In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring mi...In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks.展开更多
Background: Clinical reasoning is an essential skill for nursing students since it is required to solve difficulties that arise in complex clinical settings. However, teaching and learning clinical reasoning skills is...Background: Clinical reasoning is an essential skill for nursing students since it is required to solve difficulties that arise in complex clinical settings. However, teaching and learning clinical reasoning skills is difficult because of its complexity. This study, therefore aimed at exploring the challenges experienced by nurse educators in promoting acquisition of clinical reasoning skills by undergraduate nursing students. Methods: A qualitative exploratory research design was used in this study. The participants were purposively sampled and recruited into the study. Data were collected using semi-structured interview guides. Thematic analysis method was used to analyze the collected data The principles of beneficence, respect of human dignity and justice were observed. Results: The findings have shown that clinical learning environment, lacked material and human resources. The students had no interest to learn the skill. There was also knowledge gap between nurse educators and clinical nurses. Lack of role model was also an issue and limited time exposure. Conclusion: The study revealed that nurse educators encounter various challenges in promoting the acquisition of clinical reasoning skills among undergraduate nursing students. Training institutions and hospitals should periodically revise the curriculum and provide sufficient resources to facilitate effective teaching and learning of clinical reasoning. Nurse educators must also update their knowledge and skills through continuous professional development if they are to transfer the skill effectively.展开更多
Background: Clinical reasoning is a critical cognitive skill that enables undergraduate nursing students to make clinically sound decisions. A lapse in clinical reasoning can result in unintended harm to patients. The...Background: Clinical reasoning is a critical cognitive skill that enables undergraduate nursing students to make clinically sound decisions. A lapse in clinical reasoning can result in unintended harm to patients. The aim of the study was to assess and compare the levels of clinical reasoning skills between third year and fourth year undergraduate nursing students. Methods: The study utilized a descriptive comparative research design, based on the positivism paradigm. 410 undergraduate nursing students were systematically sampled and recruited into the study. The researchers used the Self-Assessment of Clinical Reflection and Reasoning questionnaire to collect data on clinical reasoning skills from third- and fourth-year nursing students while adhering to ethical principles of human dignity. Descriptive statistics were done to analyse the level of clinical reasoning and an independent sample t-test was performed to compare the clinical reasoning skills of the student. A p value of 0.05 was accepted. Results: The results of the study revealed that the mean clinical reasoning scores of the undergraduate nursing students were knowledge/theory application (M = 3.84;SD = 1.04);decision-making based on experience and evidence (M = 4.09;SD = 1.01);dealing with uncertainty (M = 3.93;SD = 0.87);reflection and reasoning (M = 3.77;SD = 3.88). The mean difference in clinical reasoning skills between third- and fourth-year undergraduate nursing students was not significantly different from an independent sample t-test scores (t = −1.08;p = 0.28);(t = −0.29;p = 0.73);(t = 1.19;p = 0.24);(t = −0.57;p = 0.57). Since the p-value is >0.05, the null hypothesis (H0) “there is no significantno significant difference in clinical reasoning between third year and fourth year undergraduate nursing students”, was accepted. Conclusion: This study has shown that the level of clinical reasoning skills of the undergraduate nursing students was moderate to low. This meant that the teaching methods have not been effective to improve the students clinical reasoning skills. Therefore, the training institutions should revise their curriculum by incorporating new teaching methods like simulation to enhance students’ clinical reasoning skills. In conclusion, evaluating clinical reasoning skills is crucial for addressing healthcare issues, validating teaching methods, and fostering continuous improvement in nursing education.展开更多
