AI Scientific Illustration Gallery

Control flowchart of a fractional-order fuzzy neural network with dead-zone output.

Schematic Title: Schematic Diagram of Microneedle Patch Preparation Process Overall Style and Layout: Style: Simple, modern, with technological line illustrations or flat vector graphics. Use a refreshing color scheme, mainly blue, gray, and white, with key parts highlighted in orange or green. Layout: A horizontal flowchart from left to right, including 4 core steps. Each step is clearly marked with a numbered module (Step 1-4). Detailed Description of Steps: Step 1: Mold Casting Scene: On the left is a transparent/translucent polymer microneedle mold (such as a PDMS mold), with a regularly arranged array of inverted pyramid or conical pits visible on its surface. Action: A pipette or dropper is dropping a drop of viscous, translucent mixed solution onto the surface of the mold. The solution can be symbolically dotted with tiny nanoparticles (NPs) to represent DNase-PDA@C-176 NPs. Label: Label the solution as "HA and DNase-PDA@C-176 NPs mixed solution". Label the mold as "Microneedle Mold". Step 2: Vacuum Filling Scene: The mold is placed in a transparent vacuum chamber. State: The solution on the surface of the mold has spread and covered the entire mold area. A vacuum pump icon (or a simple downward arrow indicating air extraction) is connected above the vacuum chamber. Key Visual: In the pits of the mold, use an enlarged partial cross-sectional view to show that the solution is being drawn down under the action of vacuum negative pressure, completely filling every tiny corner of the pit and expelling all air bubbles. A few small upward air bubble arrows can be used to indicate the dynamic process of exhaust. Label: "Vacuum Filling" or "Vacuum Degassing". Step 3: Drying and Curing Scene: The mold is transferred to a dry environment, such as being placed under a gentle warm air flow (indicated by curved arrows) or placed in a "drying oven" icon. State: The moisture in the solution in the mold evaporates, the volume shrinks, and finally solid needles are formed in the mold pits. A partial cross-sectional view can be used again to compare: from pits filled with liquid to solid needles. Label: "Drying/Curing". Step 4: Demolding and Finished Product Action: A hand or a robotic arm is carefully peeling/removing a formed microneedle patch substrate (a transparent polymer backing) from below. Finished Product Display: The peeled patch is displayed completely. Hundreds of tiny conical needle tips stand neatly on the patch substrate, forming a perfect array. Multi-angle Display (Optional): Main View: Complete patch. Side View/Magnified View: Shows the sharp needle tip and complete shape of a single microneedle, with dimensions labeled as "Height: 1000 μm, Base Width: 600 μm". Top View (Illustration): Shows the regular arrangement of microneedles, such as a "10x10 array" or similar. Final Label: Label the finished product as "DNase-PDA"

Ecological role of sesarmid crabs: Bioturbation mechanisms involving sediment reworking through burrowing, mixing, alteration, and transport, leading to improved soil aeration and oxygenation, nutrient distribution and cycling, and enhanced mangrove productivity and carbon sequestration.

A pipette with a tip attached.

Six jars of water kefir, two units of honey, two units of molasses, and two units of brown sugar were prepared and covered with cheesecloth.

