Different Types Of Users Research Articles

Crypto airdrops: An evolutionary approach

AbstractAirdrops are a mechanism by which blockchain-based projects distribute digital tokens to users at zero cost. Billions of dollars of tokens have been airdropped for reasons such as marketing and decentralisation. The distribution criteria that projects have designed (e.g. previous user activity on the platform) have become more complex as different types of users adapt to receive a larger airdrop. This paper argues that coevolutionary theory can help us to understand the unique phenomenon of airdrop design, where participants (projects and users) strategically adapt over time and across different blockchain ecosystems. Nine airdrop case studies show an expanding set of innovative airdrop strategies (e.g. airdrops requiring additional activities to claim). The evolutionary analysis also implies changes in airdrop design (e.g. multi-round airdrops to facilitate learning) and further strategic innovation. Ultimately, this paper offers a new platform to apply the tools of evolutionary economics, exploring a unique process of innovation and evolution.

Comparison of School-Attending Adolescent Areca Nut Users with Tobacco-Only Users and Those Using Both Substances on Demographic Variables and Behavioral Determinants.

Consumption of areca nut and tobacco, often initiated in adolescence, are important causes of oral cancers in India. Areca nut prevention, often subsumed into school tobacco prevention programs, assumes that users are similar. However, differences in gender or age of users could necessitate unique approaches. The study aimed to find if adolescent areca nut-only users are different from tobacco-only users and users of both areca and tobacco. A cross-sectional survey with school-students attending grades 7, 8, 9 was used to compare differences in age, gender, knowledge, and attitudes among areca nut only users, tobacco-only, users of both, and non-users. Of 1909 respondents, 464 (24.3%) used only areca nut, 25 (1.3%) used only tobacco, 177 (9.3%) used both, and 1243 (65.1%) were never-users. Females, overwhelmingly, consumed only areca nut. Users of both substances, in greater proportions, were male, older in age, and more than half believed that tobacco-users had more friends. A third of tobacco-only and users of both substances found it hard to turn down a friend's request to use compared to a fifth of areca-only and non-users. Differences in gender, age, and behavioral determinants such as subjective norms and perceived behavioral control between the different types of users underlines the need for prevention and cessation programs and policies that are specific to and tailored to their unique profiles and needs.

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

Designing an electricity price mechanism that efficiently enables users to manage their self-production and consumption behavior to accommodate renewable energy and regulate peak load is a hard task with the emergence of prosumer with photovoltaic and energy storage. To address this issue, we propose an iterative two-layer optimization to investigate customized price of electricity for different types of users in the presence of photovoltaic uncertainty. By taking into account power flow restrictions, the model is used to explore the optimal time-of-use, demand, and on-grid prices for both electricity consumers and prosumers. The consumer and prosumer peak-flat-valley periods are initially divided in the upper layer. Then, a model for distribution system operator profit maximization is proposed to minimize operation cost, power loss, and peak–valley difference. A stochastic optimization model is presented in the lower layer for consumer and prosumer to hedge against the risk of uncertain photovoltaic with the aim of minimizing electricity tariff, demand tariff, and life cycle cost of energy storage. Numerical experiments conducted on modified 15-bus and 69-bus distribution systems with different types of users show the effectiveness of the iterative two-layer optimization for customized price of electricity in peak-shaving and valley-filling while ensuring the interests of distribution system operator and users. Results show that the total profit increases by more than 4% and the net load fluctuation variance reduces higher than 20% compared with the traditional pricing method.

