High Number Of Data Points Research Articles

Analyzing the coastal sea level trends from SCMR-reprocessed altimeter data: A case study in the northern South China Sea

With improved altimeter data near the coast, it is possible to use them in analysis of coastal sea level trends. In this study, we assess the sea level trends over January 2002-April 2022 with the SCMR-reprocessed altimeter dataset in the coastal zone of the northern South China Sea (SCS). The reliability of this dataset is confirmed by the inter-comparison with the CMEMS (Copernicus Marine Environment Monitoring Service) Level-3 along-track data product and the ESA CCI (European Space Agency Climate Change Initiative) v2.2 virtual station product, along with the validation against tide gauge records. Compared to two altimeter products, the SCMR has a higher number of data points for the analysis of sea level trends in the study region (28439 vs 1408 for CMEMS and 868 for ESA CCI). The monthly SLA time series from the SCMR dataset are more consistent with those from tide gauge records in terms of higher correlation coefficients (0.37–0.83) and lower root mean square of the differences (RMSD, 33.98–92.19 mm) between altimeter and tide gauge. The along-track sea level trends within the 0–20 km coastal strip range between 2–5 mm yr−1, and the degradation of sea level trends occurs when the along-track trends show significant fluctuations at the spatial scales of ∼ 300 m. Therefore, a further data editing strategy is recommended in this regard. We also find that the ENSO-related signals mainly impact on sea levels in the west of Luzon Strait and of the Philippine coast, but are dampened along the Chinese coast over 2002–2022. The regional mean sea level trend is reduced from 3.54 ± 0.85 mm yr−1 to 3.00 ± 0.84 mm yr−1 after removing the ENSO-related low-frequency variability. Finally, the variation of sea level trends from offshore oceans to coasts is found to be nonlinear, indicating the complexity of sea level changes in the study area. The blending of tide gauge records and the SCMR dataset has been successfully conducted in four tide gauges over a longer period of January 1993-April 2022, which would contribute to the analysis of long-term sea level trends at the local scales.

An improved crack probability model for silicon oxide layers using three-parameter Weibull analysis

For semiconductor dielectric layer crack detection in real-time we developed an efficient and very accurate method, which is based on acoustic emission (AE) testing and simulates the wafer probing process. The crack probability is determined using clustered and filtered AE data, that is collected by an advanced sensor-indenter system. The derived distribution function is based on two-parameter Weibull analysis, which is commonly applied for stress-induced failures of brittle materials like silicon oxide (SiO2). During indenter experiments, we observed, that the probability model for a higher number of data points is becoming inaccurate, especially at low fracture forces. Therefore, we introduce a more accurate statistical model for probing-induced SiO2 layer cracks, which is, according to a three-parameter Weibull distribution, considering a location parameter. Experimental results have shown, that the improved distribution function fits much better for predicting low-ppm failure rates of back-end-of-line (BEOL) stacks of CMOS technologies. This enables more reliable pad stack design rules and increases the reliability of complex chip designs regarding mechanical stress during test and assembly processes.

Epileptic Seizure Detection Using Feature Importance and ML Classifiers

Epilepsy seizure (ES) monitoring and detection are only two examples of the many problems that maybe addressed by combining the Internet of Medical Things (IoMT) with machine learning (ML)techniques and cloud computing services. Epilepsy, a potentially fatal neurological disorder, is aworldwide problem that poses a significant threat to human health. There is an urgent need for areliable way of identifying epileptic seizures in their early stages to save thousands of epileptic patientsevery year. With the use of IoMT, several medical treatments, such as epileptic monitoring, diagnosis,and other procedures, may be performed remotely, hence lowering healthcare costs and enhancingservice quality. EEG datasets have made use of feature importance-based data reduction to address theproblem of a high number of data points and improve the delivery of service to the end user. In thisarticle, we use the feature importance method by applying two popular machine learning techniquesextra tree classifier (ETC) and the extreme gradient boosting classifier (XGBoost). Finally, theperformance of a number of tests is evaluated using experimental data from Bonn University. Alsoachieved is a comparison of the two approaches used. The collected findings demonstrate the efficacyof the XGBoost technique and its greater accuracy in comparison to the ETC strategy

High-speed Digital Image Correlation (DIC) for measuring deformation and vibration of fast rotating fan blades

