Ultrastable and efficient slight-interlayer-displacement 2D Dion-Jacobson perovskite solar cells.

Stability has been a long-standing concern for solution-processed perovskite photovoltaics and their practical applications. However, stable perovskite materials for photovoltaic remain insufficient to date. Here we demonstrate a series of ultrastable Dion-Jacobson (DJ) perovskites (1,4-cyclohexanedimethanammonium)(methylammonium)n-1PbnI3n+1 (n ≥ 1) for photovoltaic applications. The scalable technology by blade-coated solar cells for the designed DJ perovskites (nominal n = 5) achieves a maximum stabilized power conversion efficiency (PCE) of 19.11% under an environmental atmosphere. Un-encapsulated cells by blade-coated technology retain 92% of their initial efficiencies for over 4000 hours under ~90% relative humidity (RH) aging conditions. More importantly, these cells also exhibit remarkable thermal (85 °C) and operational stability, which shows negligible efficiency loss after exceeding 5000-hour heat treatment or after operation at maximum power point (MPP) exceeding 6000 hours at 45 °C under a 100 mW cm-2 continuous light illumination.

DyCR: A Dynamic Clustering and Recovering Network for Few-Shot Class-Incremental Learning.

Few-shot class-incremental learning (FSCIL) aims to continually learn novel data with limited samples. One of the major challenges is the catastrophic forgetting problem of old knowledge while training the model on new data. To alleviate this problem, recent state-of-the-art methods adopt a well-trained static network with fixed parameters at incremental learning stages to maintain old knowledge. These methods suffer from the poor adaptation of the old model with new knowledge. In this work, a dynamic clustering and recovering network (DyCR) is proposed to tackle the adaptation problem and effectively mitigate the forgetting phenomena on FSCIL tasks. Unlike static FSCIL methods, the proposed DyCR network is dynamic and trainable during the incremental learning stages, which makes the network capable of learning new features and better adapting to novel data. To address the forgetting problem and improve the model performance, a novel orthogonal decomposition mechanism is developed to split the feature embeddings into context and category information. The context part is preserved and utilized to recover old class features in future incremental learning stages, which can mitigate the forgetting problem with a much smaller size of data than saving the raw exemplars. The category part is used to optimize the feature embedding space by moving different classes of samples far apart and squeezing the sample distances within the same classes during the training stage. Experiments show that the DyCR network outperforms existing methods on four benchmark datasets. The code is available at: https://github.com/zichengpan/DyCR.

Single Crystal Seed Induced Epitaxial Growth Stabilizes α-FAPbI3 in Perovskite Solar Cells.

FAPbI3 stands out as an ideal candidate for the photoabsorbing layer of perovskite solar cells (PSCs), showcasing outstanding photovoltaic properties. Nonetheless, stabilizing photoactive α-FAPbI3 remains a challenge due to the lower formation energy of the competitive photoinactive δ-phase. In this study, we employ tetraethylphosphonium lead tribromide (TEPPbBr3) single crystals as templates for the epitaxial growth of PbI2. The strategic use of TEPPbBr3 optimizes the evolution of intermediates and the crystallization kinetics of perovskites, leading to high-quality and phase-stable α-FAPbI3 films. The TEPPbBr3-modified perovskite exhibits optimized carrier dynamics, yielding a champion efficiency of 25.13% with a small voltage loss of 0.34 V. Furthermore, the target device maintains 90% of its initial PCE under maximum power point (MPP) tracking over 1000 h. This work establishes a promising pathway through single crystal seed based epitaxial growth for achieving satisfactory crystallization regulation and phase stabilization of α-FAPbI3 perovskites toward high-efficiency and stable PSCs.

