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Satellite precipitation products can help improve precipitation estimates where ground-based observations are lacking; however, their relative accuracy and applicability in data-scarce areas remain unclear. Here, we evaluated the accuracy of different satellite precipitation datasets for the Lancang River Basin, Western China, including the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals (GPM IMERG), and Fengyun 2G (FY-2G) datasets. The results showed that GPM IMERG and FY-2G are superior to TRMM 3B42RT for meeting local research needs. A subsequent bias correction on these two datasets significantly increased the correlation coefficient and probability of detection of the products and reduced error indices such as the root mean square error and mean absolute error. To further improve data quality, we proposed a novel correction–fusion method based on window sliding data correction and Bayesian data fusion. Specifically, the corrected FY-2G dataset was merged with GPM IMERG Early, Late, and Final Runs. The resulting FY-Early, FY-Late, and FY-Final fusion datasets showed high correlation coefficients, strong detection performances, and few observation errors, thereby effectively extending local precipitation data sources. The results of this study provide a scientific basis for the rational use of satellite precipitation products in data-scarce areas, as well as reliable data support for precipitation forecasting and water resource management in the Lancang River Basin. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine the development of English article accuracy in second language writing following direct or indirect corrective feedback (CF). Participants were 56 middle-school-aged learners of English nested within four classrooms who were studying for the writing portion of the Secondary School Admission Test. In this longitudinal analysis, each student completed four essays, with a weekly gap between each assignment. On the measures of writing accuracy, adolescent students in the direct CF group demonstrated increasing trajectories of accuracy with English article usage compared to the indirect CF group. Additionally, there was a significant difference in improvement by proficiency level: for lower proficiency learners, but not higher proficiency learners, both indirect and direct corrective feedback resulted in improvement in English article accuracy. The findings suggest that CF treatments designed to enhance a learner's accuracy can be influenced by the learner's existing proficiency, at least among adolescents. [ABSTRACT FROM AUTHOR]

Chinese grammatical error correction (CGEC) is a significant challenge in Chinese natural language processing. Deep-learning-based models tend to have tens of millions or even hundreds of millions of parameters since they model the target task as a sequence-to-sequence problem. This may require a vast quantity of annotated corpora for training and parameter tuning. However, there are currently few open-source annotated corpora for the CGEC task; the existing researches mainly concentrate on using data augmentation technology to alleviate the data-hungry problem. In this paper, rather than expanding training data, we propose a competitive CGEC model from a new insight for reducing model parameters. The model contains three main components: a sequence learning module, a grammatical generalization module and a parameter sharing module. Experimental results on two Chinese benchmarks demonstrate that the proposed model could achieve competitive performance over several baselines. Even if the parameter number of our model is reduced by 1/3, it could reach a comparable |$F_{0.5}$| value of 30.75%. Furthermore, we utilize English datasets to evaluate the generalization and scalability of the proposed model. This could provide a new feasible research direction for CGEC research. [ABSTRACT FROM AUTHOR]

Atmospheric motion vectors, which can be used to infer wind speed and direction based on the trajectory of cloud movement, are instrumental in enhancing atmospheric wind-field insights, contributing notably to wind-field optimization and forecasting. However, a widespread problem with vector data is their inaccuracy, which, when coupled with the mediocre effectiveness of existing correction methods, limits their practical utility in forecasting, often falling short of expectations. Deep-learning techniques are used to refine atmospheric motion vector data from the FY-4A satellite, notably enhancing data quality. Post-training data undergoes a thorough analysis using a quality evaluation function, followed by its integration into a numerical weather prediction system in order to conduct forecasting experiments. Results indicate a marked improvement in data quality post-error correction by the model, characterized by a significant reduction in root mean square error and a notable increase in correlation coefficients. Furthermore, refined data demonstrate a considerable enhancement in the accuracy of meteorological element forecasts, particularly for Asian and Western Pacific regions. [ABSTRACT FROM AUTHOR]

The increasing availability of longitudinal data (repeated numerical observations of the same units at different times) requires the development of flexible techniques to automatically detect errors in such data. Besides standard types of errors, which can be treated with generic error correction techniques, large longitudinal datasets may present specific problems not easily traceable by the generic techniques. In particular, after applying those generic techniques, time series in the data may contain trends, natural fluctuations and possible surviving errors. To study the data evolution, one main issue is distinguishing those elusive errors from the rest, which should be kept as they are and not flattened or altered. This work responds to this need by identifying some types of elusive errors and by proposing a statistical-mathematical approach to capture their complexity that can be applied after the above generic techniques. The proposed approach is based on a system of indicators and works at the formal level by studying the differences between consecutive values of data series and the symmetries and asymmetries of these differences. It operates regardless of the specific meaning of the data and is thus applicable in a variety of contexts. We implement this approach in a relevant database of European Higher Education institutions (ETER) by analyzing two key variables: "Total academic staff" and "Total number of enrolled students", which are two of the most important variables, often used in empirical analysis as a proxy for size, and are considered by policymakers at the European level. The results are very promising. [ABSTRACT FROM AUTHOR]

During the cooling and solidification process of investment casting, the material experiences shrinkage due to its thermal properties. However, the shrinkage of castings often exhibits non-uniform distribution when the mold shell is removed. The reason is a contractive tendency of casting which is constrained by the mold shell, leading to uneven stress distribution. The residual stress will release when the constraint is removed, which induces the inhomogeneous shrinkage distribution. The inhomogeneous shrinkage distribution is not conducive to the control of dimension accuracy of castings. Thus, it is necessary to predict the inhomogeneous shrinkage for the subsequent compensation of cavity contour in the mold cavity design process. In the current relevant research, predicting nonlinear shrinkage in advance based on the geometric structure is also inadequacy. A shrinkage prediction method based on model automated self-correction strategy is proposed to predict nonlinear shrinkage with high accuracy in advance. The innovation of this article is to present a prediction model modeling method for geometric structure and shrinkage and to improve accuracy based on the deviation correction prediction model. The model automated self-correction strategy was correct model parameters according to the modeling prediction deviation. Besides, the shrinkage of the verification pieces was measured. The measured and predicted values of the verification piece were compared. The result shows that the predicted values by the shrinkage prediction method are in agreement with the measured values, and it is better than that without self-correction. [ABSTRACT FROM AUTHOR]

