• Computers and Concrete
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• v.5 no.3
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• pp.261-277
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• 2008

This paper presents methodologies for remaining life prediction of plain concrete structural components considering tension softening effect. Non-linear fracture mechanics principles (NLFM) have been used for crack growth analysis and remaining life prediction. Various tension softening models such as linear, bi-linear, tri-linear, exponential and power curve have been presented with appropriate expressions. A methodology to account for tension softening effects in the computation of SIF and remaining life prediction of concrete structural components has been presented. The tension softening effects has been represented by using any one of the models mentioned above. Numerical studies have been conducted on three point bending concrete structural component under constant amplitude loading. Remaining life has been predicted for different loading cases and for various tension softening models. The predicted values have been compared with the corresponding experimental observations. It is observed that the predicted life using bi-linear model and power curve model is in close agreement with the experimental values. Parametric studies on remaining life prediction have also been conducted by using modified bilinear model. A suitable value for constant of modified bilinear model is suggested based on parametric studies.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5599-5605
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• 2015

In oncology various imaging modalities play a crucial role in diagnosis, staging, restaging, treatment monitoring and follow up of various cancers. Stand-alone morphological imaging like computerized tomography (CT) and magnetic resonance imaging (MRI) provide a high magnitude of anatomical details about the tumor but are relatively dumb about tumor physiology. Stand-alone functional imaging like positron emission tomography (PET) and single photon emission tomography (SPECT) are rich in functional information but provide little insight into tumor morphology. Introduction of first hybrid modality PET/CT is the one of the most successful stories of current century which has revolutionized patient care in oncology due to its high diagnostic accuracy. Spurred on by this success, more hybrid imaging modalities like SPECT/CT and PET/MR were introduced. It is the time to explore the potential applications of the existing hybrid modalities, developing and implementing standardized imaging protocols and train users in nuclear medicine and radiology. In this review we discuss three existing hybrid modalities with emphasis on their technical aspects and clinical applications in oncology.

• Parasites, Hosts and Diseases
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• v.47 no.4
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• pp.377-380
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• 2009

Growing patterns of pediculocidal drug resistance towards head louse laid the foundation for research in exploring novel anti-lice agents from medicinal plants. In the present study, various extracts of Pongamia pinnata leaves were tested against the head louse Pediculus humanus capitis. A filter paper diffusion method was conducted for determining the potential pediculocidal and ovicidal activity of chloroform, petroleum ether, methanol, and water extracts of P. pinnata leaves. The findings revealed that petroleum ether extracts possess excellent anti-lice activity with values ranging between 50.3% and 100% where as chloroform and methanol extracts showed moderate pediculocidal effects. The chloroform and methanol extracts were also successful in inhibiting nymph emergence and the petroleum ether extract was the most effective with a complete inhibition of emergence. Water extract was devoid of both pediculocidal and ovicidal activities. All the results were well comparable with benzoyl benzoate (25% w/v). These results showed the prospect of using P. pinnata leave extracts against P. humanus capitis in difficult situations of emergence of resistance to synthetic anti-lice agents.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.204-210
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• 2019

Mobile Crowd Computing is one of the most efficient and effective way to collect the Electronic health records and they are very intelligent in processing them. Mobile Crowd Computing can handle, analyze and process the huge volumes of Electronic Health Records (EHR) from the high-performance Cloud Environment. Electronic Health Records are very sensitive, so they need to be secured, authenticated and processed efficiently. However, security, privacy and authentication of Electronic health records(EHR) and Patient health records(PHR) in the Mobile Crowd Computing Environment have become a critical issue that restricts many healthcare services from using Crowd Computing services .Our proposed Efficient Multi-layer Encryption Framework(MLEF) applies a set of multiple security Algorithms to provide access control over integrity, confidentiality, privacy and authentication with cost efficient to the Electronic health records(HER)and Patient health records(PHR). Our system provides the efficient way to create an environment that is capable of capturing, storing, searching, sharing, analyzing and authenticating electronic healthcare records efficiently to provide right intervention to the right patient at the right time in the Mobile Crowd Computing Environment.

