Jiangmen City Research Articles

Analysis of Influencing Factors of Work Motivation of Government Employees Based on MaxDiff Model: Evidence from Jiangmen City

The purpose of this paper is to use the MaxDiff model to explore the factors influencing the work motivation of staff in certain government agencies in Jiangmen City, and to conduct relevant empirical analyses. The study aims to propose strategies for improving work motivation among these staff members. The results indicate that the most significant factors affecting the work motivation of staff in Jiangmen government agencies are salary and remuneration, additional welfare benefits, and the power to make decisions about their own work. In contrast, factors such as a sense of belonging to the work unit, recognition of their work, and job security are less important. Therefore, it is recommended that government agencies in Jiangmen City increase the salaries and benefits of their staff and provide them with more decision-making power. This approach will stimulate their desire to work, enhance their motivation, improve the efficiency of government agencies, and elevate the level of public service. Ultimately, these measures will support the construction of a harmonious, stable, and orderly society.

The moderating effect of altruism on the relationship between occupational stress and turnover intentions: a cross-sectional study of community rehabilitation workers in China

BackgroundIn China, community rehabilitation workers are facing a growing challenge related to heavy occupational stress, which is having an impact on employment turnover. Previous studies have explored the effect of the public service motivation of workers in “helping” jobs on occupational stress or turnover intention, but there is a lack of clarification of the impact of altruism on turnover intention in the case of complex pathways involving various factors.MethodsA stratified sampling method was used, and a total of 82 community rehabilitation workers who assist disabled people from 34 community health centres in Jiangmen city were included in the study from August to October 2022. The turnover intention, occupational stress, burnout, quality of life, altruism, and certain sociodemographic information of community rehabilitation workers were measured using a structured questionnaire. The partial least squares method was employed to construct and test the structural equation model.ResultsAlthough altruism had no direct impact on occupational stress or turnover intention, altruism moderated the effect of occupational stress on burnout (βMod = −0.208) and quality of life (βMod = 0.230) and weakened the mediation of burnout and quality of life between occupational stress and turnover intention.ConclusionsThis study proposes to address the dilemma of “strong function” and “weak specialty” in community rehabilitation services and to conduct positive psychological interventions for community rehabilitation workers through the guidance of altruistic values.

Gemmobacter denitrificans sp. nov., a denitrifying bacterium, isolated from pond water for Litopenaeus vannamei.

A novel Gram-stain-negative strain, designated JM10B15T, was isolated from pond water for Litopenaeus vannamei collected from Jiangmen City, Guangdong province, south PR China. Cells of the strain were aerobic, rod-shaped, and motile by lateral flagella. JM10B15T could grow at 15-40 °C, pH 6.0-9.5, and in 0-3.0 % NaCl, with optimal growth at 25-35 °C, pH 7.5-8.5, and in 0 % NaCl, respectively. Furthermore, this strain grew well on Reasoner's 2A agar but not on nutrient broth agar or Luria-Bertani agar. JM10B15T was a denitrifying bacterium capable of removing nitrites and nitrates, and three key functional genes, nasA, nirS, and nosZ, were identified in its genome. The results of phylogenetic analyses based on the 16S rRNA gene and genome sequences indicated that JM10B15T belonged to the genus Gemmobacter. JM10B15T showed the highest 16S rRNA sequence similarity to Gemmobacter lutimaris YJ-T1-11T (98.8 %), followed by Gemmobacter aquatilis IFAM 1031T (98.6 %) and Gemmobacter serpentinus HB-1T (98.1 %). The average nucleotide identity and digital DNA-DNA hybridization values between JM10B15T and the other type strains of genus Gemmobacter were 78.1-82.1 % and 18.4-22.1 %, respectively. The major fatty acids of strain JM10B15T were summed feature 8 (C18 : 1 ω6c and/or C18 : 1 ω7c) and C18 : 1 ω7c 11-methyl. In addition, the major respiratory quinone of this novel strain was Q-10, and the predominant polar lipids were phosphatidylglycerol, phosphatidylethanolamine, four unidentified phospholipids, three unidentified lipids, and an unidentified aminophospholipid. Results of analyses of the phylogenetic, genomic, physiological, and biochemical characteristics indicated that JM10B15T represents a novel species of the genus Gemmobacter, for which the name Gemmobacter denitrificans sp. nov. is proposed. The type strain is JM10B15T (=GDMCC 1.4148T=KCTC 8140T).

