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A Simulation-Based Teaching Strategy to Achieve Competence in Learners
No full textBackground: Simulation-based education has become the mainstay of clinical education in health sciences and medical education. A simulation-based education is a result of work hour restriction placed on graduate learners, increased number of students requiring clinical experience, decreased number of clinical sites and lack of the availability to perform certain procedures by learners. Research has demonstrated that integration of a simulation-based educational teaching strategy in a curriculum and throughout continued learning achieves competence in learners. Methods: The review of the literature highlighted the following topics: (a) history of medical simulation, (b) fidelity used in simulation training, devices and equipment, (c) learning theories associated with simulation-based education, (d) role of simulation training in medical and health sciences education, e) advantages and disadvantages of simulation training, f) competence in simulation-based education, g) debriefing/reflection in simulation. Results: An extensive review of the literature supports the use of a simulation-based teaching strategy in health sciences and medical education. Learning theories associated with simulation-based education allow educators to provide teaching strategies that align with learner’s ability to achieve competence in learning clinical and procedural skills required for their profession. Conclusion: A simulation-based education integrated in all stages of learner education that provides deliberate/repetitive practice and feedback achieves competence in learners throughout a life-time of learning
Arduino-Based Remote-Controlled Robotic Arm with Recording and Repeating Capabilities
No full textRobotic arms are intensively utilized for various industrial applications such as assembly lines. They can also be used to perform operations similar to human arms to accomplish tasks under hazardous environment. In this poster, an Arduino-based infrared (IR) remote-controlled robotic arm which can record and repeat a sequence of movements is proposed. It utilizes servo-motors, which have integrated gears and a shaft that can be precisely controlled to change the position of objects, to rotate and move arms and legs of robots. By setting the angle value for each motor's shaft using input from the IR transmitter located within 80cm from receiver, it can change the position of corresponding links of the arm. When robotic arm is in the desired position, corresponding links of the arm. When robotic arm is in the desired position, corresponding IR remote button can be pressed to record the angles of all motors with LED lights indicating the number of saved positions. Up to five position savings can be recorded and repeated in a loop so that the robotic arm can be taught to perform certain functions as needed. The Arduino-based smart robotic arm is implemented and it is verified to be able to perform recording and repeating functions as designed. More complicated movements can be programmed so that the robotic arm can be used to perform required operations for potential industrial applications
R, SAS or Python: Let’s Compare!
No full textThe global analytics industry is dynamic and the trends in the languages keep changing. There are attributes that help make the comparison easy. There are five attributes that I have chosen and used a scoring scale to rank the three languages. Across all the attributes the scoring for Python and R is higher when compared to Statistical Analysis System (SAS)
Financial Well-Being: Individualistic Behavior
No full textNamrata Jain's poster on a survey of people's financial well-being
Bio-Gas Plant Project for Waste Management and Energy Generation
No full textUB faces food wastage issues, with a huge amount of food being wasted at various dinning locations on campus. The proposal is to set-up a Bio-Gas plant, which will utilize this waste and generate electrical power, that can be used by the university. The plant will be located close to the Bridgeport landfill and solar plant. The plant can be fed with waste generated from nearby hotels and educational institutions
Enhanced Deep Network Designs Using Mitochondrial DNA Based Genetic Algorithm And Importance Sampling
No full textMachine learning (ML) is playing an increasingly important role in our lives. It has already made huge impact in areas such as cancer diagnosis, precision medicine, self-driving cars, natural disasters predictions, speech recognition, etc. The painstakingly handcrafted feature extractors used in the traditional learning, classification and pattern recognition systems are not scalable for large-sized datasets or adaptable to different classes of problems or domains. Machine learning resurgence in the form of Deep Learning (DL) in the last decade after multiple AI (artificial intelligence) winters and hype cycles is a result of the convergence of advancements in training algorithms, availability of massive data (big data) and innovation in compute resources (GPUs and cloud). If we want to solve more complex problems with machine learning, we need to optimize all three of these areas, i.e., algorithms, dataset and compute. Our dissertation research work presents the original application of nature-inspired idea of mitochondrial DNA (mtDNA) to improve deep learning network design. Additional fine-tuning is provided with Monte Carlo based method called importance sampling (IS). The primary performance indicators for machine learning are model accuracy, loss and training time. The goal of our dissertation is to provide a framework to address all these areas by optimizing network designs (in the form of hyperparameter optimization) and dataset using enhanced Genetic Algorithm (GA) and importance sampling. Algorithms are by far the most important aspect of machine learning. We demonstrate the application of mitochondrial DNA to complement the standard genetic algorithm for architecture optimization of deep Convolution Neural Network (CNN). We use importance sampling to reduce the dataset variance and sample more often from the instances that add greater value from the training outcome perspective. And finally, we leverage massive parallel and distributed processing of GPUs in the cloud to speed up training. Thus, our multi-approach method for enhancing deep learning combines architecture optimization, dataset optimization and the power of the cloud to drive better model accuracy and reduce training time
Adaptive Parameter Selection for Deep Brain Stimulation in Parkinson’s Disease
