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The triboelectric nanogenerator (TENG) is a promising technology for self‐powered sensors and vibration‐driven energy harvesting, with its development especially influenced by the characteristics of tribomaterials. However, developing tribopositive materials with a strong cationic effect and high electron‐donating capability remains challenging. In this study, a novel polyethylene oxide@poly(diallyldimethylammonium chloride) (PEO@Poly‐DADMAC) composite nanofiber mat (NFM) is successfully fabricated using the electrospinning technique. It functions as a highly tribopositive material, particularly enhancing the performance of nanofiber‐based TENGs (NF‐TENGs). Through investigation of its physical and triboelectric properties, the study revealed that incorporating cationic Poly‐DADMAC with PEO effectively boosts NF‐TENG output by increasing the dielectric constant (twofold), electron‐donating affinities, and surface charge trapping capability. The fabricated NF‐TENG shows excellent output performance generating 980 V, a maximum power density of 5.6 Wm−2, and an ultrahigh sensitivity of 8.923 V kPa−1 which endows composite reliability for power supply and sensing capability. More importantly, the PEO@Poly‐DADMAC‐based self‐powered motion sensor with an NF‐TENG array is successfully demonstrated in multifunctional applications such as athlete activity tracking, and wireless gaming interface control. This study not only offers new insights into optimizing performance and selecting alternative tribomaterials but also provides valuable guidance for developing high‐output TENGs and highly sensitive self‐powered sensors. [ABSTRACT FROM AUTHOR]

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts. Chest X-ray (CXR) images of lung diseases were collected from several publicly available databases. A lightweight convolutional neural network (CNN) has been used to extract characteristic features from the raw pixel values of the CXR images. The best feature subset has been identified using the Pearson Correlation Coefficient (PCC). Finally, the extreme learning machine (ELM) has been used to perform the classification task to assist faster learning and reduced computational complexity. The proposed CNN-PCC-ELM model achieved an accuracy of 96.22% with an Area Under Curve (AUC) of 99.48% for eight class classification. The outcomes from the proposed model demonstrated better performance than the existing state-of-the-art (SOTA) models in the case of COVID-19, pneumonia, and tuberculosis detection in both binary and multiclass classifications. For eight class classification, the proposed model achieved precision, recall and fi-score and ROC are 100%, 99%, 100% and 99.99% respectively for COVID-19 detection demonstrating its robustness. Therefore, the proposed model has overshadowed the existing pioneering models to accurately differentiate COVID-19 from the other lung diseases that can assist the medical physicians in treating the patient effectively., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Sir Isaac Newton noticed that the values of the first five rows of Pascal's triangle are each formed by a power of 11, and claimed that subsequent rows can also be generated by a power of 11. Literally, the claim is not true for the 5 t h row and onward. His genius mind might have suggested a deep relation between binomial coefficients and a power of some integer that resembles the number 11 in some form. In this study, we propose and prove a general formula to generate the values in any row of Pascal's triangle from the digits of ( 1 0 ⋯ 0 ︸ Θ zeros 1 ) n . It can be shown that the numbers in the cells in n t h row of Pascal's triangle may be achieved from Θ + 1 partitions of the digits of the number ( 1 0 ⋯ 0 ︸ Θ zeros 1 ) n , where Θ is a non-negative integer. That is, we may generate the number in the cells in a row of Pascal's triangle from a power of 11, 101, 1001, or 10001 and so on. We briefly discuss how to determine the number of zeros Θ in relation to n , and then empirically show that the partition really gives us binomial coefficients for several values of n . We provide a formula for Θ and prove that the ( n + 1 ) t h row of Pascal's triangle is simply Θ + 1 partitions of the digits of ( 1 0 ⋯ 0 ︸ Θ zeros 1 ) n from the right., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors.)