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Handwriting is a natural way to communicate and exchange ideas, but converting handwritten diagrams to application‐specific digital formats requires skill and time. Automatic handwritten document conversion can save time, but diagrams and text require different recognition engines. Since accurate segmentation of handwritten diagrams can improve the accuracy of later diagram recognition steps, the authors propose to solve the problem of segmentation of text and non‐text elements of handwritten diagrams using deep semantic segmentation. The model, DeepDP is a flexible U‐net style architecture that can be tuned in complexity to a level appropriate for a particular dataset and diagram type. Experiments on a public hand‐drawn flowchart dataset and a business process diagram dataset show excellent performance, with a per pixel accuracy of 98.6% on the public flowchart datasets and improvement over the 99.3% text stroke accuracy and 96.6% non‐text stroke accuracy obtained by state of the art methods that use online stroke information. On the smaller offline business process diagram dataset, the method obtains a per‐pixel accuracy of 96.9%. [ABSTRACT FROM AUTHOR]

The Inner Northern Apennines (Italy) are a region with a dominant N-S to NNW-SSE fault system, but dissected and offset by several E-W to NE-SW trending structures and lineaments. The knowledge about the nature of these transverse structures, their origin, activity and role in current tectonic motions is limited and debated. To better establish the location, subsurface shape, and kinematics of faults related to the Livorno-Empoli lineament, one of the major transverse structures in the Northern Apennines, we analysed the seismicity in western Tuscany. In the Viareggio Basin we identified and relocated two distinct earthquake clusters as well as calculated 12 new focal mechanisms. The results show that the clusters consisted of several swarms from the years 2006, 2015, 2016 and 2021. The events had a depth between 2 and 15 km and were located along a NE-SW oriented, SE dipping fault system dissecting the Viareggio Basin. Focal mechanisms show oblique normal slip. We interpret the fault system to form a connection between the Viareggio Basin and the Lucca Basin to the east as well as continuing offshore. The results show that the transversal faults of the Inner Northern Apennines are seismogenic, with the length, position and onshore to offshore nature of the fault suggesting reactivation of pre-existing structures. [ABSTRACT FROM AUTHOR]

The relevance of this study is conditioned by the significant importance of the organisation of electronic document management in the Republic of Kazakhstan and the broad prospects that open up during the digitalisation of state archives. The purpose of the study is to investigate the key aspects of the digitalisation of archives in Kazakhstan at the current stage of the country's economic development. The basis of its methodological approach was a theoretical investigation of the processes of digitalisation of archival information under the conditions of practical application of the electronic document management system in the Republic of Kazakhstan, with the results obtained indicating the major significance of the digital transformation of archives in the context of the programmes Resolution of the Government of the Republic of Kazakhstan No. 827 "About Approval of the State Program "Digital Kazakhstan" (2017) and "Information Kazakhstan-2020": State Program (2021) adopted at the state level. The sequential transfer of archival information to digital media for storage offers numerous advantages over traditional paper media, with the state level in Kazakhstan regulating the digital transformation of archives and the adoption and use of a unified electronic document management system by enacting a number of laws that regulate a wide range of related issues. The introduction of an electronic document management system has also established numerous advantages, largely determining the significant prospects for the development of this system of interaction between organisations (state, public, legal entities, and individuals). The practical significance of the results obtained lies in the possibility of their application in the organisation of the activities of enterprises and organisations of the Republic of Kazakhstan that implement measures to digitalise archival data within the unified electronic document management system. [ABSTRACT FROM AUTHOR]

The construction sector's pivotal role in the global economy faces challenges due to its dynamic nature. Inaccurate documentation impacts project cost management, underscoring the need for effective document management systems (DMS), including electronic document management systems (EDMS). This study conducts a systematic literature review to comprehensively examine EDMS implementation, utilization, and effectiveness across sectors. Analyzing peer-reviewed articles and scholarly sources reveals key themes, trends, and findings, providing insights into successful EDMS adoption and best practices. The review contributes evidence-based insights for practitioners, researchers, and policymakers, addressing gaps in knowledge and advancing understanding of EDMS in modern information management. Additionally, it presents a detailed breakdown of publication distribution across sectors, highlighting significant research areas like companies and businesses, education, and information technology and software. Furthermore, analysis of factors influencing employee behavior, including technical factors, employee's personal characteristics, organizational factors, and trust, offers valuable insights into workplace dynamics. Overall, the study offers comprehensive insights into EDMS implementation, guiding future research, and organizational strategies. [ABSTRACT FROM AUTHOR]

This paper introduces a robust and inhanced version of dual control approach based on sequence decomposition loops in order to limit transient over-currents and improving the wind turbine's operation during asymmetrical voltage dips faults. Indeed, the doubly fed induction generator (DFIG), employed in wind energy conversion system (WECS) to produce electrical energy from wind, highly susceptible to voltage drops, which can cause transient overcurrents in both the stator and the rotor. Without any protection system, these over-currents might damage the DFIG and its converters. The strategy put forward in this work minimizes the consequences of voltage dips on wind system functioning, such as electromagnetic torque fluctuations and it enables wind turbines to maintain electrical connection with the grid in the occurrence of such disturbances. The carried-out results are promising and showed that the proposed control method can effectively minimise the oscillations in electromagnetic torque, reactive power and DC bus voltage during the asymmetrical voltage dip. This analysis also demonstrated its ability to limit the transient over-currents of the DFIG. [ABSTRACT FROM AUTHOR]

