Core Insights - Qingyue Intelligent, a commercial cleaning robot company, has completed a 300 million yuan Series B+ financing round, led by Guotou Innovation, with participation from Shenzhen Capital Group and Sequoia China [1] - The funds will be used to expand global sales networks, build a comprehensive service system, and enhance research on embodied intelligent algorithms to solidify the company's leading position in the commercial cleaning robot sector [1][5] Industry Overview - The global commercial service robot industry is experiencing rapid growth, driven by rising labor costs, an aging population, and the demand for improved service quality [3] - It is projected that by 2030, the global commercial service robot market will exceed $10 billion, with a compound annual growth rate (CAGR) of over 20%, with commercial cleaning and delivery robots being key growth drivers [3] Company Development - Established just over three years ago, Qingyue Intelligent has completed five rounds of financing, demonstrating strong business growth and technological barriers that attract top-tier capital [4] - The company focuses on the autonomous research and global layout of commercial cleaning robots, aiming to transition the industry from automation to intelligence [4] Product Innovation - Qingyue Intelligent has developed a diverse product matrix, including flagship products like the SP50 commercial sweeping and suction robot, and the L50 and L4 commercial floor washing robots, designed for large commercial spaces [4] - The newly launched L3 commercial floor washing robot features advanced capabilities such as automatic map updates and intelligent path decision-making, catering to small and medium-sized commercial environments [4][5] Future Plans - Following the recent financing, Qingyue Intelligent plans to increase R&D investment, accelerate the iteration of new technologies and products, and expand its overseas market presence [5] - The company aims to drive technological innovation to facilitate the intelligent transformation of the commercial cleaning robot industry, enhancing cost efficiency and quality in global commercial services [5]

Core Insights - The implementation of data lakes addresses the limitations of traditional databases in handling the explosive growth of data volume and complexity, providing a unified and scalable infrastructure for storing structured, semi-structured, and unstructured data [2][7] - Data lakes serve as the foundation for modern analytics and artificial intelligence, enabling real-time insights, self-service business intelligence, and predictive modeling [2][6] Group 1: Definition and Importance of Data Lakes - A data lake is a centralized storage system that allows organizations to store all types of data in its raw format until needed for analysis, contrasting with traditional data warehouses that require data to be structured before storage [6][7] - The construction of a data lake is crucial for organizational success, as it provides a flexible, cost-effective, and future-proof solution for data storage and analysis [7][10] - Data lakes enable organizations to combine historical and real-time data, supporting advanced use cases such as predictive analytics and fraud detection [6][10] Group 2: Core Architecture of Data Lakes - Data lakes are organized into multiple layers that work together to transform raw information into valuable business insights, including ingestion, storage, processing, governance, and consumption layers [11][20] - The ingestion layer brings data from various sources into the data lake, preserving its original format for later analysis [12] - The storage layer holds raw data in scalable and cost-effective repositories, supporting all data types [13][14] - The processing layer cleans, validates, and enriches data, organizing it into different zones for business analysis [15] - The governance layer ensures data remains trustworthy, secure, and compliant throughout its lifecycle [16] - The consumption layer provides tools for users to extract value from data, enabling self-service analytics while maintaining governance controls [17] Group 3: Implementation Steps and Best Practices - The first step in implementing a data lake is to clarify objectives and identify key use cases, translating them into key performance indicators (KPIs) [23] - Selecting the appropriate cloud platform is crucial, with options like AWS, Azure, and GCP offering various tools for storage, analysis, and governance [24][26] - Designing a layered architecture helps maintain data organization and trustworthiness, with clear definitions for raw, refined, and business-ready data [27][28][29] - Implementing governance and security measures from the outset is essential, including data ownership, access controls, and compliance tracking [31] - Continuous monitoring, optimization, and documentation of data processes are necessary to ensure the data lake remains scalable and efficient [33][42] Group 4: Real-World Case Studies - Shell Energy built a data lake on Microsoft Azure to integrate IoT, operations, and energy management data, reducing data preparation time by 60% and enhancing collaboration between data scientists and business teams [55] - Comcast utilized a Databricks data lake to integrate customer interaction, billing, and service data, enabling near-real-time analysis and improving customer retention rates [56] - HSBC adopted a cloud-based data lake to upgrade its risk management and compliance framework, enhancing the accuracy and transparency of regulatory reporting [57]

