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Perturbation theory (PT) might be one of the most powerful and fruitful tools for both physicists and chemists, which has led to a wide variety of applications. Over the past decades, advances in quantum computing provide opportunities for alternatives to classical methods. Recently, a general quantum circuit estimating the low order PT corrections has been proposed. In this article, we revisit the quantum circuits for PT calculations, and develop the methods for higher order PT corrections of eigenenergy, especially the 3rd and 4th order corrections. We present the feasible quantum circuit to estimate each term in these PT corrections. There are two the fundamental operations in the proposed circuit. One approximates the perturbation terms, the other approximates the inverse of unperturbed energy difference. The proposed method can be generalized to higher order PT corrections. [ABSTRACT FROM AUTHOR]

Due to the complex interactions between multiple infectious diseases, the spreading of diseases in human bodies can vary when people are exposed to multiple sources of infection at the same time. Typically, there is heterogeneity in individuals' responses to diseases, and the transmission routes of different diseases also vary. Therefore, this paper proposes an SIS disease spreading model with individual heterogeneity and transmission route heterogeneity under the simultaneous action of two competitive infectious diseases. We derive the theoretical epidemic spreading threshold using quenched mean-field theory and perform numerical analysis under the Markovian method. Numerical results confirm the reliability of the theoretical threshold and show the inhibitory effect of the proportion of fully competitive individuals on epidemic spreading. The results also show that the diversity of disease transmission routes promotes disease spreading, and this effect gradually weakens when the epidemic spreading rate is high enough. Finally, we find a negative correlation between the theoretical spreading threshold and the average degree of the network. We demonstrate the practical application of the model by comparing simulation outputs to temporal trends of two competitive infectious diseases, COVID-19 and seasonal influenza in China. [ABSTRACT FROM AUTHOR]

Cellular senescence is an irreversible cell arrest process, which is determined by a variety of complicated mechanisms, including telomere attrition, mitochondrial dysfunction, metabolic disorders, loss of protein homeostasis, epigenetic changes, etc. Cellular senescence is causally related to the occurrence and development of age-related disease. The elderly is liable to suffer from disorders such as neurodegenerative diseases, cancer, and diabetes. Therefore, it is increasingly imperative to explore specific countermeasures for the treatment of age-related diseases. Numerous studies on humans and mice emphasize the significance of metabolic imbalance caused by short telomeres and mitochondrial damages in the onset of age-related diseases. Although the experimental data are relatively independent, more and more evidences have shown that there is mutual crosstalk between telomeres and mitochondrial metabolism in the process of cellular senescence. This review systematically discusses the relationship between telomere length, mitochondrial metabolic disorder, as well as their underlying mechanisms for cellular senescence and age-related diseases. Future studies on telomere and mitochondrial metabolism may shed light on potential therapeutic strategies for age-related diseases. [ABSTRACT FROM AUTHOR]

Video captioning aims to generate a natural language sentence to describe the main content of a video. Since there are multiple objects in videos, taking full exploration of the spatial and temporal relationships among them is crucial for this task. The previous methods wrap the detected objects as input sequences, and leverage vanilla self-attention or graph neural network to reason about visual relations. This cannot make full use of the spatial and temporal nature of a video, and suffers from the problems of redundant connections, over-smoothing, and relation ambiguity. In order to address the above problems, in this paper we construct a long short-term graph (LSTG) that simultaneously captures short-term spatial semantic relations and long-term transformation dependencies. Further, to perform relational reasoning over the LSTG, we design a global gated graph reasoning module (G3RM), which introduces a global gating based on global context to control information propagation between objects and alleviate relation ambiguity. Finally, by introducing G3RM into Transformer instead of self-attention, we propose the long short-term relation transformer (LSRT) to fully mine objects’ relations for caption generation. Experiments on MSVD and MSR-VTT datasets show that the LSRT achieves superior performance compared with state-of-the-art methods. The visualization results indicate that our method alleviates problem of over-smoothing and strengthens the ability of relational reasoning. [ABSTRACT FROM AUTHOR]

With the continuous reform of intelligent manufacturing, industrial production has gradually developed from automation to intelligence. The fusion of vision technology and industrial machines has become a hot research direction in current intelligent transformation. However, machines are not as flexible as humans when grabbing, and still have great limitations. Affected by various characteristics of target objects, such as shape, material, weight and other factors, as well as complex and changeable environmental factors, the research of machine grabbing still faces severe challenges. For the actual complex working conditions, the poor target detection effect leads to the inability to complete accurate grabbing, which affects the production efficiency. This paper proposes a grabbing system with convolutional neural network, which can achieve target detection, classification, positioning and grabbing tasks. First, by comparing the current mainstream target recognition and detection algorithms, select SSD that have both real-time performance and accuracy. Then make specific network structure improvements according to the detection requirements, and insert the Inception structure. At the same time optimize its loss function and nonmaximum suppression. The improved recognition rate is higher, and the target detection frame is closer to the real part, which greatly reduces the recognition error. Second, this research proposes an algorithm model for regional posture detection and grabbing positioning, which uses the output of the previous stage as input to perform posture detection and grabbing positioning of the grabbed target. In the network, the posture angle of the grabbing target is output in a classified manner, and the position coordinates of the grabbing point are output using a regression method. Experiments have proved that our method can perform efficient target recognition and grabbing positioning. [ABSTRACT FROM AUTHOR]

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]

