2023年

Money drives the world and in times of crisis, money is more important than ever. Most political, economic and, as we have seen recently, health crises are accompanied by pressure on the economy. With such pressure, ordinary citizens are worried about their money, which is at risk either from inflation or from an uncertain economic outlook. In such times, some people resort to appreciating their money by investing. It is wise investments that can protect savings from inflation, or at least mitigate the effects of inflation. Investing in the stock market is among the most popular ways of investing. During the global coronavirus crisis, the number of small investors more than doubled (Galik and Brody [2022]), and in 2021, Covid (new) investors accounted for 15 % of investors in the U.S. market (Schwab [2021]). It is this phenomenon that inspired the author to write this paper. Investing in the stock market is one of the riskiest forms of investment, which means that there is a high probability of losing the originally invested capital. Some companies that allow retail investors to invest state that more than 90 % of their users lose their capital when investing in the stock market. On the other hand, investing in the stock market has one of the greatest potentials for profit. In the case of a long-term investment in index funds, for example, an average annual appreciation of between 8 % and 12 % can be expected (the average appreciation of the S&P 500, the most famous US index). However, it is necessary to wait several years for stable results. To see appreciation in a long-term portfolio, it is recommended to wait at least 12 years. This is because if a recession or a simple market correction comes, said portfolio can lose up to 50 % of its value in a year. But as history shows, markets do rise over the long term, and that is what long-term investors bet on, waiting for their capital to appreciate over time. But the market does not grow all the time. The market often changes price trends, and in some rare cases a long-term trend can reverse for up to several tens of months. This phenomenon, volatility, is attempted to be exploited by so-called speculators. A speculator is an investor who sells and buys in short periods of time and thus speculates on price movements. The speculator therefore needs a way to determine the likely behaviour of the price in the foreseeable future in order to make their trades profitable. The two main ways of analysing price behaviour are fundamental and technical analysis. And since fundamental analysis incorporates a large amount of diverse information into its predictions, this paper concentrates on working with technical analysis.

This research work is devoted to the stages of development and creation of a prototype of a laser 3D scanner model, programming of a controlling microcontroller, construction of 3D models of a scanned object. In the course of the work, the market of 3D scanners, which are used to build three-dimensional models of premises, was analyzed, the equipment necessary for the development and creation of the prototype was analyzed, as well as the software necessary for the operation of the prototype. The result of the work was the creation of a laser 3D scanner based on an Arduino microcontroller using a Lidar type sensor that scans and builds 3D models of objects. This working model of the 3D scanner demonstrates good capabilities and turned out to be easy to use.

The research project has an extremely relevant topic - the creation of an air quality monitoring system for general secondary education. In the context of the Covid 19 pandemic, proper air sanitation is a determining factor in counteracting the spread of coronavirus infection. Special requirements for the procedure of systematic ventilation of educational premises are set before teachers and technical staff of schools "Sanitary Regulations for General Secondary Education Institutions", which is mandatory for implementation in general secondary education institutions. Together with measures to counter the Covid 19 pandemic, the new health regulations somewhat neglect resource conservation and energy efficiency issues: ventilation during the heating season can lead to wasteful heat losses. Monitoring the quality of air purification is simply necessary if teachers and parents care about creating a safe educational environment for students at school. Requirements for air safety determine and regulate its characteristics such as temperature, humidity, the presence of dust particles of different sizes, the concentration of carbon dioxide (CO2), carbon monoxide (CO) and formaldehyde vapors. The level of hazardous substances such as formaldehyde, which can be released from building materials, carbon monoxide and carbon dioxide, should be closely monitored in accordance with the recommendations of sanitary regulations and hygiene requirements of health care facilities. With increased concentrations of carbon dioxide above normal, the human body will experience the negative effects of hypoxia: poor health, drowsiness, decreased cognitive processes, learning becomes less effective. Carbon monoxide is a poisonous substance that can damage the body, poison hemoglobin and cause constant hypoxia of all organs and systems, long stay indoors with it leads to death. Formaldehyde causes carcinogenic effects, and the excess concentration of dust particles worsens the condition of people with allergies and bronchial asthma, as well as dust promotes bacterial growth and the spread of viral particles, which is especially dangerous in a coronavirus pandemic. Our solution allows for a transparent permanent monitor of air quality in the school, makes this monitoring completely inclusive - because any user from among the participants in the educational process has access to up-to-date information about the air in the school and can make informed decisions about proper behavior Our system allows students to become more responsible, independent in terms of sanitary requirements and compliance. Therefore, the main idea of our project is extremely relevant today - to organize a digitalized system for monitoring the quality of air purification in the school, thus preventing inadequate ventilation with wasteful heat loss and reduced energy efficiency of the school building. The project has signs of sustainability - it is in line with the Sustainable Development Goals – 3 «Good health and well-being», 4 «Quality education», 5 «Gender equality», 11 «Sustainable cities and communities», 12 «Responsible consumption and production» and 13 «Climate action»