LncRNA LINC00667 gets involved in clear cell renal cell carcinoma development and chemoresistance by regulating the miR-143-3p/ZEB1 axis.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is identified as a malignant tumor in the urinary tract. The research was an attempt to probe the biological function and molecular mechanism of lncRNA LINC00667 in ccRCC development. METHODS: qRT-PCR monitored LINC00667, miR-143-3p, and ZEB1 levels. The models of LINC00667, miR-143-3p, and ZEB1 overexpression or knockdown were constructed in ccRCC cells. Cell proliferation, apoptosis, migration, and invasion of the cells were detected. The levels of apoptosis-associated proteins and epithelial-mesenchymal transition (EMT)-related proteins, and ZEB1 were detected by WB. Dual-luciferase reporter assay and RNA pull-down assay identified the binding association between LINC00667 and miR-143-3p, miR-143-3p and ZEB1. Moreover, a xenograft tumor model in nude mice was used for evaluating tumor growth in vivo. RESULTS: LINC00667 and ZEB1 displayed high expression in ccRCC tissues and cells. miR-143-3p was lowly expressed in ccRCC tissues and cells. LINC00667 targeted and repressed miR-143-3p, which inhibited ZEB1 expression in a targeted manner. Overexpression of LINC00667 facilitated ccRCC cell proliferation, migration, invasion and EMT and retarded apoptosis, whereas LINC00667 knockdown or miR-143-3p overexpression exerted reverse effects. The rescue experiments indicated that overexpressing miR-143-3p dampened LINC00667-mediated oncogenic effects. Overexpressing ZEB1 diminished miR-143-3p-mediated tumor-suppressive effects. In-vivo experiments displayed that overexpression of LINC00667 contributed to the tumor growth of ccRCC cells, in contrast to miR-143-3p overexpression, which restrained the tumor growth. CONCLUSIONS: LINC00667 is up-regulated in ccRCC and enhances the ZEB1 expression by targeting miR-143-3p, which in turn accelerates ccRCC progression and induces chemoresistance.

Urological Tumor: A Narrative Review of Tertiary Lymphatic Structures.

BACKGROUND: Tertiary lymphoid structures (TLSs), as ectopic lymphoid-like tissues, are highly similar to secondary lymphoid organs and are not only involved in chronic inflammation and autoimmune responses but are also closely associated with tumor immunotherapy and prognosis. The complex composition of the urological tumor microenvironment not only varies greatly in response to immunotherapy, but the prognostic value of TLSs in different urological tumors remains controversial. SUMMARY: We searched PubMed, Web of Science, and other full-text database systems. TLSs, kidney cancer, uroepithelial cancer, bladder cancer, and prostate cancer as keywords, relevant literature was searched from the time the library was built to 2023. Systematically explore the role and mechanism of TLSs in urological tumors. It includes the characteristics of TLSs, the role and mechanism of TLSs in urological tumors, and the clinical significance of TLSs in urological tumors. KEY MESSAGES: The prognostic role of TLSs in different urological tumors was significantly different. It is not only related to its enrichment in the tumor but also highly correlated with the location of the tumor. In addition, autoimmune toxicity may be a potential barrier to its role in the formation of TLSs through induction. Therefore, studying the mechanisms of TLSs in autoimmune diseases may help in the development of antitumor target drugs.

CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma.

BACKGROUND: Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high-risk patients who may be suitable to receive de-escalation or intensified treatment approaches. PURPOSE: To develop a deep learning (DL)-based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT). METHODS: Two patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre-treatment CT-scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2-year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), disease-specific survival (DSS), overall survival (OS), and disease-free survival (DFS). We proposed DL outcome prediction models with the multi-label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT-scans. RESULTS: The multi-label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2-year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2-year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low-risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set. CONCLUSION: MLL models demonstrated better discriminative ability for all 2-year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set.

Image Intensity Variation Information for Interest Point Detection.

