Creating New Knowledge while Solving a Relevant Practical Problem: Success Factors for an Action Research-Based PhD Thesis in Business and Management

This paper focuses on university–firm relationships in terms of individual interactions between researchers and practitioners. More specifically, we focus on an analysis of the main factors that influence the use of the action research (AR) to achieve a successful doctoral thesis. In order to achieve this, we developed a Delphi study with 15 panelists whose common characteristic is that they defended or supervised an AR-based thesis in the field of business and management. The primary contribution of the research is the development of a reference framework that should be considered in the design of a doctoral thesis for which an AR methodology is put into practice. Four dimensions were defined: profiles of both the PhD candidate and supervisor, PhD program/university, and firm/organization. Three main conclusions were reached. First, it is crucial to have a cooperative “eye-to-eye” relationship between the university and the company. Second, the AR process must respond unequivocally to its own dichotomous nature. Third, there must be a straightforward academic process for the PhD thesis. We believe that this study may impel the development of doctoral theses based on AR as a tool to potentiate collaborative university–firm relationships

Low delay network attributes randomization to proactively mitigate reconnaissance attacks in industrial control systems

Industrial Control Systems are used in a wide variety of industrial facilities, including critical infrastructures, becoming the main target of multiple security attacks. A malicious and successful attack against these infrastructures could cause serious economic and environmental consequences, including the loss of human lives. Static networks configurations and topologies, which characterize Industrial Control Systems, represent an advantage for attackers, allowing them to scan for vulnerable devices or services before carrying out the attack. Identifying active devices and services is often the first step for many attacks. This paper presents a proactive network reconnaissance defense mechanism based on the temporal randomization of network IP addresses, MAC addresses and port numbers. The obtained information distortion minimizes the knowledge acquired by the attackers, hindering any attack that relies on network addressing. The temporal randomization of network attributes is performed in an adaptive way, minimizing the overhead introduced in the network and avoiding any error and latency in communications. The implementation as well as the tests have been carried out in a laboratory with real industrial equipment, demonstrating the effectiveness of the presented solution

A pricing model to monetize your industrial data

Data monetization has become a relevant aspect of the industrial manufacturing. Consequently, this paper proposes a theoretical framework as well as a mathematical model to price industrial data. For this purpose, three characteristics of the data were considered, i.e. 1) quality; 2) entropy and 3) value. Besides, the role of data marketplace’s players was analyzed. In order to validate the economic equation, a case study was carried out by a Spanish manufacturer

Improving fuzzing assessment methods through the analysis of metrics and experimental conditions

Fuzzing is nowadays one of the most widely used bug hunting techniques. By automatically generating malformed inputs, fuzzing aims to trigger unwanted behavior on its target. While fuzzing research has matured considerably in the last years, the evaluation and comparison of different fuzzing proposals remain challenging, as no standard set of metrics, data, or experimental conditions exist to allow such observation. This paper aims to fill that gap by proposing a standard set of features to allow such comparison. For that end, it first reviews the existing evaluation methods in the literature and discusses all existing metrics by evaluating seven fuzzers under identical experimental conditions. After examining the obtained results, it recommends a set of practices –particularly on the metrics to be used–, to allow proper comparison between different fuzzing proposals

Data‐Driven Low‐Frequency Oscillation Event Detection Strategy for Railway Electrification Networks

Low-frequency oscillations (LFO) occur in railway electrification systems due to the incorporation of new trains with switching converters. As a result, the increased harmonic content can cause catenary stability problems under certain conditions. Most of the research published on this topic to date is focused on modelling the event and analysing it using frequency spectrums. However, in recent years, due to the new technologies linked to Big Data (BD) and data mining (DM), a new opportunity to study and detect LFO events by means of machine-learning (ML) methods has emerged. Trains continuously collect data from the most important catenary variables, which offers new resources for analysing this type of event. Therefore, this article presents the design and implementation of a data-driven LFO event detection strategy for AC railway network scenarios. Compared to previous investigations, a new approach to analyse and detect LFO events, based on field data and ML, is presented. To obtain the most appropriate detection approach for the context of this application, on the one hand, this investigation includes a comparison of machine-learning algorithms (support vector machine, logistic regression, random forest, k-nearest neighbours, naïve Bayes) which have been trained with real field data. On the other hand, an analysis of key parameters and features to optimize event detection is also included. Thus, the most significant result of this work is the high metric values of the solution, reaching values above 97% in accuracy and 93% in F-1 score with the random forest algorithm. In addition, the applicability and training of data-driven methods with real field data are demonstrated. This automatic detection strategy can help with speeding up and improving LFO detection tasks that used to be performed manually. Finally, it is worth mentioning that this research has been structured based on the CRISP-DM methodology, established as the de facto approach for industrial DM projects

Evaluation of User Experience in Human–Robot Interaction : A Systematic Literature Review

Industry 4.0 has ushered in a new era of process automation, thus redefining the role of people and altering existing workplaces into unknown formats. The number of robots in the manufacturing industry has been steadily increasing for several decades and in recent years the number and variety of industries using robots have also increased. For robots to become allies in the day-to-day lives of operators, they need to provide positive and fit-for-purpose experiences through smooth and satisfying interactions. In this sense, user experience (UX) serves as the greatest link between persons and robots. Essential to the study of UX is its evaluation. Therefore, the aim of this study is to identify methodologies that evaluate the human–robot interaction (HRI) from a human-centred approach. A systematic literature review has been carried out, in which 24 articles have been identified. Among these, are 15 experimental studies, in addition to theoretical frameworks and tools. The review has provided insight into how evaluations are conducted in HRI. The results show the most evaluated factors and how they are measured considering different types of measurements: qualitative and quantitative, objective and subjective. Research gaps and future directions are correspondingly identified

