Guidance Scheme Research Articles

A Digital-Twin-Based Traffic Guidance Scheme for Autonomous Driving

A Digital-Twin-Based Traffic Guidance Scheme for Autonomous Driving

Examining dimensions of career intentions: insights from medical and nursing students at a private not-for profit university in Vietnam.

Understanding students' career intentions through evidence-based approaches is crucial for developing effective career guidance and intervention strategies. Although there has been considerable attention in this field, research remains limited, particularly in low- and middle-income countries (LMICs) in Asia. These regions have unique socioeconomic conditions and cultural norms that potentially shape decision-making processes. This study provides insights into the career intentions of medical and nursing students and identifies the key determinants influencing their choices at a newly established private institution (VinUniversity) in Vietnam. This study employs a mixed-methods design. The quantitative phase involved a survey of all undergraduate medical and nursing students. The qualitative phase consisted of in-depth 11 individual interviews with a purposive sample until data saturation. A total of 198 students (77%) participated in the quantitative phase. The majority of students (75.8%) expressed interest in clinical practice, with preferences notably high for surgical specialties (30.9%), internal medicine (10.1%), and pediatrics (10.1%). 22.7% of students expressed a desire to work overseas, and a significant number (70.7%) indicated the lack of a career guidance program. Qualitative data revealed that personal interest, family influence, financial stability, and societal appreciation were primary influencers in career choice. The study underscores the complex interplay between personal interests, family influence, financial considerations, and societal expectations in shaping career choices. The persistent low interest in family medicine and primary care continued to be a concern. This study provides further insights into the career intentions and influencing factors among medical and nursing students at VinUniversity. The implications for practice include implementing tailored career guidance programs, offering diverse elective tracks that incorporate global health experiences, and establishing structured mentorship programs. It is also recommended to measure the impact of these interventions through longitudinal follow-up studies to enhance career satisfaction, better prepare future healthcare professionals, and contribute to the improvement of healthcare systems.

Research on Public Opinion Guidance Strategies of Political Microblogs in the Context of Emergencies

This study takes "Gansu 12-18 Jishishan Earthquake" as an example to explore the strategies of guiding public opinion on government microblogs in the context of emergencies. Using the life cycle theory of public opinion, the study divides the development process of public opinion into four stages: latency, growth, maturity and decline, and systematically analyzes the performance of government microblogs, such as @Gansu Firefighting, @Gansu Public Security, and @Gansu Publishing, in the development of public opinion on emergencies, as well as their guiding effects in the various stages of public opinion development. The study shows that governmental microblogs perform better in terms of timeliness and authority of information release in emergencies, and are able to rapidly convey official information and stabilize public sentiment. However, there are still deficiencies in the interactive mechanism, public opinion monitoring and early warning, and evaluation and feedback. Based on this, it is proposed to optimize the public opinion monitoring and warning mechanism, enhance public interaction, and improve the feedback and evaluation system, so as to improve the public opinion management ability of government microblogs in emergencies, and to ensure the efficient communication of government information and the effective guidance of public opinion.

The Adolescent Mental Health Consequences of Social Media Usage

This paper looks into the impact of social media on an individual’s mental health status in the adolescent population. The scope of the study includes both addiction and recovery, addressing risks such as anxiety, depression, and distorted self-perception associated with the continuous use of social platforms like Instagram, TikTok, Facebook, etc. Besides recognizing the detrimental impacts of bullying, body image comparison, and excessive use of screens, the paper also underlines some advantages of social media, such as connecting people and access to assistance. In the present research, numerical indicators and empirical observations are used, i.e. the nvestigation is conducted on the basis of the mixed research method. It reveals variations in social media effects amongst adolescents based on the moderating variables of individual characteristics and situational context. The paper ends with solutions to alleviate the negative effects, such as digital literacy education, parental guidance, and mental health strategies while fostering the healthy usage of social media regarding mental health. These findings suggest the need for a longitudinal study in order to assess the changes over time in this somewhat new aspect.

