Practical Guidelines Research Articles

Use of corticosteroids in non-infectious uveitis – expert consensus in Taiwan

Purpose To offer consensus on the utilization of corticosteroids (CS) for treating non-infectious uveitis in the context of clinical practice in Taiwan. This entails examining the different administration methods, their advantages and disadvantages, and considering alternative treatments according to the prevailing evidence and health policies. Methods Ten ophthalmologists and one rheumatologist convened on December 11, 2022, to review and discuss literature on the topic. The databases explored were the Central Cochrane library, EMBASE, Medline, PUBMED, and Web of Science using relevant keywords. The search spanned from January 1996 to June 2023. After the initial results of the literature review were presented, open voting determined the final statements, with a statement being accepted if it secured more than 70% agreement. This consensus was then presented at significant meetings for further discussions before the final version was established. Results A flow chart and nine statements emerged from the deliberations. They address the importance of CS in uveitis management, guidelines for using topical CS, indications for both periocular or intravitreal and systemic therapies, and tapering and discontinuation methods for both topical and systemic CS. Conclusion While CS are a cornerstone for non-infectious uveitis treatment, their administration requires careful consideration, depending on the clinical situation and the specific type of uveitis. The consensus generated from this article provides a guideline for practitioners in Taiwan, taking into account local health policies and the latest research on the subject. It emphasizes the significance of strategic tapering, the potential for alternative therapies, and the importance of patient-centric care.

리빙랩에서의 집단학습과 매개 활동에 대한 사례연구

Objectives A group of various stakeholders itself cannot automatically guarantee learning for co-creation. For the creation of collective knowledge, intermediary activities that can interconnect and integrate several subjects with different interests, backgrounds, experiences, and knowledge are essential. Living Lab is a place of collective learning based on the horizontal cooperative relationship between private, public, industry, and academia, and is a representative form of co-creation. However, in the case of Living Lab in Korea, research from the perspective of collective learning through co-creation has hardly been conducted. Therefore, this study aims to analyze the collective learning of Living Lab and derive implications, focusing on intermediary activities. Methods A qualitative case study was conducted on the ‘Smart Farm Planning Living Lab for Revitalizing the Old Urban Community’ held in Innovation District, Incheon. In order to structure interactions for collective learning, ac-tivities and tasks for each stage of the Living Lab were organized around the process and results of collective learn-ing, and then classified and analyzed as detailed intermediary activities of facilitating, configuring, brokering ac-cording to the definition of Hakkarainen & Hyysalo(2016). Results In all three stages of Living Lab, the proportion of facilitating was the highest, and brokering were common. This can be interpreted as a result showing that intermediary activities that facilitate actors' activities and broker connections between actors are essential in the process of co-creation to solve social problems. Next, through the three stages of Living Lab, all detailed intermediary activities of the facilitating, configuring, brokering appeared, and it was confirmed that the proportion of detailed intermediary activities was different for each stage of progress. This shows that it is necessary to pay attention to the importance and multifacetedness of interme-diary activities that are overlooked in co-creation through Living Lab. Conclusions This study supports the results of previous studies that the intermediary activities that contribute to collecitve learning and co-creation of Living Lab need to be extended and understood into three types: configuring and brokering as well as facilitating. In addition, the diversity of intermediary activities has important implications for the practical guidelines for Living Lab operation, which are characterized by a spiral development structure that changes according to the results of interaction and learning. Finally, the recognition of the intermediary's unique interactional expertise and the need for research on practical curriculum development and field operation guide-lines based on it were proposed.

Developing an integrated framework for risk management in policing: crisis management using big data analytics as a case study

