Rational manipulation of frontier orbital distribution and singlet‐triplet splitting is crucial to exploit the luminescent properties of organic molecules. To realize ultra‐blue luminescence, both ...blue‐shifted wavelength peak (λpeak) and narrow full‐width at half‐maximum (FWHM) are required. Herein, a new thermally activated delayed fluorescence (TADF) skeleton by inserting the diphenyl methylene intramolecular‐lock to adjust the torsion angles and restrict the intramolecular relaxation is developed. Two rigid emitters, incorporating phenoxazine (PXZN‐B) and acridine (DMACN‐B) as donors and mesitylboron as an acceptor, exhibit narrow FWHMs (<50 nm) with deep‐blue (0.133, 0.147) and violet‐blue emission (0.151, 0.045), respectively. In particular, the Commission Internationale de l'Eclairage (CIE) coordinates of a DMACN‐B‐based device closely approach the Rec.2020 standard (0.131, 0.046). Moreover, both of the organic light‐emitting diodes (OLEDs) based on PXZN‐B and DMACN‐B show TADF character, with high external quantum efficiencies (EQEs) exceeding 10%. Furthermore, owing to the large orbital overlap, these TADF emitters own a fast S1–S0 transition rate exceeding 108 s–1, thereby exhibiting marked amplified spontaneous emission (ASE) with low thresholds. Therefore, the intramolecular‐lock strategy provides not only innovation for realizing high‐efficiency deep‐blue TADF emission with high color purity but also an avenue for a TADF‐based ASE and lasing application.
An “intramolecular‐lock” is proposed as part of the thermally activated delayed fluorescence (TADF) molecular design for manipulating torsion angles and wave function distributions. The quasi‐planar TADF emitters lead to ultrapure violet‐blue TADF electroluminescence with CIE‐(0.151, 0.045), approaching the Rec. 2020 standard. Furthermore, a TADF‐based amplified spontaneous emission with low thresholds is triggered, which paves the way for future TADF‐based lasing application.
•Deep learning-based methods are developed for building cooling load prediction.•Accurate predictions on 24-h ahead building cooling load profiles are achieved.•The potentials of supervised and ...unsupervised deep learning is investigated.•Features extracted by unsupervised deep learning can improve model performance.•Supervised deep learning does not show obvious advantages in model development.
Short-term building cooling load prediction is the essential foundation for many building energy management tasks, such as fault detection and diagnosis, demand-side management and control optimization. Conventional methods, which heavily rely on physical principles, have limited power in practice as their performance is subject to many physical assumptions. By contrast, data-driven methods have gained huge interests due to their flexibility in model development and the rich data available in modern buildings. The rapid development in data science has provided advanced data analytics to tackle prediction problems in a more convenient, efficient and effective way.
This paper investigates the potential of one of the most promising techniques in advanced data analytics, i.e., deep learning, in predicting 24-h ahead building cooling load profiles. Deep learning refers to a collection of machine learning algorithms which are powerful in revealing nonlinear and complex patterns in big data. Deep learning can be used either in a supervised manner to develop prediction models with given inputs and output (i.e., cooling load), or in an unsupervised manner to extract meaningful features from raw data as model inputs. This study exploits the potential of deep learning in both manners, and compares its performance in cooling load prediction with typical feature extraction methods and popular prediction techniques in the building field. The results show that deep learning can enhance the performance of building cooling load prediction, especially when used in an unsupervised manner for constructing high-level features as model inputs. Using the features extracted by unsupervised deep learning as inputs for cooling load prediction can evidently enhance the prediction performance. The findings are enlightening and could bring more flexible and effective solutions for building energy predictions.
A multiple resonance thermally activated delayed fluorescence (MR‐TADF) molecule with a fused, planar architecture tends to aggregate at high doping ratios, resulting in broad full width at half ...maximum (FWHM), redshifting electroluminescence peaks, and low device efficiency. Herein, we propose a mono‐substituted design strategy by introducing spiro‐9,9′‐bifluorene (SBF) units with different substituted sites into the MR‐TADF system for the first time. As a classic steric group, SBF can hinder interchromophore interactions, leading to high device efficiency (32.2–35.9 %) and narrow‐band emission (≈27 nm). Particularly, the shield‐like molecule, SF1BN, seldom exhibits a broadened FWHM as the doping ratio rises, which differs from the C3‐substituted isomer and unhindered parent emitter. These results manifest an effective method for constructing highly efficient MR‐TADF emitters through a spiro strategy and elucidate the feasibility for steric modulation of the spiro structure in π‐framework.