2.3.1 Control of Roasting Temperature and Time The roasting temperature is the most critical factor determining the final consolidation strength of the pellets. An appropriate roasting temperature (typically between 1200-1250°C) promotes the recrystallization of magnetite (Fe₃O₄) into hematite (Fe₂O₃) within the pellets, forming a dense interlocked crystal structure, thereby imparting sufficient mechanical strength to the pellets. If the temperature is too low, consolidation is insufficient, leading to inadequate pellet strength. If the temperature is too high, over-melting may occur, causing liquid phase adhesion of the pellets, which deteriorates the permeability of the burden, increases energy consumption and FeO content, and reduces the reducibility of the pellets. Temperature control is primarily achieved by directly adjusting the gas flow rate and indirectly by adjusting the combustion air volume and the temperature and air volume of each wind box. However, strong coupling relationships exist between these variables, and adjusting one parameter often affects others. This requires the control system to have a high degree of coordination and precision, a process that often relies on various data analysis methods. For example, Liu Piliang et al. studied the roasting temperature of a Baotou Steel 624m2 D-L type belt roasting machine and found, through correlation analysis using SPSS and regression analysis using MATLAB, that the factors significantly affecting the roasting temperature were the temperature of the 14# hood and the temperature of the 14# wind box. In actual production, the temperature of each burner is adjusted to ensure that each process section of the pellet roasting process reaches the required temperature and temperature gradient. Therefore, accurate and stable control of the roasting machine temperature is crucial for improving the roasting process and the quality of the pellets. [1] Yu Haizhao, Liao Jiyong, Fan Xiaohui. Application and Research Progress of Pellet Technology in Belt Roasting Machine [J]. Sintering Pellet, 2020, 45(04):47-54+70. DOI:10.13403/j.sjqt.2020.04.054. The roasting time is determined by the length of the belt roasting machine and the running speed of the trolley. The faster the machine speed, the higher the output, but the shorter the residence time in each process section. The machine speed must be matched with the thermal regime to ensure that the pellets complete all necessary physical and chemical changes within a limited time. Frequent adjustments to the machine speed indicate production instability. Ideally, a constant machine speed is maintained under a stable thermal regime. The roasting machine transmission system typically includes a motor, reducer, and drive shaft. The reliability of each component directly affects the smooth operation of the trolley, including the conveyor belt and drive drum. Equipment failures can cause fluctuations in operating conditions and, in severe cases, can lead to the shutdown of the entire transmission system. 2.3.2 Setting of Furnace Atmosphere, Air Speed, and Air Volume The process air system is the "respiratory system" of the belt roasting machine, responsible for transporting heat, controlling the atmosphere, and removing exhaust gas. Therefore, the failure and shutdown of any fan will have a very serious impact on the entire roasting process. In particular, problems with the cooling fan and main induced draft fan are likely to cause the roasting machine temperature to become too high, leading to serious equipment damage. [1] Chang Tao. Functional Overview of Process Air Fans in Belt Roasting Machine [J]. Shanxi Metallurgy, 2016, 39(04):116-117. DOI:10.16525/j.cnki.cn14-1167/tf

Automated deployment and maintenance are fundamental to reducing operational costs and improving efficiency. The primary goal is to replace traditional manual, repetitive tasks with standardized, automated processes for system deployment, version upgrades, security patch distribution, and equipment maintenance. This approach reduces the risk of human error, enhances operational efficiency, and lowers labor costs. Traditional manual methods suffer from complex processes, difficulties in cross-system collaboration, and long execution cycles, leading to high labor costs and inefficient resource allocation. The core operational logic of this scenario follows a closed-loop management system: "Request Initiation - Solution Orchestration - Resource Scheduling - Automated Execution - Result Verification - Log Archiving." This creates a multi-level collaboration mechanism involving front-line execution and feedback, second-line solution design, and third-line standards development. Key constraints include task execution timeliness (efficient completion during off-peak hours), resource utilization thresholds (controlling hardware resource consumption), and process compliance rates. Standardized operation allows for the precise collection of core data such as hardware resource usage, labor input, and process efficiency, providing support for extracting cost-related metrics for feature systems. [An example diagram illustrating the closed-loop process of automated deployment and maintenance and multi-level collaboration should be inserted here, clearly showing the flow logic of each stage and the operational responsibilities of the first, second, and third lines of support.]

Visual Abstract Description: The visual abstract should be designed as follows: * Left Side: Illustrate the cyclic (aromatic) structure of hydroquinone. * Right Side: Illustrate the cyclic (aromatic) structure of benzoquinone. * Center (Top): Draw an arrow pointing from the hydroquinone structure to the benzoquinone structure. Above this arrow, indicate 'H₂O₂' to represent the oxidation process. * Center (Bottom): Below this reaction scheme, depict a graphene sheet. Disperse Fe₂N nanoparticles uniformly on this sheet, covering approximately 20% of the total surface area. Ensure the particles are well-dispersed across the entire graphene surface. Technical Specifications: * Use high-quality, clear graphics, avoiding excessive detail. * Final graphic dimensions: 920 x 300 pixels. * Maximum file size: 150KB. * Acceptable file formats: JPEG, PNG, or SVG. * Text should be limited to labeling compounds, reaction arrows, and diagrams.

