High-level Guidance Research Articles

The Experience of Non-Full-Time Nursing Graduate Students in China: A Qualitative Study

To address the disconnection between the educational structure of nursing teams and the rapid development of China's medical and health industry, there is a shortage of highly educated nursing personnel. In 2016, Chinese universities introduced a new training model for nursing graduate students: non-full-time nursing graduate students. Due to its late development, this model needs continuous improvement. Understanding the training experience of non-full-time nursing graduate students is crucial for refining the training model. This study explores the experiences of non-full-time nursing graduate students and provides a basis for improving their training in China. Semi-structured qualitative interviews were conducted with 20 non-full-time nursing graduate students. Data were collated and analyzed using Colaizzi's method. The analysis identified five themes related to the study experiences of non-full-time nursing graduate students: (1) maladaptation to diversified roles; (2) weak scientific research foundation; (3) lack of high-level guidance in clinical practice; (4) concern about social recognition; and (5) lack of educational and training resources. Competent government and training units should fully understand the experiences of non-full-time nursing graduate students and improve their training model from the students' perspective to promote the development of high-quality nursing talents. This will ultimately benefit employers, training units, individuals, and patients.

Uncovering Complexities in Reducing Aggression, Conflict and Restrictive Practices in Acute Mental Healthcare Settings: An Overview of Reviews.

Aggression, conflict and restrictive practices present complexities in acute mental health services, as do implementing service changes to reduce them. Existing published literature needs to offer more high-level guidance on the effectiveness of these service changes and their associated implementation factors. As a result, an overview of systematic reviews was undertaken to identify (i) nonpharmacological interventions to reduce conflict, aggression and restrictive practices in acute mental health settings, and (ii) their effects across different clinical outcomes. A parallel re-extraction from primary studies was then utilised (iii) to identify factors influencing successful intervention implementation. Of 124 articles sourced from nine databases and registries, four reviews were retained for the final analysis, using the direction of effect and tabular and narrative summaries. These reviews included programmes or interventions focused on inpatient adolescent, adult and older adult populations. They reported on alternative containment strategies, risk assessments, Safewards, sensory rooms and equipment, Six Core Strategy-based interventions and staff training. The overview found that a combination of interventions intended to improve relationships and reduce interpersonal conflict may help reduce aggression, conflict and restrictive practices. At the same time, stand-alone staff training and sensory rooms and equipment may have mixed effects. The quality of the evidence linking these interventions to reductions in aggression, conflict and restrictive practices is limited. Successful implementation hinges on multiple factors: intervention characteristics, preparation and planning, evaluation and monitoring, outcome interpretation, stakeholder involvement/investment, staff-related factors and contextual factors. Any implementation initiative may benefit from using pragmatic and complexity-informed research methodologies, including integrating meaningful involvement with service users, peer workers and culturally diverse groups.

Current state of implementation of in silico tools in the biopharmaceutical industry-Proceedings of the 5th modeling workshop.

The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for "mechanistic modeling." Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.

A general framework for developing computable clinical phenotype algorithms.

To present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machine learning and natural language processing methods to incorporate rich electronic health record data. Drawing on extensive prior phenotyping experiences and insights derived from 3 algorithm development projects conducted specifically for this purpose, our team with expertise in clinical medicine, statistics, informatics, pharmacoepidemiology, and healthcare data science methods conceptualized stages of development and corresponding sets of principles, strategies, and practical guidelines for improving the algorithm development process. We propose 5 stages of algorithm development and corresponding principles, strategies, and guidelines: (1) assessing fitness-for-purpose, (2) creating gold standard data, (3) feature engineering, (4) model development, and (5) model evaluation. This framework is intended to provide practical guidance and serve as a basis for future elaboration and extension.

Fostering Digital Communities

As online education expands, fostering digital communities that support networked learning becomes imperative. However, many universities lack comprehensive digital strategies to facilitate engagement and connections in distributed environments. This research examines the development of an inaugural digital master plan at a university where half of the student population is online. Guided by Moore's theory of interactions (1989) and Garrison's Community of Inquiry framework (2017), the plan aimed to enable meaningful student-to-content, student-to-instructor, and student-to-student interactions. The digital master plan was designed to provide high-level guidance on the intentional and purposeful use of the educational technologies, tools, platforms, and systems necessary for a robust online learning experience. It creates a cohesive vision and strategy for the digital learning experience, just as a campus master plan aligns the physical learning spaces. The digital master plan leverages the Community of Inquiry framework as a key reference point in its comprehensive gap analysis. The collaborative process involved multiple methods to assess gaps in the university’s infrastructure including: National Survey of Student Engagement (NSSE) data, three internal surveys, an external diagnostic analysis and plan, and audits of the learning management system's tools and usage. Quantitative data provided insights into student perceptions, while qualitative findings explored the depth of social relationships and learning networks enabled through digital platforms. Throughout the process, the research team aimed to understand the social learning processes that support how learners develop and utilize technology to learn and foster a sense of belonging (Garrison, 2017). The process harnesses the natural motivations underpinning social network formation - namely information sharing, support seeking, and sense of belonging. Further, it operationalizes these motivations to achieve CoI's high-order learning community goals. Analysis revealed key gaps related to: clearly defining and measuring online student success, expanding engagement opportunities in courses, leveraging technologies to facilitate connections, building a collaborative faculty culture, and improving communication. In response, the final digital master plan put forth five recommendations focused on these priority areas. Implementation and evaluation of digital platforms aligned to the recommendations indicate positive impacts on online students' sense of connectedness, learning experience, and academic performance. This research makes important theoretical and practical contributions by developing a replicable institutional process for assessing and strategically enhancing a digital ecosystem to foster networked learning. The digital master plan provides guidance for the adoption of social technologies and pedagogies to increase community, connectivity, and collaboration. As online education continues to expand, insights from this case study can inform strategic digital planning efforts at other universities striving to overcome transactional distance and cultivate impactful virtual learning communities.

A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis

Well-designed prompts have demonstrated the potential to guide text-to-image models in generating amazing images. Although existing prompt engineering methods can provide high-level guidance, it is challenging for novice users to achieve the desired results by manually entering prompts due to a discrepancy between novice-user-input prompts and the model-preferred prompts. To bridge the distribution gap between user input behavior and model training datasets, we first construct a novel Coarse-Fine Granularity Prompts dataset (CFP) and propose a novel User-Friendly Fine-Grained Text Generation framework (UF-FGTG) for automated prompt optimization. For CFP, we construct a novel dataset for text-to-image tasks that combines coarse and fine-grained prompts to facilitate the development of automated prompt generation methods. For UF-FGTG, we propose a novel framework that automatically translates user-input prompts into model-preferred prompts. Specifically, we propose a prompt refiner that continually rewrites prompts to empower users to select results that align with their unique needs. Meanwhile, we integrate image-related loss functions from the text-to-image model into the training process of text generation to generate model-preferred prompts. Additionally, we propose an adaptive feature extraction module to ensure diversity in the generated results. Experiments demonstrate that our approach is capable of generating more visually appealing and diverse images than previous state-of-the-art methods, achieving an average improvement of 5% across six quality and aesthetic metrics. Data and code are available at https://github.com/Naylenv/UF-FGTG.

Type 1 Diabetes in Ontario Schools: Policy and Practice

ObjectivesType 1 diabetes (T1D) is a challenging chronic condition. Young children with T1D require daily support to manage their condition while at school. In 2018, Ontario established a provincial policy to ensure safe and equitable school participation for children with diabetes. Despite this, children and parents describe very different school experiences. In this qualitative study we describe the interpretation and implementation of school board policy related to the care of children with T1D from the perspective of school educators (principals/teachers). MethodsPolicy documents were reviewed employing a qualitative descriptive research design using directed qualitative content analysis. Semistructured interviews were conducted with 13 teachers and principals from 10 publicly funded elementary schools across the Hamilton and Toronto District School Boards in 2021. ResultsThere are major differences in how policies regarding T1D are being implemented in schools. This includes how school staff are educated about T1D, and how they interpret and act on blood glucose information. Although educators often play an active role in supporting children, many face barriers, including competing priorities, fear, lack of information, and lack of support. Facilitators include effective communication/collaboration, actionable information, time, and a diabetes “champion.” In some instances, access to nursing support could help to resolve barriers or create care gaps. ConclusionsSchool board policy provides high-level guidance on how to support children with T1D in school, but gaps remain. We provide specific recommendations regarding policy, staff education/training, roles and responsibilities, and future research.

Exploring the High-Quality Development of Smart City Tourism in the Information Age

Abstract This article discusses the high-level guidance strategy and deep-level value embodiment of tourism informatization construction for smart city tourism. Based on the existing evaluation system of intelligent city tourism development, the article utilizes BP neural network for data screening. It combines the entropy weight method to complete the index assignment, and evaluates and analyzes the development of innovative tourism services in six intelligent cities. In the area of brilliant tourism information service, Beijing scored 94%. In clever catering and accommodation services, Beijing leads with a score of 90%, while Nanjing and Chengdu rank second and third with scores of 81% and 80% respectively. Regarding overall innovative tourism development, Beijing scored 0.4731, 0.1762, and 0.1168 in terms of scientific and technological innovation, economic development, and general development security, respectively. In addition, the intelligent tourism development trend from 2018 to 2022 shows that Beijing’s tourism revenue will reach 622.46 billion yuan in 2022, which is 30.36 billion yuan more than the previous year. By evaluating the development of intelligent city tourism services through BP neural network, this paper effectively identifies the critical factors for developing clever tourism construction. It puts forward effective strategies to improve the level of intelligent services. These findings have important theoretical and practical significance for the development of intellectual city tourism field, and provide an essential reference for the further optimization of smart city tourism services.

Model-free autonomous control of four-wheel steering using artificial flow guidance

Four-wheel steering (4WS) is an effective technique to improve handling performance and lateral stability of road vehicles. Conventionally, controllers utilise the driver's actions and vehicle dynamics to coordinate front and rear-axle steering. This paper proposes a novel approach for 4WS controller design, based on the concept of Artificial Flow Guidance (AFG), which relies on a spatially distributed motion reference through a two-dimensional vector field. This field provides high-level guidance while lower-level steering controllers to control axle centres motions relative to the flow. These flow vectors, computed in real-time via simple geometric construction, can be pre-computed globally to evaluate the guidance algorithm's efficacy. When controlling only the front axle, this same approach can function as an autonomous driving system. Relying solely on a spatial reference field and control targets' velocities enables the controller to work in a simple and robust fashion, without using a reference vehicle dynamics model or lengthy parameter tuning. The proposed approach's effectiveness is validated through co-simulation with MATLAB/Simulink and the CarMaker simulation platform. AFG control performance is found to be at least comparable to that of more complex 4WS controllers using methods such as MPC; in the cases considered, AFG provides superior path-tracking performance.

Skill assessment of models relevant for the implementation of ecosystem-based fisheries management

The advance of ecosystem-based fisheries management worldwide has made scientific advice on fisheries related questions more complex. However, despite the need to take interactions between fish stocks, and between stocks and their environment into account, multispecies and ecosystem models are still hardly used as a basis for fishery advice. Although reasons are numerous, the lack of high-level guidance for target-oriented skill assessments of such models contributes to the mistrust to use such models for advice. In this study, we propose a framework of guiding questions for a pragmatic and target-oriented skill assessment. The framework is relevant for all models irrespective of their complexity and approach. It starts with general questions on the advice purpose itself, the type of model(s) and data available for performance testing. After this, the credibility of the hindcasts are evaluated. A special emphasis is finally put on testing predictive skills. The skill assessment framework proposed provides a tool to evaluate a model’s suitability for the purpose of providing specific advice and aims to avoid the bad practice of incomplete skill assessments. In the case of multiple models available, it can facilitate the evaluation or choosing of the best model(s) for a given advice product and intends to ensure a level playing field between models of different complexities. The suite of questions proposed is an important step to improve the quality of advice products for a successful implementation of ecosystem-based fisheries management.

Lessons learned from EV charging infrastructure in mega cities: A data-driven approach

E-mobility has witnessed a major evolution in the last couple of years due to climate action concerns. Cities that have employed e-mobility across the globe can be classified into two groups: e-mobility forerunners and cities at the early market stages of e-mobility. Electric Vehicle Charging Infrastructure (EVCI) is one of the main pillars of e-mobility rollout. Thus, it is important to investigate the lessons learned from EVCI installation across those forerunner cities around the world. This research addresses a data-driven multi-dimensional serviceability analysis of EVCI in 25 cities across the globe, to unveil many of those complex patterns associated with EVCI planning by discussing: EVCI service coverage, radius, and standard evaluation metrics. City comparisons, market patterns, and worldwide trends have been observed and geo-spatially analyzed including: 1) EVCI coverage vs EVs sales, 2) density distribution of charging pools/1km2 covered cell, 3) the analogy between demand-driven networks and density distribution of EVCI locations, 4) the structure of EVCI covered areas in relation to cities’ urban fabric, 5) charging pools/million population in relation to the type of parking citizens have access to and drivers’ charging preferences, 6) the effect of road network topology on EVCI service radius, 7) the effect of prevalent degree of electrification and traffic conditions on EVCI service coverage and radius, and 8) the effect of ambient temperature on EVCI service radius. Other factors related to e-mobility public policies (such as: EVCI incentives) have resulted in – along with cities’ characteristics – different levels of success across the studied cities. The provided analysis shall help in: 1) providing e-mobility early adopters’ cities with a high-level guidance on the best practices for EVCI planning, 2) spreading the praiseworthy EVCI installation practices among e-mobility forerunners to further optimize EVCI deployment.

