Trends, periodicities and ENSO relationship of New Zealand rainfall

The rainfall series for six homogeneous regions of New Zealand for 1901-1996 were not well intercorrelated (maximum correlation +0.6). Rainfalls were almost equally spread in all months. Trends (total changes over about 90 years) were ~0, +11, +2, -6, +1, +8 (±~4)% for the six regions. For seasonal rainfall, large trends were -19% for DJF and +16% for MAM of region 1. Spectral analysis showed peaks in QBO (Quasi-biennial oscillations, 2-3 years) range and near 3, 4-5, 6-9, 10-11 years and higher periodicities. ENSO relationships were not clear-cut. In individual El Niño events, only the very strong events of 1972-73, 1982-83 and 1997-98 were associated with widespread droughts in New Zealand, while the 1940-41 El Niño event was associated with excess rainfall. During the durations of all other El Niño events, New Zealand rainfalls were excess or deficit for a few months, followed by deficit or excess for the next few months (oscillatory nature), similar in all regions in some events, dissimilar in others, with no preference for any season. During La Niña (anti-El Niño) events also, oscillations were observed.

Tree Rings Reveal the Impact of Soil Temperature on Larch Growth in the Forest-Steppe of Siberia

Dendroclimatology has focused mainly on the tree growth response to atmospheric variables. However, the roots of trees directly sense the “underground climate,” which can be expected to be no less important to tree growth. Data from two meteorological stations approximately 140 km apart in southern Siberia were applied to characterize the spatiotemporal dynamics of soil temperature and the statistical relationships of soil temperature to the aboveground climate and tree-ring width (TRW) chronologies of Larix sibirica Ledeb. from three forest–steppe stands. Correlation analysis revealed a depth-dependent delay in the maximum correlation of TRW with soil temperature. Temperatures of both the air and soil (depths 20–80 cm) were shown to have strong and temporally stable correlations between stations. The maximum air temperature is inferred to have the most substantial impact during July–September (R = −0.46–−0.64) and early winter (R = 0.39–0.52). Tree-ring indices reached a maximum correlation with soil temperature at a depth of 40 cm (R = −0.49–−0.59 at 40 cm) during April–August. High correlations are favored by similar soil characteristics at meteorological stations and tree-ring sites. Cluster analysis of climate correlations for individual trees based on the K-means revealed groupings of trees driven by microsite conditions, competition, and age. The results support a possible advantage of soil temperature over air temperature for dendroclimatic analysis of larch growth in semiarid conditions during specific seasons.

Early Detection of COVID-19 Waves From Cases in a Neighboring Country With an Open Border

Border closure or travel restriction is a critical issue as closing the border early can badly affect the economy of the country, whereas substantial delay can put human lives at stake. While many papers discuss closing the border early in the pandemic, the question of when to close the border has not been addressed well. We have tried to estimate a date of closing the border by taking the reference of a neighboring country with a high correlation in Covid-19 incidence. Here we have used non-linear methods to probe the landscape of correlation between temporal COVID-19 incidences and deaths. We have tested our method on two neighboring countries, Nepal and India, with open borders, where closing the borders are among the top priorities to reduce the spread and spill-out of variants. We have selected these countries as they have close connectivity and intertwined socio-economic network with thousands of people crossing the border every day. We found the distance correlation for COVID-19 incidence between these countries to be statistically significant (p < 0.001) and there is a lag of 6 days for maximum correlation. In addition, we analyzed the correlation for each wave and found the distance correlation for the first phase is 0.8145 (p < 0.001) with a lag of 2 days, and the distance correlation for the second wave is 0.9685 (p < 0.001) without any lag. This study can be a critical planning tool for policymakers and public health practitioners to make an informed decision on border closure in the early days as it is critically associated with the legal and diplomatic agreements and regulations between two countries.

Design Method for a Higher Order Extended Kalman Filter Based on Maximum Correlation Entropy and a Taylor Network System

This paper proposes one new design method for a higher order extended Kalman filter based on combining maximum correlation entropy with a Taylor network system to create a nonlinear random dynamic system with modeling errors and unknown statistical properties. Firstly, the transfer function and measurement function are transformed into a nonlinear random dynamic model with a polynomial form via system identification through the multidimensional Taylor network. Secondly, the higher order polynomials in the transformed state model and measurement model are defined as implicit variables of the system. At the same time, the state model and the measurement model are equivalent to the pseudolinear model based on the combination of the original variable and the hidden variable. Thirdly, higher order hidden variables are treated as additive parameters of the system; then, we establish an extended dimensional linear state model and a measurement model combining state and parameters via the previously used random dynamic model. Finally, as we only know the results of the limited sampling of the random modeling error, we use the combination of the maximum correlation estimator and the Kalman filter to establish a new higher order extended Kalman filter. The effectiveness of the new filter is verified by digital simulation.

