Measurement Geometry Research Articles

Retinal imaging biomarkers of Alzheimer’s disease: clinical evidence and perspectives

AbstractA growing body of research is devoted to retinal imaging biomarkers of Alzheimer’s disease (AD). Imaging methods such as optical coherence tomography (OCT) provide qualitative and quantitative measures of retinal and optic nerve neurodegeneration, whereas OCT‐angiography yields depth‐resolved maps of the retinal microvasculature in exquisite detail. Similarly, conventional and widefield retinal photography enable measurements of the retinal vascular geometry. Other emerging technologies, including hyperspectral imaging, retinal curcumin‐fluorescence imaging and fluorescence lifetime imaging ophthalmoscopy, provide insights into retinal changes at the molecular level, such as the accumulation of amyloid beta.Navigating the evidence in this field is challenging. Of the many studies that have examined associations between retinal imaging findings and AD, most have been based on clinical case ascertainment in the absence of biomarker findings. In this presentation, we will provide an accessible overview of this rapidly evolving field, with an emphasis on studies of retinal imaging biomarkers of the disease that align with the NIA‐AA Research Framework. Underpinning this are the findings of a systematic review and meta‐analysis that we have recently conducted (in press). Drawing on the anatomical and pathological insights provided by fellow speakers in the symposium, we highlight opportunities for further clinicopathological correlation of key imaging biomarkers. Finally, as a prelude to the last talk in the symposium, we highlight gaps in the existing clinical literature and point to efforts to harmonise study methods to generate the evidence needed for adoption of retinal imaging biomarkers of AD in practice.

Cable Robots as Conventional Linear Stage Alternatives for the Investigation of Complex-Shaped Objects via Macroscopic X-ray Fluorescence Imaging

The acquisition of elemental and chemical distribution images on the surface of cultural heritage objects has provided us new insights into our past. The techniques commonly employed, such as macroscopic X-ray fluorescence imaging (MA-XRF), in general require pointwise or whisk-broom scanning of an object under constant measurement geometry for optimal results. Most scanners in this field use stacked linear motorized stages, which are a proven solution for 2D sample positioning. Instead of these serial systems, we propose the use of a parallel cable robot to position the measurement head relative to the object investigated. In this article, we illustrate the significance of the issue and present our own cable robot prototype and test its capabilities, but also discuss the current shortcomings of the concept. With this, we demonstrate the potential of cable robots as platforms for MA-XRF and similar imaging techniques.

An approach for the estimation of lateral track irregularity using axle-box acceleration

PurposeThis article aims to predict the rapid track geometry change in the short term with a higher detection frequency, and realize the monitoring and maintenance of the railway state.Design/methodology/approachFirstly, the ABA data needs to be filtered to remove the DC component to reduce the drift due to integration. Secondly, the quadratic integration in frequency domain for concern components of the vertical and lateral ABA needs to be done. Thirdly, the displacement in lateral of the wheelset to rail needs to be calculated. Then the track alignment irregularity needs to be calculated by the integration of lateral ABA and the lateral displacement of the wheelset to rail.FindingsBy comparing with a commercial track geometry measurement system, the high-speed railway application results in different conditions, after removal of the influence of LDWR, identified that the proposed method can produce a satisfactory result.Originality/valueThis article helps realize detection of track irregularity on operating vehicle, reduce equipment production, installation and maintenance costs and improve detection density.

Depth-selective method for time-domain diffuse reflectance measurements: validation study of the dual subtraction technique.

