cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5996575
title: Corporate Governance, Corporate Social Responsibility and Corporate Sustainability: The Moderating Role of Top Management Commitment

description: <strong>Design/Methodology/Approach:</strong> We used the non-probability sampling technique by employing convenience sampling for data collection. By employing a survey questionnaire, data were collected from 397 employees of SMEs in Ghana. The IBM Statistical Package for Social Science (SPSS) version 25.0 and IBM's Analysis of Moments of Structures (AMOS) version 24 software packages were employed as analytical tools in this investigation.
DOI: 10.48370/ofd/dzxvtg/coufxx
title: Rosenscheldia abundans (Dobrozr.) Petr. F-3453.tif

description: :unav
DOI: 10.5281/zenodo.5996574
title: Corporate Governance, Corporate Social Responsibility and Corporate Sustainability: The Moderating Role of Top Management Commitment

description: <strong>Design/Methodology/Approach:</strong> We used the non-probability sampling technique by employing convenience sampling for data collection. By employing a survey questionnaire, data were collected from 397 employees of SMEs in Ghana. The IBM Statistical Package for Social Science (SPSS) version 25.0 and IBM's Analysis of Moments of Structures (AMOS) version 24 software packages were employed as analytical tools in this investigation.
DOI: 10.48370/ofd/dzxvtg
title: Rosenscheldia abundans (Dobrozr.) Petr., 3453

description: Fungarium Mikromycetes
DOI: 10.48370/ofd/qf9ls9/2ctliu
title: Rosenscheldia abundans (Dobrozr.) Petr. F-3317.tif

description: :unav
DOI: 10.48370/ofd/qf9ls9
title: Rosenscheldia abundans (Dobrozr.) Petr., 3317

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.19234256.v1
title: Additional file 1 of Complete genome sequence of biocontrol strain Paenibacillus peoriae HJ-2 and further analysis of its biocontrol mechanism

description: Additional file 1: Table 1. Genome sequences used for analysis in this study. Table 2. The antifungal activity of HJ-2 against Fusarium spp. Table 3. Chemotaxis and assembly of flagella. Table 4. Secondary metabolite clusters identified in this study by using antiSMASH database. Table 5. IAA biosynthesis and nitrogen fixation. Table 6. The genes cording for endoglucanase in the P. peoriae HJ-2.
DOI: 10.6084/m9.figshare.15041461
title: All of the supplementary tables from Forensic characteristics and population construction of two major minorities from southwest China revealed by a novel 37 Y-STR loci system

description: Table S1 Allele frequencies and gene diversity of Miao population; Table S2 Allele frequencies and gene diversity of Tujia population; Table S3 Haplotypes of Miao population; Table S4 Haplotypes of Tujia population; Table S5 Pairwise Rst
DOI: 10.6084/m9.figshare.11370699.v1
title: Elman from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. BP, RBF and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.
DOI: 10.6084/m9.figshare.7176368
title: Table S1 - Specimen Info from Specimen-based analysis of morphology and the environment in ecologically dominant grasses: the power of the herbarium

description: Collector, locality, and GenBank information for specimens used in this study
DOI: 10.6084/m9.figshare.11370696
title: SVM from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. BP, RBF and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.
DOI: 10.6084/m9.figshare.11370702.v1
title: RBF from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. BP, RBF and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.
DOI: 10.6084/m9.figshare.11370705
title: The compressor characteristics prediction points data from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: Different algorithms in this paper are used to predict compressor characteristic curves at different rotational speeds.
DOI: 10.6084/m9.figshare.11370717
title: Compressor characteristic maps from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. BP, RBF and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.
DOI: 10.6084/m9.figshare.11370717.v1
title: Compressor characteristic maps from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. BP, RBF and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.
DOI: 10.34740/kaggle/dsv/7661747
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.10680919
title: Data gathered during the second stage of carrying out the NCN research project "Epidemics and Communities in Critical Theories, Artistic Practices and Speculative Fabulations of the Last Decades" (year 2023)

