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Simple Summary: Veterinary students are required to determine an animal's heart rate and diagnose heart murmurs and arrhythmias by the time of graduation. Accurate assessment of the heart sounds of horses is considered as a day one clinical competency. Limited opportunities exist in the veterinary curriculum to develop these competencies, especially during COVID-19 times. This research aimed to determine if a multimodal learning resource consisting of diagrams, heart sound recordings and visual representation of the sounds would assist in the development of veterinary students' confidence and ability to recognize normal and abnormal heart sounds of horses. Students were invited to utilize the teaching resource and voluntarily complete surveys about their confidence in recognizing normal heart sounds as well as various murmurs and important arrhythmias of horses. The survey results were analyzed by qualitative and quantitative means. Over a two-year period, 231 fourth-year and 222 fifth-year veterinary students had access to the resource; 89 completed the initial survey and 57 completed a second survey after using the resource. Results confirmed that after access to the resource students' understanding and perception of their auscultation abilities improved. Auscultation of heart sounds is an important veterinary skill requiring an understanding of anatomy, physiology, pathophysiology and pattern recognition. This cross-sectional study was developed to evaluate a targeted, audio-visual training resource for veterinary students to improve their understanding and auscultation of common heart conditions in horses. Fourth- and fifth-year 2021 and 2022 Bachelor of Veterinary Science students at the University of Queensland (UQ) were provided the learning resource and surveyed via online pre- and post-intervention surveys. Results were quantitatively analyzed using descriptive statistics and Mann–Whitney U tests. Open-ended survey questions were qualitatively analyzed by thematic analysis and Leximancer™ Version 4 program software analysis. Over the two-year period, 231 fourth-year and 222 fifth-year veterinary students had access to the resource; 89 completed the pre-intervention survey and 57 completed the post-intervention survey. Quantitative results showed the resource helped students prepare for practicals and their perception of competency and confidence when auscultating equine cardiac sounds improved (p < 0.05). Compared to fifth-year students, fourth-year students felt less competent at identifying murmurs and arrythmias prior to accessing the learning resource (p < 0.05). Fourth-year and fifth-year students' familiarity with detection of murmurs improved after completing the learning resource (p < 0.001). Qualitative analysis demonstrated a limited number of opportunities to practice equine cardiac auscultation throughout the veterinary degree, especially during the COVID-19 pandemic, and that integrated audio-visual resources are an effective means of teaching auscultation. [ABSTRACT FROM AUTHOR]

On-line learning has been adopted as a major educational technique and method due to the COVID-19 pandemic. Even though COVID-19 led to many problems such as faculty's burden and stress during the early stage of COVID-19, both students and faculties have gradually become familiar with on-line educational environment. The high technology development and social requirement for new educational methods have paved the way for on-line education as well. Therefore, on-line learning or blended learning will be likely to go on after the end of COVID-19 pandemic. It seems that college will use both traditional in-person class and on-line class and develop various types of classes aligning them. In accordance with those social change and requirement of on-line education, it is necessary to prepare guidelines for effective utilizing on-line learning. The main purpose of this study is to examine the learning behaviors of the students in on-line learning and analyze the learning effects by comparing with learning behaviors in higher education. Given the lack of research on how online learning behaviors and patterns affect academic performance, this study will analyze the academic achievement by comparing with learning behaviors such as the number and duration of on-line learning sessions. The study includes theory classes and computer practical classes. The findings of empirical analysis will provide insights that the effective planning and designing on-line classes based on learning behaviors are key to enhancing learning effects and learner's satisfaction. [ABSTRACT FROM AUTHOR]

