User Demands Research Articles

Research on harmonious design of chairs based on the Kano model and analytic hierarchy process

To meet the functional and emotional needs of users for outdoor leisure chairs, the three-level theory of harmonious design was applied. A product design process was put together based on the Kano model and analytic hierarchy process (AHP). The Kano model obtained demand attributes and influence coefficients, while the AHP obtained total weights. The target products were designed and evaluated based on the three-level theory of harmonious design to improve the user experience and satisfaction of outdoor leisure chairs. The attribute categories of harmonious demand were obtained based on the Kano model, and the harmonious demands were ranked by importance. The design analysis and design practice were conducted with the goal of harmonious design. The AHP was used to analyze the comprehensive weights of the index factors, evaluate the user satisfaction of the three design schemes, and conduct consistency test and feasibility verification of the design schemes. The optimal design scheme was selected based on the total weight mean of three design schemes. The design and analysis method based on the Kano-AHP model can focus on user demand. It can objectively and efficiently analyze design pain spots, and effectively guide the harmonious design practice, which improves user satisfaction and market transformation efficiency of creative products.

New Methods for Optimal Power Allocation and Joint Resource Scheduling in 5G Network which Use Mobile Edge Computing

Mobile Edge Computing (MEC) is considered one of the enabling and promising technologies in 5G networks, especially with the massive data movement of various devices and the increased demand for computing. Here, computational offloading of tasks to edge clouds provides an effective, flexible, low-latency solution for mobile users in the network. However, the limited computing resources in edge clouds and the dynamic demands of mobile users make it difficult to schedule computing requests to appropriate edge clouds, and make the offloading process energetically expensive for devices. Therefore, it is very important to design an energy-efficient offloading strategy. To this end, we first formulate the transmission power allocation (PA) problem for mobile phone users to minimize power consumption. Using a quasi-convex technique, we address the (PA) problem using a (Hybrid GAPSO) algorithm resulting from combining the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) algorithm. Next, we model the joint request offloading and resource scheduling (JRORS) problem as a mixed- nonlinear program to minimize the response delay of requests. The (JRORS) problem can be divided into two problems, namely the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a dual decision problem and propose a multi-objective particle swarm optimization algorithm referred as (MO-PSO). The simulation results show that (HGAPSO) can save transmission power consumption and has good convergence property, and (MO-PSO) outperforms existing methods in terms of response rate and can maintain good performance in a dynamic network.

Shared autonomous vehicle operational decisions with vehicle movement and user travel behaviour

Shared Autonomous Vehicle (SAV) has many impacts on the transport development, such as saving parking space. However, SAV meets a huge challenge in terms of vehicle supply and user demand imbalance. The traditional mathematical optimization method cannot be well used due to the computational burden. Hence, this paper proposes a Reinforcement Learning (RL) based SAV relocation approach. First, two types of RL agents, car-based and zone-based agents, are developed as agents for vehicles and stations, respectively. Then, the RL scheme is trained by using historical demand data to facilitate real-time carsharing relocation. Finally, to compare the proposed two types of RL methods, three scenarios are used: small-scale, middle-scale, and large-scale networks. Solutions indicate that the enhanced zone-based method achieves an additional 146% profit compared to traditional threshold-based relocation strategies. The user travel behaviour impacts are provided by analyzing parking demand and travel movements among residential, industrial and commercial zones.

Visualizing the construction of subject librarianship in chinese higher education libraries: A knowledge graph-based analysis

This study investigates the evolution and current status of subject librarianship in Chinese higher education spanning the past 25 years. Utilizing scientometric analysis, we construct knowledge graphs, encompassing publication trends, co-citation and co-occurrence networks, and the burstiness of keywords. The data are drawn from 504 articles in the Chinese Social Science Citation Index (CSSCI) database. The analysis sheds light on knowledge domains, evolutionary routes, and research hotspots within this field. The graphs delineate intricate connections among subject librarianship in Chinese higher education, involving universities, faculties, students, cooperative enterprises, and government. Significantly, these services play pivotal roles in information resource construction, scientific research, intellectual property support, and propaganda. The progression of subject librarianship in Chinese higher education is driven by innovations in information technology, user demands and policy advocacy. The knowledge graph-based exploration of subject librarianship holds substantial implications for libraries further constructions, particularly in enriching content and enhancing quality, aligning with the criteria of the new education public policy outlined by the Ministry of Education of the People’s Republic of China.

