Types Of Users Research Articles

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.

Thermal runaway of Li-ion battery with different aging histories

Safety issues associated with thermal runaway (TR) in batteries have attracted extensive research attention. However, understanding and predicting TR encounter significant challenges due to the complexities of chemical reactions, venting events, thermal stratification, multiphase interactions, and variable boundary conditions. These challenges are further complicated by intrinsic battery degradation, a phenomenon often insufficiently characterized and governed by multiple mechanisms. These primarily include the depletion of electrolyte and active materials, thickening of the solid-electrolyte-interphase (SEI), and formation of lithium plating and dendrites. Such degradation alters the battery's materials and structure, directly causing electrochemical performance penalties and potentially affecting its TR behavior. This study examines the TR behavior of commercial 18,650 cells with lithium nickel manganese cobalt oxide (NMC) cathodes, focusing on degradation associated typical usage scenarios: long-term cycling, low-temperature cycling, dynamic cycling with calendar aging. We employ Electrochemical Impedance Spectroscopy (EIS) to probe internal changes in the electrochemical properties of aged cells compared to fresh counterparts. The EIS results reveal pronounced SEI impedance and substantially increased charge transfer impedance in long-term cycled cells. TR tests were conducted using an Accelerating Rate Calorimeter (EV+ ARC, Thermal Hazard Technology) following the heat-wait-seek (HWS) strategy. Our findings indicate a significant impact of aging mechanisms on second-life battery safety. Specifically, cells subjected to low-temperature cycling demonstrated a considerably lower onset temperature for exothermic reactions, and shorter TR delay time, despite all aged cells showing similar state of health (SOH). Furthermore, the temperature corresponding to the cell voltage drop is consistently much lower for aged cells with dynamic cycling and calendar aging.

Reorienting information searching research by applying a situated abilities perspective

Introduction. In studies of information searching, people are often characterised with perpetual physical, sensory and cognitive abilities. Situational factors that may affect human abilities are less considered, although they may have an impact on information searching behaviour. For example, a person with dyslexia may struggle with inputting correctly spelled queries. Spelling skills, however, may also be influenced by fatigue, illness, or using a mobile phone while walking. All types of users can therefore at certain times experience challenges related to query input. Method. This theoretical paper explores the concept of situated abilities in the context of information searching. Eight personas are constructed and used in a discussion of how a change of perspective on human abilities can provide a valuable contribution to research. Analysis. The personas are based upon empirical findings of user behaviour and are discussed in relation to theoretical frameworks and models. Results. Human abilities are dynamic and affected by a variety of situational factors. All people experience temporary impairments during their lives. It would therefore be purposeful to reorient information searching research by applying a situated abilities perspective. Conclusion. A situated abilities perspective may result in more inclusive search systems for all types of users.

Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine.

Cocaine abuse represents a major public health concern. The social perception of cocaine has been changing over the decades, a phenomenon closely tied to its patterns of use and abuse. Twitter is a valuable tool to understand the status of drug use and abuse globally. However, no specific studies discussing cocaine have been conducted on this platform. 111,508 English and Spanish tweets containing "cocaine" from 2018 to 2022 were analyzed. 550 were manually studied, and the largest subset underwent automated classification. Then, tweets related to cocaine were analyzed to examine their content, types of Twitter users, usage patterns, health effects, and personal experiences. Geolocation data was also considered to understand regional differences. A total of 71,844 classifiable tweets were obtained. Among these, 15.95% of users discussed the harm of cocaine consumption to health. Media outlets had the highest number of tweets (35.11%) and the most frequent theme was social/political denunciation (67.88%). Regarding the experience related to consumption, there are more tweets with a negative sentiment. The 9.03% of tweets explicitly mention frequent use of the drug. The continent with the highest number of tweets was America (55.44% of the total). The findings underscore the significance of cocaine as a current social and political issue, with a predominant focus on political and social denunciation in the majority of tweets. Notably, the study reveals a concentration of tweets from the United States and South American countries, reflecting the high prevalence of cocaine-related disorders and overdose cases in these regions. Alarmingly, the study highlights the trivialization of cocaine consumption on Twitter, accompanied by a misleading promotion of its health benefits, emphasizing the urgent need for targeted interventions and antidrug content on social media platforms. Finally, the unexpected advocacy for cocaine by healthcare professionals raises concerns about potential drug abuse within this demographic, warranting further investigation.

Alrecon: computed tomography reconstruction web application based on Solara.

Synchrotron X-ray computed tomography is a non-destructive 3D imaging technique that offers the possibility to study the internal microstructure of samples with high spatial and temporal resolution. Given its unmatched image quality and acquisition speed, and the possibility to preserve the specimens, there is an increasing demand for this technique, from scientific users from innumerable disciplines. Computed tomography reconstruction is the computational process by which experimental radiographs are converted to a meaningful 3-dimensional image after the scan. The procedure involves pre-processing steps for image background and artifact correction on raw data, a reconstruction step approximating the inverse Radon-transform, and writing of the reconstructed volume image to disk. Several open-source Python packages exist to help scientists in the process of tomography reconstruction, by offering efficient implementations of reconstruction algorithms exploiting central or graphics processing unit (CPU and GPU, respectively), and by automating significant portions of the data processing pipeline. A further increase in productivity is attained by scheduling and parallelizing demanding reconstructions on high performance computing (HPC) clusters. Nevertheless, visual inspection and interactive selection of optimal reconstruction parameters remain crucial steps that are often performed in close interaction with the end-user of the data. As a result, the reconstruction task involves more than one software. Graphical user interfaces are provided to the user for fast inspection and optimization of reconstructions, while HPC resources are often accessed through scripts and command line interface. We propose Alrecon, a pure Python web application for tomographic reconstruction built using Solara. Alrecon offers users an intuitive and reactive environment for exploring data and customizing reconstruction pipelines. By leveraging upon popular 3D image visualization tools, and by providing a user-friendly interface for reconstruction scheduling on HPC resources, Alrecon guarantees productivity and efficient use of resources for any type of beamline user.

Automatically Expanding User-Management System for Massive Users in the Cloud Platform

Cloud computing has become one of the key technologies used for big data processing and analytics. User management on cloud platforms is a growing challenge as the number of users and the complexity of systems increase. In light of the user-management system provided by major cloud service providers, which cannot manage multiple types of user systems, this article proposed scale-out automated expansion user management for authorization synchronization to improve the efficiency and scalability of user management on cloud platforms. Three modules for user-automated expansion were designed and implemented to synchronize the authentication information from the cloud platform resource user to the data-processing user. Additionally, an optimized dynamically weighted load-balancing algorithm in Nginx is presented in this article that adjusts the weight according to load information such as CPU and memory usage, and a better load balance can be achieved. The effectiveness of the proposed user-management system is substantiated by comparing it with two existing infrastructures, including multiple data centers and the Huawei cloud platform. The experimental results validate the finding that scale-out automated expansion user management across the Huawei cloud platform can effectively synchronize data accessing authority with cloud resource utilizing authority. Furthermore, the optimized weighted load-balancing algorithm is also valuable for massive concurrent user registration based on limited cloud resources. In the future, this scale-out user-management system could be applied to other cloud platforms and extended by database synchronization to satisfy the needs of data sharing among multiple types of users belonging to different cloud platforms.

Visual analytics system for understanding DeepRL-based charged particle tracking

AbstractIn this work, we propose a visual analytics system to analyze deep reinforcement learning (deepRL) models working on the track reconstruction of charged particles in the field of particle physics. The data of these charged particles are in the form of point clouds with high-dimensional features. We use one of the existing post hoc saliency methods of explainable artificial intelligence (XAI) and extend its adaptation to compute saliency attributions for the input data corresponding to the output of the model. Our proposed system helps users to explore these saliency attributions corresponding to the high-dimensional input data of the machine learning model and interpret the decision-making process of the model. In particular, we provide the users with multiple task-oriented components, different types of linked views and interactive tools to analyze the model. We explain how to use the system by outlining a typical user workflow and demonstrate the system’s usefulness using several case studies which address specific analysis tasks.

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.

Automatyczna platforma testowa dla front-end stron internetowych oraz aplikacji sieciowych w modelu SaaS – BrowserSpot

The article presents a modern tool created in a SaaS model for automating tests of front-end for websites and network applications, BrowserSpot. The key elements and functions of the tool were presented, as well as the industry premises and issues that initiated the start of work on the solution. All eight stages of research and development project were discussed in detail, along with the results and key achievements of each of them. The tool was created in a SaaS model to maximize its accessibility for every type of user, and is available on the market as a product by ClickRay Sp. z o.o.

