Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “process influence”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Prospects and limitations for constraining light relics with primordial abundance measurements

The light relic density affects the thermal and expansion history of the early Universe, leaving a number of observable imprints. We focus on the primordial abundances of light elements produced during the process of big bang nucleosynthesis that are influenced by the light relic density. Primordial abundances can be used to infer the density of light relics and thereby serve as a probe of physics beyond the standard model. We calculate the observational uncertainty on primordial light element abundances and associated quantities that would be required in order for these measurements to achieve sensitivity to the light relic density comparable to that anticipated from upcoming cosmic microwave background surveys. We identify the nuclear reaction rates that need to be better measured to maximize the utility of future observations. In this work, we show that improved measurements of the primordial helium-4 abundance can improve constraints on light relics, while more precise measurements of the primordial deuterium abundance are unlikely to be competitive with cosmic microwave background measurements of the light relic density.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Solar Cell Metallization Wear is Sensitive to Loading Frequency

Here, we performed cyclic loading of photovoltaic laminates with precracked silicon cells to explore if and how loading frequency and contact pressure influence the ensuing gridline wear-out process. A measurement of parallel resistance across cracked gridlines on a laminated cell coupon was used as the metric for gridline electrical contact degradation. A statistical analysis of variance (ANOVA) analysis of the experimental results discerned that loading frequency is a more significant factor than contact pressure for gridline degradation.

14 SOLAR ENERGY↗
DOE OSTI2024

Feedstock variability impacts the bioconversion of sugar and lignin streams derived from corn stover by Clostridium tyrobutyricum and engineered Pseudomonas putida

Abstract Feedstock variability represents a challenge in lignocellulosic biorefineries, as it can influence both lignocellulose deconstruction and microbial conversion processes for biofuels and biochemicals production. The impact of feedstock variability on microbial performance remains underexplored, and predictive tools for microbial behaviour are needed to mitigate risks in biorefinery scale‐up. Here, twelve batches of corn stover were deconstructed via deacetylation, mechanical refining, and enzymatic hydrolysis to generate lignin‐rich and sugar streams. These batches and their derived streams were characterised to identify their chemical components, and the streams were used as substrates for producing muconate and butyrate by engineered Pseudomonas putida and wildtype Clostridium tyrobutyricum , respectively. Bacterial performance (growth, product titers, yields, and productivities) differed among the batches, but no strong correlations were identified between feedstock composition and performance. To provide metabolic insights into the origin of these differences, we evaluated the effect of twenty‐three isolated chemical components on these microbes, including three components in relevant bioprocess settings in bioreactors, and we found that growth‐inhibitory concentrations were outside the ranges observed in the streams. Overall, this study generates a foundational dataset on P. putida and C. tyrobutyricum performance to enable future predictive models and underscores their resilience in effectively converting fluctuating lignocellulose‐derived streams into bioproducts.

09 BIOMASS FUELS↗
DOE OSTI2025

Improving leaf spring phenology modelling for temperate tree species: An integration of the Farquhar–Medlyn photosynthesis model with the optimality‐based approach

Spring leaf phenology in temperate tree species is highly sensitive to climate change and significantly affects plant photosynthetic performance, resource utilization, competition and trophic interactions, thereby impacting various ecosystem functions. Although optimality-based (OPT) approaches for modelling spring phenology are increasingly recognized, the optimal representation of the underlying principle (balancing photosynthesis gains with chilling risks) remains controversial. Here, we integrated a coupled Farquhar–Medlyn photosynthesis model into an existing OPT model, and termed the resulting model R-OPT, and evaluated its performance using the PEP725 dataset, which includes 409,144 site-species-year records from across Europe. Our results show that R-OPT outperforms both the default OPT and non-optimality-based models (e.g. the chilling-forcing trade-off and growing degree day models). This improved performance is consistent within and across five focal tree species but varies by region: R-OPT excels in lowland, moist environments but is less effective in high-altitude, cold, and dry areas, possibly due to an incomplete representation of environmental constraints on photosynthetic carbon gain in these regions. Our research advances leaf spring phenology modelling by emphasizing an optimality principle that balances photosynthetic carbon gain with chilling risk, improving the representation of plant photosynthesis processes and enhancing understanding of environmental factors influencing phenology in the context of climate change.

