Filecoin is the most impactful storage-oriented cryptocurrency. In this system, miners dedicate their storage space to the network and verify transactions to earn rewards. Nowadays, Filecoin’s network capacity has surpassed 15 exbibytes.

While previous works have discussed the network delay upper bound that guarantees the consistency of Nakamoto consensus, measuring the actual network latencies and evaluating their impact on miners/pools in Bitcoin remain open questions. This paper fills this gap by: (1) defining metrics that quantify the impact of network latency on the mining network; (2) developing a tool, named miner entanglement (ME), to experimentally evaluate these metrics with a focus on the network latency of the top mining pools; and (3) quantifying the impact of the current network delays on Bitcoin's mining network. For example, we evaluated that Poolin, a Bitcoin mining pool, was able to gain between 0.5% and 1.9% of blocks in addition (i.e., from 36.27 BTC to 137.83 BTC) per week thanks to its low network latency. Moreover, as pools are rational in Bitcoin, we model the strategy a pool would follow to improve its network latency (e.g., by leveraging our ME tool) as a two party game. We show that a Bitcoin mining pool could improve its effective hash rate by up to 4.5%. For a multi-party game, we use a state-of-the-art Bitcoin mining simulator to study the situation where all pools attempt to improve their network latency and show that the largest mining pools would improve their revenue and reach a Nash equilibrium while the smaller mining pools would suffer from a decreased access to the network, and therefore a decreased revenue. These conclusions further incentivize the centralisation of the mining network in Bitcoin, and provide an empirical explanation for the observed tendency of pools to design and rely on low latency private networks.

Depression is a common mental disorder with growing prevalence, however current diagnoses of depression face the problem of patient denial, clinical experience and subjective biases from self-report. Our study aims to develop an objective approach to depression detection that supports the process of diagnosis and assists the monitoring of risk factors. By classifying eye movement features during free viewing tasks, an accuracy of 80.1% was achieved using Random Forest to discriminate depressed and nondepressed subjects. Results indicate that eye movement features hold the potential to form a complimentary method of detection, having a relatively low computation overhead. Furthermore, given the proliferation of cheap internet eye movement detection technologies, the method offers the possibility of cost effective remote sensing of the patient mental state.

The Markov chain method has been applied to develop dynamical models for land use patterns from the point of time series since early time. Later, spatio-temporal transition models were established to study the spatial change in the land use by taking the spatial information into account. However, the methods of the spatial neighborhood effect and the appropriate number of neighbors for spatial analysis are still under research. Our effort is to find the best way to study urban sprawl by integrating the time series approach and the neighborhood effect. In this article, two spatio-temporal models were developed, by combining weighted distance approach and direct neighborhood approach with Markov chain approach respectively. As a case study, based on classified TM images in 1995, 1996, 1997 and 2001, we simulated land use transition of Shunyi Country near to Beijing City in China with both two simulation models using 8, 48, 120 and 224 neighbors respectively in 1996 and 1997. Comparing the simulated images with classified images, the results showed the spatio-temporal model with 48 or 80 neighbors of weighted distance neighborhood approach is the best for modeling urban sprawl.