Adaptive Knowledge Research Articles

Facilitating learning for innovation in a climate-stressed context: insights from flash flood-affected rice farming in Bangladesh

ABSTRACT Purpose Facilitation of learning enhances innovation through overcoming innovation barriers and supporting learning outcomes. However, little is known about how public Extension and Advisory Services (EAS) facilitate learning to help adapt to particular climate stressors. This article investigates the role of public EAS in facilitating learning to enhance innovation in a flash flood-affected farming context. Design/methodology/approach The research adopted flash flood-affected rice farming in Bangladesh as a case and collected data with actors involved in various extension approaches using interviews and focus group discussions. Findings Public EAS should involve a range of relevant actors, including the private sector and scientists, and jointly evaluate with farmers and provide feedback on the effectiveness of various crop cultivation strategies for flash flood adaptation. Public EAS needs to deliver the necessary instrumental support and resources to achieve learning outcomes and enable farmers to make desirable changes to farm activities. Practical implications Policy makers need to develop policies for the capacity development of public EAS staff and provide adequate resources so that public EAS can facilitate learning approaches to support discussions on local concerns and the use of local knowledge, experiences, and resources for flash flood adaptation. Theoretical implications Facilitation of learning to support adoption of technological innovations is not sufficient in the context of flash flood adaptation. Facilitation should support discussions on effective utilisation of natural and common resources for the flash flooding context. Originality/ value The study investigated the ways public EAS can facilitate learning to overcome barriers to innovation and support learning outcomes in a flash flooding context.

THEORETICAL AND METHODOLOGICAL ANALYSIS OF PREPARATION OF FUTURE SOCIAL WORKERS AND PSYCHOLOGISTS TO WORK WITH STUDENTS HAVING FUNCTIONAL HEALTH LIMITATIONS

The article presents the results of a study on the formation of professional competencies of future social workers and psychologists to work with students with functional health limitations. The aim of the study was to analyze the theoretical and methodological approaches in the training of social workers, psychologists and development, justification of organizational and content of training, determining the criteria and indicators of readiness of future social workers and psychologists to work with students with functional health limitations in higher education. The main research methods were to determine a group of theoretical methods (retrospective-comparative, systematization, synthesis, generalization, abstraction, theoretical modeling) and empirical (pedagogical experiment, expert questioning and evaluation, psychological and pedagogical observation, conversation, testing, questionnaires, interviews), as well as methods of mathematical statistics. The essence of the main criteria of readiness of future social workers and psychologists to work with students with functional limitations are following: information-cognitive, technological-procedural, organizational-integration, psychological-personal and readiness indicators. Thus, the indicators of information and cognitive criteria we have identified: the possession of basic knowledge of adaptation, socialization, integration of students with functional health limitations. Indicators of technological and procedural criteria determine: the use of computer information, Internet technology, social pedagogical interactive technologies, and also technologies of creation of barrier-free space of higher educational institutios. Organizational and integration criteria - readiness for professional social activity in the higher educational institutios; volunteer activity; scientific, academic socio-pedagogical mobility of future social workers. Psychological and personal criteria – the formation of emotional stability, positive life orientation and leadership qualities of the future social worker. The obtained data of the formative experiment confirmed the effectiveness of the proposed system of professional and pedagogical training of future social workers and psychologists to work with students with functional health limitations in higher education. Keywords: socialization, integration, adaptation, professional training, functional limitations.

Language-Agnostic Knowledge Representation for a Truly Multilingual Semantic Web

As the Internet users’ communities and the information on the web are increasing exponentially, that too associated with a wide range of different cultures and languages; meaningful search in a multilingual setting is required. It is crucial to develop a simple to use, granular, and adaptable knowledge representation system that could constitute the core of Semantic Web. It should be well-prepared for multilingual and multicultural settings by virtue of internationalizing the code. In this work, a model for ontology-based language-agnostic Semantic Web application architecture is offered which is independent of today’s heterogeneous encoding of language resources and provides smooth exploitation. The suggested system architecture enables a quick and meaningful search for information independent of the end user’s preferred language and cultural conventions, an easy extension of the application with new information, as well as its easy localization for new language contexts. The model’s usability is proven by a prototype application simulation in the domain of Indian biodiversity.

