Profile Error Research Articles

The influence of different load distribution considering geometric error on the fatigue life of ball screw

The ball screw pair is a precision drive component that converts rotary motion into linear motion. In practical applications, because the feed system usually has rotational torque and geometric errors, it may increase the contact load of the ball screw pair and reduce the fatigue life. In order to study the influence of different loads and geometric errors on the maximum contact load and fatigue life of ball screw pairs, the following research work was carried out. Firstly, based on the composite load and rotational torque, the ball load distribution model is established, and the accuracy of the model is verified by finite element modeling analysis. Then, the influence of different composite loads, rotation times and geometric errors (including ball size error, lead error and raceway tooth profile error) on the ball load distribution is analyzed. Finally, the influence of compound load, rotating torque and geometric error on the fatigue life of ball screw pairs is studied. The results show that the rotational torque and lead error have a great influence on the load distribution and fatigue life of the ball, and the influence of lead error on load distribution is greater than that of dimension error and tooth profile error. The fatigue life of the ball screw pair with non-uniform load distribution is shorter than that of the ball screw pair with uniform load distribution.

Fabrication of the optical lens on single-crystal germanium surfaces using the laser-assisted diamond turning

Single-crystal germanium, as an excellent infrared optical material, has been widely applied in X-ray monochromators, night vision systems, and gamma radiation detectors. However, how to obtain high-quality optical lenses on their surfaces still faces challenges due to their hard and brittle properties. To this end, this paper proposes the in situ laser-assisted diamond turning (ILADT) process, which is the combination of a laser heating technique and a single-point diamond turning process. The in situ laser heating technique is employed to enhance the surface quality of the workpiece material, while the single-point diamond turning process is utilized to fabricate optical lenses. Experimental results showed that optical lenses with high surface quality were successfully machined. The profile error is 0.135 μm, indicating the high machining accuracy. The surface roughness Sa of the aspheric lens is 0.909 nm, indicating the high machining quality achieved by the proposed ILADT process. Therefore, this study provides an effective approach for producing high-quality optical lenses on single-crystal germanium surfaces, which holds great promise for future applications in the manufacturing of optical lenses with exceptional quality.

Vision-related quality of life in children: Cross-cultural adaptation and test-retest reliability of the Danish version of the paediatric refractive error profile 2.

To translate and cross-culturally adapt PREP2 into Danish and to investigate the face validity and reliability of Danish PREP2 through cognitive interviewing, Rasch and reliability analyses. The Danish PREP2 was translated using a standardized procedure and then pretested following the Three-Steps-Interviews (TSTI) process. A total of 15 myopic children aged 7-14 wearing either orthokeratology lenses (ortho-k) or single-vision spectacles (SVS) were included in pretesting comprising cognitive interviews and Rasch analysis. Data from cognitive interviewing was analysed thematically according to Collins. Rasch analysis was used to pretest the psychometric properties in terms of person- and item-fit statistics. Reliability was assessed via test-retest using Intra-class correlation coefficient (ICC) in the CONTROL study population, which consisted of 60 Danish children aged 7-14 years wearing either ortho-k or SVS. Fifteen children participated in pilot studies comprising of cognitive interviewing and Rasch analysis and 44 out of 60 CONTROL children participated in test-retest reliability analysis. The translation process resulted in a Danish version of PREP2 corroborating the original. Pretesting highlighted issues in the contextualization of items and in marking responses. Thus, we introduced a digital format with help texts. Cognitive interviewing identified issues in the following Collins' themes: comprehension (understanding of concepts), judgement (ambiguity of items) and response (selecting answers). Rasch analysis indicated that help texts were useful for clarifying context. The ICC was 0.77 (95% CI: 0.66-0.85). The cross-cultural adaptation of PREP2 was satisfactory and issues were identified and corrected through pretesting. The test-retest reliability showed substantial consistency. The instrument could be validated in a more generalizable setting in future studies. NCT03246464 (CONTROL study).

An optimization model for monthly time-step drilling schedule under planned field production

The field production profile over the yearly horizon is planned for a balance between economy, security, and sustainability of energy. An optimal drilling schedule is required to achieve the planned production profile with minimized drilling frequency and summation. In this study, we treat each possible production process of each well as a dependent time series and the basic unit. Then we ensemble all of them into a tensor. Based on formulated tensor calculation and Lasso regularization, a linear mathematical optimization model for well drilling schedule was developed. The model is aimed at minimizing production profile error while optimizing drilling frequency and summation. Although the model proposed in this work requires more memory consumption to be solved using a computer, it is assured as a linear model and could be numerically globally solved in a stable and efficient way using gradient descent, avoiding complex nonlinear programming problems. Main input data and parameters involved in the model are analyzed in detail to understand the effects of different production parameters on the drilling schedule and production profile. The proposed model in this work can evaluate the manual drilling schedule and automatically generate an optimized drilling schedule for the gas field, significantly reducing development plan formulation time.

Accelerating multivariate functional approximation computation with domain decomposition techniques

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain-decomposed method for computing the MFA with B-spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees-of-freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high-order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C0 or at best C1 continuity for arbitrary B-spline degree expansions, or those that require post-processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real-world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest-neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

A novel method for helical gear modeling with an experimental validation

Dynamic models are important for developing gear diagnostics methods since they allow physical phenomena occurring during operation to be studied in a relatively simple environment. The main challenge in gear modeling is the calculation of the time-variant gear mesh stiffness, and this challenge is even greater in helical gears. The mechanism of helical gears is more complex than in spur gears; the helix angle both adds an axial component to the contact force and also makes the contact line three-dimensional. This study suggests a novel dynamic model for helical gear vibrations that combines an existing validated dynamic model for spur gears with a unique extension for helical gears. The extension is based on a common method called “multi-slice”, according to which the helical tooth width is divided into infinitesimal slices, and each slice is treated as spur tooth. The suggested model introduces a novel implementation of the multi-slice method that overcomes the aforementioned challenges with only few parameters and calculations, depends on the tooth geometry. Furthermore, for the first time in helical gear modeling, the manufacturing profile errors are integrated to the model to generate scatter in the data that can better reflect the reality. The model is validated experimentally and for two different test-rigs by a qualitative comparison of the RMS of the vibration signal. The simulations and the measured data show similar behavior at different ranges of rotational speed and applied load, emphasizing the potential inherent in the model for future work on gear fault diagnosis.

Abstract 909: Enhancing genomic analysis in cancer diagnostics: A machine learning approach for removing artifacts in FFPE specimens

