Current Landscapes Research Articles

Decolonising Science Education: A Bibliometric Analysis of Indigenous Knowledge Integration in Global STEM

This study presents a systematic bibliometric review of research on Indigenous knowledge integration within global STEM education between 2015 and 2025. Using keyword co-occurrence mapping and cluster analysis, the study identifies thematic patterns, research trajectories, and conceptual gaps in the emerging discourse on decolonizing science education. The findings reveal that integration efforts remain fragmented, often limited to isolated case studies or tokenistic curriculum adaptations. Genuine transformation requires coherent frameworks that position Indigenous epistemologies as foundational rather than peripheral to scientific inquiry. The study highlights the critical need for community collaboration, contextualised pedagogy, and systemic reform to foster epistemic justice and educational equity. By mapping current research landscapes, this review contributes to advancing more inclusive and pluralistic models of science education worldwide.

Emerging Biomarkers and Innovative Therapeutic Strategies in Diabetic Kidney Disease: A Pathway to Precision Medicine.

Diabetes mellitus (DM) has emerged as the most common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) globally in recent years. Diabetic nephropathy (DN), or diabetic kidney disease (DKD) that occurs as a direct consequence of DM, has complex pathophysiological mechanisms, such as various inflammatory processes and genetic and epigenetic factors, often accentuated by comorbid illnesses like hypertension and dyslipidemia. Therefore, management of DKD involves targeting these etio-pathological processes. Various medications with remarkable disease modifying properties have been introduced for treatment of DN in recent years. We update the current and future diagnostic and therapeutic landscapes against DKD in this article.

Antimicrobial peptides: from discovery to developmental applications.

Antimicrobial resistance (AMR) has emerged as a significant crisis in global health. Due to their advantageous properties, antimicrobial peptides (AMPs) have garnered considerable attention as a potential alternative therapy to address the AMR crisis. These peptides might disrupt cell membranes or cell walls to exhibit antimicrobial activity, or modulate the immune response to promote recovery from diseases. In recent years, significant progress has been made in the research of AMPs, alongside the emergence of new challenges. This review first systematically summarizes and critically discusses recent advancements in understanding the characteristics and current landscapes of AMPs, as well as their regulatory mechanisms of action and practical applications, particularly those reported or approved within the last 5 years. Additionally, the principles, paths for their identification, and future research trends in AMPs are also analyzed following a discussion of the advantages and disadvantages of AMPs in comparison to conventional antibiotics. Unlike significant prior literature in this field, this report has summarized the latest major discovery methods for AMPs and, more importantly, emphasized their practical applications by supporting various viewpoints using selected examples of AMPs' applications in real-life scenarios. Besides, some emerging hot topics of AMPs, including those derived from gut microbiota and their potential synergistic effects in combating AMR, were profiled. All of these indicate the originality of the report and provide valuable references for future AMP discoveries and applications.

168 Modeling biomedical graduate student career development needs and training contexts

Objectives/Goals: To design and implement programming that better prepares graduate students for diverse roles in a variety of workforce environments, our study models the training landscape and programming needs of graduate students in behavioral, clinical, and biomedical graduate programs at a large Midwestern school of medicine and public health. Methods/Study Population: We conducted six focus groups (two graduate program manager focus groups and four graduate student focus groups), to assess the programming, career development, and training needs of graduate students. Using a grounded theory approach, we first engaged in open coding of a sample of transcripts. After developing a codebook, we continued with an iterative coding process interspersed with coder meetings to discuss emerging and changing codes. Using the framework of landscape analysis allowed our coding and modeling to go beyond graduate student needs and study the varying relationships and contexts that impact graduate students throughout their training, such as relationship to supervisor or institutional policies. Results/Anticipated Results: Preliminary results indicate that students wrestle with their status as both students and workers. Specifically, conflict arises between graduate and supervisor expectations around time spent in class, lab, and other career development activities based on these divergent roles. Students and program managers also note the disparities that arise from the university’s lack of standard, formalized policy on labor issues, such as paid leave. Data also suggest that students on training grants note the difference in access to career development resources compared to colleagues. In many cases, students themselves coordinate ad hoc programming to better suit their career and professional development needs, although this work is not a required aspect of their training. Discussion/Significance of Impact: We characterize current graduate training landscapes, which continue to shift as graduate student bodies diversify, unionize, and express interest in increasingly varied biomedical careers. Data from multiple perspectives facilitate creating, implementing, and evaluating supportive training programs that meet identified student needs.

