Abstract
Viticulturists need to obtain the estimation of productivity map during the grape vine harvesting, to understand in detail the vineyard variability. An accurate productivity map will support the farmer to take more informed and accurate intervention in the vineyard in line with the precision viticulture concept. This work presents a novel solution to measure the productivity during vineyard harvesting operation realized by a grape harvesting machine. We propose 2D LIDAR sensor attached to low cost IoT module located inside the harvesting machine, to estimate the volume of grapes. Besides, it is proposed data methodology to process data collected and productivity map, considering GIS software, expecting to support the winemakers decisions. A PCD map is also used to validate the method developed by comparison. © Springer Nature Switzerland AG 2019.

Abstract
To properly validate wireless networking solutions we depend on experimentation. Simulation very often produces less accurate results due to the use of models that are simplifications of the real phenomena they try to model. Networking experimentation may offer limited repeatability and reproducibility. Being influenced by external random phenomena such as noise, interference, and multipath, real experiments are hardly repeatable. In addition, they are difficult to reproduce due to testbed operational constraints and availability. Without repeatability and reproducibility, the validation of the networking solution under evaluation is questionable. In this paper, we show how the Trace-based Simulation (TS) approach can be used to accurately repeat and reproduce real experiments and, consequently, introduce a paradigm shift when it comes to the evaluation of wireless networking solutions. We present an extensive evaluation of the TS approach using the Fed4FIRE+ w-iLab.2 testbed. The results show that it is possible to repeat and reproduce real experiments using Network Simulator 3 (ns-3) trace-based simulations with more accuracy than in pure simulation, with average accuracy gains above

Abstract
Current Maximum Satisfiability (MaxSAT) algorithms based on successive calls to a powerful Satisfiability (SAT) solver are now able to solve real-world instances in many application domains. Moreover, replacing the SAT solver with a Satisfiability Modulo Theories (SMT) solver enables effective MaxSMT algorithms. However, MaxSMT has seldom been used in debugging multi-threaded software. Multi-threaded programs are usually non-deterministic due to the huge number of possible thread operation schedules, which makes them much harder to debug than sequential programs. A recent approach to isolate the root cause of concurrency bugs in multi-threaded software is to produce a report that shows the differences between a failing and a non-failing execution. However, since they rely solely on heuristics, these reports can be unnecessarily large. Hence, reports may contain operations that are not relevant to the bug's occurrence. This paper proposes the use of MaxSMT for the generation of minimal reports for multi-threaded software with concurrency bugs. The proposed techniques report situations that the existing techniques are not able to identify. Experimental results show that using MaxSMT can significantly improve the accuracy of the generated reports and, consequently, their usefulness in debugging the root cause of concurrency bugs.

Abstract
Hiring outstanding employees is the goal of every company. Digitalization impacted the way companies work and the environment they are surrounded by. Considering this change, employees are facing new challenges for which a concrete skill set is needed. By conducting qualitative interviews, a distinct skill set required by companies was identified. The outcome is that soft skills moved to the forefront, playing a major role when coping with the digital future. In a further step, recommendations for talent management regarding recruitment and development of high potentials are given. With the implementation of technology and the adaption to the human resources perspective, companies will be able to master digital transformation. © Springer Nature Switzerland AG 2019.

Abstract
After making an overview on the most recent progresses regarding the optical immersion treatment technique, we use this chapter to look to the future and perspectives of the following developments and benefits that can be achieved. The increasing number of publications on OC in the last 30 years, which we present in Sect. 9.1, indicates that this is a promising method to aid in the application of optical techniques in clinical practice for diagnosis or treatment purposes. Since several spectroscopy, fluorescence, or imaging methods have recently been used to test and validate the OC effects in various human and animal tissues, a collection of OCAs and OC protocols have been developed. Section 9.2 shows that to get even better results in tissue OC, the discovery of new agents and establishment of new protocols is a work in progress. Section 9.3 indicates the future perspectives for tissue spectroscopy during OC treatment and that the potential of the refractive index matching mechanism can also be evaluated in the ultraviolet range. Section 9.4 discusses the future perspectives of tissue imaging and OC. The establishment of new and faster OC protocols for tissue imaging is suggested, and indication for the necessary efforts to adapt the light-sheet technique to image in vivo is also made. Finally, Sect. 9.5 presents other applications of tissue OC and suggests the cooperation between research fields to increase knowledge in the use of OCAs and their benefits for each field. © 2019, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic disease that propagates from one family generation to the next. The disease can have severe effects on the life of patients after the first symptoms (onset) appear. Accurate prediction of the age of onset for these patients can help the management of the impact. This is, however, a challenging problem since both familial and non-familial characteristics may or may not affect the age of onset. In this work, we assess the importance of sets of genealogical features used for Predicting the Age of Onset of TTR-FAP Patients. We study three sets of features engineered from clinical and genealogical data records obtained from Portuguese patients. These feature sets, referred to as Patient, First Level and Extended Level Features, represent sets of characteristics related to each patient's attributes and their familial relations. They were compiled by a Medical Research Center working with TTR-FAP patients. Our results show the importance of genealogical data when clinical records have no information related with the ancestor of the patient, namely its Gender and Age of Onset. This is suggested by the improvement of the estimated predictive error results after combining First and Extended Level with the Patients Features.