Adaptive Knowledge Research Articles

MFWOA: Multifactorial Whale Optimization Algorithm

Multi-task optimization is an emerging research topic in the field of evolutionary computation, which can exploit the synergy between tasks to solve multiple optimization problems simultaneously and efficiently. However, the correlation and negative transfer problems between tasks are the main challenges faced by multi-task optimization. To this end, this paper proposes a new multi-task optimization algorithm, named Multifactorial Whale Optimization Algorithm (MFWOA). MFWOA uses the Whale Optimization Algorithm (WOA) as a search mechanism and designs an adaptive knowledge transfer strategy to effectively exploit the correlation between tasks. This strategy includes two ways: one is to exchange search experience by adding distance terms from other tasks; the other is to generate new random individuals or optimal individuals through crossover and mutation operations and use them to guide position updates. By combining these two methods, MFWOA can explore a wider area. In addition, in order to better balance the useful information transfer between and within tasks, MFWOA also designs a random mating probability parameter adaptive strategy. Experimental results show that MFWOA can achieve effective and efficient knowledge transfer, and outperforms other multi-task optimization algorithms in terms of convergence speed and accuracy. It is a promising multi-task optimization algorithm.

Evolution of g-type lysozymes in metazoa: insights into immunity and digestive adaptations

Exploring the evolutionary dynamics of lysozymes is critical for advancing our knowledge of adaptations in immune and digestive systems. Here, we characterize the distribution of a unique class of lysozymes known as g-type, which hydrolyze key components of bacterial cell walls. Notably, ctenophores, and choanoflagellates (the sister group of Metazoa), lack g-type lysozymes. We reveal a mosaic distribution of these genes, particularly within lophotrochozoans/spiralians, suggesting the horizontal gene transfer events from predatory myxobacteria played a role in their acquisition, enabling specialized dietary and defensive adaptations. We further identify two major groups of g-type lysozymes based on their widespread distribution in gastropods. Despite their sequence diversity, these lysozymes maintain conserved structural integrity that is crucial for enzymatic activity, underscoring independent evolutionary pathways where g-type lysozymes have developed functionalities typically associated with different lysozyme types in other species. Specifically, using Aplysia californica as a reference species, we identified three distinct g-type lysozyme genes: two are expressed in organs linked to both feeding and defense, and the third exhibits broader distribution, likely associated with immune functions. These findings advance our understanding of the evolutionary dynamics shaping the recruitment and mosaic functional diversification of these enzymes across metazoans, offering new insights into ecological physiology and physiological evolution as emerging fields.

Cold Tolerance and Physiological Response of Natural Overwintering Pomacea canaliculata in South China.

Pomacea canaliculata (Lamarck 1822), a freshwater gastropod indigenous to lower Del Plata Basin of Argentina, has become the most destructive and invasive rice pests in south China since its introduction in the 1980s. In Guangdong, the main production areas for double rice, most of P. canaliculata overwinter in paddy field ditches after late-rice harvesting in mid-November and diapause to temporarily to avoid the damaging effects of extreme low temperatures. This pest aroused from diapause and migrated to the paddy field after early-rice reviving in next late March. Overwintering and cold tolerance of natural P. canaliculata have a non-negligible impact on population dynamics and distribution in the following year. We tested the supercooling capability, levels of cryoprotectant synthesis, activity of antioxidant defense system (antioxidant enzymes and reduced glutathione), and degree of oxidative damage (concentration of malondialdehyde as an index of lipid peroxidation) monthly, using natural P. canaliculata samples with a size-gender structure (i.e., juveniles, female, and male adults) from experimental ponds during the period of mid-November to the following April. P. canaliculata survived the winter with a monthly death rate of 7%-16.5% in coldest January. The supercooling point (SCP) of overwintering P. canaliculata decreased initially before increasing subsequently with monthly changes in water temperature. P. canaliculata accumulated a high glycogen content before December, which depleted towards the end of January, while lipid content reached peak in January and depleted since February. Activity of antioxidant defense system of P. canaliculata exhibited significant monthly differences and showed relatively higher size heterogeneity than monthly variations. The results contribute to the knowledge of adaptability in overwintering P. canaliculata and help to understand the mechanism of the invasive success of this species.

Corrections to "Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation".

Presents corrections to the paper, (Corrections to "Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation").

Adaptive Knowledge Recomposition for Personalized Federated Learning via Discrete Wavelet Transform

Adaptive Knowledge Recomposition for Personalized Federated Learning via Discrete Wavelet Transform

Integrating small data and shape prior knowledge with gradient-enhanced Kriging through adaptive knowledge sampling

Integrating prior knowledge into surrogate models constitutes an advanced approach to tackling the challenges posed by small data. The existing gradient-enhanced Kriging (GEK) method can enhance accuracy by incorporating gradient points of shape prior knowledge. Nonetheless, our findings indicate that the strategy employed for setting gradient points significantly impacts accuracy, even when the number of gradient points remains constant. To enhance the efficient utilization of gradient information, we propose a novel Adaptive Knowledge Sampling (AKS) strategy with an innovative acquisition function that balances local exploitation and global exploration, effectively extracting gradient points from shape prior knowledge. The acquisition function consists of three parts: (1) The Local Information Filling Criterion (LIFC), designed to mitigate model error; (2) The Global Information Filling Criterion (GIFC), aimed at reducing model uncertainty in data-sparse areas; and (3) The Minimum Distance Constraint (MDC), implemented to prevent excessive clustering of gradient points. To strike a balance between model accuracy and computational cost, an adaptive stopping condition is introduced to regulate the number of gradient points sampled. The impact of hyperparameters in the AKS strategy was analyzed in detail, and the method’s performance was compared to existing approaches using six benchmark functions and a missile aerodynamic prediction case. The results demonstrate that AKS-GEK offers higher prediction accuracy and robustness when integrating small data with shape prior knowledge.

Mobilizing Global Change Science for Effective Multi-Actor Governance in the Laguna San Rafael and Guayaneco Biosphere Reserve

The 1950s initiated transformative shifts in human interactions and societal behaviors, exacerbating global environmental challenges—notably, biodiversity loss. The post-2020 Kunming–Montreal Global Biodiversity Framework (GBF) addressed these challenges with ambitious plans to halt and reverse biodiversity losses. Supported by initiatives like UNESCO’s Man and the Biosphere program, the GBF seeks to enhance sustainability through country-level strategies that will mainstream nature-positive policies and expand multi-actor conservation governance. This study supports the local-level implementation of the GBF through a roadmap for the initial phase of the knowledge-action network creation. Through a case study of the Laguna San Rafael and Guayaneco Biosphere Reserve (LSRGBR) in Chilean Patagonia, this research explores the potential for inexpensive, readily available methods to support local decision makers by increasing access to and the visibility of relevant sustainability research. The study analyzes two decades of global change (GC) research within LSRGBR zones to understand spatial trends and identify applied insights with the potential to inform governance and management strategies. Findings highlight where GC research has occurred, areas of GC research interest, how applied content has manifested, and how existing research can inform and support governance action plans. Ultimately, this research proposes an adaptable knowledge mobilization framework for the LSRGBR that can be applied to a variety of place-based needs and contexts to mobilize science for broader sustainability objectives and enhance the potential for multi-actor collaboration and governance.

Overcoming Catastrophic Forgetting in Continual Fine-Grained Urban Flow Inference

Citywide fine-grained urban flow inference (FUFI) problem aims to infer the high-resolution flow maps from the coarse-grained ones, which plays an important role in sustainable and economic urban computing and intelligent traffic management. Previous models tackle this problem from spatial constraint, external factors, and memory cost. However, utilizing the new urban flow maps to calibrate the learned model is very challenging due to the “catastrophic forgetting” problem and is still under-explored. In this article, we make the first step in FUFI and present CUFAR—Continual Urban Flow inference with augmented Adaptive knowledge Replay—a novel framework for inferring the fine-grained citywide traffic flows. Specifically, (1) we design a spatial-temporal inference network that can extract better flow map features from both local and global levels; (2) then, we present an augmented adaptive knowledge replay (AKR) training algorithm to selectively replay the learned knowledge to facilitate the learning process of the model on new knowledge without forgetting. We apply several data augmentation techniques to improve the generalization capability of the learning model, gaining additional performance improvements. We also propose a knowledge discriminator to avoid the “negative replaying” issue introduced by noisy urban flow maps. Extensive experiments on two large-scale real-world FUFI datasets demonstrate that our proposed model consistently outperforms strong baselines and effectively mitigates the forgetting problem.

