Rapid Iteration Research Articles

Transition of a Collaborative In-Person Health Care Innovation Course to Online Learning.

As COVID-19 forced in-person courses to transition online, an active learning course focused on design thinking in health and health care embraced the challenge. Lessons learned, mistakes made, and thoughts on the future of online education in nursing are discussed. During the online transition, it was thought the flipped-classroom approach would transition well using the same design thinking methodology taught in the course. Because the course promoted rapid innovation and iteration, such a challenge served as a call to action, and the course became a valuable real-time case study. Based on student surveys, the overall quality of the transitioned course increased slightly compared with the previous semester's in-person course, indicating schools of nursing can innovate both the way students are taught as well as what students are taught. Rather than mourn the loss of in-person learning, the newfound possibilities of virtual education should celebrated. [J Nurs Educ. 2021;60(5):298-300.].

Participatory design to create a VR therapy for psychosis

This paper describes how participatory design was employed in the design of an automated Virtual Reality (VR) psychological therapy (gameChange), putting people with lived experience of psychosis at the heart of the process. Solutions to complex challenges invariably need to include the expertise and ideas of specialists from a broad variety of disciplines and experiences. The design of gameChange relied on the insights of clinical psychologists, programmers, animators, designers, product managers, producers, writers, researchers, 3 D artists, mental health advocates, and people with lived experience of psychosis. This involved a considerable diversity of working cultures, professional disciplines, and vocabulary. A transdisciplinary, participatory design process was established during the project. It allowed for rapid iteration, meaningful input from people with lived experience of psychosis, and delivered a VR psychological therapy with robust cognitive therapeutic principles. The structures put in place to support the different disciplines working together on the design, particularly people with lived experience of psychosis, are detailed in this paper, with examples of how decisions were made and their outcomes. The clinical effectiveness of the gameChange VR therapy is now being tested in a randomized controlled trial with several hundred patients with psychosis. https://gamechangevr.com/intro_video/

Characterization of Inkjet-Printed Digital Microfluidics Devices.

Digital microfluidics (DMF) devices enable precise manipulation of small liquid volumes in point-of-care testing. A printed circuit board (PCB) substrate is commonly utilized to build DMF devices. However, inkjet printing can be used to fabricate DMF circuits, providing a less expensive alternative to PCB-based DMF designs while enabling more rapid design iteration cycles. We demonstrate the cleanroom-free fabrication process of a low-cost inkjet-printed DMF circuit. We compare Kapton and polymethyl methacrylate (PMMA) as dielectric coatings by measuring the minimal droplet actuation voltage for a range of actuation frequencies. A minimum actuation voltage of 5.6 V was required for droplet movement with the PMMA layer thickness of 0.2 μm and a hydrophobic layer of 0.17 μm. Significant issues with PMMA dielectric breakdown were observed at actuation voltages above 10 V. In comparison, devices that utilized Kapton were found to be more robust, even at an actuation voltage up to 100 V.

Generating Intervention Concepts for Reducing Adolescent Relationship Abuse Inequities Among Sexual and Gender Minority Youth: Protocol for a Web-Based, Longitudinal, Human-Centered Design Study.

