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Background: Patient reported outcome measures (PROMs) provide valuable insight on patients' well-being and facilitates communication between healthcare providers and their patients. The increased integration of the technology within the healthcare setting presents the opportunity to collect PROMs electronically, rather than on paper. The Childhood Health Assessment Questionnaire (CHAQ) and Quality of My Life (QoML) are common PROMs collected from pediatric rheumatology patients. The objectives of this study are to (a) determine the equivalence of the paper and electronic forms (e-form) of CHAQ and QoML questionnaires; (b) identify potential benefits and barriers associated with using an e-form to capture PROMs; and (c) gather feedback on user experience., Methods: Participants completed both a paper and an e-form of the questionnaires in a randomized order, following which they completed a feedback survey. Agreement of the scores between the forms were statistically analyzed using the intraclass correlation coefficient (ICC) (95 % Confidence Interval (CI)) and bias was assessed using a Bland-Altman plot. Completion and processing times of the forms were compared using mean and median measures. Quantitative analysis was performed to assess user experience ratings, while comments were qualitatively analyzed to identify important themes., Results: 196 patients participated in this project. Scores on the forms had high ICC agreement > 0.9. New patients took longer than returning patients to complete the forms. Overall, the e-form was completed and processed in a shorter amount of time than the paper form. 83 % of survey respondents indicated that they either preferred the e-form or had no preference. Approximately 10 % of respondents suggested improvements to improve the user interface., Conclusions: E-forms collect comparable information in an efficient manner to paper forms. Given that patients and caregivers indicated they preferred completing PROMs in this manner, we will implement their suggested changes and incorporate e-forms as standard practice for PROMs collection in our pediatric rheumatology clinic.

During graduate education, postgraduates have to spend considerable time finding papers to explore the development branches of their field. However, existing paper recommendation methods focus on several attributes (title, author, keyword, venue, etc.). The network schema constructed by these attributes is extremely sparse, which easily causes the loss of important semantic paths between attributes. This results in a lack of correlations among relevant papers, which affects paper recommendation efficiency. Moreover, the relationships between multiple semantic paths can be found through common homogeneous and heterogeneous attributes. These relationships can establish many correlations among relevant papers. To address the above problems, this paper proposes a new approach to fuse multi-semantic paths into a heterogeneous educational network (HEN) for personalized paper recommendation. After data processing, a new HEN schema is built by enriching nodes and edges in heterogeneous networks. Then, different semantic meta-paths are generated by projection sub-nets. Next, a new HEN embedding method is proposed by multi-semantic path fusion to generate rich HEN node sequences. Finally, personalized paper recommendation for postgraduates by targeted path similarity. The proposed method was performed on two paper datasets in the fields of educational intergenerational mobility from 1987 to 2021 and data mining and intelligent media from 1997 to 2021. Substantial experiments demonstrate that the proposed approach is effective. [ABSTRACT FROM AUTHOR]

Introduction: This study compared the frequency of oral counseling and written information by primary care physicians at paper medical record (PMR) clinics and electronic medical record (EMR) clinics, and assessed relationships between medication counseling and medication outcomes (knowledge, questions, reported adherence and side effects, and medication fill)., Methods: A cross-sectional study with two convenience samples of English-speaking adult patients receiving > or =1 prescription at the primary care index visit was conducted in two PMR clinics, with 184 (48% response) patients seen by one of 22 physicians, and in two EMR clinics, with 249 (37% response) patients seen by one of 25 physicians. Data were from medical record reviews of the index visit and 2-week post-visit telephone interviews., Results: Three mutually exclusive counseling categories were evaluated. Patients received 1,095 prescriptions, 61% with oral counseling for indications, 21% with oral counseling for indications and side effects, and 12% with written information plus oral ("multi-mode") counseling. General linear mixed models found 1) less multi-mode counseling in PMR clinics (2%) than EMR clinics (20%); 2) PMR and EMR clinics were similar in oral counseling for indications and side effects; and 3) PMR clinics provided more oral counseling only for indications (69%) than EMR (53%) clinics. The impact of receiving oral or written counseling on patients' reports of having questions about their medications was inconclusive. Not receiving oral counseling for indications was associated with more questions, but not receiving written information was associated with fewer questions. Filling a prescription was lower when no oral counseling for indications and side effects was reported, but the absence of written information was associated with more prescriptions fills., Conclusions: Physicians' use of EMR to print medication information did not seem to compromise their oral counseling for medication indications and side effects. This feature of the EMR was underutilized by physicians; however, future studies addressing patient recall and evaluating the quality and content of medication counseling are needed.

