Building Semantic Communication System via Molecules:An End-to-End Training Approach

The concept of semantic communication provides a novel approach for applications in scenarios with limited communication resources.In this paper,we propose an end-to-end(E2E)semantic molecular communication system,aiming to enhance the efficiency of molecular communication systems by reducing the transmitted information.Specifically,following the joint source channel coding paradigm,the network is designed to encode the task-relevant information into the concentration of the information molecules,which is robust to the degradation of the molecular communication channel.Furthermore,we propose a channel network to enable the E2E learning over the non-differentiable molecular channel.Experimental results demonstrate the superior performance of the semantic molecular communication system over the conventional methods in classification tasks.

Heterostructured nickel-cobalt metal alloy and metal oxide nanoparticles as a polysulfide mediator for stable lithium-sulfur full batteries with lean electrolyte

Batteries that utilize low-cost elemental sulfur and light metallic lithium as electrodes have great potential in achieving high energy density.However,building a lithium-sulfur(Li-S)full battery by controlling the electrolyte volume generally produces low practical energy because of the limited electrochemical Li-S redox.Herein,the high energy/high performance of a Li-S full battery with practical sulfur loading and minimum electrolyte volume is reported.A unique hybrid architecture configured with Ni-Co metal alloy(NiCo)and metal oxide(NiCoO_(2))nanoparticles heterogeneously anchored in carbon nanotube-embedded selfstanding carbon matrix is fabricated as a host for sulfur.This work demonstrates the considerable improvement that the hybrid structure's high conductivity and satisfactory porosity promote the transport of electrons and lithium ions in Li-S batteries.Through experimental and theoretical validations,the function of NiCo and NiCoO_(2) nanoparticles as an efficient polysulfide mediator is established.These particles afford polysulfide anchoring and catalytic sites for Li-S redox reaction,thus improving the redox conversion reversibility.Even at high sulfur loading,the nanostructured Ni-Co metal alloy and metal oxide enable to have stable cycling performance under lean electrolyte conditions both in half-cell and full-cell batteries using a graphite anode.

Metal-free two-dimensional phosphorene-based electrocatalyst with covalent P-N heterointerfacial reconstruction for electrolyte-lean lithium-sulfur batteries

The use of lithium-sulfur batteries under high sulfur loading and low electrolyte concentrations is severely restricted by the detrimental shuttling behavior of polysulfides and the sluggish kinetics in redox processes.Two-dimensional(2D)few layered black phosphorus with fully exposed atoms and high sulfur affinity can be potential lithium-sulfur battery electrocatalysts,which,however,have limitations of restricted catalytic activity and poor electrochemical/chemical stability.To resolve these issues,we developed a multifunctional metal-free catalyst by covalently bonding few layered black phosphorus nanosheets with nitrogen-doped carbon-coated multiwalled carbon nanotubes(denoted c-FBP-NC).The experimental characterizations and theoretical calculations show that the formed polarized P-N covalent bonds in c-FBP-NC can efficiently regulate electron transfer from NC to FBP and significantly promote the capture and catalysis of lithium polysulfides,thus alleviating the shuttle effect.Meanwhile,the robust 1D-2D interwoven structure with large surface area and high porosity allows strong physical confinement and fast mass transfer.Impressively,with c-FBP-NC as the sulfur host,the battery shows a high areal capacity of 7.69 mAh cm^(−2) under high sulfur loading of 8.74 mg cm^(−2) and a low electrolyte/sulfur ratio of 5.7μL mg^(−1).Moreover,the assembled pouch cell with sulfur loading of 4 mg cm^(−2) and an electrolyte/sulfur ratio of 3.5μL mg^(−1) shows good rate capability and outstanding cyclability.This work proposes an interfacial and electronic structure engineering strategy for fast and durable sulfur electrochemistry,demonstrating great potential in lithium-sulfur batteries.

Effect of nano-CaO particle on the microstructure,mechanical properties and corrosion behavior of lean Mg-1Zn alloy

The effects of nano-CaO contents on the microstructure,mechanical properties and corrosion resistance of lean Mg-1Zn alloy were investigated.The results showed that the addition of nano-CaO significantly refined the grain size and improved mechanical properties of the Mg-1Zn alloy.At the same time,CaO reacted with molten Mg in situ to form nano-MgO,whose corrosion product in SBF solution was the same with the degradation product of Mg matrix,resulting in the enhanced compactness of the Mg(OH)_(2) layer and reduced corrosion rate of matrix.The Mg-1Zn alloy had lower corrosion resistance due to excessively large grain size and shedding of corrosion products.The composite with 0.5 wt.%CaO had the best corrosion resistance with a weight loss of 9.875 mg·y^(-1)·mm^(-2)due to the small number of Ca_(2)Mg_(6)Zn_(3) phase and suitable grain size.While for composites with high content of CaO(0.7 wt.%and 1.0 wt.%),they had lower corrosion resistance due to the coexistence of large number of Ca_(2)Mg_(6)Zn_(3) and Mg_(2)Ca at grain boundaries,especially for 1.0 wt.%CaO composite,resulting from the strong micro-galvanic corrosion.

Design Strategies for Leaning-Type Arch Bridges

This paper presents a study on the design strategy of leaning-type arch bridges.The main characteristics of leaning-type arch bridges are first introduced;Kunshan Yufeng Bridge is taken as an example to discuss different aspects of a design strategy,which includes self-system optimization,selection of beam length and bridge deck position,and other aspects.This paper can be used as a reference to further improve and develop bridge design.

Update in lean metabolic dysfunction-associated steatotic liver disease

BACKGROUND A new nomenclature consensus has emerged for liver diseases that were previously known as non-alcoholic fatty liver disease(NAFLD)and metabolic dysfunction-associated fatty liver disease(MAFLD).They are now defined as metabolic dysfunction-associated steatotic liver disease(MASLD),which includes cardiometabolic criteria in adults.This condition,extensively studied in obese or overweight patients,constitutes around 30%of the population,with a steady increase worldwide.Lean patients account for approximately 10%-15%of the MASLD population.However,the pathogenesis is complex and is not well understood.AIM To systematically review the literature on the diagnosis,pathogenesis,characteristics,and prognosis in lean MASLD patients and provide an interpretation of these new criteria.METHODS We conducted a comprehensive database search on PubMed and Google Scholar between January 2012 and September 2023,specifically focusing on lean NAFLD,MAFLD,or MASLD patients.We include original articles with patients aged 18 years or older,with a lean body mass index categorized according to the World Health Organization criteria,using a cutoff of 25 kg/m2 for the general population and 23 kg/m2 for the Asian population.RESULTS We include 85 studies in our analysis.Our findings revealed that,for lean NAFLD patients,the prevalence rate varied widely,ranging from 3.8%to 34.1%.The precise pathogenesis mechanism remained elusive,with associations found in genetic variants,epigenetic modifications,and adaptative metabolic response.Common risk factors included metabolic syndrome,hypertension,and type 2 diabetes mellitus,but their prevalence varied based on the comparison group involving lean patients.Regarding non-invasive tools,Fibrosis-4 index outperformed the NAFLD fibrosis score in lean patients.Lifestyle modifications aided in reducing hepatic steatosis and improving cardiometabolic profiles,with some medications showing efficacy to a lesser extent.However,lean NAFLD patients exhibited a worse prognosis compared to the obese or overweight counterpart.CONCLUSION MASLD is a complex disease comprising epigenetic,genetic,and metabolic factors in its pathogenesis.Results vary across populations,gender,and age.Limited data exists on clinical practice guidelines for lean patients.Future studies employing this new nomenclature can contribute to standardizing and generalizing results among lean patients with steatotic liver disease.

