Adaptive Knowledge Research Articles

Challenges for expanding inventories of climate possibilities through indigenous and local knowledges in rural Zambia

This article explores the integration (or marginality) of indigenous and local knowledge (IKL) in donor-driven community climate adaptation (CDCA) projects and the extent to which this helps expand inventories of adaptation possibilities for host communities and strengthen climate adaptation resilience in Zambia. Through multi-level qualitative research design, this study reveals that, even where climate interventions are intentional about being inclusive of community knowledge, they are likely to promote policy-centric knowledges and interventions that invisibilises ILK. Empirical evidence shows the application of CDCA expresses top-down assumptions of livelihood resilience and embeds uncritical views of what community is, including what might be socially and culturally appropriate forms of adaptation. CDCA implementation strategy is exclusionary and misaligned with ILK, affecting possibilities of knowledge intersection. This article elucidates how climate adaptation that marginalises ILK fails to expand inventories of climate adaptation possibilities for communities supposed to be adapting and proposes how this gap could be bridged. (This article is published in the thematic collection ‘African ecologies: the value and politics of indigenous knowledges’, edited by Adriaan van Klinken, Simon Manda, Damaris Parsitau and Abel Ugba.)

Using mixed methods and partnership to develop a program evaluation toolkit for organizations that provide physical activity programs for persons with disabilities

BackgroundThe purpose of this paper is to report on the process for developing an online RE-AIM evaluation toolkit in partnership with organizations that provide physical activity programming for persons with disabilities.MethodsA community-university partnership was established and guided by an integrated knowledge translation approach. The four-step development process included: (1) identify, review, and select knowledge (literature review and two rounds of Delphi consensus-building), (2) adapt knowledge to local context (rating feasibility of outcomes and integration into online platform), (3) assess barriers and facilitators (think-aloud interviews), and (4) select, tailor, implement (collaborative dissemination plan).ResultsStep 1: Fifteen RE-AIM papers relevant to community programming were identified during the literature review. Two rounds of Delphi refined indicators for the toolkit related to reach, effectiveness, adoption, implementation, and maintenance. Step 2: At least one measure was linked with each indicator. Ten research and community partners participated in assessing the feasibility of measures, resulting in a total of 85 measures. Step 3: Interviews resulted in several recommendations for the online platform and toolkit. Step 4: Project partners developed a dissemination plan, including an information package, webinars, and publications.DiscussionThis project demonstrates that community and university partners can collaborate to develop a useful, evidence-informed evaluation resource for both audiences. We identified several strategies for partnership when creating a toolkit, including using a set of expectations, engaging research users from the outset, using consensus methods, recruiting users through networks, and mentorship of trainees. The toolkit can be found at et.cdpp.ca. Next steps include disseminating (e.g., through webinars, conferences) and evaluating the toolkit to improve its use for diverse contexts (e.g., universal PA programming).

Fuzzy Adaptive Knowledge-Based Inference Neural Networks: Design and Analysis.

A novel fuzzy adaptive knowledge-based inference neural network (FAKINN) is proposed in this study. Conventional fuzzy cluster-based neural networks (FCBNNs) suffer from the challenge of a direct extraction of fuzzy rules that can capture and represent the interclass heterogeneity and intraclass homogeneity when the data possess complex structures. Moreover, the capability of the cluster-based rule generator in FCBNNs may decrease with the increase of data dimensionality. These drawbacks impede the generation of desired fuzzy rules, and affect the inference results depending on the fuzzy rules, thereby limiting their generalization ability. To address these drawbacks, an adaptive knowledge generator (AKG), consisting of the observation paradigm (OP) and clustering strategy (CS), is effectively designed to improve the generalization ability in FAKINN. The OP distills the characteristic information (CI) from data to highlight the homogeneity and heterogeneity of objects, and the CS, viz., the weighted condition-driven fuzzy clustering method (WCFCM), is proposed to summarize the CI to construct fuzzy rules. Moreover, the feedback between the OP and CS can control the dimensionality of CI, which endows FAKINN with the potential to tackle high-dimensional data. The main originality of the study focuses on the AKG and WCFCM that are proposed to develop the structural design methodology of FNNs. The performance of FAKINN is evaluated on various benchmarks with 27 comparative methods, and two real-world problems are adopted to validate its effectiveness. Experimental results show that FAKINN outperforms the comparison methods.

Rural migrants in urban centers: Sources of vulnerability or agents of adaptive capacity?

Research on climate migration is increasingly analyzing not only the role of climate as a migration driver but also migration's adaptive and maladaptive outcomes. However, despite broad recognition that climate-related migration is overwhelmingly rural-to-urban, migration's effects on the vulnerability and adaptive capacity of the receiving urban destinations, many of which struggle to adapt to climate change, have received scant attention. To begin addressing this gap, this study examines how urban planners and policymakers in flood-prone Dar es Salaam, Tanzania's main migration destination and Africa's fastest-growing metropolis, perceive these effects. To this end, we utilize semi-structured interviews to examine three interrelated dimensions: migration's effects on flooding, the potential responses to ameliorate its adverse effects, and migrants' capabilities and the ways the city can harness those capabilities to reduce flooding.The results show that most planners and policymakers view migration as mainly exacerbating flooding yet also perceive migrants as possessing the potential to contribute to urban adaptation. This potential encompasses aspects recognizing migrants' agency, such as adaptation knowledge and planning skills, alongside ‘physical-economic’ elements linked with the use of migrants as labor for maintaining drainage channels and their contribution to enlarging the city's tax base, which may assist in funding flood-prevention infrastructure. However, the results also point to Dar es Salaam's inaction to exploit this potential, accompanied by a perceived lack of responsibility for advancing adaptation. We conclude by highlighting the importance of adopting a proactive approach to mapping and harnessing migrants' capabilities, ultimately contingent on cities' willingness to assume this responsibility.

Few-shot adaptation of multi-modal foundation models: a survey

Multi-modal (vision-language) models, such as CLIP, are replacing traditional supervised pre-training models (e.g., ImageNet-based pre-training) as the new generation of visual foundation models. These models with robust and aligned semantic representations learned from billions of internet image-text pairs and can be applied to various downstream tasks in a zero-shot manner. However, in some fine-grained domains like medical imaging and remote sensing, the performance of multi-modal foundation models often leaves much to be desired. Consequently, many researchers have begun to explore few-shot adaptation methods for these models, gradually deriving three main technical approaches: (1) prompt-based methods, (2) adapter-based methods, and (3) external knowledge-based methods. Nevertheless, this rapidly developing field has produced numerous results without a comprehensive survey to systematically organize the research progress. Therefore, in this survey, we introduce and analyze the research advancements in few-shot adaptation methods for multi-modal models, summarizing commonly used datasets and experimental setups, and comparing the results of different methods. In addition, due to the lack of reliable theoretical support for existing methods, we derive the few-shot adaptation generalization error bound for multi-modal models. The theorem reveals that the generalization error of multi-modal foundation models is constrained by three factors: domain gap, model capacity, and sample size. Based on this, we propose three possible solutions from the following aspects: (1) adaptive domain generalization, (2) adaptive model selection, and (3) adaptive knowledge utilization.Kindly check and confirm the edit made in the title.The title is correct.

Enhanced Agile Methodology for Ontology Development in E-Learning Environments

This study explores the use of agile approaches to the creation of ontologies for e-learning, evaluating the benefits and drawbacks as well as the impact on information display. Traditional strategies conflict with the need to fulfill the ever-evolving expectations of users and adapt to the ever-changing features of e-learning environments. The challenge aims to encourage cooperation and versatility in the creation of ontologies for e-learning through the use of Agile standards. Because they make it simpler to organize relationships and statistics, ontologies are vital elements in e-mastering domain names due to the fact that they permit adaptive knowledge of structures and individualized learning experiences. Agile ontology engineering approaches are proposed as a choice for one’s problems, emphasizing flexibility and response. This study highlights the need to work together with customers and incorporate their input into the advent of ontologies. It notably emphasizes using established feedback loops and cooperation with e-learning platform companies. The sensible usefulness and effectiveness of agile methodology for ontology development (AMOD) in e-learning settings are shown through validation efforts in real-global conditions.

KNOWLEDGE AND ATTITUDE OF CLIMATE CHANGE ADAPTATION: THE VIEW OF MALAYSIAN UNIVERSITY STUDENTS

Sustainable consumption involves climate change adaptation. International organisations are emphasising and encouraging youth climate change preparation. This study examines undergraduate students' climate change adaptation knowledge and attitudes. A total of 384 undergraduate students from Universiti Malaya, Universiti Putra Malaysia, and Universiti Kebangsaan Malaysia responded to the survey. The findings showed a high level of knowledge and attitude among the students. The study also discovered significant and positive relationships between knowledge and attitude towards climate change. Furthermore, construct basic knowledge has a greater impact on climate change adaptation attitudes than risk perception. Their primary source of information is the internet. In light of these findings, internet information sources have an effect on students' knowledge and attitudes. This study provides an indicator of the youth's knowledge and attitude patterns, in order for policymakers and educational institutions to consider climate change adaptation more effectively.

