Benchmark Experiments Research Articles

Learning and forecasting open quantum dynamics with correlated noise

The development of practical quantum processors relies on the ability to control and predict their functioning despite the presence of noise. This is particularly challenging for temporarily correlated noise. Here we propose a physics-inspired supervised machine learning approach to efficiently and accurately predict the functioning of quantum processors in the presence of correlated noise, which only requires data from randomized benchmarking experiments. To demonstrate the efficacy of our technique, we analyze the data from a superconducting quantum processor with tunable correlated noise. We produce training data by evolving the system for a number of time steps, and with this, we fully quantify the correlated noise and accurately predict the dynamics of the system for times beyond the training data. This approach shows a path towards efficient and effective learning of noisy quantum dynamics and optimally control quantum processors over long and complex computations even in the presence of correlated noise.

Varying Projection Quality of Good Local Electric Field Gradients of Monochlorobenzaldehydes.

Rotational spectroscopy is an excellent tool for structure determination, which can provide additional insights into local electronic structure by investigating the hyperfine pattern due to nuclear quadrupole coupling. Jet-cooled molecules are good experimental benchmark targets for electronic structure calculations, as they are free of environmental effects. We report the rotational spectra of 2-chlorobenzaldehyde, 3-chlorobenzaldehyde, and 4-chlorobenzaldehyde, including a complete experimental description of the nuclear quadrupole coupling constants, which were previously not experimentally determined. We identified two conformers for 3-chlorobenzaldehyde and one conformer each for 2-chlorobenzaldehyde and 4-chlorobenzaldehyde. Rigorous structure fitting of 4-chlorobenzaldehyde was performed to determine bond lengths for r0, rs, rese, and rm(1) structures. Comparing experimental nuclear quadrupole coupling constants to computational results showed agreement in the nuclear axis system, but the accuracy of the projection into the principal axis system decreases in near-oblate 2-chlorobenzaldehyde. The experimental angle Θaz = 19.16° between the principal a-axis and nuclear z-axis is larger than predicted by multiple computational methods by ≥4°. It is attributed to the high sensitivity of 2-chlorobenzaldehyde to low-energy vibrational contributions.

Metal chalcogenide quantum dots for photochemical and electrochemical hydrogen generation: recent advancements and technological challenges.

The performance of heterogeneous catalysis, specifically photochemical and electrochemical hydrogen evolution reaction fundamentally relies upon the prudent choice of catalytic systems with ideal optoelectronic and surface properties. Progressive research in materials processing has hinted at the large-scale applicability of 2D materials for achieving higher activity in the HER process. Among 2D materials, transition metal chalcogenides have emerged as the advanced materials to enhance the rate of HER on account of their layered structure and chalcogen-sites that exhibit favourable hydrogen binding energies. Developing quantum dots is the state-of-the-art methodological approach to tuning the physicochemical properties of TMCs. Herein, we aim to encompass the latest advancements in the TMCs QDs for green hydrogen upscaling with special attention given to the comprehensive understanding of physicochemical properties and experimental benchmarks. Furthermore, we have accounted the major challenges associated with the exploitation of TMCs QDs in HER operations and future perspectives for subscribing to the overall water splitting for hydrogen synthesis in the light of TMCs QDs.

A Patch-Based Method for Underwater Image Enhancement With Denoising Diffusion Models.

The enhancement of underwater images has emerged as a significant technological challenge in advancing marine research and exploration tasks. Due to the scattering of suspended particles and absorption of light in underwater environments, underwater images tend to present blurriness and predominantly color distortion. In this study, we propose a novel approach utilizing denoising diffusion models to improve underwater degraded images. After training the noise estimation network of the denoising diffusion models, we accelerate the deterministic sampling process with denoising diffusion implicit models. We also propose a patch-based method by implementing average sampling between overlapping image patches at each sampling step, enabling the generation of images at arbitrary resolution while preserving their natural appearance and details. Through benchmark experiments, we illustrate that our method outperforms or closely approaches state-of-the-art techniques in terms of effectiveness and performance. We demonstrate that our approach reduces the interference of underwater environments with the semantic information of the images by salient object detection experiments.