Effect of Aging Path on Degradation Characteristics of Lithium-Ion Batteries in Low-Temperature Environments

Typical usage scenarios for energy storage and electric vehicles (EVs) require lithium-ion batteries (LIBs) to operate under extreme conditions, including varying temperatures, high charge/discharge rates, and various depths of charge and discharge, while also fulfilling vehicle-to-grid (V2G) interaction requirements. This study empirically investigates the impact of ambient temperature, charge/discharge rate, and charge/discharge cut-off voltage on the capacity degradation rate and internal resistance growth of 18,650 commercial LIBs. The charge/discharge rate was found to have the most significant influence on these parameters, particularly the charging rate. These insights contribute to a better understanding of the risks associated with low-temperature aging and can aid in the prevention or mitigation of safety incidents.

Is it all about fun? Self-service technology acceptance in Germany

ABSTRACT The introduction of self-service technology (SST), particularly self-checkout and self-scan services, has gained increasing attention from German retail. But to date, end-customer SST use has not developed extensively. The majority of German customers still prefer to use the traditional checkout service counter. The purpose of this paper is to explore how trust, fun, and usefulness can attract inexperienced prospective adopters and experienced customers to use SST. For this, our study investigated the intention to use an SST as a holistic system (self-scan and self-checkout) and considered trust in the SST technology for the first time. Also the interplay between fun and perceived usefulness has not been investigated before. The data were collected using an online questionnaire and in front of a German hypermarket that offers SST. The sample (n = 222) consists of both inexperienced and experienced customers. The data were analyzed using structural equation modeling in SmartPLS4. Results demonstrate fun is a decisive factor for a holistic SST system that incorporates the task of self-scanning and self-checkout. This also holds for trust and perceived usefulness. With regard to experienced and prospective users, the results show different effects in the importance of both groups. Fun is crucial for prospective users. For experienced users, benefits and trust become more important for long-term usage. Trust is an essential aspect for both user types. Managerial implications are discussed.

Trends and types of dietary supplement usage amongst people visiting fitness centers in the Northern Border Region of Saudi Arabia

Purpose: To determine the trends in dietary supplement (DS) usage and its socio-demographic determinants amongst fitness centre users in Arar, Saudi Arabia. 
 Methods: This cross-sectional survey was conducted among people visiting different fitness centers in Arar, Saudi Arabia, between December 2022 and March 2023. The sample comprised 150 people (male and female) aged ≥ 18 years, who completed a self-administered questionnaire. 
 Results: More than half (63.3 %) of the gym users regularly used DS, while the rest used DS occasionally. Young age (p < 0.001), female gender (p = 0.04), higher educational level (p = 0.006), single status (p = 0.001) and schooling (p < 0.001) had significant positive correlations with regular DS use, relative to occasional use. The most important reason for using DS was recommendation by physician (30.7 %), while the most common supplements used were multivitamins (alone or in combination with other drugs; 39.3 %). The participants were well-versed in the supportive role of DS, as well as the risks and adverse effects related to DS overuse. 
 Conclusion: The prevalence of DS usage is high among Saudi fitness center attendees, and it was significantly associated with socio-demographic and lifestyle variables. Furthermore, there are high levels of awareness among participants regarding the benefits and negative effects of DS.

Measuring the Convergence and Divergence in Urban Street Perception among Residents and Tourists through Deep Learning: A Case Study of Macau

In today’s context of flourishing tourism, the development of urban tourism leads to a continuous influx of population. Existing empirical evidence highlights the interaction between tourists’ and residents’ perception of urban spaces and the local society and living spaces. This study, focusing on Macau, utilizes the region’s streetscape images to construct a deep learning-based model for quantifying the urban street perception of tourists and local residents. To obtain more refined perceptual evaluation data results, during the training phase of the model, we intentionally categorized tourist activities into natural landscape tours, historical sightseeing, and entertainment area visits, based on the characteristics of the study area. This approach aimed to develop a more refined perception evaluation method based on the classification of urban functional areas and the types of urban users. Further, to improve the streetscape environment and reduce visitor and resident dissatisfaction, we delved into the differences in perception between tourists and residents in various functional urban areas and their relationships with different streetscape elements. This study provides a foundational research framework for a comprehensive understanding of residents’ and tourists’ perceptions of diverse urban street spaces, emphasizing the importance of exploring the differentiated perceptions of streetscapes held by tourists and residents in guiding scientific urban tourism development policies and promoting social sustainability in cities, particularly those where tourism plays a significant role.

