Measured Data Points Research Articles

Two-group interfacial area concentration model for dispersed gas-liquid flows in large-diameter pipes

Interfacial area concentration (IAC) plays a critical role in heat and mass transfers between gas and liquid phases through the interfaces. As interfacial area increases, mass and heat transfers between phases increase. Hence, a reliable IAC estimation is important for two-phase flow numerical simulations to conduct engineering designs and safety analyses. Gas bubbles show different behavior according to their shapes and sizes. Based on the size, gas bubbles can be classified into two groups that show different characteristics, such as IAC. Hence, two-group modeling helps to achieve a general approach for estimating the IAC in bubbly to beyond-bubbly flow regimes. Available models in the literature use empirical correlations to obtain group one and group two void fractions. These empirical approaches are not reliable for different flow conditions. In addition, these models are usually complex with several empirical constants. This study aims to develop a two-group area-averaged IAC model for upward vertical dispersed flows through large-diameter pipes. In addition, this study proposes using a general approach based on the two-group drift-flux model to calculate two-group void fractions rather than applying empirical correlations. The models are evaluated using 156 two-group measured data points in dispersed flow conditions through large-diameter pipes. The resulting prediction errors for group one and group two IAC models are 24.1 % and 34.2 %, and the errors for group one and group two void fraction predictions are 22.8 % and 25.6 %, respectively.

A 6 month-long real-time time scale steered to NIM-Sr1 optical lattice clock

Abstract NIM-Sr1 optical lattice clock has been operated intermittently for seven consecutive months with an operation uptime around 13.8%, which serves as the reference to steer a hydrogen maser for generating a real-time time scale TS(Sr1). The peak-to-peak time difference of TS(Sr1) compared to UTC is 1.8 ns within 180 days and is less than 0.5 ns within the last month, after correcting the time error induced by a time transfer link abnormality. A dedicated automation system with hardware and software is developed to monitor the operation status of NIM-Sr1, process the measurement data, and periodically predict the hydrogen maser frequency. According to the simulation results, a composite algorithm based on both Kalman filtering (KF) and weighted least squares fitting (WLSF) is utilized for generating TS(Sr1). The KF is utilized when the unavailability of NIM-Sr1 is shorter than one day, and the WLSF with fitting interval T fit=30 d is utilized in other conditions leveraging the long-term predictability of the hydrogen maser. In addition, it is demonstrated by simulation and the post-processing of earlier experimental data that, a time scale with smaller time error can be achieved by shortening the WLSF interval from 30 days to about 5 days under the condition of sufficient measurement data points for reducing the statistical error.

Mapping low-lying states and [formula omitted] in even-even nuclei with machine learning

A machine-learning algorithm, Light Gradient Boosting Machine, was applied for the first time to investigate the fundamental experimental observables in even-even nuclei over the Segrè chart. Specifically, we focused on the excitation energies of the 21+ and 41+ states, and the reduced electric quadrupole transition probability B(E2;01+→21+). Present obtained results well reproduced experimental data within an accuracy of 1.07, 1.05, and 1.14 times for the 21+ and 41+ states as well as B(E2;01+→21+), respectively, being significantly precise than the results from any state-of-the-art nuclear models and from any machine-learning-based approaches. The predictive capability of our machine learning methodology was further validated using 17 newly measured data points which were not used in the training set. Taking O, Ca, Sn, and Pb isotopes as examples, it has been found that our methodology precisely captures both the isotopic trend and absolute values, surpassing all theoretical models hitherto. Our findings reveal the double-magic nature of 100Sn and the disappearance of the N=20 shell in 28O.

