The thermodynamic systems and dynamic model suitable for determining the nonlinear chemical fingerprints of samples were analyzed.The results indicated that the damp nonlinear chemical reactions in close systems away ...The thermodynamic systems and dynamic model suitable for determining the nonlinear chemical fingerprints of samples were analyzed.The results indicated that the damp nonlinear chemical reactions in close systems away from the equilibrium and open systems without the complementarity of the dissipation substances have important significance for the throng characterization and whole content analysis of chemical components in samples.Various factors influencing on nonlinear chemical fingerprint,such as reactant species and their concentrations,electrode types,temperature,stir rate,the sort,dosage and granularity of the sample,etc.were amply researched by a nonlinear chemistry reaction,namely,damp B-Z oscillation which used acetone and glucose as the main dissipative substances.In addition,the quantitative information on the whole of chemical components in samples and the traits and applications of the fingerprint were investigated.The method and its important conditions for determining nonlinear chemistry fingerprint used in distinguishing and evaluating complex samples were successfully put forward.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
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
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
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
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
Debris flow susceptibility mapping(DFSM)has been reported in many studies,however,the irrational use of the same conditioning factor system for DFSM in regional-scale has not been thoroughly resolved.In this paper,a r...Debris flow susceptibility mapping(DFSM)has been reported in many studies,however,the irrational use of the same conditioning factor system for DFSM in regional-scale has not been thoroughly resolved.In this paper,a region-partitioning method that is based on the topographic characteristics of watershed units was developed with the objective of establishing multiple conditioning factor systems for regional-scale DFSM.First,watershed units were selected as the mapping units and created throughout the entire research area.Four topographical factors,namely,elevation,slope,aspect and relative height difference,were selected as the basis for clustering watershed units.The k-means clustering analysis was used to cluster the watershed units according to their topographic characteristics to partition the study area into several parts.Then,the information gain ratio method was used to filter out superfluous factors to establish conditioning factor systems in each region for the subsequent debris flow susceptibility modeling.Last,a debris flow susceptibility map of the whole study area was acquired by merging the maps from all parts.DFSM of Yongji County in Jilin Province,China was selected as a case study,and the analytical hierarchy process method was used to conduct a comparative analysis to evaluate the performance of the region-partitioning method.The area under curve(AUC)values showed that the partitioning of the study area into two parts improved the prediction rate from 0.812 to 0.916.The results demonstrate that the region-partitioning method on the basis of topographic characteristics of watershed units can realize more reasonable regional-scale DFSM.Hence,the developed region-partitioning method can be used as a guide for regional-scale DFSM to mitigate the imminent debris flow risk.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
A study on the food and feeding habit of Chrysichthys nigrodigitatus （Lacepede） was conducted in River Niger within the region of Oshimili local government area of Delta State Nigeria. A total of 90 specimens were c...A study on the food and feeding habit of Chrysichthys nigrodigitatus （Lacepede） was conducted in River Niger within the region of Oshimili local government area of Delta State Nigeria. A total of 90 specimens were collected with the help of fishermen using gill net, cast net and traps. The fish samples were immediately taken to the laboratory for analysis. Morphometric characteristics such as weight, length, condition factor, egg weight, Gonado Somatic Index （GSI） and sex ratio were determined. The stomach content was analyzed using numerical method to determine the food content. The total length of the fish sampled ranged from 14.40-44.60 cm, while the standard length varied from 11.00 cm to 47.00 cm and the fish body weight ranged between 19.00 g to 503.20 g. Sex ratio 1 female to 1 male was observed. The mean condition factor for both male and female obtained was 1.67. Of all the 90 fishes sampled, none had empty gut representing 100%. The major food items were phytoplankton, plant part and Detritus. Out of 41 females sampled, only 17 had eggs and the eggs were matured at stage IV.展开更多
