Probability theory and polymer physics

This study applies the theory of stochastic processes to the equilibrium statistical physics of polymers in solution. The topics treated include random copolymers and randomly branching polymers, with self-consistent mean field effects. A new and more natural way of dealing with Boltzmann weighting is discussed, which makes it possible from the beginning of a calculation to consider only the physical polymer conformations. We also show that in general the random copolymer problem can be reduced to the ordinary polymer problem, and treat the self-consistent field problem for a general branching polymer.     

Chaotic oscillations and noise transformations in a simple dissipative system with delayed feedback

We analyze the statistical behavior of signals in nonlinear circuits with delayed feedback in the presence of external Markovian noise. For the special class of circuits with intense phase mixing we develop an approach for the computation of the probability distributions and multitime correlation functions based on the random phase approximation. Both Gaussian and Kubo-Andersen models of external noise statistics are analyzed and the existence of the stationary (asymptotic) random process in the long-time limit is shown. We demonstrate that a nonlinear system with chaotic behavior becomes a noise amplifier with specific statistical transformation properties.     

强激光场下的双光子自电离理论

本文研究了包括二阶离化过程的强激光场下的双光子自电离。导出了光电子谱的解析表达式,并对一些特殊情况下的光电子谱进行了分析。     

An Edge Server Placement Method Based on Reinforcement Learning

In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. To overcome these defects, we propose a novel edge server placement algorithm based on deep q-network and reinforcement learning, dubbed DQN-ESPA, which can achieve optimal placements without relying on previous placement experience. In DQN-ESPA, the edge server placement problem is modeled as a Markov decision process, which is formalized with the state space, action space and reward function, and it is subsequently solved using a reinforcement learning algorithm. Experimental results using real datasets from Shanghai Telecom show that DQN-ESPA outperforms state-of-the-art algorithms such as simulated annealing placement algorithm (SAPA), Top-K placement algorithm (TKPA), K-Means placement algorithm (KMPA), and random placement algorithm (RPA). In particular, with a comprehensive consideration of access delay and workload balance, DQN-ESPA achieves up to 13.40% and 15.54% better placement performance for 100 and 300 edge servers respectively.     

The effect of post-metal annealing on the electrical performance and stability of two-step-annealed solution-processed In2O3 thin film transistors

In this work, solution-processed indium oxide (In₂O₃) thin film transistors (TFTs) were fabricated by a two-step annealing method. The influence of post-metal annealing (PMA) temperatures on the electrical performance and stability is studied. With the increase of PMA temperatures, the on-state current and off-state current (I_on/I_off) ratio is improved and the sub-threshold swing (SS) decreased. Moreover, the stability of In₂O₃ TFTs is also improved. In all, In₂O₃ TFT with post-metal annealing temperature of 350°С exhibits the best performance (a threshold voltage of 4.75 V, a mobility of 13.8 cm²/V, an I_on/I_off ratio of 1.8 × 10⁶, and a SS of 0.76 V/decade). Meanwhile, the stability under temperature stress (TBS) and positive bias stress (PBS) also show a good improvement. It shows that the PMA treatment can effectively suppress the interface trap and bulk trap and result in an obviously improvement of the In₂O₃ TFTs performance.     

Hotel Performance in the UK: The Role of Information Entropy in a Novel Slack-Based Data Envelopment Analysis

Previous hotel performance studies neglected the role of information entropy in feedback processes between input and output management. This paper focuses on this gap by exploring the relationship between hotel performance at the industry level and the capability of learning by doing and adopting best practices using a sample of 153 UK hotels over a 10-year period between 2008–2017. Besides, this research also fills a literature gap by addressing the issues of measuring hotel performance in light of negative outputs. In order to achieve this, we apply a novel Modified slack-based model for the efficiency analysis and Least Absolute Shrinkage and Selection Operator to examine the influence of entropy related variable on efficiency score. The Results indicate that less can be learnt from inputs than from outputs to improve efficiency levels and resource allocation is more balanced than cash flow and liquidity. The findings suggest that market dynamics explains the cash flow generation potential and liquidity. We find that market conditions are increasingly offering the opportunities for learning and improving hotel efficiency. The results report that the distinctive characteristic of superior performance in hotel operations is the capability to match the cash flow generation potential with market opportunities.     

Transient Dynamics in the Random Growth and Reset Model

A mean-field type model with random growth and reset terms is considered. The stationary distributions resulting from the corresponding master equation are relatively easy to obtain; however, for practical applications one also needs to know the convergence to stationarity. The present work contributes to this direction, studying the transient dynamics in the discrete version of the model by two different approaches. The first method is based on mathematical induction by the recursive integration of the coupled differential equations for the discrete states. The second method transforms the coupled ordinary differential equation system into a partial differential equation for the generating function. We derive analytical results for some important, practically interesting cases and discuss the obtained results for the transient dynamics.     

Tracking an Auto-Regressive Process with Limited Communication per Unit Time

Samples from a high-dimensional first-order auto-regressive process generated by an independently and identically distributed random innovation sequence are observed by a sender which can communicate only finitely many bits per unit time to a receiver. The receiver seeks to form an estimate of the process value at every time instant in real-time. We consider a time-slotted communication model in a slow-sampling regime where multiple communication slots occur between two sampling instants. We propose a successive update scheme which uses communication between sampling instants to refine estimates of the latest sample and study the following question: Is it better to collect communication of multiple slots to send better refined estimates, making the receiver wait more for every refinement, or to be fast but loose and send new information in every communication opportunity? We show that the fast but loose successive update scheme with ideal spherical codes is universally optimal asymptotically for a large dimension. However, most practical quantization codes for fixed dimensions do not meet the ideal performance required for this optimality, and they typically will have a bias in the form of a fixed additive error. Interestingly, our analysis shows that the fast but loose scheme is not an optimal choice in the presence of such errors, and a judiciously chosen frequency of updates outperforms it.     

Direct contact ultrasound assisted freezing of chicken breast samples

Nowadays, rapid freezing is sought to favor the formation of small ice crystals. Several studies have shown that the application of ultrasounds (US) accelerates the processes of energy and mass transfer when they are applied through immersion systems. However, there are hardly any studies on its application in direct systems without the use of a liquid medium for its transmission. Therefore, the objective of this work was to evaluate the potential of the application of US for improving the freezing process of chicken breast samples. First, the application of intermittent US treatments at different net sonication times of 7, 17, 37, 50 and 67% during the freezing of distilled water samples in a conventional freezer was evaluated. It was observed that net sonication times of 37, 50 and 67% reduced the phase change period by 30.0, 21.4, 27.0%, respectively. The effective freezing time was also reduced by 12.4 and 12.8% by applying net sonication times of 37 and 50%. Considering these results, an intermittent US treatment with a net sonication time of 37% was chosen for chicken breast freezing in an air-forced cooling tunnel at ambient temperatures from −13 to −22 °C. The length of all the freezing phases was reduced upon application of US, leading to an overall process time reduction of approx. 11%. On the other hand, no significant differences were found either in the Water Holding Capacity (WHC) or Cooking Loss (CL) values between control and US assisted frozen chicken breast samples. Furthermore, in vitro experiments showed that US-assisted freezing did not influence protein digestibility of chicken meat samples.This study demonstrates the potential of the application of US by direct contact to favor energy transfer processes during freezing of water and chicken breasts samples. However, its effect on the quality of the frozen products should be further studied.