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6,467 results for “process”
Online repository for: "Dielectrophoretic particle chromatography: From batch processing to semi-continuous high throughput separation"
<p>Please check the readme.odt file for details.</p>
Observed data and processed data
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PROBLEMS IN THE PROCESS OF INFORMATION EXCHANGE IN COMMUNICATION
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COOPERATIVE EDUCATION IS THE BASIS OF ENSURING THE EFFECTIVENESS OF THE INDEPENDENT EDUCATIONAL PROCESS AND THE DEVELOPMENT OF SELF-READINESS OF STUDENTS.
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IMPROVEMENT ACCOUNTING OF PROCESS REALIZATION IN FARM ENTERPRISES
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METHODS OF PREPARING FUTURE TEACHERS FOR PEDAGOGICAL ACTIVITY IN THE PROCESS OF STUDY PRACTICE
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IMPROVING THE METHODOLOGY OF TRAINING FUTURE TEACHERS FOR PEDAGOGICAL ACTIVITIES IN THE PROCESS OF EDUCATIONAL PRACTICE
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STUDYING OPPORTUNITIES TO PREPARE FUTURE TEACHERS FOR PEDAGOGICAL ACTIVITY IN THE PROCESS OF STUDY PRACTICE
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Implementation of Artificial Intelligence from The Perspective of Activity Theory to Determine Contradictions in The Sales Process with The Etom Framework
<p><span>This research is a continuation of previous research that discusses the relationship between the Marketing process based on the General Procedure of the Sales or Marketing process with Activity Theory (AT) for the development of Artificial Intelligence (AI). The previous discussion requires a deeper understanding of contradictions in business process frameworks. In this case, the eTOm (enhanced Telecom Operational Map) Business Framework is used, especially in telecommunication companies. Thus, quantitative research will be obtained so that there is the potential for contradictions to arise from the socio-cultural side based on AT.</span></p> <p><span>AT in the design methodology is used on a collaborative scale. The activity system is intended to systematically understand the eTOM business framework, which has been built historically and relates to each other in its design towards the use of AI technology. By using AT as a theoretical framework, it's hoped that it will offer a wealthy holistic know-how of ways humans collaborate, i.e., perform useful collective activities, with the assistance of state-of-the-art tools, in this case, AI, withinside the complicated dynamic surroundings of contemporary-day organizations.</span></p>
Determination of process parameters and precipitation methods for potential large-scale production of sugar beet leaf protein concentrate
<p>Abstract</p> <p><br>BACKGROUND</p> <p>Sugar beet is one of the most produced industrial plants in the world, and during manufacturing, it produces a large quantity of leaf waste. Because this waste is rich in protein, this study aimed to identify an efficient method for producing large-scale protein concentrate from sugar beet leaves.</p> <p><br>RESULTS</p> <p>Results showed that protein extraction from fresh leaves was more effective than dried leaves. Maximum protein extraction was achieved at pH 9, compared with pH 7 or 8. Blanching as a pretreatment reduced protein yield during isoelectric precipitation, with a yield of 2.31% compared to 20.20% without blanching. Consequently, blanching was excluded from the extraction process. After extraction, isoelectric precipitation, heat coagulation, and isoelectric–ammonium sulfate precipitation were compared. Although the latter resulted in the highest protein yield, Fourier transform infrared analysis revealed that excessive salt was not removed during dialysis, making it unsuitable for scale-up due to its additional cost and complexity. Therefore, isoelectric precipitation was selected as the appropriate method for protein precipitation from sugar beet leaves. To increase yield, extractions were assisted by ultrasound or enzyme addition. Ultrasound-assisted extraction resulted in an increased protein yield from 20.20% to 28.60%, while Pectinex Ultra SP-L-assisted extraction was the most effective, increasing protein yield from 20.20% to 38.09%.</p> <p><br>CONCLUSION</p> <p>Proteins were extracted from fresh sugar beet leaves using optimum conditions (50 °C, 30 min, pH 9) and precipitated at the isoelectric point, with enzymatic-assisted extraction yielding the maximum protein recovery.<br><br></p>
Data for "The increase in prevalence of natural products through the drug discovery process"
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Characteristics, Origins, and Atmospheric Processes of Amines in Fine Aerosol Particles in Winter in China
<p>data</p>
Insight into the impact of viruses on biogeochemical processes in agricultural soils
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Processed CODEX multiplexed imaging data of cellular microenvironment around T cell stimulating hydrogels
