Asynchronous Version Research Articles

Development and Implementation of a Family Presence Facilitator Curriculum for Interprofessional Use in Pediatric Medical Resuscitations.

Family presence during pediatric medical resuscitation has myriad benefits. However, there is significant heterogeneity in provider acceptance and implementation of the family support role. We designed this curriculum to teach all members of the health care team best practices in the Family Presence Facilitator (FPF) role during pediatric medical resuscitations. We applied Kern's six-step approach to develop an FPF curriculum comprising didactic and interactive elements, along with training for simulated participants. We implemented the curriculum through (a) live sessions (30-minute didactic or 90-minute workshop) for learners; (b) a 20-minute asynchronous version of the didactic curriculum for self-directed learning; and (c) a 1-hour, monthly, in situ simulation curriculum in a pediatric emergency department setting. Curriculum evaluation surveys queried self-reported engagement, satisfaction, relevance, confidence, commitment, knowledge, skills, and attitudes in a retrospective pre/post format. We collected data from 153 learners, including attendings, fellows, residents, advanced practice providers, medical students, and child life specialists, between October 2022 and September 2023. Only 22% of participants had received similar prior training. One hundred percent of learners found the curriculum enjoyable and engaging; learners also agreed the curriculum improved their knowledge and skills in providing empathetic and respectful communication (99%); nonspeculative, clear information (100%); and nonverbal support (99%). Of respondents, 100% believed the curriculum would improve the patient care experience. Facilitating family presence during pediatric medical resuscitations is a crucial skill. Our curriculum improves self-reported confidence, knowledge, and skills among interprofessional learners. Next steps include expanding this curriculum beyond the pediatric setting.

The asynchronous DeGroot dynamics

AbstractWe analyze the asynchronous version of the DeGroot dynamics: In a connected graph with nodes, each node has an initial opinions in and an independent Poisson clock. When a clock at a node rings, the opinion at is replaced by the average opinion of its neighbors. It is well known that the opinions converge to a consensus. We show that the expected time to reach ‐consensus is poly in undirected graphs and in Eulerian digraphs, but for some digraphs of bounded degree it is exponential. Our main result is that in undirected graphs and Eulerian digraphs, if the degrees are uniformly bounded and the initial opinions are i.i.d., then for every fixed . We give sharp estimates for the variance of the limiting consensus opinion, which measures the ability to aggregate information (“wisdom of the crowd”). We also prove generalizations to non‐reversible Markov chains and infinite graphs. New results of independent interest on fragmentation processes and coupled random walks are crucial to our analysis.

Data-Driven Optimal Bipartite Consensus Control for Second-Order Multiagent Systems via Policy Gradient Reinforcement Learning.

This article investigates the optimal bipartite consensus control (OBCC) problem for unknown second-order discrete-time multiagent systems (MASs). First, the coopetition network is constructed to describe the cooperative and competitive relationships between agents, and the OBCC problem is proposed by the tracking error and related performance index function. Based on the distributed policy gradient reinforcement learning (RL) theory, a data-driven distributed optimal control strategy is obtained to guarantee the bipartite consensus of all agents' position and velocity states. In addition, the offline data sets ensure the learning efficiency of the system. These data sets are generated by running the system in real time. Besides, the designed algorithm is an asynchronous version, which is essential to solve the challenge caused by the computational ability difference between nodes in MASs. Then, by means of the functional analysis and Lyapunov theory, the stability of the proposed MASs and the convergence of the learning process are analyzed. Furthermore, an actor-critic structure containing two neural networks is used to implement the proposed methods. Finally, a numerical simulation shows the effectiveness and validity of the results.

The newsvendor problem with GHG emissions from waste disposal under cap-and-trade regulation

We study a newsvendor problem with greenhouse gas (GHG) emissions at the disposal stage regulated by a cap-and-trade policy. We first consider a synchronous version of the problem where the replenishment period and the emission assessment period are the same. For this problem, we propose simple algorithms to derive the optimal quantity and cost for both continuous and discrete demand distributions. We show that ignoring the carbon tax may lead to large overcost, especially when the GHG quota is small. For a normally distributed demand, we also show that the demand standard deviation has a non-linear and non-monotonic effect on the order quantity. We then investigate an asynchronous version of the problem where replenishment decisions occur at a higher frequency than the quota allocation. We formulate this lot sizing problem as a Markov decision process and solve it with a dynamic programming algorithm whose complexity is assessed. Through a numerical experimentation and theoretical results, we demonstrate how and when the flexibility of the optimal dynamic replenishment decisions allows to reduce costs. When approaching the end of the time horizon, the uncertainty reduction allows the decision maker to adopt a riskier policy and order larger quantities.

