Search results for: precision molding

Authors: S. Aswath, Chinmaya Krishna Tilak, Amal Suresh, Ganesh Udupa

Abstract:

There are technologies to control a humanoid robot in many ways. But the use of Electromyogram (EMG) electrodes has its own importance in setting up the control system. The EMG based control system helps to control robotic devices with more fidelity and precision. In this paper, development of an electromyogram based interface for human gesture recognition for the control of a humanoid robot is presented. To recognize control signs in the gestures, a single channel EMG sensor is positioned on the muscles of the human body. Instead of using a remote control unit, the humanoid robot is controlled by various gestures performed by the human. The EMG electrodes attached to the muscles generates an analog signal due to the effect of nerve impulses generated on moving muscles of the human being. The analog signals taken up from the muscles are supplied to a differential muscle sensor that processes the given signal to generate a signal suitable for the microcontroller to get the control over a humanoid robot. The signal from the differential muscle sensor is converted to a digital form using the ADC of the microcontroller and outputs its decision to the CM-530 humanoid robot controller through a Zigbee wireless interface. The output decision of the CM-530 processor is sent to a motor driver in order to control the servo motors in required direction for human like actions. This method for gaining control of a humanoid robot could be used for performing actions with more accuracy and ease. In addition, a study has been conducted to investigate the controllability and ease of use of the interface and the employed gestures.

Keywords: electromyogram, gesture, muscle sensor, humanoid robot, microcontroller, Zigbee

Procedia PDF Downloads 383

Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

Abstract:

Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

Procedia PDF Downloads 563

Authors: S. Visetpotjanakit, N. Nakkaew

Abstract:

Determination of ⁹⁰Sr in environmental samples has been widely developed with several radioanlytical methods and radiation measurement techniques since ⁹⁰Sr is one of the most hazardous radionuclides produced from nuclear reactors. Liquid extraction technique using di-(2-ethylhexyl) phosphoric acid (HDEHP) to separate and purify ⁹⁰Y and Cherenkov counting using liquid scintillation counter to determine ⁹⁰Y in secular equilibrium to ⁹⁰Sr was developed and performed at our institute, the Office of Atoms for Peace. The approach is inexpensive, non-laborious, and fast to analyse ⁹⁰Sr in environmental samples. To validate our analytical performance for the accurate and precise criteria, determination of ⁹⁰Sr using the IAEA-TEL-2016-04 ALMERA proficiency test samples were performed for statistical evaluation. The experiment used two spiked tap water samples and one naturally contaminated spruce needles sample from Austria collected shortly after the Chernobyl accident. Results showed that all three analyses were successfully passed in terms of both accuracy and precision criteria, obtaining “Accepted” statuses. The two water samples obtained the measured results of 15.54 Bq/kg and 19.76 Bq/kg, which had relative bias 5.68% and -3.63% for the Maximum Acceptable Relative Bias (MARB) 15% and 20%, respectively. And the spruce needles sample obtained the measured results of 21.04 Bq/kg, which had relative bias 23.78% for the MARB 30%. These results confirm our analytical performance of ⁹⁰Sr determination in water and spruce needles samples using the same developed method.

Keywords: ALMERA proficiency test, Cerenkov counting, determination of 90Sr, environmental samples

Procedia PDF Downloads 210

Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

Abstract:

A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

Procedia PDF Downloads 88

Authors: Dionisio Andujar, Juan lópez-Correa, Hugo Moreno, Angela Ri

Abstract:

The increased number of weeds in tomato crops highly lower yields. Weed identification with the aim of machine learning is important to carry out site-specific control. The last advances in computer vision are a powerful tool to face the problem. The analysis of RGB (Red, Green, Blue) images through Artificial Neural Networks had been rapidly developed in the past few years, providing new methods for weed classification. The development of the algorithms for crop and weed species classification looks for a real-time classification system using Object Detection algorithms based on Convolutional Neural Networks. The site study was located in commercial corn fields. The classification system has been tested. The procedure can detect and classify weed seedlings in tomato fields. The input to the Neural Network was a set of 10,000 RGB images with a natural infestation of Cyperus rotundus l., Echinochloa crus galli L., Setaria italica L., Portulaca oeracea L., and Solanum nigrum L. The validation process was done with a random selection of RGB images containing the aforementioned species. The mean average precision (mAP) was established as the metric for object detection. The results showed agreements higher than 95 %. The system will provide the input for an online spraying system. Thus, this work plays an important role in Site Specific Weed Management by reducing herbicide use in a single step.

