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High-strength materials are essential for applications in the structural industry. However, their limited formability restricts broader industrial use. This study proposes a novel methodology to achieve an excellent combination of formability and strength. Surface heterostructuring through laser treatment induces local austenitization on the as-rolled martensitic stainless steel. The resultant bendability was significantly improved, enabling its bending to ∼180° folding while maintaining high tensile strength. Consequently, the present surface heterostructuring suggests achieving the superior combination of strength and formability by tailoring the microstructure specifically to forming. This surface heterostructuring will explore the empty area of the formability-strength relationship.

The microstructure and mechanical properties of adjustable-ring-mode (ARM) laser-welded, hot-stamped 22MnB5 steel joints were analyzed and compared with those of Al–Si coated 22MnB5. Al segregation into the fusion zone (FZ) was suppressed by a ring beam introduced via ring-beam-mode (RBM) and dual-beam-mode (DBM) laser welding. Dilution of the Al–Si coating in the FZ induced α-ferrite formation during hot stamping. After hot stamping, the α-ferrite fraction in the FZ decreased (from 23 vol% to 4.27 vol%) more than that in the center-beam-mode (CBM) welded joints owing to the ring beam. Electron backscattered diffraction analysis indicated the distribution of high-density and uniform geometrically necessary dislocations, which caused coordinated deformation in the martensite grains, in the RBM and DBM FZs. The average microhardness in the FZ in the tailor-welded blank (TWB) joints was ∼30–40 HV lower than that in the RBM and DBM welded base metal (480 HV) and that in the FZ of the CBM reduced to 295 HV because of the formation of a large amount of ductile α-ferrite. The tensile strength and elongation of the TWB joint in the CBM were ∼989 MPa and 1.1 %, respectively, which were significantly lower than those of the RBM (1245 MPa and 1.96 %, respectively) and DBM (1321 MPa and 2.12 %, respectively). Finally, the 22MnB5 steel sheets were subjected to ARM laser welding without removing the coating layer to evaluate the effect of the ring beam on Al segregation suppression in the FZ and mechanical properties of the TWB joints.

The present study concerns an open water performance of a partially submerged propeller. To investigate the free surface effects on the propulsion performance, the force and moment were measured, and the ventilation phenomena were visualized with respect to submergence ratios and advance coefficients. The thrust loss in heavy load conditions due to ventilation at the suction side of the propeller blade was observed. The extent of the ring-shaped ventilation phenomena increased as the advance coefficients decreased, resulting in thrust loss. Using the underwater camera and the fast Fourier transform results of the thrust, the ventilation phenomena were classified into five types. Also, the shaft excitation force of the propeller was analyzed. The shaft-bearing load was approximately 100% greater than the propeller weight in the ballast draft condition. A larger load was applied to the shaft due to the movement of the thrust eccentricity near the free surface.

TRIM46 is a RING finger E3 ligase which belongs to TRIM (tripartite motif-containing) protein family. TRIM46 is required for neuronal polarity and axon specification by driving the formation of parallel microtubule arrays, whereas its embryological functions remain to be determined yet. Expression patterns and biological functions of trim46a, a zebrafish homologue of TRIM46, were studied in zebrafish embryo. First, maternal transcripts of trim46a were present at 1 cell stage whereas zygotic messages were abundant in the eyes, MHB (Midbrain-Hindbrain Boundary) and hindbrain at 24 hpf (hours post fertilization). Second, transcriptional regulatory region of trim46a contains cis-acting elements binding a transcriptional factor Foxa2. Transcription of foxa2 is positively regulated by Sonic Hedgehog (SHH), and treatment of cyclopamine, an SHH inhibitor, represses transcription of foxa2 in 4 hpf through 24 hpf embryos. Third, the transcriptional repression of foxa2 inhibited transcription of trim46a to cause developmental defects in the midbrain and MHB. Finally, spatiotemporal expression patterns of a midbrain marker otx2b in the developmental defects confirmed inhibition of SHH by cyclopamine caused underdevelopment of the midbrain and MHB at 24 hpf. We propose a signaling network where trim46a contributes to development of the midbrain and MHB via Foxa2, a downstream element of SHH signaling in zebrafish embryogenesis.

This study proposes a 2D surface correlation-based indoor localization technology using LTE fingerprinting with an accuracy of several meters. The most important problem with RF fingerprinting is that the location discernment of signal strength becomes exceedingly low as the distance from the RF signal source increases. Instantaneous RSS measurement based conventional fingerprinting involves the installation of several signal sources to improve location discernment. However, additional installations of LTE base stations (BSs) are impossible. To improve location discernment, the proposed technology utilizes a spatial RSS pattern extracted using the Pedestrian-Dead Reckoning during user movement. The use of the proposed technology greatly improves the accuracy and availability of LTE signals using the pattern. Additionally, the following two points should be considered. First, the spatially accumulated pattern contains location errors that can cause pattern distortion. The proposed technology performs pattern correction through feature matching using RSS mark and crossroad locations. Second, the accuracy of pattern matching may be decreased prior to sufficient pattern accumulation. For the rapid convergence of the pattern matching, the proposed technology performs correlation pattern analysis. This approach detects the point in which the discernment is increased by pattern accumulation and limits the search range around the matching point. To verify the performance, we conducted tests in a shopping mall where only one LTE BS ID is available. Consequently, the convergence distance of pattern matching was improved by 69% after pattern analysis. Furthermore, it was confirmed that the localization error after convergence improved from 4.16 m to 2.82 m.