Interest point detection methods are gaining more attention and are widely applied in computer vision tasks such as image retrieval and 3D reconstruction. However, there still exist two main problems to be solved: (1) from the perspective of mathematical representations, the differences among edges, corners, and blobs have not been convincingly explained and the relationships among the amplitude response, scale factor, and filtering orientation for interest points have not been thoroughly explained; (2) the existing design mechanism for interest point detection does not show how to accurately obtain intensity variation information on corners and blobs. In this paper, the first- and second-order Gaussian directional derivative representations of a step edge, four common genres of corners, an anisotropic-type blob, and an isotropic-type blob are analyzed and derived. Multiple interest point characteristics are discovered. The characteristics for interest points that we obtained help us describe the differences among edges, corners, and blobs, explain why the existing interest point detection methods with multiple scales cannot properly obtain interest points from images, and present novel corner and blob detection methods. Extensive experiments demonstrate the superiority of our proposed methods in terms of detection performance, robustness to affine transformations, noise, image matching, and 3D reconstruction.

Traffic flow digital twin generation for highway scenario based on radar-camera paired fusion.

Autonomous driving is gradually moving from single-vehicle intelligence to internet of vehicles, where traffic participants can share the traffic flow information perceived by each other. When the sensing technology is combined with the internet of vehicles, a sensor network all over the road can provide a large-scale of traffic flow data, thus providing a basis for building a traffic digital twin model. The digital twin can enable the traffic system not only to use past and present information, but also to predict traffic conditions, providing more effective optimization for autonomous driving and intelligent transportation, so as to make long-term rational planning of the overall traffic state and enhance the level of traffic intelligence. The current mainstream traffic sensors, namely radar and camera, have their own advantages, and the fusion of these two sensors can provide more accurate traffic flow data for the generation of digital twin model. In this paper, an end-to-end digital twin system implementation approach is proposed for highway scenarios. Starting from a paired radar-camera sensing system, a single-site radar-camera fusion framework is proposed, and then using the definition of a unified coordinate system, the traffic flow data between multiple sites is combined to form a dynamic real-time traffic flow digital twin model. The effectiveness of the digital twin building is verified based on the real-world traffic data.

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review.

Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for fifteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated.

Sulfonium-Cations-Assisted Intermediate Engineering for Quasi-2D Perovskite Solar Cells.

Quasi-2D Ruddlesden-Popper (RP) perovskites with superior stability are admirable candidates for perovskite solar cells (PSCs) toward commercialization. However, the device performance remains unsatisfactory due to the disordered crystallization of perovskites. In this work, the effects of sulfonium cations on the evolution of intermediates and photovoltaic properties of 2D RP perovskites are investigated. The introduction of sulfonium cations leads to preferred intermediate transformation and improved film quality of perovskites. The resulting devices deliver a champion efficiency of 19.08% at room temperature and 20.52% at 180 K, due to reduced recombination and enhanced charge transport. More importantly, the unencapsulated device maintains 84% of the initial efficiency under maximum power point (MPP) tracking at 40 °C for 1000 h. This work helps to gain a comprehensive understanding of the crystallization process of quasi-2D perovskites and provides a simple strategy to modulate the intermediates of perovskites.

Image local structure information learning for fine-grained visual classification.

Learning discriminative visual patterns from image local salient regions is widely used for fine-grained visual classification (FGVC) tasks such as plant or animal species classification. A large number of complex networks have been designed for learning discriminative feature representations. In this paper, we propose a novel local structure information (LSI) learning method for FGVC. Firstly, we indicate that the existing FGVC methods have not properly considered how to extract LSI from an input image for FGVC. Then an LSI extraction technique is introduced which has the ability to properly depict the properties of different local structure features in images. Secondly, a novel LSI learning module is proposed to be added into a given backbone network for enhancing the ability of the network to find salient regions. Thirdly, extensive experiments show that our proposed method achieves better performance on six image datasets. Particularly, the proposed method performs far better on datasets with a limited number of images.

Microstructure and Key Properties of Phosphogypsum-Red Mud-Slag Composite Cementitious Materials.

Due to the low content of silicon and aluminum in red mud and the low reaction activity of red mud, when it was used to prepare composite cementitious materials, it was necessary to assist other aluminosilicates and improve their activity by certain methods. In this study, it was proposed to add slag to increase the percentage of silicon and aluminum in the system, and to improve the reactivity of the system through the activation effect of sulfate in phosphogypsum. The effects of slag and phosphogypsum contents on the mechanical properties and microstructures of composite cementitious materials were studied. X-ray diffraction analysis (XRD), thermogravimetric analysis (TG-DTG), and scanning electron microscopy (SEM) were used to analyze the effects of slag and phosphogypsum contents on the hydration products, microstructure, and strength formation mechanism of composite cementitious materials. The results show that with the increase of slag, the strength of the composite cementitious material increases gradually. When the slag content is 50%, the 28-day compressive strength reaches a maximum of about 14 MPa. Compared with the composite material without phosphogypsum, the composite cementitious material with 10-20% phosphogypsum showed higher strength properties, in which the 28-day compressive strength exceeds 24 MPa. The main reason for this is that the sulfate in phosphogypsum can cause the composite cementitious material to generate a large amount of ettringite and accelerate the dissolution of red mud and slag, increasing the release of aluminates, silicates, and Ca2+ to form more C-(A)-S-H and ettringite. In addition, a large amount of C-(A)-S-H makes ettringite and unreacted particles combine into a uniform and compact structure, thus improving the strength. When the content of phosphogypsum exceeds 40%, the 28-day compressive strength of the composite cementitious material drops below 12 MPa due to the presence of fewer hydration products and the expansion of ettringite.

Centimeter-Sized Single Crystal of a One-Dimensional Lead-Free Mixed-Cation Perovskite Ferroelectric for Highly Polarization Sensitive Photodetection.

Linear dichroic anisotropic photonic materials are highly attractive due to their great potentials in many applications, which in combination with the ferroelectric properties could broaden their research and applications. However, to date, the linear dichroism conversion phenomenon has not been observed in one-dimensional (1D) large-size single-crystal materials: in particular, lead-free perovskite ferroelectric crystals. Here, we propose a new ferroelectric design strategy: namely, partial organic cation substitution for precisely designing 1D polarization-sensitive perovskite ferroelectrics. As an example, the 1D mixed-cation perovskite ferroelectric (n-propylammonium)(methylammonium)SbBr5 was synthesized, which exhibits a fascinating ferroelectricity with a notable reversible polarization of 2.9 µC/cm2 and a large ferroelectricity-driven polarization ratio of 6.9. Importantly, the single-crystalline photodetectors also exhibit superior optoelectronic anisotropic performances at the paraelectric phase, having a large photoelectric anisotropy ratio (∼35), an excellent polarization-sensitive dichroism ratio (∼1.31), highly sensitive detectivity up to ∼109 Jones, and a fast response rate (∼45/68 µs). This finding provides a significant and effective pathway for the targeted design of new functional lead-free linear dichroic anisotropic photonic ferroelectrics.

Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography.