The current study examines the diet of Serranus scriba （Osteichtyes of the family Serranidae）. This species of fish was collected using gillnets at the sea near the village of Sigri which is located in the South-Wes...The current study examines the diet of Serranus scriba （Osteichtyes of the family Serranidae）. This species of fish was collected using gillnets at the sea near the village of Sigri which is located in the South-West side of the island of Lesvos. Based on the results, Serranus scriba is characterized as carnivorous with a preference in Decapods. Also, the GSI （Gonado Somatic Index）, HSI （Hepato Somatic Index） and CF （Condition Factor） index were calculated, with the estimation of numbers of females and males and separation of mature stages using the scale Nikolsky. Majority of the individuals of this species are females with a ratio of 88% versus male with 12%. And based on the calculation of indicators they are characterized quite mature, with good percentage of stored energy and good condition of healthiness. Moreover, it is concluded, based on the above analysis, in the area there was plenty of food for the individuals of this species in their breeding season （April-August breeding season）.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
A study on seasonal abundance,morphometric and meristic data,growth pattern,condition fector,sex ratio and gonadosomatic index of Liza falcip-innis(Wlenciennes,1836)from the Ojo axis of Badagry creek,Nigeria was condu...A study on seasonal abundance,morphometric and meristic data,growth pattern,condition fector,sex ratio and gonadosomatic index of Liza falcip-innis(Wlenciennes,1836)from the Ojo axis of Badagry creek,Nigeria was conducted from May 2019 to March 2020.A total of 1012 species were randomly selected,having 499 females and 513 males.The length frequency analysis and length-weight relationships(LWR)were determined.Sex ratio was determined by Chi-square analysis.The results showed that morphometric data are:0.5-2.5 mm for ED,2.1-12 mm for HL,1.7-8.1 mm for HD,2.5-11.7 for BD,2.6-233.3 mm for TL and 9.23-1006 g for BW for the combined sexes.The slope(b)shows an allometric growth pattern.The intercept'a'and slope'b'of the LWR(LogW=a+bLogL)were Log W=15.39+0.34 LogL(r=0.54)for combined sexes,Log W=12.49+0.02 log L(r=0.38)for males and Log W=18.23+0.01 log L(r=0.16)for females.The length frequency distribution indicated that species were dominated by two year classes(Ages 1 and 2).Condition factors were generally low.The values ranged between 0.68-0.85 for combined sexes.The gonadosomatic index for female was highest in August,2019(17.77%)with Mean±SD of 2.88±0.75;which indicated the peak of spawning period in the study area.Sex ratio difference was significant(P<0.05).Sexual differences were significant;the females are phenotypically larger than the male.展开更多
The present work studies water deterioration, fish survival and production as a result of effluents discharged directly without prior treatments into lake Qaroun at Fayoum governorate, Egypt. Lake Qaroun represents he...The present work studies water deterioration, fish survival and production as a result of effluents discharged directly without prior treatments into lake Qaroun at Fayoum governorate, Egypt. Lake Qaroun represents heavily polluted wild habitat for both studied fish species, Tilapia zillii and Solea aegyptiaca, the most abundant species. Results revealed deterioration in water quality (low dissolved oxygen but high ammonia, nitrite, copper, lead and cadmium) of Northeastern sector where El-Bats drain discharge its effluents without prior treatment followed by that of eastern sector at four km from the point of El-Bats discharge. Water salinity and dissolved oxygen values were in the following order: Western lake Qaroun sector > Eastern lake Qaroun sector > Northeastern lake Qaroun sector. However, Ammonia and nitrite readings were in the following order: Northeastern sector of Lake Qaroun is followed by the Eastern sector, then the Western sector. Moreover, results of the present field study revealed a decrease in fish production with the lowest condition factor and a deterioration in meat quality (an increase in muscle water content and ash but a decrease total protein and total lipids) in case of fish collected from the polluted sites along the lake (Northeastern and eastern sectors). However, fish collected from the unpolluted western sector of the lake showed condition factor and chemical muscle composition more or less similar to normal healthy fish.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
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
In this note, we show that if N is a proper subfactor of a factor M of type Ⅱ1 with finite Jones index, then there is a maximal abelian self-adjoint subalgebra （masa） A of N that is not a masa in ,M. Popa showed th...In this note, we show that if N is a proper subfactor of a factor M of type Ⅱ1 with finite Jones index, then there is a maximal abelian self-adjoint subalgebra （masa） A of N that is not a masa in ,M. Popa showed that there is a proper subfactor R0 of the hyperfinite type Ⅱ1 factor R such that each masa in R0 is also a masa in R. We shall give a detailed proof of Popa＇s result.展开更多
