Development Of Best Practices Research Articles

Tumor-On-A-Chip Models for Predicting In Vivo Nanoparticle Behavior.

Nanosized drug formulations are broadly explored for the improvement of cancer therapy. Prediction of in vivo nanoparticle (NP) behavior, however, is challenging, given the complexity of the tumor and its microenvironment. Microfluidic tumor-on-a-chip models are gaining popularity for the in vitro testing of nanoparticle targeting under conditions that simulate the 3D tumor (microenvironment). In this review, following a description of the tumor microenvironment (TME), the state of the art regarding tumor-on-a-chip models for investigating nanoparticle delivery to solid tumors is summarized. The models are classified based on the degree of compartmentalization (single/multi-compartment) and cell composition (tumor only/tumor microenvironment). The physiological relevance of the models is critically evaluated. Overall, microfluidic tumor-on-a-chip models greatly improve the simulation of the TMEin comparison to 2D tissue cultures and static 3D spheroid models and contribute to the understanding of nanoparticle behavior. Interestingly, two interrelated aspects have received little attention so far which are the presence and potential impact of a protein corona as well as nanoparticle uptake through phagocytosing cells. A better understanding of their relevance for the predictive capacity of tumor-on-a-chip systems and development of best practices will be a next step for the further refinement of advanced in vitro tumor models.

Ethical Implication of Artificial Intelligence (AI) Adoption in Financial Decision Making

The integration of artificial intelligence (AI) into the financial sector has raised ethical concerns that need to be addressed. This paper analyzes the ethical implications of using AI in financial decision-making and emphasizes the importance of an ethical framework to ensure its fair and trustworthy deployment. The study explores various ethical considerations, including the need to address algorithmic bias, promote transparency and explainability in AI systems, and adhere to regulations that protect equity, accountability, and public trust. By synthesizing research and empirical evidence, the paper highlights the complex relationship between AI innovation and ethical integrity in finance. To tackle this issue, the paper proposes a comprehensive and actionable ethical framework that advocates for clear guidelines, governance structures, regular audits, and collaboration among stakeholders. This framework aims to maximize the potential of AI while minimizing negative impacts and unintended consequences. The study serves as a valuable resource for policymakers, industry professionals, researchers, and other stakeholders, facilitating informed discussions, evidence-based decision-making, and the development of best practices for responsible AI integration in the financial sector. The ultimate goal is to ensure fairness, transparency, and accountability while reaping the benefits of AI for both the financial sector and society.

Mental capacity assessment in the multi-professional real world: a qualitative study of six areas of uncertainty

Background The Mental Capacity Act 2005 of England and Wales is a ground-breaking piece of legislation with reach into healthcare, social care and legal settings. Professionals have needed to develop skills to assess mental capacity and handle malign influence, but it is unclear how assessments are implemented in real world settings. Our previously reported survey found professionals juggling competing resources in complex systems, often struggling to stay up to date with law. The current follow-up study uses one-to-one interviews of professionals to characterise in detail six areas of uncertainty faced when assessing mental capacity, whilst suggesting ways to make improvements. Methods Forty-four healthcare, social care and legal professionals were interviewed, using a semi-structured topic guide. Transcripts were analysed using framework analysis: a qualitative technique built to investigate healthcare policy. Results Our topic guide generated 21 themes. In relation to the six areas of uncertainty: 1) Many participants stressed the importance of capturing a holistic view, adding that their own profession was best-placed for this - although a medical diagnosis was often needed. 2) The presumption of capacity was a laudable aim, though not always easy to operationalise and occasionally being open to abuse. 3) There was cautious interest in psychometric testing, providing a cognitive context for decisions. 4) Undue influence was infrequent, but remained under-emphasised in training. 5) Multi-professional assessments were common, despite doubts about fitting these within local resources and the law. 6) Remote assessment was generally acceptable, if inadequate for identifying coercion. Conclusions Practical constraints and competing demands were reported by professionals working within real world systems. Assessment processes must be versatile, equally applicable in routine and emergency settings, across diverse decisional types, for both generalist and specialist assessors, and able to handle coercion. Recognising these challenges will guide development of best practices in assessment and associated policy.

