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Cryptocurrency trading: a comprehensive survey
In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading (
e
.
g
., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.
Journal Article
A Tale of Three Excellent Chinese EFL Teachers: Unpacking Teacher Professional Qualities for Their Sustainable Career Trajectories from an Ecological Perspective
Teachers’ quality has long been researched in the field of general education. However, little attention has been paid to the professional qualities of excellent English as a foreign language (EFL) teachers in the context of English curriculum reform, especially from an ecological perspective. To address this gap, this study adopted a qualitative approach to characterise the qualities of excellent senior high school EFL teachers in China and the development of their professional qualities using Bronfenbrenner’s ecological systems model. Four interconnected dimensions of excellent EFL teachers’ professional qualities were confirmed: English language pedagogical content competence, beliefs about the foreign language teaching profession and professional ethics, beliefs about foreign language teaching and learning, and beliefs about language teacher learning and development. Meanwhile, the EFL teachers constructed and developed their professional qualities in their dynamic interaction with the complex ecological systems where they lived. The paper considers these various teacher-related factors in the ecological systems and provides some suggestions for sustaining EFL teachers’ professional development.
Journal Article
Role of pontine sub‐laterodorsal tegmental nucleus (SLD) in rapid eye movement (REM) sleep, cataplexy, and emotion
Pontine sub‐laterodorsal tegmental nucleus (SLD) is crucial for REM sleep. However, the necessary role of SLD for REM sleep, cataplexy that resembles REM sleep, and emotion memory by REM sleep has remained unclear. To address these questions, we focally ablated SLD neurons using adenoviral diphtheria‐toxin (DTA) approach and found that SLD lesions completely eliminated REM sleep accompanied by wake increase, significantly reduced baseline cataplexy amounts by 40% and reward (sucrose) induced cataplexy amounts by 70% and altered cataplexy EEG Fast Fourier Transform (FFT) from REM sleep‐like to wake‐like in orexin null (OXKO) mice. We then used OXKO animals with absence of REM sleep and OXKO controls and examined elimination of REM sleep in anxiety and fear extinction. Our resulted showed that REM sleep elimination significantly increased anxiety‐like behaviors in open field test (OFT), elevated plus maze test (EPM) and defensive aggression and impaired fear extinction. The data indicate that in OXKO mice the SLD is the sole generator for REM sleep; (2) the SLD selectively mediates REM sleep cataplexy (R‐cataplexy) that merges with wake cataplexy (W‐cataplexy); (3) REM sleep enhances positive emotion (sucrose induced cataplexy) response, reduces negative emotion state (anxiety), and promotes fear extinction.
Journal Article
Electrically driven single-photon emission from an isolated single molecule
Electrically driven molecular light emitters are considered to be one of the promising candidates as single-photon sources. However, it is yet to be demonstrated that electrically driven single-photon emission can indeed be generated from an isolated single molecule notwithstanding fluorescence quenching and technical challenges. Here, we report such electrically driven single-photon emission from a well-defined single molecule located inside a precisely controlled nanocavity in a scanning tunneling microscope. The effective quenching suppression and nanocavity plasmonic enhancement allow us to achieve intense and stable single-molecule electroluminescence. Second-order photon correlation measurements reveal an evident photon antibunching dip with the single-photon purity down to
g
(2)
(0) = 0.09, unambiguously confirming the single-photon emission nature of the single-molecule electroluminescence. Furthermore, we demonstrate an ultrahigh-density array of identical single-photon emitters.
Molecular emitters offer a promising solution for single-photon generation. Here, by exploiting electronic decoupling by an ultrathin dielectric spacer and emission enhancement by a resonant plasmonic nanocavity, the authors demonstrate electrically driven single-photon emission from a single molecule.
Journal Article
Probing intramolecular vibronic coupling through vibronic-state imaging
Vibronic coupling is a central issue in molecular spectroscopy. Here we investigate vibronic coupling within a single pentacene molecule in real space by imaging the spatial distribution of single-molecule electroluminescence via highly localized excitation of tunneling electrons in a controlled plasmonic junction. The observed two-spot orientation for certain vibronic-state imaging is found to be evidently different from the purely electronic 0–0 transition, rotated by 90°, which reflects the change in the transition dipole orientation from along the molecular short axis to the long axis. Such a change reveals the occurrence of strong vibronic coupling associated with a large Herzberg–Teller contribution, going beyond the conventional Franck–Condon picture. The emergence of large vibration-induced transition charges oscillating along the long axis is found to originate from the strong dynamic perturbation of the anti-symmetric vibration on those carbon atoms with large transition density populations during electronic transitions.