Effect of chloride ion corrosion on the microstructure of multiple interfaces of alkali-activated GGBS/FA based recycled concrete

In this paper, the resistance to chloride ion permeability of multiple interfaces in alkali-activated slag/fly ash (GGBS/FA) based full recycled concrete was investigated using the immersion method. In order to objectively and accurately reflect the microhardness and width of the interface transition zone (ITZ), box plots were used to process the measured point data. The results indicated that the microhardness of the interface was significantly lower than that of the matrix. The microhardness of the old aggregate interface (ITZOA-OM), the new aggregate interface (ITZOA-NM) and the new mortar interface (ITZOM-NM) all decreased to varying degrees with the extension of the immersion ages, but remained higher than that of the uneroded specimens. Chloride ions penetrated into ITZOA-OM and reacted with a large amount of enriched Ca (OH)2 to form an expansive composite salt (CaCl2.Ca(OH)2.H2O), which improved the microstructure of the ITZ, increased the microhardness and decreased the width of the ITZ. The dense structure of ITZOA-NM and ITZOM-NM inhibited the migration of chloride ions to a certain extent. leading to minimal changes in the width and microhardness of the ITZ. The micro mechanisms of chloride ion penetration resistance at the interfaces of alkali-activated GGBS/FA-based recycled concrete were investigated using BSE and EDS. More reaction products were generated at the interfaces after chloride ion erosion, and the newly generated substances continuously filled the microcracks, resulting in a more uniform bond at the interfaces. This contributed to the improvement of the microstructure and the resistance to chloride ion penetration of alkali-activated GGBS/FA based recycled concrete.

Integrated torque sensors for humanoid robot joints

Abstract Torque sensors are becoming increasingly important in the joints of humanoid robots, serving as an indispensable part of the joint system. This paper designs a strain-based torque sensor integrated into the output flange of a cycloidal reducer. Through mechanical analysis, the relationship between strain and torque is derived. Then, through model simulation analysis, the optimal placement for the strain gauge is determined. A combination of experimental design and response surface analysis is used to optimize the structure’s parameters. Finally, finite element simulation is conducted using ANSYS to obtain the optimized performance parameters. Subsequent steps include the static calibration of the integrated sensor. Based on the measured data points, a linear relationship between the sensor’s output signal and torque is established.

Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids

This study explores the development of predictive models for carbon dioxide (CO2) solubility in ionic liquids based on a compiled dataset of 10,116 experimentally measured data points involving four input variables: pressure (P), temperature (T), cation type, and anion type. The deep-learning (DL) predictive models evaluated are standalone and hybrid versions of convolutional neural network (CNN) and long short-term memory (LSTM) algorithms with cuckoo optimization algorithm (COA) and gradient-based optimization (GBO). The laboratory-measured data was separated into training and test categories, and each category was normalized separately to improve the performance of the deep learning algorithms. The Mahalanobis distance-based quantile method was utilized to identify any outliers in the training data. Once identified, the outlier data points were eliminated from the training dataset. The control parameters of the deep learning algorithms were optimized using COA to enhance their efficiency, and the algorithms were hybridized with optimization algorithms to further improve their performance. The resulting models were analyzed to assess their accuracy, degree of overfitting, and the importance of input features. The study found that using 80% of the data for training and 20% for testing results in more accurate and generalizable models. Using the outlier detection method on the training data led to 307 data points being eliminated as outliers. Developing CO2-solubility predictive model showed that, the CNNCOA model had the lowest RMSE and highest R2 among the developed models, indicating high generalizability for data unseen by the trained model. The analysis revealed that using optimization algorithms increased the CO2-solubility prediction performance of DL algorithms and reduced overfitting. T and cation type were the most and least important input features, respectively. Simultaneous changes in cation and anion type on CO2-solubility predictions displayed no systematic pattern. For increases in T, CO2 solubility typically decreased, whereas for increases in P CO2 solubility always increased but at variable rates. The results of this study can be used to develop accurate and generalizable CO2-solubility predictive models for various applications.

A spin on active learning analysis for health monitoring.

Decisions on the operation and maintenance of high-valued structures are critical, collecting and analysing data that can inform these decisions motivates the development of structural health monitoring (SHM) and tool monitoring (TM) systems. Descriptive health-state information labels for measured data points are crucial for successful monitoring systems. However, the labels can be expensive to collect and are often unavailable. Due to this problem, fully-supervised machine learning is limited in it’s application to monitoring systems which gives rise to unsupervised and semi-supervised approaches, one of these approaches is active learning. Active learning aims to highlight which unlabelled data points have the highest value of information and would be most informative to the model if they were labelled. For risk-based active learning in SHM, the model is used to advise some maintenance decision process where querying a data point means an expert inspecting the structure to determine its health-state. The current paper applies an active learning regression technology to a machining tool health case study. The algorithm queries data points that are most important in helping determine the health-state of the tool. Querying the most informative datapoints can drastically reduce the costs of a monitoring system. The regression analysis results are used to estimate tool wear and inform a risk-based maintenance decision process. This addresses the problem of when to replace a tool based on its health status, which has applications for many monitoring system problems.