<p>Our research used CODEX (Co-Detection by Indexing) multiplexed imaging to gain insights into the cellular microenvironment surrounding T cell stimulating hydrogels. These hydrogels were engineered with signals that could locally expand antigen-specific T cells for use in tumor immunotherapy. CODEX imaging involves an iterative process of annealing and stripping fluorophore-labeled oligonucleotide barcodes, complementing the barcodes attached to over 40 antibodies used for tissue staining. Subsequently, images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue.</p> <p>Our datasets comprise individual cells as rows, each characterized by 40+ antibody fluorescence values quantified from various markers evaluated for each study. These markers correspond to the antibodies targeting specific proteins within the tissue, quantified at the single-cell level. The values represent per-cell/area-averaged fluorescent intensities, z-normalized along each column. Each cell is mapped with its cell type, defined by x and y coordinates representing pixel locations in the original image. </p> <p>We then used this data to investigate how different proportions of the cell types change over time in response to the stimulating hydrogel injection with antigen-specific T cells. These data could be used to understand the cellular interactions, composition, and structure of T cell stimulating biomaterials for antigen-specific immunotherapy and with adoptive T cell transfer. These datasets offer valuable insights for researchers interested in engineering T cell stimulating microenvironments, immune responses, and therapeutic interventions such as T cell therapies.</p> <p>We investigate the dynamic interplay between immune responses, antigen-specific T cell interactions, and hydrogel environment in a murine melanoma model. We injected antigen-specific T cells with microparticle T cell stimulating hydrogels into mice subcutaneously. Injection sites were take out at different time points day=0 (just after injection), day=3, and day=9 (n=3-6 per time point). Our 51-plex CODEX antibody panel characterizes immune cell types, T cell phenotypes, and stromal cell types, resulting in a rich dataset of 241,685 cells across 51 marker channels.</p>
Coastal Wetland Crab Image Dataset and Image Processing Model Weights
<p>This data was collected from the distribution range of mangroves along the coast of China and has been randomly selected and manually corrected to be labeled as a crab image analysis dataset. The weights of deep learning models trained on YOLOv5/v8 and EfficientNet are also uploaded simultaneously. Additionally, it includes the necessary test datasets and some test results. Please cite when using this dataset, and contact the administrator if you need help. The specific directories are as follows:</p> <p>- R-crab: Contains code needed to test model performance, with the subfolder data containing the test dataset required. It includes (1) crab-man as human-marked references, crab-ref as model detection results. (2) luoyuan-burrow for the detection results of crab burrows in the Luoyuan area case study, and luoyuan-crab for crab detection results, including crab classification, localization, and carapace width information. (3) method-test for testing different methods, i.e., whether to use a two-stage detection model. (4) size-conf-test records the model detection results under different image input sizes and confidence threshold levels. (1), (3), and (4) are completed in Out-of-sample data, while (2) is completed in Luoyuan. fig_attr.csv records the test results of image attributes on detection accuracy. label_results.csv records the comparison results between traits measured manually using ImageJ software and our designed model for detecting crab carapace width. luoyuan_list.csv records the numbering information of Luoyuan sampling plots.<br>- Aiweights: Contains model weights trained based on Object detection data, with cpm-model under v8n-seg-crab.pt for YOLOv8 trained to extract crab carapace width. Sfc-model under adam20.pth is a two-stage detection model trained based on EfficientNet. Trained_weights under burrow-baseline is a crab burrow detection model trained based on our improved YOLOv5 (improvements stored at https://github.com/GuuX29/crab-yolo-add, same below); frame-s6-2560.pt is a plot frame detection model for obtaining standard 50*50cm plot images; s-simam-20.pt and x-simam-20.pt are both for crab detection and classification models, with x having higher accuracy.<br>- Luoyuan: crop stores standardized processed Luoyuan image data, numbered as above, test stores detection results, same as R-crab.<br>- Object detection: Contains cropped 640 pixels crab field sampling images, with images in the images folder and bounding box labels in the labels folder, divided into training and testing at a 9:1 ratio, train.txt and val.txt record the allocation information.<br>- Out-of-sample: Records 100 independent test images not used to train the model, stored in images, crab-man, and crab-ref are the same as in R-crab.<br>- Segmentation: Stores image data used to test model detection of crab carapace width, with results also stored in R-crab.</p> <p>We hope this data and method will benefit the progress of research in this field. For any suggestions for improvement and help with usage, please contact the administrator. guuxuan1994@gmail.com </p>
EXPLORING THE ACTUAL PROCESSES OF CONCEPTUAL FIELD EVOLUTION IN MODERN CONDITIONS.
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EO4EU-UC6-Processed-Data
<p>Processed data of ERA5 land for EO4EU UC5 use case.</p>
Observation and Simulation of Dart Leader-M Component Processing in Rocket Triggered Lightning
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Mouse Stereo-seq - processed data
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THE IMPORTANCE OF TIME MANAGEMENT IN TEACHING PROCESS
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.