3,787 research outputs found
CD24 Oct4 Immunohistochemical Expression in HNSCC
No full textThis SPSS datasheet contains data of demographics, clinical parameters and immunohistochemical expression related findings of CD24 and Oct4 in head and neck squamous cell carcinoma (HNSCC) patients. These two cancer stem markers expression correlates with histological subtyping of HNSCC
Differential privacy preserving based framework using blockchain for internet-of-things
No full textThe Internet of Things (IoT) has enabled the collection of vast amounts of data that can be used to improve various aspects of our lives. However, the astronomical volume of data generated by these IoT devices has raised significant concerns pertaining to privacy preservation. The amalgamation of the Internet of Things (IoT) with blockchain technology has engendered a promising solution for securing and managing IoT data, but it is still susceptible to privacy breaches. Recently, differential privacy (DP) has been proposed as a promising technique to alleviate these issues. In this paper, we design and propound a complete end-to-end blockchain-based architecture by implementing differential privacy at the stream level generated by IoT devices by deploying Laplace noise and Gaussian noise utilizing low complex cryptography mechanism and fast convergence consensus protocol to surmount the privacy preservation issues in IoT based blockchain network. Our novel DP-based framework introduces the concept of privacy levels as low, medium, and high as set by the data owner and also analyzes the impact of different parameters on the effectiveness of the approach and provides recommendations for tuning them. The workflow of our proposed framework consists of three phases: Data generation phase, Data Sharing phase, and Data Analysis phase. During the Data generation phase, the data owner will first determine the desired level of privacy protection (low, medium, high) and set the privacy budget (epsilon) and sensitivity (delta) of the data. Based on the budget value, the privacy module will generate noise from either Laplace or Gaussian distribution as requested by the data owner. The Data Sharing phase is mainly responsible for transmitting and processing the transactions inside the blockchain network. This is followed by the data analysis phase, which will check for the budget value and the amount of noise added to the data before the noisy data is handed over to the end user. We demonstrate the efficacy of our approach through multiple experimental evaluations and simulation results evince that our approach attains high levels of privacy preservation while upholding data utility and blockchain consistency. Overall, our proposed framework provides a promising solution to the privacy challenges in IoT-based blockchain systems, offering adjustable privacy levels to accommodate different privacy requirements. This DP-based approach and the adjustable privacy levels ensure alignment with the growing regulatory requirements for data privacy, such as GDPR, demonstrating compliance with these regulations and building trust with customers. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024
Application of Stochastic Optimal Control in Finance
No full textAsset allocation theory and practice has been applied to many problems of institutional investors. In this dissertation, we consider the following two problems:
Abstract i) Optimal portfolio and spending rules for endowment funds.
Abstract ii) Capital adequacy management for banks in the Lévy market.
Abstract Part I: We investigate the role of different spending rules in a dynamic asset allocation model for an endowment fund. In particular, we derive the optimal portfolios under the consumption-wealth ratio rule (CW strategy) and the hybrid rule (hybrid strategy) and compare them with a theoretically optimal (Merton's) strategy for both spending and portfolio allocation. Furthermore, we show that the optimal portfolio is less risky with habit as compared with the optimal portfolio without habit. Similarly, the optimal portfolio under hybrid strategy is less risky than both CW and Merton's strategy for given set of constant parameters. Thus, endowments following hybrid spending rule use asset allocation to protect spending. Our calibrated numerical analysis on US data shows that the consumption under hybrid strategy is less volatile as compared to other strategies. However, hybrid strategy comparatively outperforms the conventional Merton's strategy and CW strategy when the market is highly volatile but under-performs them when there is a low volatility. Overall, the hybrid strategy is effective in terms of stability of spending and intergenerational equity because, even if it allows fluctuation in spending in the short run, it guarantees the convergence of spending towards its long term mean.
