User Demands Research Articles

Research on Function Optimization of Electric Vehicle Charging Stations Based on User Demand Analysis: An Empirical Study Using the Kano Model and AHP Method

With the widespread adoption of electric vehicles (EVs), the demand for reliable and user-friendly charging infrastructure has increased significantly. However, user dissatisfaction with public EV charging stations has also intensified, and the level of satisfaction with charging stations directly influences the development of the EV market. This study aimed to identify and prioritize user needs for EV charging stations to improve their design and functionality, ultimately enhancing user satisfaction and effectively promoting the sustainable development of the EV market. Using the KJ method, this study identified 23 key user needs and categorized them into must-be, one-dimensional, attractive, and indifferent requirements using the Kano model. The analytic hierarchy process (AHP) was subsequently applied to rank these requirements by their importance. The results indicate that, in the optimization of charging station functionality, the most critical user requirements include C1 charging gun stability (0.3176), C2 system stability (0.2822), C7 safety performance (0.0885), C15 payment convenience (0.0648), and C8 accurate feedback on charging station status (0.0501). This study provides valuable insights for designers and developers, offering a user-centered approach to optimizing public EV charging stations and improving the overall charging experience.

Sustainable Packaging Solutions: Food Engineering and Biodegradable Materials

In modern, fast-paced lifestyles, food containers have become a popular solution for quick meals; however, they have significantly contributed to the increase in plastic packaging waste, which poses a substantial environmental risk. The prevalent use of non-biodegradable plastic containers exacerbates plastic pollution, contradicting government regulations designed to reduce plastic usage and promote sustainability. To address these issues, this study integrates TRIZ, ergonomics, inherent safety, and universal design methodologies, with an emphasis on sustainability. First, the technical contradiction matrix from TRIZ is applied to identify parameters for improvement without inducing negative side effects. Ergonomics principles ensure the development of user-friendly designs, while inherent safety and universal design approaches aim for accessibility and full biodegradability. Through a systematic innovation design process, this research proposes a biodegradable food container capable of decomposing entirely into particles and water within one month, blending naturally with the soil without compromising functionality. Moreover, the container incorporates a quick-alignment mechanism and an intuitive sealing design that ensures airtightness and spill prevention. As a result, the product not only meets user demands but also addresses pressing environmental concerns, aligning with global sustainability goals.

Athletes’ perception of perceived quality at the CESA BM 2022

Nowadays, numerous sporting events of diverse nature take place every week. This highlights the importance of conducting studies to assess their quality and determine the best event to participate in. Obtaining this information will allow organisers and managers to improve the services offered in order to meet the growing demands of users. The purpose of this study was to analyse the perception of quality by the athletes who attended and participated in the Campeonato de España de Selecciones Autonómicas de Balonmano 2022 (CESA BM 2022), with a gender breakdown. The sample consisted of 514 athletes. Data collection was carried out telematically by means of an online survey using the "Encuestas" tool of the University of Murcia. Perceived quality elements were assessed, as well as the importance of performance, segregated by gender and category of participation. The main results revealed that for athletes, the best rated dimension was "Future intentions", while the worst was "Perceived value". Some conclusions drawn from this study include that the female gender of the sample perceived higher quality compared to the male gender. It was also observed that players in the infantile category perceived higher quality than cadets and juniors, and statistically significant differences were also found between the various elements of the study.

A Mobility Handover Decision Method Based on Multi-Topology

With the emergence of new applications in mobile networks, users demand higher network stability and lower data transmission delays. When the network address of a mobile user changes, the data transmission path in the wired network may need to be switched to maintain service continuity. Traditional mobility support methods typically rely on a single switching path for all mobile data flows. However, if this path cannot meet the requirements of all the flows, it may lead to network congestion or a decline in user experience. To overcome this limitation, this paper proposes a mobility handover decision method based on multi-topology. It enables the dynamic allocation of mobile data flows across different switching paths within multiple logical topologies. The method models a multi-topology selection problem aimed at minimizing average packet transmission delay and packet loss rate, while considering network conditions and the Quality of Service (QoS) requirements for each flow. By solving the dual problem of the original optimization, a near-optimal solution is achieved. The proposed scheme and algorithm were implemented and tested using the Mininet network simulator. Results show that the proposed approach achieves low average packet transmission delay, low average packet loss rate, and high throughput, compared to traditional single-path switching methods and existing multipath routing methods.