Keywords: deep learning, object detection, cnn, tomato, weeds

Procedia PDF Downloads 82

Authors: Zoran Šverko, Saša Vlahinić, Nino Stojković, Ivan Markovinović

Abstract:

In this article, the classification of target letters is performed using data from the EEG P300 Speller paradigm. Neural networks trained with the results of dynamic connectivity analysis between different brain regions are used for classification. Dynamic connectivity analysis is based on the adaptive window size and the imaginary part of the complex Pearson correlation coefficient. Brain dynamics are analysed using the relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient method (RICI-imCPCC). The RICI-imCPCC method overcomes the shortcomings of currently used dynamical connectivity analysis methods, such as the low reliability and low temporal precision for short connectivity intervals encountered in constant sliding window analysis with wide window size and the high susceptibility to noise encountered in constant sliding window analysis with narrow window size. This method overcomes these shortcomings by dynamically adjusting the window size using the RICI rule. This method extracts information about brain connections for each time sample. Seventy percent of the extracted brain connectivity information is used for training and thirty percent for validation. Classification of the target word is also done and based on the same analysis method. As far as we know, through this research, we have shown for the first time that dynamic connectivity can be used as a parameter for classifying EEG signals.

Keywords: dynamic connectivity analysis, EEG, neural networks, Pearson correlation coefficients

Procedia PDF Downloads 176

Authors: Shaila Bahl, S. P. Lochab, Pratik Kumar

Abstract:

Nanotechnology and nanomaterials have attracted researchers from different fields, especially from the field of luminescence. Recent studies on various luminescent nanomaterials have shown their relevance in dosimetry of ionizing radiations for the measurements of high doses using the Thermoluminescence (TL) technique, where the conventional microcrystalline phosphors saturate. Ion beams have been used for diagnostic and therapeutic purposes due to their favorable profile of dose deposition at the end of the range known as the Bragg peak. While dealing with human beings, doses from these beams need to be measured with great precision and accuracy. Henceforth detailed investigations of suitable thermoluminescent dosimeters (TLD) for dose verification in ion beam irradiation are required. This paper investigates the TL response of nanocrystalline BaSO4 doped with Eu to 85 MeV carbon beam. The synthesis was done using Co-precipitation technique by mixing Barium chloride and ammonium sulphate solutions. To investigate the crystallinity and particle size, analytical techniques such as X-ray diffraction (XRD) and Transmission electron microscopy (TEM) were used which revealed the average particle sizes to 45 nm with orthorhombic structure. Samples in pellet form were irradiated by 85 MeV carbon beam in the fluence range of 1X1010-5X1013. TL glow curves of the irradiated samples show two prominent glow peaks at around 460 K and 495 K. The TL response is linear up to 1X1013 fluence after which saturation was observed. The wider linear TL response of nanocrystalline BaSO4: Eu and low fading make it a superior candidate as a dosimeter to be used for detecting the doses of carbon beam.

Keywords: radiation, dosimetry, carbon ions, thermoluminescence

Procedia PDF Downloads 267

Authors: Jianghao Hu, Hongyu Wang

Abstract:

The effective and efficient human pose estimation algorithm is an important task for real-time human pose estimation on mobile devices. This paper proposes a light-weight human key points detection algorithm, Light-Weight Network for Real-Time Pose Estimation (LWPE). LWPE uses light-weight backbone network and depthwise separable convolutions to reduce parameters and lower latency. LWPE uses the feature pyramid network (FPN) to fuse the high-resolution, semantically weak features with the low-resolution, semantically strong features. In the meantime, with multi-scale prediction, the predicted result by the low-resolution feature map is stacked to the adjacent higher-resolution feature map to intermediately monitor the network and continuously refine the results. At the last step, the key point coordinates predicted in the highest-resolution are used as the final output of the network. For the key-points that are difficult to predict, LWPE adopts the online hard key points mining strategy to focus on the key points that hard predicting. The proposed algorithm achieves excellent performance in the single-person dataset selected in the AI (artificial intelligence) challenge dataset. The algorithm maintains high-precision performance even though the model only contains 3.9M parameters, and it can run at 225 frames per second (FPS) on the generic graphics processing unit (GPU).

Keywords: depthwise separable convolutions, feature pyramid network, human pose estimation, light-weight backbone

Procedia PDF Downloads 126

Authors: D. S. Mohale, A. P. Dewani, A. S.tripathi, A. V. Chandewar

Abstract:

Present work demonstrates the applicability of high-performance liquid chromatography (HPLC) with UV-Vis detection for the quantification of malondialdehyde as malondialdehyde-thiobarbituric acid complex (MDA-TBA) in-vivo in rats. The HPLC method for MDA-TBA was achieved by isocratic mode on a reverse-phase C18 column (250mm×4.6mm) at a flow rate of 1.0mLmin−1 followed by detection at 532 nm. The chromatographic conditions were optimized by varying the concentration and pH of water followed by changes in percentage of organic phase optimal mobile phase consisted of mixture of water (0.2% triethylamine pH adjusted to 2.3 by ortho-phosphoric acid) and acetonitrile in ratio (80:20v/v). The retention time of MDA-TBA complex was 3.7 min. The developed method was sensitive as limit of detection and quantification (LOD and LOQ) for MDA-TBA complex were (standard deviation and slope of calibration curve) 110 ng/ml and 363 ng/ml respectively. Calibration studies were done by spiking MDA into rat plasma at concentrations ranging from 500 to 1000 ng/ml. The precision of developed method measured in terms of relative standard deviations for intra-day and inter-day studies was 1.6–5.0% and 1.9–3.6% respectively. The HPLC method was applied for monitoring MDA levels in rats subjected to chronic treatment of levofloxacin (LEV) (5mg/kg/day) for 21 days. Results were compared by findings in control group rats. Mean peak areas of both study groups was subjected for statistical treatment to unpaired student t-test to find p-values. The p value was <0.001 indicating significant results and suggesting increased MDA levels in rats subjected to chronic treatment of LEV of 21 days.

Keywords: malondialdehyde-thiobarbituric acid complex, levofloxacin, HPLC, oxidative stress

Procedia PDF Downloads 315

Authors: Beng Yew Low, Cher Liang Cha, Cheng Yong Teoh

Abstract:

Due to a lack of continual assessment or grade related data, identifying first-year engineering students in a polytechnic education at risk of failing is challenging. Our experience over the years tells us that there is no strong correlation between having good entry grades in Mathematics and the Sciences and excelling in hardcore engineering subjects. Hence, identifying students at risk of failure cannot be on the basis of entry grades in Mathematics and the Sciences alone. These factors compound the difficulty of early identification and intervention. This paper describes the development of a predictive analytics model in the early detection of students at risk of failing and evaluates its effectiveness. Data from continual assessments conducted in term one, supplemented by data of student psychological profiles such as interests and study habits, were used. Three classification techniques, namely Logistic Regression, K Nearest Neighbour, and Random Forest, were used in our predictive model. Based on our findings, Random Forest was determined to be the strongest predictor with an Area Under the Curve (AUC) value of 0.994. Correspondingly, the Accuracy, Precision, Recall, and F-Score were also highest among these three classifiers. Using this Random Forest Classification technique, students at risk of failure could be identified at the end of term one. They could then be assigned to a Learning Support Programme at the beginning of term two. This paper gathers the results of our findings. It also proposes further improvements that can be made to the model.

Keywords: continual assessment, predictive analytics, random forest, student psychological profile

Procedia PDF Downloads 105

Authors: Hossein Navid, Maryam Adeli Khadem, Shahin Oustan, Mahmoud Zareie

Abstract:

Among the nutrients needed by the plants, three elements containing nitrate, phosphorus and potassium are more important. The objective of this research was measuring these nutrient amounts in soil using Fourier transform infrared spectroscopy in range of 400- 4000 cm-1. Soil samples for different soil types (sandy, clay and loam) were collected from different areas of East Azerbaijan. Three types of fertilizers in conventional farming (urea, triple superphosphate, potassium sulphate) were used for soil treatment. Each specimen was divided into two categories: The first group was used in the laboratory (direct measurement) to extract nitrate, phosphorus and potassium uptake by colorimetric method of Olsen and ammonium acetate. The second group was used to measure drug absorption spectrometry. In spectrometry, the small amount of soil samples mixed with KBr and was taken in a small pill form. For the tests, the pills were put in the center of infrared spectrometer and graphs were obtained. Analysis of data was done using MINITAB and PLSR software. The data obtained from spectrometry method were compared with amount of soil nutrients obtained from direct drug absorption using EXCEL software. There were good fitting between these two data series. For nitrate, phosphorus and potassium R2 was 79.5%, 92.0% and 81.9%, respectively. Also, results showed that the range of MIR (mid-infrared) is appropriate for determine the amount of soil nitrate and potassium and can be used in future research to obtain detailed maps of land in agricultural use.

Keywords: nitrate, phosphorus, potassium, soil nutrients, spectroscopy

Procedia PDF Downloads 372

Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

Abstract:

The assessment of disease levels in crop fields is an important and time-consuming task that generally relies on expert knowledge of trained individuals. Image classification in agriculture problems historically has been based on classical machine learning strategies that make use of hand-engineered features in the top of a classification algorithm. This approach tends to not produce results with high accuracy and generalization to the classes classified by the system when the nature of the elements has a significant variability. The advent of deep convolutional neural networks has revolutionized the field of machine learning, especially in computer vision tasks. These networks have great resourcefulness of learning and have been applied successfully to image classification and object detection tasks in the last years. The objective of this work was to propose a new method based on deep learning convolutional neural networks towards the task of disease level monitoring. Common RGB images of winter wheat were obtained during a growing season. Five categories of disease levels presence were produced, in collaboration with agronomists, for the algorithm classification. Disease level tasks performed by experts provided ground truth data for the disease score of the same winter wheat plots were RGB images were acquired. The system had an overall accuracy of 84% on the discrimination of the disease level classes.

