This study presents the observation and evaluation of a meteotsunami in the Indian Ocean triggered by the Hunga‐Tonga volcanic eruption. The event was detected through tide gauges and bottom‐pressure ...recordings across the Indian Ocean, with an amplitude of 10–15 cm, lasting for a few days. A numerical model was used to understand the ocean's response to meteotsunami and evaluate the dynamics behind it. The model results show that the sea‐level oscillations result from the ocean waves generated by a propagating Lamb wave. In addition to interaction with bathymetry, refracted and reflected waves also determine the sea‐level variability. Our analysis shows that bathymetric slope plays a vital role in near‐shore processes. The spectral and spatial characteristics of the meteotsunami were reminiscent of seismic tsunamis. Our research on this rare event elucidates the unresolved issues and eventually leads to designing a blueprint for future observation and modeling of meteotsunamis and seismic tsunamis.
Plain Language Summary
This study evaluates the observation of a rare meteotsunami event that occurred in the Indian Ocean. An atmospheric pressure wave generated by the eruption of the Hunga‐Tonga volcano in the Pacific Ocean caused the meteotsunami. The signatures of this event were captured by tide gauges installed along the coast and pressure recorders moored at the ocean floor. We demonstrate using a numerical model that fast‐moving atmospheric pressure waves, Lamb waves, caused the sea‐level oscillations. Interactions with bathymetry further influenced sea‐level variability in the basin, reflected waves, and refracted waves. The vulnerable areas showed a striking resemblance to the area impacted by the tsunami that hit the Indian Ocean in 2004. This research eventually helps to identify and incorporate observational and modeling issues associated with seismic tsunamis and meteotsunami.
Key Points
We present the first comprehensive evaluation of meteotsunami in the Indian Ocean using observation and modeling
A forced wave to free wave transformation happens on the continental slope, and the bathymetry slope is crucial to near‐shore processes
The meteotsunami exhibits a spectral and spatial similarity to seismic tsunamis that previously occurred in the Indian Ocean
•Semiconductor laser irradiation assisted synthesis of CCT crystals.•FTIR and EDX confirms the presence of functional and metal–oxygen bonds.•Laser irradiated crystals show high thermal stability ...(>600 °C) in its oxide state.•The grown crystals are highly crystalline and belongs to monoclinic crystal system.
In the present investigation, the effect of semiconductor laser (GaAs) irradiation on silica gel medium during the crystal growth have been reported. After the growth, laser irradiated cadmium doped copper (CCT) tartrate single crystals were compared with without irradiate CCT single. The obtained single crystals having different morphologies, such as bluish semi-transparent, star-shaped, needle-shaped crystals. The effect of semiconductor laser irradiation during the growth, changes the crystals morphology, reduces the number and size of crystals compared to the crystals grown without irradiation. This is due to the variation of supersaturation. This effect of semiconductor laser irradiation of grown crystals was characterized using energy-dispersive X-ray (EDX) spectroscopy, Field emission scanning electron microscopy (FESEM), Thermo gravimetric analyzer (TGA), and Powder X-ray diffraction (PXRD).
This paper explores a novel metamaterial-inspired low-scattering electric quadrupole antenna in the microwave regime. The metasurface unit cell used here is the well-known stacked dogbone doublet ...which is conventionally used to get electric and magnetic resonances under plane wave illumination. An offset in the position of the upper dogbone metallization results in the excitation of the higher-order high-Q electric quadrupole resonance having insignificant scattering in comparison with the fundamental resonances. This unit cell is hence used as the base element of an antenna and electric quadrupole resonance is excited using direct probe feed while the fundamental electric and magnetic resonances are suppressed. The fabricated antenna shows a 2:1 VSWR bandwidth of 4% and a measured radiation efficiency of 42% around resonance. The experimental studies are conducted inside an anechoic chamber using a vector network analyzer, computational studies are performed using the full-wave CST Microwave Studio and these results are validated using the multipole scattering theory.