A High Gain Dual Band Hexagonal Metamaterial Inspired Antenna for 5G Applications

Microstrip patch antennas play an important role in wireless communications to improve speed. Some of their benefits are low cost, low profile, and easy to fabricate. Existing research on microstrip patch antennas examines difficulties such as limited diversity performance, low radiation characteristics, and low efficiency. To address these issues, metamaterials are used to enhance diversity. This study focuses on designing a dual-band microstrip patch antenna for 5G applications to meet user demands with high data rates and enhance communication speed. To improve isolation and gain, a hexagonal-shaped Split Ring Resonator (SRR) was added to the substrate of the antenna. The proposed antenna model operates at frequencies greater than 28 GHz. The antenna dimensions are 25×25×0.15 mm, designed on a polyamide substrate with a loss tangent tan δ of 0.004, dielectric constant εr of 4.3, and relative permeability of 1. The suggested model was evaluated using several performance parameters, such as reflection coefficient, axial ratio, Voltage Standing Wave Ratio (VSWR), gain, and radiation patterns. The proposed model has S-parameter values of -17.6192 dB and -20.3264 dB, and gain values of 7.1 dB and 7.3 dB at 28.900 and 33.7400 GHz, respectively.

Fault-tolerant topology construction and down-link rate maximization in air-ground integrated networks

Due to the advantages in flexible deployment, fast communication recovery and high quality of service (QoS), unmanned aerial vehicles (UAVs) have been widely used in wireless communications and networking. However, existing works have not sufficiently solved the problem of reliable and survivable connectivity of UAV networks. In this paper, we investigate the deployment of multiple UAVs, cooperating with a ground mobile station (GMS), to provide wireless coverage for ground users. Under the constraints of satisfying different QoS of users and the resource limitation of UAVs, our aim is to minimize the number of deployed UAVs and maximize the sum down-link rate of all users. In order to ensure the survivability of the deployed UAV networks, we also constraint that each UAV has at least two disjoint paths to GMS to form a fault-tolerant topology. Unfortunately, the formulate problem is intractable and cannot be solved directly. To solve this problem, we decouple the original problem into two subproblems: (1) minimized fault-tolerant topology construction concerning user demands; (2) maximized down-link rate with access policy optimization. Then, we propose a heuristic link-cost minimization method and a potential-game-based user rate maximization method to solve the two subproblems, respectively. The effectiveness of the solution is validated through simulations. Meanwhile, ours also outperforms some baselines in minimized UAVs, down-link transmission rate as well as running time.

Efficient Charging Prioritisation and Optimisation of Solar PV-Powered Portable Electronic Devices

Efficiently managing and prioritising the charging of portable electronic devices powered by solar photovoltaic sources in off-grid and resource-limited environments is a huge problem. Ensuring that critical devices maintain operational uptime, especially when energy resources are scarce and in instances where multiple devices compete for charging from the limited solar power available is crucial. This paper introduces an optimisation framework designed to prioritise the charging of portable electronic devices powered by solar photovoltaic sources. The approach aims to maximise operational uptime for critical loads before addressing less essential ones. By strategically allocating charging priorities based on comprehensive evaluations of battery capacities, usage patterns, and operational requirements, the optimisation process seeks to enhance overall efficiency and readiness of portable electronic devices in dynamic, austere and resource-constrained settings. The charging prioritisation problem was solved using MATLAB’s (version number 9.13.0.2193358 (R2022b)) OPTI toolbox in conjuction with the SCIP solver. A case study, involving three portable electronic devices—a cellphone, GPS and radio, demonstrated the model’s effectiveness in maximising satisfaction by aggregating device priorities over time. The model prioritised charging of the GPS due to its critical operational role, followed by the radio for its essential communication function, while the cellphone, with lower usage demands, was assigned the lowest priority. The model developed in this study is versatile and applicable to diverse demand profiles and any number of portable electronic devices. Furthermore, it can be customised to operate effectively in various geographic locations, irrespective of solar radiation levels.

