File Types Research Articles

Comparative study of dynamic cyclic fatigue resistance of two files with different kinematics

Objectives: The aim of the study was to determine the dynamic cyclic fatigue resistance (DCFR) of R-motion file and Protaper Next file (PTN) by differential scanning calorimetry (DSC) and scanning electron microscopy (SEM). Methods: R-motion file (25/.06), PTN file (25/.06) were used and divided into 2 groups (n = 28) according to the file type. DCFR was performed using an artificial canal. Files were allowed to work until separation. The numbers of cycles to failure (NCF) and the time to fracture (TTF) were calculated. The files were subjected to DSC and SEM. Results: R-motion files had higher NCF and TTF compared to PTN files. SEM examination showed that all files showed ductile fracture. DSC of the R-motion file displayed Austenitic phase at body temperature) 37 ˚C) while PTN file failed to demonstrate any peaks within the applied heat range. Conclusions: R-motion files had higher CFR and TTF than PTN files. Conclusions: R-motion files had higher CFR and TTF than PTN files.

ScatterX： A software for fast processing of high-throughput small-angle scattering data

Abstract Scattering experiments become increasingly popular in modern scientific research, including the areas of materials, biology, chemistry, physics, etc. Besides, various types of scattering facilities have been developed recently, such as lab-based X-ray scattering equipment, national synchrotron facilities and large neutron facilities. These above-mentioned trends bring up fast-increasing data amounts of scattering data, as well as different scattering types (X-ray, neutron, laser and even microwaves). This struggles with data analysis software of the last generation, where data are usually analyzed assuming certain first-principle models and given data formats. To help researchers process and analyze scattering data more efficiently, we developed a general and model-free scattering data analysis software based on matrix operation, which has the unique advantage of high throughput scattering data processing, analysis and visualization. To maximize generality and efficiency, data processing is performed based on a three-dimensional matrix, where scattering curves are saved as matrices or vectors, rather than the traditional definition of paired values. It can not only realize image batch processing, background subtraction and correction, but also analyze data according to scattering theory and model, such as radius of gyration, fractal dimension and other physical quantities. In the aspect of visualization, the software allows the modify the color maps of two-dimensional scattering images and the gradual color variation of one-dimensional curves to suit efficient data communications. In all, this new software can work as a standalone platform for researchers to process, analyze and visualize scattering data from different research facilities without considering different file types or formats. All codes in this manuscript are open-sourced and can be easily implemented in matrix-based software, such as MATLAB, Python and Igor.

National-scale 1-km maps of hospital travel time and hospital accessibility in China

Ensuring equitable access to health services is crucial for public welfare and social equity, and is a key objective of the United Nations’ Sustainable Development Goals (SDGs). However, existing datasets often define hospital accessibility using travel time to hospitals in geographic dimension only, without considering the supply (hospital capacity) and demand (population distribution) dynamics. To overcome this limitation, we developed and validated a national-scale 1 km map of both hospital travel time and hospital accessibility in China. We used the Gaussian two-step floating catchment area (Ga2SFCA) model to calculate hospital accessibility, incorporating hospital capacity and service population. Various file types and statistical indicators are provided, making the dataset highly accessible for non-specialists. The dataset fills the gap in publicly available nationwide hospital accessibility data for China and can serve as a critical tool in optimizing resource allocation and developing targeted strategies to improve healthcare equity.

Regulatory requirement and filling procedure of drug master file in India under central drug standard control organization (CDSCO) in comparison with South Korea

The Drug master file (DMF) is a type of Confidential file that submitted to regulatory authorities about the ingredients, process, packing and storage of any drugs intended to human use. Drug master file plays an important role in production of drug. The Indian regulatory agency is a CDSCO that manages and encourage DMFs in India and in South Korea MFDS manages the DMFs. Unveiling the difference in regulatory requirements and filling procedure of DMF in India and South Korea as per CDSCO and MFDS respectively. Make certain the Protection, Grade, and Effectiveness of Drug Products.