The invasive colonial tunicate, Didemnum vexillum Kott, 2022 was initially observed in Atlantic Canada in Nova Scotia in 2013 and has since been expected to spread to the western side of the Canadian Bay of Fundy due to colonies known to be present in nearby Eastport, Maine. Since 2018, we collected water environmental DNA (eDNA) samples at eight sites in the Quoddy Region in the western side of the Canadian Bay of Fundy. In 2021 and 2022 we used diver-based visual surveys and sample collection, as well as a surface-deployed near-seafloor optical imaging system to document the presence and extent of D. vexillum in the Head Harbour/West Isles/Passages Ecologically and Biologically Significant Area (EBSA) within the Quoddy Region. Forty-one dive sites were surveyed via SCUBA, and seven near-seafloor camera transects were conducted at depths deeper than dive limits (~ 30 m), collecting continuous highdefinition video and periodic high-resolution still images. Didemnum vexillum was detected at two sites from eDNA metabarcoding and quantitative PCR in 2018, 2020 and 2021, and observed by divers at 11 sites, two of which exhibited extensive tunicate coverage. Of the 1945 m² area surveyed by the near-seafloor drift camera system, D. vexillum occurred at depths to 118 m and across a spatial extent of 858 m², of which 170 m2 contained numerous tunicate patches and/or a homogenous mat. Didemnum vexillum was observed extensively overgrowing benthic substrates and fauna, possibly threatening the diversity of natural benthic habitats in the EBSA and adjacent areas supporting numerous commercial fisheries. This study is the first to report D. vexillum presence in the Canadian western Bay of Fundy, and the first to observe colonies at depths exceeding previous records of 81 m. We conclude by providing advice on how to improve coastal invasive species surveys from the combination of biodiversity metrics. [ABSTRACT FROM AUTHOR]

The compressor is the most core component of refrigeration products, and its performance directly affects the overall performance of the product. The exhaust silencing chamber and coil are key components of a refrigeration compressor, and their working stability and lifespan determine the reliability and economy of the refrigeration compressor. This paper obtains reference data for the fluid domain of the exhaust silencing chamber through simulation analysis and image processing of the existing exhaust silencing chamber structure and inner diameter size of the coil. Then, the optimal model of the prioritized exhaust chamber is obtained through pattern recognition method. Based on the image processing method, the internal flow field of the exhaust chamber are obtained, providing a strong reference for the optimal structural design of the exhaust silencing chamber and coil of the refrigeration compression body. [ABSTRACT FROM AUTHOR]

Background: The Diagnosis-Intervention Packet (DIP) payment system, initiated by China's National Healthcare Security Administration, is designed to enhance healthcare efficiency and manage rising healthcare costs. This study aims to evaluate the impact of the DIP payment reform on inpatient care in a specialized obstetrics and gynecology hospital, with a focus on its implications for various patient groups. Methods: To assess the DIP policy's effects, we employed the Difference-in-Differences (DID) approach. This method was used to analyze changes in total hospital costs and Length of Stay (LOS) across different patient groups, particularly within select DIP categories. The study involved a comprehensive examination of the DIP policy's influence pre- and post-implementation. Results: Our findings indicate that the implementation of the DIP policy led to a significant increase in both total costs and LOS for the insured group relative to the self-paying group. The study further identified variations within DIP groups both before and after the reform. In-depth analysis of specific disease groups revealed that the insured group experienced notably higher total costs and LOS compared to the self-paying group. Conclusions: The DIP reform has led to several challenges, including upcoding and diagnostic ambiguity, because of the pursuit of higher reimbursements. These findings underscore the necessity for continuous improvement of the DIP payment system to effectively tackle these challenges and optimize healthcare delivery and cost management. [ABSTRACT FROM AUTHOR]

Purpose: Patients with fever and inflammation of unknown origin (FUO/IUO) are clinically challenging due to variable clinical presentations with nonspecific symptoms and many differential diagnoses. Positron emission tomography/computed tomography (PET/CT) with 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) is increasingly used in FUO and IUO, but the optimal diagnostic strategy remains controversial. This consensus document aims to assist clinicians and nuclear medicine specialists in the appropriate use of [18F]FDG-PET/CT in FUO and IUO based on current evidence. Methods: A working group created by the EANM infection and inflammation committee performed a systematic literature search based on PICOs with "patients with FUO/IUO" as population, "[18F]FDG-PET/CT" as intervention, and several outcomes including pre-scan characteristics, scan protocol, diagnostic yield, impact on management, prognosis, and cost-effectiveness. Results: We included 68 articles published from 2001 to 2023: 9 systematic reviews, 49 original papers on general adult populations, and 10 original papers on specific populations. All papers were analysed and included in the evidence-based recommendations. Conclusion: FUO and IUO remains a clinical challenge and [18F]FDG PET/CT has a definite role in the diagnostic pathway with an overall diagnostic yield or helpfulness in 50–60% of patients. A positive scan is often contributory by directly guiding treatment or subsequent diagnostic procedure. However, a negative scan may be equally important by excluding focal disease and predicting a favorable prognosis. Similar results are obtained in specific populations such as ICU-patients, children and HIV-patients. [ABSTRACT FROM AUTHOR]

