Continuous Learning Research Articles

Continual Learning and Adaptation In Resource-Constrained Environments (CLAIRE)

As climate-related extreme events continue to increase and impact the world, of particular importance are the threats posed to food, agriculture, and water (FAW) systems. Deep learning could benefit FAW systems for classification of threats and for forecasting potential future events given historical patterns. However, many FAW systems are faced with operational environments that are resource-constrained, which could present challenges in deploying deep learning models. Continual learning offers a way to overcome certain deployment challenges by enabling deep learning models that are more robust to data distribution changes, without the need for GPUs or off-line training. We describe a continual learning approach to forecasting extreme air quality events developed for the National Oceanic and Atmospheric Administration to provide operational air quality guidance to the Continental United States. We describe how this deep learning model is resilient to future data distribution changes by performing curriculum learning, and how it can be deployed as a continual learner, offering better predictive performance for resource-constrained environments.

Expansive conceptualisation of the impact of continuous learning programmes: An analysis of research-based workshops in higher vocational education and training

Finnish higher education institutions offer continuous learning opportunities, including professional specialisation education programmes for the health and social sector. This article focuses on the conceptualisation of the impact of these programmes, which was the topic of discussion by education and working-life actors in research-based workshops. Drawing on the cultural–historical activity theory (CHAT) approach, the study seeks to answer the following research questions: What are the main themes through which the participants conceptualise the impact of education? How do the themes expand the conceptualisation of the impact of education? A qualitative study analysed data from the multi-professional discussions facilitated by researcher-developers in online workshops during the COVID-19 pandemic. The findings describe the impact-related discussion themes and analyse their expansive potential. Participants’ discursive actions in research-based online workshops manifest expansive conceptuali-sation on multiple dimensions, but also reveal dilemmatic impact-related questions that reflect the underlying contradictions of continuous learning programmes in the health and social sector.

Junior Doctor Retention Strategies: Integrating Knowledge Management with Supportive Leadership and Organizational Climate

AbstractThis study aims to evaluate interventions and strategies used to retain junior doctors, with a specific focus on the roles of organizational climate, supportive leadership, and work engagement influenced by effective knowledge management practices. We examine how knowledge management—the systematic process of creating, sharing, using, and managing the knowledge and information of an organization—contributes to creating a favorable organizational climate and supportive leadership strategies. Engaging in a quantitative study with a sample of 950 junior doctors across four Central European countries, we explore the impact of an integrated approach that combines traditional performance-oriented climate models (rational goals model) with knowledge management practices tailored to the unique demands of healthcare settings influenced by current healthcare reforms and bureaucratic systems. Our findings suggest that not only do supportive leadership and a positive organizational climate enhance work engagement among junior doctors, but robust knowledge management practices also play a crucial role in improving retention rates by facilitating continuous learning and effective information sharing. To the best of our knowledge, our research is the first to draw on the synergy between rational goals, organizational climate, supportive leadership, and knowledge management practices to explain the antecedents of junior doctor work engagement and retention. This approach offers new insights into the dynamics of employee retention and underscores fundamental factors influencing junior doctors’ retention, paving the way for a more resilient and thriving healthcare workforce amidst the ongoing turbulence in global healthcare.

Biomedical Engineering Literature: Advanced Reading Skills for Research and Practice

Medical engineering is a field of study that combines biological and medical sciences with engineering to develop improved healthcare technologies. It is imperative that you possess a deep understanding of both the material and the technical language used in this rapidly advancing field in order to effectively grasp key topics and navigate the rapidly expanding literature. This paper examines the future of the biomedical engineering profession through the lens of advanced reading strategies. It starts with the assertion that biomedical engineering literature presents three challenges: the level of density is technical, the area is interdisciplinary, and the growth of knowledge is exponential. In the view of the above challenges, the paper discusses various advanced reading skills in relation to biomedical engineers. These skills include the ability to critically read scientific texts, both at a fast pace for an overview and in detail for deeper understanding, to identify key ideas and methodologies relevant to the field, and to assess the validity of scientific claims and data representations. The paper also aims at pointing out how separate elements like the use of systematic reviews, of meta-analysis, of data visualization tools that complement reading, help the researcher in the process of managing gathered information from various sources. This paper describes teaching these skills for both academic and professional practice, with examples of how improved reading competencies can enhance research quality and contribute to innovations in medical technologies, tissue engineering, and clinical trials. Furthermore, the paper aims to discuss the future trends due to the increasing incorporation of continuous learning for addressing the challenges posed by novel technologies including artificial intelligence and nanotechnology that define the future of biomedical engineering. biomedical engineering students require the acquisition of higher order reading skills in preparation for information reading: national and international publications in the engineering profession, policy and technology in support of health care delivery systems, product development, and other purposes. The ideas stated and demonstrated in this paper should be practical to both beginning scholars and veteran professionals in the biomedical engineering field as well as useful for seminar and workshop participants in seminars and workshops on biomedical engineering literature.

