A topology optimization method based on the level set method incorporating a fictitious interface energy

This paper proposes a new topology optimization method, which can adjust the geometrical complexity of optimal configurations, using the level set method and incorporating a fictitious interface energy derived from the phase field method. First, a topology optimization problem is formulated based on the level set method, and the method of regularizing the optimization problem by introducing fictitious interface energy is explained. Next, the reaction–diffusion equation that updates the level set function is derived and an optimization algorithm is then constructed, which uses the finite element method to solve the equilibrium equations and the reaction–diffusion equation when updating the level set function. Finally, several optimum design examples are shown to confirm the validity and utility of the proposed topology optimization method.     

Robust topology optimization of phononic crystals with random field uncertainty

The uncertain spatial variation of material properties can remarkably affect the band gap characteristics of phononic crystals (PnCs). It is necessary to consider this issue when designing and manufacturing PnC materials/structures. This paper investigates a robust topology optimization method for designing the microstructures of PnCs by considering random‐field material properties. Herein, the spatial distribution of the material properties is first represented by a random field and then discretized into uncorrelated stochastic variables with the expansion optimal linear estimation method; stochastic band gap analysis is then conducted with polynomial chaos expansion. Furthermore, a robust topology optimization formulation of PnCs is proposed on the basis of the relative elemental density, where a weighted objective function handles the compromise of the mean value and standard deviation of the PnC band gap. The band gap response is analyzed, employing the finite element method for each sample of polynomial chaos expansion. In this context, the sensitivities of the stochastic band gap behaviors to the design variables are also derived. Numerical examples demonstrate that the proposed method can generate meaningful optimal topologies of PnCs with a relatively large width and less sensitive band gap. Additionally, the effects of the weight factors in the objective function and the variation coefficient of material properties are discussed.     

Use of the anion gap and intermittent hemodialysis following continuous hemodiafiltration in extremely high dose acute‐on‐chronic lithium poisoning: A case report

A 35‐year‐old woman intentionally took 40,000 mg of lithium carbonate, and she was transferred to our hospital with nausea, vomiting, and diarrhea. She was diagnosed as having bipolar disorder 10 years ago and was receiving oral lithium therapy. Blood test results on arrival were remarkable for a negative anion gap of ?2.1 and later, the serum lithium level turned out to be as high as 15.4 mEq/L. Intubation was required because of disrupted consciousness, and continuous hemodiafiltration (CHDF) was immediately started in the intensive care unit to obtain constant removal of lithium. After adding intermittent hemodialysis (IHD) twice during the daytime to accelerate the lithium clearance, CHDF became unnecessary on day 4, and she was extubated on day 6 with complete recovery of consciousness. Close monitoring of the patient data showed recovery of the decreased anion gap as indicator of the serum lithium level reduction. On day 36, she was discharged without any complication and sequela. The current case highlighted the effective use of CHDF between IHD sessions to prevent the rebound elevation of lithium and the role of the anion gap as a surrogate marker of serum lithium concentration during the treatment.     

Vacuum effects on fatigue crack growth in submicrometre‐thick freestanding copper films

To clarify vacuum effects on fatigue crack growth in freestanding metallic thin films, experiments were conducted on approximately 500‐nm‐thick copper films inside a field emission scanning electron microscope. Fatigue crack growth accompanied by intrusion/extrusion formation occurred in vacuum, and da/dN was smaller than in air in the middle‐ΔK region (ΔK ≈ 1.7‐3.1 MPam^1/2). Conversely, in the low‐ΔK region (ΔK ? 1.7 MPam^1/2), da/dN was larger in vacuum than in air. Further, fatigue crack growth in vacuum occurred below the fatigue threshold in air (ΔK_th,air). A nonpropagating crack after reaching ΔK_th,air continued to propagate in vacuum when the environment changed from air to vacuum. This indicates that fatigue crack growth resistance is smaller in vacuum than in air under the same effective driving force. The fatigue damage area near the crack paths in vacuum in the low‐ΔK region became wider, suggesting that the nucleation of fatigue damage was enhanced in vacuum.     

Microstructure, thermal properties and hardness of the CeMO3 (M=Al,Ga) synthesized by arc-melting method

Synthesis of CeMO₃ (M=Al,Ga) was attempted by the arc-melting method. In the case of M=Al, the arc-melted sample consisted typically of three regions CeAlO₃, CeO₂ and eutectic, mainly Al₂O₃. A similar texture was also observed for the case of M=Ga. Both CeAlO₃ and CeGaO₃ are crystallized in a tetragonal system (space group:P4/mmm). Lattice parameters of the tetragonal cell for CeAlO₃ and CeGaO₃ are a=0.37669(9) nm, c=0.37967(7) nm and a=0.3873(1) nm, c=0.3880(1) nm, respectively. According to thermal analysis, decomposition of 2CeMO₃+1/2O₂→2CeO₂+M₂O₃ occurs for CeAlO₃ and CeGaO₃ at about 1275°C and 833°C, respectively. The TG curve shows that the onset-temperature of oxidation for CeAlO₃ is at 1170°C and for CeGaO₃ is at 720°C. After TG-DTA measurement, both compounds are completely decomposed to starting materials. The value of the micro-Vickers hardness for the CeAlO₃ and CeGaO₃ is 9.0 (±0.2) GPa and 7.2 (±0.4) GPa, respectively.     

