Adaptability Research Articles

Ecological indication potential of oribatid mites

Ecological indication potential of oribatid mites has been emphasized by researchers for several decades. These small arthropods possess biological characteristics through which they are able to indicate different changes in their environment. Oribatid mites can be found in almost every kind of habitat worldwide: terrestrial and aquatic habitats, and a lot of types of microhabitats (e.g, soil, lichen, moss, tree bark, foliage). Apart from the diversity of habitats, their huge adaptation ability is also shown by great abundance and species richness. In most habitats, they constitute the largest proportion of microarthropods. This study deals with works whose results point to the characteristics of oribatid mites that would allow their wider use in the ecological indication than at present.

Digital knowledge management effect on enterprise technological innovation: an empirical study from China’s manufacturing industry

ABSTRACT With digital technology permeating across the layers of businesses, digital knowledge management has emerged, transforming the means through which companies acquire, integrate, share, and innovatively apply knowledge. However, the specific impact of digital knowledge management on corporate technological innovation remains unclear. Based on dynamic capabilities theory, this study delves into how digital knowledge management influences corporate technological innovation. Analyzing data from manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2007 to 2021, this study confirms that digital knowledge management significantly and positively affects corporate technological innovation by enhancing a company's absorptive capacity, adaptability, and innovation ability. Based on the findings, this study suggests that companies should leverage digital knowledge management to acquire and integrate external innovative resources, seek cooperation opportunities with external stakeholders, and jointly build a mutually beneficial innovation knowledge network, thereby boosting technological innovation capabilities. Additionally, companies should encourage employees to learn digital technologies and create a culture conducive to learning and innovation.

Exploratory and confirmatory factor analysis of digital entrepreneur skills

Research in the field of entrepreneurship has become an intriguing area for further investigation. In an era where digital advancements are ubiquitous, previous researchers have identified a gap: the lack of a specific instrument to identify digital entrepreneur skills. Therefore, this study aims to determine the constructs of students’ digital entrepreneur skills. The analysis techniques include confirmatory factor analysis (CFA), exploratory factor analysis (EFA), Pearson correlation, Kendall’s, Spearman’s, and Cronbach’s alpha. The study was conducted over five phases with 235 participants from university students in Indonesia who run or own a business. The results revealed strong instrument validity with robustness ranging from significant Pearson, Kendall’s, and Spearman’s analyses (<0.05) and Cronbach’s alpha (>0.60). The exploratory factor analysis indicated a Kaiser-Meyer-Olkin value of 0.954 (>0.05) and Bartlett’s test (0.000 < 0.05), with all items having values of anti-image (>0.50), communalities (> 0.50), and factor loadings (> 0.40), uncovering three components. Lastly, the CFA demonstrated an overall good fit model, with high first-order factor loadings (>0.60) and a second-order construct digital entrepreneur skills comprising three components: interpersonal digital skills with a factor loading of 0.890, idea and technology management skills (0.920), and adaptation and innovation skills (1.020). The study highlights the critical need for students to develop digital entrepreneurial skills encompassing interpersonal digital skills, ideation, and technology management, as well as adaptation and innovation abilities to thrive in the digital economy and enhance their personal and professional growth. AcknowledgmentI want to thank all those who have helped make this research a success, especially for Sebelas Maret University as a place of affiliation and all respondents willing to participate in my study.

Academic Buoyancy: The Necessary Ability for Teenagers to Succeed in School

With the continuous reform of the education system, the academic problems of teenagers are increasing. In recent years, academic buoyancy has attracted the attention of most scholars. At the same time, it plays a positive role in solving the academic problems of teenagers as the adaptability and coping ability shown by students in the face of academic challenges and difficulties. This paper aims to explore the influence of academic buoyancy on the academic success of adolescents. First, the relevant concepts of academic buoyancy are expounded. Secondly, it introduces the relationship between academic buoyancy, academic emotion, academic achievement and learning engagement. Finally, the author puts forward corresponding strategies to guide teenagers to cope with academic pressure from four aspects: themselves, families, schools and society, which is of great significance for teenagers to improve their academic performance.

Electrochromic and thermal-control broadband stealth device based on AZO/Ag/AZO configuration electrodes

Existing stealth technologies have limitations including single bands and functions, and static and passive applications, so that targets cannot remain stealthy if multi-source detection approaches are used. With the wide application of visible-infrared (Vis-IR) detection and guidance technologies, there is an urgent need to develop advanced techniques for visible smart stealth and Vis-IR compatible stealth. In view of this, an electrochromic and thermal-control broadband stealth device based on AZO/Ag/AZO configuration electrodes was developed. The influencing mechanisms of microstructural characteristics of the conducting layer in the AZO/Ag/AZO configuration on the visible transmission spectra and infrared (IR) reflection spectra were revealed. Furthermore, a conducting layer enabling IR stealth with high visible transmittance was optimized, on which a V2O5 multi-color electrochromic film layer was prepared via self-assembled deposition. In this way, the device integrates multiple optical mechanisms including the visible-induced transmission, IR radiation suppression, and electrochromism to modulate optical properties in multiple bands. A technical route that integrates visible smart stealth and active suppression of IR radiation was established, which is conducive to improving the adaptive fusion ability under a multi-domain background and multi-band compatible optical stealth.

Internet of Things intrusion detection: Research and practice of NSENet and LSTM fusion models

To address the problems of complex environment, limited device computational resources and limited memory resources in the existing IoT, SELSTM, an intrusion detection system composed of NSENet and LSTM fusion based on SENet, is investigated. The NSENet part of the SELSTM system is based on the squeeze-and-excitation network (SENet). The lightweight computational modules NonLocal, SKConv and inverted residuals are fused into SE blocks, and self-attention of Nonlocal is used to improve the local receptive field of feature extraction. The channel attention and spatial attention of each part of the data are strengthened by the use of SKConv To enhance the adaptive convolution ability of the model and ensure the completeness of the information, the properties of the inverted residual structure are used to ensure that the gradient of the model decreases steadily without gradient explosion or disappearance. For the problem of data imbalance, the dataset is randomly resampled using the weight resampling technique to improve the balance of the dataset to ensure that the final detection effect of the model is more effective and generalized, while the data flow is divided into two parts for processing, and the model parameters are optimized using the model gradient optimizer consisting of the optimizer Lion and the optimization function Lookahead. The model extracts the spatial and temporal features of the data through multidimensional extraction to ensure the completeness of the data feature information in multiple dimensions, thus obtaining better detection results. The results of the experiments comparing the SELSTM model with other models on the intrusion dataset show that the intrusion detection model has a higher detection precision and accuracy than the traditional deep learning intrusion detection model, which indicates that the SELSTM has better detection performance properties and better practicality and effectiveness on IoT devices.

Classification of lung cancer subtypes on CT images with synthetic pathological priors

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case’s CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the “gold standard” information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), positive predictive value (PPV) and F1-score.

Semi-supervised multi-modal medical image segmentation with unified translation

The two major challenges to deep-learning-based medical image segmentation are multi-modality and a lack of expert annotations. Existing semi-supervised segmentation models can mitigate the problem of insufficient annotations by utilizing a small amount of labeled data. However, most of these models are limited to single-modal data and cannot exploit the complementary information from multi-modal medical images. A few semi-supervised multi-modal models have been proposed recently, but they have rigid structures and require additional training steps for each modality. In this work, we propose a novel flexible method, semi-supervised multi-modal medical image segmentation with unified translation (SMSUT), and a unique semi-supervised procedure that can leverage multi-modal information to improve the semi-supervised segmentation performance. Our architecture capitalizes on unified translation to extract complementary information from multi-modal data which compels the network to focus on the disparities and salient features among each modality. Furthermore, we impose constraints on the model at both pixel and feature levels, to cope with the lack of annotation information and the diverse representations within semi-supervised multi-modal data. We introduce a novel training procedure tailored for semi-supervised multi-modal medical image analysis, by integrating the concept of conditional translation. Our method has a remarkable ability for seamless adaptation to varying numbers of distinct modalities in the training data. Experiments show that our model exceeds the semi-supervised segmentation counterparts in the public datasets which proves our network’s high-performance capabilities and the transferability of our proposed method. The code of our method will be openly available at https://github.com/Sue1347/SMSUT-MedicalImgSegmentation.

Infrared small target detection algorithm based on filter kernel combination optimization learning method

Infrared small target detection in complex backgrounds remains a big challenge due to its small size and weak energy, which makes it easy to be submerged in complex backgrounds. Common filtering algorithms are difficult to apply to various complex scenes and generate many false alarms and poor robustness. Therefore, this paper proposes an infrared small target detection algorithm based on filter kernel combination optimization learning method. For the sample image block use typical filter kernels and weight coefficients to calculate the combination of filtering results, and construct the loss function of the results and the true value. The optimal weight coefficients are learned using an optimization method, which effectively achieves the fusion of multiple features, then we build a sample library containing comprehensive priori information. In the implementation, an online inference method based on similarity measure is proposed to match test images with sample library image blocks to obtain optimal combination weights. By utilizing the comprehensive priori information to enhance the adaptive ability of the algorithm for various complex backgrounds. Finally, get the position of the infrared small target. The experimental results show that our algorithm has excellent detection performance for infrared small targets with complex background and low signal to clutter ratio, and the AUC value is 0.9980, which is more robust than other comparative algorithms.

Can a “Pandemic Life Adaptation” Digital Technology Curriculum Reduce the Digital Disadvantage of Older Adults During COVID-19? An Intervention Study from Shanghai

ABSTRACT Older adults are at a digital disadvantage because of social stereotypes and a lack of social support; however, smartphones have become a necessary technology to cope with crises and daily life in China, especially during the pandemic. This study aimed to help marginalized older adults take on new tasks by developing digital technology education that used a framework of social cognitive theory in social work. The study followed a quasi-experimental design in which 153 elderly people were recruited from three community service centers; 90 of the participants received 6-weekly intervention. Intent-to-treat analysis, effect size calculations, and sensitivity analysis were conducted. The findings show that digital education significantly enhanced two domains of digital life adaptation abilities: general digital life adaptation abilities [g = .50, 95% CI (.70, 2.69)] and pandemic digital life adaptation abilities [g = .89, 95% CI (.96, 2.07)]. The intervention also improved three domains of digital self-efficacy: sharing and communication [g = .55, 95% CI (.04, .48)], verification [g = .34, 95% CI (.01, .59)], and influencing others [g = .53, 95% CI (.13, .77)]. The study showed that the new intervention approach reduced the harm to vulnerable older adults in the digital wave, especially during the pandemic.

Application of self-learning interval type-2 fuzzy neural network in PM2.5 concentration prediction

Abstract The change of PM2.5 concentration in air quality is nonlinear and difficult to predict. Therefore, a self-learning interval type-2 fuzzy neural network (SLIT2FNN) is proposed. SLIT2FNN has two parts: online structure learning and parameter learning. In structure learning, to improve the training accuracy and speed of the model, the Possibilistic Fuzzy C-Means (PFCM) algorithm is used to process the input data and obtain the number of initial rules before model training. The PFCM algorithm introduces the concept of possibility P to Fuzzy C-Means (FCM), allowing PFCM to overcome the shortcomings of FCM that cannot accurately cluster a large number of nonlinear problems. SLIT2FNN can establish an appropriate number of rules in the preparation stage, and then use the firing strength of the antecedents of the rules to judge whether to generate fuzzy rules for online self-learning, thereby optimizing its network structure. Then, the improved Levenberg–Marquardt (ILM) algorithm is used to modify the relevant parameters of SLIT2FNN. The ILM algorithm can address the challenge of numerous parameters in the Jacobian matrix and complex calculations and improve the calculation speed and adaptive ability of SLIT2FNN parameter learning. Finally, SLIT2FNN is applied to the prediction of air quality PM2.5 concentration, and the performance is compared with other models. The experiment proves that SLIT2FNN has a high prediction accuracy and fast convergence.

