Boehmeria nivea var. strigosa (Urticaceae), a new variety from Southwest China

Boehmeria nivea var.strigosa Zeng Y.Wu&Y.Zhao,a new variety of B.nivea(Urticaceae)from Southwest China,is here described based on evidence from morphology and molecular phylogeny.This new variety is mainly characterized by its green abaxial leaf blade,partly connate stipules,and densely patent strigose hairs on stems and potioles.The phylogenetic analysis based on rbc L,nrDNA and rbc L+nrDNA datasets,revealed that all individuals of B.nivea var.strigosa formed a monophyletic group.The conservation status of B.nivea var.strigosa is assessed as“Near Threatened”(NT)according to IUCN evaluation criteria.The discovery of this new variety is not only crucial for the taxonomy of ramie,but also provides reference for the exploration and utilization of ramie.

A Survey of Lower Limb Rehabilitation Systems and Algorithms Based on Functional Electrical Stimulation

Functional electrical stimulation is a method of repairing a dysfunctional limb in a stroke patient by using low-intensity electrical stimulation.Currently,it is widely used in smart medical treatment for limb rehabilitation in stroke patients.In this paper,the development of FES systems is sorted out and analyzed in a time order.Then,the progress of functional electrical stimulation in the field of rehabilitation is reviewed in details in two aspects,i.e.,system development and algorithm progress.In the system aspect,the development of the first FES control and stimulation system,the core of the lower limb-based neuroprosthesis system and the system based on brain-computer interface are introduced.The algorithm optimization for control strategy is introduced in the algorithm.Asynchronous stimulation to prolong the function time of the lower limbs and a method to improve the robustness of knee joint modeling using neural networks.Representative applications in each of these aspects have been investigated and analyzed.

TMCA-Net: A Compact Convolution Network for Monitoring Upper Limb Rehabilitation

This study proposed a lightweight but high-performance convolu-tion network for accurately classifying five upper limb movements of arm,involving forearm flexion and rotation,arm extension,lumbar touch and no reaction state,aiming to monitoring patient’s rehabilitation process and assist the therapist in elevating patient compliance with treatment.To achieve this goal,a lightweight convolution neural network TMCA-Net(Time Mul-tiscale Channel Attention Convolutional Neural Network)is designed,which combines attention mechanism,uses multi-branched convolution structure to automatically extract feature information at different scales from sensor data,and filters feature information based on attention mechanism.In particular,channel separation convolution is used to replace traditional convolution.This reduces the computational complexity of the model,decouples the convolution operation of the time dimension and the cross-channel feature interaction,which is helpful to the target optimization of feature extraction.TMCA-Net shows excellent performance in the upper limb rehabilitation ges-ture data,achieves 99.11%accuracy and 99.16%F1-score for the classification and recognition of five gestures.Compared with CNN and LSTM network,it achieves 65.62%and 89.98%accuracy in the same task.In addition,on the UCI smartphone public dataset,with the network parameters of one tenth of the current advanced model,the recognition accuracy rate of 95.21%has been achieved,which further proves the light weight and performance characteristics of the model.The clinical significance of this study is to accurately monitor patients’upper limb rehabilitation gesture by an affordable intelligent model as an auxiliary support for therapists’decision-making.

Spreading and Wetting Behaviour of Trisiloxanes

Wetting and spreading processes which involve surfactant solutions are widely used in numerous industrial and practical applications nowadays.The performance of different non-ionic surfactants may vary significantly and so far superspreader solutions show the most promising spreading ability.The addition of trisiloxane surfactants to water was proven to enhance wetting,even on hydrophobic surfaces,on which conventional surfactants seem to have little or no effect.Although these extraordinary surfactants have been extensively studied over recent years,complete understanding of their underlying mechanisms and a suitable mathematical model are still lacking.Here we present a possible explanation for the impressive performance of trisiloxane,which is compared to wetting enhancement of a conventional surfactant.Additionally,we will explain why the hydrophobicity of the surface is a crucial factor for the spreading phenomenon.Light will be also shed on the effect of the pH of the solution to which surfactants are added.Finally,we will investigate long-term effects of the water environment on trisiloxane wetting ability and discuss if ageing may significantly affect their performance.

