Does Lower Transaction Price Attract More Customers?: An Empirical Study on the Short and Long Term Impacts of Online Brokerage Services

We examine price and quality as variables to examine the determinants of online brokerage’s market share and empirically investigate the effects using panel data during an eight-year span. Panel data not only allows us to isolate the short-term from that of the long-term impacts of online services on firm performance, but also to avoid the problem of studying “disequilibrium” in a “land rush” period of e-commerce. Our study identifies the significant role of interest revenue in the brokerage business and the central contribution of service quality in increasing interest revenue. We show that the businesses’ adequate management of the two generic strategies – price leadership and quality differentiation – can maximize aggregate revenues from various sources. Our study helps various businesses understand the underlying businesses principles by unraveling the effects of the interaction between price and quality strategies and their contribution to the respective and aggregate revenues

Strategic Trade Policies in International Rivalry When Competition Mode is Endogenous

We investigate government subsidy policies in which a home firm and a foreign firm choose to strategically set prices or quantities in a third market. We show that even though each firm can earn higher profits under Cournot competition than under Bertrand competition regardless of the nature of goods, choosing Bertrand competition is the dominant strategy for both firms. This can lead each firm to face a prisoners' dilemma in equilibrium. We also show that from the aspects of governments under subsidy regime, Cournot competition is more efficient than Bertrand competition when the goods are substitutes, and vice versa when the goods are complements. However, trade liberalization such as via free trade agreements brings about a change in the competition mode from Bertrand competition to Cournot competition if goods are substitutes. On the other hand, if goods are complements, there are no such a change in the competition mode and Bertrand competition prevails the market. Hence, a move toward free trade among countries increases not only profits of firms but also the welfare of both countries irrespective of the nature of goods

Strategic Trade Policies in International Rivalry When Competition Mode is Endogenous

We investigate government subsidy policies in which a home firm and a foreign firm choose to strategically set prices or quantities in a third market. We show that even though each firm can earn higher profits under Cournot competition than under Bertrand competition regardless of the nature of goods, choosing Bertrand competition is the dominant strategy for both firms. This can lead each firm to face a prisoners' dilemma in equilibrium. We also show that from the aspects of governments under subsidy regime, Cournot competition is more efficient than Bertrand competition when the goods are substitutes, and vice versa when the goods are complements. However, trade liberalization such as via free trade agreements brings about a change in the competition mode from Bertrand competition to Cournot competition if goods are substitutes. On the other hand, if goods are complements, there are no such a change in the competition mode and Bertrand competition prevails the market. Hence, a move toward free trade among countries increases not only profits of firms but also the welfare of both countries irrespective of the nature of goods

Spatial mapping of short-term solar radiation prediction incorporating geostationary satellite images coupled with deep convolutional LSTM networks for South Korea

A practical approach to continuously monitor and provide real-time solar energy prediction can help support reliable renewable energy supply and relevant energy security systems. In this study on the Korean Peninsula, contemporaneous solar radiation images obtained from the Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) system, were used to design a convolutional neural network and a long short-term memory network predictive model, ConvLSTM. This model was applied to predict one-hour ahead solar radiation and spatially map solar energy potential. The newly designed ConvLSTM model enabled reliable prediction of solar radiation, incorporating spatial changes in atmospheric conditions and capturing the temporal sequence-to-sequence variations that are likely to influence solar driven power supply and its overall stability. Results showed that the proposed ConvLSTM model successfully captured cloud-induced variations in ground level solar radiation when compared with reference images from a physical model. A comparison with ground pyranometer measurements indicated that the short-term prediction of global solar radiation by the proposed ConvLSTM had the highest accuracy [root mean square error (RMSE) = 83.458 Wcenterdotm−2, mean bias error (MBE) = 4.466 Wcenterdotm−2, coefficient of determination (R2) = 0.874] when compared with results of conventional artificial neural network (ANN) [RMSE = 94.085 Wcenterdotm−2, MBE = −6.039 Wcenterdotm−2, R2 = 0.821] and random forest (RF) [RMSE = 95.262 Wcenterdotm−2, MBE = −11.576 Wcenterdotm−2, R2 = 0.839] models. In addition, ConvLSTM better captured the temporal variations in predicted solar radiation, mainly due to cloud attenuation effects when compared with two selected ground stations. The study showed that contemporaneous satellite images over short-term or near real-time intervals can successfully support solar energy exploration in areas without continuous environmental monitoring systems, where satellite footprints are available to model and monitor solar energy management systems supporting real-life power grid systems

Machine learning approaches for detecting tropical cyclone formation using satellite data