The edible portion of Procambarus clarkii mainly consists of the abdominal muscles,highlighting the genetic enhancement of abdominal meat yield in breeding.To explore a selective breeding strategy for abdominal meat y...The edible portion of Procambarus clarkii mainly consists of the abdominal muscles,highlighting the genetic enhancement of abdominal meat yield in breeding.To explore a selective breeding strategy for abdominal meat yield,the correlation of morphological characteristics with abdominal meat yield based on five P.clarkii populations collected from its major production areas in China was analyzed and an optimal prediction model was constructed for predicting abdominal meat yield.With the analyses of P.clarkii morphological characteristics,Fulton's condition factor(K),which had a strong negative correlation(-0.800 in males and-0.705 in females)with abdominal meat yield and the advantage of not requiring the sacrifice of breeding candidates,was eventually selected as the morphological predictor.And the optimal prediction model constructed based on K value was a quadratic curve,with R^(2)values of 0.684 for males and 0.590 for females,and correlation coefficients of 0.827 and 0.768 between observed and predicted values for males and females,respectively.The results of the abdominal meat yield selective breeding experiment,utilizing the optimal prediction model,demonstrated that the breeding population exhibited favorable morphological variation as expected.These findings provide a morphology-based selection strategy for breeding the abdominal meat yield of P.clarkii.展开更多
This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of ...This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.展开更多
The study aims to afford a deeper knowledge on reproductive biology of Mystus bleekeri from Surma River,Bangladesh in a multidisciplinary way.Though,the species is in demand as food and ornamental fish in Bangladesh b...The study aims to afford a deeper knowledge on reproductive biology of Mystus bleekeri from Surma River,Bangladesh in a multidisciplinary way.Though,the species is in demand as food and ornamental fish in Bangladesh but still now comprehensive details on reproductive biology of this species is scanty.Total 600 fish samples were derived from commercial catches from July 2018 to June 2019.Periodic differences in sex ratio,size distribution,condition factor(Kn),fecundity,gonadosomatic index and gonadal maturation cycle were assessed.The results showed 384(64%)female and 216(36%)male with an overall sex ratio of 1.77:1(female:male).Mean total length(TL)and body weight(BW)of all fish studied were 14.85±3.38 cm TL and 27.54±15.76 g,respectively.The Kn varied with length groups,the highest(1.45±0.23)and lowest(0.89±0.18)values obtained in 16.0-16.9 and 10.0-10.9 cm TL groups,respectively.The highest Kn was documented in October while the lowest was in January.Monthly GSI values showed two peaks in July and November for both the sexes.The fecundity was found to vary from 1120 to 14790 eggs with average value of 4968±3047 with 14.4-18.3 cm in TL and 19.97-34.7g in BW.The relationship of fecundity with TL and BW was F=1.385TL13.405,r^(2)=0.885 and F=5.201BW^(4.827),r^(2)=0.944.Partial behavior of spawning together with allochronic oocytes development has also been found through histology.The results of this study could be used for predicting the response of populations of fish in Surma River and/or other where to human interferences and environmental change.展开更多
基金supported by the National Key Technologies R & DProgram (2009GJD20033)the International Scientific and Technological Cooperation Project (2007DFA40680) from the Ministry of Scienceand Technology of China
文摘The thermodynamic systems and dynamic model suitable for determining the nonlinear chemical fingerprints of samples were analyzed.The results indicated that the damp nonlinear chemical reactions in close systems away from the equilibrium and open systems without the complementarity of the dissipation substances have important significance for the throng characterization and whole content analysis of chemical components in samples.Various factors influencing on nonlinear chemical fingerprint,such as reactant species and their concentrations,electrode types,temperature,stir rate,the sort,dosage and granularity of the sample,etc.were amply researched by a nonlinear chemistry reaction,namely,damp B-Z oscillation which used acetone and glucose as the main dissipative substances.In addition,the quantitative information on the whole of chemical components in samples and the traits and applications of the fingerprint were investigated.The method and its important conditions for determining nonlinear chemistry fingerprint used in distinguishing and evaluating complex samples were successfully put forward.
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 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.
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.
