Conventional Evaluation Methods Research Articles

Bayesian Monte Carlo Evaluation of Imperfect (n, 233U) Data and Model

Conventional nuclear data evaluation methods using generalized linear least squares make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear approximation is sufficiently accurate to minimize the cost function (even for nonlinear models); the model (e.g., of neutron cross section) and experimental data (including covariance data) are without defect and prior PDFs of parameters and measured data are known perfectly. Neglect of covariance between model parameters and measured data in conventional evaluations contributes to imperfections. These assumptions are inherent to the generalized linear least squares minimization method commonly used for resolved resonance region neutron cross section evaluations but are often not justified due to the presence of non-normal PDFs, nonlinear models (e.g., R-matrix formalism), and inherent imperfections in data and models (e.g. imperfect covariance data). Here, these assumptions are removed in a mathematical framework of Bayes’ theorem, which is implemented using the Metropolis-Hastings Monte Carlo method. Most importantly, new parameters are introduced to parameterize discrepancies between the theoretical model and measured data to quantify judgement about discrepancies or imperfections in a reproducible manner. An evaluation of 233U in the eV region using the ENDF-B/VIII.0 library and transmission data (Guber et al.) is presented, and posterior parameters are compared to those obtained by conventional evaluation methods. This example illustrates the effects of removing the most harmful assumption: that of model-data perfection.

B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness.

Patients who experience upper-limb paralysis after stroke require continual rehabilitation. Rehabilitation must be evaluated for appropriate treatment adjustment; such evaluation can be performed using inertial measurement units (IMUs) instead of standard scales or subjective evaluations. However, IMUs produce large quantities of discretized data, and using these data directly is challenging. In this study, B-splines were used to estimate IMU trajectory data for objective evaluations of hand function and stability by using machine learning classifiers and mathematical indices. IMU trajectory data from a 2018 study on upper-limb rehabilitation were used to validate the proposed method. Features extracted from B -spline trajectories could be used to classify individuals in the 2018 study with high accuracy, and the proposed indices revealed differences between these groups. Compared with conventional rehabilitation evaluation methods, the proposed method is more objective and effective.

Advanced Modeling and Simulation Methods for Evaluation of Thermal Neutron Scattering Materials

With the rise of interest in thermal neutron scattering data for advanced reactor, criticality safety, and shielding applications, new experimental data are required for evaluation of new materials or for re-evaluation (or validations) of previously evaluated materials. New experimental data are evaluated in a three-step process: (1) computing the phonon characteristics, (2) computing the dynamic structure factor (DSF) from the data, and (3) using the experimental setup to simulate the experimental data. All three steps have challenges, ranging from the need for a suffciently general material simulation code—a processing code that can compute the corresponding DSF—to having a detailed layout of the instrument/beamline/facility where the data were measured. Whereas phonon characteristics of materials can be computed using various methods (molecular dynamics, density functional theory, etc.), a high-fidelity computation of the DSF and the simulation of the experiment based on the DSF is vital to the accuracy of the evaluation. The latter two steps can be achieved by using the two corresponding code systems developed by instrument scientists at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory: (1) OCLIMAX, a program that calculates the dynamic structure factor from DFT and MD simulation results, and (2) MCViNE, a Monte Carlo neutron ray-tracing program designed to simulate neutron scattering experiments. Recently, polyethylene and yttrium hydride were measured at the Wide Angular-Range Chopper (ARCS) and SEQUOIA instrument stations of the SNS. These experiments are simulated using the density functional theory code, the Cambridge Serial Total Energy Package (CASTEP), to compute its phonon characteristics (eigenvalues/vectors and PDOS), which is then processed using OCLIMAX to yield the DSF, and finally the data at each instrument station are simulated by the MCViNE for comparison to the measured data for evaluation. For comparison to conventional evaluation methods, the scattering data processed from OCLIMAX are compared against those processed from the LEAPR module of NJOY, and the results from MCViNE simulations are compared against previously used simplified beamline models implemented in the Monte Carlo N-Particle (MCNP) code.

