Search

The wave equations of a novel model for the electro-elastic Kirchhoff plate containing the flexoelectric effect are constructed using a variational formulation. Through an Improved Plane Wave Expansion (IPWE) approach, the proposed wave equations are then applied to examine the elastic wave bandgap and defect band in a phononic crystal plate structure without or with a defect. The results demonstrate that the flexoelectric effect on the bandgap is apparent when the structure is at the submicron scale and fades as the scale increases. Moreover, at all length scales, the magnitude of the bandgap varies dramatically with lattice length and inclusion volume ratio. This suggests that the frequency and broadness of the bandgap can be tuned by modifying the volume percentage of inclusion, plate size parameters and lattice length. The dispersion curves of the defect model and corresponding mode shape at the defect band are also studied, which can indicate the applicability of the current model incorporating flexoelectric effect in energy harvesting and wave focusing. The novel model and these findings would help the design of MEMS/NEMS devices for enhanced wave guiding when flexoelectric-based electro-elastic fields are involved. • Wave equations of a novel model for the electro-elastic Kirchhoff plate with flexoelectric effect are proposed. • The bandgap and defect band in a phononic crystal plate are analytically solved based on the new wave equations. • The flexoelectric effect is apparent when the plate is at the submicron scale and fades as the scale increases. • The dispersion curves of the defect model and corresponding mode shape at the defect band can be obtained. • The novel model and numerical results are useful for enhancing energy harvesting and wave focusing. [ABSTRACT FROM AUTHOR]

All-inorganic quantum dot (QD) light-emitting diodes (AI-QLEDs) with excellent stability received enormous interest in the past few years. Nevertheless, the vast energy offset and the high trap density at the NiO X /QDs interface limit hole injection leading to fluorescence quenching and hampering the performance. Here, we present self-assembled monolayers (SAMs) with phosphonic acid (PA) anchoring groups modifying NiO X hole transport layer (HTL) to tune energy level and passivate trap states. This strategy facilitates hole injection owning to the well-aligned energy level by interface dipole, downshifting the vacuum level, reducing the hole injection barrier from 0.94 eV to 0.28 eV. Meanwhile, it mitigates the interfacial recombination by passivating surface hydroxyl group (-OH) and oxygen vacancy (V O) traps in NiO X. The electron leakage from QDs toward NiO X HTL is significantly suppressed. The all-inorganic R-QLEDs exhibit one of the highest maximum luminance, external quantum efficiency and operational lifetime of 88980 cd m−2, 10.3 % and 335045 h (T 50 @100 cd m−2), respectively. The as-proposed interface engineering provides an effective design principle for high-performance AI-QLEDs for future outdoor and optical projection-type display applications. [Display omitted] • SAMs with phosphonic acid (PA) anchoring groups modify NiO X to reduce the hole barrier from 0.94 eV to 0.28 eV. • The PA passivates surface-OH and fill oxygen vacancy traps in NiO X , reducing the quenching of QDs at the interface. • The 2D GIXRD demonstrated improved QD film quality on the SAMs-modify-NiO X hole transport layer. • The QLEDs exhibit one of the highest external quantum efficiency and lifetime of 10.3 % and 335045 h (T 50 @100 cd m-2). [ABSTRACT FROM AUTHOR]

Multimodal images such as magnetic resonance imaging (MRI) and positron emission tomography (PET) could provide complementary information about the brain and have been widely investigated for the diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD). However, multimodal brain images are often incomplete in clinical practice. It is still challenging to make use of multimodality for disease diagnosis with missing data. In this paper, we propose a deep learning framework with the multi-level guided generative adversarial network (MLG-GAN) and multimodal transformer (Mul-T) for incomplete image generation and disease classification, respectively. First, MLG-GAN is proposed to generate the missing data, guided by multi-level information from voxels, features, and tasks. In addition to voxel-level supervision and task-level constraint, a feature-level auto-regression branch is proposed to embed the features of target images for an accurate generation. With the complete multimodal images, we propose a Mul-T network for disease diagnosis, which can not only combine the global and local features but also model the latent interactions and correlations from one modality to another with the cross-modal attention mechanism. Comprehensive experiments on three independent datasets (i.e., ADNI-1, ADNI-2, and OASIS-3) show that the proposed method achieves superior performance in the tasks of image generation and disease diagnosis compared to state-of-the-art methods. • A novel deep learning framework is proposed for incomplete image generation and multimodal diagnosis in Alzheimer's disease. • A multi-level guided generative adversarial network is proposed for generation of missing imaging data. • A multimodal transformer is proposed to model multimodal interactions and combine the features for disease classification. • Experiments on three independent datasets demonstrate the superior performance of our method [ABSTRACT FROM AUTHOR]

Abstract Public bike system (PBS) improves mobility in a healthy, emission-free, and congestion-reducing way. Various studies have examined the characteristics of PBS. Hitherto, there lacks a quantitative analysis directed to the effects of the newly emerged dockless bike-sharing system (DBSS) on the PBS. To fill this gap, this paper takes Nanjing PBS as a case study, to explore the changes of PBS usage after the popularity of DBSS at two levels, including user level and station level. Results show that the DBSS has a greater effect on weekdays than weekends, and in contrast with users over 40 years old, users who are younger, especially those of 21 to 25 years old, have a larger reduction in usage frequency. These findings suggest that the DBSS appeals to young commuters to some extent. Therefore, to improve the efficiency of PBS, more attention should be paid to the improvement of stations where young commuters often borrow and return bikes. As for the impacts of DBSS on the trip duration, there is no significant sexual difference. Notice that the patterns of pickups and returns over the hours of the day remain unchanged after the popularity of DBSS, but for station dynamics over the days of the week, the number of active days is reduced. [ABSTRACT FROM AUTHOR]

One of the most important factors governing the photovoltaic efficiency and environmental stability of Perovskite Solar Cells (PSCs) is the quality of their perovskite films. However, the present study demonstrates that CH 3 NH 3 PbI 3 perovskite films after anti-solvent washing are far from perfect on the surface, where the crystallization degree is quite low with complex multi-phases associated with numerous defects and notable chemical inhomogeneity. Herein, we report a novel anti-solvent washing treatment simply using Hydrogen Iodide (HI) additive in chlorobenzene (CB) which can enormously improve CH 3 NH 3 PbI 3 films surface with high crystallization phase purity and chemical homogeneity, leading to excellent structural stability under humidity, heat and even tensile force. Based on such high quality films with defect-less surface, fabricated planar PSCs with an architecture of ITO/TiO 2 /CH 3 NH 3 PbI 3 /Spiro-OMeTAD/MoO x /Au present a champion PCE of 19.94% and improved stability under humidity. Moreover, fabricated flexible planar PSCs on PET/ITO substrates with a champion PCE of 17.32% are shown to be more robust under tensile force than their control devices. The present study thus not only provides more insight into the facile defects passivation process on the surface achieved via balancing halide ions, but also provides a practical but efficient treatment which can be widely used to fabricate high quality CH 3 NH 3 PbI 3 films for environmentally stable high performance device applications. [ABSTRACT FROM AUTHOR]

Interface engineering resulting in good contact, enhanced transport capability, and matched energy levels is indispensable and critical for the development of high-performance planar perovskite solar cells (PSCs). Here, we report an excellent electron-transporting layer (ETL) that can simultaneously enhance the stability and efficiency of n-i-p planar PSCs. Large π-conjugated graphdiyne (GD) was introduced into cross-linkable fullerene [6,6]-phenyl-C61-butyric styryl dendron ester (PCBSD) to improve the film orientation. Raman spectroscopy and 2D grazing incidence X-ray diffraction (GIXRD) measurements revealed that a strong π–π stacking interaction occurred between GD and cross-linkable PCBSD (C-PCBSD), generating a face-on stacked composite film. The orientated C-PCBSD:GD films was favorable for the growth and crystallization of the subsequent perovskite films and provided the merits of superior electron mobility, efficient charge extraction and energy-level tailoring. In addition, the thermally annealed C-PCBSD:GD film provided an adhesive film network with sufficient solvent resistance. Consequently, the perovskite devices delivered a power conversion efficiency of 20.19% with obviously improved cell stability. This indicates a potential application of GD-modified cross-linkable fullerene as an ETL in n-i-p structure PSCs. The finding opens a new route to deposit the fullerene films with ordered orientation by 2D materials with large π-conjugation, and thus to control the subsequent perovskite crystallization. [ABSTRACT FROM AUTHOR]

