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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.

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.

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.

We present the performance of searches for gravitational wave bursts associated with external astrophysical triggers as a function of the search sky region. We discuss both the case of Gaussian noise and real noise of gravitational wave detectors for arbitrary detector networks. We demonstrate the ability to reach Gaussian limited sensitivity in real non-Gaussian data, and show the conditions required to attain it. We find that a single sky position search is ~20% more sensitive than an all-sky search of the same data.

Beam alignment is an important practical aspect of the application of squeezed states of light. Misalignments in the detection of squeezed light result in a reduction of the observable squeezing level. In the case of squeezed vacuum fields that contain only very few photons, special measures must be taken in order to sense and control the alignment of the essentially dark beam. The GEO600 gravitational wave detector employs a squeezed vacuum source to improve its detection sensitivity beyond the limits set by classical quantum shot noise. Here, we present our design and implementation of an alignment sensing and control scheme that ensures continuous optimal alignment of the squeezed vacuum field at GEO 600 on long time scales in the presence of free-swinging optics. This first demonstration of a squeezed light automatic alignment system will be of particular interest for future long-term applications of squeezed vacuum states of light.

The output port of GEO 600 is dominated by unwanted high order modes (HOMs). The current thermal actuation system, a ring heater behind one of the folding mirrors, causes a significant amount of astigmatism, which produces HOMs. We have built and installed an astigmatism correction system, based on heating this folding mirror at the sides (laterally). With these side heaters and the ring heater behind the mirror, it is possible to tune its radius of curvature in the horizontal and the vertical degree of freedom. We use this system to match the mirrors in the two arms of GEO 600 to each other, thereby reducing the contrast defect. The use of the side heaters reduces the power of the HOMs at the output of GEO 600 by approximately 37%.

Autonomous gravitational-wave searches -- fully automated analyses of data that run without human intervention or assistance -- are desirable for a number of reasons. They are necessary for the rapid identification of gravitational-wave burst candidates, which in turn will allow for follow-up observations by other observatories and the maximum exploitation of their scientific potential. A fully automated analysis would also circumvent the traditional \"by hand\" setup and tuning of burst searches that is both labourious and time consuming. We demonstrate a fully automated search with X-Pipeline, a software package for the coherent analysis of data from networks of interferometers for detecting bursts associated with GRBs and other astrophysical triggers. We discuss the methods X-Pipeline uses for automated running, including background estimation, efficiency studies, unbiased optimal tuning of search thresholds, and prediction of upper limits. These are all done automatically via Monte Carlo with multiple independent data samples, and without requiring human intervention. As a demonstration of the power of this approach, we apply X-Pipeline to LIGO data to search for gravitational-wave emission associated with GRB 031108. We find that X-Pipeline is sensitive to signals approximately a factor of 2 weaker in amplitude than those detectable by the cross-correlation technique used in LIGO searches to date. We conclude with the prospects for running X-Pipeline as a fully autonomous, near real-time triggered burst search in the next LSC-Virgo Science Run.