Objective: The study seeks to develop an integrated framework for enhancing risk management and crisis management in policing through the utilization of big data analytics, using the Dubai police force as a case study. Theoretical Framework: This research is grounded in the intersection of big data analytics and risk management within the field of policing, focusing on how data-driven approaches can innovate and improve security crisis management. Method: Data for the study was collected via questionnaires distributed to 450 police officers across all departments in Dubai. The analysis was conducted using AMOS software within a Structural Equation Modeling framework to assess the impact of big data analytics on police risk and crisis management. Results and Discussion: The results indicate that big data analytics significantly enhances risk management and crisis management in policing. It was found that police risk management serves as a mediating factor between big data analytics and security crisis management. These findings suggest that the application of big data can substantially improve knowledge and operational performance in police organizations. Research Implications: The study emphasizes the crucial role of big data analytics in revolutionizing police risk and crisis management processes. It highlights the potential for these technologies to provide substantial improvements in the efficiency and effectiveness of police operations. Originality/Value: This research is valuable as it provides empirical evidence of the benefits of big data analytics in the context of policing. It offers practical guidelines to police departments, particularly within the UAE, on leveraging big data to enhance their operational performance in managing risks and crises. The study contributes to the broader understanding of integrating advanced data analytics into public safety and emergency response strategies.

A general framework for developing computable clinical phenotype algorithms.

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 three 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 five 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.

The Effect of Big Data Analytics on Predictive Policing: The Mediation Role of Crisis Management

Objective: The objective of this study is to evaluate the impact of big data analytics (BDA) on predictive policing, particularly examining the mediating role of crisis management in this relationship. Theoretical Framework: The research is anchored in the domain of big data analytics, focusing on its application within law enforcement for enhancing predictive policing capabilities. The study explores how crisis management serves as a linkage between data analytics and predictive policing practices. Method: The study gathered data from 450 individuals working across various police departments in Dubai, utilizing a questionnaire to collect responses. The analytical approach was based on Structural Equation Modeling, conducted using AMOS software. Results and Discussion: Findings from the research indicate that big data analytics significantly boosts predictive policing and crisis management. Importantly, crisis management was identified as a mediating factor between big data analytics and its efficacy in predictive policing. These results suggest that big data analytics not only directly enhances predictive policing but also improves it indirectly through effective crisis management. Research Implications: This study underscores the importance of integrating big data analytics into police operations to advance predictive policing capabilities. It highlights the dual benefits of big data analytics in both direct application and enhancement through crisis management processes. Originality/Value: This research contributes to the limited but growing body of literature on the application of big data analytics in predictive policing. It offers practical guidelines for police forces, especially within the UAE, to better harness big data for improving their operational effectiveness and crisis management strategies. The study also discusses broader implications for both practice and ongoing research in this evolving field.

The mirror effect in online survey data: Evidence and implications for marketing theory and strategy

AbstractThis research reveals the presence, in online survey data, of a key pattern documented in psychology lab research: the Mirror Effect. The Mirror Effect occurs when unfamiliar stimuli are unexpectedly recognized as accurately as familiar stimuli, or more accurately. Using a set of familiar and unfamiliar words (as determined by lexical frequency), we first report that we can robustly replicate psychology lab research in an online survey, detecting the Mirror Effect. We then apply the same analytical approach to surveying consumer recognition of everyday brands (supermarkets, banks and car brands). We find that unfamiliar brands can be recognized with the same level of accuracy as familiar brands, and this effect is stronger than age and gender memory biases present in the data. However, we detect a boundary condition for branded apps, which are extremely unfamiliar brands competing in highly fragmented marketplaces, so very few get downloaded or used. For these digital brands, we find a Concordant Effect, as most respondents find it difficult to recognize highly unfamiliar branded apps (i.e., those with fewer than 5000 downloads). The Mirror Effect re‐emerges for highly experienced app users. These results demonstrate the implications of a generalizable empirical pattern from cognitive psychology for branding and advertising theory. The outcomes of this research also translate into practical guidelines for brand performance measurement via online surveys, mitigating recognition memory bias for the development of marketing strategies based on more accurate interpretation of empirical evidence.

AABP Guidelines for the Humane Euthanasia of Cattle

For stockman, farmers, veterinarians and animal caretakers who choose to work with cattle there are many rewards, but there is also the responsibility to provide the animals in their care with a good a life, and when that is no longer possible, to alleviating the suffering of a slow and painful death. This is not just between farmers and their cattle it is also part of the social compact farmers have with consumers to produce the food that consumers purchase for their families in a way that is ethical. The requirements for humane euthanasia are also codified in industry animal welfare standards. But in reviewing recent studies on the number of cows and calves that die unassisted or were shipped to slaughter with serious ailments, the reali­ties on farm do not always live up to this expectation. To help to address this the AABP has developed and recently updated the American Association of Bovine Practitioners (AABP) Guidelines for the Humane Euthanasia of Cattle as a tool to help bovine prac­titioners and farmers provide timely and humane euthanasia for the cattle to prevent needless pain and suffering.

