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Efficiently identifying suitable solvents for active pharmaceutical ingredients (APIs) is critical in drug formulation, yet the vast number of possible solvent-solute combinations presents a significant experimental challenge. This study addresses this by developing a robust machine learning (ML) model for accurately predicting the solubility of three phenolic acids (syringic, p-coumaric, and caffeic) in various deep eutectic solvents (DESs), integrating both experimental and computational investigations. Measured solubility data showed that the choline chloride combined with triethylene glycol in a 1:2 molar ratio was the most efficient system for the dissolution of the studied APIs. Different ML models, utilizing nu-Support Vector Regression (nuSVR) as the core regressor and based on descriptor sets derived from COSMO-RS (Conductor-like Screening Model for Real Solvents) computations, were systematically evaluated. A novel methodology termed DOO-IT (Dual-Objective Optimization with ITerative feature pruning) was employed to address the common challenges of model development with limited, high-value datasets. The final optimal 10-descriptor nuSVR model, selected from an exhaustive, multi-run search, demonstrated outstanding predictive power, offering a highly reliable computational tool for guiding experimental screening, significantly accelerating the exploration of DES-based formulations. This research also provides a strong foundation for future machine learning-guided discovery of chemicals, offering an effective and transferable framework for developing QSPR models for various chemical systems.

Advanced Virgo is a 2nd-generation laser interferometer based in Cascina (Italy) aimed at the detection of gravitational waves (GW) from astrophysical sources. Together with the two USA-based LIGO interferometers they constitute a network which operates in coincidence. The three detectors observed the sky simultaneously during the last part of the second Observing Run (O2) in August 2017, and this led to two paramount discoveries: the first three-detector observation of gravitational waves emitted from the coalescence of a binary black hole system (GW170814), and the first detection ever of gravitational waves emitted from the coalescence of a binary neutron star system (GW170817). Coincident data taking was re-started for the third Observing Run (O3), which started on 1st April 2019 and lasted almost one year. This paper will describe the new techniques implemented for the longitudinal controls with respect to the ones already in use during O2. Then, it will present an extensive description of the full scheme of the angular controls of the interferometer, focusing on the different control strategies that are in place in the different stages of the lock acquisition procedure, which is the complex sequence of operations by which an uncontrolled, “free” laser interferometer is brought to the final working point, which allows the detector to reach the best sensitivity.

On 10 April 2024 the Virgo interferometer joined the international gravitational-wave network for the second and third parts of the fourth observing run, O4b and O4c, the latter of which will end on 18 November 2025. The Virgo detector is the first large-scale detector running with a marginally stable Signal Recycling Cavity, a configuration which poses significant challenges from the optical, thermal and control points of view. In this article a description of this configuration of the Virgo detector will be given. As a follow-up to the configuration in use at the beginning of the Run, an extensive report of the main commissioning activities performed during the run will be given. The goal for these activities is two-fold: one important aspect is maintaining, if not improving, the performance of the detector over time, profiting of the longer time scales during the observing run to better understand the behaviour of the detector. The second important aspect is to characterize better the noise content of the detector, and study in advance its characteristics in view of the future developments which are already planned for the next observing run, O5. Both aspects will therefore shape the Virgo detector for the last part of the observing run, and will link to its evolution for the next years.

Backscatter limits many interferometric measurements, including gravitational wave detectors, by creating spurious interference. We describe an experimental method to directly and quantitatively measure the backscatter interference. We derive and verify experimentally a relation between backscatter interference, beam radius and the scattering sample bidirectional reflectance distribution function. We also demonstrate that our method is able to measure backscatter from high quality optics for angles as low as 500 urad with a 160 urad angular resolution.

Advanced Virgo end benches were a significant source of scattered light noise during the third observing run that lasted from April 1 2019 until March 27 2020. We describe how that noise could be subtracted using auxiliary channels during the online strain data reconstruction. We model in detail the scattered light noise coupling and demonstrate that further noise subtraction can be achieved. We also show that the fitted model parameters can be used to optically characterized the interferometer and in particular provide a novel way of establishing an absolute calibration of the detector strain data.

Accurate forecasting of electricity production is crucial for the stability of the entire energy sector. However, predicting future renewable energy production and its value is difficult due to the complex processes that affect production using renewable energy sources. In this article, we examine the performance of basic deep learning models for electricity forecasting. We designed deep learning models, including recursive neural networks (RNNs), which are mainly based on long short-term memory (LSTM) networks; gated recurrent units (GRUs), convolutional neural networks (CNNs), temporal fusion transforms (TFTs), and combined architectures. In order to achieve this goal, we have created our benchmarks and used tools that automatically select network architectures and parameters. Data were obtained as part of the NCBR grant (the National Center for Research and Development, Poland). These data contain daily records of all the recorded parameters from individual solar and wind farms over the past three years. The experimental results indicate that the LSTM models significantly outperformed the other models in terms of forecasting. In this paper, multilayer deep neural network (DNN) architectures are described, and the results are provided for all the methods. This publication is based on the results obtained within the framework of the research and development project “POIR.01.01.01-00-0506/21”, realized in the years 2022–2023. The project was co-financed by the European Union under the Smart Growth Operational Programme 2014–2020.

A methodology of adaptive time series analysis, based on Empirical Mode Decomposition (EMD), and on its time varying version tvf-EMD has been applied to strain data from the gravitational wave interferometer (IFO) Virgo in order to characterise scattered light noise affecting the sensitivity of the IFO in the detection frequency band. Data taken both during hardware injections, when a part of the IFO is put in oscillation for detector characterisation purposes, and during periods of science mode, when the IFO is fully locked and data are used for the detection of gravitational waves, were analysed. The adaptive nature of the EMD and tvf-EMD algorithms allows them to deal with nonlinear non-stationary data and hence they are particularly suited to characterise scattered light noise which is an intrinsically nonlinear and non-stationary noise. Obtained results show that tvf-EMD algorithm allows to obtain more precise results compared to the EMD algorithm, yielding higher cross-correlation values with the auxiliary channels that are the culprits of scattered light noise.

We derive a Bayesian criterion for assessing whether signals observed in two separate data sets originate from a common source. The Bayes factor for a common vs. unrelated origin of signals includes an overlap integral of the posterior distributions over the common source parameters. Focusing on multimessenger gravitational-wave astronomy, we apply the method to the spatial and temporal association of independent gravitational-wave and electromagnetic (or neutrino) observations. As an example, we consider the coincidence between the recently discovered gravitational-wave signal GW170817 from a binary neutron star merger and the gamma-ray burst GRB 170817A: we find that the common source model is enormously favored over a model describing them as unrelated signals.

In the quest for sustainable energy solutions, predicting electricity prices for renewable energy sources plays a pivotal role in efficient resource allocation and decision making. This article presents a novel approach to forecasting electricity prices for renewable energy sources using deep learning models, leveraging historical data from the power system operator (PSE). The proposed methodology encompasses data collection, preprocessing, feature engineering, model selection, training, and evaluation. By harnessing the power of recurrent neural networks (RNNs) and other advanced deep learning architectures, the model captures intricate temporal relationships, weather patterns, and demand fluctuations that impact renewable energy prices. The study demonstrates the applicability of this approach through empirical analysis, showcasing its potential to enhance energy market predictions and aid in the transition to more sustainable energy systems. The outcomes underscore the importance of accurate renewable energy price predictions in fostering informed decision making and facilitating the integration of renewable sources into the energy landscape. As governments worldwide prioritize renewable energy adoption, this research contributes to the arsenal of tools driving the evolution towards a cleaner and more resilient energy future.

Forecasting electricity demand is of utmost importance for ensuring the stability of the entire energy sector. However, predicting the future electricity demand and its value poses a formidable challenge due to the intricate nature of the processes influenced by renewable energy sources. Within this piece, we have meticulously explored the efficacy of fundamental deep learning models designed for electricity forecasting. Among the deep learning models, we have innovatively crafted recursive neural networks (RNNs) predominantly based on LSTM and combined architectures. The dataset employed was procured from a SolarEdge designer. The dataset encompasses daily records spanning the past year, encompassing an exhaustive collection of parameters extracted from solar farm (based on location in Central Europe (Poland Swietokrzyskie Voivodeship)). The experimental findings unequivocally demonstrated the exceptional superiority of the LSTM models over other counterparts concerning forecasting accuracy. Consequently, we compared multilayer DNN architectures with results provided by the simulator. The measurable results of both DNN models are multi-layer LSTM-only accuracy based on R2—0.885 and EncoderDecoderLSTM R2—0.812.