Multi-Defect Detection in Additively Manufactured Lattice Structures Using 3D Electrical Resistance Tomography

Cellular lattice structures possess high strength-to-weight ratios suitable for advanced lightweight engineering applications. However, their quality and mechanical performance can degrade because of defects introduced during manufacturing or in-service. Their complexity and small length scale features make defects difficult to detect using conventional nondestructive evaluation methods. Here we propose a current injection-based method, electrical resistance tomography (ERT), that can be used to detect damaged struts in conductive cellular lattice structures with their intrinsic electromechanical properties. The reconstructed conductivity distributions from ERT can reveal the severity and location of damaged struts without having to probe each strut. However, the low central sensitivity of ERT may result in image artifacts and inaccurate localization of damaged struts. To address this issue, this study introduces an absolute, high throughput, conductivity reconstruction algorithm for 3D ERT. The algorithm incorporates a strut-based normalized sensitivity map to compensate for lower interior sensitivity and suppresses reconstruction artifacts. Numerical simulations and experiments on fabricated representative cellular lattice structures were performed to verify the ability of ERT to quantitatively identify single and multiple damaged struts. The improved performance of this method compared with classical ERT was observed, based on greatly decreased imaging and reconstructed value errors.

A process capability index for zero-inflated processes

The proportion of zero defect (ZD) outputs is as an integral characteristic of a zero-inflated (ZI) process or high quality process. Different ZI processes can almost equally satisfy the same USL of number of defects but they can produce substantially different proportions of ZD products. The application of conventional method for process capability evaluation fails to discriminate these processes because in the conventional method, the process capability is evaluated taking into consideration the USL of number of defects only. In this paper, a new measure of process capability for ZI processes is proposed that can truly discriminate different ZI processes taking into account the USL of number of defects as well as the proportion of ZD units produced in these processes. In the proposed approach, at first a measure of process capability index (PCI) with respect to the USL is computed, and then the overall PCI is obtained by multiplying it with an appropriately defined multiplying factor. A real-life application is presented.

Reliability evaluation method of PMFSM system based on hidden Markov

To solve the complex calculation in the reliability evaluation of the motor system with the Markov model, this study developed a reliability evaluation method based on the hidden Markov model (HMM). Next, the developed method was employed in a type of permanent magnet hybrid segmented flux-switching machine (PMFSM). By analyzing and simplifying the components of the PMFSM system, the reliability measurement standard and the failure modes of the respective component were examined, and the relationships between the failure modes of each component and the system parameters were built. The optimal parameter observation sequence was determined by calculating the Minkowski distance, and the HMM was iteratively calculated by using the Baum–Welch algorithm. Moreover, the state transition probability matrix of the system was built, and the system reliability was evaluated. As revealed from the calculation results, the reliability of the PMFSM system is significantly higher than that of the conventional structure at the same time, and the mean time to failure (MTTF) of the PMFSM system is 14.8% higher than that of the conventional structure. Compared with the conventional evaluation method based on the Markov model, the two methods have similar reliability results for conventional and novel PMFSM systems, and the variation trend is the same. It shows that the proposed reliability evaluation method is feasible and effective, and the calculation is simple. The work in this study provides a basis for the design of the novel PMFSM system, and a practical engineering method to efficiently evaluate the reliability of other motor systems.

