● Development and deployment of Earth observation satellites and other space-based platforms, such as the International Space Station, to gather data and imagery of the Earth from space.● Use of remote sensing technologies, including radar and optical sensors, to capture high-resolution images and data on various aspects of the Earth, such as weather patterns, land use, and natural resources.● Data analysis and interpretation using advanced algorithms and machine learning techniques to extract meaningful insights and patterns from the vast amounts of data collected from space.● Provision of data products and services to a range of industries, including agriculture, forestry, energy, and environmental management, to support decision-making and improve operational efficiency.● Collaboration with government agencies and research institutions to develop and implement space-based observation and monitoring programs to address global challenges, such as climate change, natural disasters, and ecosystem management.● Development of new technologies and solutions to improve the accuracy and precision of Earth observation and remote sensing data, such as new sensors and platforms, and advanced signal processing and data analysis techniques.● Promotion of public awareness and education on the value of space-based observation and monitoring, and the potential for these technologies to address critical global challenges and support sustainable development.
摘要 - 边缘情报的出现使智能物联网服务(例如,视频/音频监视,自动驾驶和智能城市)成为现实。To ensure the quality of service, edge service providers train unbiased models of distributed machine learning jobs over the local datasets collected by edge networks, andusuallyadopttheparameterserver(PS)architecture.However, the training of unbiased distributed learning (UDL) depends on geo-distributed data and edge resources, bringing a new challenge for service providers: how to effectively schedule and price UDL jobs such that the long-term系统实用程序(即社会福利)可以最大化。在本文中,我们提出了一种基于在线拍卖的调度算法ERIS,该算法确定了每个到达UDL作业的数据工作负载,并发工人和PSS的数量和位置,并且基于当前资源消耗的基于当前资源消耗而动态价格有限。eris应用了一个原始的双重框架,该框架称为有效的双重子例程来安排UDL作业,实现了良好的竞争比率和伪多项式时间的复杂性。为了评估ERIS的有效性,我们同时实施了一个测试床和大型模拟器。结果表明,与当今云系统中的最新算法相比,ERIS优于表现和实现多达44%的社会福利。
混合学习已成为护理教育不可或缺的一部分,提供了在线和面对面教学的融合,可以增强学习体验,尤其是在技能实验室内。但是,混合学习的有效性在很大程度上取决于护士教育者在使用数字工具和方法方面的能力。这项研究评估了乌干达公立护理学校的护士教育工作者在采用混合学习方面的能力。Data was collected from 40 randomly selected nursing educators in 4 public nursing schools in Uganda The findings indicate significant inadequacies: only 25% are proficient with Learning Management Systems (LMS) like Moodle, 20% can effectively use platforms like Zoom and Microsoft Teams for synchronous sessions, 30% are adept at using social media platforms such as WhatsApp and YouTube for informal learning and networking, and merely 12.5% are proficient在使用电视视频作为补充教育资源时。这些结果突出了针对性的专业发展和培训计划的关键需求,以增强护士教育者的数字能力。解决这些差距对于有效实施混合学习至关重要,最终旨在提高乌干达的护理教育质量并促进熟练的护理专业人员的发展。
The data on China's domestic novel pipelines were collected from the Pharmcube database (one of the most authoritative platforms of drug information in China), curated from over 57 sources, including Chinese NMPA's Registration and Information Disclosure Platform for Drug Clinical Studies, Chinese Clinical Trial Register (ChiCTR), ClinicalTrials.gov clinical trial registries, scientific conferences, company press releases, published reports, investor presentations and other来源。药物已包含在我们的分析中,并具有以下资格标准:研究性治疗和疫苗治疗任何疾病,不包括仿制药或生物仿制药,这些疾病在中国被发现或在中国公司内被确认,但尚未进入临床发展,但在任何临床发展中都没有在任何国家/地区获得营销授权。不再为此不再活跃的开发的药物被排除在外。此分析中总共包括2251个候选人。数据由Tsinghua临床研究所(TCRI)和PharmCube进行手动验证,并进一步分类,并具有药物靶标的参数,药物类型,创新类型,中国和国外的开发阶段,起源的指示和位置。可能不会公开披露某些产品信息,这可能会偏向单个产品的分类。
人脑的基本组织是在出生前建立的,在出生后的最初几年持续增长。在出生前或之后,患有各种生物学(例如,物质暴露)或心理社会危害(例如虐待)的儿童处于偏离典型的发育轨迹的可能性升高,而典型的发育轨迹又可能与心理,行为,行为和身体健康频率有关。在健康的脑和儿童发展(HBCD)研究中,一项多站点的前瞻性纵向队列研究,大脑,身体,生物学,生物学,认知,行为,社交和情感发展从怀孕开始,并计划在10岁之间进行(数据是根据年龄的不同程度的,根据年龄的不同程度,与更早的寿命相比,数据是在不同程度上取样的)。HBCD旨在确定各种有害和保护因素(包括产前药物使用)对幼儿期发育轨迹的短期和长期影响。HBCD研究将在27个站点上作为全国财团组织,将通过数据使用应用程序和批准过程收集每年公开提供的多模式数据。在这里,我们提供了HBCD研究设计,采样,协议开发和数据管理的概述。Data collected through HBCD will be fundamental to informing future prenatal and early childhood interventions and policies to promote wellbeing and resilience in all children.
