Jawaharlal Nehru Technological University, Kakinada

FibroScan, initially designed for assessing cheese maturity, has evolved into a crucial medical tool for liver fibrosis diagnosis. This systematic review explores its development history, functionality, and pros and cons compared to traditional liver biopsy. Precision in various clinical settings is scrutinised, emphasising FibroScan’s accuracy in conditions like NAFLD and viral-induced liver disease. The article also delves into its potential in paediatrics, its relevance in monitoring COVID-19-related liver complications, and its role in predicting hepatocellular carcinoma risk, Technical aspects, including transducers, imaging integration, and portability, are examined. Various methods for evaluating liver fibrosis are discussed, highlighting FibroScan’s suitability for advanced stages, contrasting with the gold standard of liver biopsy for early stages. The impact of FibroScan on long-term liver conditions is emphasised, focusing on early detection, progression monitoring, reduced invasive biopsies, and hepatocellular carcinoma risk prediction. This systematic review underscores FibroScan’s transformative potential in liver disease treatment and predicts ongoing research to enhance early detection, disease monitoring, and explore new clinical applications. Anticipated advances include FibroScan-guided liver biopsy, artificial intelligence data analysis, and point-of-care device development, promising a further revolution in liver disease management. The article concludes with optimistic prospects for FibroScan’s future.

As the text is written on a special digitizer or Personal Digital Assistance (PDA) in which a sensor picks up the pen-tip movements along with the pen-up/pen-down switching, its automatic conversion is performed in the online Handwriting Recognition (HR). There are several works related to the online recognition of Devanagari as well as Tamil scripts. Meanwhile, the online recognition works associated with other Indian languages, specifically Telugu, which is complex in its structure together with style, are very few. Our work emphasizes the development of an online handwritten Telugu character recognition system using dominant points with the combination of SVM and performance analysis of various other features. Three classifiers namely, SVM, K-NN and MLP are used to examine the performance of the feature vectors. The proposed research is verified with HP-Lab data available in the UNIPEN format.

A new series of various aryl amide derivatives of imidazo[1,5‐a]pyridine‐1,2,4‐thiadiazoles (15a‐j) were designed, synthesized and evaluated for their cytotoxic profiles against four human cancer cell lines such as breast cancer (MCF‐7), lung cancer (A549), colon cancer (Colo‐205) and ovarian cancer (A2780) by using of MTT assay with etoposide as standard known chemotherapeutic agent. The five compounds 15a, 15b, 15c, 15f and 15j were exhibited more potent cytotoxic effect compared with etoposide. Among them, compound 15a exhibited potent cytotoxic effect against MCF‐7, A549, Colo‐205, and A2780 cell lines with IC50 values of 0.11±0.045 µM, 0.94±0.047 µM, 0.39±0.023 µM, and 0.77±0.062 µM respectively. Though docking simulations of Human Topoisomerase IIβ, it is apparent that compounds 15a, 15b, 15c, and 15f manifested exceptional binding affinity and interaction profiles, surpassing other compounds evaluated in this in silico study.

Colorectal cancer is the second leading cause of death worldwide. Chemotherapy for cancer often results in severe side effects and has a higher mortality rate compared to alternative treatment methods. Mesenchymal stem cells (MSCs) have been adopted as a potential drug delivery system targeting cancer and tumor cells. MSCs possess special features such as low immunogenicity, homing ability, and tumor tropism. There are several approaches for cancer therapy using MSCs, including migration towards irradiated tumors, chemotaxis-mediating factors, genetic engineering, priming with anticancer drugs, and derivation of Micro vesicles. These MSCs are formulated with chemotherapeutic agents to deliver drugs directly to the tumor site. With increasing rates of unhealthy lifestyles, poor food habits, and people may more easily accept genetic predispositions, mesenchymal stem cell therapy as it reduces the frequent use of conventional medications. Nanotechnology has emerged as a favorable approach in cancer treatment, managing the limitations of existing therapies. In recent decades, inorganic nanoparticles have demonstrated significant potential in the fight against colorectal cancer. Unlike organic nanoparticles, inorganic nanoparticles possess unique characteristics such as thermal efficiency, electrical conductivity, magnetic properties, and photosensitivity. These nanoparticles primarily made from materials like carbon, silica, metals and metal oxides are known for their enhanced dru-loading capabilities and effectiveness in advanced photo thermal and photodynamic therapies. This review summarize the pathophysiology of colorectal cancer, loading ability of MSCs and highlight the crucial role of inorganic nanoparticles in photo thermal and photodynamic therapy and drug delivery, while also searching several types of inorganic nanoparticles utilized in colorectal cancer treatment based on recent studies. Key words: Colorectal Cancer, Chemotaxis, Micro vesicles, Tumour tropism, photo thermal therapy, photodynamic therapy