Keywords: crop disease assessment, deep learning, precision agriculture, residual neural networks

Procedia PDF Downloads 299

Authors: Alba Luna Jimenez, Maria Dolores Hernando

Abstract:

The administration of veterinary treatments at higher doses than the recommended Varroa mite control in beehive matrices has the potential to generate residues in the honeybee colony and in the derived products for consumption. Honeybee colonies can also be indirectly exposed to residues of plant protection products when foraging in crops, wildflowers near the crops, or in urban gardens just after spraying. The study evaluates the presence of both types of residues, veterinary treatments, and pesticides in beeswax, bee bread, and honey. The study was carried out in apiaries located in agricultural zones and forest areas in Andalusia, Spain. Up to nineteen residues were identified above LOQ using gas chromatography-triple quadrupole-mass spectrometry analysis (GC-MS/MS). Samples were extracted by a modified QuEChERs method. Chlorfenvinphos was detected in beeswax and bee bread despite its use is not authorized for Varroa mite control. Residues of fluvalinate-tau, authorized as veterinary treatment, were detected in most of the samples of beeswax and bee bread, presumably due to overdose or also to its potential for accumulation associated with its marked liposolubility. Residues of plant protection products were also detected in samples of beeswax and bee bread. Pesticide residues were detected above the LOQ that was established at 5 µg.kg⁻¹, which is the minimum concentration that can be quantified with acceptable accuracy and precision, as described in the European guidelines for pesticide residue analysis SANTE/11945/2015. No residues of phytosanitary treatments used in agriculture were detected in honey.

Keywords: honeybee colony, mass spectrometry analysis, pesticide residues, Varroa destructor, veterinary treatment

Procedia PDF Downloads 132

Authors: Nagalingam Arun Prasanth, Ahmed Syed Adnan, S. H. Yeo

Abstract:

Surface topography plays a significant role in the functional performance of engineered parts. It is important to have a control on the surface geometry and understanding on the surface details to get the desired performance. Hence, in the current research contribution, a non-contact micro-texturing technique has been explored and developed. The technique involves ultrasonic excitation of a tool as a prime source of surface texturing for aluminum alloy workpieces. The specimen surface is polished first and is then immersed in a liquid bath containing 10% weight concentration of Ti6Al4V grade 5 spherical powders. A submerged slurry jet is used to recirculate the spherical powders under the ultrasonic horn which is excited at an ultrasonic frequency and amplitude of 40 kHz and 70 µm respectively. The distance between the horn and workpiece surface was remained fixed at 200 µm using a precision control stage. Texturing effects were investigated for different process timings of 1, 3 and 5 s. Thereafter, the specimens were cleaned in an ultrasonic bath for 5 mins to remove loose debris on the surface. The developed surfaces are characterized by optical and contact surface profiler. The optical microscopic images show a texture of circular spots on the workpiece surface indented by titanium spherical balls. Waviness patterns obtained from contact surface profiler supports the texturing effect produced from the proposed technique. Furthermore, water droplet tests were performed to show the efficacy of the proposed technique to develop hydrophilic surfaces and to quantify the texturing effect produced.

Keywords: surface texturing, surface modification, topography, ultrasonic

Procedia PDF Downloads 203

Authors: Hang Ju, Changmin Zhu

Abstract:

The development level of the BeiDou Navigation Satellite System (BDS) can strongly reflect national defense strength as an important spatial information infrastructure. BDS can be not only used for military purposes, such as intelligence gathering, nuclear explosion monitoring, emergency communications, but also for location services, transportation, mapping, precision agriculture. In order to ensure the national defense security and the wide application of BDS in civil and military areas, BDS must be autonomous and controllable. As a complex system of knowledge-intensive, knowledge transformation runs through the whole process of research and development, production, operation, and maintenance of BDS. Based on the perspective of knowledge transformation, this paper expounds on the meaning of socialization, externalization, combination, and internalization of knowledge transformation, and the coupling relationship of autonomy and control on the basis of analyzing the status quo and problems of the autonomy and control of BDS. The autonomous and controllable framework of BDS based on knowledge transformation is constructed from six dimensions of management capability, R&D capability, technical capability, manufacturing capability, service support capability, and application capability. It can provide support for the smooth implementation of information security policy, provide a reference for the autonomy and control of the upstream and downstream industrial chains in Beidou, and provide a reference for the autonomous and controllable research of aerospace components, military measurement test equipment, and other related industries.