A functional trait-based assessment of urban street tree selection for Ethiopia

Urbanization in Ethiopia is rapidly reducing green space and biodiversity, and placing ecological stress on trees. Urban street trees (UST) can provide multiple ecosystem services, but need to cope with challenging street ecology and future climate risks. However, UST selection in Ethiopia is poor, and conventional trials and modern breeding are costly and time consuming to apply in Ethiopia. The main objective of the study was to identify potential UST species for two Ethiopian cities; Addis Ababa, and Arba Minch, by studying interspecific differences in functional traits. For the purpose, we created a database with list of 120 candidate UST gathered from multi-stakeholder workshop, literature review, and senior experts survey, and their ecological with their phenological and morphological features. Then we filtered 25 potential UST through framework analysis considering eco-morphological features of the trees. For the 25 species, we measured three types of plant functional traits: conservative traits (leaf mass per area and leaf dry matter content), acquisitive traits (leaf area, specific leaf area and leaf water content) and drought tolerance traits (leaf turgor pressure loss point, and leaf succulence index). Our results showed a strong trade-off between conservative and acquisitive traits, particularly between leaf dry matter content (LDMC) and specific leaf area (SLA) (r = −0.71), consistent with plant economic spectrum theory. Using PCA analysis, we identified three groups of potential USTs (Type I, II and III) that exhibit different adaptation strategies, consistent with Grime’s trait-based classification of universal plant adaptation strategies (CSR). Compared to species with high acquisitive traits (Type II and Type III), Type I species have high conservative and drought-tolerant traits shows better adaptation to harsh road environments, whereas Type II and Type III species may be more advantageous for urban parks and other resource-rich components of urban ecology. We also found significant variation in conservative, acquisitive and drought-tolerant traits among different species, indicating their different adaptation strategies. Our research advances the knowledge of plant adaptation in urban environments and provides a useful method for UST selection.

Promoting Machine Abilities of Discovering and Utilizing Knowledge in a Unified Zero-shot Learning Paradigm

Knowledge discovery and utilization are two essential cognitive processes that enable humans to understand the world and extract new insights from their surroundings. These processes have motivated machine learning studies, particularly zero-shot learning (ZS), which seeks to identify unseen concepts through the use of side information. Previous ZS studies primarily focused on utilizing existing knowledge to infer unseen events, yet they overlook the crucial process of knowledge discovery and the integrated modeling of these knowledge-aware processes. In this study, we present a comprehensive ZS learning approach that explores and evaluates the machine's abilities of discovering and utilizing knowledge. More specifically, to emulate human-like knowledge discovery and utilization processes, we propose a novel visual-aware ZS knowledge graph completion task for evaluation, incorporating a traditional ZS image classification task. Technically, we develop a unified ZS learning paradigm named Cognitive Learner (CoLa) to foster the two knowledge-aware abilities. Including a knowledge representation learning (KRL) module and a knowledge adaptation (KA) module, CoLa adapts well to the two specified tasks with the corresponding data. Extensive experiments on large-scale datasets demonstrate CoLa models’ outstanding performance over compared methods in the two ZS tasks, illustrating their superior ability of discovering and utilizing knowledge.

Accumbal Dopamine Responses Are Distinct between Female Rats with Active and Passive Coping Strategies.

