Self-Adaptation in Highly Distributed Dynamic Systems: part of Report from the GI Dagstuhl Seminar 14433: Software Engineering for Self-Adaptive Systems

International audienceThis breakout group focused on identifying the challenges of performing self-adaptation in highly distributed dynamic systems. This is a pressing issue in self-adaptive systems research, as proposed ``smart" systems are increasingly built out of disparate entities (sensors and actuators) that feature a close connection to the physical world -- so-called cyber-physical systems (CPSs). Examples are numerous: intelligent vehicle navigation, fleets of autonomous robots, emergency coordination systems, to mention just a few. CPSs are typically distributed at the physical space and feature no firm boundaries -- they are open-ended. They are composed of loosely connected entities, that are often mobile. Grafting such systems with self-adaptive capabilities is a distinct challenge, which projects itself in all phases of the autonomic loop

Adaptive Exchange of Distributed Partial [email protected] for Highly Dynamic Systems

International audienceWe are experiencing a world where Cyber-Physical Systems (CPSs) play a more and more crucial role. CPSs integrate computational, physical, and networking elements; they comprise a number of subsystems, or entities, that are connected and work together. The open and highly distributed nature of the resulting system gives rise to unanticipated runtime management issues such as the organization of subsystems and resource optimization.In this paper, we focus on the problem of knowledge sharing among cooperating entities of a highly distributed and self- adaptive CPS. Specifically, the research question we address is how to minimize the knowledge that needs to be shared among the entities of a CPS. If all entities share all their knowledge with each other, the performance, energy and memory consumption as well as privacy are unnecessarily negatively impacted. To reduce the amount of knowledge to share between CPS entities, we envision a role-based adaptive knowledge exchange technique working on partial runtime models, i.e., models reflecting only part of the state of the CPS. Our approach supports two adaptation dimensions: the runtime type of and conditions over the knowledge. We illustrate the feasibility of our technique by discussing its realization based on two state-of-the-art approaches

Adaptive Exchange of Distributed Partial [email protected] for Highly Dynamic Systems

International audienceWe are experiencing a world where Cyber-Physical Systems (CPSs) play a more and more crucial role. CPSs integrate computational, physical, and networking elements; they comprise a number of subsystems, or entities, that are connected and work together. The open and highly distributed nature of the resulting system gives rise to unanticipated runtime management issues such as the organization of subsystems and resource optimization.In this paper, we focus on the problem of knowledge sharing among cooperating entities of a highly distributed and self- adaptive CPS. Specifically, the research question we address is how to minimize the knowledge that needs to be shared among the entities of a CPS. If all entities share all their knowledge with each other, the performance, energy and memory consumption as well as privacy are unnecessarily negatively impacted. To reduce the amount of knowledge to share between CPS entities, we envision a role-based adaptive knowledge exchange technique working on partial runtime models, i.e., models reflecting only part of the state of the CPS. Our approach supports two adaptation dimensions: the runtime type of and conditions over the knowledge. We illustrate the feasibility of our technique by discussing its realization based on two state-of-the-art approaches

A Parellel two Stage Classifier for Breast Cancer Prediction and Comparison with Various Ensemble Techniques

Life is a blessing but some diseases snatch human life away before even they are being diagnosed. One such horrifying disease is cancer. Among cancer, the most leading and common type is breast cancer.  The actual problem lies in the fact that it is very hard and time consuming for even the most experienced medical specialist to detect the disease with high accuracy but the machines and modern computer science techniques have increased the accuracy and reduced the amount of time taken to diagnose cancer. In the subject paper, a new parallel machine learning technique called the two-stage classifier for identifying breast cancer is presented and compared with various existing techniques in terms of accuracy and percentage error reduction. The proposed technique turns out to be better not only in terms of parallelism but also in terms of the evaluated metrics and reduced the error percentage to almost 50% in one of the cases

Intra-operative use of biological products - are we aware of their derivatives?

INTRODUCTION Global medical advances within healthcare have subsequently led to the widespread introduction of biological products such as grafts, haemostats, and sealants. Although these products have been used for many decades, this subject is frequently not discussed during the consent process and remains an area of contention. METHODS A nationwide confidential online survey was distributed to UK-based junior registrars (ST3-5), senior registrars (ST6-8), post-CCT fellows, specialist associates/staff grade doctors and consultants working in general/vascular surgery, neurosurgery, otolaryngology, oral & maxillofacial surgery and plastic surgery. RESULTS Data was collected from a total of 308 survey respondents. Biological derivatives were correctly identified in surgical products by only 25% of survey respondents, only 19% stated that they regularly consent for use of these products. Our results demonstrate that most participants in this study do not routinely consent (81%) to the intra-operative use of biological materials. An overwhelming 74% of participants agreed that further education on the intra-operative use of biological materials would be valuable. DISCUSSION This study highlights deficiencies in knowledge that results in potential compromise of the consenting process for surgical procedures. A solution to this would be for clinicians to increase their awareness via educational platforms and to incorporate an additional statement on the consent form which addresses the potential intraoperative use of biological products and what their derivatives may be. CONCLUSION Modernising the current consent process to reflect the development and use of surgical biological products will help to ensure improved patient satisfaction, fewer future legal implications as well as a better surgeon-patient relationship

Assessing Meteorological and Agricultural Drought in Chitral Kabul River Basin Using Multiple Drought Indices

Drought is a complex and poorly understood natural hazard in complex terrain and plains lie in foothills of Hindukush-Himalaya-Karakoram region of Central and South Asia. Few research studied climate change scenarios in the transboundary Chitral Kabul River Basin (CKRB) despite its vulnerability to global warming and importance as a region inhabited with more than 10 million people where no treaty on use of water exists between Afghanistan and Pakistan. This study examines the meteorological and agricultural drought between 2000 and 2018 and their future trends from 2020 to 2030 in the CKRB. To study meteorological and agricultural drought comprehensively, various single drought indices such as Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI) and Vegetation Condition Index (VCI), and combined drought indices such as Scaled Drought Condition Index (SDCI) and Microwave Integrated Drought Index (MIDI) were utilized. As non-microwave data were used in MIDI, this index was given a new name as Non-Microwave Integrated Drought Index (NMIDI). Our research has found that 2000 was the driest year in the monsoon season followed by 2004 that experienced both meteorological and agricultural drought between 2000 and 2018. Results also indicate that though there exists spatial variation in the agricultural and meteorological drought, but temporally there has been a decreasing trend observed from 2000 to 2018 for both types of droughts. This trend is projected to continue in the future drought projections between 2020 and 2030. The overall study results indicate that drought can be properly assessed by integration of different data sources and therefore management plans can be developed to address the risk and signing new treaties