Polar format algorithm (PFA) is a widely used high-resolution SAR imaging algorithm that can be implemented in advanced widefield staring synthetic aperture radar (WFS-SAR). However, existing algorithms have limited analysis in wavefront curvature error (WCE) and are challenging to apply to WFS-SAR with high-resolution and large-swath scenes. This paper proposes an extended polar format algorithm for joint envelope and phase error correction in WFS-SAR imaging with maneuvering trajectory. The impact of the WCE and residual acceleration error (RAE) are analyzed in detail by deriving the specific wavenumber domain signal based on the mapping relationship between the geometry space and wavenumber space. Subsequently, this paper improves the traditional WCE compensation function and introduces a new range cell migration (RCM) recalibration function for joint envelope and phase error correction. The 2D precisely focused SAR image is acquired based on the spatially variant inverse filtering in the final. Simulation experiments validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Since its first adoption as a computational model for language learning, evidence has accumulated that Rescorla–Wagner error‐correction learning (Rescorla & Wagner, 1972) captures several aspects of language processing. Whereas previous studies have provided general support for the Rescorla–Wagner rule by using it to explain the behavior of participants across a range of tasks, we focus on testing predictions generated by the model in a controlled natural language learning task and model the data at the level of the individual learner. By adjusting the parameters of the model to fit the trial‐by‐trial behavioral choices of participants, rather than fitting a one‐for‐all model using a single set of default parameters, we show that the model accurately captures participants' choices, time latencies, and levels of response agreement. We also show that gender and working memory capacity affect the extent to which the Rescorla–Wagner model captures language learning. A one‐page Accessible Summary of this article in non‐technical language is freely available in the Supporting Information online and at https://oasis‐database.org [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the usefulness of examples that show typical learner errors in online pedagogical dictionaries of English for the accuracy of error correction as well as immediate and delayed retention of usage. The optimal positioning of examples of errors in entries is also researched. In an online experiment, participants did a sentence correction exercise with the help of purpose-built monolingual dictionary entries, where the provision and positioning of examples showing errors were controlled. Two test versions were created, which differed only in the presence of examples of errors in the entries. Usage retention was observed immediately after the test and two weeks later. The results indicate that it is worthwhile to include examples of errors in online learners' dictionaries because they contribute greatly to the retention of usage in the long run. They also help to rectify errors, though the effect is not statistically significant. The positioning of examples showing errors in entries has no influence on error correction accuracy or usage retention. The study reveals examples of errors to be a valuable induction-oriented stand-alone dictionary component placed outside warning boxes, which typically include explicit grammar rules and promote deduction. [ABSTRACT FROM AUTHOR]

The steady airflow field on a ship is affected by structure and motion and challenged by phenomena such as the low measurement accuracy of the wind field caused by the occlusion of the anemometer. In this work, an improvement in the accuracy of wind measurements affected by structure is proposed, and a method for combining anemometer and X-band marine radar (RCRF) data is designed to further obtain wind parameters. The first step is to use the multivariate bias strategy to achieve the optimal layout of multiple anemometers based on computational fluid dynamics (CFD) numerical simulation data. Then, random forest (RF) is employed to train the wind parameter estimation model. Finally, the wind parameters are optimally estimated by combining the anemometer with the X-band radar. Under the ideal simulation, noise, and temporal uncertainty combined with anemometer noise conditions, the RCRF algorithm performance is evaluated. Compared with the bias correction combination four-anemometer weighted fusion algorithm (FAF-BC) and the BP neural network algorithm for radar wind measurement combination (RCBP), the mean errors in wind direction and speed are reduced by 1.99° and 6.99% at most. The maximum errors are reduced by 14.46° and 15.81% at most, respectively. [ABSTRACT FROM AUTHOR]

This work presents a framework aimed at mitigating adverse effects of high-amplitude drone noise ranging from hearing loss to reduced productivity in human–robot collaborative environments by infusing acoustic awareness in a path planning algorithm without imposing any additional design layers or hardware to an operational drone. Following a detailed outline of the proposed approach, it is shown that a significant reduction of noise levels perceived by human workers at noise-sensitive locations is realized via a path planner which generates optimal paths ranging from quietest to shortest paths. The approach is then augmented with a path-correction mechanism which accounts for noise exposure duration to ensure the aforementioned optimal paths are compliant with a given industrial/environmental standard. The correction mechanism enforces an adjustment of subsets of the planned paths inside quiet zones designated around noise-sensitive locations. The presented concepts were verified using numerical simulations conducted for a 2-dimensional rasterized obstacle field followed by a statistical design of experiments. The proposed framework is highly versatile and integrable with widely used industrial path planners, rendering it a highly valuable tool for noisy collaborative workplaces. [ABSTRACT FROM AUTHOR]

Bitcoin has attracted incessant attentions in recent times. Studies have completed models to examine the relationship between Bitcoin and other multiple attendant variables. This paper considers a simple and direct price-volume relation. The paper offers causality evidence according to the dynamic asymmetric causality test. Based on available monthly data spanning 2010:M7-2022:M10, the paper shows that Bitcoin price and volume are integrated, both been I(0)'s. Moreover, the paper discloses the short- and long-term price-volume behaviors of Bitcoin using the cointegration test and vector error correction model (VECM). Taken together, the study first confirms long run relations and presents the estimates of the parsimonious VECM. The results show short run evidence of positive price-volume relations, and in the long run, the disequilibria are as well corrective and mean reversing. The outcomes of the Hatemi-J's causality testing suggest likely evidence of bidirectional causality between the positive and negative fragments of the shocks of Bitcoin price and volume during the periods. [ABSTRACT FROM AUTHOR]

Accurate four-hour-ahead PV power prediction is crucial to the utilization of PV power. Conventional methods focus on using historical data directly. This paper addresses this issue from a new perspective of Numerical Weather Prediction (NWP) optimization. This paper refers to the predicted PV power given by NWP minus the actual PV power as PV NWP error, analyzes the temporal statistical characteristics of PV NWP error time series through the Ljung-Box test and autocorrelation function, reveals the significant temporal autocorrelation in PV NWP error and verifies the statistical predictability of PV NWP error for the first time. Meanwhile, this paper introduces transfer entropy to verify that introducing multiple meteorological variables including temperature, solar irradiation, clear-sky solar irradiation, cloud thickness, etc. can help predict future PV NWP error better for the first time. Based on the fusion of multiple meteorological variables and artificial neural networks, this paper proposes multiple meteorological variables-aided PV NWP error correction to realize more accurate four-hour-ahead PV power prediction for the first time. Kernel density estimation is used to obtain the probabilistic forecast results. Through experiments in three-year actual data of Brussels, the superiorities of the proposed method and the significant improvement over conventional methods are verified. In comparison to the original NWP method, the proposed approach demonstrates significant improvements in accuracy. Specifically, it achieves a reduction in mean absolute error ranging from 25.04% to 48.12% for 1–4 step predictions, 14.80% to 21.27% for 5–8 step predictions, 6.40% to 11.10% for 9–12 step predictions, and 2.18% to 4.45% for 13–16 step predictions. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that it is possible to combine machine learning with data assimilation to reconstruct the dynamics of a physical model partially and imperfectly observed. The surrogate model can be defined as an hybrid combination where a physical model based on prior knowledge is enhanced with a statistical model estimated by a neural network (NN). The training of the NN is typically done offline, once a large enough data set of model state estimates is available. By contrast, with online approaches the surrogate model is improved each time a new system state estimate is computed. Online approaches naturally fit the sequential framework encountered in geosciences where new observations become available with time. In a recent methodology paper, we have developed a new weak‐constraint 4D‐Var formulation which can be used to train a NN for online model error correction. In the present article, we develop a simplified version of that method, in the incremental 4D‐Var framework adopted by most operational weather centers. The simplified method is implemented in the European Center for Medium‐Range Weather Forecasts (ECMWF) Object‐Oriented Prediction System, with the help of a newly developed Fortran NN library, and tested with a two‐layer two‐dimensional quasi geostrophic model. The results confirm that online learning is effective and yields a more accurate model error correction than offline learning. Finally, the simplified method is compatible with future applications to state‐of‐the‐art models such as the ECMWF Integrated Forecasting System. Plain Language Summary: We have recently proposed a general framework for combining data assimilation (DA) and machine learning (ML) techniques to train a neural network for online model error correction. In the present article, we develop a simplified version of this online training method, compatible with future applications to more realistic models. Using numerical illustrations, we show that the new method is effective and yields a more accurate model error correction than the usual offline learning approach. The results show the potential of incorporating DA and ML tightly, and pave the way toward an application to the Integrated Forecasting System used for operational numerical weather prediction at the European Centre for Medium‐Range Weather Forecasts. Key Points: Variants of weak‐constraint 4D‐Var can be used to train neural networks for online model error correctionOnline learning yields a more accurate model error correction than offline learningThe new, simplified method, developed in the incremental 4D‐Var framework, can be easily applied in operational weather models [ABSTRACT FROM AUTHOR]

In 2020, the BeiDou-3 global navigation satellite system (BDS-3) was officially completed and put into service. Currently, network real-time kinematic (RTK) technology is considered the main means through which to improve the positioning accuracy of the BeiDou navigation satellite system (BDS). This paper proposes a long-range undifferenced network RTK (URTK) algorithm, based on multi-frequency observation data of the BDS. First, the multi-frequency phase integer ambiguity resolution (AR) model considering atmospheric error parameters is designed, and the multi-frequency phase integer ambiguity of the long-range BDS reference station is determined. Then, the undifferenced integer ambiguity of each reference station is obtained, using linear variation based on the accurately determined phase integer ambiguity between reference stations, and the undifferenced observation error of each reference station is calculated. Considering the weakening spatial correlation of the observation errors between long-range stations, undifferenced classification error corrections of a reference station network are separated, according to different error characteristics. Finally, the inverse distance weighting method is employed to calculate the classification undifferenced error correction of the rover station. The rover station corrects the observation error through applying the undifferenced error correction to achieve high-precision positioning. The measured data of a long-range continuous operation reference station (CORS) network are selected for an experiment. The results show that the proposed algorithm can quickly and accurately realize the resolution of the BDS integer ambiguity of a reference station network and establish an undifferenced area error correction model in order to achieve accurate classification of undifferenced error correction values for a rover station. In China, the BDS-3 is superior to the global positioning system (GPS) in terms of the satellite number, position dilution of precision (PDOP) value, AR success rate, stability, and convergence time. The results show that the AR success rate, stability, and convergence time increase with the operational frequency, and the BDS-3 can achieve centimeter-level positioning of single-system rover stations without relying on the GPS. [ABSTRACT FROM AUTHOR]

A reconfiguration error correction model for an FBG shape sensor (FSS) is proposed. The model includes curvature, bending direction error correction, and the self-correction of the FBG placement angle and calibration error based on an improved sparrow search algorithm (SSA). SSA could automatically correct the placement angle and calibration direction of the FBG, and then use the corrected placement angle and calibration direction to correct the curvature and bending direction of the FSS, thereby improving the accuracy of shape reconfiguration. After error correction, the tail point reconfiguration errors of different shapes were reduced from 2.56% and 4.96% to 1.12% and 2.45%, respectively. This paper provides a new reconfiguration error correction method for FSS that does not require a complicated experimental calibration process, is simpler, more efficient, and more operable than traditional methods, and has great potential in FSS application scenarios. [ABSTRACT FROM AUTHOR]

Context and objectives : Labour costs are a complex and multifaceted element that can have a significant impact on employment, business competitiveness and worker well-being. It is important to note that the impact of labour costs on employment can vary according to specific economic contexts, the policies in place and the characteristics of different sectors of activity. In other words, the effects of labour costs on employment are complex and depend on many other economic, social and political factors. The aim of the study is to analyse the influence of labour costs on production in industries such as trade, transport and communication, and hotels and restaurants in Benin. Methods : This study is based on heterogeneous quantitative data from the Direction Générale des Impôts, the Institut National de la Statistique et de la Démographie (INStaD) and the BCEAO website for the period 2014 to 2021. Our base is a sample representing 90% of companies in the formal sector, sorted in descending order according to the company paying the employer's payment on wages (VPS). We performed a descriptive analysis followed by an econometric analysis using Eviews9 and STATA 15 software. Results : The results of estimating the model show that the labour cost index (LCI) in the three sectors has a significant and positive effect on the value added of companies only in the long term. Labour costs therefore have a positive effect on industry output. However, the labour cost index (LCI) in the three sectors has no significant effect on value added in the short term. With a view to maintaining the evolutionary trend of the labour cost index (LCI) on the one hand, and preserving the positive effect of the labour cost index (LCI) on business output on the other, operational recommendations have been made based on the results of our analyses. Conclusion : An Error Correction Model (ECM) was used to validate the three models for the three industries covered by our study. On the basis of our results, our study has made a number of recommendations, the implementation of which will make it possible to increase the effect of labour costs on production. Limitations of the study : The study suffers from certain limitations, namely the non-significance of certain variables. Other studies need to be carried out on the same subject in order to identify other factors involved in the design of the labour cost index. [ABSTRACT FROM AUTHOR]

Chinese spelling error correction is a task to detect and correct spelling errors in text. Most Chinese spelling errors are the misuse of semantically, phonetically or morphologically similar characters, so it is common to extract features for mode-ling different modalities. However, the direct fusion of different features or summation using fixed weights prevent the model from learning in an efficient way by ignoring the importance relationship between the information of different modalities and the bias of the model in identifying errors. This paper proposed a new model to improve this problem, called the Chinese error correction algorithm based on the fusion of text sequence error probability and Chinese spelling error probability. The method used the text sequence error probability as the dynamic weight and the common Chinese spelling error probability as the fixed weight to efficiently fuse semantic, phonetic and morphologic information. The model was able to reasonably control the inflow of different modal information into the mixed modal representation and learnt more specifically where the errors occurred. Experiments conducted on the SIGHAN benchmark show that all evaluation scores of the proposed model are improved on different datasets, which validates the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

