A dual-RPA based lateral flow strip for sensitive,on-site detection of CP4-EPSPS and Cry1Ab/Ac genes in genetically modified crops

Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSPS and Cry1Ab/Ac was proposed and combined with a lateral flow immunochromatographic assay,named“Dual-RPA-LFD”,to visualize the dual detection of genetically modified(GM)crops.In which,the herbicide tolerance gene CP4-EPSPS and the insect resistance gene Cry1Ab/Ac were selected as targets taking into account the current status of the most widespread application of insect resistance and herbicide tolerance traits and their stacked traits.Gradient diluted plasmids,transgenic standards,and actual samples were used as templates to conduct sensitivity,specificity,and practicality assays,respectively.The constructed method achieved the visual detection of plasmid at levels as low as 100 copies,demonstrating its high sensitivity.In addition,good applicability to transgenic samples was observed,with no cross-interference between two test lines and no influence from other genes.In conclusion,this strategy achieved the expected purpose of simultaneous detection of the two popular targets in GM crops within 20 min at 37°C in a rapid,equipmentfree field manner,providing a new alternative for rapid screening for transgenic assays in the field.

Cheesy material on macroscopic on-site evaluation after endoscopic ultrasound-guided fine-needle biopsy:Don't miss the tuberculosis

Endoscopic ultrasound-guided fine-needle biopsy(EUS-FNB)is an excellent investigation to diagnose pancreatic lesions and has shown high accuracy for its use in pathologic diagnosis.Recently,macroscopic on-site evaluation(MOSE)performed by an endoscopist was introduced as an alternative to rapid on-site cytologic evaluation to increase the diagnostic yield of EUS-FNB.The MOSE of the biopsy can estimate the adequacy of the sample directly by the macroscopic evaluation of the core tissue obtained from EUS-FNB.Isolated pancreatic tuberculosis is extremely rare and difficult to diagnose because of its non-specific signs and symptoms.Therefore,this challenging diagnosis is based on endoscopy,imaging,and the bacteriological and histological examination of tissue biopsies.This uncommon presentation of tuberculosis can be revealed as pancreatic mass mimicking cancer.EUS-FNB can be very useful in providing a valuable histopathological diagnosis.A calcified lesion with a cheesy core in MOSE must be suggestive of tuberculosis,leading to the request of the GeneXpert,which can detect Mycobacterium tuberculosis deoxyribonucleic acid and resistance to rifampicin.A decent diagnostic strategy is crucial to prevent unnecessary surgical resection and to supply conservative management with antitubercular therapy.

Assessment of Appropriateness of Diagnostic Lymph Node Tissue Collection from the Operating Theater and by Interventional Radiologist. A Clinical Audit

Background: Appropriate sample requesting, collecting and timely dispatch to the appropriate laboratory is essential in establishing diagnosis of pathologies with lesions. Much time and effort may be wasted if this is not done according to certain standards. We conducted this study to assess the route of lymph node samples from requests to reaching the laboratories. Methods: We conducted an audit over a period from 4th June until 10th Aug 2023. Data for all the procedures performed over this period on lymph node samples (was entered into and analysed using Excel. Results: A total of eighteen samples for sixteen patients were obtained during this period. Median age of the patients was 34 years (19 - 73) with a M:F ratio of 5:11. Among the IR samples, nine samples were from the neck, three from inguinal area and one from axilla. Seven samples (53.8%) were tru-cut biopsies, six samples (46.15%) were FNA. All samples were sent to the pathology laboratory fixed in formalin. Samples for TB were sent only for five cases (31.25%) and for only two cases (12.5%) were samples sent for bacterial culture. For the OR samples, none were sent for either bacterial culture or TB. Overall, eight patients (50%) were not investigated for any infectious etiologies like brucella, toxoplasmosis, CMV, EBV plus other possible causes. Repeat sampling was required for 25% of patients (within and out of the audit period). Conclusions: to avoid delays in making diagnoses, it is paramount to consider infectious etiologies as possible diagnosis for lymphadenopathy and request appropriate investigations. This requires liaising with infectious diseases/clinical microbiology experts to guide regarding types of samples, types of media and timely dispatch to the correct laboratory.

A Federated Learning Framework with Blockchain-Based Auditable Participant Selection

Federated learning is an important distributed model training technique in Internet of Things(IoT),in which participant selection is a key component that plays a role in improving training efficiency and model accuracy.This module enables a central server to select a subset of participants to performmodel training based on data and device information.By doing so,selected participants are rewarded and actively perform model training,while participants that are detrimental to training efficiency and model accuracy are excluded.However,in practice,participants may suspect that the central server may have miscalculated and thus not made the selection honestly.This lack of trustworthiness problem,which can demotivate participants,has received little attention.Another problem that has received little attention is the leakage of participants’private information during the selection process.We will therefore propose a federated learning framework with auditable participant selection.It supports smart contracts in selecting a set of suitable participants based on their training loss without compromising the privacy.Considering the possibility of malicious campaigning and impersonation of participants,the framework employs commitment schemes and zero-knowledge proofs to counteract these malicious behaviors.Finally,we analyze the security of the framework and conduct a series of experiments to demonstrate that the framework can effectively improve the efficiency of federated learning.

Sec-Auditor:A Blockchain-Based Data Auditing Solution for Ensuring Integrity and Semantic Correctness

Currently,there is a growing trend among users to store their data in the cloud.However,the cloud is vulnerable to persistent data corruption risks arising from equipment failures and hacker attacks.Additionally,when users perform file operations,the semantic integrity of the data can be compromised.Ensuring both data integrity and semantic correctness has become a critical issue that requires attention.We introduce a pioneering solution called Sec-Auditor,the first of its kind with the ability to verify data integrity and semantic correctness simultaneously,while maintaining a constant communication cost independent of the audited data volume.Sec-Auditor also supports public auditing,enabling anyone with access to public information to conduct data audits.This feature makes Sec-Auditor highly adaptable to open data environments,such as the cloud.In Sec-Auditor,users are assigned specific rules that are utilized to verify the accuracy of data semantic.Furthermore,users are given the flexibility to update their own rules as needed.We conduct in-depth analyses of the correctness and security of Sec-Auditor.We also compare several important security attributes with existing schemes,demonstrating the superior properties of Sec-Auditor.Evaluation results demonstrate that even for time-consuming file upload operations,our solution is more efficient than the comparison one.

SHT-based public auditing protocol with error tolerance in FDL-empowered IoVs

With the intelligentization of the Internet of Vehicles(lovs),Artificial Intelligence(Al)technology is becoming more and more essential,especially deep learning.Federated Deep Learning(FDL)is a novel distributed machine learning technology and is able to address the challenges like data security,privacy risks,and huge communication overheads from big raw data sets.However,FDL can only guarantee data security and privacy among multiple clients during data training.If the data sets stored locally in clients are corrupted,including being tampered with and lost,the training results of the FDL in intelligent IoVs must be negatively affected.In this paper,we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs.Specifically,the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance,including efficient corrupted data locating and recovery.In addition,a novel data structure,Skip Hash Table(SHT)is designed to optimize data dynamics.Finally,we illustrate the security of the scheme with the Computational Diffie-Hellman(CDH)assumption on bilinear groups.Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.

Impact of an official accountability audit on industrial structure adjustment:a case study of environmental regulation in China

As a unique environmental regulation in China,the official accountability audit was piloted in 2014.With a focus on prioritizing the ecological environment,officials in pilot districts have implemented economic construction,adjusted industrial structures,and promoted coordinated development between the economy and environment.The effects of implementation have garnered widespread attention from society.However,there is limited research on the impact of an accountability audit on industrial structure adjustments.Using the“Accountability Audit of Officials for Natural Resource Assets(Trial)”released in 2015 as a quasi-natural experiment,this study collected panel data from 279 cities between 2013 and 2017.It then empirically analyzed the impact mechanism and effects of the accountability audit on industrial structure adjustment using the Propensity Score Matching and Difference-in-Differences model.The research findings indicate that the accountability audit directly impacted industrial structure adjustment,promoting the upgrading of the primary industry to the secondary industry and restricting the development of the tertiary industry.In addition,the audit is beneficial for enterprise entry,but not conducive to technological innovation,and has no significant impact on foreign direct investment.This conclusion fills a gap in the existing research and provides valuable insights for policymakers.

