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At least 397 records · Page 22

An atypical BRCT–BRCT interaction with the XRCC1 scaffold protein compacts human DNA Ligase IIIα within a flexible DNA repair complex

The XRCC1–DNA ligase IIIα complex (XL) is critical for DNA single-strand break repair, a key target for PARP inhibitors in cancer cells deficient in homologous recombination. Here, we combined biophysical approaches to gain insights into the shape and conformational flexibility of the XL as well as XRCC1 and DNA ligase IIIα (LigIIIα) alone. Structurally-guided mutational analyses based on the crystal structure of the human BRCT–BRCT heterodimer identified the network of salt bridges that together with the N-terminal extension of the XRCC1 C-terminal BRCT domain constitute the XL molecular interface. Coupling size exclusion chromatography with small angle X-ray scattering and multiangle light scattering (SEC-SAXS–MALS), we determined that the XL is more compact than either XRCC1 or LigIIIα, both of which form transient homodimers and are highly disordered. The reduced disorder and flexibility allowed us to build models of XL particles visualized by negative stain electron microscopy that predict close spatial organization between the LigIIIα catalytic core and both BRCT domains of XRCC1. Together our results identify an atypical BRCT–BRCT interaction as the stable nucleating core of the XL that links the flexible nick sensing and catalytic domains of LigIIIα to other protein partners of the flexible XRCC1 scaffold.

59 BASIC BIOLOGICAL SCIENCES↗

Hybrid Entanglement between Optical Discrete Polarizations and Continuous Quadrature Variables

By coherently combining advantages while largely avoiding limitations of two mainstream platforms, optical hybrid entanglement involving both discrete and continuous variables has recently garnered widespread attention and emerged as a promising idea for building heterogenous quantum networks. In contrast to previous results, here we propose a new scheme to remotely generate hybrid entanglement between discrete polarization and continuous quadrature optical qubits heralded by two-photon Bell-state measurement. As a novel nonclassical light resource, we further use it to discuss two examples of ways—entanglement swapping and quantum teleportation—in which quantum information processing and communications could make use of this hybrid technique.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Evaluating the contribution of plant metabolic pathways in the light to the ATP:NADPH demand using a meta-analysis of isotopically non-stationary metabolic flux analyses

Abstract Balancing the ATP: NADPH demand from plant metabolism with supply from photosynthesis is essential for preventing photodamage and operating efficiently, so understanding its drivers is important for integrating metabolism with the light reactions of photosynthesis and for bioengineering efforts that may radically change this demand. It is often assumed that the C3 cycle and photorespiration consume the largest amount of ATP and reductant in illuminated leaves and as a result mostly determine the ATP: NADPH demand. However, the quantitative extent to which other energy consuming metabolic processes contribute in large ways to overall ATP: NADPH demand remains unknown. Here, we used the metabolic flux networks of numerous recently published isotopically non-stationary metabolic flux analyses (INST-MFA) to evaluate flux through the C3 cycle, photorespiration, the oxidative pentose phosphate pathway, the tricarboxylic acid cycle, and starch/sucrose synthesis and characterize broad trends in the demand of energy across different pathways and compartments as well as in the overall ATP:NADPH demand. These data sets include a variety of species including Arabidopsis thaliana , Nicotiana tabacum , and Camelina sativa as well as varying environmental factors including high/low light, day length, and photorespiratory levels. Examining these datasets in aggregate reveals that ultimately the bulk of the energy flux occurred in the C3 cycle and photorespiration, however, the energy demand from these pathways did not determine the ATP: NADPH demand alone. Instead, a notable contribution was revealed from starch and sucrose synthesis which might counterbalance photorespiratory demand and result in fewer adjustments in mechanisms which balance the ATP deficit.

59 BASIC BIOLOGICAL SCIENCES↗

A goldilocks computational protocol for inhibitor discovery targeting DNA damage responses including replication-repair functions

