Search

Natural phenazines are a class of multifunctional secondary metabolites of bacteria that play an important role in the biocontrol of plant pathogens. In this paper, a novel bioactive phenazine derivative was isolated from

Wei AH platform is a platform composed of 8 horizontal wells in Weiyuan shale gas area. The number of faults in the area is small, and natural fractures are locally developed. Therefore, it is necessary to take volume reconstruction as the core mining technology and establish the geology-engineering integration model for the platform according to the geology-engineering integration concept. On the basis of geological model, combined with one-dimensional rock mechanics profile, the geomechanical model of Wei AH platform is established by means of deep diagenetic theory. Based on geological model and geomechanical model, the well trajectory design model, fracturing design model and reservoir numerical simulation model are modeled and optimized respectively. Finally, it can realize the comprehensive application of geophysical, geological, mechanical and seepage data, and complete the mutual call and matching of all kinds of understanding and models.

Passivating surface and bulk defects of perovskite films has been proven to be an effective way to minimize nonradiative recombination losses in perovskite solar cells (PVSCs). The lattice interference and perturbation of atomic periodicity at the perovskite surfaces often significantly affect the material properties and device efficiencies. By tailoring the terminal groups on the perovskite surface and modifying the surface chemical environment, the defects can be reduced to enhance the photovoltaic performance and stability of derived PVSCs. Here, we report a rationally designed bifunctional molecule, piperazinium iodide (PI), containing both R2NH and R2NH2+ groups on the same six-membered ring, behaving both as an electron donor and an electron acceptor to react with different surface-terminating ends on perovskite films. The resulting perovskite films after defect passivation show released surface residual stress, suppressed nonradiative recombination loss, and more n-type characteristics for sufficient energy transfer. Consequently, charge recombination is significantly suppressed to result in a high open-circuit voltage (VOC) of 1.17 V and a reduced VOC loss of 0.33 V. A very high power conversion efficiency (PCE) of 23.37% (with 22.75% certified) could be achieved, which is the highest value reported for inverted PVSCs. Our work reveals a very effective way of using rationally designed bifunctional molecules to simultaneously enhance the device performance and stability.

Background We have recently published SL-BioDP, a web resource for querying, exploration and visualization of potential synthetic lethal targets and possible synergistic drug combinations for 18 cancer types. Methods From our predictive synthetic lethality model used in SL-BioDP, we inferred TP53 mutation lead to potential synergistic drug combination of Bortezomib and Vorinostat. Here we show, how to extrapolate the drug combination results by combining drug screening data from cancer cell lines and showed the potential synergy of the drug targets, proteasome, and histone deacetylase (HDAC) pathways respectively, for patient survival advantage. Results We found that TP53 mutation is potentially synthetic lethal with multiple genes from the proteasome and HDAC pathways exclusively in many cancer types. Also, HDAC and proteasomes were found to have potential synthetic lethal relationship. Using drug screening data in cancer cell line, the sensitivity of the HDAC inhibitor drug Vorinostat was found to be increased in TP53 mutated cells where the proteasome pathway was downregulated. Conclusions Our in-silico pharmacogenomic study indicates that the potential synergistic drug combination of proteasome and HDAC inhibitors may be considered as potential treatment for TP53-mutant cancers.

Deep neural networks (DNNs) have been widely and successfully applied to various applications, but they require large amounts of memory and computational power. This severely restricts their deployment on resource-limited devices. To address this issue, many efforts have been made on training low-bit weight DNNs. In this paper, we focus on training ternary weight \{-1, 0, +1\} networks which can avoid multiplications and dramatically reduce the memory and computation requirements. A ternary weight network can be considered as a sparser version of the binary weight counterpart by replacing some -1s or 1s in the binary weights with 0s, thus leading to more efficient inference but more memory cost. However, the existing approaches to training ternary weight networks cannot control the sparsity (i.e., percentage of 0s) of the ternary weights, which undermines the advantage of ternary weights. In this paper, we propose to our best knowledge the first sparsity-control approach (SCA) to training ternary weight networks, which is simply achieved by a weight discretization regularizer (WDR). SCA is different from all the existing regularizer-based approaches in that it can control the sparsity of the ternary weights through a controller $\alpha$ and does not rely on gradient estimators. We theoretically and empirically show that the sparsity of the trained ternary weights is positively related to $\alpha$. SCA is extremely simple, easy-to-implement, and is shown to consistently outperform the state-of-the-art approaches significantly over several benchmark datasets and even matches the performances of the full-precision weight counterparts., Comment: version 1 of SCA; accepted by journal "Neural Networks"; the final version could be a little different from this version

