Facilitate Knowledge Transfer Research Articles

A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis

High-quality labeled data are crucial prerequisites for ensuring the effectiveness of fault diagnosis methods based on deep learning technology. However, in practical scenarios, providing abundant training data with accurate labels for these approaches is unfeasible owing to the constraints imposed by the operating and working conditions. To tackle this realistic challenge, we propose an innovative feature separation simulation-assisted transfer framework (FSSATF) for the fault diagnosis of rotating machinery. The primary concept of FSSATF is to leverage dynamic simulation-assisted data as a surrogate for the labeled data of actual equipment and integrate the feature separation network to explicitly extract domain-independent and fault-discriminative features from the simulated and actual domains, facilitating knowledge transfer and enhancing fault diagnosis capabilities. Specifically, we design a feature separation network consisting of two feature extractors. The special feature extractor is trained with the proposed target domain classification loss to explicitly separate the noisy features from the actual data. Moreover, our proposed domain adaptive loss function effectively narrows the distribution discrepancy between the simulated and actual data, promoting the shared feature extractor to capture domain-invariant and fault-discriminative features. Additionally, clustering learning is embedded into FSSATF to minimize the distance between samples of the same category, strengthening the model’s capabilities for feature extraction, and improving its performance in real machinery fault diagnosis. Artificially damaged and run-to-failure datasets were employed to validate the effectiveness and superiority of FSSATF. The comparative analysis results demonstrate that the fault diagnosis performance surpasses those of other advanced transfer learning fault diagnosis methods.

Model for Knowledge Transfer in Agent Organizations: a Case Study on Moise+

Knowledge transfer enables the development of complex multi-agent systems featuring agents that share knowledge to execute tasks. Environments shaped by knowledge transfer involve agents assuming specific roles, reasoning about the environment and other agents, and forming organizations that transfer knowledge through well-defined plans and strategies. This paper presents an organizational model for knowledge transfer, introducing a centralizing role, named organizer, responsible for managing relations between the system, agents, and their roles. An interaction protocol is established to guide the step-by-step communication between the organizer and other agents in the system during knowledge transfer. To model knowledge transfer within agent organizations, we employ a dynamic implementation of the Moise+ organizational model, called MoiseLight, enabling the creation of organizations at runtime. We validate the proposal by adapting the model to MoiseLight, demonstrating the organizer's ability to facilitate knowledge transfer among agents following the specified interaction protocol.

Introducing a Novel Concept for an Integrated Demolition Waste Recycling Center and the Establishment of a Stakeholder Network: A Case Study from Germany

Using recycled aggregates has many positive environmental impacts because of the conservation of natural resources and minimization of waste. The use of recycled aggregates in downcycling processes is already common in Germany, whereas utilizing them to produce high-quality recycled concrete is rarely applied in practice. The reasons behind this lag have been investigated based on surveys and interviews with stakeholders. Miscommunication and missing information were identified in all stakeholder groups. Therefore, establishing a robust network and facilitating knowledge transfer by specifying the demand for recycled aggregates in the case study region have been considered as prerequisites. Therefore, the paper presents a novel concept of a stakeholder network for an integrated construction and demolition waste center. The conceptualization integrates the recycling companies and construction product manufacturers in one venue with research, service, and educational divisions. The design of the facilities is based on calculations regarding future construction activities and the demand for concrete production. The proposed concept aims to supply the region in the west of Germany with high-quality recycled products while also establishing a robust network that offers benefits in terms of logistical optimization and knowledge transfer.

Leveraging liminality: how the interim manager’s liminal position facilitates knowledge transfer to client organizations

Purpose With the rise of the gig economy, management positions are increasingly staffed with flexible labor, so-called interim managers. They plunge into organizations for a limited period, operating in a liminal position as partly insider, partly outsider. Although several contributions to their client organizations are acknowledged, it is unknown how the interim manager’s knowledge from previous assignments is made useful in the new context under these particular working conditions. Therefore, the purpose of this paper is to increase the understanding of how the interim manager’s knowledge is transferred to the client organization while operating from a liminal position. Design/methodology/approach This paper presents an interview-based multiple case study of six interim assignments where knowledge transfer is considered a social and context-dependent process. Findings The findings unveil the multifaceted nature of the liminal position, which consists of task orientation, time limitation, political detachment and cultural distance. These facets contribute to knowledge transfer in terms of new shared understandings and joint interests, which in turn might create new practices that augment continuous knowledge-sharing patterns. Originality/value The results contribute to the research on flexible work arrangements by shedding light on how the liminal position, predominantly depicted as an obstacle for the individual, might facilitate knowledge transfer. Through the process of knowledge generation, it is shown how a short-term engagement might enable the organization to increase its knowledge over time.

Sustaining Family Businesses through Business Incubation: An Africa-Focused Review

The influence of business incubation systems on family businesses in African economies has not been thoroughly investigated despite the potential contribution of family businesses to Africa’s economic expansion and the attainment of development goals outlined in the Africa Development Agenda 2063 and the Sustainable Development Goals. Therefore, this study investigates the potential benefits that family businesses in Africa can derive from engaging in business incubation. This study utilised an integrative literature review methodology to investigate the research question. Twenty-three peer-reviewed articles were systematically selected from the Scopus, Web of Science, and Google Scholar databases using the following combination of phrases: “family business” and either “business incubation” or “business incubator”. The findings suggest ways to create a mutually beneficial relationship between family businesses and business incubators to improve long-term sustainability, promote collaboration, facilitate knowledge transfer, and foster an entrepreneurial ecosystem. It also recognises challenges, such as cultural alignment in family businesses. Business incubators in Africa can improve the sustainability of family businesses, such as during the succession, by offering support, resources, and guidance. The South African experience is a role model for the rest of the continent, in this regard. Future research should broaden the sources beyond the three databases utilised, including non-peer-reviewed sources such as grey literature, and extend the focus beyond developing economies.

Manifold regularized deep canonical variate analysis with interpretable attribute guidance for three-phase flow process monitoring

Oil–gas–water three-phase flow has multiple flow states, exhibiting dynamic, nonlinear, and instantaneous behaviors. Monitoring and analysis of flow state are crucial for ensuring safe operation of industrial processes. However, the absence of a universal definition for three-phase flow states, owing to the complex flow characteristics and structures, leads to that labeling three-phase flow states by categories is impractical. For comprehensive monitoring and analysis, the flow states should be described from both global (e.g., the dominant phase) and local (e.g., the interphase structure) aspects. Given the aforementioned challenges, the work aims to address the accurate identification of typical three-phase flow states and to meticulously monitor and analyze the continuous evolutionary process of three-phase flow. To achieve it, the manifold regularized deep canonical variate analysis with attribute guidance (Ag-MRDCVA) is proposed, which not only introduces an innovative monitoring paradigm that designs state attributes for process description but also facilitates knowledge transfer from typical flow states to transition states. For enhancing the model’s feature embedding capabilities, an attribute guidance module is introduced to increase supervision information at both class and attribute levels. Then, convolutional neural network (CNN) backbones are developed with a dual objective of global serial correlation and local manifold regularization, which capture spatiotemporal information at both global and local scales, facilitating effective attribute encoding and characterization of flow states. Finally, the attribute evolution heat map and a monitoring metric (MM) collectively offer a compelling and comprehensive analysis of the three-phase flow process. Extensive experiments demonstrate that Ag-MRDCVA surpasses existing methods, showcasing its ability to minutely monitor the process while providing reasonable explanations.

Optimizing Image Recognition Efficiency using Sparse Representation Learning and Transfer Learning for Resource-Constrained Environments

Image recognition in resource-constrained environments is crucial for mobile devices, IoT, and embedded systems. It reduces the need for internet connectivity and enhances privacy and security. Real-time processing and decision-making are critical for applications such as facial recognition, object detection, and augmented reality. This approach is efficient, making it accessible in remote areas or situations with limited internet access. Our study proposes a novel approach using a network architecture with four modules, emphasizing sparse representation learning and transfer learning to improve image recognition efficiency in resource-constrained environments. The Domain-Adaptive Feature Extractor () facilitates effective sparse representation learning by projecting data from diverse domains into a shared space. The Transferable Affine Decoder () captures affine relationships between domains to facilitate knowledge transfer, while the Cross-Domain Correspondence Network () enforces pixel-level correspondence to extract shared intrinsic representations. The Efficient Classifier Network () enhances classification accuracy using efficient CNNs. The baseline model achieved an accuracy of 0.89. Improved Model 1, leveraging transfer learning, attained 0.92 accuracy, while Improved Model 2 with the Cross-Domain Correspondence Network reached 0.91. The Final Model, amalgamating all methodologies, excelled with the highest accuracy of 0.94. This holistic approach optimizes resource usage and enables real-time processing, thus empowering a diverse array of applications in resource-limited environments.

SFDA-CD: A Source-Free Unsupervised Domain Adaptation for VHR Image Change Detection

Deep models may have disappointing performance in real applications due to the domain shifts in data distributions between the source and target domain. Although a few unsupervised domain adaptation methods have been proposed to make the pre-train models effective on target domain datasets, constraints like data privacy, security, and transmission limits restrict access to VHR remote sensing images, making existing unsupervised domain adaptation methods almost ineffective in specific change detection areas. Therefore, we propose a source-free unsupervised domain adaptation change detection structure to complete specific change detection tasks, using only the pre-trained source model and unlabelled target data. The GAN-based source generation component is designed to generate synthetic source data, which, to some extent, reflects the distribution of the source domain. Moreover, these data can be utilised in model knowledge transfer. The model adaptation component facilitates knowledge transfer between models by minimising the differences between deep features, using AAM (Attention Adaptation Module) to extract the difference between high-level features, meanwhile we proposed ISM (Intra-domain Self-supervised Module) to train target model in a self-supervised strategy in order to improve the knowledge adaptation. Our SFDA-CD framework demonstrates superior accuracy over existing unsupervised domain adaptation change detection methods, which has 0.6% cIoU and 1.5% F1 score up in cross-regional tasks and 1.4% cIoU and 1.9% F1 score up in cross-scenario tasks, proving that it can effectively reduce the domain shift between the source and target domains even without access to source data. Additionally, it can facilitate knowledge transfer from the source model to the target model.

Contrastive domain-adaptive graph selective self-training network for cross-network edge classification

The performance of graph Neural Network (GNNs) can degrade significantly when trained on the graphs with noisy edges connecting nodes from different classes. To mitigate the negative effect of noisy edges, previous studies have largely focused on predicting the label agreement between node pairs within a single network. So far, predicting noisy edges across different networks remains largely underexplored. To bridge this gap, our work studies a novel problem of cross-network homophilous and heterophilous edge classification (CNHHEC), aiming to predict the label agreement of edges in an unlabeled target network by transferring the knowledge from a labeled source network. A novel Contrastive Domain-adaptive Graph Self-training Network (CDGSN) is proposed. Firstly, CDGSN learns node and edge embeddings end-to-end by a GNN encoder with adaptive edge weights during neighborhood aggregation. Secondly, to facilitate knowledge transfer across networks, CDGSN employs an adversarial domain adaptation module to align edge embeddings across networks, and also designs a novel contrastive domain adaptation module to conduct class-aware cross-network alignment of node embeddings. As a result, the intra-class domain divergence can be mitigated while the inter-class domain discrepancy can be enlarged to yield network-invariant and label-discriminative node and edge embeddings. Moreover, CDGSN designs a selective positive and negative pseudo-labeling strategy to assign positive (negative) pseudo-labels to the target nodes with extremely high (low) prediction confidence of belonging to each specific class. Such pseudo-labeled target nodes would be employed to iteratively re-train the model in a self-training manner, so as to obtain more reliable target pseudo-labels progressively to guide class-aware domain alignment. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed CDGSN on the CNHHEC problem. The performance of CDGSN is robust against various types of edge embeddings. When adopting three operators to construct edge embeddings, the proposed CDGSN can improve the state-of-the-art method for CNHHEC by an average of 0.5 %, 2.8 %, and 10.8 % in terms of AUC, and 1.3 %, 3.0 %, and 6.3 % in terms of AP, respectively.

Assessing wildfire exposure and social vulnerability at the local scale using a GIS-based approach

Exposure and vulnerability analysis are valuable tools for wildfire management, especially important for local communities that suffer from very destructive events and that require mitigation approaches adjusted to their abilities and needs. We present a methodological procedure to analyze wildfire exposure levels, social vulnerability conditions and coping capacity at the local scale, for villages or small human settlements. The procedure was developed using GIS (Geographic Information Systems) and programming tools in R and Python, which can be adapted and updated depending on the data available. The development of accessible procedures and easily replicable methodologies facilitates knowledge transfer and supports the application of mitigation and adaptation strategies, tailored to the conditions of the exposed areas.•A step-by-step procedure for the assessment of Exposure, Vulnerability, and Coping Capacity, using Python and R programming language.•Automated processes, easily replicable and adjustable to other areas.•Indications for adapting the methodology using European/international databases.

