Verification Of Accuracy Research Articles

First PWV Retrieval Using MERSI‐LL Onboard FY‐3E and Cross Validation With Co‐Platform Occultation and Ground GNSS

AbstractFY‐3E plays a vital role in the meteorological global earth observing system. Precipitable water vapor (PWV) is an essential parameter for the water cycle and global climate change. Here, we carry out the PWV retrieval using the MERSI‐LL sensor onboard the FY‐3E satellite for the first time. The retrieval accuracy under different cloudage conditions is validated by the extra PWV from ground‐based GNSS and spaceborne occultation. For the results against ground‐based GNSS, the total accuracy shows an RMSE of 2.69–3.36 mm as the clouds increase, and correlation coefficients higher than 0.95. The spatial accuracy distribution indicates that inland stations have higher accuracy than the coast and island stations. As for the results against spaceborne occultation, the verification accuracy varies with the spatial pairing distance, showing poor accuracy in the low latitude area. This study can provide an essential reference for the community to understand the current water vapor inversion performance of MERSI‐LL.

Shape functions to scanner comparison

In the optical scanning, the processed data are in the form of point clouds that can be organised into the triangular meshes and can represent the surface with the given inaccuracy of the scanner. Meshes of special surfaces (such as sphere or ball bar) are used for the scanner calibration, qualification and verification. These artefacts restrict the calibration process, because their sizes must be periodically calibrated by the relevant institutions.The aim of this paper is to present new possibilities for optical laser scanner accuracy comparison. Three types of laser scanners with different accuracies were used. The calibration sphere was used to compare the results of the deviations (the commonly used method for scanner accuracy verification) from calibrated value with the results of new methods that use shape functions measuring distances, cone heights or curvatures. The measurement system analysis was used to verify that these functions can be used for scanners comparison. The comparison of shape distributions obtained from results of shape functions in the form of polylines was used to compare scanners according to their accuracies. The comparison of shape distribution results was done using ranges and Minkowski L1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${L}_{1}$$\\end{document} norm.

Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction

Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue. Approach. We proposed a Recon-Enhance two-stage deep learning method. In the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from raw acoustic signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification. Main results. The results evaluated on a dataset of 126 prostate cancer patients achieved an average root mean squared errors (RMSE) of 0.0292, and an average structural similarity index measure (SSIM) of 0.9618, out-performing related start-of-the-art methods. Qualitative results also demonstrated that our approach addressed the limit-view issue with more details reconstructed. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. Notably, the processing time was reduced to 6 s, demonstrating its feasibility for online 3D dose verification for prostate proton therapy. Significance. Our study achieved start-of-the-art performance in the challenging task of direct reconstruction from radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool for in vivo 3D proton dose verification to minimize the range uncertainties of proton therapy to improve its precision and outcomes.

Verification of neuronavigated TMS accuracy using structured-light 3D scans

Objective. To investigate the reliability and accuracy of the manual three-point co-registration in neuronavigated transcranial magnetic stimulation (TMS). The effect of the error in landmark pointing on the coil placement and on the induced electric and magnetic fields was examined. Approach. The position of the TMS coil on the head was recorded by the neuronavigation system and by 3D scanning for ten healthy participants. The differences in the coil locations and orientations and the theoretical error values for electric and magnetic fields between the neuronavigated and 3D scanned coil positions were calculated. In addition, the sensitivity of the coil location on landmark accuracy was calculated. Main results. The measured distances between the neuronavigated and 3D scanned coil locations were on average 10.2 mm, ranging from 3.1 to 18.7 mm. The error in angles were on average from two to three degrees. The coil misplacement caused on average a 29% relative error in the electric field with a range from 9% to 51%. In the magnetic field, the same error was on average 33%, ranging from 10% to 58%. The misplacement of landmark points could cause a 1.8-fold error for the coil location. Significance. TMS neuronavigation with three landmark points can cause a significant error in the coil position, hampering research using highly accurate electric field calculations. Including 3D scanning to the process provides an efficient method to achieve a more accurate coil position.