To be global or personalized: Generalized federated learning with cooperative adaptation for data heterogeneity

As federated learning (FL) continues to advance, research has branched into two major directions: enhancing the individual global model and developing multiple personalized models. However, few studies took both orientations into consideration in parallel. The underlying reason lies in the objective heterogeneity between global and personalized models, where the former emphasizes generalization, while the latter focuses on specialization. The dilemma of sacrificing the personalized deviation in exchange for stable global enhancement, or forsaking the global model for personalization, is particularly acute in highly heterogeneous data scenarios (as a curse). To establish the coexistence of global and personalized models in highly heterogeneous data scenarios, we propose FedAKD, a generalized federated learning framework with adaptive knowledge distillation to optimizes both global and personalized models. Specifically, based on the similarity of predictive distributions over a reference dataset, two distinct adaptive weighting strategies are employed to match the divergent focuses of global and personalized optimization, leveraging data heterogeneity to foster cooperative adaptation and establish their coexistence (as a blessing). The client-side strategy through cluster-oriented optimization to facilitate the domain adaptation in local tasks. Meanwhile, the server-side strategy adjusts the global model to accommodate varying optimization directions from diverse implicit clusters. Experiments in label and feature shift settings demonstrate that our method outperforms state-of-the-art methods in both global and personalized performance with faster convergence [e.g. the accuracy improvements on the physical activity monitoring dataset (PAMAP2) for global and personalized models exceed 26.51% and 11.00%, respectively]. Furthermore, our method is equally applicable to real-world healthcare scenarios.

DISAIN KURIKULUM INTER-DISIPLINER BERBASIS KKNI: PROGRAM STUDI ILMU KOMPUTER DENGAN FOKUS SOFT COMPUTING DAN INTELLIGENT SYSTEM

The development of technology demands Computer Science graduates to have interdisciplinary knowledge and skills. Traditional curricula are less adaptive in addressing the dynamic industry needs and focus on developing soft skills. This article discusses the design of an interdisciplinary curriculum based on KKNI for Computer Science programs with a focus on soft computing and intelligent systems. This curriculum design integrates knowledge and skills from various disciplines, with an emphasis on the development of soft skills. The curriculum structure is divided into two tracks: the Mandatory Track focusing on the fundamental foundations of Computer Science and soft skills, and the Elective Track focusing on deepening interests and talents in the field of soft computing and intelligent systems, such as Artificial Neural Networks, Microcontrollers, Data Mining, Embedded Systems, Fuzzy Logic, and Computer Vision. This research adopts a Research and Development (R&D) approach with the ADDIE model. Data are collected through surveys, interviews, and document analysis. It is expected that this curriculum design will produce Computer Science graduates with comprehensive, adaptive knowledge and skills, ready for the digital era workforce, particularly in the field of soft computing and intelligent systems.

Unlocking the Mysteries of Reproduction: Exploring Fecundity and Gonadosomatic Index in the Enigmatic Bornean Pygmy Halfbeak, Dermogenys colletei (Meisner, 2001) (Beloniformes: Zenarchopteridae)

The pygmy halfbeak Dermogenys colletei is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.

Examining site intervention efficacy and uncertainties with conceptual Bayesian networks: preventing offsite migration of DNAPL and contaminated groundwater.

For contaminated sites, conceptual site models (CSMs) guide the assessment and management of risks, including remediation strategies. Recent research has expanded diagrammatic CSMs with structural causal modeling to develop what are nominally called conceptual Bayesian networks (CBNs) for environmental risk assessment. These CBNs may also be useful for problems of controlling and preventing offsite contaminant migration, especially for sites containing dense nonaqueous phase liquids (DNAPLs). In particular, the CBNs provide greater clarity on the causal relationships between source term, onsite and offsite migration, and remediation effectiveness characterization for contaminated DNAPL sites compared to traditional CSMs. These ideas are demonstrated by the inclusion of modifying variables, causal pathway analysis, and interventions in CBNs. Additionally, several new extensions of the CBN concept are explored including the representation of measurement variables as lines of evidence and alignment with conventional pictorial CSMs for groundwater modeling. Taken as a whole, the CBNs provide a powerful and adaptable knowledge representation tool for remediating subsurface systems contaminated by DNAPL.