Does the Internal Audit Function Play a Role in Mitigating Earnings Management?

This paper aims to investigate the effect of the characteristics of the internal audit function(IAF)on earnings management(EM)for a sample of 27 Tunisian listed companies.The authors employed the Correlated Panels Corrected Standard Errors model to estimate the regression equation.The results showed that EM is negatively associated with internal audit effectiveness,frequency of audit committee meetings with Chief Audit Executives(CAEs),the partial outsourcing of internal audit,and the firm’s size.Moreover,the authors found a positive relationship between EM and the use of IAF as a training ground for management and the private sector.This study is important to academics,regulators,and policymakers in introducing new governance reforms to strengthen the IAF as an important internal governance mechanism to reduce earnings management practices in emerging countries.The results also provide useful information for investors to examine the effect of internal audit characteristics on earnings management.

The Effects of Competence and Auditor Training on Fraud Detection Within Multinational Companies in Sub-Saharan Africa

The aim of this study is to examine the qualities that auditors engaged in detecting potential fraud within multinational corporations in Sub-Saharan Africa should possess.To achieve this goal,a quantitative approach was used to develop and test a research model based on three theories:agency theory,attribution theory,and cognitive dissonance theory.Responses from a panel of two hundred and nine(209)auditors who conducted a legal audit mission in a Sub-Saharan multinational were analyzed using SmartPLS 3.3.3 software.The results emphasize the crucial importance of auditors’competence and continuous training in fraud detection.However,professional skepticism and time pressure were found to be non-significant in this context.This conclusion provides essential insights for auditors,highlighting the key qualities needed to effectively address fraud detection within multinational corporations in Sub-Saharan Africa.

Innovation of Audit Business Management Models under the Conditions of Information Technology

This paper focuses on the innovation of audit business management in the information technology era.In the wave of digitalization,audit companies need to adjust their management mode to take advantage of the advantages of informatization.The key is to use digital tools to strengthen data analysis and reshape the audit process.This not only improves efficiency and accuracy but also improves the overall audit quality.The article also highlights the importance of developing a culture of continued auditor learning and skills development to ensure competitiveness in the digital age.At the end of the paper,the success of audit companies depends on innovation ability and the importance of combining management mode with technological innovation.

Analysis of the Impact of Big Data Technology on Environmental Pollution Control Audit

As China strives towards the second centenary goal,increasing attention is being paid to environmental pollution and other related issues.Concurrently,with the rapid development of big data technology,many big data solutions have been applied to environmental pollution control audits,exerting a significant impact.This paper presents the current situation of environmental pollution audits,summarizing the application of big data from the perspectives of both domestic and international research.In terms of data collection and data analysis for environmental pollution audits,cloud platform technology,and visualization technology are selected based on multiple data sources.The impact in the field of environmental pollution control audits is further analyzed.It is found that the environmental pollution audit cloud platform is not yet perfect,the technical skills of audit personnel are insufficient,and some technologies are not mature.Relevant suggestions are put forward to provide a reference for the future development of big data technology and its integration with environmental pollution control audits.

Audit审核在车辆质检工作中的使用方法研析

Audit审核起源于德国大众,是企业模拟用户对自己的产品质量进行内部监督的觉行为。通过Audit审核在车辆质检工作中的应用,企业可以更加全面地了解车辆的质量状况,及时发现并解决问题,提升车辆的整体质量水平,从而满足消费者的需求,增强市场竞争力。Audit审核是站在用户的角度对成品车辆进行质量检查/测量的方法(内部和外部),获得汽车制造厂的广泛应用。Audit审核是在与用户相对的汽车制造厂里进行的审核检验,只有站在用户角度提升产品质量,才能发挥Audit审核的最佳作用。

Rapid on-site evaluation of endoscopic-ultrasound-guided fine-needle aspiration diagnosis of pancreatic masses

Endoscopic ultrasound (EUS) has become an essential tool for the study of pancreatic diseases. Specifically, EUS plays a pivotal role evaluating patients with a known or suspected pancreatic mass. In this setting, differential diagnosis remains a clinical challenge. EUS-guided fine-needle aspiration (FNA) and fine-needle biopsy (FNB) have been proven to be safe and useful tools in this setting. EUS-guided FNA and FNB, by obtaining cytological and/or histological samples, are able to diagnose pancreatic lesions with high sensitivity and specificity. In this context, several methodological features, trying to increase the diagnostic yield of EUS-guided FNA and FNB, have been evaluated. In this review, we focus on the role of rapid on-site evaluation (ROSE). From data reported in the literature, ROSE may increase diagnostic yield of EUS-FNA specimens by 10%-30%, and thus, diagnostic accuracy. However, we should point out that many recent studies have reported adequacy rates of > 90% without ROSE, indicating that, perhaps, at high-volume centers, ROSE may not be indispensable to achieve excellent results. The use of ROSE can be considered important during the learning curve of EUS-FNA, and also in hospital with diagnostic accuracy rates < 90%.

Improved edge lightweight YOLOv4 and its application in on-site power system work

A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on-site risk assessment and alert.Owing to its lightweight and fast speed,YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection;however,its accuracy is relatively low.This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods,achieving higher accuracy without compromising the speed.Specifically,a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization.Moreover,three optimized training methods:transfer learning,mosaic data augmentation,and label smoothing are used to improve the training effect of this improved algorithm.Finally,an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it.According to the results,the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS,and the mean average precision(mAP)is increased from 70.89%to 85.03%.

Random Forests Algorithm Based Duplicate Detection in On-Site Programming Big Data Environment

On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is essential for on-site programming big data.Duplicate data detection is an important step in data cleaning,which can save storage resources and enhance data consistency.Due to the insufficiency in traditional Sorted Neighborhood Method(SNM)and the difficulty of high-dimensional data detection,an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed.The efficiency of the algorithm can be elevated by improving the method of the key-selection,reducing dimension of data set and using an adaptive variable size sliding window.Experimental results show that the improved SNM algorithm exhibits better performance and achieve higher accuracy.

Developing a GIS Audit Framework in the Context of Information Technology though a Reductive Model Approach

A GIS audit framework is necessary considering the diverse nature of GIS with regard to components, applications and industry. In practice, checklists are generated during the audit process based on specific objectives. There is no standardized list of items that can be used as a reference. The purpose of this study was to develop a GIS audit framework as a foundation for GIS audits. The framework provides that comprehensive approach to various GIS aspects during the audit process. The design builds on a developed conceptual framework where most significant categories of GIS audit parameters namely data quality, software utilization, GIS competency and procedures (work flows) were identified. The study adopted a reductive model approach to simplify the complexity associated with each category of GIS audit parameter. The resultant audit elements for each category are organized in a matrix that forms an integral part of the framework. The columns comprise audit goal, audit questions and audit subjects as indicators which are qualitatively measured. The rows comprise the parameters (data quality, software utilization, personnel competency and procedure (workflows)). To use the framework, an auditor only needs to create an audit checklist that consists of particular parameters and indicators from the framework depending on audit objective. As part of an on-going research, the next step will involve validating the framework through a mock testing process.

