PRM or placebo nightly. PRM patients continued whereas placebo completers were re-randomized 1:1 to PRM or placebo for 26 weeks with 2 weeks of single-blind placebo run-out. Main outcome measures were sleep latency derived from a sleep diary, Pittsburgh Sleep Quality Index (PSQI), Quality of Life (World Health Organzaton-5) Clinical Global Impression of Improvement (CGI-I) and adverse effects and vital signs recorded at each visit.</p> <p>Results: On the primary efficacy variable, sleep latency, the effects of PRM (3 weeks) in patients with low endogenous melatonin (6-sulphatoxymelatonin [6-SMT] ≤8 μg/night) regardless of age did not differ from the placebo, whereas PRM significantly reduced sleep latency compared to the placebo in elderly patients regardless of melatonin levels (-19.1 versus -1.7 min; P = 0.002). The effects on sleep latency and additional sleep and daytime parameters that improved with PRM were maintained or enhanced over the 6-month period with no signs of tolerance. Most adverse events were mild in severity with no clinically relevant differences between PRM and placebo for any safety outcome.</p> <p>Conclusions: The results demonstrate short- and long-term efficacy and safety of PRM in elderly insomnia patients. Low melatonin production regardless of age is not useful in predicting responses to melatonin therapy in insomnia. The age cut-off for response warrants further investigation.</p&gt