Verification Time Research Articles

Performance verification and latency time evaluation of hardware image processing module for appearance inspection systems using FPGA

Performance verification and latency time evaluation of hardware image processing module for appearance inspection systems using FPGA

Enhancing neurosurgical navigation operation flow management through personal digital assistant technology: a prospective cohort study.

This prospective cohort study aims to evaluate the impact of digital health technology especially Personal Digital Assistants (PDA) in neurosurgical procedure management, focusing on surgical safety check accuracy, efficiency, and patient satisfaction. The study included 211 neurosurgical cases from January to December 2022. The control group of 106 patients followed traditional verification methods, while the experimental group of 105 patients used PDA. The PDA system facilitated real-time data collection, verification, and transmission. The study compared both groups in terms of check times, accuracy rates, and patient satisfaction, and used multivariate regression to assess the impact of baseline parameters on these outcomes. The study found that the experimental group using the PDA system reduced the average verification time by approximately 8 min, achieving 100.0% accuracy in preoperative and postoperative checks, significantly better than the control group (91.5% pre- and post-operation). Multivariate regression confirmed a 48.1% reduction in postoperative verification time due to the PDA system (p < 0.001), with the model showing high explanatory power (R2 = 0.911). Other examined factors, including patient age and nurse experience, had no significant effects. Similarly, the PDA's introduction markedly improved verification accuracy, with no significant impact from other variables (p = 0.010). The application of the PDA system in neurosurgical operations significantly enhanced the accuracy and efficiency of surgical safety checks, reduced nursing errors, optimized nursing workflows, and improved patient satisfaction. These results provide valuable insights for the application of PDA technology in high-risk medical fields, demonstrating potential of digital health tools in enhancing surgical safety and efficiency.

Impact of an inpatient pharmacist-driven renal dosing policy on order verification time and patient safety.

Research regarding pharmacist-driven renal dosing policies has focused on cost savings or prevention of adverse drug events. However, little is known about how these policies influence time from order signature to order verification or how this efficiency may reduce the incidence of adverse outcomes. Objectives: The primary endpoint compared time from prescriber electronic order signature to pharmacist electronic order verification between pre- and post-renal dosing policy implementation. The secondary endpoint evaluated electrocardiogram QTc prolongation attributed to fluconazole accumulation in renal impairment. Methods: This retrospective analysis included adults with a creatine clearance ⩽50 mL/min who received at least two inpatient doses from a 34-medication renal dosing protocol between January-February 2020 and April-May 2020. Results: 502 patients met eligibility for the primary outcome. The pre- and post-policy cohorts shared similar baseline characteristics. Time from order signature to verification was 9 and 8 min in the pre- and post-policy groups, respectively (p = 0.0861). In all, 56 patients met inclusion criteria for the secondary outcome. The QTc interval during fluconazole increased relative to baseline in 3 of 7 (43%) pre-policy and 4 of 5 (80%) post-policy. The QTc interval exceeded 500 ms in two patients, both in the post-policy cohort. Conclusions: There was no difference in order signature to verification time. Post-policy fluconazole renal adjustment did not reduce QTc prolongation.

EDUCATIONAL CERTIFICATE VERIFICATION SYSTEM USING BLOCK CHAIN

In this project to secure academic certificate and for accurate management and to avoid forge certificate we are converting all certificates into digital signatures and these digital signatures will be stored in Block chain server as this Block chain server support tamper proof data storage and nobody can hack or alter its data and if by a chance if its data alter then verification get failed at next block storage and user may get intimation about data alter. In Block chain technology same transaction data stored at multiple server with hash code verification and if data alter at one server then it will be detected from other server as for same data hash code will get different. For example, in Block chain technology data will be stored at multiple servers and if malicious users alter data at one server then its hash code will get changed in one server and other servers left unchanged and this changed hash code will be detected at verification time and future malicious user changes can be prevented. In Block chain each data will be stored by verifying old hash codes and if old hash codes remain unchanged then data will be considered as original and unchanged and then new transaction data will be appended to Block chain as new block. For each new data storage all blocks hash code will be verified.

Research on the Application of Blockchain Technology in the Carbon Trading Market of Substations

Abstract This study examines the entire lifecycle carbon emissions from the construction of substations, emphasizing the demand response from the carbon emission trading market. We use blockchain technology to establish an intelligent contract framework to manage and control substation configurations within digital substations’ physical environments. Based on Blockchain, a regional substation intelligent control model is developed to facilitate optimal decision-making in the carbon trading market. Simulation analyses reveal that adjustments in capacity to 150MW can enhance the peak range from 041.2% to 042.0%, indicating a 0.8% improvement in efficiency. Additionally, this research assesses the carbon emission savings, correlating the volume of carbon transactions to the verification time, particularly noting a significant increase in time cost within the first 200 transactions. This paper contributes to understanding how digital technologies can optimize carbon emissions management and energy savings in substation construction.

Data structure and privacy protection analysis in big data environment based on blockchain technology

In today's digital world, the rapid advancement of Information Technology (IT) has made it crucial to prioritize the protection and management of data storage and retrieval. It is vital to challenge the difficulties related to dispersed and decentralized data to build strong mechanisms for access control and provide effective authorization and authentication in data processing. In the contemporary context of IT, the imperative to secure data storage and retrieval has become distinctly observed. The challenges posed by distributed and decentralized data demand the development of robust mechanisms for access control, demanding a focus on proper authorization and authentication in transaction processing. This research addresses the existing gap by comprehensively adapting data structures effectively to the evolving needs of secure access and storage control. It uses the Enhanced Merkle Tree (EMT) as a novel data structure. This article initially modifies the conventional Merkle Tree (MT) structure used in Blockchain technology to suit e-healthcare Systems (e-HS) requirements. The EMT enhances data security in access and storage and significantly improves data integrity management. Its constant three-degree MT with multiple leaves, branches, and a single root node enables updated data authentication, verification, and validation procedures. The proposed method is applied to the e-HS scenario, and the proposed EMT outperforms existing state-of-the-art techniques, achieving a minimal verification time of 14.26 m for 100 transactions. This research, therefore, contributes to the discourse on data security by presenting an innovative and efficient solution tailored to the unique challenges of e-health systems.

Abeto: An automated benchmarking tool to manage heterogeneous IP core databases

System-level design makes use of building blocks, known as soft IP cores, to build complex developments. The usage of these IP cores allows to reduce design and verification time, and also to save costs. However, the use of third-party IP cores tends to present difficulties because of a lack of standardization in their organization, distribution and management, which derive in heterogeneous databases. Most of the time, system developers need to describe some additional code to enable the integration, verification and validation of the IP core, which is not available as part of their distribution. This implies acquiring a deep knowledge of each IP core, often with a large learning curve.In this work Abeto is presented, a new software tool for IP core databases management. It allows to easily integrate and use a heterogeneous group of IP cores, described in VHDL, with a unified set of instructions or commands. In order to do so, Abeto requires from every IP core some side information about its packaging and how to operate with the IP. Currently, Abeto provides support for a set of well-known EDA tools and has been successfully applied to the European Space Agency portfolio of IP cores for benchmarking purposes. To demonstrate its performance, mapping results for these IP cores on the novel NanoXplore BRAVE FPGA family are provided.

An Approach to the State Explosion Problem: SOPC Case Study

The system on a programmable chip (SOPC) architecture is better than traditional central processing unit (CPU) + field-programmable gate array (FPGA) architecture. It forms an efficient coupling between processor software and programmable logic through an on-chip high-speed bus. The SOPC architecture is resource-rich and highly customizable. At the same time, it combines low power consumption and high performance, making it popular in the field of high reliability and other new industrial fields. The SOPC architecture system is complex and integrates multiple forms of intellectual property (IP). Because of this, the traditional dynamic test and the static test cannot meet the requirements for test depth. To solve the problem of verification depth, we should introduce formal verification. But there are some types of IP forms that formal tools cannot recognize. These include black box IP, encrypted IP, and netlist IP in the SOPC model. Also, the state space explosion caused by the huge scale of the SOPC model cannot be formally verified. In this paper, we propose a modeling method using SOPC architecture. The model solves the problem of formal tools not recognizing multi-form IPs. To compress the state space, we propose reducing SOPC variables and branch relationships based on verification properties. Then, we conduct a property verification experiment on the reduced SOPC model. The experiment result shows that the model can significantly reduce the verification time.

Handwriting identification and verification using artificial intelligence-assisted textural features

Intelligent process control and automation systems require verification authentication through digital or handwritten signatures. Digital copies of handwritten signatures have different pixel intensities and spatial variations due to the factors of the surface, writing object, etc. On the verge of this fluctuating drawback for control systems, this manuscript introduces a Spatial Variation-dependent Verification (SVV) scheme using textural features (TF). The handwritten and digital signatures are first verified for their pixel intensities for identification point detection. This identification point varies with the signature’s pattern, region, and texture. The identified point is spatially mapped with the digital signature for verifying the textural feature matching. The textural features are extracted between two successive identification points to prevent cumulative false positives. A convolution neural network aids this process for layered analysis. The first layer is responsible for generating new identification points, and the second layer is responsible for selecting the maximum matching feature for varying intensity. This is non-recurrent for the different textures exhibited as the false factor cuts down the iterated verification. Therefore, the maximum matching features are used for verifying the signatures without high false positives. The proposed scheme’s performance is verified using accuracy, precision, texture detection, false positives, and verification time.

Keep it real, keep it simple: the effects of icon characteristics on visual search

ABSTRACT Previous research examining how icons’ concreteness, visual complexity, and distinctiveness influence visual search performance have led to disagreements over which icon characteristic most affects behaviour. These icon characteristics are often poorly defined and interrelated, particularly concreteness. Accordingly, drawing strong inferences about the robustness of concreteness as a factor in search for visual icons is challenging. Here, we operationalised concreteness into three distinct levels: concrete icons were images of real-world objects, photorealistic icons were drawings of the object, and abstract icons were images with no conceptual information. Across two experiments, participants rated each icon on various icon characteristics (e.g. concreteness, visual complexity) to provide a ground truth for these factors and to validate our concreteness manipulation. In a separate study, naive participants performed a visual search task for a target icon. Oculomotor measures were utilised to elucidate how various icon characteristics affected search performance. Although we were unable to fully disassociate concreteness from visual complexity, we found that icons high in concreteness improved search performance, but as visual complexity increased, object identification became slower. This was largely demonstrated through increased verification times for complex targets. The present set of studies indicate that highly concrete and simple icons engender search benefits.

Study on departure passenger movement during security check process at airport terminal based on an improved cellular automata model

Airport security check is a crucial part of air travel, and the efficiency of this process directly impacts the overall airport experience. This article presents a study on the modelling and simulation of movement of departure passengers during security check at airport terminals, with a particular emphasis on elucidating microscopic properties such as passenger flow patterns, individual movement dynamics and especially queuing phenomena. In order to solve the unreasonable path problem caused by direct application of cellular automata model, we propose an improved cellular automata model in which a new concept of horizontal restrain floor field is innovatively introduced. Considering the factors of spatial distance, number of queuing people and potential queuing people, a checkpoint selection probability model is embedded into the CA model. The results show that the queuing behavior of passengers is influenced by various factors from three aspects: (i) selection strategy of checkpoint, (ii) spatial layout of inspection area, (iii) arrival interval and verification time. The findings of this study can be used by airport operators to have better understanding of departure passengers’ movements at a micro level, improve the efficiency of the security check process and enhance the airport experience for passengers. Some measures can be taken to reduce the queue waiting time, such as personnel guidance, information signage, planning wider entrance and more queue lanes, replacing the manual verification with ticket inspection machines and so on.

Evaluation and analysis of a secure voting-based aggregated signatures authentication and revocation protocol for 5 G-V2X communication

Security concerns in 5 G-V2X communication, including message authenticity, entity identification, and efficient malicious entity detection, necessitate innovative solutions. This paper introduces a novel protocol aimed at addressing security challenges in 5 G-V2X communication, focusing on authentication and misbehaving entity revocation. The protocol employs short bilinear pairing digital signatures for message authentication and pseudo-identity for entity verification. Key generation utilizes a Secret Splitting-based approach, distributing partial keys to vehicles within the same group, ensuring secure future communication. In terms of performance, our protocol excels in communication efficiency, costing only 280 bytes per message, the lowest among evaluated protocols. It also demonstrates competitive computation efficiency, with a signing time of 0.614 ms per message and a verification time of 5.035 ms per signature. Our innovative revocation method ensures timely detection and reporting of misbehaving entities, with an average detection time of 2.3 s and an average revocation time of 1.8 s. Our protocol enhances security in 5 G-V2X communication while maintaining superior communication and computation efficiency.

Enhancing ocean environment prediction in Yellow Sea through targeted observation using ocean acoustic tomography

Ocean Acoustic Tomography (OAT) is an efficient and economical marine acoustic observation technique. Targeted observation is an appealing procedure to reduce the uncertainty of ocean environment prediction through additional observation. This study aimed to assess the validity of OAT as an observation method for targeted observation. OAT based on Niche Genetic Algorithm was employed to extract sound speed and temperature profiles from acoustic transmission time, utilizing data from the 2019 Yellow Sea experiment. The inversion results were compared with measurement data, which are found to be accurate and reliable. To further evaluate OAT as targeted observation method, the vertical bias structure of OAT was added on synchronous measurement data in the sensitive area of targeted observation to simulate OAT observation in sensitive area. This simulated data was then incorporated into a 3D-Var assimilation system to improve the short-term prediction of the target region. Comparing the predictions derived with the measurement data at the verification time, it shows that the simulated OAT observation improved the quality of target region prediction, indicating that OAT can be an effective observation method for targeted observation. An Observing System Simulation Experiment was conducted to assess the impact of OAT characteristics on prediction improvement. The results show that both adding observation nodes and extending the observation duration have positive effects, while extending the observation duration performs better.