Abstract Formalin-Fixed Paraffin-Embedded (FFPE) specimens, widely utilized in clinical cancer diagnostics, present significant challenges by introducing artifacts into genomic data. This study aimed to profile these FFPE-induced genomic alterations, with a particular focus on single-nucleotide variants (SNVs), small insertions and deletions (indels), and copy-number variations (CNVs), and to develop computational methods for filtering out such artifacts. Our primary focus was twofold: first, to comprehensively characterize the unique error profile of FFPE specimens observed through whole-genome sequencing (WGS), and second, to construct artifact classifiers and noise filters for SNV/indels and CNVs. We utilized machine learning (ML) and signal-processing techniques on a dataset of FFPE and matched fresh-frozen (FF) samples. The dataset of 52 FFPE-FF pairs were obtained from four different medical institutes and from various cancers including liver, breast, colon, stomach, and lung cancer, with varied FFPE archiving times. We also analyzed additional FFPE-only samples to refine our methods. Our methodology incorporated advanced computational approaches, including stacking ensemble, transfer learning, and wavelet transform, to enhance robustness and accuracy. The method's design was centered around the notion of not only achieving high performance in distinguishing true signals from FFPE-induced artifacts but also addressing the real-world challenges posed by the varying quality and conditions of clinical FFPE samples. In the analysis, we found peculiar patterns of FFPE-specific error profile, including well-known cytosine deamination and novel mutational signatures. The classifier, building on our findings, effectively differentiated true genomic variants from FFPE-induced artifacts for both SNVs and indels, demonstrating a sensitivity of 0.97, specificity of 0.87, and an F1 score of 0.94 for SNVs. For indels, it achieved a sensitivity of 0.91, specificity of 0.91, and an F1 score of 0.91. The CNV filter notably enhanced the signal-to-noise ratio (SNR) of CNV depth profiles, increasing it from 13dB to 17.5dB on average. Furthermore, we conducted evaluations on two critical measures in cancer and clinical genomics: homologous recombination deficiency (HRD) and tumor mutational burden (TMB), achieving post-filtering concordance rates of 0.99 for HRD—correctly identifying all 8 HRD-positive patients in our dataset—and 0.96 for SNV-based TMB and 0.87 for indel-based TMB. Additionally, a post hoc procedure for sensitive detection of cancer driver mutations resulted in concordance rates of 0.94 for SNVs, 0.91 for indels, and 0.95 for oncogene amplification. Taken together, our study advances FFPE WGS analysis in cancer diagnostics by effectively filtering artifacts and addressing challenges with older, degraded samples, enhancing clinical applicability. Citation Format: Joonoh Lim, Seongyeol Park, Won-Chul Lee, Ryul Kim, Sangmoon Lee, Jeong Seok Lee, Brian Baek-Lok Oh, Young Seok Ju. Enhancing genomic analysis in cancer diagnostics: A machine learning approach for removing artifacts in FFPE specimens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 909.

Evaluation of surface quality and error compensation for optical aspherical surface grinding

Optical aspherical lenses are widely used in the aerospace, electronics, and optical communication industries. Grinding is the primary processing method used to ensure their precision. It is difficult for traditional evaluation indexes to fully reflect the optical properties of optical surfaces after grinding. This paper presents a novel method for characterizing surface quality of optical aspherical surface oriented to the application of optical properties. The proposed evaluation indicators include the profile error, surface roughness, slope error, and projection-area scale-deviation value, which can be used to comprehensively evaluate the surface quality. Subsequently, a novel grinding compensation method for the optical aspherical surface is proposed, and its compensation precision is evaluated using the abovementioned evaluation indicators. The results show that the maximum surface-shape error decreases to less than 150 nm; additionally, the central cusp point is restrained. The precision and efficiency of error compensation for the optical aspherical surface are significantly better than those of the conventional method. The proposed evaluation indicators can be applied to reflect the features of optical aspheric surface directly. The high efficiency and precision compensation of optical aspherical surface can be achieved with the proposed evaluation method and modified error compensation algorithm.

Experimental investigation on the surface quality of WEDM machined 20MnCr5 steel for gear application

This experimental study investigates the Wire-cut Electrical Discharge Machining (WEDM) of 20MnCr5 steel, a low alloy steel grade material which is intended for the production of gears, shafts and other parts which need high surface hardness and wear resistance. WEDM is a crucial manufacturing process for producing gears, shafts, spindles, and various mechanical components. The investigation focuses on three key performance parameters: profile error, material removal rate (MRR), and surface roughness (SR), employing five WEDM parameters—wire feed (Wf), wire tension (Wt), servo voltage (SV), discharge arc-off time (Toff), and arc-on time (Ton) - by employing the Taguchi L27 orthogonal array. The study reveals that, in comparison to arc-on time and servo voltage, wire feed rate and tension have a lesser impact. Higher arc-on time and servo voltage result in increased profile error, while reduced wire tension values elevate surface roughness. Recast layer formation has been explored under different WEDM input parameters, revealing its dependence on arc-on time, voltage, wire tension, and feed rate. Higher voltage leads to uneven surfaces with larger globules and deep craters, contributing to a thicker recast layer at higher arc-on time and servo voltage.

Examining different cost ratio frameworks for decision rule machine learning algorithms in diagnostic application.