Global centre for climate change impacts on transboundary waters

Global centre for climate change impacts on transboundary waters Dr. Gail Krantzberg and Dr. Andrew Gronewold, unpack the priorities of the Global Centre for Climate Change Impacts on Transboundary Waters. Climate change threatens water resources across North America and worldwide. It intensifies floods and droughts, worsens water quality, exacerbates shoreline erosion, and damages homes and other infrastructure. Communities must learn to adapt to these worsening extremes. However, the tools and knowledge for adaptation are often non-existent, fragmented across jurisdictional boundaries, or simply too difficult to access and use.

Omics Approaches For Understanding Plant Adaptation To Climate Change

Background: The climate change can be noticed through the effects of it on the species worldwide, so knowledge of plant's adaptation to the change is a must. This research is applied through a critical omics method, which is a system that is designed to recognize the molecular mechanisms of the plants' adaptability.

Forest floor environment overrules global change treatment effects on understorey communities in a mesocosm experiment.

Light availability profoundly influences plant communities, especially below dense tree canopies in forests. Canopy disturbances, altering forest floor light conditions, together with other environmental changes such as climate change, nitrogen deposition and legacy effects from previous land-use will simultaneously impact forest understorey communities. Yet, knowledge on the individual effects of these drivers and their potential interactions remains scarce. Here we performed a forest mesocosm experiment to assess the influence of warming, illumination (simulating canopy opening), nitrogen deposition and soil land-use history (comparing ancient and post-agricultural forest soil) on understorey community composition trajectories over a 7-year period. Strikingly, understorey communities primarily evolved in response to the deeply shaded ambient forest conditions, with experimental treatments exerting only secondary influences. The overruling trajectory steered all mesocosms towards slow-colonizing forest specialist communities dominated by spring geophytes with lower nutrient-demand. The illumination treatment and, to a lesser extent, warming and agricultural land-use legacy slowed down this trend by advancing fast-growing resource-acquisitive generalist species. Warm ambient temperatures induced thermophilization of plant communities in all treatments, including control plots, towards higher dominance of warm-adapted species. Nitrogen addition accelerated this thermophilization process and increased the community light-demand signature. Land-use legacy effects were limited in our study. Our findings underscore the essential role of limited light availability in preserving forest specialists in understorey communities and highlight the importance of maintaining a dense canopy cover to attenuate global change impacts. It is crucial to integrate this knowledge in forest management adaptation to global change, particularly in the face of increasing demands for wood and wood products and intensified natural canopy disturbances.

Adaptive Knowledge Distillation-Based Lightweight Intelligent Fault Diagnosis Framework in IoT Edge Computing

Adaptive Knowledge Distillation-Based Lightweight Intelligent Fault Diagnosis Framework in IoT Edge Computing

Climate Change and Social-ecological Vulnerability in the Himalaya: A Study of the Kaligandaki Basin, Nepal

This research examines the implications and opportunities for climate change adaptation in the Kaligandaki Basin, Nepali Himalaya. The study adopts a holistic approach to explain human-environmental interrelationships. The Sustainable Livelihood Approach and the system approach of analyzing Change-Impact-Response are integrated to construct a dynamic framework that assesses household and sub-regional social-ecological vulnerability. Findings suggest that climate-sensitive social-ecological systems of the Himalayas are highly exposed to both climate change and non-climatic stressors. Households are trying to adapt to rapid change; however, their adaptation knowledge is not sufficiently translated into actions. Consequently, food and livelihood systems are insecure, and social ecosystems in the Basin are highly vulnerable to change.

Journey From a Digital Innovation to a Sustainable Health Worker Capacity-Building App in India: Experiences, Challenges, and Lessons Learned.

Health workers, especially auxiliary nurse midwives (ANMs), are among the most critical resources in improving the quality of immunization services and reducing vaccine hesitancy under the Universal Immunization Programme (UIP) in India. To improve health worker immunization skills, UIP trainings in India are primarily conducted through instructor-led classroom, cascade trainings. However, a 2018 capacity-building need assessment revealed several challenges involved in traditional classroom training, such as a single-time exposure to new guidelines, complicated logistics arrangements, a lack of refresher training, and varying quality of training. These complexities make it difficult to meet the timely knowledge and skill needs of every health worker effectively and uniformly in a rapidly changing scenario of UIP. To meet health worker capacity-building needs and address these challenges, Rapid Immunization Skill Enhancement (RISE), a learning management system (LMS) application, was conceptualized. The RISE LMS application was developed as a human-centered, interactive, continuous, and adaptable knowledge and skill-building platform for health workers engaged in the UIP. RISE complements existing classroom-based cascade training for health workers by leveraging digital technologies for faster, easier, and more effective knowledge transfer to accommodate the fast-changing needs of a dynamic health program like UIP. In this article, we share the challenges and strategic solutions to digital training applications, lessons learned, sustainability of the application, and the impact RISE has made in India, all of which stemmed from leadership, coordinated efforts from a team of skilled professionals, government acceptance, detailed planning, and continued stakeholder engagement.

