Search Results (20)

This study aimed to develop a large language model (LLM) chatbot for radiation therapy education and compare the performance of portable document format (PDF)- and webpage-based question-and-answer (Q&A) chatbots. An LLM chatbot was created using the EmbedChain framework, OpenAI GPT-3.5-Turbo API, and Gradio UI. The performance of both chatbots was evaluated based on 10 questions and their corresponding answers, using the parameters of accuracy, semantic similarity, consistency, and response time. The accuracy scores were 0.672 and 0.675 for the PDF- and webpage-based Q&A chatbots, respectively. The semantic similarity between the two chatbots was 0.928 (92.8%). The consistency score was one for both chatbots. The average response time was 3.3 s and 2.38 s for the PDF- and webpage-based chatbots, respectively. The LLM chatbot developed in this study demonstrates the potential to provide reliable responses for radiation therapy education. However, its reliability and efficiency must be further optimized to be effectively utilized as an educational tool. Full article

Quartz crystal out-of-plane vibration units are critical components of QMEMS devices. However, the fabrication of their 3D sidewall electrode structures presents significant challenges, particularly within ultrafine etched grooves. These challenges seriously limit further miniaturization, which is critical for portable and wearable electronic applications. In this paper, we propose a novel 3D self-masking fabrication strategy that enables the precise formation of sidewall electrodes by using the etched beam structure as a self-aligned pattern transfer medium. Based solely on photolithography and wet etching processes, this approach overcomes the limitations of the conventional shadow mask technique by improving alignment accuracy, process efficiency, and fabrication yields. In addition, a predictive mathematical model was developed to guide process optimization, enabling adaptive and reliable fabrication. Sidewall electrodes were successfully achieved in etched grooves as narrow as 45 μm, closely matching the theoretical predictions. To validate the approach, an ultra-miniaturized out-of-plane vibration unit with a beam spacing of just 150 μm—the narrowest reported to date—was fabricated, representing an 80% reduction compared to previously documented structures. The unit exhibited a repeatability error below 1.13%, confirming the precision and reliability of the proposed fabrication strategy. Full article

Objectives: Buccal cortical bone dimensions are crucial in dental radiology, as they impact orthodontic treatment outcomes. Changes in alveolar bone dimensions can result in malocclusion and require interdisciplinary approaches for correction. The accurate quantification of buccal bone dimensions is crucial for appropriate treatment planning and avoiding medico-legal issues. This study aimed to compare buccal bone height measurements between three-dimensional (3D) digital imaging and communications in medicine (DICOM) data and portable document format (PDF) cone beam computer topography reports for mandibular frontal teeth, testing the hypothesis of no difference in values between the two modalities. Methods: Each of the five observers performed a total of 720 height measurements (360 by DICOM and 360 by PDF), yielding a total of 3600 measurements overall. Results: Compared with the DICOM format, using PDF files was associated with a significantly greater rate of inability to carry out the measurements (8.8% vs. 3%, respectively, p < 0.001, chi-square). The average buccal bone height measured in the DICOM was 11.51 mm, which was significantly greater than the 10.35 mm measured in the PDF (p < 0.001). The mean height measured by the DICOM was consistently greater than that measured by the PDF, with highly significant differences in the findings of four of the examiners (p < 0.001). Conclusions: Viewing modality significantly affected the height of the buccal bone in the frontal mandibular area. Compared with the generated PDF reports, the 3D DICOM viewer performed better than the printed PDF and enabled more measurements in the target area. Full article

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced an optical character recognition (OCR) system for text detection and recognition that leverages transformer-based generative deep learning models and transfer learning approaches to enhance text recognition accuracy in engineering documents. The proposed system was evaluated on a dataset collected from ships’ engineering drawings provided by a U.S. agency. Experimental results demonstrated that the proposed transformer-based OCR model significantly outperformed pretrained off-the-shelf OCR models. Full article

Diagnosing congenital heart disease (CHD) remains challenging because of its complex morphology. Representing the intricate structures of CHD on conventional two-dimensional flat screens is difficult owing to wide variations in the pathologies. Technological advancements, such as three-dimensional-printed heart models (3DPHMs) and virtual reality (VR), could potentially address the limitations of viewing complex structures using conventional methods. This study aimed to investigate the usefulness and clinical value of four visualization modalities across three different cases of CHD, including ventricular septal defect, double-outlet right ventricle, and tetralogy of Fallot. Seventeen cardiac specialists were invited to participate in this study, which was aimed at assessing the usefulness and clinical value of four visualization modalities, namely, digital imaging and communications in medicine (DICOM) images, 3DPHM, VR, and 3D portable document format (PDF). Out of these modalities, 76.4% of the specialists ranked VR as the best for understanding the spatial associations between cardiac structures and for presurgical planning. Meanwhile, 94.1% ranked 3DPHM as the best modality for communicating with patients and their families. Of the various visualization modalities, VR was the best tool for assessing anatomical locations and vessels, comprehending the spatial relationships between cardiac structures, and presurgical planning. The 3DPHM models were the best tool for medical education as well as communication. In summary, both 3DPHM and VR have their own advantages and outperform the other two modalities, i.e., DICOM images and 3D PDF, in terms of visualizing and managing CHD. Full article

Background/Objectives: Imaging studies are often an integral part of patient evaluation and serve as the primary means of communication between radiologists and referring physicians. This study aimed to evaluate brain Magnetic Resonance Imaging (MRI) reports and to determine whether these reports follow a standardized or narrative format. Methods: A series of 466 anonymized MRI reports from an academic hospital were downloaded from the Picture Archiving and Communication System (PACS) in portable document format (pdf) for the period between August 2017 and March 2018. Two hundred brain MRI reports, written by four radiologists, were compared to a structured report template from the Radiology Society of North America (RSNA) and were included, whereas MR-modified techniques, such as MRI orbits and MR venography reports, were excluded (n = 266). All statistical analyses were conducted using Statistical Package for the Social Sciences (SPSS) statistical software (version 16.4.1, MedCalc Software). Results: None of the included studies used the RSNA template for structured reports (SRs). The highest number of brain-reported pathologies was for vascular disease (24%), while the lowest was for infections (3.5%) and motor dysfunction (5.5%). Radiologists specified the Technique (n = 170, 85%), Clinical Information (n = 187, 93.5%), and Impression (n = 197, 98.5%) in almost all reports. However, information in the Findings section was often missing. As hypothesized, radiologists with less experience showed a greater commitment to reporting additional elements than those with more experience. Conclusions: The SR template for medical imaging has been accessible online for over a decade. However, many hospitals and radiologists still use the free-text style for reporting. Our study was conducted in an academic hospital with a fellowship program, and we found that structured reporting had not yet been implemented. As the health system transitions towards teleservices and teleradiology, more efforts need to be put into advocating standardized reporting in medical imaging. Full article

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. Full article

In just one year, Spaceopal and its partners developed the Galileo HAS Performance Characterization User Algorithm for the EU Agency for the Space Programme (EUSPA). The Galileo HAS User Terminal (HAUT) hosts the Galileo HAS Performance Characterization User Algorithm. The Galileo HAS User Terminal is a portable, configurable and autonomous device powered by a triple-frequency Galileo and GPS receiver and calculates a single- (Galileo) or multi-constellation (Galileo + GPS) Galileo HAS and Open Service (OS) positioning, velocity and time (PVT) solution. The User Terminal can be configured to retrieve Galileo HAS corrections either from Galileo Signal-in-Space (SIS) over E6-B or Internet Data Distribution (IDD) over NTRIP in an RTCM3 format and works in different frequency combinations that can be configured by the user. The User Terminal is a robust device (IP64) with multiple communication and logging capabilities. The Galileo HAS Initial Service was declared on 24 January by the European Commission, and provides free-of-charge, high-accuracy Precise Point Positioning (PPP) corrections (orbits, clocks) and code biases for Galileo and GPS to achieve real-time improved user positioning performance. The Galileo HAS Service Definition Document (SDD) and the HAS SIS Interface Control Document (HAS SIS ICD) are freely available to users on the web portal of the European GNSS Service Centre and HAS Internet Data Distribution Interface Control Documents (HAS IDD ICD) are available after registration. Using the Galileo HAS User Terminal, this article presents the results of Galileo HAS User Terminal’s performance, configuring the User Algorithm to assume static dynamics. It is to be noted that this configuration provides a significant performance benefit with respect to a configuration compatible with kinematic operation. Preliminary results indicate the Galileo HAS User Terminal achieves excellent accuracy. Full article

Portable document format (PDF) files are widely used in file transmission, exchange, and circulation because of their platform independence, small size, good browsing quality, and the ability to place hyperlinks. However, their security issues are also more thorny. It is common to distribute printed PDF files to different groups and individuals after printing. However, most PDF watermarking algorithms currently cannot resist print–scan attacks, making it difficult to apply them in leak tracing of both paper and scanned versions of PDF documents. To tackle this issue, we propose an invisible digital watermarking technology based on modifying the edge pixels of text strokes to hide information in PDFs, which achieves high robustness to print–scan attacks. Moreover, it cannot be detected by human perception systems. This method focuses on the representation of text by embedding watermarks by changing the features of the text to ensure that changes in these features can be reflected in the scanned PDF after printing. We first segment each text line into two sub-blocks, then select the row of pixels with the most black pixels, and flip the edge pixels closest to this row. This method requires the participation of original PDF documents in detection. The experimental results show that all peak signal-to-noise ratio (PSNR) values of our proposed method exceed 32 dB, which indicates satisfactory invisibility. Meanwhile, this method can extract the hidden information with 100% accuracy under the JPEG compression attack, and has high robustness against noise attacks and print–scan attacks. In the case of no attacks, the watermark can be recovered without any loss. In terms of practical applications, our method can be applied in the practical leak tracing of official paper documents after distribution. Full article

