Technology opportunity discovery of proton exchange membrane fuel cell from the perspective of papers-patents correlation analysis

Papers and patents can respectively present the latest progress of scientific research and technological development. Combining the two for correlation analysis has certain reference significance for technology opportunity discovery. Therefore, this paper proposes a method for technology opportunity discovery of proton exchange membrane fuel cell from the perspective of papers-patents correlation analysis. Firstly, papers and patent data are collected from papers and patent databases and the LDA model is applied to extract paper and patent topics. Secondly, the indicators of topic heat and novelty are calculated to select high-value paper topics. After that, the similarity analysis between high-value paper topics and all patent topics is considered to select the patent topics with development potential. Finally, the patent texts with high relevance to these patent topics with development potential are positioned. The technical keywords in them are extracted by text mining tool and classified into innovation dimensions according to their attributes and the TEMPEST model. After that, the innovation rules in the SCAMPER model are introduced to combine with the technical keywords in each innovation dimension to identify specific technology opportunities. An empirical study on the technology of proton exchange membrane fuel cell provides sufficient evidence of the method’s ability to discover technology opportunities. This paper contributes by generating four specific technology opportunities, which can make up for the existing defects of proton exchange membrane fuel cells and provide useful guidance for companies to carry out technological innovation

Effect of nematic order on the low-energy spin fluctuations in detwinned BaFe1.935_{1.935}Ni0.065_{0.065}As2_2

The origin of nematic order remains one of the major debates in iron-based superconductors. In theories based on spin nematicity, one major prediction is that the spin-spin correlation length at (0,$\pi$) should decrease with decreasing temperature below the structural transition temperature $T_s$. Here we report inelastic neutron scattering studies on the low-energy spin fluctuations in BaFe$_{1.935}$Ni$_{0.065}$As$_2$ under uniaxial pressure. Both intensity and spin-spin correlation start to show anisotropic behavior at high temperature, while the reduction of the spin-spin correlation length at (0,$\pi$) happens just below $T_s$, suggesting strong effect of nematic order on low-energy spin fluctuations. Our results favor the idea that treats the spin degree of freedom as the driving force of the electronic nematic order.Comment: 5 pages, 4 figure

Tertiary Regulation of Cascaded Run-of-the-River Hydropower in the Islanded Renewable Power System Considering Multi-Timescale Dynamics

To enable power supply in rural areas and to exploit clean energy, fully renewable power systems consisting of cascaded run-of-the-river hydropower and volatile energies such as pv and wind are built around the world. In islanded operation mode, the primary and secondary frequency control, i.e., hydro governors and automatic generation control (AGC), ensure the frequency stability. However, due to limited water storage capacity of run-of-the-river hydropower and river dynamics constraints, without coordination between the cascaded plants, the traditional AGC with fixed participation factors cannot fully exploit the adjustability of cascaded hydropower. When imbalances between the volatile energy and load occur, load shedding can be inevitable. To address this issue, this paper proposes a coordinated tertiary control approach by jointly considering power system dynamics and the river dynamics that couples the cascaded hydropower plants. The timescales of the power system and river dynamics are very different. To unify the multi-timescale dynamics to establish a model predictive controller that coordinates the cascaded plants, the relation between AGC parameters and turbine discharge over a time interval is approximated by a data-based second-order polynomial surrogate model. The cascaded plants are coordinated by optimising AGC participation factors in a receding-horizon manner, and load shedding is minimised. Simulation of a real-life system shows a significant improvement in the proposed method in terms of reducing load shedding.Comment: Submitted to IET Renewable Power Generation; 11 page

Pleistocene glacial cycle effects on the phylogeography of the Chinese endemic bat species, Myotis davidii

<p>Abstract</p> <p>Background</p> <p>Global climatic oscillations, glaciation cycles and the unique geographic topology of China have profoundly influenced species population distributions. In most species, contemporary distributions of populations cannot be fully understood, except in a historical context. Complex patterns of Pleistocene glaciations, as well as other physiographic changes have influenced the distribution of bat species in China. Until this study, there had been no phylogeographical research on <it>Myotis davidii</it>, an endemic Chinese bat. We used a combination of nuclear and mitochondrial DNA markers to investigate genetic diversity, population structure, and the demographic history of <it>M. davidii</it>. In particular, we compared patterns of genetic variation to glacial oscillations, topography, and environmental variation during the Pleistocene in an effort to explain current distributions in light of these historical processes.</p> <p>Results</p> <p><it>M. davidii </it>comprises three lineages (MEP, SWP and SH) based on the results of molecular variance analysis (AMOVA) and phylogenetic analyses. The results of a STRUCTURE analysis reveal multi-hierarchical population structure in <it>M. davidii</it>. Nuclear and mitochondrial genetic markers reveal different levels of gene flow among populations. In the case of mtDNA, populations adhere to an isolation-by-distance model, whereas the individual assignment test reveals considerable gene flow between populations. MDIV analysis indicate that the split of the MEP and SWP/SH lineages, and from the SWP and SH lineages were at 201 ka BP and 158 ka BP, respectively. The results of a mismatch distribution analysis and neutrality tests indicate a population expansion event at 79.17 ka BP and 69.12 ka BP in MEP and SWP, respectively.</p> <p>Conclusions</p> <p>The complex demographic history, discontinuous extant distribution of haplotypes, and multiple-hierarchy population structure of <it>M. davidii </it>appear associated with climatic oscillations, topography and eco-environmental variation of China. Additionally, the three regions are genetically differentiated from one another in the entire sample set. The degree of genetic differentiation, based on the analysis of mtDNA and nDNA, suggests a male-mediated gene flow among populations. Refuges were in the MEP, SH and the lower elevations of SWP regions. This study also provides insights for conservation management units (MEP, SWP and SH).</p

