. In vitro propagation of the new orchid Dendrobium trankimianum T. Yukawa

Dendrobium trankimianum T. Yukawa is a beautiful, endemic orchid of Vietnam, a new species with a first - published description in 2004. It is very rare and expected to be added to the IUCN Red List status - CR. In vitro studies of orchid D. trankimianum T. Yukawa were conducted in order to conserve and increase the genetic pool of this precious wild orchid species. The results showed that full-strength MS medium supplemented with 2.0 mg/L BA and 0.5 mg/L NAA (10.24 PLBs/explant; 90.11% explants formed PLBs) or full-strength MS medium supplemented with 1.5 mg/L TDZ and 0.5 mg/L NAA (14.11 PLBs/explant; 92.06% explants formed PLBs) were the most suitable for protocorm formation. For subculture, suitable growth of shoots were obtained on full-strength MS medium supplemented 1.5 mg/L BA (22.35 shoots/explant; shoots length of 1.96 cm) and full-strength MS medium supplemented with 60 g ripe banana per liter (25.11 shoots/explant; shoots length of 2.12 cm). The shoots in vitro were transferred to half-strength MS supplemented with different concentrations of IAA, IBA and NAA to investigate root formation. The best rooting occurred at 0,5 mg/L NAA (7.91 roots/shoot; root length of 4.01 cm; 98.51% root formation). The plantlets with uniform growth were planted on different substrate: Eco clean soil, Coconut fiber, Fern fiber, 50% Rice husk in combination with 50% Eco clean soil for research the most suitable substrate. After 60 days of transplantion and acclimatization, the result showed that Fern fiber was suitable substrate for plantlet growth in a nursery garden (8.0 roots/ explant; root length of 5.5 cm; survival rate of 93.29%)

A robust sequential hypothesis testing method for brake squeal localisation

This contribution deals with the in situ detection and localisation of brake squeal in an automobile. As brake squeal is emitted from regions known a priori, i.e., near the wheels, the localisation is treated as a hypothesis testing problem. Distributed microphone arrays, situated under the automobile, are used to capture the directional properties of the sound field generated by a squealing brake. The spatial characteristics of the sampled sound field is then used to formulate the hypothesis tests. However, in contrast to standard hypothesis testing approaches of this kind, the propagation environment is complex and time-varying. Coupled with inaccuracies in the knowledge of the sensor and source positions as well as sensor gain mismatches, modelling the sound field is difficult and standard approaches fail in this case. A previously proposed approach implicitly tried to account for such incomplete system knowledge and was based on ad hoc likelihood formulations. The current paper builds upon this approach and proposes a second approach, based on more solid theoretical foundations, that can systematically account for the model uncertainties. Results from tests in a real setting show that the proposed approach is more consistent than the prior state-of-the-art. In both approaches, the tasks of detection and localisation are decoupled for complexity reasons. The localisation (hypothesis testing) is subject to a prior detection of brake squeal and identification of the squeal frequencies. The approaches used for the detection and identification of squeal frequencies are also presented. The paper, further, briefly addresses some practical issues related to array design and placement. (C) 2019 Author(s)

Buckling and vibration analysis of laminated composite plate/shell structures via a smoothed quadrilateral flat shell element with in-plane rotations

This paper presents buckling and free vibration analysis of composite plate/shell structures of various shapes, modulus ratios, span-to-thickness ratios, boundary conditions and lay-up sequences via a novel smoothed quadrilateral flat element. The element is developed by incorporating a strain smoothing technique into a flat shell approach. As a result, the evaluation of membrane, bending and geometric stiffness matrices are based on integration along the boundary of smoothing elements, which leads to accurate numerical solutions even with badly-shaped elements. Numerical examples and comparison with other existing solutions show that the present element is efficient, accurate and free of locking

Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting

This article presents a research approach to enhancing the quality of short-term power output forecasting models for photovoltaic plants using a Long Short-Term Memory (LSTM) recurrent neural network. Typically, time-related indicators are used as inputs for forecasting models of PV generators. However, this study proposes replacing the time-related inputs with clear sky solar irradiance at the specific location of the power plant. This feature represents the maximum potential solar radiation that can be received at that particular location on Earth. The Ineichen/Perez model is then employed to calculate the solar irradiance. To evaluate the effectiveness of this approach, the forecasting model incorporating this new input was trained and the results were compared with those obtained from previously published models. The results show a reduction in the Mean Absolute Percentage Error (MAPE) from 3.491% to 2.766%, indicating a 24% improvement. Additionally, the Root Mean Square Error (RMSE) decreased by approximately 0.991 MW, resulting in a 45% improvement. These results demonstrate that this approach is an effective solution for enhancing the accuracy of solar power output forecasting while reducing the number of input variables

Detection of co-infection and recombination cases with Omicron and local Delta variants of SARS-CoV-2 in Vietnam

The first nationwide outbreak of COVID-19 in Vietnam started in late April 2021 and was caused almost exclusively by a single Delta lineage, AY.57. In early 2022, multiple Omicron variants co-circulated with Delta variants and quickly became dominant. The co-circulation of Delta and Omicron happened leading to possibility of co-infection and recombination events which can be revealed by viral genomic data. From January to October 2022, a total of 1028 viral RNA samples out of 4852 positive samples (Ct < 30) were sequenced by the long pooled amplicons method on Illumina platforms. All sequencing data was analysed by the workflow for SARS-CoV-2 on CLC genomics workbench and Illumina Dragen Covid application. Among those sequenced samples, we detected a case of Delta AY.57/Omicron BA.1 co-infection and two cases of infection with Delta AY.57/Omicron BA.2 recombinants which were nearly identical and had different epidemiological characteristics. Since the AY.57 lineage circulated almost exclusively in Vietnam, these results strongly suggest domestic events of co-infection and recombination. These findings highlight the strengths of genomic surveillance in monitoring the circulating variants in the community enabling rapid identification of viral changes that may affect viral properties and evolutionary events

Library Design in Combinatorial Chemistry by Monte Carlo Methods

Strategies for searching the space of variables in combinatorial chemistry experiments are presented, and a random energy model of combinatorial chemistry experiments is introduced. The search strategies, derived by analogy with the computer modeling technique of Monte Carlo, effectively search the variable space even in combinatorial chemistry experiments of modest size. Efficient implementations of the library design and redesign strategies are feasible with current experimental capabilities.Comment: 5 pages, 3 figure

Pre-verbal infants perceive emotional facial expressions categorically

Adults perceive emotional expressions categorically, with discrimination being faster and more accurate between expressions from different emotion categories (i.e. blends with two different predominant emotions) than between two stimuli from the same category (i.e. blends with the same predominant emotion). The current study sought to test whether facial expressions of happiness and fear are perceived categorically by pre-verbal infants, using a new stimulus set that was shown to yield categorical perception in adult observers (Experiments 1 and 2). These stimuli were then used with 7-month-old infants (N  =  34) using a habituation and visual preference paradigm (Experiment 3). Infants were first habituated to an expression of one emotion, then presented with the same expression paired with a novel expression either from the same emotion category or from a different emotion category. After habituation to fear, infants displayed a novelty preference for pairs of between-category expressions, but not within-category ones, showing categorical perception. However, infants showed no novelty preference when they were habituated to happiness. Our findings provide evidence for categorical perception of emotional expressions in pre-verbal infants, while the asymmetrical effect challenges the notion of a bias towards negative information in this age group

Correlation between centrality metrics and their application to the opinion model

In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes. In addition to considering the widely used centrality metrics, we introduce a new centrality measure, the degree mass. The m order degree mass of a node is the sum of the weighted degree of the node and its neighbors no further than m hops away. We find that the B_{n}, the closeness, and the components of x_{1} are strongly correlated with the degree, the 1st-order degree mass and the 2nd-order degree mass, respectively, in both network models and real-world networks. We then theoretically prove that the Pearson correlation coefficient between x_{1} and the 2nd-order degree mass is larger than that between x_{1} and a lower order degree mass. Finally, we investigate the effect of the inflexible antagonists selected based on different centrality metrics in helping one opinion to compete with another in the inflexible antagonists opinion model. Interestingly, we find that selecting the inflexible antagonists based on the leverage, the B_{n}, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks. This observation is supported by our previous observations, i.e., that there is a strong linear correlation between the degree and the B_{n}, as well as a high centrality similarity between the leverage and the degree.Comment: 20 page