Electrochemical Investigation of High-Performance Dye-Sensitized Solar Cells Based on Molybdenum for Preparation of Counter Electrode

In order to improve the photocurrent conversion efficiency of dye-sensitized solar cells (DSSCs), we studied an alternative conductor for the counter electrode and focused on molybdenum (Mo) instead of conventional fluorine-doped tin oxide (FTO). Because Mo has a similar work function to FTO for band alignment, better formability of platinum (Pt), and a low electric resistance, using a counter electrode made of Mo instead of FTO lead to the enhancement of the catalytic reaction of the redox couple, reduce the interior resistance of the DSSCs, and prevent energy-barrier formation. Using electrical measurements under a 1-sun condition (100 mW/cm(2), AM 1.5), we determined that the fill factor (FF) and photocurrent conversion efficiency (eta) of DSSCs with a Mo electrode were respectively improved by 7.75% and 5.59% with respect to those of DSSCs with an FTO electrode. Moreover, we have investigated the origin of the improved performance through surface morphology analyses such as scanning electron microscopy and electrochemical analyses including cyclic voltammetry and impedance spectroscopy

Electric field control of nonvolatile four-state magnetization at room temperature

We find the realization of large converse magnetoelectric (ME) effects at room temperature in a multiferroic hexaferrite Ba$_{0.52}$Sr$_{2.48}$Co$_{2}$Fe$_{24}$O$_{41}$ single crystal, in which rapid change of electric polarization in low magnetic fields (about 5 mT) is coined to a large ME susceptibility of 3200 ps/m. The modulation of magnetization then reaches up to 0.62 $\mu$$_{B}$/f.u. in an electric field of 1.14 MV/m. We find further that four ME states induced by different ME poling exhibit unique, nonvolatile magnetization versus electric field curves, which can be approximately described by an effective free energy with a distinct set of ME coefficients

Large-scale Text-to-Image Generation Models for Visual Artists' Creative Works

Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating high-quality open-domain images from multi-modal input. Although they can even produce anthropomorphized versions of objects and animals, combine irrelevant concepts in reasonable ways, and give variation to any user-provided images, we witnessed such rapid technological advancement left many visual artists disoriented in leveraging LTGMs more actively in their creative works. Our goal in this work is to understand how visual artists would adopt LTGMs to support their creative works. To this end, we conducted an interview study as well as a systematic literature review of 72 system/application papers for a thorough examination. A total of 28 visual artists covering 35 distinct visual art domains acknowledged LTGMs' versatile roles with high usability to support creative works in automating the creation process (i.e., automation), expanding their ideas (i.e., exploration), and facilitating or arbitrating in communication (i.e., mediation). We conclude by providing four design guidelines that future researchers can refer to in making intelligent user interfaces using LTGMs.Comment: 15 pages, 3 figure

Luciferase-Rose Bengal Conjugates for Single Oxygen Generation by Bioluminescence Resonant Energy Transfer

Conjugates of Rose Bengal and Renilla luciferase generated singlet oxygen upon binding with coelenterazine via bioluminescence resonance energy transfer (BRET). Since the applications of conventional PDT have been limited to superficial lesions due to the limited light penetration in tissue, BRET activated PDT which does not require external light illumination may overcome the limitations of conventional PDT.115Ysciescopu

Sink-oriented Dynamic Location Service Protocol for Mobile Sinks with an Energy Efficient Grid-Based Approach

Sensor nodes transmit the sensed information to the sink through wireless sensor networks (WSNs). They have limited power, computational capacities and memory. Portable wireless devices are increasing in popularity. Mechanisms that allow information to be efficiently obtained through mobile WSNs are of significant interest. However, a mobile sink introduces many challenges to data dissemination in large WSNs. For example, it is important to efficiently identify the locations of mobile sinks and disseminate information from multi-source nodes to the multi-mobile sinks. In particular, a stationary dissemination path may no longer be effective in mobile sink applications, due to sink mobility. In this paper, we propose a Sink-oriented Dynamic Location Service (SDLS) approach to handle sink mobility. In SDLS, we propose an Eight-Direction Anchor (EDA) system that acts as a location service server. EDA prevents intensive energy consumption at the border sensor nodes and thus provides energy balancing to all the sensor nodes. Then we propose a Location-based Shortest Relay (LSR) that efficiently forwards (or relays) data from a source node to a sink with minimal delay path. Our results demonstrate that SDLS not only provides an efficient and scalable location service, but also reduces the average data communication overhead in scenarios with multiple and moving sinks and sources

Benefit Transfer for Water Management along the Han River in South Korea Using Meta-Regression Analysis

This study estimates the magnitude of economic benefits that are justified in transfer from downstream users to upstream users for the use of the Han River in South Korea in terms of foregone economic benefits by regulations. Based on the existing non-market valuation studies associated with water management issues in South Korea from 1997 to 2014, a meta-regression analysis was performed to provide alternatives for regional benefit sharing of water resource use. The benefits from the use of water resource along the Han River are estimated on average to be KRW 7,728 (US $7.7) per household per month. The total net benefits are estimated to be about KRW449 billion (US$449 million) per year. Following the principle regarding equal distribution of benefits, the stakeholders who received more net benefits than others should return their extra net benefits to other stakeholders through a policy tool such as tradable development rights. The results of our study provide economic indicators useful for the establishment of common resource policy and to consider stakeholders' rights within the framework of regional benefits. This study also provides practical solutions that could be used as a valid policy instrument to mediate the conflicts and disputes associated with water resource use.Kangwon National UniversityUniversity of Bayreut

EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based models not only provide better accuracy than recurrent neural networks (RNNs) but also make it possible to process irregular time-series. In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. NCDEs typically use interpolation algorithms to convert discrete time-series samples to continuous paths. However, we propose to i) generate another latent continuous path using an encoder-decoder architecture, which corresponds to the interpolation process of NCDEs, i.e., our neural network-based interpolation vs. the existing explicit interpolation, and ii) exploit the generative characteristic of the decoder, i.e., extrapolation beyond the time domain of original data if needed. Therefore, our NCDE design can use both the interpolated and the extrapolated information for downstream machine learning tasks. In our experiments with 5 real-world datasets and 12 baselines, our extrapolation and interpolation-based NCDEs outperform existing baselines by non-trivial margins.Comment: main 8 page