Firstly, input an integer � N, which represents a total of � N students. Then enter � N lines, each containing the student's name, class, exam 1 score, and exam 2 score. Count the average and total scores of each exam for each class, and sort the results by class name before outputting them to the console. Output includes class, score1_mean, score1_sum, score2_mean, score2_sum.

时间: 2024-02-05 15:04:54 浏览: 85
Sure, I can help you with that. Here's an example Python code that solves the problem you stated: ``` # Input the total number of students N = int(input()) # Create a dictionary to store the results for each class class_results = {} # Loop through each student and store their results in the dictionary for i in range(N): name, class_name, exam1, exam2 = input().split() exam1 = int(exam1) exam2 = int(exam2) if class_name not in class_results: class_results[class_name] = {'exam1_total': 0, 'exam1_count': 0, 'exam2_total': 0, 'exam2_count': 0} class_results[class_name]['exam1_total'] += exam1 class_results[class_name]['exam1_count'] += 1 class_results[class_name]['exam2_total'] += exam2 class_results[class_name]['exam2_count'] += 1 # Sort the results by class name class_names = sorted(class_results.keys()) # Print the results for each class for class_name in class_names: exam1_mean = class_results[class_name]['exam1_total'] / class_results[class_name]['exam1_count'] exam2_mean = class_results[class_name]['exam2_total'] / class_results[class_name]['exam2_count'] exam1_sum = class_results[class_name]['exam1_total'] exam2_sum = class_results[class_name]['exam2_total'] print(f"{class_name} {exam1_mean:.2f} {exam1_sum} {exam2_mean:.2f} {exam2_sum}") ``` Here's how you can use this code: 1. Copy and paste the code into a Python file (e.g. `main.py`). 2. Run the Python file using the command `python main.py`. 3. Enter the total number of students when prompted. 4. Enter the details (name, class, exam 1 score, exam 2 score) for each student, one by one. 5. The program will print the results for each class, sorted by class name. I hope this helps! Let me know if you have any questions.
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Implementing the UAV waypoint planning algorithm in MATLAB can be achieved through a variety of methods to ensure precise and efficient results. Firstly, we can design a user-friendly interface using MATLAB's GUI function. This intuitive interface allows users to easily input flight mission parameters and flight environment models. Secondly, we can take advantage of MATLAB's matrix operations and graphic drawing functions. These tools enable us to calculate the waypoint planning algorithm and provide a comprehensive visualization of the results. Thirdly, we can use MATLAB's optimization toolbox, which includes powerful functions like fmincon. These tools allow us to optimize the results of the waypoint planning algorithm. By defining suitable optimization objectives, such as minimizing total distance or energy consumption, we can find the optimal set of waypoints. Finally, we can verify the accuracy and feasibility of the waypoint planning results by conducting realistic UAV flight simulations using MATLAB's simulation capabilities, such as Simulink. By inputting the calculated waypoints into the UAV flight model, we can observe and analyze the flight trajectory and the UAV's state to ensure the planning results are accurate and reliable. By carefully choosing the right algorithms, optimizing the planning process, and fully utilizing the capabilities of MATLAB, we can achieve high-quality waypoint planning results that meet the specific requirements of each flight mission. These methods, integrated within MATLAB, provide a human-like approach to UAV waypoint planning, ensuring accuracy, effectiveness, and detection avoidance.还能检测出来,这个基础上再修改

The LULC simulation data we utilized to create future EN maps was produced by X. Liu et al. (2017), which was conducted at the national level. The reason we apply national-level simulated data to a local area is as follows. Firstly, China has a top-down land use planning system (also known as spatial planning) with five levels. The quantitative objectives in national plans are handed down to county-level plans through provincial and prefectural level plans (Zhong et al., 2014). That means land use patterns of nine cities in WUA are required to reflect relevant upper-level plans, for example, to satisfy the land use quota made by Hubei provincial plans and the national plans. Secondly, there are interdependencies across places so what happens in one region produces effects not only on this location but on other regions (Overman et al., 2010). And the increase of construction land in one place will shift protection pressure on natural ecosystems elsewhere for a sustainable goal. The land use simulation at the national level allocated land resources from a top-down perspective and links land use changes in a region to events taking place in other locations through global simulation. However, the Kappa coefficient of the simulated data in WUA is 0.55 and the overall accuracy is 0.71, which is lower than the statistic value at the national-level data. Although the Kappa between 0.4~0.6 is moderate and at an acceptable level (Appiah et al., 2015; Ding et al., 2013; Ku, 2016), the simulated accuracy of the land use data needs to be improved. Future work on exploring the impact of LULC dynamics on EN will develop based on the high-accuracy simulated data and updating the initial simulated time to 2020, by integrating the impacts of socioeconomic factors, climate change, regional planning, land use policy, etc.

润色下面英文:The controlled drug delivery systems, due to their precise control of drug release in spatiotemporal level triggered by specific stimulating factors and advantages such as higher utilization ratio of drug, less side-effects to normal tissues and so forth, provide a new strategy for the precise treatment of many serious diseases, especially tumors. The materials that constitute the controlled drug delivery systems are called “smart materials” and they can respond to the stimuli of some internal (pH, redox, enzymes, etc.) or external (temperature, electrical/magnetic, ultrasonic and optical, etc.) environments. Before and after the response to the specific stimulus, the composition or conformational of smart materials will be changed, damaging the original balance of the delivery systems and releasing the drug from the delivery systems. Amongst them, the photo-controlled drug delivery systems, which display drug release controlled by light, demonstrated extensive potential applications, and received wide attention from researchers. In recent years, photo-controlled drug delivery systems based on different photo-responsive groups have been designed and developed for precise photo-controlled release of drugs. Herein, in this review, we introduced four photo-responsive groups including photocleavage groups, photoisomerization groups, photo-induced rearrangement groups and photocrosslinking groups, and their different photo-responsive mechanisms. Firstly, the photocleavage groups represented by O-nitrobenzyl are able to absorb the energy of the photons, inducing the cleavage of some specific covalent bonds. Secondly, azobenzenes, as a kind of photoisomerization groups, are able to convert reversibly between the apolar trans form and the polar cis form upon different light irradiation. Thirdly, 2-diazo-1,2-naphthoquinone as the representative of the photo-induced rearrangement groups will absorb specific photon energy, carrying out Wolff rearrangement reaction. Finally, coumarin is a promising category photocrosslinking groups that can undergo [2+2] cycloaddition reactions under light irradiation. The research progress of photo-controlled drug delivery systems based on different photo-responsive mechanisms were mainly reviewed. Additionally, the existing problems and the future research perspectives of photo-controlled drug delivery systems were proposed.

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