The oscillator strengths and relative vibronic intensity distribution of the ${(}^2$ ${\mathit{F}}_{5/2}$)${\mathrm{\Gamma }}_8$,${\mathrm{\Gamma }}_7$←${\mathrm{\Gamma }}_6$ ${(}^2$ ${\mathit{F}}_{7/2}$) transitions of the ${\mathrm{YbCl}}_6^{3\mathrm{-}}$ complex ion at an octahedral symmetry site in the ${\mathrm{Cs}}_2$ ${\mathrm{NaYbCl}}_6$ lattice have been measured experimentally and calculated using a combined vibronic crystal-field–ligand polarization approach. The vibronic crystal-field contribution to the total transition dipole moment of the various excitations was worked out both with and without invoking closure over the central metal ion intermediate electronic states and the intensity was assumed to be derived from both a parity and a spin-allowed d←f transition with the cooperation of the odd-parity vibrational modes of the complex ion. Quadrupole and hexadecapole terms have been included in the ligand polarization contribution. Attention has been given to the correct choice of phases for both the electronic and the vibrational wave functions in order to ensure the right sign for the cross term which couples together the crystal field and the ligand polarization transition dipole vectors. The ab initio formalism employed avoids the use of any adjustable parameters in calculating the vibronic intensities. The calculated oscillator strengths of vibronic transitions are within order of magnitude agreement with experimental values. The sensitivity of the calculated values to the use of different force fields has been investigated. The experimentally measured total oscillator strengths for the ${(}^2$ ${\mathit{F}}_{5/2}$)${\mathrm{\Gamma }}_8$,${\mathrm{\Gamma }}_7$←${\mathrm{\Gamma }}_6$ ${(}^2$ ${\mathit{F}}_{7/2}$) transitions of ${\mathrm{YbCl}}_6^{3\mathrm{-}}$ diluted into the transparent ${\mathrm{Cs}}_2$ ${\mathrm{NaGdCl}}_6$ host remain constant with change in ${\mathrm{Yb}}^{3+}$ concentration although deviation of the chromophore from octahedral symmetry is evident at intermediate concentrations. A comparison with the vibronic sidebands of ${\mathrm{Cs}}_2$ ${\mathrm{LiYbCl}}_6$ and ${\mathrm{Cs}}_2$ ${\mathrm{KYbF}}_6$ is made. © 1996 The American Physical Society.

The direct tactile assessment of surface textures during palpation is an essential component of open surgery that is impeded in minimally invasive and robot-assisted surgery. When indirectly palpating with a surgical instrument, the structural vibrations from this interaction contain tactile information that can be extracted and analysed. This study investigates the influence of the parameters contact angle $\alpha$ and velocity $\overrightarrow{v}$ on the vibro-acoustic signals from this indirect palpation. A 7-DOF robotic arm, a standard surgical instrument, and a vibration measurement system were used to palpate three different materials with varying $\alpha$ and $\overrightarrow{v}$. The signals were processed based on continuous wavelet transformation. They showed material-specific signatures in the time–frequency domain that retained their general characteristic for varying $\alpha$ and $\overrightarrow{v}$. Energy-related and statistical features were extracted, and supervised classification was performed, where the testing data comprised only signals acquired with different palpation parameters than for training data. The classifiers support vector machine and k-nearest neighbours provided $99.67\%$ and $96.00\%$ accuracy for the differentiation of the materials. The results indicate the robustness of the features against variations in the palpation parameters. This is a prerequisite for an application in minimally invasive surgery but needs to be confirmed in realistic experiments with biological tissues.

Reducing environmental noise in urban settings, i.e., unwanted or harmful outdoor sounds produced by human activity, has become an important issue in recent years. Most countries have established regulations that set maximum permitted noise levels. However, enforcing these regulations effectively remains challenging as it requires active monitoring networks and audio analysis performed by trained specialists. The manual evaluation of the audio recordings is laborious, time-consuming, and inefficient since many audios exceeding the noise level threshold do not correspond to a sound event considered by the regulation. To address this challenge, this work proposes a computational pipeline to assist specialists in detecting noise sources in the built environment that do not comply with the Chilean noise regulation. The system incorporates a deep neural model following a pre-trained audio neural network architecture transferred to a dataset compiled from public sources and recordings in Valdivia, Chile. The target dataset follows a customized taxonomy of urban sound events. The system also uses a public API so potential users can post audio files to obtain a prediction matrix reporting the presence of noise sources contributing to environmental noise pollution. Experiments using recordings from two continuous noise monitoring stations showed that the amount of data to be inspected by the specialist is decreased by 97% when the deep-learning tools are used. Therefore, this system efficiently assists trained experts in enforcing noise legislation through machine-assisted environmental noise monitoring.

Object detection methods based on deep learning have significantly reduced time-consuming tasks. Semantic segmentation has shown remarkable progress in the study of rocks, especially when applied to petrographic thin sections. Despite the development of various models for specific applications in this field with promising results, their widespread adoption remains limited. This hesitation is largely due to a lack of user confidence stemming from the absence of explainability in the outcomes provided by these models. This study explores the explainability of the state-of-the-art YOLOv11 model in detecting andalusite, biotite, and grains with oolitic textures. We trained three models using plane-polarized-light thin-section microphotographs of the selected targets. Subsequently, we applied color and singular value perturbations to the annotated images using color masks and analyzed the model’s inference through connected region heatmaps. Our findings suggest that the trained models prioritize low-frequency attributes like shape, predominant colors, and contrast over the studied targets’ characteristic tones. These insights contribute to the practical application of deep learning for detecting and segmenting grains and minerals in thin sections.