Research on the spatial distribution of sensitivity of time-domain near infrared diffuse reflectance measurement is reported in this paper. The main objective of the investigation is to validate theoretically calculated sensitivity profiles for a measurement geometry with two detectors and two sources in which sensitivity profiles of statistical moments of distributions of time of flight of photons (DTOFs) are spatially restricted to a region underneath the detectors. For this dual subtraction method, smaller sensitivities to changes appearing in the superficial layer of the medium were observed compared to the single distance and single subtraction methods. Experimental validation of this approach is based on evaluation of changes in the statistical moments of DTOFs measured on a liquid phantom with local absorption perturbations. The spatial distributions of sensitivities, depth-related sensitivity and depth selectivities were obtained from the dual subtraction method and compared with those from single distance and single subtraction approaches. Also, the contrast to noise ratio (CNR) was calculated for the dual subtraction technique and combined with depth selectivity in order to assess the overall performance (product of CNR and depth selectivity) of the method. Spatial sensitivity profiles from phantom experiments are in a good agreement with the results of theoretical studies and feature more locally restricted sensitivity volume with the point of maximal sensitivity located deeper. The highest value of overall performance was obtained experimentally for the second statistical moment in the dual subtraction method (∼10.8) surpassing that of the single distance method (∼8.7). This confirms the advantage of dual subtraction measurement geometries in the suppression of optical signals originated in the superficial layer of the medium.

Field Manipulation of Band Properties in Infrared Spectra of Thin Films

This comprehensive optical study analyzes field manipulations of bands in infrared (IR) spectra of thin films and functional surfaces for varying measurement and sample conditions. Band variations related to the materials dielectric functions, the measurement geometry, the film thickness as well as the direction dependence of the probing electromagnetic fields are demonstrated. Examples are discussed for isotropic polymer films (≈200 nm polymethylmethacrylate [PMMA]) on gold and silicon as well as an anisotropic hydrogen monolayer on a Si(111) surface, characterized by IR–attenuated total reflection, IR microscopy, and incidence‐angle‐dependent IR polarimetry. Even for fixed optical material properties, significant manipulations of band frequency and shape (shifts up to ≈14 cm−1 for PMMA, up to ≈3 cm−1 for H–Si) occur in not only polarization‐dependent but also unpolarized spectra. The shown data underline that polarimetric measurements and optical analyses are essential for a detailed interpretation of band shapes.

Low contact resistance of NiGeSn on n-GeSn

A systematic investigation of the NiGeSn formation and its contact resistivity with GeSn semiconductors are studied. The method of investigation is based on the circular transmission line measurement (CTLM) geometry. The optimum NiGeSn formation temperature is 325 °C, offering a lower contact resistivity of 4.15 × 10−5 Ω·cm2 on n-GeSn. The elemental diffusion mechanism during the NiGeSn formation is also discussed. In line with the research of new materials for cryogenic electronics, GeSn shows a low contact resistivity of 1.79 × 10−5 Ω·cm2 at 5 K. These findings offer valuable insights into optimizing GeSn-based contact technologies for both low-power and cryogenic applications.

Prediction of railroad track geometry change using a hybrid CNN-LSTM spatial-temporal model

Track geometry is highly important for ensuring railroad safety. Predicting track geometry degradation can support preventive maintenance by identifying and prioritizing track segments that are more prone to potential track geometry defects. This paper develops a novel machine learning approach that can simultaneously geospatially align track geometry data from multiple inspections accounting for positional errors, and also predict foot-by-foot track geometry change over time. The proposed position correction method considers multiple geometry parameters to provide highly accurate positional information for geometry measurements, which is a prerequisite for foot-by-foot track geometry prediction. A hybrid CNN (convolutional neural network)-LSTM (long short-term memory neural network) machine learning model is developed to account for spatial–temporal dependence with respect to foot-by-foot track geometry change. Specifically, CNN is used to incorporate spatial dependence of track geometry on adjacent segments. LSTM is applied to learn dynamic changes in track geometry data over time to consider the temporal dependence. The hybrid CNN-LSTM model is validated using track inspection data provided by one major freight railroad in the United States. The results show that our positional error correction method can reduce the relative positional error to less than 1 foot with a 99% confidence interval. Our proposed CNN-LSTM model outperforms four other models, including a naïve model (using the last observation), multi-layer perceptron (MLP), plain CNN, and plain LSTM, for both short-term and long-term prediction periods. The proposed hybrid machine learning methodology can be adapted to various other freight and passenger rail lines. The predictive track geometry change information can be used by the industry to plan and prioritize resources for preventative maintenance, yielding benefits in safety and operational efficiency.

