A manual evaluation of the CI electrode position from CT and DVT scans may be affected by diagnostic errors due to cognitive biases. The aim of this study was to compare the CI electrode localization using an automated method (image-guided cochlear implant programming, IGCIP) with the clinically established manual method. This prospective experimental study was conducted on a dataset comprising N=50 subjects undergoing cochlear implantation with a Nucleus® CI532 or CI632 Slim Modiolar electrode. Scalar localization, electrode-to-modiolar axis distances (EMD) and angular insertion depth (aDOI) were compared between the automated IGCIP tool and the manual method. Two raters made the manual measurements, and the interrater reliability (±1.96·SD) was determined as the reference for the method comparison. The method comparison was performed using a correlation analysis and a Bland-Altman analysis. Concerning the scalar localization, all electrodes were localized both manually and automatically in the scala tympani. The interrater differences ranged between ±0.2 mm (EMD) and ±10° (aDOI). There was a bias between the automatic and manual method in measuring both localization parameters, which on the one hand was smaller than the interrater variations. On the other hand, this bias depended on the magnitude of the EMD respectively aDOI. A post-hoc analysis revealed that the deviations between the methods were likely due to a different selection of mid-modiolar axis. The IGCIP is a promising tool for automated processing of CT and DVT scans and has useful functionality such as being able to segment the cochlear using post-operative scans. When measuring EMD, the IGCIP tool is superior to the manual method because the smallest possible distance to the axis is determined depending on the cochlear turn, whereas the manual method selects the helicotrema as the reference point rigidly. Functionality to deal with motion artifacts and measurements of aDOI according to the consensus approach are necessary, otherwise the IGCIP is not unrestrictedly ready for clinical use.

OBJECTIVES To inform and optimise a cochlear implant (CI) fitting software design through an analysis of big data to define array-specific comfort (C) level profiles, frequently-used MAP parameters, and the minimum number of Neural Response Telemetry thresholds (tNRT) needed to create an accurate profile. To evaluate the software's ease of use and completion time for AutoNRT®s. DESIGN MAPs analysis. Clinical study evaluating software use in creating MAPs, addressing sound-quality issues and setting patient goals. STUDY SAMPLE MAPs (N = 39,885); CI recipients (N = 47) and clinicians (N = 19). RESULTS Distinct C-level profiles were observed for lateral-wall, contour, and slim-modiolar electrode arrays. Default settings were used for most MAP parameters (13/16) except for Pulse Width, Rate, and Maxima. Nine tNRT measurements were required for an accurate C-level profile. Measurement-time of nine tNRTs via the new algorithm was comparable to five tNRTs using the previous algorithm. Nearly all (99%) clinical tasks were completed by clinicians with the first use of the software. Most CI recipients (79.5%) rated goal-setting as valuable. CONCLUSION Custom Sound Pro fitting software developed based on big data analysis incorporates a guided fitting workflow and expected fitting ranges. It helps to improve clinical efficiency, is easy to use and supports patient-centred care.

This study examines a diverse set of nearly 100 private institutions that adopted test-optional undergraduate admissions policies between 2005–2006 and 2015–2016. Using comparative interrupted time series analysis and difference-in-differences with matching, I find that test-optional policies were associated with a 3% to 4% increase in Pell Grant recipients, a 10% to 12% increase in first-time students from underrepresented racial/ethnic backgrounds, and a 6% to 8% increase in first-time enrollment of women. Overall, I do not detect clear evidence of changes in application volume or yield rate. Subgroup analyses suggest that these patterns were generally similar for both the more selective and the less selective institutions examined. These findings provide evidence regarding the potential—and the limitations—of using test-optional policies to improve equity in admissions.