基于E-leaning的体育课程网络化教学改革

E-leaning可以优化教育资源、提高学习效率、降低学习成本,对于体育课程教学而言,其灵活便捷的教学方式能够解决"学训矛盾",方便学生自主学习。学习过程记录方便、教学管理和课程资源及时有效的更新,既方便了教学管理,也为体育远程教育创造了条件。E-leaning借助网络跨越了校园空间的限制,给学习者提供了一个全新的学习环境,促使了学校与学习者之间距离的缩短,让学校教育向更广泛的地区辐射。借助E-leaning进行体育精品课程和网络课程整合,开展体育远程教育有助于现有体育精品课程和网络课程的可持续发展,有利于体育教育资源共享,是未来学校体育教育和终身体育教育的重要途径。

《Leaning to do》的启示:把价值观教育与TVET结合起来

这几个模块相关联的核心价值是健康,与自然和谐,达到人的身体、智力、情感、社会和精神的全面发展,实现一种人与人、人与自然环境和谐的状态.要对自身健康负责,对所有形态的生命关爱,成为一个环境的看护者.

新时代学习方法的主流——M-Leaning

在现今知识信息的新时代,随着学习需求的与日俱增,现有的教育体系、模式与人们对教育的需求之间的矛盾也逐渐暴露出来,人们渴望更新、更灵活的学习模式的出现。因此,在任何时间(Anytime)和任何地点(Anywhere)利用手持的、无线移动通讯设备(如手机、PDA)学习任何内容(Anything)的M-leaning的出现是一种必然趋势。

Boosting Lean Electrolyte Lithium-Sulfur Battery Performance with Transition Metals: A Comprehensive Review

Lithium–sulfur(Li–S) batteries have received widespread attention, and lean electrolyte Li–S batteries have attracted additional interest because of their higher energy densities. This review systematically analyzes the effect of the electrolyte-to-sulfur(E/S) ratios on battery energy density and the challenges for sulfur reduction reactions(SRR) under lean electrolyte conditions. Accordingly, we review the use of various polar transition metal sulfur hosts as corresponding solutions to facilitate SRR kinetics at low E/S ratios(< 10 μL mg~(-1)), and the strengths and limitations of different transition metal compounds are presented and discussed from a fundamental perspective. Subsequently, three promising strategies for sulfur hosts that act as anchors and catalysts are proposed to boost lean electrolyte Li–S battery performance. Finally, an outlook is provided to guide future research on high energy density Li–S batteries.

基于Lean管理理念的门诊医疗服务质量提升研究

目的分析在Lean管理理念基础上实施门诊护理管理对门诊医疗服务质量提升的影响。方法选取2021年1月—2022年12月在南京医科大学附属逸夫医院门诊工作的34名护理人员作为研究对象,2021年1—12月实施常规管理(实施前组),2022年1—12月实施Lean管理理念(实施后组)。比较实施前后护理质量、不良事件发生率及交接班质量评分。结果实施后组护理质量评分高于实施前组,差异有统计学意义(P<0.05)。实施后组护理差错、缺陷、纠纷发生率均低于实施前组,差异有统计学意义(P均<0.05)。实施后组护理人员交接班质量评分高于实施后组,差异有统计学意义(P<0.05)。结论Lean管理理念在门诊护理管理中的应用能有效提升护理质量,降低护理差错发生率,减少护患纠纷和投诉发生率,对提升门诊医疗服务质量具有积极意义。

End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.

An End-to-End Machine Learning Framework for Predicting Common Geriatric Diseases

Interdisciplinary applications between information technology and geriatrics have been accelerated in recent years by the advancement of artificial intelligence,cloud computing,and 5G technology,among others.Meanwhile,applications developed by using the above technologies make it possible to predict the risk of age-related diseases early,which can give caregivers time to intervene and reduce the risk,potentially improving the health span of the elderly.However,the popularity of these applications is still limited for several reasons.For example,many older people are unable or unwilling to use mobile applications or devices(e.g.smartphones)because they are relatively complex operations or time-consuming for older people.In this work,we design and implement an end-to-end framework and integrate it with the WeChat platform to make it easily accessible to elders.In this work,multifactorial geriatric assessment data can be collected.Then,stacked machine learning models are trained to assess and predict the incidence of common diseases in the elderly.Experimental results show that our framework can not only provide more accurate prediction(precision:0.8713,recall:0.8212)for several common elderly diseases,but also very low timeconsuming(28.6 s)within a workflow compared to some existing similar applications.

