Room-Temperature Sputtered SnO2 as Robust Electron Transport Layer for Air-Stable and Efficient Perovskite Solar Cells on Rigid and Flexible Substrates.

Extraordinary photovoltaic performance and intriguing optoelectronic properties of perovskite solar cells (PSCs) have aroused enormous interest from both academic research and photovoltaic (PV) industry. In order to bring PSC technology from laboratory to market, material stability, device flexibility, and scalability are important issues to address for vast production. Nevertheless, PSCs are still primarily prepared by solution methods which limit film scalability, while high-temperature processing of metal oxide electron transport layer (ETL) makes PSCs costly and incompatible with flexible substrates. Here, we demonstrate rarely-reported room-temperature radio frequency (RF) sputtered SnO2 as a promising ETL with suitable band structure, high transmittance, and excellent stability to replace its solution-processed counterpart. Power conversion efficiencies (PCEs) of 12.82% and 5.88% have been achieved on rigid glass substrate and flexible PEN substrate respectively. The former device retained 93% of its initial PCE after 192-hour exposure in dry air while the latter device maintained over 90% of its initial PCE after 100 consecutive bending cycles. The result is a solid stepping stone toward future PSC all-vapor-deposition fabrication which is being widely used in the PV industry now

Direct Acyclic Graph based Ledger for Internet of Things: Performance and Security Analysis

Direct Acyclic Graph (DAG)-based ledger and the corresponding consensus algorithm has been identified as a promising technology for Internet of Things (IoT). Compared with Proof-of-Work (PoW) and Proof-of-Stake (PoS) that have been widely used in blockchain, the consensus mechanism designed on DAG structure (simply called as DAG consensus) can overcome some shortcomings such as high resource consumption, high transaction fee, low transaction throughput and long confirmation delay. However, the theoretic analysis on the DAG consensus is an untapped venue to be explored. To this end, based on one of the most typical DAG consensuses, Tangle, we investigate the impact of network load on the performance and security of the DAG-based ledger. Considering unsteady network load, we first propose a Markov chain model to capture the behavior of DAG consensus process under dynamic load conditions. The key performance metrics, i.e., cumulative weight and confirmation delay are analysed based on the proposed model. Then, we leverage a stochastic model to analyse the probability of a successful double-spending attack in different network load regimes. The results can provide an insightful understanding of DAG consensus process, e.g., how the network load affects the confirmation delay and the probability of a successful attack. Meanwhile, we also demonstrate the trade-off between security level and confirmation delay, which can act as a guidance for practical deployment of DAG-based ledgers.Comment: accepted by IEEE Transactions on Networkin

Performance analysis and comparison of PoW, PoS and DAG based blockchains

In the blockchain, the consensus mechanism plays a key role in maintaining the security and legitimation of contents recorded in the blocks. Various blockchain consensus mechanisms have been proposed. However, there is no technical analysis and comparison as a guideline to determine which type of consensus mechanism should be adopted in a specific scenario/application. To this end, this work investigates three mainstream consensus mechanisms in the blockchain, namely, Proof of Work (PoW), Proof of Stake (PoS), and Direct Acyclic Graph (DAG), and derives their performances in terms of the average time to generate a new block, the confirmation delay, the Transaction Per Second (TPS) and the confirmation failure probability. The results show that the consensus process is affected by both network resource (computation power/coin age, buffer size) and network load conditions. In addition, it shows that PoW and PoS are more sensitive to the change of network resource while DAG is more sensitive to network load conditions

Expanding Small-Scale Datasets with Guided Imagination

The power of DNNs relies heavily on the quantity and quality of training data. However, collecting and annotating data on a large scale is often expensive and time-consuming. To address this issue, we explore a new task, termed dataset expansion, aimed at expanding a ready-to-use small dataset by automatically creating new labeled samples. To this end, we present a Guided Imagination Framework (GIF) that leverages cutting-edge generative models like DALL-E2 and Stable Diffusion (SD) to "imagine" and create informative new data from the input seed data. Specifically, GIF conducts data imagination by optimizing the latent features of the seed data in the semantically meaningful space of the prior model, resulting in the creation of photo-realistic images with new content. To guide the imagination towards creating informative samples for model training, we introduce two key criteria, i.e., class-maintained information boosting and sample diversity promotion. These criteria are verified to be essential for effective dataset expansion: GIF-SD obtains 13.5% higher model accuracy on natural image datasets than unguided expansion with SD. With these essential criteria, GIF successfully expands small datasets in various scenarios, boosting model accuracy by 36.9% on average over six natural image datasets and by 13.5% on average over three medical datasets. The source code is available at https://github.com/Vanint/DatasetExpansion.Comment: NeurIPS 2023. Source code: https://github.com/Vanint/DatasetExpansio

Knee loading protects against osteonecrosis of the femoral head by enhancing vessel remodeling and bone healing

Osteonecrosis of the femoral head is a serious orthopedic problem. Moderate loads with knee loading promote bone formation, but their effects on osteonecrosis have not been investigated. Using a rat model, we examined a hypothesis that knee loading enhances vessel remodeling and bone healing through the modulation of the fate of bone marrow-derived cells. In this study, osteonecrosis was induced by transecting the ligamentum teres followed by a tight ligature around the femoral neck. For knee loading, 5 N loads were laterally applied to the knee at 15 Hz for 5 min/day for 5 weeks. Changes in bone mineral density (BMD) and bone mineral content (BMC) of the femur were measured by pDEXA, and ink infusion was performed to evaluate vessel remodeling. Femoral heads were harvested for histomorphometry, and bone marrow-derived cells were isolated to examine osteoclast development and osteoblast differentiation. The results showed that osteonecrosis significantly induced bone loss, and knee loading stimulated both vessel remodeling and bone healing. The osteonecrosis group exhibited the lowest trabecular BV/TV (p b 0.001) in the femoral head, and lowest femoral BMD and BMC (both p b 0.01). However, knee loading increased trabecular BV/TV (p b 0.05) as well as BMD (pb 0.05) and BMC (p b 0.01). Osteonecrosis decreased the vessel volume (pb 0.001), vessel number (pb 0.001) and VEGF expression (p b 0.01), and knee loading increased them (pb 0.001, pb 0.001 and p b 0.01). Osteonecrosis activated osteoclast development, and knee loading reduced its formation, migration, adhesion and the level of “pit” formation (pb 0.001, pb 0.01, pb 0.001 and pb 0.001). Furthermore, knee loading significantly increased osteoblast differentiation and CFU-F (both p b 0.001). A significantly positive correlation was observed between vessel remodeling and bone healing (both p b 0.01). These results indicate that knee loading could be effective in repair osteonecrosis of the femoral head in a rat model. This effect might be attributed to promoting vessel remodeling, suppressing osteoclast development, and increasing osteoblast and fibroblast differentiation. In summary, the current study suggests that knee loading might potentially be employed as a non-invasive therapy for osteonecrosis of the femoral head

Efficient metal halide perovskite light-emitting diodes with significantly improved light extraction on nanophotonic substrates.

Metal halide perovskite has emerged as a promising material for light-emitting diodes. In the past, the performance of devices has been improved mainly by optimizing the active and charge injection layers. However, the large refractive index difference among different materials limits the overall light extraction. Herein, we fabricate efficient methylammonium lead bromide light-emitting diodes on nanophotonic substrates with an optimal device external quantum efficiency of 17.5% which is around twice of the record for the planar device based on this material system. Furthermore, optical modelling shows that a high light extraction efficiency of 73.6% can be achieved as a result of a two-step light extraction process involving nanodome light couplers and nanowire optical antennas on the nanophotonic substrate. These results suggest that utilization of nanophotonic structures can be an effective approach to achieve high performance perovskite light-emitting diodes

Sora Generates Videos with Stunning Geometrical Consistency

The recently developed Sora model [1] has exhibited remarkable capabilities in video generation, sparking intense discussions regarding its ability to simulate real-world phenomena. Despite its growing popularity, there is a lack of established metrics to evaluate its fidelity to real-world physics quantitatively. In this paper, we introduce a new benchmark that assesses the quality of the generated videos based on their adherence to real-world physics principles. We employ a method that transforms the generated videos into 3D models, leveraging the premise that the accuracy of 3D reconstruction is heavily contingent on the video quality. From the perspective of 3D reconstruction, we use the fidelity of the geometric constraints satisfied by the constructed 3D models as a proxy to gauge the extent to which the generated videos conform to real-world physics rules. Project page: https://sora-geometrical-consistency.github.io/Comment: 5 pages, 3 figure

Effects of knee loading on obesity‐related nonalcoholic fatty liver disease in an ovariectomized mouse model with high fat diet

Aim Hormonal and nutritional disorders are the main causes of obesity and nonalcoholic fatty liver disease, especially in the elderly and postmenopausal women. Although physical activity may alleviate these disorders, the elderly may often have difficulty in conducting physical exercise. The purpose of this study was to investigate the therapeutic effect of knee loading, a new form of physical stimulation, on the symptom of obesity and fatty liver. Methods Using ovariectomized mice with high fat diet, we evaluated the effect of knee loading that applies gentle cyclic loads to the knee. Female C57BL/6 mice were divided into five groups: control (SCD), high fat diet (HF), HF with loading (HF+L), HF with ovariectomy (HF+OVX), and HF+OVX with loading (HF+OVX+L). Except for SCD, mice underwent sham operation or ovariectomy and maintained on high fat diet. After 6 weeks, the mice in HF+L and HF+OVX+L were treated with 6‐week knee loading. Results Compared to the obesity groups (HF and HF+OVX), knee loading significantly decreased a gain in body weight, liver weight, and white adipose tissue (all P<0.01). It also reduced the lipid level in the serum (P<0.01) and histological severity of hepatic steatosis (P<0.01). Furthermore, knee loading downregulated biomarkers related to the endoplasmic reticulum stress (GRP78, p‐eIF2α and ATF4) and altered biomarkers in autophagy (LC3 and p62). Conclusions Knee loading suppressed obesity‐associated metabolic alterations and hepatic steatosis, the effect with knee loading might be associated with suppression of the ER stress and promotion of autophagy