Modulating secondary growth of perovskite grains through residual solvent evaporation

Over the past decade, perovskite solar cells (PSCs) have attracted enormous attention due to their high performance. One key to fabricating high-quality perovskite films lies in controlling the volatilization rate of residual solvents during the annealing process. This study systematically investigates how different protective substrates affect the volatilization rate of residual solvent in perovskite films. By adjusting the direction and rate of evaporation, the supersaturation time of the solution was precisely controlled, leading to effective recrystallization of the grains. Concurrently, the annealing time was optimized to enhance film quality further. This optimization aimed to increase crystallinity, reduce defects, and thereby minimize non-radiative recombination centers. Implementing these methodologies, particularly the use of filter paper as a protective substrate during a 2-minute annealing process, significantly improved the fill factor (FF) and open-circuit voltage (VOC) of the PSCs. This led to a remarkable 5.26% improvement in power conversion efficiency (PCE) compared to control devices. The strategies employed in this work demonstrate significant potential in improving PSC film quality. This approach not only advances our understanding of film formation dynamics but also provides a practical guideline for future PSC fabrication.

Continuum Growth Mechanics: Reconciling Two Common Frameworks.

The objective of this study was to investigate whether the two most common growth mechanics modeling frameworks, the constrained-mixture growth model and the kinematic growth model, could be reconciled mathematically. The purpose of this effort was to provide practical guidelines for potential users of these modeling frameworks. Results showed that the kinematic growth model is mathematically consistent with a special form of the constrained-mixture growth model, where only one generation of a growing solid exists at any given time, overturning its entire solid mass at each instant of growth in order to adopt the reference configuration dictated by the growth deformation. The thermodynamics of the kinematic growth model, along with the specialized constrained-mixture growth model, requires a cellular supply of chemical energy to allow deposition of solid mass under a stressed state. A back-of-the-envelope calculation shows that the amount of chemical energy required to sustain biological growth under these models is negligibly small, when compared to the amount of energy normally consumed daily by the human body. In conclusion, this study successfully reconciled the two most popular growth theories for biological growth and explained the special circumstances under which the constrained-mixture growth model reduces to the kinematic growth model.

Effect of limited proteolysis and CaCl2 on the rheology, microstructure and in vitro digestibility of pea protein-carboxymethyl cellulose mixed gel

Limited proteolysis, CaCl2 and carboxymethyl cellulose (CMC) have individually demonstrated ability to increase the gel strength of laboratory-extracted plant proteins. However, the syneresis effects of their combination on the gelling capacity of commercial plant protein remains unclear. This was investigated by measuring the rheological property, microstructure and protein–protein interactions of gels formed from Alcalase hydrolyzed or intact pea proteins in the presence of 0.1 % CMC and 0–25 mM CaCl2. Sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) showed the molecular weight of pea protein in the mixture were < 15 kDa after hydrolysis. The hydrolysates showed higher intrinsic fluorescence intensity and lower surface hydrophobicity than the intact proteins. Rheology showed that the storage modulus (G') of hydrolyzed pea protein (PPH)-based gels sightly decreased compared to those of native proteins. 5–15 mM CaCl2 increased the G’ for both PP and PPH-based gels and decreased the strain in the creep-recovery test. Scanning electron microscopy (SEM) showed the presence of smaller protein aggregates in the PPH-based gels compared to PP gels and the gel network became denser, and more compact and heterogenous in the presence of 15 and 25 mM CaCl2. The gel dissociation assay revealed that hydrophobic interactions and hydrogen bonds were the dominant forces to maintain the gel structure. In vitro digestion showed that the soluble protein content in PPH-based gels was 10 ∼ 30 % higher compared to those of the PP counterpart. CaCl2 addition reduced protein digestibility with a concentration dependent behavior. The results obtained show contrasting effects of limited proteolysis and CaCl2 on the gelling capacity and digestibility of commercial pea proteins. These findings offer practical guidelines for developing pea protein-based food products with a balanced texture and protein nutrition through formulation and enzymatic pre-treatment.

