This review explores the evolution of the textile handicraft industry in Saudi Arabia, emphasizing its cultural and economic significance. The study highlights the transition from traditional practices to modern innov...This review explores the evolution of the textile handicraft industry in Saudi Arabia, emphasizing its cultural and economic significance. The study highlights the transition from traditional practices to modern innovations and examines the impact of globalization and technological advancements on the industry. Key innovations are discussed, demonstrating their role in enhancing textile production while preserving cultural heritage. Major challenges, such as competition from industrial textiles and the need for sustainable practices, are identified. Opportunities for growth are explored, including leveraging tourism and international markets to promote Saudi handicrafts. The social and cultural impacts of the sector are underscored, particularly in sustaining community traditions and providing economic opportunities for artisans. Strategic recommendations for supporting and advancing the industry are offered, ensuring its continued relevance and sustainability in a rapidly changing global market. This analysis provides a robust framework for understanding the current state and future potential of Saudi Arabia’s textile handicraft industry.展开更多
单脉冲搜索作为脉冲星探测的有力工具,在探测旋转射电暂现源以及快速射电暴中扮演着重要角色。为了从海量的射电巡天数据中快速筛选出最有价值的单脉冲搜索候选体,候选体识别已经从早期启发式阈值判断发展到基于机器学习自动识别。对于F...单脉冲搜索作为脉冲星探测的有力工具,在探测旋转射电暂现源以及快速射电暴中扮演着重要角色。为了从海量的射电巡天数据中快速筛选出最有价值的单脉冲搜索候选体,候选体识别已经从早期启发式阈值判断发展到基于机器学习自动识别。对于FAST观测,研究了基于机器学习的单脉冲搜索候选体识别应用到CRAFTS(the commensal radio astronomy FAST survey)超宽带脉冲星数据的性能表现。在评估过程中,使用单脉冲事件组识别(SPEGID)和单脉冲搜索器(SPS)两类自动识别方法,通过7种不同机器学习分类器对CRAFTS基准数据集产生的单脉冲搜索候选体进行自动识别;作为对比,也使用了启发式阈值判断的方法(RRATtrap和Clusterrank)。结果表明,SPEGID具有最好的性能表现(最高的F1-score值95.1%、次高的召回率95.4%、最低的假阳性率4.7%),SPS具有最快的筛选速度(平均每小时筛选4010个候选体)。通过对比分析结果,探讨了如何基于FAST观测数据开展高效的单脉冲搜索候选体识别。展开更多
文摘This review explores the evolution of the textile handicraft industry in Saudi Arabia, emphasizing its cultural and economic significance. The study highlights the transition from traditional practices to modern innovations and examines the impact of globalization and technological advancements on the industry. Key innovations are discussed, demonstrating their role in enhancing textile production while preserving cultural heritage. Major challenges, such as competition from industrial textiles and the need for sustainable practices, are identified. Opportunities for growth are explored, including leveraging tourism and international markets to promote Saudi handicrafts. The social and cultural impacts of the sector are underscored, particularly in sustaining community traditions and providing economic opportunities for artisans. Strategic recommendations for supporting and advancing the industry are offered, ensuring its continued relevance and sustainability in a rapidly changing global market. This analysis provides a robust framework for understanding the current state and future potential of Saudi Arabia’s textile handicraft industry.
文摘单脉冲搜索作为脉冲星探测的有力工具,在探测旋转射电暂现源以及快速射电暴中扮演着重要角色。为了从海量的射电巡天数据中快速筛选出最有价值的单脉冲搜索候选体,候选体识别已经从早期启发式阈值判断发展到基于机器学习自动识别。对于FAST观测,研究了基于机器学习的单脉冲搜索候选体识别应用到CRAFTS(the commensal radio astronomy FAST survey)超宽带脉冲星数据的性能表现。在评估过程中,使用单脉冲事件组识别(SPEGID)和单脉冲搜索器(SPS)两类自动识别方法,通过7种不同机器学习分类器对CRAFTS基准数据集产生的单脉冲搜索候选体进行自动识别;作为对比,也使用了启发式阈值判断的方法(RRATtrap和Clusterrank)。结果表明,SPEGID具有最好的性能表现(最高的F1-score值95.1%、次高的召回率95.4%、最低的假阳性率4.7%),SPS具有最快的筛选速度(平均每小时筛选4010个候选体)。通过对比分析结果,探讨了如何基于FAST观测数据开展高效的单脉冲搜索候选体识别。
