Correction Method Research Articles

Analysis of the influence of macroeconomic factor on long-term and short-term fluctuation of the Indonesian stock exchange (BEI) using the error correction method (ECM) approach

This research aims to empirically test the influence of the short-term equilibrium relationship and long-term equilibrium relationship of economic growth, interest rate, inflation, and exchange rate on IHSG (IDX Composite) using empirical Error Correction Model (ECM) data from 2000 to 2023. During the observation period, the macroeconomic variables showed no significant influence on the movement of the IHSG (IDX Composite) in the short-term equilibrium relationship, whereas in the long-term equilibrium relationship almost all variables, namely economic growth, interest rate, and exchange rate, had a significant influence on the IHSG ( IDX Composite), while inflation has no short or long term effect on the IHSG.

Automatic Defects Recognition of Lap Joint of Unequal Thickness Based on X-Ray Image Processing

It is difficult to automatically recognize defects using digital image processing methods in X-ray radiographs of lap joints made from plates of unequal thickness. The continuous change in the wall thickness of the lap joint workpiece causes very different gray levels in an X-ray background image. Furthermore, due to the shape and fixturing of the workpiece, the distribution of the weld seam in the radiograph is not vertical which results in an angle between the weld seam and the vertical direction. This makes automatic defect detection and localization difficult. In this paper, a method of X-ray image correction based on invariant moments is presented to solve the problem. In addition, a novel background removal method based on image processing is introduced to reduce the difficulty of defect recognition caused by variations in grayscale. At the same time, an automatic defect detection method combining image noise suppression, image segmentation, and mathematical morphology is adopted. The results show that the proposed method can effectively recognize the gas pores in an automatic welded lap joint of unequal thickness, making it suitable for automatic detection.

Knowledge, attitudes and practices of the healthcare staff and students about disposal of unwanted medications: a systematic review

Abstract Introduction Improper disposal of medications poses a significant environmental risk on the ecosystems and human beings1. When medicines are discarded by flushing them down the toilet or throwing them in the household trash, their active components may leak into the water supply2. Aim This systematic review seeks to compile studies that examine healthcare professionals' and students' knowledge, attitudes, and practices (KAP) regarding medication disposal. It also aims to explore recommendations and barriers related to appropriate medication disposal. Methods A systematic review was conducted by adhering to guidelines outlined in Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 and the Joanna Briggs Institute. The primary outcome is to measure the levels of the healthcare staff and students' knowledge, as well as their attitudes, and practices about disposal of unwanted medications. Authors included qualitative, quantitative, and mixed-method primary research studies. Participants included pharmacists, doctors, nurses, and both undergraduate and postgraduate medical, pharmacy, and nursing students due to their significant involvement with medication and its disposal in healthcare settings. Data extraction was conducted by four of the researchers independently. The study details were categorised into three main domains i.e., knowledge, attitude, and practice using the KAP model. Other relevant information was also extracted, thematically, such as challenges and recommendations. Ethical approval was not required as this systematic review used previously published data. Results 37 studies from 18 countries (21 from Asia, 7 from the US, 5 from Africa, and 2 each from the EU and South America) met the inclusion criteria. 86.5% (n=32) investigated participants' knowledge of medication disposal. Although there is a good level of awareness about the environmental impacts, there are significant gaps in knowledge regarding correct disposal methods, available services, guidelines, and training. 30 studies explored participants' attitudes toward medication disposal. The results indicate a generally positive attitude towards the need for environmentally safe disposal practices. 35 studies evaluated participants' practices in relation to medication disposal. Although there was generally a positive attitude and some understanding of appropriate disposal methods, majority of the participants do not follow the practice guidelines, especially outside healthcare settings. This inconsistency may be due to numerous factors such as inadequate practical training, limited resources, or unclear guidelines. Discussion/ Conclusion Overall, it was found that while healthcare staff and students have fair knowledge and positive attitudes toward medicine disposal, their actual practices are lacking. One significant challenge identified is the limited awareness about proper disposal methods coupled with a lack of established services or guidelines in many regions. Even in cases where take-back programs are available, they often face issues with accessibility. To tackle these challenges, it is suggested by the studies that governmental bodies should establish and enforce clear policies on medication disposal while also expanding educational initiatives to increase understanding among healthcare professionals and students about appropriate disposal methods. Furthermore, improving access to take-back programs is crucial for ensuring safe medication disposal and minimising potential environmental and health hazards.

