教育建筑的建筑教育——以本科四年级学校设计教学实践为例

从东南大学建筑学院本科四年级教育建筑设计系列课程的教学实践出发,对教育制度和空间模式相互关系展开深入研究。教育建筑设计系列课程通过立足社会问题的题目设置,职业建筑师的介入以及交叉学科的设计研究来指导教学实践,研究与总结其成果,对于发展更具有创新意识及职业性的建筑教育有重要的参考和借鉴意义。

具有时滞效应的air2stream河流水温模型及应用研究

高精度河流水温模型对于深入了解水温的时空变化特征和河流生态修复具有重要意义。基于数据驱动的air2stream模型在保证预测精度的同时,避免了计算的复杂性,已成为河流水温模拟常用的模型。由于水的热惯性及水文条件等的影响,河流水温变化往往显著滞后于气温变化,而air2stream原模型并未考虑滞后效应,导致该模型在流量未知情况下实际精度偏低。为解决该问题,采用气温-水温皮尔逊相关系数计算时滞天数,构建具有时滞的air2stream新模型,进一步根据长江中下游地区两个监测站的多年实测数据验证新模型的有效性和稳定性。结果表明:新模型在不引进额外观测数据的条件下具有更高精度且性能更稳定。相比原模型,在两个监测站新模型的均方根误差分别降低约4.29%和5.85%。新模型具有精度高、水文要素需求少的特点,可为长江中下游的水环境影响评价和生态保护提供依据。

Physical Layer Encryption of OFDM-PON Based on Quantum Noise Stream Cipher with Polar Code

Orthogonal frequency division multiplexing passive optical network(OFDM-PON) has superior anti-dispersion property to operate in the C-band of fiber for increased optical power budget. However,the downlink broadcast exposes the physical layer vulnerable to the threat of illegal eavesdropping. Quantum noise stream cipher(QNSC) is a classic physical layer encryption method and well compatible with the OFDM-PON. Meanwhile, it is indispensable to exploit forward error correction(FEC) to control errors in data transmission. However, when QNSC and FEC are jointly coded, the redundant information becomes heavier and thus the code rate of the transmitted signal will be largely reduced. In this work, we propose a physical layer encryption scheme based on polar-code-assisted QNSC. In order to improve the code rate and security of the transmitted signal, we exploit chaotic sequences to yield the redundant bits and utilize the redundant information of the polar code to generate the higher-order encrypted signal in the QNSC scheme with the operation of the interleaver.We experimentally demonstrate the encrypted 16/64-QAM, 16/256-QAM, 16/1024-QAM, 16/4096-QAM QNSC signals transmitted over 30-km standard single mode fiber. For the transmitted 16/4096-QAM QNSC signal, compared with the conventional QNSC method, the proposed method increases the code rate from 0.1 to 0.32 with enhanced security.

基于ZYNQ的高精度震动探测系统设计

在地下浅层爆炸震动探测中,测试环境噪声大,并且爆炸地震波信号频率较高,而常规的地震探测仪器底噪较大、采样率较低,难以满足爆炸震动探测需求,因此,本文设计了一种基于ZYNQ的震动探测系统。首先,采用高精度传感器ADXL354B获取爆炸震动信号,并设计调理电路滤除高频噪声,提高信号获取精度,同时采用高精度、高采样率的24位模/数转换器AD7768进行数据转换;然后,在ZYNQ芯片的PL部分进行逻辑设计,在PS部分使用驱动分离的方式定制Linux系统进行数据处理和传输;最后,搭建半实物仿真平台对震动探测节点进行功能验证。实验结果表明,该系统在采样率与系统噪声方面优于国内同类地震勘探仪器,最高能够以256 ksps、24位、八通道同步采集加速度信号,并实时传输至上位机,该成果在地下浅层震动探测领域具有较高的应用价值。

