The influence of the sales force on buyers in business-to-business market transactions is continuously waning. Particularly due to various new digital information channels buyers are in their decision-making processes only to a limited extent still dependent on the information provided by the sales persons. This development is putting sales forces increasingly under pressure as it is restricting their range of actions. In this conceptual paper it will be shown that regaining influence in the sales process will only be possible by the implementation of an integrated market intelligence system.

With the emergence of self-driving technology and the ever-increasing demand of bandwidth-hungry applications, providing the required latency, security and computational capabilities is becoming a challenging task. Although being evolving, traditional vehicular radio access technologies, namely WLAN/IEEE 802.11p and cellular networks cannot meet all the requirements of future Cooperative, Connected and Automated Mobility (CCAM). In addition, current vehicular architectures are not sufficiently flexible to support the highly heterogeneous landscape of emerging communication technologies, such as mmWave, Cellular Vehicle-to-Everything (C-V2X), and Visible Light Communication (VLC). To this aim, Multi-access Edge Computing (MEC) has been recently proposed to enhance the quality of passengers experience in delay-sensitive applications. In this paper, we discuss the in-premises features of MEC and the need of supporting technologies, such as Software Defined Networking (SDN) and Network Function Virtualization (NFV), to fulfil the requirements in terms of responsiveness, reliability and resiliency. The latter is of paramount importance for automated services, which are supposed to be always-on and always-available. We outline possible solutions for mobility-aware computation offloading, dynamic spectrum sharing, and interference mitigation. Also, by revealing MEC-inherent security vulnerabilities, we argue for the need of adequate security and privacy-preserving schemes in MEC-enabled vehicular architectures.

Since Critical Infrastructures (CIs) use systems and equipment that are separated by long distances, Supervisory Control And Data Acquisition (SCADA) systems are used to monitor their behaviour and to send commands remotely. For a long time, operator of CIs applied the air gap principle, a security strategy that physically isolates the control network from other communication channels. True isolation, however, is difficult nowadays due to the massive spread of connectivity: using open protocols and more connectivity opens new network attacks against CIs. To cope with this dilemma, sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety. However, traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. To this end, we assess in this paper Machine Learning (ML) techniques for anomaly detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), Random Forest (RF), Bidirectional Long Short Term Memory (BLSTM) are assessed in terms of accuracy, precision, recall and F1 score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF and BLSTM detect intrusions effectively, with an F1 score of respectively > 99% and > 96%.

Objectives The purpose of this work is the analysis of migraine attack reports collected online within the project Migraine Radar in respect to the distribution of the migraine attacks over the week on a single-participant level. Materials & Methods Recording data using a web app as well as smartphone apps made it possible to collect data of 44 639 migraine attacks of 1085 participants who reported seven or more attacks over a participation period of at least 90 days. This allows the investigation of attack distributions on a single-participant level. Considering the day of the week with the highest attack frequency for each participant—the mode of the individual distribution—allows identifying participants suffering from weekend migraines. Namely, a weekend pattern is assumed if the mode falls on a Saturday or Sunday. Results For 15.9% of the participants, the attacks were not distributed equally (P < 0.05) over the days of the week. Instead, participants show different individual patterns for the distribution of their migraine attacks. Furthermore, the modes of the individual distributions are not distributed equally over the week. In fact, Saturday seems to be the predominant day for migraine attacks for a greater proportion of participants (195 of 1085). Conclusions Concerning the individual attack distributions, we found that participants show individual attack patterns and weekend migraine can be determined for a subgroup of participants, while other participants show accumulations of their attacks on other days of the week.