Diverse User Research Articles

Redefining Hospital Accessibility: A Service Design Framework for Inclusive Healthcare

Abstract In the framework of becoming a carbon neutral city by 2030, Bologna faces challenges related to enhancing the quality and accessibility of its built environment to improve the quality of life and well-being of its residents. The IRCCS Azienda Ospedaliero-Universitaria Sant’Orsola Polyclinic is a key agent of change within this initiative. The hospital has initiated a comprehensive renovation project aligned with its commitment to the United Nations Sustainable Development Goals (SDGs), particularly SDG 3, which addresses Good Health and Well-being, and SDG 10, which addresses Reduce Inequalities. To address these goals, the hospital has engaged a research team from the University of Bologna to apply Service Design methodologies. The research aims to reshape accessibility and user experience systematically and sustainably, adhering to the principles of Design for All. The study maps diverse user groups, analyses circulation paths, and identifies physical and procedural barriers affecting vulnerable individuals, especially those with disabilities. The research introduces a novel approach by developing a comprehensive strategy rather than isolated solutions, focusing on interconnected accessibility services across pavilions. Dedicated hubs within each pavilion facilitate resource and support device exchange, improving users’ experiences. A pilot project, initially targeting wheelchair accessibility hubs, employs innovative technologies to enhance independent tool usage and monitor distribution and maintenance efficiently. The project is now entering a testing phase, during which the solution will be refined based on user feedback.

다이나믹어포던스와 디지털파라메트릭 융합디자인 연구 - 공기 청정기 홀커버 디자인을 중심으로

This study was inspired by the trend in contemporary production design that is encapsulated by the term “convergence.” Design, technology, and sensibility are extensively integrated into product design, forming a new methodology that enables designers to effectively attract and engage consumers. This methodology uses behavior induction, based on the concept of affordance from cognitive psychology, and a mathematical-function method called digital parametrics to amplify the affordance. This allows designers to focus on logical, mathematical methods and move away from traditional sensitive approaches. This represents the merging of analog and digital technologies and serves as a foundational effort to integrate artificial intelligence into the design field. A new concept for an air-cleaner design was developed by incorporating various functions into the basic pattern design. This parametric concept is expected to find wide application in product design in the future, as user diversity and cultural shifts become even more pronounced.

Stair Climbing Electric Wheelchair

From 18 century many types of wheelchairs had been designed, by developing its functionality. Our project focuses on addressing the mobility challenges faced by individuals with disabilities, particularly those who encounter difficulties navigating staircases. The project introduces a novel solution in the form of an electric stair- climbing wheelchair designed to provide enhanced accessibility in both indoor and outdoor environments. The wheelchair incorporates innovative technologies, including electric motors and intelligent control systems, to enable seamless and safe navigation on various types of stairs. The important parts of this product are conveyor belt, frame and driving mechanism climbing wheelchair for regular use by old disabled people. The design of frame will be done by considering various loads, stresses at various positions. The main factor of wheelchair are the angle of stair and center of gravity of whole system. The design process involves considerations for user comfort, safety, and ease of operation, with a particular emphasis on accommodating diverse user needs. Through a combination of both electrical and mechanical engineering principles and modern electronics, the wheelchair is equipped to ascend and descend stairs smoothly, offering a versatile solution for individuals with mobility impairments. We also plan to explore user feedback and ergonomic evaluations to refine the wheelchair's design and functionality. Preliminary testing would be done to demonstrates the feasibility and effectiveness of the proposed solution in real-world scenarios. The findings of this study contribute to the ongoing efforts to improve the quality of life for individuals with disabilities by expanding their mobility options and promoting inclusivity in various environments.

Energy and time-aware scheduling in diverse virtualized cloud computing environments using optimized self-attention progressive generative adversarial network

ABSTRACT The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively.

