Although from the perspective of modern mainstream Earth science, the Earth is not "growing", in this article, after analyzing some stratum structures, we discovered the fact that the stratum structure has periodic characteristics. Based on the philosophical perspective of analogical reasoning, we compare the periodic stratum structure with tree rings, thereby put forward a new viewpoint. The new viewpoint holds that there is a process of material accumulation on the Earth's surface. The sources of materials are from two aspects. The first is that plants on the Earth absorb sunlight through photosynthesis and convert light energy into substances. These substances gradually deposit, thereby continuously increasing the mass of the Earth. The second is that the Earth receives meteorites and cosmic dust from the universe every day. During its revolution around the Milky Way, the Sun, like the Earth, experiences different "galactic seasons". These "galactic seasons" cause the Earth to deposit materials at different speeds at various times, thus making the stratum structure periodic.
Chen's Plate Tectonics Theory identifies nuclear fission in the Earth's inner core as the sole dynamic source of plate motion; it overturns the concept of continental drift as an erroneous construct and refutes the notion of rigid plates, positing that the so-called rigid plates in conventional theory are in a continuous process of consumption. In doing so, the theory thoroughly upends the traditional framework of plate tectonics. Its core tenets include the following: Periodic bursts of nuclear fission in the Earth's inner core drive volume changes in the mantle via thermal expansion and contraction, thereby triggering expansion-contraction cycles in the crust. The frequency of nuclear fission events in the inner core is transmitted to the crust through the mantle with a time delay, while the fundamental frequency remains unchanged. The mantle acts as a thermal reservoir, mitigating the impact of explosive thermal energy from the inner core on the crust, a process that generates co-frequency thermal waves. The crustal rifting and contraction cycles are nested across multiple frequency bands, with the intermediate-period cycle of 20-200 days identified as the critical cycle driving crustal deformation. Crustal expansion and contraction are localized at pre-existing crustal weak zones: during the thermal expansion phase, failure and rupture are consistently and periodically localized at mid-ocean ridges, while during the cooling contraction phase, stress release is periodically fixed at convergent plate boundaries. This explains the concentration of earthquake events along convergent plate zones. The self-locking structure of rock wedges formed by cooled magma enables irreversible expansion of the crust, with variations in rock wedge thickness determining the spreading rate of mid-ocean ridges. Rock wedge self-locking at mid-ocean ridges governs the long-term directional migration of tectonic plates. The progressive growth of microcracks within plates and the formation of interconnected magma-filled fractures drive reversals in plate motion direction. At the time of its formation, the Earth was not composed of rigid continental plates and oceanic plates, but rather a uniform primitive crust covering the entire globe. This primitive crust was subsequently rifted into multiple plates, and through successive expansion-contraction cycles, the primitive crust has been continuously consumed while new crust is persistently generated. Only planets with an active inner core heat source are geologically "alive" and capable of hosting life.
The paper draws on two different reflective band-ratio algorithms, namely the Maximum Chlorophyll-a Index (MCI) and New Three Band Algorithm (N3B) to estimate Chlorophyll-a (Chl-a) concentrations from Landsat-8 images and spectrometric water samples. Band tuning procedures was performed to find optimal peak wavelengths suitable for the estimation of Chl-a from Landsat-8 satellite image and spectrometric data. Additionally, the MCI and N3B were applied on both in-situ and Landsat-8 data and compared using statistical regression models such as the coefficient of determination (R2), relative mean absolute error (rMAE), and root mean square error (RMSE) to find the best performing algorithm in estimating Chl-a pigments. The results demonstrates that the MCI algorithm performed sensitively in the estimation of Chl-a as compared to the N3B, after data regression. The MCI algorithm obtained a higher R2 of 0.69, with a minimal percentage error (rMAE) of 18.34% and RMSE of 1.85 m-1 when applied on in-situ data. A similar result was obtained when MCI was applied on Landsat-8 data with a higher R2 of 0.75 and a minimal percentage error (rMAE) of 21.29% and an RMSE of 0.97 m-1, respectively. However, the N3B algorithm returned a lower R2 of 0.54 and 0.65 when applied on both in-situ and Landsat-8 data concurrently. The standard errors for MCI were comparatively lower than that of the N3B. Hence, in this study, the MCI algorithm performed better because it has less predictive error. In all, although both algorithms were able to estimate Chl-a pigments, the MCI algorithm is more sensitive in the retrieval of Chl-a concentration from Case-2 inland waters using both in-situ and Landsat-8 data. The results indicate the high potential of analytical algorithms to estimate Chl-a concentration in turbid and eutrophic productive (Case II) waters using satellite data, which will be of immense value to scientists, natural resource managers, and decision makers involved in managing the inland aquatic ecosystems.
This study employed seven machine-learning algorithms: Random Forest, XGBoost, LightGBM, Support Vector Machine (SVM-RBF), Elastic Net, Multilayer Perceptron (MLP), and hybrid PCA-enhanced models to predict the Livelihood Vulnerability Index (LVI-IPCC) of smallholder farmers in Southern Zambia’s Agro-Ecological Region I. Using grouped cross-validation to prevent spatial bias, the PCA-MLP and Random Forest models emerged as top performers, achieving R² values above 0.95 and RMSE below 0.05. These models effectively captured nonlinear socio-ecological interactions that influence vulnerability. Feature importance analyses identified education, income, water access, and drought exposure as key predictors. The integration of dimensionality reduction (PCA) improved model stability and interpretability. These findings demonstrate that hybrid machine-learning approaches outperform traditional LVI aggregation in predicting household vulnerability, providing scalable, data-driven insights for climate adaptation planning. The results highlight the potential of artificial intelligence to revolutionize vulnerability assessments and inform targeted resilience strategies in regions affected by climate change.
Residents in hilly areas often select sites for housing on floodplains, riverbanks, and similar locations, which makes them highly vulnerable to casualties and property damage during flood events. Flood inundation analysis for mountainous rivers can provide critical data for urban flood prevention and disaster mitigation. This study takes the mountainous Gulin River as a case study, employing the hydrological analogy method and the inference formula method to calculate design peak discharges for recurrence intervals of 2, 5, 10, 20, and 50 years. Comparative analysis demonstrated that the hydrological analogy method yields results that are more representative of the design peak discharges across various river segments. Utilizing the derived design flood data and measured DEM data, a hydrodynamic model was developed using HEC-RAS for the Jinlan and Yongle reaches of Gulin River. The model computed key hydraulic parameters, including the inundation elevation and flow velocity under different recurrence intervals. The flood inundation extents were subsequently delineated using ArcGIS. The simulation results indicate that: (1) the study reaches begin to experience bank inundation under the 10-year flood scenario; (2) the maximum inundation depths for Jinlan reach reach 6.74 m, 7.81 m, and 8.92 m for 10-, 20-, and 50-year floods respectively; (3) the Yongle reach exhibits more severe flooding with maximum depths of 9.63 m, 11.45 m, and 16.77 m for the corresponding scenarios. The inundation depth in residential areas ranges from 0 to 2 m, with Xinhe Village and Yongle Town being the most severely affected; and (4) Building upon the one-dimensional model, two-dimensional hydrodynamic modeling was conducted for the river bend near Xinhe Village and the river reach near Yongle Town. The results indicate that particular attention should be given to floodplain inundation on the inner side of the bend at Xinhe Village, where flow deceleration may lead to extensive overbank flooding. In Yongle Town, riverside buildings and infrastructure are exposed to significant inundation risks under medium- to high-return period flood events. To mitigate these risks, structural flood protection measures such as levee construction along the riverbanks are recommended.
