Deep Q‐network implementation for simulated autonomous vehicle control

Deep reinforcement learning is poised to be a revolutionised step towards newer possibilities in solving navigation and autonomous vehicle control tasks. Deep Q‐network (DQN) is one of the more popular methods of deep reinforcement learning that allows the agent that controls the vehicle to learn through its mistakes based on its actions and interactions with the environment. This paper presents the implementation of DQN to an autonomous self‐driving vehicle control in two different simulated environments; first environment is in Python which is a simple 2D environment and then advanced to Unity software separately which is a 3D environment. Based on the scores and pixel inputs, the agent in the vehicle learns and adapts to its surrounding. It develops the best solution strategy to direct itself in the environment where its task is to manoeuvre the vehicle from point to point on a simulated highway scenario. The implemented DQN technique approximates the action value function with convolutional neural network. This evaluates the Q‐function for the Q‐learning architecture and updates the action value function. This paper shows that DQN is an effective learning method for the agent of an autonomous vehicle. In both simulated environments, the autonomous vehicle gradually learnt the manoeuvre operations and progressively gained the ability to successfully navigate itself and avoid obstacles without prior information of the surrounding

Towards a global partnership model in interprofessional education for cross-sector problem-solving

Objectives A partnership model in interprofessional education (IPE) is important in promoting a sense of global citizenship while preparing students for cross-sector problem-solving. However, the literature remains scant in providing useful guidance for the development of an IPE programme co-implemented by external partners. In this pioneering study, we describe the processes of forging global partnerships in co-implementing IPE and evaluate the programme in light of the preliminary data available. Methods This study is generally quantitative. We collected data from a total of 747 health and social care students from four higher education institutions. We utilized a descriptive narrative format and a quantitative design to present our experiences of running IPE with external partners and performed independent t-tests and analysis of variance to examine pretest and posttest mean differences in students’ data. Results We identified factors in establishing a cross-institutional IPE programme. These factors include complementarity of expertise, mutual benefits, internet connectivity, interactivity of design, and time difference. We found significant pretest–posttest differences in students’ readiness for interprofessional learning (teamwork and collaboration, positive professional identity, roles, and responsibilities). We also found a significant decrease in students’ social interaction anxiety after the IPE simulation. Conclusions The narrative of our experiences described in this manuscript could be considered by higher education institutions seeking to forge meaningful external partnerships in their effort to establish interprofessional global health education

Students’ interaction anxiety and social phobia in interprofessional education in Hong Kong: mapping a new research direction

AbstractBackground Interprofessional education (IPE) has been promoted as a breakthrough in healthcare because of the impact when professionals work as a team. However, despite its inception dating back to the 1960s, its science has taken a long time to advance. There is a need to theorize IPE to cultivate creative insights for a nuanced understanding of IPE. This study aims to propose a research agenda on social interaction by understanding the measurement scales used and guiding researchers to contribute to the discussion of social processes in IPE.Method This quantitative research was undertaken in a cross-institutional IPE involving 925 healthcare students (Medicine, Nursing, Social Work, Chinese Medicine, Pharmacy, Speech Language Pathology, Clinical Psychology, Food and Nutritional Science and Physiotherapy) from two institutions in Hong Kong. Participants completed the Social Interaction Anxiety Scale (SIAS-6) and Social Phobia Scale (SPS-6). We applied a construct validation approach: within-network and between-network validation. We performed confirmatory factors analysis, t-test, analysis of variance and regression analysis.Results CFA results indicated that current data fit the a priori model providing support to within-network validity [RMSEA=.08, NFI=.959, CFI=.965, IFI=.965, TLI=.955]. The criteria for acceptable fit were met. The scales were invariant between genders, across year levels and disciplines. Results indicated that social interaction anxiety and social phobia negatively predicted behavioural engagement (F = 25.093, p<.001, R2=.065) and positively predicted behavioural disaffection (F = 22.169, p<.001, R2=.057) to IPE, suggesting between-network validity.Conclusions Our data provided support for the validity of the scales when used among healthcare students in Hong Kong. SIAS-6 and SPS-6 have sound psychometric properties based on students’ data in Hong Kong. We identified quantitative, qualitative and mixed methods research designs to guide researchers in getting involved in the discussion of students’ social interactions in IPE.Key MessagesThe Social Anxiety Scale (SIAS-6) and Social Phobia Scale (SPS-6) scales have sound psychometric properties based on the large-scale healthcare students’ data in IPE in Hong Kong.Social interaction anxiety and social phobia negatively predicted students’ behavioural engagement with IPE and positively predicted behavioural disaffection. The scales are invariant in terms of gender, year level and discipline.Quantitative, qualitative and mixed methods studies are proposed to aid researchers to contribute in healthcare education literature using the SIAS-6 and SPS-6

Towards a global partnership model in interprofessional education for cross-sector problem-solving

Abstract Objectives A partnership model in interprofessional education (IPE) is important in promoting a sense of global citizenship while preparing students for cross-sector problem-solving. However, the literature remains scant in providing useful guidance for the development of an IPE programme co-implemented by external partners. In this pioneering study, we describe the processes of forging global partnerships in co-implementing IPE and evaluate the programme in light of the preliminary data available. Methods This study is generally quantitative. We collected data from a total of 747 health and social care students from four higher education institutions. We utilized a descriptive narrative format and a quantitative design to present our experiences of running IPE with external partners and performed independent t-tests and analysis of variance to examine pretest and posttest mean differences in students’ data. Results We identified factors in establishing a cross-institutional IPE programme. These factors include complementarity of expertise, mutual benefits, internet connectivity, interactivity of design, and time difference. We found significant pretest–posttest differences in students’ readiness for interprofessional learning (teamwork and collaboration, positive professional identity, roles, and responsibilities). We also found a significant decrease in students’ social interaction anxiety after the IPE simulation. Conclusions The narrative of our experiences described in this manuscript could be considered by higher education institutions seeking to forge meaningful external partnerships in their effort to establish interprofessional global health education

Guidelines for the use and interpretation of assays for monitoring autophagy

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Guidelines for the use and interpretation of assays for monitoring autophagy

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Guidelines for the use and interpretation of assays for monitoring autophagy

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field