Fault prediction is a proactive approach to operations and maintenance (O&M) that aims to reduce repair costs. The core objective is to leverage historical data and real-time status information to identify potential system vulnerabilities and weaknesses in advance, predict failure types and their impact, and reduce unexpected failures through preventative maintenance, thereby lowering O&M costs and business losses. Traditional O&M lacks the means to identify hidden risks and relies on periodic maintenance, which is costly and has limited effectiveness. The core operational logic of this approach is "data acquisition - feature extraction - model prediction - early warning notification - optimization and improvement." Data sources include historical failure records and equipment operating data. Front-line personnel are responsible for troubleshooting and rectification, second-line personnel are responsible for model optimization, and third-line personnel are responsible for strategy development. Key constraints include prediction accuracy, early warning lead time, and vulnerability identification coverage. Application of this approach can significantly reduce failure rates and repair costs, providing support for feature system extraction of hardware resource status and system loss-related indicators. [An example diagram illustrating the fault prediction technical architecture and data flow is needed here, showing data input, prediction process, and early warning notification path.]

Methods: Adult male Wistar rats were subjected to diffuse severe traumatic brain injury (TBI) using the Marmarou weight-drop model under anesthesia. Thirty minutes post-injury, pinocembrin (Pino) was administered intraperitoneally at doses of 25, 50, and 100 mg/kg. Neurological function was assessed using the Veterinary Coma Scale, beam walk, and beam balance tests at baseline, immediately after recovery from anesthesia, and at 24, 48, and 72 hours post-trauma. Cerebrospinal fluid (CSF) samples were collected for ELISA analysis of matrix metalloproteinase-9 (MMP-9), and brain tissues were fixed in 10% formalin for histological evaluation using hematoxylin and eosin staining. Results: Severe TBI induced significant neurological deficits, increased cerebral edema, disruption of blood-brain barrier (BBB) integrity, and impaired balance and motor performance. Treatment with pinocembrin at 25 and 50 mg/kg significantly attenuated these pathological changes compared with controls (p < 0.001). These doses also reduced cerebrospinal fluid MMP-9 levels, which were markedly elevated following TBI.

A blindfolded participant faces a box with two compartments. Small colored cubes are present in the right compartment. A partition separates the two compartments.

Subject: Experiment Flowchart of Bacterial Activation and Phage Treatment of Biofilm Formation Style: Concise, scientific experimental flowchart, black lines on a white background, clear icons Requirements: Use clear arrows to connect each step. All key nouns must be represented by icons/pictures, and ensure the icons are easy to understand. The process layout is from left to right and from top to bottom, with clear logic. Steps and Icon List: Start [Icon: Stock tube] (containing target bacteria) Use [Icon: Inoculation loop] to pick up bacterial solution from [Icon: Stock tube]. Perform [Icon: Streak plating] on [Icon: 90mm petri dish] (containing solid medium) to activate bacteria. Incubate in an incubator until [Icon: Single colony] grows. Use [Icon: Inoculation loop] to pick a [Icon: Single colony]. Inoculate the picked colony into [Icon: Erlenmeyer flask] containing [Icon: 2216E liquid medium] and shake culture. Measure periodically until [Icon: Spectrophotometer] shows the bacterial solution [Icon: OD600=0.1]. Branch point: Dispense the bacterial solution into two new, sterile [Icon: Erlenmeyer flasks]. Control group: Keep one [Icon: Erlenmeyer flask] as is. Experimental group: Add [Icon: Bacteriophage] to the other [Icon: Erlenmeyer flask]. Use [Icon: Pipette] to transfer the liquid from the two Erlenmeyer flasks to the wells of 4 [Icon: 24-well plates] respectively (the liquid from each Erlenmeyer flask corresponds to 2 well plates). Parallel processing: Place these 4 [Icon: 24-well plates] in [Icon: Different environments] (e.g., different temperatures or shakers) and incubate for [Icon: 24 hours]. After incubation, remove all well plates. Collect the [Icon: Biofilm] formed in each well plate. Place the collected [Icon: Biofilm] samples in [Icon: 1.5ml centrifuge tube]. End (for subsequent analysis). Layout: Please ensure that steps 1-7 are arranged vertically, and after step 8 (dispensing), the control group and the experimental group expand downwards in parallel, and steps 9-13 converge again.

