Accurate Correction Research Articles

Does aligner refinement have the same efficiency in deep bite correction?: A retrospective study

BackgroundRefinements are very common in clear aligner treatments. The aim of this study is to assess whether the predictability of deep overbite correction is similar over several refinements using clear aligners (Invisalign, Align Technology, San Jose, Calif) and examine the accuracy of vertical movement and inclination change of individual teeth.MethodsThis retrospective study included 20 deep bite patients (7M and 13F; 32.63 ± 11.88 years old; an initial overbite of 5.09 ± 0.98 mm), consecutively treated from September 2016 and March 2023, who completed at least two sets of aligners, including refinements. The initial, predicted, and achieved models were exported from ClinCheck or OrthoCAD (Cadent Inc, Carlstadt, NJ) and superimposed via best-fit surface-based registration using SlicerCMF (version 4.9.0; cmf.slicer.org). We also examined 15 out of 20 patients who completed treatments. The overbite correction and changes in vertical movement and inclination for individual teeth were measured. Descriptive statistics and a paired t-test or Wilcoxon signed-rank test were performed. P < 0.05 was considered statistically significant.ResultsThe mean accuracy of overbite correction was 37.63% after 1st set, followed by 11.19%, 6.32%, and 13.80% (2nd-4th sets), respectively. There were statistically significant differences between the predicted and achieved vertical movements and inclination changes for all teeth for the 1st and 2nd sets. For the completed cases, the mean overbite correction was 38.54% compared to the initially planned overbite correction, which is similar to one of the 1st set. Still, the vertical movements and inclination changes of all teeth present statistically significant differences between the initially planned and finally achieved movements except for maxillary lateral incisor torque.ConclusionsThe most overbite correction occurs during the 1st set of aligners, and refinement treatment does not significantly improve the deep bite correction.

Decoding topological XYZ 2 codes with reinforcement learning based on attention mechanisms

Quantum error correction, a technique that relies on the principle of redundancy to encode logical information into additional qubits to better protect the system from noise, is necessary to design a viable quantum computer. For this new topological stabilizer code-XYZ 2 code defined on the cellular lattice, it is implemented on a hexagonal lattice of qubits and it encodes the logical qubits with the help of stabilizer measurements of weight six and weight two. However topological stabilizer codes in cellular lattice quantum systems suffer from the detrimental effects of noise due to interaction with the environment. Several decoding approaches have been proposed to address this problem. Here, we propose the use of a state-attention based reinforcement learning decoder to decode XYZ 2 codes, which enables the decoder to more accurately focus on the information related to the current decoding position, and the error correction accuracy of our reinforcement learning decoder model under the optimisation conditions can reach 83.27% under the depolarizing noise model, and we have measured thresholds of 0.18856 and 0.19043 for XYZ 2 codes at code spacing of 3–7 and 7–11, respectively. our study provides directions and ideas for applications of decoding schemes combining reinforcement learning attention mechanisms to other topological quantum error-correcting codes.

BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework.

Removal of batch effect between multiple datasets from different experimental platforms has become an urgent problem, since single-cell RNA sequencing (scRNA-seq) techniques developed rapidly. Although there have been some methods for this problem, most of them still face the challenge of under-correction or over-correction. Specifically, handling batch effect in highly nonlinear scRNA-seq data requires a more powerful model to address under-correction. In the meantime, some previous methods focus too much on removing difference between batches, which may disturb the biological signal heterogeneity of datasets generated from different experiments, thereby leading to over-correction. In this article, we propose a novel multi-layer adaptation autoencoder with dual-channel framework to address the under-correction and over-correction problems in batch effect removal, which is called BERMAD and can achieve better results of scRNA-seq data integration and joint analysis. First, we design a multi-layer adaptation architecture to model distribution difference between batches from different feature granularities. The distribution matching on various layers of autoencoder with different feature dimensions can result in more accurate batch correction outcome. Second, we propose a dual-channel framework, where the deep autoencoder processing each single dataset is independently trained. Hence, the heterogeneous information that is not shared between different batches can be retained more completely, which can alleviate over-correction. Comprehensive experiments on multiple scRNA-seq datasets demonstrate the effectiveness and superiority of our method over the state-of-the-art methods. The code implemented in Python and the data used for experiments have been released on GitHub (https://github.com/zhanglabNKU/BERMAD) and Zenodo (https://zenodo.org/records/10695073) with detailed instructions.

