Abstract
Marine aquaculture, particularly in the Mediterranean region, faces the challenge of minimizing growth dispersion, which has a direct impact on the production cycle, market value and sustainability of the sector. Conventional grading methods are resource intensive and potentially detrimental to fish health. The current study presented an innovative approach in predicting fish weight dispersion in European seabass (Dicentrarchus labrax) aquaculture. Seabass is one of the two major fish species cultivated on the Mediterranean coast, with a fattening cycle of 18-24 months. During this period, several grading operations are carried out to minimize growth dispersion. The intricate feed-fish-water system, characterized by complex interactions among feeding regimes, fish behavior, individual metabolism and environmental factors, is the focus of the study. The comprehensive, five-step methodology addresses this complexity. The process begins with a Discrete Event System (DES) model that simulates the feed-fish-water dynamics, taking into account individual fish metabolism. This is followed by the development of a surrogate machine learning (ML) regressor model, which is trained on DES simulation data to efficiently predict growth distribution. The model is then calibrated and customized for specific fish stocks and production tanks. The preliminary results from 21 tanks in two trials with European seabass (D. labrax) showed the effectiveness of the method. The results from the simulation models achieved a R2 of 99.9 % and a Mean Absolute Percentage Error (MAPE) of 1.1 % for the prediction of mean final weight and a R2 of 90.3 % with a MAPE of 8.1 % for the standard deviation of final weight. In summary, this study represents a significant advance in the planning and management of seabass aquaculture. Given the lack of effective prediction tools in the aquaculture industry, the proposed methodology has the potential to reduce risks and inefficiencies, thus possibly optimizing aquaculture practices by increasing sustainability and profitability.

Abstract
Reconfigurable Intelligent Surfaces (RISs) are in significant focus within 6G research. However, RISs face a power consumption challenge in the reconfigurable elements which may restrict its future scale-up to large areas. We address this issue by proposing a unit cell based on a non-volatile memristor-based switching mechanism. A 1-bit memristor-based reconfigurable RIS unit cell was designed in the Ka-band, and validated using CST and HFSS simulation platforms. The required control circuit to enable the digital control of the memristor has also been proposed. The proposed unit cell achieves losses of less than 1 dB over a frequency band of 25 - 28.3 GHz and a phase difference of 180 degrees +/- 20 degrees at a central frequency of 26.7 GHz, with an operational bandwidth of approximately 1 GHz. Furthermore, an exemplary 16x16 RIS was designed and simulated based on the proposed unit cell to demonstrate its capability to achieve beam steering.

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Abstract
Over the last decades, the interface between two immiscible electrolyte solutions (ITIES) attracted considerable attention of the scientific community due to their vast applications, such as extraction, catalysis, partition studies and sensing. The aim of this Review is to highlight the potential of electrochemistry at the ITIES for analytical purposes, focusing on ITIES-based sensors for detection and quantification of chemically and biologically relevant (bio)molecules. We start by addressing the evolution of ITIES in terms of number of publications over the years along with an overview of their main applications (Chapter 1). Then, we provide a general historical perspective about pioneer voltammetric studies at water/oil systems (Chapter 2). After that, we discuss the most impacting improvements on ITIES sensing systems from both perspectives, set-up design (interface stabilization and miniaturization, selection of the organic solvent, etc.) and optimization of experimental conditions to improve selectivity and sensitivity (Chapter 3). In Chapter 4, we discuss the analytical applications of ITIES for electrochemical sensing of several types of analytes, including drugs, pesticides, proteins, among others. Finally, we highlight the present achievements of ITIES as analytical tool and provide future challenges and perspectives for this technology (Chapter 5).

Abstract
The Transformer-based neural machine translation (NMT) model has been very successful in recent years and has become a new mainstream method. However, using them in lowresourced languages requires large amounts of data and efficient model configuration (hyperparameter tuning) mechanisms. The scarcity of parallel texts is a bottleneck for high quality (N) MTs, especially for under resourced languages like Amharic. As a result, this paper presents an attempt to improve English-Amharic MT by introducing three different vanilla Transformer architectures, with different hyper-parameter values. To obtain additional training material, offline token level corpus augmentation was applied to the previously collected English-Amharic parallel corpus. Compared to previous work on Amharic MT, the best of the three Transformer models have achieved state-of-the-art BLEU scores. In fact, we were able to achieve this result by employing corpus augmentation techniques and hyper-parameter tuning.

Abstract