全國中小學科展

2025年

大「逆」不道—局部逆境下植物體內傳訊與物質分配機制

When a leaf of a plant encounters stress, how does the plant convey the stress signal to other tissues and manage nutrient distribution? This field of study has been largely unexplored. However, the unique interconnected frond structure of Lemna trisulca, along with the use of a divided Petri dish, is very suitable for handling localized stress and investigating the mechanisms of intracellular signaling and nutrient distribution. Research has shown that when the mother leaf experiences localized stress, it releases healthy daughter leaves to minimize collateral damage to the daughter leaves. Conversely, when the daughter leaves face localized stress, the mother leaf chooses to retain them and continues supplying them with nutrients to support their survival. In-depth studies revealed that stressed daughter leaves accumulate Reactive Oxygen Species (ROS), triggering nutrient distribution by sending a distress signal to the mother leaf. This prompts the mother leaf to use Ca2+ as a signaling molecule to deliver nutrients to the daughter leaves. Selective detachment is regulated and triggered by the interaction between Ca2+ and ROS within the mother leaf. When the mother leaf undergoes stress, Ca2+ acts upstream to induce ROS accumulation at the nodes, sending a unidirectional detachment signal to the daughter leaves. This causes ROS accumulation at the daughter leaf nodes, inducing detachment and thereby reducing the collateral damage the daughter leaf could experience due to the mother leaves.

PiezoPioggia: Energy Harvesting with Raindrops

MAGALH˜AES, Eduardo De Mˆonaco. PiezoPioggia: Energy Harvesting with Raindrops. 2024. 24 p. Research report – Scientific Apprentice Program, Col´egio Dante Alighieri, S˜ao Paulo, 2024. This project wishes to study and analyze the possibility of generating clean and accessible energy with the plain impact of droplets in the ground. Therefore, it was necessary to use piezoelectric devices in order to convert the kinetic energy of each droplet into electric energy throughout piezoelectric energy harvesting processes, (PEH). Piezoelectricity is a method of clean and sustainable energy generation, developed and explored by several scientists worldwide. Thus, while studying the proprieties of those devices, the project evaluates the present situation of electricity harvesting in Brazil, the benefits of piezoelectric technology and the possibilities it presents to economy and society. Throughout the development the project builds itself upon mathematical equations and experimental results, analyzing the deformation and generated tensions of piezos. Brand new data on the behavior of rain, as well as about the potential it presents for PEH are highlighted throughout the research, reinforcing the value of such process as a sustainable energy generation method alongside with its investment potential, both from governmental and private institutions. The project also deeply characterizes the piezoelectric device studied, diving deeply in its characteristics and evaluating the deformation of the device and treating the data sets with statistical analysis methods, in order to improve the precision of the data presented. All in all, the opportunities of piezoelectric energy harvesting in the rain, nella pioggia, shall be discussed profoundly throughout the project.

Wibrazz

Wibrazz is a wearable communication tool that allows the teacher, the therapist, the parent to communicate information to the child remotely using the device. Haptic (vibrationbased) feedback is becoming increasingly important in everyday life. A vibrating device that transmits information through clothing can help people with disabilities who have no or limited sensory use to live an integrated life in society without barriers.

Let There Be (Optimal) Light

On average, the agricultural sector uses 70% of water withdrawals worldwide to produce crops1 and contributes to the eutrophication of lakes by using nutrients that are leached from the soils into lakes and reservoirs2. Vertical farming has great potential to remedy some of these issues. By growing plants vertically in controlled environments with artificial light and reusing the water, vertical farms use op to 99% less water3 and can produce up to 10 times the yield per square meter4 compared to traditional greenhouses. This improved efficiency comes at a cost; on average, vertical farms use more than 600% more energy per kilogramme of crop compared to traditional greenhouses5. 55% of this energy use is due to the use of artificial lighting6. Even though a lot of research is conducted on yield optimisation of crops in vertical farming, few research articles focus on the growth efficiency of crops to reduce the energy use in vertical farms. Only a few previous studies have tested photoperiods under 10 h·d-1. This study focuses on reducing the energy costs of light use in vertical farms by finding the photoperiod with highest energy use efficiency for the leafy vegetable arugula (eruca sativa). Energy use efficiency is defined as fresh mass per unit of electricity input (measured in kWh). In this study, arugula plants were exposed to LED growth light, with photoperiods ranging from 0 h·d-1 to 24 h·d-1 (0 h·d-1, 4 h·d-1, 7 h·d-1, 9 h·d-1, 12 h·d-1, 14 h·d-1, 16 h·d-1 and 24 h·d-1) and a PPFD of 800 μmol·m-2·s-1. The photoperiod 7 h·d-1 had the highest energy use efficiency of all photoperiods and, if used in vertical farms, this could account for approximately a 10 percent decrease in energy per kilogramme used in vertical farms (a 4 kWh decrease), with the planting density of 1400 plants per m2. This could amount to a yearly energy saving of 4,000,000 kWh per vertical farm (based on the yearly harvest of the vertical farm Nordic Harvest). This could help make vertical farming a more sustainable plant production for the future and in turn, help farming protect our water resources instead of consuming and polluting.