Core Insights - The article highlights the successful implementation of a day trading strategy using machine learning and pressure factors to identify trading opportunities in the futures market [2][4]. Strategy Overview - The day trading strategy, initiated in February 2023, employs a machine learning framework to analyze market data and predict daily returns for selected futures [2]. - The strategy focuses on 40 to 50 mainstream commodity futures but trades only the top five predicted by the model, optimizing for a higher Sharpe ratio during backtesting [2]. - Position allocation follows an "equal market value" principle, which has shown comparable performance to a "strong signal high position" model while simplifying operations [2]. Data Utilization - The strategy captures short-term fluctuations using 1-minute K-line data while also considering daily data for long-term trends, generating signals twice a day [3]. - The approach avoids frequent predictions to reduce model complexity, especially in the absence of significant incremental information during trading hours [3]. Risk Management - A multi-layered risk control framework is established, including mandatory position closing before market close to avoid overnight risks and immediate liquidation in case of market reversals [4]. - The strategy has demonstrated strong drawdown control, with a real trading drawdown rate of 5% to 6% and a maximum drawdown of 28.95% during a specific competition [4]. - The strategy is best suited for volatile markets, relying on a reversal effect, and incorporates traditional trend sub-strategies to mitigate risks [4]. Future Plans - The company plans to expand the trading universe to 10 to 15 products, which will enhance capital capacity while maintaining profitability through diversified order placements [5]. - A new product based on the day trading strategy has been registered, indicating a move towards a broader asset management market [5]. - The focus will remain on the commodity futures CTA sector, with ongoing investments in factor exploration and model optimization to ensure robust performance for clients [5].

撰文丨王聪 2025 年 11 月 25 日， 遵义医科大学第二附属医院 马虎 教授、 周建国 副主任医师作为共同通讯作者，在 Therapeutic Advances in Medical Oncology 期刊发 表了题为： CircRNA signature predicts immunotherapy response in advanced non-small cell lung cancer 的研究论文。 该研究系统性分析了两项 非小细胞肺癌 （NSCLC） 临床研究中的 circRNA 表达谱，识别出差异表达的 circRNA，进而 确定了一个由 11 个 circRNA 组成的 circRNA 特征谱—— circRNA-Sig ， 可用于预测晚期 非小细胞肺癌 （NSCLC） 对免疫治疗的响应，有望为肺癌的临床治疗提供指导。 编辑丨王多鱼 排版丨水成文 扫描 文末二维码 ，获取 生物世界 专属投稿折扣 肺癌 是全世界范围内最常见的恶性肿瘤，也是癌症相关死亡的首要原因，其中 非小细胞肺癌 （NSCLC） 占肺癌病例总数的 85%以上。尽管临床管理方面有所 进步，但非小细胞肺癌患者的 5 年总生 ...

● 本报记者 王婧涵 在数字经济与科技自立自强双重浪潮下，深耕检验检测与数据资产管理软件近三十年的三维天地正以 AI重绘发展蓝图。2025年三季报显示，公司营收增长而净利润承压，前三季度共实现营收1.34亿元，同 比增长5.85%；归母净利润亏损7521.66万元，亏损同比收窄。但年内，三维天地研发投入空前聚焦，密 集推出了AI智能体平台SunwayLink、国产质量分析软件QRS等多款智能化新品。 "持续专业深耕、坚持创新引领、始终由使命驱动，这是我心中三维天地最重要的特质。"公司研发总 监、副总经理张京日对记者表示，公司目标是成为"AI驱动的数据开发服务商"，在智能化升级与国产化 突破上持续发力。 大胆进行技术重构 软件技术的更新换代速度常超出人们的预期，这不仅要求从业者对技术时刻保持敏锐洞察、持续学习， 还要在必要时有破釜沉舟的勇气和战略定力。 "通过提供统一的开发框架、可视化编排能力和可复用的核心模块，我们使团队摆脱了重复劳动与技术 碎片化，大幅提升了交付效率与系统稳定性，为业务的高速发展构建了坚实可靠的技术基座。" 张京日表示，这不仅是技术的胜利，更是团队由职业码农蜕变为系统建筑师的成长缩影。它深刻 ...

Core Viewpoint - Apple is actively engaging in promotional activities and store openings in China, while facing increasing competition from domestic brands and internal management changes [2][6][7]. Group 1: Store Openings and Promotions - Apple opened its 6th store in Beijing on December 6, marking the 59th store in Greater China, with significant customer turnout [2]. - On December 8, Apple launched year-end discounts on its official e-commerce platform, reducing prices of iPhone 17 Pro and iPhone 17 Pro Max by 300 yuan, with starting prices of 8699 yuan and 9699 yuan respectively [2]. Group 2: Sales Performance and Market Position - The iPhone 17 series has seen a 12% year-on-year increase in actual shipments in October, achieving a market share of 24.2%, the highest for a single month [6]. - Analysts predict that Apple may surpass Samsung in global smartphone shipments by 2025, marking its first time at the top since 2011 [6]. Group 3: Product Strategy - The iPhone 17 series maintains its starting price while offering significant upgrades, such as a 48-megapixel camera and a new processor, effectively being termed as "implicit price reduction" [6]. Group 4: Competitive Landscape - Apple faces intensified competition from Huawei and other domestic brands, particularly in the high-end market, as these brands enhance their offerings with AI capabilities [7]. - The rise of "AI smartphones" is becoming a focal point in the industry, with domestic brands leading in AI integration, while Apple has yet to launch its AI features in the domestic market [7]. Group 5: Management Changes - Apple is experiencing significant management turnover, with recent announcements of multiple executive departures, raising concerns about its future direction [7][8]. - Speculation about CEO Tim Cook's potential departure has surfaced, although he is expected to remain in his position until at least 2028 [8]. Group 6: Financial Performance - In the third fiscal quarter of 2025, Apple reported total revenue of $94.04 billion, a 10% increase year-on-year, with iPhone revenue reaching $44.58 billion, up 13% [9]. - The services segment continues to be a strong performer, with revenue of $27.42 billion, reflecting a 13% growth and a gross margin of 75.6% [9]. Group 7: Stock Performance - Prior to August, Apple's stock had dropped approximately 15% due to concerns over its slow AI advancements, but has since rebounded, with a year-to-date increase of over 11% as of December 5 [10][11].

宁波诺丁汉大学金融科技助理教授、诺丁汉大学商学院(中国)金融科技硕士项目负责人周航指出，机器 学习能够帮助投资者拓展信息源并提升决策能力。 自动播放 ...

苹果公司领导层可能很快会再次发生变化，有传言称芯片主管约翰尼·斯鲁吉（Johny Srouji）正在考 虑离开这家 iPhone 制造商。 苹果公司 Johny Srouji 公众号记得加星标⭐️，第一时间看推送不会错过。 继其他离职人员之后 此前，一系列其他员工相继宣布退休或离职，而这条新消息正是在此之后传出的。 苹果公司的管理层在短时间内经历了多次变动。如果一份最新报告属实，那么新一轮的人事变动可能 即将到来。 据消息人士透露，苹果公司硬件技术高级副总裁约翰尼·斯鲁吉已告知首席执行官蒂姆·库克，他正在 考虑在不久的将来离开苹果公司。据悉，斯鲁吉还告诉同事，如果他真的离开，他希望加入另一家公 司，但目前尚不清楚具体是哪家公司。 苹果公司拒绝就该报道置评。 一个值得怀疑的说法 对 苹 果 公 司 而 言 ， Johny Srouji 的 离 职 可 能 会 对 其 创 新 硬 件 造 成 巨 大 打 击 。 作 为 苹 果 芯 片 （ 包 括 Apple Silicon系列芯片）的设计者，他的离开可能会在几代芯片迭代后带来重大变革。 然而，与此同时，这一离职传闻也令人感到蹊跷，原因有很多。首先，它紧随其他类似 ...

● 本报记者 刘英杰 日前，"中国银河证券·中国证券报私募行业星耀领航计划"调研团队走进国内知名量化私募机构启林投 资，就量化投资如何助力科技创新、私募与科创企业双向赋能路径以及私募机构的社会责任等议题展开 深入交流，共同探索国有背景私募机构在支持科技创新与服务地方经济中的独特路径。 "星耀领航计划"致力于打造国内最具影响力的科创类私募赋能平台，聚焦挖掘并培育兼具专业投资能力 与合规治理水平的私募管理机构。本次调研旨在推动多元投资理念的行业共享，助力构建科技、资本与 实体经济良性循环的生态体系。 将科技基因融入投资血脉 共筑科创生态圈 在王鸿勇看来，"星耀领航计划"准确把握了国家鼓励科技创新的战略方向，而量化私募本质上是技术驱 动型机构，与科技企业基因相近，双方具备合作基础。 "量化公司在研发投入强度上甚至高于许多上市科技公司，这为双方对话与合作提供了可能。"王鸿勇表 示，期待通过"星耀领航计划"搭建的平台，能够促进量化机构与科创企业之间更多元的交流，包括商业 合作与技术探讨，最终形成科技、资本与实体经济之间的良性循环。 启林投资在投资科创方面形成了一套自己的体系。首先，产品布局紧密贴合科创导向。公司管理规模 ...

来源：一年打卡100场路演 诚奇量化总结：截至25年12月，规模470亿；两位管理人分别曾在千禧年和世坤工作；23年以后逐步转 向机器学习为主的非线性建模框架 贝小塔(VX:BetaRicher)整理, 仅供合格投资者审阅。 此举出于三方面考虑: 一是上海金融人才储备更丰富,便于招募高质量研究员; 二是员工更倾向在上海落户与发展,尤其在应届生招聘中更具吸引力; 三是上海作为国家金融中心,在政策支持与监管沟通上具备相对优势, 此次变更为非新设主体,统一社会信用代码及中基协登记编号均保 业务与投资运作不受任何影响。 截至25年12月初:基协显示该公司员工数为36人,其中 高度数量为3人。 正在运作产品为313个,延期清算为0,提前清算为104个,正常清算为6个。 公司曾于2022年底达到500亿规模高点,当前体置并未触及策略容量上限,在 现有约2万亿日均成交环境下运作舒适。内部设定若未来规模通近 700 亿将启动 封盘或参数调整机制,但目前多头策略额度充足,无主动控规模计划。 股权占比:股权渗透后分析得出,何文奇 占比 50.5089%,张万成 占比 49.4911%,两人合计绝对控股。为激励年轻化的核心投研队伍 ...