Attention-based neural network for end-to-end music separation

The end-to-end separation algorithm with superior performance in the field of speech separation has not been effectively used in music separation.Moreover,since music signals are often dual channel data with a high sampling rate,how to model longsequence data and make rational use of the relevant information between channels is also an urgent problem to be solved.In order to solve the above problems,the performance of the end-to-end music separation algorithm is enhanced by improving the network structure.Our main contributions include the following:(1)A more reasonable densely connected U-Net is designed to capture the long-term characteristics of music,such as main melody,tone and so on.(2)On this basis,the multi-head attention and dualpath transformer are introduced in the separation module.Channel attention units are applied recursively on the feature map of each layer of the network,enabling the network to perform long-sequence separation.Experimental results show that after the introduction of the channel attention,the performance of the proposed algorithm has a stable improvement compared with the baseline system.On the MUSDB18 dataset,the average score of the separated audio exceeds that of the current best-performing music separation algorithm based on the time-frequency domain(T-F domain).

DTHN: Dual-Transformer Head End-to-End Person Search Network

Person search mainly consists of two submissions,namely Person Detection and Person Re-identification(reID).Existing approaches are primarily based on Faster R-CNN and Convolutional Neural Network(CNN)(e.g.,ResNet).While these structures may detect high-quality bounding boxes,they seem to degrade the performance of re-ID.To address this issue,this paper proposes a Dual-Transformer Head Network(DTHN)for end-to-end person search,which contains two independent Transformer heads,a box head for detecting the bounding box and extracting efficient bounding box feature,and a re-ID head for capturing high-quality re-ID features for the re-ID task.Specifically,after the image goes through the ResNet backbone network to extract features,the Region Proposal Network(RPN)proposes possible bounding boxes.The box head then extracts more efficient features within these bounding boxes for detection.Following this,the re-ID head computes the occluded attention of the features in these bounding boxes and distinguishes them from other persons or backgrounds.Extensive experiments on two widely used benchmark datasets,CUHK-SYSU and PRW,achieve state-of-the-art performance levels,94.9 mAP and 95.3 top-1 scores on the CUHK-SYSU dataset,and 51.6 mAP and 87.6 top-1 scores on the PRW dataset,which demonstrates the advantages of this paper’s approach.The efficiency comparison also shows our method is highly efficient in both time and space.

End-to-End Auto-Encoder System for Deep Residual Shrinkage Network for AWGN Channels

With the rapid development of deep learning methods, the data-driven approach has shown powerful advantages over the model-driven one. In this paper, we propose an end-to-end autoencoder communication system based on Deep Residual Shrinkage Networks (DRSNs), where neural networks (DNNs) are used to implement the coding, decoding, modulation and demodulation functions of the communication system. Our proposed autoencoder communication system can better reduce the signal noise by adding an “attention mechanism” and “soft thresholding” modules and has better performance at various signal-to-noise ratios (SNR). Also, we have shown through comparative experiments that the system can operate at moderate block lengths and support different throughputs. It has been shown to work efficiently in the AWGN channel. Simulation results show that our model has a higher Bit-Error-Rate (BER) gain and greatly improved decoding performance compared to conventional modulation and classical autoencoder systems at various signal-to-noise ratios.

成果导向下“精益”教学资源库建设模式的构建与实践

针对高职专业教学资源库建设中资源与岗位对接度不够、资源利用率不高、学习达成度评价体系不全等问题,提出了一种成果导向下的“精益”建设模式。“精益”建设模式以成果为导向,反向设计资源模块,项目式串联建设可复用颗粒化资源,精准化设置学习目标达成评价体系,教师可以基于学情数据,利用资源库资源搭建在线课程,满足不同学习者的学习需求,实现分层次差异化教学。实践表明该模式能有效服务于专业人才培养需求,高效益建设资源库资源,提高学生学习达成度,更好的服务于教师的教学需求,为职业教育资源库提供了一种参考模式。

