Lewis Acid Catalyzed Additions to Unsaturated Fatty Compounds. II: Alkylaluminium Halide Catalyzed Ene Reactions of Unsaturated Fatty Compounds and Formaldehyde

We are presenting our results on the alkylaluminium halide catalyzed ene addition of formaldehyde to readily available unsaturated fatty compounds to give primary homoallylic alcohols. The reaction of oleic acid and 10-undecenoic acid with formaldehyde gives (E)-9(10)-(hydroxymethyl)octadec-10(8)-enoic acid and 12-hydroxydodec-9-enoic acid, respectively, in high yields. Formaldehyde can be added also to oleyl alcohol to give the very interesting diol (E)-9(10)-(hydroxymethyl)oxtadec-10(8)-en-l-ol. Me₂AlCl and EtAl-sesquichloride are the most suitable catalysts for these reactions. The addition of formaldehyde to methyl oleate and methyl 10-undecenoate, respectively, to give the corresponding ene products is catalyzed by EtAlCl₂.     

On the errors that learning machines will make

Associated with each learning system there is a class of learnable behaviors. If the target behavior to be acquired is in the learnable class, it will be learned perfectly. If it is outside that class, the machine will only be able to acquire a behavior that approximates the target and it will always make errors. It is desirable for a learning machine to have a large learnable class to maximize the chances of acquiring the unknown behavior and to minimize the expected error when only an approximation is possible. However, it is also desirable to have a small learnable class so that learning can be achieved rapidly. Thus the design of learning machines involves selecting a position on the spectrum: minimum error and slow learning time versus larger error and faster learning time. A computational method is given for finding where a given learning machine is on this spectrum. Machines that have fast learning times, relatively small learnable classes, and thus relatively large expected errors are called realization sparse in this article. These machines do little better than a random coin flipping algorithm in many situations. It is shown that many common learning systems are of this type including signature tables, linear system models, and conjunctive normal form expression based systems. These studies lead to the concept of an “optimum” machine which spreads its learnable behaviors across the behavior space in a manner to minimize the expected error. an approximation to such optimum machines is presented and its behavior is compared to the more traditional learning machines. © 1994 John Wiley & Sons, Inc.     

Reinforcement Learning with Immediate Rewards and Linear Hypotheses

We consider the design and analysis of algorithms that learn from the consequences of their actions with the goal of maximizing their cumulative reward, when the consequence of a given action is felt immediately, and a linear function, which is unknown a priori, (approximately) relates a feature vector for each action/state pair to the (expected) associated reward. We focus on two cases, one in which a continuous-valued reward is (approximately) given by applying the unknown linear function, and another in which the probability of receiving the larger of binary-valued rewards is obtained. For these cases we provide bounds on the per-trial regret for our algorithms that go to zero as the number of trials approaches infinity. We also provide lower bounds that show that the rate of convergence is nearly optimal.     

Characterization of stress–strain behavior of a cast TRIP steel under different biaxial planar load ratios

This paper investigates the stress–strain curves of different load paths and the initial yield surface of a metastable austenitic stainless cast steel under biaxial planar loading using cruciform specimens. These tests were carried out on a 250 kN biaxial servohydraulic tension-compression testing machine. The laborious stress determination was undertaken with a new testing method by using global elastic unloading to measure the local stiffness. Isotropic yielding and mainly isotropic strain hardening were found. To evaluate the microstructure, the martensite formation was detected by use of a ferrite sensor and scanning electron microscopy and electron backscatter diffraction were applied.     

Achieving Small Structures in Thin NiTi Sheets for Medical Applications with Water Jet and Micro Machining: A Comparison

NiTi shape memory alloys (SMA) are used for a variety of applications including medical implants and tools as well as actuators, making use of their unique properties. However, due to the hardness and strength, in combination with the high elasticity of the material, the machining of components can be challenging. The most common machining techniques used today are laser cutting and electrical discharge machining (EDM). In this study, we report on the machining of small structures into binary NiTi sheets, applying alternative processing methods being well-established for other metallic materials. Our results indicate that water jet machining and micro milling can be used to machine delicate structures, even in very thin NiTi sheets. Further work is required to optimize the cut quality and the machining speed in order to increase the cost-effectiveness and to make both methods more competitive.     

