How peer assessment could be interactive and effective

In practical courses, students consider teachers’ assessment of various skills to be baseless and unfair. Unfortunately, due to a lack of equipment, only a few students take part in practical skills performance, while the rest of the students remain passive in learning and assessment. In this paper we suggest an original design to use peer assessment as an interactive strategy and examine its efficiency to improve students’ individual skills, teamwork skills and practical performance in an educational technology course. In the study reported on here, a quasi-experimental design was used, which included a sample of 73 female students divided into experimental and control groups. The treatment tools were provided to the experimental group while the assessment tools were applied to both groups before and after the intervention. Data analysis revealed that an interactive peer assessment strategy was effective in improving individual skills, teamwork skills and practical performance. We recommend that this suggested strategy is used widely in practical courses. Keywords: individual skills; interactive peer assessment strategy; peer tutoring; practical performance; teamwork skill

Driving microfluidic flows with three dimensional electrodes.

Most of the structures in submillimeter-scale engineering are created from thin films, making them essentially two-dimensional (2D). Significant work has been done to fabricate 3D structures using self-folding, a deterministic form of self-assembly, and three dimensional lithographic and non-lithographic patterning. The objective of this work is to propose different fabrication and patterning strategies of 3D structures used as pumping electrodes for micro fluidic applications. 3D electrodes drive flows over the whole channel height while 2D electrodes stay near one wall. The first application of the 3D electrodes is mixing chemical or biological samples with reagents for chemical analysis which is one of the most time consuming operations in microfluidic platforms. The mixer used is based on the electrokinetic phenomenon of induced charge electro-osmosis (ICEO). ICEO creates microvortices around polarized posts with gold coated sidewalls, connected to embedded electrodes, by application of alternating current (AC) electric fields. These microvortices around posts help in mixing the two reagents very quickly. These vertical sidewall gold coated posts and embedded electrodes are fabricated using 3D photolithographic patterning and an ion milling fabrication technique. The second application is fast ac electro-osmotic (ACEO) pumps using 3D electrodes. These 3D electrodes dramatically improve the flow rate and frequency range of ACEO pumps over the planar electrodes. A non-photolithographic electrode patterning method is proposed to fabricate such electrodes. The method is based on shadowed evaporation of metal on an insulating substrate. This method is considered to be simple and cost effective compared to others used to create these stepped 3D electrodes. Finally, a self-folding technique is proposed to create out-of plane three dimensional electrodes for ACEO tube pumps. The technique depends on the strain mismatch between two different layered sheets of material. One layer usually has compressive stress, i.e. thermally grown Si02, and the other has relatively tensile stress, i.e. metals. The design is similar to the planar electrodes design in the literature, except as a 3D electrode it interacts with a larger volume of fluid for a more efficient pump

Addressing Challenges of Ultra Large Scale System on Requirements Engineering

AbstractAccording to the growing evolution in complex systems and their integrations, Internet of things, communication, massive information flows and big data, a new type of systems has been raised to software engineers known as Ultra Large Scale (ULS) Systems. Hence, it requires dramatic change in all aspects of “Software Engineering” practices and their artifacts due to its unique characteristics.Attendance of all software development members is impossible to meet in regular way and face-to-face, especially stakeholders from different national and organizational cultures. In addition, huge amount of data stored, number of integrations among software components and number of hardware elements. Those obstacles constrict design, development, testing, evolution, assessment and implementation phases of current software development methods.In this respect, ULS system that's considered as a system of systems, has gained considerable reflections on system development activities, as the scale is incomparable to the traditional systems since there are thousands of different stakeholders are involved in developing software, were each of them has different interests, complex and changing needs beside there are already new services are being integrated simultaneously to the current running ULS systems.The scale of ULS systems makes a lot of challenges for Requirements Engineers (RE). As a result, the requirements engineering experts are working on some automatic tools to support requirement engineering activities to overcome many challenges.This paper points to the limitations of the current RE practices for the challenges forced by ULS nature, and focus on the contributions of several approaches to overcome these difficulties in order to tackle unsolved areas of these solutions.As a result, the current approaches for ULS miss some RE essential practices related to find vital dependent requirements, and are not capable to measure the changes impact on ULS systems or other integrated legacy systems, in addition the requirements validation are somehow depended on the user ratings without solid approval from the stakeholders

Effect of Self Care Management on Nursing-Sensitive Patients’ Outcomes after Permanent Pacemaker Implantation

