Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

BACKGROUND: The Department of Veterans Affairs Health Care System (VA) is the largest integrated single payer system in the United States. To date, there has been no systematic measurement of health status in the VA. The Veterans Health Study has developed methods to assess patient-based health status in ambulatory populations. OBJECTIVES: To describe the health status of veterans and examine the relationships between their health-related quality of life, age, comorbidity, and socioeconomic and service-connected disability status. METHODS: Participants in the Veterans Health Study, a 2-year longitudinal study, were recruited from a representative sample of patients receiving ambulatory care at 4 VA facilities in the New England region. The Veterans Health Study patients received questionnaires of health status, including the Medical Outcomes Study Short Form 36-Item Health Survey; and a health examination, clinical assessments, and medical history taking. Sixteen hundred sixty-seven patients for whom we conducted baseline assessments are described. RESULTS: The VA outpatients had poor health status scores across all measures of the Medical Outcomes Study Short Form 36-Item Health Survey compared with scores in non-VA populations (at least 50% of 1 SD worse). Striking differences also were found with the sample stratified by age group (20-49 years, 50-64 years, and 65-90 years). For 7 of the 8 scales (role limitations due to physical problems, bodily pain, general health perceptions, vitality, social functioning, role limitations due to emotional problems, and mental health), scores were considerably lower among the younger patients; for the eighth scale (physical function), scores of the young veterans (aged 20-49 years) were almost comparable with the levels in the old veterans (>65 years). The mental health scores of young veterans were substantially worse than all other age groups (P<.001) and scores of screening measures for depression were significantly higher in the youngest age group (51%) compared with the oldest age groups (33% and 16%) (P<.001). CONCLUSIONS: The VA outpatients have substantially worse health status than non-VA populations. Mental health differences between the young and old veterans who use the VA health care system are sharply contrasting; the young veterans are sicker, suggesting substantially higher resource needs. Mental health differences may explain much of the worse health-related quality of life in young veterans. As health care systems continue to undergo a radical transformation, the Department of Veterans Affairs should focus on the provision of mental health services for its younger veteran.

Alzheimer's Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one's ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classifying cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconsistent due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) is utilized to create a framework that can be used to detect specific Alzheimer's disease characteristics from MRI images. By considering four stages of dementia and conducting a particular diagnosis, the proposed model generates high-resolution disease probability maps from the local brain structure to a multilayer perceptron and provides accurate, intuitive visualizations of individual Alzheimer's disease risk. To avoid the problem of class imbalance, the samples should be evenly distributed among the classes. The obtained MRI image dataset from Kaggle has a major class imbalance problem. A DEMentia NETwork (DEMNET) is proposed to detect the dementia stages from MRI. The DEMNET achieves an accuracy of 95.23%, Area Under Curve (AUC) of 97% and Cohen's Kappa value of 0.93 from the Kaggle dataset, which is superior to existing methods. We also used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to predict AD classes in order to assess the efficacy of the proposed model.

The Singanallur Sub-basin is one of the major waterways and it supplies water to the Coimbatore city. Currently, it is vulnerable to pollution due to an increase of unplanned urban developments, industrial, and agricultural activities that compromise both the quality and quantity. In the present study three major hydrochemical facies were identified (mixed Ca-Mg-Cl, Ca-Cl, and Ca-HCO3). Irrigation suitability indexes are specifies that the groundwater in the areas has very high salinity hazard and low to medium alkali hazard. The mechanism controlling groundwater chemistry originally regulated by the evaporation process is dominated by reason of arid condition and anthropogenic activities existing throughout the region. The multivariate statistical analysis (Correlation analysis (CA), principal component analysis (PCA) and Hierarchical cluster analysis (HCA)) indicates, most of the variations are elucidated by the anthropogenic pollutant predominantly due to population growth, industrial effluents, and irrigation water return flow. This study demonstrates enhanced information of evolution of groundwater quality by integrating hydrochemical data and multivariate statistical methods are used to understand the factors influencing contamination due to natural and anthropogenic impacts.