Total harmonic distortion is an important issue in integrated distributed generation energy systems which affect the performance. Interline hybrid continuous converter impedance control-based tuned filter is proposed to mitigate the harmonics, i.e., 10–12% reduction in integrated energy system. Effective transformer impedance is used instead of entire grid network impedance. Transformer impedance control from 0–20% is used in testing of the reactive voltage drop at point of common coupling. Transformer impedance is proportional to the turns ratio and corresponding voltage changes at the load center and it is imposed by the hybrid tuned filer. Hybrid tuned filter is designed with the impedance of transformer along with converter circuit impedance to control harmonic currents and voltage profile of integrated system. Proposed hybrid filter design consists of both instantaneous capacitor voltage vc(t)$v_c(t)$ component and combined inductor–capacitor filter voltage component vf(t)$v_f(t)$ to mitigate the harmonic component and improve power factor. Proposed hybrid tuned filter is tested over a strong grid conditions that deliver the power to all loads. As well as weak grid condition is also tested with proposed tuning filter. Testing environments, like linear loading and nonlinear loading, are validated in all grid operating conditions.

Hybrid composites have attracted considerable interest from researchers due to their balanced combination of ecological and mechanical properties. This paper examines the physical and mechanical behavior of hybrid composites reinforced with nano‐silica and woven jute/kenaf fiber. The hybrid composites were fabricated using hand layup techniques with varying weight percentages of nano‐silica (1.5, 3, 4.5, and 6 wt%) and three different fiber layering sequences (JKJK, JKKJ, and KJJK). The mechanical properties, including tensile, flexural, interlaminar shear strength, impact resistance, and hardness, along with the physical properties such as density and moisture absorption, are evaluated for the fabricated composite samples. The jute/kenaf composite fabricated with KJJK layering sequence and 4.5 wt% nano‐silica particles exhibited reduced void content (1.983%) and low moisture absorption (4.87%). The addition of 4.5 wt% nano‐silica to KJJK composites achieved a maximum tensile strength of 86.38 ± 2.9 MPa and a maximum tensile modulus of 4.238 ± 0.094 GPa. In KJJK hybrid composites, the incorporation of 4.5 wt% nano‐silica led to a 70.11% increase in flexural strength and a 47.05% increase in flexural modulus. Surface morphological analysis of the specimens was conducted using SEM to examine cracks, fiber pullout, and different failure modes within the hybrid composite. Highlights Developed the jute/kenaf composites with 1.5, 3, 4.5, and 6 wt% of nano‐silica. Nano‐silica into jute/kenaf composites reduced void content and moisture absorption. The composites with 4.5 wt% nano‐silica had better mechanical properties. Layering sequence jute and kenaf affect the mechanical properties. Morphological analysis confirms the improved adhesion with nano‐silica.

An important process for underwater acoustic signal is noise reduction. In ocean exploration and the military, the minimization of noise in the underwater environment is essential to provide a significant impact in our society. While considering the different acoustic channels and complexity of marine environment, the noise reduction process in acoustic signals is always difficult. Owing to these complexities, an advanced noise reduction mechanism for underwater acoustic signal denoising is performed using adaptive deep learning method is developed. The proposed model involves finding and analyzing the noises present in the underwater acoustic signal, which is helpful for underwater target detection, recognition and acoustic communication quality. Here, a Advanced Recurrent Neural Network with Novel Loss Function (ARRNN-NLF) is implemented for reducing noises in underwater acoustic signals. Hence, the input signal is given to the reduction process where the ARRNN-NLF network is utilized for densification. The high-order nonlinear features from the original signal are extracted and converted into subvectors with fixed lengths based on temporal dimension. Finally, the denoised signal is obtained from the developed ARRNN with a minimum loss between the predicted and target output. Here, the parameters from ARRNN-NLF are optimized by the Enhanced Osprey Optimization Algorithm (EOOA) for enhancing the denoising model. The resultant results are evaluated by diverse conventional denoising models to prove efficiency.

