Group 1 - Major European stock indices opened slightly lower, with Germany's DAX30 index down by 0.28% [1] - The UK's FTSE 100 index opened down by 0.36% [1] - The Euro Stoxx 50 index opened down by 0.60% [1] - France's CAC40 index opened down by 0.54% [1]

收评：沪指冲高回落涨0.32% 全市场超4100只个股下跌 智通财经7月4日电，市场全天冲高回落，三大指数涨跌不一，沪指一度涨超1%逼近3500点。沪深两市 全天成交额1.43万亿，较上个交易日放量1188亿。盘面上，市场热点较为杂乱，个股跌多涨少，全市场 超4100只个股下跌。从板块来看，银行股再度走强，浦发银行等多股再创历史新高。稳定币概念股震荡 反弹，京北方等涨停。电力股持续活跃，华银电力等涨停。下跌方面，固态电池概念股展开调整，信宇 人等跌超10%。板块方面，稳定币、游戏、银行、电力等板块涨幅居前，固态电池、美容护理、稀土永 磁、有色金属等板块跌幅居前。截至收盘，沪指涨0.32%，深成指跌0.25%，创业板指跌0.36%。 ...

此外，据经济参考报，第十八届国际太阳能光伏与智慧能源大会暨展览会（SNEC 2025）日前在上海举 行。在这场被誉为光伏行业"风向标"的盛会上，"产能出清""行业整合""创新突围"成为关键词。多方信 息显示，目前从上游硅料到中下游拉晶切片、电池组件，相关整合正在谋划，预计今年下半年到明年一 季度是光伏供给侧改革的关键窗口期。 7月4日，光伏概念翻红后再度回调。 相关ETF中，光伏ETF（159857）截至发稿跌0.51%，成交额突破1亿元，换手率超4%。成分股中，林 洋能源、大全能源涨超4%，科华数据、协鑫集成、太阳能、浙江新能等股跟涨。 值得一提的是，Wind数据显示，截至7月3日，光伏ETF（159857）最新份额为39.42亿份，居深市同标 的产品第一；年内份额增幅达32.51%。此外，截至7月3日，该ETF近5个交易日获超1.2亿元资金净流 入，居深市同标的产品第一。 光伏ETF（159857）紧密跟踪中证光伏产业指数，该指数将主营业务涉及光伏产业链上、中、下游的上 市公司股票作为待选样本，选取不超过50家最具代表性公司作为样本股，以反映光伏产业公司的整体表 现，为投资者提供新的投资标的。光伏ETF（ ...

Core Points - Vanguard Mining Corp. is applying to list 12,690,001 common share purchase Warrants on the Canadian Securities Exchange (CSE) [1][2] - Each Warrant allows the holder to acquire one common share at an exercise price of $0.10 per share before February 6, 2028 [2] - The Warrants are expected to trade under the symbol UUU.WT and will be governed by a warrant indenture with Endeavor Trust Corporation [2][3] Company Overview - Vanguard Mining Corp. is a Canadian mineral exploration company focused on discovering and developing high-value strategic minerals [4] - The company is advancing uranium exploration projects in the United States and Paraguay, targeting assets critical to the global energy transition [4] - Vanguard is committed to responsible exploration and value creation through the acquisition and advancement of highly prospective uranium properties [4]

2025年7月4日 来自厦门海沧的 安井食品集团股份有限公司 （以下简称"安井食品"） 成功在港交所发行上市 股票代码：2648.HK 发行价格：60.00港元/股 这也是国内首家"A+H"上市的速冻食品龙头 截至午间收盘 安井食品H股报58.4港元/股 成交额超5.42亿港元 ▲安井食品H股上市鸣锣 此次安井食品（2648.HK）全球发售H股基础发行量约为3999.47万股，募集资金约为23.9968亿港元（约合21.91亿元人民币）。高盛和中金公司为其联席 保荐人。安井食品于2017年在A股上市（603345.SH），登陆港股后成为福建第一家实现"A+H"上市的食品公司。 ▲安井食品集团股份有限公司 图源：公司官网 本次港股上市安井食品获得了多位基石投资者的追捧，共计认购9000万美元（约合7.06亿港元，或6.45亿人民币）。基石投资者包括上海景林（景林场外 掉期，华泰国际非全权委托持有）、香港景林、Hosen Capital（厚生投资）、QRT、FCP Domains、NonaVerse等。 安井食品希望借助此次赴港上市推动国际化战略及海外业务布局，建立与国际资本市场紧密相连的资本平台，并进一步提高综 ...

在服务效率方面，幸福人寿凭借客服人员技能精进与流程优化双轮驱动，将平均服务时长压缩至8.88分钟，时效较去年同期 提升19%，真正实现"秒级响应、分钟级办理"。数据背后，是12,778笔保全业务的精准处理，是每一份保单的严谨审核，是 每一个环节的精益求精。 客服人员主动放慢语速、耐心细致讲解，用通俗易懂的语言帮助老年客户理解条款内容；在办理业务时，采用"一步一确 认"的方式，确保每个环节都清晰明了，让1684位银发长辈在云端也能享受到贴心、安心的服务体验。 对于港澳及外籍客户，幸福人寿凭借专业能力精准对接需求，累计服务167人次，有效打破地域壁垒，保障跨境服务高效流 畅。 半载时光深耕服务，万次托付见证初心！ 客户满意度高达99.8%的结果是幸福人寿最珍视的勋章。这背后，是客服团队365天无休的坚守，是每一次通话中"您好，很 高兴为您服务"的真诚问候，是每一份诉求后"您放心，我们立即处理"的坚定回应。 2025年上半年，幸福人寿空中客服团队以云端为舞台，以专业为底色，以高效为准则，以温暖为纽带，在数字服务的浪潮 中破浪前行，成功为超10,000名客户搭建起便捷、安心的服务桥梁，圆满完成保全理赔服务任务。这一数 ...

冰场上，两支队伍的身影交织如梭。韩国队员身着亮黄队服，滑行时如疾风掠过，起跑爆发力与弯道控 制尽显专业风范；延庆少年则以白绿色战衣相迎，他们紧随其后，目光始终锁定"对手"的每一个动作细 节。檀国大学队主教练吴龙锡手持秒表，目光如炬，时而高声提醒："重心前压！冰刀切角再精准！"而 延庆队教练孟美君则在场边记录数据，适时为小队员调整技术动作。 "延庆的冰面质量超过很多韩国专业场馆，这里的孩子对冰雪的热爱更让我们感动。""延庆气候宜人， 环境优美，特别适合训练。"檀国大学队师生们高度评价延庆的训练条件和组织效率。 高頔/摄 转自：北京日报客户端 韩方运动员展现的高度自觉性、职业精神，以及技术细节的提升，比如在弯道处的速度控制与身体平衡 能力，让延庆的青少年们收获颇多，孩子们对短道速滑的理解也更深了。 近日，延庆区梦起源滑冰馆内的冰上训练热火朝天，一场跨越国界的中韩短道速滑合训正在此上演。近 日，韩国檀国大学短道速滑队3名教练带领12名运动员受邀来到延庆，与延庆本土少年滑冰队员展开为 期10天的技术交流，双方在冰面上并肩切磋，以技术为纽带，续写冬奥冰雪情缘。 2024-2025赛季国际滑联短道速滑世界青年锦标赛男子1 ...

Core Insights - DeepSeek has significantly disrupted the large model market within 128 days of its launch, notably reducing the prices of inference models, with OpenAI's June update pricing dropping to 20% of its previous version [1][2] - The usage of DeepSeek models on third-party platforms has surged nearly 20 times since its release, benefiting numerous cloud computing providers [3] - However, DeepSeek's own website and API market share have been declining, unable to keep pace with the rapid growth of AI products in the first half of the year [4][6] Group 1 - DeepSeek's model usage on its own platform has decreased, with its share of tokens generated dropping to only 16% by May [10][11] - The traffic for DeepSeek's web-based chatbot has also significantly declined, while other major models have seen increases in traffic [13] - Monthly active users for DeepSeek dropped from 614.7 million in February to 436.2 million in May, a decrease of 29% [14] Group 2 - DeepSeek's strategies to reduce costs have led to compromises in service quality, resulting in longer wait times for users on its official platform [15][26] - Other platforms, despite being more expensive, offer much faster response times, with some achieving near-zero latency [16][18] - DeepSeek's context window is limited to 64k, which is among the smallest in the industry, making it inadequate for certain applications [22][23] Group 3 - DeepSeek's approach involves bundling user requests, which lowers the cost per token but increases individual wait times [26] - The company appears to prioritize internal research and development over user experience, focusing on achieving AGI rather than monetizing its services [27][28] - The competition in the AI space is heavily reliant on computational resources, with DeepSeek's strategies reflecting a focus on optimizing these resources [30] Group 4 - Other model providers, like Claude, are also adjusting their speeds to manage computational strain while trying to maintain user experience [31] - Claude's output speed has decreased by 40% since the release of its latest version, yet it remains faster than DeepSeek [32] - The industry is evolving to enhance the intelligence provided by each token, rather than just increasing the overall model capabilities [36]