Acidic and Alkaline Conditions Affect the Growth of Tree Peony Plants via Altering Photosynthetic Characteristics, Limiting Nutrient Assimilation, and Impairing ROS Balance.

Exposure to acidic and alkaline conditions were found to cause the excess accumulation of reactive oxygen species in tree peony, thereby causing damage and inhibiting plant growth and development. The activities of antioxidant enzymes were also found to be significantly up-regulated, especially under alkaline conditions; this explained why tree peony is better adapted to alkaline than to acidic conditions. Through pairwise comparisons, 144 differentially expressed genes (DEGs) associated with plant growth, photosynthesis, and stress were identified. The DEGs related to stress were up-regulated, whereas the remaining DEGs were almost all down-regulated after acid and alkaline treatments. The nutrient assimilation was greatly inhibited. Chlorophyll synthesis genes were suppressed, and chlorophyll content was reduced. The development and structures of stomata and chloroplasts and the transcription of related genes were also influenced. Among photosynthesis-related DEGs, electron transport chains were the most sensitive. The suppressed expression of photosynthesis genes and the reduced light-harvesting capacity, together with the impairment of chloroplasts and stomata, finally led to a sharp decrease in the net photosynthetic rate. Carbohydrate accumulation and plant biomass were also reduced. The present study provides a theoretical basis for the response mechanisms of tree peony to adverse pH conditions and enriches knowledge of plant adaptation to alkaline conditions.

Problem-based Learning: Improving Students’ Concept Mastery and Learning Activities

Concept mastery is the based ability of higher-order thinking skills in the era of society 5.0. Increased understanding of the concept is in line with the practice of dependent on active learning activities by students. The purpose of the study was to determine the effect of problem-based learning on the concept mastery and learning activities of students. The research method used is a quasi-experimental research design with a non-equivalent pretest-posttest control group. The sample had selected through purposive random sampling. The instrument used is a conceptual understanding test instrument with an indicator of adaptation knowledge dimensions from Bloom's taxonomy and a learning activity instrument of a questionnaire. In the results of this study, there are differences between the experimental class and the control class on understanding concepts and learning activities of students; the PBL model increase students' concept mastery

Editorial: High-Quality Knowledge for Climate Adaptation: Revisiting Criteria of Credibility, Legitimacy, Salience, and Usability

Editorial: High-Quality Knowledge for Climate Adaptation: Revisiting Criteria of Credibility, Legitimacy, Salience, and Usability

Communication-efficient federated learning via knowledge distillation

Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw data. However, model updates can be extremely large if they contain numerous parameters, and many rounds of communication are needed for model training. The huge communication cost in federated learning leads to heavy overheads on clients and high environmental burdens. Here, we present a federated learning method named FedKD that is both communication-efficient and effective, based on adaptive mutual knowledge distillation and dynamic gradient compression techniques. FedKD is validated on three different scenarios that need privacy protection, showing that it maximally can reduce 94.89% of communication cost and achieve competitive results with centralized model learning. FedKD provides a potential to efficiently deploy privacy-preserving intelligent systems in many scenarios, such as intelligent healthcare and personalization.

Perception, physiological and psychological impacts, adaptive awareness and knowledge, and climate justice under urban heat: A study in extremely hot-humid Chongqing, China