Artificial Intelligence (AI) plays a pivotal role in the diagnosis of health conditions ranging from general well-being to critical health issues. In the realm of health diagnostics, an often overlooked but critical aspect is the consideration of cost-sensitive learning, a facet that this study prioritizes over the non-invasive nature of the diagnostic process whereas the other standard metrics such as accuracy and sensitivity reflect weakness in error profile. This research aims to investigate the total cost of misclassification (Total Cost) by decision rule Machine Learning (ML) algorithms implemented in Java platforms such as DecisionTable, JRip, OneR, and PART. An augmented dataset with conjunctiva images along candidates' demographic and anthropometric features under supervised learning is considered with a specific emphasis on cost-sensitive classification. The opted decision rule classifiers use the text features, additionally the image feature 'a* value of CIELAB color space' extracted from the conjunctiva digital images as input attributes. The pre-processing consists of amalgamating text and image features on a uniform scale, normalizing. Then the 10-fold cross-validation enables the classification of samples into two categories: the presence or absence of the anemia. This study utilizes the Cost Ratio (ρ) extracted from the cost matrix to meticulously monitor the Total Cost in four different cost ratio methodologies namely Uniform (U), Uniform Inverted (UI), Non-Uniform (NU), and Non-Uniform Inverted (NUI). It has been established that the PART classifier stands out as the top performer in this binary classification task, yielding the lowest mean total cost of 629.9 compared to other selected classifiers. Moreover, it demonstrates a comparatively lower standard deviation 335.9, and lower total cost across all four different cost ratio methodologies. The ranking of algorithm performance goes as follows: PART, JRIP, DecisionTable, and OneR. The significance of adopting a cost-sensitive learning approach is emphasized showing the PART classifier's consistent performance within the proposed framework for learning the anemia dataset. This emphasis on cost-sensitive learning not only enhances the recommendations in diagnosis but also holds the potential for substantial cost savings and makes it a noteworthy focal point in the advancement of AI-driven health care.

Prediction of profile error in aspheric grinding of spherical fused silica by ensemble learning regression methods

Given the low thermal expansion coefficient and excellent optical properties of fused silica in UV regions, it is widely used in lenses and other optical components. A precision grinding machine equipped with a cup grinding wheel has been employed for the aspheric grinding of spherical fused silica elements. This study aims to achieve two main objectives: firstly, to identify the contributions of feed speed and rotational speed ratio (RSR) on the profile and waviness errors of the workpieces and, secondly, to improve the prediction of profile errors by developing a high-fidelity data-driven machine learning framework. Accurate prediction of profile errors of the workpieces can be used to correct manufacturing errors. Furthermore, assessing the impact of input parameters on surface waviness errors is a viable approach to optimize the manufacturing process. Full-factorial experiments were designed for three levels of feed speed and RSR to find the sensitivity of modeling and predictions to these pertinent design variables. The trigonometric Fourier series model was used to fit form errors onto profile errors. The form errors evaluation revealed that RSR and the interaction between feed speed and RSR significantly affected the workpiece's form error. Reducing the feed speed led to a decrease in profile and waviness errors. The minimum waviness and profile errors were attained at an RSR of 50.25, regardless of the feed speed. The ensemble learning regression framework was subsequently trained using three sets of experimental datasets encompassing diverse combinations of RSR and feed speed values to model profile errors. While the profile model underwent validation on previously unseen segments of the datasets the network had been exposed to during training, the final tests were carried out using six datasets featuring entirely different profile errors that the networks had not seen before. The ensemble learning regression framework's performance tests were assessed using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and R-squared metrics. The framework demonstrated considerable predictive efficacy for the six left profile errors (unseen data), as evidenced by the calculated R-squared values from 0.767 to 0.965, signifying the lower and upper bounds of precision, respectively.