BEV-CFKT: A LiDAR-camera cross-modality-interaction fusion and knowledge transfer framework with transformer for BEV 3D object detection

The BEV-CFKT proposed in this paper leverages knowledge transfer through transformers for LiDAR-Camera fusion in the Bird’s-Eye-View (BEV) space, aiming to achieve accurate and robust 3D object detection. BEV-CFKT comprises three main components, which include the generation of BEV features from images and point clouds, cross-modality interaction, and hybrid object queries using a monocular detection head. By unifying features from both point clouds and images into the BEV space, we simplify modal interaction, facilitate knowledge transfer, and extract richer structural and semantic information from multimodal data. This effectively enhances the network’s performance. To further improve detection performance, BEV-CFKT incorporates a temporal fusion module. Additionally, a hybrid object queries module based on a monocular detection head accelerates the convergence of our model. We demonstrate the effectiveness of our approach through an extensive set of experiments.

Communities of learning and practice: a research agenda in tourism

Developing and coordinating knowledge within the tourism industry, education, and academia, as well as facilitating knowledge transfer, present significant challenges. This study introduces a novel concept – communities of learning and practice – to highlight the interconnected membership of the educational, academic, and industrial sectors within the same community. Additionally, this study proposes four research agendas in the field of tourism. Practically, this study suggests a pathway to bridge knowledge gaps among education, academia, and industry. Theoretically, it conceptualizes the notion of communities of learning and practice, expanding the current discourse on learning communities and communities of practice. Moreover, it provides a conceptual framework for future research concerning collaboration between education, academia, and industry.

Facilitating knowledge transfer to policy makers and front-line workers during a pandemic: Implementation, impact and lessons learned.

Stakeholders working on the COVID-19 pandemic response needed access to evidence, requiring a systematic approach to identify and disseminate relevant research. Outlinethe stages of development of a COVID-19 Literature Digest; demonstrate the impact the Digest had on decision-making and knowledge gain; identify the lessons learned. A standardised process was developed to identify and select papers. The main sources for content were PubMed, bioRxiv and medRxiv. A shared EndNote library was used to deduplicate and organise papers. Three user surveys obtained feedback from subscribers to determine if the Digest remained valuable, and explore the benefits to individuals. 40-60 papers were summarised each week. 211 Digests were produced from March 2020 to March 2022, with around 10,000 papers included altogether. Survey results suggest benefits of the Digest were gaining new knowledge, saving time and contributing to evidence-based decision making. Digest procedures constantly evolved and were adapted in response to survey feedback. Lessons identified: learn from failure, communication is key, measure your impact, work collaboratively, reflect and be flexible. The Digest was successfully produced within the limits of available resource. The learning from this Digest will inform evidence monitoring, selection and dissemination for future health crises.

Physics difficulty and problem-solving: Exploring the role of mathematics and mathematical symbols

Reports of difficulty in physics has been documented over the years, especially at the senior secondary level. The application of mathematics as a tool for understanding physical phenomena and problem-solving is well-established. The use of symbols and mathematical rigour is essential for effective problem-solving in physics. However, the teaching and learning of physics have encountered barriers, as highlighted in the literature on competencies in this field. This study focuses on exploring the interlink, context, and associated barriers in the teaching and learning of physics by reviewing existing literature on the application of mathematics and mathematical symbols. Through a theory synthesis design, the study examined the current state of literature on mathematical problem-solving in physics, as well as the differences between mathematising and the application of mathematics in physics. The competencies required of teachers and students were also highlighted in order to better equip physical sciences teachers to address the challenges faced by students in learning physics. The literature suggests that a well-sequenced approach to topics by both mathematics and physics teachers can facilitate knowledge transfer among students. Teachers are encouraged to provide step-by-step guidance to address students' mathematical deficiencies, particularly in the physics aspect of the physical sciences curriculum at the further education and training (FET) phase. It is recommended that topics between physics and mathematics be aligned and mathematical concepts be pre-teach to enhance students' contextual knowledge transfer.

