Flexible Interface Research Articles

Prism-SSD: A Flexible Storage Interface for SSDs

The rapid adoption of solid-state drives (SSDs) as a major storage component has been made possible thanks to their ability to export a standard block I/O interface to file system and application developers. Meanwhile, this high-level abstraction has been shown to limit the utilization of the devices and the performance of applications running on top of them. Indeed, many optimizations of performance-critical applications bypass the standard block interface and rely on low-level control over SSD internal processes. However, the need to directly manage the physical device significantly increases development complexity and cost, and reduces its portability. Thus, application developers must choose between two extreme options, either easy development or optimal performance, without a real possibility to balance between these two objectives. To bridge this gap, we propose a flexible storage interface that exports the SSD hardware in three levels of abstraction: as a raw flash media with its low-level details, as a group of functions to manage flash capacity, or as a configurable block device. This multi-level abstraction allows developers to choose the degree in which they desire to control the flash hardware in a manner that best suits the applications’ semantics and performance objectives. We demonstrate the usability of this new model with Prism-SSD—a prototype of this interface as a user-level library on the Open-Channel SSD platform. We use each of the interface’s three abstraction levels to modify the I/O module of three representative applications: a key-value cache system, a user-level file system, and a graph processing engine. Prism-SSD improves application performance by 5% to 27%, at varying development costs, between 200 and 3,500 lines of code.

Printed Stretchable Liquid Metal Electrode Arrays for In Vivo Neural Recording.

The adoption of neural interfacing into neurological diagnosis is severely hampered by the complex, costly, and error-prone manufacturing methods, requiring new fabrication processes and materials for flexible neural interfacing. Here a strategy for fabricating highly stretchable neural electrode arrays based on screen printing of liquid metal conductors onto polydimethylsiloxane substrates is presented. The screen-printed electrode arrays show a resolution of 50µm, which is ideally applicable to neural interfaces. The integration of liquid metal-polymer conductor enables the neural electrode arrays to retain stable electrical properties and compliant mechanical performance under a significant (≈108%) strain. Taking advantage of its high biocompatibility, liquid metal electrode arrays exhibit excellent performance for neurite growth and long-term implantation. The stretchable electrode arrays can spontaneously conformally come in touch with the brain surface, and high-throughput electrocorticogram signals are recorded. Based on stretchable electrode arrays, real-time monitoring of epileptiform activities can be provided at different states of seizure. The method reported here offers a new fabrication strategy to manufacture stretchable neural electrodes, with additional potential utility in diagnostic brain-machine interfaces.

Spinney: Post-processing of first-principles calculations of point defects in semiconductors with Python

Understanding and predicting the thermodynamic properties of point defects in semiconductors and insulators would greatly aid in the design of novel materials and allow tuning the properties of existing ones. As a matter of fact, first-principles calculations based on density functional theory (DFT) and the supercell approach have become a standard tool for the study of point defects in solids. However, in the dilute limit, of most interest for the design of novel semiconductor materials, the “raw“ DFT calculations require an extensive post-processing. Spinney is an open-source Python package developed with the aim of processing first-principles calculations to obtain several quantities of interest, such as the chemical potential limits that assure the thermodynamic stability of the defect-laden system, defect charge transition levels, defect formation energies, including electrostatic corrections for finite-size effects, and defect and carrier concentrations. In this paper we demonstrate the capabilities of the Spinney code using c-BN, GaN:Mg, TiO2 and ZnO as examples. Program summaryProgram Title:SpinneyCPC Library link to program files:https://doi.org/10.17632/2xp4ddwmgx.1Developer’s repository link:https://gitlab.com/Marrigoni/spinneyCode Ocean capsule:https://codeocean.com/capsule/4970623Licensing provisions: MITProgramming language: Python 3External libraries: NumPy [1], SciPy [2], Pandas [3], Matplotlib [4], ASE [5]Nature of problem: Post-processing of first-principles calculations in order to obtain important properties of defect laden systems in the dilute-limit: chemical potential values ensuring thermodynamic stability, thermodynamic charge transition levels, defect formation energies and corrections thereof using state-of-the-art corrections schemes for electrostatic finite-size effects, equilibrium defect and carriers concentrations.Solution method: Flexible low-level interface for allowing the post-processing of the raw fist-principles data provided by any computer code. High-level interface for parsing and post-processing the first-principles data produced by the popular computer codes VASP and WIEN2k.Additional comments including restrictions and unusual features: An extensive documentation is available at: https://spinney.readthedocs.io