Applicability of the generalized wind profile model over mountainous forests.

The analytical equation based on Monin-Obukhov (M-O) similarity theory (i.e., wind profile equation) has been adopted since 1970s for using in the prediction of wind vertical profile over flat terrains, which is mature and accurate. However, its applicability over complex terrains remains unknown. This applicability signifies the accuracy of the estimations of aerodynamic parameters for the boundary layer of non-flat terrain, such as zero-displacement height (d) and aerodynamic roughness length (z0), which will determine the accuracy of frequency correction and source area analysis in calculating carbon, water, and trace gas fluxes based on vorticity covariance method. Therefore, the validation of wind profile model in non-flat terrain is the first step to test whether the flux model needs improvement. We measured three-dimensional wind speed data by using the Ker Towers (three towers in a watershed) at Qingyuan Forest CERN in the Mountainous Region of east Liaoning Province, and compared them with data from Panjin Agricultural Station in the Liaohe Plain, to evaluate the applicability of a generalized wind profile model based on the Monin-Obukhov similarity theory on non-flat terrain. The results showed that the generalized wind profile model could not predict wind speeds accurately of three flux towers separately located in different sites, indicating that wind profile model was not suitable for predicting wind speeds in complex terrains. In the leaf-off and leaf-on periods, the coefficient of determination (R2) between observed and predicted wind speeds ranged from 0.12 to 0.30. Compared to measured values, the standard error of the predicted wind speeds was high up to 2 m·s-1. The predicted wind speeds were high as twice as field-measured wind speed, indicating substantial overestimation. Nevertheless, this model correctly predicted wind speeds in flat agricultural landscape in Panjin Agricultural Station. The R2 between observed wind speeds and predicted wind speed ranged from 0.90 to 0.93. The standard error between observed and predicted values was only 0.5 m·s-1. Results of the F-test showed that the root-mean-square error of the observed and predicted wind speeds in each secondary forest complex terrain was much greater than that in flat agricultural landscape. Terrain was the primary factor affecting the applicability of wind profile model, followed by seasonality (leaf or leafless canopy). The wind profile model was not applicable to the boundary-layer flows over forest canopies in complex terrains, because the d was underestimated or both the d and z0 were underestimated, resulting in inaccurate estimation of aerodynamic height.

Fast and accurate transcranial ultrasound simulation using the asymptotic model of the Civa Healthcare platform

To ensure its efficacy and safety, transcranial ultrasound therapy treatment planning requires accurate pressure field simulations and phase law corrections. Despite their long computation time and high memory usage, full numerical methods are often used since they are considered more accurate than semi-analytical methods. This work will present the so-called “pencil method“, a fast asymptotic model embedded in the CIVA HealthCare simulation platform. It allows computation in harmonic and impulse mode and the consideration of complex configurations, including solid obstacles, considering, at each interface, refractions and reflections with or without mode conversion of the acoustic field. This model was successfully compared to a recent collaborative work by Aubry et al. that presented a set of numerical benchmarks for transcranial propagation, to allow comparisons between various modeling tools. It was used to investigate the influence of parametric variation of skull material properties on the quality of acoustic focusing through the human skull. Its ability to predict the thermal rise at the intracranial target was validated against experimental data obtained ex-vivo through human skulls. Finally, works in progress will be shared about its connection to the open-source Kranion software developed at the FUS Foundation to facilitate comparison between clinical and simulated data.

