Drinking water is supplied through a centralized water supply system and may not be accessed by communities in rural areas of Malaysia.This study investigated the performance of a low-cost, self-prepared combined acti...Drinking water is supplied through a centralized water supply system and may not be accessed by communities in rural areas of Malaysia.This study investigated the performance of a low-cost, self-prepared combined activated carbon and sand filtration(CACSF) system for roofharvested rainwater and lake water for potable use. Activated carbon was self-prepared using locally sourced coconut shell and was activated using commonly available salt rather than a high-tech procedure that requires a chemical reagent. The filtration chamber was comprised of local,readily available sand. The experiments were conducted with varying antecedent dry intervals(ADIs) of up to 15 d and lake water with varying initial chemical oxygen demand(COD) concentration. The CACSF system managed to produce effluents complying with the drinking water standards for the parameters p H, dissolved oxygen(DO), biochemical oxygen demand(BOD5), COD, total suspended solids(TSS), and ammonia nitrogen(NH_3-N). The CACSF system successfully decreased the population of Escherichia coli(E. coli) in the influents to less than 30 CFU/m L. Samples with a higher population of E. coli(that is, greater than 30 CFU/m L) did not show 100% removal. The system also showed high potential as an alternative for treated drinking water for roof-harvested rainwater and class II lake water.展开更多
The concept of creating a Topography integrated urban center is to create an urban center that integrated with the city.The first step of the Concept is to settle by using the natural elevation in the land and to crea...The concept of creating a Topography integrated urban center is to create an urban center that integrated with the city.The first step of the Concept is to settle by using the natural elevation in the land and to create volumes compatible with land by raising together with the elevation.While the passenger circulation at starting elevation is moved into a project with the cavestyle volume settled in land elevation.The new area of the square to be defined in the center of the building is intended to form an area combining the neighboring squares Kartal Square and Freedom Square,as well as contributing to the silhouette of Kartal from the sea with the location of the square and building.The project is a central complex design that deals with various urban problems thanks to professionals,local people of Kartal,and clubs which established with the municipality in a comprehensive way to search for solutions to be organized urban workshops and conferences.展开更多
Roof falls due to geological conditions are major hazards in the mining industry,causing work time loss,injuries,and fatalities.There are roof fall problems caused by high horizontal stress in several largeopening lim...Roof falls due to geological conditions are major hazards in the mining industry,causing work time loss,injuries,and fatalities.There are roof fall problems caused by high horizontal stress in several largeopening limestone mines in the eastern and midwestern United States.The typical hazard management approach for this type of roof fall hazards relies heavily on visual inspections and expert knowledge.In this context,we proposed a deep learning system for detection of the roof fall hazards caused by high horizontal stress.We used images depicting hazardous and non-hazardous roof conditions to develop a convolutional neural network(CNN)for autonomous detection of hazardous roof conditions.To compensate for limited input data,we utilized a transfer learning approach.In the transfer learning approach,an already-trained network is used as a starting point for classification in a similar domain.Results show that this approach works well for classifying roof conditions as hazardous or safe,achieving a statistical accuracy of 86.4%.This result is also compared with a random forest classifier,and the deep learning approach is more successful at classification of roof conditions.However,accuracy alone is not enough to ensure a reliable hazard management system.System constraints and reliability are improved when the features used by the network are understood.Therefore,we used a deep learning interpretation technique called integrated gradients to identify the important geological features in each image for prediction.The analysis of integrated gradients shows that the system uses the same roof features as the experts do on roof fall hazards detection.The system developed in this paper demonstrates the potential of deep learning in geotechnical hazard management to complement human experts,and likely to become an essential part of autonomous operations in cases where hazard identification heavily depends on expert knowledge.Moreover,deep learning-based systems reduce expert exposure to hazardous conditions.展开更多
基金supported by the Universiti Kebangsaan Malaysia Grant(Grant No.GUP-2014-077)
文摘Drinking water is supplied through a centralized water supply system and may not be accessed by communities in rural areas of Malaysia.This study investigated the performance of a low-cost, self-prepared combined activated carbon and sand filtration(CACSF) system for roofharvested rainwater and lake water for potable use. Activated carbon was self-prepared using locally sourced coconut shell and was activated using commonly available salt rather than a high-tech procedure that requires a chemical reagent. The filtration chamber was comprised of local,readily available sand. The experiments were conducted with varying antecedent dry intervals(ADIs) of up to 15 d and lake water with varying initial chemical oxygen demand(COD) concentration. The CACSF system managed to produce effluents complying with the drinking water standards for the parameters p H, dissolved oxygen(DO), biochemical oxygen demand(BOD5), COD, total suspended solids(TSS), and ammonia nitrogen(NH_3-N). The CACSF system successfully decreased the population of Escherichia coli(E. coli) in the influents to less than 30 CFU/m L. Samples with a higher population of E. coli(that is, greater than 30 CFU/m L) did not show 100% removal. The system also showed high potential as an alternative for treated drinking water for roof-harvested rainwater and class II lake water.
文摘The concept of creating a Topography integrated urban center is to create an urban center that integrated with the city.The first step of the Concept is to settle by using the natural elevation in the land and to create volumes compatible with land by raising together with the elevation.While the passenger circulation at starting elevation is moved into a project with the cavestyle volume settled in land elevation.The new area of the square to be defined in the center of the building is intended to form an area combining the neighboring squares Kartal Square and Freedom Square,as well as contributing to the silhouette of Kartal from the sea with the location of the square and building.The project is a central complex design that deals with various urban problems thanks to professionals,local people of Kartal,and clubs which established with the municipality in a comprehensive way to search for solutions to be organized urban workshops and conferences.
基金partially supported by the National Institute for Occupational Safety and Health,contract number 0000HCCR-2019-36403。
文摘Roof falls due to geological conditions are major hazards in the mining industry,causing work time loss,injuries,and fatalities.There are roof fall problems caused by high horizontal stress in several largeopening limestone mines in the eastern and midwestern United States.The typical hazard management approach for this type of roof fall hazards relies heavily on visual inspections and expert knowledge.In this context,we proposed a deep learning system for detection of the roof fall hazards caused by high horizontal stress.We used images depicting hazardous and non-hazardous roof conditions to develop a convolutional neural network(CNN)for autonomous detection of hazardous roof conditions.To compensate for limited input data,we utilized a transfer learning approach.In the transfer learning approach,an already-trained network is used as a starting point for classification in a similar domain.Results show that this approach works well for classifying roof conditions as hazardous or safe,achieving a statistical accuracy of 86.4%.This result is also compared with a random forest classifier,and the deep learning approach is more successful at classification of roof conditions.However,accuracy alone is not enough to ensure a reliable hazard management system.System constraints and reliability are improved when the features used by the network are understood.Therefore,we used a deep learning interpretation technique called integrated gradients to identify the important geological features in each image for prediction.The analysis of integrated gradients shows that the system uses the same roof features as the experts do on roof fall hazards detection.The system developed in this paper demonstrates the potential of deep learning in geotechnical hazard management to complement human experts,and likely to become an essential part of autonomous operations in cases where hazard identification heavily depends on expert knowledge.Moreover,deep learning-based systems reduce expert exposure to hazardous conditions.