Due to ecological requirements, the bypass-ratio of future civil turbofan engines will be increased. This leads to ultra-high bypass ratio (UHBR) engines, which bring in new challenges concerning the blade material, structural integration, and aeroelastic behaviour of the fan. The ambition of the project Composite fan Aerodynamic, Aeroelastic, and Aeroacoustic Validation Rig (CA3ViAR) is to design and test an open-test-case fan that experiences instability mechanisms, which are representative for UHBR fans of civil aircrafts. The optical measurement technique Digital Image Correlation (DIC) allows for the spatial measurement of deformations. The aim is to apply this technique to measure rotor blade deformation under loading and its vibration due to aeroelastic phenomena to get a better understanding of the structural and aeroelastic behaviour of the composite fan. However, the high rotational speeds, blade vibration frequencies, and expected amplitudes pose challenges for the measurement setup. In this work, a high-speed DIC system is prepared and tested to measure deformation and vibration of a fast rotating blade. Additionally, the requirements for the DIC setup are defined and presented. The expected blade tip speeds in the CA3ViAR project are up to 295 m/s and the blade frequencies of interest up to 650 Hz. Therefore, particular emphasis must be given to the setup in order to eliminate motion blur due to the rotation and to achieve the required frequency. This leads to a setup with synchronised high-speed cameras and a laser to measure frequency vibrations up to 1 kHz with an exposure time below 210 ns. Test measurements are conducted on a stationary beam and an axial blower with a max. blade tip speed of 50 m/s. A data analysis method is developed and described to eliminate the rigid body rotation, analyse the deformation of each blade compared to a reference condition, and analyse the spatial vibration in the frequency domain for a high number of data points per time step. The results of the structural beam are in agreement with the reference measurements and numerical simulations. By analysing the spatial vibration modes of the axial blower, the 1F-flap mode is identified at 38 Hz. In conclusion, this DIC setup shows promising results for future deformation and vibration measurements on a scaled UHBR fan.

Time-Series Prediction of Gamma-Ray Counts Using XGB Algorithm

Radioactivity is spontaneous and thus not easy to predict when it will occur. The average number of decay events in a given interval can lead to accurate projection of the activity of a sample. The possibility of predicting the number of events that will occur in a given time using machine learning has been investigated. The prediction performance of the Extreme gradient boosted (XGB) regression algorithm was tested on gamma-ray counts for K-40, Pb-212 and Pb-214 photo peaks. The accuracy of the prediction over a six-minute duration was observed to improve at higher peak energies. The best performance was obtained at 1460keV photopeak energy of K-40 while the least is at 239keV peak energy of Pb-212. This could be attributed to higher number of data points at higher peak energies which are broad for NaITi detector hence the model had more features to learn from. High R-squared values in the order of 0.99 and 0.97 for K-40 and Pb-212 peaks respectively suggest model overfitting which is attributed to the small number of detector channels. Although radioactive events are spontaneous in nature and not easy to predict when they will occur, it has been established that the average number of counts during a given period of time can be modelled using the XGB algorithm. A similar study with a NaITi gamma detector of high channel numbers and modelling with other machine learning algorithms would be important to compare the findings of the current study.

Concept splatters: Exploration of latent spaces based on human interpretable concepts

Similarity maps show dimensionality-reduced activation vectors of a high number of data points and thereby can help to understand which features a neural network has learned from the data. However, similarity maps have severely limited expressiveness for large datasets with hundreds of thousands of data instances and thousands of labels, such as ImageNet or word2vec. In this work, we present “concept splatters” as a scalable method to interactively explore similarities between data instances as learned by the machine through the lens of human-understandable semantics. Our approach enables interactive exploration of large latent spaces on multiple levels of abstraction. We present a web-based implementation that supports interactive exploration of tens of thousands of word vectors of word2vec and CNN feature vectors of ImageNet. In a qualitative study, users could effectively discover spurious learning strategies of the network, ambiguous labels, and could characterize reasons for potential confusion.

