Characterization of crude lipoxygenase extract from green pea using a modified spectrophotometric method

In the spectrophotometric assay of lipoxygenase (LOX), the buffered linoleic acid solution used as the reaction medium is not optically clear enough at neutral or lower pH values, due to its limited solubility, to provide a precise, accurate and reproducible estimation of activity as the increase in absorbance at 234 nm. Therefore, an optically clear solution was obtained by formation of the Na-salt of unreacted linoleic acid before absorbance measurement. The modified method was then used to characterize crude LOX from green peas in terms of pH and temperature optima, thermal stability, and kinetic parameters. The optimum pH and temperature for the activity of LOX from green peas were determined to be 6.0 and 30 °C, respectively. LOX was found to be very stable at 60 °C, but much less stable at 65 °C or higher temperatures. The Michaelis constant (K_m) and maximum rate (V_max) for linoleic acid were calculated to be 1666 units per mg protein per min and 2.33 mM, respectively.     

The optimization of the surface roughness in the process of tangential turn-milling using genetic algorithm

Turn-milling is a relatively new process in manufacturing technology, where both the workpiece and the tool are given a rotary movement simultaneously. This paper presents an approach for optimization of cutting parameters at cylindrical workpieces leading to minimum surface roughness by using genetic algorithm in the tangential turn-milling process. During testing, the effects of the cutting parameters on the surface roughness were investigated. Additionally, by using genetic algorithms for each of the cutting parameters (depth of cut, workpiece speed, tool speed and feed rate) minimum surface roughness for the process of tangential turn-milling was determined according to the cutting parameters.     

Laser conservation of art

Focusing a laser on the dirt covering a precious work of art may seem like a dangerous thing to do, but this unexpected technique has found a variety of cleaning applications. Analogies from other fields of materials science can provide guidance for its use, and model experiments ensure it doesn't all end in disaster.     

CAIMAN: An online algorithm repository for Cancer Image Analysis

CAIMAN (CAncer IMage ANalysis: http://www.caiman.org.uk) is an online algorithm repository that provides specifically designed algorithms to analyse the images produced by experiments relevant to Cancer Research and Life Sciences, especially vascular biology. CAIMAN is accessed through a user-friendly website where researchers can upload their images and the results are returned by email. CAIMAN does not intend to replace more sophisticated software solutions such as ImageJ, Matlab, or commercial packages, but it will provide a first stop where any researcher can upload images and can obtain quantitative results without having to do any programming at all.     

Finding optimum route of electrical energy transmission line using multi-criteria with Q-learning

Due to an increasing energy requirement the consideration of route determination is becoming important. The aim of this project is to find an optimum result considering its important criteria. Finding an optimum route is a complex problem. It does not mean the shortest path to the problem. It is important to find the best way under the criterion that is determined by experts. Because of this we did not use the classical shortest path algorithm and we applied one of algorithms of the Artificial Intelligence. In this work, Geographic Information System (GIS)-based energy transmission route planning had been performed. In this optimization, using Multiagent Systems (MAS) which is a subdirectory of Distributed Artificial Intelligence the multi-criteria affecting energy transmission line (ETL) had been severally analyzed. The application had been actualized on the Selcuk University Campus Area. Therefore, the digital map of the campus area particularly had been composed containing of relevant criteria. Using Q- learning Algorithm of Multiagent System the optimum route had been determined.     

Experimental investigation of cutting parameters in machining of 100Cr6 with tangential turn-milling method

The turn-milling methods for machining operation have been developed to increase efficiency of conventional machines recently. These methods are used especially by coupling some apparatuses on the computer numerical control (CNC) machine to decrease the production time and machine costs, ensure the maximum production and increase the quality of machining. In this study, 100Cr6 bearing steel extensively used in industry has been machined by tangential turn-milling method. This paper presents an approach for optimization of the effects of the cutting parameters including cutter speed, workpiece speed, axial feed rate, and depth of cut on the surface roughness in the machining of 100Cr6 steel with tangential turn-milling method by using genetic algorithm (GA). Tangential turning-milling method has been determined to have optimum effects of cutting parameters on the machining of 100Cr6 steel. The experimental results show that the surface roughness quality is close to that of grinding process.     

