User's Guide Research Articles

Monitoring fast‐moving animals—Building a customized camera system and evaluation toolset

Abstract Automated cameras (including camera traps) are an established observation tool, allowing, for example the identification of behaviours and monitoring without harming organisms. However, limitations including imperfect detection, insufficient data storage and power supply restrict the use of camera traps, making inexpensive and customizable solutions desirable. We describe a camera system and evaluation toolset based on Raspberry Pi computers and YOLOv5 that can overcome those shortcomings with its modular properties. We facilitate the set‐up and modification for researchers via detailed step‐by‐step guides. A customized camera system prototype was constructed to monitor fast‐moving organisms on a continuous schedule. For testing and benchmarking, we recorded mason bees (Osmia cornuta) approaching nesting aids on 20 sites. To efficiently process the extensive video material, we developed an evaluation toolset utilizing the convolutional neural network YOLOv5 to detect bees in the videos. In the field test, the camera system performed reliably for more than a week (2 h per day) under varying weather conditions. YOLOv5 detected and classified bees with only 775 original training images. Overall detection reliability varied depending on camera perspective, site and weather conditions, but a high average detection precision (78%) was achieved, which was confirmed by a human observer (80% of algorithm‐based detections confirmed). The customized camera system mitigates several disadvantages of commercial camera traps by using interchangeable components and incorporates all major requirements a researcher has for working in the field including moderate costs, easy assembly and an external energy source. We provide detailed user guides to bridge the gap between ecology, computer science and engineering.

Learning from conect4children: A Collaborative Approach towards Standardisation of Disease-Specific Paediatric Research Data

The conect4children (c4c) initiative was established to facilitate the development of new drugs and other therapies for paediatric patients. It is widely recognised that there are not enough medicines tested for all relevant ages of the paediatric population. To overcome this, it is imperative that clinical data from different sources are interoperable and can be pooled for larger post hoc studies. c4c has collaborated with the Clinical Data Interchange Standards Consortium (CDISC) to develop cross-cutting data resources that build on existing CDISC standards in an effort to standardise paediatric data. The natural next step was an extension to disease-specific data items. c4c brought together several existing initiatives and resources relevant to disease-specific data and analysed their use for standardising disease-specific data in clinical trials. Several case studies that combined disease-specific data from multiple trials have demonstrated the need for disease-specific data standardisation. We identified three relevant initiatives. These include European Reference Networks, European Joint Programme on Rare Diseases, and Pistoia Alliance. Other resources reviewed were National Cancer Institute Enterprise Vocabulary Services, CDISC standards, pharmaceutical company-specific data dictionaries, Human Phenotype Ontology, Phenopackets, Unified Registry for Inherited Metabolic Disorders, Orphacodes, Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP), and Observational Medical Outcomes Partnership. The collaborative partners associated with these resources were also reviewed briefly. A plan of action focussed on collaboration was generated for standardising disease-specific paediatric clinical trial data. A paediatric data standards multistakeholder and multi-project user group was established to guide the remaining actions—FAIRification of metadata, a Phenopackets pilot with RDCA-DAP, applying Orphacodes to case report forms of clinical trials, introducing CDISC standards into European Reference Networks, testing of the CDISC Pediatric User Guide using data from the mentioned resources and organisation of further workshops and educational materials.

Introducing re-weighted range voting in clinical practice guideline prioritization: Development and testing of the re-weighted priority-setting (REPS) tool.

We aimed to develop and test a tool based on the re-weighted range voting mechanism to prioritize items (i.e. key questions) in a priority-setting assessment for clinical practice guidelines. The secondary aim was to provide methodological context of the tool. We iteratively developed the tool and used qualitative methods (i.e. think-aloud and semi-structured interviews) to test the tool's usability and make adjustments accordingly. An observational approach was used to test the tool's outcome satisfaction in a real-world priority-setting assessment within a rare-disease guideline of a European Reference Network and under four different conditions in the tool. Four guideline methodologists tested the usability of the tool. The real-world testing was performed with a guideline panel consisting of a core working group, five expertise working groups, and a working group with patient representatives. Thirty-one panel members assigned scores in the priority-setting assessment. Seventeen panel members rated the priority-setting outcome, and sixteen panel members rated the outputs generated under the four conditions. Upon initial use, guideline methodologists found the tool to be quite overwhelming. However, with some initial effort they were able to easily identify the tool's structure. Based on observations and feedback, the tool was further refined and user guidance was developed. Guideline panel members expressed (high) satisfaction with the priority-setting outcome. They particularly preferred the condition when using mean subgroup scores as input or employing aggressive penalties in the weighting method to determine the outputs. The tool generates a ranked list of items and offers flexibility for different choices in priority-setting assessments as long as its input format requirements are met. Although it is not a consensus method, the tool assists in narrowing down a set of priority items. Additional steps in the priority-setting assessment can lead to a consensus being reached regarding the final outcome.