Novel crystals of pure nickel cadmium oxalate (NCO) and calcium-doped nickel cadmium oxalate (CNCO) were grown by single diffusion method in silica hydrogel by optimizing the growth parameters. The ...grown crystals were characterized using field-emission scanning electron microscope, energy-dispersive X-ray (EDX) analysis, Fourier transform infrared spectroscope, X-ray diffraction (XRD), thermogravimetric analysis (TGA) and UV–visible spectrometer. Ca
2+
ions were used to occupy the vacancies of intrinsically available Ni
2+
and Cd
2+
ions in the lattice of NCO crystals. This causes change in morphology of NCO crystals and resulted in the growth of CNCO. Crystallinity and lattice parameters of the grown crystals are analysed by XRD technique. Thermal studies show the thermal stability of grown crystals. Number of water molecules present and molecular weight of the crystals were also determined using EDX and TGA studies. Electrical susceptibility, real and imaginary parts of the dielectric constant, energy gap of the as-grown crystals were calculated using the UV–visible spectroscopy. The results of doped crystal were compared with undoped NCO crystal.
Aim: Chronic kidney disease (CKD) is among the main 20 reasons for death worldwide and influences around 10% of the world grown-up populace. CKD is an issue that upsets typical kidney work. The main ...objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. We will be comparing Novel Decision Tree with Linear Regression to find out which of these can give us the best accuracy. Material and Methods: The study used 322 samples with Novel Decision Tree and Linear Regression is executed with varying training and testing splits for predicting the accuracy for kidney disease prediction with the G-power value of 80% and the kidney datasets were collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the classifiers are evaluated based on their accuracy rate using the chronic kidney disease dataset. Results: The accuracy of predicting kidney disease in Novel Decision Tree (96.66%) and Linear Regression (85.25%) is obtained. There is a statistical 2-tailed significant difference in accuracy for two algorithms is 0.000 (p<0.05) by performing independent samples t-tests. Conclusion: This study concludes that the Prediction of Kidney disease using the Novel Decision Tree (DT) algorithm appears to be significantly better than the Linear Regression(LR) with improved accuracy.
Aim: Currently kidney disease is a major problem. Because there are so many people with this disease. Kidney disease is very dangerous if not immediately treated on time, and may be fatal. The main ...objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. The Novel Decision Tree is compared to Logistic regression to find out which of these can give us the best accuracy. Material and Methods: The study used 220 samples with Novel Decision Tree and Logistic regression is executed with varying training and testing splits for predicting the accuracy for kidney disease prediction with the G-power value of 80% and the kidney datasets were collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the classifiers are evaluated based on their accuracy rate using the chronic kidney disease dataset. Results: The accuracy of predicting kidney disease in Novel Decision Tree (96.66%) and Logistic regression (85.25%) is obtained. There is a statistical 2-tailed significant difference in accuracy for two algorithms is 0.000 (p<0.05) by performing independent samples t-tests. Conclusion: This study concludes that the Prediction of Kidney disease using the Novel Decision Tree (DT) algorithm appears to be significantly better than the Logistic regression (LR) with improved accuracy.
Aim: Chronic Kidney Disease also referred to as long-term nephrotic syndrome, has risen exponentially in importance. A person may only remain missing their kidneys for an estimate of 18 days, which ...creates a huge need for hemodialysis and kidney replacements. The main objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. We will be comparing Novel Decision Tree with Random forest to find out which of these can give us the best accuracy. Material and Methods: The study used 143 samples with Novel Decision Tree and Random Forest is executed with varying training and testing splits for predicting the accuracy for kidney disease prediction with the G-power value of 80% and the kidney datasets were gathered from different websites, together with data from more recent studies, a criterion of 0.05%, a reliability range of 95%, a means, and a confidence interval. The performance of the classifiers are evaluated based on their accuracy rate using the chronic kidney disease dataset. Results: The accuracy of predicting kidney disease in Novel Decision Tree (96.66%) and Random Forest (62.25%) is obtained. By using independent samples t-tests, it can be shown that there is a statistically 2-tailed notable change in efficiency seen between two algorithms of 0.000 (p<0.05). Conclusion: The report's findings suggest that the Innovative can be used to predict kidney illness Decision Tree (DT) algorithm appears to be significantly better than the Random Forest (RF) with improved accuracy.