Auscultation of heart sounds is an important veterinary skill requiring an understanding of anatomy, physiology, pathophysiology and pattern recognition. This cross-sectional study was developed to evaluate a targeted, audio-visual training resource for veterinary students to improve their understanding and auscultation of common heart conditions in horses. Fourth- and fifth-year 2021 and 2022 Bachelor of Veterinary Science students at the University of Queensland (UQ) were provided the learning resource and surveyed via online pre- and post-intervention surveys. Results were quantitatively analyzed using descriptive statistics and Mann–Whitney U tests. Open-ended survey questions were qualitatively analyzed by thematic analysis and Leximancer™ Version 4 program software analysis. Over the two-year period, 231 fourth-year and 222 fifth-year veterinary students had access to the resource; 89 completed the pre-intervention survey and 57 completed the post-intervention survey. Quantitative results showed the resource helped students prepare for practicals and their perception of competency and confidence when auscultating equine cardiac sounds improved (p < 0.05). Compared to fifth-year students, fourth-year students felt less competent at identifying murmurs and arrythmias prior to accessing the learning resource (p < 0.05). Fourth-year and fifth-year students’ familiarity with detection of murmurs improved after completing the learning resource (p < 0.001). Qualitative analysis demonstrated a limited number of opportunities to practice equine cardiac auscultation throughout the veterinary degree, especially during the COVID-19 pandemic, and that integrated audio-visual resources are an effective means of teaching auscultation.

Although isolating, lockdown also created unexpected opportunities for connection and inspiration. This article describes the lockdown literacies of a sibling pair, Marco and Mara, as they wrote digital texts/books for Literacy-Cast, a virtual, interactive literacy space offered by Appalachian State University from March 2020-present. Since lockdown began, this virtual space has been enacted 4-5 days weekly with 70-250 participants logging in from "home" to co-construct a multigenerational, multilingual, geographically-dispersed community engaged in reading, writing/composing, making, speaking, and listening. Literacy-Cast was imagined, built, and enacted collaboratively among faculty, laboratory school teachers, graduate-level teacher candidates, and children (and families) in grades K-5. We hear a lot about the limitations of virtual classrooms/learning (e.g., COVID "learning loss," lack of engagement, unequal access), particularly in relation to historically marginalized communities, but rarely are we offered counter-narratives: examples where young children who live and go to school in these communities shaped the creation of new virtual spaces/places by making visible meaningful "at home" literacy/language practices, cultural artifacts, and people. Through invitations embedded in the multimodal texts/books shared on Literacy-Cast's digital bookshelf, children brought the community into their homes–bedrooms, kitchens, backyards, back porches, and backseats, reframing "home visits'' as sites/events for new kinds of community knowledge production. Research about home visits, educators visiting students' homes to learn about children's lives, has documented the impact of home environment awareness on school interactions, improved relationships between caregivers and teachers, typically focused on intervention supporting school-based achievement and school practices, often with unidirectional flow from school-to-home; however we conceptualize Literacy-Cast's daily activity as multidirectional "home visits," where invitations to come over and play, read, and write together brokered relationships and strengthened a gamut of literacy practices for all participants. Through collaborative ethnography, we explore ways "home" (e.g., objects/people/practices/languages/events) became tools/co-authors for children's digital composing/making and, ultimately, home/community-making. [ABSTRACT FROM AUTHOR]

This paper aims to get an insight into students' perceptions regarding hybrid learning/online learning, in the aftermath of the COVID pandemic. This seems to be increasingly important, as decision-making in any university takes into account the viewpoints of all the important stakeholders, in this particular case, professors and students alike. In this research study, I focused on the first year students of only one faculty of Bucharest University of Economic Studies, namely International Business and Economics. [ABSTRACT FROM AUTHOR]

With the rapid advancement of intelligent transportation systems (ITS) and vehicular communications, vehicular edge computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment. [ABSTRACT FROM AUTHOR]

Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large scales that are both flexible and efficient have been hindered by a lack of a suitable algorithmic framework. As a result, most neuromorphic hardware are trained off-line on large clusters of dedicated processors or GPUs and transferred post hoc to the device. We address this by introducing the neural and synaptic array transceiver (NSAT), a neuromorphic computational framework facilitating flexible and efficient embedded learning by matching algorithmic requirements and neural and synaptic dynamics. NSAT supports event-driven supervised, unsupervised and reinforcement learning algorithms including deep learning. We demonstrate the NSAT in a wide range of tasks, including the simulation of Mihalas-Niebur neuron, dynamic neural fields, event-driven random back-propagation for event-based deep learning, event-based contrastive divergence for unsupervised learning, and voltage-based learning rules for sequence learning. We anticipate that this contribution will establish the foundation for a new generation of devices enabling adaptive mobile systems, wearable devices, and robots with data-driven autonomy.