AN APPROACH TO TECHNOLOGICAL EQUIPMENT SELECTION FOR MEASURING RAILWAY TRACK GEOMETRY

The most-used railway track geometry measurement systems are considered in this study. The advantages and disadvantages of considered systems and the necessity and application of these technologies in European railways were examined. The decisive factors of the operation of measurement equipment were considered. Real-time railway track condition monitoring systems were evaluated by 14 experts according to a scale of 13 criteria. Questionnaire data were processed using Kendall’s rank correlation method, and the mean ranks were normalized using the average rank transformation into weight method. The most relevant evaluation criteria describing the principles of operation and quality of the most promising technologies were defined. The most important criteria for assessing the suitability of technologies were determined by applying the technique for order of preference by similarity to ideal solution. Finally, basic conclusions and recommendations were formulated.

Inferring local molecular dynamics from the global actin network structure: A case study of 2D synthetic branching actin networks

Cells rely on their cytoskeleton for key processes including division and directed motility. Actin filaments are a primary constituent of the cytoskeleton. Although actin filaments can create a variety of network architectures linked to distinct cell functions, the microscale molecular interactions that give rise to these macroscale structures are not well understood. In this work, we investigate the microscale mechanisms that produce different branched actin network structures using an iterative classification approach. First, we employ a simple yet comprehensive agent-based model that produces synthetic actin networks with precise control over the microscale dynamics. Then we apply machine learning techniques to classify actin networks based on measurable network density and geometry, identifying key mechanistic processes that lead to particular branched actin network architectures. Extensive computational experiments reveal that the most accurate method uses a combination of supervised learning based on network density and unsupervised learning based on network symmetry. This framework can potentially serve as a powerful tool to discover the molecular interactions that produce the wide variety of actin network configurations associated with normal development as well as pathological conditions such as cancer.

Connection Between Track Geometry Quality and Dynamic Vehicle Response at Various Speeds

Track geometry measurements are widely used for describing track quality. However, derailments and track deterioration are caused by forces arising in vehicle-track system. This research focuses on two types of vehicle response. Firstly, the influence of the longitudinal level irregularities on the vertical wheel-rail forces was examined. Secondly, the correlation between the lateral axle box acceleration and the cross level irregularities was investigated. Track geometry and vehicle response data were acquired simultaneously by a track recording car, formed from a passenger car, at various speeds up to 130 km/h. Vehicle-track forces were calculated based on accelerometers mounted on the car body, bogies and axle boxes, considering mass and moment of inertia. Non-linear regressions resulted in vertical vehicle-track force estimation functions. It was proven that the use of second spatial derivatives of the longitudinal level gave a better estimation than the use of reference TQIs according to European Standard EN 13848-6. A linear relationship was found between the speed and standard deviation of vertical vehicle-track forces. On straight sections with constant speed, correlation coefficients of around 0.8 were found between second spatial derivatives of cross level and lateral axle box acceleration.

A sparse-point guided photometric stereo method for the metal complex surfaces measurement and high-fidelity geometry recovery

Accurate recovery of complex surfaces with dense points and normals often requires informative measurement data, posing a great challenge for measurement. In this paper, a contact sensor and a photometric stereo (PS) vision device are integrated together to recover the geometry with high-fidelity details. A two-branch deep PS neural network is developed and the Gaussian process regression (GPR) is used to reconstruct a global geometric shape from some sparse points captured by the contact probe. This prior geometry is sent to the proposed cross-convolution attention residual block (CCARB) based network to guide the PS for better normal recovery. The resulting normals are further fused with the sparse points by a normal integral model to obtain dense and high-accuracy points. Synthetic datasets are constructed to train the network. Experiments are conducted on both synthetic and real data, and show the superiority in the high-fidelity reconstruction for the measurement of complex surfaces.