While many researchers can design knockdown and knockout methodologies to remove a gene product, this is mainly untrue for new chemical inhibitor designs that empower multifunctional DNA Damage Response (DDR) networks. Here, we present a robust Goldilocks (GL) computational discovery protocol to efficiently innovate inhibitor tools and preclinical drug candidates for cellular and structural biologists without requiring extensive virtual screen (VS) and chemical synthesis expertise. By computationally targeting DDR replication and repair proteins, we exemplify the identification of DDR target sites and compounds to probe cancer biology. Our GL pipeline integrates experimental and predicted structures to efficiently discover leads, allowing early-structure and early-testing (ESET) experiments by many laboratories. By employing an efficient VS protocol to examine protein-protein interfaces (PPIs) and allosteric interactions, we identify ligand binding sites beyond active sites, leveraging in silico advances for molecular docking and modeling to screen PPIs and multiple targets. A diverse 3,174 compound ESET library combines Diamond Light Source DSI-poised, Protein Data Bank fragments, and FDA-approved drugs to span relevant chemotypes and facilitate downstream hit evaluation efficiency for academic laboratories. Two VS per library and multiple ranked ligand binding poses enable target testing for several DDR targets. This GL library and protocol can thus strategically probe multiple DDR network targets and identify readily available compounds for early structural and activity testing to overcome bottlenecks that can limit timely breakthrough drug discoveries. By testing accessible compounds to dissect multi-functional DDRs and suggesting inhibitor mechanisms from initial docking, the GL approach may enable more groups to help accelerate discovery, suggest new sites and compounds for challenging targets including emerging biothreats and advance cancer biology for future precision medicine clinical trials.

59 BASIC BIOLOGICAL SCIENCES↗

Boosting background suppression in the NEXT experiment through Richardson-Lucy deconvolution

Next-generation neutrinoless double beta decay experiments aim for half-life sensitivities of ~ 10 27 yr, requiring suppressing backgrounds to < 1 count/tonne/yr. For this, any extra background rejection handle, beyond excellent energy resolution and the use of extremely radiopure materials, is of utmost importance. The NEXT experiment exploits differences in the spatial ionization patterns of double beta decay and single-electron events to discriminate signal from background. While the former display two Bragg peak dense ionization regions at the opposite ends of the track, the latter typically have only one such feature. Thus, comparing the energies at the track extremes provides an additional rejection tool. The unique combination of the topology-based background discrimination and excellent energy resolution (1% FWHM at the Q-value of the decay) is the distinguishing feature of NEXT. Previous studies demonstrated a topological background rejection factor of ~ 5 when reconstructing electron-positron pairs in the 208Tl 1.6 MeV double escape peak (with Compton events as background), recorded in the NEXT-White demonstrator at the Laboratorio Subterráneo de Canfranc, with 72% signal efficiency. This was recently improved through the use of a deep convolutional neural network to yield a background rejection factor of ~ 10 with 65% signal efficiency. Here, we present a new reconstruction method, based on the Richardson-Lucy deconvolution algorithm, which allows reversing the blurring induced by electron diffusion and electroluminescence light production in the NEXT TPC. The new method yields highly refined 3D images of reconstructed events, and, as a result, significantly improves the topological background discrimination. When applied to real-data 1.6 MeV e - e + pairs, it leads to a background rejection factor of 27 at 57% signal efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Enhancing traffic safety analysis with digital twin technology: integrating vehicle dynamics and environmental factors into microscopic traffic simulation

Traffic safety is a critical concern in transportation engineering and urban planning. Traditional traffic safety analysis requires trained observers to collect data in the field, which is time-consuming, labor-intensive, and sometimes inaccurate. In recent years, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, have been utilized to study traffic safety. However, microscopic traffic simulation only focuses on traffic-related factors, such as traffic volume, traffic signals, and lane configurations, neglecting vehicle dynamics and environment-related factors like weather and lighting conditions, which can significantly impact traffic safety. In light of this, this paper explores the application of digital twin technology in traffic safety analysis, integrating vehicle simulators, which consider vehicle dynamics and environmental factors, and microscopic traffic simulators, which simulate the operations of traffic flow, for enhanced safety evaluations. Various scenarios, including different weather conditions and visibility levels, are simulated using a digital twin of a road segment in Tuscaloosa, Alabama. The simulations employ Surrogate Safety Measures (SSMs) like Time to Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) to assess safety under varying conditions. The results demonstrate that traffic digital twin can identify potential safety issues that traditional microscopic simulation cannot, providing insights for improving traffic control strategies and transportation infrastructure to enhance traffic safety.

Civil engineering↗

Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs

Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. Here, in this study, we report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core–shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in fields such as biosensing, super-resolution microscopy and three-dimensional printing, can emit visible and ultraviolet light from near-infrared excitations. We report a large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use these data to train a heterogeneous graph neural network using a physically motivated representation of UCNP nanostructure. Applying gradient-based optimization on the trained graph neural network, we identify structures with 6.5× higher predicted emission under 800-nm illumination than any UCNP in our training set. Our work reveals design principles for UCNP heterostructures and presents a roadmap for DL-based inverse design of nanomaterials.

