Arrows Prompts

Highly details, HRD, 12K, stunningly beautiful.. The image features a woman dressed in a white dress, standing on a rocky surface and holding a bow and arrow. She appears to be in the middle of an action scene, possibly taking aim at a target or preparing to shoot an arrow. The woman's outfit and the presence of the bow and arrow suggest that she might be a warrior or an archer. The scene takes place in a desert-like environment, with a mountainous background and a sandy surface. controlnet_mode:canny realmixXL, sdxl-1. 0. 0. 9. safetensors, SeargeSDXL4. 2-Llama2 prompt)

Create a BioRender-style, publication-ready vector infographic titled “研究内容框架图” for a grant proposal. Use clean flat BioRender vectors, thick outlines, minimal shadows, consistent spacing, and a readable sans-serif font (Microsoft YaHei). Use a 16:10 landscape canvas (taller than 16:9). All text inside boxes must be Chinese exactly as specified. Do not include any mathematical letters, symbols, or formulas. Layout The figure has two main sections: Section A (left/center): Research Content Framework (main flowchart) A large framed panel with a top-down or left-to-right flow of four major blocks (Step 1 → Step 2 → Step 3 → Step 4). Each block is a rounded rectangle with a short title plus 2–4 bullet points. Add clear arrows between steps. Add a small triangle badge near Step 3 showing the trade-off. Section B (right side): Three embedded mini-schematics aligned vertically, each framed, with titles: “闭环控制框架（流程图）” “耦合误差示意（维恩图）” “深度递归神经网络示意（时间展开）” Use thin dashed connectors from the main Step 1–3 blocks to the corresponding mini-schematics to show correspondence. Icons (flat, minimal) Multi-agent network graph (nodes + edges), drones and mobile robots, wireless signal, clock/bell for event-triggering, sample-and-hold icon, neural network/RNN icon, Lyapunov/stability icon, and a balance scale icon (performance vs communication vs energy). Keep icons minimal and consistent. Chinese text to place in boxes (exact) Title (top center) “学习辨识—事件触发耦合下非线性多智能体系统分布式一致性控制与收敛性/有界性分析：研究内容框架图” Section A: Main research content framework (4 steps) Step 1 (Block 1) Title: “一致性误差机理刻画” Bullets: “建立统一闭环误差建模框架” “刻画学习误差、触发保持误差与拓扑耦合误差的交叉作用” “解释收敛退化、触发频繁与性能下降的成因” “覆盖无领导一致、领导跟随一致与协同跟踪场景” Step 2 (Block 2) Title: “低保守收敛性与有界性分析” Bullets: “显式利用触发区间信息构造分析工具” “建立收敛性与有界性判据并降低保守性” “推导误差上界、无有限时间无限触发条件与触发间隔下界” “刻画触发间隔与拓扑、触发参数、辨识精度的定量关系” Step 3 (Block 3) Title: “协同设计与权衡机制” Bullets: “协同设计学习辨识器、动态事件触发与分布式控制协议” “保证学习参数与内部递归状态有界” “揭示学习率、触发参数、拓扑特征与一致性性能的定量关系” “建立一致性性能—通信次数—能耗开销的可计算权衡” Add-on icon near Step 3: A small triangular trade-off badge with vertex labels (Chinese): “一致性性能 / 通信次数 / 能耗开销” Caption next to triangle: “可计算权衡” Step 4 (Block 4) Title: “仿真分析与实验验证” Bullets: “搭建含未知非线性、扰动与通信约束的仿真平台” “对比不同触发规则、拓扑与学习精度下的性能与通信开销” “在多无人机与多机器人平台开展验证” “形成可推广的低通信、高可靠、可验证方法” Section B: Three mini-schematics (right side) Mini-panel 1: “闭环控制框架（流程图）” Draw a left-to-right flowchart with rounded blocks and arrows: Blocks (in order, Chinese text exact): “非线性多智能体系统” → “局部/邻域信息获取” → “一致性误差计算” → “学习辨识器（深度递归神经网络）” → “分布式控制器” → “动态事件触发器” → “网络传输与采样保持” → Back arrow to “非线性多智能体系统” Add two dashed feedback arrows from “一致性误差计算” to: “学习辨识器（深度递归神经网络）” (label: “误差驱动更新”) “动态事件触发器” (label: “误差驱动更新”) Add small notes: Under “动态事件触发器”: “按需通信/按需更新” Near “网络传输与采样保持”: “触发保持误差” Add a small timeline icon with ticks labeled in Chinese: “触发时刻…下一次触发时刻” and label “触发间隔”. Mini-panel 2: “耦合误差示意（维恩图）” Draw a three-circle Venn diagram with semi-transparent circles: Circle labels (Chinese): “学习辨识误差” (with RNN icon) “触发保持误差” (with clock + sample-and-hold icon) “拓扑耦合误差” (with network graph icon) Pairwise overlap labels: “学习更新×非均匀更新” “异步通信×拓扑传播” “分布式辨识×邻域耦合” Center overlap (bold): “耦合项集合” Under it: “影响一致性误差演化” Arrow from center to a right-side box titled “结果表征” with bullets: “收敛退化（渐近→最终有界）” “触发频繁/触发间隔变小” “稳态误差界增大/性能下降” Mini-panel 3: “深度递归神经网络示意（时间展开）” Draw a time-unrolled recurrent network schematic along a horizontal timeline labeled in Chinese: “上一时刻 → 当前时刻 → 下一时刻” At each time slice, show stacked recurrent blocks: Input label: “本体状态与邻域信息” → Middle label: “递归记忆状态” → Output label: “未知非线性与不确定项的在线辨识输出” Connect time slices with arrows labeled: “共享参数” Add a side arrow from “一致性误差” into a small box: “参数更新（投影/正则化/学习率调度）” Then arrow into: “学习参数更新” Style constraints BioRender clean scientific infographic, no photorealism, no clutter, high readability. Strict rule: do not include any math symbols, letters, equations, or subscripts. Negative prompt: Avoid photorealistic style, avoid dense paragraphs, avoid handwritten fonts, avoid low resolution, avoid formulas, avoid math letters.

realistic rendering, digital electronic display, geometric composition, pixel art, large, bold black arrow upwards, surrounding background consists of a precise pattern of luminous yellow dots arranged in a uniform grid, high contrast between the black arrow shape and the glowing yellow background dots, clean --ar 9:16 --raw

twisted arrow

An isometric illustration of a CAD design process. Starting with an outline of a metal bracket labeled 'CAD', there is an arrow sequence illustrating the steps: 1. Uploading CAD files, represented by dotted arrows pointing upwards. 2. Configuring the order, depicted by a dotted arrow pointing right. 3. Determining the delivery date, shown by another dotted arrow pointing right. 4. Order verification and confirmation, represented by yet another dotted arrow pointing right. 5. Finally, the manufacturing and delivery of parts, symbolized by a metal bracket in full color and detail, with a downward pointing arrow. The entire process is displayed on a white background

avatar aang, the last airbender, in the style of furaffinity, orange robes, red, #vfxfriday, massurrealism, explosive pigmentation, spiritualcore, cinematic lighting, bioluminescent arrow, (arrow pointing downwards), ((full body portrait)) in the style of martial arts meditative pose, photorealism, water bending, water flowing in the background, water flowing orb, blue bioluminescent, highly detailed, 8k sharp focus

Create a BioRender-style, publication-ready vector infographic titled “研究内容框架图” for a grant proposal. Use clean flat BioRender vectors, thick outlines, minimal shadows, consistent spacing, and a readable sans-serif font (Microsoft YaHei). Use a 16:10 landscape canvas (taller than 16:9). All text inside boxes must be Chinese exactly as specified. Do not include any mathematical letters, symbols, or formulas. Layout The figure has two main sections: Section A (left/center): Research Content Framework (main flowchart) A large framed panel with a top-down or left-to-right flow of four major blocks (Step 1 → Step 2 → Step 3 → Step 4). Each block is a rounded rectangle with a short title plus 2–4 bullet points. Add clear arrows between steps. Add a small triangle badge near Step 3 showing the trade-off. Section B (right side): Three embedded mini-schematics aligned vertically, each framed, with titles: “闭环控制框架（流程图）” “耦合误差示意（维恩图）” “深度递归神经网络示意（时间展开）” Use thin dashed connectors from the main Step 1–3 blocks to the corresponding mini-schematics to show correspondence. Icons (flat, minimal) Multi-agent network graph (nodes + edges), drones and mobile robots, wireless signal, clock/bell for event-triggering, sample-and-hold icon, neural network/RNN icon, Lyapunov/stability icon, and a balance scale