Enhancing Open-Space Gas Detection Limit: A Novel Environmentally Adaptive Infrared Temperature Prediction Method for Uncooled Spectroscopy

Gas cloud imaging with uncooled infrared spectroscopy is influenced by ambient temperature, complicating the quantitative detection of gas concentrations in open environments. To solve the aforementioned challenges, the paper analyzes the main factors influencing detection errors in uncooled infrared spectroscopy gas cloud imaging and proposes a temperature correction method to address them. Firstly, to mitigate the environmental effects on the radiative temperature output of uncooled infrared detectors, a snapshot-based, multi-band infrared temperature compensation algorithm incorporating environmental awareness was developed. This algorithm enables precise infrared radiation prediction across a wide operating temperature range. Validation tests conducted over the full temperature range of 0 °C to 80 °C demonstrated that the prediction error was maintained within ±0.96 °C. Subsequently, temperature compensation techniques were integrated, resulting in the development of a comprehensive uncooled infrared spectroscopy gas cloud imaging detection method. Ultimately, the detection limits for SF6, ethylene, cyclohexane, and ammonia were enhanced by 50%, 33%, 25%, and 67%, respectively.

A partitioned functional-decomposition scheme for modelling wave-ship-sloshing interaction

A novel partitioned function decomposition method is proposed and validated for accurately and effectively solving the interaction between waves and ships with sloshing effect. This scheme employs a hybrid functional-decomposition method that utilizes both potential and viscous flows to accurately and efficiently solve the wave-ship interaction in the external domain, while employing the original viscous method for simulating sloshing in the internal domain. To further enhance computational efficiency, a single-phase Level-set method is employed. Firstly, the issue of non-conservation in the level-set model is addressed through the application of a mass correction method. Additionally, based on the principle of mass conservation, modifications are made to the boundary conditions for free surface motion. As a result, an improved single-phase Level-set method is developed, which combines jump condition correction and mass correction. Through simulations involving linear and nonlinear free surfaces, impulsive pressures, overall forces on the tank, as well as comparisons with experimental data, it is observed that the proposed Level-set method effectively solves the sloshing in the internal domain, a problem for which the traditional single-phase Level-set method often fails to tackle with. Subsequently, by adopting a multi-block grid technique to mark tank and non-tank grid blocks and integrating the improved single-phase Level-set method with the SWENSE model, a partitioned function decomposition method is established to handle ship motions with sloshing effect in waves. It is found that simultaneously solving the internal and external flow problems using traditional implicit and explicit motion-solving methods poses certain challenges. Therefore, an implicit-inner-iteration solution method is proposed. By combining the proposed motion-solving method with the partitioned function decomposition model, satisfactory results are achieved for the wave-ship-sloshing interaction.

A second-order correction method for loosely coupled discretizations applied to parabolic–parabolic interface problems

A second-order correction method for loosely coupled discretizations applied to parabolic–parabolic interface problems

Color‐Accurate Camera Capture with Multispectral Illumination and Multiple Exposures

AbstractCameras cannot capture the same colors as those seen by the human eye because the eye and the cameras' sensors differ in their spectral sensitivity. To obtain a plausible approximation of perceived colors, the camera's Image Signal Processor (ISP) employs a color correction step. However, even advanced color correction methods cannot solve this underdetermined problem, and visible color inaccuracies are always present. Here, we explore an approach in which we can capture accurate colors with a regular camera by optimizing the spectral composition of the illuminant and capturing one or more exposures. We jointly optimize for the signal‐to‐noise ratio and for the color accuracy irrespective of the spectral composition of the scene. One or more images captured under controlled multispectral illuminants are then converted into a color‐accurate image as seen under the standard illuminant of D65. Our optimization allows us to reduce the color error by 20–60% (in terms of CIEDE 2000), depending on the number of exposures and camera type. The method can be used in applications in which illumination can be controlled, and high colour accuracy is required, such as product photography or with a multispectral camera flash. The code is available at https://github.com/gfxdisp/multispectral_color_correction.

