User Experience Research Articles

Deep Learning Model for Cosmetic Gel Classification Based on a Short-Time Fourier Transform and Spectrogram.

Cosmetics and topical medications, such as gels, foams, creams, and lotions, are viscoelastic substances that are applied to the skin or mucous membranes. The human perception of these materials is complex and involves multiple sensory modalities. Traditional panel-based sensory evaluations have limitations due to individual differences in sensory receptors and factors such as age, race, and gender. Therefore, this study proposes a deep-learning-based method for systematically analyzing and effectively identifying the physical properties of cosmetic gels. Time-series friction signals generated by rubbing the gels were measured. These signals were preprocessed through short-time Fourier transform (STFT) and continuous wavelet transform (CWT), respectively, and the frequency factors that change over time were distinguished and analyzed. The deep learning model employed a ResNet-based convolution neural network (CNN) structure with optimization achieved through a learning rate scheduler. The optimized STFT-based 2D CNN model outperforms the CWT-based 2D and 1D CNN models. The optimized STFT-based 2D CNN model also demonstrated robustness and reliability through k-fold cross-validation. This study suggests the potential for an innovative approach to replace traditional expert panel evaluations and objectively assess the user experience of cosmetics.

Informing Digital Programs for Lupus Self-Management Education: A Systematic Scoping Review.

We describe the characteristics, content, and effectiveness of digital self-management (SM) education programs for lupus and other chronic conditions to identify gaps and inform the improvement of future programs in lupus. Three bibliographic databases were searched for articles published between May 2012 and April 2022. The search was cast to capture the breadth of digital SM education programs in the following conditions: lupus, epilepsy, fibromyalgia, multiple sclerosis, sickle cell anemia, Sjogren's syndrome, psoriatic arthritis, and rheumatoid arthritis. Title and abstract screening, as well as full-text review, was conducted by two independent reviewers. Data extraction was first completed by one author charting all studies and then, a second time, by four members of the research team charting collaboratively. Of the 1,969 articles identified through the search, 14 met inclusion criteria. Two additional articles were included following bibliography review. The 16 articles represented 12 unique digital SM education programs. Programs covered five conditions: epilepsy (n=3), fibromyalgia (n=2), multiple sclerosis (n=4), lupus (n=1), and rheumatoid arthritis (n=2). Most programs were asynchronous and internet-based (n=9) with a prescribed sequence of content (n=8). Peer, technical, or specialist support was offered in seven programs. Most programs demonstrated statistically significant improvement of symptoms in the intervention group (n=8). This scoping review summarizes the current landscape for digital SM education programs in lupus and similar conditions. In lupus, further investigation will fill in the gaps around digital SM education needs, user experience and evaluation of outcomes.

Identifying the deficiencies of currently available CGM to improve uptake and benefit.

The use of diabetes technologies is increasing worldwide, with health systems facilitating improved access to devices. Continuous glucose monitoring is a complex intervention that provides information on glucose concentration, rate and direction of change, historical data and alerts and alarms for extremes of glucose. These data do not themselves change glycaemia and require translation to a meaningful action for impact. It is, therefore, crucial that such systems advance to better meet the needs of individuals using them. Narrative review of the use of, engagement with, limitations and unmet needs of continuous glucose monitoring systems. CGM devices have made a significant contribution to the self-management of diabetes; however, challenges with access and user experience persist, with multiple limitations to uptake and benefit. These limitations include physical size and implementation, with associated stigma, alarm fatigue, sleep disturbance and the challenge of addressing large volumes of real-time data. Greater personalisation throughout the continuous glucose monitoring journey, with a focus on usability, may improve the benefits derived from the device and reduce the burden of self-management. Healthcare professionals may have unconscious biases that affect the provision of continuous glucose monitors due to deprivation, education, age, ethnicity and other characteristics. Continuous glucose monitoring exerts a dose-dependent response; the more it is used, the more effective it is. For optimal use, continuous glucose monitors must not just reduce the burden of management in one dimension but facilitate net improvement in all domains of self-management for all users.