An error compensation method for single point oblique axis grinding considering the grinding wheel wear

With the increasing amount of high quality optical products, the manufacturing requirement for the high-precision optical glass lenses is on the rise. Grinding is typically the main manufacturing process for the optical mold. The degree of wheel wear has a direct impact on the precision of the grinding process. In this paper, the wear rule of grinding wheel in the grinding process of optical aspheric surface is studied with the in-site wear measurement system. A new image processing method and a wear degree evaluation method are proposed. The wear prediction model for the grinding wheel is established. Then a modified compensation method of optical aspheric surface considering grinding wheel wear is proposed. The optical aspheric surface is then compensated using a modified multinomial equation considering grinding wheel wear. The results demonstrate that the profile error of optical aspheric surface can be reduced to less than 150 nm. The proposed compensation method that takes into account the grinding wheel wear can highly improve the grinding precision and efficiency of optical aspheric surface as compared with the conventional compensation method.

Fast, intelligent and high-precision adaptive null interferometry for optical freeform surfaces by backpropagation.

In the past 10 years, adaptive wavefront interferometry (AWI) has been employed for measuring freeform surface profiles. However, existing AWI techniques relying on stepwise and model-free stochastic optimizations have resulted in inefficient tests. To address these issues, deterministic adaptive wavefront interferometry (DAWI) is firstly introduced in this paper based on backpropagation (BP), which employs a loss function to simultaneously reconstruct and sparsify initial incomplete interferometric fringes until they are nulled. Each iteration of BP requires two phase shifts. Through simulations, we have verified that freeform wavefront error with a peak-to-valley (PV) of up to 168 λ can be fully compensated in tens of iterations using a 1024 × 1024 pixel area of a liquid-crystal spatial light modulator. In experiments, we accomplished a null test of a freeform surface with 80% missing interference fringes in 39 iterations, resulting in a surface profile error PV of 66.22 λ and measurement error better than λ/4. The DAWI has at least 20 times fewer iterations in fringe reconstruction than the 3-step AWI methods, and nearly an order of magnitude fewer iterations in the whole process, paving the way for significantly enhanced efficiency, generality and precision in freeform surface adaptive interferometry.

ВІДНОВЛЕННЯ НЕОДНОРІДНОГО ПРОФІЛЮ ІНТЕНСИВНОСТІ ДОЩУ ШЛЯХОМ РОЗВ’ЯЗАННЯ ІНТЕГРАЛЬНОГО РІВНЯННЯ РОЗСІЯННЯ ПРИ ЗАСТОСУВАННІ ДВОЧАСТОТНОГО ЗОНДУВАННЯ

Subject and Purpose. The methods for precipitation remote sensing continue to be developed. One of the trends has been assessing amounts of precipitations to determine whether to issue alerts for large-scale floods and landslides or to make decisions regarding the agricultural land irrigation. The present work focuses on the inverse problem of dual-frequency sensing and its solution through Tikhonov regularization. The goal is to improve the algorithm for solving the inverse problem and retrieve a non-uniform rainfall intensity profile. The retrieval results are enhanced by taking into account the impacts of the signal attenuation and errors in the received power measurements. Methods and Methodology. The retrieval of a non-uniform spatial profile of rain intensity is carried out by the numerical simulation using the integral scattering equation solution via Tikhonov regularization and employing dual-frequency sensing data. Results. The numerical simulations of a non-uniform spatial profile of the rain intensity in the range 0…30 mm/h have been performed at the operating wavelengths 8.2 mm and 3.2 cm and for various errors of the received power measurement and complete attenuation of the signals. It has been observed that the signal attenuation at the shorter (8.2 mm) operating wavelength has a significant effect on the retrieved intensity of rains heavier than 5 mm/h and more distant than 1 km. For the longer (3.2 cm) operating wavelength, the signal attenuation can be neglected when the maximum intensity in the profile is under 30 mm/h and the distance is less than 20 km. The retrieval error of a non-uniform rain intensity profile is shown to be no worse than 40% for the intensities in the profile above 3 mm/h and measurement errors of ±20%. Conclusions. The analysis of the results has shown that the proposed dual-frequency approach to solving the integral scattering equation makes it possible to retrieve a non-uniform rain intensity profile with a maximum error no worse than 40% for rain intensities above 3 mm/h and with the proviso that the error of the received power measurement and complete attenuation of signals at the shorter (8.2 mm) operating wavelength is no worth than ±20%.