There is a gap in existing knowledge of stress-triggered neurochemical and behavioral adaptations in females. This study was designed to explore the short-term consequences of a single social defeat (SD) on accumbal dopamine (DA) dynamics and related behaviors in female Wistar rats. During the SD procedure, rats demonstrated different stress-handling strategies, which were defined as active and passive coping. The "active" subjects expressed a significantly higher level of activity directed toward handling stress experience, while the "passive" ones showed an escalated freezing pattern. Remarkably, these opposite behavioral manifestations were negatively correlated. Twenty-four hours following the SD exposure, decreased immobility latency in the Porsolt test and cognitive augmentation in the new object recognition evaluation were evident, along with an increase in electrically evoked mesolimbic DA release in passive coping rats. Rats exhibiting an active pattern of responses showed insignificant changes in immobility and cognitive performance as well as in evoked mesolimbic DA response. Furthermore, the dynamics of the decline and recovery of DA efflux under the depletion protocol were significantly altered in the passive but not active female rats. Taken together, these data suggest that female rats with a passive coping strategy are more susceptible to developing behavioral and neurochemical alterations within 24 h after stress exposure. This observation may represent both maladaptive and protective responses of an organism on a short timescale.

Novel Sex-Specific Genes and Diverse Interspecific Expression in the Antennal Transcriptomes of Ithomiine Butterflies.

The olfactory sense is crucial for organisms, facilitating environmental recognition and interindividual communication. Ithomiini butterflies exemplify this importance not only because they rely strongly on olfactory cues for both inter- and intra-sexual behaviors, but also because they show convergent evolution of specialized structures within the antennal lobe, called macroglomerular complexes (MGCs). These structures, widely absent in butterflies, are present in moths where they enable heightened sensitivity to, and integration of, information from various types of pheromones. In this study, we investigate chemosensory evolution across six Ithomiini species and identify possible links between expression profiles and neuroanatomical. To enable this, we sequenced four new high-quality genome assemblies and six sex-specific antennal transcriptomes for three of these species with different MGC morphologies. With extensive genomic analyses, we found that the expression of antennal transcriptomes across species exhibit profound divergence, and identified highly expressed ORs, which we hypothesize may be associated to MGCs, as highly expressed ORs are absent in Methona, an Ithomiini lineage which also lacks MGCs. More broadly, we show how antennal sexual dimorphism is prevalent in both chemosensory genes and non-chemosensory genes, with possible relevance for behavior. As an example, we show how lipid-related genes exhibit consistent sexual dimorphism, potentially linked to lipid transport or host selection. In this study, we investigate the antennal chemosensory adaptations, suggesting a link between genetic diversity, ecological specialization, and sensory perception with the convergent evolution of MCGs. Insights into chemosensory gene evolution, expression patterns, and potential functional implications enhance our knowledge of sensory adaptations and sexual dimorphisms in butterflies, laying the foundation for future investigations into the genetic drivers of insect behavior, adaptation, and speciation.

Towards Defining Climate Adaptation Literacy in the Context of Urban Resilience

Abstract The climate crisis intensifies rapidly, straining the urban environment and society. Addressing climate change requires collaborative efforts, integrating physical resilience with knowledge empowerment. Formal and informal knowledge are vital for effective adaptation. In the updated Intergovernmental Panel on Climate Change (IPCC) AR6 Report an emphasis on knowledge and its role in reducing response risk was highlighted. At the urban scale, adaptation knowledge is essential, bridging social and technical systems. Here, community knowledge evolves from a passive component within a greater adaptation strategy to an active, integral element. This paper develops a unique definition of climate adaptation literacy for the urban built environment based on an extensive literature review on urban climate adaptation, guided by the PISA (Program for International Student Assessment) 2025 Framework. The results of this work highlight existing gaps in climate adaptation knowledge and present urban climate adaptation literacy as a means to measure the response risk. These insights aid the formulation of resilience policies and urban adaptation strategies and contribute to the safety, resilience, sustainability, and functionality of the material and non-material values of the urban environment

Constructing Ubudehe? Farmers’ perceptions of drought impacts and resilience capacities in Bugesera, Rwanda

Purpose This paper analyses Rwandan farmers’ perceptions of historical drivers of landscape vulnerability (past), current livelihood assets (present) and existing or potential capacities (future) to increase resilience to drought. The specific focus is on linking experiences from the past and present with ideas for a drought-resilient future. It explores how farmers' perceptions of past droughts and future visioning can contribute to rural development policy and multi-level collaborations. Design/methodology/approach This study was conducted in Bugesera, a drought-prone district in south-eastern Rwanda. Empirical data was collected through participatory observation, semi-structured interviews and focus groups. The analytical points of departure are based on sustainable landscapes and livelihood approaches, combining spatial and temporal perspectives on challenges and opportunities identified by farmers’ communities in addressing droughts. Findings All respondents had a high awareness of the impact of droughts. Perceived drivers of landscape change include historical climate events, such as droughts and floods, immigration and agricultural expansion, which have led to demographic pressure on land, deforestation and infringement on natural resources. Factors enhancing resilience capacities include access to diversified sources of livelihood, knowledge of appropriate irrigation techniques and availability of safety nets and credits. Furthermore, farmers identified collaborative opportunities as important for resilience capacity, including peer learning, and sharing best practices through knowledge exchange and on-field training. In addition, farmers brought up the need for innovative institutions that can facilitate access to markets and enable collaboration between different agricultural sectors. Originality/value This study analyses farmers’ perceptions of resilience capacities to droughts through a spatiotemporal lens of past droughts, present capital and future challenges by linking scales, knowledge and human–environment nexus. This paper contributes to the knowledge of climate adaptation in Rwanda and to discussions about smallholder farming in the literature on climate change adaptation.

Predictors of climate change literacy in the era of global boiling: a cross-sectional survey of Egyptian nursing students

BackgroundClimate changes have led to health and environmental risks, so it has become essential to measure climate change literacy among the entire population, especially nursing students. The significant role of nursing students in raising public awareness and future healthcare roles emphasizes assessing the predictors of climate change literacy among nursing students.AimsThis study seeks to identify the predictors of climate change literacy among nursing students in A Multi-Site Survey.DesignA multi-site descriptive cross-sectional study adheres to the guidelines outlined in A Consensus-Based Checklist for Reporting Survey Studies collected for five months, from the 1st of July 2023 to November 2023. The study participants comprise 10,084 nursing students from all 27 governments in Egypt. The researcher used the Predictors of Nursing Students’ Climate Change Literacy scale in this study. Data was collected, with 25 min average time to complete. Backward multiple linear regression was used to identify these predictors.ResultsIn the current study, nursing students demonstrated a moderate understanding of climate science (mean score 14.38), communication and advocacy skills (mean score 14.41), and knowledge of adaptation and mitigation strategies (mean score 13.33). Climate health impacts (mean score 17.72) emerged as the domain with the highest level of knowledge. No significant differences in climate literacy were observed across diverse student backgrounds (all p-values were > 0.05). Perceived faculty knowledge of climate change positively correlated with all four domains of climate literacy and emerged as a significant predictor in multiple linear regression analyses (all p-values were < 0.001).Implication.While our findings highlight significant predictors of climate literacy, it is essential to recognize that these results identify associations rather than causal relationships. Based on these associations, it is recommended that nursing professionals be equipped with comprehensive knowledge of climate adaptation strategies to better advocate for and implement effective public health measures.

Revisiting oxygen toxicity: evolution and adaptation to superoxide in a SOD-deficient bacterial pathogen.

Defenses against oxidants are crucial for the virulence of pathogens, with superoxide scavenging enzymes (SOSEs) playing a vital role for most aerobes. However, our knowledge of superoxide adaptation primarily stems from the study of SOSE-encoding bacteria. Here, we investigated the evolution of a naturally SOSE-deficient pathogen ( Leptospira spp.), along with the alternative mechanisms it recruits to combat superoxide stress. We demonstrate that emergence of pathogenic Leptospira correlated with SOD loss, but that a long-lasting adaptation to superoxide remains possible. We reveal that cysteine and leucine biosynthesis are the most induced pathways in response to superoxide and demonstrate the importance of sulfur metabolism in superoxide adaptation in this SOSE-deficient model. We also propose cysteine oxidation as a key mediator of superoxide toxicity in the absence of SOSEs. This study challenges our conventional understanding of the oxygen toxicity theory and proposes a new model of superoxide adaptation through metabolic rewiring in bacteria.

Possibilities of Application of Adaptive Knowledge Testing Using Artificial Neural Networks in Training Economics Students

Possibilities of Application of Adaptive Knowledge Testing Using Artificial Neural Networks in Training Economics Students

Climate Change Impact and Traditional Adaptation Practices in Northeast India: A Review

The review explores the intricate traditional knowledge (TK) nurtured by the diverse indigenous communities of Northeast (NE) India, emphasizing on their adaptive strategies amidst the region's evolving climate patterns. NE India faces a multitude of climate change-induced risks, including earthquakes, landslides, flash floods, and thunderstorms/lightning, highlighting the critical role of indigenous knowledge in mitigating these challenges. Through generations of experience, indigenous cultures have amassed a treasure trove of traditional ecological knowledge, particularly pertinent in NE India's context. Given the region's vulnerability to climate change impacts, these indigenous practices are indispensable. The review provides an in-depth analysis of these aspects, shedding light on recent climate change impacts, prevalent issues, and the indigenous adaptation strategies in NE India. Recognizing the significance of indigenous knowledge in climate change adaptation, the review stresses the importance of integrating it with scientific knowledge to enhance adaptation effectiveness. It extensively covers various aspects of traditional farming systems, watershed governance, the preservation of biodiversity, and traditional medical practices in NE India. However, the region faces challenges from growing development, the industrial revolution, and devastation, emphasizing an urgent need to document, evaluate, and preserve indigenous cultures' traditional ecological knowledge (TEK).

Effects of extension service on the uptake of climate-smart sorghum production practices: Insights from drylands of Ethiopia

The promotion of climate-resilient practices (CRPs) requires the development of the capacity of farmers to adopt these practices owing to the knowledge-intensive nature of technologies. Extension services serve as a conduit for facilitating the conceptualization of CRPs and are instrumental in improving the resiliency and mitigation of climate change. We used a social-ecological framework and a multivariate probit model to analyze the drivers of the CRP uptake in moisture-stressed areas in Ethiopia, with a particular focus on extension services. Unlike previous studies that investigated a single technology, we considered a bundle of technologies. We focused on the use of two capital-intensive CRPs (drought-resistant seed and inorganic fertilizer) and four knowledge-intensive CRPs (minimum tillage, farmyard manure, water-saving technology, and crop residue retention). The role of extension services in promoting other CRPs beyond input and capital-intensive technologies was insignificant. Heterogeneity analysis revealed that the correlation between extension services and the adoption of other knowledge-intensive natural resource management practices holds irrespective of the proximity to the extension service providers. This finding highlights the need for targeted and tailored interventions that support farmers to address the challenges faced by them in moisture-stressed areas. Accordingly, we propose continuously improving the ability of the extension service providers to promote climate-change adaptation knowledge and practices. This should be accompanied by efforts to strengthen a pluralistic extension system, improve land tenure security, and decrease transaction costs for farmers through output market linkages.

Adaptive Knowledge Contrastive Learning with Dynamic Attention for Recommender Systems

Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and supervised labels. This paper argues that existing knowledge-graph-based recommendation methods still suffer from insufficiently exploiting sparse information and the mismatch between personalized interests and general knowledge. This paper proposes a model named Adaptive Knowledge Contrastive Learning with Dynamic Attention (AKCL-DA) to address the above challenges. Specifically, instead of building contrastive views by randomly discarding information, in this study, an adaptive data augmentation method was designed to leverage sparse information effectively. Furthermore, a personalized dynamic attention network was proposed to capture knowledge-aware personalized behaviors by dynamically adjusting user attention, therefore alleviating the mismatch between personalized behavior and general knowledge. Extensive experiments on Yelp2018, LastFM, and MovieLens datasets show that AKCL-DA achieves a strong performance, improving the NDCG by 4.82%, 13.66%, and 4.41% compared to state-of-the-art models, respectively.