Keywords: knowledge transformation, BeiDou Navigation Satellite System, autonomy and control, framework

Procedia PDF Downloads 158

Authors: P. S. Jain, K. D. Bobade, S. J. Surana

Abstract:

A simple, selective, precise and Stability-indicating High-performance thin-layer chromatographic method for analysis of Naftopidil both in a bulk and in pharmaceutical formulation has been developed and validated. The method employed, HPTLC aluminium plates precoated with silica gel as the stationary phase. The solvent system consisted of hexane: ethyl acetate: glacial acetic acid (4:4:2 v/v). The system was found to give compact spot for Naftopidil (Rf value of 0.43±0.02). Densitometric analysis of Naftopidil was carried out in the absorbance mode at 253 nm. The linear regression analysis data for the calibration plots showed good linear relationship with r2=0.999±0.0001 with respect to peak area in the concentration range 200-1200 ng per spot. The method was validated for precision, recovery and robustness. The limits of detection and quantification were 20.35 and 61.68 ng per spot, respectively. Naftopidil was subjected to acid and alkali hydrolysis, oxidation and thermal degradation. The drug undergoes degradation under acidic, basic, oxidation and thermal conditions. This indicates that the drug is susceptible to acid, base, oxidation and thermal conditions. The degraded product was well resolved from the pure drug with significantly different Rf value. Statistical analysis proves that the method is repeatable, selective and accurate for the estimation of investigated drug. The proposed developed HPTLC method can be applied for identification and quantitative determination of Naftopidil in bulk drug and pharmaceutical formulation.

Keywords: naftopidil, HPTLC, validation, stability, degradation

Procedia PDF Downloads 378

Authors: Wiem Zaabi, Yemna Bensalem, Hafedh Trabelsi

Abstract:

The paper presents a finite element (FE) based efficient analysis procedure for induction machine (IM). The FE formulation approaches are proposed to achieve this goal: the magnetostatic and the non-linear transient time stepped formulations. The study based on finite element models offers much more information on the phenomena characterizing the operation of electrical machines than the classical analytical models. This explains the increase of the interest for the finite element investigations in electrical machines. Based on finite element models, this paper studies the influence of the stator and the rotor faults on the behavior of the IM. In this work, a simple dynamic model for an IM with inter-turn winding fault and a broken bar fault is presented. This fault model is used to study the IM under various fault conditions and severity. The simulation results are conducted to validate the fault model for different levels of fault severity. The comparison of the results obtained by simulation tests allowed verifying the precision of the proposed FEM model. This paper presents a technical method based on Fast Fourier Transform (FFT) analysis of stator current and electromagnetic torque to detect the faults of broken rotor bar. The technique used and the obtained results show clearly the possibility of extracting signatures to detect and locate faults.

Keywords: Finite element Method (FEM), Induction motor (IM), short-circuit fault, broken rotor bar, Fast Fourier Transform (FFT) analysis

Procedia PDF Downloads 274

Authors: Carl van Walraven, Meltem Tuna

Abstract:

Background: Network meta-analysis (NMA) quantifies the relative efficacy of 3 or more interventions from studies containing a subgroup of interventions. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct inclusion criteria that are evaluated on a common population (‘concurrent external validation’). Methods: We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre-specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed-effects methods that accounted for model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which ranges from -∞ through 0 (accuracy of random guessing) to 1 (accuracy of most accurate model in concurrent external validation). Results: The unbiased prediction model had the highest SBS. The NeRMA score correctly ranked all simulated prediction models by the extent of bias from the known risk function. A SAS macro and R-function was created to implement the NeRMA Score. Conclusions: The NeRMA Score makes it possible to quantify the accuracy of binomial prediction models having distinct inclusion criteria in a concurrent external validation.

Keywords: prediction model accuracy, scaled brier score, fixed effects methods, concurrent external validation

Procedia PDF Downloads 196

Authors: Ragy Raafat Gaber Attaalla

Abstract:

Despite having the greatest rates of mortality and morbidity in the world, low- and middle-income (LMIC) nations trail high-income nations in terms of the number of clinical trials, the number of qualified researchers, and the amount of research information specific to their people. Health inequities and the use of precision medicine may be hampered by a lack of local genomic data, clinical pharmacology and pharmacometrics competence, and training opportunities. These issues can be solved by carrying out health care infrastructure development, which includes data gathering and well-designed clinical pharmacology training in LMICs. It will be advantageous if there is international cooperation focused at enhancing education and infrastructure and promoting locally motivated clinical trials and research. This paper outlines various instances where clinical pharmacology knowledge could be put to use, including pharmacogenomic opportunities that could lead to better clinical guideline recommendations. Examples of how clinical pharmacology training can be successfully implemented in LMICs are also provided, including clinical pharmacology and pharmacometrics training programmes in Africa and a Tanzanian researcher's personal experience while on a training sabbatical in the United States. These training initiatives will profit from advocacy for clinical pharmacologists' employment prospects and career development pathways, which are gradually becoming acknowledged and established in LMICs. The advancement of training and research infrastructure to increase clinical pharmacologists' knowledge in LMICs would be extremely beneficial because they have a significant role to play in global health.

