Gold Nanoparticles with Self-Assembled Cysteine Monolayer Coupled to Nitrate Reductase in Polypyrrole Matrix Enhanced Nitrate Biosensor

We have developed here a novel, highly sensitive and selective nitrate (NO– 3) biosensor by covalent immobilization of nitrate reductase (NaR) in self-assembled monolayer (SAM) of cysteine on gold nanoparticles (GNP)-polypyrrole (PPy) modified platinum electrode. Incorporation of GNP in highly microporous PPy matrix was confirmed by morphological scanning electron microscope (SEM) images. The electrochemical behavior of the NaR modified electrode exhibited the characteristic reversible redox peaks at the potential, –0.76 and –0.62 V versus Ag/AgCl. Further, the GNP-PPy nanocomposite enhanced the current response by 2-fold perhaps by enhancing the immobilization of NaR and also direct electron transfer between the deeply buried active site and the electrode surface. The common biological interferences like ascorbic acid, uric acid were not interfering with the NO– 3 measurement at low concentration levels. This biosensor showed a wide linear range of response over the concentration of NO– 3 from 1 μM to 1 mM, with higher sensitivity of 84.5 nA μM–1 and a detection limit of 0.5 μM. Moreover, the NO– 3 level present in the nitrate-rich beetroot juice and the NO– 3 release from the lipopolysaccharide treated human breast cancer cells were estimated

Federated learning optimization: A computational blockchain process with offloading analysis to enhance security

The Internet of Things (IoT) technology in various applications used in data processing systems requires high security because more data must be saved in cloud monitoring systems. Even though numerous procedures are in place to increase the security and dependability of data in IoT applications, the majority of outside users can decode any transferred data at any time. Therefore, it is essential to include data blocks that, under any circumstance, other external users cannot understand. The major significance of proposed method is to incorporate an offloading technique for data processing that is carried out by using block chain technique where complete security is assured for each data. Since a problem methodology is designed with respect to clusters a load balancing technique is incorporated with data weights where parametric evaluations are made in real time to determine the consistency of each data that is monitored with IoT. The examined outcomes with five scenarios process that projected model on offloading analysis with block chain proves to be more secured thereby increasing the accuracy of data processing for each IoT applications to 89%

Preparation and performance evaluation of 3D printed poly lactic acid composites reinforced with silane functionalized walnut shell for food packaging applications

The main goal of this work is to develop silane-grafted Poly Lactic Acid (PLA) bio-composites reinforced by various compositions of 0, 5, 10, and 15 wt% Walnut shell (WAL) particles and 3D printed by Fused Filament Fabrication (FFF) technique. The composite filaments are extruded by filament extrusion technique, and the 3D printed Walnut shell/PLA (WAL/PLA) bio-composite samples are evaluated for various mechanical, water absorption and biodegradation properties. The effect of silane grafting increases the crystallinity index value of 61.2% for the silane-grafted WAL particles. The mechanical property results reveal that using WAL particles reduces the strength value and improves the modulus of both untreated and silane-treated WAL/PLA composites. The silane grafted 15% WAL/PLA samples show the highest shore hardness value of 71 MPa and the heat deflection temperature of 63.79 ℃. The biodegradation test results reveal that the untreated 15% WAL/PLA composites have a higher mass loss of 6.4% and 19.1% for 30 and 60 days, respectively. Fractographical results of silane-treated 10% WAL/PLA composites exhibit a uniform distribution of WAL particles with minimum particle pull-out from the polymeric matrix. The findings of this study affirm the potential of WAL/PLA bio-composites as a viable and sustainable material for application in food storage and service

QoS enhancement in wireless ad hoc networks using resource commutable clustering and scheduling

Effective management and control of large-scale networks can be challenging in the absence of appropriate resource allocation. This paper presents a framework for highlighting the significance of resource allocation in mobile, wireless, and ad hoc networks. The model has been designed to incorporate a clustering protocol and a schedule-based resource allocation algorithm, resulting in the establishment of a multi-objective framework. The proposed framework places a significant emphasis on the allocation of energy and distance, with a focus on minimizing these objectives. Each node is separated into several clusters where individual energy is allocated and the cluster head in each cluster allows the nodes to communication with shortest distance. For the transmitted information the speed of transmission is maximized thus more amount of time period is saved where stability factor is maximized. To test the allocated resources in the network the proposed method compares and evaluates the parametric outcomes with existing method based on five scenarios. In the comparative analysis it is observed that proposed method can able to maximize the life time and quality of service for all networks with optimized range of 84%

