Statistical theory of fluids confined in quenched disordered porous media.

We develop a theory to calculate structural correlations and thermodynamic properties of a fluid confined in a random porous solid medium (matrix). We used density functional formalism to derive an annealed averaged expression for the density profile and excess free energy of fluid arising due to random fields of a particular realization of the matrix. After performing the second average over the quenched-disordered variables, the excess free energy is organized to give one- and two-body potentials for fluid particles. The average over disorder reduces the system to an effective one-component system of fluid in which particles feel one-body (external) potential and interact via effective pair potential. The effective pair potential is a sum of the bare (the one in the pure fluid) and the matrix-induced potential. The resulting partition function involves only fluid variables. Equations are derived for fluid-fluid and fluid-matrix correlation functions and for free energy, pressure, and chemical potential of the fluid. The theory is applied to a model system of hard spheres and results for the effective pair potential, correlation functions, and thermodynamic properties are reported. The effective pair potential is found to be attractive at the contact and develops a repulsive peak before decaying to zero. Results for pair correlation function and structure factor are compared with simulation results for several fluid densities at two matrix densities. In all the cases, a very good agreement has been found.

Defending the Defender: Adversarial Learning Based Defending Strategy for Learning Based Security Methods in Cyber-Physical Systems (CPS).

Cyber-Physical Systems (CPS) are prone to many security exploitations due to a greater attack surface being introduced by their cyber component by the nature of their remote accessibility or non-isolated capability. Security exploitations, on the other hand, rise in complexities, aiming for more powerful attacks and evasion from detections. The real-world applicability of CPS thus poses a question mark due to security infringements. Researchers have been developing new and robust techniques to enhance the security of these systems. Many techniques and security aspects are being considered to build robust security systems; these include attack prevention, attack detection, and attack mitigation as security development techniques with consideration of confidentiality, integrity, and availability as some of the important security aspects. In this paper, we have proposed machine learning-based intelligent attack detection strategies which have evolved as a result of failures in traditional signature-based techniques to detect zero-day attacks and attacks of a complex nature. Many researchers have evaluated the feasibility of learning models in the security domain and pointed out their capability to detect known as well as unknown attacks (zero-day attacks). However, these learning models are also vulnerable to adversarial attacks like poisoning attacks, evasion attacks, and exploration attacks. To make use of a robust-cum-intelligent security mechanism, we have proposed an adversarial learning-based defense strategy for the security of CPS to ensure CPS security and invoke resilience against adversarial attacks. We have evaluated the proposed strategy through the implementation of Random Forest (RF), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) on the ToN_IoT Network dataset and an adversarial dataset generated through the Generative Adversarial Network (GAN) model.

Structure ordering and glass transition in size-asymmetric ternary mixtures of hard spheres: Variation from fragile to strong glasses.

We investigate the structure and activated dynamics of a binary mixture of colloidal particles dispersed in a solvent of much smaller-sized particles. The solvent degrees of freedom are traced out from the grand partition function of the colloid-solvent mixture which reduces the system from ternary to effective binary mixture of colloidal particles. In the effective binary mixture colloidal particles interact via effective potential that consists of bare potential plus the solvent-induced interaction. Expressions for the effective potentials and pair correlation functions are derived. We used the result of pair correlation functions to determine the number of particles in a cooperatively reorganizing cluster (CRC) in which localized particles form "long-lived" nonchemical bonds with the central particle. For an event of relaxation to take place these bonds have to reorganize irreversibly, the energy involved in the processes is the effective activation energy of relaxation. Results are reported for hard sphere colloidal particles dispersed in a solvent of hard sphere particles. Our results show that the concentration of solvent can be used as a control parameter to fine-tune the microscopic structural ordering and the size of CRC that governs the glassy dynamics. We show that a small variation in the concentration of solvent creates a bigger change in the kinetic fragility which highlights a wide variation in behavior, ranging from fragile to strong glasses. We conclude that the CRC which is determined from the static pair correlation function and the fluctuations embedded in the system is probably the sole player in the physics of glass transition.

SALT: transfer learning-based threat model for attack detection in smart home.

The next whooping revolution after the Internet is its scion, the Internet of Things (IoT), which has facilitated every entity the power to connect to the web. However, this magnifying depth of the digital pool oil the wheels for the attackers to penetrate. Thus, these threats and attacks have become a prime concern among researchers. With promising features, Machine Learning (ML) has been the solution throughout to detect these threats. But, the general ML-based solutions have been declining with the practical implementation to detect unknown threats due to changes in domains, different distributions, long training time, and lack of labelled data. To tackle the aforementioned issues, Transfer Learning (TL) has emerged as a viable solution. Motivated by the facts, this article aims to leverage TL-based strategies to get better the learning classifiers to detect known and unknown threats targeting IoT systems. TL transfers the knowledge attained while learning a task to expedite the learning of new similar tasks/problems. This article proposes a learning-based threat model for attack detection in the Smart Home environment (SALT). It uses the knowledge of known threats in the source domain (labelled data) to detect the unknown threats in the target domain (unlabelled data). The proposed scheme addresses the workable differences in feature space distribution or the ratio of attack instances to a normal one, or both. The proposed threat model would show the implying competence of ML with the TL scheme to improve the robustness of learning classifiers besides the threat variants to detect known and unknown threats. The performance analysis shows that traditional schemes underperform for unknown threat variants with accuracy dropping to 39% and recall to 56.

Energy-Efficient and Secure Opportunistic Routing Protocol for WSN: Performance Analysis with Nature-Inspired Algorithms and Its Application in Biomedical Applications.

Wireless sensor network (WSN) is made up of tiny sensor nodes. The application of WSN in diverse fields has seen a tremendous escalation in recent years. WSN applications are constrained by the limited set of computing resources possessed by the sensor nodes and the security aspects of data communication in the WSN. Many algorithms based on nature-inspired optimization (NIO) have been proposed in the past to optimize the issue of energy efficiency and security in WSN. In the proposed work, two opportunistic routing algorithms, i.e., intelligent opportunistic routing protocol (IOP) and trust-based secure intelligent opportunistic routing protocol (TBSIOP), are compared against two NIO algorithms developed for achieving energy efficiency and security in WSN for performance analysis. The performance is evaluated by simulating the algorithms on MATLAB and comparing the obtained results with existing ACO-based and PSO-based routing algorithms. It is observed that the TBSIOP outperforms the NIO-based algorithms in terms of energy efficiency, network lifetime, packet delivery ratio, end-to-end delay, and average risk level. All the parameters under consideration are recorded in the presence of a maximum of 50% malicious nodes for 25, 50, and 100 nodes' test cases. The increasing size of the network has a significant effect on the performance of TBSIOP, as the packet delivery ratio is close to 100%. Also, TBSIOP can easily avoid malicious nodes during the routing process as reflected from the results. This will improve the network lifetime of TBSIOP compared to other protocols. As far as the application of the work is concerned, it would be beneficial for smart healthcare services. It can also help in better communication during the sharing of data by providing energy-efficient services and keeping the network alive for a longer period.

How attractive and repulsive interactions affect structure ordering and dynamics of glass-forming liquids.

The theory developed in our previous papers [Phys. Rev. E 99, 030101(R) (2019)10.1103/PhysRevE.99.030101; Phys. Rev. E 103, 032611 (2021)10.1103/PhysRevE.103.032611] is applied in this paper to investigate the dependence of slowing down of dynamics of glass-forming liquids on the attractive and repulsive parts of intermolecular interactions. Through an extensive comparison of the behavior of a Lennard-Jones glass-forming liquid and that of its WCA reduction to a model with truncated pair potential without attractive tail, we demonstrate why the two systems exhibit very different dynamics despite having nearly identical pair correlation functions. In particular, we show that local structures characterized by the number of mobile and immobile particles around a central particle markedly differ in the two systems at densities and temperatures where their dynamics show large difference and nearly identical where dynamics nearly overlap. We also show how the parameter ψ(T) that measures the role of fluctuations embedded in the system on size of the cooperatively reorganizing cluster (CRC) and the crossover temperature T_{a} depend on the intermolecular interactions. These parameters stemming from the intermolecular interactions characterize the temperature and density dependence of structural relaxation time τ_{α}. The quantitative and qualitative agreements found with simulation results for the two systems suggest that our theory brings out the underlying features that determine the dynamics of glass-forming liquids.

Emergence of cooperatively reorganizing cluster and super-Arrhenius dynamics of fragile supercooled liquids.

In this paper, we develop a theory to calculate the structural relaxation time τ_{α} of fragile supercooled liquids. Using the information of the configurational entropy and structure, we calculate the number of dynamically free, metastable, and stable neighbors around a central particle. In supercooled liquids, the cooperatively reorganizing clusters (CRCs) in which the stable neighbors form "stable" nonchemical bonds with the central particle emerge. For an event of relaxation to take place, these bonds have to reorganize irreversibly; the energy involved in the processes is the effective activation energy of relaxation. The theory brings forth a temperature T_{a} and a temperature-dependent parameter ψ(T) which characterize slowing down of dynamics on cooling. It is shown that the value of ψ(T) is equal to 1 for T>T_{a}, indicating that the underlying microscopic mechanism of relaxation is dominated by the entropy-driven processes, while for T

Internet of Things: Evolution, Concerns and Security Challenges.

