基于WIA-PA传感器网络的能量有效时间同步算法

WIA-PA协议是一种新型工业级无线传感器网络国际标准。当WIA-PA网络内节点数目增多时,传统的参考广播同步算法RBS以及改进型ERBS算法网络开销非常大。针对这一严峻的问题,提出了一种能量有效的局部最优时间同步算法。该算法对相邻的接收节点在多个参考广播消息的条件下求取平均相位偏差,然后对此平均相位偏差进行最大后验估计,最后根据此估计值调整本地时钟。仿真结果表明,本文所提时间同步算法在同步精度方面比RBS约提高168%,比ERBS约提高24%;同时,同步过程中的网络能耗也大大减少。     

Effect of oblique incidence angle of sunlight on the optimized folding angle of V-shaped organic solar cells

We numerically investigate the effect of the incidence angle of sunlight on the optimized folding angle of V-shaped organic solar cells (VOSCs) to obtain the best power conversion efficiency in a realistically operating environment. The light absorbance at the active layer is calculated based on the finite element method with respect to the incidence angle of sunlight and the folding angle of the VOSC. We calculate the generation energy density per day at each folding angle by integrating the angular response of the short-circuit current and the open-circuit voltage with the consideration of the variation of the incidence angle during daytime. We show that the optimized folding angle of the VOSC based on the variation of the generation energy density per day is close to that determined from the variation of the electric power density in normal incidence, which has been widely used to optimize the folding angle of VOSCs.     

Tiling solutions for optimal biological sensing

Biological systems, from cells to organisms, must respond to the ever-changing environment in order to survive and function. This is not a simple task given the often random nature of the signals they receive, as well as the intrinsically stochastic, many-body and often self-organized nature of the processes that control their sensing and response and limited resources. Despite a wide range of scales and functions that can be observed in the living world, some common principles that govern the behavior of biological systems emerge. Here I review two examples of very different biological problems: information transmission in gene regulatory networks and diversity of adaptive immune receptor repertoires that protect us from pathogens. I discuss the trade-offs that physical laws impose on these systems and show that the optimal designs of both immune repertoires and gene regulatory networks display similar discrete tiling structures. These solutions rely on locally non-overlapping placements of the responding elements (genes and receptors) that, overall, cover space nearly uniformly.     

Combinatıon of a Box-Behnken design technique with response surface methodology for optimization of the photocatalytic mineralization of C.I. Basic Red 46 dye from aqueous solution

The photodegradation of an industrial azo dye C.I Basic Red 46 was examined in a fixed-bed photoreactor using UV-lamps simulated to the solar irradiation. In our photodecolorization study, the UV/TiO₂ process was optimized using the Box-Behnken approach to evaluate the synergistic effects of three independent parameters (initial concentration of the dye, flow rate, and UV intensity) on mineralization effectiveness. The response surface methodology was in good promise with the prediction model (coefficients of determination of decolorization and mineralization were R²_Dec = 0.997 and R²_TOC = 0.994, respectively). The effects of the factors could be estimated from a second–order polynomial equation and student’s t-test. The optimal parameters of decolorization and mineralization were as follows: initial concentration of colorant 25 mg L⁻¹, rate of fluid flow 0.3 L min⁻¹, and ultraviolet light intensity 38.1 W m⁻². The decolorization and mineralization removal efficiency under these optimal conditions were 100% and 57.63% respectively. These results indicate that optimization using response surface methodology, based on the Box-Behnken approach, is an excellent tool for determining the optimal conditions, and the process can be easily extrapolated for a specific treatment of real waste water containing the azo dye C.I Basic Red 46. Also, the intermediates that were produced during photodegradation process of Basic Red 46 were determined by GC/MS.     