APPROVED This is the abstract of my project proposal. Please generate a scheme for it. Abstract: Asymmetric segregation of protein damage and "aged proteins" during cell division is considered a key mechanism in the initiation of aging and cellular rejuvenation. However, existing studies mainly rely on observing a few fluorescently labeled molecules, lacking the ability to globally analyze at the molecular level and making it difficult to distinguish between new, old, and damaged proteins. To address these issues, this project proposes to use human cells as a model, combining live-cell fluorescence imaging, metabolic isotope labeling, and single-cell proteomics to systematically study the differential segregation patterns of protein abundance, new/old ratios, and damaged components between sister daughter cells during cell division. By constructing a SNAP-Omp25 mitochondrial temporal labeling system, we aim to capture asymmetric segregation events and perform Astral DIA single-cell proteomic analysis on strictly paired sister daughter cells to obtain high-resolution molecular readouts. Furthermore, we will introduce machine learning methods to extract stable asymmetrically segregated proteins and functional modules, and verify their regulatory roles through genetic and pharmacological interventions. This study will elucidate the molecular mechanisms of protein homeostasis resetting and damage isolation in human cells, laying the foundation for understanding aging mechanisms and intervention strategies for related diseases.

Title: Surface Chemistry Orchestrates the Immunological Identities of Nanoplastics via Protein Corona Formation Scientific Question: How do the surface functional groups of nanoplastics (PS) exposed via the bloodstream specifically encode the protein corona formed in serum? How does this differentiated protein corona, generated by surface chemistry, determine the immun recognition patterns, cellular responses, and ultimate toxic effects of nanoparticles in mice? Methods: First, 200 nm polystyrene nanoparticles with carboxyl (PS-COOH), amino (PS-NH2), and unmodified (PS) surface modifications were selected and characterized. They were then incubated with mouse serum in vitro, and proteomic analysis was performed using liquid chromatography-tandem mass spectrometry to identify the differential expression of proteins in the three protein coronas. This comparison clarified the specific regulatory effects of surface functional groups on protein corona composition. Subsequently, a blood exposure model (tail vein injection) was established, and transcriptome sequencing was performed on mouse peripheral blood cells at the single-cell level. Differential expression gene analysis and KEGG pathway enrichment analysis were conducted on the sequencing data for each cell subpopulation to systematically map the immune cell-specific transcription profiles induced by nanoplastics with different protein coronas. Finally, using mouse macrophages as an in vitro model, bare particle groups and pre-coated protein corona particle groups were established. Cell viability was assessed using the CCK-8 method, cellular uptake efficiency was quantified by flow cytometry, and inflammatory cytokine levels were measured by ELISA. Spearman correlation analysis was performed to integrate protein corona composition data with cellular function data, establishing a quantitative relationship between key protein adsorption and biological effects, with the aim of revealing the complex interaction mechanisms between surface functional groups, protein adsorption, and immune responses. Conclusions: The study demonstrates that surface charge is a crucial factor in regulating protein corona composition. Carboxylation leads to the specific enrichment of complement and coagulation proteins, while amination primarily adsorbs apolipoproteins and albumin. This specific adsorption is not random but is driven by molecular properties such as surface potential and protein isoelectric point, achieving a precise conversion of surface chemical information into biological molecular identity. PS-COOH with a complement protein corona is efficiently recognized and endocytosed by macrophages through complement receptors and other pathways, specifically activating the NF-κB signaling axis and driving classical inflammatory pathways such as TNF and IL-17, triggering a strong immune response. In contrast, PS-NH2 with an apolipoprotein corona mimics endogenous lipoprotein particles, reducing rapid clearance by the endothelial phagocytic system, prolonging blood half-life, and primarily inducing upregulation of mitochondrial respiratory chain genes and disruption of oxidative phosphorylation pathways.