Introduction: Transimpedance measurements from cochlear implant electrodes have the potential to identify anomalous electrode array placement, such as tip fold-over (TFO) or fold-back, basal electrode kinking, or buckling. Analysing transimpedance may thus replace intraoperative or post-operative radiological imaging to detect any potential misplacements. A transimpedance algorithm was previously developed to detect deviations from a normal electrode position with the aim of intraoperatively detecting TFO. The algorithm had been calibrated on 35 forced, tip folded electrode arrays in six temporal bones to determine the threshold criterion required to achieve a sensitivity of 100%. Our primary objective here was to estimate the specificity of this TFO algorithm in patients, in a prospective study, for a series of electrode arrays shown to be normally inserted by post-operative imaging. Methods: Intracochlear voltages were intraoperatively recorded for 157 ears, using Cochlear’s Custom Sound™ EP 5 electrophysiological software (Cochlear Ltd., Sydney, NSW, Australia), for both Nucleus® CI512 and CI532 electrode arrays. The algorithm analysed the recorded 22 × 22 transimpedance matrix (TIM) and results were displayed as a heatmap intraoperatively, only visible to the technician in the operating theatre. After all clinical data were collected, the algorithm was evaluated on the bench. The algorithm measures the transimpedance gradients and corresponding phase angles (θ) throughout the TIM and calculates the gradient phase range. If this was greater than the predetermined threshold, the algorithm classified the electrode array insertion as having a TFO. Results: Five ears had no intraoperative TIM and four anomalous matrices were identified from heatmaps and removed from the specificity analysis. Using the 148 remaining data sets (n = 103 CI532 and n = 45 CI512), the algorithm had an average specificity of 98.6% (95.80%–99.75%). Conclusion: The algorithm was found to be an effective screening tool for the identification of TFOs. Its specificity was within acceptable levels and resulted in a positive predictive value of 76%, with an estimated incidence of fold-over of 4% in perimodiolar arrays. This would mean 3 out of 4 cases flagged as a fold-over would be correctly identified by the algorithm, with the other being a false positive. The measurements were applied easily in theatre allowing it to be used as a routine clinical tool for confirming correct electrode placement.

Many states are redesigning their college remediation policies to increase postsecondary degree completion. In 2012, Tennessee began waiving college math remediation for high school students who completed a computer-based remedial math course (SAILS) during their senior year. Using a regression discontinuity design, we find that the high school remedial course did not improve students' math achievement any more than the typical senior year math course (although it did allow students to avoid the cost and delay of remedial math in college). Using a difference-in-difference design, we find that completing SAILS boosted enrollment in college-level math among first-year community college students by nearly 30 percentage points, with nearly half of new enrollees passing the college-level course. Such students had, however, only completed 1.5 additional college courses after two years. In 2015, Tennessee community colleges implemented "co-requisite" remediation, allowing students to complete remediation alongside college-level math. Under the co-requisite policy, completing SAILS no longer produced any boost in college credits. Although both alternatives to pre-requisite remediation produced modest gains for students and taxpayers, remediation requirements are not a primary driver of low degree completion rates.

Prior research demonstrates the important role that financial considerations play in prospective students’ decision making when applying to and enrolling in graduate school. Racially/ethnically minoritized students, in particular, face persistent challenges during the graduate application and enrollment process. Capitalizing on a natural experiment, we identify the effects of introducing a PhD fellowship on the composition of applicants and enrolling students in PhD programs at a large public university’s graduate school of education. Using administrative data from 9 years of applications, we use difference-in-differences and event study analyses to show that the fellowship increased the number of applicants overall, as well as the share of Black applicants and enrollees in impacted cohorts, with no significant effects on academic preparation. To better understand why and how a PhD fellowship might impact students’ application behaviors and experiences once in graduate school, we supplement our primary findings with survey responses from current PhD students at the graduate school of education.

In recent decades, several dozen colleges and universities have instituted loan-reduction initiatives (LRIs), such as "no-loan" programs. Institutions frequently cast such initiatives as efforts to increase socioeconomic diversity on campus. Using a difference-in-differences analytic strategy with national institution-level data, we examine the effect of LRI adoption at 54 institutions on three sets of outcomes: student borrowing, admission metrics, and campus diversity. Our analysis suggests LRIs decreased institution-level borrowing rates at private institutions, with no detected change at public institutions. Consistent with stated program goals, LRI adoption increased the number of Pell Grant recipients at both public and private institutions. However, adopting LRIs at public institutions reduced racial/ethnic diversity, suggesting possible trade-offs for LRI adoption in terms of student body diversity.

One explanation for negative or null findings in prior research on postsecondary remediation is that college may be too late to address issues of academic underpreparedness. This study evaluates the impact on student outcomes when college math remediation is offered in the senior year of high school. The Seamless Alignment and Integrated Learning Support (SAILS) program in Tennessee targets students with low eleventh-grade ACT math scores. Students who pass SAILS in twelfth grade can enroll directly in college-level math courses at any Tennessee community college. Using a triple-difference design, we exploit variation in students' treatment status based on ACT math scores (remediation-eligible versus remediation-ineligible), high school adoption of SAILS (first cohort versus later cohort), and senior year (before versus during first SAILS year). We find that SAILS-eligible students in the first cohort were significantly less likely to enroll in remedial math courses in college, and more likely to enroll in and pass college-level math overall. These students also earn 2.8 additional credits by their second year. We detect no significant differences in high school graduation rates, college enrollment, or postsecondary credential attainment within two years. The program advanced progress toward several, but not all, of the potential goals examined.

