Application of maximum entropy principle for reliability-based design optimization

The maximum entropy principle (MEP) is used to generate a natural probability distribution among the many possible that have the same moment conditions. The MEP can accommodate higher order moment information and therefore facilitate a higher quality PDF model. The performance of the MEP for PDF estimation is studied by using more than four moments. For the case with four moments, the results are compared with those by the Pearson system. It is observed that as accommodating higher order moment, the estimated PDF converges to the original one. A sensitivity analysis formulation of the failure probability based on the MEP is derived for reliability-based design optimization (RBDO) and the accuracy is compared with that by finite difference method (FDM). Two RBDO examples including a realistic three-dimensional wing design are solved by using the derived sensitivity formula and the MEP-based moment method. The results are compared with other methods such as TR-SQP, FAMM + Pearson system, FFMM + Pearson system in terms of accuracy and efficiency. It is also shown that an improvement in the accuracy by including more moment terms can increase numerical efficiency of optimization for the three-dimensional wing design. The moment method equipped with the MEP is found flexible and well adoptable for reliability analysis and design.     

Proteomic analysis of sera of asymptomatic,early‐stage patients with Wilson's disease

Wilson's disease (WD) is characterized by excessive accumulation of intracellular copper in liver and extrahepatic tissues, leading to significant oxidative stress and tissue damage. To date, several diagnostic biomarkers for WD such as serum ceruloplasmin, serum or urine copper levels and copper content in liver have been identified. However, these biomarkers may not be convincing for the diagnosis in some WD patients. To identify additional novel diagnostic biomarkers, we compared the serum protein profiles of asymptomatic childhood WD patients (n=20), without neurologic manifestation or liver cirrhosis, with normal controls (n=13). Fourteen spots, five up‐regulated and nine down‐regulated (>2‐fold), were differentially expressed in WD patients in comparison to normal control on 2‐DE. Among them, three spots were down‐regulated in both male and female WD. MS/MS analysis revealed that the three spots were complement component C3, complement factor B and alpha‐2 macroglobulin. By comparative proteome analysis, complement component C3, complement factor B and alpha‐2 macroglobulin, which are related to oxidative stress and inflammation, turned out to be good candidates for novel diagnostic biomarkers for early stages of WD.     

Invited article: development of high-field superconducting Ioffe magnetic traps

We describe the design, construction, and performance of three generations of superconducting Ioffe magnetic traps. The first two are low current traps, built from four racetrack shaped quadrupole coils and two solenoid assemblies. Coils are wet wound with multifilament NbTi superconducting wires embedded in epoxy matrices. The magnet bore diameters are 51 and 105 mm with identical trap depths of 1.0 T at their operating currents and at 4.2 K. A third trap uses a high current accelerator-type quadrupole magnet and two low current solenoids. This trap has a bore diameter of 140 mm and tested trap depth of 2.8 T. Both low current traps show signs of excessive training. The high current hybrid trap, on the other hand, exhibits good training behavior and is amenable to quench protection.     

Metal Single-Site Molecular Complex–MXene Heteroelectrocatalysts Interspersed Graphene Nanonetwork for Efficient Dual-Task of Water Splitting and Metal–Air Batteries

Development of multifunctional electrocatalysts with high efficiency and stability is of great interest in recent energy conversion technologies. Herein, a novel heteroelectrocatalyst of molecular iron complex (Fe_MC)-carbide MXene (Mo₂TiC₂T_x) uniformly embedded in a 3D graphene-based hierarchical network (GrH) is rationally designed. The coexistence of Fe_MC and MXene with their unique interactions triggers optimum electronic properties, rich multiple active sites, and favorite free adsorption energy for excellent trifunctional catalytic activities. Meanwhile, the highly porous GrH effectively promotes a multichannel architecture for charge transfer and gas/ion diffusion to improve stability. Therefore, the Fe_MC–MXene/GrH results in superb performances towards oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) in alkaline medium. The practical tests indicate that Zn/Al–air batteries derived from Fe_MC–MXene/GrH cathodic electrodes produce high power densities of 165.6 and 172.7 mW cm⁻², respectively. Impressively, the liquid-state Zn–air battery delivers excellent cycling stability of over 1100 h. In addition, the alkaline water electrolyzer induces a low cell voltage of 1.55 V at 10 mA cm⁻² and 1.86 V at 0.4 A cm⁻² in 30 wt.% KOH at 80 °C, surpassing recent reports. The achievements suggest an exciting multifunctional electrocatalyst for electrochemical energy applications.     