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Conifers are a common type of plant used in ornamental horticulture. The prompt diagnosis of the phenological state of coniferous plants using remote sensing is crucial for forecasting the consequences of extreme weather events. This is the first study to identify the “Vegetation” and “Dormancy” states in coniferous plants by analyzing their annual time series of spectral characteristics. The study analyzed Platycladus orientalis, Thuja occidentalis and T. plicata using time series values of 81 vegetation indices and 125 spectral bands. Linear discriminant analysis (LDA) was used to identify “Vegetation” and “Dormancy” states. The model contained three to four independent variables and achieved a high level of correctness (92.3 to 96.1%) and test accuracy (92.1 to 96.0%). The LDA model assigns the highest weight to vegetation indices that are sensitive to photosynthetic pigments, such as the photochemical reflectance index (PRI), normalized PRI (PRI_norm), the ratio of PRI to coloration index 2 (PRI/CI2), and derivative index 2 (D2). The random forest method also diagnoses the “Vegetation” and “Dormancy” states with high accuracy (97.3%). The vegetation indices chlorophyll/carotenoid index (CCI), PRI, PRI_norm and PRI/CI2 contribute the most to the mean decrease accuracy and mean decrease Gini. Diagnosing the phenological state of conifers throughout the annual cycle will allow for the effective planning of management measures in conifer plantations.

Nuclease-free precise genome editing corrects MECP2 mutations associated with Rett syndrome.

Rett syndrome is an acquired progressive neurodevelopmental disorder caused by de novo mutations in the X-linked MECP2 gene which encodes a pleiotropic protein that functions as a global transcriptional regulator and a chromatin modifier. Rett syndrome predominantly affects heterozygous females while affected male hemizygotes rarely survive. Gene therapy of Rett syndrome has proven challenging due to a requirement for stringent regulation of expression with either over- or under-expression being toxic. Ectopic expression of MECP2 in conjunction with regulatory miRNA target sequences has achieved some success, but the durability of this approach remains unknown. Here we evaluated a nuclease-free homologous recombination (HR)-based genome editing strategy to correct mutations in the MECP2 gene. The stem cell-derived AAVHSCs have previously been shown to mediate seamless and precise HR-based genome editing. We tested the ability of HR-based genome editing to correct pathogenic mutations in Exons 3 and 4 of the MECP2 gene and restore the wild type sequence while preserving all native genomic regulatory elements associated with MECP2 expression, thus potentially addressing a significant issue in gene therapy for Rett syndrome. Moreover, since the mutations are edited directly at the level of the genome, the corrections are expected to be durable with progeny cells inheriting the edited gene. The AAVHSC MECP2 editing vector was designed to be fully homologous to the target MECP2 region and to insert a promoterless Venus reporter at the end of Exon 4. Evaluation of AAVHSC editing in a panel of Rett cell lines bearing mutations in Exons 3 and 4 demonstrated successful correction and rescue of expression of the edited MECP2 gene. Sequence analysis of edited Rett cells revealed successful and accurate correction of mutations in both Exons 3 and 4 and permitted mapping of HR crossover events. Successful correction was observed only when the mutations were flanked at both the 5' and 3' ends by crossover events, but not when both crossovers occurred either exclusively upstream or downstream of the mutation. Importantly, we concluded that pathogenic mutations were successfully corrected in every Rett line analyzed, demonstrating the therapeutic potential of HR-based genome editing.

Performance characteristics of the 5-ring GE Discovery MI PET/CT scanner using AAPM TG-126 report.

To report on the performance characteristics of the 5-ring GE Discovery MI PET/CT systems using the AAPM TG-126 report and compare these results to NEMA NU 2-2012 where applicable. TG-126 testing was performed on two GE 5-Rings Discovery MI scanners. Tests performed included spatial resolution, PET/CT image-registration accuracy, sensitivity, count rate performance, accuracy of corrections, image contrast, scatter/attenuation correction, and image uniformity. All acquired data were analyzed using scanner console or free software tools as described by TG-126 and the results were then compared to published NEMA NU 2-2012 values. Both scanners gave similar resolution results for TG-126 and NEMA NU 2-2012 and were within manufacturer specifications. Image-registration accuracy between PET and CT using our clinical protocol showed excellent results with values≤1mm. Sensitivity using TG-126 was 19.43cps/kBq while for NEMA the value was 20.73cps/kBq. The peak noise-equivalent counting rate was 2174 kcps at 63.1 kBq/mL and is not comparable to NEMA NU 2-2012 due to differences in phantoms and methods used to measure and calculate this parameter. The accuracy of corrections for count losses for TG-126 were expressed in SUV values and found to be within 10% of the expected SUV measurement of 1. Image contrast and scatter/attenuation correction using the TG-126 method gave acceptable results. Image uniformity assessment resulted in values within the recommended±5% limits. These results show that the 5-ring GE Discovery MI PET/CT scanner testing using TG-126 is reproducible and has similar results to NEMA NU 2-2012 tests where applicable. We hope these results start to form the basis to compare PET/CT systems using TG-126.

Accuracy of correction of a hexapod frame, patient-specific osteotomy and reduction guides, and hinged circular external fixation in a 3D-printed canine antebrachial deformity model.

This study aimed to objectively define whether human hexapod fixation (Maxframe), with or without the use of 3D-printed positioning guides, can correct a canine antebrachial deformity with greater accuracy than the clinically established techniques of 3D patient-specific osteotomy and reduction guides (3D-PSORG) or hinged circular external skeletal fixation (CESF). CT of a canine antebrachium was manipulated to induce distal radial deformity of the valgus, external torsion, and procurvatum, each of magnitude 20o. Five experiments were performed to correct the deformity via a distal radial and ulna opening osteotomy using: (1) A 3D-PSORG with the application of a locking plate, (2) hinged CESF, (3) Maxframe standard protocol, (4) Maxframe applied with patient-specific positioning guides (PSPGs), and (5) Maxframe with frame adjustment calculated from post-application CT. Following correction, all constructs were optically scanned, and objective measurement of the correction achieved was performed. No construct returned the distal bone segment to its preoperative position in all planes. Translational malalignment in the sagittal plane had the highest magnitude of error for all constructs, with the Maxframe standard protocol showing the greatest error. Maxframe (PSPGs) showed the minimum error of all constructs in the frontal and sagittal planes. In this 3D-printed model of antebrachial deformity correction, the hexapod frame with the use of PSPGs achieved better accuracy than 3D-PSORG and hinged CESF and may be a technique of future interest and development in the management of canine antebrachial limb deformity.

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.

A meta-cognitive Wisconsin Card Sorting Test in people with schizophrenia and bipolar disorder: Self-assessment of sorting performance

People with serious mental illness have challenged self-awareness, including momentary monitoring of performance. A core feature of this challenge is in the domain of using external information to guide behavior, an ability that is measured very well by certain problem-solving tasks such as the Wisconsin Card Sorting Test (WCST) . We used a modified WCST to examine correct sorts and accuracy decisions regarding the correctness of sort. Participants with schizophrenia (n = 99) or bipolar disorder (n = 76) sorted 64 cards and then made judgments regarding correctness of each sort prior to feedback. Time series analyses examined the course of correct sorts and correct accuracy decisions by examining the momentary correlation and lagged correlation on the next sort. People with schizophrenia had fewer correct sorts, fewer categories, and fewer correct accuracy decisions (all p<.001). Positive response biases were seen in both groups. After an incorrect sort or accuracy decision, the groups were equally likely to be incorrect on the next sort or accuracy decision. Following correct accuracy decisions, participants with bipolar disorder were significantly (p=.003) more likely to produce a correct sort or accuracy decision. These data are consistent with previous studies implicating failures to consider external feedback for decision making. Interventions aimed at increasing consideration of external information during decision making have been developed and interventions targeting use of feedback during cognitive test performance are in development.