Investigation of creep behaviours of gypsum specimens with flaws under different uniaxial loads

The aim of this study is to identify the influence of the dip angle of a pre-existing macrocrack on the lifetime and ultimate deformation of rock-like material. Prediction of lifetime has been studied for three groups of specimens under axial static compressive load levels. The specimens were investigated from 65% to 85% of UCS(uniaxial compressive strength) at an interval of 10% of UCS for the groups of specimens with a single modelled open flaw with a dip angle to the loading direction of 30°(first group), at an interval of 5% of UCS increment for the groups of specimens with single(second group), and double sequential open flaws with a dip angle to the loading direction of 60°(third group). This study shows that crack propagation in specimens with a single flaw follows the same sequences. At first, wing cracks appear, and then shear crack develops from the existing wing cracks. Shear cracking is responsible for specimen failure in all three groups. A slip is expected in specimens from the third group which connects two individual modelled open flaws. The moment of the slip is noticed as a characteristic rise in the axial deformation at a constant load level. It is also observed that axial deformation versus time follows the same pattern, irrespective of local geometry. Specimens from the first group exhibit higher axial deformation under different load levels in comparison with the specimens from the second and third groups.

Analysis of the Efficiency-Energy with Regression and Classification in Household Using K-NN

This paper aims to study energy consumption in a house. Home energy managementsystem (HEMS) has become very important, because energy consumption of aresidential sector accounts for a significant amount of total energy consumption.However, a conventional HEMS has some architectural limitations among dimensionalvariables reusability and interoperability. Furthermore, the cost of implementation inHEMS is very expensive, which leads to the disturbance of the spread of a HEMS.Therefore, this study proposes an Internet of Things (IoT) based HEMS with lightweightphotovoltaic (PV) system over dynamic home area networks (DHANs), which enablesthe construction of a HEMS to be scalable reusable and interoperable. The study suggestsa technique for decreasing cost of energy that HEMS is using and various perspectives insystem. The method that proposed is K-NN (K-Nearest Neighbor) which helps us toanalyze the classification and regression datasets. This paper has the result from the datarelevant in October 2018 from some buildings of Nanjing University of InformationScience and Technology.

Modelling of Eruptive Fire Occurrence and Behaviour

Eruptive fires are one of the main causes of human losses in forest fire fighting. The sudden change in fire behaviour due to a fire eruption is extremely dangerous for fire-fighters because it is unpredictable. Very little literature is available to support either modelling or occurrence prediction for this phenomenon. In this study, an unsteady physical model of fire spread is detailed, which describes the initiation and development of eruptive fires with an induced wind sub-model. The latter phenomenon is proposed as the mainspring of fire eruptions. Induced wind is proportional to the rate of spread and the rate of spread is in a non-linear relationship with induced wind. This feedback can converge or diverge depending on the conditions. The model allows both explaining why an eruption can occur and predicting explicitly its occurrence according to meteorological conditions, topographic parameters, fuel bed properties and fire front width. The model is tested by comparing its results to a set of experiments carried out at laboratory scale and during an outdoor wildfire, the Kornati accident.

An Experimental Study of the Thermal Behavior of the Courtyard Style Cave Dwelling

The effect of the courtyard on moderating the hush climate and improving the thermal environment of the courtyard style cave dwelling, and the interaction amongst the ambient, the courtyard and its surrounding cave rooms were investigated. A field measurement was carried out in such a typical dwelling in the winter. The results presented in this paper prove the ability of this dwelling to transform an extreme winter environment outside cave rooms into a better outdoor community space and will be helpful for modern architects to seek a low cost housing solution with concern for sustainable development.

Monkeypox:A comprehensive review of a multifaceted virus

In May 2022,the world witnessed the re-emergence of the zoonotic disease monkeypox.While this was not the first epidemic of this disease,what differentiated the outbreak was the rapid global spread and increase of cases,which led the WHO to declare monkeypox a global health emergency.Although the disease spreads mainly through inadequately cooked meat of various rodent species,this virus also shows droplet,respiratory,sexual,and even vertical transmission.Monkeypox further multiplies in lymphoproliferative organs and presents with a classical smallpox-like rash,fever,headache,and muscle aches.Diagnosis is confirmed with a polymerase-chain-reaction test and is managed largely supportively with possible usage of some antivirals and immunoglobulins.Moreover,some pre-exposure and postexposure prophylactic vaccines have been developed.This paper aims to conduct an in-depth review of the historical epidemics,transmission,pathophysiology,clinical presentation,and management of the monkeypox disease.

Characterizing Human Collective Behaviors During COVID-19 — Hong Kong SAR, China, 2020

Summary What is already known about this topic?People are likely to engage in collective behaviors online during extreme events,such as the coronavirus disease 2019(COVID-19)crisis,to express awareness,take action,and work through concerns.