A model for community-driven development of best practices: the Ocean Observatories Initiative Biogeochemical Sensor Data Best Practices and User Guide

The field of oceanography is transitioning from data-poor to data-rich, thanks in part to increased deployment of in-situ platforms and sensors, such as those that instrument the US-funded Ocean Observatories Initiative (OOI). However, generating science-ready data products from these sensors, particularly those making biogeochemical measurements, often requires extensive end-user calibration and validation procedures, which can present a significant barrier. Openly available community-developed and -vetted Best Practices contribute to overcoming such barriers, but collaboratively developing user-friendly Best Practices can be challenging. Here we describe the process undertaken by the NSF-funded OOI Biogeochemical Sensor Data Working Group to develop Best Practices for creating science-ready biogeochemical data products from OOI data, culminating in the publication of the GOOS-endorsed OOI Biogeochemical Sensor Data Best Practices and User Guide. For Best Practices related to ocean observatories, engaging observatory staff is crucial, but having a “user-defined” process ensures the final product addresses user needs. Our process prioritized bringing together a diverse team and creating an inclusive environment where all participants could effectively contribute. Incorporating the perspectives of a wide range of experts and prospective end users through an iterative review process that included “Beta Testers’’ enabled us to produce a final product that combines technical information with a user-friendly structure that illustrates data analysis pipelines via flowcharts and worked examples accompanied by pseudo-code. Our process and its impact on improving the accessibility and utility of the end product provides a roadmap for other groups undertaking similar community-driven activities to develop and disseminate new Ocean Best Practices.

A guide to critical appraisal of quality improvement reports

Quality improvement (QI) has become an integral part of healthcare. Despite efforts to improve the reporting of QI through frameworks such as the SQUIRE 2.0 guidelines, there is no standard or well-accepted guide to evaluate published QI for rigor, validity, generalizability, and applicability. User's Guides for evaluation of published clinical research have been employed routinely for over 25 years; however, similar tools for critical appraisal of QI are limited and uncommonly used. In this article we propose an approach to guide the critical review of QI reports focused on evaluating the methodology, improvement results, and applicability and feasibility for implementation in other settings. The resulting Quality Improvement Critical Knowledge (QUICK) Tool can be used by those reviewing manuscripts submitted for publication, as well as healthcare providers seeking to understand how to apply published QI to their local context.

Translating Suicide Safety Planning Components Into the Design of mHealth App Features: Systematic Review.

Suicide safety planning is an evidence-based approach used to help individuals identify strategies to keep themselves safe during a mental health crisis. This study systematically reviewed the literature focused on mobile health (mHealth) suicide safety planning apps. This study aims to evaluate the extent to which apps integrated components of the safety planning intervention (SPI), and if so, how these safety planning components were integrated into the design-based features of the apps. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically analyzed 14 peer-reviewed studies specific to mHealth apps for suicide safety planning. We conducted an analysis of the literature to evaluate how the apps incorporated SPI components and examined similarities and differences among the apps by conducting a comparative analysis of app features. An independent review of SPI components and app features was conducted by downloading the available apps. Most of the mHealth apps (5/7, 71%) integrated SPI components and provided customizable features that expanded upon traditional paper-based safety planning processes. App design features were categorized into 5 themes, including interactive features, individualized user experiences, interface design, guidance and training, and privacy and sharing. All apps included access to community supports and revisable safety plans. Fewer mHealth apps (3/7, 43%) included interactive features, such as associating coping strategies with specific stressors. Most studies (10/14, 71%) examined the usability, feasibility, and acceptability of the safety planning mHealth apps. Usability findings were generally positive, as users often found these apps easy to use and visually appealing. In terms of feasibility, users preferred using mHealth apps during times of crisis, but the continuous use of the apps outside of crisis situations received less support. Few studies (4/14, 29%) examined the effectiveness of mHealth apps for suicide-related outcomes. Positive shifts in attitudes and desire to live, improved coping strategies, enhanced emotional stability, and a decrease in suicidal thoughts or self-harm behaviors were examined in these studies. Our study highlights the need for researchers, clinicians, and app designers to continue to work together to align evidence-based research on mHealth suicide safety planning apps with lessons learned for how to best deliver these technologies to end users. Our review brings to light mHealth suicide safety planning strategies needing further development and testing, such as lethal means guidance, collaborative safety planning, and the opportunity to embed more interactive features that leverage the advanced capabilities of technology to improve client outcomes as well as foster sustained user engagement beyond a crisis. Although preliminary evidence shows that these apps may help to mitigate suicide risk, clinical trials with larger sample sizes and more robust research designs are needed to validate their efficacy before the widespread adoption and use.