No clinical outcome difference between varus phenotypes after medial opening-wedge high tibial osteotomy at 2 years follow-up.

Clinical studies regarding medial open-wedge high tibial osteotomy (MOWHTO) often analyse a large group of mechanical varus knees rather than differentiating for its primary varus-inducing component. This study aims to compare the radiological and clinical outcomes of the most prevalent varus malalignment phenotypes using the coronal plane alignment of the knee (CPAK) classification. MOWHTO cases with minimal 2-year clinical follow-up were retrospectively selected from a knee osteotomy database (2016-2020). Based on the medial proximal tibial angle (MPTA) and lateral distal femoral angle (LDFA), subjects were allocated to the correct CPAK phenotype pre- and postoperatively. Clinical outcomes were the numeric rating scale (NRS), the knee injury and osteoarthritis outcome score (KOOS) and the therapeutic response rate (TRR) at 2-year follow-up. Inter-observer correlation coefficient (ICC) and unpaired student t test were performed for cross-phenotype comparison. One hundred thirty-five (135) subjects were found eligible (53.0 years old ±9.6 [19-77], 72% male, 53% left-sided). The most prevalent preoperative phenotype was CPAK 1 (n = 70 (52%)) and the postoperative phenotype was CPAK 6 (n = 66 (49%)). All CPAK phenotypes improved significantly relative to baseline but cross-phenotype comparison yielded no significant differences in clinical outcome. The TRR at 2 years was 67% for CPAK 1, 69% for CPAK 2and 87% for CPAK 4. The TRR for CPAK 6 was 64% compared with 80% for CPAK 9, which was not significantly different. At 2-yearfollow-up, no clinically significant differences are observed between different CPAK phenotypes. Accurate MOWHTO corrections provide significant clinical improvement even in the femoral-driven varus knee and the constitutional varus knee dominated by intra-articular wear. The clinical indication for MOWHTO performance should not be reduced to the medial arthritic varus knee with underlying tibial varus alone. Level IV, retrospective comparative study.

Fourier Ptychographic Neural Network Combined with Zernike Aberration Recovery and Wirtinger Flow Optimization.

Fourier ptychographic microscopy, as a computational imaging method, can reconstruct high-resolution images but suffers optical aberration, which affects its imaging quality. For this reason, this paper proposes a network model for simulating the forward imaging process in the Tensorflow framework using samples and coherent transfer functions as the input. The proposed model improves the introduced Wirtinger flow algorithm, retains the central idea, simplifies the calculation process, and optimizes the update through back propagation. In addition, Zernike polynomials are used to accurately estimate aberration. The simulation and experimental results show that this method can effectively improve the accuracy of aberration correction, maintain good correction performance under complex scenes, and reduce the influence of optical aberration on imaging quality.

Transient thermal stress analysis of a thin rotating FGM blade based on the exact shear correction factor: a comparison of ROM and LRVE homogenization models

This work is an attempt to develop a simple and accurate finite element formulation based on first-order shear deformation theory (FSDT) for the transient thermal stress analysis of a pre-twisted and tapered thin rotating functionally graded material (FGM) blade, induced due to the thermal shock. The neutral surface of the FGM blade is taken as the reference plane and the exact shear correction factor is obtained from the equivalent energy principle. The temperature-dependent material property at a point in the thickness direction is estimated according to the rule of mixture (ROM) and the Local representative volume elements (LRVE) homogenization model. A finite element formulation using FSDT in conjunction with the eight-noded isoparametric element, taking five degrees of freedom is developed. The top layer of the thin FGM blade is subjected to a thermal shock and the bottom layer is kept at the ambient temperature. The governing equation for the present formulation is obtained using Hamilton’s principle. The accuracy of the present finite element formulation is established by comparing the results with the benchmark results. The parametric studies are conducted to investigate the effect of the angle of twist, variable chord and span, rotational speed, and accurate shear correction factor on the transient thermal stresses due to the thermal shock. Notably, there can be significant differences in the transient thermal stresses calculated by the two homogenization models viz., ROM and LRVE. Also, it is important to consider the exact shear correction factor in the formulation.

