Traditional Processing Methods Research Articles

Revealing the optimal traditional processing methods and its protective effects against febrile seizures of Arisaema cum bile.

Arisaema cum bile (known as Dan Nanxing in Chinese, DNX) is a herbal medicine used for treating febrile seizure (FS), which commonly prepared by using Arisaematis Rhizoma and animal bile. This study was designed to explore the optimal processing time of DNX and its potential mechanism on the anti-FS effect. A total of 17 volatile organic compounds (VOCs) were the characteristic ones to distinguish different fermentation stages of DNX by using gas chromatography-ion mobility spectrometry (GC-IMS), such as 2-heptanone monomer, and heptanal monomer. DNX with fermentation for 3 months had an obvious pattern of VOCs with others, which could be regarded as the optimal fermentation time. The Enterococcus and Staphylococcus might be the core bacteria on the production of VOCs. Additionally, DNX (2.8 g/kg, p.o.) reversed hot water bath-induced FSs of rats, as indicated by increased seizure latency and decreased seizure duration time. It also prevented hippocampal neuronal loss, increased GABAAR, and decreased GRIA1 expression. At the genus level, relative abundance of Enterococcus and Akkermansia were enriched after DNX treatment. These findings suggested that fermentation for 3 months might be the optimal process time for DNX, and DNX possess an anti-FS effect through regulating neurotransmitter disorder and gut microbiota.

Extraction of Substance Use Information From Clinical Notes: Generative Pretrained Transformer-Based Investigation.

Understanding the multifaceted nature of health outcomes requires a comprehensive examination of the social, economic, and environmental determinants that shape individual well-being. Among these determinants, behavioral factors play a crucial role, particularly the consumption patterns of psychoactive substances, which have important implications on public health. The Global Burden of Disease Study shows a growing impact in disability-adjusted life years due to substance use. The successful identification of patients' substance use information equips clinical care teams to address substance-related issues more effectively, enabling targeted support and ultimately improving patient outcomes. Traditional natural language processing methods face limitations in accurately parsing diverse clinical language associated with substance use. Large language models offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of the generative pretrained transformer (GPT) model in specific GPT-3.5 for extracting tobacco, alcohol, and substance use information from patient discharge summaries in zero-shot and few-shot learning settings. This study contributes to the evolving landscape of health care informatics by showcasing the potential of advanced language models in extracting nuanced information critical for enhancing patient care. The main data source for analysis in this paper is Medical Information Mart for Intensive Care III data set. Among all notes in this data set, we focused on discharge summaries. Prompt engineering was undertaken, involving an iterative exploration of diverse prompts. Leveraging carefully curated examples and refined prompts, we investigate the model's proficiency through zero-shot as well as few-shot prompting strategies. Results show GPT's varying effectiveness in identifying mentions of tobacco, alcohol, and substance use across learning scenarios. Zero-shot learning showed high accuracy in identifying substance use, whereas few-shot learning reduced accuracy but improved in identifying substance use status, enhancing recall and F1-score at the expense of lower precision. Excellence of zero-shot learning in precisely extracting text span mentioning substance use demonstrates its effectiveness in situations in which comprehensive recall is important. Conversely, few-shot learning offers advantages when accurately determining the status of substance use is the primary focus, even if it involves a trade-off in precision. The results contribute to enhancement of early detection and intervention strategies, tailor treatment plans with greater precision, and ultimately, contribute to a holistic understanding of patient health profiles. By integrating these artificial intelligence-driven methods into electronic health record systems, clinicians can gain immediate, comprehensive insights into substance use that results in shaping interventions that are not only timely but also more personalized and effective.

Dynamic Changes in Microbial Communities and Chemical Compounds during the Semi-Dry Fermentation Processing of Coffea arabica

The semi-dry fermentation processing (SFP) of Coffea arabica is a traditional primary processing method in the coffee industry, which crucially impacts the coffee’s flavor. To further obtain useful information on microbial communities and chemical compounds during the SFP of C. arabica from Yunnan, China, the microbial community structures and the differentially changed non-volatile compounds (DCnVCs) were comprehensively analyzed. The results showed that Tatumella, Staphylococcus, Klebsiella, Brevundimonas, and Gluconobacter were the most prevalent bacteria genera, and Candida, Hannaella, Hanseniaspora, Pichia, and Lachancea were the most abundant fungal genera. Furthermore, 117 DCnVCs were found in the fermentation-finished samples compared to the raw materials. Therefore, this study can provide useful information for understanding the SFP of coffee beans, and its impact on coffee’s quality parameters.