SCARF: Scalable Continual Learning Framework for Memory‐efficient Multiple Neural Radiance Fields

AbstractThis paper introduces a novel continual learning framework for synthesising novel views of multiple scenes, learning multiple 3D scenes incrementally, and updating the network parameters only with the training data of the upcoming new scene. We build on Neural Radiance Fields (NeRF), which uses multi‐layer perceptron to model the density and radiance field of a scene as the implicit function. While NeRF and its extensions have shown a powerful capability of rendering photo‐realistic novel views in a single 3D scene, managing these growing 3D NeRF assets efficiently is a new scientific problem. Very few works focus on the efficient representation or continuous learning capability of multiple scenes, which is crucial for the practical applications of NeRF. To achieve these goals, our key idea is to represent multiple scenes as the linear combination of a cross‐scene weight matrix and a set of scene‐specific weight matrices generated from a global parameter generator. Furthermore, we propose an uncertain surface knowledge distillation strategy to transfer the radiance field knowledge of previous scenes to the new model. Representing multiple 3D scenes with such weight matrices significantly reduces memory requirements. At the same time, the uncertain surface distillation strategy greatly overcomes the catastrophic forgetting problem and maintains the photo‐realistic rendering quality of previous scenes. Experiments show that the proposed approach achieves state‐of‐the‐art rendering quality of continual learning NeRF on NeRF‐Synthetic, LLFF, and TanksAndTemples datasets while preserving extra low storage cost.

Faculty experiences in publishing in Scopus-indexed journals: Challenges and strategies among higher education institutions in the Philippines

Research is a cornerstone of education, with publication output often used as a key metric for research performance. This study investigates the experiences of Philippine Higher Education Institution (HEI) faculty as they work to publish in Scopus-indexed journals. By exploring faculty members' challenges, strategies, and perspectives, this research aims to inform how HEIs can enhance support for faculty in contributing to global scholarship and advancing academic careers. Using Husserl’s descriptive phenomenology, 12 faculty members from HEIs in Eastern Visayas, Philippines, were purposively selected as participants, with theoretical saturation guiding the sample size. Data was gathered through in-depth interviews and analyzed using Colaizzi's method. The study reveals four primary themes regarding the publication journey: motivation for publishing, preparation and publication process, challenges faced, and support systems. Faculty perceptions of publishing centered around five themes: career advancement and promotion, increased visibility and credibility, personal and professional satisfaction, institutional expectations, and incentives. Key challenges included financial barriers, the rigorous peer review and revision process, submission and formatting requirements, and emotional strain. To address these, participants highlighted strategies involving financial and personal investment, mentorship and collegial support, institutional resources, continuous learning, strategic journal selection, and maintaining resilience. Findings indicate that publishing in Scopus-indexed journals requires a blend of motivation, planning, and resilience, as faculty strive to contribute to knowledge and further their careers. Yet, they face substantial barriers, especially high publication fees and stringent formatting standards. Institutional support—especially through funding, mentorship, and workshops—is vital to overcoming these obstacles. For greater faculty success, institutions should cultivate a supportive research culture that recognizes and addresses the logistical and emotional demands of academic publishing.