Events Management Practices and Performance of Selected HEIs in National Capital Region: Inputs for Sustainable Business Generating Projects

Events management practices played a crucial role in the success and performance of higher education institutions (HEIs) in implementing sustainable business generating projects. This study examined the inputs related to events management practices and their impact on the performance of selected HEIs in the context of sustainable business generating projects. The research aimed to provide insights into effective strategies and best practices that could enhance the success of these projects within HEIs. The study adopted a quantitative research approach, utilizing surveys to gather data from a sample of selected HEIs. The study identified key success factors and challenges faced by HEIs in managing events and sustaining their business generating initiatives. It also provided practical recommendations and guidelines for HEIs to optimize their events management practices and improve project performance. The research outcomes were valuable for HEI administrators, event managers, and other stakeholders involved in sustainable business generating projects within the higher education sector. By identifying effective strategies and practices, this study aimed to enhance the ability of HEIs to generate sustainable business opportunities through events and contribute to the overall development and reputation of these institutions. In conclusion, this study sought to bridge the gap in knowledge regarding events management practices and their impact on the performance of HEIs in sustainable business generating projects. The findings informed decision-making processes and enabled HEIs to implement effective and sustainable events management strategies, ultimately leading to enhanced project performance and long-term success.

Solving a class of Fredholm integral equations of the first kind via Wasserstein gradient flows

Solving Fredholm equations of the first kind is crucial in many areas of the applied sciences. In this work we consider integral equations featuring kernels which may be expressed as scalar multiples of conservative (i.e. Markov) kernels and we adopt a variational point of view by considering a minimization problem in the space of probability measures with an entropic regularization. Contrary to classical approaches which discretize the domain of the solutions, we introduce an algorithm to asymptotically sample from the unique solution of the regularized minimization problem. As a result our estimators do not depend on any underlying grid and have better scalability properties than most existing methods. Our algorithm is based on a particle approximation of the solution of a McKean–Vlasov stochastic differential equation associated with the Wasserstein gradient flow of our variational formulation. We prove the convergence towards a minimizer and provide practical guidelines for its numerical implementation. Finally, our method is compared with other approaches on several examples including density deconvolution and epidemiology.

SynergyAnalyzer: A Matlab toolbox implementing mixed-matrix factorization to identify kinematic-muscular synergies

Background and ObjectiveA new direction in the study of motor control was opened about two decades ago with the introduction of a model for the generation of motor commands as combination of muscle synergies. Muscle synergies provide a simple yet quantitative framework for analyzing the hierarchical and modular architecture of the human motor system. However, to gain insights on the functional role of muscle synergies, they should be related to the task space. The recently introduced mixed-matrix factorization (MMF) algorithm extends the standard approach for synergy extraction based on non-negative matrix factorization (NMF) allowing to factorize data constituted by a mixture of non-negative variables (e.g. EMGs) and unconstrained variables (e.g. kinematics, naturally including both positive and negative values). The kinematic-muscular synergies identified by MMF provide a direct link between muscle synergies and the task space. In this contribution, we support the adoption of MMF through a Matlab toolbox for the extraction of kinematic-muscular synergies and a set of practical guidelines to allow biomedical researchers and clinicians to exploit the potential of this novel approach. MethodsMMF is implemented in the SynergyAnalyzer toolbox using an object-oriented approach. In addition to the MMF algorithm, the toolbox includes standard methods for synergy extraction (NMF and PCA), as well as methods for pre-processing EMG and kinematic data, and for plotting data and synergies. ResultsAs an example of MMF application, kinematic-muscular synergies were extracted from EMG and kinematic data collected during reaching movements towards 8 targets on the sagittal plane. Instructions and command lines to achieve such results are illustrated in detail. The toolbox has been released as an open-source software on GitHub under the GNU General Public License. ConclusionsThanks to its ease of use and adaptability to a variety of datasets, SynergyAnalyzer will facilitate the adoption of MMF to extract kinematic-muscular synergies from mixed EMG and kinematic data, a useful approach in biomedical research to better understand and characterize the functional role of muscle synergies.