基于鞍山式铁矿成像光谱的融合算法研究

铁矿资源是我国经济发展和社会进步的物质基础。在铁矿开采过程中,快速精准地确定铁矿品位,对矿山开采决策及经济效益具有重要影响。高光谱成像技术具有影像覆盖范围广、精度高等优势,已广泛应用于矿石分类及成分反演等领域。然而目前高光谱成像传感器的波段范围主要为可见短近红外(Vis-SWIR)和近红外(NIR)两类,且两类数据多为独立获取,缺乏连续性,采用单一数据所建模型的精度往往偏低。因此融合多传感器所获光谱数据,可有效解决单一传感器波段范围小、包含目标特征波段少等问题,提高基于高光谱成像技术的铁矿品位反演精度。使用Pika L与Pika NIR-320高光谱成像仪,分别在Vis-SWIR与NIR两个波段范围内采集鞍山式铁矿的成像光谱数据,提出了基于互信息(MI)的光谱串联融合方法,该方法首先对两组光谱数据进行预处理,然后对处理后的数据进行互信息计算以此对光谱数据进行串联融合。最后分别以Vis-SWIR、NIR以及基于不同波段串联融合的光谱数据为数据源,建立RBF神经网络品位反演模型,并以融合前后光谱数据所建模型的准确性与精度为融合算法有效性的判别指标。结果表明,光谱数据串联融合后所建模型的准确性与精度高于单独使用Vis-SWIR、NIR光谱数据所建模型的准确性与精度。与基于其余波段串联融合的光谱数据相比,在基于互信息计算得出的959.89nm处串联融合后光谱数据所建模型的准确性与精度最高,R2为0.88,RPD为2.97,RMSE为4.464,MAE为3.32。该研究针对多传感器光谱融合提出了一种新思路,对成像光谱技术应用于铁矿品位反演具有现实指导意义。

《我本是高山》的类型建构、上镜头性与伦理化叙事

《我本是高山》沿袭了新主流电影的创作规律和类型化特征,通过经典类型化叙事来塑造人物性格,以一种近乎质朴的影像表达、上镜头性与伦理化叙事,塑造人物形象,表现时空,承载着电影价值观和情感弥合点。影片在网络上引发的巨大争议,则提醒人们反思电影批评对于创作者、对于观众及对于批评本身的重要意义。

基于关联规则提取的外语翻译信息密度检测

为了提高外语翻译信息密度检测的有效性,提出基于关联规则提取的外语翻译信息密度检测方法。构建语料特征信息挖掘的二元模型,并采用关联规则提取方法计算关联规则分布特征参数,完成语料特征的提取。根据语料特征提取结果,计算信息密度检测中心点与信息增量特征的多维尺度信息。计算信息的平均可达密度,将其作为密度检测结果进行输出。实验结果表明,相较于传统对比方法,所提方法可以提高外语翻译信息密度检测精度,检测精度在97%左右。

制造发展的三个范式:制造发展规律的研究

基于对人类社会发展历史和科学技术发展内在规律的认识,本文详细分析了制造发展的三个范式,论证了原子及近原子尺度制造是制造范式III的核心使能技术。文章回顾了国内外各机构对原子级制造研发规划现状,指出我国目前处于原子级制造技术发展的重要战略机遇期,并从设计、材料、加工和检测等角度分析了原子级制造的技术体系内涵;梳理了原子级表面制造、原子级结构制造、原子级测量与表征等领域的研究进展,呈现了当前具有原子级制造能力的部分代表性技术,包括原子级切削、原子级抛光、电化学加工、等离子体原子级加工技术、原子精准操控以及原子分辨测量与表征技术,并对原子级制造战略规划提出发展建议。

基于CNN–LSTM的风电场发电功率迁移预测方法

随着能源消耗的持续增长和全球气候问题的日趋严峻,以风能为代表的清洁能源装机容量正在稳步提升。为更好地消纳风电,需要准确的风电场发电功率预测为配套设施建设和未来规划制定提供有效依据。针对在缺少风电历史运行数据时预测精度较低的问题,提出一种基于卷积神经网络–长短期记忆神经网络(CNN–LSTM)的规划阶段风电场发电功率预测模型。首先,基于参考电站历史数据提取风速–风电功率实测数据点,采用3次样条插值进行风电功率曲线建模。然后,采用K–means聚类算法,根据风速–风电功率的特性关系划分参考风电场的区域类别。综合考虑风电功率与多维气象因素的特征关系和功率的时序特性,构建CNN–LSTM预测模型,提出基于功率曲线的预测结果修正方法。最后,基于某地风电场实际数据进行算例分析,并与使用标准功率曲线和未进行修正时的预测结果进行对比分析。结果表明:基于风速–风电功率特性的风电场聚类可以实现参考风电场的优化识别;所提模型预测结果优于传统标准功率曲线预测方法,基于功率曲线的修正方法进一步提升了预测效果。基于深度学习算法的规划阶段风电场发电功率迁移预测模型综合考虑了风力发电特性和多维环境因素,其有效性得到了验证,可以为提高规划阶段风电场发电功率的预测精度提供新思路。

办人民满意的学校教育路径研究

办人民满意的学校教育,是针对青少年身心健康方面存在的各种不利因素,结合我国社会经济发展需要提出的新时代教育理念。办人民满意的学校教育,需要深入领会教育的本质,明确学校教育的责任与使命;准确把握人民对学校教育的需求和期望,深入剖析影响人民对学校教育满意度的因素。在此基础上,正确认识和理解学校教育制度的制定与实施,坚持高等学校教育的差异化发展,规范学校教育信息的传递并确保信息的客观、真实。

相对剥夺感与大学生网络欺凌的关系:道德推脱的中介作用与道德认同的调节作用

为探讨相对剥夺感与大学生网络欺凌的关系及其作用机制,本研究构建了一个有调节的中介模型,重点考察道德推脱在二者关系中的中介作用以及道德认同的调节作用。以575名大学生为被试,采用问卷法对其相对剥夺感、道德推脱、道德认同和网络欺凌进行调查。结果表明:(1)在控制性别、年龄、主观阶层等级后,相对剥夺感能够显著正向预测大学生网络欺凌;(2)相对剥夺感通过道德推脱的中介作用预测网络欺凌;(3)道德推脱的中介作用受到道德认同的调节,即相较于高道德认同,低道德认同水平的大学生的道德推脱对网络欺凌的预测作用更大。研究结果有助于揭示大学生网络欺凌的形成机制,对大学生网络欺凌的干预具有启示意义。

面向采购文件的跨模态图片文本命名实体识别

智慧供应链的数智化采购环节能够提高采购工作效率,节省大量人力成本。采购文件中包括大量证照资质等文件,针对其中图片文本中文字排版参差不齐、扫描图像不清晰等问题,设计了基于深度学习的端到端跨模态命名实体识别模型O2V2BLC(OCR-Vector-Bi-LSTM-CRF),从图片文本中识别命名实体。该模型针对光学字符识别技术识别出的图片文本字符,定义连续文本字符边界,将边界内每个文本字符映射为向量,设计双向长短期记忆(Bi-LSTM)网络捕获边界内字符序列的上下文语义,计算字符状态分数矩阵,并通过条件随机场约束字符标记序列规则,获得全局最优标记序列。针对训练集计算命名实体预测误差,动态优化O2V2BLC模型的参数,实现命名实体识别。将该方法应用于采购文件资质类型等图片文本数据,能够有效识别图片中的投标单位、专家姓名、专业名称等命名实体,与条件随机场、隐马尔可夫算法、Bert-Bi-LSTM-CRF模型进行对比,显著提高了实体识别准确率,为智慧供应链的数智化采购提供支持。

交通基础设施建设与企业信息披露质量——基于市场化程度和投资者实地调研的中介效应分析

本文基于高铁开通这一准自然实验,使用2012~2022年我国深圳证券交易所A股上市公司作为研究样本,构建双重差分模型探究高铁开通对上市公司信息披露质量的影响。研究表明,高铁开通能够显著提高我国上市公司信息披露质量,该结论在控制了内生性问题后依然成立。同时,本文发掘了高铁开通影响信息披露质量的作用机理,结果显示,市场化程度的提升和投资者实地调研次数的增加是高铁开通提高信息披露质量的内在机制。本文研究为交通基础设施建设在促进信息传播和推动资本市场有效性方面提供了微观经验证据。

ChatGPT在幼儿教育领域中的潜力:支持职前幼儿园教师教学活动设计的研究

ChatGPT的问题导向生成能力与幼儿园教师以儿童为本生成活动的能力相似,使职前幼儿园教师专业发展的机遇和风险并存。采用混合研究方法,探讨职前幼儿教师对同一个教学目标下教科书活动案例和ChatGPT生成的活动的质量评估。研究发现:1)ChatGPT生成的活动在目标契合度和主体体验方面获得较高评价,但在实施可行性和语言表达方面受到质疑;2)ChatGPT具有支持职前幼儿园教师创建活动设计的能力,其所生成的活动质量在总体上得到了认可并且教师的主观建构能力也在对其质量评价中得以体现;3)为了最大程度发挥ChatGPT支持职前幼儿园教师创设活动计划的潜力,未来职前幼儿园教师教育应走向“问学”和教学实践的有机结合。