The Development of a Prototype Solution for Collecting Information on Cycling and Hiking Trail Users

Hiking and cycling have gained popularity as ways of promoting well-being and physical activity. This has not gone unnoticed by Portuguese authorities, who have invested in infrastructure to support these activities and to boost sustainable and nature-based tourism. However, the lack of reliable data on the use of these infrastructures prevents us from recording attendance rates and the most frequent types of users. This information is important for the authorities responsible for managing, maintaining, promoting and using these infrastructures. In this sense, this study builds on a previous study by the same authors which identified computer vision as a suitable technology to identify and count different types of users of cycling and hiking routes. The performance tests carried out led to the conclusion that the YOLOv3-Tiny convolutional neural network has great potential for solving this problem. Based on this result, this paper describes the proposal and implementation of a prototype demonstrator. It is based on a Raspberry Pi 4 platform with YOLOv3-Tiny, which is responsible for detecting and classifying user types. An application available on users’ smartphones implements the concept of opportunistic networks, allowing information to be collected over time, in scenarios where there is no end-to-end connectivity. This aggregated information can then be consulted on an online platform. The prototype was subjected to validation and functional tests and proved to be a viable low-cost solution.

Children's Acceptance of a Domestic Social Robot: How It Evolves over Time

Little is known about children's long-term acceptance of social robots; whether different types of users exist; and what reasons children have not to use a robot. Moreover, the literature is inconclusive about how the measurement of children's robot acceptance (i.e., self-report or observational) affects the findings. We relied on both self-report and observational data from a six-wave panel study among 321 children aged eight to nine, who were given a Cozmo robot to play with at home over the course of 8 weeks. Children's robot acceptance decreased over time, with the strongest drop after 2–4 weeks. Children rarely rejected the robot (i.e., they did not stop using it already prior to actual adoption). They rather discontinued its use after initial adoption or alternated between using and not using the robot. The competition of other toys and lacking motivation to play with Cozmo emerged as the strongest reasons for not using the robot. Self-report measures captured patterns of robot acceptance well but seemed suboptimal for precise assessments of robot use.

Tracking and Profiling Repeated Users Over Time in Text-Based Counseling: Longitudinal Observational Study With Hierarchical Clustering.

Due to their accessibility and anonymity, web-based counseling services are expanding at an unprecedented rate. One of the most prominent challenges such services face is repeated users, who represent a small fraction of total users but consume significant resources by continually returning to the system and reiterating the same narrative and issues. A deeper understanding of repeated users and tailoring interventions may help improve service efficiency and effectiveness. Previous studies on repeated users were mainly on telephone counseling, and the classification of repeated users tended to be arbitrary and failed to capture the heterogeneity in this group of users. In this study, we aimed to develop a systematic method to profile repeated users and to understand what drives their use of the service. By doing so, we aimed to provide insight and practical implications that can inform the provision of service catering to different types of users and improve service effectiveness. We extracted session data from 29,400 users from a free 24/7 web-based counseling service from 2018 to 2021. To systematically investigate the heterogeneity of repeated users, hierarchical clustering was used to classify the users based on 3 indicators of service use behaviors, including the duration of their user journey, use frequency, and intensity. We then compared the psychological profile of the identified subgroups including their suicide risks and primary concerns to gain insights into the factors driving their patterns of service use. Three clusters of repeated users with clear psychological profiles were detected: episodic, intermittent, and persistent-intensive users. Generally, compared with one-time users, repeated users showed higher suicide risks and more complicated backgrounds, including more severe presenting issues such as suicide or self-harm, bullying, and addictive behaviors. Higher frequency and intensity of service use were also associated with elevated suicide risk levels and a higher proportion of users citing mental disorders as their primary concerns. This study presents a systematic method of identifying and classifying repeated users in web-based counseling services. The proposed bottom-up clustering method identified 3 subgroups of repeated users with distinct service behaviors and psychological profiles. The findings can facilitate frontline personnel in delivering more efficient interventions and the proposed method can also be meaningful to a wider range of services in improving service provision, resource allocation, and service effectiveness.