BRDF representation in response to the build orientation in 3D-printed digital materials

Additively manufactured (AM) parts still lack a thorough understanding of their optical properties, particularly surface texture and reflectance characteristics at different viewing angles. This study examines the reflectance properties of material jetting (MJT) parts using bidirectional reflectance distribution functions (BRDFs). The visual appearance of the MJT parts was analyzed using a gonio-spectrophotometer at 328 unique incidence and viewing geometries for seven different wedge angles for build orientation (BO) from 0° to 90° at 15° intervals. The redundancy analysis (RDA) and principal component analysis (PCA) were used to study BO and to determine the prominent measurement geometries. The results indicate higher BOs resulted in more color and texture variation and rougher surfaces, with Sq 4.21 μm vertical compared to 1.42 μm for horizontal BOs. Furthermore, it affected the visual representation and parametric estimation of BRDF, where significantly lower luminance, more diffuse reflection, and less hue distribution were observed for all CMYK resins printed at higher BOs. Accordingly, vertically printed surfaces showed a wider near-to-specular luminance area than other BOs. An analysis of the bidirectional reflectance property suggests that a gonio-spectrophotometer can be embedded in the printing process and quality assurance in AM as a computationally efficient model.

Measurement of Geometric Variations of a Railway Truss Bridge (Case Study: BH77 Railway Bridge)

Monitoring the condition of the railway track on a regular basis needs to be carried out regularly to minimize risk. One of the causes of the decrease in the strength of the bridge structure can be caused by changes in cross-sectional dimensions. The existing dimensions of the bridge structure need to be known because they will affect the steel frame profile area, where the steel frame area will affect the size or the small value of the deflection and stress of the bridge structure. The dimensions of the frame need to be seriously considered so that the stress and deflection values ??of the bridge structure remain constant. This research was conducted at the BH 77 Railway Bridge in Tegineneng, Tanjungkarang-Martapura, Lampung, which uses a type frame configuration warren truss. This location was chosen to fit the research that was also conducted by Badan Riset dan Inovasi Nasional (National Research and Innovation Agency of Indonesia). Based on the measurement of the dimensions of the bridge truss that has been carried out, the results show that there is a difference in the value of the circumference of each rod with the standard deviation for each similar profile. The biggest difference in the circumference of the truss is the profile of H beam, 113 and 115 rods with dimensions of 340×300×15×18, the initial circumference value is 1,280 mm, and after the measurement is 1,289.42 mm with a standard deviation value of 0.91 mm. while for the smallest difference, namely the H beam profile, rod 110 with dimensions of 340×310×39×21, the initial circumference value is 1,300 mm and after the measurement is 1,300.33 mm with a standard deviation value of 0.24 mm.

Generation, validation, and benchmarking of a commercial independent Monte Carlo calculation beam model for multi-target SRS

BackgroundDosimetric validation of single isocenter multi-target radiosurgery plans is difficult due to conditions of electronic disequilibrium and the simultaneous irradiation of multiple off-axis lesions dispersed throughout the volume. Here we report the benchmarking of a customizable Monte Carlo secondary dose calculation algorithm specific for multi-target radiosurgery which future users may use to guide their commissioning and clinical implementation. PurposeTo report the generation, validation, and clinical benchmarking of a volumetric Monte Carlo (MC) dose calculation beam model for single isocenter radiosurgery of intracranial multi-focal disease. MethodsThe beam model was prepared within SciMoCa (ScientificRT, Munich Germany), a commercial independent dose calculation software, with the aim of broad availability via the commercial software for use with single isocenter radiosurgery. The process included (1) definition & acquisition of measurement data required for beam modeling, (2) tuning model parameters to match measurements, (3) validation of the beam model via independent measurements and end-to-end testing, and finally, (4) clinical benchmarking and validation of beam model utility in a patient specific QA setting. We utilized a 6X Flattening-Filter-Free photon beam from a TrueBeam STX linear accelerator (Siemens Healthineers, Munich Germany). ResultsIn addition to the measured data required for standard IMRT/VMAT (depth dose, central axis profiles & output factors, leaf gap), beam modeling and validation for single-isocenter SRS required central axis and off axis (5 cm & 9 cm) small field output factors and comparison between measurement and simulation of backscatter with aperture for jaw much greater than MLCs. Validation end-to-end measurements included SRS MapCHECK in StereoPHAN geometry (2%/1 mm Gamma = 99.2% ± 2.2%), and OSL & scintillator measurements in anthropomorphic STEEV phantom (6 targets, volume = 0.1–4.1cc, distance from isocenter = 1.2–7.9 cm) for which mean difference was −1.9% ± 2.2%. For 10 patient cases, MC for individual PTVs was −0.8% ± 1.5%, −1.3% ± 1.7%, and −0.5% ± 1.8% for mean dose, D95%, and D1%, respectively. This corresponded to custom passing rates action limits per AAPM TG-218 guidelines of ±5.2%, ±6.4%, and ±6.3%, respectively. ConclusionsThe beam modeling, validation, and clinical action criteria outlined here serves as a benchmark for future users of the customized beam model within SciMoCa for single isocenter radiosurgery of multi-focal disease.