ScAnnoX: an R package integrating multiple public tools for single-cell annotation.

Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking. This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms. The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.

Prompt Optimization Methods for Large Language Models with Long Text Input

When faced with long text input, the generated results from large language models sometimes fail to meet user expectations. Due to the length and complexity of the input content, users often do not know how to modify the input to obtain the desired results. To address this dilemma, we propose a Prompt optimization method for large language models with long text input. This method determines the influence weights of different semantic segments on the results, providing guidance for users to generate desired text using large language models. Experimental results show that by evaluating the importance of different semantic segments in military question-answering system text and improving the input content, the quality and usability of the generated military question-answering text can be enhanced.

Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance

Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality. Recently, deep learning based terrain generation has emerged, notably the ones based on generative adversarial networks (GAN). However, these methods often struggle to fulfill the requirements of flexible user control and maintain generative diversity for realistic terrain. Therefore, we propose a novel diffusion-based method, namely terrain diffusion network (TDN), which actively incorporates user guidance for enhanced controllability, taking into account terrain features like rivers, ridges, basins, and peaks. Instead of adhering to a conventional monolithic denoising process, which often compromises the fidelity of terrain details or the alignment with user control, a multi-level denoising scheme is proposed to generate more realistic terrains by taking into account fine-grained details, particularly those related to climatic patterns influenced by erosion and tectonic activities. Specifically, three terrain synthesisers are designed for structural, intermediate, and fine-grained level denoising purposes, which allow each synthesiser concentrate on a distinct terrain aspect. Moreover, to maximise the efficiency of our TDN, we further introduce terrain and sketch latent spaces for the synthesizers with pre-trained terrain autoencoders. Comprehensive experiments on a new dataset constructed from NASA Topology Images clearly demonstrate the effectiveness of our proposed method, achieving the state-of-the-art performance. Our code is available at https://github.com/TDNResearch/TDN.

Benchmarking Combinations of Learning and Testing Algorithms for Automata Learning

Automata learning enables model-based analysis of black-box systems by automatically constructing models from system observations, which are often collected via testing. The required testing budget to learn adequate models heavily depends on the applied learning and testing techniques. Test cases executed for learning (1) collect behavioural information and (2) falsify learned hypothesis automata. Falsification test-cases are commonly selected through conformance testing. Active learning algorithms additionally implement test-case selection strategies to gain information, whereas passive algorithms derive models solely from given data. In an active setting, such algorithms require external test-case selection, like repeated conformance testing to extend the available data. There exist various approaches to learning and conformance testing, where interdependencies among them affect performance. We investigate the performance of combinations of six learning algorithms, including a passive algorithm, and seven testing algorithms by performing experiments using 153 benchmark models. We discuss insights regarding the performance of different configurations for various types of systems. Our findings may provide guidance for future users of automata learning. For example, counterexample processing during learning strongly impacts efficiency, which is further affected by testing approach and system type. Testing with the random Wp-method performs best overall, while mutation-based testing performs well on smaller models.