The algorithm journey map: a tangible approach to implementing AI solutions in healthcare

When integrating AI tools in healthcare settings, complex interactions between technologies and primary users are not always fully understood or visible. This deficient and ambiguous understanding hampers attempts by healthcare organizations to adopt AI/ML, and it also creates new challenges for researchers to identify opportunities for simplifying adoption and developing best practices for the use of AI-based solutions. Our study fills this gap by documenting the process of designing, building, and maintaining an AI solution called SepsisWatch at Duke University Health System. We conducted 20 interviews with the team of engineers and scientists that led the multi-year effort to build the tool, integrate it into practice, and maintain the solution. This “Algorithm Journey Map” enumerates all social and technical activities throughout the AI solution’s procurement, development, integration, and full lifecycle management. In addition to mapping the “who?” and “what?” of the adoption of the AI tool, we also show several ‘lessons learned’ throughout the algorithm journey maps including modeling assumptions, stakeholder inclusion, and organizational structure. In doing so, we identify generalizable insights about how to recognize and navigate barriers to AI/ML adoption in healthcare settings. We expect that this effort will further the development of best practices for operationalizing and sustaining ethical principles—in algorithmic systems.

The Current State of Neonatal Neurodevelopmental Follow-up Programs in North America: A Children's Hospitals Neonatal Consortium Report.

This study aimed to determine neonatal neurodevelopmental follow-up (NDFU) practices across academic centers. This study was a cross-sectional survey that addressed center-specific neonatal NDFU practices within the Children's Hospitals Neonatal Consortium (CHNC). Survey response rate was 76%, and 97% of respondents had a formal NDFU program. Programs were commonly staffed by neonatologists (80%), physical therapists (77%), and nurse practitioners (74%). Median gestational age at birth identified for follow-up was ≤32 weeks (range 26-36). Median duration was 3 years (range 2-18). Ninety-seven percent of sites used Bayley Scales of Infant and Toddler Development, but instruments used varied across ages. Scores were recorded in discrete electronic data fields at 43% of sites. Social determinants of health data were collected by 63%. Care coordination and telehealth services were not universally available. NDFU clinics are almost universal within CHNC centers. Commonalities and variances in practice highlight opportunities for data sharing and development of best practices. · Neonatal NDFU clinics help transition high-risk infants home.. · Interdisciplinary neonatal intensive care unit follow-up brings together previously separated outpatient service lines.. · This study reviews the current state of neonatal NDFU in North America..

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.

Personalised learning in higher education for health sciences: a scoping review protocol

BackgroundPersonalised learning, an educational approach that tailors teaching and learning to individual needs and preferences, has gained attention in recent years, particularly in higher education. Advances in educational technology have facilitated the implementation of personalised learning in various contexts. Despite its potential benefits, the literature on personalised learning in health sciences higher education remains scattered and heterogeneous. This scoping review aims to identify and map the current literature on personalised learning in health sciences higher education and its definition, implementation strategies, benefits, and limitations.MethodsA comprehensive search of electronic databases, PubMed, Scopus, Google Scholar, Educational Research Complete, and Journal Storage (JSTOR), will be conducted to identify relevant articles. The search will be limited to articles published in the English language between 2000 and 2023. The search strategy will be designed and adapted for each database using a combination of keywords and subject headings related to personalised learning and health sciences higher education. Eligibility criteria will be applied to screen and select articles. Data extraction and quality assessment will be performed, and thematic synthesis will be used to analyse the extracted data.DiscussionThe results of the scoping review will present a comprehensive and coherent overview of the literature on personalised learning in health sciences higher education. Key themes and topics related to personalised learning, its definitions, models, implementation strategies, benefits, and limitations, will be identified. The geographical and temporal distribution of research on personalised learning in health sciences higher education will also be described. This scoping review will provide a structured synthesis of the available evidence on personalised learning in health sciences higher education, highlighting potential gaps and areas for future research. The findings will contribute to ongoing scholarly and policy debates on personalised learning in higher education, informing the development of best practices, guidelines, and future research agendas.

