Background: In recent years more and more electronic health behaviour interventions have been developed in order to reach individuals with an unhealthy behaviour such as risky drinking. This is especially relevant in ...Background: In recent years more and more electronic health behaviour interventions have been developed in order to reach individuals with an unhealthy behaviour such as risky drinking. This is especially relevant in university students who are among those who most frequently are risky drinkers. This study explored the acceptability and feasibility, in an unselected group of university students, of a fully automated multiple session alcohol intervention offering different modes of delivery such as email, SMS and Android. Material and Methods: A total of 11,283 students at Link?ping University in Sweden were invited to perform a single session alcohol intervention and among those accepting this (4916 students) a total of 24.7% accepted to further participate in the extended multiple intervention lasting 3 - 6 weeks. The students could choose mode of delivery, total length of the intervention (between 3 - 6 weeks) and number of messages per week (3, 5, or 7 per week). A follow-up questionnaire was applied after the intervention to which 82.7% responded. Results: most students wanted to receive the messages by email with the shortest intervention length (3 weeks) and as few messages as possible per week (3 messages). However, no major difference was seen regarding satisfaction with the length and frequency of the intervention despite chosen length and frequency. Most students also expressed satisfaction with the content of the messages and would recommend the intervention to a fellow student in need of reducing drinking. Discussion and Conclusion: Based upon feedback from the students, a multiple push-based intervention appears to be feasible to offer students interested in additional support after a single session alcohol intervention. In a forthcoming study we will further explore the optimal mode of delivery and length of intervention and number of messages per week.展开更多
Feature selection(FS)is essential in machine learning(ML)and data mapping by its ability to preprocess high-dimensional data.By selecting a subset of relevant features,feature selection cuts down on the dimension of t...Feature selection(FS)is essential in machine learning(ML)and data mapping by its ability to preprocess high-dimensional data.By selecting a subset of relevant features,feature selection cuts down on the dimension of the data.It excludes irrelevant or surplus features,thus boosting the performance and efficiency of the model.Particle Swarm Optimization(PSO)boasts a streamlined algorithmic framework and exhibits rapid convergence traits.Compared with other algorithms,it incurs reduced computational expenses when tackling high-dimensional datasets.However,PSO faces challenges like inadequate convergence precision.Therefore,regarding FS problems,this paper presents a binary version enhanced PSO based on the Support Vector Machines(SVM)classifier.First,the Sand Cat Swarm Optimization(SCSO)is added to enhance the global search capability of PSO and improve the accuracy of the solution.Secondly,the Latin hypercube sampling strategy initializes populations more uniformly and helps to increase population diversity.The last is the roundup search strategy introducing the grey wolf hierarchy idea to help improve convergence speed.To verify the capability of Self-adaptive Cooperative Particle Swarm Optimization(SCPSO),the CEC2020 test suite and CEC2022 test suite are selected for experiments and applied to three engineering problems.Compared with the standard PSO algorithm,SCPSO converges faster,and the convergence accuracy is significantly improved.Moreover,SCPSO’s comprehensive performance far exceeds that of other algorithms.Six datasets from the University of California,Irvine(UCI)database were selected to evaluate SCPSO’s effectiveness in solving feature selection problems.The results indicate that SCPSO has significant potential for addressing these problems.展开更多
Longevity is regarded as the most important functional trait in cattle breeding with high economic value yet low heritability. In order to identify genomic regions associated with longevity, a genome wise association ...Longevity is regarded as the most important functional trait in cattle breeding with high economic value yet low heritability. In order to identify genomic regions associated with longevity, a genome wise association study was performed using data from 4887 Fleckvieh bulls and 33,556 SNPs after quality control. Single SNP regression was used for identification of important SNPs including eigenvectors as a means of correction for population structure. SNPs selected with a false discovery rate threshold of 0.05 and with local false discovery rate identified genomic regions associated with longevity which were subsequently cross checked with the National Center for Biotechnology Information (NCBI) database. This, to identify interesting genes in cattle and their homologue forms in other species. The most notable genes were SYT10 located on chromosome 5, ADAMTS3 on chromosome 6, NTRK2 on chromosome 8 and SNTG1 on chromosome 14 of the cattle genome. Several of the genes found have previously been associated with cattle fertility. Poor fertility is an important culling reason and thereby affects longevity in cattle. Several signals were located in regions sparse with described genes, which suggest that there might be several other non-identified genetic pathways for this important trait.展开更多
human-automation collaboration.This problem is particularly pronounced in time-constrained safety critical domains such as in Air Traffic Management.A visual representation should aid operators understanding why the s...human-automation collaboration.This problem is particularly pronounced in time-constrained safety critical domains such as in Air Traffic Management.A visual representation should aid operators understanding why the system initiates the communication,when the operator must act,and the consequences of not responding to the cue.Data glyphs can be used to present multidimensional data,including temporal data in a compact format to facilitate this type of communication.In this paper,we propose a glyph design for communication initialization for highly automated systems in Air Traffic Management,Vessel Traffic Service,and Train Traffic Management.The design was assessed by experts in these domains in three workshop sessions.The results showed that the number of glyphs to be presented simultaneously and the type of situation were domain-specific glyph design aspects that needed to be adjusted for each work domain.The results also showed that the core of the glyph design could be reused between domains,and that the operators could successfully interpret the temporal data representations.We discuss similarities and differences in the applicability of the glyph design between the different domains,and finally,we provide some suggestions for future work based on the results from this study.展开更多
摘要Background: In recent years more and more electronic health behaviour interventions have been developed in order to reach individuals with an unhealthy behaviour such as risky drinking. This is especially relevant in university students who are among those who most frequently are risky drinkers. This study explored the acceptability and feasibility, in an unselected group of university students, of a fully automated multiple session alcohol intervention offering different modes of delivery such as email, SMS and Android. Material and Methods: A total of 11,283 students at Link?ping University in Sweden were invited to perform a single session alcohol intervention and among those accepting this (4916 students) a total of 24.7% accepted to further participate in the extended multiple intervention lasting 3 - 6 weeks. The students could choose mode of delivery, total length of the intervention (between 3 - 6 weeks) and number of messages per week (3, 5, or 7 per week). A follow-up questionnaire was applied after the intervention to which 82.7% responded. Results: most students wanted to receive the messages by email with the shortest intervention length (3 weeks) and as few messages as possible per week (3 messages). However, no major difference was seen regarding satisfaction with the length and frequency of the intervention despite chosen length and frequency. Most students also expressed satisfaction with the content of the messages and would recommend the intervention to a fellow student in need of reducing drinking. Discussion and Conclusion: Based upon feedback from the students, a multiple push-based intervention appears to be feasible to offer students interested in additional support after a single session alcohol intervention. In a forthcoming study we will further explore the optimal mode of delivery and length of intervention and number of messages per week.
