Biodegradation of Post-Cured Photopolymeric Resin of Stereolithography 3D Printers Using Galleria mellonella Larva.
The present research has as main objective to degrade the post-cured photopolymer of the stereolithography 3D printer resin using Galleria mellonella larvae. It is necessary to consider that the use of materials from 3D printers tends to increase considerably and in approximately seven years about 10% of everything that will be produced in the world will come from this type of printing. Considering also that the increase in population growth and technological development are directly linked to the increase of solid waste on the planet, in particular to polymeric materials, there is a need to degrade and give an adequate end to waste, avoiding a notorious accumulation along the time. For this purpose, Galleria mellonella larvae will be used because of it's comprovated capacity to degrade polyethylene, to find out if it is capable of biodegrading the post-cured resin of the printer. To carry out the research, compositional tests were done in partnership with the SENAI Institute for Innovation in Polymer Engineering, located in São Leopoldo, Rio Grande do Sul, and the creation of the larvae and degradation of the photopolymer will be carried out in partnership with the University Federal University of Health Sciences of Porto Alegre (UFCSPA). The data analysis will be based on the crystallinity determination tests by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and attenuated total reflectance spectroscopy (ATR) that will also be applied in the larvae feces after contact with the polymer to assess for degradation. As a result of the compositional tests, the ATR showed predominantly characteristic absorptions of acrylic resin; in the TGA test, the loss of mass described in the test is related to the loss of mass of organic material, mainly polymer. Finally, in the DSC test a thermal event was observed in the heating of the sample, with peaks at 125 ° C (Tpm), characteristic of fusion, and a thermal event in the cooling of the sample, in 112 ° C (Tpc), characteristic of crystallization. Based on the analysis of the results obtained, it is possible to infer that most of the composition of the photopolymer is acrylic resin, widely used in stereolithography 3D printers. The research has the future objective of isolating the substance into the larvae responsible for degradation so that it can be degraded on industrial scales. The research started in March 2020 and is still under development due to the COVID-19 pandemic, which compromised the planned tests.
HoneySurfer: Intelligent Web-Surfing Honeypots
In Singapore’s evolving cyber landscape, 96% of organisations have suffered at least one cyber attack and 95% of organisations have been reporting more sophisticated attacks in the frame of one year according to a 2019 report[1] by Carbon Black. As such, more tools must be utilised to counter increasingly refined attacks performed by malicious actors. Honeypots are effective tools for studying and mitigating these attacks. They work as decoy systems, typically deployed alongside real systems to capture and log the activities of the attacker. These systems are useful as they can actively detect potential attacks, help cybersecurity specialists study an attacker’s tactics and even misdirect attackers from their intended targets. Honeypots can be classified into two main categories: 1. Low-interaction honeypots merely emulate network services and internet protocols, allowing for limited interaction with the attacker. 2. High-interaction honeypots emulate operating systems, allowing for much more interaction with the attacker. Although honeypots are powerful tools, its value diminishes when its true identity is uncovered by attackers. This is especially so with attackers becoming more skilled through system fingerprinting or analysing network traffic from targets and hence, hindering honeypots from capturing more experienced attackers. While substantial research has been done to defend against system fingerprinting scans (see 1.1 Related Work), not much has been done to defend against network traffic analysis. As pointed out by Symantec[2][3], when attackers attempt to sniff network traffic of the system in question, the lack of network traffic raises a red flag, increasing the likelihood of the honeypot’s true identity being discovered. In addition, the main concern with regards to honeypot deployment being their ability to attract and engage attackers for a substantial period of time, an increased ability to interest malicious actors is invaluable. Producing human-like network activity on a honeypot would appeal to more malicious actors. Hence, this research aims to build an intelligent web-surfer which can learn and thus simulate human web-surfing behaviour, creating evidence of human network activities to disguise the identity of honeypots as production systems and luring in more attackers interested in packet sniffing for malicious purposes.
多邊形的剖分圖形數量之探討
從參考資料[1]可知，將凸n+2邊形利用n-1條不相交的對角線剖分成n個三角形的圖形數量即為卡特蘭數Cn。而我利用不相交的對角線把n+2邊形剖分成數個多邊形和三角形的組合，並從此類的剖分圖形與三角剖分圖形之關聯，進而由卡特蘭數的一般式推導出此類剖分圖形數量的一般式。在本研究中可得，若到把n+2邊形剖分成一個k+2邊形和多個三角形的圖形數量是(2n-k+1 n+1) ；把n+2邊形剖分成一個k+2邊形、一個m+2邊形和多個三角形的圖形數量，當m≠k，數量為n+2/2(2n-k-m+2 n+2) ，當m=k時，數量為n+2/2(2n-2k+2 n+2) ；把n+2邊形剖分成一個k1+2邊形、一個k2+2邊形、一個k3+2邊形、和n-k1-k2-k3 個三角形的剖分圖形，當k1,k2,k3兩兩相異時，數量為(n+2)(n+3)(2n-k1-k2-k3+3 n+3) ；把n+2邊形剖分成一個K1+2邊形、一個K2+2邊形、一個K3+2邊形、一個K4+2邊形和n-K1-k2-k3-k4個三角形的剖分圖形當k1,k2,k3,k4兩兩相異，數量為(n+2)(n+3)(n+4)(2n-k1-k2-k3-k4+4 n=4)。並猜測若k1,k2,...,ki兩兩相異時，把n+2邊形剖分成一個k1+2邊形、一個k2+2邊形、…、一個ki+2邊形、和n-Σkj 個三角形的剖分圖形數量為(n+i)!/(n+1)!(2n-Σkj+i n+i) 。
Predicting the Binding Affinity between Medicine and Estrogen Receptor Beta
Recent studies showed that the probability of Taiwanese females developing breast cancer has risen dramatically over the past 30 years. We are now facing younger and more breast cancer patients in Taiwan. What makes the matter even more severe, is the fact that patients that take cancer treating medicine will suffer from its serious side effects, some may even lose the ability to reproduce. We hope to develop a new system that can help doctors and researchers develop new medicine for treating breast cancer, the way medicine cures cancer tumors are by attaching onto the infected cells’ receptors. After collecting MACCS data (converted from SMILES), the dataset will be used for training the machine learning program. Due to the problem of insufficient training data, we used an ensemble method to generate our machine learning model. Among the three basic ensemble techniques, Max Voting, Averaging, and Weighted Averaging. we selected the max voting technique to perform the prediction for this research. We created two separate datasets, positive and negative, the two datasets will later be used as training data for the program. We weren’t sure of the ratio of positive and negative in the training data, therefore we compare 40 different ratios and evaluate the results. By comparing the accuracy of the models, we found out that when the ratio between positive data and negative data is 1:3000, the machine learning program will have the highest precision. After we created the final model through voting among the 1000 models generated, we evaluate the precision of the model through the following methods, AUC, precision, recall. The ultimate goal of this research is to assist doctors and researchers shorten the process of developing and testing new medicines.