Developing a digital twin of centrifugal pump for performance evaluation

Performance Evaluation of a physical asset can be made effortless with a headway to its virtual replica “A Digital Twin”. This article focuses on a thorough review of a digital twin of a centrifugal pump for performance evaluation. An exception to conventional performance evaluation method of a centrifugal pump can be made by its digital twin. This also proves beneficial for higher capacity pumps with appropriate scaling of the twin as the variant changes. A digital twin system may be useful to eliminate the setup and installation time, and the capital cost to develop product specific test bench. A twin which can be simulated and evaluated using MATLAB can be developed. The process that was used for creating a digital twin of centrifugal pump involves steps like selection of parameters to be measured, selection of microcontroller for data acquisition, IOT cloud interfacing using ThingSpeak, simulation of MATLAB model of centrifugal pump in ideal environment for flow rate measurement using Simscape. For obtaining the real-time data for flow rate of the centrifugal pump, a water flow sensor was attached to the pump and the sensor data was obtained and uploaded to ThingSpeak using ESP32 WiFi module. The ideal data for the flow rate of the centrifugal pump was obtained by creating a SimScape model of the centrifugal pump and flow sensor arrangement and simulating it in ideal environment. This will include, receiving sensor data to and during the operation of the pump and monitored on ThingSpeak IOT platform. The result of this entire process will be a statistical twin of a centrifugal pump for its performance evaluation based on the performance parameter- flow rate.

In silico tools to assess the potential allergenicity of shiitake mushrooms (Lentinula edodes).

Computational tools may have an edge over conventional methods for the preliminary evaluation of food allergenicity. In this study, the allergenic potential of Lentinula edodes was evaluated and validated using in silico tools. The potential cross-reactivity of mushroom proteins with fungal allergens was determined using sequence alignment - the Fast Alignment(FASTA) and Basic Local Alignment Search Tool (BLAST) algorithm. Eight L. edodes proteins were cross-reactive with allergens from fungal origin, showing 52%-89% sequence identity using FASTA algorithm-based alignment. The BLAST data were corroborated by percentage identity and query coverage. Physico-chemical property-based allergenicity was deciphered by AlgPred, Allermatch, and AllergenFP software, which predicted six out of eight proteins as potential allergens. Sequence alignment showed 66%-86% conservancy between mushroom protein and known fungal allergens. Secondary structure and amino acid composition supported structural affinity between query and fungal proteins. Three-dimensional structures of five mushroom proteins were generated, quality assessed, and superimposed with fungal allergens, suggesting possible allergenicity of mushroom proteins. An enzyme-linked immunosorbent assay (ELISA) demonstrated immunoglobulin E (IgE) binding in 13 out of 21 food-hypersensitive patients' sera. In silico tools provide preliminary indications about the potential allergenicity and cross-reactivity of mushroom proteins. This approach may be used for the prelusive allergenicity assessment of allergen sources. © 2022 Society of Chemical Industry.

Analysis of the spatial and temporal distribution of a spray cloud using commercial LiDAR

Pesticide spray drift is an important cause of environmental damage. It can also cause phytotoxicity in non-target areas, and reduce efficacy. Suitable methods for evaluating the spray drift are necessary to reduce these hazards and improve the utilisation of pesticides. However, most conventional spray drift evaluation methods based on sampling patterns that are labour-intensive, expensive, and time-consuming. Moreover, the temporal distribution information of drifting droplets cannot be easily obtained. In this study, a spray cloud evaluation method is investigated based on a common commercially available light detection and ranging (LiDAR) system. Its feasibility was verified in terms of evaluating a spray cloud, and the effect of the wind speed on the relationship between the number of cloud points and deposit volume was experimentally studied. The optimal scanning area of the LiDAR was explored and a simple statistical model developed based on the experimental data developed. The model was validated and had a high coefficient of determination (R 2 = 0.71). The deposit volume predicted by the model also exhibited a high correlation coefficient (r = 0.89) and coefficient of determination (R 2 = 0.79) with respect to the measured deposit volume on the collector, and it satisfies the significance test at the 0.01 level. The LiDAR point cloud facilitated an intuitive analysis of the spatiotemporal distribution of drifting droplets in the air, which is difficult to achieve using passive sampling. • Commercial LiDAR system used to evaluate pesticide spray cloud. • Number of LiDAR cloud points correlated with volume measured on polyethylene lines. • Spatiotemporal distribution of drifting spray can aid suitable buffer zone setting. • Laboratory experiments explore optimal scanning area for LiDAR. • Results validate the feasibility of model to predict spray volume airborne.

