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This research proposes the development of a next generation airline reservation system that incorporates the Cloud microservices, distributed artificial intelligence modules and the blockchain technology to improve on the efficiency, safety and customer satisfaction. The traditional reservation systems encounter issues related to the expansion of the systems, the integrity of the data provided and the level of service offered to the customers, which is the main focus of this architecture through the modular and data centric design approaches. This will allow different operations such as reservations, payments, and customer data management among others to be performed separately thereby facilitating high availability of the system by 30% and enhancing performance of the system by 40% on its scalability. Such systems contain AI driven modules that utilize the past booking patterns along with the profile of the customer to estimate the demand and make recommendations, which increases to 25 % of customer engagement. Moreover, blockchain is effective in engaging an incorruptible ledger system for the all transactions therefore mitigating fraud incidences and increasing the clarity by 20%. The system was subjected to analysis using a simulator and using machine learning evaluations that rated it against other conventional systems. The results show that there were clear enhancements in the speed of transactions where the rates of secure data processing rose by 35%, and the system response time by 15 %. The system can also be used for other high transaction industries like logistics and hospitality. This structural design is indicative of how the use of advanced technologies will revolutionize the airline reservation sector. The implications are growing effectiveness, improvement in security and greater customer contentment., Comment: 25 pages, 8 figures

This research investigates the implementation of a real-time, microservices-oriented dynamic pricing system for the travel sector. The system is designed to address factors such as demand, competitor pricing, and other external circumstances in real-time. Both controlled simulation and real-life application showed a respectable gain of 22% in revenue generation and a 17% improvement in pricing response time which concern the issues of scaling and flexibility of classical pricing mechanisms. Demand forecasting, competitor pricing strategies, and event-based pricing were implemented as separate microservices to enhance their scalability and reduce resource consumption by 30% during peak loads. Customers were also more content as depicted by a 15% increase in satisfaction score post-implementation given the appreciation of more appropriate pricing. This research enhances the existing literature with practical illustrations of the possible application of microservices technology in developing dynamic pricing solutions in a complex and data-driven context. There exist however areas for improvement for instance inter-service latency and the need for extensive real-time data pipelines. The present research goes on to suggest combining these with direct data capture from customer behavior at the same time as machine learning capacity developments in pricing algorithms to assist in more accurate real time pricing. It is determined that the use of microservices is a reasonable and efficient model for dynamic pricing, allowing the tourism sector to employ evidence-based and customer centric pricing techniques, which ensures that their profits are not jeopardized because of the need for customers., Comment: 19 pages, 18 figures

This paper introduces a microservices architecture for the purpose of enhancing the flexibility and performance of an airline reservation system. The architectural design incorporates Redis cache technologies, two different messaging systems (Kafka and RabbitMQ), two types of storages (MongoDB, and PostgreSQL). It also introduces authorization techniques, including secure communication through OAuth2 and JWT which is essential with the management of high-demand travel services. According to selected indicators, the architecture provides an impressive level of data consistency at 99.5% and a latency of data propagation of less than 75 ms allowing rapid and reliable intercommunication between microservices. A system throughput of 1050 events per second was achieved so that the acceptability level was maintained even during peak time. Redis caching reduced a 92% cache hit ratio on the database thereby lowering the burden on the database and increasing the speed of response. Further improvement of the systems scalability was done through the use of Docker and Kubernetes which enabled services to be expanded horizontally to cope with the changes in demand. The error rates were very low, at 0.2% further enhancing the efficiency of the system in handling real-time data integration. This approach is suggested to meet the specific needs of the airline reservation system. It is secure, fast, scalable, all serving to improve the user experience as well as the efficiency of operations. The low latency and high data integration levels and prevaiing efficient usage of the resources demonstrates the architecture ability to offer continued support in the ever growing high demand situations., Comment: 20 pages, 12 figures