High-Precision Monitoring Method for Bridge Deformation Measurement and Error Analysis Based on Terrestrial Laser Scanning

In bridge structure monitoring and evaluation, deformation data serve as a crucial basis for assessing structural conditions. Different from discrete monitoring points, spatially continuous deformation modes provide a comprehensive understanding of deformation and potential information. Terrestrial laser scanning (TLS) is a three-dimensional deformation monitoring technique that has gained wide attention in recent years, demonstrating its potential in capturing structural deformation models. In this study, a TLS-based bridge deformation mode monitoring method is proposed, and a deformation mode calculation method combining sliding windows and surface fitting is developed, which is called the SWSF method for short. On the basis of the general characteristics of bridge structures, a deformation error model is established for the SWSF method, with a detailed quantitative analysis of each error component. The analysis results show that the deformation monitoring error of the SWSF method consists of four parts, which are related to the selection of the fitting function, the density of point clouds, the noise of point clouds, and the registration accuracy of point clouds. The error caused by point cloud noise is the main error component. Under the condition that the noise level of point clouds is determined, the calculation error of the SWSF method can be significantly reduced by increasing the number of points of point clouds in the sliding window. Then, deformation testing experiments were conducted under different measurement distances, proving that the proposed SWSF method can achieve a deformation monitoring accuracy of up to 0.1 mm. Finally, the proposed deformation mode monitoring method based on TLS and SWSF was tested on a railway bridge with a span of 65 m. The test results showed that in comparison with the commonly used total station method, the proposed method does not require any preset reflective markers, thereby improving the deformation monitoring accuracy from millimeter level to submillimeter level and transforming the discrete measurement point data form into spatially continuous deformation modes. Overall, this study introduces a new method for accurate deformation monitoring of bridges, demonstrating the significant potential for its application in health monitoring and damage diagnosis of bridge structures.

Deep-Learning-Based Automatic Extraction of Aquatic Vegetation from Sentinel-2 Images—A Case Study of Lake Honghu

Aquatic vegetation is an important component of aquatic ecosystems; therefore, the classification and mapping of aquatic vegetation is an important aspect of lake management. Currently, the decision tree (DT) classification method based on spectral indices has been widely used in the extraction of aquatic vegetation data, but the disadvantage of this method is that it is difficult to fix the threshold value, which, in turn, affects the automatic classification effect. In this study, Sentinel-2 MSI data were used to produce a sample set (about 930 samples) of aquatic vegetation in four inland lakes (Lake Taihu, Lake Caohai, Lake Honghu, and Lake Dongtinghu) using the visual interpretation method, including emergent, floating-leaved, and submerged vegetation. Based on this sample set, a DL model (Res-U-Net) was used to train an automatic aquatic vegetation extraction model. The DL model achieved a higher overall accuracy, relevant error, and kappa coefficient (90%, 8.18%, and 0.86, respectively) compared to the DT method (79%, 23.07%, and 0.77) and random forest (78%,10.62% and 0.77) when utilizing visual interpretation results as the ground truth. When utilizing measured point data as the ground truth, the DL model exhibited accuracies of 59%, 78%, and 91% for submerged, floating-leaved, and emergent vegetation, respectively. In addition, the model still maintains good recognition in the presence of clouds with the influence of water bloom. When applying the model to Lake Honghu from January 2017 to October 2023, the obtained temporal variation patterns in the aquatic vegetation were consistent with other studies. The study in this paper shows that the proposed DL model has good application potential for extracting aquatic vegetation data.

