Ningde City Research Articles

Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction

Reliable and accurate traffic flow prediction is crucial for the construction and operation of smart highways, supporting scientific traffic management and planning. However, accurately predicting spatiotemporal traffic flow in non-stationary and unprecedented traffic patterns scenarios, such as holidays and adverse weather conditions, remains a challenging task. Considering that (1) Koopman theory effectively captures the underlying time-variant dynamics of the non-stationary temporal sequence (2) Graph convolutional network (GCN) effectively extracts complex spatial dependencies, combining the strengths of both is a promising solution. Therefore, this paper proposes a spatiotemporal prediction network that integrates Koopman theory and GCN, named KoopGCN, for predicting non-stationary and inexperienced highway traffic flow. KoopGCN decomposes the input into time-invariant and time-variant components based on Fast Fourier Transform. The dual engine block consisting of KoopGCN InvarEngine and KoopGCN VarEngine is designed to predict two types of components separately. And the dual engine block also passes the residual to the next block for modeling. The experiment is conducted on real monitored highway data in Ningde City, Fujian Province, China. The results indicate that even if there is a significant distribution difference between the training and testing sets, KoopGCN can achieve accurate prediction, significantly outperforms state-of-the-art baselines.

PI-STGnet: Physics-integrated spatiotemporal graph neural network with fundamental diagram learner for highway traffic flow prediction

At present, traffic state prediction primarily relies on purely data-driven methods, ignoring the incorporation of physical constraints within the field of traffic flow. Taking this as a starting point, this paper endeavors to embed the physical mechanism of the traffic flow fundamental graph into the deep learning model, and proposes a physics-integrated spatiotemporal graph neural network with fundamental diagram learner (PI-STGnet) for highway traffic flow prediction. The PI-STGnet mainly comprises a semantic enhancement module (SE), a spatiotemporal correlation extraction module (ST-Block), and a fundamental diagram learner module (FD-learner). These modules are strategically designed to sequentially enhance the contextual semantic relationships of the traffic flow and speed inputs, extract intricate spatiotemporal correlations of traffic states, and adaptively learn the dynamic evolution of the physical relationships between traffic flow and speed. The real-world dataset employed to assess the performance of the PI-STGnet is sourced from the monitoring data of highway gantry sensors located in Ningde City, Fujian Province, China. The experimental results demonstrate that the proposed PI-STGnet has the advantages to extract spatiotemporal features and achieve prediction, surpassing the accuracy of cutting-edge baseline models. In summary, as an exploratory work, this paper provides a novel approach that driven by the fusion of deep learning and physical mechanisms for traffic flow prediction to a certain extent.

The effect of core stability training combined with fascial release on patients with nonspecific low back pain.

Non-specific lower back pain (NLBP) is treated with a variety of therapies, including health education, exercise therapy, soft tissue release, psychological interventions, and shockwave therapy. However, some studies have shown that core stability training or fascial release therapy alone is not effective in the treatment of low back pain. The aim of this study was to investigate the effects of core stability training on patients' inflammatory cytokine levels and lumbar muscle temperature when combined with fascial release for the treatment of non-specific low back pain. In this study, a total of 60 patients with non-specific low back pain who were treated in Mindong Hospital of Ningde City between December 2021 and January 2023 were selected and randomly and equally divided into a control group (30 cases) and an experimental group (30 cases). The control group received core stability training, while the experimental group added fascial release surgery to this. We compared and assessed the pain visual analog score (VAS), Oswestry dysfunction index (ODI), lumbar spine mobility (including anterior flexion, posterior extension, left flexion, and right flexion), as well as levels of inflammatory factors IL-6, TNF-a, and muscle tissue temperature in the two groups. This study has been successfully implemented and covered 60 patients throughout the trials. Upon comparison, the two groups did not show statistically significant differences in baseline data such as age, gender and duration of disease (p> 0.05). After four weeks of treatment, the test group showed statistically significant (p< 0.05) differences in VAS scores, ODI scores, and IL-6 and TNF-a levels that were significantly lower than those of the control group. It is worth mentioning that the muscle tissue temperature of the patients in the test group, as well as their performance in lumbar anterior flexion, posterior extension, left flexion, and right flexion mobility, were significantly better than those of the control group, and these differences also showed statistical significance (p< 0.05). The combination of core stability training and fascial release demonstrates significant clinical results in the treatment of nonspecific lower back pain. Through medical thermography and serum inflammatory factor testing, we were able to assess the treatment effect more objectively, providing a strong basis for future clinical practice.