Numerical and experimental benchmark study of nonspherical cavitation bubble dynamics near a rigid wall

Numerical and experimental benchmark study of nonspherical cavitation bubble dynamics near a rigid wall

Unlocking multiscale metallic metamaterials via lithography additive manufacturing

ABSTRACT Metamaterials possess properties not found in nature and are expected to revolutionise the design of structural components. However large-scale production of metallic metamaterials remains locked due to the compromise between print size and resolution in existing metal 3D printing methods. We unlock the possibility of 3D printing of stainless steel metamaterials across scales using lithography metal manufacturing, a vat photopolymerisation technology that uses digital light processing (DLP) on metal-filled resin to 3D print a green body for further debinding and sintering in a furnace. Here in, were explore the effects of energy dose on overpolymerisation, minimal feature size, and print resolution as well as the effects of sintering temperature on microstructure, shape stability, and mechanical properties of 3D printed metamaterials. It has become possible to 3D print steel metamaterials with a twist and auxetic metamaterials with micro-scaled structures on a decimetre scale. Our benchmarking experiments demonstrate that lithography metal manufacturing competes with laser powder bed fusion regarding print accuracy, surface roughness, and design freedom and provides a viable solution for translating metallic metamaterials from laboratories to markets.

Machine learning-optimized targeted detection of alternative splicing.

RNA sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases that hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local splicing variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTExRNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.

Availability of Critical Benchmark Experiments for the Pebble Tanker Transportation Model for Nuclear Criticality Safety Validation of TRISO Pebbles

This study addresses the need for comprehensive investigations into TRi-structural ISOtropic (TRISO) fuel pebble transportation validation. In this work, an exploratory model, the pebble tanker(PT), was developed with the aim of facilitating the validation of nuclear criticality safety calculations in the context of industrial-scale transportation of TRISO fuel. The PT model was designed to investigate the availability and applicability of critical benchmark experiments crucial for assessing the transportation of these pebbles. This work incorporated sensitivity/uncertainty (S/U) similarity studies to quantify the applicability of critical benchmark experiments and to address nuclear data uncertainties in the context of TRISO transportation. Two container models were investigated: one for the Hermes-type pebble and one for the Pebble Bed Modular Reactor (PBMR)–type pebble. The models were simplified, considering fuel, containment, and either water or air, to enable a focus on the underlying physics of applications involving TRISO fuel pebbles using the PT model. A crucial aspect under consideration was the capacity of the transport package to hold pebbles while ensuring subcriticality in the flooded state. An approach in the criticality validation process involves assessing the similarity between systems through an integral index parameter evaluation. This involves calculating a correlation coefficient (referred to as ck) based on shared nuclear data–induced uncertainty between a benchmark experiment and the application of the PT model. To facilitate this analysis, the SCALE tools, particularly the CSAS6-Shift, TSUNAMI-3D-Shift, and TSUNAMI-IP sequences, were employed for comprehensive studies in neutronics and S/U analysis. Our findings showed that there are sufficient critical experimental benchmarks to perform this validation of the PT model in the most reactive state, i.e. when the tanker is flooded. This paper provides valuable insights into validating a transport package for Generation IV TRISO fuel pebbles.

Editorial: Experimental benchmark control problem on multi-axial real-time hybrid simulation

Editorial: Experimental benchmark control problem on multi-axial real-time hybrid simulation

Energy-Efficient Collision-Free Machine/AGV Scheduling Using Vehicle Edge Intelligence.