StarTracer is an accelerated approach for precise marker gene identification in single-cell RNA-Seq analysis

Revealing the heterogeneity among tissues is the greatest advantage of single-cell-sequencing. Marker genes not only act as the key to correctly identify cell types, but also the bio-markers for cell-status under certain experimental imputations. Current analysis methods such as Seurat and Monocle employ algorithms which compares one cluster to all the rest and select markers according to statistical tests. This pattern brings redundant calculations and thus, results in low calculation efficiency, specificity and accuracy. To address these issues, we introduce starTracer, a novel algorithm designed to enhance the efficiency, specificity and accuracy of marker gene identification in single-cell RNA-seq data analysis. starTracer operates as an independent pipeline, which exhibits great flexibility by accepting multiple input file types. The primary output is a marker matrix, where genes are sorted by the potential to function as markers, with those exhibiting the greatest potential positioned at the top. The speed improvement ranges by 2 ~ 3 orders of magnitude compared to Seurat, as observed across three independent datasets with lower false positive rate as observed in a simulated testing dataset with ground-truth. It’s worth noting that starTracer exhibits increasing speed improvement with larger data volumes. It also excels in identifying markers in smaller clusters. These advantages solidify starTracer as an important tool for single-cell RNA-seq data, merging robust accuracy with exceptional speed.

SIFT: Sifting file types—application of explainable artificial intelligence in cyber forensics

Artificial Intelligence (AI) is being applied to improve the efficiency of software systems used in various domains, especially in the health and forensic sciences. Explainable AI (XAI) is one of the fields of AI that interprets and explains the methods used in AI. One of the techniques used in XAI to provide such interpretations is by computing the relevance of the input features to the output of an AI model. File fragment classification is one of the vital issues of file carving in Cyber Forensics (CF) and becomes challenging when the filesystem metadata is missing. Other major challenges it faces are: proliferation of file formats, file embeddings, automation, We leverage and utilize interpretations provided by XAI to optimize the classification of file fragments and propose a novel sifting approach, named SIFT (Sifting File Types). SIFT employs TF-IDF to assign weight to a byte (feature), which is used to select features from a file fragment. Threshold-based LIME and SHAP (the two XAI techniques) feature relevance values are computed for the selected features to optimize file fragment classification. To improve multinomial classification, a Multilayer Perceptron model is developed and optimized with five hidden layers, each layer with i×n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$i \ imes n$$\\end{document} neurons, where i = the layer number and n = the total number of classes in the dataset. When tested with 47,482 samples of 20 file types (classes), SIFT achieves a detection rate of 82.1% and outperforms the other state-of-the-art techniques by at least 10%. To the best of our knowledge, this is the first effort of applying XAI in CF for optimizing file fragment classification.

Implementation of the Frictionless data standard for linked data

Objective and ApproachData linkage centres receive, hold and provision data in a wide variety of formats. Using a consistent, flexible and open metadata standard for data holdings can enable a center to clearly and unambiguously describe their data holdings and facilitate processing pipelines. One such framework is the Frictionless data standard. We present our experience at our data centre implementing this standard for both internal data management and for communication with external data providers and third parties. ResultsOur team has replaced tool-specific metadata files with standard-compliant files to make our pipelines more modular and less fragile. Since the standard is based on JSON, most programming languages will support reading and writing metadata files and we are not restricted to any software. This work has also made our internal tooling interoperable with that of external partners. We find that the standard is well equipped to handle CSV and other delimited files. Our center uses a mix of file types including fixed-width text files which do not have an explicit specification within the standard, but its extensibility has allowed us to define our own file type while staying within the ecosystem. We have also developed tools to convert JSON metadata files to excel workbooks for a more human-friendly format. ConclusionsThe Frictionless data standard can be a powerful tool for data centers in organizing data and building processing pipelines. ImplicationsAdopting the Frictionless data standard for metadata files has streamlined our internal processes and improved internal and external communication.

Artificial optoelectronic synapse based on CdSe nanobelt photosensitized MoS2 transistor with long retention time for neuromorphic application

Abstract Optoelectronic synaptic devices have been regarded as the key component in constructing neuromorphic computing systems. However, the optoelectronic synapses based on conventional 2D transistor are still suffering low photosensitivity and volatile retention behavior, which can affect the recognition accuracy and long-term memory. Here, a novel optoelectronic synaptic device based on surface-state-rich CdSe nanobelt photosensitized 2D MoS2 transistor is demonstrated. Benefiting from the excellent light absorption of CdSe and effective charge trapping at the hetero-interface, the device exhibits not only high photosensitivity but also long retention time (>1,500 s). In addition, typical synaptic functions including the excitatory postsynaptic current, paired-pulse facilitation, the transformation from short-term to long-term plasticity, the transformation from short-term to long-term plasticity, spike-amplitude-dependent plasticity, and learning-forgetting-relearning process are successfully simulated and modulated by light stimulation. Most importantly, an artificial neural network is simulated based on the optical potentiation and electrical habituation characteristics of the synaptic devices, with recognition accuracy rates of 89.2, 93.8, and 91.9 % for file type datasets, small digits, and large digits are achieved. This study demonstrates a simple and efficient way to fabricate highly photosensitive optoelectronic synapse for artificial neural networks by combining the merits of specific materials and device architecture.