Emerging roles of plant transcriptional gene silencing under heat.

Plants continuously endure unpredictable environmental fluctuations that upset their physiology, with stressful conditions negatively impacting yield and survival. As a contemporary threat of rapid progression, global warming has become one of the most menacing ecological challenges. Thus, understanding how plants integrate and respond to elevated temperatures is crucial for ensuring future crop productivity and furthering our knowledge of historical environmental acclimation and adaptation. While the canonical heat-shock response and thermomorphogenesis have been extensively studied, evidence increasingly highlights the critical role of regulatory epigenetic mechanisms. Among these, the involvement under heat of heterochromatic suppression mediated by transcriptional gene silencing (TGS) remains the least understood. TGS refers to a multilayered metabolic machinery largely responsible for the epigenetic silencing of invasive parasitic nucleic acids and the maintenance of parental imprints. Its molecular effectors include DNA methylation, histone variants and their post-translational modifications, and chromatin packing and remodeling. This work focuses on both established and emerging insights into the contribution of TGS to the physiology of plants under stressful high temperatures. We summarized potential roles of constitutive and facultative heterochromatin as well as the most impactful regulatory genes, highlighting events where the loss of epigenetic suppression has not yet been associated with corresponding changes in epigenetic marks.

Integrative Metabolomics, Enzymatic Activity, and Gene Expression Analysis Provide Insights into the Metabolic Profile Differences between the Slow-Twitch Muscle and Fast-Twitch Muscle of Pseudocaranx dentex.

The skeletal muscles of teleost fish encompass heterogeneous muscle types, termed slow-twitch muscle (SM) and fast-twitch muscle (FM), characterized by distinct morphological, anatomical, histological, biochemical, and physiological attributes, driving different swimming behaviors. Despite the central role of metabolism in regulating skeletal muscle types and functions, comprehensive metabolomics investigations focusing on the metabolic differences between these muscle types are lacking. To reveal the differences in metabolic characteristics between the SM and FM of teleost, we conducted an untargeted metabolomics analysis using Pseudocaranx dentex as a representative model and identified 411 differential metabolites (DFMs), of which 345 exhibited higher contents in SM and 66 in FM. KEGG enrichment analysis showed that these DFMs were enriched in the metabolic processes of lipids, amino acids, carbohydrates, purines, and vitamins, suggesting that there were significant differences between the SM and FM in multiple metabolic pathways, especially in the metabolism of energy substances. Furthermore, an integrative analysis of metabolite contents, enzymatic activity assays, and gene expression levels involved in ATP-PCr phosphate, anaerobic glycolysis, and aerobic oxidative energy systems was performed to explore the potential regulatory mechanisms of energy metabolism differences. The results unveiled a set of differential metabolites, enzymes, and genes between the SM and FM, providing compelling molecular evidence of the FM achieving a higher anaerobic energy supply capacity through the ATP-PCr phosphate and glycolysis energy systems, while the SM obtains greater energy supply capacity via aerobic oxidation. These findings significantly advance our understanding of the metabolic profiles and related regulatory mechanisms of skeletal muscles, thereby expanding the knowledge of metabolic physiology and ecological adaptation in teleost fish.

Adaptive Knowledge Distillation for High-Quality Unsupervised MRI Reconstruction With Model-Driven Priors.

Magnetic Resonance Imaging (MRI) reconstruction has made significant progress with the introduction of Deep Learning (DL) technology combined with Compressed Sensing (CS). However, most existing methods require large fully sampled training datasets to supervise the training process, which may be unavailable in many applications. Current unsupervised models also show limitations in performance or speed and may face unaligned distributions during testing. This paper proposes an unsupervised method to train competitive reconstruction models that can generate high-quality samples in an end-to-end style. Firstly teacher models are trained by filling the re-undersampled images and compared with the undersampled images in a self-supervised manner. The teacher models are then distilled to train another cascade model that can leverage the entire undersampled k-space during its training and testing. Additionally, we propose an adaptive distillation method to re-weight the samples based on the variance of teachers, which represents the confidence of the reconstruction results, to improve the quality of distillation. Experimental results on multiple datasets demonstrate that our method significantly accelerates the inference process while preserving or even improving the performance compared to the teacher model. In our tests, the distilled models show 5%-10% improvements in PSNR and SSIM compared with no distillation and are 10 times faster than the teacher.