In this study we aimed to analyze the effects of water temperature and diet on the length-weight rela- tionship and condition of juvenile Malabar blood snapper Lutjanus malabaricus over a 30-d experimental period. The...In this study we aimed to analyze the effects of water temperature and diet on the length-weight rela- tionship and condition of juvenile Malabar blood snapper Lutjanus malabaricus over a 30-d experimental period. The experiment was conducted in the laboratory using a flow-through-sea-water system. The fish were subjected to four different temperatures （22, 26, 30, and 34 ℃） and two diets （commercial pellet and natural shrimp）. Fish were fed twice daily. L. malabancus exhibited negative allometric growth （b〈3） at the beginning of the experiment （Day 0） at all temperatures and both diets except for 22 ℃ fed with shrimp, which showed isometric growth （b=3）. Conversely, at the end of the experiment （Day 30） fish showed isometric growth （b=3） at 30 ℃ fed with the pellet diet, indicating that the shape of the fish did not change with increasing weight and length, and a positive allometric growth （b〉3） at 30 ℃ fed with shrimp diet, which indicated that fish weight increases faster than their length. The rest of the temperatures represented negative allometric growth （b〈3） on both diet, meaning that fish became lighter with increasing size. The condition factors in the initial and final measurements were greater than 1, indicating the state of health of the fish, except for those fed on a pellet diet at 34 ℃. However, the best condition was obtained at 30 ℃ on both diets. Nev- ertheless, diets did not have a significant effect on growth and condition of juvenile L. malabaricus. The data obtained from this study suggested culturing L. malabaricus at 30 ℃ and feeding on the pellet or shrimp diet, which will optimize the overall production and condition of this commercially important fish species.展开更多
Several dams have been constructed in Ethiopia, East Africa to support electricity and/or irrigation. Fishes were introduced to some of these dams. Thus, the objective of this study was to assess the dynamics and cond...Several dams have been constructed in Ethiopia, East Africa to support electricity and/or irrigation. Fishes were introduced to some of these dams. Thus, the objective of this study was to assess the dynamics and condition factor of Oriochromis niloticus in Korir and Lailay Wukro Dams, Northern Ethiopias. The study was conducted by deploying two gill net, every month in the littoral and pelagic zones of the two dams from August 2011 to May 2012. A total of 524 O. niloticus, 278 from Lailay Wukro and 246 from Korir dams were collected. The monthly catch per unit effort （CPUE） showed significant variation among months, the highest catch was in May and the least was in January 2012 （P 〈 0.000）. Catches of fish encountered higher in the littoral （69.1%） than in the pelagic zones （30.9%） （P 〈 0.000）. The condition factor of O. niloticus in the two reservoirs remains high, in Korir 2.05 and in Lailay Wukro 1.65 （P 〈 0.000）. In these small tropical dams, O. niloticus mature as they are smaller in size （Ls0： TL average 22.5 cm）. The ratio of male to female was 1.3：1 （P 〈 0.016）. The two dams have favorable condition for high production of O. niloticus. This high potential for fish production in the dams may be sustainable if the local authorities set a regulation to control the illegal fishing activity.展开更多
Length-weight relationship(LWR),condition factor(k)of the black chin tilapia,Sarotherodon melanotheron(Rüppel,1852)from Forcados River estuary Nigeria was investigated.The fish were collected monthly from fisherm...Length-weight relationship(LWR),condition factor(k)of the black chin tilapia,Sarotherodon melanotheron(Rüppel,1852)from Forcados River estuary Nigeria was investigated.The fish were collected monthly from fishermen for a period of 24 months(between April 2012 and March 2014).699 specimens of the fish species were collected.The Length-weight relationship(LWR)of the fish was evaluated using the equation:W=a L^(b) while the condition factor of the fish was determined using the equation;K=100W L^(b).The standard length of sampled S.melanotheron ranged from 4.15 to 18.92 cm,total length 6.01 and 22.5 cm while the weight ranged from 7.85-286.71 g.The b value 2.1299 was less than 3 indicating that the growth pattern of the fish was allometric.The correlation co-efficient(r)value for S.melanotheron was 0.7528.The condition factor for the combined sexes fluctuated monthly.The length-weight relationships and condition factor of S.melanotheron in Forcados river estuary indicated that the fish were above average condition.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a...In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.展开更多