This study reports on the perceived effectiveness of electronic document management system functionalities and their relationship with information quality, system quality, perceived usefulness, and satisfaction using the Delone and McLean IS success model. Conducted within Sarawak government agencies, the research adopted a quantitative method with data collected via an online questionnaire. The target respondents were employees who used electronic document management systems. The setting used was considered non-contrived, as a natural environment was preferred to analyse the assessment of the electronic document management system. The unit of analysis is conducted at the individual level, assessing the performance and effectiveness of electronic document management systems. The analysis of the structural model found that four hypotheses (H1, H2, H3, and H4) were supported. The study found that System Quality had a significant positive relationship on both Perceived Usefulness (T-Value: 9.407) and Satisfaction (T-Value: 1.989), highlighting the importance of system quality in influencing user satisfaction. It was revealed that the electronic document management system functionalities and the information system effectiveness had not been previously tested. As a result, this study provides evidence of a new construct for research on the contribution of functionalities within an electronic document management system to the effectiveness or success of the information system. [ABSTRACT FROM AUTHOR]

This compendium review focuses on the spatial distribution of sensitivity to localized absorption changes in optically diffuse media, particularly for measurements relevant to near-infrared spectroscopy. The three temporal domains, continuous wave, frequency domain, and time domain, each obtain different optical data types whose changes may be related to effective homogeneous changes in the absorption coefficient. Sensitivity is the relationship between a localized perturbation and the recovered effective homogeneous absorption change. Therefore, spatial sensitivity maps representing the perturbation location can be generated for the numerous optical data types in the three temporal domains. The review first presents a history of the past 30 years of work investigating this sensitivity in optically diffuse media. These works are experimental and theoretical, presenting one-, two-, and three-dimensional sensitivity maps for different Near-Infrared Spectroscopy methods, domains, and data types. Following this history, we present a compendium of sensitivity maps organized by temporal domain and then data type. This compendium provides a valuable tool to compare the spatial sensitivity of various measurement methods and parameters in one document. Methods for one to generate these maps are provided in Appendix A, including the code. This historical review and comprehensive sensitivity map compendium provides a single source researchers may use to visualize, investigate, compare, and generate sensitivity to localized absorption change maps. [ABSTRACT FROM AUTHOR]

Two moderate earthquakes struck the South-Central part of Java Island (in Indonesia's archipelago) with Mw 5.7 and Mw 5.9 on June 06 and June 30, 2023, respectively. Both earthquakes were followed by ~100 aftershocks with widespread and strong impacts along Java Island where some regions suffered several damages. In this study, both earthquake mechanisms were derived from the Bayesian moment tensor inversion and configure a unique faulting with a thrusting mechanism that is striking perpendicularly with the trench in the N–S direction. The results of the hypocenter relocation, using an updated 1-D velocity model, show that the aftershocks of the Mw 5.9 occurred deeper than the interface zone, while the aftershocks of the Mw 5.7 were located shallower above the slab. Both earthquakes can robustly confirm possible evidence of different faulting vertically clustered above the intraslab zone. The Mw 5.9 can be assumed as the backstop system dipping to the east–west direction, while Mw 5.7 is the backthrust system with south-dipping. Furthermore, a broad impact of both earthquakes on the resilience of MMI VI intensity with PGA value > 100 gal can be used to update the mitigation plan for the intraslab earthquake in the near future. [ABSTRACT FROM AUTHOR]

Layout analysis is the main component of a typical Document Image Analysis (DIA) system and plays an important role in pre-processing. However, regarding the Pashto language, the document images have not been explored so far. This research, for the first time, examines Pashto text along with graphics and proposes a deep learning-based classifier that can detect Pashto text and graphics per document. Another notable contribution of this research is the creation of a real dataset, which contains more than 1,000 images of the Pashto documents captured by a camera. For this dataset, we applied the convolution neural network (CNN) following a deep learning technique. Our intended method is based on the development of the advanced and classical variant of Faster R-CNN called Single-Shot Detector (SSD). The evaluation was performed by examining the 300 images from the test set. Through this way, we achieved a mean average precision (mAP) of 84.90%. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks widely known as GANs, are deep learning models that has gained a lot of popularity in the last few years due to their promising nature. One of their major applications can be found in the healthcare sector for various purposes like image reconstruction, image enhancement, image segmentation, image processing, image analysis etc. The objective of this paper is to bring forward some of the recent research on the application of GANs in the health care domain during 2017 – 2022. A total of 168 papers were screened out of which 77 relevant to the present study were finalized for this study. Bibliometric analysis was also performed on these documents, to obtain an overall picture of Generative Adversarial Networks (GANs) in the healthcare domain. This analysis is expected to help the future researcher and developers in targeting the areas of the healthcare sector which are likely to grow in the coming future using GANs algorithms. [ABSTRACT FROM AUTHOR]