目的:比较使用不同模式Er:YAG激光以及传统车针去龋后牙本质与复合树脂的粘接强度。方法:选用人类离体磨牙模拟龋坏,分别采用Er:YAG激光中短脉冲(medium short pulse,MSP)模式、Er:YAG激光超短脉冲(super short pulse,SSP)模式和传统车...目的:比较使用不同模式Er:YAG激光以及传统车针去龋后牙本质与复合树脂的粘接强度。方法:选用人类离体磨牙模拟龋坏,分别采用Er:YAG激光中短脉冲(medium short pulse,MSP)模式、Er:YAG激光超短脉冲(super short pulse,SSP)模式和传统车针去除模拟的龋坏后,采用自酸蚀粘接剂将牙体标本与复合树脂粘接制成试件。使用万能试验机对试件进行拉伸试验,测得断裂负荷和粘接强度,并采用单因素方差分析和Tukey多重比较进行统计学分析。采用扫描电子显微镜观察3种不同去龋方式处理后的牙本质表面形态,以及涂布自酸蚀粘接剂并固化后试件的横截面形态。结果:使用Er:YAG激光MSP模式处理后牙本质与复合树脂的粘接强度最高,SSP模式处理后次之,传统车针处理后最低,但差异无统计学意义(P>0.05)。扫描电子显微镜图像显示,Er:YAG激光MSP模式处理后的牙本质表面较平坦,牙本质小管内几乎没有残屑;Er:YAG激光SSP模式处理后的牙本质表面呈现鳞片状,牙本质小管内可见少量碎屑;而传统车针处理后牙本质小管大部分处于被表面牙本质部分甚至完全遮盖的状态,牙本质小管内充满残屑。结论:使用Er:YAG激光去龋相比传统车针去龋可以获得较好的牙本质粘接强度,且对牙本质小管的处理深度和洁净度明显优于传统车针去龋,其中MSP模式更佳。展开更多
转镜调Q无插入损耗,是获得窄脉冲、高峰值功率输出激光的直接方式。纳秒脉冲需要使用高速转镜调Q,并精准控制电机转速与氙灯放电延时,以使激光介质上能级粒子数反转最大,获得最大激光能量输出。本文设计了以Arduino mega 2560单片机为...转镜调Q无插入损耗,是获得窄脉冲、高峰值功率输出激光的直接方式。纳秒脉冲需要使用高速转镜调Q,并精准控制电机转速与氙灯放电延时,以使激光介质上能级粒子数反转最大,获得最大激光能量输出。本文设计了以Arduino mega 2560单片机为核心的高速转镜调Q控制系统,通过精确单片机解析串口屏指令控制激光电源的充放电和高速电机启停,同时通过对转镜脉冲信号整合降频控制氙灯放电时刻,实现对延迟时间的精准控制,实现了灯泵Er,Cr:YSGG激光纳秒窄脉冲调Q输出。在5 Hz重复频率下,转镜转速为650 r/s时,获得的最高单脉冲激光能量为45.7 mJ、脉冲宽度为86.2 ns,相应的峰值功率为530.2 kW。展开更多
With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecas...With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecast potential threats.However,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods.To address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the reasoning.The reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical paths.The proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the KG.We emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate reasoning.In order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related semantics.Some critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation validity.In particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning performance.Meanwhile,the proposed algorithm avoids redundant paths between the given pairs of entities.Experimental results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.展开更多
基金supported by Key Laboratory of Information System Requirement,No.LHZZ202202Natural Science Foundation of Xinjiang Uyghur Autonomous Region(2023D01C55)Scientific Research Program of the Higher Education Institution of Xinjiang(XJEDU2023P127).
文摘In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks.
文摘Background: Clinical reasoning is an essential skill for nursing students since it is required to solve difficulties that arise in complex clinical settings. However, teaching and learning clinical reasoning skills is difficult because of its complexity. This study, therefore aimed at exploring the challenges experienced by nurse educators in promoting acquisition of clinical reasoning skills by undergraduate nursing students. Methods: A qualitative exploratory research design was used in this study. The participants were purposively sampled and recruited into the study. Data were collected using semi-structured interview guides. Thematic analysis method was used to analyze the collected data The principles of beneficence, respect of human dignity and justice were observed. Results: The findings have shown that clinical learning environment, lacked material and human resources. The students had no interest to learn the skill. There was also knowledge gap between nurse educators and clinical nurses. Lack of role model was also an issue and limited time exposure. Conclusion: The study revealed that nurse educators encounter various challenges in promoting the acquisition of clinical reasoning skills among undergraduate nursing students. Training institutions and hospitals should periodically revise the curriculum and provide sufficient resources to facilitate effective teaching and learning of clinical reasoning. Nurse educators must also update their knowledge and skills through continuous professional development if they are to transfer the skill effectively.