BackgroundSexual and gender minority youth (SGMY; eg, lesbian, gay, bisexual, and transgender youth) are at greater risk than their cisgender heterosexual peers for adolescent relationship abuse (ARA; physical, sexual, or psychological abuse in a romantic relationship). However, there is a dearth of efficacious interventions for reducing ARA among SGMY. To address this intervention gap, we designed a novel web-based methodology leveraging the field of human-centered design to generate multiple ARA intervention concepts with SGMY.ObjectiveThis paper aims to describe study procedures for a pilot study to rigorously test the feasibility, acceptability, and appropriateness of using web-based human-centered design methods with SGMY to create novel, stakeholder-driven ARA intervention concepts.MethodsWe are conducting a longitudinal, web-based human-centered design study with 45-60 SGMY (aged between 14 and 18 years) recruited via social media from across the United States. Using MURAL (a collaborative, visual web-based workspace) and Zoom (a videoconferencing platform), the SGMY will participate in four group-based sessions (1.5 hours each). In session 1, the SGMY will use rose-thorn-bud to individually document their ideas about healthy and unhealthy relationship characteristics and then use affinity clustering as a group to categorize their self-reported ideas based on similarities and differences. In session 2, the SGMY will use rose-thorn-bud to individually critique a universal evidence-based intervention to reduce ARA and affinity clustering to aggregate their ideas as a group. In session 3, the SGMY will use a creative matrix to generate intervention ideas for reducing ARA among them and force-rank the intervention ideas based on their potential ease of implementation and potential impact using an importance-difficulty matrix. In session 4, the SGMY will generate and refine intervention concepts (from session 3 ideations) to reduce ARA using round robin (for rapid iteration) and concept poster (for fleshing out ideas more fully). We will use content analyses to document the intervention concepts. In a follow-up survey, the SGMY will complete validated measures about the feasibility, acceptability, and appropriateness of the web-based human-centered design methods (a priori benchmarks for success: means >3.75 on each 5-point scale).ResultsThis study was funded in February 2020. Data collection began in August 2020 and will be completed by April 2021.ConclusionsThrough rigorous testing of the feasibility of our web-based human-centered design methodology, our study may help demonstrate the use of human-centered design methods to engage harder-to-reach stakeholders and actively involve them in the co-creation of relevant interventions. Successful completion of this project also has the potential to catalyze intervention research to address ARA inequities for SGMY. Finally, our approach may be transferable to other populations and health topics, thereby advancing prevention science and health equity.International Registered Report Identifier (IRRID)DERR1-10.2196/26554

Practical Efficient Regional Land-Use Planning Using Constrained Multi-Objective Genetic Algorithm Optimization

Practical efficient regional land-use planning requires planners to balance competing uses, regional policies, spatial compatibilities, and priorities across the social, economic, and ecological domains. Genetic algorithm optimization has progressed complex planning, but challenges remain in developing practical alternatives to random initialization, genetic mutations, and to pragmatically balance competing objectives. To meet these practical needs, we developed a Land use Intensity-restricted Multi-objective Spatial Optimization (LIr-MSO) model with more realistic patch size initialization, novel mutation, elite strategies, and objectives balanced via nominalizations and weightings. We tested the model for Dapeng, China where experiments compared comprehensive fitness (across conversion cost, Gross Domestic Product (GDP), ecosystem services value, compactness, and conflict degree) with three contrast experiments, in which changes were separately made in the initialization and mutation. The comprehensive model gave superior fitness compared to the contrast experiments. Iterations progressed rapidly to near-optimality, but final convergence involved much slower parent–offspring mutations. Tradeoffs between conversion cost and compactness were strongest, and conflict degree improved in part as an emergent property of the spatial social connectedness built into our algorithm. Observations of rapid iteration to near-optimality with our model can facilitate interactive simulations, not possible with current models, involving land-use planners and regional managers.

Ultra-fast charging in aluminum-ion batteries: electric double layers on active anode

With the rapid iteration of portable electronics and electric vehicles, developing high-capacity batteries with ultra-fast charging capability has become a holy grail. Here we report rechargeable aluminum-ion batteries capable of reaching a high specific capacity of 200 mAh g−1. When liquid metal is further used to lower the energy barrier from the anode, fastest charging rate of 104 C (duration of 0.35 s to reach a full capacity) and 500% more specific capacity under high-rate conditions are achieved. Phase boundaries from the active anode are believed to encourage a high-flux charge transfer through the electric double layers. As a result, cationic layers inside the electric double layers responded with a swift change in molecular conformation, but anionic layers adopted a polymer-like configuration to facilitate the change in composition.

Research on the Construction of Configuration Software for Building Electrical Equipment Computer Internet of Things System

With the rapid iteration of computer information technology, the application of computer-based configuration software in building electrical equipment can further improve the functionality of the building itself, meet people’s needs for building residence, office and other activities, improve people’s living and use experience and feelings, and help to achieve energy conservation and emission reduction, and reduce the use cost of buildings, therefore, it has important research value. Based on this, this paper first analyses the characteristics and application process of the configuration software of the computer Internet of things system, and then gives the construction and application strategy of the configuration software of the computer Internet of things for building electrical equipment.