548 Plain language summaries (PLS): Practices, limitations, and strategies

Objectives/Goals: This literature review examines the current landscapes of plain language summaries (PLS), which are used to make research accessible to nonexpert audiences. It aims to identify gaps in their implementation by focusing on challenges related to consistency, accessibility, and quality across fields. Methods/Study Population: A systematic search was conducted using databases such as PubMed and Google Scholar, employing key search terms like “plain language summaries,” “scientific communication,” “health literacy,” “patient education,” “knowledge translation,” “accessibility in research,” “public engagement,” “lay-person,” and “lay summaries.” Literature from multiple sources (pharmaceutical companies/industry, nonprofit organizations, private–public partnerships, and government) was compared to assess the gaps in current PLS best practices. Results/Anticipated Results: Search results yielded 95 articles. Of those, 37 articles fit the criteria, highlighting critical gaps in PLS implementation for clinical research. Preliminary findings suggest a lack of standards and guidelines, as well as a need for more research on the effectiveness of PLS for improving knowledge transfer and patient engagement. Key limitations were identified for investigator-initiated trials (IITs). A best practice table, comparing recommendations from each group of sources, was developed with suggestions for writing effective PLS. While there is consensus on some principles (i.e., importance of simplicity), differences emerge regarding optimal length and the use of layperson glossaries and graphics. The table aims to serve as a guide for creating effective and standardized PLS across fields. Discussion/Significance of Impact: There are limited PLS best practice resources tailored for IITs. These findings could lead to more practical tools and a streamlined approach to enhance communication strategies for lay audiences. This would benefit trial participants and community members who rely on this information and bridge the gap between scientific communities and the public.

Navigating the High Costs of Energy Storage: Challenges and Solutions for Sustainable Integration

As global energy consumption continues to rise, it is imperative that energy supply systems are aligned to meet this growing demand. However, the current state of the energy industry is characterized by prohibitively high costs associated with energy storage systems, alongside significant inefficiencies and energy wastage. This paper first introduce two main energy storage system: Lithium-ion battery, Compressed Air Energy Storage(CAES), explores the underlying factors contributing to these elevated costs and examines the current energy landscapes of key regions, including Asian countries, particularly China, the United States, and Europe. Through a comparative analysis, the paper identifies the differences and underlying causes that drive these disparities. Furthermore, it proposes strategic solutions to address these cost challenges, such as reforming tax systems to incentivize innovation, enhancing supply chain efficiency through better integration and localization, and investing in research and development to drive technological advancements. By tackling these issues, the global energy sector can advance towards more cost-effective and sustainable energy storage solutions, thereby supporting the transition to a low-carbon future and ensuring energy security for generations to come.

The Trail of Axonal Protein Synthesis: Origins and Current Functional Landscapes.

The Trail of Axonal Protein Synthesis: Origins and Current Functional Landscapes.

Transforming Cognition and Human Society in the Digital Age

AbstractSince the onset of the digital revolution, humankind has experienced an unprecedented acceleration of changes triggered by technological advancements. Frequently used digital media have unquestionably penetrated our everyday life, shaping human cognition in multiple ways. The rise of artificial intelligence, which coevolved with a new, interdisciplinary field of cognitive science, has amplified these effects, contributing new ways of affecting human society, in terms of efficient human-machine interaction and knowledge generation and accumulation, at an exponential rate. Simultaneously, cultural shifts driven by globalization and social media have fostered new modes of communication, identity formation, and knowledge dissemination on a global scale. Understanding the intricate dynamics of techno-cultural evolution and its influence on cognition is essential for comprehending the contemporary society and preparing it for the future challenges. We need to adapt for the current and future information environments and digital landscapes, improving human resilience to new technologies and addressing core human vulnerabilities. Thanks to human flexibility, enabled by neural plasticity, that may be feasible, ideally combined with alleviation of known negative effects of digital technologies.

