Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations ...Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations are usually represented by the probability distributions. However, precise characterization of uncertainty becomes challenging due to imperfect knowledge about some key aspects, such as birth targets and sensor detection profiles. To overcome this challenge, this paper proposes a multi-sensor possibility PHD filter based on the theory of outer probability measures. An effective compensation method is introduced to tackle variations in the fields of view of SSA sensors or instances of missed detections, aiming to mitigate the inconsistency in localized information. The proposed method is adapted to centralized and distributed sensor networks, offering effective solutions for multi-sensor multi-target tracking. The major innovation of the proposed method compared with typical methods is the proper description of epistemic uncertainty, which yields more robust performance in the scenarios of lacking some information about the system.The effectiveness of the multi-sensor possibility PHD filter is demonstrated by a comparison with conventional methods in two simulated scenarios.展开更多
Background:Despite access to university counseling services,many students underutilize them due to cultural stigma,language barriers,and perceived irrelevance.As a result,ChatGPT has emerged as an informal,always-avai...Background:Despite access to university counseling services,many students underutilize them due to cultural stigma,language barriers,and perceived irrelevance.As a result,ChatGPT has emerged as an informal,always-available support system.This study investigates how international PhD students in Malaysia navigate loneliness,mental well-being,and social disconnection through interactions with Generative AI(mainly ChatGPT).Methods:Using a mixed-methods design,the study surveyed 155 international doctoral students and analyzed quantitative responses across four dimensions:loneliness,well-being(WHO-5),perceived social support,and AI-facilitated emotional support.Additionally,open-ended responses were examined using Latent Dirichlet Allocation(LDA)to identify emergent themes.Results:Quantitative findings showed that ChatGPT use was modestly associated with greater loneliness(r=0.17)and lower perceived social support(r=−0.16),with only a weak positive link to well-being(r=0.11).Regression analysis confirmed these small effects,while qualitative themes revealed that students used ChatGPT mainly for emotional venting and productivity,underscoring its value as short-term support but also its potential to displace human interaction.More specifically,thematic analysis revealed two dominant student experiences:(1)emotional venting and calmness,and(2)productivity through non-judgmental dialogue.Conclusions:These findings suggest that while ChatGPT offers emotional reprieve and academic clarity,it may also displace human interaction.This study highlights the promise and pitfalls of AI-driven mental support in higher education.It urges institutions to enhance peer networks,foster culturally responsive mentoring,and develop ethical AI usage guidelines to support international doctoral students holistically.展开更多
基金funded by the National Natural Science Foundation of China(No.12202049)the Beijing Institute of Technology Research Fund Program for Young Scholars,China.
文摘Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness(SSA) applications. In this process, the uncertainties of targets,dynamics, and observations are usually represented by the probability distributions. However, precise characterization of uncertainty becomes challenging due to imperfect knowledge about some key aspects, such as birth targets and sensor detection profiles. To overcome this challenge, this paper proposes a multi-sensor possibility PHD filter based on the theory of outer probability measures. An effective compensation method is introduced to tackle variations in the fields of view of SSA sensors or instances of missed detections, aiming to mitigate the inconsistency in localized information. The proposed method is adapted to centralized and distributed sensor networks, offering effective solutions for multi-sensor multi-target tracking. The major innovation of the proposed method compared with typical methods is the proper description of epistemic uncertainty, which yields more robust performance in the scenarios of lacking some information about the system.The effectiveness of the multi-sensor possibility PHD filter is demonstrated by a comparison with conventional methods in two simulated scenarios.
文摘Background:Despite access to university counseling services,many students underutilize them due to cultural stigma,language barriers,and perceived irrelevance.As a result,ChatGPT has emerged as an informal,always-available support system.This study investigates how international PhD students in Malaysia navigate loneliness,mental well-being,and social disconnection through interactions with Generative AI(mainly ChatGPT).Methods:Using a mixed-methods design,the study surveyed 155 international doctoral students and analyzed quantitative responses across four dimensions:loneliness,well-being(WHO-5),perceived social support,and AI-facilitated emotional support.Additionally,open-ended responses were examined using Latent Dirichlet Allocation(LDA)to identify emergent themes.Results:Quantitative findings showed that ChatGPT use was modestly associated with greater loneliness(r=0.17)and lower perceived social support(r=−0.16),with only a weak positive link to well-being(r=0.11).Regression analysis confirmed these small effects,while qualitative themes revealed that students used ChatGPT mainly for emotional venting and productivity,underscoring its value as short-term support but also its potential to displace human interaction.More specifically,thematic analysis revealed two dominant student experiences:(1)emotional venting and calmness,and(2)productivity through non-judgmental dialogue.Conclusions:These findings suggest that while ChatGPT offers emotional reprieve and academic clarity,it may also displace human interaction.This study highlights the promise and pitfalls of AI-driven mental support in higher education.It urges institutions to enhance peer networks,foster culturally responsive mentoring,and develop ethical AI usage guidelines to support international doctoral students holistically.
