•We study knowledge continuity (KC): passing knowledge from departing employees to their successors.•We examine the role of org. knowledge management (KM) culture, and supervisors’ KM behavior in ...KC.•We consider both the departing employee’s and the successor’s points of view.•We develop and validate a scale for measuring KC.•We find and discuss the duality of responses to a work environment that supports KC.
This study attempts to identify factors influencing knowledge continuity (KC), the passing of knowledge from a departing employee to his or her successor. Considering the perspectives of both the departing employee and the successor, we examine how employee perceptions of KC quality are affected by two normative influences: organizational knowledge management (KM) culture, and the KM behavior of the employee’s current supervisor. Data were collected from 44 departing employees (who transitioned to new jobs) and their 44 successors, up to 6 months following job transition. Participants were full-time engineers employed in a large high- technology firm in Israel. The extent to which departing employees perceived the organization as fostering KM culture, and the extent to which they perceived their current supervisors as engaging in KM behavior, were, respectively, negatively and positively associated with KC quality as perceived by successors. Successors’ perceived organizational KM culture was positively related to their perceptions of KC quality. Successors’ perceptions of their own supervisors’ KM behavior were not significantly associated with their perceptions of KC quality. We discuss the potential duality of responses to a work environment that supports KM, and ways to synchronize opposing effects. We also develop and validate a scale for measuring KC.
Virtual diagnostic (VD) is a computational tool based on deep learning that can be used to predict a diagnostic output. VDs are especially useful in systems where measuring the output is invasive, ...limited, costly or runs the risk of altering the output. Given a prediction, it is necessary to relay how reliable that prediction is, i.e., quantify the uncertainty of the prediction. In this paper, we use ensemble methods and quantile regression neural networks to explore different ways of creating and analyzing prediction’s uncertainty on experimental data from the Linac Coherent Light Source at SLAC National Lab. We aim to accurately and confidently predict the current profile or longitudinal phase space images of the electron beam. The ability to make informed decisions under uncertainty is crucial for reliable deployment of deep learning tools on safety-critical systems as particle accelerators.
We introduce a general approach to determine the optimal charge, efficiency and gradient for laser driven accelerators in a self-consistent way. We propose a way to enhance the operational gradient ...of dielectric laser accelerators by leverage of beam-loading effect. While the latter may be detrimental from the perspective of the effective gradient experienced by the particles, it can be beneficial as the effective field experienced by the accelerating structure, is weaker. As a result, the constraint imposed by the damage threshold fluence is accordingly weakened and our self-consistent approach predicts permissible gradients of∼10GV/m, one order of magnitude higher than previously reported experimental results—with unbunched pulse of electrons. Our approach leads to maximum efficiency to occur for higher gradients as compared with a scenario in which the beam-loading effect on the material is ignored. In any case, maximum gradient does not occur for the same conditions that maximum efficiency does—a trade-off set of parameters is suggested.
We examine the drivers of the convergence of the hourly wage distributions of males and females in Israel between 1995 and 2008. Israel is an interesting case study in this respect, since it ...experienced declining wage inequality in recent decades, as opposed to most developed countries. We found that the gender differences in both average wages and wage inequality declined over time. In particular, average wages increased faster for females than for males, while wage inequality declined faster for males than for females. We decomposed these distributional changes into the contributions of worker and job attributes, the returns on these attributes and residuals using a Shapley approach applied to counterfactual simulated wage distributions. We found that most of the increase in male wages was due to the increase in wages of workers in high-wage occupations and industries, while female wages increased mostly due to the increase in the returns to experience. The decline in wage inequality was driven mostly by changes in attributes, the decline in the returns to education, and the catching-up of immigrant workers, and each of these components was stronger for males than for females. We conclude that the convergence of the male and female wage distributions was due to both changes in the supply of labor, especially among females, and changes in the demand for labor leading to changes in the returns to various skills. Keywords Decomposition * Inequality * Shapley * Simulation
Dynamic assessment (DA) of self-regulation and planning behavior are neglected area of research. The objective of this study is to present a novel DA method of executive functions using the
...Seria-Think Instrument-Revised
(STI-R). The reliability and validity the STI-R was examined with children in grades 3–6. Children were randomly assigned to an experimental (
n
= 85) and control (
n
= 85) groups and administered the STI-R and the
Seriation Math Problems Test
before and after the STI-R teaching phase. In the teaching phase children in the experimental group were taught problem-solving strategies while children in the control group received a substitute intervention. The STI-R yields four scores: performance, number of insertions (NINS, indicating impulsivity), number of measurements (NMES, indicating planning), and efficiency index (EFFIN). Children in the experimental group showed a significant decrease in NINS and an increase in performance, NMES, and EFFIN from pre- to post-teaching. In the transfer phase they showed higher performance and EFFIN and lower NINS than children in the control group. The findings indicate that NINS is negatively correlated with NMES and that the correlation between the pre- and post-teaching phases in all variables were lower in the experimental than in the control group. The typology of Reflective, Impulsive, Effective, and Non-effective children was also confirmed. Regression analysis showed that NINS significantly predicted math problem-solving score before and after teaching. The findings support the reliability and validity of the STI-R and that cognitive modifiability of executive functions is a promising domain of DA complementing DA of performance scores.
With the advent of increased computational resources and improved algorithms, machine learning-based models are being increasingly applied to complex problems in particle accelerators. However, such ...data-driven models may provide overly confident predictions with unknown errors and uncertainties. For reliable deployment of machine learning models in high-regret and safety-critical systems such as particle accelerators, estimates of prediction uncertainty are needed along with accurate point predictions. In this investigation, we evaluate Bayesian neural networks (BNN) as an approach that can provide accurate predictions along with reliably quantified uncertainties for particle accelerator problems, and compare their performance with bootstrapped ensembles of neural networks. We select three accelerator setups for this evaluation: a storage ring, a photoinjector, and a linac. The problems span different data volumes and dimensionalities (e.g., scalar predictions as well as image outputs). It is found that BNN provide accurate predictions of the mean along with reliable estimates of predictive uncertainty across the test cases. In this vein, BNN may offer an attractive alternative to deterministic deep learning tools to generate accurate predictions with quantified uncertainties in particle accelerator applications.
Abstract
Background
Although the stethoscope is used for auscultation in the diagnosis of valvular heart disease for more than 200 years, its diagnostic accuracy is limited and highly dependent on ...clinical expertise and acoustic range of the human ear.
Purpose
To develop an electronic stethoscope, based on artificial intelligence (AI), for the diagnosis of aortic stenosis (AS).
Methods
We developed an electronic stethoscope (VoqxTM, Sanolla) with subsonic capabilities and acoustic range of 0–2,000 Hz. Using the VoqxTM, we recorded heart sounds from 100 patients referred for echocardiography (derivation group), 50 with moderate or severe AS (aortic valve area (AVA) ≤1.5 cm2) and 50 without valvular disease, using the 5 standard auscultation points. An AI based supervised learning model was applied to the auscultation data from the first 100 patients, to construct a diagnostic algorithm that was then tested on a validation group (50 other patients, 25 with AS and 25 without AS).
Results
The derivation group included 50 patients with AS (age 78±9 years, 28 males, AVA=0.87±0.19 cm2, mean gradient 44±16 mmHg, 39 with severe AS) and 50 without AS (age 58±16 years, 39 males). The AI based algorithm using 1–4 auscultation position (all except the mitral position), correctly identified 47/50 AS patients (sensitivity 94%) and 49/50 patients without AS (specificity 98%), total accuracy 96% (Table).
The algorithm was then applied to the validation group (AS: n=25, age 76±9 years,12 males, AVA=0.88±0.21 cm2, mean gradient 41±15 mmHg, 18 with severe AS; No AS: n=25, age = 59±18 years, 17 males). The algorithm correctly identified 21/25 AS patients (sensitivity 84%) and 24/25 patients without AS (specificity 96%), total accuracy 90% (Table 1).
Conclusion
Our initial findings show that an AI based stethoscope can accurately diagnose AS. AI based electronic auscultation is a promising new tool for automatic diagnosis of valvular heart disease.
Funding Acknowledgement
Type of funding sources: Private company. Main funding source(s): Sanolla Company Table 1
We present a self-consistent analysis to determine the optimal charge, gradient, and efficiency for laser driven accelerators operating with a train of microbunches. Specifically, we account for the ...beam loading reduction on the material occurring at the dielectric-vacuum interface. In the case of a train of microbunches, such beam loading effect could be detrimental due to energy spread, however this may be compensated by a tapered laser pulse. We ultimately propose an optimization procedure with an analytical solution for group velocity which equals to half the speed of light. This optimization results in a maximum efficiency 20% lower than the single bunch case, and a total accelerated charge of106electrons in the train. The approach holds promise for improving operations of dielectric laser accelerators and may have an impact on emerging laser accelerators driven by high-power optical lasers.
PDF
