Cu_(3)Mo_(2)O_(9)/Mn_(0.3)Cd_(0.7)S S型异质结的构筑及其光解水产氢性能研究

采用超声辅助研磨煅烧法制备了Cu_(3)Mo_(2)O_(9)/Mn_(0.3)Cd_(0.7)S S型异质结光催化剂,通过X射线粉末衍射仪、扫描电子显微镜、X射线光电子能谱、固体紫外漫反射等测试技术表征了样品的物相、形貌、化学元素组成以及光吸收能力等物理化学性质。在可见光加近红外光照射下进行了光催化析氢性能测试,结果表明,Cu_(3)Mo_(2)O_(9)/Mn_(0.3)Cd_(0.7)S复合材料的光解水产氢性能均优于纯相的Mn_(0.3)Cd_(0.7)S和Cu_(3)Mo_(2)O_(9),其中Cu 3Mo 2O 9的质量分数为1%的Cu_(3)Mo_(2)O_(9)/Mn_(0.3)Cd_(0.7)S显示出最佳的产氢速率(1.554 mmol·h^(-1)·g^(-1)),是Mn 0.3 Cd_(0.7)S的5.5倍。且经过四次循环实验后仍保持较好的光催化活性。此外,根据电化学测试以及红外热成像结果提出了合理的机理,Cu 3Mo 2O 9的光热效应与S型异质结的协同作用是Cu_(3)Mo_(2)O_(9)/Mn_(0.3)Cd_(0.7)S光催化活性提高的关键。

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.

Challenging situation of coronary artery anomaly associated with ischemia and/or risk of sudden death

Coronary artery anomaly is known as one of the causes of angina pectoris and sudden death and is an important clinical entity that cannot be overlooked.The incidence of coronary artery anomalies is as low as 1%-2%of the general population,even when the various types are combined.Coronary anomalies are practically challenging when the left and right coronary ostium are not found around their normal positions during coronary angiography with a catheter.If there is atherosclerotic stenosis of the coronary artery with an anomaly and percutaneous coronary intervention(PCI)is required,the suitability of the guiding catheter at the entrance and the adequate back up force of the guiding catheter are issues.The level of PCI risk itself should also be considered on a caseby-case basis.In this case,emission computed tomography in the R-1 subtype single coronary artery proved that ischemia occurred in an area where the coronary artery was not visible to the naked eye.Meticulous follow-up would be crucial,because sudden death may occur in single coronary arteries.To prevent atherosclerosis with full efforts is also important,as the authors indicated admirably.

A Security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks

Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misreporting of normal data,which will impact the normal operation of IoT.To mitigate the impact caused by the high false positive rate of ADS,this paper proposes an ADS management scheme for clustered IoT.First,we model the data transmission and anomaly detection in clustered IoT.Then,the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device.In the presence of a high false positive rate in ADSs,to deal with the trade-off between the security and availability of data,we develop a linear programming model referred to as a security trade-off(ST)model.Next,we develop an analysis framework for the ST model,and solve the ST model on an IoT simulation platform.Last,we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis.Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.

Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.

Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.

Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

In situ formation of multiple catalysts for enhancing the hydrogen storage of MgH_(2) by adding porous Ni_(3)ZnC_(0.7)/Ni loaded carbon nanotubes microspheres

MgH_(2) is considered one of the most promising hydrogen storage materials because of its safety,high efficiency,high hydrogen storage quantity and low cost characteristics.But some shortcomings are still existed:high operating temperature and poor hydrogen absorption dynamics,which limit its application.Porous Ni_(3)ZnC_(0.7)/Ni loaded carbon nanotubes microspheres(NZC/Ni@CNT)is prepared by facile filtration and calcination method.Then the different amount of NZC/Ni@CNT(2.5,5.0 and 7.5 wt%)is added to the MgH_(2) by ball milling.Among the three samples with different amount of NZC/Ni@CNT(2.5,5.0 and 7.5 wt%),the MgH_(2)-5 wt%NZC/Ni@CNT composite exhibits the best hydrogen storage performances.After testing,the MgH_(2)-5 wt%NZC/Ni@CNT begins to release hydrogen at around 110℃ and hydrogen absorption capacity reaches 2.34 wt%H_(2) at 80℃ within 60 min.Moreover,the composite can release about 5.36 wt%H_(2) at 300℃.In addition,hydrogen absorption and desorption activation energies of the MgH_(2)-5 wt%NZC/Ni@CNT composite are reduced to 37.28 and 84.22 KJ/mol H_(2),respectively.The in situ generated Mg_(2)NiH_(4)/Mg_(2)Ni can serve as a"hydrogen pump"that plays the main role in providing more activation sites and hydrogen diffusion channels which promotes H_(2) dissociation during hydrogen absorption process.In addition,the evenly dispersed Zn and MgZn2 in Mg and MgH_(2) could provide sites for Mg/MgH_(2) nucleation and hydrogen diffusion channel.This attempt clearly proved that the bimetallic carbide Ni_(3)ZnC_(0.7) is a effective additive for the hydrogen storage performances modification of MgH_(2),and the facile synthesis of the Ni_(3)ZnC_(0.7)/Ni@CNT can provide directions of better designing high performance carbide catalysts for improving MgH_(2).

