Flexible Interface Research Articles

ACE-Molecule: An open-source real-space quantum chemistry package.

ACE-Molecule (advanced computational engine for molecules) is a real-space quantum chemistry package for both periodic and non-periodic systems. ACE-Molecule adopts a uniform real-space numerical grid supported by the Lagrange-sinc functions. ACE-Molecule provides density functional theory (DFT) as a basic feature. ACE-Molecule is specialized in efficient hybrid DFT and wave-function theory calculations based on Kohn-Sham orbitals obtained from a strictly localized exact exchange potential. It is open-source oriented calculations with a flexible and convenient development interface. Thus, ACE-Molecule can be improved by actively adopting new features from other open-source projects and offers a useful platform for potential developers and users. In this work, we introduce overall features, including theoretical backgrounds and numerical examples implemented in ACE-Molecule.

Buckling analysis of layered composite beams with interlayer slip: A force-based finite element formulation

A force-based finite element formulation for the buckling analysis of two-layer composite beam structures with partial interaction is presented. The geometrically nonlinear beam elements possess a single flexible shear interface and each layer is modelled by means of Timoshenko’s theory. The formulation relies on a hybrid variational principle of complementary energy only involving force/moment-like variables as fundamental unknown fields. The approximate field variables are selected such that all equilibrium differential equations are satisfied in strong form. The inter-element equilibrium, as well as Neumann boundary conditions are enforced by means of the Lagrangian-multiplier method. The accuracy and effectiveness of the proposed formulation is demonstrated through the analysis of several numerical tests.

Phase-sensitive optical neural recording of cerebellum tissue on a flexible interface

Knowing an increased number of patients suffering from mental disorders, neural signal recording and imaging have become highly prerequisite challenges for providing healing procedures. Despite the fact that novel optical techniques provide highly resolved imaging/recording of large neuron population, most of them suffer from insertion damage, tethering connection, labeling, and photobleaching deficiencies, among which plasmonic ellipsometry is a highly sensitive and label-free platform for detecting neural activity both quantitatively and qualitatively. In this paper, a flexible patterned plasmonic substrate is used as a sensing surface for phase-sensitive neural recording of a cerebellum tissue slice under electrical and chemical stimulations. Although the traditional reflection spectrum cannot represent the changes in neural activity with high precision, phase-sensitive neuroplasmonics can not only reveal the neural activity level but also distinguish different electrical and chemical stimulation types with a considerable phase splitting factor. This study can open up a new insight into label-free and flexible biological sensors with neuroscience applications.

MPI-RCDD: A Framework for MPI Runtime Communication Deadlock Detection

The message passing interface (MPI) has become a de facto standard for programming models of high-performance computing, but its rich and flexible interface semantics makes the program easy to generate communication deadlock, which seriously affects the usability of the system. However, the existing detection tools for MPI communication deadlock are not scalable enough to adapt to the continuous expansion of system scale. In this context, we propose a framework for MPI runtime communication deadlock detection, namely MPI-RCDD, which contains three kinds of main mechanisms. Firstly, MPI-RCDD has a message logging protocol that is associated with deadlock detection to ensure that the communication messages required for deadlock analysis are not lost. Secondly, it uses the asynchronous processing thread provided by the MPI to implement the transfer of dependencies between processes, so that multiple processes can participate in deadlock detection simultaneously, thus alleviating the performance bottleneck problem of centralized analysis. In addition, it uses an AND⊕OR model based algorithm named AODA to perform deadlock analysis work. The AODA algorithm combines the advantages of both timeout-based and dependency-based deadlock analysis approaches, and allows the processes in the timeout state to search for a deadlock circle or knot in the process of dependency transfer. Further, the AODA algorithm cannot lead to false positives and can represent the source of the deadlock accurately. The experimental results on typical MPI communication deadlock benchmarks such as Umpire Test Suit demonstrate the capability of MPI-RCDD. Additionally, the experiments on the NPB benchmarks obtain the satisfying performance cost, which show that the MPI-RCDD has strong scalability.