Vibronic coupling is a key feature of molecular electronic transitions, but its visualization in real space is an experimental challenge. Here the authors, using scanning tunneling microscopy induced luminescence, resolve the effect of vibronic coupling with different modes on the electron distributions in real space in a single pentacene molecule.
Journal Article
An Enhanced Intrusion Detection Model Based on Improved kNN in WSNs
Aiming at the intrusion detection problem of the wireless sensor network (WSN), considering the combined characteristics of the wireless sensor network, we consider setting up a corresponding intrusion detection system on the edge side through edge computing. An intrusion detection system (IDS), as a proactive network security protection technology, provides an effective defense system for the WSN. In this paper, we propose a WSN intelligent intrusion detection model, through the introduction of the k-Nearest Neighbor algorithm (kNN) in machine learning and the introduction of the arithmetic optimization algorithm (AOA) in evolutionary calculation, to form an edge intelligence framework that specifically performs the intrusion detection when the WSN encounters a DoS attack. In order to enhance the accuracy of the model, we use a parallel strategy to enhance the communication between the populations and use the Lévy flight strategy to adjust the optimization. The proposed PL-AOA algorithm performs well in the benchmark function test and effectively guarantees the improvement of the kNN classifier. We use Matlab2018b to conduct simulation experiments based on the WSN-DS data set and our model achieves 99% ACC, with a nearly 10% improvement compared with the original kNN when performing DoS intrusion detection. The experimental results show that the proposed intrusion detection model has good effects and practical application significance.
Journal Article
GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs
Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.
Journal Article
Peripheral blood neurotrophic factor levels in children with autism spectrum disorder: a meta-analysis
Increasing evidence suggests that abnormal regulation of neurotrophic factors is involved in the etiology and pathogenesis of Autism Spectrum Disorder (ASD). However, clinical data on neurotrophic factor levels in children with ASD were inconsistent. Therefore, we performed a systematic review of peripheral blood neurotrophic factors levels in children with ASD, and quantitatively summarized the clinical data of peripheral blood neurotrophic factors in ASD children and healthy controls. A systematic search of PubMed and Web of Science identified 31 studies with 2627 ASD children and 4418 healthy controls to be included in the meta-analysis. The results of random effect meta-analysis showed that the peripheral blood levels of brain-derived neurotrophic factor (Hedges’ g = 0.302; 95% CI = 0.014 to 0.591;
P
= 0.040) , nerve growth factor (Hedges’ g = 0.395; 95% CI = 0.104 to 0.686;
P
= 0.008) and vascular endothelial growth factor (VEGF) (Hedges’ g = 0.097; 95% CI = 0.018 to 0.175;
P
= 0.016) in children with ASD were significantly higher than that of healthy controls, whereas blood neurotrophin-3 (Hedges’ g = − 0.795; 95% CI = − 1.723 to 0.134;
P
= 0.093) and neurotrophin-4 (Hedges’ g = 0.182; 95% CI = − 0.285 to 0.650;
P
= 0.445) levels did not show significant differences between cases and controls. Taken together, these results clarified circulating neurotrophic factor profile in children with ASD, strengthening clinical evidence of neurotrophic factor aberrations in children with ASD.
Journal Article
Metal-free oxidative cross-coupling enabled practical synthesis of atropisomeric QUINOL and its derivatives
As an important platform molecule, atropisomeric QUINOL plays a crucial role in the development of chiral ligands and catalysts in asymmetric catalysis. However, efficient approaches towards QUINOL remain scarce, and the resulting high production costs greatly impede the related academic research as well as downstream industrial applications. Here we report a direct oxidative cross-coupling reaction between isoquinolines and 2-naphthols, providing a straightforward and scalable route to acquire the privileged QUINOL scaffolds in a metal-free manner. Moreover, a NHC-catalyzed kinetic resolution of QUINOL N-oxides with high selectivity factor is established to access two types of promising axially chiral Lewis base catalysts in optically pure forms. The utility of this methodology is further illustrated by facile transformations of the products into QUINAP, an iconic ligand in asymmetric catalysis.
1-(Isoquinolin-1-yl)naphthalen-2-ol (QUINOL) is an atropisomeric heterobiaryl that serves as a platform for the synthesis of other biaryl ligands useful in asymmetric catalysis. Here, the authors report a straightforward oxidative cross-coupling reaction between isoquinolines and 2-naphthols to efficiently access the QUINOL scaffolds in a metal-free manner.
Journal Article