文摘Background: Clinical reasoning is a critical cognitive skill that enables undergraduate nursing students to make clinically sound decisions. A lapse in clinical reasoning can result in unintended harm to patients. The aim of the study was to assess and compare the levels of clinical reasoning skills between third year and fourth year undergraduate nursing students. Methods: The study utilized a descriptive comparative research design, based on the positivism paradigm. 410 undergraduate nursing students were systematically sampled and recruited into the study. The researchers used the Self-Assessment of Clinical Reflection and Reasoning questionnaire to collect data on clinical reasoning skills from third- and fourth-year nursing students while adhering to ethical principles of human dignity. Descriptive statistics were done to analyse the level of clinical reasoning and an independent sample t-test was performed to compare the clinical reasoning skills of the student. A p value of 0.05 was accepted. Results: The results of the study revealed that the mean clinical reasoning scores of the undergraduate nursing students were knowledge/theory application (M = 3.84;SD = 1.04);decision-making based on experience and evidence (M = 4.09;SD = 1.01);dealing with uncertainty (M = 3.93;SD = 0.87);reflection and reasoning (M = 3.77;SD = 3.88). The mean difference in clinical reasoning skills between third- and fourth-year undergraduate nursing students was not significantly different from an independent sample t-test scores (t = −1.08;p = 0.28);(t = −0.29;p = 0.73);(t = 1.19;p = 0.24);(t = −0.57;p = 0.57). Since the p-value is >0.05, the null hypothesis (H0) “there is no significantno significant difference in clinical reasoning between third year and fourth year undergraduate nursing students”, was accepted. Conclusion: This study has shown that the level of clinical reasoning skills of the undergraduate nursing students was moderate to low. This meant that the teaching methods have not been effective to improve the students clinical reasoning skills. Therefore, the training institutions should revise their curriculum by incorporating new teaching methods like simulation to enhance students’ clinical reasoning skills. In conclusion, evaluating clinical reasoning skills is crucial for addressing healthcare issues, validating teaching methods, and fostering continuous improvement in nursing education.
文摘目的:比较使用不同模式Er:YAG激光以及传统车针去龋后牙本质与复合树脂的粘接强度。方法:选用人类离体磨牙模拟龋坏,分别采用Er:YAG激光中短脉冲(medium short pulse,MSP)模式、Er:YAG激光超短脉冲(super short pulse,SSP)模式和传统车针去除模拟的龋坏后,采用自酸蚀粘接剂将牙体标本与复合树脂粘接制成试件。使用万能试验机对试件进行拉伸试验,测得断裂负荷和粘接强度,并采用单因素方差分析和Tukey多重比较进行统计学分析。采用扫描电子显微镜观察3种不同去龋方式处理后的牙本质表面形态,以及涂布自酸蚀粘接剂并固化后试件的横截面形态。结果:使用Er:YAG激光MSP模式处理后牙本质与复合树脂的粘接强度最高,SSP模式处理后次之,传统车针处理后最低,但差异无统计学意义(P>0.05)。扫描电子显微镜图像显示,Er:YAG激光MSP模式处理后的牙本质表面较平坦,牙本质小管内几乎没有残屑;Er:YAG激光SSP模式处理后的牙本质表面呈现鳞片状,牙本质小管内可见少量碎屑;而传统车针处理后牙本质小管大部分处于被表面牙本质部分甚至完全遮盖的状态,牙本质小管内充满残屑。结论:使用Er:YAG激光去龋相比传统车针去龋可以获得较好的牙本质粘接强度,且对牙本质小管的处理深度和洁净度明显优于传统车针去龋,其中MSP模式更佳。
基金Our work is supported by the National Key R&D Program of China(2021YFB2012400).
文摘With the growing discovery of exposed vulnerabilities in the Industrial Control Components(ICCs),identification of the exploitable ones is urgent for Industrial Control System(ICS)administrators to proactively forecast potential threats.However,it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods.To address these challenges,we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph(KG)in which relation paths contain abundant potential evidence to support the reasoning.The reasoning task in this work refers to determining whether a specific relation is valid between an attacker entity and a possible exploitable vulnerability entity with the help of a collective of the critical paths.The proposed method consists of three primary building blocks:KG construction,relation path representation,and query relation reasoning.A security-oriented ontology combines exploit modeling,which provides a guideline for the integration of the scattered knowledge while constructing the KG.We emphasize the role of the aggregation of the attention mechanism in representation learning and ultimate reasoning.In order to acquire a high-quality representation,the entity and relation embeddings take advantage of their local structure and related semantics.Some critical paths are assigned corresponding attentive weights and then they are aggregated for the determination of the query relation validity.In particular,similarity calculation is introduced into a critical path selection algorithm,which improves search and reasoning performance.Meanwhile,the proposed algorithm avoids redundant paths between the given pairs of entities.Experimental results show that the proposed method outperforms the state-of-the-art ones in the aspects of embedding quality and query relation reasoning accuracy.