Upscaling cargo bike sharing in cities: A comparative case study

Urban mobility transitions are essential to mitigate climate change and improve the quality of life in cities. Cargo bikes (CBs) show promise in replacing motorised vehicles due to their lower carbon intensity, space efficiency, and ability to reduce air and noise pollution. CB sharing relieves users from purchase and maintenance costs. However, it is mainly adopted by individuals with high environmental awareness. Barriers to CB sharing stem from the urban infrastructure, individual mobility choices, and the design of CB sharing organisations (CBSOs). This article analyses upscaling pathways for CB sharing, i.e., increasing their use by a broader audience and reshaping the urban mobility regime. It delivers a comparative case study analysis of two CBSOs: Grätzlrad, Vienna, and LastenVelo e.V., Freiburg. Data was collected through academic and grey literature review and 15 semi-structured interviews with CB sharing stakeholders and experts. The analytical framework is informed by strategic niche management, viewing CB sharing as a niche innovation in the urban mobility regime. The article enhances understanding of CB sharing, the interaction of CBSOs with key actors, and their scalability. CBSOs should increase the availability of shared CBs while reducing the organisational effort required from users. New ways of CB sharing, e.g., integration in shared mobility hubs, should be explored. Municipal actors play a crucial role in upscaling and ensuring that CB sharing reaches a diverse user base. The findings are useful for academia, practitioners and policymakers working with CB sharing and other sustainable urban mobility solutions.

Dataset Expansion with Pseudo-Labeling: Case Study for Optimizing Chatbot Intent Recognition

Purpose of the study: This study presents an approach for improving the performance of natural language processing (NLP) models in pseudo-labeling tasks, with a particular focus on enhancing chatbot model intent recognition for business use cases. Methodology: The employed case study approach explores the pseudo-labeling technique and demonstrates a practical and efficient way to iteratively expand the original set of labeled data for the purpose of refining model training to achieve superior intent recognition accuracy in chatbots. Main Findings: The approach results in notable increases in macro-averaged F1 score and overall accuracy, particularly by iteratively re-training the model with progressively larger datasets. While enhancing the model's ability to generalize through difficult cases was effective, the study found that incorporating a full range of examples, including easy ones, yielded the best results. This comprehensive approach made the model better suited for real-world applications. Applications of the study: As chatbots are increasingly deployed in various sectors, including business, customer service, healthcare, and education, it becomes crucial for research to examine their long-term impact, scalability, and adaptability to ensure their effectiveness and sustainability in diverse contexts. Therefore, building more accurate chatbots, capable of understanding a wide range of user intents, is particularly valuable in real-world applications where chatbots need to respond to diverse, often complex and unpredictable user queries. Novelty/Originality of the study: Unlike traditional approaches, this study introduces a novel strategy of filling low-density regions in the dataset with pseudo-labels, allowing the model to better separate classes and handle semantically similar but varied messages. These advancements contribute to a more effective and scalable chatbot solution across diverse industries.

Implementation of an electronic prospective surveillance model for cancer rehabilitation: a mixed methods study protocol

IntroductionAn electronic prospective surveillance model (ePSM) uses patient-reported outcomes to monitor impairments along the cancer pathway for timely management. Randomised controlled trials show that ePSMs can effectively manage cancer-related impairments....

Transforming Academic Libraries into Hubs of Innovation and Entrepreneurship: A Case Study of a New Library Model

In a data-driven era, academic libraries must evolve beyond traditional roles as spaces for reading and storing information resources to become platforms promoting learning, research, innovation, future skills, and entrepreneurship. Based on the triple helix model of innovation, this paper highlights the new library model in a case study aimed at transforming traditional academic library services into enterprising, innovation-driven activities. For over ninety years, Thammasat University Library has been located on Rattanakosin Island, an historical area of Thailand. More recently, the library business model has evolved to meet modern needs. This qualitative research uses a case study to explore library strategies to create space meeting requirements of diverse user groups: students, faculty, staff, alumni, businesspeople and the community, including older residents. Service innovations responding to user demands are prioritized by drawing on the expertise of librarians. These changes generate new revenue for the library and enhance the space design and use through the Life Space concept. Co-working spaces, learning areas, inspirational corners, and performance venues make the library a platform for inspiring creativity, learning, life enhancement, and business ventures. The Thammasat University Library has also become an innovative bridge linking university research with small and medium-sized enterprises (SMEs), facilitating knowledge transfer and research commercialization. These findings may serve as guidelines for academic libraries seeking to adapt the Library of Life approach to be centres catering to readers of all generations and stations in life.

The Effect of Increasing Aggregation Levels of Electrical Consumption Data on Renewable Energy Community (REC) Analyses

The growing number of renewable energy communities (RECs) exemplifies the potential of citizen-driven actions towards a more sustainable future. However, obtaining hourly measured consumption data from REC members remains challenging, hindering accurate feasibility studies for the development of communities. This study examines the impact of estimating hourly consumption from aggregated data on REC analysis results. A case study with real consumption data from diverse users, representative of a typical community in Tuscany, Italy, was analysed to investigate various influencing factors. Multiple scenarios were simulated: two open-source tools estimated energy production from the community’s PV plants, and two REC configurations were considered—one with consumers and prosumers and another with consumers and a producer (with the same total installed power). Additionally, three locations were evaluated to consider the importance of geographical positioning. The study revealed that the impact of consumption data aggregation is more significant in scenarios with low energy sharing, such as the scenario where prosumers were replaced with a producer. Geographical positioning showed no major discrepancies in energy and economic outcomes, implying that using estimated hourly consumption data from aggregated data has a relevant impact regardless of location. Furthermore, different weather files did not affect the impact of aggregated consumption data.

Intersections of Genre and Identity in Contraceptive Health Discourses

This study aims to examine online contraception texts as a way to interrogate the intersections of identity, inclusivity, and access in contraception and reproductive health discourses. At the center of this project is the understanding that, while many contraceptive technologies are designed for and marketed towards "women" for the sole use of preventing pregnancy, the actual users of contraception and their purposes for its use are diverse and involve considerations of sexuality, gender identity, socioeconomic status, ability, cultural and religious norms, and access to healthcare. By examining the genre of contraception texts through systematized coding and rhetorical analysis, this study examines how the constitutive genre features of these texts do and do not recognize the diversity of users, with a particular focus on users in the trans community.

Toward a personalized autonomous transportation system: Vision, challenges, and solutions

The fragmented design of Intelligent Transportation Systems creates isolated intelligent systems. Resource competition and information gaps are fierce and widespread, worsening traffic issues and degrading overall service levels. Therefore, empowered by advanced technologies, an evolution towards Autonomous Transportation System (ATS) is observed. This evolution aims to develop a collaborative and sustainable ecosystem, prompting interoperability within the cloud-edge-device continuum. It can, accordingly, dismantle internal resource barriers and achieve a systematic balance between demand and supply with less human intervention. Despite the promising vision of ATS, it encounters three key challenges: disparate data, deficient models, and conflicting interests in supporting autonomous and personalized mobility. Hence, as an innovative solution, a trustworthy, private, and equal-serving framework called TPE is designed. It seamlessly integrates Blockchain, Federated Learning, and Large-scale Model to deploy a trustworthy operating environment, process private data for globally shareable knowledge, and develop a foundation model for personalized adaptation, respectively. Consequently, ATS empowered by TPE can serve diverse user groups both privately and equally.

Modeling the impact of noise and uncertain operational environment on software release decisions considering testing coverage-based SRGM

The operational environment of software differs from the debugging environment. Therefore, the study explores the impact of irregular consumption by diverse users on the development cost of software, reliability of software, and release decisions. To accomplish this, a release model has been formulated considering logistic testing coverage-based reliability growth model built as a stochastic process including the error generation. The stochastic nature has been captured by considering the noise factor due to irregular fluctuations occurring during testing while usage uncertainty has been captured by introducing a constant parameter in the cost function of the operational phase. It is assumed that the testing phase cost is affected by the noise and the operation phase cost is affected by the severity of noise which is the result of uncertain usage by users. The model was evaluated against a real failure dataset. The release model creates a trade-off between software development cost, release timing, and reliability aspirations. This study contributes to software reliability literature and provides insights to practitioners to make software release decisions. The sensitivity analysis results give information about various aspects during the operational phase that affect the overall development cost.

A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems

As the world braces for an era of ubiquitous and seamless connectivity, hybrid beamforming stands out as a beacon guiding the evolutionary path of wireless communication technologies. Several hybrid beamforming technologies are explored for millimeter-wave multiple-input multi-output (MIMO) communication. The aim is to provide a roadmap for hybrid beamforming that enhances wireless fidelity. In this systematic review, a detailed literature review of algorithms/techniques used in hybrid beamforming along with performance metrics, characteristics, limitations, as well as performance evaluations are provided to enable communication compatible with modern Wireless Intelligent Transport Systems (WITSs). Further, an in-depth analysis of the mmWave hybrid beamforming landscape is provided based on user, link, band, scattering, structure, duplex, carrier, network, applications, codebook, and reflecting intelligent surfaces to optimize system design and performance across diversified user scenarios. Furthermore, the current research trends for hybrid beamforming are provided to enable the development of advanced wireless communication systems with optimized performance and efficiency. Finally, challenges, solutions, and future research directions are provided so that this systematic review can serve as a touchstone for academics and industry professionals alike. The systematic review aims to equip researchers with a deep understanding of the current state of the art and thereby enable the development of next-generation communication in WITSs that are not only adept at coping with contemporary demands but are also future-proofed to assimilate upcoming trends and innovations.

Adaptive Control for Underwater Simultaneous Lightwave Information and Power Transfer: A Hierarchical Deep-Reinforcement Approach

In this work, we consider a point-to-point underwater optical wireless communication scenario where an underwater sensor (US) transmits its sensing data to a remotely operated vehicle (ROV). Before the US transmits its data to the ROV, the ROV performs simultaneous lightwave information and power transfer (SLIPT), delivering both control data and lightwave power to the US. Under the considered scenario, our objective is to maximize energy harvesting at the US while supporting predetermined communication performance between the two nodes. To achieve this objective, we develop a hierarchical deep Q-network (DQN)–deep deterministic policy gradient (DDPG)-based online algorithm. This algorithm involves two reinforcement learning agents: the ROV and US. The role of the ROV agent is to determine an optimal beam-divergence angle that maximizes the received optical signal power at the US while ensuring a seamless optical link. Meanwhile, the US agent, which is influenced by the decision of the ROV agent, is responsible for determining the time-switching and power-splitting ratios to maximize energy harvesting without compromising the required communication performance. Unlike existing studies that do not account for adaptive parameter control in underwater SLIPT, the proposed algorithm’s adaptive nature allows for the dynamic fine-tuning of optimization parameters in response to varying underwater environmental conditions and diverse user requirements.

“The seven principles of universal design of research”: how to increase user diversity in mainstream research

PurposeUser diversity has been discussed in many fields of study for decades. Nevertheless, some cohorts are rarely included in mainstream research. According to the concept of universal design of research (UDR), people with disabilities should be more often included in participant pools. There is, however, a need for certain methodological considerations to achieve UDR.Design/methodology/approachThe suggested guidelines are based upon existing guidelines and research in addition to key takeaways from studies conducted by the authors over the past 15 years. Six studies are used to show how the guidelines can be applied in research involving three example cohorts, namely people with dyslexia, aphasia and intellectual disability.FindingsExtensive guidelines are needed to increase user inclusion in mainstream studies and support researchers in conducting purposeful and ethical research. The suggested guidelines should be examined for other cohorts to ensure that all types of user needs are accommodated.Research limitations/implicationsThis study contributes with methodological approaches to enhance inclusion of all types of participants in mainstream research.Originality/valueThe main contribution is The seven principles of UDR, a further development of the original principles for UDR.

Integrating artificial intelligence for optimal thermal comfort: A design approach for electric heating textiles aligned with user preferences

Human thermal comfort, crucial for well-being and productivity, is often improved by personal comfort systems that offer tailored control over environmental conditions while promoting energy efficiency. Previous studies have explored various textile technologies in thermoregulation systems according to user preferences. However, limited research has focused on temperature prediction by artificial intelligence to maximize thermal comfort for varied users. This study proposes a design approach to optimize thermal comfort in electric heating textiles using artificial intelligence, considering user preferences related to age and gender differences. A fuzzy logic model is established as a proof of concept for temperature regulation by varying ambient temperature, followed by developing an artificial neural network model to predict the optimal temperature for maximum comfort. Subsequently, a smart electric heating jacket is fabricated to assess preferred heating temperatures among 50 subjects with varying ages and genders. Results from the artificial neural network model show promising temperature prediction, while subject tests reveal significant differences in skin temperatures based on gender. This emphasizes the need for artificial intelligence-based heating e-textiles to accommodate diverse user needs. The study’s findings are expected to contribute to intelligent temperature regulation in thermal textiles and wearables, benefitting both the industry and consumers through customized heating products.

Learnable and Expressive Visualization Authoring through Blended Interfaces.

A wide range of visualization authoring interfaces enable the creation of highly customized visualizations. However, prioritizing expressiveness often impedes the learnability of the authoring interface. The diversity of users, such as varying computational skills and prior experiences in user interfaces, makes it even more challenging for a single authoring interface to satisfy the needs of a broad audience. In this paper, we introduce a framework to balance learnability and expressivity in a visualization authoring system. Adopting insights from learnability studies, such as multimodal interaction and visualization literacy, we explore the design space of blending multiple visualization authoring interfaces for supporting authoring tasks in a complementary and fexible manner. To evaluate the effectiveness of blending interfaces, we implemented a proof-of-concept system, Blace, that combines four common visualization authoring interfaces-template-based, shelf confguration, natural language, and code editor-that are tightly linked to one another to help users easily relate unfamiliar interfaces to more familiar ones. Using the system, we conducted a user study with 12 domain experts who regularly visualize genomics data as part of their analysis workfow. Participants with varied visualization and programming backgrounds were able to successfully reproduce unfamiliar visualization examples without a guided tutorial in the study. Feedback from a post-study qualitative questionnaire further suggests that blending interfaces enabled participants to learn the system easily and assisted them in confdently editing unfamiliar visualization grammar in the code editor, enabling expressive customization. Refecting on our study results and the design of our system, we discuss the different interaction patterns that we identifed and design implications for blending visualization authoring interfaces.

Exploring additive manufacturing in assistive technologies to transform the educational experience: empowering inclusion

This study explores additive manufacturing's potential in creating tailored assistive technologies, fostering inclusion in education. To design ergonomic solutions for diverse user needs, promoting inclusion and autonomy. A sequential approach involves assessment, 3D modeling, additive manufacturing, and aligning with the Inclusion Support and Accessibility Promotion Program (PIPA). Various assistive technologies were developed, addressing specific needs. The “Ergonomic Pen and Pencil Reamer” improves grip accuracy, and the “Folding Handle for Folding Cup” offers an ergonomic solution. The “Guideline Ruler” supports music education, with accessories like the “Template for Clefs and Musical Notes” and “Thimble with Support Base for Musical Instrument Strings” enhancing versatility. The “Ring with Front Support” aids art education, and the “Support for Scissors” promotes independence. These innovations contribute to inclusion and autonomy. The research underscores the importance of additive manufacturing in crafting personalized solutions, propelling inclusion in education and daily activities. Identified challenges, including material selection and adaptation to diverse needs, signal areas for future research. Continuous collaboration with end-users and professionals remains crucial for enhancing usability and effectiveness, reinforcing the commitment to promoting inclusion and autonomy.

Exploring the Effectiveness of Shallow and L2 Learner-Suitable Textual Features for Supervised and Unsupervised Sentence-Based Readability Assessment

Simplicity in information found online is in demand from diverse user groups seeking better text comprehension and consumption of information in an easy and timely manner. Readability assessment, particularly at the sentence level, plays a vital role in aiding specific demographics, such as language learners. In this paper, we research model evaluation metrics, strategies for model creation, and the predictive capacity of features and feature sets in assessing readability based on sentence complexity. Our primary objective is to classify sentences as either simple or complex, shifting the focus from entire paragraphs or texts to individual sentences. We approach this challenge as both a classification and clustering task. Additionally, we emphasize our tests on shallow features that, despite their simplistic nature and ease of use, seem to yield decent results. Leveraging the TextStat Python library and the WEKA toolkit, we employ a wide variety of shallow features and classifiers. By comparing the outcomes across different models, algorithms, and feature sets, we aim to offer valuable insights into optimizing the setup. We draw our data from sentences sourced from Wikipedia’s corpus, a widely accessed online encyclopedia catering to a broad audience. We strive to take a deeper look at what leads to greater readability classification in datasets that appeal to audiences such as Wikipedia’s, assisting in the development of improved models and new features for future applications with low feature extraction/processing times.

People, Places, and Perceptions: Assessing Spatial Quality Attributes of Urban Third-place With Projective Survey

Urban third places, such as cafes, parks, and plazas, are integral to the social fabric of urban environments, providing spaces for informal social interaction outside of home and work. This study explores the spatial quality attributes that define these third places and their influence on user perceptions and experiences. By employing a projective survey, a qualitative research method utilizing ambiguous stimuli to reveal deeper user sentiments, the research identifies key characteristics that contribute to the desirability of these spaces. Findings highlight the significance of physical, functional, and social dimensions, including furniture design, spatial layout, and opportunities for social interaction. These attributes are shown to vary significantly across different age groups, with notable differences in preferences between younger and older participants. The study's insights inform urban designers and architects on creating inclusive, vibrant third places that enhance urban livability and economic vitality. The research underscores the need for a human-centric approach in urban design, emphasizing the importance of accommodating diverse user needs and preferences to foster a sense of community and well-being in urban settings. By understanding the nuanced relationships between spatial attributes and user perceptions, the study contributes to the development of more effective urban design strategies.