Organizational Readiness to Adopt Artificial Intelligence in the Library and Information Sector of Pakistan

Objective – This study investigates the readiness for artificial intelligence (AI) adoption in library and information centres of Pakistani universities. The projected outcomes of this study are expected to contribute to the development of best practices for effectively motivating university administrators and preparing librarians for adopting AI in library and information centres. Methods – A theoretical framework combining the technology-organization-environment (TOE) framework and the Technology Readiness Index (TRI) guided this qualitative study. Interviews were conducted with 27 senior representatives, including library managers and registrars, from 27 universities across four provinces and the capital city, Islamabad. A systematic approach was employed to analyze the data. Results – The findings indicate that the concept of AI adoption in Pakistani university libraries is new. The library and information sector of Pakistan is slow in adopting AI, which could have implications for its future competitiveness, despite the push for AI adoption by university librarians and administrators. The readiness for AI adoption in this sector is influenced by factors such as organizational technological practices, financial resources, university size, and data management and protection concerns. Conclusion – Library managers and researchers can implement the TOE framework and TRI scale to facilitate AI adoption in a manner that is relevant to library and information settings in Pakistan as well as other parts of the world. Our research indicates that most adoptions are still in their nascent phases, and numerous library managers feel uneasy due to either uncertainties about the precise benefits AI can bring to their libraries or a lack of knowledge and skills for its effective implementation. To manage the networks of internal and external stakeholders essential for successful AI adoption, universities should consider appointing individuals with a specialized knowledge of AI within their libraries.

Revolutionizing Frontend Development: Embracing Micro UI Architecture with Cloud Integration

Purpose: The primary aim of this study is to dissect and highlight the ongoing transformative shift in frontend development practices, predominantly spurred by the adoption of Micro UI architecture and its synergistic integration with cloud services. This transformation is poised to address critical challenges in scalability, modularity, and development efficiency that currently beset web application development. By investigating the theoretical underpinnings and practical applications of Micro UI architecture in tandem with cloud technologies, the study elucidates its potential to revolutionize frontend development, enhance team dynamics, and elevate application performance. Methodology: This article adopts a comprehensive approach to explore the theoretical foundations and practical implications of Micro UI architecture and cloud integration in frontend development. The methodology encompasses a comparative analysis with the microservices approach in backend development, detailed case studies on the implementation of Micro UI with cloud services, and an empirical examination of its effects on development processes and application performance. Through this multifaceted methodology, the article aims to provide a holistic understanding of Micro UI architecture’s role in modern web development. Findings: The findings reveal that Micro UI architecture, characterized by its decoupled, component-based strategy, significantly contributes to simplifying the complexities inherent in modern web development. This architectural approach, when combined with the expansive capabilities of cloud computing, facilitates the development of more resilient, scalable, and performant frontend applications. Additionally, the study finds that this convergence enhances development efficiency, fosters better team collaboration, and promotes a more agile development environment. The integration with cloud services further ensures a seamless and robust user experience globally, underscoring the transformative potential of embracing Micro UI architecture in frontend development. Unique Contribution to Theory, Policy, and Practice: The article makes a unique contribution to the theoretical framework of frontend development by systematically articulating the concept of Micro UI architecture and delineating its integration with cloud technologies. On a practical level, it offers actionable insights and guidelines for development teams and organizations looking to adopt this innovative approach. Policy-wise, it suggests frameworks for standardizing practices and protocols in deploying Micro UI components and integrating cloud services. Recommendations include the adoption of Micro UI architecture as a standard practice in frontend development, the development of best practices for its integration with cloud services, and the encouragement of collaborative efforts across teams to leverage the full potential of this transformative approach. By doing so, the study paves the way for a new era of frontend development that is more scalable, efficient, and conducive to innovation.

Machine learning in financial markets: A critical review of algorithmic trading and risk management

The integration of machine learning (ML) techniques in financial markets has revolutionized traditional trading and risk management strategies, offering unprecedented opportunities and challenges. This paper provides a comprehensive and critical review of the application of ML in algorithmic trading and risk management within the realm of financial markets. The review begins by exploring the evolution of algorithmic trading, highlighting the paradigm shift from traditional rule-based strategies to ML-driven approaches. Various ML algorithms, including neural networks, decision trees, and ensemble methods, are examined in the context of their application to predictive modeling, pattern recognition, and signal generation for trading purposes. The paper also delves into the challenges and limitations associated with the adoption of ML in financial markets. Issues such as overfitting, data bias, and model interpretability are discussed, emphasizing the importance of addressing these concerns to ensure robust and reliable trading systems. Furthermore, ethical considerations and potential regulatory implications of ML-driven trading strategies are considered in the context of market fairness and stability. In the realm of risk management, the review scrutinizes the role of ML in assessing and mitigating financial risks. The paper evaluates the effectiveness of ML models in identifying market trends, measuring portfolio risk, and optimizing asset allocation. Additionally, it examines the potential impact of ML on systemic risk and the need for adaptive risk management frameworks in dynamic market conditions. The synthesis of findings underscores the transformative impact of ML on financial markets, showcasing its potential to enhance trading strategies and risk management practices. However, the review also highlights the importance of addressing inherent challenges and ethical considerations to ensure the responsible and sustainable integration of ML in the financial domain. This critical review provides valuable insights into the current state of machine learning in financial markets, offering a foundation for future research directions and the development of best practices in algorithmic trading and risk management.

