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question:<!DOCTYPE html> <html> <head> <script type="text/javascript" src="https://cdn.scaledrone.com/scaledrone.min.js"></script> <meta charset="UTF-8"/> <title>Multiplayer chat server</title> <style> .nickname { color: blue; } .message { color: black; } .timestamp { color: gray; } </style> </head> <body> <h1>Multiplayer Chat Server</h1> <p>Welcome to the chat server! Enter your nickname to join the chat:</p> <input type="text" id="nicknameInput"> <button onclick="joinChat()">Join Chat</button> <div id="chatLog"></div> <p>Send a message:</p> <input type="text" id="messageInput"> <button onclick="sendMessage()">Send</button> <script>
answer:const nicknameInput = document.getElementById("nicknameInput"); const messageInput = document.getElementById("messageInput"); const chatLog = document.getElementById("chatLog"); const drone = new Scaledrone("YOUR_CHANNEL_ID_HERE"); drone.on("open", function(error) { if (error) { console.error(error); } else { console.log("Successfully connected to Scaledrone"); } }); drone.on("message", function(message) { const p = document.createElement("p"); const nicknameSpan = document.createElement("span"); nicknameSpan.classList.add("nickname"); nicknameSpan.textContent = message.clientId.substring(0, 8) + ": "; const messageSpan = document.createElement("span"); messageSpan.classList.add("message"); messageSpan.textContent = message.data; const timestampSpan = document.createElement("span"); timestampSpan.classList.add("timestamp"); timestampSpan.textContent = " (" + new Date(message.timestamp).toLocaleString() + ")"; p.appendChild(nicknameSpan); p.appendChild(messageSpan); p.appendChild(timestampSpan); chatLog.appendChild(p); }); function joinChat() { const nickname = nicknameInput.value; drone.publish({ room: "observable-room", message: nickname + " has joined the chat", }); } function sendMessage() { const message = messageInput.value; drone.publish({ room: "observable-room", message: message, }); } </script> </body> </html> <!-- Remember to replace "YOUR_CHANNEL_ID_HERE" with your actual Scaledrone channel ID -->
question:Summarize the content for 10 marks - BLOCKING AND CONFOUNDING SYSTEM FOR TWO-LEVEL FACTORIALS 13.1.1. Introduction Under homogeneous conditions, there are many conditions in which it is not possible to perform all runs in a 2 k factorial experiment. For example, a single group of raw materials may not be sufficient to make other requirements in process. There are many case where it may be desirable to vary the experimental conditions to ensure that treatments are equally effective in many situations, likely to come into practice. For example, an engineer may run a pilot plant experiment with multiple batches of raw materials as he knows quality grades of raw materials that are likely to be used in the process. Bloacking is a design tehcnique that can be used in these situations. It is a method of dealing controllable nuisance variables. 13.1.2. Blocking a Replicated 2 k Factorial Design Suppose the 2 k factorial design is repeated n times. In case, there exist n replicas, then every non-homogeneous conditions defines a block, and each replication is run in a block. The replicates (or runs) in each block will be created randomly. Design analysis is similar to any blocked factorial experiment. Example 1: Let us suppose that the chemical process experiment. Consider that only four experimental tests can be conducted from a single batch of raw materials. Thus, three batches would be required to run all three replicates. Table 13.1 illustrates the design, where every batch of raw materials matches to one block. Table 13.1:Chemical Process Experiment in Three Blocks Block 1 Block 2 Block 3 (1) = 28 a = 36 b = 18 (D = 25 a = 32 b = 19 (1) = 27 a = 32 b = 23 ab = 31 ab = 30 ab = 29 Block Totals B| = 113 B2 = 106 B3= lll For this blocking design, the analysis of variance is shown in table 13.2. 