Application of fuzzy Delphi technique to identify analytical lenses for determining the preparation of free and open source software projects for user experience maturity

User eXperience (UX) significantly influences the success of free and open source software (FOSS) projects and is measured using UX capability maturity models (UXCMMs). Every organization desires higher levels of UX maturity; however, it requires upfront preparations and process quality control.Harmonizing processes and analytical lenses for determining preparation for UX maturity are still challenging, and studies to create them are limited. The analysis is ad hoc and based on the actors’ will and experiences. This study proposes and validates analytical lenses.Findings show that UX experts agreed that the lenses could be used with a consensus percentage of 81 %, the threshold value (d) = 0.112, and crisp values greater than α-cut = 0.5. On validation, 47.57 % of stakeholders agreed, and 52.43 % strongly agreed they were relevant. Results help evaluate the status quo and change culture and policies toward ideal preparation. Two areas are suggested for future research.

Mobile Data Network Performance Review – A Perspective of Zambia

High speed mobile internet results in better user experience and satisfied subscriber. However factors that affect the download speed include transfer technology, number of subscribers sharing the connection, device capability and radio conditions at the subscriber location. This research was conducted to review the mobile internet performance in Zambia and determine its conformity to the regulator, ZICTA, QoS regulation. Two sets of empirical data were collected in the capital city, first in 2021 during the covid pandemic period and second in 2024. The data was compared to check for network operator developments. The results reveal that two MNOs acquired new 2600MHz spectrum, however only one MNO drew maximum benefit resulting in improved 4G download speeds. This improvement was despite an increase in market share over the same period. The second operator that deployed the 2600MHz LTE TDD mode spectrum recorded a degradation in overall downloads speeds when compared to the initial data set in spite of a reduction in market share over the same period. Thus the steps to improve the LTE network are to add more spectrum to at least reach the 3GPP Rel 10 standard of 5 carrier configuration (5CC) to reach a total 100MHz and configure network features like Carrier Aggregation (CA) to provide for higher throughputs for capable devices. Other ways to expand LTE network include add more antennas to allow for higher spectral efficiency using 8 x 8 MIMO or more and thirdly deploy Small cells solution to enhance coverage and capacity in both indoor and outdoor environment.

Map retrieval intention recognition based on relevance feedback and geographic semantic guidance: For better understanding user retrieval demands

Effective retrieval is essential for finding resources in demand handily amidst extensive data records in data warehouse. Mainstream map retrieval methods suffer from intention gap problem and are incapable to describe sophisticated user demands precisely due to the limits of low- and middle-level text or visual feature matching, resulting in unsatisfactory retrieval results. Such limitations are more marked when map retrieval demands were characterized with joint constraints of geographic concepts. To address this issue, we propose a map retrieval intention recognition method to perceive user demands with relevance feedback samples and geographic semantics guidance. Specifically, we construct a hierarchical intention expression model to describe retrieval goals and their multi-dimensional attribute constrains; incorporate geographic ontologies to provide semantic guidance and facilitate recognition; utilize the frequent itemset mining (FIM) algorithm Apriori to generate intention candidates from relevance feedback samples, and search for the optimal intention set by adopting the minimum description length (MDL) principle. The experiments verify the effectiveness of Apriori algorithm and MDL principle on intention recognition. The proposed method outperforms the FIM algorithm Gene Ontology (RuleGO) and the Decision Tree algorithm with Hierarchical Features (DTHF) with higher recognition accuracy and noise tolerance. Furthermore, through our sample augmentation strategy, the method yields promising recognition accuracy even when the feedback sample size is as low as ten, substantially reducing the feedback burden in human-computer interactions. We envision that the application of our method in spatial data infrastructures (SDIs), such as geoportals and catalogue services, could enhance the quality of service and user experience in geospatial data discovery.

