Small Input Research Articles

Development, verification, and validation of comprehensive acoustic fluid‐structure interaction capabilities in an open‐source computational platform

AbstractThe acoustic fluid‐structure interaction (FSI) formulation is a practical numerical approach for the seismic analysis of fluid‐filled tanks. However, there are no verification and validation studies reported in the literature that demonstrate the ability of an acoustic FSI numerical model to predict responses important to structural and mechanical design for intense translational and rotational earthquake inputs. Herein, an acoustic FSI formulation is implemented in the open‐source Multiphysics Object‐Oriented Simulation Environment (MOOSE), and is formally verified and validated using analytical solutions and code‐to‐code verification, and experimental data, respectively. The analytical solutions are for small amplitude, unidirectional seismic inputs. The code‐to‐code verification utilizes a previously verified and validated Arbitrary Lagrangian‐Eulerian (ALE) numerical model in the commercial finite element code LS‐DYNA. The validation studies utilize a comprehensive data set assembled from results of 3D earthquake‐simulator tests of a fluid‐filled vessel. The acoustic numerical model in MOOSE is verified and validated for hydrodynamic pressures and support reactions except for cases that involve significant convective response. For small amplitude inputs, numerically predicted wave heights match those of the analytical solutions. The numerical model is not verified and validated for wave height calculations under intense 3D seismic inputs. The run times for the acoustic FSI simulations in MOOSE are an order of magnitude, or more, shorter than for the corresponding ALE simulations in LS‐DYNA. The utility of the MOOSE acoustic FSI implementation is demonstrated by seismic analysis of a building equipped with a fluid‐filled, advanced nuclear reactor.

Spontaneous dynamics of synaptic weights in stochastic models with pair-based spike-timing-dependent plasticity.

We investigate spike-timing dependent plasticity (STPD) in the case of a synapse connecting two neuronal cells. We develop a theoretical analysis of several STDP rules using Markovian theory. In this context there are two different timescales, fast neuronal activity and slower synaptic weight updates. Exploiting this timescale separation, we derive the long-time limits of a single synaptic weight subject to STDP. We show that the pairing model of presynaptic and postsynaptic spikes controls the synaptic weight dynamics for small external input on an excitatory synapse. This result implies in particular that mean-field analysis of plasticity may miss some important properties of STDP. Anti-Hebbian STDP favors the emergence of a stable synaptic weight. In the case of an inhibitory synapse the pairing schemes matter less, and we observe convergence of the synaptic weight to a nonnull value only for Hebbian STDP. We extensively study different asymptotic regimes for STDP rules, raising interesting questions for future work on adaptative neuronal networks and, more generally, on adaptative systems.

A Compact 60GHz Power Amplifier in 65nm CMOS Technology

A compact 60GHz power amplifier chip in 65nm CMOS technology of three-stage common source structure is presented. The first two amplifiers offer sufficient gain to pre-amplify the small input power. The third stage amplifier uses two sets of differential pairs to achieve power synthesis. On-chip transformer coupling is adopted to realize inter-stage impedance matching as well as input and output matching. The compact structure of on-chip transformer can reduce the chip size and improve the integration degree. The measured small signal gain at 60GHz of 22.3dB, the saturation power of 20.2dBm, the output P-1dB of 17.3dBm, the PAE of 14%, and the core area is 0.19mm2. The power amplifier can be used in high-speed short-range wireless communication system to enhance communication data transmission capacity.

Crowdsourced RNA design discovers diverse, reversible, efficient, self-contained molecular switches.

Internet-based scientific communities promise a means to apply distributed, diverse human intelligence toward previously intractable scientific problems. However, current implementations have not allowed communities to propose experiments to test all emerging hypotheses at scale or to modify hypotheses in response to experiments. We report high-throughput methods for molecular characterization of nucleic acids that enable the large-scale video game–based crowdsourcing of RNA sensor design, followed by high-throughput functional characterization. Iterative design testing of thousands of crowdsourced RNA sensor designs produced near–thermodynamically optimal and reversible RNA switches that act as self-contained molecular sensors and couple five distinct small molecule inputs to three distinct protein binding and fluorogenic outputs. This work suggests a paradigm for widely distributed experimental bioscience.

