-
likefu说:
帮忙写一篇中国龙的由来
英语作文
The Chinese Dragon is an important symbol in Chinese culture, and has a long history. It is believed that the Chinese Dragon originated from the totems of ancient Chinese people, which were used to worship the gods and ward off evil.
The Chinese Dragon is usually depicted as a long and powerful creature with scales, whiskers, and horns. It is usually shown with five claws, which symbolize the power of the Emperor. According to legend, the Chinese Dragon is said to be able to control the weather, bring rain to the crops, and even create floods.
The Chinese Dragon is also thought to bring luck and good fortune to those who respect it. In traditional Chinese culture, people believed that the dragon was responsible for protecting their homes and families from bad luck.
The Chinese Dragon is also a symbol of strength and power. It is believed to be able to bring success and honor to those who possess it.
In modern times, the Chinese Dragon is often seen as a symbol of Chinese culture and national pride. It is a popular icon used in art, literature, and popular culture, and is frequently used as a mascot for Chinese sports teams.
Overall, the Chinese Dragon has a long and rich history. It is a powerful symbol of Chinese culture, and is still revered and respected today.
-
senooo说:
Write an example of Recursive Flow Classification algorithm using python
Below is an example of the Recursive Flow Classification algorithm written in Python:
def recursive_flow_classification(node, edges):
"""
Recursive Flow Classification Algorithm
Args:
node (string): The node to classify
edges (dict): A dictionary of edges, with the key being the node and the value the weight
Returns:
int: The classification of the node
"""# base case: if node is a leaf node, its classification is 0 if not edges[node]: return 0 # recursively classify the adjacent nodes adjacent_classifications = [recursive_flow_classification(adj_node, edges) for adj_node in edges[node]] # calculate the classification of the current node classification = max(adjacent_classifications) + 1 return classification -
@ChatGPT 如何解决中国出生人口断崖式下滑的问题？
slackingfred说:
如何解决中国出生人口断崖式下滑的问题？
一、加强少生婴儿教育。提高公众认识，注重婴儿教育，让每个家庭有能力让自己的孩子受到全面的教育，特别是对于低收入家庭，应当提供必要的援助，以便他们的孩子能够得到更好的教育。
二、改善生育环境。政府应加强财政和社会政策的支持，改善家庭生育环境，建立一套全面的有利于生育的政策，利用市场机制和政府资金支持家庭生育。
三、提高人口出生率。通过宣传、教育、社会福利等政策，引导家庭按照规定的标准生育，提高出生率，促进人口的增长。
四、加强计划生育政策的实施。加强计划生育政策的实施，以限制家庭的生育次数，限制高龄妇女的生育，限制低收入家庭的生育，提高出生率，避免人口过快增长，达到控制人口数量的目的。
-
guest说:
有中国年夜饭的饭菜做法视频链接？
-
guest说:
/*
- Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
*/
#ifndef PLAT_TEGRA_PRIVATE_H
#define PLAT_TEGRA_PRIVATE_H#include
/*******************************************************************************
- Tegra194 specific defines
******************************************************************************/
#define TEGRA_194_CCPLEX_CLUSTER_COUNT U(2)
#define TEGRA_194_CLUSTER_CORE_COUNT U(6)
#define TEGRA_194_MAX_CPUS_PER_CLUSTER U(6)
#define TEGRA_194_MAX_PE_PER_CPU U(1)
#define TEGRA_194_CLUSTER_COUNT U(2)
#define TEGRA_194_CCPLEX_CLUSTER0_BASE U(0x0)
#define TEGRA_194_CCPLEX_CLUSTER1_BASE U(0x20000)#define TEGRA_194_CLUSTER0_BASE U(0x0)
#define TEGRA_194_CLUSTER1_BASE U(0x10000)#define TEGRA_194_CLUSTER_SIZE U(0x10000)
#define TEGRA_194_CLUSTER_CORE_MASK ULL(0x3f)
#define TEGRA_194_CLUSTER_CORE_SHIFT U(0x4)
#define TEGRA_194_CLUSTER_AFFINITY_MASK U(0xf)#define TEGRA_194_CCPLEX_CLUSTER_SHIFT U(0x10)
#define TEGRA_194_CLUSTER_MASK ULL(0xffff0000)
#define TEGRA_194_CPU_MASK ULL(0xff00)
#define TEGRA_194_PE_MASK ULL(0xff)#define TEGRA_194_CLUSTER_SHIFT U(0x10)
#define TEGRA_194_CPU_SHIFT U(0x8)
#define TEGRA_194_PE_SHIFT U(0x0)#define TEGRA_194_TRUSTED_MAILBOX_BASE U(0x60040000)
#define TEGRA_194_TRUSTED_MAILBOX_SIZE U(0x1000)
#define TEGRA_194_TRUSTED_MAILBOX_CLUSTER_OFFSET U(0x400)#define TEGRA_194_BPMP_CLUSTER_SHIFT U(0x18)
#define TEGRA_194_CCPLEX_LOCAL_TIMER_COUNT U(2)
#define TEGRA_194_CLUSTER_TIMER_COUNT U(2)#define TEGRA_194_CLUSTER_LOCAL_TIMER_BASE U(0x60005000)
#define TEGRA_194_CLUSTER_GLOBAL_TIMER_BASE U(0x60005020)#define TEGRA_194_CLUSTER_TIMER_OFFSET U(0x20)
#define TEGRA_194_CCPLEX_LOCAL_TIMER_BASE U(0x60005000)
#define TEGRA_194_CCPLEX_GLOBAL_TIMER_BASE U(0x60005020)#define TEGRA_194_CCPLEX_TIMER_OFFSET U(0x20)
#define TEGRA_194_IRQ_SEC_PHY_TIMER U(29)
#define TEGRA_194_IRQ_SEC_SGI_0 U(8)/*******************************************************************************
- Tegra186 specific defines
******************************************************************************/
#define TEGRA_186_CCPLEX_CLUSTER_COUNT U(2)
#define TEGRA_186_CLUSTER_CORE_COUNT U(4)
#define TEGRA_186_MAX_CPUS_PER_CLUSTER U(4)
#define TEGRA_186_MAX_PE_PER_CPU U(1)
#define TEGRA_186_CLUSTER_COUNT U(2)
#define TEGRA_186_CCPLEX_CLUSTER0_BASE U(0x0)
#define TEGRA_186_CCPLEX_CLUSTER1_BASE U(0x10000)#define TEGRA_186_CLUSTER0_BASE U(0x0)
#define TEGRA_186_CLUSTER1_BASE U(0x8000)#define TEGRA_186_CLUSTER_SIZE U(0x8000)
#define TEGRA_186_CLUSTER_CORE_MASK ULL(0xf)
#define TEGRA_186_CLUSTER_CORE_SHIFT U(0x3)
#define TEGRA_186_CLUSTER_AFFINITY_MASK U(0x7)#define TEGRA_186_CCPLEX_CLUSTER_SHIFT U(0x10)
#define TEGRA_186_CLUSTER_MASK ULL(0xffff0000)
#define TEGRA_186_CPU_MASK ULL(0xff00)
#define TEGRA_186_PE_MASK ULL(0xff)#define TEGRA_186_CLUSTER_SHIFT U(0x10)
#define TEGRA_186_CPU_SHIFT U(0x8)
#define TEGRA_186_PE_SHIFT U(0x0)#define TEGRA_186_TRUSTED_MAILBOX_BASE U(0x0)
#define TEGRA_186_TRUSTED_MAILBOX_SIZE U(0x1000)
#define TEGRA_186_TRUSTED_MAILBOX_CLUSTER_OFFSET U(0x0)#define TEGRA_186_BPMP_CLUSTER_SHIFT U(0x18)
#define TEGRA_186_CCPLEX_LOCAL_TIMER_COUNT U(2)
#define TEGRA_186_CLUSTER_TIMER_COUNT U(2)#define TEGRA_186_CLUSTER_LOCAL_TIMER_BASE U(0x60005000)
#define TEGRA_186_CLUSTER_GLOBAL_TIMER_BASE U(0x60005020)#define TEGRA_186_CLUSTER_TIMER_OFFSET U(0x20)
#define TEGRA_186_CCPLEX_LOCAL_TIMER_BASE U(0x60005000)
#define TEGRA_186_CCPLEX_GLOBAL_TIMER_BASE U(0x60005020)#define TEGRA_186_CCPLEX_TIMER_OFFSET U(0x20)
#define TEGRA_186_IRQ_SEC_PHY_TIMER U(27)
#define TEGRA_186_IRQ_SEC_SGI_0 U(8)/*******************************************************************************
- Tegra general defines
******************************************************************************/
#define TEGRA_CCPLEX_CLUSTER_SHIFT U(16)
#define TEGRA_MAX_CLUSTER_COUNT U(2)
#define TEGRA_MAX_CPUS_PER_CLUSTER U(6)
#define TEGRA_MAX_PE_PER_CPU U(1)
#define TEGRA_CLUSTER_SIZE U(0x10000)
#define TEGRA_CLUSTER_MASK ULL(0xffff0000)
#define TEGRA_CPU_MASK ULL(0xff00)
#define TEGRA_PE_MASK ULL(0xff)
#define TEGRA_CLUSTER_SHIFT U(0x10)
#define TEGRA_CPU_SHIFT U(0x8)
#define TEGRA_PE_SHIFT U(0x0)
#define TEGRA_CCPLEX_LOCAL_TIMER_COUNT U(2)
#define TEGRA_CLUSTER_TIMER_COUNT U(2)
#define TEGRA_CCPLEX_TIMER_OFFSET U(0x20)
#define TEGRA_CLUSTER_TIMER_OFFSET U(0x20)
#define TEGRA_IRQ_SEC_PHY_TIMER U(29)
#define TEGRA_IRQ_SEC_SGI_0 U(8)
#endif /* PLAT_TEGRA_PRIVATE_H */
-
guest说:
Java实现监听 tibco queue
1、通过JMS API来实现：
（1）首先需要获取JMS连接工厂，并使用JMS连接工厂实例化一个JMS连接。
（2）然后，使用连接工厂和连接实例化一个会话（session），同时，创建一个Queue对象实例。
（3）下一步，创建一个MessageConsumer实例，并使用Session和Queue作为参数来初始化该MessageConsumer实例。
（4）最后，使用MessageConsumer实例的receive()方法来接收来自tibco queue的消息，并处理接收到的消息。
