Error Detection Tool Research Articles

Effects of Web-Based Survey Tool and Public Health Services for Low-Cost Detection of Vulnerable Medication Errors Using Telepharmacy

Telepharmacy, in which prescription requests are reviewed and approved online by a pharmacist in a different geography, is an effective way to lower the number of mistakes made when giving medicines. However, not enough studies have been done on the benefits of error reduction and the possible cost savings that come with these teleservices. This paper discusses a study examining what happened when a web-based survey tool and telepharmacy solutions were used to find Vulnerable Medication (VM) errors and low-cost Public Health (PH) services. It is very important to get each patient's drug records when they come into the Emergency Room (ER) so that any mistakes in their current medication record can be found. The trained pharmacists were interviewed after using a safe online tool to keep track of drug information. Details about each patient were gathered, along with the amount and type of VM and any PH errors found during the data-gathering procedure. From May 2022 to November 2022, 190 patient files were successfully filled up utilizing the survey instrument throughout the experimental period. Among the 1090 drugs documented, 41.38% were classified as drugs of high risk. 42.33% of possible prescription mistakes were categorized as VM errors. This online survey tool has enhanced the caliber and effectiveness of identifying possible errors during the gathering of medication records by pharmacists. This data may be readily accessed and contribute to debates about the reconciliation of drugs at the management level. It can also positively affect patient care outcomes by facilitating the development of virtual procedures that may reduce drug-related incidents.

Thought Experiments as an Error Detection and Correction Tool.

The ability to recognize and correct errors in one's explanatory understanding is critically important for learning. However, little is known about the mechanisms that determine when and under what circumstances errors are detected and how they are corrected. The present study investigated thought experiments as a potential tool that can reveal errors and trigger belief revision in the service of error correction. Across two experiments, 1149 participants engaged in reasoning about force and motion (a domain with well-documented misconceptions) in a pre-training-training-post-training design. The two experiments manipulated the type of mental model manipulated in the thought experiments (i.e., whether participants reasoned about forces acting on their own bodies vs. on external objects), as well as the level of relational and argumentative reasoning about the outcomes of the thought experiments. The results showed that: (i) thought experiments can serve as a tool to elicit inconsistencies in one's representations; (ii) the level of relational and argumentative reasoning determines the level of belief revision in the service of error correction; and (iii) the type of mental model manipulated in a thought experiment determines its outcome and its potential to initiate belief revision. Thought experiments can serve as a valuable teaching and learning tool, and they can help us better understand the nature of error detection and correction systems.

In silico molecular docking studies of 2, 4 and 2, 6-di-tert-butylphenol against NAGK and SPP1 proteins to recuperate rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that primarily affects the synovial joints. It is connected to progressive disability, early death and socioeconomic burdens. So, there is an urgent need to develop novel drugs that effectively treat patients at each stage of disease progression. N-acetylglucosamine kinase (NAGK) and secreted phosphoprotein 1(SPP1) are two attractive drug targets for RA. NAGK protein converts endogenous N-acetyl-D-glucosamine (GlcNAc) into GlcNAc 6-phosphate. GlcNAc has suppressive effects on experimental RA in mouse models. SPP1 stimulates the synthesis of a key proinflammatory cytokine implicated in the pathogenesis of RA. In order to design new drug candidates, crystal structure of human NAGK (PDB Id: 2CH5) was retrieved from the protein data bank. Amino acid sequence of SPP1 (Uniprot Id: P10451) was retrieved from uniprot database and modeled by the Phyre2 server. Computed SPP1 model energy was minimized using the YASARA energy minimization server and validated by the Ramachandran plot and the ProSA-web error-detection tool. 2, 4-Di-tert-butylphenol and 2, 6-Di-Tert-butylphenol are known compound isolates from bark extract of Schleichera oleosa having anti-arthritic and anti-inflammatory activities as accessed by PASS online server. These compounds were docked against active site of NAGK and SPP1 proteins using MTiAutoDock server. The binding affinity of these compounds against NAGK and SPP1 proteins ranges from -6.12 to -7.17 kcal/mol. The findings of this study will open the way for the development of herbal remedies for rheumatoid arthritis based on the Schleichera oleosa plant, potentially leading to the development of novel drugs.

Detecting Floating-Point Expression Errors Based Improved PSO Algorithm

The use of floating-point numbers inevitably leads to inaccurate results and, in certain cases, significant program failures. Detecting floating-point errors is critical to ensuring that floating-point programs outputs are proper. However, due to the sparsity of floating-point errors, only a limited number of inputs can cause significant floating-point errors, and determining how to detect these inputs and to selecting the appropriate search technique is critical to detecting significant errors. This paper proposes characteristic particle swarm optimization (CPSO) algorithm based on particle swarm optimization (PSO) algorithm. The floating-point expression error detection tool PSOED is implemented, which can detect significant errors in floating-point arithmetic expressions and provide corresponding input. The method presented in this paper is based on two insights: (1) treating floating-point error detection as a search problem and selecting reliable heuristic search strategies to solve the problem; (2) fully utilizing the error distribution laws of expressions and the distribution characteristics of floating-point numbers to guide the search space generation and improve the search efficiency. This paper selects 28 expressions from the FPBench standard set as test cases, uses PSOED to detect the maximum error of the expressions, and compares them to the current dynamic error detection tools S3FP and Herbie. PSOED detects the maximum error 100% better than S3FP, 68% better than Herbie, and 14% equivalent to Herbie. The results of the experiments indicate that PSOED can detect significant floating-point expression errors.

Trustful Test Suites for Natural Language Processing

Machine Translation (MT) research has witnessed continuous growth, accompanied by an increasing demand for automated error detection and correction in textual content. In response, Unbabel has developed a hybrid approach that combines machine translation with human editors in post-edition (PE) to provide high-quality translations. To facilitate the tasks of post-editors, Unbabel has created a proprietary error detection tool named Smartcheck, designed to identify errors and provide correction suggestions. Traditionally, the evaluation of translation errors relies on carefully curated annotated texts, categorized based on error types, which serve as the evaluation standard or Test Suites for assessing the accuracy of machine translation systems. However, it is crucial to consider that the effectiveness of evaluation sets can significantly impact the outcomes of evaluations. In fact, if evaluation sets do not accurately represent the content or possess inherent flaws, the decisions made based on such evaluations may inadvertently yield undesired effects. Hence, it is of utmost importance to employ suitable datasets containing representative data of the structures needed for each system, including Smartcheck. In this paper we present the methodology that has been developed and implemented to create reliable and revised Test Suites specifically designed for the evaluation process of MT systems and error detection tools. By using these meticulously curated Test Suites to evaluate proprietary systems and tools, we can ensure the trustworthiness of the conclusions and decisions derived from the evaluations. This methodology accomplished robust identification of problematic error types, grammar-checking rules, and language- and/or register-specific issues, leading to the adoption of effective production measures. With the integration of Smartcheck’s reliable and accurate correction suggestions and the improvements made to the post-edition revision process, the work presented herein led to a noticeable improvement in the translation quality delivered to customers.

CuCatch: A Debugging Tool for Efficiently Catching Memory Safety Violations in CUDA Applications

CUDA, OpenCL, and OpenACC are the primary means of writing general-purpose software for NVIDIA GPUs, all of which are subject to the same well-documented memory safety vulnerabilities currently plaguing software written in C and C++. One can argue that the GPU execution environment makes software development more error prone. Unlike C and C++, CUDA features multiple, distinct memory spaces to map to the GPU’s unique memory hierarchy, and a typical CUDA program has thousands of concurrently executing threads. Furthermore, the CUDA platform has fewer guardrails than CPU platforms that have been forced to incrementally adjust to a barrage of security attacks. Unfortunately, the peculiarities of the GPU make it difficult to directly port memory safety solutions from the CPU space. This paper presents cuCatch, a new memory safety error detection tool designed specifically for the CUDA programming model. cuCatch combines optimized compiler instrumentation with driver support to implement a novel algorithm for catching spatial and temporal memory safety errors with low performance overheads. Our experimental results on a wide set of GPU applications show that cuCatch incurs a 19% runtime slowdown on average, which is orders of magnitude faster than state-of-the-art debugging tools on GPUs. Moreover, our quantitative evaluation demonstrates cuCatch’s higher error detection coverage compared to prior memory safety tools. The combination of high error detection coverage and low runtime overheads makes cuCatch an ideal candidate for accelerating memory safety debugging for GPU applications.

