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The optimal ship hull form in contemporary design practice primarily consists of three parts: hull form modification, performance prediction, and optimization. Hull form modification is a crucial step to affect optimization efficiency because the baseline hull form is varied to search for performance improvements. The conventional hull form modification methods mainly rely on human decisions and intervention. As a direct expression of the three-dimensional hull form, the lines are not appropriate for machine learning techniques. This is because they do not explicitly express a meaningful performance metric despite their relatively large data dimension. To solve this problem and develop a novel machine-based hull form design technique, an autoencoder, which is a dimensional reduction technique based on an artificial neural network, was created in this study. Specifically, a convolutional autoencoder was designed; firstly, a convolutional neural network (CNN) preprocessor was used to effectively train the offsets, which are the half-width coordinate values on the hull surface, to extract feature maps. Secondly, the stacked encoder compressed the feature maps into an optimal lower dimensional-latent vector. Finally, a transposed convolution layer restored the dimension of the lines. In this study, 21 250 hull forms belonging to three different ship types of containership, LNG carrier, and tanker, were used as training data. To describe the hull form in more detail, each was divided into several zones, which were then input into the CNN preprocessor separately. After the training, a low-dimensional manifold consisting of the components of the latent vector was derived to represent the distinctive hull form features of the three ship types considered. The autoencoder technique was then combined with another novel approach of the surrogate model to form an objective function neural network. Further combination with the deterministic particle swarm optimization method led to a successful hull form optimization example. In summary, the present convolutional autoencoder has demonstrated its significance within the machine learning-based design process for ship hull forms.Graphical Abstract

Human embryonic stem cells (hESCs) are naturally equipped to maintain genome integrity to minimize genetic mutations during early embryo development. However, genetic aberration risks and subsequent cellular changes in hESCs during in vitro culture pose a significant threat to stem cell therapy. While a few studies have reported specific somatic mutations and copy number variations (CNVs), the molecular mechanisms underlying the acquisition of ‘culture-adapted phenotypes’ by hESCs are largely unknown. Therefore, we conducted comprehensive genomic, single-cell transcriptomic, and single-cell ATAC-seq analyses of an isogenic hESC model displaying definitive ‘culture-adapted phenotypes’. We found that hESCs lacking TP53, in which loss-of-function mutations were identified in human pluripotent stem cells (hPSCs), presented a surge in somatic mutations. Notably, hPSCs with a copy number gain of 20q11.21 during early passage did not present ‘culture-adapted phenotypes’ or BCL2L1induction. Single-cell RNA-seq and ATAC-seq analyses revealed active transcriptional regulation at the 20q11.21 locus. Furthermore, the induction of BCL2L1and TPX2to trigger ‘culture-adapted phenotypes’ was associated with epigenetic changes facilitating TEA domain (TEAD) binding. These results suggest that 20q11.21 copy number gain and additional epigenetic changes are necessary for expressing ‘culture-adapted phenotypes’ by activating gene transcription at this specific locus.

Automatic login is a commonly used feature of smartphones, because their small keyboards make it difficult to key in user credential information. However, this feature may pose a serious risk to smartphone users? privacy. The stored data for automatic login could be stolen by an attacker, resulting in identity theft. In this article, we demonstrate an execution of this attack in a systematic manner through two real-world Android application case studies by implementing a prototype. We also discuss five possible defense strategies to mitigate the risk of user credential data being stolen from the application files.

The Bio-Fenton reaction was applied to degrade bisphenol A (BPA, 0.1mM), a commonly used plastic additive, via in-situproduced hydrogen peroxide (H2O2) from glucose oxidase (GOx, 10 U), glucose (32mM), and Fe(III)-citrate (0.5mM) at pH 5.3. The reaction produced 0.15-1.1 × 10−16M of •OH at a steady-state ([•OH]ss), which contributed significantly to BPA degradation. The second-order rate constant (k) for BPA degradation was 1.39-2.38 × 109M−1s−1, with 80% degradation of 0.1mM BPA in 10 days of incubation and the production of hydroxylated intermediates, 4-hydroxyacetophenone, and hydroquinone, which were further filtered into small carboxylic acids via •OH-mediated reactions: hydroxylation, oxidation, scission of the C-C bond between the two BPA aromatic rings, and ring cleavage. Based on the successful application of the Bio-Fenton reaction to the radical scavenger and plastic additive BPA, microorganisms capable of producing H2O2through diverse oxidase enzymatic systems can be applied to degrade diverse pollutants in environments where ubiquitous Fe(III) with organic iron chelators is present.

Cations in an electrolyte modulate microenvironments near the catalyst surface and affect product distribution from an electrochemical CO2reduction reaction, and thus, their interaction with intermediate states has been tried to be probed. Herein, we directly observed the cation effect on *CO intermediates on the Cu(OH)2-derived catalyst in real time through operando surface-enhanced Raman spectroscopy at high overpotentials (−1.0 VRHE). Atop *CO peaks are composed of low-frequency binding *CO (*COLFB) and high-frequency binding *CO (*COHFB) because of their adsorption sites. These two *CO intermediates are found to have different sensitivities to the cation-induced field, and each *CO is proposed to be suitably stabilized for efficient C–C coupling. The proportions between *COHFBand *COLFBare dependent on the type of alkali cations, and the increases in the *COHFBratio have a high correlation with selective C2H4production under K+and Cs+, indicating that *COHFBis the dominant and fast active species. In addition, as the hydrated cation size decreases, *COLFBis more sensitively red-shifted than *COHFB, which promotes C–C coupling and suppresses C1products. Through time-resolved operando measurements, dynamic changes between the two *CO species are observed, showing the rapid initial adsorption of *COHFBand subsequently reaching a steady ratio between *COLFBand *COHFB.