Suitability of performance adaptation methods for updating the thermodynamic cycle model of a turboprop engine

A model of the thermodynamic cycle can be used in various fields, such as engine operation and control optimization, diagnosis, and maintenance. It is therefore important to develop a thermodynamic cycle model with high accuracy, and for this purpose, studies have been conducted to increase the accuracy of the model using measurement data. This study was conducted to identify the most effective method of updating the thermodynamic cycle model based on measurement data for the turboprop engine. A performance adaptation approach was applied to update the thermodynamic cycle model using measured data, and adaptation factors were used to adjust the component performance and sensor values. We considered three methods of calculating the adaptation factors and generating functions to apply to an existing engine model, which was a non-adapted model. The first was based on the Newton-Raphson (NR) method, in which the factors were generated as a function through regression curve fitting (RCF). In the second method, adaptation factors were calculated using a genetic algorithm (GA)-based optimization technique, and were generated as a function using RCF. The third method involved optimizing the function of the adaptation factor through GA. The accuracy and calculation cost of each method were compared using steady-state data measured using an in-flight monitoring sensor for the engine condition. All three methods yielded better prediction accuracy than the existing engine model; particularly, the first approach, based on the NR and RCF, showed the highest improvement in accuracy and the lowest number of required calculations. The maximum absolute values of the relative errors for the fuel mass flow rate, low-pressure shaft power, compressor exit pressure, turbine exit temperature, and exhaust gas temperature for the adapted thermodynamic cycle engine model with the NR and RCF were 4.29%, 2.83%, 1.36%, 1.80%, and 3.49%, respectively. As a result, we believe that the NR and RCF can be effectively used to calculate new adaptation factors for added measurement data points and to create a new function including the added factors.

Glass Fibre-Reinforced Extrusion 3D-Printed Composites: Experimental and Numerical Study of Mechanical Properties.

The properties of 3D-printed bodies are an essential part of both the industrial and research sectors, as the manufacturers try to improve them in order to make this now additive manufacturing method more appealing compared to conventional manufacturing methods, like injection moulding. Great achievements were accomplished in both 3D printing materials and machines that made 3D printing a viable way to produce parts in recent years. However, in terms of printing parameters, there is still much room for advancements. This paper discusses four of the 3D printing parameters that affect the properties of the final products made by chopped glass fibre-filled nylon filaments; these parameters are the printing temperature, nozzle diameter, layer height, and infill orientation. Furthermore, a polynomial function was fitted to the measured data points, which made it possible to calculate the tensile strength, flexural strength, and Young's modulus of the 3D-printed samples based on their printing parameters. A Pearson correlation analysis was also carried out to determine the impact of each parameter on all three mechanical properties studied. Both the infill orientation and printing temperature had a significant effect on both strengths and Young's modulus, while the effect of nozzle diameters and layer heights were dependent on the infill orientation used. Also, a model with excellent performance was established to predict the three mechanical properties of the samples based on the four major parameters used. As expected from a fibre-reinforced material, the infill orientation had the most significant effect on the tensile strength, flexural strength, and Young's modulus. The temperature was also quite significant, while the nozzle diameters and layer height effect were situational. The highest values for the tensile strength, flexural strength, and Young's modulus were 72 MPa, 78.63 MPa, and 4243 MPa, respectively, which are around the same values the manufacturer states.

A simple and accurate model to predict pressure drop in vertical gas wells

ABSTRACT Predicting pressure drop in gas wells is one of the most important issues for designing deliquification technologies and optimizing gas production. Current pressure-drop prediction methods can be divided into analytical and empirical models. Although various models have been proposed to predict pressure drop in wellbores, no model has offered stable performance over wide liquid and gas flowrate ranges. Especially for gas wells with low liquid – gas ratios, very few models have been developed specifically. In this study, a simple and accurate three-point model is developed for predicting liquid holdup in wellbores. The model is based on the liquid holdups and gas velocities at the annular – churn, churn – slug and slug – bubble boundaries and is comprehensive for calculating the liquid holdup in vertical wellbores at different liquid/gas flowrates and pressures. The proposed method was verified with 39 collected laboratory and 182 field measured data points from the literature, which have wide parameter ranges. For comparison, some widely used models in the petroleum industry were also evaluated. In validation with the laboratory data with pipe diameters of 50.8 and 101.6 mm, although the new model has an average percentage error (E3) of 220.1% in the churn flow, ranking fourth of all the evaluated models, the other five indicators in the churn flow and six indicators in the annular flow are lowest of all these evaluated models, resulting in relative performance factors (RPFs) of 0.2 and 0, respectively. In the field measured data, gas and liquid productions range between 0.34 × 104—77.6 × 104 m3/d and 1—347.8 m3/d, tubinghead pressure ranges from 0.7 to 84.7 MPa, which can cover most gas wells. The validation demonstrates that the new model still has good performance with five indicators being lowest. The RPF of the new pressure-drop model in the field measured data ranges is 0.29, still lowest of all these evaluated models, showing better performance than these widely used models in petroleum industry for vertical gas wells. This indicates that the new model can accurately predict pressure drop under different conditions in vertical gas wells.