Labeo victorianus(Boulenger,1901)is one of the endemic fishes in Lake Victoria Basin(LVB)but is now threatened by multiple stressors caused by human activities.We investigated spatial and temporal variability in food ...Labeo victorianus(Boulenger,1901)is one of the endemic fishes in Lake Victoria Basin(LVB)but is now threatened by multiple stressors caused by human activities.We investigated spatial and temporal variability in food composition and condition of L.victorianus in influent rivers of Lake Victoria,Kenya.Sampling was done during the dry and wet seasons by electrofishing.Food composition analysis showed that L.victorianus is a benthophagus and omnivorous species whose diet is dominated by detritus,periphyton and insects.There were differences in food composition among rivers,with significant river X season interactions(PERMANOVA F=11.6,df=4,p=0.001),suggesting that the diet depended on prevailing environmental conditions.In turbid rivers,the diet was dominated by detritus while in less turbid rivers it was dominated by insects and periphyton.Sand and mud also formed a significant part of the diet,which was an indication of a limited occurrence of preferable food items.There were ontogenetic shifts in food composition(PERMANOVA F=4.6,df=3,p=0.001),but also with a spatial interaction(PERMANOVA F=5.6,df=7,p=0.001),further indicating the role of environmental conditions in determining the diet for different size classes.Interestingly,the fish condition did not differ among rivers.This study shows that turbidity and organic matter and nutrient loading determine the diet of L.victorianus in LVB rivers,and provides further justification for the maintenance of water quality as a conservation measure for threatened species.展开更多
To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method propose...To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.展开更多
Flood is one of the most devastating natural hazards.Employing machine learning models to construct flood susceptibility maps has become a pivotal step for decision-makers in disaster prevention and management.Existin...Flood is one of the most devastating natural hazards.Employing machine learning models to construct flood susceptibility maps has become a pivotal step for decision-makers in disaster prevention and management.Existing flood conditioning factors inadequately account for regional characteristics of flood in the depiction of topography,potentially leading to an overestimation of flood susceptibility in flat areas.Addressing this gap,this study proposes a novel flood conditioning factor,local convexity factor(LCF),to enhance the accuracy of flood susceptibility modeling.Initially,LCF is computed based on a standard normal Gaussian surface to highlight elevation variations in local terrain.Subsequently,LCF is applied to flood susceptibility modeling using seven machine learning models across four distinct basins.Comparative analysis is conducted between flood susceptibility maps with and without the application of LCF to evaluate its impact on flood susceptibility modeling.The results demonstrate that the proposed LCF can enhance the accuracy of flood susceptibility modeling to varying degrees,across the four basins investigated.The Fujiang basin exhibited the most substantial improvement,with its AUC improved from 0.861 to 0.886,Producer’s Agreement improved from 0.869 to 0.899,and Overall Agreement improved from 0.778 to 0.811.Comparation with hydrodynamic inundation maps shows that particularly in relatively flat terrain areas,flood susceptibility maps incorporating LCF offer more precise delineation between flood-prone and non-flood-prone zones.This research holds potential for widespread application in the prediction of flood susceptibility using machine learning models,providing a novel perspective for enhancing their accuracy.展开更多
Aim: To investigate the effect of air-conditioner exposure on semen quality. Methods: The data came from the healthy male volunteers, aged 22 to 30 years, who went to centers for maternity and children health for prem...Aim: To investigate the effect of air-conditioner exposure on semen quality. Methods: The data came from the healthy male volunteers, aged 22 to 30 years, who went to centers for maternity and children health for premarital physical examination in Shanghai, Henan, Zhejiang and Hebei from December 1998 to February 2000. The sampling size is 304. Results: Among the subjects, 90 (29.6 %) had air-conditioner at home and the rest did not. X2-test and multiple logistic regression analyses showed that, the difference between the exposure and control groups was statistically significant in semen volume, sperm density and proportion of sperm with normal morphology. The three indexes were lower in the exposure group. Conclusion: Air-conditioner exposure possibly influences the semen quality.展开更多