Dynamic edge clustering and task scheduling for edge assisted metaverse system in the field of remote work and collaboration

SummaryThe metaverse, a future digital world for living, working, learning, and interacting, is rapidly gaining significance, particularly in the domain of remote work and collaboration. This emerging digital landscape demands high‐performance, low‐latency, and scalable services to provide an immersive user experience. The current technology used in metaverse systems has some limitations, which emphasize the importance of adopting emerging technologies like Edge Computing (EC). However, as the number of users and data volume increases, it can impact both system performance and scalability of the edge‐assisted metaverse system. Additionally, the uneven distribution of edge servers can cause inconsistencies and result in high latency. To overcome these challenges, this paper proposes a dynamic edge clustering and task scheduling approach for edge‐assisted metaverse systems (DTAM) in the field of remote work and collaboration. The proposed approach addresses the challenges of high user volume and uneven resource distribution by incorporating dynamic clustering and edge server assistance to improve clustering performance. Furthermore, a Prioritized Experience Replay‐based Deep Q‐learning algorithm with state augmentation (PERDQSA) for task scheduling is introduced to improve sample efficiency and performance. The performance of the proposed DTAM is evaluated against existing techniques, and experimental results demonstrate its significant superiority in terms of specific metrics such as bandwidth, task response time, energy efficiency, and latency. The experiments demonstrated that DTAM outperforms Transformation‐based Edge Computing Deep Q‐Learning (TransEC‐DQL) in several key metrics. Specifically, DTAM achieves 28.5% reduction in latency, 13.7% reduction in response time, and 6.4% improvement in bandwidth compared to TransEC‐DQL. These results signify that DTAM can deliver a significantly enhanced user experience in the metaverse, particularly in the context of remote work and collaboration.

Movie Ticket Booking Application

In this digital age, buying movie tickets the old-fashioned way has been replaced by an efficient online experience. The main characteristics and advantages of a cinema ticket booking system are described in this abstract. This system provides a feature-rich online platform that may be accessed by mobile and web applications. Customers may quickly peruse a huge library of films, check the showtimes, and instantly select the seats they want. A seamless and safe transaction process is ensured by the system's integration of secure payment gateways. Customers can book tickets whenever and wherever they choose thanks to the system's simplicity and flexibility. Customized recommendations, dynamic pricing, and seat selection are examples of advanced features that improve the user experience overall. Theatre employees can effectively manage ticket inventory, track sales analytics, and optimise seat allocation with the system's comprehensive management interface. Administrators are informed and empowered to make data-driven choices thanks to real-time updates and notifications. Additionally, the system places a high priority on inclusion and accessibility, with features like multilingual help and seating accommodations for those with special needs. In conclusion, this technology offers a smooth, user-centric experience for both patrons and theatre operators, signalling a paradigm shift in the movie ticket booking industry. With its cutting-edge capabilities and user-friendly interface, it raises the bar for watching movies on a digital device

A STUDY ON MARKETING STRATEGIES USED BY boAt

boAt is an Indian startup which was established in the year 2016 by its founders Aman Gupta and Sameer Mehta with the objective to provide high quality products at an affordable and suitable price for the consumers. The company offers to its consumers an affordable range of headphones, earphones, speakers and other accessories. The reason for the companys success can solely be credited to its affordable and cheap prices than compared to other competitors in the market and also because it caters to needs and preferences of the younger generations or the Gen-Z s in India. It also lays strong emphasis on design, boAt products often feature trendy aesthetics, vibrant colors, and unique designs, appealing to fashion-conscious consumer preferences. The companys ongoing product development demonstrates its dedication to innovation and technology , adding features like Bluetooth connectivity, waterproofing, and noise cancellation to enhance the user experience. KEYWORDS: Marketing Strategies, boAt, Consumer Preferences

