Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with ...Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with Tajikistan representing a typical example of such disparities.Based on 81 SDG indicators for Tajikistan from 2001 to 2023,this study applied a three-level coupling network framework:at the microscale,it identified synergies and trade-offs between indicators;at the mesoscale,it examined the strength and direction of linkages within four SDG-related components(society,finance,governance,and environment);and at the global level,it focused on the overall SDG interlinkages.Spearman’s rank correlation,sliding window method,and topological properties were employed to analyze the coupling dynamics of SDGs.Results showed that over 70.00%of associations in the global SDG network were of medium-to-low intensity,alongside extremely strong ones(|r|value approached 1.00,where r is the correlation coefficient).SDG interactions were generally limited,with stable local synergy clusters in core livelihood sectors.Network modularity fluctuated,reflecting a cycle of differentiation,integration,and fragmentation,while coupling efficiency varied with the external environment.Each component exhibited distinct functional characteristics.The social component maintained high connectivity through the“poverty alleviation-education-healthcare”loop.The environmental component shifted toward coordinated eco-economic governance.The governance-related component broke interdepartmental barriers,while the financial component showed weak links between resource-based indicators and consumption/employment indicators.Tajikistan’s SDG coupling evolved through three phases:survival-oriented(2001–2012),policy integration(2013–2018),and shock adaptation(2019–2023).These phases were driven by policy changes,resource industries,governance optimization,and external factors.This study enriches the analytical framework for understanding the dynamic coupling of SDGs in mountainous resource-dependent countries and provides empirical evidence to support similar countries in formulating phase-specific SDG promotion strategies.展开更多
增强安徽省可持续发展动能对推动长三角更高质量一体化发展和服务国家新发展格局具有重要意义。本文构建了面向联合国2030可持续发展目标(Sustainable Development Goals,SDGs)的可持续发展水平评估框架和指标体系,对2011~2021年安徽省...增强安徽省可持续发展动能对推动长三角更高质量一体化发展和服务国家新发展格局具有重要意义。本文构建了面向联合国2030可持续发展目标(Sustainable Development Goals,SDGs)的可持续发展水平评估框架和指标体系,对2011~2021年安徽省及其16个地级市可持续发展水平进行测算,计算各市经济、社会和环境维度可持续发展的耦合协调度,分析安徽省可持续发展水平的时空演化特征,利用Dagum基尼系数深入探究安徽省可持续发展水平区域差异的原因,并基于灰色关联度分析方法进一步揭示影响安徽省不同城市可持续发展水平的关键因素。研究结果表明:安徽省可持续发展综合指数以及3个子系统间的耦合协调度呈现不断提高的趋势,但可持续发展水平空间差异明显。安徽省可持续发展水平空间差异原因主要来自区域之间的差异,“皖中-皖北”和“皖中-皖南”的区域间差异明显高于“皖南-皖北”。不同维度下不同指标对各市可持续发展综合水平的影响表现出明显差异。对省会合肥而言,研发经费支出占比、公共服务支出比重、生活垃圾无害化处理率分别是经济、社会和环境子系统影响其可持续发展综合水平的首要因素。研究结果可为安徽省加快实现2030可持续发展目标和推动高质量发展提供科学支撑。展开更多
The rapid urbanization and increasing challenges are faced by cities globally,including climate change,population growth,and resource constraints.Sustainable smart city(also referred to as“smart sustainable city”)ca...The rapid urbanization and increasing challenges are faced by cities globally,including climate change,population growth,and resource constraints.Sustainable smart city(also referred to as“smart sustainable city”)can offer innovative solutions by integrating advanced technologies to build smarter,greener,and more livable urban environments with significant benefits.Using the Web of Science(WoS)database,this study examined:(i)the mainstream approaches and current research trends in the literature of sustainable smart city;(ii)the extent to which the research of sustainable smart city aligns with Sustainable Development Goals(SDGs);(iii)the current topics and collaboration patterns in sustainable smart city research;and(iv)the potential opportunities for future research on the sustainable smart city field.The findings indicated that research on sustainable smart city began in 2010 and gained significant momentum in 2013,with China leading,followed by Italy and Spain.Moreover,59.00%of the selected publications on the research of sustainable smart city focus on SDG 11(Sustainable Cities and Communities).Bibliometric analysis outcome revealed that artificial intelligence(AI),big data,machine learning,and deep learning are emerging research fields.The terms smart city,smart cities,and sustainability emerged as the top three co-occurring keywords with the highest link strength,followed by frequently co-occurring keywords such as AI,innovation,big data,urban governance,resilience,machine learning,and Internet of Things(IoT).The clustering results indicated that current studies explored the theoretical foundation,challenges,and future prospects of sustainable smart city,with an emphasis on sustainability.To further support urban sustainability and the attainment of SDGs,the future research of sustainable smart city should explore the application and implications of AI and big data on urban development including cybersecurity and governance challenges.展开更多
Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN model...Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.展开更多
Biodiversity is a critical component for sustainable human development.The recently concluded Sixteenth Conference of Parties to the Convention on Biological Diversity 2024 highlighted the need for whole of society mo...Biodiversity is a critical component for sustainable human development.The recently concluded Sixteenth Conference of Parties to the Convention on Biological Diversity 2024 highlighted the need for whole of society mobilization to address the global biodiversity crisis by translating international conservation commitments into effective local actions.A study to understand the linkages between ecological conservation measures in Aba Tibetan and Qiang Autonomous Prefecture and the United Nations Sustainable Development Goal(SDG)15 target 15.5,was undertaken,using the content analysis method that reviewed international conventions,national policies,and local government measures and practices.The study revealed that there was a strong link with between Aba’s conservation strategies and SDG 15 particularly target 15.5 in reducing natural habitat degradation,curbing biodiversity loss,and protecting endangered species.The Aba Prefecture has established 25 nature reserves,that are regulated by stringent wetland protection measures,and comprehensive legal frameworks for biodiversity conservation which is in line with SDG 15.The findings further show that that the Aba Prefecture’s efforts in ecosystem conservation,species protection,and sustainable resource utilization can be used to help meet SDG 15 target 15.5.The study also identified steps to help localize SDG aspirations and goals,by strengthening long-term data monitoring and local herder participation.These insights can be used to support other initiatives and measures in other similar biodiversity-rich regions seeking to implement global conservation goals at the local level,particularly in ecologically sensitive mountainous areas.展开更多
随着全球经济可持续发展需求的日益增长,环境、社会和公司治理(Environment,Social and Governa-nce;ESG)理念和联合国可持续发展目标(Sustainable Development Goals,SDGs)框架逐渐成为企业运营的重要指引,注册会计师审计作为保障财务...随着全球经济可持续发展需求的日益增长,环境、社会和公司治理(Environment,Social and Governa-nce;ESG)理念和联合国可持续发展目标(Sustainable Development Goals,SDGs)框架逐渐成为企业运营的重要指引,注册会计师审计作为保障财务信息质量的核心机制,也需要顺应这一趋势。然而,其在适应ESG理念与SDGs框架的过程中面临诸多现实困境。文章深入探讨注册会计师审计在该背景下面临的困境,包括审计标准与评价体系缺失、信息披露不充分、审计人员的专业能力与知识结构不足、利益相关方的期望与审计责任冲突等,然后提出完善审计标准与评价体系、加强信息披露与监管、提升审计人员的专业素养、协调利益相关方期望等对策,以期为注册会计师审计在ESG理念与SDGs框架下的发展提供有益参考,推动审计实践更好地服务于企业和社会可持续发展。展开更多
Assessment of SDG11.3.1 indicator of the United Nations Sustainable Development Goals(SDGs)is a valuable tool for policymakers in urban planning.This study aims to enhance the accuracy of the SDG11.3.1 evaluation and ...Assessment of SDG11.3.1 indicator of the United Nations Sustainable Development Goals(SDGs)is a valuable tool for policymakers in urban planning.This study aims to enhance the accuracy of the SDG11.3.1 evaluation and explore the impact of varying precision levels in urban built-up area on the indicator’s assessment outcomes.We developed an algorithm to generate accurate urban built-up area data products based on China’s Geographical Condition Monitoring data with a 2 m resolution.The study evaluates urban land-use efficiency in China from 2015 to 2020 across different geographical units using both the research product and data derived from other studies utilizing medium and low-resolution imagery.The results indicate:(1)A significant improvement in the accuracy of our urban built-up area data,with the SDG11.3.1 evaluation results demonstrating a more precise reflection of spatiotemporal characteristics.The indicator shows a positive correlation with the accuracy level of the built-up area data;(2)From 2015 to 2020,Chinese prefecture-level cities have undergone faster urbanization in terms of land expansion relative to population growth,leading to less optimal land resource utilization.Only in extra-large cities does urban population growth show a relatively balanced pattern.However,urban popula tion growth in other regions and cities of various sizes lags behind land urbanization.Notably,Northeast China and small to medium cities encounter significant challenges in urban population growth.The comprehensive framework developed for evaluating SDG11.3.1 with high-precision urban built-up area data can be adapted to different national regions,yielding more accurate SDG11.3.1 outcomes.Our urban area and built-up area data products provide crucial inputs for calculating at least four indicators related to SDG11.展开更多
文摘Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with Tajikistan representing a typical example of such disparities.Based on 81 SDG indicators for Tajikistan from 2001 to 2023,this study applied a three-level coupling network framework:at the microscale,it identified synergies and trade-offs between indicators;at the mesoscale,it examined the strength and direction of linkages within four SDG-related components(society,finance,governance,and environment);and at the global level,it focused on the overall SDG interlinkages.Spearman’s rank correlation,sliding window method,and topological properties were employed to analyze the coupling dynamics of SDGs.Results showed that over 70.00%of associations in the global SDG network were of medium-to-low intensity,alongside extremely strong ones(|r|value approached 1.00,where r is the correlation coefficient).SDG interactions were generally limited,with stable local synergy clusters in core livelihood sectors.Network modularity fluctuated,reflecting a cycle of differentiation,integration,and fragmentation,while coupling efficiency varied with the external environment.Each component exhibited distinct functional characteristics.The social component maintained high connectivity through the“poverty alleviation-education-healthcare”loop.The environmental component shifted toward coordinated eco-economic governance.The governance-related component broke interdepartmental barriers,while the financial component showed weak links between resource-based indicators and consumption/employment indicators.Tajikistan’s SDG coupling evolved through three phases:survival-oriented(2001–2012),policy integration(2013–2018),and shock adaptation(2019–2023).These phases were driven by policy changes,resource industries,governance optimization,and external factors.This study enriches the analytical framework for understanding the dynamic coupling of SDGs in mountainous resource-dependent countries and provides empirical evidence to support similar countries in formulating phase-specific SDG promotion strategies.
文摘增强安徽省可持续发展动能对推动长三角更高质量一体化发展和服务国家新发展格局具有重要意义。本文构建了面向联合国2030可持续发展目标(Sustainable Development Goals,SDGs)的可持续发展水平评估框架和指标体系,对2011~2021年安徽省及其16个地级市可持续发展水平进行测算,计算各市经济、社会和环境维度可持续发展的耦合协调度,分析安徽省可持续发展水平的时空演化特征,利用Dagum基尼系数深入探究安徽省可持续发展水平区域差异的原因,并基于灰色关联度分析方法进一步揭示影响安徽省不同城市可持续发展水平的关键因素。研究结果表明:安徽省可持续发展综合指数以及3个子系统间的耦合协调度呈现不断提高的趋势,但可持续发展水平空间差异明显。安徽省可持续发展水平空间差异原因主要来自区域之间的差异,“皖中-皖北”和“皖中-皖南”的区域间差异明显高于“皖南-皖北”。不同维度下不同指标对各市可持续发展综合水平的影响表现出明显差异。对省会合肥而言,研发经费支出占比、公共服务支出比重、生活垃圾无害化处理率分别是经济、社会和环境子系统影响其可持续发展综合水平的首要因素。研究结果可为安徽省加快实现2030可持续发展目标和推动高质量发展提供科学支撑。
文摘The rapid urbanization and increasing challenges are faced by cities globally,including climate change,population growth,and resource constraints.Sustainable smart city(also referred to as“smart sustainable city”)can offer innovative solutions by integrating advanced technologies to build smarter,greener,and more livable urban environments with significant benefits.Using the Web of Science(WoS)database,this study examined:(i)the mainstream approaches and current research trends in the literature of sustainable smart city;(ii)the extent to which the research of sustainable smart city aligns with Sustainable Development Goals(SDGs);(iii)the current topics and collaboration patterns in sustainable smart city research;and(iv)the potential opportunities for future research on the sustainable smart city field.The findings indicated that research on sustainable smart city began in 2010 and gained significant momentum in 2013,with China leading,followed by Italy and Spain.Moreover,59.00%of the selected publications on the research of sustainable smart city focus on SDG 11(Sustainable Cities and Communities).Bibliometric analysis outcome revealed that artificial intelligence(AI),big data,machine learning,and deep learning are emerging research fields.The terms smart city,smart cities,and sustainability emerged as the top three co-occurring keywords with the highest link strength,followed by frequently co-occurring keywords such as AI,innovation,big data,urban governance,resilience,machine learning,and Internet of Things(IoT).The clustering results indicated that current studies explored the theoretical foundation,challenges,and future prospects of sustainable smart city,with an emphasis on sustainability.To further support urban sustainability and the attainment of SDGs,the future research of sustainable smart city should explore the application and implications of AI and big data on urban development including cybersecurity and governance challenges.
基金funded by Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney.Moreover,Ongoing Research Funding Program(ORF-2025-14)King Saud University,Riyadh,Saudi Arabia,under Project ORF-2025-。
文摘Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.
文摘Biodiversity is a critical component for sustainable human development.The recently concluded Sixteenth Conference of Parties to the Convention on Biological Diversity 2024 highlighted the need for whole of society mobilization to address the global biodiversity crisis by translating international conservation commitments into effective local actions.A study to understand the linkages between ecological conservation measures in Aba Tibetan and Qiang Autonomous Prefecture and the United Nations Sustainable Development Goal(SDG)15 target 15.5,was undertaken,using the content analysis method that reviewed international conventions,national policies,and local government measures and practices.The study revealed that there was a strong link with between Aba’s conservation strategies and SDG 15 particularly target 15.5 in reducing natural habitat degradation,curbing biodiversity loss,and protecting endangered species.The Aba Prefecture has established 25 nature reserves,that are regulated by stringent wetland protection measures,and comprehensive legal frameworks for biodiversity conservation which is in line with SDG 15.The findings further show that that the Aba Prefecture’s efforts in ecosystem conservation,species protection,and sustainable resource utilization can be used to help meet SDG 15 target 15.5.The study also identified steps to help localize SDG aspirations and goals,by strengthening long-term data monitoring and local herder participation.These insights can be used to support other initiatives and measures in other similar biodiversity-rich regions seeking to implement global conservation goals at the local level,particularly in ecologically sensitive mountainous areas.
文摘随着全球经济可持续发展需求的日益增长,环境、社会和公司治理(Environment,Social and Governa-nce;ESG)理念和联合国可持续发展目标(Sustainable Development Goals,SDGs)框架逐渐成为企业运营的重要指引,注册会计师审计作为保障财务信息质量的核心机制,也需要顺应这一趋势。然而,其在适应ESG理念与SDGs框架的过程中面临诸多现实困境。文章深入探讨注册会计师审计在该背景下面临的困境,包括审计标准与评价体系缺失、信息披露不充分、审计人员的专业能力与知识结构不足、利益相关方的期望与审计责任冲突等,然后提出完善审计标准与评价体系、加强信息披露与监管、提升审计人员的专业素养、协调利益相关方期望等对策,以期为注册会计师审计在ESG理念与SDGs框架下的发展提供有益参考,推动审计实践更好地服务于企业和社会可持续发展。
基金funded by the National Key Research and De-velopment Program of China(Grant No.2023YFC3804001)the Natural Resources Planning and Management Project(Grant No.A2417,A2418)the Fundamental Scientific Research Funds for Central Public Wel-fare Research Institutes(Grant No.AR2409).
文摘Assessment of SDG11.3.1 indicator of the United Nations Sustainable Development Goals(SDGs)is a valuable tool for policymakers in urban planning.This study aims to enhance the accuracy of the SDG11.3.1 evaluation and explore the impact of varying precision levels in urban built-up area on the indicator’s assessment outcomes.We developed an algorithm to generate accurate urban built-up area data products based on China’s Geographical Condition Monitoring data with a 2 m resolution.The study evaluates urban land-use efficiency in China from 2015 to 2020 across different geographical units using both the research product and data derived from other studies utilizing medium and low-resolution imagery.The results indicate:(1)A significant improvement in the accuracy of our urban built-up area data,with the SDG11.3.1 evaluation results demonstrating a more precise reflection of spatiotemporal characteristics.The indicator shows a positive correlation with the accuracy level of the built-up area data;(2)From 2015 to 2020,Chinese prefecture-level cities have undergone faster urbanization in terms of land expansion relative to population growth,leading to less optimal land resource utilization.Only in extra-large cities does urban population growth show a relatively balanced pattern.However,urban popula tion growth in other regions and cities of various sizes lags behind land urbanization.Notably,Northeast China and small to medium cities encounter significant challenges in urban population growth.The comprehensive framework developed for evaluating SDG11.3.1 with high-precision urban built-up area data can be adapted to different national regions,yielding more accurate SDG11.3.1 outcomes.Our urban area and built-up area data products provide crucial inputs for calculating at least four indicators related to SDG11.