Decoding Van der Waals Impact on Chirality Transfer in Perovskite Structures: Density Functional Theory Insights.

Chiral organic-inorganic perovskites exhibit unique physicochemical properties driven by the symmetry of monovalent organic cations. However, an atomistic understanding of how chiral cations transfer their chirality to the inorganic framework and the role played by van der Waals (vdW) interactions in this process is still incomplete. In this work, we report a theoretical investigation, based on density functional theory calculations within the Perdew-Burke-Ernzerhof (PBE) formulation for the exchange-correlation functional, into the role of the vdW interactions in the chirality transfer process. For that, we selected several vdW corrections, namely, Grimme (D2, D3, D3(BJ)), Tkatchenko-Scheffler (TS, TS+SCS, TS+HSI), density-dependent energy correction (dDsC), and many-body scattering (MBD) energy method correction. For the chiral perovskite systems, we selected a set of chiral organic-inorganic perovskites with several dimensions, namely, from zero-dimensional to three-dimensional, each having enantiomers with R and S configurations. Based on a statistical treatment of the relative errors of all lattice parameters with respect to experimental data, we found that D3, D3(BJ), TS, TS+SCS, TS+HSI, and MBD vdW are the most accurate corrections to describe the equilibrium structural properties of chiral perovskites using the PBE method. We identify chirality-induced sequential asymmetries of distorted octahedrons and propose angular descriptors to quantify them, where the orientations of these distortions depend on the R or S nature of the chiral cations. Furthermore, we demonstrate the importance of accurate vdW interactions in precisely describing these asymmetric distortions. By means of binding energies and charge-transfer analysis, we show that the impact of vdW corrections on the charge distribution leads to a subtle strengthening of hydrogen bonds between chiral cations and inorganic octahedra, resulting in an increase in the binding energy. Finally, we identified that the Rashba-Dresselhaus effect in two-dimensionality is refined by vdW interactions.

Effect of using K.W.L strategy on developing some motor abilities and response speed with precision aiming with hand ball

&#x0D; Research objective: To identify the effect of using K.W.L strategy in developing some motor abilities and speed of response with precision aiming with handball. The imposition of the research: There is no statistically significant relationship between exercises to develop some motor abilities and response speed in the accuracy of correction in handball. Research methodology and its sample: The researcher used the experimental approach with two pre and post tests to suit the suitability and nature of the problem, and the research was chosen from third-grade students 2016-2017, where the sample was chosen from two divisions of students and the students were excluded because they were not participating in the research, and the research sample was divided into two control and experimental groups. The total number of the sample is (50) students, and the researcher excluded non-committed students and their number was (8) students and absentees were excluded (5) students and (3) repeaters were excluded, and (10) students were excluded for their participation in the exploratory experiment, where the research sample consisted of (25) A student formed a proportion of (50%) of the research community, and each group was divided (12) students, and the researcher used the statistical bag (SPSS) for the purpose of data processing.&#x0D; &#x0D;

Validation of satellite-derived water-leaving reflectance in contrasted French coastal waters based on HYPERNETS field measurements