Investigation of Potential Factors on South Atlantic Magnetic Anomaly

The first part of this investigation analyzes the deep earthquake occurrences in Nazca subducting under South America. The depth taken is to get information about possible influences from the unknown materials and formations under the crust. The results revealed the presence of malleable material, which is unbreakable and, therefore, unable to trigger earthquakes. The structure of those elements is diamagnetic, attracting ionized particles from the Van Allen belt region in the ionosphere. The charged particles travel towards Earth’s surface, enhanced during the geomagnetic storms. The South Atlantic Magnetic Anomaly (SAMA) found that the deformation suffered by the anomaly moving from South Africa to South America is, possibly due to a bulge of unknown flexible material buried underneath the oceanic and continental crust. The continental part is strengthening in weakness because the background also has a high amount of diamagnetic material in this region, and it would not happen over the Atlantic Ocean, where part of the deformation is placed.

Investigation of helicity-dependent photocurrent of surface states in(Bi_(0.7)Sb_(0.3))_(2)Te_(3)nanoplate

Helicity-dependent photocurrent(HDPC)of the surface states in a high-quality topological insulator(Bi_(0.7)Sb_(0.3))_(2)Te_(3)nanoplate grown by chemical vapor deposition(CVD)is investigated.By investigating the angle-dependent HDPC,it is found that the HDPC is mainly contributed by the circular photogalvanic effect(CPGE)current when the incident plane is perpendicular to the connection of the two contacts,whereas the circular photon drag effect(CPDE)dominates the HDPC when the incident plane is parallel to the connection of the two contacts.In addition,the CPGE of the(Bi_(0.7)Sb_(0.3))_(2)Te_(3)nanoplate is regulated by temperature,light power,excitation wavelength,the source–drain and ionic liquid top-gate voltages,and the regulation mechanisms are discussed.It is demonstrated that(Bi_(0.7)Sb_(0.3))_(2)Te_(3)nanoplates may provide a good platform for novel opto-spintronics devices.

Electron G-Factor Anomaly and the Charge Thickness

The electron g-factor relates the magnetic moment to the spin angular momentum. It was originally theoretically calculated to have a value of exactly 2. Experiments yielded a value of 2 plus a very small fraction, referred to as the g-factor anomaly. This anomaly has been calculated theoretically as a power series of the fine structure constant. This document shows that the anomaly is the result of the electron charge thickness. If the thickness were to be zero, g = 2 exactly, and there would be no anomaly. As the thickness increases, the anomaly increases. An equation relating the g-factor and the surface charge thickness is presented. The thickness is calculated to be 0.23% of the electron radius. The cause of the anomaly is very clear, but why is the charge thickness greater than zero? Using the model of the interior structure of the electron previously proposed by the author, it is shown that the non-zero thickness, and thus the g-factor anomaly, are due to the proposed positive charge at the electron center and compressibility of the electron material. The author’s previous publication proposes a theory for splitting the electron into three equal charges when subjected to a strong external magnetic field. That theory is revised in this document, and the result is an error reduced to 0.4% in the polar angle where the splits occur and a reduced magnetic field required to cause the splits.

The 0.7-Anomaly in Quantum Point Contact;Many-Body or Single-Electron Effect?

Apart from usual quantization steps on the ballistic conductance of quasi-one-dimensional conductor, an additional plateau-like feature appears at a fraction of about 0.7 below the first conductance step in GaAs-based quantum point contacts (QPCs). Despite a tremendous amount of research on this anomalous feature, its origin remains still unclear. Here, a unique model of this anomaly is proposed relying on fundamental principles of quantum mechanics. It is noticed that just after opening a quasi-1D conducting channel in the QPC a single electron travels the channel at a time, and such electron can be—in principle—observed. The act of observation destroys superposition of spin states, in which the electron otherwise exists, and this suppresses their quantum interference. It is shown that then the QPC-conductance is reduced by a factor of 0.74. “Visibility” of electron is enhanced if the electron spends some time in the channel due to resonant transmission. Electron’s resonance can also explain an unusual temperature behavior of the anomaly as well as its recently discovered feature: oscillatory modulation as a function of the channel length and electrostatic potential. A recipe for experimental verification of the model is given.

面向水平GAA内侧墙模块的干法Si_(0.7)Ge_(0.3)选择性刻蚀研究

针对环栅(Gate-All-Around,GAA)Si_(0.7)Ge_(0.3)的内侧墙(Inner spacer)空腔刻蚀难以精确控制尺寸和形貌的问题,本研究基于常规电感耦合等离子体(inductively coupled plasma,ICP)刻蚀设备,采用CF_(4)/O_(2)/He混合气体进行Si_(0.7)Ge_(0.3)干法各向同性选择性刻蚀实验,探究了包括激励射频源(source radio frequency,SRF)功率、气压、刻蚀前侧壁清洗工艺等因素对刻蚀结果的影响与机制。研究结果表明,SRF对刻蚀深度的影响是存在线性区与准饱和区的,气压与刻蚀深度在实验区间内呈二次函数关系,稀释的氢氟酸(Diluted HF,DHF)与O_(3)交替清洗相对单一的DHF清洗方案在界面钝化层的去除与刻蚀形貌的控制上有更优的表现。经工艺方案优化,最终获得良好的工艺结果:刻蚀精度达到了0.61 nm/s,最优粗糙度Rq为0.101 nm,刻蚀轮廓矩形度高(d/t~83.3%)。本研究为内侧墙空腔刻蚀提供了一种解决方案。

WO_(3)/BiOCl_(0.7)I_(0.3)光催化剂的制备及其光催化降解机理

通过简单的煅烧法和原位沉淀法制备了WO_(3)/BiOCl_(0.7)I_(0.3)复合光催化材料,利用X射线衍射(XRD)、扫描电子显微镜(SEM)、X射线能谱(XPS)、紫外可见漫反射光谱(UV-Vis DRS)等表征手段对合成材料的微观形貌、化学组成等进行表征。并通过可见光催化降解20mg/L盐酸四环素来评价材料的光催化性能,结果显示,WO_(3)/BiOCl_(0.7)I_(0.3)复合材料较单一的BiOCl_(0.7)I_(0.3)和WO_(3)具有更优的光催化性能,其中W与Bi摩尔比为1∶15时,复合材料具有最高的光降解率,可见光下60min达到最高值93.84%,且四次循环试验后仍具有较好的光催化活性。对自由基捕获试验结果及电子自旋共振光谱进行分析,明确了h^(+)和·O_(2)^(-)为光催化主要活性物质,提出WO_(3)/BiOCl_(0.7)I_(0.3)复合材料Z型异质结构的光催化作用机理。研究为新型可见光光催化材料的开发提供了新思路,为抗生素类废水的处理提供了新途径。

Scanning gate imaging of quantum point contacts and the origin of the 0.7 anomaly

出现在传导力 G 0.7 睯物 ? 的异常运输特征的起源 ???????????? 瀠潲慰慧楴湯景 ? 慮潮楷敲？？

P元素对Sn-0.3Ag-0.7Cu钎料抗氧化性、润湿性以及显微组织的影响

为改善Sn-0.3Ag-0.7Cu钎料抗氧化性和润湿性差的问题,在钎料中添加了P元素,研究了微量P元素对Sn-0.3Ag-0.7Cu钎料抗氧化性、润湿性以及显微组织的影响。制备了含不同质量分数P元素的Sn-0.3Ag-0.7Cu-xP(x=0%,0.005%,0.010%,0.015%,0.020%)钎料。以实验的方式测试了钎料的抗氧化性以及润湿性,并使用扫描电镜(SEM)和能谱仪(EDS)观察了显微组织。结果表明:随着P含量的增加,钎料的抗氧化性和润湿性显著提高。当P含量达到0.020%时,与不添加P元素的钎料相比,在250℃保温30 min后的氧化渣质量由2.25 g减少到0.79 g。此时,钎料的铺展面积和润湿力也达到最大,分别为65.74 mm^(2)和5.04 mN,润湿时间为0.96 s。添加微量P元素未改变钎料的相组成,其显微组织主要由β-Sn、Ag_(3)Sn与Cu_(6)Sn_(5)三种相构成。随着P含量的增加,Ag_(3)Sn的形态由粒状向片状转变。