One of the main indicators for detecting changes in climate and marine ecosystems around the world is the sea surface temperature (SST). Even with several models presented in the literature, it is still a challenging task when only a single model is considered for SST forecasting. In this context, hybrid approaches that combine statistical models (to estimate linear dependencies) and machine learning models (to estimate nonlinear dependencies from residuals) have attained highlighted accuracy in several time series forecasting problems. In this way, this paper proposes a hybrid system that combines the autoregressive integrated moving average (ARIMA) model with a deep morphological neural network model, which employs dilation-erosion operators to estimate ARIMA's model residuals. In this context, the proposed hybrid system is composed of three stages: (1) time series forecast using the ARIMA model, (2) residuals forecast using the deep morphological neural network and (3) a linear combination of the stages (1) and (2). Three SST time series are used in the experimental analysis. Moreover, the achieved results are evaluated using three relevant statistical measures showing that the proposed hybrid system attains higher accuracy when compared to other hybrid models in the literature. [ABSTRACT FROM AUTHOR]

The combination of large tooling size, environmental vibration, and equipment errors at the aircraft assembly site leads to errors in the enhanced reference system (ERS) point measurement information. ERS point errors directly reduce the accuracy of the assembly measurement field. This paper proposes ERS point error prediction and registration compensation based on the neural network to address this problem. First, the effects of equipment measurement errors and environmental vibration factors on the measurement field are studied. The ERS point error prediction model based on the neural network is established. On this basis, model evaluation is used to assess the prediction model of this paper. Then, a measurement field registration compensation model is constructed based on the neural network error results for ERS point compensation analysis. Finally, an experimental validation platform was built to predict the ERS point errors and compensate for the constructed measurement fields using the method in this paper. The experimental results show that, compared with the conventional method, the maximum registration errors in the X, Y, and Z directions are reduced from 0.0812, −0.0565, and −0.2810 to −0.0184, −0.0010, and 0.0022 mm, respectively, after compensation in this paper. The method proposed in this paper can not only predict the ERS point error state and provide a reference for designers but also guide the selection of appropriate ERS points when constructing the measurement field. The compensation method in this paper effectively reduces the measurement field registration error. [ABSTRACT FROM AUTHOR]

Effective prediction of carbon prices matters a great deal for risk management in the carbon financial market. This article designs a blended approach incorporating secondary decomposition and nonlinear error-correction technology to predict the regional carbon price in China. Firstly, the variational mode decomposition (VMD) method is used to decompose the carbon price, and then, the time-varying filter-based empirical mode decomposition (TVFEMD) is introduced to decompose the residual term generated by VMD, and the multiple kernel-based extreme learning machine (MKELM) optimized by the sparrow search algorithm (SSA) is innovatively built to forecast the carbon subsequences. Finally, in order to mine the hidden information contained in the forecasted error, the nonlinear error-correction method based on the SSA-MKELM model is introduced to correct the initial prediction of carbon price. The empirical results show that the proposed model improves the prediction accuracy of carbon prices, with RMSE, MAE, MAPE, and DS up to 0.1363, 0.1160, 0.0015, and 0.9231 in Guangdong, respectively. In the case of the Hubei market, the model also performs best. This research innovatively expands the prediction theory and method of China's regional carbon price. [ABSTRACT FROM AUTHOR]

Phase measurement profilometry (PMP) is widely used to reconstruct three-dimensional (3D) shape of objects. However, errors are introduced during standard phase unwrapping process, which considerably brings down measurement accuracy and lowers method reliability. This paper proposes a novel method to identify the errors, and subsequently correct these errors through erecting adaptive local windows. Under proposal, two robust algorithms are developed to correct these errors in initial rudimentary correction as well as in subsequent refined error removal. The adaptive local window is designed as such that the fringe order corresponding to each period of the wrapping phase is independent of each other. Therefore, it avoids the error transmission in the correction process. Experiments are carried out on absolute phase error correction and 3D point cloud reconstruction. Experimental results verified the developed method in terms of reducing the stratification of 3D point cloud and improving the quality of 3D measurement. [ABSTRACT FROM AUTHOR]

Accurate wind power forecasting is an important factor in ensuring the stable operation of a power system. In this paper, we propose a wind power forecasting method based on feature analysis and error correction in order to further improve its accuracy. Firstly, the correlation analysis is carried out on the features using the maximal information coefficient (MIC), and the main features are selected as the model input items. Then, the two primary factors affecting wind power forecasting—the wind speed and wind direction provided by numerical weather prediction (NWP)—are analyzed, and the data are divided and clustered from the above two perspectives. Next, the bidirectional long short-term memory network (BiLSTM) is used to predict the power of each group of sub data. Finally, the error is forecasted by a light gradient boosting machine (LightGBM) in order to correct the prediction results. The calculation example shows that the proposed method achieves the expected purpose and improves the accuracy of forecasting effectively. [ABSTRACT FROM AUTHOR]

The experience of various crises that have occurred, including the impact of the Covid-19 pandemic, presents a challenge to implement macroprudential policies to ensure the financial system survives and continues to carry out its function in driving the economy. The existing macroprudential policies tend to be individual and focus on prudent banking and other financial institutions. Economic fluctuations that occur on the macro side will greatly impact, either directly or indirectly, the stock price index, as well as the company's internal indicators which are considered to have a major influence on the decisions of investors and potential investors to take action on the stock exchange. The type of research used in this research is quantitative research. The nature of this research is descriptive with a quantitative approach. The data collection technique in this research is Literature Study. The test carried out in this study is the multiple linear regression analysis test (multiple linear regression method), this study uses the ECM model to obtain the best model which includes the classical assumption test. The results of this study based on the partial short-term relationship test, it can be concluded that the Exchange Rate, Inflation, and TPF in the short term have no significant effect on the PNBS Stock Price Index. Meanwhile, short-term CAR has a significant positive effect on the PNBS Stock Price Index. Based on the results of the partial long-term relationship test, it can be concluded that in the long term, the Exchange Rate has a significant negative effect and TPF and CAR have a significant positive effect on the PNBS Stock Price Index while Inflation has no significant effect on the PNBS Stock Price Index. Based on the output results of the simultaneous short-term and long-term F test, it shows that all independent variables simultaneously have a significant effect on the PNBS Stock Price Index in the short term. Based on the provisions of the MUI DSN through the issued fatwas related to the Sharia capital market and Sharia shares, it is explained that Sharia stock investment to invest according to the perspective of Sharia economic law is allowed. [ABSTRACT FROM AUTHOR]

We implement Epstein–Nesbet perturbative corrections in the third-order for the initiator approximation of the configuration space quantum Monte Carlo. An improved sampling algorithm is proposed to address the stochastic noise of the corrections. The stochastic error for the perturbative corrections is considerably larger than that for the reference energy, and it fails to provide reasonable results unless a very long imaginary time integration is performed. The new sampling algorithm accumulates rejected walkers from multiple independent steps to cover a larger portion of the secondary space. The performance of the perturbative corrections is demonstrated for small molecules. [ABSTRACT FROM AUTHOR]