[Objective] The paper was to analyze the meteorological epidemic factors for occurrence and prevalence of tobacco bacterial wilt （ Ralstonia solanaca- rum）, and to study control effects of different soil conditioner...[Objective] The paper was to analyze the meteorological epidemic factors for occurrence and prevalence of tobacco bacterial wilt （ Ralstonia solanaca- rum）, and to study control effects of different soil conditioners on the bacterial disease in Gacligongshan demonstration area of green, ecological, high quality tobac- co leaf production. [Method] The plots attacked by tobacco bacterial wilt over the years were selected and the incidence of the disease was periodically surveyed in tobacco growth period in 2012, 2103 and 2014, respectively. 10 d Effective accumulated temperature and rainfall were counted according to the meteorological data, and the relationship between meteorological factors and disease index was analyzed. The control effects of three kinds of soil conditioners ＂Zhuanggenfeng＂, refined fulvic acid and lime on tobacco bacterial wilt were tested. [ Result] The analysis results of meteorological factors showed that 10 d effective accumulated temperature and rainfall were positively correlated to disease index. The variation curve of 10 d effective accumulated temperature and rainfall reflected the change trend of disease index. The pH values were increased by 0.57, 0.50 and 0.72 respectively after applying ＂Zhuanggenfeng＂, refined fulvic acid and lime. The aver- age control effects on tobacco bacterial wilt were 60.74% -62. 18%, 53.05% -59.53%, and 48.59% -58.53%, respectively. [ Conclusion] 10 d Effective accumulated temperature and rainfall could be used as important reference for disease forecasting and controUing. The usage of soil conditioner has a certain preven- tion and control effect on tobacco bacterial wilt disease by forming soil conditions conducive to flue-cured tobacco growth but adverse to disease survival, which is an effective auxiliary method against the disease.展开更多
Anthropogenic activities have greatly affected water resources on a global scale where the world is experiencing water quality and resources issues. Heavy metal is a crucial group of pollutants that is toxic to the en...Anthropogenic activities have greatly affected water resources on a global scale where the world is experiencing water quality and resources issues. Heavy metal is a crucial group of pollutants that is toxic to the environment even at low concentrations due to its bioaccumulation and biomagnification capabilities in living organisms. The detrimental effects of heavy metals on living organisms are due to their bioaccumulation in the aquatic ecosystem. Cadmium may result in adverse health effects due to its high toxicity. The study is conducted to determine the cadmium exposure effects on the morphometric indices of Anabas testudineus which are the Scaling Coefficient (SC) and Condition Factor (K) of such species. Anabas testudineus is exposed to four different cadmium treatment groups namely the control group, cadmium treatment group of 0.005 mg/L, 0.010 mg/L, and 0.015 mg/L for 16 weeks. The findings of the study have reported inconsistent trends in the values of SC and a decrease in the value of K with increasing cadmium concentration. The trend for the average SC has shown an overall decrease in value while the pattern of the K value is inconsistent in each treatment group with exposure time. Collectively, no significant differences for SC and K of A. testudineus in different treatment groups as well as comparison between treatment groups with time exposure.展开更多
Mapping floods is crucial for effective disaster management. This study focuses on flood assessment in northern Morocco, specifically Tangier, Tetouan, and Larache. Due to the lack of a comprehensive flood inventory m...Mapping floods is crucial for effective disaster management. This study focuses on flood assessment in northern Morocco, specifically Tangier, Tetouan, and Larache. Due to the lack of a comprehensive flood inventory map, we used unsupervised learning techniques, such as K-means clustering and fuzzy logic algorithms, to predict flood-prone areas. We identified nine conditioning factors influencing flood risk: elevation, slope, aspect, plan curvature, profile curvature, land use, soil type, normalized difference vegetation index(NDVI), and topographic position index(TPI). Using Landsat-8 imagery and a Digital Elevation Model(DEM) within a Geographic Information System(GIS), we analyzed topographic and geo-environmental variables. K-means clustering achieved silhouette scores of 0.66 in Tangier and 0.70 in Tetouan, while the fuzzy logic method in Larache produced a Davies-Bouldin Index(DBI) score of 0.35. The maps classified flood risk levels into low, moderate, and high categories. This research demonstrates the integration of machine learning and remote sensing for predicting flood-prone areas without existing flood inventory maps. Our findings highlight the main factors contributing to flash floods and assess their impact, enhancing the understanding of flood dynamics and improving flood management strategies in vulnerable regions.展开更多