PURPOSE: Classic encoder-decoder-based convolutional neural network (EDCNN) approaches cannot accurately segment detailed anatomical structures of the mandible in computed tomography (CT), for instance, condyles and coronoids of the mandible, which are often affected by noise and metal artifacts. The main reason is that EDCNN approaches ignore the anatomical connectivity of the organs. In this paper, we propose a novel CNN-based 3D mandible segmentation approach that has the ability to accurately segment detailed anatomical structures. METHODS: Different from the classic EDCNNs that need to slice or crop the whole CT scan into 2D slices or 3D patches during the segmentation process, our proposed approach can perform mandible segmentation on complete 3D CT scans. The proposed method, namely, RCNNSeg, adopts the structure of the recurrent neural networks to form a directed acyclic graph in order to enable recurrent connections between adjacent nodes to retain their connectivity. Each node then functions as a classic EDCNN to segment a single slice in the CT scan. Our proposed approach can perform 3D mandible segmentation on sequential data of any varied lengths and does not require a large computation cost. The proposed RCNNSeg was evaluated on 109 head and neck CT scans from a local dataset and 40 scans from the PDDCA public dataset. The final accuracy of the proposed RCNNSeg was evaluated by calculating the Dice similarity coefficient (DSC), average symmetric surface distance (ASD), and 95% Hausdorff distance (95HD) between the reference standard and the automated segmentation. RESULTS: The proposed RCNNSeg outperforms the EDCNN-based approaches on both datasets and yields superior quantitative and qualitative performances when compared to the state-of-the-art approaches on the PDDCA dataset. The proposed RCNNSeg generated the most accurate segmentations with an average DSC of 97.48%, ASD of 0.2170 mm, and 95HD of 2.6562 mm on 109 CT scans, and an average DSC of 95.10%, ASD of 0.1367 mm, and 95HD of 1.3560 mm on the PDDCA dataset. CONCLUSIONS: The proposed RCNNSeg method generated more accurate automated segmentations than those of the other classic EDCNN segmentation techniques in terms of quantitative and qualitative evaluation. The proposed RCNNSeg has potential for automatic mandible segmentation by learning spatially structured information.

Bromine-Substitution-Induced High-Tc Two-Dimensional Bilayered Perovskite Photoferroelectric.

High-Curie-temperature (Tc) ferroelectrics have exhibited broad applications in optoelectronic devices. Recently, two-dimensional multilayered perovskite ferroelectrics with excellent photoelectric attributes are attracting increasing interest as new systems of photoferroelectrics. However, the effective tuning of the Tc value of a multilayered perovskite photoferroelectric system still remains a huge challenge. Here, by a halogen substitution strategy to introduce bromine atoms on n-propylamine cations, the hybrid perovskite photoferroelectric (3-bromopropylaminium)2(formamidinium)Pb2Br7 (BFPB) with a high Tc value (348.5 K) was obtained. It is notable that BFPB adopts a two-dimensional bilayered inorganic framework, with tight linking to the organic cation by C-Br···Br-Pb halogen···halogen interactions and N-H···Br hydrogen bonds. Intriguingly, in comparison with the prototypical compound (n-propylaminium)2(formamidinium)Pb2Br7, a remarkable augmentation of 85.2 K in the resulting Tc value of BFPB is clearly observed, which further broadens the temperature range of its application. In combination with the remarkable ferroelectric and semiconducting attributes, the reversible bulk photovoltaic effect was realized in single crystals of BFPB. This finding can not only enhance the hybrid perovskite ferroelectric family but also further promote the photoelectric application of ferroelectrics.

Corner Detection Using Second-Order Generalized Gaussian Directional Derivative Representations.

Corner detection is a critical component of many image analysis and image understanding tasks, such as object recognition and image matching. Our research indicates that existing corner detection algorithms cannot properly depict the difference between edges and corners and this results in wrong corner detections. In this paper, the capability of second-order generalized (isotropic and anisotropic) Gaussian directional derivative filters to suppress Gaussian noise is evaluated. The second-order generalized Gaussian directional derivative representations of step edge, L-type corner, Y- or T-type corner, X-type corner, and star-type corner are investigated and obtained. A number of properties for edges and corners are discovered which enable us to propose a new image corner detection method. Finally, the criteria on detection accuracy and average repeatability under affine image transformation, JPEG compression, and noise degradation, and the criteria on region repeatability are used to evaluate the proposed detector against nine state-of-the-art methods. The experimental results show that our proposed detector outperforms all the other tested detectors.

Indoor formaldehyde removal by three species of Chlorophytum comosum under dynamic fumigation system: part 2-plant recovery.