Effects of User Experience in Automated Information Processing on Perceived Usefulness of Digital Contact-Tracing Apps: Cross-Sectional Survey Study.

In pandemic situations, digital contact tracing (DCT) can be an effective way to assess one's risk of infection and inform others in case of infection. DCT apps can support the information gathering and analysis processes of users aiming to trace contacts. However, users' use intention and use of DCT information may depend on the perceived benefits of contact tracing. While existing research has examined acceptance in DCT, automation-related user experience factors have been overlooked. We pursued three goals: (1) to analyze how automation-related user experience (ie, perceived trustworthiness, traceability, and usefulness) relates to user behavior toward a DCT app, (2) to contextualize these effects with health behavior factors (ie, threat appraisal and moral obligation), and (3) to collect qualitative data on user demands for improved DCT communication. Survey data were collected from 317 users of a nationwide-distributed DCT app during the COVID-19 pandemic after it had been in app stores for >1 year using a web-based convenience sample. We assessed automation-related user experience. In addition, we assessed threat appraisal and moral obligation regarding DCT use to estimate a partial least squares structural equation model predicting use intention. To provide practical steps to improve the user experience, we surveyed users' needs for improved communication of information via the app and analyzed their responses using thematic analysis. Data validity and perceived usefulness showed a significant correlation of r=0.38 (P<.001), goal congruity and perceived usefulness correlated at r=0.47 (P<.001), and result diagnosticity and perceived usefulness had a strong correlation of r=0.56 (P<.001). In addition, a correlation of r=0.35 (P<.001) was observed between Subjective Information Processing Awareness and perceived usefulness, suggesting that automation-related changes might influence the perceived utility of DCT. Finally, a moderate positive correlation of r=0.47 (P<.001) was found between perceived usefulness and use intention, highlighting the connection between user experience variables and use intention. Partial least squares structural equation modeling explained 55.6% of the variance in use intention, with the strongest direct predictor being perceived trustworthiness (β=.54; P<.001) followed by moral obligation (β=.22; P<.001). Based on the qualitative data, users mainly demanded more detailed information about contacts (eg, place and time of contact). They also wanted to share information (eg, whether they wore a mask) to improve the accuracy and diagnosticity of risk calculation. The perceived result diagnosticity of DCT apps is crucial for perceived trustworthiness and use intention. By designing for high diagnosticity for the user, DCT apps could improve their support in the action regulation of users, resulting in higher perceived trustworthiness and use in pandemic situations. In general, automation-related user experience has greater importance for use intention than general health behavior or experience.

Preserving Heritage Riverine Bridges: A Hydrological Approach to the Case Study of the Grau Bridge in Peru

Heritage bridges constitute an integral feature of the urban landscape in numerous cities. However, it is common for these structures to surpass their life cycle, rendering them ill-equipped to withstand the dynamic demands of users and extreme events, particularly hydrological occurrences. This research presents a methodology for the assessment of heritage riverine bridges, with a focus on the Grau Bridge in Peru as a case study. The investigation commences with an exhaustive literature review, complemented by a historical examination, followed by a preliminary diagnosis. Subsequently, hydrological and hydraulic studies are presented, encompassing drone surveys of the riverbed and the bridge, soil analyses, and the application of 1D and 2D models in HEC-RAS. The outcomes of this comprehensive analysis reveal the high vulnerability of the Grau Bridge. Finally, strategic interventions for its conservation are recommended.