Carbon Emissions and Intensity of Land Use: A Rural Setting Analysis in Ningde City, China

Carbon emissions and land use intensity serve as crucial indicators of land management. This paper proposes a methodological framework to elucidate the sustainability of carbon emissions in rural areas via a coordination model, scrutinizes the correlation with land use intensity, and investigates the significance of influential factors. The study focuses on village-level units within Ningde City, and finds the pronounced spatial heterogeneity characterizing the distribution of carbon emissions across different villages: (1) Villages exhibiting high levels of carbon emissions are predominantly concentrated in the southeast region, whereas those with low carbon emission levels are primarily clustered in the northwest region. The majority of the villages serve as net carbon emission sources. (2) Spatial disparities exist in the impact of land use intensity on economic benefits from carbon sources, ecological benefits from carbon sinks, and carbon emission sustainability. (3) Significant variations exist in the influence of factors affecting land use intensity, economic benefits of carbon sources, ecological benefits of carbon sinks, and the sustainability of carbon emissions in rural areas. These findings could guide governments in implementing distinct land use control policies and provide a framework for assessing carbon emission sustainability within land management strategies.

Analysis of the Near-Ground Wind Field Characteristics during Typhoon Soulik

In 2013, during Typhoon Soulik, wind data were collected at various heights above the ground (15, 27, 53, 67, and 82 m) on the 550 kV 52# pole transmission tower in Ningde City, Fujian Province. The wind speed profile, turbulence intensity, gust factor, crest factor, and power spectrum were analyzed using 10 min interval wind speed records. The results show the following: (1) the average wind velocity of Typhoon Soulik varies in accordance with both the power law and the logarithmic law, but the Deaves–Harris model exhibits significant discrepancies; (2) the turbulence intensity in u, v, and w orientations decreases with the average wind velocity at each height. Exponential fitting is conducted on the strength of turbulence and gust factor profiles in each direction based on the standards of different countries, resulting in the derivation of empirical expressions; (3) the integral scale components of turbulence in u, v, and w orientations exhibit a positive correlation with both average wind velocity and height. The turbulence integral scale ratios in the longitudinal, transverse, and vertical directions at heights of 15, 53, and 82 m are 1:0.68:0.11, 1:0.67:0.27, and 1:0.67:0.30, respectively; (4) the Von Karman empirical spectrum and the modified Kaimal cross-spectrum model closely match the observed wind power spectrum of Typhoon Soulik. The presented results contribute to furthering references for wind-resistant design of structures in typhoon-prone areas and prevention of typhoon-related disasters.

Land-Use Transitions Impact the Ecosystem Services Value in a Coastal Region by Coupling the Geo-Informatic Tupu and Benefit-Transfer Method: The Case of Ningde City, China

Exploring the mechanisms and processes of land-use transitions (LUTs) and their impact on ecosystem services can effectively elucidate the intricate interactions between human and natural systems, which is pivotal for advancing the sustainable development of regional economies and enhancing ecological environments. However, the existing literature lacks comprehensive analysis regarding the spatial and temporal evolution of LUTs, with insufficient integration of the “spatial pattern” and “time process”. Moreover, traditional assessments of the ecosystem services value (ESV) often overlook their negative costs. To address these gaps, this study first utilized the Google Earth Engine (GEE) cloud platform and employed the random forest algorithm to conduct supervised classification on Landsat remote-sensing images from the years 2000, 2010, and 2020 within the research area, thereby obtaining land-use data for three distinct periods. And then, we investigated the geographic features of LUTs and their ecological effects in the Ningde City of China from 2000 to 2020. The geo-informatic Tupu model and a newly revised method of benefit transfer were primarily employed for this purpose. The findings indicate the following: (1) Over the study period, the land-use structure of Ningde City predominantly comprised cultivated land and forest land, with continuous decreases in both types and a concurrent increase in built-up land. (2) Significant disparities exist in the spatial distribution of Tupu units, notably with “forest land → cultivated land” and “cultivated land → built-up land” as crucial units influencing ESV changes. (3) The ESV in Ningde City decreased from CNY 1105.54 × 108 to CNY 1020.47 × 108 over 2000–2020, while the ecosystem dis-services value exhibited an opposing trend, rising from CNY 12.68 × 108 to CNY 20.39 × 108. (4) The net ESV in Ningde City showed a decline over the same period, indicating a certain vulnerability in the city’s ecological system structure. This study aims to enhance our understanding of the influence of land-use patterns on ESV, offering valuable insights for regional ecological–environment management and land-use policy formulation, thereby fostering sustainable development in ecological, environmental, and socio-economic dimensions. Furthermore, the results serve as a reference for evaluating net ecosystem services value in other countries/regions.

Price Forecasting of Marine Fish Based on Weight Allocation Intelligent Combinatorial Modelling.

China is a major player in the marine fish trade. The price prediction of marine fish is of great significance to socio-economic development and the fisheries industry. However, due to the complexity and uncertainty of the marine fish market, traditional forecasting methods often struggle to accurately predict price fluctuations. Therefore, this study adopts an intelligent combination model to enhance the accuracy of food product price prediction. Firstly, three decomposition methods, namely empirical wavelet transform, singular spectrum analysis, and variational mode decomposition, are applied to decompose complex original price series. Secondly, a combination of bidirectional long short-term memory artificial neural network, extreme learning machine, and exponential smoothing prediction methods are applied to the decomposed results for cross-prediction. Subsequently, the predicted results are input into the PSO-CS intelligence algorithm for weight allocation and to generate combined prediction results. Empirical analysis is conducted using data illustrating the daily sea purchase price of larimichthys crocea in Ningde City, Fujian Province, China. The combination prediction accuracy with PSO-CS weight allocation is found to be higher than that of single model predictions, yielding superior results. With the implementation of weight allocation intelligent combinatorial modelling, the prediction of marine fish prices demonstrates higher accuracy and stability, enabling better adaptation to market changes and price fluctuations.

Reconstruction of Single-Bay Buddhist Architecture Based on Stylistic Comparisons in Northeast Fujian, the Core Hinterland of the Changxi River Basin—Using Gonghoulong Temple as an Example

In the Changxi River Basin in eastern Fujian, a few stone elements remain and Buddhist buildings with one bay in width and three bays in depth have been preserved dating from the timespan of the Tang to the Song dynasty. These features are characterized by a regional form of early Buddhist architecture seldom seen in Chinese history. The article focuses on the reconstruction of a Song dynasty Buddhist building at the Gonghoulou Temple site in Huotong Town, Jiaocheng District, Ningde City, and aims to analyze the potential characteristics and rules of single-bay Buddhist architecture. From the styles of the remaining stone columns, the direction of the lotus carving at the column base, and the mortises of the plinth stone, a spatial arrangement is indicated that includes an open front corridor and a closed rear section. A “reconstruction” of the ruler used in the original building reveals the possibility that a local Fujian ruler was used, shorter than the standard measurement device employed elsewhere. The analysis of the frame construction indicates that this hip-gable roof-covered Buddhist hall utilizes the horizontally layered logic of multi-storied palatial-style halls. Key elements include its gentle roof slope, restraint from the practice of shortening the roof ridge, use of the traditional chuji method, and the interior columns use of internal longitudinal architraves secured to beam supporting brackets. This research brings to light a unique architectural type that has disappeared in the course of history and was previously unknown to the academic community. It holds significant importance and value for deepening the understanding of the history of timber frame architecture technology in Fujian.