Utilizing Video Feedback for Effective Motor Skill Learning in Online Physical Education

ABSTRACT Video feedback is a commonly recommended tool for online physical education (OLPE) instructors. However, there is little evidence to support its effectiveness in motor skill learning. This study investigated the impact of different forms of pedagogical guidance delivered alongside video feedback on the acquisition and retention of two-disc golf skills: the spin putt (SP) and backhand throw (BT). Forty-three college students were randomly assigned to one of the three video feedback groups: visual cue sheet (VCS), self-assessment (SA), or delayed teacher feedback (TF). During four online practice sessions, they trained on the spin putt and backhand throw techniques. Pre-, post-, and retention tests assessed their performance on both skills. The results indicate significant improvements in BT technique at posttest for both SA and VCS groups. All participants exhibited significant gains in SP technique at posttest; however, only the VCS group sustained these improvements for both disc golf skills at the retention test. No significant differences were observed between the groups for both disc golf skills at all three measurements. These findings suggest that video feedback, particularly when combined with in-the-moment guidance like VCS or SA, can effectively enhance motor skill learning in online settings. This result underscores the potential of OLPE to go beyond fitness-based activities and effectively address psychomotor learning standards. Future research should explore the long-term impact of video feedback in OLPE and identify optimal guidance strategies for diverse motor skills and demographics.

Guiding Students in Using LLMs in Supported Learning Environments: Effects on Interaction Dynamics, Learner Performance, Confidence, and Trust

Personalized chatbot-based teaching assistants can be crucial in addressing increasing classroom sizes, especially where direct teacher presence is limited. Large language models (LLMs) offer a promising avenue, with increasing research exploring their educational utility. However, the challenge lies not only in establishing the efficacy of LLMs but also in discerning the nuances of interaction between learners and these models, which impact learners' engagement and results. We conducted a formative study in an undergraduate computer science classroom (N=145) and a controlled experiment on Prolific (N=356) to explore the impact of four pedagogically informed guidance strategies on the learners' performance, confidence and trust in LLMs. Direct LLM answers marginally improved performance, while refining student solutions fostered trust. Structured guidance reduced random queries as well as instances of students copy-pasting assignment questions to the LLM. Our work highlights the role that teachers can play in shaping LLM-supported learning environments.

Trajectory tracking for autonomous surface ships using Gaussian process regression and model predictive control with BVS strategy

Autonomous navigation is critical to the development of next-generation shipping systems. The proposal of intelligent ships enables innovation in the shipping and shipbuilding industry and increases the safety and efficiency of ship operations. Autonomous control of surface ships to follow a prescribed trajectory is critical in a variety of maritime applications. This paper proposes a trajectory tracking control strategy for autonomous surface ships that combines nonparametric modelling using Gaussian Process Regression (GPR) with the Model Predictive Control (MPC) framework. A Bézier curve-based Virtual Ship(BVS) guidance strategy is proposed to convert dynamic trajectory points into reference heading angles and speeds, such that the trajectory tracking problem can be decomposed into heading control and speed control problems. Gaussian process regression is utilised to identify the correlation between propeller revolution speed and ship speed, as well as the correlation between rudder angle and heading angle based on experimental data. Two GPR models are therefore constructed as the prediction models for designing MPC controllers for heading control and speed control, respectively. Nonlinear optimisation algorithms are utilised to search for optimal control commands in each sampling interval to solve the optimisation problem in MPC with GPR models and input constraints. Simulations are carried out to evaluate the effectiveness of the proposed method.

Guidance Strategy and Fast Robustness Analysis for Space Debris Flyby

Envisioning the scenario of space debris inspection by orbital flyby, this paper investigates the guidance strategy and fast robustness analysis method considering the relative state constraints. First, the corrected state transition matrix is derived to predict and correct the perturbed relative state at the flyby moment. Based on the state transition matrix, a free-flight corridor model is established to characterize the maximum allowed magnitude of the deviation, which can quickly estimate the robustness of the trajectory under uncertainties and circumvent intensive numerical sampling. The corresponding optimization, with four norm-extreme constraints, can be solved by a proposed double-layer algorithm with proven convergence. Numerical simulations demonstrate that the guidance algorithm is effective, and the free-flight corridor can accurately and efficiently reflect the robustness of the nominal trajectory.

Experimental study on the synchronization mechanism and trigger characteristic density of vertical evacuation in crowds.