Due to the scarcity of human identities labels, unsupervised person re-identification (re-ID) draws much attention recently, which attempts to learn discriminative person representation without labels. Domain adaptation based methods utilize the labeled sample in source domain and transfer the knowledge to the unlabeled target domain. However, for person re-ID, no identities overlapping between source and target domain leads to the difficulty during adaptation. To address this problem, we propose an unsupervised adversarial domain adaptation method for person re-ID which exploits the pixel level and feature level alignments. Specifically, we utilize CycleGAN to transfer the target domain style to the source domain for the pixel level, and we propose an adversarial metric adaptation method which aligns between the source domain and target domain for the feature level. We further explore the feature similarity laying on manifold structure revealed by the features through the similarity diffusion. To verify the efficacy of our proposed method, we conduct extensive experiments on three benchmark datasets: Market1501, Duke-MTMC, and MSMT17. Comparing with state-of-the-art unsupervised domain adaptation approaches, we have comparable performance on ranking metric and significant improvement on mAP metric, which validates the efficacy of the proposed technique for person re-identification tasks. [ABSTRACT FROM AUTHOR]

The article delves into the efficacy of Sampled Softmax (SSM) loss in item recommendation, emphasizing its advantages over conventional pointwise and pairwise losses. It addresses key questions regarding the suitability of SSM loss for recommendation tasks and examines its conceptual benefits. Topics include its ability to mitigate popularity bias, facilitate informative gradient mining, and enhance top-K performance, shedding light on its potential to revolutionize recommendation systems.

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]

Correlation Filter (CF) based algorithms play an important role in the field of Visual Object Tracking (VOT) due to their high accuracy and low computational complexity. While existing CF tracking algorithms suffer performance degradation due to inaccurate object modeling. In this paper, we improve the object modeling accuracy in both CF training stage and target detection procedure to preventing the drift problem. Specifically, we propose a multi-model structure for CF trackers to capture the target appearance changes, where different appearance models are trained with specific samples to catch the salient features of the target and reduce the computational cost. Furthermore, a space filter for detection features is designed to suppress the boundary effect under Gaussian motion prior, which contributes to improving the accuracy of position estimation. We deploy our method to three hand-crafted features based CF trackers to perform real-time visual tracking on popular benchmarks. The experimental results demonstrate the efficacy of our proposed scheme and the efficiency of our trackers. In addition, we provide a comprehensive analysis of the proposed method to facilitate application. [ABSTRACT FROM AUTHOR]

Clouds lead to missing or distorted land-related information in impacted areas in optical remote sensing images. Cloud masking, which labels cloud-contaminated pixels, forms the basis for subsequent image utilization, such as excluding the distorted pixel or filling in the missing area. However, due to the diverse spectral, textural, and shape characteristics of different clouds and complicated combinations with the underlying land surfaces, cloud masking has become a challenge in remote sensing image processing. In recent years, the Mask region-based convolutional neural network (R-CNN) method, which performs instance segmentation from a complex background and generates a pixelwise mask for the object of interest, has been used widely in object segmentation tasks. When the Mask R-CNN method is used for cloud masking, the mask result has certain problems, such as failing to extract uncommon clouds and outputting inaccurate mask boundaries for large clouds. To address these problems, we introduce two strategies, group training and boundary optimization, to improve the Mask R-CNN. For group training, samples are divided into several groups. The samples in the first group are used for the initial training, and the samples in the next group are used for evaluation. Only samples with missing or falsely detected clouds are used for tuning the classifier; then, these processes are repeated until all groups have been used or the detection precision becomes stable. For boundary optimization, a block-by-block mask strategy is adopted to guarantee that clouds with diverse sizes have similar performances. Finally, two open data sets and one data set labelled by ourselves are selected to test the proposed method, and the results demonstrate that our method can produce cloud masks for different cloud types and diverse underlying land surfaces and can achieve high accuracies, thereby providing an effective alternative for cloud masking. Compared with the original Mask R-CNN method, our method improves the average recall, average precision, and intersection over union by 5.88%, 2.4%, and 0.071 in pixel level, respectively, demonstrating the effectiveness of our improvement. [ABSTRACT FROM AUTHOR]

Quantum transducers can transfer quantum information between different systems. Microwave–optical photon conversion is important for future quantum networks to interconnect remote superconducting quantum computers with optical fibers. Here, a high‐speed quantum transducer based on a single‐photon emitter in an atomically thin membrane resonator, that can couple single microwave photons to single optical photons, is proposed. The 2D resonator is a freestanding van der Waals heterostructure (which may consist of hexagonal boron nitride, graphene, or other 2D materials) that hosts a quantum emitter. The mechanical vibration (phonon) of the 2D resonator interacts with optical photons by shifting the optical transition frequency of the single‐photon emitter with strain or the Stark effect. The mechanical vibration couples to microwave photons by shifting the resonant frequency of an LC circuit that includes the membrane. Thanks to the small mass of the 2D resonator, both the single‐photon optomechanical coupling strength and the electromechanical coupling strength can reach the strong coupling regime. This provides a way for high‐speed quantum state transfer between a microwave photon, a phonon, and an optical photon. [ABSTRACT FROM AUTHOR]

Correlation filter (CF) is a critical technique to improve accuracy and speed in the field of visual object tracking. Despite being studied extensively, most existing CF methods suffer from failing to make the most of the inherent spatial-temporal prior of videos. To address this limitation, as consecutive frames are eminently resemble in most videos, we investigate a novel scheme to predict targets’ future state by exploiting previous observations. Specifically, in this paper, we propose a prediction based CF tracking framework by learning the spatial-temporal similarity of consecutive frames for sample managing, template regularization, and training response pre-weighting. We model the learning problem theoretically as a novel objective and provide effective optimization algorithms to solve the learning task. In addition, we implement two CF trackers with different features. Extensive experiments are conducted on three popular benchmarks to validate our scheme. The encouraging results demonstrate that the proposed scheme can significantly boost the accuracy of CF tracking, and the two trackers achieve competitive performances against state-of-the-art trackers. We finally present a comprehensive analysis on the efficacy of our proposed method and the efficiency of our trackers to facilitate real-world visual tracking applications. [ABSTRACT FROM AUTHOR]