Finding Compiler Bugs Duplicates by Generating Witness Programs

Programming language compilers are complex software projects, the quality of which directly affects the quality of programs created by them. Therefore, compilers are subject to increased requirements for such software quality characteristics as functional suitability, reliability, performance level, safety, etc. To ensure quality, various methods are used: user and manual testing, tools for automatic error detection. And due to many methods of searching for bugs often situations arise when the same error, especially if its cause is trivial, is detected repeatedly, and the test cases that lead to it can be completely different from each other. Such test cases are called duplicates, and their determination is an urgent and acute problem, since their manual search requires a large amount of human resources. Algorithms for automatically finding duplicates would greatly simplify the process of developing and maintaining a compiler. The main idea of the approach for finding duplicates presented in the article is that the causes of the same errors are located in the same place in the compiler source code. To search for this place, the method of generating witness programs is used: for each test program containing an error, similar programs are generated that do not contain it. After that, metrics are calculated based on the source code coverage of the compiler and a list of source code files that are potentially causing the compiler to fail is formed. If these lists for two test programs are similar in terms of the proximity metric, then the test programs are considered duplicates. The proposed approach was developed and implemented for the compiler of the Kotlin programming language. Testing has shown the applicability of the proposed approach for solving the problem of finding duplicate errors of compilers of programming languages

Ideas and Tools for Error Detection in Opacity Databases

In this article, we propose several ideas and tools in order to check the reliability of radiative opacity and atomic physics databases. We first emphasize that it can be useful to verify that mathematical inequalities, which impose lower and upper bounds on the Rosseland and/or Planck mean opacities, are satisfied, either for pure elements or mixtures. In the second part, we discuss the intriguing law of anomalous numbers, also named Benford’s law, which enables one to detect errors in line-strength collections, required in order to perform fine-structure calculations. Finally, we point out and illustrate the importance of quantifying the uncertainties due to interpolations in the density-temperature opacity (or more generally atomic-data) tables and performing convergence checks, which are crucial in the accuracy-completeness compromise inherent in opacity computations.

Delta checks.

Delta check is an electronic error detection tool. It compares the difference in sequential results within a patient against a predefined limit, and when exceeded, the delta check rule is considered triggered. The patient results should be withheld for review and troubleshooting before releasing to the clinical team for patient management. Delta check was initially developed as a tool to detect wrong-blood-in-tube (sample misidentification) errors. It is now applied to detect errors more broadly within the total testing process. Recent advancements in the theoretical understanding of delta check has allowed for more precise application of this tool to achieve the desired clinical performance and operational set up. In this Chapter, we review the different pre-implementation considerations, the foundation concepts of delta check, the process of setting up key delta check parameters, performance verification and troubleshooting of a delta check flag.

Grammarly: An instructional intervention for writing enhancement in management education

To ensure that graduates are adequately prepared for the workplace, writing skills is increasingly regarded as an important outcome of management education; hence, there is a need to improve students' writing skills alongside giving ongoing scholarly attention to providing pedagogical and practical guidance for management educators. In this paper, we present the findings of a study that examined the usefulness of Grammarly – a digital writing assistance tool – for supporting management educators and students. Using a sample of one hundred 2000-word group reports from a masters business analysis subject, we examine feedback from Grammarly to determine its usefulness as an instructional tool for improving students' written work. While Grammarly detects twelve types of writing issues (i.e. formality, variety in vocabulary, concision, clarity, conventions, grammar, sensitivity, fluency, readability, punctuation, and spelling) our results demonstrate that the breadth and depth of feedback provided by Grammarly can be used as more than an error detection tool. We discuss some of the implications for higher education arising from this, and offer suggestions for incorporating Grammarly as an instructional tool in management education to enhance students' writing skills.