Ergo, SMIRK is safe: a safety case for a machine learning component in a pedestrian automatic emergency brake system

Integration of machine learning (ML) components in critical applications introduces novel challenges for software certification and verification. New safety standards and technical guidelines are under development to support the safety of ML-based systems, e.g., ISO 21448 SOTIF for the automotive domain and the Assurance of Machine Learning for use in Autonomous Systems (AMLAS) framework. SOTIF and AMLAS provide high-level guidance but the details must be chiseled out for each specific case. We initiated a research project with the goal to demonstrate a complete safety case for an ML component in an open automotive system. This paper reports results from an industry-academia collaboration on safety assurance of SMIRK, an ML-based pedestrian automatic emergency braking demonstrator running in an industry-grade simulator. We demonstrate an application of AMLAS on SMIRK for a minimalistic operational design domain, i.e., we share a complete safety case for its integrated ML-based component. Finally, we report lessons learned and provide both SMIRK and the safety case under an open-source license for the research community to reuse.

Valuation of Urban Public Bus Electrification with Open Data and Physics-Informed Machine Learning

This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for their environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification require granular and bespoke data on bus operation that can be difficult to procure. This strict requirement on data and modeling can hinder potential collaborators on bus electrification, such as electric vehicle manufacturers or electricity providers, from selecting the candidate public transit agencies in the initial phase of the market investigation. Our valuation tool uses General Transit Feed Specification, a standard data format used by transit agencies worldwide, to provide high-level guidance on developing a prioritization strategy for electrifying a bus fleet. We develop physics-informed machine learning models to evaluate energy consumption, carbon emissions, health impacts, and the total cost of ownership for each transit route. We demonstrate our tool with a case study of the bus lines in the Greater Boston and Milan metropolitan areas. While the capital cost of electric buses and the price of diesel fuel may impact the economic benefit of electrification, we expect significant improvements in health and environmental performances. We estimate about 50% health cost reduction by electrifying only 4 routes out of 15 to 19 routes in each city. For Boston and Milan, some routes are estimated to produce less than 5% of the diesel bus emissions and 14%, respectively, for which investment in electrification can be prioritized.

World Heritage and the challenge of climate change: a reform agenda

ABSTRACT The General Assembly of States Parties to the World Heritage Convention adopted a policy and strategy on climate change in 2007. Many of its actions remain to be delivered. Climate change has highlighted the limits of a system under stress to respond to an immense challenge. The General Assembly is again considering the issue, and a second World Heritage climate change policy is being developed to provide high-level guidance on response measures. The draft second policy emphasises the role of individual States Parties in addressing climate impacts on their World Heritage sites but says less about the responsibilities of the World Heritage Committee, World Heritage Centre or Advisory Bodies to the Convention in meeting the goals of climate action and achieving an equitable, international and shared response. Nor does it tackle issues related to Outstanding Universal Value, the core of any response to climate change. We develop a conceptual framework for substantive reform and propose actions to enable the World Heritage system to effectively respond to climate change and support the resolution of longstanding, systemic issues. Meaningful response to climate change needs to involve strategic as well as operational elements in a staged response with measurable outcomes and outputs.

A Scoping Review of Non-Structural Airway Disease as a Cause of Poor Performance in Racehorses.

The association between poor performance and respiratory disease in Thoroughbred racehorses that do not have a structural abnormality of the respiratory tract, is often based on anecdotal evidence. The objective of this scoping review was to examine the scientific evidence for such associations. Publications were selected based on a search of three databases (PubMed, Scopus, and CAB Direct), in English and without date restriction, followed by a screening process to exclude non-relevant papers, duplicates, and reviews. This process identified 996 publications of which 20 were analysed using the Quality in Prognosis Studies (QUIPS) tool. The results indicated that the evidence supporting the relationship between proposed diagnostic indicators and poor performance is variable. There is a need for better quality evidence. In particular, there are conflicting reports relating to the impact of equine asthma and EIPH on athletic performance. Furthermore, a lack of standardisation in the measurement of racehorse performance makes it difficult to compare findings from different studies. The industry would benefit from high-level guidance concerning the design of controlled performance studies in Thoroughbred racehorses to collect comprehensive data and facilitate targeted interventions.