Epidemiological characteristics of typhoid fever in Fujian Province, 2011-2022

Objective: To analyze the incidence trend and epidemiological characteristics of typhoid fever in Fujian Province from 2011 to 2022, and understand the high-incidence population and hotspot areas, and provide evidences to develop more targeted prevention and control measures. Methods: The surveillance data of typhoid fever during 2011-2022 in Fujian Province were obtained from the National Disease Reporting Information System and analyzed with SAS 9.4. The spatial autocorrelation analysis of typhoid fever incidence at county/district levels was performed with ArcGlS 10.8. Results: A total of 5 126 cases of typhoid fever were reported in Fujian Province from 2011 to 2022, with an average annual incidence rate of 1.10/100 000. The average annual incidence rate was 0.96/100 000 from 2011 to 2015, 1.49/100 000 from 2016 to 2019, and 0.81/100 000 from 2020 to 2022. The disease occurred all the year round, with high epidemic season from May to September. A total of 23.59% (1 209/5 126) of the cases occurred at the age of 0-4, and 9.62% (493/5 126) at the age of 5-9. The male to female ratio of the cases was 0.97∶1 (2 524∶2 602) for the whole population, 1.19∶1 (925∶777) for people under 10 years old, 0.75∶1 (1 060∶1 404) for people between 10 and 54 years old, and 1.28∶1 (539∶421) for people over 55 years old. Cases in Ningde City accounted for 30.65% (1 571/5 126) of the total cases. Most hotspots were occurred in Ningde City. Recurrent and clustered cases were found in family members. Conclusions: Typhoid fever was prevalent at a low level in Fujian Province during 2011-2022, indicating that strengthening the prevention and control measures should target key areas and populations. The incidence of typhoid fever in Fujian Province showed spatial aggregation phenomenon, and most cases gathered in Ningde City. Intensive study for the influencing factors of spatial clustering should be conducted.

Study on Improving the Mechanism for Assessing and Realizing the Value of Forest Ecological Products in Fujian Province

Abstract The key to grasping and practicing the important concept of “green mountains are golden mountains” is to internalize the construction of ecological civilization into the operation mechanism of the market economy and to stimulate the endogenous motivation of the whole society to engage in the construction of ecological civilization. The study focuses on the value of forest ecological products in Fujian Province and evaluates these products from four perspectives: support services, regulating services, supply services, and cultural services. Subsequently, we constructed the suitability evaluation index system for the realization mode of forest ecological product value in Fujian Province. We weighted the indexes by combining the entropy value method and the expert scoring method, and evaluated the suitability of the realization mode using the structural equation model. The suitability assessment results show that Zhouning County, Ningde City - ecological compensation &gt; Shunchang County, Nanping City - ecological tenure trading &gt; Changting County, Longyan City - ecological restoration &gt; Jiangle County, Sanming City - ecological industrialization model. The regression estimates of ecological product production potential factor (β=0.221, p&lt;0.001), ecological environment constraints (β=0.145, p&lt;0.05), social environment constraints (β=0.178, p&lt;0.001), and regression estimation of the realization of the value of the ecological products of forests in Fujian Province are significant. The mechanism for integrating synergy and coordination, localization, and differentiation for the realization of sustainable forest ecological product value has been improved.

Predicting risk of obesity in overweight adults using interpretable machine learning algorithms.

To screen for predictive obesity factors in overweight populations using an optimal and interpretable machine learning algorithm. This cross-sectional study was conducted between June 2011 and January 2012. The participants were randomly selected using a simple random sampling technique. Seven commonly used machine learning methods were employed to construct obesity risk prediction models. A total of 5,236 Chinese participants from Ningde City, Fujian Province, Southeast China, participated in this study. The best model was selected through appropriate verification and validation and suitably explained. Subsequently, a minimal set of significant predictors was identified. The Shapley additive explanation force plot was used to illustrate the model at the individual level. Machine learning models for predicting obesity have demonstrated strong performance, with CatBoost emerging as the most effective in both model validity and net clinical benefit. Specifically, the CatBoost algorithm yielded the highest scores, registering 0.91 in the training set and an impressive 0.83 in the test set. This was further corroborated by the area under the curve (AUC) metrics, where CatBoost achieved 0.95 for the training set and 0.87 for the test set. In a rigorous five-fold cross-validation, the AUC for the CatBoost model ranged between 0.84 and 0.91, with an average AUC of ROC at 0.87 ± 0.022. Key predictors identified within these models included waist circumference, hip circumference, female gender, and systolic blood pressure. CatBoost may be the best machine learning method for prediction. Combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.