Fine-grained lineage for safer notebook interactions

Computational notebooks have emerged as the platform of choice for data science and analytical workflows, enabling rapid iteration and exploration. By keeping intermediate program state in memory and segmenting units of execution into so-called "cells", notebooks allow users to enjoy particularly tight feedback. However, as cells are added, removed, reordered, and rerun, this hidden intermediate state accumulates, making execution behavior difficult to reason about, and leading to errors and lack of reproducibility. We present nbsafety, a custom Jupyter kernel that uses runtime tracing and static analysis to automatically manage lineage associated with cell execution and global notebook state. nbsafety detects and prevents errors that users make during unaided notebook interactions, all while preserving the flexibility of existing notebook semantics. We evaluate nbsafety's ability to prevent erroneous interactions by replaying and analyzing 666 real notebook sessions. Of these, nbsafety identified 117 sessions with potential safety errors, and in the remaining 549 sessions, the cells that nbsafety identified as resolving safety issues were more than 7X more likely to be selected by users for re-execution compared to a random baseline, even though the users were not using nbsafety and were therefore not influenced by its suggestions.

Computerized Care-Pathways (CCPs) System to Support Person-Centered, Integrated, and Proactive Care in Home-Care Settings

ABSTRACT This paper describes the software design/development process leading to an improved computerized clinical/management solution—RSIPA (2016 version)—integrating care pathways (CPs) specifically designed to meet the needs of frail and disabled older adults in home care. The development methodology used Soft Systems Methodology (SSM) for the initial system design and participatory design (PD) to involve stakeholders and end users, along with AGILE SCRUM methodology to provide rapid iterations in adapting to new requests. Given scarce project resources, we opted to combine methodologies to efficiently deliver a fully functional system for three of the five CP clinical phases. The development methodology aggregated assessment-based data to identify risk factors and assist in needs prioritization leading to care plans and addressed in the current system. The new Quebec RSIPA solution incorporating CCPs is a promising example of technologies that support person-centered care, clinical and management processes, and proactive care in home-care settings.

ProtoChat

Similar to a design process for designing graphical user interfaces, conversation designers often apply an iterative design process by defining a conversation flow, testing with users, reviewing user data, and improving the design. While it is possible to iterate on conversation design with existing chatbot prototyping tools, there still remain challenges in recruiting participants on-demand and collecting structured feedback on specific conversational components. These limitations hinder designers from running rapid iterations and making informed design decisions. We posit that involving a crowd in the conversation design process can address these challenges, and introduce ProtoChat, a crowd-powered chatbot design tool built to support the iterative process of conversation design. ProtoChat makes it easy to recruit crowd workers to test the current conversation within the design tool. ProtoChat's crowd-testing tool allows crowd workers to provide concrete and practical feedback and suggest improvements on specific parts of the conversation. With the data collected from crowd-testing, ProtoChat provides multiple types of visualizations to help designers analyze and revise their design. Through a three-day study with eight designers, we found that ProtoChat enabled an iterative design process for designing a chatbot. Designers improved their design by not only modifying the conversation design itself, but also adjusting the persona and getting UI design implications beyond the conversation design itself. The crowd responses were helpful for designers to explore user needs, contexts, and diverse response formats. With ProtoChat, designers can successfully collect concrete evidence from the crowd and make decisions to iteratively improve their conversation design.

A Novel Numerical-Analytical Approach Using FIPIM for Critical Eigenvalue Investigation Meanwhile Solving the Corresponding Bratu's Problem

In this paper, a novel numerical-analytical method is proposed, using Function Iteration based on Precise Integration Method (FIPIM) and variational method, to solve the critical point (the critical eigenvalue and the corresponding solution) of one-dimensional nonlinear Bratu's boundary value problem. The proposed method is supported by the numerical examples with high accuracy results (arbitrary high order precise) and rapid iteration convergence.

Intelligent vehicle modeling design based on image processing

With the urgent demand of consumers for diversified automobile modeling, simple, efficient, and intelligent automobile modeling analysis and modeling method is an urgent problem to be solved in current automobile modeling design. The purpose of this article is to analyze the modeling preference and trend of the current automobile market in time, which can assist the modeling design of new models of automobile main engine factories and strengthen their branding family. Intelligent rapid modeling shortens the current modeling design cycle, so that the product rapid iteration is to occupy an active position in the automotive market. In this article, aiming at the family analysis of automobile front face, the image database of automobile front face modeling analysis was created. The database included two data sets of vehicle signs and no vehicle signs, and the image data of vehicle front face modeling of most models of 22 domestic mainstream brands were collected. Then, this article adopts the image classification processing method in computer vision to conduct car brand classification training on the database. Based on ResNet-8 and other model architectures, it trains and classifies the intelligent vehicle brand classification database with and without vehicle label. Finally, based on the shape coefficient, a 3D wireframe model and a curved surface model are obtained. The experimental results show that the 3D curve model can be obtained based on a single image from any angle, which greatly shortens the modeling period by 92%.

