An effective cryptanalysis of DES for secure communication using hybrid cryptanalysis and deep neural network

In today's world, information security plays a key role in data storage and communication owing to the modern evolution of digitized data interchange in electronic mode. Cryptography is a widely preferred technique for securing transmitted information by transforming the original text into cipher text. A few cryptographic techniques have the inability to protect the data which are vulnerable to a distinct class of attacks. Therefore, a reliable cryptographic technique is necessitated for enlarging information security. In this paper, a new hybrid cryptanalysis (HCA) model has been proposed to acquire optimal cryptanalysis of data encryption standard (DES). It combines linear cryptanalysis (LCA) and neural cryptanalysis (NCA) for enhancing the performance of the cryptographic system with minimal time complexity. Primarily, the HCA model employs LCA to break cryptographic codes by analyzing linear approximations of the cryptographic algorithm. NCA is then applied with the aid of a deep neural network for identifying patterns in larger datasets. These patterns can be further utilized to break encryption. The efficacy of the proposed HCA model will be assessed through the assessment of three different datasets. The results manifest that the accuracy of the proposed model is increased up to 97.66% and the time needed to break encryption can be reduced.     

A hybrid energy harvesting framework and graphene-based supercapacitor for 6G and smart grid networks

This paper proposes a novel hybrid energy harvesting framework integrated with a graphene-based supercapacitor to address the energy demands of 6G communication systems and smart grid networks. To ensure consistent power generation, the proposed framework combines diverse energy harvesting mechanisms, including acoustic, vibration, and radio frequency (RF) energy sources. These harvested energies are then efficiently managed and stored in a proposed graphene-based supercapacitor compared with existing Li-ion batteries, due to its high energy density and rapid charge-discharge capabilities. Smart grids and 6G networks can utilize the energy that hybrid systems harvest. This paper examines the analysis of RF energy extracted from various antenna configurations. In the acoustic energy harvesting system, energy extraction has been achieved by subjecting it to acoustic pressure and impedance. Notably, the inclusion of acoustic impedance has resulted in a greater amount of harvested energy. The vibration energy harvesting analysis with angular frequencies under various force conditions is employed. The magnitude of force applied to the harvesting system significantly influences the efficiency of the harvested energy. The three-dimensional structure and voltage levels of the graphene-based supercapacitor have been examined for various electrodes. For maximizing the harvested hybrid energy, convex optimization is formulated due to its ability to efficiently navigate complex design spaces while ensuring reliable and globally optimal solutions. CVX tool in MATLAB is used for validation. The results improve the energy harvesting efficiency and enhance the power availability of all the harvesting sources. Furthermore, the compatibility of the proposed framework with existing network infrastructure highlights its potential for seamless integration into real-world communication and energy distribution systems.     

Natural citric acid (lemon juice) assisted synthesis of ZnO nanostructures: Evaluation of phase composition,morphology, optical and thermal properties

In recent years, because of their excellent electrocatalytic action and applications in different fields, metal oxide nanostructures have received massive consideration from scientists. Zinc oxide nanostructures are useful materials for a range of sensing applications and possess admirable electrocatalytic properties and stability. The current research presents the natural citric acid assisted synthesis of ZnO nanostructures and their structural, optical, morphological and thermal properties. X-ray diffraction was studied for the phase assessment of as prepared (Z1) and annealed ZnO (Z2) nanostructures and the crystallite sizes of the Z1 and Z2 samples were also located in the range between 35 nm and 38 nm. FESEM and TEM experiments were carried out to explore the surface features of Z1 and Z2 samples. The polycrystalline existence of the samples is demonstrated by the hexagonal, cubic and spherical shaped ZnO nanostructures. The energy band gap of Z1 and Z2 samples was determined (3.16 eV for Z1 and 3.12 eV for Z2) from the UV spectrum. The impact of annealing treatment on the thermal stability of ZnO nanostructures was studied and the main peak was observed for the Z1 sample at ~249 °C and for the Z2 sample at ~289 °C.     

N-doped reduced graphene oxide anchored with δTa2O5 for energy and environmental remediation: Efficient light-driven hydrogen evolution and simultaneous degradation of textile dyes

Precipitation assisted facile hydrothermal method has been developed for anchoring nitrogen-doped reduced graphene oxide (NRGO) with tantalum pentoxide (TO) denoted as TO@NRGO. Synthesized materials were characterized using spectroscopic, optical and microscopic techniques and confirm transforming TO from orthorhombic to hexagonal phase (δ) upon anchoring with NRGO. TO@NRGO, TO and NRGO have been evaluated for photocatalytic hydrogen evolution, degradation of Methylene blue (MB) and Rhodamine B (RhB). The enhanced photocatalytic activity was observed in TO@NRGO nanocomposite compared to TO and NRGO due to decreased bandgap (2.5 eV) and increased surface area (312 m² g^?1). TO@NRGO evolved 19,500 µmol g^?1 of hydrogen for 3 h. TO@NRGO showed better degradation efficiency of 94 and 88% at a time of 100 min for MB and RhB, respectively. The parameters involved in photocatalytic dye degradation, like the effect of pH, catalyst dosage, and initial concentration of dyes, have been carefully optimized to achieve maximum performance of the catalyst. The stability and reusability of TO@NRGO are good and managed to degrade dyes effectively even after the 5th run. Thus, TO@NRGO could serve as a choice of material in resolving the issues related to energy and the environment.     