Based on the transport equation of the semiconductor device model for 0.524 eV GeSn alloy and the experimental parameters of the material, the thermal–electricity conversion performance governed by a GeSn diode has been systematically studied in its normal and inverted structures. For the normal p+/n (n+/p) structure, it is demonstrated here that an optimal base doping N d(a) = 3 (7) × 1018 cm−3 is observed, and the superior p+/n structure can achieve a higher performance. To reduce material consumption, an economical active layer can comprise a 100 nm–300 nm emitter and a 3 μm–6 μm base to attain comparable performance to that for the optimal configuration. Our results offer many useful guidelines for the fabrication of economical GeSn thermophotovoltaic devices.

Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema. In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (StruG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus. We identify a set of novel prediction tasks: column grounding, value grounding and column-value mapping, and leverage them to pretrain a text-table encoder. Additionally, to evaluate different methods under more realistic text-table alignment settings, we create a new evaluation set Spider-Realistic based on Spider dev set with explicit mentions of column names removed, and adopt eight existing text-to-SQL datasets for cross-database evaluation. STRUG brings significant improvement over BERT-LARGE in all settings. Compared with existing pretraining methods such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms all baselines on more realistic sets. The Spider-Realistic dataset is available at https://doi.org/10.5281/zenodo.5205322., Accepted to NAACL 2021. The Spider-Realistic dataset is available at https://doi.org/10.5281/zenodo.5205322

Voice Activity Detection (VAD) plays an important role in audio processing, but it is also a common challenge when a voice signal is corrupted with strong and transient noise. In this paper, an accurate and causal VAD module using a long short-term memory (LSTM) deep neural network is proposed. A set of features including Gammatone cepstral coefficients (GTCC) and selected spectral features are used. The low complex structure allows it can be easily implemented in speech processing algorithms and applications. With carefully pre-processing and labeling the collected training data in the classes of speech or non-speech and training on the LSTM net, experiments show the proposed VAD is able to distinguish speech from different types of noisy background effectively. Its robustness against changes including varying frame length, moving speech sources and speaking in different languages, are further investigated.

The authors have requested that this preprint be removed from Research Square.

Echovirus is an important cause of viral pneumonia and encephalitis in infants, neonates, and young children worldwide. However, the exact mechanism of its pathogenesis is still not well understood. Here, we established an echovirus type 9 infection mice model, and performed two-dimensional gel electrophoresis (2DE) and tandem mass spectrometry (MS/MS)-based comparative proteomics analysis to investigate the differentially expressed host proteins in mice brain. A total of 21 differentially expressed proteins were identified by MS/MS. The annotation of the differentially expressed proteins by function using the UniProt and GO databases identified one viral protein (5%), seven cytoskeletal proteins (33%), six macromolecular biosynthesis and metabolism proteins (28%), two stress response and chaperone binding proteins (9%), and five other cellular proteins (25%). The subcellular locations of these proteins were mainly found in the cytoskeleton, cytoplasm, nucleus, mitochondria, and Golgi apparatus. The protein expression profiles and the results of quantitative RT-PCR in the detection of gene transcripts were found to complement each other. The differential protein interaction network was predicted using the STRING database. Of the identified proteins, heat shock protein 70 (Hsp70), showing consistent results in the proteomics and transcriptomic analyses, was analyzed through Western blotting to verify the reliability of differential protein expression data in this study. Further, evaluation of the function of Hsp70 using siRNA and quercetin, an inhibitor of Hsp70, showed that Hsp70 was necessary for the infection of echovirus type 9. This study revealed that echovirus infection could cause the differential expression of a series of host proteins, which is helpful to reveal the pathogenesis of viral infection and identify therapeutic drug targets. Additionally, our results suggest that Hsp70 could be a useful therapeutic host protein target for echovirus infection.

Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such tables, there has been tremendous progress on a variety of tasks in the area of table understanding. However, existing work generally relies on heavily-engineered task-specific features and model architectures. In this paper, we present TURL, a novel framework that introduces the pre-training/fine-tuning paradigm to relational Web tables. During pre-training, our framework learns deep contextualized representations on relational tables in an unsupervised manner. Its universal model design with pre-trained representations can be applied to a wide range of tasks with minimal task-specific fine-tuning. Specifically, we propose a structure-aware Transformer encoder to model the row-column structure of relational tables, and present a new Masked Entity Recovery (MER) objective for pre-training to capture the semantics and knowledge in large-scale unlabeled data. We systematically evaluate TURL with a benchmark consisting of 6 different tasks for table understanding (e.g., relation extraction, cell filling). We show that TURL generalizes well to all tasks and substantially outperforms existing methods in almost all instances., Accepted to VLDB 2021. Extended version with experiments added during revision. Our source code, benchmark, as well as pre-trained models will be available on https://github.com/sunlab-osu/TURL

Today, environmental protection has become a global issue, and various environmental regulations have been actively adopted. However, are these measures promoting or harming enterprise values? Is this effect the same for enterprises with different ownership backgrounds? In order to address these problems, we conducted an empirical analysis of China&rsquo, s A-share market to investigate the relationship between the New Environmental Protection Law (NEPL) launched in China and corporate financial performance, and further explore the impact of environmental supervision intensity (ESI) from the perspective of ownership. The empirical results show that there is a negative correlation between NEPL and the financial performance of high pollution enterprises. Further analysis demonstrates that there is an inverted U-shape relationship between ESI and corporate financial performance for both state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs), while the financial performance of SOEs is more sensitive and tolerant to environmental regulation than that of non-SOEs. Finally, we make recommendations for the future direction of China&rsquo, s ecological civilization construction and sustainable development of enterprises based on three aspects: environmental awareness, policy considerations, and sustainable development. The innovation of this paper lies in putting NEPL and corporate financial performance in the same analytical framework for the first time, which enriches the research in this field. Meanwhile, it provides a new perspective for understanding the relationship between ESI and corporate financial performance through the analysis of nonlinearity and owner heterogeneity.

Stress transfer and redistribution always accompany with the evolution of landslides. However, previous literature studies have mainly focused on stages of stress variation, and far too little attention has been paid to detailed transfer and redistribution process analysis on stress variation. In this paper, a large-scale clay model slope with masonry slide bed and prefabricated cambered slip surface was constructed. Earth pressure cells were embedded into slip mass to monitor vertical and horizontal stresses in different parts of the test soils under the set load sequence. Stress transfer efficiency (STE) indicators based on qualified stress monitoring datasets (tested by Shapiro-Wilk method) were established to quantify the stress transfer process. Staged development of stress inside the clay slope was analyzed through extracting slopes of stress curves and limit loads. The stress redistribution process was analyzed using STE and deflection of stress isolines derived from numerical simulation. Moreover, to study the influence of loading position on stress variation, geometry partitioning has also been discussed. Results showed that vertical and horizontal stresses had different growth trends on both sides of 80 kN and 60 kN, respectively. Horizontal stress growth has two stages; vertical stress growth has two stages in soils close to slope surface and shear outlet, while there are three stages in other soils. Vertical stress transfer efficiency (VSTE) and horizontal stress transfer efficiency (HSTE) are recommended to quantify stress transfer and redistribution process. Based on VSTEs and HSTEs, the slip mass could be partitioned into three parts: loading zone, transfer zone, and free zone. Deflecting amplitudes of stress isolines were in consistency with the results revealed by STEs.

Traditional air-conditioning control system assumes indoor air temperature to be uniformly distributed. However, indoor air is often not completely well-mixed. The air temperature and speed data obtained from sensors cannot represent thermal state of the whole room. This paper aims to investigate real thermal perception of the occupants in an air-conditioned office model. A Computational Fluid Dynamics (CFD) based method is utilised for building modelling. The temperature and air speed calibrations are adopted to offset the temperature and air speed difference between the actual sensors (i.e. temperature and speed sensors) and the virtual sensors (i.e. temperature and speed sensors) located in the occupied zone. Predicted Mean Vote (PMV) method is employed to evaluate the thermal comfort performance under different indoor air conditions. Simulation results show that indoor thermal comfort performance in a specified office model is well improved by calibrating the differences between the actual and virtual temperature sensors.

Deep neural networks have been successfully deployed in various domains of artificial intelligence, including computer vision and natural language processing. We observe that the current standard procedure for training DNNs discards all the learned information in the past epochs except the current learned weights. An interesting question is: is this discarded information indeed useless? We argue that the discarded information can benefit the subsequent training. In this paper, we propose learning with retrospection (LWR) which makes use of the learned information in the past epochs to guide the subsequent training. LWR is a simple yet effective training framework to improve accuracies, calibration, and robustness of DNNs without introducing any additional network parameters or inference cost, only with a negligible training overhead. Extensive experiments on several benchmark datasets demonstrate the superiority of LWR for training DNNs., Comment: Accepted to AAAI2021

Aorta segmentation is clinically important as it is a necessary step towards accurate assessments of some aorta disease. In the paper, we present a graph cut based method for automated aorta segmentation. In our method, a discriminative integrated feature (DIF) and a novel adaptive smoothness constraint are designed. DIF consists of low level features and other discriminative features including eigenvalues of Hessian and local self-similarity descriptor. DIF and random forests (RFs) are used to generate the probability maps. The probability maps containing learning information from RFs are more accurate than traditional probability maps generated based on intensities directly. The negative logarithm of the probability maps serves as the penalty term in a cost function. Additionally, a novel adaptive smoothness constraint is imposed to ensure a smooth solution. The adaptive smoothness term is constructed by DIFs and data-driven weights. Two kinds of data-driven weights are developed based on the idea that the discontinuity of two neighboring voxels with different labels should be distinct with two neighboring voxels with the same label. The final segmentation is obtained by optimizing the cost function using graph cuts. We evaluate the proposed method through challenging task of abdominal aorta segmentation in 3D CT images. With average dice metric (DM) > 0.9690 on the test set, our experimental results demonstrate that our method achieves higher aorta segmentation accuracy than existing methods.

Conductivity is an important parameter for characterizing dissolved solids and salinity in water, and is also one of the routinely measured parameters in water quality monitoring. To reveal temporal and spatial variations in conductivity in different media in Taihu Lake, historical data (1980-2009) were collected and field monitoring data (2009-2018) were analyzed. The results indicated that water conductivity in Taihu Lake has shown a significant increasing trend over the past 40 years and diverged in 1996-1997. Conductivity values increased from (239.43±70.60)μS·cm

β-Amyloid (Aβ) plays an important role in the pathogenesis of Alzheimer's disease (AD). However, there is still no effective Aβ-targeting drugs for AD treatment. In this study, we explored the effect and mechanism of Sodium Tanshinone IIA Sulfonate (STS) on AD. Aβ-treated HT22 cells, an immortalized mouse hippocampal neuronal cell line, were employed. Different dosages of STS (0.1, 1 and 10 μM) were selected. STS improved cell viability and protected against Aβ-induced apoptosis in a dose-dependent manner. Furthermore, the levels of reactive oxygen species (ROS) and malondialdehyde (MDA) were decreased, while the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) were significantly increased after STS treatment. STS decreased the levels of phosphorylate PKR-like (p-PERK), phosphorylate eukaryotic initiation factor 2 (p-eIF2α), phosphorylate inositol-requiring enzyme (p-IRE1α), X-box binding protein 1 (XBP1) and binding immunoglobulin heavy chain protein (Bip), while increased protein disulfide isomerase (PDI) levels in Aβ-treated HT22 cells. In addition, the levels of insulin degrading enzymes (IDE) and Nepterrilysin (NEP) (or call it CD10) were significantly increased after STS treatment. Taken together, these results indicated that STS might be effective in treating AD via increasing the levels of Aβ-degrading enzymes.