coupled Farquhar–Medlyn photosynthesis model↗
DOE OSTI2020

Effects of nitrogen enrichment on tree carbon allocation: A global synthesis

Aim: Increased atmospheric nitrogen deposition may have profound effects on tree carbon allocation dynamics. However, a comprehensive understanding of how nitro-gen (N) enrichment influences carbon (C) allocation across plant functional processes and tree organs in individual trees remains elusive. Location: Global forest ecosystems. Time period: 1990–2018. Major taxa studied: Trees. Methods: We compiled data from 75 N addition experiments and conducted a meta-analysis to evaluate the responses of C source (photosynthesis), sinks (growth and respiration) and storage (non-structural carbohydrate concentrations) in different tree organs (foliage, above-ground wood and roots) to N enrichment. Results: N enrichment significantly enhanced C supply via photosynthesis (+39.6%, n = 128). C allocation to growth (biomass increment/production) significantly in-creased in foliage (+15.9%, n = 68) and above-ground wood (+31.8%, n = 64; bole, branch, stem and/or twig) with increasing N availability, but not in roots, whereas allocation increased in roots via increasing fine root turnover rate (+22.6%, n = 11). N fertilization significantly increased C allocation to respiration in above-ground wood (+46.6%, n = 12) and roots (+5.5%, n = 57), but not in foliage. N addition decreased non-structural carbohydrate (NSC) concentrations in foliage (-5.4%, n = 16) and roots (-5.0%, n = 21), but increased NSC in above-ground wood (+6.1%, n = 22). In addition, N enrichment effects were strongly affected by moderator variables. Main conclusions: Our results demonstrate that N addition increased C allocation to growth and respiration more strongly than C allocation to NSC storage, and increased C allocation to above-ground parts more strongly than to below-ground parts. Our results are useful for better understanding the response of tree functional processes at organ level to N enrichment. The existing data also reveal that more long-term ex-perimental studies on mature trees in tropical and boreal forests are urgently needed to provide a basis for forecasting tree responses to N enrichment at the global scale.

Li, Weibin↗
DOE OSTI2021

Controlled synthesis and ordered assembly of Co 3 O 4 nanowires using genetically engineered bacterial flagella as biotemplates (Final Technical Report)

The major goals of the project are to gain a fundamental understanding on the influence of the displayed peptides and other processing parameters on the flagella assembly and the resulting biomimetic control over the synthesis and assembly of Co 3 O 4 nanoparticles (NPs) and nanowires (NWs). To pursue these goals, we designed two specific aims: Aim 1: Understanding the self-assembly of genetically engineered flagella and its outcome for site-specific nucleation and growth of Co 3 O 4 NPs and NWs. Aim 2: Understanding controllable alignment of 1D Co 3 O 4 -flagella NWs into highly ordered 2D and 3D structures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Verifying LLM generative agents reflect human behavior in contested information environments to effectively simulate disinformation campaigns (Proteus)

Disinformation poses a significant and evolving threat to today’s online environment. Individuals encounter challenges in detecting disinformation, subsequently influencing their behavior and decision-making processes. Our research examines the potential use of large language model (LLM) generative agents (LGAs) to replicate human behavior to better understand how disinformation is spread in online environments. Using human subjects research, we first investigate how personality traits, individual differences, and demographic factors relate to decision-making in simulated online disinformation environments. Then, we examine whether LGAs can effectively replicate human responses in the same simulated online environments when assigned personality traits, demographic characteristics and behavioral attributes. Our findings indicate that LGAs can align with human decisions in these scenarios; however, alignment is contingent upon scenario context, persona settings and LLM selection. Results provide valuable insights for methodology refinement in future research and in utilizing LGAs to model complex national security challenges such as disinformation campaigns.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Entry and Disposition of Zika Virus Immune Complexes in a Tissue Culture Model of the Maternal-Fetal Interface