Transformative Electronics: Design Strategy for Transformative Electronic System toward Rapid, Bidirectional Stiffness Tuning using Graphene and Flexible Thermoelectric Device Interfaces (Adv. Mater. 10/2021)

In article number 2007239, Byung Jin Cho, Jae-Woong Jeong, and co-workers present a design strategy for transformative electronics capable of rapid bidirectional stiffness tuning. Transformative electronics are built on a gallium platform interfaced with graphene and a flexible thermoelectric device (f-TED). The graphene works as a catalyst to mitigate the supercooling phenomenon in the liquid-to-solid transition of gallium, and the f-TED offers active temperature control to help accelerate the rigid–soft transformation of the overall system.

An automated approach to determine antibody endpoint titers for COVID-19 by an enzyme-linked immunosorbent assay.

Abstract While a variety of therapeutic options continue to emerge for COVID-19 treatment, convalescent plasma (CP) has been used as a possible treatment option early in the pandemic. One of the most significant challenges with CP therapy, however, both when defining its efficacy and implementing its approach clinically, is accurately and efficiently characterizing an otherwise heterogenous therapeutic treatment. Given current limitations, our goal is to leverage a SARS antibody testing platform with a newly developed automated endpoint titer analysis program to rapidly define SARS-CoV-2 antibody levels in CP donors and hospitalized patients. A newly developed antibody detection platform was used to perform a serial dilution enzyme-linked immunosorbent assay (ELISA) for immunoglobulin (Ig)G, IgM, and IgA SARS-CoV-2 antibodies. Data were then analyzed using commercially available software, GraphPad Prism, or a newly developed program developed in Python called TiterScape, to analyze endpoint titers. Endpoint titer calculations and analysis times were then compared between the two analysis approaches. Serial dilution analysis of SARS-CoV-2 antibody levels revealed a high level of heterogeneity between individuals. Commercial platform analysis required significant time for manual data input and extrapolated endpoint titer values when the last serial dilution was above the endpoint cutoff, occasionally producing erroneously high results. By contrast, TiterScape processed 1008 samples for endpoint titer results in roughly 14 minutes compared with the 8 hours required for the commercial software program analysis. Equally important, results generated by TiterScape and Prism were highly similar, with differences averaging 1.26 ± 0.2 percent (mean ± SD). The pandemic has created unprecedented challenges when seeking to accurately test large numbers of individuals for SARS-CoV-2 antibody levels with a rapid turnaround time. ELISA platforms capable of serial dilution analysis coupled with a highly flexible software interface may provide a useful tool when seeking to define endpoint titers in a high-throughput manner. Immunohematology 2021;37:33–43.

Optimization of vehicular ADHOC network performance using cloud computing model over mobility model

Vehicular ad hoc networks (VANET) is more dynamic in nature and rapidly changing the topology structure as the vehicles are always on the move. Due to this nature of Vehicular ad hoc networks, Security is a major concern for the network resources. Traffic jam detection and safest route finding is a prior goal for the Vehicular ad hoc networks protocol design. Objective of this paper is to provide an Effective Data Communication Model for Vehicular ad hoc networks with Cloud Computing. Processing Units of Vehicles are provided with a flexible interface system with Cloud servers for communicating between the vehicles. This model assures to give low cost, fast computing and extended storage for the nodes without requiring any additional infrastructures with in the network. The services include data aggregation, resource management and privacy. The results obtained from the simulation are tabulated and compared with the existing mobility model. The graph clearly shows the improvement in the performance in terms of reduced packet loss compared with the existing approach. Thus the Cloud based Vehicular ad hoc networks model out performs the Mobility model by certain level of performance attainment.