By incorporating a three‐dimensional spiro unit into multiple resonance thermally activated delayed fluorescence emitters, the device efficiency is increased to nearly 1.5 times that of the unhindered emitter. Notably, the linkage pattern with spatial interaction and hindrance can maintain the narrow FWHM and curb unfavorable redshifts at a high doping ratio.
•Various strategies have been proposed for short-term building energy predictions.•Three inference approaches have been exploited for multi-step ahead predictions.•Advanced techniques have been ...utilized for the development of recurrent models.•Model performance is evaluated based on prediction accuracy and computation loads.•The results can provide valuable insights for developing deep recurrent models.
Accurate and reliable building energy predictions can bring significant benefits for energy conservations. With the development in smart buildings, massive amounts of building operational data are being collected and available for analysis. It is desired to develop big data-driven methods to fully realize the potential of building operational data in energy predictions. This paper investigates the usefulness of advanced recurrent neural network-based strategies for building energy predictions. Each strategy presents unique characteristics at two levels. At the high level, three inference approaches are used for generating short-term predictions, including the recursive approach, the direct approach and the multi-input and multi-output (MIMO) approach. At the low level, the state-of-the-art techniques are utilized for recurrent model development, such as the use of one-dimensional convolutional operations, bidirectional operations, and different types of recurrent units. The performance of different strategies has been assessed from different perspectives based on real building operational data. The research results help to bridge the knowledge gap between building professionals and advanced big data analytics. The insights obtained can be used as guidelines and references for developing advanced deep recurrent models for short-term building energy predictions.
This paper proposes an efficient method to modify histograms and enhance contrast in digital images. Enhancement plays a significant role in digital image processing, computer vision, and pattern ...recognition. We present an automatic transformation technique that improves the brightness of dimmed images via the gamma correction and probability distribution of luminance pixels. To enhance video, the proposed image-enhancement method uses temporal information regarding the differences between each frame to reduce computational complexity. Experimental results demonstrate that the proposed method produces enhanced images of comparable or higher quality than those produced using previous state-of-the-art methods.
•This paper proposes an applicable framework for mining BAS data sets using data mining (DM) techniques.•Cluster analysis identifies three daily power consumption patterns.•Association rules can be ...used to detect operation abnormalities.•DM techniques are valuable for discovering knowledge underlying massive BAS data sets.•Domain knowledge is still needed for the application of knowledge discovered using DM techniques.
Today's building automation system (BAS) provides us with a tremendous amount of data on actual building operation. Buildings are becoming not only energy-intensive, but also information-intensive. Data mining (DM) is an emerging powerful technique with great potential to discover hidden knowledge in large data sets. This study investigates the use of DM for analyzing the large data sets in BAS with the aim of improving building operational performance. An applicable framework for mining BAS database is proposed. The framework is implemented to mine the BAS database of the tallest building in Hong Kong. After data preparation, clustering analysis is performed to identify the typical power consumption patterns of the building. Then, association rule mining is adopted to unveil the associations among power consumptions of major components in each cluster. Lastly, post-mining is conducted to interpret the rules. 457 rules are obtained in association rule mining, of which the majority can be easily deduced from domain knowledge and hence be ignored in this study. Four of the rules are used for improving building performance. This study shows that DM techniques are valuable for knowledge discovery in BAS database; however, solid domain knowledge is still needed to apply the knowledge discovered to achieve better building operational performance.
•A data mining based method is proposed to predict building energy consumption.•The outlier detection method can identify abnormal building operating patterns.•He recursive feature elimination ...technique is effective in selecting optimal inputs.•The prediction performances of eight popular predictive algorithms are studied.•Ensemble models built on the eight base models have the best performances.
This paper presents a data mining (DM) based approach to developing ensemble models for predicting next-day energy consumption and peak power demand, with the aim of improving the prediction accuracy. This approach mainly consists of three steps. Firstly, outlier detection, which merges feature extraction, clustering analysis, and the generalized extreme studentized deviate (GESD), is performed to remove the abnormal daily energy consumption profiles. Secondly, the recursive feature elimination (RFE), an embedded variable selection method, is applied to select the optimal inputs to the base prediction models developed separately using eight popular predictive algorithms. The parameters of each model are then obtained through leave-group-out cross validation (LGOCV). Finally, the ensemble model is developed and the weights of the eight predictive models are optimized using genetic algorithm (GA).
The approach is adopted to analyze the large energy consumption data of the tallest building in Hong Kong. The prediction accuracies of the ensemble models measured by mean absolute percentage error (MAPE) are 2.32% and 2.85% for the next-day energy consumption and peak power demand respectively, which are evidently higher than those of individual base models. The results also show that the outlier detection method is effective in identifying the abnormal daily energy consumption profiles. The RFE process can significantly reduce the computation load while enhancing the model performance. The ensemble models are valuable for developing strategies of fault detection and diagnosis, operation optimization and interactions between buildings and smart grid.
In prostate cancer (PCa), similar to many other cancers, distant organ metastasis symbolizes the beginning of the end disease, which eventually leads to cancer death. Many mechanisms have been ...identified in this process that can be rationalized into targeted therapy. Among them, epithelial-to-mesenchymal transition (EMT) is originally characterized as a critical step for cell trans-differentiation during embryo development and now recognized in promoting cancer cells invasiveness because of high mobility and migratory abilities of mesenchymal cells once converted from carcinoma cells. Nevertheless, the underlying pathways leading to EMT appear to be very diverse in different cancer types, which certainly represent a challenge for developing effective intervention. In this article, we have carefully reviewed the key factors involved in EMT of PCa with clinical correlation in hope to facilitate the development of new therapeutic strategy that is expected to reduce the disease mortality.
Recently, strong polymer‐based hydrogels have been intensively investigated. However, the development of tough protein hydrogels with controlled degradation for bone regeneration has rarely been ...reported. Here, regenerated silk fibroin/gelatin (RSF/G) hydrogels with both strength and controlled degradation are prepared via physically and chemically double‐crosslinked networks. As a representative example, the 9%RSF/3%G hydrogel shows approximately 80% elongation and a compressive and tensile modulus of up to 0.25 and 0.21 MPa, respectively. It also shows a degradation rate that can be adjusted to approximately three months in vivo, a value between that of the rapidly degrading gelatin hydrogel and the slowly degrading RSF hydrogel. The 9%RSF/3%G hydrogel has good biocompatibility and promotes the proliferation and differentiation of bone marrow–derived stem cells compared with the control and pure RSF hydrogels. At 12 weeks after implantation of the gel in a calvarial defect, micro‐computed tomography shows greater bone volume and bone mineral density in the 9%RSF/3%G group. More importantly, histology reveals more mineralization and enhancements in the quality and rate of bone regeneration with less of a tissue response in the 9%RSF/3%G group. These results indicate the promising potential of this tough protein hydrogel with controlled degradation for bone regeneration applications.
A novel facile strategy is developed to fabricate strong and tough hydrogels with controlled degradation by immersing an enzymatically crosslinked regenerated silk fibroin/gelatin (RSF/G) hydrogel in a salt solution, forming a double‐crosslinked network. The resulting 9%RSF/3%G hydrogel shows high strength, good biocompatibility, a suitable degradation rate, and capacity for promoting osteogenic differentiation and bone regeneration.
A recurrence of hepatocellular carcinoma (HCC) after living donor liver transplantation (LDLT) is one of the major concerns reflecting the higher mortality of HCC. This study aimed to explore the ...impact of circulating exosomes on HCC development and recurrence. One‐shot transfusion of hepatoma serum to naïve rats induced liver cancer development with gradual elevation of alpha‐fetoprotein (AFP), but exosome‐free hepatoma serum failed to induce AFP elevation. The microarray analysis revealed miR‐92b as one of the highly expressing microribonucleic acids in hepatoma serum exosomes. Overexpression of miR‐92b enhanced the migration ability of liver cancer cell lines with active release of exosomal miR‐92b. The hepatoma‐derived exosomal miR‐92b transferred to natural killer (NK) cells, resulting in the downregulation of CD69 and NK cell‐mediated cytotoxicity. Furthermore, higher expression of miR‐92b in serum exosomes was confirmed in HCC patients before LDLT, and its value at 1 month after LDLT was maintained at a higher level in the patients with posttransplant HCC recurrence. In summary, we demonstrated the impact of circulating exosomes on liver cancer development, partly through the suppression of CD69 on NK cells by hepatoma‐derived exosomal miR‐92b. The value of circulating exosomal miR‐92b may predict the risk of posttransplant HCC recurrence.
This study demonstrates the impact of circulating exosomes on liver cancer development in rats, explores functional roles of exosomal miR‐92b in the tumor microenvironment, and verifies its clinical value for early prediction of posttransplant hepatocellular carcinoma recurrence.
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