Effectiveness of Sensor-Based Media to Improve Referee Education

The purpose of this research is to produce a new product in the form of censorship media and video assistant referee (VAR) in assisting referee leadership in a championship to achieve fair play. This research uses the Research and Development (RD) method from Sugiyono using ten steps. The place of research was carried out at the All-Indonesian Volleyball Association, Deli Serdang Regency. The population and sample in this study were 36 Deli Serdang Regency volleyball players with a small sample of 6 people, a large sample of 16, and a comparison sample of 14. For the referee sample, six nationally licensed and nine regional licensed referees. The results of this study include Net and VAR sensors used to develop a player error detection tool when smashing and blocking. The results are obtained from the data from expert validation, including: (1) Material experts 92.3% with the "Very Eligible" category. (2) Media Experts 92.5% with the "Very Eligible" category. In large-scale trials on referees and athletes, the resulting percentages were 90.8% and 92.7%, respectively, while in the comparison trial, the percentage produced was 92.5%.

Kex: A Platform For Analysis Of JVM Programs

Introduction: Over the last years program analysis methods were widely used for software quality assurance. Different types of program analysis require various levels of program representation, analysis methods, etc. Platforms that provide utilities to implement different types of analysis on their basis become very important because they allow one to simplify the process of development. Purpose: Development of a platform for analysis of JVM programs. Results: In this paper we present Kex, a platform for building program analysis tools for JVM bytecode. Kex provides three abstraction levels. First is Kfg, which is an SSA-based control flow graph representation for bytecode-level analysis and transformation. Second is a symbolic program representation called Predicate State, which consists of first order logic predicates that represent instructions of the original program, constraints, etc. The final level is SMT integration layer for constraint solving. It currently provides an interface for interacting with three SMT solvers. Practical relevance: We have evaluated our platform by considering two prototypes. First prototype is an automatic test generation tool that has participated in SBST 2021 tool competition. Second prototype is a tool for detection of automatic library integration errors. Both prototypes have proved that Kex can be used to implement competitive and powerful program analysis tools.

Improving the laboratory result release process in the light of ISO 15189:2012 standard

The ISO 15189:2012 standard section 5.9.1 requires laboratories to review results before release, considering quality control, previous results, and clinical information, if any, and to issue documented procedures about it. While laboratory result reporting is generally regarded as part of the post-analytical phase, the result release process requires a general view of the total examination process. Reviewing test results may follow with troubleshooting and test repetition, including reanalyzing an individual sample or resampling. A systematic understanding of the result release may help laboratory professionals carry out appropriate test repetition and ensure the plausibility of laboratory results. In this paper, we addressed the crucial steps in the result release process, including evaluation of sample quality, critical result notification, result reporting, and recommendations for the management of the result release, considering quality control alerts, instrument flags, warning messages, and interference indexes. Error detection tools and plausibility checks mentioned in the present paper can support the daily practice of results release.

Actualities in neonatal endocrine and metabolic screening.

Neonatal metabolic screening has proven to be an important tool for the early detection of innate metabolic errors. Despite the fact that simple and effective methods of testing for metabolic diseases have been identified since the middle of the twentieth century, no consensus has been reached so far on the content of neonatal metabolic screening panels. There are large differences between countries in the number of metabolic diseases identified through national metabolic screening programs, ranging from zero to several tens, the most common testing being for phenylketonuria and congenital hypothyroidism (including in Romania). Given the fact that rare but treatable diseases have been identified in recent decades, reducing the financial burden on the health system, it would be useful to include them in the national neonatal metabolic screening program.