Impacts of an expert’s opinion on the collective performance of a competing population for limited resources

The dissemination of information and opinion to the public has never been easier due to the easy access to platforms, making it necessary to study the responses to public information by a competing population. We study the impact of an expert’s opinion on the performance of a population including some die-hard fans of the expert and cautious agents who will adapt to trust or to ignore the expert’s opinion, within the framework of the minority game (MG). The expert collects information by probing a subpopulation for its minority opinion without the knowledge of its composition. When the information is not disseminated, the expert’s opinion has a higher accuracy than the intrinsic success rate of the population. When the information is disseminated, the population could enhance its success rate by making use of the wasted winning quotas through the actions of the fans and the adaptive ability of the cautious agents, as long as the fraction of the fans is not too big to turn the expert’s opinion into the losing option. There exists an optimal fraction of followers, the fans plus those cautious agents adapted to trust the expert, for the population to be the most harmonious with the highest success rate allowed by MG. The phenomena observed by numerical simulations motivated us to develop a theory, using the intrinsic success rate SrI, a quantity related to how the expert collects his information, and the average adapted level of trust f¯ among the cautious agents as inputs. The theory gives excellent agreement with simulation results, even when an approximated form of f¯ is invoked. The work paves the way for studying and formulating theories for the dissemination of other forms of information and opinion.

A Dynamic Network with Transformer for Image Denoising

Deep convolutional neural networks (CNNs) can achieve good performance in image denoising due to their superiority in the extraction of structural information. However, they may ignore the relationships between pixels to limit effects for image denoising. Transformer, focusing on pixel to pixel relationships can effectively solve this problem. This article aims to make a CNN and Transformer complement each other in image denoising. In this study, we propose a dynamic network with Transformer for image denoising (DTNet), with a residual block (RB), a multi-head self-attention block (MSAB), and a multidimensional dynamic enhancement block (MDEB). Firstly, the RB not only utilizes a CNN but also lays the foundation for the combination with Transformer. Then, the MSAB adds positional encoding and applies multi-head self-attention, which enables the preservation of sequential positional information while employing the Transformer to obtain global information. Finally, the MDEB uses dimension enhancement and dynamic convolution to improve the adaptive ability. The experiments show that our DTNet is superior to some existing methods for image denoising.

A new method for unmanned aerial vehicle path planning in complex environments

To solve the problems of UAV path planning, such as low search efficiency, uneven path, and inability to adapt to unknown environments, this paper proposes A double-layer optimization A* and dynamic window method for UAV path planning. Firstly, the neighboring node clip-off rule is defined to optimize the node expansion mode of the A* algorithm, and the obstacle coverage model is designed to dynamically adjust the heurizing function of the A* algorithm to improve the path search efficiency. Then, the Bresenham algorithm is adopted for collision detection and critical path nodes are extracted to significantly reduce the number of path turning points. Secondly, a new tracking index is proposed to optimize the evaluation function of the dynamic window method to make the local path fit the global path further. By detecting the dangerous distance, the dynamic adaptive method of evaluation function weight is designed to improve the fixed weight of the dynamic window method. Finally, the key turning point of optimizing the A* algorithm is taken as the temporary target point to improve the DWA algorithm, and the local part follows the global part, and the fusion of the two algorithms is realized. Simulation results show that the proposed method can significantly improve the efficiency and smoothness of mobile robot path planning, enhance the real-time obstacle avoidance and adaptive ability of unknown environments, and better meet the requirements of complex planning tasks.

The development of research skills in physics laboratory works of secondary school students in an information and education environment

Relevance. The relevance of this research lies in the indispensable role of laboratory work in physics education. Laboratory sessions facilitate the integration of knowledge, research, and practical skills, fostering essential qualities such as independence, analytical thinking, and the application of theoretical concepts to real-world situations. As comprehensive schools evolve, there is a growing need to employ laboratory work to enhance students' independence, adaptability, and creative problem-solving abilities in physics. Laboratory work is a critical method for developing research skills and occupies a leading position in organizing activities that develop complex skills. Purpose. The purpose of this study is to analyze the relevance and feasibility of using mobile technology in physics laboratory work, considering current societal and technological advancements. The study aims to explore how contemporary information learning tools can enhance the cognitive and thinking functions of students and establish an interactive dialogue for effective information perception. Methodology. The research uses analytical methods to explore integrating mobile technology in physics labs. Results. The study finds that the comprehensive application of technical tools, in combination with traditional laboratory and demonstration equipment, can introduce new didactic possibilities. These possibilities are currently underdeveloped but hold significant potential for enhancing interactive interaction within the information and educational environment. The use of mobile technology in laboratory work motivates students and ensures a more engaging and effective learning experience. Conclusions. The research concludes that mobile technology in physics labs is an effective alternative to traditional methods. It creates an engaging learning environment, bridging the gap between theory and practice, and enhances student motivation, critical thinking, and problem-solving skills. The study highlights the importance of innovative teaching methods in modern education. Keywords: laboratory work; physics; smartphone; mobile applications

