Context. The Near-InfraRed Planet Searcher (NIRPS) is a high-resolution, high-stability near-infrared (NIR) spectrograph equipped with an adaptive optics (AO) system. Installed on the ESO 3.6-m telescope at La Silla Observatory, Chile, it was developed to enable radial velocity (RV) measurements of low-mass exoplanets around M dwarfs and to characterise exoplanet atmospheres in the NIR. Aims. This paper provides a comprehensive design overview and characterisation of the NIRPS instrument, reporting on its on-sky performance, advising on how to carry out observations, and presenting its guaranteed time observation (GTO) programme. Methods. Intensive on-sky testing phases were conducted between November 2019 and March 2023. The instrument started its operations on 1 April 2023. Results. The spectral range continuously covers the Y, J, and H bands from 972.4 to 1919.6 nm. The thermal control system maintains 1 mK stability over several months, thereby minimising drift. The NIRPS’s AO-assisted fibre link improves coupling efficiency and offers a unique high-angular resolution capability with a fibre acceptance of only 0.4″. A high spectral resolving power of R ~ 90 000 and R ~ 75 000 is provided in high-accuracy (HA) and high-efficiency (HE) modes, respectively. The overall throughput from the top of the atmosphere to the detector peaks at 13%. The RV precision, measured on the bright star Proxima with a known exoplanetary system, is 77 cms⁻¹. NIRPS and HARPS can be used simultaneously, offering unprecedented spectral coverage for spectroscopic characterisation and stellar activity mitigation. Modal noise can be aptly mitigated by the implementation of fibre stretchers and AO scanning mode. Conclusions. Initial results confirm that NIRPS opens new possibilities for RV measurements, stellar characterisation, and exoplanet atmosphere studies with high precision and high spectral fidelity. NIRPS demonstrated stable RV precision at the level of 1 m s⁻¹ over several weeks. The instrument’s high throughput, particularly in the H band, offers a notable improvement over previous spectrographs, enhancing our ability to detect small exoplanets.

Vehicular acoustic noise evaluations are a concern of researchers due to health and comfort effects on humans and are fundamental for anyone interested in mitigating audio noise. This paper focuses on the evaluation of the noise level inside a vehicle by using statistical tools. First, an experimental setup was developed with microphones and a microcomputer located strategically on the car’s panel, and measurements were carried out with different conditions such as car window position, rain, traffic, and car speed. Regression analysis was performed to evaluate the similarity of the noise level from those conditions. Thus, we were able to discuss the relevance of the variables that contribute to the noise level inside a car. Finally, our results revealed that the car speed is strongly correlated to interior noise levels, suggesting the most relevant noise sources are in the vehicle itself.

We obtained 420 high-resolution spectra of Proxima, over 159 nights, using the Near Infra Red Planet Searcher (NIRPS). We derived 149 nightly binned radial velocity measurements with a standard deviation of 1.69 ms ⁻¹ and a median uncertainty of 55 cms ⁻¹ , and performed a joint analysis combining radial velocities, spectroscopic activity indicators, and ground-based photometry, to model the planetary and stellar signals present in the data, applying multi-dimensional Gaussian process regression to model the activity signals. We detect the radial velocity signal of Proxima b in the NIRPS data. All planetary characteristics are consistent with those previously derived using visible light spectrographs. In addition, we find evidence of the presence of the sub-Earth Proxima d in the NIRPS data. When combining the data with the HARPS observations taken simultaneous to NIRPS, we obtain a tentative detection of Proxima d and parameters consistent with those measured with ESPRESSO. By combining the NIRPS data with simultaneously obtained HARPS observations and archival data, we confirm the existence of Proxima d, and demonstrate that its parameters are stable over time and against change of instrument. We refine the planetary parameters of Proxima b and d, and find inconclusive evidence of the signal attributed to Proxima c (P = 1900 d) being present in the data. We measure Proxima b and d to have minimum masses of 1.055 ± 0.055 M _⊕ , and 0.260 ± 0.038 M _⊕ , respectively. Our results show that, in the case of Proxima, NIRPS provides more precise radial velocity data than HARPS, and a more significant detection of the planetary signals. The standard deviation of the residuals of NIRPS after the fit is ~80 cm s ⁻¹ , showcasing the potential of NIRPS to measure precise radial velocities in the near-infrared.

San-Francisco in the US and Natal in Brazil are two coastal cities which are known rather for its tech scene and natural beauty than for its criminal activities. We analyze characteristics of the urban environment in these two cities, deploying a machine learning model to detect categories and hotspots of criminal activities. We propose an extensive set of spatio-temporal & urban features which can significantly improve the accuracy of machine learning models for these tasks, one of which achieved Top 1% performance on a Crime Classification Competition by kaggle.com. Extensive evaluation on several years of crime records from both cities show how some features - such as the street network - carry important information about criminal activities.

The growth of smart city applications is increasingly around the world, many cities invest in the development of these systems intending to improve the management and life of their residents. This increase is mainly due to the emergence of new technologies such as Big Data and Internet of Things (IoT). Some of the biggest challenges in applying these systems, relate to the processing, visualization, and analysis of the generated data, since most systems tend to work connected, thus generating a large mass of data that deviates from the standard of previously used systems. For data visualization, one of the main devices used is the reduction of dimensionality, in an attempt to bring data from one dimension N to two or three dimensions and thus be noticeable to human eyes. There are several algorithms used for dimensionality reduction, the linear ones that as the name implies, solve linearly separable problems and so these are very limited and the nonlinear ones, that solve more complex problems, but usually have an excessive runtime, making them or often inappropriate to apply. This article presents the parallel implementation of the nonlinear dimension reduction algorithm t-Distributed Stochastic Neighbor Embedding (t-SNE), showing better results than its conventional version in terms of runtime, thus showing that parallelism can make the problem of dimensionality reduction treatable, bringing greater scalability and delivering results within an acceptable time frame.

Electroencephalography (EEG) is a fundamental tool for understanding the brain’s electrical activity related to human motor activities. Brain-Computer Interface (BCI) uses such electrical activity to develop assistive technologies, especially those directed at people with physical disabilities. However, extracting signal features and patterns is still complex, sometimes delegated to machine learning (ML) algorithms. Therefore, this work aims to develop a ML based on the Random Forest algorithm to classify EEG signals from subjects performing real and imagery motor activities. The interpretation and correct classification of EEG signals allow the development of tools controlled by cognitive processes. We evaluated our ML Random Forest algorithm using a consumer and a research-grade EEG system. Random Forest efficiently distinguishes imagery and real activities and defines the related body part, even with consumer-grade EEG. However, interpersonal variability of the EEG signals negatively affects the classification process.