Adapting and translating the 'Hep B Story' App the right way: A transferable toolkit to develop health resources with, and for, Aboriginal people.

In 2014 the 'Hep B Story App', the first hepatitis B educational app in an Aboriginal language was released. Subsequently, in 2018, it was assessed and adapted before translation into an additional 10 Aboriginal languages. The translation process developed iteratively into a model that may be applied when creating any health resource in Aboriginal languages. The adaptation and translation of the 'Hep B Story' followed a tailored participatory action research (PAR) process involving crucial steps such as extensive community consultation, adaptation of the original material, forward and back translation of the script, content accuracy verification, voiceover recording, and thorough review before the publication of the new version. Iterative PAR cycles shaped the translation process, leading to a refined model applicable to creating health resources in any Aboriginal language. The community-wide consultation yielded widespread chronic hepatitis B education, prompting participants to share the story within their families, advocating for hepatitis B check-ups. The project offered numerous insights and lessons, such as the significance of allocating sufficient time and resources to undertake the process. Additionally, it highlighted the importance of implementing flexible work arrangements and eliminating barriers to work for the translators. Through our extensive work across the Northern Territory, we produced an educational tool for Aboriginal people in their preferred languages and developed a translation model to create resources for different cultural and linguistic groups. SO WHAT?: This translation model provides a rigorous, transferable method for creating accurate health resources for culturally and linguistically diverse populations.

Image enhancement in palmprint recognition: a novel approach for improved biometric authentication

Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8×7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.

Evaluation of 3D C-Arm Fluoroscopy versus Diagnostic CT for Deep Brain Stimulation Stereotactic Registration and Post-Operative Lead Localization

Introduction: DBS efficacy depends on accuracy. CT-MRI fusion is established for both stereotactic registration and electrode placement verification. The desire to streamline DBS workflows, reduce operative time, and minimize patient transfers has increased interest in portable imaging modalities such as the Medtronic O-arm® and mobile CT. However, these remain expensive and bulky. 3D C-arm fluoroscopy (3DXT) units are a smaller and less costly alternative, albeit incompatible with traditional frame-based localization and without useful soft tissue resolution. We aimed to compare fusion of 3DXT and CT with pre-operative MRI to evaluate if 3DXT-MRI fusion alone is sufficient for accurate registration and reliable targeting verification. We further assess DBS targeting accuracy using a 3DXT workflow and compare radiation dosimetry between modalities. Methods: Patients underwent robot-assisted DBS implantation using a workflow incorporating 3DXT which we describe. Two intra-operative 3DXT spins were performed for registration and accuracy verification followed by conventional CT post-operatively. Post-operative 3DXT and CT images were independently fused to the same pre-operative MRI sequence and co-ordinates generated for comparison. Registration accuracy was compared to 15 consecutive controls who underwent CT-based registration. Radial targeting accuracy was calculated and radiation dosimetry recorded. Results: Data were obtained from 29 leads in 15 consecutive patients. 3DXT registration accuracy was significantly superior to CT with mean error 0.22 ± 0.03 mm (p < 0.0001). Mean Euclidean electrode tip position variation for CT to MRI versus 3DXT to MRI fusion was 0.62 ± 0.40 mm (range 0.0 mm–1.7 mm). In comparison, direct CT to 3DXT fusion showed electrode tip Euclidean variance of 0.23 ± 0.09 mm. Mean radial targeting accuracy assessed on 3DXT was 0.97 ± 0.54 mm versus 1.15 ± 0.55 mm on CT with differences insignificant (p = 0.30). Mean patient radiation doses were around 80% lower with 3DXT versus CT (p < 0.0001). Discussion: Mobile 3D C-arm fluoroscopy can be safely incorporated into DBS workflows for both registration and lead verification. For registration, the limited field of view requires the use of frameless transient fiducials and is highly accurate. For lead position verification based on MRI co-registration, we estimate there is around a 0.4 mm discrepancy between lead position seen on 3DXT versus CT when corrected for brain shift. This is similar to that described in O-arm® or mobile CT series. For units where logistical or financial considerations preclude the acquisition of a cone beam CT or mobile CT scanner, our data support portable 3D C-arm fluoroscopy as an acceptable alternative with significantly lower radiation exposure.

Estimation of ecosystem water consumption and the suitable scale of cultivated land in the Karamay region and Muzat River basin based on the optimized SEBAL energy balance model and water resource constraint model

Food security is closely related to the development of human society, and the root of food production lies in cultivated land, with water conservancy as its lifeline. This study estimates the ecological water consumption of located in the arid region of Northwest China (the Karamay region and Muzat River basin) from 1990 to 2020 based on the optimized Land Surface Energy Balance Algorithm. The verification accuracy of SEBAL energy balance model is greatly improved after optimization. It was showed an increasing trend in the Karamay region and Muzat River basin, increasing at the rates of 2.84 mm/year and 2.86 mm/year respectively. The suitability of cultivated land was evaluated by combining four periods of 30 m spatial resolution land use/land cover data from 1990, 2000, 2010, and 2020, as well as the Hydrological Statistical Yearbook and Xinjiang Statistical Yearbook. Through the analysis of spatial optimization for cultivated land, it can be inferred that the primary limiting factors affecting cultivated land suitability in the Karamay region are irrigation guarantee rate (54.03%) and soil salinity (11.98%). Muzat River region are irrigation guarantee rate (32.19%) and soil salinity (18.62%). By comparing the scenarios of setting ecological priority and cultivated land priority, it is concluded that under the conditions of water resource constraints and 50%, 75% and 90% design irrigation assurance rates, Karamay still has cultivated land expansion potential, which can be used as the main preparatory reclamation area. In addition to the traditional agriculture irrigation area, the Muzat River basin still has development potential under the condition of ecological priority and no more than 75% irrigation design assurance rate. The study on the cultivated land suitability under the condition of water resource constraints can provide new ideas for food security.

Research on spacecraft in orbit perception based on artificial neural networks and digital twin technology using grating arrays

In order to improve the safety of spacecraft, the research on artificial neural network and digital twin technology based on, to our best knowledge, a novel fiber Bragg grating (FBG) sensor array is proposed for intelligent sensing monitoring of spacecraft on-orbit collisions. Femtosecond FBG arrays were fabricated on the novel oxide-doped fiber by point-by-point writing technique. The femtosecond FBG is analyzed using the time-dependent perturbation theory of quantum mechanics. The FBG array can achieve high-temperature measurement of 1100 °C and large strain measurement of 15000 µε. The sensing arrays were deployed on the surface of the spacecraft. Constructed the multi-layer perceptron neural network structure and convolutional neural network structure. 1200 samples were trained. Conducted model accuracy testing. The accuracy rate is above 98%, and accuracy verification has been implemented. The digital twin model was designed based on various data such as strain and temperature of the spacecraft structure under impact monitored by FBG sensors. A precise mapping has been formed between the physical entities of spacecraft and digital twins. Empower spacecraft with functions such as self-monitoring, judgment, and response. To ensure the stable and safe operation of spacecraft.

