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

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.

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

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.

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.

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.

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).

Interface design: An educational perspective

Information and Communication Technology (ICT) advancements have eased the life of mankind in many ways. Modern-day gadgets and tools have helped immensely in our daily life and have increased the life standards. Many tools and techniques are being used to communicate and disseminate information in the form of text, audio, video and what not. Effects of ICT can be visualized in home and domestic activities, education healthcare, commerce, banking and employment. Medical fraternity across the globe has heavily invested in ICT to provide better quality health-related services throughout the world in general and within their respective countries in particular. Medical experts uses e-healthcare systems, a combination of multimedia and digital technologies to facilitate health improvement and healthcare services which include diagnosis, application, training and implementation. The medical information stored in electronic form known as electronic health record have replaced traditional paper-based systems. Electronic health record systems, a critical part of e-health system, are prone to the threats which make security and privacy of patient’s health data vulnerable. Unauthorized access of electronic gadgets, data manipulation, denial-of-service attacks and phishing are common threats to the e-healthcare systems. Different types of users are accessing this vital information from the electronic healthcare systems which make it imperative to implement role-based access control mechanism. In this chapter, privacy-related issues of e-health data and security models used at different phases have been discussed.KeywordsElectronic Health Records (EHR)ICTTPBSAccess ControlCloudMultimediaElectronic Healthcare Systems

Serious problems such as the shortage of medical settings and materials, as well as a nosocomial infection, have been recognized in hospitals of the world during the COVID-19 pandemic. Emergency medical space was the key material guarantee for the outbreak, and its systematic planning and reserve were needed in the pre-disaster phase. The purpose of the study is to chart the orientation and road for future research on standardized emergency medical space system design methods and R&D strategies on a global scale. Improve the global resource allocation ability of disaster emergency medical care and optimize the international emergency rescue guarantee system. In this study from the perspective of user demand, three types of field hospital cases that were widely used during COVID-19 were analyzed by analogy. It was found that the three types of emergency medical space had their strengths and weaknesses in the emergency response capability and treatment efficiency. The contents and results of the study are as follows.<BR> Firstly, combined with disaster emergency management and medical-related literature research. Medical staff, CDC managers, and inpatients who were involved in the treatment in Huoshenshan Hospital, Wuhan, China were interviewed and investigated. The types, behaviors, and needs of emergency medical space users were analyzed.<BR> Secondly, combined with the needs of users, the advantages that can be used for reference in the case were analyzed and integrated, and the design requirements of emergency medical space were deduced, including the structural design requirements for the whole disaster cycle, planning and design requirements for multi-type disaster treatment strategies, and functional module design requirements according to needs of different types of users.<BR> Thirdly, the development trend of the future emergency medical space was discussed, including the establishment of a global reserve mechanism, standardized design system and research and development platform, and information system to deal with the whole disaster cycle. The purpose was to provide a systematic method and strategy reference for the future development of the globalization of emergency medical space.

Analysis and research on library user behavior based on apriori algorithm

As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the ‘how’ and ‘why’ of human-computer interaction (HCI) within these frameworks, both current and expected. Such a discussion is necessary for optimal system design, leveraging advanced data-processing capabilities to support decision-making involving humans, but it is also key to identifying the opportunities and risks presented by ever-increasing levels of machine autonomy. Within this context, we focus on the following questions: (i) What does HCI currently look like for state-of-the-art AutoML algorithms, especially during the stages of development, deployment, and maintenance? (ii) Do the expectations of HCI within AutoML frameworks vary for different types of users and stakeholders? (iii) How can HCI be managed so that AutoML solutions acquire human trust and broad acceptance? (iv) As AutoML systems become more autonomous and capable of learning from complex open-ended environments, will the fundamental nature of HCI evolve? To consider these questions, we project existing literature in HCI into the space of AutoML; this connection has, to date, largely been unexplored. In so doing, we review topics including user-interface design, human-bias mitigation, and trust in artificial intelligence (AI). Additionally, to rigorously gauge the future of HCI, we contemplate how AutoML may manifest in effectively open-ended environments. This discussion necessarily reviews projected developmental pathways for AutoML, such as the incorporation of high-level reasoning, although the focus remains on how and why HCI may occur in such a framework rather than on any implementational details. Ultimately, this review serves to identify key research directions aimed at better facilitating the roles and modes of human interactions with both current and future AutoML systems.

The daily travel patterns (DTPs) present short-term and timely characteristics of the users’ travel behaviour, and they are helpful for subway planners to better understand the travel choices and regularity of subway users (SUs) in details. While several well-known subway travel patterns have been detected, such as commuting modes and shopping modes, specific features of many patterns are still confused or omitted. Now, based on the automatic fare collection (AFC) system, a data-mining procedure to recognize DTPs of all SUs has become possible and effective. In this study, DTPs are identified by the station sequences (SSs), which are modelled from smart card transaction data of the AFC system. The data-mining procedure is applied to a large weekly sample from the Beijing Subway to understand DTPs. The results show that more than 93% SUs of the Beijing Subway travel in 7 DTPs, which are remarkably stable in share and distribution. Different DTPs have their own unique characteristics in terms of time distribution, activity duration and repeatability, which provide a wealth of information to calibrate different types of users and characterize their travel patterns.