268 Biostatistics & Research Methodology Table 13.2: Analysis of Variance for the Chemical Process Experiment in Three Blocks Source of Variation Sum of Squares Degrees of Freedom Mean Square F. PValue Blocks 6.50 2 3.25 A 208.33 208.33 1 50.32 0.0004 (Concentration) B (catalyst) 75.00 1 75.00 18.12 0.0053 AB 8.33 1 8.33 2.01 0.2060 Error 24.84 6 4.14 Total 323.00 11 All sums of squares are calculated similar to a standard unblocked 2 k design. From the block sum, the sum of squares for a block is calculated. B| , B2 and B^ shows block totals as shown in table 13.1: 3 r 2 ..2 = y y ... s ' 4 12 i=l ^ (113) 2 +(106) 2 +(111) 2 (330) 2 SSBlock = 6.50 4 12 These three blocks have two degrees of freedom. Table 13.2 shows that the summary from this analysis, the design was run in blocks and the block effect is relatively small. 13.1.3. Confounding in the 2 k Factorial Design There are many situations where it is not possible to perform a complete replication of a factorial design in a block. Confounding is a design technique in which complete factorial experiment is arranged in blocks and the block size is smaller than the number of treatment combinations in one replica. The technique causes information about certain treatment effects to be confounded with block. For 2 k factorial design, we typically focus on confounding systems. Sometimes the designs shown are incomplete block designs as every block does not include all treatments combinations. The particular structure of a 2 k factorial system permits a simple method of analysis. We generally focus on design and analysis of 2 k factorial design in 2 P incomplete blocks having condition, p < k. These designs can be generally run with two blocks, four blocks, eight blocks and so on. 13.1.4. Confounding the 2 k Factorial Design in Two Blocks Assume that someone wants to run a single replica of 22 designs. Each of the 22 = 4 treatment combinations needs the same amount of raw material. For example, each batch of raw materials is sufficient to test two treatment combinations. Therefore, two batches of raw materials are needed. In case, batches of raw materials are taken as blocks, we must specify two of four treatment combinations in every block. For this problem,figure 13.1 shows a possible design. Figure 13.1a shows the geometric view and indicates that the treatment combination is assigned to different blocks on opposing diagonals. Figure 13.1b shows that that Block 1 contains the treatment combination ( 1 ) and ab and Block 2 contains q and b. The order is Blocking and Confounding System (Chapter 13) 269 randomly defined for running treatment combinations within a block. The block which to run first is decided by random manner. We estimate the main effects of A and B by assuming that there is no blocking. We get: A=- ^ [ab+a -b-(l)] B=- ^ [ab+ b-a-(l)] We see that both A and B are not affected by blocking as each estimate has a plus and a minus treatment combination from every block. This means that there is no difference between Block 1 and Block 2. Let us assume that we have AB interaction: ^ AB = [ab+(l)-a -b] The two treatment with the minus sign (A and B) are in block 2 and the two with the plus sign [Ab and (1)] are in block 1, the block effect and AB interaction are the identical. This shows that AB is confounded with blocks. The reason is shown in the table of plus and minus signs for 22 designs. It is illustrated in table 13.3. In table, we see that all treatment combinations with a plus sign on AB are assigned to block 1. Similarly, all treatment combinations with a minus sign on AB are placed in block 2. This method can be used to confound any effect A, B, or AB with blocks. For example, in case, (1) and b are assigned to block 1 and a and ab to block 2, then the main effect A will be confounded with block. +<> Block 1 Block 2 ( 1 ) a B ab b A + b) Assignment of the Four •= Run in block 1 Runs to Two Blocks o — Run in block 2 a) Geometric View Figure 13.1: A 21 Design in Two Blocks Table 13.3: Table of Plus and Minus Signs for the 21 Design Treatnient Combination Factorial Effect 1 A B AB ( 1 ) + a + + b + + ab + + + + This method can be very useful to confound any 2 k design in two blocks. For example, let us assume that a 23 design run in two blocks. Let we confound the three-factor interaction ABC with blocks. Table 13.4 shows plus and minus signs. 270 Biostatistics & Research Methodology We assign treatment combinations of minus to block 1 on ABC and plus to block on ABC. Table 13.4: Table of Plus and Minus Signs for the 23 Design Treatment Combination Factorial Effect I A B AB C AC BC ABC ( 1) + + a + + + + b + + + + ab + + + + c + + + ac + + + be + + + abc + + + + + + + + Figure 13.2 illustrates the resultant design. The treatments within a block are again run randomly. Block 1 Block 2 (1) a # = Run in block 1 ab b O = Run in block 2 ac c ,0 be abc a) Geometric View b) Assignment of the Eight Runs to Two Blocks Figure 13.2: 23 Design in Two Blocks with ABC Confounded Other Methods for Constructing the Blocks These designs can be constructed by using another method. The method applies linear combination: L = aiXi + (X2X2 + + akxk, . . (1) Where, Xj = The level of i ,h factor in a specific treatment combination a = Exponent appearing on the ilh factor in the effect to be confounded. Note: For the 2 k system, we have a. = 0 or 1 and x, = 0 (low level) or x4 = 1 (high level). Equation 1 is known as defining contrast. Treatment combinations that give the similar value of L (mod 2) will be assigned in the similar block. Since 0 and 1 are the only possible values of L (mod 2), it will specify 2k treatment combinations in exactly two segments. To see the approach, assume 23 designs with ABC, confounded by blocks. Where Xi corresponds to A, x2 to B, X3 to C, and oil = 012 = 013 = 1 . Therefore, the contrast corresponding to ABC is as follows: L = X|+ X2 + X3 The treatment combination ( 1 ) is written 000 in the (0, 1 ) notation, thus we get: L = 1 (0) + 1 (0) + 1(0) = 0 = 0 (mod 2) Blocking and Confounding System (Chapter 13) 271 Similarly, the treatment combination is 100, obtaining: L = 1(1) + 1(0) + 1 (0) = 1 = 1 (mod 2) Thus, (1) and 'a' will be run in different blocks. For the remaining treatment combinations, we get: b: L = 1(0) + 1(1) + 1(0) = 1 = 1 (mod 2) ab: L = 1(1) + 1(1) + 1(0) = 2 = 0 (mod 2) c: L = 1(0) + 1(0) + 1(1) = 1 = 1 (mod 2) ac: L = 1 (1) + 1(0) + 1(1 ) = 2 = 0 (mod 2) be: L = 1(0) + 1(1) + 1(1) = 2 = 0 (mod 2) abc: L = 1(1) + 1(1) + 1(1 ) = 3 = 1 (mod 2) Thus (1), ab, ac, and be are run in blocks 1 and a, b, c. abc are run in block 2.This is the same design shown in Figure 13.3, which originated from the table of plus and minussigns. Additional methods can be used to make these designs. The block containing the treatment combination (1) is known as the principal block. This block has a useful group-theoretic property in treatment combinations. That is, they form a group against the multiplication modulus. This implies that if good quality material was used then any element except the original (1 ) would be principal. Figure 13.3b shows the resulting responses. These can be found by subtracting the block effect from the original observations. This shows that the original response to treatment combination ( 1) was 45, and it was calculated (1) = 25 ( = 45 - 20) in figure 13.3b. Other responses can also be obtained in this block. After being tested in block 1, block 2 performed eight trials. D + Block 1 Block 2 a = 71 b = 48 c = 68 d = 43 abc = 65 bed = 86 abd = 86 abd = ( 1 ) = 25 ab = 45 ac = 40 be = 60 ad = 80 bd = 25 cd = 55 abed = 76 104 # = Runs in block 1 C b) Assignment of the 16 Runs to Two O = Blocks Runs in block 2 a) Geometric View Figure 13J: 24 Design in Two Blocks Table 13.5 shows the effect estimates for this “modified” version. The estimates of 4 main effects, 6 two-factor interactions, and 4 three-factor interactions are similar to affect estimates where there was no block effect. Similar to the original experiment, the factors A, C, D and AC and AD interactions emerge as significant effects when a normal probability of effect estimates is constructed. In the original experiment, this effect was estimated as ABCD = 1.375. In this example, the estimate of the ABCD = -18.625. Since ABCD is confounded with blocks, the ABCD interaction estimates the original interaction effect (1.375) with addition to the block effect (-20), hence ABCD = 1.375 + (-20) =-18.625. 