description: There is a set of various data (bibliographical, textual) gathered during the second stage of carrying out the NCN research project, related to one online workshop "Elements of Contagion" (18-20.01.2023), international conference "Contagion: Between Contiguity and Community" (24-26.05.2023) organised during that time as well as to a paper presented during an international conference "Genealogies of Memory. Pandemics, famines and industrial disasters of the 20th and 21st centuries" (22-24.11.2023).
DOI: 10.5281/zenodo.10680920
title: Data gathered during the second stage of carrying out the NCN research project "Epidemics and Communities in Critical Theories, Artistic Practices and Speculative Fabulations of the Last Decades" (year 2023)

description: There is a set of various data (bibliographical, textual) gathered during the second stage of carrying out the NCN research project, related to one online workshop "Elements of Contagion" (18-20.01.2023), international conference "Contagion: Between Contiguity and Community" (24-26.05.2023) organised during that time as well as to a paper presented during an international conference "Genealogies of Memory. Pandemics, famines and industrial disasters of the 20th and 21st centuries" (22-24.11.2023).
DOI: 10.6084/m9.figshare.11370702
title: RBF from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. BP, RBF and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.
DOI: 10.6084/m9.figshare.11370708
title: BP from Compressor performance modelling method based on support vector machine nonlinear regression algorithm

description: To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. BP, RBF and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.
DOI: 10.15151/esrf-es-1437867929
title: Detemination of the spin and orbital moment of the photomagnetic octacyanotungstate(IV) in high-temperature photomagnets

description: Coordination polymers based on octacyanotungstate(IV) and magnetically non-innocent manganese(II) complex cations establish a new class of high-temperature photomagnets with photo-induced magnetic ordering temperatures exceeding the boiling point of liquid nitrogen. In this proposal we aim to fully determine the mechanism of the reversible photomagnetic switching in these unique compounds, which seem to be associated with the reversible ligand dissociation at the tungsten center. The XAS/XMCD measurements seem to be the only technique that enables direct insight into the electronic/magnetic structure of W, where the photo-induced changes take place. The unequivocal determination of the photoswitching mechanism was not possible using other general techniques such as single-crystal and powder XRD or IR spectroscopy. The new knowledge gained through the implementation of the proposal will enable a rational approach to the design of the 2nd generation of photomagnets working at RT.
DOI: 10.6084/m9.figshare.25003345
title: Using detrital zircon systematics to trace the sediment sources of Jurassic uranium-bearing sandstones in the western Ordos Basin, China: implications for uranium exploration in a sedimentary basin

description: The Jurassic Zhiluo Formation in the western Ordos Basin is known for hosting economically significant sandstone-type uranium deposits. However, the understanding of sediment sources for this formation, crucial for guiding uranium exploration, has been a subject of controversy. This study aims to address this issue by presenting a systematic dataset derived from detrital zircons found in the uranium-bearing sandstones of the Zhiluo Formation. The dataset includes U‒Pb dating, trace element analyses, and in-situ Hf isotope compositions. The detrital zircons analysed are predominantly of magmatic origin, as indicated by a Th/U ratio greater than 0.4. Their U‒Pb ages can be broadly categorized into a predominant population ranging from 170 to 500 Ma, with a peak ca. 300 Ma. Additionally, there are two minor age populations: 1600–2050 Ma (peaking at ca. 1850 Ma) and 2300–2650 Ma (peaking at ca. 2500 Ma). The εHf(t) values of these zircons exhibit a range from −27 to 7, concentrating mostly from −16 to 5. Importantly, none of them aligns above the deficit mantle evolutionary line in an εHf(t) vs. age covariant diagram. A comprehensive comparison of the analytical U‒Pb ages and Hf isotopic results with previously reported data from adjacent geological units surrounding the western Ordos Basin suggests that the primary provenance supply area for the sandstones is the Alxa Block, particularly the uranium-rich felsic rocks within it. Minor contributions are inferred from the Yinshan Block and the Qilian Orogenic Belt. The northwestern region of the study appears to be the primary prospect area for uranium exploration, and we have proposed effective measures to refine the exploration target area and enhance exploration efficiency. Overall, the study provided crucial insights into deducing provenance supply areas and transport paths, with implications for uranium exploration in sedimentary basins worldwide.
DOI: 10.6084/m9.figshare.25003345.v2
title: Using detrital zircon systematics to trace the sediment sources of Jurassic uranium-bearing sandstones in the western Ordos Basin, China: implications for uranium exploration in a sedimentary basin

