Superconducting joints using reacted multifilament MgB_(2)wires:A technology toward cryogen-free MRI magnets

The development of superconducting joining technology for reacted magnesium diboride(MgB_(2))conductors remains a critical challenge for the advancement of cryogen-free MgB_(2)-based magnets for magnetic resonance imaging(MRI).Herein,the fabrication of superconducting joints using reacted carbon-doped multifilament MgB_(2)wires for MRI magnets is reported.To achieve successful superconducting joints,the powder-in-mold method was employed,which involved tuning the filament protection mechanism,the powder compaction pressure,and the heat treatment condition.The fabricated joints demonstrated clear superconducting-to-normal transitions in self-field,with effective magnetic field screening up to 0.5 T at 20 K.To evaluate the interface between one of the MgB_(2)filaments and the MgB_(2)bulk within the joint,serial sectioning was conducted for the first time in this type of superconducting joint.The serial sectioning revealed space formation at the interface,potentially caused by the volume shrinkage associated with the MgB_(2)formation or the combined effect of the volume shrinkage and the different thermal expansion coefficients of the MgB_(2)bulk,the filament,the mold,and the sealing material.These findings are expected to be pivotal in developing MgB_(2)superconducting joining technology for MRI magnet applications through interface engineering.

Suboptimal Feature Selection Techniques for Effective Malicious Traffic Detection on Lightweight Devices

With the advancement of wireless network technology,vast amounts of traffic have been generated,and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated.While signature-based detection methods,static analysis,and dynamic analysis techniques have been previously explored for malicious traffic detection,they have limitations in identifying diversified malware traffic patterns.Recent research has been focused on the application of machine learning to detect these patterns.However,applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process.In this study,we examined methods for effectively utilizing machine learning-based malicious traffic detection approaches for lightweight devices.We introduced the suboptimal feature selection model(SFSM),a feature selection technique designed to reduce complexity while maintaining the effectiveness of malicious traffic detection.Detection performance was evaluated on various malicious traffic,benign,exploits,and generic,using the UNSW-NB15 dataset and SFSM sub-optimized hyperparameters for feature selection and narrowed the search scope to encompass all features.SFSM improved learning performance while minimizing complexity by considering feature selection and exhaustive search as two steps,a problem not considered in conventional models.Our experimental results showed that the detection accuracy was improved by approximately 20%compared to the random model,and the reduction in accuracy compared to the greedy model,which performs an exhaustive search on all features,was kept within 6%.Additionally,latency and complexity were reduced by approximately 96%and 99.78%,respectively,compared to the greedy model.This study demonstrates that malicious traffic can be effectively detected even in lightweight device environments.SFSM verified the possibility of detecting various attack traffic on lightweight devices.

Multi-Binary Classifiers Using Optimal Feature Selection for Memory-Saving Intrusion Detection Systems

With the rise of remote work and the digital industry,advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics,rendering them difficult to detect with conventional intrusion detection methods.Signature-based intrusion detection methods can be used to detect attacks;however,they cannot detect new malware.Endpoint detection and response(EDR)tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques.However,EDR tools are restricted by the continuous generation of unnecessary logs,resulting in poor detection performance and memory efficiency.Machine learning-based intrusion detection techniques for responding to advanced cyberattacks are memory intensive,using numerous features;they lack optimal feature selection for each attack type.To overcome these limitations,this study proposes a memory-efficient intrusion detection approach incorporating multi-binary classifiers using optimal feature selection.The proposed model detects multiple types of malicious attacks using parallel binary classifiers with optimal features for each attack type.The experimental results showed a 2.95%accuracy improvement and an 88.05%memory reduction using only six features compared to a model with 18 features.Furthermore,compared to a conventional multi-classification model with simple feature selection based on permutation importance,the accuracy improved by 11.67%and the memory usage decreased by 44.87%.The proposed scheme demonstrates that effective intrusion detection is achievable with minimal features,making it suitable for memory-limited mobile and Internet of Things devices.

