Abstract
Big data is one of the hottest topics nowadays. Under the background of smart city and intelligent public transport, a large number of scholars begin to use big data of public transport in ...urban transport planning. This paper, combined with dozens of the latest journal papers, combined with their own understanding, focuses on the development of bus IC card big data, application status and research prospects, forming a reading report.
Construction is associated with quarrying as well as heavy transportation of soil and rock materials, in and out of construction sites. Both quarrying and transportation of the excavated materials ...result in negative environmental impact due to energy use and greenhouse gas (GHG) emissions. Moreover, soil and rock materials of suitable geotechnical quality for construction are a scarce natural resource in some urban regions. These issues have urged the need to optimize the use of quarry materials on-site and thereby reduce transportation. Still, internal flows of soil and rock materials in urban areas have not been well analyzed. This study presents a model to analyze future soil and rock flows in terms of material quality and quantities in urban areas. Furthermore, the study analyses the possibility of recycling excavated soil and rock and thereby reduce transportation and transport-related GHG emissions. The study applies the model in a case study to analyze integrating future residential and non-residential developments and a highway project. The case study revealed that excavated material would be generated in enough volumes to potentially cover the quarry materials demanded for providing stability and permeability to buildings, streets and highway. The scenario analysis showed that provision of strategically located recycling sites for material coordination could reduce the demand for soil and rock transportation as well as transport-related GHG emissions i.e. by 23–36% per area, compared to a business as usual scenario. The study shows that internal soil and rock flows within regions can be modelled by using data from development plans and geological maps. The model results may serve as a basis for decision making regarding strategic material management in urban planning.
•A model for quantifying soil and rock flows from urban construction was presented.•Environmental gains of local recycling sites for rock and soil was analyzed in scenarios.•Recycling reduced soil and rock transportation and its GHG emissions by 23–36%.•The model can improve studies of urban metabolism and strategical urban planning.
Abstract
This study aims to know the susceptible disaster region based on peak ground acceleration and earthquake intensity on earthquake study cases in Mamasa 2018. Data that is used in this ...research is data on earthquake cases in Mamasa 2018 that is gained from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) district IV Makassar, it included data of the time when the earthquake happened, latitude, longitudinal (, depth (< 60 km) and magnitude (≥ 5) SR. The calculation of on peak ground acceleration was conducted by using Donovan and Mc.Guirre methods, whereas to calculate the value of intensity, it is using the Wald method (1999). The result from the calculation of on peak ground acceleration by Donovan is 95.903 gal to 377.094 gals with the intensity VI to VIII MMI. Whereas the calculation result of on peak ground acceleration by Mc.Guirre method is 58.981 gal to 227.39 gals with the intensity V to VII MMI. MMI intensity of the Wald method is obtained by using the PGA value conversation Mc.Guirre method that is closer to the intensity of the result of the macroseismic survey in Mamasa District and relates with the damage that happened on location. The mapping of earthquake intensity based on PGA Mc.Guirre method shows that 3 (three) sub-districts that have an intensity of VII MMI, namely Tawalian Sub-district, Mamasa Sub-district, and Sesean Padang Sub-district. The area is the most vulnerable. Mamasa has low-risk level from the earthquake, although sometimes an earthquake happened until now. This result is supported by a geological map in the Mamasa regions that showed source rock in that area is a type of hard rock that consis of granitic materials.
Abstract
The implementation of this study aims to present information not only in the text/tabular form but also in the spatial/digital map form to support the poverty alleviation programs in East ...Kalimantan, especially in Samarinda and Balikpapan, hence information obtained can be more easily analyzed, identified in terms of the poor population distribution in the visualization form. It presented as a list an area, graph, detail data and geographical map integrated with Google Maps. This application was developed using PHP scripts, Apache Webserver, Codeigniter Framework, CCS Bootstrap, MySQL database, and google maps API. Poor data collected from the Central Bureau of Statistics and the results of the survey through questioner and interview respondents. The information presented in this system is the resident name, identification number, address, employment, income, education, residence status and photo of living conditions/person.