Dynamic local heating performances of typical heating terminals: Evaluation and comparison based on heat matching coefficient

Building space heating accounts for an increasing portion of energy utilization in China. To address this problem, the occupant-oriented control part-time-local-space (PTLS) heating strategy, and various novel heating terminal forms have been developed. However, conventional evaluation methods take inadequate consideration of personal space demand, making them unreasonable to reflect the local heating performance of terminals for PTLS mode. Thus, we developed an index—heat matching coefficient (HMC)—from the perspective of matching degree between heat distribution and personal space demand. The concept of space volume was introduced in the performance evaluation, which quantitatively weighs the heat utilization efficiency of demand zone. With this index, the local heating performance of typical convective and radiative terminals (fan coil, radiator, and floor heating) were compared, and a sensitivity analysis of the effects of terminal features and heating loads on the heating performance were conducted. Further, correlations between local heating performances with energy consumption, response time and local discomfort were evaluated. Results suggested that the installation position is crucial to improve the local heating performance for both convective and radiative terminals. The heat consumption and response time showed a negative correlation with the local heating performance. When the HMC reached 1, approximately 34.6% heat was expected to be saved than the uniform built environment for the analyzed scenario. Overall, the HMC is a reasonable index to quantify the local heating performance of terminals, which is of great significance for terminal application and optimization to improve energy-saving and comfort potential.

Application of comprehensive evaluation methods in the clinical efficacy evaluation of traditional Chinese medicine: a narrative review

Background and Objective: Clinical effect is necessary for the inheritance and development of traditional Chinese medicine (TCM). The conventional single primary outcome evaluation method is difficult to reflect the advantages of multi-target and multi-dimensional treatment of TCM. The comprehensive evaluation method can combine multiple dimensional outcomes such as Western medicine-related outcomes, TCM-related syndromes, and patient report outcomes into a single-dimensional comprehensive outcome, which can reflect the advantages of the multi-dimensional efficacy of TCM, and has been gradually applied to the clinical efficacy of TCM in recent years. In this study, we aimed to introduce comprehensive evaluation methods and guide TCM researchers use these methods correctly, then provide methodological aid for the comprehensive and objective evaluation of true clinical effect of TCM.

Sonication versus the conventional method for evaluation of the dental microbiome: a prospective pilot study

ObjectivesTo investigate sonication as a new tool in microbiological probing of dental infections.MethodsComparison of a standard probing method: intraoperative swab, with sonication, and vortex of the removed tooth, was performed on 20 carious destructed teeth. Illumina high throughput sequencing of the 16S-rRNA-gene was used for assessing the microbial composition. Antibiotic susceptibility has been assigned based on known resistances of each detected species. Probing procedures were compared using Bland–Altmann-Test, and antibiotic susceptibility using the Friedmann-Test and alpha-adjusted post-hoc-analysis.ResultsIn total, 60 samples were analysed: 20 intraoperative swabs, 20 vortex fluids, and 20 sonication fluids. Sonication fluid yielded the highest number of bacterial sequencing reads in all three procedures. Comparing the operational taxonomic units (OTUs) of the identified bacteria, significantly more OTUs were found in sonication fluid samples. Phylum and order abundances varied between the three procedures. Significantly more Actinomycetales have been found in sonication fluid samples compared to swab samples. The assigned resistance rates for the identified bacteria (1.79–31.23%) showed no differences between the tested probing procedures. The lowest resistance rates were found for amoxicillin + clavulanate (3.95%) and levofloxacin (3.40%), with the highest in amoxicillin (30.21%) and clindamycin (21.88%).ConclusionsBy using sonication on extracted teeth, it is possible to get a more comprehensive image of the residing microbial flora compared to the standard procedure. If sonication is not available, vortexing is a potential alternative. In immunocompromised patients, especially when actinomycosis is suspected, sonication should be considered for a more detailed microbiological evaluation of the potential disease-causing microbiome. Due to the high rates of antibiotic resistance, a more targeted antibiotic therapy is favourable. Levofloxacin should be considered as a first-line alternative to amoxicillin + clavulanate in patients with an allergy to penicillin.

