Objectives We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, a...
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Manganese-rich layered oxide cathodes of sodium-ion batteries (SIBs) are extremely promising for large-scale energy storage owing to their high capacities and cost effectiveness, while the Jahn–Teller (J–T) distorti...
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Manganese-rich layered oxide cathodes of sodium-ion batteries (SIBs) are extremely promising for large-scale energy storage owing to their high capacities and cost effectiveness, while the Jahn–Teller (J–T) distortion and low operating potential of Mn redox largely hinder their practical applications. Herein, we reveal that annealing in argon rather than conventional air is a universal strategy to comprehensively upgrade the Na-storage performance of Mn-based oxide cathodes. Bulk oxygen vacancies are introduced via this method, leading to reduced Mn valence, lowered Mn 3 d- orbital energy level, and formation of the new-concept Mn domains. As a result, the energy density of the model P2-Na 0.75 Mg 0.25 Mn 0.75 O 2 cathode increases by ≈50 % benefiting from the improved specific capacity and operating potential of Mn redox. The Mn domains can disrupt the cooperative J–T distortion, greatly promoting the cycling stability. This exciting finding opens a new avenue towards high-performance Mn-based oxide cathodes for SIBs.
With the development of sensor networks in the past two decades, synchronization of complex dynamical networks has received an increasing attention and has been widely studied both in theoretical research and in pract...
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With the development of sensor networks in the past two decades, synchronization of complex dynamical networks has received an increasing attention and has been widely studied both in theoretical research and in practical applications. This paper concerns the synchronization protocol design problem of nonlinear dynamical complex networks with stochastic ***-integral(PI) control protocols are designed to guarantee that complex dynamical networks with stochastic coupling can achieve global synchronization. Sufficient condition for choice of PI control gains are derived. Numerical example is included to demonstrate the efficiency of the theoretical results.
Wide-band gap (1.68 eV) perovskite solar cells (PSCs) are important components of perovskite/Si tandem devices. However, the efficiency of wide band gap PSCs has been limited by their huge open-circuit voltage ( V oc ...
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Wide-band gap (1.68 eV) perovskite solar cells (PSCs) are important components of perovskite/Si tandem devices. However, the efficiency of wide band gap PSCs has been limited by their huge open-circuit voltage ( V oc ) deficit due to non-radiative recombination. Deep-level acceptor defects are identified as the major killers of V oc , and they can be effectively improved by passivation with ammonium salts. Theoretical calculation predicts that increasing the distance between F and −NH 3 + of fluorinated ammonium can dramatically enhance the electropositivity of −NH 3 + terminals, thus providing strong adsorption onto the negatively charged I A and I Pb anti-site defects. Characterizations further confirm that surface gradient passivation employing p -FPEAI demonstrates the most efficient passivation effect. Consequently, a record-efficiency of 21.63 % with the smallest V oc deficit of 441 mV is achieved for 1.68 eV-band gap inverted PSCs. Additionally, a flexible PSC and 1 cm 2 opaque device also deliver the highest PCEs of 21.02 % and 19.31 %, respectively.
advanced persistent threat (APT) as a new form of cyber attack has posed a severe threat to modern organizations. When an APT has been detected, the target organization has to develop a response resource allocation st...
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Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in deve...
Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from t...
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.
Agriculture has been the backbone of the Indian economy. Automation in the agriculture field helps to improve productivity and economic growth. The export market for fresh produce requires fast and reliable fruit and ...
Agriculture has been the backbone of the Indian economy. Automation in the agriculture field helps to improve productivity and economic growth. The export market for fresh produce requires fast and reliable fruit and vegetable quality detection techniques. The traditional manual system of quality assessment is a time consuming and tedious task which is more prone to error. The goal of supply-chain management is to add product value by maintaining quality, reduce wastage of fresh produce, retain consumers by keeping customer satisfaction, and increase the profitability. Researches are going on in various domain to develop a fast and reliable automated fruit quality grading system which helps to meet the food value goals of supply-chain. This paper focuses on a detailed survey of the researches being carried out on various techniques used in post-harvest grading of fresh produces using computer vision, image processing and machine learning techniques. Few papers have been reviewed and discussed here pertaining to the above techniques. The advantages and shortcomings of various methods are also mentioned. This comprehensive paper will give researchers a deeper insight to the state of the art technologies in fresh produce grading system.
The large-scale deployment of CO 2 electroreduction is hampered by deficient carbon utilization in neutral and alkaline electrolytes due to CO 2 loss into (bi)carbonates. Switching to acidic media mitigates carbonatio...
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The large-scale deployment of CO 2 electroreduction is hampered by deficient carbon utilization in neutral and alkaline electrolytes due to CO 2 loss into (bi)carbonates. Switching to acidic media mitigates carbonation, but suffers from low product selectivity because of hydrogen evolution. Here we report a crown ether decoration strategy on a Cu catalyst to enhance carbon utilization and selectivity of CO 2 methanation under acidic conditions. Macrocyclic 18-Crown-6 is found to enrich potassium cations near the Cu electrode surface, simultaneously enhancing the interfacial electric field to stabilize the *CO intermediate and accelerate water dissociation to boost *CO protonation. Remarkably, the mixture of 18-Crown-6 and Cu nanoparticles affords a CH 4 Faradaic efficiency of 51.2 % and a single pass carbon efficiency of 43.0 % toward CO 2 electroreduction in electrolyte with pH=2. This study provides a facile strategy to promote CH 4 selectivity and carbon utilization by modifying Cu catalysts with supramolecules.
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