Abstract

In this work, Nested Monte Carlo (NMC) simulation was done for symmetric and asymmetric ionic systems where the energy function is described by a combination of hard-sphere and Coulomb interaction (the primitive model). In the NMC method, Monte Carlo (MC) moves for the primary chain (where the energy function is the primitive model) is given by running a short MC trajectory using an auxiliary potential reducing the computational cost significantly. The Debye-Hückel (DH) potential was used as the auxiliary potential in our work. It is shown that with a careful choice of Debye length in the DH potential, and length of the short MC run in the auxiliary chain, the NMC method gives the same result as the more expensive MC simulation using full Ewald summation in the primitive model. Implementing

Abstract

In recent days, the application of cloud computing has been gaining significant popularity among people. A considerable amount of data are being stored in the cloud server. However, data owners outsource their encrypted data to the cloud for various security reasons. Unfortunately, encrypted data cannot be searched, like plaintext data. So how to search encrypted data is an interesting problem in this era. Many public key encryption with keyword search (PEKS) schemes have been designed in the literature. However, most of them cannot prevent keyword-guessing attacks. In this paper, we develop a provably secure PEKS scheme in the random oracle model. This scheme may be used for secure email access from an email server containing a list of encrypted keywords. The proposed scheme can resist keyword-guessing attacks, and offer ciphertext and trapdoor indistinguishability properties. We use the data owner’s private key during encryption to prevent keyword-guessing attacks. Using the data owner’s public key in the verification phase ensures the resilience of keyword-guessing attacks. Finally, the proposed scheme has been tested on a real testbed, and the results show that it can be used in the cloud computing scenario to search for keywords on encrypted data

Abstract

Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging is the most well-known screening approach used for detecting pneumonia in the early stages. While chest-Xray images are mostly blurry with low illumination, a strong feature extraction approach is required for promising identification performance.

Abstract

Signal propagation in complex networks drives epidemics, is responsible for information going viral, promotes trust and facilitates moral behavior in social groups, enables the development of misinformation detection algorithms, and it is the main pillar supporting the fascinating cognitive abilities of the brain, to name just some examples. The geometry of signal propagation is determined as much by the network topology as it is by the diverse forms of nonlinear interactions that may take place between the nodes. Advances are therefore often system dependent and have limited translational potential across domains. Given over two decades worth of research on the subject, the time is thus certainly ripe, indeed the need is urgent, for a comprehensive review of signal propagation in complex networks. We here first survey different models that determine the nature of interactions between the nodes, including epidemic models, Kuramoto models, diffusion models, cascading failure models, and models describing neuronal dynamics. Secondly, we cover different types of complex networks and their topologies, including temporal networks, multilayer networks, and neural networks. Next, we cover network time series analysis techniques that make use of signal propagation, including network correlation analysis, information transfer and nonlinear correlation tools, network reconstruction, source localization and link prediction, as well as approaches based on artificial intelligence. Lastly, we review applications in epidemiology, social dynamics, neuroscience, engineering, and robotics. Taken together, we thus provide the reader with an up-to-date review of the complexities associated with the network’s role in propagating signals in the hope of better harnessing this to devise innovative applications across engineering, the social and natural sciences as well as to inspire future research.

Abstract

We investigate epidemic spreading in a deterministic susceptible-infected-susceptible model on uncorrelated heterogeneous networks with higher-order interactions. We provide a recipe for the construction of one-dimensional reduced model (resilience function) of the N-dimensional susceptible-infected-susceptible dynamics in the presence of higher-order interactions. Utilizing this reduction process, we are able to capture the microscopic and macroscopic behavior of infectious networks. We find that the microscopic state of nodes (fraction of stable healthy individual of each node) inversely scales with their degree, and it becomes diminished due to the presence of higher-order interactions. In this case, we analytically obtain that the macroscopic state of the system (fraction of infectious or healthy population) undergoes abrupt transition. Additionally, we quantify the network’s resilience, i.e., how the topological changes affect the stable infected population. Finally, we provide an alternative framework of dimension reduction based on the spectral analysis of the network, which can identify the critical onset of the disease in the presence or absence of higher-order interactions. Both reduction methods can be extended for a large class of dynamical models.