description: The Jurassic Zhiluo Formation in the western Ordos Basin is known for hosting economically significant sandstone-type uranium deposits. However, the understanding of sediment sources for this formation, crucial for guiding uranium exploration, has been a subject of controversy. This study aims to address this issue by presenting a systematic dataset derived from detrital zircons found in the uranium-bearing sandstones of the Zhiluo Formation. The dataset includes U‒Pb dating, trace element analyses, and in-situ Hf isotope compositions. The detrital zircons analysed are predominantly of magmatic origin, as indicated by a Th/U ratio greater than 0.4. Their U‒Pb ages can be broadly categorized into a predominant population ranging from 170 to 500 Ma, with a peak ca. 300 Ma. Additionally, there are two minor age populations: 1600–2050 Ma (peaking at ca. 1850 Ma) and 2300–2650 Ma (peaking at ca. 2500 Ma). The εHf(t) values of these zircons exhibit a range from −27 to 7, concentrating mostly from −16 to 5. Importantly, none of them aligns above the deficit mantle evolutionary line in an εHf(t) vs. age covariant diagram. A comprehensive comparison of the analytical U‒Pb ages and Hf isotopic results with previously reported data from adjacent geological units surrounding the western Ordos Basin suggests that the primary provenance supply area for the sandstones is the Alxa Block, particularly the uranium-rich felsic rocks within it. Minor contributions are inferred from the Yinshan Block and the Qilian Orogenic Belt. The northwestern region of the study appears to be the primary prospect area for uranium exploration, and we have proposed effective measures to refine the exploration target area and enhance exploration efficiency. Overall, the study provided crucial insights into deducing provenance supply areas and transport paths, with implications for uranium exploration in sedimentary basins worldwide.
DOI: 10.6084/m9.figshare.7177682
title: TGF-b1 or hypoxia enhance glucose metabolism and lactate production via HIF1A signaling in tendon cells

description: <b>Purpose/Aim of the study</b>: Healthy tendons are maintained in homeostasis through controlled usage of glucose for energy and redox equilibrium. Tendon cell stress imposed by overuse injury or vascular insufficiency is accompanied by activation of wound healing pathways which facilitate an adaptive response and the restoration of homeostasis. To understand this response at the gene expression level we have studied the in vivo effects of injected TGF-β1 in a murine model of tendinopathy, as well as treatment of murine tendon explants with either TGF-β1 or hypoxia in vitro. <b>Methods and Results</b>: We provide evidence (from expression patterns and immunohistochemistry) that both in vivo and in vitro, the stress response in tendon cells may be metabolically controlled in part by glycolytic reprogramming. A major feature of the response to TGF-β1 or hypoxia is activation of the Warburg pathway which generates lactate from glucose under normoxia and thereby inhibits mitochondrial energy production. <b>Conclusions</b>: We discuss the likely outcome of this major metabolic shift in terms of the potential benefits and damage to tendon and suggest how incorporation of this metabolic response into our understanding of initiation and progression of tendinopathies may offer new opportunities for diagnosis and the monitoring of therapies.
DOI: 10.6084/m9.figshare.7176368.v1
title: Table S1 - Specimen Info from Specimen-based analysis of morphology and the environment in ecologically dominant grasses: the power of the herbarium

description: Collector, locality, and GenBank information for specimens used in this study