Abstract
The automotive industry has an extremely high-quality product standard, not just for the security risks each faulty component can present, but the very brand image it must uphold at all times to stay competitive. In this paper, a prototype model is proposed for smart quality inspection using machine vision. The engine labels are detected using Faster-RCNN and YOLOv3 object detection algorithms. All the experiments were carried out using a custom dataset collected at an automotive assembly plant. Eight engine labels of two brands (Citroën and Peugeot) and more than ten models were detected. The results were evaluated using the metrics Intersection of Union (IoU), mean of Average Precision (mAP), Confusion Matrix, Precision and Recall. The results were validated in three folds. The models were trained using a custom dataset containing images and annotation files collected and prepared manually. Data Augmentation techniques were applied to increase the image diversity. The result without data augmentation was 92.5%, and with it the value was up-to 100%. Faster-RCNN has more accurate results compared to YOLOv3. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Growing demand for energy carriers has led to an increased interest in developing and managing multiple energy carrier microgrids. Furthermore, the volatile nature of renewable resources as well as the uncertain electrical and thermal demands imposes significant challenges for the operation of microgrids. Motivated by this, the paper leverages a min max min robust framework for short-term operation of microgrids with natural gas network to capture the uncertainty of wind generation and electrical/thermal loads. The proposed model is linearized and solved using the column-and-constraint generation (C&CG) procedure that decomposes the framework into a master problem and a subproblem. The master problem minimizes the unit commitment cost, while the sub-problem determines the dispatch cost associated with the worst realization of uncertainties via a max min objective function. Also, polyhedral uncertainty sets are defined with budget of uncertainty parameter that adjusts the trade-off between the operation cost and the degree of robustness. The effectiveness of the framework is assessed and discussed via a 21-node energy hub-based microgrid. It can be seen that the solution immunizes against all realizations of uncertainties, whereby increasing the budget of uncertainty and the forecast error, the system robustness is improved. Moreover, the dual variables of the subproblem are converted to the primary variables in order to evaluate the unit commitment and energy dispatch results.

Abstract
Energy demand in the world is mostly met by conventional sources that cause carbon emissions. Considering environmental problems and the depletion of these sources in the near future, there is a trend towards renewable energy sources (RESs). Also, countries are implementing policies such as investment support, production support, quantity target, and limiting carbon emissions to increase the number of RESs. When these policies are compared, one of them can be superior to another in different countries. Also, superficial supports can cause an excessive financial burden on the governments. RESs have inherently intermittent power generation and in this respect, it is important to correctly estimate the RESs whose production changes with environmental conditions and to offer to the electricity markets optimally. For this reason, it is also important to know the structures of the electricity markets in bidding. Besides, RESs can come together to take an effective position in the market in terms of price and manage their imbalances. These structures can take names such as aggregator, virtual power plant (VPP), and portfolio. Considering the above-mentioned issues, this study aims to investigate in detail the methods applied to increase the number of RESs and the ways these resources participate in the electricity markets. In this context, subjects of policies promoting RESs, electricity market structures, development of the electricity market, optimum bidding strategy and ways of collective participation of RESs in the electricity markets are comprehensively examined under different sections.

Abstract
Deep Neural Networks using histopathological images as an input currently embody one of the gold standards in automated lung cancer diagnostic solutions, with Deep Convolutional Neural Networks achieving the state of the art values for tissue type classification. One of the main reasons for such results is the increasing availability of voluminous amounts of data, acquired through the efforts employed by extensive projects like The Cancer Genome Atlas. Nonetheless, whole slide images remain weakly annotated, as most common pathologist annotations refer to the entirety of the image and not to individual regions of interest in the patient's tissue sample. Recent works have demonstrated Multiple Instance Learning as a successful approach in classification tasks entangled with this lack of annotation, by representing images as a bag of instances where a single label is available for the whole bag. Thus, we propose a bag/embedding-level lung tissue type classifier using Multiple Instance Learning, where the automated inspection of lung biopsy whole slide images determines the presence of cancer in a given patient. Furthermore, we use a post-model interpretability algorithm to validate our model's predictions and highlight the regions of interest for such predictions.

Abstract
This paper explores the problem of model-based detecting and reconstructing occurring actuator and sensor faults in direct current (DC) microgrids (MGs) connected to resistive and constant power loads (CPLs) and energy storage units. Both the actuator and sensor faults are modeled as an additive time-varying term in the state-space representation, which highly degrade the system response performance if they are not compensated. In this paper, a novel advanced extended Kalman filter (EKF), called dualEKF (D-EKF) is proposed to estimate the system states as well as the accruing actuator and sensor faults. The main property of the developed approach is that it offers a systematic estimation procedure by dividing the estimating parameters into three parts and these parts are estimated in parallel. A first-order filter is utilized to turn the sensor faulty system into an auxiliary sensor faults-free representation. Thereby, the artificial output contains the filter states. The proposed D-EKF estimator does not require restrictive assumptions on the power system matrices and is highly robust against stochastic Gaussian noises. At the end, the proposed approach is applied on a practical faulty DC MG benchmark connected to a CPL, a resistive load, and an energy storage system and the obtained simulation results are analyzed form the accuracy and convergence speed viewpoints.

Abstract
Tourism is an information-intensive sector and today's tourist is hungry for information about everything that surrounds him and is increasingly demanding about the mechanisms that are made available for access and interaction with information. This new reality requires rethinking many of the existing solutions. In this context, the Internet of Things (IoT) is revolutionizing the way we think, design and implement Information and Communication Technologies (ICT) solutions for the tourism sector, opening up unprecedented opportunities in terms of how we can provide information and services. This new reality is enabling reengineering the interaction-process between tourists and its surrounding space. For heritage spaces, typically visited by countless tourists, there is an opportunity to rethink the entire process of supporting the interpretation and fruition of heritage, carried out by tourists. In order to understand how this reengineering can be carried out, a review of the state of the art is carried out with regard to how the IoT has been applied in the context of tourism. Then, the methodology that governed the creation of a conceptual model based on IoT is clearly defined, capable of transforming the way physical spaces of tourist interest can be interpreted and how their fruition can be improved. Particular importance is given to the contextualization of the experience, since the information provided must be adjusted to the visitor, according to their profile, which may necessarily reflect different types of interest or prior knowledge about the space. Finally, this article presents a conceptual model where its components are described and where it is discussed how the model can transform the experience of visiting touristic spaces and how tourists can access information and services that entities promoters of these spaces wish to make available. In the dissertation carried out, important aspects of the model and the gains it may generate for the revitalization and promotion of heritage are discussed.