Artificial Intelligence, Innovation, and Copyright: Comparing Intellectual Property Law in Indonesia and South Korea

The rapid advancement of Artificial Intelligence (AI) is reshaping industries globally, raising critical questions about the intersection of technology, law, and innovation. In particular, AI's ability to autonomously generate creative works challenges traditional concepts of authorship, ownership, and intellectual property (IP) rights. As AI technologies continue to evolve, existing legal frameworks, particularly in the realm of copyright, struggle to keep pace. This issue is especially pronounced in countries like Indonesia, where traditional copyright laws fail to address the complexities introduced by AI-generated content. In contrast, South Korea has made notable strides in reforming its IP laws to accommodate technological advancements, offering a more adaptive approach to regulating AI-driven innovation. This study critically analyzes the adequacy of copyright regulations in Indonesia in the context of AI and compares them with South Korea's more progressive legal responses. The research addresses the urgent need for legal reform in Indonesia to ensure that its IP laws remain relevant in an era of rapid technological change. By examining the regulatory responses of both countries, this study highlights the potential risks and opportunities for innovation that arise from the current legal landscapes. The contribution of this research lies in its comparative approach, shedding light on how differing legal systems address the challenges posed by AI. Through this analysis, the study provides valuable insights into how Indonesia can evolve its copyright framework to foster innovation while protecting the rights of creators, offering a model for countries facing similar technological and legal challenges.

Effectiveness of Student Teams: The Role of Team Personality Diversity

In current educational landscapes, group work has emerged as a cornerstone of pedagogical strategies, offering students opportunities to cultivate essential skills for future professional endeavors. Central to the success of collaborative efforts lies the intricate interplay of team dynamics, with team personality diversity emerging as a significant factor influencing team effectiveness. Building upon existing research highlighting the critical role of team personality in driving team effectiveness (Kozlowski and Bell, 2003), this study delves into the nuanced relationship between team personality diversity, team processes and emergent states, and collaborative outcomes within student teams. Acknowledging the multifaceted nature of team dynamics, this research adopts a comprehensive approach, drawing upon established theoretical frameworks, such as the input-process-output model (Hackman, 1987; Ilgen et al, 2005), to elucidate the mechanisms through which team personality diversity influences team effectiveness. Through a thoroughly designed two-wave methodology and a group-level analytical approach, data were collected from 57 student teams enrolled in undergraduate and graduate courses across diverse academic disciplines during semester 2 of 2022/2023. Our findings reveal that emotional stability diversity within teams exerts a significant yet indirect influence on team satisfaction through its impact on communication openness. Specifically, greater emotional stability diversity leads to diminished communication openness, subsequently leading to reduced levels of team satisfaction. This intricate chain of effects underscores the importance of managing team personality diversity to foster open communication and ultimately enhance satisfaction among team members. While our results align with established theoretical underpinnings, they also prompt critical reflections on team interdependence and the duration of collaborative engagements within academic settings. Consequently, we advocate for further exploration into the potential of team personality diversity within student teams, with an emphasis on longitudinal studies to capture the temporal dynamics of collaborative interactions. This study contributes to the growing body of literature on team dynamics in educational settings, offering valuable insights for educators and practitioners seeking to optimize student experiences, enhance group learning outcomes, and prepare students for their future careers.

Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach

The integration of artificial intelligence (AI) in healthcare management marks a significant advance in technological innovation, promising transformative effects on healthcare processes, patient care, and the efficacy of emergency responses. The scientific novelty of the study lies in its integrated approach, combining systematic review and predictive algorithms to provide a comprehensive understanding of AI’s role in improving healthcare management across different contexts. Covering the period between 2019 and 2023, which includes the global challenges posed by the COVID-19 pandemic, this research investigates the operational, strategic, and emergency response implications of AI adoption in the healthcare sector. It further examines how the impact of AI varies across temporal and geographical contexts. The study addresses two main research objectives: to explore how AI influences healthcare management in operational, strategic, and emergency response domains, and to identify variations in the impact of AI on healthcare management based on temporal and geographical contexts. Utilizing an integrated approach, we compared various prediction algorithms, including logistic regression, and interpreted the results through SHAP (SHapley Additive exPlanations) analysis. The findings reveal five key thematic areas: AI’s role in enhancing quality assurance, resource management, technological innovation, security, and the healthcare response to the COVID-19 pandemic. The study highlights AI’s positive influence on operational efficiency and strategic decision making, while also identifying challenges related to data privacy, ethical considerations, and the need for ongoing technological integration. These insights provide opportunities for targeted interventions to optimize AI’s impact in current and future healthcare landscapes. In conclusion, this work contributes to a deeper understanding of the role of AI in healthcare management and provides insights for policymakers, healthcare professionals, and researchers, offering a roadmap for addressing both the opportunities and challenges posed by AI integration in the healthcare sector.

Articial Intelligence - Driven Prediction of Health Issues in Infants - A Review

Advances in technology and data availability have helped in improving the quality of care and in predicting health issues in infants. Currently, Information and Communication technology aids in reaching the essentiality and the applications of infant health to a greater extent. Over a few decades, researchers have dived into sensing and the prediction of Artificial Intelligence (AI) for infant health. Since these healthcare systems deal with large amounts of data, significant development is seen in several computing platforms. AI, including both machine learning (ML) and deep learning (DL), plays a crucial role in the medical industry in the prediction and classification of various infant diseases. The prediction of diseases in infants using extubation readiness and their utility ranges is still lacking. Thus, the present study aims to present a complete review of the adaption of ML and DL approaches to infant health prediction. The current review paper provides a complete overview of the research predicting infant health issues. Effectual comparisons are made among the AI approaches performing infant disease prediction. Furthermore, the paper identifies the research gaps and the future direction of the research in the present domain. A comprehensive form of analysis of the current landscapes involved in predicting infant health issues using AI is presented.

Human Factors Considerations in Artificial Intelligence Applications for Nuclear Power Plants

In recent years, there has been a wave of artificial intelligence (AI) technologies that offer to solve problems from shopping habits to mortgage approvals to critical systems operations. The rapidity of the development of these systems has led to both excitement and apprehension about the roles these systems should play in our modern societies. This paper focuses on the critical infrastructure industry, in general, and nuclear power generation, in particular, and seeks to scrutinize how we can leverage these novel technologies in human-centered ways to maintain or enhance the established high levels of reliability and resilience in these industries. First, we discuss the broader aspects of cognitive systems and activities that are critical to understanding the human-AI space. Then we explore different approaches to explainability in AI and the notions of trust. We then move on to discuss several human factors concepts and methods and how they can support the design of human-AI teams. We then explore recent research related to nuclear power that has been undertaken and evaluate the current industry and regulatory landscapes. Finally, we discuss identified research gaps and recommendations for solving these for the critical infrastructure space.

Evolving oncology care management trends in the United States: A survey among health care decision makers.

There is limited knowledge of how US managed care professionals view and prioritize quality metrics/performance measures, care models, alternative payment models, and clinical pathways in oncology settings. To characterize payor perspectives on, and the use of, oncology clinical pathways and performance measures in their reimbursement/access decision-making process. A survey was implemented via SurveyMonkey software and distributed electronically to a national sample of the Academy of Managed Care Pharmacy (AMCP) Market Insights Panel members from July 11 through August 5, 2022. The survey was created by a steering committee based on literature reviews of the current and future oncology care landscapes. The survey consisted of 47 questions, including those to establish respondents' position, responsibilities, and demographics. The results are presented as descriptive statistics for 7 key questions that covered the perceptions and use of quality metrics/performance measures, alternative payment models, and oncology care pathways as prioritized by the steering committee. Among the 695 AMCP panel members who were sent the survey, 73 responded (response rate 10.5%), 54 were eligible to continue, and 31 completed the entire questionnaire; the low response rate may limit generalizability of the survey results. Specific oncology clinical and economic measures of performance were currently used (70%-88%) but generally received less endorsement for future use (39%-49%) except for chemotherapy during end of life, which was considered for future use by 80% of respondents but was only currently used by 31%. Benchmarking was the primary reason for the use of performance measures; only 27% used these to inform value-based agreements. Real-world data tracked by respondents' institutions primarily focused on managed care and pharmacy utilization (39%-85%), with patient-reported and clinical outcomes tracked by only 17%-34%. Almost one-third (31%) did not use clinical oncology pathways, and among those who did, fewer than half (48%) reported that their organization tracks whether treatment decisions agree with the oncology care pathways, and only 26% reported feedback to oncology providers on how often their treatment decisions agree with the pathways. When considering alternative payment models, patient-related components received lower rankings in importance than clinical relevance, actionability, and costs. Variation among payors regarding current trends in oncology care management, including on the importance of patient-centric outcomes and the use of oncology clinical pathways, suggests the need to focus on value-based health care and greater uptake of oncology clinical pathways.

How can geomorphology facilitate a better understanding of glacier and ice sheet behaviour?

AbstractGlaciers and ice sheets are an integral part of Earth's system, advancing and retreating in response to changes in climate. Clues about the past, present and future behaviour of these ice masses are found throughout current and former glaciated landscapes. In this commentary, we outline recent scientific advances from a collection of articles in which geomorphological evidence is used to inform us about the behaviour of glaciers and ice sheets across a range of spatial (landform to continent) and temporal (seasons to millennia) scales. Through a diversity of approaches including field measurements, remote sensing and numerical modelling, these studies build on an extensive background literature to deepen our understanding of how ice flows, how glaciers and ice sheets respond to climate change, and of the processes of ice advance and retreat and the stability of the system. Further integration of knowledge across the fields of geomorphology and glaciology will have tangible benefits for managing the societal and environmental impacts of glacier change and for improved projections of sea‐level rise over the coming decades to centuries.

The Communal Roots of Mexico’s Maquila Industry: Urbanization, Land, and Inequality in Ciudad Juárez, 1960–2000

Abstract This article analyzes the urbanization and privatization of communal lands (ejidos) in Ciudad Juárez, Mexico, via a computer vision model that utilizes Google Street View (GSV) and Geographic Information System (GIS) imagery. Our innovative methodology reveals how processes of ejidal urbanization in Mexico’s northern borderlands contributed to the rise of multinational factories (maquiladoras) and geographies of inequality and violence. Past scholarship on the (d)evolution of ejidal land tenure details how periurban ejidal lands throughout Mexico were often sites of impoverishment and a lack of investment, featuring informal settlement and rapid or chaotic urbanization following the country’s 1960 urban turn. Through its use of novel sources and methods, this article demonstrates that the urbanization process in Juárez’s principal periurban ejidos diverged from this classic model in specific ways. By combining conventional historical sources with visual data like GSV and GIS imagery, Juárez’s former and current ejidal landscapes reveal high levels of investment, formal planning, and infrastructure. We argue that Juárez’s distinctive physical, political, and economic geographies shaped the overwhelmingly industrial, private, and invested character of the city’s (former and current) periurban ejidal lands. This process occurred via a globalized modernization regime that forged disparate landscapes of investment and inequality beginning in the 1950s.

