A Neural Network Approach For Estimating Examinees' Proficiency Levels in Computerized Adaptive Testing

This paper studies the potential of using neural network models for estimating examinees' proficiency levels in computerized adaptive testing. Computerized adaptive testing (CAT) has recently become increasingly important to standardized testing. An essential constituent of CAT is the estimation of each examinee proficiency level. Previously, this estimation has been carried out using the maximum likelihood estimator (MLE) or a Bayesian procedure. As being parametric techniques, the quality of estimates strongly depends on some restrictive assumptions. Neural network, with its strong theoretical background and ability to learn and generalize, provides a more flexible non-parametric function approximation and tends to be more efficient in estimation accuracy. It can be used for estimating the proficiency levels of examinees. In this work, several models have been simulated and compared namely multi-layer perceptron (MLP), principal-component analysis (PCA), radial-basis function (RBF) and support-vector machines (SVM). Simulation results reveal that neural-network models are capable of providing good estimates of examinees' proficiecy levels. The accuracy of the classification estimation varies based on the network complexity and the size of the training data set

A Neural Network Approach For Estimating Examinees' Proficiency Levels in Computerized Adaptive Testing

This paper studies the potential of using neural network models for estimating examinees' proficiency levels in computerized adaptive testing. Computerized adaptive testing (CAT) has recently become increasingly important to standardized testing. An essential constituent of CAT is the estimation of each examinee proficiency level. Previously, this estimation has been carried out using the maximum likelihood estimator (MLE) or a Bayesian procedure. As being parametric techniques, the quality of estimates strongly depends on some restrictive assumptions. Neural network, with its strong theoretical background and ability to learn and generalize, provides a more flexible non-parametric function approximation and tends to be more efficient in estimation accuracy. It can be used for estimating the proficiency levels of examinees. In this work, several models have been simulated and compared namely multi-layer perceptron (MLP), principal-component analysis (PCA), radial-basis function (RBF) and support-vector machines (SVM). Simulation results reveal that neural-network models are capable of providing good estimates of examinees' proficiecy levels. The accuracy of the classification estimation varies based on the network complexity and the size of the training data set

Adaptive dissection based subword segmentation of printed Arabic text

Numerous segmentation and recognition techniques have been proposed in literature for Arabic OCR system. Correct and efficient segmentation of Arabic text into characters is considered to be a fundamental problem. While OCR systems for other languages do not need segmentation for printed text for successful recognition, it is essential to design robust and powerful segmentation algorithms or employ segmentation free recognition schemes for printed Arabic text. Even more, in recognition of handwritten characters, segmentation is considered to be indispensable. Most of current segmentation technique suffers from over segmentation and under segmentation in addition to not being adaptive in nature. In this paper, we have proposed a new sub-word segmentation scheme, which is independent of font size and font type

Adaptive dissection based subword segmentation of printed Arabic text

Numerous segmentation and recognition techniques have been proposed in literature for Arabic OCR system. Correct and efficient segmentation of Arabic text into characters is considered to be a fundamental problem. While OCR systems for other languages do not need segmentation for printed text for successful recognition, it is essential to design robust and powerful segmentation algorithms or employ segmentation free recognition schemes for printed Arabic text. Even more, in recognition of handwritten characters, segmentation is considered to be indispensable. Most of current segmentation technique suffers from over segmentation and under segmentation in addition to not being adaptive in nature. In this paper, we have proposed a new sub-word segmentation scheme, which is independent of font size and font type

Risk factors and outcomes associated with recurrent autoimmune hepatitis following liver transplantation