User-Type Differential Paths for a Media Effect Model: A Test of Self-Regulation Deficiency in Drama Watching for Different Motive-Driven Users

ABSTRACT Media users are not homogeneous, and research that treats them as such tends to yield inconsistent findings. More nuanced insights into media effect processes might emerge from an identification of qualitatively distinct subgroups, for whom unique media effect processes lead to distinctive outcomes as well. Building on an existing differential susceptibility to media effects model, the current research adds the possibility of differential paths defined by user types. In this revised model, the triggers (e.g. individual characteristics) that lead to media effects and their outcomes (e.g. sociopsychological states) vary across different groups of media users. In the context of problematic drama watching, as a type of media effect, panel data gathered from a representative sample and a latent class analysis reveal that drama viewers can be grouped into types, according to the distinct constellations of motives they have for watching. Individual characteristics identified for such groups can lead to problematic drama watching and associated negative outcomes in the future, which also vary with the types of viewers. The findings affirm that the distinct processes linked to each viewer type correspond well with their distinctive motives.

Robots still outnumber humans in web archives in 2019, but less than in 2015 and 2012

The significance of the web and the crucial role of web archives in its preservation highlight the necessity of understanding how users, both human and robot, access web archive content, and how best to satisfy this disparate needs of both types of users. To identify robots and humans in web archives and analyze their respective access patterns, we used the Internet Archive’s (IA) Wayback Machine access logs from 2012, 2015, and 2019, as well as Arquivo.pt’s (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based on their browsing behavior. To better understand how users navigate through the web archives, we evaluated these sessions to discover user access patterns. Based on the two archives and between the three years of IA access logs (2012 vs. 2015 vs. 2019), we present a comparison of detected robots vs. humans and their user access patterns and temporal preferences. The total number of robots detected in IA 2012 (91% of requests) and IA 2015 (88% of requests) is greater than in IA 2019 (70% of requests). Robots account for 98% of requests in Arquivo.pt (2019). We found that the robots are almost entirely limited to “Dip” and “Skim” access patterns in IA 2012 and 2015, but exhibit all the patterns and their combinations in IA 2019. Both humans and robots show a preference for web pages archived in the near past.

Centering School Leaders’ Expertise: Usability Evaluation of a Leadership-Focused Implementation Strategy to Support Tier 1 Programs in Schools

Although there are a growing number of implementation strategies to increase the adoption, fidelity, and sustainment of evidence-based practices (EBP), they are often complex and bulky, which can interfere with their widespread application. To address these challenges, the Helping Educational Leaders Mobilize Evidence (HELM) strategy was created as an adaptation of the evidence-based Leadership and Organizational Change for Implementation (LOCI) implementation strategy to enhance elementary school principals’ use of strategic implementation leadership to support the adoption and delivery of Tier 1 (i.e., universal social, emotional, and behavioral) EBP. In service of its iterative development, a human-centered design methodology was employed to increase the successful uptake and use of HELM. The Cognitive Walkthrough for Implementation Strategies (CWIS), a novel mixed-methods approach to evaluate implementation strategy usability, was applied to identify and test HELM strategy tasks of critical importance. A sample of 15 elementary school principals participated in group cognitive walkthrough testing as either school principal recipients or HELM coaches. Both user types rated the strategy as acceptable (principal M = 77.8, SD = 15.5; coach M = 87.5, SD = 7.9). Five usability issues were identified using a highly structured common usability issue framework and provided direction for the generation of redesign solutions to be incorporated in a subsequent version of the HELM strategy. The evaluation of the strategy’s usability improved its alignment with user needs, expectations, and contextual constraints, rendering a more usable strategy and broadly applicable information surrounding the development of psychosocial implementation strategies in real-world settings.