Abstract Part II: We investigate the capital adequacy management and asset allocation problems for a bank whose risk process follows a jump-diffusion process. Capital adequacy management problem is based on regulations in Basel III Capital Accord such as the capital adequacy ratio (CAR) which is calculated by the dividing the bank capital by total risk-weighted assets (TRWAs). Capital adequacy management requires a bank to reserve a certain amount for liquidity. We derive the optimal investment portfolio for a bank with constant absolute risk aversion (CARA) preferences and then the capital adequacy ratio process of the bank is derived, conditional on the optimal policy chosen
BCPriPIoT: BlockChain Utilized Privacy-Preservation Mechanism for IoT Devices
No full textSecurity and privacy are the primary concerns for IoT devices but because of their inherent limitation both in terms of processing and energy, IoT devices cannot be deployed at their full scale. To alleviate such security and privacy issues, the interaction of blockchain with IoT systems has acquired significant attention these days because blockchain has presented an underlying mechanism of immutability, audibility, and transparency for data storage. However due to the intrinsic nature of a blockchain containing complex mathematical proof concepts such as Merkle Hash Tree and Proof of Work (PoW) which demands high computation power making it less viable for IoT devices to be connected with. To overcome these issues, a novel scheme is proposed in this paper, which deploys private by design based blockchain architecture for IoT devices using low complex consensus algorithm and low computation cryptographic mechanism which suits best for IoT devices to address the privacy concerns. Unlike the traditional blockchain network in which every node maintained a copy of the transaction, we have proposed a new architecture in which block validation and block generation logic has been modified so that a transaction will be limited to the trusted recipient only. The proposed scheme outperforms the contemporary approaches both in terms of throughput and latency as observed through simulation results as well as maintaining the privacy concerns which will encourage the actual implementation of IoT applications in the real world. Moreover, the evaluation analysis demonstrate that the approach has major potential in a trusted network computing system and provides a substantial secure environment for IoT users
EPIoT: Enhanced privacy preservation based blockchain mechanism for internet-of-things
No full textWith the increasing popularity of the Internet of things (IoT) and giving the end users the opportunity of collecting and analyzing the data by these IoT devices give rise to ultimate privacy concern and is attracting significant attention nowadays. These IoT devices may contain highly sensitive data and data sharing processes which may lead to security and privacy concerns. To surmount these issues, the interaction of IoT with blockchain for a secure transaction is accepted as a candidate solution. However, the innate behavior of blockchain containing complex mathematical proofs and consensus protocol requires high computational power making it less favorable for IoT devices to be connected with. Motivated by a private by-design framework and emphasizing greater control and setting of privacy preferences by the data owner, this paper complements our previous work on privacy preservation in IoT networks. In this paper, we design and propound a complete blockchain-based privacy-preserving framework by deploying service-oriented layers concepts and low computation cryptography, and a less complex consensus protocol to address the privacy concern. Moreover, this paper will unravel the complete end-to-end architecture of IoT-based blockchain purposely build for secure transactions in IoT networks. Security analysis is conducted using AVISPA tool to show that the proposed algorithms attain the desired security goals. This is followed by extensive simulation experiments and ultimate output results cultivating it much favorably for the deployment of IoT applications in real life
Joining of Dissimilar Materials
No full textMaterial manufacturers and engineering structure designers are currently focusing new ways to exploit the benefits of light-weight, hybrid materials with improved properties at a low cost. The ability to join dissimilar materials is enabling the design engineers to develop light-weight and efficient automobiles, aircraft and space vehicles.
The objective of this PhD research study was to produce alternative and efficient joining solutions for automotive and aerospace applications. The joining of dissimilar material was experimented to obtain light-weight Fibre Reinforced Polymer (FRP) sandwich composites, Al-foam sandwich (AFS) composites, hybrid dynamic FRP epoxy/polyurethane composites and the joining of Ti6Al4V alloy with and without surface modification to Ceramic Matrix Composite (CMC) and itself.
The joining of Al-foam and Al-honeycomb to FRP skins was performed. The experimental results show that higher flexural properties can be achieved by replacing Al-honeycomb with low-cost Al-foam as a core material in the sandwich structures. Compared to FRP-honeycomb sandwich panels, FRP-Al foam sandwich panels display ~25 % and ~65 % higher flexural strength in a long and short span three-point bending tests respectively.
AFS composites with complete metallic character, to withstand high-temperature application conditions, were produced by soldering/brazing techniques using Zn-based and Al-based joining alloys. A post-brazing thermal treatment was designed to recover the mechanical properties of AFS composites, lost during the soldering/brazing process. The microstructural analysis of the Al-skin/Al-foam interface revealed that the diffusion of joining materials into the joining substrates (Al-sheet and Al-foam) was achieved. Around 80% higher bending load before failure was observed when the AFS specimens produced with Zn-based joining alloys were subjected to flexural load compared to those produced with Al-based joining alloys.
Hybrid dynamic Carbon Fibre Reinforced Polymer (CFRP) composites with enhanced impact properties were produced by exploiting the reversible cross-linking functionalities of dynamic epoxy and dynamic PU resin systems. By joining dynamic CFRP-epoxy and dynamic CFR-PU laminates, hybrid dynamic composite in three different configurations and a non-hybrid composite were obtained. The four dynamic composites were characterised for structural, thermal, flexural and impact properties. The damage initiation upon impact was observed at around 95% higher energy level in the hybrid configuration (CFRP-4), compared to the non-hybrid configuration. The hybrid configuration CFRP-3 responded with around 55% higher perforation threshold energy compared the non-hybrid configuration.
