Electronic Friction Near Metal Surface: Incorporating Nuclear Quantum Effect with Ring Polymer Molecular Dynamics

Molecular dynamics with electronic friction (MDEF) approach can describe nonadiabatic effects accurately at metal surfaces in the weak nonadiabatic limit. That being said, MDEF treats nuclear motion classically, such that the nuclear quantum effects are missing completely in the approach. To address this limitation, we combine electronic friction with Ring Polymer Molecular Dynamics (RPMD). In particular, we apply the averaged electronic friction from the metal surface to the centroid mode of the ring polymer. We benchmark our approach against quantum dynamics to show that electronic friction with RPMD (EF-RPMD) can capture zero-point energy as well as transition dynamics accurately. In addition, we show EF-RPMD can correctly predict the electronic transfer rate near metal surfaces in the tunneling limit as well as the barrier crossing limit. We expect our approach will be very useful to study nonadiabatic dynamics near metal surface when nuclear quantum effects become essential

Product Demand Forecasting and Dynamic Pricing considering Consumers' Mental Accounting and Peak-End Reference Effects

We introduce a demand forecasting model for a monopolistic company selling products to consumers with double-entry mental accounting, which means consumers experience pleasure when consuming goods or service and feel pains when paying for them. Moreover, as the monopolist changes prices, consumers form a reference price that adjusts an anchoring standard based on the lowest price that they perceived, namely, the peak-end anchoring. We obtain the steady state prices under three different payment schemes for two-and infinite-period. We also analyze the relationship between these steady prices and maximal profit and compare the steady state prices of different payment schemes by changing the double-entry mental accounting's parameters through numerical examples. The proposed model is computationally tractable for demand forecasting of realistic size

Product Demand Forecasting and Dynamic Pricing considering Consumers’ Mental Accounting and Peak-End Reference Effects

We introduce a demand forecasting model for a monopolistic company selling products to consumers with double-entry mental accounting, which means consumers experience pleasure when consuming goods or service and feel pains when paying for them. Moreover, as the monopolist changes prices, consumers form a reference price that adjusts an anchoring standard based on the lowest price that they perceived, namely, the peak-end anchoring. We obtain the steady state prices under three different payment schemes for two- and infinite-period. We also analyze the relationship between these steady prices and maximal profit and compare the steady state prices of different payment schemes by changing the double-entry mental accounting’s parameters through numerical examples. The proposed model is computationally tractable for demand forecasting of realistic size

Observation of a thermoelectric Hall plateau in the extreme quantum limit

The thermoelectric Hall effect is the generation of a transverse heat current upon applying an electric field in the presence of a magnetic field. Here we demonstrate that the thermoelectric Hall conductivity $\alpha_{xy}$ in the three-dimensional Dirac semimetal ZrTe$_5$ acquires a robust plateau in the extreme quantum limit of magnetic field. The plateau value is independent of the field strength, disorder strength, carrier concentration, or carrier sign. We explain this plateau theoretically and show that it is a unique signature of three-dimensional Dirac or Weyl electrons in the extreme quantum limit. We further find that other thermoelectric coefficients, such as the thermopower and Nernst coefficient, are greatly enhanced over their zero-field values even at relatively low fields.Comment: 17+21 pages, 3+14 figures; published versio

Automated zooplankton size measurement using deep learning: Overcoming the limitations of traditional methods