Spider plants (Chlorophytum comosum) are known to be among the most common easy mountable indoor plants capable of purifying indoor air by absorbing carbon monoxide, formaldehyde, xylene, and many other hazardous gases. In addition, these plants are non-toxic and safe for pets and children. This project is focused on the investigation of the spider plants' capability of the formaldehyde purification under laboratory-controlled parameters of the indoor air environment. Two scenarios including employment of fresh plants as well as recovered ones damaged by 7-day exposure of formaldehyde were considered. A special attention was made to the investigation of physiological indexes of the plant leaves after damage, and whether the spider plant could be reused after its recovery. The physiological characteristics of the recovery period of potted Chlorophytum comosum immediately after 7 days of fumigation with formaldehyde were studied. Eight physiological indexes of leaves including chlorophyll, free protein, relative conductivity, MDA (malondialdehyde, lipid peroxidation), SOD (superoxide dismutase), POD (peroxidase), T-AOC (total antioxidant capacity), and stomata were selected to monitor plants' recovery processes. The results of 30-day experimental runs showed that three species of spider plants were mostly recovered within 15 days. Repeated 7-day fumigation of plants, conducted to study their ability to effectively clean the air after regeneration, confirmed such ability; the efficiency at the first day was similar to the performance of the fresh plant. However, from the second day, the efficiency was dropped by 35-50% and remained at these levels for the rest of the exercise.

Halide Double Perovskite Ferroelectrics.

Halide double perovskites have recently emerged as a promising environmentally friendly optoelectronic and photovoltaic material for their inherent thermodynamic stability, high defect tolerance, and appropriate band gaps. However, to date, no ferroelectric material based on halide double perovskites has been discovered. Herein, by hetero-substitution of lead and cation intercalation of n-propylamine, the first halide double perovskite ferroelectric, (n-propylammonium)2 CsAgBiBr7 (1), is reported and it exhibits distinct ferroelectricity with a notable saturation polarization of about 1.5 µC cm-2 . More importantly, single-crystal photodetectors of 1 exhibit extraordinary performance with containing high on/off ratios of about 104 , fast response rates of 141 µs, and detectivity as high as 5.3×1011  Jones. This finding opens a new way to design high-performance perovskite ferroelectrics, and provides a viable approach in the search for stable and lead-free optoelectronic materials as an alternative to the lead-containing system.

Large-Area Exfoliated Lead-Free Perovskite-Derivative Single-Crystalline Membrane for Flexible Low-Defect Photodetectors.

Wide applications of personal consumer electronics have tended to cause a huge demand for smart and portable electronics, featuring mechanical flexibility, lightweight, and environmental friendliness. However, most of the recently reported flexible photodetectors based on microcrystalline and amorphous components commonly suffer from severe drawbacks, including plenty of grains, boundaries, and surface defects. Here, we present a new lead-free chiral perovskite-derivative light absorber of (aminoguanidinium)3Bi2I9 (AG3Bi2I9), which displays a narrow direct band gap of about 1.89 eV. High-quality bulk single crystals were successfully grown with dimensions up to 24 × 12 × 5 mm3. Emphatically, as-grown bulk single crystals are easy to be exfoliated for large-area ultrathin wafers with an exfoliated area up to 0.6 cm2, showing promise for low-defect flexible optoelectronic applications. The remarkable surface smoothness and crystalline quality of single-crystalline thin layers were further confirmed by TEM, HRTEM, AFM, single-crystalline X-ray diffraction, and space-charge limited current (SCLC) measurements. As expected, the planar photodetectors based on flexible exfoliated wafers are first fabricated and exhibit notable photoelectric performance. This work represents an important step forward as it offers an effective strategy for the fabrication of high-quality large-area flexible exfoliated wafer devices.

Tunable optical absorption in lead-free perovskite-like hybrids by iodide management.

We present an instructional design strategy to tune the optical absorption ability of organic-inorganic hybrids by managing the internal iodide state. Three bismuth-based hybrids with different internal iodide states were synthesized and display tunable bandgaps from 1.91 eV to 1.87 eV and finally to 1.59 eV.