Finding passages related to a sentence over a large collection of text documents is a fundamental task for claim verification and open-domain question answering. For instance, a common approach for verifying a claim is to extract short snippets of relevant text from a collection of reference documents and provide them as input to a natural language inference machine that determines whether the claim can be deduced or refuted. Available approaches struggle when several pieces of evidence from different documents need to be combined to make an inference, as individual documents often have a low relevance with the input and are therefore excluded. We propose GRAAL (GRAph-based retrievAL), a novel graph-based approach that outlines the relevant evidence as a subgraph of a large graph that summarizes the whole corpus. We assess the validity of this approach by building a large graph that represents co-occurring entity mentions on a corpus of Wikipedia pages and using this graph to identify candidate text relevant to a claim across multiple pages. Our experiments on a subset of FEVER, a popular benchmark, show that the proposed approach is effective in identifying short passages related to a claim from multiple documents. [ABSTRACT FROM AUTHOR]

The article discusses the journal Preservation, Digital Technology & Culture (PDT&C) and its focus on the preservation of digital content from various perspectives. It argues that the journal should go beyond the theory and practice of digital preservation and also explore emerging technologies such as AI, ML, AR/VR, and blockchain. The article then provides summaries of six articles from the journal's third volume, covering topics such as the portrayal of cultural heritage in video games, film preservation in India, digitalization of archives in Kazakhstan, immersive technology for cultural preservation, the use of digital technology in minority language preservation, and the training needs of US cultural heritage professionals in digital preservation. [Extracted from the article]

Twitter boasts 319 million daily active users, making it an invaluable asset for public figures and businesses looking to cultivate positive public image. Businesses can leverage sentiment analysis for real-time polling on various social media platforms allowing them to gauge public sentiment and opinion accurately. Recently, academic researchers focused on sentiment analysis as an approach for Twitter propaganda analysis. Text sentiment analysis is an automated process that offers valuable insight into the content of a text segment. It can reveal whether it conveys factual or subjective information and reveal its polarity; for Twitter sentiment classification this goal primarily lies with determining whether tweets have positive or negative undertones; researchers utilize various Machine Learning (ML), Deep Learning (DL) and other models to accomplish this task. Present research work utilising classification algorithms such as Support Vector Machines are among the most frequently utilized ML/DL models for sentiment analysis, along with Random Forest, Ensemble Machine Learning, Artificial Neural Networks, Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) for effective classification. Also, for preprocessing the tweets API, techniques such as filtering, tokenization, removal of stopwords, stemming and lemmatization have been used. Then preprocessed input is fed as input to the TF-IDF and Bag of Words for vectorize the input. Then classification has been performed with aforementioned models. Finally, performance evaluation metrices have been perfomed, from that out of all these models used for sentiment analysis on Twitter dataset, Bidirectional LSTM has proven itself most accurate at detecting sentiment with an accuracy rate of 98.14%, 98.39% in vectorize techniques includes TF-IDF and Bag of Words—making this tool invaluable when conducting voice analyses on this platform. [ABSTRACT FROM AUTHOR]

In public procurement, a multitude of data is used, produced, and exchanged in various ways and formats. Laws and regulations often require that the exchange of information takes place through signed and recorded documents, following specific authorization processes. On the other hand, many modern Project Management techniques rely on accurate knowledge of project data, which requires information flow to occur in a structured and digital manner. The concept of an Electronic Document and Records Management System was created for this purpose, to combine document flow and structured information flow. However, in practice, it is difficult to find ready-to-use commercial solutions, and many administrations choose to use traditional document management systems. This work aims to investigate the two requirements independently and then jointly, proposing a clean and simple approach to align them. The chosen approach envisions a two-tier system with defined functional specifications, where the first tier coincides with an existing document management system. The outcome demonstrates how to implement an Electronic Document and Records Management System by retrofitting a traditional document engine. The described approach will be tested through a case study related to a large public construction contract. [ABSTRACT FROM AUTHOR]

Conventional techniques for sharing paper documents in teleconferencing tend to introduce two inconsistencies: 1) media inconsistency: a paper document is converted into a digital image on the remote site; 2) space inconsistency: a workspace deliberately inverts the partner's handwriting to make a document easy to read. In this paper, we present a novel system that eliminates these inconsistencies. The media and space inconsistencies are resolved by reproducing a real paper document on a remote site and allowing a user to handover the paper document to a remote partner across a videoconferencing display. From a series of experiments, we found that reproducing a real paper document contributes to a higher sense of information sharing. We also found that handing over a document enhances a sense of space sharing, regardless of whether the document is digital or paper-based. These findings provide insights into designing systems for sharing paper documents across distances. [ABSTRACT FROM AUTHOR]

The robotic drilling of assembly holes is a crucial process in aerospace manufacturing, in which measuring the normal of the workpiece surface is a key step to guide the robot to the correct pose and guarantee the perpendicularity of the hole axis. Multiple laser displacement sensors can be used to satisfy the portable and in-site measurement requirements, but there is still a lack of accurate analysis and layout design. In this paper, a simplified parametric method is proposed for multi-sensor normal measurement devices with a symmetrical layout, using three parameters: the sensor number, the laser beam slant angle, and the laser spot distribution radius. A normal measurement error distribution simulation method considering the random sensor errors is proposed. The measurement error distribution laws at different sensor numbers, the laser beam slant angle, and the laser spot distribution radius are revealed as a pyramid-like region. The influential factors on normal measurement accuracy, such as sensor accuracy, quantity and installation position, are analyzed by a simulation and verified experimentally on a five-axis precision machine tool. The results show that increasing the laser beam slant angle and laser spot distribution radius significantly reduces the normal measurement errors. With the laser beam slant angle ≥15° and the laser spot distribution radius ≥19 mm, the normal measurement error falls below 0.05°, ensuring normal accuracy in robotic drilling. [ABSTRACT FROM AUTHOR]

The article is aimed at studying the current state of the digitalization of customs procedures in Ukraine and determining the prospects for the development of this process in Ukraine. Improving the electronic document management system at customs is consistent with Ukraine's European integration aspirations. Electronic declarations can not only minimize the time of customs procedures, but also fight corruption, develop international cooperation, and interact with the information systems of government organizations in the country. The purpose of the study is to determine the impact of the digital economy on the transformation of customs in Ukraine and to analyze the existing shortcomings of this system. The article describes the features and current state of digitalization of customs procedures in the context of simplification of customs control. The state of implementation of modern electronic services for the automation of customs procedures in Ukraine is analyzed. The authors emphasize the need for the development of world trade and the implementation of customs control procedures using communication technologies and electronic communications. The positive aspects of the introduction of information technology are identified. The achievements and problems of the process of reforming modern information technologies in the work of the State Customs Service of Ukraine within the framework of the strategic course of the state development are outlined. The reduction of customs clearance times can be achieved by introducing such new customs methods: 1) modern risk management processes using a multifunctional declaration system; 2) the widest possible use of post-clearance control methods; 3) gradual transition from transactional control to control based on audit methods; 4) independent control by participants of foreign economic activity over compliance with the law through the introduction of the institute of authorized economic operators. It is emphasized that the introduction of modern digital service technologies into the practice of the State Customs Service of Ukraine will improve the image of this state body both in Ukraine and in the international arena. [ABSTRACT FROM AUTHOR]

A logotype is a rectangular region defined by a set of characteristics, which come from the pixel information and region shape, that differ from those of the text. In this paper, a new method for automatic logo detection is proposed and tested using the public Tobacco800 database. Our method outputs a set of regions from an official document with a high probability to contain a logo using a new approach based on the variation of the feature rectangles method available in the literature. Candidate regions were computed using the longest increasing run algorithm over the document blank lines' indices. Those regions were further refined by using a feature-rectangle-expansion method with forward checking, where the rectangle expansion can occur in parallel in each region. Finally, a C4.5 decision tree was trained and tested against a set of 1291 official documents to evaluate its performance. The strategic combination of the three previous steps offers a precision and recall for logo detention of 98.9% and 89.9%, respectively, being also resistant to noise and low-quality documents. The method is also able to reduce the processing area of the document while maintaining a low percentage of false negatives. [ABSTRACT FROM AUTHOR]

The growing integration of converter‐interfaced renewable energy sources (RESs) utilizing Grid‐Following (GFL) converters has displaced conventional synchronous generators (SGs) in central generation units. This shift presents challenges, including diminished system inertia, lower fault levels, and implications for system strength and network resilience. The propagation of voltage dips, particularly during disturbances like system Short Circuit (SC) faults, is adversely affected by the increased penetration of such RESs. This is attributed to the limited support capability of these sources and their distinct SC response compared to SGs. In response to these challenges, Grid‐Forming (GFM) converters emerge as a promising technology equipped with advanced functionalities that emulate SG operation. Consequently, they hold potential for mitigating the effects of voltage dip propagation in renewable‐rich power systems. This study aims to assess the impact of employing GFM converters in renewable‐rich power systems on voltage dip propagation across the network. The authors' investigation begins by examining the SC response of GFM converters and comparing it with the responses of traditional GFL converters and SGs. The paper proceeds to analyze voltage dip propagation, considering various penetration scenarios involving RESs based on GFL and GFM converters. The IEEE 9‐BUS test system, implemented in the DIgSILENT PowerFactory software, serves as the basis for these evaluations. Through extensive simulations and analysis, the authors' research provides valuable insights into the effectiveness of GFM converters in enhancing the network's response to voltage dips. [ABSTRACT FROM AUTHOR]

Shadows cause a reduction of text legibility and contrast of the overall document, leading to poor recognition rates by Optical Character Recognition (OCR). Shadows were cast during photo capturing because of object interferences due to projection angle from illumination sources and are challenging to handle by OCR. This work proposes an automated technique to detect the shadows caused by object interferences and eliminate them from document images. The proposed algorithm generates two reference masks of shadow and non-shadow regions using a global thresholding method to detect and remove shadow. Initially, the document image is pre-processed, followed by mask generation for shadow and non-shadow regions, and finally, a global thresholding strategy that removes the shadow using an image mapping technique. To evaluate the effectiveness of the proposed approach, a dataset of real-world smartphone-captured document images of about 300 is considered. The datasets encompass Mobile Dataset 3 and Mobile Dataset 4 from DIBCO datasets. Experiments are conducted to quantify the efficiency of the proposed approach using performance measures such as recognition rate by OCR, presentation score, legibility score, skew, and overall document quality score. From the results, the proposed approach collectively produces an average recognition rate of 87.15% and 81.50% towards word-level recognition of Mobile Datasets 3 and 4. The text quality of the document image post shadow removal procedure achieves a higher performance score with an average presentation score of 0.35, legibility score of 0.78, skew as 73.45 degrees from the horizontal axis, and overall document quality score above six towards datasets considered. Based on qualitative analysis, comparative analysis proves that the proposed is simple yet more proficient than existing methods. [ABSTRACT FROM AUTHOR]

