曼彻斯特大学（The University of Manchester，简称曼大，缩写为UoM）始建于1824年，是一所著名的英国研究型大学。曼大是英国的六所红砖大学之一，亦为英国罗素集团的创始成员。曼大位于英格兰第三大城市曼彻斯特[1]，是英国最大的单一校址大学。曼彻斯特大学综合学术实力出色，在2021年的QS世界大学排名及泰晤士高等教育世界大学排名中，曼大分别排名27（全英第6）[2]及51（全英第8）[3]。在由泰晤士高等教育于2021的世界大学影响力排名中，曼大位列全球第1（2020排名中，曼大位列全球第8）[4]。迄今，曼大校友、教授和研究员中共有25位诺贝尔奖得主；其现任教职员中有4位诺贝尔奖得主，为全英之冠[5]。曼大是英国获得女王周年奖次数第二多的大学（7次），仅次于牛津大学（10次）。在2014年英国官方组织的研究卓越框架（REF）评估中，曼大的研究实力位居全英第5名，其83%的科研项目被评为“世界领先”或“国际优秀”[6]。此外，曼大旗下的曼彻斯特商业管理学院所提供的MBA课程在2020年的QS全球MBA排名及金融时报全球MBA排名两个榜单上分列48[7]和45[8]。根据高等教育统计局所作之统计，曼彻斯特大学于2018年时连研究生在内共有40,140名学生修读其所提供之逾1,000个学科种类。曼大拥有全英国最大的学生会及最国际化的学生群体（11,050名非本土学生，约28%），除上述四万多名在校学生外，曼大还有3,450名海外注册学生修读其所提供的课程。至今，曼大有约480,000名校友遍布于190个国家[9]。财政方面，曼彻斯特大学于2017/18年总收入为十亿英镑，其中近三亿英镑为外来赞助研究经费[10]，数额为全英第6。坐落在贯穿市中心南北的牛津路上，曼彻斯特大学占地共270公顷，并拥有229座教学设施[9]。曼大的大学图书馆为全英第三大学术类图书馆，仅次于牛津大学和剑桥大学。曼大其余资产包括曼彻斯特博物馆、卓瑞尔河岸天文台及约翰·莱兰兹图书馆等地标性建筑。
The relationship between self-reported falls and fracture risk was estimated in an international meta-analysis of individual-level data from 46 prospective cohorts. Previous falls were associated with an increased fracture risk in women and men and should be considered as an additional risk factor in the FRAX® algorithm. Previous falls are a well-documented risk factor for subsequent fracture but have not yet been incorporated into the FRAX algorithm. The aim of this study was to evaluate, in an international meta-analysis, the association between previous falls and subsequent fracture risk and its relation to sex, age, duration of follow-up, and bone mineral density (BMD). The resource comprised 906,359 women and men (66.9
The concept of immunity debt is a phenomenon resulting from the suppression of endemic pathogens during the COVID-19 pandemic due to non-pharmaceutical interventions. The reduced circulation of various pathogens during the pandemic, particularly respiratory syncytial virus (RSV), altered typical infectious disease dynamics by reducing levels of population immunity usually acquired through exposure to infection. This concept is demonstrated through the post-pandemic resurgence of diseases such as RSV and group A Streptococcus, and highlights the interplay between reduced pathogen exposure and increased susceptibility in populations. The complexities and nonlinear dynamics of seasonal transmission are observed in differences in pathogen resurgence across regions. These issues highlight the importance of comprehensive disease surveillance and public health strategies in mitigating these long-term epidemiological impacts.
As 6G evolves and moves towards denser heterogeneous networks, advanced technologies like non-orthogonal multiple access (NOMA), reconfigurable intelligent surfaces (RIS), and integrated sensing and communication (ISAC) are becoming increasingly important to enhance network efficiency. This paper presents a new analytical framework to assess downlink transmissions in heterogeneous networks (HetNets). The framework uses homogeneous Poisson point processes (PPP) to model the spatial distribution of base stations (BSs) and users. In the t-th tier, the BS utilizes a superimposed NOMA signal for target sensing. RISs, also modeled with homogeneous PPP, are deployed to address blockage issues when the direct link from BSs to users is unavailable. The approximated outage probability expressions are derived for two distinct scenarios: one where a typical user has a direct link to a tagged BS and one where the direct link is absent. The analysis also incorporates practical challenges such as imperfect successive interference cancellation and hardware impairments on both the transmitter and the receiver sides. The results demonstrate the benefits of RIS-NOMA over traditional orthogonal multiple access HetNets, with the performance improving as the number of RIS elements increases. Additionally, approximated expressions for ergodic rates, system throughput, and the probability of detection for sensing performance are derived.
The remarkable progress in Large Language Models (LLMs) opens up new avenues for addressing planning and decision-making problems in Multi-Agent Systems (MAS). However, as the number of agents increases, the issues of hallucination in LLMs and coordination in MAS have become increasingly prominent. Additionally, the efficient utilization of tokens emerges as a critical consideration when employing LLMs to facilitate the interactions among a substantial number of agents. In this paper, we develop a modular framework called LLaMAC to mitigate these challenges. LLaMAC implements a value distribution encoding similar to that found in the human brain, utilizing internal and external feedback mechanisms to facilitate collaboration and iterative reasoning among its modules. Through evaluations involving system resource allocation and robot grid transportation, we demonstrate the considerable advantages afforded by our proposed approach.
Researchers, engineers, and entrepreneurs are enthusiastically exploring and promoting ways to apply generative artificial intelligence (GenAI) tools to qualitative data analysis. From promises of automated coding and thematic analysis to functioning as a virtual research assistant that supports researchers in diverse interpretive and analytical tasks, the potential applications of GenAI in qualitative research appear vast. In this paper, we take a step back and ask what sort of technological artifact is GenAI and evaluate whether it is appropriate for qualitative data analysis. We provide an accessible, technologically informed analysis of GenAI, specifically large language models (LLMs), and put to the test the claimed transformative potential of using GenAI in qualitative data analysis. Our evaluation illustrates significant shortcomings that, if the technology is adopted uncritically by management researchers, will introduce unacceptable epistemic risks. We explore these epistemic risks and emphasize that the essence of qualitative data analysis lies in the interpretation of meaning, an inherently human capability.