Fusing binocular vision and deep learning to detect dynamic wheel-rail displacement of high-speed trains

Large displacements between the wheel and rail lead to track damage or even train derailment. Detecting dynamic wheel-rail displacement (DWRD) can provide a technical means for discriminating the operational health status of rail vehicles and formulating active control strategies. In this paper, a new method, which fuses binocular vision (BV) and deep learning (DL), is proposed to detect the DWRD online, the method is abbreviated as BV-DL. First, a binocular camera is used to capture the dynamic wheel-rail contact video online, while two lasers are equipped to focus on the wheel-rail contact region to locate some keypoints in the wheel-rail contact images. Then, an ROI (region of interest) detection network is proposed to recognize the wheel-rail contact region automatically, and a keypoint detection network is proposed to detect the wheel-rail pixel displacement (WRPD). Finally, a 3D coordinate transformation (3DCT) model is built to calculate the wheel-rail world displacement in real-time, i.e., the DWRD between the keypoints. To test the performance of the method, a laser displacement sensor-based method is used to verify the reliability of the 3DCT model. Meanwhile, a traditional image processing method, a MobileNet V3-based method, and a YOLOX-s-based method are introduced to predict the WRPD. Experimental results show that compared with the four methods, the proposed BV-DL method can achieve optimal results in pixel coordinate calculation and 3D coordinate computation.

Traditional Processing Can Enhance the Medicinal Effects of Polygonatum cyrtonema by Inducing Significant Chemical Changes in the Functional Components in Its Rhizomes.

The aims of this study were to explore the significant chemical changes in functional components induced by the traditional processing method and evaluate whether this method based on nine cycles of steaming and drying can effectively enhance the medicinal effects of Polygonatum cyrtonema rhizome. A global analysis on dynamic changes in secondary metabolites during nine processing cycles was performed, and the significantly differentially accumulated secondary metabolites were initially identified based on the secondary metabolome. Unsupervised principal component analysis (PCA), hierarchical clustering analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLA-DA) on secondary metabolites clearly showed that processing significantly increased the global accumulation of secondary metabolites in processed P. cyrtonema rhizomes compared to unprocessed crude rhizomes. The first six processing cycles induced drastic changes in the accumulation of functional components, while the last three did not induce further changes. The accumulations of most functional components were significantly enhanced after the first three cycles and stabilized after six cycles; meanwhile, the first three cycles also led to numerous new components. However, the enhancing effects were unavoidably reversed or weakened under continued processing lasting 6-9 cycles. Furthermore, continued processing also reduced the contents of a small number of original components to undetectable levels. Processing induced some significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, among which the first three processing cycles enhanced the synthesis of various secondary metabolites and significantly affected the metabolisms of amino acids. In conclusion, this study not only reveals that processing can effectively enhance the medicinal effects, by two main mechanisms including enhancing chemical synthesis and inducing structural transformation of functional components, but also provides theoretical guidance for the optimization of the traditional processing method based on nine cycles of steaming and drying for achieving optimal effects on enhancing the medicinal effects of P. cyrtonema rhizome.

Fabrication of controllable porous tungsten tips for indium FEEP by dynamic reciprocating electrochemical etching combined with ultrasonic cleaning method

The porous tungsten emitter is a core component of field emission electric propulsion (FEEP). It combines the advantages of sharp tip and internal propellant transport. The emission performance of a FEEP thruster is highly depended on the emitter's tip apex radius. Traditional processing methods for solid tungsten tips are unsuitable for porous tungsten due to its unique structure and electrochemical properties. This paper introduces a novel approach combining dynamic reciprocating electrochemical etching with ultrasonic cleaning to fabricate cone-shaped porous tungsten tips with controlled apex radii. By alternating between 5 cycles of etching and an ultrasonic cleaning, a 1.9 μm apex radius is achieved after 90 repetitions. The etching current waveform reveals the material dissolution mechanism. The electrochemical characteristics of porous tungsten are investigated, with an optimal processing voltage of 2 V identified. To refine the apex radius and prevent tip collapse, a 1.9 V etching voltage is used in the final stages, maintaining the apex within 2 μm. The effectiveness of the fabricated tip is confirmed through wetting in indium and ignition tests.