回流区稳定燃烧的近熄火特性理论分析

基于火焰稳定在回流区剪切层中的假设,建立了预混火焰近贫燃熄火极限特性的理论分析模型.一旦火焰稳定在剪切层中,由于剪切层同时从两侧卷吸流体,所以进入火焰的流体将是自由流与回流区流体的混合物.如果是贫燃火焰,回流区流体由燃烧产物和多余的氧化剂组成而没有燃料,因此进入剪切层火焰的混合物当量比将低于自由流的当量比.如此一来,即使自由来流是可燃的,剪切层中混合物的当量比也可能超出可燃极限.分析表明,对于回流区稳定的贫燃火焰,进入剪切层火焰的混合物有效当量比将低于自由来流的当量比.根据理论分析建立了近贫燃熄火极限的回流区稳定燃烧模式图,其中涉及4个参数:自由来流当量比、剪切层卷吸比、吹熄极限和再点火极限.分析揭示出4种可能的燃烧模式:超稳定火焰、亚稳定火焰、振荡火焰和熄火.特别地,在参数空间发现了一个振荡区,它可能为近吹熄火焰引入一种新的固有不稳定性机制.在这种机制中,剪切层中的当量比振荡实际上是由燃烧过程本身驱动的,因为火焰的位置/振荡影响着进入回流区的流体成分进而影响剪切层中的当量比.因此,当量比振荡与燃烧过程之间的反馈循环由于周期性的火焰吹熄和闪回/再稳定而得以封闭.

企业—用户互动、敏捷开发与数字产品创新绩效

数字技术的可重新编程性、开放性等特征增强了市场需求的不确定性,企业通过与用户互动进行敏捷开发以提升数字产品创新绩效,成为获取竞争优势的关键。基于精益创业理论和利益相关者理论,深入分析企业—用户互动、敏捷开发与数字产品创新绩效间的作用机理。结果表明:企业—用户互动通过敏捷开发积极影响数字产品创新绩效,且组织创新氛围将增强三者间的作用关系。研究有助于丰富对于数字情境下企业—用户互动创新的理解,推动敏捷开发研究的发展,为解释数字产品创新绩效差异化提供有价值的研究思路,也为数字产品创新绩效的提升提供重要的管理启示。

基于DMAIC模型的妊娠合并脑血管病急诊就诊流程优化研究

目的利用精益六西格玛(lean six sigma,LSS)管理中的界定、测量、分析、改进和控制(define,measure,analyze,improve,control;DMAIC)模型,优化妊娠合并脑血管病患者就医流程,提高患者就医效率,保障母婴安全。方法2021年12月,首都医科大学附属北京天坛医院根据DMAIC模型,对危重孕产妇的就诊流程进行优化:梳理就诊流程,明确到院—医嘱开立、采血—送检、医嘱开立—影像学检查为院内延误的关键环节,对上述环节进行流程跟踪及分析,找出延误原因,并采取改进措施,优化流程。本研究回顾性纳入流程优化前(2019年1月—2021年12月)的妊娠合并脑血管病患者为优化前组,流程优化后(2022年1月—2023年12月)的患者为优化后组。比较两组患者就诊流程中的到院—医嘱开立、采血—送检、医嘱开立—影像学检查、到院—办理住院的时间。结果急诊就诊流程优化后,妊娠合并脑血管病患者的总体就诊效率提高。到院—医嘱开立[24.0(13.5~38.5)min vs.39.0(17.5~98.0)min,P=0.027]、医嘱开立—影像学检查[48.0(10.0~73.0)min vs.65.5(22.7~90.7)min,P=0.025]以及到院—办理入院总时间[120.0(93.0~149.0)min vs.218.0(123.0~382.7)mi n,P<0.001]均较优化前缩短,差异有统计学意义;采血—送检时间有缩短趋势,但优化前后差异无统计学意义。结论使用DMAIC模型能够明确流程优化的关键环节,优化妊娠合并脑血管病患者的急诊就诊流程。