41 research outputs found
Evaluation of pH, Calcium Ion Release, and Dimensional Stability of an Experimental Silver Nanoparticle-Incorporated Calcium Silicate-Based Cement
No full textAn experimental calcium silicate-based root-end filling material incorporated with silver nanoparticles intended for use in periapical surgeries was developed with the purpose to overcome the drawbacks of existing materials and to satisfy the ideal requirements of root-end filling materials. This study was designed to evaluate the physicochemical properties, pH, calcium ion release, and dimensional stability of the experimental cement, and compare the results with commercially available ProRoot MTA (Dentsply). An independent sample test was used to analyze the data. Mean initial pH (immediately after mixing) of the experimental cement was 10.42 ± 0.04 which was higher than that of MTA. However, there was a significant increase in pH of MTA at 1 day, 2 days, and 7 days. Presence of calcium chloride favored the release of calcium ions which was significantly increased in the experimental group at 24 hours. At the end of 30 days, MTA showed a significant expansion when compared to the experimental cement p<0.001. In conclusion, the experimental nanoparticle-incorporated calcium silicate-based cement showed clinically acceptable physicochemical properties
Sentiment analysis of big data with intensity analysis by rule engine, 2015
No full textThe use of social media is an emerging way for the public to express their views on companies and other organizations. The success of these entities can depend on a positive presence on social media, leading to an increasing interest in understanding public opinion expressed there. This thesis presents a method for gathering and storing a large number of social media posts, analyzing the sentiments expressed, and further classifying the specific emotions conveyed. The social media platform Twitter was used as a source of millions of publicly viewable posts. The big data software tools Twitter4j, Apache Hadoop, and Apache Hive were used to gather and store these posts. These were then classified as communicating a positive, negative, or neutral sentiment through the technique of sentiment analysis, performed using the tool Lingpipe. To further identify the particular emotions expressed in the Tweet, a rule engine, specifically the DROOLS software, was used
Assessment of sealing efficacy, radiopacity, and surface topography of a bioinspired polymer for perforation repair
No full textBackground Root perforation repair presents a significant challenge in dentistry due to inherent limitations of existing materials. This study explored the potential of a novel polydopamine-based composite as a root repair material by evaluating its sealing efficacy, radiopacity, and surface topography. Methods Confocal microscopy assessed sealing ability, comparing the polydopamine-based composite to the gold standard, mineral trioxide aggregate (MTA). Radiopacity was evaluated using the aluminium step wedge technique conforming to ISO standards. Surface roughness analysis utilized atomic force microscopy (AFM), while field emission scanning electron microscopy (FESEM) visualized morphology. Results The polydopamine-based composite exhibited significantly superior sealing efficacy compared to MTA (P < 0.001). Radiopacity reached 3 mm aluminium equivalent, exceeding minimum clinical requirements. AFM analysis revealed a smooth surface topography, and FESEM confirmed successful composite synthesis. Conclusion This study demonstrates promising properties of the polydopamine-based composite for root perforation repair, including superior sealing efficacy, clinically relevant radiopacity, and smooth surface topography. Further investigation is warranted to assess its clinical viability and potential translation to endodontic practice
Awareness of Symptoms and Risk Factors of Ovarian Cancer Among Healthy Women Aged 18 Years and Above: A Narrative Review
No full textIntroduction: Ovarian cancer is often termed a silent killer due to its late presentation and poor prognosis. Awareness of its symptoms and risk factors is crucial for early healthcare intervention, which can significantly increase survival rates. This review article focuses on awareness of ovarian cancer, the risk factors, symptoms and factors influencing awareness among women.
Methods: Articles published in English between 2009 and 2024 were included, focusing on awareness of ovarian cancer symptoms and risk factors among healthy women aged 18 years and above in a community-based or facility-based study setting.
Results: Overall, awareness of ovarian cancer symptoms and risk factors was low among women. Common symptoms identified included pelvic or abdominal pain, increased abdominal size, fatigue, and unexplained weight loss. Key risk factors were a family history of breast/ovarian cancer, smoking, over 50 years, and ovarian cysts. The studies also highlighted the influence of demographic factors such as education, occupation, marital status, and age on awareness levels.
Conclusion: This review highlights the critical need for impactful interventions to raise awareness about ovarian cancer symptoms and risk factors globally. Increasing awareness is essential for early detection and better management, which can reduce the disease\u27s incidence. Enhanced education and health programs for women are crucial to significantly improve survival rates and overall health outcomes
A Comparative Study to Assess the Accuracy of the Lifting Line Code AWSM for Simulating Winglets on Wind Turbines
No full textWind turbine power output has grown massively over the past few decades, and this has been achieved in part by increasing the size of rotors. But the size of rotors is now limited by structural constraints as well as space constraints in wind farms. It is therefore important to use other innovative methods to increase wind turbine capacity without increasing size. One way to achieve this is by the use of winglets. Winglets increase power output by reducing tip effects, thereby producing a more efficient distribution of forces over the blade. The art of designing winglets is to find the best trade-off between the increase in profile drag of the winglet itself and the reduction of induced drag that the winglet provides. To do this, it is very important to fully understand the aerodynamics of winglets on wind turbine blades.High-fidelity methods like CFD are capable of producing accurate and detailed flow fields and are able to offer greater insight into the complex aerodynamics of winglets on rotors. However, this comes at great computational cost which might be infeasible in the design and optimization of winglets. More common and cheaper models like the BEM method are incapable of modelling winglets and other out-of-plane features. The Lifting Line Method is a middle ground that is capable of simulating winglets but is also comparatively inexpensive. The goal of this thesis is to study the performance of the Lifting Line method, in particular, ECN Aeromodule's AWSM Free-Wake Vortex Lifting Line code in simulating the case of winglets mounted on wind turbines. AWSM results are compared with results of normal and tangential forces and circulation distribution from a validated OpenFOAM model. The results show that over the outboard section of the blade and over the span of the winglet, AWSM performs well in predicting the performance of the blade-winglet configuration. This study shows that AWSM is a reliable tool for the design and optimization of winglets on wind turbine blades at a much lower cost than higher fidelity methods like CFD.Aerospace Engineerin
Chewing Detection on Low Power Embedded Systems
No full textAnalyzing food consumption patterns can provide valuable insights into the development of obesity and eating disorders. The detection and quantification of chewing strokes are essential to facilitate this analysis. One approach to food intake analysis involves evaluating chewing sounds generated during the eating process. These sounds were recorded by microphones placed to the user’s outerear canal. Aside from discovering the most accurate solution, the algorithms used must demonstrate sufficient efficiency to operate on low-power embedded ear-worn hardware. Three algorithms for automated chewing detection were evaluated with the help of two datasets. The first dataset consists of the food intake sounds from the consumption of three types of food. The second dataset consists of environmental noise. The data were manually labeled by recognizing mastication sounds’ visual and audio characteristics. Precision of over 80%was achieved by all algorithms in the dataset consisting of only chewing sounds. Finally, an efficient solution has been developed to distinguish between speech and chewing sounds.CSE3000 Research ProjectComputer Science and Engineerin
To identify a correlation between IMU and microphone data in earable computing with regards to chewing
No full textThis research explores the correlation between chewing activities and non-chewing activities using an Arduino microcontroller. Chewing samples are recorded by attaching the microcontroller to the back of the jaw, underneath the ear. The microcontroller collects data from a microphone capturing audio data of chewing sounds, as well as an Inertial Measurement Unit (IMU) collecting motion and orientation data of jaw movements.The collected data is processed using signal processing techniques, extracting relevant features from the microphone data, such as intensity, frequency content, and features related to acceleration, orientation, and jaw movement patterns from the IMU data. Statistical analysis, employing correlation metrics like Pearson correlation coefficient and Spearman's rank correlation coefficient, determines the correlation between the extracted features from the microphone and IMU data.Conclusions from the analysis indicate that pre-processing and feature extraction techniques are needed to establish meaningful correlations between the IMU and microphone data. The sliding window approach shows promising results, particularly in correlating the sum of energy from the audio with the sum of gyro data, specifically in the y- and z-axes. The accelerometer data does not exhibit significant correlations, but it can be useful as a threshold for detecting the start of chewing events based on zero crossings.Furthermore, the findings reveal that food texture and density play a larger role than anticipated in determining the correlation between chewing patterns and sensory data. The research outcomes contribute to various fields, including dentistry, nutrition, and human-computer interaction.CSE3000 Research ProjectComputer Science and Engineerin
SILENCING THE TRUTH - STUDY ON MEN
No full textThis article highlights the tactics used by vested interest stake holders and well-wishers of Feminists to silence the truth when they are exposed. This paper specifically demonstrates how SSRN (Social Science Research Network) handled the articles written and published and then on a flimsy accusation deleted them to hide the truth.
These study articles/reports/Papers were based on reviews of literature obtained from legal journals, medical journals, main stream media articles, NCRB and references from other study reports and published on SSRN from August 2020 onwards. Total of 14 articles were submitted and out of 14, only 7 were peer reviewed and accepted for publication by SSRN.
Total 14 articles were written by the Author and seven of them were peer reviewed and accepted for publication. Most of the papers were liked and downloaded many times making them the TOP 10 articles within 60 days. However, this instigated the feminists to take it down from the site at any cost