Cellular Computational Logic Using Toehold Switches.

The development of computational logic that carries programmable and predictable features is one of the key requirements for next-generation synthetic biological devices. Despite considerable progress, the construction of synthetic biological arithmetic logic units presents numerous challenges. In this paper, utilizing the unique advantages of RNA molecules in building complex logic circuits in the cellular environment, we demonstrate the RNA-only bitwise logical operation of XOR gates and basic arithmetic operations, including a half adder, a half subtractor, and a Feynman gate, in Escherichia coli. Specifically, de-novo-designed riboregulators, known as toehold switches, were concatenated to enhance the functionality of an OR gate, and a previously utilized antisense RNA strategy was further optimized to construct orthogonal NIMPLY gates. These optimized synthetic logic gates were able to be seamlessly integrated to achieve final arithmetic operations on small molecule inputs in cells. Toehold-switch-based ribocomputing devices may provide a fundamental basis for synthetic RNA-based arithmetic logic units or higher-order systems in cells.

Practical parallel self-testing of Bell states via magic rectangles

Self-testing is a method to verify that one has a particular quantum state from purely classical statistics. For practical applications, such as device-independent delegated verifiable quantum computation, it is crucial that one self-tests multiple Bell states in parallel while keeping the quantum capabilities required of one side to a minimum. In this work, we use the $3 \times n$ magic rectangle games (generalizations of the magic square game) to obtain a self-test for $n$ Bell states where the one side needs only to measure single-qubit Pauli observables. The protocol requires small input sizes [constant for Alice and $O(\log n)$ bits for Bob] and is robust with robustness $O(n^{5/2} \sqrt{\varepsilon})$, where $\varepsilon$ is the closeness of the ideal (perfect) correlations to those observed. To achieve the desired self-test, we introduce a one-side-local quantum strategy for the magic square game that wins with certainty, we generalize this strategy to the family of $3 \times n$ magic rectangle games, and we supplement these nonlocal games with extra check rounds (of single and pairs of observables).

Photoinduced Single-Crystal to Single-Crystal Transformation via Conformational Change with Turn-On Fluorescence

Single-crystal to single-crystal (SCSC) transformation by phototrigger is presently attracting immense research interest in many fields such as sensors, actuators, artificial muscles, soft robotics, and energy harvesting. However, the only success of rare examples has been achieved by SCSC transformation so far, involving a change in molecular composition based on photocyclization reactions. Here, we show photoinduced SCSC transformation of 3-methylene-2-(quinolin-8-yl) isoindolin-1-one (MQIO) derivatives from monoclinic to triclinic phase via conformational change, just as “small input and big output”. UV light induces a molecular conformation change of MQIO-H with rotation (11.3°) of the carbon–nitrogen single bond. The conformational changes of MQIO-H lead to the slides of molecular packing in crystals, finally resulting in the SCSC transformation from the monoclinic to the triclinic phase. In addition, the SCSC transformation was accompanied by turn-on fluorescence and shape changes of single crystal. The shape changes of single crystals can be clearly observed by UV light triggering under an optical microscope. This work provides a strategy to achieve SCSC transformation via photoinduction.

Miscanthus Sinensis is as Efficient as Miscanthus × Giganteus for Nitrogen Recycling in spite of Smaller Nitrogen Fluxes

Nitrogen (N) recycling is a key mechanism to ensure the sustainability of miscanthus production with no or small fertiliser inputs, but little is known on the subject in miscanthus species other than the most cultivated Miscanthus × giganteus. This field experiment on Miscanthus × giganteus and Miscanthus sinensis quantified plant biomass and N stock dynamics during two years. Endogenous net N fluxes, calculated from the evolution of plant N content throughout time, were higher in Miscanthus × giganteus than in Miscanthus sinensis. Indeed, 79 kg N ha−1 and 105 to 197 kg N ha−1 were remobilised during spring and autumn, respectively, for Miscanthus × giganteus, as opposed to 13 to 25 kg N ha−1 and 46 to 128 kg N ha−1 for Miscanthus sinensis. However, N recycling efficiency, defined as the ratio between N remobilisation fluxes and the maximum above-ground N content, did not differ significantly between the two species. N recycling efficiency ranged from 8 to 27% for spring remobilisation and from 63 to 74% and 24 to 38% for autumn remobilisation calculated on above-ground and below-ground N, respectively. Exogenous N, the main source of N to constitute maximum plant N content for all genotypes, was provided by fertilisation (22 to 24%) and organic matter mineralisation or other sources (43 to 59%). During winter, 42 to 56% of plant N content was lost. Only a small part of these plant N losses was due to abscised leaves (6–12% of the maximum plant N content). Our results show that Miscanthus sinensis is as efficient as Miscanthus × giganteus in N recycling and N use efficiency and as performant as other perennial species.

