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 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.

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.

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.

GAGA 序列為生物發育重要順式作用子； BPC (BASIC PENTACYSTEINE) 則為植物特有 GAGA 結合蛋白。已知 bpc 突變體具多效性，其生理時鐘相關之發育有多重缺陷。阿拉伯芥BPC家族中 BPC1, BPC2, BPC3 為第一亞群，且 BPC 群間和群內有重疊與拮抗作用。為探究第一群 BPC 是否調控生理時鐘，本實驗以 3D 影像觀察 bpc1 bpc2、bpc1 bpc2 bpc3 及野生型之晝夜運動，並誘導 BPC 過量表現以檢測時鐘基因反應，發現 bpc 突變體之晝夜運動與時鐘節律皆有缺陷，顯示 BPC 能影響生理時鐘運行。透過一系列對第一群 BPC 突變體與過量表現植株的 RT-qPCR 檢測，可歸結第一群 BPC 是能調控生理時鐘與葉片生長的中心。

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.

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.

「零元效應」所指的是消費者面對同類性質之高低價兩商品，當低價商品從原價降至零元，而高價商品同時等量降價時，消費者對低價商品之偏好將顯著增加，並且因確定性效應、損失規避及稟賦效應造成。研究目的驗證零元效應的強度與成因，設計實驗零元效應於不同消費領域、族群是否皆成立；設計兩種商品，A為高品質高價位，B為低品質低價位，兩種定價情況，定價情況1，A商品為A元，B商品為B元，定價情況2，A商品為A-B元，B商品為B-B元（零元）。實驗結果使用Wilcoxon 等級和檢定，以需求光譜進行分析，並與 Dan Ariely (2007) 實驗結果進行比較，發現其差異不大；結果發現受試者身分不影響零元效應存在，除「必需品」外，其他品項皆存在零元效應，並且發現零元效應的強度受需求強度影響，若需求越強，零元效應將越弱。需求遂受試者環境影響改變，故不同國家將有不同結果。

Infectious diseases caused by bacteria from biological pollutants pose a great burden in terms of diagnosis and treatment, and millions of people worldwide die from bacterial infections. Detection of bacteria plays a critical role in clinical diagnosis and control of contamination, but is not accessible due to the high cost, complex devices and equipment required. In the project, an alternative to existing methods, a paper-based biosensor for the detection of model organism E. coli bacteria, which is visible, low cost, easy to use, can be integrated with a smartphone, is based on rapid color change in the exposed environments, drinking and pool water, wastewater, beverage products. platforms were developed. For the specific detection of E.coli bacteria, two different biosensors have been developed that can perform colorimetric detection in a user-friendly origami design, minimizing microchip and processing steps based on antibody-bound PVDF membrane and filter paper-based immunological method. In the presence and absence of target bacteria E.coli, the lowest detection limit of the biosensors obtained by using paper-based platforms that create a distinctive color on them, depending on the concentration, was 0.9x103 bacteria/ml for origami biosensor, 2.7x103 bacteria/ml for microchip biosensor and the widest dynamic linear operating range was calculated as 103-107 bacteria/ml. With the biosensor platforms we have developed, the use of only one smartphone for both qualitative and quantitative, visible results and analysis within minutes constitutes the originality of our project. With these promising results, the biosensors we have developed can also be used for the detection of different biological pollutants, do not contain complex devices and can be easily produced in large scales. We believe that the biosensors we have developed for the detection of biological pollutants in water and beverages, especially in regions where test laboratory infrastructure is not available, will contribute to the literature, public health, health economy and sustainable development goals such as clean water and sanitation, health and quality life, and life in water.

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.