Abstract
The Industry 4.0 (i4.0) paradigm was conceived bearing smart machines enabling capabilities, mostly through real-time communication both between smart equipment on a shop floor and decision-aiding software at the business level. This interoperability is achieved mostly through a reference architecture specifically designed for i4.0, which is aimed at devising the information architecture with real-time capabilities. From such architectures, the Reference Architectural Model for Industrie 4.0 (RAMI 4.0) is considered the preferred approach for implementation purposes, especially within Small and Medium Enterprises (SMEs). Nevertheless, the implementation of RAMI 4.0 is surrounded with great challenges when considering the current industrial landscape, which requires retrofitting of existing equipment and the various communication needs. Through three different case studies conducted within footwear and cork industries, this research proposes a RAMI 4.0 SME implementation methodology that considers the initial stages of equipment preparation to enable smart communications and capabilities. The result is a methodological route aimed for SMEs’ implementation of smart machines, based on RAMI 4.0, which considers both the technological aspects as well as the business requirements. © 2020, Springer Nature Switzerland AG.

Abstract
The development of efficient sensing technologies and the maturation of the Internet of Things (IoT) paradigm and related protocols have considerably fostered the expansion of sensor-based monitoring applications. A great number of those applications has been developed to monitor a set of information for better perception of the environment, with some of them being dedicated to identifying emergency situations. Current IoT-based emergency systems have limitations when considering the broader scope of smart cities, exploiting one or just a few monitoring variables or even allocating high computational burden to regular sensor nodes. In this context, we propose a distributed multi-tier emergency alerting system built around a number of sensor-based event detection units, providing real-time georeferenced information about the occurrence of critical events, while taking as input a configurable number of different scalar sensors and GPS data. The proposed system could then be used to detect and to deliver emergency alarms, which are computed based on the detected events, the previously known risk level of the affected areas and temporal information. Doing so, modularized and flexible perceptions of critical events are provided, according to the particularities of each considered smart city scenario. Besides implementing the proposed system in open-source electronic platforms, we also created a real-time visualization application to dynamically display emergency alarms on a map, demonstrating a feasible and useful application of the system as a supporting service. Therefore, this innovative approach and its corresponding physical implementation can bring valuable results for smart cities, potentially supporting the development of adaptive IoT-based emergency-aware applications.

Abstract
The Vehicle Routing Problem with Selective Backhauls (VRPSB) aims to minimize the total routing costs minus the total revenue collected at backhaul customers. We explore a VRPSB under uncertain revenues. A deterministic VRPSB is formulated as a mixed-integer programming problem and two robust counterparts are derived. A novel method to estimate the probabilistic bounds of constraint violation is designed. A robust metaheuristic is developed, requiring little time to obtain feasible solutions with average gap of 1.40%. The robust approach studied demonstrates high potential to tackle the problem, requiring similar computing effort and maintaining the same tractability as the deterministic modeling.

Abstract
Data races, a condition where two memory accesses to the same memory location occur concurrently, have been shown to be a major source of concurrency bugs in distributed systems. Unfortunately, data races are often triggered by non-deterministic event orderings that are hard to detect when testing complex distributed systems. In this paper, we propose Spider, an automated tool for identifying data races in distributed system traces. Spider encodes the causal relations between the events in the trace as a symbolic constraint model, which is then fed into an SMT solver to check for the presence of conflicting concurrent accesses. To reduce the constraint solving time, Spider employs a pruning technique aimed at removing redundant portions of the trace. Our experiments with multiple benchmarks show that Spider is effective in detecting data races in distributed executions in a practical amount of time, providing evidence of its usefulness as a testing tool. © Springer Nature Switzerland AG 2020.

Abstract
Demand Response (DR) is known as an effective solution for many power grid problems such as high operating cost as well as high peak demand. In order to achieve full potential of DR programs, DR must be implemented optimally. On this basis, determining optimal DR location, appropriate DR program and efficient penetration rate for each DR program is of great practical interest due to the fact that it guides the system operators to choose proper DR strategies. This paper presents a novel linear framework for DR optimization problem incorporated into the network-constrained unit commitment with the aim of determining optimal location, type and penetration rate of DR programs considering several practical limitations. To this end, a number of tariff-based and incentive-based DR programs have been taken into account according to the customer's benefit function based on the price elasticity concept. The IEEE 24 bus Reliability Test System (RTS 24-bus) is used to demonstrate the applicability of the proposed model. Finally, DR optimization is analyzed with regards to different customer's elasticity values and also different number of candidate load buses which reveal the applicability and effectiveness of the proposed methodology.

Abstract
In today’s software industry, systems are constantly changing. To maintain their quality and to prevent failures at controlled costs is a challenge. One way to foster quality is through thorough and systematic testing. Therefore, the definition of adequate tests is crucial for saving time, cost and effort. This paper presents a framework that generates software test cases automatically based on user interaction data. We propose a data-driven software test generation solution that combines the use of frequent sequence mining and Markov chain modeling. We assess the quality of the generated test cases by empirically evaluating their coverage with respect to observed user interactions and code. We also measure the plausibility of the distribution of the events in the generated test sets using the Kullback-Leibler divergence. © 2020, Springer Nature Switzerland AG.