Search results for: precision grinding

Authors: A. Mandić, M. Sakač, A. Mišan, B. Šojić, L. Petrović, I. Lončarević, B. Pajin, I. Sedej

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Aldehydes as secondary lipid oxidation products are highly specific to the oxidative degradation of particular polyunsaturated fatty acids present in foods. Gas chromatographic analysis of those volatile compounds has been widely used for monitoring of the deterioration of food products. Developed static headspace gas chromatography method using flame ionization detector (SHS GC FID) was applied to monitor the aldehydes present in processed foods such as bakery, meat and confectionary products. Five selected aldehydes were determined in samples without any sample preparation, except grinding for bakery and meat products. SHS–GC analysis allows the separation of propanal, pentanal, hexanal, heptanal and octanal, within 15min. Aldehydes were quantified in fresh and stored samples, and the obtained range of aldehydes in crackers was 1.62±0.05-9.95±0.05mg/kg, in sausages 6.62±0.46-39.16±0.39mg/kg; and in cocoa spread cream 0.48±0.01-1.13±0.02mg/kg. Referring to the obtained results, the following can be concluded, proposed method is suitable for different types of samples, content of aldehydes varies depending on the type of a sample, and differs in fresh and stored samples of the same type.

Keywords: lipid oxidation, aldehydes, crackers, sausage, cocoa cream spread

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Authors: Ali Amiri, Arash Esfandiari, Elham Zarenezhad

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We report here an efficient and rapid method for the preparation of 3,4-dihydro-1H-quinoxalin-2-ones and 1H‑quinolin-2-ones that involves grinding of o-, m-, or p‑phenylenediamine and three dialkyl acetylenedicarboxylates using a pestle and mortar. This solvent-free approach requires only a few minutes of reaction time. This type of reaction is expected to be the most economical method since neither catalyst nor solvent is used. Finally, all synthesised compounds were screened for antimicrobial activity against two Gram-positive bacteria (Pseudomonas aeruginosa PTCC 1077, Escherichia coli PTCC1330) and two Gram-negative bacteria (Staphylococcus aureus PTCC 1133, Bacillus cereus PTCC 1015) and their activity. Compared with gentamycin and ampicillin as reference drugs for Gram-negative and Gram-positive bacteria, respectively. The minimum inhibitory concentration (MIC) of the synthesised compounds and reference drugs were determined by the microdilution method. Good antibacterial activity was observed for 3,4-dihydro-1H-quinoxalin-2-ones against all species of Gram-positive and Gram-negative bacteria, and1H‑quinolin-2-ones showed good antibacterial activity against two Gram-positive bacteria.

Keywords: quinolin, quinoxalin, anti-bacterial activity, minimum inhibitory concentration (MIC)

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Authors: Jenaidullah Batur, Sebghatullah Mudaber

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Currently, there is a list of catalysts that can reduce CO₂ to valuable chemicals and fuels, among them metal oxides such as TiO₂, known as promising photocatalysts to produce hydrogen and CO unless they are at an earlier age and still need to promote activity to able for produce fabricated values. Herein, in this work, we provided a novel, facile and eco-friendly synthesis strategy to synthesize more effective TiO₂-organic composite materials to selectively reduce CO₂ to CO. In this experiment, commercial nanocrystalline TiO₂ and saccharin with Li (LiBr, LiCl) were synthesized using the facile physical grinding in the motel pestle for 10 minutes, then added 10 mL of deionized water (18.2 megaohms) on the 300mg composite catalyst before samples moving for hydrothermal heating for 24 hours at 80 C in the oven. Compared with nanosized TiO₂, the new TiO₂-Sac-Li indeed displays a high CO generation rate of 70.83 μmol/g/h, which is 7 times higher than TiO₂, which shows enhancement in CO₂ reduction and an apparent improvement in charge carrier dynamic. The CO₂ reduction process at the gas-solid interface on TiO₂-Sac-Li composite semiconductors is investigated by functional calculations and several characterization methods. The results indicate that CO₂ can be easily activated by the TiO₂-Sac-Li atoms on the surface. This work innovatively investigates CO₂ reduction in novel composite materials and helps to broaden the applications of composite materials semiconductors.

Keywords: green chemistry, green synthesis, TiO₂, photocatalyst

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Authors: Novia Dwi Prabandari, Diah Ayu Asmarani

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Soluble fiber can accelerate the metabolism of cholesterol. Pectin and gum has been used in the form of substance additive for material stabilizer and emulsifier. Pectin supplementation in laying hens can decimate the cholesterol content in egg yolk and muscle. Therefore, this laying hens’ feed is regular feed chickens enriched with soluble fiber (Pectin, Xanthan gum, and Guar gum) to produce eggs and muscle with lower cholesterol than usual.The ingredients are mixed in the ratio of concentrate 45%, corn flour 25%, soybean meal 20%, and extract of soluble fiber 10%. Once all the ingredients are mixed and then evaporated with temperature < 80 °C. Then put in the grinding machine resulting in a circular shape with holes 2-3 mm in diameter, after it dried up the water content in the feed is less than 14%. Eggs from laying hen with soluble fiber fortification feed intake will have lower cholesterol levels in eggs than regular feed. So even with the cholesterol content in the muscle, it is because chicken feed fortified with soluble fiber will accelerate the metabolism of cholesterol and cause cholesterol deposits in the chicken less. The use of this kind of laying hens feed is produce eggs with high protein content can be consumed more for people who have hypercholesterolemia.

Keywords: pectin, xanthan gum, guar gum, laying hen, cholesterol

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Cristina-Corina Bențea, Teodora-Mihaela Iconomescu, Laurențiu-Gabriel Talaghir, Claudiu Mereuță, Anamaria Berdilă

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The study aims to examine the relationships between motor abilities, self-evaluation of athletic competence, and demographic characteristics in a sample of late-childhood participants. Defined as physical elements that enable the movements, motor skills are classified according to movement precision as gross and fine motor skills. Across their development, children enhance their ability to coordinate the limbs to produce different actions. In educational settings, they perform various instructional activities that involve the improvement of their athletic prowess and are taught how to strengthen their gross and fine motor abilities. Also, in relation to their activities, children tend to evaluate themselves differently across the various domains of their life. Starting from childhood, athletic competence is one of the area-specific evaluations of competence that refers to one’s ability to do well at sports, including outdoor games. Method: The sample consisted of fifty-eight primary school children, thirty girls, and twenty-eight boys, with ages between 8-10 years. The Bruininks-Oseretsky test of motor proficiency was used to assess both gross and fine motor skills in eight specific areas (fine motor precision, fine motor integration, manual dexterity, bilateral coordination, balance, running speed and agility, upper-limb coordination, strength). Athletic competence self-perceived was assessed with one of the six subscales of the Self-Perception Profile for Children. Results: Were examined both the relationships between each motor skills scale and subscales and between motor skills and general self-perceived athletic competence. Results indicated correlations between the athletic competence and four motor skills subscales depending on the gender and age of the children. The findings of the study were discussed related to the possibility to improve children's physical proficiency in educational settings according to the level of self-perceived athletic competence.

Keywords: gross motor skills, fine motor skills, athletic competence, self-evaluation, children, education

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Authors: Eman Alzahrani

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Date palm trees are abundant and cheap natural resources in Saudi Arabia. In this study, an activated carbon was prepared from palm trunks by chemical processes. The chemical activation was performed by impregnation of the raw materials after grinding with H3PO4 solution (63%), followed by placing of the sample solution on a muffle furnace at 400ºC for 30 min, and then at 800ºC for 10 min. The morphology of the fabricated material was checked using scanning electron microscopy that showed the rough surfaces on the carbon samples. The use of fabricated activated carbon for removal of eosin dye from aqueous solutions at different contact time, initial dye concentration, pH and adsorbent doses was investigated. The experimental results show that the adsorption process attains equilibrium within 20 min. The adsorption isotherm equilibrium was studied by means of the Langmuir and Freundlich isotherms, and it was found that the data fit the Langmuir isotherm equation with maximum monolayer adsorption capacity of 126.58 mg g-1. The results indicated that the home made activated carbon prepared from palm trunks has the ability to remove eosin dye from aqueous solution and it will be a promising adsorbent for the removal of harmful dyes from waste water.