The Interrelation Between Peripersonal Action Space and Interpersonal Social Space: Psychophysiological Evidence and Clinical Implications.

The peripersonal space is an adaptive and flexible interface between the body and the environment that fulfills a dual-motor function: preparing the body for voluntary object-oriented actions to interact with incentive stimuli and preparing the body for defensive responses when facing potentially harmful stimuli. In this position article, we provide arguments for the sensorimotor rooting of the peripersonal space representation and highlight the variables that contribute to its flexible and adaptive characteristics. We also demonstrate that peripersonal space represents a mediation zone between the body and the environment contributing to not only the control of goal-directed actions but also the organization of social life. The whole of the data presented and discussed led us to the proposal of a new theoretical framework linking the peripersonal action space and the interpersonal social space and we highlight how this theoretical framework can account for social behaviors in populations with socio-emotional deficits.

Flexible thermal interface based on self-assembled boron arsenide for high-performance thermal management

Thermal management is the most critical technology challenge for modern electronics. Recent key materials innovation focuses on developing advanced thermal interface of electronic packaging for achieving efficient heat dissipation. Here, for the first time we report a record-high performance thermal interface beyond the current state of the art, based on self-assembled manufacturing of cubic boron arsenide (s-BAs). The s-BAs exhibits highly desirable characteristics of high thermal conductivity up to 21 W/m·K and excellent elastic compliance similar to that of soft biological tissues down to 100 kPa through the rational design of BAs microcrystals in polymer composite. In addition, the s-BAs demonstrates high flexibility and preserves the high conductivity over at least 500 bending cycles, opening up new application opportunities for flexible thermal cooling. Moreover, we demonstrated device integration with power LEDs and measured a superior cooling performance of s-BAs beyond the current state of the art, by up to 45 °C reduction in the hot spot temperature. Together, this study demonstrates scalable manufacturing of a new generation of energy-efficient and flexible thermal interface that holds great promise for advanced thermal management of future integrated circuits and emerging applications such as wearable electronics and soft robotics.

Design Strategy for Transformative Electronic System toward Rapid, Bidirectional Stiffness Tuning using Graphene and Flexible Thermoelectric Device Interfaces.

Electronics with tunable shape and stiffness can be applied in broad range of applications because their tunability allows their use in either rigid handheld form or soft wearable form, depending on needs. Previous research has enabled such reconfigurable electronics by integrating a thermally tunable gallium-based platform with flexible/stretchable electronics. However, supercooling phenomenon caused in the freezing process of gallium impedes reliable and rapid bidirectional rigid-soft conversion, limiting the full potential of this type of "transformative" electronics. Here, materials and electronics design strategies are reported to develop a transformative system with a gallium platform capable of fast reversible mechanical switching. In this electronic system, graphene is used as a catalyst to accelerate the heterogeneous nucleation of gallium to mitigate the degree of supercooling. Additionally, a flexible thermoelectric device is integrated as a means to provide active temperature control to further reduce the time for the solid-liquid transition of gallium. Analytical and experimental results establish the fundamentals for the design and optimized operation of transformative electronics for accelerated bidirectional transformation. Proof-of-concept demonstration of a reconfigurable system, which can convert between rigid handheld electronics and a flexible wearable biosensor, demonstrates the potential of this design approach for highly versatile electronics that can support multiple applications.

A Light-Driven Vibrotactile Actuator with a Polymer Bimorph Film for Localized Haptic Rendering.