We study various discrete nonlinear combinatorial optimization problems in an online learning framework. In the first part, we address the computational complexity of designing vanishing regret (and vanishing approximate regret) algorithms. We provide a general reduction showing that many (min-max) polynomial time solvable problems not only do not have a vanishing regret, but also no vanishing approximation α -regret, for some α , unless N P = R P. In particular, for the min-max version of the vertex cover problem, which is solvable in polynomial time in the offline case, our reduction implies that there is no (2 − ϵ) -regret online randomized algorithm unless Unique Game is in RP. Besides, we prove that the bound is tight by providing an online efficient algorithm based on the online gradient descent method. In the second part, we turn our attention to online learning algorithms that are based on an offline optimization oracle that, given a set of multiple instances of the problem, is able to compute the optimum static solution that performs best on the set of instances overall. We show that for several min-max (nonlinear) discrete optimization problems, it is strongly NP -hard to solve the offline optimization oracle, even for problems that can be solved in polynomial time in the single-instance static case (e.g. min-max vertex cover, min-max perfect matching, etc.). This also provides a useful insight into the connection between the non-linear nature of some problems and the drastic change of their computational hardness when moved to an online learning setting. [ABSTRACT FROM AUTHOR]

Due to COVID-19, an annual interactive statistics and research methodology workshop for radiation oncology trainees was adapted at short notice into a live virtual format. This study aimed to evaluate trainee opinions around the educational value of the workshop, logistical aspects and impact on interactivity. A post-course on-line survey was completed by 26/42 trainee attendees (response rate 62%). For five pre-specified learning outcomes (LO), 58 to 69% of trainees agreed that the LO was completely or largely met (Likert scores 6 and 7 on a scale 1 = not met at all; 7 = completely met). All trainees felt that logistical aspects of the workshop including organisation, accessibility to the platform and sound/image quality were good or excellent. With regard to opportunities for interaction and suitability for small-group 'break-out' sessions, the majority felt that interaction could be adequately maintained whilst just under a quarter felt the delivery method was not fit for the purpose. Networking/social engagement with peers and teachers was the factor most impaired using the live virtual delivery format. Over three-quarters of trainees replied they would favour the current event or other educational sessions being offered (at least as an option) in a virtual format in the future. Cost and convenience were given as the major non-COVID-19–related benefits of virtual on-line learning. These preliminary findings provide valuable feedback to help adapt or develop further on-line educational and training initiatives that will be necessary in the COVID-19 pandemic period and beyond. [ABSTRACT FROM AUTHOR]

This paper presents how Q-learning algorithm can be applied as a general-purpose selfimproving controller for use in industrial automation as a substitute for conventional PI controller implemented without proper tuning. Traditional Q-learning approach is redefined to better fit the applications in practical control loops, including new definition of the goal state by the closed loop reference trajectory and discretization of state space and accessible actions (manipulating variables). Properties of Q-learning algorithm are investigated in terms of practical applicability with a special emphasis on initializing of Q-matrix based only on preliminary PI tunings to ensure bumpless switching between existing controller and replacing Q-learning algorithm. A general approach for design of Q-matrix and learning policy is suggested and the concept is systematically validated by simulation in the application to control two examples of processes exhibiting first order dynamics and oscillatory second order dynamics. Results show that online learning using interaction with controlled process is possible and it ensures significant improvement in control performance compared to arbitrarily tuned PI controller.

The study determined the students' adaptability challenges on online learning. It specifically identified the profile of the respondents in terms of age, sex, socio-economic status, educational attainment of the respondents' parents and the campus they belong. It also identified the academic performance of the student-respondents. Furthermore, it determined the problems faced by the students during online class along geographical location, adaptability struggle, technical issues, computer literacy, time management and self-motivation. Descriptive correlation design was utilized. Pearson-R correlation was used to determine the relationship of the respondents' profile and their academic performance and ANOVA was used to compare the problems faced by the students during online when grouped according to campus. Students' motivation also has an impact on their academic performance since they fall behind and are more likely to entertain the notion of quitting up as a result of the difficulties associated with navigating a technical medium during an online class that appears insurmountable. They are less driven to keep up with current educational trends, and they are not well prepared to face the problems of the future. They experienced burnout when taking online classes, and they were unable to comprehend online lectures because they were not sufficiently motivated to participate in online courses. [ABSTRACT FROM AUTHOR]