In the current international context, the ways in which financial technology (FinTech) affects sustainable development need to be urgently identified. However, relevant studies are rare and there is no consensus on the optimal indicator system for sustainable development. Therefore, this study proposes an indicator system to evaluate sustainability and conducts in-depth analysis of the relationship between FinTech and sustainable development based on data of peer-to-peer platforms (P2P) in 31 Chinese provinces. The empirical results show the existence of a U-shaped relationship between FinTech and sustainable development, mainly determined by&nbsp, the pattern of extensive economic growth. Furthermore, heterogeneity analysis indicates that there are significant regional differences in its impact on sustainable development, being significant in China&rsquo, s eastern and central regions and insignificant in the western region, moreover, the impact on the central region is significantly higher than that on the eastern region. Our research not only has strong practical significance but also contributes significantly to the literature on FinTech and sustainable development.

Objective To design a WeChat mini program called Chinese Syndrome Differentiation Learning Platform (CSDLP) on smartphone to improve health literacy.Methods The developer tools of WeChat (Version: v1.01.170925) were used for designing and debugging the mini program. SPSS17.0 was used for statistical purposes. “View container”“Basic content”“Form component”“Navigation” and “Media components” were used for the development of the WeChat mini program. The detailed method was referred to https://mp.weixin.qq.com/debug/wxadoc/dev/.Results A WeChat mini program called CSDLP was developed. This program has three major functions which are WeChat reading, WeChat class and WeChat syndrome differentiation. The official test report showed that there were no functionality errors for the seven android smartphones (referred to as A, B, C, D, E, F and G) that CSDLP was tested on. Statistical analysis results showed that the average memory in D, E, F and G was lower than in A, B and C. Average ratio was the highest in F and the lowest in G. The average loading time was the same for all smartphones. The audio database for diagnostics using traditional Chinese medicine (TCM) and a lecture video database were based on diagnostic textbook. Our team built a syndrome differentiation database which included 51 diseases.Conclusion CSDLP can improve knowledge visualization, studying process, and information sharing in terms of the training and development of new techniques for syndrome differentiation and treatment in TCM, and it can provide a better illustration for people to understand TCM. Keywords: Syndrome differentiation and treatment, Learning platform, Smartphone, WeChat Program

Extensive scar tissue formation often occurs after severe burn injury, trauma, or as one of complications after surgical intervention. Despite significant therapeutic advances, it is still a significant challenge to manage massive scar tissue formation while also promoting normal wound healing. The goal of this study was to investigate the therapeutic effect of bone mesenchymal stem cells (BMSCs) that were genetically modified to overexpress transforming growth factor-beta 3 (TGF-β3), an inhibitor of myofibroblast proliferation and collagen type I deposition, on full-thickness cutaneous wound healing in a rabbit model. Twenty-four rabbits with surgically-induced full-thickness cutaneous wounds created on the external ear (1.5 × 1.5 cm, two wounds/ear) were randomized into four groups: (G1), wounds with no special treatment but common serum-free culture medium as negative controls; (G2), topically-applied recombinant adenovirus, expressing TGF-β3/GFP; (G3), topically-applied BMSCs alone; (G4), topically-applied BMSCs transfected with Ad-TGF-β3/GFP (BMSCsTGF-β3); and (G5), an additional normal control (n = 2) with neither wound nor treatment on the external ear skin. The sizes of wounds on the ear tissues were grossly examined, and the scar depth and density of wounds were histologically evaluated 21, 45, and 90 days after surgical wound creation. Our results demonstrated that G4 significantly reduced the wound scar depth and density, compared to G1~3. Numbers of cells expressing GFP significantly increased in G4, compared to G2. The protein expression of TGF-β3 and type III collagen in G4 significantly increased, while the ratio of type I to type III collagen was also significantly reduced, which is similar to the tissue architecture found in G5, as compared the other treatment groups. In conclusion, transplantation of BMSCsTGF-β3 remarkably improves wound healing and reduces skin scar tissue formation in an animal model, which may potentially provide an alternative in the treatment of extensive scar tissue formation after soft tissue injury.

In this study, the characteristic of three transformants named as B1C106-1, B1C106-2, and B1C106-3 were studied that carried three innate resistant genes Bph14, Bph15, and Xa23, and two enthetic resistant genes Cry1Ca# and Bar. The five resistant genes were all verified by PCR and the two enthetic genes were identified in single copy insertion by Southern blot. At tillering stage, the Cry1C and PAT (phosphinothricin acetyl transferase) protein contents in leaf, sheath, and stem of T2 generation were in the similar pattern: leaf>stem>sheath, and showed significant difference (P

A novel side-chain polymer was designed and synthesized by integrating a methoxytriphenylamine-conjugated-thiophene moiety as the hole transporting material (HTM) unit on a polystyrene side-chain (HTM–P1) by the radical polymerization of its relevant monomer (HTM–M1). These two materials and their similar model compound without the styrene moieties (HTM2) were used as the HTMs for perovskite solar cells (PSCs). The results showed that with similar device preparation and measurement conditions, the hole mobility of polymer HTM–P1 was about 3 times and 2.5 times that of HTM2 and HTM–M1, respectively. PSCs based on HTM–P1 as the dopant-free HTM afford an impressive highest power conversion efficiency of 17.2%, which is much higher than those obtained from HTM2 (3.2% without the dopant and 14.7% with the dopant) and HTM–M1 (9.7%). Moreover, the devices based on the HTM–P1 presented significantly higher stability than the device based on its model compound of HTM2. Hence, this study demonstrates that the side-chain polymer strategy is an effective approach to achieve high efficiency and highly stable PSCs using dopant-free HTMs.

An ultrasensitive sandwich-type electrochemiluminescent (ECL) immunosensor suitable for identifying carcinoembryonic antigens (CEAs) was developed, using a metal–organic framework nanomaterial (AuNP@NPCGO) as an ideal loading platform and Ru(phen)32+-doped silica nanoparticles (RuSiNPs) as ECL signaling units. The AuNP@NPCGO nanocomposites were synthesized via in situ deposition of AuNPs on NPCGO as this configuration exhibits high conductivity, strong fixed ability, and excellent stability. The use of RuSiNPs as signaling units provided numerous luminophores that enhanced the ECL signals. Besides the aminated RuSiNPs, which had a large specific surface area and good biocompatibility, functioned as a superior matrix for immobilizing numerous secondary antibodies (Ab2). The proposed ECL immunosensor demonstrated excellent performance for CEA detection at concentrations of 0.01 ng mL−1 to 80 ng mL−1 with a detection limit of 0.003 ng mL−1 at a signal-to-noise ratio of three.

Organic-rich shale deposited in a marine-continental transitional facies was well developed in the upper Yangtze area during the Late Permian. It is considered to contain a large amount of shale hydrocarbon resources. This paper takes the Upper Permian Longtan shale in the central Guizhou uplift as an example to characterize the transitional shale reservoir. Based on organic geochemistry data, X-ray diffraction (XRD) analysis, and scanning electron microscopy (SEM) observation, the geochemical characteristics of this transitional shale were systematically studied and the shale gas potential was investigated. The results indicate that the Longtan shale has fair to good hydrocarbon potential with high total organic carbon (TOC) and contains type III gas-prone organic matter. Its high thermal maturity favors the generation of gas (Ro, 1.10%–2.74%). XRD analyses show that the mineral components of the transitional shale are dominated by high clay content (58.7 wt%). The clay minerals are dominated by illite (44%) and mixed illite/smectite (43%). SEM observation shows that intra-particle pores, inter-particle pores and microfracture were well developed in the Longtan shale, which can provide reservoir space for the accumulation of shale gas. Organic matter pores appear rare due to its organic type. In summary, the Longtan shale has high organic content, high maturation, and high gas content, all of which demonstrate its great potential as a shale-gas resource. But the high clay content of the Longtan shale may be a challenge to shale-gas production.

This note consists of two parts . In the first part, we provide a pedagogic review on the multivariate generalized hyperbolic (MGH) distribution. We show that this probability family is clo...

By using density functional theory in combination with nonequilibrium Green's functions method, we design a molecular rectifier base on acene molecule, which is connected by the zigzag and armchair graphene nanoribbons (GNRs). The spin dependent electron transport studies show that the Large Rectification Ratios (10 4 ) and High-efficiency spin-filtering (100%) can be observed at low bias voltages. To further modulate the transport properties, we doped the device with N-pair in the different positions of acene molecule, and found that the rectification ratios are strongly dependent on the doping position. Interestingly, the perfect spin-filtering is still maintained in the doped devices. Our simulations suggest that the 4-acene supramolecular can be formed a promising nanoscale device.