Disordered Structure and Reversible Phase Transformation from K-Birnessite to Zn-Buserite Enable High-Performance Aqueous Zinc-lon Batteries

The layeredδ-MnO_(2)(dMO)is an excellent cathode material for rechargeable aqueous zinc-ion batteries owing to its large interlayer distance(~0.7 nm),high capacity,and low cost;however,such cathodes suffer from structural degradation during the long-term cycling process,leading to capacity fading.In this study,a Co-doped dMO composite with reduced graphene oxide(GC-dMO)is developed using a simple cost-effective hydrothermal method.The degree of disorderness increases owing to the hetero-atom doping and graphene oxide composites.It is demonstrated that layered dMO and GC-dMO undergo a structural transition from K-birnessite to the Zn-buserite phase upon the first discharge,which enhances the intercalation of Zn^(2+)ions,H_(2)O molecules in the layered structure.The GC-dMO cathode exhibits an excellent capacity of 302 mAh g^(-1)at a current density of 100 mAg^(-1)after 100 cycles as compared with the dMO cathode(159 mAhg^(-1)).The excellent electrochemical performance of the GC-dMO cathode owing to Co-doping and graphene oxide sheets enhances the interlayer gap and disorderness,and maintains structural stability,which facilitates the easy reverse intercalation and de-intercalation of Zn^(2+)ions and H_(2)O molecules.Therefore,GC-dMO is a promising cathode material for large-scale aqueous ZIBs.

Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.Sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

Depth First:Optimal Path Discovery Between Designated Nodes in Random Ring-Based Graphs

This paper focuses on optimally determining the existence of connected paths between some given nodes in random ring-based graphs.Serving as a fundamental underlying structure in network modeling,ring topology appears as commonplace in many realistic scenarios.Regarding this,we consider graphs composed of rings,with some possible connected paths between them.Without prior knowledge of the exact node permutations on rings,the existence of each edge can be unraveled through edge testing at a unit cost in one step.The problem examined is that of determining whether the given nodes are connected by a path or separated by a cut,with the minimum expected costs involved.Dividing the problem into different cases based on different topologies of the ring-based networks,we propose the corresponding policies that aim to quickly seek the paths between nodes.A common feature shared by all those policies is that we stick to going in the same direction during edge searching,with edge testing in each step only involving the test between the source and the node that has been tested most.The simple searching rule,interestingly,can be interpreted as a delightful property stemming from the neat structure of ring-based networks,which makes the searching process not rely on any sophisticated behaviors.We prove the optimality of the proposed policies by calculating the expected cost incurred and making a comparison with the other class of strategies.The effectiveness of the proposed policies is also verified through extensive simulations,from which we even disclose three extra intriguing findings:i)in a onering network,the cost will grow drastically with the number of designated nodes when the number is small and will grow slightly when that number is large;ii)in ring-based network,Depth First is optimal in detecting the connectivity between designated nodes;iii)the problem of multi-ring networks shares large similarity with that of two-ring networks,and a larger number of ties between rings will not influence the expected cost.

Real-Time Tunable Gas Sensing Platform Based on SnO_(2) Nanoparticles Activated by Blue Micro-Light-Emitting Diodes

Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power consumption.However,despite these benefits,challenges still exist such as a limited range of detectable gases and slow response.In this study,we present a blueμLED-integrated light-activated gas sensor array based on SnO_(2)nanoparticles(NPs)that exhibit excellent sensitivity,tunable selectivity,and rapid detection with micro-watt level power consumption.The optimal power forμLED is observed at the highest gas response,supported by finite-difference time-domain simulation.Additionally,we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO_(2)NPs.The noble metals induce catalytic interaction with reducing gases,clearly distinguishing NH3,H2,and C2H5OH.Real-time gas monitoring based on a fully hardwareimplemented light-activated sensing array was demonstrated,opening up new avenues for advancements in light-activated electronic nose technologies.

Two-Terminal Lithium-Mediated Artificial Synapses with Enhanced Weight Modulation for Feasible Hardware Neural Networks

Recently,artificial synapses involving an electrochemical reaction of Li-ion have been attributed to have remarkable synaptic properties.Three-terminal synaptic transistors utilizing Li-ion intercalation exhibits reliable synaptic characteristics by exploiting the advantage of nondistributed weight updates owing to stable ion migrations.However,the three-terminal configurations with large and complex structures impede the crossbar array implementation required for hardware neuromorphic systems.Meanwhile,achieving adequate synaptic performances through effective Li-ion intercalation in vertical two-terminal synaptic devices for array integration remains challenging.Here,two-terminal Au/LixCoO_(2)/Pt artificial synapses are proposed with the potential for practical implementation of hardware neural networks.The Au/LixCoO_(2)/Pt devices demonstrated extraordinary neuromorphic behaviors based on a progressive dearth of Li in LixCoO_(2)films.The intercalation and deintercalation of Li-ion inside the films are precisely controlled over the weight control spike,resulting in improved weight control functionality.Various types of synaptic plasticity were imitated and assessed in terms of key factors such as nonlinearity,symmetricity,and dynamic range.Notably,the LixCoO_(2)-based neuromorphic system outperformed three-terminal synaptic transistors in simulations of convolutional neural networks and multilayer perceptrons due to the high linearity and low programming error.These impressive performances suggest the vertical two-terminal Au/LixCoO_(2)/Pt artificial synapses as promising candidates for hardware neural networks.