The current public debate on the possibilities and effectiveness of online learning, its perspective in the foreseeable future and the public opinion “forˮ and “againstˮ this educational model, are sufficient grounds for presenting such a topic. The publication reflects opinions, assessments and conclusions based on the results of studies and reports on online learning and my personal observations and shared experience. Special emphasis is placed on the ethical aspect of online learning, related to overcoming such a dangerous phenomenon as school violence. [ABSTRACT FROM AUTHOR]

We present a processing-in-memory (PIM)-based hardware platform, referred to as MONETA, for on-chip acceleration of inference and learning in hybrid convolutional spiking neural network. MONETAuses 8T static random-access memory (SRAM)-based PIM cores for vector matrix multiplication (VMM) augmented with spike-time-dependent-plasticity (STDP) based weight update. The spiking neural network (SNN)-focused data flow is presented to minimize data movement in MONETAwhile ensuring learning accuracy. MONETAsupports on-line and on-chip training on PIM architecture. The STDP-trained convolutional neural network within SNN (ConvSNN) with the proposed data flow, 4-bit input precision, and 8-bit weight precision shows only 1.63% lower accuracy in CIFAR-10 compared to the STDP accuracy implemented by the software. Further, the proposed architecture is used to accelerate a hybrid SNN architecture that couples off-chip supervised (back propagation through time) and on-chip unsupervised (STDP) training. We also evaluate the hybrid network architecture with the proposed data flow. The accuracy of this hybrid network is 10.84% higher than STDP trained accuracy result and 1.4% higher compared to the backpropagated training-based ConvSNN result with the CIFAR-10 dataset. Physical design of MONETAin 65 nm complementary metal-oxide-semiconductor (CMOS) shows 18.69 tera operation per second (TOPS)/W, 7.25 TOPS/W and 10.41 TOPS/W power efficiencies for the inference mode, learning mode, and hybrid learning mode, respectively. [ABSTRACT FROM AUTHOR]

Blended instruction integrating off-line and on-line teaching has become an important instrument for promoting educational reform and innovation. However, the results of current empirical studies diverge on the effect of blended instruction on student performance, which necessitates further research on the effectiveness of blended instruction and related factors. This study, using an evidence-based meta-analytical approach, conducts a quantitative analysis of 106 experimental and quasi-experimental studies published from January 2000 to September 2021 in China and abroad, and systematically examines the effectiveness of blended instruction. The research finds that: i) The summary effect size (ES) of the included sample is 0.669 (n=142), indicating that blended instruction has above-moderate positive effects on student performance, especially on student learning motivation and academic emotions and attitude; ii) In terms of education levels, experimental periods and class sizes, blended instruction has the most significant positive effect on junior and senior secondary school students, on a teaching period from one to three months, and on a class size of 51 to 100 students; iii) Regarding the proportion and interactive patterns of online teaching, 50% composition of online teaching and synchronous or synchronous + asynchronous interaction exert the most significant positive effects on student learning. iv) Teaching methods including task-driven learning, role-playing, inquiry-based teaching, and case-based teaching have greater positive effects on student performance than other methods. Group study yields a greater effect on promoting student learning compared to individual study. Based on the findings, the present study also makes suggestions for the effective practice of blended instruction. [ABSTRACT FROM AUTHOR]

We present a processing-in-memory (PIM)-based hardware platform, referred to as MONETA, for on-chip acceleration of inference and learning in hybrid convolutional spiking neural network. MONETAuses 8T static random-access memory (SRAM)-based PIM cores for vector matrix multiplication (VMM) augmented with spike-time-dependent-plasticity (STDP) based weight update. The spiking neural network (SNN)-focused data flow is presented to minimize data movement in MONETAwhile ensuring learning accuracy. MONETAsupports on-line and on-chip training on PIM architecture. The STDP-trained convolutional neural network within SNN (ConvSNN) with the proposed data flow, 4-bit input precision, and 8-bit weight precision shows only 1.63% lower accuracy in CIFAR-10 compared to the STDP accuracy implemented by the software. Further, the proposed architecture is used to accelerate a hybrid SNN architecture that couples off-chip supervised (back propagation through time) and on-chip unsupervised (STDP) training. We also evaluate the hybrid network architecture with the proposed data flow. The accuracy of this hybrid network is 10.84% higher than STDP trained accuracy result and 1.4% higher compared to the backpropagated training-based ConvSNN result with the CIFAR-10 dataset. Physical design of MONETAin 65 nm complementary metal-oxide-semiconductor (CMOS) shows 18.69 tera operation per second (TOPS)/W, 7.25 TOPS/W and 10.41 TOPS/W power efficiencies for the inference mode, learning mode, and hybrid learning mode, respectively.