Molecular dynamics simulation approach to explore atomistic molecular mechanism of peroxidase activity of apoptotic cytochrome c mutants

Mutations in cytochrome c (Cyt c) have been reported in tuning peroxidase activity, which in-turn cause Cyt c release from mitochondria and early apoptosis. However, the molecular tuning mechanism underlying this activity remains elusive. Herein, multiple 20 ns molecular dynamics (MD) simulations of wild type (WT), Y67F and K72W mutated Cyt c in aqueous solutions have been carried out to study how the changes in structural features alters the peroxidase activity of the protein. MD simulation results indicate that Y67F mutation caused, (i) increased distances between critical electron-transfer residues, (ii) higher fluctuations in omega loops, and (iii) weakening of intraprotein hydrogen bonds result in open conformation at heme crevice loop in Cyt c leading to an enhanced peroxidase activity. Interestingly, the aforementioned structural features are strengthened in K72W compared to WT and Y67F, which triggers K72W mutated Cyt c into a poor peroxidase. Essential dynamics results unveil that first two eigenvectors are responsible for overall motions of WT, Y67F and K72W mutated Cyt c. This study thus provides atomic level insight into molecular mechanism of peroxidase activity of Cyt c, which will help in designing novel Cyt c structures that is more desirable than natural Cyt c for biomedical and industrial processe

Predictive modeling of compressive strength for additively manufactured PEEK spinal fusion cages using machine learning techniques

The current study delves into the utilization of Machine Learning (ML) algorithms to evaluate the mechanical properties of additively manufactured PEEK spinal fusion cages. In this research, a range of ML models, including Linear Regression (LiR), Lasso Regression (LaR), Decision tree (DT), and K-Nearest Neighbor (KNN) are harnessed to enhance compressive strength prediction. Ensemble learning techniques such as bagging, boosting, and stacking are applied to identify the most accurate ML model in terms of achieving heightened accuracy and minimized errors. To facilitate this, spinal fusion cages are 3D printed using the Fused Filament Fabrication (FFF) technique and subsequently tested using a Universal Testing Machine (UTM). The development of ML models involves the exploration of independent material-extrusion factors, encompassing layer height (0.1 mm, 0.2 mm, 0.3 mm), printing temperature (370℃, 390℃, 410℃), printing speed (10 mm/sec, 30 mm/sec, 50 mm/sec), infill density (40%, 70%, 100%), build orientation (0º, 45º, 90º), and line width (0.1 mm, 0.2 mm, 0.3 mm). The robustness and effectiveness of the developed ML models in predicting compressive strength properties are optimized through comprehensive error metric analysis. The results indicate that the LiR model, particularly when implemented under the boosting ensemble technique, demonstrates the highest accuracy with a Mean Absolute Error (MAE) of 0.657, Root Mean Square Error (RMSE) of 0.758, and Median Absolute Error (MedAE) of 0.634. This underscores the potential of LiR for precise compressive strength prediction in 3D-printed PEEK spinal fusion cages for spinal and maxillomandibular reconstruction

Development of edge computing and classification using The Internet of Things with incremental learning for object detection

The edge computing method and Internet of Things (IoT), which offers significantly shorter inactivity intervals, is one of the promising network technologies in today's generation of systems. There is no need to process the data using a cloud platform whenever an edge computing technology is used; alternative ways employing offline IoT and incremental learning techniques can be used. Using IoT, the incremental learning process transfers all essential data within a specific device. Thus, edge computing, IoT and incremental learning techniques are combined in the proposed method to detect numerous objects with varying weights. An analytical model that minimizes the parametric values and has various objectives is used to carry out the object detection process. Additionally, by utilizing evaluation metrics from five different case studies that were simulated using the MATLAB computing toolkit, the proposed method was tested. The efficacy of the proposed method rises to 62% when the simulated results are compared with the current method. The suggested method can accurately identify several objects in real-time when operating in a multi-object mode