icon (performance vs communication vs energy). Keep icons minimal and consistent. Chinese text to place in boxes (exact) Title (top center) “学习辨识—事件触发耦合下非线性多智能体系统分布式一致性控制与收敛性/有界性分析：研究内容框架图” Section A: Main research content framework (4 steps) Step 1 (Block 1) Title: “一致性误差机理刻画” Bullets: “建立统一闭环误差建模框架” “刻画学习误差、触发保持误差与拓扑耦合误差的交叉作用” “解释收敛退化、触发频繁与性能下降的成因” “覆盖无领导一致、领导跟随一致与协同跟踪场景” Step 2 (Block 2) Title: “低保守收敛性与有界性分析” Bullets: “显式利用触发区间信息构造分析工具” “建立收敛性与有界性判据并降低保守性” “推导误差上界、无有限时间无限触发条件与触发间隔下界” “刻画触发间隔与拓扑、触发参数、辨识精度的定量关系” Step 3 (Block 3) Title: “协同设计与权衡机制” Bullets: “协同设计学习辨识器、动态事件触发与分布式控制协议” “保证学习参数与内部递归状态有界” “揭示学习率、触发参数、拓扑特征与一致性性能的定量关系” “建立一致性性能—通信次数—能耗开销的可计算权衡” Add-on icon near Step 3: A small triangular trade-off badge with vertex labels (Chinese): “一致性性能 / 通信次数 / 能耗开销” Caption next to triangle: “可计算权衡” Step 4 (Block 4) Title: “仿真分析与实验验证” Bullets: “搭建含未知非线性、扰动与通信约束的仿真平台” “对比不同触发规则、拓扑与学习精度下的性能与通信开销” “在多无人机与多机器人平台开展验证” “形成可推广的低通信、高可靠、可验证方法” Section B: Three mini-schematics (right side) Mini-panel 1: “闭环控制框架（流程图）” Draw a left-to-right flowchart with rounded blocks and arrows: Blocks (in order, Chinese text exact): “非线性多智能体系统” → “局部/邻域信息获取” → “一致性误差计算” → “学习辨识器（深度递归神经网络）” → “分布式控制器” → “动态事件触发器” → “网络传输与采样保持” → Back arrow to “非线性多智能体系统” Add two dashed feedback arrows from “一致性误差计算” to: “学习辨识器（深度递归神经网络）” (label: “误差驱动更新”) “动态事件触发器” (label: “误差驱动更新”) Add small notes: Under “动态事件触发器”: “按需通信/按需更新” Near “网络传输与采样保持”: “触发保持误差” Add a small timeline icon with ticks labeled in Chinese: “触发时刻…下一次触发时刻” and label “触发间隔”. Mini-panel 2: “耦合误差示意（维恩图）” Draw a three-circle Venn diagram with semi-transparent circles: Circle labels (Chinese): “学习辨识误差” (with RNN icon) “触发保持误差” (with clock + sample-and-hold icon) “拓扑耦合误差” (with network graph icon) Pairwise overlap labels: “学习更新×非均匀更新” “异步通信×拓扑传播” “分布式辨识×邻域耦合” Center overlap (bold): “耦合项集合” Under it: “影响一致性误差演化” Arrow from center to a right-side box titled “结果表征” with bullets: “收敛退化（渐近→最终有界）” “触发频繁/触发间隔变小” “稳态误差界增大/性能下降” Mini-panel 3: “深度递归神经网络示意（时间展开）” Draw a time-unrolled recurrent network schematic along a horizontal timeline labeled in Chinese: “上一时刻 → 当前时刻 → 下一时刻” At each time slice, show stacked recurrent blocks: Input label: “本体状态与邻域信息” → Middle label: “递归记忆状态” → Output label: “未知非线性与不确定项的在线辨识输出” Connect time slices with arrows labeled: “共享参数” Add a side arrow from “一致性误差” into a small box: “参数更新（投影/正则化/学习率调度）” Then arrow into: “学习参数更新” Style constraints BioRender clean scientific infographic, no photorealism, no clutter, high readability. Strict rule: do not include any math symbols, letters, equations, or subscripts. Negative prompt: Avoid photorealistic style, avoid dense paragraphs, avoid handwritten fonts, avoid low resolution, avoid formulas, avoid math letters.