We perform molecular dynamics simulations to model density as a function of temperature for 74 alkanes with 5–10 carbon atoms and non-equilibrium molecular dynamics simulations in the NVT ensemble to model the kinematic viscosity of 10 linear alkanes as a function of molecular weight, pressure, and temperature. To model density, we perform simulations in the NPT ensemble before applying correction factors to exploit the systematic error in the SciPCFF force field and compare the results to experimental values, obtaining an average absolute deviation of 3.4 g l at 25 °C and of 7.2 g l at 100 °C. We develop a sampling algorithm that automatically selects good shear rates at which to perform viscosity simulations in the NVT ensemble and use the Carreau model with weighted least squares regression to extrapolate Newtonian viscosity. Viscosity simulations are performed at experimental densities and show an excellent agreement with experimental viscosities, with an average percent deviation of −1% and an average absolute percent deviation of 5%. Future plans to study and apply the sampling algorithm are outlined. [ABSTRACT FROM AUTHOR]

We extend to strongly correlated molecular systems the recently introduced basis-set incompleteness correction based on density-functional theory (DFT) [E. Giner et al., J. Chem. Phys. 149, 194301 (2018)]. This basis-set correction relies on a mapping between wave-function calculations in a finite basis set and range-separated DFT (RSDFT) through the definition of an effective non-divergent interaction corresponding to the electron–electron Coulomb interaction projected in the finite basis set. This enables the use of RSDFT-type complementary density functionals to recover the dominant part of the short-range correlation effects missing in this finite basis set. To study both weak and strong correlation regimes, we consider the potential energy curves of the H10, N2, O2, and F2 molecules up to the dissociation limit, and we explore various approximations of complementary functionals fulfilling two key properties: spin-multiplet degeneracy (i.e., independence of the energy with respect to the spin projection Sz) and size consistency. Specifically, we investigate the dependence of the functional on different types of on-top pair densities and spin polarizations. The key result of this study is that the explicit dependence on the on-top pair density allows one to completely remove the dependence on any form of spin polarization without any significant loss of accuracy. Quantitatively, we show that the basis-set correction reaches chemical accuracy on atomization energies with triple-ζ quality basis sets for most of the systems studied here. In addition, the present basis-set incompleteness correction provides smooth potential energy curves along the whole range of internuclear distances. [ABSTRACT FROM AUTHOR]

We present a method that exploits self-consistent simulation of coarse-grained and fine-grained models in order to analyze properties of physical systems. The method uses the coarse-grained model to obtain a first estimate of the quantity of interest, before computing a correction by analyzing properties of the fine system. We illustrate the method by applying it to the Asakura-Oosawa model of colloid-polymer mixtures. We show that the liquid-vapor critical point in that system is affected by three-body interactions which are neglected in the corresponding coarse-grained model. We analyze the size of this effect and the nature of the three-body interactions. We also analyze the accuracy of the method as a function of the associated computational effort. [ABSTRACT FROM AUTHOR]

The accurate carbon price prediction is of significance to decrease investment risks, make scientific decisions and improve production efficiency. As matters stand, most studies focusing on carbon price prediction are following the preprocessing models, while the postprocessing models based on the error correction are rarely applied. To enhance the forecasting robustness and provide a relatively comprehensive comparison between the preprocessing and postprocessing model, this research proposes a novel hybrid model KELM-VMD-KELM by combining variational mode decomposition (VMD) and the kernel-based extreme learning machine (KELM), in which the KELM is firstly employed to forecast the daily carbon price series and obtain the initial prediction results, and then the VMD-KELM is utilized to build the predicting models for the residual error series to implement the process of error correction. The particle swarm optimization (PSO) algorithm is made a use to determine the optimal parameters of KELM and VMD. The daily average carbon price from Beijing, Guangdong and Shanghai market are selected to test the validity of the model. The results indicate that there is heterogeneity of the optimal model in different datasets. Both KELM-VMD-KELM and VMD-KELM perform well in the daily carbon price prediction. The postprocessing model can guarantee a high stability in different datasets. [ABSTRACT FROM AUTHOR]

Real-time error correction is an effective measure to improve forecast accuracy. This paper develops a real-time error correction model based on machine learning (ML) ensemble method, including the establishment of base learners, heterogeneity test, and Stacking combination. Then the Copula-based Bayesian processor of forecast (BPF) is adopted for probabilistic forecasts, which quantitatively describes the uncertainty of the correction results. Finally, the model performance is comprehensively evaluated from accuracy and stability, with deterministic metrics used to evaluate the correction effect and uncertain metrics for assessing probabilistic forecasts. The proposed model is applied to the multireservoir system in the Huai River Basin, and the results reveal the following: (1) Unlike the single ML algorithm with the performance of oscillations, the Stacking ensemble method can aggregate the advantages of multiple learners, showing robust correction effect and high adaptability across all data samples. (2) The forecasting error is significantly reduced by the Stacking method, with the average Nash–Sutcliffe efficiency coefficient ( NSE ) value increasing above 0.9, which is 4.93% higher than that of the autoregressive (AR) method. The greater superiority is also shown in the remaining evaluation metric values. Moreover, as the lead time increases, the performance of the stacking method tends to have a slower decline trend than the AR method. (3) The changes in the structure of the Stacking method have a relatively small influence on the forecast uncertainty, with all the Containing ratio ( CR ) values over 80% for different samples. The flexible combination of ML algorithms in the ensemble method will not add additional uncertainty factors and ensure the stability of the correction performance. The framework for real-time error correction and its uncertainty assessment has overall optimal correction performance with less observed data and lower computational cost required, which is promising for further improving the accuracy and reliability of real-time flood forecasting. [ABSTRACT FROM AUTHOR]

Synchronization (insertions–deletions) errors are still a major challenge for reliable information retrieval in DNA storage. Unlike traditional error correction codes (ECC) that add redundancy in the stored information, multiple sequence alignment (MSA) solves this problem by searching the conserved subsequences. In this paper, we conduct a comprehensive simulation study on the error correction capability of a typical MSA algorithm, MAFFT. Our results reveal that its capability exhibits a phase transition when there are around 20% errors. Below this critical value, increasing sequencing depth can eventually allow it to approach complete recovery. Otherwise, its performance plateaus at some poor levels. Given a reasonable sequencing depth (≤ 70), MSA could achieve complete recovery in the low error regime, and effectively correct 90% of the errors in the medium error regime. In addition, MSA is robust to imperfect clustering. It could also be combined with other means such as ECC, repeated markers, or any other code constraints. Furthermore, by selecting an appropriate sequencing depth, this strategy could achieve an optimal trade-off between cost and reading speed. MSA could be a competitive alternative for future DNA storage. [ABSTRACT FROM AUTHOR]

Do party policy offerings simply reflect public opinion or do parties shape public demand for policies? Theories of party position-taking and the operation of democracy expect parties to track their supporters' positions, while scholarship of public opinion has shown voters often adopt the position of their preferred parties. We apply both of these theoretical expectations to the relationship between citizen polarization and party polarization and additionally argue that the relationship between them should be stronger among politically more engaged and sophisticated citizens. We draw on aggregated survey data from 174 cross-national and national election studies from 19 established democracies, to assess the extent to which citizen polarization responds to party polarization, the extent to which parties respond to changes in citizen polarization, and whether these relationships differ across different groups of citizens. We estimate seemingly unrelated error-correction models employing data on party and citizen positions from 1971 to 2019. Our findings suggest that citizen polarization follows party polarization and also that politically engaged and sophisticated citizens are more responsive to changes in party polarization than the politically less engaged and unsophisticated. In contrast, we find little evidence that party polarization responds to changes in citizen polarization. [ABSTRACT FROM AUTHOR]

A distinctive feature of modern Global Navigation Satellite System (GNSS) signals is that they transmit multiple signal components at the same carrier frequencies. The idea of joints across the signal channels from the same carrier frequency and even across different frequencies has been presented in many studies for tracking purposes. Carrier joint tracking is required on the premise that the frequency and phase relationship between signals are nominal values, and the bias of carrier characteristics between signals is drowned in noise as the signal reaches the ground, which requires high-gain receiving equipment to restore the original signal. The space signal-quality monitoring and evaluation system built by the National Timing Center of the Chinese Academy of Sciences is based on a 40 m dish antenna, which can automatically track a single satellite and achieve a high-fidelity reception of navigation signals to a certain extent, realizing fine signal quality monitoring (SQM) of GNSS satellites. Based on this platform, we discuss four types of time distributions of the combined signals among different signal components and provide a method to estimate the carrier characteristic bias between GNSS signals. We derived the correction method of carrier characteristic bias in the joint reception by the joint tracking mathematical model. Under the conditions of narrow correlation and unobstructed case, the carrier characteristic deviation does not vary significantly with the correlator interval and the satellite elevation angle. Based on the results of stability analysis, it is recommended that the receivers should update the carrier frequency bias correction number of the intra-frequency signal and carrier phase bias correction number of the intra-frequency signal monthly. The carrier phase deviation correction number of the inter-frequency signal is performed daily. The measured data from satellites show that the phase accumulation error of the joint tracking carrier loop can be eliminated to achieve long-term stable tracking after frequency bias correction. After the carrier phase bias correction, the joint positioning accuracy of the B2a and B2b signals was improved by 0.81%, and those of the B1C, L1C, E1C, and B2a signals were improved by 0.35%, 0.04%, 0.20%, and 0.11%, respectively. The positioning accuracy improvement effect of inter-frequency signals was greater than that of intra-frequency signals after carrier phase correction. [ABSTRACT FROM AUTHOR]

To realize monitoring of trenching depth, this paper built an online monitoring system and measured depth at three levels of 25 cm, 30 cm, and 35 cm. The test results show that the measured data have large fluctuations and low precision and are smaller than the actual value. For the above problems, this paper proposes a comprehensive noise reduction filtering algorithm based on wavelet noise reduction and Kalman filtering, in which the output of the preprocessing part of the wavelet noise reduction is used as the input of the postprocessing part of the Kalman filter. This article first selects the optimal combination of wavelet threshold denoising parameters such as wavelet function, decomposition level, and threshold denoising rule. Then the optimal Q/R value and the combination of Q and R parameters for the Kalman filter reprocessing part are explored. Finally, the comprehensive filtering correction algorithm and Kalman filtering algorithm are used to process the above-measured data. By comparing the results of the two algorithms, we found that for the local data, the variance decreased by 0.0207, 0.0398, and 0.0609, and the mean square deviation decreased by 0.0609, 0.0854, and 0.0457. The mean values of the two algorithms are very close. Similarly, for the global data, the variance decreased by 0.0182, 0.0549, 0.0428, and the mean square deviation decreased by 0.0359, 0.041, 0.0323. There is no significant difference in means between the two algorithms. The results show that the comprehensive algorithm improves the quality of trenching depth data and has certain significance for precision agriculture. [ABSTRACT FROM AUTHOR]

To obtain an accurate prediction model of surface roughness on the premise of low experimental cost in precision machining processes, this paper proposes two error correction models of the subsection theoretical model of arithmetic mean height roughness (Ra) in turning. The prediction performance of the two error correction models was evaluated by 25 groups of turning data of AISI1045 steel. The determination coefficient (R2) of the 10 random runs of the two correction models are both above 0.9, the mean squared error (MSE) is lower than 0.05, and the standard deviations (StdDev) of R2 and MSE are 0.01. The experimental results show that the two error correction models significantly improve the prediction accuracy and stability of the subsection theoretical model. Moreover, the influence of turning parameters and tool geometry on Ra based on the two correction models and the subsection theoretical model as well as the advantages and disadvantages of the three models is analysed, which provide an effective guidance for the selection of parameters and the prediction model in actual machining. [ABSTRACT FROM AUTHOR]

Error correction has been one of the central themes in accounting for potential gains in second language (L2) writing. While a wide range of empirical studies focused on the effects of written corrective feedback (WCF) on learners' writing outcomes, few of them have specifically examined the feedback process, or its potential role as a shaping force in learner development. Underpinned by a sociocultural framework of L2 acquisition, the present study examines learner development as mutually mediated by teacher feedback and learner response during dyadic sessions of error correction. The socioculturally informed approach, dynamic corrective feedback (DCF), operates via dialogic, collaborative negotiation over errors between an expert and a novice, in which feedback is scaffolded and tailored toward learner competence. Data collected from three Chinese EFL learners' written texts and writing tutorial sessions show that learner idiosyncrasies are evident in development trajectory, and that development is not always linear but recursive. It is suggested that teachers attend to individual needs and abilities when providing feedback so as to facilitate sustainable development. [ABSTRACT FROM AUTHOR]

To solve the problems of the existing indoor localization methods with high cost, low precision and accumulated error, a vehicle localization method integrating dead reckoning (DR) algorithm, motion state identification and correction points and sensors information was proposed. The DR algorithm was used to calculate the approximate position of the vehicle for detecting the vehicle whether at correction point, and the trajectory was corrected according to the correction point information and sensor data by the motion state recognition technology. The data collected by low precision inertial sensor were simulated, and the trajectory was tracked. The results show that by eliminating the cumulative error at the correction point, the proposed method can effectively improve the localization accuracy and meet the localization needs of vehicles using low-accuracy inertial sensor without global positioning system (GPS). [ABSTRACT FROM AUTHOR]