Physicochemical parameter influences and their optimization on the biosynthesis of MnO2 nanoparticles using Vernonia amygdalina leaf extract

The manganese dioxide nanoparticles (MnO₂ NPs) were synthesized using Vernonia amygdalina leaf extract which was used as a reducing, capping, and stabilizing agents due to the presence of bioactive phytochemical compounds. Twenty five runs were designed to investigate the effect of V. amygdalina leaf extract ratio (A), initial potassium permanganate (KMnO₄) concentration (B), pH (C), and reaction time (D) on the biosynthesized MnO₂ NPs using 4-factor, 4-level D-Optimal Response Surface Quadratic Design Model approach. The relationship between physicochemical variables and absorption responses were established using transform second degree polynomial quadratic model. The effects of each absorption responses were analyzed by ANOVA principle using quadratic equations. A very low p-values (<0.0001), non-significant Lack of Fit F-values, and reasonable regression coefficient values (coefficient R² = 0.9790, adjusted R² = 0.9496, and predicted R² = 0.8452) suggested that there is an effective correlation between experimental results and predicted values. Numerical and graphical optimized results demonstrated that the optimized conditions for the predicted absorbance at 320 nm (1.095) were suggested at 43.72%, 1.81 mM, 6.02, and 103.42 min for V. amygdalina leaf extract ratio, initial KMnO₄ concentration, pH, and reaction time, respectively. Under these optimal conditions, the average absorbance from four experimental run was recorded to be 0.9678. This result was very closest to the predicted values. The average size elucidated by X-ray diffraction (XRD) analysis was found in the range between 20 nm and 22 nm. The stretching/or and vibrational, surface topography, thermal, and surface roughness as well as its porosity distributions were investigated by UV–Vis spectroscopy, Fourier transforms infrared (FTIR), scanning electron microscopy (SEM), differential scanning calorimeter (DSC), and Gwyddion software analysis.     

Mathematical Analysis of caching policies and cooperation in YouTube-like services

Currently, most video on-demand services offered over the Internet do not exploit the idle resources available from end-users. We discuss the benefits of user-assistance in video on-demand systems, where users are both clients and servers, helping with the task of video distribution. The mathematical machinery for the systematic analysis of video on-demand services is not mature yet. In this paper we develop a deterministic fluid model to determine the expected evolution of user-assisted on-demand video streaming services. We theoretically prove that cooperative systems always outperform non-cooperative solutions. A combinatorial optimization problem is proposed, where the goal is to distribute a set of video items into repositories trying to offer the minimum waiting times to end-users. This combinatorial problem is proved to be in the class of NP-Complete computational problem, and is heuristically solved with a GRASP methodology. Predictions inspired in YouTube scenarios suggest the introduction of cooperation is both robust and extremely attractive from an economical viewpoint as well.     

A Branch-and-price Algorithm for the Multi-Vehicle Covering Tour Problem

In this paper, we propose a Branch-and-price (BP) algorithm and a Column Generation Heuristic (CGH) for the Multi-Vehicle Covering Tour Problem (m-CTP). Specific dominance and extension pruning rules are introduced to accelerate the resolution of the pricing problems. To the best of our knowledge, this is the first work dedicated to the exact resolution of m-CTP. The algorithm managed to solve about 30% of the instances in our test bed, within a 4 hour CPU time limit. Our preliminary computational experiments suggest that both the lower bounds provided by the formulation behind BP and the CGH upper bounds are of good quality.     

New Quasi-Newton Equation and Related Methods for Unconstrained Optimization

In unconstrained optimization, the usual quasi-Newton equation is B _k+1 s _k=y _k, where y _k is the difference of the gradients at the last two iterates. In this paper, we propose a new quasi-Newton equation, , in which is based on both the function values and gradients at the last two iterates. The new equation is superior to the old equation in the sense that better approximates ² f(x _k+1)s _k than y _k. Modified quasi-Newton methods based on the new quasi-Newton equation are locally and superlinearly convergent. Extensive numerical experiments have been conducted which show that the new quasi-Newton methods are encouraging.     

Numerical experience with the truncated Newton method for unconstrained optimization

The truncated Newton algorithm was devised by Dembo and Steihaug (Ref. 1) for solving large sparse unconstrained optimization problems. When far from a minimum, an accurate solution to the Newton equations may not be justified. Dembo's method solves these equations by the conjugate direction method, but truncates the iteration when a required degree of accuracy has been obtained. We present favorable numerical results obtained with the algorithm and compare them with existing codes for large-scale optimization.     

Position of starting spot in TLC: Influence on average plate height

Summary The influence of the distance between the solvent entry and the starting zone positions, Z₀, on the average plate height was studied. This factor was found to be of major importance and could be optimized. The Guiochon and Siouffi model greatly underestimates the plate height for low values of Z₀.