Emergency ventilator for COVID-19.

The COVID-19 pandemic disrupted the world in 2020 by spreading at unprecedented rates and causing tens of thousands of fatalities within a few months. The number of deaths dramatically increased in regions where the number of patients in need of hospital care exceeded the availability of care. Many COVID-19 patients experience Acute Respiratory Distress Syndrome (ARDS), a condition that can be treated with mechanical ventilation. In response to the need for mechanical ventilators, designed and tested an emergency ventilator (EV) that can control a patient’s peak inspiratory pressure (PIP) and breathing rate, while keeping a positive end expiratory pressure (PEEP). This article describes the rapid design, prototyping, and testing of the EV. The development process was enabled by rapid design iterations using additive manufacturing (AM). In the initial design phase, iterations between design, AM, and testing enabled a working prototype within one week. The designs of the 16 different components of the ventilator were locked by additively manufacturing and testing a total of 283 parts having parametrically varied dimensions. In the second stage, AM was used to produce 75 functional prototypes to support engineering evaluation and animal testing. The devices were tested over more than two million cycles. We also developed an electronic monitoring system and with automatic alarm to provide for safe operation, along with training materials and user guides. The final designs are available online under a free license. The designs have been transferred to more than 70 organizations in 15 countries. This project demonstrates the potential for ultra-fast product design, engineering, and testing of medical devices needed for COVID-19 emergency response.

Covid-19 ICU remote-learning course (CIRLC): Rapid ICU remote training for frontline health professionals during the COVID-19 pandemic in the UK.

The unprecedented increase in critically ill patients due to the COVID-19 pandemic mandated rapid training in critical care for redeployed staff to work safely in intensive care units (ICU). The COVID-19 ICU Remote-Learning Course (CIRLC) is a remote delivery course developed in response to the pandemic. This was a one-day course focused on the fundamentals of Intensive Care. The course used blended learning with recorded lectures and interactive tutorials delivered by shielding and frontline ICU trained professionals. The course was developed within one week and piloted at three NHS Trusts. It was then made publicly available free of charge to redeployed healthcare professionals across the UK and Ireland. An iterative cycle of improvement was used to update the course content weekly. A course confidence questionnaire with quantitative and qualitative questions was used to evaluate effectiveness. Data is reported as n (%), means (SD) and thematic analysis was used for the open questions. 1,269 candidates from 171 organisations completed the course, with 99 volunteer trainers. 96% of respondents rated the course as very or extremely useful. 86% rated the online platform as excellent. Overall confidence improved from 2.7/5 to 3.9/5. Qualitative data showed that the course was pitched at the appropriate level, accessible and built clinicians confidence to work in intensive care. This model of educational delivery with a rapid iteration cycle was a pragmatic, effective solution to knowledge-based training under social distancing measures. Whilst full course evaluation was not possible, we believe that this work demonstrates practical guidance on educational response in a pandemic as well as highlighting the altruistic nature of the critical care community.

Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy.

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue.

P.652 Hypernutrition due to litter reduction induces depressive behavior and decreased GFAP astrocyte expression in prepubertal rats with no oxytocin influence

Simultaneous measurements of glucose, lactate, and neurotransmitters (e.g., glutamate) in cell culture over hours and days can provide a more dynamic and longitudinal perspective on ways neural cells respond to various drugs and environmental cues. Compared with conventional microfabrication techniques, direct writing of conductive ink is cheaper, faster, and customizable, which allows rapid iteration for different applications. Using a simple direct writing technique, we printed biosensor arrays onto cell culture dishes, flexible laminate, and glass to enable multianalyte monitoring. The ink was a composite of PEDOT:PSS conductive polymer, silicone, activated carbon, and Pt microparticles. We applied 0.5% Nafion to the biosensors for selectivity and functionalized them with oxidase enzymes. We characterized biosensors in phosphate-buffered saline and in cell culture medium supplemented with fetal bovine serum. The biosensor arrays measured glucose, lactate, and glutamate simultaneously and continued to function after incubation in cell culture at 37 °C for up to 2 days. We cultured primary human astrocytes on top of the biosensor arrays and placed arrays into astrocyte cultures. The biosensors simultaneously measured glucose, glutamate, and lactate from astrocyte cultures. Direct writing can be integrated with microfluidic organ-on-a-chip platforms or as part of a smart culture dish system. Because we print extrudable and flexible components, sensing elements can be printed on any 3D or flexible substrate.