Core Insights - The article discusses the increasing prevalence of AI-assisted writing in biomedical research, highlighting that over 20% of the 1.5 million abstracts published on PubMed in 2024 exhibit characteristics typical of large language models (LLMs) [1][3]. Group 1: AI Usage in Biomedical Research - A significant portion of biomedical papers, approximately 14%, has been identified as having been written with the assistance of LLMs within a year [1]. - The use of LLMs in certain countries and disciplines has surpassed 20%, indicating a growing trend in AI-assisted writing [3][5]. - The study found that 10%-11% of abstracts in 2024 utilized LLMs, with some sub-corpora showing rates as high as 30% [11][15]. Group 2: Characteristics of LLM Writing - LLMs tend to favor stylistic verbs and adjectives that do not contribute to the content but alter the writing style, leading to the frequent use of words like "intricate" and "notably" [2][11]. - The analysis revealed that 66% of the overused words in 2024 were verbs, while 16% were adjectives, indicating a preference for certain types of language [11][21]. - Authors are beginning to adjust their writing to avoid obvious LLM markers, which complicates the assessment of LLMs' impact on academic output [5][21]. Group 3: Variability in LLM Usage - The reliance on LLMs varies significantly across disciplines, with fields like computational biology and bioinformatics seeing around 20% usage due to rapid technological advancements [15][16]. - In non-English speaking countries, such as China and South Korea, LLM usage can reach 15% as researchers seek assistance with English writing [16]. - Open-access journals, particularly those with simpler review processes, show higher LLM usage rates (up to 24%) compared to prestigious journals like Nature and Science, which have rates between 6% and 8% [16]. Group 4: Detection and Adaptation - Researchers are exploring methods to detect LLM-generated text, but current detection tools may not accurately differentiate between human and AI-generated content [27][28]. - The study indicates that while authors can modify their writing to reduce LLM characteristics, complete avoidance is challenging [25][28]. - Future research aims to quantify the impact of AI on academic literature more comprehensively, moving beyond single text analyses to broader statistical evaluations [28].

Core Viewpoint - Public fund institutions have purchased over 2 billion yuan in equity funds this year, reflecting their confidence in the Chinese capital market and investment management capabilities [1][3][10]. Group 1: Fund Purchases - On July 4, Dacheng Fund announced a minimum investment of 10 million yuan in its Dacheng Insight Advantage Fund, with a commitment to hold for at least one year [2]. - Several public fund institutions have collectively purchased over 2 billion yuan in their equity funds this year [3]. - Dacheng Fund's recent purchase of its Dacheng Insight Advantage Fund marks its second self-purchase, following a 20 million yuan investment in the Dacheng Ultimate Return Fund on June 7 [5]. Group 2: Recent Trends in Fund Purchases - Public fund institutions have been actively purchasing their own funds, with Huashang Fund announcing a 20 million yuan investment in its Huashang Zhiyuan Return A Fund on July 1 [6]. - In May and June, a wave of self-purchases occurred alongside the issuance of new floating-rate funds, with various fund management companies announcing investments [7]. - Notable self-purchases include 10 million yuan from Dongfanghong Asset Management and 20 million yuan from Jiaoyin Schroder Fund for their respective new floating-rate funds [8]. Group 3: Fundraising and Market Sentiment - As of July 4, public fund institutions have net subscribed over 2 billion yuan in equity funds this year, with stock funds net subscriptions at approximately 1.32 billion yuan and mixed funds at about 1.18 billion yuan [11]. - Tianhong Fund led stock fund subscriptions with around 295 million yuan, while Industrial Bank of China and Bank of China also showed significant net subscriptions in mixed funds [12]. - The market has shown resilience, with recent rebounds exceeding expectations, driven by changing investor confidence in Chinese assets [13][14].