Context: Nursing is striving to build a knowledge base that supports professional practice and improves the quality of care.Aim: This study aimed to evaluate the effect of self-care management guidelines on nursing-sensitive patients' outcomes after permanent pacemaker implantation.Methods: A quasi-experimental design was utilized in this study. A purposive sample of 50 patients was admitted to the cardiac catheterization unit at Ain Shams University Hospital after permanent pacemaker implantation during their follow-up visit. They are divided into two matched groups, study and control groups. Their mean age ±SD was 45.37±5.76, and 48.75±4.27 successively. Patient socio-demographic characteristic and medical data sheet, self-care management level assessment scale, and nursing-sensitive outcomes measuring scale were utilized to achieve the study aim.Results: The study results revealed positive outcomes for patients of the study group compared to the controls and their pre-implementation level of self-care guidelines.Conclusion: The study concludes that implementing self-care management guidelines positively enhances all dimensions of nursing-sensitive patients' outcomes, recommending that it be applied in all cardiac catheterization units and should be updated periodically to enhance self-care management for those patients based on nursing-sensitive outcomes classification

Medical Image Classification using Deep Learning Techniques and Uncertainty Quantification

The emergence of medical image analysis using deep learning techniques has introduced multiple challenges in terms of developing robust and trustworthy systems for automated grading and diagnosis. Several works have been presented to improve classification performance. However, these methods lack the diversity of capturing different levels of contextual information among image regions, strategies to present diversity in learning by using ensemble-based techniques, or uncertainty measures for predictions generated from automated systems. Consequently, the presented methods provide sub-optimal results which is not enough for clinical practice. To enhance classification performance and introduce trustworthiness, deep learning techniques and uncertainty quantification methods are required to provide diversity in contextual learning and the initial stage of explainability, respectively. This thesis aims to explore and develop novel deep learning techniques escorted by uncertainty quantification for developing actionable automated grading and diagnosis systems. More specifically, the thesis provides the following three main contributions. First, it introduces a novel entropy-based elastic ensemble of Deep Convolutional Neural Networks (DCNNs) architecture termed as 3E-Net for classifying grades of invasive breast carcinoma microscopic images. 3E-Net is based on a patch-wise network for feature extraction and image-wise networks for final image classification and uses an elastic ensemble based on Shannon Entropy as an uncertainty quantification method for measuring the level of randomness in image predictions. As the second contribution, the thesis presents a novel multi-level context and uncertainty-aware deep learning architecture named MCUa for the classification of breast cancer microscopic images. MCUa consists of multiple feature extractors and multi-level context-aware models in a dynamic ensemble fashion to learn the spatial dependencies among image patches and enhance the learning diversity. Also, the architecture uses Monte Carlo (MC) dropout for measuring the uncertainty of image predictions and deciding whether an input image is accurate based on the generated uncertainty score. The third contribution of the thesis introduces a novel model agnostic method (AUQantO) that establishes an actionable strategy for optimising uncertainty quantification for deep learning architectures. AUQantO method works on optimising a hyperparameter threshold, which is compared against uncertainty scores from Shannon entropy and MC-dropout. The optimal threshold is achieved based on single- and multi-objective functions which are optimised using multiple optimisation methods. A comprehensive set of experiments have been conducted using multiple medical imaging datasets and multiple novel evaluation metrics to prove the effectiveness of our three contributions to clinical practice. First, 3E-Net versions achieved an accuracy of 96.15% and 99.50% on invasive breast carcinoma dataset. The second contribution, MCUa, achieved an accuracy of 98.11% on Breast cancer histology images dataset. Lastly, AUQantO showed significant improvements in performance of the state-of-the-art deep learning models with an average accuracy improvement of 1.76% and 2.02% on Breast cancer histology images dataset and an average accuracy improvement of 5.67% and 4.24% on Skin cancer dataset using two uncertainty quantification techniques. AUQantO demonstrated the ability to generate the optimal number of excluded images in a particular dataset

Validation of a method to elute viruses from different types of face masks

Due to the SARS-CoV-2 pandemic, it is crucial to study the efficiency of face masks in retaining viruses for the upcoming years. The first objective of this study was to validate a method to elute viruses from polyester and cotton face masks. We observed that deionized water followed by 3% beef glycine (pH 9.5 or pH 7.2) was significantly more efficient (p < 0.05) in eluting the bacteriophage phiX174 virus from polyester (4.73% ± 0.25% to 28.67% ± 1.89%), polyester/cotton (3% ± 0.33%), and cotton (1.7% ± 0.21%) face masks than 3% beef glycine only (pH 9.5 or pH 7.2) as a single eluent (3.4% ± 0.16% to 21.33% ± 0.94% for polyester, 1.91% ± 0.08% for polyester/cotton, and 1.47% ± 0.12% for cotton face masks). Also, deionized water was significantly less efficient as a single eluent for eluting bacteriophage phiX174 from all the studied face mask types. The polyethylene glycol (PEG) precipitation method was substantially more efficient (p < 0.05) as a second step concentration method for the viruses in the eluates than the organic flocculation (OF) method. Higher viral loads were eluted from polyester face masks than cotton ones. We also found varying viral loads in the eluate solutions from different commercial polyester face masks, with the highest percentage seen for the N95 face mask. The second objective was to apply the validated method to study the effect of autoclaving on the different face mask materials. Results of the study did not show any significant differences in the viral loads eluted from the studied face masks before and after one and five autoclaving cycles. Moreover, a scanning electron microscope (SEM) analysis revealed no changes in the yarns, elongation, tensile strength, and contact angle measurements of the polyester or cotton materials after one or five autoclaving cycles

Calcium availability regulates antioxidant system, physio-biochemical activities and alleviates salinity stress mediated oxidative damage in soybean seedlings

Salinity is considered as one of the devastating abiotic stress factors and global climate change has further worsened the situation. Present experiments were aimed to evaluate the role of calcium (Ca) availability on growth and salinity tolerance mechanisms in soybean. Seedlings were grown with (2 mM Ca) and without Ca supplementation and modulation in key physiological and biochemical parameters were studied. Salinity (100 mM NaCl) stress resulted in growth reduction in terms of height and biomass accumulation, which was more pronounced in Ca-deficient plants. Relative to control (Ca deficient) and NaCl stressed plants, Ca supplemented seedlings exhibited higher relative water content, pigment synthesis and the photosynthetic efficiency. Ca availability affected the synthesis of proline, glycine betaine and soluble sugars under normal and saline growth conditions. Optimal Ca supplementation up-regulated the activities of antioxidant enzymes assayed and the contents of non-enzymatic antioxidants (ascorbate, glutathione, and tocopherol) thereby reflecting in amelioration of NaCl induced oxidative damage. Moreover, increased accumulation of phenols due to Ca supplementation and the amelioration of NaCl mediated decline if nitrate reductase activity was observed. More importantly, Ca availability reduced the accumulation of Na under control and NaCl stressed conditions restricting the damging effects on metabolism. Availability of optimal Ca potentially regulates the salinity tolerance mechanisms in soybean by maintaining osmoregulation and antioxidant metabolism

AUQantO: Actionable Uncertainty Quantification Optimization in deep learning architectures for medical image classification

Deep learning algorithms have the potential to automate the examination of medical images obtained in clinical practice. Using digitized medical images, convolution neural networks (CNNs) have demonstrated their ability and promise to discriminate among different image classes. As an initial step towards explainability in clinical diagnosis, deep learning models must be exceedingly precise, offering a measure of uncertainty for their predictions. Such uncertainty-aware models can help medical professionals in detecting complicated and corrupted samples for re-annotation or exclusion. This paper proposes a new model and data-agnostic mechanism, called Actionable Uncertainty Quantification Optimization (AUQantO) to improve the performance of deep learning architectures for medical image classification. This is achieved by optimizing the hyperparameters of the proposed entropy-based and Monte Carlo (MC) dropout uncertainty quantification techniques escorted by single- and multi-objective optimization methods, abstaining from the classification of images with a high level of uncertainty. This helps in improving the overall accuracy and reliability of deep learning models. To support the above claim, AUQantO has been validated with four deep learning architectures on four medical image datasets and using various performance metric measures such as precision, recall, Area Under the Receiver Operating Characteristic (ROC) Curve score (AUC), and accuracy. The study demonstrated notable enhancements in deep learning performance, with average accuracy improvements of 1.76% and 2.02% for breast cancer histology and 5.67% and 4.24% for skin cancer datasets, utilizing two uncertainty quantification techniques, and AUQantO further improved accuracy by 1.41% and 1.31% for brain tumor and 4.73% and 1.83% for chest cancer datasets while allowing exclusion of images based on confidence levels

TOPOLOGY OPTIMIZATION OF A MEMS RESONATOR USING HYBRID FUZZY TECHNIQUES

This paper introduces a new methodology for the design of structures by geometry and topology optimization accounting for loading and boundary conditions as well as material properties. The Fuzzy Heuristic Gradient Projection (FHGP) method is used as a direct search technique for the geometry optimization, while the Complex Method (CM) is used as a random search technique for the topology optimization. In the proposed method, elements are designed such that they all have the same amount of stresses using the Fuzzy Heuristic Gradient Projection method. On the other hand, the complex method is used for the topology optimization step satisfying any constraint other than the stress constraint. The developed hybrid fuzzy technique is applied for different applications ranging from micro-scale to macro-scale applications. The method is applied to a micro-mechanical resonator as a microelectro-mechanical system (MEMS). The resonator is solved for minimum weight and is subjected to an equality frequency constraint and an inequality stress constraint. The proposed method is compared with the Multi-objective Genetic Algorithms (MOGAs) on solving the MEMS resonator. Results showed that the proposed hybrid fuzzy technique converges to optimum solutions faster than (MOGAs). The time consumed is improved by a 77%