CRITICAL E-LEARNING VALUES AND ATTRIBUTES: PERSPECTIVES FROM VOCATIONAL TEACHERS AND STUDENTS

ABSTRACT E-learning platforms are designed to meet user demands and enhance value, making it crucial to identify their key attributes. In vocational institutions, where teachers and students are integral co-creators of value, e-learning tools are used to enrich technical education. This study utilizes a means-end-chain approach to explore the critical attributes, values, and impacts of e-learning from the perspectives of vocational teachers and students. The study uses laddering techniques to identify the co-creation value based on the principles of Service-Dominant Logic, Means-End Chain Theory, and a list of values. A tool called APT Data Laddering Col&Cal was developed to analyze data on the laddering technique. The data is also validated with LadderUX software and subsequently used to generate a Hierarchical Value Map. The conceptual analysis results and the Hierarchical Value Map form the components of the developed model. By mapping the relationships between these attributes and the resulting values, the research identifies nine significant values: accomplishment, self-fulfillment, and a high-quality lifestyle, which stand out as the most prominent. The study also highlights that interactive elements such as games, quizzes, and videos play a significant role in facilitating value co-creation. The importance of this research lies in its ability to pinpoint how specific e-learning attributes contribute to value creation, offering valuable insights for designing e-learning models that better serve the needs of both educators and learners. Understanding these dynamics can significantly enhance how e-learning platforms are developed to effectively engage and benefit youth in vocational settings. Keywords: Value Co-Creation, Means-End-Chain, E-learning, Vocational, Youth

In Pursuit of Architectural Happiness, on the Potential for Change in Legal Frameworks for Accessibility.

Modern architecture has generated measurable requirements about users' interactions with the built environment to create user-oriented architecture through meticulous full-scale testing with subsequent standardization. These requirements have entered the legal framework as minimum threshold values for basic demands that regulate building and physical planning. In the construction of the modern welfare society, building-related requirements have been associated with fundamental ethical values for the societal construction, e.g., equity, health, inclusion, and sustainable development. The programming of architecture through building requirements can be seen as giving the users a dosage of happiness through architecture. The modern welfare state defines an architectural happiness through is legal frameworks. This study analyzed thirteen questions from a 37-question questionnaire, distributed to 122 informants, in thirteen countries. The study aimed at establishing the contemporaneous understandings and uses of the concept of accessibility. The study concluded that, currently, the concept of accessibility has turned into a technical instrument that limits the quest for an increased fit between user demands and needs with the architectural design. Furthermore, there is a loose link between accessibility and sustainability. In conclusion, architectural happiness for the upcoming sustainable society of the 2030s needs to update the concept of accessibility so that the outcome - usability for future users - becomes apparent.

EdgeCross: Cloud Scale Traffic Management at Peering Edges

Cloud providers deployed dozens of PoPs and data centers globally to serve billions of geo-distributed users. The traffic management at peering edges has become a key capability of cloud network operators to meet the diverse demands of users. With the rapid growth of cloud applications, users have recently announced new performance requirements, e.g., achieving latency as low as possible instead of maintaining a specified delay. The conventional inter-domain bandwidth allocation approach, which aims to reduce the high operating expenditures of bandwidth usage, fails to meet these new requirements. We further reveal that the flow scheduling among PoPs may fail due to the limited link capacity hidden by the cloud private backbone network controller. Therefore we seek a new traffic management at peering edges. We propose a new controller framework, EdgeCross, that satisfies not only users' emerging demands but maintains low operating costs. The large number of fine-grain application-aware flows and the consideration of backbone links' capacity lead to very high complexity of routing computation and verification for the controller. EdgeCross introduces a two-phase operation that first achieves the low-expense bandwidth allocation according to the standard 95th percentile billing model and then allocates specified flows to peering edges based on users' requirements. EdgeCross further reduces large memory consumption by proposing an effective routing table compression approach. The evaluation based on a production network with 16 PoPs has shown that EdgeCross can successfully process the routes of 1 billion flows in 10 seconds, reduce the average delay for performance-sensitive flows by 2 milliseconds compared to traditional BGP, and is able to save the bandwidth cost by 10-26% compared to the state-of-the-art Cascara.

