Abstract
Ester-based dielectric fluids have now been on the market for several decades, providing fire-safe and environmentally friendly alternatives to mineral oils, which have traditionally been used in transformers and other electrical equipment. This opens the door to innovation in power transformers. However, the use of esters-based dielectrics in power transformers is still very limited, especially for the higher voltage levels. The usage of these esters-based dielectrics in higher voltage power transformers is not yet consensual. this work present results with the use of natural esters in power distribution transformers. Tests carried out on mineral oil and natural ester oil found that the ester-based dielectric can withstand higher voltage thresholds for AC and Impulses tests, mainly within the specs of destructive tests, e.g., the natural ester was able to withstand a 185kV impulse without registering dielectric rupture while the natural oil registered a dielectric rupture with a 160kV impulse. Heating and mechanical tests demonstrated that ester-based dielectric oils for power transformers lead to a flow reduction between 16,8% and 18,2% in the cooling system that was design for mineral oils but they achieve a higher heat transfer coefficient, between 0,5% to 5% depending on the location of measurement. © Published under licence by IOP Publishing Ltd.

Abstract
Tampered multimedia content is increasingly being used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and ransomware-related crimes are among the most recurrent crimes in which manipulated digital photos are being used as an attacking vector. One of the linchpins of accurately detecting manipulated multimedia content is the use of machine learning and deep learning algorithms. This work proposed a dataset of photos and videos suitable for digital forensics, which has been used to benchmark Support Vector Machines (SVM) and Convolution Neural Networks algorithms (CNN). An SVM-based module for the Autopsy digital forensics open-source application has also been developed. This was evaluated as a very capable and useful forensic tool, winning second place on the OSDFCon international Autopsy modules competition.

Abstract
The number of smartphone users has increased significantly, and the development of mobile applications has brought convenience to daily life. However, large numbers of users who have various barriers, such as visual or hearing impairments, and physical disorders, are not able to fully access and use the referred applications, which is unfair to them, especially when considering its use by students in a university campus where all users should be able to enjoy equal opportunities and experiences. The main goal of this study is to assess accessibility in mobile applications of the education sector. Thus, an evaluation model is also proposed to assess the accessibility of the applications from two perspectives, which are the inherent properties of the applications and the user experience of different disability categories. 46 official mobile applications were tested which related to 23 universities and institutes of Portugal, using automatic and manual testing methods. Several frequently occurring accessibility issues in the apps were identified and summarized, such as color contrast, touch target, missing focus. The results of the accessibility testing showed that the status of web accessibility of mobile applications in the higher education sector in Portugal is unsatisfactory. Most apps have multiple accessibility issues, and they are extremely unfriendly to the users with visual impairments. In addition, the study also proposed a series of accessibility recommendations for mobile application designers and developers, with the purpose of improving the accessibility of apps and providing an equitable user experience for all users. © 2022, Springer Nature Switzerland AG.

Abstract
Probabilistic load forecasting (PLF) is necessary for power system operations and control as it assists in proper scheduling and dispatch. Moreover, PLF adequately captures the uncertainty whether that uncertainty is related to load data or the forecasting model. And there are not many PLF models, and those which exist are very complex or difficult to interpret. This paper proposes a novel neuroevolution algorithm for handling the uncertainty associated with load forecasting. In this paper, a new modified evolutionary algorithm is proposed which is used to find the optimal hyperparameters of 1D-Convolutional neural network (CNN). The probabilistic forecasts are produced by minimizing the mean scaled interval score loss function at 50%, 90% and 95% prediction intervals. The proposed neuroevolution algorithm is tested on a global energy forecasting competition (GEFCom-2014) load dataset, and two different experiments are conducted considering load only and one with load and temperature. Strong conclusions are drawn from these experiments. Also, the proposed model is compared with other benchmark models, and it has been shown that it outperforms the other models.

Abstract
Purpose The purpose of this study is to explore the human resource management practices and the associated dimensions of quality of employment in university spin-offs. Through this, it becomes possible to explore and recognize the practices and difficulties placed on the employees of university spin-offs. Design/methodology/approach The United Nations Economic Commission for Europe (UNECE) framework to assess the quality of work in the European Union is adopted. It is used a qualitative approach through the development of four case studies at university spin-offs located in Portugal. These case studies relate to four sectors of activity, such as information technology, urban mobility, health and electronics. Findings The findings reveal that most of the challenges of quality of work in a spin-off university are common to those in an SME or micro company. Among these factors, the authors highlight the lack of job security, reduced or no social protection and very low income and nonwage pecuniary benefits. Other factors specific to university spin-offs also emerge, such as the numerous opportunities for skills development and training, the potentialities to become an entrepreneur and the high number of working hours that are necessary to face the vibrant market dynamics. Originality/value The study aims to contribute, in a theoretical and empirically grounded basis, to the knowledge about the quality of employment in a spin-off university. This work becomes relevant for policymakers to understand in depth the specific challenges faced by employees of a spin-off university.

Abstract
Refactoring helps improve the design of software systems, making them more understandable, readable, maintainable, cleaner, and self-explanatory. Many refactoring tools allow developers to select and execute the best refactorings for their code. However, most of them lack quick and continuous feedback, support, and guidance, leading to a poor refactoring experience. To fill this gap, we are researching ways to increase liveness in refactoring. Live Refactoring consists of continuously knowing, in real-time, what and why to refactor. To explore the concept of Live Refactoring and its main components - recommendation, visualization, and application, we prototyped a Live Refactoring Environment focused on the Extract Method refactoring. With it, developers can receive recommendations about the best refactoring options and have support to apply them automatically. This work helped us reinforce the hypothesis that early and continuous refactoring feedback helps to shorten the time needed to create high-quality systems. © 2022 ACM.