Keywords: activated carbon, date palm trunks, H3PO4 activation, adsorption, dye removal, eosin dye, isotherm

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Authors: Ladda Silanoi

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One of the main purposes in establishment of ASEAN Community is educational development. All countries in ASEAN shall then prepare for plans and strategies for country development. Therefore, Thailand set up the policy concerning educational management for all educational institutions to understand about ASEAN Community. However, some educational institutions lack of precision in determining the curriculums of ASEAN Community, especially schools in rural areas, for example, schools along the common border with Laos, and Cambodia. One of the effective methods to promote the precision in ASEAN Community is to design additional learning courses. The important process of additional learning courses design is to provide learning outcomes of ASEAN Community for course syllabus determination. Therefore, the researcher is interested in developing teachers in the schools of common border with Laos, and Cambodia to provide learning outcomes and learning process. This research has the objective of developing the learning outcomes and learning process management of basic education along Thailand, Laos, and Cambodia Common Border for the ASEAN Community Preparation. Research methodology consists of 2 steps. Step 1: Delphi Technique was used to provide guidelines in development of learning outcomes and learning process. Step 2: Action Research procedures was employed to study the result of additional learning courses design. Result of the study: By using Delphi technique, consensus is expected to be achieved, from 50 experts in the study within 3 times of the survey. The last survey found that experts’ opinions were compatible on every item (inter-quartile range = 0) leading to the arrangement of training courses in step of Action Research. The result from the workshop found that teachers in schools of Srisaket and Bueng Kan provinces could be able to provide learning outcomes of all courses.

Keywords: learning outcome and learning process, basic education, ASEAN Community preparation, Thailand Laos and Cambodia common border

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Authors: M. Varin, A. M. Dubois, R. Gadbois-Langevin, B. Chalghaf

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Good knowledge of the physical environment is essential for an integrated forest planning. This information enables better forecasting of operating costs, determination of cutting volumes, and preservation of ecologically sensitive areas. The use of satellite images in stereoscopic pairs gives the capacity to generate high precision 3D models, which are scale-adapted for harvesting operations. These models could represent an alternative to 3D LiDAR data, thanks to their advantageous cost of acquisition. The objective of the study was to assess the quality of stereo-derived canopy height models (CHM) in comparison to a traditional LiDAR CHM and ground tree-height samples. Two study sites harboring two different forest stand types (broadleaf and conifer) were analyzed using stereo pairs and tri-stereo images from the WorldView-3 satellite to calculate CHM. Acquisition of multispectral images from an Unmanned Aerial Vehicle (UAV) was also realized on a smaller part of the broadleaf study site. Different algorithms using two softwares (PCI Geomatica and Correlator3D) with various spatial resolutions and band selections were tested to select the 3D modeling technique, which offered the best performance when compared with LiDAR. In the conifer study site, the CHM produced with Corelator3D using only the 50-cm resolution panchromatic band was the one with the smallest Root-mean-square deviation (RMSE: 1.31 m). In the broadleaf study site, the tri-stereo model provided slightly better performance, with an RMSE of 1.2 m. The tri-stereo model was also compared to the UAV, which resulted in an RMSE of 1.3 m. At individual tree level, when ground samples were compared to satellite, lidar, and UAV CHM, RMSE were 2.8, 2.0, and 2.0 m, respectively. Advanced analysis was done for all of these cases, and it has been noted that RMSE is reduced when the canopy cover is higher when shadow and slopes are lower and when clouds are distant from the analyzed site.

Keywords: very high spatial resolution, satellite imagery, WorlView-3, canopy height models, CHM, LiDAR, unmanned aerial vehicle, UAV

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Authors: Verena M. Moock, Darien E. Arce Chávez, Mariana M. Espejel González, Leopoldo Ruíz-Huerta, Crescencio García-Segundo

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Uncertainty analysis in industrial computed tomography is commonly related to metrological trace tools, which offer precision measurements of external part features. Unfortunately, there is no such reference tool for internal measurements to profit from the unique imaging potential of X-rays. Uncertainty approximations for computed tomography are still based on general aspects of the industrial machine and do not adapt to acquisition parameters or part characteristics. The present study investigates the impact of the acquisition time on the dimensional uncertainty measuring a stainless steel cylinder with a circular tomography scan. The authors develop the figure difference method for X-ray radiography to evaluate the volumetric differences introduced within the projected absorption maps of the metal workpiece. The dimensional uncertainty is dominantly influenced by photon energy dissipated as heat causing the thermal expansion of the metal, as monitored by an infrared camera within the industrial tomograph. With the proposed methodology, we are able to show evolving temperature differences throughout the tomography acquisition. This is an early study showing that the number of projections in computer tomography induces dimensional error due to energy absorption. The error magnitude would depend on the thermal properties of the sample and the acquisition parameters by placing apparent non-uniform unwanted volumetric expansion. We introduce infrared imaging for the experimental display of metrological uncertainty in a particular metal part of symmetric geometry. We assess that the current results are of fundamental value to reach the balance between the number of projections and uncertainty tolerance when performing analysis with X-ray dimensional exploration in precision measurements with industrial tomography.