The irradiation-induced defects strongly influence the mechanical behaviors of zirconium (Zr) and its alloys in nuclear reactors. In this work, we focus on how the vacancies change the mechanical properties of α-Zr through density functional theory (DFT) calculations. Both uniformly distributed vacancies and vacancy clusters were considered. And a wide range of vacancy concentrations from 0.005 to 0.063 (molar fraction) was modeled. The most stable configurations of di- and trivacancy clusters were predicted, which correspond to the most compact distribution of vacancies. Mechanical properties were explored in terms of single-crystal elastic constants, based on which the polycrystalline elastic moduli, Pugh's ratio for ductility and Vickers hardness were derived. Our results show that the existence of uniformly distributed vacancies can reduce the ductility, while enhance the hardness in general. However, when the vacancy concentration is larger than a critical value, a rise in the ductility and a reduction in the hardness occur, which may contribute to the degeneration of the material. Compared with the uniform distribution of vacancies, clustering of vacancies strengthens the above changes of ductility and hardness. Moreover, it was found that the anisotropy of Young's modulus decreases with increasing vacancy concentration. [ABSTRACT FROM AUTHOR]

Nearshore tsunami hazard can be greatly affected by the tidal level, especially when large tsunami waves meet the high tidal level. This study aims to quantify the probability of the peak nearshore tsunami elevation (PNTE) caused by seismic tsunamis considering the randomness of event occurrence time and the variation of tidal level. The coast around the Pearl River Estuary has been chosen as the study area to show the influence of tidal level on the probability of nearshore tsunami hazard. Two approaches are proposed to calculate the PNTE considering the effect of tide in a probabilistic tsunami assessment framework. One approach is a linear superposition of tsunami wave upon the tidal level, and the other considering the influence of tidal level on tsunami nearshore amplification. The probability of the PNTE along the concern coast is obtained. The results considering the effect of tide show that the PNTE corresponding to short return period is obviously larger than that based on the mean sea level, but no obvious difference in the case of long return period. Moreover, in the study area, the two approaches considering the effect of tide show only slight difference in estimating the probability of PNTE, which suggests the applicability of the linear superposition method in nearshore tsunami hazard assessments. • Incorporate tidal uncertainty into probabilistic tsunami hazard assessment. • Data-based support for the arrival time of tsunami is uniformly distributed. • Confirm that ignoring tides may underestimate tsunami risk. [ABSTRACT FROM AUTHOR]

The optical filter effect of the metal–dielectric–metal (MDM) waveguide with stub structure is investigated with surface plasmons (SPs) interference theory and simulated using finite difference time domain (FDTD) method. The transmission process of the surface plasmons propagating through the stub structure is discussed in detail, and testified by different time step field intensity images of FDTD simulation. It is verified by the FDTD simulation that the oscillation periods of the transmission curves are proportional to the wavelengths, and the number of the wavelength of the standing wave in the stub is linear to the stub length d . When the monochromatic continuous waves are incident, the transmissions are also periodically changed with the length of the stub. The shorter wavelengths present better filter sensitivity than longer wavelengths. The above optical filter effect implies that the MDM waveguide with stub structure can be applied in the fields of photonic integration devices and sensors. [ABSTRACT FROM AUTHOR]

Abnormal activity recognition has been paid much attention in the field of healthcare and related applications, especially for the elderly people׳s physical and mental health, the high risk of the fall accident and its caused injures have gradually attracted more and more concerns. At present, wearable devices based fall detection technology can effectively and timely monitor the occurrence of fall accidents and help the injured person receive the first aid. However, the built classifiers of traditional approaches for fall detecting and monitoring suffer from a high false-alarm rate though they can reach a relatively high detection accuracy, further they have to face with the imbalance problem because sensor data of abnormal activities are usually rare in the realistic application. To address this challenge, we propose two-stage adaptive weighted extreme learning machine (AWELM) method for eyeglass and watch wearables based fall detecting and monitoring. Experimental results validate and illustrate significant efficiency and effectiveness of the proposed method and show that, our approach firstly achieves a good balance between high detection accuracy and low false-alarm rate based on our two-stage recognition scheme; secondly enables our imbalance learning approach for scarce abnormal activity data by two-stage adaptive weighted method; thirdly provides a light-weight classifier solution to resource constrained wearable devices using extreme learning machine with the fast training speed and good generalization capability, which enables large-scale mHealth applications and especially helps the elderly people to greatly reduce the risk of fall accidents finally. [ABSTRACT FROM AUTHOR]