基金supported by the Ministry of Science Technology and Innovation Malaysia(MOSTI)(No.04-01-02-SF1208)
文摘In this study we aimed to analyze the effects of water temperature and diet on the length-weight rela- tionship and condition of juvenile Malabar blood snapper Lutjanus malabaricus over a 30-d experimental period. The experiment was conducted in the laboratory using a flow-through-sea-water system. The fish were subjected to four different temperatures （22, 26, 30, and 34 ℃） and two diets （commercial pellet and natural shrimp）. Fish were fed twice daily. L. malabancus exhibited negative allometric growth （b〈3） at the beginning of the experiment （Day 0） at all temperatures and both diets except for 22 ℃ fed with shrimp, which showed isometric growth （b=3）. Conversely, at the end of the experiment （Day 30） fish showed isometric growth （b=3） at 30 ℃ fed with the pellet diet, indicating that the shape of the fish did not change with increasing weight and length, and a positive allometric growth （b〉3） at 30 ℃ fed with shrimp diet, which indicated that fish weight increases faster than their length. The rest of the temperatures represented negative allometric growth （b〈3） on both diet, meaning that fish became lighter with increasing size. The condition factors in the initial and final measurements were greater than 1, indicating the state of health of the fish, except for those fed on a pellet diet at 34 ℃. However, the best condition was obtained at 30 ℃ on both diets. Nev- ertheless, diets did not have a significant effect on growth and condition of juvenile L. malabaricus. The data obtained from this study suggested culturing L. malabaricus at 30 ℃ and feeding on the pellet or shrimp diet, which will optimize the overall production and condition of this commercially important fish species.
文摘Several dams have been constructed in Ethiopia, East Africa to support electricity and/or irrigation. Fishes were introduced to some of these dams. Thus, the objective of this study was to assess the dynamics and condition factor of Oriochromis niloticus in Korir and Lailay Wukro Dams, Northern Ethiopias. The study was conducted by deploying two gill net, every month in the littoral and pelagic zones of the two dams from August 2011 to May 2012. A total of 524 O. niloticus, 278 from Lailay Wukro and 246 from Korir dams were collected. The monthly catch per unit effort （CPUE） showed significant variation among months, the highest catch was in May and the least was in January 2012 （P 〈 0.000）. Catches of fish encountered higher in the littoral （69.1%） than in the pelagic zones （30.9%） （P 〈 0.000）. The condition factor of O. niloticus in the two reservoirs remains high, in Korir 2.05 and in Lailay Wukro 1.65 （P 〈 0.000）. In these small tropical dams, O. niloticus mature as they are smaller in size （Ls0： TL average 22.5 cm）. The ratio of male to female was 1.3：1 （P 〈 0.016）. The two dams have favorable condition for high production of O. niloticus. This high potential for fish production in the dams may be sustainable if the local authorities set a regulation to control the illegal fishing activity.
文摘Length-weight relationship(LWR),condition factor(k)of the black chin tilapia,Sarotherodon melanotheron(Rüppel,1852)from Forcados River estuary Nigeria was investigated.The fish were collected monthly from fishermen for a period of 24 months(between April 2012 and March 2014).699 specimens of the fish species were collected.The Length-weight relationship(LWR)of the fish was evaluated using the equation:W=a L^(b) while the condition factor of the fish was determined using the equation;K=100W L^(b).The standard length of sampled S.melanotheron ranged from 4.15 to 18.92 cm,total length 6.01 and 22.5 cm while the weight ranged from 7.85-286.71 g.The b value 2.1299 was less than 3 indicating that the growth pattern of the fish was allometric.The correlation co-efficient(r)value for S.melanotheron was 0.7528.The condition factor for the combined sexes fluctuated monthly.The length-weight relationships and condition factor of S.melanotheron in Forcados river estuary indicated that the fish were above average condition.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
基金This work is funded by the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the National Science Fund for Distinguished Young Scholars of China(Grant No.52222905).