Influence Of Marine Sediments On The Rheological Properties Of Bricks

The practices of dredging constitute a challenge for the development and maintenance of port activities. Millions of cubic meters are regularly extracted from the Algerian ports, it is necessary to value them. This study focuses on the valorization of marine sediments from the port of Bethioua for making bricks. A rheological characterization was performed with great care for these marine sediments and for the brick slip. The rheological study was conducted for different concentrations using the rheometer using a measurement geometry coaxial cylinder. The study of the rheological parameters of the brick slip , marine sediments and the brick slip with addition of these marine sediments has enabled to determine the ideal composition and concentrations ideal for making bricks. The results of the rheological parameters obtained from these marine sediments show a percentage of valorization recovery up to 15% which allows that these sediments can be valued in the field of manufacture of brick.

Computed tomography to guide transcatheter aortic valve implantation.

Since its introduction in 2022, transcatheter aortic valve implantation (TAVI) has revolutionized the treatment and prognosis of patients with aortic stenosis. Robust clinical trial data and awealth of scientific evidence support its efficacy and safety. One of the key factors for success of the TAVI procedure is careful preprocedural planning using imaging. Computed tomography (CT) has developed into the standard imaging method for comprehensive patient assessment in this context. Suitability of the femoral and iliac arteries for transfemoral access, exact measurement of aortic annulus size and geometry as the basis for prosthesis selection, quantification of the spatial relationship of the coronary ostia to the aortic annular plane, and identification of optimal fluoroscopic projection angles for the implantation procedure are among the most important information that can be gained from preprocedural CT. Further research is aimed at improving risk stratification, for example, with respect to annular perforation, periprosthetic aortic regurgitation, and need for postprocedural implantation of apermanent pacemaker.

The Study of the Bistatic Cross-Correlation Function of Two Signals Separated in Frequency Reflected by the Water Surface

The purpose of this study is to analyze the applicability conditions for the significant wave height (SWH) measurement approach based on measuring the cross-correlation function of two signals with similar frequencies reflected by the sea surface in the bistatic problem statement (the transmitting antenna and the receiving antenna are separated in space). When implementing this approach, the modulus of the normalized cross-correlation function for several pairs of signals with different frequency bases will be measured in the experiment. The advantage of this approach over the traditional method for radar altimetry, based on the analysis of the shape of the reflected pulse, is the high accuracy in measuring the SWH for weak waves. In the bistatic formulation of the problem, an important advantage of the approach under study is the possibility of obtaining analytical formulas for solving the direct problem. This paper presents the derivation of a formula for the modulus of the normalized cross-correlation function of reflected signals, which expresses an explicit relationship with the parameters of sea waves and the measurement geometry in the bistatic formulation of the problem. This paper considers the influence on the modulus of the normalized cross-correlation function of the antenna patterns of the transmitting and receiving antennas, the distances to the sea surface, the wave slope variances, the SWH and the frequency base of the transmitted signals. The optimal variants of the measurement scheme are discussed. The results and conclusions obtained can be easily expanded to underwater acoustic sounding.