Sivonxay, Eric [Lawrence Berkeley National Laborat↗

High-bandwidth CMOS-voltage-level electro-optic modulation of 780 nm light in thin-film lithium niobate

Integrated photonics operating at visible-near-infrared (VNIR) wavelengths offer scalable platforms for advancing optical systems for addressing atomic clocks, sensors, and quantum computers. The complexity of free-space control optics causes limited addressability of atoms and ions, and this remains an impediment on scalability and cost. Networks of Mach-Zehnder interferometers can overcome challenges in addressing atoms by providing high-bandwidth electro-optic control of multiple output beams. Here, we demonstrate a VNIR Mach-Zehnder interferometer on lithium niobate on sapphire with a CMOS voltage-level compatible full-swing voltage of 4.2 V and an electro-optic bandwidth of 2.7 GHz occupying only 0.35 mm 2 . Our waveguides exhibit 1.6 dB/cm propagation loss and our microring resonators have intrinsic quality factors of 4.4 × 10 5 . This specialized platform for VNIR integrated photonics can open new avenues for addressing large arrays of qubits with high precision and negligible cross-talk.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

A 3-Year Sample of Almost 1,600 Elves Recorded Above South America by the Pierre Auger Cosmic-Ray Observatory

Elves are a class of transient luminous events, with a radial extent typically greater than 250 km, that occur in the lower ionosphere above strong electrical storms. We report the observation of 1,598 elves, from 2014 to 2016, recorded with unprecedented time resolution (100 ns) using the fluorescence detector (FD) of the Pierre Auger Cosmic-Ray Observatory. The Auger Observatory is located in the Mendoza province of Argentina with a viewing footprint for elve observations of 3∙10sup>6 km ₂, reaching areas above the Pacific and Atlantic Oceans, as well as the Córdoba region, which is known for severe convective thunderstorms. Primarily designed for ultrahigh energy cosmic-ray observations, the Auger FD turns out to be very sensitive to the ultraviolet emission in elves. The detector features modified Schmidt optics with large apertures resulting in a field of view that spans the horizon, and year-round operation on dark nights with low moonlight background, when the local weather is favorable. The measured light profiles of 18% of the elve events have more than one peak, compatible with intracloud activity. Within the 3-year sample, 72% of the elves correlate with the far-field radiation measurements of the World Wide Lightning Location Network. The Auger Observatory plans to continue operations until at least 2025, including elve observations and analysis. To the best of our knowledge, this observatory is the only facility on Earth that measures elves with year-round operation and full horizon coverage.

79 ASTRONOMY AND ASTROPHYSICS↗

Design of a neutral thermal scattering (NeTS) module for hydrogen in light water

The accurate representation of thermal scattering law (TSL) data is integral to the design and characterization of many modern nuclear systems, particularly those using light water as a moderator/coolant. As a material-dependent distribution over energy-momentum phase space, the TSL may exhibit a variety of unique and relevant conditional (e.g., temperature, pressure) and compositional (e.g., porosity, stoichiometry, radiation damage) dependencies. Currently, there are various approaches to incorporate temperature dependence, which is especially important in coupled neutronic-thermal hydraulic simulations. In each approach, there is an inherent tradeoff between memory consumption and accuracy. Some techniques require tens to hundreds of MBs or more, while others fail to reproduce the underlying data to within 10% error over the considered input domain, despite having a reduced storage burden. This work aims to address both sides of the tradeoff simultaneously by implementing a novel deep learning (DL) approach to TSL representation. The neural thermal scattering (NeTS) concept, which is amenable to an arbitrary number of dependencies, is demonstrated via the inclusion of temperature dependence into a highly compact, highly accurate functional form of the multi- variate TSL (i.e., S(α, β, T)) for hydrogen in light water. Resulting storage requirements are on the order of 100 kB, and median and maximum percent deviations are on the order of 0.1% and 1%, respectively. These measures represent a step improvement over previous techniques. Notably, the developed neural network and feature methodology build on those employed in prior work on beryllium oxide. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Soil carbon stocks in temperate grasslands differ strongly across sites but are insensitive to decade-long fertilization

