This paper introduces a unified mathematical definition for describing commonly used terms encountered in systematical analysis of automated driving systems in mixedtraffic simulations. The most significant contribution of this work is in translating the terms that are clarified previously in literature into a mathematical set and function based format. Our work can be seen as an incremental step towards further formalisation of Domain-Specific-Language (DSL) for scenario representation. We also extended the previous work in the literature to allow more complex scenarios by expanding the model-incompliant information using set-theory to represent the perception capacity of the road-user agents. With this dynamic perception definition, we also support interactive scenarios and are not limited to reactive and pre-defined agent behavior. Our main focus is to give a framework to represent realistic roaduser behavior to be used in simulation or computational tool to examine interaction patterns in mixed-traffic conditions. We believe that, by formalising the verbose definitions and extending the previous work in DSL, we can support automatic scenario generation and dynamic/evolving agent behavior models for simulating mixed traffic situations and scenarios. In addition, we can obtain scenarios that are realistic but also can represent rare-conditions that are difficult to extract from field-tests and real driving data repositories.

Abstract. This paper describes our experience with the first steps towards integrating pathway and protein interaction data with other data sets within the framework of a federated database system based on the functional data model. We have made use of DTD and XML files produced by the BIND project. The DTD provides a specification for information about biomolecular interactions, complexes and pathways, and can be translated semi-automatically to a database schema. The load utility uses metadata derived from this schema to help identify data items of interest when recursively traversing a Prolog tree structure representing the XML data. We also show how derived functions can be used to make explicit those relationships that are present in data sets but which are not fully described in DTD files.

Asma merupakan penyakit saluran napas obstruktif intermiten, reversibel, di mana bronkus dan saluran udara menjadi terlalu aktif dalam menanggapi rangsangan tertentu, ditandai dengan gejala seperti mengi, sesak napas, sesak dada dan/atau batuk, sehingga dapat menyebabkan saluran pernafasan menjadi sempit. Tujuan penelitian ini adalah untuk menggambarkan penerapan batuk efektif dalam meningkatkan bersihan jalan nafas pada pasien Asma Bronkial. Desain penelitian menggunakan metode deskriptif dengan pendekatan case study research (studi kasus) di ruang Keremunting Rumah Sakit Umum dr. H. Marsidi Judono Kabupaten Belitung. Penelitian ini dilakukan pada bulan Januari 2022 – Juni 2022. Subjek kasus adalah satu individu dengan diagnosis medis Asma Bronkial yang mengalami masalah gangguan pernafasan (bersihan jalan nafas tidak efektif) fokus studi pada kasus ini adalah penerapan batuk efektif dalam meningkatkan bersihan jalan nafas pada pada pasien asma bronkial. pengambilan data dengan pengkajian, wawancara, pengukuran, dokumentasi, dan literature. Instrumen pengumpulan data menggunakan menggunakan format asuhan keperawatan medikal bedah, SOP batuk efektif, lembar observasi. Hasil intervensi pasien mampu melakukan batuk efektif, setiap hari jumlah sputum yang dikeluarkan mengalami peningkatan. Penerapan batuk efektif dapat meningkatkan bersihan jalan nafas pada pasien asma bronkial dengan masalah bersihan jalan nafas tidak efektif. Saran bagi penulis diharapkan dapat menambah wawasan dan sebagai sarana untuk menerapkan ilmu dalam bidang keperawatan tentang penerapan prosedur batuk efektif pada pasien asma bronkial.

Naturalistic driving studies (NDS) generate tremendous amounts of traffic data and constitute an important component of modern traffic safety research. However, analysis of the entire NDS database is rarely feasible, as it often requires expensive and time-consuming annotations of video sequences. We describe how automatic measurements, readily available in an NDS database, may be utilized for selection of time segments for annotation that are most informative with regards to detection of potential associations between driving behavior and a consecutive safety critical event. The methodology is illustrated and evaluated on data from a large naturalistic driving study, showing that the use of optimized instance selection may reduce the number of segments that need to be annotated by as much as 50%, compared to simple random sampling.

Purpose This paper aims to explore whether drivers would adapt their behavior when they drive among automated vehicles (AVs) compared to driving among manually driven vehicles (MVs).Understanding behavioral adaptation of drivers when they encounter AVs is crucial for assessing impacts of AVs in mixed-traffic situations. Here, mixed-traffic situations refer to situations where AVs share the roads with existing nonautomated vehicles such as conventional MVs. Design/methodology/approach A driving simulator study is designed to explore whether such behavioral adaptations exist. Two different driving scenarios were explored on a three-lane highway: driving on the main highway and merging from an on-ramp. For this study, 18 research participants were recruited. Findings Behavioral adaptation can be observed in terms of car-following speed, car-following time gap, number of lane change and overall driving speed. The adaptations are dependent on the driving scenario and whether the surrounding traffic was AVs or MVs. Although significant differences in behavior were found in more than 90% of the research participants, they adapted their behavior differently, and thus, magnitude of the behavioral adaptation remains unclear. Originality/value The observed behavioral adaptations in this paper were dependent on the driving scenario rather than the time gap between surrounding vehicles. This finding differs from previous studies, which have shown that drivers tend to adapt their behaviors with respect to the surrounding vehicles. Furthermore, the surrounding vehicles in this study are more “free flow'” compared to previous studies with a fixed formation such as platoons. Nevertheless, long-term observations are required to further support this claim.