Clay micromechanics: Experimental challenges and perspectives

The predictive capability of machine learning plays a crucial role in aiding forensic practitioners in decision-making regarding opinions. However, the intricate specialization and complexity involved in developing machine learning models impede their comprehensive utilization within forensic science research and practical identification. The utilisation of Advanced Data Analysis (ADA) tools based on the ChatGPT-4 provides strategies to address this challenge by simplifying the machine learning process. The objective of this study was to assess the efficacy of autonomously machine learning models for ADA in diverse tasks by providing ADA with an array of data types, with postmortem interval (PMI), injury time, and sudden cardiac death (SCD) serving as illustrative examples. ChatGPT ADA is capable of autonomously conducting data standardization and selecting the optimal machine learning model based on the raw data. A comparison of the prediction results of ADA with those generated by machine learning models developed by professional data analysts revealed that ADA demonstrated robust predictive performance across diverse datasets. Furthermore, no statistically significant differences were observed in the evaluation metrics across the models when compared to those constructed by data analysts. In conclusion, for the forensic field with a greater number of applications, ChatGPT ADA simplifies the intricate construction process of machine learning and offers a prospective instrument for the comprehensive implementation of machine learning in forensic research and practice by emulating human discourse. However, ADA should not supplant researchers but rather serve as a supplementary tool for research, avoiding its misuse as an "all in" predatory analysis instrument.

The micromechanical behaviour of clays cannot be investigated experimentally in a direct fashion due to the small size of clay particles. An insight into clay mechanical behaviour at the particle scale can be gained by way of virtual experiments based on the discrete-element method (DEM). So far, most DEM models for clays have been designed on the basis of theoretical formulations of inter-particle interactions, with limited experimental evidence of their actual control over the clay's macroscopic response. This paper presents a simplified two-dimensional DEM framework where contact laws were inferred from indirect experimental evidence at the microscale provided by Pedrotti and Tarantino in 2017 (particle-to-particle interactions were probed experimentally by varying the pore-fluid chemistry, and the resulting effect was explored by way of scanning electron microscopy and mercury intrusion porosimetry). The proposed contact laws were successfully tested against their ability to reproduce qualitatively the compression behaviour of clay with pore fluids of varying pH and dielectric permittivity. The DEM framework presented in this work was intentionally kept simple in order to demonstrate the robustness of the micromechanical concept underlying the proposed contact laws. It is anticipated that a satisfactory quantitative prediction would be achieved by moving to a three-dimensional formulation, by considering polydisperse specimens and by refining the contact laws.

Total‐body positron emission tomography (TB‐PET) has ultra‐high sensitivity and the unique ability to conduct dynamic imaging of the entire body. Both the hardware configuration and the data acquired from a TB‐PET scanner differ from those of the conventional short axial field‐of‐view scanners. Therefore, various aspects concerning data processing need careful consideration when implementing TB‐PET in clinical settings. Additionally, advances in data analysis are needed to fully uncover the potential of these systems. Although some progress has been achieved, further research and innovation in scan data management are necessary. In this report, we provide a comprehensive overview of the current progress, challenges, and possible future directions for TB‐PET data processing and analysis. For a review of clinical applications, please find the other review accompanying this paper.

Although the gills have been reported as a site for settlement by sea lice attached stages in experimental challenges, the prevalence and abundance of infection on gills have not been reported in farmed fish and have been reported only rarely in wild salmonids. Disproportionate attachment to the gills may be significant in evaluation of new therapeutants and this study asks whether the efficacy data from studies of therapeutants based on experimental challenge can be extrapolated to the fish farm arena. The distribution of chalimus on gills, body and fins of farmed first sea year Atlantic salmon were examined in two Scottish sea lochs, each on two occasions (30 fish on each sampling date), fish from one farm with infection with Lepeophtheirus salmonis only and the other farm with exclusively Caligus elongatus. The dorsal fin was the primary attachment site of L. salmonis (50% and 33% in two samples respectively) with 29% and 32% on other fins and 20% and 29% on the body and head. Only one of 56 chalimus (1.8%) and four of 75 chalimus (5.3%) were attached to the gills. In contrast, the chalimus distribution of C. elongatus was predominantly on fins other than the dorsal, including the tail, 74% and 71%, on two sampling occasions, respectively, with only 4.2% and 3.6% of chalimus located on the dorsal fin. The pectoral fins were the most common location with 32%, followed by the tail 23%, pelvic fins 14% and only 3% on the anal fin. None of 287 and 111 chalimus in two samples, respectively, of C. elongatus was attached to the gills. The low abundance of lice on the gills indicated that non-destructive sampling methods adequately represented lice infection. In three experimental challenges with L. salmonis, the gills were a major attachment location with 19.9–36.3% of chalimus, 9.9–26.9% on the body, 22–27% on the dorsal fin and 19–23% on the other fins. These differences with natural challenge in L. salmonis reported here and also in the literature suggest that the importance of the gills for chalimus location in the experimental model is atypical and an artefact of reduced host swimming speed in tanks that permits copepodids to attach to the gills. In natural infection, the flow over the gills is high and reduced flow is required for copepodid settlement. Greatest reductions (96%) in chalimus bath treated with cypermethrin occurred on the gills with no significant reductions in those located on the dorsal and other fins. It is possible that the efficacy of therapeutants may be higher in chalimus attached to the gills because of the higher concentration encountered of bath therapeutant and extrapolation of efficacy to farms may be misleading and progression to field testing is essential.