Background: Although most health-related quality of life questionnaires are self-administered by means of paper and pencil, new technologies for automated computer administration are becoming more readily available. Novel methods of instrument administration must be assessed for score equivalence in addition to consistency in reliability and validity., Objectives: The present study compared the psychometric characteristics (score equivalence and structure, internal consistency, and reproducibility reliability and construct validity) of the Quality of Life in Reflux And Dyspepsia (QOLRAD) questionnaire when self-administered by means of paper and pencil versus touch-screen computer. The influence of age, education, and prior experience with computers on score equivalence was also examined., Research Design: This crossover trial randomized 134 patients with gastroesophageal reflux disease to 1 of 2 groups: paper-and-pencil questionnaire administration followed by computer administration or computer administration followed by use of paper and pencil. To minimize learning effects and respondent fatigue, administrations were scheduled 3 days apart. A random sample of 32 patients participated in a 1-week reproducibility evaluation of the computer-administered QOLRAD., Results: QOLRAD scores were equivalent across the 2 methods of administration regardless of subject age, education, and prior computer use. Internal consistency levels were very high (alpha = 0.93-0.99). Interscale correlations were strong and generally consistent across methods (r = 0.7-0.87). Correlations between the QOLRAD and Short Form 36 (SF-36) were high, with no significant differences by method. Test-retest reliability of the computer-administered QOLRAD was also very high (ICC = 0.93-0.96)., Conclusions: Results of the present study suggest that the QOLRAD is reliable and valid when self-administered by means of computer touch-screen or paper and pencil.

Background: Electronic implementation of questionnaires has many advantages, but there may be concerns that it alters versions that were validated on paper., Objective: To determine whether electronic implementation alters responses to the SF-36 and asthma quality of life questionnaire (AQLQ), compared to paper implementation., Methods: Patients with asthma presenting to a pneumologist were asked for consent to participate. Each patient completed both forms of each questionnaire. The order of presentation was alternated sequentially, with the first patient completing the electronic version first. Each patient waited at least 2 hours between completions to minimize recollection of answers. For both the SF-36 and AQLQ, intraclass correlations coefficients were calculated to compare patients' scores, for each scale and overall, on the electronic and paper versions., Results: Sixty-eight patients (mean age: 48 years, 50 females) of 311 contacted were enrolled. Overall intraclass correlation coefficients for the SF-36 and AQLQ were excellent (0.965 and 0.991 respectively). For paper versions, eight questions (AQLQ) and 24 (SF-36) were left blank and nine questions (SF-36) were answered incorrectly by patients selecting more than one answer. Electronic data for one patient could not be retrieved., Conclusion: Collecting SF-36 and AQLQ data electronically can decrease the number of spoiled responses without altering the results. Successful implementation depends on proper instruction of the respondent in the handling of the electronic instrument.

The effort to standardize electronic claims and other transactions continues to move ahead as HIPAA rules near completion. The goal: a simpler, less paper-intensive system that saves money.

Even though much of the quality-assurance process had been automated at Kettering Medical Center (KMC) in Dayton, Ohio, reviewers still had to key in data from paper records each day. An extensive paper trail continued until Kettering took the leap to total paperless review using eight laptop computers with internal modems.

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The overall purpose of this paper is to demonstrate how data preprocessing, training size variation, and subsampling can dynamically change the performance metrics of imbalanced text classification. The methodology encompasses using two different supervised learning classification approaches of feature engineering and data preprocessing with the use of five machine learning classifiers, five imbalanced sampling techniques, specified intervals of training and subsampling sizes, statistical analysis using R and tidyverse on a dataset of 1000 portable document format files divided into five labels from the World Health Organization Coronavirus Research Downloadable Articles of COVID-19 papers and PubMed Central databases of non-COVID-19 papers for binary classification that affects the performance metrics of precision, recall, receiver operating characteristic area under the curve, and accuracy. One approach that involves labeling rows of sentences based on regular expressions significantly improved the performance of imbalanced sampling techniques verified by performing statistical analysis using a t-test documenting performance metrics of iterations versus another approach that automatically labels the sentences based on how the documents are organized into positive and negative classes. The study demonstrates the effectiveness of ML classifiers and sampling techniques in text classification datasets, with different performance levels and class imbalance issues observed in manual and automatic methods of data processing. [ABSTRACT FROM AUTHOR]