A vibrotactile actuator driven by light energy is developed to produce dynamic stimulations for haptic rendering on a thin-film structure. The actuator is constructed by adopting a thermal bimorph membrane structure of poly(3,4-ethylenedioxythiophene) doped with p-toluenesulfonate (PEDOT-Tos) coated onto a polyethylene terephthalate (PET) film. Upon irradiation of near-infrared (NIR) light, the light energy absorbed at the PEDOT-Tos layer is converted into thermoelastic bending deformation due to the mismatch in coefficient of thermal expansion between PEDOT-Tos and PET. Since the light-induced deformation is reversible, spatially localized, and rapidly controllable with designed light signals, the proposed actuator can produce vibrotactile stimulation over 10 dB at arbitrary areas in the human-sensitive frequency range from 125 to 300 Hz using a low input power of ∼2.6 mW mm-2, as compared with a complex electrical circuit and high input power needed to achieve such actuation performance. Together with its simple structure based on light-driven actuation, the advent of this actuator could open up new ways to achieve substantial advances in rendering textures at a flexible touch interface.

Diamonds are not forever: liveness in reactive programming with guarded recursion

When designing languages for functional reactive programming (FRP) the main challenge is to provide the user with a simple, flexible interface for writing programs on a high level of abstraction while ensuring that all programs can be implemented efficiently in a low-level language. To meet this challenge, a new family of modal FRP languages has been proposed, in which variants of Nakano's guarded fixed point operator are used for writing recursive programs guaranteeing properties such as causality and productivity. As an apparent extension to this it has also been suggested to use Linear Temporal Logic (LTL) as a language for reactive programming through the Curry-Howard isomorphism, allowing properties such as termination, liveness and fairness to be encoded in types. However, these two ideas are in conflict with each other, since the fixed point operator introduces non-termination into the inductive types that are supposed to provide termination guarantees. In this paper we show that by regarding the modal time step operator of LTL a submodality of the one used for guarded recursion (rather than equating them), one can obtain a modal type system capable of expressing liveness properties while retaining the power of the guarded fixed point operator. We introduce the language Lively RaTT, a modal FRP language with a guarded fixed point operator and an `until' type constructor as in LTL, and show how to program with events and fair streams. Using a step-indexed Kripke logical relation we prove operational properties of Lively RaTT including productivity and causality as well as the termination and liveness properties expected of types from LTL. Finally, we prove that the type system of Lively RaTT guarantees the absence of implicit space leaks.

Graph-driven Federated Data Management

Modern data analysis applications, require the ability to provide on-demand integration of data sources while offering a flexible and user-friendly query interface. Traditional techniques for answering queries using views, focused on a rather static setting, fail to address such requirements. To overcome these issues, we propose a fully-fledged data integration approach based on graph-based constructs. The extensibility of graphs allows us to extend the traditional framework for data integration with view definitions. Furthermore, we also propose a query language based on subgraphs. We tackle query answering via a query rewriting algorithm based on well-known algorithms for answering queries using views. We experimentally show that the proposed method yields good performance and does not introduce a significant overhead.

Channel Modeling and Over-the-Air Signal Quality at 3.5 GHz for 5G New Radio

With the next generation of mobile communication being trialled across the world, 5G New Radio (NR) promises to provide a flexible radio interface that fits a diverse range of use cases. As trials and pilots progress, propagation studies are required that evaluate electromagnetic (EM) propagation effects for 5G NR signals. In this article, 5G NR signals are used to evaluate the over-the-air (OTA) error vector magnitude (EVM) within a line-of-sight (LoS) rural/urban environment. For the same receiver locations, time dispersion and propagation loss were measured via the root mean square (RMS) delay spread and path loss. The transmitter−receiver distance investigated ranged from $\mathrm {60~m}$ to $\mathrm {450~m}$ . From the measurement campaign, the path loss exponent (PLE) using a directional antenna at the receiver was 1.98, and 1.82 with an omnidirectional antenna. The root mean square error (RMSE) of the floating intercept/close-in (FI/CI) models for the path loss was 4.96/4.74 and 3.84/3.23 for directional and omnidirectional receivers. The delay spread using a cross polar configuration showed significant increase and a dependency of the delay spread on the path loss was observed. For the measurement route, the mean delay spread was $\mathrm {24~ns}$ and $\mathrm {35~ns}$ for directional and omnidirectional measurements. With regards to the EVM, 16 and 64-QAM transmissions were robust along the entire route for directional and omnidirectional antennas, whereas 256-QAM worked in locations where there was minimal obstruction to the propagation path. The average EVM (%) for 16, 64 and 256-QAM measured along the route was 4.5/5.3/3.9 and 3.4/4.3/3.6 for omnidirectional and directional antennas. The OTA results also show that using a directional antenna (as the receiver) significantly improves the obtainable signal quality of 5G NR signals and also reduces outages for higher modulation schemes. With the measurement results presented, system designers can design efficient receivers, estimate coverage and adequately provision services using 5G NR on the sub-6 GHz frequency band.