Machine Learning Based Image Feature Recognition and Clinical Nursing of Children’s Rheumatoid Arthritis-Related Lung Injury

This work was aimed at investigating image feature recognition and clinical nursing of children’s rheumatoid arthritis- (CRA-) related lung injury under maximum correlation minimum redundancy algorithm of machine learning. In this study, 18 children with CRA in the hospital were selected as the rheumatoid group to explore the nursing method, and 18 healthy children were selected as the control group. The maximum correlation minimum redundancy algorithm of machine learning was compared with the information gain algorithm and the Fisher score algorithm and applied in computed tomography (CT) images of 18 CRA children. The classification accuracy of the algorithm in this study (94.52%) was higher than that of the information gain algorithm (88.64%) and Fisher score algorithm (81.24%). CT alveolitis score (2.35 ± 0.72 points) of children from the rheumatoid group was markedly higher than that of the control group (1.21 ± 0.24 points) (t = 2.147 and P < 0.05 ). The nitric oxide level (14.00 ppb) of children from the rheumatoid group increased greatly compared with the control group (10.00 ppb) ( P < 0.05 ). CRA can cause a decline of lung function in children, while the nitric oxide level exhaled by children can assess the activity of RA. In addition, adopting active nursing methods can help children get better.

Research on Fault Extraction Method of CYCBD Based on Seagull Optimization Algorithm

Maximum cyclostationarity blind deconvolution (CYCBD) can recover the periodic impulses from mixed fault signals comprised by noise and periodic impulses. In recent years, blind deconvolution has been widely used in fault diagnosis. However, it requires a preset of filter length, and inappropriate filter length may cause the inaccurate extraction of fault signal. Therefore, in order to determine filter length adaptively, a method to optimize CYCBD by using the seagull optimization algorithm (SOA) is proposed in this paper. In this method, the ratio of SNR to kurtosis is used as the objective function; firstly, SOA is used to search the optimal filter length in CYCBD by iteration, and then it uses the optimal filter length to perform CYCBD; finally, the frequency-domain waveform is determined through Fourier transformation. The method proposed is applied to the fault extraction of a simulated signal and a test vibration signal of the closed power flow gearbox test bed, and the fault frequency is successfully extracted, in addition, using maximum correlation kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) to compare with CYCBD-SOA, which validated availability of the proposed method.

Machine Learning Based Essay Grading System

Machine Learning and automation has progressed immensely over the years and has tend to make human lives simpler with reducing human effort and time on tasks by enabling a machine to perform them. One such task is to grade essays. Essay writing is an integral part for anyone willing to learn a language or skill or to simply exhibit one’s thoughts and ideas on a topic. This leads us to the reason why essay grading is important. When a work is scored against some parameters, a scope of improvement is possible. Hence, when essays are graded and feedbacks are provided, it guides the writer to analyse the work and to have a better understanding of the topic in general. Although, manual grading of essays could create discrepancy because of being graded by different individuals having different perceptions of the same content. It also consumes a lot of human time and effort. Therefore, automatic grading of essays could prove to be the saviour. In this project, we build a machine learning model which grades essays based on various features extracted using Natural Language Processing. We also test the model’s performance using several regression models like Linear, Lasso, and Ridge, and methods like Artificial Neural Network to find the best fit giving the maximum correlation with human grades.

Prediction of Biometric Variables Through Multispectral Images Obtained From Uav in Beans (Phaseolus vulgaris L.) During Ripening Stage

Here, we report the prediction of vegetative stages variables of canary bean crop by means of RGB and multispectral images obtained from UAV during the ripening stage, correlating the vegetation indices with biometric variables measured manually in the field. Results indicated a highly significant correlation of plant height with eight RGB image vegetation indices for the canary bean crop, which were used for predictive models, obtaining a maximum correlation of R2 = 0.79. On the other hand, the estimated indices of multispectral images did not show significant correlations.