Additive manufacturing is a cutting-edge process that enables the creation and development of complex shapes, opening new avenues for advanced heat sink designs that maximize heat transfer while minimizing pressure drop. This review provides an overview of recent results and insights from global researchers on the additive manufacturing of microchannel heat sinks. It discusses various novel microchannel heat sink types fabricated using additive manufacturing and the complexities involved in their fabrication. Additionally, the review explores geometric configurations, the application of computational optimization methods, and the impact of surface roughness on heat transfer and pressure drop. The primary focus is on the fundamental advantages of additive manufacturing in addressing complex heat transfer designs. However, the review also highlights limitations restricting its benefits in specific applications, such as material handling and availability. This review identifies future challenges for creating novel heat sinks with complex geometries and opportunities for advancements in heat transfer technology.

Now-a-days, the generation of videos has increased dramatically due to the quick growth of multimedia and the internet. The need for effective ways to store, manage, and index the massive numbers of videos has become imperative due to this expansion. As a result, a method needs to be proposed that collects only the necessary data from the original recording. In computer vision, Video summarization is a significant task, and its primary goal is to give a quick summary of the video by removing irrelevant information and capturing key frames from the video. Many approaches have developed over the last several decades, using the most recent deep neural network architectures that represent the current state-of-the-art. Our method involves extracting vital key frames from the input video using the MobileNetSSD model, which is well-known for its efficient recognition and localization of objects of interest. These highlighted frames are essential in creating a detailed video summary. Furthermore, a method of temporal analysis is applied to guarantee that the summary accurately reflects the relevant events in the order in which they occurred, contributing to a coherent and meaningful representation of the information. We evaluated the proposed approach on TV Sum and SUM me video datasets, comparing the results against cutting-edge video summarization techniques. Our approach works effectively to produce clear and meaningful video summaries.

The paper addresses the challenges posed by the increasing integration of Direct Current (DC) transmission lines and system controllers into power systems. It presents a novel AC-DC load flow methodology designed to resolve the limitations of traditional AC-only models. The methodology integrates traditional AC load flow techniques with tailored DC system models to better address the complexities of mixed AC-DC networks. The proposed methodology, utilizing the Newton-Raphson algorithm, systematically evaluates performance by comparing it with conventional AC-only models and other hybrid methods through detailed test cases. The results show that the AC-DC load flow method significantly enhances both the accuracy and computational efficiency of power flow calculations. By effectively addressing the complexities of integrating AC and DC components, this method offers a robust and innovative solution for modern power system analysis, representing a notable advancement in the field.

A person's Mental Health (MH) dramatically influences their complete evolution in life, including their cognitive, emotional, and psychomotor components. A person with good MH is content with life and can be creative, learn new things, and take risks to accomplish more significant objectives. Currently, college students are dealing with MH concerns for various causes, which affect their academic performance and significantly contribute to poor academic results. Therefore, encouraging MH in college students presents a significant problem for educators, parents, teacher educators, and governments. Adolescence is a crucial and delicate time characterized by considerable physical, emotional, social, and religious changes. The physical, social, and psychological facets of an individual's growth are laid out in this period, with mental health as a crucial factor in promoting these gains. Therefore, it becomes crucial for researchers to use Deep Learning (DL) algorithms to study the association between MH and vital psychological characteristics, such as emotional intelligence, personality traits, and intelligence. The personal aspects, namely personality, emotional intelligence, and MH are all related ideas that influence one another. Individuals must have mental well-being and emotional harmony to have a good personality. The current study uses DL techniques to investigate the relationship between college students' MH, emotional intelligence, and personality features. To perform a thorough study on emotion identification and Mental Health Prediction (MHP) among college students, this project investigates the integration of edge computing enabled by the Internet of Things (IoT) in the context of intelligent systems. Innovative treatments are urgently needed due to this population's rising prevalence of MH issues. This paper aims to continuously monitor and predict college students' MH using Edge Computing (EC) and IoT technology.

Epilepsy is a neurological condition that is found in most people all over the world, and the ability to accurately anticipate seizures in epileptic patients has a significant impact on both their level of protection and their overall quality of life. This research proposes a unique patient specific seizure prediction approach based on Deep Learning (DL) using long-term scalp electroencephalogram (EEG) recordings to predict seizure onset. Preictal brain states should be adequately detected and differentiated from the prevalent interictal brain states as early as possible to make this technology acceptable for real-time use. A single automated system has been designed for the Features Extraction (FE) and classification processes. The raw EEG signal that has not been pre-processed is considered the input to the system, and the signal is further reduced using subsequent computations. An innovative reconstruction approach using Variational Auto-Encoder Generative Adversarial Networks (VAE+C+GAN) with the Cramer Distance (CD) and a Temporal-Spatial-Frequency (TSF) loss function is presented in this research work. The machine that discriminates receives instructions to differentiate between created tests and actual samples, while the generator is verified to produce false samples that the discriminator does not recognize as fake. The proposed VAE+C+GAN’s experimental results have been examined, and a classification accuracy of 95% has been achieved. According to the experiment's findings, the VAE-C-GAN performs better than the current EEG classification system and has excellent potential for real-time applications.