Experimental analysis of chemical removal effect in RISA grinding of optical glass

In the large-aperture lens manufacturing, not only high surface quality but also high productivity is required. From this perspective, Reaction-Induced-Slurry-Assisted grinding (RISA grinding), in which cerium oxide slurry is supplied instead of using general grinding fluid, has been developed to achieve high surface quality at high removal rate. However, the surface profile error becomes large due to chemical removal effect of the cerium oxide slurry. Therefore, the purpose of this study is to experimentally investigate the influence of the chemical removal effect on the surface profile error using AE signals and servo current signals in RISA grinding. First, relation between dwell time and removal depth is analyzed by conducting a basic grinding test without the workpiece rotation. The result shows that the actual removal depth increases with increase of dwell time despite applying the constant depth of cut. Then, zero-cut RISA grinding test is conducted. It is found that the removal process proceeds even though zero-cut grinding, and its removal rate varies according to the wheel feed rate. Furthermore, the amount of cerium oxide abrasives adhered to the grinding wheel can be estimated from the AE signals and servo current signals.

Determination of Estimation Accuracy of Voltage Profiles Required for Proper Voltage Control by On-Load Tap Changer in Distribution Systems

The introduction of a large number of photovoltaic systems to distribution systems has increased the complexity of the voltage profile. It is thus necessary to manage voltage at a higher system level. For this purpose, the voltage profile of the distribution system can be calculated using state estimation and the voltage can be controlled based on the estimated values. Many methods for estimating voltage profiles using state estimation have been developed. However, the estimation accuracy of voltage profiles required for proper voltage control has not been discussed. If the assumed required estimation accuracy is too high, then more meters than necessary will be installed, unnecessarily increasing costs. Conversely, if the assumed required estimation accuracy is too low, voltage control equipment will not operate properly, and voltage violations may occur. Thus, it is important to determine the required estimation accuracy of voltage profiles for proper voltage control. In this paper, the estimation error of the voltage profile is expressed using a probability density function, and the estimation accuracy of voltage profiles required for proper voltage control is examined. A numerical case study was performed on a distribution network model with 2160 consumers, and revealed the following. To prevent voltage violations, an estimation accuracy of voltage profile that is approximately 10 times lower than that of the estimation accuracy based on current measurement equipment data is sufficient. To minimize the number of tap operations, an estimation accuracy needs to be improved by 70% compared with the estimation accuracy based on current measurement equipment data.

Uncertainty-aware error modeling and hierarchical redundancy optimization for robotic surface machining

Industrial robots are commonly utilized in in-situ machining, but their inherent limitations in stiffness and positioning accuracy can pose challenges in achieving precise freeform surface milling. Previous research primarily focused on robot stiffness optimization and positioning error prediction separately, neglecting the uncertainty of robot errors and the modeling of surface profile errors. To address these issues, this work introduces a novel evaluation method for surface profile errors in robotic milling. It combines geometric error prediction models, based on kinematic and stiffness models, with non-geometric errors using Gaussian process regression. Moreover, collaborative optimization of multiple redundancies in robotic surface milling systems is crucial for reducing machining errors effectively. To achieve this, this work proposes a collaborative optimization method for optimizing the robot’s posture change sequence and the workpiece’s orientation, considering the motion constraints and error requirements of the robot. Among them, this work introduces the improved Whale Optimization Star Algorithm (WOA*) method to address the optimization problem of coupling robot posture and workpiece’s orientation. The proposed method’s effectiveness is confirmed through simulations and experiments, which clearly demonstrate its capability in improving error prediction and machining accuracy in robotic milling.

How much Romanian does Google Translate know?

