Abstract
The creation of university spin-offs plays a fundamental role in the technology transfer process between universities and the business field. Universities have developed for themselves internal processes within their technology transfer offices in order to collect this information. However, this data is only available locally and often outdated. Furthermore, the number of frameworks that allow the study and simultaneously access to this data in an integrated way is still small on a global scale, and in Portugal, there isn't a repository which contains this information. This study proposes the establishment of a framework for longitudinal analysis that could identify and characterize the evolution and performance of Portuguese university spin-offs. The repository is built exclusively in open source technologies and offers the potential to characterize and analyze the evolution and performance of university spin-offs in different perspectives, such as individual factors; organizational factors; institutional factors; and scientific, educational, and commercial outputs.

Abstract
This paper reports on-chip rail-to-rail timing signals generation thin-film circuits for the first time. These circuits, based on a-IGZO thin-film transistors (TFTs) with a simple staggered bottom gate structure, allow row and column selection of a sensor matrix embedded in a flexible radiation sensing system. They include on-chip clock generator (ring oscillator), column selector (shift register) and row-selector (a frequency divider and a shift register). They are realised with rail-to-rail logic gates with level-shifting ability that can perform inversion and NAND logic operations. These logic gates are capable of providing full output swing between supply rails, $V_{DD}$ and $V_{SS}$ , by introducing a single additional switch for each input in bootstrapping logic gates. These circuits were characterised under normal ambient atmosphere and show an improved performance compared to the conventional logic gates with diode connected load and pseudo CMOS counterparts. By using these high-performance logic gates, a complete rail-to-rail frequency divider is presented from measurements using D-Flip Flop. In order to realize a complete compact system, an on-chip ring oscillator (output clock frequency around 1 kHz) and a shift register are also presented from simulations, where these circuits show a power consumption of 1.5 mW and 0.82 mW at a supply voltage of 8 V, respectively. While the circuit concepts described here were designed for an X-ray sensing system, they can be readily expanded to other domains where flexible on-chip timing signal generation is required, such as, smart packaging, biomedical wearable devices and RFIDs.

Abstract
Since wind turbines or photovoltaic (PV) panels are generally connected to the power grid by power electronic inverters, the power system inertia is gradually decreasing along with the growing share of renewable energy. This jeopardizes the system frequency response dynamics so that the corresponding frequency security issue is becoming the bottle-neck factor that restricts the development of high renewable energy penetration. Consequently, power system scheduling models need to incorporate frequency dynamics. The difficulty lies in how to formulate the frequency security constraints from the perspective of hourly load-generation balance since the frequency dynamics have a shorter time scale (5 similar to 30 s). Several modeling methods have been proposed based on different assumptions and simplifications. However, their accuracy is not clear. We first propose a novel method to formulate linear frequency security constraints, which considers more details of frequency response dynamics. Then, an evaluation methodology is designed to quantify the accuracy of those frequency constraints. Using this evaluation method, we compare two typical methods in recent literature with the proposed method. The results show the effectiveness and superiority of our proposed method.

Abstract
In tonal music, musical tension is strongly associated with musical expression, particularly with expectations and emotions. Most listeners are able to perceive musical tension subjectively, yet musical tension is difficult to be measured objectively, as it is connected with musical parameters such as rhythm, dynamics, melody, harmony, and timbre. Musical tension specifically associated with melodic and harmonic motion is called tonal tension. In this article, we are interested in perceived changes of tonal tension over time for chord progressions, dubbed tonal tension profiles. We propose an objective measure capable of capturing tension profile according to different tonal music parameters, namely, tonal distance, dissonance, voice leading, and hierarchical tension. We performed two experiments to validate the proposed model of tonal tension profile and compared against Lerdahl's model and MorpheuS across 12 chord progressions. Our results show that the considered four tonal parameters contribute differently to the perception of tonal tension. In our model, their relative importance adopts the following weights, summing to unity: dissonance (0.402), hierarchical tension (0.246), tonal distance (0.202), and voice leading (0.193). The assumption that listeners perceive global changes in tonal tension as prototypical profiles is strongly suggested in our results, which outperform the state-of-the-art models.

Abstract

Abstract
The past years have witnessed a boost in fall detection-related research works, disclosing an extensive number of methodologies built upon similar principles but addressing particular use-cases. These use-cases frequently motivate algorithm fine-tuning, making the modelling stage a time and effort consuming process. This work contributes towards understanding the impact of several of the most frequent requirements for wearable-based fall detection solutions in their performance (usage positions, learning model, rate). We introduce a new machine learning pipeline, trained with a proprietary dataset, with a customisable modelling stage which enabled the assessment of performance over each combination of custom parameters. Finally, we benchmark a model deployed by our framework using the UMAFall dataset, achieving state-of-the-art results with an F1-score of 84.6% for the classification of the entire dataset, which included an unseen usage position (ankle), considering a sampling rate of 10 Hz and a Random Forest classifier.