272 Biostatistics & Research Methodology Table 13.5: Effect Estimates for the Blocked 24 Design Regression Coefficient Sum of Squares Per cent Contribution Model Term Effect Estimate 21.625 3.125 9.875 14.625 0.125 -18.125 16.625 2.375 -0.375 -1.125 1.875 4.125 -1.625 -2.625 -18.625 1870.5625 39.0625 390.0625 855.5625 0.0625 1314.0625 1105.5625 22.5625 0.5625 5.0625 14.0625 68.0625 10.5625 27.5625 1387.5625 A 10.81 26.30 B 1.56 0.55 C 4.94 5.49 7.31 12.03 <0.01 18.48 15.55 D 0.062 -9.06 AB AC AD 8.31 BC 1.19 0.32 BD -0.19 -0.56 <0.01 CD 0.07 ABC 0.94 0.20 ABD 2.06 0.96 ACD -0.81 -1.31 0.15 BCD 0.39 Blocks (ABCD) 19.51 The block effect can be calculated between two blocks as follows: Block Effect -y Block! -y Block2 406 555 -149 8 ~~ 8 ” 8 =-18.625 These effects are estimates as Block + ABCD. Table 13.6 gives the summary of the analysis of variance. Effects with large estimates are included in the model, and the block sum of squares is as follows: (406 ) 2 +(555 ) 2 (961 ) 2 SS =1387.5625 Blocks 8 16 Table 13.6: Analysis of Variance Source of Variation Degrees of Freedom Sum of Squares Mean Square F„ P-Value Blocks (ABCD) 1387.5625 1 1870.5625 390.0625 855.5625 1314.0625 1105.5625 187.5625 7111.4375 1870.5625 390.625 855.5625 1314.0625 1105.5625 20.8403 1 89.76 18.72 41.05 63.05 53.05 <0.0001 0.0019 0.0001 <0.0001 <0.0001 A c 1 D 1 AC 1 AD 1 Error 9 Total 15 The findings of this experiment match those exactly where no block effect was present. Note that in case, the experiment was not run in blocks, and in case the magnitude (-20) had affected the first eight trials, then the results vary. Blocking and Confounding System (Chapter 13) 13.1.5. Confounding the 2 k Factorial Design in Four Blocks A 2 k factorial design confounded in four sections of 2 k‘2 observations can be constructed easily. These designs are useful when the number of factors is moderately large (k > 4), and the block sizes are very small. For example, assume 25 designs. In case, each block would have only eight runs, then four blocks would be useful. This design can be constructed in straightforward. Assume two effects confounded with blocks, according to ADE and BCE. Such effects have two defining contrasts associated with them. L, = x, + x4 + x5 Li = x2 + x3 + x5 273 Every treatment combination will give a particular values of Li (mod 2) and L2 (mod 2) like (L|, L2 ) = (0, 0), (0, 1 ) ( 1 , 0), or ( 1 , 1 ). The treatment combinations having the same values are assigned to a single block. In this example we get: Li = 0, L2 = 0 For ( 1 ), ad, be, abed, abe, ace, ede, bde L| = 1 , Li = 0 For a, d, abc, bed, be, abde, ce, aede Li = 0, L2= 1 For b, abd, c, acd, ae, de, abce, bede L, = 1 , L2 = 1 For e, ade, bee, abede, ab, bd, ac, cd The above combinations will be assigned to different blocks. Figure 13.4 shows the complete design. Block 1 L, = 1 Lz = l Block 1 L, = 0 L2 = 0 Block 1 L, = 1 Lj= 0 Block 1 L, = 0 L2 = 1 (1 ) abc be e abede a b abce ad ace d abde ade bd abd ae be ede bee ac abc ce c bede abed bde bed aede ab cd acd de Figure 13.4: 25 Design in Four Blocks with ADE, BCE and ABCD Confounded In addition to ADE and BCE, another effect must be confounded with the block. Another effect of one degree of freedom must be confounded because there are four blocks of three degrees of freedom between them and because ADE and BCE each have only one degree of freedom. This effect is the generalised form of ADE and BCE and can be determined by product of ADE and BCE modulus 2. Therefore generalised interaction (ADE) (BCE) = ABCDE2 = ABCD is also confounded with blocks. This can be easily verified for 25 designs, like a table of plus and minus signs. The treatment combination assigned to blocks is shown in the table below: Treatment Combinations In Sign on ADE Sign on BCE Sign on ABCD Block 1 Block 2 Block 3 Block 4 + + + + + + The product of the signs of any two effects for a block (e.g., ADE and BCE) gives additional effects for that block (ABCD). Therefore, ADE, BCE and ABCD are all confounded with blocks. 