User Requirements Research Articles

URDO&RG: A Tool for User Role Discovery and Optimization & Requirement Generation Based on User Check-in Data in LBSN

The regularities shared by a set of users in a long-term in Location-Based Social Network (LBSN) can be abstracted as user roles. Although methods are proposed to identify user roles, the quantitative analysis of the effectiveness of the method is insufficient, and as far as we know, tools to generate user roles automatically from user data are missing. Therefore, a tool named URDO&RG (User Role Discovery and Optimization & Requirements Generation) is realized. The tool includes four main functions: (1) visualization of the data from multiple dimensions in diverse display diagram, (2) discovery of user roles using various algorithms, (3) optimization of the method to achieve better user role discovery outcome. (4) generation of user requirements from user data. Data set is used to validate the effectiveness of the tool. Result shows that, the tool can discover user roles and generate user requirements effectively, and the optimization method outperforms the original methods in many evaluation metrics.

A DRL-based RAQ-GERT dynamic resource allocation algorithm considering utility for multibeam satellite system

With the evolution and popularity of smart devices, the demand and requirement (e.g., communication, file transfer) of satellite users have increased rapidly. Moreover, users have different preferences for services and the quality of service (QoS), like delay and throughput, which leads to user heterogeneity. Facing numerous, time-varying, and heterogeneous users, how to dynamically allocate limited spectrum and on-board power while satisfying user requirements is the major challenge for the multibeam satellite system (MSS). Aiming to seek a solution, firstly, the resource allocation queue graphical evaluation and review technique (RAQ-GERT) network is constructed to describe the service process of the MSS, as well as to compute the channel condition parameters during the whole process. Next, appropriate QoS indicators are selected based on user requirements. Then, QoS indicators are calculated from the results of the RAQ-GERT network, which are combined to form the optimization objective of the MSS by drawing on the Cobb–Douglas utility function. After that, guided by the utility of the MSS, the proximal policy optimization (PPO) algorithm is applied to explore the optimal resource allocation scheme in this heterogeneous user scenario. Finally, the simulation comparisons show that the proposed scheme has enhancements in several performances, up to 42.19 % in service rate, 53.58 % in system capacity, and 3.42 % in throughput with minimal increase in latency.

Research on Sustainable Design of Smart Charging Pile Based on Machine Learning

With the rapid growth of the electric vehicle market, the importance of the user experience and product sustainability requirements for intelligent charging stations has become increasingly significant. However, accurately capturing the complex associations between design features and sustainability elements remains challenging. Therefore, this study aims to balance user needs and environmental standards in designing smart charging piles, ensuring adherence to symmetry principles. This balance addresses the growing demand for personalization and ensures sustainability. In this paper, the semiotic approach to product construction (SAPAD) model is introduced to analyze the user behavioral process in depth and clarify the core needs of users. Subsequently, these core needs are translated into specific technical requirements for products, and a correlation matrix linking user needs with product technical requirements is constructed using fuzzy quality function deployment (FQFD) to identify design features that fulfill the user requirements. The sustainability factors are then comprehensively evaluated and prioritized based on three dimensions: economic, environmental, and social, i.e., the triple bottom line (TBL). Furthermore, a mapping matrix is developed to connect the design features and sustainability factors, which is combined with the particle swarm optimization–random forest (PSO-RF) algorithm to predict the sustainability factors associated with design features that meet users’ needs. The number of branches m and the maximum depth d of the random forest (RF) algorithm are optimized using the particle swarm optimization (PSO) method. The results indicate that the SAPAD-FQFD model effectively identifies the user needs and relevant product design features. In contrast, the PSO-RF model adeptly manages the nonlinear relationships between charging pile design features and various sustainability factors, e.g., aesthetics and material selection, ensuring that the intelligent charging pile meets users’ core needs in terms of form and function, while embodying the principles of design symmetry. This integrated approach effectively bridges the gap between user needs analysis and product functional design, ensuring the sustainability of the design solution. This study contributes a sustainable framework for the development and design of smart charging piles and related products, further promoting the adoption of green design principles and symmetry design concepts within the supporting infrastructure of new energy vehicles.