Development of a 10 K automated sample exchange cryostat for SANS_CSNS

The Small-angle Neutron Spectrometer at China Spallation Neutron Source (SANS_CSNS) is a functional apparatus utilized for the examination of structures and inhomogeneities within the size range of 1 to 100 nm. The development of sample environments for in situ experiments at low temperatures at SANS_CSNS is urgent in order to address the increasing demands of users. The CSNS Sample Environment Group has successfully designed and constructed an automated sample exchange cryostat for SANS experiments, capable of operating in a temperature range of 10 ∼ 500 K. This cryostat has been specifically engineered to accommodate up to four samples simultaneously and exchange samples automatically in order to optimize the utilization of neutron beams by minimizing the downtime associated with manual sample handling. The results of simulation and temperature measurement proved that sample temperature could be accurately controlled from 10 K to 500 K through the incorporation of a 4 K GM cryocooler and a heater. Furthermore, neutron scattering studies conducted on SANS_CSNS proved that this cryostat exhibits commendable temperature control capabilities and minimal background interference. This automated sample exchange cryostat will be available to researchers of SANS_CSNS in the following user programs.

A demand-based three-stage seismic resilience assessment and multi-objective optimization method of community water distribution networks

This paper proposes a three-stage resilience concept that takes into account reliability, vulnerability and recovery resilience of water distribution networks (WDNs) from three phases, namely, pre-disaster preparation (PDP) phase, disaster response (DR) phase and the post-disaster recovery (PDR) phase. Based on the three-stage resilience concept, the initial performance, robustness and multi-objective optimization recovery method are proposed. In PDP phase, based on the reliability concept and the user demand, the initial performance of WDNs is defined, and the method is proposed based on Monte Carlo simulation method combined with seismic fragility of pipelines. In DR phase, a comprehensive quantitative index is proposed to quantify the robustness of WDNs by integrating the structural attributes, topological attributes and operational characteristics of WDNs. Finally, on the basis of the previous two stages, the disaster recovery process of failed pipelines of WDNs is further analyzed, and a multi-objective recovery optimization model of failed pipelines with consideration of users’ demands under the resources constraints is proposed to achieve the resilience enhancement. Through a case, the three-stage resilience assessment optimization enhancement of WDNs is implemented using the improved NSGA-II. The proposed method can be used as a basis for disaster prevention planning and seismic measures.

GeoGPT: An assistant for understanding and processing geospatial tasks

Decision-makers in GIS often need to combine multiple spatial algorithms and operations to solve geospatial tasks. While professionals can understand and solve these tasks by using GIS tools sequentially, developing workflows for various tasks can be inefficient, as even slight differences in tasks require corresponding adjustments in the workflow. Recently, large language models (e.g., ChatGPT) presented a strong performance in semantic understanding and reasoning. Especially, AutoGPT can further extend the capabilities of large language models (LLMs) by automatically reasoning and calling externally defined tools. Inspired by these studies, we attempt to increase the efficiency of developing workflows for handling geoprocessing tasks by integrating the semantic understanding ability inherent in LLMs with mature tools within the GIS community. Specifically, we develop a new framework called GeoGPT that can conduct geospatial data collection, processing, and analysis in an autonomous manner. In this framework, a LLM is used to understand the demands of users, and then think, plan, and execute defined GIS tools sequentially to output final effective results. In this process, our framework is user-friendly, accepting natural language instructions as input and adapting to various geospatial tasks, which can serve as an assistant for GIS professionals. A systemic evaluation and several cases, including geospatial data crawling, spatial query, facility siting, and mapping, validate the effectiveness of our framework. Though limited cases are presented in this paper, GeoGPT can be further extended to various tasks by equipping with more GIS tools, and we think the paradigm of “foundational plus professional” implied in GeoGPT provides an effective way to develop next-generation GIS in this era of large foundation models.

Demand Response Power-Gas Interconnection Energy System and Power Metering Based on SOGA

Under the double background of global energy crisis and environmental pollution, China vigorously develops renewable energy while accelerating the construction of energy Internet. Taking demand response into account, this paper proposes the optimal design of power-gas interconnection energy system and power metering, and establishes the optimization research model of IEGES (integrated electricity-gas energy system) with demand response. Firstly, the structure model of the electric-gas interconnection energy system with CHP (Cogeneration, combined heat and power) as the core is constructed, and the energy conversion relationship and different energy flow directions of the coupling equipment are expounded from three aspects. The natural gas source point, pipeline equation, power side branch equation, voltage and current equation are modeled and sorted out, and the square term in the equation is linearized by second-order cone programming method, and the mixed integer nonlinear programming problem is transformed into mixed integer linear programming problem. A single objective genetic algorithm with “elite strategy” was selected to solve the equipment capacity optimization problem of IEGES system with system economy as the optimization objective. After a long time of parameter combination attempts, the current population size is 30, the number of iterations is 600, the crossover rate is 0.8, and the heritability is 0.3. The above parameters can obtain better convergence results on the basis of considering the operation time. Finally, a stochastic optimization method of energy Internet considering integrated demand response and uncertainty of wind power is proposed, which aims to meet the energy demand of end users while minimizing the operating cost of the system. The comprehensive demand response strategy including internal and external demand response is considered in the model. Internal demand response is realized by adjusting the internal operation mode of EH, while external demand response is implemented by the end user’s active response, and the load is time-shifted or interrupted under the guidance of external signals.

Multi-protocol updating for seamless key negotiation in quantum metropolitan networks

Quantum key distribution (QKD) can provide long-term security for numerous users. Currently, quantum networks are still in the early stages of small-scale deployment, most of which can only support a single QKD protocol (QKDP). However, with the advancement of various QKDPs, a single-protocol quantum network is no longer sufficient to meet the demands of multiple users, prompting the emergence of multi-protocol quantum networks. Nevertheless, the transition from a single-protocol to a multi-protocol quantum network still faces many challenging issues, such as key negotiation interruptions due to device initialization and channel calibration during protocol updating. To address the quantum key negotiation interruption problem, we propose a seamless key negotiation oriented multi-protocol updating algorithm in this work, which can fulfill the protocol updating requests of different users in quantum metropolitan networks. Furthermore, to better improve network performance while meeting diverse user demands, we propose four heuristic algorithms for optimal QKDP recommendation, focusing on their applications for multi-protocol updating in different types of typical networks. We perform the simulations with different QKDP recommendation algorithms to analyze the impact of the cache time of the key cache area on the key negotiation interruption probability and the time resource utilization. Simulation results demonstrate that the proposed algorithm can reduce the key negotiation interruption probability by 77.7% while increasing the time resource utilization by 15.3% compared to no key cache area.

Initiating and integrating user-centred design in upgrading the e-learning layout: a case study on FlexLearn (Brightspace) 2.0 template

