Abstract
Simple Summary Neoplastic diseases are among the leading causes of death worldwide and constitute the main health problem in both human and veterinary medicine, particularly as the occurrence of the disease continues to increase. Comparative oncology is a quickly expanding field that examines both cancer risk and tumor development across species. Characterized by interdisciplinary collaboration, its goal is the improvement of both human and animal health. Canine neoplastic disease occurs spontaneously and has comparable clinical presentation and pathophysiology to corresponding human cancers. Since the nature of the disease is spontaneous, the complex interactions between tumor cells, tissues and the immune system can be better studied. Such relations are otherwise difficult to study in other experimental animal models. Raman spectroscopy has proved to be a suitable technique to detect and study breast microcalcifications. Raman spectroscopy is a specific and sensitive tool for identifying biomarkers of oncologic disease and also shows further potential in differentiating malignant and benign tumors, and these tumors from healthy tissue. Breast cancer is a health problem that affects individual life quality and the family system. It is the most frequent type of cancer in women, but men are also affected. As an integrative approach, comparative oncology offers an opportunity to learn more about natural cancers in different species. Methods based on Raman spectroscopy have shown significant potential in the study of the human breast through the fingerprinting of biological tissue, which provides valuable information that can be used to identify, characterize and discriminate structures in breast tissue, in both healthy and carcinogenic environments. One of the most important applications of Raman spectroscopy in medical diagnosis is the characterization of microcalcifications, which are highly important diagnostic indicators of breast tissue diseases. Raman spectroscopy has been used to analyze the chemical composition of microcalcifications. These occur in benign and malignant lesions in the human breast, and Raman helps to discriminate microcalcifications as type I and type II according to their composition. This paper demonstrates the recent progress in understanding how this vibrational technique can discriminate through the fingerprint regions of lesions in unstained histology sections from canine mammary glands.

Abstract
The need for flexibility in power system operation gradually increases regarding more renewable energy integration, load growth, etc., and the system operators already invest in this manner to enhance the power system operation. Besides, the power system has thermally sensitive assets such as lines, transformers, etc. that are normally operated under highly conservative static ratings. There is a growing trend in this regard to use the actual capacity of such assets dynamically under varying operating conditions leading to a dynamic thermal rating concept which is referred as dynamic line rating (DLR) approach specifically for lines. This study provides a comprehensive overview of existing perspectives on DLR and combination with other flexibility options from an operational point of view. Apart from the existing review studies more focused on implementation category of DLR concept, the concentration on more operational stage from the power system operation point of view leads the difference of this study compared to the mentioned studies. A categorization of the DLR implementation for either being sole or combined usage as a flexibility option is further realized. Besides, a geographically categorized analysis on existing practical evidence on DLR concept and implementations is also presented in this study.

Abstract

Abstract
Prediction of the energy consumption is a key aspect of home energy management systems, whose aim is to increase the occupant’s comfort while reducing the energy consumption. This work, employing three years measured data, uses radial basis function neural networks, designed using a multi-objective genetic algorithm (MOGA) framework, for the prediction of total electric power consumption, HVAC demand and other loads demand. The prediction horizon desired is 12 h, using 15 min step ahead model, in a multi-step ahead fashion. To reduce the uncertainty, making use of the preferred set MOGA output, a model ensemble technique is proposed which achieves excellent forecast results, comparing additionally very favorably with existing approaches.

Abstract
In this paper, an integrated approach for optimal planning and operation of energy hubs is provided considering the effects of wind energy resources. Inevitable uncertainties of electrical, heating, cooling demands as well as the wind power generation are considered in this study. The proposed model is based on two-stage optimization problems and represented as a stochastic programming problem to address the effects of uncertain parameters. In order to address the uncertain parameters in the model, different scenarios have been generated by Monte-Carlo Simulation approach and then the scenarios are reduced by applying K-means method. In addition, the effects of demand response programs on the operational sub-problem are taken into account. Benders decomposing approach is adopted in this research to solve the complex model of coordinated planning and operation problem. The master problem is supposed to determine the type and capacity of hub equipment, while the operating points of these assets are the decision variables of the operational slave problem. As a result, the proposed mathematical model is expressed as a linear model solved in GAMS. The simulation results confirm that the Benders decomposition method offers extremely high levels of accuracy and power in solving this problem in the presence of uncertainties and numerous decision variables. Moreover, the convergence time is drastically decreased using Benders decomposition method.

Abstract
Determining the optimal management in terms of operative decisions (charging, discharging, or disconnection) as well as their magnitudes (charging/discharging current/power), considering the nonlinearities of battery energy storage system (BESS) is a crucial process on the successful acceptance of energy storage technologies. This work presents an optimization model for the management of BESS operating in real-time electricity markets in order to maximize the economic profits by energy arbitrage. The optimization model proposed combines genetic algorithm (GA) with gravitational search algorithm (GSA). On one hand, GA uses an integer codification, where charging, discharging, and disconnection are represented. On the other hand, GSA optimizes the maximum charging or discharging energy. The proposed combination of optimization algorithms allows determining the integer and continuous variables involved in the management problem, taking into account the nonlinear behavior of BESS. The proposed approach was implemented considering lead acid and vanadium redox flow batteries under the conditions of Spanish electricity market.