Learning from graph data by putting graphs on the lattice

Graph data have been of common practice in many application domains. However, it is very difficult to deal with graphs due to their intrinsic complex structure. In this paper, we propose to apply Formal Concept Analysis (FCA) to learning from graph data. We use subgraphs appearing in each of graph data as its attributes and construct a lattice based on FCA to organize subgraph attributes which are too numerous. For statistical learning purpose, we propose a similarity measure based on the concept lattice, taking into account the lattice structure explicitly. We prove that, the upper part of the lattice can provide a reliable and feasible way to compute the similarity between graphs. We also show that the similarity measure is rich enough to include some other measures as subparts. We apply the measure to a transductive learning algorithm for graph classification to prove its efficiency and effectiveness in practice. The high accuracy and low running time results confirm empirically the merit of the similarity measure based on the lattice.     

Theoretical conformational analysis of poly(val-pro-gly-gly) with cis peptide bond at val-pro portion

Summary Theoretical conformational analysis was carried out for Ac-(Val-Pro-Gly-Gly) ₆-NHMe with cis peptide bond at Val-Pro portion. A right-handed ^11.6-helix was found as the lowest-energy helical conformation for this polypeptide with cis peptide bond. The energy difference between ^11.6-helix and -helix, which is the lowest-energy helical conformation with trans peptide bond, is 3.08 kcal/mol per repeating unit(Val-Pro-Gly-Gly sequence). This value almost corresponds to the energy difference between the most stable cis and trans conformations of Ac-Val-Pro-Gly-Gly-NHMe (2.75 kcal/mol). Obtained results indicate that -helix is the most stable helical conformation of poly(Val-Pro-Gly-Gly) which is a model polypeptide of elastin, and also that relative stabilities of trans and cis conformations of polypeptide are essentially estimated by short-range interactions.     

Learning concepts and their unions from positive data with refinement operators

This paper is concerned with a sufficient condition under which a concept class is learnable in Gold’s classical model of identification in the limit from positive data. The standard principle of learning algorithms working under this model is called the MINL strategy, which is to conjecture a hypothesis representing a minimal concept among the ones consistent with the given positive data. The minimality of a concept is defined with respect to the set-inclusion relation – the strategy is semantics-based. On the other hand, refinement operators have been developed in the field of learning logic programs, where a learner constructs logic programs as hypotheses consistent with given logical formulae. Refinement operators have syntax-based definitions – they are defined based on inference rules in first-order logic. This paper investigates the relation between the MINL strategy and refinement operators in inductive inference. We first show that if a hypothesis space admits a refinement operator with certain properties, the concept class will be learnable by an algorithm based on the MINL strategy. We then present an additional condition that ensures the learnability of the class of unbounded finite unions of concepts. Furthermore, we show that under certain assumptions a learning algorithm runs in polynomial time.     

A design of generalized minimum-state-change FSSP algorithms and their implementations

The firing squad synchronization problem (FSSP) on cellular automata has been studied extensively for more than 50 years, and a rich variety of FSSP algorithms has been proposed. Here we consider the FSSP from a view point of state-change complexity that models the energy consumption of SRAM-type storage with which cellular automata might be built. In the present paper, we propose minimum-state-change generalized FSSP (GFSSP) algorithms for synchronizing any one-dimensional (1D) cellular automaton, where the initial synchronization operation is started by any cell in the array. First, we construct two minimum-time, minimum-state-change GFSSP implementations on finite state automata: one is based on Goto’s algorithm, known as the first minimum-time FSSP algorithm that was reconstructed again recently in Umeo et al. (A new reconstruction and the first implementation of Goto’s FSSP algorithm, 2017), and the other is based on Gerken’s (Diplomarbeit, Institut für Theoretische Informatik, Technische Universität Braunschweig, pp 1–50, 1987) one. These implementations are optimal not only in time but also in the state-change complexity. The implementations of the minimum-time GFSSP algorithms are the first ones having the minimum-state-change complexity. In addition, we also present a six-state 145-rule non-minimum-time, minimum-state-change GFSSP implementation. The implemented GFSSP algorithm is the smallest one, known at present, in number of states of the finite state automaton.