In this study, estimates of inhomogeneous integral scales are derived from rapid distortion theory (RDT) for the case of wall-bounded high-Reynolds-number turbulence and from large-eddy simulation (LES) of a neutrally stratified atmospheric boundary layer (ABL). As for any inhomogeneous flow, integral scales in different directions are introduced. Downward integral scales are introduced since they differ from the usual vertical integral scales because of the presence of the wall. The study concentrates on the length scales based on the vertical velocity, which are the most affected by blocking by the wall, which is assumed to be horizontal. It is shown from RDT that the asymptotic behaviour of the integral length scales for small heights depends crucially on the spectrum power law $-2p$. When $2p>1$ there is always one length scale which does not scale with the distance to the wall $z$. Only the downward integral scale is proportional to $z$ for any $2p$. These results show that the assumption, often made in studies of boundary layers, that all the lengths are proportional to $z$, is not compatible with the assumption of a spectrum decaying according to Kolmogorov's law, but rather with a spectrum following a $-1$ power law. It is an encouraging result since there is now widespread theoretical, experimental and numerical evidence that such a $-1$ power-law subrange exists in the spectra of high-Reynolds-number wall-bounded turbulence, for eddies larger than $z$. The RDT results allow an interpretation of the vertical profiles of the integral length scales computed from the LES outputs: above the third grid point, the vertical profiles of the integral length scales have a linear shape, as expected for high-Reynolds-number turbulence and $2p=1$. Very close to the surface, the upward integral length scales decreases with $z$ because of the fast decay of the spectrum ($2p>2$) from the LES subgrid model. The longitudinal-to-transverse integral length scale ratio is computed using RDT and LES. This ratio is interpreted as the aspect ratio of elongated near-wall large eddies, which are ubiquitous features of LES of boundary layers in which shear plays an important role in the dynamics. The LES shows that the longitudinal-to-transverse integral length scale ratio is an increasing function of $z$, ranging between 1 and 3, which is of the order of magnitude of the published theoretical value of 3.5. From RDT, the evolution with $z$ of the longitudinal-to-transverse integral length scale ratio means either that the velocity shear $\beta$ decreases with $z$ and the spectral power law $2p$ varies in a non-trivial manner, or if both the RDT and LES are valid that the scale of the large eddies is proportional to $\beta z$ with $\beta$ varying from 1.3 to about 4.

Biophysical studies by ultracentrifugation

Compact SERS detection system enabling automated assay on disc combined with advanced data analysis – A case study of methotrexate

Halide Perovskites – Optoelectronic and Structural Characterization Methods

The length scales of film thickness nonuniformities, commonly observed in polymer colloid (i.e., latex) films, are predicted. This prediction is achieved by investigating the stability behavior of drying latex films. A linear stability analysis is performed on a base solution representing a uniformly drying latex film containing a surfactant. The analysis identifies film thickness nonuniformities over two length scales: long (millimeter) range (from lubrication theory) and short (micrometer) range (from nonlubrication theory). Evaporation and surfactant desorption into the bulk film are identified as the primary destabilizing mechanisms during drying. Experimental evidence through direct visualization and atomic force microscopy confirm the existence of nonuniformities over both length scales, which are shown to be functions of parameters such as initial particle volume fraction, surfactant amount, and desorption strength, while being independent of drying rate. © 2008 American Institute of Chemical Engineers AIChE J, 2008

Among the many additive manufacturing (AM) processes for metallic materials, selective laser melting (SLM) is arguably the most versatile in terms of its potential to realize complex geometries along with tailored microstructure. However, the complexity of the SLM process, and the need for predictive relation of powder and process parameters to the part properties, demands further development of computational and experimental methods. This review addresses the fundamental physical phenomena of SLM, with a special emphasis on the associated thermal behavior. Simulation and experimental methods are discussed according to three primary categories. First, macroscopic approaches aim to answer questions at the component level and consider for example the determination of residual stresses or dimensional distortion effects prevalent in SLM. Second, mesoscopic approaches focus on the detection of defects such as excessive surface roughness, residual porosity or inclusions that occur at the mesoscopic length scale of individual powder particles. Third, microscopic approaches investigate the metallurgical microstructure evolution resulting from the high temperature gradients and extreme heating and cooling rates induced by the SLM process. Consideration of physical phenomena on all of these three length scales is mandatory to establish the understanding needed to realize high part quality in many applications, and to fully exploit the potential of SLM and related metal AM processes.

Recent advances in data analysis for electron spectroscopy

River water quality (RWQ) data has obvious characteristics of spatial and temporal distribution, and tables are conventionally exploited for storage of multi-period monitoring data of RWQ; however, neither effective visualization nor accurate analysis of the obtained data can be realized due to its dispersion character. In this paper, a novel spatiotemporal data model is proposed for RWQ data to realize conveniently data representation and spatiotemporal analysis. In this model, a spatial point, containing both location and dynamic water quality information, is considered as the basic element of river spaces, and methods of expanding a point to a line segment, a flat surface and a cube are designed respectively so as to make this model be applicable to different generalizations of river spaces. Moreover, a temporal data storage structure is designed so that efficient inquiry and advanced analysis of RWQ data can be guaranteed and the occupied memory space can be reduced. Finally, case studies are conducted by performing 3D visualization, trend analysis and anomaly identification on RWQ data, the result of which showing that tridimensional representation of RWQ data can be realized efficiently, the computational complexity is reduced significantly and the occupied memory space of monitoring data is effectively economized. Accordingly, the proposed spatiotemporal data model can contribute to the efficient visualization and advanced analysis of RWQ data.

X-ray scanning diffraction can be used to raster-scan tissues to elucidate structural organization and other physical properties. A comprehensive data analysis package targeted to take raster X-ray diffraction (XRD) scans is vital in integrating data at multiple length and resolution scales. MuscleX-DI is an open-source software suite designed for the analysis and visualization of scanning XRD data. Developed in Python and compatible with multiple operating systems, MuscleX-DI provides an end-to-end pipeline for processing diffraction patterns and generating 2D visualizations of calculated measurements. The software supports both a graphical user interface and a command-line interface, offering tools for calibration, masking, azimuthal and radial integration, and heatmap generation. By streamlining the analysis of large XRD datasets, MuscleX-DI facilitates the extraction of structural information from heterogeneous samples, enabling new insights into tissue architecture and biomolecular organization. The scanning diffraction methodology can be applied to study complex tissues and assemblies at various length scales. Example applications include characterizing myelin organization in brain tissue and observing improved drug availability in tumors when used with ECM-degrading enzymes.

Machine learning techniques have been used extensively in several domains of Science and Engineering for decades. These powerful tools have been applied also to the domain of high-energy physics, in the analysis of the data from particle collisions, for years already. Accelerator physics, however, has not started exploiting machine learning until very recently. Several activities are flourishing in this domain, in view of providing new insights to beam dynamics in circular accelerators, in different laboratories worldwide. This is, for instance, the case for the CERN Large Hadron Collider, where since a few years exploratory studies are being carried out. A broad range of topics have been addressed, such as anomaly detection of beam position monitors, analysis of optimal correction tools for linear optics, optimisation of the collimation system, lifetime and performance optimisation, and detection of hidden correlations in the huge data set of beam dynamics observables collected during the LHC Run 2. Furthermore, very recently, machine learning techniques are being scrutinised for the advanced analysis of numerical simulations data, in view of improving our models of dynamic aperture evolution.

With the advent of Machine Learning a few decades ago, Science and Engineering have had new powerful tools at their disposal. Particularly in the domain of particle physics, Machine Learning techniques have become an essential part in the analysis of data from particle collisions. Accelerator physics, however, only recently discovered the possibilities of using these tools to improve its analysis. In different laboratories worldwide, several activities are being carried out, typically in view of providing new insights to beam dynamics in circular accelerators. This is, for instance, the case for the CERN Large Hadron Collider, where, since a few years, exploratory studies are being carried out, covering a broad range of topics. These include the optimisation of the collimation system, the anomaly detection of beam position monitors, analysis of optimal correction tools for linear optics, lifetime and performance optimisation, and detection of hidden correlations in the huge data set of beam dynamics observables collected during the LHC Run 2. Furthermore, very recently, ML techniques are being scrutinised for the advanced analysis of numerical simulations data, in view of improving our models of dynamic aperture evolution.