Multiple Verification Research Articles

Log Volume Measurement and Counting Based on Improved Cascade Mask R-CNN and Deep SORT

Logs require multiple verifications to ensure accurate volume and quantity measurements. Log end detection is a crucial step in measuring log volume and counting logs. Currently, this task primarily relies on the Mask R-CNN instance segmentation model. However, the Feature Pyramid Network (FPN) in Mask R-CNN may compromise accuracy due to feature redundancy during multi-scale fusion, particularly with small objects. Moreover, counting logs in a single image is challenging due to their large size and stacking. To address the above issues, we propose an improved log segmentation model based on Cascade Mask R-CNN. This method uses ResNet for multi-scale feature extraction and integrates a hierarchical Convolutional Block Attention Module (CBAM) to refine feature weights and enhance object emphasis. Then, a Region Proposal Network (RPN) is employed to generate log segmentation proposals. Finally, combined with Deep SORT, the model tracks log ends in video streams and counts the number of logs in the stack. Experiments demonstrate the effectiveness of our method, achieving an average precision (AP) of 82.3, APs of 75.3 for small, APm of 70.9 for medium, and APl of 86.2 for large objects. These results represent improvements of 1.8%, 3.7%, 2.6%, and 1.4% over Mask R-CNN, respectively. The detection rate reached 98.6%, with a counting accuracy of 95%. Compared to manually measured volumes, our method shows a low error rate of 4.07%.

Establishment and Research of Cotton Stalk Moisture Content–Discrete Element Parameter Model Based on Multiple Verification

Establishment and Research of Cotton Stalk Moisture Content–Discrete Element Parameter Model Based on Multiple Verification

A Non-Transferable Trade Scheme of Green Power Based on Blockchain

Power consumers can obtain authoritative green environmental value certification through green electricity trading, which plays an important role in improving the production competitiveness of enterprises, especially for international product trade affairs. However, the credibility of green electricity transactions faces serious challenges in the enterprise green authentication affairs, especially the user’s identity authentication, the traceability of green electricity transactions, and the standardization of green electricity transactions. Aiming to solve the certification and traceability problem of tradable green certificates, this paper proposes an integrated green certificate trading protocol, which solves its double-trading problem and helps to improve the credibility of renewable energy use. The main contribution is providing a solution based on the consortium blockchain technology to solve the main challenges mentioned above. The main solved scheme designs a series of protocols, which includes a purchase protocol, payment protocol, and non-transferable protocol. The whole process ensures the credibility, traceability, and non-transferability of green certificate trading. Multiple verification measures are adopted to address security and privacy challenges in green certificate management. Through security analysis, the protocol effectively defends against attacks such as double payments, transaction rollback, and transaction replays while ensuring users’ privacy.

Design of 1.1T/300mm Bitter-type magnet for gyro-traveling-wave tube with power consumption optimization theory.

To improve the portability of magnets in gyrotron devices, we designed a compact Bitter-type magnet with power consumption optimization theory. This magnet operates at room temperature in a small volume. The theory revises existing electromagnetic theory for non-uniform structural Bitter-type magnets and achieves the lowest energy consumption through iterative optimization. To extend the magnetic field homogeneity region, the ferromagnetic material armature is applied to the Bitter-type system without additional power consumption. Unlike previous manual designs, the proposed Bitter-type magnets can obtain optimal parameters with a significant reduction in computing time. Through the introduction of correction factors, we improve accuracy through multiple verifications of simulations and experiments. On this basis, a room-temperature Bitter-type magnet system for Ka-band fundamental mode gyrotron amplifiers is designed. Its maximum magnetic field strength is 1.1T, and the length of the homogeneity region is 300mm. Through optimization, its energy consumption is only 27.5 kW.

Exploring the Usefulness of Machine Learning Severe Weather Guidance in the Warn-on-Forecast System: Results from the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment

Abstract Artificial intelligence (AI) is gaining popularity for severe weather forecasting. Recently, the authors developed an AI system using machine learning (ML) to produce probabilistic guidance for severe weather hazards, including tornadoes, large hail, and severe winds, using the National Severe Storms Laboratory’s (NSSL) Warn-on-Forecast System (WoFS) as input. Known as WoFS-ML-Severe, it performed well in retrospective cases, but its operational usefulness had yet to be determined. To examine the potential usefulness of the ML guidance, we conducted a control and treatment (experimental) group experiment during the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (HWT-SFE). The control group had full access to WoFS, while the experimental group had access to WoFS and ML products. Explainability graphics were also integrated into the WoFS web viewer. Both groups issued 1-h convective outlooks for each hazard. After issuing their forecasts, we surveyed participants on their confidence, the number of products viewed, and the usefulness of the ML guidance. We found the ML-based outlooks outperformed non-ML-based outlooks for multiple verification metrics for all three hazards and were rated subjectively higher by the participants. However, the difference in confidence between the two groups was not significant, and the experimental group self-reported viewing more products than the control group. Participants had mixed sentiments toward explainability products as it improved their understanding of the input/output relationships, but viewing them added to their workload. Although the experiment demonstrated the usefulness of ML guidance for severe weather forecasting, there are avenues to improve upon the ML guidance, and more training and exposure are needed to exploit its benefits fully. Significance Statement We developed an artificial intelligence (AI) system to predict tornadoes, large hail, and damaging straight-line winds. The AI system was leveraged in real time during the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. This study reveals that forecasters using AI guidance produced more reliable and spatially accurate outlooks than those without. While AI and complementary explainability products did not reduce forecaster workload, both demonstrated great potential for improving severe weather forecasting. This research also highlights the importance of user feedback in refining AI tools for severe weather forecasting.

Evaluation of the consistency of the MRI- based AI segmentation cartilage model using the natural tibial plateau cartilage.

The study aims to evaluate the accuracy of an MRI-based artificial intelligence (AI) segmentation cartilage model by comparing it to the natural tibial plateau cartilage. This study included 33 patients (41 knees) with severe knee osteoarthritis scheduled to undergo total knee arthroplasty (TKA). All patients had a thin-section MRI before TKA. Our study is mainly divided into two parts: (i) In order to evaluate the MRI-based AI segmentation cartilage model's 2D accuracy, the natural tibial plateau was used as gold standard. The MRI-based AI segmentation cartilage model and the natural tibial plateau were represented in binary visualization (black and white) simulated photographed images by the application of Simulation Photography Technology. Both simulated photographed images were compared to evaluate the 2D Dice similarity coefficients (DSC). (ii) In order to evaluate the MRI-based AI segmentation cartilage model's 3D accuracy. Hand-crafted cartilage model based on knee CT was established. We used these hand-crafted CT-based knee cartilage model as gold standard to evaluate 2D and 3D consistency of between the MRI-based AI segmentation cartilage model and hand-crafted CT-based cartilage model. 3D registration technology was used for both models. Correlations between the MRI-based AI knee cartilage model and CT-based knee cartilage model were also assessed with the Pearson correlation coefficient. The AI segmentation cartilage model produced reasonably high two-dimensional DSC. The average 2D DSC between MRI-based AI cartilage model and the tibial plateau cartilage is 0.83. The average 2D DSC between the AI segmentation cartilage model and the CT-based cartilage model is 0.82. As for 3D consistency, the average 3D DSC between MRI-based AI cartilage model and CT-based cartilage model is 0.52. However, the quantification of cartilage segmentation with the AI and CT-based models showed excellent correlation (r = 0.725; P values < 0.05). Our study demonstrated that our MRI-based AI cartilage model can reliably extract morphologic features such as cartilage shape and defect location of the tibial plateau cartilage. This approach could potentially benefit clinical practices such as diagnosing osteoarthritis. However, in terms of cartilage thickness and three-dimensional accuracy, MRI-based AI cartilage model underestimate the actual cartilage volume. The previous AI verification methods may not be completely accurate and should be verified with natural cartilage images. Combining multiple verification methods will improve the accuracy of the AI model.

A Comparison of State-Specific Pediatric Emergency Medical Facility Recognition Programs, 2020.

Prior research suggests that the presence of state-specific pediatric emergency medical facility recognition programs (PFRPs) is associated with high emergency department (ED) pediatric readiness. The PFRPs aim to improve the quality of pediatric emergency care, but individual state programs differ. We aimed to describe the variation in PFRP characteristics and verification requirements and to describe the availability of pediatric emergency care coordinators (PECCs) in states with PFRPs. In mid-2020, we collected information about each PFRP from 3 sources: the state Emergency Medical Services for Children (EMSC) website, the EMSC Innovation and Improvement Center website, or via communication with the state's EMSC program manager. For each state with a PFRP, we documented program characteristics, including program start date, number of tiers, whether participation was required/optional, and requirements for verification. Overall, we identified 17 states with active PFRPs. Five states had only 1 tier or level of recognition whereas the others had multiple. All programs did require presence of a PECC for verification. However, some PRFPs with multiple verification tiers did not require presence of a PECC to achieve each level of verification. In states with PFRPs, EDs with higher total visit volumes, a separate pediatric ED area, located in the Northeast, and earlier program start date were all more likely to have a PECC. There is variation in state PFRPs, although all prioritize the presence of a PECC. We encourage further research on the effect of different aspects of PFRPs on patient outcomes.

Trust System- and Multiple Verification Technique-Based Method for Detecting Wormhole Attacks in MANETs

Trust System- and Multiple Verification Technique-Based Method for Detecting Wormhole Attacks in MANETs

Bilingual problem size effect: an ERP study of multiplication verification and production in two languages.

The problem size effect (PSE) is defined by better performance solving small problems (e.g., 2x4) than large problems (e.g., 8x9). For monolinguals, the PSE is larger when problems are presented in unfamiliar formats (e.g., written words), reflecting increased processing difficulty. Bilinguals are typically faster and more accurate at retrieving multiplication facts in the language of learning (LA+) than in their other language (LA-). We hypothesized that the less familiar arithmetic language (i.e., LA-) would elicit larger PSEs than LA+. Here, fluent Spanish-English bilingual adults verified spoken multiplication problems presented in LA+ and LA- while event-related potentials (ERPs) were recorded (Experiment 1A). To further promote language differences, we increased task difficulty by presenting problems at a faster pace (Experiment 1B) and requiring bilinguals to verbally produce solutions (Experiment 2). Language differences in performance were only observed for Experiment 2, where solutions were produced more slowly in LA- than LA+. In the ERPs, a PSE was driven by larger P300s for small than large solutions. A language effect was only observed under time pressure where LA- elicited a PSE at the 2nd operand. Additionally, the PSE was smaller for LA- at the solution. This suggests that categorizing multiplication facts is more effortful in LA-. In sum, very subtle language differences arise in fluent bilinguals when problems are more difficult, such as larger problems presented under time pressure in a weaker language. Critically, the effect of LA+ is at the level of response production and not access to the facts from memory.

An Enigma Called Homeopathy

When I speak of an enigma, I adhere to its definition; the one given by the RAE (Royal Spanish Academy), which means: a statement whose meaning is artificially concealed to make it difficult to understand or interpret. Homeopathy (pathos=disease, homoios=similar); was created by the German Doctor Samuel Hahnemann in 1796. He was a man with a restless spirit and deeply dissatisfied with late 18th century medicine. After multiple verifications, Hahnemann creates the law that supports the homeopathic doctrine, the Law of Similars. It states that: ‘‘in order to cure a given illness, a remedy must be taken which, when administered to a healthy subject, will cause illnesses ‘and if it is capable of making one sick, it is also capable of curing. This principle of dilution accompanies the Law of Similars [1].