Keywords: computed tomography, digital metrology, infrared imaging, thermal expansion

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Shohreh Ghasemi

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Introduction: Managing oral and maxillofacial trauma is a multifaceted challenge, as it can have life-threatening consequences and significant functional and aesthetic impact. Navigation techniques have been introduced to improve surgical precision to meet this challenge. A machine learning algorithm was also developed to support clinical decision-making regarding treating oral and maxillofacial trauma. Given these advances, this systematic meta-analysis aims to assess the efficacy of navigational techniques in treating oral and maxillofacial trauma and explore the impact of machine learning on their management. Methods: A detailed and comprehensive analysis of studies published between January 2010 and September 2021 was conducted through a systematic meta-analysis. This included performing a thorough search of Web of Science, Embase, and PubMed databases to identify studies evaluating the efficacy of navigational techniques and the impact of machine learning in managing oral and maxillofacial trauma. Studies that did not meet established entry criteria were excluded. In addition, the overall quality of studies included was evaluated using Cochrane risk of bias tool and the Newcastle-Ottawa scale. Results: Total of 12 studies, including 869 patients with oral and maxillofacial trauma, met the inclusion criteria. An analysis of studies revealed that navigation techniques effectively improve surgical accuracy and minimize the risk of complications. Additionally, machine learning algorithms have proven effective in predicting treatment outcomes and identifying patients at high risk for complications. Conclusion: The introduction of navigational technology has great potential to improve surgical precision in oral and maxillofacial trauma treatment. Furthermore, developing machine learning algorithms offers opportunities to improve clinical decision-making and patient outcomes. Still, further studies are necessary to corroborate these results and establish the optimal use of these technologies in managing oral and maxillofacial trauma

Keywords: trauma, machine learning, navigation, maxillofacial, management

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Authors: Sofiqul Islam, V. Murugan, Prema Kumari, Hari

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Gentamicin is a broad spectrum water-soluble aminoglycoside antibiotics produced by the fermentation process of microorganism known as Micromonospora purpurea. It is widely used for the treatment of infection caused by both gram positive and gram negative bacteria. Gentamicin consists of a mixture of aminoglycoside components like C1, C1a, C2a, and C2. The molecular structure of Gentamicin and its related substances showed that it has lack of presence of chromophore group in the molecule due to which the detection of such components were quite critical and challenging. In this study, a simple Reversed Phase-High Performance Liquid Chromatographic (RP-HPLC) method using ultraviolet (UV) detector was developed and validated for quantification of the related substances present in Gentamicin drug substances. The method was achieved by using Thermo Scientific Hypersil Gold analytical column (150 x 4.6 mm, 5 µm particle size) with isocratic elution composed of methanol: water: glacial acetic acid: sodium hexane sulfonate in the ratio 70:25:5:3 % v/v/v/w as a mobile phase at a flow rate of 0.5 mL/min, column temperature was maintained at 30 °C and detection wavelength of 330 nm. The four components of Gentamicin namely Gentamicin C1, C1a, C2a, and C2 were well separated along with the related substance present in Gentamicin. The Limit of Quantification (LOQ) values were found to be at 0.0075 mg/mL. The accuracy of the method was quite satisfactory in which the % recovery was resulted between 95-105% for the related substances. The correlation coefficient (≥ 0.995) shows the linearity response against concentration over the range of Limit of Quantification (LOQ). Precision studies showed the % Relative Standard Deviation (RSD) values less than 5% for its related substance. The method was validated in accordance with the International Conference of Harmonization (ICH) guideline with various parameters like system suitability, specificity, precision, linearity, accuracy, limit of quantification, and robustness. This proposed method was easy and suitable for use for the quantification of related substances in routine analysis of Gentamicin formulations.

