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3,688 results for “Milling”
Fig. 2 in Circuitry and coding used in a flight mill system to study flight performance of Halyomorpha halys (Hemiptera: Pentatomidae)
Fig. 2. The coding used to record the number of rotations flown by the insect on the flight mill.
Video for Robotic assisted milling for increased productivity
<p>Video in MP4 - Robotic Assisted Milling.mp4</p>
Is tree age or tree size reducing height increment in Abies alba Mill. at its southernmost distribution limit?
<p>Many factors limit height increment of trees when age and size increase in large-statured tree species. Height-diameter allometric relationships are commonly used measures of tree growth.</p> <p>The data were collected in a silver fir forest located in Southern Italy, at the southernmost distribution limit for this species. Through a stratified random sampling, 100 trees were selected. All the selected trees were then felled and the total tree height, height increments (internode distances), diameter at breast height, and diameter increments (ring widths) were measured.</p> <p>Conventional methods for estimating the current annual increment of stand volume are based on the uncertain assumption that height increment decreases with tree age. Conversely, size, rather than age, should be accounted for the observed senescence-related declines in relative growth rate and, consequently, implemented in silvicultural manuals. Results stem from a study on Abies alba Mill. at its southern limit of distribution.</p>
MADFORWATER: WP2: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task 2.3–Agro-industrial wastewater treatment: Subtask 2.3.1. –Treatment of olive mill wastewater (OMWW): Aerobic biological treatment in sequenced batch reactors (SBRs): Subset 1
<p>This dataset contains the data underlying the following publication: Fatma AROUS, Chadlia HAMDI, Souhir KMIHA, Nadia KHAMMASSI, Amani AYARI, Mohamed NEIFAR, Tahar MECHICHI, Atef JAOUANI (2018). Treatment of olive mill wastewater through employing sequencing batch reactor: Performance and microbial diversity assessment. 3 Biotech 2018, 8, 481. https://doi.org/10.1007/s13205-018-1486-6.</p>
Data for the prediction of chatter vibrations in robotic milling of aluminium parts based on previous experiences using neural network
<p>This data has been used for the validation of the software developed by DFKI in collaboration with IDEKO for the prediction of stability in robotic milling of aluminium parts, in the framework of COROMA research project funded by the European Union. www.coroma-project.eu</p> <p>The source of information is stability lobes obtained from FRFs obtained mixing by receptance coupling experimental FRFs of the robot, spindle and toolholder with FRFs of the tool obtained analitycally using beams theory. Real machinings have not been done since they would be very time consuming. Once the stability lobes where available random sampling has been done in the lobes between certain boundaries of axial depth of cut and spindle speed to represent machining with different conditions.</p> <p>The information contained here includes:</p> <p>- Data sets for different conditions, with tools of different diameters and different number of cutting teeth. (in the naming of the folder D represents diameter, Z represents number of teeth).</p> <p>- Most of the data sets also include figures with the milling stability lobe charts for different radial depths of cut and different diameters and number of teeth. In these figures the random sampling representing machining tests has been marked with a black X.</p> <p>- There are also versions of the data sets with different number of samples (20 or 40) in order to test the prediction algorithm with a different number of information.</p> <p>- In the data sets an extended version has been created, representing the know-how of the operator that if a machining is unstable all the machinings with higher axial depth of cut will be unstable, and if a machining is stable all the machinings with lower axial depth of cut will be stable.</p> <p>- Companion documents in PDF format in order to provide more detailed information on the datasets and results.</p> <p>Keywords: Milling, machining, vibration, chatter, stability, prediction, neural network, robot, robotic, AI, artificial intelligence.</p> <p>www.ideko.es<br> www.dfki.de</p> <p>Asier Barrios<br> IDEKO research centre<br> Arriaga Kalea, 2<br> Elgoibar 20870, Spain<br> Phone: +34 943748000<br> abarrios@ideko.es</p> <p>October 2019</p>
Figure 1 in Effects of nanoparticles treatments and salinity stress on the genetic structure and physiological characteristics of Lavandula angustifolia Mill.
Figure 1. The XRD pattern for the used Fe O nanoparticles.
Figure 2 in Effects of nanoparticles treatments and salinity stress on the genetic structure and physiological characteristics of Lavandula angustifolia Mill.
Figure 2. The XRD pattern for the used ZnO nanoparticles.