Robotic archer slightly adjusts its aim and shoots off an arrow in a crisp, precise quick release motion, then reloads and repeats the action with a second arrow, while the camera performs an extremely slow cinematic pan around the character, capturing glinting metal details, the taut bowstring, and the arrows streaking through the air in smooth, dramatic motion.

avatar aang, the last airbender, in the style of furaffinity, orange robes, red, #vfxfriday, massurrealism, explosive pigmentation, spiritualcore, cinematic lighting, bioluminescent arrow, (arrow pointing downwards), portrait photorealism, highly detailed, 8k sharp focus

### **Image Generation Prompt for "Memory Me Objects Ka Behavior Kya Hoga?"** **Prompt:** Create a detailed and visually intuitive diagram to explain the behavior of objects in memory during a `while` loop that iterates over a database `ResultSet`. The diagram should include the following elements: 1. **Heap Memory Section:** - Show how new `User` objects are created in the heap memory during each iteration of the loop. - Highlight that each object corresponds to a row from the `ResultSet`. 2. **Garbage Collection:** - Illustrate how objects lose their reference after the loop ends and become eligible for garbage collection. - Use an arrow or icon to represent the Java Garbage Collector cleaning up unused objects. 3. **Permanent Storage (Optional):** - Show a scenario where objects are stored in a `List<User>` for permanent use. - Highlight the difference between temporary objects (eligible for garbage collection) and permanently stored objects. 4. **Flow of Execution:** - Include a flowchart-like representation of the `while` loop: - Start with `rs.next()` moving the cursor to the next row. - Show the creation of a new `User` object for each row. - End with either garbage collection or storage in a `List`. 5. **Annotations:** - Add labels and arrows to explain each step clearly. - Use Hindi/Hinglish annotations like: - "Har row ke liye naya object banega." - "Object ka reference lost ho jayega, to Garbage Collector clean kar dega." - "Agar List mei store kiya, to object permanent rehta hai." 6. **Color Coding:** - Use different colors for: - Heap memory (e.g., light blue). - Garbage-collected objects (e.g., grayed out). - Permanently stored objects (e.g., green). 7. **Database Table Example:** - Include a small table representation (e.g., `users` table with columns `id` and `name`) to show the source of data. --- ### **Expected Output:** The image should look like this: 1. **Top Section:** - A small database table (`users`) with rows and columns. 2. **Middle Section:** - A heap memory area showing multiple `User` objects being created during each iteration of the loop. - Arrows pointing from the `ResultSet` rows to the corresponding `User` objects in heap memory. 3. **Bottom Section:** - Two paths: - Path 1: Objects losing reference and being garbage collected (grayed out). - Path 2: Objects being stored in a `List<User>` for permanent use (highlighted in green). 4. **Annotations:** - Clear Hindi/Hinglish explanations for each step. --- This prompt will help generate a visually rich and easy-to-understand diagram for explaining the behavior of objects in memory! 😊

"A female warrior walks directly toward the camera, carrying her bow in her left hand and holding an arrow in her right hand, preparing to aim. She takes steady steps forward on the cinematic path, maintaining strong eye contact with the camera. As she stops, she raises the bow, nocks the arrow, and shoots upwards into the sky in a powerful motion. Hyper-realistic, cinematic depth of field, Canon 5D Mark IV style, 8K, dramatic lighting, slow-motion effect as the arrow is released."

"A highly detailed, zoomed-in view of a Forex candlestick chart displaying a clearly defined Bullish Engulfing Pattern. The first candle is red (bearish), small, and represents a downward price movement. The second candle is a larger green (bullish) candle that completely engulfs the body of the previous red candle, symbolizing a strong reversal signal. The chart is set against a dark, sleek background with a modern, professional aesthetic. Chart Details: The chart includes thin, crisp support and resistance lines in white or light gray, with subtle transparency for a clean look. The timeframe (e.g., 1-hour or 4-hour) is displayed in the bottom corner, and the price axis is clearly labeled on the right side. Highlighted Pattern: The Bullish Engulfing Pattern is highlighted with a soft glow or outline in bright green to draw attention, while the rest of the chart remains slightly muted for contrast. Reversal Signal: A bold, upward arrow in neon green or gold is placed above the green candle, pointing upwards to emphasize the reversal. The arrow has a subtle shadow or glow effect to make it stand out. Additional Elements: Include minimalistic grid lines in the background for structure, and add a faint, futuristic holographic effect to the chart for a modern, high-tech vibe. Text Overlay: In the top-left corner, include the text ‘Bullish Engulfing Pattern – Reversal Signal’ in a bold, modern sans-serif font (e.g., Helvetica or Futura). The text should be white or light gray with a subtle shadow for readability. Color Palette: Use a dark blue and black theme for the background, with neon green accents for the pattern and arrow. The overall design should feel sleek, professional, and visually engaging. Lighting: Add soft neon lighting around the edges of the chart and subtle reflections to give it a polished, futuristic look. Mood: The image should evoke a sense of precision, opportunity, and confidence, appealing to traders and financial enthusiasts."