Malicious Traffic Compression and Classification Technique for Secure Internet of Things

With the introduction of 5G technology,the application of Internet of Things(IoT)devices is expanding to various industrial fields.However,introducing a robust,lightweight,low-cost,and low-power security solution to the IoT environment is challenging.Therefore,this study proposes two methods using a data compression technique to detect malicious traffic efficiently and accurately for a secure IoT environment.The first method,compressed sensing and learning(CSL),compresses an event log in a bitmap format to quickly detect attacks.Then,the attack log is detected using a machine-learning classification model.The second method,precise re-learning after CSL(Ra-CSL),comprises a two-step training.It uses CSL as the 1st step analyzer,and the 2nd step analyzer is applied using the original dataset for a log that is detected as an attack in the 1st step analyzer.In the experiment,the bitmap rule was set based on the boundary value,which was 99.6%true positive on average for the attack and benign data found by analyzing the training data.Experimental results showed that the CSL was effective in reducing the training and detection time,and Ra-CSL was effective in increasing the detection rate.According to the experimental results,the data compression technique reduced the memory size by up to 20%and the training and detection times by 67%when compared with the conventional technique.In addition,the proposed technique improves the detection accuracy;the Naive Bayes model with the highest performance showed a detection rate of approximately 99%.

Wake-Up Security:Effective Security ImprovementMechanism for Low Power Internet of Things

As time and space constraints decrease due to the development of wireless communication network technology,the scale and scope of cyber-attacks targeting the Internet of Things(IoT)are increasing.However,it is difficult to apply high-performance security modules to the IoT owing to the limited battery,memory capacity,and data transmission performance depend-ing on the size of the device.Conventional research has mainly reduced power consumption by lightening encryption algorithms.However,it is difficult to defend large-scale information systems and networks against advanced and intelligent attacks because of the problem of deteriorating security perfor-mance.In this study,we propose wake-up security(WuS),a low-power security architecture that can utilize high-performance security algorithms in an IoT environment.By introducing a small logic that performs anomaly detection on the IoT platform and executes the security module only when necessary according to the anomaly detection result,WuS improves security and power efficiency while using a relatively high-complexity security module in a low-power environment compared to the conventional method of periodically exe-cuting a high-performance security module.In this study,a Python simulator based on the UNSW-NB15 dataset is used to evaluate the power consumption,latency,and security of the proposed method.The evaluation results reveal that the power consumption of the proposed WuS mechanism is approxi-mately 51.8%and 27.2%lower than those of conventional high-performance security and lightweight security modules,respectively.Additionally,the laten-cies are approximately 74.8%and 65.9%lower,respectively.Furthermore,the WuS mechanism achieved a high detection accuracy of approximately 96.5%or greater,proving that the detection efficiency performance improved by approximately 33.5%compared to the conventional model.The performance evaluation results for the proposed model varied depending on the applied anomaly-detection model.Therefore,they can be used in various ways by selecting suitable models based on the performance levels required in each industry.

Tailored BiVO_(4)/In_(2)O_(3)nanostructures with boosted charge separation ability toward unassisted water splitting

The development of new heterostructures with high photoactivity is a breakthrough for the limitation of solar-driven water splitting.Here,we first introduce indium oxide(In_(2)O_(3))nanorods(NRs)as a novel electron transport layer for bismuth vanadate(BiVO_(4))with a short charge diffusion length.In_(2)O_(3)NRs reinforce the electron transport and hole blocking of BiVO_(4),surpassing the state-of-the-art photoelectrochemical performances of BiVO_(4)-based photoanodes.Also,a tannin-nickel-iron complex(TANF)is used as an oxygen evolution catalyst to speed up the reaction kinetics.The final TANF/BiVO_(4)/In_(2)O_(3)NR photoanode generates photocurrent densities of 7.1 mAcm^(−2) in sulfite oxidation and 4.2 mA cm^(−2) in water oxidation at 1.23 V versus the reversible hydrogen electrode.Furthermore,the“artificial leaf,”which is a tandem cell with a perovskite/silicon solar cell,shows a solar-to-hydrogen conversion efficiency of 6.2%for unbiased solar water splitting.We reveal significant advances in the photoactivity of TANF/BiVO_(4)/In_(2)O_(3)NRs from the tailored nanostructure and band structure for charge dynamics.

