Face Editing Using Part-Based Optimization of the Latent Space

We propose an approach for interactive 3D face editing based on deep generative models. Most of the current face modeling methods rely on linear methods and cannot express complex and non-linear deformations. In contrast to 3D morphable face models based on Principal Component Analysis (PCA), we introduce a novel architecture based on variational autoencoders. Our architecture has multiple encoders (one for each part of the face, such as the nose and mouth) which feed a single decoder. As a result, each sub-vector of the latent vector represents one part. We train our model with a novel loss function that further disentangles the space based on different parts of the face. The output of the network is a whole 3D face. Hence, unlike part-based PCA methods, our model learns to merge the parts intrinsically and does not require an additional merging process. To achieve interactive face modeling, we optimize for the latent variables given vertex positional constraints provided by a user. To avoid unwanted global changes elsewhere on the face, we only optimize the subset of the latent vector that corresponds to the part of the face being modified. Our editing optimization converges in less than a second. Our results show that the proposed approach supports a broader range of editing constraints and generates more realistic 3D faces.     

Predictive model for shear strength estimation in reinforced concrete beams with recycled aggregates using Gaussian process regression

Neural Computing and Applications - In order to attain sustainable development, recycled concrete aggregates (RCAs) are increasingly utilized in civil engineering projects. Therefore, it is vital...     

Free vibration analysis of double-viscoelastic nano-composite micro-plates reinforced by FG-SWCNTs based on the third-order shear deformation theory

Microsystem Technologies - In this paper, free vibration analysis of a double viscoelastic nano-composite plate system reinforced by functionally graded single-walled carbon nanotubes (FG-SWCNT)...     

Glyphosate Adsorption from Water Using Hierarchically Porous Metal–Organic Frameworks

The selective removal of one ligand in mixed-ligand MOFs upon thermolysis provides a powerful strategy to introduce additional mesopores without affecting the overall MOF structure. By varying the initial ligand ratio, MOFs of the MIL-125-Ti family with two distinct hierarchical pore architectures are synthesized, resembling either large cavities or branching fractures. The performance of the resulting hierarchically porous MOFs is evaluated toward the adsorptive removal of glyphosate (N-(phosphonomethyl)glycine) from water, and the adsorption kinetics and mechanism are examined. Due to their strong affinity for phosphoric groups, the numerous Ti–OH groups resulting from the selective ligand removal act as natural anchor points for effective glyphosate uptake. The relationships between contact duration, glyphosate concentration, and adsorbent dosage are investigated, and the impact of these parameters on the effectiveness of glyphosate removal from contaminated water samples is examined. The introduction of additional mesopores has increased the adsorption capacities by nearly 3 times with record values exceeding 440.9 mg g⁻¹, which ranks these MOFs among the best-reported adsorbents.     

Giant Anomalous Hall and Nernst Conductivities in Magnetic All-d Metal Heusler Alloys

All-d Heuslers are a category of novel compounds combining versatile functionalities such as caloric responses and spintronics with enhanced mechanical properties. Despite the promising transport properties (anomalous Hall (AHC) and anomalous Nernst (ANC) conductivities) shown in the conventional Co₂XY Heuslers with p-d hybridization, the all-d Heuslers with only d-d hybridization open a new horizon to search for new candidates with outstanding transport properties. In this work, the AHC and ANC are evaluated for thermodynamically stable ferro/ferri-magnetic all-d-metal regular Heusler compounds based on high-throughput first-principles calculations. It is observed that quite a few materials exhibit giant AHCs and ANCs, such as cubic Re₂TaMn with an AHC of 2011 S cm^-1, and tetragonal Pt₂CrRh with an AHC of 1966 S cm^-1 and an ANC of 7.50 A m^-1K^-1. Comprehensive analysis on the electronic structure reveals that the high AHC can be attributed to the occurrence of the Weyl nodes or gapped nodal lines in the neighborhood of the Fermi level. The correlations between such transport properties and the number of valence electrons are also thoroughly investigated, which provides a practical guidance to tailor AHC and ANC via chemical doping for transverse thermoelectric applications.     

Plasma Enhanced Lithium Coupled with Cobalt Fibers Arrays for Advanced Energy Storage

Construction of high efficiency and stable Li metal anodes is extremely vital to the breakthrough of Li metal batteries. In this study, for the first time, groundbreaking in situ plasma interphase engineering is reported to construct high-quality lithium halides-dominated solid electrolyte interphase layer on Li metal to stabilize & protect the anode. Typically, SF₆ plasma-induced sulfured and fluorinated interphase (SFI) is composed of LiF and Li₂S, interwoven with each other to form a consecutive solid electrolyte interphase. Simultaneously, brand-new vertical Co fibers (diameter: ≈5 µm) scaffold is designed via a facile magnetic-field-assisted hydrothermal method to collaborate with plasma-enhanced Li metal anodes (SFI@Li/Co). The Co fibers scaffold accommodates active Li with mechanical integrity and decreases local current density with good lithiophilicity and low geometric tortuosity, supported by DFT calculations and COMSOL Multiphysics simulation. Consequently, the assembled symmetric cells with SFI@Li/Co anodes exhibit superior stability over 525 h with a small voltage hysteresis (125 mV at 5 mA cm⁻²) and improved Coulombic efficiency (99.7%), much better than the counterparts. Enhanced electrochemical performance is also demonstrated in full cells with commercial cathodes and SFI@Li/Co anode. The research offers a new route to develop advanced alkali metal anodes for energy storage.     

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.     

Passivating Defects of Perovskite Solar Cells with Functional Donor-Acceptor–Donor Type Hole Transporting Materials

In this study, a series of donor–acceptor–donor (D-A-D) type small molecules based on the fluorene and diphenylethenyl enamine units, which are distinguished by different acceptors, as holetransporting materials (HTMs) for perovskite solar cells is presented. The incorporation of the malononitrile acceptor units is found to be beneficial for not only carrier transportation but also defects passivation via Pb–N interactions. The highest power conversion efficiency of over 22% is achieved on cells based on V1359, which is higher than that of spiro-OMeTAD under identical conditions. This st shows that HTMs prepared via simplified synthetic routes are not only a low-cost alternative to spiro-OMeTAD but also outperform in efficiency and stability state-of-art materials obtained via expensive cross-coupling methods.     

Agent-based time delay margin in consensus of multi-agent systems by an event-triggered control method: Concept and computation

This paper deals with defining the concept of agent-based time delay margin and computing its value in multi-agent systems controlled by event-triggered based controllers. The agent-based time delay margin specifying the time delay tolerance of each agent for ensuring consensus in event-triggered controlled multi-agent systems can be considered as complementary for the concept of (network) time delay margin, which has been previously introduced in some literature. In this paper, an event-triggered control method for achieving consensus in multi-agent systems with time delay is considered. It is shown that the Zeno behavior is excluded by applying this method. Then, in a multi-agent system controlled by the considered event-triggered method, the concept of agent-based time delay margin in the presence of a fixed network delay is defined. Moreover, an algorithm for computing the value of the time delay margin for each agent is proposed. Numerical simulation results are also provided to verify the obtained theoretical results.     

Magnetic and photocatalytic properties of CoFe2O4/Ni nanocomposites

Journal of Electroceramics - In this research, hard/soft CoFe2O4/Ni magnetic nanocomposite samples with different concentrations of Ni were successfully produced by a two-step mechanical alloying...