Evaluating long‐term introduction success of the Florida scrub endemic Crotalaria avonensis (Fabaceae) using a decade of demographic data

Introductions are a critical tool in the recovery of many imperiled species, yet adequate evaluation and development of best practices has lagged. Importantly, long‐term post‐introduction data are typically lacking, as well as suitable comparisons to wild populations to provide a baseline against which to assess performance. Here, we report on three experimental introductions of Crotalaria avonensis (Fabaceae), a federally endangered perennial herb that is narrowly endemic to scrub of the Lake Wales Ridge in peninsular Florida, U.S.A. We synthesize 10 years of post‐introduction monitoring at both the introduced and a nearby, protected wild population to (1) develop best practices for conservation, and (2) evaluate the success of the introduction. First, our study identified best practices that included using transplants propagated from stem cuttings, as well as several factors that may increase seed germination such as habitat choice, seed burial, and litter addition. Second, during the 10 years following the introduction, population density in the introduced population was higher than in a nearby protected, wild population, and a comparison of vital rates revealed that this result was due to relatively high clonal and seedling recruitment rates in the introduced population. Furthermore, the source population, which occurred on unprotected lands, precipitously declined during this time period, further highlighting the importance of safeguarding plants from that population. We report that a new, growing population of C. avonensis has been established to date, with important implications for the species' conservation as well as how introductions are evaluated.

Are Emotions Conveyed Across Machine Translations? Establishing an Analytical Process for the Effectiveness of Multilingual Sentiment Analysis with Italian Text


 
 
 Abstract Natural language processing (NLP) is being widely used globally for a variety of value-creation tasks ranging from chat-bots and machine translations to sentiment and topic analysis and multilingual large language models (LLMs). However, most of the advances are initially implemented within the English language framework, and it takes time and resources to develop comparable resources in other languages. The advances in machine translations have enabled the rapid and effective conversion of content in global languages into English and vice-versa. This creates potential opportunities to apply English language NLP methods and tools to other languages via machine translations. However, although this idea is powerful, it needs to be validated and processes and best practices need to be developed and kept updated. The present research is an effort to contribute to the development of best practices and an evaluation framework. We present a systematic and repeatable state-of-the-art process to evaluate the viability of applying English language sentiment analysis tools to Italian text by using multiple English language machine translation mechanisms such that it can be easily extended to other languages.
 
 

@INNOVATIVEESCAPE: Students’ perception and perceived learning outcome using an online exhibition via Instagram platforms

Instagram's @innovateescape channel provides social, educational, and technological opportunities. The objective of this study is to enhance comprehension of students' perspectives and perceived educational outcomes through the use of Instagram as a platform for issue-based learning. Students used issue-based learning using the Instagram feed @innovativeescape for this assignment. The findings give empirical evidence that students’ perceptions of the Instagram learning activity were favourable, resulting in a satisfactory learning outcome. These findings prompted the development of best practices for utilising the Instagram platform @innovativeescape to improve students' learning perceptions.

Faculty and Student Perceptions of Open Pedagogy: A Case Study From British Columbia, Canada

A transformation in teaching and learning happens when students move from being consumers to creators of knowledge. While there is a growing body of research available on the use of open education resources by faculty and students, there is comparatively little research available with regards to open pedagogy (OP) in higher education. The few studies that have explored the perceptions of OP have focused on one specific OP practice in a small context (one or two course sections). The present review study surveyed the perceptions of faculty and students at a Canadian university across several courses and a range of types of OP. Quantitative and qualitative analyses revealed students and faculty alike were positive about the benefits and impacts of engaging in OP, but each expressed challenges with needing greater time for OP. Additionally, while students experienced challenges with process, faculty experienced challenges with supports. Recommendations are provided for ways faculty can support students when engaging in OP and ways institutions can support faculty who engage in OP. Ultimately, knowing more about the experiences and perspectives of students and faculty could help inform the development of best practices for faculty who wish to use OP with students.

