Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region

Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.

Understanding crop processing and its social meaning in the Xinzhai period (1850–1750 cal bce): a case study on the Xinzhai site, China

Although the Xinzhai period (1850–1750 cal bce) has been widely regarded as a critical time for the development of urbanization in China, little is known about the labour and social organization of the time. In this paper, archaeobotanical assemblages have been used to explore evidence of crop processing and they have provided further insights into the organization of labour and society at the Xinzhai site on the Central Plain of China. This is the first case study linking agricultural activities and social organization in the Xinzhai period. By discussing macro-botanical and phytolith results together, we conclude that the hulled cereals Setaria italica (foxtail millet), Panicum miliaceum (common or broomcorn millet) and Oryza sativa (rice), and the free-threshing pulse Glycine max (soybean) were all partly processed before storage to reduce labour demand in the harvest period. Since these summer-sown crops are all harvested in autumn, the practice of partial processing might imply that less labour was needed before storage. Thus, the labour for crop processing appears to have been organized on the basis of small production units such as households. This pattern is different from the communal bulk processing of crops before storage by the contemporary inhabitants of Dongzhao. Different patterns of social organization in various settlements in the Xinzhai period can thus be suggested. This conclusion contributes to a comprehensive understanding of the social development of communities living on the Central Plain and indicates that a steady increase in social complexity was very likely in the period before urbanization.

Quantifying Floating Plastic Debris at Sea Using Vessel-Based Optical Data and Artificial Intelligence

Despite recent advances in remote sensing of large accumulations of floating plastic debris, mainly in coastal regions, the quantification of individual macroplastic objects (>50 cm) remains challenging. Here, we have trained an object-detection algorithm by selecting and labeling footage of floating plastic debris recorded offshore with GPS-enabled action cameras aboard vessels of opportunity. Macroplastic numerical concentrations are estimated by combining the object detection solution with bulk processing of the optical data. Our results are consistent with macroplastic densities predicted by global plastic dispersal models, and reveal first insights into how camera recorded offshore macroplastic densities compare to micro- and mesoplastic concentrations collected with neuston trawls.

Principles for managing innovative activities of entities in Russia during the transition to the digital economy

Subject. Budgeting as a management technology is highly sought after by enterprises and groups of companies all over the globe regardless of their industry affiliation and scale of operations. The budgeting methodology is a dynamically developing field of scientific research; it provides tools enabling to solve urgent problems, namely those related to data bulk processing at the stage of planning and generation of budget versions. Objectives. The study aims to justify principles of data modeling in management accounting and budgeting; to develop data modeling tools aimed at achieving the objectives of an enterprise or a group of companies. Methods. In the study, I employ logical analysis methods and a systems approach. Results. The paper substantiates data modeling principles in management accounting and budgeting systems of enterprises and groups of companies. Based on the formulated principles, I developed data modeling tools for implementation in spreadsheets, special applications, and cloud-based technologies. Conclusions. Developing and elaborating the budgeting system of enterprises and groups of companies should be based on data modeling under the principles and approaches to their systematization and structuring formulated in the paper. The offered data modeling tools may be useful in the organization of management accounting and formation of a budget model on all automation platforms, as well as in Microsoft Excel, using Power Pivot and Power Query add-ins.

PartSeg: a tool for quantitative feature extraction from 3D microscopy images for dummies

Background Bioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high-resolution 3D microscopy provides access to an ever-increasing amount of high-quality images, there arises a need for their analysis in an automated, unbiased, and simple way. Segmentation of structures within the cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time, the available segmentation tools provide a steep learning curve for new users with a limited technical background. This is especially apparent in the bulk processing of image sets, which requires the use of some form of programming notation. Results In this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of the cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer an ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists, the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures. Conclusions In this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for the bulk processing of multiple images and its components can be reused in other systems or computational experiments.

Accurate bulk quantitation of droplet digital PCR

Droplet digital PCR provides superior accuracy in nucleic acid quantitation. The requirement of microfluidics to generate and analyze the emulsions, however, is a barrier to its adoption, particularly in low resource or clinical settings. Here, we report a novel method to prepare ddPCR droplets by vortexing and readout the results by bulk analysis of recovered amplicons. We demonstrate the approach by accurately quantitating SARS-CoV-2 sequences using entirely bulk processing and no microfluidics. Our approach for quantitating reactions should extend to all digital assays that generate amplicons, including digital PCR and LAMP conducted in droplets, microchambers, or nanoliter wells. More broadly, our approach combines important attributes of ddPCR, including enhanced accuracy and robustness to inhibition, with the high-volume sample processing ability of quantitative PCR.