In order to help financial users to invest, and to provide users with comprehensive and accurate information about financial securities, information about financial securities from multi-heterogeneous information is obtained. The characteristics of financial information are analyzed to provide valuable investment advice to users. According to the financial characteristics of the user's interest, the characteristics of the investor's interest are extracted from the heterogeneous information. Then, the multi-level model is proposed to analyze the characteristics. Through the multi-level model, the conversion of convertible bonds and the net value of closed funds are predicted. In the first level, based on the characteristics of convertible bonds and closed funds, three models of trend evaluation model, SVR (Support Vector Regression) model and neural network backpropagation network (BPN) model are used to predict financial characteristics. In the second level, the results produced by the three models in the first level are fused by the neural network. The third level optimizes the neural network based on the second level. The optimal initial weights and thresholds are selected by genetic algorithm to obtain better prediction results. The results show that the model can predict the characteristics of convertible bonds and closed funds more accurately. Therefore, the model provides a certain reference for financial users' investment. [ABSTRACT FROM AUTHOR]

Abstract As green behavior plays an increasingly important role today, in this paper, we aimed at investigating the spreading process of city resident green behavior. We applied classical threshold model and modified contagion model into a multiplex network to illustrate this dynamic process. Since people always act in conformity with the majorities when they make decisions or take actions, we consider the effects on negative individuals from their positive friends. Only when the proportion of centrist's positive friends surpassed the local awareness ratio α , will he become positive. Also, we consider the influence from green awareness, which facilitate the green behavior. Last but not least, compulsory policy is also taken into account because it will force specific negative individuals to take such green behavior. The theoretical analysis is conducted by microscopic Markov chain approach and the numerical simulations are performed based on the Monte Carlo simulation. The results show that the intensity of policy regulation θ plays a vital role in both spreading threshold and final green behavior size. We attain a new explanation on the extinction of some behaviors and may provide advice on policy modification. Highlights • We construct a model for city resident green awareness–behavior spreading with policy regulation. • The promotion from government policy plays the most important role in city resident green behavior spreading. • Under the directly impact from government, green awareness hardly works on city resident green behavior. [ABSTRACT FROM AUTHOR]

The skin is the largest organ of the human body and the first line of defense against environmental hazards. Many factors, including internal factors such as natural aging and external factors such as ultraviolet radiation and air pollution, can lead to skin aging. Mitochondria provide sufficient energy to maintain the high-speed turnover capacity of the skin, so the quality control of mitochondria plays an indispensable role in this process. Mitochondrial dynamics, mitochondrial biogenesis and mitophagy are the key steps in mitochondrial quality surveillance. They are coordinated to maintain mitochondrial homeostasis and restore damaged mitochondrial function. All of the mitochondrial quality control processes are related to skin aging caused by various factors. Therefore, fine-tuning regulation of the above process is of great significance to the skin aging problem that needs to be solved urgently. This article mainly reviews the physiological and environmental factors causing skin aging, the effects of mitochondrial dynamics, mitochondrial biogenesis and mitophagy on skin aging, as well as their specific regulatory mechanisms. Finally, mitochondrial biomarkers for diagnosis of skin aging, and therapeutic approaches of skin aging via mitochondrial quality control were illustrated. The homeostasis of mitochondrial quality helps to ameliorate skin aging caused by internal and external factors. Both external factors such as ultraviolet (UV) radiation and pollution and internal factors such as natural aging and genetic inheritance can induce skin aging. These factors enhance the production of reactive oxygen species (ROS), promote inflammatory reaction, destroy DNA synthesis, increase matrix metalloproteinases (MMPs) in the skin, and thus accelerate skin aging. Mitochondrial dynamics, mitochondrial biogenesis and mitophagy are collectively involved in the regulation of skin aging. The balance between mitochondrial fission and fusion sustains the stability of mitochondria and attenuates skin aging. Mitochondrial biogenesis improves mitochondrial function and retards skin aging via generating new and healthy mitochondria. Mitophagy engulfs and degrades the damaged proteins and mitochondria, thus ensuring the mitochondrial quality and rescue skin aging (Created with Biorender.com). [Display omitted] • Internal and external factors accelerate the aging process of skin. • The homeostasis of mitochondrial quality affects skin aging. • Mitochondrial dynamics, mitochondrial biogenesis and mitophagy are mutually regulated. • Treatment via targeting mitochondrial quality control contributes to tackling skin aging. [ABSTRACT FROM AUTHOR]

Learning image similarity plays a critical role in real-world multimedia information retrieval applications, especially in Content-Based Image Retrieval (CBIR) tasks, in which an accurate retrieval of visually similar objects largely relies on an effective image similarity function. Crafting a good similarity function is very challenging because visual contents of images are often represented as feature vectors in high-dimensional spaces, for example, via bag-of-words (BoW) representations, and traditional rigid similarity functions, for example, cosine similarity, are often suboptimal for CBIR tasks. In this article, we address this fundamental problem, that is, learning to optimize image similarity with sparse and high-dimensional representations from large-scale training data, and propose a novel scheme of Sparse Online Learning of Image Similarity (SOLIS). In contrast to many existing image-similarity learning algorithms that are designed to work with low-dimensional data, SOLIS is able to learn image similarity from large-scale image data in sparse and high-dimensional spaces. Our encouraging results showed that the proposed new technique achieves highly competitive accuracy as compared to the state-of-the-art approaches but enjoys significant advantages in computational efficiency, model sparsity, and retrieval scalability, making it more practical for real-world multimedia retrieval applications. [ABSTRACT FROM AUTHOR]