To compensate, even if on a small scale, for the scarcity of investigations of English-into-Romanian machine translations from a corpus-based genre-specific perspective, this case study concerns the quality of such translation outcomes in the particular case of two different, but closely related, text genres – everyday and newspaper/ news releases language, that have been translated with the help of Google Translate. The paper starts from Keshavarz’s (1999) very general model of error analysis to create a linguistic error profile in the two genres taken into consideration. The errors identified and discussed are illustrated with small-scale corpus examples. Since they reflect the capabilities of the Google translation platform, the findings of this paper may be relevant for its developers to get a clearer picture of its strengths and limitations and suggest ways of improving it so that it can ultimately provide higher quality translations. It may also contribute to raising translators’ attention to which areas are potentially problematic when they work, in the post-editing stage, with the English-Romanian language pair, with the particular text genres considered here.

Evaluating pooled testing for asymptomatic screening of healthcare workers in hospitals

BackgroundThere is evidence that during the COVID pandemic, a number of patient and HCW infections were nosocomial. Various measures were put in place to try to reduce these infections including developing asymptomatic PCR (polymerase chain reaction) testing schemes for healthcare workers. Regularly testing all healthcare workers requires many tests while reducing this number by only testing some healthcare workers can result in undetected cases. An efficient way to test as many individuals as possible with a limited testing capacity is to consider pooling multiple samples to be analysed with a single test (known as pooled testing).MethodsTwo different pooled testing schemes for the asymptomatic testing are evaluated using an individual-based model representing the transmission of SARS-CoV-2 in a ‘typical’ English hospital. We adapt the modelling to reflect two scenarios: a) a retrospective look at earlier SARS-CoV-2 variants under lockdown or social restrictions, and b) transitioning back to ‘normal life’ without lockdown and with the omicron variant. The two pooled testing schemes analysed differ in the population that is eligible for testing. In the ‘ward’ testing scheme only healthcare workers who work on a single ward are eligible and in the ‘full’ testing scheme all healthcare workers are eligible including those that move across wards. Both pooled schemes are compared against the baseline scheme which tests only symptomatic healthcare workers.ResultsIncluding a pooled asymptomatic testing scheme is found to have a modest (albeit statistically significant) effect, reducing the total number of nosocomial healthcare worker infections by about 2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} in both the lockdown and non-lockdown setting. However, this reduction must be balanced with the increase in cost and healthcare worker isolations. Both ward and full testing reduce HCW infections similarly but the cost for ward testing is much less. We also consider the use of lateral flow devices (LFDs) for follow-up testing. Considering LFDs reduces cost and time but LFDs have a different error profile to PCR tests.ConclusionsWhether a PCR-only or PCR and LFD ward testing scheme is chosen depends on the metrics of most interest to policy makers, the virus prevalence and whether there is a lockdown.

An Improved 3D OPC Method for the Fabrication of High-Fidelity Micro Fresnel Lenses.

Based on three-dimensional optical proximity correction (3D OPC), recent advancements in 3D lithography have enabled the high-fidelity customization of 3D micro-optical elements. However, the micron-to-millimeter-scale structures represented by the Fresnel lens design bring more stringent requirements for 3D OPC, which poses significant challenges to the accuracy of models and the efficiency of algorithms. Thus, a lithographic model based on optical imaging and photochemical reaction curves is developed in this paper, and a subdomain division method with a statistics principle is proposed to improve the efficiency and accuracy of 3D OPC. Both the simulation and the experimental results show the superiority of the proposed 3D OPC method in the fabrication of Fresnel lenses. The computation memory requirements of the 3D OPC are reduced to below 1%, and the profile error of the fabricated Fresnel lens is reduced 79.98%. Applying the Fresnel lenses to an imaging system, the average peak signal to noise ratio (PSNR) of the image is increased by 18.92%, and the average contrast of the image is enhanced by 36%. We believe that the proposed 3D OPC method can be extended to the fabrication of vision-correcting ophthalmological lenses.