Due to simultaneous horizontal and vertical displacement during vertical evacuation, the consequences of stampede congestion accidents can be more severe. Generally, pedestrians trigger a synchronization mechanism at some point during the vertical evacuation process. This synchronization behavior helps prevent stampede congestion and improves evacuation efficiency. This paper designs a well-controlled single-file vertical evacuation experiment. After the experiment, the video footage is imported into the TRACKER system to extract the coordinates of pedestrian step movements, after which the experimental data undergo calculations and visual analysis. The research findings indicate the following: Firstly, when the crowd coordinates trigger the synchronization mechanism, this behavior remains stable as long as pedestrian speed and direction are unchanged; Secondly, the variation in footstep speed over time is not directly related to the footstep synchronization rate of the crowd; Lastly, this study calculated the characteristic density value most likely to trigger the synchronization mechanism during vertical evacuation. This research deepens our understanding of crowd dynamics, reveals the characteristics of pedestrian movement during vertical evacuation, and proposes evacuation guidance strategies based on these features.

Strategies for Enhancing Young Children's Scientific Inquiry Skills

In recent years, with the continuous advancement of educational reform, the importance of scientific education in early childhood has become increasingly evident. Early childhood is a critical period for cognitive development and interest cultivation. Through scientific inquiry activities, young children’s cognitive development, problem-solving abilities, and curiosity about the surrounding world can be effectively promoted. However, the implementation of early childhood scientific inquiry education still faces numerous challenges, such as a lack of systematic theoretical guidance and insufficient strategies for improvement. This study aims to enhance the existing theoretical foundations of young children's scientific inquiry abilities and proposes specific strategies for improvement to foster the comprehensive development of children's scientific literacy.

Dynamic Self‐Triggered Parallel Control of Guidance Intercept Systems With Constrained Inputs via Adaptive Dynamic Programming

ABSTRACTThis paper introduces an anti‐saturation optimization algorithm based on dynamic self‐triggered adaptive dynamic programming (ADP) for bounded acceleration guidance interception. By establishing a nonlinear input‐constrained guidance intercept and control system, the smooth bounded function is used to constrain the system input to ensure that the system operates in a controllable range. Subsequently, the appropriate performance function that can accurately reflect the system is designed. In alignment with the advantages of parallel control and ADP, a group of parallel systems are constructed by modeling the derivation of control input. A self‐learning control framework is explored, facilitating virtual‐actual interaction and mutual reinforcement between multiple controllers, optimizing the management of the interception system. Furthermore, event‐triggered control (ETC) policies are devised, which can be updated only when needed by setting trigger conditions, thus saving data resources. The stability proof of the closed‐loop system is given to ensure that the system can keep stable operation when the trigger condition is satisfied. On this basis, a dynamic self‐triggered control (DSTC) with soft computing is put forward, enabling trigger instant calculation without real‐time system monitoring and further extending the trigger interval. Simulation results evidence that the devised guidance scheme can intercept the maneuvering target at a reduced expenditure of communication resources.

Exploring the Determinants of Chinese Tourists' Shopping Behavior During Shopping Tourism in Korea.

This study aimed to examine the factors influencing Chinese tourists' shopping behavior during shopping tourism in Korea using a theoretical framework based on the modified theory of planned behavior (TPB). A quantitative research method was used; the questionnaire for this study was posted on Wenjuanxing for data collection and 269 valid questionnaires were analyzed in September 2024 using partial least-squares structural equation modeling (PLS-SEM). The results of analyzing the 269 questionnaires showed that attitude, conformity, perceived behavioral control, and perceived quality of goods influenced shopping intention. However, face consciousness did not affect shopping intentions. Additionally, Chinese tourists' shopping intentions influenced their shopping behavior during shopping tourism in Korea. The results of this study reveal the potential mechanisms of the influence of shopping intentions with conformity (a factor of Chinese-subjective norms) and the perceived quality of goods (a new factor) in shopping behavior, and they provide theoretical guidance and appropriate marketing strategies for companies in the Korean tourism field.

Quaternion-based non-singular nonlinear impact angle guidance for three-dimensional engagements

This paper proposes a three-dimensional impact angle-constrained nonlinear guidance strategy against different types of targets. To avoid the occurrence of singularities, in Euler angle-based approaches, the development of this guidance strategy is solely based on quaternion dynamics. An explicit relationship between the quaternions representing impact orientation and line-of-sight orientation at the time of collision is derived. Based on the obtained relation, a guidance law is derived using a sliding mode control technique to track the desired line-of-sight orientation for target interception. The performance of the proposed guidance strategy is evaluated for constant-speed and different time-varying-speed interceptor models accounting for the effect of varying aerodynamic parameters on interceptor speed. The numerical simulations performed show satisfactory results for all engagements, validating the efficacy and robustness of the proposed guidance law.