Magnetic flux ropes, often observed during intervals of interplanetary coronal mass ejections, have long been recognized to be critical in space weather. In this work, we focus on magnetic flux rope structure but on a much smaller scale, and not necessarily related to interplanetary coronal mass ejections. Using near-Earth solar wind advanced composition explorer (ACE) observations from 1998 to 2016, we identified a total of 309 small-scale magnetic flux ropes (SMFRs). We compared the characteristics of identified SMFR events with those of normal magnetic cloud (MC) events available from the existing literature. First, most of the MCs and SMFRs have similar values of accompanying solar wind speed and proton densities. However, the average magnetic field intensity of SMFRs is weaker (~7.4 nT) than that of MCs (~10.6 nT). Also, the average duration time and expansion speed of SMFRs are ~2.5 hr and 2.6 km/s, respectively, both of which are smaller by a factor of ~10 than those of MCs. In addition, we examined the geoeffectiveness of SMFR events by checking their correlation with magnetic storms and substorms. Based on the criteria Sym-H < -50 nT (for identification of storm occurrence) and AL < -200 nT (for identification of substorm occurrence), we found that for 88 SMFR events (corresponding to 28.5 % of the total SMFR events), substorms occurred after the impact of SMFRs, implying a possible triggering of substorms by SMFRs. In contrast, we found only two SMFRs that triggered storms. We emphasize that, based on a much larger database than used in previous studies, all these previously known features are now firmly confirmed by the current work. Accordingly, the results emphasize the significance of SMFRs from the viewpoint of possible triggering of substorms.

Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learning solutions employing deep representations frequently have been reported to greatly outperform those using hand-crafted feature representations. At the same time, they may pick up on aspects that are predominant in the data, yet not actually meaningful or interpretable. In this paper, we therefore propose a systematic way to test the trustworthiness of deep music representations, considering musical semantics. The underlying assumption is that in case a deep representation is to be trusted, distance consistency between known related points should be maintained both in the input audio space and corresponding latent deep space. We generate known related points through semantically meaningful transformations, both considering imperceptible and graver transformations. Then, we examine within- and between-space distance consistencies, both considering audio space and latent embedded space, the latter either being a result of a conventional feature extractor or a deep encoder. We illustrate how our method, as a complement to task-specific performance, provides interpretable insight into what a network may have captured from training data signals.

Profiled side-channel analysis based on deep learning, and more precisely Convolutional Neural Networks, is a paradigm showing significant potential. The results, although scarce for now, suggest that such techniques are even able to break cryptographic implementations protected with countermeasures. In this paper, we start by proposing a new Convolutional Neural Network instance able to reach high performance for a number of considered datasets. We compare our neural network with the one designed for a particular dataset with masking countermeasure and we show that both are good designs but also that neither can be considered as a superior to the other one. Next, we address how the addition of artificial noise to the input signal can be actually beneficial to the performance of the neural network. Such noise addition is equivalent to the regularization term in the objective function. By using this technique, we are able to reduce the number of measurements needed to reveal the secret key by orders of magnitude for both neural networks. Our new convolutional neural network instance with added noise is able to break the implementation protected with the random delay countermeasure by using only 3 traces in the attack phase. To further strengthen our experimental results, we investigate the performance with a varying number of training samples, noise levels, and epochs. Our findings show that adding noise is beneficial throughout all training set sizes and epochs.

Interplanetary scintillation-driven (IPS-driven) ENLIL model was jointly developed by University of California, San Diego (UCSD) and National Aeronaucics and Space Administration/Goddard Space Flight Center (NASA/GSFC). The model has been in operation by Korean Space Weather Cetner (KSWC) since 2014. IPS-driven ENLIL model has a variety of ambient solar wind parameters and the results of the model depend on the combination of these parameters. We have conducted researches to determine the best combination of parameters to improve the performance of the IPS-driven ENLIL model. The model results with input of 1,440 combinations of parameters are compared with the Advanced Composition Explorer (ACE) observation data. In this way, the top 10 parameter sets showing best performance were determined. Finally, the characteristics of the parameter sets were analyzed and application of the results to IPS-driven ENLIL model was discussed.

In wireless sensor networks, the clustering methods proposed to improve the energy efficiency of a sensor network can lead to severe energy consumption by the cluster heads. Furthermore, in the case where an unbalanced cluster structure is generated, the overall lifetime of the sensor networks is significantly reduced. Moreover, existing cooperative communication methods, for example, CoopLEACH, waste energy because of the unilateral cooperative communication method, which does not consider the distance between the sink and cluster heads. To solve these problems, this paper proposes a novel method that transmits and collects data according to the density of the nodes and the distance between the sink and cluster heads. Through extensive performance studies, we observed that our method is 12% more energy-efficient than CoopLEACH.