Abstract

Our need for a waste-derived bioeconomy culminates from multiple areas of concern. These include, firstly, the well-documented non-renewability of our principal sources of energy and industrially important chemicals, i.e., petroleum-based products, and their effects on the environment and climate change [1]. Secondly, the drastic increase in waste production (50 million dry tons in the US alone in 2017) and unsustainable disposal strategies, such as landfilling or incineration [2]. Thirdly, to meet the increased demand for alternative renewable products and chemicals, it is important to develop sustainable methods and processes [3]. Taking a waste-to-treasure approach towards producing environmentally benign solutions can be sustainable only if an economy is modeled around it, with the potential to create a green world and solve problems, such as food security. Several types of organic waste that can be used as renewable feedstock include food waste, agricultural, forestry, and animal waste, and sludges [4]. The conversion of different kinds of biomass to produce energy has been known for a long time, but the biochemical conversion of waste for this purpose has gained importance in the last century [5,6]. Typically, wastes are categorized

Abstract

Understanding the frequency and structural context of discrete noncovalent interactions between nucleotides is of pivotal significance in establishing the rules that govern RNA structure and dynamics. Although T-shaped contacts (i.e., perpendicular stacking contacts) between aromatic amino acids and nucleobases at the nucleic acid-protein interface have recently garnered attention, the analogous contacts within the nucleic acid structures have not been discussed. In this work, we have developed an automated method for identifying and unambiguously classifying T-shaped interactions between nucleobases. Using this method, we identified a total of 3261 instances of T-shaped (perpendicular stacking) contacts between two nucleobases in an array of RNA structures from an up-to-date data set of ≤3.5 Å resolution crystal structures deposited in the Protein Data Bank.

Abstract

Designing navigation strategies for search-time optimization remains of interest in various interdisciplinary branches in science. Herein, we focus on active Brownian walkers in noisy and confined environments, which are mediated by one such autonomous strategy, namely stochastic resetting. As such, resetting stops the motion and compels the walkers to restart from the initial configuration intermittently. The resetting clock is operated externally without any influence from the searchers. In particular, the resetting coordinates are either quenched (fixed) or annealed (fluctuating) over the entire topography. Although the strategy relies upon simple governing laws of motion, it shows a significant ramification for the search-time statistics, in contrast to the search process conducted by the underlying reset-free dynamics. Using extensive numerical simulations, we show that the resetting-driven protocols enhance the performance of these active searchers. This, however, depends robustly on the inherent search-time fluctuations, measured by the coefficient of variation of the underlying reset-free process. We also explore the effects of different boundaries and rotational diffusion constants on the search-time fluctuations in the presence of resetting. Notably, for the annealed condition, resetting is always found to expedite the search process. These features, as well as their applicability to more general optimization problems from queuing systems, computer science and randomized numerical algorithms, to active living systems such as enzyme turnover and backtracking recovery of RNA polymerases in gene expression, make resetting-based strategies universally promising.

Abstract

The rampant adoption of digital technologies made momentous changes in all economic sectors. The agriculture sector cannot abstain from the digital revolution. Agriculture and farming are one of the oldest and most important professions in India. The sector remains the backbone of the Indian rural economy, which desperately demands technological impetus for the socio-economic development of rural areas. Smart agriculture is a revolution in the agriculture industry which helps to guide the actions that are required to modify and reorient the agricultural systems. The paper made an extensive survey of various technologies proposed for the agriculture sector. We have surveyed various smart agricultural applications developed and proposed a taxonomy for classifying them. Network infrastructure and connectivity remains the major challenge for rural areas. The paper explores the viability of

Abstract

Kalman filter (KF) and its variants are commonly used in estimating states of dynamical systems. For accurate estimation of states, researchers have developed intelligent KFs by combining them with machine learning algorithms. The inherent variations in the vehicle dynamics and uncertainties in the environment require the development of an autonomous sequential framework for continuous estimation. Further, the need for real-time estimation of states in certain applications requires low computer memory usage and low computational cost while implementing autonomous-structured learning algorithms. In this paper, a parsimonious autonomous sequential estimator (PASE) is proposed, which combines the KF-based estimator and autonomous-structured recurrent parsimonious learning machine (rPALM) in a sequential manner. The rPALM overcomes the dependency on target variables while point-to-hyperplane distance calculation. The performance of PASE has been evaluated extensively by comparing it with various batch-learning algorithms and single-pass learning-based intelligent estimators. The results clearly indicate that PASE provides better estimation accuracy with a compact architecture for both linear and nonlinear dynamical systems. Finally, the performance of PASE has been evaluated with experimental data for the state estimation of an unmanned ground vehicle while the training of the learning machine is performed with the simulated data. The estimation accuracy in such a scenario is justifying its appropriateness in real-