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 National Natural Science Foundation of China(Grant Nos.41977221 and 41202197)Jilin Provincial Science and Technology Department(No.20190303103SF,No.20170101001JC)。
文摘Debris flow susceptibility mapping(DFSM)has been reported in many studies,however,the irrational use of the same conditioning factor system for DFSM in regional-scale has not been thoroughly resolved.In this paper,a region-partitioning method that is based on the topographic characteristics of watershed units was developed with the objective of establishing multiple conditioning factor systems for regional-scale DFSM.First,watershed units were selected as the mapping units and created throughout the entire research area.Four topographical factors,namely,elevation,slope,aspect and relative height difference,were selected as the basis for clustering watershed units.The k-means clustering analysis was used to cluster the watershed units according to their topographic characteristics to partition the study area into several parts.Then,the information gain ratio method was used to filter out superfluous factors to establish conditioning factor systems in each region for the subsequent debris flow susceptibility modeling.Last,a debris flow susceptibility map of the whole study area was acquired by merging the maps from all parts.DFSM of Yongji County in Jilin Province,China was selected as a case study,and the analytical hierarchy process method was used to conduct a comparative analysis to evaluate the performance of the region-partitioning method.The area under curve(AUC)values showed that the partitioning of the study area into two parts improved the prediction rate from 0.812 to 0.916.The results demonstrate that the region-partitioning method on the basis of topographic characteristics of watershed units can realize more reasonable regional-scale DFSM.Hence,the developed region-partitioning method can be used as a guide for regional-scale DFSM to mitigate the imminent debris flow risk.
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.
文摘A study on the food and feeding habit of Chrysichthys nigrodigitatus （Lacepede） was conducted in River Niger within the region of Oshimili local government area of Delta State Nigeria. A total of 90 specimens were collected with the help of fishermen using gill net, cast net and traps. The fish samples were immediately taken to the laboratory for analysis. Morphometric characteristics such as weight, length, condition factor, egg weight, Gonado Somatic Index （GSI） and sex ratio were determined. The stomach content was analyzed using numerical method to determine the food content. The total length of the fish sampled ranged from 14.40-44.60 cm, while the standard length varied from 11.00 cm to 47.00 cm and the fish body weight ranged between 19.00 g to 503.20 g. Sex ratio 1 female to 1 male was observed. The mean condition factor for both male and female obtained was 1.67. Of all the 90 fishes sampled, none had empty gut representing 100%. The major food items were phytoplankton, plant part and Detritus. Out of 41 females sampled, only 17 had eggs and the eggs were matured at stage IV.
文摘The current study examines the diet of Serranus scriba （Osteichtyes of the family Serranidae）. This species of fish was collected using gillnets at the sea near the village of Sigri which is located in the South-West side of the island of Lesvos. Based on the results, Serranus scriba is characterized as carnivorous with a preference in Decapods. Also, the GSI （Gonado Somatic Index）, HSI （Hepato Somatic Index） and CF （Condition Factor） index were calculated, with the estimation of numbers of females and males and separation of mature stages using the scale Nikolsky. Majority of the individuals of this species are females with a ratio of 88% versus male with 12%. And based on the calculation of indicators they are characterized quite mature, with good percentage of stored energy and good condition of healthiness. Moreover, it is concluded, based on the above analysis, in the area there was plenty of food for the individuals of this species in their breeding season （April-August breeding season）.
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A study on seasonal abundance,morphometric and meristic data,growth pattern,condition fector,sex ratio and gonadosomatic index of Liza falcip-innis(Wlenciennes,1836)from the Ojo axis of Badagry creek,Nigeria was conducted from May 2019 to March 2020.A total of 1012 species were randomly selected,having 499 females and 513 males.The length frequency analysis and length-weight relationships(LWR)were determined.Sex ratio was determined by Chi-square analysis.The results showed that morphometric data are:0.5-2.5 mm for ED,2.1-12 mm for HL,1.7-8.1 mm for HD,2.5-11.7 for BD,2.6-233.3 mm for TL and 9.23-1006 g for BW for the combined sexes.The slope(b)shows an allometric growth pattern.The intercept'a'and slope'b'of the LWR(LogW=a+bLogL)were Log W=15.39+0.34 LogL(r=0.54)for combined sexes,Log W=12.49+0.02 log L(r=0.38)for males and Log W=18.23+0.01 log L(r=0.16)for females.The length frequency distribution indicated that species were dominated by two year classes(Ages 1 and 2).Condition factors were generally low.The values ranged between 0.68-0.85 for combined sexes.The gonadosomatic index for female was highest in August,2019(17.77%)with Mean±SD of 2.88±0.75;which indicated the peak of spawning period in the study area.Sex ratio difference was significant(P<0.05).Sexual differences were significant;the females are phenotypically larger than the male.