Meta-interaction: Deployable framework integrating the metaverse and generative AI for participatory building design

Much has been exhorted to pursue participatory building design (PBD) but little has been done to enhance it owing to difficulties such as participant inclusion and clarity of expression. An opportunity is enabled by metaverse and generative AI technologies. This paper aims to explore this by developing a framework that integrates metaverse and generative AI. To start, a desktop study is conducted to examine the challenges related to PBD. Based on this, a PBD framework called Meta-interaction was developed. It incorporates metaverse in enabling users to participate in the immersive design process, and generative AI for automated user requirement extraction, design feature option generation, and visualization. Finally, the prototype was implemented in five pilot cases. Results show that the framework is applicable and highly valued by most designers and end-users. This study enriches the applications of metaverse and generative AI in building design and offers insights for future PBD initiatives.

Innovation in Digitalization of UI/UX Design with User Centered Design to Increase Customer Satisfaction

In the era of rapid digitalization, the economic sector, especially small and medium enterprises, is required to adapt to the development of digitalization. SDS YPMM Cooperative is one example of a small business that still relies on traditional systems in its business processes. To overcome this problem, a website-based User Interface (UI) and User Experience (UX) design was carried out to digitize the SDS YPMM cooperative business process. In the design, the User Centered Design method is used which focuses on user needs. This method is done by Specify the context of use, specify user and organizational requirements, produce design solutions, and evaluate design against user requirements. At the evaluation stage, the System Usability Scale (SUS) was used to measure the level of usability of the resulting design. The evaluation results obtained 179 respondents out of 533 population for the user interface showed a score of 84.97, which falls into the “acceptable” category and a rating of B or “Excellent”. Meanwhile, the admin interface received 5 respondents from 5 populations and scored 93, which falls into the “acceptable” category and an A or “Best Imaginable” rating. These findings indicate that the UI/UX design is highly accepted by both categories of users, thereby improving operational efficiency and shopping experience, which creates satisfaction with SDS YPMM cooperative services.

Self-Identification Phenomenon in A Temporary Shelter Unit After A Disaster: AFAD Containers

Container homes used to meet the shelter needs of disaster victims in the post-disaster period are often foreign environments, which can create psychological challenges. The process of individuals adapting to a space begins with personalization. This study examines the physical and psychosocial needs of disaster victims through the 'phenomenon of personalization.' Implementing different forms of personalization to overcome alienation from the space can facilitate the acceptance process by making the environment familiar. The research addresses the personalization processes of disaster victims in containers and their user requirements, collecting data through observations and semi-structured interviews in the AFAD container city in İskenderun. As a result, the personalization that disaster victims engage in within the container contributes to psychological, social, and physical recovery processes. Therefore, it is essential that post-disaster shelter spaces are designed to allow for personalization and adopt a user-centered approach.

Can GitHub Issues Help in App Review Classifications?

App reviews reflect various user requirements that can aid in planning maintenance tasks. Recently, proposed approaches for automatically classifying user reviews rely on machine learning algorithms. A previous study demonstrated that models trained on existing labeled datasets exhibit poor performance when predicting new ones. Therefore, a comprehensive labeled dataset is essential to train a more precise model. In this paper, we propose a novel approach that assists in augmenting labeled datasets by utilizing information extracted from an additional source, GitHub issues, that contains valuable information about user requirements. First, we identify issues concerning review intentions (bug reports, feature requests, and others) by examining the issue labels. Then, we analyze issue bodies and define 19 language patterns for extracting targeted information. Finally, we augment the manually labeled review dataset with a subset of processed issues through the Within-App , Within-Context , and Between-App Analysis methods. We conducted several experiments to evaluate the proposed approach. Our results demonstrate that using labeled issues for data augmentation can improve the F1-score to 6.3 in bug reports and 7.2 in feature requests. Furthermore, we identify an effective range of 0.3 to 0.7 for the auxiliary volume, which provides better performance improvements.

Organizing an Interdisciplinary Platform for Knowledge Sharing on a Class of Compounds of Natural Origin.

Sesterterpenoids, a subset of the terpene family, exhibit notable biological activities. These natural compounds are present in a variety of organisms such as plants, fungi, bacteria, insects and marine life. The therapeutic potential and structural diversity of sesterterpenoids have attracted considerable interest in pharmacological and chemical research. This study illustrates the development of a database to structure and manage data on these compounds. The design process involves the collection of user requirements, creation of a conceptual model with and Entity-Relationship Diagram (ERD), development of a logical model, and implementation in Microsoft SQL Server 2022. Data collection began with an extensive literature review and organization in an Excel spreadsheet. The resulting database improves data acquisition, organization, and accessibility. Future work will include building a website to facilitate data entry, editing, reading and extraction, and automation of data updates via external web services.