Improving Cooperative Transmission Feasibility by Network Reconfiguration in Limited Backhaul Networks

For coordinated multi-point (CoMP), sets of base stations (BSs) have to be selected to jointly serve user equipments (UEs). These sets are typically selected based on wireless characteristics only. However, using CoMP also poses strict capacity and latency requirements on the backhaul network. Hence, these requirements additionally need to be taken into account when selecting BSs for CoMP. We have developed a mixed integer linear program and a BSs selection heuristic for CoMP that takes into account both aspects: the wireless channels and the backhaul network status. This heuristic can also identify which bottlenecks in the backhaul network make a desired BSs selection infeasible. We exploit this to dynamically reconfigure the backhaul network to the wireless requirements. Our simulations show that the heuristic’s solution quality is close to the optimum while execution time and memory consumption are reduced by multiple orders of magnitude compared to solving the problem via mathematical optimization. In addition, we simulate the network reconfiguration in different backhaul network scenarios. The results illustrate how our approach helps to better exploit available backhaul resources.     

Simulation of grinding processes using finite element analysis and geometric simulation of individual grains

The wear-resistance of sheet metal forming tools can be increased by thermally sprayed coatings. However, without further treatment, the high roughness of the coatings leads to poor qualities of the deep drawn sheet surfaces. In order to increase the surface quality of deep drawing tools, grinding on machining centers is a suitable solution. Due to the varying engagement situations of the grinding tools on free-formed surfaces, the process forces vary as well, resulting in inaccuracies of the ground surface shape. The grinding process can be optimized by means of a simulative prediction of the occurring forces. In this paper, a geometric-kinematic simulation coupled with a finite element analysis is presented. Considering the influence of individual grains, an additional approximation to the resulting topography of the ground surface is possible. By using constructive solid geometry and dexel modeling techniques, multiple grains can be simulated with the geometric-kinematic approach simultaneously. The process forces are predicted with the finite element method based on an elasto-plastic material model. Single grain engagement experiments were conducted to validate the simulation results.     

Sweetness concentration-response behavior of rebiana at room and refrigerator temperatures

Rebiana is a zero-calorie, natural, high-potency sweetener derived from Stevia rebaudiana Bertoni and comprising almost pure rebaudioside A. Reliable information on its sweetness concentration-response (C-R) behavior is fundamental to rebiana's use as an ingredient. The response curve of rebiana in room-temperature (21 °C) and refrigerated (5 °C) water was determined using 2-alternative forced choice discrimination tests with a minimum of 70 tasters. From a series of panels the proportion of tasters finding different sucrose concentrations sweeter than a fixed concentration of rebiana was plotted against sucrose concentration. The resultant sigmoid curves were linearized by transforming the ordinate axis to a probability scale. This aided experimental design and determination of isosweet concentrations. The latter were deemed to be the sucrose concentration at which 50% of tasters found it to be the sweeter of the pair. Isosweet concentrations of sucrose for seven rebiana concentrations up to 600 mg/L were used to construct a C-R curve for each temperature. Equations were derived for the resultant hyperbolic curves. Rebiana is significantly more potent in cold water. PRACTICAL APPLICATION: Rebiana is a new, zero-calorie, natural, high-potency sweetener derived from the Stevia plant. We have measured the sweetness of rebiana over a range of concentrations at room and refrigerator temperatures. This information will help developers of low-calorie products get the right sweetness level when replacing sugar with rebiana.     

Optimization of guide pads for the BTA deep hole drilling of high alloyed steels by microfinishing

The boring and trepanning association deep hole drilling of materials with a high tendency to adhesion, such as high alloyed-steels, is characterized by a poor surface quality of the bore hole. Material particles adhere to the guide pads that are positioned on the circumference of the drill head and that are normally responsible for the outstanding workpiece quality. In order to prevent this mechanism the guide pads were coated with an innovative amorphous and tetrahedral bonded (ta-C)-coating. This coating has a reduced friction coefficient of 0.1 against steel and a hardness coefficient of about 7,000 HV. To use the benefits of this ta-C-coating the pre- and the after-treatment of the uncoated carbide substrate and ta-C-coated guide pads are essential. For these process steps a microfinishing process was carried out as an alternative to the conventional treatment by polishing and brushing. The microfinishing of the uncoated guide pads effects a smooth surface that is necessary for an optimum bonding strength of the ta-C-coating at the carbide substrate. Furthermore the chamfer edge in the lead-in-area is rounded which reduces the mechanical load at this specific area during the process. The finishing process of the coated guide pads reduces the coating defects and improves the friction coefficient. Thus, the wear behavior of the guide pads is improved because of the better friction conditions during the drilling process of high alloyed steels.