A complete season with attendance restrictions confirms the relevant contribution of spectators to home advantage and referee bias in association football

Due to the unfortunate pandemic situation, the phenomena of home advantage and referee bias in sports have recently received a particular research attention, especially in association football. In this regard, several studies were conducted on the last portion of the 2019-20 season: the majority of them suggests a reduction-but not the elimination-of the two phenomena, with some exceptions in which no reduction was found or, at the other extreme, the phenomena were not observed at all

An empirical study of social networks metrics in object-oriented software

We study the application to object-oriented software of new metrics, derived from Social Network Analysis. Social Networks metrics, as for instance, the EGO metrics, allow to identify the role of each single node in the information flow through the network, being related to software modules and their dependencies. These metrics are compared with other traditional software metrics, like the Chidamber-Kemerer suite, and software graph metrics. We examine the empirical distributions of all the metrics, bugs included, across the software modules of several releases of two large Java systems, Eclipse and Netbeans. We provide analytical distribution functions suitable for describing and studying the observed distributions. We study also correlations among metrics and bugs. We found that the empirical distributions systematically show fat-tails for all the metrics. Moreover, the various metric distributions look very similar and consistent across all system releases and are also very similar in both the studied systems. These features appear to be typical properties of these software metrics

The advantages of context specific language models:the case of the Erasmian Language Model

The current trend to improve language model performance seems to be based on scaling up with the number of parameters (e.g. the state of the art GPT4 model has approximately 1.7 trillion parameters) or the amount of training data fed into the model. However this comes at significant costs in terms of computational resources and energy costs that compromise the sustainability of AI solutions, as well as risks relating to privacy and misuse. In this paper we present the Erasmian Language Model (ELM) a small context specific, 900 million parameter model, pre-trained and fine-tuned by and for Erasmus University Rotterdam. We show how the model performs adequately in a classroom context for essay writing, and how it achieves superior performance in subjects that are part of its context. This has implications for a wide range of institutions and organizations, showing that context specific language models may be a viable alternative for resource constrained, privacy sensitive use cases

Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings

In the field of vibration monitoring and control, the use of low-cost multicomponent MEMS-based accelerometer sensors is nowadays increasingly widespread. Such sensors allow implementing lightweight monitoring systems with low management costs, low power consumption and a small size. However, for the monitoring systems to provide trustworthy and meaningful data, the high accuracy and reliability of sensors are essential requirements. Consequently, a metrological approach to the calibration of multi-component accelerometer sensors, including appropriate uncertainty evaluations, are necessary to guarantee traceability and reliability in the frequency domain of data provided, which nowadays is not fully available. In addition, recently developed metrological characterizations at the microscale level allow to provide detailed and accurate quantification of the enhanced technical performance and the responsiveness of these sensors. In this paper, a dynamic calibration procedure is applied to provide the sensitivity parameters of a low-cost, multicomponent MEMS sensor accelerometer prototype (MDUT), designed, developed and realized at the University of Siena, conceived for rolling bearings vibration monitoring in a broad frequency domain (from 10 Hz up to 25 kHz). The calibration and the metrological characterization of the MDUT are carried out by comparison to a reference standard transducer, at the Primary Vibration Laboratory of the National Institute of Metrological Research (INRiM)

Challenges for Children with Cochlear Implants in Everyday Listening Scenarios: The Competitive Effect of Noise and Face Masks on Speech Intelligibility

Speech intelligibility (SI) tests under realistic acoustic scenarios are complex tasks to perform. Optimal acoustics, in terms of reverberation and noise, are thus needed. This is particularly true in the presence of young hard-of-hearing (HoH) children equipped with cochlear implants who need speech to be highly intelligible to learn. During the COVID-19 pandemic starting in early 2020, wearing face masks became common to avoid the spread of infection, mainly impacting the increasingly challenging task of listening for HoH listeners. This study investigated the influence of different types of face masks on speech intelligibility and listening difficulty under competitive noise scenarios. Fourteen children with cochlear implants were involved, as well as six children with typical hearing. Three types of face masks with different acoustic, filtration, and breathability characteristics were considered; three signal-to-noise ratios (SNR) of +10 dB, +5 dB, and 0 dB were used. As expected, lower SNRs corresponded to lower speech intelligibility, and SI without a mask was similar to that obtained with a mask at the lowest acoustic attenuation, albeit with a low filtration efficiency. These preliminary outcomes help improve speech communication strategies in classrooms to support optimal listening conditions

Ambipolar light-emitting organic field-effect transistor

We demonstrate a light-emitting organic field-effect transistor (OFET) with pronounced ambipolar current characteristics. The ambipolar transport layer is a coevaporated thin film of α-quinquethiophene (α-5T) as hole-transport material and N,N'-ditridecylperylene-3,4,9,10-tetracarboxylic diimide (P13) as electron-transport material. The light intensity is controlled by both the drain–source voltage VDS and the gate voltage VG. Moreover, the latter can be used to adjust the charge-carrier balance. The device structure serves as a model system for ambipolar light-emitting OFETs and demonstrates the general concept of adjusting electron and hole mobilities by coevaporation of two different organic semiconductors