Lead halide perovskite and organic solar cells (PSCs and OSCs) are considered as the prime candidates currently for clean energy applications due to their solution and low-temperature processibility. Nevertheless, the substantial photon loss in near-infrared (NIR) region and relatively large photovoltage deficit need to be improved to enable their uses in high-performance solar cells. To mitigate these disadvantages, low-bandgap organic bulk-heterojunction (BHJ) layer into inverted PSCs to construct facile hybrid solar cells (HSCs) is integrated. By optimizing the BHJ components, an excellent power conversion efficiency (PCE) of 23.80%, with a decent open-circuit voltage (Voc ) of 1.146 V and extended photoresponse over 950 nm for rigid HSCs is achieved. The resultant devices also exhibit superior long-term (over 1000 h) ambient- and photostability compared to those from single-component PSCs and OSCs. More importantly, a champion PCE of 21.73% and excellent mechanical durability can also be achieved in flexible HSCs, which is the highest efficiency reported for flexible solar cells to date. Taking advantage of these impressive device performances, flexible HSCs into a power source for wearable sensors to demonstrate real-time temperature monitoring are successfully integrated.

Since the influence of building types has not taken into consideration, the current climatic zoning scheme of China may not fully reveal the real energy requirements, especially the annual energy consumption variation of different types of buildings. This study proposes a novel zoning method based on the clustering of building thermal load data of a typical office building. Thermal load simulations were performed under 274 cities’ meteorological conditions across China. Two widely used clustering algorithms : K-Means and Agglomerative Hierarchical Clustering were adopted and compared. Based on the inter-city clustering results, a new zoning map with five distinct zones for building thermal design was developed. To demonstrate the thermal load patterns, especially its dynamic characteristics, three representative cities from the three most densely populated zones were selected for intra-city clustering. The results demonstrate that K-Means method performed better in building thermal design zoning and thermal load pattern analysis, and the optimal thermal zone number is 5. The present zoning method could be applied to other building types and promotes an improvement of the existing zoning strategy. It benefits not only architects, but also building operators and policy makers for energy efficiency design and even green urban design.

An all-digital agile dual auroral radar network (AgileDARN) was developed by the National Space Science Center, Chinese Academic of Sciences (NSSC, CAS). AgileDARN can achieve higher performance and greater flexibility based on highly digitized hardware and its distributed digital signal processing system. Multiple receiving beams can be created within the transmitting beam pattern better to determine the direction of arrival (DOA). In each beam, seven real-time azimuthal sub-beams are synthesized simultaneously with 16 main digital receiver channels. Each sub-beam detects the echo around the transmitting boresight with an angular separation of 0.46°. Comparing the echoes from different sub-beams, the azimuthal geolocation of the irregularities can be improved when their volume is smaller than the beamwidth. Furthermore, the Chebyshev window enables the control of the sidelobes and antenna pattern, effectively suppressing interference. To implement multi-beam forming, extra processing resources of the field-programmable gate array (FGPA) are required. This paper introduces the signal processing procedure and analyzes the requirements on the FPGA resources，and the technique is validated with meteor echoes received by the AgileDARN radar. The azimuthal geolocation of echoes increases by seven times compared with the single beamforming SuperDARN radar.

Natural ventilation can be used in residential buildings in several Australian climatic zones and well-designed windows have the potential to facilitate indoor thermal comfort by allowing occupants to control volumetric outdoor airflow and indoor air velocities. Top-hung window is one of the most popular window types in Australia. This paper investigates the effect of the attributes of top-hung windows (i.e. window length, aspect ratio, height above the ground, window opening angle and the fly screen porosity) and outdoor air conditions (i.e. outdoor air temperature, wind speed and direction) on indoor thermal comfort during cross ventilation using CFD simulations. The Taguchi method was used to design the simulation scenarios and analysis of variance was used to determine the most significant factors influencing thermal comfort optimisation so as to reduce the number of CFD simulation cases. For the range of parameters considered and a particular case study building, results show that outdoor air temperature, window height and window opening angle are the most important factors influencing indoor thermal comfort in this room. The optimal window configurations for indoor thermal comfort of the case study building are also identified using a signal-to-noise ratio analysis.