Analysis of the Current Situation and Development Strategies of Marine Fisheries in Jiangmen City

Based on the statistical data of marine fisheries in Jiangmen City in the past three years, this paper analyzes the development status and trend of marine fisheries. By combining the information obtained from enterprise research, it deeply analyzes the main problems existing in the development of marine fisheries in Jiangmen City and puts forward corresponding optimization countermeasures, which provide suggestions for promoting the high-quality development of marine fisheries in Jiangmen City.

基于ARIMA模型的江门市GDP分析与预测

基于ARIMA模型的江门市GDP分析与预测

Molecular characterization of a novel 83.9-kb deletion of the α-globin upstream regulatory elements by long-read sequencing

Inherited deletions of upstream regulatory elements of α-globin genes give rise to α-thalassemia, which is an autosomal recessive monogenic disease. However, conventional thalassemia target diagnosis often fails to identify these rare deletions. Here we reported a family with two previous pregnancies of Hb Bart's hydrops fetalis and was seeking for prenatal diagnosis during the third pregnancy. Both parents had low level of Hemoglobin A2 indicating α-thalassemia. Conventional Gap-PCR and PCR-reverse dot blot showed the father carried –SEA deletion but did not identify any variants in the mother. Multiplex ligation-dependent probe amplification identified a deletion containing two HS-40 probes but could not determine the exact region. Finally, a long-read sequencing (LRS)-based approach directly identified that the exact deletion region was chr16: 48,642-132,584, which was located in the α-globin upstream regulatory elements and named (αα)JM after the Jiangmen city. Gap-PCR and Sanger sequencing confirmed the breakpoint. Both the mother and fetus from the third pregnancy carried heterozygous (αα)JM, and the fetus was normally delivered at gestational age of 39 weeks. This study demonstrated that LRS technology had great advantages over conventional target diagnosis methods for identifying rare thalassemia variants and assisted better carrier screening and prenatal diagnosis of thalassemia.

Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization

Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative samples used for machine learning. Recurrent neural network (RNN) has a memory function, so when using an RNN for landslide susceptibility mapping purposes, the input order of the landslide-influencing factors affects the resulting quality of the model. The information value analysis calculates the landslide-influencing factors, determines the input order of data based on the importance of any specific factor in determining the landslide susceptibility, and improves the prediction potential of recurrent neural networks. The simple recurrent unit (SRU), a newly proposed variant of the recurrent neural network, is characterized by possessing a faster processing speed and currently has less application history in landslide susceptibility mapping. This study used recurrent neural networks optimized by information value analysis for landslide susceptibility mapping in Xinhui District, Jiangmen City, Guangdong Province, China. Four models were constructed: the RNN model with optimized negative sample selection, the SRU model with optimized negative sample selection, the RNN model, and the SRU model. The results show that the RNN model with optimized negative sample selection has the best performance in terms of AUC value (0.9280), followed by the SRU model with optimized negative sample selection (0.9057), the RNN model (0.7277), and the SRU model (0.6355). In addition, several objective measures of accuracy (0.8598), recall (0.8302), F1 score (0.8544), Matthews correlation coefficient (0.7206), and the receiver operating characteristic also show that the RNN model performs the best. Therefore, the information value analysis can be used to optimize negative sample selection in landslide sensitivity mapping in order to improve the model’s performance; second, SRU is a weaker method than RNN in terms of model performance.

