a Molecular Oncology Group, Cancer Research UK Manchester Institute, The University of Manchester, Alderley Park SK10 4TG, United Kingdom b The Christie NHS Foundation Trust, 550 Wilmslow Road, Manchester M20 4BX, United Kingdom c Department of Surgery, Manchester University NHS Foundation Trust, Manchester, United Kingdom d Molecular Biology Core Facility, Cancer Research UK Manchester Institute, The University of曼彻斯特,奥尔德利公园SK10 4TG,英国E免疫疗法和稀有肿瘤部门,伊斯蒂托托科学家公司Romagnolo per lo studio lo studio e lo cura cura dei dei tumori(IRST)“ Dino Amadori” IRCCS,MELDOLA(MELDOLA),MELDOLA(fc),曼彻斯特型曼彻斯特型,曼彻斯特型,曼彻斯特型曼彻斯特型曼彻斯特型,曼彻斯特大学SK10 4TG,英国G计算生物学支持团队,曼彻斯特癌症研究所,曼彻斯特大学,曼彻斯特大学,Alderley Park SK10 4TG,英国生物科学学院,生物学,医学和健康学院,英国曼彻斯特大学,英国
8.) Martínez Vivot, R.、Pallavicini, C.、Zamberlan, F.、Vigo, D. 和 Tagliazucchi, E. (2020)。冥想增加大脑振荡活动的熵。神经科学,431,40–51。https://doi.org/10.1016/j.neuroscience.2020.01.033 9.) memories, C. (2018 年 1 月 28 日)。从科学上讲,冥想对我们的大脑有多大帮助?🧠。Medium。2022 年 2 月 27 日检索自 https://medium.com/@christiandag/scientifically-how-much-meditation-helps-our-brain-459dc021925b 10.) P Harne, B. (2014)。 Higuchi 对吟诵 om 之前和之后的 EEG 信号进行分形维数分析以观察对大脑的整体影响。国际电气和计算机工程杂志 (IJECE),4 (4)。https://doi.org/10.11591/ijece.v4i4.5800 11.) Shaw, L.,& Routray, A. (2016)。SVM 和 K-SVM 在克里亚瑜伽冥想状态相关 EEG 分类中的关键比较。2016 年 IEEE 国际 WIE 电气和计算机工程会议 (WIECON-ECE)。https://doi.org/10.1109/wiecon-ece.2016.8009103 12.) Rodriguez-Larios, J.、Faber, P.、Achermann, P.、Tei, S. 和 Alaerts, K. (2020)。从无思虑的意识到费力的认知:经验丰富的冥想者在冥想、休息和算术过程中的 Alpha - theta 跨频率动态 13.) Xue, S.-W.、Tang, Y.-Y.、Tang, R. 和 Posner, MI (2014)。短期冥想会引起大脑静息 EEG Theta 网络的变化。脑与认知,87,1-6。https://doi.org/10.1016/j.bandc.2014.02.008 14.) Young, JH、Arterberry, ME 和 Martin,JP(2021)。对比脑电图衍生的熵和神经振荡与高技能冥想者。人类神经科学前沿,15。
在这里,我们研究解码通过未知量子态传输的信息的问题。我们假设 Alice 将字母表编码为一组正交量子态,然后将其传输给 Bob。然而,介导传输的量子通道将正交状态映射到非正交状态,可能混合。如果没有准确的通道模型,那么 Bob 收到的状态是未知的。为了解码传输的信息,我们建议训练测量设备以在鉴别过程中实现尽可能最小的误差。这是通过用经典通道补充量子通道来实现的,经典通道允许传输训练所需的信息,并采用抗噪声优化算法。我们在最小误差鉴别策略的情况下演示了训练方法,并表明它实现了非常接近最优误差概率。特别是,在两个未知纯态的情况下,我们的建议接近 Helstrom 界限。对于更高维度中的大量状态,类似的结果也成立。我们还表明,减少训练过程中使用的搜索空间可以大大减少所需资源。最后,我们将我们的建议应用于相位翻转通道达到最佳误差概率的准确值的情况。
摘要。- 目标:更年期是女性重生生活中的重要过渡时期,在此期间,激素会改变,导致心脏瓦斯加斯加疾病和2型糖尿病的风险增加。在这项研究中,我们评估了使用胰岛素抵抗(IR)的替代表现来预测甲膜膜上苏联妇女胰岛素抵抗的风险的可能性。患者和方法:研究涉及252名居住在西部波美拉尼亚案的终止妇女。本研究中采用的方法是基于原始问卷,拟人测量和实验室测试的诊断调查,用于确定所选生物化学参数的水平。结果:在整个研究人群中,曲线下的最高面积是稳态模型评估 - 胰岛素分解(HOMA-IR)和定量的胰岛素剂量检查指数(QUICKI)。甘油三酸酯指数(TYG指数)显示出比其他标记物中糖尿病和糖尿病之间的区别诊断值,这是糖尿病和糖尿病之间的区别工具。homa-ir与禁食的血液胶质(r = 0.72; p = 0.001),糖化血红蛋白(HbA1c,r = 0.74; p = 0.001),甘油三酸酯,甘油三酸酯(TG,r = 0.18; p <0.005)和Sure-sure(sbp = 0.02)(r = 0.001;用高密度脂蛋白(HDL,r = -0.28; p = 0.001)负否。结论:发现人体测量法和二进时间代谢参数与IR标记显着相关。homa-beta,Quicki与快速血液(r = -0.051; p = 0.001),HBA1C(R = -0.51; P = 0.001),TG(R = -0.25; P = 0.001),低密度脂蛋白(LDL,R = -0.13; P = 0.045),R = 0.045),; 0.011),并用HDL积极(r = 0.39; p = 0.001)。
ChatGPT 等生成式人工智能 (AI) 聊天机器人日益流行,对社交媒体产生了变革性的影响。随着人工智能生成内容的普及,人们对网络隐私和错误信息的担忧不断增加。在社交媒体平台中,Discord 支持人工智能集成——这使得其主要的“Z 世代”用户群特别容易接触到人工智能生成的内容。我们调查了 Z 世代的个人 (n = 335),以评估他们在 Discord 上区分人工智能生成文本和人类撰写的文本的能力。调查采用了 ChatGPT 的一次性提示,伪装成在 Discord.com 平台上收到的短信。我们探讨了人口统计因素对能力的影响,以及参与者对 Discord 和人工智能技术的熟悉程度。我们发现 Z 世代的人无法辨别人工智能和人类编写的文本(p = 0.011),而那些自称对 Discord 熟悉程度较低的人与那些自称有人工智能使用经验的人相比,在识别人类编写文本方面表现出更高的能力(p << 0.0001)。我们的结果表明,人工智能技术与 Z 世代流行的沟通方式之间存在微妙的关系,为人机交互、数字通信和人工智能素养提供了宝贵的见解。
Main Capabilities • Maritime dominance and A2/AD missions • Deep strike at land • GNSS-independent • All-weather operation • Discriminate hostile targets from non- combatants and decoys • Low observability and passive • Reduced demand from platform (size, interfaces) • Advanced mission planning • Man-in-the-loop for decision backup and BDA • Open architecture, simple integration • Mission agility
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