The post-HapMap era has witnessed a significant expansion in resources available for genome analysis and interpretation, alongside the development of numerous databases and consortia dedicated to personal genomics. The emergence of next-generation sequencing technologies has accelerated the growth of personal genomics, while also indirectly advancing the field of pharmacogenomics (PGx). Modern pharmacogenomics has progressed beyond merely identifying variations in common genes related to frequently used drugs, evolving into a comprehensive study of the interactions between single genes and multiple drugs, as well as the effects of combinatorial drug use. The vast increase in genomic data has prompted the growth of specialized databases and consortia focused on PGx and the curation of this data. This review provides an overview of key online resources and tools essential for interpreting personal genomes, with a focus on pharmacogenetically relevant data. The resources are categorized into five main sections according to their content and utility, encompassing pharmacogenomics databases, variation databases, tools and resources for analyzing pharmacogenomic data, community efforts and consortia, as well as standards for data representation. Key words: Pharmacogenomics, Single-nucleotide variations, Data Bases, Datasets

Continuous fire models are not suitable for the implementation of hardware units for applications and hence, suitable discrete versions need to be selected. Moreover, the nonlinear components in the neuronal equations reduce system performance (in the case of frequency and number of resources). This research paper focuses on implementing efficient Spiking Neural Networks (SNNs) using Field-Programmable Gate Array (FPGA), with a specific emphasis on the Leaky Integrate and Fire (LIF) neuron model. Its objective is to optimize the mathematical equations of the LIF model by approximating nonlinear functions. This approach enables the development of a simple, cost-effective and high-speed design. Existing LIF Neuron Hardware Blocks (NHBs) are based on the approximation of continuous models by standard difference schemes such as the Euler method or R–K method etc. Mathematically, such approximations do not exactly represent all dynamics of continuous systems. There are good approximations for small step sizes but they behave oddly when the approximation step size increases. Hence, the corresponding discrete, digital versions are not suitable for applications in all cases. This paper utilizes a Nonstandard Finite Difference (NSFD) scheme for the hardware (FPGA) implementation of the exact model of LIF-based NHB that works for all step sizes. The model presented here has a speed of 438.686[Formula: see text]MHz which is more than other existing models presented in this paper. It is multiplier-less, unlike earlier models. Further, it is implemented for SNN for basic pattern recognition and established that the proposed model works properly for given patterns. The system was evaluated using large datasets such as MNIST handwritten digit recognition, achieving a classification accuracy of 97.8%. Additionally, it underwent testing for COVID-19 chest CT scan image classification, demonstrating an 84% accuracy rate which is 6% more compared to existing Spiking Neural Networks (SNNs).

The real-time monitoring of electrical activities of the heart using a wearable electrocardiogram (ECG) sensor plays a vital role in providing real-time data and allows for the immediate detection of arrhythmia events for patients with high risk of cardiac diseases. This work presents an architecture that capitalizes on multiscale convolutional neural networks (CNNs) and gated recurrent units (GRUs), augmented with a self-attention mechanism, to thoroughly analyze both spatial and temporal aspects of ECG signals. This innovative integration enables the model to detect nuanced arrhythmic patterns effectively, thereby addressing the complex nature of ECG interpretation. The proposed model’s performance is substantiated by its high diagnostic accuracy, reaching a peak accuracy of 99.63%, which is a marked improvement over the existing models. It is optimized for real-time analysis, featuring a significant reduction in computational complexity and memory usage, distinguishing it from other high-performing but computationally intensive frameworks. Moreover, this article delineates the signal lengths and datasets, ensuring a comprehensive validation against established benchmarks. The system demonstrates 98.39%, 99.63%, and 99.00% of precision, recall, and ${F}1$ -scores, respectively. The work also elucidate the importance of sensor technology in enhancing diagnostic precision, detailing the role of sensor sensitivity and specificity in our system’s overall efficacy.