汽车防抱死制动系统MK70性能检测分析

论述了汽车防抱死制动系统的概念,分析了ABS的工作原理,并通过对具体的一种车型ABS的性能进行检测、分析,得出了该车型ABS能够达到预定的要求,并起到作为汽车安全附加装置的目的。     

Improvement of grinding performance and investigation of cutting parameters using a grinding mechanism with secondary rotational axis

“Grinding Mechanism having Advanced Secondary Rotational Axis” (GMASRA) is one of the newer plane surface grinding methods that have an uncommon abrasion mechanism. Unlike conventional methods, in GMASRA, there are two rotations of a wheel. The first rotation is the same as in conventional grinding methods, which is the circumferential rotation. The other rotation is the newly developed axial rotation, where the wheel rotates around itself perpendicular to its radial axis. In this study, the effects of certain cutting parameters on arithmetical mean deviation of the assessed profile (the R_a parameter) were investigated. Particularly, the effects of cutting parameters on R_a in the GMASRA grinding process were examined. The selected cutting parameters were the depth of cut, the number of axial revolutions of the wheel, and the stepover distance of the wheel. Five wheels with different properties were chosen. Additionally, GMASRA was modeled using the Taguchi orthogonal test design. In this orthogonal design, the depth of cut, the spindle speed, and the type of grinding wheel were chosen as the control factors. The effect of the specified control factors on the surface roughness was demonstrated using an analysis of variance (ANOVA) test. Results show that GMASRA produced better R_a values than the conventional method. R_a values were very close to each other in every part of the ground workpieces. According to the modeling results, the spindle speed had the highest effect on R_a, followed by the depth of cut and the type of grinding wheel. GMASRA is also very cost effective and can be adapted to most milling machines and CNC milling machines.     

Analysis of different approaches to cross-dock truck scheduling with truck arrival time uncertainty

This paper studies scheduling of inbound trucks at the inbound doors of a cross-dock facility under truck arrival time uncertainty. Arrival time of an inbound truck is considered to be unknown. In particular, the cross-dock operator only acknowledges the arrival time window of each truck, i.e., the lower and upper bounds of any inbound truck’s arrival time. In absence of any additional information, the cross-dock operator may use three approaches to determine a scheduling strategy: deterministic approach (which assumes expected truck arrival times are equal to their mid-arrival time windows), pessimistic approach (which assumes the worst truck arrivals will be realized), and optimistic approach (which assumes the best truck arrivals will be realized). In this paper, a bi-level optimization problem is formulated for pessimistic and optimistic approaches. We discuss a Genetic Algorithm (GA) to solve the truck-to-door assignments for given truck arrival times, which solves the deterministic approach. Then the GA is modified to solve the bi-level formulations of the pessimistic and the optimistic approaches. Our numerical studies show that an hybrid approach regarding the pessimistic and the optimistic approaches may outperform all of the three approaches in certain cases.     

Formal verification of a pervasive messaging system

As ubiquitous computing becomes a reality, its applications are increasingly being used in business-critical, mission-critical and even in safety-critical, areas. Such systems must demonstrate an assured level of correctness. One approach to the exhaustive analysis of the behaviour of systems is formal verification, whereby each important requirement is logically assessed against all possible system behaviours. While formal verification is often used in safety analysis, it has rarely been used in the analysis of deployed pervasive applications. Without such formality it is difficult to establish that the system will exhibit the correct behaviours in response to its inputs and environment. In this paper, we show how model-checking techniques can be applied to analyse the probabilistic behaviour of pervasive systems. As a case study we apply this technique to an existing pervasive message-forwarding system, Scatterbox. Scatterbox incorporates many typical characteristics of pervasive systems, such as dependence on sensor reliability and dependence on context. We assess the dynamic temporal behaviour of the system, including the analysis of probabilistic elements, allowing us to verify formal requirements even in the presence of uncertainty in sensors. We also draw some tentative conclusions concerning the use of formal verification for pervasive computing in general.