Flexible Interface Design for Stress Regulation of a Silicon Anode toward Highly Stable Dual-Ion Batteries.

Dual-ion batteries (DIBs) have attracted increasing attention due to their high working voltage, low cost, and environmental friendliness, yet their development is hindered by their limited energy density. Pairing silicon-a most promising anode due to its highest theoretical capacity (4200 mAh g-1 )-with a graphite cathode is a feasible strategy to address the challenge. Nevertheless, the cycling stability of silicon is unsatisfactory due to the loss of electrical contact resulting from the high interface stress when using rigid current collectors. In this work, a flexible interface design to regulate the stress distribution is proposed, via the construction of a silicon anode on a soft nylon fabric modified with a conductive Cu-Ni transition layer, which endows the silicon electrode with remarkable flexibility and stability over 50 000 bends. Assembly of the flexible silicon anode with an expanded graphite cathode yields a silicon-graphite DIB (SGDIB), which possesses record-breaking rate performance (up to 150 C) and cycling stability over 2000 cycles at 10 C with a capacity retention of 97%. Moreover, the SGDIB shows a high capacity retention of ≈84% after 1500 bends and a low self-discharging voltage loss of 0.0015% per bend after 10 000 bends, suggesting high potential for high-performance flexible energy-storage applications.

Protein Docking Refinement with Systematic Conformational Search - Application to Models Inside the Docking Funnel

Structural refinement of modeled protein-protein complexes is an essential step in protein docking. Such refinement becomes increasingly challenging when the interacting proteins undergo significant conformational changes upon binding (unbound/bound interface RMSD>3 Å). We developed a flexible interface refinement protocol and benchmarked it on a set of top 100 docking models, which are inside the docking funnel (C-alpha ligand interface RMSD ≤ 10 Å [1]), generated for binary protein-protein complexes from the Dockground X-ray unbound set 4.0 (http://dockground.compbio.ku.edu). The refinement of the docking models was performed by a systematic local conformational search using previously developed Atom-Atom Contact Energy (AACE18) [2]. The average numbers of models of acceptable or better quality (according to CAPRI criteria [3]) in top-10 refined structures were 4.2, 3.0, and 1.7, for easy (unbound/bound interface RMSD ≤ 1.5 Å), medium (1.5 - 2.2 Å) and difficult (> 2.2 Å) docking targets, respectively. The results showed that the procedure performs better than other widely used refined protocols benchmarked on the same set of protein-protein complexes. 1. Hunjan et al. Proteins, 2008, 72: 344-352. 2. Anishchenko et al. Biophys. J., 2018, 115: 809-821. 3. Mendez et al. Proteins, 2003, 52: 51-67.

A 2020 perspective on “Optimistic overconfidence in electronic reverse auctions: A research commentary on behavioral OR methods”

Abstract This paper revisits the paper “Optimistic overconfidence in electronic reverse auctions” published in this journal in 2019 and discusses its contributions and possible impact. The paper established a connection between individual characteristics of decision makers (bidders in auctions), and biases such as overconfidence. One conclusion that can be drawn from its results is that online systems such as electronic auction systems should provide a flexible user interface that allows to adapt the form in which information is presented to the cognitive style of users.

Mathematics Innovative Gamification for At-Risk Students

Many researches emphasize the importance of developing students’ ability to solve problems related to number or algebraic equations. As at-risk students’ understandings of the algebraic concepts are in need of guidance, this study intends to innovative gamification for at-risk students to solve number equations through the use of Flexi-EQ Solver. In this quantitative study, a randomly selected 161 at-risk students answered the test questions. From the test answers, students’ difficulties in learning mathematics concepts were analyzed. On the other hand, another 56 randomly selected at-risk students answered a questionnaire. From the test analysis, the students face major difficulties in factorization on algebraic expression, linear equations in one variable, transformation of formula and solving logarithmic equations. An innovative gamification framework embedded in Flexi-EQ Solver was then proposed for atrisk students. Flexi-EQ Solver which also called as Flexible-Equations Solver is a flexible interface featuring step-by-step exploration solutions for exponential, logarithmic, square and square root, inequality, decimal, percentage, fraction and trigonometric equations. Flexi-EQ Solver helps at-risk students to visualize number equations by seeing, touching, hands-on and manipulating it. On the average, the respondents agreed on the use of Flexi-EQ Solver for learning mathematics. With respect to the usage of Flexi-EQ Solver, 57.1% of the respondents highly recommended the use of Flexi-EQ Solver. Majority of at-risk students proposed the incorporation of visual learning theory and multiple representations as an innovative gamification framework in learning mathematical skills.