Mosquito-larvicidal efficacy of gold nanoparticles synthesized from the seaweed,Turbinaria ornata (Turner) J.Agardh 1848

Malaria is considered a dreadful mosquito-borne infectious disease of human beings caused and spread by biting of the female mosquito Anopheles stephensi infected with a parasitic protozoan Plasmodium falciparum. Continuous application of chemicals/synthetic insecticides for vector control causes various problems such as resistant mechanism of mosquito, toxicity to nontarget aquatic organisms and disturbance to the microbial community of the soil. Currently, green synthesized nanoparticles are being employed in various biological processes including insect and pest control. The present investigation focused on the mosquito-larvicidal property of Turbinaria ornata-mediated gold nanoparticles (To-AuNPs) and its boiled aqueous extract (To-AE) against the malarial vector A. stephensi. The recorded lethal concentration (LC₅₀ and LC₉₀) values (µg/ml) of To-AE and To-AuNPs against fourth instar larvae of A. stephensi were 37.77 and 159.55 and 12.79 and 78.70, respectively. The To-AuNPs were characterized through UV-visible spectroscopy, Fourier transform infrared spectroscopy (FTIR), x-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), high-resolution transmission electron microscopy (HRTEM), energy dispersive x-ray spectroscopy (EDX), zeta potential and dynamic light scattering (DLS) method. The presently synthesized gold nanoparticles through the single-step, eco-friendly method is a potentially effective mosquitocidal agent.     

Disc bulge diagnostic model in axial lumbar MR images using Intervertebral disc Descriptor (IdD)

Multimedia Tools and Applications - One of the common types of lumbar disc disease is bulging which cause low back pain, tingling, and numbness. An automatic diagnostic system to detect the disc...     

Fabrication of a Chitosan‐Coated Magnetic Nanobiocatalyst for Starch Hydrolysis

The preparation of chitosan‐coated magnetic nanoparticles (MNPs) and covalent immobilization of α‐amylase for starch hydrolysis was investigated. Surface morphology, chemical composition, and structural characteristics of the MNPs were analyzed by scanning electron microscopy, energy dispersion spectrometry, and X‐ray diffractometry, respectively. Surface functional groups of MNPs, chitosan‐coated MNPs, and α‐amylase‐immobilized MNPs were characterized by Fourier transform infrared spectroscopy. Response surface methodology based on three levels was implemented to optimize three immobilization conditions and a regression model was developed. α‐Amylase‐immobilized MNPs provided better stability towards pH and temperature. The prepared thermostable nanobiocatalyst is well‐suited for industrial processes involving starch hydrolysis.     

TelMED: Dynamic User Clustering Resource Allocation Technique for MooM Datasets Under Optimizing Telemedicine Network

Wireless Personal Communications - Tele-Medical data communication via general purpose networking protocols and techniques are major set-back under low line channels and adequate resources such as...     

Cover Picture: Inhibition of Peroxidase‐Catalyzed Iodination by Cephalosporins: Metallo‐β‐Lactamase‐Induced Antithyroid Activity of Antibiotics (ChemMedChem 4/2009)

The cover picture shows some commonly used antibiotics with heterocyclic side chains inhibit peroxidase‐catalyzed iodination of L‐tyrosine upon hydrolysis by the bacterial enzyme metallo‐beta‐lactamase. This suggests that the production of metallo‐beta‐lactamase and subsequent hydrolysis of antibiotics would affect thyroid activity. For more details, see the Communication by A. Tamilselvi and G. Mugesh on p. 512 ff.

     

Node ID based detection of Sybil attack in mobile wireless sensor network

Security is the major issue in wireless sensor networks and many defence mechanisms have been developed to secure the network from these alarming attacks by detecting the malicious nodes which hinder the performance of the network. Sybil attack can make the network vulnerable. Sybil attack means a node which illegitimately claims multiple identities. This attack threatens wireless sensor network in routing, voting system, fair resource allocation, data aggregation and misbehaviour detection. Hence, the research is carried out to prevent the Sybil attack and improve the network performance. The node ID-based scheme is proposed, where the detection is based on node registration, consisting of two phases and the assignment of ID to the node is done dynamically. The ID's corresponding to the nodes registered is at the base station and the node active time is monitored, any abnormalities in the above phases confirm the presence of Sybil nodes in the network. The scheme is simulated using NS2. The energy consumed for this algorithm is 2.3?J. The proposed detection scheme is analysed based on the network's PDR and found that the throughput has improved, which prove that this scheme may be used in the environment where security is needed.