Three six-degree-of-freedom dynamic descent convex optimization guidance methods

A key technology for soft landing of powered descent of aircraft is to solve the six-degree-of-freedom powered descent guidance problem in real time, which can be described as the fuel-saving trajectory optimization problem under multiple constraints. Three optimization models are established by selecting three independent variables: flight time, time substitution variable, and trajectory height. The original trajectory optimization problem is transformed into a solvable form of sequential convex optimization for iterative solution, forming three online guidance methods. The three guidance methods are compared in terms of convergence, real-time performance, optimality, and solution accuracy. The results show that all three guidance methods can solve the six-degree-of-freedom powered descent problem. The guidance method with flight time as the independent variable has the shortest calculation time and the least fuel consumption, but the powered descent flight time needs to be determined in advance. The guidance methods based on the other two types of independent variables can optimize the powered descent flight time, but they are all suboptimal solutions, and the calculation time is significantly increased. The solution accuracy of the three methods is similar under the same number of discrete points. If sequential convex optimization is used as the online guidance scheme for powered descent, how to determine the optimal flight time, approach the optimal solution for fuel, and further shorten the calculation time still needs to be further studied.

FTDZOA: An Efficient and Robust FS Method with Multi-Strategy Assistance.

Feature selection (FS) is a pivotal technique in big data analytics, aimed at mitigating redundant information within datasets and optimizing computational resource utilization. This study introduces an enhanced zebra optimization algorithm (ZOA), termed FTDZOA, for superior feature dimensionality reduction. To address the challenges of ZOA, such as susceptibility to local optimal feature subsets, limited global search capabilities, and sluggish convergence when tackling FS problems, three strategies are integrated into the original ZOA to bolster its FS performance. Firstly, a fractional order search strategy is incorporated to preserve information from the preceding generations, thereby enhancing ZOA's exploitation capabilities. Secondly, a triple mean point guidance strategy is introduced, amalgamating information from the global optimal point, a random point, and the current point to effectively augment ZOA's exploration prowess. Lastly, the exploration capacity of ZOA is further elevated through the introduction of a differential strategy, which integrates information disparities among different individuals. Subsequently, the FTDZOA-based FS method was applied to solve 23 FS problems spanning low, medium, and high dimensions. A comparative analysis with nine advanced FS methods revealed that FTDZOA achieved higher classification accuracy on over 90% of the datasets and secured a winning rate exceeding 83% in terms of execution time. These findings confirm that FTDZOA is a reliable, high-performance, practical, and robust FS method.

Alpha Hydroxyl Acids from Mume Fructus and Schisandrae Chinensis Fructus Prevent Obesity by Inhibiting Intestinal Lipase in Diet-Induced Obese Mice.

Restriction of lipid uptake and absorption from the diet is regarded as an efficient strategy for the management of obesity, while lipase inhibition could successfully block the digestion of dietary lipids. Mume Fructus (MF) and Schisandrae Chinensis Fructus (SCF) were used as fruits, the biological effect of which on obesity desired more attention. Herein, the extracts of MF and SCF displayed significant efficacy in managing obesity in mice fed with a high-fat diet, via the inhibition of hydrolase activity of lipase in the digestive tract. Using the bioactivity guidance strategy, citric acid and malic acid were identified as the major lipase inhibitors from MF and SCF, respectively, which could prevent body weight gain, along with adipose tissue formation, and alleviate hyperlipidemia and hepatic steatosis in obese mice. Above all, MF and SCF could be used for the management of obesity via the lipase inhibition by citric acid and malic acid, displaying potential applications in healthy foods, nutritional supplements, and pharmaceutical materials.