Building A Simple Weather Forecast Application using Python Django

Weather forecasting plays a crucial role in various sectors such as agriculture, aviation, transportation, and disaster management. Accurate and timely weather predictions are essential for making informed decisions and mitigating potential risks. This project presents a comparative study of different Application Programming Interfaces (APIs) for weather data retrieval and their effectiveness in weather forecasting. The objective is to evaluate the performance, reliability, and accuracy of various APIs in providing weather information for accurate forecasting. This project presents the development of a sophisticated weather forecasting application using the Python Django framework. In response to the growing demand for accurate and accessible weather information, this application provides users with timely updates on current weather conditions, temperature variations, and detailed forecasts for the upcoming six hours. The application dynamically adapts its interface to reflect hourly changes in weather patterns, ensuring that users receive up-to-date and reliable information at all times. One of the key features of this application is its user-friendly interface, which allows users to conveniently search for weather data anytime and anywhere. Whether planning a trip, scheduling outdoor activities, or simply staying informed about local weather conditions, users can rely on this application to deliver precise and relevant information tailored to their needs. Furthermore, the application enhances user experience by providing comprehensive coverage of global weather data. Users can easily access weather information for any location worldwide, enabling them to plan travel itineraries, explore new destinations, and make informed decisions about their future holiday plans.

Refining Travel Suggestions using Python

In the era of personalized travel experiences, the need for intelligent and adaptive planning tools is paramount. This is developed using Python, harnesses the power of machine learning to analyse traveller preferences and patterns, offering customized destination suggestions. The core of this study is to demonstrate how machine learning can transform traditional travel planning into a more efficient, personalized experience. By integrating user data and preferences, the system proposes itineraries that not only align with individual interests but also enhance the overall travel experience. This details the development process, the machine learning techniques employed, and the efficacy of Python in creating a dynamic and responsive travel planning website. The goal is to enhance the user experience in planning trips by providing optimizing destination suggestions, considering factors like historical preferences, budget constraints, and time availability.

Evaluating the Energy Efficiency of Popular US Smartphone Health Care Apps: Comparative Analysis Study Toward Sustainable Health and Nutrition Apps Practices.

The emergence of smartphones has sparked a transformation across multiple fields, with health care being one of the most notable due to the advent of mobile health (mHealth) apps. As mHealth apps have gained popularity, there is a need to understand their energy consumption patterns as an integral part of the evolving landscape of health care technologies. This study aims to identify the key contributors to elevated energy consumption in mHealth apps and suggest methods for their optimization, addressing a significant void in our comprehension of the energy dynamics at play within mHealth apps. Through quantitative comparative analysis of 10 prominent mHealth apps available on Android platforms within the United States, this study examined factors contributing to high energy consumption. The analysis included descriptive statistics, comparative analysis using ANOVA, and regression analysis to examine how certain factors impact energy use and consumption. Observed energy use variances in mHealth apps stemmed from user interactions, features, and underlying technology. Descriptive analysis revealed variability in app energy consumption (150-310 milliwatt-hours), highlighting the influence of user interaction and app complexity. ANOVA verified these findings, indicating the critical role of engagement and functionality. Regression modeling (energy consumption = β₀ + β₁ × notification frequency + β₂ × GPS use + β₃ × app complexity + ε), with statistically significant P values (notification frequency with a P value of .01, GPS use with a P value of .05, and app complexity with a P value of .03), further quantified these bases' effects on energy use. The observed differences in the energy consumption of dietary apps reaffirm the need for a multidisciplinary approach to bring together app developers, end users, and health care experts to foster improved energy conservation practice while achieving a balance between sustainable practice and user experience. More research is needed to better understand how to scale-up consumer engagement to achieve sustainable development goal 12 on responsible consumption and production.

The Impact of ChatGPT on Finance Technology

With the rapid development of science and technology, financial technology has risen rapidly around the world and has become a key driving force in the financial field. The emergence of natural language processing models such as ChatGPT, has brought new possibilities to financial services. This paper mainly studies the role and impact of ChatGPT in financial technology. Focus includes its applications in customer service, investment advice and risk management, as well as the potential impact of these applications on financial operations and user experience. The purpose of the research is to gain an in-depth understanding of the application of ChatGPT in the financial field, and reveal its potential contribution to improving financial service efficiency. This study uses literature analysis, case analysis and data analysis. Through systematic combing of relevant literature, we can understand the development status of ChatGPT in financial services. The research of this paper found that ChatGPT has a significant influence in the field of financial technology. Its application in customer service can improve service efficiency and provide users with faster and more personalized services. However, to ensure information security and privacy protection, financial institutions need to carefully weigh the pros and cons when adopting ChatGPT technology.