Evaluation of the efficiency of implementation of asynchronous computing algorithms using coroutines and threads in С++

Modern multi-core systems are most effective when used in large server centers and for cloud computing. However, despite the known complexity of software implemen-tation, parallel computing on multiprocessors is increasingly used in computer model-ling. Advanced mechanisms of synchronous and multithreaded programming are in-creasingly used to improve the productivity of numerical studies, reducing the time of computer models implementation. One such mechanism is coroutines, a convenient tool for managing asynchronous operations introduced in the C++20 standard. A special feature of coroutines is the ability to suspend a function at a certain stage, saving its state, and after some time resume its execution from the previous stop. The aim of this research is to improve the performance of computer modelling by using coroutines and data threads. As a result of the work, a test algorithm for multiplying a matrix by a vector and its modified asynchronous version using the coroutine mechanism and splitting into two data threads was developed, which allowed to achieve 1.94 times increase in the com-puting speed when the matrix dimension is 15000 (2.25×106 elements). It has been found that at a small matrix dimension, the developed asynchronous algorithm using coroutines and splitting into two threads is less efficient than the single thread algo-rithm. This is due to the fact that the compiler needs some time to create threads and start execution simultaneously. With a large dimensionality, the performance of the asynchronous algorithm increases significantly. With a matrix dimension of more than 1200, the use of an asynchronous algorithm divided into two threads is guaranteed to be more efficient than a single-threaded. The data obtained are consistent with the results of similar studies of the problem of increasing the efficiency of computer modelling using alternative software and hard-ware. The new method of solving the problems of asynchronous programming provides a more efficient and simple mechanism for managing asynchronous operations.

Penerapan Blended Learning pada Pembelajaran Tematik di Sekolah Dasar Daerah Aliran Sungai Kahayan Kota Palangka Raya

This study aimed to determine the learning model at SDN 1 Menteng, Palangka Raya City during the COVID-19 pandemic. This study used a qualitative approach. The type of research used is descriptive. Sources of data taken from teachers, principals and documentation. Data collection methods were observation, interviews, and documentation. Test the validity of the data using Source Triangulation and Technique Triangulation. The methods of analysis were Data Reduction, Data Presentation, and Verification. The results of the study were: 1). The teacher prepared a blended learning lesson plan 2). Blended learning was carried out with a synchronous version, direct learning at school and through google meet, and the results of assignments were collected in boxes in classroom; and the asynchronous version, they used the whatsapp or google classroom application, 3). Evaluation of the application of blended learning used WhatsApp in giving questions to students then the results of student work were collected in boxes in classroom. 4). Supporting factors were the whatsapp application, google classroom, google meet and student handbooks. The inhibiting factor was the regulation of mass accumulation when collecting assignments.

Asynchronising five-fold symmetry sequence for better homonuclear polarisation transfer in magic-angle-spinning solid-state NMR

Recoupling, decoupling, and multidimensional correlation experiments in magic-angle-spinning (MAS) solid-state NMR can be designed by exploiting the symmetry of internal spin interactions. One such scheme, namely, C521, and its supercycled version SPC521, notated as a five-fold symmetry sequence, is widely used for double-quantum dipole-dipole recoupling. Such schemes are generally rotor synchronised by design. We demonstrate an asynchronous implementation of the SPC521 sequence leading to higher double-quantum homonuclear polarisation transfer efficiency compared to the normal synchronous implementation. Rotor-synchronisation is broken in two different ways: lengthening the duration of one of the pulses, denoted as pulse-width variation (PWV), and mismatching the MAS frequency denoted as MAS variation (MASV). The application of this asynchronous sequence is shown on three different samples, namely, U–13C-alanine and 1,4-13C-labelled ammonium phthalate which include 13Cα-13Cβ, 13Cα-13Co, and 13Co–13Co spin systems, and adenosine 5′- triphosphate disodium salt trihydrate (ATP⋅3H2O). We show that the asynchronous version performs better for spin pairs with small dipole-dipole couplings and large chemical-shift anisotropies, for example, 13Co–13Co. Simulations and experiments are shown to corroborate the results.