The angular dependence of magnetic dichroism in photoemission (MDAD) from valence bands is more complicated than that from core levels. It gives access to spin-resolved information about the bands involved in the photoexcitation process. By using a unique ellipsoidal-mirror analyzer, we are able to observe MDAD from valence bands as a two-dimensional image, which gives a direct view of their angular dependence over a large slice through the Brillouin zone. In this work we have investigated Co films grown on a Cu(001) substrate. By using either circularly polarized light or linearly polarized light at a photon energy of 21 eV, magnetic circular dichroism and magnetic linear dichroism (MLD) from the Fermi edge shows different angular dependencies. This is due to the different excitation processes involved with the different light polarizations and it reflects the symmetry of the 3d bands of the Co. Furthermore, we also find that the sign of the MLD can change if differing initial states are involved. First principles calculations of Co bands and their photoelectron angular distributions with the different light polarizations will be presented. This shows that dichroism in valence band photoemission can provide valuable additional information on the electronic and magnetic properties of ultrathin films. [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]

This study focuses on the coast around the Pearl River Estuary, and assesses tsunami hazard posed by the earthquakes along the Manila Trench. A probabilistic tsunami hazard assessment (PTHA) has been conducted based on 1,380,000 potential earthquake scenarios, in order to consider the uncertainty of seismic parameters as much as possible. To achieve such a large number of scenario simulations, the tsunami at an offshore position is firstly simulated based on a superposition approach proposed in previous studies, and then the joint probability density of incident tsunami period and height at the offshore position is obtained. After that, an amplification factor is adopted to estimate nearshore tsunami height probability from the offshore position. The amplification factor is the main concern in this study. It is commonly estimated from the Green' law based on the linear wave shoaling theory, which is a simple and effective way. However, it may loss accuracy in the coast with complicate topography and tortuous shoreline. This study numerically obtained a dataset of wave amplification factors along the concerned coast, using the two-dimensional FUNWAVE-TVD model based on the Boussinesq equations. The results confirm the significance of incident wave period and height on the amplification factor, and suggest a more carefully estimation on the amplification factor should be taken into consideration. Based the newly obtained dataset of wave amplification factors, the quantified tsunami hazard probability along the concerned coast is given. •1,380,000 potential scenarios are computed to assess nearshore tsunami hazard. • Joint probability density of tsunami period and height at offshore site is given. • Effects of wave period and height on coastal amplification factor are clarified. • Tsunami hazard around the coast of the Pearl River Estuary is presented. [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]

With the explosion of multimedia data, it is usual that different multimedia data often coexist in web repositories. Accordingly, it is more and more important to explore underlying intricate cross-media correlation instead of single-modality distance measure so as to improve multimedia semantics understanding. Cross-media distance metric learning focuses on correlation measure between multimedia data of different modalities. However, the existence of content heterogeneity and semantic gap makes it very challenging to measure cross-media distance. In this paper, we propose a novel cross-media distance metric learning framework based on sparse feature selection and multi-view matching. First, we employ sparse feature selection to select a subset of relevant features and remove redundant features for high-dimensional image features and audio features. Secondly, we maximize the canonical coefficient during image-audio feature dimension reduction for cross-media correlation mining. Thirdly, we further construct a Multi-modal Semantic Graph to find embedded manifold cross-media correlation. Moreover, we fuse the canonical correlation and the manifold information into multi-view matching which harmonizes different correlations with an iteration process and build Cross-media Semantic Space for cross-media distance measure. The experiments are conducted on image-audio dataset for cross-media retrieval. Experiment results are encouraging and show that the performance of our approach is effective. [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]

Multiple scattering theory (MST) is one of the most widely used methods in electronic structure calculations. It features a perfect separation between atomic configurations and site potentials and hence provides an efficient way to simulate defected and disordered systems. This work studies MST methods from a numerical point of view and shows convergence with respect to the truncation of the angular momentum summations, which is a fundamental approximation parameter for all MST methods. We provide both rigorous analysis and numerical experiments to illustrate the efficiency of MST methods within the angular momentum representations. [ABSTRACT FROM AUTHOR]

A solid solution is one of the important ways to enhance the structural and functional performance of materials. In this work, we develop a structural modeling approach to solid solutions based on the similar atomic environment (SAE). We propose a similarity function associated with any type of atom cluster to describe quantitatively the configurational deviation from the desired solid-solution structure that is fully disordered or contains short-range order (SRO). In this manner, the structural modeling for solid solutions is transferred to a minimization problem in the configuration space. Moreover, we strive to enhance the practicality of this approach. The approach and implementation are demonstrated by cross validations with the special quasi-random structure method. We apply the SAE method to the typical quinary CoCrFeMnNi high-entropy alloy, continuous binary Ta–W alloy, and ternary CoCrNi medium-entropy alloy with SRO as prototypes. In combination with ab initio calculations, we investigate the structural properties and compare the calculation results with experiments. [ABSTRACT FROM AUTHOR]

The paper adopted the method of using "the first successfully matched table" for the data extraction which is correct in cases where the consolidated statement comes first and the parent company statement comes second. However, using this method some of the financial data used came from the parent company while some of the data came from consolidated statements. [Extracted from the article]

Abstract: The plasmonic characteristics of four types of nanorods based metallic nanostructures, including parallel-nanorod, U-shaped, ∩-shaped and π-shaped nanostructures, are numerically investigated using finite difference time domain (FDTD) method. In the visible and near infrared regions, their extinction spectra are different according to the morphologies of the nanostructures and the separations of their longitudinal nanorods. Under the same plane continuous wave (CW) illumination, their electromagnetic field distributions show nodal fields inside the nano-waveguides formed by the nanostructures. Under the same focal beam illumination, all of the four nanostructures exhibit waveguiding effect which transports the energy of the focuses to a long distance, but by different resonance modes which depending their configurations. The proposed nanorod-based nanostructures behave as surface enhanced nano-waveguides which can find intriguing application in nano-photonic devices. [Copyright &y& Elsevier]