HYBRID DEEP LEARNING APPROACH TO IDENTIFY INTRUSION DETECTION WITH IMBALANCE DATASETS

The intrusion detection system is a computer-based system that constantly identifies all types of malicious activities by monitoring the network traffic. These intrusions and doubtful activities disturb all business activities performed over the public network, such as the Internet and all connected networks. It is an essential system to provide consistent and reliable transfer of information to complete e-commerce and e-business transactions and private communication using social sites. Various deep learning techniques are used to identify security attacks by observing the typical system usage profile and to restrict all of the network traffic if it is outside the scope of the standard profile. Our proposed system is used to combine various deep learning techniques to develop a hybrid deep learning model to identify any security attack in the network. The proposed hybrid deep learning model is trained using an integrated and balanced dataset by merging already available imbalanced benchmark datasets such as NSL-KDD, ISCX, CICIDS2017, and UNSWNB15. Our proposed system is limited to identifying security attacks in benchmark datasets and restricted to available deep-learning techniques and algorithms.

Assessing the Energy Performance and Retrofit Potential of the 1980–1990s’ Residential Building Stock in China’s Jiangsu Province: A Simulation-Based Study

The building operation sector in China represents 22% of the national energy consumption and 22% of the carbon emission, of which urban residential buildings accounted for 24% in 2019. Such figures for the housing sector are projected to increase sharply in the near future, while China aims to peak CO2 emissions by 2030 and reach neutrality before 2060. To reduce the impacts of the urban housing sector and address the energy use and waste generated by large-scale demolition and reconstruction, the central government started promoting the energy retrofit of urban residential buildings, raising such policies to the national strategic level. Jiangsu Province is one of the most urbanised, with a rapid growth in the energy consumption of residential buildings. The Multi-Danyuan and Single-Danyuan Apartment built in 1980–1999 are the most representative residential types in its urban areas. While still adequate functionally, they were designed and built to low energy standards and show significant potential for energy retrofit. Nonetheless, their current performance and energy-saving potential are under-researched, while more detailed and reliable data would be critical to support retrofit design and policy making. This study investigates and characterises the typical use and energy performance of the two building types. Additionally, seven measures and six retrofit scenarios were identified based on the optimal energy reductions and regulations from selected countries. The simulations indicate that, without intervention, the energy consumption of the typical urban residential buildings can reach 122 kWh/m2 under the typical high-energy user scenario. By selecting a set of effective energy-saving measures, the operational energy use for heating and cooling can be reduced by up to 52.4%. Current local standards prove cost-efficient, although less effective in reducing energy use compared to international best practices, indicating potential improvements to the contribution of building retrofit towards achieving the national carbon reduction goals.

Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review.

International advances in information communication, eHealth, and other digital health technologies have led to significant expansions in the collection and analysis of personal health data. However, following a series of high-profile data sharing scandals and the emergence of COVID-19, critical exploration of public willingness to share personal health data remains limited, particularly for third-party or secondary uses. This systematic review aims to explore factors that affect public willingness to share personal health data for third-party or secondary uses. A systematic search of 6 databases (MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and SocINDEX) was conducted with review findings analyzed using inductive-thematic analysis and synthesized using a narrative approach. Of the 13,949 papers identified, 135 were included. Factors most commonly identified as a barrier to data sharing from a public perspective included data privacy, security, and management concerns. Other factors found to influence willingness to share personal health data included the type of data being collected (ie, perceived sensitivity); the type of user requesting their data to be shared, including their perceived motivation, profit prioritization, and ability to directly impact patient care; trust in the data user, as well as in associated processes, often established through individual choice and control over what data are shared with whom, when, and for how long, supported by appropriate models of dynamic consent; the presence of a feedback loop; and clearly articulated benefits or issue relevance including valued incentivization and compensation at both an individual and collective or societal level. There is general, yet conditional public support for sharing personal health data for third-party or secondary use. Clarity, transparency, and individual control over who has access to what data, when, and for how long are widely regarded as essential prerequisites for public data sharing support. Individual levels of control and choice need to operate within the auspices of assured data privacy and security processes, underpinned by dynamic and responsive models of consent that prioritize individual or collective benefits over and above commercial gain. Failure to understand, design, and refine data sharing approaches in response to changeable patient preferences will only jeopardize the tangible benefits of data sharing practices being fully realized.

Health Metaphor and Embodied Meaning in English Economic Discourse

In economic discourse, economists use conceptual metaphors to explain complex and abstract economic concepts or theories. Among them, health metaphors based on physical experience are widely used. Therefore, this article selects health metaphors with the highest frequency in the British National Corpus as research object, and conducts analysis on its typical usage. Results show that the vast majority of health metaphors tend to map economy as a healthy organism. Moreover, the health metaphors that appear in economic discourse have signifi-cant embodied meanings.