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The tremendous progress of 5G technology has transformed the landscape of the Internet of Things (IoT), allowing for fast data speeds, low delay, and widespread connection that is crucial for a variety of applications, including smart cities and industrial automation. In the context of 5G enabled IoT networks, colored noise introduces varying levels of interference across different frequency bands, which can significantly degrade the performance of 5G LDPC decoding. This paper presents a novel Deep learning approach for 5G channel LDPC code decoding tailored for next-generation IoT applications. The proposed method integrates an Iterative Normalized Min-Sum (NMS) algorithm with a Convolutional Neural Network (CNN) to enhance the performance of LDPC decoding in the presence of colored noise, a common interference in real-world communication channels. Through extensive simulations and analysis, our approach demonstrates a significant performance improvement, achieving a 3.8 dB enhancement at a Bit error rate of $10^{-6}$ . This is achieved by accurately estimating and mitigating channel noise, thereby ensuring reliable data transmission for critical IoT applications. The findings indicate that our approach to decoding technique not only enhances error correction capabilities but also adapts to varying channel conditions, optimizing IoT network performance and efficiency. This research contributes a robust solution to the challenges posed by colored noise in 5G-enabled IoT networks, promoting the deployment of more reliable and efficient IoT systems.

Vaishnavi Ravikumar,1 Ivan Mijakovic,1,2 Santosh Pandit2 1Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; 2Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, 41296, SwedenCorrespondence: Santosh Pandit, Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Göteborg, 41296, Sweden, Tel +46 729484011, Fax +46 317723801, Email pandit@chalmers.seIntroduction: Antibacterial activity of graphene oxide (GO) has been extensively studied, wherein penetration of the bacterial cell membrane and oxidative stress are considered to play a major role in the bactericidal activity of GO. However, the specific mechanism responsible for the antibacterial activity of GO remains largely unknown. Hence, the goal of this study was to explore the mode of action of GO, via an in-depth proteomic analysis of the targeted bacteria.Methods: Staphylococcus aureus was grown in the presence of GO and samples were collected at different growth phases to examine the cell viability and to analyze the changes in protein expression. Antimicrobial efficiency of GO was tested by assessing bacterial viability, live/dead staining and scanning electron microscopy. The intracellular reactive oxygen species (ROS) induced by GO treatment were examined by fluorescence microscopy. Label-free quantitative proteomics analysis was performed to examine the differentially regulated proteins in S. aureus after GO treatment.Results: GO treatment was observed to reduce S. aureus viability, from 50 ± 17% after 4 h, to 93 ± 2% after 24 h. The live/dead staining confirmed this progressive antimicrobial effect of GO. SEM images revealed the wrapping of bacterial cells and their morphological disruption by means of pore formation due to GO insertion. GO treatment was observed to generate intracellular ROS, correlating to the loss of cell viability. The proteomics analysis revealed alteration in the expression of cell membrane, oxidative stress response, general stress response, and virulence-associated proteins in GO-treated bacterial cells. The time-dependent bactericidal activity of GO correlated with a higher number of differentially regulated proteins involved in the above.-mentioned processes.Conclusion: The obtained results suggest that the time-dependent bactericidal effect of GO is attributed to its wrapping/trapping ability, ROS production and due to physical disruption of the cell membrane.Keywords: cell wrapping, membrane disruption, oxidative stress, proteomics

An increase in the development of Industries and rapid growth in the population has led to an increase in the power demand in the distribution network. With these increased needs, the existing distribution transformer have become overloaded conditions. Due to overload on the transformer, the efficiency and power factor drops and also it led to increase in the transformer voltage regulation and windings get overheated. This paper presents a novel topology called transformer auto stop-start that will automatically energise and de-energise, one pair of transformers at a kV/V Distribution network. In this way, the proposed technique reduces overall electric losses. Performance of transformer under different load conditions are illustrated by simulation.

A large number of organisms are known to cause acute encephalitic syndrome (AES). A number of diagnostic tests have to be performed in order to arrive at a probable pathogen causing AES thus making it a very time consuming, laborious and expensive. The problem is further compounded by the lack of availability of sufficient volume of Cerebrospinal fluid (CSF). Thus, there is an urgent need of a diagnostic tool for the simultaneous detection of all probable pathogens responsible for causing AES. Here we report the development of a novel diagnostic method, Syndrome Evaluation System (SES) for the simultaneous detection of 22 pathogens including RNA and DNA Viruses, bacteria, fungi, and parasite all endemic to India and Southeast Asia in a single sample using a novel multiplexing strategy. Syndrome Evaluation System (SES) involves isolation of nucleic acid, multiplex amplification of the DNA, and cDNA followed by identification of the amplified product by sequence specific hybridization on SES platform with the final read out being a visually recordable colored signal. The total time required to carry out this diagnostic procedure is 7 h. The SES was standardized using the commercially available vaccines, panels and cell culture grown quantified viruses/bacteria/fungi. The limit of detection (LOD) of SES ranged between 0.1 and 50 viral particles per ml of CSF and 100 to 200 bacterial cells or 5 parasites per ml of CSF, along with 100% specificity. Precision studies carried out as per the Clinical Laboratory Improvement Amendments (CLIA) guidelines, using two concentrations of each pathogen one the LOD and the other double the LOD, clearly demonstrated, that inter/intra assay variability was within the limits prescribed by the guidelines. SES is a rapid molecular diagnostic tool for simultaneous identification of 22 etiological agents of AES encountered both in sporadic and outbreak settings.