Part cleaning is very important for the assembly of precision machinery. After cleaning, the parts are randomly distributed in the collection area, which makes it difficult for a robot to collect them. Common robots can only collect parts located in relatively fixed positions, and it is difficult to adapt these robots to collect at randomly distributed positions. Therefore, a rapid part classification method based on a non-pooling YOLOv5 network for the recognition of randomly distributed multiple types of parts is proposed in this paper; this method classifies parts from their two-dimensional images obtained using industrial cameras. We compared the traditional and non-pooling YOLOv5 networks under different activation functions. Experimental results showed that the non-pooling YOLOv5 network improved part recognition precision by 8% and part recall rate by 3% within 100 epochs of training, which helped improve the part classification efficiency. The experiment showed that the non-pooling YOLOv5 network exhibited improved classification of industrial parts compared to the traditional YOLOv5 network. [ABSTRACT FROM AUTHOR]

Abstract: We design a Kohn–Sham equation solver based on hexahedral finite element discretizations. The solver integrates three schemes proposed in this paper. The first scheme arranges one a priori locally-refined hexahedral mesh with appropriate multiresolution. The second one is a modified mass-lumping procedure which accelerates the diagonalization in the self-consistent field iteration. The third one is a finite element recovery method which enhances the eigenpair approximations with small extra work. We carry out numerical tests on each scheme to investigate the validity and efficiency, and then apply them to calculate the ground state total energies of nanosystems C60, C120, and C275H172. It is shown that our solver appears to be computationally attractive for finite element applications in electronic structure study. [Copyright &y& Elsevier]

Abstract: To investigate the initial growth of Fe films on Si(0 0 1) and the Fe/Si interface, Fe films at various thicknesses have been systematically studied by soft X-ray absorption spectroscopy (XAS) and X-ray photoemission spectroscopy (XPS). The Fe L edge XAS spectrum shows a strong thickness dependence with broader line-width for thinner films. Detailed analysis of the Fe absorption signal as a function of the thickness shows that the broad linewidth of Fe L edge XAS spectra is mostly contributed by the first Fe layer at the Fe/Si interface. In contrast to XAS, Fe 2p photoemission spectra for these films are identical. However, valence band photoemission also shows a strong thickness dependence. Comparing the valence band photoemission spectra of the thin Fe/Si(0 0 1) films with that of pure Si and the thickest Fe film, the difference spectra at all thicknesses show almost identical shape indicating the same origin: the Fe/Si interface. Thus, it is mainly the first Fe layer at Fe/Si layer that is reactive with the Si substrate changing its electronic structure. [Copyright &y& Elsevier]

Abstract: Soft-magnetic FeCo alloy nanoparticles with diameters less than 100 nm are prepared by ball milling. X-ray photoemission spectroscopy (XPS) and X-ray magnetic circular dichroism (XMCD) are used to characterize these particles. While the XPS spectrum from the as-prepared sample clearly shows Co photoemission peaks, no sign of Fe is observed in the same spectrum. However, Fe photoemission peaks appear after 1 h of Ar ion sputtering. A quantitative analysis of the XPS spectra shows an increase of Fe concentration versus sputtering time until the Fe:Co ratio of the bulk alloy is reached. In addition, the narrow scan Fe and Co 2p XPS spectra show that Co is more oxidized than Fe. All these measurements indicate that the nanoparticles have a Co shell and an Fe-rich core. They further demonstrate the usefulness of XPS combined with depth-profiling via sputtering to obtain element- and chemically-sensitive structural information on nanoparticles. XMCD as an element-specific magnetic analysis tool further reveals that Fe and Co are ferromagnetically coupled in these particles. The information obtained is useful for establishing a structure–property relation for the studied material that is expected to have applications as a soft magnetic material at high temperatures. [Copyright &y& Elsevier]

In order to test the presence of interatomic multi-atom resonant photoemission (MARPE) in 3d magnetic ultrathin films, 2p photoelectron emission of various ferromagnetic 3d metals was studied while the excitation energy was tuned around the L2,3 core level absorption edge of a different 3d metal. Both elements were in direct contact either as bilayer or as alloys to maximize this possible effect. Systems under study were Fe/Co, Fe/Ni, Fe30Ni70, Co, Co90Mn10, and Ni90Mn10, all grown as epitaxial films on Cu(001). No measurable change in intensity of the 2p photoemission intensities in normal emission for 45° incidence of more than 2.5–5% was found as a result of resonant excitation of neighboring atoms in any of these systems. We can thus exclude the presence of significant MARPE in these metallic systems in the present experimental geometry. This is further confirmed by the photon energy dependence of the linear magnetic dichroism in the Fe 2p photoemission of a Fe/Co film, which is not influenced by Co L2,3 edge resonant absorption. [Copyright &y& Elsevier]

The scientific judgement of the structural abrupt transition characteristics of the carbon market price is an important means to comprehensively analyze its fluctuation law and effectively prevent carbon market risks. However, the existing methods for identifying structural changes of the carbon market based on carbon price data mostly regard the carbon price series as a deterministic time series and pay less attention to the uncertainty implied by the carbon price series. We propose a framework for identifying abrupt transitions in the carbon market from the perspective of a complex network by considering the influence of random factors on the carbon price series, expressing the carbon price series as a sequence of probability density functions, using the distribution of probability density to reveal the uncertainty information implied by carbon price series and constructing a recurrence network of carbon price probability density. Based on the community structure, the break index and statistical test method are defined. The simulation verifies the effectiveness and superiority of the method compared with traditional methods. An empirical analysis uses the carbon price data of the European Union carbon market and seven pilot carbon markets in China. The results show many abrupt transitions in the carbon price series of the two markets, whose occurrence period is closely related to major events. [ABSTRACT FROM AUTHOR]