The spatial distribution, health risk, and cytotoxicity of metal(loid)s in contaminated field soils: The role of Cd in human gastric cells damage

The spatial distribution and pollution level of heavy metal(loid)s in soil (0–6 m) from a typical industrial region in Jiangmen City, Southeast China was investigated. Their bioaccessibility, health risk, and human gastric cytotoxicity in topsoil were also evaluated using an in vitro digestion/human cell model. The average concentrations of Cd (87.52 mg/kg), Co (106.9 mg/kg), and Ni (1007 mg/kg) exceeded the risk screening values. The distribution profiles of metal(loid)s showed a downward migration trend to reach a depth of 2 m. The highest contamination was found in topsoil (0–0.5 m), with the concentrations of As, Cd, Co, and Ni being 46.98, 348.28, 317.44, and 2395.60 mg/kg, respectively, while Cd showed the highest bioaccessibility in the gastric phase (72.80 %), followed by Co (21.08 %), Ni (18.27 %), and As (5.26 %) and unacceptable carcinogenic risk. Moreover, the gastric digesta of topsoil suppressed the cell viability and triggered cell apoptosis, evidenced by disruption of mitochondrial transmembrane potential and increase of Cytochrome c (Cyt c) and Caspases 3/9 mRNA expression. Bioaccessible Cd in topsoil was responsible for those adverse effects. Our data suggest the importance to reduce Cd in the soil to decrease its adverse impacts on the human stomach.

Multi-spatiotemporal AOD trends and association with land use changes over the Guangdong-Hong Kong-Macao Greater Bay Area during 2001-2021.

Analyzing long-term variations of aerosol optical depth (AOD) is beneficial for determining high-pollutant-risk areas and formulating mitigation policies. In this study, multi-spatiotemporal trends and periodicity of AOD, as well as the persistence over the Guangdong-Hong Kong-Macao Greater Bay Area from 2001 to 2021, were investigated by the extreme-point symmetric mode decomposition (ESMD), Theil-Sen Median trend analysis and Hurst exponent. The results elucidate that AOD exhibits fluctuant variations during the 21-year period with the year 2012 as the turning point. There is a slight upward tendency (0.009year-1) in the pre-2012 period but a pronounced downward trend (- 0.03year-1) in the post-2012 period, suggesting an overall declining trend in the study area. The northern cities in the area present an increasing-stable-decreasing trend of monthly average AOD, whereas other cities have an increasing-fluctuating-decreasing trend over the study period. The decreasing rate in the western parts is higher than that in the eastern parts, like Zhaoqing, Jiangmen and Foshan city. A continuous decline of AOD is dominated over the study area, whereas an anti-persistence tendency is accumulated in the northeastern parts. Additionally, elevated AOD can be observed in unused land, water bodies and construction land, while grassland, cropland and woodland have lower AOD. The decreasing rate is larger when land-use types with high AOD are converted to those with low AOD; otherwise, the decreasing rate is smaller. The results have a great significance for improving the understanding of long-term variations of AOD, as well as providing a scientific basis to formulate environmental protection and mitigation practices.

Research on Image Classification and Retrieval Using Deep Learning with Attention Mechanism on Diaspora Chinese Architectural Heritage in Jiangmen, China

The study of the architectural heritage of the Chinese diaspora has an important role and significance in China’s historical and cultural background in the preservation of cultural data, the restoration of images, and in the analysis of human social and ideological conditions. The images from the architectural heritage of the Chinese diaspora usually include frescos, decorative patterns, chandelier base patterns, various architectural styles and other major types of architecture. Images of the architectural heritage of the Chinese diaspora in Jiangmen City, Guangdong Province, China are the research object of this study. A total of 5073 images of diaspora Chinese buildings in 64 villages and 16 towns were collected. In view of the fact that different types of image vary greatly in features while there are only small differences among the features of the same type of image, this study uses the depth learning method to design the Convolutional Neural Network Attention Retrieval Framework (CNNAR Framework). This approach can be divided into two stages. In the first stage, the transfer learning method is used to classify the image in question by transferring the trained parameters of the Paris500K datasets image source network to the target network for training, and thus the classified image is obtained. The advantage of this method is that it narrows the retrieval range of the target image. In the second stage, the fusion attention mechanism is used to extract the features of the images that have been classified, and the distance between similar images of the same type is reduced by loss of contrast. When we retrieve images, we can use the features extracted in the second stage to measure the similarities among them and return the retrieval results. The results show that the classification accuracy of the proposed method reaches 98.3% in the heritage image datasets of the JMI Chinese diaspora architectures. The mean Average Precision (mAP) of the proposed algorithm can reach 76.6%, which is better than several mainstream model algorithms. At the same time, the image results retrieved by the algorithm in this paper are very similar to those of the query image. In addition, the CNNAR retrieval framework proposed in this paper achieves accuracies of 71.8% and 72.5% on the public data sets Paris500K and Corel5K, respectively, which can be greatly generalized and can, therefore, also be effectively applied to other topics datasets. The JMI architectural heritage image database constructed in this study, which is rich in cultural connotations of diaspora Chinese homeland life, can provide strong and reliable data support for the follow-up study of the zeitgeist of the culture reflected in architecture and the integration of Chinese and Western aesthetics. At the same time, through the rapid identification, classification, and retrieval of precious architectural images stored in the database, similar target images can be retrieved reasonably and accurately; then, accurate techniques can be provided to restore old and damaged products of an architectural heritage.

Machine learning based on the graph convolutional self-organizing map method increases the accuracy of pollution source identification: A case study of trace metal(loid)s in soils of Jiangmen City, south China

Rapid economic development and industrialization may include environmentally harmful human activities that cause heavy-metal accumulation in soils, ultimately threatening the quality of the soil environment and human health. Therefore, accurate identification of pollution sources is an important weapon in efforts to control and prevent pollution. The self-organizing map (SOM) method is widely used in pollution source identification because of its capacity for visualization of high-dimensional data. The SOM ignores the graph structure relationship among chemical elements in soils; the SOM analysis of pollution sources has high uncertainty. Here, we propose a new analysis method, i.e., the graph convolutional self-organizing map (GCSOM), which uses a graph convolutional network (GCN) to extract the graph structure relationship among the chemical elements in soils, then performs data visualization using an SOM. We compared the performances of GCSOM and SOM, then assessed the pollution source characteristics of trace metal(loid)s (TMs, mostly heavy metals) in Jiangmen City using the GCSOM. Our experimental results showed that the GCSOM is superior to the SOM for identification of TM sources, while the TMs in the soil of Jiangmen originate from three main sources: agricultural activities (mainly in Taishan City, Jiangmen), traffic emissions (mainly in Xinhui and Pengjiang Districts), and industrial activities (mainly in Xinhui District). The risk assessment indicated that the risk of all TMs was within threshold.

Quantitative risk assessment of typhoon storm surge for multi-risk sources

Typhoon storm surge (TSS) is a complex marine disaster affected by multi-risk sources. Quantitative risk assessment is an important prerequisite for identifying risk areas and designing risk reduction strategies. This paper aims to propose a rapid, accurate, and comprehensive quantitative risk assessment method for TSS under multi-risk sources, including disaster occurrence probability and severity. First, identify the primary risk sources according to the disaster-causing mechanism of TSS. Then, based on the official public data from 1989 to 2020, the dependence structure among multi-risk sources is constructed using Copulas to calculate the probability of each superposition scenario. Meanwhile, build visual scenario databases employing Geographical Information System (GIS) techniques. Subsequently, the extent and depth of inundation are translated into economic risk and population risk using GIS and depth-damage functions. Finally, taking the “Mangkhut” as a case study, the method’s feasibility and accuracy are verified. The results show that the primary risk sources of TSS are storm tide, astronomical tide and coastal waves. The Gumbel Copula is optimal, with OLS (ordinary least squares) and D of 0.0186 and 0.1831, respectively. The probability assessment under different superposition scenarios indicates that the greatest threat of TSS in Guangzhou comes from the storm tide and the astronomical tide. As for the “Mangkhut” case study in Jiangmen City, the assesses occurrence probability is 0.0355%, the accuracy of economic risk assessment (except mariculture) is 95.28%, and the accuracy of population risk assessment is 98.60%. Residences and the disaster-bearing bodies in 0–3 m inundation depth are most severely affected by TSS disasters. Measures such as locating residential and important buildings away from the shoreline (at least 10 km) and ground (above 3 m), formulating disaster emergency plans, and developing the forecast and prevention of storm tides and astronomical tides will help ensure the safety of residents’ life and property. This paper provides an efficient and accurate method, which is of great significance for disaster control, sustainable development, and decision-making.