Many U.S. students arrive on college campus lacking the skills expected for college-level work. As state leaders seek to increase postsecondary enrollment and completion, public colleges have sought to lessen the delays created by remedial course requirements. Tennessee has taken a novel approach by allowing students to complete their remediation requirements in high school. Using both a difference-in-differences and a regression discontinuity design, we evaluate the program’s impact on college enrollment and credit accumulation, finding that the program boosted enrollment in college-level math during the first year of college and allowed students to earn a modest 4.5 additional college credits by their second year. We also report the first causal evidence on remediation's impact on students' math skills, finding that the program did not improve students’ math achievement, nor boost students’ chances of passing college math. Our findings cast doubt on the effectiveness of the current model of remediation—whether in high school or college—in improving students’ math skills. They also suggest that the time cost of remediation—whether pre-requisite or co-requisite remediation—is not the primary barrier causing low degree completion for students with weak math preparation.

This paper aims to investigate a method of peak load shaving through the utilization of solar PV and battery energy storage whilst creating a cost effective Energy Management System (EMS). This is achieved by utilizing a rule-sets to manage and optimize a scheduling system with a forecasting algorithm. As Time of Use (ToU) tariffs change throughout the day, a cost benefit can be achieved when a smart energy storage system is appropriately employed. The EMS operation is tested on an experimental microgrid with commercial load considering payback period calculation.

Three phase battery energy storage (BES) installed in the residential low voltage (LV) distribution network can provide functions such as peak shaving and valley filling (i.e. charge when demand is low and discharge when demand is high), load balancing (i.e. charge more from phases with lower loads and discharge more to phases with higher loads) and management of distributed renewable energy generation (i.e. charge when rooftop solar photovoltaics are generating). To accrue and enable these functions an intelligent scheduling system was developed. The scheduling system can reliably schedule the charge and discharge cycles and operate the BES in real time. The scheduling system is composed of three integrated modules: (1) a load forecast system to generate next-day load profile forecasts; (2) a scheduler to derive an initial charge and discharge schedule based on load profile forecasts; and (3) an online control algorithm to mitigate forecast error through continuous schedule adjustments. The scheduling system was applied to an LV distribution network servicing 128 residential customers located in an urban region of South East Queensland, Australia. (c) 2015 Elsevier Ltd. All rights reserved.

In this paper, the communication architecture of an experimental MicroGrid is designed and implemented. Modbus TCP/IP is used as the communication protocol in the MicroGrid to receive data and send commands to different components within the MicroGrid. HTTP TCP/IP is applied as a back-up protocol in case of no availability of Modbus TCP/IP, and also serves as the protocol platform for displaying meaningful data regarding the MicroGrid. Implementation of data acquisition in a database for control and forecasting purposes and data exhibition in a website for monitoring purposes are also presented in this paper. A design for a future implementation of an AC/DC Hybrid Multi-MicroGrid with an advanced communication architecture is proposed as the next phase of the experimental MicroGrid.

There is an opportunity for commercial customers to use energy storage to charge during low load periods and discharge during peak load periods to reduce demand charges. Energy storage control systems that incorporate load forecasts have an economic relationship with forecast error. The less the forecast error is, the more economically feasible energy storage will be. A range of time series forecast models and exponential smoothing forecast algorithms were compared to determine their applicability for use in these energy storage control systems. Model coefficients were estimated by regression and an optimization algorithm. The ARIMA model and double exponential smoothing algorithm performed the best out of the developed set of models.

This paper discusses design methodologies for implementing Smart-MicroGrid testing facility. A Smart- MicroGrid testing facility provides a test-bench environment to develop and analyse new technologies, topologies and control systems for MicroGrid and SmartGrid architectures. These different systems can be analysed with varying levels and types of communication, control, energy storage, distributed generation and mode settings. By testing numerous types of MicroGrid system structures, inferences can be made regarding the optimal design of a Smart-MicroGrid. A MicroGrid testing facility was created as part of the Queensland Government's Peak Demand Energy Management Project. Initial design results for the first set of test systems are presented in the paper. The encouraging results of the initial testing facility design outline the need for such a testing system for future advanced development and implementation of a Smart-MicroGrid. Further expansion of the testing facility with larger energy storage, generation supply and a diversification of loads is the next phase enabling testing of the interconnection methods between MicroGrids.