Assessment of the Traditional Processing Methods in the Reduction of Aflatoxin Levels in Maize and Maize Products in Kenya

Aflatoxins are fungal secondary toxic metabolites produced predominantly by certain strains of the Aspergillus molds. The objective of this study was to investigate the effectiveness of Kenyan traditional processing methods in reduction of aflatoxin in maize. The traditional methods used included; alkaline cooking using Magadi soda and maize cob ash, decortication by pounding wet maize with mortar and pestle, dehulling in mechanical mill and exposure to solar radiation. Standardization of alkali cooking was done before actual treatment of the contaminated samples. Naturally contaminated samples of maize were sampled and used in the experiment. Aflatoxin analysis before and after each of the treatments was done using ELISA method and total aflatoxins expressed in parts per billion (ppb) (dry weight basis). The results were subjected to statistical analysis at 5% significance levels (p≤0.05). The reduction of aflatoxin was found to be highest when maize was boiled in maize cob ash solution which resulted in loss of aflatoxin from 83.1±0.3 ppb to 7.0±3.9 ppb. Dry decortication reduced aflatoxin from 51.3±15.3 to 9.6±0.8 ppb, boiling in Magadi soda led to a drop from 59.5±3.818 ppb to 13.4±0.424 ppb, solar irradiation caused a drop from 60.8±1.8 ppb to 13.7±0.1 ppb and pounding in Magadi soda resulted in least loss of aflatoxin from 81.5±0.3 to 72.7±0.2ppb. Aflatoxin loss in all the treatments was significant except for dry decortication. Only dry decortication and boiling in maize cob ash solution brought down the aflatoxin levels to below the maximum allowable limit as set by the Kenya Bureau of Standards (10ppb)

Planetary gearbox fault classification based on tooth root strain and GAF pseudo images

Traditional signal processing methods based on acceleration signals can determine whether a fault has occurred in a planetary gearbox. However, acceleration signals are severely affected by interference, causing difficulties in fault identification. This study proposes a gear fault classification method based on root strain and pseudo images. Firstly, fiber optic sensors are employed to directly acquire strain data from the ring gear root. Next, the strain signals are preprocessed using resampling and a time-domain synchronous averaging algorithm. The processed signals are encoded into two-dimensional images using Gramian Angular Fields (GAF). Then, CN-EfficientNet with contrast learning is proposed to analyze and extract deeper fault features from the image texture features. In the classification experiments for different types of faults, the accuracy reached 96.84%. The results indicate that the method can effectively accomplish the task of fault classification in planetary gearboxes. Comparative experiments with other common classification models further indicate the superior performance of the proposed learning model. Visualization based on Grad-CAM provides interpretability for the fault recognition network’s results and reveals the underlying mechanism for its excellent classification performance.

Image Reconstruction in Ultrasonic Speed-of-Sound Computed Tomography Using Time of Flight Estimated by a 2D Convolutional Neural Networks

In ultrasonic nondestructive testing (NDT), accurately estimating the time of flight (TOF) of ultrasonic waves is crucial. Traditionally, TOF estimation involves the signal processing of a single measured waveform. In recent years, deep learning has also been applied to estimate the TOF; however, these methods typically process only single waveforms. In contrast, this study acquired fan-beam ultrasonic waveform profile data from 64 paths using an ultrasonic-speed computed tomography (CT) simulation of a circular column and developed a TOF estimation model using two-dimensional convolutional neural networks (CNNs) based on these data. We compared the accuracy of the TOF estimation between the proposed method and two traditional signal processing methods. Additionally, we reconstructed ultrasonic-speed CT images using the estimated TOF and evaluated the generated CT images. The results showed that the proposed method could estimate the longitudinal TOF more accurately than traditional methods, and the evaluation scores for the reconstructed images were high.

Nighttime wildlife object detection based on YOLOv8‐night

AbstractMonitoring nocturnal animals in the field is an important task in ecological research and wildlife conservation, but the complexity of nocturnal images and low light conditions make it difficult to cope with traditional image processing methods. To address this problem, researchers have introduced infrared cameras to improve the accuracy of nocturnal animal behaviour observations. Object detection in nighttime images captured by infrared cameras faces several challenges, including low image quality, animal scale variations, occlusion, and pose changes. This study proposes the YOLOv8‐night model, which effectively overcomes these challenges by introducing a channel attention mechanism in YOLOv8. The model is more focused on capturing animal‐related features by dynamically adjusting the channel weights, which improves the saliency of key features and increases the accuracy rate in complex backgrounds. The main contribution of this study is the introduction of the channel attention mechanism into the YOLOv8 framework to create a YOLOv8‐night model suitable for object detection in nighttime images. When tested on nighttime images, the model performs well with a significantly higher mAP (0.854) than YOLOv8 (0.831), and YOLOv8‐night scores 0.856 on mAP_l, which is obviously better than YOLOv8 (0.833) in terms of processing large objects. The study provides a reliable technical tool for ecological research, wildlife conservation and environmental monitoring, and offers new methods and insights for the study of nocturnal animal behaviour.

Culinary Potential in The Colorful Village of Tigarihit in Simalungun District

This research has three research objectives, namely: (1) Looking at the condition of the facilities and supporting facilities that currently exist in the colorful village tourism of Tigarihit, (2) compiling a detailed inventory of culinary potential which includes: (a) Types of traditional food (b) Traditional food processing methods, (c) Economic aspects and (d) Documentation, (3) Arrange alternative development of culinary potential in the colorful village tourism village. This type of research is survey research with descriptive methods. Based on the survey conducted in Parapat City, a total of 6 types of traditional food were identified. The main ingredients most widely used to make traditional foods are chicken and fish (80%), then other ingredients (20%). Traditional Simalungun food, based on the way it is processed can be categorized into four, namely: (1) steamed, (2) fried, (3) grilled, and (4) boiled. Based on the results of interviews with traditional food sources in Simalungun, some alternative developments that can be done are the Identification and inventory of traditional foods, compiling profiles of traditional foods in the region as information and promotion materials, encouraging the development of traditional food centers with related agencies, the private sector and the community; Providing soft credit for additional capital

Research on Molten Iron Quality Prediction Based on Machine Learning

The quality of molten iron not only has a significant impact on the strength, toughness, smelting cost and service life of cast iron but also directly affects the satisfaction of users. The establishment of timely and accurate blast furnace molten iron quality prediction models is of great significance for the improvement of the production efficiency of blast furnace. In this paper, Si, S and P content in molten iron is taken as the important index to measure the quality of molten iron, and the 989 sets of production data from a No.1 blast furnace from August to October 2020 are selected as the experimental data source, predicting the quality of molten iron by the I-GWO-CNN-BiLSTM model. First of all, on the basis of the traditional data processing method, the missing data values are classified into correlation data, temporal data, periodic data and manual input data, and random forest, the Lagrangian interpolation method, the KNN algorithm and the SVD algorithm are used to complete them, so as to obtain a more practical data set. Secondly, CNN and BiLSTM models are integrated and I-GWO optimized hyperparameters are used to form the I-GWO-CNN-BiLSTM model, which is used to predict Si, S and P content in molten iron. Then, it is concluded that using the I-GWO-CNN-BiLSTM model to predict the molten iron quality can obtain high prediction accuracy, which can provide data support for the regulation of blast furnace parameters. Finally, the MCMC algorithm is used to analyze the influence of the input variables on the Si, S and P content in molten iron, which helps the steel staff control the quality of molten iron in a timely manner, which is conducive to the smooth running of blast furnace production.

Water Conservancy Hub Geometric Modeling Based on Point Clouds

For hydraulic engineering, accurate geometric modeling is crucial for design, analysis, and maintenance. In recent years, point cloud technology, with its high precision and detailed data capture capabilities, has gradually become the preferred method for geometric modeling in hydraulic engineering. However, traditional point cloud processing methods exhibit slow data processing speeds and poor mesh quality when reconstructing 3D models. Therefore, we have studied the meshing method for the 3D reconstruction of large-scale discrete point cloud data and proposed a meshing method based on the Delaunay criterion and the growth method to achieve rapid meshing of discrete point cloud data. To demonstrate the efficiency and convenience of the proposed method, point cloud reconstruction experiments are conducted on various buildings. We compare our approach with the Poisson, BPA, and ONet methods. Additionally, we apply it to the Jiangsu Gaogang Water Conservancy Hub in China. The results show that our proposed method is superior in both reconstruction quality and efficiency, providing a promising solution for the reconstruction of 3D building models from point clouds.