Courier Service System

The demand for efficient courier services is growing rapidly, and traditional systems often struggle to meet these demands. This project introduces an Enhanced Courier Service System Application, utilizing cutting-edge technologies such as ReactJS for the frontend, Java Spring Boot for the backend, and MongoDB for the database. The primary objective is to develop a robust and scalable courier service system that offers enhanced features and functionalities to meet the evolving needs of users. The system architecture facilitates seamless communication between frontend, backend, and database components, ensuring smooth data flow and efficient processing. ReactJS for the frontend enhances user experience, while Java Spring Boot provides scalability, security, and performance. MongoDB, a NoSQL database, offers flexibility and scalability for large data storage and retrieval. The modular design allows for easy maintenance and future enhancements, adapting to changing requirements and technologies. By incorporating industry-standard practices like UML diagrams, data flow diagrams, and sample test cases, the project aims to deliver a high-quality, reliable, and user-friendly courier service system application.

An appraisal-based chain-of-emotion architecture for affective language model game agents.

The development of believable, natural, and interactive digital artificial agents is a field of growing interest. Theoretical uncertainties and technical barriers present considerable challenges to the field, particularly with regards to developing agents that effectively simulate human emotions. Large language models (LLMs) might address these issues by tapping common patterns in situational appraisal. In three empirical experiments, this study tests the capabilities of LLMs to solve emotional intelligence tasks and to simulate emotions. It presents and evaluates a new Chain-of-Emotion architecture for emotion simulation within video games, based on psychological appraisal research. Results show that it outperforms control LLM architectures on a range of user experience and content analysis metrics. This study therefore provides early evidence of how to construct and test affective agents based on cognitive processes represented in language models.

Prediction of Submergence of Islands Due to Glacier Melting & Sea Level Rise using Deep Learning

Prediction of Submergence of Island Due to Glacier Melting & Sea Level Rise will have significant contribution in the field of Environmental Engineering. This research paper present comprehensive overview of development and implementation this project, which designed to predict the submergence of island especially Lakshadweep Islands which is union territory of India. Capitalizing technologies like LSTM for forecasting sea level rise over period of time and Linear Regression for forecasting glacier melting and python for model training. To enhance the user experience also making it easy to understand the prediction we integrated JavaScript via python ELL framework to show charts and outputs. This paper discusses about the technical innovations, methodology and result of the project. It also spotlights its potential to help people living in islands which are submerging to evacuate them before this threat. This project aims to develop system to migrate people living on island and saving thousands of lives. Key Words: Submergence, LSTM, JavaScript, EEL, Model Training, Linear Regression, User Interface

Analyzing Neuroarchitecture to Enhance User Experience in Sacred Spaces

This paper explores the analysis of neuroarchitecture principles to enhance the user experience in sacred spaces, aiming to deepen spiritual engagement, emotional resonance, and overall well-being. Sacred paces play a pivotal role in human societies, serving as places for worship, contemplation, and community gathering. By integrating insights from neuroscience into architectural design, it is possible to optimize spatial layouts, sensory stimuli, and environmental factors to foster a deeper sense of connection, tranquility, and spiritual fulfillment. Through a multidisciplinary approach encompassing neuroscience, architecture, and environmental psychology, this research examines the impact of architectural elements on neural processes and subjective experiences within sacred environments. Case studies of sacred spaces from diverse cultural and religious traditions are analyzed to elucidate effective design strategies and best practices. The effectiveness of neuroarchitecture principles in enhancing the user experience and promoting a sense of connection, reverence, and transcendence in sacred environments is assessed through empirical studies and user feedback. The findings highlight the potential of neuroarchitecture to enrich the design of sacred spaces, offering insights that contribute to the creation of environments that inspire awe, foster contemplation, and deepen the human spirit.

