Auricular Transcutaneous Electrical Stimulation: A Review

Pain has oppressed man for centuries and is the most common symptom for which patients seek medical treatment. The goal of medical research has been to discover the simplest method of pain relief with the least detrimental or undesirable side effects. If pain is not treated successfully, it can lead to a chronic condition with crippling effects that can profoundly affect the individual\u27s quality of life. Pain can also prevent the successful treatment of other disorders. Auricular transcutaneous electrical stimulation (ATENS) is on~ of the most recent methods of pain management to arise from research. It combines both acupuncture and electrotherapy as a simple, non-invasive method of pain relief that does not have the detrimental or undesirable side effects that accompany other methods of pain management such as surgery or drugs. The purpose of this literature review is to present the history by which ATENS has evolved, the theories for its effectiveness, and, finally, the clinical applications of ATENS for the physical therapist as suggested by research in multiple professions

Bootstrap Markov chain Monte Carlo and optimal solutions for the Law of Categorical Judgment (Corrected)

A novel procedure is described for accelerating the convergence of Markov chain Monte Carlo computations. The algorithm uses an adaptive bootstrap technique to generate candidate steps in the Markov Chain. It is efficient for symmetric, convex probability distributions, similar to multivariate Gaussians, and it can be used for Bayesian estimation or for obtaining maximum likelihood solutions with confidence limits. As a test case, the Law of Categorical Judgment (Corrected) was fitted with the algorithm to data sets from simulated rating scale experiments. The correct parameters were recovered from practical-sized data sets simulated for Full Signal Detection Theory and its special cases of standard Signal Detection Theory and Complementary Signal Detection Theory

Effective learning and teaching of reading

This was the largest study in Britain to date of the strategies used to teach reading in adult literacy classes (some classes were integrated with ICT and financial literacy for example), and the first attempt to correlate that evidence with measures of change in learners' reading attainment and attitudes to literacy. We observed and recorded (in writing) over 472 hours of teaching and learning. Our sample of learners is broadly representative of the national distribution, and the data gathered on 454 learners in 59 classes represents a wealth of information: about teaching and learning, effective and promising practices, and also areas where it is a priority for teachers and teacher trainers to engage in further training and development

Shadow IT Use, Outcome Effects, and Subjective Performance Evaluation

Abstract: The use of shadow IT (information technology systems not sanctioned or monitored by a company’s IT department) may be seen as either a form of organizational misbehavior or proactive and creative problem-solving. We examine whether these differing possible perceptions have implications for the subjective evaluation of subordinate performance. In our experiment, participants choose whether to award a bonus to an employee when different IT systems are used (normal vs. shadow IT) across different outcome levels (high vs. low outcomes). We find that employees using shadow IT are less likely to receive the bonus in both high and low outcome conditions relative to employees using the normal IT system. Our results suggest that shadow IT usage is more likely to be viewed as organizational misbehavior and to cast a negative light on employee performance

Qualitative Factors as Determinants of Continued Success:An Examination of eBusiness Entrepreneurial Firms Using the NewVenture Template

In this study, we analyze eBusiness entrepreneurs nominated by the Ernst & Young Entrepreneur of the Year Award program to ascertain whether qualitative factors are correlated with success. We find qualitative factors are incrementally informative above and beyond the information provided by quantitative factors. More specifically, firms that are able to maintain their innovative strategies by improving upon the product (or service) they offer and are able to meet the long-term needs of the customer are more likely to experience increased sales growth and have greater access to capital which results in a successful harvest strategy

Shadow IT Use, Outcome Effects, and Subjective Performance Evaluation

Using shadow IT may be seen as either a form of organizational misbehavior or proactive and creative problem-solving. These perceptions have implications for the subjective evaluation of subordinate performance. In our experiment, participants choose whether to award a bonus to an employee when different IT systems are used across different outcome levels. We find that employees using shadow IT are less likely to receive the bonus in both high and low outcome conditions relative to employees using the normal IT system, suggesting that managers consider shadow IT usage an organizational misbehavior which casts a negative light on employee performance

An artificial neural network approach to payload estimation in four wheel drive loaders

ABSTRACT Estimation of the manipulated payload mass in offhighway machines is made non-trivial by the nonlinearities associated with the hydraulic systems used to actuate the linkage of the machine in addition to the nonlinearity of the kinematics of the linkage itself. Hydraulic cylinder friction, hydraulic conduit compressibility, linkage machining variation and linkage joint friction all make this a complex task under even ideal (machine static) conditions. This problem is made even more difficult when the linkage is mobile as is often the case with off-highway equipment such as four-wheel-drive loaders, cranes, and excavators. The rigid body motion of this type of equipment affects the gravitational loads seen in the linkage and impacts the payload estimate. The commercially available state-of-the-art load estimation solutions rely on the mobile machine becoming pseudo-static in order to maintain accuracy. This requirement increases the time required to move the material and decreases the productivity of the machine. An artificial neural network solution to this problem that enables the machine to remain dynamic and still accurately estimate the payload is discussed in this paper. Development and implementation on an actual four-wheel-drive loader is shown