Abstract
Digital transformation is progressing exponentially. Given the importance of this transformation, managerial strategies and practices need to be adapted to meet the new challenges. While countries are on a journey toward a process where human interactions and transactions—with the government, businesses—and consumption of goods, services, and ideas primarily conducted through the use of the internet and internet-based technologies, they are all traveling at different speeds. Based on the theory, drawing from the Global Innovation Index (GII) input-output framework and literature review on innovation, the chapter intends to answer the question: What is the impact of management and strategies for digital enterprise transformation on welfare?

Abstract
This paper presents an effort to timely handle 400+ GBytes of sensor data in order to produce Predictive Maintenance (PdM) models. We follow a data-driven methodology, using state-of-the-art python libraries, such as Dask and Modin, which can handle big data. We use Dynamic Time Warping for sensors behavior description, an anomaly detection method (Matrix Profile) and forecasting methods (AutoRegressive Integrated Moving Average - ARIMA, Holt-Winters and Long Short-Term Memory - LSTM). The data was collected by various sensors in an industrial context and is composed by attributes that define their activity characterizing the environment where they are inserted, e.g. optical, temperature, pollution and working hours. We successfully managed to highlight aspects of all sensors behaviors, and produce forecast models for distinct series of sensors, despite the data dimension.

Abstract
Despite decades of research and development in named entity resolution, dealing with name ambiguity is still a challenging issue for many bibliometric-enhanced information retrieval (IR) tasks. As new bibliographic datasets are created as a result of the upward growth of publication records worldwide, more problems arise when considering the effects of errors resulting from missing data fields, duplicate entities, misspellings, extra characters, etc. As these concerns tend to be of large-scale, both the general consistency and the quality of electronic data are largely affected. This paper presents an approach to handle these name ambiguity problems through the use of crowdsourcing as a complementary means to traditional unsupervised approaches. To this end, we present "AuthCrowd", a crowdsourcing system with the ability to decompose named entity disambiguation and entity matching tasks. Experimental results on a real-world dataset of publicly available papers published in peer-reviewed venues demonstrate the potential of our proposed approach for improving author name disambiguation. The findings further highlight the importance of adopting hybrid crowd-algorithm collaboration strategies, especially for handling complexity and quantifying bias when working with large amounts of data.

Abstract
This paper presents a quadratically constrained programming (QCP) model to tackle the optimal power flow (OPF) problem in distribution networks. The proposed model is fast, reliable, and precise enough to be embedded into the multi-emporal power system analysis. The proposed model benefits from a standard QCP to solve the branch active and reactive power flows. The second-order conic programming (SOCP) approach has been applied to address the quadratic constraints. The nonconvex feature of the OPF problem has been relaxed utilizing the McCormick envelopes. To find the minimum current of each branch, the lossless power flow model has been first solved and the obtained results have been considered for solving the OPF problem. The IEEE 33-bus test system has been selected as the benchmark to verify the efficient performance of the proposed OPF model. The simulation study confirms that the McCormick envelopes used in the QCP approach lead to precise results with a very fast convergence time. Overall, the presented model for the OPF can be extended for both planning and operation purposes in distribution system studies.

Abstract
This paper presents the initial developments of new hardware devices targeted for CAN (Controller Area Network) bus communications in forest machines. CAN bus is a widely used protocol for communications in the automobile area. It is also applied in industrial vehicles and machines due to its robustness, simplicity, and operating flexibility. It is ideal for forestry machinery producers who need to couple their equipment to a machine that allows the transportation industry to recognize the importance of standardizing communications between tools and machines. One of the problems that producers sometimes face is a lack of flexibility in commercialized hardware modules; for example, in interfaces for sensors and actuators that guarantee scalability depending on the new functionalities required. The hardware device presented in this work is designed to overcome these limitations and provide the flexibility to standardize communications while allowing scalability in the development of new products and features. The work is being developed within the scope of the research project "SMARTCUT-Remote Diagnosis, Maintenance and Simulators for Operation Training and Maintenance of Forest Machines ", to incorporate innovative technologies in forest machines produced by the CUTPLANT S.A. It consists of an experimental system based on the PIC18F26K83 microcontroller to form a CAN node to transmit and receive digital and analog messages via CAN bus, tested and validated by the communication between different nodes. The main contribution of the paper focuses on the presentation of the development of new CAN bus electronic control units designed to enable remote communication between sensors and actuators, and the main controller of forest machines.

Abstract