Dynamics of iron and aluminum storages in a subtropical forest headwater stream

The forest headwater streams are important hubs for connecting terrestrial and aquatic ecosystems,with plant litter and sediments as the major carriers for material migrations;however,until now we knew little about the dynamics of trace elements such as iron(Fe)and aluminum(Al)in forest headwater streams.Here,we quantitatively identified the spatiotemporal dynamics of Fe and Al storages in plant litter and sediments and their influencing factors in a subtropical forest headwater stream,and assessed the potential pollution risk.The results showed that:(1)the mean concentrations of Fe and Al in plant litter(sediments)were 5.48 and 8.46(7.39 and 47.47)g·kg^(-1),and the mean storages of Fe and Al in plant litter(sediments)were 0.26 and 0.43(749.04 and 5030.90)g·m^(-2),respectively;(2)the storages of Fe and Al in plant litter and sediments significantly fluctuated from January to December,and showed a decreasing pattern from the source to mouth;and(3)storages of Fe and Al had no significant correlation with riparian forest type and the present of tributary and the Fe and Al storages in plant litter were mainly affected by water temperature and water alkalinity,and their storages in sediments were mainly affected by water temperature and frequency of rainfall;and(4)there were no anthropogenic pollution in Fe and Al in the forest headwater stream.Our study revealed the primary factors of concentrations and storages of Fe and Al in plant litter and sediments in a forest headwater stream,which will improve our understanding of the role of headwater streams in forest nutrient storage and cycling along with hydrological processes.

Enhancing Geological Understanding and Identifying Gold Anomalies in the Ailaoshan Orogen

The Ailaoshan Orogen in the southeastern Tibet Plateau,situated between the Yangtze and Simao blocks,underwent a complex structural,magmatic,and metamorphic evolution resulting in different tectonic subzones with varying structural lineaments and elemental concentrations.These elements can conceal or reduce anomalies due to the mutual effect between different anomaly areas.Dividing the whole zone into subzones based on tectonic settings,ore cluster areas,or sample catchment basins(Scb),geochemical and structural anomalies associated with gold(Au)mineralization have been identified utilizing mean plus twice standard deviations(Mean+2STD),factor analysis(FA),concentration-area(CA)modeling of stream sediment geochemical data,and lineament density in both the Ailaoshan Orogen and the individual subzones.The FA in the divided 98 Scbs with 6 Scbs containing Au deposits can roughly ascertain unknown rock types,identify specific element associations of known rocks and discern the porphyry or skarn-type Au mineralization.Compared with methods of Mean+2STD and C-A model of data in the whole orogen,which mistake the anomalies as background or act the background as anomalies,the combined methods of FA and C-A in the separate subzones or Scbs works well in regional metallogenic potential analysis.Mapping of lineament densities with a 10-km circle diameter is not suitable to locate Au deposits because of the delineated large areas of medium-high lineament density.In contrast,the use of circle diameters of 1.3 km or 1.7 km in the ore cluster scale delineates areas with a higher concentration of lineament density,consistent with the locations of known Au deposits.By analyzing the map of faults and Au anomalies,two potential prospecting targets,Scbs 1 and 63 with a sandstone as a potential host rock for Au,have been identified in the Ailaoshan Orogen.The use of combined methods in the divided subzones proved to be more effective in improving geological understanding and identifying mineralization anomalies associated with Au,rather than analyzing the entire large area.

An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data

Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.

The viscous strip approach to simplify the calculation of the surface acoustic wave generated streaming

In recent decades,the importance of surface acoustic waves,as a biocompatible tool to integrate with microfluidics,has been proven in various medical and biological applications.The numerical modeling of acoustic streaming caused by surface acoustic waves in microchannels requires the effect of viscosity to be considered in the equations which complicates the solution.In this paper,it is shown that the major contribution of viscosity and the horizontal component of actuation is concentrated in a narrow region alongside the actuation boundary.Since the inviscid equations are considerably easier to solve,a division into the viscous and inviscid domains would alleviate the computational load significantly.The particles'traces calculated by this approximation are excellently alongside their counterparts from the completely viscous model.It is also shown that the optimum thickness for the viscous strip is about 9-fold the acoustic boundary layer thickness for various flow patterns and amplitudes of actuation.

Features of sampling stream sediments of large river valleys under cryolithogenesis conditions in the Balygychan-Sugoy trough,North-East of Russia

Comprehensive research has been implemented to raise the efficiency of the geochemical survey of stream sediments(SSs)that formed under the cryolithogenesis conditions.The authors analysed the composition,structure and specific features of the formation of exogenous anomalous geochemical fields(AGFs)identified through SSs of large river valleys of IV order.In our case,these were the valleys of Maly Ken,Ken and Tap Rivers.These rivers are located in the central and southern parts of the Balygychan-Sugoy trough enclosed in the Magadan region,North-East of Russia.The authors proposed a new technique to sample loose alluvium of SSs in the large river valleys along the profiles.The profiles were located across the valleys.The AGFs of Au,Ag,Pb,Zn,Sn,Bi,Mo and W were studied.Correlations between elements have been established.These elements are the main indicator elements of Au-Ag,Ag-Pb,Sn-Ag,Mo-W and Sn-W mineralization occurring on the sites under study.The results obtained were compared with the results of geochemical surveys of SSs.It is concluded that the AGFs recognized along the profiles reflect the composition and structure of eroded and drained ore zones,uncover completely and precisely the pattern of element distribution in loose sediments of large water flows.The alluvium fraction<0.25 mm seems to be most significant in a practical sense,as it concentrated numerous ore elements.Sampling of this fraction in the river valleys of IV order does not cause any difficulty,for this kind of material is plentiful.The developed technique of alluvium sampling within large river valleys is efficient in searching for diverse mineralization at all stages of prognostic prospecting.It is applicable for geochemical survey of SSs performed at different scales both in the North-East of Russia,as well as other regions with similar climatic conditions,where the SSs are formed under the cryolithogenesis conditions.