ALICE Run 3 Analysis Framework

In LHC Run 3 the ALICE Collaboration will have to cope with an increase of lead-lead collision data of two orders of magnitude compared to the Run 1 and 2 data-taking periods. The Online-Offline (O2) software framework has been developed to allow for distributed and efficient processing of this unprecedented amount of data. Its design, which is based on a message-passing back end, required the development of a dedicated Analysis Framework that uses the columnar data format provided by Apache Arrow. The O2 Analysis Framework provides a user-friendly high-level interface and hides the complexity of the underlying distributed framework. It allows the users to access and manipulate the data in the new format both in the traditional “event loop” and a declarative approach using bulk processing operations based on Arrow’s Gandiva sub-project. Building on the well-tested system of analysis trains developed by ALICE in Run 1 and 2, the AliHyperloop infrastructure is being developed. It provides a fast and intuitive user interface for running demanding analysis workflows in the GRID environment and on the dedicated Analysis Facility. In this document, we report on the current state and ongoing developments of the Analysis Framework and of AliHyperloop, highlighting the design choices and the benefits of the new system.

Translating Fieldwork into Datasets: The Development of a Corpus for the Quantitative Investigation of Grammatical Phenomena in Eibela

This extended abstract details the process of constructing an annotated XML corpus suitable for quantitative analysis of morphosyntactic and phonetic phenomena in the Eibela language of Papua New Guinea. Preliminary results will also be included, which investigate the semantic, phonetic, and discourse correlates of argument realization. The goal of this paper is to illustrate how legacy materials can be enriched and investigated using computational methodologies including forced alignment of phonetic segments using bulk processing of data in Python and R, the Montreal Forced Aligner (MFA), and morphosyntactic annotation developed as part of the Multilingual Corpus of Annotated Spoken Texts (Multi-CAST).

Fluidization Dynamics of Hydrophobic Nanosilica with Velocity Step Changes

Nanosilica is widely used in various applications, with its market expected to grow over USD 5 billion by 2025. The fluidized bed technology, owing to its intimate contact and efficient mixing of phases, is ideally suited for the large scale processing of powders. However, the bulk processing and dispersion of ultrafine nanosilica using the fluidized bed technology are critically affected by the interparticle forces, such that the hydrophilic nanosilica shows agglomerate bubbling fluidization (ABF), while the hydrophobic nanosilica undergoes agglomerate particulate fluidization (APF). This study carried out a detailed investigation into the fluidization hydrodynamic of the hydrophobic nanosilica by monitoring the region-wise dynamics of the fluidized bed subjected to a regular step change of fixed duration in the gas velocity. The gas flow was controlled using a mass controller operated with an analog output signal from a data acquisition system. The analog input data were acquired at the sampling rate of 100 Hz and analyzed in both time and temporal frequency domains. The effect of velocity transients on the bed dynamics was quickly mitigated and appeared as lower frequency events, especially in regions away from the distributor. Despite the apparent particulate nature of the fluidization, strong hysteresis was observed in both pressure drop and bed expansion. Moreover, the fully fluidized bed’s pressure drop was less than 75% of the theoretical value even though the bed appeared to free from non-homogeneities. Key fluidization parameters, e.g., minimum fluidization velocity (Umf) and the agglomerate size, were evaluated, which can be readily used in the large scale processing of nanosilica powders using fluidized bed technology.

PartSeg, a Tool for Quantitative Feature Extraction From 3D Microscopy Images for Dummies

BackgroundBioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high resolution 3D microscopy provides access to an ever-increasing amount of high quality images, there arises a need for their analysis in an automated, unbiased and simple way.Segmentation of structures within cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap.At the same time the available segmentation tools provide a steep learning curve for new users with limited technical background. This is especially apparent in bulk processing of image sets, which requires the use of some form of programming notation.ResultsIn this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images.The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results.Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins.For biologists the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures.ConclusionsIn this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multiscale segmentation algorithms best-suited for this task. PartSeg can also be used for bulk processing of multiple images and its components can be reused in other systems or computational [email protected], [email protected], [email protected]