The restoration of degraded document images is a critical aspect of document image processing. In this study, we present a rigorous comparative analysis of two denoising techniques applied to degraded document images within the HSV and UV color spaces. The primary objective is to quantitatively assess the efficacy of these methods in enhancing the quality of degraded document images. Experiments were conducted using an extensive dataset of degraded document images, and a comprehensive evaluation was performed employing established metrics, including peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean square error (MSE). Results unequivocally demonstrate the superiority of the UV-based approach over the HSV-based method across all evaluation criteria. Specifically, the UV-based approach consistently outperforms the HSV-based approach in PSNR, SSIM, and MSE assessments. Qualitative analysis further underscores the remarkable effectiveness of both techniques in elevating the quality of degraded document images. This study addresses a pressing problem in document image processing by delivering invaluable insights into the comparative efficacy of these denoising techniques. It offers a useful tool for academics and professionals who want to improve the clarity and legibility of deteriorated document images. [ABSTRACT FROM AUTHOR]

Purpose: Develop an efficient, interactive, and instructive checklist document for the management of implanted electronic medical devices in a multimodality radiotherapy clinic. Methods: The built-in scripting and interactivity of a popular commercial word processor was used to develop an interactive document that changes the information presented to the user based on drop-down selections. The interactivity and scripting were compatible with the radiation oncology information system (ROIS) which allows the document to be accessible by all team members and serve as a permanent record in a patient's electronic chart. Results: The final interactive document,which was clinically deployed after beta testing with a group consisting of nurses and medical physicists, presents information and action plans to the user based on multiple departmental medical device decision trees that are specific to the combination of device, treatment modality, rhythm-pacing dependence for cardiac devices, and distance from the device to the treatment volume. Conclusion: A script-enabled interactive document was developed for a busy multimodality clinic, condensing multiple comprehensive departmental guidelines spanning multiple device types and treatment modalities into a single interactive checklist accessible within the ROIS. Given the wide accessibility of the commercial word processor, this approach could be adopted by other clinics to streamline their own respective workflows. [ABSTRACT FROM AUTHOR]

DC microgrids (DC MGs) offer advantages such as efficiency, control, cost, reliability, and size compared to AC MGs. However, they often operate with numerous constant power loads (CPLs), exhibiting a negative incremental impedance characteristic that can lead to instability. This instability weakens stability boundaries and reduces system damping, especially when dealing with pulsed power loads (PPLs) on electric aircraft, ships, and cars. Linear controllers may not ensure stability across various operations, causing voltage dips and potential system instability. To secure DC/DC converter functionality and comply with impedance specifications, it is crucial to consider minor loop gain in control strategies and stabilization techniques. Employing diverse methods to decrease minor loop gain in DC/DC converters is essential. A comprehensive evaluation, including strengths, weaknesses, opportunities, and threats (SWOT) analysis, is conducted to assess control strategies, stabilization techniques, and stability standards for different DC/DC converters, identifying SWOT. [ABSTRACT FROM AUTHOR]

This document contains abstracts from the CardioVascular & Interventional Radiology journal. The abstracts cover a range of topics including survival rates of patients undergoing endovascular aneurysm repair, safety and efficacy of new EVAR follow-up guidelines, a modified technique for treating Type B aortic dissection, incidence of aortic stent graft infections, interventional radiological treatment for bronchopleural fistulas, and the safety and effectiveness of GAE for knee osteoarthritis. Other abstracts discuss non-surgical treatments for lung cancer, selective internal radiation therapy for hepatocellular carcinoma, pulmonary ablation for colorectal metastases, cryoablation for renal cell carcinoma, and endovascular salvage procedures for thrombosed dialysis access. The article also includes a study on radiation exposure among medical specialists and abstracts on supervised exercise programs for intermittent claudication, an Atherectomy Program for advanced CLI, interventional radiology for managing venous leg ulceration, percutaneous transhepatic biliary interventions, portal vein embolization, and oesophageal stents for oesophageal malignancy. [Extracted from the article]

Object detection methods based on deep learning have been used in a variety of sectors including banking, healthcare, e-governance, and academia. In recent years, there has been a lot of attention paid to research endeavors made towards text detection and recognition from different scenesor images of unstructured document processing. The article's novelty lies in the detailed discussion and implementation of the various transfer learning-based different backbone architectures for printed text recognition. In this research article, the authors compared the ResNet50, ResNet50V2, ResNet152V2, Inception, Xception, and VGG19 backbone architectures with preprocessing techniques as data resizing, normalization, and noise removal on a standard OCR Kaggle dataset. Further, the top three backbone architectures selected based on the accuracy achieved and then hyper parameter tunning has been performed to achieve more accurate results. Xception performed well compared with the ResNet, Inception, VGG19, MobileNet architectures by achieving high evaluation scores with accuracy (98.90%) and min loss (0.19). As per existing research in this domain, until now, transfer learningbased backbone architectures that have been used on printed or handwritten data recognition are not well represented in literature. We split the total dataset into 80 percent for training and 20 percent for testing purpose and then into different backbone architecture models with the same number of epochs, and found that the Xception architecture achieved higher accuracy than the others. In addition, the ResNet50V2 model gave us higher accuracy (96.92%) than the ResNet152V2 model (96.34%). [ABSTRACT FROM AUTHOR]

