4,218 publications from this institution
The triboelectric nanogenerator (TENG) is a promising mechanical energy harvesting technology, but its pulsed output and the instability of input energy sources make associated energy‐storage devices necessary for real applications. In this work, feasible and efficient charging of Li‐ion batteries by a rotating TENG with pulsed output current is demonstrated. In‐depth discussions are made on how to maximize the power‐storage efficiency by achieving an impedance match between the TENG and a battery with appropriate design of transformers. With a transformer coil ratio of 36.7, ≈72.4% of the power generated by the TENG at 250 rpm can be stored in an LiFePO 4 –Li 4 Ti 5 O 12 battery. Moreover, a 1 h charging of an LiCoO 2 –C battery by the TENG at 600 rpm delivers a discharge capacity of 130 mAh, capable of powering many smart electronics. Considering the readily scale‐up capability of the TENG, promising applications in personal electronics can be anticipated in the near future.
Background: Long noncoding RNAs (lncRNAs) crucially modulate DNA damage responses/repair in cancer cells. However, the underlying regulatory role of genome integrity and its clinical value in colon adenocarcinoma (COAD) remains unclear. This study links genome instability to lncRNA using computational biology techniques, in attempt to propose novel biomarkers of immunotherapy outcome, and investigated a potential competing endogenous RNA (ceRNA) as a molecular regulatory mechanism. Methods: TCGA-COAD patients were divided into genome unstable (GU)-like and genome stable (GS)-like clusters via hierarchical clustering to predict immunotherapy outcomes. Multivariate Cox model was established to predict the overall survival rate in COAD patients. Additionally, SVM and LASSO algorithms were applied to obtain hub lncRNAs. A novel genome instability-related ceRNA network was predicted with the Starbase 2.0 database. To better understand how these genes fundamentally interact during tumor progression and development, the mutation analysis and single-gene analysis for each gene was performed. Results: In contrast to those in the GS-like cluster, GU-like-cluster patients demonstrated a higher tumor mutational burden (TMB)/microsatellite instability (MSI), DNA polymerase epsilon (POLE) mutation rate, and immune checkpoint expression, all indicate a greater predictive power for response rate for immunotherapy. The novel prognostic signature demonstrated an outstanding predictive performance (AUC > 0.70). The genes in the genome insatiability-related ceRNA network (including four axes: AL161772.1-has-miR-671-5p (hsa-miR-181d-5p, has-miR-106a-5p)-NINL, AL161772.1-has-miR-106a-5p-TNFSF11, AC124067.4-hsa-miR-92b-3p (hsa-miR-589-5p)-PHYHIPL, and BOLA3-AS1-has-miR-130b-3p-SALL4) were identified as critical regulators of tumor microenvironment infiltration, cancer stemness, and drug resistance. qPCR was performed to validate the expression patterns of these genes. Furthermore, the MSI-high proportion was greater in patients with mutated type than in those with the wild type according to all four target genes, indicating that these four genes modulate genomic integrity and could serve as novel immunotherapy biomarkers. Conclusion: We demonstrated that genome instability-related lncRNA is a novel biomarker for immunotherapy outcomes and prognosis. A novel ceRNA network that modulates genomic integrity, including four lncRNA-miRNA-mRNA axes, was proposed.
The atmosphere is a complex nonlinear system, with the information of its temperature, water vapor, pressure, and cloud being crucial aspects of remote-sensing data analysis. There exist intricate interactions among these internal components, such as convection, radiation, and humidity exchange. Atmospheric phenomena span multiple spatial and temporal scales, from small-scale thunderstorms to large-scale events like El Niño. The dynamic interactions across different scales, along with external disturbances to the atmospheric system, such as variations in solar radiation and Earth surface conditions, contribute to the chaotic nature of the atmosphere, making long-term predictions challenging. Grasping the intrinsic chaotic dynamics is essential for advancing atmospheric analysis, which holds profound implications for enhancing meteorological forecasts, mitigating disaster risks, and safeguarding ecological systems. To validate the chaotic nature of the atmosphere, this paper reviewed the definitions and main features of chaotic systems, elucidated the method of phase space reconstruction centered on Takens’ theorem, and categorized the qualitative and quantitative methods for determining the chaotic nature of time series data. Among quantitative methods, the Wolf method is used to calculate the Largest Lyapunov Exponents, while the G–P method is used to calculate the correlation dimensions. A new method named Improved Saturated Correlation Dimension method was proposed to address the subjectivity and noise sensitivity inherent in the traditional G–P method. Subsequently, the Largest Lyapunov Exponents and saturated correlation dimensions were utilized to conduct a quantitative analysis of FY-4A and Himawari-8 remote-sensing infrared observation data, and ERA5 reanalysis data. For both short-term remote-sensing data and long-term reanalysis data, the results showed that more than 99.91% of the regional points have corresponding sequences with positive Largest Lyapunov exponents and all the regional points have correlation dimensions that tended to saturate at values greater than 1 with increasing embedding dimensions, thereby proving that the atmospheric system exhibits chaotic properties on both short and long temporal scales, with extreme sensitivity to initial conditions. This conclusion provided a theoretical foundation for the short-term prediction of atmospheric infrared radiation field variables and the detection of weak, time-sensitive signals in complex atmospheric environments.
Elevated-temperature synthesis has been used to generate side-by-side biaxially structured silicon carbide–silica nanowires. The axial growth direction approaches [311] for nanowires with a high density of microtwins and is [211] for defect-free nanowires. The structure of these nanowires, their cross-sectional shape, and their structural transformation between a biaxial and coaxial configuration have been studied by transmission electron microscopy. The Young’s modulus of the biaxially structured nanowires was measured to be 50–70 GPa depending on the size of the nanowire.
