This paper proposes an effective material delivery algorithm to address the challenges associated with Unmanned Aerial Vehicle (UAV) material transportation and delivery, which include complex route planning, low detection precision, and hardware limitations. This novel approach integrates the Whale-Swarm Hybrid Algorithm (WSHA) with the APCR-YOLOv8 model to enhance efficiency and accuracy. For path planning, the placement paths are transformed into a Generalized Traveling Salesman Problem (GTSP) to be able to compute solutions. The Whale Optimization Algorithm (WOA) is improved for balanced global and local searches, combined with an Artificial Bee Colony (ABC) Algorithm and adaptive weight adjustment to quicken convergence and reduce path costs. For precise placement, the YOLOv8 model is first enhanced by adding the SimAM attention mechanism to the C2f module in the detection head, focusing on target features. Secondly, GhoHGNetv2 using GhostConv is the backbone of YOLOv8 to ensure accuracy while reducing model Params and FLOPs. Finally, a Lightweight Shared Convolutional Detection Head (LSCDHead) further reduces Params and FLOPs through shared convolution. Experimental results show that WSHA reduces path costs by 9.69% and narrows the gap between the best and worst paths by about 34.39%, compared to the Improved Whale Optimization Algorithm (IWOA). APCR-YOLOv8 reduces Params and FLOPs by 44.33% and 34.57%, respectively, with mAP@0.5 increasing from 88.5 to 92.4 and FPS reaching 151.3. This approach can satisfy the requirements for real-time responsiveness while effectively preventing missed, false, and duplicate detections during the inspection of emergency airdrop stations. In conclusion, combining bionic optimization algorithms and image processing significantly enhances the efficiency and precision of material placement in emergency management.

This paper presents a lightweight convolutional neural network model based on DeepLabV3+. By collecting and annotating an image dataset, sufficient sample support is provided for the model’s training. In order to enhance the model’s training speed and applicability, this study proposes replacing the backbone network Xception with MobileNetv2. This successfully reduces the number of model parameters, making the training process more rapid and efficient. Additionally, by refining the model structure and incorporating Coordinate Attention (CA) and Cascaded Feature Fusion (CFF) for feature integration, the feature map processing is further optimized. This allows the model to more accurately identify in complex background environments. Verification shows that the improved model performs excellently in metrics such as mIoU, mPA, mPrecision, and mRecall. During testing, mIoU reached 82.66%, mPA reached 88.20%, mPrecision reached 92.26%, and mRecall reached 87.67%. These results fully demonstrate the feasibility, accuracy, and robustness of the proposed method, providing strong support in practical applications.

With the improvement of people’s living quality and technology level, video games have become the product and trend which formed by the gradual evolution of people’s entertainment forms. The New York Time currently offers a popular puzzle called Wordle everyday, where each player gets 6 chances to guess a 5-letter word ^[1] . A scatter plot has been drawn about the number of reported results varies over time according to the data file, fitting the curve. Therefore this passage finds that the number of reported results had risen sharply over time first in a short time, then it declines slowly over time. Its influence has been declined. Then the ARIMA time series model ^[2] is used to predict the interval for the number of reported results on March 1, 2023, that is, [10409, 10446]. At the same time, the word attribute is defined, which includes the frequency of word in books, the repetition of letters in a word and the part of speech. For the frequency of word, the data from Goole Books Ngram Viewer ^[3] has been found, standardized method is considering the number of occurrences per million words as frequency of word. The passage establishes the Pearson model ^[4] to analyse the correlation between attributes of words and the percentage of scores reported that were played in Hard Mode. The three attributes of the word do affect the percentage of scores reported that were played in Hard Mode and the the frequency of word has the greatest influence. Then, The TOPSIS model ^[5] was used to classify the words according to their difficulty, which is divided three types:hard, moderate difficulty, easy. Categorize each word by giving it an overall score. The wore"EERIE"belongs to the hard, which conform the result studied before. Hence the accuracy of the model is high. The above can help game developers or decision makers make more accurate decisions, thus increasing the popularity of the game.