A novel approach for designing delay efficient path for mobile sink in wireless sensor networks

In the recent years, the use of mobile sink has drawn enormous attention for data collection in wireless sensor networks (WSNs). Mobile sink is well known for solving hotspot or sinkhole problem. However, the design of an efficient path for mobile sink has tremendous impact on network lifetime and coverage in data collection process of WSNs. This is particularly an important issue for many critical applications of WSNs where data collection requires to be carried out in delay bound manner. In this paper, we propose a novel scheme for delay efficient trajectory design of a mobile sink in a cluster based WSN so that it can be used for critical applications without compromising the complete coverage of the target area. Given a set of gateways (cluster heads), our scheme determines a set of rendezvous points for designing path of the mobile sink for critical applications. The scheme is based on the Voronoi diagram. We also propose an efficient method for recovery of the orphan sensor nodes generated due to the failure of one or more cluster heads during data collection. We perform extensive simulations over the proposed algorithm and compare its results with existing algorithms to demonstrate the efficiency of the proposed algorithm in terms of network lifetime, path length, average waiting time, fault tolerance and adaptability etc. For the fault tolerance, we simulate the schemes using Weibull distribution and analyze their performances.     

Adsorption characterisation of the dealumination effect on H-mordenites

Adsorption and desorption isotherms of nitrogen at 77 K, on H-mordenite having various SiO₂/Al₂O₃ ratio have been studied. The isotherms have been treated in the BET, Langmuir, Kiselev, Barrett-Joyner-Halenda, and Dubinin-Raduschkevich equation, determining the effect of the dealumination on texture parameters, as well as the limits of applicability for the BET equations in comparison with the Langmuir and Dubinin-Polanyi theories.The presented data confirmed that the BET equation for calculation of the specific surface area does not apply to solids with most of their pares having diameters smaller than four times the molecular diameter of the adsorbed gas; applying it to zeolites lacks any physical support.The adsorbtion potential and the Dubinin-Raduschkevich maximum adsorbtion volume are more suitable for the characterisation of these microporous systems.The texture parameters exhibit maximum values for the sample with the SiO₂/Al₂O₃ ratio equal to 100.     

A novel SPN-based video steganographic scheme using Sudoku puzzle for secured data hiding

Innovations in Systems and Software Engineering - Video steganography is the method of hiding the presence of data within the cover video. In this paper, a novel video steganography technique is...     

An Ultrasmall SnFe2O4 Nanozyme with Endogenous Oxygen Generation and Glutathione Depletion for Synergistic Cancer Therapy

The tumor microenvironment (TME) with the characteristics of severe hypoxia, overexpressed glutathione (GSH), and high levels of hydrogen peroxide (H₂O₂) dramatically limits the antitumor efficiency by monotherapy. Herein, a novel TME-modulated nanozyme employing tin ferrite (SnFe₂O₄, abbreviated as SFO) is presented for simultaneous photothermal therapy (PTT), photodynamic therapy (PDT), and chemodynamic therapy (CDT). The as-fabricated SFO nanozyme demonstrates both catalase-like and GSH peroxidase-like activities. In the TME, the activation of H₂O₂ leads to the generation of hydroxyl radicals (•OH) in situ for CDT and the consumption of GSH to relieve antioxidant capability of the tumors. Meanwhile, the nanozyme can catalyze H₂O₂ to generate oxygen to meliorate the tumor hypoxia, which is beneficial to achieve better PDT. Furthermore, the SFO nanozyme irradiated with 808 nm laser displays a prominent phototherapeutic effect on account of the enhanced photothermal conversion efficiency (η  = 42.3%) and highly toxic free radical production performance. This “all in one” nanozyme integrated with multiple treatment modalities, computed tomography, and magnetic resonance imaging properties, and persistent modulation of TME exhibits excellent tumor theranostic performance. This strategy may provide a new dimension for the design of other TME-based anticancer strategies.     

Existence of χ‐efficient solution of multiobjective fractional programming problem with bounded parameters

In this paper, a methodology is developed to solve a multiobjective fractional programming problem in which the coefficients of the objective functions and constraints are intervals. This model is transformed into an interval‐free equivalent optimization problem. A new partial ordering is introduced and the relation between the original problem and the transformed problem is established using this partial ordering. The proposed methodology is illustrated through a numerical example.     

Dual Emission of Water‐Stable 2D Organic–Inorganic Halide Perovskites with Mn(II) Dopant

Moisture‐delicate and water‐unstable organic–inorganic halide perovskites (OI‐HPs) create huge challenges for the synthesis of highly efficient water‐stable light‐emitting materials for optoelectronic devices. Herein, a simple acid solution–assisted method to synthesize quantum confined 2D lead perovskites through Mn doping is reported. The efficient energy transfer between host and dopant ions in orange light‐emitting Mn²⁺‐doped OI‐HPs leads to the most efficient integrated luminescence with a photoluminescence quantum yield over 45%. The Mn²⁺ substitution of Pb²⁺ and passivation with low dielectric constant molecules such as phenethylamine, benzylamine, and butylamine enhance water resistivity, leading to water stability. The dual emission process of this water‐stable 2D Mn‐doped perovskite will help in developing highly efficient 2D water‐stable perovskites for practical applications.     

Nonlinear control of a multicomponent distillation process coupled with a binary distillation model as an EKF predictor

The work is devoted to design the globally linearizing control (GLC) strategy for a multicomponent distillation process. The control system is comprised with a nonlinear transformer, a nonlinear closed-loop state estimator [extended Kalman filter (EKF)], and a linear external controller [conventional proportional integral (PI) controller]. The model of a binary distillation column has been used as a state predictor to avoid huge design complexity of the EKF estimator. The binary components are the light key and the heavy key of the multicomponent system. The proposed GLC-EKF (GLC in conjunction with EKF) control algorithm has been compared with the GLC-ROOLE [GLC coupled with reduced-order open-loop estimator (ROOLE)] and the dual-loop PI controller based on set point tracking and disturbance rejection performance. Despite huge process/predictor mismatch, the superiority of the GLC-EKF has been inspected over the GLC-ROOLE control structure.     

Neuro-estimator based GMC control of a batch reactive distillation

In this paper, an artificial neural network (ANN)-based nonlinear control algorithm is proposed for a simulated batch reactive distillation (RD) column. In the homogeneously catalyzed reactive process, an esterification reaction takes place for the production of ethyl acetate. The fundamental model has been derived incorporating the reaction term in the model structure of the nonreactive distillation process. The process operation is simulated at the startup phase under total reflux conditions. The open-loop process dynamics is also addressed running the batch process at the production phase under partial reflux conditions. In this study, a neuro-estimator based generic model controller (GMC), which consists of an ANN-based state predictor and the GMC law, has been synthesized. Finally, this proposed control law has been tested on the representative batch reactive distillation comparing with a gain-scheduled proportional integral (GSPI) controller and with its ideal performance (ideal GMC).