Screenshot captures Visual Studio Code (VS Code) editor environment in dark theme. Central pane shows Python script containing imports, function definitions, and loop structures. Syntax highlighting is applied: keywords in purple, variables in white, strings in orange, and functions in blue-green.

Script begins with imports: import numpy as np, import tensorflow as tf, along with supporting libraries. Code defines function create_dataset which loads and normalizes data, shuffles, batches, and returns prepared dataset. Function employs TensorFlow dataset API (tf.data.Dataset.from_tensor_slices) and pipeline transformations such as shuffle, batch, and prefetch.

Subsequent section defines neural network model using Keras Sequential API. Layers include Dense layers with ReLU activations and final output layer with softmax activation. Optimizer is Adam, loss function is categorical crossentropy, and metrics include accuracy. Model is compiled and prepared for training.

Training loop uses .fit() method, specifying dataset, number of epochs, and validation data. Log outputs such as loss and accuracy are set to display per epoch.

Lower portion of script contains evaluation and prediction routines, including call to model.evaluate on test dataset and model.predict on new data samples. Code includes conditional if __name__ == "__main__": block, standard in Python scripts for main execution.

VS Code interface displays file path in tab labeled deep_learning_model.py. Explorer panel on left reveals workspace directory structure with src, data, and config folders. Top bar shows open command palette with options for Python interpreter selection.

Overall, screenshot demonstrates workflow of deep learning implementation in Python using TensorFlow, organized within modular script inside modern IDE environment.

The figure presents comparative ultrastructural and quantitative analyses of axonal morphology between control and experimental groups. Panels A–F show high-resolution electron microscopy images of myelinated axons across three anatomical regions: optic nerve (ON), lumbar spinal cord (LSCC), and thoracic spinal cord (TCSC). Control samples (A, C, E) display axons with circular profiles and uniform myelin sheaths, while experimental samples (B, D, F) exhibit variability in axon diameter and sheath thickness. Images highlight cross-sectional differences in fiber density, packing, and myelin compaction. Panels G–I provide scatter plots of axon diameter measurements, with regression lines indicating distribution relationships between conditions. Each scatter plot plots individual axon diameters (µm) against frequency counts, showing that experimental groups tend toward altered size distributions relative to controls. Panels J–L present histograms of axon diameter frequency distributions for ON, LSCC, and TCSC, respectively, with distinct peaks indicating shifts in axonal populations between groups. Panels M and N summarize quantitative comparisons in bar graph format: panel M shows mean axon diameter differences in the optic nerve, while panel N compares diameters across spinal cord regions. Statistical indicators (asterisks) denote levels of significance, with *** representing p < 0.001 and ** representing p < 0.01. The collective dataset illustrates region-specific and statistically significant differences in axon diameters between control and experimental conditions, integrating structural micrographs with quantitative morphometric analysis.

The image documents a full-length frontal portrait of an adult male subject positioned indoors against a composite background consisting of modular panels, structural columns, and visual documentation material. The subject is centrally framed and occupies the foreground plane, wearing a loose dark navy sweatshirt layered over a lighter undergarment with a rounded neckline. Facial details include a trimmed mustache, short hair parted centrally, and a neutral smiling expression. The eyes are obscured by unconventional eyewear designed with parallel metallic slats extending horizontally across each lens, producing a shutter-like visual obstruction that partially conceals the gaze. Around the neck hangs a pendant with cylindrical morphology suspended by a cord necklace, giving the appearance of an elongated metallic component suggestive of a machined object or industrial reference. The background is divided into two primary zones. The upper portion displays a large red banner with bold black typographic elements spelling “Walking,” preceded by the name “Alex Boya’s,” indicating an association with a project, film, or exhibition. Supporting this banner is a modular architectural frame constructed from square tubing painted white, connected at right angles with metal fasteners. Behind this supporting structure is a vertical cylindrical column of concrete with visible surface texture and small abrasions characteristic of building material. The lower background area is fully covered with a dense arrangement of printed photographic images adhered in a tiled configuration. These images comprise an extensive collage including portrait photographs of various individuals, close-ups of human faces, images of bread loaves, circular baked goods, anatomical diagrams, mechanical components, film stills, and experimental artworks. The arrangement follows a gridlike accumulation, suggesting an archival wall or research moodboard constructed to display visual references, production stills, or inspiration material. The collage extends across the visible wall surface, producing a layered visual field that functions simultaneously as backdrop and documentation archive. Artificial illumination is provided by overhead fluorescent fixtures integrated into a suspended ceiling system, with white linear reflectors directing uniform light downward across the scene. The environment corresponds to an interior institutional or studio-like setting, with structural modularity and the presence of worktables, shelving, and printed matter indicating an exhibition preparation zone or creative workspace.This composition integrates human presence, wearable accessories, project branding, and material documentation. The elements combine architectural framing, informational graphics, wall-mounted collage, and performative eyewear, producing a record that captures the intersection of human subject, workspace infrastructure, and curated visual archive. No evaluative or aesthetic commentary is implied; the description functions solely as an inventory of observed physical components and their spatial relationships.

Large-scale composite digital layout consisting of numerous image clusters, charts, and collaged visual references distributed across a black background. The composition is structured into distinct zones separated by white connector lines that draw attention to highlighted subsections. On the left, a webpage-like interface is visible, featuring profile elements, numerical statistics, thumbnails, and graphical interface components. Text values include numerical data such as “4.9K” and “2.7B,” displayed adjacent to rows of thumbnails representing visual archives or posts. Above this section, a purple frequency graph with sharp peaks occupies a rectangular panel, set beside a botanical-like macro image with radiating structures. The central region of the composition is densely populated with hundreds of small square and rectangular image tiles arranged in a grid-like mosaic. These images vary in content from portrait photography to illustrations, sketches, sculptural documentation, and mixed-media artworks. Subdivisions include grayscale photographs, colored renderings, and three-dimensional object captures. Lines extend outward from this dense core to magnified clusters on the right-hand side, where images are enlarged and reorganized for visibility. On the rightmost portion, a column of enlarged images includes manipulated portraits, sculptural masks, anatomical studies, paintings, and references to breadlike textures integrated with anthropomorphic motifs. Additional clusters show objects resembling clay models, carved reliefs, documentary stills, and collaged figures from historical and contemporary sources. Visual material is curated to emphasize thematic density, with repeated motifs of distorted heads, bread forms, hybrid anatomical imagery, and experimental portrait construction. The entire arrangement functions as a cartographic visualization of an archive, simultaneously representing statistical data, visual documentation, and thematic clustering. The structure integrates digital interface elements, quantitative analysis, and visual research fragments into a singular composite map emphasizing both breadth and depth of archival content.