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Whole Body Vibration Impact Assessment on Dumper Operator Using Computational Learning Technique
Kaviraj Ramar,L. A. Kumaraswamidhas,P. S. Balaji,A. Agasthian 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.24 No.2
In the mining industry, the dumper vehicle plays a vital role in material handling tasks. During the various operations, the dumper operators are subjected to whole-body vibrations (WBV) which also affects their physiological factors. The present study investigates the dumper operator discomfort during various dumper operations such as material-loading (ML), loaded-travel (LT), material-unloading (MU) and unloaded-travel (UT). As per ISO 2631:1 specified, limit during the ML and MU task, the measured crest factor value is greater than the recommended values. However, the measured aw(8) and VDV(8) magnitude are within the specified limit. In the LT, it is observed that the VDV is within the limit, although the measured value is 1.39 times greater than MU task. The maximum WBV is observed during the UT, and the measured value of VDV falls above the specified limit, and experience a greater amplification of source vibration at 1.6 Hz. Moreover, the demand for high number of operation cycle increases the risk of neck pain and back pain among the study population. Operator physiological stress under WBV exposure showed a significant increase in heart rate by 2.04 bpm. Whereas, no significant influence on the increase in blood pressure (SYS/DIA: 1.56/0.76 mmHg) and a decrease in oxygen saturation level (SpO2) by 1% was observed. Therefore, to optimize the performance of seat design under different operation cycle using computational learning technique support vector machine classifier with quadratic preset model provides a best accuracy of 98.5% over the other machine learning algorithm. The study reveals that the prolonged sitting and constant experience of WBV could increase the job work stress, the computational learning technique warranted to prevent the operator from high WBV exposures.
Dinesh Devadoss,Manikandan Ramar,Arulvasu Chinnasamy 대한약학회 2018 Archives of Pharmacal Research Vol.41 No.3
The aim of present study was to elucidate antiinitiatingefficacy of galangin against benzo(a)pyrene(B(a)P)-induced lung carcinogenesis in male Swiss albinomice. Therefore, the activities of xenobiotic metabolicenzymes such as phase I and II were examined in lung aswell as liver tissues (to compare the effects between targetand non-target organs). Besides, the activities/levels oftissue marker enzymes, antioxidants, lipid peroxidation(LPO), cytochrome P450 1A1 (CYP1A1) expressions andhistological observation of lungs were also analyzed. B(a)P(50 mg/kg body weight) was administered to male Swissalbino mice (20–25 g) to experimentally induce lung cancer. B(a)P-induced animals showed increased activity ofphase I (Cytochrome P450, Cytochrome b5, NADPHCytochrome P450 redcutase and NADH Cytochrome b5reductase) drug metabolic enzymes, LPO levels, tissuemarker enzymes and decreased activity of phase II metabolicenzymes (glutathione-S-transferase, DT-diaphoraseand UDP-glucuronyl transferase) as well as antioxidantlevels. Histological examination of lungs revealed severealveolar and bronchiolar damages in B(a)P-induced mice. Immunohistochemical and western blot analysis ofCYP1A1 increased significantly in lung tissues of B(a)Pinducedanimals. Treatment with galangin (20 mg/kg bodyweight) efficiently counteracted all the above anomaliesand restored cellular homeostasis. Our results demonstratethat galangin can modify xenobiotic enzymes in murinemodel of pulmonary tumorigenesis.
Raj, Kathamuthu Gokul,Sambantham, Shanmugam,Manikanadan, Ramar,Arulvasu, Chinnansamy,Pandi, Mohan Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.16
Purpose: The present study concerns molecular mechanisms involved in induction of apoptosis by a fungal taxol extracted from the fungus Cladosporium oxysporum in T47D human breast cancer cells. Materials and Methods: Apoptosis-induced by the fungal taxol was assessed by MTT assay, nuclear staining, DNA fragmentation, flow cytometry and pro- as well as anti-apoptotic protein expression by Western blotting. Results: Our results showed inhibition of T47D cell proliferation with an $IC_{50}$ value of $2.5{\mu}M/ml$ after 24 h incubation. It was suggested that the extract may exert its anti-proliferative effect on human breast cancer cell line by suppressing growth, arresting through the cell cycle, increase in DNA fragmentation as well as down-regulation of the expression of NF-${\kappa}B$, Bcl-2 and Bcl-XL and up-regulation of pro-apoptotic proteins like Bax, cyt-C and caspase-3. Conclusions: We propose that the fungal taxol contributes to growth inhibition in the human breast cancer cell through apoptosis induction via a mitochondrial mediated pathway, with possible potential as an anticancer therapeutic agent.
Multivariate Congestion Prediction using Stacked LSTM Autoencoder based Bidirectional LSTM Model
Vijayalakshmi B,Thanga Ramya S,Ramar K 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.1
In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.