An Analysis Method of Multi Round Interactive Semantics for Power Enterprises Based on Solr Engine

For visually impaired people, interaction with the Web can be a challenging task; this is due to poor accessibility, usability, and User eXperience (UX) of websites. Therefore, it is necessary to conduct a thorough investigation on the UX of websites, especially considering new trends on the Web. In this study, we identified, classified, and analyzed 34 906 scientific papers published in the last eight years to show the originality and relevance of investigating the UX of visually impaired and blind users. We considered new Web trends, focusing on Flat Design and Responsive Web Design. We performed a systematic review and selected 1 015 scientific papers that addressed the importance of UX or other issues relevant to UX. In this literature, we found only five articles addressing issues faced by blind or visually impaired users. None of the studies compared the experiences of users who are blind or visually impaired with those of sighted users. Considering the lack of scientific studies that address the impact of new web trends on UX and studies that examine the importance of UX, we concluded that it is original and relevant to investigate the UX of visually impaired, blind, and sighted users on the Web. From our research, it was possible to identify recurrent subjects of research relevant to UX; among them were UX methods applied to mobile, Approach to Agile UX, Usability techniques UX, and Centered Design to User.

One form of information technology advancement is the use of AI in website development, one of which is ChatGPT website. ChatGPT has a poor user experience in various aspects, so it is necessary to evaluate the user experience on the ChatGPT website using User Experience Questionnaire (UEQ). The attractiveness variable obtained a positive evaluation value of 1.503. The perspicuity variable obtained a positive evaluation value of 1.661. The efficiency variable obtained a positive evaluation value of 1.615. The dependability variable obtained a positive evaluation value with an overall average value of 1.286. The stimulation variable obtained a positive evaluation value of 1.182. The novelty variable obtained a positive evaluation value of 0.942. The ChatGPT website has shown good quality because it has a positive evaluation value from user assessments. However, in the attractiveness, dependability, and novelty variables, there are still several items that get neutral ratings. So product improvements are still needed to increase user satisfaction.

In the era of digitalization, medical websites have rapidly expanded their healthcare market share due to their convenience. However, with this user-base expansion, issues with poor user experience have surfaced. To address this, we developed and ranked usability indicators for medical websites, aiming to improve their design and development from a user experience perspective, thereby improving user satisfaction and the website’s usability. Initially, we reviewed the relevant literature and summarized 30 usability indicators. Subsequently, we formed a Delphi panel of 20 experts and preliminarily identified 24 usability indicators through the Delphi survey method. Using data from 300 valid user surveys, we applied the Exploratory Factor Analysis (EFA) method to categorize these 24 indicators into four groups. Finally, we assessed the relative importance and priorities of these indicators using the Analytic Hierarchy Process (AHP) method. The results showed that, in terms of criterion layer weight priorities, Trust and Security (0.5494), Basic Performance (0.2710), and Features and Technology (0.1355) exhibited higher proportions. For the solution layer, Property Protection (0.1894), Credibility (0.1852), Privacy Protection (0.1194), Effectiveness (0.0932), and Findability (0.0579) exhibited higher weight proportions. The findings of this study will assist in future usability assessments and enhancements of medical websites. By optimizing the usability, we can both advance the digitalization of medical websites and improve the usability of medical websites, and enhance the service experience and satisfaction of your users.