The project proposes an efficient implementation for IoT (Internet of Things) used for monitoring and controlling the home appliances via World Wide Web. Home automation system uses the portable devices as a user interface. They can communicate with home automation network through an Internet gateway, by means of low power communication protocols like Zigbee, Wi-Fi etc. This project aims at controlling home appliances via Smartphone using Wi-Fi as communication protocol and raspberry pi as server system. The user here will move directly with the system through a web-based interface over the web, whereas home appliances like lights, fan and door lock are remotely controlled through easy website. An extra feature that enhances the facet of protection from fireplace accidents is its capability of sleuthing the smoke in order that within the event of any fireplace, associates an alerting message and an image is sent to Smartphone. The server will be interfaced with relay hardware circuits that control the appliances running at home. The communication with server allows the user to select the appropriate device. The communication with server permits the user to pick out the acceptable device. The server communicates with the corresponding relays. If the web affiliation is down or the server isn't up, the embedded system board still will manage and operate the appliances domestically. By this we provide a climbable and price effective Home Automation system.

Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India’s total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers’ reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS).

In the past few decades, the development of chemosensors for neurotransmitters has emerged as a research area of significant importance, which attracted a tremendous amount of attention due to its high sensitivity and rapid response. This current review focuses on various neurotransmitter detection based on fluorescent or colorimetric spectrophotometry published for the last 12 years, covering biogenic amines (dopamine, epinephrine, norepinephrine, serotonin, histamine and acetylcholine), amino acids (glutamate, aspartate, GABA, glycine and tyrosine), and adenosine.

Magical spell have the ability to turn everything touched into gold, in real time scenario one such spell is “Nanotechnology” which has the mysterical power to revolutionize every field touched by it. Nanotechnology is now invading the food industry and establishing great potential.Nanotechnology applications in food industry include: encapsulation and delivery of substances in targeted sites, increasing the flavor,introducing antibacterial nanoparticles into food, enhancement of shelf life, sensing contamination, improved food storage, tracking, tracing and brand protection. Nano food processing and products can change the color, flavor, or sensory characteristics; they also change the nutritional functionality, removes chemicals or pathogens from food. Nano food packaging materials may extend food life due to high barrier packaging, improve food safety, alert consumers that food is contaminated or spoiled, repair tears in packaging, and even release preservatives to extend the life of the food in the package. Nanobarcodes are used for safety labeling and monitor distribution of food products. Nanosupplements can be easily incorporated by encapsulation techniques for nutritional and drug delivery systems effectively. And as health plays a major role in food the disadvantages of the technology is to be concerned.

The present work reports a simple, cost-effective, and ecofriendly method for the synthesis of silver nanoparticles (AgNPs) using Chrysanthemum indicum and its antibacterial and cytotoxic effects. The formation of AgNPs was confirmed by color change, and it was further characterized by ultraviolet-visible spectroscopy (435 nm). The phytochemical screening of C. indicum revealed the presence of flavonoids, terpenoids, and glycosides, suggesting that these compounds act as reducing and stabilizing agents. The crystalline nature of the synthesized particles was confirmed by X-ray diffraction, as they exhibited face-centered cubic symmetry. The size and morphology of the particles were characterized by transmission electron microscopy, which showed spherical shapes and sizes that ranged between 37.71-71.99 nm. Energy-dispersive X-ray spectroscopy documented the presence of silver. The antimicrobial effect of the synthesized AgNPs revealed a significant effect against the bacteria Klebsiella pneumonia, Escherichia coli, and Pseudomonas aeruginosa. Additionally, cytotoxic assays showed no toxicity of AgNPs toward 3T3 mouse embryo fibroblast cells (25 μg/mL); hence, these particles were safe to use.