An organosynthetic dynamic heart model with enhanced biomimicry guided by cardiac diffusion tensor imaging.

The complex motion of the beating heart is accomplished by the spatial arrangement of contracting cardiomyocytes with varying orientation across the transmural layers, which is difficult to imitate in organic or synthetic models. High-fidelity testing of intracardiac devices requires anthropomorphic, dynamic cardiac models that represent this complex motion while maintaining the intricate anatomical structures inside the heart. In this work, we introduce a biorobotic hybrid heart that preserves organic intracardiac structures and mimics cardiac motion by replicating the cardiac myofiber architecture of the left ventricle. The heart model is composed of organic endocardial tissue from a preserved explanted heart with intact intracardiac structures and an active synthetic myocardium that drives the motion of the heart. Inspired by the helical ventricular myocardial band theory, we used diffusion tensor magnetic resonance imaging and tractography of an unraveled organic myocardial band to guide the design of individual soft robotic actuators in a synthetic myocardial band. The active soft tissue mimic was adhered to the organic endocardial tissue in a helical fashion using a custom-designed adhesive to form a flexible, conformable, and watertight organosynthetic interface. The resulting biorobotic hybrid heart simulates the contractile motion of the native heart, compared with in vivo and in silico heart models. In summary, we demonstrate a unique approach fabricating a biomimetic heart model with faithful representation of cardiac motion and endocardial tissue anatomy. These innovations represent important advances toward the unmet need for a high-fidelity in vitro cardiac simulator for preclinical testing of intracardiac devices.

A water resource simulator in Python

A new generalised water resource network modelling Python library, Pywr, is presented. Given hydrological inflows, Pywr simulates customisable water allocation and operation rules throughout complex multi-purpose managed water systems at each user-defined time-step. The model uses a low-level interface to existing linear programming solvers for fast priority-based optimisation-driven simulation. The library uses an object based system for users to provide input data and record simulation outputs. A novel multi-scenario simulation method provides an almost 4-fold improvement in model run-times and supports calculating robustness metrics across scenarios. A flexible interface to specify multi-objective optimisation formulations as part of a model’s input file is included. These features enable analysts to apply advanced water planning approaches, such as robust decision making and robust optimisation, to real systems. The library is available under the GPLv3 open source licence, includes several examples and a regression test suite.

Behaviour and mechanism of fatigue crack growth in aramid-fibre-reinforced styrene–butadiene rubber composites

Aramid fibres (AF)/styrene–butadiene rubber (SBR) filled with carbon black (CB) exhibit different fatigue behaviours with changes in fatigue strain. A mechanism for the effect of the fibres attracting fatigue cracks in the process of crack growth was proposed. The fibre effect parameter, α, was calculated from the power law relationship between the crack growth rate and the tearing energy, to evaluate the comprehensive effect of the AFs on fatigue crack growth rate. Furthermore, the effect of the attraction of fatigue cracks by fibres with a good bonded flexible interface layer was more prominent, and the fatigue crack propagation could be effectively suppressed.

Flexible and stretchable opto-electric neural interface for low-noise electrocorticogram recordings and neuromodulation in vivo

Optogenetic-based neuromodulation tools is evolving for the basic neuroscience research in animals combining optical manipulation and electrophysiological recordings. However, current opto-electric integrated devices attaching on cerebral cortex for electrocorticogram (ECoG) still exist potential damage risks for both brain tissue and electrode, due to the mechanical mismatch and brain deformation. Here, we propose a stretchable opto-electric integrated neural interface by integrating serpentine-shaped electrodes and multisite micro-LEDs onto a hyperelastic substrate, as well as a serpentine-shaped metal shielding embedded in recording electrode for low-noise signal acquisition. The delicate structure design, ultrasoft encapsulation and independent fabrication followed by assembly are beneficial to the conformality, reliability and yield. In vitro accelerated deterioration and reciprocating tensile have demonstrated good performance and high stability. In vivo optogenetic activation of focal cortical areas of awaked mouse expressing Channelrhodopsin-2 is realized with simultaneous high-quality recording. We highlight the potential use of this multifunctional neural interface for neural applications.

Flexible, Self-Adaptive Sense-and-Compress SoC for Sub-microWatt Always-On Sensory Recording

Miniaturized sensory systems for IoT applications experience a severe power burden from their wireless link and/or embedded storage system. Compressive sensing techniques target data compression before storage and transmission to save power, while minimizing information loss. This work proposes a self-adaptive sense-and-compress system, which consumes only 45-884n W while continuously recording and compressing signals with a bandwidth up to 5kHz. The flexible system uses a combination of off-line Evolutionary Algorithms, and on-line self-adaptivity to constantly adapt to the incoming sensory data statistics, and the current application quality requirements. The 0.27mm2 sense-and-compress interface is integrated in a 65nm CMOS technology, together with an on-board temperature sensor, or can interface with any external sensor. The scalable, self-adaptive system is moreover heavily optimized for low-power and low-leakage, resulting in a tiny, efficient, yet flexible interface allowing always-on sensory monitoring, while consuming 2.5X less power compared to the current State-of-the-Art.

Movable-Type Transfer and Stacking of van der Waals Heterostructures for Spintronics

The key to achieving high-quality van der Waals heterostructure devices made from various two-dimensional (2D) materials lies in the control over clean and flexible interfaces. However, existing transfer methods based on different mediators possess insufficiencies including the presence of residues, the unavailability of flexible interface engineering, and the selectivity towards materials and substrates since their adhesions differ considerably with the various preparation conditions, from chemical vapor deposition (CVD) growth to mechanical exfoliation. In this paper, we introduce a more universal method using a prefabricated polyvinyl alcohol (PVA) film to transfer and stack 2D materials, whether they are prepared by CVD or exfoliation. This peel-off and drop-off technique promises an ideal interface of the materials without introducing contamination. In addition, the method exhibits a micron-scale spatial transfer accuracy and meets special experimental conditions such as the preparation of twisted graphene and the 2D/metal heterostructure construction. We illustrate the superiority of this method with a WSe2 vertical spin valve device, whose performance verifies the applicability and advantages of such a method for spintronics. Our PVA-assisted transfer process will promote the development of high-performance 2D-material-based devices.

Capacitor Voltage Ripple Minimization of a Modular Three-Phase AC/DC Power Electronics Transformer With Four-Winding Power Channel

Power electronic transformer (PET) with modular cascaded structure has become a flexible interface between AC and DC grids. Nonetheless, the notable 2nd-order ripple power introduced from the AC source requires considerable energy storage in the module capacitors, which is likely to degrade the overall power density and increase system cost. In this paper, a power channel based four-winding PET (PC-PET) is proposed to address such an issue. Owing to the phase-crossing power channels, the low-frequency ripples can cancel each other through magnetic flux counteracting. To improve the accuracy of power decoupling, the effect compromised by inconsistent windings is considered. Accordingly, a proportional-resonant (PR) control based power decoupling strategy for PC-PET incorporated with power balance is proposed to decouple the three-phase low-frequency ripple powers, and minimize the voltage ripple in module capacitors. Additionally, the improvement of power density is discussed based on analyses on power losses, capacitor volume and transformer volume. Simulation and experimental results verified the validity of proposed control strategies.

Integrated querying and version control of context-specific biological networks.

MotivationBiomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks.ResultsWe present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases.ConclusionOur results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications.Availability and Implementation VerTIoN is implemented as a C++ library and is available at http://compbio.case.edu/omics/software/vertion and https://github.com/tjcowman/vertionContacttyler.cowman@case.edu

Development and Stability Analysis of an Imitation Learning-Based Pose Planning Approach for Multi-Section Continuum Robot

Recently, continuum flexible robots have been designed for the use in diverse applications; including the exploration of confined static and dynamic environments. One of the challenging tasks for those robots is planning optimal trajectories due to, not only the redundant Degrees of Freedom (DOF) they own but also their compliant behaviour. In this paper, an Imitation-based Pose Planning (IbPP) approach is proposed to teach a two-section continuum robot the motion primitives that will facilitate achieving and generalizing for spatial point-to-point motion which involves both position and orientation goals encoded in a dual quaternion form. Two novel approaches are proposed in this research to intuitively generate the motion demonstrations that will be used in the proposed IbPP. Namely, a flexible input interface, acting as a twin robot, is designed to allow a human to demonstrate different motions for the robot end-effector. Alternatively, as a second approach, the Microsoft Kinect sensor is used to provide motion demonstrations faster via human arm movements. Based on the kinematic model of the two-section continuum robot, a Model Reference Adaptive Control (MRAC) algorithm is developed to achieve tracking the generated trajectory from the IbPP and to guarantee the robustness against the model uncertainties and external disturbances. Moreover, controller stability analysis is developed based on Lyapunov criteria. Finally, simulations are conducted for the two-section continuum robot to prove the ability of the proposed IbPP with the two proposed inputs to learn and generalize for spatial motions, which in future could be easily accommodated for robots with multiple sections. In addition, the proposed MRAC shows a significant performance towards following the required trajectory and rejecting the external disturbance.

Development of a Remote Industrial Laboratory for Automatic Control based on Node-RED

In this paper, we propose a remote laboratory for automatic control that enables an easier interconnection and integration of its elements. It is based on Node-RED and the MQTT protocol, which enable the easy development of laboratories and an intuitive but flexible user interface for the students. Node-RED is an open-source programming platform oriented to easily connect hardware devices, APIs and online services. The remote laboratory uses three different elements: an instructor’s client, one student’s client for each student and a central broker. The instructor’s client is the only one with direct access to the plant, through a Modbus TCP connection to the PLC, so it decides which student can manage the system and monitor their actions, hiding the complexity of automation to the students and providing an additional layer for safety and security. The students’ clients are installed in the laboratory or students’ own PCs. To perform the tasks, the students use a predefined dashboard or the Node-RED editor for easy graphical programming. In any case, the process is as follows: instructor’s client reads/writes variables of the plant, publishes their values using a multi-level hierarchical structure, subscribes to the control actions published by the students’ clients and manages how and when those actions are sent to the corresponding pilot plant. This approach has been assessed through the implementation of a hands-on task where students need to set the parameters of a PID to appropriately control the level of a tank in a real pilot plant.

Post-Silicon Microarchitecture

Microprocessors are designed to provide good general performance across a range of benchmarks. As such, microarchitectural techniques which provide good speedup for only a small subset of applications are not attractive when designing a general-purpose core. We propose coupling a reconfigurable fabric with the CPU, on the same chip, via a simple and flexible interface to allow post-silicon development of application-specific microarchitectures. The interface supports observation and intervention at key pipeline stages of the CPU, so that exotic microarchitecture designs (with potentially narrow applicability) can be synthesized in the reconfigurable fabric and seem like components that were hardened into the core.

PDEparams: parameter fitting toolbox for partial differential equations in python.

Partial differential equations (PDEs) is a well-established and powerful tool to simulate multi-cellular biological systems. However, available free tools for validation against data are on development. The PDEparams module provides a flexible interface and readily accommodates different parameter analysis tools in PDE models such as computation of likelihood profiles, and parametric bootstrapping, along with direct visualization of the results. To our knowledge, it is the first open, freely available tool for parameter fitting of PDE models. PDEparams is distributed under the MIT license. The source code, usage instructions and examples are freely available on GitHub at github.com/systemsmedicine/PDE_params. Supplementary data are available at Bioinformatics online.