The increasing demand for intelligent and sustainable energy management within higher education institutions has encouraged the adoption of IoT-based solutions; however, traditional IoT dashboards typically rely on text-based device lists and non-intuitive identifiers that lack spatial context. As a result, users often struggle to understand which physical devices they are controlling, leading to confusion, poor user experience, and a higher risk of operational errors when managing smart campus facilities. This study aims to develop and validate a Digital Twin–based Smart Campus system capable of synchronizing physical electronic devices with an interactive 3D virtual environment in real time, providing a spatially accurate digital representation of the lecturer room that mirrors the real-world layout. The research employs a systematic workflow that includes problem identification, literature analysis, installation of IoT devices such as Zigbee smart switches and ESP32 IR blasters, creation of a web-based Digital Twin interface, and development of optimized 3D room models using Blender. System testing was conducted to evaluate physical-to-digital and digital-to-physical synchronization performance, and FPS benchmarking was performed to assess usability across high-end, mid-range, and entry-level devices. The results show that the Digital Twin maintains 100% synchronization accuracy with millisecond-level response times and runs smoothly on diverse hardware. By enabling users to interact with devices directly through a virtual environment that visually matches the real room, the system reduces operational mistakes, improves user experience, and enhances awareness of energy usage. The study concludes that the proposed Digital Twin approach effectively overcomes key limitations of traditional IoT dashboards and offers a scalable, practical framework for Smart Campus implementations.

The performance of data restore is one of the key indicators of user experience for backup storage systems. Compared to the traditional offline restore process, online restore reduces downtime during backup restoration, allowing users to operate on already restored files while other files are still being restored. This approach improves availability during restoration tasks but suffers from a critical limitation: inconsistencies between the access sequence and the restore sequence. In many cases, the file a user needs to access at a given moment may not yet be restored, resulting in significant delays and poor user experience. To this end, we present Histore, which builds on the user’s historical access sequence to schedule the restore sequence, in order to reduce users’ access delayed time. Histore includes three restore approaches: (i) the frequency-based approach, which restores files based on historical file access frequencies and prioritizes ensuring the availability of frequently accessed files; (ii) the graph-based approach, which preferentially restores the frequently accessed files as well as their correlated files based on historical access patterns, and (iii) the trie-based approach, which restores particular files based on both users’ real-time and historical access patterns to deduce and restore the files to be accessed in the near future. We implement a prototype of Histore and evaluate its performance from multiple perspectives. Trace-driven experiments on two datasets show that Histore significantly reduces users’ delay time by 4-700 with only 1.0%–14.5% additional performance overhead.

With the development of internet, the problem of information overload is becoming more and more serious. Recommendation technology can screen out information that is useful to people. Therefore, many scholars pay attention to it. There are two types of internet services: online and offline. At present, recommendation technology has become more and more mature in purely online applications, including news recommendation and commodity recommendation. However, O2O service recommendation needs the support of offline resources. Owing to the constraints of limited resources, many users adopt recommendation result at the same time, which often leads to crowded service points, useless recommendations and poor user experience. How to improve the effectiveness of O2O service recommendation under condition of resource constraints is a crucial issue. This paper proposes a group of O2O service recommendation strategies from the prospect of supply and demand matching to solve the problem step by step. Furthermore, we utilise computational experiment to perform performance comparison analysis for these service strategies. The results show that the adaptive adjustment mechanism based on current supply and demand conditions is conductive to improving effectiveness of O2O service recommendation so as to increase profit of the merchant and improve user experience.

The open and distributed connection of the power system makes it vulnerable to various potential cyber-attacks, which may lead to power outages and even casualties. Therefore, the construction of attack and defense drill (ADD) platforms for attack mechanism investigation and protection strategy evaluation has become a research hotspot. However, for the massive and heterogeneous security analysis data generated during the drill, it is rare to have a comprehensive and intuitive method to visually and efficiently display the perspective of the attacker and defender. In order to solve this problem, this paper proposes a visual analysis scheme of an ADD framework for a grid cyber-physical system (GCPS) based on the interactive visual analysis method. Specifically, it realizes system weakness discovery based on knowledge visualization, optimization of the detection model and visualization interaction. Finally, the case study on the simulation platform of ADD proves the effectiveness of the proposed method.