Feasibility considerations for free-fall tests of gravitational decoherence

Space offers exciting opportunities to test the foundations of quantum physics using macroscopic quantum superpositions. It has been proposed to perform such tests in a dedicated space mission (MAQRO) using matter-wave interferometry with massive test particles or monitoring how the wave function of a test particle expands over time. Such experiments could test quantum physics with sufficiently high precision to resolve potential deviations from its unitary evolution due to gravitational decoherence. For example, such deviations have been predicted by the Diósi–Penrose (DP) model and the Károlyházy (K) model. The former predicts the collapse of massive or large superpositions due to a nonlinear modification of quantum evolution. The latter predicts decoherence because of an underlying uncertainty of space time. Potential advantages of a space environment are (1) long free-fall times, (2) low noise, and (3) taking a high number of data points over several years in a dedicated space mission. In contrast to interferometric tests, monitoring wave function expansion is less complex, but it does face some practical limitations. Here, we will discuss limitations of such non-interferometric experiments due to the limited number of data points achievable during a mission lifetime. Our results show that it will require an interferometric approach to conclusively test for gravitational decoherence as predicted by the DP or K models. In honor of the Nobel prize of Sir Roger Penrose, we will focus our discussion on the Diósi–Penrose model.

Massively scalable density based clustering (DBSCAN) on the HPCC systems big data platform

<span id="docs-internal-guid-919b015d-7fff-56da-f81d-8f032097bce2"><span>Dealing with large samples of unlabeled data is a key challenge in today’s world, especially in applications such as traffic pattern analysis and disaster management. DBSCAN, or density based spatial clustering of applications with noise, is a well-known density-based clustering algorithm. Its key strengths lie in its capability to detect outliers and handle arbitrarily shaped clusters. However, the algorithm, being fundamentally sequential in nature, proves expensive and time consuming when operated on extensively large data chunks. This paper thus presents a novel implementation of a parallel and distributed DBSCAN algorithm on the HPCC Systems platform. The algorithm seeks to fully parallelize the algorithm implementation by making use of HPCC Systems optimal distributed architecture and performing a tree-based union to merge local clusters. The proposed approach* was tested both on synthetic as well as standard datasets (MFCCs Data Set) and found to be completely accurate. Additionally, when compared against a single node setup, a significant decrease in computation time was observed with no impact to accuracy. The parallelized algorithm performed eight times better for higher number of data points and takes exponentially lesser time as the number of data points increases.</span></span>

Hyperspectral Imaging Tera Hertz System for Soil Analysis: Initial Results.

Analyzing soils using conventional methods is often time consuming and costly due to their complexity. These methods require soil sampling (e.g., by augering), pretreatment of samples (e.g., sieving, extraction), and wet chemical analysis in the laboratory. Researchers are seeking alternative sensor-based methods that can provide immediate results with little or no excavation and pretreatment of samples. Currently, visible and infrared spectroscopy, electrical resistivity, gamma ray spectroscopy, and X-ray spectroscopy have been investigated extensively for their potential utility in soil sensing. Little research has been conducted on the application of THz (Tera Hertz) spectroscopy in soil science. The Tera Hertz band covers the frequency range between 100 GHz and 10 THz of the electromagnetic spectrum. One important feature of THz radiation is its correspondence with the particle size of the fine fraction of soil minerals (clay < 2 µm to sand < 2 mm). The particle size distribution is a fundamental soil property that governs soil water and nutrient content, among other characteristics. The interaction of THz radiation with soil particles creates detectable Mie scattering, which is the elastic scattering of electromagnetic waves by particles whose diameter corresponds approximately to the wavelength of the radiation. However, single-spot Mie scattering spectra are difficult to analyze and the understanding of interaction between THz radiation and soil material requires basic research. To improve the interpretation of THz spectra, a hyperspectral imaging system was developed. The addition of the spatial dimension to THz spectra helps to detect relevant features. Additionally, multiple samples can be scanned in parallel and measured under identical conditions, and the high number of data points within an image can improve the statistical accuracy. Technical details of the newly designed hyperspectral imaging THz system working from 250 to 370 GHz are provided. Results from measurements of different soil samples and buried objects in soil demonstrated its performance. The system achieved an optical resolution of about 2 mm. The sensitivity of signal damping to the changes in particle size of 100 µm is about 10 dB. Therefore, particle size variations in the µm range should be detectable. In conclusion, automated hyperspectral imaging reduced experimental effort and time consumption, and provided reliable results because of the measurement of hundreds of sample positions in one run. At this stage, the proposed setup cannot replace the current standard laboratory methods, but the present study represents the initial step to develop a new automated method for soil analysis and imaging.

