Vertically aligned InGaN nanowires with engineered axial In composition for highly efficient visible light emission.

We report on the fabrication of novel InGaN nanowires (NWs) with improved crystalline quality and high radiative efficiency for applications as nanoscale visible light emitters. Pristine InGaN NWs grown under a uniform In/Ga molar flow ratio (UIF) exhibited multi-peak white-like emission and a high density of dislocation-like defects. A phase separation and broad emission with non-uniform luminescent clusters were also observed for a single UIF NW investigated by spatially resolved cathodoluminescence. Hence, we proposed a simple approach based on engineering the axial In content by increasing the In/Ga molar flow ratio at the end of NW growth. This new approach yielded samples with a high luminescence intensity, a narrow emission spectrum, and enhanced crystalline quality. Using time-resolved photoluminescence spectroscopy, the UIF NWs exhibited a long radiative recombination time (τr) and low internal quantum efficiency (IQE) due to strong exciton localization and carrier trapping in defect states. In contrast, NWs with engineered In content demonstrated three times higher IQE and a much shorter τr due to mitigated In fluctuation and improved crystal quality

Spatial-temporal Vehicle Re-identification

Vehicle re-identification (ReID) in a large-scale camera network is important in public safety, traffic control, and security. However, due to the appearance ambiguities of vehicle, the previous appearance-based ReID methods often fail to track vehicle across multiple cameras. To overcome the challenge, we propose a spatial-temporal vehicle ReID framework that estimates reliable camera network topology based on the adaptive Parzen window method and optimally combines the appearance and spatial-temporal similarities through the fusion network. Based on the proposed methods, we performed superior performance on the public dataset (VeRi776) by 99.64% of rank-1 accuracy. The experimental results support that utilizing spatial and temporal information for ReID can leverage the accuracy of appearance-based methods and effectively deal with appearance ambiguities.Comment: 10 pages, 6 figure

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

Relationships of walking activity with depressed mood and suicidal ideation among the middle-aged Korean population: a nationwide cross-sectional study

IntroductionThe suicide rate of middle-aged adults has increased rapidly, which is a significant public health concern. A depressed mood and suicidal ideation are significant risk factors for suicide, and non-pharmacological interventions such as exercise therapy have been suggested as potential treatments. Walking is a feasible and accessible form of exercise therapy for middle-aged adults.MethodsWe conducted a study based on the Seventh Korea National Health and Nutrition Examination Survey (2016–2018) data of 6,886 general middle-aged adults in South Korea to investigate the relationships of walking exercise with depressed mood and suicidal ideation. Multiple logistic regression analysis was used to adjust for confounding variables. Sampling weights were applied to obtain estimates for the general Korean population.ResultsParticipants who walked ≥5 days per week had a significantly lower odds ratio (OR) for depressed mood [OR = 0.625, 95% confidence interval (CI): 0.424–0.921, p = 0.018] and suicidal ideation (OR = 0.252, 95% CI: 0.125–0.507, p < 0.001) compared to those who never walked, regardless of the duration of exercise. The same results were obtained for males after stratifying the data by sex and suicidal ideation was associated with walking in females.ConclusionRegular walking exercise was associated with diminished mental health problems in middle-aged adults. Light walks may serve as a useful starting point for patients with serious mental health issues, such as suicidal ideation

High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R-2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area

Polyelectrolyte complex micelles by self-assembly of polypeptide-based triblock copolymer for doxorubicin delivery

AbstractPolyelectrolyte complex micelles were prepared by self-assembly of polypeptide-based triblock copolymer as a new drug carrier for cancer chemotherapy. The triblock copolymer, poly(l-aspartic acid)-b-poly(ethylene glycol)-b-poly(l-aspartic acid) (PLD-b-PEG-b-PLD), spontaneously self-assembled with doxorubicin (DOX) via electrostatic interactions to form spherical micelles with a particle size of 60–80 nm (triblock ionomer complexes micelles, TBIC micelles). These micelles exhibited a high loading capacity of 70% (w/w) at a drug/polymer ratio of 0.5 at pH 7.0. They showed pH-responsive release patterns, with higher release at acidic pH than at physiological pH. Furthermore, DOX-loaded TBIC micelles exerted less cytotoxicity than free DOX in the A-549 human lung cancer cell line. Confocal microscopy in A-549 cells indicated that DOX-loaded TBIC micelles were transported into lysosomes via endocytosis. These micelles possessed favorable pharmacokinetic characteristics and showed sustained DOX release in rats. Overall, these findings indicate that PLD-b-PEG-b-PLD polypeptide micelles are a promising approach for anti-cancer drug delivery

Preparation and evaluation of solid-self-emulsifying drug delivery system containing paclitaxel for lymphatic delivery

Solid-self-emulsifying drug delivery system (S-SEDDS) of paclitaxel (Ptx) was developed by the spray drying method with the purpose of improving the low bioavailability (BA) of Ptx. 10% oil (ethyl oleate), 80% surfactant mixture (Tween 80: Carbitol, 90: 10, w/w), and 10% cosolvent (PEG 400) were chosen according to their solubilizing capacity. The mean droplet size, zeta potential, and encapsulation efficiency of the prepared S-SEDDS were 16.9 ± 1.53 nm, 12.5 ± 1.66 mV, and 56.2 ± 8.1%, respectively. In the S-SEDDS, Ptx presents in the form of molecular dispersion in the emulsions or is distributed in an amorphous state or crystalline with very small size. The prepared S-SEDDS formulation showed 70 and 75% dissolution in 60 and 30 min in dissolution medium pH 1.2 and 6.8, respectively. Significant increase (P ≤ 0.05) in the peak concentration (C m a x), the area under the curve (A U C 0 - ∞), and the lymphatic targeting efficiency of Ptx was observed after the oral administration of the Ptx-loaded S-SEDDS to rats (20 mg/kg as Ptx). Our research suggests the prepared Ptx-loaded S-SEDDS can be a good candidate for the enhancement of BA and targeting drug delivery to the lymphatic system of Ptx