A Bayesian Approach Toward Robust Multidimensional Ellipsoid-Specific Fitting.

This work presents a novel and effective method for fitting multidimensional ellipsoids (i.e., ellipsoids embedded in [Formula: see text]) to scattered data in the contamination of noise and outliers. Unlike conventional algebraic or geometric fitting paradigms that assume each measurement point is a noisy version of its nearest point on the ellipsoid, we approach the problem as a Bayesian parameter estimate process and maximize the posterior probability of a certain ellipsoidal solution given the data. We establish a more robust correlation between these points based on the predictive distribution within the Bayesian framework, i.e., considering each model point as a potential source for generating each measurement. Concretely, we incorporate a uniform prior distribution to constrain the search for primitive parameters within an ellipsoidal domain, ensuring ellipsoid-specific results regardless of inputs. We then establish the connection between measurement point and model data via Bayes' rule to enhance the method's robustness against noise. Due to independent of spatial dimensions, the proposed method not only delivers high-quality fittings to challenging elongated ellipsoids but also generalizes well to multidimensional spaces. To address outlier disturbances, often overlooked by previous approaches, we further introduce a uniform distribution on top of the predictive distribution to significantly enhance the algorithm's robustness against outliers. Thanks to the uniform prior, our maximum a posterior probability coincides with a more tractable maximum likelihood estimation problem, which is subsequently solved by a numerically stable Expectation Maximization (EM) framework. Moreover, we introduce an ε-accelerated technique to expedite the convergence of EM considerably. We also investigate the relationship between our algorithm and conventional least-squares-based ones, during which we theoretically prove our method's superior robustness. To the best of our knowledge, this is the first comprehensive method capable of performing multidimensional ellipsoid-specific fitting within the Bayesian optimization paradigm under diverse disturbances. We evaluate it across lower and higher dimensional spaces in the presence of heavy noise, outliers, and substantial variations in axis ratios. Also, we apply it to a wide range of practical applications such as microscopy cell counting, 3D reconstruction, geometric shape approximation, and magnetometer calibration tasks. In all these test contexts, our method consistently delivers flexible, robust, ellipsoid-specific performance, and achieves the state-of-the-art results.

A Preclinical Blinded Randomized-Controlled Trial Evaluating the Clinical Relevance of Polyp Size Measurement Using a Virtual Scale Endoscope.

The virtual scale endoscope (VSE) helps endoscopists measure colorectal polyp size more accurately compared to visual assessment (VA). However, previous studies were not adequately powered to evaluate the sizing of polyps at clinically relevant size thresholds and relative accuracy for size subgroups. We created 64 artificial polyps of varied sizes and Paris class morphology, randomly assigned 1:1 to be measured (383 total measurement datapoints with VSE and VA by 6 endoscopists blinded to true size) in a colon model. We added data from two previous trials (480 measurement datapoints). We evaluated for correct classification of polyps into size groups at 3 mm, 5 mm, 10 mm, and 20 mm size thresholds and the relative size measurement accuracy for diminutive polyps (≤5 mm), small polyps (5-9 mm), large polyps at 10-19 mm, and polyps (≥20). VSE had significantly less size group misclassifications at the 5 mm, and 10 mm thresholds (28 percent vs. 45 percent, P = 0.0159 and 26 percent vs. 44 percent, P = 0.0135, respectively). For the 3 mm and 20 mm thresholds, VSE had lower misclassifications; however, this was not statistically significant (36 percent vs. 46 percent, P = 0.3853 and 38 percent vs. 41 percent, P = 0.2705, respectively). The relative size measurement accuracy was significantly higher for VSE compared to VA for all size subgroups (diminutive (P < 0.01), small polyps (P < 0.01), 10-19 mm (P < 0.01), and ≥20 mm (P < 0.01)). VSE outperforms VA in categorizing polyps into size groups at the clinically relevant size thresholds of 5 mm and 10 mm. Using VSE resulted in significantly higher relative measurement accuracy for all size subgroups.