Review of Hybrid Denoising Approaches in Face Recognition

Statistically, image denoising is one of the key pillars of image processing and picture acquisition, which also is utilized to clear the noisy images. Over the last years, there is an increase of study subjects that are devoting to designing and making noise cancellation methods. This study reviews all major image denoising techniques, with a special emphasis on integrated deep learning approaches as well as traditional signal processing methods. The review presents a broad array of techniques for instance convolutional neural networks (CNNs), wavelet transforms, hybrid models, and their emendations. The lecturer will focus on the advantages, as well as the disadvantages, of each methodology along with their appropriateness in various fields, from which the current state of the art image denoising can be concluded. On the other hand, the paper discusses critical barriers leading to further prospects of research in cybersecurity and cybercrime prevention This review is important in that it aims to serve researchers, practitioners, and enthusiasts who would like to peer into the new trends and developments in denoise image generation.

Освоение моделирования как элемента подготовки студентов-гуманитариев в условиях развития технологии больших данных

g Data is a qualitatively new information object that requires special methods of analysis and processing. The peculiarity of Big Data is the following: traditional methods of data processing in the case of Big Data may not be effective, since they require very large time frames and computing resources; Big Data, in many ways, loses its discrete structure and approaches its properties to structures of continuity, which complicates the use of traditional methods of discrete analysis. The article discusses approaches to the study of Big Data in humanitarian universities. The sources of the emergence of big data in this area are discussed as an imbalance between the syntactic and semantic aspects of the information object. It is substantiated that in humanitarian universities it is advisable to place emphasis on mastering the modeling methodology, which, in many respects, allows eliminating this imbalance.

Tracking Metabolite Variations during the Degradation of Vegetables in Rice Bran Bed with Intact-State Nuclear Magnetic Resonance Spectroscopy.

Fermentation-a process of compound degradation by microorganisms-is a traditional food processing method utilized worldwide for the long-term preservation of fresh foods. In recent years, fermented foods have gained attention as health foods. Fermentation increases the nutritional value of ingredients, producing complex flavors and aromas. To identify unknown components in fermented foods, it is necessary to analyze compounds and conditions nondestructively and comprehensively. We performed intact-state nuclear magnetic resonance (NMR) spectroscopy using intermolecular single quantum coherence (iSQC) to detect the degradation of vegetables directly and nondestructively. We used two types of vegetables and a rice bran bed (nukazuke), which is used for traditional vegetable fermentation in Japan. Major metabolites such as saccharides, organic acids, and amino acids were identified in iSQC-sliced spectra. Comparing NMR signal intensities during degradation revealed the transition of metabolites characteristic of lactic acid fermentation. A pathway-based network analysis showed pathways involved in amino acid metabolism and lactic acid fermentation. Our analytical approach with intact-state NMR spectroscopy using iSQC demonstrated that it may be effective in other experimental systems, allowing for the evaluation of phenomena that have been conventionally overlooked in their true state.

3D-printed polymer-derived ceramics with tunable cellular architectures

Ceramic materials possess high mechanical strength and environmental stability, but their brittleness limits their suitability for structural applications. A solution lies in using polymer-derived ceramics (PDCs), which offer enhanced toughness and versatility in shaping unlike traditional ceramic processing methods. This study explores tunable ceramic cellular architectures based on triply periodic minimal surface (TPMS) designs, fabricated via stereolithography (SLA) using a silicon oxycarbide precursor formulated for vat photopolymerization. By combining the preceramic polymer with a photoinitiator, crosslinkers, and other additives, intricate shapes are 3D-printed and then pyrolyzed under nitrogen, resulting in PDCs with complex TPMS geometries. The toughness, strength, and stiffness of the 3D-printed structures are evaluated through quasi-static compression experiments. Comprehensive material and microstructural characterizations of the PDCs are performed pre- and post-pyrolysis, employing visual inspection, X-ray micro-tomography, thermogravimetric analysis, energy dispersive X-ray spectroscopy, density, and rheological measurements. Optimization of 3D printing and pyrolysis parameters yields ceramic structures with 2.2 MPa compressive strength and 330 MPa stiffness with a lattice density of 0.5 g cm-3. The ceramic material, including porosity, had a maximum density of 1.63 ± 0.01 g cm-3. This low-cost SLA 3D printing technique is ideal for creating thin features and customized structures of bio-inspired, architectured ceramics. Furthermore, the process exhibits excellent printability, being compatible with common and cost-effective SLA, DLP, and LCD 3D printers.

Recent Advances in Non-Contact Food Decontamination Technologies for Removing Mycotoxins and Fungal Contaminants.

Agricultural food commodities are highly susceptible to contamination by fungi and mycotoxins, which cause great economic losses and threaten public health. New technologies such as gamma ray irradiation, ultraviolet radiation, electron beam irradiation, microwave irradiation, pulsed light, pulsed electric fields, plasma, ozone, etc. can solve the problem of fungal and mycotoxin contamination which cannot be effectively solved by traditional food processing methods. This paper summarizes recent advancements in emerging food decontamination technologies used to control various fungi and their associated toxin contamination in food. It discusses the problems and challenges faced by the various methods currently used to control mycotoxins, looks forward to the new trends in the development of mycotoxin degradation methods in the future food industry, and proposes new research directions.

Obtention of Suitable Pregnant Leach Solution (PLS) for Copper Solvent Extraction Plants from Copper Concentrate Using Hydrogen Peroxide and Iodine in a Sulfuric Acid–Chloride Medium

Copper leaching presents an environmentally friendly alternative to traditional sulfide ore processing methods. This study investigates an efficient leaching process for copper concentrate, utilizing a solution of sulfuric acid (H2SO4) and potassium iodide (KI) in a chloride medium (NaCl), enhanced by hydrogen peroxide (H2O2) at room temperature. A significant aspect of this research was optimizing the KI concentration to minimize iodide sublimation into iodine gas (I2). Through the experimental design, the optimal dosages of reagents were determined, leading to maximized copper extraction of approximately 27% in 45 min of testing at room temperature. The results showed that it is possible to obtain a suitable pregnant leach solution (PLS) (i.e., in the range of 3 to 8 g/L of Cu) for treatment in available copper solvent extraction (SX) plants with a cost of less than 4.5 USD/t Cu, according to the economic analysis carried out. The results of this study determine the most effective operational conditions for leaching and ensure a suitable PLS for SX plants in a cost-effective and environmentally friendly manner. This approach could significantly contribute to more sustainable practices in the mining and processing of copper ores.

Digital Transformation Techniques for Manuscripts

The world is witnessing continuous technological developments that have an impact on societies and institutions. Therefore, all countries seek to keep pace with these developments and introduce them into various fields. Digital transformation has become one of the most important necessities that all institutions must adopt today, as digital transformation is capable of creating a competitive technological environment to achieve the highest levels of quality at the lowest costs. Manuscripts are of interest to many scholars and researchers due to their scientific and artistic value. Preserving them means preserving national identity, especially with the emergence of technology. It has become imperative to preserve manuscripts from damage and loss and make them available to the largest number of beneficiaries. In the past, Arabs were among the most important peoples who contributed to the preservation of manuscripts. One of the most important challenges facing modern society is managing, preserving, and retrieving information from the vast amount of paper documents and files that are increasing daily. By using technology, the time taken to search for the required document or distribute documents to the relevant parties leads to a significant increase in the productivity of the institution. Information technology and electronic communication systems have revolutionized traditional methods of processing, preserving, and exchanging information. The presence of advanced technologies provides us with ease and accuracy in accessing information. Archiving as a general concept relies on storing and classifying papers in a way that makes it easy to refer back to them when needed. With the advent of computers, the process has become easier, providing more flexibility and ease in retrieval, as well as reducing the costs of preservation and storage. This technology has a direct impact on the archival profession, as archivists have turned to electronic means and imaging storage systems, as well as audiovisual methods, in an attempt to solve the storage and preservation problem. Modern digital technology affects all areas of life due to the integrated scientific environment it provides. All societies have begun to transition towards the digital age because of its remarkable development in information and communication technology. Keywords: Information, Manuscript, Technology, Scanning, Digitization