PurposeThe purpose of this paper is to articulate how the user experience (UX) approach was initiated and integrated into the centre’s scope of operations with the objective of improving the e-learning layout on the D2L learning management system (LMS). One of the most effective ways to collect user feedback has historically been to evaluate user interfaces using strategies from user testing. The integration of a UX approach by the Centre for ODL Experiences (COLE) at Wawasan Open University has led to a more user-oriented design of FlexLearn by conducting user testing on students as the target users of the platform and gathering course leaders’ (CLs) feedback after the presentation of the new template.Design/methodology/approachSince the process of design and development is a looping process, the first user testing methods employed were observation and interviews, which were conducted over the course of numerous sessions. The data collection used a mixed-methods approach, combining quantitative demographic and background data with qualitative feedback from open-ended questions and real-time interview responses. A standardized questionnaire gathered demographic information, while questions for feedback forms and interviews were adjusted based on specific tasks to explore usability and user interactions comprehensively.FindingsThe findings revealed overall positive feedback, with some concerns highlighted by the students who claimed to have trouble navigating the courses during the initial prototype. In addition to the qualitative data from the user testing session, a quantitative method based on an online questionnaire was also utilised for the CLs after the presentation of the final layout. Positive responses were received from the CLs, and constructive suggestions were considered for FlexLearn 3.0.Research limitations/implicationsThis paper is among the first that articulates the process of initiating and integrating user-centred design in an effort to improve the user experience of online and ODL platforms and LMSs. It will contribute to a dialogue on investigating and prioritising learners’ ODL experiences to ensure education equity across all levels or categories of students, which aligns with the United Nations Sustainable Development Goals.Practical implicationsThe integration of UX and user testing allows us to better identify what users like, their concerns and their needs. We gain important input on how easy or difficult it is to use the system, move around it and how much they enjoy using it. This feedback helps us make changes to the design so that the final product is more in line with what users want. It also allows us to discover problems before they become major, saving time and effort later on. Finally, integrating user input improves the LMS, delivering a more fun and successful learning experience for everyone.Social implicationsUser-friendly systems arise as institutions prioritise user-centred design, breaking down barriers for various learners. This develops an innovative culture, improving present learning experiences and setting a precedent for future generations. The emphasis on user demands helps to create a more accessible, adaptive and egalitarian educational landscape by connecting education with current technological trends. As education becomes more inclusive, the broader community benefits, emphasising the beneficial social impact of LMS user testing.Originality/valueBy articulating the process of integrating user testing on an LMS/e-learning prototype, helps us understand what users like, where they face problems and what needs improvement. By involving users in testing, we get valuable feedback on how easy it is to use the system, navigate around and overall, how much they enjoy using it. Case studies like this also offer universities concrete examples of real-world challenges and successes.

Automatic house location identification using location service based VANET

SummaryThe vehicular ad hoc network (VANET) provides a variety of applications and is gaining popularity due to the reuse of network resources. Exact location identification with optimum delay is the demand of all vehicle users. Currently, individuals are utilizing the Global Positioning System (GPS) to ascertain the precise geographical coordinates of a given location. The drawback of GPS technology is its inability to accurately determine location and use GPS in some remote areas. Searching for the location of a particular house randomly incurs a loss of fuel as well as increases delay. This motivates us to propose one automated method through VANET using location service‐based routing for the location identification of a house. The proposed work involves searching for the location of a house using the open‐source MongoDB database, and the operations on the database are performed using the tool Node‐Red. By simulation using SUMO and Network Simulator 2.35, the proposed work is evaluated and compared with existing location service‐based routing like geographic location service (GLS) and hierarchical location service (HLS). The proposed work performs better in terms of routing efficiency (end‐to‐end latency, packet delivery rate), location efficiency (request sends, query success rate, and request travel time), and routing and location overhead (MAC bandwidth consumption). Also, the performance of the proposed work is presented as stable by increasing the number of vehicles. The statistical analysis of the packet delivery ratio and CBR end‐to‐end latency is carried out using T score.

Identification of User Requirements and their Influencing Factors Based on Online Reviews and Operational Data

It is crucial for enterprises to clearly identify user needs during the process of formulating product design improvement plans. Therefore, it is essential to comprehensively and accurately identify user needs, explore the reasons behind the emergence of these needs, and incorporate user opinions into the process of product design improvement. A method is proposed to comprehensively and accurately capture user requirements and address the challenge of identifying the underlying causes of user requirements. This method utilizes online comments and operational data to identify user requirements and their influencing factors. First, text sentiment analysis techniques are employed to quantify the importance and performance values of product feature topics. Second, we construct a quadrant model to identify product features requiring improvement, and the original negative comments related to these features are traced. However, the quadrant model alone is insufficient to reflect specific product issues that users are concerned about. Therefore, a functional structure model based on product issues is designed to filter and identify factors that influence user requirements using operational data. Finally, a Bayesian network inference approach is utilized to identify the key influencing factors on user requirements, enabling analysis of the causes behind user requirements and the proposal of product design improvement strategies. The feasibility and effectiveness of the proposed method are validated through experiments conducted on heavy-duty truck data. By analyzing the original negative comments related to the power characteristics, specific user demands regarding the insufficient power of the product were identified, such as “obviously insufficient power when climbing slopes” and other issues. Based on the vehicle power system functional structure model, combined with expert knowledge and operational data, factors related to the state of parts and user behavior that may affect “insufficient vehicle power” were identified. Based on the analysis results, suggestions were made to improve the engine intake air temperature control strategy and to enhance vehicle performance by promoting correct user behavior through informational campaigns.

Learning-driven service caching in MEC networks with bursty data traffic and uncertain delays

Mobile edge computing (MEC) provides extremely low-latency services for mobile users, by attaching computing resources to 5G base stations in an MEC network. Network service providers can cache their services from remote data centers to base stations to serve mobile users within their proximity, thereby reducing service latencies. However, mobile users of network services usually have bursty requests that require immediate processing in the MEC network. The data traffic of such requests is bursty, since mobile users have various hidden features including locations, group tags, and mobility patterns. Furthermore, such bursty data traffic causes uncertain congestion at base stations and thus leads to uncertain processing delays. Considering the limited resources of base stations, network services may not be able to be placed into base stations permanently to handle the bursty data traffic. As such, network services need to be dynamically cached in the MEC network to fully address the bursty data traffic and uncertain processing delays.In this paper, we investigate the problem of dynamic service caching and task offloading in an MEC network, by adopting the online learning technique to harness the challenges brought by bursty data traffic and uncertain processing delays. We first propose an online learning algorithm for the problem with uncertain processing delays, by utilizing the multi-armed bandits technique, and analyze the regret bound of the proposed algorithm. We then propose another online learning algorithm for the problem with bursty data traffic and uncertain processing delays, which adaptively learns the bursty data traffic of requests, based on small samples of mobile users’ hidden features. We also propose a novel architecture of Generative Adversarial Network (GAN) to accurately predict user demands using small samples of mobile users’ hidden features. Based on the proposed GAN model, we then devise an efficient heuristic for the problem with the uncertainties of both bursty data traffic and uncertain delays. We finally evaluate the performance of the proposed algorithms by simulations, using a real data trace. Experimental results show that the performance of the proposed algorithms outperforms existing ones by up to 44% in terms of average delay.

Towards Energy Efficient Cloud: A Green and Intelligent Migration of Traditional Energy Sources

Geographically distributed cloud data centers (DCs) consume enormous amounts of energy to meet the ever-increasing processing and storage demands of users. The brown energy generated using fossil fuels is expensive and significantly contributes to global warming. Considering the environmental impact caused by the high carbon emissions and relatively high energy cost of brown energy, we propose the integration of renewable energy sources (RES), especially solar and wind energy, with brown energy to power cloud data centers. In our earlier study, we addressed the intermittency of renewable energy sources, where we replaced the random initialization of artificial neural network (ANN) edge weights with the harmony search algorithm (HSA)-optimized assignment of weights. This study incorporated reliably forecast solar and wind energy into the input parameters of our proposed green energy manager (GEM), for cost minimization, carbon emission minimization, and better energy management of cloud DCs, to make our current study more reliable and trustworthy. Four power sources, on-site solar energy and wind energy, off-site solar energy and wind energy, energy stored in energy storage devices, and brown energy, were considered in this study and simulations were carried out for three different cases. The simulation results showed that case 1 (all brown) was 58% more expensive and caused 71% higher carbon emissions than case 2.1 (cost minimization). Case 1 (all brown) was 39% more expensive and had 80% higher carbon emissions than case 2.2 (carbon emission minimization). The simulation results justify the necessity and importance of the GEM, and finally the results proved that our proposed GEM is less expensive and more environmentally friendly.