To ensure high-quality educational processes, more educational research should be developed about student experiences during the COVID-19 pandemic. Furthermore, the student's perception about distance learning education (under the current situation) could be used as input for instructors and educational institutions to guide their distance learning process. The primary aim of this research is to explore engineering undergraduate student's perceptions about the online assessment methods during the COVID-19 age. This paper presents an exploratory-descriptive study based on content and quantitative analysis tools. The main findings of this research concern the factors perceived as the main differences between the face-to-face and online assessment by a group of engineering students during the current pandemic. These factors are: teaching presence, self-efficacy, autonomy, teamwork, and coherence between assessment and class. Furthermore, in the analyzed state-of-the-art, the last three factors have not been reported. These results will be used to guide the improvement of future online assessment methods in our engineering school. [ABSTRACT FROM AUTHOR]

This study investigates student readiness for self-directed learning and its impact on academic performance. Changes in course delivery and modality have been widespread due to governmental mandates. Courses that were traditional face-to-face or blended learning moved to online learning. In this study, survey data from 350 upper-level business students at an AACSB accredited institution were collected prior to and after the online teaching mandates. This data measured students' level of self-directed learning readiness compared to their final grade percentage. An analysis of overall self-directed learning readiness, the sub-scales of self-management, desire to learn, and self-regulation, course content, and course modality was conducted. The results showed a significant correlation between the self-management sub-scale score and the final grade percentage. No significant differences were found for the other comparisons or demographic data. The results may allow students and faculty to improve self-management behaviours before or early in the course semester to improve learning and academic performance. [ABSTRACT FROM AUTHOR]

A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft. [ABSTRACT FROM AUTHOR]

In this paper, a neuroadaptive robust trajectory tracking controller is utilized to reduce speed ripples of permanent magnet synchronous machine (PMSM) servo drive under the presence of a fracture or fissure in the rotor and external disturbances. The dynamics equations of PMSM servo drive with the presence of a fracture and unknown frictions are described in detail. Due to inherent nonlinearities in PMSM dynamic model, in addition to internal and external disturbances; a traditional PI controller with fixed parameters cannot correctly regulate the PMSM performance under these scenarios. Hence, a neuroadaptive robust controller (NRC) based on a category of on-line trained artificial neural network is used for this purpose to enhance the robustness and adaptive abilities of traditional PI controller. In this paper, the moth-flame optimization algorithm provides the optimal weight parameters of NRC and three PI controllers (off-line) for a PMSM servo drive. The performance of the NRC is evaluated in the presence of a fracture, unknown frictions, and load disturbances, likewise the result outcomes are contrasted with a traditional optimized PID controller and an optimal linear state feedback method. [ABSTRACT FROM AUTHOR]

This paper presents how Q-learning algorithm can be applied as a general-purpose selfimproving controller for use in industrial automation as a substitute for conventional PI controller implemented without proper tuning. Traditional Q-learning approach is redefined to better fit the applications in practical control loops, including new definition of the goal state by the closed loop reference trajectory and discretization of state space and accessible actions (manipulating variables). Properties of Q-learning algorithm are investigated in terms of practical applicability with a special emphasis on initializing of Q-matrix based only on preliminary PI tunings to ensure bumpless switching between existing controller and replacing Q-learning algorithm. A general approach for design of Q-matrix and learning policy is suggested and the concept is systematically validated by simulation in the application to control two examples of processes exhibiting first order dynamics and oscillatory second order dynamics. Results show that online learning using interaction with controlled process is possible and it ensures significant improvement in control performance compared to arbitrarily tuned PI controller. [ABSTRACT FROM AUTHOR]