With the widespread use of autonomous guided vehicles (AGVs), avoiding collisions has become a challenging problem. Addressing the issue is not straightforward since production efficiency, collision avoidance, and energy consumption are conflicting factors. This paper proposes a novel edge computing method based on vehicle edge intelligence to solve the energy-efficient collision-free machine/AGV scheduling problem. First, a vehicle edge intelligence architecture was built, and the corresponding state transition diagrams for collision-free scheduling were developed. Second, the energy-efficient collision-free machine/AGV scheduling problem was modeled as a multi-objective function with electric capacity constraints, where production efficiency, collision prevention, and energy conservation were comprehensively considered. Third, an artificial plant community algorithm was explored based on the edge intelligence of AGVs. The proposed method utilizes a heuristic search and the swarm intelligence of multiple AGVs to realize energy-efficient collision-free scheduling and is suitable for deploying on embedded platforms for edge computing. Finally, a benchmark dataset was developed, and some benchmark experiments were conducted, where the results revealed that the proposed heuristic method could effectively instruct multiple automatic guided vehicles to avoid collisions with high energy efficiency.

Lambda Authorizer Benchmarking Tool with Aws Sam And Artillery Framework

This paper proposes a novel Lambda Authorizer Benchmarking Tool utilizing AWS Serverless Application Model (SAM) and Artillery framework. The tool evaluates the performance of serverless functions implementing Lambda Authorizers. We focus on three key performance parameters: cold start behaviour, programming language runtimes, and authorization types (e.g., token-based, claim-based). By employing load testing with Artillery, the tool measures the impact of these factors on authorization processing speed and resource consumption. The findings from the benchmarking experiments offer valuable insights for developers building secure and cost-effective serverless APIs with Lambda Authorizers. The tool empowers and developers to make informed decisions regarding programming language selection, authorization strategy, and optimization techniques for their serverless applications.

METHODS AND TOOLS USED TO INVESTIGATE INTERACTION BETWEEN AEROSOLS AND CLOUDS

The interaction between aerosols and clouds is a relevant factor in global, regional and local climate change. The scientific community is trying to provide relevant theoretical and experimental benchmarks on aerosol loading, cloud formation patterns and trends. Aerosol–cloud interactions play a vital role in global climate change and are associated with one of the greatest uncertainties. In recent decades, due to its unique geographical location, the North Indian Ocean (NIO) has been gaining significant attention among scientific communities. Deep understanding of aerosols and their interaction with clouds in this region is very important both regionally and globally. The paper provides both an overview of the most used methods and tools used in experimental approaches in the field, as well as an analysis methodology regarding aerosol-cloud interaction scenarios.

Analyzing real-time communication: Arduino-based Controller Area Network (CAN) in electric vehicle

The increasing complexity of electric vehicles (EVs) and the growing demand for propulsion and driver assistance have strengthened the need for CAN networks. This necessitates a detailed analysis of communication and CAN bus utilization. A low bus load does not guarantee a valid design if response times are insufficient. This paper presents a response time analysis (RTA) of a distributed real-time control system that uses an Arduino-based CAN network and an MCP2515 interface to check if the system can be scheduled. The RTA is based on calculating the worst-case response time of the message (WCRT), which is its longest response time. The prototype EV houses a system consisting of four (4) real-time embedded CAN nodes. We have implemented a PID controller within the motor speed control node. Based on existing literature, we proposed an algorithm to calculate the WCRT of the control system messages and another to address the priority inversion issue in network communication. The experimental results for the PID controller are satisfactory. The controller reaches the target speed with minimal oscillations with values 478ms, 31.81s, 0.62%, and 2.57%, corresponding to time rise, settling time, steady-state error, and overshoot at 90 rpm, demonstrating its robust performance. Experimental results confirm the scheduling analysis of the system with a low bus load (0.9% at 500 kbps). The tests conducted on the experimental and SAE benchmark data show that validity depends on message schedulability, bus load, and bandwidth. A system may be schedulable at 500 kbps but not at 125 kbps with the same traffic.

Balancing Group 1 Monoatomic Ion-Polar Compound Interactions in the Polarizable Drude Force Field: Application in Protein and Nucleic Acid Systems.