This paper presents geographic maps of the profitability of PV and CPV solar projects over 1,960,000 km2. Such maps supplement the irradiance maps used in solar project planning and are relevant to ...government policy makers to assess the impact of incentives on profitability. Also, they are useful to solar project developers to identify geographical areas in which a required rate of financial return can be expected. Eight such maps of central Canada are presented in this paper and show the impact on profitability of different levels of government incentives, the difference in profitability between PV and CPV and the impact on profitability of delaying a project start date from 2016 to 2020. Government incentives of 10%–30% of the capital cost improve the internal rate of return (IRR) by between 0.7 and 4.67. For PV, the orientation of the modules is optimized to achieve maximum IRR resulting in an IRR increase of between 1.3% and 8.2%. Since CPV is a more recent technology than PV, its capital costs are projected to decline faster and we quantify the impact on IRR by showing that CPV has a higher IRR than PV of between 0.55 in 2016 and 3.6 in 2020.
Display omitted
•Geographic maps of the profitability of PV and CPV over 1,960,000 km2 are produced.•Maps show how profitability increases with project start dates up to 2020.•Maps show how profitability increases with government subsidy level up to 30%.•Profitability maps are useful to policy makers as well as solar project developers.•Profitability for CPV is in general higher than for PV.
Buildings are responsible for a major share of global final energy consumption and carbon dioxide (CO2) emissions. An analysis of the worldwide observed drivers of demand can highlight the policy ...actions most suited to drive the decarbonization of the building sector. To contribute to such an analysis, we carry out a mapping of the literature on determinants of energy demand and CO2 emissions from buildings. The work includes a list and classification of relevant studies in an on-line geographical map, a description of trends and gaps, and a narrative review. We identify 4080 articles in the Scopus and the Web of Science databases, of which 712 are relevant after screening at the title and abstract level, and 376 are included for data extraction. The literature base mostly addresses electricity and water use, in North America and Europe (57% of the literature) and Asia (27%). Econometric modeling approaches using panel data to calculate demand elasticities, dominate. These findings highlight gaps in terms of the studied variables (only 5% focus on CO2 emissions while a mere 1% have a lifecycle perspective), geographical scope (only 5% of the articles focus on Africa, 7% on Latin America and the Caribbean, and 5% on Oceania), and methodological approach (only 5% use qualitative methods). We confirm that worldwide, income, energy price and outdoor temperature are unequivocal drivers of buildings energy demand and CO2 emissions, followed by other indicators of scale such as population or heated floor area. Our analysis makes it clear that decoupling from rising wealth levels has not been observed. This will continue to challenge reductions in energy use and CO2 emissions from buildings in line with climate targets. Macroeconomic policies focusing on the impacts of income, energy price, population and growing floor area are needed in combination with technical policy to reduce the impact of outdoor climate.
Schistosomiasis japonicum is a major zoonosis that seriously harms human health and affects social and economic development in China. The control of Oncomelania Hupensis, the only intermediate host ...of schistosome japonicum, is one of the integrated measures for schistosomiasis control in China. Acquiring updated elevation data of snail habitat environment, as well as it's spatial analysis, play an important role for the risk evaluation and precise control of schistosomiasis transmission and prevalence. Currently, the elevation database of snail habitat environment in schistosomiasis epidemic areas has not been available in the world, which affects the development of research and application work regarding to snail control. Google Earth(GE) can provide massive information related to topography, geomorphology and ground objects of a region due to its indisputable advantages such as wide use, free charge and rapidly updating. In this paper, taking the Poyang lake region as a example, we extracted elevation data of snail-inhabited environment of the lake from GE and established a elevation correction regression model(CRM) for acquiring accurate geospatial elevations, so as to provide a decision-making reference for snail control and risk evaluation of schistosomiasis in China. We developed a GE Application Programming Interface(API) program to extract elevation data from GE, which was compared with the actual elevation data obtained from topographic map of the Poyang Lake bottom. Then, a correction regression model was established and evaluated by 3 index, Mean Absolute Error(MAE), Root Mean Squared Error(RMSE) and Index of Agreement(IOA) for the accuracy of the model. The elevation values extracted from GE in 15086 sample grid points of the lake ranged from 8.5 m to 24.8 m. After the sample points were divided randomly to three groups, the mean elevations of three groups were 13.49 m, 13.52 m and 13.65 m, respectively, with standard deviation ranged from 2.04-2.06. The mean elevation among three groups has no statistic difference (F = 1.536, P = 0.215). A elevation correction regression model was established as y = 6.228 + 0.485×. the evaluation results for the accuracy of the model showed that the MAE and RMSE before correction was 1.28 m and 3.95 m respectively, higher than that after correction, which were 0.74 and 1.30 m correspondingly. The IOA before correction (-0.40)was lower than that after correction(0.34). Google Earth can directly or indirectly get access to massive information related to topography, geomorphology and ground objects due to its indisputable advantages. However, it still needs to be converted into more reliable and accurate data by combining with pre-processing tools. This study used self-developed API program to extract elevation data from GE through precisely locating and improved the accuracy of elevation by using a correction regression model, which can provide reliable data sources for all kinds of spatial data researches and applications.
In recent years, there has been a surge of research in the field of medical image segmentation using hybrid CNN‐Transformer network architectures. Most of these studies leverage the attention ...mechanism of ViT to overcome the limitations of CNN architectures in capturing long‐range dependencies. However, these hybrid model approaches also have some potential drawbacks. First, due to the heavy reliance of the Transformer's attention mechanism on global information, it can lead to a significant increase in computational cost when dealing with high‐resolution input images. Furthermore, the convolutional and attention mechanisms in hybrid models have different interpretability in information extraction and decision‐making, which poses a challenge for the interpretability of the convolutional part. Our proposed model, DWHA, addresses these limitations and outperforms state‐of‐the‐art models in a range of medical image segmentation tasks, such as abdominal multiorgan segmentation, automatic cardiac diagnosis, neurostructure segmentation, and skin lesion segmentation, achieving significantly superior performance. Specifically, on the abdominal multiorgan segmentation dataset, DWHA outperformed the previous state‐of‐the‐art baseline by 0.57%; on the neurostructure segmentation dataset, it achieved an improvement of 1.17%; and on the skin lesion segmentation dataset, it achieved an improvement of 0.91%. These significant improvements suggest that DWHA may become the preferred model in the field of medical image segmentation.
•We have developed and built a data acquisition system for grid-connected PV plants.•It is accurate despite its low cost arising from the use of a microcomputer board and inexpensive sensors.•We have ...described the system architecture and the data collection methods used.•It has been used for grid-connected PV systems spread over 20 Moroccan cities for more than one year.•We have also compared our data with those obtained with calibrated commercial sensors.
A low-cost monitoring system for grid-connected PV system is presented. It is based on an inexpensive and available hardware, as well as easy software which does not require any high specific skills and which can be written in different programming languages (C/C++, python, etc). The proposed system uses a 16-bits resolution with a good accuracy and enough reliability responding to the most IEC standards requirements on PV applications. It is composed of a group of sensors measuring meteorological parameters (Solar irradiance, ambient temperature, wind speed and direction) and module temperatures while electrical parameters (currents and voltages in both sides, DC and AC) are numerically taken from the inverter. The collected data are first conditioned using precision electronic circuits, processed by the PcDuino board, and then stored in an SD card in a form that can be easily used, not requiring any external computer. The system allows local storage and distance data transfer through Internet. The PV system users are informed about its operation and each incident is reported by an e-mail message generated by an error notification routine. The proposed architecture can be built easily; it has low energy consumption and enough flexibility for potential future modifications. The system has been installed to monitor 20 outdoor grid-connected PV systems more than one year ago and all its units operate in a satisfying manner.
PDF
