CARBON NANOTUBES AS A THERAGNOSTIC TOOL AGAINST BREAST CANCER（和訳：乳癌の診断・治療のためのカーボンナノチューブの開発）

東洋大学201

Non-linear finite element of analysis of reinforced concrete members

A finite element analysis of reinforced concrete members is used to study the initiation and propagation of cracks and the behavior at the interface of concrete and steel. The increase of concrete strength due to the biaxial compression and the shear capacity of the cracked elements are also considered in the analysis. The results of the present analysis are compared with some available experimental data in beam tests. It has been found that the present analysis is capable of calculating the stress ( or moment ) and strain ( or deformation ) from zero load upto the ultimate load

Behaviors of the Curtain Wall Façade in the Tall Building Under Dynamic Actions- An Overview and Recommendations

The flourishing growth of tall building construction with advanced technologies adopted façade structures is the new trend around the world. This study is mainly focused on the design and construction techniques of the curtain wall façade system and its behaviors under dynamic actions. Generally, a unitized curtain wall system is used as a building lope in the tall building due to its faster installation techniques. The provision of stack joints is a crucial part, especially at the design stage to accommodate the horizontal and vertical movement of the tall building under cyclic wind and seismic action. The effect of the deflection, story drift index, and column shortening of the tall building is analyzed using Finite Element Analysis (FEA) with 41 store building model on the façade wall is well discussed and recommended suitable measures to increase the life span of the façade wall under heavy dynamic repeated loading. On the other hand, lateral torsional buckling of the mullion, especially for the long span plays a vital role in the material and cost of the curtain wall panel. Therefore, this study is focused on the provision of the Anti-Buckling Clip (ABC) to increase the lateral torsional buckling capacity as well as flexural buckling capacity without increasing the stiffeners of the mullion

An E-Passport System with Multi-Stage Authentication : A Casestudy of the Security of Sri Lankaas E-Passport

E-passport or Electronic passport is one of the newly established research areas, especially since in the last few years there have been numerous reported attempts of illegal immigration across a number of country borders. Therefore, many countries are choosing to introduce electronic passports for their citizens and to automate the verification process at their border control security. The current e-passport systems are based on two technologies: RFID and Biometrics. New applications of RFID technology have been introduced in various aspects of people2019;s lives. Even though this technology has existed for more than a decade, it still holds considerable security and privacy risks. But together with RFID and biometric technologies an e-passport verification system can reduce fraud, identity theft and will help governments worldwide to improve security at their country borders. In 2017 Sri Lankan government proposed to introduce a new epassport scheme which will contain embedded RFID tags for person identification purpose. Therefore, this paper proposes a novel multi-stage e-passport verification scheme based on watermarking, biometrics and RFID

Assessing the loading rate dependence of hydrogen environment-assisted cracking behavior in a wide-range of engineering alloys

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Preserving individual privacy in ubiquitous e-commerce environments: a utility preserving approach for user-based privacy control

Applications such as e-commerce, smart home appliances, and healthcare systems, amongst other things, have become part and parcel of our daily lives. The data aggregated through these applications combined with state-of-the-art machine learning approaches have even increased the widespread uptake of these applications. However, such data aggregation and analytical practices have raised privacy concerns among users. Privacy-preserving machine learning models mitigate these concerns through private data aggregation and analytical techniques. The first objective of this thesis is to design a privacy preserving data aggregation and analytical approach for recommendation systems. Recommendation systems rely heavily on behavioural and preferential data of a user to produce accurate recommendations. Aggregation of such data can reveal sensitive information about users to the Third-Party Service Providers (TPSPs). We start with designing a recommendation system that uses Local Differential Privacy (LDP) based input data perturbation mechanism to perturb users’ ratings locally before sending it to the TPSP. Hence, the TPSP aggregates only the perturbed ratings and has no access to original ratings. This approach protects a user’s privacy from TPSPs who aggregate ratings to infer any sensitive information. Next, we propose an LDP-based hybrid recommendation framework to protect users’ privacy from TPSPs who aggregate both ratings and reviews. We propose to perturb user ratings and pre-process user reviews at the user-side before sending them to the TPSP. Such an approach ensures that the TPSP cannot aggregate the original ratings or reviews from the users. However, these approaches still do not protect a user’s privacy from TPSPs who collect implicit feedback to predict a user’s preferences. Hence, we design an LDP-based federated matrix factorization for implicit feedback. We motivate the idea of stochastic gradient perturbation using the Bounded Laplace (BLP) mechanism to ensure strong privacy protection for users. The second objective of this thesis is to design a privacy preserving untraceable TPSP-based payment protocol. A TPSP based payment system does not protect a customer’s privacy in the face of an untrustworthy TPSP. Customers cannot make transactions anonymously as the TPSP collects detailed transaction-related information. TPSP uses this information to create a comprehensive behaviour profile of each customer, based on which TPSP can deduce sensitive information about a customer’s lifestyle. Hence we propose an untraceable payment system in this thesis to tackle this problem

Effect of laser surface treatment on the corrosion and fatigue performance of aa5456-h116 alloys

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Private and Utility Enhanced Recommendations with Local Differential Privacy and Gaussian Mixture Model

Recommendation systems rely heavily on behavioural and preferential data (e.g. ratings and likes) of a user to produce accurate recommendations. However, such unethical data aggregation and analytical practices of Service Providers (SP) causes privacy concerns among users. Local differential privacy (LDP) based perturbation mechanisms address this concern by adding noise to users' data at the user-side before sending it to the SP. The SP then uses the perturbed data to perform recommendations. Although LDP protects the privacy of users from SP, it causes a substantial decline in recommendation accuracy. We propose an LDP-based Matrix Factorization (MF) with a Gaussian Mixture Model (MoG) to address this problem. The LDP perturbation mechanism, i.e., Bounded Laplace (BLP), regulates the effect of noise by confining the perturbed ratings to a predetermined domain. We derive a sufficient condition of the scale parameter for BLP to satisfy ε -LDP. We use the MoG model at the SP to estimate the noise added locally to the ratings and the MF algorithm to predict missing ratings. Our LDP based recommendation system improves the predictive accuracy without violating LDP principles. We demonstrate that our method offers a substantial increase in recommendation accuracy under a strong privacy guarantee through empirical evaluations on three real-world datasets, i.e., Movielens, Libimseti and Jester