apex arrows written in a fashionable way

Create a meaningful, symbolic logo for "MST Private Limited" — a software house that transforms businesses through modern web development (MERN Stack) and AI architectures. CORE SYMBOLISM TO EMBED: - The logo MUST visually communicate: "transformation through technology" - Show progression: traditional business → modern digital solution (like Amazon's arrow) - Embed the letters M, S, T in a clever way that tells a story PRIMARY CONCEPT (Recommended): An abstract "MST" monogram where: - The letter "M" forms the left side of a bracket < (like code) - The letter "S" flows upward like a rising graph or digital pathway (growth/transformation) - The letter "T" tops it as a peak/arrow pointing up (success, trust, leadership) - Together they form a subtle upward-pointing triangle or arrow (progress, elevation) - Negative space between letters forms a subtle "play button" or "forward arrow" (moving businesses forward) ALTERNATIVE CONCEPT 2 (AI + Web Fusion): - A stylized "M" that looks like both: a) Two code brackets < > facing each other (web development) b) Two connected neural network nodes (AI) - The "S" curves through the middle like a digital circuit path - The "T" sits atop as a cornerstone/stable foundation - Subtle gradient from blue (trust) to purple (innovation/AI) ALTERNATIVE CONCEPT 3 (Minimalist Wordmark with Meaning): - Bold "MST" where the crossbar of the "T" extends right like an arrow → - The arrow subtly forms the shape of a checkmark (✓) = "we deliver results" - Color gradient along the arrow: dark blue → bright blue = transformation COLOR PSYCHOLOGY: - Deep Navy Blue (#1A237E): Trust, professionalism, corporate credibility - Electric Blue (#2979FF): Technology, innovation, modern web - Accent Teal (#00BFA5): AI, fresh thinking, growth - White background for clean versatility TYPOGRAPHY: - "MST" in custom geometric sans-serif (bold, strong, modern) - "Private Limited" in lighter weight below, smaller (12-15% of MST size) - Font should feel like: Inter Bold, Poppins SemiBold, or custom geometric KEY REQUIREMENTS (Non-negotiable): ✓ Logo must work at 16x16px (favicon) and 10ft billboard ✓ Must look professional on dark AND light backgrounds ✓ Must convey: "We transform businesses with web + AI" in under 2 seconds ✓ Not generic — should be memorable like Amazon's arrow ✓ Clean enough for LinkedIn profile pic, bold enough for website header ✓ No clichés: avoid generic globes, basic circuit boards, or overused tech icons MOOD: - Confident but approachable (like Facebook) - Innovative but trustworthy (like Amazon) - Premium but not arrogant (like Apple) - Modern Pakistani tech company serving Dubai/UK/Pakistan markets

Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.

Robotic archer slightly adjusts its aim and shoots off an arrow in a crisp, precise motion, then reloads and repeats the action with a second arrow, while the camera performs an extremely slow cinematic pan around the character, capturing glinting metal details, the taut bowstring, and the arrows streaking through the air in smooth, dramatic motion.

avatar aang, the last airbender, in the style of furaffinity, orange robes, red, #vfxfriday, massurrealism, explosive pigmentation, spiritualcore, cinematic lighting, bioluminescent arrow, (arrow pointing downwards), portrait photorealism, highly detailed, 8k sharp focus