Deep Proteomics-Discovered Molecular Identities Reveal Biological Functions of Nanoparticle Protein Corona Key Scientific Questions: 1. Missing Causal Mechanisms: How do the key physicochemical properties of nanoparticles (surface charge, PEGylation, morphology, material) systematically and causally determine their protein corona composition, and in turn, regulate biological fates such as cellular uptake and immune recognition? 2. Data Resource Bottleneck: How can we overcome the fragmentation and low quality of existing public proteomics data to establish a high-quality, standardized nanobio interaction database that can support reliable mechanism discovery and model prediction? Research Methods: This study employs an integrated "data mining-guided experimental construction" strategy. First, a literature-mined nanoparticle protein corona database (LM-NPC-DB) was constructed through text mining and literature data integration, systematically evaluating the field's research paradigm and data quality defects. Based on this analysis, a standardized nanoparticle library covering 42 different materials, charges, PEGylation states, and morphologies was rationally designed and synthesized. Subsequently, a high-quality in-house nanoparticle protein corona database (IH-NPC-DB) was constructed by strictly following uniform standard operating procedures. This database, with its high reproducibility, high protein coverage, and minimized missing values, serves as the core data foundation for this study. On this basis, combined with bioinformatics analysis (differential protein analysis, pathway enrichment, network analysis), machine learning models (predicting morphology-specific adsorption), and functional cell experiments (such as using gene knockout cell models to verify specific uptake pathways), the quantitative relationship between nanoparticle properties, protein corona composition, and biological effects was systematically decoded. Conclusions: This study is expected to establish and validate a clear causal framework of "nanoparticle properties → protein corona composition → biological fate." Specific conclusions include: 1. Surface charge guides protein adsorption through electrostatic-hydrophobic synergistic effects. Negatively charged particles enrich adhesion proteins and mediate efficient cell uptake via Itgav, while positively charged particles preferentially bind to apolipoproteins. 2. PEGylation actively reduces the adsorption of immune-related proteins such as complement/coagulation factors, reconstructing the protein corona to achieve "immune stealth" and effectively inhibit macrophage inflammatory responses. 3. Particle morphology shapes a unique protein adsorption fingerprint. Spherical particles enrich adhesion-related proteins, while rod-shaped particles exhibit high immunogenic potential, both achieved through different physical interactions and interfacial geometric effects. 4. Different materials exhibit complementary protein adsorption profiles, which can be used as "molecular amplifiers" to specifically enrich low-abundance disease biomarkers, providing a theoretical basis for constructing multi-material combined liquid biopsy panels. Ultimately, this study not only provides a standardized database (IH-NPC-DB) that surpasses the quality of existing public data, but also enables rational...

This document describes the creation of an abstract vector art flowchart illustrating a six-step sequential process. The desired style is minimalistic, modern, and icon-based, employing a limited color palette of technological blues, neutral grays, and a single accent color such as green or orange. Each step is to be clearly represented by an icon or symbol, connected to the subsequent step via smooth, flowing arrows. The steps are as follows: 1. **Weighing CNT:** Depict a high-precision digital analytical balance displaying a value of `0.15 g`. Black CNT particles should be visible on the weighing pan. 2. **Adding to Solvent:** Show a glass beaker containing a blue liquid (representing distilled water). Black CNT particles are illustrated falling from a weighing spatula into the beaker. The volume `30 mL` should be indicated next to the beaker. 3. **Probe Sonication:** Illustrate a probe sonicator device with the probe tip immersed in the liquid. Ultrasonic waves should be abstractly represented.

This document describes the creation of an abstract vector art flowchart illustrating a six-step sequential process. The desired style is minimalistic, modern, and icon-based, employing a limited color palette of technological blues, neutral grays, and a single accent color such as green or orange. Each step is to be clearly represented by an icon or symbol, connected to the subsequent step via smooth, flowing arrows. The steps are as follows: 1. **Weighing CNT:** Represented by a symbol of a high-precision digital analytical balance displaying a value of `0.15 g`. Black CNT particles should be visible on the weighing pan, indicating the transfer from paper to a beaker. 2. **Adding to Solvent:** A glass beaker containing a blue liquid, symbolizing distilled water. Black CNT particles are depicted falling from a weighing spatula into the beaker. The volume `30 mL` should be indicated adjacent to the beaker. 3. **Probe Sonication:** Symbolized by a probe sonicator device with the probe tip immersed in the liquid. Ultrasonic waves should be abstractly represented.

A schematic diagram illustrating magma evolution. It contains two magma chambers. The first magma chamber is located at a depth of 10 km, and the other is at 13 km; these two magma chambers are depicted in a vertical arrangement. The lower magma chamber has a higher degree of crystallization and is in a crystal mush state. Upwelling magma from deeper sources carries plagioclase crystals, which have crystallized in the lower magma chamber, into the upper magma chamber.