DASCore: a Python Library for Distributed Fiber Optic Sensing.

In the past decade, distributed acoustic sensing (DAS) has enabled many new monitoring applications in diverse fields including hydrocarbon exploration and extraction; induced, local, regional, and global seismology; infrastructure and urban monitoring; and several others. However, to date, the open-source software ecosystem for handling DAS data is relatively immature. Here we introduce DASCore, a Python library for analyzing, visualizing, and managing DAS data. DASCore implements an object-oriented interface for performing common data processing and transformations, reading and writing various DAS file types, creating simple visualizations, and managing file system-based DAS archives. DASCore also integrates with other Python-based tools which enable the processing of massive data sets in cloud environments. DASCore is the foundational package for the broader DAS data analysis ecosystem (DASDAE), and as such its main goal is to facilitate the development of other DAS libraries and applications.

Expert System for Extracting Hidden Information from Electronic Documents during Outgoing Control

For confidential and sensitive electronic documents within enterprises and organizations, failure to conduct proper checks before sending can easily lead to incidents such as security degradation. Sensitive information transmission has become one of the main ways of internal data leakage. However, existing methods or systems cannot extract hidden data and do not support mining the binary structure of hidden information in files. In this paper, an expert system for mining electronic document hidden information is designed for various office documents, compressed files, and image files. This system can quickly mine various forms of concealment in more than 40 common types of electronic documents and extract hidden information, such as file type tampering, encryption concealment, structure concealment, redundant data concealment, etc. Additionally, feature information in the binary structure of the document is extracted to form a feature information base. Subsequently, an expert knowledge base is constructed. Finally, a hidden information mining engine is designed using the knowledge base to realize the security control of corresponding outgoing files with good expansibility and integration. By controlling the exit of documents through scanning for sensitive information contained within them, the security level contents can be obtained effectively, avoiding data leakage by technical means while also facilitating forensics. The actual test result proves that this system can quickly mine various means used to conceal information, extract their respective information, and provide a fast, practical diagnostic way for outgoing control over these electronic documents.

The effect of irrigation solution temperature and novel heat-treated rotary files on apical debris extrusion and canal preparation time.

This study aimed to compare apical debris extrusion and canal preparation time with ProTaper Gold (PTG) and ProTaper Ultimate (PTU) files at different temperatures. Mesio-buccal roots of 60 mandibular first molars were distributed into six groups depending on the file type (PTG, PTU) and irrigation solution temperature (20, 37, 45°C). During instrumentation, extruded debris were collected and weighed to measure the mass in milligrams. The canal preparation time was recorded in seconds. ANOVA followed by Bonferroni post-hoc tests were used for analysis. The amount of debris extrusion was significantly higher in PTU, which was affected by the irrigation solution temperature (p < 0.05). The difference in canal preparation time was not significant between the two file systems, however, it was significant between the different temperatures (p = 0.001). Both file systems had shorter canal preparation times at 20°C. The irrigation solution temperature could influence the debris extrusion and time of canal preparation.

Relevant Aspects in the Mechanical and Aging Degradation of NiTi Alloy with R-Phase in Endodontic Files.

One of the most important challenges in endodontics is to have files that have excellent flexibility, toughness, and high fatigue life. Superelastic NiTi alloys have been a breakthrough and the new R-phase NiTi alloys promise to further optimize the good properties of NiTi alloys. In this work, two austenitic phase endodontic files with superelastic properties (Protaper and F6) and two austenitic phase files with the R-phase (M-wire and Reciproc) have been studied. The transformation temperatures were studied by calorimetry. Molds reproducing root canals at different angles (30, 45, and 70°) were obtained with cooling and loads simulating those used in the clinic. Mechanical cycles of different files were realized to fracture. Transformation temperatures were determined at different number of cycles. The different files were heat treated at 300 and 500 °C as the aging process, and the transformation temperatures were also determined. Scanning and transmission electron microscopy was used to observe the fractography and precipitates of the files. The results show that files with the R-phase have higher fracture cycles than files with only the austenitic phase. The fracture cycles depend on the angle of insertion in the root canal, with the angle of 70° being the one with the lowest fracture cycles in all cases. The R-Phase transformation increases the energy absorbed by the NiTi to produce the austenitic to R-phase and to produce the martensitic transformation causing the increase in the fracture cycles. Mechanical cycling leads to significant increases in the transformation temperatures Ms and Af as well as Rs and Rf. No changes in the transformation temperatures were observed for aging at 300 °C, but the appearance of Ni4Ti3 precipitates was observed in the aging treatments to the Nickel-rich files that correspond to those with the R transition. These results should be considered by endodontists to optimize the type of files for clinical therapy.