Development and Governance of FAIR Thresholds for a Data Federation

The FAIR (findable, accessible, interoperable, and re-usable) principles and practice recommendations provide high level guidance and recommendations that are not research-domain specific in nature. There remains a gap in practice at the data provider and domain scientist level demonstrating how the FAIR principles can be applied beyond a set of generalist guidelines to meet the needs of a specific domain community. We present our insights developing FAIR thresholds in a domain specific context for self-governance by a community (agricultural research). ‘Minimum thresholds’ for FAIR data are required to align expectations for data delivered from providers’ distributed data stores through a community-governed federation (the Agricultural Research Federation, AgReFed). Data providers were supported to make data holdings more FAIR. There was a range of different FAIR starting points, organisational goals, and end user needs, solutions, and capabilities. This informed the distilling of a set of FAIR criteria ranging from ‘Minimum thresholds’ to ‘Stretch targets’. These were operationalised through consensus into a framework for governance and implementation by the agricultural research domain community. Improving the FAIR maturity of data took resourcing and incentive to do so, highlighting the challenge for data federations to generate value whilst reducing costs of participation. Our experience showed a role for supporting collective advocacy, relationship brokering, tailored support, and low-bar tooling access particularly across the areas of data structure, access and semantics that were challenging to domain researchers. Active democratic participation supported by a governance framework like AgReFed’s will ensure participants have a say in how federations can deliver individual and collective benefits for members.

The National Blood Shortage-An Impetus for Change.

The National Blood Shortage-An Impetus for Change.

Graph ranking based butterfly segmentation in ecological images

An accurate cultural insect detection and recognition relies mainly on a proper automatic segmentation. This paper deals with butterfly segmentation in ecological images characterized by several artifacts like the complexity of environmental decors and cluttered backgrounds. The distractors contained in the rich ecological environment and the huge difference between butterfly species complicate severely the segmentation and make it a challenging task. As butterflies appears to be well contrasted from their surrounding, we suggest to explore the saliency property to delineate accurately the butterfly boundaries. In this vein, we perform a graph ranking process with high level guidance according to foreground and background cues to improve the quality of segmentation. The ranking accuracy is improved through a weighting scheme that combines accurately color, texture and spatial information. The contribution of each used feature is controlled according to its relevance in highlighting butterfly regions. After that, we initialize foreground seeds from most salient pixels and background seeds from less salient pixels as an input for a Graph-cut algorithm to extract the butterfly from the background. Comparative evaluation has shown that our segmentation scheme outperforms some existing segmentation methods that provide high segmentation scores.

The Need to Prioritize Action to Address Climate Change Risks by All Means

This article describes some high-level guidance for actions to be taken immediately in order to address the ever-increasing climate change risks. The approaches considered span the full range as described in the literature and are described as the four pillars of climate policy. Pillar 1 describes mitigation, the reduction of greenhouse gas emissions, pillar 2 adaptation, the incremental or transformational change of systems to enhance resilience of natural and human systems against current and future impacts of climate change, pillar 3 carbon dioxide removal (CDR) in order to reduce the concentration of this long-living greenhouse gas in the atmosphere to a level that avoids global warming that might result in dangerous interference with the climate system, and pillar 4 radiative forcing management (RFM) which has been recently addressed in a draft technical report by the International Standardization Institute (ISO14082-TR), an approach that aims to reduce global warming quite fast, however without eliminating the main cause of global warming.

Arbitrary-Scale Texture Generation From Coarse-Grained Control.

Existing deep-network based texture synthesis approaches all focus on fine-grained control of texture generation by synthesizing images from exemplars. Since the networks employed by most of these methods are always tied to individual exemplar textures, a large number of individual networks have to be trained when modeling various textures. In this paper, we propose to generate textures directly from coarse-grained control or high-level guidance, such as texture categories, perceptual attributes and semantic descriptions. We fulfill the task by parsing the generation process of a texture into the three-level Bayesian hierarchical model. A coarse-grained signal first determines a distribution over Markov random fields. Then a Markov random field is used to model the distribution of the final output textures. Finally, an output texture is generated from the sampled Markov random field distribution. At the bottom level of the Bayesian hierarchy, the isotropic and ergodic characteristics of the textures favor a construction that consists of a fully convolutional network. The proposed method integrates texture creation and texture synthesis into one pipeline for real-time texture generation, and enables users to readily obtain diverse textures with arbitrary scales from high-level guidance only. Extensive experiments demonstrate that the proposed method is capable of generating plausible textures that are faithful to user-defined control, and achieving impressive texture metamorphosis by interpolation in the learned texture manifold.