SUSTAINABLE DEVELOPMENT: A VIABLE SOLUTION TO REDUCE POVERTY

Poverty is one of the world’s greatest economic and social problems of all time. Therefore, sustainable solutions to reduce it are essential to increase the well-being of individuals and society. In this paper, the various causes of poverty, such as social and economic inequalities, limited access to education and health, will be analysed. After an introduction, the concept of sustainable development will be presented, underlying the virtues, as well as limits of it. There will be explored sustainable solutions for poverty reduction, which focus on sustainable development and the promotion of social and economic inclusion. Such solutions could be the creation of jobs and investment in the agricultural sector, the development of infrastructure and public services, the promotion of access to education and health services, and support for local communities and the social and solidarity economy. To validate these findings, qualitative methodology based on content analysis of sustainable development literature will allow to identify and evaluate good practices in sustainable development and poverty reduction. Therefore, it will be possible to show that sustainable solutions for poverty reduction are not only feasible but also effective in terms of social and economic impact. The paper will end with conclusions, arguing that sustainable solutions to poverty reduction are the key to increasing the well-being of individuals and society, and showing that interconnectivity, when considered in a sustainable approach, can improve the benefits for economic and social life.

IoT Integration in Telemedicine: Investigating the Role of Internet of Things Devices in Facilitating Remote Patient Monitoring and Data Transmission

Introduction: The integration of Internet of Things (IoT) technology with telemedicine has ushered in a groundbreaking era for healthcare, allowing for remote monitoring and data transmission of patients. This research paper aims to explore the complex relationship between IoT devices and telemedicine, examining their combined potential to improve patient care and transform healthcare workflows. Method: To gain a deeper understanding of the impact of IoT-powered telemedicine, a qualitative study was conducted to explore the experiences and perspectives of both healthcare practitioners and patients. Using in-depth interviews and content analysis, we examined the integration of IoT devices, such as wearables and remote sensors, into telemedicine platforms. This allowed us to uncover real-time health data collection and transmission from patients' homes to medical professionals. Result: This study's analysis of the benefits, challenges, and user satisfaction indicates that IoT-powered remote patient monitoring significantly enhances patient care by facilitating timely interventions and reducing hospital admissions, ultimately leading to improved healthcare outcomes and highlighting the effectiveness of IoT-powered remote patient monitoring in relation to healthcare outcomes. Furthermore, we analyze the complex technical factors that influence the effective deployment of IoT devices in telehealth settings. The integration of telemedicine with IoT technology is a complex process that requires careful consideration of several factors, including data security, interoperability, and data analytics. These factors play a crucial role in ensuring the effective and secure exchange of health information between different systems. The insights gained from this research can help to inform the development of best practices for telemedicine, ultimately improving patient care and outcomes. The findings also have implications for healthcare providers, technology developers, and policymakers who seek to leverage IoTintegrated telehealth solutions in their work.

Developing public health competency statements and frameworks: a scoping review and thematic analysis of approaches

Competencies ensure public health students and professionals have the necessary knowledge, skills, values, and behaviours to do their jobs effectively. Public health is a dynamic and complex field requiring robust competency statements and frameworks that are regularly renewed. Many countries have public health competencies, but there has been no evidence synthesis on how these are developed. Our research aim was to synthesize the extent and nature of the literature on approaches and best practices for competencies statement and framework development in the context of public health, including identifying the relevant literature on approaches for developing competency statements and frameworks for public health students and professionals using a scoping review; and, synthesizing and describing approaches and best practices for developing public health competency statements and frameworks using a thematic analysis of the literature identified by the scoping review. We conducted a scoping review and thematic analysis of the academic and grey literature to synthesize and describe approaches and best practices for developing public health competency statements and frameworks. A systematic search of six databases uncovered 13 articles for inclusion. To scope the literature, articles were assessed for characteristics including study aim, design, methods, key results, gaps, and future research recommendations. Most included articles were peer-reviewed journal articles, used qualitative or mixed method design, and were focused on general, rather than specialist, public health practitioners. Thematic analysis resulted in the generation of six analytical themes that describe the multi-method approaches utilized in developing competency statements and frameworks including literature reviews, expert consultation, and consensus-building. There was variability in the transparency of competency framework development, with challenges balancing foundational and discipline-specific competencies. Governance, and intersectoral and interdisciplinary competency, are needed to address complex public health issues. Understanding approaches and best practices for competency statement and framework development will support future evidence-informed iterations of public health competencies.