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.

NFA:A neural factorization autoencoder based online telephony fraud detection

The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal activity on the network.To reduce these losses,a new fraud detection approach is required.Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic.Developing an effective strategy to combat fraud has become challenging.Although much effort has been made to detect fraud,most existing methods are designed for batch processing,not real-time detection.To solve this problem,we propose an online fraud detection model using a Neural Factorization Autoencoder(NFA),which analyzes customer calling patterns to detect fraudulent calls.The model employs Neural Factorization Machines(NFM)and an Autoencoder(AE)to model calling patterns and a memory module to adapt to changing customer behaviour.We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods.Our results show that our approach outperforms the baselines,with an AUC of 91.06%,a TPR of 91.89%,an FPR of 14.76%,and an F1-score of 95.45%.These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

Digital twin-and extended reality-based telepresence for collaborative robot programming in the 6G perspective

In the context of Industry 4.0,a paradigm shift from traditional industrial manipulators to Collaborative Robots(CRs)is ongoing,with the latter serving ever more closely humans as auxiliary tools in many production processes.In this scenario,continuous technological advancements offer new opportunities for further innovating robotics and other areas of next-generation industry.For example,6G could play a prominent role due to its human-centric view of the industrial domains.In particular,its expected dependability features will pave the way for new applications exploiting highly effective Digital Twin(DT)-and eXtended Reality(XR)-based telepresence.In this work,a novel application for the above technologies allowing two distant users to collaborate in the programming of a CR is proposed.The approach encompasses demanding data flows(e.g.,point cloud-based streaming of collaborating users and robotic environment),with network latency and bandwidth constraints.Results obtained by analyzing this approach from the viewpoint of network requirements in a setup designed to emulate 6G connectivity indicate that the expected performance of forthcoming mobile networks will make it fully feasible in principle.

Distribution of antioxidants and phenolic compounds in flour milling fractions from hard red winter wheat

Mature wheat kernels contain three main parts:endosperm,bran,and germ.Flour milling results in multiple streams that are chemically different;however,the distribution of antioxidants and phenolic compounds has not been well documented in terms of conventional milling by-product streams.In this study,multiple analytical methods were used to investigate antioxidant activity and phenolic compound compositions of hard red winter wheat(whole ground wheat),the parts of a wheat kernel(bran,flour,germ),and wheat by-product streams(mill feed,red dog,shorts)for the first time.For each mill stream,phenolic compounds(total,flavonoid,and anthocyanin contents)were determined and antioxidant activities were evaluated with 1,1-diphenyl-2-picrylhydrazyl(DPPH)radical-scavenging activity,ferric reducing/antioxidant power(FRAP),and total antioxidant capacity assays.Significant differences(P<0.05)were observed in phenolic concentrations among fractions of bran,flour,and germ milled from the same kernels and noted that germ accounts for the majority of antioxidant properties,whereas bran contains a substantial portion of phenolic compounds and anthocyanins.Mill feed was high in phenolic content(5.29 mg FAE/g),total antioxidant capacity(866 mg/g),and antioxidant activity(up to 75% DPPH inhibition and 20.26μmol FeSO_(4)/g).The comprehensive information on distribution of antioxidants and phenolic compounds provides insights for future human consumption of commonly produced co-products from flour milling,and for selecting and using different milling fractions to make foods with improved nutritional properties.

