Today,urban traffic,growing populations,and dense transportation networks are contributing to an increase in traffic incidents.These incidents include traffic accidents,vehicle breakdowns,fires,and traffic disputes,re...Today,urban traffic,growing populations,and dense transportation networks are contributing to an increase in traffic incidents.These incidents include traffic accidents,vehicle breakdowns,fires,and traffic disputes,resulting in long waiting times,high carbon emissions,and other undesirable situations.It is vital to estimate incident response times quickly and accurately after traffic incidents occur for the success of incident-related planning and response activities.This study presents a model for forecasting the traffic incident duration of traffic events with high precision.The proposed model goes through a 4-stage process using various features to predict the duration of four different traffic events and presents a feature reduction approach to enable real-time data collection and prediction.In the first stage,the dataset consisting of 24,431 data points and 75 variables is prepared by data collection,merging,missing data processing and data cleaning.In the second stage,models such as Decision Trees(DT),K-Nearest Neighbour(KNN),Random Forest(RF)and Support Vector Machines(SVM)are used and hyperparameter optimisation is performed with GridSearchCV.In the third stage,feature selection and reduction are performed and real-time data are used.In the last stage,model performance with 14 variables is evaluated with metrics such as accuracy,precision,recall,F1-score,MCC,confusion matrix and SHAP.The RF model outperforms other models with an accuracy of 98.5%.The study’s prediction results demonstrate that the proposed dynamic prediction model can achieve a high level of success.展开更多
It has become general for surface waters being polluted by micro organic compounds. In order to know the current pollution situation and the properties of micro organic compounds in the Changjiang River, a test was pe...It has become general for surface waters being polluted by micro organic compounds. In order to know the current pollution situation and the properties of micro organic compounds in the Changjiang River, a test was performed on micro organic compounds in the water, bottom material and fish bodies which were sampled from major city river reaches of the Changjiang River. Based on the test result, researchers described and analyzed the sorts, concentration level and distribution features of micro organic compounds. A comprehensive evaluation was conducted by adopting the method of MEG (Multimedia Environmental Goals). The study indicated that ① the water body of major city river reaches of the Changjiang River has been generally polluted. In the test, totally 12 types with 308 kinds of organic compounds were detected. The main pollutants were paraffins, PAHs and lipids; and ② micro organic pollutant content in fish bodies was generally higher than that in bottom material which is in turn higher than that in water; and ③ pollution is relatively severe in the river reaches of mid-to-large comprehensive industrial cities with fairly great TAS (Total Ambient Severity) of public health and ecological system.展开更多
There was elaborated a method for calculating magnetic fields of the Solar System planets. It is based on the quantum theory of electroconductivity of metals and semiconductors. The latter helps to calculate thermoele...There was elaborated a method for calculating magnetic fields of the Solar System planets. It is based on the quantum theory of electroconductivity of metals and semiconductors. The latter helps to calculate thermoelectrical processes, always taking place in the bowels of “hot” planets. Main elements of those processes are planetary temperature gradients, thermo electromotive force and radially directed thermoelectrical currents, which are associated with Seebeck effect. Thermo electromotive force causes directional movement of planetary thermoelectrical currents both in metal cores and other conductive shells of planets. Those currents are big and they generate magnetic fields of proportional intensity. The capacities of the calculation method were tested while finding the reason why the Jupiter magnetic field is such complicated. As a result it was specified that the source of the main magnetic field of a planet is its metal core and the source of an additional magnetic field is the layer of liquid metal hydrogen. There was also found the third source of a local magnetic field of low intensity along the circular zone of the equatorial region. The conclusion that the Jupiter’s main magnetic field has a polarity opposite to the Earth’s one.展开更多
