Hydrotropism is the movement or growth of a plant towards water. It is a type of tropism, or directional growth response, that is triggered by water. Plants are able to detect water through various stimuli, including changes in moisture levels and changes in water potential. The purpose of this study is to provide an overview of how root movement towards water and plant water uptake are stabilized. The impact of hydrotropism on plants can be significant. It can help plants to survive in environments where water is scarce, and it can also help them to grow more efficiently by directing their roots towards the most nutrient-rich soil. To make sure that plant growth and water uptake are stabilized, plants must sense water. Flowing down the roots, being absorbed by roots, and evaporating from the leaves are all processes that are governed by plant physiology and soil science. Soil texture and moisture affect water uptake. Hydraulic resistances can impede plants’ water absorption, while loss of water and water movement can change plants’ water potential gradients. Growth causes water potential gradients. Plants respond to gradient changes. Stomata and aquaporins govern water flow and loss. When water is scarce, stomatal closure and hydraulic conductance adjustments prevent water loss. Plants adapt to water stream changes by expanding their roots towards water and refining the architecture of their roots. Our study indicates that water availability, or gradients, are impacted by systemic and local changes in water availability. The amount of water available is reflected in plant turgor. There is still a lot of work to be done regarding the study of how the loss and availability of water affect plant cells, as well as how biophysical signals are transformed in a certain way during their transmission into chemical signals so that pathways such as abscisic acid response or organ development can be fed with information.

It is often said that mathematical modeling is an implementation of mathematics in real-world problems with the aim of better understanding them, so we can say that mathematical modeling is linked to the solution of problems. Some of the essential principles and procedures of mathematical modeling are discussed using formulas and equations. We investigate the stability and convergence characteristics and demonstrate the suitability of different mathematical methods in a set of numerical examples. The described methods in our paper are the best choices for the simulation of linear phenomena and are more efficient for use with high-order spatial discretization. We emphasized the importance of mathematical modeling technologies used in computational tools. Our study shows that these new methods are more stable with lower errors.

Deepspeech was very useful for development IoT devices that need voice recognition. One of the voice recognition systems is deepspeech from Mozilla. Deepspeech is an open-source voice recognition that was using a neural network to convert speech spectrogram into a text transcript. This paper shows the implementation process of speech recognition on a low-end computational device. Development of English-language speech recognition that has many datasets become a good point for starting. The model that used results from pre-trained model that provide by each version of deepspeech, without change of the model that already released, furthermore the benefit of using raspberry pi as a media end-to-end speech recognition device become a good thing, user can change and modify of the speech recognition, and also deepspeech can be standalone device without need continuously internet connection to process speech recognition, and even this paper show the power of Tensorflow Lite can make a significant difference on inference by deepspeech rather than using Tensorflow non-Lite.This paper shows the experiment using Deepspeech version 0.1.0, 0.1.1, and 0.6.0, and there is some improvement on Deepspeech version 0.6.0, faster while processing speech-to-text on old hardware raspberry pi 3 b+.

Computational biology blends mechanistic models with data-driven AI. In this survey, we focus on plant systems, with occasional pointers to human biology that were useful. We review four advances, organ-level growth models that allocate resources among leaves, stems, and fruit (e.g., GreenLab), whole-tree models linking physiology and form (e.g., L-PEACH), plant microbe models for nitrogen fixation (e.g., AMIGO), and AI workflows from high-throughput phenotyping to protein-structure prediction (e.g., AlphaFold). For each topic, we outline the core equations in plain terms, state key assumptions and identifiability limits, and give practical validation steps using open datasets, standard metrics, and simple ablations. We also clearly separate the survey from a forward-looking idea mimicking plant metabolism, which we present as future work, not a finished result. We end with concrete next steps: calibrating across scales, quantifying uncertainty, testing models across species and sites, and sharing reproducible pipelines that connect simulations with AI. Our aim is a tighter, clearer guide for both method builders and applied researchers. To bridge plant science and computer science, we suggest modeling plant metabolism mathematically. Such models mirror biochemical pathways, deepen biological insight, and open new avenues for algorithm design in computational biology. We argue that the convergence of computational biology and AI will advance plant science and, more broadly, benefit medicine, ecology, and environmental science. This paper summarizes the current state of the field and outlines a practical path for future work