Recreational Fishery Environmental Suggestions of Sandu Island, Ningde, Fujian Province Based on KANO Model and Expert Verification

Tourist satisfaction is an important reference in destination management decisions, but on-site validation is often lacking. By exploring the relationship between the current situation of recreational fishery and tourism satisfaction evaluation, we can accurately understand tourists’ consumption preferences and obtain product/service quality information. After on-site verification, such validation is conducive to promoting the high-quality development of regional recreational fisheries. The study area is located on Sandu Island, Ningde City, Fujian Province. This study examines how each item of service quality may exhibit different impacts on customer satisfaction by using a questionnaire survey of consumers in the recreational fisheries. This study combines the Kano model and the Better–Worse index method to investigate the critical service quality. The results show that among the 19 quality indicators of visitor satisfaction, environmental and landscape quality elements are the most important. In addition, through expert verification, it can be confirmed that the improvement of the water environment is of great help to the industry. To improve and maintain customer satisfaction, the above attributes are combined with underlying quality attributes as strategic weapons to highlight competitive advantages.

Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort.

The identification of associated overweight risk factors is crucial to future health risk predictions and behavioral interventions. Several consensus problems remain in machine learning, such as cross-validation, and the resulting model may suffer from overfitting or poor interpretability. This study employed nine commonly used machine learning methods to construct overweight risk models. The general community are the target of this study, and a total of 10,905 Chinese subjects from Ningde City in Fujian province, southeast China, participated. The best model was selected through appropriate verification and validation and was suitably explained. The overweight risk models employing machine learning exhibited good performance. It was concluded that CatBoost, which is used in the construction of clinical risk models, may surpass previous machine learning methods. The visual display of the Shapley additive explanation value for the machine model variables accurately represented the influence of each variable in the model. The construction of an overweight risk model using machine learning may currently be the best approach. Moreover, CatBoost may be the best machine learning method. Furthermore, combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.

A dataset of the visible light remote sensing images for offshore maritime targets in Sanduao from 2019 to 2021

The intelligent detection of offshore maritime targets using computer vision technology can provide a scientific basis for marine administrative management, marine environmental supervision and management as well as the formulation of marine environmental protection policies, providing a powerful environmental information reference for the steady development of the economy. The dataset includes the data collected from Sanduao Harbor in the southeast of Ningde City, Fujian Province, China, with Google Earth serving as the primary data source and a time span from 2019 to 2021. This dataset comprises 1,761 visible light remote sensing images acquired under different seasons, backgrounds and illumination conditions, and corresponding horizontal object detection labels, rotational object detection labels and semantic segmentation labels, covering three types of offshore maritime targets, namely ships, fish row cage culture areas, and raft culture areas. After screening and correction, it can meet the current mainstream deep learning model training needs. This dataset can provide basic data for the semantic segmentation, horizontal object detection, rotational object detection and other research fields of offshore maritime target images.

How Does the Intelligent Monitoring System Influence the Sense of Security of the Elderly in Home Care? A Case Study of the Hubin Community in Ningde City

Under the background of rapid aging in China, the security of the elderly in home care has become the focus of social attention. The present study takes the Hubin community of Ningde City as the case study area; and uses hierarchical regression analysis, structural equation model, and other methods to explore the influence path of the sense of security of the elderly in home care under the moderating effect of the intelligent monitoring system, and the subjective and objective demands for such equipment among the elderly. The results showed that compared with family care and social support, the influence of individual health on the sense of security of the elderly in home care was dominant. The intelligent monitoring system can play a negative moderating effect in the process of individual health directly affecting the sense of security of the elderly in home care, and indirectly improving the sense of security of the elderly in home care. Therefore, under the premise of paying attention to the individual health of the elderly at home, the reasonable application of the intelligent monitoring system and full play to its moderating effect is a practical way to improve the sense of security of the elderly in home care. In addition, the family and society should also pay more attention to the security of the elderly in home care, so as to promote the overall improvement of their sense of security.

Ecological restoration approaches for degraded muddy coasts: Recommendations and practice

Scientific selection of an ecological restoration approach from among the three common approaches: natural restoration, assisted restoration, and ecological reconstruction is the first step to successful ecological restoration. This study chose the appropriate ecological restoration approach for the study area from the following two perspectives. The threshold effect considers the degree of damage to the muddy coast and makes an estimate of its resilience. The development needs of the coastal zone consider the needs of muddy coasts for ecological protection, safety, and culture and aesthetics; development planning aims to determine the priority of these three needs. This study also assessed common technical measures for ecological restoration of muddy coasts and analyzed the applicability and advantages and disadvantages of each perspective to assist in the selection of specific ecological restoration measures suited to local conditions. The target area for ecological restoration was the muddy coastal area around Yundanmen Island, Sansha Bay, Ningde City, China, which had been degraded by reclamation and aquaculture. Based on conceptual models for selection, ecological restoration approaches for the study area were determined, technical measures were selected, and the plan was developed and implemented. We analyzed the initial effects of ecological restoration on birds, vegetation, and benthic organisms to verify the effectiveness of the chosen ecological restoration approach. This study provides scientific support for rationally planning the ecological restoration of muddy coasts and a reference for the selection of other coastal ecological restoration approaches and technical measures.

Analysis of blood carnitine profile and SLC22A5 gene variants in 17 neonates with Primary carnitine deficiency

To analyze the blood free carnitine (C0) level and SLC22A5 gene variants in 17 neonates with Primary carnitine deficiency (PCD) and to determine its incidence in local area and explore the correlation between C0 level and genotype. 148 043 newborns born in 9 counties (cities and districts) of Ningde city from September 2016 to June 2021 were selected as study subjects. Blood free carnitine and acyl carnitine of 148 043 neonates were analyzed. Variants of the SLC22A5 gene were screened in those with blood C0 < 10 µmol/L, or C0 between 10 ∼ 15 µmol/L. Correlation between the free carnitine level and genetic variants was analyzed. In total 17 neonates were diagnosed with PCD, which yielded a prevalence of 1/8 707 in the region. Twelve variants of the SLC22A5 gene were identified, with the common ones including c.760C>T, c.1400C>G and c.51C>G. Compared with those carrying other variants of the gene, children carrying the c.760C>T variant had significantly lower C0 values (P < 0.01). The prevalence of PCD is relatively high in Ningde area, and intervention measures should be taken to prevent and control the disease. The c. 760C>T variant is associated with lower level of C0, which can provide a clue for the diagnosis.

Ecological Environment Evaluation Based on Remote Sensing Ecological Index: A Case Study in East China over the Past 20 Years

East China is one of the most active regions in terms of economic and social development, and with the accelerated urbanization process, environmental problems are becoming increasingly prominent. The objective, quantitative, and timely evaluation of spatial and temporal changes in ecological quality is of great significance for environmental protection and decision making. The remote sensing ecological index (RSEI) is an objective, fast, and easy ecological quality monitoring and evaluation technique which has been widely used in the field of ecological research, but it often involves problems of cloud occlusion and stitching difficulties when used to conduct large-scale and long-term monitoring. In this paper, based on the Google Earth Engine (GEE) platform, an RSEI was constructed using MODIS data products to evaluate the spatial and temporal changes in ecological quality in East China over the past 20 years. The study shows the following: (1) The mean RSEI values in 2000, 2005, 2010, 2015, and 2020 were 0.67, 0.55, 0.59, 0.58, and 0.63, respectively, with the mean values first decreasing and then showing a stable increasing trend. In Shanghai and Jiangsu, the mean RSEI values show a fluctuating characteristic of “falling and then rising”, and large respective decreases of 32.4% and 25.8% throughout the monitoring period. The RSEI values in Fujian Province showed a relatively stable upward trend during the study period (19% increase). (2) The RSEI spatially correlated clustering maps of the local indicators showed that the regions with a high degree of clustering are mainly located in Quzhou City, Zhejiang Province, Ningde City, Fujian Province, and northern Anhui Province (Bozhou and Huabei). With the promotion of ecological civilization and the enhancement of environmental protection awareness, the vegetation cover has significantly increased, which has led to the rise in RSEI values. The low values are mainly distributed in densely populated areas with more human activity, such as the central-eastern part of Jiangsu Province, central Anhui Province, Shanghai, and northern Zhejiang Province. With the development of cities, impervious surfaces occupy more and more ecological land, which eventually affects the regional RSEI values. (3) This research provides a promising method for the evaluation of spatial and temporal changes in ecological environment quality based on an RSEI and GEE. The image processing, based on GEE cloud computing, can help overcome the problems of missing remote sensing data, chromatic aberrations, and spatial and temporal inconsistency, which could greatly improve the efficiency of image processing and extend the application of the remote sensing ecological index to large-scale, long-term ecological monitoring. The research results can provide a reference for improving the applicability and accuracy of remote sensing ecological indices and provide a theoretical basis for ecological conservation and land management in the context of rapid urbanization.