Aim: Individuals at high-hazard of cardiovascular sickness are no doubt defenseless against ongoing kidney diseases, and historical clinical records can assist with turning away complicated kidney ...issues. The main objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. We will be comparing Novel Decision Tree with Naive Bayes to find out which of these can give us the best accuracy. Material and Methods: The study used 540 samples with Novel Decision Tree and Naive Bayes is executed with varying training and testing splits for predicting the accuracy for kidney disease prediction with the G-power value of 80% and the kidney datasets were collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the classifiers are evaluated based on their accuracy rate using the chronic kidney disease dataset. Results: The accuracy of predicting kidney disease in Novel Decision Tree (96.66%) and Naive Bayes (90.83%) is obtained. There is a statistical significant difference in accuracy for two algorithms is 0.001 (p<0.05) by performing independent samples t-tests. Conclusion: This study concludes that the Prediction of Kidney disease using the Novel Decision Tree (DT) algorithm appears to be significantly better than the Naive Bayes (NB) with improved accuracy.
Cadmium magnesium oxalate (CdMgO) crystals were grown by silica gel technique. Crystals were optimized by parameters like specific gravity of sodium meta silicate (SMS), concentration of the oxalic ...acid (C2H2O4·2H2O) solution, pH of the gel, gel setting time, concentration of the cadmium chloride (CdCl2·2H2O) and Magnesium chloride (MgCl2·6H2O) solutions. Grown oxalate crystals examined under Powder X-Ray Diffraction (PXRD), Fourier Transform Infrared (FTIR) spectrum, Raman spectrum and Z-scan instruments. Grown materials are crystallizes in the triclinic system space group P1 with lattice parameters, a = 5.9906 Å, b = 6.6215 Å, c = 8.4736 Å and α = 74.55°, β = 74.091°, γ = 80.911°. Comparison study of FTIR and Raman spectrum of grown crystals were discussed. Third order non-linear optical parameters such as nonlinear absorption coefficient (β=∼10−4cm/W), nonlinear refractive index (n2 = ∼10 −9 cm2 /W), susceptibility (χ(3) =∼10−7esu) and molecular hyperpolarizability (hγ =∼10−27esu) were determined from Z-scan experimental data analysis.
The air pressure system (APS) is an integral component of Scania trucks and other heavy machinery. Because the brakes on these vehicles use air pressure, keeping the APS in good working order is ...crucial. Automakers can save money on repairs and boost vehicle efficiency with predictive maintenance. This can be done manually or using an automated system. Predictive maintenance that is performed manually requires human interaction and, as a result, introduces room for error. When humans are involved, there is always a chance that something may be missed or misunderstood, which might compromise the reliability of the maintenance procedures. Several benefits may be gained by employing automatic predictive maintenance strategies, such as artificial intelligence (AI), to investigate the underlying reasons for failure in the APS of Scania trucks. The company relies heavily on the dataset since it pinpoints the faulty parts. Predicting the root cause of failure is made more difficult if the dataset has missing values and unbalanced class issues. To overcome these issues, the data are preprocessed by many resampling techniques such as under-sampling, over-sampling, and the synthetic minority over-sampling technique (SMOTE), and imputation techniques such as KNNImputer and SimpleImputer for mean, mode, and constant strategies, multivariate imputation by chained equations (MICE), and principal component analysis (PCA), to balance the entire data set. After preprocessing, implementation of eight different machine learning algorithms, namely random forest, decision tree, gradient boosting, logistic regression,
k
-nearest neighbors classifier, AdaBoost classifier, CatBoost classifier, and XGB classifier, is carried out, and then the cost, accuracy metrics, and confusion matrices are analyzed. The results from the experimental analysis show that the XGB classifier is the best model, with accuracy of 99.6241% along with cost-effectiveness.