Shadow removal for document images is an essential task for digitized document applications. Recent shadow removal models have been trained on pairs of shadow images and shadow-free images. However, obtaining a large, diverse dataset for document shadow removal takes time and effort. Thus, only small real datasets are available. Graphic renderers have been used to synthesize shadows to create relatively large datasets. However, the limited number of unique documents and the limited lighting environments adversely affect the network performance. This paper presents a large-scale, diverse dataset called the Synthetic Document with Diverse Shadows (SynDocDS) dataset. The SynDocDS comprises rendered images with diverse shadows augmented by a physics-based illumination model, which can be utilized to obtain a more robust and high-performance deep shadow removal network. In this paper, we further propose a Dual Shadow Fusion Network (DSFN). Unlike natural images, document images often have constant background colors requiring a high understanding of global color features for training a deep shadow removal network. The DSFN has a high global color comprehension and understanding of shadow regions and merges shadow attentions and features efficiently. We conduct experiments on three publicly available datasets, the OSR, Kligler's, and Jung's datasets, to validate our proposed method's effectiveness. In comparison to training on existing synthetic datasets, our model training on the SynDocDS dataset achieves an enhancement in the PSNR and SSIM, increasing them from 23.00 dB to 25.70 dB and 0.959 to 0.971 on average. In addition, the experiments demonstrated that our DSFN clearly outperformed other networks across multiple metrics, including the PSNR, the SSIM, and its impact on OCR performance. [ABSTRACT FROM AUTHOR]

Document image retrieval (DIR) systems simplify access to digital data within printed documents by capturing images. These systems act as bridges between print and digital realms, with demand in organizations handling both formats. In education, students use DIR to access online materials, clarify topics, and find solutions in printed textbooks by photographing content with their phones. DIR excels in handling complex figures and formulas. We propose using ensembles of DIR systems instead of single-feature models to enhance DIR's efficacy. We introduce "Vote-Based DIR" and "The Strong Decision-Based DIR". These ensembles combine various techniques, like optical code reading, spatial analysis, and image features, improving document retrieval. Our study, using a dataset of university exam preparation materials, shows that ensemble DIR systems outperform individual ones, promising better accuracy and efficiency in digitizing printed content, which is especially beneficial in education. [ABSTRACT FROM AUTHOR]

Considering the toxicity of traditional resorcinol‐formaldehyde‐latex (RFL) dipping solution used to prepare fiber‐ reinforced rubber composites, a novel eco‐friendly dopamine (DA)‐supported dipping system was developed in this study. Based on the interactions between DA, glycerol triglycidyl ether (GTE), and m‐xylylenediamine (MXDA), the dipping solution successfully coated the surface of the nylon‐66 (PA66), and the resulting GTE/DA/MXDA/latex‐treated PA66 (PA66‐GDML) possessed better thermal stability than untreated PA66. The interfacial adhesion between PA66 and neoprene was significantly improved by using the appropriate interfacial layer and the synergistic effect during co‐vulcanization. The adhesive strength between PA66‐GDML and neoprene was comparable to that obtained in an RFL system, especially when the ratio of GTE/DA/MXDA was 5:1:2. Highlights: An eco‐friendly dopamine‐supported dipping system was developed.The dipping solution (GTE/DA/MXDA) could successfully coat nylon 66 fibers.Treated nylon 66 possessed better thermal stability than untreated nylon 66.The adhesive strength was comparable to that obtained in a RFL system.The new dipping system shows great potential to replace RFL system. [ABSTRACT FROM AUTHOR]

This document is a book review section from the journal Ibis, which provides information about the Alexander Library at the Edward Grey Institute of Field Ornithology in Oxford, UK. The review section is organized and edited by Dr. Richard Sale, with contributions from various reviewers. The document includes reviews of specific books related to birdwatching and bird photography. Each book is briefly described, highlighting the main themes and perspectives presented by the authors. The reviews provide valuable insights into bird species found in Italy, Buckton, England, and Colombia, as well as practical advice for photographing garden birds. [Extracted from the article]

This work is devoted to improving the quality of segmentation of images of various scientific papers and legal acts by neural network models by training them using modified loss functions that take into account special features of images of the appropriate subject domain. The analysis of existing loss functions is carried out, and new functions are proposed that work both with the coordinates of bounding boxes and use information about the pixels of the input image. To assess the quality, a neural network segmentation model with modified loss functions is trained, and a theoretical assessment is carried out using a simulation experiment showing the convergence rate and segmentation error. As a result of the study, rapidly converging loss functions are created that improve the quality of document image segmentation using additional information about the input data. [ABSTRACT FROM AUTHOR]

