Silt erosion and cavitation impact on hydraulic turbines performance: An in-depth analysis and preventative strategies.

The primary issues in the Himalayan Rivers are sediment and cavitation degradation of the hydroelectric power turbine components. During the monsoon season, heavy material is transported by streams in hilly areas like the Himalayas through regular rainfalls, glacial and sub-glacial hydrological activity, and other factors. The severe erosion of hydraulic turbines caused by silt abrasion in these areas requires hydropower facilities to be regularly shut down for maintenance, affecting the plant's overall efficiency.

Chemical Composition, Larvicidal and Molluscicidal Activity of Essential Oils of Six Guava Cultivars Grown in Vietnam.

Diseases transmitted by mosquitoes and snails cause a large burden of disease in less developed countries, especially those with low-income levels. An approach to control vectors and intermediate hosts based on readily available essential oils, which are friendly to the environment and human health, may be an effective solution for disease control. Guava is a fruit tree grown on a large scale in many countries in the tropics, an area heavily affected by tropical diseases transmitted by mosquitoes and snails.

Physico-chemical and biological remediation techniques for the elimination of endocrine-disrupting hazardous chemicals.

Due to their widespread occurrence and detrimental effects on human health and the environment, endocrine-disrupting hazardous chemicals (EDHCs) have become a significant concern. Therefore, numerous physicochemical and biological remediation techniques have been developed to eliminate EDHCs from various environmental matrices. This review paper aims to provide a comprehensive overview of the state-of-the-art remediation techniques for eliminating EDHCs.

Chemistry and Bioactivity of Essential Oils: Literature Survey and from Vietnam.

Using essential oils to control vectors, intermediate hosts, and disease-causing microorganisms is a promising approach. The genus in the family Euphorbiaceae is a large genus, with many species containing large amounts of essential oils, however, essential oil studies are limited in terms of the number of species investigated. In this work, the aerial parts of growing wild in Vietnam were collected and analyzed by gas chromatography/mass spectrometry (GC/MS).

A transformer-Based neural language model that synthesizes brain activation maps from free-form text queries.

Neuroimaging studies are often limited by the number of subjects and cognitive processes that can be feasibly interrogated. However, a rapidly growing number of neuroscientific studies have collectively accumulated an extensive wealth of results. Digesting this growing literature and obtaining novel insights remains to be a major challenge, since existing meta-analytic tools are constrained to keyword queries.

Predicting individual task contrasts from resting-state functional connectivity using a surface-based convolutional network.

Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior localization. In this paper, we build on recent deep learning methods to create a model that predicts task-based contrast maps from resting-state fMRI scans.

Cortical response to naturalistic stimuli is largely predictable with deep neural networks.

Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm.

Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis.

Coordinate-based meta-analysis can provide important insights into mind-brain relationships. A popular approach for curated small-scale meta-analysis is activation likelihood estimation (ALE), which identifies brain regions consistently activated across a selected set of experiments, such as within a functional domain or mental disorder. ALE can also be utilized in meta-analytic co-activation modeling (MACM) to identify brain regions consistently co-activated with a seed region.

Machine learning in resting-state fMRI analysis.

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in resting-state fMRI.

Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems.

The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.