Zika virus (ZIKV) infections have been associated with an increased incidence of severe microcephaly and other neurodevelopmental disorders in newborn babies. Passive immunization with anti-ZIKV neutralizing antibodies has the potential to become a feasible treatment or prophylaxis option during pregnancy. Prior to clinical use, such antibodies should be assessed for their ability to block ZIKV passage to the fetus. We used human placental and mammalian cell monolayers that express FcRn and laboratory preparations of anti-ZIKV antibodies as a model system to investigate the disposition of ZIKV/antibody immune complexes (ICs) at the maternal-fetal interface. We further characterized solution properties of the ICs to evaluate whether these are related to in vitro effects. We found that both ZIKV and ZIKV envelope glycoprotein can enter and passage through epithelial cells, especially those that overexpress FcRn. In the presence of ZIKV antibodies, Zika virus entry was bimodal, with reduced entry at the lowest (0.3–3 ng/mL) and highest (µg/mL) antibody concentrations. Intermediate concentrations attenuated inhibition or enhanced viral entry. With respect to anti-ZIKV antibodies, we found that their degradation was accelerated when presented as ICs containing increased amounts of ZIKV immunogen. Of the two monoclonal antibodies tested, the preparation with higher aggregation also exhibited higher degradation. Our studies confirm that intact Zika virus and its envelope immunogen have the potential to enter and be transferred across placental and other epithelial cells that express FcRn. Presence of anti-ZIKV IgG antibodies can either block or enhance cellular entry, with the antibody concentration playing a complex role in this process. Physicochemical properties of IgG antibodies can influence their degradation in vitro.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Advancing automated classification of atmospheric aerosols from Single-Particle Mass Spectrometry (SPMS) data remains challenging due to overlapping ion signatures, compositional diversity, and limited labeled data. This study evaluates supervised and semi-supervised learning frameworks to enhance aerosol identification by jointly leveraging labeled and unlabeled spectra. Four models were compared: a supervised Support Vector Machine (SVM), a self-training SVM, a stacked autoencoder classifier, and a stacked autoencoder trained using a temporal-ensembling Mean Teacher approach. All models achieved high and stable accuracies (90.0 %–91.1 %), surpassing previous results on the same dataset (87 %) and matching the performance of state-of-the-art deep learning methods. Despite small global metric differences (≤ 1 %), semi-supervised variants yielded up to 5 %–10 % improvements for compositionally rare particle types – such as soot (0.77 % of spectra, F1-score: 0.93–0.97) and hazelnut pollen (0.98 % of spectra, F1-score: 0.97–1.00) – equating to roughly ∼ 187 additional correctly classified spectra. These gains are scientifically significant, as such rare particles exert disproportionate influence on radiative absorption and ice nucleation processes; their improved detection reduces modeled uncertainties in aerosol absorption optical depth and mixed-phase cloud ice nucleation rates. The models' residual misclassifications (≈ 9 %) largely arise from true spectral overlap among chemically adjacent species (e.g., Na- vs. K-feldspar, coated vs. uncoated feldspars), reflecting physical compositional continuity rather than algorithmic error. Collectively, these findings demonstrate that leveraging unlabeled data to learn robust spectral representations and refine classification enhances both fidelity and interpretability, bridging data-driven analysis with aerosol–climate process understanding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Recovery of valuable metals from electronic waste using a novel ammonia-based hydrometallurgical process

The growing quantity of waste electrical and electronic equipment (WEEE), also known as electronic waste (E-waste), has been an area of growing public concern. The abundance of valuable metals contained in waste printed circuit boards (WPCBs) has made it a promising secondary resource, especially for Cu and Au. Although the recovery of metals from WPCBs via hydrometallurgical routes has been studied extensively over the past 20 years, most of the research has been limited in the laboratory. In current hydrometallurgical processes, strong acids and expensive oxidizers are often used to ensure a high recovery of metals without considering the sustainability aspects of the environment and economics. To improve upon current hydrometallurgical offerings, the current study seeks to develop an energy-saving, environmentally friendly, economic and sustainable process to efficiently recover the valuable metals from real-world end-of-life WPCBs. The new contributions presented in this study are 1) design and evaluation of a comprehensive hydrometallurgical flowsheet; 2) employment of real end-of-life PCBs as feed materials in an investigation on Cu-NH3 leaching kinetics; 3) further application of kinetic model on a counter flow process simulation; and 4) evaluation of the influences by co-existing metals in Au-S2O3 leaching and recommendation for favorable leaching conditions.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Influence of Shear Strength Assumptions on BISON Debonding Simulations

Accurately predicting the thermomechanical response of buffer–IPyC debonding in TRISO fuel particles requires reliable mechanical property inputs for each coating layer, particularly the normal and shear strengths that influence interlayer delamination and stress concentrations. Micro tensile testing of AGR-2 fuel particles provided experimentally measured normal strengths for the buffer, IPyC, and buffer–IPyC interface; however, shear strength was not measured. As a result, BISON simulations of interface debonding must rely on assumed shear strength values, typically estimated as 20–40% of the measured ultimate tensile strength. This study evaluates how these assumed shear strength values influence cohesive zone model (CZM) predictions of buffer–IPyC separation in AGR 2 TRISO particles. Using micro tensile data from three AGR 2 compacts (2 1 3, 5 1 3, and 6 3 3), BISON simulations were performed with multiple shear strength assumptions to quantify their effect on radial and tangential stress evolution, debonding, and gap propagation. The results show that shear strength is a high sensitivity parameter: increasing the assumed shear strength significantly alters the stress distribution at the buffer–IPyC junction, shifts the predicted debonding location, and changes the extent of partial gap formation. While normal strength controls the initiation of interface separation, shear strength strongly influences the mode mixity of the failure process and the resulting stress concentrations transmitted to the IPyC and SiC layers. These findings highlight a critical gap in current TRISO mechanical characterization. Without experimentally measured shear strength, BISON simulations must rely on approximations that introduce uncertainty into predictions of coating layer integrity and fission product barrier performance. Future fuel qualification campaigns should therefore consider measurement of shear strength at the interlayer interfaces to reduce model uncertainty and improve the fidelity of TRISO fuel performance simulations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

The hidden influence of terrestrial groundwater on salt marsh function and resilience

Salt marshes are hotspots of nutrient processing and carbon sequestration. So far, studies addressing spatiotemporal variability in and drivers of salt marsh biogeochemical function, carbon storage and resilience have focused on ocean-driven surface hydrologic influences, neglecting effects of terrestrial hydrology through subsurface connections. Here we evaluate drivers of salt marsh redox potential, a proxy for biogeochemical state, through wavelet analyses and information theory using data from seven marshes. The results point to terrestrial groundwater level as a dominant control on redox variability across all sites. Because redox is a key driver of biogeochemical processes, and specifically oxidation of organic matter that sequesters carbon and maintains marsh elevation, these terrestrial influences are critical to understanding marsh function and evolution. Further, the newly identified links between onshore groundwater levels and marsh redox conditions shift the traditional paradigm and suggest that terrestrial hydrology is a primary control on salt marsh carbon sequestration potential and resilience.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Soil Organic Matter Dynamics in the Critical Zone

Soil is the thin, vital layer of Earth’s surface that forms the foundation of the Critical Zone and sustains life. This chapter explores the intricate dynamics of soil organic matter within the critical zone, focusing on three key thematic areas: deep soil organic matter, wildfire organic matter interactions, and organic matter erosion. Soil organic matter, although a small fraction of soil mass, plays a crucial role in soil function and ecosystem stability. The complexity of soil organic matter arises from its diverse chemical composition and interactions with minerals, which influence its persistence in the environment. This chapter begins by examining deep soil organic matter, which constitutes a significant portion of global carbon storage. We discuss how deep soil organic matter, typically isolated from surface processes, may become vulnerable to decomposition and carbon release due to disturbances. Next, we explore the impacts of fire on soil organic matter, particularly the formation and stability of pyrolyzed organic matter. The decomposition of pyrolyzer organic matter is influenced by its chemical composition and the surrounding environmental conditions, with implications for carbon cycling and soil fertility in post-fire ecosystems. Finally, the chapter addresses the role of erosion in soil organic matter dynamics. Erosion, accelerated by human activities, redistributes soil organic matter across landscapes, affecting its turnover and the broader biogeochemical cycles. We consider how micro-topographic features and erosion processes interact to influence soil OM stability and carbon sequestration. By synthesizing recent advances in these areas, this chapter provides a comprehensive overview of the complex and dynamic nature of soil organic matter in the critical zone, highlighting its importance in understanding the critical zone in the face of environmental change.

Moreland, Kimber↗
DOE OSTI2020

GPS-Based Gamma Survey for Characterizing and Decommissioning NORM Sites - 20389

Gamma survey techniques are an especially powerful decommissioning tool at naturally occurring radioactive material (NORM) sites due to both the low cost to obtain data over a large spatial scale and the abundance of gamma emitters in the uranium and thorium decay series. Gamma surveys are executed by coupling a detector - most often a sodium iodide crystal - to a global positioning system (GPS), then reporting a location and gross gamma reading coincidentally to a data logger. Systems may be carried by workers or mounted to a car, all-terrain vehicle, or unmanned aerial system (UAS). The resulting data set provides a high-resolution but low precision map of the gamma radiation field over the area surveyed. Frequently this map is also correlated to soil concentrations of NORM radionuclides (most often, Ra-226) and/or exposure rate. Gamma survey parameters such as movement speed, transect spacing, and data logging frequency define the spatial resolution of the resulting surface, and can be optimized depending on the desired survey sensitivity. This paper examines gamma survey as a tool for decommissioning NORM sites and provides an overview of current gamma survey technology designed to improve the efficiency and effectiveness of the decommissioning process. Topics to be discussed in the paper include: - An overview of gamma survey systems, and the utility of different delivery vehicles depending on desired cost, desired spatial resolution, and site topography. - The influence of physical detector characteristics on detection sensitivity and survey planning. - The tradeoff between high-resolution and large spatial extent, but inherently uncertain data, and low-resolution, low spatial extent, but highly certain data, as well as the specific utility of each of these types of data during NORM facility decommissioning. - Confounding variables that may limit the utility of gamma survey at some sites (e.g., radon gas and spatial heterogeneity / hot spots), and methods to plan for and control these conditions. Results show that the confounding variables, such as radon and data output can greatly influence the overall data quality associated with the decommissioning process. In addition, the use of real-time and aerial survey platforms provides a method for ensuring proper spatial extent of the data. When applied thoughtfully, gamma survey is a powerful tool for detecting NORM radionuclides in the environment and a cost-effective technique for identifying areas requiring remediation. However, entities performing or using gamma survey as a decommissioning tool must be aware of both its advantages and its limitations before basing remediation or regulatory action on gamma survey results. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Support Vector Machines for Classification of Direct Energy Deposition Standoff Distance for Improved Process Control

A critical factor in the implementation of direct energy deposition is the ability to maintain the standoff distance between the nozzle and the build surface, as this influences powder capture efficiency and overall part quality. Due to process-related variations, layer height may vary, causing unintended variation in standoff distance and poor build quality. While prior work has utilized contact probing to qualify standoff distance during processing, in situ methods for qualification of standoff distance are of major interest. The present work seeks to understand efficacy of image-based methods for classifying standoff distance variation in real-time using support vector machines (SVMs). It was hypothesized that the size of the melt pool and the amount of spatter will have significant correlations with deviations in the standoff distance; thus, SVMs were used on a dataset that is comprised of morphological features of melt pool size and image entropy. The SVM model was used to classify melt pool images into categories according to standoff distance variation from nominal. K-folds cross validation was used to find the optimal hyperparameters for the SVM model. To understand the impact of the selected features on the classification performance and inference speed, multiple models were trained with differing numbers of included features. Results for classification score, inference time, and image preprocessing/feature extraction from these data are reported. The present results show that the SVM model was able to predict the standoff distance classification with an accuracy of 97 percent and a speed of 0.122 s per image, making it a viable solution for real-time control of standoff distance.

Klesmith, Zoe↗
DOE OSTI2022

KATRIN background due to surface radioimpurities

The goal of the KArlsruhe TRItrium Neutrino (KATRIN) experiment is the determination of the effective electron antineutrino mass with a sensitivity of 0.2 eV/c 2 at 90 % C.L. 1 . This goal can only be achieved with a very low background level in the order of 10 mcps 2 in the detector region of interest. A possible background source are α-decays on the inner surface of the KATRIN Main Spectrometer. Rydberg atoms, produced in sputtering processes accompanying the α-decays, are not influenced by electric or magnetic fields and freely propagate inside the vacuum of the Main Spectrometer. Here, they can be ionized by thermal radiation and the released electrons directly contribute to the KATRIN background. Two α-sources, 223 Ra and 228 Th, were installed at the Main Spectrometer with the purpose of temporarily increasing the background in order to study α-decay induced background processes. In this paper, we present a possible background generation mechanism and measurements performed with these two radioactive sources. Our results show a clear correlation between α-activity on the inner spectrometer surface and background from the volume of the spectrometer. Two key characteristics of the Main Spectrometer background – the dependency on the inner electrode offset potential, and the radial distribution – could be reproduced with this artificially induced background. These findings indicate a high contribution of α-decay induced events to the residual KATRIN background.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Martensite decomposition during rapid heating of Ti-6Al-4V studied via in situ synchrotron X-ray diffraction

Martensite, α‘, commonly appears in Ti-6Al-4V upon rapid cooling from above the β-transus temperature. It is known that α‘ decomposes into α and β at high temperatures but well below the β-transus temperature. Here, we study the decomposition of martensitic Ti-6Al-4V under rapid laser heating, employing in situ synchrotron X-ray diffraction. A comparison is made with post-annealed Ti-6Al-4V under heating to elucidate changes without martensite decomposition. The fast acquisition of X-ray diffraction data at 250 Hz temporally resolves the decomposition process initiated by annihilating dislocations in α‘. The recovery process is accompanied by structural changes in martensite, followed by the phase transformation to β. Thermal profiles estimated from the lattice parameter data reveal the influence of heating rates and dislocation densities on the decomposition process. Throughout the analysis of the diffraction profiles with respect to estimated temperature, we propose a straightforward method for approximating the initiation temperature of martensite decomposition.

42 ENGINEERING↗