An accurate force field (FF) is the foundation of reliable results from molecular dynamics (MD) simulations. In our recently published work, we developed a protocol to generate atom pair-specific Lennard-Jones (known as NBFIX in CHARMM) and through-space Thole dipole screening (NBTHOLE) parameters in the context of the Drude polarizable FF based on readily accessible quantum mechanical (QM) data to fit condensed phase experimental thermodynamic benchmarks, including the osmotic pressure, diffusion coefficient, ionic conductivity, and solvation free energy, when available. In the present work, the developed protocol is applied to generate NBFIX and NBTHOLE parameters for interactions between monatomic ions (specifically Li+, Na+, K+, Rb+, Cs+, and Cl-) and common functional groups found in proteins and nucleic acids. The parameters generated for each ion-functional group pair were then applied to the corresponding functional groups within proteins or nucleic acids followed by MD simulations to analyze the distribution of ions around these biomolecules. The modified FF successfully addresses the issue of overbinding observed in a previous iteration of the Drude FF. Quantitatively, the model accurately reproduces the effective charge of proteins and demonstrates a level of charge neutralization for a double-helix B-DNA in good agreement with the counterion condensation theory. Additionally, simulations involving ion competition correlate well with experimental results, following the trend Li+ > Na+ ≈ K+ > Rb+. These results validate the refined model for group 1 ion-biomolecule interactions that will facilitate the application of the polarizable Drude FF in systems in which group 1 ions play an important role.

Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions

Electrochemical C‐H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet identifying suitable substrates and optimizing synthesis remain challenging. We report an integrated approach combining machine learning (ML) and large language models (LLMs) to streamline the exploration of electrochemical C‐H oxidation reactions. Utilizing a batch rapid screening electrochemical platform, we evaluated a wide range of reactions, initially classifying substrates by their reactivity, while LLMs text‐mined literature data to augment the training set. The resulting ML models for reactivity prediction achieved high accuracy (>90%) and enabled virtual screening of a large set of commercially available molecules. To optimize reaction conditions for selected substrates, LLMs were prompted to generate code that iteratively improved yields. This human‐AI collaboration proved effective, efficiently identifying high‐yield conditions for 8 drug‐like substances or intermediates. Notably, we benchmarked the accuracy and reliability of 10 different LLMs ‐ including LLaMA, Claude, and GPT‐4 ‐ on generating and executing codes related to ML based on natural language prompts given by chemists to showcase their tool‐making (code generation) and tool‐use (function calling) capabilities and potentials for accelerating research across four diverse tasks. We also collected an experimental benchmark dataset comprising 1071 reaction conditions and yields for electrochemical C‐H oxidation reactions.

Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions.

Electrochemical C-H oxidation reactions offer a sustainable route to functionalize hydrocarbons, yet identifying suitable substrates and optimizing synthesis remainchallenging. We report an integrated approach combining machine learning (ML) and large language models (LLMs) to streamline the exploration of electrochemical C-H oxidation reactions. Utilizing a batch rapid screening electrochemical platform, we evaluated a wide range of reactions, initially classifying substrates by their reactivity, while LLMs text-mined literature data to augment the training set. The resulting ML models for reactivity prediction achieved high accuracy (>90%) and enabled virtual screening of a large set of commercially available molecules. To optimize reaction conditions for selected substrates, LLMs were prompted to generate code that iteratively improved yields. This human-AI collaboration proved effective, efficiently identifying high-yield conditions for 8 drug-like substances or intermediates. Notably, we benchmarked the accuracy and reliability of 10 different LLMs -including LLaMA, Claude, and GPT-4 -on generating and executing codes related to ML based on natural language prompts given by chemists to showcase their tool-making (code generation) and tool-use (function calling) capabilities and potentials for accelerating research across four diverse tasks. We also collected an experimental benchmark dataset comprising 1071 reaction conditions and yields for electrochemical C-H oxidation reactions.