Please help me create a technical roadmap for a paper. Technical Roadmap Structure Design: Top Title Bar: Research on the Impact of National Scholarship on the Learning and Development of Awarded Undergraduate Students Main Structure (Three-Column Layout): | **Left Column: Research Ideas** | **Middle Column: Research Content** | **Right Column: Research Methods** | #### Top Legend Bar [ **Research Ideas** ] | [ **Research Content** ] | [ **Research Methods** ] Detailed Flowchart Content Design (I) First Stage: Problem Formulation Left Side (Research Context): * [ **Problem Formulation** ] * *(Downward Arrow)* * **Middle (Research Content):** * *(Start of Top Dashed Box)* * [ Research Background ] —> [ **Focus on Research Question** (System-Subject-Impact) ] <— [ Literature Review and Theoretical Framework ] * *(Downward Arrow Pointing to Content 1)* * **Right Side (Research Methods):** * *(Corresponding to Top Dashed Box)* * Literature Research Method (II) Second Stage: Problem Analysis * **Left Side (Research Context):** * *(Large Arrow Downward)* * [ **Problem Analysis** (Following Logical Chain) ] * *(Downward Arrow)* * **Middle (Research Content):** * **[Sub-module 1: "Why the System?"]** * *(Dashed Box Enclosing)* * [ **Content 1: Practical Forms and Cognition of the National Scholarship System** (RQ1) ] * *(Downward Branching Arrow)* * [ **Policy Evolution Review** (Diachronic Analysis) ] —> [ **Analysis of Evaluation Rules of University Departments** (Index Structure/Difference H University) ] <— [ **Analysis of Students' "National Scholarship Experiences"** (System Cognition/Effect Perception) ] * *(Downward Total Arrow)* * **[Sub-module 2: "Who is Selected?"]** * *(Dashed Box Enclosing)* * [ **Content 2: Heterogeneous Characteristics and Type Identification of Awarded Undergraduate Students** (RQ2) ] * *(Downward Branching Arrow)* * [ Overall Characteristics of Learning Development (Descriptive Statistics) ] <—> [ **Typical Type Identification** (Latent Profile Analysis LPA) ] —> [ **Revelation of Inter-group Heterogeneity** (Gender/Discipline/Class Type Difference Test) ] * *(Downward Total Arrow, Leading to a Line Pointing to the Case Selection of Content 3)* * **[Sub-module 3: "What is the Impact?"]** * *(Dashed Box Enclosing, Clearly Indicating Mixed Research)* * [ **Content 3: Impact Effects and Mechanisms of National Scholarship on Students' Learning Development** (RQ3) ] * *(Internal Process: Exploratory Sequential Mixed Design)*

"Rich and mellow" is a core term used to describe the taste of high-quality green tea. "Richness" mainly refers to the intensity and thickness of the tea soup, which is a direct reflection of the high content of water extract, rich in tea polyphenols, caffeine and other substances that provide a strong sensory impact. "Mellow" emphasizes the mellowness and coordination of the tea soup, referring to the moderate bitterness and astringency, balanced with freshness and sweetness, resulting in a comfortable aftertaste in the mouth after swallowing, rather than a stimulating astringent sensation. It is not limited to a single taste dimension, but is the product of the synergistic effect of multi-sensory signals such as taste, smell and oral tactile sensation, and its perceived physiological basis covers a multi-dimensional complex mechanism. First, from the perspective of taste, the formation of a rich and mellow taste is closely related to basic tastes such as sweetness and umami, and sweetness is the cornerstone of its core taste experience. For example, during the long-term withering process of white tea, the content of soluble sugar inside the tea leaves accumulates significantly, and the sweetness increases, and the mellow and smooth taste also follows, which together creates its unique rich and mellow flavor [4]; sweetness perception is mediated by the T1R2/T1R3 heterodimeric receptor, which makes the brain feel pleasant about the stimulation of sweetness, which further enhances the overall perception of the rich and mellow taste. In addition, there is a two-way interaction between sweetness and salty, sour, bitter, umami and other tastes, such as adding sugar and milk to coffee to adjust the tribological properties of the system, weakening the bitterness while enhancing the sweetness and mellowness, which confirms the regulatory effect of taste interaction on the rich and mellow flavor. Second, the sense of smell plays an indispensable synergistic role in the perception of rich and mellow taste. Volatile compounds in food combine with olfactory receptors through the orthonasal and retronasal olfactory pathways, and the rich aroma signals generated are integrated with taste information in the cerebral cortex to form a complete flavor perception. For example, the sweet floral, warm fruity, or aged woody aromas in tea drinks can complement the taste signals and significantly enhance the overall richness and mellowness [9]; the rich and mellow experience of coffee is also inseparable from complex volatile components, and different roasting degrees create differentiated flavors, such as the chocolate and caramel aromas produced by medium-roasted coffee beans, which not only enrich the flavor level, but also enhance the rich and mellow texture through the synergistic effect of aroma and taste. Third, oral tactile sensation is the key physical basis for the perception of rich and mellow taste, and its core is directly related to the physical properties of food such as texture, viscosity, smoothness and granularity. For example, the rich and mellow taste of dairy products is derived from the synergistic effect of their milky aroma, milk flavor, viscosity and smoothness, in which viscosity and smoothness can directly enhance the oral tactile perception of richness and mellowness. Relevant studies have made it clear that creaminess and smoothness are the core dimensions for evaluating the rich and mellow taste of food, and the difference in their physical properties will directly affect the perception intensity and comfort of the rich and mellow taste. In summary, the perception of rich and mellow taste is a complex physiological process of multi-sensory synergistic integration of taste, smell and oral tactile sensation, which relies on the specific recognition of multiple chemical components by taste receptors, the precise capture of volatile aroma substances by the olfactory system, the intuitive perception of the physical texture of food by oral tactile sensation, and the integration, processing and comprehensive analysis of these multi-source sensory signals by the cerebral cortex.