We report enhancing soil carbon (C) storage has the potential to offset human-caused increases in atmospheric CO 2 . Rising CO 2 has occurred concurrently with increasing supply rates of biologically limiting nutrients such as nitrogen (N) and phosphorus (P). However, it is unclear how increased supplies of N and P will alter soil C sequestration, particularly in grasslands, which make up nearly a third of non-agricultural land world-wide. Here, we leverage a globally distributed nutrient addition experiment (the Nutrient Network) to examine how a decade of N and P fertilization (alone and in combination) influenced soil C and N stocks at nine grassland sites spanning the continental United States. We measured changes in bulk soil C and N stocks and in three soil C fractions (light and heavy particulate organic matter, and mineral-associated organic matter fractions). Nutrient amendment had variable effects on soil C and N pools that ranged from strongly positive to strongly negative, while soil C and N pool sizes varied by more than an order of magnitude across sites. Piecewise SEM clarified that small increases in plant C inputs with fertilization did not translate to greater soil C storage. Nevertheless, peak season aboveground plant biomass (but not root biomass or production) was strongly positively related to soil C storage at seven of the nine sites, and across all nine sites, soil C covaried with moisture index and soil mineralogy, regardless of fertilization. Overall, we show that site factors such as moisture index, plant productivity, soil texture, and mineralogy were key predictors of cross-site soil C, while nutrient amendment had weaker and site-specific effects on C sequestration. This suggests that prioritizing the protection of highly productive temperate grasslands is critical for reducing future greenhouse gas losses arising from land use change.

59 BASIC BIOLOGICAL SCIENCES↗

Optimization of Energy Flow through Synthetic Metabolic Modules and Regulatory Networks in a Model Photosynthetic Eukaryotic Microbe

Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methods, coupled with functional and synthetic genomics, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require a detailed systems biology understanding of photosynthetic processes and cellular metabolism. Genome-scale metabolic models (GEMs) are at the core of systems analysis of cellular processes and form a common organizational framework for analyses of data resulting from functional genomics experimental work and computational studies. Therefore, there is a clear demand for high quality photosynthetic model organisms and the appropriate computational tools that enable systems analysis of light-driven metabolism. Through research conducted we expanded the currently available repertoire of photosynthetic GEMs to include the commercially valuable model diatom Phaeoctylum tricornutum. Diatoms have a peculiar and distinct evolutionary footprint and represent a major eukaryotic lineage that is taxonomically and functionally distinct from green and red algae and vascular plants. Therefore, the true potential for light-driven metabolism aimed at biofuel production remains poorly understood at a systems level for a large subset of the global diversity of photosynthetic organisms. The metabolic capabilities of P. tricornutum were comparatively modeled with those from other photosynthetic groups in order to elucidate the occurrence of metabolic traits within and between phototrophs. Additionally, this research resulted in significant extension of the COnstraints Based Reconstruction and Analysis (COBRA) Toolbox to accommodate the crucial need for infrastructure required for ‘omics data integration and analysis in the context of genome-scale models. Therefore, the proposed research achieved two important goals. First, within the broad scope of photosynthetic organisms, we functionally compared and, as a result, identified cellular processes that require optimization in order to enable deployment as biofuel feedstock. Second, the proposed research resulted in development of key computational infrastructure, which can be further extended to other biological systems, that is currently lacking but necessary for multiple ‘omics data integration.

59 BASIC BIOLOGICAL SCIENCES↗

Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks, Frontiers in Earth Science: Dataset

This data package contains watershed modeling inputs and outputs as well as deep neural networks training and testing results used in "Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks" (Cromwell et al., 2021). We train various deep neural network models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models. For detailed information regarding watershed model description and DNNs setup, please refer to Cromwell et al., 2021. The deep_learning.zip file contains the inputs and outputs for the different deep neural networks. The watershed_model.zip file contains the inputs and outputs for watershed simulation using ATS. The figures.zip file contains raw figures and their corresponding scripts. For a more detailed description, please see the README.md file within each compressed file.

54 ENVIRONMENTAL SCIENCES↗

Structural Dynamics in UV Curable Resins Resolved by In Situ 3D Printing X-ray Photon Correlation Spectroscopy

Additive manufacturing (AM) is a promising technique to rapidly produce polymeric materials into complex 3-dimensional (3D) geometries. While AM is widespread and relevant for a range of applications, implementation in industry has outpaced our fundamental understanding of polymer dynamics and structure development during the printing process. Characterization and quantification of such dynamics is necessary to optimize final material properties and design future materials and processes for 3D printing. Here, we utilize X-ray photon correlation spectroscopy (XPCS) to measure spatial and time-resolved, out-of-equilibrium dynamics during direct ink write (DIW) 3D printing. Specifically, we investigate the progression of structural dynamics in a dual cure (UV/thermal) nanocomposite during and directly after printing. As the filament is printed and cured in situ, the relaxation processes of the cross-linking network are measured through the dynamics of inorganic filler particles. The characteristic relaxation time of the dynamics is calculated through the intensity–intensity autocorrelation function g 2 and directly correlated to the printing process parameters, such as printhead velocity and UV light intensity. The time-resolved evolution of nanoscale dynamics follows a power-law dependence as the filament is cured. Additionally, bulk rheological characterizations reveal the macroscopic solidification of the resin, providing correlation of material properties across a wide range of length and time scales. Overall, the measurement of multiscale, out-of-equilibrium dynamics provides insight into the development of structure in polymer nanocomposite filaments during 3D printing and is used to further understand the influence of such parameters on the AM process.