Far-reaching displacement effects of artificial light at night in a North American bat community

Artificial light at night (ALAN) is a global pollutant that disrupts circadian rhythmicity and a broad range of physiological processes and behaviors in animals. However, ALAN sensitivity can vary greatly even among closely related species and urgently needs study for much of the world’s nocturnal wildlife, including bats. While an increasing number of bat species have recently been assessed for light tolerance, the spatial extent of ALAN’s influence on bats has received little attention. This information need is a barrier to the protection of bats from ALAN in land-use planning and policy, and the development of best practices that effectively buffer bat habitat from light trespass. To help address this information gap for North America, we experimentally tested the distances up to which ALAN affects presence and activity of light-averse little brown bats (Myotis lucifugus) and big brown bats (Eptesicus fuscus), and the composition of a foraging bat assemblage in Connecticut, USA. We used three residential-scale, white, LED floodlights to expose bat foraging habitat to ALAN and compared acoustic activity of bats at distances of 0, 25, 50, and 75 m from the lights between nights when the lights were on versus off. Little brown bats were present on significantly fewer light than dark nights at every distance. Lighting significantly reduced little brown bat activity overall and at the farthest location from the lights (75 m), where it was only 43% of dark-night activity despite < 1 lx of illuminance. Presence of big brown bats was not significantly affected at any distance. Big brown bat activity on light nights averaged 48–75% of dark-night activity at each distance but was significantly lower only at 0 m. As expected, lighting had no effect on eastern red (Lasiurus borealis) or hoary bat (L. cinereus) presence or activity. Community composition on dark and light nights had an average dissimilarity of 38% and significantly differed at 0 m and 25 m. We conclude the type of ALAN we studied has a disturbance radius of at least 75 m for the little brown bat and up to 25 m for the big brown bat, with a resulting influence on community composition for up to 50 m. Cumulative habitat loss for the imperiled little brown bat caused by ALAN could therefore be substantial. We urge planners and natural resources regulators to consider these footprints when evaluating indirect impacts to bat habitat from current and future sources of ALAN across these species’ ranges.

The appreciation of the collaboration agreements used to prevent intrafamilial homicides

Purpose The purpose of this paper is to improve the understanding of how collaboration agreements function and their benefits for the environments where they are implemented. The goal of these initiatives is to bring together in partnerships the actors concerned by domestic violence and coordinate their actions to ensure the safety of persons at risk of intrafamilial homicides through an effective collaboration structure. Design/methodology/approach The synthesis data originates from four research projects research work with the partners of four studied agreements: A-GIR (Arrimage-Groupe d’Intervention Rapide [Rapid Intervention Unit]) in Laval, Alerte-Lanaudière [Lanaudière Alert] in the Lanaudière region, P.H.A.R.E. (Prévention des homicides intrafamiliaux par des Actions Rapides et Engagées [Domestic Homicide Prevention through Rapid and Committed Action]) in South Western Montérégie and the Rabaska Protocol in Abitibi-Témiscamingue. Findings Overall, the interveners agree on the positive impacts resulting from the relationships between the partners, the development of a common expertise and the collective responsibility acting to prevent intrafamilial homicides, while highlighting the challenges met and the essential conditions for the success of these collaboration initiatives. Research limitations/implications Findings are drawn from participants in a particular locale – i.e. French–Canada, and may not entirely apply to other regions and cultures. Additional research should be conducted with similar methodology in other regions of Canada and elsewhere. Practical implications The findings should help in the further development of best practices for IPH prevention and therefore protect potential victims from lethal assaults of domestic violence. Originality/value Few studies have been conducted on how stakeholders involved in IPH prevention actually work together in collaborative efforts, and none, as far as we know, specifically on drawing up formal agreements.

The first Global e-Competition on Astronomy and Astrophysics

The first global e-competition on astronomy and astrophysics was held online in September–October 2020 as a replacement for the International Olympiad on Astronomy and Astrophysics, which was postponed due to the COVID-19 pandemic. Despite the short time available for organization, 8 weeks, the competition was run successfully, with 325 students from over 42 countries participating with no major issues. The feedback from the participants was positive and reflects the ways in which such events can boost interest in astronomy and astronomy education. With online activities set to become more prevalent in the future, we present an overview of the competition process, the challenges faced, and some of the lessons learned, aiming to contribute to the development of best practices for organizing online competitions.