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 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»

本次研究主要探討中型及長圓金蛛蛛網隱帶的結構不同，所造成的振動模式差異。蜘蛛網面的結構會直接影響到蛛網結構的振動方式，以及能量散布的情形。為了了解蜘蛛網有無隱帶結構對於獵物衝擊網面的影響，使用釣魚線及蠶絲進行模擬蛛網。實驗結果顯示，不論長圓或是中型金蛛其隱帶的長度與體長成正相關。金蛛隱帶的長度與環境溫度、照度和風速的相關性不高。而在複式顯微鏡下發現金蛛隱帶的構造有疏密之分。仿生金蛛網不論是何種類型隱帶，皆無法減少受模擬風吹吹拂網面的振幅，僅有蠶絲密十字型隱帶能夠有效減少受模擬獵物撞擊網面的振幅。此外，蠶絲仿生網的振動幅度較釣魚線小，穩固性較佳。

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.

溫熱化學治療(Hyperthermic Intraperitoneal Chemotherapy, 簡稱HIPEC)，為將化療藥物加熱後灌注到腹腔內殺死癌細胞，但溫熱化學治療只能在開刀時使用，治療次數有限。為達到多次的熱化療效果，本研究開發奈米氧化鐵藥物磁性複合微粒(Nano iron oxide magnetic drug complex particles, NIOMP)，可利用開刀時包覆複合微粒於腹腔，術後用磁場加熱，加上微粒化療藥物釋放，達到多次溫熱化學治療。NIOMP以鐵氧化物奈米粒子為核心，海藻酸鈉為外殼體，利用電紡法將前其與氯化鈣交聯產生凝聚性微粒。數據顯示最佳製作條件為16 kv高電壓、距離4 cm、噴速5μm/s，可得直徑大小約為200~450 μm穩定性高微粒。藥物釋放實驗顯示，NIOMP於2~7小時藥物釋放速率較穩定。利用可變磁場之電磁效應加熱NIOMP中氧化鐵磁顆粒，可於30分鐘內快速升溫至43℃。細胞相容性測試結果得知，NIOMP對細胞無毒性。本研究改善HIPEC的治療限制，可多次針對腫瘤患部局部熱化學治療，未來將可改善病人存活率。

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.

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.

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 ceRNAs as a class of RNAs act by competitively binding to miRNAs and limiting their regulatory effect on the target genes. Increasing evidences point to the role of ceRNAs in glioma cancer. So far, limited studies have been reported on the role of ceRNA in the development in glioma cancer. In this study, we have analyzed online RNA sequencing data in order to predict the ceRNAs which are putative regulators of in glioma cancer.

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.

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.