Body awareness, activity trackers, and the cognitive unconscious

This paper investigates the role activity trackers play for their users, focusing on the transformative role of wearable technology in shaping our perceptions of our bodies and health. Drawing on autophenomenography, in-depth interviews and online content analysis, it questions the neutrality of tracking data and interpretation, by drawing on Annemarie Mol’s concept of ontonorms and highlighting the built-in normativity of the assemblage that makes it constantly prone to enacting a certain kind of optimal performance. Using Katherine Hayles’ concept of cognitive assemblages, it defines trackers as being specific types of cognizers, entering alongside humans in assemblages in which their role sometimes ends up dethroning the body’s own perceptual systems and changing the way we are aware of our own bodies. Throughout the research, three different types of users are identified and described, for whom the cognitive assemblage takes a different shape: regular users, users with medical issues and power users.

RFM user value tags and XGBoost algorithm for analyzing electricity customer demand data

With the increasing demand for electricity, predicting user electricity demand has become an essential task. The electricity demand characteristics of users in the electricity market are different, so it is necessary to classify and predict users. Aiming at the above problems, a classified forecasting model of electricity demand based on recent consumption, frequency, monetary (RFM), K-means, XGBoost and dynamic time warping (DTW) algorithm is proposed. The experiment showcases that among the electricity consumption of commercial users, the first type of load has the lowest proportion in autumn, at around 18.6 %; The second type of load has the highest proportion in autumn, about 81.3 %; Accurate classification has been made for the consuming quantity of electricity of commercial users. The average error in the forecasting results of the RFM-KM-XGboost model and the actual value of commercial electricity demand is about 0.07 kW; The average errors between the forecasting results of SVM model and RF model and the true values are about 0.2 kW and 0.14 kW, respectively; It indicates that the forecasting error of the RFM-KM-XGBoost model is smaller. The above results indicate that the RFM-KM-XGBoost model can extract users' electricity demand characteristics by classifying user types and load types, and make more accurate predictions of electricity demand for different types of users.

Using Computer Vision to Collect Information on Cycling and Hiking Trails Users

Hiking and cycling have become popular activities for promoting well-being and physical activity. Portugal has been investing in hiking and cycling trail infrastructures to boost sustainable tourism. However, the lack of reliable data on the use of these trails means that the times of greatest affluence or the type of user who makes the most use of them are not recorded. These data are of the utmost importance to the managing bodies, with which they can adjust their actions to improve the management, maintenance, promotion, and use of the infrastructures for which they are responsible. The aim of this work is to present a review study on projects, techniques, and methods that can be used to identify and count the different types of users on these trails. The most promising computer vision techniques are identified and described: YOLOv3-Tiny, MobileNet-SSD V2, and FasterRCNN with ResNet-50. Their performance is evaluated and compared. The results observed can be very useful for proposing future prototypes. The challenges, future directions, and research opportunities are also discussed.

Learning-based Relaxation of Completeness Requirements for Data Entry Forms

Data entry forms use completeness requirements to specify the fields that are required or optional to fill for collecting necessary information from different types of users. However, because of the evolving nature of software, some required fields may not be applicable for certain types of users anymore. Nevertheless, they may still be incorrectly marked as required in the form; we call such fields obsolete required fields. Since obsolete required fields usually have “not-null” validation checks before submitting the form, users have to enter meaningless values in such fields to complete the form submission. These meaningless values threaten the quality of the filled data and could negatively affect stakeholders or learning-based tools that use the data. To avoid users filling meaningless values, existing techniques usually rely on manually written rules to identify the obsolete required fields and relax their completeness requirements. However, these techniques are ineffective and costly. In this article, we propose LACQUER, a learning-based automated approach for relaxing the completeness requirements of data entry forms. LACQUER builds Bayesian Network models to automatically learn conditions under which users had to fill meaningless values. To improve its learning ability, LACQUER identifies the cases where a required field is only applicable for a small group of users and uses SMOTE, an oversampling technique, to generate more instances on such fields for effectively mining dependencies on them. During the data entry session, LACQUER predicts the completeness requirement of a target based on the already filled fields and their conditional dependencies in the trained model. Our experimental results show that LACQUER can accurately relax the completeness requirements of required fields in data entry forms with precision values ranging between 0.76 and 0.90 on different datasets. LACQUER can prevent users from filling 20% to 64% of meaningless values, with negative predictive values (i.e., the ability to correctly predict a field as “optional”) between 0.72 and 0.91. Furthermore, LACQUER is efficient; it takes at most 839 ms to predict the completeness requirement of an instance.

