Abstract
Foraminifers are widespread, highly abundant protists and active participants in marine carbon cycling. Their biomass might represent almost half of the total meiobenthic biomass in the deep sea. Foraminiferal biomass is frequently assessed through geometric models and biovolume estimates due to its non-destructive nature, which allows estimates of individuals from palaeoecological, museum, and living samples. To increase the accuracy of foraminiferal biovolume and biomass assessment we evaluate and propose geometric models for 207 foraminiferal taxa and the species’ average cell occupancy of the test. Individual test dimensions were measured to calculate volume (µm3), and the percent of cell occupancy (PCO) of the test was measured to assess the biovolume (µm3). These data were converted into individual biomass measurements (µg Corg ind-1). Our high intra- and interspecific PCO variance suggest that a mean PCO for each species represents the natural variability of occupancy more accurately than a predetermined fixed percentage for the whole assemblage, as previously asserted in the literature. Regression equations based on the relationship between test dimensions and volumes are presented. The geometric models, the PCO adjustment, and the equations will reduce time, effort, and discrepancies in foraminiferal biovolume and biomass assessments. Therefore, these results can improve the use and reliability of foraminiferal biomass in the future, facilitating its use in (1) distinct approaches including carbon flux estimations, (2) determining the effects of climate change on the marine trophic webs, and (3) environmental monitoring programs.

Abstract
ABSTRACT: This report covers the implementation of temporal flexibility options in TradeRES’ agent-based electricity market simulations models. Within this project, the term “temporal flexibility option” was defined as an asset or measure supporting the power system to balance electric demand and supply and compensate for their stochastic fluctuations stemming from, e.g., weather or consumer behaviour by adjusting demand and/or supply as a function over time or by reducing their forecast uncertainty. Other reports from the same work package of TradeRES are published almost simultaneously, each focussing on another aspect of market model enhancements. These accompanying reports address sectoral flexibility, spatial flexibility, actor types, and modelling requirements for market designs. Flexibility options covered in this report were selected with regard to a predominantly temporal characteristic, a contribution to TradeRES’ assessment of market designs, and the feasibility to be implemented in at least one of the agent based models (ABM) during the project’s lifetime. The technical aspects of “Load shedding”, “Load shifting”, “Electricity storage”, and “Real-time pricing” were selected for implementation. In addition, the following new electricity market products were selected for implementation: “Rolling market clearing”, “Trading with shorter time units”, and “Variable market closure lead times”.

Abstract
Surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) are among the most common and powerful label-free refractive index-based biosensing techniques available nowadays. Focusing on LSPR sensors, their performance is highly dependent on the size, shape, and nature of the nanomaterial employed. Indeed, the tailoring of those parameters allows the development of LSPR sensors with a tunable wavelength range between the ultra-violet (UV) and near infra-red (NIR). Furthermore, dealing with LSPR along optical fiber technology, with their low attenuation coefficients at NIR, allow for the possibility to create ultra-sensitive and long-range sensing networks to be deployed in a variety of both biological and chemical sensors. This work provides a detailed review of the key science underpinning such systems as well as recent progress in the development of several LSPR-based biosensors in the NIR wavelengths, including an overview of the LSPR phenomena along recent developments in the field of nanomaterials and nanostructure development towards NIR sensing. The review ends with a consideration of key advances in terms of nanostructure characteristics for LSPR sensing and prospects for future research and advances in this field.

Abstract
Mobile handheld devices, such as smartphones and tablets, have become some of the most prominent ubiquitous terminals within the information and communication technology landscape. Their transformative power within the digital music domain changed the music ecosystem from production to distribution and consumption. Of interest here is the ever-expanding number of mobile music applications. Despite their growing popularity, their design in terms of interaction perception and control is highly arbitrary. It remains poorly addressed in related literature and lacks a clear, systematized approach. In this context, our paper aims to provide the first steps towards defining guidelines for optimal sonic interaction design practices in mobile music applications. Our design approach is informed by user data in appropriating mobile handheld devices. We conducted an experiment to learn links between control gestures and musical parameters, such as pitch, duration, and amplitude. A twofold action-reflection protocol and tool-set for evaluating the aforementioned links-are also proposed. The results collected from the experiment show statistically significant trends in pitch and duration control gesture mappings. On the other hand, amplitude appears to elicit a more diverse mapping approach, showing no definitive trend in this experiment.

Abstract
Autonomous navigation in agriculture is very challenging as it usually takes place outdoors where there is rough terrain, uncontrolled natural lighting, constantly changing organic scenarios and sometimes the absence of a Global Navigation Satellite System (GNSS). In this work, a single camera and a Google coral dev Board Edge Tensor Processing Unit (TPU) setup is proposed to navigate among a woody crop, more specifically a vineyard. The guidance is provided by estimating the vanishing point and observing its position with respect to the central frame, and correcting the steering angle accordingly. The vanishing point is estimated by object detection using Deep Learning (DL) based Neural Networks (NN) to obtain the position of the trunks in the image. The NN's were trained using Transfer Learning (TL), which requires a smaller dataset than conventional training methods. For this purpose, a dataset with 4221 images was created considering image collection, annotation and augmentation procedures. Results show that our framework can detect the vanishing point with an average of the absolute error of 0.52. and can be considered for autonomous steering.

Abstract
Probabilistic inductive logic programming (PILP) is a statistical relational learning technique which extends inductive logic programming by considering probabilistic data. The ability to use probabilities to represent uncertainty comes at the cost of an exponential evaluation time when composing theories to model the given problem. For this reason, PILP systems rely on various pruning strategies in order to reduce the search space. However, to the best of the authors' knowledge, there has been no systematic analysis of the different pruning strategies, how they impact the search space and how they interact with one another. This work presents a unified representation for PILP pruning strategies which enables end-users to understand how these strategies work both individually and combined and to make an informed decision on which pruning strategies to select so as to best achieve their goals. The performance of pruning strategies is evaluated both time and quality-wise in two state-of-the-art PILP systems with datasets from three different domains. Besides analysing the performance of the pruning strategies, we also illustrate the utility of PILP in one of the application domains, which is a real-world application.