Practical Approach to Nuclear Shelter Wall Thickness Estimation

Abstract The authors goal was to provide a practical guideline on nuclear shelter wall thickness estimation. The shelter wall thickness is assessed on the basis of published data on radiation dose calculations, corroborated by field data from nuclear tests, and information or calculations of the dose attenuation in the shelter wall. Dose criterion is selected as the acceptable dose behind the shield. Doses from nuclear tests or simulations are summarized in tables in relation to bomb yield and distance from the explosion point. Neutron dose transmission factors, defined as ratio of the dose behind the shielding to the dose without shielding, and their interdependence with shield thickness for different materials were found in the references. Based on the required dose transmission factor, calculated with the selected dose criterion, the corresponding shelter concrete wall thickness is assessed, taken into account the relation with the dose transmission factors from the literature. For gamma rays the shield thickness is calculated on the basis of analytical functions describing the dependence between dose without shield, dose criterion and shield thickness. Data tables are provided with the assessed concrete wall thickness in relation to bomb yield and distance.

Exploring spatial machine learning techniques for improving land surface temperature prediction

Land Surface Temperature (LST) is a crucial parameter in Earth observation and environmental studies due to its significance in various fields. The purpose of this study is to investigate the effects of including spatial information into the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models for forecasting LST. The significance and impact of each input parameter on the models' predictive capabilities are assessed using the SHAP (SHapley Additive exPlanations) approach and the model intercomparisons were done using the error evaluation metrices. The predictions were further validated using the Pearson correlation, independent samples t-test and potential geographic anomalies in the predictions are examined by spatial comparison of predicted errors using classification maps and error envelopes. The projected errors are within the acceptable range and range from −2.267 °C to 1.292 °C for the spatially enhanced RF model and from −1.675 °C to 1.439 °C for the spatially enhanced XGBoost model. These error ranges closely align with the training data's quality flag of ±2 °C, demonstrating the models' capability to predict LST accurately and within a reasonable error range. The findings show the significance of adding spatial information for precise LST prediction and draw attention to possible uses for such models in environmental monitoring and management. The work advances our understanding of spatial modelling strategies and offers practical guidelines for enhancing LST forecasts.

What to translate and how to translate in audio description: a case study of the Oscar-winning animated film Feast

ABSTRACT Introduction: Audio description (AD) enables access to blind/low vision audiences by translating visual content into narrated descriptions. This study examines AD translation approaches for the Oscar-winning animated short Feast. Methods: Chinese and English AD scripts from Feast were analyzed to compare priorities in content selection and patterns in translation techniques. Results: Both versions prioritized graphics and dynamic symbols when deciding what to translate. Shared translation techniques included compensation, iconic description, and substitution. Differences emerged with more reduction, adaptation, and technical descriptions in the Chinese AD, versus more generalization in the English AD. Discussion: Disparities highlight the linguistic and cultural factors integral to effective AD translation. Implications: This analysis offers practical customization guidelines for AD providers and theoretical insights into intersemiotic translation complexity. Further comparative research could support tailored AD practices and accessibility for diverse global audiences.

CyberFusion Protocols: Strategic Integration of Enterprise Risk Management, ISO 27001, and Mobile Forensics for Advanced Digital Security in the Modern Business Ecosystem

This research paper explores the integration of Enterprise Risk Management (ERM), the ISO 27001 standard, and mobile forensics methodologies as a comprehensive framework for enhancing digital security measures within modern business ecosystems. Employing a quantitative research design, this paper utilized a survey methodology, gathering data from 372 professionals across various sectors including risk management, IT/security, and forensic analysis. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the research hypotheses and assess the impact of the integrated approach on organizational digital security capabilities. The findings reveal a significant positive effect of integrating ERM, ISO 27001, and mobile forensics on an organization’s ability to manage digital risks effectively. Specifically, the integrated approach was found to enhance strategic digital security management, improve the identification, assessment, and mitigation of digital risks, strengthen information security management practices, and elevate the effectiveness and efficiency of digital crime investigation processes. These outcomes underscore the value of a cohesive strategy that leverages the strengths of ERM, ISO 27001, and mobile forensics in addressing the complex and interconnected digital threat landscape. Based on the results, the study recommends adopting a holistic security framework, investing in continuous professional development, leveraging technological advancements for proactive security management, and fostering a culture of security and collaboration. Such measures are crucial for organizations aiming to enhance their resilience against cyber threats and protect their digital assets in the face of sophisticated cyber-attacks. This research contributes to the field of cybersecurity by providing empirical evidence on the benefits of an integrated approach to digital security, offering practical guidelines for organizations seeking to improve their digital security measures, and highlighting the need for continuous adaptation and collaboration in the fight against cyber threats.

How to describe a new species in zoology and avoid mistakes

Abstract Taxonomy is the science of discovering, naming, describing, diagnosing, identifying, and classifying different kinds of taxa, from species to families. It lays the foundation for all of the biological sciences. The rapid increase in both taxonomic descriptions and malpractice in recent decades indicates a need for consistency in the procedure and quality of taxonomic research publications dealing with recognition of new taxa, name changes, and nomenclatural acts. Indeed, there are numerous examples in the recent literature of taxonomic works that fall short of the basic procedures and minimum standards required for naming new species according to the mandatory provisions and recommendations of the International Code of Zoological Nomenclature, accepted standards of taxonomic best practice, and journal editorial policy. Here, we provide practical guidelines of the procedure and key elements required to name, describe, and publish a new animal species or revise the taxonomic status of a species. We then discuss some of the common pitfalls that should be avoided. Mistakes commonly made include failure to read the Code, to review the primary taxonomic literature, to examine type material, to construct the taxon name correctly, to explicitly establish the new taxon, to provide adequate typification, to clearly differentiate the new taxon, to register the publication of the new taxon name in ZooBank, or to publish the name of the new taxon in a manner that is compliant with the Code. We provide some examples of these mistakes, mainly from butterflies (Insecta: Lepidoptera: Papilionoidea) and, to some extent, reptiles because these taxon groups seem to have an unusually high level of poor taxonomic practice.

Digital transformation, incentives, and pro-environmental behaviour: Assessing the uptake of sustainability in companies' transition towards circular economy

In an era marked by technological progress and escalating environmental concerns, businesses must swiftly reconcile sustainability with digital innovation and evolving consumer expectations. This study takes a distinct consumer-oriented approach to assess the adoption of a circular economy across businesses. It specifically explores the integration of pro-environmental behaviour, digital transformation, and incentives as pivotal dimensions in enhancing corporate circularity.Employing a mixed-method approach, this research introduces a novel circularity readiness index specifically developed to quantitatively assess the integration of circular economy principles across the corporate landscape in Norway. Analysing 12 companies within the energy, transportation, fashion, food, and retail sectors, the methodology evaluates companies based on their environmental initiatives, the extent of digital technology integration, and the effectiveness of their incentive mechanisms that promote sustainable practices among their customers. This methodology reveals sector-specific insights regarding circular economy practices, with the energy sector demonstrating notable advancements due to the thorough integration of pro-environmental behaviour, digital transformation, and incentives. On the other hand, the food industry encounters challenges when implementing circular economy practices, highlighting the necessity of targeted initiatives.This study emphasizes the relevance of strategic integration of pro-environmental behaviour, digital transformation, and incentives for progressing towards a circular economy. Businesses should leverage the circularity readiness index to benchmark their sustainability efforts and pinpoint areas for enhancement. Policymakers should establish favourable circumstances by implementing specific policies and incentives, thereby enhancing the engagement of both companies and consumers in circular economy practices. This research contributes to the academic discourse on circular economy and offers practical guidelines for stakeholders aiming to foster sustainable development.

Roadmap on Perovskite light-emitting diodes

Abstract In recent years, the field of metal-halide perovskite emitters has rapidly emerged as a new community in solid-state lighting. Their exceptional optoelectronic properties have contributed to the rapid rise in external quantum efficiencies (EQEs) in perovskite light-emitting diodes (PeLEDs) from &lt;1% (in 2014) to approaching 30% (in 2023) across a wide range of wavelengths. However, several challenges still hinder their commercialization, including the relatively low EQEs of blue/white devices, limited EQEs in large-area devices, poor device stability, as well as the toxicity of the easily accessible lead components and the solvents used in the synthesis and processing of PeLEDs. This roadmap addresses the current and future challenges in PeLEDs across fundamental and applied research areas, by sharing the community's perspectives. This work will provide the field with practical guidelines to advance PeLED development and facilitate more rapid commercialization.