Mitochondrial DNA and Microsatellite Analyses Showed Panmixia between Temporal Samples in Endangered Anguilla japonica in the Pearl River Basin (China).

The Japanese eel (Anguilla japonica) is a commercially important species in East Asia, the abundance of which has rapidly decreased in recent decades. The fishery resource in the Pearl River basin has mainly deteriorated due to overexploitation and habitat degradation. Knowledge on its genetic status is indispensable for resource management. In this study, we explored the temporal genetic structure of A. japonica on the basis of the concatenated sequences of two mitochondrial fragments (mtDNA) and eight microsatellite markers. A total of nine temporal samples (N = 127) were collected during 2019 and 2021 from Jiangmen City, China, which is located in the Pearl River estuary. mtDNA sequence analysis showed a high level of haplotype diversity, and yielded 124 haplotypes with ranging from 9 to 19 in temporal samples. All microsatellite loci were polymorphic among each of the nine temporal samples, with 150 alleles identified across all samples. Pairwise FST values were low and nonsignificant according to both mtDNA and microsatellite markers. STRUCTURE analysis showed that all temporal samples were not clearly differentiated from each other. The yielded outcomes supported a panmictic pattern in different temporal A. japonica samples. Therefore, our results call for the management of A. japonica as a single unit and joint conservation strategy of the species, since overexploitation in any region will decrease its global resource.

Etiology of acute febrile illnesses in Southern China: Findings from a two-year sentinel surveillance project, 2017-2019.

Southern China is at risk for arborvirus disease transmission, including Zika virus and dengue. Patients often present to clinical care with non-specific acute febrile illnesses (AFI). To better describe the etiology of AFI, we implemented a two-year AFI surveillance project at five sentinel hospitals in Yunnan and Guangdong Provinces. Between June 2017 and August 2019, we enrolled patients between 2 and 65 years of age presenting at one sentinel hospital in Mengla County, Yunnan, and four in Jiangmen City, Guangdong, with symptoms of AFI (acute onset of fever ≥ 37.5°C within the past 7 days) without respiratory symptoms or diarrhea. Demographic, epidemiologic, and clinical information was obtained and entered into a web-based AFI surveillance database. A custom TaqMan Array card (TAC) was used to test patients' whole blood specimens for 27 different pathogens using real-time polymerase chain reaction assays. During the two-year project period, 836 patients were enrolled; 443 patients from Mengla County and 393 patients from Jiangmen City. The median age was 33 years [range: 2-65], and most were hospitalized [641, 77%]. Of 796 patients with valid TAC results, 341 (43%) were positive for at least one of the 10 unique pathogens detected. This included 205 (26%) patients positive for dengue virus, 60 (8%) for Orientia tsutsugamushi, and 42 (5%) for Coxiella burnetii. Ten patients (1%) in Jiangmen City tested positive for malaria, 8 of whom reported recent travel outside of China. TAC results were negative for 455 (57%) patients. None of the patients had a positive TAC detection for Zika virus. The project detected variability in the etiology of AFI in Southern China and highlighted the importance of differential diagnosis. Dengue, O. tsutsugamushi, and C. burnetii were the most frequently identified pathogens among enrolled AFI patients. As a non-notifiable disease, the frequent detection of C. burnetii is noteworthy and warrants additional investigation. The project provided a framework for routine surveillance for persons presenting with AFI.