SummaryService‐oriented paradigm presents numerous new software development patterns and idioms. Software systems are implemented by composing existing third‐party services deployed in the open environment, which is significantly different from traditional software development methodologies in which systems are built through developing modules after system design in a closed environment. Therefore, it is urgent to raise a new design method to adapt to this new circumstance. We concentrate on reusing as many deployed services as possible then introduce a new life cycle model named Taiji model to illustrate this development process. The iteration‐based interactive analysis method following the model is proposed to design service‐oriented systems based on the view of extracting non‐creative activities from a creative activity through defining new notations or applying new rules. The method includes the interactive analysis process that analyzes requirements with deployed services in a local point of view and the iterative analysis process that redesigns system with new knowledge in a global perspective. Meanwhile, the reusable service threshold value is defined to build the uncertain candidate service set (UCSS) of each module in analysis process. The reliability and flexibility of systems can be improved through the quantitative static structure analysis on the basis of the UCSS of systems. Meanwhile, a practical dynamic service binding method that selects services according to actual states of invoking them is presented on the basis of the UCSS containing them. Finally, we also give a case study to illustrate the feasibility of this method. Copyright © 2017 John Wiley & Sons, Ltd.

For chatbots to be broadly adopted by users, it is critical that they are experienced as useful and pleasurable. While there is an emerging body of research concerning user uptake and use of chatbots, there is a lack of theoretically grounded studies detailing what constitutes good or poor chatbot user experiences. In this paper, we present findings from a questionnaire study involving more than 200 chatbot users who reported on episodes of chatbot use that they found particularly satisfactory or frustrating. The user reports were analysed with basis in theory on user experience, with particular concern for pragmatic and hedonic attributes. We found that pragmatic attributes such as efficient assistance (positive) and problems with interpretation (negative) were important elements in user reports of satisfactory and frustrating episodes. Hedonic attributes such as entertainment value (positive) and strange and rude responses (negative) were also frequently mentioned. Older participants tended to report on pragmatic attributes more often, whereas younger participants tended to report on hedonic attributes more often. Drawing on the findings, we propose four high-level lessons learnt that may benefit chatbot service providers, and we suggest relevant future research.

This research explores the development of a novel method and apparatus for creating spherical panoramas enhanced with high dynamic range (HDR) for high fidelity Virtual Reality 360 degree (VR360) user experiences. A VR360 interactive panorama presentation using spherical panoramas can provide virtual interactivity and wider viewing coverage; with three degrees of freedom, users can look around in multiple directions within the VR360 experiences, gaining the sense of being in control of their own engagement. This degree of freedom is facilitated by the use of mobile displays or head-mount-devices. However, in terms of image reproduction, the exposure range can be a major difficulty in reproducing a high contrast real-world scene. Imaging variables caused by difficulties and obstacles can occur during the production process of spherical panorama facilitated with HDR. This may result in inaccurate image reproduction for location-based subjects, which will in turn result in a poor VR360 user experience. In this article we describe a HDR spherical panorama reproduction approach (workflow and best practice) which can shorten the production processes, and reduce imaging variables, and technical obstacles and issues to a minimum. This leads to improved photographic image reproduction with fewer visual abnormalities for VR360 experiences, which can be adaptable into a wide range of interactive design applications. We describe the process in detail and also report on a user study that shows the proposed approach creates images which viewers prefer, on the whole, to those created using more complicated HDR methods, or to those created without the use of HDR at all.

Artificial Intelligence (AI) agents must interact with users across a fragmented landscape of messaging platforms, including SMS, WhatsApp, and RCS. Current agent communication strategies often rely on static preferences or simplistic fallback logic, failing to adapt to the dynamic context of interactions and the heterogeneous capabilities, costs, and reliability of available channels. This deficiency leads to suboptimal communication, increased operational costs, and poor user experience. This paper proposes the Intelligent Messaging Channel Selector (IMCS), a novel framework enabling AI agents to perform dynamic, context-aware selection of the optimal communication channel in real-time. IMCS integrates a multi-dimensional context vector-comprising message urgency, user preferences, and real-time platform performance-into a multi-criteria decision engine. Through a simulated evaluation processing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0 0, 0 0 0}$</tex> user interactions, IMCS demonstrated a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4. 5 \%}$</tex> improvement in message delivery success rate and a 22 % reduction in cost per interaction compared to a standard static fallback strategy. This paper details the IMCS architecture, its decision-making logic, and the quantitative results that validate its superiority over conventional static approaches in complex, multi-channel environments.