Plaid fabric image retrieval based on hand-crafted features and relevant feedback

Common fabric image retrieval methods ignore the diversity and dynamism of user demands, the results are determined by the query image and cannot be dynamically adjusted. To solve this problem, this study proposes a novel image retrieval method for plaid fabrics based on hand-crafted features and relevant feedback. First, local texture descriptors are extracted by the local binary pattern on the separated images which are processed by Fourier transform. Global texture descriptors are extracted by scale-invariant feature transform (SIFT) and vector of locally aggregated descriptors (VLAD). Second, color moments with image partitioning are extracted to characterize spatial color information of plaid fabric images. Third, the extracted features are fused by the weight allocation for similarity measurement. Finally, the relevant feedback based on meta learning is involved to realize personalized adjustment and optimization of retrieval results. An image retrieval database is built as the benchmark by collecting over 44, 000 plaid fabric samples from the factory. Experiments show that precision and recall at rank eight reach to 70.6% and 62.6%, respectively, and mAP reaches to 0.690. Results prove that the proposed strategy is feasible and effective, which can realize plaid fabric image retrieval fast and efficiently.

Focus on user micro multi-behavioral states: Time-sensitive User Behavior Conversion Prediction and Multi-view Reinforcement Learning Based Recommendation Approach

In recommender systems, user behavior conversion implies user interest drifts and behavior patterns. However, current research has paid little attention to the correlation between target behavior conversion rate and user behavior patterns, and the impact of highly time-sensitive multi-behavior analysis on target behavior conversion rate is neglected. Meanwhile, compared to normal behavior conversions, user deviant behavior conversions are seldom studied. The behavior conversion rate that balances normal behavior patterns and deviant behavior patterns can more accurately reflect user interest drifts and real-time needs, thereby improving recommendation performance. Based on the above motivations, we propose a Time-sensitive Behavior Conversion Prediction and Multi-view Reinforcement Learning Based Recommendation Approach (TCMR), aiming to achieve more accurate and adaptive recommendations by analyzing user interest drifts, demand timings and behavior stability. First, we construct a hyper-behavior spatial model of highly collaborative temporal signals, and propose a subnet collaborative method to obtain normal behavior patterns, in which, core subnet, similarity subnet and behavior subnet are extracted from the hyper-behavior spatial model. Subsequently, we design a multi-level user behavior trajectory tree to perceive potential user deviant behaviors by comparing behavior conversions within the single behavior modality and across different behavior modality. By integrating normal behaviors and deviant behaviors, we evaluate user interest drifts, demand timings, and behavior stability, and ultimately obtain a prediction of behavior conversion rate. Finally, a multi-perspective asynchronous reinforcement learning is proposed, enabling TCMR to provide recommendations by considering multiple user perspectives and purposes. Experimental results demonstrate that TCMR exhibits superior recommendation performance and effectiveness.

Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems

This paper presents an advanced AI-based optimization framework for Electric Vehicle (EV) smart charging systems, focusing on efficient energy distribution to meet dynamic user demand. The study leverages machine learning models such as Random Forest, Support Vector Regression (SVR), Gradient Boosting Regressor, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) to forecast user demand and optimize energy allocation. Among the models, XGBoost demonstrated superior predictive performance, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), making it the most effective for real-time user demand prediction in smart charging scenarios. The framework introduces proportional and priority-based allocation strategies to distribute available energy effectively, with a focus on minimizing energy shortfalls and balancing supply with user demand. Results from the XGBoost model reduced prediction error by 15% compared to other models, significantly improving the station’s ability to meet user demand efficiently. The proposed AI framework enhances charging station operations, supports grid stability, and promotes sustainability in the context of increasing EV adoption.