A robust approach for balancing the color and light integrity of underwater images

Abstract As light travels under the deep water, it scatters and is absorbed, resulting in a loss of intensity and altered color perception—a phenomenon known as underwater light attenuation. Images captured under these low light conditions suffered from color distortions, as you go deeper, colors fade in this order: red, orange, and yellow, while green and blue become more prominent. The red channel experiences significant attenuation due to the scattering properties of light under the deep water. As a consequence, deep water images often display noticeable color casts. Researchers encounter various challenges when enhancing low-light underwater images, such as reduced contrast, detail loss, artifacts, noise, and color distortion. In this paper, we present a novel Adaptive Color and Light Correction (ACLC) method for color correction and an Intuitionistic Fuzzy Generator (IFG) for enhancing low-light underwater images. The proposed Adaptive Color and Light Correction (ACLC) method tackles color casts on individual pixels by considering the scene depth. The proposed Intuitionistic Fuzzy Generator (IFG) method balances the image contrast by computing an intuitionistic fuzzy image representation using the proposed IFG approach. Experimental results reveal that the proposed approach significantly improves the color quality and contrast of the output image. The proposed ACLC and IFG methods exceed existing underwater color correction and low-light image enhancement techniques in visual and quantitative evaluations, as evidenced by extensive experimentation on well-established underwater image datasets, such as UIEB, Ocean dark, and LSUI.

Real-time correction of gain nonlinearity in electrostatic actuation for whole-angle micro-shell resonator gyroscope

MEMS gyroscopes are well known for their outstanding advantages in Cost Size Weight and Power (CSWaP), which have inspired great research attention in recent years. A higher signal-to-noise ratio (SNR) for MEMS gyroscopes operating at larger vibrating amplitudes provides improved measuring resolution and ARW performance. However, the increment of amplitude causes strong nonlinear effects of MEMS gyroscopes due to their micron size, which has negative influences on the performance. This paper carries out detailed research on a general nonlinear mechanism on the sensors using parallel-plate capacitive transducers, which is called the gain nonlinearity in electrostatic actuation. The theoretical model established in this paper demonstrates the actuation gain nonlinearity causes the control-force coupling and brings extra angle-dependent bias with the 4th component for the whole-angle gyroscopes, which are verified by the experiments carried out on a micro-shell resonator gyroscope (MSRG). Furthermore, a real-time correction method is proposed to restore a linear response of the electrostatic actuation, which is realized by the gain modification with an online parameter estimation based on the harmonic-component relationship of capacitive detection. This real-time correction method could reduce the 4th component of the angle-dependent bias by over 95% from 0.003°/s to less than 0.0001°/s even under different temperatures. After the correction of actuation gain nonlinearity, the bias instability (BI) of whole-angle MSRG is improved by about 3.5 times from 0.101°/h to 0.029°/h and the scale factor nonlinearity (SFN) is reduced by almost one order of magnitude from 2.02 ppm to 0.21 ppm.

Nonlinear correction method of digital fringe projection 3D measurement system based on precise precoding

Nonlinear correction method of digital fringe projection 3D measurement system based on precise precoding

Structural epilepsy: current state of the problem

This review emphasizes modern conceptions in the fundamental and clinical aspects of structural epilepsy. Highly informative neuroimaging, electrophysiological and pathomorphological diagnostic methods contribution to the study of this problem is noted. Current understanding of the pathogenesis and clinical picture of the most common types of drug resistant epilepsy are presented: hippocampal sclerosis, focal cortical dysplasia and their surgical correction methods. We considered perspective directions of fundamental clinical and experimental research, focusing on the study of epileptogenesis mechanisms and developing effective methods for prevention and treatment.

A gas-surface interaction algorithm for discrete velocity methods in predicting rarefied and multi-scale flows: For Maxwell boundary model

The discrete velocity method (DVM) for rarefied flows and the unified methods (based on the DVM framework) for flows in all regimes, from continuum one to free molecular one, have worked well as precise flow solvers over the past decades and have been successfully extended to other important physical fields. Both DVM and unified methods endeavor to model the gas-gas interaction physically. However, for the gas-surface interaction (GSI) at the wall boundary, they have only use the full accommodation boundary up to now, which can be viewed as a rough Maxwell boundary with a fixed accommodation coefficient (AC) at unity, deviating from the real value. For example, the AC for metal materials typically falls in the range of 0.8 to 0.9. To overcome this bottleneck and extend the DVM and unified methods to more physical boundary conditions, an algorithm for Maxwell boundary with an adjustable AC is established into the DVM framework. The Maxwell boundary model splits the distribution of the bounce-back molecules into specular ones and Maxwellian (normal) ones. Since the bounce-back molecules after the spectral reflection does not math with the discrete velocity space (DVS), both macroscopic conservation (from numerical quadrature) and microscopic consistency in the DVS are hard to achieve in the DVM framework. In this work, this problem is addressed by employing a combination of interpolation methods for mismatch points in DVS and an efficient numerical error correction method for micro-macro consistency. On the other hand, the current Maxwell boundary for DVM takes the generality into consideration, accommodating both the recently developed efficient unstructured velocity space and the traditional Cartesian velocity space. Moreover, the proposed algorithm allows for calculations of both monatomic gases and diatomic gases with internal degrees in DVS. Finally, by being integrated with the unified gas-kinetic scheme within the DVM framework, the performance of the present GSI algorithm is validated through a series of benchmark numerical tests across a wide range of Knudsen numbers.

Relative Radiometric Correction Method Based on Temperature Normalization for Jilin1-KF02

The optical remote sensors carried by the Jilin-1 KF02 series satellites have an imaging resolution better than 0.5 m and a width of 150 km. There are radiometric problems, such as stripe noise, vignetting, and inter-slice chromatic aberration, in their raw images. In this paper, a relative radiometric correction method based on temperature normalization is proposed for the response characteristics of sensors and the structural characteristics of optical splicing of Jilin-1 KF02 series satellites cameras. Firstly, a model of temperature effect on sensor output is established to correct the variation of sensor response output digital number (DN) caused by temperature variation during imaging process, and the image is normalized to a uniform temperature reference. Then, the horizontal stripe noise of the image is eliminated by using the sensor scan line and dark pixel information, and the vertical stripe noise of the image is eliminated by using the method of on-orbit histogram statistics. Finally, the method of superposition compensation is used to correct the vignetting area at the edge of the image due to the lack of energy information received by the sensor so as to ensure the consistency of the image in color and image quality. The proposed method is verified by Jilin-1 KF02A on-orbit images. Experimental results show that the image response is uniform, the color is consistent, the average Streak Metrics (SM) is better than 0.1%, Root-Mean-Square Deviation of the Mean Line (RA) and Generalized Noise (GN) are better than 2%, Relative Average Spectral Error (RASE) and Relative Average Spectral Error (ERGAS) are greatly improved, which are better than 5% and 13, respectively, and the relative radiation quality is obviously improved after relative radiation correction.

A day-ahead wind speed correction method: Enhancing wind speed forecasting accuracy using a strategy combining dynamic feature weighting with multi-source information and dynamic matching with improved similarity function

Forecasting error of day-ahead wind speed (WS) seriously affects wind power integration and power system security and stability. In this regard, this paper fully considers the spatiotemporal correlation of wind farms (WFs) in different geographical locations, and proposes a day-ahead WS combined correction method that integrates multi-source station dynamic information weighting. Different from the previous WS correction methods, this paper fully considers the dynamic correlation of WS between the WFs, introduces an improved weighted similarity function to screen and dynamically weight the information of WFs with dynamic correlation, and introduces the dynamic weighting feature into the WS correction process. A combined decomposition mechanism is proposed, which combines sequential variational mode decomposition (SVMD) and feature mode decomposition (FMD) models to extract the most relevant trend components and non-stationary components of forecasted and measured WS. A combined correction model is introduced, and a combined architecture of Non-stationary Transformer combined with bidirectional long short-term memory network (Ns-Transformer-BILSTM) is used to correct the stationary WS component. A dynamic matching mechanism of fluctuation components considering improved similarity is proposed for the correction of non-stationary components. The proposed method is applied to several regional WFs in China. The experimental results show that the average correction of NRMSE, NMAE and R can reach 2.4 % ∼ 3.7 %, 2.0 % ∼ 3.0 % and 3.3 % ∼ 9.7 %, respectively. The NRMSE and NMAE corresponding to the corrected WS of certain individual WFs can be reduced by 10 % and 9 %, respectively, and R can be increased by 33 %.

Study on runoff forecasting and error correction driven by atmosphere–ocean-land dataset

Accurate runoff forecasting results can not only provide an important basis for flood control scheduling, but also provide scientific support for water resources optimization, which promotes the maximization of the overall benefits of the basin. To further explore the inherent mechanisms of the atmosphere–ocean-land factors driving runoff changes, this study proposes the factors dimension reduction and interpretation framework based on Pearson, eXtreme Gradient Boosting and SHapley Additive exPlanations (P-XGBoost-SHAP). Base on this, the Gaussian Process Regression (GPR), Long Short-Term Memory neural network (LSTM) and Support Vector Machine (SVM) models are used to construct the atmospheric-ocean-land data-driven runoff prediction model. Meanwhile, for the runoff prediction residuals, this paper proposes an error multi-step correction framework based on ensemble empirical mode decomposition and autoregressive model (EEMD-AR). The case study of Lianghekou hydrological station shows that the factor dimension reduction and interpretation framework can greatly reduce the input dimension of the model, and explain the factors globally and locally by using SHAP Value. Compared with the traditional Random Forest (RF) dimension reduction method, it shows higher prediction accuracy. The Nash-Sutcliffe efficiency coefficient (NSE) can be increased to about 0.93, which is 4.91 % and 1.97 % higher than the series–parallel coupling (AR-Parallel) and empirical mode decomposition-autoregressive (EMD-AR) correction methods, respectively. The accuracy of the runoff forecasting prediction is improved while reducing the input dimension of the model.

Qubit: The Leap into Quantum Computation

Abstract. This article delves into the fundamental aspects of qubits in quantum computation, emphasizing their coordination, mathematical representation through the Bloch Sphere, and the unique advantages and challenges of superconducting, trapped ion, and photonic qubits. It highlights the transformative potential of quantum algorithms in solving complex problems in cryptography and information security, while addressing key sources of quantum errors such as decoherence and quantum noise. The study discusses advanced error correction methods and underscores the necessity of improving qubit coherence, stability, scalability, and integration for the practical application and commercialization of quantum computers. Ongoing research and collaboration are deemed essential for overcoming technical challenges and unlocking the full potential of quantum computation across various industries.

Generalized, Three-Dimensional Wind Tunnel Wall Correction Using Nonlinear Least-Squares Optimization

A novel three-dimensional technique for correcting wind tunnel measurements for wall interference has been developed. The flow around the test body and its wake is approximated as a series of inviscid singularities, while the wind tunnel walls and model support are approximated using a panel method. A nonlinear least-squares approach with trust-region reflective optimization is then used to obtain the strength and locations of singularities together with the domain boundary panel strengths by fitting to measured wall pressures. The technique is demonstrated using computational fluid dynamics for the test case of a well-documented slender delta wing body with high blockage and validated against both experiments and legacy data. The nonlinear least-squares wall correction method is shown to demonstrate a significant improvement in wall corrections compared to the classical two-variable technique.

