Iterative Process Research Articles

Lobbying in the Sunlight: A Scoping Review of Frameworks to Measure the Accessibility of Lobbying Disclosures

Background: While anyone can lobby governments, most lobbying is driven by commercial interests. Due to limited government disclosures, it is often challenging to get a clear picture of who is lobbying whom or why. To help make lobbying more visible to the public, we set out to develop a framework of key criteria for best practice government lobbying disclosures. Methods: We undertook a systematic scoping review of peer-reviewed and grey literature to identify frameworks for measuring or evaluating lobbying transparency. We screened the titles and abstracts of 1727 peer-reviewed and 184 grey literature articles, assessing 230 articles for eligibility. Following screening, we included 15 frameworks from six peer-reviewed and nine grey literature articles in our review. To create our framework of lobbying disclosures, we thematically coded the 15 included frameworks and used an iterative process to synthesise categories. Results: The 15 frameworks covered more than only lobbying disclosures, with the most common other theme about enforcement and compliance. Most frameworks were developed to evaluate lobbying transparency in particular jurisdictions, with the United States the most common. Of the 15 frameworks analysed, those developed by non-governmental organizations focused mainly on improving lobbying regulations, while most peer-reviewed studies developed frameworks to measure, compare and evaluate lobbying regulations. We developed a Framework fOr Comprehensive and Accessible Lobbying (FOCAL). It comprised eight primary categories (scope, timeliness, openness, descriptors, revolving door, relationships, financials and contact log) covering 50 total indicators. Conclusion: Government transparency plays a crucial role in facilitating access to information about commercial political activities like lobbying. Our framework (FOCAL) offers a template for policy makers to develop or strengthen regulations to improve lobbying transparency so commercial political influence strategies are more visible and subject to public scrutiny. This is an important step towards rebalancing influence toward the public interest.

Abstract B089: A community-based participatory approach to developing a salon-based HPV self-collection intervention

Abstract Background: Black women in the US experience higher rates of cervical cancer mortality due to delayed diagnosis from lower screening rates. Contributing to this disparity, especially for those who are 40-64 years old, are provider bias, costs, and limited access. Recent FDA approval of HPV self-collection and current innovative research efforts for clinical and simulated home- based HPV self-collection bring promise of increasing access. However, like other innovations, equitable dissemination of HPV self-collection will be challenging. Our project seeks to distribute HPV self-collection kits in unconventional settings like hair salons. We conducted a community-based participatory study using concept mapping to gather diverse insights to design a salon-based HPV self-collection intervention. Methods: We employed GroupWisdom and REDCap online platforms to engage with our participants. Participants were members of the Black Community Advisory Council of Tucson (Black CACTus). Concept mapping steps involved: 1) Brainstorming statements from a prompt, 2) Sorting statements into piles according to meaning by each participant, 3) Rating the importance and feasibility of statements, 4) Reviewing the cluster map, and 5) Reviewing a draft/proposed intervention. Results: Black CACTus members (n=10) included hairstylists (n=3), salon clients (n=4), and healthcare providers (n=3). All participants identified as Black women, with two also identifying as Latina and White. Participants were 23-53 years old. Brainstorming yielded 39 statements. A six-cluster map was generated using the sorting data. Cost-related factors like "free" or "covered by insurance," as well as convenience, were rated as highly important by all participants. Development of clear communication and education about screening benefits, test results, and timely delivery of results were regarded as the most feasible. Conclusions: To equitably increase cervical cancer screening we must look beyond traditional clinical settings to trusted community locales like hair salons. Including the voice of the community and cultural and contextual factors is vital when tailoring cancer screening initiatives, particularly for Black women. Our study used concept mapping, in a collaborative, iterative process with vested community members to identify intervention elements and implementation strategies that have the potential to increase access to and uptake of cervical cancer screening among Black women. Citation Format: Kelly N.B. Palmer, Namoonga M. Mantina, Adebola Adegboyega, Itunu O. Sokale, Kathleen Pryor, Azaria Suero-Davis, Trevon Brooks, Jennifer Hatcher. A community-based participatory approach to developing a salon-based HPV self-collection intervention [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B089.

A Pn-wave spectral inversion technique based on trust region reflective algorithm

This paper suggests utilizing the trust region reflective (TRR) algorithm to address the inverse problem related to seismic spectrum analysis of Pn waves. By applying this approach, it becomes feasible to simultaneously deduce the seismic moment, corner frequency, tomographic model of Pn-wave attenuation, and site responses while incorporating appropriate constraints based on prior knowledge to ensure solution accuracy. The efficacy of this method has been validated using synthetic data. Specifically, the recorded Pn waves from four underground explosions in North Korea were employed to evaluate the performance of the proposed technique. The derived seismic moment and corner frequency for these events were found to align with the characteristics of the seismic data, earthquake magnitude calculations, and empirical data. The resulting tomographic model of Pn-wave attenuation revealed a spatial distribution of high and low attenuation, indicating significant lithospheric heterogeneity in northeastern China. Areas with low Q0 and low η, such as the Xialiaohe Basin and southwestern Changbai Mountain, suggest a high-temperature environment in the upper mantle cap layer or the presence of molten substances. Conversely, regions with high Q0 and high η, like the northern Changbai Mountain and the vicinity of the eastern Tanlu Fault Zone, characterized by high Pn-wave velocity and thick crust, indicate minimal modification or destruction in the lithosphere. Furthermore, site responses were determined for 75 seismic stations in northeastern China, with their characteristics preliminarily analyzed and interpreted in conjunction with geological context. The iterative process based on the TRR algorithm for Pn-wave spectral inversion proposed in this study demonstrates robust convergence and enhances the fitting accuracy of the observed spectrum. The inversion outcomes from this spectral technique yielded synthetic Pn-wave spectra that closely matched the observed spectra of the four underground explosions in North Korea across various frequency bands for over 90 % of the stations.

Multi-Weather Visibility Restoration using MPRNet - Multi-Stage Progressive Image Restoration

This paper introduces a novel approach, termed Multi-Stage Progressive Image Restoration (MSPIR), aimed at addressing the challenging task of restoring images degraded by various adverse weather conditions. Leveraging a combination of advanced algorithms including HI-Net, DCP, and MSCIA, MSPIR offers a comprehensive solution for mitigating the effects of fog, haze, rain streaks, and other atmospheric distortions on image visibility. The methodology follows a multi-stage iterative process, progressively refining image quality and enhancing visibility through successive stages of processing. By integrating deep learning techniques and adaptive context aggregation mechanisms, MSPIR achieves superior results in restoring image clarity and preserving fine details across diverse weather conditions. Experimental evaluations demonstrate the efficacy and robustness of the proposed framework, highlighting its potential for real-world applications in autonomous navigation, surveillance, and remote sensing. Looking ahead, future research directions include optimizing MSPIR for real-time applications, exploring novel deep learning architectures, and integrating additional sensory modalities to further enhance visibility restoration capabilities. Overall, MSPIR represents a significant advancement in multi-weather visibility restoration, offering promising prospects for clearer and safer image processing in various domains.

Collective Translation as Forking (分岔)

