Abstract
This work presents a framework of dynamic structural models with covariates for short-term forecasting of time series with complex seasonal patterns. The framework is based on the multiple sources of randomness formulation. A noise model is formulated to allow the incorporation of randomness into the seasonal component and to propagate this same randomness in the coefficients of the variant trigonometric terms over time. A unique, recursive and systematic computational procedure based on the maximum likelihood estimation under the hypothesis of Gaussian errors is introduced. The referred procedure combines the Kalman filter with recursive adjustment of the covariance matrices and the selection method of harmonics number in the trigonometric terms. A key feature of this method is that it allows estimating not only the states of the system but also allows obtaining the standard errors of the estimated parameters and the prediction intervals. In addition, this work also presents a non-parametric bootstrap approach to improve the forecasting method based on Kalman filter recursions. The proposed framework is empirically explored with two real time series.

Abstract
Artificial Intelligence (AI) is continuously improving several aspects of our daily lives. There has been a great use of gadgets & monitoring devices for health and physical activity monitoring. Thus, by analyzing large amounts of data and applying Machine Learning (ML) techniques, we have been able to infer fruitful conclusions in various contexts. Activity Recognition is one of them, in which it is possible to recognize and monitor our daily actions. The main focus of the traditional systems is only to detect pre-established activities according to the previously configured parameters, and not to detect novel ones. However, when applying activity recognizers in real-world applications, it is necessary to detect new activities that were not considered during the training of the model. We propose a method for Novelty Detection in the context of physical activity. Our solution is based on the establishment of a threshold confidence value, which determines whether an activity is novel or not. We built and train our models by experimenting with three different algorithms and four threshold values. The best results were obtained by using the Random Forest algorithm with a threshold value of 0.8, resulting in 90.9% of accuracy and 85.1% for precision.

Abstract
Thin graphene oxide (GO) film layers are being widely used as sensing layers in different types of electrical and optical sensor devices. GO layers are particularly popular because of their tuned interface reflectivity. The stability of GO layers is fundamental for sensor device reliability, particularly in complex aqueous environments such as wastewater. In this work, the stability of GO layers in layer-by-layer (LbL) films of polyethyleneimine (PEI) and GO was investigated. The results led to the following conclusions: PEI/GO films grow linearly with the number of bilayers as long as the adsorption time is kept constant; the adsorption kinetics of a GO layer follow the behavior of the adsorption of polyelectrolytes; and the interaction associated with the growth of these films is of the ionic type since the desorption activation energy has a value of 119 +/- 17 kJ/mol. Therefore, it is possible to conclude that PEI/GO films are suitable for application in optical fiber sensor devices; most importantly, an optical fiber-based interrogation setup can easily be adapted to investigate in situ desorption via a thermally stimulated process. In addition, it is possible to draw inferences about film stability in solution in a fast, reliable way when compared with the traditional ones.

Abstract
This paper presents a stochastic planning algorithm to plan an operation of a multi-microgrid (MMG) in an electricity market considering the integration of stochastic renewable energy resources (RERs). The proposed planning algorithm investigates the optimal operation of resources (i.e., wind turbine (WT), fuel cell (FC), Electrolyzer, photovoltaic (PV) panel, and microturbine (MT)) and energy storage (ES). Various uncertainties (e.g., the power production of WT, the power production of PV, the departure time of electric vehicle (EV), the arrival time of EV, and the traveled distance of EV) are initially forecasted according to the observed data. The prediction error is estimated by fitting the forecasted data and observed data using a Copula method. A Cournot equilibrium and game theory (GT) are applied to model the real-time electricity market and its interactions with the MMG. The proposed algorithm is examined in a sample MMG to determine the operation of uncertain resources and ES. The obtained results are compared with a baseline and the other conventional optimization methods to verify the effectiveness of the proposed algorithm. The obtained results authenticate the importance of modeling the interaction between the MMG and electricity market, especially under the high integration of uncertain RERs, resulting in above 8% cost reduction in the MMG.

Abstract
We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfilment of job deadlines.

Abstract
Reference evapotranspiration (ETo) estimations may be used to improve the efficiency of irrigated agriculture. However, its computation can be complex and could require numerous weather data that are not always available for many locations. Different methods are available to estimate ETo when limited data are available, and the assessment of the most accurate one can be difficult and time consuming. There are some standalone softwares available for computing ETo but none of them allow for the comparison of different methods for the same or different datasets simultaneously. This paper aims to present an application for estimating ETo using several methods that require different levels of data availability, namely FAO-56 Penman-Monteith (PM), the Original and the three modified Hargreaves-Samani (HS and MHS1, MHS2 and MHS3), Trajkovic (TR) and the single temperature procedure (MaxTET). Also, it facilitates the comparison of the accuracy estimation of two selected methods. From an example case, for where the application was used to compute ETo for three different locations, results show that the application can easily and successfully estimate ETo using the proposed methods, allowing for statistical comparison of those estimations. HS proves to be the most accurate method for the studied locations; however, the accuracy of all methods tends to be lower for costal locations than for more continental sites. With this application, users can select the best ETo estimation methods for a specific location and use it for irrigation purposes.