Debate: Supporting the mental health of school students in the COVID-19 pandemic in New Zealand - a digital ecosystem approach.

The pandemic is creating unprecedented demand for mental health support for young people. While schools often facilitate mental health support for their students, the demands for online teaching and the uncertainty created by the pandemic make traditional delivery of support through schools challenging. Technology provides a potential way forward. We have developed a digital ecosystem, HABITS, that can be integrated into school and healthcare systems. This has allowed us to deploy specific evidence‐based interventions directly, and through schools, to students and to parents in New Zealand during the current pandemic. Chatbot architecture is particularly suited to rapid iteration to provide specific information while apps can provide more generalised support. While technology can provide some solutions, it is important to be aware of the potential to increase current inequities, with those facing the greatest challenges to health and well‐being, also least able to afford the resources to access digital interventions. Development of an integrated and equitable digital system will take time and collaboration.

Research on Brand Image Design Innovation and Practice under the New Retail Trend Based on Computer Technology

With the rapid iteration of the computer Internet, the conventional brand image design has been unable to adapt to the development of market retail mode and mode, and innovation and reform are urgently needed. Based on this, this paper first analyzes the challenges of brand image design under the background of new retail, then studies the innovative principles of brand image design under the new retail trend based on computer technology, and finally gives the ideas and methods of innovation and practice.

Testing mHealth solutions at the last mile: insights from a study of technology-assisted community health referrals in rural Kenya.

mHealth technologies are already disrupting conventional healthcare delivery by making innovative solutions more accessible in terms of reach and price across reach and price across the developing world. However, much less has been documented on the process of mHealth innovation introduction in the context of rural communities of Africa. Pending still is the widespread adoption of standards and the removal of barriers to introduction, testing and scale. This paper documents the innovation process of technology introduction, results and lessons learned through a case study of two mHealth initiatives: closed-loop referrals for maternal and child health; and HIV self-testing. Both initiatives were implemented and evaluated in Kisii County, Kenya by Living Goods. Living Goods applied an innovation framework to introduce and evaluate two interventions integrated into the Living Goods Smart Health app, a smartphone-based digital health application designed to carry out household registration, assessment, and diagnosis at community level. Community health workers (CHWs) used digitally assisted, standardized Ministry of Health algorithms to assess and refer clients to the nearest health facility for diagnosis confirmation and treatment as appropriate. Routine data as well as periodic household surveys were captured to incorporate performance data and outcomes into activity management. A quasi-experimental evaluation was carried out using a Propensity Score Matching (PSM) methodology to evaluate intervention arms for each intervention. Findings suggest that the initiatives increased the frequency of visits to households with participants in the treatment groups being more likely to have been visited more than six times within the last six months. The interventions contributed in part to an increase in the frequency of CHW follow-up visits within the treatment group. Attitudes of trust and confidence in CHWs were high but limited to referral services and not to diagnostic and curative services. The innovation process effectively positioned and tested at community level the two interventions to address key barriers to service delivery acceptance and uptake. Despite extensive pre-testing and field iterations to adapt the solutions to the local context, behavioral and technology barriers persisted. The study highlights important implications for both innovators and service providers: technology introduction and adaptation at community level requires multiple, rapid iteration loops to ensure product refinement and user-acceptance; behavioral assessments of acceptability require a wholistic approach to ensure effective alignment of senders, receivers and trusted intermediaries of novel services.

Effectuation or Causation? Configurational Causes of Entrepreneurial Logic

More entrepreneurs are increasingly interested in such a way of “crossing the river by feeling the stones” to realize rapid iteration of enterprises. In this study, we explored simultaneously impacts of entrepreneurial experience, environmental uncertainty, organizational slack and centralization of desicion making on entrepreneurial logic. To do so, we then use clear-set approach to qualitative comparative analysis (csQCA) to compare the different configurations affecting effectuation and causation under an integrated perspective. Our findings suggest that entrepreneurs preferred to use causation logic for decision-making while the level of environmental uncertainty is low. Whereas, when the environmental uncertainty is higher, entrepreneurial experience, organizational slack and decision concentration become the decisive factors for entrepreneurial logic. As such, this study offers novel theoretical and empirical insights to scholars as well as implications for practitioners.