Involvement of Academic Staff in the Practice of Professional Development

The article explores the level of involvement of scientific and pedagogical professionals in various social activities for professional development compared to other employee groups. It argues that obtaining additional professional training can be a valuable tool for career growth. The author sees professional development as an integral part of academic growth for scientific and educational workers, along with personal and career development. The findings are based on a combination of theoretical and empirical research conducted by domestic and international scholars, as well as official statistical data and the results of an original sociological study. The study involved a questionnaire survey of scientific and educational professionals (n = 223), as well as other employee groups (n = 833), in the Tyumen and Sverdlovsk regions. As a result, it was found that scientific and pedagogical workers are more likely to engage in professional development activities compared to other categories of the employed population. Continuing professional education programs appear to be the most common form of professional growth. Due to their specialized activities, academic staff tend to develop digital skills more often, while other groups develop personal skills to a greater degree. Based on the level of involvement in professional development, we identified five groups of respondents: “active and engaged”, “achieving the desired level or feeling disappointed”, “questioning or confused”, “recognizing the importance of professional development”, and “rejecting professional development”. The theoretical and empirical findings led to the development of potential strategies to enhance the engagement of scientific and educational professionals in ongoing skill development and acquisition of new competencies. This article is aimed at university management responsible for professional development of academic staff. The findings presented here may have practical implications for the development of personnel strategies at universities with the goal of increasing human capital and professional competence among academic staff, as well as motivating them to engage in continuous learning. The results of this study could be beneficial for participants in the academic community, as well as for specialists involved in developing educational programs and planning the future of universities strategically. The material presented in this paper is relevant not only for academic staff, but also for graduate students who are focused on their continuous professional growth within the academic sector.

A Review of Self-regulated Learning and Lifelong Learning in Chinese Vocational Education

This literature review explores the relationship between self-regulated learning and lifelong learning in the context of Chinese vocational education. Self-regulated learning is discussed as a foundational component in educational psychology. The relationship between self-regulated learning and lifelong learning is examined, illustrating how self-regulation skills foster continuous learning. Moreover, this review addresses the role of lifelong learning in vocational education in China, emphasizing the need for flexible, skills-based learning pathways to support economic and personal growth. In addition, the paper explores lifelong learning policies in China. The literature review culminates in a discussion on the challenges and opportunities in Chinese vocational education.

Talent Management in Healthcare: The New Strategic Objective

In today’s rapidly evolving healthcare landscape, effective talent management has become a critical priority for any organization seeking exceptional and safe care, whilst driving a positive outcome and impact in people’s lives. This article delves into the intricacies of talent management in healthcare, from an operational standpoint, aiming to capture how the frontline workforce perceives talent management. It is hypothesized that by implementing comprehensive talent management strategies, healthcare institutions would be able to address the current challenges posed by the worldwide workforce shortages. The increasing demand for healthcare services has prompted organizations and governments to proactively approach talent management to attract and retain the right individuals with the right skills to remain competitive and even functional. Employee engagement initiatives, along with a robust culture of continuous learning and phycological safety, where individuals can be at optimal performance and positively impact patient experience and outcome, significantly reduce turnover rates and ensure workforce stability (Dyrbye et al., 2017).

Driving a three-age learning curriculum from local wisdom under the “YANGKHINOK Model” in Thailand

This study had the objective of developing a three-age learning curriculum from local wisdom under the YANGKHINOK Model which focuses on time, resources, knowledge, skills, and sincerity to enhance competency and leverage social networks in Khueang Nai District, Ubon Ratchathani Province, Thailand. Due to the dynamics of global society influencing Thai education, integrating local wisdom in curriculum development is essential for preserving cultural heritage and community sustainability. A mixed method of qualitative case studies and quantitative evaluation was employed to assess the efficacy of the local wisdom project. Key findings spotlighted the importance of community participation, educational coordination, and the appropriate development of a curriculum for all age groups by the YANGKHINOK Model (Youth, Adult, Narration, Gathering, Knowledge, Hearkening, Impetus, Network, Older Persons, Keeping). This inclusive approach emphasizes the critical role of collaboration among local communities and educational institutions to ensure that the curriculum remains relevant and adaptable across different generations. The approach also aims to create a contemporary democratic society by promoting continuous learning and sustainable professional development that fosters long-term community resilience. It was concluded that a sustainable community curriculum must include these components to meet the actual needs of the community, as well as being adaptable to social and economic changes. Effective use of limited local resources, strategic planning, and broad participation for practical results lead to lasting sustainability, providing a framework for continuous community improvement.