Multiplexed droplet single-cell sequencing (Mux-Seq) of normal and transplant kidney

Maintenance of systemic homeostasis by kidney requires the coordinated response of diverse cell types. The use of single-cell RNA sequencing (scRNAseq) for patient tissue samples remains fraught with difficulties with cell isolation, purity, and experimental bias. The ability to characterize immune and parenchymal cells during transplant rejection will be invaluable in defining transplant pathology where tissue availability is restricted to needle biopsy fragments. Herein, we present feasibility data for multiplexing approach for droplet scRNAseq (Mux-Seq). Mux-Seq has the potential to minimize experimental batch bias and variation even with very small sample input. In this first proof-of-concept study for this approach, explant tissues from six normal and two transplant recipients after multiple early post-transplant rejection episodes leading to nephrectomy due to aggressive antibody mediated rejection, were pooled for Mux-Seq. A computational tool, Demuxlet was applied for demultiplexing the individual cells from the pooled experiment. Each sample was also applied individually in a single microfluidic run (singleplex) to correlate results with the pooled data from the same sample. Our applied protocol demonstrated that data from Mux-Seq correlated highly with singleplex (Pearson coefficient 0.982) sequencing results, with the ability to identify many known and novel kidney cell types including different infiltrating immune cells. Trajectory analysis of proximal tubule and endothelial cells demonstrated separation between healthy and injured kidney from transplant explant suggesting evolving stages of cell- specific differentiation in alloimmune injury. This study provides the technical groundwork for understanding the pathogenesis of alloimmune injury and host tissue response in transplant rejection and normal human kidney and provides a protocol for optimized processing precious and low input human kidney biopsy tissue for larger scale studies.

Texture Synthesizability Assessment via Deep Siamese-Type Network

Example-based texture synthesis plays a significant role in many fields, including computer graphics, computer vision, multimedia, and image and video editing and processing. However, it is not easy for all textures to synthesize high-quality outputs of any size from a small input example. Hence, the assessment of the synthesizability of the example textures deserves more attention. Inspired by the broad studies in image quality assessment, we propose a texture synthesizability assessment approach based on a deep Siamese-type network. To our best knowledge, this is the first attempt to evaluate the synthesizability of sample textures through end-to-end training. We first train a Siamese-type network to compare the example texture and the synthesized texture in terms of their similarity and then transfer the experience knowledge obtained in the Siamese-type network to a traditional CNN by fine-tuning, so that to give an absolute score to a single example texture, representing its synthesizability. Not relying on laborious human selection and annotation, these synthesized textures can be generated automatically by example-based synthesis algorithms. We demonstrate that our approach is completely data-driven without hand-crafted features and/or prior knowledge in the field of expertise. Experiments show that our approach improves the accuracy of texture synthesizability assessment qualitatively and quantitatively and outperforms the manual feature-based method.

A 0.6-V 12-bit 13.2-fJ/conversion-step SAR ADC with time-domain VCDL-based comparator and metastability immunity technique

A 0.6-V 12-bit successive-approximation-register (SAR) analog-to-digital converter (ADC) is presented in this paper. Compared with the conventional time-domain comparator, the latch-like phase detector can reduce the dead zone voltage because of the positive feedback structure. Furthermore, a low power metastability immunity technique is proposed to suppress the uncertain decision-making behavior at the small input differences. The simulation results show that the proposed SAR ADC can achieve a 73.75-dB signal-to-noise and distortion ratio (SNDR) and consume 3.6 μW power consumption at the Nyquist input and sampling rate of 100 kS/s, resulting in a figure-of-merit (FOM) of 13.2 fJ/Conversion-step. Meanwhile, the core area is only 0.197 mm2 with the fully customized MOM capacitor array in a 0.18-μm CMOS process.

Techno-Economic Evaluation of Novel Hybrid Biomass and Electricity-Based Ethanol Fuel Production

In order to limit climate change, fast greenhouse gas reductions are required already before 2030. Ethanol commonly produced by fermentation of sugars derived either from starch-based raw material such as corn, or lignocellulosic biomass is an established fuel decarbonizing the transport sector. We present a novel selective and flexible process concept for the production of ethanol with electricity and lignocellulosic biomass as main inputs. The process consists of several consecutive steps. First synthesis gas from gasification of biomass is purified by filtration and reforming and fed to methanol synthesis. The produced methanol is fed to acetic acid synthesis, together with a carbon monoxide-rich stream separated from the synthesis gas by membranes. Finally, acetic acid is hydrogenated to yield ethanol. With the exception of acetic acid hydrogenation, the overall process consists of technically mature subprocesses. Each process step was modelled in Aspen Plus to generate the mass and energy balances for the overall process. Additionally, the CO2 emissions and economic feasibility were assessed. Three separate cases were investigated. In the first two cases, the syngas carbon (CO and CO2) was split between methanol and acetic acid synthesis. The cases included either allothermal (case A) or electrically heated reforming (case B). In case C, maximum amount of CO was sent to acetic acid synthesis to maximize the acetic acid output, requiring a small additional carbon dioxide input to methanol synthesis. In all cases, additional hydrogen to methanol synthesis was provided by water electrolysis. Each case was designed at biomass input of 27.9 MW and the electrolyzer electricity requirement between 36 and 43.5 MW, depending on the case. The overall energy efficiency was calculated at 53–57%, and carbon efficiencies were above 90%. The lowest levelized cost of ethanol was 0.65 €/l, at biomass cost of 20 €/MWh and electricity cost of 45 €/MWh and production scale of approximately 42 kt ethanol per year. The levelized cost is competitive with the current biological route for lignocellulosic ethanol production. The ethanol price is very sensitive to the electricity cost, varying from 0.56 to 0.74 €/l at ±30% variation in electricity cost.

Physics Informed Deep Neural Net Inverse Modeling for Estimating Model Parameters in Permeable Porous Media Flows

Abstract We present a method that combines a physics-informed deep neural network and Stokes' second problem to estimate the porosity and the permeability of a porous medium. Particularly, we investigate the accuracy of physics-informed deep neural networks in predicting the hidden quantities of interest, such as velocity and unknown parameters, including permeability and porosity, by employing different network architectures and different sizes of input data sets. The employed neural network is jointly trained to match the essential class of physical laws governing fluid motion in porous media (Darcy's law and mass conservation) and the fluid velocities in the domain or region of interest. Therefore, the described approach allows the estimation of hidden quantities of interest. This strategy conditions the neural network to honor physical principles. Thus, the model adapts to fit best the data provided while striving to respect the governing physical laws. Results show that the proposed approach achieves significant accuracy in estimating the velocity, permeability, and porosity of the media, even when the neural network is trained by a relatively small input data-set. Also, results demonstrate that using the optimal neural network architecture is indispensable to increase the porosity and permeability prediction accuracy.

A Procedure for Tracing Chain of Custody in Digital Image Forensics: A Paradigm Based on Grey Hash and Blockchain

Digital evidence is critical in cybercrime investigations because it is used to connect individuals to illegal activity. Digital evidence is complicated, diffuse, volatile, and easily altered, and as such, it must be protected. The Chain of Custody (CoC) is a critical component of the digital evidence procedure. The aim of the CoC is to demonstrate that the evidence has not been tampered with at any point throughout the investigation. Because the uncertainty associated with digital evidence is not being assessed at the moment, it is impossible to determine the trustworthiness of CoC. As scientists, forensic examiners have a responsibility to reverse this tendency and officially confront the uncertainty inherent in any evidence upon which they base their judgments. To address these issues, this article proposes a new paradigm for ensuring the integrity of digital evidence (CoC documents). The new paradigm employs fuzzy hash within blockchain data structure to handle uncertainty introduced by error-prone tools when dealing with CoC documents. Traditional hashing techniques are designed to be sensitive to small input modifications and can only determine if the inputs are exactly the same or not. By comparing the similarity of two images, fuzzy hash functions can determine how different they are. With the symmetry idea at its core, the suggested framework effectively deals with random parameter probabilities, as shown in the development of the fuzzy hash segmentation function. We provide a case study for image forensics to illustrate the usefulness of this framework in introducing forensic preparedness to computer systems and enabling a more effective digital investigation procedure.

A scaled down method for identifying the optimum range of L/S ratio in twin screw wet granulation using a regime map approach

Twin screw wet granulation (TSWG) has gained momentum in the pharmaceutical industry for effective continuous granulation of solid dosage products. Liquid-to-solid (L/S) ratio is a key process parameter affecting granule properties. Identifying an optimum range of L/S ratio while reducing the number of full scale experiments can minimize material requirements and streamline formulation and process development. In this work, microcrystalline cellulose-based (MCC) formulations of varying drug loads were granulated using a kneading element screw configuration at a wide range of L/S ratios until pasting was visually determined. Quantitative criteria based on process relevant granule size and mass % of fines were established to identify undesirable granulation conditions. Key mechanical properties of wet compacts measured using a small scale approach are discussed. The stress–strain behavior is used to predict pasting, and natural strain at peak yield stress and total work of deformation are used to identify undergranulation and overgranulation respectively. The small scale method is used to establish viable ranges of L/S ratios in advance of at-scale experiments. A quantitative predictive growth regime map is proposed based fully on small scale experiments and input process parameters. Strategies for establishing a generalized growth regime map for various systems of interest are discussed.

Patch-based weighted SCAD prior for compressive sensing

The nuclear norm-based convex surrogate of the rank function has been widely used in compressive sensing (CS) to exploit the sparsity of nonlocal similar patches in an image. However, this method treats different singular values equally and thus may produce a result far from the optimum one. In order to alleviate the limitations of the nuclear norm, different singular values should be treated differently. The reason is that large singular values can be used to retrieve substantial contents of an image, while small ones may contain noisy information. In this paper, we propose a model via non-convex weighted Smoothly Clipped Absolute Deviation (SCAD) prior. Our motivation is that SCAD shrinkage behaves like a soft shrinkage operator for small enough inputs, whereas for large enough ones, it leaves the input intact and behaves like hard shrinkage. For moderate input values, SCAD makes a good balance between soft shrinkage and hard shrinkage. Numerically, the alternating direction method of multiplier (ADMM) is adopted to split the original problem into several sub-problems with closed-form solutions. We further analyze the convergence of the proposed method under mild conditions. Various experimental results demonstrate that the proposed model outperforms many existing state-of-the-art CS methods.

Technical note: High-accuracy weighing micro-lysimeter system for long-term measurements of non-rainfall water inputs to grasslands

Abstract. Non-rainfall water (NRW), defined here as dew, hoar frost, fog, rime, and water vapour adsorption, might be a relevant water source for ecosystems, especially during summer drought periods. These water inputs are often not considered in ecohydrological studies, because water amounts of NRW events are rather small and therefore difficult to measure. Here we present a novel micro-lysimeter (ML) system and its application which allows us to quantify very small water inputs from NRW during rain-free periods with an unprecedented high accuracy of ±0.25 g, which corresponds to ±0.005 mm water input. This is possible with an improved ML design paired with individual ML calibrations in combination with high-frequency measurements at 3.3 Hz and an efficient low-pass filtering to reduce noise level. With a set of ancillary sensors, the ML system furthermore allows differentiation between different types of NRW inputs, i.e. dew, hoar frost, fog, rime, and the combinations among these, but also additional events when condensation on leaves is less probable, such as water vapour adsorption events. In addition, our ML system design allows one to minimize deviations from natural conditions in terms of canopy and soil temperatures, plant growth, and soil moisture. This is found to be a crucial aspect for obtaining realistic NRW measurements in short-statured grasslands. Soil temperatures were higher in the ML compared to the control, and thus further studies should focus on improving the thermal soil regime of ML. Our ML system has proven to be useful for high-accuracy, long-term measurements of NRW on short-statured vegetation-like grasslands. Measurements with the ML system at a field site in Switzerland showed that NRW input occurred frequently, with 127 events over 12 months with a total NRW input of 15.9 mm. Drainage-water flow of the ML was not measured, and therefore the NRW inputs might be conservative estimates. High average monthly NRW inputs were measured during summer months, suggesting a high ecohydrological relevance of NRW inputs for temperate grasslands.