Air Quality Measurement using Computer Vision and CCTV Footage of Road Traffic

Air Quality is at a steady state of decline throughout the world. While the Indian government, in particular, has been deploying monitoring stations across multiple cities to not only monitor but also establish a cause and effect relationship when it comes to air pollution, these monitoring stations clearly, don’t suffice the actual demands for building a robust model for Air Quality Index. Our goal here is to reduce costs in terms of hardware deployment while, at the same time, provide a higher number of data points of collection on pre-existing infrastructure. The project aims at calculating the air pollution factors at the suburban level using Vehicular Emissions. The idea is to identify the number and type of vehicles from a video feed and then estimate the vehicular pollution levels using the data collected.

Potential for Re-use of Oil and Gas Infrastructure on the Norwegian Continental Shelf

Re-use of existing oil and gas infrastructure (pipelines, installations, wells) is often mentioned as an option to reduce the costs associated to Carbon Capture and Storage (CCS) deployment. The re-use of infrastructure entails, however, some challenges (e.g. infrastructure availability, capacity limitation, state). Total E&P Norge, together with Carbon Limits and NORCE, have looked more in detail at this topic in the 2nd Life CO2 project presented here. The aim of the 2nd Life CO2 project was to study, at a high level and not on a specific case-basis, the potential for re-use of offshore oil and gas pipelines, platforms and wells for CO2 transport throughout the North Sea in general, and the specific storage potential on the Norwegian Continental Shelf based on The Norwegian CO2 Storage Atlas developed by the Norwegian Petroleum Directorate. Alongside, an assessment of the economic and environmental benefits of such re-use was performed, and knowledge and data gaps were identified. In some cases, these gaps will require further de-risking to set up successful CCS projects involving re-use of infrastructure. For pipelines, open data sources from various countries (Norway, United Kingdom, Germany, Denmark and the Netherlands) were used in the project. Several tasks of the project had to be done in an automated way considering the high number of data points. This approach entails some limitations. Examples include specific aspects of the infrastructure, such as flow direction and to what extent pipelines are linked, which have an important impact on availability. The core idea of the approach was to estimate the year in which hydrocarbon fields are depleted (referred to its ‘end of life’) to determine the time at which the connected infrastructure is available. The end of life of each field was estimated based on resource depletion rather than economic cash-flow evaluations. The collected data set of remaining producible hydrocarbon volume was divided by data of current yearly production rate to estimates how long the fields could operate under current conditions. The assumption is that after this time the connected infrastructure (platform, wells and pipelines) becomes available. The corresponding infrastructure was classified and mapped, and only those available in the upcoming 20 years were studied further. Based on the availability mapping exercise, focus was put on the pipelines that link existing CO2 sources / potential hubs and CO2 storages. Both deep saline aquifers and depleted hydrocarbon fields were considered for CO2 storage. The storage potential for relevant storage sites in the vicinity of the reusable infrastructure was evaluated based on results from The Norwegian CO2 Storage Atlas and specific logs in the considered area. The results from this project displayed that overall, the Norwegian Continental Shelf (NCS) showed less infrastructure re-use potential in the coming 20 years compared to the UK continental shelf and offshore Netherlands. This finding was mainly related to the larger and less mature oil and gas fields on NCS. Thus, overall, the Netherlands and the UK seem more suited for smaller-scale re-use projects in a nearer future such as the Acorn project, whilst the Norwegian Continental Shelf seems more suited for larger-scale infrastructure re-use projects in more distant time-horizons. Some interesting cases on the Norwegian Continental Shelf were, however, singled out to be timely available of pipelines linking potential CO2 storage areas to shore. Given the limited number of large emitters in Norway in the vicinity of those pipelines, the best way to re-use those facilities would likely be to build an onshore hub that could import CO2 from large European emitters with an easy access to waterways or seaports. A few relevant cases were selected for economic and environmental assessment to compare the cost and environmental impact of re-use of oil and gas infrastructure for CCS with new built. The results of these assessments are presented in this article. Before launching CCS projects that make use of existing oil and gas infrastructure in the North Sea, there are, however, still significant data and technological uncertainties which need to be resolved. To ensure a satisfactory state of the re-used infrastructure as well as its availability, future re-use projects are recommended to align with decommissioning plans for oil and gas fields and related infrastructure.

Upsampling for Improved Multidimensional Attribute Space Clustering of Multifield Data

Clustering algorithms in the high-dimensional space require many data to perform reliably and robustly. For multivariate volume data, it is possible to interpolate between the data points in the high-dimensional attribute space based on their spatial relationship in the volumetric domain (or physical space). Thus, sufficiently high number of data points can be generated, overcoming the curse of dimensionality for this particular type of multidimensional data. We applies this idea to a histogram-based clustering algorithm. We created a uniform partition of the attribute space in multidimensional bins and computed a histogram indicating the number of data samples belonging to each bin. Without interpolation, the analysis was highly sensitive to the histogram cell sizes, yielding inaccurate clustering for improper choices: Large histogram cells result in no cluster separation, while clusters fall apart for small cells. Using an interpolation in physical space, we could refine the data by generating additional samples. The depth of the refinement scheme was chosen according to the local data point distribution in attribute space and the histogram’s bin size. In the case of field discontinuities representing sharp material boundaries in the volume data, the interpolation can be adapted to locally make use of a nearest-neighbor interpolation scheme that avoids averaging values across the sharp boundary. Consequently, we could generate a density computation, where clusters stay connected even when using very small bin sizes. We exploited this result to create a robust hierarchical cluster tree, apply our technique to several datasets, and compare the cluster trees before and after interpolation.

Generic and Automated Data Evaluation in Analytical Measurement.

In the past year, automation has become more and more important in the field of elemental and structural chemical analysis to reduce the high degree of manual operation and processing time as well as human errors. Thus, a high number of data points are generated, which requires fast and automated data evaluation. To handle the preprocessed export data from different analytical devices with software from various vendors offering a standardized solution without any programming knowledge should be preferred. In modern laboratories, multiple users will use this software on multiple personal computers with different operating systems (e.g., Windows, Macintosh, Linux). Also, mobile devices such as smartphones and tablets have gained growing importance. The developed software, Project Analytical Data Evaluation (ADE), is implemented as a web application. To transmit the preevaluated data from the device software to the Project ADE, the exported XML report files are detected and the included data are imported into the entities database using the Data Upload software. Different calculation types of a sample within one measurement series (e.g., method validation) are identified using information tags inside the sample name. The results are presented in tables and diagrams on different information levels (general, detailed for one analyte or sample).

The Phobos geodetic control point network and rotation model

A new global control point network was derived for Phobos, based on SRC (Mars Express), Phobos-2, and Viking Orbiter image data. We derive 3-D Cartesian coordinates for 813 control points as well as improved pointing data for 202 SRC and Viking images in the Phobos-fixed coordinate system. The point accuracies vary from 4.5m on the Phobos nearside, to up to 67.0m on the farside, where we rely on Viking images (average point accuracy: 13.7m). From tracking of the control points we detect a librational motion synchronous to the Phobos orbital period and measure libration amplitude of 1.09°, in agreement with predictions from shape information assuming a uniform interior. This suggests that the interior of Phobos is homogeneous – but small local mass anomalies, e.g., associated with crater Stickney, cannot be ruled out. Our new control point network has a higher number of data points and higher point accuracy than previous data products and will be an important basis for accurate shape models and maps.

PTMScan Direct: Identification and Quantification of Peptides from Critical Signaling Proteins by Immunoaffinity Enrichment Coupled with LC-MS/MS

Proteomic studies of post-translational modifications by metal affinity or antibody-based methods often employ data-dependent analysis, providing rich data sets that consist of randomly sampled identified peptides because of the dynamic response of the mass spectrometer. This can complicate the primary goal of programs for drug development, mutational analysis, and kinase profiling studies, which is to monitor how multiple nodes of known, critical signaling pathways are affected by a variety of treatment conditions. Cell Signaling Technology has developed an immunoaffinity-based LC-MS/MS method called PTMScan Direct for multiplexed analysis of these important signaling proteins. PTMScan Direct enables the identification and quantification of hundreds of peptides derived from specific proteins in signaling pathways or specific protein types. Cell lines, tissues, or xenografts can be used as starting material. PTMScan Direct is compatible with both SILAC and label-free quantification. Current PTMScan Direct reagents target key nodes of many signaling pathways (PTMScan Direct: Multipathway), serine/threonine kinases, tyrosine kinases, and the Akt/PI3K pathway. Validation of each reagent includes score filtering of MS/MS assignments, filtering by identification of peptides derived from expected targets, identification of peptides homologous to expected targets, minimum signal intensity of peptide ions, and dependence upon the presence of the reagent itself compared with a negative control. The Multipathway reagent was used to study sensitivity of human cancer cell lines to receptor tyrosine kinase inhibitors and showed consistent results with previously published studies. The Ser/Thr kinase reagent was used to compare relative levels of kinase-derived phosphopeptides in mouse liver, brain, and embryo, showing tissue-specific activity of many kinases including Akt and PKC family members. PTMScan Direct will be a powerful quantitative method for elucidation of changes in signaling in a wide array of experimental systems, combining the specificity of traditional biochemical methods with the high number of data points and dynamic range of proteomic methods.

