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Irreversible phase transition caused capacity fading has been considered as an obstacle for rechargeable batteries. An in-depth investigation of the irreversible phase transition is critical for understanding the reaction mechanism and developing advanced batteries. In this work, taking vanadium oxide and its alkali ion-stabilized intercalation compounds (A-V-O, A = Li, Na, K) as prototypes, utilizing operando characterizations, we discovered coupled discontinuous (interlayer)-continuous (intralayer) transitions in the stabilized multielectron intercalation cathodes. The highly ordered crystal of vanadium pentoxide irreversibly transfers to a disordered/amorphous structure after the first cycle, whereas A-V-O enables reversible discontinuous lattice transitions at the interlayer pathway for facilitating lithium diffusion. Among the A-V-O family, K-V-O, with the highest capacity retention, shows a coupled discontinuous-continuous transition, which exhibits a continuous transition and the minimum volume change at the V-O intralayer during lithium intercalation/deintercalation. These coupled discontinuous-continuous lattice transitions were captured for the first time in cathode materials. It implies that the suitable ion intercalation induced continuous intralayer transition inhibits the irreversible ion intercalation and phase transition. [ABSTRACT FROM AUTHOR]

Recently, intrusion detection methods based on supervised deep learning techniques (DL) have seen widespread adoption by the research community, as a result of advantages, such as the ability to learn useful feature representations from input data without excessive manual intervention. However, these techniques require large amounts of data to generalize well. Collecting a large-scale malicious sample is non-trivial, especially in the modern day with its constantly evolving landscape of cyber-threats. On the other hand, collecting a few-shot of malicious samples is more realistic in practical settings, as in cases such as zero-day attacks, where security agents are only able to intercept a limited number of such samples. Hence, intrusion detection methods based on few-shot learning is emerging as an alternative to conventional supervised learning approaches to simulate more realistic settings. Therefore, in this paper, we propose a novel method that leverages discriminative representation learning with a supervised autoencoder to achieve few-shot intrusion detection. Our approach is implemented in two stages: we first train a feature extractor model with known classes of malicious samples using a discriminative autoencoder, and then in the few-shot detection stage, we use the trained feature extractor model to fit a classifier with a few-shot examples of the novel attack class. We are able to achieve detection rates of 99.5% and 99.8% for both the CIC-IDS2017 and NSL-KDD datasets, respectively, using only 10 examples of an unseen attack. [ABSTRACT FROM AUTHOR]

The Ti–Al3Ti–Al laminated composites with different Al contents were prepared by vacuum hot pressing sintering technology. The effects of Al content on the dynamic mechanical properties of the composites were studied using the combination of Split Hopkinson Pressure Bar experiment and finite element analysis. The results showed that different Al content changes the fracture mode of the composites. The laminated composites without Al have higher brittleness and lower fracture strain. The Ti–Al3Ti–Al laminated composites containing 10–15%Al have better dynamic mechanical properties than those without Al, but the subsequent increase of Al content is not conducive to the improvement of strength. However, when the Al content in the specimen reaches 30%, the dynamic mechanical properties of the composites decrease, multi-crack phenomenon and relatively large strain occur, and the Al extruded from the layers fills the crack. [ABSTRACT FROM AUTHOR]

Machine learning models often converge slowly and are unstable due to the significant variance of random data when using a sample estimate gradient in SGD. To increase the speed of convergence and improve stability, a distributed SGD algorithm based on variance reduction, named DisSAGD, is proposed in this study. DisSAGD corrects the gradient estimate for each iteration by using the gradient variance of historical iterations without full gradient computation or additional storage, i.e., it reduces the mean variance of historical gradients in order to reduce the error in updating parameters. We implemented DisSAGD in distributed clusters in order to train a machine learning model by sharing parameters among nodes using an asynchronous communication protocol. We also propose an adaptive learning rate strategy, as well as a sampling strategy, to address the update lag of the overall parameter distribution, which helps to improve the convergence speed when the parameters deviate from the optimal value—when one working node is faster than another, this node will have more time to compute the local gradient and sample more samples for the next iteration. Our experiments demonstrate that DisSAGD significantly reduces waiting times during loop iterations and improves convergence speed when compared to traditional methods, and that our method can achieve speed increases for distributed clusters. [ABSTRACT FROM AUTHOR]

In recent years, the application of traceability systems in the food and drug industry has developed rapidly, but it is rarely used for wind turbines. From the aspects of low information transparency and information islands in the supply chain process for wind turbines, a reliable traceability system is essential. However, the existing traceability systems are not suitable to be directly applied to wind turbines. Consequently, according to the characteristics of the wind power industry, a semi-centralized traceability architecture based on Internet of Things technology was proposed. Furthermore, a traceability platform was constructed by analyzing the information collected in each stage related to various user needs of wind turbines, and various applications, including manufacturing management and spare parts management, were developed. Compared with the existing systems, the proposed platform was wind-turbine-oriented, effectively improved traceability efficiency and enterprises' information security, and extended the length of the traceability chain by integrating the after-sales information. The traceability of key components of wind turbines during their life cycle provides a useful reference for further improving the parts quality management system of the wind power industry. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) is conducive to the change in the wind power industry management model and is beneficial to the green design of products. Nowadays, none of the LCA systems are for wind turbines and the concept of Internet of Things (IoT) in LCA is quite a new idea. In this paper, a four-layer LCA platform of wind turbines based on IoT architecture is designed and discussed. In the data transmission layer, intelligent sensing of wind turbines can be achieved and their status and location can be monitored. In the data transmission layer, the LCA platform can be effectively integrated with enterprise information systems through the object name service (ONS) and directory service (DS). In the platform layer, a model based on IMPACT 2002+ is developed, and four management modules are designed. In the application layer, different from other systems, energy payback time (EPBT) is selected as an important evaluation index for wind turbines. Compared with the existing LCA systems, the proposed system is specifically for wind turbines and can collect data in real-time, leading to improved accuracy and response time. [ABSTRACT FROM AUTHOR]

Cu-containing CHA type (Cu-CHA) zeolites have been widely investigated owing to their excellent low-temperature activity and high hydrothermal stability in selective catalytic reduction of NOx with NH3 (NH3-SCR). Herein, a series of Cu-SAPO-44 zeolites were prepared by one-pot method with dual-amine templates and the subsequent ion exchange (IE) with NH4NO3. The effect of NH4NO3 treatment on Cu species was investigated by X-ray powder diffraction (XRD), N2 adsorption-desorption isotherm, inductively coupled plasma (ICP); field-emission scanning electron microscope (FE-SEM), high-resolution transmission electron microscope (HRTEM), X-ray absorption fine structure (XAFS), and H2-temperature-programmed reduction (H2-TPR). The results indicated that—besides the main SAPO-44 structure—the CuO phase was detected by XRD in original samples. After IE with NH4NO3, the Cu contents decreased greatly from ICP analysis. The removal of CuO agglomerations and the presence of highly dispersed CuO nanoparticles (~2.36 nm) were confirmed by SEM, TEM and H2-TPR. Furthermore, a significant increase in the proportion of isolated Cu2+ was derived from XAFS. As a result, the activity at higher temperature (≥350 °C) was improved a lot. [ABSTRACT FROM AUTHOR]

Power MOSFETs specially designed for space power systems are expected to simultaneously meet the requirements of electrical performance and radiation hardness. Radiation-hardened (rad-hard) power MOSFET design can be achieved via cell structure optimization. This paper conducts an investigation of the cell geometrical parameters with major impacts on radiation hardness, and a rad-hard power MOSFET is designed and fabricated. The experimental results validate the devices' total ionizing dose (TID) and single event effects (SEE) hardness to suitably satisfy most space power system requirements while maintaining acceptable electrical performance. [ABSTRACT FROM AUTHOR]

This paper presents the "model construction method", an optimization method and industrial internet of things (IIoT) technology that is proposed for nearly zero energy buildings (nZEB), providing a comfortable visual environment by only utilizing natural light while improving its induced indoor air conditioner energy consumption (ACEC). The incident light is sampled by light sensors, and this data is sent to the cloud server. The visual comfort and indoor ACEC, both of which are induced by incident light, are combined as the optimization objective, and the area of windows covered by curtains is used as the optimal parameter in the particle swarm optimization (PSO). The visual comfort and indoor ACEC induced by incident light are modeled, and the construction method is independent of the geographical location. Five modes are defined for applications with different purposes, the performance of which are investigated and compared carefully. The result shows that natural light could provide comfortable visual comfort, while the ACEC induced by it could be reduced effectively. [ABSTRACT FROM AUTHOR]

With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75⁻80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system's effectiveness.