Together with a Taiwanese working group, we have been producing a Chinese translation of Aesthetic Programming: A Handbook of Software Studies, a free and open source book first written by Winnie Soon and Geoff Cox in 2020, and released in English in a git repository, dynamic website, downloadable PDF and printed form. Apart from learning to code in p5.js, the book addresses the cultural and aesthetic dimensions of programming from its insides, as a means to think and act critically, and to understand the importance of programming as a cultural practice that can develop discussion of issues that are relatively under-acknowledged in technical subjects such as gender, race and sexuality. Importantly, the book is understood as a computational object, not released as a fixed and universal teaching resource, but rather a situated curriculum with the potential for extension and customization with other arts and coding communities. The use of Git has allowed the authors to formalize its production as an iterative process, allowing for reversioning and for others to fork a copy and customize with different references, examples, critical reflections and even new chapters. The interest is in forking a book like forking software, and incorporating local knowledge and examples, and how this resonates with a politics of cultural translation. This essay will elaborate on the process of running two open participatory workshops that were conducted in Taipei and London (in 2023) with the aim to challenge some of the normative social relations of production associated with translation, and explore other possibilities of collective practice. The politics of translation has been well-established in general, but what of the specifics of translating a book such as this? Aside from the technical and aesthetic challenges and implications, this raises the question of how the Chinese language model enforces particular hegemonic worldviews that occlude differences. With all the variants of Chinese language, how is this tied to expressions of colonial power that resonates with our use of English? Given the rich variations of Chinese and indigenous languages (not least in a Taiwanese context), we are curious how we might be sensitive to language diversity that challenges the Western-centrism of programming in English (and inherent nationalisms). We are also mindful of the way that “queer” politics has informed the way that terms can be appropriated/expropriated, as a means to “talk back” to the source codes of oppression. What are the implications of drawing the practices of forking and translating together?

Innovative Content Production in Marketing Communication Through AI

The integration of artificial intelligence (AI) into marketing communications has changed the way businesses communicate with their customers. The present paper explores the potential of AI-driven systems to automate content creation, highlighting the role of AI in increasing customer engagement and strategic decision making. AI offers significant benefits by leveraging customer data to predict their behaviour, optimize marketing strategies, and personalize content. By systematically creating and evaluating content, AI enables marketers to customize messages to specific audiences, increasing the effectiveness of campaigns. The research employs a mixed methods approach that combines qualitative and quantitative analyses to assess the potential use of AI in content marketing. The qualitative aspect involves a thorough review of existing literature, identifying key trends and relationships in the use of AI for marketing communications. On the other hand, the quantitative aspect of the study involves a process of experimental validation using Python and APIs such as OpenAI and Graph APIs to automate content creation and measure its effectiveness. The findings confirm that AI can be utilized to personalize content using the input data in the form of brand-voice, target group, and the evaluation of current communication. The iterative process allows for continuous improvement of quality and relevance of content, which is important in the dynamic environment of marketing communications. The study highlights the importance of accurately defining the target audience and brand-voice, as the effectiveness of AI depends heavily on the accuracy of the input data. However, the study also acknowledges limitations such as the potential inconsistency of the content generated through AI and the challenges associated with maintaining a coherent brand-voice. Future research should focus on developing advanced machine learning methods to better understand context and improve the alignment of content with brand values and customer expectations. In conclusion, the integration of AI into marketing communications offers a powerful tool for businesses to optimize content creation, increase customer engagement, and achieve strategic goals in an increasingly competitive digital environment. AI's ability to create, evaluate and refine content in real-time provides a significant advantage and ensures that marketing communications remain adaptive and effective.

A Thermal Model for a Hybrid Gas Bearing System in Oil-Free Turbochargers

Abstract With little drag friction, gas bearings operate at high rotor speed and high temperature and thus enable long operating life along with material damping for mechanical energy dissipation. However, most computational tools for modeling gas bearings are not accessible to bearing manufacturers or potential end-users, and the models are restricted to specific geometries and operating conditions or are missing important features, thus limiting their utility. This paper presents the thermal energy transport in a hybrid gas bearing for oil-free turbochargers with integrated heat and fluid flow models to produce a comprehensive thermo-hydrodynamic analysis predictive tool, and to design and evaluate air-lubricated gas bearings by predictions and experiments. An efficient algorithm couples the solution of the Reynolds equations and the thermal energy transport equations in the film between the rotor and a pad of the hybrid gas bearing, and updates the temperature-dependent viscosity and film thicknesses during the iterative process. The test repeats and averaged for each rotor speed from 2 krpm to 22 krpm at intervals of 2 krpm, for supply pressure varying along 25 psi to 100 psi and predicted film temperature matches well with the measured one. The parameters of interest in this analysis include the gas supply condition, bearing configuration, and the gas properties at high altitudes and high rotation speeds. The parametric study results show the density and kinematic viscosity are the most dominant properties of the gas lubrication for the film temperature.