Asynchronous global–local non-invasive coupling for linear elliptic problems

This paper presents the first asynchronous version of the Global/Local non-invasive coupling, capable of dealing efficiently with multiple, possibly adjacent, patches. We give a new interpretation of the coupling in terms of primal domain decomposition method, and we prove the convergence of the relaxed asynchronous iteration. The asynchronous paradigm lifts many bottlenecks of the Global/Local coupling performance. We illustrate the method on several linear elliptic problems as encountered in thermal and elasticity studies.

TELE-SAVVY@HOME: CONVERTING TO A COMPLETELY ASYNCHRONOUS DELIVERY APPROACH

Abstract It is well established that family caregiving is taxing and stressful. Group-based psychoeducational programs such as the Tele-Savvy program demonstrated that the acquisition of skills, knowledge, and caregiving mastery can ameliorate caregiving stress – and enhance PLWD quality of life. Many factors, however, preclude caregivers’ attendance in the synchronous portions of Tele-Savvy limiting the program’s scalability. To make Tele-Savvy more accessible and available to a growing number of dementia family caregivers, we designed a fully asynchronous version of the Tele-Savvy program, Tele-Savvy@Home. To accomplish this, we consulted facilitators and caregivers who participated in Tele-Savvy. We also reviewed the existing curriculum, re-wrote portions, reordered modules, and created new videos to cover synchronous content. In addition, we partnered with education design specialists to create interactive and reflection exercises to maintain a psychoeducation orientation and strengthen caregivers’ self-efficacy. This strategy may help investigators in their augmentation of the delivery strategies of established interventions.

METAMORPHOSES: THE CONTINUING EVOLUTION AND ADAPTATION OF THE SAVVY CAREGIVER PROGRAM

Abstract This symposium traces the nearly 30 years of development projects, trials, and transformations that continue to occur in efforts to make an evidence-based psychoeducation program (Savvy Caregiver) available to family members and friends who provide care to community-dwelling persons living with dementia illnesses. Savvy seeks to enhance caregivers’ knowledge and skills for providing care and to enhance their competence and confidence (sense of mastery) in being able to guide their persons through days that are as safe, calm, and pleasant as possible. Over this period of time, Savvy has clarified its target participant group (focusing on primary caregivers rather than larger family groups), moved from having concurrent sessions for caregivers and persons living with dementia, continued to develop strategies that enable broad implementation while maintaining program fidelity, demonstrated the effectiveness of a synchronous/asynchronous online version that extends program reach (Tele-Savvy), and is currently exploring in an ongoing pilot how best to deliver a fully asynchronous version of the program that employs continuing education principles (Tele-Savvy@Home). The individual presentations will describe the main developmental milestones (investigator led to protocol-trained to online trained interventionist; in-person to synchronous/asynchronous to fully asynchronous) and the structural adaptations that enabled them. Particular emphasis will be placed on design decisions linked to maintaining a psychoeducational orientation and to program emphasis on developing or strengthening caregivers’ self-efficacy for providing effective day-to-day care. Savvy’s experience may help investigators in designing new interventions, particularly online interventions or in considering augmentation or delivery platform change of established interventions.

Instructional media selection principles for online medical education and emerging models for the new normal

The COVID-19 pandemic, and the resulting need to avoid in-person classes, compelled many faculty members to convert to a completely online instructional format. The literature on selecting media for medical educators, however, provided little assistance for them to make choices that facilitated learning through using alternative online instruction practices. In this study, we addressed the lack of guidance for the use of media to facilitate the effective online medical education. To optimise the transition from face-to-face educational modalities to online learning, we incorporated insights from theories of media synchronicity and learning. We considered the value of existing learning theories in influencing how we could guide entrenched face-to-face educators to online learning practice. Therefore, we employed existing theories and practice to assist in developing an algorithmic approach to guiding these educators. We reassessed the way taxonomies of learning objectives, practice-oriented learning experiences, the social and collaborative features of learning activities, and media synchronicity theory could have augmented face-to-face teaching, and influenced how these could be reconfigured to assist in the transition to online learning. Consequently, we have developed key principles to inform the continuity of design and selection of instructional media in the transition to medical online learning. We have constructed specific criteria for media selection that correspond to the 12 goals of medical learning. We found that the majority of the goals can be more enhanced by synchronous media than asynchronous versions. We discuss the role of instructional media in emergency online medical education as well as emerging models of media selection for the new normal in medical education and future directions for medical education media research.