Highlights: High-quality large-area perovskite films are prepared using a solid–liquid two-step film formation method combined with CsBr modification for the buried interface and Urea additive for perovskite crystallization. The inverted perovskite solar modules' performance is enhanced to 20.56% in 61.56 cm2 with improved stability. A considerable efficiency gap exists between large-area perovskite solar modules and small-area perovskite solar cells. The control of forming uniform and large-area film and perovskite crystallization is still the main obstacle restricting the efficiency of PSMs. In this work, we adopted a solid–liquid two-step film formation technique, which involved the evaporation of a lead iodide film and blade coating of an organic ammonium halide solution to prepare perovskite films. This method possesses the advantages of integrating vapor deposition and solution methods, which could apply to substrates with different roughness and avoid using toxic solvents to achieve a more uniform, large-area perovskite film. Furthermore, modification of the NiOx/perovskite buried interface and introduction of Urea additives were utilized to reduce interface recombination and regulate perovskite crystallization. As a result, a large-area perovskite film possessing larger grains, fewer pinholes, and reduced defects could be achieved. The inverted PSM with an active area of 61.56 cm2 (10 × 10 cm2 substrate) achieved a champion power conversion efficiency of 20.56% and significantly improved stability. This method suggests an innovative approach to resolving the uniformity issue associated with large-area film fabrication. [ABSTRACT FROM AUTHOR]

The treadmill exercise test (TET) serves as a non-invasive method for the diagnosis of coronary artery disease (CAD). Despite its widespread use, TET reports are susceptible to external influences, heightening the risk of misdiagnosis and underdiagnosis. In this paper, we propose a novel automatic CAD diagnosis approach. The proposed approach introduces a customized preprocessing method to obtain clear electrocardiograms (ECGs) from individual TET reports. Additionally, it presents TETDiaNet, a novel neural network designed to explore the temporal relationships within TET ECGs. Central to TETDiaNet is the TETDia block, which mimics clinicians' diagnostic processes to extract essential diagnostic information. This block encompasses an intra-state contextual learning module and an inter-state contextual learning module, modeling the temporal relationships within a single state and between states, respectively. These two modules help the TETDia block to capture effective diagnosis information by exploring the temporal relationships within TET ECGs. Furthermore, we establish a new TET dataset named TET4CAD for CAD diagnosis. It contains simplified TET reports for 192 CAD patients and 224 non-CAD patients, and each patient undergoes coronary angiography for labeling. Experimental results on TET4CAD underscore the superior performance of the proposed approach, highlighting the discriminative value of the temporal relationships within TET ECGs for CAD diagnosis. [ABSTRACT FROM AUTHOR]

Background: Stem cell senescence is considered as a significant driver of organismal aging. As individuals age, the number of stem cells is declined, and the ability to proliferate and survive is also weakened. It has been reported that metabolism plays an important role in stem cell self-renewal, multilineage differentiation, senescence and fate determination, which has aroused widespread concerns. However, whether metabolism-related genes or signalling pathways are involved in physiological aging remain largely undetermined. Results: In the current study, we showed 868 up-regulated and 2006 down-regulated differentially expressed genes (DEGs) in bone marrow mesenchymal stem cells (MSCs) from old rats in comparison with that from young rats by performing RNA sequence. And DEGs functions and pathways were further selected by function enrichment analysis. The results indicated that the high expression of DEGs might participate in cell differentiation, growth factor binding and etc., while the down-regulated DEGs were majorly enriched in metabolism process, such as the cellular metabolic process and mitochondria. Then, we screened and verified DEGs related to glucose metabolism and investigated the glycolysis levels. We identified that glucose uptake, lactate secretion, ATP production and relative extracellular acidification rates (ECAR) were all diminished in MSCs from old rats. More importantly, we conducted microRNA prediction on the key DEGs of glycolysis to elucidate the potential molecular mechanisms of glucose metabolism affecting MSC senescence. Conclusions: Our study unravelled the profiles of DEGs in age-associated MSC senescence and their functions and pathways. We also clarified DEGs related to glucose metabolism and down-regulated glycolysis level in age-associated MSC senescence. This study will uncover the metabolic effects on regulating stem cell senescence, and provide novel therapeutic targets for ameliorating age-associated phenotypes. [ABSTRACT FROM AUTHOR]

Through the K-means clustering analysis, it divides the regions of China into four clusters according to the differences in high-tech industry development level between 2008 and 2016. Considering "environmental pollution" and "innovation failure", an improved SBM-DEA efficiency measurement model was constructed to measure the green technology innovation efficiency of China's high-tech industry clusters. Lasso regression was used to screen out the factors affecting the green technology innovation efficiency of high-tech industry in each cluster area. On this basis, quantile regression method is used to study the influence degree and regional differences of various influencing factors on green innovation efficiency of high-tech industry at different quantile. Meanwhile, DEA-tobit model is used for robustness test. The research shows that in each cluster area, the factors that significantly affect the green innovation efficiency of high-tech industry are different, and the degree of influence of each factor on the innovation efficiency at different quantile is also different. Combining the empirical results with the reality of high-tech industries in various regions, the corresponding policy recommendations are put forward. [ABSTRACT FROM AUTHOR]

