1,721,297 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
Full text linkAsset 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
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
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
Design and characterization of resonant devices for optical applications
Full text linkPeriodic structures have attracted huge research interest over the past many years due to their interesting electromagnetic properties. There are lots of useful applications in the fields of photonics and microwave engineering that come from periodic structures. Examples of periodic structures include diffraction gratings, photonic crystals, phased array antennas, frequency selective surfaces, and metamaterials. A diffraction grating is composed of diffracting elements arranged periodically. The spacing between these elements is comparable to the wavelength of the incident light. The amplitude, or phase, or both, of the diffracted electromagnetic radiation from a diffraction grating, can be modified in a controlled and predictable manner. Another interesting phenomenon is the presence of sharp resonant features in the optical spectra of the gratings such as Guided-Mode Resonances (GMR). GMR gratings have been employed in wide-ranging applications such as sensors for biosensing, optical absorbers, efficient photodetectors and tunable filters for optical communication systems, reflection mirrors for lasers, and spectrometers.
The first part of this work focuses on the design, fabrication, and characterization of resonant pillar gratings. This is further split into two parts. The first part describes the graphene-based pillar grating for optical absorber applications. The performance of the proposed periodic structure is investigated through numerical simulations. The proposed design exploits the guided mode resonances of the structure to achieve enhanced absorption in the monolayer graphene. In the second part, a phase change material vanadium-dioxide (VO2) is integrated with the pillar grating structure to achieve the thermal tuning of the optical response exploiting the phase change properties of VO2. The grating has been fabricated utilizing a nanoimprint lithography system exploiting a silicon mold. VO2 nano-powders have been deposited by spin-coating. In addition to the experimental tests, the proposed structure is simulated using the RCWA method. Next, plasmonic grating structures on planar as well curved surfaces are designed and analyzed through numerical simulations for sensing and Surface Enhanced Raman Spectroscopy (SERS) applications. The work related to plasmonic structures has also been further split into two parts. In the first part, a planar plasmonic grating is designed and synthesized for sensing applications in the transmission domain exploiting Extraordinary Transmittance (EOT) properties of the plasmonic modes as well as the sensitivity of these modes to the changes in the refractive index of the surrounding media. A Finite Difference Time Domain (FDTD) model of the finite set of nanoplatelets has been developed to theoretically investigate and optimize the nanostructure as well as validate the experimental results. Plasmonic modes can concentrate light to much smaller locations creating field hotspots. This makes plasmonic structures a suitable platform for SERS. In the second part of the work, plasmonic gratings on planar and curved surfaces are investigated as SERS platforms
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
Tunable Nanoislands Decorated Tapered Optical Fibers Reveal Concurrent Contributions in Through-Fiber SERS Detection
No full text: Creating plasmonic nanoparticles on a tapered optical fiber (TF) tip enables a remote surface-enhanced Raman scattering (SERS) sensing probe, ideal for challenging sampling scenarios like biological tissues, site-specific cells, on-site environmental monitoring, and deep brain structures. However, nanoparticle patterns fabricated from current bottom-up methods are mostly random, making geometry control difficult. Uneven statistical distribution, clustering, and multilayer deposition introduce uncertainty in correlating device performance with morphology. Ultimately, this limits the design of the best-performance remote SERS sensing probe. Here we employ a tunable solid-state dewetting method to create densely packed monolayer Au nanoislands with varied geometric parameters in direct contact with the silica TF surface. These patterns exhibit analyzable nanoparticle sizes, densities, and uniform distribution across the entire taper surface, enabling a systematic investigation of particle size, density, and analyte effects on the SERS performance of the through-fiber detection system. The study is focused on the SERS response of a widely employed benchmark molecule, rhodamine 6G (R6G), and serotonin, a highly relevant neurotransmitter for the neuroscience field. The numerical simulations and limit of detection (LOD) experiments on R6G show that the increase of the total near-field enhancement volume promotes the SERS sensitivity of the probe. However, we observed a different behavior for serotonin linked to its interaction with the nanoparticle's surface. The obtained LOD is as low as 10-7 M, a value not achieved so far in a through-fiber detection scheme. Therefore, our work offers a strategy to design nanoparticle-based remote SERS sensing probes and provides new clues to discover and understand intricate plasmonic-driven chemical reactions
What Hinders or Enables Knowledge Sharing in Swedish-based Multinational Corporations from a Cultural, Motivational and Trust Perspectives?
No full textAbstract Date: January 17, 2013 Level: Bachelor thesis in business administration, 15 ECTS Institution: School of Sustainable Development of Society and Technology, Mälardalen University Authors: AZIZ Najibullah, GLEESON Darren and KASHIF Muhammad 28th August 1980, 29th November 1977, 22nd July 1984 Tutor: Eva Maaninen-Olsson Keywords: Knowledge, Knowledge Management, Knowledge Sharing, Factors Affecting Knowledge Sharing Research Question: What hinders or enables knowledge sharing in Swedish-based multi-national corporations from a cultural, motivational and trust perspective? Purpose: The purpose of this thesis is to investigate the barriers and enablers of knowledge sharing within multi-national corporations. Method: The research method chosen to fulfill the purpose of the thesis is a qualitative approach. In order to achieve the purpose both primary and secondary data was sought. In accordance with the qualitative approach, interviews have been carried out with senior managers in ABB, Bombardier, Ericsson and Siemens. Data collected from these interviews represents the primary data. Secondary data has been gathered from company websites. Conclusion: The results from the studied multi-national organizations suggest that knowledge sharing culture is influenced by communication, rules, regulations and routines (sub-factors of culture). This study shows that communication, rules, regulations and routines are enablers of knowledge sharing in the organizations. However, language and technology (sub-factors of culture) as collaborative tools are proven to be problematic; consequently creating hindrances to knowledge sharing. When it comes to motivational factors (rewards, power and reciprocity), this thesis shows that none of the studied companies offer rewards for knowledge sharing. This confirms the controversy connected with rewards which can either enable or cause hindrance to knowledge sharing. Reciprocity seems to enable knowledge sharing in the studied organizations whereas power remains controversial. The existence of power can either be a barrier or an enabler for knowledge sharing depending on the individual’s perception of power. This thesis also shows that the existence of trust enables knowledge sharing between employees, but the difficulties of building this trust is akey problem for management
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