Keywords: reversed phase-high performance liquid chromatographic (RP-HPLC), high performance liquid chromatography, gentamicin, isocratic, ultraviolet

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Authors: Kaye Angelica Lacurom, Keziah Macahilo

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One of the most important and in demand products in the Philippines is Scylla serrata. Despite the increasing demand in the market today, the cost of feeds corresponds to a fraction of 40%-50% of the entire operational of crab production. Raisers and suppliers are seeking alternative ways to lessen their expense with more effective enhancers than the usual feeds. This study aimed to enhance the growth and immune system of the mud crabs using natural antioxidants from plant powders that are available in the locality. There were four treatments: Diet 1: commercially available feeds for the positive control, Diet 2: 1,200 mg/kg Euphorbia hirta , Diet 3: 1,600 mg/kg of Citrus microcarpa, Diet 4: Mixed 1,400 of Euphorbia hirta and Citrus microcarpa. Air-drying was done first-hand followed by the grinding of plants. After which the plants were stored in a container and was added to the feed formulation given. Mud crabs were fed twice a day for 30 days for better results. For inferential analysis, weight gain and survivability were measured, hemolymph was extracted and the Total Hemocycte Count (THC) was determined analyzed. Results showed that the highest THC mean (9.0 x 105 ± 7.1 x 104) and weight gain mean (2.9 x 10± 1.9 x 10) was achieved by Diet 3 with the same survivability rates among other treatments and positive control. While Diet 2 presented the lowest THC mean (7.2 x 105 ±3.5 x 104) and weight gain mean (1.0 x 10± 7.0 x 10-1).

Keywords: fed additives, Scylla serrata, enhanced growth, innate immune response

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Authors: Sukhwinder Singh Jolly

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Metal spinning is one such process in which the stresses are localized to a small area and the material is made to flow or move over the mandrel with the help of spinning tool. Spinning of tubular products can be performed by two techniques, forward spinning and backward spinning. Many researchers have studied the process both experimentally and analytically. An effort has been made to apply the process to the spinning of thin wall, highly precision, small bore long tube in hard-to-work materials such as titanium.

Keywords: metal spinning, hard-to-work materials, roller diameter, power consumption

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Authors: Alexander A. Karabutov, Natalia B. Podymova, Elena B. Cherepetskaya

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The theoretical analysis is carried out to get the relation between the ultrasonic wave velocity and the value of residual stresses. The laser-ultrasonic method is developed to evaluate the residual stresses and subsurface defects in metals. The method is based on the laser thermooptical excitation of longitudinal ultrasonic wave sand their detection by a broadband piezoelectric detector. A laser pulse with the time duration of 8 ns of the full width at half of maximum and with the energy of 300 µJ is absorbed in a thin layer of the special generator that is inclined relative to the object under study. The non-uniform heating of the generator causes the formation of a broadband powerful pulse of longitudinal ultrasonic waves. It is shown that the temporal profile of this pulse is the convolution of the temporal envelope of the laser pulse and the profile of the in-depth distribution of the heat sources. The ultrasonic waves reach the surface of the object through the prism that serves as an acoustic duct. At the interface ‚laser-ultrasonic transducer-object‘ the conversion of the most part of the longitudinal wave energy takes place into the shear, subsurface longitudinal and Rayleigh waves. They spread within the subsurface layer of the studied object and are detected by the piezoelectric detector. The electrical signal that corresponds to the detected acoustic signal is acquired by an analog-to-digital converter and when is mathematically processed and visualized with a personal computer. The distance between the generator and the piezodetector as well as the spread times of acoustic waves in the acoustic ducts are the characteristic parameters of the laser-ultrasonic transducer and are determined using the calibration samples. There lative precision of the measurement of the velocity of longitudinal ultrasonic waves is 0.05% that corresponds to approximately ±3 m/s for the steels of conventional quality. This precision allows one to determine the mechanical stress in the steel samples with the minimal detection threshold of approximately 22.7 MPa. The results are presented for the measured dependencies of the velocity of longitudinal ultrasonic waves in the samples on the values of the applied compression stress in the range of 20-100 MPa.

