With the growing economy of India, banking sector growth has led to installation of thousands of Automatic Teller Machines (ATMs) throughout the country. ATMs provide 24 × 7 services as well as operate at low-tem...With the growing economy of India, banking sector growth has led to installation of thousands of Automatic Teller Machines (ATMs) throughout the country. ATMs provide 24 × 7 services as well as operate at low-temperature ranges of cooling, hence have high operating energy costs. Insulating an ATM’s envelope is not a prevalent technique in India. In the present study, an effort has been made to determine the optimum insulation thickness for three different insulation materials for the typical ATM envelope in four different climatic zones of India. Life cycle savings and payback periods for various insulation materials are also evaluated. Further, these optimally insulated ATM envelopes can be integrated with grid connected rooftop solar PV systems. The energy saving and emissions reduction potential due to these two interventions have been estimated on the national basis. Altogether in the four selected climate zones, energy saving of 17% - 30% provides the annual economic benefit of Indian National Rupees (Rs.) 3570 million with annual carbon reduction potential of about 0.60 million tCO<sub>2</sub>. From this study, it is observed that properly insulated ATMs integrated with rooftop solar PV systems, can significantly reduce the energy costs as well as carbon emissions in India’s context.展开更多
The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,com...The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,complex fabrics,and varying degrees of contact states,characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging.This study proposes an analytical method to address this issue,based on geological fieldwork and relevant experimental results.The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters.The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC,enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station.The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5e1 times the main powerhouse span,showing local activation.Furthermore,the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.展开更多
In very short time today web has become an enormously important tool for communicating ideas, conducting business and entertainment. At the time of navigation, web users leave various records of their action. This vas...In very short time today web has become an enormously important tool for communicating ideas, conducting business and entertainment. At the time of navigation, web users leave various records of their action. This vast amount of data can be a useful source of knowledge for predicting user behavior. A refined method is required to carry out this task. Web usages mining (WUM) is the tool designed to do this task. WUM system is used to extract the knowledge based on user behavior during the web navigation. The extracted knowledge can be used for predicting the users’ future request when user is browsing the web. In this paper we advanced the online recommender system by using a Longest Common Subsequence (LCS) classification algorithm to classify users’ navigation pattern. Classification using the proposed method can improve the accuracy of recommendation and also proposed an algorithm that uses LCS method to know the user behavior for improvement of design of a website.展开更多
Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are prese...Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are present in adequate amounts as required by the plant.On the other hand,if the factors are inadequate,they may also support the growth of a disease in the plants.The current study focuses on the Rust disease in Aonla fruits and leaves by utilizing a real time dataset of weather parameters.Fifteen different models are tested for spray prediction on conducive days.Two resampling techniques,random over sampling(ROS)and synthetic minority oversampling technique(SMOTE)have been used to balance the dataset and five different classifiers:support vector machine(SVM),logistic regression(LR),k-nearest neighbor(kNN),decision tree(DT)and random forest(RF)have been used to classify a particular day based on weather conditions as conducive or non-conducive.The classifiers are then evaluated based on four performance metrics:accuracy,precision,recall and F1-score.The results indicate that for imbalanced dataset,kNN is appropriate with high precision and recall values.Considering both balanced and imbalanced dataset models,the proposed model SMOTE-RF performs best among all models with 94.6%accuracy and can be used in a real time application for spray prediction.Hence,timely fungicide spray prediction without over spraying will help in better productivity and will prevent the yield loss due to rust disease in Aonla crop.展开更多
Change in global climate is primarily due to rising concentrations of greenhouse gases in the atmosphere that is mostly caused by human activities.The important factors affecting the occurrence and spread of the plant...Change in global climate is primarily due to rising concentrations of greenhouse gases in the atmosphere that is mostly caused by human activities.The important factors affecting the occurrence and spread of the plant diseases are temperature,moisture,light,and CO_(2) concentration.These factors cause physiological changes in plants that result in increase in intensity of crop diseases.Climate change causes a significant impact on germination,reproduction,sporulation and spore dispersal of pathogens.Climate change affects all life stages of the pathogen as well as its host to cause impact on host-pathogen interaction which facilitates the emergence of new races of the pathogen ultimately breakdowns the host resistance.It also affects the microbial community in the soil which is beneficial to the plants in various aspects.The minor diseases become major ones due to alteration in climatic parameters thus posing a threat to the food security.展开更多
文摘With the growing economy of India, banking sector growth has led to installation of thousands of Automatic Teller Machines (ATMs) throughout the country. ATMs provide 24 × 7 services as well as operate at low-temperature ranges of cooling, hence have high operating energy costs. Insulating an ATM’s envelope is not a prevalent technique in India. In the present study, an effort has been made to determine the optimum insulation thickness for three different insulation materials for the typical ATM envelope in four different climatic zones of India. Life cycle savings and payback periods for various insulation materials are also evaluated. Further, these optimally insulated ATM envelopes can be integrated with grid connected rooftop solar PV systems. The energy saving and emissions reduction potential due to these two interventions have been estimated on the national basis. Altogether in the four selected climate zones, energy saving of 17% - 30% provides the annual economic benefit of Indian National Rupees (Rs.) 3570 million with annual carbon reduction potential of about 0.60 million tCO<sub>2</sub>. From this study, it is observed that properly insulated ATMs integrated with rooftop solar PV systems, can significantly reduce the energy costs as well as carbon emissions in India’s context.
