Search

Recently, a renewed interest in adopting data analytics to help solving HR problems and to make more informed and effective choices appeared in the literature. One of the greatest challenges for organizations is employee turnover because of its adverse impact in many areas, such as productivity, performance and reputation. In case of attrition, one of the problems is that data from HRIS are complex, full of sensitive information (GDPR) and of useless data. Once data are clean, they can be analyzed by using statistical approaches such as machine learning. This study is about predicting employee attrition using machine learning models on a real dataset of a large Italian financial company, and, in particular, we focus on choosing the best. This contrasts with much extant research which is based on artificial datasets. To address this issue, machine learning tools have been developed for investigating and predicting employee attrition, as well as methods for evaluating their predictive power. Evidence on what are the most important predictors that lead to attrition and in what areas it is more likely to happen enable HR managers to implement targeted retention policies and practices. The contribution of this paper is to explore and compare the performance of several common models which are found in the literature on real data. Then, we focus on the results of the best performing model and identify some groups of employees who have a high risk of attrition on which the company could intervene to reduce voluntary resignation.

This paper presents a methodology to support the decision-making process during the planning of ship operations. The methodology is designed with the aim of identifying and providing the operator with the best Estimated Time of Departure (ETD)–Estimated Time of Arrival (ETA) window of opportunity to execute the journey/operation between two predefined locations. To achieve this purpose, the International Maritime Organization (IMO) stability criteria are exploited in the process to formulate an operational safety criterion based on fuzzy reasoning as a function of the METeorological and OCeanographic (METOC) and sailing conditions. This allows for the analysis of the set of Pareto routes computed by a weather routing systems relying on a multi-objective set-up. The proposed methodology is tested in an operational scenario in the Mediterranean Sea.

Robotics competitions have the potential of engaging the future engineers and improving their technical and soft skills. In competitions, students are faced with unique challenges, usually not present in theoretical lectures. CMRE has been organising marine robotics competitions since 2010. Along the years, these have become more complex, including multi-domain cooperation and have become more applicative (from search and rescue to oil & gas). Similarly, there were changes in the scoring methodology and purpose of the competition. While the European Robotics League (ERL) Emergency has been running for a few years with search and rescue scenarios, recently, CMRE has been working on a new competition, the Robotics for Asset Maintenance and Inspection (RAMI), which expands the precise scientific scoring method of ERL Emergency to a metrological evaluation. RAMI competition inaugurates a new concept of having both physical and cascade (based on acquired data) competitions. This enlarges the competition to research communities typically not engaged in marine robotics such as the Artificial Intelligence (AI) community. This paper reports on the latest ERL Emergency event (held in 2019) and on the ongoing implementation of the new RAMI competition.

The idea that the individualistic-contractualistic view of the work relationship has insufficient regulatory power is attracting renewed attention from jurists, especially in Italy. Consequently, a number of prominent labor law scholars are calling for a dedicated thematization of the “third,” or “collective-relational” dimension of the work relationship, as a distinct component from the individual and the collective-trade union dimension, concerning the coordination of interdependent workers and jobs in the organization. With a view to contributing to this debate, this chapter aims at shedding light on the organizational roots of that dimension. To this end, it focuses on and illustrates two options for interpreting coordination drawn from organizational theory—i.e., predetermination and processual regulation—which reflect two different ideas of the organization and of the workers’ participation in the regulation/coordination of interdependent/cooperative actions. The chapter argues that only by means of an epistemological turn to a processual perspective on organization and regulation/coordination, scholars can go beyond the long-standing and still unresolved opposition, in labor law debate, between contract and organization.

This paper investigates how robot navigation strategies can improve the communication performance among the nodes of a deployed underwater autonomous network (UAN). To this aim, we present an algorithm that evaluates the impact of environmental conditions and network configurations in the connectivity performance of an UAN. The algorithm exploits acoustic propagation models and data from experimental campaigns for the definition of the so-called communication performance function (CPF). The CPF estimates the probability of successful packet delivery by correlating it with the environmental conditions and the output of acoustic propagation models with past communication data. Once the CPF is designed, it offers a way to assess the communication performance of certain geometric configurations. The environmental and communication data from the littoral continuous active sonar (LCAS18) sea-trial are analysed for the definition and estimation of the CPF. The resulting CPF shows the coherence achieved with the LCAS18 dataset.

Knowledge of the underwater environment is synonymous of accurate understanding of how sound propagates. This fact is very important in naval operations, such as mine-counter measures (MCM) and anti-submarine warfare (ASW). In order to better understand the underwater environment, this paper presents a simple and fast approach for clustering sound speed profile (SSP) shapes towards inform decision makers in other tasks and operations. The proposed method is to define the shape of a SSP using a set of parameters. These parameters are then used for defining the different SSP clusters/groups. The technique is characterized by two phases: the linearisation, where a piecewise linear approximation is applied on each SSP, and the clustering phase, where SSP clusters are grouped and identified. The method is applied to live and real SSPs data available from the Gibraltar Strait, a challenging area from the point of view of the acoustic propagation, mixing fresh waters from the Atlantic Ocean and warm waters from the Mediterranean Sea.