Diagnostics for optimised dengue surveillance: a qualitative focus group study to investigate user experience and requirements in Thailand

ObjectivesEffective, real-time surveillance of dengue may provide early warning of outbreaks and support targeted disease-control intervention but requires widespread accurate diagnosis and timely case reporting. Research directing innovation in diagnostics...

Acoustic Transmission Loss of a Cylindrical Silencer Filled with Multilayer Poroelastic Materials Based on Mode-Matching Method

The efficacy of silencers in reducing piping noise is contingent upon the specific installation and operating environment. Among the various forms of silencers, the acoustic characteristics of dissipative silencers with sound-absorbing materials attached internally exist in an area that is difficult to explain by existing theories. This is dependent upon the specific type and placement of the attached sound-absorbing materials. This paper presents a methodology for calculating the acoustic transmission loss (TL) of a cylindrical silencer filled with a multilayer poroelastic material, employing the mode-matching method. To describe the numerical process of treating waves propagating within a poroelastic material and determine the modes in accordance with the boundary conditions necessary for analyzing the acoustic performance of the silencer, the Biot model and the Johnson–Champoux–Allard–Lafarge model were employed. The obtained modes were utilized to calculate the acoustic TL of silencers filled with single, double, and triple layers of poroelastic materials. In particular, the results obtained for the single layer were validated by comparing them with the results of a finite element analysis, and the results obtained for multiple layers with the same material were validated by comparing them with the equivalent single-layer results. Moreover, the results of the numerical calculations of the acoustic TLs of the silencer for three distinct types of poroelastic materials, including those with varying degrees of frame rigidity or softness, were compared, and the acoustic characteristics were analyzed in relation to the intrinsic properties of the materials and their arrangement. It is anticipated that the methodology presented in this paper will facilitate the design of silencers using poroelastic materials in accordance with the specific requirements of users or designers by allowing for a comprehensive consideration of the thickness of layers and the arrangement of materials.

Simplification of Product Design to Enhance the Sustainability Product Service System (SPSS)

In today’s society, many donation programs still operate using a linear economy (LE) business model. This approach restricts donated products to their intended recipients without returning them to the manufacturer or distributor for repair, maintenance, or product enhancement. Unfortunately, this often leads to more waste when products can no longer be reused and are immediately disposed of. Our research aims to simplify innovative products and strengthen the connection between products and sustainable services to establish a circular economy (CE) using the product service system (PSS) business model. We utilized design thinking, a four-stage method, to accomplish our goal. We began by analyzing products available for sale and considering the perceptions of users, manufacturers, and distributors. In the defining stage, we formulated product design specifications (PDS), followed by prototyping and testing. Our research results focus on the requirements of users and manufacturers and the qualitative amalgamation of product services in the form of PDS. These findings have the potential to impact the development of a more efficient sustainable product service system (SPSS). Keywords: linear economy, circular economy, product service system, product design specifications

Lyapunov-like conditions for prescribed-time stability of perturbed impulsive systems

The present work deals with the problem of prescribed-time control of non-linear impulsive systems consisting of external perturbations. Lyapunov-like sufficient conditions for prescribed-time and practical prescribed-time stability are provided for vanishing and non-vanishing perturbations, respectively. Depending on the user's requirements, some sequences of stabilizing impulses are constructed in this regard. It is shown that the systems consisting of vanishing-type disturbances can attain prescribed-time stabilization at the origin. On the other hand, in the presence of non-vanishing disturbances, which can consist of bounded or unbounded disturbances, the trajectories of the system can enter a stable region only within the prescribed time. Moreover, the stable region is independent of the impulsive strength and the prescribed-time. The efficacy of the proposed results is provided through various examples and their numerical simulations.

“I wish I could use any language as it comes to mind”: User experience in digital platforms in the context of multilingual personal information management

AbstractIn today's linguistically diverse world, managing personal information across multiple languages presents a challenge. This study engaged 16 multilingual participants to explore their user experience in the context of multilingual personal information management (MPIM), with a focus on inclusivity, universality, and equity. Addressing two main questions, the study explores the challenges users face on digital platforms in MPIM contexts and their ideal platform features. Findings highlight key issues in MPIM platform design, including unsupported languages and integration of visual aesthetics. We also identify user preferences for ideal platform features, such as language flexibility and efficient information retrieval. The study suggests the need for more inclusive, universal, and equitable platform designs that cater to the specific requirements of multilingual users. Ultimately, this study underscores the critical need for improved MPIM support and emphasizes the significance of continued exploration in this area, establishing it as a vital field of future research.

The Influence of Employees’ Attitudes Towards Implementing Financial Management Information Systems In Public Institutions In Tanzania

Employees are the key players in successfully implementing the Financial Management Information Systems (FMIS) in public institutions. Therefore, it is important to understand and address the attitudes of employees towards behavioural intentions to use these systems. However, the empirical evidence specifically targeting FMIS implementation in public institutions is limited. This study determined the influence of employees' attitudes towards implementing FMIS in public institutions. The study adopted a quantitative research approach and the Decomposed Theory of Planned Behaviour (DTPB) in which a structured questionnaire was used to collect primary data. The study applied data analysis through SPSS to test Pearson Chi-Square, One-Way ANOVA and Ordinal Regression Analysis. The study found that attitudes influence the implementation of FMIS in public institutions. The findings suggest that public institutions have to strategise how to change the attitudes of employees to adapt to the use of information systems. The institutions should work towards complete participation of employees in the requirements of system users, adequate communication during implementation, hands-on training, and full support from developers of such systems. Also, the government must ensure that all the employees who are the principal users of these systems know the benefits of these accounting systems in day-to-day operations.

Intention to adopt blockchain technology for zakat management in Indonesia

Purpose This paper aims to analyze intention for applying blockchain-based technology in zakat management regarding the readiness of zakat institutions and muzakki in Indonesia. Design/methodology/approach This research is a mixed method, namely, using qualitative research to examine the readiness of practitioner and academics in welcoming blockchain-based financial technology and using quantitative research to measure people’s knowledge and intentions to adopt zakat using blockchain technology based on UTAUT theory (Unified Theory of Acceptance and Use of Technology). Findings The findings indicate that performance expectancy, social influence, facilitating conditions and sentiment positively affect the intention to adopt blockchain technology in zakat payments, while effort expectancy do not. Additionally, interviews reveal that practitioners and academics support blockchain technology but are unprepared for its implementation. Practical implications The implications of this study suggest important directions for policy makers, notably zakat institutions, supporting the application of blockchain technology and the adaptation of user-friendly information system services to meet the requirements of zakat service users. Originality/value This study fills the gap in previous research by offering a more comprehensive analysis of the collaboration between zakat and blockchain technology through three different perspectives, namely, practitioners (BAZNAS), academics and users of zakat services (muzakki).

Integrating symbolic reasoning into neural generative models for design generation

Design generation requires tight integration of neural and symbolic reasoning, as good design must meet explicit user needs and honor implicit rules for aesthetics, utility, and convenience. Current automated design tools driven by neural networks produce appealing designs, but cannot satisfy user specifications and utility requirements. Symbolic reasoning tools, such as constraint programming, cannot perceive low-level visual information in images or capture subtle aspects such as aesthetics. We introduce the Spatial Reasoning Integrated Generator (SPRING) for design generation. SPRING embeds a neural and symbolic integrated spatial reasoning module inside the deep generative network. The spatial reasoning module samples the set of locations of objects to be generated from a backtrack-free distribution. This distribution modifies the implicit preference distribution, which is learned by a recurrent neural network to capture utility and aesthetics. The sampling from the backtrack-free distribution is accomplished by a symbolic reasoning approach, SampleSearch, which zeros out the probability of sampling spatial locations violating explicit user specifications. Embedding symbolic reasoning into neural generation guarantees that the output of SPRING satisfies user requirements. Furthermore, SPRING offers interpretability, allowing users to visualize and diagnose the generation process through the bounding boxes. SPRING is also adept at managing novel user specifications not encountered during its training, thanks to its proficiency in zero-shot constraint transfer. Quantitative evaluations and a human study reveal that SPRING outperforms baseline generative models, excelling in delivering high design quality and better meeting user specifications.

Future-Proof Homes. Design Support System for Selecting Age-Adaptive Building Elements and Finishes.

The paper illustrates the definition of a home adaptation design support tool for selecting interior finishes and partition systems based on multicriteria evaluations. In line with Universal Design principles, the selection criteria promote the creation of homes that respond to the changing needs of people over their life course, allowing for aging in place. Based on a literature review on the design of age-friendly living spaces, optimal requirements for safety, comfort, usability, management, environmental protection and integrability related to partitioning elements and interior finishes were identified. Then, through the multicriteria analysis technique of the Analytic Hierarchy Process, selection criteria were established, and weights were assigned to classify market-available products according to performance, thereby informing the choices of designers and end users.

Evaluating the implementations of big data analytics in academic libraries: a structural equation model-based approach

PurposeThe rapid evolution of technological infrastructure and analytical capabilities has facilitated the integration of big data analytics (BDA) across various sectors. This study aims to investigate the suitability of implementing BDA within academic libraries, addressing the demanding need for effective data utilization in contemporary educational environments.Design/methodology/approachThe research is grounded in five critical components: data-driven culture, organizational infrastructure, employee responsibilities, management capabilities and the successful deployment of technology for BDA. An extensive literature review led to the development of a Likert scale-based questionnaire distributed on social media to collect data from university librarians in Pakistan. The authors were able to collect the data from 211 librarians. Descriptive statistics were employed to analyze the variables, while confirmatory factor analysis (CFA) was conducted using the AMOS to validate the research model.FindingsThe findings from the measurement model reveal significant positive correlations among all five components, underscoring their collective importance in facilitating the implementation of BDA. This formation is essential for addressing the evolving needs and academic requirements of users in the context of big data in a digital environment.Research limitations/implicationsThe study acknowledges limitations about its focus on a single country’s perspective, which may affect the generalizability of the findings regarding the implementation process of BDA in academic libraries.Originality/valueThis research contributes to the existing body of knowledge by highlighting the practices and capabilities of librarians in the era of big data as well as the requisite organizational infrastructure for the effective implementation of analytics in academic libraries.

Activity-based measures of landscape fragmentation

ContextLandscape fragmentation, which has demonstrated links to habitat loss, increased isolation, a loss of connectivity, and decreased biodiversity, is difficult to quantify. Traditional pattern-based approaches to measuring fragmentation use landscape metrics to quantify aspects of the composition or configuration of landscapes.ObjectiveThe objective of this study was to examine the relative improvements of an alternative activity-based approach using the cost of traversing a landscape as a proxy for fragmentation and compare it with the traditional approach.MethodsOne thousand binary landscapes varying in composition and configuration were simulated, and least-cost path analysis provided the data to calculate the activity-based metrics, which were compared with computed traditional pattern-based metrics.ResultsActivity-based fragmentation assessments were sensitive to levels of landscape fragmentation, but offered improvements over exiting pattern-based methods in that some metrics varied monotonically across the spectrum of landscape configurations and thus makes their interpretation more holistically meaningful.ConclusionsThis study provides a modular conceptual framework for assessing fragmentation using activity-based metrics that offer functional improvements over existing pattern-based approaches. While we present a focused theoretical implementation, the process to be measured and the scale of observation can be altered to suit specific user requirements, ecosystems, or species of interest.

Literacy Deep Reinforcement Learning-Based Federated Digital Twin Scheduling for the Software-Defined Factory

As user requirements become increasingly complex, the demand for product personalization is growing, but traditional hardware-centric production relies on fixed procedures that lack the flexibility to support diverse requirements. Although bespoke manufacturing has been introduced, it provides users with only a few standardized options, limiting its ability to meet a wide range of needs. To address this issue, a new manufacturing concept called the software-defined factory has emerged. It is an autonomous manufacturing system that provides reconfigurable manufacturing services to produce tailored products. Reinforcement learning has been suggested for flexible scheduling to satisfy user requirements. However, fixed rule-based methods struggle to accommodate conflicting needs. This study proposes a novel federated digital twin scheduling that combines large language models and deep reinforcement learning algorithms to meet diverse user requirements in the software-defined factory. The large language model-based literacy module analyzes requirements in natural language and assigns weights to digital twin attributes to achieve highly relevant KPIs, which are used to guide scheduling decisions. The deep reinforcement learning-based scheduling module optimizes scheduling by selecting the job and machine with the maximum reward. Different types of user requirements, such as reducing manufacturing costs and improving productivity, are input and evaluated by comparing the flow-shop scheduling with job-shop scheduling based on reinforcement learning. Experimental results indicate that in requirement case 1 (the manufacturing cost), the proposed method outperforms flow-shop scheduling by up to 14.9% and job-shop scheduling by 5.6%. For requirement case 2 (productivity), it exceeds the flow-shop method by up to 13.4% and the job-shop baseline by 7.2%. The results confirm that the literacy DRL scheduling proposed in this paper can handle the individual characteristics of requirements.