Zooplankton size is a crucial indicator in marine ecosystems, reflecting demographic structure, species diversity and trophic status. Traditional methods for measuring zooplankton size, which involve direct sampling and microscopic analysis, are laborious and time-consuming. In situ imaging systems are useful sampling tools; however, the variation in angles, orientations, and image qualities presented considerable challenges to early machine learning models tasked with measuring sizes.. Our study introduces a novel, efficient, and precise deep learning-based method for zooplankton size measurement. This method employs a deep residual network with an adaptation: replacing the fully connected layer with a convolutional layer. This modification allows for the generation of an accurate predictive heat map for size determination. We validated this automated approach against manual sizing using ImageJ, employing in-situ images from the PlanktonScope. The focus was on three zooplankton groups: copepods, appendicularians, and shrimps. An analysis was conducted on 200 individuals from each of the three groups. Our automated method's performance was closely aligned with the manual process, demonstrating a minimal average discrepancy of just 1.84%. This significant advancement presents a rapid and reliable tool for zooplankton size measurement. By enhancing the capacity for immediate and informed ecosystem-based management decisions, our deep learning-based method addresses previous challenges and opens new avenues for research and monitoring in zooplankton

Hyperglycemia Induced by Chronic Restraint Stress in Mice Is Associated With Nucleus Tractus Solitarius Injury and Not Just the Direct Effect of Glucocorticoids

Chronic restraint stress (CRS) can affect hypothalamic-pituitary-adrenal (HPA) axis activity and increase glucocorticoid levels. Glucocorticoids are stress hormones that regulate multiple aspects of energy homeostasis. Stress also impairs glucose tolerance. The aim of this study was to investigate the cause of insulin-resistant hyperglycemia during CRS. We produced the CRS models (a 7-day restraint followed by a 3-day free moving procedure, total of 4 cycles for 40 days) in mice, detected the parameters related to glucose metabolism, and compared them to those of the dexamethasone (DEX) injection (0.2 mg/kg i.p., also a 4 cycle procedure as the CRS). The results showed that the CRS induced a moderate (not higher than 11 mmol/L) and irreversible insulin-resistant hyperglycemia in about 1/3 of the individuals, and all the restrained mice had adrenal hypertrophy. CRS induced the apoptosis of neurons in the anterior part of commissural subnucleus of nucleus tractus solitarius (acNTS) in the hyperglycemic mice, and acNTS mechanical damage also led to insulin-resistant hyperglycemia. In contrast, in the DEX-treated mice, adrenal gland atrophy was evident. The glucose and insulin tolerance varied with the delay of determination. DEX exposure in vivo does not induce the apoptosis of neurons in NTS. This study indicates that restraint stress and DEX induce metabolic disorders through different mechanisms. During CRS, injury (apoptosis) of glucose-sensitive acNTS neurons cause dysregulation of blood glucose. This study also suggests the mouse restraint stress model has value as a potential application in the study of stress-induced hyperglycemia

Neuraminidase and Hemagglutinin Matching Patterns of a Highly Pathogenic Avian and Two Pandemic H1N1 Influenza A Viruses

BACKGROUND: Influenza A virus displays strong reassortment characteristics, which enable it to achieve adaptation in human infection. Surveying the reassortment and virulence of novel viruses is important in the prevention and control of an influenza pandemic. Meanwhile, studying the mechanism of reassortment may accelerate the development of anti-influenza strategies. METHODOLOGY/PRINCIPAL FINDINGS: The hemagglutinin (HA) and neuraminidase (NA) matching patterns of two pandemic H1N1 viruses (the 1918 and current 2009 strains) and a highly pathogenic avian influenza A virus (H5N1) were studied using a pseudotyped particle (pp) system. Our data showed that four of the six chimeric HA/NA combinations could produce infectious pps, and that some of the chimeric pps had greater infectivity than did their ancestors, raising the possibility of reassortment among these viruses. The NA of H5N1 (A/Anhui/1/2005) could hardly reassort with the HAs of the two H1N1 viruses. Many biological characteristics of HA and NA, including infectivity, hemagglutinating ability, and NA activity, are dependent on their matching pattern. CONCLUSIONS/SIGNIFICANCE: Our data suggest the existence of an interaction between HA and NA, and the HA NA matching pattern is critical for valid viral reassortment

One-year delayed effect of fog on malaria transmission: a time-series analysis in the rain forest area of Mengla County, south-west China

Background: Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China. Methods: Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Results: At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria. Conclusion: Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide