Training Kuwaiti Teachers to Lead: A Case Study of Competitive Debate at the Basic Education College

This paper presents a case study of a competitive debate program designed for teachers-in-training at the Basic Education College in Kuwait. Stakeholders at different levels have expressed an interest in introducing more constructivist-based pedagogies into the Kuwaiti national education system, but institutional and ideological challenges have hindered implementation. Teachers at the college designed and implemented a debate program based on constructivist principles of authenticity, student meaning-making, collaboration, and high performance expectations. Survey data suggest that participants experienced debate as a transformative experience, changing their perception of themselves, of the world, and of their ability to effect change in it. Participants came to imagine themselves as future system leaders preparing future generations with higher-order skills involving complex solving, which an increasingly complex social reality demanded. From 2015 to 2018, a group of professors formed debate teams at the Kuwait University National English Debate League. This endeavour formed the empirical research presented here as evidence to support a move from instructivist teaching to constructivist learning for future teachers in Kuwait.

Sexual health assessment as part of a whole health model to care: Improving communication and technological barriers

Sexual health is the state of well-being regarding one’s sexuality, not just the absence of disease or dysfunction. Sexual health is highly valued by most people and associated with overall health. Assessment of sexual health, including sexual orientation and gender identity, should be integrated into the whole health approach to care. Addressing sexual health will enhance preventive care, promote healthy sexual functioning, and optimize overall health and well-being.Unfortunately, sexual health is not routinely assessed in clinical practice. One of the primary barriers is a gap in communication between patients and providers. Providers cite beliefs that patients will bring up sexual concerns themselves, or that patients might be offended by discussing sexual health. By contrast, patients often report an expectation that providers will bring up sexual health, and be comfortable with sexual health discussions in the clinic setting.Within the Veterans Health Administration (VHA), the lack of a sexual health template within the electronic health record (EHR) adds an additional technological barrier. The VHA’s transition toward whole health and updates to its EHR will provide unique opportunities to integrate sexual health assessment into routine care. We highlight future system modifications to address this within the VHA.Given the multifaceted barriers to sexual health assessment, it will be vital for healthcare systems integrating a whole health approach to develop both practical and educational interventions to address the communication gap. VHA’s expertise in developing and implementing EHR-based quality improvements and health education interventions may help inform interventions beyond VHA.

Supplemental Material: Learning from the COVID-19 Pandemic: How Faculty Experiences Can Prepare Us for Future System-Wide Disruption

Demographic information for participants in all phases of the study and the survey and interview questions for all phases of the study

Supplemental Material: Learning from the COVID-19 Pandemic: How Faculty Experiences Can Prepare Us for Future System-Wide Disruption

Demographic information for participants in all phases of the study and the survey and interview questions for all phases of the study

Measuring health science research and development in Africa: mapping the available data

Background In recent years there have been calls to strengthen health sciences research capacity in African countries. This capacity can contribute to improvements in health, social welfare and poverty reduction through domestic application of research findings; it is increasingly seen as critical to pandemic preparedness and response. Developing research infrastructure and performance may reduce national economies’ reliance on primary commodity and agricultural production, as countries strive to develop knowledge-based economies to help drive macroeconomic growth. Yet efforts to date to understand health sciences research capacity are limited to output metrics of journal citations and publications, failing to reflect the complexity of the health sciences research landscape in many settings. Methods We map and assess current capacity for health sciences research across all 54 countries of Africa by collecting a range of available data. This included structural indicators (research institutions and research funding), process indicators (clinical trial infrastructures, intellectual property rights and regulatory capacities) and output indicators (publications and citations). Results While there are some countries which perform well across the range of indicators used, for most countries the results are varied—suggesting high relative performance in some indicators, but lower in others. Missing data for key measures of capacity or performance is also a key concern. Taken as a whole, existing data suggest a nuanced view of the current health sciences research landscape on the African continent. Conclusion Mapping existing data may enable governments and international organizations to identify where gaps in health sciences research capacity lie, particularly in comparison to other countries in the region. It also highlights gaps where more data are needed. These data can help to inform investment priorities and future system needs.

Methodology on Establishing Multivariate Failure Thresholds for Improved Remaining Useful Life Prediction in PHM

Prognostics and Health Management (PHM) methodologies and techniques have been much widely studied in the academia and practiced by the industry in recent years. Prognostic approaches commonly try to establish the relationship between Remaining Useful Life (RUL) and a single variable or health indicator (HI) which can be obtained from multi-sensor fusion or data-driven models. However, simply relying on a single variable could reduce RUL prediction robustness when it is less representative of the system health conditions. Taking multiple variables into consideration for RUL prediction, quantifying operating risks and determining multivariate failure threshold is essential yet rarely studied. Generally, there are three major challenges that limit the practicality of this topic. 1) How to determine the multivariate failure threshold? 2) How to quantify operation risks based on multiple variables? 3) How to make reliable extrapolations of future conditions? To address these questions, this paper proposes 1) a novel copula model to determine multivariate failure threshold, and 2) a Maximum Likelihood Estimation enhanced similarity-based Particle Filter (MLE-SMPF) to predict future system conditions. In the proposed methodology, the health assessment is firstly performed to obtain HI trajectory. The copula risk quantification model is then trained by two variables HI and life. The proposed copula model can easily include multiple variables compared with our previously published approach using bivariate Weibull Distribution[1]. Afterward, MLE-SMPF is used to extrapolate future HI for testing data. The prediction capability is further improved compared with [2] by introducing MLE for Particle Filter transition function parameter initialization. Finally, the system RUL is determined from the failure threshold which is obtained according to the quantified operation risk. The proposed methodology is validated on the C-MAPSS data from the PHM data competition 2008 hosted by PHM society. The result outperforms most of the benchmarks from recent publications. The proposed methodology is easy to transfer to other potential machine prognostic applications.

