Financial Technology dalam Industri Finansial: Survey Paper

Pada makalah ini akan menjelaskan kegunaan serta pengertian dan kegunaan mengenai financial technology dalam industri keuangan. Dalam bidang keuangan banyak yang menggunakannya sebagai inovasi. Fintech merupakan sebuah istilah baru terhadap sebuah teknologi maju yang memanfaatkan internet. Financial Technology juga mampu membantu layanan untuk mengelola keuangan dengan memanfaatkan digital berupa data besar, rantai blok dan investasi dalam bidang keuangan. Dalam studi ini menyimpulkan bahwa dalam penerapan Financial Technology terdapat teknology yang dapat membantu dalam pelayanan untuk algoritmanya menggunakan Artifical intelligence (AI), mengetahui perilaku pelangkan menggunakan Big Data dan Blockchain untuk menghubungkan jaringan yang ikut untuk membantu pelayanan

Modeling cost and time uncertainty in rail line construction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 437-443).Transportation construction projects are often plagued by cost overruns and delays. Technical, economic-political, psychological, and legal causes explain the frequent underestimations. To counteract such underestimations, the author developed an innovative approach to capture cost and time uncertainty in rail line projects, and applied this to the construction of a new high speed rail line in Portugal. The construction of the four main types of structures in rail lines (tunnels, viaducts, cuts and embankments) is modeled bottom-up from the single activity to the entire rail line. Sub-networks of activities are combined in structure networks to model the rail line structures; in turn, the structure networks are organized in the construction network to represent the rail line. For the first time, three sources of uncertainty (variability in the construction process, correlations between the costs of repeated activities, and disruptive events) are modeled jointly at the level of the single activity. These uncertainties are propagated to the total construction cost and time through the combination of the individual activity costs and times. The Construction and Uncertainty Models are integrated in the Decision Aids for Tunneling (DAT), which have been extended beyond tunneling to consider different structures and different uncertainty types. Based on historical input data and expert estimations, the cost and time uncertainty in the construction of four alignments of the new Portuguese high speed rail line is simulated. The three sources of uncertainty cause different cost and time impacts depending on the type of structure suggesting structure specific mitigation measures. Most importantly, their cumulative impact causes significant increases in construction cost and time compared to the deterministic estimates: 58% in the construction cost of tunnels, and 94% in the construction time of cuts and embankments. The Construction and Uncertainty Models and their integrated implementation in the DAT provide transportation agencies with a modeling tool to tackle cost and time uncertainty in the construction of rail lines and other linear/networked infrastructure projects.by Yvonne Moret.Ph.D

Numerical modelling of crack propagation in quasi-brittle heterogeneous materials : a stochastic approach

Deformation and damage processes in brittle and quasi-brittle materials, such as rock and concrete, are strongly influenced by their heterogeneous nature, related to their formation processes. The presence of heterogeneities leads in fact to noticeable variation in material properties values: it is of extreme importance that a numerical model which aims to realistically, reliably reproduce with low computational effort deformation and damage processes is able to include the effect of laminations, micro-cracks, voids and other types of heterogeneities; this is even more important when a numerical models has to reproduce the propagation of fractures. This thesis presents the development of a numerical framework for the simulation of crack propagation in shale rocks and concrete which also looks at the optimisation problem in the sense of computational efficiency (defined as optimal computational time needed to obtain realistic and accurate results). The numerical framework for crack propagation developed in this thesis is a variational phase-field model based on a finite elements smeared approach, able to automatically and realistically capture crack initiation processes for a variety of loading conditions; this numerical framework is based on the relation between potential energy associated to body deformation and the energy released during fracture formation. Heterogeneity is considered in the model by means of a stochastic approach based on the assumption that some mechanical properties of heterogeneous brittle materials (such as fracture energy) follow a non-Gaussian Weibull distribution. To guarantee adequate convergence of the results, Monte Carlo Simulation (MCS) method has been used in combination with the developed stochastic methodology. A non-linear dimensionality reduction technique has been developed and incorporated in the algorithm to reduce the computational effort required for the generation of sample realisations. The methodology has been validated using experimental results from both laboratory tests on shale rocks and literature on fracture in concrete. Results show that the developed algorithm is capable of realistically reproducing the mechanical behaviour of the chosen case studies, showing an applicability to problems where cracks propagate in mode-I, mode-II and mixed-mode I and II, guaranteeing a fast generation of sampling realisations of realistic stochastic fields and convergence of results after a maximum of 130 MCS analyses. This methodology can be applied to materials with random spatially-distributed variations of mechanical properties and to those showing laminar natural formations

Proceedings of the 4th Workshop of the MPM4CPS COST Action

Proceedings of the 4th Workshop of the MPM4CPS COST Action with the presentations delivered during the workshop and papers with extended versions of some of them

Impact of peer-to-peer trading and flexibility on local energy systems

To meet the 2050 net zero emission targets, energy systems around the globe are being revisited to achieve multi-vector decarbonisation in terms of electricity, transport, heating and cooling. As energy systems become more decentralised and digitised, local energy systems will have greater potential to self-sustain and hence, decrease reliance on fossil-fuelled central generation. While the uptake of electric vehicles, heat pumps, solar and battery systems offer a solution, the increase in electricity demand poses challenges in terms of higher peak demand, imbalance and overloading. Additionally, the current energy market structure prevents these assets in the distribution network from reaching their true techno-economic potential in flexibility services and energy trading. Peer-to-peer energy trading and community-level control algorithms achieve better matching of local demand and supply through the use of transactive energy markets, load shifting and peak shaving techniques. Existing research addresses the challenges of local energy markets and others investigate the effect of increased distributed assets on the network. However, the combined techno-economic effect requires the co-simulation of both market and network levels, coupled with simultaneous system balance, cost and carbon intensity considerations. Using bottom-up coordination and user-centric optimisation, this project investigated the potential of network-aware peer-to-peer trading and community-level control to increase self-sufficiency and self-consumption in energy communities. The techno-economic effects of these strategies are modelled while maintaining user comfort levels and healthy operation of the network and assets. The proposed strategies are evaluated according to their economic benefit, environmental impact and network stress. A case study in Scotland was employed to demonstrate the benefits of peer-to-peer trading and community self-consumption using future projections of demand, generation and storage. Additionally, the concept of energy smart contracts, embedded in blockchains, are proposed and demonstrated to overcome the major challenges of monitoring and contracting. The results indicate benefits for various energy systems stakeholders. Distribution system end-users benefit from lower energy costs while system operators obtain better visibility of the local-level flexibility along with the associated technical challenges in terms of losses, imbalance and loading. From a commercial perspective, community energy companies may utilise this study to inform investment decisions regarding storage, distributed generation and transactive market solutions. Additionally, the insights about the energy smart contracts allow blockchain and relevant technology sectors to recognise the opportunities and challenges of smart contracts and distributed ledger technologies that are specific to the energy sector. On the broader scale, energy system operators, regulators and high-level decision-makers can compare the simulated impact of community-led energy transition on the net zero goals with large-scale top-down initiatives

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph