Short sales in Log-robust portfolio management

This paper extends the Log-robust portfolio management approach to the case with short sales, i.e., the case where the manager can sell shares he does not yet own. We model the continuously compounded rates of return, which have been established in the literature as the true drivers of uncertainty, as uncertain parameters belonging to polyhedral uncertainty sets, and maximize the worst-case portfolio wealth over that set in a one-period setting. The degree of the manager’s aversion to ambiguity is incorporated through a single, intuitive parameter, which determines the size of the uncertainty set. The presence of short-selling requires the development of problem-specific techniques, because the optimization problem is not convex. In the case where assets are independent, we show that the robust optimization problem can be solved exactly as a series of linear programming problems; as a result, the approach remains tractable for large numbers of assets. We also provide insights into the structure of the optimal solution. In the case of correlated assets, we develop and test a heuristic where correlation is maintained only between assets invested in. In computational experiments, the proposed approach exhibits superior performance to that of the traditional robust approach.     

Synthesis and in vitro protein tyrosine kinase inhibitory activity of furan-2-yl(phenyl)methanone derivatives

A series of novel furan-2-yl(phenyl)methanone derivatives were synthesized, and their structures were established on the basis of 1H-NMR, 13C-NMR and mass spectral data. All the prepared compounds were screened for their in vitro protein tyrosine kinase inhibitory activity and several new derivatives exhibited promising activity, which, in some cases, was identical to, or even better than that of genistein, a positive reference compound. The preliminary structure-activity relationships of these compounds were investigated and are discussed.     

Combination of fluid and solid mechanical stresses contribute to cell death and detachment in a microfluidic alveolar model

Studies using this micro-system demonstrated significant morphological differences between alveolar epithelial cells (transformed human alveolar epithelial cell line, A549 and primary murine alveolar epithelial cells, AECs) exposed to combination of solid mechanical and surface-tension stresses (cyclic propagation of air-liquid interface and wall stretch) compared to cell populations exposed solely to cyclic stretch. We have also measured significant differences in both cell death and cell detachment rates in cell monolayers experiencing combination of stresses. This research describes new tools for studying the combined effects of fluid mechanical and solid mechanical stress on alveolar cells. It also highlights the role that surface tension forces may play in the development of clinical pathology, especially under conditions of surfactant dysfunction. The results support the need for further research and improved understanding on techniques to reduce and eliminate fluid stresses in clinical settings.     

Multi-Task Learning for Compositional Data via Sparse Network Lasso

Multi-task learning is a statistical methodology that aims to improve the generalization performances of estimation and prediction tasks by sharing common information among multiple tasks. On the other hand, compositional data consist of proportions as components summing to one. Because components of compositional data depend on each other, existing methods for multi-task learning cannot be directly applied to them. In the framework of multi-task learning, a network lasso regularization enables us to consider each sample as a single task and construct different models for each one. In this paper, we propose a multi-task learning method for compositional data using a sparse network lasso. We focus on a symmetric form of the log-contrast model, which is a regression model with compositional covariates. Our proposed method enables us to extract latent clusters and relevant variables for compositional data by considering relationships among samples. The effectiveness of the proposed method is evaluated through simulation studies and application to gut microbiome data. Both results show that the prediction accuracy of our proposed method is better than existing methods when information about relationships among samples is appropriately obtained.     

Correlating conductivity and Seebeck coefficient to doping within crystalline and amorphous domains in poly(3-(methoxyethoxyethoxy)thiophene)

Molecular doping of conjugated polymers (CPs) plays a vital role in optimizing organic electronic and energy applications. For the case of organic thermoelectrics, it is commonly believed that doping CPs with a strong dopant could result in higher conductivity (σ) and thus better power factor (PF). Herein, by investigating thermoelectric performance of a polar side-chain bearing CP, poly(3-(methoxyethoxyethoxy)thiophene) (P3MEET), vapor doped with fluorinated-derivative of tetracyanoquinodimethane FnTCNQ (n = 1, 2, 4), we show that using strong dopants can in fact have detrimental effects on the thermoelectric performance of CPs. Despite possessing higher electron affinity, doping P3MEET with F4TCNQ only results in a σ (27.0 S/cm) comparable to samples doped with other two weaker dopants F2TCNQ and F1TCNQ (26.4 and 20.1 S/cm). Interestingly, F4TCNQ-doped samples display a marked reduction in the Seebeck coefficient (α) compared to F1TCNQ- and F2TCNQ-doped samples from 42 to 13 μV/K, leading to an undesirable suppression of the PF. Structural characterizations coupled with Kang-Snyder modeling of the α–σ relation show that the reduction of α in F4TCNQ-doped P3MEET samples originates from the generation of low mobility carrier within P3MEET's amorphous domain. Our results demonstrate that factors such as dopant distribution and doping efficiency within the crystalline and amorphous domains of CPs should play a crucial role in advancing rational design for organic thermoelectrics.     

Antioxidant product analysis of Folium Hibisci Mutabilis

Folium Hibisci Mutabilis, a new member of Chinese Pharmacopoeia, can treat some diseases induced by reactive oxygen species. The study prepared a lyophilized aqueous extract of Folium Hibisci Mutabilis (LAFHM). LAFHM was found to enrich eight flavonoids (i.e., quercetin, luteolin, hyperoside, isoquercitrin, rutin, kaempferol, tiliroside, and vitexin) by HPLC analysis. These flavonoids were further compared using antioxidant assays, where triliroside and vitexin always exhibited higher IC₅₀ values than the others. In ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry analysis, these flavonoids could basically give two characteristic m/z values (226 and 196) and their corresponding double m/z values (i.e., 602, 570, 926, 926, 570, 1186, and 862), when treated by 1,1-diphenyl-2-picryl-hydrazl radical (DPPH^?). Finally, the coupling products of DPPH^?-treated triliroside were investigated using computational chemistry. It was found that the –OH in para-coumaroyl moiety to have the lowest bond disassociation energy among all phenolic -OHs in the triliroside. In conclusion, Folium Hibisci Mutabilis contains the above eight antioxidant flavonoids. Despite of the different antioxidant levels, they can generally produce flavonoid-radical coupling product and flavonoid-flavonoid homodimer during antioxidant process. Especially, tiliroside uses para-coumaroyl as linker to construct a tiliroside-radical coupling product at the meta-carbon atom.     

Functionalization of a simple dithienylethene via palladium-catalyzed regioselective direct arylation

The direct arylation on the thienyl groups of a diarylethene with various aryl iodides efficiently provided arylated dithienylethenes under palladium catalysis. Unsymmetrically substituted dithienylethenes were also synthesized by this protocol. This procedure allows a rapid access to a variety of aryl-substituted dithienylethenes from a single substrate of a simple dithienylethene.     

Application of Automatic Chromatic Dispersion Compensation for Realization of All-Optical Network

We have studied the algorithm for the automatic chromatic dispersion compensation using bit error rate (BER) and Q-factor optimization for realization of dynamically reconfigurable all-optical network. We have made sure good performance using the compensation system by laboratory experiments.