Conjugates of Unsaturated Fatty Acids with Propylene Glycol as Potentially Less-Irritant Skin Penetration Enhancers

Fatty acids (FA) are well known as efficient enhancers for transdermal delivery of drugs; however, their frequent dermal toxicity limits their regular use. In order to utilize the fatty acid as a safe enhancer devoid of its irritant effect, we have synthesized and evaluated a series of fatty acids conjugated to propylene glycol (FA-PG). Each one of the conjugates was prepared as a mono- or di- acyl ester derivative. The effects of the synthetic enhancers on the porcine skin permeability were evaluated in a diffusion cell system using lidocaine as the model drug. In addition, in vivo examinations in rabbits were preformed for skin toxicological evaluation. The results indicate that among the FA-PG conjugates, oleic acid (C18:1_n–9)-PG, linoleic acid (C18:2_n–6)-PG and α-linolenic acid (C18:3_n–3)-PG, mono- or di-esters, enhance the penetration of lidocaine relatively to the vehicle (without enhancer). The conjugates of oleic acid (C18:1_n–9) and linoleic acid (C18:2_n–6) with PG have demonstrated a similar enhancing effect as the corresponding free fatty acids. Interestingly, although the mono- or the di- conjugates of α-linolenic acid (C18:3_n–3) with PG enhanced the lidocaine flux as the other two fatty acid conjugates, they resulted in a reduced permeability as compared to the action of their free acid. In addition, the mono-conjugates of α-linolenic acid (C18:3_n–3) with PG exhibited elevated skin irritation in rabbits (relative to the fatty acid alone) compared to the significantly reduced irritation of oleate-PG and linoeate-PG mono-conjugates. In conclusion, except saturated FA-PG and α-linolenic acid (C18:3_n–3)—PG mono-conjugates, unsaturated fatty acids (e.g., oleic and linoleic acids) after conjugation to PG may be safe and effective enhancers for delivering topical drugs.     

Critical factors in the empirical performance of temporal difference and evolutionary methods for reinforcement learning

Temporal difference and evolutionary methods are two of the most common approaches to solving reinforcement learning problems. However, there is little consensus on their relative merits and there have been few empirical studies that directly compare their performance. This article aims to address this shortcoming by presenting results of empirical comparisons between Sarsa and NEAT, two representative methods, in mountain car and keepaway, two benchmark reinforcement learning tasks. In each task, the methods are evaluated in combination with both linear and nonlinear representations to determine their best configurations. In addition, this article tests two specific hypotheses about the critical factors contributing to these methods’ relative performance: (1) that sensor noise reduces the final performance of Sarsa more than that of NEAT, because Sarsa’s learning updates are not reliable in the absence of the Markov property and (2) that stochasticity, by introducing noise in fitness estimates, reduces the learning speed of NEAT more than that of Sarsa. Experiments in variations of mountain car and keepaway designed to isolate these factors confirm both these hypotheses.     

监测喷射混凝土的硬化和强度发展的一种新方法(英文)

提出了一种新的监测喷射混凝土硬化和强度发展的方法。该方法在"Modiin-Jerusalem"铁路隧道建设工程中得到了实际应用。其最初目的是用来预测评估混凝土的拆模时间。基于直接测量混凝土构件中的电阻率和温度的发展,估测了隧道结构中混凝土的硬化和强度发展。通过组合分析电阻率和温度的发展曲线,构建了一个通用的"强度--电阻率"关系,并依此开发了用于监测早龄期(24h内)喷射混凝土的硬化和强度发展的算法。这个算法可以用于估算混凝土构件达到一定强度所需要的时间,也可用于评估混凝土搅拌站供应到施工现场的混凝土的性能稳定性。     

Formation of ArCl(B) molecules in a transverse volume discharge

Results of optimizing a λ=175 nm ArCl(B-X) emitter pumped by a transverse volume discharge are presented. The formation of ArCl(B) molecules was investigated in a plasma formed by mixtures of rare gases with HCl and Cl₂ molecules. It is shown that the Cl₂ molecule is the most efficient chlorine carrier and that neon at pressures ≥ 50 kPa is the most efficient buffer gas. Pis’ma Zh. Tekh. Fiz. 25, 29–33 (June 12, 1999)     

A statistical analysis of interference and effective deployment strategies for facility-specific wireless sensor networks

Sensors are essential to industrial automation as they provide vital links between control systems and the physical world. Recently, wireless sensor networks (WSNs) attract more attention as they become feasible solutions for facility management. Unlike simulated environments, however, there are challenges in developing reliable WSNs for monitoring real facilities, including reduced accuracy, reliability and performance due to unpredictable interferences. This paper investigates deployment of automation facility-specific WSNs, called facility sensor networks (FSNs). First, interferences at multiple sensing nodes are analyzed to see if FSNs are vulnerable to interference. Second, interference sources are identified by applying statistical methods to collected data, in order to find the appropriate FSN configuration. Finally, an interference model is proposed to obtain optimal deployment strategies that minimize influence of interference. The strategy yields the lowest interference level compared to others. The results also suggest the appropriate number of sensors to be deployed.