Inverted polymer fullerene solar cells exceeding 10% efficiency with poly(2-ethyl-2-oxazoline) nanodots on electron-collecting buffer layers

Polymer solar cells have been spotlighted due to their potential for low-cost manufacturing but their efficiency is still less than required for commercial application as lightweight/flexible modules. Forming a dipole layer at the electron-collecting interface has been suggested as one of the more attractive approaches for efficiency enhancement. However, only a few dipole layer material types have been reported so far, including only one non-ionic (charge neutral) polymer. Here we show that a further neutral polymer, namely poly(2-ethyl-2-oxazoline) (PEOz) can be successfully used as a dipole layer. Inclusion of a PEOz layer, in particular with a nanodot morphology, increases the effective work function at the electron-collecting interface within inverted solar cells and thermal annealing of PEOz layer leads to a state-of-the-art 10.74% efficiency for single-stack bulk heterojunction blend structures comprising poly[4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b:4,5-b′]dithiophene-alt-3-fluorothieno[3,4-b]thiophene-2-carboxylate] as donor and [6,6]-phenyl-C71-butyric acid methyl ester as acceptor

PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling

As the size of accessible compound libraries expands to over 10 billion, the need for more efficient structure-based virtual screening methods is emerging. Different pre-screening methods have been developed for rapid screening, but there is still a lack of structure-based methods applicable to various proteins that perform protein-ligand binding conformation prediction and scoring in an extremely short time. Here, we describe for the first time a deep-learning framework for structure-based pharmacophore modeling to address this challenge. We frame pharmacophore modeling as an instance segmentation problem to determine each protein hotspot and the location of corresponding pharmacophores, and protein-ligand binding pose prediction as a graph-matching problem. PharmacoNet is significantly faster than state-of-the-art structure-based approaches, yet reasonably accurate with a simple scoring function. Furthermore, we show the promising result that PharmacoNet effectively retains hit candidates even under the high pre-screening filtration rates. Overall, our study uncovers the hitherto untapped potential of a pharmacophore modeling approach in deep learning-based drug discovery.Comment: 21 pages, 5 figure

The impact of Arctic sea ice loss on mid-Holocene climate.

Mid-Holocene climate was characterized by strong summer solar heating that decreased Arctic sea ice cover. Motivated by recent studies identifying Arctic sea ice loss as a key driver of future climate change, we separate the influences of Arctic sea ice loss on mid-Holocene climate. By performing idealized climate model perturbation experiments, we show that Arctic sea ice loss causes zonally asymmetric surface temperature responses especially in winter: sea ice loss warms North America and the North Pacific, which would otherwise be much colder due to weaker winter insolation. In contrast, over East Asia, sea ice loss slightly decreases the temperature in early winter. These temperature responses are associated with the weakening of mid-high latitude westerlies and polar stratospheric warming. Sea ice loss also weakens the Atlantic meridional overturning circulation, although this weakening signal diminishes after 150-200 years of model integration. These results suggest that mid-Holocene climate changes should be interpreted in terms of both Arctic sea ice cover and insolation forcing

Mid-Holocene Northern Hemisphere warming driven by Arctic amplification.

The Holocene thermal maximum was characterized by strong summer solar heating that substantially increased the summertime temperature relative to preindustrial climate. However, the summer warming was compensated by weaker winter insolation, and the annual mean temperature of the Holocene thermal maximum remains ambiguous. Using multimodel mid-Holocene simulations, we show that the annual mean Northern Hemisphere temperature is strongly correlated with the degree of Arctic amplification and sea ice loss. Additional model experiments show that the summer Arctic sea ice loss persists into winter and increases the mid- and high-latitude temperatures. These results are evaluated against four proxy datasets to verify that the annual mean northern high-latitude temperature during the mid-Holocene was warmer than the preindustrial climate, because of the seasonally rectified temperature increase driven by the Arctic amplification. This study offers a resolution to the "Holocene temperature conundrum", a well-known discrepancy between paleo-proxies and climate model simulations of Holocene thermal maximum

Andro-Simnet: Android Malware Family Classification Using Social Network Analysis

While the rapid adaptation of mobile devices changes our daily life more conveniently, the threat derived from malware is also increased. There are lots of research to detect malware to protect mobile devices, but most of them adopt only signature-based malware detection method that can be easily bypassed by polymorphic and metamorphic malware. To detect malware and its variants, it is essential to adopt behavior-based detection for efficient malware classification. This paper presents a system that classifies malware by using common behavioral characteristics along with malware families. We measure the similarity between malware families with carefully chosen features commonly appeared in the same family. With the proposed similarity measure, we can classify malware by malware's attack behavior pattern and tactical characteristics. Also, we apply a community detection algorithm to increase the modularity within each malware family network aggregation. To maintain high classification accuracy, we propose a process to derive the optimal weights of the selected features in the proposed similarity measure. During this process, we find out which features are significant for representing the similarity between malware samples. Finally, we provide an intuitive graph visualization of malware samples which is helpful to understand the distribution and likeness of the malware networks. In the experiment, the proposed system achieved 97% accuracy for malware classification and 95% accuracy for prediction by K-fold cross-validation using the real malware dataset.Comment: 13 pages, 11 figures, dataset link: http://ocslab.hksecurity.net/Datasets/andro-simnet , demo video: https://youtu.be/JmfS-ZtCbg4 , In Proceedings of the 16th Annual Conference on Privacy, Security and Trust (PST), 201