文摘In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.
基金support was provided by the National Research Fund(NRF),Kenya through the KISS Project.
文摘Labeo victorianus(Boulenger,1901)is one of the endemic fishes in Lake Victoria Basin(LVB)but is now threatened by multiple stressors caused by human activities.We investigated spatial and temporal variability in food composition and condition of L.victorianus in influent rivers of Lake Victoria,Kenya.Sampling was done during the dry and wet seasons by electrofishing.Food composition analysis showed that L.victorianus is a benthophagus and omnivorous species whose diet is dominated by detritus,periphyton and insects.There were differences in food composition among rivers,with significant river X season interactions(PERMANOVA F=11.6,df=4,p=0.001),suggesting that the diet depended on prevailing environmental conditions.In turbid rivers,the diet was dominated by detritus while in less turbid rivers it was dominated by insects and periphyton.Sand and mud also formed a significant part of the diet,which was an indication of a limited occurrence of preferable food items.There were ontogenetic shifts in food composition(PERMANOVA F=4.6,df=3,p=0.001),but also with a spatial interaction(PERMANOVA F=5.6,df=7,p=0.001),further indicating the role of environmental conditions in determining the diet for different size classes.Interestingly,the fish condition did not differ among rivers.This study shows that turbidity and organic matter and nutrient loading determine the diet of L.victorianus in LVB rivers,and provides further justification for the maintenance of water quality as a conservation measure for threatened species.
基金funded by the Natural Science Foundation of China(Grant Nos.41807285,41972280 and 52179103).
文摘To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.
基金supported by National Natural Science Foundation of China(Grant No.U2243227)the Science and Technology Program of Guangdong Province(No.2020B1515120079)National Flash Flood Investigation and Evaluation Project(Grant No.SHZH-IWHR-57).
文摘Flood is one of the most devastating natural hazards.Employing machine learning models to construct flood susceptibility maps has become a pivotal step for decision-makers in disaster prevention and management.Existing flood conditioning factors inadequately account for regional characteristics of flood in the depiction of topography,potentially leading to an overestimation of flood susceptibility in flat areas.Addressing this gap,this study proposes a novel flood conditioning factor,local convexity factor(LCF),to enhance the accuracy of flood susceptibility modeling.Initially,LCF is computed based on a standard normal Gaussian surface to highlight elevation variations in local terrain.Subsequently,LCF is applied to flood susceptibility modeling using seven machine learning models across four distinct basins.Comparative analysis is conducted between flood susceptibility maps with and without the application of LCF to evaluate its impact on flood susceptibility modeling.The results demonstrate that the proposed LCF can enhance the accuracy of flood susceptibility modeling to varying degrees,across the four basins investigated.The Fujiang basin exhibited the most substantial improvement,with its AUC improved from 0.861 to 0.886,Producer’s Agreement improved from 0.869 to 0.899,and Overall Agreement improved from 0.778 to 0.811.Comparation with hydrodynamic inundation maps shows that particularly in relatively flat terrain areas,flood susceptibility maps incorporating LCF offer more precise delineation between flood-prone and non-flood-prone zones.This research holds potential for widespread application in the prediction of flood susceptibility using machine learning models,providing a novel perspective for enhancing their accuracy.
文摘Aim: To investigate the effect of air-conditioner exposure on semen quality. Methods: The data came from the healthy male volunteers, aged 22 to 30 years, who went to centers for maternity and children health for premarital physical examination in Shanghai, Henan, Zhejiang and Hebei from December 1998 to February 2000. The sampling size is 304. Results: Among the subjects, 90 (29.6 %) had air-conditioner at home and the rest did not. X2-test and multiple logistic regression analyses showed that, the difference between the exposure and control groups was statistically significant in semen volume, sperm density and proportion of sperm with normal morphology. The three indexes were lower in the exposure group. Conclusion: Air-conditioner exposure possibly influences the semen quality.