An Effective Threat Detection Framework for Advanced Persistent Cyberattacks

Recently,with the normalization of non-face-to-face online environments in response to the COVID-19 pandemic,the possibility of cyberattacks through endpoints has increased.Numerous endpoint devices are managed meticulously to prevent cyberattacks and ensure timely responses to potential security threats.In particular,because telecommuting,telemedicine,and teleeducation are implemented in uncontrolled environments,attackers typically target vulnerable endpoints to acquire administrator rights or steal authentication information,and reports of endpoint attacks have been increasing considerably.Advanced persistent threats(APTs)using various novel variant malicious codes are a form of a sophisticated attack.However,conventional commercial antivirus and anti-malware systems that use signature-based attack detectionmethods cannot satisfactorily respond to such attacks.In this paper,we propose a method that expands the detection coverage inAPT attack environments.In this model,an open-source threat detector and log collector are used synergistically to improve threat detection performance.Extending the scope of attack log collection through interworking between highly accessible open-source tools can efficiently increase the detection coverage of tactics and techniques used to deal with APT attacks,as defined by MITRE Adversarial Tactics,Techniques,and Common Knowledge(ATT&CK).We implemented an attack environment using an APT attack scenario emulator called Carbanak and analyzed the detection coverage of Google Rapid Response(GRR),an open-source threat detection tool,and Graylog,an open-source log collector.The proposed method expanded the detection coverage against MITRE ATT&CK by approximately 11%compared with that conventional methods.

Machine Learning-Based Efficient Discovery of Software Vulnerability for Internet of Things

With the development of the 5th generation of mobile communi-cation(5G)networks and artificial intelligence(AI)technologies,the use of the Internet of Things(IoT)has expanded throughout industry.Although IoT networks have improved industrial productivity and convenience,they are highly dependent on nonstandard protocol stacks and open-source-based,poorly validated software,resulting in several security vulnerabilities.How-ever,conventional AI-based software vulnerability discovery technologies cannot be applied to IoT because they require excessive memory and com-puting power.This study developed a technique for optimizing training data size to detect software vulnerabilities rapidly while maintaining learning accuracy.Experimental results using a software vulnerability classification dataset showed that different optimal data sizes did not affect the learning performance of the learning models.Moreover,the minimal data size required to train a model without performance degradation could be determined in advance.For example,the random forest model saved 85.18%of memory and improved latency by 97.82%while maintaining a learning accuracy similar to that achieved when using 100%of data,despite using only 1%.

Wearable and stretchable conductive polymer composites for strain sensors:How to design a superior one?

Wearable and stretchable strain sensors have potential values in the fields of human motion and health monitoring,flexible electronics,and soft robotic skin.The wearable and stretchable strain sensors can be directly attached to human skin,providing visualized detection for human motions and personal healthcare.Conductive polymer composites(CPC)composed of conductive fillers and flexible polymers have the advantages of high stretchability,good flexibility,superior durability,which can be used to prepare flexible strain sensors with large working strain and outstanding sensitivity.This review has put forward a comprehensive summary on the fabrication methods,advanced mechanisms and strain sensing abilities of CPC strain sensors reported in recent years,especially the sensors with superior performance.Finally,the structural design,bionic function,integration technology and further application of CPC strain sensors are prospected.

碳纳米管/氯化镁负载钛基Ziegler-Natta催化剂在异戊二烯聚合中的应用（英文）

以大内径碳纳米管、羟基化碳纳米管及氯化镁作载体,采用高能球磨技术制备了纳米级钛系ZieglerNatta催化剂,研究了该催化剂对反式聚异戊二烯聚合过程的影响。结果表明,反式聚异戊二烯/碳纳米管复合材料具有管状结构,碳纳米管颗粒分散良好,并且明显提高了反式聚异戊二烯的热稳定性。

共价键合二氧化硅/氧化石墨烯对天然橡胶的多尺度和界面增强及在环保高性能轮胎胎面中的应用（英文）

将零维(0 D) Si 69改性的二氧化硅纳米颗粒(MS)和KH 550官能化的二维(2 D)多层氧化石墨烯(KGO)组成的纳米复合物通过胶乳法制备了天然橡胶(NR)纳米材料,研究了纳米材料的多尺度增强和界面强化。结果表明,通过MS和KGO表面活性极性官能团之间良好的H键相互作用形成了0 D/2 D杂化复合物(即MSKGO),后者与异戊二烯单元通过Si 69的硫化物相互作用和非极性相互作用与氧化石墨烯(GO)形成NRMSKGO的三维网络结构;在干燥和固化过程中,H键网络结构转化为共价键网络结构,同时GO部分转变为还原氧化石墨烯(RGO)。含有质量分数为3%GO的复合材料的200%定伸应力和拉伸强度分别提高9. 47倍和1. 36倍;仅使用10份含质量分数1%GO的复合材料替代10份NR制备的轮胎胎面,不仅使炭黑降低5份,且耐磨性和抗湿滑性分别提高了44. 5%和14. 6%,滚动阻力下降了5. 1%