A new marine epipsammic diatom species, Ambodajingensis sp. nov. (Bacillariophyceae), from the coast of Southeast China.

Ambodajingensis HH Liu, Z Wang, YH Gao & CP Chen, sp. nov. is described as a new species in samples collected from sand grains at Dajing Beach, Ningde City, Fujian Province, China. Morphological details of the new species with respect to valve shape, size and valve ultrastructure are presented based on light microscopy (LM) and scanning electron microscopy (SEM). The main features of Ambodajingensis under a light microscope are elongated elliptic valves with rounded apices, two internal costae on the valve and rectangular in girdle views. SEM observation showed that externally, the frustules are comprised of two valves with a relatively deep mantle and a transition between the valve faces. Small, flabelliform spines are present along the valve margin. Internally, the valves are divided into three sectors by robust costae, which penetrate the whole valve lumen and are thickest at the mantle interior and thinner toward the center. The sternum is narrow and linear, visible only in the valve apex, set off by costae. The striae are comprised of small, round areolae and they are parallel in the middle to slightly radiate at the apices. The new species is compared with other species in the genus Ambo.

Assessment on the Spatial Distribution Suitability of Ethnic Minority Villages in Fujian Province Based on GeoDetector and AHP Method

Ethnic minority villages are important resources for the economy and social development of ethnic minority areas because they preserve ethnic minorities’ culture. With the rapid development of industrialization and urbanization in China, the factors affecting the development of villages have changed. With the help and guidance of the government, the gap between villages has increased. According to the development conditions of ethnic minority villages at the present stage, the suitability of their spatial distribution has been studied, the existing problems in the current development have been explored, and the development laws and future development trends have been found. To make the evaluation results more scientific and objective, Geographical Detector (Geodetector) and Absorbent Hygiene Product (AHP) methods are used to establish the evaluation model. Taking 567 ethnic minority villages in Fujian Province as the research object, 13 factors are selected from the aspects of natural geographical, socio-economy and cultural life to construct the evaluation indicator system of Fujian ethnic minority villages, and the spatial distribution suitability of Fujian ethnic minority villages is quantitatively evaluated. The findings indicated the following: (1) The per capita income of villages has the most important impact on the suitability of spatial distribution of Fujian minority villages. Through comprehensive evaluation, the impact of cultural life indicators on the suitability of the spatial distribution of the village is greater than that of socio-economic indicators and natural geographical indicators. The intensity relationship is 9:7:10. (2) The high suitability value is concentrated in Fujian Province’s southeast coastal and central areas, gradually decreasing from east to west. In Fujian Province, 82.84%of the land is suitable for the development of ethnic minority villages, with 89% of ethnic minority villages. The unsuitable areas are mostly in Fujian’s north and west. (3) The most suitable cities for the number of ethnic minority villages are Ningde City and Quanzhou City because ethnic minority villages in these two cities are mostly distributed in areas relatively close to the central urban area, with good economic conditions, flat terrain, and easy transportation. The cities of Nanping and Sanming are the least suitable for many ethnic minority villages, which are primarily limited by topographic conditions, have a backward economy, a sparse road network, and have experienced significant population loss. In the context of urbanization, the evaluation results can provide a reference for the precise development and protection of minority villages. Governments at all levels in Fujian Province can adjust and optimize the development strategies of minority villages according to the evaluation results.