Selection of optimal exciting frequency and Lamb wave mode for detecting wall-thinning in plates using scanning laser Doppler vibrometer

The continuous excitation-based scanning laser Doppler vibrometer (SLDV) has been gaining prominence as a defect-detecting tool because of its superior performance in discovering faults, such as thinning of structures, compared to conventional non-destructive evaluation methods. To date, the optimal test mode and frequency for interdigital transducers have been heuristically selected. Although this approach is effective for problems involving a small number of modes, heuristic algorithms have limitations with respect to problems involving multiple complex modes. Therefore, in this study, the determination of the inspection mode and frequency is formulated as an optimization problem, and the effective wavenumber and bandwidth of the high-pass and bandpass filters in signal processing are also considered. Numerical optimization examples show that the proposed method is effective and robust. An experiment with a thick plate with shallow defects was performed, and the quantitative images obtained showed the correct location of the defects with the proposed numerical optimal solution.

An Intelligent Rice Yield Trait Evaluation System Based on Threshed Panicle Compensation.

High-throughput phenotyping of yield-related traits is meaningful and necessary for rice breeding and genetic study. The conventional method for rice yield-related trait evaluation faces the problems of rice threshing difficulties, measurement process complexity, and low efficiency. To solve these problems, a novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy. To improve the threshed panicle detection accuracy, the Region of Interest Align, Convolution Batch normalization activation with Leaky Relu module, Squeeze-and-Excitation unit, and optimal anchor size have been adopted to optimize the Faster-RCNN architecture, termed ‘TPanicle-RCNN,’ and the new model achieved F1 score 0.929 with an increase of 0.044, which was robust to indica and japonica varieties. Additionally, AI cloud computing was adopted, which dramatically reduced the system cost and improved flexibility. To evaluate the system accuracy and efficiency, 504 panicle samples were tested, and the total spikelet measurement error decreased from 11.44 to 2.99% with threshed panicle compensation. The average measuring efficiency was approximately 40 s per sample, which was approximately twenty times more efficient than manual measurement. In this study, an automatic and intelligent system for rice yield-related trait evaluation was developed, which would provide an efficient and reliable tool for rice breeding and genetic research.

Permeability extraction from multiple well logs using particle swarm optimization based factor analysis

In this paper, we present an innovative factor analysis algorithm for hydrocarbon exploration to estimate the intrinsic permeability of reservoir rocks from well logs. Unlike conventional evaluation methods that employ a single or a limited number of data types, we process simultaneously all available data to derive the first statistical factor and relate it to permeability by regression analysis. For solving the problem of factor analysis, we introduce an improved particle swarm optimization method, which searches for the global minimum of the distance between the observed and calculated data and gives a quick estimation for the factor scores. The learning factors of the intelligent computational technique such as the cognitive and social constants are specified as hyperparameters and calculated by using simulated annealing algorithm as heuristic hyperparameter estimator. Instead of the arbitrary fixation of these hyperparameters, we refine them in an iterative process to give reliable estimation both for the statistical factors and formation permeability. The estimated learning parameters are consistent with literature recommendations. We demonstrate the feasibility of the proposed well-log analysis method by a Hungarian oilfield study involving open-hole wireline logs and core data. We determine the spatial distribution of permeability both along a borehole and between more wells using the factor analysis approach, which serves as efficient and reliable multivariate statistical tool for advanced formation evaluation and reservoir modeling.

Method for evaluating the performance of catalytic reactions using renewable-energy-derived materials

Hydrogen produced by electrolysis using electricity derived from renewable energy sources has a fluctuating supply. However, conventional catalyst evaluation methods cannot evaluate catalyst synthesis assuming a fluctuating feedstock. This paper investigates a simple screening method for catalysts that can be used for renewable energy by using a combination of three catalyst evaluation methods: Light-off Performance, equilibrium achievement degree, and maximum ammonia concentration. We examined the combination of evaluation methods and trends for each element, and finally concluded that a three-axis graph combining the three is the easiest graph to obtain the information necessary for catalyst screening intuitively rather than quantitatively.

Evaluation of alumina slurry dispersion by nuclear magnetic resonance relaxation as a comparison to conventional dispersion evaluation methods.

In this paper, we evaluated the particle dispersion degree of alumina slurry containing a dispersant by solvent nuclear magnetic resonance (NMR) relaxation and compared it with conventional dispersion evaluation methods such as viscosity, particle size, and sedimentation height measurements. The dispersion of slurry was evaluated via numerical analysis of the transverse relaxation time (T2 ). The effect of the changes in different parameters of the experiment in terms of milling time, solid loading, and dispersant amount was investigated by NMR relaxation as well as conventional methods. The results of NMR relaxation measurements revealed that T2 correlates well with other dispersion evaluation methods; thus, it is an efficient technique to evaluate the dispersion of alumina slurry, specifically, when studying the effect of the change in milling time and dispersion amount.

Use of electrochemical techniques for determining the effect of brewing techniques (espresso, Turkish and filter coffee) and roasting levels on total antioxidant capacity of coffee beverage

In this study, we determined how roasting levels (light, medium, and dark) of Arabica coffee seed and three brewing techniques—decoction methods (Turkish coffee), infusion method (filter coffee), and pressure methods (Espresso)—affect the total antioxidant capacity in a cup of coffee beverage by electrochemical methods such as square wave stripping voltammetry (SWSV), differential pulse stripping voltammetry (DPSV), and cyclic voltammetry (CV). The highest antioxidant capacity was found in espresso coffee prepared with light roasted coffee seeds, as equivalent of rutin and caffeic acid at 9.4 ± 0.2 g/L and 19.7 ± 0.7 g/L, respectively with SWSV on a carbon paste electrode (CPE). The antioxidant capacity of coffee beverages was influenced by the roasting degree, extraction time, and brewing methods, significantly. SWSV, DPSV, and CV voltammetric methods, fast, reliable, fully validated and without any pretreatment, are alternatives to conventional analytical methods for evaluation of antioxidant values in coffee brews. Practical applications This research will contribute to the literature considerably since we have established that the antioxidant capacity can be measured by electrochemical methods rapidly with high reliable results. According to the study, the brewing method and roasting temperature significantly affected the antioxidant capacity, and thus, it is important to know how brewing methods, roasting temperatures, and other conditions changes the coffee quality. The results can be used to prepare healthy coffee beverages.

Characterization of deep traps in the near-interface oxide of widegap metal–oxide–semiconductor interfaces revealed by light irradiation and temperature change

To evaluate oxide trap state density in the near-interface region of silicon carbide metal–oxide-semiconductor (SiC MOS) stacks, photo-assisted capacitance–voltage measurements at various temperatures were performed. The difference between the deep trap profiles at SiC MOS interfaces treated with two kinds of post-oxidation-annealing was revealed, which cannot be detected by conventional evaluation methods of interface state density. With this method, the differences in the energy profile of trap levels together with their spatial distribution in the near interface region of oxide were investigated.

In Situ Small-Angle Neutron Scattering Analysis of Water Evaporation from Porous Exhaust-Gas-Catalyst Supports.

Porous media as catalyst supports are key to developing automotive exhaust purification systems. In particular, the water content of these porous media is attracting research attention because catalyst supports containing condensed water vapor at the early stage of cold start require a longer warm-up period. In this regard, water isotherms and evaporation in porous Al2O3 were investigated in this study using in situ small-angle neutron scattering (SANS) experiments. Unlike conventional evaluation methods, such as weighing and X-ray tomography, SANS distinguishes water in the primary and secondary pores using a contrast-matching method. Time-resolved measurements showed that water started to evaporate from the secondary pores in tens of seconds and subsequently from the primary pores in a hundred seconds. Exhaustive experiments conducted using nine alumina-based samples revealed that the drying rate depended on the secondary pore size of the porous Al2O3. The proposed approach can enable the evaluation of controlling factors to additionally optimize the performance of automotive exhaust gas catalysts, especially during cold start.