Keywords: laser-ultrasonic method, longitudinal ultrasonic waves, metals, residual stresses

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Manpreet Kaur, Cheol-Hong Min

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The increasing advancement in the robotics equipped with machine vision sensors applied to precision agriculture is a demanding solution for various problems in the agricultural farms. An important issue related with the machine vision system concerns crop row and weed detection. This paper proposes an automatic furrow detection system based on real-time processing for identifying crop rows in maize fields in the presence of weed. This vision system is designed to be installed on the farming vehicles, that is, submitted to gyros, vibration and other undesired movements. The images are captured under image perspective, being affected by above undesired effects. The goal is to identify crop rows for vehicle navigation which includes weed removal, where weeds are identified as plants outside the crop rows. The images quality is affected by different lighting conditions and gaps along the crop rows due to lack of germination and wrong plantation. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The proposed algorithm used HSV color space to discriminate crops, weeds and soil. The region of interest is defined by filtering each of the HSV channels between maximum and minimum threshold values. Then the noises in the images were eliminated by the means of hybrid median filter. Further, mathematical morphological processes, i.e., erosion to remove smaller objects followed by dilation to gradually enlarge the boundaries of regions of foreground pixels was applied. It enhances the image contrast. To accurately detect the position of crop rows, the region of interest is defined by creating a binary mask. The edge detection and Hough transform were applied to detect lines represented in polar coordinates and furrow directions as accumulations on the angle axis in the Hough space. The experimental results show that the method is effective.

Keywords: furrow detection, morphological, HSV, Hough transform

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Authors: A. Sopharak, B. Uyyanonvara, S. Barman

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Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

Keywords: diabetic retinopathy, microaneurysm, naive Bayes classifier, SVM classifier

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Authors: L. Capitanu, V. Floresci, L. L. Badita

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Starting from the idea that the greatest pressure and velocity of composite melted is in the die nozzle, was an experimental nozzle with wear samples of sizes and weights which can be measured with precision as good. For a larger accuracy of measurements, we used a method for radiometric measuring, extremely accurate. Different nitriding steels have been studied as nitriding treatments, as well as some special steels and alloyed steels. Besides these, there have been preliminary attempts made to describe and checking corrosive action of thermoplastics on metals.

Keywords: plastics, composites with short glass fibres, moulding, wear, experimental modelling, glass fibres content influence

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Authors: AnjalI A. Prasad

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Nowadays, social media played an important role in spreading misinformation or fake news. This study analyzes the fake news related to the COVID-19 pandemic spread in social media. This paper aims at evaluating and comparing different approaches that are used to mitigate this issue, including popular deep learning approaches, such as CNN, RNN, LSTM, and BERT algorithm for classification. To evaluate models’ performance, we used accuracy, precision, recall, and F1-score as the evaluation metrics. And finally, compare which algorithm shows better result among the four algorithms.

Keywords: BERT, CNN, LSTM, RNN

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Authors: J. Alcisto, M. Papakyriakou, J. Guerra, A. Dominguez, M. Miller, J. Foyos, E. Jones, N. Ula, M. Hahn, L. Zeng, Y. Li, O. S. Es-Said

Abstract:

Current methods of testing for hydrogen charging are slow and time consuming. The objective of this paper was to determine if hydrogen charging can be detected quantitatively through the use of Charpy Impact (CI) testing. CI is a much faster and simpler process than current methods for detecting hydrogen charging. Steel plates were Electro Discharge Machined (EDM) into ninety-six 4340 steel CI samples and forty-eight tensile bars. All the samples were heat treated at 900°C to austentite and then rapidly quenched in water to form martensite. The samples were tempered at eight different target strengths/target temperatures (145, 160, 170, 180, 190, 205, 220, to 250KSI, thousands of pounds per square inch)/(1100, 1013, 956, 898, 840, 754, 667, 494 degrees Celsius). After a tedious process of grinding and machining v-notches to the Charpy samples, they were divided into four groups. One group was kept as received baseline for comparison while the other three groups were sent to Alcoa (Fasteners) Inc. in Torrance to be cadmium coated. The three groups were coated with three thicknesses (2, 3 and 5 mils). That means that the samples were charged with ascending hydrogen levels. The samples were CI tested and tensile tested, and the data was tabulated and compared to the baseline group of uncharged samples of the same material. The results of this study were successful and indicated that CI testing was able to quantitatively detect hydrogen charging.

Keywords: Charpy impact toughness, hydrogen charging, 4340 steel, Electro Discharge Machined (EDM)

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Authors: S. Srikiran, Dharmala Venkata Padmaja, P. N. L. Pavani, R. Pola Rao, K. Ramji

Abstract:

Technological advancements in the development of cutting tools and coolant/lubricant chemistry have enhanced the machining capabilities of hard materials under higher machining conditions. Generation of high temperatures at the cutting zone during machining is one of the most important and pertinent problems which adversely affect the tool life and surface finish of the machined components. Generally, cutting fluids and solid lubricants are used to overcome the problem of heat generation, which is not effectively addressing the problems. With technological advancements in the field of tribology, nano-level particulate solid lubricants are being used nowadays in machining operations, especially in the areas of turning and grinding. The present investigation analyses the effect of using nano-particulate graphite powder as lubricant in the turning of AISI 1040 steel under variable machining conditions and to study its effect on cutting forces, tool temperature and surface roughness of the machined component. Experiments revealed that the increase in cutting forces and tool temperature resulting in the decrease of surface quality with the decrease in the size of nano-particulate graphite powder as lubricant.

Keywords: solid lubricant, graphite, minimum quantity lubrication (MQL), nano–particles

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Authors: Mehmet Erdi Korkmaz, Mustafa Günay

Abstract:

Nowadays, grinding is frequently replaced with hard turning for reducing set up time and higher accuracy. This paper focused on mathematical modeling of average surface roughness (Ra) in hard turning of AISI L2 grade (DIN 1.2210) cold work tool steel with ceramic tools. The steel was hardened to 60±1 HRC after the heat treatment process. Cutting speed, feed rate, depth of cut and tool nose radius was chosen as the cutting conditions. The uncoated ceramic cutting tools were used in the machining experiments. The machining experiments were performed according to Taguchi L27 orthogonal array on CNC lathe. Ra values were calculated by averaging three roughness values obtained from three different points of machined surface. The influences of cutting conditions on surface roughness were evaluated as statistical and experimental. The analysis of variance (ANOVA) with 95% confidence level was applied for statistical analysis of experimental results. Finally, mathematical models were developed using the artificial neural networks (ANN). ANOVA results show that feed rate is the dominant factor affecting surface roughness, followed by tool nose radius and cutting speed.

Keywords: ANN, hard turning, DIN 1.2210, surface roughness, Taguchi method

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Authors: Morsli Mourad, Tahiri Mohamed, Samdi Azzeddine

Abstract:

Casablanca is the biggest city in Morocco. It concentrates more than 60% of the economic and industrial activity of the kingdom. Its building and public works (BTP) sector is the leading source of inert waste scattered in open areas. This inert waste is a major challenge for the city of Casablanca, as it is not properly managed, thus causing a significant nuisance for the environment and the health of the population. Hence the vision of our project is to recycle and valorize concrete waste. In this work, we present concrete results in the exploitation of this abundant and permanent deposit. Typical wastes are concrete, clay and concrete bricks, ceramic tiles, marble panels, gypsum, scrap metal, wood . The work performed included: geolocation with a combination of artificial intelligence and Google Earth, estimation of the amount of waste per site, sorting, crushing, grinding, and physicochemical characterization of the samples. Then, we proceeded to the exploitation of the types of substrates to be developed: light cement, coating, and glue for ceramics... The said products were tested and characterized by X-ray fluorescence, specific surface, resistance to bending and crushing, etc. We will present in detail the main results of our research work and also describe the specific properties of each material developed.

Keywords: déchets de démolition et des chantiers de construction, logiciels de combinaison SIG, valorisation de déchets inertes, enduits, ciment leger, casablanca

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Authors: Pei-Hsing Huang, Wei-Ju Huang

Abstract:

Investment casting is commonly used in the production of metallic components with complex shapes, due to its high dimensional precision, good surface finish, and low cost. However, the process is cumbersome, and the period between trial casting and final production can be very long, thereby limiting business opportunities and competitiveness. In this study, we replaced conventional wax injection with stereolithography (SLA) 3D printing to speed up the trial process and reduce costs. We also used silicone molds to further reduce costs to avoid the high costs imposed by photosensitive resin.

Keywords: investment casting, stereolithography, wax molding, 3D printing

Procedia PDF Downloads 372

Authors: Idowu Olugbenga Adewumi, Foluke Iyabo Oluwatoyinbo

Abstract:

Presently, there are several discussions on the food security with increase in yield of arable crop throughout the world. This article, briefly present research efforts to create digital interfaces to nature, in particular to area of crop production in agriculture with increase in yield with interest on pervasive computing. The approach goes beyond the use of sensor networks for environmental monitoring but also by emphasizing the development of a system architecture that detect intruder (Intrusion Process) which reduce the yield of the farmer at the end of the planting/harvesting period. The objective of the work is to set a model for setting up the hand held or portable device for increasing the quality and quantity of arable crop. This process incorporates the use of infrared motion image sensor with security alarm system which can send a noise signal to intruder on the farm. This model of the portable image sensing device in monitoring or scaring human, rodent, birds and even pests activities will reduce post harvest loss which will increase the yield on farm. The nano intelligence technology was proposed to combat and minimize intrusion process that usually leads to low quality and quantity of produce from farm. Intranet system will be in place with wireless radio (WLAN), router, server, and client computer system or hand held device e.g PDAs or mobile phone. This approach enables the development of hybrid systems which will be effective as a security measure on farm. Since, precision agriculture has developed with the computerization of agricultural production systems and the networking of computerized control systems. In the intelligent plant production system of controlled greenhouses, information on plant responses, measured by sensors, is used to optimize the system. Further work must be carry out on modeling using pervasive computing environment to solve problems of agriculture, as the use of electronics in agriculture will attracts more youth involvement in the industry.

Keywords: pervasive computing, intrusion detection, precision agriculture, security, arable crop

Procedia PDF Downloads 376

Authors: Hung-Yuan Chang, Wen-Lung Chiang, Kuo-Liang Wu, Chen-Tsung Lin

Abstract:

National Space Organization (NSPO) has completed in 2014 the development of a space-borne GPS receiver, including design, manufacture, comprehensive functional test, environmental qualification test and so on. The main performance of this receiver include 8-meter positioning accuracy, 0.05 m/sec speed-accuracy, the longest 90 seconds of cold start time, and up to 15g high dynamic scenario. The receiver will be integrated in the autonomous FORMOSAT-7 NSPO-Built satellite scheduled to be launched in 2019 to execute pre-defined scientific missions. The flight model of this receiver manufactured in early 2015 will pass comprehensive functional tests and environmental acceptance tests, etc., which are expected to be completed by the end of 2015. The space-borne GPS receiver is a pure software design in which all GPS baseband signal processing are executed by a digital signal processor (DSP), currently only 50% of its throughput being used. In response to the booming global navigation satellite systems, NSPO will gradually expand this receiver to become a multi-mode, multi-band, high-precision navigation receiver, and even a science payload, such as the reflectometry receiver of a global navigation satellite system. The fundamental purpose of this extension study is to port some software algorithms such as signal acquisition and correlation, reused code and large amount of computation load to the FPGA whose processor is responsible for operational control, navigation solution, and orbit propagation and so on. Due to the development and evolution of the FPGA is pretty fast, the new system architecture upgraded via an FPGA should be able to achieve the goal of being a multi-mode, multi-band high-precision navigation receiver, or scientific receiver. Finally, the results of tests show that the new system architecture not only retains the original overall performance, but also sets aside more resources available for future expansion possibility. This paper will explain the detailed DSP/FPGA architecture, development, test results, and the goals of next development stage of this receiver.

Keywords: space-borne, GPS receiver, DSP, FPGA, multi-mode multi-band

Procedia PDF Downloads 342

Authors: Cristian Chacha, David Asiain, Jesús Ponce de León, José Ramón Beltrán

Abstract:

This work explores and characterizes the behavior of the AFE AD5941 in impedance measurement using an embedded algorithm with a constant current AC source. The main aim of this research is to improve the exact measurement of impedance values for their application in EDA-focused wearable devices. Through comprehensive study and characterization, it has been observed that employing a measurement sequence with a constant current source produces results with increased dispersion but higher accuracy. As a result, this approach leads to a more accurate system for impedance measurement.

Keywords: EDA, constant current AC source, wearable, precision, accuracy, impedance

Procedia PDF Downloads 65