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

Proactive Recommendation with Iterative Preference Guidance

Recommender systems mainly tailor personalized recommendations according to user interests learned from user feedback. However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems like filter bubbles and opinion polarization. To counteract this, proactive recommendation actively steers users towards developing new interests in a target item or topic by strategically modulating recommendation sequences. Existing work for proactive recommendation faces significant hurdles: 1) overlooking the user feedback in the guidance process; 2) lacking explicit modeling of the guiding objective; and 3) insufficient flexibility for integration into existing industrial recommender systems. To address these issues, we introduce an Iterative Preference Guidance (IPG) framework. IPG performs proactive recommendation in a flexible post-processing manner by ranking items according to their IPG scores that consider both interaction probability and guiding value. These scores are explicitly estimated with iteratively updated user representation that considers the most recent user interactions. Extensive experiments validate that IPG can effectively guide user interests toward target interests with a reasonable trade-off in recommender accuracy. The code is available at https://github.com/GabyUSTC/IPG-Rec.Comment: Accepted by WWW 2024 (Short

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

Molecular tracing of a suspected foodborne disease event caused by Bacillus cereus

ObjectiveTo trace Bacillus cereus （B. cereus） from foodborne disease outbreaks toidentify pathogens and cut off transmission.MethodsPulsed-field gel electrophoresis （PFGE） was performed. Furthermore， 12 isolates of B. cereus were subjected to PFGE. Subsequently， whole-genome sequencing （WGS） analysis was conducted on ten of these isolates. The WGS data were analyzed and assembled using BioNumerics software. Multilocus sequence typing （MLST）， virulence gene profiles， and single nucleotide polymorphisms （SNPs） were analyzed using assembled sequences.ResultsPFGE analysis classified the 12 B. cereus strains into nine pulsotypes. The three B. cereus isolates with the same PFGE pattern belonged to ST1435， and there were only three SNPs in the three ST1435 strains. The two B. cereus isolates with the same PFGE patterns were ST24 with one SNP between them， and the two ST24 isolates harbored hlbACD. These results indicate that the B. cereus isolates belonged to the same clone. The remaining three B. cereus strains also contained hlbACD.ConclusionFood-borne illness events caused by B. cereus are complex and are sources of contamination. Therefore， it will be necessary to strengthen the hygiene surveillance of food sources and workers and to pay more attention to cleaning and disinfecting environments and facilities， which will be important for preventing and controlling foodborne diseases