End-use Profiles Research Articles

A Survey on IoT Programming Platforms: A Business-Domain Experts Perspective

The vast growth and digitalization potential offered by the Internet of Things (IoT) is hindered by substantial barriers in accessibility, interoperability, and complexity, mainly affecting small organizations and non-technical entities. This survey paper provides a detailed overview of the landscape of IoT programming platforms, focusing specifically on the development support they offer for varying end-user profiles, ranging from developers with IoT expertise to business experts willing to take advantage of IoT solutions to automate their organization processes. The survey examines a range of IoT platforms, classified according to their programming approach between general-purpose programming solutions, model-driven programming, mashups and end-user programming. Necessary IoT and programming backgrounds are described to empower non-technical readers with a comprehensive field summary. In addition, the paper compares the features of the most representative platforms and provides decision insights and guidelines to support end-users in selecting appropriate IoT platforms for their use cases. This work contributes to narrowing the knowledge gap between IoT specialists and end users, breaking accessibility barriers and further promoting the integration of IoT technologies in various domains.

Exploiting high-resolution data to investigate the characteristics of water consumption at the end-use level: A Dutch case study

In the water industry, an accurate estimation of end-use water consumption is helpful for the implementation of efficient water systems and water-saving technologies. This study aimed to explore the characteristics of water consumption at nine households north of Amsterdam (the Netherlands), subjected to water consumption monitoring at high temporal resolution (i.e. 1 s). Overall, 36,297 water-use events monitored over about 447 days were automatically segmented into 44,115 individual events by means of a new rule-based filtering algorithm, and then labelled by expert analysts. A multi-stage analysis was then conducted in order to evaluate daily per capita end-use water consumption, daily end-use profiles, average end-use parameter average, and their statistical distributions. The results achieved provide insight into the features of end-use consumption, confirming that the largest components are typically related to showers/bathtubs, toilets, and washing machines, whereas different end-use parameter distributions can emerge.

Anomaly detection on household appliances based on variational autoencoders

Electrical anomalies in residential buildings represent a serious problem that can unpredictably change the power profiles of end-users, causing a sub-optimal energy distribution. In addition, electrical faults can cause unnoticed energy wastages and higher energy bills, or even severe damages for properties and people in the most critical cases. In this paper, we introduce a novel anomaly detection method for detecting electrical faults in household appliances based on the analysis of their power signatures with unsupervised deep learning techniques. For this purpose, we trained a variational autoencoder to reconstruct the power signatures of three commonly used devices: the dishwasher, the washing machine and the dryer. For each use case, we injected several randomly generated anomalies that simulate to our best realistic electrical faults in these devices. To demonstrate the effectiveness of our method, we compared the accuracy of the variational autoencoder with the classification performance of a one-class support vector machine (OC-SVM) trained with two manual features: the energy consumption and duration of the appliance’s operations. The variational autoencoder showed higher classification accuracy with respect to the OC-SVM, reporting an F1-score greater than 90% in all the use cases. Most importantly, the results demonstrate that deep anomaly detection methods outperform traditional algorithms based on handcrafted features, allowing to better characterize the set of normal cycles and produce more precise alerts for the monitored devices.

Participation: A Disciplinary Border for Architectural Research and Practice

This editorial note provides an extended summary and transversal analysis of ten articles gathered for the 2022 Special Issue on participation in contemporary architecture. The call for contribution circulated in June 2021 attracted papers from Central Europe (n = 8) and North America (n = 2), and presents an overview of ongoing practices and research in participatory architecture in these areas. The Special Issue aimed to study the connections between disciplines and gathered nine empirical cases and one literature review. In this editorial note, we first analyze these contributions to better understand the nature of architecture in participating in the profiles of end-users and project teams, and the scale of the projects. Secondly, we highlight four lessons taken from these practices and studies: we emphasize how participation in architecture (1) emerges and operates in interstitial spaces; (2) often deployed for and with “vulnerable” end-user groups, this “vulnerability” provides power and originality to processes and outcomes; (3) inspirational principles, guides, and frameworks are produced as outcomes; and finally, (4) social architectures are deployed beyond tangible concepts through a multilevel relationship to pedagogy. Finally, we observe that reflections on gender, politics, decoloniality, and disciplinary transfers remain underexplored and need to be explicitly studied and integrated.

SimEx: A Tool for the Rapid Evaluation of the Effects of Explosions

The dynamic response of structural elements subjected to blast loading is a problem of growing interest in the field of defense and security. In this work, a novel computational tool for the rapid evaluation of the effects of explosions, hereafter referred to as SimEx, is presented and discussed. The classical correlations for the reference chemical (1 kg of TNT) and nuclear (106 kg of TNT) explosions, both spherical and hemispherical, are used together with the blast wave scaling laws and the International Standard Atmosphere (ISA) to compute the dynamic response of Single-Degree-of-Freedom (SDOF) systems subject to blast loading. The underlying simplifications in the analysis of the structural response follow the directives established by UFC 3-340-02 and the Protective Design Center Technical Reports of the US Army Corps of Engineers. This offers useful estimates with a low computational cost that enable in particular the computation of damage diagrams in the Charge Weight–Standoff distance (CW–S) space for the rapid screening of component (or building) damage levels. SimEx is a computer application based on Matlab and developed by the Fluid Mechanics Research Group at University Carlos III of Madrid (UC3M). It has been successfully used for both teaching and research purposes in the Degree in Security Engineering, taught to the future Guardia Civil officers at the Spanish University Center of the Civil Guard (CUGC). This dual use has allowed the development of the application well beyond its initial objective, testing on one hand the implemented capacities by undergraduate cadets with the end-user profile, and implementing new functionalities and utilities by Masters and PhD students. With this experience, the application has been continuously growing since its initial inception in 2014 both at a visual and a functional level, including new effects in the propagation of the blast waves, such as clearing and confinement, and incorporating new calculation assistants, such as those for the thermochemical analysis of explosive mixtures; crater formation; fragment mass distributions, ejection speeds and ballistic trajectories; and the statistical evaluation of damage to people due to overpressure, body projection, and fragment injuries.

Türkiye'de Müstakil Bir Konut İçin Kurşun-Asit Pil Destekli Yakıt Pilinin Tekno-Ekonomik Analizi

The penetration rate of renewable energy sources is increasing day by day. However, most depend on the weather conditions such as solar, wind, precipitation, etc. This dependency also creates reliability problems for stand-alone systems. Fuel cells are essential solutions to overcome these issues since they need hydrogen to produce electrical energy, one renewable energy source that does not depend on weather conditions. In this research, an end-user profile, isolated from the grid and provides its energy by using a battery-backed PEM fuel cell, has been technically investigated in the simulation environment within the scope of the scenarios created. So, the systems intended to be implemented in reality have been transferred to the simulation environment. During the operation, to prevent deep discharge and overcharge of the battery pack, the fuel cell is kept in operation between a minimum of 40% and a maximum 90% State of Charge (SoC). Based on this system, initial investment, operation, and maintenance costs for a battery-backed fuel cell supply system are calculated and presented. The battery-supported fuel cell system is 46% expensive for lower consumption cases and 63% more expensive for higher consumption cases than grid prices.

Is it possible to recruit HIV self-test users for an anonymous phone-based survey using passive recruitment without financial incentives? Lessons learned from a pilot study in C\xf4te d\u2019Ivoire

BackgroundDue to the discreet and private nature of HIV self-testing (HIVST), it is particularly challenging to monitor and assess the impacts of this testing strategy. To overcome this challenge, we conducted a study in Côte d’Ivoire to characterize the profile of end users of HIVST kits distributed through the ATLAS project (AutoTest VIH, Libre d’Accéder à la connaissance de son Statut). Feasibility was assessed using a pilot phone-based survey.MethodsThe ATLAS project aims to distribute 221300 HIVST kits in Côte d’Ivoire from 2019 to 2021 through both primary (e.g., direct distribution to primary users) and secondary distribution (e.g., for partner testing). The pilot survey used a passive recruitment strategy—whereby participants voluntarily called a toll-free survey phone number—to enrol participants. The survey was promoted through a sticker on the HIVST instruction leaflet and hotline invitations and informal promotion by HIVST kit-dispensing agents. Importantly, participation was not financially incentivized, even though surveys focussed on key populations usually use incentives in this context.ResultsAfter a 7-month period in which 25,000 HIVST kits were distributed, only 42 questionnaires were completed. Nevertheless, the survey collected data from users receiving HIVST kits via both primary and secondary distribution (69% and 31%, respectively).ConclusionThis paper provides guidance on how to improve the design of future surveys of this type. It discusses the need to financial incentivize participation, to reorganize the questionnaire, the importance of better informing and training stakeholders involved in the distribution of HIVST, and the use of flyers to increase the enrolment of users reached through secondary distribution.

Heterogeneity in preferences for renewable home heating systems among Irish households

This paper aims to understand heterogeneity in preferences for renewable home heating systems such as heat pump systems, using a combination of psychological construct statements and a discrete choice experiment. We employ a latent class analysis for a nationally representative sample of Irish households to identify and characterize distinct respondent classes based on their responses to statements on attitude, subjective norms, perceived behavioral control, and intention to install a heat pump that are constructed based on the theory of planned behavior. This allows us to identify four types of people with different viewpoints towards heat pumps. Furthermore, we conducted a discrete choice experiment with a subsample of randomly selected respondents to estimate preferences for different attributes of a new home heating system and the corresponding marginal willingness to pay. Our estimated results show that, in addition to upfront cost and bill savings, non-monetary attributes such as installation hassle, environmental sustainability, and an increase in home comfort of use are important determinants of the uptake of new home heating systems, including heat pumps. The findings of this study should provide useful information to policymakers and companies on end-user profiles, heterogeneity in preferences, and willingness to pay estimates for new home heating alternatives.

Designing an eHealth Coaching Solution to Improve Transitional Care of Seniors' with Heart Failure: End-User Needs.

Based on scientific studies, heart failure is the principal cause of hospitalization among seniors. More than 50% of elderly with heart failure are readmitted to hospital within six months. Readmission is linked with poor compliance with medical treatment and recommendations, emphasizing the need for a tool to help seniors better comply with post-discharge measures. The goal of this study was to identify end-user needs for the development of a coaching solution aiming to support elderly patients but also formal and informal caregivers. End-user needs were identified through interviews with the three end-user profiles: seniors with heart failure and formal and informal caregivers. The results present six categories of needs: daily treatment follow-up; healthcare network communication; transfer of information; synchronization with current digital tools; information access; and psychosocial support. The identified needs will help to develop an eHealth solution to improve care management and coaching after discharge.

A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation

This article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor (1) the drivers’ behaviors and (2) the situational criticality to manage in real time the Human-Machine Interactions (HMI). This Human-Centered AI (HCAI) approach was designed from real drivers’ needs, difficulties and errors observed at the wheel of an instrumented car. Then, the HCAI algorithm was integrated into demonstrators of Advanced Driving Aid Systems (ADAS) implemented on a driving simulator (dedicated to highway driving or to urban intersection crossing). Finally, user tests were carried out to support their evaluation from the end-users point of view. Thirty participants were invited to practically experience these ADAS supported by the HCAI algorithm. To increase the scope of this evaluation, driving simulator experiments were implemented among three groups of 10 participants, corresponding to three highly contrasted profiles of end-users, having respectively a positive, neutral or reluctant attitude towards vehicle automation. After having introduced the research context and presented the HCAI algorithm designed to contextually manage HMT with vehicle automation, the main results collected among these three profiles of future potential end users are presented. In brief, main findings confirm the efficiency and the effectiveness of the HCAI algorithm, its benefits regarding drivers’ satisfaction, and the high levels of acceptance, perceived utility, usability and attractiveness of this new type of “adaptive vehicle automation”.

Privacy-Preserving Similarity Computation in Cloud-Based Mobile Social Networks

A growing number of mobile social network applications are taking advantage of cloud computing to store profiles of end users and run protocols which are compute-intensive. We focus on an application scenario of similarity computation between two users. To protect data security and privacy, mobile users encrypt their sensitive profiles before outsourcing to the cloud and different users choose different encryption keys. Three challenges need attention - how to compute on encrypted profiles under different keys, how to allow mobile users to stay offline during execution of the protocol, and how to select similarity metric. Existing schemes either rely on multi-key fully homomorphic encryption with one server or partially homomorphic encryption with two non-colluding servers. To balance computational complexity on one server and communication overhead between two servers, we put forward a privacy-preserving similarity computation protocol which supports both homomorphic additions and one homomorphic multiplication. We conduct security analysis and experimental evaluation of our scheme. The results show that our protocol is provably secure and runs reasonably fast, and thus can be applied in practice.

Game Theoretic Spectrum Allocation in Femtocell Networks for Smart Electric Distribution Grids

Ever growing penetration of the behind-the-meter technologies is changing the electricity consumption profiles of end-users. Intelligent coordination of these emerging technologies through a robust communication infrastructure enables their seamless integration with electric utilities’ operation. In this context, an efficient and reliable communication infrastructure plays a pivotal role in enabling optimal integration of emerging resources. In this paper, we propose a game-theory based method to enhance efficiency of the underlying communication network. Specifically, we focus on Femtocell communication technology which is one the promising options for improving poor indoor communication coverage. The major drawback for using femtocell communication technology is cross-layer interference of femto users (FUs) and macro users (MUs) which adversely impact network performance. In this paper, we propose a novel approach for sharing spectrum in a cognitive radio system with FUs and MUs as primary and secondary users, respectively. The underlying problem is formulated as Stackelberg game that is joined with a convex optimization problem. In this study, MUs and FUs are assumed to be selfish, rational and motivated to achieve maximum utility function, while MUs are competing to obtain maximum bandwidth. Finally, we present a closed form solution for the proposed approach which obtains a unique Nash Equilibrium and prioritizes the access of MUs to femto-base stations. Simulation results provide proof of concept and verify the effectiveness of our mathematical modeling.

Seasonal fuel consumption, stoves, and end-uses in rural households of the far-western development region of Nepal

Understanding how fuels and stoves are used to meet a diversity of household needs is an important step in addressing the factors leading to continued reliance on polluting devices, and thereby improving household energy programs. In Nepal and many other countries dependent on solid fuel, efforts to mitigate the impacts of residential solid fuel use have emphasized cooking while focusing less on other solid fuel dependent end-uses. We employed a four-season fuel assessment in a cohort of 110 households residing in two elevation regions of the Far-Western Development Region (Province 7) of Nepal. Household interviews and direct fuel weights were used to assess seasonality in fuel consumption and its association with stoves that met cooking and non-cooking needs. Per-capita fuel consumption in winter was twice that of other measured seasons, on average. This winter increase was attributed to greater prevalence of use and fuel consumption by supplemental stoves, not the main cooking stove. End-use profiles showed that fuel was used in supplemental stoves to meet the majority of non-meal needs in the home, notably water heating and preparation of animal food. This emphasis on fuels, stoves, and the satisfaction of energy needs—rather than just stoves or fuels—leads to a better understanding of the factors leading to device and fuel choice within households.

On the Spatio-Temporal End-User Energy Demands of a Dense Urban Environment

Buildings in major metropolitan centers face increased peak electrical load during the warm season, especially during extreme heat events. City-wide, the increased demand for space cooling can stress the grid, increasing generation costs. It is therefore imperative to better understand building energy consumption profiles at the city scale. This understanding is not only paramount for users to avoid peak demand charges but also for utilities to improve load management. This study aims to develop a city-scale energy demand forecasting tool using high resolution weather data interfaced with a single building energy model. The forecasting tool was tested in New York City (NYC) due to the availability of building morphology data. We identified 51 building archetypes, based on the building function (residential, educational, or office), the age of the building, and the land use type. The single building simulation software used is energyplus which was coupled to an urbanized weather research and forecasting (uWRF) model for weather forecast input. Individual buildings were linked to the archetypes and scaled using the building total floor area. The single building energy model is coupled to the weather model resulting in energy maps of the city. These maps provide an energy end-use profile for NYC for total and individual components including lighting, equipment and heating, ventilation, and air-conditioning (HVAC). The methodology was validated with single building energy data for a particular location, and with city-scale electric load archives, showing good agreements in both cases.

Profile of Patients Opting for Palliative Care in India.

Introduction:Understanding the profile of end users is a key input for designing a service. Centers that provide full-scale palliative care are few and far between in our country, and there is a need to open more centers to extend the services to a larger percentage of patients.Methods:Patient data from the inception of the center till January 2016 have been stored in a database. The data were mined, exported to Excel, and analyzed.Results:The data of 11,258 patients revealed the profile of patients, including their age, gender distribution, marital status, symptomology, and outcome. The present paper describes the profile of patients opting for palliative care in our country.Conclusions:The profile of patients registered at this center validates certain assumptions while debunking others. It is hoped that this profile gives some guidance to those desirous of setting up palliative care services.

Coaching or gaming? Implications of strategy choice for home based stroke rehabilitation.

BackgroundThe enduring aging of the world population and prospective increase of age-related chronic diseases urge the implementation of new models for healthcare delivery. One strategy relies on ICT (Information and Communications Technology) home-based solutions allowing clients to pursue their treatments without institutionalization. Stroke survivors are a particular population that could strongly benefit from such solutions, but is not yet clear what the best approach is for bringing forth an adequate and sustainable usage of home-based rehabilitation systems. Here we explore two possible approaches: coaching and gaming.MethodsWe performed trials with 20 healthy participants and 5 chronic stroke survivors to study and compare execution of an elbow flexion and extension task when performed within a coaching mode that provides encouragement or within a gaming mode. For each mode we analyzed compliance, arm movement kinematics and task scores. In addition, we assessed the usability and acceptance of the proposed modes through a customized self-report questionnaire.ResultsIn the healthy participants sample, 13/20 preferred the gaming mode and rated it as being significantly more fun (p < .05), but the feedback delivered by the coaching mode was subjectively perceived as being more useful (p < .01). In addition, the activity level (number of repetitions and total movement of the end effector) was significantly higher (p < .001) during coaching. However, the quality of movements was superior in gaming with a trend towards shorter movement duration (p = .074), significantly shorter travel distance (p < .001), higher movement efficiency (p < .001) and higher performance scores (p < .001). Stroke survivors also showed a trend towards higher activity levels in coaching, but with more movement quality during gaming. Finally, both training modes showed overall high acceptance.ConclusionsGaming led to higher enjoyment and increased quality in movement execution in healthy participants. However, we observed that game mechanics strongly determined user behavior and limited activity levels. In contrast, coaching generated higher activity levels. Hence, the purpose of treatment and profile of end-users has to be considered when deciding on the most adequate approach for home based stroke rehabilitation.

On a fair distribution of consumer's flexibility between market parties with conflicting interests

Summary The present work deals with social welfare maximization for energy scheduling. The parties involved in this study are a utility company, a distribution system operator and residential end-users. It allows the different participants to share a common electrical grid, without violating network constraints. A smart grid, equipped with a two-way communication system on a district level, is considered. For this purpose, each participant exchanges information, in order to establish a power consumption schedule that optimizes its own objectives, within the grid constraints. This strategy is illustrated on a district level to avoid overloading the distribution network. The consumption profiles of end-users consist of background consumption; some end-users are equipped with a heat pump and/or an electric vehicle, each of which is capable of responding to price variations. The impact of several tariff structures on the stability of the grid is explored. First of all, flat and day/night tariff structures are considered, and it is shown that price variation provokes load synchronization and leads to network overloads. Next, a real-time pricing tariff structure is used. If each end-user optimizes its behaviour to minimize personal costs, the grid is unable to supply all power during peak moments. In conclusion, if power constraints are imposed, the same grid can be stabilized, even if power demands increase by a factor of three or four. In a final example, the impact of imbalance costs is examined. The cluster can be guided to maximize profits for the utility company from the imbalance mechanism. Copyright © 2016 John Wiley & Sons, Ltd.

Development of an Energy Benchmarking, Measurement and Verification Program for an Existing Laboratory Building

ABSTRACTThis article describes the development of an energy benchmarking, measurement, and verification (M&V) program for an existing research laboratory (RL) building located in western New York.The results of the development of the RL building energy end-use profiles and energy benchmarking using the Labs21 benchmarking tool are presented. This article describes how targeted energy efficiency measures (EEMs) can noticeably reduce an existing lab building's annual energy consumption. The article demonstrates that even with a traditional HVAC design, the optimization of operational system parameters can result in measurable energy savings.The significance of energy data normalization using regression analysis for evaluating RL building energy performance is emphasized. The results can be used as a case study for streamlining energy performance evaluations and minimizing energy use of existing laboratories.

Strategies for Medical Data Extraction and Presentation Part 3: Automated Context- and User-Specific Data Extraction

In current medical practice, data extraction is limited by a number of factors including lack of information system integration, manual workflow, excessive workloads, and lack of standardized databases. The combined limitations result in clinically important data often being overlooked, which can adversely affect clinical outcomes through the introduction of medical error, diminished diagnostic confidence, excessive utilization of medical services, and delays in diagnosis and treatment planning. Current technology development is largely inflexible and static in nature, which adversely affects functionality and usage among the diverse and heterogeneous population of end users. In order to address existing limitations in medical data extraction, alternative technology development strategies need to be considered which incorporate the creation of end user profile groups (to account for occupational differences among end users), customization options (accounting for individual end user needs and preferences), and context specificity of data (taking into account both the task being performed and data subject matter). Creation of the proposed context- and user-specific data extraction and presentation templates offers a number of theoretical benefits including automation and improved workflow, completeness in data search, ability to track and verify data sources, creation of computerized decision support and learning tools, and establishment of data-driven best practice guidelines.