사용자 중심의 응급의료 공간 디자인 요구와 발전 방향 - COVID-19에 기초한 이동형 응급병원의 비교와 융합

Travel behaviour market segmentations have become a popular method of identifying different types of car users, bicyclists or public transport users. However, while previous studies have looked at different types of users within single modes, such as the car, little research has explored the existence of traveller types transcending modes. The study presented here is an extension of an earlier segmentation study that distinguished travellers based on their individual preferences, yet did so independent of their current mode choice. The data came from a travel survey at a middle-sized UK university and were analysed using a combination of hierarchical and iterative partitioning methods. Crucially, however, the current study uses a different theoretical framework to previous segmentation research—goal framing theory—which may more adequately explain the findings than models used in the past such as the theory of planned behaviour. The findings supported earlier work, suggesting the presence of seemingly stable traveller types that cut across modes and can be distinguished based on gain, hedonic and normative goals. This has important implications for policies aimed at encouraging mode change which may have been too preoccupied with changing people’s attitudes rather than paying attention to people’s underlying travel preferences.

The countries along the Belt and Road are located in regions highly prone to various natural disasters. From 1900 to 2022, these areas experienced 9,595 major natural disasters, resulting in over 16 million deaths and economic losses exceeding two trillion USD. This article first reviews global disaster risk assessment and governance frameworks, then examines the development and practical achievements of the Belt and Road International Cooperation Framework for Disaster Risk Reduction and Emergency Management (BRIDRREM). Using policy analysis and case study methods, it evaluates the framework’s effectiveness at multilateral, regional, and bilateral levels, and delves into the three key challenges: evolving climate risks, capacity disparities among nations, and institutional gaps. Finally, policy recommendations are proposed, including alignment with the Sendai Framework, strengthened top-level design and legal and institutional processes, and enhanced practical cooperation with neighbouring countries, offering a Chinese approach to improve regional disaster control resilience and emergency response capabilities. Keywords: Belt and Road; Natural Disasters; Emergency Management; International Cooperation; Framework Development.

Telehealth has played a critical role in supporting healthcare delivery during the COVID-19 pandemic. Mitigation practices of physical distancing and sheltering-in-place continue to impose a significant burden on face-to-face healthcare visits. As a result, telehealth adoption and utilization have increased significantly after the U.S. Department of Health and Human Services waived telehealth payment requirements. These changes have been rapid and affect both individuals and organizations, ranging from healthcare and technology to news and policy. We turn to Twitter, as a broad-reaching communication platform, to elucidate and characterize Telehealth communication patterns. While different aspects of tweeting about telehealth have been used to answer specific questions, we sought to understand the collective telehealth tweeting behavior as a system of different types of users tweeting with various intent and sentiment. Such an approach combines the typically independent analysis of structure, using social network analysis, and purpose or function of communication, with topic analysis and sentiment analysis. In this paper, we apply a systems-based approach-called hetero-functional graph theory-to comprehensively describe the structure and function of the Twitter Telehealth communication system. We modeled 13 different types of telehealth users and 15 combinations of tweet intent and sentiment, which together describe 195 system capabilities. We applied machine learning to classify individual user tweeting behavior for different classes of telehealth stakeholders. We identified several patterns of longitudinal tweeting behaviors that varied by system form and function. By applying an interdependent methodology to understand both form and function, we provide deeper insights into tweeting behaviors between different classes of stakeholders. In doing so, we achieve a greater understanding of the various needs of different stakeholders, which can be leveraged as a valuable resource in informing more equitable policies as telehealth becomes a more permanent and scaled healthcare delivery service.

Considering the characteristics of different types of users in hybrid carsharing systems, in which sharing autonomous vehicles (SAVs) and conventional sharing cars (CSCs) coexist, a tailored pricing strategy (TPS) is proposed to maximize the operator’s profit and minimize all users’ costs. The fleet sizes and sizes of SAVs’ stations are also determined simultaneously. A bi-objective nonlinear programming model is established, and a genetic algorithm is applied to solve it. Based on the operational data in Lanzhou, China, carsharing users are clustered into three types. They are loyal users, losing users, and potential users, respectively. Results show the application of the TPS can help the operator increase profit and attract more users. The loyal users are assigned the highest price, while they still contribute the most to the operator’s profit with the highest number of carsharing trips. The losing users and potential users are comparable in terms of the number of trips, while the latter still makes more profit.

An information-pattern-based approach to novelty detection

The analysis of electrical signals is a pressing requirement for the optimal design of power distribution. In this context, this paper illustrates how to use a variety of numerical tools, such as the Fourier, wavelet, and Hilbert-Huang transforms, to obtain information relating to the active and reactive power absorbed by different types of users. In particular, the Fourier spectrum gives the most important frequency components of the electrical signals, and the wavelet analysis highlights the non-stationarity of those frequency contributions, whereas the Hilbert-Huang transform, by means of the Empirical Mode Decomposition, provides a more complete spectrum of frequencies.

This study explores whether social roles mentioned in jihadist literature can be used linguistically to profile the behavior of its consumers. It seeks to empirically understand extremist forums, where such ideological communication is widely disseminated, and to identify different types of user behavior through their discussions in these communities. An Arabic-language corpus was constructed with 1050 users categorized into seven behavioral profiles derived from distinct patterns of communication observed among users in forum networks. Statistical key term extraction was used to find significant social roles referenced in users’ posts as these were proposed to differentiate their behavioral profiles. Multinomial logistic regression and post hoc tests were used to find the frequency of identified roles as the highest-ranking terms capable of prediction. Role terms produced high accuracy scores across classification experiments in identifying behavioral profiles (95% CI, 0.92–0.98), with varying intra- and inter-behavioral differentiation abilities emerging from the authority, religion, closeness, and conflict themes of social roles. This suggests simple and complex predictive potential exist in these constructs for separating profiles based on nuanced expressions of social roles by different types of users in extremist contexts, as was the case for distinguishing extremist organizations through their ideological communication in earlier work.

Towards optimal customized electricity pricing via iterative two-layer optimization for consumers and prosumers

Climate change has increased the frequency of various types of meteorological disasters in recent years. Finding the primary factors that limit the emergency response capability of meteorological disasters through the evaluation of that capability and proposing corresponding improvement measures in order to increase that capability is of great practical importance. The evaluation of meteorological disaster emergency response capability still has some issues. The majority of research methods use qualitative analysis, which makes it challenging to deal with fuzzy factors, leading to conclusions that are subjective and insufficiently rigorous. The evaluation models themselves are also complex and challenging to simulate and analyze, making it challenging to promote and use them in practice. Deep learning techniques have made it easier to collect and process large amounts of data, which has opened new avenues for advancement in the emergency management of weather-related disasters. In this paper, we suggest a Recurrent Neural Network (RNN)-based dynamic capability feature extraction method. The process of evaluation content determination and index selection is used to build a meteorological disaster emergency response capability evaluation index system before an encoder, based on the encoder–decoder architecture, is built for dynamic feature extraction. The RNN autoencoder deep learning ability dynamic rating method used in this paper has been shown through a series of experiments to be able to not only efficiently extract ability features from time series data and reduce the dimensionality of ability features, but also to reduce the focus of the ability evaluation model on simple and abnormal samples, concentrate the model learning on difficult samples, and have a higher accuracy. As a result, it is more suitable for the problem situation at evaluation of the disaster capability.

Based on the investigation and analysis of typical users in Beijing and Guangzhou, a comparative analysis of the load characteristics of different types of users is carried out, and the basic characteristics of the load characteristics of various types of users are summarized. In the calculation example, the hourly load of the typical buildings in Beijing and Guangzhou is simulated and analyzed for the whole year. The load stability of the hotel and hospital buildings is poor and the continuity is strong. The load stability of shopping malls, residential and office buildings Stronger and weaker in persistence; the imbalance rate of the cumulative cooling and heating load in the summer hot winter and warm winter area and the cumulative cooling and heating load in the air conditioning season is higher, while the unbalance rate in the cold area is lower.

We propose a framework for describing, comparing and understanding tools for the mining of software repositories. The fundamental premise of this framework is that mining should be done by considering the specific needs of the users and the tasks to be supported by the mined information. First, different types of users have distinct needs, and these needs should be taken into account by tool designers. Second, the data sources available, and mined, will determine if those needs can be satisfied. Our framework is based upon three main principles: the type of user, the objective of the user, and the mined information. This framework has the following purposes: to help tool designers in the understanding and comparison of different tools, to assist users in the assessment of a potential tool; and to identify new research areas. We use this framework to describe several mining tools and to suggest future research directions.

This research is an evaluation of disaster emergency management policies in Bangkalan Regency, East Java Province.Using a qualitative research approach. The results showed that the flood disaster management by Bangkalan BPBD had not run optimally, there were still problems and obstacles faced in the implementation. This is marked by Disaster Prevention (floods, earthquakes, etc.) Disaster emergency management, disaster rehabilitation and reconstruction, many problems are still found.The conclusion is that the Disaster Emergency Management Policy in Bangkalan Regency, East Java Province, is already good, only at the time of implementation it still needs to be reevaluated, because the community still complains, the apparatus handling it has not performed their duties optimally (less professional).Researcher's suggestion: to optimize the prevention of natural disasters, prevention needs to be done as best as possible by implementing prone maps, maximum socialization; There must be personnel who support the implementation of tasks and adequate equipment so that they can carry out their duties as best they can; The need for facilities and infrastructure in order to provide good treatment for disaster victims; There must be coordination between the government and the community in accompanying disaster management; The need for equity in the implementation of disaster management both in terms of prevention, emergency or rehabilitation and reconstruction. Keywords : evaluation, policy, disaster emergency DOI : 10.7176/PPAR/10-1-02 Publication date: January 31 st 2020

Because of the rapidly growing access of social media platforms, it provides convenient means for users to participate in multiple online activities on various contents and create fast widespread interactions. However, characterizing user types to understand people’s lifestyle decisions shared in social media is challenging task due to diverse information. In this paper, we propose a weakly supervised graph embedding based framework for creating user representation to understand user types. We evaluate the user embedding learned using weak supervision over well-being related tweets from Twitter, focusing on `Yoga’, `Keto diet’. Experiments on real-world datasets demonstrate that the proposed framework outperforms the baselines for detecting user types. Finally, we illustrate data analysis on different types of users (e.g., practitioner vs. promotional) from our dataset. While we focus on lifestyle-related tweets (i.e., yoga, keto), our method for constructing user representation readily generalizes to other domains.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于多源遥感数据的农业干旱监测模型构建及应用 DOI: 10.5846/stxb201711202068 作者: 作者单位: 安徽师范大学国土资源与旅游学院,安徽师范大学国土资源与旅游学院,北京师范大学减灾与应急管理研究院,北京师范大学减灾与应急管理研究院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41601023）；国家杰出青年科学基金项目（51425903）；地表过程与资源生态国家重点实验室开放基金资助项目（2017-KF-04）；安徽省自然科学基金（1808085QD117） An integrated agricultural drought monitoring model based on multi-source remote sensing data: model development and application Author: Affiliation: College of Territorial Resources and Tourism,College of Territorial Resources and Tourism,Academy of Disaster Reduction and Emergency Management，Beijing Normal University,Academy of Disaster Reduction and Emergency Management，Beijing Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:干旱监测问题是干旱灾害模拟与预警及旱灾防灾减灾的关键。基于2001-2013年淮河流域40个气象站资料、28个土壤墒情站点和中分辨率成像光谱仪（MODIS）多源遥感数据，采用SEN趋势法和标准化降水蒸散指数（SPEI）等方法，综合了大气-植被-土壤相互作用等多元成因，构建了适用于淮河流域多源综合遥感干旱监测模型，探讨淮河流域干旱时空规律。研究表明：（1）基于多源数据构建综合干旱监测模型，利用土壤墒情和典型年份干旱监测对综合干旱监测模型适用性进行评价，通过了P < 0.01的显著性检验，构建的模型可综合反映出农业和气象干旱多源信息；（2）淮河流域干旱面积和干旱频率大都集中在4-5月和7-9月，9月份受旱面积最大。河南省是淮河流域受旱频率最高，其干旱面积占淮河流域多年平均干旱面积比重最大（38%），其次是安徽（22%），旱地受旱面积比重大于水田受旱面积比重；（3）淮河流域2、3和5月干旱有显著减弱趋势；而1、4和6月则有增强趋势。淮河流域小麦灌浆-成熟时期（4-6月）缺水对小麦粮食产量影响显著，综合淮河流域干旱趋势变化，需强化淮河流域4月份小麦的干旱监测与旱灾预警。 Abstract:Drought monitoring is crucial for the early warning of drought hazards. In this study, an attempt to develop an Integrated Remote Sensing Drought Monitoring Index (IRSDI) was made, based on multi-source remote sensing data, with the aim of investigating drought conditions across the Huai River basin in both space and time. The IRSDI relied on:meteorological data recorded from 2003 to 2013 at 40 stations, soil moisture data recorded at 16 stations, Moderate Resolution Imaging Spectroradiometer (MODIS) data obtained by linear trend detection, and on the Standardized Precipitation Evapotranspiration Index (SPEI). The results of this study indicate that:(1) the proposed IRSDI is able to describe and monitor drought conditions well in both space and time; (2) droughts and high drought frequency occur in the periods April-May and July-September. In particular, September is the month during which the largest area is dominated by droughts. In particular, droughts affect Henan province, which accounts for 38% of the total drainage area of the Huai River basin, followed by Anhui province, which accounts for 22% of the total drainage area of the Huai River basin; (3) droughts tend to decrease during February, March and May, whereas they increase during January, April and June. Wheat grouting and ripening occur in the period from April to June. Thus, droughts in these months can seriously affect wheat production, and a more careful allocation of water resources and irrigation management should be performed during this period. Nevertheless, spring droughts and autumn-winter droughts should receive proper attention as well, in order to manage and reduce drought-induced losses. 参考文献 相似文献 引证文献

Impact of Information and Communications Technologies (ICTs) on sustainable transport development is indisputable. It can be tested through various methods. The identification and detailed analysis of specific types of ICT solutions, case studies approach or scenarios analysis are examples of perspectives for impact assessment that can lead to the proof that successful applications of ICT solutions entails optimised, more accessible, less resource consuming and less emission intensive transport. The impact of ICTs in travel induction and substitution and resulting decarbonisation effect is difficult to assess isolated from other social, economic and technologic drivers. Financial applicability reflects initial investment cost as well as operation maintenance costs. Organizational feasibility refers primarily to the necessary changes in organisation and conduct of operations created by introduction of ICTs. The social factor results from user and public acceptance or lack of it. User acceptance is combination of many sub factors of which most important are: D2D travel time, D2D travel costs, comfort and convenience, safety and security. The objective of the article is to present the applicability of ICT solutions in passenger transport from the perspective of transport users taking into consideration real case studies from different European background. These case studies were identified as a result of specific selection process in order to receive a complex and differentiated sample. The first element of the selection process was the identification of ICT solutions to be applied in case studies. The next element was the choice of regions corresponding to varied economic and geographic characteristics. In addition, cultural factors have a strong impact on the behaviour of users. The resulting multidimensional factor matrix allows for the selection of best fitting case studies, which have potential to cover most of the ICT applications. Five European settings are selected for in-depth research. Each case represents different geographic, social and economic area, which forms a good European sample across differentiated setups. Case studies are supported by user surveys. Surveys allow for direct answers regarding user attitudes towards proposed ICTs. Based on this approach findings could be reported. The main conclusion is that users in various regions with very different characteristics as to the wealth, GDP levels, geography, and cultural backgrounds represent surprisingly similar attitudes towards ICTs. Specific findings regarding different types of solutions and different type of users are presented in the article. The analysis of selected case studies shows also that they could offer solution to many of transport sector problems including key issues from transport policy agenda: environment, mobility or accessibility. The paper is based on the authors research conducted within COMPASS project (Optimised co-modal passenger transport for reducing carbon emissions, project co-funded by the European Commission within the Seventh Framework Programme).