Urban heat, the combined effect of heatwaves and urban heat islands (UHIs), is a severe challenge for many cities around the world. While there have been numerous studies on urban heat, society's understanding of it is still insufficient, hindering its mitigation and adaptation. This paper aims to investigate people's vulnerability to, and perception, awareness and knowledge of, urban heat. A questionnaire survey was conducted with 562 respondents in the hot and humid city of Chongqing, China in the summer of 2020. Data were analysed using descriptive statistics, Mann-Whitney U test, Kruskal-Wallis H test and logistic regression. Results indicated that urban heat is generally understood as having a moderate severity, while there is limited knowledge of heat-related risks. The perceived heat-related psychological impacts are more severe than physiological impacts. There is limited awareness and knowledge of heat-impact reduction methods. Nevertheless, people's awareness, perception and knowledge of urban heat increase once they suffer heat-induced impacts, indicating exposure/experience-driven awareness and knowledge. Moreover, climate injustice among different groups (e.g. gender, age, education, income, health) of people related to heat challenges was identified. People's perception, vulnerability, awareness and knowledge increased with age, but interestingly decreased with increasing education level and improved health conditions. Economic factor was not critical to heat-related responses. Men could be more vulnerable to physiological symptoms and daily functioning than women. The results of this study provide an understanding of urban heat perception and adaptive knowledge, enabling practitioners and policy makers to formulate effective urban heat mitigation and adaptation policies and regulations.

The Livelihood of Chinese Migrants in Timor-Leste

This article aims to describe, in depth, the experiences of migrant workers from China in maintaining their livelihoods in Timor-Leste through entrepreneurial activities before and during the COVID-19 pandemic. Specifically, this study discusses sustainable livelihoods with the knowledge of migration, adaptation, and entrepreneurship in Timor-Leste. To fulfill these aims, a qualitative methodology with a phenomenological approach was adopted by interviewing eight Chinese migrants who run businesses in Timor-Leste. Furthermore, the framework for the concept of sustainable livelihoods of the Department for International Development (DFID) was adapted to explain, in depth, the phenomena. The finding reveals that economic reasons encourage and attract Chinese migrant workers and entrepreneurs to Timor-Leste. The adaptation process of migrants in Timor-Leste relies on social capital to synergize with local communities in terms of culture, further affecting the comfort and security of entrepreneurship. From the perspective of sustainable livelihoods, migrant workers take advantage of human capital in the process of accumulation of financial capital to enhance their business activities. Meanwhile, migrant entrepreneurship expands their financial capital and human capital into social capital, physical capital, and natural capital. The findings also explain that the mobility and access to financial capital of Chinese migrants are supported through institutional structures, entrepreneurship activities, and policies. Furthermore, to ensure livelihood sustainability and business, Chinese migrants in Timor-Leste can carry out business intensification, extensification, diversification, and transformation strategies.

460 Adaptive Capacity and Preparedness of CTSAs: The Environmental Scan Approach and Findings

OBJECTIVES/GOALS: Illuminate processes and findings of the Environmental Scan of Adaptive Capacity and Preparedness of CTSAs done by the CTSA Working Group. Share challenges, strategies, and lessons learned for CTSAs to build Capacity to address clinical and translational barriers while responding to emergencies. METHODS/STUDY POPULATION: An Environmental Scan approach for searching, collecting, analyzing and using information from diverse sources regarding CTSA hubs experiences during emergency as related to research implementation, translation, support, adaptation, and preparedness: - Triangulating multiple data sources and mixed methods (e.g., literature review, document/RPPR analysis, and expert review); - Secondary analysis of the JCTS COVID-19 Survey of the CTSAs: challenges, lessons learned, and practices that work in various program components/areas; - Using feedback of CTSA professionals from multiple disciplines to enhance our knowledge of emergency preparedness and efficient adaptation to public health and other crises. RESULTS/ANTICIPATED RESULTS: The E-scan approach helped identify challenges, successful practices, and evidence-based strategies for building adaptive capacity and preparedness of CTSAs across various scientific sectors of the translational science spectrum. Some of the findings include: - Roadmaps for the creation of new collaborative research resources (biobanks; data repositories, etc.); - Rapid clinical and research decision making during public health crises; - New community-based research strategies to facilitate communication, research dissemination, and participant recruitment based on existing trust-based networks; - Innovative resource allocation to guarantee continuity of training and research opportunities for trainees. DISCUSSION/SIGNIFICANCE: The Environmental Scan of the Adaptive Capacity of CTSA hubs provides useful knowledge and tools to diverse clinical research stakeholders for mitigating the impact of a disaster via adjusting programs, practices, and processes, and building capacity for effective, emergency-ready and responsive research, training, and community engagement.

