Dodsonflynn5850

No significant reciprocal effects and genotype-by-environment (G×E) interactions were found for HF in both experiments, but HMF showed significant effects for both in one of the experiments. The GCA effects were more important than the SCA effects for HMF and HF across environments, implying that selection could facilitate their improvement. The high correlations between F1-hybrid performance and mid-parent values, as well as that between F1-hybrid performance and GCA effects, also supports the assumption that these traits are controlled by a few genes. SCA effects also played a role, especially when lines with low spontaneous doubling were used as parents. A2ti-2 Overall, spontaneous doubling can be introgressed and improved in elite germplasm with selection, and it has the potential to be employed in DH pipelines.Most households with a smoker do not implement comprehensive smoke-free rules (smoke-free homes and cars), and secondhand smoke (SHS) exposure remains prevalent among children and low-socioeconomic status (SES) populations. This pilot project aimed to assess implementation feasibility and impact of an intervention designed to increase smoke-free rules among socioeconomically disadvantaged households with children. The pilot was implemented through Minnesota's National Breast and Cervical Cancer Early Detection Program (NBCCEDP). NBCCEDPs provide cancer prevention services to low-income individuals experiencing health disparities. We successfully utilized and adapted the Smoke-Free Homes Program (SFHP) to address comprehensive smoke-free rules among households with children. We used two recruitment methods (a) direct mail (DM) and (b) opportunistic referral (OR) by patient navigators in the NBCCEDP call center. We used descriptive statistics to assess implementation outcomes and hierarchical logistic regressior program, but the current pilot demonstrated recruitment is a challenge. DM produced a low response rate and therefore OR is the recommended recruitment route. Despite low recruitment rates, we conclude that the SFHP can successfully increase comprehensive smoke-free rules and reduce SHS exposure among socioeconomically disadvantaged households with children recruited through a NBCCEDP.At a time when a global pandemic rightly holds our collective attention, environmental issues have taken a backseat to the ongoing battle against Covid-19 [...].This work presents a non-invasive and low-cost alternative to traditional methods for measuring the performance of machining processes directly on existing machine tools. A prototype measuring system has been developed based on non-contact microphones, a custom designed signal conditioning board and signal processing techniques that take advantage of the underlying physics of the machining process. Experiments have been conducted to estimate the depth of cut during end-milling process by means of the measurement of the acoustic emission energy generated during operation. Moreover, the predicted values have been compared with well established methods based on cutting forces measured by dynamometers.Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocardiogram (EEG), electroencephalogram (ECG), skin temperature, along with facial expressions, voice, posture to name a few, in order to classify emotions. Spiking neural networks (SNNs) represent the third generation of neural networks and employ biologically plausible models of neurons. SNNs have been shown to handle Spatio-temporal data, which is essentially the nature of the data encountered in emotion recognition problem, in an efficient manner. In this work, for the first time, we propose the application of SNNs in order to solve the emotion recognition problem with the multimodal dataset. Specifically, we use the NeuCube framework, which employs an evolving SNN architecture to classify emotional valence and evaluate the performance of our approach on the MAHNOit suitable for practical and on-line applications. These features are not manifested in other methods for this problem.Artificial membranes are models for biological systems and are important for applications. We introduce a dry two-step self-assembly method consisting of the high-vacuum evaporation of phospholipid molecules over silicon, followed by a subsequent annealing step in air. We evaporate dipalmitoylphosphatidylcholine (DPPC) molecules over bare silicon without the use of polymer cushions or solvents. High-resolution ellipsometry and AFM temperature-dependent measurements are performed in air to detect the characteristic phase transitions of DPPC bilayers. Complementary AFM force-spectroscopy breakthrough events are induced to detect single- and multi-bilayer formation. These combined experimental methods confirm the formation of stable non-hydrated supported lipid bilayers with phase transitions gel to ripple at 311.5 ± 0.9 K, ripple to liquid crystalline at 323.8 ± 2.5 K and liquid crystalline to fluid disordered at 330.4 ± 0.9 K, consistent with such structures reported in wet environments. We find that the AFM tip induces a restructuring or intercalation of the bilayer that is strongly related to the applied tip-force. These dry supported lipid bilayers show long-term stability. These findings are relevant for the development of functional biointerfaces, specifically for fabrication of biosensors and membrane protein platforms. The observed stability is relevant in the context of lifetimes of systems protected by bilayers in dry environments.The application of deep learning (DL) algorithms to non-destructive evaluation (NDE) is now becoming one of the most attractive topics in this field. As a contribution to such research, this study aims to investigate the application of DL algorithms for detecting and estimating the looseness in bolted joints using a laser ultrasonic technique. This research was conducted based on a hypothesis regarding the relationship between the true contact area of the bolt head-plate and the guided wave energy lost while the ultrasonic waves pass through it. First, a Q-switched NdYAG pulsed laser and an acoustic emission sensor were used as exciting and sensing ultrasonic signals, respectively. Then, a 3D full-field ultrasonic data set was created using an ultrasonic wave propagation imaging (UWPI) process, after which several signal processing techniques were applied to generate the processed data. By using a deep convolutional neural network (DCNN) with a VGG-like architecture based regression model, the estimated error was calculated to compare the performance of a DCNN on different processed data set.