The Logic and Mechanism of VR Marketing: Market Shaping in the Perspective of Virtual Reality

With the rapid development of technology, VR technology has become an emerging trend in the field of marketing. This paper explores the application of VR technology in marketing and analyzes its impact on consumer behavior and marketing Ddelves into two successful cases of VR in marketing: the Volvo XC90 VR test drive and Audi's virtual showroom, revealing the practical application of VR in improving user engagement and creating a unique shopping experience. VR technology provides an immersive experience that enhances the user's interaction with the product and brand and improves the user's behavior and marketing strategy. VR technology provides an immersive experience that enhances user interaction with products and brands, increasing user retention and advertising effectiveness. VR marketing is more effective than traditional marketing in increasing user engagement, emotional connection, memory, and satisfaction, thus increasing user conversion and loyalty. The article gives the advantages and disadvantages of VR marketing. The advantages of VR marketing are more realistic product demonstrations or trial experiences, the ability to create in-depth, emotional brand storytelling narratives, and expanding global markets and audiences. Its disadvantages are high technology costs, user access thresholds, technical limitations consumer adaptation issues, and limitations of the experience. In addition, this paper discusses the challenges faced by VR technology in marketing: including user acceptance, quality of experience, clarity of user needs, protection of user rights and interests, and future development potential: improving user education and guidance, optimizing user experience, conducting in-depth research on user needs, and protecting user rights and privacy.

Differences in Preferences for Drug Therapy Between Patients with Metastatic Versus Early-Stage Breast Cancer: A Systematic Literature Review.

Compared with early stages (eBC) metastatic BC (mBC) is incurable. In mBC, aggressive treatment may increase the duration of survival but may also cause severe treatment side effects. A better understanding how patients with BC value different aspects of drug therapy might improve treatment effectiveness, satisfaction and adherence. This systematic review aims to identify and summarise studies evaluating patient preferences for drug therapy of BC and to compare preferences of patients with eBC and mBC. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The electronic databases PubMed and Web of Science were searched on 22 June 2023. All studies published to this point were considered. Original studies reporting patient preferences on BC drug therapy determined by any type of choice experiment were eligible. A narrative synthesis of the effect measures presented as relative importance ratings, trade-offs (required benefit to make a therapy worthwhile) or monetary values of the treatment attributes was reported for each study. Risk of bias assessment for individual studies was performed using the checklist for observational studies from the STROBE Statement and the checklist from 'Conducting Discrete Choice Experiments to Inform Healthcare Decision Making: A User's Guide'. The study protocol was registered at the PROSPERO database (CRD42022377031). A total of 34 studies met the inclusion criteria were included in the analysis evaluating the preferences of patients with eBC (n = 18), mBC (n = 10) or any stage BC (n = 6) on, for example, chemotherapy, endocrine therapy, hormonal therapy or CKD4/6-inhibitors using different types of choice experiments. Regardless of the stage, most patients valued treatment effectiveness in terms of survival gains higher than potential adverse drug reactions (ADRs). Treatment cost, mode of administration, treatment regimen and monitoring aspects were considered as least important treatment attributes. In addition, preferences concerning 16 different types of ADRs were described, showing high heterogeneity within BC stages. Yet, comparable results across BC stages were observed. Regardless of the stage, patients with BC consistently valued survival gains as the most important attribute and were willing to accept the risk of potential ADRs. Incorporating patient preferences in shared decision making may improve the effectiveness of interventions by enhancing adherence to drug therapy in patients suffering from BC.

Four decades of global surface albedo estimates in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) climate data record

Abstract. We present the surface albedo data in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) data record family. The temporal coverage of this edition is extended from 1979 until the near-present day. The core algorithms and data format remain unchanged from previous editions, but now white- and blue-sky albedo estimates are also available for the first time in CLARA data. We present an overview of the retrieval, followed by an assessment of the accuracy and stability of the data record, based on collocated comparisons with reference surface albedo measurements and intercomparisons with preceding satellite-based albedo data records. Specific attention is paid to addressing the spatial representativeness problem inherent in the “point-to-pixel” validation of satellite-based coarse surface albedo estimates against in situ measurements. We find the CLARA-A3 albedo data to match or improve upon the accuracy and robustness of the predecessor record (CLARA-A2), with good agreement found when compared to in situ measurements. In cases of a large bias, the spatial representativeness of the measurement site typically explains most of the increase. We conclude with a summarizing discussion on the observed strengths and weaknesses of the new data record, including guidance for potential users. The data are available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (Karlsson et al., 2023b).

Effectiveness and visual performance assessment of anti-peeping films

Some private information inevitably becomes more visible when using wide-angle electronic devices in public places. Thus, some people use anti-peeping films to achieve privacy protection. To examine the effectiveness and its influence on the visual performance of the anti-peeping film, we investigated the color gamut, luminance, contrast ratio, Bhattacharyya coefficient and structural similarity characteristics of a smartphone without-film screen and with two-sided (left and right) of 25°, 30°, and 45° anti-peeping films, respectively, and four-sided (up, down, left, and right) anti-peeping films. The results show that the film reduces the screen luminance and contrast ratio except for the color gamut. At 0° viewing angle, the luminance decreases by 24.8%, 25.2%, 18.6%, and 53.8%, respectively. For the 25°, 30°, 45°, and 360° anti-peeping films, the luminance and contrast ratio decrease to 50% at 24°, 25°, 19°, and 20° horizontal viewing angles. The effectiveness of four-sided anti-peeping film is the best, followed by the two-sided ones, and the smaller the viewing angle, the better the effect. The Bhattacharyya coefficients of the white images’ pixel micrographs are 0.887, 0.896, 0.898, and 0.878, respectively, of which the smallest four-sided anti-peeping film has the greatest influence on screen clarity. Under the 0-40° viewing angle, the structural similarity continuously decreases, and the screen becomes blurred due to the anti-peeping film. This work investigates the optical characteristics of anti-peeping film and examines its influence on visual performance, and provides strategy for manufacturers and use guidance for users.

Optimized parallelization of boundary integral Poisson-Boltzmann solvers

The Poisson-Boltzmann (PB) model governs the electrostatics of solvated biomolecules, i.e., potential, field, energy, and force. These quantities can provide useful information about protein properties, functions, and dynamics. By considering the advantages of current algorithms and computer hardware, we focus on the parallelization of the treecode-accelerated boundary integral (TABI) PB solver using the Message Passing Interface (MPI) on CPUs and the direct-sum boundary integral (DSBI) PB solver using KOKKOS on GPUs. We provide optimization guidance for users when the DSBI solver on GPU or the TABI solver with MPI on CPU should be used depending on the size of the problem. Specifically, when the number of unknowns is smaller than a predetermined threshold, the GPU-accelerated DSBI solver converges rapidly thus has the potential to perform PB model-based molecular dynamics or Monte Carlo simulation. As practical applications, our parallelized boundary integral PB solvers are used to solve electrostatics on selected proteins that play significant roles in the spread, treatment, and prevention of COVID-19 virus diseases. For each selected protein, the simulation produces the electrostatic solvation energy as a global measurement and electrostatic surface potential for local details.

Component-resolved diagnosis of tree pollen allergen: identify key allergens to develop treatment plans

With the increasing global prevalence of tree pollen allergies, there has been a significant impact on the quality of life for populations. In North and Central China, birch pollen, cypress pollen, and plane tree pollen are the most common allergens for springtime pollen allergy sufferers. The distribution of plants and patterns of pollen transmission in different geographical areas result in varying pollen exposure outcomes, further complicating the challenges in diagnosis and individualized treatment. This article delves into the research progress and clinical application of tree pollen allergies based on the "Molecular Allergology User's Guide 2.0 (MAUG 2.0) " published by the European Academy of Allergy and Clinical Immunology (EAACI). It discusses major allergen families and component proteins of tree pollen such as PR-10 proteins, profilins, polcalcins, as well as cross-reactive components that may cause pollen-food allergy syndrome. Allergen component diagnostics can distinguish true allergy sufferers from those with multiple allergen reactions, enabling more targeted selection of allergens for specific immunotherapy, thus enhancing treatment effectiveness. Bet v 1 and Cup a 1, for instance, are specific indicators for immunotherapy in birch and cypress allergy patients. Overall, this article provides cutting-edge information for professionals in the field of tree pollen allergies, offering in-depth exploration of tree pollen allergen component proteins, clinical manifestations, and treatment-related research, aiding in better understanding and addressing the challenges of tree pollen allergies.

Development of Online NAS Application User Guide Website using NextJS 13 and REST API to Enhance User Understanding

This research focuses on developing a user guide for the Nusantara Certification Application System (NAS) through an online platform, utilizing NextJS 13 technology and REST API to enhance comprehensive understanding and optimal utilization of NAS features. The implementation of NextJS 13 ensures a responsive and adaptive interface, while the REST API optimizes speed and smooth access to required data. Through a combination of structured video tutorials and information sources in PDF format, the objective of this guide is to provide comprehensive insight into each feature offered within the NAS application. Research findings confirm the success of integrating NextJS 13 technology and REST API, significantly enhancing understanding and efficiency in NAS application use. The primary role of this implementation lies in aiding users to deeply comprehend and optimize the benefits of the NAS application while expediting access to necessary information. In conclusion, the utilization of cutting-edge technology in user guide development has proven to have a positive impact by broadening user insights into the application and enhancing productivity and efficiency in its use. This underscores the significance of employing advanced technology in delivering information to modern application users.

Uncertainty analysis and updated user guidance for interspecies correlation estimation models and low toxicity compounds.

Interspecies correlation estimation (ICE) models are log-linear relationships of acute sensitivity between two species that estimate the sensitivity of an untested species from the known sensitivity of a surrogate. As ICE model use increases globally, additional user guidance is required to ensure consistent use across chemicals and applications. The present study expands ICE uncertainty analyses and user guidance with a focus on low toxicity compounds whose acute values (i.e., reported as mg/L) can be greater than those used to develop a model. In these cases, surrogate values may be outside the ICE model domain and require additional extrapolations to predict acute toxicity. We use the extensive, standardized acute toxicity database underlying ICE models to broadly summarize inter-test variability of acute toxicity data as a measure by which model prediction accuracy can be evaluated. Using the data and models found on the USEPA Web-ICE (www3.epa.gov/webice), we created a set of "truncated" models from data corresponding to the lower 75th percentile of surrogate toxicity. We predicted toxicity for chemicals in the upper 25th percentile as both μg/L beyond the model domain and converted to mg/L (i.e., "scaled" value) and compared these predictions with those from cross-validation of whole ICE models and to the measured value. For ICE models with slopes in the range 0.66-1.33, prediction accuracy of scaled values did not differ from the accuracy of the models when data were entered as μg/L within or beyond the model domain. An uncertainty analysis of ICE confidence intervals was conducted and an interval range of two orders of magnitude was determined to minimize type I and II errors when accepting or rejecting ICE predictions. We updated the ICE user guidance based on these analyses to advance the state of the science for ICE model application and interpretation. Integr Environ Assess Manag 2024;20:1554-1565. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

GLOW IN THE DARK MEDIAN FRONTAGE AND PEDESTRIAN AS A GUIDE FOR DRIVERS AT NIGHT

The condition of the road plays a significant role in the driving experience and traffic safety. However, excessive use of Street Lighting (LPJU) can have an impact on global warming. This research utilizes a Research and Development (R&D) approach to develop phosphorescent paint technology for curbstones. The main goal of this research is to create a solution that aids the visualization of drivers and pedestrians in dark road conditions. Additionally, the research aims to reduce excessive electricity consumption, which is a crucial step in supporting efforts to mitigate global climate change. The research results show that the application of phosphorescent paint on curbstones can enhance visibility at night and provide clear visual guidance for road users. This technology also successfully reduces reliance on external street lighting, resulting in significant electricity savings. Thus, this research makes a positive contribution to improving traffic safety and supporting energy conservation efforts to protect the environment.

Growth of condition-related knowledge among youth with spina bifida: associations with neurocognitive functioning and self-management skills.

This study aims to characterize the growth in condition-related knowledge in youth with spina bifida (SB), identify neurocognitive predictors of growth, and examine associations between growth in knowledge and subsequent levels of medical self-management skills. Participants were recruited from a larger longitudinal study involving 140 youth with SB and caregivers, who completed questionnaires and interviews every 2 years over 8 years. The current study included the youth report of condition-related knowledge and medical self-management skills. Youth attention and executive functioning were assessed via parent and teacher reports and performance-based assessment. Latent growth curves were conducted in Mplus Version 8 (Múthen, L. K., & Múthen, B. O. [1998]. Mplus User's Guide. [Eighth]. Muthén & Muthén) to examine change over time in youth-reported condition-related knowledge. Neurocognitive variables were included as predictors of growth in knowledge and regression analyses were used to predict medical self-management skills from growth in condition-related knowledge. Youth condition-related knowledge increased linearly. Better youth performance on working memory and attention performance-based tasks predicted a higher intercept for condition-related knowledge at T1, but not slope. Teacher and parent reports of inattention and executive dysfunction were not consistent predictors of intercept and growth. Slope of condition-related knowledge was not predictive of subsequent youth self-management skills. Youth with SB gain condition-related knowledge over time. However, executive dysfunction and inattention may impede gains in condition-related knowledge. Thus, executive functioning supports, attention-related interventions, and psychoeducation may support condition-related knowledge gains and later medical self-management skills, but further research assessing family and cultural factors is needed.