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Trade tariffs could worsen deforestation in South America
Global classification of river morphology based on inland water dynamics characterization and digital elevation data
Abstract Classifying river morphology is crucial for fluvial geomorphology and hydrology. River morphology reflects hydrodynamic and sedimentary processes, providing critical insights into the diversity of global river systems. This study establishes a global framework for river morphology classification based on remote sensing and topographic data. Using the Global Inland Water Dynamics Characterization dataset and the global digital elevation model ASTER GDEM V3, a river spatial image decomposition process was developed, dividing global river data into tens of thousands of image blocks containing dynamic imagery and elevation information. A ResNet-50 deep neural network was employed to construct an image-elevation fusion classification model, classifying global rivers into five major types: meandering rivers, braided rivers, straight rivers, anastomosing rivers, and anabranching rivers. These types were further divided into 17 subtypes to capture finer morphological variations. The spatial distribution patterns and morphological features of these river types were analyzed, providing a comprehensive understanding of the global distribution of river planforms. This framework advances the knowledge of river systems at a global scale and lays the foundation for future studies in fluvial geomorphology and hydrology.
A signature based on efferocytosis-related genes for the evaluation of prognosis and the tumour microenvironment in gastric cancer
The global assault on universities is an attack on democracy
A novel approach for music genre identification using ZFNet, ELM, and modified electric eel foraging optimizer
Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study
Arctic researchers need to find ways to keep working together
EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA
Top of the crops: rice scientists seek to meet global food demands
In vivo and in vitro spermatogenesis in prepubertal mouse testes exposed to low gonadotoxic doses of cytarabine or Daunorubicin
Defend scientific integrity and academic freedom
Relationship of the lumbar Lordotic angle to the abdominal aortic deviation and abdominal aortic diameter
Small parallel residual convolutional neural network and traffic congestion detection
Research on rolling bearing compound fault diagnosis based on AMOMCKD and convolutional neural network
Europe must become a research epicentre as US system gets undermined
Characterization of talc deposits in ultramafic rocks of Gebel El Maiyit and its economic feasibility
Abstract A comprehensive, multiscale investigation, integrating remote sensing, mineralogy, whole rock chemistry, Electron Microprobe (EMP), and stable isotopes (oxygen-18O and carbon-13C), was undertaken to assess the feasibility of talc deposits and their host serpentinite at Gebel El-Maiyit in the Eastern Desert of Egypt. Sentinel 2 remote sensing images were applied to discriminate talc from serpentinites followed by geochemical study of serpentinites using RO`/SiO2 ratios, AFM diagram and MgO versus SiO2 relationship indicates a peridotite origin formed at low temperature Alpine type. Our study revealed that talc deposit has a varied mineralogical composition and according to the predominant talc and gangue minerals three main types have been distinguished: 1- pure talc, 2- tremolite talc and 3- chlorite talc. Paragenetically, talc is derived from serpentine minerals, tremolite and chlorite. The latter is formed at about 231 °C. The chemical data of talc deposit reveals that the summation of talc components (SiO2 + MgO + H2O) is 92.68%, while that of impurity oxides (Al2O3 + CaO + Fe2O3 + FeO) is 5.56%. The carbon13C) and oxygen18O) contents of pure magnesite revealed that the pure phase of Gebel El-Maiyit was formed at low temperature (around 100 °C) while magnesite contained in talc carbonate rock was formed at high temperature (140–175 °C). In terms of source fluids, the metamorphic and /or magmatic water was supposed to be the main fluids which are circulated during the hydrothermal alteration. Although S and P are very minor components in all the talc ore types of the considered area and do not affect their industrial use. Copper (Cu) was not detected. Iron (Fe) and manganese (Mn) concentrations are significantly high, necessitating treatment to reduce these elements for the ore to be suitable as an electrical insulator. Arsenic (As) levels are consistently below 5 ppm, indicating the ore’s potential use in the cosmetic industry without further processing.
How India rewrote the rules of space travel when it launched its first satellite
A comprehensive study of recent maximum power point tracking techniques for photovoltaic systems
Abstract The percentage of renewable energy in the global mix of energy sources is rising annually, with solar photovoltaics (PVs) accounting for most capacity expansions due to their widespread availability, safety, and cleanliness. Because the amount of energy generated is limited by the poor efficiency of the photovoltaic cells and the characteristics of the connected load and weather fluctuation, maximum power point tracking (MPPT) strategies are crucial for maximizing the power delivered in PV production systems. These MPPT techniques face several issues and limitations, so this paper has focused more on modeling and developing the MPPT techniques in PV systems. The MPPT-based methodologies fall into three categories: artificial intelligence (AI), metaheuristic, and conventional. Five of these techniques have been proposed here to solve the MPPT problem. The perturb & observe (P&O) and incremental conductance (INC) methods have been used as conventional methods. In contrast, particle swarm optimization (PSO) has been used as a metaheuristic method. Finally, the artificial neural network (ANN) and fuzzy logic control (FLC) techniques have been used as AI methods. Each technique is analyzed critically in terms of tracking speed, algorithm complexity, and dynamic tracking in different environmental conditions. Furthermore, this comprehensive study of MPPT methods aims to be a guideline for selecting the best MPPT method for optimal operation under the environmental conditions of PV systems by employing multi-criteria decision-making (MCDM) based on AHP and CRITIC weighting methods, as well as the ranking method (VIKOR), to compare and rank the MPPT methods based on their effectiveness and economic feasibility. The results show AI techniques have a tracking efficiency of almost 99% when compared to other examined approaches, and they give quick and efficient tracking speed.