Keywords: low- and middle-income, clinical pharmacology, pharmacometrics, career development pathways

Procedia PDF Downloads 48

Authors: S. Visetpotjanakit, C. Khrautongkieo

Abstract:

Radiation monitoring in the environment and foodstuffs is one of the main responsibilities of Office of Atoms for Peace (OAP) as the nuclear regulatory body of Thailand. The main goal of the OAP is to assure the safety of the Thai people and environment from any radiological incidents. Various radioanalytical methods have been developed to monitor radiation and radionuclides in the environmental and foodstuff samples. To validate our analytical performance, several proficiency test exercises from the International Atomic Energy Agency (IAEA) have been performed. Here, the results of a proficiency test exercise referred to as the Proficiency Test for Tritium, Cobalt, Strontium and Caesium Isotopes in Seawater 2017 (IAEA-RML-2017-01) are presented. All radionuclides excepting ³H were analysed using various radioanalytical methods, i.e. direct gamma-ray counting for determining ⁶⁰Co, ¹³⁴Cs and ¹³⁷Cs and developed radiochemical techniques for analysing ¹³⁴Cs, ¹³⁷Cs using AMP pre-concentration technique and 90Sr using di-(2-ethylhexyl) phosphoric acid (HDEHP) liquid extraction technique. The analysis results were submitted to IAEA. All results passed IAEA criteria, i.e. accuracy, precision and trueness and obtained ‘Accepted’ statuses. These confirm the data quality from the OAP environmental radiation laboratory to monitor radiation in the environment.

Keywords: international atomic energy agency, proficiency test, radiation monitoring, seawater

Procedia PDF Downloads 152

Authors: Saurabh Satija, Munish Garg

Abstract:

Present work reports an efficient extraction method using microwaves based solvent–sample duo-heating mechanism, for the extraction of an important anti-diabetic plant Tinospora cordifolia from AYUSH system for estimation of berberine content. The process is based on simultaneous heating of sample matrix and extracting solvent under microwave energy. Methanol was used as the extracting solvent, which has excellent berberine solubilizing power and warms up under microwave attributable to its great dispersal factor. Extraction conditions like time of irradition, microwave power, solute-solvent ratio and temperature were optimized using Taguchi design and berberine was quantified using high performance thin layer chromatography. The ranked optimized parameters were microwave power (rank 1), irradiation time (rank 2) and temperature (rank 3). This kind of extraction mechanism under dual heating provided choice of extraction parameters for better precision and higher yield with significant reduction in extraction time under optimum extraction conditions. This developed extraction protocol will lead to extract higher amounts of berberine which is a major anti-diabetic moiety in Tinospora cordifolia which can lead to development of cheaper formulations of the plant Tinospora cordifolia and can help in rapid prevention of diabetes in the world.

Keywords: berberine, microwave, optimization, Taguchi

Procedia PDF Downloads 316

Authors: Rajesh Kumar G

Abstract:

A challenging aspect of any clinical trial is to carefully plan the study design to meet the study objective in optimum way and to validate the assumptions made during protocol designing. And when it is a pediatric study, there is the added challenge of stringent guidelines and difficulty in recruiting the necessary subjects. Unlike adult trials, there is not much historical data available for pediatrics, which is required to validate assumptions for planning pediatric trials. Typically, pediatric studies are initiated as soon as approval is obtained for a drug to be marketed for adults, so with the adult study historical information and with the available pediatric pilot study data or simulated pediatric data, the pediatric study can be well planned. Generalizing the historical adult study for new pediatric study is a tedious task; however, it is possible by integrating various statistical techniques and utilizing the advantage of hybrid study design, which will help to achieve the study objective in a smoother way even with the presence of many constraints. This research paper will explain how well the hybrid study design can be planned along with integrated technique (SEV) to plan the pediatric study; In brief the SEV technique (Simulation, Estimation (using borrowed adult data and applying Bayesian methods)) incorporates the use of simulating the planned study data and getting the desired estimates to Validate the assumptions.This method of validation can be used to improve the accuracy of data analysis, ensuring that results are as valid and reliable as possible, which allow us to make informed decisions well ahead of study initiation. With professional precision, this technique based on the collected data allows to gain insight into best practices when using data from historical study and simulated data alike.

Keywords: adaptive design, simulation, borrowing data, bayesian model

Procedia PDF Downloads 50

Authors: Omid Ataei Nia, Karim Salahshoor

Abstract:

In this paper, a new precise and reliable sensor network methodology is introduced for unit processes and operations using the Constriction Coefficient Particle Swarm Optimization (CPSO) method. CPSO is introduced as a new search engine for optimal sensor network design purposes. Furthermore, a Square Root Unscented Kalman Filter (SRUKF) algorithm is employed as a new data reconciliation technique to enhance the stability and accuracy of the filter. The proposed design procedure incorporates precision, cost, observability, reliability together with importance-of-variables (IVs) as a novel measure in Instrumentation Criteria (IC). To the best of our knowledge, no comprehensive approach has yet been proposed in the literature to take into account the importance of variables in the sensor network design procedure. In this paper, specific weight is assigned to each sensor, measuring a process variable in the sensor network to indicate the importance of that variable over the others to cater to the ultimate sensor network application requirements. A set of distinct scenarios has been conducted to evaluate the performance of the proposed methodology in a simulated Continuous Stirred Tank Reactor (CSTR) as a highly nonlinear process plant benchmark. The obtained results reveal the efficacy of the proposed method, leading to significant improvement in accuracy with respect to other alternative sensor network design approaches and securing the definite allocation of sensors to the most important process variables in sensor network design as a novel achievement.

Keywords: constriction coefficient PSO, importance of variable, MRMSE, reliability, sensor network design, square root unscented Kalman filter

Procedia PDF Downloads 141

Authors: Murat Durucu

Abstract:

Glaucoma is a condition of irreversible blindness, early diagnosis and appropriate interventions to make the patients able to see longer time. In this study, it addressed that the importance of developing a decision support system for glaucoma diagnosis. Glaucoma occurs when pressure happens around the eyes it causes some damage to the optic nerves and deterioration of vision. There are different levels ranging blindness of glaucoma disease. The diagnosis at an early stage allows a chance for therapies that slows the progression of the disease. In recent years, imaging technology from Heidelberg Retinal Tomography (HRT), Stereoscopic Disc Photo (SDP) and Optical Coherence Tomography (OCT) have been used for the diagnosis of glaucoma. This better accuracy and faster imaging techniques in response technique of OCT have become the most common method used by experts. Although OCT images or HRT precision and quickness, especially in the early stages, there are still difficulties and mistakes are occurred in diagnosis of glaucoma. It is difficult to obtain objective results on diagnosis and placement process of the doctor's. It seems very important to develop an objective decision support system for diagnosis and level the glaucoma disease for patients. By using OCT images and pattern recognition systems, it is possible to develop a support system for doctors to make their decisions on glaucoma. Thus, in this recent study, we develop an evaluation and support system to the usage of doctors. Pattern recognition system based computer software would help the doctors to make an objective evaluation for their patients. It is intended that after development and evaluation processes of the software, the system is planning to be serve for the usage of doctors in different hospitals.

Keywords: decision support system, glaucoma, image processing, pattern recognition

Procedia PDF Downloads 267

Authors: Y. Mehdipour, S. Ebrahimi, A. Jahanpour, F. Seyedzaei, B. Sabayan, A. Karimi, H. Amirifard

Abstract:

Ischemic stroke is one of the common reasons for disability and mortality. The fourth leading cause of death in the world and the third in some other sources. Only 1/3 of the patients with ischemic stroke fully recover, 1/3 of them end in permanent disability and 1/3 face death. Thus, the use of predictive models to predict stroke has a vital role in reducing the complications and costs related to this disease. Thus, the aim of this study was to specify the effective factors and predict ischemic stroke with the help of DM methods. The present study was a descriptive-analytic study. The population was 213 cases from among patients referring to Ali ibn Abi Talib (AS) Hospital in Zahedan. Data collection tool was a checklist with the validity and reliability confirmed. This study used DM algorithms of decision tree for modeling. Data analysis was performed using SPSS-19 and SPSS Modeler 14.2. The results of the comparison of algorithms showed that CHAID algorithm with 95.7% accuracy has the best performance. Moreover, based on the model created, factors such as anemia, diabetes mellitus, hyperlipidemia, transient ischemic attacks, coronary artery disease, and atherosclerosis are the most effective factors in stroke. Decision tree algorithms, especially CHAID algorithm, have acceptable precision and predictive ability to determine the factors affecting ischemic stroke. Thus, by creating predictive models through this algorithm, will play a significant role in decreasing the mortality and disability caused by ischemic stroke.

Keywords: data mining, ischemic stroke, decision tree, Bayesian network

Procedia PDF Downloads 151

Authors: Sadegh Sadeghi, Negar Shabani

Abstract:

From a thermal point of view, zeotropic mixtures are likely to be more efficient than azeotropic fluids in low-temperature thermodynamic cycles due to their suitable boiling characteristics. In this study, performance of a low-temperature Kalina cycle with R717/water working fluid used in different existing power plants is mathematically investigated. To analyze the behavior of the cycle, mass conservation, energy conservation, and exergy balance equations are presented. With regard to the similarity in molar mass of R717 (17.03 gr/mol) and water (18.01 gr/mol), there is no need to alter the size of Kalina system components such as turbine and pump. To optimize the cycle energy and exergy efficiencies simultaneously, a constrained multi-objective optimization is carried out applying an Artificial Bee Colony algorithm. The main motivation behind using this algorithm lies on its robustness, reliability, remarkable precision and high–speed convergence rate in dealing with complicated constrained multi-objective problems. Convergence rates of the algorithm for calculating the optimal energy and exergy efficiencies are presented. Subsequently, due to the importance of exergy concept in Kalina cycles, exergy destructions occurring in the components are computed. Finally, the impacts of pressure, temperature, mass fraction and mass flow rate on the energy and exergy efficiencies are elaborately studied.