274 Biostatistics & Research Methodology The group-theory properties of principal block also hold. For example, the product of principal block of the two treatment combination gives another element of the principal block. This gives: ad • be = abed and abe • bde = ab2 =de2 = ad and so on. To design another block, we have to select that treatment combination that is not in the principal block such as b and multiply b by all the treatment combinations in the principal block. It provides: b (1) = b b • ad = abd b • be = b 2 c = c b • abed = ab2 cd = acd And so on, that is, it would produce eight treatment combinations in block 3. The principal blocks can be derived from the defined contrasts and group-theoretic property. The remaining blocks can be determined from these treatment combinations by the method given above. The typical process of building a 2 k design divided into four segments is to select two effects to make blocks, automatically confounding a third effect which is a generalized form of the first two. Then, the design is constructed using the group-theory properties of the principal block and two defined contrasts (L| , Li). To select confounded effects with blocks, precaution must be done to obtain a design that does not confound effects that may be of interest. For example, in 25 designs, we may select ABCDE and ABD, which automatically confounds CE, an effect of interest. A best option is to confound ADE and BCE, which confounds ABCD automatically. It is preferable to focus on three-factor interactions ADE and BCE rather than two-factor interaction CE.
answer:This section delves into advanced concepts in experimental design, specifically addressing issues encountered when conducting 2^k factorial experiments under non-homogeneous conditions that prevent the execution of all runs simultaneously. It introduces blocking and confounding as strategies to navigate these challenges, ensuring experiments are robust across varied conditions. **Blocking** is presented as a technique to handle nuisance variables by grouping experimental runs into blocks where these variables are held constant. An example with a chemical process experiment illustrates how blocking is implemented in practice. It involves dividing the experiment into blocks (in this case, based on batches of raw materials) to mitigate the effects of variability not related to the experimental factors. Analysis of variance is used to analyze the data from such blocked designs, highlighting how blocking affects the calculation and interpretation of experimental results. **Confounding** is introduced as a technique applied when it’s impractical to replicate a factorial design within a single block due to constraints, leading to some treatment effects being intentionally mixed (or confounded) with block effects. The text describes how to set up a 2^k factorial design in two blocks using a geometrical and tabular approach, demonstrating the process with a 2^2 design and expanding on more complex designs like 2^3. Confounding enables a reduction in the complexity of the experiment by blending specific effects with block effects, thus reducing the experimental runs needed. However, it requires careful selection of which effects to confound to avoid obscuring effects of interest. Further, the discussion expands into confounding in four blocks, showcasing how designs can manage a larger number of factors by confounding more effects while being cautious to not confound key effects of interest such as main effects or lower-order interactions that are crucial to the experimental goals. The principle of defining contrasts and the group-theoretic properties of blocks are elucidated to guide the correct assignment of treatment combinations to blocks, ensuring that every block design is systematically constructed to achieve the desired experimental objectives while accommodating practical constraints. In essence, this section provides a comprehensive overview of employing blocking and confounding within 2^k factorial designs to handle external variability and practical limitations, respectively. It does so through illustrative examples and detailed explanations of the underlying principles and methodologies, including the calculation and analysis steps involved in interpreting results from such advanced experimental designs.
question:As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image. Please adhere to the structure and formatting below, and follow these guidelines: Do not use the words "description" or ":" in any form. Do not place a comma between [ar] and [v]. Write each prompt in one line without using return. Structure: [1] = 一只红色狐狸，不舍的向着森林跑去，夜晚，中国乡村恐怖漫画风格 [2] = a detailed description of [1] with specific imagery details. [3] = a detailed description of the scene's environment. [4] = a detailed description of the compositions. [5] = a detailed description of the scene's mood, feelings, and atmosphere. [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1]. [7] = a detailed description of the scene's mood, feelings, and atmosphere. [ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images. [v] = Use "--niji" for Japanese art style, or "--v 5" for other styles. Formatting: Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]". Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization. Write your prompts in english. Do not describe unreal concepts as "real" or "photographic". Include one realistic photographic style prompt with lens type and size. Separate different prompts with two new lines. Example Prompts: /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2 /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2 /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5
answer:/imagine prompt: A red fox, with piercing eyes and a mischievous grin, darting through a dense forest under the veil of night, in a style reminiscent of traditional Chinese countryside horror comics. The fox's fur is vibrant against the dark backdrop, its every movement exuding reluctance and urgency, creating an eerie and suspenseful atmosphere. --ar 16:9 --v 5 /imagine prompt: An intense scene featuring a scarlet fox, its coat gleaming in the moonlit shadows as it dashes through a spooky woodland, trees casting sinister silhouettes against the midnight sky. The forest is alive with a cacophony of sounds, invoking a sense of foreboding with every step the fox takes, in a style akin to eerie Japanese manga. --ar 16:9 --v 5 /imagine prompt: In the heart of a dense wooded area, a crimson fox darts through the tangled undergrowth as if pursued by unseen forces, the night alive with mysterious sounds that add a chilling overtone to the scene. The fox's vivid fur stands out amid the darkness, its determined gaze hinting at a deeper narrative in a style resembling dark and vibrant contemporary illustration. --ar 16:9 --v 5 /imagine prompt: A scarlet fox races through a twisted forest at night, its movements filled with a sense of urgency and trepidation, the moon casting an eerie glow on the twisted branches and dense foliage. The scene is teeming with a sense of haunting beauty and malevolent energy, captured in a photography style with a Canon EOS 5D Mark IV camera, using a 50mm prime lens to highlight the fox's vivid colors and expressions. --ar 16:9 --v 5.2
question:<!DOCTYPE html> <html> <head> <script type=“text/javascript” src=“https://cdn.scaledrone.com/scaledrone.min.js”></script> <meta charset=“UTF-8”/> <title>Multiplayer chat server</title> <style> .nickname { color: blue; } .message { color: black; } .timestamp { color: gray; } </style> </head> <body> <h1>Multiplayer Chat Server</h1> <p>Welcome to the chat server! Enter your nickname to join the chat:</p> <input type=“text” id=“nicknameInput”> <button onclick=“joinChat()”>Join Chat</button> <div id=“chatLog”></div> <p>Send a message:</p> <input type=“text” id=“messageInput”> <button onclick=“sendMessage()”>Send</button> <script>
answer:const nicknameInput = document.getElementById('nicknameInput'); const messageInput = document.getElementById('messageInput'); const chatLog = document.getElementById('chatLog'); const client = new Scaledrone('YOUR_CHANNEL_ID'); function joinChat() { const nickname = nicknameInput.value; client.publish({ room: 'observable-room', message: { nickname: nickname, body: `{nickname} has joined the chat.` } }); } function sendMessage() { const nickname = nicknameInput.value; const message = messageInput.value; client.publish({ room: 'observable-room', message: { nickname: nickname, body: message } }); } client.on('message', message => { const { nickname, body } = message.data; const messageElement = document.createElement('div'); messageElement.innerHTML = `<span class="nickname">{nickname}</span>: <span class="message">{body}</span> <span class="timestamp">{(new Date()).toLocaleTimeString()}</span>`; chatLog.appendChild(messageElement); }); </script> </body> </html>