Planning for Service Line Material Identification and Lead Service Line Replacement Costs

Key TakeawaysA utility survey was performed to gather cost data and information on identification and verification methods.In compiling a service line inventory, water utilities need to account for using multiple verification methods at some locations to improve accuracy.Costs vary significantly between verification methods for developing accurate service line inventories and should be accounted for in a water system's planning efforts.The costs for lead service line replacements are estimated to be higher than originally reported when accounting for auxiliary costs to utilities.

Exploring Key Biomarkers and Common Pathogenesis of Seven Digestive System Cancers and Their Correlation with COVID-19.

Digestive system cancer and COVID-19 significantly affect the digestive system, but the mechanism of interaction between COVID-19 and the digestive system cancers has not been fully elucidated. We downloaded the gene expression of COVID-19 and seven digestive system cancers (oral, esophageal, gastric, colorectal, hepatocellular, bile duct, pancreatic) from GEO and identified hub differentially expressed genes. Multiple verifications, diagnostic efficacy, prognostic analysis, functional enrichment and related transcription factors of hub genes were explored. We identified 23 common DEGs for subsequent analysis. CytoHubba identified nine hub genes (CCNA2, CCNB1, CDKN3, ECT2, KIF14, KIF20A, KIF4A, NEK2, TTK). TCGA and GEO data validated the expression and excellent diagnostic and prognostic ability of hub genes. Functional analysis revealed that the processes of cell division and the cell cycle were essential in COVID-19 and digestive system cancers. Furthermore, six related transcription factors (E2F1, E2F3, E2F4, MYC, TP53, YBX1) were involved in hub gene regulation. Via in vitro experiments, CCNA2, CCNB1, and MYC expression was verified in 25 colorectal cancer tissue pairs. Our study revealed the key biomarks and common pathogenesis of digestive system cancers and COVID-19. These may provide new ideas for further mechanistic research.

75‐3: Investigation of Influence and Improvement of Dry Etch on Glass Strength

None film surface of TFT glass will be damaged varying degrees due to physical contact with array equipment, which will seriously reduce the strength of none film surface. In this paper, we clarified the physical damage caused by the dry etch equipment on none film surface of TFT and its impact on the strength reduction through multiple verifications in array process. It is proved that strength reduction is mainly related to the back electrode material of dry etch. By changing the back electrode material from M to N(code name), the strength of the acrylic ball drop increased by more than 500%, and the strength of the cell ROR increased by 57%. This research has greatly improved the strength of none film surface of LCD, which is great significant to product performance improvement and new product certification.

Efficient Convolutional Neural Network-Based Keystroke Dynamics for Boosting User Authentication

The safeguarding of online services and prevention of unauthorized access by hackers rely heavily on user authentication, which is considered a crucial aspect of security. Currently, multi-factor authentication is used by enterprises to enhance security by integrating multiple verification methods rather than relying on a single method of authentication, which is considered less secure. Keystroke dynamics is a behavioral characteristic used to evaluate an individual’s typing patterns to verify their legitimacy. This technique is preferred because the acquisition of such data is a simple process that does not require any additional user effort or equipment during the authentication process. This study proposes an optimized convolutional neural network that is designed to extract improved features by utilizing data synthesization and quantile transformation to maximize results. Additionally, an ensemble learning technique is used as the main algorithm for the training and testing phases. A publicly available benchmark dataset from Carnegie Mellon University (CMU) was utilized to evaluate the proposed method, achieving an average accuracy of 99.95%, an average equal error rate (EER) of 0.65%, and an average area under the curve (AUC) of 99.99%, surpassing recent advancements made on the CMU dataset.

The Cuproptosis-Related Long Noncoding RNA Signature Predicts Prognosis and Immune Cell Infiltration in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC), ranking as one of the most common malignant tumors, is one of the leading causes of cancer death, with a poor prognosis. Cuproptosis, a novel programmed cell death modality that has just been confirmed recently, may play an important role in HCC prognosis. Long noncoding RNA (LncRNA) is a key participant in tumorigenesis and immune responses. It may be of great significance to predict HCC based on cuproptosis genes and their related LncRNA. The sample data on HCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Combined with cuproptosis-related genes collected from the literature search, expression analysis was carried out to find cuproptosis genes and their related LncRNAs significantly expressed in HCC. The prognostic model was constructed by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression. The feasibility of these signature LncRNAs used for the evaluation of the overall survival rate in HCC patients as independent factors was investigated. The expression profile of cuproptosis, immune cell infiltration, and the status of somatic mutation were analyzed and compared. A prognostic model of HCC consisting of seven cuproptosis gene-related LncRNA signatures was constructed. Multiple verification methods have showed that this model can accurately predict the prognosis of HCC patients. It was showed that the classified high-risk group under the risk score of this model had worse survival status, more significant expression of the immune function, and higher mutation frequency. During the analysis, the cuproptosis gene CDKN2A was found to be most closely related to LncRNA DDX11-AS1 in the expression profile of HCC patients. The cuproptosis-related signature LncRNA in HCC was identified, on the basis of which a model was constructed, and it was verified that it can be used to predict the prognosis of HCC patients. The potential role of these cuproptosis-related signature LncRNAs as new targets for disease therapy in antagonizing HCC development was discussed.

Development is in the details: Event-related theta oscillations reveal children and adults verify multiplication facts differently.

When verifying the correctness of single-digit multiplication problems, children and adults show a robust ERP correctness effect thought to reflect similar cognitive processes across groups. Recent studies suggest that this effect is instead a modulation of the negative-going N400 component in children, reflecting access to semantic memory, and the positive-going P300 component in adults, reflecting stimulus categorization. However, the relative difference in ERP amplitude is the same for both components, more positive for correct than incorrect solutions, presenting a challenge to ascertaining the appropriate interpretation. Time-frequency analysis (TFA) of the N400/P300 window provides an objective approach to dissociating these effects. TFA measured from solution onset during single-digit multiplication verification revealed significant modulations of event-related as theta power (3-6Hz) in both groups. Correct trials elicit less power in children (9-12 years) and more power in adults relative to incorrect trials. These findings are consistent with modulations of the N400 and P300, respectively, where opposite effects were predicted for spectral power. The ERP results further support a reinterpretation of the multiplication correctness effect. In contrast, TFA of the N400 effect elicited to a word-picture verification task revealed the same event-related theta effect in both groups, with increased power for mismatched than matched pictures. Together, these findings provide evidence for a developmental shift in cognitive processing specific to the multiplication task. Models of arithmetic should account for this overlooked difference in cognitive processing between children and adults.

Just What Is “Good”? Musings on Hail Forecast Verification through Evaluation of FV3-HAILCAST Hail Forecasts

Abstract Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFEs). As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verification methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast. The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1- and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement, agreeing with the HWT SFE participants’ recommendations for multiple verification methods. Significance Statement “Good” forecasts of hail can be determined in multiple ways and must depend on both the performance of the guidance and the perspective of the end-user. This work looks at different verification strategies to capture the performance of the CAM-HAILCAST hail forecasting model across three years of the Spring Forecasting Experiment (SFE) in different parent models. Verification strategies were informed by SFE participant input via a survey. Skill variability among models decreased in SFE 2021 relative to prior SFEs. The FV3 model in 2021, compared to 2019, provided improved forecasts of both convective distribution and 38-mm (1.5 in.) hail size, as well as less overforecasting of convection from 1900 to 2300 UTC.

Retrieving Vertical Cloud Radar Reflectivity from MODIS Cloud Products with CGAN: An Evaluation for Different Cloud Types and Latitudes

Retrieving cloud vertical structures with satellite remote-sensing measurements is highly desirable and technically challenging. In this paper, the conditional adversarial neural network (CGAN) for retrieving the equivalent cloud radar reflectivity at 94 GHz of the Cloud Profile Radar (CPR) onboard CloudSat is extended and evaluated comprehensively for different cloud types and geographical regions. The CGAN-based retrieval model was extended with additional data samples and improved with a new normalization adjustment. The model was trained with the labeled datasets of the moderate-resolution imaging spectroradiometer (MODIS) cloud top pressure, cloud water path, cloud optical thickness, and effective particle radius data, and the CloudSat/CPR reflectivity from 2010 to 2017 over the global oceans. The test dataset, containing 24,427 cloud samples, was statistically analyzed to assess the performance of the model for eight cloud types and three latitude zones with multiple verification metrics. The results show that the CGAN model possesses good reliability for retrieving clouds with reflectivity &gt; −25 dBZ. The model performed the best for deep convective systems, followed by nimbostratus, altostratus, and cumulus, but presented a very limited ability for stratus, cirrus, and altocumulus. The model performs better in the low and middle latitudes than in the high latitudes. This work demonstrated that the CGAN model can be used to retrieve vertical structures of deep convective clouds and nimbostratus with great confidence in the mid- and lower latitude region, laying the ground for retrieving reliable 3D cloud structures of the deep convective systems including convective storms and hurricanes from MODIS cloud products and used for predicting these storms.

A Data Integrity Verification Scheme for Centralized Database Using Smart Contract and Game Theory

Currently, many companies and institutions use centralized or distributed databases to store massive amounts of data. However, the use of untrusted centralized third-party auditors can result in security issues because these auditors may be malicious and tamper with or delete user data. This poses a significant challenge for ensuring the reliability of the data verification results. Although introducing a third-party auditor can help address this issue, it may also be untrustworthy and collude with the database service provider to forge false data verification results. In this study, we propose a data integrity verification scheme using smart contracts (DIV-SC) to address this challenge in a centralized database environment. Our approach utilizes blockchain technology as a decentralized third-party auditor, ensuring that the information stored on the blockchain is immutable and cannot be tampered with maliciously. In addition, smart contracts deployed on the blockchain can ensure that the verification procedures are correct and are not affected by any malicious party. We also leverage game theory to improve the reliability of multiple verifications, reduce verification time and improve overall performance. Our proposed scheme reduces the total verification time consumption by up to 53.44% while increasing the number of verifiable times by nearly 3.8 times, compared to conventional data integrity verification schemes.

Sub-Seasonal Experiment (SubX) Model-based Assessment of the Prediction Skill of Recent Multi-Year South Korea Droughts

Reliable sub-seasonal forecast of precipitation is essential to manage the risk of multi-year droughts in a timely manner. However, comprehensive assessments of sub-seasonal prediction skill of precipitation remain limited, particularly during multi-year droughts. This study used various verification metrics to assess the sub-seasonal prediction skill of hindcasts of five Sub-seasonal Experiment (SubX) models for precipitation during two recent multi-year South Korea droughts (2007 − 10 and 2013 − 16). Results show that the sub-seasonal prediction skill of the SubX models were stage-, event-, and model-dependent over the recent multi-year droughts. According to the Brier skill scores, SubX models show a more skillful in one to four lead weeks during the drought onset and persistence stages, than the recovery stage. While the prediction skill of the SubX models in the first two initial weeks show more skillful prediction during the 2007–10 drought, the impact of the forecast initial time on the prediction skill is relatively weak during the 2013–16 drought. Overall, the EMC-GEFSv12 model with the 11 ensemble members (the largest among the five SubX models) show the most skillful forecasting skill. According to the sensitivity test to the ensemble member size, the EMC-GEFSv12 model had no gain for biweekly precipitation forecast with the nine ensemble members or more. This study highlights the importance of a robust evaluation of the predictive performance of sub-seasonal climate forecasts via multiple verification metrics.