Preliminary work on Adhesive joining of the Ti6Al4V alloy to itself was performed to analyse the effect micro-machining on adhesion and the effect of shape/design of micro-slots on an adhesive joint strength. Three types of micro-slots: V, semi-circle and U-shaped micro-slots were produced on Ti6Al4V sheet surface by using an in-house developed Micro-Electro-Discharge Machining (Micro-EDM) setup. Ti6Al4V alloy specimens with and without micro-machined surfaces were bonded together using a commercial epoxy adhesive. The Single Lap Offset (SLO) shear test results revealed that the micro-slot oriented perpendicular to the applied load displayed ~23 % higher joining strength compared to when the micro-slots were oriented parallel to the applied load. U-shaped micro-slots configuration displayed ~30 % improvement in the joint shear strength compared to the specimens with un-modified surfaces. The fractured surfaces analysis revealed mix (adhesive-cohesive) with cohesive dominated failure in bonded specimens with micro-machined surfaces compared to the as-received where pure adhesive failure was observed.
The joining of CMCs (C/SiC and SiC/SiC) to Ti6Al4V alloy was experimented using active brazing alloy (Cusil-ABA) and Zr-based brazing alloy (TiB590) in a pressure-less argon atmosphere. The CMC-Ti6Al4V joint strength was further improved by modifying the surface of Ti6Al4V alloy using an in-house built Micro-EDM setup. Around 40% higher joining strength was recorded when the Zr-based brazing alloy was used as a joining material compared to the conventional active brazing alloy, Cusil-ABA. Improvement in the joining strength was noticed when the Ti6Al4V surface was modified prior to joining
Joining of AL-6016 to Al-foam using Zn-based joining materials
No full textTo obtain an Aluminium Foam Sandwich (AFS), Al-6016 sheets were successfully joined to a 9 mm thick Aluminium (Al) foam, by using Zinc (Zn) based joining materials (pure Zn and Zn alloy with 2% Al) at 430 °C in argon atmosphere. The microstructure of the joints was analysed by Optical Microscope (OM), Scanning Electron Microscope (SEM) and Energy Dispersive Spectroscopy (EDS). Moreover, three-point bending tests were carried out to evaluate the flexural properties of the AFS components. Current experimental work is focused on optimization of the AFS joining process and the mechanical properties of AFS components
Consumer environmental awareness and purchase intentions. A survey
No full textBesides posing a serious threat to the health and life of the world population, COVID-19, a severe acute respiratory syndrome, has also changed significantly people’s habits concerning transportation. Indeed, the fear of getting infected has provoked a shift from public to personal transportation, which may lead consumers to prefer buying their own cars, rather than using public transport, with a negative impact on the environment. At the same time, however, recent research suggests that COVID-19 might have increased consumers’ environmental awareness, thus paving the way for increased diffusion of electric cars. Nevertheless, we know little about whether and how the growing environmental awareness created by COVID-19 (EAC-19) affects consumers’ (electric vs. gasoline) car purchase intention (EvsG-CPI). To investigate such an impact, we frame the decision to buy an electric vs. gasoline car as an ethical decision, and argue that it is influenced by both rational or deliberated (i.e., individual judgement) and non-rational or emotional (i.e., ethical obligations and subjective norms) factors. Structural Equation Modelling (SEM) results reveal that both a rational path and a non-rational path are at work in explaining the influence of EAC-19 on EvsG-CPI. On the one hand, a rational path is confirmed as the effects of EAC-19 on EvsG-CPI is mediated by ethical judgement. On the other hand, we find that both ethical obligation and subjective norms influence the relationship between EAC-19 and EvsG-CPI by acting simultaneously as mediators in a serial mediation model. Overall, our findings allow marketing scholars, electric car companies, and policy-makers to develop a better understanding of customers’ preferences regarding the purchase of cars in the new normal after the COVID-19 pandemic and, more generally, of the rational and non-rational factors underlying the purchase of environmentally-friendly products. Limitations of the study and avenues for further research are also discussed
Machine learning based activity learning for behavioral contexts in Internet of things (IoT)
No full textOntology based activity learning models play a vital role in diverse fields of Internet of Things (IoT) such as smart homes, smart hospitals or smart communities etc. The prevalent challenges with ontological models are their static nature and inability of self-evolution. The models cannot be completed at once and smart home inhabitants cannot be restricted to limit their activities. Also, inhabitants are not predictable in nature and may perform "Activities of Daily Life (ADL)" not listed in ontological model. This gives rise to the need of developing an integrated framework based on unified conceptual backbone (i.e. activity ontologies), addressing the lifecycle of activity recognition and producing behavioral models according to inhabitant's routine. In this paper, an ontology evolution process has been proposed that learns particular activities from existing set of activities in daily life (ADL). It learns new activities that have not been identified by the recognition model, adds new properties with existing activities and learns inhabitant's newest behavior of performing activities through Artificial Neural Network (ANN). The better degree of true positivity is evidence of activity recognition with detection of noisy sensor data. Effectiveness of proposed approach is evident from improved rate of activity learning, activity detection and ontology evolution
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