The advent of distributed renewable energy supply sources and storage systems has placed a greater degree of focus on the operations of the LV (low voltage) electricity distribution network. However, LV networks are characterised by having much higher variability in time series demand meaning that modelling techniques solely relying on iterative forecasts to produce a next day demand profile forecast are insufficient. To cater for the complexity of LV network demand, a novel hybrid expert system comprised of three modules, namely, correlation clustering, discrete classification neural network, and a post-processing procedure was developed. The system operates by classifying a set of key variables associated with a future day and refining a recalled historical demand profile as the forecast. The expert system exhibited high hindcast accuracy when trained with a residential LV transformer's demand data with R2 values ranging from 0.86 to 0.87 and MAPE (mean absolute percentage error) ranging from 11% to 12% across the three phases of the network. Under simulated real world conditions the R2 statistic reduced slightly to 0.81–0.84 and the MAPE increased to 12.5–13.5%. Future work will involve integrating the developed expert system for forecasting next day demand in an LV network into a comprehensive distributed energy resource management algorithm.

This paper set out to identify the significant variables which affect residential low voltage (LV) network demand and develop next day total energy use (NDTEU) and next day peak demand (NDPD) forecast models for each phase. The models were developed using both autoregressive integrated moving average with exogenous variables (ARIMAX) and neural network (NN) techniques. The data used for this research was collected from a LV transformer serving 128 residential customers. It was observed that temperature accounted for half of the residential LV network demand. The inclusion of the double exponential smoothing algorithm, autoregressive terms, relative humidity and day of the week dummy variables increased model accuracy. In terms of R2 and for each modelling technique and phase, NDTEU hindcast accuracy ranged from 0.77 to 0.87 and forecast accuracy ranged from 0.74 to 0.84. NDPD hindcast accuracy ranged from 0.68 to 0.74 and forecast accuracy ranged from 0.56 to 0.67. The NDTEU models were more accurate than the NDPD models due to the peak demand time series being more variable in nature. The NN models had slight accuracy gains over the ARIMAX models. A hybrid model was developed which combined the best traits of the ARIMAX and NN techniques, resulting in improved hindcast and forecast fits across the all three phases.

This paper describes the method of a prototype forecast component of the energy resource management control algorithm for STATCOMs with battery energy storage. It is desired to be computationally efficient and of minimal complexity due to the desired purposes of forecasting each load in a LV network. The forecast model is comprised of a basis structure selected from observed electricity demand data and an electricity demand difference forecasting component estimated by the autoregressive method. The produced forecasting model had a R2 of 0.65 and a standard error of 368.55 W. During validation of the model, discrepancies between the forecasted and observed electricity demand profiles were observed. To overcome forecast model limitations, future work will involve more precise clustering of demand profiles according to additional temporal and environmental variables. This is to enable forecasts under a more diverse range of electricity demand profiles. The final developed forecasting model will be a core component of the firmware controlling STATCOMS with energy storage systems.

Bottom-up urban water demand forecasting based on empirical data for individual water end uses or micro-components (e.g., toilet, shower, etc.) for different households of varying characteristics is undoubtedly superior to top-down estimates originating from bulk water metres that are currently performed. Residential water end-use studies partially enabled by modern smart metering technologies such as those used in the South East Queensland Residential End Use Study (SEQREUS) provide the opportunity to align disaggregated water end-use demand for households with an extensive database covering household demographic, socio-economic and water appliance stock efficiency information. Artificial neural networks (ANNs) provide the ideal technique for aligning these databases to extract the key determinants for each water end-use category, with the view to building a residential water end-use demand forecasting model. Three conventional ANNs were used: two feed-forward back propagation networks and one radial basis function network. A sigmoid activation hidden layer and linear activation output layer produced the most accurate forecasting models. The end-use forecasting models had R^2 values of 0.33, 0.37, 0.60, 0.57, 0.57, 0.21 and 0.41 for toilet, tap, shower, clothes washer, dishwasher, bath and total internal demand, respectively. All of the forecasting models except the bath demand were able to reproduce the means and medians of the frequency distributions of the training and validation sets. This study concludes with an application of the developed forecasting model for predicting the water savings derived from a citywide implementation of a residential water appliance retrofit program (i.e., retrofitting with efficient toilets, clothes washers and shower heads).