The concentration of air pollutants in ambient air is governed by the meteorological parameters such as atmospheric wind speed, wind direction, relative humidity, and temperature. This study analyses the influence of temperature and relative humidity on ambient SO 2 , NO x , RSPM, and SPM concentrations at North Chennai, a coastal city in India, during monsoon, post-monsoon, summer, and pre-monsoon seasons for 2010-11 using regression analysis. The results of the study show that both SO 2 and NO x were negatively correlated in summer ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mi>r</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>0.25</mml:mn></mml:mrow></mml:math> for SO 2 and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msup><mml:mi>r</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>0.15</mml:mn></mml:mrow></mml:math> for NO x ) and moderately and positively correlated ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msup><mml:mi>r</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>0.32</mml:mn></mml:mrow></mml:math> for SO 2 and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:msup><mml:mi>r</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>0.51</mml:mn></mml:mrow></mml:math> for NO x ) during post-monsoon season with temperature. RSPM and SPM had positive correlation with temperature in all the seasons except post-monsoon one. These findings indicate that the influence of temperature on gaseous pollutant (SO 2 &amp; NO x ) is much more effective in summer than other seasons, due to higher temperature range, but in case of particulate, the correlation was found contradictory. The very weak to moderate correlations existing between the temperature and ambient pollutant concentration during all seasons indicate the influence of inconstant thermal variation in the coastal region. Statistically significant negative correlations were found between humidity and particulates (RSPM and SPM) in all the four seasons, but level of correlation was found moderate only during monsoon ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mrow><mml:msup><mml:mi>r</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>0.51</mml:mn></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mrow><mml:msup><mml:mi>r</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>0.41</mml:mn></mml:mrow></mml:math> ) in comparison with other three seasons and no significant correlation was found between humidity and SO 2 , NO x in all the seasons. It is suggested from this study that the influence of humidity is effective on subsiding particulates in the coastal region.

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

In this research, we proposed a novel 14-layered deep convolutional neural network (14-DCNN) to detect plant leaf diseases using leaf images. A new dataset was created using various open datasets. Data augmentation techniques were used to balance the individual class sizes of the dataset. Three image augmentation techniques were used: basic image manipulation (BIM), deep convolutional generative adversarial network (DCGAN) and neural style transfer (NST). The dataset consists of 147,500 images of 58 different healthy and diseased plant leaf classes and one no-leaf class. The proposed DCNN model was trained in the multi-graphics processing units (MGPUs) environment for 1000 epochs. The random search with the coarse-to-fine searching technique was used to select the most suitable hyperparameter values to improve the training performance of the proposed DCNN model. On the 8850 test images, the proposed DCNN model achieved 99.9655% overall classification accuracy, 99.7999% weighted average precision, 99.7966% weighted average recall, and 99.7968% weighted average F1 score. Additionally, the overall performance of the proposed DCNN model was better than the existing transfer learning approaches.

The growing interest in renewable energy and the falling prices of solar panels place solar electricity in a favourable position for adoption. However, the high-rate adoption of intermittent renewable energy introduces challenges and the potential to create power instability between the available power generation and the load demand. Hence, accurate solar Photovoltaic (PV) power forecasting is essential to maintain system reliability and maximize renewable energy integration. The current solar PV power forecasting approaches are an essential tool to maintain system reliability and maximize renewable energy integration. This paper presents a comprehensive and comparative review of existing Machine Learning (ML) based approaches used in PV power forecasting, focusing on short-term horizons. We provide an overview of factors affecting solar PV power forecasting and an overview of existing PV power forecasting methods in the literature, with a specific focus on ML-based models. To further enhance the comparison and provide more insights into the advancement in the area, we simulate the performance of different ML methods used in solar PV power forecasting and, finally, a discussion on the results of the work.