Scaling and Dynamic Resource Reallocation in NFV

Network Function Virtualization (NFV) has brought incredible experiences for Internet users and network operators. NFV enables the implementation of Virtualized Network Functions (VNFs) as software running in High Volume Servers (HVSs) to execute a Service Function Chain (SFC) to satisfy service demands of Internet users. During the execution of SFCs, VNFs and Virtual Links (VLs) tend to change their resource requirements due to the dynamic nature of the end user's demands. In this paper, we focus on dynamic resource allocation to the elements of SFC throughout the SFC process to adapt to the elasticity in demand from users by providing an overall picture of NFV and the scaling problem of SFC. We then review and analyze related studies on dynamic resource allocation of NFV systems during SFC operation and analyze the results of these projects. The most recent works are also classified based on several criteria to highlight their approaches, achievements, and also shortcomings. Finally, we introduce some research directions to deal with the scaling problem during SFC operation that needs more attention from researchers to inspire future work in the elastic operation of NFV-enabled systems.

Perceptions of healthcare professionals and patients with cardiovascular diseases on mHealth lifestyle apps: A qualitative study

BackgroundCardiovascular disease (CVD) is the leading cause of death globally and is predominantly associated with a cluster of lifestyle risk factors. Mobile health (mHealth) apps offer the potential to overcome traditional barriers by supporting healthy lifestyle behaviours. However, knowledge about perceptions of mHealth lifestyle apps among healthcare professionals and people with CVD is sparse. This study examined barriers and facilitators of using the mHealth apps for healthy lifestyle support among healthcare professionals and CVD patients. MethodsWe conducted an in-depth qualitative study employing individual semi-structured interviews with 8 CVD healthcare professionals and 4 patients with CVD in Australia. Participants were asked open-ended questions about their perceptions and experiences with mHealth lifestyle apps. A thematic analysis approach was used to establish perceived barriers and facilitators for mHealth lifestyle apps. ResultsMost participants perceived mHealth lifestyle apps as useful. The key perceptions of facilitators included features for tailoring to personal needs, low costs and wide availability of the apps and addressing barriers to use like reminders. Both healthcare professionals and patients identified the strengths and weaknesses regarding the usefulness of mHealth lifestyle apps. Healthcare professionals and patients perceived several barriers to mHealth app use including trustworthiness, scientific validity, language barriers, the capability of using an app or digital literacy, costs for some commercial apps, and accessibility of an app for low-income groups. ConclusionLifestyle apps provide an opportunity for better patient and healthcare professional communications, however, several barriers including improving digital health literacy and scientific validations of the apps are required before being recommended in clinical practice. Findings from this study can inform potential mHealth lifestyle app design to meet the demands of users. Addressing these barriers effectively can enhance the adoption and efficacy of mHealth apps, ultimately contributing to improved CVD management and healthier lifestyle behaviours.

Post occupancy evaluation of the relocatable modular construction method for temporary educational facilities

PurposeThis study aims to evaluate user satisfaction and perceptions of the relocatable modular school (RMS) system in South Korea. It seeks to identify key factors for improvement to enhance the performance of this innovative construction method, particularly in the temporary educational sector.Design/methodology/approachA comprehensive post occupancy evaluation (POE) was conducted, considering functional spaces, aesthetics, indoor environmental quality and technical aspects. This study also includes a comparative analysis with conventional school buildings to provide insights into the efficiency and potential of RMS.FindingsRelocatable modular schools can achieve the equivalent quality of building performance and user satisfaction as those built using traditional construction methods. The satisfaction performance of both methods falls within the “satisfied” level, with mean scores ranging from 3.35 to 3.90. Notably, when compared to ES, RMS exhibits a marginally lower satisfaction level in functional performance but a higher satisfaction level in the areas of aesthetics, IEQ and technical performance. Key factors identified for enhancement include optimizing classroom shape, balancing the window-to-wall ratio, enhancing natural ventilation and addressing floor noise and vibration concerns.Originality/valueThis research is pioneering in evaluating user satisfaction and perceptions of RMS in South Korea, a relatively unexplored area. By taking a holistic approach and incorporating comparative analysis with traditional school buildings, the study provides valuable insights into the practical application and potential of modular construction in educational facilities. The results contribute significantly to the advancement of high-performance modular educational buildings, informing practitioners and stakeholders of user demands and guiding future developments in the field.

