V for variety: Lessons learned from complex smart cities data harmonization and integration

With emerging trends for Internet of Things (IoT) and Smart Cities, complex data transformation, aggregation and visualization problems are becoming increasingly common. These tasks support improved business intelligence, analytics and enduser access to data. However, in most cases developers of these tasks are presented with challenging problems including noisy data, diverse data formats, data modeling and increasing demand for sophisticated visualization support. This paper describes our experiences with just such problems in the context of Household Travel Surveys data integration and harmonization. We describe a common approach for addressing these harmonizations. We then discuss a set of lessons that we have learned from our experience that we hope will be useful for others embarking on similar problems. We also identify several key directions and needs for future research and practical support in this area.

Rethinking value in the stack: smart cities and digital currencies

O Seminário Estratégico "A Reinvenção do Dinheiro e a Emergência das Moedas Criativas" reunirá 15 especialistas de diferentes áreas para discutir as transformações contemporâneas implicadas na digitalização do dinheiro e na emergência resultante de novos circuitos de monetização da produção, do consumo, da distribuição, do financiamento e da reciclagem de todos os bens e serviços, assim como da própria informação.O debate marca o lançamento da campanha MIL CLICKS de monetização de projetos de Media and Information Literacy (MIL) pela UNESCO na USP, plataforma desenhada pelo grupo de pesquisa Cidade do Conhecimento em parceria com Dentsu Aegis Network e UNESCO, integrada à Global MIL Week UNESCO 2016 na Escola de Comunicações e Artes da USP (2 a 6 de novembro de 2016).

Integration of electric vehicles for load frequency output feedback H ∞ control of smart grids

This study considers a novel application of electric vehicles (EVs) to quickly help reheated thermal turbine units to provide the stability fluctuated by load demands. A mathematical model of a power system with EVs is first derived. This model contains the dynamic interactions of EVs and multiple network-induced time delays. Then, a dynamic output feedback H∞ controller for load frequency control of power systems with multiple time delays in the control input is proposed. To address the multiple time delays issue, a refined Jensen-based inequality, which encompasses the Jensen inequality, is used to derive less conservative synthesis conditions in terms of tractable linear matrix inequalities. A procedure is given to parameterise an output feedback controller to guarantee stability and H∞ performance of the closed-loop system. Extensive simulations are conducted to validate the proposed control method.

Identifying destination distances that support walking trips in local neighborhoods

When examining associations between local destinations and walking it is common to count local destinations using street network buffers measured at various distances to mitigate spatial data aggregation issues caused by scale and the Modifiable Areal Unit Problem. However, it remains unclear whether a particular buffer size is preferred since large buffers may mask important effects whilst small buffers may not accurately represent a neighborhood area. Furthermore, the use of various buffer distances in measuring destination counts does not yield specific information on distances where destinations could be placed in order to increase levels of walking. This paper extends current methods to address these issues by using a new method to define network buffers to identify threshold distances for walking to seven destination types using multilevel models. Donut-buffers are introduced as a method of counting destinations between distances of 401. m-800. m and 801. m-1200. m which are compared to standard network buffers at distances of 400. m, 800. m and 1200. m respectively. We found that destinations within 401. m-800. m could be responsible for associations found at a network buffer of 1200. m. Specifically, the odds of walking increased when local food outlets including supermarkets, cafés/takeaway stores, and small food stores, were located within 401. m-800. m but not 801. m-1200. m, suggesting that these destinations encourage walking when placed at 401. m-800. m away. Consequently we argue that donut-buffers offer greater specificity than standard network buffers for geographic measurement of destinations. This warrants further investigation to inform urban policy guidelines for designing walkable environments.

A digital signal processor controlled smart battery charger for photovoltaic power systems

An off-grid photovoltaic power system requires an energy storage system, especially batteries, for mitigation of variability and intermittency problems, and for assured service reliability and availability. The longevity and reliability of such batteries depend on the effectiveness of the charging system. This paper presents the modelling, simulation and hardware implementation of a four-stage switch-mode charger based on the single-ended primary inductance converter. The digital signal processor based controller implements algorithms for the system's power balance control, maximum power point tracking to improve charging speed and efficiency, four-stage optimal charging, and system's protection. The protection algorithm provides over-charge, overdischarge, over-temperature and short circuit protection capabilities. The proposed system has the following advantages: ability to continuously charge the batteries even at reduced solar irradiation, higher efficiency, and use of adaptive thermally compensated set points for optimum performance. A prototype is built and experimental results are presented to validate the simulation results.

Randomised controlled trial of a digitally assisted low intensity intervention to promote personal recovery in persisting psychosis: SMART-Therapy study protocol

BACKGROUND: Psychosocial interventions have an important role in promoting recovery in people with persisting psychotic disorders such as schizophrenia. Readily available, digital technology provides a means of developing therapeutic resources for use together by practitioners and mental health service users. As part of the Self-Management and Recovery Technology (SMART) research program, we have developed an online resource providing materials on illness self-management and personal recovery based on the Connectedness-Hope-Identity-Meaning-Empowerment (CHIME) framework. Content is communicated using videos featuring persons with lived experience of psychosis discussing how they have navigated issues in their own recovery. This was developed to be suitable for use on a tablet computer during sessions with a mental health worker to promote discussion about recovery.

METHODS/DESIGN: This is a rater-blinded randomised controlled trial comparing a low intensity recovery intervention of eight one-to-one face-to-face sessions with a mental health worker using the SMART website alongside routine care, versus an eight-session comparison condition, befriending. The recruitment target is 148 participants with a schizophrenia-related disorder or mood disorder with a history of psychosis, recruited from mental health services in Victoria, Australia. Following baseline assessment, participants are randomised to intervention, and complete follow up assessments at 3, 6 and 9 months post-baseline. The primary outcome is personal recovery measured using the Process of Recovery Questionnaire (QPR). Secondary outcomes include positive and negative symptoms assessed with the Positive and Negative Syndrome Scale, subjective experiences of psychosis, emotional symptoms, quality of life and resource use. Mechanisms of change via effects on self-stigma and self-efficacy will be examined.

DISCUSSION: This protocol describes a novel intervention which tests new therapeutic methods including in-session tablet computer use and video-based peer modelling. It also informs a possible low intensity intervention model potentially viable for delivery across the mental health workforce.

I-Smart Date Entry User Guide, May 2015

I-SMART is an Internet-based client management system that allows the State of Iowa and its licensed substance abuse treatment providers to administer, manage and provide cost efficient and quality substance abuse assessment and treatment services. Implementation of the I-SMART System is a key product in meeting the federal government requirements for National Outcome Monitoring System (NOMS).

Steady tourists’ relationship with a mature destination. The case of Portugal

This article assesses how the last-visit features and the socio-demographic profile of tourists moderate repeat-visit patterns to Portugal, a mature destination where the persistence of loyal visitors has made its mark on tourism development. The methodology used is a survival analysis to assess the tourists’ repeat patterns. To test the model, a database of 4612 observations was employed, which was obtained from a survey of international tourists. Only repeat visitors with more than two visits over the years were considered for the purpose of the research. The study finds that a combination of socio-demographic characteristics, expectation/satisfaction, trip purpose, pull motivations and regional destination has a positive effect on repeat patterns, confirming that tourists’ willingness to repeat visits to Portugal is far from ceasing. Based on those tourists to Portugal who declared when they started to visit the country, and the number of years of their repeat visits, the article contributes to the literature by introducing new methods of assessing tourists’ repeat patterns for destinations.

Classi cation of new electricity customers based on surveys and smart metering data

This paper proposes a process for the classifi cation of new residential electricity customers. The current state of the art is extended by using a combination of smart metering and survey data and by using model-based feature selection for the classifi cation task. Firstly, the normalized representative consumption profi les of the population are derived through the clustering of data from households. Secondly, new customers are classifi ed using survey data and a limited amount of smart metering data. Thirdly, regression analysis and model-based feature selection results explain the importance of the variables and which are the drivers of diff erent consumption profi les, enabling the extraction of appropriate models. The results of a case study show that the use of survey data signi ficantly increases accuracy of the classifi cation task (up to 20%). Considering four consumption groups, more than half of the customers are correctly classifi ed with only one week of metering data, with more weeks the accuracy is signifi cantly improved. The use of model-based feature selection resulted in the use of a signifi cantly lower number of features allowing an easy interpretation of the derived models.

Computational Models Development and Demand Response Application for Smart Grids

This paper focuses on computational models development and its applications on demand response, within smart grid scope. A prosumer model is presented and the corresponding economic dispatch problem solution is analyzed. The prosumer solar radiation production and energy consumption are forecasted by artificial neural networks. The existing demand response models are studied and a computational tool based on fuzzy clustering algorithm is developed and the results discussed. Consumer energy management applications within the InovGrid pilot project are presented. Computation systems are developed for the acquisition, monitoring, control and supervision of consumption data provided by smart meters, allowing the incorporation of consumer actions on their electrical energy management. An energy management system with integration of smart meters for energy consumers in a smart grid is developed.

Smart mail – a smart platform for mail management

Email is a key communication format in a digital world, both for professional and/or personal usage. Exchanged messages (both human and automatically generated) have reached such a volume that processing them can be a great challenge for human users that try to do it on a daily basis and in an efficient manner. In fact, a significant amount of their time is spent searching and getting context information (normally historic information) in order to prepare a reply message or to take a decision/action, when compared to the actual time required for writing a reply. Therefore, it is of utmost importance for this process to use both automatic and semi-automatic mechanisms that allow to put email messages into context. Since context information is given, not only by historical email messages but also inferred from the relationship between contacts and/or organizations present in the messages, the existence of navigation mechanisms (and even exploration ones) between contacts and entities associated to email messages, is of fundamental importance. This is the main purpose of the SMART Mail prototype, which architecture, data visualization and exploration components and AI algorithms, are presented throughout this paper.