La Fageda enpresa soziala, Mondragoneko kooperatibismoaren begiradatik

Artikulu honetan La Fageda (Katalunia) esperientziaren kasu azterketa egin dugu, MONDRAGONeko esperientzia kooperatiboaren begiradatik. Esperientzia horren ezaugarri na-gusiak azaldu ditugu, bilakaera historikoan izan dituen erronkak eta dilemak, eta Mondrago-neko kooperatibismoaren begiradatik interesgarriak iruditu zaizkigun gako nagusiak.This article presents a case analysis of the experience of La Fageda (Catalonia) from the perspective of the cooperative experience of MONDRAGON. The main character-istics of this experience, the challenges and dilemmas it has had in the historical evolution, and the main keys that we found interesting from the point of view of Mondragon’s coop-erativism

A review on reinforcement learning for contact-rich robotic manipulation tasks

Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors has led reinforcement learning agents to be increasingly applied in real-life scenarios. However, there is still a long way ahead for reinforcement learning to become a core element in industrial applications. This paper examines the landscape of reinforcement learning and reviews advances in its application in contact-rich tasks from 2017 to the present. The analysis investigates the main research for the most commonly selected tasks for testing reinforcement learning algorithms in both rigid and deformable object manipulation. Additionally, the trends around reinforcement learning associated with serial manipulators are explored as well as the various technological challenges that this machine learning control technique currently presents. Lastly, based on the state-of-the-art and the commonalities among the studies, a framework relating the main concepts of reinforcement learning in contact-rich manipulation tasks is proposed. The final goal of this review is to support the robotics community in future development of systems commanded by reinforcement learning, discuss the main challenges of this technology and suggest future research directions in the domain

Design and 3D printing of an electrochemical sensor for Listeria monocytogenes detection based on loop mediated isothermal amplification

The aim of this work is the design and 3D printing of a new electrochemical sensor for the detection of Listeria monocytogenes based on loop mediated isothermal amplification (LAMP). The food related diseases involve a serious health issue all over the world. Listeria monocytogenes is one of the major problems of contaminated food, this pathogen causes a disease called listeriosis with a high rate of hospitalization and mortality. Having a fast, sensitive and specific detection method for food quality control is a must in the food industry to avoid the presence of this pathogen in the food chain (raw materials, facilities and products). A point-of-care biosensor based in LAMP and electrochemical detection is one of the best options to detect the bacteria on site and in a very short period of time. With the numerical analysis of different geometries and flow rates during sample injection in order to avoid bubbles, an optimized design of the microfluidic biosensor chamber was selected for 3D-printing and experimental analysis. For the electrochemical detection, a novel custom gold concentric-3-electrode consisting in a working electrode, reference electrode and a counter electrode was designed and placed in the bottom of the chamber. The LAMP reaction was optimized specifically for a primers set with a limit of detection of 1.25 pg of genomic DNA per reaction and 100% specific for detecting all 12 Listeria monocytogenes serotypes and no other Listeria species or food-related bacteria. The methylene blue redox-active molecule was tested as the electrochemical transducer and shown to be compatible with the LAMP reaction and very clearly distinguished negative from positive food samples when the reaction is measured at the end-point inside the biosensor

Influence of cryogenic grinding surface on fatigue performance of carburised 27MnCr5

Automotive transmission components are subjected to cyclic loads and, thus, must have a reliable fatigue performance. Since fatigue cracks nucleate at the surface, it is necessary to guarantee that its surface integrity accomplishes the required specifications. Typically, those components are finished by wet grinding after carburising heat treatment. However, there is an increasing demand to reduce pollutants and hazardous lubricants in the industry, and eco-friendly finishing operations have been highly encouraged. To this end, it is necessary to understand the effect of these novel finishing processes on surface integrity and, consequently, on fatigue behaviour. This study aims to assess the surface integrity and the fatigue performance of cryo-ground surfaces of 27MnCr5 steel, extensively used in fabricating shafts and gears for gearboxes. Fatigue specimens for pure torsion tests were initially case-hardened and afterwards finished using two different cryogenic grinding conditions applying liquid N2 and, as a reference, using the conventional wet grinding process. First, the surface integrity was analysed in terms of texture, residual stresses, microstructure, and microhardness. Second, the batches of specimens were tested under pure torsion fatigue. Surface residual stress relaxation was also measured during fatigue tests. Finally, fracture surfaces were observed to identify crack initiation sites and establish correlations with the surface integrity. Specimens produced by cryogenic and conventional wet grinding did not show microstructural defects or hardness reductions in the carburised layer. All conditions induced compressive residual stresses, and they barely relaxed during fatigue tests. Compressive residual stresses induced by cryogenic grinding were 10–20% lower than those generated by conventional wet grinding. This decrease resulted in a minor reduction of the fatigue resistance (4–6%) compared to the wet grinding. Importantly, this study demonstrates that with a slight geometrical radio correction in the design of the mechanical components (around 2.2%), cryogenic grinding generates pieces with the same fatigue strength as conventional grinding. Therefore, it confirms that cryogenic cooling could be a potential solution to replace pollutant coolant/lubricant fluid in grinding operations