• Mass Spectrometry Letters
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• v.13 no.3
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• pp.49-57
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• 2022

During electrospray ionization mass spectrometry (ESI-MS) analysis of proteins, the addition of supercharging agents allows for adjusting the maximal charge state, affecting the charge state distribution, and increases the number of ions reaching the detector thus, improving signal detection. We postulate that in di-substituted arene isomers, molecules with higher polarizability values should generate greater interactions and hence elicit higher signal intensities. Polarizability is an electronic parameter which has been demonstrated to predict many chemical interactions. Many properties can be predicted based on charge polarization. Molecular polarizability is a vital descriptor for explaining intermolecular interactions. We employed DFT (density functional/Hartree-Fock hybrid model, B3LYP)-derived descriptors and computed molecular polarizability for ten disubstituted arene reagents, each set made up of three (ortho, meta, para) isomers, with reported use as supercharging reagents during ESI experiments. The atomic electronic inputs were ionization potential (IP), electron affinity (EA), electronegativity (𝛘), hardness (η), chemical potential (µ), and dipole moment (D). We determined that the para isomers showed the highest polarizability values in nine of the ten sets. There was no difference between the ortho and meta isomers. Polarizability also increased with increasing complexity of the substituents on the benzene ring. Polarizability correlated positively with IP, EA, 𝛘, η, and D but correlated negatively with chemical potential. This DFT study predicts that the para isomers of di-substituted arene isomers should elicit the strongest ESI responses. An experimental comparison of the three isomers, especially of larger supercharging molecules, could be carried out to establish this premise.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.300-308
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• 2018

Objectives: Prompt detection is a cornerstone in the control and prevention of infectious diseases. The Integrated Disease Surveillance Project of India identifies outbreaks, but it does not exactly predict outbreaks. This study was conducted to assess temporal correlation between Google Trends and Integrated Disease Surveillance Programme (IDSP) data and to determine the feasibility of using Google Trends for the prediction of outbreaks or epidemics. Methods: The Google search queries related to malaria, dengue fever, chikungunya, and enteric fever for Chandigarh union territory and Haryana state of India in 2016 were extracted and compared with presumptive form data of the IDSP. Spearman correlation and scatter plots were used to depict the statistical relationship between the two datasets. Time trend plots were constructed to assess the correlation between Google search trends and disease notification under the IDSP. Results: Temporal correlation was observed between the IDSP reporting and Google search trends. Time series analysis of the Google Trends showed strong correlation with the IDSP data with a lag of -2 to -3 weeks for chikungunya and dengue fever in Chandigarh (r > 0.80) and Haryana (r > 0.70). Malaria and enteric fever showed a lag period of -2 to -3 weeks with moderate correlation. Conclusions: Similar results were obtained when applying the results of previous studies to specific diseases, and it is considered that many other diseases should be studied at the national and sub-national levels.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.538-541
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• 2009

Increasing of number of people who suffered from long term chronic diseases which required frequent daily health monitoring and body check up in conjunction with the trendy uses of mobile phones and Personal Digital Assistants (PDAs) in various ubiquitous computing had make portable healthcare system a well known application today. A mobile phone based portable healthcare monitoring system with multiple vital signals monitoring ability at real time in WSN and CDMA network is developed. This system carries out real time monitoring and local data analysis process in the mobile phone. Any detection of abnormal health condition and diagnosis at earlier stage will reduce the risk of patient's life. As an extension to the existing model, a pre-diagnosis management system (PDMS) is designed to minimize the time consuming in pre-diagnosis process in the hospital or healthcare center. An alert is sent to the web server at the healthcare center when the patient detects his health is at critical state where the immediate diagnosis is needed. Preparation of diagnosis equipments and arrangement of doctor and nurses at the hospital side can be done earlier before the arrival of patient at the hospital with the help of PDMS. An efficient pre-diagnosis management increases the chances of diseases recovery rate as well.

• Parasites, Hosts and Diseases
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• v.53 no.4
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• pp.493-496
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• 2015

Toxoplasmosis, caused by Toxoplasma gondii, is a parasitic zoonosis with worldwide distribution. The present study investigated the prevalence of T. gondii in dogs in Zhanjiang city, southern China, using both serological and molecular detection. A total of 364 serum samples and 432 liver tissue samples were collected from the slaughter house between December 2012 and January 2013 and were examined for T. gondii IgG antibody by ELISA and T. gondii DNA by semi-nested PCR based on B1 gene, respectively. The overall seroprevalence of T. gondii IgG antibody was 51.9%, and T. gondii DNA was detected in 37 of 432 (8.6%) liver tissue samples. These positive DNA samples were analyzed by PCR-RFLP at 3'- and 5'-SAG2. Only 8 samples gave the PCR-RFLP data, and they were all classified as type I, which may suggest that the T. gondii isolates from dogs in Zhanjiang city may represent type I or type I variant. This study revealed the high prevalence of T. gondii infection in dogs in Zhanjiang city, southern China. Integrated measures should be taken to prevent and control toxoplasmosis in dogs in this area for public health concern.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.199-208
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• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.

• Environmental Analysis Health and Toxicology
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• v.29
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• pp.16.1-16.9
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• 2014

Objectives Rapid increase in engineered nanoparticles (ENPs) in many goods has raised significant concern about their environmental safety. Proper methodologies are therefore needed to conduct toxicity and exposure assessment of nanoparticles in the environment. This study reviews several analytical techniques for nanoparticles and summarizes their principles, advantages and disadvantages, reviews the state of the art, and offers the perspectives of nanometrology in relation to ENP studies. Methods Nanometrology is divided into five techniques with regard to the instrumental principle: microscopy, light scattering, spectroscopy, separation, and single particle inductively coupled plasma-mass spectrometry. Results Each analytical method has its own drawbacks, such as detection limit, ability to quantify or qualify ENPs, and matrix effects. More than two different analytical methods should be used to better characterize ENPs. Conclusions In characterizing ENPs, the researchers should understand the nanometrology and its demerits, as well as its merits, to properly interpret their experimental results. Challenges lie in the nanometrology and pretreatment of ENPs from various matrices; in the extraction without dissolution or aggregation, and concentration of ENPs to satisfy the instrumental detection limit.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.4
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• pp.535-550
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• 2016

Securing the safety and the reliability of liquid-propellant rocket engines (LREs) for space vehicles is indispensable as engines consist of many complex components and operate under extremely high energy-dense conditions. Thus, health monitoring has become a mandatory requirement, especially for the reusable LREs that are currently being developed. In this context, a dynamic simulation program based on MATLAB/Simulink was developed in the current research on the Space Shuttle Main Engine (SSME), a partly reusable engine. Then, a series of fault simulations using this program was conducted: at a steady state operating condition (104% Rated Propulsion Level), various simulated fault conditions were artificially injected into the simulation models for the five major valves, the pumps, and the turbines of the SSME. The consequent effects due to each fault were analyzed based on the time responses of the major parameters of the engine. It is believed that this research topic is an essential pre-step for the development of fault detection and diagnosis algorithms for reusable engines in the future.

• Smart Structures and Systems
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• v.21 no.1
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• pp.1-14
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• 2018

A method is proposed to detect structural damages in the presence of damping using noisy data. This method uses Frequency Response Function (FRF) and Mode-Shapes as the input parameters for a system of Couple Sparse Coding (CSC) to study the healthy state of the structure. To obtain appropriate patterns of FRF for CSC training, Principal Component Analysis (PCA) technique is adopted to reduce the full-size FRF to overcome over-fitting and convergence problems in machine-learning training. To verify the proposed method, a numerical two-story frame structure is employed. A system of individual CSCs is trained with FRFs and mode-shapes, and then termed ensemble to detect the health condition of the structure. The results demonstrate that the proposed method is accurate in damage identification even in presence of up to 20% noisy data and 5% unconsidered damping ratio. Furthermore, it can be concluded that CSC ensemble is highly efficient to detect the location and the severity of damages in comparison to the individual CSC trained only with FRF data.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Smart Structures and Systems
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• v.5 no.1
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• pp.95-117
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• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.