Determination of Sugar Level and the Existence of Magic Sugar in Various Beverages using a Glucose Meter with Four-Point Probe and Electrochemical Impedance

Nowadays, people are being inconsiderate about the healthy lifestyle that might lead us to be unhealthy and be prone to developing tumors in the kidney due to some kind of sugar being used. In order to minimize these problems, the team will raise people awareness. Raising people awareness is not the same as telling them what to do. It is about giving them the knowledge to let them decide for themselves. This is why the team developed a device that can measure the sugar level and determine the existence of magic sugar in various beverages. The device is composed of two major parts: first is the circuit that will measure the impedance of a liquid sample and second, is a four-point probe, which includes a microcontroller that will display and interpret the results. The Four-point probe applies the concept of Wenner method and Electrochemical Impedance. After constructing the device, the team performed its calibration that requires different liquid samples. Based on its gathered data, different graphical representations were formulated and translated into mathematical equations in order to integrate it onto the microcontroller. Whenever the microcontroller encounters an unknown solution, it can determine the sugar level and classify the type of sugar being used

Personalized Photo Enhancement Using Artificial Neural Network

Artificial Neural Network (ANN) is applied to create a photo enhancement program that automatically adjusts image parameters on the face based on the preference of its own user. Viola Jones algorithm was used for face detection, and a Graphical User Interface (GUI) is created to enable users to edit the photos easily. Input data sets are essential in the learning progress of ANN. Variety of users inputted their respective image data into the program for training the neural network. Regression plot developed will be used to determine the performance of the system. The authors would relate the consistency of the users in editing their photos to the produced regression plot. On the other hand, actual tests were conducted to determine the time spent editing the photos manually and the amount of time the system automatically adjusted the photo. There is a difference in editing time between average manual adjustment and automatic adjustment by the ANN

Implementation of a Professional Society Core Curriculum and Integrated Maintenance of Certification Program

Medical professional societies exist to foster collaboration, guide career development, and provide continuing medical education opportunities. Maintenance of certification is a process by which physicians complete formal educational activities approved by certifying organizations. The American Thoracic Society (ATS) established an innovative maintenance of certification program in 2012 as a means to formalize and expand continuing medical education offerings. This program is unique as it includes explicit opportunities for collaboration and career development in addition to providing continuing medical education and maintenance of certification credit to society members. In describing the development of this program referred to as the “Core Curriculum,” the authors highlight the ATS process for content design, stages of curriculum development, and outcomes data with an eye toward assisting other societies that seek to program similar content. The curriculum development process described is generalizable and positively influences individual practitioners and professional societies in general, and as a result, provides a useful model for other professional societies to follow

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Machine Learning-Based Classification of Mango Pulp Weevil Activity Utilizing an Acoustic Sensor

The mango pulp weevil (MPW) is an aggressive pest that mates seasonally according to the cycle of the mango fruit. After discovering the existence of the mango pulp weevil in Palawan, the island has been under quarantine for exporting mangoes. Detection of the pest proves difficult as the pest does not leave a physical sign that the mango has been damaged. Infested mangoes are wasted as they cannot be sold due to damage. This study serves as a base study for non-invasive mango pulp weevil detection using MATLAB machine learning and audio feature extraction tools. Acoustic sensors were evaluated for best-fit use in the study. The rationale for selecting the acoustic sensors includes local availability and accessibility. Among the three sensors tested, the MEMS sensor had the best result. The data for acoustic frequency are acquired using the selected sensor, which is placed inside a soundproof chamber to minimize the noise and isolate the sound produced by each activity. The identified activity of the adult mango pulp weevil includes walking, resting, and mating. The Mel-frequency cepstral coefficient (MFCC) was used for feature extraction of the recorded audio and training of the SVM classifier. The study achieved 89.81% overall accuracy in characterizing mango pulp weevil activity

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson’s correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson’s correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality

Neuro-Particle Swarm Optimization Based In-Situ Prediction Model for Heavy Metals Concentration in Groundwater and Surface Water

Limited monitoring activities to assess data on heavy metal (HM) concentration contribute to worldwide concern for the environmental quality and the degree of toxicants in areas where there are elevated metals concentrations. Hence, this study used in-situ physicochemical parameters to the limited data on HM concentration in SW and GW. The site of the study was Marinduque Island Province in the Philippines, which experienced two mining disasters. Prediction model results showed that the SW models during the dry and wet seasons recorded a mean squared error (MSE) ranging from 6 &times; 10&minus;7 to 0.070276. The GW models recorded a range from 5 &times; 10&minus;8 to 0.045373, all of which were approaching the ideal MSE value of 0. Kling&ndash;Gupta efficiency values of developed models were all greater than 0.95. The developed neural network-particle swarm optimization (NN-PSO) models for SW and GW were compared to linear and support vector machine (SVM) models and previously published deterministic and artificial intelligence (AI) models. The findings indicated that the developed NN-PSO models are superior to the developed linear and SVM models, up to 1.60 and 1.40 times greater than the best model observed created by linear and SVM models for SW and GW, respectively. The developed models were also on par with previously published deterministic and AI-based models considering their prediction capability. Sensitivity analysis using Olden&rsquo;s connection weights approach showed that pH influenced the concentration of HM significantly. Established on the research findings, it can be stated that the NN-PSO is an effective and practical approach in the prediction of HM concentration in water resources that contributes a solution to the limited HM concentration monitored data

Multi-scale vehicle classification using different machine learning models

The focus of this paper is to explore multi-scale vehicle classification based on the histogram of oriented gradient features. Several literatures have used these features together with different classification models, however, there is a need to compare different models suited for vehicle classification application. In order to quantify the results a common dataset was used for the machine learning models: logistic regression, k-nearest neighbor, and support vector machine. However, since the classification of the support vector machine is based on the type of kernel (linear, polynomial, and Gaussian) used, additional tests were conducted. Thus, this study provides the following contributions: (1) comparison of machine learning models for vehicle classification; and (2) comparison of the best type of kernel function. © 2018 IEEE