Analyzing the Current Situation of High-Quality Development and Its Optimization Path: A Case Study of Xinhui District

Based on the social and economic development data of Xinhui District in Jiangmen City from 2016 to 2020, as well as the characteristics and connotation of high-quality development, this study constructs a high-quality development evaluation index system, explores the current situation of high-quality development in Xinhui District from a quantitative perspective by factor analysis, compares it with the high-quality development situation of other regions, so as to ascertain the gap existing in Xinhui District, and finally, proposes corresponding improvement measures to provide reference for local the government to formulate policies.

First full-scale application of electron beam technology for treating dyeing wastewater (30,000 m3/d) in China

Electron beam technology has been widely investigated for the degradation of recalcitrant organic pollutants in water and wastewater in laboratory scale. However, it needs verification in practical application for long-term operation. Strikingly, for the first time, we have successfully applied electron beam technology to treat the actual dyeing wastewater in capacity of 30,000 m3/d in Jiangmen city, Guangdong Province, China. The continuous monitoring data of the effluent water quality showed that electron beam technology can be used as advanced treatment process to effectively improve the removal efficiency of COD and color. The effluent COD of electron beam unit stayed between 20 and 50 mg/L when the influent COD ranged from 180 to 280 mg/L, and the color of the treated dyeing wastewater decreased to about 10 times. The operational cost of whole wastewater treatment processes was about 2.0–2.5 ¥/m3, including electron beam unit. Based on the monitoring data of more than one year's operation, it can be concluded that electron beam technology could provide a solution for refractory industrial wastewater treatment, which is a high efficient and cost-effective advanced treatment process.

JUNO Oscillation Physics

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton multi-purpose liquid scintillator detector with an expected energy resolution, under construction in a 700 m underground laboratory in the south of China (Jiangmen city, Guangdong province). The exceptional energy resolution and the massive fiducial volume of the JUNO detector offer great opportunities for addressing many essential topics in neutrino and astroparticle physics. JUNO’s primary goals are to determine the neutrino mass ordering and precisely measure the related neutrino oscillation parameters. With six years of data taking with reactor antineutrinos, JUNO can determine the mass ordering at a 3-4σ significance and the neutrino oscillation parameters sin2 θ 12, , and to a precision of better than 0.6%. In addition, the atmospheric neutrino and solar neutrino measurement at JUNO can also provide complementary and important information for neutrino oscillation physics. This work focuses on the oscillation physics of JUNO, which includes measurement and analysis of the reactor neutrinos, the atmospheric neutrinos, and the solar neutrinos. With the delicate energy response calibration and event reconstruction potential, JUNO will make a world-leading measurement on the neutrino oscillation parameters and neutrino mass ordering in the near future.

First report of Curvularia gladioli causing leaf spots on Gladiolus gandavensis in China.