Social Implementation and Measurement Accuracy Verification of Non-contact Biological Monitors

In this research, for the purpose of social implementation, we conducted a near-miss demonstration experiment using a car driving in the city and a drive-simulator. Next, we conducted a demonstration experiment to evaluate the reliability of biometric information measured on patients in a medical facility's recuperation ward and residents of a special nursing home. NBC-1100 emits radar waves from a distance of up to 3 meters from an object and uses the reflected waves to measure biological information such as pulse waves and breathing waves. The multi-element pyroelectric effect was used to measure body temperature by detecting infrared radiation emitted from distant objects. This device is unique in that it can measure biological information without being restrained while wearing clothes. In the demonstration test, simultaneous measurements were conducted on nine healthy men aged 45 to 65 using a pharmaceutically approved product (μBP-mp) and a prototype non-contact biological monitor (NBC-1100 manufactured by K&amp;S Co., Ltd.). The demonstration experiments at medical institutions and nursing care facilities were conducted with the consent of residents and their families and were conducted on 30 men and women between the ages of 70 and 94 who were undergoing treatment or in need of care. The tests were conducted on residents with chronic diseases such as dilated gastrostomies, symptomatic epilepsy, hypertension, Alzheimer's disease, and progressive supranuclear palsy. The evaluation method was simultaneous measurement using a master meter (μBP-mp) and a test meter (NBC-1100).

Revisiting the Information Capacity of Neural Network Watermarks: Upper Bound Estimation and Beyond

To trace the copyright of deep neural networks, an owner can embed its identity information into its model as a watermark. The capacity of the watermark quantify the maximal volume of information that can be verified from the watermarked model. Current studies on capacity focus on the ownership verification accuracy under ordinary removal attacks and fail to capture the relationship between robustness and fidelity. This paper studies the capacity of deep neural network watermarks from an information theoretical perspective. We propose a new definition of deep neural network watermark capacity analogous to channel capacity, analyze its properties, and design an algorithm that yields a tight estimation of its upper bound under adversarial overwriting. We also propose a universal non-invasive method to secure the transmission of the identity message beyond capacity by multiple rounds of ownership verification. Our observations provide evidence for neural network owners and defenders that are curious about the tradeoff between the integrity of their ownership and the performance degradation of their products.

Research on the parameter design and calibration and control of EV in-vehicle active sound generation system

In order to improve the interior sound quality of electric vehicles (EVs) under acceleration and uniform speed conditions, to balance the comfort and dynamics of the interior sound, and to improve the accuracy and performance of the active sound generation system (ASGS), this article carries out the research related to the parameter design, sound calibration, evaluation methodology, and control system of the EV ASGS. Propose an in-vehicle sound design method focusing on three dimensions, including engine order composition, spectral energy distribution, and sound amplitude enhancement in the typical speed range, and determine the in-vehicle sound design scheme and the total sound value target. Focus on the sound parameter design, calibration and evaluation methods of EV ASGS considering the frequency response characteristics of the loudspeaker, sound amplitude control accuracy, sound quality, and psychoacoustic parameters, clarify the active sound parameter settings of EVs, complete the analysis of sound extraction methods, complete the engine order sound fitting, and design the ASGS of the EV interior by combining the subjective and objective evaluations. Develop the control software and hardware of the ASGS, complete the construction and accuracy verification of the ASGS based on the in-vehicle sound system, and realize the sound calibration of the ASGS under the static conditions of the real vehicle and the verification of the target achievement. The real-vehicle test shows that the ASGS reduces the sharpness of 1.0 acum and 0.52 acum under acceleration and constant speed conditions, respectively, and improves the comfort and dynamics of in-vehicle sound. The objective and subjective evaluation results show that the parameter design, selection and accuracy of the sound calibration and evaluation methods of the ASGS in the EV determines the accuracy and effect of the ASGS.

The Investigation of Multiple Optimization Methods on Convolutional Neural Network

In this study, the optimization of a Convolutional Neural Network (CNN) was conducted using the Fruits 360 dataset, with a specific emphasis on the impacts of Spatial Transformer Network (STN) and Stochastic Gradient Descent (SGD) optimization methods. Firstly, a baseline CNN model is built, which achieves 97.84% accuracy with a loss of 0.0999 after 50 epochs. Then, the impact of integrating STN and SGD into CNN models separately is investigated. The addition of STN slightly increased the accuracy to 97.92%, reduced the loss to 0.0994, and decreased the validation accuracy. This result suggests that while STN enhances the model's generalization ability, it may slightly reduce the maximum accuracy achievable on the validation set. After SGD optimization, the verification accuracy is increased to 98.19%, the loss is reduced to 0.0537, and the verification accuracy is increased to 98.40%. These results highlight the effectiveness of SGD in fine-tuning model parameters, resulting in more accurate models and improved generalization capabilities. A comparative analysis of these methods highlights their respective advantages. The effectiveness of the STN is rooted in its capacity to improve model generalization and mitigate overfitting, which is particularly beneficial in situations that demand robustness against varied data sets. In contrast, SGD stands out for its ability to significantly improve model accuracy and reduce loss, making it a balanced choice for comprehensive model optimization. Future research directions include exploring these optimization techniques on various datasets and investigating the potential of combining STN and SGD to achieve higher performance in CNN models.

Application of Amorphous and Nanocrystalline Soft Magnetic Materials in Balanced-Force-Type Electromagnetic Relay.

The magnetic properties of soft magnetic materials, including their saturation magnetic induction strength and permeability, significantly affect the dynamic characteristics of electromagnetic relays. However, the soft materials most commonly used for relays in the magnetic conductive components of electromagnetic systems, such as electrical pure iron, limit further relay design improvement and optimization to a certain extent. Thus, this paper proposes the use of amorphous and nanocrystalline soft magnetic materials with good high-frequency magnetic properties in magnetic circuits. A wavelet analysis was conducted on the high-frequency components of the coil current while the relay operated, and the corresponding magnetic materials were selected. Considering the challenges in processing amorphous and nanocrystalline materials and collecting test data for the accuracy verification of simulation methods, we prepared a scaled-up prototype for use in dynamic characteristic tests. The simulation method was improved, yielding more accurate simulation results regarding the relay's dynamic characteristics. On this basis, six replacement schemes using amorphous and nanocrystalline materials were considered. The test results proved that this application could improve the relay's dynamic characteristics. Finally, a full-size sample with an iron core consisting of nanocrystalline alloy 1K107B was prepared, and the conclusions were verified in tests.

A blockchain system supporting cross-border data protection and consistency verification in unified global carbon emissions trading framework

The Carbon Emission Trading (CET) system has been independently implemented in dozens of countries to protect the environment. Meanwhile, there are efforts underway to establish a Unified Global CET (UG-CET) system, which aims to better coordinate emission reduction efforts worldwide and protect the global environment. However, these efforts overlook the issue of data security, which may raise concerns for countries considering participation in such a system. In this paper, we propose a UG-CET system that addresses data security concerns. Firstly, it safeguards cross-border data by not requiring countries to share detailed data, but only providing generalized data. Secondly, it is capable of auditing data, ensuring data consistency even in the absence of shared original data. Therefore, it respects the interests of participating countries by not only protecting their own data but also ensuring the authenticity of data shared by other countries. Finally, the proposed scheme is validated using authentic CET data, and the results demonstrated that it can maintain a high efficiency while achieving a verification accuracy of 99.6%, effectively ensuring the security and consistency of the data flow in carbon emission trading.

Facial augmented reality based on hierarchical optimization of similarity aspect graph

BackgroundThe existing face matching method requires a point cloud to be drawn on the real face for registration, which results in low registration accuracy due to the irregular deformation of the patient's skin that makes the point cloud have many outlier points. MethodsThis work proposes a non-contact pose estimation method based on similarity aspect graph hierarchical optimization. The proposed method constructs a distance-weighted and triangular-constrained similarity measure to describe the similarity between views by automatically identifying the 2D and 3D feature points of the face. A mutual similarity clustering method is proposed to construct a hierarchical aspect graph with 3D pose as nodes. A Monte Carlo tree search strategy is used to search the hierarchical aspect graph for determining the optimal pose of the facial 3D model, so as to realize the accurate registration of the facial 3D model and the real face. ResultsThe proposed method was used to conduct accuracy verification experiments on the phantoms and volunteers, which were compared with four advanced pose calibration methods. The proposed method obtained average fusion errors of 1.13 ± 0.20 mm and 0.92 ± 0.08 mm in head phantom and volunteer experiments, respectively, which exhibits the best fusion performance among all comparison methods. ConclusionsOur experiments proved the effectiveness of the proposed pose estimation method in facial augmented reality.

A novel subset assignment and matching method for DIC measurement of complex deformation in aircraft braking process

This paper proposes a measurement method related to the braking deformation of a complex motion. During the braking process, the deformation of the wheel includes large amounts of movement, vibration, warping, and distortion. A novel subset assignment and correlation method is proposed to measure the complex deformation. The proposed method can greatly improve the accuracy and stability of the calculation of complex deformations by simplifying the complex deformation into translational deformations in logarithmic coordinate system. According to the simulation and actual experiments, the proposed method can be utilized to measure the deformations of up to 100% tensile strain under complex deformation. According to the accuracy verification experiment, the error of the proposed method is less than 50 με. The results show that the proposed method can effectively carry out structural deformation measurement in the complex motion and deformation process. The proposed method has great significance for structural performance analysis and optimization design considering complex motion and deformation.

High Accuracy of Clinical Verification of Electrohydrodynamic-Driven Nanobox-on-Mirror Platform for Molecular Identification of Respiratory Viruses.

The molecular detection of multiple respiratory viruses provides evidence for the rational use of drugs and effective health management. Herein, we developed and tested the clinical performance of an electrohydrodynamic-driven nanobox-on-mirror platform (E-NoM) for the parallel, accurate, and sensitive detection of four respiratory viral antigens. The E-NoM platform uses gold-silver alloy nanoboxes as the core material with the deposition of a silver layer as a shell on the core surfaces to amplify and enable a reproducible Raman signal readout that facilitates accurate detection. Additionally, the E-NoM platform employs gold microelectrode arrays as the mirror with electrohydrodynamics to manipulate the fluid flow and enhance molecular interactions for an improved biosensing response. The presence of viral antigens binds the nanobox-based core-shell nanostructure on the gold microelectrode and creates the nanocavity with extremely strong "hot spots" to benefit sensitive analysis. Significantly, in a large clinical cohort with 227 patients, the designed E-NoM platform demonstrates the capability of screening respiratory infection with achieved clinical specificity, sensitivity, and accuracy of 100.0, 96.48, and 96.91%, respectively. It is anticipated that the E-NoM platform can find a position in clinical usage for respiratory disease diagnosis.

Wind resistance performance analysis of metal roof system of the long-span integrated photovoltaic building

Wind resistance is an important factor in the operation of Building Integrated Photovoltaic (BIPV) systems, especially for long-span roofs, where lifting of the roof can result in significant economic losses. This paper proposes a periodic boundary numerical simulation method for long-span metal roof systems to address the problem of meshes and contact pairs in the numerical simulation of wind resistance. In order to calibrate the BIPV metal roof's material parameters, indoor tensile test was conducted, and the results of the global metal roof model were compared with those of the single metal roof model with periodic boundaries for verification of periodic boundary efficiency and accuracy. The wind-resistant capacity of laboratory and long-span sizes for the BIPV metal roof system are compared, and a conversion formula between them is proposed to enable the rapid calculation from the laboratory to long-span roof system. The results show that the periodic boundary is suitable for the analysis of wind resistance of the long-span BIPV metal roof. Wind resistance of the long-span BIPV metal roof system is 1.77 kPa, whereas that of the laboratory size is 4.50 kPa. According to the proposed formula, the wind-resistant capacity of the long-span metal roof can be converted from the laboratory test, which can provide technical support for the design of BIPV systems.

Normalized coefficients of prediction accuracy for comparative forecast verification and modeling

Normalized coefficients of prediction accuracy for comparative forecast verification and modeling