Keywords: artificial bee colony algorithm, binary zeotropic mixture, constrained multi-objective optimization, energy efficiency, exergy efficiency, Kalina cycle

Procedia PDF Downloads 127

Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

Abstract:

This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

Procedia PDF Downloads 10

Authors: Abdelkader Elhanaoui, Mhamed Hadji, Rachid Skouri, Said Agounad

Abstract:

The purpose of this paper is to study the phenomenon of acoustic scattering by using a new method. The signal processing (Fast Fourier Transform FFT Inverse Fast Fourier Transform iFFT and BESSEL functions) is widely applied to obtain information with high precision accuracy. Signal processing has a wider implementation in general-purpose pro-cessors. Our interest was focused on the use of FPGAs (Field-Programmable Gate Ar-rays) in order to minimize the computational complexity in single processor architecture, then be accelerated on FPGA and meet real-time and energy efficiency requirements. Gen-eral-purpose processors are not efficient for signal processing. We implemented the acous-tic backscattered signal processing model on the Altera DE-SOC board and compared it to Odroid xu4. By comparison, the computing latency of Odroid xu4 and FPGA is 60 sec-onds and 3 seconds, respectively. The detailed SoC FPGA-based system has shown that acoustic spectra are performed up to 20 times faster than the Odroid xu4 implementation. FPGA-based system of processing algorithms is realized with an absolute error of about 10⁻³. This study underlines the increasing importance of embedded systems in underwater acoustics, especially in non-destructive testing. It is possible to obtain information related to the detection and characterization of submerged cells. So we have achieved good exper-imental results in real-time and energy efficiency.

Keywords: DE1 FPGA, acoustic scattering, form function, signal processing, non-destructive testing

Procedia PDF Downloads 53

Authors: Djamel Fawzi Hadj Sadok, Marrone Silvério Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner

Abstract:

A radio base station (RBS), part of the radio access network, is a particular type of equipment that supports the connection between a wide range of cellular user devices and an operator network access infrastructure. Nowadays, most of the RBS maintenance is carried out manually, resulting in a time consuming and costly task. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. This paper proposes and compares two deep learning solutions to identify attached RJ45 connectors on network ports. We named connector detection, the solution based on object detection, and connector classification, the one based on object classification. With the connector detection, we get an accuracy of 0:934, mean average precision 0:903. Connector classification, get a maximum accuracy of 0:981 and an AUC of 0:989. Although connector detection was outperformed in this study, this should not be viewed as an overall result as connector detection is more flexible for scenarios where there is no precise information about the environment and the possible devices. At the same time, the connector classification requires that information to be well-defined.

Keywords: radio base station, maintenance, classification, detection, deep learning, automation

Procedia PDF Downloads 173

Authors: Hoda Mahgoub, Abeer Hanafy

Abstract:

Linagliptin (LNG) belongs to dipeptidyl-peptidase-4 (DPP-4) inhibitor class. DPP-4 inhibitors represent a new therapeutic approach for the treatment of type 2 diabetes in adults. The aim of this work was to develop and validate an accurate and reproducible HPLC method for the determination of LNG with high sensitivity in rat plasma. The method involved separation of both LNG and pindolol (internal standard) at ambient temperature on a Zorbax Eclipse XDB C18 column and a mobile phase composed of 75% methanol: 25% formic acid 0.1% pH 4.1 at a flow rate of 1.0 mL.min-1. UV detection was performed at 254nm. The method was validated in compliance with ICH guidelines and found to be linear in the range of 5–1000ng.mL-1. The limit of quantification (LOQ) was found to be 5ng.mL-1 based on 100µL of plasma. The variations for intra- and inter-assay precision were less than 10%, and the accuracy values were ranged between 93.3% and 102.5%. The extraction recovery (R%) was more than 83%. The method involved a single extraction step of a very small plasma volume (100µL). The assay was successfully applied to an in-vivo pharmacokinetic study of LNG in rats that were administered a single oral dose of 10mg.kg-1 LNG. The maximum concentration (Cmax) was found to be 927.5 ± 23.9ng.mL-1. The area under the plasma concentration-time curve (AUC0-72) was 18285.02 ± 605.76h.ng.mL-1. In conclusion, the good accuracy and low LOQ of the bioanalytical HPLC method were suitable for monitoring the full pharmacokinetic profile of LNG in rats. The main advantages of the method were the sensitivity, small sample volume, single-step extraction procedure and the short time of analysis.

Keywords: HPLC, linagliptin, pharmacokinetic study, rat plasma

Procedia PDF Downloads 223