No full textEach year, around 60,000 people are diagnosed with Parkinson’s disease (PD) and the economic burden of PD is at least 12,000 to $6,000 per year with the addition of neuromodulation therapies such as Deep Brain Stimulation (DBS), transcranial Direct Current Stimulation (tDCS), Transcranial Magnetic Stimulation (TMS), etc. In neurodegenerative disorders such as PD, deep brain stimulation (DBS) is a desirable approach when the medication is less effective for treating the symptoms. DBS incorporates transferring electrical pulses to a specific tissue of the central nervous system and obtaining therapeutic results by modulating the neuronal activity of that region. The hyperkinetic symptoms of PD are associated with the ensembles of interacting oscillators that cause excess or abnormal synchronous behavior within the Basal Ganglia (BG) circuitry. Delayed feedback stimulation is a closed loop technique shown to suppress this synchronous oscillatory activity. Deep Brain Stimulation via delayed feedback is known to destabilize the complex intermittent synchronous states. Computational models of the BG network are often introduced to investigate the effect of delayed feedback high frequency stimulation on partially synchronized dynamics. In this work, we developed several computational models of four interacting nuclei of the BG as well as considering the Thalamo-Cortical local effects on the oscillatory dynamics. These models are able to capture the emergence of 34 Hz beta band oscillations seen in the Local Field Potential (LFP) recordings of the PD state. Traditional High Frequency Stimulations (HFS) has shown deficiencies such as strengthening the synchronization in case of highly fluctuating neuronal activities, increasing the energy consumed as well as the incapability of activating all neurons in a large-scale network. To overcome these drawbacks, we investigated the effects of the stimulation waveform and interphase delays on the overall efficiency and efficacy of DBS. We also propose a new feedback control variable based on the filtered and linearly delayed LFP recordings. The proposed control variable is then used to modulate the frequency of the stimulation signal rather than its amplitude. In strongly coupled networks, oscillations reoccur as soon as the amplitude of the stimulus signal declines. Therefore, we show that maintaining a fixed amplitude and modulating the frequency might ameliorate the desynchronization process, increase the battery lifespan and activate substantial regions of the administered DBS electrode. The charge balanced stimulus pulse itself is embedded with a delay period between its charges to grant robust desynchronization with lower amplitude needed. The efficiency and efficacy of the proposed Frequency Adjustment Stimulation (FAS) protocol in a delayed feedback method might contribute to further investigation of DBS modulations aspired to address a wide range of abnormal oscillatory behaviors observed in neurological disorders. Adaptive stimulation can open doors towards simultaneous stimulation with MRI recordings. We additionally propose a new pipeline to investigate the effect of Transcranial Magnetic Stimulation (TMS) on patient specific models. The pipeline allows us to generate a full head segmentation based on each individual MRI data. In the next step, the neurosurgeon can adaptively choose the proper location of stimulation and transmit accurate magnetic field with this pipeline
Strange Conceptual Bedfellows: Assessing Grounded Theory For Effective Virtual Student Team Project Delivery Via Knowledge Management, Qualitative Research and Management Theory
No full textThis case study tests the efficacy of a grounded theoretical model and related best practices developed to assist online instructors in facilitating virtual student team projects. The lived experience of two successful project teams comprised of seven students charged with delivering a term paper was analyzed to confirm the validity of the theoretical model. The findings exhibit commonality with previous findings in the knowledge management, qualitative research and management theory literature and reinforce and extend the findings of a previous case study focused on the lived experience of an unsuccessful project team
Community College Freshmen's Perceptions of In-Class Faculty Microaggressions and Their Intent to Persist
No full textFaculty microaggressive behavior was reported to be pervasive in the community college classroom (Casanova, McGuire, & Martin, 2018; Suarez-Orozco et al., 2015). However, not much research focused on how community college freshmen’s perceptions of faculty’s microaggressive behaviors related to their intent to persist in the college environment. The study’s purposes were to: (a) examine any relationship between community college freshmen’s perceptions of in-class faculty racial and gender microaggressions, and their intent to persist beyond the second semester of their freshman year, (b) examine whether differences existed in the racial and gender groups’ intent to persist, and in their perceptions of faculty classroom microaggressions, and (c) explore students’ perceptions of their experiences with classroom faculty-student interactions. The study used a convergent mixed-methods approach to inquiry; Tinto’s (1975) interactionalist model of student persistence as a theoretical foundation that has been widely validated and tested by others; and Sue’s (2010) microaggression taxonomy and themes as a conceptual framework that connected ideas in the study within the theoretical framework. Surveys were administered to 311 eligible participants, and quantitative results were analyzed at a significance level of alpha .05. Qualitative data collected from three open-ended survey questions were coded for emergent themes related to faculty microaggression, using Sue (2010) as a guide, and disconfirming results were analyzed and resolved. Key results at alpha .05 included: (a) no statistically significant difference in intent to persist in the college environment for all racial and gender student groups; (b) statistically significant differences in perception of faculty in-class racial microaggression between non-White and White freshmen; (c) no statistically significant difference in perception of faculty in-class gender microaggression between females and males; and (d) statistically significant relationships between perceived microaggression and intent to persist for Asian/Pacific Islander and female freshmen. Non-White and female participants also reported feeling demeaned and/or ignored by faculty, and White and male participants largely did not perceive faculty microaggressions. Examining coping mechanisms used to blunt the effects of perceived faculty classroom microaggressions, and supporting positive classroom environments were identified as important for student success
A Dog's Impact on Empathy and Decision Making
No full textA poster discussing Briana Livingston's study on empathy bias for pets vs adolescents