Gladiolus (Gladiolus gandavensis Van Houtte) is a perennial plant in the family Iridaceae, which shows sword-shaped leaves and spikes of brilliantly colored irregular flowers arising from corms. It is one of the most important fresh cut flowers and is widely cultivated worldwide, including in China. In September 2020, white pinpoints were first observed on gladiolus leaves in Jiangmen City, Guangdong Province, China. The white spots eventually turned brown. The lesions then developed into oval to circular spots, which were surrounded with an obvious yellow halo. The spots expanded and coalesced, causing leaf blight. These symptoms were observed on approximately 10% of gladiolus plants in fields measuring ca. 70 ha. Symptomatic leaves were sampled from fields, surface sterilized in 75% ethanol for 30 s, submerged in a 2% NaOCl solution for 10 min, and rinsed three times with sterile water. The samples were then cut into pieces (5 × 5 mm) and incubated for 4 d on potato dextrose agar (PDA) at 25°C. A representative fungal colony was subcultured onto new PDA and grown for another 7 d, and its mycelium appeared to be grayish-black and villiform. This strain was named as Cg_TS. Its conidiophores were simple, septate, cylindrical in shape, and moderate brown in color. They occurred singly or in groups. They were straight or slightly flexuous and ranged in size from 57.0 to 80.0 μm × 4.0 to 8.0 μm. Conidia were 3-distoseptate and curved at the third cell from the base. The third cell was swollen to one side and larger than other cells. These conidia ranged in size from 23.5 to 32.0 μm × 11.5 to 16.0 μm. These morphological characteristics were consistent with the description of Curvularia gladioli Boerema & Hamers (Boerema and Hamers 1989). Using primer pair ITS1 and ITS4, PCR was applied to amplify the internal transcribed spacer (ITS) region of rDNA. This sequence (GenBank accession No. MW426196.1) was subjected to BLAST in GenBank, suggesting that it was most similar to C. gladioli sequences, LT631345.1 and HG778987.1, with both of 99.49% of similarity. To fulfill Koch's postulates, healthy two-month-old gladiolus plants were used for pathogenicity testing, and the leaves were wounded by pressing slightly with a pipette tip. Mycelium disks (3 mm diameter) were applied onto wounded leaves of 10 plants. Another 10 healthy plants were inoculated with PDA disks which served as control. Inoculated samples were placed in a greenhouse at 25°C and 90% relative humidity. After 3 d, brown leaf spots appeared on all of pathogen-inoculated leaves. The symptoms were consistent with those initially observed and C. gladioli was re-isolated from the symptomatic tissue. Identification was confirmed by morphological observation and ITS sequencing. Control leaves remained symptomless. The curvularia fungus was firstly reported on gladiolus in Florida in 1947 and spread globally via infected corms (Torre et al. 2015), it was also reported to cause leaf spots on gladiolus in Brazil in 2013 (Torres et al. 2013). Although C. gladioli had been recorded as a Curvularia species occurring in China (Zhang et al. 2006), it was not reported to cause leaf spots on gladiolus in Guangdong Province and elsewhere in China. To our knowledge, this is the first report of Curvularia gladioli causing leaf spots on gladiolus in China. Identification of this pathogen will help develop diagnostic methods for corms and seedlings, and may lead to the development of appropriate chemical management strategies.

Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir

In this study, an inland reservoir water quality parameters’ inversion model was developed using a back propagation (BP) neural network to conduct reservoir eutrophication evaluation, according to multi-temporal remote sensing images and field observations. The inversion model based on the BP neural network (the BP inversion model) was applied to a large inland reservoir in Jiangmen city, South China, according to the field observations of five water quality parameters, namely, Chlorophyl-a (Chl-a), Secchi Depth (SD), total phosphorus (TP), total nitrogen (TN), and Permanganate of Chemical Oxygen Demand (CODMn), and twelve periods of Landsat8 satellite remote sensing images. The reservoir eutrophication was evaluated. The accuracy of the BP inversion model for each water parameter was compared with that of the linear inversion model, and the BP inversion models of two parameters (i.e., Chl-a and CODMn) with larger fluctuation range were superior to the two multiple linear inversion models due to the ability of improving the generalization of the BP neural network. The Dashahe Reservoir was basically in the state of mesotrophication and light eutrophication. The area of light eutrophication accounted for larger proportions in spring and autumn, and the reservoir inflow was the main source of nutrient salts.

Statistical Evaluation of Green Economic Development Based on Rough Set Calculation Method

In the context of big data, decisions and evaluations in business, economics and other fields increasingly rely on data analysis rather than experience and intuition. Data grows exponentially, but not all of it is useful. Rough set is a data mining method to deal with data classification, which can effectively remove redundant information. Green economy is an important proposition of the current world development. The evaluation of green economy should not only focus on economic data, but also environmental and social data. How to filter the data and establish a green economic index evaluation model is the problem this paper wants to solve. Based on the data of green economy and rough set algorithm, this paper evaluates the development level of green economy. Taking the data of Jiangmen city as an example, the evaluation model is tested and analyzed.