The Response of Anomalous Vertically Integrated Moisture Flux Patterns Related to Drought and Flood in Southern China to Sea Surface Temperature Anomaly

With the extreme drought(flood)event in southern China from July to August in 2022(1999)as the research object,based on the comprehensive diagnosis and composite analysis on the anomalous drought and flood years from July to August in 1961-2022,it is found that there are significant differences in the characteristics of the vertically integrated moisture flux(VIMF)anomaly circulation pattern and the VIMF convergence(VIMFC)anomaly in southern China in drought and flood years,and the VIMFC,a physical quantity,can be regarded as an indicative physical factor for the"strong signal"of drought and flood in southern China.Specifically,in drought years,the VIMF anomaly in southern China is an anticyclonic circulation pattern and the divergence characteristics of the VIMFC are prominent,while those are opposite in flood years.Based on the SST anomaly in the typical draught year of 2022 in southern China and the SST deviation distribution characteristics of abnormal draught and flood years from 1961 to 2022,five SST high impact areas(i.e.,the North Pacific Ocean,Northwest Pacific Ocean,Southwest Pacific Ocean,Indian Ocean,and East Pacific Ocean)are selected via the correlation analysis of VIMFC and the global SST in the preceding months(May and June)and in the study period(July and August)in 1961-2022,and their contributions to drought and flood in southern China are quantified.Our study reveals not only the persistent anomalous variation of SST in the Pacific and the Indian Ocean but also its impact on the pattern of moisture transport.Furthermore,it can be discovered from the positive and negative phase fitting of SST that the SST composite flow field in high impact areas can exhibit two types of anomalous moisture transport structures that are opposite to each other,namely an anticyclonic(cyclonic)circulation pattern anomaly in southern China and the coastal areas of east China.These two types of opposite anomalous moisture transport structures can not only drive the formation of drought(flood)in southern China but also exert its influence on the persistent development of the extreme weather.

Anomaly detection on displacement rates and deformation pattern features using tree-based algorithm in Japan and Indonesia

Research on strain anomalies and large earthquakes based on temporal and spatial crustal activities has been rapidly growing due to data availability, especially in Japan and Indonesia. However, many research works used local-scale case studies that focused on a specific earthquake characteristic using knowledgedriven techniques, such as crustal deformation analysis. In this study, a data-driven-based analysis is used to detect anomalies using displacement rates and deformation pattern features extracted from daily global navigation satellite system(GNSS) data using a machine learning algorithm. The GNSS data with188 and 1181 continuously operating reference stations from Indonesia and Japan, respectively, are used to identify the anomaly of recent major earthquakes in the last two decades. Feature displacement rates and deformation patterns are processed in several window times with 2560 experiment scenarios to produce the best detection using tree-based algorithms. Tree-based algorithms with a single estimator(decision tree), ensemble bagging(bagging, random forest and Extra Trees), and ensemble boosting(AdaBoost, gradient boosting, LGBM, and XGB) are applied in the study. The experiment test using realtime scenario GNSSdailydatareveals high F1-scores and accuracy for anomaly detection using slope windowing 365 and 730 days of 91-day displacement rates and then 7-day deformation pattern features in tree-based algorithms. The results show the potential for medium-term anomaly detection using GNSS data without the need for multiple vulnerability assessments.

Deep Domain-Adversarial Anomaly Detection With One-Class Transfer Learning

Despite the big success of transfer learning techniques in anomaly detection,it is still challenging to achieve good transition of detection rules merely based on the preferred data in the anomaly detection with one-class classification,especially for the data with a large distribution difference.To address this challenge,a novel deep one-class transfer learning algorithm with domain-adversarial training is proposed in this paper.First,by integrating a hypersphere adaptation constraint into domainadversarial neural network,a new hypersphere adversarial training mechanism is designed.Second,an alternative optimization method is derived to seek the optimal network parameters while pushing the hyperspheres built in the source domain and target domain to be as identical as possible.Through transferring oneclass detection rule in the adaptive extraction of domain-invariant feature representation,the end-to-end anomaly detection with one-class classification is then enhanced.Furthermore,a theoretical analysis about the model reliability,as well as the strategy of avoiding invalid and negative transfer,is provided.Experiments are conducted on two typical anomaly detection problems,i.e.,image recognition detection and online early fault detection of rolling bearings.The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in terms of detection accuracy and robustness.