Determination of volterra transfer functions of non-linear multiport networks

A method is presented to determine frequency domain Volterra transfer functions of non-linear multiport networks containing non-linear multiport subsystems (elements). the method is based on an extension of the probing method to allow arbitrary (also commensurate) frequencies. A recursively based algorithm for the determination of Volterra transfer functions of non-linear multiport networks is derived. A computer implementation of the method in a symbolic programming language is presented. This makes it possible to determine algebraic expressions of the Volterra transfer functions. Three examples are considered.     

Lipase-catalyzed esterification of lactic acid with straight-chain alcohols

Enzymatic synthesis of esters of lactic acid and straight-chain alcohols with different chain lengths (C₆–C₁₈) were investigated in batch reactions with hexadecanol (C₁₆) as the model alcohol. Cyclohexane was the best solvent for higher ester yields, and the best biocatalyst was the immobilized Candida antarctica lipase B (Novozym 435) as well as the textile-immobilized Candida sp. lipase. A method was established to obtain ester yields in the range of 71 to 82% for the different alcohols, and the most favorable conditions for the esterification reaction using Novozym 435 were an equimolar ratio of lactic acid to alcohol, each at a concentration of 120 mM each; a 50°C reaction temperature; 190 rpm shaking speed; and the addition of 100 mg molecular sieves (4 Å) for drying. The ester yield increased with increasing lipase load, and a yield of 79.2% could be obtained after 24 h of reaction at 20 wt% of Novozym 435. The immobilized Candida sp. lipase prepared in the laboratory also could be used to produce esters of lactic acid and straight-chain alcohols, but it had a much lower activity than Novozym 435 with a temperature optimum of 40°C.     

Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In-Sensor Neuromorphic Computation

Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra-thin (<3 nm) doped indium oxide (In₂O₃) layers are engineered to demonstrate a monolithic two-terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof-of-concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real-time, in-sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand.     

Linear-Time Self-Interpretation of the Pure Lambda Calculus

We show that linear-time self-interpretation of the pure untyped lambda calculus is possible, in the sense that interpretation has a constant overhead compared to direct execution under various execution models. The present paper shows this result for reduction to weak head normal form under call-by-name, call-by-value and call-by-need.We use a self-interpreter based on previous work on self-interpretation and partial evaluation of the pure untyped lambda calculus.We use operational semantics to define each reduction strategy. For each of these we show a simulation lemma that states that each inference step in the evaluation of a term by the operational semantics is simulated by a sequence of steps in evaluation of the self-interpreter applied to the term (using the same operational semantics).By assigning costs to the inference rules in the operational semantics, we can compare the cost of normal evaluation and self-interpretation. Three different cost-measures are used: number of beta-reductions, cost of a substitution-based implementation (similar to graph reduction) and cost of an environment-based implementation.For call-by-need we use a non-deterministic semantics, which simplifies the proof considerably.     

Casting traceability with direct part marking using reconfigurable pin-type tooling based on paraffin–graphite actuators

Green sand moulding machines for cast iron foundries are presently unable to uniquely identify individual castings. An insert tool concept is developed and tested via incremental mock-up development. The tool is part of the pattern plate and changes shape between each moulding, thus giving each mould a unique ID by embossing a Data Matrix symbol into the sand. In the process of producing the mould, each casting can be given a unique (DPM), enabling part tracking throughout the casting's life cycle. Sand embossing is achieved with paraffin-actuated reconfigurable pin-type tooling under simulated processing conditions. The marker geometry limitations have been tested using static symbol patterns, both for sand embossing and actual casting marking. The marked castings have successfully been identified with decoding software. The study shows that the function of each element of this technology can be successfully applied within the foundry industry.     

Augmented semantic segmentation for the digitization of grinding tools based on deep learning

In order to analyze various process characteristics, grinding simulations can be used, which need accurate models of the tool and the individual grains. For this purpose, grinding tools can be digitized. To identify characteristic grains from a large number of measurements, each individual grain has to be analyzed and separated from the bond manually. Therefore, a deep learning-based methodology was developed to achieve a high segmentation accuracy of the grain boundaries efficiently. Additionally, a data augmentation approach was investigated to limit the data necessary for learning. The model transferability was quantified by analyzing different states of tool wear.     

Raft crystals of poly(isoprene)-block-poly(ferrocenyldimethylsilane) and their surface wetting behavior during melting as observed by AFM and NanoTA

We report on the morphology evolution during heating and melting of lamellar poly(isoprene)-block-poly(ferrocenyldimethylsilane) (PI₇₆-b-PFDMS₇₆) raft crystals deposited at the native oxide surface of silicon (SiO₂) or at a highly ordered pyrolytic graphite (HOPG) surface, studied by in situ temperature controlled atomic force microscopy. Crystals deposited on hydrophilic SiO₂ surfaces revealed an irreversible decrease in length at temperatures of up to tens of degrees above their expected melting temperature, while maintaining their platelet-like structure. Crystals deposited on hydrophobic HOPG surfaces initially decreased in length below their expected melting temperature, while at 120 °C and above a typical molten morphology was observed. In addition, the irreversible formation of a PI₇₆-b-PFDMS₇₆ wetting layer around the crystals was observed upon increasing the temperature. These observations in the morphological behavior upon heating emphasize the role of interfacial energy between a surface deposited block copolymer based macromolecular nanostructure and its supporting substrate.     

Synthesis and thermal decomposition of potassium tetraamidoboranealuminate,K[Al(NH2BH3)4]

A new potassium tetraamidoboranealuminate, K[Al(NH₂BH₃)₄], has been synthesized by a mechanochemical reaction between KAlH₄ and NH₃BH₃. The compound, K[Al(NH₂BH₃)₄], crystallizes in a triclinic unit cell with space group symmetry P?1. The crystal structure consists of [K(NH₂BH₃)₆]^5? octahedra which facilitate the bridging between K⁺ in 1D chains, while also bridging K⁺ to Al³⁺ to connect the 1D chains in a 3D network. Thermal analysis reveals that K[Al(NH₂BH₃)₄] decomposes in two exothermic steps at T ～ 94 and 138 °C and releases primarily hydrogen. The total gas release amounts to ～6.0 wt% H₂. The decomposition products are investigated ex situ by powder X-ray diffraction, infrared spectroscopy, and ¹¹B and ²⁷Al NMR and identified as KBH₄ and amorphous phases, possibly BN₃, N₂BH, and/or NBH₂ whereas aluminum is found in four-, five-, and six-fold coordination. Unfortunately, the decomposed sample shows no hydrogen absorption at T = 260 °C and p(H₂) = 110 bar.