• Journal of IKEEE
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• v.22 no.4
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• pp.1031-1035
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• 2018

A compensation algorithm targeting for torque development from a SPMSM including a low speed operation is presented in this paper. As known, PM flux linkage in SPMSM is varied by temperature. Maximum Torque per Ampere (MTPA) uses the calculated PM flux linkage, and torque error occurs due to change of PM flux linkage. In the manuscript, estimated PM flux linkage is obtained using a stator flux observer. The torque error is corrected using the estimated PM flux linkage. The proposed algorithm is implemented and verified in simulation and experiment.

• Journal of IKEEE
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• v.26 no.4
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• pp.653-658
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• 2022

This paper presents a Maximum Torque per Ampere (MTPA) control algorithm for an interior permanent magnet synchronous motor (IPMSM) drive considering the permanent magnet (PM) flux linkage variations due to PM temperature variation. PM flux linkage are estimated in real time via a Gopinath style stator flux linkage observer and a torque error correction factor is calculated from the estimated PM flux linkage. A 2-dimensional (2D) MTPA look-up table (LUT) is developed to achieve the MTPA trajectory reflecting PM flux linkage variation for compensating torque error occurred by parameter variation. The proposed IPMSM control algorithm is verified through simulations.

• Mass Spectrometry Letters
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• v.14 no.1
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• pp.9-23
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• 2023

Plants are able to produce a large number of diverse bioactive compounds. Solvent extraction is used for isolation of plant metabolites. The extract yield for plant metabolite extraction strongly depends on the nature of solvent. A review showed the methanol can yield more bioactive compounds. Drying of the sample material is also important for the extraction of plant material. The present study was carried out to analyze the phytocomponents of 5 different latex producing plants. The plants like Calotropis gigantea, Carica papaya, Nerium oleander, Ficus benghalensis and Plumeria alba leaves and latex. The GC-MS analysis of the metabolites revealed phytocomponents. Calotropis gigantea leaves showed 14 compounds and latex produced 5 compounds out of this 4,4,6A,6B,8A,11,11,14B-Octamethyl-1,4,4A,5,6,6A,6B,7,8,8A,9,10,11,12,12A,14,14A,14B-Octadeca-hydro-2 and 2R- Acetoxymethyl-1,3,3-trimethyl-4T-(3-Methyl-2-Buten-1-Yl)-1T-Cyclohexanol compound was present in both latex and leaf extraction. Beta. -carotene compound was present in both latex and leaf of Carica papaya. It was observed that Ficus benghalensis contained 2R-Acetoxymethyl-1,3,3-trimethyl-4T-(3-Methyl-2-Buten-1-Yl)-1T-Cyclohexanol was same in latex and leaf extraction.

• International Journal of Industrial Entomology and Biomaterials
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• v.23 no.2
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• pp.187-191
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• 2011

An attempt was made to assess the potentiality of bivoltine double hybrids under simulated conditions of farmers to identify the suitable bivoltine double hybrid combination. Four bivoltine double hybrids developed at Central Sericultural Research and Training Institute (CSRTI), Mysore along with popular single hybrid, $CSR2{\times}CSR4$ as control was assessed for economic traits. The rearing results showed significant improvement of 20-24% in fecundity of the double hybrids studied over single hybrid. Among the double hybrids, $[D7{\times}S5]{\times}[D13{\times}S1]$ recorded significantly higher survival (89.58 %), cocoon yield (76.328 kg/ 50,000 eggs), cocoon price (Rs. 180.87/kg) and lower cocoon leaf ratio of 1: 21.80. The performance of the reeling traits were also found significantly superior in $[D7{\times}S5]{\times}[D13{\times}S1]$ with higher filament length (1100 m), reelability (88%), raw silk (18.55%) and neatness (92 points) compared to $CSR2{\times}CSR4$ and other double hybrids evaluated. Besides, the cocoons of $[D7{\times}S5]{\times}[D13{\times}S1]$ exhibit uniformity in size with a standard deviation of < 8. Overall data indicated the superiority of $[D7{\times}S5]{\times}[D13{\times}S1]$ compared to the other hybrids evaluated and it has profound influence in expressing the full potentiality in the field.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.167-176
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• 2020

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient's feedback with their information regarding their treatment. Patient's preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient's feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor's in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients' health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.