A sustainable Bitcoin blockchain network through introducing dynamic block size adjustment using predictive analytics

Bitcoin is the largest cryptocurrency in the market, which uses blockchain technology to bring in features like decentralization, anonymity, and trust. However, it still struggles with broader adaptation due to long verification times and high transaction fees. As a result, it is lagging behind competitors. We need to provide faster confirmations to tackle these issues while ensuring stable earnings for the miners. However, it is challenging to increase the block sizes or decrease the average block creation time without affecting the stability and security of the network. To address this conundrum, firstly, an optimization problem is formulated where the objective is to increase the transaction count in every cycle. Based on that, a comprehensive learning framework is developed to solve the formulated problem since the issue is intractable and hard to solve in polynomial time. The proposed learning framework includes (i) implementing a viable data-driven infrastructure with a machine learning (ML) root, (ii) training learning models with efficient generalization capability, and (iii) predicting the ideal block size in every block generation cycle. Our concept uses extreme gradient boost (XGB) as its core algorithm, which analyzes nine attributes associated with the Bitcoin network. These network-allied data points assist the model in creating an adaptive block size in the blockchain. XGB, trained using the last four years of real-world data, can predict block sizes with a 63.41% accuracy. The model ensures an all-around positive change in Bitcoin with a 12.29% increase in block size, a 13.45% increase in transaction fee (USD), and a 14.88% increase in transaction approval rate and transaction count, thus addressing the long wait time and broader adaption issue.

Bank Loan Fraud Detection with Integrated KYC Verification System

In today's technologically-driven financial landscape, bank loan fraud poses a significant threat to both financial institutions and their customers. While traditional methods of fraud detection have been moderately effective, the increasing sophistication of fraudsters necessitates the implementation of more advanced measures. This research proposes an integrated system for bank loan fraud detection that leverages the robustness of Know Your Customer (KYC) verification. By combining traditional KYC processes with advanced machine learning algorithms, this system seeks to provide a more comprehensive approach to detecting and preventing fraudulent loan applications. Initial results indicate a marked reduction in successful fraud attempts, as well as a decrease in false positives compared to conventional systems. Furthermore, the integrated system offers enhanced customer experience by streamlining the loan application process, reducing verification times, and ensuring greater security of personal data. As financial institutions continue to grapple with the challenges of fraud, this research underscores the importance of integrating traditional verification methods with cutting-edge technological solutions for optimal results

Predictors of therapeutic response to specific therapy in distal chronic thromboembolic pulmonary hypertension

Aim. To assess therapeutic response to specific therapy in patients with distal CTEPH who are ineligible for surgical management as pulmonary endarterectomy (PEA) or balloon pulmonary angioplasty (BPA).Materials and methods. The study included inoperable patients with CTEPH (n=53) who are ineligible for surgical management as pulmonary endarterectomy or balloon pulmonary angioplasty. The effectiveness of specific therapy were assessed after dividing the patients into improvement or nonimprovement groups based on the following indicators: functional class I-II (WHO), mean pulmonary artery pressure according to right heart catheterization ≤40 mm Hg, pulmonary vascular resistance ≤400 dyn×s×cm-5, cardiac index ≥2,5 L/min/m2 , decrease in N-terminal brain natriuretic peptide &gt;30%, and increase in 6-minute test distance &gt;30 m at 12th-month follow-up compared to baseline parameters.Results. There were no differences in groups by gender, age, and received specific therapy. Multiple logistic regression was performed, resulting in a model for predicting clinical improvement with sensitivity (57,1%) and specificity (87,1%), p=0,002. It was revealed, that the absence of the loss of pulmonary vascularity by X-ray at the time of verification of the diagnosis in patients with CTEPH by 5,91 times increases the chance of achieving clinical improvement after 12 months of the specific therapy, while the absence of signs of heart failure by 5,79 times, and degree of pulmonary valve insufficiency 0-1 according to echocardiography by 9,2 times, respectively.Conclusion. Early initiation of specific therapy in inoperable CTEPH patients due to distal lesions with the absence of severe vascular remodeling and heart failure symptoms leads to the better therapeutic response after 12 months of treatment.

Assessment of technology-assisted technician verification of compounded intravenous sterile preparations versus pharmacist verification.

This study is an evaluation of technology-assisted technician verification (TATV) of the compounded sterile product (CSP) preparation process as an alternative to final verification by a pharmacist. A 2-phase, single-center noninferiority study was conducted to assess the accuracy and CSP processing time with TATV versus pharmacist verification. Phase I of the study was a validation of the internal pharmacist accuracy rate in which 2 pharmacists checked each CSP. In phase II, prepared CSPs were first checked by a technician and then checked by a pharmacist. Technicians were required to complete baseline credentialing and training requirements to participate in the study. The primary outcome was the error rate for the pharmacist check in phase I and the error rate of the technician check in phase II. Secondary outcomes included total verification time and total dose processing time in each phase. The Farrington-Manning test was used for noninferiority assessment of accuracy, and the Wilcoxon rank sum test was used to detect a difference between the processing times. A total of 4,000 doses were checked in each phase. Pharmacist accuracy was 99.600% in phase I, compared to TATV accuracy of 99.575% in phase II. TATV of CSPs was noninferior to pharmacist verification (absolute difference in accuracy, 0.025%; 95% CI, -0.26% to 0.31%; P = 0.0016). Total verification time and total dose processing times were significantly lower in Phase II. This study showed that TATV of CSPs is noninferior to pharmacist final verification and does not negatively impact the time to check CSPs or total CSP processing time.

Application of deep metric learning in the verification process of wheel design similarity: Hyundai motor company case

AbstractThe global automobile market experiences quick changes in design preferences. In response to the demand shifts, manufacturers now try to apply new technologies to bring a novel design to market faster. In this paper, we introduce a novel AI application that performs a similarity verification task of wheel designs that aims to solve the real‐world problem. Through the deep metric learning approach, we empirically prove that the cross‐entropy loss does similar tasks as the pairwise losses do in the embedding space. On Jan 2022, we successfully transitioned the verification system to the wheel design process of Hyundai Motor Company's design team and shortened the verification time by 90% to a maximum of 10 min. With a few clicks, the designers at Hyundai Motor could take advantage of our verification system.

Impact of the US Accelerated Approval for New Anticancer Drugs on Time to Verification of Benefit and Regulatory Approval in the EU and Japan.

The accelerated approval (AA) program in the USA has succeeded in expediting the regulatory approval of new cancer drugs based on surrogate endpoint data. It is unclear whether the AA program promotes overall drug development, including verification of the clinical benefit, as the verification of drugs granted AA often takes long time. To determine the impact of the AA program on overall drug development, the time required for verification of clinical benefits was compared between anticancer drugs that initially received AA and those that received regular approval (RA). It was found that anticancer drugs that were approved under the AA program took longer time for verification, suggesting that the program may delay the start of a confirmatory study, and there may be room for speeding up the process. In addition, discordance was found in the pivotal study between the USA and the EU and the USA and Japan for obtaining the indication for which AA was granted in the USA and a delay in the start of the confirmatory study for the AA indication was considered to lead to a delay in approval in the EU and Japan. Early initiation of confirmatory studies for AA indications is recommended to reduce the time that patients receive drugs with unproven benefit in the USA, as well as to deliver innovative new drugs to patients earlier in the EU and Japan.

Addressing the Transaction Validation Issue in IOTA Tangle: A Tip Selection Algorithm Based on Time Division

IOTA is a new public chain system specifically designed for the Internet of Things (IoT), which provides strong support for the high concurrency, scalability, and zero handling fees of the IoT. The distributed ledger of IOTA, called the tangle, adopts a Directed Acyclic Graph (DAG) structure. However, compared to the single-chain architecture, the tangle is more complex and highly vulnerable to security threats. The existing transaction verification methods still cannot simultaneously meet the need for accelerating approval speed and improving security to resist illegal transactions, such as lazy tips and permanent tips. In this work, we propose TDTS, a tip-selection algorithm based on time division to improve the efficiency of transaction verification. The main idea of the algorithm is to quickly determine two tips of an incoming transaction that need to be confirmed by sorting tip values in a time slot. It shortens the transaction verification time and reduces the number of lazy tips and permanent tips. A comprehensive theoretical analysis confirmed the effectiveness of our proposed algorithm. Based on 1000 IOTA nodes, the evaluations showed that TDTS can select tips quickly like URTS and resist lazy tips like MCMC.