Automatic data processing represents the future for the development of any system, especially in scientific research. In this paper, we describe one of the automatic classification methods applied to scientific research as a supervised learning task. Throughout the process, we identify the main features that are used as keys to play a significant role in terms of predicting the new rank under the supervised learning setup. First, we propose an overview of the work that has been realized in ranking scientific research papers. Second, we evaluate and compare some of state-of-the-art for the classification by supervised learning, semi-supervised learning and non-supervised learning. During the preliminary tests, we have obtained good results for performance on realistic corpus then we have compared performance metrics, such as NDCG, MAP, GMAP, F-Measure, Precision and Recall in order to define the influential features in our work. [ABSTRACT FROM AUTHOR]

The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how science works and where innovation occurs. Yet as datasets grow, it becomes increasingly difficult to track available sources and linkages across datasets. Here we present SciSciNet, a large-scale open data lake for the science of science research, covering over 134M scientific publications and millions of external linkages to funding and public uses. We offer detailed documentation of pre-processing steps and analytical choices in constructing the data lake. We further supplement the data lake by computing frequently used measures in the literature, illustrating how researchers may contribute collectively to enriching the data lake. Overall, this data lake serves as an initial but useful resource for the field, by lowering the barrier to entry, reducing duplication of efforts in data processing and measurements, improving the robustness and replicability of empirical claims, and broadening the diversity and representation of ideas in the field. [ABSTRACT FROM AUTHOR]

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care. [ABSTRACT FROM AUTHOR]

Designing clean, reusable, and repeatable experiments for a research paper does not have to be difficult. We report on our efforts to create an integrated toolchain for running, processing, and including the results of computer experiments in scientific publications. [ABSTRACT FROM AUTHOR]

Folk art is a "living fossil" of hisory, whose existence and evolution reflect the way people live and think. It is a one-of-a-kind way of communicating the process of human development to the rest of the world. However, as China's economic development and cultural globalization accelerate, many folk arts are being severely harmed and lost. The emergence of virtual reality technology, which benefits from the advantages of virtual reality technology, has greatly strengthened the utilization and protection of folk art resources in recent years, with the rapid development of Internet technology. Virtual reality technology is a human-computer interaction experience technology that uses complex calculations to provide corresponding operational feedback to the senses of touch, hearing, vision, and balance. It is more reasonable and perfect to reconstruct and integrate the resource content and realize the digital utilization of folk art resources in this paper, which is based on the ALS collaborative filtering algorithm. This paper will make some necessary improvements to the original algorithm, which has met the requirements of virtual reality technology, through the comparison of algorithms. Finally, experiments show that the proposed algorithm's accuracy is 85 percent higher than the original algorithms, and the data retrieval processing interval is more reasonable. [ABSTRACT FROM AUTHOR]

This study aims to explore digital transformation and the level of sophistication in the digital data handling strategy by agri-food small and medium-sized enterprises (SMEs). By developing an exploratory multiple-case study that involves fourteen food processing SMEs, this paper identifies a data processing flow made up of the following phases: data generation, data acquisition, data storage, data analysis, and data exploitation. In addition, it shows how data processing flow may occur in a number of ways, thereby classifying the agri-food SMEs' digital transformation as paper master, digital wannabe, and digital champion. Hence, the article contributes to providing the conceptualization of a data processing flow, which includes the progressive stages in data management identified across firms by proposing a data handling strategy framework. Such a framework offers a snapshot of firms at various levels of digitization in their handling of various kinds of data, thus clarifying how the strategies of data processing flow occur. [ABSTRACT FROM AUTHOR]

The preservation of paper-based artifacts is an essential issue for collection management in academic libraries. In recent years, the library science profession has often favored reformatting through microfilm or digitization, assuming too quickly that information matters, whereas an artifact's medium does not. However, much recent humanities scholarship has demonstrated the significance of physical artifacts per se. In fact, it is often the most cutting-edge scholarship on race, gender, and history that requires the physical artifact itself for research. Although librarians obviously cannot save every physical artifact, any decision on which artifacts to preserve, deaccession, or reformat (and, for that matter, on how to reformat) requires an understanding of the importance of the original physical format. Since texts acquire meaning through an intricate interplay between physical form and abstract information, the reduction of texts to abstract information alone evinces a misunderstanding of the artifacts in our care and an inadequate theory on which to base preservation decisions. [ABSTRACT FROM AUTHOR]

