Detector-Device-Independent Quantum Key Distribution

Recently, a quantum key distribution (QKD) scheme based on entanglement swapping, called measurement-device-independent QKD (mdiQKD), was proposed to bypass all detector side-channel attacks. While mdiQKD is conceptually elegant and offers a supreme level of security, the experimental complexity is challenging for practical systems. For instance, it requires interference between two widely separated independent single-photon sources, and the rates are dependent on detecting two photons - one from each source. Here we experimentally demonstrate a QKD scheme that removes the need for a two-photon system and instead uses the idea of a two-qubit single-photon (TQSP) to significantly simplify the implementation and improve the efficiency of mdiQKD in several aspects.Comment: 5 pages + 3 figure

Controlling Your Own Story Using a Digital Identity Solution: Creation Of Economic Identity for Financial Inclusion and Protection

An economic identity that we take for granted in many developed countries is often elusive for large segments of the population who are at risks or under political oppression. These segments are often physically located in geographical jurisdiction that are politically unstable and are not connected to conventional banking infrastructure and have little means to access or afford financial services that some of us take for granted. Therefore, in this research proposal, we posit that the starting point of activating one’s digital identity and the subsequent creation of an economy identity is the proper management of the elements of their life stories associated with their identity. We are thus interested in exploring the design principles underlying a digital identity solution (DIS) built on a decentralized blockchain that would bestow upon the individual the affordances to curate, manage, and formulate their life stories through the mechanisms of the narrative persuasion theory for an optimal economic identity

A Study of Mobile Robot Control using EEG Emotiv Epoc Sensor

The study was using an EEG Emotiv Epoc+ sensor to recognize brain activity for controlling a mobile robots movement. The study used Emotiv Control Panel software for EEG command identification. The commands will be interfaced inside Mind Your OSCs software and processing software which processed inside an Arduino Controller. The output of the Arduino is a movement command (ie. forward, backward, turn left, and turn right). The training methods of the system composed of three sets of thinking mode. First, thinking with doing facial expressions. Second, thinking with visual help. Third, thinking mentally without any help. In the first set, there are two configurations thinking with facial expression help as command of the mobile robot. Final results of the system are the second facial expressions configuration as the best facial expressions method with success rate 88.33 %. The second facial expression configuration has overall response time 1.60175 s faster than the first facial expressions configuration. In these two methods have dominant signals on the frontal lobe. The second facial expressions method have overall respond time 6.12 and 9.53 s faster than thinking with visual, and thinking without help respectively

ANALISIS PENGARUH KONSTRUKSI PADA PENINGKATAN PDRB KOTA BATAM

The economic development of each region can be seen from the increase in the regional GRDP. Currently, Batam City has achieved a fairly good economic  growth, which is assisted by construction factors. This development was investigated by the authors using qualitative research methods and the researchers also analyzed the effect of construction on PDRB growth. From the research that has been conducted, it shows that the results obtained are explaining the growth of PDRB in 2015-2020 which has experienced a significant increase and PDRB of Batam City has decreased in 2020 due to the spread of Covid-19. In 2020 there was a decline in PDRB in batam, which was the impact of Covid-19 which required a temporary stop of development

Multivariate Inputs on a MIMO Neuro-Fuzzy structure with LMA training. A study case: Indonesian Banking Stock Market

The paper describes the design and implementation of the multivariate inputs of multi-input-multi-output neuro-fuzzy with Levenberg-Marquardt algorithm training (MIMO neuro-fuzzy with accelerated LMA) to forecast stock market of Indonesian Banking. The accelerated LMA is efficient in the sense that it can bring the performance index of the network, such as the root mean squared error (RMSE), down to the desired error goal, more efficiently than the standard Levenberg-Marquardt algorithm. The MIMO neuro-fuzzy method is a hybrid intelligent system which combines the human-like reasoning style of fuzzy systems with the learning ability of neural nets. The main advantages of a MIMO neuro-fuzzy system are: it interprets IF-THEN rules from input-output relations and focuses on accuracy of the output network and offers efficient time consumption for on-line computation. The proposed architectures of this paper are a MIMO-neuro-fuzzy structure with multivariate input such as fundamental quantities as inputs network (High, Low, Open and Close) and a MIMO-neuro-fuzzy structure with other multivariate inputs, which is a combination inputs between two fundamental quantities (High and Low) and two inputs from technical indicator Exponential Moving Average (EMA High and EMA Low). Both proposed learning procedures, which are using accelerated LMA with optimal training parameters with at least one million iterations with different 16 membership functions, employ 12% of the input-output correspondences from the known input-output dataset. For experimental database, both structures are trained using the seven-year period (training data from 2 Oct 2006 to 28 Sept 2012) and tested using two-weeks period of the stock price index (prediction data from 1 Oct 2012 to 16 Oct 2012) and the proposed models are evaluated with a performance indicator, root mean squared error (RMSE) for mid-term forecasting application. The simulation results show that the MIMO-neuro-fuzzy structure with combination of fundamental quantities and technical indicators has better performance (RMSE) for two-weeks forecast. Key words: MIMO neuro-fuzzy; accelerated Levenberg-Marquardt algorithm; multivariate inputs, fundamental quantities; technical indicator

Neuronal Models for Studying Tau Pathology

Alzheimer's disease (AD) is the most frequent neurodegenerative disorder leading to dementia in the aged human population. It is characterized by the presence of two main pathological hallmarks in the brain: senile plaques containing β-amyloid peptide and neurofibrillary tangles (NFTs), consisting of fibrillar polymers of abnormally phosphorylated tau protein. Both of these histological characteristics of the disease have been simulated in genetically modified animals, which today include numerous mouse, fish, worm, and fly models of AD. The objective of this review is to present some of the main animal models that exist for reproducing symptoms of the disorder and their advantages and shortcomings as suitable models of the pathological processes. Moreover, we will discuss the results and conclusions which have been drawn from the use of these models so far and their contribution to the development of therapeutic applications for AD