One size does not fit all: Three tools and approaches for soundscape simulations

We present a reflection on three prototypes of real-time interactive soundscape simulators aimed at supporting participatory urban sound planning and interventions. These prototypes were developed as part of the Sounds in the City cross-sectoral partnership through an iterative process involving various stakeholders. Each prototype enables different ways to manipulate soundscapes through tailored interfaces and audio/visual outputs, targeting different types of users. The first version is audio-only and uses live music tools for Ambisonics spatialization, with limited environmental modeling for co-design exercises with urban and sound professionals. The second version builds on the idea of the first and adds acoustic modeling. It was used to assess the impact of sound installations in public spaces through research-creation involving sound artists and residents. The last version utilizes (desktop or head-mounted) virtual reality with binaural rendering, immersing the user in an audio-visual city to raise sound awareness and support urban soundscape design. We emphasize that there is no one-size-fits-all tool. Rather, we highlight how different tools are needed for different auralization tasks and target user groups. These tools are presented through examples of early-stage conceptualization, educational components, creative processes, laboratory-based soundscape assessments, and both individual and participatory design sessions.

Models database of information technology for valuation of real estate

The goal of the research is to find and analyze ways to improve the results of real estate valuation based on a comparative approach. The chosen approach suggests the use of an information technology for the valuation of residential real estate, which is built on the basis of a database of models of decision support systems (DSS). The problem solved with the use of the discussed approach is to establish the degree of similarity between the object under consideration and its analogs by determining the value as an integral quality indicator based on the analysis of value-creating factors. This is followed by the construction of a generalized relationship between the value of this indicator and the prices of analogs. According to the logic of the comparative approach, it is assumed that the obtained relationship holds for the evaluated object as well and can be used in the calculation of its value.
 The formation of the database of models was based on the analysis of information technology processes, where details of functional aspects of process implementation, informational and logical connections between them, rights of access to information environment, and the implementation of processes by different types of users (their roles) were considered. The proposed database of models is built as a mathematical and logical basis of information technology that is applied as a computer tool for appraisers and aims to maximize formalization and efficiency improvement of process implementation based on expert evaluation methods. The construction of the database of models is based on methods of value estimation using multiple criteria, with consideration of their significance, analysis of hierarchies, ranking, Delphi, and user access control based on defined roles.

A Systematic Review on Pill and Medication Dispensers from a Human-Centered Perspective

As medication adherence represents a critical challenge in healthcare, pill and medication dispensers have gained increasing attention as potential solutions to promote adherence and improve patient outcomes. Following the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) methodology, we carried out a systematic literature review on papers indexed in Scopus and PubMed, which present solutions for pill or medication dispensers. Given the importance of user acceptance for these solutions, the research questions of the survey are driven by a human-centered perspective. We first provide an overview of the different solutions, classifying them according to their stage of development. We then analyze each solution considering its hardware/software architecture. Finally, we review the characteristics of user interfaces designed for interacting with pill and medication dispensers and analyze the involvement of different types of users in dispenser management. On the basis of this analysis, we draw findings and indications for future research that are aimed to provide insights to healthcare professionals, researchers, and designers who are interested in developing and using pill and medication dispensers.

NAVMAT: an AI-powered pathway to knowledge sharing on material failures

The paper describes the product development methodology, the architecture, the modules and features of the operating prototype, as well as future challenges of NAVMAT, a naval materials failure management system. The fundamental compound of the knowledge management platform is the recording and classification of a failure incident. NAVMAT supports different types of users, from the first Reporter to the Analyst and Forensic Engineer through appropriate workflows. In these workflows the incident-related information can be provided as text, files, images and videos, which can be easily associated to the incident and provide structured information. NAVMAT provides a number of Artificial Intelligence (AI)-enabled helpers. During the incident recording, NAVMAT brings into play reactive real-time search which suggests related incidents and literature to facilitate the editor. It also speeds up the classification of incidents by providing AI-suggested labels, chosen from the (multi-lingual) concepts contained in the system Ontology. On the other hand, the intelligent indexing and search infrastructure of the system supports easy identification and retrieval of past incidents, reports and publications, by applying Natural Language Processing. The prototype of the described system has been embedded as a web application validated by potential Users and is being prepared for Operation in a fleet environment.

Understanding socio-demographic factors associated with shared-use-paths (SUPs) utilization

Shared Use Paths (SUPs) are becoming very popular in North America due to the current initiatives that promote active travel. SUPs can accommodate different types of users, including pedestrians, bicyclists, scooterists, and skateboarders. Although the interest in SUPs continues to increase, relatively less research has been performed on their utilization, especially using revealed preferences. Therefore, this study utilizes the survey data collected from Edmonton, Canada, between June 12th to 19th 2018 to explore the likelihood of utilizing the SUPs and the associated frequency of use. Results indicate that not all variables associated with the likelihood of utilization are also associated with the frequency of use. Specifically, higher levels of education influence the likelihood of SUP utilization, while the higher frequency of SUP usage is influenced by the secondary modes of transportation. On the other hand, as the age increases, the likelihood and frequency of SUP usage decreases. Further, households with higher income are associated with a higher likelihood of SUP utilization, male residents are likely to use the SUPs more frequently compared to their female counterparts. Other variations are also observed for home ownership and whether the resident resides in a downtown area. The application of the findings to the city planners and active travel initiatives have been provided to improve the planning and installation/construction of the SUPs facilities.

Evaluation of Surabaya population administration & civil registration systems using DeLone & McLean information system success model

This paper aims to prove that different types of users influence the degree of user satisfaction with the Surabaya Population Administration & Civil Registration Systems (KNG) and confirm that there are differences in satisfaction between internal and external users. D&M IS Success Model is used to evaluate user satisfaction. The data were collected via a survey and analyzed by the PLS-SEM method. The results revealed that service quality has the most significant influence on user satisfaction. The results also revealed that there are differences in variables that influence user satisfaction with the KNG system between different types of users.

Accessibility of Virtual Museum Spaces in the 21st Century in Turkey

Museums, which are the embodiment of art, exhibition, culture, and science, have started to offer virtual space experiences as well as traditional physical space experiences in recent years. In addition to the development of technology, the pandemic, which started in 2019 and affected the whole world, has also been effective in accelerating the transformation of museums from physical space to virtual space. The increasing use of virtual museums directly concerns different user groups in terms of accessibility. In a physical museum experience, solutions that appeal to different types of users are generally produced. Thanks to these solutions, an individual with any disability can be included in the space with all senses such as sight, touch, smell, and hearing during a museum visit, while the lack of inclusion of all senses in the virtual space experience causes the experience to be more limited. This study discusses this limitation in terms of accessibility. How can the virtual space experience be more accessible for different types of users? Can different solutions be offered to all user groups for a better understanding of the space and the art product? These questions constitute the research questions of this study. In this context, this study aims to examine how accessible virtual museums are. The study has conducted this inquiry through 59 virtual museums in Turkey. As a method, virtual museums were classified and tabulated in terms of their accessibility levels in line with their characteristics. It was concluded that there are deficiencies in the experience of different types of users and various suggestions were made in terms of virtual accessibility.

Healing Applications in Hospital Interiors: Ceramic Art

Healthcare facilities include complex partnerships that accommodate different types of users to meet the needs of the healthcare sector and require the collaboration of many disciplines to meet these needs. Hospitals, which contain the outputs of different fields of expertise from city scale to industrial product scale, are fed by the fields of engineering, architecture, and interior architecture in terms of space. Interior design, on the other hand, is divided into theoretical and practical specializations related to building types. The fact that health buildings have significant differences from other buildings due to their function and the obligations that would be met is of great importance in terms of the benefit to be provided to the public by the studies to be carried out in the field. Hospital buildings and spaces are constructed and designed according to the standards determined by the shade's laws, regulations, or guidelines where they are to be built. In this context, the study aims to provide a public contribution with art outputs that would positively affect the recovery of the user for the spaces that are generally open to the use of users in hospital interiors by researching the standards in the framework of international standards. However, since traditional and modern art contents have different spatial needs; ceramic art outputs, which are included in traditional art, focus on the specific evaluation of the research with its visual and tactile character. Using qualitative research methods; hospital construction guidelines and literature research were carried out by data collection and inductive methods. The research aims to make theoretical and practical contributions to the healing space with ceramic artworks/objects in the field of architecture and design.

Information System for the Educational Center

The goal of the research of the qualification thesis is the development of an intelligent information system of the educational centre using the Python programming language and the Django framework, the SQLite database system, and the chatbot with artificial intelligence ChatGPT. The developed system should facilitate the interaction of different types of users with learning/training centres in order to acquire new necessary skills. As you know, from now on every day more and more educational centres are opening, which provide their services for the study of this or that ability. Due to the onset of the pandemic period, all such educational centres are transferring their classes to the online mode, and the actual need to solve this type of issue arises more and more often. In this work, an analytical review of literary sources is carried out, a comparison of known means of implementation, i.e., so-called analogue systems, is carried out; the actual functioning of this type of system is checked. In addition, a goal tree is being built, which clearly outlines the general goal, namely, the optimization of all processes of the educational centre. In addition, an equally important step is the description of requirements, which is carried out in this work using the RUP methodology for the intelligent information system of the educational centre. The category of users is also determined and a generalized model of system functioning in the environment is built. To develop the system, an analysis of possible methods and software tools for creating and deploying the system is carried out. The most optimal methods for this case are chosen, namely the method of software synthesis and the method of using ready-made solutions, because their application will provide an opportunity to develop a system from scratch using the necessary components and provide an opportunity to integrate an already ready-made system with an intelligent component. Next, a recalculation of possible options among the integration of ready-made intelligent components is carried out, and a chat system with artificial intelligence is chosen, because it will give users the opportunity to search for answers to their questions without leaving the system and facilitate the learning process for each of the users.

Two-level collaborative demand-side management for regional distributed energy system considering carbon emission quotas

In demand response programs, users typically modify their energy consumption behavior in response to the grid. Distributed energy systems (DES) also need users' participation to ensure the efficient and stable operation of the system, especially under government-imposed carbon emission quotas. This study proposes a two-level collaborative demand-side management framework that allows users to participate in demand response in distributed energy systems, thereby ensuring that the systems' carbon emission remain within the quota while minimizing the impact on thermal comfort. Firstly, the framework realizes coordination between the supply and demand side. Changing energy use behavior not only reduces the amount of load but also enhances the system's efficiency. Secondly, different type of users can cooperatively adjust their air conditioners according to their load characteristics to achieve optimal overall thermal comfort. The effectiveness of the proposed method is demonstrated in a DES with four types of buildings, evaluated using two indoor thermal environment indexes. Under optimal guidance, the system efficiency is improved by 1.9%. Additionally, there is a 3.3% reduction in carbon emissions, along with a 13.2% improvement in overall indoor thermal comfort compared with unified temperature regulation. This study holds positive implications for users' participation in distributed energy systems to achieve energy saving and carbon reduction.