Create a BioRender-style vector infographic. Place the panel header “研究目标” as a small caption in the upper-left corner (not a large title). Set its font size to ~80% of the main box titles, use regular weight (not bold), and keep it visually subtle. Use a clean professional layout, flat colors, thick outlines, minimal shadows, and consistent sans-serif font (Microsoft YaHei). Canvas: 16:9 or 16:10 landscape. The figure should be non-technical (no equations), focusing on goals hierarchy. All text inside boxes must be Chinese exactly as provided. Layout Split the figure into two parts: Left Part (Overall Goals Pyramid): Draw a 3-layer stacked pyramid (or three stacked rounded rectangles) labeled from top to bottom: “理论目标”, “方法目标”, “应用目标”. Use subtle distinct colors for each layer. Add a small left-side label “总体目标” above the pyramid. Right Part (Three Specific Objectives Cards): Place three numbered rounded-rectangle cards vertically aligned (G1, G2, G3). Each card contains the specific objective text. Draw thin arrows from each card to the related pyramid layer(s): G1 arrows to “理论目标” (primary) and slightly to “方法目标” (secondary) G2 arrows to “方法目标” (primary) and slightly to “理论目标” (secondary) G3 arrows to “应用目标” (primary) and slightly to “方法目标” (secondary) Bottom Bar (One-line Summary): Add a wide rounded rectangle at the bottom spanning the width, labeled “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系”. Icons (small, minimal, optional) Next to “理论目标”: a Lyapunov/analysis icon (V(x)) Next to “方法目标”: a neural network + event-trigger clock icon Next to “应用目标”: drone + robot icons Next to bottom bar: a balance scale icon labeled “性能—通信—能耗” Chinese text to place (exact) Overall Goals (left pyramid): Top layer title: “理论目标” Text: “建立一致性误差动力学描述与统一的收敛/有界性分析框架；给出误差判据、误差上界、无Zeno与IET下界等可验证结论。” Middle layer title: “方法目标” Text: “提出以DRNN为核心的学习辨识器、动态事件触发机制与分布式控制协议协同设计方法；揭示触发参数、拓扑、辨识误差与一致性性能的定性—定量关系；建立‘一致性性能—通信资源与能耗开销’权衡机制。” Bottom layer title: “应用目标” Text: “依托多无人机与多机器人平台开展仿真与实验验证；形成可推广的低通信、高可靠、可验证协同控制方法；服务无人系统集群、网络化制造单元及分布式能源等场景。” Right cards (specific objectives): Card 1 header: “具体目标1（G1）” Body: “构造显式利用触发区间信息的looped-functional型Lyapunov分析工具，降低收敛性与有界性结论的保守性；推导误差上界、无Zeno条件与IET下界；刻画IET与拓扑结构、触发参数及系统状态之间的定性—定量关系。” Card 2 header: “具体目标2（G2）” Body: “建立基于DRNN学习辨识的分布式事件触发一致性控制框架；研究学习误差、触发误差与拓扑耦合误差对闭环性能的影响；设计权值更新律与触发机制，实现对未知非线性/不确定项的在线补偿并提升鲁棒性。” Card 3 header: “具体目标3（G3）” Body: “面向模型信息不完备与参数不确定等情形，研究模型弱依赖的学习辨识–事件触发一致性控制方法；给出一致性误差收敛性与有界性条件，以及DRNN权值与内部递归状态的有界性结论；提升方法的工程可实施性与适用范围。” Bottom bar text: “预期形成：低通信、高可靠、可验证的一致性控制理论与方法体系” Negative prompt Avoid photorealism, avoid dense paragraphs, avoid tiny illegible text, avoid complex mathematical derivations, avoid cluttered decorative elements.

a man stands surprised, arrows sticking out of him, and arrows pointing at him from all sides. cartoon style

add an arrow to the right

Acrylic and chalk highly detailed digital painting colorized vintage-style action scene from a 1930s adventure film, featuring a female heroic archer Robin Hood. She stands in a dynamic pose in a sunlit forest clearing, wearing a classic green tunic with leather lacing, a belt with a sword, and a quiver of arrows on her back. Her expression is bold and determined, one hand gripping a longbow, the other reaching for an arrow. The background features soft, hand-tinted colours and a painterly style.

a mascot of green arrow with apex arrow as font

close up shot of Ashwatthama in indian armor, stormy night on hill, aiming a charged arrow upward with chanted lights at the sky, red energy, realistic, cinematic, gem on forehead, glowing eyes and gem on forehead, shot from chest to head holding arrow using bow to aim at sky, gem glowing, hyperrealism, 4k, upscaled, real character, looking at sky

High resolution image as pictogram, easy arrows, modeern gray color - left, right, up and down without backround, same large and styl