The relationship between dynamic capacity and innovation: the case of startup hand talk

This theoretical essay explores the relationship between dynamic capacity and innovation based on the case study of the startup Hand Talk published by the Revista de Ciências Empresariais da UNIPAR, in 2022. The company's business is to use technology to overcome barriers between deaf and hearing people with innovation and digital accessibility. A qualitative approach was used, with bibliographical research on the concepts of dynamic capabilities and single case study analysis, aiming to explore and describe observable characteristics of the theories of dynamic capabilities, effectuation and frugal innovation in the period 2012 to 2022. The result showed that the company identified an opportunity and through dynamic capacity, created a business model capable of absorbing user demands and implementing continuous innovations in its services to enable digital accessibility. of deaf people to the market, through an application for mobile devices that converts texts, images and audio into Brazilian Sign Language (LIBRAS). The company was able to realize its ideas and put effectuation into practice, in addition to adopting a dynamic capacity approach, through organizational reconfiguration and skills development, observing the environment and organizational resources.

Exploration of Librarians' Assertive Behaviour in Improving Service Quality in the Digital Age

This article explores the assertive behaviour of librarians in an effort to improve service quality in the digital era. It is expected that librarians can adjust to the rapid development of technology and the increasingly complex demands of users. To face these challenges, assertive behaviour is required, which includes effective communication skills, the ability to express opinions assertively, and self-confidence. This research uses the literature study method to look at various scientific sources on assertive behaviour and its impact on library service quality. The results show that assertive behaviour helps create good interactions between librarians and users, increases user satisfaction with services, and supports the transformation of libraries towards digital-based services. It is hoped that this research will provide insights for librarians on how to develop assertive skills to improve the quality of services provided by digital libraries.

Incentivized BiLSTM with Triplet Attention for Predecting Congestion in Network Traffic

In the world of online live video streaming, network traffic congestion poses a serious risk to user experience and service quality. Several factors contributing to the congestion include insufficient bandwidth, an increase in user demand, as well as ineffective data routing. The incapacity of the prediction algorithms to Adjust to modifications in network circumstances and challenges in detecting long-range relationships may restrict their ability to effectively estimate congestion in dynamic contexts when it comes to online streaming video. In order to overcome these constraints, this study created an Incentivized-RF-Triplet ASTM (“Incentivized learning-based triplet attention enabled rat fierce hunting optimized Bidirectional Long Short-Term Memory) for network traffic congestion prediction in online streaming video. A reward” scheme built into the Incentivized learning mechanism pushes the algorithm to prioritize online live video streaming congestion prediction. The sequential structure of network traffic data is handled by the BiLSTM architecture, that is well-known for capturing temporal dependencies. By utilizing the triplet Attention method, the model is better able to identify relevant regions within the input data as well as identify congestion patterns more successfully. The RFSO method incorporates social behavior with selection and searching qualities to further improve the classifier's parameters. Therefore, the characteristics of the model can be tuned more effectively and robustly, improving its effectiveness in congestion detection. The outcomes of the experiment show how well the Incentivized-RF-Triplet ASTM technique works to precisely estimate traffic congestion for the Darpa99week1 dataset. 95.97% accuracy, 96.08% specificity, and 0.22 mean square error are reported.

SDN-controlled multi-user NOMA-OFDM VLC system based on a resource allocation algorithm.

We propose a novel, to our knowledge, resource allocation algorithm (RAA) for a multi-user non-orthogonal multiple access orthogonal frequency division multiplexing (NOMA-OFDM) visible light communication (VLC) system. The proposed algorithm considers both the interference between users and their channel diversity. Bit allocation to users in each iteration is based on the ratio between the already allocated number of bits and the demanded number of bits of users, rather than their channel gains. In this way, the inherent issue of prioritizing strong users in a conventional RAA is avoided. We implement a software-defined-network (SDN)-controlled VLC system with real-time signal generation to validate the proposed RAA. The allocation results in the SDN platform are used to configure a FPGA-based transmitter. Experiments of a two-user NOMA-OFDM system with 115-191 Mbit/s throughput show that the system can dynamically react to the change of data demand and channel responses of users. We further demonstrate that the proposed algorithm outperforms conventional RAAs regardless of the data demand, receiving angles, and distances of users.