基金support from the Key Projects of the Yalong River Joint Fund of the National Natural Science Foundation of China(Grant No.U1865203)the Innovation Team of Changjiang River Scientific Research Institute(Grant Nos.CKSF2021715/YT and CKSF2023305/YT)。
文摘The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,complex fabrics,and varying degrees of contact states,characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging.This study proposes an analytical method to address this issue,based on geological fieldwork and relevant experimental results.The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters.The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC,enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station.The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5e1 times the main powerhouse span,showing local activation.Furthermore,the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.
文摘In very short time today web has become an enormously important tool for communicating ideas, conducting business and entertainment. At the time of navigation, web users leave various records of their action. This vast amount of data can be a useful source of knowledge for predicting user behavior. A refined method is required to carry out this task. Web usages mining (WUM) is the tool designed to do this task. WUM system is used to extract the knowledge based on user behavior during the web navigation. The extracted knowledge can be used for predicting the users’ future request when user is browsing the web. In this paper we advanced the online recommender system by using a Longest Common Subsequence (LCS) classification algorithm to classify users’ navigation pattern. Classification using the proposed method can improve the accuracy of recommendation and also proposed an algorithm that uses LCS method to know the user behavior for improvement of design of a website.
文摘Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are present in adequate amounts as required by the plant.On the other hand,if the factors are inadequate,they may also support the growth of a disease in the plants.The current study focuses on the Rust disease in Aonla fruits and leaves by utilizing a real time dataset of weather parameters.Fifteen different models are tested for spray prediction on conducive days.Two resampling techniques,random over sampling(ROS)and synthetic minority oversampling technique(SMOTE)have been used to balance the dataset and five different classifiers:support vector machine(SVM),logistic regression(LR),k-nearest neighbor(kNN),decision tree(DT)and random forest(RF)have been used to classify a particular day based on weather conditions as conducive or non-conducive.The classifiers are then evaluated based on four performance metrics:accuracy,precision,recall and F1-score.The results indicate that for imbalanced dataset,kNN is appropriate with high precision and recall values.Considering both balanced and imbalanced dataset models,the proposed model SMOTE-RF performs best among all models with 94.6%accuracy and can be used in a real time application for spray prediction.Hence,timely fungicide spray prediction without over spraying will help in better productivity and will prevent the yield loss due to rust disease in Aonla crop.
文摘Change in global climate is primarily due to rising concentrations of greenhouse gases in the atmosphere that is mostly caused by human activities.The important factors affecting the occurrence and spread of the plant diseases are temperature,moisture,light,and CO_(2) concentration.These factors cause physiological changes in plants that result in increase in intensity of crop diseases.Climate change causes a significant impact on germination,reproduction,sporulation and spore dispersal of pathogens.Climate change affects all life stages of the pathogen as well as its host to cause impact on host-pathogen interaction which facilitates the emergence of new races of the pathogen ultimately breakdowns the host resistance.It also affects the microbial community in the soil which is beneficial to the plants in various aspects.The minor diseases become major ones due to alteration in climatic parameters thus posing a threat to the food security.