Adapting to Change: A Qualitative Study on Continuous Professional Learning among Adult Learners

Adapting to Change: A Qualitative Study on Continuous Professional Learning among Adult Learners

Early years practitioners in Poland – thematic analysis on professional identities

ABSTRACT This research explores perceptions of practitioners’ professional identity in early childhood education and care (ECEC) in Poland. Igor Knez’s theory on professional identity was applied. Qualitative methodology was used to gather personal perspectives and thematic analysis as a tool to define professional identity. Practitioners’ self-constructions of their own childhood were imagined as “romanticized constructions” and “childhood on the margins.” Practitioners see themselves as professionals committed to continuous learning. Participants in this study felt less self-efficacy with respect to working with parents than with children. Practitioners preferred to discuss negative relationships with parents.

Market-oriented entrepreneurship and the impact of social media and crowdsourcing on continuous organizational learning. An empirical study

This research highlights the relationship between the market orientation of entrepreneurs and continuous organizational learning, as well as the influence of the use of social media and crowdsourcing as ideal tools for the implementation of the market orientation strategy for entrepreneurs. The empirical study was carried out in four and five-star hotels. The conclusions also recommend the widespread use of crowdsourcing as an indispensable tool for solving entrepreneurs' problems, capturing new ideas from Internet users and obtaining opinions on products and services, thus collaborating in continuous innovation in entrepreneurs. These tools represent a very important source of information for the market orientation strategy for entrepreneurs, which in turn encourages continuous organizational learning, ensuring that sustainable competitive advantages are gained over time.

Revolutionizing DevOps Security: AI and ML-Enabled Automated Testing Approaches

In modern DevOps environments, the integration of security practices poses significant challenges due to the fast-paced nature of Continuous Integration/Continuous Deployment (CI/CD) pipelines. Traditional security testing methods are usually too slow and reactive to address vulnerabilities effectively in such dynamic settings. To overcome these challenges, organizations are increasingly adopting automated security testing solutions that leverage Artificial Intelligence (AI) and Machine Learning (ML). This paper discusses AI and ML capabilities in automating security testing during DevOps. It talks about how these technologies can improve security by enabling real-time threat detection, reducing false positives, and adapting to new vulnerabilities through continuous learning. Key AI/ML-based tools and techniques, along with their integration into DevOps workflows, are also discussed in detail. It also covers the integration challenges and the potential of AI/ ML in security testing in the coming years.

Building a Resilient and Adaptive Organization Through Mindset Shifts and Continuous Learning

In a world characterized by constant change and disruption, organizations must embrace adaptability, resilience, and continuous learning to remain competitive. This article explores how leaders can build "future-fit" organizations through strategic mindset shifts and by fostering cultures of experimentation and growth. A focus on learning orientation, growth mindset, and adaptive problem-solving equips organizations to navigate challenges and seize emerging opportunities. The article also examines key practices such as scenario planning, agile methodologies, and internal learning programs that ensure continuous development and operational flexibility. Case studies of organizations like Amazon, Anthropic, and Netflix demonstrate how these principles translate into sustained success. Ultimately, organizations that cultivate future-readiness through innovation, reinvention, and learning will thrive in the face of uncertainty and lead in an ever-changing landscape.

Hyperspectral Inversion of Soil Cu Content in Agricultural Land Based on Continuous Wavelet Transform and Stacking Ensemble Learning

Heavy metal pollution in agricultural land poses significant threats to both the ecological environment and human health. Therefore, the rapid and accurate prediction of heavy metal content in agricultural soil is crucial for environmental protection and soil remediation. Acknowledging the limitations of traditional single linear or nonlinear machine learning models in terms of prediction accuracy, this study developed an ensemble learning model that integrates multiple linear or nonlinear learning models with a random forest (RF) model to improve both the prediction accuracy and reliability. In this study, we selected a typical copper (Cu) polluted area in the Pearl River Delta of Guangdong Province as the research site and collected Cu content data and indoor soil reflectance spectral data from 269 surface soil samples. First, the soil spectral data were preprocessed using Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC), and continuous wavelet transform (CWT) to reduce noise interference. Next, principal components analysis (PCA) was employed to reduce the dimensionality of the preprocessed spectral data, eliminating redundant features and lowering the computational complexity. Finally, based on the dimensionality-reduced data and Cu content, we established a stacked ensemble learning model, where the base models included SVR, PLSR, BPNN, and XGBoost, with RF serving as the meta-model to estimate the soil heavy metal content. To evaluate the performance of the stacking model, we compared its prediction accuracy with that of individual models. The results indicate that, compared to the traditional machine learning models, the prediction accuracy of the stacking model was superior (R2 = 0.77; RMSE = 7.65 mg/kg; RPD = 2.29). This suggests that the integrated algorithm demonstrates a greater robustness and generalization capability. This study presents a method to improve soil heavy metal content estimation using hyperspectral technology, ensuring a robust model that supports policymakers in making informed decisions about land use, agriculture, and environmental protection.

MICL: Mutual Information Guided Continual Learning for LiDAR Place Recognition

MICL: Mutual Information Guided Continual Learning for LiDAR Place Recognition

“Navigating The High-Stress World Of IT: Strategies For Managing Work Pressure”

The IT industry is characterized by rapid change and high demands, often resulting in significant stress for professionals. The fast-paced and demanding nature of the IT industry often leads to significant work-related stress. The IT industry is marked by high levels of stress due to rapid technological advancements, demanding project timelines, and the complexity of problem-solving. Professionals face the challenge of continuous learning to keep pace with evolving technologies while managing the expectations for immediate results. The omnipresence of connectivity exacerbates work-life balance issues, often leading to burnout. Additionally, the pressure to ensure cybersecurity and navigate team dynamics adds further strain. This article explores these stressors, highlighting their impact on IT professionals and emphasizing the need for effective coping strategies to foster resilience and well-being in a high-pressure environment. This article also offers practical strategies for managing work-related pressure. Key approaches include prioritizing tasks, establishing boundaries, and fostering open communication within teams. Stress management techniques such as mindfulness, physical activity, and continuous skill development are emphasized as vital for maintaining well-being. By adopting these strategies, IT professionals can enhance their resilience, boost productivity, and cultivate a healthier work-life balance, effectively navigating the complexities of their fast-paced field

Person Re-Identification for Robot Person Following With Online Continual Learning

Person Re-Identification for Robot Person Following With Online Continual Learning

Bridging Human and Machine Intelligence: Reverse-Engineering Radiologist Intentions for Clinical Trust and Adoption

In the rapidly evolving landscape of medical imaging, the integration of artificial intelligence (AI) with clinical expertise offers unprecedented opportunities to enhance diagnostic precision and accuracy. Yet, the "black box" nature of AI models often limits their integration into clinical practice, where transparency and interpretability are important. This paper presents a novel system leveraging the Large Multimodal Model (LMM) to bridge the gap between AI predictions and the cognitive processes of radiologists. This system consists of two core modules, Temporally Grounded Intention Detection (TGID) and Region Extraction (RE). The TGID module predicts the radiologist's intentions by analyzing eye gaze fixation heatmap videos and corresponding radiology reports. Additionally, the RE module extracts regions of interest that align with these intentions, mirroring the radiologist’s diagnostic focus. This approach introduces a new task, radiologist intention detection, and is the first application of Dense Video Captioning (DVC) in the medical domain. By making AI systems more interpretable and aligned with radiologist’s cognitive processes, this proposed system aims to enhance trust, improve diagnostic accuracy, and support medical education. Additionally, it holds the potential for automated error correction, guiding junior radiologists, and fostering more effective training and feedback mechanisms. This work sets a precedent for future research in AI-driven healthcare, offering a pathway towards transparent, trustworthy, and human-centered AI systems. We evaluated this model using NLG(Natural Language Generation), time-related, and vision-based metrics, demonstrating superior performance in generating temporally grounded intentions on REFLACX and EGD-CXR datasets. This model also demonstrated strong predictive accuracy in overlap scores for medical abnormalities and effective region extraction with high IoU(Intersection over Union), especially in complex cases like cardiomegaly and edema. These results highlight the system's potential to enhance diagnostic accuracy and support continuous learning in radiology. We are also releasing the source code for our project, available here.