Becoming More Resilient during COVID-19: Insights from a Process Evaluation of Digital Resilience Training.

Resilience training is gaining attention as a strategy to build students’ resistance to adversity and promote their mental well-being. However, owing to inconsistencies and variations in the content and delivery of resilience training, more work is needed to examine students’ experiences and preferences to address issues relating to intervention fidelity. This study adopted a qualitative approach in exploring students’ experience of synchronous and asynchronous versions of a digital resilience training program. Seventeen students were interviewed using a semi-structured virtual face-to-face interview via Zoom. The thematic analyses unveiled four themes: embarking on a journey toward resilience, discovering strategies to develop resilience, finding a balance to benefit from resilience skill enhancement, and instilling resilience in the everyday. Future resilience training should consider students’ workload and interactivity to enhance their engagement. As being resilient is associated with better mental well-being, the findings of this study may support the development of future wellness programs.

An efficient distributed algorithm for economic dispatch considering communication asynchrony and time delays

The increasing integration of distributed energy resources (DERs) to power grids has fostered the emergence of advanced control technologies for energy management. This study focuses on distributed solutions to economic dispatch considering asynchrony and non-ideal communications. A distributed consensus-based algorithm is developed for general directed networks, where heavy-ball momentum is incorporated into the algorithm design to accelerate convergency. Subsequently, an asynchronous version is proposed based on a general asynchronous communication model with unpredictable, non-uniform, and bounded time delays. Without a global clock in conventional synchronous distributed algorithms, distributed generators (DGs) are allowed to exchange information through local communications at any moment and use out-sync information to execute new updates. Simulations are presented to demonstrate the performance of the proposed algorithm and verify its robustness against time delays.

Distributed Primal–Dual Splitting Algorithm for Multiblock Separable Optimization Problems

This article considers the distributed structured optimization problem of collaboratively minimizing the global objective function composed of the sum of local cost functions. Each local objective function involves a Lipschitz-differentiable convex function, a nonsmooth convex function, and a linear composite nonsmooth convex function. For such problems, we derive the synchronous distributed primal–dual splitting (S-DPDS) algorithm with uncoordinated stepsizes. Meanwhile, we develop the asynchronous version of the algorithm in light of the randomized block-coordinate method (A-DPDS). Further, the convergence results show the relaxed range and concise form of the acceptable parameters, which indicates that the algorithms are conducive to the selection of parameters in practical applications. Finally, we demonstrate the efficiency of S-DPDS and A-DPDS algorithms by numerical experiments.

Data-Based Optimal Consensus Control for Multiagent Systems With Policy Gradient Reinforcement Learning.

This article investigates the optimally distributed consensus control problem for discrete-time multiagent systems with completely unknown dynamics and computational ability differences. The problem can be viewed as solving nonzero-sum games with distributed reinforcement learning (RL), and each agent is a player in these games. First, to guarantee the real-time performance of learning algorithms, a data-based distributed control algorithm is proposed for multiagent systems using offline system interaction data sets. By utilizing the interactive data produced during the run of a real-time system, the proposed algorithm improves system performance based on distributed policy gradient RL. The convergence and stability are guaranteed based on functional analysis and the Lyapunov method. Second, to address asynchronous learning caused by computational ability differences in multiagent systems, the proposed algorithm is extended to an asynchronous version in which executing policy improvement or not of each agent is independent of its neighbors. Furthermore, an actor-critic structure, which contains two neural networks, is developed to implement the proposed algorithm in synchronous and asynchronous cases. Based on the method of weighted residuals, the convergence and optimality of the neural networks are guaranteed by proving the approximation errors converge to zero. Finally, simulations are conducted to show the effectiveness of the proposed algorithm.

A Modified Progressive Hedging Approach for Multistage Intraday Trade of EV aggregators

The growing prominence of electric vehicle (EV) aggregators in the modern power system is drawing more attention towards modeling their behavior in the short-term electricity markets. The demand-side flexibility offered by the EVs can be leveraged to reduce their charging costs. In this paper, the participation of an EV aggregator in the intraday and balancing market is modeled as a multistage stochastic programming problem. The computational complexity introduced by the peculiarities of the intraday market is solved by a progressive hedging algorithm (PHA), a scenario-based decomposition technique. A randomized scenario sampling approach is implemented to accelerate the PHA which is further improved with a parallel randomized PHA. Finally, an asynchronous version of the parallel randomized PHA is leveraged to speed up the multistage model of EV aggregator trading. We compare the computation time of the modified versions of the PHA algorithm with the conventional PHA for the proposed EV aggregator model. Furthermore, we also show the value of EV aggregator trading in the intraday and balancing markets by comparing its cost to baseline models.