Business Intelligence System and Firms’ Adaptive Ability: Evidence from Developing Country

Purpose: This study assessed the relationship between business intelligence system and firm ability for adaptation Method: A survey research design and sample of one hundred and twenty-four (124) employees of Fintech firms were used. Essentially, three (3) business intelligence systems were used such as technology skill, business forecasting and strategic planning. Data obtained in the survey were analyzed using independent sample t-test statistical too Results and Conclusion: Findings revealed strong and significant variations in business forecasting, strategic planning and technology skill and ability of Fintech companies to adapt. The study concludes that there is relationship between business intelligence system and firm ability for adaptation. Implication of the Research: The implication of the finding is that systems of business intelligence are vital particularly in areas of technology skill, forecasting and strategic planning which aid the ability of Fintech companies to adapt Originality/Value: This study assessed how business forecasting, strategic planning and technology skills which are vital elements of business intelligence can be carried out constantly and collaboratively by including all units/departments concerned with process and implementation of business intelligence

Analysis of Disease Adaptation Ability in Patients with Hepatolenticular Degeneration: Current Status and Influencing Factors

Objective To investigate This study aims to examine the disease adaptation ability capabilities of hepatolenticular degeneration patients during their treatment treatment, and explore to identify the factors influencing these affecting their adaptation ability. abilities. Methods A total of 210 hepatolenticular degeneration patients in treatment were selected by convenience sampling method, method and was employed to select 210 patients undergoing treatment for hepatolenticular degeneration. The investigation utilized a general information questionnaire, the Disease Acceptance Scale AIS-CHI questionnaire, and the Chinese version of the Simple Roy Coping with Adaptation Scale Scale. were used for investigation. Results The results of multiple Multiple linear regression analysis showed revealed that factors such as place of residence, education level, occupation, medical payment mode, and average monthly income of family members significantly influenced were the main factors affecting the disease adaptation ability capabilities of these patients. In conclusion, patients withpatients. In conclusion, patients with hepatolenticular degeneration exhibit suboptimal patients. Conclusion Hepatolenticular degeneration patients show poor adaptation ability abilities during treatment. Therefore, Consequently, it is incumbent upon the responsibility of medical staff to guide assist patients and their families to in actively managing cope with the their disease status status, thereby facilitating so as to better adapt to the disease adaptation and ensuring thus ensure the physiological and psychological health well-being of patients post-discharge.

STM1863, a Member of the DUFs Protein Family, Is Involved in Environmental Adaptation, Biofilm Formation, and Virulence in Salmonella Typhimurium.

Salmonella Typhimurium (STM) is an important zoonotic Gram-negative pathogen that can cause infection in a variety of livestock and poultry. Meanwhile, as an important foodborne pathogen, the bacterium can survive in various stressful environments and transmits through the fecal-oral route, posing a serious threat to global food safety. To investigate the roles of STM1863, a member of the DUFs protein family, involved in STM environmental adaptation, biofilm formation, and virulence. We analyzed the molecular characteristics of the protein encoded by STM1863 gene and examined intra- and extracellular expression levels of STM1863 gene in mouse macrophages. Furthermore, we constructed STM1863 gene deletion and complementation strains and determined its environmental adaptation under stressful conditions such as acid, alkali, high salt, bile salt, and oxidation. And the capacity of biofilm formation and pathogenicity of those strains were analyzed and compared. In addition, the interaction between the promoter of STM1863 gene and RcsB protein was analyzed using DNA gel electrophoresis migration assay (electrophoretic mobility shift assay [EMSA]). The experiments revealed that acid adaptability and biofilm formation ability of STM1863 gene deletion strain were significantly weakened compared with the parental and complementary strains. Moreover, the adhesion and invasion ability of STM1863 deletion strain to mouse macrophages was significantly decreased, while the median lethal dose (LD50) increased by 2.148-fold compared with the parental strain. In addition, EMSA confirmed that RcsB protein could bind to the promoter sequence of STM1863 gene, suggesting that the expression of STM1863 gene might be modulated by RcsB. The present study demonstrated for the first time that STM1863, a member of the DUFs protein family, is involved in the modulation of environmental adaptation, biofilm formation, and virulence.

Resistance of an intertidal oyster(Saccostrea mordax)to marine heatwaves and the implication for reef building

Marine heatwaves (MHWs) have a significant impact on intertidal bivalves and the ecosystems they sustain, causing the destruction of organisms' original habitats. Saccostrea mordax mainly inhabits the intertidal zone around the equator, exhibiting potential tolerance to high temperatures and maybe a species suitable for habitat restoration. However, an understanding about the tolerance mechanism of S. mordax to high temperatures is unclear. It is also unknown the extent to which S. mordax can tolerate repeated heatwaves of increasing intensity and frequency. Here, we simulated the effects of two scenarios of MHWs and measured the physiological and biochemical responses and gene expression spectrum of S. mordax. The predicted responses varied greatly across heatwaves, and no heatwave had a significant impact on the survival of S. mordax. Specifically, there were no statistically significant changes apparent in the standard metabolic rate and the activities of enzymes of the oyster during repeated heatwaves. S. mordax exposed to high-intensity heatwaves enhanced their standard metabolic rate to fuel essential physiological maintenance and increasing activity of SOD and expression of HSP70/90. These strategies are presumably at the expense of functions related to immunity and growth, as best exemplified by significant depressions in activities of enzymes (NaK, CaMg, T-ATP, and AKP) and expression levels of genes (Rab, eEF-2, HMGR, Rac1, SGK, Rab8, etc.). The performance status of S. mordax tends to improve by implementing a suite of less energy-costly compensatory mechanisms at various levels of biological organization when re-exposed to heatwaves. The adaptive abilities shown by S. mordax indicate that they can play a crucial role in the restoration of oyster reefs in tropical seas.

Climate risk assessment and adaption ability in China's coastal urban agglomerations - A case study of Guangdong-Hong Kong-Macao greater bay area

As climate change intensifies, extreme weather events, especially the coastal flooding is becoming increasingly severe. Carbon emissions exacerbate climate change, leading to increased frequency and intensity of extreme precipitation, which may result in flooding. This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a coastal city cluster in China, and adopts the scenario analysis approach to evaluate flood risks in the different carbon mitigation scenario from the perspectives of dangerousness, and adaption ability. The result shows that the Huizhou in the east of the GBA and the Zhaoqing in the northwest have the highest level of flood disaster risk. However, the central region of the GBA (Guangzhou, Dongguan, Zhongshan, and Foshan) is very vulnerable to disaster-inducing environments and hazard-affected bodies, which means that the flooding risk and the impact of such risk in the GBA display regional differentiation characteristics and the spatial patterns of them are different. According to the carbon neutrality scenario, the flood risk in the GBA peaks in 2030, and decreases during 2030–2090. In contrast, under the uneven development and high carbon pathways, the risk peaks in 2060, and the overall risk by 2090 is roughly the same as that in 2060. In addition, the regions with the highest flood disaster risk are Guangzhou, Foshan, Dongguan and Zhongshan along the Pearl River estuary. Overall, it shows that areas with higher levels of urbanization and more developed economies will face higher flooding risks. The “dangerousness-adaption ability” structure can reflect the spatial distribution pattern of flood disaster risks, identify the priority areas for flood prevention and control, and is conducive to risk management. At the same time, the research results show that ecological protection have a positive effect on reducing flood risk. In addition, corresponding policy recommendations for pre-disaster, mid-disaster and post-disaster have been proposed, providing references for flood risk management in the GBA.