Knowledge innovation, as a kind of green economic behavior, maximizes its benefits in the process of knowledge diffusion. In previous studies, the impairment or promotion of existing knowledge under different relationships on the diffusion of new knowledge and its mechanism has not been considered. On the basis of epidemic spreading model, knowledge diffusion in complex networks with interaction between two kinds of relevant knowledge is investigated in this article. Here we bridge this theoretical gap by dividing the interaction between two different kinds of knowledge into three types: interdependent, competitive and decoupled. A two-layer multiplex network is constructed to model the dynamic process. Here we assume one of the two kinds of knowledge has diffused to steady state already and pay attention to the impact from the steady knowledge on the diffusing knowledge. We derive the theoretical threshold and conduct numerical simulation of knowledge diffusion dynamic process in homogeneous network, heterogeneous network and a real citation network respectively. Theoretical results suggest that the interaction term from the steady layer is inversely proportional to the diffusion threshold in another layer. Numerical simulation shows that knowledge diffuses easier in heterogeneous networks and nodes with higher degrees. Furthermore, the final knower size of the steady knowledge impacts the diffusing knowledge positively when they are interdependent while interaction impairs both their diffusion compared with decoupled condition. According to the results, before the diffusion of innovative knowledge, the diffusion state of relevant knowledge and the influence of individual who releases innovative knowledge should be fully considered to minimize their suppression on green behavior. [ABSTRACT FROM AUTHOR]

Correlation filter (CF) has received increasing attention in online visual object tracking. By accelerating the correlation in the frequency domain, CF trackers have achieved superior performance. However, existing CF trackers have a common shortcoming in that tracking models are prone to drift due to error accumulation. To address this problem, we propose a two-stage cascaded framework for accurate object modeling. In the first stage, we propose a passive–aggressive correlation filter (PACF) tracker to reduce error accumulation. In the subsequent stage, an online refinement algorithm is used to calibrate the tracking model by exploiting both long-term and short-term cues. In order to achieve high efficiency, our scheme reuses the PACF tracking response in the following stage. Extensive experiments were conducted on both long-term and short-term visual tracking benchmarks. The experimental results demonstrate that our tracker outperforms the state-of-the-art online tracking schemes in both long-term and short-term settings. Finally, we present a comprehensive analysis to validate the efficacy of our proposed method. [ABSTRACT FROM AUTHOR]

Metal halide perovskite light‐emitting diodes (PeLEDs) are gaining increasing attention as a promising candidate for the new‐generation display technology. Although tremendous progress has been witnessed in this field, the device performance of blue PeLEDs still lags far behind that of the green and red counterparts. In this work, an effective interfacial engineering is employed to boost the radiative recombination of blue perovskite film by introducing a multifunctional tetraethyl orthosilicate (TEOS) network on the grain‐growth substrate. Benefitting from the strong interaction between the TEOS molecule and perovskite nuclei, the perovskite crystallization dynamics are effectively regulated, contributing to significantly improved emitting film with uniformly distributed halogen and concentrated low‐dimensional domain. Additionally, the TEOS network can distinctly passivate the crystal defects at the buried perovskite interface owing to its adequate electron‐donating sites. Consequently, the target blue PeLEDs featuring a stable emission peak at 488 nm exhibit a champion external quantum efficiency of 17.3%, which is among the highest values to date. The results demonstrate the critical role of the surface molecular characteristics of the grain‐growth substrate in regulating the mixed‐halide blue perovskite crystallization. [ABSTRACT FROM AUTHOR]

Phase transition during annealing in the two‐step sequential deposition has drawn intensive attention as its significance in fabricating superior perovskite films. However, previous works have not paid enough attention to the importance of the purified phase transition path in the crystallization process. Herein, different from the mixed paths in the conventional cognition, purified phase transition path for α‐FAPbI3 crystallization is achieved by introducing dimethylurea (DMU) into lead iodide (PbI2) precursor solution. The multifunctional molecule is found to design a penetrable porous PbI2 film and fundamentally regulate the perovskite crystallization by forming single phase transition path via the complete δ‐phase during annealing of perovskite. The role of DMU in purified transition path for α‐FAPbI3 crystallization is unraveled with in situ photoluminescence and grazing incidence wide‐angle x‐ray scattering (GIWAXS) investigation. The crystal quality of perovskite films is significantly improved, resulting in single crystal‐like film. The best‐performing perovskite solar cells (PSCs) deliver a power conversion efficiency of 24.75%, resulting from the higher FF of 83.25% and an improved long‐term stability up to 3600 h. This work highlights the importance of purified phase transition path for the superior crystal quality toward high‐performance perovskite photovoltaics. [ABSTRACT FROM AUTHOR]

Quasi‐two‐dimensional (quasi‐2D) perovskites are emerging as efficient emitters in blue perovskite light‐emitting diodes (PeLEDs), while the imbalanced crystallization of the halide‐mixed system limits further improvements in device performance. The rapid crystallization caused by Cl doping produces massive defects at the interface, leading to aggravated non‐radiative recombination. Meanwhile, unmanageable perovskite crystallization is prone to facilitate the formation of nonuniform low‐dimensional phases, which results in energy loss during the exciton transfer process. Here, we propose a multifunctional interface engineering for nucleation and phase regulation by incorporating the zwitterionic additive potassium sulfamate into the hole transport layer. By using potassium ions (K+) as heterogeneous nucleation seeds, finely controlled growth of interfacial K+‐guided grains is achieved. The sulfamate ions can simultaneously regulate the phase distribution and passivate defects through coordination interactions with undercoordinated lead atoms. Consequently, such synergistic effect constructs quasi‐2D blue perovskite films with smooth energy landscape and reduced trap states, leading to pure‐blue PeLEDs with a maximum external quantum efficiency (EQE) of 17.32 %, spectrally stable emission at 478 nm and the prolonged operational lifetime. This work provides a unique guide to comprehensively regulate the halide‐mixed blue perovskite crystallization by manipulating the characteristics of grain‐growth substrate. [ABSTRACT FROM AUTHOR]