Background & Aims: Autoimmune hepatitis can recur after liver transplantation (LT), though the impact of recurrence on patient and graft survival has not been well characterized. We evaluated a large, international, multicenter cohort to identify the probability and risk factors associated with recurrent AIH and the association between recurrent disease and patient and graft survival.Methods: We included 736 patients (77% female, mean age 42 +/- 1 years) with AIH who underwent LT from January 1987 through June 2020, among 33 centers in North America, South America, Europe and Asia. Clinical data before and after LT, biochemical data within the first 12 months after LT, and immunosuppression after LT were analyzed to identify patients at higher risk of AIH recurrence based on histological diagnosis.Results: AIH recurred in 20% of patients after 5 years and 31% after 10 years. Age at LT <= 42 years (hazard ratio [HR] 3.15; 95% CI 1.22-8.16; p = 0.02), use of mycophenolate mofetil post-LT (HR 3.06; 95% CI 1.39-6.73; p = 0.005), donor and recipient sex mismatch (HR 2.57; 95% CI 1.39-4.76; p = 0.003) and high IgG pre-LT (HR 1.04; 95% CI 1.01-1.06; p = 0.004) were associated with higher risk of AIH recurrence after adjusting for other confounders. In multivariate Cox regression, recurrent AIH (as a time-dependent covariate) was significantly associated with graft loss (HR 10.79, 95% CI 5.37-21.66, p <0.001) and death (HR 2.53, 95% CI 1.48-4.33, p = 0.001).Conclusion: Recurrence of AIH following transplant is frequent and is associated with younger age at LT, use of mycophenolate mofetil post-LT, sex mismatch and high IgG pre-LT. We demonstrate an association between disease recurrence and impaired graft and overall survival in patients with AIH, highlighting the importance of ongoing efforts to better characterize, prevent and treat recurrent AIH.Lay summary: Recurrent autoimmune hepatitis following liver transplant is frequent and is associated with some recipient features and the type of immunosuppressive medications use. Recurrent autoimmune hepatitis negatively affects outcomes after liver transplantation. Thus, improved measures are required to prevent and treat this condition. (C) 2022 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.Cellular mechanisms in basic and clinical gastroenterology and hepatolog

OPTIMAL PROSTHESIS TEMPERATURE IN CEMENTED TOTAL HIP ARTHROPLASTY

Introduction: Aseptic loosening is the commonest complication of cemented total hip arthroplasy. Gaseous voids within the cement mantle are thought to act as stress concentrators and points of origin and preferential fracture propagation at the cement stem interface. Assuming a bone tempereature of 37°C, Bishop recommended heating the prosthesis to 44°C, thereby effecting a reduction in cement-prosthesis interface porosity.The aim of this study was to (I) determine the intra-operative temperature of the femoral cancellous bed prior to insertion of prosthesis, (II) to investigate whether the magnitude of the temperature gradient effects interface porosity (III) to develop clinically relevant recommendations.Materials and Methods: (I) The intra-operative determination of femoral cancellous boney bed temperature. Sterile, single use thermocouples (Mon-a-therm) were used to record interface temperature in six patients, after canal preparation and lavage. (II) A simulated femoral model was designed consisting of a waterbath, set at temperature determined by (I) with an inner water-tight chamber formed by 19mm diameter polyethylene tubing. Cement (Palacos) was non-vacuum mixed (to exaggerate porosity) for 1 minute and injected in a retrograde manner into the inner tube at 3 minutes. Femoral stems (Exeter) were pre-heated in a second waterbath to 18, 32,35,37,40,44°C, were thoroughly dried and lowered into the inner tube by a Lloyd universal testing machine via a custom jig. The cement was left to polymerise.The cement mantle was sectioned transversely, then longitudinally to expose the cement-prosthesis interface. This was stained with acrylic dye to facilitate image analysis. Three mantles for each temperature were produced.Results: (I) The mean femoral canal temperature was 32.3°C, (II) the effect of stem temperature on interface porosity is shown in fig1.Conclusions: Bone temperature is 32°C after canal preparation using contemporary cementing techniques. Heating to 35°C reduces interface porosity, heating to 40°C is optimal

Management of chronic hepatitis B in challenging patient populations

10.1111/j.1478-3231.2006.01375.xLiver International26SUPPL. 238-46LIIN