结构体~!!!

PM2.5

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/65536 K (Java/Others)
Total Submission(s): 317    Accepted Submission(s): 191


Problem Description
Nowadays we use content of PM2.5 to discribe the quality of air. The lower content of PM2.5 one city have, the better quality of air it have. So we sort the cities according to the content of PM2.5 in asending order.

Sometimes one city’s rank may raise, however the content of PM2.5 of this city may raise too. It means that the quality of this city is not promoted. So this way of sort is not rational. In order to make it reasonable, we come up with a new way to sort the cityes. We order this cities through the diffrence between twice measurement of content of PM2.5 (first measurement – second measurement) in descending order, if there are ties, order them by the second measurement in asending order , if also tie, order them according to the input order.
 

Input
Multi test cases (about 100), every case contains an integer n which represents there are n cities to be sorted in the first line.
Cities are numbered through 0 to n1.
In the next n lines each line contains two integers which represent the first and second measurement of content of PM2.5
The ith line describes the information of city i1
Please process to the end of file.

[Technical Specification]
all integers are in the range [1,100]
 

Output
For each case, output the cities’ id in one line according to their order.
 

Sample Input
2 100 1 1 2 3 100 50 3 4 1 2
 

Sample Output
0 1 0 2 1
题记:题目的意思为:给一组数据城市排序,打印出其编号;首先根据两次雾霾含量差值进行排序,若差值相同则根据第二次测量的
数据,谁比较小,就靠前。本题目只能用结构体解答:下面附上代码解析
#include<stdio.h>
#include<string.h>
#include<math.h>
#include<algorithm>
using namespace std;
struct pp
{
    int er,cha,id;
}b[110];//结构体的申明
int cmp(const pp &a,const pp &b)
{
    if(a.cha==b.cha)
    {
    if(a.er==b.er)
    return a.id<b.id;
    return a.er<b.er;
    }

    return a.cha<b.cha;
}//判定cmp算法应当注意所有情况的考虑
int main()
{
    int i,j,k,first,se,n;
    while(scanf("%d",&n)!=EOF)
    {
        for(i=0;i<n;i++)
        {
            scanf("%d%d",&first,&se);
            b[i].er=se;b[i].cha=se-first;b[i].id=i;
        }
        sort(b,b+n,cmp);//调用sort算法
        printf("%d",b[0].id);
        for(i=1;i<n;i++)
        printf(" %d",b[i].id);//打印
        printf("\n");
    }
    return 0;

}

内容概要:该论文探讨了一种基于粒子群优化(PSO)的STAR-RIS辅助NOMA无线通信网络优化方法。STAR-RIS作为一种新型可重构智能表面,能同时反射和传输信号,与传统仅能反射的RIS不同。结合NOMA技术,STAR-RIS可以提升覆盖范围、用户容量和频谱效率。针对STAR-RIS元素众多导致获取完整信道状态信息(CSI)开销大的问题,作者提出一种在不依赖完整CSI的情况下,联合优化功率分配、基站波束成形以及STAR-RIS的传输和反射波束成形向量的方法,以最大化总可实现速率并确保每个用户的最低速率要求。仿真结果显示,该方案优于STAR-RIS辅助的OMA系统。 适合人群:具备一定无线通信理论基础、对智能反射面技术和非正交多址接入技术感兴趣的科研人员和工程师。 使用场景及目标:①适用于希望深入了解STAR-RIS与NOMA结合的研究者;②为解决无线通信中频谱资源紧张、提高系统性能提供新的思路和技术手段;③帮助理解PSO算法在无线通信优化问题中的应用。 其他说明:文中提供了详细的Python代码实现,涵盖系统参数设置、信道建模、速率计算、目标函数定义、约束条件设定、主优化函数设计及结果可视化等环节,便于读者理解和复现实验结果。此外,文章还对比了PSO与其他优化算法(如DDPG)的区别,强调了PSO在不需要显式CSI估计方面的优势。
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