New Explorations of Laboratory and On-site Testing of Broadband Seismographs in China

According to different testing purposes, methods and available environmental conditions, the seismograph testing can be divided into laboratory and on-site testing, respectively. The testing of the seismograph's key parameters and other concerning technical specifications are well described in guide documents(China Earthquake Administration, 2017). This includes seismometer sensitivity, linearity and clip levels based on the shake table test, as well as the seismometer natural period, damping constant based on electrical calibration(Wang Guangfu,1986; Ple?inger A.,1993) and instrumental self-noise collocation estimation(Holcomb L.G., 1989; Sleeman R. et al., 2006). However, with the development of seismic observation technology, many new requirements for the performance evaluation of seismographs have been put forward, and new testing items and methods have emerged.

Dynamical energy equipartition of the Toda model with additional on-site potentials

We study the dynamical energy equipartition properties in the integrable Toda model with additional uniform or disordered on-site energies by extensive numerical simulations. The total energy is initially equidistributed among some of the lowest frequency linear modes. For the Toda model with uniform on-site potentials, the energy spectrum keeps its profile nearly unchanged in a relatively short time scale. On a much longer time scale, the energies of tail modes increase slowly with time. Energy equipartition is far away from being attached in our studied time scale. For the Toda model with disordered on-site potentials, the energy transfers continuously to the high frequency modes and eventually towards energy equipartition. We further perform a systematic study of the equipartition time teq depending on the energy density ε and the nonlinear parameter α in the thermodynamic limit for the Toda model with disordered on-site potentials. We find teq∝ （1/ε）^a（1/α）^b, where b≈ 2a. The values of a and b are increased when increasing the strengths of disordered on-site potentials or decreasing the number of initially excited modes.

Increased Effluent Dosage Effects on On-Site Wastewater Treatment Systems of Differing Architecture Type

Approximately 20% of homes nationwide use an on-site treatment system as a form of household wastewater management. However, approximately 10% to 20% of on-site treatment systems malfunction each year, many of which have either failed or exceeded the soil’s long-term acceptance rate (LTAR), causing environmental and human health risks. The objective of this field study was to evaluate the effects of soil condition (e.g., wet and dry) and product architecture type [i.e., chamber, gravel-less-pipe (GLP), polystyrene-aggregate, and pipe-and-aggregate] on in-product solution storage and biomat thickness in a profile-limited soil in northwest Arkansas under increased loading rates and to estimate the LTAR for each product. During Phase I of this study (March 13 to October 4, 2013), effluent loading rates were approximately doubled, while rates were approximately quadrupled during Phase II (October 8, 2013 to May 29, 2014), from the maximum allowable loading rate for each product. The pipe-and-tire-chip, 46-cm-wide trench pipe-and-gravel, and the 25-cm diameter GLP products had the greatest (p < 0.001), while the 31-cm-width and the 5.4-m-long chambers had the lowest (p < 0.001) in-product solution storage during wet-soil conditions of Phase I monitoring. The 25-cm diameter GLP product had the greatest (p < 0.001), while the 61-cm-width, 5.4-m-long chamber had the lowest (p < 0.001) in-product solution storage during Phase II. Results of this study indicate that some alternative products may be able to effectively handle effluent loading rates in excess of those currently allowed by the State of Arkansas. Further research will be required to confirm these interpretations.

A rapid on-site calibration method for measuring dynamic envelope curve of high-speed trains

Dynamic envelope curve is a significant parameter to assess the running safety of high-speed trains.Up to now the method based on binocular stereo vision is the only way available to measure the dynamic envelope curve of a train,the speed of which is over200km/h.Nevertheless the method has two limitations,one is large field-〇f-view(FO V),the other is calibration time.Hence portable calibration equipment,easy-t〇-build target and rapid calibration algorithm are required to complete the calibration.In this paper,a new rapid on-site calibration method with large FOV based on binocular stereo vision is proposed.To address these issues,a light target has been designed,the rail coordinate system(RCS)is represented by40fixed retroreflective points on the target,and they are utilized to calibrate the parameters of two cameras.In addition,two cameras merely capture a single image of the target simultaneously,and the intrinsic and extrinsic parameters of the cameras can be calculated rapidly.To testify the proposed method,the experiments have been conducted and the results reveal that the accuracy can reach+1mm,which can meet the measurement requirement.