Portable and Efficient Implementation of CRYSTALS-Kyber Based on WebAssembly

With the rapid development of quantum computers capable of realizing Shor’s algorithm, existing public key-based algorithms face a significant security risk. Crystals-Kyber has been selected as the only key encapsulation mechanism (KEM) algorithm in the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography (PQC) competition. In this study, we present a portable and efficient implementation of a Crystals-Kyber post-quantum KEM based on WebAssembly (Wasm), a recently released portable execution framework for high-performance web applications. Until now, most Kyber implementations have been developed with native programming languages such as C and Assembly. Although there are a few previous Kyber implementations based on JavaScript for portability, their performance is significantly lower than that of implementations based on native programming languages. Therefore, it is necessary to develop a portable and efficient Kyber implementation to secure web applications in the quantum computing era. Our Kyber software is based on JavaScript and Wasm to provide portability and efficiency while ensuring quantum security. Namely, the overall software is written in JavaScript, and the performance core parts (secure hash algorithm-3-based operations and polynomial multiplication) are written in Wasm. Furthermore, we parallelize the number theoretic transform (NTT)-based polynomial multiplication using single instruction multiple data (SIMD) functionality, which is available in Wasm. The three steps in the NTT-based polynomial multiplication have been parallelized with Wasm SIMD intrinsic functions. Our software outperforms the latest reference implementation of Kyber developed in JavaScript by ×4.02 (resp. ×4.32 and ×4.1), ×3.42 (resp. ×3.52 and ×3.44), and ×3.41 (resp. ×3.44 and ×3.38) in terms of key generation, encapsulation, and decapsulation on Google Chrome (resp. Firefox, and Microsoft Edge). As far as we know, this is the first software implementation of Kyber with Wasm technology in the web environment.     

Android IoT Lifelog System and Its Application to Motion Inference

In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users’ daily lives. However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services. Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users’ daily lives. The proposed system consists of an Android IoT client application, an oneM2M-compliant IoT server, and an ML server whose high-level functional architecture was carefully designed to be open, accessible, and internationally recognized in accordance with the oneM2M standards. In particular, we explain implementation details of activity diagrams for the Android IoT client application, the primary component of the proposed system. Experimental results verified that this application could work with the oneM2M-compliant IoT server normally and provide corresponding class labels properly. As an application of the proposed system, we also propose motion inference based on three multi-class ML classifiers (i.e., k nearest neighbors, Naive Bayes, and support vector machine) which were created by using only motion and location data (i.e., acceleration force, gyroscope rate of rotation, and speed) and motion class labels (i.e., driving, cycling, running, walking, and stilling). When compared with confusion matrices of the ML classifiers, the k nearest neighbors classifier outperformed the other two overall. Furthermore, we evaluated its output quality by analyzing the receiver operating characteristic (ROC) curves with area under the curve (AUC) values. The AUC values of the ROC curves for all motion classes were more than 0.9, and the macro-average and micro-average ROC curves achieved very high AUC values of 0.96 and 0.99, respectively.     

Joint frame rate adaptation and object recognition model selection for stabilized unmanned aerial vehicle surveillance

We propose an adaptive unmanned aerial vehicle (UAV)-assisted object recognition algorithm for urban surveillance scenarios. For UAV-assisted surveillance, UAVs are equipped with learning-based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self-adaptive control strategy to maximize the time-averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real-world data demonstrate that the proposed algorithm achieves the desired performance improvements.     

Feature matching and instance reweighting with transfer learning for human activity recognition using smartphone

Human activity recognition using smartphone has been attracting great interest. Since collecting large amount of labeled data is expensive and time-consuming for conventional machine learning techniques, transfer learning techniques have been proposed for activity recognition. However, existing transfer learning techniques typically rely on feature matching based on global domain shift and lack considering the intra-class knowledge transfer. In this paper, a novel transfer learning technique is proposed for cross-domain activity recognition, which can properly integrate feature matching and instance reweighting across the source and target domain in principled dimensionality reduction. The experiments using three real datasets demonstrate that the proposed scheme can achieve much higher precision (92%), recall (91%), and F1-score (92%), in comparison with the existing schemes.

     

Photonic Reactions Leading to Fluorescence in a Polymeric System Induced by the Photothermal Effect of Magnetite Nanoparticles Using a 780 nm Multiphoton Laser

Recently, polymer‐coated magnetite (Fe₃O₄) nanoparticles (NPs) are extensively studied for applications in therapeutics or diagnostics using photothermal effect. Therefore, it is essential to understand the interactions between Fe₃O₄ NPs and polymers when optical stimuli are applied. Herein, the photonic reactions of Fe₃O₄ NPs and polymer composites upon application of a 780 nm multiphoton laser are analyzed. The photonic reactions produce unique results including fluorescence from conformationally changed polymer and low‐temperature phase transformation of Fe₃O₄ NPs. Typically, π‐conjugated chains are formed, inducing fluorescence through a series of main and side‐chain cleavage reactions of polymers with the aliphatic chain. In addition, fluorescence is detected in the cellular system by photonic reactions between Fe₃O₄ NPs and biomolecules. After multiphoton laser irradiation, light emission is detected near the intracellular Fe₃O₄ NPs, and a stronger intensity is observed in large‐sized NPs.