Decentralized Dual Operator Splitting for Nonsmooth Composite Optimization

Inspired by the advantages of the decentralized network structure in large-scale data processing and system robustness, this paper considers a fully distributed computation strategy for a nonsmooth composite optimization problem. The goal is to solve the problem only by resorting to local computation and local communications. Here, the local objective function is composed of two possibly nonsmooth functions where one is strongly convex and the other is proper closed convex (not necessarily strongly convex). Through associating variables with the agents and edges of the network, a decentralized synchronous algorithm is developed based on the dual operator splitting method. The asynchronous version of the proposed algorithm is further developed based on the randomized block coordinate descent technique, where each agent performs the algorithm only under its independent activation. Then the convergence analysis is provided depending on the fixed point iterations and the property of strong duality. Simulations on regularized problems demonstrate the effectiveness of the proposed decentralized algorithm.

A Study on School-Based Teacher Collaboration in Designing and Implementing Online English Classes for University Students

This qualitative case study investigates a school-based teacher learning community’s efforts in conducting online synchronous and asynchronous English classes for college students. It analyzes English instructors’ experiences and opinions as well as students’ responses. It explores important considerations for effective online teaching in a school-based learning environment. To this end, this study conducted interviews with four college English professors who are members of the teacher learning community and gave questionnaires to 23 female college students who enrolled in College English (Listening and Speaking) during the COVID-19 pandemic to determine how satisfied they were with the online classes. The results show that students were satisfied with both asynchronous and synchronous online English classes although they reported that their workload increased for the asynchronous version. Designing and implementing the online English classes was challenging for the instructors, although the teachers reported having enhanced their expertise. Based on these results, this study makes suggestions regarding the enhancement of teacher professional development through a teacher learning community. It also provides implications for how a teacher learning community can take shape, function, grow, and impact learning, and how it can support teaching online English classes. Further research is required, particularly in regard to teacher professional development through a school-based teacher learning community.

Synchronous and asynchronous optimized Schwarz methods for Poisson's equation in rectangular domains

Convergence results for optimized Schwarz methods (OSM) applied as solvers for Poisson's equation in a bounded rectangular domain with Dirichlet (physical) boundary conditions and zeroth-order (Robin) artificial transmission conditions between subdomains are presented. The analysis presented applies to a continuous formulation on an arbitrary number of subdomains with cross points. Both synchronous and asynchronous versions of OSM are discussed. Convergence theorems are presented, and it is shown numerically that the hypotheses of these theorems are satisfied for certain configurations of the subdomains. Additional numerical experiments illustrate the practical behavior of the methods discussed.

A Comparison of Family Medicine Resident Documentation Following Implementation of an Asynchronous vs Synchronous Telemedicine Curriculum.

Prior to the start of the 2020 COVID pandemic, the use of telemedicine among family physicians was limited; telemedicine curriculum in undergraduate and graduate medical education (GME) was even more scarce. In response to the need for training, we developed synchronous and asynchronous versions of a telemedicine curriculum focused on documentation, communication, and virtual physical exam. As the evaluation of the curriculum, this study compares the documentation behaviors of the clinicians participating in the curriculum. We compared the documentation practice of asynchronous learners to those participating in synchronous learning over 1 month. We reviewed each clinical note for five practice behaviors: (1) consent for delivery of care via telemedicine, (2) time on the phone, (3) physical examination, (4) procedure code, and (5) billing code. We reviewed notes from 11 interns (synchronous) and 22 senior residents (asynchronous). Notes written by an intern were significantly more likely to include documentation of consent and a focused exam. Notes written by senior resident were significantly more likely to include documentation of length of the encounter. We detected no significant differences for documenting the billing or procedure code. Our analysis determined that correct documentation behaviors can be taught through asynchronous mediums. Components requiring effective communication (consent for care and a virtual physical exam) are more effectively taught when there is deliberate practice and immediate feedback on the skills.