Can Quantum Mechanical Two-State Theory model Coulomb’s Force?

The quantum mechanical description of the four fundamental forces of nature is very important for the decryption of the rules which underlie our world. While Quantum Electrodynamics (QED) describes the electromagnetic force in great detail, it also uses complex mathematical techniques and advanced physical concepts. In the following, I will analyze to what extent a quantum mechanical two-state model can be used to describe the Coulomb interaction between two charged particles. To do so, I will exclusively focus on the electrostatic interaction, leaving dynamics aside. Furthermore, the analysis is nonrelativistic and does not consider the spin of the particles. Finally, using discrete state theory allows to explore the strength of the basic concepts of early quantum mechanics. In this sense, I will try to develop a simpli ed model for the quantum mechanical description of the electrostatic force. However, the analysis is not simplistic, since the traditional formalism of quantum mechanics will be used, including Dirac's Bra-ket notation, probability amplitudes, the Hamiltonian matrix as well as the Schrödinger equation. To understand the framework of my project, it may be helpful to take a look at the source of inspiration for my analysis: In Chapter 10 of the third volume of the well-known textbook series The Feynman Lectures on Physics[4], the force holding the hydrogen molecular ion together is explained in terms of a two-state system. The electron of the molecular ion can be either at the rst proton or at the second one. The exchange of the electron between both protons leads to an attractive force between them. It is known from QED that the electrostatic interaction between two charged particles is due to the exchange of a virtual photon which acts as force carrier. The idea of my work is to explore whether the electrostatic force can be described by a very similar model, replacing the electron acting as force carrier in the molecular ion by a virtual photon for the description of the electrostatic force between two charged particles. To describe a system consisting of charged particles, I will make the assumption that a charged particle can appear in two states. Either it is in state e where it can emit a photon or it is in state a which enables it to absorb a photon. Upon emission or absorption of a photon the charged particle transitions to the respective other state. This makes the approach analyzed in my work an element of discrete state theory, since two di erent states of the particle are used to store information about it. Of course such a model cannot be compared to the sophisticated theory of Quantum Electrodynamics. The point is, however, that it is interesting to explore the power of the most fundamental concepts of quantum mechanics and to show that such an analysis can lead to inspiring results.

雙向隨機生成數列的長度探討

本研究探討隨機生成數列的長度期望值。一個籤筒中有n支籤,編號分別為1,2,3,…,n,每抽出一支籤,就將抽取的編號寫在紙上,形成一個數列。數列只能向左右兩端添加項,不能從中插入。抽出的籤若大於目前數列的最大項,則將抽出的數寫在目前數列右邊;抽出的籤若小於目前數列的最小項,則將抽出的數寫在目前數列左邊;抽出的籤若介於目前數列的最小與最大項之間,則操作結束。基於此想法,研究者將數列依照添加項的方向分為「單向數列」與「雙向數列」兩類。顧名思義,單向數列只能向一端延伸(本研究不失一般性討論往右延伸),雙向數列代表可以向左右兩端延伸。此外,研究者又將數列分為「嚴格遞增減」和「非嚴格遞增減」兩類。在生成原理上,嚴格遞增減等價於「抽後不放回」;非嚴格遞增減等價於「抽後放回」。在這樣的規則下,本研究探討了n支籤抽完放回與不放回時,單雙向隨機生成數列的長度期望值之通解,並成功證明了一些恆等式及性質。