Evolutionary Multitask Optimization With Adaptive Knowledge Transfer

Evolutionary multitask optimization (EMTO) studies how to simultaneously solve multiple optimization tasks via evolutionary algorithms (EAs) while making the useful knowledge acquired from solving one task to assist solving other tasks, aiming to improve the overall performance of solving each individual task. Recent years have seen a large body of EMTO works based on different kinds of EAs and studying one or more aspects in how to represent, extract, transfer, and reuse knowledge. A key challenge to EMTO is the occurrence of negative knowledge transfer between tasks, which becomes severer when the total number of tasks increases. To address this issue, we propose an adaptive EMTO (AEMTO) framework. This framework can adapt knowledge transfer frequency, knowledge source selection, and knowledge transfer intensity in a synergistic way to make the best use of knowledge transfer, especially when facing many tasks. We implement the proposed AEMTO framework and evaluate our implementation on three suites of MTO problems with 2, 10, and 50 tasks and one real-world MTO problem with 2000 tasks in comparison to several state-of-the-art EMTO methods with certain adaptation strategies regarding knowledge transfer and the single-task optimization counterpart of the proposed method. Experimental results have demonstrated the effectiveness of the adaptive knowledge transfer strategies used in AEMTO and the overall performance superiority of AEMTO.

Mitchell’s Building Construction goes to China: the impact of British Polytechnics, construction teaching and construction textbooks in Shanghai (1870-1937)

ABSTRACT Taking the localization of Mitchell’s Building Construction in Shanghai as a case study and a starting point and discussing British polytechnics, construction teaching and construction textbooks, this paper examines the transmission of technical knowledge to Chinese builders in Modern Shanghai. The paper provides the first systematic examination of Mitchell’s professional biography and the first review of the editions of his Building Construction textbooks. The paper shows that Mitchell’s textbooks built on rapidly evolving scientific knowledge in building technology in Britain, and how their success was attributed to the time, his construction pedagogy, teir constant updates and their remarkable contribution to the building industry. A comparative analysis between Mitchell’s books and Du’s technical Dictionary and Yingzao Xue from an educational perspective, reveals Du’s insights into Mitchell’s powerful pedagogical tools of construction textbook writing, bridging different construction cultures – terminology learning, visual learning, and adaptive knowledge learning. Drawing from one of the most successful models of construction textbooks, Du’s contribution was to create a wholly new work – a distinct mixture that filled in the gaps and created a new discourse of Chinese Building Construction for the new global-local building industry in Shanghai

We want our children to survive: An interview with Sharon Nelson-Barber

Sharon Nelson-Barber, a sociolinguist, directs Culture & Language in STEM Education within WestEd’s Science and Engineering content area. She is co-founder of POLARIS—Pacific/Polar Opportunities to Learn, Advance and Research Indigenous Systems—a research and development network that supports healthy communities by integrating Indigenous perspectives with new frontiers of knowledge that strengthen educational transformation. In this interview, we talk to Nelson-Barber about the Indigenous communities she works with and is a part of in the Pacific and Polar regions and the pressing climate change stories that illustrate the seriousness and urgency of adaptation. In the context of climate-induced displacement of Indigenous homelands, Nelson-Barber emphasizes the importance of engaging multiple knowledge systems when thinking and strategizing around climate change education. Thinking within the tension between standardization and localization, Nelson-Barber underscores how careful collaboration with Indigenous elders, Knowledge Keepers, and communities is vital for adaptation knowledge to be passed down within communities and for education systems to be responsive to local contexts.

Broader environmental adaptation of rare rather than abundant bacteria in reforestation succession soil

Revealing the responses of rare and abundant bacteria to environmental change is crucial for understanding microbial community assembly and ecosystem function. However, both the environmental adaptability and the ecological assembly processes exhibited by rare and abundant soil bacteria remain poorly understood. Here we investigated the assembly processes of rare and abundant bacteria along a chronosequence of a 35-year reforestation succession (8, 17, and 35 years), particularly with regard to their environmental adaptations. Compared to the abundant taxa, the phylogenetic clustering of rare taxa was tighter but their environmental breadth wider. Homogeneous selection (65.8%) belonging to deterministic processes dominated the rare bacterial assembly, whereas homogenizing dispersal and undominated process (57.9%) belonging to stochastic processes governed the abundant taxa. Neutral processes had a significant impact on shaping the rare taxa compared to the abundant taxa. Rare taxa were environmentally less constrained than abundant taxa. Soil EC was the major determinant factor for the assembly processes of both rare and abundant taxa. Ecological assembly processes showed a significant negative correlation with rare bacterial functional redundancies, while they had a significant positive correlation with the abundant taxa. Microbial network modularity further demonstrated that rare taxa developed stronger environmental adaptation strategies than their abundant counterparts. Our study significantly advances the knowledge of the environmental adaptability of rare and abundant bacteria and emphasizes their key role in reforestation ecological succession soils.

Transferring Structured Knowledge in Unsupervised Domain Adaptation of a Sleep Staging Network.

Automatic sleep staging based on deep learning (DL) has been attracting attention for analyzing sleep quality and determining treatment effects. It is challenging to acquire long-term sleep data from numerous subjects and manually labeling them even though most DL-based models are trained using large-scale sleep data to provide state-of-the-art performance. One way to overcome this data shortage is to create a pre-trained network with an existing large-scale dataset (source domain) that is applicable to small cohorts of datasets (target domain); however, discrepancies in data distribution between the domains prevent successful refinement of this approach. In this paper, we propose an unsupervised domain adaptation method for sleep staging networks to reduce discrepancies by re-aligning the domains in the same space and producing domain-invariant features. Specifically, in addition to a classical domain discriminator, we introduce local discriminators - subject and stage - to maintain the intrinsic structure of sleep data to decrease local misalignments while using adversarial learning to play a minimax game between the feature extractor and discriminators. Moreover, we present several optimization schemes during training because the conventional adversarial learning is not effective to our training scheme. We evaluate the performance of the proposed method by examining the staging performances of a baseline network compared with direct transfer (DT) learning in various conditions. The experimental results demonstrate that the proposed domain adaptation significantly improves the performance though it needs no labeled sleep data in target domain.

The influence of local background climate on the dominant factors and threshold-size of the cooling effect of urban parks

Urban parks can mitigate the urban heat island (UHI) by creating microclimates that lower in temperature than their surroundings, which are known as park cooling effect (PCE). The local background climate has a significant impact on the PCE, however the dominant factors and threshold value of efficiency (TVoE) of the PCE under different local background climates are still uncertain. Here, we selected 207 urban parks in 27 cities in East China with four different local background climates, warm temperate sub-humid monsoon (WTC), northern subtropical sub-humid monsoon (NSC), northern subtropical humid monsoon (NHC), and middle subtropical humid monsoon climate (MSC), for comparative studies. The relative contributions of multi-influencing factors to the PCE and TVoE of urban parks were quantified through a multivariate stepwise regression model and curve fitting. The results show that: (1) PCE increases from WTC, NSC, NHC to MSC, and urban parks at low latitudes have a greater cooling effect in general than those at high latitudes; (2) the area of the park is the dominant factor of PCE under four different local background climates (the explanation rate exceeds 50%) and water bodies within urban parks play a more significant role in the cooling effect in high latitudes, dry areas; (3) the TVoE of park on WTC, NSC, NHC, and MSC are 0.81, 0.71, 0.70, and 0.66 ha, respectively, revealing that the background climate significantly affects the TVoE. These findings are essential to decision-makers and can provide actionable knowledge for climate adaptation planning on a regional (climate) scale.

Automated Transformation from Competency List to Tree: Way to Competency-Based Adaptive Knowledge E-Evaluation

E-learning is rapidly gaining its application. While actively adapting student-oriented learning with the competency evaluation model, the standard of competency support in existing e-learning systems is not implemented and varies. This complicated integration of different e-learning systems or transfer from one system to another might be challenging if the student had his or her competency portfolio in list form, while another system supports tree-based competency portfolios. Therefore, in this paper, we propose a transformation model dedicated to converting the competency list to a competency tree. This solution incorporates text processing and analysis, competency ranking based on Bloom’s taxonomy, and competency topic area clustering. The case analysis illustrates the model’s capability to generate a qualitative tree from the competency list, where the average accuracy of competency assignment to appropriate parent competency is 72%, but, in some cases, it reaches just 50%.

Adaptive Knowledge Transfer by Continual Weighted Updating of Filter Kernels for Few-Shot Fault Diagnosis of Machines

Deep learning (DL) based diagnosis models have to be trained by large quantities of monitoring data of machines. However, in real-case scenarios, machines operate under the normal condition in most of their life time while faults seldom happen. Therefore, though massive data are accessible, most are data of the normal condition while fault data are still extremely limited. In other words, fault diagnosis of real machines is actually a few-shot diagnosis problem. To deal with few-shot diagnosis, this article proposes adaptive knowledge transfer with multiclassifier ensemble (AKTME) under the paradigm of continual machine learning. In AKTME, knowledge learned by DL models is considered to be represented by the learnable filter kernels (FKs). The key of AKTME is a proposed continual weighted updating (CWU) technique of FKs. By CWU, shared FKs are distilled from multiple auxiliary tasks and adaptively transferred to the target task. Then by multiclassifier ensemble, AKTME is able to recognize faults with few fault data accessible. AKTME is applied on two few-shot diagnosis cases. Results verify that AKTME achieves higher diagnosis accuracies than recently proposed methods. Moreover, AKTME tends to improve the diagnosis accuracy as it prelearns on more auxiliary tasks continually.

Population genomics provides insights into lineage divergence and local adaptation within the cotton bollworm.

The cotton bollworm Helicoverpa armigera is a cosmopolitan pest and its diverse habitats plausibly contribute to the formation of diverse lineages. Despite the significant threat it poses to economic crops worldwide, its evolutionary history and genetic basis of local adaptation are poorly understood. In this study, we de novo assembled a high-quality chromosome-level reference genome of H. a. armigera (contig N50=7.34Mb), with 99.13% of the HaSCD2 assembly assigned to 31 chromosomes (Z-chromosome + 30 autosomes). We constructed an ultradense variation map across 14 cotton bollworm populations and identified a novel lineage in northwestern China. Historical inference showed that effective population size changes coincided with global temperature fluctuation. We identified nine differentiated genes in the three H. armigera lineages (H. a. armigera, H. a. conferta and the new northwestern Chinese lineage), of which per and clk genes are involved in circadian rhythm. Selective sweep analyses identified a series of Gene Ontology categories related to climate adaptation, feeding behaviour and insecticide tolerance. Our findings reveal fundamental knowledge of the local adaptation of different cotton bollworm lineages and will guide the formulation of cotton bollworm management measures at different scales.

The Taiwan Climate Change Projection Information and Adaptation Knowledge Platform: A Decade of Climate Research

Taiwan’s climate change projections have always presented a challenge due to Taiwan’s size and unique meteorological and geographical characteristics. The Taiwan Climate Change Projection Information and Adaptation Knowledge Platform (TCCIP), funded by the Ministry of Science and Technology, Taiwan, is a decade-long climate research project with the most predominant climate data provider and national adaptation policymaking in the country. This paper outlines the evolution of the project. It describes the project’s major achievements, including climate projection arising from participation in the WCRP Coupled Model Inter-comparison Project (CMIP), dynamically and statistically downscaled data with resolutions up to 5 km grid, impact assessments of various themes, such as flooding, as well as the support of national policies through approaches including risk maps, climate data, and knowledge brokering.