The escalated growth of the Internet of Things (IoT) has started to reform and reshape our lives. The deployment of a large number of objects adhered to the internet has unlocked the vision of the smart world around us, thereby paving a road towards automation and humongous data generation and collection. This automation and continuous explosion of personal and professional information to the digital world provides a potent ground to the adversaries to perform numerous cyber-attacks, thus making security in IoT a sizeable concern. Hence, timely detection and prevention of such threats are pre-requisites to prevent serious consequences. The survey conducted provides a brief insight into the technology with prime attention towards the various attacks and anomalies and their detection based on the intelligent intrusion detection system (IDS). The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS). Additionally, a case study of healthcare in IoT is presented. The study depicts the architecture, security, and privacy issues and application of learning paradigms in this sector. The research assessment is finally concluded by listing the results derived from the literature. Additionally, the paper discusses numerous research challenges to allow further rectifications in the approaches to deal with unusual complications.

A survey of E-learning methods in nursing and medical education during COVID-19 pandemic in India.

BACKGROUND: COVID-19 pandemic has necessitated mandatory e-learning in medical and nursing education. How far are developing countries like India (with wide socioeconomic and cultural diversity) geared up for this challenge remains unexplored. At this critical juncture, we aim to evaluate if online teaching methods are as feasible, acceptable, and effective as in-class teaching for medical/nursing students. OBJECTIVES: The questionnaire captured: (1) practicability/feasibility of online classes, (2) health issues from online classes, (3) current methods for e-teaching, and (4) student attitudes and preferences. DESIGN: Cross-sectional survey. SETTINGS: Population-based study in India. PARTICIPANTS: Nursing and medical undergraduate students (I-IV year). METHODOLOGY: The online questionnaire was distributed to 200 medical and nursing colleges across India. Categorical variables were analyzed using chi-square tests. Binary logistic regression was done to analyze factors predicting health issues in students. p < 0.05 was considered significant. RESULTS: Overall, 1541 medical and 684 nursing students completed the survey from 156 cities. The availability of laptop (p < 0.0001), Wi-Fi (p < 0.0001), dedicated room (p < 0.0001), and computer proficiency was more in students of affluent families and those from cities (p < 0.0001). Class duration >4 h/day (p < 0.0001), each class >40 min (p < 0.009) and pre-existing health issues (p < 0.0001) predicted the occurrence headache, eyestrain, anxiety, neck/back pain, and sleep disturbance. Power-point presentation was the most widely (80%) used method of teaching. Only 30% got adequate time to interact with teachers. Only 20.4% felt e-learning can replace conventional teaching. Students preferred: 3-6 classes/day, each class <40 min, 10-20 min break between classes and interactive sessions. CONCLUSION: There is a need to improve information and communication infrastructure to enhance feasibility of e-learning for nursing/medical students in India. There should be guidelines (number of classes/day, length of each class, break between classes, curriculum, etc) to improve the retention capacity in students and reduce health issues. Continuous feedback from teachers and students will be required to make e-learning effective.

The solvent mediated interaction potential between solute particles: theory and applications.

In this paper we develop a theory to calculate the solvent mediated interaction potential between solute particles dispersed in a solvent. The potential is a functional of the instantaneous distribution of solute particles and is expressed in terms of the solute-solvent direct pair correlation function and the density-density correlation function of the bulk solvent. The dependence of the direct pair correlation function on multi-point correlations of the solute distribution is simplified with a mean field approximation. A self consistent approach is developed to calculate the effective potential between solute particles, the solute-solvent and the solute-solute correlation functions. The significance of the solvent fluctuations on the range of the effective potential is elucidated. The theory is applied to calculate equilibrium properties of the Asakura-Oosawa (AO) model for several values of solute and solvent densities and for several values of the particles size ratio. The results give a quantitative description of many-body effect on the effective potential and on the pair correlation functions.

An Intelligent Opportunistic Routing Algorithm for Wireless Sensor Networks and Its Application Towards e-Healthcare.

The lifetime of a node in wireless sensor networks (WSN) is directly responsible for the longevity of the wireless network. The routing of packets is the most energy-consuming activity for a sensor node. Thus, finding an energy-efficient routing strategy for transmission of packets becomes of utmost importance. The opportunistic routing (OR) protocol is one of the new routing protocol that promises reliability and energy efficiency during transmission of packets in wireless sensor networks (WSN). In this paper, we propose an intelligent opportunistic routing protocol (IOP) using a machine learning technique, to select a relay node from the list of potential forwarder nodes to achieve energy efficiency and reliability in the network. The proposed approach might have applications including e-healthcare services. As the proposed method might achieve reliability in the network because it can connect several healthcare network devices in a better way and good healthcare services might be offered. In addition to this, the proposed method saves energy, therefore, it helps the remote patient to connect with healthcare services for a longer duration with the integration of IoT services.

Super-Arrhenius behavior of molecular glass formers.

A theory is developed to calculate values of the potential-energy barriers to structural relaxation in molecular glass formers from the data of static pair-correlation function. The barrier height is shown to increase due to an increase in the number of "stable bonds" a particle forms with its neighbors and the energy of each bond as liquids move deeper into the supercooled (supercompressed) region. We present results for a system of hard spheres and compare calculated values of the structural relaxation time with experimental and simulation results.

Density-functional theory for fluid-solid and solid-solid phase transitions.

We develop a theory to describe solid-solid phase transitions. The density functional formalism of classical statistical mechanics is used to find an exact expression for the difference in the grand thermodynamic potentials of the two coexisting phases. The expression involves both the symmetry conserving and the symmetry broken parts of the direct pair correlation function. The theory is used to calculate phase diagram of systems of soft spheres interacting via inverse power potentials u(r)=ε(σ/r)^{n}, where parameter n measures softness of the potential. We find that for 1/n<0.154 systems freeze into the face centered cubic (fcc) structure while for 1/n≥0.154 the body-centred-cubic (bcc) structure is preferred. The bcc structure transforms into the fcc structure upon increasing the density. The calculated phase diagram is in good agreement with the one found from molecular simulations.

Uptake, Accumulation and Toxicity of Silver Nanoparticle in Autotrophic Plants, and Heterotrophic Microbes: A Concentric Review.

Nanotechnology is a cutting-edge field of science with the potential to revolutionize today's technological advances including industrial applications. It is being utilized for the welfare of mankind; but at the same time, the unprecedented use and uncontrolled release of nanomaterials into the environment poses enormous threat to living organisms. Silver nanoparticles (AgNPs) are used in several industries and its continuous release may hamper many physiological and biochemical processes in the living organisms including autotrophs and heterotrophs. The present review gives a concentric know-how of the effects of AgNPs on the lower and higher autotrophic plants as well as on heterotrophic microbes so as to have better understanding of the differences in effects among these two groups. It also focuses on the mechanism of uptake, translocation, accumulation in the plants and microbes, and resulting toxicity as well as tolerance mechanisms by which these microorganisms are able to survive and reduce the effects of AgNPs. This review differentiates the impact of silver nanoparticles at various levels between autotrophs and heterotrophs and signifies the prevailing tolerance mechanisms. With this background, a comprehensive idea can be made with respect to the influence of AgNPs on lower and higher autotrophic plants together with heterotrophic microbes and new insights can be generated for the researchers to understand the toxicity and tolerance mechanisms of AgNPs in plants and microbes.

Chemomodulatory effect Melastoma Malabathricum Linn against chemically induced renal carcinogenesis rats via attenuation of inflammation, oxidative stress, and early markers of tumor expansion.

Melastoma malabathricum Linn (MM) has high valued for its commercial significance. Indian market (northeast) has great demand for the plants, which extended, its use as a traditional home remedy due to its anti-inflammatory effects. In this study, we scrutinize the therapeutic and protective effect of MM against diethylnitrosamine (DEN) and ferric nitrilotriacetate (Fe-NTA)-induced renal carcinogenesis, renal hyperproliferation, and oxidative stress in rats. Liquid chromatography mass spectroscopy (LC-MS) was used for identification of phytoconstituents. Administration of DEN confirmed the initiation the renal carcinogenesis via enhancing the expansion of tumor incidence. Intraperitoneally, administration of Fe-NTA boost the antioxidant enzymes (phase I), viz., superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR), glutathione peroxidase (GPx) and phase II, viz., quinone reductase (QR) and glutathione-S-transferase (GST). It also increased the content of renal lipid peroxidation (LPO), hydrogen peroxidase (H2O2) with decrease content in glutathione content (GSH). It also increased the renal biochemical and non-biochemical parameter. It also confirmed the augment the level of thymidine [3H] incorporation into renal DNA, ornithine decarboxylase (ODC) activity and increased the generation of proinflammatory (TNF-α, IL-6 and IL-ß) and inflammatory mediator (PGE2). We also analyzed the macroscopic and histologic of renal tissue. In addition, the effect of phytoconstituent of MM extract was evaluated in silico and free radical scavenging activity against the DPPH and ABTS free radicals. LC-MS confirmed the presence of quercetin >gallic acid in MM extract. Renal carcinogenesis rats treated with MM (100, 250, and 500 mg/kg) confirmed the significantly (P < 0.001) protective effect via reduction the antioxidant (phase I and phase II) enzymes, biochemical parameter and restore the proinflammatory and inflammatory mediator at dose dependent manner. MM altered the ODC and thymidine activity in renal DNA. The chemoprotective effect of MM was confirmed via decreased the renal tumor incidence, which was confirmed by the macroscopic and histopathological observation. Consequently, our result suggests that MM is a potent chemoprotective agent and suppresses DEN+ Fe-NTA-induced renal carcinogenesis, inflammatory reaction, and oxidative stress injury in Wister rats.