This study compared detection skill for tropical cyclone (TC) formation using models based on three different machine learning (ML) algorithms-decision trees (DT), random forest (RF), and support vector machines (SVM)-and a model based on Linear Discriminant Analysis (LDA). Eight predictors were derived from WindSat satellite measurements of ocean surface wind and precipitation over the western North Pacific for 2005-2009. All of the ML approaches performed better with significantly higher hit rates ranging from 94 to 96% compared with LDA performance (~77%), although false alarm rate by MLs is slightly higher (21-28%) than that by LDA (~13%). Besides, MLs could detect TC formation at the time as early as 26-30 h before the first time diagnosed as tropical depression by the JTWC best track, which was also 5 to 9 h earlier than that by LDA. The skill differences across MLs were relatively smaller than difference between MLs and LDA. Large yearly variation in forecast lead time was common in all models due to the limitation in sampling from orbiting satellite. This study highlights that ML approaches provide an improved skill for detecting TC formation compared with conventional linear approaches

Multivalent electrostatic pi–cation interaction between synaptophysin and synapsin is responsible for the coacervation

We recently showed that synaptophysin (Syph) and synapsin (Syn) can induce liquid–liquid phase separation (LLPS) to cluster small synaptic-like microvesicles in living cells which are highly reminiscent of SV cluster. However, as there is no physical interaction between them, the underlying mechanism for their coacervation remains unknown. Here, we showed that the coacervation between Syph and Syn is primarily governed by multivalent pi–cation electrostatic interactions among tyrosine residues of Syph C-terminal (Ct) and positively charged Syn. We found that Syph Ct is intrinsically disordered and it alone can form liquid droplets by interactions among themselves at high concentration in a crowding environment in vitro or when assisted by additional interactions by tagging with light-sensitive CRY2PHR or subunits of a multimeric protein in living cells. Syph Ct contains 10 repeated sequences, 9 of them start with tyrosine, and mutating 9 tyrosine to serine (9YS) completely abolished the phase separating property of Syph Ct, indicating tyrosine-mediated pi-interactions are critical. We further found that 9YS mutation failed to coacervate with Syn, and since 9YS retains Syphs negative charge, the results indicate that pi–cation interactions rather than simple charge interactions are responsible for their coacervation. In addition to revealing the underlying mechanism of Syph and Syn coacervation, our results also raise the possibility that physiological regulation of pi–cation interactions between Syph and Syn during synaptic activity may contribute to the dynamics of synaptic vesicle clustering.This work was supported by grants from the National Research Foundation of Korea (Grants 2019R1A2C2089182 to S.C.). This work was also supported by the Education and Research Encouragement Fund of Seoul National University Hospital

Detection of tropical overshooting cloud tops using himawari-8 imagery

Overshooting convective cloud Top (OT)-accompanied clouds can cause severe weather conditions, such as lightning, strong winds, and heavy rainfall. The distribution and behavior of OTs can affect regional and global climate systems. In this paper, we propose a new approach for OT detection by using machine learning methods with multiple infrared images and their derived features. Himawari-8 satellite images were used as the main input data, and binary detection (OT or nonOT) with class probability was the output of the machine learning models. Three machine learning techniques-random forest (RF), extremely randomized trees (ERT), and logistic regression (LR)-were used to develop OT classification models to distinguish OT from non-OT. The hindcast validation over the Southeast Asia andWest Pacific regions showed that RF performed best, resulting in a mean probabilities of detection (POD) of 77.06% and a mean false alarm ratio (FAR) of 36.13%. Brightness temperature at 11.2 ??m (Tb11) and its standard deviation (STD) in a 3 ?? 3 window size were identified as the most contributing variables for discriminating OT and nonOT classes. The proposed machine learning-based OT detection algorithms produced promising results comparable to or even better than the existing approaches, which are the infrared window (IRW)-texture and water vapor (WV) minus IRW brightness temperature difference (BTD) methods

Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis

BackgroundMagnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI.MethodsThis study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation.ResultsA total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705–0.745), 0.936 (95% CI, 0.924–0.947), and 0.830 (95%CI, 0.792–0.868), respectively, at the image level and 0.947 (95% CI, 0.912–0.982), 0.691 (95% CI, 0.603–0.779), and 0.816 (95% CI, 0.776–0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493–0.780), 0.944 (95% CI, 0.933–0.955), and 0.731 (95% CI, 0.681–0.780), respectively, at the image level and 0.806 (95% CI, 0.729–0.883), 0.617 (95% CI, 0.523–0.711), and 0.711 (95% CI, 0.660–0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation.ConclusionAn AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting