Highly efficient organic thermally activated delayed fluorescence (TADF) and room‐temperature phosphorescence (RTP) emitters for organic light‐emitting diodes (OLEDs) generally consist of a twisted ...donor–acceptor skeleton with aromatic amine donors. Herein, through introducing sulfur atoms into isomeric pentaphene and pentacene frameworks, we demonstrate a set of polycyclic luminophores exhibiting efficient TADF and RTP characters. The incorporation of sulfur atoms confirms a folded molecular plane, while intensifies singlet–triplet spin‐orbit coupling. Further, the isomeric effect has a significant effect on the electronic structure of excited state, giving rise to the investigated compounds tunable luminescence mechanisms of TADF and RTP. With efficient triplet harvesting ability, maximum external quantum efficiencies up to 25.1 % and 8.7 % are achieved for the corresponding TADF and RTP OLEDs, verifying the great potential of sulfur‐bridged frameworks for highly efficient devices.
By incorporating powerful electron‐donating sulfur atoms into isomeric pentaphene and pentacene frameworks, three highly efficient polycyclic thermally activated delayed fluorescence (TADF), room‐temperature phosphorescence (RTP), and mixed TADF/RTP molecules were developed for application in organic light‐emitting diodes.
By exploiting a smart card, this paper presents a robust and efficient password-authenticated key agreement scheme. This paper strengthens the security of the scheme by addressing untraceability ...property such that any third party over the communication channel cannot tell whether or not he has seen the same (unknown) smart card twice through the authentication sessions. The proposed remedy also prevents a kind of denial of service attack found in the original scheme. High performance and other good functionalities are preserved.
Fast spin-flipping is the key to exploit the triplet excitons in thermally activated delayed fluorescence based organic light-emitting diodes toward high efficiency, low efficiency roll-off and long ...operating lifetime. In common donor-acceptor type thermally activated delayed fluorescence molecules, the distribution of dihedral angles in the film state would have significant influence on the photo-physical properties, which are usually neglected by researches. Herein, we find that the excited state lifetimes of thermally activated delayed fluorescence emitters are subjected to conformation distributions in the host-guest system. Acridine-type flexible donors have a broad conformation distribution or bimodal distribution, in which some conformers feature large singlet-triplet energy gap, leading to long excited state lifetime. Utilization of rigid donors with steric hindrance can restrict the conformation distributions in the film to achieve degenerate singlet and triplet states, which is beneficial to efficient reverse intersystem crossing. Based on this principle, three prototype thermally activated delayed fluorescence emitters with confined conformation distributions are developed, achieving high reverse intersystem crossing rate constants greater than 10
s
, which enable highly efficient solution-processed organic light-emitting diodes with suppressed efficiency roll-off.
k
-nearest neighbor (
k
-NN) query is widely applied to various networks, such as mobile Internet, peer-to-peer (P2P) network, urban road networks, and so on. The location-based service in the ...outsourced environment has become a research hotspot with the rise of cloud computing. Meanwhile, various privacy issues have been increasingly prominent. We propose an efficient privacy-preserving query protocol to accomplish the
k
-nearest neighbor (
k
-NN) query processing on outsourced data. We adopt the Moore curve to transform the spatial data into one-dimensional sequence and utilize the AES to encrypt the original data. According to the cryptographic transformation, the proposed protocol can minimize the communication overhead to achieve efficient
k
-NN query while protecting the spatial data and location privacy. Furthermore, the proposed efficient scheme offers considerable performance with privacy preservation. Experiments show that the proposed scheme achieves high accuracy and efficiency while preserving the data and location privacy when compared with the existing related approach.
Social media platforms have become inundated with offensive language. This issue must be addressed for the growth of online social networks (OSNs) and a healthy online environment. While significant ...research has been devoted to identifying toxic content in major languages like English, this remains an open area of research in the low-resource Pashto language. This study aims to develop an AI model for the automatic detection of offensive textual content in Pashto. To achieve this goal, we have developed a benchmark dataset called the Pashto Offensive Language Dataset (POLD), which comprises tweets collected from Twitter and manually classified into two categories: "offensive" and "not offensive". To discriminate these two categories, we investigated the classic deep learning classifiers based on neural networks, including CNNs and RNNs, using static word embeddings: Word2Vec, fastText, and GloVe as features. Furthermore, we examined two transfer learning approaches. In the first approach, we fine-tuned the pre-trained multilingual language model, XLM-R, using the POLD dataset, whereas, in the second approach, we trained a monolingual BERT model for Pashto from scratch using a custom-developed text corpus. Pashto BERT was then fine-tuned similarly to XLM-R. The performance of all the deep learning and transformer learning models was evaluated using the POLD dataset. The experimental results demonstrate that our pre-trained Pashto BERT model outperforms the other models, achieving an F1-score of 94.34% and an accuracy of 94.77%.
Recognizing the certain person of interest in cameras of different viewpoints is known as the task of person re-identification. It has been a challenging job considering the variation in human pose, ...the changing illumination conditions and the lack of paired samples. Previous matching techniques in the person re-identification field mainly focus on Mahalanobis-like metric learning functions. Taking advantage of the sparse representation and collaborative representation, we propose a new approach that elaborately exploits both the globality and locality of images. First, we explore multi-feature extraction with different spatial levels. The extracted features are then projected to a common subspace which handles dimension reduction. Second, we learn a single dictionary for each level that is invariant with the changing of viewpoints. Third, we adopt a weighted fusion approach that combines the dictionary learning-based sparse representation with collaborative representation. Experiments on two benchmark re-identification data sets (VIPeR and GRID) justify the advances of our integration algorithm by comparing with several state-of-the-art methods.
The development of white light emitting diodes (WLEDs) holds great promise for replacing traditional lighting devices due to high efficiency, low energy consumption and long lifetime. Metal-organic ...frameworks (MOFs) with a wide range of luminescent behaviors are ideal candidates to produce white light emission in the phosphor-converted WLEDs. Encapsulation of emissive organic dyes is a simple way to obtain luminescent MOFs. In this review, we summarize the recent progress on the design and constructions of dye encapsulated luminescent MOFs phosphors. Different strategies are highlighted where white light emitting phosphors were obtained by combining fluorescent dyes with metal ions and linkers.
Recurrent neural network (RNN) has been widely applied to many sequential tagging tasks such as natural language process (NLP) and time series analysis, and it has been proved that RNN works well in ...those areas. In this paper, we propose using RNN with long short-term memory (LSTM) units for server load and performance prediction. Classical methods for performance prediction focus on building relation between performance and time domain, which makes a lot of unrealistic hypotheses. Our model is built based on events (user requests), which is the root cause of server performance. We predict the performance of the servers using RNN-LSTM by analyzing the log of servers in data center which contains user’s access sequence. Previous work for workload prediction could not generate detailed simulated workload, which is useful in testing the working condition of servers. Our method provides a new way to reproduce user request sequence to solve this problem by using RNN-LSTM. Experiment result shows that our models get a good performance in generating load and predicting performance on the data set which has been logged in online service. We did experiments with nginx web server and mysql database server, and our methods can been easily applied to other servers in data center.
Abstract
Background
Persistent pain following back surgery called failed back surgery syndrome remains a major treatment challenge. The aim of this study is to evaluate the efficacy and safety of ...electroacupuncture on relieving back pain in FBSS patients.
Methods/design
This is a randomized, single-blind, single-site, placebo-controlled trial. A total of 144 eligible FBSS patients will be randomly assigned to the electroacupuncture, manual acupuncture, or sham acupuncture group in a 1:1:1 ratio. Each group will receive 2 treatment sessions per week for 12 weeks. The primary outcome will be low back pain intensity based on the 11-point numerical rating scale (NRS). The secondary outcomes include Oswestry Disability Index (ODI) questionnaire, Beck Depression Inventory-II (BDI-II), Pittsburgh Sleep Quality Index (PSQI), and analgesic consumption. All clinical outcomes will be collected at baseline, during the treatment phase (at 8 and 12 weeks), and at the 16-, 24- and 36-week follow-ups. All data will be analyzed based on the intention-to-treat principle and adverse events will be assessed during the trial.
Discussion
This pilot randomized controlled trial will evaluate the efficacy of electroacupuncture for treating failed back surgery syndrome. The outcomes will determine whether electroacupuncture is efficacious in relieving low back pain as well as improving the quality of life in failed back surgery syndrome patients.
Trial registration
Chinese Clinical Trial Registry
ChiCTR2000040144
. Registered on 22 November 2020
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