Evaluation of the Thermal Performance of Two Passive Facade System Solutions for Sustainable Development

Sustainable development is essential for the future of the planet. Using passive elements, like ventilated facades based on insulation and air chambers, or living walls, which are solutions based on nature, is a powerful strategy for cities to improve their thermal environment, reduce energy consumption, and mitigate the effects of climate change. This approach allows for the quantification of the influence of passive surfaces on energy fluxes compared to bare surfaces. In addition, it delves into understanding how the incorporation of vegetation on building facades alters surface energy fluxes, involving a combination of physical and biochemical processes. This comprehensive investigation seeks to harness the potential of passive and natural solutions to address the pressing challenges of urban sustainability and climate resilience. This research uses a surface energy balance model to analyze the thermal performance of two facades using experimental data from a PASLINK test cell. This study uses the grey box RC model, which links continuous-time ordinary differential equations with discrete measurement data points. This model provides insight into the complex interplay among factors that influence the thermal behavior of building facades, with the goal of comprehensively understanding how ventilated and green facades affect the dynamics of energy flow compared to conventional facades. The initial thermal resistance of the bare facade was 0.75 (°C m2)/W. The introduction of a ventilated facade significantly increased this thermal resistance to 2.47 (°C m2)/W due to the insulating capacity of the air chamber and its insulating layer (1.70 (°C m2)/W). Regarding the modular living wall, it obtained a thermal resistance value of 1.22 (°C m2)/W (this vegetated facade does not have an insulating layer). In this context, the modular living wall proved to be effective in reducing convective energy by 68% compared with the non-green facade. It is crucial to highlight that evapotranspiration was the primary mechanism for energy dissipation in the green facade. The experiments conclusively show that both the modular living wall and open-ventilated facade significantly reduce solar heat loads compared with non-passive bare wall facades, demonstrating their effectiveness in enhancing thermal performance and minimizing heat absorption.

Optimizing the efficiency of ECAP measurements due to interpolation

ABSTRACT: Background Thresholds of electrically evoked compound action potentials (TECAP) may serve as starting points for electrophysiologically based fitting of cochlear implants. Absent TECAP data at single electrodes reduces the number of data points available for fitting and can be substituted by interpolation of measured data points. Aim To compare complete TECAP profiles with interpolated TECAP profiles of 5/22 (∼22.7%) and 11/22 (50%) electrode contacts. Material and methods Single-centre, retrospective, observational study of data from 624 ears implanted with a Slim Modiolar (CI ×32) or Contour Advance (CI ×12, CI24RE(CA)) electrode array (Cochlear Ltd). The deviation of the complete measured TECAP profile from the same profile with missing and therefore interpolated TECAP values was quantified. Results Interpolated TECAP profiles significantly differ from complete measured profiles especially at the basal and apical electrodes. Reference data for Slim Modiolar and Contour Advance electrodes mean profiles are provided. Conclusions and significance Reducing the number of measured TECAP electrodes has to be weighted against losses in the TECAP accuracy of interpolated values. A clinically acceptable compromise may be a reduction from 22 to 11 even non-equidistant data points. While reducing ECAP measurement time, it is accompanied by a minimal loss of accuracy of the TECAP threshold profile.

Stromal Vascular Fraction Gel (SVF-Gel) Combined with Nanofat for Tear Trough Deformity.

