Electron transfer properties of double quantum dot system in a fluctuating environment

Using the innovative method of the additional Bloch vector,the electron transfer properties of a double quantum dot(DQD) system measured by a quantum point contact(QPC) in a fluctuating environment are investigated.The results show that the environmental noises in transverse and longitudinal directions play different roles in the dynamical evolution of the open quantum systems.Considering the DQD with symmetric energy level,the Fano factor exhibits a slight peak with the increase of transverse noise amplitude σ_(T),which provides a basis for distinguishing dynamical phenomena caused by different directional fluctuation noises in symmetric DQD structures by studying the detector output.In the case of asymmetric DQD,the dependence of a detector current involving the level displacement is distinct when increasing the transverse noise damping coefficient τ_(T) and the longitudinal noise damping coefficient τ_(ε) respectively.Meanwhile,the transverse noise damping coefficient τ_(T) could significantly reduce the Fano factor and enhance the stability of the quantum system compared with the longitudinal one.The Fano factors with stable values as the enhancement of noise amplitudes show different external influences from the detector measurement,and provide a numerical reference for adjusting the noise amplitudes in both transverse and longitudinal directions appropriately in a microscopic experimental process to offset the decoherence effect caused by the measurements.Finally,the research of average waiting time provides unique insights to the development of single electron transfer theory in the short-time limit.

Prediction of Photosynthetic Carbon Assimilation Rate of Individual Rice Leaves under Changes in Light Environment Using BLSTM-Augmented LSTM

A model to predict photosynthetic carbon assimilation rate(A)with high accuracy is important for forecasting crop yield and productivity.Long short-term memory(LSTM),a neural network suitable for time-series data,enables prediction with high accuracy but requires mesophyll variables.In addition,for practical use,it is desirable to have a technique that can predict A from easily available information.In this study,we propose a BLSTM augmented LSTM(BALSTM)model,which utilizes bi-directional LSTM(BLSTM)to indirectly reproduce the mesophyll variables required for LSTM.The most significant feature of the proposed model is that its hybrid architecture uses only three relatively easy-to-collect external environmental variables—photosynthetic photon flux density(Q_(in)),ambient CO_(2) concentration(C_(a)),and temperature(T_(air))—to generate mesophyll CO_(2) concentration(C_(i))and stomatal conductance to water vapor(g_(sw))as intermediate outputs.Then,A is predicted by applying the obtained intermediate outputs to the learning model.Accordingly,in this study,1)BALSTM(Q_(in),C_(a),T_(air))had a significantly higher A prediction accuracy than LSTM(Q_(in),C_(a),T_(air))in case of using only Q_(in),C_(a),and T_(air);2)BALSTMC_(i),g_(sw),which had C_(i) and g_(sw) as intermediate products,had the highest A prediction accuracy compared with other candidates;and 3)for samples where LSTM(Q_(in),C_(a),T_(air))had poor prediction accuracy,BALSTMC_(i),g_(sw)(Q_(in),C_(a),T_(air))clearly improved the results.However,it was found that incorrect predictions may be formed when certain factors are not reflected in the data(e.g.,timing,cultivar,and growth stage)or when the training data distribution that accounts for these factors differs from the predicted data distribution.Therefore,a robust model should be constructed in the future to improve the prediction accuracy of A by conducting gasexchange measurements(including a wide range of external environmental values)and by increasing the number of training data samples.

The 1/f noise in multiwalled carbon nanotubes

The 1/f noise in multiwalled carbon nanotubes bundles has been investigated between the frequency range of 0.1 to 30 Hz. At room temperature the noise spectrum is standard 1/f, and its level is proportional to the square of the bias voltage. With decreasing temperature the noise level also decreases. At 4.2 K the noise level follows a non-monotonic dependence against the bias voltage, showing a peak at a certain bias voltage, meanwhile its frequency dependence also deviates from the 1/f trend. This anomalous behaviour is discussed within the picture of environmental quantum fluctuation of charge transport in the samples.

Evolution of a flexible rule for foraging that copes with environmental variation

Models of adaptive behaviour typically assume that animals behave as though they have highly complex, detailed strategies for making decisions. In reality, selection favours the optimal balance between the costs and benefits of complexity. Here we investigate this trade-off for an animal that has to decide whether or not to forage for food - and so how much energy reserves to store - depending on the food availability in its environment. We evolve a decision rule that controls the target reserve level for different ranges of food availability, but where increasing complexity is costly in that metabolic rate increases with the sensitivity of the rule. The evolved rule tends to be much less complex than the optimal strategy but performs almost as well, while being less costly to implement. It achieves this by being highly sensitive to changing food availability at low food abun- dance - where it provides a close fit to the optimal strategy - but insensitive when food is plentiful. When food availability is high, the target reserve level that evolves is much higher than under the optimal strategy, which has implications for our under- standing of obesity. Our work highlights the important principle of generalisability of simple decision-making mechanisms, which enables animals to respond reasonably well to conditions not directly experienced by themselves or their ancestors.

New insights into plant nutrient signaling and adaptation to fluctuating environments

Over the past 50 years, the Green Revolution and exploitation of heterosis have allowed cereal grain yield to keep pace with world- wide population growth. Unfortunately, plant growth and crop productivity are heavily dependent on the application of synthetic fertilizers.

Phylogenetic diversity stabilizes community biomass

Aims The relationship between biodiversity and ecological stability is a long-standing issue in ecology.Current diversity–stability studies,which have largely focused on species diversity,often report an increase in the stability of aggregate community properties with increasing species diversity.Few studies have examined the linkage between phylogenetic diversity,another important dimension of biodiversity,and stability.By taking species evolutionary history into account,phylogenetic diversity may better capture the diversity of traits and niches of species in a community than species diversity and better relate to temporal stability.In this study,we investigated whether phylogenetic diversity could affect temporal stability of community biomass independent of species diversity.Methods We performed an experiment in laboratory microcosms with a pool of 12 bacterivorous ciliated protist species.To eliminate the possibility of species diversity effects confounding with phylogenetic diversity effects,we assembled communities that had the same number of species but varied in the level of phylogenetic diversity.Weekly disturbance,in the form of short-term temperature shock,was imposed on each microcosm and species abundances were monitored over time.We examined the relationship between temporal stability of community biomass and phylogenetic diversity and evaluated the role of several stabilizing mechanisms for explaining the influence of phylogenetic diversity on temporal stability.Important Findings Our results showed that increasing phylogenetic diversity promoted temporal stability of community biomass.Both total community biomass and summed variances showed a U-shaped relationship with phylogenetic diversity,driven by the presence of large,competitively superior species that attained large biomass and high temporal variation in their biomass in both low and high phylogenetic diversity communities.Communities without these species showed patterns consistent with the reduced strength of competition and increasingly asynchronous species responses to environmental changes under higher phylogenetic diversity,two mechanisms that can drive positive diversity–stability relationships.These results support the utility of species phylogenetic knowledge for predicting ecosystem functions and their stability.