High-sensitivity and spatial resolution benchtop cone beam XFCT imaging system with pixelated photon counting detectors using enhanced multipixel events correction method

Objective.High atomic number element nanoparticles have shown potential in tumor diagnosis and therapy. X-ray fluorescence computed tomography (XFCT) technology enables quantitative imaging of high atomic number elements by specifically detecting characteristic x-ray signals. The potential for further biomedical applications of XFCT depends on balancing sensitivity, spatial resolution, and imaging speed in existing XFCT imaging systems.Approach.In this study, we utilized a high-energy resolution pixelated photon-counting detector for XFCT imaging. We tackled degradation caused by multi-pixel events in the photon-counting detector through energy and interaction position corrections. Sensitivity and spatial resolution imaging experiments were conducted using PMMA phantoms to validate the effectiveness of the multi-pixel events correction algorithm.Main results.After correction, the system's sensitivity and spatial resolution have both improved. Furthermore, XFCT/CBCT dual-modality imaging of gadolinium nanoparticles within mice subcutaneous tumor was successfully achieved.Significance.These results demonstrate the preclinical research application potential of the XFCT/CBCT dual-modality imaging system in high atomic number nanoparticle-based tumor diagnosis and therapy.

Chaotic dynamics and optimal therapeutic strategies for Caputo fractional tumor immune model in combination therapy.

In this paper, a Caputo fractional tumor immune model of combination therapy is established. First, the stability and biological significance of each equilibrium point are analyzed, and it is demonstrated that chaos may arise under specific conditions. Combined with the mathematical definition of Caputo fractional differentiation (CFD), it is found that there is a high correlation between the chaotic phenomenon of the patient's condition and the sensitivity of the patient to the change in the state of the day. The bifurcation threshold of each parameter is determined through numerical simulation, and the Hopf bifurcation of direct competition coefficient and inhibition coefficient between tumor cells and host healthy cells is elaborated upon in detail. Subsequently, a novel method combining optimal control theory with the particle swarm optimization (PSO) algorithm is proposed for the optimal control of the tumor immune model in combination therapy. Finally, the Adams-Bashforth-Moulton (ABM) prediction correction method is utilized in numerical simulations which demonstrate that the introduction of the CFD alters the model dynamics. Furthermore, these results indicate that fractional calculus can effectively be applied to tumor immune models better to elucidate complex chaotic dynamics of tumor cell evolution. Concurrently, the PSO can be successfully integrated with optimal control theory to address optimization challenges in cancer treatment.

Image enhancement methods for inspection of planar and non-planar FRP structures using a noise-based microwave NDT inspection system

Microwave nondestructive testing (MNDT) includes inspection techniques that assess a particular material’s health status using low-power and contactless inspection systems. In near-field microwave inspections, imaging results are heavily influenced by the standoff distance parameter, i.e., the physical separation between the microwave sensor and the sample under test (SUT). Variations in the standoff distance during an inspection tend to cause defect masking of disbonds and delaminations in fiber-reinforced polymer (FRP) materials, causing defects to go undetected frequently. An MNDT near-field inspection system using noise waveforms is used to identify engineered internal defects within carbon fiber-reinforced polymer (CFRP) samples. Tactics utilizing Principal Component Analysis (PCA), Stacked Sparse Autoencoders (SSAEs), and an autoencoder network trained in a manner for anomaly detection are used to minimize the effects of standoff distance, reduce defect masking, and increase the ability to identify hidden defects. The samples tested are constructed to possess planar and non-planar geometries, such that the viability of the data-driven image enhancement and standoff distance correction methods are demonstrated with respect to a wide variety of in-situ inspection applications.