Exhaustive Proteolysis of Derivatives of Lysozyme

The specificity of pepsin action towards a protein substrate, 8-carboxymethyllysozyme, was investigated. The complex mixture of peptides obtained after 16hr of digestion was fractionated by high voltage paper electrophoresis. The peptide fragments were identified by a computer program and were fitted along the lysozyme sequence. The peptide bonds attacked were generally those that originate from at least one hydrophobic amino acid side chain, though some bonds involving alanine, threonine, and serine, were also found to be split.     

A Thienothiophene-Based Cation Treatment Allows Semitransparent Perovskite Solar Cells with Improved Efficiency and Stability

Perovskite surface treatment with additives has been reported to improve charge extraction, stability, and/or surface passivation. In this study, treatment of a 3D perovskite ((FAPbI₃)_1−x(MAPbBr₃)_x) layer with a thienothiophene-based organic cation (TTMAI), synthesized in this work, is investigated. Detailed analyses reveal that a 2D (n = 1) or quasi-2D layer does not form on the PbI₂-rich surface 3D perovskite. TTMAI-treated 3D perovskite solar cells (PSCs) fabricated in this study show improved fill factors, providing an increase in their power conversion efficiencies (PCEs) from 17% to over 20%. It is demonstrated that the enhancement is due to better hole extraction by drift-diffusion simulations. Furthermore, thanks to the hydrophobic nature of the TTMAI, PSC maintains 82% of its initial PCE under 15% humidity for over 380 h (the reference retains 38%). Additionally, semitransparent cells are demonstrated reaching 17.9% PCE with treated 3D perovskite, which is one of the highest reported efficiencies for double cationic 3D perovskites. Moreover, the semitransparent 3D PSC (TTMAI-treated) maintains 87% of its initial efficiency for six weeks (>1000 h) when kept in the dark at room temperature. These results clearly show that this study fills a critical void in perovskite research where highly efficient and stable semitransparent perovskite solar cells are scarce.     

RECURRENT NEURAL NETWORKS AND FINITE AUTOMATA

This article studies finite size networks that consist of interconnections of synchronously evolving processors. Each processor updates its state by applying an activation function to a linear combination of the previous states of all units. We prove that any function for which the left and right limits exist and are different can be applied to the neurons to yield a network which is at least as strong computationally as a finite automaton. We conclude that if this is the power required, one may choose any of the aforementioned neurons, according to the hardware available or the learning software preferred for the particular application.     

Upgrading automation for nuclear fuel in-core management: From the symbolic generation of configurations, to the neural adaptation of heuristics

FUELCON is an expert system in nuclear engineering. Its task is optimized refueling-design, which is crucial to keep down operation costs at a plant. FUELCON proposes sets of alternative configurations of fuel-allocation; the fuel is positioned in a grid representing the core of a reactor. The practitioner of in-core fuel management uses FUELCON to generate a reasonably good configuration for the situation at hand. The domain expert, on the other hand, resorts to the system to test heuristics and discover new ones, for the task described above. Expert use involves a manual phase of revising the ruleset, based on performance during previous iterations in the same session. This paper is concerned with a new phase: the design of a neural component to carry out the revision automatically. Such an automated revision considers previous performance of the system and uses it for adaptation and learning better rules. The neural component is based on a particular schema for a symbolic to recurrent-analogue bridge, called NIPPL, and on the reinforcement learning of neural networks for the adaptation.     

Silicon grating-based mirror for 1.3-µm polarized beams: MATLAB-aided design

A dielectric lamellar-grating layer-substrate structure is proposed to be capable, under some conditions, of acting as a 100% efficiency mirror when operated at fixed wavelengths and incidence angles. The design of such mirrors for 1.3 μm and near normal incidence is achieved with silicon as the grating-layer material and glass substrates of two types. The study is based on a new matrix-vector procedure for the solution of rigorous coupled-wave equations. The computations use MATLAB, and, in particular, its goal-attainment routine. Design parameter tolerances are also discussed.     

Improving learner quality of experience by content adaptation based on network conditions

Apart from user characteristics, properties of the network over which the content is delivered and device on which the content is displayed affect end-user perceived quality. This paper presents a learner quality of experience (QoE) model that apart from the user-related content adaptation, considers delivery performance-based content personalisation in order to improve user experience when interacting with an online learning system.A comparison-based study on the benefit of using the proposed learner QoE model in adaptive and personalized education was conducted involving the original AHA! and QoEAHA – a version of AHA! enhanced with the learner QoE model. Testing results demonstrate significant benefits in terms of learning achievement, learning performance, learner navigation and user QoE in favour of the learner QoE model-enhanced solution.     

Neural and Super-Turing Computing

``Neural computing' is a research field based on perceiving the human brain as an information system. This system reads its input continuously via the different senses, encodes data into various biophysical variables such as membrane potentials or neural firing rates, stores information using different kinds of memories (e.g., short-term memory, long-term memory, associative memory), performs some operations called ``computation', and outputs onto various channels, including motor control commands, decisions, thoughts, and feelings. We show a natural model of neural computing that gives rise to hyper-computation. Rigorous mathematical analysis is applied, explicating our model's exact computational power and how it changes with the change of parameters. Our analog neural network allows for supra-Turing power while keeping track of computational constraints, and thus embeds a possible answer to the superiority of the biological intelligence within the framework of classical computer science. We further propose it as standard in the field of analog computation, functioning in a role similar to that of the universal Turing machine in digital computation. In particular an analog of the Church-Turing thesis of digital computation is stated where the neural network takes place of the Turing machine.     

Generalization of children's preferences as a function of reinforcement and task similarity.

"An experiment was designed to study children's preferences for a series of problem-solving tasks as a function of the particular reinforcement condition associated with a training task, and the degree of similarity of each task to the training task… negative reinforcement produced in some S's approach gradients and in others avoidance gradients. The meaning of the results was discussed and related to motivation theory. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Carrier-based PWM-VSI overmodulation strategies: analysis,comparison, and design

In this paper, the overmodulation region voltage-gain characteristics and waveform quality of carrier-based pulsewidth-modulated (PWM) methods are investigated. Through detailed analytical study, voltage-gain characteristics are extracted independent of carrier frequency. The influence of blanking time and minimum pulsewidth (MPW) control on the inverter gain characteristics are studied and shown to be significant. A comparative evaluation of the modulator characteristics reveals the advantageous high-modulation-range characteristics of discontinuous PWM methods and, in particular, the superior overmodulation performance of a discontinuous PWM method. The modulation methods under consideration are tested on a PWM voltage-source inverter (VSI)-fed induction motor drive in the laboratory, and the theoretical results are verified by experiments. Also, a gain linearization technique is presented and experimentally verified. The results of this study are useful in the design, performance prediction and development of high-performance overmodulation strategies for PWM-VSI drives