Identifying Elective Induction of Labor Among a Diverse Pregnant Population from Electronic Health Records within a Large Integrated Healthcare System.

Distinguishing between medically indicated induction of labor (iIOL) and elective induction of labor (eIOL) is a daunting process for researchers. We aimed to develop a Natural Language Processing (NLP) algorithm to identify eIOLs from electronic health records (EHR) within a large integrated healthcare system. We used structured and unstructured data from Kaiser Permanente Southern California's EHR of patients who were <35 years old and had singleton deliveries between 37 and 40 gestational weeks. Induction of labor (IOL) pregnancies were identified if there was evidence of an IOL diagnosis code, procedure code, or documentation in a delivery flowsheet or progress note. A comprehensive NLP algorithm was developed and refined through an iterative process of chart reviews and adjudications, where IOL-associated reasons (medically indicated versus elective induction) were reviewed. The final algorithm was applied to discern the indications of IOLs performed during the study period. A total of 332,163 eligible pregnancies were identified between 01/01/2008-12/31/2022. Of these eligible pregnancies, 68,541 (20.6%) were IOL, of which 6,824 (10.0%) were eIOL. Validation of the NLP process against 300 randomly selected pregnancies (100 eIOL, iIOL, and non-IOL cases each) yielded a positive predictive value of 83.0% and 88.0% for eIOL and iIOL, respectively. The rates of eIOL among the maternal age groups ranged between 9.6% -10.3%, except for the < 20 years group (12.2%). Non-Hispanic White individuals had the highest rate of eIOL (13.2%), while non-Hispanic Asian/Pacific Islanders had the lowest rate of eIOL (7.8%). The rate of eIOL increased from 1.0% in the 37 weeks gestational age group to 20.6% the 40 weeks gestational age group. Findings suggest that the developed NLP algorithm effectively identifies eIOL. It can be utilized to support eIOL-related pharmaco-epidemiological studies, filling in knowledge gaps and providing content more relevant to researchers.

Flower pollination algorithm with ring topology for multi-solution spaces to solve the disassembly line balancing problem

Abstract The Disassembly Line Balancing Problem (DLBP) is of significant importance in the product recycling process. However, existing DLBP research have primarily proposed improved optimization algorithms for single solution space. To provide decision makers with more efficient disassembly solutions, this paper chooses three classical layouts (straight, U-shaped, and parallel) to broaden the solution space and expands an incomplete DLBP with multi-solution spaces (DLBP-MS). By employing the strategy of incomplete disassembly, only the essential components are retained. In DLBP-MS, the disassembly information from used products is processed and imported into the three types of disassembly spaces to be solved and compared to find suitable disassembly solutions. And a multi-objective mathematical model is developed, this includes factors such as workstation count, free time, disassembly smoothness index, carbon emission, and disassembly revenue. The part constraints are established based on directed graphs, and the encoding and decoding methods for multi-solution spaces disassembly sequences in the random incomplete case are designed respectively. A ring topology based flower pollination algorithm (RTFPA) is introduced to effectively address the DLBP-MS. The solution set obtained during the iterative process is divided into subsets based on the congestion level and the overall diversity is preserved by internal optimization of these subsets. After that, the RTFPA is applied to the DLBP-MS of waste cell phones and computers. The improvement of the algorithm's optimization ability under different solution spaces is verified by comparing the results with those obtained from four other algorithms.

Flexible Endoscopic Evaluation of Swallowing: A Proposed Checklist.

Flexible endoscopic evaluation of swallowing (FEES) is not only a well-recognized and ubiquitous tool in dysphagia research but also possesses features that make the assessment vulnerable to shortcomings in transparency and rigor in published literature. Therefore, FEES was considered an important addition to the Framework for RigOr aNd Transparency In REseaRch on Swallowing (FRONTIERS), a multisite collective effort to establish a tool for the critical appraisal of reporting in all forms of dysphagia and swallowing-related research on human subjects. From the FRONTIERS collective, a team of three clinician researchers completed a review of FEES-related literature to determine all components crucial for generalizable and reproducible reporting of FEES research. These components were developed and refined through an iterative process. This review culminated in a 26-item series of "yes/no" questions, forming the FEES section of FRONTIERS. These questions are grouped into the following five components: (a) Equipment, (b) Rater(s), (c) Rating Process, (d) Outcome Metrics, and (e) Miscellaneous Factors. The results of this review support that FEES possesses unique characteristics to other aspects of dysphagia research and is consequently a crucial addition to FRONTIERS to ensure that clinical researchers have access to critical appraisal of FEES-related research inquiry.

The user-centered design and development of a childhood and adolescent obesity Electronic Health Record tool, a mixed-methods study.

Childhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians. The objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management. We utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process. A total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process. More research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future.

A Proposed Framework for Rigor and Transparency in Dysphagia Research: Prologue.

Scientific transparency and rigor are essential for the successful translation of knowledge in clinical research. However, the field of oropharyngeal dysphagia research lacks guidelines for methodological design and reporting, hindering accurate interpretation and replication. This article introduces the Framework for RigOr aNd Transparency In REseaRch on Swallowing (FRONTIERS), a new critical appraisal tool intended to support optimal study design and results reporting. The purpose of introducing FRONTIERS at this early phase is to invite pilot use of the tool and open commentary. FRONTIERS was developed by collaborating researchers and trainees from six international dysphagia research labs. Eight domains were identified, related to study design, swallowing assessment methods, and oropharyngeal dysphagia intervention reporting. Small groups generated questions capturing rigor and transparency for each domain, based on examples from the literature. An iterative consensus process informed the refinement and organization of primary and subquestions, culminating in the current initial version of FRONTIERS. FRONTIERS is a novel tool, intended for use by oropharyngeal dysphagia researchers and research consumers across disciplines. A web application enables provisional use of the tool, and an accompanying survey solicits feedback regarding the framework. FRONTIERS seeks to foster rigor and transparency in the design and reporting of oropharyngeal dysphagia research. We encourage provisional use and invite user feedback. A future expert consensus review is planned to incorporate feedback. By promoting scientific rigor and transparency, we hope that FRONTIERS will support evidence-based practice and contribute to improved health outcomes for individuals with oropharyngeal dysphagia.

China in Africa: companies as enablers and beneficiaries? An empirical evaluation of state-business relations in the Forum on China-Africa Cooperation

Abstract China’s strong economic presence in Africa has resulted in an increased interdisciplinary debate. Our contribution is the incorporation of a business perspective by uncovering the prominence and role of business in China’s diplomatic Africa engagement. Our theoretical contribution by applying the state-business relations (SBR) literature is to examine whether established frameworks can be expanded by an international dimension through intergovernmental initiatives like the Forum on China-Africa Cooperation (FOCAC). The paper conducts a document analysis of all declarations and Action Plans of all FOCAC conferences in the period 2000–2021, combining both a content and a thematic analysis based on an explorative and iterative coding process. Our data suggests that the prominence of businesses has increased while the scope of their activities and the number of focus sectors (especially infrastructure) has risen particularly since 2012. Companies are considered as enablers for political and economic goals in the state-driven FOCAC. We find that SBR frameworks are applicable to international contexts and propose an expanded SBR approach integrating transnational intermediary institutions like the intergovernmental FOCAC and transnational business platforms which facilitate positive state-business relations across countries and a conducive business environment.

Fading regularization method for an inverse boundary value problem associated with the biharmonic equation

In this paper, we propose a numerical algorithm that combines the fading regularization method with the method of fundamental solutions (MFS) to solve a Cauchy problem associated with the biharmonic equation. We introduce a new stopping criterion for the iterative process and compare its performance with previous criteria. Numerical simulations using MFS validate the accuracy of this stopping criterion for both compatible and noisy data and demonstrate the convergence, stability, and efficiency of the proposed algorithm, as well as its ability to deblur noisy data.

Exchange of CO2 with CO as Reactant Switches Selectivity in Photoreduction on Co–ZrO2 from C1–3 Paraffin to Small Olefins

Photocatalytic reduction of CO2 into C2,3 hydrocarbons completes a C‐neutral cycle. The reaction pathways of photocatalytic generation of C2,3 paraffin and C2H4 from CO2 are mostly unclear. Herein, a Co0–ZrO2 photocatalyst converted CO2 into C1–3 paraffin, while selectively converting CO into C2H4 and C3H6 (6.0 ± 0.6 μmol h−1 gcat−1, 70 mol%) only under UV–visible light. The photocatalytic cycle was conducted under 13CO and H2, with subsequent evacuation and flushing with CO. This iterative process led to an increase in the population of C2H4 and C3H6 increased up to 61–87 mol%, attributed to the accumulation of CH2 species at the interface between Co0 nanoparticles and the ZrO2 surface. CO2 adsorbed onto the O vacancies of the ZrO2 surface, with resulting COH species undergoing hydrogenation on the Co0 surface to yield C1–3 paraffin using either H2 or H2O (g, l) as the reductant. In contrast, CO adsorbed on the Co0 surface, converted to HCOH species, and then split into CH and OH species at the Co and O vacancy sites on ZrO2, respectively. This comprehensive study elucidates intricate photocatalytic pathways governing the transformation of CO2 into paraffin and CO to olefins.

Exchange of CO2 with CO as Reactant Switches Selectivity in Photoreduction on Co-ZrO2 from C1-3 Paraffin to Small Olefins.

Photocatalytic reduction of CO2 into C2,3 hydrocarbons completes a C-neutral cycle. The reaction pathways of photocatalytic generation of C2,3 paraffin and C2H4 from CO2 are mostly unclear. Herein, a Co0-ZrO2 photocatalyst converted CO2 into C1-3 paraffin, while selectively converting CO into C2H4 and C3H6 (6.0±0.6 μmol h-1 gcat -1, 70 mol %) only under UV/Visible light. The photocatalytic cycle was conducted under 13CO and H2, with subsequent evacuation and flushing with CO. This iterative process led to an increase in the population of C2H4 and C3H6 up to 61-87 mol %, attributed to the accumulation of CH2 species at the interface between Co0 nanoparticles and the ZrO2 surface. CO2 adsorbed onto the O vacancies of the ZrO2 surface, with resulting COH species undergoing hydrogenation on the Co0 surface to yield C1-3 paraffin using either H2 or H2O (g, l) as the reductant. In contrast, CO adsorbed on the Co0 surface, converted to HCOH species, and then split into CH and OH species at the Co and O vacancy sites on ZrO2, respectively. This comprehensive study elucidates intricate photocatalytic pathways governing the transformation of CO2 into paraffin and CO to olefins.

Patient-Reported Outcome Measures for Swallowing: A Proposed Checklist.

Patient-reported outcome measures (PROMs) are an important tool both in clinical practice and research involving patients with swallowing disorders. There are several challenges to be overcome and methodological details to be adequately reported to ensure rigor and transparency in studies utilizing PROMs in dysphagia research. For this reason, the Framework for RigOr aNd Transparency In REseaRch on Swallowing (FRONTIERS), a critical appraisal tool developed to ensure rigor and transparency in dysphagia research, has devoted a domain to PROMs. The aim of this current article, as part of a larger forum of articles, is to present the PROM section of FRONTIERS and describe its development. A literature review was carried out on PROMs in the field of dysphagia by three members of the FRONTIERS collaborative to identify items that were necessary for the rigorous application and transparent reporting of PROMs utilized in research of swallowing disorders in human subjects. Framework items were then subjected to an iterative process of feedback and consensus among the three members of the working group as well as all members of the FRONTIERS collaborative. Items were flagged for review and revised as needed until consensus was achieved on the final list. The final item list was compared to existing critical appraisal tools for PROMs. The final checklist for the PROMs domain included 20 "Yes/No" questions that can be broadly divided into three categories: (a) development and validation, (b) patient population, and (c) PROM administration method. Each item is presented with a rationale for its inclusion. The use of the FRONTIERS Framework will serve researchers and those appraising quality of research involving PROMs. More broadly, the FRONTIERS Framework will facilitate improved rigor and transparency across dysphagia research. Special considerations and future goals are discussed.

Specific emitter identification unaffected by time through adversarial domain adaptation and continual learning

Timely identifying the emitter of target signals is crucial for communication security in complex electromagnetic environments. Specific emitter identification (SEI) is a technique to identify emitters using the hardware fingerprints of emitters. In this paper, the impact of changes in hardware fingerprints over time on SEI is solved, which significantly deteriorates identification performance. This issue is addressed from two aspects: one is mitigating the impact of these changes, and the other is tracking and adapting to them. For the aspect of mitigating impact, an alternating adversarial domain adaptation (AADA) method is proposed to eliminate the time-varying component in hardware fingerprints. Subsequently, a feature map calculation method using weighted Euclidean distance is designed, preserving the main parameters of feature maps for each emitter. For the aspect of tracking and adapting to changes, a continual learning method was designed based on feature maps of each emitter. This approach incorporates the selective annotation of unlabeled new data with an iterative optimization training process. To validate the effectiveness of the proposed method, we independently collected comprehensive time-variant datasets as well as simpler datasets with varying receivers and environments. The proposed method was tested on these datasets and compared with existing conventional and advanced methods. The experimental results indicate that the proposed SEI method exhibits superior recognition performance. Compared to existing methods, it achieved an average recognition accuracy improvement of over 8% on the time-variant dataset, and demonstrated enhanced robustness against these three types of variations.

Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and super large twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax’s reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science.

COVID-19 research response to immediate demands: setting priorities with key stakeholders to enable health services research in NSW, Australia

PurposeCOVID-19 has caused unprecedented disruption to health systems. There is much to be gained by capturing what was learned from changes and adaptations made by health services and systems. The Ministry of Health in New South Wales (NSW), Australia, sought to prioritise health services research (HSR) to address critical issues arising from the COVID-19 pandemic. We tested a priority setting methodology to create priorities for a specific funding opportunity and to extract generalisable lessons.Design/methodology/approachA virtual roundtable meeting of key stakeholders was held in June 2020. We used a modified Nominal Group Technique (NGT) for priority setting, with potential items (n = 35) grouped under headings. Data was analysed through a reflective deliberative process.FindingsWe engaged 89 senior policy makers, health service executives, clinicians and researchers in the roundtable. The NGT proved an efficient method with participants reaching consensus on eight priorities. Findings included strong support for learning from the rapid response to COVID-19 and addressing needs of vulnerable populations and the health workforce. Opinions differed about strategic areas investment and where learnings should be via internal evaluation rather than funded research. Three of the eight recommended priorities were included in the funding opportunity.Research limitations/implicationsCoronavirus disease 2019 (COVID-19) required unprecedented change and adaptations within health systems, and rapid, applied health services research can help to create, understand and (where relevant) sustain change beyond the immediate impact of the pandemic. While final decisions may be dependent on a wider range of considerations by government, stakeholder enthusiasm for engagement in priority setting exercises may be dampened if they do not perceive their application in decision-making.Practical implicationsA modified nominal group technique can be used to set research priorities in constrained conditions by engaging large numbers of stakeholders in rankings and then using an online delivery of a roundtable and to reach consensus on priorities in real time. Recommended priorities for health services research can be readily generated through rapid engagement but does not guarantee their application.Social implicationsAustralia’s swift response to COVID-19 pandemic in 2020 was perceived as a relative success due to the rapid public health and policy response and a relatively low number of cases. This response was underpinned by systematic knowledge mobilisation including support for targeted and prioritised health services research to fill knowledge gaps.Originality/valueSetting priority processes can provide rich, engaged input to support government funding decisions about HSR. A wide range of dynamic and iterative processes influence decision-making in a rapidly evolving situation in the health system response to COVID-19. It is crucial to consider how major investment decisions will support a value-based healthcare system.