基金Supported by Key Science and Technology Project of China National Tobacco Corporation(110201202015)Science and Technology Project of Yunnan Tobacco Company(2012YN11)
文摘[Objective] The paper was to analyze the meteorological epidemic factors for occurrence and prevalence of tobacco bacterial wilt （ Ralstonia solanaca- rum）, and to study control effects of different soil conditioners on the bacterial disease in Gacligongshan demonstration area of green, ecological, high quality tobac- co leaf production. [Method] The plots attacked by tobacco bacterial wilt over the years were selected and the incidence of the disease was periodically surveyed in tobacco growth period in 2012, 2103 and 2014, respectively. 10 d Effective accumulated temperature and rainfall were counted according to the meteorological data, and the relationship between meteorological factors and disease index was analyzed. The control effects of three kinds of soil conditioners ＂Zhuanggenfeng＂, refined fulvic acid and lime on tobacco bacterial wilt were tested. [ Result] The analysis results of meteorological factors showed that 10 d effective accumulated temperature and rainfall were positively correlated to disease index. The variation curve of 10 d effective accumulated temperature and rainfall reflected the change trend of disease index. The pH values were increased by 0.57, 0.50 and 0.72 respectively after applying ＂Zhuanggenfeng＂, refined fulvic acid and lime. The aver- age control effects on tobacco bacterial wilt were 60.74% -62. 18%, 53.05% -59.53%, and 48.59% -58.53%, respectively. [ Conclusion] 10 d Effective accumulated temperature and rainfall could be used as important reference for disease forecasting and controUing. The usage of soil conditioner has a certain preven- tion and control effect on tobacco bacterial wilt disease by forming soil conditions conducive to flue-cured tobacco growth but adverse to disease survival, which is an effective auxiliary method against the disease.
文摘Anthropogenic activities have greatly affected water resources on a global scale where the world is experiencing water quality and resources issues. Heavy metal is a crucial group of pollutants that is toxic to the environment even at low concentrations due to its bioaccumulation and biomagnification capabilities in living organisms. The detrimental effects of heavy metals on living organisms are due to their bioaccumulation in the aquatic ecosystem. Cadmium may result in adverse health effects due to its high toxicity. The study is conducted to determine the cadmium exposure effects on the morphometric indices of Anabas testudineus which are the Scaling Coefficient (SC) and Condition Factor (K) of such species. Anabas testudineus is exposed to four different cadmium treatment groups namely the control group, cadmium treatment group of 0.005 mg/L, 0.010 mg/L, and 0.015 mg/L for 16 weeks. The findings of the study have reported inconsistent trends in the values of SC and a decrease in the value of K with increasing cadmium concentration. The trend for the average SC has shown an overall decrease in value while the pattern of the K value is inconsistent in each treatment group with exposure time. Collectively, no significant differences for SC and K of A. testudineus in different treatment groups as well as comparison between treatment groups with time exposure.
文摘Mapping floods is crucial for effective disaster management. This study focuses on flood assessment in northern Morocco, specifically Tangier, Tetouan, and Larache. Due to the lack of a comprehensive flood inventory map, we used unsupervised learning techniques, such as K-means clustering and fuzzy logic algorithms, to predict flood-prone areas. We identified nine conditioning factors influencing flood risk: elevation, slope, aspect, plan curvature, profile curvature, land use, soil type, normalized difference vegetation index(NDVI), and topographic position index(TPI). Using Landsat-8 imagery and a Digital Elevation Model(DEM) within a Geographic Information System(GIS), we analyzed topographic and geo-environmental variables. K-means clustering achieved silhouette scores of 0.66 in Tangier and 0.70 in Tetouan, while the fuzzy logic method in Larache produced a Davies-Bouldin Index(DBI) score of 0.35. The maps classified flood risk levels into low, moderate, and high categories. This research demonstrates the integration of machine learning and remote sensing for predicting flood-prone areas without existing flood inventory maps. Our findings highlight the main factors contributing to flash floods and assess their impact, enhancing the understanding of flood dynamics and improving flood management strategies in vulnerable regions.