基金supported by the Fundamental Research Funds for the Central Universities of China(No.300102122105)the Natural Science Basic Research Plan in Shaanxi Province of China(2023-JC-YB-023).
摘要Feature selection(FS)is essential in machine learning(ML)and data mapping by its ability to preprocess high-dimensional data.By selecting a subset of relevant features,feature selection cuts down on the dimension of the data.It excludes irrelevant or surplus features,thus boosting the performance and efficiency of the model.Particle Swarm Optimization(PSO)boasts a streamlined algorithmic framework and exhibits rapid convergence traits.Compared with other algorithms,it incurs reduced computational expenses when tackling high-dimensional datasets.However,PSO faces challenges like inadequate convergence precision.Therefore,regarding FS problems,this paper presents a binary version enhanced PSO based on the Support Vector Machines(SVM)classifier.First,the Sand Cat Swarm Optimization(SCSO)is added to enhance the global search capability of PSO and improve the accuracy of the solution.Secondly,the Latin hypercube sampling strategy initializes populations more uniformly and helps to increase population diversity.The last is the roundup search strategy introducing the grey wolf hierarchy idea to help improve convergence speed.To verify the capability of Self-adaptive Cooperative Particle Swarm Optimization(SCPSO),the CEC2020 test suite and CEC2022 test suite are selected for experiments and applied to three engineering problems.Compared with the standard PSO algorithm,SCPSO converges faster,and the convergence accuracy is significantly improved.Moreover,SCPSO’s comprehensive performance far exceeds that of other algorithms.Six datasets from the University of California,Irvine(UCI)database were selected to evaluate SCPSO’s effectiveness in solving feature selection problems.The results indicate that SCPSO has significant potential for addressing these problems.
基金financial support of the Austrian Ministry for Transport,Innovation and Technology(BMVIT)and the Austrian Science Fund(FWF)via the project TRP46-B19Part of the study was conducted using a travel grant provided by the European Science Foundation(ESF).
摘要Longevity is regarded as the most important functional trait in cattle breeding with high economic value yet low heritability. In order to identify genomic regions associated with longevity, a genome wise association study was performed using data from 4887 Fleckvieh bulls and 33,556 SNPs after quality control. Single SNP regression was used for identification of important SNPs including eigenvectors as a means of correction for population structure. SNPs selected with a false discovery rate threshold of 0.05 and with local false discovery rate identified genomic regions associated with longevity which were subsequently cross checked with the National Center for Biotechnology Information (NCBI) database. This, to identify interesting genes in cattle and their homologue forms in other species. The most notable genes were SYT10 located on chromosome 5, ADAMTS3 on chromosome 6, NTRK2 on chromosome 8 and SNTG1 on chromosome 14 of the cattle genome. Several of the genes found have previously been associated with cattle fertility. Poor fertility is an important culling reason and thereby affects longevity in cattle. Several signals were located in regions sparse with described genes, which suggest that there might be several other non-identified genetic pathways for this important trait.
基金funded by the Swedish Transport Administration,Sweden through project F AUTO (part I:TRV 2018/41347 and part II:TRV 2020/138317).
摘要human-automation collaboration.This problem is particularly pronounced in time-constrained safety critical domains such as in Air Traffic Management.A visual representation should aid operators understanding why the system initiates the communication,when the operator must act,and the consequences of not responding to the cue.Data glyphs can be used to present multidimensional data,including temporal data in a compact format to facilitate this type of communication.In this paper,we propose a glyph design for communication initialization for highly automated systems in Air Traffic Management,Vessel Traffic Service,and Train Traffic Management.The design was assessed by experts in these domains in three workshop sessions.The results showed that the number of glyphs to be presented simultaneously and the type of situation were domain-specific glyph design aspects that needed to be adjusted for each work domain.The results also showed that the core of the glyph design could be reused between domains,and that the operators could successfully interpret the temporal data representations.We discuss similarities and differences in the applicability of the glyph design between the different domains,and finally,we provide some suggestions for future work based on the results from this study.