Error detection of arithmetic expressions

Inspecting floating-point errors is essential to floating-point operations. In this paper, we present floating-point error detector (FPED), an inspector of floating-point errors for arithmetic expressions. FPED can pick a suitable benchmark generation approach by analyzing the distribution of the expression of a floating-point operation, thereby minimizing the possibilities of underreporting floating-point errors. FPED is also able to determine the significant sources of errors in a floating-point operation according to the frequencies of computation building blocks that contribute most to the floating-point errors, benefiting the follow-up optimizations of computation accuracies. We validate the correctness and functionalities of FPED by conducting experiments on the FPBench benchmark suite. The experimental results demonstrate that FPED can obtain more accurate detection results than the random detecting approach with respect to floating-point error detection. We also compare FPED with the existing dynamic error detection tools. The experimental results show that in most of the 33 test benchmarks, the maximum error results of FPED are greater than Herbgrind and the detection performance is higher than Herbgrind.

Designing an efficient multi-epitope vaccine against Campylobacter jejuni using immunoinformatics and reverse vaccinology approach

Campylobacterjejuni causes acute diarrhea as a leading cause of morbidity and mortality in children especially in the developing countries of Asia and Africa. C.jejuni has been identified as a member of the priority pathogens category due to the sudden emergence of multidrug-resistant isolates. Therefore, it is important to develop a protective vaccine against this pathogen. In the present study, the Reverse vaccinology approach was used to identify vaccine targets from the proteome of diarrheagenic C. jejuni strain NCTC11168 for the development of chimeric vaccine candidates against C. jejuni. Pathogen proteins that have adhesin like properties and role in virulence but not present in the human host were selected for the design of a multi-epitope vaccine. MHC class I & II and B-cell epitopes present in the selected vaccine target proteins were screened using different immunoinformatics tools. The commonly predicted epitopes from their corresponding different servers were selected and further shortlisted based on their immunogenicity, antigenicity, and toxicity analysis. Shortlisted peptides were joined by GPGPG linkers to design a multi-epitope vaccine construct. Immune-modulating adjuvant monophosphoryl lipid sequence was added with the vaccine construct's N terminal using EAAAK linkers to enhance the immunogenicity. The designed vaccine construct was evaluated by antigenicity, allergenicity, solubility, and physicochemical analysis using various bioinformatics tools. A three-dimensional model of vaccine construct was modeled by the Phyre2 server and refinement by the GalaxyRefine tool. Constructed model quality was validated by the ProSA-web error-detection tool and the Ramachandran plot. After that vaccine model was docked with TLR-4 protein and complex stability confirmed by molecular dynamics simulation studies. Finally, In-silico cloning of vaccine constructs into a vector was performed to ensuring its effective expression in the microbial system.

Genre-specific Error Detection with Multimodal Feedback

A purpose-built online error detection tool was developed to provide genre-specific corpus-based feedback on errors occurring in draft research articles and graduation theses. The primary envisaged users were computer science majors studying at a public university in Japan. This article discusses the development and evaluation of this interactive, multimodal tool. An in-house learner corpus of graduation theses was annotated for errors that affect the accuracy, brevity, clarity, objectivity and formality of scientific research writing. Software was developed to identify the errors discovered and provide learners with actionable advice and multimodal explanations in both English and Japanese. Qualitative evaluation received in usability studies and focus groups from both teachers and students was extremely positive. Preliminary quantitative evaluation of the effectiveness of the error detector was conducted. Through this pedagogic tool, learners can receive immediate actionable feedback on potential errors, and their teachers no longer feel obliged to check for common genre-specific errors.

Automatic detection of contouring errors using convolutional neural networks.

PurposeTo develop a head and neck normal structures autocontouring tool that could be used to automatically detect the errors in autocontours from a clinically validated autocontouring tool.MethodsAn autocontouring tool based on convolutional neural networks (CNN) was developed for 16 normal structures of the head and neck and tested to identify the contour errors from a clinically validated multiatlas‐based autocontouring system (MACS). The computed tomography (CT) scans and clinical contours from 3495 patients were semiautomatically curated and used to train and validate the CNN‐based autocontouring tool. The final accuracy of the tool was evaluated by calculating the Sørensen–Dice similarity coefficients (DSC) and Hausdorff distances between the automatically generated contours and physician‐drawn contours on 174 internal and 24 external CT scans. Lastly, the CNN‐based tool was evaluated on 60 patients' CT scans to investigate the possibility to detect contouring failures. The contouring failures on these patients were classified as either minor or major errors. The criteria to detect contouring errors were determined by analyzing the DSC between the CNN‐ and MACS‐based contours under two independent scenarios: (a) contours with minor errors are clinically acceptable and (b) contours with minor errors are clinically unacceptable.ResultsThe average DSC and Hausdorff distance of our CNN‐based tool was 98.4%/1.23 cm for brain, 89.1%/0.42 cm for eyes, 86.8%/1.28 cm for mandible, 86.4%/0.88 cm for brainstem, 83.4%/0.71 cm for spinal cord, 82.7%/1.37 cm for parotids, 80.7%/1.08 cm for esophagus, 71.7%/0.39 cm for lenses, 68.6%/0.72 for optic nerves, 66.4%/0.46 cm for cochleas, and 40.7%/0.96 cm for optic chiasm. With the error detection tool, the proportions of the clinically unacceptable MACS contours that were correctly detected were 0.99/0.80 on average except for the optic chiasm, when contours with minor errors are clinically acceptable/unacceptable, respectively. The proportions of the clinically acceptable MACS contours that were correctly detected were 0.81/0.60 on average except for the optic chiasm, when contours with minor errors are clinically acceptable/unacceptable, respectively.ConclusionOur CNN‐based autocontouring tool performed well on both the publically available and the internal datasets. Furthermore, our results show that CNN‐based algorithms are able to identify ill‐defined contours from a clinically validated and used multiatlas‐based autocontouring tool. Therefore, our CNN‐based tool can effectively perform automatic verification of MACS contours.

The Development and Evaluation of a Corpus-based Spanish Collocation Error Detection and Revision Suggestion Tool

The topic of collocation has drawn attention for the past three decades in the lexical area of theoretical and applied linguistics. Our research team developed and evaluated a corpus-based assisted tool for collocation learning in Spanish. Based on the two constructed corpora (CEATE and CPEIC) and a Spanish collocation extraction tool (HCE), this computer-assisted learning tool, SpColEDRS, is easy to use to detect errors and also suggests revisions for Spanish collocations. Based on the evaluation, the research results indicated that the tool developed in this research can assist learners effectively, especially in the case of beginners. In addition, the results of the satisfaction survey provided positive confirmation of the effectiveness of this tool in assisting the learning of Spanish collocations. Finally, this study shed light on pedagogical applications of the constructed corpora and the learning of Spanish collocation with a corpus-based approach in a multilingual acquisition setting.

Development and Validation of a Bayesian Network Method to Detect External Beam Radiation Therapy Physician Order Errors

To investigate a Bayesian network (BN)-based method to detect errors in external beam radiation therapy physician orders. A total of 4431 external beam radiation therapy orders from 2008 to 2017 at the authors' institution were obtained from clinical treatment management systems and divided into 3 groups: single prescription, concurrent boost, and sequential boost. Multiple BNs were developed for each group to detect errors in new orders using joint posterior probabilities of the order parameters, given disease information. Each BN was trained with a group of orders using a Bayesian learning algorithm. A procedure was developed to select the optimal BN for each treatment site in each group and to determine site-specific parameters and error detection thresholds. Potential clinical errors, created both manually and automatically, were applied to test error detection performance. The average true-positive rate (TPR) and false-positive rate (FPR) of error detection were 95.72% and 1.99%, respectively, for the single-prescription cohort with 9 treatment sites. For the concurrent-boost cohort, the TPR and FPR were 92.94% and 14.53%, respectively. For the sequential-boost cohort, the TPR and FPR were 100% and 9.48%, respectively, for the prescribed dose values and 100% and 4.34%, respectively, for the remaining order parameters. For the patient simulation and imaging parameters for 9 treatment sites, the TPR and FPR were 100% and 4.96%, respectively. The probabilistic BN method was able to perform physician order error detection at a higher accuracy than previously reported in a variety of complex prescription instances, thus warranting further development in incorporating BNs into clinical error detection tools to assist manual physician order checks.