High temperature reactions between Si3N4 bonded SiC materials and Cu, Cu2O and matte

Si3N4 bonded SiC (Si3N4-SiC) is a conventional refractory material and has broad applications. In the present study, Si3N4-SiC refractory materials were systematically investigated in the copper-making environment. Si3N4SiC was reacted with Cu, Cu2O, industrial matte, Cu2S and FeS melts at 1200 degrees C in argon gas atmosphere, and all samples were directly quenched in water after the experiments. Phase changes and compositions of the phases were measured by electron probe X-ray microanalysis. The present investigations demonstrate that Cu and Cu2S do not react with Si3N4-SiC at high temperatures and the wettability between this material and the melts is low. However, significant reactions occur between Si3N4-SiC and Cu2O, industrial matte and FeS. The results imply that Si3N4-SiC material has limited oxidation-resistance and can only be used under reducing conditions

CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer

ObjectivesAlthough the preoperative assessment of whether a bladder cancer (BCa) indicates muscular invasion is crucial for adequate treatment, there currently exist some challenges involved in preoperative diagnosis of BCa with muscular invasion. The aim of this study was to construct deep learning radiomic signature (DLRS) for preoperative predicting the muscle invasion status of BCa.MethodsA retrospective review covering 173 patients revealed 43 with pathologically proven muscle-invasive bladder cancer (MIBC) and 130 with non–muscle–invasive bladder cancer (non- MIBC). A total of 129 patients were randomly assigned to the training cohort and 44 to the test cohort. The Pearson correlation coefficient combined with the least absolute shrinkage and selection operator (LASSO) was utilized to reduce radiomic redundancy. To decrease the dimension of deep learning features, Principal Component Analysis (PCA) was adopted. Six machine learning classifiers were finally constructed based on deep learning radiomics features, which were adopted to predict the muscle invasion status of bladder cancer. The area under the curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of the model.ResultsAccording to the comparison, DLRS-based models performed the best in predicting muscle violation status, with MLP (Train AUC: 0.973260 (95% CI 0.9488-0.9978) and Test AUC: 0.884298 (95% CI 0.7831-0.9855)) outperforming the other models. In the test cohort, the sensitivity, specificity and accuracy of the MLP model were 0.91 (95% CI 0.551-0.873), 0.78 (95% CI 0.594-0.863) and 0.58 (95% CI 0.729-0.827), respectively. DCA indicated that the MLP model showed better clinical utility than Radiomics-only model, which was demonstrated by the decision curve analysis.ConclusionsA deep radiomics model constructed with CT images can accurately predict the muscle invasion status of bladder cancer

Subinhibitory Concentrations of Thymol Reduce Enterotoxins A and B and α-Hemolysin Production in Staphylococcus aureus Isolates

BACKGROUND: Targeting bacterial virulence factors is now gaining interest as an alternative strategy to develop new types of anti-infective agents. It has been shown that thymol, when used at low concentrations, can inhibit the TSST-1 secretion in Staphylococcus aureus. However, there are no data on the effect of thymol on the production of other exotoxins (e.g., alpha-hemolysin and enterotoxins) by S. aureus. METHODOLOGY/PRINCIPAL FINDINGS: Secretion of alpha-hemolysin, SEA and SEB in both methicillin-sensitive and methicillin-resistant S. aureus isolates cultured with graded subinhibitory concentrations of thymol was detected by immunoblot analysis. Hemolysin and tumor necrosis factor (TNF) release assays were performed to elucidate the biological relevance of changes in alpha-hemolysin, SEA and SEB secretion induced by thymol. In addition, the influence of thymol on the transcription of hla, sea, and seb (the genes encoding alpha-hemolysin, SEA and SEB, respectively) was analyzed by quantitative RT-PCR. Thymol inhibited transcription of hla, sea and seb in S. aureus, resulting in a reduction of alpha-hemolysin, SEA and SEB secretion and, thus, a reduction in hemolytic and TNF-inducing activities. CONCLUSIONS/SIGNIFICANCE: Subinhibitory concentrations of thymol decreased the production of alpha-hemolysin, SEA and SEB in both MSSA and MRSA in a dose-dependent manner. These data suggest that thymol may be useful for the treatment of S. aureus infections when used in combination with beta-lactams and glycopeptide antibiotics, which induce expression of alpha-hemolysin and enterotoxins at subinhibitory concentrations. Furthermore, the structure of thymol may potentially be used as a basic structure for development of drugs aimed against these bacterial virulence factors