This research aims at exploiting civil and pre-treated industrial wastewaters that go into the purifier and those that come out of it after various treatments in order to build a galvanic cell with the goal of producing clean electric energy. Our background hypothesis is that it is possible to exploit the existing potential difference between these two types of water to generate electricity. In fact, the water sent for purification contains elements (carbon, nitrogen, sulphur, phosphorus, etc.) in a predominantly "reduced" state and its oxygen level is scarce. On the other hand, the water coming out of the process contains the same elements in a mostly "oxidized" state and it is rich in oxygen. Those chemical discrepancies should get the job done. In order to simulate the two types of water, two different solutions were prepared. The first one is highly concentrated with pollutants and gaseous nitrogen is insufflated in it to reproduce its anoxic environment. The second one’s pollution level is based on the Italian legislative limits of chemical contaminants for superficial waters (Legislative Decree 152/2006) and the semi-cell is insufflated with gaseous oxygen.

白花蛇舌草是中醫常使用的抗癌中藥，本研究主要探究白花蛇舌草治療大腸癌的可能效應、機轉及活性物質。本研究利用大腸癌動物實驗搭配次世代基因定序，期望發現白花蛇舌草的新穎免疫抗癌標靶，並利用氣相層析質譜儀搭配新穎免疫抗癌標靶分子對接，分析主要的活性物質。結果顯示，白花蛇舌草可以減少小鼠結直腸增生組織的數量及大小，將被影響的基因進行生物資訊程式分析，發現白花蛇舌草會干擾與免疫細胞趨化有關的基因群組以及與上皮細胞增生有關的白細胞介質IL-17訊息路徑，而且存在於白花蛇舌草的阿魏酸可以阻斷IL-17與IL-17受體的結合，減緩小鼠的大腸直腸癌。本研究將古老知識透過現代科學證實有用，呈現與發炎或與上皮細胞增生有關的細胞激素可以作為免疫抗癌標靶，也發現白花蛇舌草的免疫抗癌新機制，並由免疫抗癌新機轉成功求證白花蛇舌草的主要活性物質。

This is a first approach in the development of beekeeping and the preserving of bees, a crucial and important species in the balance of ecology on our planet. This project consists in designing and building a small affordable device that will help beekeepers keep an eye on their hives and prevent theft whenever and wherever they are by providing them with instant and continuous data and information about their beehive status through a mobile application. This IOT approach will rely on many physical variables especially the sound frequency of the bee buzz, which appears to be a way for the bees to communicate with each other in special circumstances. That is why; we aimed to analyze the sound frequencies of the bee buzz to detect beehive behavioral changes. Many other factors are also important for the keeping of a healthy beehive such us temperature, humidity, weight and fly activity. And as for security measures we are going to add a GPS tracker to the system to keep track of the hives and alert the beekeeper if there is any kind of danger. The development of this real time beehive monitoring system will not only help the beekeeper keep track of his hive and collect useful data but also increase the honey production and avoid many colony losses and thus preserve the bees and ensure their well-being.

The research work is dedicated to analyzing the impact of disposable tableware and packages made from synthetic polymers on people and the environment, and the search for ecological alternatives to synthetic polymers used in their production. Various types of disposable tableware and packages, their composition, production technology, harmful effects on the human body, environmental pollution, processing and recycling were studied. The statistics of the use of disposable tableware and packages in Ukraine and the world as a whole, the places of their use were analyzed. Ecological alternatives to disposable tableware and packages made from various natural materials have been studied. A practical study of the use of organic waste and wastepaper for the production of ecological disposable dishes and packages was carried out.

Improved methods of processing latex into rubber sheets will improve the incomes of small rubber producers. There are two ways in which latex can be processed into rubber sheets: fumigation and solar incubation. The fumigation method is expensive and produces pollution, but solar incubation can cause dark, sticky rubber sheets due to UV radiation, which reduces their value. A low-cost and environmentally-friendly solution to this problem was investigated here. A UV-protective roofing panel made using Nitrogen-doped Graphene Quantum Dots (NGQDs) was developed and tested. N-GQDs were made using the hydrothermal process for 2 and 4 hours (T2 and T4) and the solvothermal process for 4, 6, and 8 hours (TS4, TS6, and TS8). It was found that all types of N-GQDs absorbed light in the UV range, withT4 showing the greatest absorption. T4 had the greatest Fluorescent Intensity (FL) value, emitting blue light, while for the solvothermal method TS6 had the highest FL value, emitting red light. T4 and TS6 were chosen for further testing, and were applied to a clear roofing tile. After installing the roof on the chamber, the temperature inside was higher than outside. Then we measure the UV protection efficiency of the roof which was 93.27%. The average temperature was 45℃, which is the temperature for drying rubber sheets. Due to the roof’s capability to absorb UV radiation and heat the chamber, our N-GQDs roof has a great ability to produce higher-quality rubber sheets.

The Aedes aegypti mosquito is one of the main transmitters of viral diseases in countries close to the equator. This vector promotes a series of generalized endemics that are difficult to control and prevent in these regions. Furthermore, the presence of bacteria in the environment favors the proliferation of mosquito larvae, which increases the probability of Aedes aegypti reproductive success. The Araçzeiro (Psidium guineense Sw.) is a plant present throughout the Brazilian Atlantic Forest and has in its composition, especially in the leaves, several substances that can be used to solve problems. Thus, we sought to verify the activity of flavonoids and polyphenols in terms of their antibacterial potential and the performance of saponins in their larvicidal potential, as well as surfactant, in order to prevent the accommodation of the mosquito in the water at the time of egg deposition and larvae respiration. The saponins were extracted from the araçazeiro leaf using a hydroalcoholic solvent and the flavonoids/polyphenols using methanol, the latter being subsequently rotaevaporated to maintain the non-toxic nature of the extract. Through the aqueous extracts, the content of total saponins by UV-VIS spectrophotometry, surfactant activity, larvicidal activity and toxicity were determined. In relation to the ethanolic extracts, the content of polyphenols and total flavonoids by UV-VIS spectrophotometry and high performance liquid chromatography (HPLC), antibacterial activity and toxicity were determined. The results showed that the aqueous extract has a satisfactory amount of saponins, as well as a surfactant potential due to the formation of foam and larvicidal activity in the two highest concentrations of the extracts. Ethanol extracts showed phenolic acids, especially gallic and ellagic acid, and flavonoids, especially catechin and quercetin, and antibacterial activity in most of the worked concentrations. Both extracts (aqueous and ethanolic) showed a dominant nontoxic character, which favors their use without risk to the environment, having an alternative and sustainable potential for controlling the proliferation of the Aedes aegypti mosquito.

With the introduction of the transformer architecture by Vaswani et al. (2017), contemporary Text Generation Models (TGMs) have shown incredible capabilities in generating neural text that, for humans, is nearly indistinguishable from human text (Radford et al., 2019; Zellers et al., 2019; Keskar et al., 2019). Although TGMs have many potential positive uses in writing, entertainment and software development (Solaiman et al., 2019), there is also a significant threat of these models being misused by malicious actors to generate fake news (Uchendu et al., 2020; Zellers et al., 2019), fake product reviews (Adelani et al., 2020), or extremist content (McGuffie & Newhouse, 2020). TGMs like GPT-2 generate text based on a given prompt, which limits the degree of control over the topic and sentiment of the neural text (Radford et al., 2019). However, other TGMs like GROVER and CTRL allow for greater control of the content and style of generated text, which increases its potential for misuse by malicious actors (Zellers et al., 2019; Keskar et al., 2019). Additionally, many state-of-the-art pre-trained TGMs are available freely online and can be deployed by low-skilled individuals with minimal resources (Solaiman et al., 2019). There is therefore an immediate and substantial need to develop methods that can detect misuse of TGMs on vulnerable platforms like social media or e-commerce websites. Several methods have been explored in detecting neural text. Gehrmann et al. (2019) developed the GLTR tool which highlights distributional differences in GPT-2 generated text and human text, and assists humans in identifying a piece of neural text. The other approach is to formulate the problem as a classification task to distinguish between neural text and human text and train a classifier model (henceforth a ‘detector’). Simple linear classifiers on TF-IDF vectors or topology of attention maps have also achieved moderate performance (Solaiman et al., 2019; Kushnareva et al., 2021). Zellers et al. (2019) propose a detector of GROVER generated text based on a linear classifier on top of the GROVER model and argue that the best TGMs are also the best detectors. However, later results by Uchendu et al. (2020) and Solaiman et al. (2019) show that this claim does not hold true for all TGMs. Consistent through most research thus far is that fine-tuning the BERT or RoBERTa language model for the detection task achieves state-of-the-art performance (Radford et al., 2019; Uchendu et al., 2020; Adelani et al., 2020; Fagni et al., 2021). I will therefore be focussing on attacks against a fine-tuned RoBERTa model. Although extensive research has been conducted on detecting generated text, there is a significant lack of research in adversarial attacks against such detectors (Jawahar et al., 2020). However, the present research that does exist preliminarily suggests that neural text detectors are not robust, meaning that the output can change drastically even for small changes in the text input and thus that these detectors are vulnerable to adversarial attacks (Wolff, 2020). In this paper, I extend on Wolff’s (2020) work on adversarial attacks on neural text detectors by proposing a series of attacks designed to counter detectors as well as an algorithm to optimally select for these attacks without compromising on the fluency of generated text. I do this with reference to a fine-tuned RoBERTa detector and on two datasets: (1) the GPT-2 WebText dataset (Radford et al., 2019) and (2) the Tweepfake dataset (Fagni et al., 2021). Additionally, I experiment with possible defences against these attacks, including (1) using count-based features, (2) stylometric features and (3) adversarial training.