Location-allocation modeling for emergency evacuation planning with GIS and remote sensing:A case study of Northeast Bangladesh

This work developed models to identify optimal spatial distribution of emergency evacuation centers(EECs)such as schools,colleges,hospitals,and fire stations to improve flood emergency planning in the Sylhet region of northeastern Bangladesh.The use of location-allocation models(LAMs)for evacuation in regard to flood victims is essential to minimize disaster risk.In the first step,flood susceptibility maps were developed using machine learning models(MLMs),including:Levenberg-Marquardt back propagation(LM-BP)neural network and decision trees(DT)and multi-criteria decision making(MCDM)method.Performance of the MLMs and MCDM techniques were assessed considering the area under the receiver operating characteristic(AUROC)curve.Mathematical approaches in a geographic information system(GIS)for four well-known LAM problems affecting emergency rescue time are proposed:maximal covering location problem(MCLP),the maximize attendance(MA),p-median problem(PMP),and the location set covering problem(LSCP).The results showed that existing EECs were not optimally distributed,and that some areas were not adequately served by EECs(i.e.,not all demand points could be reached within a 60-min travel time).We concluded that the proposed models can be used to improve planning of the distribution of EECs,and that application of the models could contribute to reducing human casualties,property losses,and improve emergency operation.

Enigmatic Glass-Like Carbon from the Alpine Foreland, Southeast Germany: A Natural Carbonization Process

Unusual carbonaceous matter, termed here chiemite, composed of more than 90% C from the Alpine Foreland at Lake Chiemsee in Bavaria, southeastern Germany has been investigated using optical and atomic force microscopy, X-ray fluorescence spectroscopy, scanning and transmission electron microscopy, high-resolution Raman spectroscopy, X-ray diffraction and differential thermal analysis, as well as by δ13 C and 14 C radiocarbon isotopic data analysis. In the pumice-like fragments, poorly ordered carbon matter co-exists with high-ordering monocrystalline α-carbyne, and contains submicrometersized inclusions of complex composition. Diamond and carbyne add to the peculiar mix of matter. The required very high temperatures and pressures for carbyne formation point to a shock event probably from the recently proposed Holocene Chiemgau meteorite impact. The carbon material is suggested to have largely formed from heavily shocked coal, vegetation like wood, and peat from the impact target area. The carbonization/coalification high PT process may be attributed to a strong shock that instantaneously caused the complete evaporation and loss of volatile matter and water, which nevertheless preserved the original cellular structure seen fossilized in many fragments. Relatively fresh wood encapsulated in the purported strongly shocked matter point to quenched carbon melt components possibly important for the discussion of survival of organic matter in meteorite impacts, implying an astrobiological relationship.

Predicting Carpark Prices Indices in Hong Kong Using AutoML

The aims of this study were threefold:1)study the research gap in carpark and price index via big data and natural language processing,2)examine the research gap of carpark indices,and 3)construct carpark price indices via repeat sales methods and predict carpark indices via the AutoML.By researching the keyword“carpark”in Google Scholar,the largest electronic academic database that coversWeb of Science and Scopus indexed articles,this study obtained 999 articles and book chapters from 1910 to 2019.It confirmed that most carpark research threw light on multi-storey carparks,management and ventilation systems,and reinforced concrete carparks.The most common research method was case studies.Regarding price index research,many previous studies focused on consumer,stock,press and futures,with many keywords being related to finance and economics.These indicated that there is no research predicting carpark price indices based on an AutoML approach.This study constructed repeat sales indices for 18 districts in Hong Kong by using 34,562 carpark transaction records from December 2009 to June 2019.Wanchai’s carpark price was about four times that of Yuen Long’s carpark price,indicating the considerable carpark price differences inHong Kong.This research evidenced the features that affected the carpark price indices models most:gold price ranked the first in all 19 models;oil price or Link stock price ranked second depending on the district,and carpark affordability ranked third.

Factors influencing venture capital perforsmance in emerging technology:The case of China

This study aims to identify the systematic factors influencing venture capital performance in emerging technology.Emerging technologies are the driving force of contemporary economic and social development,but their technologies and markets are subject to great uncertainty.As a high-risk,high-yield investment method,venture capital plays an extremely important role in supporting technological innovation and promoting the upgrading of industrial structures.Following information processing theory,this study applies a quantitative analysis to data from 61 venture capitalists in Beijing,China using structural equation modeling(SEM).The sample firms were established since 2015 and invested mainly in emerging technology startups.The model passed the goodness-of-fit indices test and reveals that information acquisition,venture capital executives,and venture capital strategies have a positive impact on venture capital performance,and venture capital strategies played a partial mediating role.

Mushrooms in the Mountains: Assessing the Role of Fungi on the Ecosystem-based Adaptation (EbA) Practices in Nepal Himalaya

To achieve the Sustainable Development Goals(SDGs), thereby meet the post 2020 global biodiversity targets and increase resilience to climate change, nature-based approaches such as ecosystem-based adaptation(EbA) is suggested as a promising and integrated adaptation strategy. EbA comprises adaptation strategies that value the role of ecosystems in reducing social vulnerability to climate change. Among the different biological groups on earth, fungi play not only an important role to maintain the biogeochemical cycle/nutrient cycle in ecosystems(supporting and regulating services), but also contribute to the socio-economic and cultural benefits of societies(provisioning and cultural services). Here, we present our knowledge and scientific understanding on how these neglected groups of biodiversity-fungi are crucial for ecosystem-based adaptation(EbA) approach based on our field experience, review and associated expertise on caterpillar fungus(Ophiocordyceps sinensis), and other wild mushrooms found in Nepal. Several species of fungi are used by local communities as food, medicines, and environmental income. Fungi are important sources of household income for mountain communities in Nepal providing a cushion during shocks and disasters and supporting food security, health care, education and building shelter. For the holistic EbA approach, it is essential to strengthen local institutions as well as indigenous local knowledge which could be an important policy intervention for the identification, conservation, and sustainable management of ecologically, socially and economically useful fungal species.

Climate change and its association with the expansion of vectors and vector-borne diseases in the Hindu Kush Himalayan region:A systematic synthesis of the literature

Observed weather and projected climate change suggest an increase in the transmission of vector-borne diseases(VBDs)in the Hindu Kush Himalayan(HKH)region.In this study,we systematically explore the literature for empiric associations between the climate variables and specific VBDs and their vectors in the HKH region.We conducted a systematic synthesis of the published literature on climate variables,VBDs and vectors in the HKH region until the 8th of December 2020.The majority of studies show significant positive associations of VBDs with climatic factors,such as temperature,precipitation,relative humidity,etc.This systematic review allowed us to identify the most significant variables to be considered for evidence-based trend estimates of the effects of climate change on VBDs and their vectors in the HKH region.This evidence-based trend was set into the context of climate change as well as the observed expansion of VBDs and disease vectors in the HKH region.The geographic range of VBDs expanded into previously considered non-endemic areas of highlands(mountains)in the HKH region.B ased on scarce,but clear evidence of a positive relationship of most climate variables and VBDs and the observed climatic changes,we strongly recommend an expansion of vector control and surveillance programmes in areas of the HKH region that were previously considered to be non-endemic.