Deciphering Ball Milling Mechanochemistry via Molecular Simulations of Collision‐Driven and Liquid‐Assisted Reactivity
<p>The direct study of mechanical activation events in ball milling mechanochemistry is, at present, not directly accessible experimentally, which prompted us to examine it by theoretical modelling. We use molecular dynamics simulations to describe mechanical activation by fragmentation of crystal particles down to individual atoms, ions and ion pairs, and monitor subsequent product formation and agglomeration under neat and liquid-assisted conditions. We demonstrate that a small amount of the liquid additive facilitated product formation, while too much of it destabilised the product by stabilising the reactants. Our approach provides a fresh and detailed view of mechanochemistry that could allow for its theoretical optimisation including the targeted choice of a catalyst and its loading. </p>
Combined Calculation of the Yang-Mills Equation and Einstein Field Equation
<p>In this paper, we present a comprehensive analysis and calculation of the combined Yang-Mills equation and Einstein field equation. These equations are two of the fundamental building blocks of modern physics. The Einstein field equation describes gravity as the curvature of spacetime, while the Yang-Mills equation describes the dynamic interactions in quantum field theory. By combining these two equations, we aimed to explore a deeper connection between general relativity and quantum field theory. The calculations include symbolic and numerical evaluations of both equations. First, the Einstein field equation was expressed in matrix form to compute the curvature of spacetime and its relationship to the distribution of matter. Subsequently, we symbolically computed the Yang-Mills equation to describe the interactions of the fields. These symbolic expressions were then combined to obtain an extended equation that integrates the effects of the Yang-Mills fields on the curvature of spacetime. Through numerical calculations, the terms of both equations were evaluated using real physical constants. The Einstein field equation demonstrated that the cosmological constant has the most significant influence on the curvature of spacetime, while the gravitational constant, due to its minuscule size, has only a minor effect. The Yang-Mills equation provided numerical values representing the interactions of the fields, particularly regarding the dynamics of gluons in the quark-gluon plasma.</p> <p> </p> <p>Combination of the Yang-Mills Equation with the Einstein Field Equation: By combining both equations, we were able to conduct a detailed analysis of the interactions of quantum fields with the curvature of spacetime. This combination is of particular interest as it could potentially provide new insights into the structure of the universe, dark matter, and the dynamics of particles in extreme gravitational fields.</p> <p> </p> <p>Central Formula of the Work:</p> <p> </p> <p>G_{\mu\nu} + \Lambda g_{\mu\nu} = \frac{8\pi G}{c^4} T_{\mu\nu}, \quad D_{\mu}F^{\mu\nu} = J^{\nu}</p> <p> </p> <p>The numerical calculations were verified using various validation methods, including NumPy, absolute error validation, and the math.isclose function. All calculations passed these tests, confirming the accuracy of the symbolic and numerical expressions. Notably, the calculation of the eigenvalues of the Yang-Mills field strength tensor provides indications of the presence of a mass gap. This is crucial for understanding the behavior of quarks and gluons in extreme states, as they occur in particle accelerators or in the early universe. Our results suggest that the combination of the Yang-Mills equation with the Einstein field equation could provide new insights into fundamental physical processes. This work lays the foundation for future investigations, particularly regarding the simulation of complex interactions and their experimental validation in high-energy experiments.</p> <p> </p> <p> </p>
Data from paper "Impact of milling machine parameters on the properties of reclaimed asphalt pavement"
<p>The dataset includes test results of road cores and samples gathered from the same locations after milling of asphalt pavement. Milling machine parameters, including moving speed, milling depth, and drum rotational speed were intentionally varied during milling. Results from four jobsites are included in the dataset. The results include binder content, penetration, softening point, coarse and fine aggregate flow coefficients, gradation of RAP, gradation of extracted RAP aggregates, as well as the calculated chunk, breakdown, and filler increase indexes. These three indexes were used to quantitatively characterize the milling operations.</p>
FIG. 2 in A new species of Inga Mill. (Fabaceae, Mimosoideae) from the Guianas
FIG. 2. — Inga loubryana Poncy, herbarium specimen Fanshawe 2136 (= FD 4872) (Guyana).
3DED data of FIB milled lysozyme crystals
<p>This repository contains 3DED data from FIB milled lysozyme crystals. The directory contains the following data files:</p> <p><strong>FIB_Lysozyme_Data</strong></p> <ul> <li>argon <ul> <li>Ar_final.pdb</li> <li>Ar_merged.mtz</li> </ul> </li> <li>xenon <ul> <li>Xe_final.pdb</li> <li>Xe_merged.mtz</li> </ul> </li> <li>gallium <ul> <li>Ga_final.pdb</li> <li>Ga_merged.mtz</li> </ul> </li> </ul> <p>For each FIB source, the *_merged.mtz file contains the merged reflection intensities from multiple datasets milled using that source. The corresponding *_final.pdb file is the final refined model.</p>
Fig. 54 in A revision of the genus Malus Mill. (Rosaceae)
Fig. 54. Morphological characters of Malus florentina (Zuccagni) C.K.Schneid. Drawn by J.C. Li.
Fig. 7 in A revision of the genus Malus Mill. (Rosaceae)
Fig. 7. Distribution of Malus baccata (L.) Borkh. var. gracilis (Rehder) T.C.Ku.
Fig. 33 in A revision of the genus Malus Mill. (Rosaceae)
Fig. 33. Distribution of Malus sikkimensis (Wenz.) Koehne ex C.K.Schneid.
Appendix 2 in A revision of the genus Malus Mill. (Rosaceae)
Appendix 2. Frequency distribution of qualitative morphological parameters.
Fig. 39 in A revision of the genus Malus Mill. (Rosaceae)
Fig. 39. Distribution of Malus kansuensis (Batalin) C.K.Schneid.
Fig. 37 in A revision of the genus Malus Mill. (Rosaceae)
Fig. 37. Distribution of Malus honanensis Rehder.
Fig. 35 in A revision of the genus Malus Mill. (Rosaceae)
Fig. 35. Distribution of Malus tschonoskii (Maxim.) C.K.Schneid.
Fig. 31 in A revision of the genus Malus Mill. (Rosaceae)
Fig. 31. Distribution of Malus transitoria (Batalin) C.K.Schneid.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.