Unveiling the Underlying Mechanism of Transition Metal Atoms Anchored Square Tetracyanoquinodimethane Monolayers as Electrocatalysts for N2 Fixation

We for the first time systematically studied the structures and electrochemical nitrogen reduction reaction properties of two‐dimensional single transition‐metal anchored square tetracyanoquinodimethane monolayers (labeled as: TM‐sTCNQ, TM = 3d, 4d, 5d series transition metals) by employing density functional theory method. Through high‐throughput screenings and full reaction path researches, two promising electrochemical nitrogen reduction reaction catalysts Nb‐sTCNQ and Mo‐sTCNQ have been obtained. The nitrogen reduction reaction onset potential on Nb‐sTCNQ is as low as −0.48 V. Furthermore, the Nb‐sTCNQ catalyst can quickly desorb NH3 produced with a free energy of 0.65 eV, giving Nb‐sTCNQ excellent catalytic cycle performance. The high catalytic activity of the two materials might be attributed to the effective charge transfer between the active center and adsorbed N2, which enables the active center to adsorb and activate inert N2 molecules well, and the reduction processes require small energy input (i.e., the maximum free energy changes are small). This work provides insights for finding highly efficient, stable, and low‐cost nitrogen reduction reaction electrocatalysts. We hope our results can promote further experimental and theoretical research of this field.

Improving Uncertainty in Chain of Custody for Image Forensics Investigation Applications

Cybercrime investigations rely heavily on digital evidence to establish links between suspects and the criminal conduct they are allegedly involved in. As a result, digital evidence must be protected since it is complex, volatile, and susceptible to alteration. In the digital evidence method, the chain of custody (CoC) is essential. As a result of the CoC, it is possible to establish that the evidence was never tampered with. Due to the inherent uncertainty of digital evidence, the trustworthiness of the CoC cannot be judged at this time. It is the duty of forensic examiners to challenge this inclination and publicly admit the inherent ambiguity in whatever evidence they use to make their decisions. This article suggests a new paradigm for maintaining the integrity of digital evidence in order to overcome these challenges. To handle the uncertainty generated by error-prone technologies while dealing with CoC documents, the new paradigm used a fuzzy hash inside the blockchain data structure. Traditional hashing methods are only able to tell whether two inputs are precisely the same or not because they are sensitive to even the smallest input changes. Using fuzzy hash functions, we can figure out how dissimilar two images are by comparing their similarities. As an example of how this paradigm may be applied to computer systems and make digital investigations more successful, we utilize image forensics as the focus of an in-depth look at how it works.

A Novel Gray Image Denoising Method Using Convolutional Neural Network

In order to make the image denoising more effective in high noise level environment, we propose a gray image denoising method using convolutional neural network (ConvNet). By constructing symmetric and dilated convolutional residual network and combining leaky ReLU (Rectified Linear Unit) and ReLU dual-functions. Experimental data show that this method achieves better objective results based on small resolution input & large resolution input and small dataset & large dataset compared with other methods. A series of subjective visual comparison results also show that proposed method can perform image denoising well in high noise level environment, and there are no problems such as damaged edge or texture details, boundary artifacts and poor definition of the denoised image. The proposed method not only improves the image denoising ability, but also improves the denoising productivity to some extent. In the related work, we have made a comprehensive discussion on the current mainstream image denoising methods based on traditional machine learning and deep learning, which to some extent makes up for the lack of such reviews. In addition, the existing problems and future development direction of image denoising are discussed in detail.