This paper investigates the inclusion of microservices architecture in the development of scalable and reliable airline reservation systems. Most of the traditional reservation systems are very rigid and centralized which makes them prone to bottlenecks and a single point of failure. As such, systems do not meet the requirements of modern airlines which are dynamic. Microservices offer better resiliency and scalability because the services do not depend on one another and can be deployed independently. The approach is grounded on the Circuit Breaker Pattern to maintain fault tolerance while consuming foreign resources such as flight APIs and payment systems. This avoided the failure propagation to the systems by 60% enabling the systems to function under external failures. Traffic rerouting also bolstered this with a guarantee of above 99.95% uptime in systems where high availability was demanded. To address this, load balancing was used, particularly the Round-Robin method which managed to enhance performance by 35% through the equal distribution of user requests among the service instances. Health checks, as well as monitoring in real-time, helped as well with failure management as they helped to contain failures before the users of the system were affected. The results suggest that the use of microservices led to a 40% increase in system scalability, a 50% decrease in downtime and a support for 30% more concurrent users than the use of monolithic architectures. These findings affirm the capability of microservices in the development of robust and flexible airline ticket booking systems that are responsive to change and recover from external system unavailability., Comment: 18 pages, 3 figures

The objective of this research is how an implementation of AI algorithms in the microservices architecture enhances travel itineraries by cost, time, user preferences, and environmental sustainability. It uses machine learning models for both cost forecasting and personalization, genetic algorithm for optimization of the itinerary, and heuristics for sustainability checking. Primary evaluated parameters consist of latency, ability to satisfy user preferences, cost and environmental concern. The experimental results demonstrate an average of 4.5 seconds of response time on 1000 concurrent users and 92% of user preferences accuracy. The cost efficiency is proved, with 95% of provided trips being within the limits of the budget declared by the user. The system also implements some measures to alleviate negative externalities related to travel and 60% of offered travel plans had green options incorporated, resulting in the average 15% lower carbon emissions than the traditional travel plans offered. The genetic algorithm with time complexity O(g.p.f) provides the optimal solution in 100 generations. Every iteration improves the quality of the solution by 5%, thus enabling its effective use in optimization problems where time is measured in seconds. Finally, the system is designed to be fault-tolerant with functional 99.9% availability which allows the provision of services even when requirements are exceeded. Travel optimization platform is turned dynamic and efficient by this microservices based architecture which provides enhanced scaling, allows asynchronous communication and real time changes. Because of the incorporation of Ai, cost control and eco-friendliness approaches, the system addresses the different user needs in the present days travel business., Comment: 18 pages, 6 figures

This research gives a detailed analysis of the application of blockchain technology to the airline reservation systems in order to bolster trust, transparency, and operational efficiency by overcoming several challenges including customer control and data integrity issues. The study investigates the major components of blockchain technology such as decentralised databases, permanent records of transactions and transactional clauses executed via codes of programs and their impacts on automated systems and real-time tracking of audits. The results show a 30% decrease in booking variations together with greater data synchronization as a result of consensus processes and resistant data formations. The approach to the implementation of a blockchain technology for the purpose of this paper includes many APIs for the automatic multi-faceted record-keeping system including the smart contract execution and controllable end-users approach. Smart contracts organized the processes improving the cycle times by 40% on the average while guaranteeing no breach of agreements. In addition to this, the architecture of the system has no single point failure with over 98% reliability while measures taken to improve security have led to 85% of the customers expressing trust in the services provided. In summation, the results suggest that reservations in the airline sector stand a chance of being redefined with blockchain through savoring the benefits of a single source of truth while attempting to resolve this intrinsic problem of overcomplexity. Although the system improves the experience of customers and the level of operational transparency, issues concerning scalability and regulatory adherence. This research is also a stepping stone for further studies that are intended to address these challenges and more applicable to the airline industry., Comment: 17 pages, 7 Figures

Can we really "read the mind in the eyes"? Moreover, can AI assist us in this task? This paper answers these two questions by introducing a machine learning system that predicts personality characteristics of individuals on the basis of their face. It does so by tracking the emotional response of the individual's face through facial emotion recognition (FER) while watching a series of 15 short videos of different genres. To calibrate the system, we invited 85 people to watch the videos, while their emotional responses were analyzed through their facial expression. At the same time, these individuals also took four well-validated surveys of personality characteristics and moral values: the revised NEO FFI personality inventory, the Haidt moral foundations test, the Schwartz personal value system, and the domain-specific risk-taking scale (DOSPERT). We found that personality characteristics and moral values of an individual can be predicted through their emotional response to the videos as shown in their face, with an accuracy of up to 86% using gradient-boosted trees. We also found that different personality characteristics are better predicted by different videos, in other words, there is no single video that will provide accurate predictions for all personality characteristics, but it is the response to the mix of different videos that allows for accurate prediction.

The KArlsruhe TRItium Neutrino (KATRIN) experiment, which aims to make a direct and model-independent determination of the absolute neutrino mass scale, is a complex experiment with many components. More than 15 years ago, we published a technical design report (TDR) [https://publikationen.bibliothek.kit.edu/270060419] to describe the hardware design and requirements to achieve our sensitivity goal of 0.2 eV at 90% C.L. on the neutrino mass. Since then there has been considerable progress, culminating in the publication of first neutrino mass results with the entire beamline operating [arXiv:1909.06048]. In this paper, we document the current state of all completed beamline components (as of the first neutrino mass measurement campaign), demonstrate our ability to reliably and stably control them over long times, and present details on their respective commissioning campaigns., Comment: Added missing acknowledgement, corrected performance statement in chapter 4.2.5, updated author list and references

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Broadly categorized in three types (i.e., sequences, images, and signals), these data are huge in amount and complex in nature. Mining such an enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities and lately their deep architectures - known as deep learning (DL) - have been successfully applied to solve many complex pattern recognition problems. Highlighting the role of DL in recognizing patterns in biological data, this article provides - applications of DL to biological sequences, images, and signals data; overview of open access sources of these data; description of open source DL tools applicable on these data; and comparison of these tools from qualitative and quantitative perspectives. At the end, it outlines some open research challenges in mining biological data and puts forward a number of possible future perspectives., Comment: 36 pages, 8 figures, and 6 tables

The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC, balanced with the security and resilience challenges. LEGaTO is an ongoing three-year EU H2020 project started in December 2017., Comment: 6 pages, 9 figures

The theory of welded and extended welded knots is a generalization of classical knot theory. Welded (resp. extended welded) knot diagrams include virtual crossings (resp. virtual crossings and wen marks) and are equivalent under an extended set of Reidemeister-type moves. We present a new class of invariants for welded and extended welded knots and links using a multi-skein relation, following Z. Yang's approach for virtual knots. Using this skein-theoretic approach, we find sufficient conditions on the coefficients to obtain invariance under the extended Reidemeister moves appropriate to welded and extended welded links.

We present the construction and performance of an ultra-low temperature scanning tunneling microscope (STM), working in ultra-high vacuum conditions (UHV) and in high magnetic fields up to 9 T. The cryogenic environment of the STM is generated by a single shot $^3$He magnet cryostat in combination with a $^4$He dewar system. At base temperature (300~mK), the cryostat has an operation time of approximately 80 hours. The special design of the microscope allows the transfer of the STM head from the cryostat to a UHV-chamber system, where samples and STM-tips can be easily exchanged. The UHV chambers are equipped with specific surface science treatment tools for the functionalization of samples and tips, including high-temperature treatments and thin film deposition. This, particularly, enables spin-resolved tunneling measurements. We present test measurements using well known samples and tips based on superconductor and metallic materials such as LiFeAs, Nb, Fe and W, respectively. The measurements demonstrate the outstanding performance of the STM with high spatial and energy resolution as well as the spin-resolved capability., Comment: 12 pages, 16 figures

Rapid popularity of Internet of Things (IoT) and cloud computing permits neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable data communication between end-to-end (E2E) devices supported by current IoT and cloud infrastructure, trust management is needed at the IoT and user ends. This paper introduces a Neuro-Fuzzy based Brain-inspired trust management model (TMM) to secure IoT devices and relay nodes, and to ensure data reliability. The proposed TMM utilizes node behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference System and weighted-additive methods respectively to assess the nodes trustworthiness. In contrast to the existing fuzzy based TMMs, the NS2 simulation results confirm the robustness and accuracy of the proposed TMM in identifying malicious nodes in the communication network. With the growing usage of cloud based IoT frameworks in Neuroscience research, integrating the proposed TMM into the existing infrastructure will assure secure and reliable data communication among the E2E devices., Comment: 17 pages, 10 figures, 2 tables