In document forensics, identifying ink mismatches is crucial for detecting forgeries and determining document authenticity. However, identifying and separating specific inks from paper can be challenging. Hyperspectral imaging can capture unique spectral patterns exhibited by inks composed of different materials, even when their colors appear identical. Hyperspectral document analysis (HSDI) can be employed to authenticate documents by analyzing the ink. Earlier studies had insufficiently accurate black ink detection, so this paper proposes a novel approach using deep learning and pre-processing methods to improve black ink detection accuracy. The proposed approach uses supervised deep learning to identify ink mismatches in hyperspectral document images. The study evaluated the performance of the proposed model using a hyperspectral image dataset consisting of UWA (University of Western Australia) writing ink in both blue and black colors and different types of artificially identical color inks mixed in various ratios to find ink mismatches. The results showed that the proposed system performed better than other systems reported in the literature, improving the average accuracy by up to 0.18% for blue and 0.36% for black. The proposed method accurately detects ink mismatches and identifies various inks based on their unique spectral response, rendering it highly beneficial for applications in document forensics. [ABSTRACT FROM AUTHOR]

This article is a literature review that examines the impact of document management using good practices. It discusses the benefits of implementing these practices, such as cost savings, improved service speed, and increased security. The review also explores the use of good practices in various sectors, including geoscience research, healthcare, and the construction industry. It emphasizes the importance of policies, procedures, technological infrastructure, and software reliability in the success of document management systems. The article concludes that document management is a global trend, with Malaysia and Russia being prominent contributors, and highlights the need for further research in this field. [Extracted from the article]

Deletion/insertion polymorphism (DIP) is one of the more promising genetic markers in the field of forensic genetics for personal identification and biogeographic ancestry inference. In this research, we used an in-house developed ancestry-informative marker-DIP system, including 56 autosomal diallelic DIPs, three Y-chromosomal DIPs, and an Amelogenin gene, to analyze the genetic polymorphism and ancestral composition of the Chinese Korean group, as well as to explore its genetic relationships with the 26 reference populations. The results showed that this novel panel exhibited high genetic polymorphism in the studied Korean group and could be effectively applied for forensic individual identification in the Korean group. In addition, the results of multiple population genetic analyses indicated that the ancestral component of the Korean group was dominated by northern East Asia. Moreover, the Korean group was more closely related to the East Asian populations, especially to the Japanese population in Tokyo. This study enriched the genetic data of the Korean ethnic group in China and provided information on the ancestry of the Korean group from the perspective of population genetics. [ABSTRACT FROM AUTHOR]

The purpose of this article is to review the current state of legal acts on electronic document management in China. The authors analyzed its development from the point of view of state institutions, enterprises, and universities. The article is a review of the literature from open access. For many scientists, this is very valuable first-hand material that allows you to learn and understand the current state of development of legislative acts. There is little literature on electronic document management in China in current databases, especially on the state of development of electronic document management at enterprises, government institutions, and universities. The conclusion is formulated about the whole legal system of electronic document management in China, which successfully operates in the country. Nevertheless, the issue of more effective development of electronic document management in China under state regulation needs further study. In the end, constructive proposals for the further development of electronic document management in China are recommended. [ABSTRACT FROM AUTHOR]

Purpose: Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill. Design/methodology/approach: The authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources. Findings: The authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public. Originality/value: The authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them. [ABSTRACT FROM AUTHOR]

Extracting domain-specific words from the domain corpus to build vocabulary and identifying classes represented by domain-specific vocabulary is a challenging task in classifying text. Several comprehensive domain-specific techniques have been developed to build specific domain vocabulary but they lack generality and also do not consider the degree of the word's relevance. This paper proposes a supervised generic algorithmic model to create a domain-specific vocabulary. The model identifies domain-indicative word combinations from labeled real-time domain corpus, calculates their weights and logged their inclusion time to understand their semantic behavior in the present era of development. Further, a framework ADS-Classif is also developed using vocabulary to classify real-time documents extracted from different sources and test the domain-specific vocabulary's significance. Experimental results show that the approach leads to the accurate identification of domain classes for test documents compared with classical classifiers Naïve Bayes and Support Vector Machine. [ABSTRACT FROM AUTHOR]

The digital image processing has major attention towards the automatic optical character recognition system. The image analysis of ancient images is mainly focused on printed and inscription images. In printed image analysis the text is printed by using machine and in the inscription images text is written by different persons. Presently most of the research highlighted challenges related to the inscription image analysis. In the printed document images font style, variation, size, and writing direction is standard, but in case of inscription font style, size, variation and writing direction vary by person to person. Image enhancement deals with the improvement of the quality of a digital image. The quality is achieved by highlighting useful details and removing the redundant details. In this paper, image enhancement based on the Binarization technique is discussed for the improvement of ancient inscription. The binary image is an essential part of document analysis. The proposed method is composed using a colour threshold and median filter. The proposed method is tested on different sample images. To evaluate the performance of the proposed method, the MSE, PSNR, F-Measure, NRM, Accuracy is calculated. These results are compared with manual results obtained by archeologist. It is also compared with the Nick method in the literature. The results of the proposed method are effective than manual and nick method. The enhancement of ancient Marathi scripts plays an important role in the identification, classification and the recognition of ancient Marathi characters. [ABSTRACT FROM AUTHOR]