This article presents the design of the modified error detection and localization algorithm in the Residue Number System (RNS). Classical redundant RNS with one control modulus can detect one error but not localize it. Two control moduli are used to localize a single error. Presented algorithm can achieve an error correction with a single control modulus transmitted over a reliable communication channel. The proposed approach was veryfied using Verilog on ASIC in RTL and physical synthesis tool Cadence Genus Synthesis Solution. It significantly reduces the area of the hardware implementation increasing the packing density and more efficient use of silicon resource. It slightly increases the running time compared with the classical algorithm. Distributed data storage was developed to study efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In order to achieve a more efficient and accurate correction of near‐field error in the semiphysical radio frequency simulation system, the precise control parameters of the three antenna elements need to be obtained. This article is based on the method of moments electromagnetic simulation, and propose corresponding improvement ideas for the problems of limited optimization accuracy and low calculation efficiency in the near‐field error correction process. From the aspects of high‐precision intelligent inversion algorithm and high‐efficiency electromagnetic forward modeling, systematic optimization design and verification were carried out. The results prove that the control parameter filtering scheme based on PSO‐GA hybrid method has better optimization efficiency and accuracy than single genetic algorithm or differential evolution algorithm, which can provide more ideal initial amplitude and phase parameters for the subsequent selection of electromagnetic simulation and forward verification. In order to solve the problem of time‐consuming in the electromagnetic simulation, the multivariate vector forward model based on GA‐BP network and PSO‐SVM network are established, which can achieve high‐precision positioning of synthetic vector target points. The neural network method has been proved to be feasible on the basis of the current sample size. The paper selects hybrid algorithms to improve the shortcomings of single algorithm and uses algorithms to optimize neural networks, thereby obtaining better optimization results and reducing the time‐consuming of electromagnetic simulations, which can realize efficient correction of near‐field error. [ABSTRACT FROM AUTHOR]

The instability of wind power poses a great threat to the security of the power system, and accurate wind power prediction is beneficial to the large-scale entry of wind power into the grid. To improve the accuracy of wind power prediction, a short-term multi-step wind power prediction model with error correction is proposed, which includes complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), sample entropy (SE), improved beetle antennae search (IBAS) and kernel extreme learning machine (KELM). First, CEEMDAN decomposes the original wind power sequences into a set of stationary sequence components. Then, a set of new sequence components is reconstructed according to the SE value of each sequence component to reduce the workload of subsequent prediction. The new sequence components are respectively sent to the IBAS-KELM model for prediction, and the wind power prediction value and error prediction value of each component are obtained, and the predicted values of each component are obtained by adding the two. Finally, the predicted values of each component are added to obtain the final predicted value. The prediction results of the actual wind farm data show that the model has outstanding advantages in high-precision wind power prediction, and the error evaluation indexes of the combined model constructed in this paper are at least 34.29% lower in MAE, 34.53% lower in RMSE, and 36.36% lower in MAPE compared with other models. prediction decreased by 30.43%, RMSE decreased by 29.67%, and MAPE decreased by 28.57%, and the error-corrected three-step prediction decreased by 55.60%, RMSE decreased by 50.00%, and MAPE decreased by 54.17% compared with the uncorrected three-step prediction, and the method significantly improved the prediction accuracy. [ABSTRACT FROM AUTHOR]

Consider the compressed sensing setup where the support $\mathbf {s}^{\ast}$ of an $m$ -sparse $d$ -dimensional signal $\mathbf {x}$ is to be recovered from $n$ linear measurements with a given algorithm. Suppose that the measurements are such that the algorithm does not guarantee perfect support recovery and that true features may be missed. Can they efficiently be retrieved? We address this question through a simple error-correction module referred to as LiRE. LiRE takes as input an estimate $\mathbf {s}_{\text {in}}$ of the true support $\mathbf {s}^{\ast}$ , and outputs a refined support estimate $\mathbf {s}_{\text {out}}$. We establish sufficient conditions under which LiRE is guaranteed to recover the entire support, that is $\mathbf {s}_{\text {out}}\supseteq \mathbf {s}^{\ast} $. These conditions imply, for instance, that in high dimension LiRE can correct a sublinear in $m$ number of errors made by Orthogonal Matching Pursuit (OMP). The computational complexity of LiRE is ${\mathcal{O}}(m n d)$. Experimental results with random Gaussian design matrices show that LiRE substantially reduces the number of measurements needed for perfect support recovery via Compressive Sampling Matching Pursuit, Basis Pursuit (BP), and OMP. Interestingly, adding LiRE to OMP yields a support recovery procedure that is more accurate and significantly faster than BP. This observation carries over in the noisy measurement setup where the combination of LiRE and OMP is faster and more accurate than LASSO. Finally, as a standalone support recovery algorithm with a random initialization, experiments show that LiRE’s support recovery performance lies between OMP and BP. These results show that LiRE can be used generically, on top of any suboptimal baseline support recovery algorithm, to improve support recovery or to operate with a smaller number of measurements, at the cost of a relatively small computational overhead. Alternatively, LiRE may be used as a standalone support recovery algorithm that is competitive with respect to OMP. [ABSTRACT FROM AUTHOR]

Multilevel modelling (MLM) is the most frequently used approach for evaluating interventions with clustered data. MLM, however, has some limitations that are associated with numerous obstacles to model estimation and valid inferences. Longitudinal multiple-group (LMG) modelling is a longstanding approach for testing intervention effects using cluster-sampled data that has been superseded by the rise of MLM approaches, but the LMG approach can have advantages when research questions do not pertain to predicting variability at the higher levels. In this paper, we first review the advantages and limitations of MLM and LMG approaches. Second, steps in the estimation of an LMG model are presented, with some recent upgrades and changes in the modelling strategy that have particular utility for evaluating interventions. We discuss the advantages of the LMG approach as a guided confirmatory model-testing framework and how the approach places a premium on avoiding Type II errors, particularly when complex interactions are potentially at play. [ABSTRACT FROM AUTHOR]

The seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) has shown promising results in modeling small and sparse observed time-series data by capturing linear features using independent and dependent variables. Long short-term memory (LSTM) is a promising neural network for learning nonlinear dependence features from data. With the increase in wildlife roadkill patterns, the SARIMAX-only and LSTM-only models would likely fail to learn the precise endogenous and/or exogenous variables driven by this wildlife roadkill data. In this paper, we design and implement an error correction mathematical framework based on LSTM-only. The framework extracts features from the residual error generated by a SARIMAX-only model. The learned residual features correct the output time-series prediction of the SARIMAX-only model. The process combines SARIMAX-only predictions and LSTM-only residual predictions to obtain a hybrid SARIMAX-LSTM. The models are evaluated using South African wildlife–vehicle collision datasets, and the experiments show that compared to single models, SARIMAX-LSTM increases the accuracy of a taxon whose linear components outweigh the nonlinear ones. In addition, the hybrid model fails to outperform LSTM-only when a taxon contains more nonlinear components rather than linear components. Our assumption of the results is that the collected exogenous and endogenous data are insufficient, which limits the hybrid model's performance since it cannot accurately detect seasonality on residuals from SARIMAX-only and minimize the SARIMAX-LSTM error. We conclude that the error correction framework should be preferred over single models in wildlife time-series modeling and predictions when a dataset contains more linear components. Adding more related data may improve the prediction performance of SARIMAX-LSTM. [ABSTRACT FROM AUTHOR]