Current developments in nanotechnology make electromagnetic communication (EC) possible at the nanoscale for applications involving Wireless [Body] Sensor Networks (W[B]SNs). This specialized branch of WSN has emerged as an important research area contributing to medical treatment, social welfare, and sports. The concept is based on the interaction of integrated nanoscale machines by means of wireless communications. One key hurdle for advancing nanocommunications is the lack of an apposite networking protocol to address the upcoming needs of the nanonetworks. Recently, some key challenges have been identified, such as nanonodes with extreme energy constraints, limited computational capabilities, Terahertz frequency bands with limited transmission range, etc., in designing protocols for wireless nanosensor networks (WNN). This work proposes an improved performance scheme of nanocommunication over Terahertz bands for wireless BSNs making it suitable for smart e-health applications. The scheme contains -- a new energy-efficient forwarding routine for EC in WNN consisting of hybrid clusters with centralized scheduling, a model designed for channel behavior taking into account the aggregated impact of molecular absorption, spreading loss, and shadowing, and an energy model for energy harvesting and consumption. The outage probability is derived for both single and multilinks and extended to determine the outage capacity. The outage probability for a multilink is derived using a cooperative fusion technique at a predefined fusion node. Simulated using a Nano-Sim simulator, performance of the proposed model has been evaluated for energy efficiency, outage capacity, and outage probability. The results demonstrate the efficiency of the proposed scheme through maximized energy utilization in both single and multihop communication, multisensor fusion enhances the link quality of the transmission., Comment: 13 pages, 13 figures, and 2 tables, IEEE Access, 2018

Rapid advances of hardware-based technologies during the past decades have opened up new possibilities for Life scientists to gather multimodal data in various application domains (e.g., Omics, Bioimaging, Medical Imaging, and [Brain/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques. Overall, recent research in Deep learning (DL), Reinforcement learning (RL), and their combination (Deep RL) promise to revolutionize Artificial Intelligence. The growth in computational power accompanied by faster and increased data storage and declining computing costs have already allowed scientists in various fields to apply these techniques on datasets that were previously intractable for their size and complexity. This review article provides a comprehensive survey on the application of DL, RL, and Deep RL techniques in mining Biological data. In addition, we compare performances of DL techniques when applied to different datasets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives., Comment: 33 pages, 5 figures, 1 table, survey paper, IEEE Trans. Neural Netw. Learn. Syst., 2018

The SwissFEL Injector Test Facility operated at the Paul Scherrer Institute between 2010 and 2014, serving as a pilot plant and testbed for the development and realization of SwissFEL, the X-ray Free-Electron Laser facility under construction at the same institute. The test facility consisted of a laser-driven rf electron gun followed by an S-band booster linac, a magnetic bunch compression chicane and a diagnostic section including a transverse deflecting rf cavity. It delivered electron bunches of up to 200 pC charge and up to 250 MeV beam energy at a repetition rate of 10 Hz. The measurements performed at the test facility not only demonstrated the beam parameters required to drive the first stage of an FEL facility, but also led to significant advances in instrumentation technologies, beam characterization methods and the generation, transport and compression of ultra-low-emittance beams. We give a comprehensive overview of the commissioning experience of the principal subsystems and the beam physics measurements performed during the operation of the test facility, including the results of the test of an in-vacuum undulator prototype generating radiation in the vacuum ultraviolet and optical range., Comment: 60 pages, 70 figures; final version as published, except with table of contents and Fig. 68 in full resolution

The main physical results on the registration of solar neutrinos and the search for rare processes obtained by the Borexino collaboration to date are presented., Comment: 8 pages, 8 figgures, To be published as Proceedings of the Third Annual Large Hadron Collider Physics Conference, St. Petersburg, Russia, 2015

Borexino is a liquid scintillation detector located deep underground at the Laboratori Nazionali del Gran Sasso (LNGS, Italy). Thanks to the unmatched radio-purity of the scintillator, and to the well understood detector response at low energy, a new limit on the stability of the electron for decay into a neutrino and a single mono-energetic photon was obtained. This new bound, tau > 6.6 10**28 yr at 90 % C.L., is two orders of magnitude better than the previous limit., Comment: 5 pages, 1 figure

The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants in Guangdong, China. The experimental hall, spanning more than 50 meters, is under a granite mountain of over 700 m overburden. Within six years of running, the detection of reactor antineutrinos can resolve the neutrino mass hierarchy at a confidence level of 3-4$\sigma$, and determine neutrino oscillation parameters $\sin^2\theta_{12}$, $\Delta m^2_{21}$, and $|\Delta m^2_{ee}|$ to an accuracy of better than 1%. The JUNO detector can be also used to study terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard Model. The central detector contains 20,000 tons liquid scintillator with an acrylic sphere of 35 m in diameter. $\sim$17,000 508-mm diameter PMTs with high quantum efficiency provide $\sim$75% optical coverage. The current choice of the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of detected photoelectrons per MeV is larger than 1,100 and the energy resolution is expected to be 3% at 1 MeV. The calibration system is designed to deploy multiple sources to cover the entire energy range of reactor antineutrinos, and to achieve a full-volume position coverage inside the detector. The veto system is used for muon detection, muon induced background study and reduction. It consists of a Water Cherenkov detector and a Top Tracker system. The readout system, the detector control system and the offline system insure efficient and stable data acquisition and processing., Comment: 328 pages, 211 figures

Neutrino produced in a chain of nuclear reactions in the Sun starting from the fusion of two protons, for the first time has been detected in a real-time detector in spectrometric mode. The unique properties of the Borexino detector provided an oppurtunity to disentangle pp-neutrino spectrum from the background components. A comparison of the total neutrino flux from the Sun with Solar luminosity in photons provides a test of the stability of the Sun on the 10$^{5}$ years time scale, and sets a strong limit on the power production in the unknown energy sources in the Sun of no more than 4\% of the total energy production at 90\% C.L., Comment: 15 pages, 2 tables, 3 figures

We report an improved geo-neutrino measurement with Borexino from 2056 days of data taking. The present exposure is $(5.5\pm0.3)\times10^{31}$ proton$\times$yr. Assuming a chondritic Th/U mass ratio of 3.9, we obtain $23.7 ^{+6.5}_{-5.7} (stat) ^{+0.9}_{-0.6} (sys)$ geo-neutrino events. The null observation of geo-neutrinos with Borexino alone has a probability of $3.6 \times 10^{-9}$ (5.9$\sigma$). A geo-neutrino signal from the mantle is obtained at 98\% C.L. The radiogenic heat production for U and Th from the present best-fit result is restricted to the range 23-36 TW, taking into account the uncertainty on the distribution of heat producing elements inside the Earth., Comment: 4 pages, 4 figures

The vulnerability of technology on which present society relies demands that a solar event, its time of arrival at Earth, and its degree of geoeffectiveness be promptly forecasted. Motivated by improving predictions of arrival times at Earth of shocks driven by coronal mass ejections (CMEs), we have analyzed 71 Earth-directed events in different stages of their propagation. The study is primarily based on approximated locations of interplanetary (IP) shocks derived from type II radio emissions detected by the Wind/WAVES experiment during 1997-2007. Distance-time diagrams resulting from the combination of white-light corona, IP type II radio, and in situ data lead to the formulation of descriptive profiles of each CME's journey toward Earth. Furthermore, two different methods to track and predict the location of CME-driven IP shocks are presented. The linear method, solely based on Wind/WAVES data, arises after key modifications to a pre-existing technique that linearly projects the drifting low-frequency type II emissions to 1 AU. This upgraded method improves forecasts of shock arrival time by almost 50%. The second predictive method is proposed on the basis of information derived from the descriptive profiles, and relies on a single CME height-time point and on low-frequency type II radio emissions to obtain an approximate value of the shock arrival time at Earth. In addition, we discuss results on CME-radio emission associations, characteristics of IP propagation, and the relative success of the forecasting methods., Comment: Solar Physics; Accepted for publication 2015-Apr-21