Cloud data centers (CDCs) have become increasingly popular and widespread in recent years with the growing popularity of cloud computing and high-performance computing. Due to the multi-step computation of data streams and heterogeneous task dependencies, task failure frequently occurs, resulting in poor user experience and additional energy consumption. To reduce task execution failure as well as energy consumption, we propose a novel AI-driven energy-aware proactive fault-tolerant scheduling scheme for CDCs in this paper. First, a prediction model based on the machine learning approach is trained to classify the arriving tasks into "failure-prone tasks" and "non-failure-prone tasks" according to the predicted failure rate. Then, two efficient scheduling mechanisms are proposed to allocate two types of tasks to the most appropriate hosts in a CDC. The vector reconstruction method is developed to construct super tasks from failure-prone tasks and separately schedule these super tasks and non-failure-prone tasks to the most suitable physical host. All the tasks are scheduled in an earliest-deadline-first manner. Our evaluation results show that the proposed scheme can intelligently predict task failure and achieves better fault tolerance and reduces total energy consumption better than the existing schemes.

5G is growing globally, and the handover performance is needed to be updated and improved to adapt to new changes in telecommunication. 5G are considered small cell networks that are anticipated to have a short dwell time for users that move at high speed, like a vehicle traversing the 5G cell at a rate of 40km/h and above. It induces unnecessary handover that causes poor user experience and waste of network resources. This research tackles the problem by proposing a new handover algorithm that integrates a travel distance prediction method with AHP-TOPSIS (Analytic Hierarchy Process - Techniques for Order Preference by Similarity for an Ideal Solution) decision making. The proposed algorithm has successfully reduced the unnecessary handover in 5G networks up to 89.75% compared to the conventional TOPSIS method.

The explosion of internet news data has led to frequent information overload, which in turn has caused the dual problems of low recommendation quality and poor user experience. The research combines machine learning with image matching algorithms to provide users with interesting news content. Firstly, the news recommendation algorithm based on multi-task learning (MTL) constructs the input sequence through the users click history, maps the inputs of different tasks to the shared space and extracts text features. The image matching algorithm is adopted to record users historical preferences and capture the changing trends of interests. Experimental data show that the accuracy rate of the content-based recommendation (CB) algorithm reaches 78.27%. The collaborative filtering (CF) recommendation algorithm reached 87.97%. The recommendation accuracy of the joint recommendation algorithm all exceeds 90%. Moreover, the accuracy of the joint recommendation algorithm significantly exceeds that of the two traditional recommendation methods, CB and CF.

In the context of a quickly paced world of aging, intelligent elderly-care robots as a technology that is vital to overcome the servicing gap in relation to elderly-care services are enjoying new opportunities and challenges like never before. Based on an analysis of the current aging population and market demand, and integrating product innovation theory with commercialization promotion pathways, this paper systematically explores the product design system and market growth strategies for intelligent elderly-care robots. The study points out that current products suffer from core issues such as a disconnect between technology and demand, poor user experience, and low market penetration. It proposes a dual-track innovation system centered on "emotional interaction" and "physiological personalized services." By constructing a tiered product architecture (Tier 1–Tier 3) combined with the MVP development model, the usability, accessibility, and user acceptance of the products are significantly enhanced. Furthermore, market promotion strategies are proposed from perspectives such as policy coordination, ecological cooperation, experiential marketing, and new media communication, providing theoretical support and practical references for the widespread adoption and sustainable development of intelligent elderly-care robots.