Void fraction prediction using prompt gamma neutron activation analysis and artificial intelligence

This paper presents the simulation of a water-gas biphasic system in annular and stratified flow regimes using a neutron beam from a241Am–Be radiation source. The objective is to evaluate the potential of prompt gamma neutron activation analysis (PGNAA) and artificial intelligence-based algorithms in flow measurements. A measurement geometry consisted of a neutron flat source with parallel emission and a spherical detector was developed using the MCNP6 code. Prompt gamma rays spectra were used as input data of a feed-forward multilayer perceptron (MLP) artificial neural network aiming to predict void fraction and to identify the investigated flow regimes. The results showed that the MLP was able to predict void fraction, for both flow regimes, with mean absolute percentage error within 1.58%. In addition, the same MLP achieved 100% of success in flow regime identification. Therefore, it is possible to combine PGNAA with MLP in order to investigate flow characteristics.

Water-efficient genotypes along with conservation measures significantly reduce the green and blue water footprints in sugarcane (Saccharum spp.)

Sugarcane crop is irrigated using surface, overhead, and drip irrigation methods. Increased water use in sugarcane is a major concern around the world, implying the need for water accounting, developing water-efficient hybrids and water-saving agro-techniques for long-term conservation and use of water. “Water Footprint (WF)” is a measure of both direct and indirect water usage accountable for any product and/or process. In praxis, ‘Green Water Footprint’ (GWF) and ‘Blue Water Footprint’ (BWF) are extremely crucial for the restoration of essential ecosystem services (ES), such as sugarcane production. The WF metric was used as a priority tool in our study to evaluate water-efficient sugarcane hybrids, germplasm clones, deficit irrigation scheduling, crop geometry, and water conservation measures. Precise and accurate WF quantification would supplement the decision-making processes for managing available water resources in sugarcane agriculture. In split plot experimental design two research investigations on water management in sugarcane were undertaken at the ICAR-Sugarcane Breeding Institute, Coimbatore, Tamil Nadu, India. The major objective of the research trails was to find out suitable sugarcane hybrids and agronomic management practices to minimise water usage in sugarcane cultivation in water stressed and drought prone areas of tropical India. Our investigation comprised two phases; the first one being assessment of the impact of deficit irrigation scheduling, planting techniques and water conservation measures in sugarcane production, while the second phase dealt with genotypic evaluation under variable irrigation scheduling. Results showed that BWF reduced significantly in the first ratoon crop due to deficit irrigation scheduling coupled with planting of two budded setts and application of sugarcane trash at the rate of 5 t ha−1. Sugarcane hybrids viz., Co 85019, Co 10026, Co 12009, Co 13014, Co 14002, Co 14025, Co 15015, and Co 15018 were more water efficient, with a lower total WF. Among the germplasm clones, Fiji 55, ISH 111, ISH 107, Pathri, and Gungera exhibited lower GWF, BWF and total WF.

Precise geometry and pose measurement of in-hand objects with simple features using a multi-camera system

In precision pick-and-place tasks, robotic grasp errors can cause the failure of subsequent placement steps. Therefore, in-hand object pose measurement has attracted the attention of the intelligent manufacturing industry. However, existing algorithms have limited accuracy and insufficient practicality. Moreover, they often assume that the actual object geometry is identical to the CAD model. To alleviate the problems in terms of accuracy, practicality, and uncritical assumptions, this paper proposes an in-hand measurement solution based on multi-view reconstruction for objects with simple features. In this concept, multiple monocular cameras are deployed at the robot end to calculate the object’s actual geometry and 6D pose by reconstructing its key geometric features. An in-hand pose compensation strategy and a robot terminal system integrated with manipulation and measurement are developed. To verify the solution’s effectiveness, several experiments were carried out. First, grasp errors caused by multiple sources are intuitively demonstrated in a simulation environment. The in-hand measurement is validated to compensate for grasp errors through comparison experiments. Then, robots can rearrange various irregularly piled objects in real-world experiments based on the developed terminal system. Results demonstrate that maximum remaining position errors are within 2 mm with maximum angle errors of less than 1°. The maximum error compensation rate reaches 95%, which is superior to existing in-hand measurement algorithms. It can be observed that the proposed solution is valid for multiple objects. Geometry measurement can help robots calculate the in-hand object pose and identify the object’s category and availability. Therefore, the proposed measurement solution seems accurate and universal for robot applications in smart manufacturing industries, enhancing the accuracy and flexibility of robot manipulation.