The Effect of Orthodontic Appliances on the Evaluation of the Professionalism and Esthetics of an Adult Employee

This study explored the influence of fixed and removable orthodontic appliances on participants’ ratings of the job performance, intelligence, and attractiveness of an adult female. Ninety-four adult subjects were recruited from the Graduate School of Management at Marquette University. Each subject received an identical employee performance review with an attached photograph of a female employee. The smile of the photo was manipulated to represent one of four conditions: no orthodontic appliance, a metal orthodontic appliance, a ceramic orthodontic appliance, or a clear aligner. Subjects then rated the employee on three continuous Likert scales. Ratings of job performance, intelligence, and attractiveness were not correlated. There were no significant differences between the types of orthodontic appliance for overall ratings of job performance, intelligence, and attractiveness. However, when analyzed by the subject’s gender, there was a significant interaction between gender and type of orthodontic appliance pictured for intelligence ratings. Female respondents rated the photos with the metal appliance with lower intelligence than the photo with the clear aligner while male respondents answered in the opposite manner. Background facial attractiveness may be a better predictor than smile esthetics of the psychosocial ratings of individuals. However, both gender and the presence or absence of an orthodontic appliance can influence assessments of perceived intelligence or similar qualities in the workplace

A comparison of generative and discriminative appliance recognition models for load monitoring

Appliance-level Load Monitoring (ALM) is essential, not only to optimize energy utilization, but also to promote energy awareness amongst consumers through real-time feedback mechanisms. Non-intrusive load monitoring is an attractive method to perform ALM that allows tracking of appliance states within the aggregated power measurements. It makes use of generative and discriminative machine learning models to perform load identification. However, particularly for low-power appliances, these algorithms achieve sub-optimal performance in a real world environment due to ambiguous overlapping of appliance power features. In our work, we report a performance comparison of generative and discriminative Appliance Recognition (AR) models for binary and multi-state appliance operations. Furthermore, it has been shown through experimental evaluations that a significant performance improvement in AR can be achieved if we make use of acoustic information generated as a by-product of appliance activity. We demonstrate that our a discriminative model FF-AR trained using a hybrid feature set which is a catenation of audio and power features improves the multi-state AR accuracy up to 10 %, in comparison to a generative FHMM-AR model

Using hidden Markov models for iterative non-intrusive appliance monitoring

Non-intrusive appliance load monitoring is the process of breaking down a household’s total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances are iteratively separated from the aggregate load. Our approach does not require training data to be collected by sub-metering individual appliances. Instead, prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are used to estimate each appliance’s load, which is subsequently subtracted from the aggregate load. We evaluate our approach using the REDD data set, and show that it can disaggregate 35% of a typical household’s total energy consumption to an accuracy of 83% by only disaggregating three of its highest energy consuming appliances

Low-Power Appliance Monitoring Using Factorial Hidden Markov Models

To optimize the energy utilization, intelligent energy management solutions require appliance-specific consumption statistics. One can obtain such information by deploying smart power outlets on every device of interest, however it incurs extra hardware cost and installation complexity. Alternatively, a single sensor can be used to measure total electricity consumption and thereafter disaggregation algorithms can be applied to obtain appliance specific usage information. In such a case, it is quite challenging to discern low-power appliances in the presence of high-power loads. To improve the recognition of low-power appliance states, we propose a solution that makes use of circuit-level power measurements. We examine the use of a specialized variant of Hidden Markov Model (HMM) known as Factorial HMM (FHMM) to recognize appliance specific load patterns from the aggregated power measurements. Further, we demonstrate that feature concatenation can improve the disaggregation performance of the model allowing it to identify device states with an accuracy of 90% for binary and 80% for multi-state appliances. Through experimental evaluations, we show that our solution performs better than the traditional event based approach. In addition, we develop a prototype system that allows real-time monitoring of appliance states

Robust, Recognizable and Legitimate: Strengthening India's Appliance Efficiency Standards and Labels Through Greater Civil Society Involvement

Residential use accounts for 14 percent of global energy consumption. Appliance standards alone could achieve 17 percent energy reductions in the residential sector. Although appliance efficiency standards and labeling programs (AES&L) aim to influence consumer behavior, consumers and civil society often play a limited role in the design, implementation, and monitoring of these programs. This report considers the contribution that civil society organizations can make at each stage of an appliance efficiency standards and labeling program (AES&L), based on experiences in 10 developed and developing countries

Robust energy disaggregation using appliance-specific temporal contextual information

An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.Peer reviewedFinal Published versio

Portable appliance security apparatus

An apparatus for securing a small computer, or other portable appliance, against theft is described. It is comprised of a case having an open back through which the computer is installed or removed. Guide members in the form of slots are formed in a rear portion of opposite walls of the case for receiving a back plate to cover the opening and thereby secure the computer within the case. An opening formed in the top wall of the case exposes the keyboard and display of the computer. The back plate is locked in the closed position by a key-operated plug type lock. The lock is attached to one end of a hold down cable, the opposite end thereof being secured to a desk top or other stationary object. Thus, the lock simultaneously secures the back plate to the case and retains the case to the stationary object