Analysis of crack propagation due to fatigue using the Stable Generalized Finite Element Method (SGFEM)

This work aims to present a combination of the stable generalized finite element method (SGFEM) and of the displacement correlation method (DCM) for analyzing 2D crack propagation problems due to fatigue. The SGFEM allows to model fracture mechanics problems without excessive refinement and/or concern of the mesh-to-crack alignment. Additionally, this method maintains the numerical conditioning under control. The DCM, in turn, is computationally very efficient when compared to traditional energy based methods (like J integral) and herein is even improved by means of a linear least square extrapolation. The maximum circumferential tension criterion and well-known propagation laws available in literature are adopted for considering the crack propagation. Experimental and numerical benchmark examples are proposed and the obtained results are compared against results provided by a commercial software widely adopted in the aeronautics industry.

A versatile setup for hydrogen isotope permeation studies.

The Testbed for Analysis of Permeation of Atoms in Samples (TAPAS) is an experimental setup for ion-driven permeation studies with a focus on investigating wall materials for nuclear fusion devices. A monoenergetic, mass-filtered high-intensity keV ion beam is focused and directed onto the permeation sample by electrostatic ion optics and decelerated to the desired ion energy by a dedicated set of apertures close to the sample. We were able to obtain ion energies as low as 170 eV/D with a D3+ ion beam with an ion flux density of the order of 1020 D/m2s on a beam-wetted area of ∼33mm2. These conditions avoid sputtering of W targets by the ion beam and are representative of the particle flux and energy spectrum impinging on the first wall of a prospective nuclear fusion power reactor. Permeation samples can be heated up to 1000K in an ultra-high vacuum. The design of the deceleration system, together with a high pumping speed in the loading chamber, ensures a low pressure of recycling hydrogen isotope molecules in front of the sample. In addition to ion-driven permeation, TAPAS provides a limited capability for gas-driven permeation at low pressures up to nearly 1mbar. Permeating hydrogen isotopes are detected with a quadrupole mass spectrometer in the downstream ultra-high vacuum chamber. After a detailed description of the setup and calibration procedures for implanted particle flux, mass spectrometer, and neutral gas pressure, benchmark experiments on recrystallized, 50μm thick tungsten foils are shown, demonstrating that diffusion-limited boundary conditions for permeation were reached.

Predicting the subcellular location of prokaryotic proteins with DeepLocPro.

Protein subcellular location prediction is a widely explored task in bioinformatics because of its importance in proteomics research. We propose DeepLocPro, an extension to the popular method DeepLoc, tailored specifically to archaeal and bacterial organisms. DeepLocPro is a multiclass subcellular location prediction tool for prokaryotic proteins, trained on experimentally verified data curated from UniProt and PSORTdb. DeepLocPro compares favorably to the PSORTb 3.0 ensemble method, surpassing its performance across multiple metrics in our benchmark experiment. The DeepLocPro prediction tool is available online at https://ku.biolib.com/deeplocpro and https://services.healthtech.dtu.dk/services/DeepLocPro-1.0/.

Nuclear Quantum Effects in Liquid Water Are Marginal for Its Average Structure but Significant for Dynamics.

Isotopic substitution, which can be realized in both experiment and computer simulations, is a direct approach to assess the role of nuclear quantum effects on the structure and dynamics of matter. However, the impact of nuclear quantum effects on the structure of liquid water as probed in experiment by comparing normal to heavy water has remained controversial. To settle this issue, we employ a highly accurate machine-learned high-dimensional neural network potential to perform converged coupled cluster-quality path integral simulations of liquid H2O versus D2O at ambient conditions. We find substantial H/D quantum effects on the rotational and translational dynamics of water, in close agreement with the experimental benchmarks. However, in stark contrast to the role for dynamics, H/D quantum effects turn out to be small, on the order of 1/1000 Å, on both average intramolecular and H-bonding structures of water. The most probable structure of water remains nearly unaffected by nuclear quantum effects, but effects on fluctuations away from average are appreciable, rendering H2O substantially more "liquid" than D2O.