Illustrate the growth process of a mung bean from seed to the emergence of two true leaves, spanning two months, and culminating in yellowing leaves and the development of a few seed pods.

APPROVED. Conceptual diagram outline for flash drought event (FDE) detection using standardized evaporative stress ratio (SESR) and its change (ΔSESR). The figure title should be placed at the top in a small font. The conceptual workflow for FDE detection is as follows: Panel 1 (Inputs): Box label: ET & PET (daily). Subtext: → ESR = ET / PET → pentads (5-day). Icon idea: thermometer + droplet or ET arrows. Panel 2 (Standardization): Box label: SESR (pentad). Subtext: Seasonal standardization (pentad-of-year). Panel 3 (Intensification): Box label: ΔSESR. Subtext: Pentad-to-pentad change (standardized). Panel 4 (Thresholds): Box label: Seasonal thresholds. Subtext: P40 P25 P20. Panel 5 (Decision): Diamond.

APPROVED Technical Illustration Request: Multi-modal Feature Fusion Neural Network Architecture Role: Technical Illustrator for Computer Science Research. Subject: A neural network architecture diagram illustrating 'Multi-modal Feature Fusion'. Style: Academic, IEEE standard, flat 2D vector, orthogonal lines, high contrast. White background. Layout & Components (Left to Right flow): 1. Input Phase (Left): Three parallel input vectors stacked vertically: * Top: A blue vector bar labeled '$V_{sem}$ (Semantic)'. * Middle: A green vector bar labeled '$V_{graph}$ (Graph)'. * Bottom: An orange vector bar labeled '$V_{stat}$ (Statistical)'. 2. Alignment Phase (Middle-Left): * The Top ($V_{sem}$) and Middle ($V_{graph}$) vectors pass through unchanged (identity). * The Bottom ($V_{stat}$) vector passes through a small neural network block labeled 'MLP Alignment'. * The output of this block is a new vector labeled '$H_{stat}$'. 3. Fusion Phase (Center): * Show the three vectors ($V_{sem}$, $V_{graph}$, $H_{stat}$) merging into one long vertical block. * Label this merging operation with the symbol '||' (Concatenation).

Scientific illustration, graphical abstract. Split layout design depicting a water treatment process using Covalent Organic Frameworks (COFs). Left: Hexagonal COF structures with varying pore sizes, highlighting a specific pore size of 5.3 nm, functionalized with TEMPO molecules. Middle: A chemical reaction occurring within the COF pore. Purple permanganate ions (MnO4-) and organic pollutant molecules (Tetracycline) are shown. Bright yellow arrows indicate electron transfer from the pollutant to TEMPO and then to permanganate, labeled 'Electron Transfer'. Right: A continuous flow water treatment column, illustrating dirty water input and clean water output. A small graph displays rapid degradation kinetics. Professional, 3D rendering style, clean background, using a scientific color palette (purple, orange, blue, grey).