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.

Inhibitory effects of the secondary metabolite of actinomycete were examined on cell cycle of the yeasts of S. pombe and S. cerevisiae. The secondary metabolite was obtained from cultivation of the actinomycete isolated from the soil of Owakudani in Hakone, Japan. The fifth fraction of the secondary metabolite by ODS column separation (HK-T5), which was soluble to pure methanol, was used in the present experiments. The HK-T5 brought about the delay of forming colonies of S. pombe for about 11 days compared to that cultivated without the HK-T5. The delay of the colony formation was longer for the S. pombe cultivated with more amount of the HK-T5. The cultivation with HK-T5 also brought about the extension of the lifespan of the S. pombe for more than 10 weeks in a liquidus medium. The cell life recovered the ordinary manner by removal of the HK-T5, meaning that the activities of the HK-T5 is reversible. These facts confirm the suppression of cell cycle, and the delay of cell growth by the HK-T5. These phenomena were similarly observed for S. cerevisiae. Comparison of the action of HK-T5 with hydroxyurea, which is an anticancer drug inhibiting the cell cycle at S phase, clarified that the inhibitory action of HK-T5 worked at the phase earlier than S phase. The combined effects of HK-T5 on the cell cycle were evaluated with triamcinolone acetonide (TA), or aspirin, the former of which is a drug synchronizing cancer cells in S phase, and the latter keeping human cells in G1/G0 phases. The combined use of HK-T5 with TA synchronized the cells at the phase slightly proceeding from G1 to S phase without toxicity. On the other hand, the combined use with aspirin made the inhibitory effect of HK-T5 inactive. Hence, the HK-T5 is attractive as a drug for the extension of cell lifespan, and anticancer therapy.

近年入侵外來種植物問題嚴重，造成農業損失，我們也在校園中發現某些樹木下方有雜草不易生長的現象，透過文獻探討得知許多常見植物皆有毒他作用。因此決定將其應用在雜草抑制層面，我們萃取常見植物（樟樹、榕樹、鳳凰木、金絲竹、血桐）的葉，施澆在作物（小白菜、九層塔及綠豆）和入侵外來種植物（空心蓮子草及大花咸豐草），觀察其萌發率和生長狀況。從實驗結果得知有些植物之毒他物質萃取液，能抑制入侵外來種植物而不影響作物生長，同時也利用土壤分析技術證明毒他物質不會影響土壤pH及電導率。本研究旨在找出種植不同作物的最佳雜草抑制方法，找出最適合之植物毒他物質萃取液種類與濃度，並發現毒他物質會影響植物根毛和維管束細胞的生長。

In this project, we evaluate the effectiveness of Random Shuffling in the Cross Lingual Information Retrieval (CLIR) process. We extended the monolingual Word2Vec model to a multilingual one via the random shuffling process. We then evaluate the cross-lingual word embeddings (CLE) in terms of retrieving parallel sentences, whereby the query sentence is in a source language and the parallel sentence is in some targeted language. Our experiments on three language pairs showed that models trained on a randomly shuffled dataset outperforms randomly initialized word embeddings substantially despite its simplicity. We also explored Smart Shuffling, a more sophisticated CLIR technique which makes use of word alignment and bilingual dictionaries to guide the shuffling process, making preliminary comparisons between the two. Due to the complexity of the implementation and unavailability of open source codes, we defer experimental comparisons to future work.

The number of natural disasters has risen significantly in recent years, and with climate change there is no end in sight. Consequently, the demands on rescue forces around the world are increasing. For this reason, I asked myself what I can do to improve the work of rescue teams. Advances in artificial intelligence and drone technology enable new possibilities for problem solving. Based on the technological advances mentioned above, an autonomous Search and Rescue drone was developed as part of this project. The system assists rescue workers in searching for survivors of natural disasters or missing people. This paper also suggests a method for prioritizing survivors based on their vitality. The system was implemented using a commercial Parrot ANAFI drone and Python. The software was tested on a simulated drone. To simplify the development, the whole system was divided into the following subsystems: Navigation System, Search System and Mission Abort System. These subsystems were tested independently. The testing of solutions and new concepts were performed using smaller test programs on the simulated drone and finally on the physical drone. The Search and Rescue system was successfully developed. The person detection system can detect humans and distinguish them from the environment. Furthermore, based on the movements of a person, the system can distinguish whether the person is a rescuer or a victim. In addition, an area to be flown over can be defined. If something goes wrong during the mission, the mission can be aborted by the Mission Abort System. In the simulation, the predefined area can successfully be flown over. Unfortunately, controlling the physical drone does not work. It stops in the air after takeoff due to the firmware of the drone. It does not change the flight state of the drone, which results in all subsequent commands from the system being ignored. This paper shows that artificial intelligence and drone technologies can be combined to deliver better rescue services. The same system can be applied to other applications.

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.

Triage is the process by which nurses manage hospital emergency departments by assigning patients varying degrees of urgency. While triage algorithms such as the Emergency Severity Index (ESI) have been standardized worldwide, many of them are highly inconsistent, which could endanger the lives of thousands of patients. One way to improve on nurses’ accuracy is to use machine learning models (ML), which can learn from past data to make predictions. We tested six ML models: random forest, XGBoost, logistic regression, support vector machines, k-nearest neighbors, and multilayer perceptron. These models were tasked with predicting whether a patient would be admitted to the intensive care unit (ICU), another unit in the hospital, or be discharged. After training on data from more than 30,000 patients and testing using 10-fold cross-validation, we found that all six models outperformed ESI. Of the six, the random forest model achieved the highest average accuracy in predicting both ICU admission (81% vs. 69% using ESI; p<0.001) and hospitalization (75% vs. 57%; p<0.001). These models were then added to an Android application, which would accept patient data, predict their triage, and then add them to a priority-ordered waiting list. This approach may offer significant advantages over conventional triage: mainly, it has a higher accuracy than nurses and returns predictions instantaneously. It could also stand-in for triage nurses entirely in disasters, where medical personnel must deal with a large influx of patients in a short amount of time.

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.

The number of natural disasters has risen significantly in recent years, and with climate change there is no end in sight. Consequently, the demands on rescue forces around the world are increasing. For this reason, I asked myself what I can do to improve the work of rescue teams. Advances in artificial intelligence and drone technology enable new possibilities for problem solving. Based on the technological advances mentioned above, an autonomous Search and Rescue drone was developed as part of this project. The system assists rescue workers in searching for survivors of natural disasters or missing people. This paper also suggests a method for prioritizing survivors based on their vitality. The system was implemented using a commercial Parrot ANAFI drone and Python. The software was tested on a simulated drone. To simplify the development, the whole system was divided into the following subsystems: Navigation System, Search System and Mission Abort System. These subsystems were tested independently. The testing of solutions and new concepts were performed using smaller test programs on the simulated drone and finally on the physical drone. The Search and Rescue system was successfully developed. The person detection system can detect humans and distinguish them from the environment. Furthermore, based on the movements of a person, the system can distinguish whether the person is a rescuer or a victim. In addition, an area to be flown over can be defined. If something goes wrong during the mission, the mission can be aborted by the Mission Abort System. In the simulation, the predefined area can successfully be flown over. Unfortunately, controlling the physical drone does not work. It stops in the air after takeoff due to the firmware of the drone. It does not change the flight state of the drone, which results in all subsequent commands from the system being ignored. This paper shows that artificial intelligence and drone technologies can be combined to deliver better rescue services. The same system can be applied to other applications.

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»

The aim of this research was to investigate whether the ecology and biology of the Orange River mudfish Labeo capensis were suitable for the species to be used in fisheries. Three fleets of the gill nets were set, parallel to the shore. One fleet was lifted, and the fish were collected by hand. The two remaining fleets were lifted the next day. The seine net was pulled for 10 metres within the littoral zone. The net was then pulled towards the shore of the dam and the procedure was repeated four times. The four fyke nets were set parallel to the shore and were left for two nettings nights and then lifted. All fish caught were collected by hand and placed into buckets. The majority (82.93%) of the fish caught were within the 0-100 mm size class. The 101-200mm and 201-300mm size classes contain similar numbers of fish, while no fish were caught in the 301-400mm size class. The hypothesis was accepted. Allemanskraal Dam, as of the study period, has a very small juvenile fish population of L. capensis, as only 7 out of 41 fish individuals caught were within the 101- 300mm fork length size class. These results show that the population of L. capensis is not established as of yet, as the research did was right after their breeding season. Historical research has shown that sexually mature individuals of the L. capensis species tend to be a minimum of 300mm SL, 4-6 years after hatching. The population was largely young-of-the-year and may develop into an established population in 3-4 years (after sexual maturity). The L. capensis population in Allemanskraal Dam has the potential to be a fishery species if suitable conditions are maintained. Establishing this species’ potential will therefore allow economically viable fisheries to utilise them sustainably and to their full economic potential.

閱讀、記憶與理解，對莘莘學子們乃至大眾來說，都是每天經歷著的重要日常。有鑑於此，本研究驗證了一款宣稱能藉由理想的閱讀難度來啟動深度認知歷程，進而提升記憶與理解的字體：Sans Forgetica的效果，以及其設計在中文的應用可能，結果發現無論在英文或中文材料中，Sans Forgetica字體都展現出提升記憶和理解的效果。另外，本研究又根據認知心理學的完形理論，提出Sans Forgetica字體效果成因的可能解釋，並以眼動追蹤技術驗證，結果雖不夠強烈，但仍具參考價值，開啟後續研究的方向。