The wheat yield variation on solonetz and chernozem soil in six environments was in study in order to obtain information for use of genetic variability and for building strategy in plant breeding for less productive a...The wheat yield variation on solonetz and chernozem soil in six environments was in study in order to obtain information for use of genetic variability and for building strategy in plant breeding for less productive and marginal environments. The sample of eight bread wheat varieties： Rcnesansa, Pobeda, Rapsodija, Dragana, Cipovka, Evropa 90, NSR-5 and Nevesinjka, which are characterized by tolerance to stressful growing conditions and broader adaptability, was selected for the study. The trial was established by Randomized Complete Block Design in three replications at two locations in the Pannonian Plain, Northern Serbia in two vegetation periods 2004/2005 and 2008/2009. Locations differed in a soil type, primarily. The tested locality was on solonetz, while control locality was on chernozem soil type. Additive Main and Multiplicative Interaction model （AMMI） grouped varieties that exhibited strong reaction to environmental improvement （Nevesinjka and Evropa 90）, varieties showing fairly small GE interaction （Renesansa, Cipovka and Pobeda） and varieties having the ability for maximum use of less productive soil in better meteorological conditions （Dragana, Rapsodija and NSR-5）. Meteorological conditions significantly influenced the effect of soil quality variation on grain yield in trial. Varieties have interacted differently with the environment, depending on their genetic background.展开更多
Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrientmanagement,maximizing profitability,ensuring food security,and promoting environ...Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrientmanagement,maximizing profitability,ensuring food security,and promoting environmental sustainability.Weanalyzed data fromnutrient omission plot trials(NOPTs)conducted in 324 farmers'fields across ten agroecological zones(AEZs)in the Eastern Indo-Gangetic Plains(EIGP)of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields.An additive main effect and multiplicative interaction(AMMI)model was used to explain maize yield variability with nutrient addition.Interpretable machine learning(ML)algorithms in automatic machine learning(AutoML)frameworks were subsequently used to predict attainable yield relative nutrient-limited yield(RY)and to rank variables that control RY.The stack-ensemble model was identified as the best-performing model for predicting RYs of N,P,and Zn.In contrast,deep learning outperformed all base learners for predicting RYK.The best model's square errors(RMSEs)were 0.122,0.105,0.123,and 0.104 for RY_(N),RY_(P),RY_(K),and RY_(Zn),respectively.The permutation-based feature importance technique identified soil pH as the most critical variable controlling RY_(N)and RY_(P).The RY_(K)showed lower in the eastern longitudinal direction.Soil N and Zn were associated with RYZn.The predicted median RY of N,P,K,and Zn,representing average soil fertility,was 0.51,0.84,0.87,and 0.97,accounting for 44,54,54,and 48%upland dry season crop area of Bangladesh,respectively.Efforts are needed to update databases cataloging variability in land type inundation classes,soil characteristics,and INS and combine them with farmers'crop management information to develop more precise nutrient guidelines for maize in the EIGP.展开更多
文摘Today,urban traffic,growing populations,and dense transportation networks are contributing to an increase in traffic incidents.These incidents include traffic accidents,vehicle breakdowns,fires,and traffic disputes,resulting in long waiting times,high carbon emissions,and other undesirable situations.It is vital to estimate incident response times quickly and accurately after traffic incidents occur for the success of incident-related planning and response activities.This study presents a model for forecasting the traffic incident duration of traffic events with high precision.The proposed model goes through a 4-stage process using various features to predict the duration of four different traffic events and presents a feature reduction approach to enable real-time data collection and prediction.In the first stage,the dataset consisting of 24,431 data points and 75 variables is prepared by data collection,merging,missing data processing and data cleaning.In the second stage,models such as Decision Trees(DT),K-Nearest Neighbour(KNN),Random Forest(RF)and Support Vector Machines(SVM)are used and hyperparameter optimisation is performed with GridSearchCV.In the third stage,feature selection and reduction are performed and real-time data are used.In the last stage,model performance with 14 variables is evaluated with metrics such as accuracy,precision,recall,F1-score,MCC,confusion matrix and SHAP.The RF model outperforms other models with an accuracy of 98.5%.The study’s prediction results demonstrate that the proposed dynamic prediction model can achieve a high level of success.
文摘It has become general for surface waters being polluted by micro organic compounds. In order to know the current pollution situation and the properties of micro organic compounds in the Changjiang River, a test was performed on micro organic compounds in the water, bottom material and fish bodies which were sampled from major city river reaches of the Changjiang River. Based on the test result, researchers described and analyzed the sorts, concentration level and distribution features of micro organic compounds. A comprehensive evaluation was conducted by adopting the method of MEG (Multimedia Environmental Goals). The study indicated that ① the water body of major city river reaches of the Changjiang River has been generally polluted. In the test, totally 12 types with 308 kinds of organic compounds were detected. The main pollutants were paraffins, PAHs and lipids; and ② micro organic pollutant content in fish bodies was generally higher than that in bottom material which is in turn higher than that in water; and ③ pollution is relatively severe in the river reaches of mid-to-large comprehensive industrial cities with fairly great TAS (Total Ambient Severity) of public health and ecological system.
文摘There was elaborated a method for calculating magnetic fields of the Solar System planets. It is based on the quantum theory of electroconductivity of metals and semiconductors. The latter helps to calculate thermoelectrical processes, always taking place in the bowels of “hot” planets. Main elements of those processes are planetary temperature gradients, thermo electromotive force and radially directed thermoelectrical currents, which are associated with Seebeck effect. Thermo electromotive force causes directional movement of planetary thermoelectrical currents both in metal cores and other conductive shells of planets. Those currents are big and they generate magnetic fields of proportional intensity. The capacities of the calculation method were tested while finding the reason why the Jupiter magnetic field is such complicated. As a result it was specified that the source of the main magnetic field of a planet is its metal core and the source of an additional magnetic field is the layer of liquid metal hydrogen. There was also found the third source of a local magnetic field of low intensity along the circular zone of the equatorial region. The conclusion that the Jupiter’s main magnetic field has a polarity opposite to the Earth’s one.
文摘The wheat yield variation on solonetz and chernozem soil in six environments was in study in order to obtain information for use of genetic variability and for building strategy in plant breeding for less productive and marginal environments. The sample of eight bread wheat varieties： Rcnesansa, Pobeda, Rapsodija, Dragana, Cipovka, Evropa 90, NSR-5 and Nevesinjka, which are characterized by tolerance to stressful growing conditions and broader adaptability, was selected for the study. The trial was established by Randomized Complete Block Design in three replications at two locations in the Pannonian Plain, Northern Serbia in two vegetation periods 2004/2005 and 2008/2009. Locations differed in a soil type, primarily. The tested locality was on solonetz, while control locality was on chernozem soil type. Additive Main and Multiplicative Interaction model （AMMI） grouped varieties that exhibited strong reaction to environmental improvement （Nevesinjka and Evropa 90）, varieties showing fairly small GE interaction （Renesansa, Cipovka and Pobeda） and varieties having the ability for maximum use of less productive soil in better meteorological conditions （Dragana, Rapsodija and NSR-5）. Meteorological conditions significantly influenced the effect of soil quality variation on grain yield in trial. Varieties have interacted differently with the environment, depending on their genetic background.
文摘Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrientmanagement,maximizing profitability,ensuring food security,and promoting environmental sustainability.Weanalyzed data fromnutrient omission plot trials(NOPTs)conducted in 324 farmers'fields across ten agroecological zones(AEZs)in the Eastern Indo-Gangetic Plains(EIGP)of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields.An additive main effect and multiplicative interaction(AMMI)model was used to explain maize yield variability with nutrient addition.Interpretable machine learning(ML)algorithms in automatic machine learning(AutoML)frameworks were subsequently used to predict attainable yield relative nutrient-limited yield(RY)and to rank variables that control RY.The stack-ensemble model was identified as the best-performing model for predicting RYs of N,P,and Zn.In contrast,deep learning outperformed all base learners for predicting RYK.The best model's square errors(RMSEs)were 0.122,0.105,0.123,and 0.104 for RY_(N),RY_(P),RY_(K),and RY_(Zn),respectively.The permutation-based feature importance technique identified soil pH as the most critical variable controlling RY_(N)and RY_(P).The RY_(K)showed lower in the eastern longitudinal direction.Soil N and Zn were associated with RYZn.The predicted median RY of N,P,K,and Zn,representing average soil fertility,was 0.51,0.84,0.87,and 0.97,accounting for 44,54,54,and 48%upland dry season crop area of Bangladesh,respectively.Efforts are needed to update databases cataloging variability in land type inundation classes,soil characteristics,and INS and combine them with farmers'crop management information to develop more precise nutrient guidelines for maize in the EIGP.
