在互联网技术和信息技术日新月异的背景下,信息服务行业竞争格局愈加复杂,商业模式创新成为决定企业竞争优势的关键性要素。本文聚焦搜索引擎行业,总结行业常见商业盈利模式,并以Google作为典型案例,列举其在搜索领域的商业模式创新行为...在互联网技术和信息技术日新月异的背景下,信息服务行业竞争格局愈加复杂,商业模式创新成为决定企业竞争优势的关键性要素。本文聚焦搜索引擎行业,总结行业常见商业盈利模式,并以Google作为典型案例,列举其在搜索领域的商业模式创新行为,根据案例资料进行扎根分析,研究搜索引擎公司商业模式创新的机理及发展路径等问题。研究显示:行业结构和制度环境构成了搜索引擎公司商业模式创新的外部环境,核心经营层战略、技术洞见战略和市场定位战略形成了搜索引擎公司商业模式创新的内部战略基础,财务绩效和用户价值通过绩效反馈反向调节搜索引擎公司的商业模式创新行为,在线广告平台、广告联盟、软件生态等构筑了搜索引擎行业的商业生态系统。四者综合作用影响企业的商业模式创新。Against the backdrop of rapid advancements in internet and information technologies, the competitive landscape of the information services industry has grown increasingly complex, with business model innovation emerging as a critical determinant of corporate competitive advantage. This study focuses on the search engine industry, summarizing common business monetization models within the sector. Using Google as a representative case, it examines the company’s innovative practices in search-related business models. Through grounded theory analysis of case data, the research investigates the mechanisms and evolutionary pathways of business model innovation among search engine companies. Key findings reveal: 1) External Environment: Industry structure and institutional factors shape the external conditions for business model innovation in search engine firms. 2) Strategic Foundations: Core operational strategies, technology vision strategies, and market positioning strategies form the internal strategic basis for innovation. 3) Feedback Mechanisms: Financial performance and user value create reverse-moderation effects on innovation behaviors through performance feedback loops. 4) Ecosystem Architecture: Key components including online advertising platforms, ad networks, and software ecosystems constitute the industry’s commercial ecosystem. The study demonstrates how these four dimensions interact synergistically to drive business model innovation.展开更多
Floods are among the most severe and frequent natural disasters,impacting numerous countries worldwide.This study investigates flood mapping methodologies utilizing Google Earth Engine(GEE)with Sentinel-1,Sentinel-2,a...Floods are among the most severe and frequent natural disasters,impacting numerous countries worldwide.This study investigates flood mapping methodologies utilizing Google Earth Engine(GEE)with Sentinel-1,Sentinel-2,and Landsat data,focusing on the January 2021 Tetouan flood in Morocco.Three approaches were assessed:Sentinel-1 thresholding and NDWI(Normalized Difference Water Index)methods applied to Sentinel-2 and Landsat imagery.The analysis revealed flooded areas of 891 hectares(Sentinel-1),814 hectares(Sentinel-2),and 1237 hectares(Landsat),validated against ArcGIS(Geographic Information System)results estimating 900 hectares.Sentinel-1 demonstrated superior accuracy with only a 9-hectare deviation and proved effective under cloudy conditions.Sentinel-2 provided a balance between spatial resolution and error levels,with moderate commission and omission errors.Landsat detected the largest flood extent but exhibited a slight overestimation.The study emphasizes the advantages of GEE’s cloud-based platform,which significantly reduced processing time,facilitating rapid flood extent mapping.This scalability and efficiency make GEE an invaluable tool for disaster management.The results underline the potential of these methodologies for accurate and timely flood monitoring,enabling informed decision-making in resilience planning and emergency response.Such advancements are critical for mitigating the impacts of flooding and supporting sustainable disaster management strategies in vulnerable regions worldwide.展开更多
Google Gemini 1.5 Flash scores were compared with ChatGPT 4o-mini on evaluations of(a)51 of the author’s journal articles and(b)up to 200 articles in each of 34 field-based Units of Assessment(UoAs)from the UK Resear...Google Gemini 1.5 Flash scores were compared with ChatGPT 4o-mini on evaluations of(a)51 of the author’s journal articles and(b)up to 200 articles in each of 34 field-based Units of Assessment(UoAs)from the UK Research Excellence Framework(REF)2021.From(a),the results suggest that Gemini 1.5 Flash,unlike ChatGPT 4o-mini,may work better when fed with a PDF or article full text,rather than just the title and abstract.From(b),Gemini 1.5 Flash seems to be marginally less able to predict an article’s research quality(using a departmental quality proxy indicator)than ChatGPT 4o-mini,although the differences are small,and both have similar disciplinary variations in this ability.Averaging multiple runs of Gemini 1.5 Flash improves the scores.展开更多
Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status ...Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status of land covers in Hung Yen province of Vietnam using Landsat 8 OLI satellite images,a free data source with reasonable spatial and temporal resolution.The results of the study show that all three algorithms presented good classification for five basic types of land cover including Rice land,Water bodies,Perennial vegetation,Annual vegetation,Built-up areas as their overall accuracy and Kappa coefficient were greater than 80%and 0.8,respectively.Among the three algorithms,SVM achieved the highest accuracy as its overall accuracy was 86%and the Kappa coefficient was 0.88.Land cover classification based on the SVM algorithm shows that Built-up areas cover the largest area with nearly 31,495 ha,accounting for more than 33.8%of the total natural area,followed by Rice land and Perennial vegetation which cover an area of over 30,767 ha(33%)and 15,637 ha(16.8%),respectively.Water bodies and Annual vegetation cover the smallest areas with 8,820(9.5%)ha and 6,302 ha(6.8%),respectively.The results of this study can be used for land use management and planning as well as other natural resource and environmental management purposes in the province.展开更多
The article employs the wetlands of Ruoergai(i.e.,Zoige),Sichuan Province,as a case study to analyze changes over various time scales,utilizing Landsat data from 2004,2008,2012,2016,2020,and 2023.The study uses the GE...The article employs the wetlands of Ruoergai(i.e.,Zoige),Sichuan Province,as a case study to analyze changes over various time scales,utilizing Landsat data from 2004,2008,2012,2016,2020,and 2023.The study uses the GEE platform and a deep learning model,focusing on the long-term perspective.This analysis serves as a focal point for discussing sustainable development,offering ecological balance information and a realistic foundation.The paper systematically gathers remote sensing classification images resembling sample points on the GEE(Google Earth Engine)platform.Simultaneously,it develops a deep learning model for classifying land types in Ruoergai into six categories:river-wetland,lake-wetland,swamp-wetland,grassland,forest and shrubland.This classification is achieved by utilizing various bands of Landsat data as input features and assigning land cover as corresponding labels.A comparison of classification results in 2016 indicates that the approach integrating the GEE platform and the deep learning model enhances overall accuracy by 9%compared to the random forest method.Furthermore,the overall accuracy surpasses that of the support vector machine method by 16%,and the CART method by 23%.These results affirm that the combined GEE platform and deep learning model outperforms the random forest method in overall accuracy.The findings reveal a declining trend in the wetland area of Ruoergai from 2004 to 2012,with the area remaining relatively stable from 2012 to 2016.Subsequently,there is a significant increase from 2016 to 2023.These trends corroborate the positive outcomes of long-term environmental protection policies implemented by the Chinese government.Furthermore,they underscore the success and efforts exerted by both the government and society in the sustainable management of wetland ecosystems.This serves as an exemplary case for advancing the SDG 15.1 development goal.展开更多
In recent years,with the increasing attention to issues related to carbon emissions,such as carbon tariffs and government netzero carbon emission policies,carbon emissions have become an important indicator that is be...In recent years,with the increasing attention to issues related to carbon emissions,such as carbon tariffs and government netzero carbon emission policies,carbon emissions have become an important indicator that is being prioritized by governments worldwide.The Google Environmental Insights Explorer(EIE)tool has been developed to facilitate the collection and integration of data in this context.This study focuses on Tainan City and utilizes EIE to analyze greenhouse gas emissions from transportation.By using EIE,the study obtains data on greenhouse gas emissions from transportation activities in Tainan City.EIE utilizes data collected by Google and simulation functions to estimate data based on actual measurements of transportation activities.This tool saves time and resources by eliminating the need for on-site investigations while providing data that closely represent the real emissions from transportation activities in urban areas.Transportation vehicles contribute to greenhouse gas emissions in two ways:through direct combustion of fossil fuels and through the consumption of electricity in electric vehicles(EVs).The level of greenhouse gas emissions in a city’s transportation industry depends on factors such as transportation modes,fuel types,fleet age and energy efficiency,total distance traveled,and annual mileage.EIE estimates the greenhouse gas emissions from Tainan City’s transportation industry in 2022 to be 3,320,000 metric tons,including emissions from buses,motorcycles,cars,walking,railways,bicycles,and other modes of transportation.展开更多
文摘在互联网技术和信息技术日新月异的背景下,信息服务行业竞争格局愈加复杂,商业模式创新成为决定企业竞争优势的关键性要素。本文聚焦搜索引擎行业,总结行业常见商业盈利模式,并以Google作为典型案例,列举其在搜索领域的商业模式创新行为,根据案例资料进行扎根分析,研究搜索引擎公司商业模式创新的机理及发展路径等问题。研究显示:行业结构和制度环境构成了搜索引擎公司商业模式创新的外部环境,核心经营层战略、技术洞见战略和市场定位战略形成了搜索引擎公司商业模式创新的内部战略基础,财务绩效和用户价值通过绩效反馈反向调节搜索引擎公司的商业模式创新行为,在线广告平台、广告联盟、软件生态等构筑了搜索引擎行业的商业生态系统。四者综合作用影响企业的商业模式创新。Against the backdrop of rapid advancements in internet and information technologies, the competitive landscape of the information services industry has grown increasingly complex, with business model innovation emerging as a critical determinant of corporate competitive advantage. This study focuses on the search engine industry, summarizing common business monetization models within the sector. Using Google as a representative case, it examines the company’s innovative practices in search-related business models. Through grounded theory analysis of case data, the research investigates the mechanisms and evolutionary pathways of business model innovation among search engine companies. Key findings reveal: 1) External Environment: Industry structure and institutional factors shape the external conditions for business model innovation in search engine firms. 2) Strategic Foundations: Core operational strategies, technology vision strategies, and market positioning strategies form the internal strategic basis for innovation. 3) Feedback Mechanisms: Financial performance and user value create reverse-moderation effects on innovation behaviors through performance feedback loops. 4) Ecosystem Architecture: Key components including online advertising platforms, ad networks, and software ecosystems constitute the industry’s commercial ecosystem. The study demonstrates how these four dimensions interact synergistically to drive business model innovation.
文摘Floods are among the most severe and frequent natural disasters,impacting numerous countries worldwide.This study investigates flood mapping methodologies utilizing Google Earth Engine(GEE)with Sentinel-1,Sentinel-2,and Landsat data,focusing on the January 2021 Tetouan flood in Morocco.Three approaches were assessed:Sentinel-1 thresholding and NDWI(Normalized Difference Water Index)methods applied to Sentinel-2 and Landsat imagery.The analysis revealed flooded areas of 891 hectares(Sentinel-1),814 hectares(Sentinel-2),and 1237 hectares(Landsat),validated against ArcGIS(Geographic Information System)results estimating 900 hectares.Sentinel-1 demonstrated superior accuracy with only a 9-hectare deviation and proved effective under cloudy conditions.Sentinel-2 provided a balance between spatial resolution and error levels,with moderate commission and omission errors.Landsat detected the largest flood extent but exhibited a slight overestimation.The study emphasizes the advantages of GEE’s cloud-based platform,which significantly reduced processing time,facilitating rapid flood extent mapping.This scalability and efficiency make GEE an invaluable tool for disaster management.The results underline the potential of these methodologies for accurate and timely flood monitoring,enabling informed decision-making in resilience planning and emergency response.Such advancements are critical for mitigating the impacts of flooding and supporting sustainable disaster management strategies in vulnerable regions worldwide.
文摘Google Gemini 1.5 Flash scores were compared with ChatGPT 4o-mini on evaluations of(a)51 of the author’s journal articles and(b)up to 200 articles in each of 34 field-based Units of Assessment(UoAs)from the UK Research Excellence Framework(REF)2021.From(a),the results suggest that Gemini 1.5 Flash,unlike ChatGPT 4o-mini,may work better when fed with a PDF or article full text,rather than just the title and abstract.From(b),Gemini 1.5 Flash seems to be marginally less able to predict an article’s research quality(using a departmental quality proxy indicator)than ChatGPT 4o-mini,although the differences are small,and both have similar disciplinary variations in this ability.Averaging multiple runs of Gemini 1.5 Flash improves the scores.
文摘Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status of land covers in Hung Yen province of Vietnam using Landsat 8 OLI satellite images,a free data source with reasonable spatial and temporal resolution.The results of the study show that all three algorithms presented good classification for five basic types of land cover including Rice land,Water bodies,Perennial vegetation,Annual vegetation,Built-up areas as their overall accuracy and Kappa coefficient were greater than 80%and 0.8,respectively.Among the three algorithms,SVM achieved the highest accuracy as its overall accuracy was 86%and the Kappa coefficient was 0.88.Land cover classification based on the SVM algorithm shows that Built-up areas cover the largest area with nearly 31,495 ha,accounting for more than 33.8%of the total natural area,followed by Rice land and Perennial vegetation which cover an area of over 30,767 ha(33%)and 15,637 ha(16.8%),respectively.Water bodies and Annual vegetation cover the smallest areas with 8,820(9.5%)ha and 6,302 ha(6.8%),respectively.The results of this study can be used for land use management and planning as well as other natural resource and environmental management purposes in the province.
文摘The article employs the wetlands of Ruoergai(i.e.,Zoige),Sichuan Province,as a case study to analyze changes over various time scales,utilizing Landsat data from 2004,2008,2012,2016,2020,and 2023.The study uses the GEE platform and a deep learning model,focusing on the long-term perspective.This analysis serves as a focal point for discussing sustainable development,offering ecological balance information and a realistic foundation.The paper systematically gathers remote sensing classification images resembling sample points on the GEE(Google Earth Engine)platform.Simultaneously,it develops a deep learning model for classifying land types in Ruoergai into six categories:river-wetland,lake-wetland,swamp-wetland,grassland,forest and shrubland.This classification is achieved by utilizing various bands of Landsat data as input features and assigning land cover as corresponding labels.A comparison of classification results in 2016 indicates that the approach integrating the GEE platform and the deep learning model enhances overall accuracy by 9%compared to the random forest method.Furthermore,the overall accuracy surpasses that of the support vector machine method by 16%,and the CART method by 23%.These results affirm that the combined GEE platform and deep learning model outperforms the random forest method in overall accuracy.The findings reveal a declining trend in the wetland area of Ruoergai from 2004 to 2012,with the area remaining relatively stable from 2012 to 2016.Subsequently,there is a significant increase from 2016 to 2023.These trends corroborate the positive outcomes of long-term environmental protection policies implemented by the Chinese government.Furthermore,they underscore the success and efforts exerted by both the government and society in the sustainable management of wetland ecosystems.This serves as an exemplary case for advancing the SDG 15.1 development goal.
文摘In recent years,with the increasing attention to issues related to carbon emissions,such as carbon tariffs and government netzero carbon emission policies,carbon emissions have become an important indicator that is being prioritized by governments worldwide.The Google Environmental Insights Explorer(EIE)tool has been developed to facilitate the collection and integration of data in this context.This study focuses on Tainan City and utilizes EIE to analyze greenhouse gas emissions from transportation.By using EIE,the study obtains data on greenhouse gas emissions from transportation activities in Tainan City.EIE utilizes data collected by Google and simulation functions to estimate data based on actual measurements of transportation activities.This tool saves time and resources by eliminating the need for on-site investigations while providing data that closely represent the real emissions from transportation activities in urban areas.Transportation vehicles contribute to greenhouse gas emissions in two ways:through direct combustion of fossil fuels and through the consumption of electricity in electric vehicles(EVs).The level of greenhouse gas emissions in a city’s transportation industry depends on factors such as transportation modes,fuel types,fleet age and energy efficiency,total distance traveled,and annual mileage.EIE estimates the greenhouse gas emissions from Tainan City’s transportation industry in 2022 to be 3,320,000 metric tons,including emissions from buses,motorcycles,cars,walking,railways,bicycles,and other modes of transportation.
