Energy Transfer in CdSe Nanoplatelet Superlattices

Two-dimension CdSe semiconductor nanoplatelets (NPLs) exhibit unique, highly desirable optical and electronic properties, such as large absorption crossection and bright emission. Fӧrster resonance energy transfer (FRET) between NPLs is responsible for the utility of these NPLs in fields such as lasing, lighting, solar energy, and sensing. Here we study energy transfer processes in NPL superlattices using photoluminescence (PL) and time resolved PL (TRPL) spectroscopic methods. Information on the effect of thickness of NPL is obtained through correlating PL and TRPL spectra of CdSe superlattices with AFM measurements. PL spectrum showed narrow fluorescence and absorption peaks at room temperature corresponding to excitonic transitions. A FRET lifetime of 351 ps was observed. Results suggest that FRET occurs more rapidly in CdSe NPL superlattices than in isolated CdSe NPLs and that FRET lifetimes depend on available energy pathways in the surrounding environment. This is a promising new material in the field of semiconductors and optical applications

The Physiological and Psychological Benefits of CrossFit Training – A Pilot Study

CrossFit has been one of the fastest growing training methods in the fitness industry since its inception in 2000. CrossFit combines classic strength and conditioning along with gymnastics movements, Olympic weightlifting, and other functional movements into a constantly varied, high intensity workout. The success of CrossFit and what seems to be exponential growth of their over 10,000 affiliated gyms is undeniable. This popularity might be stem from two main factors: the physiological changes of training and the psychological benefits of a community emphasized, social atmosphere. However, there is very limited research evidence supporting the potential benefits of CrossFit . This study was conducted to investigate the physiological and psychological benefits of CrossFit training in a healthy adult population undergoing their first exposure to the training method. Sixteen participants were recruited from a local CrossFit affiliate in San Angelo, Texas. Participants completed a series of self-report psychological questionnaires including the Motives for Physical Activity Measures (MPAM), Mental Health Inventory 38 (MHI-38), and the Group Environment Questionnaire (GEQ). Following these questionnaires, physical metrics including: heart rate, blood pressure, height, body weight, body composition via Dual-energy X-ray Absorptiometry (DXA), along with performance measures including 1-RM back squat, 1-RM bench press, vertical jump test, and a Wingate Anaerobic Power Test were conducted. The CrossFit program was conducted for 8 weeks by certified CrossFit coaches at the local affiliate gym. After the 8-week training, the participants were reassessed using the same measures. Over the course of the study, 6 participants completed the program (2 males, 4 females, 36.2 ± 10.8 years of age, 73.6 ± 7.4 kg, 167.6 ± 5.5 cm, and 31.0 ± 9.2% body fat). Despite the large attrition rate, there were statistically significant increase of lean mass (1.44 ± 1.26 kg; p= 0.039), decrease of mean fat (1.67 ± 1.17 kg ; p= 0.017) and changes in interest subset of motivation from MPAM motivational test (p \u3c 0.05). In conclusion, this pilot study suggests that CrossFit training might be beneficial for improving body composition and concurrently changes certain motivational factors to continue engaging in the fitness activity. Further studies with a longer intervention period and a larger sample size are needed to support these findings

Mapping solar array location, size, and capacity using deep learning and overhead imagery

The effective integration of distributed solar photovoltaic (PV) arrays into existing power grids will require access to high quality data; the location, power capacity, and energy generation of individual solar PV installations. Unfortunately, existing methods for obtaining this data are limited in their spatial resolution and completeness. We propose a general framework for accurately and cheaply mapping individual PV arrays, and their capacities, over large geographic areas. At the core of this approach is a deep learning algorithm called SolarMapper - which we make publicly available - that can automatically map PV arrays in high resolution overhead imagery. We estimate the performance of SolarMapper on a large dataset of overhead imagery across three US cities in California. We also describe a procedure for deploying SolarMapper to new geographic regions, so that it can be utilized by others. We demonstrate the effectiveness of the proposed deployment procedure by using it to map solar arrays across the entire US state of Connecticut (CT). Using these results, we demonstrate that we achieve highly accurate estimates of total installed PV capacity within each of CT's 168 municipal regions

Exploring tourists’ slow food experience: perspectives from slow tourism

This paper aims to explore tourists’ slow food experience from slow tourism perspective, including authenticity, slowness, and environmental consciousness. Data will be collect in March 2021

Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform

The Hilbert-Huang Transform is a novel, adaptive approach to time series analysis that does not make assumptions about the data form. Its adaptive, local character allows the decomposition of non-stationary signals with hightime-frequency resolution but also renders it susceptible to degradation from noise. We show that complementing the HHT with techniques such as zero-phase filtering, kernel density estimation and Fourier analysis allows it to be used effectively to detect and characterize signals with low signal to noise ratio.Comment: submitted to PRD, 10 pages, 9 figures in colo

The roles of Fzy/Cdc20 and Fzr/Cdh1 in regulating the destruction of cyclin B in space and time

In Drosophila cells cyclin B is normally degraded in two phases: (a) destruction of the spindle-associated cyclin B initiates at centrosomes and spreads to the spindle equator; and (b) any remaining cytoplasmic cyclin B is degraded slightly later in mitosis. We show that the APC/C regulators Fizzy (Fzy)/Cdc20 and Fzy-related (Fzr)/Cdh1 bind to microtubules in vitro and associate with spindles in vivo. Fzy/Cdc20 is concentrated at kinetochores and centrosomes early in mitosis, whereas Fzr/Cdh1 is concentrated at centrosomes throughout the cell cycle. In syncytial embryos, only Fzy/Cdc20 is present, and only the spindle-associated cyclin B is degraded at the end of mitosis. A destruction box–mutated form of cyclin B (cyclin B triple-point mutant [CBTPM]–GFP) that cannot be targeted for destruction by Fzy/Cdc20, is no longer degraded on spindles in syncytial embryos. However, CBTPM–GFP can be targeted for destruction by Fzr/Cdh1. In cellularized embryos, which normally express Fzr/Cdh1, CBTPM–GFP is degraded throughout the cell but with slowed kinetics. These findings suggest that Fzy/Cdc20 is responsible for catalyzing the first phase of cyclin B destruction that occurs on the mitotic spindle, whereas Fzr/Cdh1 is responsible for catalyzing the second phase of cyclin B destruction that occurs throughout the cell. These observations have important implications for the mechanisms of the spindle checkpoint

Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning

For a federated learning model to perform well, it is crucial to have a diverse and representative dataset. However, the data contributors may only be concerned with the performance on a specific subset of the population, which may not reflect the diversity of the wider population. This creates a tension between the principal (the FL platform designer) who cares about global performance and the agents (the data collectors) who care about local performance. In this work, we formulate this tension as a game between the principal and multiple agents, and focus on the linear experiment design problem to formally study their interaction. We show that the statistical criterion used to quantify the diversity of the data, as well as the choice of the federated learning algorithm used, has a significant effect on the resulting equilibrium. We leverage this to design simple optimal federated learning mechanisms that encourage data collectors to contribute data representative of the global population, thereby maximizing global performance