Towards an Arabic Sign Language (ArSL) corpus for deaf drivers

Sign language is a common language that deaf people around the world use to communicate with others. However, normal people are generally not familiar with sign language (SL) and they do not need to learn their language to communicate with them in everyday life. Several technologies offer possibilities for overcoming these barriers to assisting deaf people and facilitating their active lives, including natural language processing (NLP), text understanding, machine translation, and sign language simulation. In this paper, we mainly focus on the problem faced by the deaf community in Saudi Arabia as an important member of the society that needs assistance in communicating with others, especially in the field of work as a driver. Therefore, this community needs a system that facilitates the mechanism of communication with the users using NLP that allows translating Arabic Sign Language (ArSL) into voice and vice versa. Thus, this paper aims to purplish our created dataset dictionary and ArSL corpus videos that were done in our previous work. Furthermore, we illustrate our corpus, data determination (deaf driver terminologies), dataset creation and processing in order to implement the proposed future system. Therefore, the evaluation of the dataset will be presented and simulated using two methods. First, using the evaluation of four expert signers, where the result was 10.23% WER. The second method, using Cohen’s Kappa in order to evaluate the corpus of ArSL videos that was made by three signers from different regions of Saudi Arabia. We found that the agreement between signer 2 and signer 3 is 61%, which is a good agreement. In our future direction, we will use the ArSL video corpus of signer 2 and signer 3 to implement ML techniques for our deaf driver system.

Anomaly Detection for the Centralised Elasticsearch Service at CERN

For several years CERN has been offering a centralised service for Elasticsearch, a popular distributed system for search and analytics of user provided data. The service offered by CERN IT is better described as a service of services, delivering centrally managed and maintained Elasticsearch instances to CERN users who have a justified need for it. This dynamic infrastructure currently consists of about 30 distinct and independent Elasticsearch installations, in the following referred to as Elasticsearch clusters, some of which are shared between different user communities. The service is used by several hundred users mainly for logs and service analytics. Due to its size and complexity, the installation produces a huge amount of internal monitoring data which can be difficult to process in real time with limited available person power. Early on, an idea was therefore born to process this data automatically, aiming to extract anomalies and possible issues building up in real time, allowing the experts to address them before they start to cause an issue for the users of the service. Both deep learning and traditional methods have been applied to analyse the data in order to achieve this goal. This resulted in the current deployment of an anomaly detection system based on a one layer multi dimensional LSTM neural network, coupled with applying a simple moving average to the data to validate the results. This paper will describe which methods were investigated and give an overview of the current system, including data retrieval, data pre-processing and analysis. In addition, reports on experiences gained when applying the system to actual data will be provided. Finally, weaknesses of the current system will be briefly discussed, and ideas for future system improvements will be sketched out.

Kinetics and Equilibrium Modeling of Single and Binary Adsorption of Aluminum(III) and Copper(II) Onto Calamansi (Citrofortunella microcarpa) Fruit Peels

The introduction of heavy metal wastes in the environment has posed health risks to both human and animals due to their toxicity. Since then, different studies have been explored for the possibility of utilizing new, low–cost, and sustainable adsorbent materials to get rid of heavy metals in the wastewater streams and aqueous solutions. This present study aimed to investigate and compare the adsorption ability of powdered calamansi (Citrofortunella microcarpa) fruit peels (PCFP) for the elimination of both Al(III) and Cu(II) ions in single (non–competitive) and binary (competitive) aqueous systems by batch adsorption techniques. Scanning electron microscopic and spectroscopic techniques were used to characterize the surface morphologies for the biosorbent and quantify the removal rates of heavy metal, respectively. Models were then used to describe in detail about the adsorption kinetics and isotherms for both single and binary metal systems. The influence and dependency of different experimental conditions on adsorption performance were also analyzed. The PCFP derived biosorbent was successful in removal of both Al(III) and Cu(II) ions in single (non–competitive) and binary (competitive) aqueous systems with 99, 70 and 91% adsorption rates, respectively. The biosorption process follows the Ho’s pseudo–second order kinetics. Furthermore, the Langmuir isotherm model was found helpful in explaining the adsorption mechanism. The dominating electrostatic interaction between adsorbents and adsorbates demonstrates monolayer adsorption at the binding sites on the surface of the peeling. Finally, the findings of this study will contribute to a better understanding of the adsorption process, as well as future system design applications in the treatment of heavy metal containing waste effluents.