Quasi‐two‐dimensional (quasi‐2D) perovskites are emerging as efficient emitters in blue perovskite light‐emitting diodes (PeLEDs), while the imbalanced crystallization of the halide‐mixed system limits further improvements in device performance. The rapid crystallization caused by Cl doping produces massive defects at the interface, leading to aggravated non‐radiative recombination. Meanwhile, unmanageable perovskite crystallization is prone to facilitate the formation of nonuniform low‐dimensional phases, which results in energy loss during the exciton transfer process. Here, we propose a multifunctional interface engineering for nucleation and phase regulation by incorporating the zwitterionic additive potassium sulfamate into the hole transport layer. By using potassium ions (K+) as heterogeneous nucleation seeds, finely controlled growth of interfacial K+‐guided grains is achieved. The sulfamate ions can simultaneously regulate the phase distribution and passivate defects through coordination interactions with undercoordinated lead atoms. Consequently, such synergistic effect constructs quasi‐2D blue perovskite films with smooth energy landscape and reduced trap states, leading to pure‐blue PeLEDs with a maximum external quantum efficiency (EQE) of 17.32 %, spectrally stable emission at 478 nm and the prolonged operational lifetime. This work provides a unique guide to comprehensively regulate the halide‐mixed blue perovskite crystallization by manipulating the characteristics of grain‐growth substrate. [ABSTRACT FROM AUTHOR]

The deviated crystallization kinetics of solution‐processed perovskite layers caused by the bottom interface is a barrier to comprehensively suppressing the defect formation and further to efficaciously improve the film quality and device performance. Herein, it is reported that the incorporation of a PEA2PbBr4 interlayer can effectively modify the crystallization process of the quasi‐2D mixed‐halide perovskite layers, resulting in enhanced Lewis acid‐base interaction and improved film homogeneity, which contributes to the strong defect passivation efficacy toward efficient sky‐blue emission. As a result, the devices with the bottom‐interface modification show a maximum external quantum efficiency of 14.2%, impressively an outstanding power efficiency of 17.0 lm W−1, and an external quantum efficiency of 12.3% at 1000 cd m−2. This work provides an insight into crystallization kinetics for defect suppression of the perovskite layer and further highlights the defect passivation efficacy of 2D perovskite‐based bottom‐interface modification toward high‐efficiency sky blue‐emitting diodes. [ABSTRACT FROM AUTHOR]

Based on ab initio molecular dynamics simulations, we determined the melting curve of magnesium (Mg) up to ∼460 GPa using the solid–liquid coexistence method. Between ∼30 and 100 GPa, our melting curve is noticeably lower than those from static experiments but is in good agreement with recent shock experiments. Up to ∼450 GPa, our melting curve is generally consistent with the melting points from first-principles calculations using the small-cell coexistence method. We found that, at high pressures of a few hundred GPa, due to the strong softening of interatomic interactions in the liquid phase, solid–liquid coexistence simulations of Mg show some characteristics distinctively different from other metal systems, such as aluminum. For example, at a given volume, the pressure and temperature range for maintaining a stable solid–liquid coexistence state can be very small. The strong softening in the liquid phase also causes the unusual behavior of reentrant melting to occur at very high pressures. The onset of reentrant melting is predicted at ∼305 GPa, close to that at ∼300 GPa from the small-cell coexistence method. We show that the calculated melting points, considering reentrant melting, can be excellently fitted to a low-order Kechin equation, thereby making it possible for us to obtain a first-principles melting curve of Mg at pressures above 50 GPa for the first time. Similar characteristics in solid–liquid coexistence simulations, as well as reentrant melting, are also expected for other systems with strong softening in the liquid phase at high pressures. [ABSTRACT FROM AUTHOR]

Tin halide perovskite solar cells are promising for the next generation of highly efficient photovoltaics. Their commercialization can be accelerated by increasing their stability in moisture and oxygen. Herein, an n‐type organic molecule (IO‐4Cl) is applied as an interlayer between the perovskite films and electron transport layers in p‐i‐n structured devices. The electron‐rich indacenodithieno‐[3,2‐b]thiophene enhances electron transport, while the hydrocarbon side chains and rigid conjugated backbone isolate air. It is also shown that the C═O in IO‐4Cl can coordinate with Sn2+ on perovskite films' surface and grain boundaries to enhance perovskite crystal stability. In addition, IO‐4Cl slows down crystallization dynamics, resulting in lower non‐radiation recombination. The moisture ingress in the perovskite films is tracked under high relative humidity (RH) and it is found that IO‐4Cl can mitigate moisture infiltration. Finally, the devices with IO‐4Cl maintain 95% of the initial power conversion efficiency after 1200 h of storage in a nitrogen‐filled glovebox, and their stability in ambient air (60–80% RH) is significantly improved against pristine devices, thus demonstrating the beneficial effects of IO‐4Cl interlayer on device stability. [ABSTRACT FROM AUTHOR]

AbstractArea detectors have been widely used in X-ray diffraction (XRD) to improve the acquisition speed and data quality. Here, an accurate, high efficiency, low distortion approach with a large angle range for collecting powder X-ray diffraction data is developed which is carried out by rotating a small area detector on the detector arm of the diffractometer. A series of collected images are spliced and converted into one 1D XRD pattern. The calculation of the 2θ value and intensity corresponding to every pixel are shown in detail. The calibration of the detector and experimental geometry was carried out. Finally, the reliability of this method has been demonstrated by the X-ray diffraction measurements of a standard LaB6 powder and Rietveld refinement results. [ABSTRACT FROM AUTHOR]