Load-aware continuous-time optimization for multi-agent systems: toward dynamic resource allocation and real-time adaptability

In the realm of next-generation mobile communication networks, characterized by dynamic and evolving workloads, the efficient resource allocation becomes paramount for achieving optimal performance. This paper addresses the intricate challenges of multiagent resource management through the integration of stochastic learning automata and continuous-time optimization techniques, presenting an innovative solution for real-time adaptability and dynamic load balancing. The central aim is to achieve optimal values for local time-varying cost functions while taking into account resource constraints and the feasibility of local allocation. In the context of multi-agent resource allocation systems, where conditions vary over time, the algorithm exhibits a dynamic adaptability, continuously adjusting to the evolving optimal solutions tailored for each agent. Utilizing stochastic learning automata, the algorithm effectively addresses the time-varying load-balancing function in response to dynamic user demands. Furthermore, we introduced an scalable solution for mobility robustness optimization based on deep reinforcement learning (DRL-MRO). This method dynamically identifies network-wide optimal parameter values across the entire network to accommodate diverse mobility patterns, ensuring the effectiveness of load balancing parameters that seamlessly adapt to the dynamic configuration of the network. The overarching goal is to maintain a consistent quality of service level for each agent, thereby enhancing the overall system performance. Simulation outcomes unequivocally affirm the superior performance of the proposed load balancing approach, rooted in continuous-time optimization and stochastic learning automata, surpassing existing schemes across diverse system configurations.

UniSched: A Unified Scheduler for Deep Learning Training Jobs With Different User Demands

UniSched: A Unified Scheduler for Deep Learning Training Jobs With Different User Demands

Emotional design of traditional Chinese cultural creative products based on user demand

Mortice-tenons are the main structural forms of ancient buildings, furniture, and woodwork in China. They are the inheritance of China's craft and cultural spirit, follow the principles of mechanics and have strong practicability. How-ever, mortice-tenons have gradually disappeared from modern life, mainly be-cause they cannot meet the specific needs of users. The purpose of this study is to establish a set of mortice-tenons structure innovation and product design for modern people. By analyzing the types and structural characteristics of mortice-tenon structure, based on the user's needs, using modern creative design methods, and combining with practice, we can verify how to further improve the market acceptance of mortice-tenon structure innovation prod-ucts, constantly explore the combination of modern technology and material experiments, and explore the emotional design of China traditional cultural creative products. Finally, it is proposed that further research is needed in this field to fully understand the user's behavior of using mortice-tenon structure innovation products, and at the same time, mortice-tenon structure innova-tion is applied to daily necessities and furniture products to meet the needs of a wider range of user scenarios.

The influence of guide vane opening on internal cavitation of Francis turbine

Abstract In response to the theme of national multi energy complementary power grid construction, hydropower stations need to frequently change the opening of active guide vanes. Operation deviates from the rated working condition. And output is adjusted to meet the flexible power demand of users. Cavitation phenomenon as the biggest problem restricting the eccentric operation of Francis turbine. Easily induce cavitation damage to the flow passage components of the unit and shorten the service life of the unit. Therefore, there is a good engineering research background for the cavitation characteristics inside Francis turbines in the process of exploring the variation of guide vane opening. This paper takes a high head Francis turbine as the research object, based on numerical simulation and model test. The results show that the maximum amplitude of the draft tube vortex gradually increases during the process of decreasing the opening. The degree of cavitation is gradually serious, and the streamline distribution of the flow field inside the impeller is uneven. As the opening of the guide vane gradually increases, the vortexof the draft tube gradually decreases. The cavitation degree is weakened, and the streamline distribution inside the impeller is significantly improved. With the increase of the guide vane opening, the low-speed area on the outlet surface of the impeller gradually decreases, resulting in a gradual reduction of the low-speed zone in the straight cone section of the draft tube, a smaller vortex, a weaker cavitation degree. The internal flow distribution of the impeller is significantly improved.