文摘The present work studies water deterioration, fish survival and production as a result of effluents discharged directly without prior treatments into lake Qaroun at Fayoum governorate, Egypt. Lake Qaroun represents heavily polluted wild habitat for both studied fish species, Tilapia zillii and Solea aegyptiaca, the most abundant species. Results revealed deterioration in water quality (low dissolved oxygen but high ammonia, nitrite, copper, lead and cadmium) of Northeastern sector where El-Bats drain discharge its effluents without prior treatment followed by that of eastern sector at four km from the point of El-Bats discharge. Water salinity and dissolved oxygen values were in the following order: Western lake Qaroun sector > Eastern lake Qaroun sector > Northeastern lake Qaroun sector. However, Ammonia and nitrite readings were in the following order: Northeastern sector of Lake Qaroun is followed by the Eastern sector, then the Western sector. Moreover, results of the present field study revealed a decrease in fish production with the lowest condition factor and a deterioration in meat quality (an increase in muscle water content and ash but a decrease total protein and total lipids) in case of fish collected from the polluted sites along the lake (Northeastern and eastern sectors). However, fish collected from the unpolluted western sector of the lake showed condition factor and chemical muscle composition more or less similar to normal healthy fish.
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.
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This work is supported by the National Natural Science Foundation of China (10301004)
文摘In this note, we show that if N is a proper subfactor of a factor M of type Ⅱ1 with finite Jones index, then there is a maximal abelian self-adjoint subalgebra （masa） A of N that is not a masa in ,M. Popa showed that there is a proper subfactor R0 of the hyperfinite type Ⅱ1 factor R such that each masa in R0 is also a masa in R. We shall give a detailed proof of Popa＇s result.
基金supported by the Fishery Seed Industry Joint Breeding Program of Jiangxi Province,China(2023yyzygg-07),the Key Research and Development Program of Hubei Province,China(2021BBA232).
文摘The edible portion of Procambarus clarkii mainly consists of the abdominal muscles,highlighting the genetic enhancement of abdominal meat yield in breeding.To explore a selective breeding strategy for abdominal meat yield,the correlation of morphological characteristics with abdominal meat yield based on five P.clarkii populations collected from its major production areas in China was analyzed and an optimal prediction model was constructed for predicting abdominal meat yield.With the analyses of P.clarkii morphological characteristics,Fulton's condition factor(K),which had a strong negative correlation(-0.800 in males and-0.705 in females)with abdominal meat yield and the advantage of not requiring the sacrifice of breeding candidates,was eventually selected as the morphological predictor.And the optimal prediction model constructed based on K value was a quadratic curve,with R^(2)values of 0.684 for males and 0.590 for females,and correlation coefficients of 0.827 and 0.768 between observed and predicted values for males and females,respectively.The results of the abdominal meat yield selective breeding experiment,utilizing the optimal prediction model,demonstrated that the breeding population exhibited favorable morphological variation as expected.These findings provide a morphology-based selection strategy for breeding the abdominal meat yield of P.clarkii.
基金This research is funded by the National Natural Science Foundation of China(41807285,41762020,51879127 and 51769014E)Natural Science Foundation of Hebei Province(D2022202005).
文摘This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.
基金supported by Sylhet Agricultural University Research System(SAURES)Bangladesh through the research grant#SAU/Director(Research)-80/18/351(15).
文摘The study aims to afford a deeper knowledge on reproductive biology of Mystus bleekeri from Surma River,Bangladesh in a multidisciplinary way.Though,the species is in demand as food and ornamental fish in Bangladesh but still now comprehensive details on reproductive biology of this species is scanty.Total 600 fish samples were derived from commercial catches from July 2018 to June 2019.Periodic differences in sex ratio,size distribution,condition factor(Kn),fecundity,gonadosomatic index and gonadal maturation cycle were assessed.The results showed 384(64%)female and 216(36%)male with an overall sex ratio of 1.77:1(female:male).Mean total length(TL)and body weight(BW)of all fish studied were 14.85±3.38 cm TL and 27.54±15.76 g,respectively.The Kn varied with length groups,the highest(1.45±0.23)and lowest(0.89±0.18)values obtained in 16.0-16.9 and 10.0-10.9 cm TL groups,respectively.The highest Kn was documented in October while the lowest was in January.Monthly GSI values showed two peaks in July and November for both the sexes.The fecundity was found to vary from 1120 to 14790 eggs with average value of 4968±3047 with 14.4-18.3 cm in TL and 19.97-34.7g in BW.The relationship of fecundity with TL and BW was F=1.385TL13.405,r^(2)=0.885 and F=5.201BW^(4.827),r^(2)=0.944.Partial behavior of spawning together with allochronic oocytes development has also been found through histology.The results of this study could be used for predicting the response of populations of fish in Surma River and/or other where to human interferences and environmental change.