In order to improve the effect of smart city construction, this paper combines smart buildings and ethical computing to conduct research on smart city edge computing. The new smart city architecture based on the flexible deployment of edge computing and data slicing capabilities provides support for the transformation of smart city construction from hardware embedded technology, access means, and software data processing. Moreover, this paper uses information technology to collect, process, analyze, use the information to achieve intelligence, and integrate resources and information of cities and people to build a smart city functional architecture. Moreover, this paper combines simulation technology for experimental research. Through experimental analysis, it can be seen that the smart city edge computing method based on smart buildings and ethical computing proposed in this paper has good results. [ABSTRACT FROM AUTHOR]

Abstract: MobilePaperAccess is a wearable camera-glasses system with tangible user interface allowing mobile interaction. We access the digital information from a paper interface extending the input space. The system is devised to validate our concepts of Environment Dependent Interface (EDI) and Environment Independent Interface (EII), which focus on enabling people to access their personal data as well as public resources at any time and in any place. In this paper, we propose a continuum from physical interface to digital interface in relation with EDI and EII, and we present design, implementation and evaluation aspects of our MobilePaperAccess system. We compare two interfaces (EDI and EII) and three input techniques (finger input, mask input and page input). [Copyright &y& Elsevier]

Abstract: Lepton is an automaton for literate executable papers. It enables researchers to publish their work in the form of a script or program that can generate the research paper along with the corresponding source code, input data and output results. Lepton files do not contain pre-computed results, but the full set of instructions for reproducing the results presented in the manuscript. Taking inspiration from literate programming for code review and code re-use, we have written Lepton to facilitate the review and re-use of computational methods. Lepton is designed to provide strong guarantees for the reproducibil-ity of the results, many features for easily applying the methods to new data while remaining unobtrusive and easy to deploy in any environment. Lepton is designed for writing reproducible technical reports during method development, and journal manuscripts when the research is polished. Developped independently from the Elsevier Executable Paper Grand Challenge, it addresses similar issues and objectives. This manuscript was written with Lepton. [Copyright &y& Elsevier]

Abstract: The paper describes a system for executable papers for publishers enabling them to reuse content and to generate further advances of science and engineering. The executable algorithmic descriptions within a paper are presented in natural language sentences and basic code, thereby making long term compatibility absolute. Authors are required to use publicly numerical libraries on the Internet or references to publications with executable papers. As used by authors the system automatically creates a web of algorithmic knowledge on the Internet. Novelty of new algorithms in publications can be evaluated by automated tools available to authors, reviewers and readers of scientific papers published. [Copyright &y& Elsevier]

Abstract: This paper describes how SHARE (Sharing Hosted Autonomous Research Environments) satisfies the criteria of the Elsevier 2011 Executable Paper Grand Challenge. This challenge aims at disseminating the use of systems that provide reviewers and fellow scientists a convenient way to reproduce computational results of research papers. This can involve among others the calculation of a number, the plotting of a diagram, the automatic proof of a theorem or the interactive transformation of various inputs into a complex output document. Besides reproducing the literate results, readers of an executable paper should also be able to explore the result space by entering different input parameters than the ones reported in the original text. SHARE is a web portal that enables academics to create, share, and access remote virtual machines that can be cited from research papers. By deploying in SHARE a copy of the required operating system as well as all the relevant software and data, authors can make a conventional paper fully reproducible and interactive. Shared virtual machines can also contain the original paper text— when desirable even with embedded computations. This paper shows the concrete potential of SHARE-based articles by means of an example virtual machine that is based on a conventional research article published by Elsevier recently. More generally, it demonstrates how SHARE has supported the publication workflow of a journal special issue and various workshop proceedings. Finally, it clarifies how the SHARE architecture supports among others the Elsevier challenge''s licensing and scalability requirements without domain specific restrictions. [Copyright &y& Elsevier]

Abstract: For centuries, the research paper have been the main vehicle for scientific progress. From the paper, readers in the scientific community are expected to extract all the relevant information necessary to reproduce and validate the results presented by the paper''s authors. However, the increased use of computer software in science makes reproducing scientific results increasingly difficult. The research paper in its current state is no longer sufficient to fully reproduce, validate, or review a paper''s experimental results and conclusions. This impedes scientific progress. To remedy these concerns, we introduce PaperMâché, a new system for creating dynamic, executable research papers. The key novelty of PaperMâché is its use of virtual machines, which lets readers and reviewers easily view and interact with a paper, and reproduce key experimental results. For authors, the Paper Mâché workbench provides an easy-touse interface to build an executable paper. By transforming the static research paper into a dynamic and interactive entity, Paper Mâché brings the presentation of scientific results into the 21st century. We believe that Paper Mâché will become indispensable to the scientific process, and increase the visibility of key findings among members and non-members of the scientific community. [Copyright &y& Elsevier]