運用深度學習色彩校正模型之黃疸偵測 Jaundice Detection Using Deep Learning-Based Color Correction Models

現今醫療中,黃疸的早期偵測對肝臟疾病的預防與治療至關重要,但多數人難以在症狀輕微時察覺。我們希望藉由智慧手機影像結合機器學習進行黃疸檢測,提升民眾自我監測的能力。Su 等人(2021)曾使用深度學習和機器學習進行黃疸預測,但其方法依賴專業色卡進行色彩校正,成本高且限制應用範圍。本研究提出以白平衡演算法中的白色補丁法與灰界演算法,搭配深度學習模型 DCCNM1和2 取代色卡,提升黃疸檢測的普及性與便利性。經黃疸偵測效果評估顯示,DCCNM2 在無色卡模型中表現最佳,雖然各指標略低於色卡校正,但其展現出優異的穩定性和準確性,證明其作為無色卡黃疸篩檢方案的可行性。本方法將能提供便捷的居家黃疸檢測途徑,尤其對偏鄉地區居民而言,不僅提升早期發現的機會,還能有效減輕醫護人員的負擔,推動大眾健康管理。

Real-Time Ensemble Model for Stroke, Drowsy, and Distracted Driver Detection Using Transfer Learning Models

Road safety remains a global concern, with driver-related factors like distraction, drowsiness, and medical conditions such as stroke being leading causes of accidents. In this paper, we propose a real-time ensemble learning framework that leverages transfer learning for the detection of stroke, drowsiness, and distracted driving. Our model integrates multiple Convolutional Neural Networks (CNNs) fine-tuned for each specific task, and employs a stacking method to combine the predictions of these models using a meta-classifier. Notably, the model is optimized to enhance stroke detection, minimizing false negatives— an essential aspect for timely medical intervention. Experimental evaluations on diverse datasets demonstrate the efficacy of our approach, achieving an overall accuracy of 92.5%. The results emphasize the model’s potential for real-time driver monitoring, offering critical safety features that could reduce accidents and save lives.

方格裡的秘密—隨機分布的機率探討

本文研究了一個信息完全公開的組合遊戲,探討當一群人被完全隨機的分配到模型裡時,其初始位置與特定位置所形成的包圍關係,並探討最佳的人力分配。本研究通過座標解析與不等關係的代數運算等方法,成功找出獲勝條件對於遊戲雙方的限制,並進一步解決問題。在研究的過程中,也將結論擴展到不同模型,探討不同模型對於遊戲造成的影響,並比較其結論有何區別。

Greenhouse Gases Reduction: Conversion of Methane and Carbon Dioxide into Clean Energy

In the upcoming years, both population and energy consumption are expected to increase dramatically [1]. Industrialization has led to a dramatic shift in the energy environment [2], with predictions of a 57% increase in demand for energy between 2002 and 2025 [3]. In addition to organic materials like trees and solid waste, fossil fuels like coal, natural gas, and oil provide more than 90% of the world's energy needs. Their overuse has resulted in the release of climate-altering greenhouse gases like carbon dioxide (CO2) and methane (CH4) into the atmosphere [4]. Scientists and other stakeholders are putting more emphasis on finding solutions to global warming, increasing energy production in order to meet increasing demands, and decreasing emissions of greenhouse gases. Using greenhouse gasses to make useful chemicals or fuels is one solution to both problems [5]. This motivated researchers to investigate the potential of CO2 and CH4 as clean energy sources. The process of dry reforming of methane (DRM) has been identified as a potentially successful strategy for transforming CO2 into marketable syngas with a balanced H2/CO composition [6], [7], [8], [9]. The economic viability of DRM, the reactor type, the availability of raw materials, and the intended use of the produced syngas are all-important considerations. Though DRM is gaining popularity, maintaining its long-term stability is difficult due to carbon accumulation from CO disproportionation and methane degradation [10], [11]. The catalyst used, as well as other parameters like as pressure, temperature, feed concentration, and reactor size, are critical to the process's effectiveness. In this scenario, a nickel catalyst on a La2O3/SiO2 substrate with microspheres and a core-shell structure will be developed to improve the conversion of greenhouse gases into profitable syngas. This catalyst is projected to improve the efficiency and performance of the DRM process significantly.