Fluid-solid transition in simple systems using density functional theory.

A free energy functional for a crystal which contains both the symmetry-conserved and symmetry-broken parts of the direct pair correlation function has been used to investigate the fluid-solid transition in systems interacting via purely repulsive Weeks-Chandler-Anderson Lennard-Jones potential and the full Lennard-Jones potential. The results found for freezing parameters for the fluid-face centred cubic crystal transition are in very good agreement with simulation results. It is shown that although the contribution made by the symmetry broken part to the grand thermodynamic potential at the freezing point is small compared to that of the symmetry conserving part, its role is crucial in stabilizing the crystalline structure and on values of the freezing parameters.

Communication: Integral equation theory for pair correlation functions in a crystal.

A method for calculating pair correlation functions in a crystal is developed. The method is based on separating the one- and two-particle correlation functions into the symmetry conserving and the symmetry broken parts. The conserving parts are calculated using the integral equation theory of homogeneous fluids. The symmetry broken part of the direct pair correlation function is calculated from a series written in powers of order parameters and that of the total pair correlation function from the Ornstein-Zernike equation. The results found for a two-dimensional hexagonal lattice show that the method provides accurate and detailed informations about the pair correlation functions in a crystal.

Correlation functions in liquids and crystals: free-energy functional and liquid-to-crystal transition.

A free-energy functional for a crystal that contains both the symmetry-conserved and symmetry-broken parts of the direct pair-correlation function has been used to investigate the crystallization of fluids in three dimensions. The symmetry-broken part of the direct pair-correlation function has been calculated using a series in ascending powers of the order parameters and which contains three- and higher-body direct correlation functions of the isotropic phase. It is shown that a very accurate description of freezing transitions for a wide class of potentials is found by considering the first two terms of this series. The results found for freezing parameters including the structure of the frozen phase for fluids interacting via the inverse power potential u(r)=ε(σ/r)(n) for n ranging from 4 to ∞ are in very good agreement with simulation results. It is found that for n>6.5 the fluid freezes into a face-centered cubic (fcc) structure while for n≤6 the body-centered cubic (bcc) structure is preferred. The fluid-bcc-fcc triple point is found to be at 1/n=0.158, which is in good agreement with simulation result.

Freezing of a two-dimensional fluid into a crystalline phase: density functional approach.

A free-energy functional for a crystal proposed by Singh and Singh [Europhys. Lett. 88, 16005 (2009)] which contains both the symmetry conserved and symmetry broken parts of the direct pair correlation function has been used to investigate the crystallization of a two-dimensional fluid. The results found for fluids interacting via the inverse power potential u(r)=ε(σ/r)(n) for n=3,6, and 12 are in good agreement with experimental and simulation results. The contribution made by the symmetry broken part to the grand thermodynamic potential at the freezing point is found to increase with the softness of the potential. Our results explain why the Ramakrishnan-Yussouff [Phys. Rev. B 19, 2775 (1979)] free-energy functional gave good account of freezing transitions of hard-core potentials but failed for potentials that have soft core and/or attractive tail.

Shear unzipping of double-stranded DNA.

We use a simple nonlinear scaler displacement model to calculate the distribution of effects created by a shear stress on a double-stranded DNA (dsDNA) molecule and the value of shear force F(c) that is required to separate the two strands of a molecule at a given temperature. It is shown that for molecules of base pairs fewer than than 21, the entire single strand moves in the direction of applied force, whereas for molecules having base pairs more than 21, part of the strand moves in the opposite direction under the influence of force acting on the other strand. This result as well as the calculated values of F(c) as a function of length of dsDNA molecules are in very good agreement with the experimental values of Hatch et al. [Phys. Rev. E 78, 011920 (2008)].