Molecular mechanisms of chemopreventive phytochemicals against gastroenterological cancer development

Cancer is one of the leading causes of death worldwide.Commonly used cancer treatments,including chemotherapy and radiation therapy,often have side effects and a complete cure is sometimes impossible.Therefore,prevention,suppression,and/or delaying the onset of the disease are important.The onset of gastroenterological cancers is closely associated with an individual's lifestyle.Thus,changing lifestyle,specifically the consumption of fruits and vegetables,can help to protect against the development of gastroenterological cancers.In particular,naturally occurring bioactive compounds,including curcumin,resveratrol,isothiocyanates,(-)-epigallocatechin gallate and sulforaphane,are regarded as promising chemopreventive agents.Hence,regular consumption of these natural bioactive compounds found in foods can contribute to prevention,suppression,and/or delay of gastroenterological cancer development.In this review,we will summarize natural phytochemicals possessing potential antioxidant and/or anti-inflammatory and anti-carcinogenic activities,which are exerted by regulating or targeting specific molecules against gastroenterological cancers,including esophageal,gastric and colon cancers.

Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing

Two-dimensional(2D)transition metal chalcogenides(TMC)and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices,particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems.The distinct properties such as high durability,electrical and optical tunability,clean surface,flexibility,and LEGO-staking capability enable simple fabrication with high integration density,energy-efficient operation,and high scalability.This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications,including the promise of 2D TMC materials and heterostructures,as well as the state-of-the-art demonstration of memristive devices.The challenges and future prospects for the development of these emerging materials and devices are also discussed.The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.

Development of genome engineering technologies in cattle: from random to specific

The production of transgenic farm animals(e.g., cattle) via genome engineering for the gain or loss of gene functions is an important undertaking. In the initial stages of genome engineering, DNA micro-injection into one-cell stage embryos(zygotes) followed by embryo transfer into a recipient was performed because of the ease of the procedure.However, as this approach resulted in severe mosaicism and has a low efficiency, it is not typically employed in the cattle as priority, unlike in mice. To overcome the above issue with micro-injection in cattle, somatic cell nuclear transfer(SCNT) was introduced and successfully used to produce cloned livestock. The application of SCNT for the production of transgenic livestock represents a significant advancement, but its development speed is relatively slow because of abnormal reprogramming and low gene targeting efficiency. Recent genome editing technologies(e.g.,ZFN, TALEN, and CRISPR-Cas9) have been rapidly adapted for applications in cattle and great results have been achieved in several fields such as disease models and bioreactors. In the future, genome engineering technologies wil accelerate our understanding of genetic traits in bovine and wil be readily adapted for bio-medical applications in cattle.

Effects of feeding a diet containing Gymnema sylvestre extract: attenuating progression of obesity in C57BL/6J mice

Objective: To investigate the effect of Gymnema sylvestre extract(GS) on initial anti-obesity, liver injury, and glucose homeostasis induced by a high-fat diet(HFD). Methods: The dry powder of GS was extracted with methanol, and gymnemic acid was identified by high performance liquid chromatography as deacyl gymnemic acid. Male C57BL/6J mice that fed on either a normal diet, normal diet containing 1 g/kg GS(CON+GS) HFD, or HFD containing 1.0 g/kg GS(HFD+GS) for 4 weeks were used to test the initial anti-obesity effect of GS. Body weight gain and food intake, and serum levels about lipid and liver injury markers were measured. Histopathology of adipose tissue and liver stained with hematoxylin and eosin(H&E) and oil-red O were analyzed. After 4 weeks of GS extract feeding, intraperitoneal glucose tolerance test(IPGTT) was performed. Results: The methanol extracts of GS exerted significant anti-obesity effects in HFD+GS group. They decreased body weight gain, a lower food and energy efficiency ratio, and showed lower serum levels of total cholesterol(TC), triglyceride(TG), low-density lipoprotein(LDL)-cholesterol, very-low density lipoprotein(VLDL)-cholesterol and leptin compared with the HFD group. The decreases of abdominal as well as epididymal fat weight and adipocyte hypertrophy, lipid droplets in liver, and serum levels of aspartate aminotransferase(AST) and alanine transaminase(ALT) were also observed. The CON+GS group showed an effect of glucose homeostasis compared to the CON group. Conclusions: This study shows that GS provide the possibility as a key role in an initial anti-obesity effects feeding with a HFD.