A coronal mass ejection (CME) associated with a type II burst and originating close to the center of the solar disk typically results in a shock at Earth in 2-3 days and hence can be used to predict shock arrival at Earth. However, a significant fraction (about 28%) of such CMEs producing type II bursts were not associated with shocks at Earth. We examined a set of 21 type II bursts observed by the Wind/WAVES experiment at decameter-hectometric (DH) wavelengths that had CME sources very close to the disk center (within a central meridian distance of 30 degrees), but did not have a shock at Earth. We find that the near-Sun speeds of these CMEs average to ~644 km/s, only slightly higher than the average speed of CMEs associated with radio-quiet shocks. However, the fraction of halo CMEs is only ~30%, compared to 54% for the radio-quiet shocks and 91% for all radio-loud shocks. We conclude that the disk-center radio-loud CMEs with no shocks at 1 AU are generally of lower energy and they drive shocks only close to the Sun and dissipate before arriving at Earth. There is also evidence for other possible processes that lead to the lack of shock at 1 AU: (i) overtaking CME shocks merge and one observes a single shock at Earth, and (ii) deflection by nearby coronal holes can push the shocks away from the Sun-Earth line, such that Earth misses these shocks. The probability of observing a shock at 1 AU increases rapidly above 60% when the CME speed exceeds 1000 km/s and when the type II bursts propagate to frequencies below 1 MHz., Comment: 33 pages, 11 figures, 2 tables

The distribution of dust in the ecliptic plane between 0.96 and 1.04 AU has been inferred from impacts on the two STEREO spacecraft through observation of secondary particle trails and unexpected off-points in the Heliospheric Imager (HI) cameras. This study made use of analysis carried out by members of a distributed web-based project, Solar Stormwatch. A comparison between observations of the brightest particle trails and a survey of fainter trails shows consistent distributions. While there is no obvious correlation between this distribution and the occurrence of individual meteor streams at Earth, there are some broad longitudinal features in these distributions that are also observed in sources of the sporadic meteor population. The asymmetry in the number of trails seen by each spacecraft and the fact that there are many more unexpected off-points in the HI-B than in HI-A, indicates that the majority of impacts are coming from the apex direction. For impacts causing off-points in the HI-B camera these dust particles are estimated to have masses in excess of 10-17 kg with radii exceeding 0.1 {\mu}m. For off-points observed in the HI-A images, which can only have been caused by particles travelling from the anti-apex direction, the distribution is consistent with that of secondary 'storm' trails observed by HI-B, providing evidence that these trails also result from impacts with primary particles from an anti-apex source. It is apparent that the differential mass index of particles from the apex direction is consistently above 2. This indicates that the majority of the mass is within the smaller particles of this population. In contrast, the differential mass index of particles from the anti-apex direction (causing off-points in HI-A) is consistently below 2, indicating that the majority of the mass is to be found in larger particles of this distribution., Comment: Accepted by MNRAS

We report on the radio-emission characteristics of 222 interplanetary (IP) shocks. A surprisingly large fraction of the IP shocks (~34%) is radio quiet (i.e., the shocks lacked type II radio bursts). The CMEs associated with the RQ shocks are generally slow (average speed ~535 km/s) and only ~40% of the CMEs were halos. The corresponding numbers for CMEs associated with radio loud (RL) shocks are 1237 km/s and 72%, respectively. The RQ shocks are also accompanied by lower peak soft X-ray flux. CMEs associated with RQ (RL) shocks are generally accelerating (decelerating). The kinematics of CMEs associated with the km type II bursts is similar to those of RQ shocks, except that the former are slightly more energetic. Comparison of the shock The RQ shocks seem to be mostly subcritical and quasi-perpendicular. The radio-quietness is predominant in the rise phase and decreases through the maximum and declining phases of solar cycle 23. The solar sources of the shock-driving CMEs follow the sunspot butterfly diagram, consistent with the higher-energy requirement for driving shocks.

The STEREO/WAVES instrument has detected a very large number of intense voltage pulses. We suggest that these events are produced by impact ionisation of nanoparticles striking the spacecraft at a velocity of the order of magnitude of the solar wind speed. Nanoparticles, which are half-way between micron-sized dust and atomic ions, have such a large charge-to-mass ratio that the electric field induced by the solar wind magnetic field accelerates them very efficiently. Since the voltage produced by dust impacts increases very fast with speed, such nanoparticles produce signals as high as do much larger grains of smaller speeds. The flux of 10-nm radius grains inferred in this way is compatible with the interplanetary dust flux model. The present results may represent the first detection of fast nanoparticles in interplanetary space near Earth orbit., Comment: In press in Solar Physics, 13 pages, 5 figures