Human-Robot handover task intention recognition framework by fusing human digital twin and deep domain adaptation

Industry 5.0 is conceived to achieve the expected high level of smart manufacturing and the more proactive and adaptive human-robot collaboration (HRC). In particular, human-robot handover tasks (HRHTs) are a notably crucial aspect of HRC, and resolving how to motivate robots to comprehend human handover intentions constitutes an exigent issue that demands resolution. To address these problems, A framework that integrates hierarchical human digital twin (HHDT) and deep domain adaptation is proposed, where HHDT model is built to digitise the human physical and physiological state and enhance the robot's perception of humans, a feature extractor-based on bidirectional long-short-term memory and spatio-temporal graph convolution network (BiLSTM-ST-GCN) is devised to extract latent spatio-temporal features of HRHTs intentions. Additionally, the framework incorporates a deep domain adaptation layer (DDAL) that facilitates knowledge transfer of HRHTs intentions by accounting for the distribution differences between the source and target operator objects. Extensive experiments demonstrate the superior performance of the proposed framework for HRHTs intention recognition and extraction of domain invariant features.

A configuration analysis on knowledge transfer between projects: from horizontal and vertical perspectives

PurposeDespite the knowledge transfer between projects has received increasing attention from scholars, few scholars still conduct comprehensive research on inter-project knowledge transfer from both horizontal and vertical perspectives. Besides, knowledge transfer is affected by multiple antecedent conditions, and these factors should be combined for analysis. Therefore, this paper aims to explore the key factors influencing knowledge transfer between projects using the fuzzy-set qualitative comparative analysis (fsQCA) method from both horizontal and vertical perspectives and how these factors combine to improve the effectiveness of knowledge transfer (EKT) between projects.Design/methodology/approachFirst, nine factors affecting knowledge transfer between projects were identified, which were from the four dimensions of subject, relationship, channel, and context, namely temporary nature (TN), time urgency (TU), transmit willingness (TW), receive willingness (RW), trust (TR), project-project transfer channels (PPC), project-enterprise transfer channels (PEC), organizational atmosphere (OA), and motivation system (MS). Then, the source of the samples was determined and the data from the respondents was collected for analysis. Following the operation steps of the fsQCA method, variable calibration, single condition necessity analysis, and configuration analysis were carried out. After that, the configurations of influencing factors were obtained and the robustness test was conducted.FindingsThe results of the fsQCA method show that there are five configurations that can obtain better EKT between projects. Configuration 3 (∼TN * ∼TU * TW * RW * TR * ∼PPC * PEC * MS) has the highest consistency, indicating that it has the highest degree of the explanatory variable subset. Configuration 1 (∼TN * ∼TU * TW * RW * PEC * OA * MS) has the highest coverage, meaning that this configuration can explain most cases. Also, the five configurations were divided into three types: vertical transfer, horizontal-vertical transfer, and channel-free transfer category.Originality/valueFirstly, this study explores the key factors influencing knowledge transfer between projects from four dimensions, which presents the logical chain of influencing factors more clearly. Then, this study divided the five configurations obtained into three categories according to the transfer direction: vertical, horizontal-vertical, and channel-free transfer, which gives implications to focus on both horizontal knowledge transfer (HKT) and (VKT) when studying knowledge transfer between projects. Lastly, this study helps to realize the exploration of combined improvement strategies for EKT, thereby providing meaningful recommendations for enterprises and project teams to facilitate knowledge transfer between projects.

Improving HPV-Related Oropharyngeal Cancer Knowledge and HPV Vaccination Rates via Virtual Workshops.