Dynamic Playlist Generation using Machine Learning

This study investigates the application of machine learning algorithms for dynamic playlist generation in the context of music recommendation systems. The traditional method of creating playlists frequently depends on immutable standards, like artist or genre, which might not adequately represent the unique and dynamic character of personal preferences. On the other hand, our suggested system makes use of machine learning methods to examine user behavior, preferences, and contextual elements in order to create playlists that are dynamically created and customized to each user’s individual preferences. In order to train machine learning models, the study collects user interaction data, such as listening history, skip patterns, and user feedback. To extract patterns and relationships from the data, a variety of algorithms are used, including hybrid models, content-based filtering, and collaborative filtering. After that, the models are incorporated into a dynamic playlist creation system that can eventually adjust to changing user preferences.Our test findings show how well the suggested strategy works to improve user experience by offering more interesting and customized playlists. With its ability to adjust to changing user preferences and contextual cues, the dynamic playlist generation system provides a smooth and pleasurable music discovery experience. We also talk about possible enhancements, implementation difficulties, and deployment considerations in the real world.This work adds to the ongoing efforts to improve music recommendation systems by demonstrating how machine learning can be used to develop more responsive and intelligent playlist generation systems. The results highlight how crucial customized experiences are in the constantly changing world of digital music consumption. Index Terms—hybrid mode, content-based filtering, intelligent playlist, music

Luggage Management System in order to Reduce Carbon Emissions and Ensure Sustainability

Air transport is a vital enabler of growth in the economy. It contributes to the economy by supporting trade and tourism. However, air transportation also affects our environment by increasing carbon emissions. Additionally, the use of private vehicles or taxis to access the airport also contributes to carbon emissions. With this study, considering that transportation to the airport has different difficulties in big cities, a mobile system will be developed that offers processes such as airport access, check-in, and luggage delivery in a single module, facilitates the person's transportation process to the airport and aims to protect the environment by directing them to public transportation. The main goal to be achieved with the luggage management software is to direct people's individual transportation to the airport to public transportation and thus, reduce carbon emissions. It is also aimed to automate the check-in process of luggage given to the plane and to reduce the time spent at the airport. Since the main aim of this study is to improve airport luggage handling, a business process improvement (BPM) approach will be adopted. Business process analysis and improvement will be used to identify, analyze, and optimize existing luggage processes. Additionally, user-centered design principles will be applied to add many user interface features to enhance user experience. The systems offering the same service are limited and there is a clear market in this field. The process will be digitized and streamlined. Time lost at the airport is going to be minimized and flight access is going to be facilitated.

Optimalisasi Isi Situs Informasi Magister Komunikasi Penyiaran Islam Universitas Islam Negeri Sumatera Utara Medan

This research aims to determine the strengthening of the content of the Islamic Broadcasting Communication Master's Study website, North Sumatra State Islamic University, Medan. This research uses a descriptive qualitative approach. Data sources for this research include primary data in the form of interviews, observations, and secondary data in the form of documentary studies through literature reviews. Data collection was carried out through structured interviews, observation, and documentation. This research data analysis was carried out by reducing the data, presenting it, and verifying the data. The validity of the data was tested through triangulation techniques. The results of this research show that the content on the website of the Islamic Broadcasting Communication Master's Study Program at UIN North Sumatra Medan is very important to support a targeted and efficient user experience, as well as to promote the study program through marketing and branding strategies. Strengthening content faces obstacles such as limited resources, technological challenges, and lack of involvement of lecturers and students. To overcome this, strategies such as leveraging open source, ongoing training, participation incentives, systematic evaluation, automatic content updates, and accessibility optimization are needed. By applying multimedia theory and the latest technology, study programs can increase attractiveness, provide relevant information, and build a strong reputation at national and international levels.