Approach Allowing Design Adaptive View Models

The problem of setting interface settings for global software development processes is actual now. Successful solving of this problem can be source many benefits in view models, other words interface representation designing. Thus, in turn, allow to increase amount of web-site visitors and web applications users. Other advantage is to allow different developers to understand interface well and design application using n-tired architectural style. One of the important task is to archive dynamic localization for switching interface languages. Paper represents an approach of view models designing in context of dynamic localization for desktop application using WPF technology. Paper contains the review of existing frameworks for flexible interface designing supporting localization process. Approach touches some mathematical foundation of interface components representation, recommendation for framework designing, touching design patterns. Paper contains some practical examples of preparation interface using different languages.

Phosphorus-decorated Mo-MXene/CQD hybrid: a 2D/0D architecture for bifunctional electrochemical water splitting.

The exploration of nonprecious metal-based 2D bifunctional electrocatalysts is of great significance for transforming to sustainable energies in terms of hydrogen. However, to achieve commendable electrocatalytic performance via rational design of surface-interface-engineered Mo-MXene hybrids remain challenging and highly demanding. Herein, we report large size exfoliated Mo-MXene sheets, which provide a flat flexible interface for decoration with carbon quantum dots (CQDs) and controlled surface phosphorization (denoted as Mo-MX/C/P hybrid). The resulting Mo-MX/C/P hybrid exhibited the lowest onset potentials of 14 and 58 mV at an applied current of 0.2 mA cm-2 for the HER and OER, respectively. Strikingly, the electronegative nature of phosphorous (P) and quick charge transfer between the CQDs and Mo2CTx matrix were responsible for its superior catalytic activities. Despite the superior performance, the Mo-MX/C/P hybrid can also be used for full-cell division of water with a cell voltage of 1.34 volts at 10 mA cm-2 and was found to be durable up to 12. This work provides a novel insight into the further development of surface-interface-engineered Mo-MXene hybrids for sustainable energy.

Exploiting Parallelism Opportunities with Deep Learning Frameworks

State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers. Identifying and using a performance-optimal setting in feature-rich frameworks, however, involves a non-trivial amount of performance profiling efforts and often relies on domain-specific knowledge. This article takes a deep dive into analyzing the performance impact of key design features in a machine learning framework and quantifies the role of parallelism. The observations and insights distill into a simple set of guidelines that one can use to achieve much higher training and inference speedup. Across a diverse set of real-world deep learning models, the evaluation results show that the proposed performance tuning guidelines outperform the Intel and TensorFlow recommended settings by 1.30× and 1.38×, respectively.

High-Capacity Cardiac Signal Acquisition System for Flexible, Simultaneous, Multidomain Acquisition.

Capturing cardiac electrical propagation or electrocardiographic images demands simultaneous, multidomain recordings of electrocardiographic signals with adequate spatial and temporal resolution. Available systems can be cost-prohibitive or lack the necessary flexibility to capture signals from the heart and torso. We have designed and constructed a system that leverages affordable commercial products (Intantech, CA, USA) to create a complete, cardiac signal acquisition system that includes a flexible front end, analog signal conditioning, and defibrillation protection. The design specifications for this project were to (1) record up to 1024 channels simultaneously at a minimum of 1 kHz, (2) capture signals within the range of ± 30 mV with a resolution of 1 μV, and (3) provide a flexible interface for custom electrode inputs.We integrated the Intantech A/D conversion circuits to create a novel system, which meets all the required specifications. The system connects to a standard laptop computer under control of open-source software (Intantech). To test the system, we recorded electrograms from within the myocardium, on the heart surface, and on the body surface simultaneously from a porcine experimental preparation. Noise levels were comparable to both our existing, custom acquisition system and a commercial competitor. The cost per channel was $32 USD, totaling $33,800 USD for a complete system.

Flexible user interface for machine learning techniques to enhance the complex geospatial hydro-climatic models with future perspective

Hydro-climatic (HC) models have complex environments due to the integration of hydrological processes and climate indices for the assessment of historical and future scenarios. The approximation of HC models leads to a major uncertainty in the selection of optimal methods for processing, enhancement, and assessment. The present work developed a User-Friendly Interface (UI) in the R programming platform to enhance the geospatial HC models using machine learning concepts. Here, UI complies with various technologies together to perform consistently with input control, processing, and visualization. To validate this interface, a snow-dominated alpine watershed was selected. The results showed that, (a) UI assisted to downscale of the future climatic data into finer resolution, (b) boosted the efficiency of the geospatial model by adaptive random forest regression with NSE = 0.92 and 0.84, respectively. Moreover, UI designed to apply for different geospatial optimization problems which assist academicians, scientists, decision-makers, planners, and stakeholders, etc.

Modelling the health and economic impacts of different testing and tracing strategies for COVID-19 in the UK

Background: Coronavirus disease 2019 (COVID-19) is resurgent in the UK and health and economic costs of the epidemic continue to rise. There is a need to understand the health and economic costs of different courses of action. Methods: We combine modelling, economic analysis and a user-friendly interface to contrast the impact and costs of different testing strategies: two levels of testing within the current test-trace-isolate (TTI) strategy (testing symptomatic people, tracing and isolating everyone) and a strategy where TTI is combined with universal testing (UT; i.e. additional population testing to identify asymptomatic cases). We also model effective coverage of face masks. Results: Increased testing is necessary to suppress the virus after lockdown. Partial reopening accompanied by scaled-up TTI (at 50% test and trace levels), full isolation and moderately effective coverage of masks (30% reduction in overall transmission) can reduce the current resurgence of the virus and protect the economy in the UK. Additional UT from December 2020 reduces the epidemic dramatically by Jan 2021 when combined with enhanced TTI (70% test-trace levels) and full isolation. UT could then be stopped; continued TTI would prevent rapid recurrence. This TTI+UT combination can suppress the virus further to save ~20,000 more lives and avoid ~£90bn economic losses, though costs ~£8bn more to deliver. We assume that all traced and lab-confirmed cases are isolated. The flexible interface we have developed allows exploration of additional scenarios, including different levels of reopening of society after the second lockdown in England as well as different levels of effective mask coverage. Conclusions: Our findings suggest that increased TTI is necessary to suppress the virus and protect the economy after the second lockdown in England. Additional UT from December 2020 reduces the epidemic dramatically by Jan 2021 and could then be stopped, as continued TTI would prevent rapid recurrence.

ArborX

Searching for geometric objects that are close in space is a fundamental component of many applications. The performance of search algorithms comes to the forefront as the size of a problem increases both in terms of total object count as well as in the total number of search queries performed. Scientific applications requiring modern leadership-class supercomputers also pose an additional requirement of performance portability, i.e., being able to efficiently utilize a variety of hardware architectures. In this article, we introduce a new open-source C++ search library, ArborX, which we have designed for modern supercomputing architectures. We examine scalable search algorithms with a focus on performance, including a highly efficient parallel bounding volume hierarchy implementation, and propose a flexible interface making it easy to integrate with existing applications. We demonstrate the performance portability of ArborX on multi-core CPUs and GPUs and compare it to the state-of-the-art libraries such as Boost.Geometry.Index and nanoflann.