To validate the efficacy of a virtual, interdisciplinary workshop in improving HPV knowledge, and vaccination practices among primary care and dental trainees. A virtual platform was utilized to hold HPV educational workshops with primary care and dental trainees. Online surveys were distributed before and after the 1-h interactive workshop, led by otolaryngology residents. Surveys included the workshop's impact on (a) improving knowledge of HPV-associated oropharyngeal squamous cell carcinoma (OPSCC) and (b) affecting change in HPV vaccination practices. The vaccination rates of trainees were recorded prior to and after workshop attendance. After the workshop, participants demonstrated significantly improved scores related to knowledge of HPV in the head and neck (p = 0.003) and showed an increased comfort level with counseling on HPV vaccination (p = 0.002). Respondents were also more aware that the HPV vaccine is approved to prevent OPSCC (61% vs. 95%, p < 0.05). Ninety-seven percent of respondents stated that the workshop changed their HPV vaccination practices, and 95% of those not fully vaccinated stated they would now be more likely to receive the vaccine themselves. There was a significant increase in the average number of HPV vaccines administered at the studied trainee clinic, from 16.83 vaccines/month to 37.6 vaccines/month (percent increase = 123%) in 5 months following the workshop (p = 0.002). The present interactive virtual workshop demonstrates efficacy in improving HPV-related knowledge and vaccination practices among trainees. The virtual nature of the course facilitates knowledge transfer and can be used to foster multi-institutional partnerships regarding medical education and vaccination efforts. 4 Laryngoscope, 134:3096-3101, 2024.

FedRDR: Federated Reinforcement Distillation-Based Routing Algorithm in UAV-Assisted Networks for Communication Infrastructure Failures

Traditional Internet of Things (IoT) networks have limited coverage and may experience failures due to natural disasters affecting critical IoT devices, making it difficult for them to provide communication services. Therefore, how to establish network communication service more efficiently in the presence of fault points is the problem we solve in this paper. To address this issue, this study constructs a hierarchical multi-domain data transmission architecture for an emergency network with unmanned aerial vehicles (UAVs) employed as core communication devices. This architecture expands the functionality of UAVs as key network devices and provides a theoretical basis for their feasibility as intelligent network controllers and switches. Firstly, the UAV controllers perceive the network status and learn the spatio-temporal characteristics of air-to-ground network links. Secondly, a routing algorithm within the domain based on federated reinforcement distillation (FedRDR) is developed, which enhances the generalization capability of the routing decision model by increasing the training data samples. Simulation experiments are conducted, and the results show that the average communication data size between each domain controller and the server is approximately 45.3 KB when using the FedRDR algorithm. Compared to the transmission of parameters through federated reinforcement learning algorithms, FedRDR reduces the transmitted parameter size by approximately 29%. Therefore, the FedRDR routing algorithm helps to facilitate knowledge transfer, accelerate the training process of intelligent agents within the domain, and reduce communication costs in resource-constrained scenarios for UAV networks and has practical value.

The Impact of Transfer Learning on AI Performance Across Domains

This study investigates the profound impact of transfer learning on the performance of artificial intelligence (AI) models when applied across diverse domains. Transfer learning, a machine learning technique that leverages knowledge gained from one task to improve performance on a related task, has demonstrated remarkable success in various applications. The article explores the underlying principles of transfer learning, its mechanisms, and the ways in which it enhances AI performance. The findings highlight the potential of transfer learning to facilitate knowledge transfer between domains, reduce training data requirements, and accelerate model convergence, ultimately contributing to the broader adaptability and efficiency of AI systems

Climate Modelling and Analytics for Urban Heat Risks Mitigation and Adaptation

This study highlights the need for climate-sensitive urban planning and design in the face of climate change, with a specific focus on Singapore. Rapid urbanization has led to significant warming trends, increased heat stress, and heightened electricity demand for cooling. The Urban Climate Design Lab (UCDL) at the National University of Singapore employs a multidisciplinary approach, merging urban planning, architecture, and urban climate science. The research introduces GIS-based tools to evaluate the microclimate impact of new developments, replacing time-consuming simulations and wind tunnel experiments. These tools encompass: 1) The Urban Wind Environment Model, assessing urban permeability for natural ventilation; 2) The Fine-Scale Wind Environment Model, providing high-resolution pedestrian-level wind speed data. 3) The Urban Tree-Airflow Model, aiding tree placement and species selection for optimal cooling. 4) The Anthropogenic Heat Dispersion Model, estimating the impact of human-generated heat emissions. These GIS tools are integrated into the open-access UCDL Microclimate Digital Platform, facilitating knowledge transfer and empowering stakeholders in climate-sensitive urban planning. The platform offers various climate models and visualization capabilities to enhance evidence-based decision-making for urban climate sustainability and resilience. In the future, the platform will expand its offerings, becoming a valuable resource for urban planners, engineers, health practitioners, environmental experts, and residents adapting to a changing climate.