The third-generation sequencing technology has advanced genome analysis with long-read length, but the reads need error correction due to the high error rate. Error correction is a time-consuming process especially when the sequencing coverage is high. Generally, for a pair of overlapping reads A and B, the existing error correction methods perform a base-level alignment from B to A when correcting the read A. And another base-level alignment from A to B is performed when correcting the read B. However, based on our observation, the base-level alignment information can be reused. In this article, we present a fast error correction tool Fec, using two-rounds overlapping and caching. Fec can be used independently or as an error correction step in an assembly pipeline. In the first round, Fec uses a large window size (20) to quickly find enough overlaps to correct most of the reads. In the second round, a small window size (5) is used to find more overlaps for the reads with insufficient overlaps in the first round. When performing base-level alignment, Fec searches the cache first. If the alignment exists in the cache, Fec takes this alignment out and deduces the second alignment from it. Otherwise, Fec performs base-level alignment and stores the alignment in the cache. We test Fec on nine datasets, and the results show that Fec has 1.24–38.56 times speed-up compared to MECAT, CANU and MINICNS on five PacBio datasets and 1.16–27.8 times speed-up compared to NECAT and CANU on four nanopore datasets. Availability and implementation Fec is available at https://github.com/zhangjuncsu/Fec. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

To better understand the actual rating variables that affects Auto insurance Policyholders' premium, this paper attempts to provide empirical evidence to justify which ones are significant and needed to be considered by insurers by adopting the autoregressive distributed lag (ARDL) model. In satisfying all the conditions for ARDL application, unit root, Heteroskedasticity, normality, dynamic stability and serial correlation tests were conducted. We estimate the effects of each rating variable on Premium taking into consideration whether the policy of the insured is Third-Party or Comprehensive. These rating variables in the ARDL model serves as the independent variables that establishes the short and long-run relationships between them and the Premium as the dependent variable. The results suggest that not all the classical rating variables used in the market significantly impact Premium. Whiles, to some extent, we found a varying degree of variables impact on Premium depending on the insurance type, the autos cubic capacity, which plays a cogent role on the basic Premium in Ghana, is insignificant. Also, policyholders' age characteristics are statistically significant but are excluded in the premium calculations. Thus, this paper shows the need to consider all the other possible rating variables, including policyholders' age into the Ghanaian insurance pricing system, whiles autos cubic capacity considering the weight it put on the basic Premium should be re-examined. This would help to obtain a financially balanced and optimal pricing system for policyholders. [ABSTRACT FROM AUTHOR]

Owing to the effect of data retention noise in multi-level-cell NAND flash memory, the initial threshold-voltage distributions and read voltages can no longer be used to accurately calculate log-likelihood ratios (LLRs) as the retention time increases, thus causing retention errors. To solve this problem, we first utilize the so-called “correction factors” to optimize the LLR accuracy by maximizing the achievable rate of a flash-memory system without introducing extra memory-sensing operations. We further prove that the optimization of the correction factors is a convex optimization problem and can be solved analytically. To obtain the optimal correction factors, we propose two retention-error correction schemes, referred to as offline maximum-achievable-rate correction (MARC) algorithm and online MARC algorithm, which enable the flash-memory controller to utilize the corrected LLRs that are stored in a look-up table and correct the inaccurate LLRs in real time, respectively. Motivated by the variation characteristics of the threshold-voltage distributions, we also propose an enhanced expectation–maximization (EM) algorithm to reestimate their corresponding parameters, and then adjust the read voltages. By combining the enhanced EM algorithm with the MARC algorithms, an enhanced EM-based correction strategy is developed to further boost the retention-error endurance of flash memory while avoiding excessive memory-sensing overhead. Theoretical analyses and simulation results illustrate the superiority of the proposed correction mehtods in terms of the robustness against retention errors. [ABSTRACT FROM AUTHOR]

In mobile communications, plenty of textual messages need to be transmitted and processed rapidly. However, messages usually contain noise, which will affect the performance of related applications. Thus, we investigate grammatical error correction (GEC) to correct errors in messages. Unlike recent works, we focus on improving the efficiency of GEC because low time delay is significant in mobile communications. We propose a novel multi-task learning approach to GEC by detecting errors first and then making corrections. Two classifiers are used to serially detect sentence-level and token-level errors, so the correct content can be free from correction operations. We adapt a non-autoregressive decoder to parallelly generate corrected tokens, making the correction stage efficient. Experiments show that our approach is ten times faster than the traditional approach and can achieve a comparable GEC performance. [ABSTRACT FROM AUTHOR]

Miniaturization trends in high-frequency electronics have led to accommodation challenges in the integration of the corresponding components. Size reduction thereof has become a practical necessity. At the same time, the increasing performance demands imposed on electronic systems remain in conflict with component miniaturization. On the practical side, the challenges related to handling design constraints are aggravated by the high cost of system evaluation, normally requiring full-wave electromagnetic (EM) analysis. Some of these issues can be alleviated by implicit constraint handling using the penalty function approach. Yet, its performance depends on the arrangement of the penalty factors, necessitating a costly trial-and-error procedure to identify their optimum setup. A workaround is offered by the recently proposed algorithms with automatic adaptation of the penalty factors using different adjustment schemes. However, these intricate strategies require a continuous problem-dependent adaptation of the penalty function throughout the entire optimization process. Alternative methodologies have been proposed by taking an explicit approach to handle the inequality constraints, along with correction-based control over equality conditions, the combination of which proves to be demonstrably competitive for some miniaturization tasks. Nevertheless, optimization-based miniaturization, whether using implicit or explicit constraint handling, remains a computationally expensive task. A reliable way of reducing the aforementioned costs is the incorporation of multi-resolution EM fidelity models into the miniaturization procedure. Therein, the principal operation is based on the simultaneous monitoring of factors such as quality of the constraint satisfaction, as well as algorithm convergence status. This paper provides an overview of the abovementioned size-reduction algorithms, in which theoretical considerations are illustrated using a number of antenna and microwave circuit case studies. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1038/s41598-022-11422-2. Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-022-11422-2, published online 27 May 2022 In the original version of this Article, Figures 14, 15 and 16 were incorrect. [Extracted from the article]