Abstract: We present the Lab Wiki, an executable paper platform primarily designed but not limited to networking experiment-based research. The LabWiki leverages the current state of the art tools for the orchestration of experiments in the networking community and propose a new approach to execute and reproduce experiments. We demonstrate the usability of the LabWiki through an example at the boundary between network and high performance computing researches. [Copyright &y& Elsevier]

Abstract: Adessowiki is a collaborative platform for scientific programming and document writing. It is a wiki environment that carries simultaneously documentation, programming code and results of its execution without any software con- figuration such as compilers, libraries and special tools at the client side. This combination of a collaborative wiki environment, central server and execution of code at rendering time enables the use of Adessowiki as an executable paper platform, since it fulfills the need to disseminate, validate, and archive research data. [Copyright &y& Elsevier]

Among the activities that burden capital in the supply chain of forest-based industries, the activity of road transport of wood deserves to be highlighted. Machine learning techniques are applied the knowledge extracted from real data, and support strategies that aim to maximize the resources destined for it. Based on variables inherent to the wood transport activity, we verified whether machine learning models can act as predictors of the volume of wood to be transported and support strategic decision-making. The database came from companies in the pulp and paper segments, which totaled 26,761 data instances. After the data wrangling process, machine learning algorithms were used to build models, which were optimized from the hyperparameter adjustment and selected to compose the blended learning hierarchy. In addition to belonging to different methodological basis, a CatBoost Regressor, Decision Tree Regressor, and K Neighbors Regressor were selected mainly for providing minimal values to errors metrics and maximal values to determination coefficient. The learning by stack stands out, with a coefficient of determination of 0.70 and an average absolute percentage error of 6% in the estimation of the volume of wood to be transported. Based on variables inherent to the wood transport process, we verified that machine learning models can act in the prediction of the volume of wood to be transported and support strategic decision-making. [ABSTRACT FROM AUTHOR]

Abstract: This proposal describes how data, program code, and presentation can be stored together in a single file suitable for electronic publication and permitting the reproduction of computational results.. Universality, effciency, platformindependence, automated verifiability, and provenance tracking are the major design criteria. Existing and well-tested technology is used as much as possible, the two major building blocks being the Hierarchical Data Format for storage and the Java Virtual Machine for platform-independent code representation and secure execution. [Copyright &y& Elsevier]

Abstract: We present Open Review, a web-based platform aimed at stimulating executable papers by means of post-publication peer-review. Its goal is to bring computer science researchers to collaboratively build their work upon previous research results, in such a way that transparency, reproducibility and sustainability of research results are greatly improved. The main design goals of the platform are clarity, conciseness, and reproducibility. Its main features are to: (i) provide incentives for making research communities to participate, (ii) make papers executable by means of boards’ annotations, without necessarily involving the authors of an article, and (iii) give snapshots of the current research state on any given article. [Copyright &y& Elsevier]

This study explores the development of research on the hajj's economy (pilgrimage) published in reputable journal papers. The data analysed included more than 66 indexed research publications. The entire sample of journal publications has been published during the 18 years from 2003 to 2020. The export data is then processed and analysed using the VOSviewer application to determine a bibliometric map of the research's development. The results show that the number of publications on the development of hajj's economic research from 2003-2020 experienced a fluctuating increase, and most papers were published in 2019 with 15 articles (22,7%). Network visualisation showed that the map of the research was divided into 4 clusters. Cluster 1 consists of 15 topics; cluster 2 consists of 11 topics; cluster 3 consists of 11 topics, and cluster 4 consists of 10 topics. The findings of this study are in the form of suggestions for alternative research on the hajj investment fund. [ABSTRACT FROM AUTHOR]

In this paper, research was conducted on anomaly detection of wheel flats. In the railway sector, conducting tests with actual railway vehicles is challenging due to safety concerns for passengers and maintenance issues as it is a public industry. Therefore, dynamics software was utilized. Next, STFT (short-time Fourier transform) was performed to create spectrogram images. In the case of railway vehicles, control, monitoring, and communication are performed through TCMS, but complex analysis and data processing are difficult because there are no devices such as GPUs. Furthermore, there are memory limitations. Therefore, in this paper, the relatively lightweight models LeNet-5, ResNet-20, and MobileNet-V3 were selected for deep learning experiments. At this time, the LeNet-5 and MobileNet-V3 models were modified from the basic architecture. Since railway vehicles are given preventive maintenance, it is difficult to obtain fault data. Therefore, semi-supervised learning was also performed. At this time, the Deep One Class Classification paper was referenced. The evaluation results indicated that the modified LeNet-5 and MobileNet-V3 models achieved approximately 97% and 96% accuracy, respectively. At this point, the LeNet-5 model showed a training time of 12 min faster than the MobileNet-V3 model. In addition, the semi-supervised learning results showed a significant outcome of approximately 94% accuracy when considering the railway maintenance environment. In conclusion, considering the railway vehicle maintenance environment and device specifications, it was inferred that the relatively simple and lightweight LeNet-5 model can be effectively utilized while using small images. [ABSTRACT FROM AUTHOR]

The smart grid communication parties need to process the data by the trusted central node, which will lead to security issues such as single‐point attacks and data tampering. This paper proposes a smart grid data access control scheme based on blockchain, the user completes the registration of the smart meter by three encryptions. After the registration is completed, the registration information will be uploaded to the blockchain. In the data access phase, the verification center verifies the user's data access request, the database will accept the user's request for data if the verification is passed, and that will be broadcasted on the entire network and uploaded to the blockchain. The security of the scheme is analyzed by using a random oracle model. Analysis shows that this scheme can resist public key replacement attacks and malicious key generation center (KGC) attacks. Compared with the existing scheme, this scheme can more effectively resist more types of attacks. It shows that the smart grid data access control scheme proposed in this paper is safe, reliable and efficient. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for the detection and assessment of internal corrosion in concrete bridge decks. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper analyzes some common abnormal data from actual bridges GPR data and proposes corresponding automatic algorithms for anomaly correction to enhance assessment accuracy. The automatic algorithm focuses on two main aspects: correcting anomalies in direct coupling wave amplitudes based on data statistics and mitigating the impact of abnormal data due to incorrectly picked rebar on depth correction using density clustering. The specific process of the automatic method can be divided into four steps. First, automatic rebar picking is performed based on the preprocessed GPR data. Next, data statistics analysis is implemented on the extracted rebar data to identify and correct abnormal amplitude data. Then, the true rebar data are identified for depth correction based on density clustering. Finally, the bridge deck corrosion map is generated based on the corrected rebar reflection amplitudes and rebar positions. The feasibility of this method was verified through a case study with GPR data from two in-service bridges. The results show that this method can effectively and automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by the proposed method is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR. Practical Applications: Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for concrete bridge deck corrosion detection and assessment. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper proposes a set of automatic data processing procedures for anomaly correction to improve the corrosion assessment accuracy. The feasibility of the proposed algorithms was validated through a case study with GPR data from two in-service bridges. The results show that these algorithms can effectively automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by these algorithms is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR. [ABSTRACT FROM AUTHOR]

This document is a summary of a collection of research papers in the fields of astrophysics and geophysics. The papers cover a wide range of topics, including big data, climate change, and remote sensing. They provide datasets and analyses related to landslides, detonation craters, coastal change, and spectroscopy. The research has potential applications in fields such as climate change and astronomy. The papers underwent peer review and are considered valuable contributions to the scientific community. The authors express their gratitude to the contributors and reviewers for their efforts. [Extracted from the article]

The National Oceanic and Atmospheric Administration (NOAA) has operated a network of Fischer & Porter gauges providing hourly and subhourly precipitation observations as part of the U.S. Cooperative Observer Program since the middle of the twentieth century. A transition from punched paper recording to digital recording was completed by NOAA's National Weather Service in 2013. Subsequently, NOAA's National Centers for Environmental Information (NCEI) upgraded its quality assurance and data stewardship processes to accommodate the new digital record, better assure the quality of the data, and improve the timeliness by which hourly precipitation observations are made available to the user community. Automated methods for removing noise, detecting diurnal variations, and identifying malfunctioning gauges are described along with quality control algorithms that are applied on hourly and daily time scales. The quality of the hourly observations during the digital era is verified by comparison with hourly observations from the U.S. Climate Reference Network and summary of the day precipitation totals from the Global Historical Climatology Network dataset. [ABSTRACT FROM AUTHOR]