Integrating Next-Generation SIEM with Data Lakes and AI: Advancing Threat Detection and Response

The article focuses on how Next-Gen SIEM can be extended with Data Lakes and AI to improve threat detection and response in current threat landscapes. Conventional SIEM tools have several major disadvantages: they could be more scalable, their false positive rates can be extremely high, and data processing takes too much time due to the constantly growing number and levels of sophistication in cyber threats. These limitations may result in delayed threat detection, alert fatigue, and operations nightmares for security operations. Data Lakes form the center of the proposed architecture to ensure the large raw, unstructured data from different sources are integrated and analyzed in real time. When applied, the system will be able to identify anomalies, evolve with threats, and improve on false positives with the help of superior machine learning algorithms. This integration also solves most of the inherent problems of traditional SIEM and provides more general and efficient solutions for improved security postures for organizations, as this article describes how to orientate CSFs for cybersecurity and SOCs. It demonstrates how various current integrated security technologies improve the detection rates, accuracy, the burden on the security personnel and the human information defense system.

Historical and Current Landscapes of Autonomous Quadrotor Control: An Early-Career Researchers’ Guide

The rising demand for autonomous quadrotor flights across diverse applications has led to the introduction of novel control strategies, resulting in several comparative analyses and comprehensive reviews. However, existing reviews lack a comparative analysis of experimental results from published papers, resulting in verbosity. Additionally, publications featuring comparative studies often demonstrate biased comparisons by either selecting suboptimal methodologies or fine-tuning their own methods to gain an advantageous position. This review analyzes the experimental results of leading publications to identify current trends and gaps in quadrotor tracking control research. Furthermore, the analysis, accomplished through historical insights, data-driven analyses, and performance-based comparisons of published studies, distinguishes itself by objectively identifying leading controllers that have achieved outstanding performance and actual deployment across diverse applications. Crafted with the aim of assisting early-career researchers and students in gaining a comprehensive understanding, the review’s ultimate goal is to empower them to make meaningful contributions toward advancing quadrotor control technology. Lastly, this study identifies three gaps in result presentation, impeding effective comparison and decelerating progress. Currently, advanced control methodologies empower quadrotors to achieve a remarkable flight precision of 1 cm and attain flight speeds of up to 30 m/s.

Good Intercollegiate Athlete Representation: "All Hands on Deck"

The current landscapes of intercollegiate sports and higher education are experiencing shifts toward more democratic representation. In college sport, student-athlete representatives are more engaged in policy decisions, hold voting rights, and are included on boards and committees. Despite this shift, little is known about what good intercollegiate athlete representation entails and how multi-level, democratic governance systems may support or impede good representation in the context of college sport. This paper explores qualities of good college athlete representation (CARep) and factors contributing to and/or detracting from the process of good CARep in the context of a democratic multi-level intercollegiate sport governance system. Findings showed individual attributes of good CARep, including interpersonal skills and leadership, were based on democratic representation virtues (i.e., fairmindedness, trust building, good gatekeeping) and helped foster democratic values of civic equality, self-governance, and inclusion. The intercollegiate sport governance system supports the work of athlete representatives primarily through its educative function. More specifically, administrators were key to identifying experiential learning opportunities for athlete representatives, which contributed to the process of good representation through responsiveness, inclusiveness, and egalitarianism. Lack of administrative support and education for all relevant interest groups characterized governance system inconsistencies impeding good CARep, primarily at institutional levels where the purpose of student-athlete committees varied and/or athlete representative roles were less understood. Implications for practice and directions for future research on good athlete representation are presented.

China and Italy’s Energy Development Trajectories: Current Landscapes and Future Cooperation Potential

In order to achieve the ambitious goal of “carbon neutrality”, countries around the world are striving to develop clean energy. Against this background, this paper takes China and Italy as representatives of developing and developed countries to summarize the energy structure composition and development overview of the two countries. The paper analyzes the serious challenges facing the future energy development of both countries and investigates the possibilities of energy cooperation between the two countries, taking into account their respective advantages in energy development. By comparing the policies issued by the two governments to encourage clean energy development, this paper analyzes the severe challenges faced by the two countries’ energy development in the future and combines their respective energy development advantages to look forward to the possibility of energy cooperation between the two countries in the future. This lays the foundation for China and Italy to build an “Energy Road” after the “Silk Road”.