Efficient Fault Warning Model Using Improved Red Deer Algorithm and Attention-Enhanced Bidirectional Long Short-Term Memory Network

The rapid advancement of industrial processes makes ensuring the stability of industrial equipment a critical factor in improving production efficiency and safeguarding operational safety. Fault warning systems, as a key technological means to enhance equipment stability, are increasingly gaining attention across industries. However, as equipment structures and functions become increasingly complex, traditional fault warning methods face challenges such as limited prediction accuracy and difficulties in meeting real-time requirements. To address these challenges, this paper proposes an innovative hybrid fault warning method. The proposed approach integrates a multi-strategy improved red deer optimization algorithm (MIRDA), attention mechanism, and bidirectional long short-term memory network (BiLSTM). Firstly, the red deer optimization algorithm (RDA) is enhanced through improvements in population initialization strategy, adaptive optimal guidance strategy, chaos regulation factor, and double-sided mirror reflection theory, thereby enhancing its optimization performance. Subsequently, the MIRDA is employed to optimize the hyperparameters of the BiLSTM model incorporating an attention mechanism. A predictive model is then constructed based on the optimized Attention-BiLSTM, which, combined with a sliding window approach, provides robust support for fault threshold identification. The proposed algorithm’s efficacy is demonstrated through its application to real-world gas-fired power plant equipment fault cases. Comparative analyses with other advanced algorithms reveal its superior robustness and accuracy in efficiently issuing fault warnings. This research not only provides a more reliable safeguard for the stable operation of industrial equipment but also pioneers a new avenue for the application of metaheuristic algorithms.

Beam steerable MIMO antenna based on conformal passive reflective metasurface for 5G millimeter wave applications

A conformal reflective metasurface fed by a dual-band multiple-input multiple-output (MIMO) antenna is proposed for low-cost beam steering applications in 5G Millimeter-wave frequency bands. The beam steering is accomplished by selecting a specific port of MIMO antenna. Each MIMO port is associated with a beam that points in a different direction due to a conformal reflective metasurface. This novel conformal metasurface antenna design has the advantages of higher gain, lower cost, a simpler feeding source, and a lower profile when compared to traditional reflective metasurfaces using bulky horn antennas and phased arrays with complex feeding networks and phase shifters for beam steering. The proposed beam steering antenna consists of a compact five-element dual-band MIMO and a 32×32\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$32 \ imes 32$$\\end{document} unit-cell conformal dual-band reflective metasurface placed at the top of the MIMO antenna to obtain the beam steering capability as well as gain enhancement. The proposed reflective metasurface has a stable response under oblique incidence angles of up to 600\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$60^0$$\\end{document} at 24 GHz and 38 GHz and its symmetric, single-layer structure, ensures polarization insensitivity and stable response under conformal conditions. The presented MIMO antenna design is not only compact but also offers a wideband response effectively covering the desired 5G mm-wave frequency bands. The overall size of the MIMO antenna alone is 70 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 12 mm2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mm}^2$$\\end{document} with a maximum gain of 5.4 and 7.2 dB. It is further improved up to 13.1 and 14.2 dB at 24 and 38 GHz respectively, with a beam steering range of ś400\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$40^0$$\\end{document} by using a conformal reflective metasurface. Unlike the existing beam steering strategies, the suggested method is not only cost-effective but also increases the overall directivity and gain of the source MIMO antenna. The measured results agree with the simulated results, making it a potential candidate in the 5G and beyond beam steering applications.

Employing artificial intelligence to steer exascale workflows with colmena

Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. Our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing.

Distributed cooperative guidance strategy based on virtual negotiation and rolling optimization

The problem of multi-UAV cooperative guidance under target maneuvering conditions is studied, and a distributed cooperative guidance strategy based on virtual negotiation mechanism and rolling horizon optimization is proposed. Firstly, in order to improve the time coordination and guidance control effect at the same time, local coordination variables and objective functions are designed under the rolling horizon optimization framework, and a multi-constrained cooperative guidance optimization model in a limited time domain is constructed; secondly, in order to enhance the robustness of the guidance system to maneuvering targets, a disturbance observer based on nonlinear autoregressive neural network is designed; thirdly, for the control input coupling between UAVs, a virtual negotiation mechanism is proposed to achieve synchronous decision-making and eliminate internal "differences", and the root mean square propagation algorithm of Nesterov momentum is used to design the guidance command generation strategy; finally, digital simulation and semi-physical simulation experiments are carried out respectively. The results show that the proposed guidance strategy can cope with various interferences and improve the cooperative guidance effect through online prediction and optimization, and is expected to be further applied and tested in actual tasks.