36 MATERIALS SCIENCE↗

Decoupled few-femtosecond phase transitions in vanadium dioxide

Abstract The nature of the insulator-to-metal phase transition in vanadium dioxide (VO 2 ) is one of the longest-standing problems in condensed-matter physics. Ultrafast spectroscopy has long promised to determine whether the transition is primarily driven by the electronic or structural degree of freedom, but measurements to date have been stymied by their sensitivity to only one of these components and/or their limited temporal resolution. Here we use ultra-broadband few-femtosecond pump-probe spectroscopy to resolve the electronic and structural phase transitions in VO 2 at their fundamental time scales. Our experiments show that the system transforms into a bad-metallic phase within 10 fs after photoexcitation, but requires another 100 fs to complete the transition, during which we observe electronic oscillations and a partial re-opening of the bandgap, signalling a transient semi-metallic state. Comparisons with tensor-network simulations and density-functional theory calculations show these features result from an unexpectedly fast structural transition, in which the vanadium dimers separate and untwist with two different timescales. Our results resolve the structural and electronic nature of the light-induced phase transition in VO 2 and establish ultra-broadband few-femtosecond spectroscopy as a powerful tool for studying quantum materials out of equilibrium.

Science & Technology - Other Topics↗

Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning

Here, we present the first reported quantification of trace elements in plutonium via a portable laser-induced breakdown spectroscopy (LIBS) device and demonstrate the use of chemometric analysis to enhance the handheld device's sensitivity and precision. Quantification of trace elements such as iron and nickel in plutonium metal via LIBS is a challenging problem due to the complex nature of the plutonium optical emission spectra. While rapid analysis of plutonium alloys has been demonstrated using portable LIBS devices, such as the SciAps Z300, their detection limits for trace elements are severely constrained by their achievable pulse power and length, light collection optics, and detectors. In this paper, analytical methods are evaluated as a means to circumvent the detection constraints. Three chemometric methods often used in analytical spectroscopy are evaluated; principal component regression, partial least-squares regression, and artificial neural networks. These models are evaluated based on goodness-of-fit metrics, root mean-squared error, and their achievable limits of detection (LoDs). Partial least squares proved superior for determining content of iron and nickel in plutonium metal, yielding LoDs of 15 and 20 ppm, respectively. These results of identifying the undesirable trace elements in plutonium components are critical for applications such as fabricating radioisotope thermoelectric generators or nuclear fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

A Co-Registered In-Situ and Ex-Situ Tensile Properties Dataset from a Laser Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-11)

This release contains a co-registered in-situ and ex-situ Peregrine dataset from five Concept Laser M2 Laser Powder Bed Fusion (L-PBF) stainless steel 316L builds containing 6,299 SS-J3 individually tracked tensile coupons. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise visible-light in-situ imaging data, the laser scan paths and parameters, in-situ temporal sensor data, room-temperature static tensile test results, and the target part geometries. Additionally, anomaly detections produced by a modified Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided.

36 MATERIALS SCIENCE↗

Network-Level Traffic Signal Cooperation: A Higher-Order Conflict Graph Approach

Traffic signal control and cooperation are extremely important to alleviate traffic congestion in a large traffic network. This study develops a higher-order conflict graph approach for network-wide traffic signal control and cooperation. A conflict graph is applied to model the traffic signal configurations, which identifies the conflict and unconflicted movements for each intersection. In conflict graph, the node represents each movement. The weight of each node can be defined as traffic volume, queue length, fuel consumption, or any weighted combinations of these measurements. The calculation of the optimal green light duration and green light sequence (for different movements) is equivalent to sequentially finding the maximum weight independent set (MWIS) in the conflict graph. The conflict graph also provides a uniform and efficient way to connect traffic signal operations among nearby intersections spatially. Then, we introduced the concept of the k -th order neighborhood to model the degree of connectivity between each movement to the movements at upstream or downstream intersections. The weight of each node in the higher-order conflict graph not only represents its own congestion level, but also relates to the traffic conditions of nearby intersections. Through this approach, the cooperation of multiple intersections can be realized by incorporating their spatial connectivity into conflict graph and solving the MWIS problem. A simulation network is built in SUMO to test the effectiveness of the proposed method. Results suggested that the proposed model outperformed other state-of-the-art signal control methods. Also, the scheme maintains good performance under varying traffic demands.

42 ENGINEERING↗