The Charles Sturt University (CSU) Engineering programme is a new course (degree programmme) established in 2016 by a university that had not previously taught engineering. This start from scratch occasion was taken as an opportunity to build an all-new programme structure and philosophy. Students at CSU Engineering complete a sequence of three semester-long Project-Based Learning (PBL) style challenges across their time face-to-face at the university; after this point, they commence four yearlong paid industry-based work placements and continue studies in an online mode during evenings, weekends, and scheduled study days. The underlying technical curriculum for the engineering programme at Charles Sturt University is delivered mostly on-line via the RealizeIT platform and is based on a philosophy of just-in-time, self-directed learning. Students have freedom in deciding when, how and, to a large extent, which elements of the curriculum they engage within the online environment. This freedom, along with the PBL-style challenges, is enabled by the structure of the technical curriculum which is broken down into fine-grained learning activities called 'topics'. In this paper, we summarise our experiences during the first four years, and the insights gained into student behaviours when offered an opportunity to engage in self-directed learning. [ABSTRACT FROM AUTHOR]

Covid-19 is (in) a class of its own in its influence on human lives and livelihoods globally, precipitating steep learning and psychological well-being curves for university teachers and students. This has impacted dramatically on the conditions under which higher education has had to function in regard to research and what is now referred to as ‘emergency online education’. As staff face unprecedented challenges, so too do students. Given that the consequences of these times are likely to be felt well into the future, it is important to capture what is happening now. We therefore present this perspective piece comprising 13 musings co-authored by students and educators regarding our experiences of two lockdowns within Aotearoa New Zealand in 2020 representative of the disrupted university in its adjustment to learning and teaching. In contributing to calls to develop a post-pandemic pedagogy for higher education, and better support staff and student well-being, we draw on methods that would multiply questions and invoke possibilities, as an impetus for reimagining higher education. Making use of a cogenerative dialoguing process, these musings enable multiple voices to be heard and considered. A non-representational lens enables us to explore the what and how of Covid-19 creating disruption and uncertainty for students’ and educators, influencing their psychological well-being and higher education pedagogy and practices, and becoming a contextually relevant taonga (treasure) of experiences that might inform future educational activities.

Objective: To determine the response to a virtual educational curriculum in reconstructive urology presented during the COVID-19 pandemic. To assess learner satisfaction with the format and content of the curriculum, including relevance to learners' education and practice.Materials and Methods: A webinar curriculum of fundamental reconstructive urology topics was developed through the Society of Genitourinary Reconstructive Surgeons and partnering institutions. Expert-led sessions were broadcasted. Registered participants were asked to complete a survey regarding the curriculum. Responses were used to assess the quality of the curriculum format and content, as well as participants' practice demographics.Results: Our survey yielded a response rate of 34%. Survey responses showed >50% of practices offer reconstructive urologic services, with 37% offered by providers without formal fellowship training. A difference in self-reported baseline knowledge was seen amongst junior residents and attendings (P < .05). Regardless of level of training, all participants rated the topics presented as relevant to their education/practice (median response = 5/5). Responders also indicated that the curriculum supplemented their knowledge in reconstructive urology (median response = 5/5). The webinar format and overall satisfaction with the curriculum was highly rated (median response = 5/5). Participants also stated they were likely to recommend the series to others.Conclusion: We demonstrate success of an online curriculum in reconstructive urology. Given >50% of practices surveyed offer reconstruction, we believe the curriculum's educational benefits (increasing access and collaboration while minimizing the risk of in-person contact) will continue beyond the COVID-19 pandemic and that this will remain a relevant educational platform for urologists moving forward. [ABSTRACT FROM AUTHOR]

Purpose To provide a rationale for the design and implementation of a Professional Development Program for academics and present reflections on the process and outcomes provided by the program facilitators and participants. Methods The program methodology centres on a collaborative approach to professional development. However, the focus is on student-centred rather than teacher-centred learning approaches in a manner consistent with the ideals of practice/problem-based learning philosophy and methodology. Results Reflective pieces provided at the conclusion of the program demonstrated that participants satisfied the criteria for leaner-managed learning outcomes. Conclusion The context and culture of the academic environment has continuously changed over the last three decades mainly given developments in technology. However, in contexts reliant on technology enhanced learning and teaching, successful on-line learning events still need to reflect a student-centred approach. In most instances professional development opportunities should allow academics the freedom to pursue answers to their own development needs.

We study “active” decision making over sensor networks where the sensors’ sequential probing actions are actively chosen by continuously learning from past observations. We consider two network settings: with and without central coordination. In the first case, the network nodes interact with each other through a central entity, which plays the role of a fusion center. In the second case, the network nodes interact in a fully distributed fashion. In both of these scenarios, we propose sequential and adaptive hypothesis tests extending the classic Chernoff test. We compare the performance of the proposed tests to the optimal sequential test. In the presence of a fusion center, our test achieves the same asymptotic optimality of the Chernoff test, minimizing the risk, expressed by the expected cost required to reach a decision plus the expected cost of making a wrong decision, when the observation cost per unit time tends to zero. The test is also asymptotically optimal in the higher moments of the time required to reach a decision. Additionally, the test is parsimonious in terms of communications, and the expected number of channel uses per network node tends to a small constant. In the distributed setup, our test achieves the same asymptotic optimality of Chernoff’s test, up to a multiplicative constant in terms of both risk and the higher moments of the decision time. Additionally, the test is parsimonious in terms of communications in comparison to state-of-the-art schemes proposed in the literature. The analysis of these tests is also extended to account for message quantization and communication over channels with random erasures. [ABSTRACT FROM AUTHOR]

Listening and appreciating the views of the academic staff and students is critical in a learning period characterised by abrupt changes in the mode of classroom delivery where the traditional classroom teaching and learning is replaced by virtual classes. This study explored the “carrots” and “sticks” of on-line learning experienced by both academic staff and students at Uganda Martyrs University in Uganda. The study used a qualitative research approach involving in-depth interviews for academic staff and focus groups with selected university students from three academic Faculties. The study results showed that both students and academic staff found on-line learning beneficial in terms of incurring reduced transport expenses and chances of getting infected with Covid-19 due to reduced physical mobility. Due to increased use of technologies, the participants became more innovative and conscious in time use during the teaching-learning process. However, the effectiveness of on-line learning was limited by the challenges of limited data, unreliable internet connection, failure to record lectures, few zoom links at Faculty level, limited class control, and unstable attendance by students. Results further revealed the challenges of time constraint for computational subjects, assessment challenge, limited skills and knowledge in using , limited consultations, and speedy lecturers. The results provide valuable information on the progress in adjusting to the demands of the “new normal” in higher education teaching and learning. Concerted efforts of institutional leadership, academic staff and students should be the norm especially in acquiring and using ICT Infrastructure to enable academic staff and students’ transit to the “new normal”.

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In this paper, a RISE (Robust Integral of the Sign Error) controller with adaptive feedforward compensation terms based on Associative Memory Neural Network (AMNN) type B-Spline is proposed to regulate the positioning of a Delta Parallel Robot (DPR) with three degrees of freedom. Parallel Kinematic Manipulators (PKMs) are highly nonlinear systems, so the design of a suitable control scheme represents a significant challenge given that these kinds of systems are continually dealing with parametric and non-parametric uncertainties and external disturbances. The main contribution of this work is the design of an adaptive feedforward compensation term using B-Spline Neural Networks (BSNNs). They make an on-line approximation of the DPR dynamics and integrates it into the control loop. The BSNNs' functions are bounded according to the extreme values of the desired joint space trajectories that are the BSNNs' inputs, and their weights are on-line adjusted by gradient descend rules. In order to evaluate the effectiveness of the proposed control scheme with respect to the standard RISE controller, numerical simulations for different case studies under different scenarios were performed. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic is changing profoundly the obstetrics and gynecology (OB/GYN) academic clinical learning environment in many different ways. Rapid developments affecting our learners, patients, faculty and staff require unprecedented collaboration and quick, deeply consequential readjustments, almost on a daily basis. We summarized here our experiences, opportunities, challenges and lessons learned and outline how to move forward. The COVID-19 pandemic taught us there is a clear need for collaboration in implementing the most current evidence-based medicine, rapidly assess and improve the everchanging healthcare environment by problem solving and "how to" instead of "should we" approach. In addition, as a community with very limited resources we have to rely heavily on internal expertise, ingenuity and innovation. The key points to succeed are efficient and timely communication, transparency in decision making and reengagement. As time continues to pass, it is certain that more lessons will emerge. [ABSTRACT FROM AUTHOR]