Assessment of sediment connectivity using modelling and field-based approaches in the Slavíč River catchment(MoravskoslezskéBeskydy Mts,Czech Republic)

Mountain streams act as conveyors of sediments within the river continuum,where the physical transport of sediments between river reaches through the catchment or between individual parts(e.g.,between hillslopes and channels)of the catchment is assumed.This study focused on sediment connectivity analysis in the SlavíčRiver catchment in the MoravskoslezskéBeskydy Mts in the eastern part of the Czech Republic.The connectivity index and connectivity index target modelling were combined with an analysis of anthropogenic interventions.Additionally,field mapping,grain size of bed sediments and stream power analysis were used to obtain information about connectivity in the catchment.Based on the analysis and obtained results,terrain topography is the current main driving factor affecting the connectivity of sediment movement in the SlavíčRiver catchment.However,the modelling provided valuable information about high sediment connectivity despite different recent land use conditions(highly forested area of the catchment)than those in historical times from the 16th to 19th centuries when the SlavíčRiver catchment was highly deforested and sediment connectivity was probably higher.The analysis of anthropogenic interventions,field mapping,grain size of bed sediments and stream power analysis revealed more deceleration of sediment movement through the catchment,decreased sediment connectivity with bed erosion,and gradual river channel process transformation in some reaches.Field mapping has identified various natural formations and human-induced changes impacting the longitudinal and lateral connectivity in the SlavíčRiver.For instance,embankments along 48%of the river's length,both on the right and left banks,significantly hinder lateral sediment supply to the channel.Stream power index analysis indicates increased energy levels in the flowing water in the river's upper reaches(up to 404.8 W m^(-2)).This high energy is also observed in certain downstream sections(up to 337.6 W m^(-2)),where it is influenced by human activities.These conditions lead to intensified erosion processes,playing a crucial role in sediment connectivity.Similar observations were described in recent studies that pointed out the long-term human interventions on many streams draining European mountains,where a decrease in sediment connectivity in these streams is linked with sediment deficits and the transformation of processes forming channels.

Chemical simulation teaching system based on virtual reality and gesture interaction

Background Most existing chemical experiment teaching systems lack solid immersive experiences,making it difficult to engage students.To address these challenges,we propose a chemical simulation teaching system based on virtual reality and gesture interaction.Methods The parameters of the models were obtained through actual investigation,whereby Blender and 3DS MAX were used to model and import these parameters into a physics engine.By establishing an interface for the physics engine,gesture interaction hardware,and virtual reality(VR)helmet,a highly realistic chemical experiment environment was created.Using code script logic,particle systems,as well as other systems,chemical phenomena were simulated.Furthermore,we created an online teaching platform using streaming media and databases to address the problems of distance teaching.Results The proposed system was evaluated against two mainstream products in the market.In the experiments,the proposed system outperformed the other products in terms of fidelity and practicality.Conclusions The proposed system which offers realistic simulations and practicability,can help improve the high school chemistry experimental education.

Heat and Mass Transfer on Non-Newtonian Peristaltic Flow in a Channel in Presence of Magnetic Field: Analytical and Numerical Analysis

A study has been arranged to investigate the flow of non-Newtonian fluid in a vertical asymmetrical channel using peristalsis. The porous medium allows the electrically conductive fluid to flow in the channel, while a uniform magnetic field is applied perpendicular to the flow direction. The analysis takes into account the combined influence of heat and mass transfer, including the effects of Soret and Dufour. The flow’s non-Newtonian behavior is characterized using a Casson rheological model. The fluid flow equations are examined within a wave frame of reference that has a wave velocity. The analytic solution is examined using long wavelengths and a small Reynolds number assumption. The stream function, temperature, concentration and heat transfer coefficient expressions are derived. The bvp4c function from MATLAB has been used to numerically solve the transformed equations. The flow characteristics have been analyzed using graphs to demonstrate the impacts of different parameters.

基于STREAM教育理念的初中物理实验拓展的内涵和价值研究

基于初中物理实验教学现状,结合初中物理实验教学对STREAM教育进行本土化解读,探索基于STREAM教育理念的初中物理实验拓展的内涵和价值。

Domestic Films and TV Dramas “Shining” Overseas

In recent years,Chinese films and TV dramas“going global”has become an important cultural phenomenon.The films and TV drams are released not only in Asia and Africa,but also in the European and American markets through international streaming platforms.The genres of domestic TV series“going abroad”are becoming increasingly diverse,and the channels and platforms are also becoming more diversified.

Newegg Is Taking Various Measures to Help Chinese Businesses Go Abroad Digitally—Interview with Anthony Chow,CEO of Newegg Commerce

The CES 2024(International Consumer Electronics Show)was held this January in Las Vegas,USA.Newegg Commerce,Inc.,a global well-known e-commerce platform for science and technology companies,officially collaborated with TikTok to report at the CES 2024 and introduced new representative products from high-quality Chinese tech brands to both on-site visitors and online followers via TikTok’s live streaming service,helping these tech products attract a lot of fans.