摘要。大量能源消耗吸引了利用可再生能源的关注,其中最重要的是在炎热气候中的太阳能应用,以满足冷却和功率的需求。本研究的新颖性在于在弹出器冷却循环中将瞬态自我分析应用于两个喷射器和两个蒸发器。Furthermore, the study uses solar data specific to Tehran in Iran.第三,通过吸收冷凝器热部位的废热,热电发电机系统提供了运行泵送和电气控制系统所需的能量,从而创建了一个完全自主的系统。Thermodynamic model have been designed using EES software.桑迪亚国家实验室(SNL)和国家可再生能源实验室(NERL)的结果验证了抛物线槽太阳能模型。The comparison with the experimental data collected by SNL during the LS-2 tests on the AZTRAK platform has shown good agreement.Weather conditions were analyzed as transients using Meteonorm software.The results show that the solar system produced the most heat in June and the least in December, with 816 kW and 262.3 kW, respectively.Additionally, production power and cooling in June are 5.9 kW and 86 kW, and in December: 2.7 kW and 28 kW.Regarding exergy destruction percentages, the solar collector has 86% and the storage tank has 6.5%.
围绕海洋的塑料通常是从缺乏固体废物管理系统的发展中国家的地点收集的。然后可以将回收的塑料发送到回收设施并重新利用为新材料。为了确保海洋结合塑料的有效性,几个组织证明正确收集和管理塑料废物。这些认证提供商可以验证收集材料的何处,收集其材料的位置以及如何和何处,以确保其符合质量,道德,环境和劳动力标准。
在现场工作的情况下,提及现场工作的目标和结果以及现场工作报告。1.4.1机构从各种利益相关者(例如学生,教师,雇主,校友等)那里获得有关机构的学业绩效和氛围的反馈。和有关反馈的行动报告可在机构网站的机构反馈过程中提供,如下所示:(20)A。在机构网站B上收集,分析,采取的行动和反馈。已收集,分析和行动已采取的反馈。收集和分析的D.收集的反馈(至少来自任何两个利益相关者)E。未收集反馈
土壤是对人类生活最重要的环境自然资源之一,对人类健康和生态环境的质量非常重要。重金属对土壤酶活性有直接影响,因此,酶的活性基团的空间结构被破坏,因此,微生物的生长和繁殖受到了破坏,并且减少了微生物酶的合成和代谢。土壤微生物通常用作土壤环境质量的重要指标,因为它们对土壤环境条件的敏感性大于较大的动物或植物。通过土壤微生物的变化,无论土壤被污染,土壤污染的程度。重金属对土壤微生物效应的影响主要包括重金属对土壤微生物活性的影响,对土壤酶活性的影响和土壤微生物群落的组成。重金属通过与蛋白质结合而杀死微生物,从而抑制酶活性。重金属是寡动力学的,这意味着非常小的浓度显示出明显的抗菌活性。汞是重金属,用于微生物对照,各种形式的汞通过与蛋白质中含硫的氨基酸结合而抑制微生物作用。We collected 18 soil samples from Unnao and Jajmau in which Jajmau had the highest total microbial count (bacteria) in all three layers (Upper, middle and lower) of soil and Unnao had the lowest total microbial count (bacteria) in all three layers (Upper, middle and lower) of soil but the total microbial count (fungi) in all two layers (Upper, middle) is high in Jajmau in与其他总微生物计数(下层)的比较较低。