Since 2021, two autonomous HYPERNETS (A new hyperspectral radiometer integrated in automated networks of water and land bidirectional reflectance measurements for satellite validation) stations are operated in contrasted French coastal waters: one in the center of an optically complex coastal lagoon and one at the mouth of a highly turbid estuary. These stations perform predefined sequences of above-water hyperspectral radiometric measurements following a strict viewing geometry. The data recorded by the ®HYPSTAR radiometer is automatically transmitted to servers for quality-controls then computation of the water-leaving reflectance signal. Numerous matchups were identified with high (Sentinel2-MSI and Landsat8/9-OLI) and medium (Sentinel3-OLCI and Aqua-MODIS) spatial resolution satellite data and are analyzed to assess the performance of different atmospheric correction algorithms (Sen2Cor, ACOLITE, POLYMER, iCOR, C2RCC, GRS, BPAC, NIR-SWIR). Considering the specifications of each site (i.e., spatial and temporal variations of water optical properties), optimized matchup protocols are first established to guaranty high quality comparisons between satellite products and field measurements. The matchup results highlight the failure and limits of several atmospheric correction algorithms in complex/turbid coastal waters. The importance of accurate sun glint corrections in low to moderately-turbid waters (with the good performances of POLYMER, C2RCC and GRS processors, e.g., errors (MAPE) lower than 25% in the green spectral region) is also shown while the use of dark targets and spectral fitting to estimate the aerosol contributions is proved to be the most accurate method in the case of turbid waters (with Sen2Cor and ACOLITE errors (MAPE) lower than 20% in the visible and near-infrared spectral regions).

Correction of Kerr-Sen Black Hole Temperature and Entropy by Lorentz Invariance Violation* *Supported by National Natural Science Foundation of China (Grant No.12373109) and the Natural Science Foundation of Shandong Province of China(Grant No.ZR202102220686).

In this paper, we modify the action of spin field in Kerr-Sen curved space-time through Lorentz invariance violation (LIV). The tunneling radiation rate of Fermion in the Kerr-Sen black hole and the correction of the black hole entropy are studied. In the curved space-time of the Kerr-Sen black hole, by considering the inclusion of LIV correction terms in the action of a spinor field, the modified form of the fermion dynamics equation, the tunneling radiation rate and Bekenstein-Hawking entropy of the black hole are obtained. In this paper, we further consider the modification of the results obtained under quantum perturbation theory and discuss the significance of the results obtained. We have used quantum perturbation theory to perform a more accurate correction of the above results.

Investigation of a perturbation-based model updating approach for structural health and event monitoring

With the intention to detect structural events, for example, occurrence of damage and assessing their severity under service, structural health and event monitoring proved itself as a reliable evaluation tool. While many algorithmic approaches exist, model-based monitoring offers the prominent advantage to enable extensive analysis of damage states based on a structural digital twin. In order to accurately replicate the dynamical behaviour of a real-world structure, inherent physical properties, for example, mass, stiffness and structural damping, of the digital twin might be corrected from nominal values. This can be achieved by numerical model updating procedures. In this study, the application of a model updating approach is presented to eliminate modal discrepancies. Based on the perturbated modal dynamic residual, an updating equation is formulated. Stiffness and mass correction terms with preserved orthogonality and symmetry conditions are determined by the method of least squares. A case study using computationally generated modal data is conducted, evaluating the numerical updating performance in terms of correction accuracy and reproduction of modal parameters. Finally, the transient structural response to an impulse excitation is compared between updated and reference model. The gathered results prove the method’s suitability as accurate and robust updating procedure, fostering its application in model-based monitoring frameworks.

Image Identification and Error Correction Method for Test Report Based on Deep Reinforcement Learning and IoT Platform in Smart Laboratory

In order to solve the problems that most models are complex, time-consuming, and have difficulty in identifying image errors, an image identification and error correction method of test report based on deep reinforcement learning and the internet of things platform in the smart lab was proposed. Firstly, a smart lab architecture was designed based on the internet of things platform, achieving efficient operation of the laboratory through cloud edge collaboration. Then, the depth separable convolution improved convolutional neural network is used to extract image features, and the features are input into bidirectional recurrent neural networks (BiLSTM) for analysis to complete image recognition. Finally, the ICNN-BiLSTM model is used as the agent of reinforcement learning, and image error correction is completed by identifying the distance between the image and the key points of the reference image. Based on the Python platform, the proposed method was experimentally demonstrated, and the results showed that its average error correction accuracy reached 96.75%, with a processing time of 15.37s.