Fungal bioremediation represents a promising and sustainable approach to addressing environmental pollution by exploiting the natural metabolic capabilities of fungi to degrade and detoxify a wide array of pollutants. This review provides a comprehensive overview of the mechanisms, applications, and future perspectives of fungal bioremediation. Fungi are uniquely equipped with an extensive arsenal of enzymes, including laccases, peroxidases, and hydrolases, which facilitate the breakdown of complex organic compounds, heavy metals, and xenobiotics into less harmful substances. The versatility of fungi enables their application across various environmental contexts, including soil, water, and air remediation. The efficacy of fungal bioremediation is demonstrated in its ability to degrade persistent organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and petroleum hydrocarbons, as well as to immobilize and transform heavy metals through biosorption and bioaccumulation. This review also discusses the challenges and limitations associated with fungal bioremediation, such as the need for optimized environmental conditions and potential ecological impacts. Future research directions are highlighted, including the integration of omics technologies for the elucidation of fungal metabolic pathways and the development of biotechnological innovations to scale up fungal bioremediation processes. This review underscores the critical role of fungi in environmental remediation and emphasizes the need for continued research and technological advancements to harness their full potential in addressing global environmental challenges.

Abstract A series of novel Fe-Cd co-doped ZnO nanoparticle based photocatalysts are successfully synthesized by sol-gel route and characterized using scanning electron microscopy (SEM), energy dispersive X-ray emission (EDX), transmission electron microscopy (TEM), X-ray diffraction (XRD), UV-Vis spectroscopy, X-ray photoelectron spectroscopy (XPS), and Brunauer-Emmett-Teller (BET) techniques. The photocatalytic activity of ZnO nanoparticles doped with various atomic weight fraction of Fe and Cd has been investigated under visible light irradiation using the Methylene Blue and Rhodamine B dye in aqueous solution. The FeCd (2%):ZnO (ZFC-1) exhibit the highest photocatalytic activity in terms of rate constant as K MB = 0.01153 min −1 and K RhB = 0.00916 min −1 ). Further, the re-usability of the ZFC-1 photocatalyst is studied which confirms that it can be reused up to five times with nearly negligible loss of the photocatalytic efficiency. Moreover, the role of photoactive species investigated using a radical scavenger technique. The present investigations show that the doping concentration plays significant role in photocatalytic performance. The visible light absorption shown by Fe-Cd co-doped ZnO nanoparticles is much higher than that of undoped body probably due to co-doping, and the charge carrier recombination is decreased effectively which yields a higher photocatalytic performance. The mechanism for the enhancement of photocatalytic activity under visible light irradiation is also proposed.

This paper describes an innovative protocol for full field mapping of a large civil structures involving effective use of Unmanned Aerial Vehicles (UAVs) to enable real time structural health monitoring. The proposed frameworks integrates UAVs, image processing and data acquisition procedures for crack detection and assessment of surface degradation. A novel approach is proposed combining hat transform and HSV thresholding technique for crack detection. In addition, grey scale thresholding is employed for the measurement of surface degradations. A Demonstration multi-rotor UAV model is developed to carry out full field inspection of civil structures and real time testing is performed in our large university campus. In order to provide sophisticated monitoring platform for users, a MATLAB Graphical User Interface (GUI) is developed to analyse real time as well as acquired images and the results are validated successfully. The obtained results confirms that, the envisaged approach is the foundation for cost effective and time compressing solution for monitoring of large civil structures.

Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process parameters, the appropriate tweaking of which is necessary to obtain economical and suitable results. However, the costly and time-consuming nature of the NTMs makes it a tedious and expensive task to manually investigate the appropriate process parameters. The NTM process parameters and responses are often not linearly related and thus, conventional statistical tools might not be enough to derive functional knowledge. Thus, in this paper, three popular machine learning (ML) methods (viz. linear regression, random forest regression and AdaBoost regression) are employed to develop predictive models for NTM processes. By considering two high-fidelity datasets from the literature on electro-discharge machining and wire electro-discharge machining, case studies are shown in the paper for the effectiveness of the ML methods. Linear regression is observed to be insufficient in accurately mapping the complex relationship between the process parameters and responses. Both random forest regression and AdaBoost regression are found to be suitable for predictive modelling of NTMs. However, AdaBoost regression is recommended as it is found to be insensitive to the number of regressors and thus is more readily deployable.

Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as physical, behavioral, and mental changes. In the latest days, physiological activity has been used to study emotional reactions. This study describes the electroencephalography (EEG) signals, the brain wave pattern, and emotion analysis all of these are interrelated and based on the consequences of human behavior and Post-Traumatic Stress Disorder (PTSD). Post-traumatic stress disorder effects for long-term illness are associated with considerable suffering, impairment, and social/emotional impairment. PTSD is connected to subcortical responses to injury memories, thoughts, and emotions and alterations in brain circuitry. Predominantly EEG signals are the way of examining the electrical potential of the human feelings cum expression for every changing phenomenon that the individual faces. When going through literature there are some lacunae while analyzing emotions. There exist some reliability issues and also masking of real emotional behavior by the victims. Keeping this research gap and hindrance faced by the previous researchers the present study aims to fulfill the requirements, the efforts can be made to overcome this problem, and the proposed automated CNN-LSTM with ResNet-152 algorithm. Compared with the existing techniques, the proposed techniques achieved a higher level of accuracy of 98% by applying the hybrid deep learning algorithm.

The existence of the memristor, as a fourth fundamental circuit element, by researchers at Hewlett Packard (HP) labs in 2008, has attracted much interest since then. This occurs because the memristor opens up new functionalities in electronics and it has led to the interpretation of phenomena not only in electronic devices but also in biological systems. Furthermore, many research teams work on projects, which use memristors in neuromorphic devices to simulate learning, adaptive and spontaneous behavior while other teams on systems, which attempt to simulate the behavior of biological synapses. In this paper, the latest achievements and applications of this newly development circuit element are presented. Also, the basic features of neuromorphic circuits, in which the memristor can be used as an electrical synapse, are studied. In this direction, a flux-controlled memristor model is adopted for using as a coupling element between coupled electronic circuits, which simulate the behavior of neuron-cells. For this reason, the circuits which are chosen realize the systems of differential equations that simulate the well-known Hindmarsh-Rose and FitzHugh-Nagumo neuron models. Finally, the simulation results of the use of a memristor as an electric synapse present the effectiveness of the proposed method and many interesting dynamic phenomena concerning the behavior of coupled neuron-cells.

Abstract First, this paper announces a seven-term novel 3-D conservative chaotic system with four quadratic nonlinearities. The conservative chaotic systems are characterized by the important property that they are volume conserving. The phase portraits of the novel conservative chaotic system are displayed and the mathematical properties are discussed. An important property of the proposed novel chaotic system is that it has no equilibrium point. Hence, it displays hidden chaotic attractors. The Lyapunov exponents of the novel conservative chaotic system are obtained as L 1 = 0.0395,L 2 = 0 and L 3 = −0.0395. The Kaplan-Yorke dimension of the novel conservative chaotic system is D KY =3. Next, an adaptive controller is designed to globally stabilize the novel conservative chaotic system with unknown parameters. Moreover, an adaptive controller is also designed to achieve global chaos synchronization of the identical conservative chaotic systems with unknown parameters. MATLAB simulations have been depicted to illustrate the phase portraits of the novel conservative chaotic system and also the adaptive control results.

High-rise in the air pollution levels due to combustion of the fossil fuel gives us the opportunity to discover environmentally friendly and clean fuels for the engines. Biodiesel originated from cashew nut shell oil through transesterification process can be blended or used as a neat fuel in unmodified engines. This work investigates the effect of alumina nanoparticles on emission and performance characteristics of cashew nut shell biodiesel. Neat cashew nut shell biodiesel prepared by conventional transesterification is termed as BD100 and biodiesel prepared by modified transesterification with the addition of alumina nanoparticles is termed as BD100A. Experimental results on unmodified diesel engine revealed that emission parameters such as CO, HC, NOx, and smoke were decreased by 5.3%, 7.4%, 10.23%, and 16.1% for BD100% and 8.8%, 10.1%, 12.4%, and 18.4% for B100A, respectively, compared to diesel fuel. At full load conditions, compared to diesel fuel, the BTE dropped by 1.1% and 2.3%, whereas the BSFC increased by 3.8% and 5.1% for B100A and B100 correspondingly.