The number of cyber-attacks is increasing daily, and attackers are coming up with new ways to harm their target by disseminating viruses and other malware. With new inventions and technologies appearing daily, there is a chance that a system might be attacked and its weaknesses taken advantage of. Malware is distributed through Portable Document Format (PDF) files, among other methods. These files’ adaptability makes them a prime target for attackers who can quickly insert malware into PDF files. This study proposes a model based on the Fuzzy Unordered Rule Induction Algorithm (FURIA) to detect PDF malware. The proposed model outperforms currently used methods in terms of reducing error rates and increasing accuracy. Other models, such as Naïve Bayes (NB), Decision Tree (J48), Hoeffding Tree (HT), and Quadratic Discriminant Analysis (QDA), were compared to the proposed model. The accuracy achieved by the proposed model is 99.81%, with an error rate of 0.0022. Full article

The Portable Document Format (PDF) is considered one of the most popular formats due to its flexibility and portability across platforms. Although people have used machine learning techniques to detect malware in PDF files, the problem with these models is their weak resistance against evasion attacks, which constitutes a major security threat. The goal of this study is to introduce three machine learning-based systems that enhance malware detection in the presence of evasion attacks by substantially relying on evasive data to train malware and evasion detection models. To evaluate the robustness of the proposed systems, we used two testing datasets, a real dataset containing around 100,000 PDF samples and an evasive dataset containing 500,000 samples that we generated. We compared the results of the proposed systems to a baseline model that was not adversarially trained. When tested against the evasive dataset, the proposed systems provided an increase of around 80% in the f1-score compared to the baseline. This proves the value of the proposed approaches towards the ability to deal with evasive attacks. Full article

Portable document format (PDF) files are one of the most universally used file types. This has incentivized hackers to develop methods to use these normally innocent PDF files to create security threats via infection vector PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PDF files from malware PDF files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight and accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at a high detection performance and low detection overhead. Full article

Background: Retrospective research on real-world data provides the ability to gain evidence on specific topics especially when running across different sites in research networks. Those research networks have become increasingly relevant in recent years; not least due to the special situation caused by the COVID-19 pandemic. An important requirement for those networks is the data harmonization by ensuring the semantic interoperability. Aims: In this paper we demonstrate (1) how to facilitate digital infrastructures to run a retrospective study in a research network spread across university and non-university hospital sites; and (2) to answer a medical question on COVID-19 related change in diagnostic counts for diabetes-related eye diseases. Materials and methods: The study is retrospective and non-interventional and runs on medical case data documented in routine care at the participating sites. The technical infrastructure consists of the OMOP CDM and other OHDSI tools that is provided in a transferable format. An ETL process to transfer and harmonize the data to the OMOP CDM has been utilized. Cohort definitions for each year in observation have been created centrally and applied locally against medical case data of all participating sites and analyzed with descriptive statistics. Results: The analyses showed an expectable drop of the total number of diagnoses and the diagnoses for diabetes in general; whereas the number of diagnoses for diabetes-related eye diseases surprisingly decreased stronger compared to non-eye diseases. Differences in relative changes of diagnoses counts between sites show an urgent need to process multi-centric studies rather than single-site studies to reduce bias in the data. Conclusions: This study has demonstrated the ability to utilize an existing portable and standardized infrastructure and ETL process from a university hospital setting and transfer it to non-university sites. From a medical perspective further activity is needed to evaluate data quality of the utilized real-world data documented in routine care and to investigate its eligibility of this data for research. Full article

PLATO (Polypharmacology pLATform predictiOn) is an easy-to-use drug discovery web platform, which has been designed with a two-fold objective: to fish putative protein drug targets and to compute bioactivity values of small molecules. Predictions are based on the similarity principle, through a reverse ligand-based screening, based on a collection of 632,119 compounds known to be experimentally active on 6004 protein targets. An efficient backend implementation allows to speed-up the process that returns results for query in less than 20 s. The graphical user interface is intuitive to give practitioners easy input and transparent output, which is available as a standard report in portable document format. PLATO has been validated on thousands of external data, with performances better than those of other parallel approaches. PLATO is available free of charge (http://plato.uniba.it/ accessed on 13 April 2022). Full article

In this study, Portable Document Format (PDF), Word, Excel, Rich Test format (RTF) and image documents are taken as the research objects to study a static and fast method by which to detect malicious documents. Malicious PDF and Word document features are abstracted and extended, which can be used to detect other types of documents. A universal static detection framework for malicious documents based on feature generalization is then proposed. The generalized features include specification check errors, the structure path, code keywords, and the number of objects. The proposed method is verified on two datasets, and is compared with Kaspersky, NOD32, and McAfee antivirus software. The experimental results demonstrate that the proposed method achieves good performance in terms of the detection accuracy, runtime, and scalability. The average F1-score of all types of documents is found to be 0.99, and the average detection time of a document is 0.5926 s, which is at the same level as the compared antivirus software. Full article