Tear trough deformity makes patients appear tired. Patients with less severe tear trough deformity prefer a less invasive method to correct the deformity. The infraorbital area is a multilayered tissue, and the aging of various components leads to tear trough deformity. To this end, we utilized the different characteristics of different fat derivatives to correct tear trough deformity. Thirty-two patients with Barton Grade I/II tear trough deformity were enrolled in this study between September 2020 and March 2021. We injected Stromal Vascular Fraction Gel (SVF-Gel) into the suborbicularis oculi fat layer and Nanofat into the subcutaneous. After 12 months of follow-up, we evaluated the changes using standardized clinical photogrammetric techniques, volume, global aesthetic improvement scale, and patient self-evaluation. There were no major complications in any of the 32 patients. The measured data points demonstrated improvements in all aesthetic parameters. The width of the tear trough and the distance from the pupil to the tear trough improved. The Global Aesthetic Improvement Scale (GAIS) showed a high score (2.45±0.64 points), with patient self-assessment showing satisfactory results. SVF-Gel combined with Nanofat injection can effectively correct tear trough deformities. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Millimeter Wave Wireless Channel Knowledge Map Construction Based on Path Matching and Environment Partitioning

Key technologies in 5G and future 6G, such as millimeter wave massive multiple-input multiple-output (MIMO), relies accurate channel state information (CSI). However, when the number of base station (BS) antenna increases or the number of users is large, it is rather resource-consuming to obtain the CSI. Channel knowledge map (CKM) is an emerging environment-aware wireless communication technology, which stores the physical coordinates of BS and reference locations together with the corresponding channel path information. This makes it possible to obtain CSI with light or even without pilots, which can significantly reduce the overhead of channel estimation and improve system performance, especially suitable for quasi-static wireless environments with relatively stable channels and communication systems using millimeter waves, terahertz waves, visible light, and so on. The main challenge for CKM is how to construct an accurate CKM based on finite measurement data points at limited reference locations. In this work, we proposed a novel CKM construction method based on path matching and environmental partitioning (PMEP-CC) to address the above issues. Specifically, we first sort the propagation paths between reference locations, map them to a high-dimensional space to establish the path correlation coefficient between two reference locations. Then, the communication region are divided into different subregions based on its spatial correlation. Finally, the path information at locations where no measurements are available are estimated based on the known path information within the subregion to construct CKM. Numerical results are provided to show the performance of the proposed method over related studies.

Information sharing in high-dimensional gene expression data for improved parameter estimation in concentration-response modelling.

In toxicological concentration-response studies, a frequent goal is the determination of an 'alert concentration', i.e. the lowest concentration where a notable change in the response in comparison to the control is observed. In high-throughput gene expression experiments, e.g. based on microarray or RNA-seq technology, concentration-response profiles can be measured for thousands of genes simultaneously. One approach for determining the alert concentration is given by fitting a parametric model to the data which allows interpolation between the tested concentrations. It is well known that the quality of a model fit improves with the number of measured data points. However, adding new replicates for existing concentrations or even several replicates for new concentrations is time-consuming and expensive. Here, we propose an empirical Bayes approach to information sharing across genes, where in essence a weighted mean of the individual estimate for one specific parameter of a fitted model and the mean of all estimates of the entire set of genes is calculated as a result. Results of a controlled plasmode simulation study show that for many genes a notable improvement in terms of the mean squared error (MSE) between estimate and true underlying value of the parameter can be observed. However, for some genes, the MSE increases, and this cannot be prevented by using a more sophisticated prior distribution in the Bayesian approach.

Piecewise Affine Maximum Torque per Ampere for the Wound Rotor Synchronous Machine

Power-efficient torque control of the Wound Rotor Synchronous Machine (WRSM) below base speed requires a minimization of electrical losses, namely copper losses. In this paper, the Maximum Torque per Ampere (MTPA) optimization problem is presented and solved using a convex pareto frontier of simulated or measured data points. Three filtered solution sets are mapped to current space using piecewise affine functions, which approximate the current using a piecewise linear function for a given torque. This set of piecewise linear functions enables a machine controller to implement MTPA online or as an offline lookup table. Simulated and experimental results are presented for a 65 kW, FEA-sampled WRSM. Compared to linear MTPA, the PWA MTPA functions are shown to reduce torque error by > 25%, reduce average copper loss by > 20%.