Sedimentation Equilibrium Analysis of Interference Optical Data by Systematic Noise Decomposition

An analytical method is described for removal of systematic signal offsets from interference optical data of sedimentation equilibrium gradients. It is demonstrated that the time-invariant signal contributions can be extracted from hydrodynamic modeling of interference profiles acquired during the approach to sedimentation equilibrium. This method is based on a technique for the explicit algebraic calculation of time-invariant noise components from sedimentation data, recently described for the direct modeling of sedimentation velocity experiments (P. Schuck and B. Demeler, Biophys. J. 76, 2288–2296, 1999). The calculated systematic signal offset is very well defined by the experimental data, stable over time, and its calculation is robust and to a large extent independent of the hydrodynamic model. The calculated time-invariant signal can be used to reduce the systematic errors in the measured sedimentation equilibrium profiles by more than an order of magnitude. It is shown that the resulting net equilibrium fringe profiles after subtraction of the time-invariant noise component allow equilibrium analyses consistent with those obtained from absorbance profiles. However, due to a higher dynamic range and the higher number of data points, the parameters derived from the net interference analysis can exhibit significantly improved precision. The presented study demonstrates the feasibility and potential of this analytical method for full exploitation of the remarkable precision of the interference optical data acquisition system, allowing sedimentation equilibrium experiments at loading concentrations below 0.05 mg/ml.

Exact mass determination of narrow electrophoretic peaks using an orthogonal acceleration time-of-flight mass spectrometer

High efficiency electrically driven separation techniques, such as capillary zone electrophoresis (CZE) and capillary electrochromatography (CEC), yield narrow peak widths of the order of a few seconds. Scanning instruments may have difficulty in obtaining sufficient data points across such narrow peaks (although the use of selected ion monitoring (SIM) mode may overcome this). Orthogonal acceleration time-of-flight ((oa-TOF)) mass analysers have an inherent ability for fast acquisition rates allowing a high number of data points (for example 10 spectra s−1) to be collected across a narrow peak and are capable of operation at resolutions in the region of 5000 full width half maxima (FWHM). This enables spectral data to be measured with accuracies greater than 5 ppm, allowing the molecular formula to be predicted / confirmed. We have investigated the use of an oa-TOF mass analyser coupled to CZE. The on-line separation of a drug substance and related impurities has been achieved. Copyright © 1999 John Wiley & Sons, Ltd.

Optical tomography applied to speckle photographic measurement of asymmetric flows with variable density

Optical tomography is applied to the speckle photographic measurement of an asymmetric flow field with variable fluid density. The convolution back projection algorithm is used for obtaining the 3-D density distribution. Noise in the experimental data is reduced by spline smoothing. The method is verified with a steady, laminar, axisymmetric helium jet exhausting vertically into the ambient air, and then applied to a non-axisymmetric helium jet for determining the helium concentration. It is found that speckle photographic recordings are very adequate for tomographic reconstruction, because they provide a high number of data points from each projection. The influence of the limited number of projections on the reconstruction quality is particularly investigated.

Kinetics of biomass formation and citric acid production by Aspergillus niger on pilot plant scale

AbstractCitric acid fermentation on pilot plant scale was studied, using two strains of Aspergillus niger of different productivity. Growth and product formation were analyzed from a high number of data points. Several phases could be distinguished which were described by a sequence of logarithmic, cube‐root, and linear equations. Growth and product formation could be related by a modified Luedeking‐Piret equation, taking into consideration a lag time for the organism to shift to shift into the physiological state ready for product formation. The observed differences in productivity of the two strains are reflected by the different non‐growth‐associated terms of the equation. In light of these findings the classical "type II fermentation" is redefined as a mixed type–resembling type I during trophophase and type III during idiophases.