BIBLIOMETRIC ANALYSIS OF DRAINAGE CHANNEL CAPACITY USING HEC-RAS METHOD IN FLOOD DISASTER MANAGEMENT

Drainage capacity is the ability of a channel to pass a certain amount of water or discharge through the drain. A study entitled Bibliometric Analysis of Drainage Channel Capacity Using Hec-Ras in Flood Disaster Management will map previous interrelated studies and make it easier for the author to conduct research. The method uses bibliometric analysis with the Publish or Perish 8.9 and Vos Viewer 1.6.19 software applications as visualization tools with an output of 2 file types, namely RIS and CSV. From the analysis, it can be found that there are 38 keywords with 3 clusters with the most research peaks in 2022. In addition, the most publishers identified in the IEEE publisher type as many 284, with the dominant type of publication being Journals-article.

Multi-file dynamic compression method based on classification algorithm in DNA storage.

The exponential growth in data volume has necessitated the adoption of alternative storage solutions, and DNA storage stands out as the most promising solution. However, the exorbitant costs associated with synthesis and sequencing impeded its development. Pre-compressing the data is recognized as one of the most effective approaches for reducing storage costs. However, different compression methods yield varying compression ratios for the same file, and compressing a large number of files with a single method may not achieve the maximum compression ratio. This study proposes a multi-file dynamic compression method based on machine learning classification algorithms that selects the appropriate compression method for each file to minimize the amount of data stored into DNA as much as possible. Firstly, four different compression methods are applied to the collected files. Subsequently, the optimal compression method is selected as a label, as well as the file type and size are used as features, which are put into seven machine learning classification algorithms for training. The results demonstrate that k-nearest neighbor outperforms other machine learning algorithms on the validation set and test set most of the time, achieving an accuracy rate of over 85% and showing less volatility. Additionally, the compression rate of 30.85% can be achieved according to k-nearest neighbor model, more than 4.5% compared to the traditional single compression method, resulting in significant cost savings for DNA storage in the range of $0.48 to 3 billion/TB. In comparison to the traditional compression method, the multi-file dynamic compression method demonstrates a more significant compression effect when compressing multiple files. Therefore, it can considerably decrease the cost of DNA storage and facilitate the widespread implementation of DNA storage technology.

Digital Forensic Research for Analyzing Drone and Mobile Device: Focusing on DJI Mavic 2 Pro

With the frequent occurrence of drone-related criminal cases, drone forensics has become a hot spot of concern. During drone-related criminal investigations, the implicated drones are often forcibly brought down, which poses significant challenges in conducting forensic analysis. In order to restore the truth of criminal cases, it is necessary to extract data not only from the external TF card but also from internal chip memory in drone forensics. To address this issue, a drone data parser (DRDP) is proposed to extract internal and external data from criminal-implicated drones. In this paper, we present comprehensive forensics on the DJI Mavic 2 Pro, analyzing the main file structure and encryption model. According to its file structures, three case studies are conducted on various file types (DAT files, TXT files, and default files) to verify the effectiveness and applicability of the designed procedure. The results show that the encrypted data of the implicated drone, such as GPS information, flight time, flight altitude, flight distance, three velocity components (x, y, z) and other information can be extracted and decrypted correctly, which provides evidence for the identification of the case facts.

Optimized intuitionistic fuzzy enriched honey badger algorithm for cloud network-based work load scheduling

The difficulty of scheduling jobs or workloads increases due to the stochastic and transient characteristics of the cloud network. As a key prerequisite for establishing QoS, it asserts that effective work scheduling must be developed and executed. Maximum profit is made possible for cloud service providers by proper resource management. The most effective scheduling algorithm considers resources given by providers rather than the task set that users have accumulated. This paper developed a model that works in a two-level hierarchical model comprising global scheduling and local schedules to handle the heterogeneous type of request in real-time. These two levels of scheduling communicate with each other to produce an optimal scheduling scheme. Initially, all the requests are passed to the global scheduler, whose task is to categorize the type of request and pass it to the corresponding queue for assigning it to the related local scheduler using a parabolic intuitionistic fuzzy scheduler. In this work, the heterogeneous types of files are handled by maintaining different queues, in which each queue handles only a specific type of file like text doc, audio, image and video. Once the type of req is initiated by the clients, the global scheduler identifies the type of request and passes it to their relevant queue. In the next level, the local scheduler is assigned to each type of web server cluster. Once the work request is dispatched from the global workload scheduler, it is allocated to the local queue of the local scheduler, which allocates the resources of web servers by adapting the Quantum Honey Badger Algorithm, which searches the best-suited server for completing the assigned work based on the available resource parameters.

RiboGraph: an interactive visualization system for ribosome profiling data at read length resolution.

Ribosome profiling is a widely-used technique for measuring ribosome occupancy at nucleotide resolution. However, the need to analyze this data at nucleotide resolution introduces unique challenges in data visualization and analyses. In this study, we introduce RiboGraph, a dedicated visualization tool designed to work with .ribo files, a specialized and efficient format for ribosome occupancy data. Unlike existing solutions that rely on large alignment files and time-consuming preprocessing steps, RiboGraph operates on a purpose designed compact file type. This efficiency allows for interactive, real-time visualization at ribosome-protected fragment length resolution. By providing an integrated toolset, RiboGraph empowers researchers to conduct comprehensive visual analysis of ribosome occupancy data. Source code, step-by-step installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling/ribograph. On the same page, we provide test files and a step-by-step tutorial highlighting the key features of RiboGraph.

BIBLIOMETRIC ANALYSIS OF RESEARCH DEVELOPMENTS IN THE FIELD OF PUBLIC ADMINISTRATION

The development of science which is essentially towards the process of improvement will continue throughout the human thought process. This also occurs in the paradigm of public administration development so that it attracts the attention of researchers to analyze the development of public administration research. In this study using bibliometric analysis to describe the results of journal publications with data search tools using Publish or Perish produced 996 meta data and analysis tools using Microsoft Excel, and VosViewer to visualize meta data. The results showed that from 996 meta data with the keyword "paradigm of public administration development" obtained a publication range from 1995-2024, with the most documents being the type of book file published on books. google.com with a total of 83 book titles and the highest publication development trend occurred in 2020 with 129 publications while the results of network mapping using VosViewer obtained 4 authors, namely Ismail, I; Sos, S; Irawan B; Larasati, E. which is the most productive author in the field of public administration development paradigms. The research development map using VosViewer is grouped into 5 clusters. Fluctuating research shows that the paradigm of public administration development is a research topic that attracts researchers to write, study, and analyze the topic of the paradigm of public administration development so that the implications of this research are to help other researchers to further study the development of the paradigm of public administration and be associated with the latest phenomena.

Large file encryption in a reduced-round permutation-based AES file management system

In the rapid evolving digital landscape, the imperative to ensure data security has never been more crucial. This paper addresses the pressing challenges in data security by introducing a file encryption management system, leveraging a modified advanced encryption standard (AES) algorithm with reduced round iterations and bit permutation. This system aims to comprehensively secure various file types, providing a dependable solution for file exchange. Our findings reveal substantial improvements in both encryption and decryption processes using the reduced-round permutation-based AES (RRPBA). The adapted algorithm demonstrates a significant 38.8% acceleration in encryption time and a remarkable 44.86% improvement in decryption time, positioning it as a pivotal component for efficient file operations within the management system. Moreover, the throughput assessments showcase a remarkable 33.73% improvement in encryption and 23.72% in decryption, outperforming the original AES, emphasizing the algorithm's superior computational effectiveness, signaling positive implications for future high-performance applications. In conclusion, the study not only addresses critical security challenges but also presents a viable solution with tangible speed advantages for file encryption and decryption processes within digital file management systems.

The HR Insight Sidebar Extension for LinkedIn Profile

This paper explores the "HR Insights Sidebar Extension," which enhances LinkedIn profile evaluation through a chatbot, aiding informed decision-making with real-time analysis. AI-driven algorithms extract key information like skills, experience, and endorsements, generating concise summaries to streamline evaluations. Leveraging the OpenAI Assistant API's Information Retrieval and Natural Language Processing, the system extracts data from various file types. Users query the chatbot via a natural language interface, receiving direct answers and saving time. Tools like ChatGPT and Proxy curl improve profile evaluation efficiency and effectiveness for students, researchers, and professionals. Keywords- large language models, GPT-3 summarization, proxy curl, implementation, and integration.